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    <title>Charles Sieg's Latest Posts</title>
    <link>https://charlessieg.com</link>
    <description><![CDATA[RSS feed for Charles Sieg's blog]]></description>
    <language>en-us</language>
    <lastBuildDate>Tue, 01 Sep 2026 23:59:00 GMT</lastBuildDate>
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    <item>
      <title><![CDATA[Leverage Record: September 1, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-09-01-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-09-01-leverage-record.html</guid>
      <pubDate>Tue, 01 Sep 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">4 tasks. September 1, 2026 closed at 22.1x aggregate execution leverage across 88.0 qualified-senior-equivalent hours in 239 agent-session minutes. Covered operator leverage was 330.0x.</p>
<p class="mb-4 font-light font-serif">That is 2.2 qualified-senior-equivalent weeks. Task factors ranged from 13.2x to 30.0x. The largest project grouping contained 2 entries.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Senior Est.</th>
      <th>Agent Session</th>
      <th>Operator</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Testing</td>
      <td>32.0h</td>
      <td>64m</td>
      <td>3m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Deployment</td>
      <td>26.0h</td>
      <td>72m</td>
      <td>5m</td>
      <td>21.7x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Documentation</td>
      <td>21.0h</td>
      <td>62m</td>
      <td>5m</td>
      <td>20.3x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Testing</td>
      <td>9.0h</td>
      <td>41m</td>
      <td>3m</td>
      <td>13.2x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>4</td>
    </tr>
    <tr>
      <td>Qualified-senior-equivalent hours</td>
      <td>88.0</td>
    </tr>
    <tr>
      <td>Agent-session minutes</td>
      <td>239</td>
    </tr>
    <tr>
      <td>Covered operator minutes</td>
      <td>16</td>
    </tr>
    <tr>
      <td>Recorded tokens</td>
      <td>340,000</td>
    </tr>
    <tr>
      <td>Aggregate execution leverage</td>
      <td>22.1x</td>
    </tr>
    <tr>
      <td>Covered operator leverage</td>
      <td>330.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="interpretation">Interpretation</h2>
<p class="mb-4 font-light font-serif">The qualified-senior hours are counterfactual estimates and inherit that uncertainty. Execution, token, and operator-time values retain their recorded provenance, which is incomplete for some legacy rows. Summed agent-session minutes are not serial wall-clock time when sessions overlap.</p>
<p class="mb-4 font-light font-serif">This is one unusually experienced operator&#39;s record under unusual working conditions, not a general benchmark. The complete reconciled snapshot is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 31, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-31-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-31-leverage-record.html</guid>
      <pubDate>Mon, 31 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">28 tasks. August 31, 2026 closed at 11.2x aggregate execution leverage across 921.0 qualified-senior-equivalent hours in 4,953 agent-session minutes. Covered operator leverage was 343.2x.</p>
<p class="mb-4 font-light font-serif">That is 23.0 qualified-senior-equivalent weeks. Task factors ranged from 3.2x to 76.7x. The largest project grouping contained 24 entries.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Senior Est.</th>
      <th>Agent Session</th>
      <th>Operator</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Deployment</td>
      <td>78.0h</td>
      <td>61m</td>
      <td>10m</td>
      <td>76.7x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>/findings round-18 on [product]: verified and fixed all 7 findings from the follow-up review of the round-17 fixes (G1 balanced command grammar — star/bracket args, whitelisted single-letter commands,…</td>
      <td>24.0h</td>
      <td>33m</td>
      <td>2m</td>
      <td>43.6x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>/findings run 2 on notification-service: verified (4 by runtime probe) and resolved all 15 findings from the concurrency/state-machine review of the prior remediation — P1s: deferred-webhook/queued-resume…</td>
      <td>30.0h</td>
      <td>42m</td>
      <td>3m</td>
      <td>42.9x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>/findings round 2 on @[product]/activity-ui: resolved 10 findings (V15-V24) from a review gate against the just-shipped 2.7.0. All verified against source before any fix, ranked by severity rather than the…</td>
      <td>24.0h</td>
      <td>37m</td>
      <td>2m</td>
      <td>38.9x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>/findings round 2 on [product] (@[product]/about-dialog): verified 6/6 reviewer findings (focus-return-to-wrong-element reproduced via programmatic click; Escape leak + missing inert/scroll-lock reproduced;…</td>
      <td>12.0h</td>
      <td>19m</td>
      <td>6m</td>
      <td>37.9x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Deployment</td>
      <td>16.0h</td>
      <td>26m</td>
      <td>2m</td>
      <td>36.9x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Testing</td>
      <td>11.0h</td>
      <td>20m</td>
      <td>6m</td>
      <td>33.0x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Testing</td>
      <td>40.0h</td>
      <td>89m</td>
      <td>3m</td>
      <td>27.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>/findings on @[product]/activity-ui: resolved 14 findings end to end — 12 from an adversarial Fable review of the shipped 2.6.0, plus 2 found during the run. All verified against source first, ranked by…</td>
      <td>28.0h</td>
      <td>64m</td>
      <td>2m</td>
      <td>26.2x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Testing</td>
      <td>16.0h</td>
      <td>38m</td>
      <td>2m</td>
      <td>25.3x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Infrastructure</td>
      <td>28.0h</td>
      <td>71m</td>
      <td>2m</td>
      <td>23.7x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Infrastructure</td>
      <td>46.0h</td>
      <td>118m</td>
      <td>6m</td>
      <td>23.4x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>/findings round 2 on [product]: verified and fixed all 17 review findings plus 2 discovered en route — payment phase machine ending coupon/confirm races, stale-validation resurrection killed with…</td>
      <td>32.0h</td>
      <td>85m</td>
      <td>3m</td>
      <td>22.6x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>/findings run on @[product]/activity-ui ([product]): verified and resolved six defects in the client-resident deterministic verification engine and the audio primitive. V47 (P1) the symbolic activity inferred…</td>
      <td>24.0h</td>
      <td>71m</td>
      <td>4m</td>
      <td>20.3x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>/findings run on [product] math pipeline: verified and fixed all 8 owner-reported findings (L1-L8) — currency scanner preserving authored digit-leading $...$ math, non-overlap interval invariant + affix guard…</td>
      <td>20.0h</td>
      <td>60m</td>
      <td>3m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>/findings run on [product] (@[product]/about-dialog): verified 9/9 owner-reported findings real (require() empty-module repro, publint+attw, jsdom defect tests, bundle grep, npm audit), fixed all of them plus…</td>
      <td>10.0h</td>
      <td>31m</td>
      <td>5m</td>
      <td>19.4x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>[product] /findings round 5 — resolved 4 reported findings in the Markdown→unicode-math→KaTeX pipeline (src/lib/unicodeMath.ts), then found and resolved 4 more by adversarially probing the fixes: two-letter…</td>
      <td>14.0h</td>
      <td>45m</td>
      <td>2m</td>
      <td>18.7x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>/findings round 3 on @[product]/activity-ui: 11 findings resolved (V25-V34 from a review gate vs 2.7.0, plus 1 found during verification) — first round past the audio stack into the math and chart activities.…</td>
      <td>28.0h</td>
      <td>97m</td>
      <td>4m</td>
      <td>17.3x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Testing</td>
      <td>38.0h</td>
      <td>175m</td>
      <td>6m</td>
      <td>13.0x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Findings run 3 on notification-service: verified and resolved all 15 review findings (H1-H15) against the runs-1/2 hardening — migration 028 JSONB bind rewrite with frozen normalizer, identity-only…</td>
      <td>35.0h</td>
      <td>185m</td>
      <td>2m</td>
      <td>11.4x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>[product] /findings round 4: resolved all 18 owner findings (9 P1 cross-account/async-tail defects incl. receiving-tab state erasure, signOut revoking a newer account, offline-queue cross-account replay,…</td>
      <td>48.0h</td>
      <td>273m</td>
      <td>2m</td>
      <td>10.5x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Infrastructure</td>
      <td>38.0h</td>
      <td>330m</td>
      <td>6m</td>
      <td>6.9x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Formation program ([product]): manifold replication &amp; HA correctness. Orchestrated a 6-phase, 27-task remediation closing all 17 findings (12 owner + 5 research) across the cross-instance replication…</td>
      <td>170.0h</td>
      <td>1535m</td>
      <td>12m</td>
      <td>6.6x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Crosscheck program completion ([product]) — Phases 2-5, following the partial record dfe77014 which covered Phases 0-1. Resolved the R1 tripwire that had stopped the program (a prior-round test went red once…</td>
      <td>25.0h</td>
      <td>260m</td>
      <td>12m</td>
      <td>5.8x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Four consecutive /findings rounds against @[product]/activity-ui (React 19 + TS component library, ~2,240 tests): ~44 review-gate findings verified against source before any fix, ranked by severity, and each…</td>
      <td>36.0h</td>
      <td>433m</td>
      <td>30m</td>
      <td>5.0x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Clean Sweep — [product] erasure &amp; deletion integrity program, orchestrated end to end. Eleven tasks across four phases in two repositories (core/[product], automations/automation-account-deletion), closing…</td>
      <td>28.0h</td>
      <td>380m</td>
      <td>6m</td>
      <td>4.4x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>Deployment</td>
      <td>18.0h</td>
      <td>300m</td>
      <td>15m</td>
      <td>3.6x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Testing</td>
      <td>4.0h</td>
      <td>75m</td>
      <td>3m</td>
      <td>3.2x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>28</td>
    </tr>
    <tr>
      <td>Qualified-senior-equivalent hours</td>
      <td>921.0</td>
    </tr>
    <tr>
      <td>Agent-session minutes</td>
      <td>4,953</td>
    </tr>
    <tr>
      <td>Covered operator minutes</td>
      <td>161</td>
    </tr>
    <tr>
      <td>Recorded tokens</td>
      <td>2,484,000</td>
    </tr>
    <tr>
      <td>Aggregate execution leverage</td>
      <td>11.2x</td>
    </tr>
    <tr>
      <td>Covered operator leverage</td>
      <td>343.2x</td>
    </tr>
  </tbody>
</table>
<h2 id="interpretation">Interpretation</h2>
<p class="mb-4 font-light font-serif">The qualified-senior hours are counterfactual estimates and inherit that uncertainty. Execution, token, and operator-time values retain their recorded provenance, which is incomplete for some legacy rows. Summed agent-session minutes are not serial wall-clock time when sessions overlap.</p>
<p class="mb-4 font-light font-serif">This is one unusually experienced operator&#39;s record under unusual working conditions, not a general benchmark. The complete reconciled snapshot is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 30, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-30-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-30-leverage-record.html</guid>
      <pubDate>Sun, 30 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">23 tasks. August 30, 2026 closed at 10.3x aggregate execution leverage across 448.5 qualified-senior-equivalent hours in 2,608 agent-session minutes. Covered operator leverage was 228.1x.</p>
<p class="mb-4 font-light font-serif">That is 11.2 qualified-senior-equivalent weeks. Task factors ranged from 2.7x to 27.7x. The largest project grouping contained 20 entries.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Senior Est.</th>
      <th>Agent Session</th>
      <th>Operator</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Testing</td>
      <td>6.0h</td>
      <td>13m</td>
      <td>2m</td>
      <td>27.7x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Testing</td>
      <td>9.0h</td>
      <td>20m</td>
      <td>2m</td>
      <td>27.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Documentation</td>
      <td>12.0h</td>
      <td>27m</td>
      <td>6m</td>
      <td>26.7x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Fable-planner: twelfth-pass remediation program for @[product]/activity-ui — independently re-verified all 12 owner-review findings (pause propagation, AudioCapture lifecycle races, TTS ownership, response…</td>
      <td>6.0h</td>
      <td>14m</td>
      <td>2m</td>
      <td>25.7x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>[product] /findings round 2: verified and fixed 12 defects across the Markdown/unicode-math pipeline and the component layer. All 10 owner-reported findings reproduced executably before any fix; 2 more found…</td>
      <td>18.0h</td>
      <td>44m</td>
      <td>3m</td>
      <td>24.5x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Deployment</td>
      <td>34.0h</td>
      <td>86m</td>
      <td>4m</td>
      <td>23.7x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Documentation</td>
      <td>14.0h</td>
      <td>39m</td>
      <td>4m</td>
      <td>21.5x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Full WCAG 2.2 AA accessibility audit and same-session remediation of [product] web, including shared activity components, six rendered axe matrices, responsive and unit verification, and a validated PubMark…</td>
      <td>40.0h</td>
      <td>123m</td>
      <td>1m</td>
      <td>19.5x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Infrastructure</td>
      <td>7.0h</td>
      <td>26m</td>
      <td>2m</td>
      <td>16.2x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>[product]: /findings run 11 — 2 owner findings plus 2 found during the work, all fixed. F19: round 10 replaced a hand-written URL regex with the real Markdown parser but applied the new boundary inside only…</td>
      <td>5.0h</td>
      <td>21m</td>
      <td>3m</td>
      <td>14.3x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Design and frontend</td>
      <td>6.0h</td>
      <td>26m</td>
      <td>4m</td>
      <td>13.8x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>/findings tenth pass on @[product]/activity-ui ([product]). 23 owner-reported findings verified by a 46-agent fan-out (independent verifier + adversarial challenger each), 0 refutations. 21 confirmed, 1…</td>
      <td>62.0h</td>
      <td>281m</td>
      <td>3m</td>
      <td>13.2x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Testing</td>
      <td>72.0h</td>
      <td>360m</td>
      <td>45m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>[product] /findings: verified and fixed 9 defects in the Markdown/unicode-math normalization pipeline. All 7 owner-reported findings reproduced executably before any fix; 2 more found during the run (Greek…</td>
      <td>14.0h</td>
      <td>73m</td>
      <td>4m</td>
      <td>11.5x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Deployment</td>
      <td>26.0h</td>
      <td>139m</td>
      <td>6m</td>
      <td>11.2x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Testing</td>
      <td>15.0h</td>
      <td>87m</td>
      <td>4m</td>
      <td>10.3x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Deployment</td>
      <td>34.0h</td>
      <td>218m</td>
      <td>3m</td>
      <td>9.4x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>[product]: fixed a concurrency-arbitration defect in the retirement-transfer endpoint. The idempotency verdict (<code>transferred</code>) was derived from whether the call inserted the successor enrollment row, a proxy…</td>
      <td>5.0h</td>
      <td>43m</td>
      <td>3m</td>
      <td>7.0x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>[product] /findings round 4: four owner-reported findings, each with a regression test observed failing first. (1) P1 — every request-scoped DB session committed in FastAPI&#39;s dependency exit stack, which…</td>
      <td>7.0h</td>
      <td>89m</td>
      <td>3m</td>
      <td>4.7x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Content production</td>
      <td>3.5h</td>
      <td>47m</td>
      <td>2m</td>
      <td>4.5x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Deployment</td>
      <td>9.0h</td>
      <td>124m</td>
      <td>3m</td>
      <td>4.4x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>[product] ninth-pass /findings run, released as @[product]/activity-ui 2.3.0: 33 findings resolved and 1 disproved — the owner&#39;s 14 plus 20 found while verifying and fixing them, four of which were…</td>
      <td>34.0h</td>
      <td>489m</td>
      <td>5m</td>
      <td>4.2x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Deployment</td>
      <td>10.0h</td>
      <td>219m</td>
      <td>4m</td>
      <td>2.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>23</td>
    </tr>
    <tr>
      <td>Qualified-senior-equivalent hours</td>
      <td>448.5</td>
    </tr>
    <tr>
      <td>Agent-session minutes</td>
      <td>2,608</td>
    </tr>
    <tr>
      <td>Covered operator minutes</td>
      <td>118</td>
    </tr>
    <tr>
      <td>Recorded tokens</td>
      <td>6,225,000</td>
    </tr>
    <tr>
      <td>Aggregate execution leverage</td>
      <td>10.3x</td>
    </tr>
    <tr>
      <td>Covered operator leverage</td>
      <td>228.1x</td>
    </tr>
  </tbody>
</table>
<h2 id="interpretation">Interpretation</h2>
<p class="mb-4 font-light font-serif">The qualified-senior hours are counterfactual estimates and inherit that uncertainty. Execution, token, and operator-time values retain their recorded provenance, which is incomplete for some legacy rows. Summed agent-session minutes are not serial wall-clock time when sessions overlap.</p>
<p class="mb-4 font-light font-serif">This is one unusually experienced operator&#39;s record under unusual working conditions, not a general benchmark. The complete reconciled snapshot is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 29, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-29-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-29-leverage-record.html</guid>
      <pubDate>Sat, 29 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">15 tasks. August 29, 2026 closed at 6.8x aggregate execution leverage across 260.5 qualified-senior-equivalent hours in 2,283 agent-session minutes. Covered operator leverage was 197.8x.</p>
<p class="mb-4 font-light font-serif">That is 6.5 qualified-senior-equivalent weeks. Task factors ranged from 1.7x to 19.0x. The largest project grouping contained 13 entries.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Senior Est.</th>
      <th>Agent Session</th>
      <th>Operator</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Deployment</td>
      <td>20.0h</td>
      <td>63m</td>
      <td>5m</td>
      <td>19.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>[product]: /findings run 8 — verified, ranked and resolved all 12 reported findings (2 P1, 8 P2, 2 P3) end to end. Every finding reproduced with executable evidence before any fix; none dismissed. Re-ranked…</td>
      <td>15.0h</td>
      <td>52m</td>
      <td>4m</td>
      <td>17.3x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>[product]: 14 findings resolved across two adversarial review rounds (teardown singleton leak, six AST guard holes, a coverage floor met only by leaked state, and a self-inflicted cross-repo event-contract…</td>
      <td>34.0h</td>
      <td>124m</td>
      <td>15m</td>
      <td>16.5x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>[product]: resolved three reported defects in the repo&#39;s own meta-test guards, plus a fourth found during verification. (1) The pytest collection oracle rejected real parameterized tests — the matcher…</td>
      <td>11.0h</td>
      <td>42m</td>
      <td>5m</td>
      <td>15.7x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Testing</td>
      <td>5.5h</td>
      <td>23m</td>
      <td>3m</td>
      <td>14.3x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>[product] round 3: generalized three test guards from case-enumeration to rule-shape (position-agnostic hash ban, pytest-as-oracle for collection, ownership-by-binding-source), plus one self-found defect…</td>
      <td>11.0h</td>
      <td>53m</td>
      <td>6m</td>
      <td>12.5x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Testing</td>
      <td>7.0h</td>
      <td>44m</td>
      <td>3m</td>
      <td>9.5x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>[product]: resolved a 10-finding queue end to end (8 reported + 2 discovered). Verified every finding executably before any fix, ranked by severity, then fixed most-severe-first with a regression test…</td>
      <td>11.0h</td>
      <td>84m</td>
      <td>4m</td>
      <td>7.9x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>/findings run 6 on @[product]/ui ([product]): 6 reported findings verified and resolved, 22 more found during the run and also resolved - 28 total across 12 repos. P1: a canvas that never painted when its…</td>
      <td>46.0h</td>
      <td>430m</td>
      <td>6m</td>
      <td>6.4x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>/findings run 7 on @[product]/ui: 14 reported findings verified (all 14 confirmed, none refuted) plus 16 found during the run; 30 total, all fixed. The P1s: every Mermaid diagram and Timeline in every lesson…</td>
      <td>52.0h</td>
      <td>505m</td>
      <td>3m</td>
      <td>6.2x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>[product] eighth-pass /findings run, released as @[product]/activity-ui 2.2.0: 17 findings resolved (the owner&#39;s 9 plus 8 found in-run), each of the nine reproduced with an out-of-tree probe before any fix…</td>
      <td>26.0h</td>
      <td>329m</td>
      <td>4m</td>
      <td>4.7x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Testing</td>
      <td>9.0h</td>
      <td>148m</td>
      <td>9m</td>
      <td>3.6x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>[product] + [product] /findings round 2: 2 owner-reported findings plus 2 found during the run, each with a regression test observed failing first. (1) GET /users/me/enrollments declared status with a string…</td>
      <td>2.5h</td>
      <td>45m</td>
      <td>2m</td>
      <td>3.3x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>[product] /findings run: resolved 3 owner-reported findings plus 3 discovered during the run, each with a regression test observed failing first. (1) POST /telemetry published activity.started reading…</td>
      <td>3.5h</td>
      <td>90m</td>
      <td>3m</td>
      <td>2.3x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>[product] + [product] re-audit /findings run: verified 5 enumerated findings, resolved 4 with red-first regression tests and confirmed 2 already fixed. Included reverting my own same-day regression…</td>
      <td>7.0h</td>
      <td>251m</td>
      <td>7m</td>
      <td>1.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>15</td>
    </tr>
    <tr>
      <td>Qualified-senior-equivalent hours</td>
      <td>260.5</td>
    </tr>
    <tr>
      <td>Agent-session minutes</td>
      <td>2,283</td>
    </tr>
    <tr>
      <td>Covered operator minutes</td>
      <td>79</td>
    </tr>
    <tr>
      <td>Recorded tokens</td>
      <td>3,930,000</td>
    </tr>
    <tr>
      <td>Aggregate execution leverage</td>
      <td>6.8x</td>
    </tr>
    <tr>
      <td>Covered operator leverage</td>
      <td>197.8x</td>
    </tr>
  </tbody>
</table>
<h2 id="interpretation">Interpretation</h2>
<p class="mb-4 font-light font-serif">The qualified-senior hours are counterfactual estimates and inherit that uncertainty. Execution, token, and operator-time values retain their recorded provenance, which is incomplete for some legacy rows. Summed agent-session minutes are not serial wall-clock time when sessions overlap.</p>
<p class="mb-4 font-light font-serif">This is one unusually experienced operator&#39;s record under unusual working conditions, not a general benchmark. The complete reconciled snapshot is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 28, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-28-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-28-leverage-record.html</guid>
      <pubDate>Fri, 28 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">23 tasks. August 28, 2026 closed at 14.1x aggregate execution leverage across 352.0 qualified-senior-equivalent hours in 1,498 agent-session minutes. Covered operator leverage was 352.0x.</p>
<p class="mb-4 font-light font-serif">That is 8.8 qualified-senior-equivalent weeks. Task factors ranged from 4.0x to 51.4x. The largest project grouping contained 18 entries.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Senior Est.</th>
      <th>Agent Session</th>
      <th>Operator</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>[product] /findings run 3: resolved 17 findings (16 reported + 1 found in the gate). All 16 reproduced; six were regressions from the two earlier runs the same day. Two left a canvas BLANK, not stale: the…</td>
      <td>24.0h</td>
      <td>28m</td>
      <td>3m</td>
      <td>51.4x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Deployment</td>
      <td>26.0h</td>
      <td>33m</td>
      <td>3m</td>
      <td>47.3x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>[product] /findings run 4 — resolved 10 findings (9 reported + 1 discovered) end to end, each with a regression test observed failing first. S1: stale button-initiated audio play() promise flipping a…</td>
      <td>24.0h</td>
      <td>39m</td>
      <td>3m</td>
      <td>36.9x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Infrastructure</td>
      <td>27.0h</td>
      <td>49m</td>
      <td>3m</td>
      <td>33.1x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Documentation</td>
      <td>22.0h</td>
      <td>56m</td>
      <td>3m</td>
      <td>23.6x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Testing</td>
      <td>34.0h</td>
      <td>98m</td>
      <td>4m</td>
      <td>20.8x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>@[product]/activity-ui sixth-pass /findings run: 6 reported findings resolved plus 4 found during the work, each with a regression test observed failing first. Four P1 session/telemetry races: a REST 401 held…</td>
      <td>19.0h</td>
      <td>60m</td>
      <td>4m</td>
      <td>19.0x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Deployment</td>
      <td>11.0h</td>
      <td>39m</td>
      <td>3m</td>
      <td>16.9x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Deployment</td>
      <td>9.0h</td>
      <td>37m</td>
      <td>0m</td>
      <td>14.6x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Deployment</td>
      <td>11.0h</td>
      <td>47m</td>
      <td>0m</td>
      <td>14.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Fixed a P1 where a misconfigured OPTIONAL dependency could fail every learner answer before grading. get_<a href="" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">product</a> is called as an ARGUMENT to the service call in submit_answer and to spawn_background in…</td>
      <td>5.0h</td>
      <td>23m</td>
      <td>0m</td>
      <td>13.0x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Testing</td>
      <td>5.0h</td>
      <td>23m</td>
      <td>0m</td>
      <td>13.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Card-execution round on [product] (owner: &quot;need to do those cards and this one: 9ba3ecc8&quot;). W2 / card 9ba3ecc8 (high, part 3 of 3 of the live cross-device activity timeline): built the web consumer —…</td>
      <td>6.0h</td>
      <td>33m</td>
      <td>2m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Deployment</td>
      <td>26.0h</td>
      <td>143m</td>
      <td>4m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Deployment</td>
      <td>7.0h</td>
      <td>39m</td>
      <td>0m</td>
      <td>10.8x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>/findings round 4 on [product] — the owner&#39;s sixth-pass review of the Clearance remediation: 1 P2 + 1 P3, both fixed with regression tests observed failing first. G1 (P2, gate reliability):…</td>
      <td>3.5h</td>
      <td>20m</td>
      <td>2m</td>
      <td>10.5x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Testing</td>
      <td>54.0h</td>
      <td>371m</td>
      <td>8m</td>
      <td>8.7x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>[product] /findings run: 3 owner findings verified by mutation and fixed, plus 4 uncovered along the way. F1 (S2): the rest_gateway split&#39;s citation guard joined all exemption reasons and required one match…</td>
      <td>10.0h</td>
      <td>71m</td>
      <td>3m</td>
      <td>8.5x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Testing</td>
      <td>3.0h</td>
      <td>22m</td>
      <td>2m</td>
      <td>8.2x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>[product] /findings round 2: 6 review findings verified and fixed, plus 3 uncovered in-run; 5 of the 6 were regressions from round 1. R1 (S1, not mine): reset_engine_context cleared only _context, leaving two…</td>
      <td>9.0h</td>
      <td>75m</td>
      <td>4m</td>
      <td>7.2x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>[product] /findings run: verified and resolved 11 findings (8 in [product] incl. broken production build, cross-account live-row leak, dead enrollment realtime wire, course filter vs production events,…</td>
      <td>14.0h</td>
      <td>158m</td>
      <td>7m</td>
      <td>5.3x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>/findings round 6 on [product] — the owner&#39;s eighth-pass review: 2 P3 findings in the two guards added the day before, both fixed with tests observed failing first. E1: the parity parser&#39;s tuple regex…</td>
      <td>1.5h</td>
      <td>19m</td>
      <td>1m</td>
      <td>4.7x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Testing</td>
      <td>1.0h</td>
      <td>15m</td>
      <td>1m</td>
      <td>4.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>23</td>
    </tr>
    <tr>
      <td>Qualified-senior-equivalent hours</td>
      <td>352.0</td>
    </tr>
    <tr>
      <td>Agent-session minutes</td>
      <td>1,498</td>
    </tr>
    <tr>
      <td>Covered operator minutes</td>
      <td>60</td>
    </tr>
    <tr>
      <td>Recorded tokens</td>
      <td>4,789,498</td>
    </tr>
    <tr>
      <td>Aggregate execution leverage</td>
      <td>14.1x</td>
    </tr>
    <tr>
      <td>Covered operator leverage</td>
      <td>352.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="interpretation">Interpretation</h2>
<p class="mb-4 font-light font-serif">The qualified-senior hours are counterfactual estimates and inherit that uncertainty. Execution, token, and operator-time values retain their recorded provenance, which is incomplete for some legacy rows. Summed agent-session minutes are not serial wall-clock time when sessions overlap.</p>
<p class="mb-4 font-light font-serif">This is one unusually experienced operator&#39;s record under unusual working conditions, not a general benchmark. The complete reconciled snapshot is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 27, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-27-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-27-leverage-record.html</guid>
      <pubDate>Thu, 27 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">36 tasks. August 27, 2026 closed at 13.4x aggregate execution leverage across 1,096.0 qualified-senior-equivalent hours in 4,900 agent-session minutes. Covered operator leverage was 644.7x.</p>
<p class="mb-4 font-light font-serif">That is 27.4 qualified-senior-equivalent weeks. Task factors ranged from 4.2x to 120.0x. The largest project grouping contained 33 entries.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Senior Est.</th>
      <th>Agent Session</th>
      <th>Operator</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Author the Finance total-coverage corpus specification (specs/professional/Finance/Finance_Domain_Specification.json — 13 clusters, 4,950 leaf goals, 5,667 nodes, 31 anchors, 15 course shapes from the SIE…</td>
      <td>60.0h</td>
      <td>30m</td>
      <td>1m</td>
      <td>120.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Author the Insurance total-coverage corpus specification (specs/professional/Insurance/Insurance_Domain_Specification.json — 10 national clusters plus 51 jurisdiction-tagged state clusters, 2,954 leaf goals,…</td>
      <td>45.0h</td>
      <td>25m</td>
      <td>1m</td>
      <td>108.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Author the Medicine total-coverage corpus specification (specs/professional/Medicine/Medicine_Domain_Specification.json — 23 clusters, 6,493 leaf goals, 7,033 nodes, 117 anchors, 16 course shapes; the…</td>
      <td>80.0h</td>
      <td>45m</td>
      <td>1m</td>
      <td>106.7x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Documentation</td>
      <td>64.0h</td>
      <td>45m</td>
      <td>1m</td>
      <td>85.3x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Author the Management total-coverage corpus specification (specs/professional/Management/Management_Domain_Specification.json — 8 clusters, 1,495 leaf goals, 1,592 nodes, 22 anchors, 10 course shapes;…</td>
      <td>24.0h</td>
      <td>25m</td>
      <td>1m</td>
      <td>57.6x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Deployment</td>
      <td>16.0h</td>
      <td>22m</td>
      <td>2m</td>
      <td>43.6x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Deployment</td>
      <td>15.0h</td>
      <td>25m</td>
      <td>1m</td>
      <td>36.0x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Author the US Law total-coverage corpus specification (specs/professional/Law/US_Law_Domain_Specification.json — 18 clusters, 3,242 leaf goals, 3,464 nodes, 58 anchors, 5 course shapes) in the corpora…</td>
      <td>40.0h</td>
      <td>70m</td>
      <td>1m</td>
      <td>34.3x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Testing</td>
      <td>28.0h</td>
      <td>49m</td>
      <td>8m</td>
      <td>34.3x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Documentation</td>
      <td>12.0h</td>
      <td>22m</td>
      <td>1m</td>
      <td>32.7x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Deployment</td>
      <td>28.0h</td>
      <td>61m</td>
      <td>4m</td>
      <td>27.5x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Deployment</td>
      <td>30.0h</td>
      <td>88m</td>
      <td>8m</td>
      <td>20.5x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Testing</td>
      <td>24.0h</td>
      <td>78m</td>
      <td>10m</td>
      <td>18.5x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Testing</td>
      <td>24.0h</td>
      <td>82m</td>
      <td>0m</td>
      <td>17.6x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Testing</td>
      <td>24.0h</td>
      <td>83m</td>
      <td>4m</td>
      <td>17.3x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>/findings run on [product]: verify, rank, and resolve a 5-finding review. All 5 confirmed with reproductions before any fix; 1 new S2 found during verification; all 6 fixed worst-first, each with a regression…</td>
      <td>12.0h</td>
      <td>44m</td>
      <td>5m</td>
      <td>16.4x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Infrastructure</td>
      <td>20.0h</td>
      <td>77m</td>
      <td>6m</td>
      <td>15.6x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Deployment</td>
      <td>10.0h</td>
      <td>41m</td>
      <td>6m</td>
      <td>14.6x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Real Estate total-coverage corpus (corpora program, wave 13): the 12-national-cluster skeleton (corpus.json, OUTLINES.md, 32 anchors, 109 course shapes — every state&#39;s salesperson and broker examination, the…</td>
      <td>52.0h</td>
      <td>270m</td>
      <td>1m</td>
      <td>11.6x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Behavioral Health total-coverage corpus (corpora program, wave 10): the 16-national-cluster skeleton + the 51 jurisdiction-tagged state law-and-ethics clusters generated from state_template.json (corpus.json,…</td>
      <td>56.0h</td>
      <td>300m</td>
      <td>1m</td>
      <td>11.2x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>/findings round 2 on [product] — the owner&#39;s second same-evening review of the Clearance remediation (1 P2, 1 P3) plus an owner order to self-reaudit before claiming done. R1 (P2): testing-strategy.md and…</td>
      <td>4.0h</td>
      <td>22m</td>
      <td>2m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Education total-coverage corpus (corpora program, wave 12): the 14-national-cluster skeleton (corpus.json, OUTLINES.md, 25 anchors, 19 course shapes — Praxis PLT ×4/Core/ParaPro/SpEd/ESOL, SLLA/SSA, TExES…</td>
      <td>46.0h</td>
      <td>280m</td>
      <td>1m</td>
      <td>9.9x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Public Administration total-coverage corpus (corpora program, wave 5): the 8-cluster skeleton (corpus.json, OUTLINES.md, 21 anchors, 15 course shapes — the CGFM&#39;s three examinations as three clusters, the…</td>
      <td>24.0h</td>
      <td>152m</td>
      <td>1m</td>
      <td>9.5x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Public Service total-coverage corpus (corpora program, wave 4): the 11-cluster skeleton (corpus.json, OUTLINES.md, 21 anchors, 15 course shapes — three shared ability clusters, six occupational clusters, the…</td>
      <td>30.0h</td>
      <td>195m</td>
      <td>1m</td>
      <td>9.2x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Dental total-coverage corpus (corpora program, wave 4): the 14-cluster skeleton (corpus.json, 10,963-word OUTLINES.md, 40 anchors, 23 course shapes over DANB&#39;s 7 credentials + 13 component exams, the NBDHE,…</td>
      <td>36.0h</td>
      <td>235m</td>
      <td>1m</td>
      <td>9.2x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Deployment</td>
      <td>36.0h</td>
      <td>240m</td>
      <td>15m</td>
      <td>9.0x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>/findings run on [product] — resolve the owner&#39;s 2026-08-27 code review of the Clearance review remediation (&quot;not fully closed&quot;: 0 P0, 0 P1, 4 P2, 2 P3). All six resolved in one session, each verified with…</td>
      <td>10.0h</td>
      <td>73m</td>
      <td>3m</td>
      <td>8.2x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Safety total-coverage corpus (corpora program, wave 7): the 14-cluster skeleton (corpus.json, OUTLINES.md, 28 anchors, 16 course shapes — the seven BCSP examinations as tier cuts, the free OSHA-fundamentals…</td>
      <td>34.0h</td>
      <td>255m</td>
      <td>1m</td>
      <td>8.0x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>Deployment</td>
      <td>9.0h</td>
      <td>70m</td>
      <td>4m</td>
      <td>7.7x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>Nursing total-coverage corpus (corpora program, wave 6): the 22-cluster skeleton (corpus.json, OUTLINES.md, 69 anchors, 25 course shapes — NCLEX-RN/PN, REx-PN, CPNRE, NNAAP, MACE, the nursing-school companion…</td>
      <td>48.0h</td>
      <td>390m</td>
      <td>1m</td>
      <td>7.4x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>Engineering total-coverage corpus + the two Mathematics course specs (corpora program, wave 9): the 14-cluster skeleton (corpus.json, 9,425-word OUTLINES.md, 35 anchors, 18 course shapes — FE…</td>
      <td>40.0h</td>
      <td>350m</td>
      <td>1m</td>
      <td>6.9x</td>
    </tr>
    <tr>
      <td>32</td>
      <td>Testing</td>
      <td>10.0h</td>
      <td>89m</td>
      <td>3m</td>
      <td>6.7x</td>
    </tr>
    <tr>
      <td>33</td>
      <td>Airmen total-coverage corpus (corpora program, wave 11): the 16-cluster skeleton (corpus.json, 14,400-word OUTLINES.md, 39 anchors, 16 course shapes — every FAA airman knowledge test private through ATP,…</td>
      <td>42.0h</td>
      <td>380m</td>
      <td>1m</td>
      <td>6.6x</td>
    </tr>
    <tr>
      <td>34</td>
      <td>Valuation total-coverage corpus (corpora program, wave 14): the 9-cluster skeleton (corpus.json, OUTLINES.md, 18 anchors, 3 course shapes — the AQB Licensed Residential, Certified Residential, and Certified…</td>
      <td>18.0h</td>
      <td>165m</td>
      <td>1m</td>
      <td>6.5x</td>
    </tr>
    <tr>
      <td>35</td>
      <td>Aircraft Maintenance total-coverage corpus (corpora program, wave 16): the 13-cluster skeleton (corpus.json, OUTLINES.md, 21 anchors, 5 course shapes — the FAA mechanic General, Airframe, and Powerplant…</td>
      <td>30.0h</td>
      <td>310m</td>
      <td>2m</td>
      <td>5.8x</td>
    </tr>
    <tr>
      <td>36</td>
      <td>Property Inspection total-coverage corpus (corpora program, wave 15): the 12-cluster skeleton (corpus.json, OUTLINES.md, 18 anchors, 3 course shapes — the NHIE, the Texas TREC examination, the New York…</td>
      <td>15.0h</td>
      <td>212m</td>
      <td>1m</td>
      <td>4.2x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>36</td>
    </tr>
    <tr>
      <td>Qualified-senior-equivalent hours</td>
      <td>1,096.0</td>
    </tr>
    <tr>
      <td>Agent-session minutes</td>
      <td>4,900</td>
    </tr>
    <tr>
      <td>Covered operator minutes</td>
      <td>102</td>
    </tr>
    <tr>
      <td>Recorded tokens</td>
      <td>0</td>
    </tr>
    <tr>
      <td>Aggregate execution leverage</td>
      <td>13.4x</td>
    </tr>
    <tr>
      <td>Covered operator leverage</td>
      <td>644.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="interpretation">Interpretation</h2>
<p class="mb-4 font-light font-serif">The qualified-senior hours are counterfactual estimates and inherit that uncertainty. Execution, token, and operator-time values retain their recorded provenance, which is incomplete for some legacy rows. Summed agent-session minutes are not serial wall-clock time when sessions overlap.</p>
<p class="mb-4 font-light font-serif">This is one unusually experienced operator&#39;s record under unusual working conditions, not a general benchmark. The complete reconciled snapshot is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 26, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-26-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-26-leverage-record.html</guid>
      <pubDate>Wed, 26 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">23 tasks. August 26, 2026 closed at 28.5x aggregate execution leverage across 956.0 qualified-senior-equivalent hours in 2,010 agent-session minutes. Covered operator leverage was 651.8x.</p>
<p class="mb-4 font-light font-serif">That is 23.9 qualified-senior-equivalent weeks. Task factors ranged from 7.1x to 96.7x. The largest project grouping contained 12 entries.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Senior Est.</th>
      <th>Agent Session</th>
      <th>Operator</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Deployment</td>
      <td>58.0h</td>
      <td>36m</td>
      <td>9m</td>
      <td>96.7x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Audit and review</td>
      <td>40.0h</td>
      <td>25m</td>
      <td>1m</td>
      <td>96.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Deployment</td>
      <td>40.0h</td>
      <td>27m</td>
      <td>1m</td>
      <td>88.9x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Deployment</td>
      <td>40.0h</td>
      <td>27m</td>
      <td>1m</td>
      <td>88.9x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Design and frontend</td>
      <td>40.0h</td>
      <td>27m</td>
      <td>1m</td>
      <td>88.9x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Infrastructure</td>
      <td>40.0h</td>
      <td>27m</td>
      <td>1m</td>
      <td>88.9x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Testing</td>
      <td>40.0h</td>
      <td>31m</td>
      <td>1m</td>
      <td>77.4x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Deployment</td>
      <td>40.0h</td>
      <td>32m</td>
      <td>1m</td>
      <td>75.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Deployment</td>
      <td>40.0h</td>
      <td>34m</td>
      <td>1m</td>
      <td>70.6x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Deployment</td>
      <td>40.0h</td>
      <td>35m</td>
      <td>1m</td>
      <td>68.6x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Infrastructure</td>
      <td>40.0h</td>
      <td>37m</td>
      <td>1m</td>
      <td>64.9x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Infrastructure</td>
      <td>40.0h</td>
      <td>37m</td>
      <td>1m</td>
      <td>64.9x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Documentation</td>
      <td>40.0h</td>
      <td>41m</td>
      <td>1m</td>
      <td>58.5x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Documentation</td>
      <td>40.0h</td>
      <td>50m</td>
      <td>4m</td>
      <td>48.0x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Testing</td>
      <td>28.0h</td>
      <td>46m</td>
      <td>2m</td>
      <td>36.5x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Deployment</td>
      <td>34.0h</td>
      <td>61m</td>
      <td>4m</td>
      <td>33.4x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Deployment</td>
      <td>15.0h</td>
      <td>33m</td>
      <td>3m</td>
      <td>27.3x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Deployment</td>
      <td>32.0h</td>
      <td>76m</td>
      <td>8m</td>
      <td>25.3x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Deployment</td>
      <td>125.0h</td>
      <td>375m</td>
      <td>12m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Deployment</td>
      <td>48.0h</td>
      <td>207m</td>
      <td>15m</td>
      <td>13.9x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Infrastructure</td>
      <td>10.0h</td>
      <td>55m</td>
      <td>1m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>[product]: re-certified commit bb6c883, which a storm outage had forced onto main behind SKIP_TESTS=1. The gate had never actually run against that content. Running it found SIX defects, stacked so each hid…</td>
      <td>16.0h</td>
      <td>101m</td>
      <td>6m</td>
      <td>9.5x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Clearance program — orchestrated the full remediation of a 14-finding Fable release-readiness review of [product] (3 P1, 7 P2, 4 P3), across four repos, as an 8-phase gated program with 20 delegated agent…</td>
      <td>70.0h</td>
      <td>590m</td>
      <td>12m</td>
      <td>7.1x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>23</td>
    </tr>
    <tr>
      <td>Qualified-senior-equivalent hours</td>
      <td>956.0</td>
    </tr>
    <tr>
      <td>Agent-session minutes</td>
      <td>2,010</td>
    </tr>
    <tr>
      <td>Covered operator minutes</td>
      <td>88</td>
    </tr>
    <tr>
      <td>Recorded tokens</td>
      <td>5,630,000</td>
    </tr>
    <tr>
      <td>Aggregate execution leverage</td>
      <td>28.5x</td>
    </tr>
    <tr>
      <td>Covered operator leverage</td>
      <td>651.8x</td>
    </tr>
  </tbody>
</table>
<h2 id="interpretation">Interpretation</h2>
<p class="mb-4 font-light font-serif">The qualified-senior hours are counterfactual estimates and inherit that uncertainty. Execution, token, and operator-time values retain their recorded provenance, which is incomplete for some legacy rows. Summed agent-session minutes are not serial wall-clock time when sessions overlap.</p>
<p class="mb-4 font-light font-serif">This is one unusually experienced operator&#39;s record under unusual working conditions, not a general benchmark. The complete reconciled snapshot is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 25, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-25-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-25-leverage-record.html</guid>
      <pubDate>Tue, 25 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">21 tasks. August 25, 2026 closed at 55.6x aggregate execution leverage across 1,084.0 qualified-senior-equivalent hours in 1,169 agent-session minutes. Covered operator leverage was 448.6x.</p>
<p class="mb-4 font-light font-serif">That is 27.1 qualified-senior-equivalent weeks. Task factors ranged from 7.3x to 300.0x. The largest project grouping contained 11 entries.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Senior Est.</th>
      <th>Agent Session</th>
      <th>Operator</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Content production</td>
      <td>120.0h</td>
      <td>24m</td>
      <td>8m</td>
      <td>300.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Testing</td>
      <td>120.0h</td>
      <td>24m</td>
      <td>5m</td>
      <td>300.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Testing</td>
      <td>120.0h</td>
      <td>36m</td>
      <td>10m</td>
      <td>200.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Documentation</td>
      <td>40.0h</td>
      <td>24m</td>
      <td>3m</td>
      <td>100.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Deployment</td>
      <td>40.0h</td>
      <td>27m</td>
      <td>1m</td>
      <td>88.9x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Deployment</td>
      <td>40.0h</td>
      <td>31m</td>
      <td>2m</td>
      <td>77.4x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Deployment</td>
      <td>52.0h</td>
      <td>46m</td>
      <td>12m</td>
      <td>67.8x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Deployment</td>
      <td>40.0h</td>
      <td>36m</td>
      <td>1m</td>
      <td>66.7x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Documentation</td>
      <td>40.0h</td>
      <td>36m</td>
      <td>3m</td>
      <td>66.7x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Testing</td>
      <td>40.0h</td>
      <td>37m</td>
      <td>3m</td>
      <td>64.9x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Content production</td>
      <td>40.0h</td>
      <td>43m</td>
      <td>3m</td>
      <td>55.8x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Deployment</td>
      <td>40.0h</td>
      <td>45m</td>
      <td>3m</td>
      <td>53.3x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Deployment</td>
      <td>40.0h</td>
      <td>45m</td>
      <td>3m</td>
      <td>53.3x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Deployment</td>
      <td>54.0h</td>
      <td>63m</td>
      <td>13m</td>
      <td>51.4x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Critical Path — the [product] AI Business vertical plan (vertical-plans program, line 6 of 18). client_record_id: f279cad6-43b6-4b33-bd05-948a2659d0b0. Three research agents (local grounding on the 2026-08-24…</td>
      <td>40.0h</td>
      <td>58m</td>
      <td>2m</td>
      <td>41.4x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Testing</td>
      <td>36.0h</td>
      <td>54m</td>
      <td>12m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Debugging</td>
      <td>52.0h</td>
      <td>83m</td>
      <td>14m</td>
      <td>37.6x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Documentation</td>
      <td>40.0h</td>
      <td>66m</td>
      <td>3m</td>
      <td>36.4x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Testing</td>
      <td>42.0h</td>
      <td>148m</td>
      <td>16m</td>
      <td>17.0x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Deployment</td>
      <td>34.0h</td>
      <td>128m</td>
      <td>14m</td>
      <td>15.9x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Deployment</td>
      <td>14.0h</td>
      <td>115m</td>
      <td>14m</td>
      <td>7.3x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>21</td>
    </tr>
    <tr>
      <td>Qualified-senior-equivalent hours</td>
      <td>1,084.0</td>
    </tr>
    <tr>
      <td>Agent-session minutes</td>
      <td>1,169</td>
    </tr>
    <tr>
      <td>Covered operator minutes</td>
      <td>145</td>
    </tr>
    <tr>
      <td>Recorded tokens</td>
      <td>0</td>
    </tr>
    <tr>
      <td>Aggregate execution leverage</td>
      <td>55.6x</td>
    </tr>
    <tr>
      <td>Covered operator leverage</td>
      <td>448.6x</td>
    </tr>
  </tbody>
</table>
<h2 id="interpretation">Interpretation</h2>
<p class="mb-4 font-light font-serif">The qualified-senior hours are counterfactual estimates and inherit that uncertainty. Execution, token, and operator-time values retain their recorded provenance, which is incomplete for some legacy rows. Summed agent-session minutes are not serial wall-clock time when sessions overlap.</p>
<p class="mb-4 font-light font-serif">This is one unusually experienced operator&#39;s record under unusual working conditions, not a general benchmark. The complete reconciled snapshot is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 24, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-24-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-24-leverage-record.html</guid>
      <pubDate>Mon, 24 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">6 tasks. August 24, 2026 closed at 14.6x aggregate execution leverage across 264.0 qualified-senior-equivalent hours in 1,088 agent-session minutes. Covered operator leverage was 465.9x.</p>
<p class="mb-4 font-light font-serif">That is 6.6 qualified-senior-equivalent weeks. Task factors ranged from 10.4x to 45.0x. The largest project grouping contained 6 entries.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Senior Est.</th>
      <th>Agent Session</th>
      <th>Operator</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Content remediation phase orchestrator ([product]): a Workflow script (phase.workflow.js) that dispatches one Opus agent per work package with a lane-capped, dependency-gated scheduler (≤10 concurrent, one…</td>
      <td>12.0h</td>
      <td>16m</td>
      <td>3m</td>
      <td>45.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Documentation</td>
      <td>150.0h</td>
      <td>590m</td>
      <td>15m</td>
      <td>15.3x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Testing</td>
      <td>22.0h</td>
      <td>91m</td>
      <td>4m</td>
      <td>14.5x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Test Prep Fidelity program — Fable-grade execution plan (fable-planner): ground-truth research across 11 [product] repos (6 parallel sweeps + live verification of SAT/PSAT/ACT/GRE/GMAT blueprints incl.…</td>
      <td>32.0h</td>
      <td>135m</td>
      <td>2m</td>
      <td>14.2x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Testing</td>
      <td>28.0h</td>
      <td>141m</td>
      <td>6m</td>
      <td>11.9x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Testing</td>
      <td>20.0h</td>
      <td>115m</td>
      <td>4m</td>
      <td>10.4x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>6</td>
    </tr>
    <tr>
      <td>Qualified-senior-equivalent hours</td>
      <td>264.0</td>
    </tr>
    <tr>
      <td>Agent-session minutes</td>
      <td>1,088</td>
    </tr>
    <tr>
      <td>Covered operator minutes</td>
      <td>34</td>
    </tr>
    <tr>
      <td>Recorded tokens</td>
      <td>2,000,000</td>
    </tr>
    <tr>
      <td>Aggregate execution leverage</td>
      <td>14.6x</td>
    </tr>
    <tr>
      <td>Covered operator leverage</td>
      <td>465.9x</td>
    </tr>
  </tbody>
</table>
<h2 id="interpretation">Interpretation</h2>
<p class="mb-4 font-light font-serif">The qualified-senior hours are counterfactual estimates and inherit that uncertainty. Execution, token, and operator-time values retain their recorded provenance, which is incomplete for some legacy rows. Summed agent-session minutes are not serial wall-clock time when sessions overlap.</p>
<p class="mb-4 font-light font-serif">This is one unusually experienced operator&#39;s record under unusual working conditions, not a general benchmark. The complete reconciled snapshot is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 23, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-23-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-23-leverage-record.html</guid>
      <pubDate>Sun, 23 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">4 tasks. August 23, 2026 closed at 16.9x aggregate execution leverage across 349.0 qualified-senior-equivalent hours in 1,240 agent-session minutes. Covered operator leverage was 3490.0x.</p>
<p class="mb-4 font-light font-serif">That is 8.7 qualified-senior-equivalent weeks. Task factors ranged from 11.0x to 40.4x. The largest project grouping contained 3 entries.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Senior Est.</th>
      <th>Agent Session</th>
      <th>Operator</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Deployment</td>
      <td>64.0h</td>
      <td>95m</td>
      <td>1m</td>
      <td>40.4x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Deployment</td>
      <td>86.0h</td>
      <td>150m</td>
      <td>2m</td>
      <td>34.4x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Testing</td>
      <td>34.0h</td>
      <td>95m</td>
      <td>3m</td>
      <td>21.5x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Deployment</td>
      <td>165.0h</td>
      <td>900m</td>
      <td>0m</td>
      <td>11.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>4</td>
    </tr>
    <tr>
      <td>Qualified-senior-equivalent hours</td>
      <td>349.0</td>
    </tr>
    <tr>
      <td>Agent-session minutes</td>
      <td>1,240</td>
    </tr>
    <tr>
      <td>Covered operator minutes</td>
      <td>6</td>
    </tr>
    <tr>
      <td>Recorded tokens</td>
      <td>0</td>
    </tr>
    <tr>
      <td>Aggregate execution leverage</td>
      <td>16.9x</td>
    </tr>
    <tr>
      <td>Covered operator leverage</td>
      <td>3490.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="interpretation">Interpretation</h2>
<p class="mb-4 font-light font-serif">The qualified-senior hours are counterfactual estimates and inherit that uncertainty. Execution, token, and operator-time values retain their recorded provenance, which is incomplete for some legacy rows. Summed agent-session minutes are not serial wall-clock time when sessions overlap.</p>
<p class="mb-4 font-light font-serif">This is one unusually experienced operator&#39;s record under unusual working conditions, not a general benchmark. The complete reconciled snapshot is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 22, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-22-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-22-leverage-record.html</guid>
      <pubDate>Sat, 22 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">9 tasks. August 22, 2026 closed at 7.5x aggregate execution leverage across 568.0 qualified-senior-equivalent hours in 4,545 agent-session minutes. Covered operator leverage was 757.3x.</p>
<p class="mb-4 font-light font-serif">That is 14.2 qualified-senior-equivalent weeks. Task factors ranged from 2.7x to 184.6x. The largest project grouping contained 4 entries.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Senior Est.</th>
      <th>Agent Session</th>
      <th>Operator</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Implement the Glidepath content-addressed media composition service and portable playback engine, including persistence, provider adapters, REST and MCP interfaces, observability UI, migrations,…</td>
      <td>120.0h</td>
      <td>39m</td>
      <td>1m</td>
      <td>184.6x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Testing</td>
      <td>68.0h</td>
      <td>134m</td>
      <td>6m</td>
      <td>30.4x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Testing</td>
      <td>24.0h</td>
      <td>55m</td>
      <td>15m</td>
      <td>26.2x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Hero-image color-block defect: fleet-wide fix + reusable herokit toolkit</td>
      <td>40.0h</td>
      <td>109m</td>
      <td>0m</td>
      <td>22.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>[product] P2 close + P3 contracts/boundaries (4 Opus subagents); critical pre-commit gate defect found, fixed, proven</td>
      <td>92.0h</td>
      <td>417m</td>
      <td>0m</td>
      <td>13.2x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Infrastructure</td>
      <td>28.0h</td>
      <td>146m</td>
      <td>5m</td>
      <td>11.5x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>[product] marketing-site overhaul: tabs, per-product counts, nav, Features restructure, Certs rewrite, [unreleased product]/Enterprise design system — shipped to prod</td>
      <td>34.0h</td>
      <td>195m</td>
      <td>0m</td>
      <td>10.5x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Deployment</td>
      <td>16.0h</td>
      <td>150m</td>
      <td>3m</td>
      <td>6.4x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Deployment</td>
      <td>146.0h</td>
      <td>3300m</td>
      <td>15m</td>
      <td>2.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>9</td>
    </tr>
    <tr>
      <td>Qualified-senior-equivalent hours</td>
      <td>568.0</td>
    </tr>
    <tr>
      <td>Agent-session minutes</td>
      <td>4,545</td>
    </tr>
    <tr>
      <td>Covered operator minutes</td>
      <td>45</td>
    </tr>
    <tr>
      <td>Recorded tokens</td>
      <td>0</td>
    </tr>
    <tr>
      <td>Aggregate execution leverage</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>Covered operator leverage</td>
      <td>757.3x</td>
    </tr>
  </tbody>
</table>
<h2 id="interpretation">Interpretation</h2>
<p class="mb-4 font-light font-serif">The qualified-senior hours are counterfactual estimates and inherit that uncertainty. Execution, token, and operator-time values retain their recorded provenance, which is incomplete for some legacy rows. Summed agent-session minutes are not serial wall-clock time when sessions overlap.</p>
<p class="mb-4 font-light font-serif">This is one unusually experienced operator&#39;s record under unusual working conditions, not a general benchmark. The complete reconciled snapshot is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 21, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-21-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-21-leverage-record.html</guid>
      <pubDate>Fri, 21 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">7 tasks. August 21, 2026 closed at 18.6x aggregate execution leverage across 177.5 qualified-senior-equivalent hours in 572 agent-session minutes. Covered operator leverage was 289.4x.</p>
<p class="mb-4 font-light font-serif">That is 4.4 qualified-senior-equivalent weeks. Task factors ranged from 4.7x to 82.9x. The largest project grouping contained 3 entries.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Senior Est.</th>
      <th>Agent Session</th>
      <th>Operator</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Testing</td>
      <td>72.0h</td>
      <td>52m</td>
      <td>0m</td>
      <td>82.9x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Testing</td>
      <td>36.0h</td>
      <td>70m</td>
      <td>1m</td>
      <td>30.8x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Coding</td>
      <td>28.0h</td>
      <td>64m</td>
      <td>0m</td>
      <td>26.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Flightline closeout amendment (Fable planning): ground-truth sweep (memory-fix commit state, decontam wiring branch, activity-ui august divergence, palette/requirements trace, client type gaps, live prod…</td>
      <td>2.5h</td>
      <td>9m</td>
      <td>1m</td>
      <td>16.7x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Infrastructure</td>
      <td>7.0h</td>
      <td>40m</td>
      <td>3m</td>
      <td>10.5x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Deployment</td>
      <td>14.0h</td>
      <td>105m</td>
      <td>6m</td>
      <td>8.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Debugging</td>
      <td>18.0h</td>
      <td>231m</td>
      <td>25m</td>
      <td>4.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>7</td>
    </tr>
    <tr>
      <td>Qualified-senior-equivalent hours</td>
      <td>177.5</td>
    </tr>
    <tr>
      <td>Agent-session minutes</td>
      <td>572</td>
    </tr>
    <tr>
      <td>Covered operator minutes</td>
      <td>37</td>
    </tr>
    <tr>
      <td>Recorded tokens</td>
      <td>0</td>
    </tr>
    <tr>
      <td>Aggregate execution leverage</td>
      <td>18.6x</td>
    </tr>
    <tr>
      <td>Covered operator leverage</td>
      <td>289.4x</td>
    </tr>
  </tbody>
</table>
<h2 id="interpretation">Interpretation</h2>
<p class="mb-4 font-light font-serif">The qualified-senior hours are counterfactual estimates and inherit that uncertainty. Execution, token, and operator-time values retain their recorded provenance, which is incomplete for some legacy rows. Summed agent-session minutes are not serial wall-clock time when sessions overlap.</p>
<p class="mb-4 font-light font-serif">This is one unusually experienced operator&#39;s record under unusual working conditions, not a general benchmark. The complete reconciled snapshot is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 20, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-20-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-20-leverage-record.html</guid>
      <pubDate>Thu, 20 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">16 tasks. August 20, 2026 closed at 11.3x aggregate execution leverage across 310.0 qualified-senior-equivalent hours in 1,640 agent-session minutes. Covered operator leverage was 320.7x.</p>
<p class="mb-4 font-light font-serif">That is 7.8 qualified-senior-equivalent weeks. Task factors ranged from 4.0x to 46.7x. The largest project grouping contained 7 entries.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Senior Est.</th>
      <th>Agent Session</th>
      <th>Operator</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Documentation</td>
      <td>14.0h</td>
      <td>18m</td>
      <td>2m</td>
      <td>46.7x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Infrastructure</td>
      <td>32.0h</td>
      <td>43m</td>
      <td>1m</td>
      <td>44.7x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Infrastructure</td>
      <td>18.0h</td>
      <td>45m</td>
      <td>2m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Deployment</td>
      <td>22.0h</td>
      <td>75m</td>
      <td>4m</td>
      <td>17.6x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Deployment</td>
      <td>9.0h</td>
      <td>35m</td>
      <td>1m</td>
      <td>15.4x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Testing</td>
      <td>8.0h</td>
      <td>35m</td>
      <td>1m</td>
      <td>13.7x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Deployment</td>
      <td>11.0h</td>
      <td>50m</td>
      <td>2m</td>
      <td>13.2x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>[product] Infinite — re-tier the iOS test suite to unit-tests-only per owner directive, with an empirically-derived integration tier and a coverage ratchet. Classified all 53 test classes by running the full…</td>
      <td>9.0h</td>
      <td>41m</td>
      <td>0m</td>
      <td>13.2x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Deployment</td>
      <td>22.0h</td>
      <td>132m</td>
      <td>0m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Deployment</td>
      <td>12.0h</td>
      <td>72m</td>
      <td>1m</td>
      <td>9.9x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Migrate the 901-document [product] corpus to PubMark: flatten unit folders, absorb metadata sidecars into front matter, retire docgen/HTML/PDF, fold in and retire the [product] repo, relocate…</td>
      <td>58.0h</td>
      <td>380m</td>
      <td>12m</td>
      <td>9.2x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Corpus-wide staleness review and repair of the [product] documentation corpus (816 units): 30-rule staleness scanner, _staging reconciliation, 25 repair agents across 3 waves, 210 units repaired (471…</td>
      <td>65.0h</td>
      <td>432m</td>
      <td>5m</td>
      <td>9.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Deployment</td>
      <td>3.0h</td>
      <td>20m</td>
      <td>1m</td>
      <td>9.0x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Deployment</td>
      <td>14.0h</td>
      <td>108m</td>
      <td>21m</td>
      <td>7.8x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Testing</td>
      <td>7.0h</td>
      <td>64m</td>
      <td>3m</td>
      <td>6.6x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Deployment</td>
      <td>6.0h</td>
      <td>90m</td>
      <td>2m</td>
      <td>4.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>16</td>
    </tr>
    <tr>
      <td>Qualified-senior-equivalent hours</td>
      <td>310.0</td>
    </tr>
    <tr>
      <td>Agent-session minutes</td>
      <td>1,640</td>
    </tr>
    <tr>
      <td>Covered operator minutes</td>
      <td>58</td>
    </tr>
    <tr>
      <td>Recorded tokens</td>
      <td>2,361,229</td>
    </tr>
    <tr>
      <td>Aggregate execution leverage</td>
      <td>11.3x</td>
    </tr>
    <tr>
      <td>Covered operator leverage</td>
      <td>320.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="interpretation">Interpretation</h2>
<p class="mb-4 font-light font-serif">The qualified-senior hours are counterfactual estimates and inherit that uncertainty. Execution, token, and operator-time values retain their recorded provenance, which is incomplete for some legacy rows. Summed agent-session minutes are not serial wall-clock time when sessions overlap.</p>
<p class="mb-4 font-light font-serif">This is one unusually experienced operator&#39;s record under unusual working conditions, not a general benchmark. The complete reconciled snapshot is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 19, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-19-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-19-leverage-record.html</guid>
      <pubDate>Wed, 19 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">11 tasks. August 19, 2026 closed at 11.0x aggregate execution leverage across 607.0 qualified-senior-equivalent hours in 3,298 agent-session minutes. Covered operator leverage was 1251.5x.</p>
<p class="mb-4 font-light font-serif">That is 15.2 qualified-senior-equivalent weeks. Task factors ranged from 5.9x to 158.8x. The largest project grouping contained 8 entries.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Senior Est.</th>
      <th>Agent Session</th>
      <th>Operator</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Documentation</td>
      <td>72.0h</td>
      <td>27m</td>
      <td>0m</td>
      <td>158.8x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Infrastructure</td>
      <td>6.0h</td>
      <td>11m</td>
      <td>1m</td>
      <td>32.7x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Documentation</td>
      <td>10.0h</td>
      <td>33m</td>
      <td>1m</td>
      <td>18.2x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Deployment</td>
      <td>72.0h</td>
      <td>270m</td>
      <td>4m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Code-Review Remediation ([ip-cluster]-service) — CR-P2 Wave B to its exit gate: R5 settings/startup boundary, R6 kind catalog + injected registries + composition root (stub-success deleted, exposing 7 of 83…</td>
      <td>80.0h</td>
      <td>311m</td>
      <td>1m</td>
      <td>15.4x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Deployment</td>
      <td>120.0h</td>
      <td>522m</td>
      <td>5m</td>
      <td>13.8x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Testing</td>
      <td>48.0h</td>
      <td>243m</td>
      <td>2m</td>
      <td>11.9x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Testing</td>
      <td>10.0h</td>
      <td>79m</td>
      <td>1m</td>
      <td>7.6x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Web Audit Remediation follow-on + production deploy ([product], [product]) — cleared every card the parent program&#39;s own execution surfaced, after the owner judged it not review-ready. Fixed a CRITICAL live…</td>
      <td>50.0h</td>
      <td>466m</td>
      <td>4m</td>
      <td>6.4x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Deployment</td>
      <td>115.0h</td>
      <td>1090m</td>
      <td>5m</td>
      <td>6.3x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Deployment</td>
      <td>24.0h</td>
      <td>246m</td>
      <td>5m</td>
      <td>5.9x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>11</td>
    </tr>
    <tr>
      <td>Qualified-senior-equivalent hours</td>
      <td>607.0</td>
    </tr>
    <tr>
      <td>Agent-session minutes</td>
      <td>3,298</td>
    </tr>
    <tr>
      <td>Covered operator minutes</td>
      <td>29</td>
    </tr>
    <tr>
      <td>Recorded tokens</td>
      <td>26,057,647</td>
    </tr>
    <tr>
      <td>Aggregate execution leverage</td>
      <td>11.0x</td>
    </tr>
    <tr>
      <td>Covered operator leverage</td>
      <td>1251.5x</td>
    </tr>
  </tbody>
</table>
<h2 id="interpretation">Interpretation</h2>
<p class="mb-4 font-light font-serif">The qualified-senior hours are counterfactual estimates and inherit that uncertainty. Execution, token, and operator-time values retain their recorded provenance, which is incomplete for some legacy rows. Summed agent-session minutes are not serial wall-clock time when sessions overlap.</p>
<p class="mb-4 font-light font-serif">This is one unusually experienced operator&#39;s record under unusual working conditions, not a general benchmark. The complete reconciled snapshot is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 18, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-18-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-18-leverage-record.html</guid>
      <pubDate>Tue, 18 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">23 tasks. August 18, 2026 closed at 22.5x aggregate execution leverage across 1,230.0 qualified-senior-equivalent hours in 3,283 agent-session minutes. Covered operator leverage was 1232.1x.</p>
<p class="mb-4 font-light font-serif">That is 30.8 qualified-senior-equivalent weeks. Task factors ranged from 5.8x to 202.8x. The largest project grouping contained 6 entries.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Senior Est.</th>
      <th>Agent Session</th>
      <th>Operator</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Coding</td>
      <td>24.0h</td>
      <td>7m</td>
      <td>0m</td>
      <td>202.8x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Documentation</td>
      <td>60.0h</td>
      <td>20m</td>
      <td>0m</td>
      <td>178.2x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Documentation</td>
      <td>48.0h</td>
      <td>20m</td>
      <td>0m</td>
      <td>144.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Coding</td>
      <td>56.0h</td>
      <td>24m</td>
      <td>0m</td>
      <td>139.2x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Coding</td>
      <td>180.0h</td>
      <td>95m</td>
      <td>3m</td>
      <td>113.7x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Coding</td>
      <td>48.0h</td>
      <td>26m</td>
      <td>0m</td>
      <td>111.6x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Coding</td>
      <td>40.0h</td>
      <td>25m</td>
      <td>0m</td>
      <td>97.6x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Documentation</td>
      <td>40.0h</td>
      <td>28m</td>
      <td>0m</td>
      <td>85.7x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Design and frontend</td>
      <td>40.0h</td>
      <td>37m</td>
      <td>1m</td>
      <td>64.9x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Coding</td>
      <td>32.0h</td>
      <td>42m</td>
      <td>3m</td>
      <td>45.7x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Design and frontend</td>
      <td>24.0h</td>
      <td>38m</td>
      <td>1m</td>
      <td>37.9x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Infrastructure</td>
      <td>11.0h</td>
      <td>19m</td>
      <td>3m</td>
      <td>34.7x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Testing</td>
      <td>14.0h</td>
      <td>35m</td>
      <td>3m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Documentation</td>
      <td>12.0h</td>
      <td>38m</td>
      <td>5m</td>
      <td>18.9x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Designed and implemented the PubMark Lesson Score 0.1 foundation for [product] fixed lessons: normative profile and schema, production validator and integrity sealing, real [product] conformance artifact,…</td>
      <td>24.0h</td>
      <td>91m</td>
      <td>6m</td>
      <td>15.9x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Testing</td>
      <td>270.0h</td>
      <td>1020m</td>
      <td>18m</td>
      <td>15.9x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Testing</td>
      <td>22.0h</td>
      <td>90m</td>
      <td>3m</td>
      <td>14.7x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Author the Accounting total-coverage corpus domain specification (Bar &amp; Ledger program): 17 subject clusters, 3,238 leaf goals, 60-anchor cross-cluster prerequisite DAG, 15-course coverage map (CPA…</td>
      <td>140.0h</td>
      <td>610m</td>
      <td>3m</td>
      <td>13.8x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Infrastructure</td>
      <td>68.0h</td>
      <td>382m</td>
      <td>5m</td>
      <td>10.7x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>MCP Retirement Program planning: fleet-wide research (5 parallel agents), 9-phase plan, durable state, Fable review gate</td>
      <td>14.0h</td>
      <td>83m</td>
      <td>2m</td>
      <td>10.1x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Design and frontend</td>
      <td>32.0h</td>
      <td>236m</td>
      <td>1m</td>
      <td>8.1x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Deployment</td>
      <td>3.0h</td>
      <td>29m</td>
      <td>1m</td>
      <td>6.2x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Deployment</td>
      <td>28.0h</td>
      <td>289m</td>
      <td>1m</td>
      <td>5.8x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>23</td>
    </tr>
    <tr>
      <td>Qualified-senior-equivalent hours</td>
      <td>1,230.0</td>
    </tr>
    <tr>
      <td>Agent-session minutes</td>
      <td>3,283</td>
    </tr>
    <tr>
      <td>Covered operator minutes</td>
      <td>60</td>
    </tr>
    <tr>
      <td>Recorded tokens</td>
      <td>8,222,637</td>
    </tr>
    <tr>
      <td>Aggregate execution leverage</td>
      <td>22.5x</td>
    </tr>
    <tr>
      <td>Covered operator leverage</td>
      <td>1232.1x</td>
    </tr>
  </tbody>
</table>
<h2 id="interpretation">Interpretation</h2>
<p class="mb-4 font-light font-serif">The qualified-senior hours are counterfactual estimates and inherit that uncertainty. Execution, token, and operator-time values retain their recorded provenance, which is incomplete for some legacy rows. Summed agent-session minutes are not serial wall-clock time when sessions overlap.</p>
<p class="mb-4 font-light font-serif">This is one unusually experienced operator&#39;s record under unusual working conditions, not a general benchmark. The complete reconciled snapshot is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 17, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-17-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-17-leverage-record.html</guid>
      <pubDate>Mon, 17 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">32 tasks. August 17, 2026 closed at 14.6x aggregate execution leverage across 661.5 qualified-senior-equivalent hours in 2,723 agent-session minutes. Covered operator leverage was 339.2x.</p>
<p class="mb-4 font-light font-serif">That is 16.5 qualified-senior-equivalent weeks. Task factors ranged from 3.7x to 64.9x. The largest project grouping contained 10 entries.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Senior Est.</th>
      <th>Agent Session</th>
      <th>Operator</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Debugging</td>
      <td>50.0h</td>
      <td>46m</td>
      <td>5m</td>
      <td>64.9x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>v7 mega-wave: Business split from Certs (line 24, [cost]/mo, PMI-Schweser pricing research), ratified corpus-block schedule restated, English/Languages-&gt;Oct, feature lists +English/Languages/Business…</td>
      <td>40.0h</td>
      <td>47m</td>
      <td>5m</td>
      <td>50.7x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Infrastructure</td>
      <td>32.0h</td>
      <td>40m</td>
      <td>5m</td>
      <td>48.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Cross-platform program planning for a task-management product: measured test coverage across iOS, web and backend; root-caused live client HTTP failures to client-side API route drift by diffing the served…</td>
      <td>20.0h</td>
      <td>28m</td>
      <td>5m</td>
      <td>42.9x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Debugging</td>
      <td>5.0h</td>
      <td>7m</td>
      <td>1m</td>
      <td>42.9x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>[ip-cluster] script-elimination wave 2: 6 scripts into dependency-injected tested server modules behind 3 new CLI groups, +280 tests, 3 latent defects found and fixed; NEW per-file 80% coverage ratchet; plus…</td>
      <td>42.0h</td>
      <td>62m</td>
      <td>3m</td>
      <td>40.6x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Deployment</td>
      <td>20.0h</td>
      <td>31m</td>
      <td>4m</td>
      <td>39.2x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Debugging</td>
      <td>14.0h</td>
      <td>27m</td>
      <td>3m</td>
      <td>31.1x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Testing</td>
      <td>46.0h</td>
      <td>96m</td>
      <td>2m</td>
      <td>28.8x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Accounting v5: EA (SEE) + CIA join the product — 10% Vision v5 cascade (base 190K, 19K plateau, [cost] line, [cost].58B destination), 2 new corpus clusters + 6 course specs planned, feature lists, both…</td>
      <td>10.0h</td>
      <td>22m</td>
      <td>3m</td>
      <td>27.8x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Documentation</td>
      <td>18.0h</td>
      <td>40m</td>
      <td>5m</td>
      <td>27.0x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Audit and review</td>
      <td>4.0h</td>
      <td>9m</td>
      <td>1m</td>
      <td>26.7x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Documentation</td>
      <td>24.0h</td>
      <td>56m</td>
      <td>2m</td>
      <td>25.7x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Coding</td>
      <td>18.0h</td>
      <td>48m</td>
      <td>2m</td>
      <td>22.5x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Documentation</td>
      <td>26.0h</td>
      <td>74m</td>
      <td>3m</td>
      <td>21.1x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Documentation</td>
      <td>2.0h</td>
      <td>7m</td>
      <td>1m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Infrastructure</td>
      <td>13.0h</td>
      <td>47m</td>
      <td>4m</td>
      <td>16.6x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Deploy notification-service to prod (arm64/Graviton via SSM) with the per-app APNs topic fix + migration 012; hand-verified what the deploy skipped (health 200, alembic 012 head, device-list 200 proving new…</td>
      <td>5.0h</td>
      <td>22m</td>
      <td>2m</td>
      <td>13.6x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>[ip-cluster] [product]: relevance-ranked premises at 800 chars, ungated-count wiring across all gated phases, judge wall-clock guard on a real black-holed socket, script wave 1 (65-&gt;44), benchmarks coverage,…</td>
      <td>72.0h</td>
      <td>320m</td>
      <td>12m</td>
      <td>13.5x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Debugging</td>
      <td>55.0h</td>
      <td>251m</td>
      <td>2m</td>
      <td>13.1x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Debugging</td>
      <td>9.0h</td>
      <td>46m</td>
      <td>2m</td>
      <td>11.7x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Engine vector-architecture brief + S3 Vectors costing (published artifact, 3 options costed against measured volumes and real Cost Explorer spend), then script-elimination wave 3: content_screen.py into a…</td>
      <td>26.0h</td>
      <td>138m</td>
      <td>6m</td>
      <td>11.3x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Documentation</td>
      <td>5.0h</td>
      <td>30m</td>
      <td>2m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Infrastructure</td>
      <td>11.0h</td>
      <td>74m</td>
      <td>3m</td>
      <td>8.9x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Documentation</td>
      <td>7.5h</td>
      <td>60m</td>
      <td>3m</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Deployment</td>
      <td>2.0h</td>
      <td>16m</td>
      <td>2m</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>iOS: root-caused and fixed empty course catalog (one string-typed passingScore killed Swift all-or-nothing decode); lenient+lossy catalog decoding; build-version stamping (AppBuildInfo, Home stamp, Settings…</td>
      <td>14.0h</td>
      <td>114m</td>
      <td>4m</td>
      <td>7.4x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Deployment</td>
      <td>6.0h</td>
      <td>52m</td>
      <td>4m</td>
      <td>6.9x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>iOS 18 support: root-caused back-deployed isolated-deinit aborts (nonisolated deinit on 6 classes), full suite now runs on iOS 18.4 (was truncating at 386/2042 with 8 silent crash-restarts); fixed…</td>
      <td>9.0h</td>
      <td>92m</td>
      <td>3m</td>
      <td>5.9x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>Audit and review</td>
      <td>11.0h</td>
      <td>118m</td>
      <td>5m</td>
      <td>5.6x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>Infrastructure</td>
      <td>13.5h</td>
      <td>195m</td>
      <td>8m</td>
      <td>4.2x</td>
    </tr>
    <tr>
      <td>32</td>
      <td>Hangar: vertical-scoped ownership catalog ([product] + [product]) — persistent active-vertical store (localStorage + auth-service user_state, contract-tolerant ?vertical=/?plan= capture, newer-updatedAt race…</td>
      <td>31.5h</td>
      <td>508m</td>
      <td>5m</td>
      <td>3.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>32</td>
    </tr>
    <tr>
      <td>Qualified-senior-equivalent hours</td>
      <td>661.5</td>
    </tr>
    <tr>
      <td>Agent-session minutes</td>
      <td>2,723</td>
    </tr>
    <tr>
      <td>Covered operator minutes</td>
      <td>117</td>
    </tr>
    <tr>
      <td>Recorded tokens</td>
      <td>16,576,560</td>
    </tr>
    <tr>
      <td>Aggregate execution leverage</td>
      <td>14.6x</td>
    </tr>
    <tr>
      <td>Covered operator leverage</td>
      <td>339.2x</td>
    </tr>
  </tbody>
</table>
<h2 id="interpretation">Interpretation</h2>
<p class="mb-4 font-light font-serif">The qualified-senior hours are counterfactual estimates and inherit that uncertainty. Execution, token, and operator-time values retain their recorded provenance, which is incomplete for some legacy rows. Summed agent-session minutes are not serial wall-clock time when sessions overlap.</p>
<p class="mb-4 font-light font-serif">This is one unusually experienced operator&#39;s record under unusual working conditions, not a general benchmark. The complete reconciled snapshot is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 16, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-16-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-16-leverage-record.html</guid>
      <pubDate>Sun, 16 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">34 tasks. August 16, 2026 closed at 18.6x aggregate execution leverage across 1,701.0 qualified-senior-equivalent hours in 5,479 agent-session minutes. Covered operator leverage was 523.4x.</p>
<p class="mb-4 font-light font-serif">That is 42.5 qualified-senior-equivalent weeks. Task factors ranged from 4.5x to 154.1x. The largest project grouping contained 25 entries.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Senior Est.</th>
      <th>Agent Session</th>
      <th>Operator</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Coding</td>
      <td>150.0h</td>
      <td>58m</td>
      <td>12m</td>
      <td>154.1x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Deployment</td>
      <td>24.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>80.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>WP-SYN re-aim: ported 11 language content-type generators onto merged [ip-cluster] (2-repo port incl. discovering+rescuing a hidden dependency branch in [product][engine subsystem]-runtime, production…</td>
      <td>80.0h</td>
      <td>77m</td>
      <td>2m</td>
      <td>62.3x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Infrastructure</td>
      <td>44.0h</td>
      <td>62m</td>
      <td>10m</td>
      <td>42.6x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Testing</td>
      <td>40.0h</td>
      <td>61m</td>
      <td>2m</td>
      <td>39.3x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Infrastructure</td>
      <td>16.0h</td>
      <td>25m</td>
      <td>6m</td>
      <td>38.4x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Deployment</td>
      <td>40.0h</td>
      <td>63m</td>
      <td>14m</td>
      <td>38.1x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Coding</td>
      <td>24.0h</td>
      <td>49m</td>
      <td>6m</td>
      <td>29.3x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Documentation</td>
      <td>16.0h</td>
      <td>33m</td>
      <td>2m</td>
      <td>29.1x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Documentation</td>
      <td>14.0h</td>
      <td>29m</td>
      <td>2m</td>
      <td>29.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Deployment</td>
      <td>400.0h</td>
      <td>940m</td>
      <td>12m</td>
      <td>25.5x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Fable resumption planning for iOS coverage program: plan section 16 (R0-R5 blocker-first sequencing), 3.1 two-bundle amendment, STATE reconciliation to 40.36%+14 held commits, review-prompt resumption…</td>
      <td>8.0h</td>
      <td>20m</td>
      <td>2m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>beautiful-mermaid occupancy layout engine: built to 0 defects across all 11 invariants on 192-figure [ip] corpus; closed 6 owner-reported structural defects</td>
      <td>120.0h</td>
      <td>300m</td>
      <td>15m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Documentation</td>
      <td>14.0h</td>
      <td>38m</td>
      <td>10m</td>
      <td>22.1x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>[product] iOS coverage program R0-R5: root-caused the test-host crash blocker (d5a2a720) from 37 unread crash reports - three real audio defects, two shipping crashes - fixed all plus a fourth found by…</td>
      <td>70.0h</td>
      <td>204m</td>
      <td>8m</td>
      <td>20.6x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>[product] iOS: wired TTS through the gateway (dead code, zero call sites); fixed iOS-27-only builds (XcodeGen emitted no deployment target, artifact-verified 18.0); found and fixed the ADR-0020 dead-host…</td>
      <td>34.0h</td>
      <td>100m</td>
      <td>4m</td>
      <td>20.4x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Design and frontend</td>
      <td>3.0h</td>
      <td>10m</td>
      <td>1m</td>
      <td>18.6x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Deployment</td>
      <td>14.0h</td>
      <td>47m</td>
      <td>4m</td>
      <td>17.9x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Infrastructure</td>
      <td>10.0h</td>
      <td>35m</td>
      <td>2m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Testing</td>
      <td>7.0h</td>
      <td>25m</td>
      <td>2m</td>
      <td>16.8x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Design and frontend</td>
      <td>140.0h</td>
      <td>568m</td>
      <td>9m</td>
      <td>14.8x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Root-caused the canary observability blackout across three layers (CLI never called configure_logging so lastResort discarded every INFO milestone; stdout block-buffering; unconditional in-process torch load)…</td>
      <td>14.0h</td>
      <td>58m</td>
      <td>3m</td>
      <td>14.5x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Testing</td>
      <td>28.0h</td>
      <td>118m</td>
      <td>4m</td>
      <td>14.2x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>[ip-cluster] [product]: full-suite green + 88.23% coverage baseline (P0 exit), root-caused and fixed both threads of the c70f63c7 flake family, merged P4.T1, triaged all 65 [ip-cluster] board cards with…</td>
      <td>56.0h</td>
      <td>247m</td>
      <td>6m</td>
      <td>13.6x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>beautiful-mermaid mermaid-rebuild-2 Fable planning: ground-truth measurement, four-docs update, successor plan + state + adversarial review gate</td>
      <td>10.0h</td>
      <td>45m</td>
      <td>5m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>[product] coverage overnight segment (paused session, logged per handoff): [ip-cluster] batches 1-3 + activities batch 1 (58 files to &gt;=50%), ApertureInteractions 14,379-line/85-type file split into 85 files,…</td>
      <td>150.0h</td>
      <td>742m</td>
      <td>5m</td>
      <td>12.1x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>Testing</td>
      <td>32.0h</td>
      <td>167m</td>
      <td>9m</td>
      <td>11.5x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Debugging</td>
      <td>48.0h</td>
      <td>298m</td>
      <td>6m</td>
      <td>9.7x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>Deployment</td>
      <td>10.0h</td>
      <td>70m</td>
      <td>3m</td>
      <td>8.6x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>Owner defect batch wave 1: 14 cards filed; purchase modal rebuilt (title, catalog counts, trial, spinner) via subscribe-react 0.4.1; title-case convention across 4 clients; [product] uploads routes + [model]…</td>
      <td>16.0h</td>
      <td>175m</td>
      <td>6m</td>
      <td>5.5x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>Fixed [product] Apple redirect URI (retired auth host -&gt; Concourse gate, closed an allowlist-widening side effect) + terraform comment correction, deployed [product] (td :46) and [product] to prod with live…</td>
      <td>2.0h</td>
      <td>22m</td>
      <td>2m</td>
      <td>5.5x</td>
    </tr>
    <tr>
      <td>32</td>
      <td>Coding</td>
      <td>4.0h</td>
      <td>45m</td>
      <td>4m</td>
      <td>5.3x</td>
    </tr>
    <tr>
      <td>33</td>
      <td>August I3 overnight: canary campaign root-causing ~14 pipeline defects (silent logging, unchunked [engine subsystem] dispatch + silent grounding degrade, premise truncation, content_profile/DEFAULT_PIPELINE…</td>
      <td>60.0h</td>
      <td>690m</td>
      <td>12m</td>
      <td>5.2x</td>
    </tr>
    <tr>
      <td>34</td>
      <td>Diagnosed and fixed silent Google sign-in failure in prod: CSP blocked accounts.google.com so GIS never loaded and the button container rendered empty; fixed live CloudFront policy + terraform, extended the…</td>
      <td>3.0h</td>
      <td>40m</td>
      <td>2m</td>
      <td>4.5x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>34</td>
    </tr>
    <tr>
      <td>Qualified-senior-equivalent hours</td>
      <td>1,701.0</td>
    </tr>
    <tr>
      <td>Agent-session minutes</td>
      <td>5,479</td>
    </tr>
    <tr>
      <td>Covered operator minutes</td>
      <td>195</td>
    </tr>
    <tr>
      <td>Recorded tokens</td>
      <td>41,761,000</td>
    </tr>
    <tr>
      <td>Aggregate execution leverage</td>
      <td>18.6x</td>
    </tr>
    <tr>
      <td>Covered operator leverage</td>
      <td>523.4x</td>
    </tr>
  </tbody>
</table>
<h2 id="interpretation">Interpretation</h2>
<p class="mb-4 font-light font-serif">The qualified-senior hours are counterfactual estimates and inherit that uncertainty. Execution, token, and operator-time values retain their recorded provenance, which is incomplete for some legacy rows. Summed agent-session minutes are not serial wall-clock time when sessions overlap.</p>
<p class="mb-4 font-light font-serif">This is one unusually experienced operator&#39;s record under unusual working conditions, not a general benchmark. The complete reconciled snapshot is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 15, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-15-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-15-leverage-record.html</guid>
      <pubDate>Sat, 15 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">18 tasks. August 15, 2026 closed at 33.1x aggregate execution leverage across 1,251.5 qualified-senior-equivalent hours in 2,266 agent-session minutes. Covered operator leverage was 774.1x.</p>
<p class="mb-4 font-light font-serif">That is 31.3 qualified-senior-equivalent weeks. Task factors ranged from 2.2x to 106.0x. The largest project grouping contained 13 entries.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Senior Est.</th>
      <th>Agent Session</th>
      <th>Operator</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>I3 launch-six language fan-out: 6 total-coverage corpora authored+merged (German 406/Japanese 425/Italian 385/French 402/Mandarin 414/Turkish 377 leaves, exam-formats web-verified) + AP-2027 reconciliation…</td>
      <td>500.0h</td>
      <td>283m</td>
      <td>2m</td>
      <td>106.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Fable-planned the mermaid-rebuild program for libs/beautiful-mermaid: rewrote the four docs against measured reality, authored a 10-phase 44-task gated plan, durable state file with resume protocol,…</td>
      <td>16.0h</td>
      <td>14m</td>
      <td>2m</td>
      <td>68.6x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Documentation</td>
      <td>28.0h</td>
      <td>27m</td>
      <td>2m</td>
      <td>62.2x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>I3 template phase: Spanish+English total-coverage corpora (591 leaves) + canonical concept lists (8352 vocab/1045 verbs) + pedagogy review, TOEFL-2026 rebuild, concept alignment + gap remediation (7 Fable…</td>
      <td>340.0h</td>
      <td>345m</td>
      <td>10m</td>
      <td>59.1x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Audit and review</td>
      <td>30.0h</td>
      <td>35m</td>
      <td>4m</td>
      <td>51.4x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Deployment</td>
      <td>90.0h</td>
      <td>187m</td>
      <td>8m</td>
      <td>28.9x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>[product] iOS coverage &amp; DI sub-program planned at Fable grade (ship P12.T1 via /fable-planner): ground-truth research (card, baseline provenance, per-directory LOC sweep, singleton/protocol/seam census,…</td>
      <td>16.0h</td>
      <td>38m</td>
      <td>2m</td>
      <td>25.3x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Proving Flights program planning (fable-planner): pause-file resume, routing-gate repair (28 stale assertions across 8 test files to owner-ruled values, 200 green), routing recommit relaunch, program…</td>
      <td>14.0h</td>
      <td>35m</td>
      <td>2m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Audit and review</td>
      <td>6.0h</td>
      <td>20m</td>
      <td>2m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Infrastructure</td>
      <td>32.0h</td>
      <td>120m</td>
      <td>10m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>[product] iOS Coverage &amp; DI Program: C0 (coverage pipeline + baseline v2 + seeded floor trials + checkpoint re-derivation) and C1 (4 DI seams, fixture library, 4 money assertions, card 5e762665 closed); found…</td>
      <td>36.0h</td>
      <td>139m</td>
      <td>10m</td>
      <td>15.5x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>[product] iOS: 5% breadth coverage pass across 4 batches taking overall 12.12-&gt;27.62%; split ApertureInteractions (14,379 lines/85 types) and AppState (2,097/234 members), both verified pure moves; found and…</td>
      <td>40.0h</td>
      <td>200m</td>
      <td>15m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Deployment</td>
      <td>19.5h</td>
      <td>112m</td>
      <td>8m</td>
      <td>10.4x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Documentation</td>
      <td>6.0h</td>
      <td>35m</td>
      <td>2m</td>
      <td>10.3x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>[product] iOS Coverage Program C2 wave 1 (two parallel Sonnet campaigns), salvage after both agents died to a session limit, merge+verify to 12.12%/1450 tests, then TestFlight build 3 built, uploaded,…</td>
      <td>28.0h</td>
      <td>180m</td>
      <td>4m</td>
      <td>9.3x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Deployment</td>
      <td>32.0h</td>
      <td>240m</td>
      <td>8m</td>
      <td>8.0x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Audit and review</td>
      <td>12.0h</td>
      <td>91m</td>
      <td>1m</td>
      <td>7.9x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Deployment</td>
      <td>6.0h</td>
      <td>165m</td>
      <td>5m</td>
      <td>2.2x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>18</td>
    </tr>
    <tr>
      <td>Qualified-senior-equivalent hours</td>
      <td>1,251.5</td>
    </tr>
    <tr>
      <td>Agent-session minutes</td>
      <td>2,266</td>
    </tr>
    <tr>
      <td>Covered operator minutes</td>
      <td>97</td>
    </tr>
    <tr>
      <td>Recorded tokens</td>
      <td>21,188,000</td>
    </tr>
    <tr>
      <td>Aggregate execution leverage</td>
      <td>33.1x</td>
    </tr>
    <tr>
      <td>Covered operator leverage</td>
      <td>774.1x</td>
    </tr>
  </tbody>
</table>
<h2 id="interpretation">Interpretation</h2>
<p class="mb-4 font-light font-serif">The qualified-senior hours are counterfactual estimates and inherit that uncertainty. Execution, token, and operator-time values retain their recorded provenance, which is incomplete for some legacy rows. Summed agent-session minutes are not serial wall-clock time when sessions overlap.</p>
<p class="mb-4 font-light font-serif">This is one unusually experienced operator&#39;s record under unusual working conditions, not a general benchmark. The complete reconciled snapshot is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 14, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-14-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-14-leverage-record.html</guid>
      <pubDate>Fri, 14 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">30 tasks. August 14, 2026 closed at 17.3x aggregate execution leverage across 729.5 qualified-senior-equivalent hours in 2,531 agent-session minutes. Covered operator leverage was 293.8x.</p>
<p class="mb-4 font-light font-serif">That is 18.2 qualified-senior-equivalent weeks. Task factors ranged from 4.0x to 42.4x. The largest project grouping contained 17 entries.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Senior Est.</th>
      <th>Agent Session</th>
      <th>Operator</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>I3 English+Languages Waves 1-2: 3 ADRs + canon/trademarks, catalog 57-&gt;72 with skill axis, 11 language schemas + ContentProfile threading, Spanish/English template corpora (373 leaves), 10 [content…</td>
      <td>120.0h</td>
      <td>170m</td>
      <td>3m</td>
      <td>42.4x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Documentation</td>
      <td>88.0h</td>
      <td>145m</td>
      <td>4m</td>
      <td>36.4x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Debugging</td>
      <td>32.0h</td>
      <td>55m</td>
      <td>4m</td>
      <td>34.9x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Progression hero fleet: authored 68 content-specific cinematic scene prompts, built reusable FLUX 1.1-pro renderer tool, generated + deployed photorealistic heroes for every page (steampunk plates retired),…</td>
      <td>14.0h</td>
      <td>25m</td>
      <td>3m</td>
      <td>33.6x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Progression site: Analytics nav consolidation + /analytics/ page, tree pin/freeze + keystone highlighting, WCAG 2.2 AA remediation of all 9 audit findings (320px reflow, contrast tokens, atlas text…</td>
      <td>20.0h</td>
      <td>36m</td>
      <td>6m</td>
      <td>33.3x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Progression appendix overhaul + section-paged reader: deleted appendices B/C + risk matrix, relettered A-G, linked all 63 reading/resource items, authored 404 company descriptions, deep-linked 825 chapter…</td>
      <td>30.0h</td>
      <td>55m</td>
      <td>8m</td>
      <td>32.7x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Progression Tree + publishing: authored 80-node/102-edge advance dependency DAG with build-time validation and layered layout, /tree/ SVG renderer (ancestry/unlock lighting, chapter links, nav+icon); book…</td>
      <td>26.0h</td>
      <td>50m</td>
      <td>4m</td>
      <td>31.2x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Progression ch4 prototype: photorealistic content-specific hero via Replicate FLUX, Path Forward rendered as brass-spine era timeline (generic transform, 9 chapters), Notable Players relocated pre-endnotes…</td>
      <td>18.0h</td>
      <td>45m</td>
      <td>6m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Progression staleness + typography round: ch4 Notable Players rewritten as 34 individual current-to-2026 cards (GLP-1 cardio, amyloid antibodies, liquid biopsy, new AI Drug Discovery section), 7 new players…</td>
      <td>20.0h</td>
      <td>50m</td>
      <td>6m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Progression book press round: print/ebook structure parity with the site (players pre-endnotes, plain endnote heads), small-type endnotes in both formats, elegant LaTeX design (letterspaced chapter openings,…</td>
      <td>14.0h</td>
      <td>40m</td>
      <td>4m</td>
      <td>21.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Progression ledger rework: retired reader-entered predictions; /ledger/ now a curated register of all 538 auto-extracted forecasts with stable ids + hand-maintained achievement overlay (the single post-launch…</td>
      <td>12.0h</td>
      <td>35m</td>
      <td>5m</td>
      <td>20.6x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>[product] iOS Ship Program P0+P1: environment/baseline gate (suite 1065/1065, board zero-drift reconcile, stale card closed) then killed the entityId &quot;current&quot; sentinel across 23 call sites in 14 files behind…</td>
      <td>18.0h</td>
      <td>53m</td>
      <td>10m</td>
      <td>20.4x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Progression cards follow-through: description absorption for blank-line entries (53 stranded blurbs -&gt; 0), registry sweep to 355 companies/50 aliases incl. defunct-player support (395 cards), and a prose…</td>
      <td>12.0h</td>
      <td>40m</td>
      <td>5m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Coding</td>
      <td>14.0h</td>
      <td>48m</td>
      <td>8m</td>
      <td>17.5x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Deployment</td>
      <td>30.0h</td>
      <td>105m</td>
      <td>4m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Deployment</td>
      <td>9.0h</td>
      <td>36m</td>
      <td>6m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>iOS Ship Program planning (/fable-planner): resumed pause file, mapped 60-card iOS board + prior parity program (E8/WP-4.1/WP-6.5 archaeology), verified 2026 App Store rules, authored 14-phase plan + durable…</td>
      <td>10.0h</td>
      <td>41m</td>
      <td>2m</td>
      <td>14.8x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Deployment</td>
      <td>36.0h</td>
      <td>163m</td>
      <td>6m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>charlessieg.com mobile overflow diagnosis and fix (4 distinct causes), 12 UI changes, production promotion, removal of 11 stale prod objects incl. 2 unpublished drafts serving a staging preview gate</td>
      <td>26.0h</td>
      <td>118m</td>
      <td>10m</td>
      <td>13.2x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>I2-5b: argument_map_builder de-GED gating/config + Answer-Choice Elimination Trainer full build across 5 repos (agent-built, orchestrator-supervised)</td>
      <td>28.0h</td>
      <td>138m</td>
      <td>2m</td>
      <td>12.2x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Deployment</td>
      <td>8.0h</td>
      <td>43m</td>
      <td>2m</td>
      <td>11.2x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Fixed the real cause of stray timeline year markers (entry dates used the standalone-marker class); Career tab first; [product] AI linked</td>
      <td>4.0h</td>
      <td>22m</td>
      <td>4m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>August program resume day: 10-day-gap reconciliation, august→main merges across 8 repos with suite verification, plan Rev 2.2 + tracker/ledger reconciliation, ExamAttemptRow nullable migration (4-gate saga…</td>
      <td>26.0h</td>
      <td>146m</td>
      <td>3m</td>
      <td>10.7x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Deployment</td>
      <td>14.0h</td>
      <td>79m</td>
      <td>2m</td>
      <td>10.6x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Deployment</td>
      <td>48.0h</td>
      <td>288m</td>
      <td>8m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Deployment</td>
      <td>10.0h</td>
      <td>62m</td>
      <td>3m</td>
      <td>9.7x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>Fixed 2 fleet process defects (worktree catalog-path resolution + merge commits bypassing the test gate) with mutation-verified regression tests and a 27-repo hook rollout; reviewed and merged a 1,227-line…</td>
      <td>20.0h</td>
      <td>149m</td>
      <td>5m</td>
      <td>8.1x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Audit and review</td>
      <td>5.0h</td>
      <td>40m</td>
      <td>2m</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>Progression fix round: referenced-by grid collapse for unnumbered chapters, keyboard-only section focus ring, endnote popup tick aimed at reference number on narrow viewports; headless-verified and deployed…</td>
      <td>1.5h</td>
      <td>14m</td>
      <td>2m</td>
      <td>6.4x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>Flight Plans session 2026-08-13/14: four owner-directed fixes, app.[product].ai Route53 go-live, session self-audit, 11-stage [ip-cluster] consolidation replan</td>
      <td>16.0h</td>
      <td>240m</td>
      <td>12m</td>
      <td>4.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>30</td>
    </tr>
    <tr>
      <td>Qualified-senior-equivalent hours</td>
      <td>729.5</td>
    </tr>
    <tr>
      <td>Agent-session minutes</td>
      <td>2,531</td>
    </tr>
    <tr>
      <td>Covered operator minutes</td>
      <td>149</td>
    </tr>
    <tr>
      <td>Recorded tokens</td>
      <td>22,383,600</td>
    </tr>
    <tr>
      <td>Aggregate execution leverage</td>
      <td>17.3x</td>
    </tr>
    <tr>
      <td>Covered operator leverage</td>
      <td>293.8x</td>
    </tr>
  </tbody>
</table>
<h2 id="interpretation">Interpretation</h2>
<p class="mb-4 font-light font-serif">The qualified-senior hours are counterfactual estimates and inherit that uncertainty. Execution, token, and operator-time values retain their recorded provenance, which is incomplete for some legacy rows. Summed agent-session minutes are not serial wall-clock time when sessions overlap.</p>
<p class="mb-4 font-light font-serif">This is one unusually experienced operator&#39;s record under unusual working conditions, not a general benchmark. The complete reconciled snapshot is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 13, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-13-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-13-leverage-record.html</guid>
      <pubDate>Thu, 13 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">25 tasks. August 13, 2026 closed at 9.4x weighted leverage across 554.0 human-equivalent hours in 3,538 minutes of wall-clock time. Supervisory leverage came in at 339.2x.</p>
<p class="mb-4 font-light font-serif">That is 13.8 weeks of human-equivalent throughput in 59.0 hours. The ceiling was 29.6x; the floor was 3.6x. 22 of the 25 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>[ip-cluster] P2 engine runtime + P3 web surfaces: 11 tasks via 7-way parallel worktree fan-out against a frozen cross-repo contract (RefresherConfig, staleness scorer, contradiction capture, composition…</td>
      <td>120.0h</td>
      <td>243m</td>
      <td>8m</td>
      <td>29.6x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>charlessieg.com instrument-template rebuild: 12 templates + SCSS design system, books page with 3 Replicate-generated covers + typographic compositing, context-specific post subtitles, regenerated all 157…</td>
      <td>75.0h</td>
      <td>180m</td>
      <td>12m</td>
      <td>25.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>avian-app-ios WP-2.1 Today-first Dashboard rebuild (web-parity IA + 3 latent-defect fixes)</td>
      <td>14.0h</td>
      <td>58m</td>
      <td>1m</td>
      <td>14.5x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>avian-app-ios WP-3.4 autopilot/Afterburner/Convoy + exam remediation (+decode-gap fix)</td>
      <td>18.0h</td>
      <td>79m</td>
      <td>1m</td>
      <td>13.7x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>avian-app-ios WP-2.3 Position Fix + Direct-To focus (CalibrateView flow + focus rail + bus events)</td>
      <td>12.0h</td>
      <td>66m</td>
      <td>1m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>avian-app-ios WP-2.5 Review Queue + bell (9+ cap) + Practice hub</td>
      <td>11.0h</td>
      <td>67m</td>
      <td>1m</td>
      <td>9.9x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>WP-5.2 on-device flashcard gen: multimodal iOS27 finding; My-cards deck + practice view; credit-unreachability lint; 41 tests; merge</td>
      <td>14.0h</td>
      <td>87m</td>
      <td>2m</td>
      <td>9.7x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>avian-app-ios WP-2.2 Course-detail 5-tab (fold+delete Trajectory/Curriculum/Autopilot, study-plan+exam-tips; resumed after API drop)</td>
      <td>14.0h</td>
      <td>90m</td>
      <td>1m</td>
      <td>9.3x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>WP-5.1 on-device explain-simpler: SDK-verified streaming service + seam; 2 surfaces; live rephrase proven; 19 tests; i18n; merge</td>
      <td>10.0h</td>
      <td>67m</td>
      <td>2m</td>
      <td>9.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>WP-4.2 APNs client half: per-config aps-environment across 4 targets; auth triggers; idempotent register/unregister; 20 tests; ASC push-capability fix; merge</td>
      <td>8.0h</td>
      <td>58m</td>
      <td>2m</td>
      <td>8.3x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>WP-3.3 case-study grading E2E + domain glossary: engine POST wiring; outcome seam; orphaned phase fix; 23 tests; merge — closes Phase 3</td>
      <td>9.0h</td>
      <td>68m</td>
      <td>2m</td>
      <td>7.9x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Refactoring</td>
      <td>40.0h</td>
      <td>310m</td>
      <td>14m</td>
      <td>7.7x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>WP-5.0 Xcode 27 + Liquid Glass migration: SDK raise at 755/755 parity; Swift 6.4 semantic fix; deprecations header-verified; design-system audit (zero changes); toolchain pin + beta lane constraint; merge</td>
      <td>16.0h</td>
      <td>125m</td>
      <td>3m</td>
      <td>7.7x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>avian-app-ios WP-2.6 Profile/Settings parity (self-service MFA enrollment + activity-prefs + CrossDomain decode fix + a11y fixes)</td>
      <td>12.0h</td>
      <td>95m</td>
      <td>1m</td>
      <td>7.6x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>avian-app-ios WP-3.5 onboarding resume-upload fix (real endpoint + auto-bug-file)</td>
      <td>5.0h</td>
      <td>44m</td>
      <td>1m</td>
      <td>6.8x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>App-Store prep Test Prep + AP/IB: bundle ids w/ SIWA-primary via API; AASA 4 ids; SSM v4 + avian-api deploy; new AP target (configs/icons/scheme/registry/tests)</td>
      <td>5.0h</td>
      <td>45m</td>
      <td>2m</td>
      <td>6.7x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>WP-4.3 event-bus parity audit: 27-type map vs Python source; 2 gaps wired; compiler-exhaustive enum refactor; 16 tests; cross-repo drift found+carded; merge</td>
      <td>5.0h</td>
      <td>45m</td>
      <td>2m</td>
      <td>6.7x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Documentation</td>
      <td>101.0h</td>
      <td>1012m</td>
      <td>25m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>avian-app-ios Certs TestFlight ship: pipeline+archive+ASC bundle-id debug/rename; SIWA prod infra (SSM v3 + avian-api:39 + AASA); unit-only commit gate; parity program restart</td>
      <td>7.0h</td>
      <td>72m</td>
      <td>4m</td>
      <td>5.8x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Debugging</td>
      <td>14.0h</td>
      <td>145m</td>
      <td>6m</td>
      <td>5.8x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>WP-3.6 offline credit queue: resume stopped worktree; verify inherited diff vs web parity; 15 regression tests; merge</td>
      <td>10.0h</td>
      <td>105m</td>
      <td>2m</td>
      <td>5.7x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>avian-app-ios WP-3.1 engine-driven interaction formats (5 kinds live dispatch + 2 bug fixes; stall+resume)</td>
      <td>14.0h</td>
      <td>169m</td>
      <td>1m</td>
      <td>5.0x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>avian-app-ios WP-2.4 Progress hub + Timeline + Concept Biography (fail-soft, no analytics dup)</td>
      <td>10.0h</td>
      <td>146m</td>
      <td>1m</td>
      <td>4.1x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>avian-app-ios WP-3.2 question-bank parity audit + silent-failure fix</td>
      <td>4.0h</td>
      <td>62m</td>
      <td>1m</td>
      <td>3.9x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>WP-3.7 labs hand-off: resume stopped worktree; verify vs web routes/hosts; 11 URL tests; merge</td>
      <td>6.0h</td>
      <td>100m</td>
      <td>2m</td>
      <td>3.6x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>25</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>554.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>3,538</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>98</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>24,606,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>9.4x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>339.2x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>13.8</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 29.6x and the lowest at 3.6x, a spread of 8.2 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 120.0 of the 554.0 human-equivalent hours, or 22 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 98 minutes against 3,538 minutes of execution, a ratio of about 1 to 36. Supervisory leverage of 339.2x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[The 29-Year Payback]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-13-the-29-year-payback.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-13-the-29-year-payback.html</guid>
      <pubDate>Thu, 13 Aug 2026 00:45:00 GMT</pubDate>
      <description><![CDATA[<p><img src="https://charlessieg.com/images/the-29-year-payback-hero.png" alt="The 29-Year Payback" /></p><p class="mb-4 font-light font-serif"><em>Cross-posted from the <a href="https://renkara.com/blog/build-versus-buy-line-moved.html" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">Renkara engineering blog</a>. This version is the personal one — what the numbers did to my thinking, rather than what they are.</em></p>
<p class="mb-4 font-light font-serif">I spent last night pulling apart our AWS bill, and somewhere around midnight I ended up costing out a decision I made five months ago without ever really checking it.</p>
<p class="mb-4 font-light font-serif">Since March I&#39;ve built 33 internal tools instead of buying the commercial equivalents. Defect tracking instead of Jira. Chat instead of Slack. Observability instead of Datadog. Accounting instead of QuickBooks. At the time this felt obviously correct in the way things feel obviously correct when you want to do them anyway.</p>
<p class="mb-4 font-light font-serif">So I ran the numbers properly. Three of them.</p>
<p class="mb-4 font-light font-serif"><strong>What the commercial stack would cost.</strong> Twenty-six of those tools map onto a product somebody sells by the seat. At four people, list price, that stack is $52,560 a year. Force in the two products that have no four-person price at all — Glean wants a 100-seat floor, patent portfolio management is quote-only — and it&#39;s $127,560.</p>
<p class="mb-4 font-light font-serif"><strong>What mine costs.</strong> $280 a month. $3,354 a year. Fifteen production services on six vCPUs and a small Postgres.</p>
<p class="mb-4 font-light font-serif"><strong>What it cost to build.</strong> This is the one I hadn&#39;t looked at. I log every session to my own leverage tracker with an estimate of how long the same work would have taken a senior engineer who already knew the codebase. Across 397 sessions: <strong>14,105 human-equivalent hours against 225 hours of actual wall-clock time.</strong> A factor of 62.7.</p>
<p class="mb-4 font-light font-serif">Fifteen times cheaper to run. Great. That&#39;s the number you put on a slide, and it&#39;s the least interesting thing here.</p>
<h2 id="the-number-that-stopped-me">The number that stopped me</h2>
<p class="mb-4 font-light font-serif">14,105 hours is 7.1 person-years. At a $100/hour loaded rate that&#39;s <strong>$1.41 million</strong>.</p>
<p class="mb-4 font-light font-serif">Spending $1.41M to avoid $49K a year of software is a <strong>29-year payback</strong>.</p>
<p class="mb-4 font-light font-serif">I sat with that for a while, because it means that if I&#39;d had to pay engineers to build this, building it would have been an act of genuine stupidity. Not a close call. Not &quot;defensible if you value control.&quot; Stupid. Any competent engineering leader would have killed it in the first meeting and they would have been completely right.</p>
<p class="mb-4 font-light font-serif">The reason it wasn&#39;t stupid is that I didn&#39;t spend $1.41M. I spent 225 hours and a subscription.</p>
<h2 id="what-i-actually-learned">What I actually learned</h2>
<p class="mb-4 font-light font-serif">I&#39;ve been telling myself a story where I built these tools because SaaS is overpriced, or because integration between vendors is bad, or because I wanted control over my own data. Those things are all true and none of them are the reason. Plenty of people believe all three and still buy Jira, correctly.</p>
<p class="mb-4 font-light font-serif">The real reason is that the build number moved and I hadn&#39;t consciously noticed.</p>
<p class="mb-4 font-light font-serif">Build-versus-buy has always been a comparison between one large known cost and one small recurring one. Buy won nearly every time, and it won for a good reason: the build side was enormous, because software took human-years. Every rule of thumb I absorbed over twenty years — don&#39;t build what you can buy, focus on your core competency, undifferentiated heavy lifting — is downstream of that one fact.</p>
<p class="mb-4 font-light font-serif">When the build side drops by a factor of sixty, those rules don&#39;t bend at the edges. They break for a whole category of software that was never remotely close before.</p>
<p class="mb-4 font-light font-serif">The uncomfortable part is that I made this decision on instinct in March and only checked it in August. I got the right answer for reasons I couldn&#39;t have articulated at the time. That&#39;s not judgement, that&#39;s luck with good ergonomics.</p>
<h2 id="the-caveats-i-owe-you">The caveats I owe you</h2>
<p class="mb-4 font-light font-serif">The leverage estimates are mine. I made them, about my own work, at the time I did it. They&#39;re a considered figure and not a measurement, and if you want to discount them 30% the argument survives fine.</p>
<p class="mb-4 font-light font-serif">The SaaS prices are list. Real contracts land 10–30% lower. Doesn&#39;t change the shape.</p>
<p class="mb-4 font-light font-serif">And building means owning. Every one of those 33 tools is now mine to patch and migrate and keep alive at 2am. That cost is real, it&#39;s ongoing, and it doesn&#39;t show up in the $280. It&#39;s tolerable for exactly the same reason the build was — maintenance got cheap by the same factor — but &quot;tolerable&quot; is doing real work in that sentence.</p>
<p class="mb-4 font-light font-serif">I&#39;d also add: this worked for <em>operational tooling</em>. Known requirements, one user, no compliance surface, and I can walk down the hall to the product owner because he&#39;s me. I am not about to write my own database.</p>
<h2 id="where-ive-landed">Where I&#39;ve landed</h2>
<p class="mb-4 font-light font-serif">The thing I keep turning over isn&#39;t the $49K. It&#39;s that a rule I&#39;d internalized so deeply I stopped seeing it as a rule — <em>don&#39;t build what you can buy</em> — turns out to have been a statement about the price of engineering hours, wearing a costume as a principle.</p>
<p class="mb-4 font-light font-serif">The hours got cheap. The principle was never load-bearing on its own.</p>
<p class="mb-4 font-light font-serif">I don&#39;t think that generalizes to everything, and I&#39;m suspicious of anyone who says it does. But it generalized further than I expected, and I found that out by accident rather than by checking. So: check. The arithmetic most of us are carrying around is correct against a number that stopped being true.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 12, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-12-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-12-leverage-record.html</guid>
      <pubDate>Wed, 12 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">19 tasks. August 12, 2026 closed at 18.8x weighted leverage across 578.0 human-equivalent hours in 1,843 minutes of wall-clock time. Supervisory leverage came in at 578.0x.</p>
<p class="mb-4 font-light font-serif">That is 14.4 weeks of human-equivalent throughput in 30.7 hours. The ceiling was 57.4x; the floor was 1.7x. 18 of the 19 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Infrastructure</td>
      <td>45.0h</td>
      <td>47m</td>
      <td>3m</td>
      <td>57.4x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Audit and review</td>
      <td>14.0h</td>
      <td>16m</td>
      <td>4m</td>
      <td>52.5x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>[ip-cluster] cross-domain refresher design artifact + [ip] CIP round (KK/BB/CC) + portfolio consistency sweep</td>
      <td>30.0h</td>
      <td>41m</td>
      <td>3m</td>
      <td>43.9x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>[ip-cluster] build program planning: 7-repo phased plan + durable state + Fable review gate + four-docs updates</td>
      <td>16.0h</td>
      <td>25m</td>
      <td>1m</td>
      <td>38.4x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Charter program planning — cross-repo build plan for doc-upload private domains + Concourse unified gate (4-agent research sweep; master plan + durable state + Fable review gate)</td>
      <td>32.0h</td>
      <td>52m</td>
      <td>5m</td>
      <td>36.9x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Testing</td>
      <td>120.0h</td>
      <td>214m</td>
      <td>2m</td>
      <td>33.6x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Documentation</td>
      <td>32.0h</td>
      <td>68m</td>
      <td>3m</td>
      <td>28.2x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Audit and review</td>
      <td>28.0h</td>
      <td>62m</td>
      <td>2m</td>
      <td>27.1x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>avian-admin-ios repo bootstrap: four docs + phased iOS/iPad build plan + fleet registry updates</td>
      <td>10.0h</td>
      <td>24m</td>
      <td>3m</td>
      <td>25.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>avian-admin-ios build program: phases 0-5 orchestrated with Opus agents — full iPhone+iPad admin app + OIDC auth + SSE + 237 tests</td>
      <td>140.0h</td>
      <td>406m</td>
      <td>2m</td>
      <td>20.7x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Produce first 4 demo films end-to-end (H1/H2/H3-AP/H3-TP): stand up full local stack (api+engine+content_r4+SPA); build persona seeder (570 answers + 3 graded exams); fix 5 capture defects; render 1080p mp4 +…</td>
      <td>26.0h</td>
      <td>105m</td>
      <td>3m</td>
      <td>14.9x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>avian-admin-ios Release-build fix (#Preview/nav #if DEBUG guards, 27 files) + full TestFlight delivery (archive, cloud distribution signing, app record, export compliance, internal testing) via ASC API</td>
      <td>6.0h</td>
      <td>25m</td>
      <td>10m</td>
      <td>14.4x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Fleet branch reconciliation: 140 repos audited, 78 promoted staging-&gt;main, 22 reverse-merges, 3 defects fixed, provenance breach found, tests+libs+artifact</td>
      <td>26.0h</td>
      <td>124m</td>
      <td>6m</td>
      <td>12.6x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>avian-app-ios WP-1.4 src/ reorganization (Opus agent: git-mv 200 files into src/ groupings + project.yml rebuild + cross-repo feature-registry repoint + tooling audit; orchestrator verify+merge+cross-repo…</td>
      <td>10.0h</td>
      <td>51m</td>
      <td>1m</td>
      <td>11.8x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>avian-app-ios WP-2.0 per-product build matrix + app-icon family (Opus agent: project.yml AppTargetBase + 3 targets certs/parked/skeleton + Vertical.swift gating + icon family; orchestrator verify+merge)</td>
      <td>14.0h</td>
      <td>85m</td>
      <td>1m</td>
      <td>9.9x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>avian-app-ios WP-1.2 readiness truth (Opus agent: wire predictReadiness/forecast live + regression test + cross-account leak fix; orchestrator verify+merge)</td>
      <td>8.0h</td>
      <td>58m</td>
      <td>1m</td>
      <td>8.3x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>avian-app-ios WP-1.1 assistant revival (Opus agent: mount ChatPanelView + send/history/clear + live SSE streaming parity + keychain test-leak fix; orchestrator verify+merge)</td>
      <td>12.0h</td>
      <td>214m</td>
      <td>1m</td>
      <td>3.4x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Infrastructure</td>
      <td>6.0h</td>
      <td>118m</td>
      <td>8m</td>
      <td>3.1x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>avian-app-ios WP-1.3 dead-code purge (Opus agent: delete DomainSelectView/PlaceholderActivityView/TelemetryBuffer + dead POST + stale docs, grep-verified, tombstones kept; orchestrator verify+merge)</td>
      <td>3.0h</td>
      <td>108m</td>
      <td>1m</td>
      <td>1.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>19</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>578.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,843</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>60</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>21,435,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>18.8x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>578.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>14.4</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 57.4x and the lowest at 1.7x, a spread of 34.5 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 45.0 of the 578.0 human-equivalent hours, or 8 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 60 minutes against 1,843 minutes of execution, a ratio of about 1 to 31. Supervisory leverage of 578.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 5, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-05-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-05-leverage-record.html</guid>
      <pubDate>Wed, 05 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">2 tasks. August 5, 2026 closed at 9.0x weighted leverage across 26.0 human-equivalent hours in 174 minutes of wall-clock time. Supervisory leverage came in at 222.9x.</p>
<p class="mb-4 font-light font-serif">That is 0.7 weeks of human-equivalent throughput in 2.9 hours. The ceiling was 14.6x; the floor was 4.8x. The day&#39;s work was spread across several areas.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Commit+push all 102 AVIAN repos: fixed 6 pre-existing test/type failure sets, generated 136-repo clone script + portable environment archive, triggered S3 DR sync</td>
      <td>18.0h</td>
      <td>74m</td>
      <td>6m</td>
      <td>14.6x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Deployment</td>
      <td>8.0h</td>
      <td>100m</td>
      <td>1m</td>
      <td>4.8x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>2</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>26.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>174</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>7</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>1,050,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>9.0x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>222.9x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>0.7</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 14.6x and the lowest at 4.8x, a spread of 3.0 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 18.0 of the 26.0 human-equivalent hours, or 69 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 7 minutes against 174 minutes of execution, a ratio of about 1 to 25. Supervisory leverage of 222.9x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 4, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-04-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-04-leverage-record.html</guid>
      <pubDate>Tue, 04 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">40 tasks. August 4, 2026 closed at 14.0x weighted leverage across 786.5 human-equivalent hours in 3,362 minutes of wall-clock time. Supervisory leverage came in at 386.8x.</p>
<p class="mb-4 font-light font-serif">That is 19.7 weeks of human-equivalent throughput in 56.0 hours. The ceiling was 62.8x; the floor was 1.7x. 27 of the 40 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Documentation</td>
      <td>68.0h</td>
      <td>65m</td>
      <td>2m</td>
      <td>62.8x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Audit and review</td>
      <td>28.0h</td>
      <td>30m</td>
      <td>3m</td>
      <td>56.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Documentation</td>
      <td>32.0h</td>
      <td>40m</td>
      <td>5m</td>
      <td>48.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Infrastructure</td>
      <td>28.0h</td>
      <td>38m</td>
      <td>2m</td>
      <td>44.2x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Create /fable-planner skill - Fable-gated program planner producing four docs and phased Opus/Sonnet execution plans</td>
      <td>2.5h</td>
      <td>4m</td>
      <td>5m</td>
      <td>37.5x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Infrastructure</td>
      <td>32.0h</td>
      <td>63m</td>
      <td>3m</td>
      <td>30.5x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Deployment</td>
      <td>26.0h</td>
      <td>68m</td>
      <td>3m</td>
      <td>22.9x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Fable planning: model-scorecard program (research + four docs x3 repos + phased plan + state + Fable review gate)</td>
      <td>26.0h</td>
      <td>70m</td>
      <td>2m</td>
      <td>22.3x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Documentation</td>
      <td>34.0h</td>
      <td>96m</td>
      <td>6m</td>
      <td>21.2x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Documentation</td>
      <td>14.0h</td>
      <td>42m</td>
      <td>3m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Testing</td>
      <td>16.0h</td>
      <td>50m</td>
      <td>5m</td>
      <td>19.2x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Model-Scorecard program: P0 baseline + P1.T1 benchmarks package + P4 phase-scoped routing live on the daemon + 5 production defects found/fixed with regression tests + 2 real benchmark runs</td>
      <td>130.0h</td>
      <td>445m</td>
      <td>12m</td>
      <td>17.5x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Testing</td>
      <td>16.0h</td>
      <td>57m</td>
      <td>2m</td>
      <td>16.8x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>13-site UI/UX audit remediation: canon-driven facts fleet-wide, dropdown a11y rebuild, responsive tables, CTA/posture truthing, [unreleased product] shell migration, GED+Certs compact pilots, Playwright+axe…</td>
      <td>100.0h</td>
      <td>362m</td>
      <td>3m</td>
      <td>16.6x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>iOS plan approval pass: decisions D1-D5 recorded, WP rewrites, STATE+ledger scaffolding, ASC runbook, labs-deferral card</td>
      <td>2.5h</td>
      <td>10m</td>
      <td>2m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>iOS productization steer: D9 per-product builds + icon family, D10 price parity, WP-2.0/4.0 added, ASC runbook rewrite</td>
      <td>2.0h</td>
      <td>8m</td>
      <td>2m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Coding</td>
      <td>7.0h</td>
      <td>28m</td>
      <td>2m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Electron: canon-driven LaunchPromoBanner + 7 stale-content fixes across 2 surfaces, 33 tests (A-ELEC Opus agent)</td>
      <td>8.0h</td>
      <td>33m</td>
      <td>2m</td>
      <td>14.5x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Phase-2 content repair: R4 complete, dedupe 14665-&gt;5265, deictic stems -26%/explanations -41%, math markup pushed, 2 transform bugs + 1 platform bug caught pre-push, [cost]spend (R-EXEC Opus agent)</td>
      <td>26.0h</td>
      <td>115m</td>
      <td>2m</td>
      <td>13.6x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>avian-content-checks: JudgeClient/BudgetMeter thread safety + gate_report schema-pin test (A-CHECKS Opus agent)</td>
      <td>5.0h</td>
      <td>24m</td>
      <td>2m</td>
      <td>12.5x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>run-smoke self-heal + smoke-user deletion forensics (audit_logs empty — actor unidentifiable) + 3 follow-up cards (A-SMOKE Opus agent)</td>
      <td>6.5h</td>
      <td>32m</td>
      <td>2m</td>
      <td>12.2x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>[engine subsystem]: benchmark spend-&gt;ledger + findings current-version default + hub key-scrub done right (kept benchmark keys, filed doc-contradiction card) (A-[engine subsystem] Opus agent)</td>
      <td>9.0h</td>
      <td>45m</td>
      <td>2m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>iOS plan: WP-1.4 src/ reorganization added (sequencing, registry/tooling path scope, exit gates)</td>
      <td>1.0h</td>
      <td>5m</td>
      <td>1m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Testing</td>
      <td>1.5h</td>
      <td>8m</td>
      <td>2m</td>
      <td>11.2x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>iOS parity WP-0.2: catalog stale-while-revalidate (disk cache, explicit load state, skeleton/error+Retry), host out of Swift into xcconfig, Associated Domains repointed; 20 tests</td>
      <td>12.0h</td>
      <td>64m</td>
      <td>4m</td>
      <td>11.2x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>[ip-cluster] first-3 cards: [engine subsystem] judge-role swap + regression guard; ANN debt cleared in 2 test files; [engine subsystem] CLAUDE.md benchmark-keys exception; board-state corrected (56/81 already…</td>
      <td>5.0h</td>
      <td>27m</td>
      <td>1m</td>
      <td>11.1x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>iOS parity WP-4.0: environment-scoped App Store Server Notifications V2 (per-env SignedDataVerifier + data-layer guard), 66 tests incl. real x5c chain</td>
      <td>10.0h</td>
      <td>58m</td>
      <td>4m</td>
      <td>10.3x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Testing</td>
      <td>18.0h</td>
      <td>105m</td>
      <td>6m</td>
      <td>10.3x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>Phase-2 drain: dedupe 14665-&gt;13, deictic stems -58%, explanations -89%, ISACA matcher root-cause corrected + husks repaired, closing audit locked, 3 findings filed (R-FINISH Opus agent)</td>
      <td>32.0h</td>
      <td>190m</td>
      <td>2m</td>
      <td>10.1x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>Design and frontend</td>
      <td>12.0h</td>
      <td>85m</td>
      <td>3m</td>
      <td>8.5x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>iOS parity WP-0.3: anonymous public domain catalog in avian-api (optional-user dep, public_only forced for anonymous) + iOS OIDC retirement note; live-probed electron/[unreleased product] prod sign-in breakage</td>
      <td>4.0h</td>
      <td>33m</td>
      <td>4m</td>
      <td>7.3x</td>
    </tr>
    <tr>
      <td>32</td>
      <td>Audit and review</td>
      <td>5.0h</td>
      <td>43m</td>
      <td>2m</td>
      <td>7.0x</td>
    </tr>
    <tr>
      <td>33</td>
      <td>Debugging</td>
      <td>5.0h</td>
      <td>46m</td>
      <td>2m</td>
      <td>6.5x</td>
    </tr>
    <tr>
      <td>34</td>
      <td>Debugging</td>
      <td>14.0h</td>
      <td>142m</td>
      <td>4m</td>
      <td>5.9x</td>
    </tr>
    <tr>
      <td>35</td>
      <td>Deployment</td>
      <td>10.0h</td>
      <td>105m</td>
      <td>2m</td>
      <td>5.7x</td>
    </tr>
    <tr>
      <td>36</td>
      <td>Testing</td>
      <td>5.0h</td>
      <td>59m</td>
      <td>3m</td>
      <td>5.1x</td>
    </tr>
    <tr>
      <td>37</td>
      <td>Engine: unmapped question-type WARNING + evidence-based code-scenarios-&gt;mcq mapping, fleet re-audit clean (A-ENG Opus agent)</td>
      <td>3.5h</td>
      <td>43m</td>
      <td>2m</td>
      <td>4.9x</td>
    </tr>
    <tr>
      <td>38</td>
      <td>Debugging</td>
      <td>14.0h</td>
      <td>240m</td>
      <td>2m</td>
      <td>3.5x</td>
    </tr>
    <tr>
      <td>39</td>
      <td>iOS parity WP-0.6: prod-API posture + launch-time unreachable-gateway banner (3s Task-cancellation deadline, dedicated session, offline/unreachable mutual exclusion); root-caused an XCUIAccessibilityAudit…</td>
      <td>10.0h</td>
      <td>175m</td>
      <td>4m</td>
      <td>3.4x</td>
    </tr>
    <tr>
      <td>40</td>
      <td>Product-listing regroup across 14 sites: Available Now / Free Forever / Coming Soon (launch-date order) on shared products page + gateway router, August→September launch slip via canon, full-family redeploy +…</td>
      <td>6.0h</td>
      <td>209m</td>
      <td>2m</td>
      <td>1.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>40</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>786.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>3,362</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>122</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>28,090,247</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>14.0x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>386.8x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>19.7</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 62.8x and the lowest at 1.7x, a spread of 36.4 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 68.0 of the 786.5 human-equivalent hours, or 9 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 122 minutes against 3,362 minutes of execution, a ratio of about 1 to 28. Supervisory leverage of 386.8x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 3, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-03-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-03-leverage-record.html</guid>
      <pubDate>Mon, 03 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">31 tasks. August 3, 2026 closed at 15.3x weighted leverage across 637.0 human-equivalent hours in 2,492 minutes of wall-clock time. Supervisory leverage came in at 444.4x.</p>
<p class="mb-4 font-light font-serif">That is 15.9 weeks of human-equivalent throughput in 41.5 hours. The ceiling was 48.8x; the floor was 3.5x. 25 of the 31 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>[engine subsystem] publish integrity: Meta [restricted] store fix + 98-domain registry repair + candidacy/publish-on-promote enforcement + prod orphan deletion w/ parity gate (V1 Opus agent)</td>
      <td>100.0h</td>
      <td>123m</td>
      <td>2m</td>
      <td>48.8x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Debugging</td>
      <td>20.0h</td>
      <td>28m</td>
      <td>2m</td>
      <td>42.9x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Coding</td>
      <td>24.0h</td>
      <td>41m</td>
      <td>3m</td>
      <td>35.1x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>[engine subsystem] [content generation] quality: fleet bank audit (10 checks/1.56M items) + unconditional write-time gates + prompt/judge fixes, 91 tests (P1 Opus agent)</td>
      <td>28.0h</td>
      <td>49m</td>
      <td>2m</td>
      <td>34.5x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Infrastructure</td>
      <td>14.0h</td>
      <td>25m</td>
      <td>2m</td>
      <td>33.6x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Deployment</td>
      <td>30.0h</td>
      <td>60m</td>
      <td>3m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Deployment</td>
      <td>16.0h</td>
      <td>34m</td>
      <td>3m</td>
      <td>28.2x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Deployment</td>
      <td>32.0h</td>
      <td>69m</td>
      <td>3m</td>
      <td>27.8x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Deployment</td>
      <td>13.0h</td>
      <td>33m</td>
      <td>4m</td>
      <td>23.6x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Deployment</td>
      <td>40.0h</td>
      <td>103m</td>
      <td>2m</td>
      <td>23.3x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Coding</td>
      <td>7.0h</td>
      <td>19m</td>
      <td>2m</td>
      <td>21.8x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Coding</td>
      <td>7.0h</td>
      <td>25m</td>
      <td>3m</td>
      <td>16.8x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Diagnose + fix prod admin OAuth login failure (fleet_auth missing redirect_uri); seed fix + regression tests; branch sync + deploy avian-admin</td>
      <td>6.0h</td>
      <td>22m</td>
      <td>4m</td>
      <td>16.4x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Documentation</td>
      <td>64.0h</td>
      <td>239m</td>
      <td>8m</td>
      <td>16.1x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Deployment</td>
      <td>40.0h</td>
      <td>150m</td>
      <td>3m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Engine boot halved + blue/green made permanent + rollback proven: root-caused TWO independent causes of a duplicate 7GB snapshot download (an unused gRPC server building a second engine context, and a &#39;cheap…</td>
      <td>24.0h</td>
      <td>98m</td>
      <td>2m</td>
      <td>14.7x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>[engine subsystem] actions+observability: regen fail-loud, regen_items concurrency, telemetry, knobs, env fingerprint, standalone-readability live-wired, 9 cards closed (P2 Opus agent)</td>
      <td>23.0h</td>
      <td>100m</td>
      <td>2m</td>
      <td>13.8x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Debugging</td>
      <td>4.5h</td>
      <td>22m</td>
      <td>2m</td>
      <td>12.3x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>[engine subsystem] robustness: benchmarks fail-closed + real Luna rerun ([cost]) + leases/workers/SpecIndex/resolver/reclamation + 2 forensic investigations + auto-publish armed w/ 64,930-row proof (V2 Opus…</td>
      <td>37.0h</td>
      <td>194m</td>
      <td>2m</td>
      <td>11.4x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Engine boot halved again + embedding model baked into the base image (zero HuggingFace calls at boot) + boot ceiling to 180 + investigated and closed a real data-staleness gap in blue/green: shutdown flush…</td>
      <td>20.0h</td>
      <td>111m</td>
      <td>2m</td>
      <td>10.8x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Client quick strikes: plan-slug subscription gate + prod web deploy verified + help-center canon/i18n + electron parity (F1 Opus agent)</td>
      <td>16.0h</td>
      <td>91m</td>
      <td>2m</td>
      <td>10.5x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>DMCA takedown response: identify + [restricted] entire Meta Blueprint catalog, purge 60+ objects across 4 S3 buckets, rebuild/redeploy 3 sites + web app, fix provider [restricted] gate with regression tests,…</td>
      <td>14.0h</td>
      <td>126m</td>
      <td>4m</td>
      <td>6.7x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Engine content correctness: embeddings id-binding fix + fleet npz audit (410/410 clean) + non-MCQ inventory (E1 Opus agent)</td>
      <td>12.0h</td>
      <td>110m</td>
      <td>2m</td>
      <td>6.5x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Audit and review</td>
      <td>5.0h</td>
      <td>47m</td>
      <td>3m</td>
      <td>6.4x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>deploy.sh digest assertions across all three container handlers (fargate/lambda/ec2-container) with task-def pinning and immutable deploy tags, verified live on avian-api; Valkey-vs-NATS replication transport…</td>
      <td>9.0h</td>
      <td>104m</td>
      <td>2m</td>
      <td>5.2x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Coding</td>
      <td>1.5h</td>
      <td>21m</td>
      <td>2m</td>
      <td>4.3x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>Deployment</td>
      <td>5.0h</td>
      <td>72m</td>
      <td>3m</td>
      <td>4.2x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Deployment</td>
      <td>4.0h</td>
      <td>58m</td>
      <td>4m</td>
      <td>4.1x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>Fixed deploy.sh silently shipping stale code (task-def pinned an immutable tag while deploy pushed :latest; force-new-deployment re-pulled the old image and verify only curled health) - added task-def…</td>
      <td>14.0h</td>
      <td>205m</td>
      <td>3m</td>
      <td>4.1x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>Grant permanent all-access comps (karl/thomas/charles) after finding none existed; fix revenue charts plotting every day one day early (UTC-midnight parse) + 8 tests; trace prod revenue to an E2E test payment</td>
      <td>3.0h</td>
      <td>45m</td>
      <td>3m</td>
      <td>4.0x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>Fix avian-admin Health page crash (snake_case API mapping + regression tests) and engine-proxy: missing ENGINE_ADMIN_KEY SSM param plus missing security-group path to the engine ALB</td>
      <td>4.0h</td>
      <td>68m</td>
      <td>2m</td>
      <td>3.5x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>31</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>637.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>2,492</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>86</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>14,990,716</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>15.3x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>444.4x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>15.9</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 48.8x and the lowest at 3.5x, a spread of 13.8 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 100.0 of the 637.0 human-equivalent hours, or 16 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 86 minutes against 2,492 minutes of execution, a ratio of about 1 to 29. Supervisory leverage of 444.4x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 2, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-02-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-02-leverage-record.html</guid>
      <pubDate>Sun, 02 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">16 tasks. August 2, 2026 closed at 27.9x weighted leverage across 460.0 human-equivalent hours in 988 minutes of wall-clock time. Supervisory leverage came in at 388.7x.</p>
<p class="mb-4 font-light font-serif">That is 11.5 weeks of human-equivalent throughput in 16.5 hours. The ceiling was 141.2x; the floor was 4.2x. 6 of the 16 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Documentation</td>
      <td>120.0h</td>
      <td>51m</td>
      <td>4m</td>
      <td>141.2x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Infrastructure</td>
      <td>120.0h</td>
      <td>63m</td>
      <td>3m</td>
      <td>114.3x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Audit and review</td>
      <td>20.0h</td>
      <td>27m</td>
      <td>8m</td>
      <td>44.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Documentation</td>
      <td>21.0h</td>
      <td>38m</td>
      <td>2m</td>
      <td>33.2x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Testing</td>
      <td>40.0h</td>
      <td>75m</td>
      <td>5m</td>
      <td>32.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Content production</td>
      <td>5.0h</td>
      <td>13m</td>
      <td>2m</td>
      <td>23.1x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Audit and review</td>
      <td>8.0h</td>
      <td>25m</td>
      <td>3m</td>
      <td>19.2x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Restored the production engine build path end-to-end: root-caused a cross-account teardown (staging had zero ECR repos + no CodeBuild source bucket; prod had zero CodeBuild projects), rebuilt CodeBuild in the…</td>
      <td>20.0h</td>
      <td>74m</td>
      <td>6m</td>
      <td>16.2x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Deployment</td>
      <td>9.0h</td>
      <td>35m</td>
      <td>4m</td>
      <td>15.4x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Deployment</td>
      <td>6.0h</td>
      <td>28m</td>
      <td>3m</td>
      <td>12.9x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Deployment</td>
      <td>20.0h</td>
      <td>96m</td>
      <td>4m</td>
      <td>12.5x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Testing</td>
      <td>14.0h</td>
      <td>68m</td>
      <td>6m</td>
      <td>12.4x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Infrastructure</td>
      <td>34.0h</td>
      <td>195m</td>
      <td>12m</td>
      <td>10.5x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Testing</td>
      <td>14.0h</td>
      <td>90m</td>
      <td>4m</td>
      <td>9.3x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Deployment</td>
      <td>4.0h</td>
      <td>38m</td>
      <td>3m</td>
      <td>6.3x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Kill AWS CodeArtifact fleet-wide: repoint global npmrc to Verdaccio, write global CLAUDE.md registry guidance, verify Verdaccio superset, delete CodeArtifact domain+repos, re-point 54 lockfiles (10370 URLs)…</td>
      <td>5.0h</td>
      <td>72m</td>
      <td>2m</td>
      <td>4.2x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>16</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>460.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>988</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>71</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>10,455,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>27.9x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>388.7x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>11.5</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 141.2x and the lowest at 4.2x, a spread of 33.9 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 120.0 of the 460.0 human-equivalent hours, or 26 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 71 minutes against 988 minutes of execution, a ratio of about 1 to 14. Supervisory leverage of 388.7x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: August 1, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-08-01-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-08-01-leverage-record.html</guid>
      <pubDate>Sat, 01 Aug 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">21 tasks. August 1, 2026 closed at 42.6x weighted leverage across 1,112.0 human-equivalent hours in 1,566 minutes of wall-clock time. Supervisory leverage came in at 725.2x.</p>
<p class="mb-4 font-light font-serif">That is 27.8 weeks of human-equivalent throughput in 26.1 hours. The ceiling was 160.0x; the floor was 7.9x. 16 of the 21 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Documentation consolidation: docgen pipeline (11 doc-type templates + PDF/hero/ASCII/determinism) + 17-agent cataloguing fleet over 1038 docs + 199 units rendered</td>
      <td>200.0h</td>
      <td>75m</td>
      <td>6m</td>
      <td>160.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Documentation</td>
      <td>320.0h</td>
      <td>195m</td>
      <td>10m</td>
      <td>98.5x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Testing</td>
      <td>16.0h</td>
      <td>18m</td>
      <td>4m</td>
      <td>53.3x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Documentation</td>
      <td>20.0h</td>
      <td>25m</td>
      <td>6m</td>
      <td>48.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Deployment</td>
      <td>140.0h</td>
      <td>178m</td>
      <td>2m</td>
      <td>47.2x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Stage 3 documentation repair: 54 docs repaired by 6 Opus agents with live verification + dedup of 50 redundant units + app-content guard</td>
      <td>60.0h</td>
      <td>80m</td>
      <td>4m</td>
      <td>45.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Deployment</td>
      <td>3.5h</td>
      <td>6m</td>
      <td>8m</td>
      <td>35.0x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Documentation</td>
      <td>7.0h</td>
      <td>12m</td>
      <td>2m</td>
      <td>35.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Testing</td>
      <td>14.0h</td>
      <td>24m</td>
      <td>3m</td>
      <td>35.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Deployment</td>
      <td>130.0h</td>
      <td>270m</td>
      <td>10m</td>
      <td>28.9x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Fleet registry + CI/CD sweep: destroyed 18 orphaned CodeBuild projects across 9 AWS accounts, migrated 28 repos from stale CodeArtifact to Verdaccio (mirrored 6 missing versions, relocked all, un-gitignored…</td>
      <td>40.0h</td>
      <td>90m</td>
      <td>3m</td>
      <td>26.7x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Coding</td>
      <td>28.0h</td>
      <td>71m</td>
      <td>3m</td>
      <td>23.7x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Research and file 3 single-domain-vertical defect cards for avian-app-web (dead-end Browse courses CTAs across 19 sites, singleDomain classification for LSAT/MCAT, USA to US Citizenship nav title)</td>
      <td>1.5h</td>
      <td>4m</td>
      <td>4m</td>
      <td>22.5x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Infrastructure</td>
      <td>11.0h</td>
      <td>32m</td>
      <td>4m</td>
      <td>20.6x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Debugging</td>
      <td>36.0h</td>
      <td>115m</td>
      <td>3m</td>
      <td>18.8x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Prod defect run: root-caused and fixed the certs checkout lockout (guard/unique-index mismatch), blank-card render bug, product-scoped subscribe rail, restored Apple Sign In via SSM migration + 2 ECS task-def…</td>
      <td>36.0h</td>
      <td>130m</td>
      <td>6m</td>
      <td>16.6x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Speed program: concurrency census + parallelize 9 serial LLM loops on the [content generation] path (shared order-preserving map_calls primitive + 18-case concurrency/equivalence battery + fleet restart)</td>
      <td>16.0h</td>
      <td>65m</td>
      <td>4m</td>
      <td>14.8x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Root-caused Apple Sign In popup hang across AVIAN (Apple web_message posts to the redirect_uri origin, not the opener): threaded a vetted per-origin redirect_uri through avian-app-web and avian-api, shipped…</td>
      <td>16.0h</td>
      <td>74m</td>
      <td>4m</td>
      <td>13.0x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Diagnosed and fixed Sign in with Apple failing on all 12 AVIAN product hosts: CloudFront CSP allowed applepay.cdn-apple.com (Apple Pay) but never appleid.cdn-apple.com (Sign in with Apple); reconciled…</td>
      <td>4.0h</td>
      <td>19m</td>
      <td>2m</td>
      <td>12.6x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>CodeArtifact cleanup: inventoried 27.4GB domain, identified console-sim as 93.7% of storage (222 versions), deleted 316 unreferenced private versions (~25.6GB) preserving all fleet-referenced versions +…</td>
      <td>8.0h</td>
      <td>45m</td>
      <td>2m</td>
      <td>10.7x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Documentation</td>
      <td>5.0h</td>
      <td>38m</td>
      <td>2m</td>
      <td>7.9x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>21</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>1,112.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,566</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>92</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>37,445,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>42.6x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>725.2x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>27.8</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 160.0x and the lowest at 7.9x, a spread of 20.3 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 200.0 of the 1,112.0 human-equivalent hours, or 18 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 92 minutes against 1,566 minutes of execution, a ratio of about 1 to 17. Supervisory leverage of 725.2x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 31, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-31-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-31-leverage-record.html</guid>
      <pubDate>Fri, 31 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">44 tasks. July 31, 2026 closed at 14.9x weighted leverage across 1,253.0 human-equivalent hours in 5,032 minutes of wall-clock time. Supervisory leverage came in at 414.2x.</p>
<p class="mb-4 font-light font-serif">That is 31.3 weeks of human-equivalent throughput in 83.9 hours. The ceiling was 160.0x; the floor was 2.8x. 22 of the 44 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Quorum: name + scaffold a new fleet tool for corporate governance (board meetings, quorum, resolutions, minutes, e-signature, stock ledger); researched and selected self-hosted LiveKit track-egress for…</td>
      <td>40.0h</td>
      <td>15m</td>
      <td>3m</td>
      <td>160.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Testing</td>
      <td>120.0h</td>
      <td>70m</td>
      <td>3m</td>
      <td>102.9x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Documentation</td>
      <td>28.0h</td>
      <td>32m</td>
      <td>6m</td>
      <td>52.5x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Deployment</td>
      <td>38.0h</td>
      <td>48m</td>
      <td>3m</td>
      <td>48.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Coding</td>
      <td>30.0h</td>
      <td>40m</td>
      <td>1m</td>
      <td>45.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Deployment</td>
      <td>32.0h</td>
      <td>55m</td>
      <td>7m</td>
      <td>34.9x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>AVIAN documentation consolidation comprehensive plan (census + plan doc)</td>
      <td>8.0h</td>
      <td>14m</td>
      <td>5m</td>
      <td>34.3x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Deployment</td>
      <td>52.0h</td>
      <td>95m</td>
      <td>4m</td>
      <td>32.8x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Deployment</td>
      <td>46.0h</td>
      <td>90m</td>
      <td>2m</td>
      <td>30.7x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>avian-app-web: nav chrome root-cause fixes (undefined DS layout tokens), avatar-&gt;profile link + design-system 0.4.4 publish, page padding sweep, prod bug-reporter allowlist, single-domain product nav/docs…</td>
      <td>26.0h</td>
      <td>51m</td>
      <td>7m</td>
      <td>30.6x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Deployment</td>
      <td>34.0h</td>
      <td>80m</td>
      <td>2m</td>
      <td>25.5x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Infrastructure</td>
      <td>22.0h</td>
      <td>55m</td>
      <td>1m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Documentation</td>
      <td>18.0h</td>
      <td>46m</td>
      <td>4m</td>
      <td>23.5x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Design and frontend</td>
      <td>28.0h</td>
      <td>75m</td>
      <td>5m</td>
      <td>22.4x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Coding</td>
      <td>26.0h</td>
      <td>70m</td>
      <td>6m</td>
      <td>22.3x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Phase 1b generation contract: prompt quality contract (both MCQ prompts), generator_model attribution (questions+lessons), mercury MCQ bar, lazy multi-provider escalation ladder (terra→grok→sol) + 12 tests +…</td>
      <td>12.0h</td>
      <td>35m</td>
      <td>1m</td>
      <td>20.6x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Remediate all 8 accessibility audit findings in avian-app-web: 2 design-system releases (modal accessible-name + Avatar role), new authenticated axe sweep (18 routes) that caught a serious latent defect,…</td>
      <td>80.0h</td>
      <td>240m</td>
      <td>3m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Audit and review</td>
      <td>22.0h</td>
      <td>72m</td>
      <td>4m</td>
      <td>18.3x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Documentation</td>
      <td>16.0h</td>
      <td>55m</td>
      <td>2m</td>
      <td>17.5x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Deployment</td>
      <td>20.0h</td>
      <td>78m</td>
      <td>8m</td>
      <td>15.4x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Root-caused + fixed 33 unloadable [pipeline stage] (760 orphaned nodes): diagnosed stale materialization not missing embeddings, flipped the WP2 packages/ gate with regression tests, 2 materialize passes to…</td>
      <td>24.0h</td>
      <td>95m</td>
      <td>5m</td>
      <td>15.2x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Wire [engine subsystem] worker daemon onto models.yaml registry (category/content-type routing + key_env-first credentials + mercury amplifier flip + tests + fleet restart)</td>
      <td>12.0h</td>
      <td>50m</td>
      <td>3m</td>
      <td>14.4x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Staging defect batch: 15 reported issues across avian-engine/avian-api/avian-app-web + 2 shared libs — root-caused, fixed with regression tests, published libs, deployed and verified on staging</td>
      <td>40.0h</td>
      <td>178m</td>
      <td>6m</td>
      <td>13.5x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Deployment</td>
      <td>160.0h</td>
      <td>732m</td>
      <td>12m</td>
      <td>13.1x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Testing</td>
      <td>4.0h</td>
      <td>19m</td>
      <td>2m</td>
      <td>12.6x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Content production</td>
      <td>64.0h</td>
      <td>310m</td>
      <td>5m</td>
      <td>12.4x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>Handoff block P1-P3: parity-check root-cause fix (id-list vs vector rows) + incident regression tests + COMPLETE contract; f675fc77 store-safe manifest projection; pointer-lag investigation (410/412, median…</td>
      <td>20.0h</td>
      <td>105m</td>
      <td>3m</td>
      <td>11.4x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Phase 2 block: client-wedge root cause + never-wired llm_hard_call_timeout_s + client.py 85→95% wedge-guard battery; reindex exit-139 root-caused (faiss/torch dup libomp, both orders reproduced) +…</td>
      <td>22.0h</td>
      <td>120m</td>
      <td>1m</td>
      <td>11.0x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>Prod content-DB cutover + deterministic receipted release: restore content_r4 to prod RDS + content contract system + 5 release-script fixes + verify-only auditor + verdaccio-only lockfile migration +…</td>
      <td>28.0h</td>
      <td>155m</td>
      <td>12m</td>
      <td>10.8x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>Audit and review</td>
      <td>14.0h</td>
      <td>80m</td>
      <td>3m</td>
      <td>10.5x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>Phase 3 block: llm_content_pool_size knob; #17 caching/batching audit (agent-swept 39 files) + 2 cache-violation fixes + speed-program blueprint card; [engine subsystem] MCP admin-key confirmed; registry…</td>
      <td>8.0h</td>
      <td>50m</td>
      <td>0m</td>
      <td>9.6x</td>
    </tr>
    <tr>
      <td>32</td>
      <td>Purge fixture-wp6-smoke registry row (90 rows across 7 tables) + canonical-UUID domain-id guard + regression tests</td>
      <td>4.0h</td>
      <td>25m</td>
      <td>1m</td>
      <td>9.6x</td>
    </tr>
    <tr>
      <td>33</td>
      <td>Deployment</td>
      <td>14.0h</td>
      <td>95m</td>
      <td>5m</td>
      <td>8.8x</td>
    </tr>
    <tr>
      <td>34</td>
      <td>Prod outage: app.accelastudy.ai was serving a staging-mode bundle (login broken 5 days) — root-caused, fixed CSP, cherry-pick-promoted the defect batch to prod, fixed 2 prod-release.sh defects, deployed and…</td>
      <td>7.0h</td>
      <td>48m</td>
      <td>3m</td>
      <td>8.8x</td>
    </tr>
    <tr>
      <td>35</td>
      <td>Deployment</td>
      <td>30.0h</td>
      <td>213m</td>
      <td>4m</td>
      <td>8.5x</td>
    </tr>
    <tr>
      <td>36</td>
      <td>Provision classroom prod (ungated) + hyphenated staging from scratch; build/deploy both; capture hero; fix capture script DNS and load-wait bugs</td>
      <td>4.0h</td>
      <td>32m</td>
      <td>2m</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>37</td>
      <td>classroom.app host + banner overlap/light-mode fixes + timeline 422; caught and restored a prod API outage (desiredCount=0) via the new deploy smoke; reverse-merged main-&gt;staging across 7 repos</td>
      <td>9.0h</td>
      <td>72m</td>
      <td>4m</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>38</td>
      <td>Deployment</td>
      <td>6.0h</td>
      <td>50m</td>
      <td>2m</td>
      <td>7.2x</td>
    </tr>
    <tr>
      <td>39</td>
      <td>Content production</td>
      <td>5.0h</td>
      <td>42m</td>
      <td>4m</td>
      <td>7.1x</td>
    </tr>
    <tr>
      <td>40</td>
      <td>Infrastructure</td>
      <td>8.0h</td>
      <td>75m</td>
      <td>3m</td>
      <td>6.4x</td>
    </tr>
    <tr>
      <td>41</td>
      <td>Benchmark fail-open forensics + fail-closed guards (checks-lib/[engine subsystem]) + Temporal ceiling fix + 5 model registrations + 4-set Luna campaign rerun + comparison</td>
      <td>16.0h</td>
      <td>155m</td>
      <td>3m</td>
      <td>6.2x</td>
    </tr>
    <tr>
      <td>42</td>
      <td>Promoted US Citizenship to live and shipped it to production: [engine subsystem] registry promote (admin REST, MCP operator key 403s), spec+manifest status flip preserving the immutable store, surgical…</td>
      <td>4.0h</td>
      <td>40m</td>
      <td>2m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>43</td>
      <td>US Citizenship package to CANDIDATE: corpus surgery (7-goal [engine subsystem] via synthetic checkpoint) + 78/78 lessons via escalation ladder + question regen/reanchor + pair [engine subsystem] + scenarios +…</td>
      <td>40.0h</td>
      <td>720m</td>
      <td>15m</td>
      <td>3.3x</td>
    </tr>
    <tr>
      <td>44</td>
      <td>Infrastructure</td>
      <td>12.0h</td>
      <td>255m</td>
      <td>4m</td>
      <td>2.8x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>44</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>1,253.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>5,032</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>182</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>25,139,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>14.9x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>414.2x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>31.3</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 160.0x and the lowest at 2.8x, a spread of 56.7 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 40.0 of the 1,253.0 human-equivalent hours, or 3 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 182 minutes against 5,032 minutes of execution, a ratio of about 1 to 28. Supervisory leverage of 414.2x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 30, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-30-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-30-leverage-record.html</guid>
      <pubDate>Thu, 30 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">20 tasks. July 30, 2026 closed at 45.1x weighted leverage across 1,243.0 human-equivalent hours in 1,654 minutes of wall-clock time. Supervisory leverage came in at 745.8x.</p>
<p class="mb-4 font-light font-serif">That is 31.1 weeks of human-equivalent throughput in 27.6 hours. The ceiling was 160.0x; the floor was 6.5x. 16 of the 20 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Parental-consent + guardian design: located learner-history artifact, raised 14 open questions cross-checked vs avian-classroom-web, then resolved all into ADR-0017 (minor identity, guardian projection,…</td>
      <td>120.0h</td>
      <td>45m</td>
      <td>8m</td>
      <td>160.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Unified design + layer-grouped phased implementation plan for AccelaStudy AI English (IELTS/TOEFL) and Languages (30 world languages): ground-truth survey of 9 repos correcting 5 stale planning docs,…</td>
      <td>80.0h</td>
      <td>35m</td>
      <td>5m</td>
      <td>137.1x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>AVIAN Enterprise: four-doc set (requirements/design/testing-strategy/README) + phased implementation plan organized by layer for cross-product batching</td>
      <td>56.0h</td>
      <td>28m</td>
      <td>4m</td>
      <td>120.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>AVIAN web UI/UX redesign program: 45-package overnight multi-agent orchestration (visual stability, bundle 3.1MB-&gt;508KB, Today-first Dashboard, course 5-tab redesign, Progress hub, Review Queue, nav…</td>
      <td>400.0h</td>
      <td>400m</td>
      <td>8m</td>
      <td>60.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Deployment</td>
      <td>280.0h</td>
      <td>300m</td>
      <td>10m</td>
      <td>56.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Manual test plan for auth/sign-in/compliance/GDPR/purchasing: 5-agent codebase recon + 155 grounded test cases…</td>
      <td>32.0h</td>
      <td>35m</td>
      <td>3m</td>
      <td>54.9x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Review 5 initiative doc sets and write consolidated August master build plan with remote-[content generation] track</td>
      <td>32.0h</td>
      <td>45m</td>
      <td>5m</td>
      <td>42.7x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>LSAT domain spec + content + activity roster audit and remediation plan</td>
      <td>14.0h</td>
      <td>25m</td>
      <td>3m</td>
      <td>33.6x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Audit and review</td>
      <td>14.0h</td>
      <td>26m</td>
      <td>4m</td>
      <td>32.3x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Documentation</td>
      <td>12.0h</td>
      <td>26m</td>
      <td>3m</td>
      <td>27.7x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>onboarding-service deprecation: repoint electron/iOS/GDPR-Lambda to avian-api, sweep references across 14 repos, reconcile canonical counts, archive prep</td>
      <td>12.0h</td>
      <td>30m</td>
      <td>2m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Deployment</td>
      <td>90.0h</td>
      <td>230m</td>
      <td>15m</td>
      <td>23.5x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>AccelaStudy AI v1.1 release notes: review ~400 commits across 9 repos (web/engine/api/domains/sims/activities/auth/notifications/site), roll up to 16 impact-ranked customer-facing features, rewrite…</td>
      <td>10.0h</td>
      <td>32m</td>
      <td>5m</td>
      <td>18.8x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Revise master build plan to Rev 2: shipping increments + august branch + [ip-cluster] track + artifacts backlog sweep</td>
      <td>10.0h</td>
      <td>35m</td>
      <td>4m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Debugging</td>
      <td>20.0h</td>
      <td>77m</td>
      <td>3m</td>
      <td>15.6x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Deployment</td>
      <td>40.0h</td>
      <td>155m</td>
      <td>10m</td>
      <td>15.5x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Rev 2.1 + durable august program tracker (STATE ledger prompt pack resume wiring)</td>
      <td>6.0h</td>
      <td>25m</td>
      <td>4m</td>
      <td>14.4x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Infrastructure</td>
      <td>4.0h</td>
      <td>22m</td>
      <td>1m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Deployment</td>
      <td>5.0h</td>
      <td>28m</td>
      <td>1m</td>
      <td>10.7x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Design and frontend</td>
      <td>6.0h</td>
      <td>55m</td>
      <td>2m</td>
      <td>6.5x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>20</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>1,243.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,654</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>100</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>28,155,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>45.1x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>745.8x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>31.1</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 160.0x and the lowest at 6.5x, a spread of 24.4 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 120.0 of the 1,243.0 human-equivalent hours, or 10 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 100 minutes against 1,654 minutes of execution, a ratio of about 1 to 17. Supervisory leverage of 745.8x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 29, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-29-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-29-leverage-record.html</guid>
      <pubDate>Wed, 29 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">35 tasks. July 29, 2026 closed at 30.1x weighted leverage across 1,621.0 human-equivalent hours in 3,236 minutes of wall-clock time. Supervisory leverage came in at 720.4x.</p>
<p class="mb-4 font-light font-serif">That is 40.5 weeks of human-equivalent throughput in 53.9 hours. The ceiling was 205.7x; the floor was 2.8x. 26 of the 35 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>60-month execution plan for 10% Vision - model + monthly deliverables (md/PDF/artifact)</td>
      <td>120.0h</td>
      <td>35m</td>
      <td>2m</td>
      <td>205.7x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>10% Vision doc - market-share model + 5-year plan (md/PDF/artifact)</td>
      <td>72.0h</td>
      <td>30m</td>
      <td>3m</td>
      <td>144.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Documentation</td>
      <td>20.0h</td>
      <td>13m</td>
      <td>4m</td>
      <td>92.3x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>GED launch campaign waves 1-2: orchestration plan + Tier C generators with gate stack + contracts/ADR/checks + engine scoring schema + Tier A web renderers + ged.accelastudy.ai site and infra live…</td>
      <td>460.0h</td>
      <td>342m</td>
      <td>15m</td>
      <td>80.7x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Documentation</td>
      <td>80.0h</td>
      <td>63m</td>
      <td>6m</td>
      <td>76.2x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Author sanitized AI Venture Builder domain spec (146 goals) with canonical count updates across 3 repos</td>
      <td>12.0h</td>
      <td>11m</td>
      <td>2m</td>
      <td>65.5x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Corporate philanthropy plan (The 1% Commitment) — reviewed 12 artifacts via agents; budget model from cash walk; entity + programs + timeline; published artifact + corpus doc 14</td>
      <td>20.0h</td>
      <td>19m</td>
      <td>3m</td>
      <td>63.2x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Design and frontend</td>
      <td>75.0h</td>
      <td>76m</td>
      <td>6m</td>
      <td>59.2x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Deployment</td>
      <td>100.0h</td>
      <td>113m</td>
      <td>6m</td>
      <td>53.1x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Aug-2028 schedule shift + headcount plan + campus concept studies (docs/PDFs/3 artifacts)</td>
      <td>30.0h</td>
      <td>35m</td>
      <td>3m</td>
      <td>51.4x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Coding</td>
      <td>64.0h</td>
      <td>77m</td>
      <td>5m</td>
      <td>49.9x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Boston facilities + physical-security program integrated into 10% Vision docs (md/PDF/artifacts)</td>
      <td>16.0h</td>
      <td>20m</td>
      <td>2m</td>
      <td>48.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Campus program v2 - Restaurant/Fieldhouse/Undercroft across docs + concept boards + 3 renders</td>
      <td>20.0h</td>
      <td>25m</td>
      <td>3m</td>
      <td>48.0x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Campus doc set split - master plan + 8 building docs + partition itemization (9 PDFs)</td>
      <td>20.0h</td>
      <td>25m</td>
      <td>2m</td>
      <td>48.0x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Documentation</td>
      <td>60.0h</td>
      <td>76m</td>
      <td>4m</td>
      <td>47.4x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Coding</td>
      <td>32.0h</td>
      <td>42m</td>
      <td>3m</td>
      <td>45.7x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Coding</td>
      <td>50.0h</td>
      <td>66m</td>
      <td>3m</td>
      <td>45.5x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Engagement notifications: 4-agent fleet audit + phased multi-channel implementation plan (SMS/APNs/FCM/web push/email/watch)</td>
      <td>20.0h</td>
      <td>27m</td>
      <td>4m</td>
      <td>44.4x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Photorealistic campus renders generated + embedded in concept-studies artifact</td>
      <td>8.0h</td>
      <td>12m</td>
      <td>1m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Documentation</td>
      <td>12.0h</td>
      <td>20m</td>
      <td>2m</td>
      <td>36.0x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Campus v3 - Migration + underground parking + cistern + reactor assessment + linked artifacts x2 variants</td>
      <td>24.0h</td>
      <td>40m</td>
      <td>4m</td>
      <td>36.0x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Documentation</td>
      <td>16.0h</td>
      <td>30m</td>
      <td>5m</td>
      <td>32.0x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Build and launch free.accelastudy.ai — free-tier catalog site (150 courses / 18 focus areas / 193 pages) with derived-catalog pipeline, 4 templates, Terraform infra, four docs, and both stages deployed</td>
      <td>30.0h</td>
      <td>65m</td>
      <td>2m</td>
      <td>27.7x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>US Citizenship free-tier domain spec (78 leaves; USCIS-verified 2025+2008 tests; multilingual Spanish-first design) + canonical/adjacency fleet wiring</td>
      <td>16.0h</td>
      <td>37m</td>
      <td>3m</td>
      <td>25.9x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Deployment</td>
      <td>16.0h</td>
      <td>37m</td>
      <td>2m</td>
      <td>25.9x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Rotating patriotic hero photography for usa.accelastudy.ai (10 FLUX day/night pairs via heroes_orchestrator; template rotation wiring; both stages redeployed+verified)</td>
      <td>8.0h</td>
      <td>25m</td>
      <td>1m</td>
      <td>19.2x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>Split accelastudy.ai into a brand gateway + certs.accelastudy.ai: shared catalog generator + network partition (407 courses across 4 sites) / new certs site with infra / catalogs added to ap and test-prep /…</td>
      <td>60.0h</td>
      <td>205m</td>
      <td>4m</td>
      <td>17.6x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Staging refresh: content_r3 release + PP-25 lazy hydration (19.22-&gt;6.4 GiB) + v16 writer torn-pair fix + g4dn.xlarge downsize + engine builds moved to CodeBuild</td>
      <td>34.0h</td>
      <td>150m</td>
      <td>8m</td>
      <td>13.6x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>Documentation</td>
      <td>30.0h</td>
      <td>210m</td>
      <td>3m</td>
      <td>8.6x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>Infrastructure</td>
      <td>6.0h</td>
      <td>45m</td>
      <td>3m</td>
      <td>8.0x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>Staging full update: restamp completion 405/405 + fleet stale-hash repair 408 domains + materialization gap closure (409-domain tree) + engine/api/content/boot-cache staging deploy</td>
      <td>24.0h</td>
      <td>250m</td>
      <td>3m</td>
      <td>5.8x</td>
    </tr>
    <tr>
      <td>32</td>
      <td>Staging web deploy + CDN cache forensics (version-checker no-cache repair) + staging-app host retirement (DNS+CloudFront+3 repos)</td>
      <td>4.0h</td>
      <td>45m</td>
      <td>4m</td>
      <td>5.3x</td>
    </tr>
    <tr>
      <td>33</td>
      <td>v16 sharded boot cache shipped end-to-end + tiered-store cutover (content_r2, store ON) + cross-type dedup exemption (2 repos) + engine host disk-exhaustion incident chain (root cause + remediation +…</td>
      <td>40.0h</td>
      <td>520m</td>
      <td>8m</td>
      <td>4.6x</td>
    </tr>
    <tr>
      <td>34</td>
      <td>Wave-2 live-HIGH campaign: serialized babysitter + restore_quality_report action ([engine subsystem]) + disk-full recovery + convergence proof</td>
      <td>8.0h</td>
      <td>150m</td>
      <td>2m</td>
      <td>3.2x</td>
    </tr>
    <tr>
      <td>35</td>
      <td>Deployment</td>
      <td>14.0h</td>
      <td>300m</td>
      <td>4m</td>
      <td>2.8x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>35</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>1,621.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>3,236</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>135</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>27,735,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>30.1x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>720.4x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>40.5</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 205.7x and the lowest at 2.8x, a spread of 73.5 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 120.0 of the 1,621.0 human-equivalent hours, or 7 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 135 minutes against 3,236 minutes of execution, a ratio of about 1 to 24. Supervisory leverage of 720.4x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 28, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-28-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-28-leverage-record.html</guid>
      <pubDate>Tue, 28 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">18 tasks. July 28, 2026 closed at 27.9x weighted leverage across 864.5 human-equivalent hours in 1,856 minutes of wall-clock time. Supervisory leverage came in at 710.5x.</p>
<p class="mb-4 font-light font-serif">That is 21.6 weeks of human-equivalent throughput in 30.9 hours. The ceiling was 46.5x; the floor was 4.3x. 15 of the 18 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Testing</td>
      <td>100.0h</td>
      <td>129m</td>
      <td>3m</td>
      <td>46.5x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Unified LMS/Enterprise/[unreleased product] platform requirements doc (business/09): merged 4 PRDs + Instructure analysis into single requirements base + PDF</td>
      <td>10.0h</td>
      <td>15m</td>
      <td>3m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Practice exam subsystem analysis + authored-bank sourcing/figures/all-type grading in engine + client renderers + realistic CHEM1B 73-question final blueprint</td>
      <td>88.0h</td>
      <td>145m</td>
      <td>7m</td>
      <td>36.4x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Practice-exam campaign + learner-history Phase 1: 12 Opus agents across 8 repos (exam rebuild for 405 live domains blueprint/aids/lifecycle backfill; exam-start briefing; aids panel w/ periodic table;…</td>
      <td>408.0h</td>
      <td>690m</td>
      <td>14m</td>
      <td>35.5x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Master rollout plan (business/10) + canonical count re-sync across 07/08 with dependent-math audit + 4 PDF regens</td>
      <td>14.0h</td>
      <td>25m</td>
      <td>2m</td>
      <td>33.6x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Notification-service operator console SPA (orchestrated Opus agent) + reviewed its output and fixed a real gap it surfaced in my own multi-brand work: templates.brand was only half-wired, the REST routes…</td>
      <td>12.0h</td>
      <td>26m</td>
      <td>2m</td>
      <td>27.7x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Collect all package-pipeline simulation configs into avian-simulations as authoritative store (collector script + 364-domain collection + index)</td>
      <td>4.0h</td>
      <td>9m</td>
      <td>1m</td>
      <td>26.7x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Deployment</td>
      <td>30.0h</td>
      <td>68m</td>
      <td>3m</td>
      <td>26.5x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Design and frontend</td>
      <td>34.0h</td>
      <td>78m</td>
      <td>6m</td>
      <td>26.2x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Author KB-1..15 KnowBe4 displacement requirements + content-state reconciliation in unified platform requirements doc (9 sections + PDF regen)</td>
      <td>3.0h</td>
      <td>7m</td>
      <td>1m</td>
      <td>25.7x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Restate entire avian-planning business corpus to canonical (18 docs): launch-date correction, [ip]/content/build re-sync, IP valuation recompute, new Campus/Enterprise lines, revenue model rework, 18 PDFs +…</td>
      <td>40.0h</td>
      <td>95m</td>
      <td>4m</td>
      <td>25.3x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Deployment</td>
      <td>48.0h</td>
      <td>114m</td>
      <td>4m</td>
      <td>25.3x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Apply owner product directive across corpus: 12 lines, repricing, compressed schedule, Classroom K-12 + [unreleased product] launch, [ip] filing-status correction, throughput-based plan revision,…</td>
      <td>24.0h</td>
      <td>60m</td>
      <td>5m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Wave B LLM content-repair completion: 5 families / 1322 jobs dispatched+verified (goal starvation; per-goal content; answer-length tell; scenario rubrics; stem-quality lint) incl. outage recovery +…</td>
      <td>24.0h</td>
      <td>145m</td>
      <td>6m</td>
      <td>9.9x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Replace hand-maintained dashboard MCP catalog with live-introspection generator (stdio+HTTP MCP clients, 28 servers/1341 tools, bind-mounted json + fetch render) + Directory tools grid refresh + 60 stale…</td>
      <td>4.0h</td>
      <td>25m</td>
      <td>2m</td>
      <td>9.6x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Deployment</td>
      <td>16.0h</td>
      <td>150m</td>
      <td>3m</td>
      <td>6.4x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Remove 4 app services from supporting-services; reclaim images; move MySQL behind compose profile</td>
      <td>2.5h</td>
      <td>33m</td>
      <td>3m</td>
      <td>4.5x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Restore supporting-services stack: 3 compose defects blocking docker compose up -d + regression gate</td>
      <td>3.0h</td>
      <td>42m</td>
      <td>4m</td>
      <td>4.3x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>18</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>864.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,856</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>73</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>17,592,511</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>27.9x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>710.5x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>21.6</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 46.5x and the lowest at 4.3x, a spread of 10.9 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 100.0 of the 864.5 human-equivalent hours, or 12 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 73 minutes against 1,856 minutes of execution, a ratio of about 1 to 25. Supervisory leverage of 710.5x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Roost: One Hub, Every Session]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-28-roost-one-hub-every-session.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-28-roost-one-hub-every-session.html</guid>
      <pubDate>Tue, 28 Jul 2026 22:30:00 GMT</pubDate>
      <description><![CDATA[<p><img src="https://charlessieg.com/images/roost-one-hub-every-session-hero.png" alt="Roost: One Hub, Every Session" /></p><p class="mb-4 font-light font-serif"><em>Cross-posted from the <a href="https://renkara.com/blog/roost-one-hub-every-session.html" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">Renkara engineering blog</a>. This one is personal: it happened on my desk, in one day, mostly while I watched.</em></p>
<h2 id="the-problem-nobody-budgets-for">The problem nobody budgets for</h2>
<p class="mb-4 font-light font-serif">Our development workstation routinely runs a dozen Claude Code sessions. Each session talks to the Renkara tools fleet — Docket, Fulcrum, Beacon, Courier, Aviary, and eighteen more — through MCP servers. And until this week, every session spawned its own private copy of every server.</p>
<p class="mb-4 font-light font-serif">We measured it before touching anything: <strong>92 processes and 5.85 GB of resident memory at just four sessions</strong>, extrapolating to roughly 270 processes and 17 GB at a normal working load. Every one of those processes was a byte-identical, stateless Python wrapper translating MCP tool calls into REST calls. Twelve copies of the same idle proxy, per server, holding no state whatsoever — plus twenty-two Python interpreter startups every single time a new session opened.</p>
<h2 id="the-design-insight">The design insight</h2>
<p class="mb-4 font-light font-serif">Two facts made the fix obvious once stated. First, the wrappers are stateless — sharing one instance across every session is semantically free. Second, in Claude Code the tool-name prefix comes from the <em>config key</em>, not the transport — which means you can swap a stdio spawn for an HTTP URL and every tool name, permission rule, and document referencing <code>mcp__docket__*</code> keeps working, unchanged.</p>
<p class="mb-4 font-light font-serif">So Roost is one daemon on localhost. Each server lives at <code>/&amp;lt;name&amp;gt;/mcp</code> over Streamable HTTP; sessions carry dumb URL entries. Behind the router, Roost spawns <strong>at most one</strong> child per backend — lazily, on the first actual tool call — reaps it after thirty idle minutes, and wraps it in a circuit breaker so one wedged backend can&#39;t poison the rest. The centerpiece is the catalog snapshot: tool listings are persisted to disk, so a new session&#39;s <code>tools/list</code> is answered in microseconds without waking anything. Session startup went from twenty-two interpreter forks to a handful of cached handshakes.</p>
<p class="mb-4 font-light font-serif">We wrote the architecture up as a formal decision record, considered and rejected the alternatives (a merged mega-server breaks every tool name; containerizing is structurally impossible — a Linux container cannot exec macOS virtualenvs, and moving the memory into a VM would defeat the point), and claimed a port. The whole design took an afternoon.</p>
<h2 id="built-by-three-agents-before-dinner">Built by three agents before dinner</h2>
<p class="mb-4 font-light font-serif">The implementation plan pinned every contract — config schema, module APIs, log format, CLI behaviors, test matrix — precisely so the build could be parallelized. Three Claude Opus agents did the work: one built the daemon core, one built the CLI and launchd lifecycle scripts, and a third ran adversarial integration against the real fleet. The packages integrated on the first try; the two parallel builders even met in the middle, one consuming the other&#39;s test fixture mid-flight without coordination.</p>
<p class="mb-4 font-light font-serif">The integration phase earned its keep: it caught a real bug where child-process identification failed for twenty of twenty-two production servers (macOS framework Python rewrites argv in ways no unit test predicted), fixed it, and re-verified fleet-wide. Final gate before the switch: tool-list parity for all twenty-two servers, verified three separate times, plus a live end-to-end tool call through the relay. Then one command flipped the fleet config — with a timestamped backup and a one-command revert path. Total wall-clock from &quot;we have a memory problem&quot; to &quot;every new session rides the hub&quot;: about two hours.</p>
<h2 id="the-challenges-were-not-where-we-expected">The challenges were not where we expected</h2>
<p class="mb-4 font-light font-serif"><strong>The SDK had been rewritten.</strong> The installed MCP SDK was a 2.0 release whose API matched almost nothing in public documentation. The agents adapted by reading the installed source instead of trusting priors — the plan&#39;s rule that</p>
<p class="mb-4 font-light font-serif"><em>behavior contracts are binding, symbol names are not</em> paid for itself.</p>
<p class="mb-4 font-light font-serif"><strong>macOS had opinions.</strong> Getting Roost supervised by launchd became a small detective story. First failure: launchd can&#39;t open log files on an external volume. Second failure: our fix-attempt wrapped the daemon in a shell wrapper — which silently defeated the Full Disk Access grant, because TCC roots a launchd job&#39;s disk rights at the job&#39;s <em>program binary</em>. The wrapper made bash responsible instead of the granted Python. The fix was almost poetic: run the real binary directly, keep logs on the boot volume. We then proved the supervision honestly — killed the daemon with <code>kill -9</code> and watched launchd resurrect it in six seconds.</p>
<p class="mb-4 font-light font-serif"><strong>Production found the bug the tests missed.</strong> Hours after shipping, a rolling maintenance operation crashed a child during its restart window, the cleanup timed out, and an obscure asyncio property turned that into a daemon-wide outage: a task that fails <em>after</em> signaling readiness poisons its entire task group, permanently. Worse, the health endpoint kept smiling — the process was alive while its spawn machinery was dead. The same night, the fix shipped with two containment layers, a regression test proven by reverting the fix and watching it fail, and a health endpoint that now tells the truth: it returns 503 when the machinery is actually broken, not just when the process is gone.</p>
<p class="mb-4 font-light font-serif"><strong>And because we upgrade Homebrew constantly</strong>, we removed the last fragility: brew upgrades move interpreter paths, which silently voids macOS disk-access grants. Twenty of twenty-three servers now run on version-manager-owned Python paths that Homebrew can never touch, and the daemon&#39;s <code>doctor</code> command flags the stragglers — including one hiding behind a script&#39;s shebang line.</p>
<h2 id="where-it-landed">Where it landed</h2>
<p class="mb-4 font-light font-serif">One daemon at 59–83 MB plus a small warm set, at <em>any</em> session count, versus seventeen projected gigabytes. New sessions start instantly. By the same evening the hub had grown config hot-reload (registering a new MCP server is now a config stanza and one <code>reload</code> — no restart) and live tool-roster change notifications pushed to connected sessions. The best validation arrived unprompted: mid-build, a different AI session registered a brand-new MCP server through the freshly written recipe — it appeared in the hub with 115 tools, verified clean, and nobody involved in building Roost had touched it.</p>
<p class="mb-4 font-light font-serif">About 5,600 lines of production Python and 111 tests, from measurement to battle-hardened, in one day. The memory graph is the least interesting part; the interesting part is that the constraint that used to make &quot;a dozen sessions, every tool available&quot; expensive is simply gone.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 27, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-27-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-27-leverage-record.html</guid>
      <pubDate>Mon, 27 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">36 tasks. July 27, 2026 closed at 21.5x weighted leverage across 811.5 human-equivalent hours in 2,261 minutes of wall-clock time. Supervisory leverage came in at 368.9x.</p>
<p class="mb-4 font-light font-serif">That is 20.3 weeks of human-equivalent throughput in 37.7 hours. The ceiling was 250.0x; the floor was 0.7x. 33 of the 36 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Documentation</td>
      <td>100.0h</td>
      <td>24m</td>
      <td>5m</td>
      <td>250.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Deployment</td>
      <td>26.0h</td>
      <td>23m</td>
      <td>3m</td>
      <td>67.8x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>12-defect AVIAN staging punch list across 6 repos: cross-vertical SSO, demo profile parity+reload, dashboard tour persistence, modal stacking, chart/card layout, cross-domain-transfer 404, assistant…</td>
      <td>110.0h</td>
      <td>110m</td>
      <td>7m</td>
      <td>60.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Deployment</td>
      <td>120.0h</td>
      <td>155m</td>
      <td>4m</td>
      <td>46.5x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Testing</td>
      <td>36.0h</td>
      <td>55m</td>
      <td>4m</td>
      <td>39.3x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Testing</td>
      <td>100.0h</td>
      <td>155m</td>
      <td>3m</td>
      <td>38.7x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>TX source library: full TEKS (61 PDFs, ch110-130) + STAAR all grades/EOCs (108) + TTU K-12 catalog completion (190 new, 210 total) + state-grouped inbox restructure + gitignore fix, committed/pushed</td>
      <td>16.0h</td>
      <td>27m</td>
      <td>2m</td>
      <td>35.6x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Infrastructure</td>
      <td>34.0h</td>
      <td>82m</td>
      <td>4m</td>
      <td>24.9x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Top-5 TX district collections (Houston/Dallas/Cy-Fair/Northside/Katy, 6 parallel collectors) + Prosper completion + master 1208-district matrix from TAPR, committed/pushed</td>
      <td>14.0h</td>
      <td>35m</td>
      <td>2m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Wave B dispatch: 615 jobs across 4 families, per-item regen_kind + gate_job_id chaining discovered, fam4/fam5 coordinators built</td>
      <td>18.0h</td>
      <td>60m</td>
      <td>4m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Staging punch list: 21 owner-reported defects — web app (18 UI/UX fixes incl. session-machine expiry rework, viewer revert, K12 verbiage, sim drawer/completion), avian-api scenario timeout fix, activity-ui…</td>
      <td>20.0h</td>
      <td>73m</td>
      <td>10m</td>
      <td>16.4x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>ISACA CISA KG-cap investigation: bimodal distribution proof, 8-domain comparison, verdict KEEP no resynthesis</td>
      <td>6.0h</td>
      <td>22m</td>
      <td>3m</td>
      <td>16.4x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>German AP registry row repair: backfill re-run ruled out as history-corrupting, code-derived targeted UPDATE w/ optimistic concurrency, registry-completeness check spec</td>
      <td>3.0h</td>
      <td>11m</td>
      <td>3m</td>
      <td>16.4x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Close all annotation gaps to 100% across avian-engine + avian-api (91 real gaps, 29 files); exclude protobuf codegen; find+fix silently-broken SOC2 audit logging with 5 regression tests</td>
      <td>14.0h</td>
      <td>52m</td>
      <td>3m</td>
      <td>16.2x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Six new repair actions across [engine subsystem]+[engine subsystem] (goal_similarity, gw reconcile, glossary merge, topic_area, flashcard bounds, pair-row reconcile) + 104 tests + 2 design docs</td>
      <td>28.0h</td>
      <td>115m</td>
      <td>10m</td>
      <td>14.6x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Keller ISD YAG completion (all 7 workbooks, 95 tabs) + 138 CTE learning plans + Carroll ISD (Southlake) APG collection, committed/pushed</td>
      <td>5.0h</td>
      <td>21m</td>
      <td>1m</td>
      <td>14.3x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Infrastructure</td>
      <td>7.0h</td>
      <td>30m</td>
      <td>3m</td>
      <td>14.0x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Remove [ip-cluster] from supporting-services (superseded by [ip-cluster]) + fix dashboard services.json envsubst defect + containerize codex frontend (nginx static, npm ci, auth-disabled) and add…</td>
      <td>4.0h</td>
      <td>18m</td>
      <td>2m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Plano ISD collection (HS catalog + online-surface mapping) + Prosper ISD incidental pickup, committed/pushed</td>
      <td>2.0h</td>
      <td>9m</td>
      <td>1m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Coding</td>
      <td>8.0h</td>
      <td>37m</td>
      <td>1m</td>
      <td>13.0x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Testing</td>
      <td>13.0h</td>
      <td>65m</td>
      <td>3m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Testing</td>
      <td>4.0h</td>
      <td>20m</td>
      <td>1m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Testing</td>
      <td>2.5h</td>
      <td>13m</td>
      <td>2m</td>
      <td>11.5x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Audit and review</td>
      <td>16.0h</td>
      <td>85m</td>
      <td>6m</td>
      <td>11.3x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Follow-ups 1+3: [engine subsystem] failed-status honesty fix (subprocess exit + errored-flag mapping + 7 tests + stale registry test repin) and [ip-cluster] lesson slicing end-to-end (engine mastery-annotated…</td>
      <td>10.0h</td>
      <td>55m</td>
      <td>2m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Five [engine subsystem] hub bug fixes: planner repair-mode + regen-kind ordering + same-goal fallback + waive-sacrosanct upserts + restamp routing w/ param injection</td>
      <td>8.0h</td>
      <td>50m</td>
      <td>5m</td>
      <td>9.6x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>Testing</td>
      <td>1.5h</td>
      <td>10m</td>
      <td>1m</td>
      <td>9.0x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Deployment</td>
      <td>50.0h</td>
      <td>355m</td>
      <td>5m</td>
      <td>8.5x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>Infrastructure</td>
      <td>11.0h</td>
      <td>85m</td>
      <td>3m</td>
      <td>7.8x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>Pre-Algebra spec graft revert: taxonomy restored to live-package form, resolver_key + subscription_group bugs fixed, hub spec-caching discovered</td>
      <td>4.0h</td>
      <td>35m</td>
      <td>10m</td>
      <td>6.9x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>Staging live-verification audit: root-caused + fixed Sign In button (TopNav icon-sizer specificity), browser-automation sweep of all 21 punch-list items on staging-k12 (logged-out + logged-in), found…</td>
      <td>5.0h</td>
      <td>45m</td>
      <td>2m</td>
      <td>6.7x</td>
    </tr>
    <tr>
      <td>32</td>
      <td>Multi-instance [engine subsystem] worker scripts: --instance/--all/--dry-run, FDA+bash-3.2 preserved, sandbox-tested kill logic, live workers untouched</td>
      <td>3.0h</td>
      <td>28m</td>
      <td>3m</td>
      <td>6.4x</td>
    </tr>
    <tr>
      <td>33</td>
      <td>Embedder adapter worker-killer fix: unwired lmstudio_url env default root-caused across 2 repos, forced [model] routing + bounded batching, 16 regression tests</td>
      <td>6.0h</td>
      <td>65m</td>
      <td>6m</td>
      <td>5.5x</td>
    </tr>
    <tr>
      <td>34</td>
      <td>audit_specs null-tier crash fix + IB Global Politics spec repair + pre-commit regression gate</td>
      <td>3.5h</td>
      <td>38m</td>
      <td>3m</td>
      <td>5.5x</td>
    </tr>
    <tr>
      <td>35</td>
      <td>Debugging</td>
      <td>1.0h</td>
      <td>18m</td>
      <td>2m</td>
      <td>3.3x</td>
    </tr>
    <tr>
      <td>36</td>
      <td>L5 continuation: 402/402 goal-linkage dispatch, 5 domains verified 50/50 clean, fleet worker-stall diagnosed via disciplined polling</td>
      <td>2.0h</td>
      <td>180m</td>
      <td>3m</td>
      <td>0.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>36</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>811.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>2,261</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>132</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>22,666,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>21.5x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>368.9x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>20.3</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 250.0x and the lowest at 0.7x, a spread of 375.0 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 100.0 of the 811.5 human-equivalent hours, or 12 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 132 minutes against 2,261 minutes of execution, a ratio of about 1 to 17. Supervisory leverage of 368.9x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 26, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-26-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-26-leverage-record.html</guid>
      <pubDate>Sun, 26 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">18 tasks. July 26, 2026 closed at 11.8x weighted leverage across 217.0 human-equivalent hours in 1,107 minutes of wall-clock time. Supervisory leverage came in at 183.4x.</p>
<p class="mb-4 font-light font-serif">That is 5.4 weeks of human-equivalent throughput in 18.4 hours. The ceiling was 48.9x; the floor was 2.3x. 16 of the 18 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Documentation</td>
      <td>22.0h</td>
      <td>27m</td>
      <td>7m</td>
      <td>48.9x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Design and frontend</td>
      <td>16.0h</td>
      <td>32m</td>
      <td>3m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Deployment</td>
      <td>20.0h</td>
      <td>50m</td>
      <td>4m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>6AM staging content deploy automation — staging-snapshot-deploy.sh + launchd agent + installer + docs (TCC-aware; shellcheck/plutil/dry-run validated)</td>
      <td>6.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Lane 1 onesies repair: 6 families fixed w/ evidence (pair coverage 19-24pct to 100pct x4), 2 waived, 6 catalog gaps root-caused, 4 [engine subsystem] tooling bugs found</td>
      <td>19.0h</td>
      <td>65m</td>
      <td>5m</td>
      <td>17.5x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Deploy-tooling migrations gap — fargate migrate support + gated staging-migrate.sh (700 lines) + docs; caught [unreleased product] chain 4 behind</td>
      <td>8.0h</td>
      <td>33m</td>
      <td>2m</td>
      <td>14.5x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Testing</td>
      <td>6.0h</td>
      <td>25m</td>
      <td>2m</td>
      <td>14.4x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Coding</td>
      <td>34.0h</td>
      <td>150m</td>
      <td>2m</td>
      <td>13.6x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Full GO program: ship difficulty ladder + wave sizing + overtime grace + dynamic sprint clock + sprint-in-autopilot + convoy interleaving + endless afterburner + [ip-cluster] injection; web+engine prod…</td>
      <td>30.0h</td>
      <td>140m</td>
      <td>5m</td>
      <td>12.9x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Infrastructure</td>
      <td>9.0h</td>
      <td>45m</td>
      <td>3m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Engine boot cross-domain vector collision fix — if_exists=skip on auto-load path + 6 regression tests (10773 passed 93.14% cov)</td>
      <td>4.0h</td>
      <td>25m</td>
      <td>2m</td>
      <td>9.6x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Lane 6 LLM-content repair: 3 catalog gaps root-caused via source, 31 domains repaired/verified, spend recon for 4 big families, [cost]actual</td>
      <td>9.0h</td>
      <td>80m</td>
      <td>5m</td>
      <td>6.8x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Lane 5 goal-remap triage: Pre-Algebra spec-graft root cause via git archaeology, tier-normalization false positive found, cs.goal_linkage mechanism validated + 105 domains dispatched</td>
      <td>6.0h</td>
      <td>60m</td>
      <td>5m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Lane 4 dedupe/normalize: cross-lane version-race root-caused w/ evidence, dedupe_flashcards validated, 4 families source-verified + chunked</td>
      <td>8.0h</td>
      <td>80m</td>
      <td>5m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Catalog-overrides precedence fix (web+electron) + KaTeX/t-deps component defect + electron pre-commit gate — 16 tests</td>
      <td>6.0h</td>
      <td>65m</td>
      <td>2m</td>
      <td>5.5x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Lane 2 manifests/packaging: 141/141 rebuilds verified clean, frozen packages-tree + stray-classes gaps found, materializer race diagnosed</td>
      <td>8.0h</td>
      <td>105m</td>
      <td>5m</td>
      <td>4.6x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Fleet content audit re-run 411/411 + findings analysis + AP French/German candidacy-gate diagnosis + hub restart</td>
      <td>4.0h</td>
      <td>54m</td>
      <td>5m</td>
      <td>4.4x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Land orphaned backfill_node_embeddings action: [engine subsystem] worker action + [engine subsystem] hub registration, 2 suites green, both repos pushed</td>
      <td>2.0h</td>
      <td>53m</td>
      <td>6m</td>
      <td>2.3x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>18</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>217.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,107</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>71</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>7,313,025</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>11.8x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>183.4x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>5.4</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 48.9x and the lowest at 2.3x, a spread of 21.6 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 22.0 of the 217.0 human-equivalent hours, or 10 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 71 minutes against 1,107 minutes of execution, a ratio of about 1 to 16. Supervisory leverage of 183.4x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 25, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-25-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-25-leverage-record.html</guid>
      <pubDate>Sat, 25 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">9 tasks. July 25, 2026 closed at 35.9x weighted leverage across 581.0 human-equivalent hours in 971 minutes of wall-clock time. Supervisory leverage came in at 871.5x.</p>
<p class="mb-4 font-light font-serif">That is 14.5 weeks of human-equivalent throughput in 16.2 hours. The ceiling was 50.0x; the floor was 6.0x. 9 of the 9 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Overnight iOS program run: tokens+XcodeGen/RenkaraKit/Swift6+IAP backend+gateway client+SIWA/IAP client+29 components+design/a11y sweep+AASA fixes</td>
      <td>400.0h</td>
      <td>480m</td>
      <td>3m</td>
      <td>50.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>avian-app-web two-batch owner defect sweep: autopilot exam-date silent overwrite, lesson markdown/LaTeX/commentary sanitizers, narration moved off macOS voice to Rime/ElevenLabs gateway, dashboard tour…</td>
      <td>48.0h</td>
      <td>62m</td>
      <td>14m</td>
      <td>46.5x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Debugging</td>
      <td>40.0h</td>
      <td>75m</td>
      <td>2m</td>
      <td>32.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Testing</td>
      <td>12.0h</td>
      <td>28m</td>
      <td>2m</td>
      <td>25.7x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Deploy avian.renkara.com to prod; build+deploy shared VelvetRope CloudFront edge gate across 23 staging distributions in 2 AWS accounts (closing a fleet-wide unauthenticated-read exposure); rebuild 16 staging…</td>
      <td>40.0h</td>
      <td>128m</td>
      <td>6m</td>
      <td>18.8x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Restore VelvetRope gate UI: diagnosed 10KB CloudFront Functions code cap as the cause of the stripped gate, ported the full 26.5KB animated gate to Lambda@Edge with edge-side key validation (key never sent to…</td>
      <td>16.0h</td>
      <td>52m</td>
      <td>3m</td>
      <td>18.5x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Testing</td>
      <td>14.0h</td>
      <td>46m</td>
      <td>5m</td>
      <td>18.3x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Testing</td>
      <td>6.0h</td>
      <td>50m</td>
      <td>4m</td>
      <td>7.2x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>B4 [ip-cluster] lane integration: merge 25 interaction kinds, 3 wiring edits, fix manifest scanner uppercase blind spot (iOS 96-&gt;97/97), correct 55 stale registry paths, fold 6 status flips</td>
      <td>5.0h</td>
      <td>50m</td>
      <td>1m</td>
      <td>6.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>9</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>581.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>971</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>40</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>9,040,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>35.9x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>871.5x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>14.5</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 50.0x and the lowest at 6.0x, a spread of 8.3 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 400.0 of the 581.0 human-equivalent hours, or 69 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 40 minutes against 971 minutes of execution, a ratio of about 1 to 24. Supervisory leverage of 871.5x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 24, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-24-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-24-leverage-record.html</guid>
      <pubDate>Fri, 24 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">19 tasks. July 24, 2026 closed at 21.7x weighted leverage across 394.0 human-equivalent hours in 1,088 minutes of wall-clock time. Supervisory leverage came in at 295.5x.</p>
<p class="mb-4 font-light font-serif">That is 9.8 weeks of human-equivalent throughput in 18.1 hours. The ceiling was 77.1x; the floor was 9.0x. 18 of the 19 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Documentation</td>
      <td>36.0h</td>
      <td>28m</td>
      <td>8m</td>
      <td>77.1x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Post-crash recovery: root-caused WindowServer abort from crash report + session transcripts, resumed and completed the 411-domain duplication adjudication campaign from checkpoint, built and published the…</td>
      <td>13.0h</td>
      <td>15m</td>
      <td>3m</td>
      <td>52.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Coding</td>
      <td>40.0h</td>
      <td>55m</td>
      <td>3m</td>
      <td>43.6x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Three self-contained HTML doc pages for Colibri git archive (timeline + AI-productivity + finding aid; custom design system; validated palette; 4 SVG charts; dual theme)</td>
      <td>28.0h</td>
      <td>40m</td>
      <td>3m</td>
      <td>42.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Documentation triage across avian-architecture + avian-architecture-client: classify every doc (keep/archive/merge/delete), create Unbuilt Work Register, move 5 completed campaigns to history, rebuild…</td>
      <td>28.0h</td>
      <td>40m</td>
      <td>5m</td>
      <td>42.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Doc-vs-code fact-check across avian-architecture: 10 Opus agents verified every architecture doc + all 13 ADRs against real code; applied verified corrections across 6 repos; built pre-commit drift gate;…</td>
      <td>72.0h</td>
      <td>150m</td>
      <td>8m</td>
      <td>28.8x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Infrastructure</td>
      <td>7.0h</td>
      <td>17m</td>
      <td>5m</td>
      <td>24.7x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>avian-api absorbed-architecture cutover: 3-DB prod migration, sandbox Stripe+webhook, 4 deploy-defect fixes (notification key, RESP3 pin, JWT keys, OIDC issuer), plans seed, [unreleased product] migration…</td>
      <td>20.0h</td>
      <td>55m</td>
      <td>4m</td>
      <td>21.8x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Widget audit follow-up: diagnosed 12 failing interaction e2e tests across 96 widgets; fixed 3 real component defects (svg-plot 1M-tick tab hang, ForceDiagram asymmetric angle grading, mixed numeral systems…</td>
      <td>18.0h</td>
      <td>53m</td>
      <td>3m</td>
      <td>20.4x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Build action.exclude_questions versioned-exclusion ([engine subsystem]; 34 tests; audit sidecar; npz row-subset)</td>
      <td>5.0h</td>
      <td>16m</td>
      <td>4m</td>
      <td>18.8x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Build term.case_consistency lexicon+detector in checks-lib (105 tests; sentence-initial forgiveness rule)</td>
      <td>9.0h</td>
      <td>31m</td>
      <td>4m</td>
      <td>17.4x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Add dup-scores sidecar checks (score_present; over_threshold) + mcq.answer_length_tell lint to avian-content-checks</td>
      <td>6.0h</td>
      <td>25m</td>
      <td>3m</td>
      <td>14.4x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Overnight war-room recovery: crash forensics+guards; Fix2+lint+4 platform fixes landed; 86-domain AP/IB burn to full promotion; 85 widget e2e tests+5 defect fixes; docs remediation; 3 staging deploys with 4…</td>
      <td>70.0h</td>
      <td>330m</td>
      <td>10m</td>
      <td>12.7x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Build action.score_duplication ([engine subsystem]; 30 tests; 5-point registry wiring) + ADR-0013 duplication scoring</td>
      <td>6.0h</td>
      <td>30m</td>
      <td>4m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Removed 7 policy-violating CI test workflows across 6 repos (delete pure-test, strip test steps from build/publish pipelines); fixed Session Composition dead proxy — 501 + deleted doomed EngineClient methods…</td>
      <td>9.0h</td>
      <td>45m</td>
      <td>3m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Wave 1 pilot gate: found and fixed 3 [engine subsystem] hub defects blocking the dup campaign (missing catalog entries, missing job-kind/TTL registration, rebuild_manifest preview ignoring _source_hashes)…</td>
      <td>9.0h</td>
      <td>48m</td>
      <td>2m</td>
      <td>11.2x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Engine session dup-exclusion scheduler (3 choke points; flag off; 46 new tests; byte-identical proof)</td>
      <td>10.0h</td>
      <td>58m</td>
      <td>4m</td>
      <td>10.3x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Add sched_grp session-exclusion clusters to dup_scores sidecar (union-find; schema 1.1.0; 13 tests)</td>
      <td>2.0h</td>
      <td>12m</td>
      <td>2m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>chem1b_ttu exam_tips population: TTU exam-guide research (agent), spec enrichment + consolidation-leftover domain_id restamp, worker-pool prereq-flag fix, 3 platform defects found (content-gen store seeding,…</td>
      <td>6.0h</td>
      <td>40m</td>
      <td>2m</td>
      <td>9.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>19</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>394.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,088</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>80</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>9,838,752</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>21.7x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>295.5x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>9.8</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 77.1x and the lowest at 9.0x, a spread of 8.6 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 36.0 of the 394.0 human-equivalent hours, or 9 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 80 minutes against 1,088 minutes of execution, a ratio of about 1 to 14. Supervisory leverage of 295.5x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 23, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-23-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-23-leverage-record.html</guid>
      <pubDate>Thu, 23 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">5 tasks. July 23, 2026 closed at 19.2x weighted leverage across 168.0 human-equivalent hours in 525 minutes of wall-clock time. Supervisory leverage came in at 336.0x.</p>
<p class="mb-4 font-light font-serif">That is 4.2 weeks of human-equivalent throughput in 8.8 hours. The ceiling was 28.8x; the floor was 10.1x. 5 of the 5 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Feedback batch 1: in-flow header vertical tag + DS 0.2.4/0.2.5 (Brand.subtitle + Button nowrap) + engine math study-plan variety + calibration skip discount + unit descriptions (snapshot v15) + unicode-math…</td>
      <td>24.0h</td>
      <td>50m</td>
      <td>5m</td>
      <td>28.8x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>7-lane Opus orchestration (17-item feedback batch): session auto-open+Convoy+math wiring, lesson viewer+math rendering+sim modal, dashboard/course-detail 10-defect sweep, sim chrome gating, autopilot date…</td>
      <td>80.0h</td>
      <td>170m</td>
      <td>10m</td>
      <td>28.2x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Zero-floor evening: silent no-op regen defect root-caused+fixed (hub item_refs nesting + worker guard), scenario_rubric_fields generator, criticals spec-sync fix both sides, 9 repair chains -&gt;…</td>
      <td>28.0h</td>
      <td>105m</td>
      <td>4m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Design and frontend</td>
      <td>20.0h</td>
      <td>105m</td>
      <td>6m</td>
      <td>11.4x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Content burn-downs to 0 HIGH: CHEM1B (17H→0, 88M→58M, promoted), Algebra II anaphoric stems (promoted), AP Macro+Micro full chains (promoted) + embedder-seam and regen-kind-inference platform fixes</td>
      <td>16.0h</td>
      <td>95m</td>
      <td>5m</td>
      <td>10.1x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>5</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>168.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>525</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>30</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>2,925,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>19.2x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>336.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>4.2</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 28.8x and the lowest at 10.1x, a spread of 2.9 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 24.0 of the 168.0 human-equivalent hours, or 14 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 30 minutes against 525 minutes of execution, a ratio of about 1 to 18. Supervisory leverage of 336.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 22, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-22-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-22-leverage-record.html</guid>
      <pubDate>Wed, 22 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">4 tasks. July 22, 2026 closed at 91.4x weighted leverage across 1,461.0 human-equivalent hours in 959 minutes of wall-clock time. Supervisory leverage came in at 2578.2x.</p>
<p class="mb-4 font-light font-serif">That is 36.5 weeks of human-equivalent throughput in 16.0 hours. The ceiling was 195.4x; the floor was 16.5x. 3 of the 4 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Testing</td>
      <td>1130.0h</td>
      <td>347m</td>
      <td>15m</td>
      <td>195.4x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Infrastructure</td>
      <td>200.0h</td>
      <td>152m</td>
      <td>6m</td>
      <td>78.9x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Demo-2 autonomous overnight run (full authority): fixed+deployed+live-verified all 6 founder defects — the keystone auth/logout bug (email logins never armed boot-refresh; new-tab logout),…</td>
      <td>120.0h</td>
      <td>420m</td>
      <td>8m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Reviewed crypto-investing book first draft; generated 14 AI illustrations via Replicate Recraft V3 and 8 styled matplotlib data charts, then assembled a professional 110-page PDF (pandoc/xelatex) with full…</td>
      <td>11.0h</td>
      <td>40m</td>
      <td>5m</td>
      <td>16.5x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>4</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>1,461.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>959</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>34</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>28,950,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>91.4x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>2578.2x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>36.5</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 195.4x and the lowest at 16.5x, a spread of 11.8 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 1,130.0 of the 1,461.0 human-equivalent hours, or 77 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 34 minutes against 959 minutes of execution, a ratio of about 1 to 28. Supervisory leverage of 2578.2x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 21, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-21-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-21-leverage-record.html</guid>
      <pubDate>Tue, 21 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">4 tasks. July 21, 2026 closed at 15.3x weighted leverage across 114.5 human-equivalent hours in 450 minutes of wall-clock time. Supervisory leverage came in at 327.1x.</p>
<p class="mb-4 font-light font-serif">That is 2.9 weeks of human-equivalent throughput in 7.5 hours. The ceiling was 20.4x; the floor was 6.0x. 3 of the 4 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Convert Colibri work-history docs (README/TIMELINE/AI_PRODUCTIVITY) into three cross-linked HTML artifacts with SVG charts plus bright template redesign</td>
      <td>8.5h</td>
      <td>25m</td>
      <td>3m</td>
      <td>20.4x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>CHEM1B post-demo directive: 31-defect fix wave across 9 repos (engine session-durability+chat-scoping+DTO-flags, api 6 proxies+CORS-safe-500, web 16 flow fixes+FAQ/[unreleased product]/demo chrome, content…</td>
      <td>90.0h</td>
      <td>300m</td>
      <td>10m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>E2E activity coverage: 10 new Playwright specs (glossary match_board/cloze_sprint + 8 uncovered activities) driven to completion + activity-matrix rebuilt as anti-regression meta-test; surfaced…</td>
      <td>9.0h</td>
      <td>55m</td>
      <td>4m</td>
      <td>9.8x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Deployment</td>
      <td>7.0h</td>
      <td>70m</td>
      <td>4m</td>
      <td>6.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>4</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>114.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>450</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>21</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>1,710,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>15.3x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>327.1x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>2.9</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 20.4x and the lowest at 6.0x, a spread of 3.4 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 8.5 of the 114.5 human-equivalent hours, or 7 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 21 minutes against 450 minutes of execution, a ratio of about 1 to 21. Supervisory leverage of 327.1x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 20, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-20-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-20-leverage-record.html</guid>
      <pubDate>Mon, 20 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">16 tasks. July 20, 2026 closed at 30.0x weighted leverage across 692.5 human-equivalent hours in 1,383 minutes of wall-clock time. Supervisory leverage came in at 784.0x.</p>
<p class="mb-4 font-light font-serif">That is 17.3 weeks of human-equivalent throughput in 23.1 hours. The ceiling was 74.1x; the floor was 8.7x. 16 of the 16 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Build all 120 earth_space-cluster interactive simulations on the generalized harness (earth/space contract; build-&gt;verify workflow; recovered from 3 session-limit cutoffs + 2 ECONNRESET blips via…</td>
      <td>420.0h</td>
      <td>340m</td>
      <td>3m</td>
      <td>74.1x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Content production</td>
      <td>24.0h</td>
      <td>48m</td>
      <td>10m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Fleet units distillation: cert golden gate 96pct + 73 packages stamped + hardlink bug found; orchestrator store restore 73/73 via opus agent</td>
      <td>20.0h</td>
      <td>43m</td>
      <td>2m</td>
      <td>27.9x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Syllabus goal matcher + golden benchmark + course compiler in [engine subsystem] (88 tests; 86.2pct golden accuracy) via opus agent</td>
      <td>36.0h</td>
      <td>105m</td>
      <td>2m</td>
      <td>20.6x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Engine de-flag: collapse 5 ship-dark flags to always-on + dead-seam and surrogate deletion via opus agent</td>
      <td>14.0h</td>
      <td>41m</td>
      <td>2m</td>
      <td>20.5x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Content topology map: read-only inventory of all local (~70GB) + cloud (~216GB) AVIAN content copies with inode/hardlink/manifest verification, source-of-truth vs derived vs rollback-buffer classification,…</td>
      <td>12.0h</td>
      <td>40m</td>
      <td>5m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Close out 2026-07-19 audit: 3 Info items incl. a REAL IDOR fix across 27 avian-api routes (entitlement audit → require_own_entity + Tenant-Isolation Contract) + stale-docs rewrites + web mobile-axe-runner…</td>
      <td>20.0h</td>
      <td>70m</td>
      <td>2m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Fix all 14 Low staging-audit findings across 6 repos (engine/avian-api/auth/notification/web + onboarding-lows-in-avian-api) + build NEW automation-notification-retention EventBridge Lambda from scratch (repo…</td>
      <td>28.0h</td>
      <td>100m</td>
      <td>2m</td>
      <td>16.8x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Course-flow smoke harness web+electron with isolated 3-service stack; found entity-id 403 + boot-hang root cause via opus agent</td>
      <td>20.0h</td>
      <td>80m</td>
      <td>2m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Glossary plumbing: [engine subsystem]-runtime Phase 0 + hybrid checks migration + [content generation] lane across 3 repos (170 new tests) via opus agent</td>
      <td>22.0h</td>
      <td>96m</td>
      <td>2m</td>
      <td>13.8x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Honest full-corpus package audit (408 pkgs: 251 clean, 9 real defects) + verified [restricted] enforcement airtight end-to-end (wiring + 3 enforcement paths — contractual content cannot leak) + queued…</td>
      <td>16.0h</td>
      <td>70m</td>
      <td>10m</td>
      <td>13.7x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Glossary foundations: ADR-0012 acceptance + gl.* check family (8 checks; 65 tests; 97.88% cov) via opus agent</td>
      <td>10.0h</td>
      <td>50m</td>
      <td>2m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>WP-C4 course catalog surfacing web+electron with prepared engine patch + warm-start un-gate via opus agent</td>
      <td>12.0h</td>
      <td>65m</td>
      <td>2m</td>
      <td>11.1x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Smoke re-run: file:// catalog defect found+fixed in electron; all 5 course flows PASS both clients; CHEM1B free-tier confirmed via opus agent</td>
      <td>18.0h</td>
      <td>100m</td>
      <td>2m</td>
      <td>10.8x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Engine lane: boot-hang [engine subsystem]-sweep root cause + skip/scope fix (measured 2.4x) and WP-C4 catalog projection via opus agent</td>
      <td>12.5h</td>
      <td>80m</td>
      <td>2m</td>
      <td>9.4x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Deployment</td>
      <td>8.0h</td>
      <td>55m</td>
      <td>3m</td>
      <td>8.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>16</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>692.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,383</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>53</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>41,442,958</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>784.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>17.3</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 74.1x and the lowest at 8.7x, a spread of 8.5 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 420.0 of the 692.5 human-equivalent hours, or 61 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 53 minutes against 1,383 minutes of execution, a ratio of about 1 to 26. Supervisory leverage of 784.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 19, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-19-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-19-leverage-record.html</guid>
      <pubDate>Sun, 19 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">12 tasks. July 19, 2026 closed at 51.8x weighted leverage across 1,310.5 human-equivalent hours in 1,517 minutes of wall-clock time. Supervisory leverage came in at 1355.7x.</p>
<p class="mb-4 font-light font-serif">That is 32.8 weeks of human-equivalent throughput in 25.3 hours. The ceiling was 86.2x; the floor was 11.7x. 12 of the 12 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Build all 138 computer_science-cluster interactive simulations on the generalized harness (CS contract with real-algorithm correctness bar; build-&gt;verify workflow; recovered from 2 session-limit cutoffs via…</td>
      <td>460.0h</td>
      <td>320m</td>
      <td>3m</td>
      <td>86.2x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Build all 131 core_math-cluster interactive simulations on the generalized harness (math contract with exact-arithmetic correctness bar, jsxgraph-&gt;SVG/canvas, build-&gt;verify workflow, hand-recovered 2 partials…</td>
      <td>430.0h</td>
      <td>300m</td>
      <td>3m</td>
      <td>86.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Remediate all 3 Criticals + 22 Highs from staging readiness audit across 8 repos (engine, avian-api, auth, notification, onboarding, deletion-Lambda, web app, deploy infra): health-check auth exemption,…</td>
      <td>80.0h</td>
      <td>60m</td>
      <td>4m</td>
      <td>80.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Full readiness audit: orchestrated 5 parallel deep security+a11y sub-audits (engine/api/web-app/notification/onboarding), reconciled ~75 findings with the canonical pipeline report, produced a consolidated…</td>
      <td>40.0h</td>
      <td>50m</td>
      <td>5m</td>
      <td>48.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Full-monorepo canonical-values reconciliation via 36-agent workflow (recompute every value from source: content aggregates across 407 packages, specs, labs, 112-repo build metrics, [ip] from application…</td>
      <td>30.0h</td>
      <td>55m</td>
      <td>8m</td>
      <td>32.7x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Fix all 20 Medium staging-audit findings across 5 repos (engine/avian-api/auth/notification/web) + repair avian-api uncallable onboarding module + port S3 PII-delete endpoint + decommission onboarding-service…</td>
      <td>42.0h</td>
      <td>80m</td>
      <td>5m</td>
      <td>31.5x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Deploy avian-engine to AVIAN staging from staging branch: fixed the image build (uv --no-sources for vendored path-deps, numba&gt;=0.61 for arm64 llvmlite wheel), added a CPU &#39;staging&#39; profile (cloud d=512 +…</td>
      <td>40.0h</td>
      <td>95m</td>
      <td>4m</td>
      <td>25.3x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Deployment</td>
      <td>140.0h</td>
      <td>360m</td>
      <td>15m</td>
      <td>23.3x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Fix engine SQLAlchemy boot error + migrate engine to GPU in AVIAN staging: resolved branched-alembic-heads (ran &#39;upgrade heads&#39; -&gt; created governance_audit_log + all engine tables), confirmed GPU quota…</td>
      <td>24.0h</td>
      <td>85m</td>
      <td>4m</td>
      <td>16.9x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Populate AVIAN staging with full prod content + fix engine cold-boot OOM: server-side synced 6370 objects/16.5GB domain packages prod-&gt;staging, diagnosed boot-cache-miss forcing a full in-RAM parse (prod…</td>
      <td>16.0h</td>
      <td>70m</td>
      <td>3m</td>
      <td>13.7x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Prep omniscient sweep for all live/candidate/beta packages (403 domains, 3-at-a-time): root-caused engine symlink-overlay data-dir trap in gen_omni_sweep.py readiness gate; added start-temporal-worker.sh…</td>
      <td>5.0h</td>
      <td>24m</td>
      <td>2m</td>
      <td>12.5x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Debugging</td>
      <td>3.5h</td>
      <td>18m</td>
      <td>2m</td>
      <td>11.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>12</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>1,310.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,517</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>58</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>67,850,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>51.8x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>1355.7x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>32.8</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 86.2x and the lowest at 11.7x, a spread of 7.4 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 460.0 of the 1,310.5 human-equivalent hours, or 35 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 58 minutes against 1,517 minutes of execution, a ratio of about 1 to 26. Supervisory leverage of 1355.7x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 18, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-18-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-18-leverage-record.html</guid>
      <pubDate>Sat, 18 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">19 tasks. July 18, 2026 closed at 34.3x weighted leverage across 1,143.0 human-equivalent hours in 1,998 minutes of wall-clock time. Supervisory leverage came in at 672.4x.</p>
<p class="mb-4 font-light font-serif">That is 28.6 weeks of human-equivalent throughput in 33.3 hours. The ceiling was 87.1x; the floor was 3.3x. 19 of the 19 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Build all 102 physics-cluster interactive simulations on the generalized domain-agnostic harness (build-&gt;verify workflow, physics authoring contract with real constants + numerical integration,…</td>
      <td>370.0h</td>
      <td>255m</td>
      <td>3m</td>
      <td>87.1x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Build all 139 biology-cluster interactive simulations on the generalized domain-agnostic harness (biology authoring contract with real base-pairing/codons/Nernst/Hardy-Weinberg, rive/lottie-&gt;procedural…</td>
      <td>500.0h</td>
      <td>350m</td>
      <td>3m</td>
      <td>85.7x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>AVIAN root cleanup: 14-agent triage of 11 root docs vs codebase + 5-agent root-file dependency mapping; 4 designs converted to artifacts, 4 plans moved into repos, 6 files deleted (verified); refs repointed…</td>
      <td>20.0h</td>
      <td>50m</td>
      <td>3m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Phase 8.1 npm-workspace elimination: Verdaccio registry stood up in supporting-services (pinned image + config + idempotent publish-libs.sh w/ frozen historical versions), 13 pkg versions published, 19…</td>
      <td>32.0h</td>
      <td>105m</td>
      <td>3m</td>
      <td>18.3x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Feature-flag cleanup: cut 16 stale/dead+retire flags across 6 repos (gateway/web/android/supporting-services/[engine subsystem]/engine) with per-flag consumer checks + suite runs; reviewed 7 ADR ship-dark…</td>
      <td>16.0h</td>
      <td>55m</td>
      <td>4m</td>
      <td>17.5x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Deployment</td>
      <td>10.0h</td>
      <td>35m</td>
      <td>4m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>CHEM1B web-serving audit: 10 gaps (2 engine inference bugs + catalog typo + unreachable sims/cases/scenarios + dead exam tips)</td>
      <td>18.0h</td>
      <td>65m</td>
      <td>4m</td>
      <td>16.6x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Coverage agent B: api secondary +659 statements (214 tests; composite exam_metadata bugfix)</td>
      <td>28.0h</td>
      <td>105m</td>
      <td>5m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>WP-3.5 electron Direct-To parity (unit rail/chip/nudge/badges) + NextActionsResponse bug fixed in 2 places (53 tests)</td>
      <td>14.0h</td>
      <td>55m</td>
      <td>4m</td>
      <td>15.3x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Phase 8.3 root CLAUDE/AGENTS stub-shrink (content -&gt; docs/avian-architecture project-reference docs, @import stub + plain stub) + full Verdaccio pipeline proof: publish script hardened (legacy-tag retry,…</td>
      <td>6.0h</td>
      <td>25m</td>
      <td>2m</td>
      <td>14.4x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>WP-3.4 web Direct-To UI: UnitRail/FocusChip/nudge/badges + advance passthrough + NextActions shape bugfix (53 tests)</td>
      <td>22.0h</td>
      <td>100m</td>
      <td>5m</td>
      <td>13.2x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>CHEM1B content repair: 389 validated questions (9 goals; 99.74% pass) + combined gas law lesson + exam tips + corpus v42 pipeline + engine reload</td>
      <td>20.0h</td>
      <td>100m</td>
      <td>8m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Deployment</td>
      <td>24.0h</td>
      <td>120m</td>
      <td>12m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Catalog schema completeness (sims/cases/scenarios fields) + loud categoryKey validation (15 tests; surfaced 15-course drift)</td>
      <td>6.0h</td>
      <td>33m</td>
      <td>4m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>CHEM1B serving fixes: manifest-first category/tier + sims/case/scenario grants + exam-tips route+UI across 4 repos</td>
      <td>20.0h</td>
      <td>115m</td>
      <td>15m</td>
      <td>10.4x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>WP-2.5 electron parity: Position Fix flow + IPC 5-layer port + QA gate + tips card (514 tests; found domainId wiring bug)</td>
      <td>7.0h</td>
      <td>70m</td>
      <td>5m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Prevention system: per-goal coverage checker + assembler density backstop + [engine subsystem] invariant + fleet census (11 true zero-question instances) + ChemFund v42 local serve (49 tests)</td>
      <td>21.0h</td>
      <td>225m</td>
      <td>6m</td>
      <td>5.6x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Engine /domains/load symlink containment + idempotency verification; golden harness 8/8 (12 tests)</td>
      <td>4.0h</td>
      <td>45m</td>
      <td>4m</td>
      <td>5.3x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Console-error fixes (TTS wrong-origin 404, bug-report 401, banners 502 degradation) + full activity-type vocabulary inventory (476 usages, 2 lists) + ship-now planSession compat fix so study-plan…</td>
      <td>5.0h</td>
      <td>90m</td>
      <td>8m</td>
      <td>3.3x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>19</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>1,143.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,998</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>102</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>68,892,048</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>34.3x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>672.4x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>28.6</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 87.1x and the lowest at 3.3x, a spread of 26.1 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 370.0 of the 1,143.0 human-equivalent hours, or 32 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 102 minutes against 1,998 minutes of execution, a ratio of about 1 to 20. Supervisory leverage of 672.4x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 17, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-17-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-17-leverage-record.html</guid>
      <pubDate>Fri, 17 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">20 tasks. July 17, 2026 closed at 13.2x weighted leverage across 279.2 human-equivalent hours in 1,265 minutes of wall-clock time. Supervisory leverage came in at 197.1x.</p>
<p class="mb-4 font-light font-serif">That is 7.0 weeks of human-equivalent throughput in 21.1 hours. The ceiling was 22.7x; the floor was 3.3x. 16 of the 20 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Coverage agent A: api core +923 statements (328 tests; 2 production bugs fixed incl. never-working micro-challenge endpoint)</td>
      <td>28.0h</td>
      <td>74m</td>
      <td>6m</td>
      <td>22.7x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Scenario E PASS: real-study advancement harness (1040 lines), 3 transitions verified live, 2 methodology fixes (ground-truth polling, order-0 gate), competence-floor chain traced</td>
      <td>9.0h</td>
      <td>28m</td>
      <td>1m</td>
      <td>19.3x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Fix loop: 4 CONCERN packages root-caused (store/staging sync gap, NOT content) + fixed free via re-ingest/materialize/reload — re-sweep 4/4 at 100% (1363/1363)</td>
      <td>8.0h</td>
      <td>29m</td>
      <td>1m</td>
      <td>16.6x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Fix local avatar upload: endpoint-aware S3 helper in avian-api routing avatars/resume uploads to the local MinIO emulator; auth-service parity; provisioned buckets + policies</td>
      <td>5.5h</td>
      <td>20m</td>
      <td>3m</td>
      <td>16.5x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>WP-3.1 engine focus store + dual-pool planner: 009 focus table + FocusConfig + frontier/maintenance budgets + CRUD (78 tests)</td>
      <td>36.0h</td>
      <td>135m</td>
      <td>6m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Coverage agent C: manifold/ring/training/governance +634 statements (159 tests; hnswlib unlock)</td>
      <td>20.0h</td>
      <td>75m</td>
      <td>5m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>WP-2.1 engine placement service: 3-layer seeding + entity_placements + TT endpoints + posterior placement priors (85 tests)</td>
      <td>28.0h</td>
      <td>110m</td>
      <td>6m</td>
      <td>15.3x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Fix profile avatar upload (local S3 emulator) + P0 gateway entitlement bug blocking all paid courses + remove fabricated-question fallback + honest error UI, then audit &amp; fix all 17 web-app error-maskings…</td>
      <td>26.0h</td>
      <td>110m</td>
      <td>5m</td>
      <td>14.2x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>SAP-C02 scenario fix loop: 4 ceilings (pace/decay/seeding-trap/gate-deadlock) + 2 engine features + scenario C exam PASS</td>
      <td>32.0h</td>
      <td>150m</td>
      <td>12m</td>
      <td>12.8x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Debugging</td>
      <td>6.0h</td>
      <td>29m</td>
      <td>1m</td>
      <td>12.4x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>WP-2.2 seeded-prior decay blend: two-gate root cause (honesty clamp dominant), patch-package application + fixture repairs, ADR-0011 — suite 6999 green</td>
      <td>16.0h</td>
      <td>79m</td>
      <td>2m</td>
      <td>12.2x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Scenario B: 9-journey mid-course omniscient sweep + comparator — 18/18 readiness, 6/8 faster-assertion misses root-caused to unbuilt WP-2.2 seeded-prior cliff (telemetry archaeology + engine source trace)</td>
      <td>5.0h</td>
      <td>28m</td>
      <td>1m</td>
      <td>10.7x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>WP-2.4 web Position Fix flow + CHEM1B catalog surfacing + QA-status gate (351 tests green)</td>
      <td>20.0h</td>
      <td>115m</td>
      <td>10m</td>
      <td>10.4x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Audit and review</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>WP-3.3 gateway focus proxies + focus.updated SSE + completion-candidate seam (23 tests)</td>
      <td>5.0h</td>
      <td>30m</td>
      <td>5m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>WP-3.2 unit completion gate + consent-gated advancement (62 tests; effort-bounded amendment)</td>
      <td>16.0h</td>
      <td>105m</td>
      <td>7m</td>
      <td>9.1x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>WP-2.3 gateway placement proxies + SSE placement.completed + enrollment seed (29 tests + pre-existing coverage gate fix)</td>
      <td>6.0h</td>
      <td>40m</td>
      <td>4m</td>
      <td>9.0x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>WP-3.2 follow-up: unit.completion-candidate relay wired to real engine payload (4 tests)</td>
      <td>2.5h</td>
      <td>17m</td>
      <td>3m</td>
      <td>8.8x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>[engine subsystem] telemetry WP: RAG/omni telemetry parity + comparator snapshot fallback + snapshot-dir relocation + Temporal DB persistence (59 tests)</td>
      <td>6.0h</td>
      <td>50m</td>
      <td>3m</td>
      <td>7.2x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Fleet sweep 100% closure: final 3 stale-staging misses fixed (signature-verified, push+materialize+reload, re-sweep 3/3 at 100%) — 406/406 final tally</td>
      <td>1.2h</td>
      <td>23m</td>
      <td>1m</td>
      <td>3.3x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>20</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>279.2</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,265</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>85</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>12,122,660</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>13.2x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>197.1x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>7.0</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 22.7x and the lowest at 3.3x, a spread of 7.0 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 28.0 of the 279.2 human-equivalent hours, or 10 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 85 minutes against 1,265 minutes of execution, a ratio of about 1 to 15. Supervisory leverage of 197.1x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 16, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-16-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-16-leverage-record.html</guid>
      <pubDate>Thu, 16 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">22 tasks. July 16, 2026 closed at 17.5x weighted leverage across 251.0 human-equivalent hours in 859 minutes of wall-clock time. Supervisory leverage came in at 485.8x.</p>
<p class="mb-4 font-light font-serif">That is 6.3 weeks of human-equivalent throughput in 14.3 hours. The ceiling was 36.9x; the floor was 6.5x. 22 of the 22 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>WP-1.1 course assembler v1: 10 modules + CLI + 99 tests, CHEM1B dry run verified (determinism, integrity, weights)</td>
      <td>24.0h</td>
      <td>39m</td>
      <td>1m</td>
      <td>36.9x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>WP-0.2 hand-authored CHEM1B course spec (units 11-17, 69-leaf exact coverage, drift reconciliation, chem-nuc verification)</td>
      <td>3.0h</td>
      <td>5m</td>
      <td>1m</td>
      <td>36.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>beta→candidate fleet migration: 69 specs+registry+staging coherently, ~407 reconciliation (dir-count myth), 2 production bugs found ([quality threshold] NULL, funnel-apply promotion integrity)</td>
      <td>16.0h</td>
      <td>31m</td>
      <td>1m</td>
      <td>31.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>WP-0.6+0.7: checker blindspot fix (4-layout validation, 3 real defects surfaced), hardlink-safe npz migration script (guardrail-halted), atoms_v2 de-flag, images glob recursion, waiver lifecycle resolution</td>
      <td>14.0h</td>
      <td>31m</td>
      <td>2m</td>
      <td>27.1x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>WP-0.5 engine simulation-&gt;lab rename + assign-simulation-to-goal path (false-premise correction, 3 latent catalog regressions caught, 29 tests)</td>
      <td>22.0h</td>
      <td>50m</td>
      <td>1m</td>
      <td>26.4x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>[engine subsystem] WP-7.1+7.2: units-aware loaders, StudentProfile mid-course fields, graded-fidelity unit_placement bootstrap, seed-from-mastery hook (+104 tests)</td>
      <td>14.0h</td>
      <td>35m</td>
      <td>1m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Scenario B/C/D kit: [engine subsystem] fixes (lessons-root, exam-label null), goal-level mid-course fallback, 10 scenario profiles, readiness comparator validated on live sweep data (+80 tests)</td>
      <td>20.0h</td>
      <td>52m</td>
      <td>1m</td>
      <td>23.1x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>WP-0.1 course-spec schema/parser/validator across 2 repos + WP-0.4 status investigation (refused stale reclassification via ADR archaeology)</td>
      <td>6.0h</td>
      <td>16m</td>
      <td>1m</td>
      <td>22.5x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Fleet omniscient sweep prep: 406 profiles, Temporal runner readiness + gotchas (flat-dir, --clouds AND), harvest script (found Temporal path drops verification rows), pilot config</td>
      <td>7.0h</td>
      <td>21m</td>
      <td>1m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Audit and review</td>
      <td>40.0h</td>
      <td>125m</td>
      <td>3m</td>
      <td>19.2x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>WP-1.2+1.3: CHEM1B registered via real [engine subsystem] ingest/materialization + golden E2E harness (correctly blocked on corpus embedding gap; status-model finding)</td>
      <td>8.0h</td>
      <td>31m</td>
      <td>1m</td>
      <td>15.5x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Content production</td>
      <td>12.0h</td>
      <td>50m</td>
      <td>2m</td>
      <td>14.4x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>WP-1.4: embeddings backfill (identity-gated e5 backfill of 40 nodes/40 pairs), corpus v41 + course package v2 via real ingest, golden E2E 8/8 PASS, amplify_pairs root cause</td>
      <td>12.0h</td>
      <td>52m</td>
      <td>1m</td>
      <td>13.8x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Engine fix batch: taxonomy topic-key alias (58 pkgs), /domains/load idempotency, exam passed:null default+provenance (+15 tests, fixture blast-radius audit, restart forensics)</td>
      <td>6.0h</td>
      <td>28m</td>
      <td>1m</td>
      <td>12.9x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Migration completion: principled gate fix (shared REGISTRY, ALWAYS_OPTIONAL), 74 domains to candidate (399 total), regen pilot (identity-gated, 54 stems/s, legacy-embedding non-reproducibility finding)</td>
      <td>10.0h</td>
      <td>48m</td>
      <td>2m</td>
      <td>12.5x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>[engine subsystem]-service: fix stale MCQ mock fixtures dropped by new structural shape gate (root-caused to 58d2ce3, verified not a production regression)</td>
      <td>2.0h</td>
      <td>11m</td>
      <td>1m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>WP-0.5b client lab/simulation split (web+electron+catalog, latent settings bug caught) + mathjs/jsxgraph dependency fix + rescue of 4 stranded lib commits</td>
      <td>6.0h</td>
      <td>35m</td>
      <td>1m</td>
      <td>10.3x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>FOCPRO re-embed (broken node/pair embeddings) + 83 non-serving finding waivers + fixture soft-delete; waive-logic caught 5 held real gaps</td>
      <td>5.0h</td>
      <td>30m</td>
      <td>2m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>WP-0.3 engine units-awareness: authored manifest units + fallback, unit filter fix, per-unit rollups, snapshot v10 (patch-verified handoff applied, 6933 tests green)</td>
      <td>7.0h</td>
      <td>42m</td>
      <td>1m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>PMP 550 malformed-MCQ deterministic repair + structural MCQ shape generation gate (module+wiring+12 tests, committed 58d2ce3)</td>
      <td>7.0h</td>
      <td>45m</td>
      <td>3m</td>
      <td>9.3x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Fleet-engine swap + pilot omniscient sweep: 408 domains loaded (156s bulk reload, no wedge), pilot 3/3 at 100% (834/834), 2 engine bugs root-caused (exam-label null for 142 pkgs, taxonomy 500 for 58…</td>
      <td>4.0h</td>
      <td>27m</td>
      <td>1m</td>
      <td>8.9x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Atoms production-bug fix (d61123a) + generated/promoted 3 atoms domains (FOCE/Linux+/Network+); caught+fixed own finalizer premature-promotion race + coverage/criticals/dup-option fixes</td>
      <td>6.0h</td>
      <td>55m</td>
      <td>2m</td>
      <td>6.5x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>22</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>251.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>859</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>31</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>9,987,649</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>17.5x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>485.8x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>6.3</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 36.9x and the lowest at 6.5x, a spread of 5.6 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 24.0 of the 251.0 human-equivalent hours, or 10 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 31 minutes against 859 minutes of execution, a ratio of about 1 to 28. Supervisory leverage of 485.8x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 15, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-15-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-15-leverage-record.html</guid>
      <pubDate>Wed, 15 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">3 tasks. July 15, 2026 closed at 22.1x weighted leverage across 57.0 human-equivalent hours in 155 minutes of wall-clock time. Supervisory leverage came in at 190.0x.</p>
<p class="mb-4 font-light font-serif">That is 1.4 weeks of human-equivalent throughput in 2.6 hours. The ceiling was 42.0x; the floor was 5.0x. 3 of the 3 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>CHEM1B mid-course placement + unit focus: 5-repo research, [ip] gap analysis, design contracts, and course-experience master plan</td>
      <td>28.0h</td>
      <td>40m</td>
      <td>8m</td>
      <td>42.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Content perfection plan + fleet fabrication forensics + [content generation] restart with fab-gate + sims integration research</td>
      <td>24.0h</td>
      <td>55m</td>
      <td>5m</td>
      <td>26.2x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Full-fleet omniscient sweep setup ([engine subsystem]): generalized profile generator to all 407 packages, resolved 4-dir content-alignment via AVIAN_DOMAIN_ROOT, added 3rd concurrency worker, built detached…</td>
      <td>5.0h</td>
      <td>60m</td>
      <td>5m</td>
      <td>5.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>3</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>57.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>155</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>18</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>1,160,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>22.1x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>190.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>1.4</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 42.0x and the lowest at 5.0x, a spread of 8.4 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 28.0 of the 57.0 human-equivalent hours, or 49 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 18 minutes against 155 minutes of execution, a ratio of about 1 to 9. Supervisory leverage of 190.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 14, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-14-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-14-leverage-record.html</guid>
      <pubDate>Tue, 14 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">2 tasks. July 14, 2026 closed at 50.3x weighted leverage across 398.0 human-equivalent hours in 475 minutes of wall-clock time. Supervisory leverage came in at 2985.0x.</p>
<p class="mb-4 font-light font-serif">That is 9.9 weeks of human-equivalent throughput in 7.9 hours. The ceiling was 82.9x; the floor was 5.4x. 2 of the 2 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Review core/avian-simulations and build all 106 chemistry-cluster interactive simulations (SDK-based, scientifically-correct, self-validating; build-&gt;verify workflow orchestration + independent validator +…</td>
      <td>380.0h</td>
      <td>275m</td>
      <td>5m</td>
      <td>82.9x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Root-cause omniscient coverage gap ([engine subsystem]) + build/validate paraphrase-tolerant semantic option matcher; reject twin-gap guard via fidelity metric</td>
      <td>18.0h</td>
      <td>200m</td>
      <td>3m</td>
      <td>5.4x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>2</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>398.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>475</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>8</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>26,850,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>50.3x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>2985.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>9.9</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 82.9x and the lowest at 5.4x, a spread of 15.4 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 380.0 of the 398.0 human-equivalent hours, or 95 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 8 minutes against 475 minutes of execution, a ratio of about 1 to 59. Supervisory leverage of 2985.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 13, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-13-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-13-leverage-record.html</guid>
      <pubDate>Mon, 13 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">7 tasks. July 13, 2026 closed at 10.5x weighted leverage across 51.5 human-equivalent hours in 293 minutes of wall-clock time. Supervisory leverage came in at 71.9x.</p>
<p class="mb-4 font-light font-serif">That is 1.3 weeks of human-equivalent throughput in 4.9 hours. The ceiling was 21.8x; the floor was 4.0x. 7 of the 7 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>[engine subsystem] Temporal migration V1 (journey-as-activity): researched [engine subsystem] pattern + [engine subsystem] orchestration (2 agents), built workflow/activity/client/worker/config/CLI mirroring…</td>
      <td>20.0h</td>
      <td>55m</td>
      <td>4m</td>
      <td>21.8x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Gated case-study repair lane in backfill_content (agent-built)</td>
      <td>5.5h</td>
      <td>18m</td>
      <td>6m</td>
      <td>18.3x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Canonical goal-resolution library + coverage completeness check in checks-lib (agent-built)</td>
      <td>5.0h</td>
      <td>20m</td>
      <td>6m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>amplify_pairs silent-no-op fault cluster fix: taxonomy resolution + post-conditions + true cost accounting (agent-built)</td>
      <td>5.0h</td>
      <td>30m</td>
      <td>6m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>[engine subsystem] omniscient calibration sweep: 42 AWS/GCP/Azure packages (2-at-a-time) — reviewed audit + Phase 0-4 remediation, fixed generator data-dir path, ran sweep to 42/42 pass, audited 14.3k graded…</td>
      <td>8.0h</td>
      <td>50m</td>
      <td>8m</td>
      <td>9.6x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Local serving stack bring-up: engine+gateway+webapp against materialized v6 tree with repaired-content verification (agent-built)</td>
      <td>2.0h</td>
      <td>30m</td>
      <td>5m</td>
      <td>4.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Priority-tranche completion campaign: forensics + finisher drivers + 47 packages to COMPLETE (orchestration)</td>
      <td>6.0h</td>
      <td>90m</td>
      <td>8m</td>
      <td>4.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>7</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>51.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>293</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>43</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>2,140,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>10.5x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>71.9x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>1.3</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 21.8x and the lowest at 4.0x, a spread of 5.5 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 20.0 of the 51.5 human-equivalent hours, or 39 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 43 minutes against 293 minutes of execution, a ratio of about 1 to 7. Supervisory leverage of 71.9x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 12, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-12-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-12-leverage-record.html</guid>
      <pubDate>Sun, 12 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">8 tasks. July 12, 2026 closed at 26.0x weighted leverage across 282.0 human-equivalent hours in 652 minutes of wall-clock time. Supervisory leverage came in at 352.5x.</p>
<p class="mb-4 font-light font-serif">That is 7.0 weeks of human-equivalent throughput in 10.9 hours. The ceiling was 88.4x; the floor was 6.4x. 5 of the 8 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Temporal cutover forensics + verified re-cutover + 9-agent full review (job layer/design/[ip]/speed/validation) + P0 hardening batch (2 repos, 2256 tests green) + remediation artifact</td>
      <td>140.0h</td>
      <td>95m</td>
      <td>6m</td>
      <td>88.4x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Deployment</td>
      <td>49.0h</td>
      <td>65m</td>
      <td>8m</td>
      <td>45.2x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Post-crash forensics + validation screen relaunch: watchdogd panic diagnosis; ssh-agent leak fix; flashcards crash-resume; sequential load-gated runner; stack restart + commits</td>
      <td>7.0h</td>
      <td>25m</td>
      <td>3m</td>
      <td>16.8x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Validation funnel apply: per-item-verified hard-defect repairs → 538 fixes promoted live across 117 packages (gate refused bulk-trust, switched to per-item verify, 519 grounding→relink worklist); style-drip…</td>
      <td>22.0h</td>
      <td>80m</td>
      <td>4m</td>
      <td>16.5x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Infrastructure</td>
      <td>26.0h</td>
      <td>100m</td>
      <td>5m</td>
      <td>15.6x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Coding</td>
      <td>9.0h</td>
      <td>42m</td>
      <td>7m</td>
      <td>12.9x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Extended apply_verdicts.py with pairs/flashcards/prose lanes + generate_repairs.py; 4-gate addendum; caught+fixed real bug; live-tested vs production data (agent-built)</td>
      <td>13.0h</td>
      <td>95m</td>
      <td>10m</td>
      <td>8.2x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Temporal long-running-activity support (heartbeat pump + subprocess kill-on-cancel + per-kind timeouts) + Temporal-aware job resubmit (campaign-cutover enabler), 2 committed [engine subsystem] features w/ 26…</td>
      <td>16.0h</td>
      <td>150m</td>
      <td>5m</td>
      <td>6.4x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>8</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>282.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>652</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>48</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>6,795,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>26.0x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>352.5x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>7.0</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 88.4x and the lowest at 6.4x, a spread of 13.8 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 140.0 of the 282.0 human-equivalent hours, or 50 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 48 minutes against 652 minutes of execution, a ratio of about 1 to 14. Supervisory leverage of 352.5x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 11, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-11-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-11-leverage-record.html</guid>
      <pubDate>Sat, 11 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">29 tasks. July 11, 2026 closed at 21.0x weighted leverage across 762.0 human-equivalent hours in 2,172 minutes of wall-clock time. Supervisory leverage came in at 205.9x.</p>
<p class="mb-4 font-light font-serif">That is 19.1 weeks of human-equivalent throughput in 36.2 hours. The ceiling was 60.0x; the floor was 5.5x. 24 of the 29 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Deployment</td>
      <td>90.0h</td>
      <td>90m</td>
      <td>8m</td>
      <td>60.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Design and frontend</td>
      <td>140.0h</td>
      <td>140m</td>
      <td>8m</td>
      <td>60.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>TTU Biology + Anatomy &amp; Physiology full-year coverage analysis (2 courses, 4 syllabi PDFs) vs live AVIAN domains, mirroring CHEM1B playbook</td>
      <td>32.0h</td>
      <td>33m</td>
      <td>8m</td>
      <td>58.2x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>TTU Biology + Anatomy and Physiology full-year coverage analysis vs live AVIAN domains</td>
      <td>32.0h</td>
      <td>33m</td>
      <td>8m</td>
      <td>58.2x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Temporal migration design doc for [engine subsystem]/[engine subsystem] job substrate (3-way NATS/Temporal/Procrastinate research pivoted mid-task into a full Temporal migration plan per Cooper&#39;s decision:…</td>
      <td>20.0h</td>
      <td>30m</td>
      <td>12m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Temporal migration design doc ([engine subsystem]/[engine subsystem] job substrate)</td>
      <td>20.0h</td>
      <td>30m</td>
      <td>12m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Deployment</td>
      <td>44.0h</td>
      <td>100m</td>
      <td>4m</td>
      <td>26.4x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Judge-in-the-loop regeneration gate for [engine subsystem]-service action.regen_items (checks-lib mcq.judge.option_truth per-option judge + independent confirm adjudication + node_ambiguity/generation_quality…</td>
      <td>14.0h</td>
      <td>40m</td>
      <td>8m</td>
      <td>21.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Judge-in-the-loop regeneration gate ([engine subsystem] regen_items: routed judge + v4-pro confirm in retry loop, unrepairable classification)</td>
      <td>14.0h</td>
      <td>40m</td>
      <td>8m</td>
      <td>21.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Temporal migration Phase 1 for AVIAN [content generation] job layer: deploy integrated Temporal server into the shared-Postgres supporting-services stack (server+UI, namespace/720h retention/ports 7233+8080,…</td>
      <td>26.0h</td>
      <td>82m</td>
      <td>6m</td>
      <td>19.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Documentation</td>
      <td>34.0h</td>
      <td>110m</td>
      <td>18m</td>
      <td>18.5x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Infrastructure</td>
      <td>80.0h</td>
      <td>270m</td>
      <td>15m</td>
      <td>17.8x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Deployment</td>
      <td>48.0h</td>
      <td>175m</td>
      <td>3m</td>
      <td>16.5x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Bio Fundamentals + Anatomy&amp;Physiology [engine subsystem]: synthesize+validate+ship v5 (blood unit / plant systems / succession / biomolecules)</td>
      <td>24.0h</td>
      <td>90m</td>
      <td>10m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Fix systemic [engine subsystem]-service [engine subsystem]/resume [content generation] hang: scope resume validation to PENDING nodes (Defect B) + hard per-node timeout bound on Pass-3 adversarial evaluation…</td>
      <td>14.0h</td>
      <td>55m</td>
      <td>8m</td>
      <td>15.3x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Fix [engine subsystem]-service [engine subsystem]/resume [content generation] hang (scoped validation + hard per-node timeout)</td>
      <td>14.0h</td>
      <td>55m</td>
      <td>8m</td>
      <td>15.3x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>[engine subsystem] coverage baseline + latency integration test tier + 11 pre-existing test fixes</td>
      <td>14.0h</td>
      <td>60m</td>
      <td>8m</td>
      <td>14.0x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Except-stem uniqueness generation guard (mcq.gen.except_uniqueness) in [engine subsystem]-service: new [content generation]/questions/except_uniqueness.py reusing the mcq.judge.option_truth per-option judge…</td>
      <td>6.0h</td>
      <td>27m</td>
      <td>8m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Except-stem uniqueness generation guard (mcq.gen.except_uniqueness) in [engine subsystem]-service</td>
      <td>6.0h</td>
      <td>27m</td>
      <td>8m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>SAA-C03 node-edit pilot: 125-item diagnosis via 5 sub-agents + dispatch-bug discovery + judge-gated regen (61% conversion) + findings doc</td>
      <td>28.0h</td>
      <td>160m</td>
      <td>10m</td>
      <td>10.5x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Close CHEM1B hardlink-corruption hazard in [engine subsystem]-service: hard refusal + isolated-build-then-finalize default across 11 [content generation]/pipeline call sites, 29 new tests, CLAUDE.md doc;…</td>
      <td>14.0h</td>
      <td>100m</td>
      <td>4m</td>
      <td>8.4x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Close CHEM1B hardlink-corruption hazard: hard refusal + isolated-build default ([engine subsystem]-service)</td>
      <td>14.0h</td>
      <td>100m</td>
      <td>4m</td>
      <td>8.4x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Testing</td>
      <td>6.0h</td>
      <td>50m</td>
      <td>3m</td>
      <td>7.2x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Debugging</td>
      <td>6.0h</td>
      <td>50m</td>
      <td>3m</td>
      <td>7.2x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Build isolated Temporal SPIKE proving worker-heartbeat self-heal (kill-worker auto-resume) for AVIAN [content generation] layer</td>
      <td>6.0h</td>
      <td>55m</td>
      <td>8m</td>
      <td>6.5x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Root-cause + fix live regression: build_real_action_llm hardcoded pool_size=1 (discarding config.llm_pool_size) broke QuestionValidator.map_structured for [engine subsystem]-service action.regen_items…</td>
      <td>3.0h</td>
      <td>30m</td>
      <td>5m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>Root-cause + fix build_real_action_llm pool_size regression (hardcoded pool_size=1 predating regen kinds; regression tests on real construction seam)</td>
      <td>3.0h</td>
      <td>30m</td>
      <td>5m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Root-cause + fix live no-op regression: regen_items exhausted-retry logic silently discarded judge/confirm-confirmed defect evidence whenever a later attempt failed for an unrelated gate reason; reproduced…</td>
      <td>5.0h</td>
      <td>55m</td>
      <td>6m</td>
      <td>5.5x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>Root-cause + fix regen evidence-loss no-op (mixed-retry case; real-package repro; seam tests through serialize chain)</td>
      <td>5.0h</td>
      <td>55m</td>
      <td>6m</td>
      <td>5.5x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>29</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>762.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>2,172</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>222</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>21,015,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>21.0x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>205.9x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>19.1</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 60.0x and the lowest at 5.5x, a spread of 11.0 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 90.0 of the 762.0 human-equivalent hours, or 12 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 222 minutes against 2,172 minutes of execution, a ratio of about 1 to 10. Supervisory leverage of 205.9x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 10, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-10-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-10-leverage-record.html</guid>
      <pubDate>Fri, 10 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">15 tasks. July 10, 2026 closed at 22.2x weighted leverage across 319.0 human-equivalent hours in 862 minutes of wall-clock time. Supervisory leverage came in at 236.3x.</p>
<p class="mb-4 font-light font-serif">That is 8.0 weeks of human-equivalent throughput in 14.4 hours. The ceiling was 54.0x; the floor was 8.6x. 15 of the 15 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>AVIAN sim standardization: vendored amCharts5 (+Maps) offline, built the Sim SDK (shared shell/controls/theming + first-run coach-mark walkthrough + per-variable help + Guide modal), rebuilt Supply&amp;Demand on…</td>
      <td>36.0h</td>
      <td>40m</td>
      <td>4m</td>
      <td>54.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>AVIAN simulations: ElevenLabs narration pipeline (baked 6 clips + rewired blood-flow to play them, no browser TTS) + built one reference sim per interaction type (guided_builder Circuit Lab, data_widget…</td>
      <td>40.0h</td>
      <td>45m</td>
      <td>3m</td>
      <td>53.3x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Audit and review</td>
      <td>36.0h</td>
      <td>48m</td>
      <td>2m</td>
      <td>45.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Documentation</td>
      <td>30.0h</td>
      <td>45m</td>
      <td>5m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>avian-[engine subsystem] remediation: verified stale-content failure is resolved + authored 5-phase remediation plan (all findings traced, Mermaid dep map, open decisions)</td>
      <td>5.0h</td>
      <td>10m</td>
      <td>2m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>ADR-0010 fast-boot Phase-0 foundation, 5 repos: 7-agent recon + read-only AWS verification + build-readiness report; engine (3 flags, WP0.2 boot instrumentation, WP2.3 readiness bifurcation, WP1.4a loader…</td>
      <td>80.0h</td>
      <td>170m</td>
      <td>4m</td>
      <td>28.2x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>AVIAN sims: built the macro AD–AS (Aggregate Demand &amp; Supply) reference sim on the SDK+amCharts standard — AD/SRAS/LRAS curves, demand &amp; supply shocks, output gaps, animated self-correction; registered +…</td>
      <td>5.0h</td>
      <td>13m</td>
      <td>1m</td>
      <td>23.1x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>AVIAN sim SDK: added animated graph transitions + draggable curves (SimChart.dragHandle) to both amCharts econ sims, a safe markdown renderer for tour/help/guide bubbles (fixed raw HTML), and removed the…</td>
      <td>4.0h</td>
      <td>12m</td>
      <td>2m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>AVIAN sims dev experience: container-side build-watch.sh + nginx sub_filter injecting livereload.js on every page — auto-reload on any file change (fixes Safari stale cache) + on-screen build-hash badge; no…</td>
      <td>2.0h</td>
      <td>10m</td>
      <td>2m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>G1 Finisher: SAA-C03 AWS content adjudication+staging (services/[engine subsystem]) -- 780-item confirm-judge, waive 989 false-positives, found regen_items capability gap + 3 platform bugs, staged test path</td>
      <td>16.0h</td>
      <td>88m</td>
      <td>15m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>G1 finisher: SAA-C03 independent adjudication (287/1276 confirmed, 80% FP discovery) + capability-gap diagnosis + 989 waivers</td>
      <td>16.0h</td>
      <td>88m</td>
      <td>15m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Fleet campaign: deterministic sharding (CampaignConfig shards + [engine subsystem] wiring + plan generator rework, verified 4907 shards/1.14M items)</td>
      <td>9.0h</td>
      <td>50m</td>
      <td>6m</td>
      <td>10.8x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>[engine subsystem] self-recovery (lost-job auto-requeue + executor idle watchdog + infra liveness watchdog) plus supporting-services Postgres backup system + [engine subsystem]/[engine subsystem]-worker…</td>
      <td>28.0h</td>
      <td>165m</td>
      <td>12m</td>
      <td>10.2x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>ARMED Option-4 campaign ops-prep (4 items): hub_max_concurrency config wiring end-to-end, campaign heartbeat-TTL mislabel fix (async subprocess polling), fleet dispatch-plan generator + dispatcher (build…</td>
      <td>8.0h</td>
      <td>50m</td>
      <td>6m</td>
      <td>9.6x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>AVIAN sims fix: debugged (Playwright) + fixed the econ sims — HUD pointer-events swallowing chart clicks (drag blocked), amCharts snap vs a JS param-tween for animation, and mid-drag bullet recreation…</td>
      <td>4.0h</td>
      <td>28m</td>
      <td>2m</td>
      <td>8.6x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>15</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>319.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>862</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>81</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>10,795,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>22.2x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>236.3x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>8.0</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 54.0x and the lowest at 8.6x, a spread of 6.3 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 36.0 of the 319.0 human-equivalent hours, or 11 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 81 minutes against 862 minutes of execution, a ratio of about 1 to 11. Supervisory leverage of 236.3x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 9, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-09-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-09-leverage-record.html</guid>
      <pubDate>Thu, 09 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">22 tasks. July 9, 2026 closed at 24.8x weighted leverage across 593.5 human-equivalent hours in 1,435 minutes of wall-clock time. Supervisory leverage came in at 481.2x.</p>
<p class="mb-4 font-light font-serif">That is 14.8 weeks of human-equivalent throughput in 23.9 hours. The ceiling was 109.1x; the floor was 3.8x. 22 of the 22 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>AVIAN simulation master list: cataloged 96 existing interactive components, orchestrated 22-agent workflow across all 261 academic domain specs, produced 2141-simulation master list + filterable gallery…</td>
      <td>160.0h</td>
      <td>88m</td>
      <td>4m</td>
      <td>109.1x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>AVIAN simulations build-out: photoreal scriptable Three.js blood-flow reference (IBL/bloom/DoF/SSS + SimController narration-cue API, vendored offline three.js), upgraded gallery into live local test app with…</td>
      <td>48.0h</td>
      <td>55m</td>
      <td>4m</td>
      <td>52.4x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Documentation</td>
      <td>44.0h</td>
      <td>54m</td>
      <td>8m</td>
      <td>48.9x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>WP13 platform-build closer: P0 boot-cache root-cause+fix (proven round-trip), both field guides rewritten w/ what-changed, doc-sync 7/7, spend_ledger writer, fleet hooks, final all-repo sweep</td>
      <td>44.0h</td>
      <td>56m</td>
      <td>1m</td>
      <td>47.1x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>WP12 [engine subsystem] MCP catalog (66 tools, live stdio smoke) + CLI (13 groups, 2 live bugs caught) + docs set + backend punch list (batch-upsert 61-67x, spend/benchmarks/routing endpoints, severity-tile…</td>
      <td>44.0h</td>
      <td>82m</td>
      <td>1m</td>
      <td>32.2x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Option-4 round 3: voting engine + per-domain-class routing + fresh B-set protocol (checks-lib)</td>
      <td>16.0h</td>
      <td>36m</td>
      <td>4m</td>
      <td>26.4x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>M1 status-vocab migration + ADR-0007 execution across 7 repos: 5-state lifecycle live (9 candidates), 70 CE [restricted], 16 excluded, private extinct; leak gates verified; 2 pre-existing bugs fixed;…</td>
      <td>32.0h</td>
      <td>82m</td>
      <td>1m</td>
      <td>23.4x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Option-4 round 2: grounded per-option judge + LLM-mode coverage calibration (checks-lib)</td>
      <td>12.0h</td>
      <td>33m</td>
      <td>3m</td>
      <td>22.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Coding</td>
      <td>40.0h</td>
      <td>111m</td>
      <td>5m</td>
      <td>21.6x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>WP9a judge fixes + benchmark harness + real calibration (blind_compare confirm; fc check redesigned to clear bars; found the [engine subsystem] label-swap) + orchestrator [engine subsystem] server fix…</td>
      <td>28.0h</td>
      <td>95m</td>
      <td>1m</td>
      <td>17.7x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Updated both hosted field-manual artifacts ([content generation] Engine + AWS SAA-C03 worked example) to platform reality: new Registry section w/ SVG, blocking gates, 85-check framework + judge design,…</td>
      <td>6.0h</td>
      <td>25m</td>
      <td>2m</td>
      <td>14.4x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Option-4 round 7: mutator bug fixes + clean powered F-sets + pre-registered confirmation (checks-lib)</td>
      <td>10.0h</td>
      <td>45m</td>
      <td>3m</td>
      <td>13.4x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Post-[content generation] cleanup automation: [engine subsystem]-service build-dir cleanup after ingest + [engine subsystem] ingest-staging cleanup and real admin-prune delete mode</td>
      <td>14.0h</td>
      <td>65m</td>
      <td>8m</td>
      <td>12.9x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Post-[content generation] cleanup automation: [engine subsystem] build-dir cleanup + [engine subsystem] staging cleanup/real prune mode</td>
      <td>14.0h</td>
      <td>66m</td>
      <td>8m</td>
      <td>12.8x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>G1 SAA-C03 golden-package operator pass: fixed reachable deterministic defects (v2-v4 lineage), discovered + precisely documented 7 platform bugs + 2 calibration classes; zero content harmed, live untouched</td>
      <td>7.0h</td>
      <td>37m</td>
      <td>1m</td>
      <td>11.4x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Option-4 round 7-EXT: pooled measurement extension → campaign GO + arm-ready package (checks-lib)</td>
      <td>4.0h</td>
      <td>21m</td>
      <td>2m</td>
      <td>11.3x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>WP9b final: MCQ-judge escalation ladder (forced-CoT + ensemble, real calibration) — clean negative result establishing cheap-tier negation ceiling; JudgeClient hard-timeout fix; harvest economics quantified</td>
      <td>20.0h</td>
      <td>109m</td>
      <td>1m</td>
      <td>11.0x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Option-4 round 4: per-miss error analysis + negation-emphasis variant + C-set routing table (checks-lib)</td>
      <td>14.0h</td>
      <td>79m</td>
      <td>4m</td>
      <td>10.7x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Option-4 round 6: powered E-sets + Wilson bounds + mutator forensics + grok grounded probe (checks-lib)</td>
      <td>16.0h</td>
      <td>107m</td>
      <td>5m</td>
      <td>9.0x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Option-4 round 5: targeted fixes (retry/neg-scoped/derivable-support) + D-set close-out + tuning-pool bug catch (checks-lib)</td>
      <td>12.0h</td>
      <td>91m</td>
      <td>6m</td>
      <td>7.9x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>FLEET-A operator pass: complete 407-domain dry-run ledger (2849 calls, 84s) + apply inventory + independent root-cause of action-apply and validate-noop blockers + 2 new bugs; [cost]spend</td>
      <td>5.0h</td>
      <td>43m</td>
      <td>1m</td>
      <td>7.0x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>WP9a round 2: MCQ lever A/B re-benchmark (lever A reverted on evidence, lever B kept), [engine subsystem]-fix follow-through recalibration, honest no-winner verdict + escalation path</td>
      <td>3.5h</td>
      <td>55m</td>
      <td>1m</td>
      <td>3.8x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>22</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>593.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,435</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>74</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>15,552,467</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>24.8x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>481.2x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>14.8</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 109.1x and the lowest at 3.8x, a spread of 28.6 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 160.0 of the 593.5 human-equivalent hours, or 27 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 74 minutes against 1,435 minutes of execution, a ratio of about 1 to 19. Supervisory leverage of 481.2x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 8, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-08-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-08-leverage-record.html</guid>
      <pubDate>Wed, 08 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">34 tasks. July 8, 2026 closed at 41.8x weighted leverage across 1,916.5 human-equivalent hours in 2,749 minutes of wall-clock time. Supervisory leverage came in at 991.3x.</p>
<p class="mb-4 font-light font-serif">That is 47.9 weeks of human-equivalent throughput in 45.8 hours. The ceiling was 154.8x; the floor was 4.8x. 34 of the 34 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Content [content generation] Platform comprehensive design: [engine subsystem] registry + validation framework + audit-as-code + benchmarked model routing + MCP/API/console + 13-WP implementation plan</td>
      <td>80.0h</td>
      <td>31m</td>
      <td>6m</td>
      <td>154.8x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>AccelaStudy Classroom: research all 51 US homeschool jurisdictions (requirements/accreditation/lab-science/personal-finance) into state-requirements.json; author 30 world-language 102 specs + Personal Finance…</td>
      <td>180.0h</td>
      <td>90m</td>
      <td>6m</td>
      <td>120.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Coding</td>
      <td>320.0h</td>
      <td>170m</td>
      <td>1m</td>
      <td>112.9x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Review alpha K-12 domains; propose grades 5-12 curriculum for classroom.accelastudy.ai (scope-and-sequence + state-requirement mapping + published HTML artifact)</td>
      <td>18.0h</td>
      <td>13m</td>
      <td>3m</td>
      <td>83.1x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Audit AccelaStudy activity system; brainstorm + design 34-concept gamified activities playbook artifact</td>
      <td>16.0h</td>
      <td>12m</td>
      <td>2m</td>
      <td>80.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>WP2 [engine subsystem] registry core: 13-table schema + hardlink version store + real 407-pkg backfill (33.6GB) + 54-endpoint API + ingest protocol + jobs/executor + lifecycle/leases/promotion + catalog…</td>
      <td>140.0h</td>
      <td>125m</td>
      <td>1m</td>
      <td>67.2x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Documentation</td>
      <td>40.0h</td>
      <td>40m</td>
      <td>6m</td>
      <td>60.0x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>WP4 audit parity + adjudications: 299-item disposition map (zero silent losses), parity gate PASS, dup-options false-positive verdict, 34k dangling-pair confirmation, canonical duplicate-key root-cause; +…</td>
      <td>44.0h</td>
      <td>44m</td>
      <td>1m</td>
      <td>60.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>WP10 [engine subsystem] operations console: 52 components, 3 faces, 10-tab content drill-down, issue-centric flow live-verified on real fleet data, Playwright 12/12 incl mobile+dark, caught real API shape…</td>
      <td>88.0h</td>
      <td>92m</td>
      <td>1m</td>
      <td>57.4x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>WP0 content [content generation] platform: scaffold services/[engine subsystem] (FastAPI backend w/ API-key roles auth + async alembic + 63 tests/99% cov; React/Vite frontend w/ ThemeProvider + ApiKeyGate +…</td>
      <td>28.0h</td>
      <td>30m</td>
      <td>15m</td>
      <td>56.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>WP11 rubrics: backfill_rubrics action (plan/apply, schema-validated) + goal-linkage embed matcher (floors, fallback) + rubric-aware case generation; SAA-C03 pilot proven (linkage 8-to-0, coverage 100%)</td>
      <td>36.0h</td>
      <td>41m</td>
      <td>1m</td>
      <td>52.7x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>WP0 [engine subsystem] + avian-content-checks scaffold: 2 private repos + FastAPI backend (auth roles) + themed React shell + check-framework lib (170 tests; 99-100% cov; ports registered)</td>
      <td>28.0h</td>
      <td>33m</td>
      <td>1m</td>
      <td>50.9x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Remediate notification-service + onboarding-service: ~58 verified security/correctness fixes across both (all tiers), ~218 new regression tests, suites green (NS 466 / OS 395)</td>
      <td>100.0h</td>
      <td>125m</td>
      <td>3m</td>
      <td>48.0x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Coding</td>
      <td>56.0h</td>
      <td>72m</td>
      <td>1m</td>
      <td>46.7x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>WP5a: model-based checks ([engine subsystem]+LLM judges), campaign runner, WP4 diffs applied for avian-content-checks</td>
      <td>65.0h</td>
      <td>87m</td>
      <td>5m</td>
      <td>44.8x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>WP5a model-based checks: 13 [engine subsystem]/LLM judges (parse-failure-proof verdict channel) + campaign runner w/ hard budget stops + WP4 diffs applied + real calibration that caught a broken judge for…</td>
      <td>65.0h</td>
      <td>89m</td>
      <td>1m</td>
      <td>43.8x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>WP1 content [content generation] platform: CONTENT_TYPES registry + full Pydantic content schemas in avian-[engine subsystem]-runtime (35 content types, ~20 schema modules, fixture mini-corpus, real-data…</td>
      <td>36.0h</td>
      <td>50m</td>
      <td>8m</td>
      <td>43.2x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>WP8: actions catalog (13 [engine subsystem] worker-side runners incl. 3 LLM-gated + FakeLLM tests) + [engine subsystem] hub (catalog/scope/planning/API/2 hub handlers) + 12-endpoint content-browse API;…</td>
      <td>72.0h</td>
      <td>100m</td>
      <td>8m</td>
      <td>43.2x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>WP8 actions catalog (13 actions, new-version-via-ingest, parity on 6 w/ 2 real bugs caught) + content-browse API (10 endpoint groups) + 14 legacy scripts deleted across engine+[engine subsystem]</td>
      <td>72.0h</td>
      <td>100m</td>
      <td>1m</td>
      <td>43.2x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>WP1 CONTENT_TYPES registry (35 rows w/ requiredness predicates) + 18 content schema modules + fixture mini-package + writer-schema drift tests; 3 real drift bugs found+fixed; PACKAGE_FILES retired to derived…</td>
      <td>36.0h</td>
      <td>52m</td>
      <td>1m</td>
      <td>41.5x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>WP3 deterministic check catalog: 71 checks + 31 mutators + runner/CLI + first-ever 552,976-finding fleet scan (backfilled record)</td>
      <td>72.0h</td>
      <td>104m</td>
      <td>1m</td>
      <td>41.5x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Critical-goal requirement in domain-spec generation (module+gate+authoring wiring+5-layer critical-field threading), enforce in runtime_artifacts+tests, apply to 360 specs+16 packages; Class1 canonical…</td>
      <td>44.0h</td>
      <td>65m</td>
      <td>4m</td>
      <td>40.6x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>WP7 containerized job execution: real [engine subsystem]-worker image (2.27GB, 2 build bugs fixed) + container-per-job executor (kill-cancel 0.29s, restart-reattach, resource caps) + /complete client wiring;…</td>
      <td>52.0h</td>
      <td>80m</td>
      <td>1m</td>
      <td>39.0x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>WP3b wire real 71-check catalog into [engine subsystem]: RealChecksBackend + findings dedup/resolve persistence + /validate + audit job kinds + issue-centric summary endpoints; 2 real bugs fixed (candidacy…</td>
      <td>60.0h</td>
      <td>114m</td>
      <td>1m</td>
      <td>31.6x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Complete code review of notification-service and onboarding-service (8 parallel review passes + hand-verification; 62 findings)</td>
      <td>18.0h</td>
      <td>35m</td>
      <td>3m</td>
      <td>30.9x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Extend Gap 8 outcome-aware credit to all clients: electron IPC options bridge + 6 graded screens + React TDZ fix; iOS EngineClient parity + pbxproj build-break fix; web+android reconciled (case_study…</td>
      <td>13.0h</td>
      <td>30m</td>
      <td>2m</td>
      <td>26.0x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>Testing</td>
      <td>8.0h</td>
      <td>20m</td>
      <td>3m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Coding</td>
      <td>20.0h</td>
      <td>55m</td>
      <td>4m</td>
      <td>21.8x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>Debugging</td>
      <td>40.0h</td>
      <td>110m</td>
      <td>3m</td>
      <td>21.8x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>AVIAN cleanup buckets 1-2: log dumps + superseded scripts across 7 repos (grep-verified; engine suite 7294 green; ~290MB reclaimed)</td>
      <td>5.0h</td>
      <td>20m</td>
      <td>1m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>Class 4 P2.2: build goal_weights+goal_similarity runtime artifacts for 32 packages (via patched write_runtime_artifacts, criticals applied) + finish Class 3 P26.3 question-tier fix + P6/P19 forensic…</td>
      <td>7.0h</td>
      <td>35m</td>
      <td>2m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>32</td>
      <td>P4.1 [engine subsystem] (11 pkgs +5016 pairs, 0 goals starved) + P24.2 question-quality repair (160 dup-&gt;0, 33 missing expl-&gt;0) + [engine subsystem] pair-gate (16.9% weak flagged) + MCQ correctness…</td>
      <td>7.0h</td>
      <td>60m</td>
      <td>3m</td>
      <td>7.0x</td>
    </tr>
    <tr>
      <td>33</td>
      <td>Move 10 private packages (HP/ASOIAF/48-Laws ~1.1GB) out of active store to private-packages/ + durable private-status filter in content-audit pkg_dirs + canonical 417-&gt;407 + purge from both S3 backup regions…</td>
      <td>2.5h</td>
      <td>25m</td>
      <td>1m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>34</td>
      <td>Finalize 14 held IB HL @0.93 domains via scope-aware adversarial-validation fix + critique persistence + blind-panel diagnosis + provenance ledger + crash/config fixes + 2 pipeline field-manuals</td>
      <td>48.0h</td>
      <td>600m</td>
      <td>15m</td>
      <td>4.8x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>34</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>1,916.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>2,749</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>116</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>44,625,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>41.8x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>991.3x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>47.9</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 154.8x and the lowest at 4.8x, a spread of 32.3 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 80.0 of the 1,916.5 human-equivalent hours, or 4 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 116 minutes against 2,749 minutes of execution, a ratio of about 1 to 24. Supervisory leverage of 991.3x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 7, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-07-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-07-leverage-record.html</guid>
      <pubDate>Tue, 07 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">9 tasks. July 7, 2026 closed at 15.0x weighted leverage across 234.2 human-equivalent hours in 935 minutes of wall-clock time. Supervisory leverage came in at 369.9x.</p>
<p class="mb-4 font-light font-serif">That is 5.9 weeks of human-equivalent throughput in 15.6 hours. The ceiling was 80.0x; the floor was 3.6x. 7 of the 9 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>[ip] portfolio hardening: 8 CIP drafts (spec backfills; +87 claims; +10 figures; defect fixes; language hardening) + doc sync via 51-agent orchestration</td>
      <td>120.0h</td>
      <td>90m</td>
      <td>2m</td>
      <td>80.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Survey IB Spanish domain + activity/content-type systems; author ADR-0008 (world-language content types + four-skill activity model) + design-brief artifact</td>
      <td>30.0h</td>
      <td>30m</td>
      <td>4m</td>
      <td>60.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Autopilot activity-coverage audit + implement Gap1 minimal_pair_contrast dispatch (web+electron) and Gap2 credit-gate 5 types + regression test</td>
      <td>22.0h</td>
      <td>40m</td>
      <td>4m</td>
      <td>33.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Review 402-line portfolio analysis and build complete 16-week pre-filing execution plan (7 work streams to Oct 27 deadline)</td>
      <td>6.0h</td>
      <td>15m</td>
      <td>2m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Draft counsel engagement introduction email for AVIAN nonprovisional filings ([counsel] SF + Southlake variants)</td>
      <td>3.0h</td>
      <td>8m</td>
      <td>3m</td>
      <td>22.5x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Diagnose + fix VS Code blank-terminal (GPU/WebGL context loss) without losing shell sessions</td>
      <td>1.5h</td>
      <td>6m</td>
      <td>3m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Content production</td>
      <td>11.0h</td>
      <td>80m</td>
      <td>3m</td>
      <td>8.2x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Rebuild The Deferral epubs (full novel + preview + synopsis) from current manuscript; diagnose cwd-relative cover-path build failure and gitignored-but-tracked force-add; validate epub zip integrity + cover…</td>
      <td>0.8h</td>
      <td>6m</td>
      <td>2m</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>IB/AP domain [content generation] marathon: resumable harness rebuild ([content generation]-only + standalone activity backfill) + 4 non-lang academic domains shipped to beta; 29 deferred (validator/gate…</td>
      <td>40.0h</td>
      <td>660m</td>
      <td>15m</td>
      <td>3.6x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>9</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>234.2</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>935</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>38</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>11,539,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>369.9x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>5.9</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 80.0x and the lowest at 3.6x, a spread of 22.0 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 120.0 of the 234.2 human-equivalent hours, or 51 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 38 minutes against 935 minutes of execution, a ratio of about 1 to 25. Supervisory leverage of 369.9x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 6, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-06-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-06-leverage-record.html</guid>
      <pubDate>Mon, 06 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">6 tasks. July 6, 2026 closed at 8.3x weighted leverage across 72.0 human-equivalent hours in 520 minutes of wall-clock time. Supervisory leverage came in at 86.4x.</p>
<p class="mb-4 font-light font-serif">That is 1.8 weeks of human-equivalent throughput in 8.7 hours. The ceiling was 10.1x; the floor was 3.4x. 6 of the 6 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Coding</td>
      <td>16.0h</td>
      <td>95m</td>
      <td>10m</td>
      <td>10.1x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Coding</td>
      <td>20.0h</td>
      <td>130m</td>
      <td>12m</td>
      <td>9.2x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Testing</td>
      <td>28.0h</td>
      <td>185m</td>
      <td>18m</td>
      <td>9.1x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Content production</td>
      <td>3.5h</td>
      <td>35m</td>
      <td>3m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>AVIAN [engine subsystem] per-category response budget: right-sized reasoning max_tokens 16384-&gt;2048 + made [engine subsystem]_max_tokens per-category in the model registry (resolve_[engine subsystem]_config…</td>
      <td>2.5h</td>
      <td>40m</td>
      <td>3m</td>
      <td>3.8x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Coding</td>
      <td>2.0h</td>
      <td>35m</td>
      <td>4m</td>
      <td>3.4x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>6</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>72.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>520</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>50</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>2,690,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>8.3x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>86.4x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>1.8</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 10.1x and the lowest at 3.4x, a spread of 2.9 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 16.0 of the 72.0 human-equivalent hours, or 22 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 50 minutes against 520 minutes of execution, a ratio of about 1 to 10. Supervisory leverage of 86.4x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 5, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-05-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-05-leverage-record.html</guid>
      <pubDate>Sun, 05 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">2 tasks. July 5, 2026 closed at 7.3x weighted leverage across 20.0 human-equivalent hours in 165 minutes of wall-clock time. Supervisory leverage came in at 85.7x.</p>
<p class="mb-4 font-light font-serif">That is 0.5 weeks of human-equivalent throughput in 2.8 hours. The ceiling was 8.0x; the floor was 6.0x. 2 of the 2 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Debugging</td>
      <td>14.0h</td>
      <td>105m</td>
      <td>10m</td>
      <td>8.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Coding</td>
      <td>6.0h</td>
      <td>60m</td>
      <td>4m</td>
      <td>6.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>2</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>20.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>165</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>14</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>870,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>7.3x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>85.7x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>0.5</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 8.0x and the lowest at 6.0x, a spread of 1.3 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 14.0 of the 20.0 human-equivalent hours, or 70 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 14 minutes against 165 minutes of execution, a ratio of about 1 to 12. Supervisory leverage of 85.7x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 4, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-04-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-04-leverage-record.html</guid>
      <pubDate>Sat, 04 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">2 tasks. July 4, 2026 closed at 6.4x weighted leverage across 74.0 human-equivalent hours in 690 minutes of wall-clock time. Supervisory leverage came in at 185.0x.</p>
<p class="mb-4 font-light font-serif">That is 1.9 weeks of human-equivalent throughput in 11.5 hours. The ceiling was 6.7x; the floor was 5.6x. 2 of the 2 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>AVIAN overnight: 3-model benchmark harness + models.yaml phase routing + general misconceptions generator + full beta content remediation (ALL 44/44 beta candidate-ready (incl 3 IB DP math bundles))</td>
      <td>60.0h</td>
      <td>540m</td>
      <td>15m</td>
      <td>6.7x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>AVIAN [engine subsystem] overnight omniscient sweep: brought up 8-service stack, diagnosed+fixed critical stale package-path defect ([engine subsystem] read retired avian-engine/data/domains not canonical…</td>
      <td>14.0h</td>
      <td>150m</td>
      <td>9m</td>
      <td>5.6x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>2</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>74.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>690</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>24</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>3,300,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>6.4x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>185.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>1.9</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 6.7x and the lowest at 5.6x, a spread of 1.2 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 60.0 of the 74.0 human-equivalent hours, or 81 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 24 minutes against 690 minutes of execution, a ratio of about 1 to 29. Supervisory leverage of 185.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 2, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-02-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-02-leverage-record.html</guid>
      <pubDate>Thu, 02 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">3 tasks. July 2, 2026 closed at 10.3x weighted leverage across 32.0 human-equivalent hours in 187 minutes of wall-clock time. Supervisory leverage came in at 61.9x.</p>
<p class="mb-4 font-light font-serif">That is 0.8 weeks of human-equivalent throughput in 3.1 hours. The ceiling was 20.0x; the floor was 7.6x. 3 of the 3 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Debugging</td>
      <td>10.0h</td>
      <td>30m</td>
      <td>8m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Diagnose overnight kernel panic (watchdog-timeout from parallel-[content generation] OOM) and safely restart the interrupted 14-package IB DP [content generation] batch: model offload to shared [engine…</td>
      <td>8.0h</td>
      <td>47m</td>
      <td>3m</td>
      <td>10.2x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>AVIAN [content generation]: dual-run 6-wide parallelization + [engine subsystem]/engine decoupling (PACKAGES_DIR+11 scripts+test+completeness-gate fix) + [ip-cluster] isolation architecture plan</td>
      <td>14.0h</td>
      <td>110m</td>
      <td>20m</td>
      <td>7.6x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>3</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>32.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>187</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>31</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>980,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>10.3x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>61.9x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>0.8</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 20.0x and the lowest at 7.6x, a spread of 2.6 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 10.0 of the 32.0 human-equivalent hours, or 31 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 31 minutes against 187 minutes of execution, a ratio of about 1 to 6. Supervisory leverage of 61.9x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: July 1, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-07-01-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-07-01-leverage-record.html</guid>
      <pubDate>Wed, 01 Jul 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">42 tasks. July 1, 2026 closed at 29.4x weighted leverage across 518.5 human-equivalent hours in 1,059 minutes of wall-clock time. Supervisory leverage came in at 198.2x.</p>
<p class="mb-4 font-light font-serif">That is 13.0 weeks of human-equivalent throughput in 17.6 hours. The ceiling was 80.0x; the floor was 5.0x. 40 of the 42 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Author Civics and Government knowledge taxonomy for AccelaStudy AI</td>
      <td>8.0h</td>
      <td>6m</td>
      <td>3m</td>
      <td>80.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Author 94 alpha domain specs (29 languages, 10 AP gaps, 22 IB gaps, 30 K-12 grade courses, 3 pilots) via web-grounded 91-agent fan-out + generalized/hardened author_spec.py assembler; all pass structural…</td>
      <td>120.0h</td>
      <td>95m</td>
      <td>5m</td>
      <td>75.8x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Author 115 vertical certification domain specs via 2-wave web-grounded fan-out + vendor-canonical dedup gate; survived mid-run quota interrupt with zero content loss (external backup + git); all pass…</td>
      <td>150.0h</td>
      <td>120m</td>
      <td>5m</td>
      <td>75.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>The Deferral: 3-round multi-agent editorial review (30 full-manuscript reads) + surgical revision (fair-play plant, thread closures, HERA seeding, opening rework, authenticity patches, 2 prose tic passes) +…</td>
      <td>70.0h</td>
      <td>90m</td>
      <td>4m</td>
      <td>46.7x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Greek 101 knowledge taxonomy for AccelaStudy AI Languages (CEFR A1, 68 leaf goals, 8 domains)</td>
      <td>6.0h</td>
      <td>12m</td>
      <td>4m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>CAIA Level II knowledge taxonomy authorship</td>
      <td>6.0h</td>
      <td>12m</td>
      <td>3m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Author EX280 Red Hat OpenShift Specialist knowledge taxonomy with web research</td>
      <td>8.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>26.7x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>The Deferral: full 15-reader editorial review + publication-prep structural revision (Ch31 split, boardroom scene, HERA interstitial, Finn cost arc, tic pass, Book-2 seeds, audit+docs)</td>
      <td>40.0h</td>
      <td>105m</td>
      <td>3m</td>
      <td>22.9x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Author USMLE Step 2 CK knowledge taxonomy for AccelaStudy AI</td>
      <td>3.0h</td>
      <td>8m</td>
      <td>3m</td>
      <td>22.5x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Czech 101 knowledge taxonomy 68 leaf goals CEFR A1 full prereq graph</td>
      <td>4.0h</td>
      <td>12m</td>
      <td>4m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Author Hungarian 101 knowledge taxonomy (CEFR A1) — 64 leaf goals, 89 prereq edges, 6 domains</td>
      <td>4.0h</td>
      <td>12m</td>
      <td>3m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Author CFP exam knowledge taxonomy (70 leaf goals, 8 domains)</td>
      <td>4.0h</td>
      <td>12m</td>
      <td>3m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Author QSBA Qlik Sense Business Analyst knowledge taxonomy - 72 leaf goals across 4 official exam domains</td>
      <td>4.0h</td>
      <td>14m</td>
      <td>5m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Author HashiCorp Vault Associate 002 exam knowledge taxonomy (63 leaf goals, 8 domains)</td>
      <td>6.0h</td>
      <td>22m</td>
      <td>5m</td>
      <td>16.4x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Author Swedish 101 CEFR A1 knowledge taxonomy for AccelaStudy AI Languages — 8 domains, 72 leaf goals</td>
      <td>3.0h</td>
      <td>12m</td>
      <td>3m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Grade 6 Mathematics CCSS knowledge taxonomy for AccelaStudy AI</td>
      <td>3.0h</td>
      <td>12m</td>
      <td>3m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Author OSEP PEN-300 knowledge taxonomy for AccelaStudy AI (80 nodes, 69 leaf goals, 14 prereq edges)</td>
      <td>3.0h</td>
      <td>12m</td>
      <td>3m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Author Turkish 101 knowledge taxonomy for AccelaStudy AI Languages (68 leaf goals, CEFR A1, ACTFL Novice)</td>
      <td>4.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Author IB Theatre HL knowledge taxonomy with web research</td>
      <td>4.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>IB World Religions HL taxonomy authoring - 70 leaf nodes 9 domains</td>
      <td>4.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Author RHCA knowledge taxonomy for AccelaStudy AI</td>
      <td>4.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>CIA exam knowledge taxonomy authoring (3-part IIA CIA blueprint research + 70-node flat node list)</td>
      <td>4.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Thin avian-engine to code+docs: hard-gated removal of 49G package content+caches (containment analysis vs canonical+S3, 683M external backup, per-item divergence diff) + root-cause fix of relative-path…</td>
      <td>4.0h</td>
      <td>20m</td>
      <td>3m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Author IB English Literature HL knowledge taxonomy — 73 nodes, 8 domains, 12+ prereq edges, web research</td>
      <td>3.5h</td>
      <td>18m</td>
      <td>3m</td>
      <td>11.7x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Author CIPP/US knowledge taxonomy for AccelaStudy AI (60-80 leaf goals across 5 exam domains)</td>
      <td>4.0h</td>
      <td>22m</td>
      <td>3m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Hebrew 101 knowledge taxonomy for AccelaStudy AI Languages</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>Author Finnish 101 CEFR A1 knowledge taxonomy for AccelaStudy AI Languages — 8 domains 72 leaf goals</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Ukrainian 101 knowledge taxonomy authorship</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>AP Research knowledge taxonomy authorship</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>Author Databricks DE-P knowledge taxonomy with web research and official exam guide extraction</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>Author GPYC GIAC Python Coder knowledge taxonomy with web-researched exam blueprint</td>
      <td>2.0h</td>
      <td>12m</td>
      <td>3m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>32</td>
      <td>Author CAPA knowledge taxonomy for AccelaStudy AI</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>33</td>
      <td>SHRM-SCP knowledge taxonomy authoring with web research</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>34</td>
      <td>Author Praxis Core 5712-5722-5732 knowledge taxonomy for AccelaStudy AI</td>
      <td>2.5h</td>
      <td>18m</td>
      <td>5m</td>
      <td>8.3x</td>
    </tr>
    <tr>
      <td>35</td>
      <td>AP Seminar knowledge taxonomy (68 leaf goals QUEST framework)</td>
      <td>3.0h</td>
      <td>22m</td>
      <td>3m</td>
      <td>8.2x</td>
    </tr>
    <tr>
      <td>36</td>
      <td>Author CPIM knowledge taxonomy for AccelaStudy AI</td>
      <td>3.0h</td>
      <td>22m</td>
      <td>5m</td>
      <td>8.2x</td>
    </tr>
    <tr>
      <td>37</td>
      <td>Author Alteryx Designer Core (ADC) knowledge taxonomy - 69 leaf goals across 4 exam domains</td>
      <td>2.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>6.7x</td>
    </tr>
    <tr>
      <td>38</td>
      <td>Author CIPM knowledge taxonomy for AccelaStudy AI</td>
      <td>2.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>6.7x</td>
    </tr>
    <tr>
      <td>39</td>
      <td>CCSK taxonomy authoring - CCSK v5 12-domain knowledge map with 70 leaf goals</td>
      <td>2.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>6.7x</td>
    </tr>
    <tr>
      <td>40</td>
      <td>Author CFA Level III knowledge taxonomy for AccelaStudy AI</td>
      <td>2.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>6.7x</td>
    </tr>
    <tr>
      <td>41</td>
      <td>Coding</td>
      <td>4.0h</td>
      <td>45m</td>
      <td>3m</td>
      <td>5.3x</td>
    </tr>
    <tr>
      <td>42</td>
      <td>Author CIS-HRSD knowledge taxonomy for AccelaStudy AI</td>
      <td>1.5h</td>
      <td>18m</td>
      <td>3m</td>
      <td>5.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>42</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>518.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,059</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>157</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>27,965,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>29.4x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>198.2x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>13.0</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 80.0x and the lowest at 5.0x, a spread of 16.0 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 8.0 of the 518.5 human-equivalent hours, or 2 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 157 minutes against 1,059 minutes of execution, a ratio of about 1 to 7. Supervisory leverage of 198.2x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 30, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-30-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-30-leverage-record.html</guid>
      <pubDate>Tue, 30 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">13 tasks. June 30, 2026 closed at 15.0x weighted leverage across 204.0 human-equivalent hours in 816 minutes of wall-clock time. Supervisory leverage came in at 170.0x.</p>
<p class="mb-4 font-light font-serif">That is 5.1 weeks of human-equivalent throughput in 13.6 hours. The ceiling was 39.8x; the floor was 4.1x. 10 of the 13 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Full AVIAN deployment-readiness audit (71 repos, content excluded) via 16 parallel agents + central security pass; consolidated findings report; remediated 19 repos incl. backend str(exc)/assert security…</td>
      <td>96.0h</td>
      <td>145m</td>
      <td>3m</td>
      <td>39.8x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Audit and review</td>
      <td>3.0h</td>
      <td>6m</td>
      <td>3m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>The Deferral: fix nine prose continuity issues (timeline seasons, ghost-port geometry, report deadline, Marcus medevac, Ada surveillance, Ch22 location, HERA/VERA attribution, Doppelbot connectivity, doc…</td>
      <td>11.0h</td>
      <td>30m</td>
      <td>4m</td>
      <td>22.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Security hygiene edits to avian-api: sanitize error details + replace asserts</td>
      <td>5.0h</td>
      <td>14m</td>
      <td>3m</td>
      <td>21.4x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Leverage post backfill: reconciled the week&#39;s metrics-tracker records (removed 1 within-CSV duplicate, 06-23..06-30 all in sync), then generated 6 sanitized daily leverage blog posts (40 tasks via 2-agent…</td>
      <td>5.0h</td>
      <td>22m</td>
      <td>1m</td>
      <td>13.6x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Content production</td>
      <td>24.0h</td>
      <td>120m</td>
      <td>12m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>The Deferral continuity pass 2 + adversarial sweep: reconcile Sloane/VERA/HERA bait chain across 8 files, fix report-date weekday, Epilogue dating, 91,247 payoff, Sloane debt, HERA age; multi-agent sweep;…</td>
      <td>8.0h</td>
      <td>40m</td>
      <td>6m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Fix 4 HIGH spec-audit findings (goal_id dedup x2, Salesforce UUID across 15 files/9 repos, VMware description) + non-cert exam_code exemption; consolidate 11 repos to staging (merge a11y branches, resolve…</td>
      <td>7.0h</td>
      <td>40m</td>
      <td>4m</td>
      <td>10.5x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Add Phase 0.5 branch-hygiene rule to readiness audit (check-branches.sh + doc) + fleet-wide staging consolidation: 16 of 25 stray-a11y repos merged+pushed+pruned (conflict resolution, build-artifact stashing,…</td>
      <td>10.0h</td>
      <td>60m</td>
      <td>5m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Security hygiene fixes for avian-api: static error details + assert replacements + regression tests</td>
      <td>3.0h</td>
      <td>20m</td>
      <td>5m</td>
      <td>9.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Triage+fix AVIAN readiness audit: 5 repos test/build failures (npm deps, TS types, uv venv pytest, free-tier adjacency cluster, auth-service real-HTTP test hang), isolate-pushed to staging across 6 repos;…</td>
      <td>12.0h</td>
      <td>110m</td>
      <td>8m</td>
      <td>6.5x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Root-cause + fix deep [content generation]-pipeline validation deadlock (kernel SO_RCVTIMEO socket-options + bounded deadline threads on OpenAI client path; thread/fd-leak under wedged TLS reads) + IBM spec…</td>
      <td>18.0h</td>
      <td>180m</td>
      <td>15m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>avian-engine deployment-readiness audit 2026-06-30</td>
      <td>2.0h</td>
      <td>29m</td>
      <td>3m</td>
      <td>4.1x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>13</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>204.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>816</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>72</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>5,841,396</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>170.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>5.1</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 39.8x and the lowest at 4.1x, a spread of 9.6 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 96.0 of the 204.0 human-equivalent hours, or 47 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 72 minutes against 816 minutes of execution, a ratio of about 1 to 11. Supervisory leverage of 170.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 29, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-29-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-29-leverage-record.html</guid>
      <pubDate>Mon, 29 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">7 tasks. June 29, 2026 closed at 14.9x weighted leverage across 37.0 human-equivalent hours in 149 minutes of wall-clock time. Supervisory leverage came in at 79.3x.</p>
<p class="mb-4 font-light font-serif">That is 0.9 weeks of human-equivalent throughput in 2.5 hours. The ceiling was 28.6x; the floor was 7.2x. 7 of the 7 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Full readiness audit (structural) across 71 changed AVIAN repos: Phase 0 canonical validation + git hygiene + structural checks + architecture/[ip] cross-reference + report</td>
      <td>10.0h</td>
      <td>21m</td>
      <td>3m</td>
      <td>28.6x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Author IBM Storage Fusion domain specification taxonomy (68 leaves across 8 domains)</td>
      <td>6.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Web-research IBM Bob agentic enterprise development: curriculum domains and enablement structure</td>
      <td>4.0h</td>
      <td>15m</td>
      <td>5m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Triage+fix overnight math backfill: root-caused spec-shadowing resolver bug + math-filter gre/Greek substring bug, repaired 8 specs, cleaned bogus content, relaunched 10 math domains, rebuilt Slack monitor</td>
      <td>5.0h</td>
      <td>20m</td>
      <td>3m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>AVIAN readiness-audit remediation: canonical sync + architecture [ip]-doc fixes (App GG back-port + 7 API sections + subsystem reconcile) + README content (7 repos) + audit-def/repo-map onboarding + 15…</td>
      <td>6.0h</td>
      <td>32m</td>
      <td>2m</td>
      <td>11.2x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>IBM Bob Agentic Enterprise Development domain spec taxonomy (67-leaf goal tree across 8 domains)</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>AVIAN architecture doc fixes: GG to System_Architecture Appendix C + subsystem count note + Application_Interface AA-GG sections + README broken links + client README stale [ip] coverage</td>
      <td>3.0h</td>
      <td>25m</td>
      <td>5m</td>
      <td>7.2x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>7</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>37.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>149</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>28</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>1,555,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>14.9x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>79.3x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>0.9</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 28.6x and the lowest at 7.2x, a spread of 4.0 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 10.0 of the 37.0 human-equivalent hours, or 27 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 28 minutes against 149 minutes of execution, a ratio of about 1 to 5. Supervisory leverage of 79.3x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 28, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-28-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-28-leverage-record.html</guid>
      <pubDate>Sun, 28 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">3 tasks. June 28, 2026 closed at 28.1x weighted leverage across 38.0 human-equivalent hours in 81 minutes of wall-clock time. Supervisory leverage came in at 142.5x.</p>
<p class="mb-4 font-light font-serif">That is 0.9 weeks of human-equivalent throughput in 1.4 hours. The ceiling was 50.0x; the floor was 15.0x. 3 of the 3 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Audit AVIAN learning activities (51 in catalog) + generated content completeness across cert vs academic, chemistry/physics/math focus</td>
      <td>20.0h</td>
      <td>24m</td>
      <td>4m</td>
      <td>50.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Design + build automation-slack-relay (HTTP Lambda to Slack chat.postMessage): 4 docs, handler + 17 tests, Terraform stack, /slack-notify skill; located existing Slack webhook creds</td>
      <td>15.0h</td>
      <td>45m</td>
      <td>7m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>AVIAN activity catalog audit — cross-category breakdown + IB check + coverage gaps + GED deferred inventory</td>
      <td>3.0h</td>
      <td>12m</td>
      <td>5m</td>
      <td>15.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>3</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>38.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>81</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>16</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>985,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>28.1x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>142.5x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>0.9</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 50.0x and the lowest at 15.0x, a spread of 3.3 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 20.0 of the 38.0 human-equivalent hours, or 53 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 16 minutes against 81 minutes of execution, a ratio of about 1 to 5. Supervisory leverage of 142.5x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 27, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-27-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-27-leverage-record.html</guid>
      <pubDate>Sat, 27 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">2 tasks. June 27, 2026 closed at 8.3x weighted leverage across 5.0 human-equivalent hours in 36 minutes of wall-clock time. Supervisory leverage came in at 30.0x.</p>
<p class="mb-4 font-light font-serif">That is 0.1 weeks of human-equivalent throughput in 0.6 hours. The ceiling was 10.0x; the floor was 6.7x. 2 of the 2 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Audit [engine subsystem]-service [content generation] code for silent-failure bombs</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Consolidate [engine subsystem]-service [content generation] bomb report and fix plan</td>
      <td>2.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>6.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>2</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>5.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>36</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>10</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>180,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>8.3x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>0.1</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 10.0x and the lowest at 6.7x, a spread of 1.5 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 3.0 of the 5.0 human-equivalent hours, or 60 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 10 minutes against 36 minutes of execution, a ratio of about 1 to 4. Supervisory leverage of 30.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 26, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-26-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-26-leverage-record.html</guid>
      <pubDate>Fri, 26 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">4 tasks. June 26, 2026 closed at 19.7x weighted leverage across 36.5 human-equivalent hours in 111 minutes of wall-clock time. Supervisory leverage came in at 243.3x.</p>
<p class="mb-4 font-light font-serif">That is 0.9 weeks of human-equivalent throughput in 1.9 hours. The ceiling was 26.2x; the floor was 12.0x. 3 of the 4 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Testing</td>
      <td>24.0h</td>
      <td>55m</td>
      <td>3m</td>
      <td>26.2x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Bring up + verify full AVIAN local stack (engine/api/web/notification); confirm all 248 live domains loaded in engine; end-to-end auth+session smoke test through gateway</td>
      <td>3.0h</td>
      <td>11m</td>
      <td>3m</td>
      <td>16.4x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Infrastructure</td>
      <td>4.5h</td>
      <td>20m</td>
      <td>1m</td>
      <td>13.5x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Testing</td>
      <td>5.0h</td>
      <td>25m</td>
      <td>2m</td>
      <td>12.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>4</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>36.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>111</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>9</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>2,310,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>19.7x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>243.3x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>0.9</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 26.2x and the lowest at 12.0x, a spread of 2.2 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 24.0 of the 36.5 human-equivalent hours, or 66 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 9 minutes against 111 minutes of execution, a ratio of about 1 to 12. Supervisory leverage of 243.3x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 25, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-25-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-25-leverage-record.html</guid>
      <pubDate>Thu, 25 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">5 tasks. June 25, 2026 closed at 26.5x weighted leverage across 142.0 human-equivalent hours in 322 minutes of wall-clock time. Supervisory leverage came in at 473.3x.</p>
<p class="mb-4 font-light font-serif">That is 3.5 weeks of human-equivalent throughput in 5.4 hours. The ceiling was 29.1x; the floor was 10.0x. 2 of the 5 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Design and frontend</td>
      <td>120.0h</td>
      <td>247m</td>
      <td>5m</td>
      <td>29.1x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Coding</td>
      <td>12.0h</td>
      <td>28m</td>
      <td>5m</td>
      <td>25.7x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Audited all 73 AVIAN repos for stale docs and executed approved Tier 1+2 cleanup (logs/caches/backups/dups/checkpoints)</td>
      <td>5.0h</td>
      <td>20m</td>
      <td>3m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Tier3 stale-doc cleanup (clients/services/docs/websites) + per-repo staging commits &amp; pushes across 8 repos avoiding WIP contamination</td>
      <td>2.5h</td>
      <td>12m</td>
      <td>2m</td>
      <td>12.5x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Design and frontend</td>
      <td>2.5h</td>
      <td>15m</td>
      <td>3m</td>
      <td>10.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>5</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>142.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>322</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>18</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>2,690,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>26.5x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>473.3x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>3.5</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 29.1x and the lowest at 10.0x, a spread of 2.9 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 120.0 of the 142.0 human-equivalent hours, or 85 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 18 minutes against 322 minutes of execution, a ratio of about 1 to 18. Supervisory leverage of 473.3x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 24, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-24-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-24-leverage-record.html</guid>
      <pubDate>Wed, 24 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">19 tasks. June 24, 2026 closed at 15.0x weighted leverage across 117.0 human-equivalent hours in 468 minutes of wall-clock time. Supervisory leverage came in at 149.4x.</p>
<p class="mb-4 font-light font-serif">That is 2.9 weeks of human-equivalent throughput in 7.8 hours. The ceiling was 28.1x; the floor was 5.4x. 10 of the 19 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Accessibility contrast-standard rollout: codified WCAG 2.1 AA contrast standard in avian-design-system (docs+tokens) and applied uniformly across 18 repos (marketing sites, React apps, libs, services);…</td>
      <td>30.0h</td>
      <td>64m</td>
      <td>4m</td>
      <td>28.1x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Deployment</td>
      <td>14.0h</td>
      <td>35m</td>
      <td>6m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Parallel [content generation] safety audit for [engine subsystem]-service</td>
      <td>3.0h</td>
      <td>8m</td>
      <td>5m</td>
      <td>22.5x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Added 6 new fleet tools to the corporate tools site: authored 6 rich frontmatter-driven marketing pages (23-28 feature cards each, specs, flowcharts, replaces) via a 6-agent fan-out from each tool&#39;s spec,…</td>
      <td>11.0h</td>
      <td>30m</td>
      <td>1m</td>
      <td>22.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Testing</td>
      <td>4.5h</td>
      <td>16m</td>
      <td>1m</td>
      <td>16.9x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Coding</td>
      <td>3.5h</td>
      <td>14m</td>
      <td>2m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Leverage reconciliation + post backfill: resynced a divergent week of metrics-tracker records (local CSV was 34 behind the shared DB; backfilled cloud-&gt;CSV using a numeric fingerprint to survive task…</td>
      <td>5.5h</td>
      <td>25m</td>
      <td>1m</td>
      <td>13.2x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Coding</td>
      <td>6.0h</td>
      <td>28m</td>
      <td>2m</td>
      <td>12.9x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Leverage CSV maintenance: one-pass structural cleanup (reconstructed an unquoted-comma row + a field-short row from cloud-authoritative values, normalized all 2399 rows to proper CSV quoting) + full-history…</td>
      <td>2.5h</td>
      <td>12m</td>
      <td>1m</td>
      <td>12.5x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>De-risked Opus [engine subsystem]: root-caused all-reject verdicts to a transient Agent-SDK subscription error (CLI exits non-zero -&gt; &#39;error result: success&#39;) with zero judge retry; built 3 reproductions…</td>
      <td>4.5h</td>
      <td>22m</td>
      <td>1m</td>
      <td>12.3x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Diagnosed + fixed 42 un-synthesizable alpha specs surfaced by the parallel workers: backed-up data repair of 41 specs (depth_constraints/available_at list-&gt;str, passing_score float-&gt;int) + fixed the…</td>
      <td>3.5h</td>
      <td>18m</td>
      <td>1m</td>
      <td>11.7x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Coding</td>
      <td>1.5h</td>
      <td>8m</td>
      <td>3m</td>
      <td>11.2x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Testing</td>
      <td>16.0h</td>
      <td>88m</td>
      <td>3m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Diagnosed [content generation] stray-output-path bug (CWD-relative writer default sent packages 1 level too high into Projects/core); preserved 2 gate-passing packages GSEC+CSM to canonical tree; built…</td>
      <td>3.0h</td>
      <td>22m</td>
      <td>2m</td>
      <td>8.2x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Write Aegis marketing page for renkara.com tools site</td>
      <td>1.5h</td>
      <td>12m</td>
      <td>3m</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Coding</td>
      <td>1.5h</td>
      <td>12m</td>
      <td>3m</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Coding</td>
      <td>1.5h</td>
      <td>12m</td>
      <td>3m</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Write Atlas marketing page for renkara.com tools site</td>
      <td>1.5h</td>
      <td>14m</td>
      <td>4m</td>
      <td>6.4x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Testing</td>
      <td>2.5h</td>
      <td>28m</td>
      <td>1m</td>
      <td>5.4x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>19</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>117.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>468</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>47</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>4,796,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>149.4x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>2.9</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 28.1x and the lowest at 5.4x, a spread of 5.2 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 30.0 of the 117.0 human-equivalent hours, or 26 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 47 minutes against 468 minutes of execution, a ratio of about 1 to 10. Supervisory leverage of 149.4x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 23, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-23-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-23-leverage-record.html</guid>
      <pubDate>Tue, 23 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">26 tasks. June 23, 2026 closed at 19.2x weighted leverage across 325.0 human-equivalent hours in 1,015 minutes of wall-clock time. Supervisory leverage came in at 187.5x.</p>
<p class="mb-4 font-light font-serif">That is 8.1 weeks of human-equivalent throughput in 16.9 hours. The ceiling was 200.0x; the floor was 1.7x. 14 of the 26 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Testing</td>
      <td>40.0h</td>
      <td>12m</td>
      <td>3m</td>
      <td>200.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Fleet-wide test coverage for all 19 libs/ libraries (~1,750 tests via 14 parallel agents, coverage gates wired, console-sim coverage pipeline repaired) + authored libs-audit.md + dated report + ledger…</td>
      <td>135.0h</td>
      <td>120m</td>
      <td>3m</td>
      <td>67.5x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>The Deferral — extend continuity ledger: added 15 prose-anchored canonical facts (roles, names, St. Alastair facility, 24-yr institutionalization, Luthor self-destruct, Elysium, etc.) across 11 chapter parts;…</td>
      <td>3.0h</td>
      <td>9m</td>
      <td>1m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>The Deferral - implemented Phase 5 background reconciliation in the continuity-audit checker plus README link/prose repair (243/243 green) then committed and pushed</td>
      <td>3.5h</td>
      <td>12m</td>
      <td>1m</td>
      <td>17.5x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>fix-coverage-pipeline-add-view-tests-avian-console-sim-react</td>
      <td>26.0h</td>
      <td>90m</td>
      <td>10m</td>
      <td>17.3x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>avian-ui-react test coverage: 7 component test files + 2 renderer files + coverage-v8 setup (312 tests green LINES 80%)</td>
      <td>7.0h</td>
      <td>25m</td>
      <td>7m</td>
      <td>16.8x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>The Deferral - built deterministic continuity-audit harness (canonical + ledger + checker + reports) modeled on avian-audits</td>
      <td>5.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>16.7x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>The Deferral - manuscript consistency audit and Tier 3-5 fixes across 13 files</td>
      <td>10.0h</td>
      <td>40m</td>
      <td>4m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>The Deferral continuity audit: deep prose-vs-ledger verification sweep; found+fixed 2 ledger defects (fabricated Prologue L44 quote, misattributed Epilogue L75 fact) and added Phase 6 quote-fidelity check…</td>
      <td>5.0h</td>
      <td>20m</td>
      <td>2m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>The Deferral - scaled continuity-audit ledger to all 34 chapters via 8 parallel extraction agents (615 facts) plus checker hardening to a clean 225/225 audit</td>
      <td>8.0h</td>
      <td>35m</td>
      <td>2m</td>
      <td>13.7x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Refactoring</td>
      <td>8.0h</td>
      <td>36m</td>
      <td>1m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Diagnosed and fixed broken Atom/RSS feeds: root-caused malformed XML (analytics <script> injected after the feed root element by the static-site build pipeline, breaking all feed-reader parsing), guarded the…</td>
      <td>6.0h</td>
      <td>30m</td>
      <td>5m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Write 9 activity component test files for avian-activities-react</td>
      <td>4.0h</td>
      <td>22m</td>
      <td>5m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Extended the Atom/RSS feed fix to a second (articles-only) site: diagnosed its stale/empty feed (missing feed templates + content stubs, posts-only filter matching zero content), authored new Atom + RSS…</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>1m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>avian-auth-react: implement full test suite (vitest coverage)</td>
      <td>4.0h</td>
      <td>25m</td>
      <td>5m</td>
      <td>9.6x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Add pytest-cov coverage for avian-diagnostics library</td>
      <td>4.0h</td>
      <td>25m</td>
      <td>3m</td>
      <td>9.6x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>implement vitest coverage for avian-subscribe-react: SubscribeBack and EmbeddedSubscribeFlow tests + coverage config</td>
      <td>4.0h</td>
      <td>25m</td>
      <td>5m</td>
      <td>9.6x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Testing</td>
      <td>3.0h</td>
      <td>22m</td>
      <td>5m</td>
      <td>8.2x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Implement full test coverage for avian-design-system (181 tests across 7 suites; 70.66% line coverage)</td>
      <td>12.0h</td>
      <td>90m</td>
      <td>5m</td>
      <td>8.0x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>[engine subsystem] omniscient sweep of all 206 remaining live non-cloud packages: built generalized grouped profile generator + sequential group runner, debugged bash GROUPS builtin collision, ran 14 logical…</td>
      <td>4.0h</td>
      <td>30m</td>
      <td>2m</td>
      <td>8.0x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>avian-auth-client security test coverage (dpop/cookies/encoding/client-extended)</td>
      <td>4.0h</td>
      <td>35m</td>
      <td>5m</td>
      <td>6.9x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Add vitest test coverage for avian-sound-effects and shared libs</td>
      <td>4.0h</td>
      <td>35m</td>
      <td>5m</td>
      <td>6.9x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>resume-parser coverage wiring + 6 test files (pdf/docx/html/doc parsers + LLM client/normalizer/rewriter/auditor/prompts + pdf_renderer)</td>
      <td>4.0h</td>
      <td>38m</td>
      <td>3m</td>
      <td>6.3x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Write comprehensive test coverage for avian-activities-react library (9 activities + 13 primitives + hooks + providers + container)</td>
      <td>10.0h</td>
      <td>95m</td>
      <td>10m</td>
      <td>6.3x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Implement test coverage for three AVIAN React libraries (avian-about-react avian-app-shell avian-bug-reporter-react)</td>
      <td>8.0h</td>
      <td>90m</td>
      <td>5m</td>
      <td>5.3x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Wire coverage measurement for beautiful-mermaid TypeScript library</td>
      <td>0.5h</td>
      <td>18m</td>
      <td>3m</td>
      <td>1.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>26</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>325.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,015</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>104</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>7,492,300</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>19.2x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>187.5x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>8.1</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 200.0x and the lowest at 1.7x, a spread of 120.0 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 40.0 of the 325.0 human-equivalent hours, or 12 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 104 minutes against 1,015 minutes of execution, a ratio of about 1 to 10. Supervisory leverage of 187.5x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 22, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-22-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-22-leverage-record.html</guid>
      <pubDate>Mon, 22 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">12 tasks. June 22, 2026 closed at 20.2x weighted leverage across 124.7 human-equivalent hours in 371 minutes of wall-clock time. Supervisory leverage came in at 213.8x.</p>
<p class="mb-4 font-light font-serif">That is 3.1 weeks of human-equivalent throughput in 6.2 hours. The ceiling was 140.0x; the floor was 2.4x. 6 of the 12 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Reviewed test coverage across all 19 libs/ repos: stack, runner, test/source counts, coverage gates, and gaps; fanned out 10 parallel audit agents and [content generation]</td>
      <td>14.0h</td>
      <td>6m</td>
      <td>2m</td>
      <td>140.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Testing</td>
      <td>56.0h</td>
      <td>77m</td>
      <td>3m</td>
      <td>43.6x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Coding</td>
      <td>28.0h</td>
      <td>75m</td>
      <td>2m</td>
      <td>22.4x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Audit and review</td>
      <td>5.0h</td>
      <td>20m</td>
      <td>3m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Audit and review</td>
      <td>4.0h</td>
      <td>20m</td>
      <td>5m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Coding</td>
      <td>4.5h</td>
      <td>30m</td>
      <td>2m</td>
      <td>9.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Convert salvage_confirm_sweep (~2900-node 2-stage Haiku+Sonnet [quality gate]) to resumable Anthropic Batches API (50% off both stages); verified submit/poll/fetch/parse/resume live end-to-end</td>
      <td>3.5h</td>
      <td>28m</td>
      <td>2m</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>[engine subsystem] omniscient sweep prep: full local stack bring-up (infra+engine+avian-api+app-web+[engine subsystem]), GCP UUID resolution bug fix in gen_omniscient_profiles, end-to-end spot-check validation</td>
      <td>5.0h</td>
      <td>45m</td>
      <td>2m</td>
      <td>6.7x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Update [engine subsystem]-service docs for LLM caching+batching (CHANGELOG/CLAUDE/DESIGN/README)</td>
      <td>1.0h</td>
      <td>12m</td>
      <td>3m</td>
      <td>5.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Background canon reconciliation — 11 canonical values across 8 files (The Deferral)</td>
      <td>3.0h</td>
      <td>45m</td>
      <td>5m</td>
      <td>4.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Update avian-engine docs for LLM caching+batching changes</td>
      <td>0.5h</td>
      <td>8m</td>
      <td>3m</td>
      <td>3.8x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Documentation</td>
      <td>0.2h</td>
      <td>5m</td>
      <td>3m</td>
      <td>2.4x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>12</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>124.7</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>371</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>35</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>9,595,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>20.2x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>213.8x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>3.1</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 140.0x and the lowest at 2.4x, a spread of 58.3 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 14.0 of the 124.7 human-equivalent hours, or 11 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 35 minutes against 371 minutes of execution, a ratio of about 1 to 11. Supervisory leverage of 213.8x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 21, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-21-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-21-leverage-record.html</guid>
      <pubDate>Sun, 21 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">2 tasks. June 21, 2026 closed at 162.2x weighted leverage across 1,014.0 human-equivalent hours in 375 minutes of wall-clock time. Supervisory leverage came in at 3042.0x.</p>
<p class="mb-4 font-light font-serif">That is 25.4 weeks of human-equivalent throughput in 6.2 hours. The ceiling was 200.0x; the floor was 11.2x. The day&#39;s work was spread across several areas.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Infrastructure</td>
      <td>1000.0h</td>
      <td>300m</td>
      <td>12m</td>
      <td>200.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Infrastructure</td>
      <td>14.0h</td>
      <td>75m</td>
      <td>8m</td>
      <td>11.2x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>2</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>1,014.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>375</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>20</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>2,280,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>162.2x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>3042.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>25.4</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 200.0x and the lowest at 11.2x, a spread of 17.9 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 1,000.0 of the 1,014.0 human-equivalent hours, or 99 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 20 minutes against 375 minutes of execution, a ratio of about 1 to 19. Supervisory leverage of 3042.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 20, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-20-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-20-leverage-record.html</guid>
      <pubDate>Sat, 20 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">18 tasks. June 20, 2026 closed at 36.9x weighted leverage across 481.0 human-equivalent hours in 782 minutes of wall-clock time. Supervisory leverage came in at 390.0x.</p>
<p class="mb-4 font-light font-serif">That is 12.0 weeks of human-equivalent throughput in 13.0 hours. The ceiling was 90.0x; the floor was 5.6x. 7 of the 18 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Content production</td>
      <td>60.0h</td>
      <td>40m</td>
      <td>2m</td>
      <td>90.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Testing</td>
      <td>100.0h</td>
      <td>75m</td>
      <td>3m</td>
      <td>80.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Design and frontend</td>
      <td>100.0h</td>
      <td>78m</td>
      <td>4m</td>
      <td>76.9x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Coding</td>
      <td>35.0h</td>
      <td>32m</td>
      <td>2m</td>
      <td>65.6x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Design and frontend</td>
      <td>40.0h</td>
      <td>42m</td>
      <td>3m</td>
      <td>57.1x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Design and frontend</td>
      <td>35.0h</td>
      <td>40m</td>
      <td>3m</td>
      <td>52.5x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Documentation</td>
      <td>10.0h</td>
      <td>14m</td>
      <td>4m</td>
      <td>42.9x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Design and frontend</td>
      <td>9.0h</td>
      <td>13m</td>
      <td>5m</td>
      <td>41.5x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Coding</td>
      <td>12.0h</td>
      <td>18m</td>
      <td>1m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Run full AVIAN content audit: 27-phase content-audit.py + audit_specs.py + canonical validation across 973 specs/289 packages/2196 labs; wrote timestamped report</td>
      <td>4.0h</td>
      <td>6m</td>
      <td>2m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Leverage reconciliation + backfill for charlessieg.com: synced one month of metrics-tracker records bidirectionally (CSV&lt;-&gt;cloud, 13 deltas resolved, 0 dups), then generated 17 sanitized daily leverage blog…</td>
      <td>16.0h</td>
      <td>40m</td>
      <td>3m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Researched the CloudFront WebSockets-over-VPC-origins launch + AWS docs (announcement, WebSocket handshake behavior, VPC origins internals, real-world gotchas) and wrote a ~3700-word architecture deep-dive…</td>
      <td>10.0h</td>
      <td>28m</td>
      <td>2m</td>
      <td>21.4x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Infrastructure</td>
      <td>7.0h</td>
      <td>25m</td>
      <td>1m</td>
      <td>16.8x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Coding</td>
      <td>8.0h</td>
      <td>40m</td>
      <td>4m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Debugging</td>
      <td>7.0h</td>
      <td>38m</td>
      <td>6m</td>
      <td>11.1x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>[engine subsystem] validation of [model] pairs ([model] [engine subsystem]): verified working, built validate+prune+regenerate pipeline, ran loop to convergence (weak rate 10.5%-&gt;1.9% across 14,384 pairs/59…</td>
      <td>8.0h</td>
      <td>45m</td>
      <td>6m</td>
      <td>10.7x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Novel 2 background analysis and continuity review — 11 files read full plot/dependency/risk/dates report</td>
      <td>4.0h</td>
      <td>38m</td>
      <td>8m</td>
      <td>6.3x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Audit and review</td>
      <td>16.0h</td>
      <td>170m</td>
      <td>15m</td>
      <td>5.6x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>18</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>481.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>782</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>74</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>9,395,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>36.9x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>390.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>12.0</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 90.0x and the lowest at 5.6x, a spread of 15.9 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 60.0 of the 481.0 human-equivalent hours, or 12 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 74 minutes against 782 minutes of execution, a ratio of about 1 to 11. Supervisory leverage of 390.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 19, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-19-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-19-leverage-record.html</guid>
      <pubDate>Fri, 19 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">1 task. June 19, 2026 closed at 35.3x weighted leverage across 20.0 human-equivalent hours in 34 minutes of wall-clock time. Supervisory leverage came in at 600.0x.</p>
<p class="mb-4 font-light font-serif">That is 0.5 weeks of human-equivalent throughput in 0.6 hours. The ceiling was 35.3x; the floor was 35.3x. The day&#39;s work was spread across several areas.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Testing</td>
      <td>20.0h</td>
      <td>34m</td>
      <td>2m</td>
      <td>35.3x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>1</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>20.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>34</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>2</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>4,600,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>35.3x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>600.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>0.5</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 35.3x and the lowest at 35.3x, a spread of 1.0 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 20.0 of the 20.0 human-equivalent hours, or 100 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 2 minutes against 34 minutes of execution, a ratio of about 1 to 17. Supervisory leverage of 600.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 18, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-18-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-18-leverage-record.html</guid>
      <pubDate>Thu, 18 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">2 tasks. June 18, 2026 closed at 36.7x weighted leverage across 52.0 human-equivalent hours in 85 minutes of wall-clock time. Supervisory leverage came in at 624.0x.</p>
<p class="mb-4 font-light font-serif">That is 1.3 weeks of human-equivalent throughput in 1.4 hours. The ceiling was 37.9x; the floor was 36.4x. 2 of the 2 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Full AVIAN content audit (audit_specs.py + content-audit.py, 24 phases, 289 pkgs/592 specs) + mechanical fixes: canonical reconcile, 4 GED specs completed, domain docs refreshed</td>
      <td>12.0h</td>
      <td>19m</td>
      <td>2m</td>
      <td>37.9x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Full AVIAN deployment readiness audit (36 changed repos via 13 fan-out agents) + applied safe fixes across 19 repos + doc/baseline updates</td>
      <td>40.0h</td>
      <td>66m</td>
      <td>3m</td>
      <td>36.4x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>2</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>52.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>85</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>5</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>2,250,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>36.7x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>624.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>1.3</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 37.9x and the lowest at 36.4x, a spread of 1.0 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 12.0 of the 52.0 human-equivalent hours, or 23 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 5 minutes against 85 minutes of execution, a ratio of about 1 to 17. Supervisory leverage of 624.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 9, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-09-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-09-leverage-record.html</guid>
      <pubDate>Tue, 09 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">7 tasks. June 9, 2026 closed at 13.4x weighted leverage across 50.5 human-equivalent hours in 226 minutes of wall-clock time. Supervisory leverage came in at 216.4x.</p>
<p class="mb-4 font-light font-serif">That is 1.3 weeks of human-equivalent throughput in 3.8 hours. The ceiling was 16.6x; the floor was 8.4x. 5 of the 7 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Design and frontend</td>
      <td>16.0h</td>
      <td>58m</td>
      <td>1m</td>
      <td>16.6x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Deployment</td>
      <td>10.0h</td>
      <td>40m</td>
      <td>4m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Documentation</td>
      <td>7.0h</td>
      <td>30m</td>
      <td>1m</td>
      <td>14.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Infrastructure</td>
      <td>8.0h</td>
      <td>38m</td>
      <td>3m</td>
      <td>12.6x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Deployment</td>
      <td>3.5h</td>
      <td>20m</td>
      <td>1m</td>
      <td>10.5x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Deployment</td>
      <td>2.5h</td>
      <td>15m</td>
      <td>2m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Testing</td>
      <td>3.5h</td>
      <td>25m</td>
      <td>2m</td>
      <td>8.4x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>7</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>50.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>226</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>14</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>4,540,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>13.4x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>216.4x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>1.3</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 16.6x and the lowest at 8.4x, a spread of 2.0 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 16.0 of the 50.5 human-equivalent hours, or 32 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 14 minutes against 226 minutes of execution, a ratio of about 1 to 16. Supervisory leverage of 216.4x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 8, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-08-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-08-leverage-record.html</guid>
      <pubDate>Mon, 08 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">11 tasks. June 8, 2026 closed at 37.1x weighted leverage across 510.0 human-equivalent hours in 824 minutes of wall-clock time. Supervisory leverage came in at 695.5x.</p>
<p class="mb-4 font-light font-serif">That is 12.8 weeks of human-equivalent throughput in 13.7 hours. The ceiling was 120.0x; the floor was 6.9x. 4 of the 11 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Coding</td>
      <td>140.0h</td>
      <td>70m</td>
      <td>2m</td>
      <td>120.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Documentation</td>
      <td>20.0h</td>
      <td>14m</td>
      <td>4m</td>
      <td>85.7x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Full deployment readiness audit (AVIAN monorepo, 40 changed repos): Phase-0 canonical reconciliation ([ip] App GG), schema-validated 973 specs (44 invalid found), ~16110 tests run 0 failures, 93-finding…</td>
      <td>90.0h</td>
      <td>65m</td>
      <td>4m</td>
      <td>83.1x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Infrastructure</td>
      <td>16.0h</td>
      <td>18m</td>
      <td>2m</td>
      <td>53.3x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Documentation</td>
      <td>32.0h</td>
      <td>42m</td>
      <td>3m</td>
      <td>45.7x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Audit and review</td>
      <td>32.0h</td>
      <td>45m</td>
      <td>3m</td>
      <td>42.7x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Coding</td>
      <td>32.0h</td>
      <td>70m</td>
      <td>1m</td>
      <td>27.4x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Coding</td>
      <td>18.0h</td>
      <td>50m</td>
      <td>1m</td>
      <td>21.6x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Audit and review</td>
      <td>120.0h</td>
      <td>380m</td>
      <td>14m</td>
      <td>18.9x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>AWS cost-spike forensics + prod tools-box disk-full incident recovery (EBS volume rescue)</td>
      <td>6.0h</td>
      <td>35m</td>
      <td>9m</td>
      <td>10.3x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Deployment</td>
      <td>4.0h</td>
      <td>35m</td>
      <td>1m</td>
      <td>6.9x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>11</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>510.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>824</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>44</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>23,705,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>37.1x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>695.5x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>12.8</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 120.0x and the lowest at 6.9x, a spread of 17.5 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 140.0 of the 510.0 human-equivalent hours, or 27 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 44 minutes against 824 minutes of execution, a ratio of about 1 to 19. Supervisory leverage of 695.5x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 7, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-07-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-07-leverage-record.html</guid>
      <pubDate>Sun, 07 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">2 tasks. June 7, 2026 closed at 24.0x weighted leverage across 36.0 human-equivalent hours in 90 minutes of wall-clock time. Supervisory leverage came in at 240.0x.</p>
<p class="mb-4 font-light font-serif">That is 0.9 weeks of human-equivalent throughput in 1.5 hours. The ceiling was 28.4x; the floor was 17.1x. 2 of the 2 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Content remediation: strict content-audit completeness FAIL gates + remediation_inventory.py (prioritized, 0/289 pass strict bar) + duplicate MCQ option-text code-defect fix via memory-bounded [model] regen…</td>
      <td>26.0h</td>
      <td>55m</td>
      <td>6m</td>
      <td>28.4x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Executed + verified cloud question backfill: 9095 schema-faithful MCQs across 42 AWS/GCP/Azure packages, cloud node coverage 77.8%-&gt;99.98% (10 degenerate skipped), 0 dup options, peak RSS 282MB, run records…</td>
      <td>10.0h</td>
      <td>35m</td>
      <td>3m</td>
      <td>17.1x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>2</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>36.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>90</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>9</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>950,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>240.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>0.9</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 28.4x and the lowest at 17.1x, a spread of 1.7 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 26.0 of the 36.0 human-equivalent hours, or 72 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 9 minutes against 90 minutes of execution, a ratio of about 1 to 10. Supervisory leverage of 240.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 6, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-06-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-06-leverage-record.html</guid>
      <pubDate>Sat, 06 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">7 tasks. June 6, 2026 closed at 26.8x weighted leverage across 161.5 human-equivalent hours in 362 minutes of wall-clock time. Supervisory leverage came in at 312.6x.</p>
<p class="mb-4 font-light font-serif">That is 4.0 weeks of human-equivalent throughput in 6.0 hours. The ceiling was 218.2x; the floor was 6.6x. 5 of the 7 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>avian-status coverage backfill: 16% to 95% lines, 31 test files, 263 tests</td>
      <td>40.0h</td>
      <td>11m</td>
      <td>3m</td>
      <td>218.2x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Post-audit remediation + features: quick wins, backup sync of content+weights to 2 buckets, AudioRecorder web parity port + parity-matrix deferred reclassification, engine weights to Git LFS, coverage…</td>
      <td>48.0h</td>
      <td>112m</td>
      <td>10m</td>
      <td>25.7x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Full readiness audit across 52 repos (Phase 0 + 11-agent fan-out, 222 findings) then applied &amp; independently verified 11 code/test/doc fixes + canonical sync + audit-doc 35-repo expansion</td>
      <td>24.0h</td>
      <td>60m</td>
      <td>4m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Audit and review</td>
      <td>40.0h</td>
      <td>107m</td>
      <td>3m</td>
      <td>22.4x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>[engine subsystem]-service: raise test coverage from 73.17% to ≥75% by adding 85 unit tests across 4 router/CLI modules</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>4m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>clear 85% per-module coverage gate for avian/core and avian/persistence</td>
      <td>3.0h</td>
      <td>22m</td>
      <td>5m</td>
      <td>8.2x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Commit+push verified audit fixes to staging (6 repos, no prod); create private remote for aegis; create staging branches fleet-wide (89/90 repos); debug zsh :r modifier</td>
      <td>3.5h</td>
      <td>32m</td>
      <td>2m</td>
      <td>6.6x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>7</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>161.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>362</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>31</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>4,689,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>26.8x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>312.6x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>4.0</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 218.2x and the lowest at 6.6x, a spread of 33.2 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 40.0 of the 161.5 human-equivalent hours, or 25 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 31 minutes against 362 minutes of execution, a ratio of about 1 to 12. Supervisory leverage of 312.6x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 5, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-05-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-05-leverage-record.html</guid>
      <pubDate>Fri, 05 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">22 tasks. June 5, 2026 closed at 29.1x weighted leverage across 347.2 human-equivalent hours in 717 minutes of wall-clock time. Supervisory leverage came in at 281.5x.</p>
<p class="mb-4 font-light font-serif">That is 8.7 weeks of human-equivalent throughput in 11.9 hours. The ceiling was 81.8x; the floor was 1.5x. 21 of the 22 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Engine lint+type overhaul: ruff 3106-&gt;0 + mypy --strict 2097-&gt;1 (22-agent typing pass + config overrides) + per-error-code lint ratchet &amp; CI wiring + test-profile boot-skip + hnswlib; adversarial review…</td>
      <td>150.0h</td>
      <td>110m</td>
      <td>6m</td>
      <td>81.8x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Brand consolidation across [ip] portfolio: strip product brands from 8 CIP specs (brand-free, matching filed A-Y) and refresh ~40 supporting docs (add GG, fix counts/paths/footers, consolidate retired per-app…</td>
      <td>20.0h</td>
      <td>30m</td>
      <td>2m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Tier-3 cross-repo [ip] remediation Wave 5 — wire dormant validate_bundle onto production _run_atoms path with default-off rejection-enforcement flag; CC-6 cycle detector; CC-11 configurable coverage fraction;…</td>
      <td>18.0h</td>
      <td>32m</td>
      <td>2m</td>
      <td>33.8x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Audit avian-engine unit tests (234 files, ~114k LOC): heuristic AST scan + 12 parallel review agents; removed ~146 zero-value tests and rewrote 13 tautological/weak assertions across ~40 files; full unit…</td>
      <td>20.0h</td>
      <td>40m</td>
      <td>3m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Testing</td>
      <td>7.0h</td>
      <td>15m</td>
      <td>2m</td>
      <td>28.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Tier-3 cross-repo [ip] remediation phase-1 orchestration — recon across 3 repos; CC schema (CC-5/17/21) + D staleness (composition + dormant-method wiring) waves; disentangle and bank entangled WIP;…</td>
      <td>38.0h</td>
      <td>100m</td>
      <td>6m</td>
      <td>22.8x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Portfolio-wide domain-neutrality pass: remove school-specific lesson term (~75 occurrences) across 6 CIP drafts incl. AA source-type enum + BB/CC enum renames and CC deprecated-artifact filename</td>
      <td>4.5h</td>
      <td>12m</td>
      <td>1m</td>
      <td>22.5x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Engine branch-review P1 bug fixes — adaptive-intake zero-items (selector contract + min_intake_items), grpc.aio context.abort never awaited (2 servicers), ring _persist_ring_topology NameError,…</td>
      <td>9.0h</td>
      <td>28m</td>
      <td>3m</td>
      <td>19.3x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Resume crashed CompTIA Network+ [content generation]: fix structural ~0.80 validation plateau with tier-aware adversarial challenge calibration (recall-&gt;definition/terminology challenges;…</td>
      <td>6.0h</td>
      <td>20m</td>
      <td>2m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Tier-3 cross-repo [ip] remediation Wave 4 — re-baseline atoms pipeline vs WIP; implement CC-15/16/18/19 atom writers + CC-26 engine validate_collection wiring; diagnose+fix CC-21 shared-lib HTTPS-validator…</td>
      <td>22.0h</td>
      <td>75m</td>
      <td>3m</td>
      <td>17.6x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>AVIAN [ip] portfolio freshness+brand-consolidation edit pass: 9 docs, 34 filing counts, 12-cluster brand tokens, GG inventions added</td>
      <td>4.0h</td>
      <td>14m</td>
      <td>5m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Coding</td>
      <td>8.0h</td>
      <td>28m</td>
      <td>5m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Engine review Low-Med fix — tracked background-task registry: avian.core.background (strong-ref no-GC + exception-logging done-callback + shutdown cancellation) wired through all 11 fire-and-forget…</td>
      <td>6.0h</td>
      <td>22m</td>
      <td>5m</td>
      <td>16.4x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Tier-3 cross-repo [ip] remediation Waves 6+7 — B-22 cross-repo information-gain ranking ([engine subsystem] Fisher-info + engine ranking) and AA-7 [engine subsystem] configurable opt-in alignment fault;…</td>
      <td>12.0h</td>
      <td>50m</td>
      <td>2m</td>
      <td>14.4x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Engine security-review Medium fix — upload size-cap: _read_upload_capped streams uploads in 1MB chunks rejecting 413 before buffering the full payload (declared-size early-reject + running cap) wired into…</td>
      <td>3.5h</td>
      <td>16m</td>
      <td>1m</td>
      <td>13.1x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Engine security-review HIGH fixes — fail-closed API auth when AVIAN_API_KEY missing (cloud/prod 503 + test/local escape hatch) + admin-key enforcement on 3 unprotected admin/debug routes (relocate…</td>
      <td>7.0h</td>
      <td>32m</td>
      <td>3m</td>
      <td>13.1x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Freshness edit pass on AVIAN [ip] portfolio Analysis and Stage-4 business docs: add GG to all 11 target files, fix scope/counts A-GG, update CIP→Filing paths for A-Y, fix Z cluster name, recompute Stage-4…</td>
      <td>8.0h</td>
      <td>45m</td>
      <td>8m</td>
      <td>10.7x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Domain-neutrality terminology pass on BB/EE/FF [ip] specs</td>
      <td>1.0h</td>
      <td>6m</td>
      <td>3m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Content production</td>
      <td>1.0h</td>
      <td>8m</td>
      <td>3m</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>AVIAN [ip] portfolio freshness + brand consolidation edit pass (6 files)</td>
      <td>1.5h</td>
      <td>18m</td>
      <td>3m</td>
      <td>5.0x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Content production</td>
      <td>0.5h</td>
      <td>8m</td>
      <td>3m</td>
      <td>3.8x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Mechanical freshness edit pass on AVIAN Stage-3 use-case docs — update 12 stale footers + fix cluster count in Use_Case_Scenarios</td>
      <td>0.2h</td>
      <td>8m</td>
      <td>3m</td>
      <td>1.5x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>22</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>347.2</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>717</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>74</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>17,919,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>29.1x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>281.5x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>8.7</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 81.8x and the lowest at 1.5x, a spread of 54.5 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 150.0 of the 347.2 human-equivalent hours, or 43 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 74 minutes against 717 minutes of execution, a ratio of about 1 to 10. Supervisory leverage of 281.5x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 4, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-04-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-04-leverage-record.html</guid>
      <pubDate>Thu, 04 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">33 tasks. June 4, 2026 closed at 48.1x weighted leverage across 1,301.0 human-equivalent hours in 1,622 minutes of wall-clock time. Supervisory leverage came in at 500.4x.</p>
<p class="mb-4 font-light font-serif">That is 32.5 weeks of human-equivalent throughput in 27.0 hours. The ceiling was 174.2x; the floor was 5.0x. 31 of the 33 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Audit and review</td>
      <td>180.0h</td>
      <td>62m</td>
      <td>3m</td>
      <td>174.2x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Implemented ~220 AVIAN [ip] claims (Apps A-GG) from audit baseline to 441/718 wired via ~28 parallel agents + shared-file integration wiring; full unit suite 5842 passing</td>
      <td>700.0h</td>
      <td>316m</td>
      <td>12m</td>
      <td>132.9x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Audit and review</td>
      <td>100.0h</td>
      <td>55m</td>
      <td>4m</td>
      <td>109.1x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Documentation</td>
      <td>130.0h</td>
      <td>105m</td>
      <td>2m</td>
      <td>74.3x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Harden [ip]+diagram audit specs (4 gap-closing checks) and run proactive 8-agent semantic/legal sweep across all CIP drafts; apply ~28 calibrated overclaim/self-contradiction/grammar/cross-ref fixes</td>
      <td>12.0h</td>
      <td>25m</td>
      <td>3m</td>
      <td>28.8x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Testing</td>
      <td>14.0h</td>
      <td>30m</td>
      <td>1m</td>
      <td>28.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Coding</td>
      <td>8.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>26.7x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Coding</td>
      <td>16.0h</td>
      <td>38m</td>
      <td>5m</td>
      <td>25.3x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Coding</td>
      <td>12.0h</td>
      <td>42m</td>
      <td>5m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Coding</td>
      <td>8.0h</td>
      <td>35m</td>
      <td>5m</td>
      <td>13.7x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Coding</td>
      <td>8.0h</td>
      <td>35m</td>
      <td>5m</td>
      <td>13.7x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Coding</td>
      <td>4.0h</td>
      <td>22m</td>
      <td>5m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Coding</td>
      <td>8.0h</td>
      <td>45m</td>
      <td>5m</td>
      <td>10.7x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Coding</td>
      <td>8.0h</td>
      <td>45m</td>
      <td>5m</td>
      <td>10.7x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Coding</td>
      <td>8.0h</td>
      <td>45m</td>
      <td>5m</td>
      <td>10.7x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Coding</td>
      <td>8.0h</td>
      <td>45m</td>
      <td>5m</td>
      <td>10.7x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Coding</td>
      <td>3.5h</td>
      <td>22m</td>
      <td>5m</td>
      <td>9.5x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Coding</td>
      <td>6.0h</td>
      <td>40m</td>
      <td>5m</td>
      <td>9.0x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Coding</td>
      <td>8.0h</td>
      <td>55m</td>
      <td>5m</td>
      <td>8.7x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Pre-filing semantic/legal review of AVIAN CIP [ip] drafts DD ([ip-cluster]) and EE (Pylon)</td>
      <td>4.0h</td>
      <td>28m</td>
      <td>5m</td>
      <td>8.6x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Coding</td>
      <td>4.0h</td>
      <td>28m</td>
      <td>3m</td>
      <td>8.6x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Coding</td>
      <td>4.0h</td>
      <td>28m</td>
      <td>5m</td>
      <td>8.6x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Audit and review</td>
      <td>4.0h</td>
      <td>30m</td>
      <td>5m</td>
      <td>8.0x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Debugging</td>
      <td>8.0h</td>
      <td>65m</td>
      <td>5m</td>
      <td>7.4x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Coding</td>
      <td>4.0h</td>
      <td>35m</td>
      <td>5m</td>
      <td>6.9x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Coding</td>
      <td>4.0h</td>
      <td>35m</td>
      <td>5m</td>
      <td>6.9x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>Audit and review</td>
      <td>3.0h</td>
      <td>28m</td>
      <td>5m</td>
      <td>6.4x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Coding</td>
      <td>8.0h</td>
      <td>75m</td>
      <td>5m</td>
      <td>6.4x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>Coding</td>
      <td>2.0h</td>
      <td>20m</td>
      <td>5m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>Coding</td>
      <td>4.0h</td>
      <td>45m</td>
      <td>5m</td>
      <td>5.3x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>Coding</td>
      <td>3.0h</td>
      <td>35m</td>
      <td>5m</td>
      <td>5.1x</td>
    </tr>
    <tr>
      <td>32</td>
      <td>Coding</td>
      <td>2.5h</td>
      <td>30m</td>
      <td>5m</td>
      <td>5.0x</td>
    </tr>
    <tr>
      <td>33</td>
      <td>Coding</td>
      <td>5.0h</td>
      <td>60m</td>
      <td>5m</td>
      <td>5.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>33</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>1,301.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,622</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>156</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>30,435,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>48.1x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>500.4x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>32.5</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 174.2x and the lowest at 5.0x, a spread of 34.8 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 180.0 of the 1,301.0 human-equivalent hours, or 14 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 156 minutes against 1,622 minutes of execution, a ratio of about 1 to 10. Supervisory leverage of 500.4x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 3, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-03-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-03-leverage-record.html</guid>
      <pubDate>Wed, 03 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">9 tasks. June 3, 2026 closed at 40.9x weighted leverage across 617.5 human-equivalent hours in 905 minutes of wall-clock time. Supervisory leverage came in at 661.6x.</p>
<p class="mb-4 font-light font-serif">That is 15.4 weeks of human-equivalent throughput in 15.1 hours. The ceiling was 160.0x; the floor was 5.0x. 8 of the 9 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Design and frontend</td>
      <td>240.0h</td>
      <td>90m</td>
      <td>10m</td>
      <td>160.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Coding</td>
      <td>80.0h</td>
      <td>75m</td>
      <td>4m</td>
      <td>64.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Harden 8 CIP [ip] drafts (Z/AA/BB/CC/DD/EE/FF/GG) per review: soften bitwise/RAG/[engine subsystem]/guarantee overclaims to calibrated tolerances + answer-support verifier; fix stem-type count; worked-example…</td>
      <td>12.0h</td>
      <td>12m</td>
      <td>4m</td>
      <td>60.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Infrastructure</td>
      <td>40.0h</td>
      <td>42m</td>
      <td>4m</td>
      <td>57.1x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Documentation</td>
      <td>7.0h</td>
      <td>10m</td>
      <td>5m</td>
      <td>42.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Full a11y audit + fix of all 10 accelastudy.ai sites (main + 9 sisters): built axe harness, diagnosed FOUC/scroll-reveal/dark-mode/redirect/gate-timing false positives, fixed contrast tokens (light+dark),…</td>
      <td>100.0h</td>
      <td>200m</td>
      <td>3m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Audit and review</td>
      <td>120.0h</td>
      <td>300m</td>
      <td>20m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Implement 12 unimplemented [ip] claims to reduction-to-practice (BB-9/12/18/19/26, DD-17/24/25/26, Z-7/17/18) with tests+endpoints+config; prove 0% exam-bug fix with 7-test regression; App Z cleared to 0…</td>
      <td>18.0h</td>
      <td>170m</td>
      <td>3m</td>
      <td>6.4x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Coding</td>
      <td>0.5h</td>
      <td>6m</td>
      <td>3m</td>
      <td>5.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>9</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>617.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>905</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>56</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>12,118,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>40.9x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>661.6x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>15.4</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 160.0x and the lowest at 5.0x, a spread of 32.0 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 240.0 of the 617.5 human-equivalent hours, or 39 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 56 minutes against 905 minutes of execution, a ratio of about 1 to 16. Supervisory leverage of 661.6x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 2, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-02-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-02-leverage-record.html</guid>
      <pubDate>Tue, 02 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">7 tasks. June 2, 2026 closed at 27.1x weighted leverage across 124.0 human-equivalent hours in 275 minutes of wall-clock time. Supervisory leverage came in at 354.3x.</p>
<p class="mb-4 font-light font-serif">That is 3.1 weeks of human-equivalent throughput in 4.6 hours. The ceiling was 80.0x; the floor was 7.9x. 7 of the 7 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Full accessibility audit + fix across all four avian-app clients (web/electron/android/ios): jsx-a11y errors, label associations, autofocus, tablist roles, jsx-a11y plugin + axe coverage 8-&gt;40, 100+ Compose…</td>
      <td>80.0h</td>
      <td>60m</td>
      <td>3m</td>
      <td>80.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Full WCAG 2.1 AA accessibility audit across all 4 avian-app clients (web/electron source + 39-route axe sweep; 11 mechanical fixes, 3 false positives triaged, ledger reconciled, native heuristics)</td>
      <td>7.0h</td>
      <td>8m</td>
      <td>2m</td>
      <td>52.5x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Run full accessibility audit across all four avian-app clients (web/electron/iOS/Android) — hand-created Android AVD, booted sim+emulator, ran axe-sweep/vitest/XCUITest/Espresso a11y suites</td>
      <td>4.0h</td>
      <td>9m</td>
      <td>1m</td>
      <td>26.7x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Fix iOS + Android accessibility audit failures: 44pt hit-target on All Courses button + opaque-white hero subtitles (contrast) + accessibilityHidden on decorative SF Symbols; repair Android Compose a11y test…</td>
      <td>3.0h</td>
      <td>13m</td>
      <td>1m</td>
      <td>13.8x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Resume avian-[engine subsystem] OMNISCIENT-ONLY cloud sweeps: diagnose OOM root cause, generate 42 omni profiles, write concurrency-capped batching runner, bring up engine+[engine subsystem] backend at hard…</td>
      <td>5.0h</td>
      <td>27m</td>
      <td>2m</td>
      <td>11.1x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Finish AP Precalc math content: fix 3 [content generation] bugs (rep_pack schema-drop, judge-pool deadlock, id-collision), Sonnet regen of 38 goals, standalone re-judge of 381 items, prune to 0…</td>
      <td>20.0h</td>
      <td>120m</td>
      <td>6m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Audit and review</td>
      <td>5.0h</td>
      <td>38m</td>
      <td>6m</td>
      <td>7.9x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>7</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>124.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>275</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>21</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>1,789,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>27.1x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>354.3x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>3.1</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 80.0x and the lowest at 7.9x, a spread of 10.1 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 80.0 of the 124.0 human-equivalent hours, or 65 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 21 minutes against 275 minutes of execution, a ratio of about 1 to 13. Supervisory leverage of 354.3x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: June 1, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-06-01-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-06-01-leverage-record.html</guid>
      <pubDate>Mon, 01 Jun 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">5 tasks. June 1, 2026 closed at 27.4x weighted leverage across 52.0 human-equivalent hours in 114 minutes of wall-clock time. Supervisory leverage came in at 390.0x.</p>
<p class="mb-4 font-light font-serif">That is 1.3 weeks of human-equivalent throughput in 1.9 hours. The ceiling was 90.0x; the floor was 6.7x. 4 of the 5 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>ADR 0005: GED novel adult-learner activity set — triaged 16 activities into 6 tiers with [ip] considerations, implementation plan, validation metrics, and risk evaluation; updated ADR index</td>
      <td>15.0h</td>
      <td>10m</td>
      <td>2m</td>
      <td>90.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Aegis Phase 1 close-out — scan endpoint + dedup-aware persistence: 5 ORM models (repos/scans/scan_results/findings/finding_events) + Pydantic schemas + orchestrator (sequential scan execution with…</td>
      <td>22.0h</td>
      <td>26m</td>
      <td>1m</td>
      <td>50.8x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Phase 0 of ADR-0005: extended activities catalog with 16 GED novel-activity entries + 4 addons + academic/GED category + deferred runtime_source + per-activity feature-flag plumbing + library mirror sync + 8…</td>
      <td>8.0h</td>
      <td>18m</td>
      <td>1m</td>
      <td>26.7x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Forensic diagnosis of crashed cross-session [content generation] run + safe checkpoint resume (CompTIA Network+)</td>
      <td>2.0h</td>
      <td>15m</td>
      <td>3m</td>
      <td>8.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Brought IB Business + AZ-140 to pristine + beta (cohort now 30): generated ~6,157 questions from 0, reweighted both, amplified pairs (IB median 13-&gt;15, +313; AZ-140 +34), stamped AZ-140 readiness-gate…</td>
      <td>5.0h</td>
      <td>45m</td>
      <td>1m</td>
      <td>6.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>5</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>52.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>114</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>8</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>510,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>27.4x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>390.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>1.3</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 90.0x and the lowest at 6.7x, a spread of 13.5 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 15.0 of the 52.0 human-equivalent hours, or 29 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 8 minutes against 114 minutes of execution, a ratio of about 1 to 14. Supervisory leverage of 390.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 31, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-31-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-31-leverage-record.html</guid>
      <pubDate>Sun, 31 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">4 tasks. May 31, 2026 closed at 14.9x weighted leverage across 90.0 human-equivalent hours in 362 minutes of wall-clock time. Supervisory leverage came in at 450.0x.</p>
<p class="mb-4 font-light font-serif">That is 2.2 weeks of human-equivalent throughput in 6.0 hours. The ceiling was 16.0x; the floor was 8.0x. 3 of the 4 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Accessibility audit remediation — all 71 findings (5 blocker/30 serious/24 moderate/12 minor) fixed across web/iOS/Android/Electron + design-system; 4 waves; per-client build/test-gated commits</td>
      <td>80.0h</td>
      <td>300m</td>
      <td>8m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Promoted 13 alpha packages to beta (Tier A+B): reweighted 12 goal_weights, generated 621 CCSP recall questions + 17 CISM pairs, verified all 27 beta packages pristine (zero findings); produced AP Precalc +…</td>
      <td>5.0h</td>
      <td>25m</td>
      <td>2m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Brought AP Precalculus to pristine + beta: generated 3,359 questions from 0 (fixed a fresh-bank write bug mid-run), reweighted goal_weights (5 criticals), +1 pair, re-stamped, verified all 28 beta packages…</td>
      <td>3.0h</td>
      <td>22m</td>
      <td>1m</td>
      <td>8.2x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Coding</td>
      <td>2.0h</td>
      <td>15m</td>
      <td>1m</td>
      <td>8.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>4</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>90.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>362</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>12</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>810,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>14.9x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>450.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>2.2</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 16.0x and the lowest at 8.0x, a spread of 2.0 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 80.0 of the 90.0 human-equivalent hours, or 89 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 12 minutes against 362 minutes of execution, a ratio of about 1 to 30. Supervisory leverage of 450.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 30, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-30-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-30-leverage-record.html</guid>
      <pubDate>Sat, 30 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">45 tasks. May 30, 2026 closed at 26.0x weighted leverage across 701.8 human-equivalent hours in 1,620 minutes of wall-clock time. Supervisory leverage came in at 316.6x.</p>
<p class="mb-4 font-light font-serif">That is 17.5 weeks of human-equivalent throughput in 27.0 hours. The ceiling was 80.0x; the floor was 3.3x. 21 of the 45 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>[ip-cluster] interaction batch 2 — 7 interactions x 3 clients (math/chem/biology/language/history)</td>
      <td>40.0h</td>
      <td>30m</td>
      <td>3m</td>
      <td>80.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>[ip-cluster] interaction batch 3 — 8 interactions x 3 clients (econ/math/chem/biology/physics/history) + stall recovery</td>
      <td>48.0h</td>
      <td>55m</td>
      <td>4m</td>
      <td>52.4x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>[ip-cluster] V2 native host shells + 8 primitive interactions x 3 clients</td>
      <td>120.0h</td>
      <td>140m</td>
      <td>4m</td>
      <td>51.4x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>[ip-cluster] interaction batch 4 — 7 interactions x 3 clients (physics/econ/history/biology/CS) + stall recovery</td>
      <td>42.0h</td>
      <td>50m</td>
      <td>4m</td>
      <td>50.4x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>[ip-cluster] interaction batch 5 — 8 interactions x 3 clients (econ curves + CS tools) + stall recovery</td>
      <td>46.0h</td>
      <td>58m</td>
      <td>4m</td>
      <td>47.6x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>[ip-cluster] interaction batch 6 — 8 interactions x 3 clients (econ/math/language) + double-launch + stall recovery</td>
      <td>46.0h</td>
      <td>62m</td>
      <td>4m</td>
      <td>44.5x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>[ip-cluster] interaction batch 8 — 8 interactions x 3 clients (earth-sci/history/art/language/math)</td>
      <td>46.0h</td>
      <td>62m</td>
      <td>4m</td>
      <td>44.5x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>[ip-cluster] interaction batch 9 (FINAL pure-UI) — 10 interactions x 3 clients</td>
      <td>56.0h</td>
      <td>78m</td>
      <td>4m</td>
      <td>43.1x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Client parity Phase 1: iOS wrong-answer lesson-review panel wired into MCQActivityView (captures missed concept IDs, defers auto-advance to sheet onDismiss; faithful to QuestionBankMCQView) + Android 6…</td>
      <td>32.0h</td>
      <td>80m</td>
      <td>3m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Android OfflineScaffold: NetworkMonitor+NetworkObserver+ConnectivityModule+OfflineScaffold+OfflineWriteQueue+Room v2+AppViewModel+AppHost+strings (13 files)</td>
      <td>6.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>avian-app-android Phase-2 rum: PulseClient + ChronicleBeacon + TelemetryFlushWorker real POST</td>
      <td>6.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Regenerated 2 empty PMI CP nodes (re-validated 0.994) + ran LLM confirmation sweep over 2923 HIGH validation residuals (Haiku-&gt;Sonnet), confirming 282 real content errors across 101 packages (90.4% FP);…</td>
      <td>5.0h</td>
      <td>15m</td>
      <td>1m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Repaired all 125 live-domain content errors found by the confirmation sweep (Sonnet-regen seeded with each confirmed issue + local [engine subsystem] [quality gate]; 1 node re-fixed), verified 125/125,…</td>
      <td>4.0h</td>
      <td>12m</td>
      <td>1m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>[ip-cluster] interaction batch 7 — 8 interactions x 3 clients (chemistry/physics) + draft-status fix + recovery</td>
      <td>48.0h</td>
      <td>145m</td>
      <td>4m</td>
      <td>19.9x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>[ip-cluster] rive_diagram native renderer — Android + Electron (gradle + npm/WASM/CSP dep adds)</td>
      <td>12.0h</td>
      <td>40m</td>
      <td>4m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Audit and review</td>
      <td>36.0h</td>
      <td>125m</td>
      <td>5m</td>
      <td>17.3x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Retired draft status catalog-wide (631 specs + manifests + 6 code/doc + 3 UI files via subagent, tsc-verified); fixed residual live issues (3 stale checkpoints, 53 accepted atom-gaps); ranked 12 beta…</td>
      <td>7.0h</td>
      <td>25m</td>
      <td>3m</td>
      <td>16.8x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Reswept 536 MCQs derived from the 125 repaired live nodes: regenerated 535 from corrected content (Haiku), preserved IDs/tier/metadata, structurally validated + spot-checked; on disk for S3</td>
      <td>2.0h</td>
      <td>8m</td>
      <td>1m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Phase 1+2.1 live-catalog audit cleanup: re-stamped 9611 question tiers from source nodes + 277 quality_report node-counts (mtime-preserved) + 47 manifests (deterministic, backed up); reconstructed 224 empty…</td>
      <td>6.0h</td>
      <td>25m</td>
      <td>2m</td>
      <td>14.4x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>[ip-cluster] host nav rewire (iOS+Android) with audio-parity audit + workflow-recovery</td>
      <td>16.0h</td>
      <td>70m</td>
      <td>3m</td>
      <td>13.7x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Autonomously promoted 14 domains to beta with necessary [content generation]: ~6.6k tier-coverage questions (recall for 6 ISC2/ISACA certs, all-tier AP Macro), blueprint goal-weight reweight for 11…</td>
      <td>9.0h</td>
      <td>40m</td>
      <td>3m</td>
      <td>13.5x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>electron-updater spec: wire electron-updater IPC and VersionChecker UI</td>
      <td>4.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>electron-updater Phase-1: wire autoUpdater IPC end-to-end (main.ts, preload.ts, types.ts, ipc-client.ts, VersionChecker.tsx, test mock)</td>
      <td>4.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>iOS morning-briefing cinematic overlay — MorningBriefingView + ParticleLayer in DashboardView.swift</td>
      <td>3.0h</td>
      <td>14m</td>
      <td>3m</td>
      <td>12.9x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Build morning-briefing Phase-2 feature for Android client (MorningBriefingOverlay + DashboardScreen wiring)</td>
      <td>6.0h</td>
      <td>28m</td>
      <td>5m</td>
      <td>12.9x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>bug-reporter cross-client spec investigation</td>
      <td>2.0h</td>
      <td>10m</td>
      <td>3m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>Build ErrorDetectionScreen + ViewModel (Android Jetpack Compose)</td>
      <td>4.0h</td>
      <td>22m</td>
      <td>5m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Build Android service_match activity: ServiceMatchScreen + ServiceMatchViewModel (Jetpack Compose)</td>
      <td>4.0h</td>
      <td>22m</td>
      <td>5m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>Documentation</td>
      <td>7.0h</td>
      <td>40m</td>
      <td>1m</td>
      <td>10.5x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>Build Android ProceduralStepSequencingScreen + ViewModel (Jetpack Compose)</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>content data-repair (cheap/safe subset of Task 7 backfills): case-normalized 5 corrupt node tiers + 56 question tiers (40 case + 16 from valid node), backed up to data/.archive/tier-repair; investigated +…</td>
      <td>2.5h</td>
      <td>16m</td>
      <td>1m</td>
      <td>9.4x</td>
    </tr>
    <tr>
      <td>32</td>
      <td>root-caused 224 empty-[model] (10 live cert packages = 2026-05-17 recall-regen content-persistence gap; embeddings+questions survived, content never written; unrecoverable from .before-recall-regen backups) +…</td>
      <td>3.0h</td>
      <td>20m</td>
      <td>2m</td>
      <td>9.0x</td>
    </tr>
    <tr>
      <td>33</td>
      <td>Build CaseStudyAnalysisScreen + CaseStudyAnalysisViewModel for Android</td>
      <td>2.0h</td>
      <td>14m</td>
      <td>3m</td>
      <td>8.6x</td>
    </tr>
    <tr>
      <td>34</td>
      <td>status-model overhaul (#8) — beta=staging-only-not-public across engine (public_only list_domains filter + GET /api/v1/domains param), web+electron public catalog filters (applyVerticalFilter +…</td>
      <td>4.5h</td>
      <td>32m</td>
      <td>1m</td>
      <td>8.4x</td>
    </tr>
    <tr>
      <td>35</td>
      <td>avian-app-android MinimalPairContrastScreen+ViewModel</td>
      <td>3.0h</td>
      <td>22m</td>
      <td>3m</td>
      <td>8.2x</td>
    </tr>
    <tr>
      <td>36</td>
      <td>tier reclassification (option a): Haiku-classified 42 corrupt knowledge-node tiers from content + propagated to 331 questions across 21 packages (~[cost]), backed up to data/.archive/tier-reclassify; fully…</td>
      <td>1.5h</td>
      <td>11m</td>
      <td>1m</td>
      <td>8.2x</td>
    </tr>
    <tr>
      <td>37</td>
      <td>Build Android RecallSprintScreen + RecallSprintViewModel</td>
      <td>2.5h</td>
      <td>22m</td>
      <td>3m</td>
      <td>6.8x</td>
    </tr>
    <tr>
      <td>38</td>
      <td>cognitive-state iOS feature: algorithm port + EngineClient.postCognitiveState + ActiveSessionView/AppState wiring</td>
      <td>2.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>6.7x</td>
    </tr>
    <tr>
      <td>39</td>
      <td>Drove iOS CI green + Electron all-gates green. iOS: verified Xcode build of the MCQ change, fixed stale Phase unit test, rewrote run-tests.sh (dynamic sim resolution + pre-boot to kill the FBSOpenApplication…</td>
      <td>8.0h</td>
      <td>75m</td>
      <td>2m</td>
      <td>6.4x</td>
    </tr>
    <tr>
      <td>40</td>
      <td>Refactor CredentialMapping.tsx off react-router-dom onto Electron prop-router contract</td>
      <td>0.5h</td>
      <td>6m</td>
      <td>2m</td>
      <td>5.0x</td>
    </tr>
    <tr>
      <td>41</td>
      <td>Refactor PageNotFound.tsx off react-router-dom onto prop-router contract</td>
      <td>0.2h</td>
      <td>4m</td>
      <td>2m</td>
      <td>3.8x</td>
    </tr>
    <tr>
      <td>42</td>
      <td>Refactor ReadinessForecast.tsx off react-router onto prop-router contract</td>
      <td>0.2h</td>
      <td>4m</td>
      <td>2m</td>
      <td>3.8x</td>
    </tr>
    <tr>
      <td>43</td>
      <td>Refactor KnowledgeMapScreen to prop-router contract (onBack prop + back button both branches)</td>
      <td>0.2h</td>
      <td>4m</td>
      <td>2m</td>
      <td>3.8x</td>
    </tr>
    <tr>
      <td>44</td>
      <td>iOS Phase-1 parity: add loadError state + AvianInlineAlert warning banner to ResumeReviewSectionView</td>
      <td>0.5h</td>
      <td>8m</td>
      <td>3m</td>
      <td>3.8x</td>
    </tr>
    <tr>
      <td>45</td>
      <td>Coding</td>
      <td>1.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>3.3x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>45</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>701.8</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,620</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>133</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>8,985,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>26.0x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>316.6x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>17.5</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 80.0x and the lowest at 3.3x, a spread of 24.0 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 40.0 of the 701.8 human-equivalent hours, or 6 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 133 minutes against 1,620 minutes of execution, a ratio of about 1 to 12. Supervisory leverage of 316.6x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 29, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-29-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-29-leverage-record.html</guid>
      <pubDate>Fri, 29 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">7 tasks. May 29, 2026 closed at 32.2x weighted leverage across 94.0 human-equivalent hours in 175 minutes of wall-clock time. Supervisory leverage came in at 313.3x.</p>
<p class="mb-4 font-light font-serif">That is 2.4 weeks of human-equivalent throughput in 2.9 hours. The ceiling was 80.0x; the floor was 9.5x. 6 of the 7 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Aegis Phase 1 — scanner foundation (Scanner ABC + Finding model + content-addressable dedup hash + lazy registry) plus all 7 scanner integrations (semgrep/bandit/detect-secrets/pip-audit/safety/checkov/trivy)…</td>
      <td>60.0h</td>
      <td>45m</td>
      <td>1m</td>
      <td>80.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Root-caused cloud-cert quality instability (42 AWS/GCP/Azure pkgs, 3 repair campaigns) + committed 301 engine re-stamps + regenerated 121 PMP single-option questions</td>
      <td>6.0h</td>
      <td>10m</td>
      <td>3m</td>
      <td>36.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>[content generation] field-preservation remediation (task 1/7): traced parser-&gt;adapter-&gt;writer path across [ip-cluster]-runtime + [ip-cluster]; added raw exam_metadata passthrough + ConfigDict(extra=allow) +…</td>
      <td>4.5h</td>
      <td>13m</td>
      <td>2m</td>
      <td>20.8x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Testing</td>
      <td>4.0h</td>
      <td>15m</td>
      <td>2m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>origin to [engine subsystem] monorepo rename — [ip-cluster]_runtime and [ip-cluster] packages + 2 repo dirs + [ip-cluster] to [engine subsystem] CLI + path-deps/uv.locks across 8 repos — infra deferred —…</td>
      <td>9.0h</td>
      <td>35m</td>
      <td>6m</td>
      <td>15.4x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>[engine subsystem] rebuild — shared in-process pipeline_core (run_phase/run_pipeline) + made lessons/questions/[engine subsystem] stub runners real + write_question_bank + pipeline job kind + [engine…</td>
      <td>7.0h</td>
      <td>35m</td>
      <td>3m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>[engine subsystem] rebuild finish — retired subprocess paths (orchestrator in-process + JobStore-backed [content generation]/[engine subsystem] APIs + deleted SynthesisManager/TribunalManager) + standalone…</td>
      <td>3.5h</td>
      <td>22m</td>
      <td>1m</td>
      <td>9.5x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>7</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>94.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>175</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>18</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>1,398,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>32.2x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>313.3x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>2.4</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 80.0x and the lowest at 9.5x, a spread of 8.4 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 60.0 of the 94.0 human-equivalent hours, or 64 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 18 minutes against 175 minutes of execution, a ratio of about 1 to 10. Supervisory leverage of 313.3x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 28, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-28-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-28-leverage-record.html</guid>
      <pubDate>Thu, 28 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">3 tasks. May 28, 2026 closed at 49.9x weighted leverage across 104.0 human-equivalent hours in 125 minutes of wall-clock time. Supervisory leverage came in at 416.0x.</p>
<p class="mb-4 font-light font-serif">That is 2.6 weeks of human-equivalent throughput in 2.1 hours. The ceiling was 168.0x; the floor was 16.0x. 2 of the 3 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Built comprehensive GED curriculum: 4 domain specs (Math/RLA/Science/Social Studies) totaling 249 leaf goals + README with 16 novel adult-learner activities</td>
      <td>70.0h</td>
      <td>25m</td>
      <td>3m</td>
      <td>168.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Resume session: re-stamped 584 spec&lt;-&gt;manifest exam_metadata fields across 142 packages (+ restamp tool) + 3 recall backfills (+2555 Qs: PHR/PHRca/FinOps) + content-audit Phase 27 ([content generation]…</td>
      <td>22.0h</td>
      <td>55m</td>
      <td>8m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Testing</td>
      <td>12.0h</td>
      <td>45m</td>
      <td>4m</td>
      <td>16.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>3</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>104.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>125</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>15</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>3,180,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>49.9x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>416.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>2.6</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 168.0x and the lowest at 16.0x, a spread of 10.5 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 70.0 of the 104.0 human-equivalent hours, or 67 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 15 minutes against 125 minutes of execution, a ratio of about 1 to 8. Supervisory leverage of 416.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 27, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-27-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-27-leverage-record.html</guid>
      <pubDate>Wed, 27 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">7 tasks. May 27, 2026 closed at 43.6x weighted leverage across 146.0 human-equivalent hours in 201 minutes of wall-clock time. Supervisory leverage came in at 213.7x.</p>
<p class="mb-4 font-light font-serif">That is 3.6 weeks of human-equivalent throughput in 3.4 hours. The ceiling was 160.0x; the floor was 7.2x. 7 of the 7 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Phase 0 + initial Phase 1 client parity catch-up — plan doc + Android Profile/DomainSelect/Curriculum + [ip-cluster] interaction manifest across 4 repos</td>
      <td>80.0h</td>
      <td>30m</td>
      <td>3m</td>
      <td>160.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Documentation</td>
      <td>40.0h</td>
      <td>55m</td>
      <td>8m</td>
      <td>43.6x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Consolidate AVIAN cluster taxonomy from 36 to 12 commercially distinct clusters: rewrite README cluster table + [ip]_Family_Grouping intro/IP strategy + canonical.json branded_cluster_list + CLAUDE/AGENTS…</td>
      <td>6.0h</td>
      <td>18m</td>
      <td>7m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Cluster taxonomy cascade across monorepo: rewrite Cluster_Naming_Rationale for 12 clusters + global search/replace for 24 dropped brand names across 6 repos ([ip] portfolio + planning marketing + architecture…</td>
      <td>8.0h</td>
      <td>28m</td>
      <td>8m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Update avian.renkara.com Jinja templates for 12-cluster taxonomy: rewrite clusters.jinja (8 tier groups / 12 clusters with current descriptions) + architecture.jinja (13-tier descriptions with…</td>
      <td>4.0h</td>
      <td>15m</td>
      <td>5m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Documentation</td>
      <td>5.0h</td>
      <td>30m</td>
      <td>5m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Coding</td>
      <td>3.0h</td>
      <td>25m</td>
      <td>5m</td>
      <td>7.2x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>7</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>146.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>201</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>41</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>1,012,500</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>43.6x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>213.7x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>3.6</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 160.0x and the lowest at 7.2x, a spread of 22.2 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 80.0 of the 146.0 human-equivalent hours, or 55 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 41 minutes against 201 minutes of execution, a ratio of about 1 to 5. Supervisory leverage of 213.7x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 26, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-26-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-26-leverage-record.html</guid>
      <pubDate>Tue, 26 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">1 task. May 26, 2026 closed at 144.0x weighted leverage across 60.0 human-equivalent hours in 25 minutes of wall-clock time. Supervisory leverage came in at 720.0x.</p>
<p class="mb-4 font-light font-serif">That is 1.5 weeks of human-equivalent throughput in 0.4 hours. The ceiling was 144.0x; the floor was 144.0x. The day&#39;s work was spread across several areas.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Author world-class NCLEX-RN and NCLEX-PN domain specs (NCSBN 2023 + NGN + CJMM)</td>
      <td>60.0h</td>
      <td>25m</td>
      <td>5m</td>
      <td>144.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>1</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>60.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>25</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>5</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>175,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>144.0x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>720.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>1.5</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 144.0x and the lowest at 144.0x, a spread of 1.0 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 60.0 of the 60.0 human-equivalent hours, or 100 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 5 minutes against 25 minutes of execution, a ratio of about 1 to 5. Supervisory leverage of 720.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 25, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-25-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-25-leverage-record.html</guid>
      <pubDate>Mon, 25 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">11 tasks. May 25, 2026 closed at 18.6x weighted leverage across 79.5 human-equivalent hours in 256 minutes of wall-clock time. Supervisory leverage came in at 136.3x.</p>
<p class="mb-4 font-light font-serif">That is 2.0 weeks of human-equivalent throughput in 4.3 hours. The ceiling was 33.6x; the floor was 5.3x. 11 of the 11 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>[engine subsystem]: prompt caching + Anthropic Batches API integration across [content generation] scripts in core/avian-engine (1649 LOC, 5 files)</td>
      <td>28.0h</td>
      <td>50m</td>
      <td>5m</td>
      <td>33.6x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Invert leverage tracking policy: CSV first then cloud second both mandatory; patched global CLAUDE.md Rules block, /fix skill Step 6k, 16 tool-loader Step 4 blocks, and /leverage-post Phase 2 reconciliation</td>
      <td>4.0h</td>
      <td>8m</td>
      <td>2m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Infrastructure</td>
      <td>4.0h</td>
      <td>8m</td>
      <td>4m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Audit and review</td>
      <td>3.0h</td>
      <td>6m</td>
      <td>2m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Content production</td>
      <td>12.0h</td>
      <td>25m</td>
      <td>2m</td>
      <td>28.8x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>/leverage-post reconciliation Phase 1+2: backfilled 139 CSV rows across 12 days (5/14-5/25), verified all in sync, 0 stragglers remaining</td>
      <td>1.5h</td>
      <td>4m</td>
      <td>1m</td>
      <td>22.5x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>core/avian-[engine subsystem] rebuild: brain answerer switched to direct Anthropic SDK with prompt caching (257 LOC), all zero/pmp sweep profiles flipped to omniscient:false (46 files), headless runner…</td>
      <td>10.0h</td>
      <td>30m</td>
      <td>4m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Coding</td>
      <td>3.0h</td>
      <td>10m</td>
      <td>2m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>avian-audits: content-audit P4.1 pair-density check with PGWA-class detection (61 LOC), canonical.json headline counts bumped to 2026-05-25 audit snapshot, per-activity-format trackers added (scenarios,…</td>
      <td>4.0h</td>
      <td>15m</td>
      <td>3m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>core/avian-engine: autopilot_service legacy coverage-damping ceiling lifted + bulk_amplify_fleet and bulk_backfill_recall custom_id format fix with error logging</td>
      <td>2.0h</td>
      <td>10m</td>
      <td>2m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Design and build AVIAN staging environment: 4 Terraform stacks (valkey/engine/api/app-web) reusing prod ALB/RDS/S3 + /staging skill with up/down/status/extend and at-based 3h auto-teardown</td>
      <td>8.0h</td>
      <td>90m</td>
      <td>8m</td>
      <td>5.3x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>11</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>79.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>256</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>35</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>1,063,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>18.6x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>136.3x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>2.0</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 33.6x and the lowest at 5.3x, a spread of 6.3 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 28.0 of the 79.5 human-equivalent hours, or 35 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 35 minutes against 256 minutes of execution, a ratio of about 1 to 7. Supervisory leverage of 136.3x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 24, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-24-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-24-leverage-record.html</guid>
      <pubDate>Sun, 24 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">1 task. May 24, 2026 closed at 14.4x weighted leverage across 6.0 human-equivalent hours in 25 minutes of wall-clock time. Supervisory leverage came in at 120.0x.</p>
<p class="mb-4 font-light font-serif">That is 0.1 weeks of human-equivalent throughput in 0.4 hours. The ceiling was 14.4x; the floor was 14.4x. The day&#39;s work was spread across several areas.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Debugging</td>
      <td>6.0h</td>
      <td>25m</td>
      <td>3m</td>
      <td>14.4x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>1</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>6.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>25</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>3</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>80,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>14.4x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>120.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>0.1</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 14.4x and the lowest at 14.4x, a spread of 1.0 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 6.0 of the 6.0 human-equivalent hours, or 100 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 3 minutes against 25 minutes of execution, a ratio of about 1 to 8. Supervisory leverage of 120.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 23, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-23-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-23-leverage-record.html</guid>
      <pubDate>Sat, 23 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">1 task. May 23, 2026 closed at 33.6x weighted leverage across 28.0 human-equivalent hours in 50 minutes of wall-clock time. Supervisory leverage came in at 336.0x.</p>
<p class="mb-4 font-light font-serif">That is 0.7 weeks of human-equivalent throughput in 0.8 hours. The ceiling was 33.6x; the floor was 33.6x. The day&#39;s work was spread across several areas.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Math content shapes for [ip-cluster] [content generation]: three new content shapes (symbolic problems, modeling problems) + math [engine subsystem] verdict schema. libs/[ip-cluster]-runtime +…</td>
      <td>28.0h</td>
      <td>50m</td>
      <td>5m</td>
      <td>33.6x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>1</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>28.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>50</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>5</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>350,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>33.6x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>336.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>0.7</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 33.6x and the lowest at 33.6x, a spread of 1.0 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 28.0 of the 28.0 human-equivalent hours, or 100 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 5 minutes against 50 minutes of execution, a ratio of about 1 to 10. Supervisory leverage of 336.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 22, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-22-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-22-leverage-record.html</guid>
      <pubDate>Fri, 22 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">22 tasks. May 22, 2026 closed at 27.3x weighted leverage across 425.2 human-equivalent hours in 935 minutes of wall-clock time. Supervisory leverage came in at 447.6x.</p>
<p class="mb-4 font-light font-serif">That is 10.6 weeks of human-equivalent throughput in 15.6 hours. The ceiling was 161.5x; the floor was 2.5x. 22 of the 22 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Full AVIAN accessibility audit (50 repos, deterministic Phase 0 + 4 parallel LLM agents, ~288 findings) followed by full compliance audit (12 sections, 4 parallel agents, 1 CRITICAL + 5 HIGH gaps,…</td>
      <td>70.0h</td>
      <td>26m</td>
      <td>2m</td>
      <td>161.5x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Accessibility zero-disruption HIGH sweep: 4 parallel agents fixed ~135 HIGH findings across 30+ repos — Phase 0 went from 60 to 0 verified by deterministic checker; 488 scope=col + 15 aria-modal added across…</td>
      <td>80.0h</td>
      <td>35m</td>
      <td>1m</td>
      <td>137.1x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Full AVIAN readiness audit: Phase 0 canonical + 4 parallel agents across 60 repos (core+services, clients+libs, 21 tools, docs+sites+infra), consolidated report at audit-report-2026-05-22.md with 10 HIGH + 13…</td>
      <td>40.0h</td>
      <td>25m</td>
      <td>1m</td>
      <td>96.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Readiness audit rerun: 4 parallel agents verified today HIGH fixes landed clean (admin, electron, infra) + audited 42 previously-uncovered repos; consolidated to audit-report-2026-05-22-rerun.md with 2 new…</td>
      <td>12.0h</td>
      <td>14m</td>
      <td>1m</td>
      <td>51.4x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Readiness rerun3 + security audit (5 parallel agents): verified today HIGH fixes clean, agents auto-fixed 9 test failures + 1 real h1-&gt;h3 heading-skip a11y bug, surfaced 1 CRITICAL (ElevenLabs key) + 3 HIGH…</td>
      <td>35.0h</td>
      <td>50m</td>
      <td>3m</td>
      <td>42.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>4 parallel readiness remediation agents: pushed 6 automation Lambdas + [ip-cluster] to GitHub, cleaned avian-terraform (6 commits — CLAUDE.md, lock files, plan.bin removal, 5 new marketing stacks, tfvars…</td>
      <td>14.0h</td>
      <td>22m</td>
      <td>1m</td>
      <td>38.2x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Testing</td>
      <td>10.0h</td>
      <td>18m</td>
      <td>1m</td>
      <td>33.3x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Security audit HIGH fix: revoked 0.0.0.0/0 + ::/0 tcp/3306 ingress on prod-ascloud-rds-sg ([aws-id]) — Aurora MySQL no longer reachable from public internet; verified internal app/admin paths still intact via…</td>
      <td>1.0h</td>
      <td>2m</td>
      <td>1m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Playwright live-stack e2e suite for AuthModal + enrollment: register-verify-signin, signin happy path, forgot-reset-signin, dup-email error, enrollment + DB-verify, unverified-blocked. Captures emails via…</td>
      <td>16.0h</td>
      <td>34m</td>
      <td>2m</td>
      <td>28.2x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Phase 5 origin-extraction wiring: discover stub-runner gap, build runtime-to-service DomainSpecification adapter, real [content generation] runner + three math content runners (worked_examples,…</td>
      <td>16.0h</td>
      <td>35m</td>
      <td>3m</td>
      <td>27.4x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Consolidate auth+purchase under avian-api gateway and build in-modal auth UI (sign in, register, forgot/reset, MFA TOTP, verify email, Apple/Google social) replacing the hosted OIDC SPA; strip 12 legacy env…</td>
      <td>18.0h</td>
      <td>42m</td>
      <td>5m</td>
      <td>25.7x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Documentation</td>
      <td>32.0h</td>
      <td>90m</td>
      <td>6m</td>
      <td>21.3x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Testing</td>
      <td>4.0h</td>
      <td>12m</td>
      <td>1m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>post-PMP-fleet morning session: AZ-500 root-cause (snapshot serializer dropped goal_weights/goal_similarity for entire v3 schema lifetime; engine fell into legacy 0.85 clamp); fixed…</td>
      <td>24.0h</td>
      <td>90m</td>
      <td>8m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Coding</td>
      <td>8.0h</td>
      <td>30m</td>
      <td>1m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Release-test avian-app-web stack: fixed purchase-route DB binding (9 files hitting wrong DB) + rewrote purchase JWT verifier to use local public key (self-JWKS deadlock under single-worker uvicorn); verified…</td>
      <td>3.0h</td>
      <td>14m</td>
      <td>3m</td>
      <td>12.9x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Cloud-wide regression sweep: 44 students across AWS/Azure/GCP/PMP (3-batch parallel via [engine subsystem] CLI). 43/44 passed; mean predicted 89.8%, mean actual 99.7%, mean gap +9.9pt. PGWA flagged with same…</td>
      <td>18.0h</td>
      <td>90m</td>
      <td>4m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Compliance L1 (admin-service role check), L2 (audit-log profile updates), M16 (Dependabot for avian-engine + notification-service); 888 tests pass across auth-service + admin-service; readiness audit…</td>
      <td>6.0h</td>
      <td>35m</td>
      <td>2m</td>
      <td>10.3x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Readiness blockers H5 (self-assign bug), H6 (eslint-plugin-react-hooks load + Sparkline conditional useEffect fix), H9 (commit infra VPC doc comments); ESLint 13 errors -&gt; 0 errors across avian-admin +…</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>1m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Autonomous blueprint-anchor diagnosis + content-aware re-anchor script (139 domains fixed); full 47-profile confirmation sweep (43/44 passed); PGWA deep-dive identified borderline 74.7% reserved-pool accuracy…</td>
      <td>12.0h</td>
      <td>180m</td>
      <td>3m</td>
      <td>4.0x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>accelastudy.ai: PMP nav entry with Coming Monday badge, catalog search box with JSON index + JS filter, PMI June dates, refactor templates, build+deploy staging+prod</td>
      <td>2.5h</td>
      <td>55m</td>
      <td>4m</td>
      <td>2.7x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>accelastudy.ai PMP card+banner: set available_at, update category/course-page templates to render Available May 25th</td>
      <td>0.8h</td>
      <td>18m</td>
      <td>3m</td>
      <td>2.5x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>22</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>425.2</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>935</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>57</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>8,145,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>27.3x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>447.6x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>10.6</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 161.5x and the lowest at 2.5x, a spread of 64.6 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 70.0 of the 425.2 human-equivalent hours, or 16 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 57 minutes against 935 minutes of execution, a ratio of about 1 to 16. Supervisory leverage of 447.6x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 21, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-21-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-21-leverage-record.html</guid>
      <pubDate>Thu, 21 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">3 tasks. May 21, 2026 closed at 36.3x weighted leverage across 69.0 human-equivalent hours in 114 minutes of wall-clock time. Supervisory leverage came in at 318.5x.</p>
<p class="mb-4 font-light font-serif">That is 1.7 weeks of human-equivalent throughput in 1.9 hours. The ceiling was 55.4x; the floor was 8.6x. 3 of the 3 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Deployment</td>
      <td>60.0h</td>
      <td>65m</td>
      <td>6m</td>
      <td>55.4x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>AP Precalc spec audit + math content rollout plan (spec issue identification, activity catalog inventory, 8-phase plan covering spec fixes, math-specific content shapes, 5 new Tier A activities, v2 atom…</td>
      <td>4.0h</td>
      <td>14m</td>
      <td>3m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>avian-api native-mode wiring (bcrypt pin, settings hardening, certs-&gt;certifications fix, native entitlement path, commit-on-exit deps, event_type kwarg drift, secure-cookie toggle) + seed_test_user.py +…</td>
      <td>5.0h</td>
      <td>35m</td>
      <td>4m</td>
      <td>8.6x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>3</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>69.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>114</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>13</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>360,500</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>36.3x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>318.5x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>1.7</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 55.4x and the lowest at 8.6x, a spread of 6.5 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 60.0 of the 69.0 human-equivalent hours, or 87 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 13 minutes against 114 minutes of execution, a ratio of about 1 to 9. Supervisory leverage of 318.5x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 20, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-20-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-20-leverage-record.html</guid>
      <pubDate>Wed, 20 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">11 tasks. May 20, 2026 closed at 54.5x weighted leverage across 550.0 human-equivalent hours in 605 minutes of wall-clock time. Supervisory leverage came in at 1269.2x.</p>
<p class="mb-4 font-light font-serif">That is 13.8 weeks of human-equivalent throughput in 10.1 hours. The ceiling was 202.1x; the floor was 14.4x. 10 of the 11 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Testing</td>
      <td>320.0h</td>
      <td>95m</td>
      <td>1m</td>
      <td>202.1x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Debugging</td>
      <td>42.0h</td>
      <td>75m</td>
      <td>2m</td>
      <td>33.6x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Coding</td>
      <td>30.0h</td>
      <td>55m</td>
      <td>2m</td>
      <td>32.7x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Documentation</td>
      <td>20.0h</td>
      <td>40m</td>
      <td>2m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Coding</td>
      <td>40.0h</td>
      <td>80m</td>
      <td>2m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Coding</td>
      <td>16.0h</td>
      <td>35m</td>
      <td>2m</td>
      <td>27.4x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Design and frontend</td>
      <td>18.0h</td>
      <td>40m</td>
      <td>2m</td>
      <td>27.0x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Coding</td>
      <td>28.0h</td>
      <td>65m</td>
      <td>2m</td>
      <td>25.8x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Debugging</td>
      <td>12.0h</td>
      <td>35m</td>
      <td>2m</td>
      <td>20.6x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Phase 1 recommender starvation fix (lesson-first + goal-scoped saturation + weak_goal_ids surfacing) + SAP-C02 baseline scenario family (vacation, recert, convoy) + lessons-learned doc + validation sweep…</td>
      <td>18.0h</td>
      <td>60m</td>
      <td>4m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Full AVIAN content audit + generate v2 lesson atoms for AWS Solutions Architect Pro (893/894 atoms, diagnosed and fixed max_tokens truncation bug)</td>
      <td>6.0h</td>
      <td>25m</td>
      <td>5m</td>
      <td>14.4x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>11</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>550.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>605</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>26</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>3,700,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>54.5x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>1269.2x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>13.8</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 202.1x and the lowest at 14.4x, a spread of 14.0 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 320.0 of the 550.0 human-equivalent hours, or 58 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 26 minutes against 605 minutes of execution, a ratio of about 1 to 23. Supervisory leverage of 1269.2x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 19, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-19-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-19-leverage-record.html</guid>
      <pubDate>Tue, 19 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">7 tasks. May 19, 2026 closed at 47.1x weighted leverage across 182.0 human-equivalent hours in 232 minutes of wall-clock time. Supervisory leverage came in at 574.7x.</p>
<p class="mb-4 font-light font-serif">That is 4.5 weeks of human-equivalent throughput in 3.9 hours. The ceiling was 166.2x; the floor was 4.8x. 5 of the 7 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Atlas Act I Phase 0 — Python orchestrator daemon (IPC, Opus agent, MCP bus, briefing, diagnostics, test) + Swift Command Bar app (NSPanel, Carbon hotkey, NWConnection IPC client, view-model, design tokens) +…</td>
      <td>36.0h</td>
      <td>13m</td>
      <td>1m</td>
      <td>166.2x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Atlas design rewrite — Metal+Rive visual stack (§21), Fleet Integration Matrix (§22), 32-phase plan (foundation + one feature per phase), 20 invented features mapped to phases and persisted to innovation log</td>
      <td>30.0h</td>
      <td>14m</td>
      <td>2m</td>
      <td>128.6x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Atlas Act I Phase 2 — 21-peer fleet registry + httpx-probing MCP bus + Haiku/rule-based classifier + PermissionGuard with TTL + fast/slow/confirm router + 8 slash commands + IPC fleet.<em> + confirm.</em> + Mac…</td>
      <td>36.0h</td>
      <td>17m</td>
      <td>1m</td>
      <td>127.1x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Atlas Act I Phase 3 — Visual Stack Foundation: AtlasRenderEnvironment singleton (Metal device + queue + library + DisplayLink + Rive factory + energy monitor + FramePacer), MetalLayerView NSViewRepresentable,…</td>
      <td>32.0h</td>
      <td>16m</td>
      <td>1m</td>
      <td>120.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Testing</td>
      <td>24.0h</td>
      <td>14m</td>
      <td>1m</td>
      <td>102.9x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Audit and review</td>
      <td>12.0h</td>
      <td>8m</td>
      <td>3m</td>
      <td>90.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Resume autopilot cascade: diagnose+fix start_student commit-order bug, fix runs.json parallel-sweep race, fix 5 pre-existing tests, run Azure+AWS+GCP+retry sweeps; final 35/39 cloud certs passed (AWS 13/13,…</td>
      <td>12.0h</td>
      <td>150m</td>
      <td>10m</td>
      <td>4.8x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>7</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>182.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>232</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>19</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>1,433,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>47.1x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>574.7x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>4.5</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 166.2x and the lowest at 4.8x, a spread of 34.6 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 36.0 of the 182.0 human-equivalent hours, or 20 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 19 minutes against 232 minutes of execution, a ratio of about 1 to 12. Supervisory leverage of 574.7x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 18, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-18-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-18-leverage-record.html</guid>
      <pubDate>Mon, 18 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">5 tasks. May 18, 2026 closed at 30.4x weighted leverage across 190.0 human-equivalent hours in 375 minutes of wall-clock time. Supervisory leverage came in at 518.2x.</p>
<p class="mb-4 font-light font-serif">That is 4.8 weeks of human-equivalent throughput in 6.2 hours. The ceiling was 120.0x; the floor was 13.6x. 5 of the 5 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Review avian-admin and author full Stitch prompt for Westworld Delos-themed WebGL/Rive redesign covering all 24 pages, design tokens, component vocabulary, motion language, audio design, and fidelity grading…</td>
      <td>24.0h</td>
      <td>12m</td>
      <td>3m</td>
      <td>120.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>[ip-cluster] V2 viewer Phases 1-3: Three.js stage layer (paper-grain + page-turn shaders, mastery candle, postprocessing), Rive Living Diagrams integration (validator update in engine), layer-registry slot…</td>
      <td>45.0h</td>
      <td>35m</td>
      <td>1m</td>
      <td>77.1x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Design and frontend</td>
      <td>16.0h</td>
      <td>28m</td>
      <td>6m</td>
      <td>34.3x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>AVIAN autopilot Fix A: coverage damping + hard ceiling on readiness. SOA-C02 baseline 36/73 KG goals at exam_passed→ 73/73 covered + passed; 36/38 cloud certs hit full per-goal coverage across AWS/GCP/Azure…</td>
      <td>100.0h</td>
      <td>278m</td>
      <td>10m</td>
      <td>21.6x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Docstring audit Phase 7 (Protocol contract enforcement): new audit script (scripts/audit_protocol_contracts.py, 857 LoC) with AST-based one-hop expansion through same-class helpers AND field-attribute…</td>
      <td>5.0h</td>
      <td>22m</td>
      <td>2m</td>
      <td>13.6x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>5</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>190.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>375</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>22</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>1,416,500</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>30.4x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>518.2x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>4.8</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 120.0x and the lowest at 13.6x, a spread of 8.8 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 24.0 of the 190.0 human-equivalent hours, or 13 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 22 minutes against 375 minutes of execution, a ratio of about 1 to 17. Supervisory leverage of 518.2x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 17, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-17-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-17-leverage-record.html</guid>
      <pubDate>Sun, 17 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">17 tasks. May 17, 2026 closed at 10.8x weighted leverage across 309.0 human-equivalent hours in 1,723 minutes of wall-clock time. Supervisory leverage came in at 228.9x.</p>
<p class="mb-4 font-light font-serif">That is 7.7 weeks of human-equivalent throughput in 28.7 hours. The ceiling was 96.0x; the floor was 1.0x. 17 of the 17 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Origin-extract Phase 3 — populate services/[ip-cluster] with [content generation] code, merged backend, /jobs API + structlog observability, [ip-cluster] CLI, and relocated test surface (522 passing…</td>
      <td>80.0h</td>
      <td>50m</td>
      <td>5m</td>
      <td>96.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Audit other Claude&#39;s outstanding-work report against AVIAN engine codebase; corrected stale claims and re-estimated effort</td>
      <td>8.0h</td>
      <td>11m</td>
      <td>6m</td>
      <td>43.6x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Cloud deployment plan for [ip-cluster]: distilled Phases 5-7 (SQS+Fargate+Bedrock wiring, frontend refactor, deploy+cutover) + Phase 8 hygiene into a single 194-line plan doc with Mermaid flow diagram,…</td>
      <td>3.0h</td>
      <td>5m</td>
      <td>1m</td>
      <td>36.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Persistence audit follow-through: all 4 fixes shipped. (1) DeltaReplicationPublisher fail-loud in cloud profile. (2) HIGH-severity in-flight exam persistence — Alembic 007_active_exams + ActiveExamRow +…</td>
      <td>24.0h</td>
      <td>55m</td>
      <td>1m</td>
      <td>26.2x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Refresh [ip] valuations and content counts across 25 avian-planning docs (business, marketing, research, README, CHANGELOG); rebuild [ip]-portfolio valuation framework ([cost]-230M floor); scrub…</td>
      <td>14.0h</td>
      <td>40m</td>
      <td>4m</td>
      <td>21.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Origin-extract Phase 4: delete src/avian/origin + dying [engine subsystem] subdirs + origin_router + 100+ scripts; slim OriginConfig; collapse regression guard; ratchet coverage 81→82; recover 7 over-deleted…</td>
      <td>16.0h</td>
      <td>47m</td>
      <td>1m</td>
      <td>20.4x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Docstring audit Phase 3 (DOC_OVERSELLS rewrite): F1 fix in admin_events.py (module + _live_session_payload docstrings) for asymmetric user fallback (user_name-&gt;entity_id; user_email-&gt;&quot;&quot;); audit re-run…</td>
      <td>3.0h</td>
      <td>10m</td>
      <td>1m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Deterministic docstring-vs-code audit for engine: AST-driven scripts/audit_docstrings.py with 12 categories (structural + intent-vs-impl), per-finding likely_truth heuristic (fix doc / fix code / review). 65…</td>
      <td>12.0h</td>
      <td>40m</td>
      <td>2m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Docstring audit Phase 2 (FP bookkeeping): added EXCLUDED_FINDINGS set + AuditReport.add_finding() to scripts/audit_docstrings.py with 28 exact-tuple exclusions (file, line, symbol, category) retiring the 29…</td>
      <td>3.0h</td>
      <td>10m</td>
      <td>1m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>CI hardening (fixed silently-dead nightly leak gate in engine nightly.yml — wrong import path; dropped continue-on-error from memray steps; mirrored nightly to [ip-cluster] with 500MB import baseline) + full…</td>
      <td>6.0h</td>
      <td>20m</td>
      <td>1m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Documentation</td>
      <td>8.0h</td>
      <td>35m</td>
      <td>1m</td>
      <td>13.7x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>avian-engine: fix domain reload manifold dupe (if_exists policy) + 8 unit tests + endpoint regression test; live-validated by reloading 38 AWS/GCP/Azure cert packages into running engine</td>
      <td>5.0h</td>
      <td>25m</td>
      <td>6m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Deployment</td>
      <td>4.0h</td>
      <td>25m</td>
      <td>3m</td>
      <td>9.6x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Infrastructure</td>
      <td>80.0h</td>
      <td>540m</td>
      <td>12m</td>
      <td>8.9x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>[engine subsystem] zero-sweep diagnosis: fixed current_day/[scoring model] DB sync + zombie &#39;running&#39; reaper + content-density auditor, traced 365-day exam-plateau to 74% of goals lacking recall foundation</td>
      <td>15.0h</td>
      <td>210m</td>
      <td>8m</td>
      <td>4.3x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Docstring audit Phase 1: deterministic 9-step disposition pass for 30 doc-likely findings (3 batches of 10), with verbatim docstring/code citations, call-site enumeration, and per-finding justification.…</td>
      <td>24.0h</td>
      <td>360m</td>
      <td>20m</td>
      <td>4.0x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>[engine subsystem] zero-sweep on reclassified cloud cert packages: engine restart, fixed autopilot_service NameError (missing import os), ran sweep, 2 real terminals (AZ-120 crossed 0.5 readiness=0.509 day 44…</td>
      <td>4.0h</td>
      <td>240m</td>
      <td>8m</td>
      <td>1.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>17</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>309.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,723</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>81</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>10,907,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>10.8x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>228.9x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>7.7</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 96.0x and the lowest at 1.0x, a spread of 96.0 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 80.0 of the 309.0 human-equivalent hours, or 26 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 81 minutes against 1,723 minutes of execution, a ratio of about 1 to 21. Supervisory leverage of 228.9x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 16, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-16-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-16-leverage-record.html</guid>
      <pubDate>Sat, 16 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">38 tasks. May 16, 2026 closed at 23.3x weighted leverage across 393.5 human-equivalent hours in 1,012 minutes of wall-clock time. Supervisory leverage came in at 373.3x.</p>
<p class="mb-4 font-light font-serif">That is 9.8 weeks of human-equivalent throughput in 16.9 hours. The ceiling was 57.8x; the floor was 4.4x. 32 of the 38 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Design and frontend</td>
      <td>26.0h</td>
      <td>27m</td>
      <td>1m</td>
      <td>57.8x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>avian-app-android Phase 11 five [ip] screens: 4 new EngineApi endpoints (governance/trajectory/cross-domain/scenario+submit) + 4 DTO files, PatentRepository, MockEngineDispatcher Contains match mode + 5 new…</td>
      <td>26.0h</td>
      <td>28m</td>
      <td>1m</td>
      <td>55.7x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>avian-app-android Phase 10 course mode + TTS: ElevenLabsTts (Media3 ExoPlayer wrapper with callbackFlow Player.Listener bridge), PlaybackUpdate, TtsCacheStore (SHA-256-keyed disk cache +…</td>
      <td>22.0h</td>
      <td>24m</td>
      <td>1m</td>
      <td>55.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Testing</td>
      <td>28.0h</td>
      <td>31m</td>
      <td>1m</td>
      <td>54.2x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>avian-app-android Phase 13 competitive multiplayer: 2 new lobby endpoints + CompetitiveDto + CompetitiveRepository + 2 fixtures, ReconnectingEngineEventClient (exponential backoff 1/2/4/8/16s cap with…</td>
      <td>22.0h</td>
      <td>25m</td>
      <td>1m</td>
      <td>52.8x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>avian-app-android Phase 16 billing + i18n + finishing: Plus Jakarta Sans via Compose downloadable fonts + GoogleFont.Provider (5 weights, transparent SansSerif fallback), font_certs.xml documented stub,…</td>
      <td>24.0h</td>
      <td>28m</td>
      <td>1m</td>
      <td>51.4x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>avian-app-android Phase 12 Autopilot + WorkManager: AutopilotStore (encrypted prefs) + InMemoryAutopilotStore, NotificationChannels (autopilot.reminders + streak.milestones), AutopilotReminderScheduler…</td>
      <td>22.0h</td>
      <td>26m</td>
      <td>1m</td>
      <td>50.8x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Design and frontend</td>
      <td>18.0h</td>
      <td>22m</td>
      <td>1m</td>
      <td>49.1x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>avian-app-android Phase 17 macrobenchmark + baseline profile: :macrobenchmark Gradle module (com.android.test + androidx.baselineprofile + self-instrumenting + variant gating), StartupBenchmark (cold + warm ×…</td>
      <td>14.0h</td>
      <td>18m</td>
      <td>1m</td>
      <td>46.7x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Phase 6A: extract exam_service from rest_gateway (create_exam+submit_exam+get_study_plan, 800 LOC removed, 22 new unit tests)</td>
      <td>12.0h</td>
      <td>23m</td>
      <td>1m</td>
      <td>31.3x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Phase 7B: autopilot_service composite-path unit tests (compute_composite_readiness aggregation + compute_composite_next_actions cluster-dedup + diversity guard)</td>
      <td>5.0h</td>
      <td>12m</td>
      <td>0m</td>
      <td>25.0x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Phase 7D: manifold + strategy gRPC servicer tests (fixed manifold.proto deprecated option, unblocked proto codegen, 14 new tests; api 75.3-&gt;79.3%, origin 78.2-&gt;80.5%)</td>
      <td>5.0h</td>
      <td>13m</td>
      <td>0m</td>
      <td>23.1x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Phase 6H: extract composite autopilot routes + cross-domain cluster helpers to autopilot_service (359 LOC, collocates the full autopilot brain in one service)</td>
      <td>9.0h</td>
      <td>24m</td>
      <td>0m</td>
      <td>22.5x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Phase 6F: extract insights_service (compute_insights + cognitive-state classifier; 402 LOC out of rest_gateway, 16 new tests covering each card heuristic)</td>
      <td>7.0h</td>
      <td>19m</td>
      <td>0m</td>
      <td>22.1x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Phase 6C: extract question_service (get_next_pair_mcq + get_next_question) + generate_micro_challenge into autopilot_service (350 LOC, 21 new tests, fixes Phase 6B compute_next_actions regression)</td>
      <td>8.0h</td>
      <td>22m</td>
      <td>0m</td>
      <td>21.8x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Coding</td>
      <td>4.0h</td>
      <td>11m</td>
      <td>0m</td>
      <td>21.8x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>avian-engine Phase 3 heavyweight extractions: delete_entity (127 LOC) + submit_answer (313 LOC) + submit_question_answer (258 LOC) + assess_readiness (225 LOC) + get_fingerprint (85 LOC) into…</td>
      <td>18.0h</td>
      <td>50m</td>
      <td>2m</td>
      <td>21.6x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Phase 6B: extract submit_activity_credit + get_cross_domain_transfer into existing service modules (311 LOC, 12 new tests, 3 pre-existing tests updated)</td>
      <td>6.0h</td>
      <td>17m</td>
      <td>0m</td>
      <td>21.2x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Coding</td>
      <td>6.0h</td>
      <td>17m</td>
      <td>0m</td>
      <td>21.2x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>avian-engine Phase 3 final heavyweight push: get_daily_stats + get_entity_readiness_history + get_lesson + record_autopilot_activity + diagnose_root_cause + create_remediation_session (6 endpoints; ~750 LOC…</td>
      <td>14.0h</td>
      <td>40m</td>
      <td>2m</td>
      <td>21.0x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Phase 7C: snapshot_cache pure-logic unit tests (17 tests: msgpack coercion, SnapshotMeta round-trip, tensor markers, url resolution, load_snapshot error paths)</td>
      <td>3.0h</td>
      <td>9m</td>
      <td>0m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>avian-engine final autopilot brain extraction: _get_next_actions_inner (660 LOC) moved to autopilot_service.compute_next_actions. Late-imports for 7 gateway-local helpers keep helpers + brain on separate…</td>
      <td>6.0h</td>
      <td>18m</td>
      <td>2m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>avian-engine Phase 5 ratchet + client update plan: bumped fail_under 79-&gt;80 (actual 81.46%), wrote 200-line client-update-plan.md with endpoint-by-endpoint compatibility table, per-client impact assessment,…</td>
      <td>4.0h</td>
      <td>12m</td>
      <td>2m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>LLM-IT 9: ValidationPipeline integration tests (3 tests covering 3-pass validation through real embedder+[engine subsystem]+LLM; happy/empty/wrong-fragment paths)</td>
      <td>3.0h</td>
      <td>9m</td>
      <td>0m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>LLM integration test harness: 17 tests across 5 origin modules (client, synthesizer, amplifier, validator [engine subsystem], flashcard [engine subsystem]) with cost guard + auto-skip; first run cost [cost]</td>
      <td>12.0h</td>
      <td>38m</td>
      <td>2m</td>
      <td>18.9x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Origin extract Phase 2: 7 grouped commits cutting engine off avian.origin.<em> (LLM-client/embedder rewires in 9 files, composer relocation to avian.runtime, PERSONALIZATION_</em> relocation to avian.api.prompts,…</td>
      <td>8.0h</td>
      <td>26m</td>
      <td>1m</td>
      <td>18.5x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>Phase 6G: move _compute_domain_readiness from rest_gateway to services/_helpers (zero late-imports from services to rest_gateway anymore; 227 LOC, 5 new readiness-math tests)</td>
      <td>4.0h</td>
      <td>13m</td>
      <td>0m</td>
      <td>18.5x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>avian-engine Phase 5 coverage backfill: 85 new tests across snapshot_cache (msgpack default, tensor markers strip/restore, URL resolver, SnapshotPayload), scenario_seeds (normalize_difficulty, filter, tokens,…</td>
      <td>6.0h</td>
      <td>20m</td>
      <td>2m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>Phase 6D: extract shared math+taxonomy helpers into services/_helpers (eliminates late-import dance; 328 LOC out of rest_gateway, 25 new helper tests)</td>
      <td>5.0h</td>
      <td>17m</td>
      <td>0m</td>
      <td>17.6x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>Phase 7A: catalog_service unit tests (15 tests covering cache helpers, projection bundle, invalidation, both routes; lifts catalog_service from 24% to ~95%)</td>
      <td>4.0h</td>
      <td>14m</td>
      <td>0m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>Phase 7E: engine_context singleton + lab-index unit tests (6 tests; api 79.3-&gt;79.4%)</td>
      <td>2.0h</td>
      <td>7m</td>
      <td>0m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>32</td>
      <td>avian-engine Phase 5 final coverage backfill: 25 new tests for rest_gateway math helpers (poisson_binomial_pass_probability, target_per_question_probability inverse with round-trip verification,…</td>
      <td>2.0h</td>
      <td>8m</td>
      <td>2m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>33</td>
      <td>Phase 6E: move 15 inline Pydantic models from rest_gateway to api/models.py (197 LOC, 0 regressions)</td>
      <td>2.0h</td>
      <td>9m</td>
      <td>0m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>34</td>
      <td>Origin extraction Phase 0: full inventory + dependency map + 9-phase plan + 3 new lib repos + new service repo with CLI/observability skeleton + 4 existing repos updated + 7 commits</td>
      <td>14.0h</td>
      <td>95m</td>
      <td>15m</td>
      <td>8.8x</td>
    </tr>
    <tr>
      <td>35</td>
      <td>Audit-orphanfix batch complete: 9 fresh re-syntheses + 9 question banks landed at 100% graph∩pair overlap, VPR 0.87-0.98. Engine bug fix (regenerate_nodes pair-orphan) verified end-to-end across all 9…</td>
      <td>2.5h</td>
      <td>20m</td>
      <td>2m</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>36</td>
      <td>Origin extract Phase 1: populate 3 new libs from avian.origin (llm/embeddings/runtime types + schemas + parser + validator), full coverage suites, 197 tests green at ≥92% per lib, all 4 docs and commits per lib</td>
      <td>9.0h</td>
      <td>75m</td>
      <td>3m</td>
      <td>7.2x</td>
    </tr>
    <tr>
      <td>37</td>
      <td>Debugging</td>
      <td>3.0h</td>
      <td>35m</td>
      <td>4m</td>
      <td>5.1x</td>
    </tr>
    <tr>
      <td>38</td>
      <td>Diagnosed + fixed stale engine domain-cache bug (engine in-memory pairs/KG drift from disk after resynth), added /api/v1/admin/domains/reload bulk endpoint, wired [engine subsystem] zero-sweep [engine…</td>
      <td>8.0h</td>
      <td>110m</td>
      <td>12m</td>
      <td>4.4x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>38</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>393.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,012</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>63</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>5,552,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>23.3x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>373.3x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>9.8</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 57.8x and the lowest at 4.4x, a spread of 13.2 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 26.0 of the 393.5 human-equivalent hours, or 7 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 63 minutes against 1,012 minutes of execution, a ratio of about 1 to 16. Supervisory leverage of 373.3x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 15, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-15-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-15-leverage-record.html</guid>
      <pubDate>Fri, 15 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">19 tasks. May 15, 2026 closed at 21.1x weighted leverage across 378.0 human-equivalent hours in 1,075 minutes of wall-clock time. Supervisory leverage came in at 238.7x.</p>
<p class="mb-4 font-light font-serif">That is 9.4 weeks of human-equivalent throughput in 17.9 hours. The ceiling was 73.8x; the floor was 7.2x. 18 of the 19 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>avian-app-android repo skeleton: README, CLAUDE.md, and four parity docs (requirements, design, design-system, testing-strategy) translating the iOS Swift/SwiftUI client to Kotlin/Compose/AppAuth/Wear OS</td>
      <td>16.0h</td>
      <td>13m</td>
      <td>2m</td>
      <td>73.8x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>avian-app-android Phase 3 data layer: EngineApi (single Retrofit interface, all endpoint groups), 7 DTO files, EngineClient facade with HttpException/SerializationException/IOException → EngineError mapping,…</td>
      <td>30.0h</td>
      <td>28m</td>
      <td>1m</td>
      <td>64.3x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Testing</td>
      <td>22.0h</td>
      <td>25m</td>
      <td>1m</td>
      <td>52.8x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Testing</td>
      <td>14.0h</td>
      <td>16m</td>
      <td>1m</td>
      <td>52.5x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>avian-app-android Phase 7 onboarding + initialization: OnboardingViewModel (3-step state machine with DeterministicShuffle-seeded calibration quiz, 8-question SAMPLE_BANK, SavedStateHandle restoration),…</td>
      <td>14.0h</td>
      <td>16m</td>
      <td>1m</td>
      <td>52.5x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>avian-app-android Phase 6 catalog + exam info: CatalogViewModel (StateFlow combine + EngineError-to-message mapping), DomainCatalogScreen (adaptive LazyVerticalGrid 1/2/3 cols, badges, top app bar with…</td>
      <td>18.0h</td>
      <td>21m</td>
      <td>1m</td>
      <td>51.4x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>avian-app-android Phase 4 authentication: TokenStore + EncryptedTokenStore (AES-256-GCM Keystore), PendingEnrollmentStore + Encrypted impl, PkceVerifierStore + Encrypted impl with 5-min TTL, OidcConfig,…</td>
      <td>16.0h</td>
      <td>19m</td>
      <td>1m</td>
      <td>50.5x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>avian-app-android Phase 5 app shell + state machine: 28-state Phase sealed interface (all @Parcelize), ActivityModeKey + WatchPhase, AppState, AppStateHolder (StateFlow Singleton), AppViewModel (HiltViewModel…</td>
      <td>14.0h</td>
      <td>17m</td>
      <td>1m</td>
      <td>49.4x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>avian-app-android Phase 1 design system: HslColor + AvianColorScheme (light + dark, 1:1 parity with web tokens.css), AvianBrand runtime accent override, AvianTokens public surface (Composable getters +…</td>
      <td>18.0h</td>
      <td>22m</td>
      <td>1m</td>
      <td>49.1x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>avian-app-android Phase 0: phased build plan (18 phases) + Gradle multi-module skeleton (app/wear/design-system/domain/data/testing), Kotlin 2.0 + AGP 8.5 + Compose BOM, Hilt+KSP, version catalog, Hilt…</td>
      <td>12.0h</td>
      <td>18m</td>
      <td>1m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Two funding-strategy documents (pre-revenue SAFE path and growth-bridge + priced-seed path) covering consumer + [unreleased product] + enterprise markets with branded PDFs</td>
      <td>16.0h</td>
      <td>28m</td>
      <td>8m</td>
      <td>34.3x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>avian-engine: retire @pytest.mark.slow tests, add 30s default timeout + pristine RNG seeding, lift 14 of 16 packages to &gt;=85% unit-test coverage with 1,342 new fast tests across 20 files (5,010 pass / 0 fail…</td>
      <td>80.0h</td>
      <td>240m</td>
      <td>15m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Third funding-plan variant (SAFE + 2 equity-comp founding hires + native Android September 2026); PDF tooling improvements (DOC_DATE override, H2 page-break removal)</td>
      <td>6.0h</td>
      <td>22m</td>
      <td>6m</td>
      <td>16.4x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>avian-engine Phase 3 service-layer extraction: 16 endpoints across 12 service modules (sequencing, interaction, atom_service compose v1+v2, autopilot lifecycle/create/composite/list-due, operations+telemetry…</td>
      <td>32.0h</td>
      <td>130m</td>
      <td>4m</td>
      <td>14.8x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>avian-[engine subsystem]: audited UI vs post-April app-web rebuild, fixed Postgres auth + 22 stuck workers, added 4 frontend polish fixes (SSE wiring, sidebar grouping, cloud filter, per-provider calibration…</td>
      <td>22.0h</td>
      <td>95m</td>
      <td>6m</td>
      <td>13.9x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Diagnosed pair-orphan engine bug (regenerate_nodes returned only new nodes, caller looked up stale pairs by NEW id; pairs hold OLD id so intersection always empty); fixed signature + caller, added regression…</td>
      <td>5.0h</td>
      <td>35m</td>
      <td>5m</td>
      <td>8.6x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Consolidated advisor-ready funding plan 02c (5-person team, [cost] SAFE, profit-sharing, [ip]-adjusted valuations, 5-year comp tables) plus HoRO/CFO + Marketing Director job description PDFs</td>
      <td>32.0h</td>
      <td>240m</td>
      <td>35m</td>
      <td>8.0x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Infrastructure</td>
      <td>8.0h</td>
      <td>65m</td>
      <td>2m</td>
      <td>7.4x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Recovered 5 misdirected re-synth packages (scripts/data/domains -&gt; data/domains); diagnosed and fixed engine bug at loop.py:460 (_pre_validate_nodes string-not-dict crash) mirroring synthesizer/engine.py…</td>
      <td>3.0h</td>
      <td>25m</td>
      <td>3m</td>
      <td>7.2x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>19</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>378.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,075</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>95</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>5,130,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>21.1x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>238.7x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>9.4</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 73.8x and the lowest at 7.2x, a spread of 10.3 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 16.0 of the 378.0 human-equivalent hours, or 4 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 95 minutes against 1,075 minutes of execution, a ratio of about 1 to 11. Supervisory leverage of 238.7x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 14, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-14-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-14-leverage-record.html</guid>
      <pubDate>Thu, 14 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">8 tasks. May 14, 2026 closed at 31.1x weighted leverage across 189.0 human-equivalent hours in 365 minutes of wall-clock time. Supervisory leverage came in at 290.8x.</p>
<p class="mb-4 font-light font-serif">That is 4.7 weeks of human-equivalent throughput in 6.1 hours. The ceiling was 64.0x; the floor was 4.4x. 8 of the 8 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Merge auth-service + purchase-service + onboarding-service + avian-[unreleased product]-web backend into avian-api as separate logical DBs (auth_db, purchase_db, avian_[unreleased product]). Phases 0-5:…</td>
      <td>80.0h</td>
      <td>75m</td>
      <td>8m</td>
      <td>64.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Infrastructure</td>
      <td>60.0h</td>
      <td>90m</td>
      <td>6m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Implement 7-day free-trial epic: purchase-service comp endpoints + auto-revoke, auth-service signup hook + trial-started email, notification-service templates, EventBridge Lambda for T-1d + T0 sweep,…</td>
      <td>24.0h</td>
      <td>65m</td>
      <td>6m</td>
      <td>22.2x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Audit and review</td>
      <td>6.0h</td>
      <td>18m</td>
      <td>1m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Content production</td>
      <td>7.0h</td>
      <td>22m</td>
      <td>4m</td>
      <td>19.1x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Debugging</td>
      <td>5.0h</td>
      <td>18m</td>
      <td>6m</td>
      <td>16.7x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Bring full AVIAN local stack up (11 services) and fix unauth /entitlements/me + /auth/refresh 401 cascade on public dashboard</td>
      <td>3.0h</td>
      <td>22m</td>
      <td>4m</td>
      <td>8.2x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Content audit run; identified next 10 priority domains; SOA-C02 pair_id linkage repair (12.2% -&gt; 100%); diagnosed cross-domain prereq validator bug; built and launched audit-batch (5 re-syntheses + 4 question…</td>
      <td>4.0h</td>
      <td>55m</td>
      <td>4m</td>
      <td>4.4x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>8</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>189.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>365</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>39</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>2,131,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>31.1x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>290.8x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>4.7</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 64.0x and the lowest at 4.4x, a spread of 14.7 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 80.0 of the 189.0 human-equivalent hours, or 42 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 39 minutes against 365 minutes of execution, a ratio of about 1 to 9. Supervisory leverage of 290.8x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 13, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-13-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-13-leverage-record.html</guid>
      <pubDate>Wed, 13 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">3 tasks. May 13, 2026 closed at 54.5x weighted leverage across 80.0 human-equivalent hours in 88 minutes of wall-clock time. Supervisory leverage came in at 480.0x.</p>
<p class="mb-4 font-light font-serif">That is 2.0 weeks of human-equivalent throughput in 1.5 hours. The ceiling was 130.0x; the floor was 15.0x. 2 of the 3 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Audit and review</td>
      <td>65.0h</td>
      <td>30m</td>
      <td>3m</td>
      <td>130.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Deterministic diagram edge audit: Python classifier, 6 .mmd fixes, 12 per-edge exceptions, audit doc update</td>
      <td>5.0h</td>
      <td>18m</td>
      <td>2m</td>
      <td>16.7x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>AP Macro flagship: CED mapping + 10-day study plan + V2 atom interaction tagger + goal_id bug fix + repair tooling + 354 atoms tagged with 708 interactions</td>
      <td>10.0h</td>
      <td>40m</td>
      <td>5m</td>
      <td>15.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>3</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>80.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>88</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>10</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>490,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>54.5x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>480.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>2.0</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 130.0x and the lowest at 15.0x, a spread of 8.7 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 65.0 of the 80.0 human-equivalent hours, or 81 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 10 minutes against 88 minutes of execution, a ratio of about 1 to 9. Supervisory leverage of 480.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 12, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-12-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-12-leverage-record.html</guid>
      <pubDate>Tue, 12 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">24 tasks. May 12, 2026 closed at 65.7x weighted leverage across 877.0 human-equivalent hours in 801 minutes of wall-clock time. Supervisory leverage came in at 506.0x.</p>
<p class="mb-4 font-light font-serif">That is 21.9 weeks of human-equivalent throughput in 13.4 hours. The ceiling was 213.3x; the floor was 5.0x. 24 of the 24 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Documentation</td>
      <td>160.0h</td>
      <td>45m</td>
      <td>5m</td>
      <td>213.3x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>avian-app-electron full web feature parity — foundation deps + 16 IPC handlers + 8 charts + 15 components + 24 data stores + 22 i18n namespaces + readiness module + session machine + voice/TTS +…</td>
      <td>240.0h</td>
      <td>95m</td>
      <td>8m</td>
      <td>151.6x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Build remaining ~57 Tier 3-4 interaction components across 12 domains; FullComponentCatalog browse page; registry wire-up; build green</td>
      <td>160.0h</td>
      <td>85m</td>
      <td>3m</td>
      <td>112.9x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Build all 10 Tier-2 interaction components (graphing_calc, compound_interest, punnett_square, timeline, conjugation_drill, piano, map_quiz, orbital_sim, physics_sim, circuit_builder) plus shared utilities;…</td>
      <td>80.0h</td>
      <td>50m</td>
      <td>3m</td>
      <td>96.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>avian-app-electron: wire every local-only stub to real IPC — getDailyStats, postCognitiveState, patchEnrollment/archiveEnrollment, userState get/put/delete, testimonial get/upsert/delete/streaming-suggest…</td>
      <td>12.0h</td>
      <td>12m</td>
      <td>2m</td>
      <td>60.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Documentation</td>
      <td>32.0h</td>
      <td>35m</td>
      <td>4m</td>
      <td>54.9x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Testing</td>
      <td>14.0h</td>
      <td>20m</td>
      <td>1m</td>
      <td>42.9x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Port 10 screens + KnowledgeMap chart from avian-app-web to avian-app-electron (ExamResultsScreen, ReadinessForecast, CredentialMapping, Courses, FlashcardsScreen, CertificationsScreen, KnowledgeMapScreen,…</td>
      <td>8.0h</td>
      <td>12m</td>
      <td>3m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Run full [ip] and diagram audits for avian-[ip]-portfolio: 7 CIP apps (Z, AA, BB, CC, DD, EE, FF), 56 diagrams, 7 phases of [ip] checks plus per-app semantic agents. Produced timestamped report and updated…</td>
      <td>6.0h</td>
      <td>10m</td>
      <td>1m</td>
      <td>36.7x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Documentation</td>
      <td>8.0h</td>
      <td>14m</td>
      <td>2m</td>
      <td>34.3x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Seed four Entity Collections for AVIAN adaptive learning platform (periodic_elements 118, us_states 50, countries 50, historical_figures 44)</td>
      <td>20.0h</td>
      <td>35m</td>
      <td>5m</td>
      <td>34.3x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Port CourseDetail.tsx (2930 LOC, 5 tabs) from avian-app-web to CourseStructure.tsx in avian-app-electron — full feature parity including Autopilot, Study Plan, Curriculum, Activities, Labs tabs</td>
      <td>24.0h</td>
      <td>45m</td>
      <td>8m</td>
      <td>32.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Full AVIAN [ip] + diagram audit (7 CIP apps, 56 diagrams, 27 docs)</td>
      <td>6.0h</td>
      <td>12m</td>
      <td>1m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Deployment</td>
      <td>35.0h</td>
      <td>75m</td>
      <td>12m</td>
      <td>28.0x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>avian-app-electron Wave 5 parity: Help Center (10 screens), full Insights rewrite (AnalyticsPanel), Dashboard polish (DriftActionCard + ConvoyCard + DashboardAcesSection), Settings polish (tabbed layout +…</td>
      <td>24.0h</td>
      <td>55m</td>
      <td>10m</td>
      <td>26.2x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Testing</td>
      <td>8.0h</td>
      <td>25m</td>
      <td>1m</td>
      <td>19.2x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Port active-session screen from avian-app-web to avian-app-electron - full state machine with countdown/active/feedback/paused/summary phases, ActivityFrame, cognitive state, TTS narration, plan session</td>
      <td>8.0h</td>
      <td>28m</td>
      <td>5m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Build deterministic a11y audit toolchain (axe-core CLI + Playwright sweep + jsx-a11y + Python source checker, unified through stable-hash triage ledger) to eliminate cross-run finding nondeterminism. New…</td>
      <td>6.0h</td>
      <td>22m</td>
      <td>5m</td>
      <td>16.4x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Run full deterministic accessibility audit via new 3-engine toolchain (Python source + Playwright axe + static-site axe via Playwright .mjs replacing broken @axe-core/cli). Ledger bootstrapped with 185 unique…</td>
      <td>8.0h</td>
      <td>30m</td>
      <td>4m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Documentation</td>
      <td>1.5h</td>
      <td>6m</td>
      <td>2m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Port 22 utility modules (hooks, voice, sync, telemetry, app-services, a11y) from avian-app-web to avian-app-electron with IPC adaptations</td>
      <td>8.0h</td>
      <td>35m</td>
      <td>8m</td>
      <td>13.7x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Port LessonView from avian-app-web to avian-app-electron LessonScreen — full markdown/math/code rendering, collapsible sidebar taxonomy, TTS IPC audio, adaptive toggle, section pagination, completion credit,…</td>
      <td>4.0h</td>
      <td>18m</td>
      <td>4m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Port readiness and session modules (16 files) from avian-app-web to avian-app-electron with API import adaptation</td>
      <td>3.0h</td>
      <td>20m</td>
      <td>5m</td>
      <td>9.0x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Fix 8 pre-existing test failures in avian-engine API endpoint suite (route mismatches, wrong status codes, inverted diminishing_note logic)</td>
      <td>1.5h</td>
      <td>18m</td>
      <td>2m</td>
      <td>5.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>24</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>877.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>801</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>104</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>5,146,500</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>65.7x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>506.0x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>21.9</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 213.3x and the lowest at 5.0x, a spread of 42.7 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 160.0 of the 877.0 human-equivalent hours, or 18 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 104 minutes against 801 minutes of execution, a ratio of about 1 to 8. Supervisory leverage of 506.0x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Making "What If?"]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-12-making-what-if.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-12-making-what-if.html</guid>
      <pubDate>Tue, 12 May 2026 06:00:00 GMT</pubDate>
      <description><![CDATA[<p><img src="https://charlessieg.com/images/making-what-if-hero.png" alt="Making "What If?"" /></p><p class="mb-4 font-light font-serif">We launched AccelaStudy® AI today. The teaser you may have seen on Friday — the one that ends with &quot;What if?&quot; — was made over the course of about 36 hours, mostly by two people, almost entirely with off-the-shelf AI tools paid by the credit. This post is for anyone curious about what the actual workflow looked like, the prompts that didn&#39;t work included.</p>
<p class="mb-4 font-light font-serif">I&#39;m writing this from my own perspective, not the company&#39;s. The corporate version of this post lives <a href="https://www.renkara.com/blog/launching-monday-what-if/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">on Renkara</a> and the marketing version is <a href="https://www.accelastudy.ai/blog/launching-monday/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">on AccelaStudy</a>. They tell roughly the same story with different emphasis.</p>
<h2 id="i-almost-wrote-education-is-broken">I almost wrote &quot;education is broken&quot;</h2>
<p class="mb-4 font-light font-serif">The original draft of the script started with &quot;Education is broken.&quot; I wrote that line and then sat with it for ten minutes before I threw the whole thing out. Every ed-tech ad I&#39;ve ever seen opens with some variation of that line. Khan said it. Coursera said it. Every TED talk on learning says it. By the time you&#39;ve heard it, you&#39;ve already filed the next sixty seconds as &quot;another ed-tech ad.&quot;</p>
<p class="mb-4 font-light font-serif">I rewrote with a concrete image. Three short statements:</p>
<blockquote><p class="mb-4 font-light font-serif">Twenty kids. One classroom. One pace.</p></blockquote>
<p class="mb-4 font-light font-serif">That tells the audience the same thing — the system is broken — without telling them they&#39;re being marketed to. The visual does the work.</p>
<p class="mb-4 font-light font-serif">The full final script went through five revisions during production. The biggest single change was a one-word swap: &quot;That&#39;s not your fault&quot; became &quot;You&#39;re not the problem.&quot; Both lines mean the same thing structurally. The difference is that the first has a vague antecedent — fault for what? — and the second directly rebukes the system framing in the lines before it. It&#39;s also two syllables shorter, which matters when you&#39;re working on a 60-second cut.</p>
<h2 id="the-protagonist">The protagonist</h2>
<p class="mb-4 font-light font-serif">The teaser uses one face across five shots. For those shots to read as the same person across different lighting and emotional contexts, I needed a stable character reference up front.</p>
<p class="mb-4 font-light font-serif"><figure><picture><source srcset="images/launching-monday-protagonist.webp" type="image/webp"><img src="https://charlessieg.com/images/launching-monday-protagonist.png" alt="Locked protagonist reference image" /></picture><figcaption>Locked protagonist reference image</figcaption></figure></p>
<p class="mb-4 font-light font-serif">I generated five candidates with <strong>Flux 1.1 Pro Ultra</strong> ($0.30 total, ten seconds per image, parallel). Brief was deliberately ungroomed: real-looking 15-16 year old, mixed-ethnicity, soft natural light from one side, no makeup, no glamour shot framing. Flux defaulted to female across all five candidates. I picked one.</p>
<p class="mb-4 font-light font-serif">That image then became the input for <strong>Flux Kontext Max</strong> every time the protagonist needed to appear in a different scene — confused at her desk, looking down in despair, the warm-light close-up at the end. Without Kontext, the same character would have drifted into five different people across five shots.</p>
<p class="mb-4 font-light font-serif">This is the workflow I&#39;d repeat every time. <strong>Reference image first, then character lock, then animate.</strong> It costs almost nothing in API credits and saves entire days of iteration trying to make the model produce the same person twice.</p>
<h2 id="the-shots-and-the-ones-that-wouldnt-behave">The shots and the ones that wouldn&#39;t behave</h2>
<p class="mb-4 font-light font-serif">Sixteen shot positions, with sixteen-plus distinct stills generated. Each got at least three rounds of iteration.</p>
<p class="mb-4 font-light font-serif"><figure><picture><source srcset="images/launching-monday-shot-01.webp" type="image/webp"><img src="https://charlessieg.com/images/launching-monday-shot-01.png" alt="Shot 1: vast institutional classroom" /></picture><figcaption>Shot 1: vast institutional classroom</figcaption></figure></p>
<p class="mb-4 font-light font-serif">Shot 1 — the establishing wide of the institutional classroom — took five passes to get the teacher cleanly in the central walkway and not standing on top of a student. Each iteration I made the prompt more explicit (&quot;teacher at the front of the classroom on a raised platform clearly separated from the students by empty floor space&quot;). The fix that finally worked was specifying the geometry: &quot;teacher is positioned at the very front of the room only, well away from any students, NOT among them.&quot;</p>
<p class="mb-4 font-light font-serif">Shot 2 — the kid in the back row — took seven versions to get the spatial position to read. The first three takes had him in close-up with no spatial context (could have been any classroom seat). The fourth had the camera positioned wrong, so his desk appeared to face the wrong direction. The seventh was right: 3/4 front framing with a clearly visible back wall close behind him, classmates blurred in the foreground (in front of him, from his perspective), wall clock as the only institutional cue. <strong>Seven takes.</strong> Just to establish where one student was sitting.</p>
<p class="mb-4 font-light font-serif"><figure><picture><source srcset="images/launching-monday-shot-03.webp" type="image/webp"><img src="https://charlessieg.com/images/launching-monday-shot-03.png" alt="Shot 3: front-row protagonist holding pencil aloft" /></picture><figcaption>Shot 3: front-row protagonist holding pencil aloft</figcaption></figure></p>
<p class="mb-4 font-light font-serif">The shot that taught me the biggest lesson was Shot 3 — the front-row protagonist who&#39;s lost. The script beat is: she&#39;s holding a pencil, frozen in confused panic, then drops it in frustration. We generated the still. We generated the video with very strong negative prompts: <code>writing, scribbling, hand moving, pencil moving on paper, taking notes, drawing</code>. Every single take, Kling animated her writing.</p>
<p class="mb-4 font-light font-serif">I wrote the prompt stronger. I added more negation. Nothing worked.</p>
<p class="mb-4 font-light font-serif"><strong>The fix wasn&#39;t the prompt. The fix was the start frame.</strong> I regenerated the still with the pencil clearly held vertically in the air, several inches above the desk, eraser pointing up, lead pointing down. Once the geometry was right, the only motion that <em>made physical sense</em> was the drop. The model can only animate what the start frame allows.</p>
<p class="mb-4 font-light font-serif">I now generate stills before I generate videos. Every time. No exceptions.</p>
<h2 id="the-diversity-problem">The diversity problem</h2>
<p class="mb-4 font-light font-serif">The first round of background students was almost entirely white men. I added &quot;balanced mix of young men and young women in equal proportion, multiple ethnicities including Black, white, Asian, Latino&quot; to the prompt. We got better — clearly visible Black students, mixed gender — but never quite balanced. Image models reflect the training data; explicit prompting helps but doesn&#39;t override the prior. This is a known problem and not one I solved here. It&#39;s something to be aware of every time you generate a crowd.</p>
<h2 id="the-pixar-incident">The Pixar incident</h2>
<p class="mb-4 font-light font-serif">One iteration of Shot 10 (the home-learning teen) came back as a 9-year-old in stylized 3D. Full Pixar tonality. We were nowhere near asking for animation.</p>
<p class="mb-4 font-light font-serif">The cause was one word in the prompt: &quot;a faint reflection of an animated lesson in their eyes.&quot; The word &quot;animated&quot; tipped the entire image into a 3D-rendered aesthetic. Stripping that one word and adding &quot;photorealistic film still, real human, naturalistic skin texture&quot; fixed it instantly.</p>
<p class="mb-4 font-light font-serif">I&#39;d been writing prompts for image generators for two years and still walked into this trap. Words have weight even when they&#39;re not the subject of the sentence.</p>
<h2 id="audio-the-part-where-i-had-to-record-myself">Audio: the part where I had to record myself</h2>
<p class="mb-4 font-light font-serif">Music was three rounds on <strong>ElevenLabs Music</strong> at about 50 cents per generation. The prompt was a three-act structure with explicit timing markers. The first track had a built-in ramp-up that fought our visual fade-in. The second was better. The third honored the explicit instruction: &quot;<strong>START AT FULL DENSITY AND VOLUME from the very first frame at 0:00 — NO ramp-up, NO swell-in, NO crescendo intro.</strong>&quot; Even then I <code>atrim</code>&#39;d the first 3 seconds of the file in post, just in case the music wanted to do something quiet at the open.</p>
<p class="mb-4 font-light font-serif">Narration was harder. We tried four ElevenLabs voices, generated full takes with each, and they were all... fine. Cinematic, professional, hits the marks. But the AI voice doesn&#39;t <em>hesitate</em>. It doesn&#39;t take an extra millisecond on a word that matters. The script needed someone who would read &quot;What if?&quot; and let the words sit.</p>
<p class="mb-4 font-light font-serif">So I recorded the narration myself. Twelve files, one per line. SM7B at six inches off-axis (kills plosives, captures the full warmth). Take three or four for most lines. The opening line — &quot;Twenty kids. One classroom. One pace.&quot; — got nine takes. Three short statements with a specific cadence is harder than it looks.</p>
<p class="mb-4 font-light font-serif">Then I ran every file through a cleanup chain:</p>
<div class="highlight"><pre><span></span>ffmpeg<span class="w"> </span>-i<span class="w"> </span><span class="k">in</span>.mp3<span class="w"> </span>-af<span class="w"> </span><span class="s2">&quot;highpass=f=70,</span>
<span class="s2">  afftdn=nr=10:nf=-22:tn=1,</span>
<span class="s2">  adeclick,</span>
<span class="s2">  deesser=i=0.4:m=0.5:f=0.5,</span>
<span class="s2">  acompressor=threshold=-20dB:ratio=2.5:attack=5:release=80,</span>
<span class="s2">  alimiter=limit=0.95:level=disabled&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w">  </span>-c:a<span class="w"> </span>libmp3lame<span class="w"> </span>-b:a<span class="w"> </span>192k<span class="w"> </span>out.mp3
</pre></div>

<p class="mb-4 font-light font-serif">This costs zero, runs in under a second per file, and saved me probably four hours of re-recording. The chain is: kill rumble below 70Hz, denoise the room tone, remove mouth clicks and pops, soften the &quot;s&quot; sounds, compress for line-to-line evenness, limit the peaks to prevent clipping. I then normalized every cleaned line to match the level of the first one — so the listener doesn&#39;t get pulled around as the cut moves between voices.</p>
<p class="mb-4 font-light font-serif">We kept both versions. There&#39;s a <a href="https://www.accelastudy.ai/vote/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">side-by-side comparison</a> if you want to vote on which sounds better. I genuinely don&#39;t know which one&#39;s right and I want the data.</p>
<h2 id="the-end-card">The end card</h2>
<p class="mb-4 font-light font-serif">LAUNCHING / MONDAY in stacked typography. Plus Jakarta Sans Bold (AccelaStudy&#39;s brand font, lifted directly from our design system tokens). Cold blue <code>#a8c4d8</code> on black.</p>
<p class="mb-4 font-light font-serif">The animation is what makes it. Black hold for 1.3 seconds after &quot;What if?&quot; lands. LAUNCHING fades in over half a second. MONDAY fades in 300 milliseconds behind LAUNCHING. Both hold to the end of the cut.</p>
<p class="mb-4 font-light font-serif">That 300ms stagger between LAUNCHING and MONDAY is the difference between a static title card and a beat. Without it, both words land at the same moment and the eye reads them as a single unit. With the stagger, the eye gets pulled to the date — which is the entire point of the title plate.</p>
<p class="mb-4 font-light font-serif">I built the plate with PIL (transparent text layers) plus ffmpeg fade filters. About 30 lines of Python. The whole title plate sequence is generated fresh on every build, so changing the timing is a parameter swap, not a re-render.</p>
<h2 id="what-this-whole-thing-cost">What this whole thing cost</h2>
<ul class="my-6 lg:mb-0 space-y-4">
<li>Reference image generation: $0.30</li>
<li>Character-locked stills (~50 across iterations): ~$4</li>
<li>Video generation (~25 Kling 3 generations): ~$70</li>
<li>Music (3 tracks): ~$1.50</li>
<li>Narration TTS (B version comparison): ~$2</li>
<li><strong>Total external API spend: ~$80</strong></li>
</ul>
<p class="mb-4 font-light font-serif">Plus the human time: about 36 hours from first script draft to shipped cut. Two people. The slow part was iteration loops — getting the pencil orientation right took half a day, getting the back-row composition right took most of an afternoon. Total compute was well under an hour.</p>
<p class="mb-4 font-light font-serif">A traditional production for a 60-second teaser at this quality level — agency, location, talent, post-production — would be in the $50,000 to $200,000 range. We did this for $80 and the time of the people involved.</p>
<p class="mb-4 font-light font-serif">That&#39;s the leverage I keep writing about. The tools have arrived.</p>
<h2 id="what-id-do-differently">What I&#39;d do differently</h2>
<p class="mb-4 font-light font-serif">Two things, both small, both mostly already covered above:</p>
<p class="mb-4 font-light font-serif"><strong>Generate stills before generating video, every time.</strong> I learned this on Shot 2 the hard way. The first three video iterations all had the kid in the wrong spatial position because I was letting Kling guess the composition from a text prompt. Once I switched to image-to-video with deliberate stills, the bad takes stopped almost entirely.</p>
<p class="mb-4 font-light font-serif"><strong>Cleanup costs nothing.</strong> I almost re-recorded several narration lines because I thought they had too much room tone. The ffmpeg chain above ran in under a second per file and saved me hours. If you&#39;re recording your own VO at home, the cleanup pass is non-optional.</p>
<h2 id="stack">Stack</h2>
<ul class="my-6 lg:mb-0 space-y-4">
<li><strong>Reference images:</strong> Flux 1.1 Pro Ultra (Replicate)</li>
<li><strong>Character lock:</strong> Flux Kontext Max (Replicate)</li>
<li><strong>Video animation:</strong> Kling 3 Video (Replicate)</li>
<li><strong>Music:</strong> ElevenLabs Music</li>
<li><strong>Narration TTS:</strong> ElevenLabs Voice (for B-version comparison only)</li>
<li><strong>Audio cleanup + assembly:</strong> ffmpeg</li>
<li><strong>Title plate:</strong> PIL + ffmpeg fade filter</li>
<li><strong>Build orchestration:</strong> ~280 lines of Python</li>
<li><strong>Brand font:</strong> Plus Jakarta Sans, from the AccelaStudy design system</li>
</ul>
<p class="mb-4 font-light font-serif">All on Replicate or directly via vendor APIs. No agency, no rendering farm.</p>
<h2 id="watch-it">Watch it</h2>
<p class="mb-4 font-light font-serif"><video src="https://charlessieg.com/launching-monday/teaser-final.mp4" controls playsinline preload="metadata" style="width:100%;max-width:480px;border-radius:12px;display:block;margin:1.5em auto;background:#000;"></video></p>
<p class="mb-4 font-light font-serif">AccelaStudy® AI launches Monday. We&#39;ll see if it works.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 11, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-11-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-11-leverage-record.html</guid>
      <pubDate>Mon, 11 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">19 tasks. May 11, 2026 closed at 37.2x weighted leverage across 473.5 human-equivalent hours in 764 minutes of wall-clock time. Supervisory leverage came in at 263.1x.</p>
<p class="mb-4 font-light font-serif">That is 11.8 weeks of human-equivalent throughput in 12.7 hours. The ceiling was 240.0x; the floor was 7.6x. 16 of the 19 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>WCAG 2.1 AA accessibility audit across 9 properties (avian-app-web + 8 accelastudy.ai marketing sites) — ~120 concrete findings with file:line refs, severity grouping, cross-cutting themes, and 6-8 dev-day…</td>
      <td>60.0h</td>
      <td>15m</td>
      <td>2m</td>
      <td>240.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Full WCAG 2.1 AA accessibility audit on avian-app-web + 8 AccelaStudy.ai sites — deterministic checker + parallel LLM judgment phase, 56 findings (7 CRITICAL, 17 HIGH, 24 MEDIUM, 8 LOW) with sequenced…</td>
      <td>30.0h</td>
      <td>17m</td>
      <td>2m</td>
      <td>105.9x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>WCAG 2.1 AA remediation across 11 repos (avian-app-web + design-system + activities + accelastudy.ai flagship + 6 sister sites + shared template + enterprise accessibility-statement rewrite). 8 parallel fix…</td>
      <td>70.0h</td>
      <td>40m</td>
      <td>1m</td>
      <td>105.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Full WCAG 2.1 AA accessibility audit across avian-app-web and 10 AccelaStudy sites (123 findings; 13 P0 blockers identified). Consolidated report written to…</td>
      <td>24.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>80.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Debugging</td>
      <td>60.0h</td>
      <td>50m</td>
      <td>1m</td>
      <td>72.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Pre-launch security &amp; crash audit + fix sweep across auth/purchase/onboarding/notification services: 21 issues fixed (4 CRITICAL admin gaps + IDOR, MFA bypass, webhook bypass, IDOR/spam, plus 17 HIGH), 2…</td>
      <td>48.0h</td>
      <td>55m</td>
      <td>8m</td>
      <td>52.4x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Testing</td>
      <td>14.0h</td>
      <td>18m</td>
      <td>4m</td>
      <td>46.7x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>PMP demo + Adaptive Lesson Generation 2.0: plan + patentability (8 claims), atom schema+validator+composer+generator end-to-end, 6 PMP item generators producing +671 new items…</td>
      <td>50.0h</td>
      <td>90m</td>
      <td>5m</td>
      <td>33.3x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>WCAG 2.1 AA accessibility audit of shared AccelaStudy Jinja templates (30 templates + main.js, 23 issues found)</td>
      <td>12.0h</td>
      <td>28m</td>
      <td>5m</td>
      <td>25.7x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>WCAG 2.1 AA accessibility audit of accelastudy.ai and accelastudy.com — all templates, content pages, built HTML</td>
      <td>8.0h</td>
      <td>22m</td>
      <td>5m</td>
      <td>21.8x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Testing</td>
      <td>30.0h</td>
      <td>90m</td>
      <td>8m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Coding</td>
      <td>24.0h</td>
      <td>90m</td>
      <td>8m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Deployment</td>
      <td>1.5h</td>
      <td>6m</td>
      <td>1m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>avian-admin: wire hard-delete customer flow to purchase-service GDPR endpoint so subscriptions/payments/comps cascade-delete and Stripe stops billing; receipt modal now shows purchase-side counts and Stripe…</td>
      <td>2.0h</td>
      <td>8m</td>
      <td>3m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Add system snapshot purge (archived + older-than modes) to avian-admin SnapshotsTab + RPC handler; fix banner save MissingGreenlet by setting eager_defaults=True on Banner model</td>
      <td>3.0h</td>
      <td>12m</td>
      <td>4m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>CSS accessibility audit: color contrast, focus styles, motion preferences across AccelaStudy sites and avian-app-web</td>
      <td>6.0h</td>
      <td>25m</td>
      <td>10m</td>
      <td>14.4x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>WCAG 2.1 AA accessibility audit of avian-app-web React SPA</td>
      <td>8.0h</td>
      <td>35m</td>
      <td>10m</td>
      <td>13.7x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Launch-day recovery: rewrote launch schedule for post-PH-flop reality (struck dead email-blast rows, added wire spend, fixed LinkedIn post date), audited homepage email-capture gap, wrote…</td>
      <td>16.0h</td>
      <td>90m</td>
      <td>22m</td>
      <td>10.7x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Launch-night batch: fix admin delete lockup (Valkey purge timeout), unblock avian-api CI build (ruff lint), kill TanStack 401 retry storm, rebuild + upload 4.3GB boot cache to S3, author SessionStart voice…</td>
      <td>7.0h</td>
      <td>55m</td>
      <td>6m</td>
      <td>7.6x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>19</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>473.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>764</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>108</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>5,185,500</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>37.2x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>263.1x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>11.8</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 240.0x and the lowest at 7.6x, a spread of 31.4 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 60.0 of the 473.5 human-equivalent hours, or 13 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 108 minutes against 764 minutes of execution, a ratio of about 1 to 7. Supervisory leverage of 263.1x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[How I Built AccelaStudy AI]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-11-how-i-built-accelastudy-ai.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-11-how-i-built-accelastudy-ai.html</guid>
      <pubDate>Mon, 11 May 2026 12:00:00 GMT</pubDate>
      <description><![CDATA[<p><img src="https://charlessieg.com/images/how-i-built-accelastudy-ai-hero.png" alt="How I Built AccelaStudy AI" /></p><p class="mb-4 font-light font-serif">Today I launched AccelaStudy AI: what I believe is the most advanced, most capable adaptive learning platform ever created. That&#39;s a bold claim but one I believe will quickly be proven as people start using it to study.</p>
<p class="mb-4 font-light font-serif">The technology behind AccelaStudy AI is called <a href="https://avian.renkara.com/index.html" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">AVIAN — Adaptive Vector Intelligence and Network</a> — and is protected by 33 patent filings describing 192 distinct inventions. The filings run nearly 1,000 pages of documentation, with 263 technical figures, 733 claims, grouped into 36 branded platform clusters spanning a 13-tier pipeline architecture. No competitor has anything remotely like it.</p>
<p class="mb-4 font-light font-serif">I built all of this in 80 days. Solo. Bootstrapped. $0 raised, no team, no co-founders. My only collaborator was Anthropic&#39;s Claude.</p>
<p class="mb-4 font-light font-serif">This post is the story of how that happened.</p>
<h2 id="the-problem">The Problem</h2>
<p class="mb-4 font-light font-serif">I&#39;ve worn many hats in my career but the one I wear most often these days is &quot;Solution Architect,&quot; which is a somewhat generic term that means I build infrastructure in the cloud, usually the Amazon Web Services (AWS) cloud. I have passed most of the AWS certification exams, some multiple times, but in September 2025 I was preparing to study for the <a href="https://aws.amazon.com/certification/certified-advanced-networking-specialty/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">Advanced Networking Specialty (ANS)</a> exam. ANS is widely considered the most difficult of the AWS certifications to pass.</p>
<p class="mb-4 font-light font-serif">For other certifications in the past, I&#39;ve used A Cloud Guru (acquired by Pluralsight), Udemy, and other sites that are supposed to help you prepare for the exam. I hate these sites. They are all the same. An exam has a syllabus and most of the topics have videos and transcripts of the videos and simple, static quizzes at the end of each topic. After slogging through all of this, there are usually 1–3 practice exams that, assuming you pass, indicate you are ready for the real exam.</p>
<p class="mb-4 font-light font-serif">Garbage.</p>
<p class="mb-4 font-light font-serif">The first issue I have is the &quot;one size fits all&quot; curriculum model. Every class treats every student the same. And since they have to teach to the lowest common denominator, they assume you are coming at the exam with minimal prior knowledge. So they all start with refreshers on prerequisite material. You can skip these usually, but maybe I want a refresher and just don&#39;t need the WHOLE thing — just some of the more esoteric details. No way to get a refresher on just the details you need refreshed.</p>
<p class="mb-4 font-light font-serif">The primary course material is grouped into fairly broad topics. This means the course itself is largely like the refreshers: new material coupled with basic material many students already know. So you end up watching a 30-minute video to get 2 minutes of new knowledge that you need for the exam. It&#39;s not possible to skip around or you might miss the new material. To help with this, the video can often be watched at 1.5x or 2x speed. That&#39;s an awesome experience: having to focus intently on someone speaking super fast to make sure you don&#39;t miss the new material. Exhausting. The transcripts aren&#39;t much better. They are usually just blobs of text dumped out by a speech-to-text utility with zero formatting, no headers, nothing.</p>
<p class="mb-4 font-light font-serif">Some topics have practice &quot;quizzes&quot; which are essentially a handful of multiple choice questions to answer. There is only one practice quiz and it never changes, so once you&#39;ve taken it, that&#39;s it. You can take it again but it&#39;s the same questions with, maybe, the answers sorted into a different order than the first attempt. Woo!</p>
<p class="mb-4 font-light font-serif">Some topics have &quot;labs&quot; which is where they give you some instructions and then you go log into your own live cloud account and muck around following the instructions and hope you don&#39;t mess anything up or accidentally run up a bunch of charges. I&#39;ve never done a lab. I understand the value of doing things for real, but I&#39;m not messing around in my own cloud account. Forget it.</p>
<p class="mb-4 font-light font-serif">And the practice exams — these are arguably the most useful feature of these online courses. A good one simulates the format of the exam and its duration. I thought the A Cloud Guru (<a href="https://www.pluralsight.com" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">Pluralsight</a>) ones were pretty good until I passed all three available exams with near-perfect scores and then went on to fail the real exam. $300 down the drain and a serious shot to my confidence. The main problem is that these exams use a fixed battery of questions and you end up learning their practice exam and not the real material being tested.</p>
<p class="mb-4 font-light font-serif">I was not looking forward to studying for ANS with any of these sites.</p>
<h2 id="the-idea">The Idea</h2>
<p class="mb-4 font-light font-serif">I had been thinking about building my own certification prep site for awhile. I figured if I was frustrated with the existing options, others were too. I was using Sonnet 4.5 regularly to write code and was able to have it put together a basic site in a few hours. There were two major obstacles to launching a real site, though.</p>
<p class="mb-4 font-light font-serif">One, how do I make mine better and truly useful? It wouldn&#39;t be sufficient to just put out a site that was the same as the competition. It had to be measurably better. Really, it had to be revolutionary.</p>
<p class="mb-4 font-light font-serif">Two, how do I create all of that content for users to study? Even one exam required a massive amount of content, and while I like writing, no way I had the free time to write the code AND write the content. And I didn&#39;t know all of it, either. I needed content for exams I hadn&#39;t passed yet.</p>
<p class="mb-4 font-light font-serif">Fortunately, I already knew all about creating educational software. The original AccelaStudy was the first flashcard app in the App Store when it opened in July 2008. That AccelaStudy was basically just foreign-language vocabulary flashcards: &quot;Hello&quot; on one side, &quot;Hola&quot; on the other. But I didn&#39;t know all of the languages (Spanish, French, German, Italian, and Turkish on opening day), so how did I generate the translations? I didn&#39;t. I hired professors at the premier foreign-language university in the world — <a href="https://catalog24byu.catalog.prod.coursedog.com/pages/department-1234" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">Brigham Young University</a> in Utah — to do the translations. Then I simply imported them into the app. For the native speaker audio files, I hired professional voiceover artists who spoke each language natively. That was a lot of fun, actually. The voice for Japanese was done by the same actor who does voiceovers in TV commercials for Mercedes-Benz.</p>
<p class="mb-4 font-light font-serif">But this content was on a different scale. Pluralsight has over 2,500 expert authors creating their technical courses. Of course, keeping 2,500 authors around is very expensive, and probably part of the reason Pluralsight is <a href="https://nedinthecloud.com/2024/07/06/pluralsight-problems/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">struggling financially</a>. I had no money for content authors, so I needed a different solution.</p>
<h2 id="content-galore">Content Galore</h2>
<p class="mb-4 font-light font-serif">For quite awhile, myself and all of my professional colleagues had been using ChatGPT for infrastructure questions. For example: &quot;What are the options for encrypting an S3 bucket?&quot; or &quot;I&#39;m getting a 502 error on a new web service I&#39;m running in Fargate. What could be the problem?&quot; I realized that the LLM&#39;s training data included every possible detail about every resource, every service that you could use in the AWS cloud.</p>
<p class="mb-4 font-light font-serif">Or be tested on in an AWS certification exam.</p>
<p class="mb-4 font-light font-serif">A few test prompts later — &quot;Tell me everything I need to know about S3 buckets to pass the Solutions Architect Professional exam&quot; — and I knew that AI had all the knowledge I needed to generate content for the site.</p>
<p class="mb-4 font-light font-serif">But how to handle hallucinations? How to make sure the content is accurate? These are tough problems with LLMs today. The solution to these issues is quite complicated but achievable. The solution that evolved became part of the AVIAN Origin and AVIAN Preflight patents, two of the 33 AVIAN patent filings, in the Content Creation architectural tier. AVIAN can generate the entire content of an AWS certification course in about 8 hours for around $100. And if the exam changes? A new version can be ready in 30 minutes.</p>
<p class="mb-4 font-light font-serif">But I&#39;m getting ahead of myself.</p>
<h2 id="adaptive-learning-solved">Adaptive Learning, Solved</h2>
<p class="mb-4 font-light font-serif">For over 10 years, I had been working on an adaptive learning patent. It started out as an idea to improve on the <a href="https://subjectguides.york.ac.uk/study-revision/leitner-system" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">Leitner spaced-repetition algorithm</a>. That improvement proved unpatentable but it was a real improvement, and it shipped in AccelaStudy years ago. So I kept working on it. By 2020 or so, I had a draft of <em>The AccelaStudy Method</em>, which captured most of the ideas I had around adaptive learning. Alas, that document was heavy on the concepts and light on the technical implementation. Not patentable.</p>
<p class="mb-4 font-light font-serif">Then, last September, when I was getting started on a proof of concept for what would eventually become AccelaStudy AI, I entered a fateful prompt:</p>
<blockquote><p class="mb-4 font-light font-serif">I&#39;m working on an educational site and I&#39;ve got some ideas in this document, <code>accelastudy_method.md</code>. What would it take to make this a real patent?</p></blockquote>
<p class="mb-4 font-light font-serif">And so it began. What started off as a single Markdown file describing an array of ideas for making online learning adaptive and personalized became 33 separate patents, not just the one I thought I had. The first patent was filed in October 2025, another 25 in March and April 2026, and 7 more in early May.</p>
<p class="mb-4 font-light font-serif">One of the key aspects of the patent portfolio is that it applies to ANYTHING that can be learned. As long as the AI has a deep knowledge of the subject, curriculum can be created. And given that the training data for OpenAI and Anthropic models (and Grok and Gemini and others) includes essentially every document ever written by humans, the AI has far deeper knowledge than even the most experienced content author.</p>
<h2 id="code-warrior">Code Warrior</h2>
<p class="mb-4 font-light font-serif">On February 16, 2026, it was time to build it. The patents were mostly done, but I wanted to ensure they worked before I went to all the trouble and expense of filing them.</p>
<p class="mb-4 font-light font-serif">The first task was to build the AVIAN engine itself. This meant taking all of that patent documentation and extracting a system architecture, and then an implementation and testing plan. That work was done in an afternoon.</p>
<p class="mb-4 font-light font-serif">The next several weeks were a sustained sprint of building, in roughly this order: the engine, the content synthesis pipeline, the web application, the API, the admin tooling, the marketing site, the press kit, the iOS app, the desktop apps for macOS / Windows / Linux, and the entire supporting infrastructure to run all of it. Then, in parallel with the customer-facing product, I built out a fleet of internal tools to actually operate the company: a CMS, an email client, a CRM, an accounting system, a calendar, an analytics platform, a service-health monitor, a leverage-metrics tracker, and more than a dozen others. Each one is a real production application. Each one was 100% built with Claude Code.</p>
<p class="mb-4 font-light font-serif">I&#39;ll write a longer technical post about the architecture choices that made this pace possible. But the single biggest workflow unlock was something simple and structural: I used 57 nested <code>CLAUDE.md</code> constraint files as a per-repo knowledge graph that Claude Code walks before any edit. Plan mode and parallel sub-agents rode on top of that. It felt like handing Claude a map of the entire monorepo. Every constraint I would have wanted to enforce as a code reviewer — coding style, architectural rules, naming conventions, testing requirements, what NOT to touch — lives in those files. The agent reads them. The agent respects them.</p>
<p class="mb-4 font-light font-serif">I ran 2–3 concurrent Claude Max subscriptions for most of the build window so I could fan out work across multiple repos at once. I typically had 10-12 terminals up, each doing work in a different repo. Through the API, the content-synthesis pipeline ran independently — various Anthropic models orchestrated in sequence to yield the most accurate and comprehensive course material. That synthesis spend lives in a separate stack of credit-recharge invoices: 80+ at roughly $50 each, $4,000+ documented. The coding spend through Claude Code lives in <a href="https://fulcrum.renkara.com/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">Fulcrum</a>, the leverage tracker, which is itself one of the <a href="https://renkara.com/tools.html" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">19 internal tools</a> I built along the way.</p>
<h2 id="by-the-numbers">By the Numbers</h2>
<p class="mb-4 font-light font-serif">Eighty days. Solo. The tracker captured every non-trivial task as a row: estimated human-equivalent hours, actual Claude wall-clock minutes, tokens consumed, leverage factor, supervisory leverage. Here is what 80 days of compressed work looks like:</p>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Days of build</td>
      <td>80 (Feb 23 → May 13, 2026)</td>
    </tr>
    <tr>
      <td>Measured tasks</td>
      <td>2,115</td>
    </tr>
    <tr>
      <td>Human-equivalent work hours</td>
      <td>~50,319</td>
    </tr>
    <tr>
      <td><strong>Human-equivalent work-years</strong></td>
      <td><strong>24.2</strong></td>
    </tr>
    <tr>
      <td>Claude wall-clock</td>
      <td>~1,061 hours</td>
    </tr>
    <tr>
      <td>My supervisory time (writing prompts)</td>
      <td>~148 hours</td>
    </tr>
    <tr>
      <td>Average task leverage</td>
      <td>51.5×</td>
    </tr>
    <tr>
      <td>Average supervisory leverage (personal ROI)</td>
      <td>432.4×</td>
    </tr>
    <tr>
      <td>Maximum single-task leverage</td>
      <td>240×</td>
    </tr>
    <tr>
      <td>Claude Code tokens consumed</td>
      <td>~360 million</td>
    </tr>
  </tbody>
</table>
<p class="mb-4 font-light font-serif">The full record set has been published daily since early April at <a href="https://charlessieg.com/leverage/all/index.html" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">charlessieg.com/leverage/all</a>. Every task, every estimate, every minute of Claude wall-clock. Nothing redacted. Each day&#39;s post also includes an analytical writeup of which task patterns produced the highest leverage and which were still gated by human review.</p>
<p class="mb-4 font-light font-serif">And here is what those 24 work-years of compressed effort produced:</p>
<ul class="my-6 lg:mb-0 space-y-4">
<li><strong>AccelaStudy AI</strong> — the customer product. Over 900 certifications, standardized tests, and other courses covered, 1.4 million synthesized questions, sub-2-millisecond knowledge updates, root-cause prerequisite-gap detection, pass-probability forecasting before you spend hundreds of dollars on an exam voucher. Live on the web today at <a href="https://accelastudy.ai" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">accelastudy.ai</a>; native iOS / iPadOS / macOS / Windows / Linux apps follow on June 1.</li>
<li><strong>AVIAN</strong> — the patent portfolio behind it. 33 USPTO filings, 192 distinct inventions, 733 claims (68 independent + 665 dependent), 263 technical figures, organized into 36 platform clusters across 13 pipeline tiers. <a href="https://avian.renkara.com" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">avian.renkara.com</a>, also built by Claude.</li>
<li><strong>74 repositories</strong>, 1.27 million lines of code, 25,000+ automated tests.</li>
<li><strong>19 production Renkara internal tools</strong> — listed publicly at <a href="https://renkara.com/tools.html" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">renkara.com/tools</a>, with each tool&#39;s page tagged &quot;100% Built by Claude&quot; alongside the commercial SaaS category it replaces: Narrative (static site generator), Courier (email client), Tribe (CRM), Trellis (cloud accounting), Vigil (uptime monitoring), Cadence (calendar), Pulse (web analytics), Fulcrum (leverage tracker), Docket (issue tracking), Chronicle (observability), Beacon (marketing automation), Herald (newsletter platform), and seven more. Together they expose <strong>800+ MCP tools</strong> to any Claude session — so the entire fleet is agent-addressable through Anthropic&#39;s own protocol, not just human-addressable. That fleet is the operational backbone that lets one person run a 74-repo monorepo.</li>
<li><strong>21 production websites</strong> — 16 AVIAN/Renkara properties plus four fictional in-world sites and the book&#39;s own site for the novel below, all generated by Narrative.</li>
<li><strong>19,000+ pages of Markdown documentation</strong> — 3,513 files, 4.85 million words. Including the 57 nested <code>CLAUDE.md</code> constraint files.</li>
</ul>
<h2 id="fulcrum-and-other-side-quests">Fulcrum, and Other Side Quests</h2>
<p class="mb-4 font-light font-serif">Fulcrum, the leverage tracker, deserves its own paragraph. As I was starting the build I realized that nobody had ever produced a longitudinal dataset on a single solo developer&#39;s actual productivity with an AI coding agent. Most &quot;AI productivity&quot; claims are marketing. I wanted real data — task by task, hour by hour, dollar by dollar — and I wanted it public. So I built <a href="https://renkara.com/tools/fulcrum.html" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">Fulcrum</a>. It records every non-trivial task as a row, computes leverage factor and supervisory ROI per task, and publishes a daily blog post with analytical commentary. As of today: 2,115 records, 51.5× weighted leverage, 432.4× supervisory ROI, 24.2 work-years compressed into 80 calendar days. If anyone wants to challenge the numbers, the records are there.</p>
<p class="mb-4 font-light font-serif">The other side quest is a novel.</p>
<p class="mb-4 font-light font-serif">In parallel with the AVIAN build, I co-wrote a 67,000-word literary novel with Claude called <a href="https://the-deferral.com" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600"><em>The Deferral</em></a>. As part of the world-building, Claude designed and built four in-world fictional company websites — <a href="https://strataforge-robotics.com/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">Strataforge Robotics</a>, <a href="https://luthan-dynamics.com" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">Luthan Dynamics</a>, <a href="https://elysium-atelier.com" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">Elysium Atelier</a>, and <a href="https://mercer-institute.com" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">MIDAS</a> — each with its own brand identity and full marketing copy, plus the book&#39;s own site at <a href="https://the-deferral.com" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">the-deferral.com</a>. We even wrote a <a href="https://strataforge-robotics.com/engram-fabric.html" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">fake patent</a> to deepen the world. The novel announcement and a behind-the-scenes writeup live <a href="https://charlessieg.com/posts/2026/2026-04-02-announcing-the-deferral.html" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">here</a>. Total wall-clock cost: a side hobby on weekends. The point: this isn&#39;t just about code. Working with Claude expands what one person can attempt across every creative discipline at once.</p>
<h2 id="accessibility">Accessibility</h2>
<p class="mb-4 font-light font-serif">Most software fails accessibility. I didn&#39;t want AccelaStudy AI to be most software.</p>
<p class="mb-4 font-light font-serif">In the final weeks before launch I ran a series of <a href="https://www.w3.org/TR/WCAG21/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">WCAG 2.1 AA</a> audits across the web client and all 16 marketing-site properties — a deterministic Python checker plus a parallel LLM-judgment phase. The first deep audit found 123 findings, with 13 P0 blockers. I then dispatched eight parallel Claude Code sub-agents to fix them in the order an accessibility consultant would prioritize them: token contrast, focus management, ARIA wiring, keyboard navigation, focus traps, animation guards, touch targets, document titles, modal labelling, custom tablists, FAQ semantic structure, and the long tail of smaller issues. Across the fleet of 56 UI repos, the final sweep cleared 2,460 HIGH findings, 2,553 MEDIUM, and a long tail of LOW findings.</p>
<p class="mb-4 font-light font-serif">This work is invisible to most users. But it is the entire experience for users who depend on screen readers, who navigate by keyboard only, who need reduced motion, who use voice control. There is no chance I could have manually audited 16 marketing sites + a complex React SPA + a Swift iOS app + four desktop builds for full WCAG 2.1 AA compliance in a week. With Claude Code, it was tightly scoped, parallelizable, and verifiable. The deterministic checker is itself open-source, lives in the monorepo, and runs on every CI build.</p>
<p class="mb-4 font-light font-serif">That last detail matters. The audits are reproducible. Anyone can rerun them.</p>
<h2 id="built-with-claude">Built with Claude</h2>
<p class="mb-4 font-light font-serif">I want to be honest about what this actually was.</p>
<p class="mb-4 font-light font-serif">I didn&#39;t write a single line of production code in 80 days. I wrote prompts, I wrote <code>CLAUDE.md</code> constraint files, I wrote <a href="https://martinfowler.com/bliki/ArchitectureDecisionRecord.html" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">architecture decision records</a>, I reviewed pull requests, I made judgment calls about what to build next and what to defer. Claude wrote the code. Claude helped me turn my ideas into patents and did the grunt work of hardening the language, working examples, constructing diagrams, and checking the math. Claude wrote the marketing copy (with my voice). Claude wrote the documentation. Claude designed the UIs. Claude wrote the synthesis pipeline that wrote the learning content. Claude wrote the leverage tracker that documented Claude writing everything else.</p>
<p class="mb-4 font-light font-serif">A few specific observations from the 80 days, for anyone curious about what working at this scale with Claude is actually like:</p>
<ul class="my-6 lg:mb-0 space-y-4">
<li><strong>Plan mode is the highest-leverage feature</strong> for any change touching more than three files. It surfaces dependency cycles and forces explicit reasoning about ordering. Twice it caught a circular import my own static analysis had missed.</li>
<li><strong><code>CLAUDE.md</code> constraint files are dramatically underused.</strong> 57 of them across 74 repos formed a knowledge graph the agent navigated before any edit. The agent&#39;s adherence to nuanced architectural rules tracked almost perfectly with whether those rules were written down. If a rule wasn&#39;t in a <code>CLAUDE.md</code> file, it might as well not have existed.</li>
<li><strong>Parallel sub-agents change the work model.</strong> For the synthesis pipeline, three or four sub-agents could fan out across distinct learning domains and produce independent drafts in 10 minutes. The bottleneck moves from &quot;writing the content&quot; to &quot;specifying what the content should be.&quot;</li>
<li><strong>Hooks reduce approval-cycle friction more than any other optimization.</strong> A small <code>settings.json</code> hook that runs my test suite after every edit saved an enormous amount of manual cycling.</li>
</ul>
<p class="mb-4 font-light font-serif">AccelaStudy AI is, in the end, an incredible product, and I didn&#39;t write a single line of its code. It is Claude&#39;s masterpiece. I am the operator who pointed the model at the target.</p>
<h2 id="create-like-a-god-command-like-a-king-work-like-a-machine">&quot;Create like a god; command like a king; work like a machine.&quot;</h2>
<p class="mb-4 font-light font-serif">This philosophy comes from the famous Romanian sculptor <a href="https://en.wikiquote.org/wiki/Constantin_Brâncuși" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">Constantin Brâncuși</a> and is what I now live by.</p>
<p class="mb-4 font-light font-serif">Claude Code has given me the power of creation, to transform world-changing ideas into stunning reality.</p>
<p class="mb-4 font-light font-serif">Claude followed command after command after command, over 2,000 of them, tirelessly working to execute my vision.</p>
<p class="mb-4 font-light font-serif">However, I did work like a machine.</p>
<p class="mb-4 font-light font-serif">In my favorite scene from Jurassic Park, John Hammond says memorably that <a href="https://www.youtube.com/watch?v=Z3oVUmfKHNE" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">&quot;creation is an act of sheer will&quot;</a>. Delivering AccelaStudy AI, even with the work being done almost entirely by Claude Code, required the mental resolve and determination to sit at my desk an average of 120+ hours a week for almost 12 weeks, prompting Claude along, reviewing the work. That left only a handful of hours a day for sleep, eating, exercising, and spending time with family and friends. I should mention that I also worked a full-time job during 8 of those daily hours.</p>
<p class="mb-4 font-light font-serif">It was my deadline, optimistically set early on when it seemed like I&#39;d be done in no time at Claude Code pace. But, like any project that has to go to production, the <a href="https://en.wikipedia.org/wiki/Pareto_principle" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">80/20 rule</a> applies and it was clearly evident in this effort. It&#39;s the kind of ballooning that happens when the &quot;user sign up&quot; feature expands to include social media sign-ups, forgot password and MFA flows, and regulatory account closure requirements. In the end, even with all the hours, I still had to move the launch by 3 weeks. But it did launch.</p>
<h2 id="giving-back">Giving Back</h2>
<p class="mb-4 font-light font-serif">Middle school and high school curriculum is free. For students. For schools. For homeschoolers. For anyone teaching kids who deserve adaptive, personalized learning without a paywall. The K-12 curriculum rolls out across summer and fall 2026, available to any student, school, or family at no cost. Pass-probability forecasting, root-cause gap detection, real adaptive sequencing — at no cost, ever, full stop.</p>
<p class="mb-4 font-light font-serif">Adaptive learning shouldn&#39;t be a luxury good. The kids whose families can afford $4,000 tutors have always had the edge over the kids whose families can&#39;t. AccelaStudy AI doesn&#39;t know what a family&#39;s bank balance looks like, and that&#39;s the point.</p>
<p class="mb-4 font-light font-serif">The paid products fund the free K-12 work. We are launching with professional certifications to kickstart revenue. The AP catalog, AccelaStudy AI Languages, AccelaStudy AI English (IELTS + TOEFL, coming this summer), and the graduate-and-professional tests (GRE, GMAT, MCAT, and LSAT, coming in October) are all paid products. The college-entrance tests (SAT, ACT, PSAT) may also go free — that call is still open.</p>
<p class="mb-4 font-light font-serif">A solo founder, working with Claude, can build all of this in 80 days. The implication for what the rest of us — teachers, students, families — can attempt is what I want people to take from this story.</p>
<p class="mb-4 font-light font-serif">The ceiling moved. Look up.</p>
<hr>
<p class="mb-4 font-light font-serif"><em>Charles Sieg is the founder of Renkara Media Group. AccelaStudy AI is live at <a href="https://accelastudy.ai" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">accelastudy.ai</a>. The full daily leverage dataset is public at <a href="https://charlessieg.com/leverage" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">charlessieg.com/leverage</a>. The 19 internal Renkara tools, each tagged &quot;100% Built by Claude,&quot; are listed at <a href="https://renkara.com/tools.html" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">renkara.com/tools</a>. The AVIAN patent portfolio summary lives at <a href="https://avian.renkara.com" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">avian.renkara.com</a>.</em></p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 10, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-10-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-10-leverage-record.html</guid>
      <pubDate>Sun, 10 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">27 tasks. May 10, 2026 closed at 21.3x weighted leverage across 524.0 human-equivalent hours in 1,478 minutes of wall-clock time. Supervisory leverage came in at 251.5x.</p>
<p class="mb-4 font-light font-serif">That is 13.1 weeks of human-equivalent throughput in 24.6 hours. The ceiling was 68.6x; the floor was 2.2x. 27 of the 27 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Compliance HIGH remediation: bumped Aurora cluster RDS retention 1d→7d, removed localhost from admin-service prod CORS, added auth to 7 unauth anomalies endpoints in avian-admin (421 tests pass), wrote…</td>
      <td>32.0h</td>
      <td>28m</td>
      <td>4m</td>
      <td>68.6x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Audit findings remediation: BLOCKER fixes (onboarding-service test threshold + 21 orphan adjacency entries removed), CRITICAL #2 fix (HttpOnly refresh-cookie + in-memory tokenStore across auth-service +…</td>
      <td>120.0h</td>
      <td>110m</td>
      <td>10m</td>
      <td>65.5x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Run all 9 AVIAN audits (canonical, ecosystem inventory, content, accessibility, health-check, security, documentation, compliance, full-readiness) — 7 reports written to avian-audits/reports/</td>
      <td>80.0h</td>
      <td>95m</td>
      <td>1m</td>
      <td>50.5x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>AccelaStudy AI press-kit features 1/2/4/5/6: mastery seal, transfer-credit banner, root-cause diagnosis modal+endpoint, Monte Carlo distribution chart, past-readiness trend chart+endpoint — 5 UI components, 2…</td>
      <td>50.0h</td>
      <td>75m</td>
      <td>3m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Fix all HIGH/MEDIUM/LOW findings from AVIAN documentation audit (2026-05-10): README Features/Tech sections, stale CHANGELOGs, missing CI/CD sections, cross-reference links, missing docs for libs</td>
      <td>20.0h</td>
      <td>45m</td>
      <td>3m</td>
      <td>26.7x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Post-practice-exam autopilot remediation: submit_exam auto-injects wrong-node IDs into sequencing remediation queue; new POST /entities/{id}/remediation-session endpoint; ExamResults rewritten with…</td>
      <td>14.0h</td>
      <td>32m</td>
      <td>2m</td>
      <td>26.2x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Roll the new email design across the remaining 22 transactional templates: welcome, invitation, comp-welcome, account-update/closed/deleted, daily-study-reminder, streak-at-risk, [scoring…</td>
      <td>11.0h</td>
      <td>28m</td>
      <td>2m</td>
      <td>23.6x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Generate full launch demo: lived-in Charles ANS-C01 dashboard via engine seeding + DEV auth bypass, 14 retina press-kit screenshots, 64 site feature-mock screenshots (32 labels × 2 themes), ElevenLabs…</td>
      <td>14.0h</td>
      <td>40m</td>
      <td>5m</td>
      <td>21.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Rebuild shared feature page template Supernova-style: strip fake browser chrome (red/yellow/green dot row + URL chip), move hero shot below H1/subtitle/CTA at full container width, pair each how-it-works step…</td>
      <td>7.0h</td>
      <td>22m</td>
      <td>2m</td>
      <td>19.1x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Infrastructure</td>
      <td>30.0h</td>
      <td>95m</td>
      <td>5m</td>
      <td>18.9x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Remediation video + plan-preview modal + ExamReview fix + delete-entity completeness audit &amp; fix (engine multi-layer purge + admin cascade) — RemediationPlanModal, Exam.tsx review payload, target_concepts…</td>
      <td>22.0h</td>
      <td>70m</td>
      <td>4m</td>
      <td>18.9x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Testing</td>
      <td>18.0h</td>
      <td>60m</td>
      <td>6m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Brand pass on english.accelastudy.ai (always AccelaStudy AI, never AccelaStudy alone), repricing to [cost]/[cost]from [cost]/[cost]across site.yml, content stubs, both templates, README, comparison tables,…</td>
      <td>1.5h</td>
      <td>6m</td>
      <td>2m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Deployment</td>
      <td>18.0h</td>
      <td>75m</td>
      <td>5m</td>
      <td>14.4x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Shared overlay i18n full rollout via tiered approach: Tier A (full conditional i18n on about/accessibility/platforms/faq with translations across 7 languages, ~400 string-language pairs), Tier B (chrome i18n…</td>
      <td>14.0h</td>
      <td>60m</td>
      <td>4m</td>
      <td>14.0x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Infrastructure</td>
      <td>8.0h</td>
      <td>35m</td>
      <td>3m</td>
      <td>13.7x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Move Whats New release notes out of the SPA bundle: new GET /api/v1/whats-new route in avian-api proxies markdown from assets.accelastudy.ai/whats-new.md (engine content bucket) with 60s cache; new…</td>
      <td>4.0h</td>
      <td>18m</td>
      <td>2m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Fleet-wide nav + CSS + content sweep: (1) hide desktop CTA on &lt;lg viewport so mobile right-toolbar fits + hamburger becomes hit-targetable; (2) add .dark .bg-gradient-accent variant with lifted blues; (3)…</td>
      <td>6.0h</td>
      <td>28m</td>
      <td>5m</td>
      <td>12.9x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Generate two missing daily leverage blog posts (May 8 + May 9): fetch records from Leverage Manager API, sanitize 48 task descriptions for public disclosure, write Python sanitization pass with ~80…</td>
      <td>6.0h</td>
      <td>30m</td>
      <td>1m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Four avian-app-web UI fixes: (1) AnalyticsPanel restack — Accuracy/Drift/Recs stacked left, wider Learning Style Fingerprint right with wrapping legend labels; (2) added productLabel slot to design-system…</td>
      <td>6.0h</td>
      <td>32m</td>
      <td>4m</td>
      <td>11.2x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Deployment</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>1m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Email template polish + Stripe PDF invoice capture wired through purchase-service. Templates: drop Manage Notifications link, swap billing email to accelastudy.ai, rebuild receipt as edge-to-edge full-width…</td>
      <td>5.0h</td>
      <td>30m</td>
      <td>4m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Fleet sweep: disable pricing/subscribe CTAs across all 6 sister sites (mcat/lsat/ap/test-prep/english/languages) — pricing.jinja Start-Monthly/Annual/Product CTAs and home Get-Started buttons all swapped to…</td>
      <td>5.0h</td>
      <td>30m</td>
      <td>3m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Coding</td>
      <td>4.0h</td>
      <td>28m</td>
      <td>6m</td>
      <td>8.6x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Hide placeholder testimonials across all accelastudy sister sites — audit identified mcat/ap/test-prep with ungated TESTIMONIALS sections (lsat/english/[unreleased product]/enterprise clean; accelastudy.ai…</td>
      <td>2.5h</td>
      <td>18m</td>
      <td>1m</td>
      <td>8.3x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Testing</td>
      <td>14.0h</td>
      <td>130m</td>
      <td>25m</td>
      <td>6.5x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>Pre-launch calibration iteration: diagnosed v11 inverse-formula regression, designed and tested asymmetric-sigma fixes (v12, v13) via 12-journey AWS subset sweeps, reverted v13 to v12, built + pushed cloud…</td>
      <td>9.0h</td>
      <td>240m</td>
      <td>12m</td>
      <td>2.2x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>27</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>524.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,478</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>125</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>6,963,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>21.3x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>251.5x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>13.1</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 68.6x and the lowest at 2.2x, a spread of 30.5 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 32.0 of the 524.0 human-equivalent hours, or 6 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 125 minutes against 1,478 minutes of execution, a ratio of about 1 to 12. Supervisory leverage of 251.5x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 9, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-09-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-09-leverage-record.html</guid>
      <pubDate>Sat, 09 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">38 tasks. May 9, 2026 closed at 26.9x weighted leverage across 632.5 human-equivalent hours in 1,410 minutes of wall-clock time. Supervisory leverage came in at 223.2x.</p>
<p class="mb-4 font-light font-serif">That is 15.8 weeks of human-equivalent throughput in 23.5 hours. The ceiling was 85.7x; the floor was 6.7x. 22 of the 38 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>iOS web-parity rebuild: 8 phases — phase machine restructure, AvianAppShell+TopNav, launch routing fix, HomeView (slim hub), multi-course Dashboard, CoursesView+CourseDetailView, SettingsView split,…</td>
      <td>50.0h</td>
      <td>35m</td>
      <td>8m</td>
      <td>85.7x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Debugging</td>
      <td>60.0h</td>
      <td>44m</td>
      <td>6m</td>
      <td>81.8x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Infrastructure</td>
      <td>80.0h</td>
      <td>70m</td>
      <td>8m</td>
      <td>68.6x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>iOS Help fixes: tab strikethrough fix (overlay alignment), port 40 help guides verbatim from helpCenterDocs.ts → HelpDocs.swift (sidebar+content layout, iPhone sheet), embed 5 legal docs (privacy, terms,…</td>
      <td>24.0h</td>
      <td>22m</td>
      <td>4m</td>
      <td>65.5x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>iOS app facelift: SwiftUI design system port (tokens, typography, 14 components), Plus Jakarta Sans bundling, AvianTheme shim, migrate 6 high-traffic views (LoginView, ResultsView, WelcomeView, ProfileView,…</td>
      <td>40.0h</td>
      <td>50m</td>
      <td>6m</td>
      <td>48.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>iOS Settings/Profile/Help web parity: fix sign-in button (TopNav overflow on iPhone), build new HelpView (5-tab Overview/FAQ/Guides/WhatsNew/Legal + .help phase + bug-report bridge), refactor ProfileView into…</td>
      <td>18.0h</td>
      <td>24m</td>
      <td>5m</td>
      <td>45.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Deployment</td>
      <td>9.0h</td>
      <td>14m</td>
      <td>4m</td>
      <td>38.6x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Upgrade english.accelastudy.ai (IELTS/TOEFL) to multi-page subscription product site: standalone /pricing/ page with comparison table &amp; FAQ, switched nav to standalone routes, live header CTA, sister-site…</td>
      <td>5.0h</td>
      <td>8m</td>
      <td>3m</td>
      <td>37.5x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Major engine fix + audit expansion. (1) Built backfill_question_pair_ids.py - deterministic pair_id linkage backfill across 234 [content generation] domain packages. Drove pair_id coverage from 32.3% to 54.1%…</td>
      <td>16.0h</td>
      <td>28m</td>
      <td>6m</td>
      <td>34.3x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Deployment</td>
      <td>32.0h</td>
      <td>65m</td>
      <td>2m</td>
      <td>29.5x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>MEDIUM cleanup wave: 9 reduced-motion guards + 8 sr-only utilities + 594 h1-&gt;h2 codemod demotions across 241 files + 50 input-adjacent-label codemod pairings + 5 hand-fixes (BillingPage h1, Blog.jsx h1s,…</td>
      <td>14.0h</td>
      <td>30m</td>
      <td>1m</td>
      <td>28.0x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Fleet-wide a11y fix sweep across 56 UI repos: 2,460 HIGH findings fixed (2,235 via console-sim label-pairing codemod + 17 manual + 5 wave-1 activities-react + 59 wave-3 client apps + 136 wave-4 tools fleet +…</td>
      <td>60.0h</td>
      <td>130m</td>
      <td>5m</td>
      <td>27.7x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Final wave: clear remaining 192 HIGH a11y findings — patched 71 stale cloudops dist HTML files with lang=en (Python sed), dispatched focused subagent to fix 118 of 120 console-sim view-level…</td>
      <td>16.0h</td>
      <td>35m</td>
      <td>1m</td>
      <td>27.4x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Port web Help Center guide articles to iOS (40 docs, 7 categories) and rebuild Guides tab with sidebar+content layout</td>
      <td>8.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>26.7x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Drafted Making AccelaStudy AI Accessible to All across 3 sites — charlessieg.com (~3500-word technical deep-dive with mermaid wave diagram + 6 reference tables + concrete codebase counts: 2185 TSX/JSX files,…</td>
      <td>12.0h</td>
      <td>28m</td>
      <td>2m</td>
      <td>25.7x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Drove the avian-domains spec audit from 257 LOW (post-prior-pass) to absolute zero across all four severities. Tightened audit_specs.py (broadened verb whitelist, fixed cross-domain prefix detection,…</td>
      <td>14.0h</td>
      <td>35m</td>
      <td>4m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Refactor CLAUDE.md chain: extract [ip] checklist, repo map, ADR rules, SSM, domain inventory, [engine subsystem] into subtree files; relocate API keys to mode-600 env file outside prompt</td>
      <td>2.5h</td>
      <td>7m</td>
      <td>3m</td>
      <td>21.4x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Built deterministic Python accessibility-audit checker (15 rules, 56-repo discovery, brace/quote-aware JSX tokeniser, JSON+MD output, mode-aware exit codes); updated accessibility-audit.md with Phase 0 spec…</td>
      <td>24.0h</td>
      <td>70m</td>
      <td>4m</td>
      <td>20.6x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Content audit reconciliation: dropped 30 of 40 findings (all 11 CRITICALs + all 14 catalog/canonical MEDIUMs + 5 LOWs). Wrote _fix_exam_code_dups.py to remap 33 collided exam_code values to vendor-correct…</td>
      <td>4.0h</td>
      <td>12m</td>
      <td>2m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Restructure iOS ProfileView to mirror web Profile 4-tab layout (hero + Profile/Resume/Subscription/Account)</td>
      <td>6.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Design and frontend</td>
      <td>18.0h</td>
      <td>55m</td>
      <td>5m</td>
      <td>19.6x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Infrastructure</td>
      <td>16.0h</td>
      <td>50m</td>
      <td>4m</td>
      <td>19.2x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Build CoursesView and CourseDetailView for AccelaStudy iOS app (web parity)</td>
      <td>6.0h</td>
      <td>22m</td>
      <td>8m</td>
      <td>16.4x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Port web legal docs into iOS HelpView — embed privacy policy, terms, accessibility, trademarks, and credits as scrollable in-app LegalNode tree; rebuild Legal tab with iPad sidebar and iPhone sheet flow</td>
      <td>6.0h</td>
      <td>22m</td>
      <td>5m</td>
      <td>16.4x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Deployment</td>
      <td>24.0h</td>
      <td>90m</td>
      <td>8m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Coding</td>
      <td>4.0h</td>
      <td>16m</td>
      <td>2m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>Mode-1 a11y audit on avian-app-web + 6 React libs: 11 HIGH fixes (RemoteBanners aria-live, HelpCenter dialog focus, LabConsole tab pattern, ProgressBar/ExamScoreReport/Sidebar progress+log roles,…</td>
      <td>12.0h</td>
      <td>55m</td>
      <td>3m</td>
      <td>13.1x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Deployment</td>
      <td>7.5h</td>
      <td>36m</td>
      <td>4m</td>
      <td>12.5x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>Build real SettingsView for iOS app mirroring web settings page (appearance, language, study prefs, voice, accessibility, privacy, about sections)</td>
      <td>3.0h</td>
      <td>15m</td>
      <td>5m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>Migrate WelcomeView.swift to AVIAN iOS design system (AvianTokens, AvianFont, AvianButton, AvianCard, AvianEmptyState)</td>
      <td>1.5h</td>
      <td>8m</td>
      <td>3m</td>
      <td>11.2x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>Rebuild DashboardView.swift to multi-course portfolio matching web Dashboard.tsx</td>
      <td>4.0h</td>
      <td>22m</td>
      <td>5m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>32</td>
      <td>Migrate DashboardView.swift (1521 lines) to AVIAN iOS design system tokens, typography, and components</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>33</td>
      <td>accelastudy.ai canon-swap propagation: replace hardcoded counts with [value] placeholders across press, about, how-it-works, faq, accessibility, pricing, courses, free, 5 feature pages and shared pricing-card…</td>
      <td>5.0h</td>
      <td>35m</td>
      <td>2m</td>
      <td>8.6x</td>
    </tr>
    <tr>
      <td>34</td>
      <td>Testing</td>
      <td>4.0h</td>
      <td>30m</td>
      <td>1m</td>
      <td>8.0x</td>
    </tr>
    <tr>
      <td>35</td>
      <td>Activities catalog reorg (default+5 addons across 62 categories) + 4 web bug fixes (data-driven Service Match applicability, Privacy footer link, Bio Profile→Resume, unenroll→autopilot cascade) + Settings…</td>
      <td>14.0h</td>
      <td>110m</td>
      <td>18m</td>
      <td>7.6x</td>
    </tr>
    <tr>
      <td>36</td>
      <td>Infrastructure</td>
      <td>5.0h</td>
      <td>40m</td>
      <td>2m</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>37</td>
      <td>Design and frontend</td>
      <td>3.0h</td>
      <td>25m</td>
      <td>3m</td>
      <td>7.2x</td>
    </tr>
    <tr>
      <td>38</td>
      <td>Restructure SettingsView.swift to 4-tab layout (General/Autopilot/Accessibility/Privacy) with Audio section, extended-time toggle, Privacy Policy link, and SoundManager integration</td>
      <td>2.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>6.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>38</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>632.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,410</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>170</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>6,445,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>26.9x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>223.2x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>15.8</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 85.7x and the lowest at 6.7x, a spread of 12.9 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 50.0 of the 632.5 human-equivalent hours, or 8 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 170 minutes against 1,410 minutes of execution, a ratio of about 1 to 8. Supervisory leverage of 223.2x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Making AccelaStudy AI Accessible to All]]></title>
      <link>https://charlessieg.com/articles/making-accelastudy-ai-accessible-to-all.html</link>
      <guid>https://charlessieg.com/articles/making-accelastudy-ai-accessible-to-all.html</guid>
      <pubDate>Sat, 09 May 2026 00:00:00 GMT</pubDate>
      <description><![CDATA[<p><img src="https://charlessieg.com/images/making-accelastudy-ai-accessible-to-all-hero.png" alt="Making AccelaStudy AI Accessible to All" /></p><p class="mb-4 font-light font-serif">Accessibility is the kind of work that gets pushed to the next sprint until something forces the issue. The forcing function for AccelaStudy AI was the launch window. We had fifty-six user-facing repositories across consumer apps, enterprise tools, internal back-office surfaces, and marketing sites, and we had a brand promise that adaptive learning works for every learner. Every learner means every learner; a screen-reader user, a switch-device user, a keyboard-only user, a person magnifying the screen 200%. None of those people care that we built a beautiful adaptive engine if the buttons aren&#39;t reachable, the inputs aren&#39;t labeled, or the focus ring is invisible.</p>
<p class="mb-4 font-light font-serif">This article is the autopsy of one day&#39;s accessibility work. The numbers are real. The patterns are reusable. If you have a JavaScript codebase north of a thousand files and you&#39;ve been treating WCAG as a checklist instead of as engineering, this is the playbook.</p>
<h2 id="what-fully-accessible-actually-means">What &quot;fully accessible&quot; actually means</h2>
<p class="mb-4 font-light font-serif">Plenty of apps claim to be accessible. Far fewer pass an actual audit. Almost none have an audit that runs in CI and refuses to ship regressions. The difference matters. <strong>WCAG 2.1 Level AA</strong> is a moving target made up of about fifty success criteria; &quot;we tested with VoiceOver once&quot; is not a strategy for staying compliant as a codebase grows.</p>
<p class="mb-4 font-light font-serif">I draw a hard line between three states a codebase can be in:</p>
<table>
  <thead>
    <tr>
      <th>State</th>
      <th>What it means</th>
      <th>What it requires</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Aspirationally accessible</td>
      <td>Some components are good; some aren&#39;t; nobody knows which</td>
      <td>Hope and a styleguide</td>
    </tr>
    <tr>
      <td>Audited accessible</td>
      <td>A scan happened once and findings got fixed</td>
      <td>Discipline for a sprint</td>
    </tr>
    <tr>
      <td>Continuously accessible</td>
      <td>Every commit is gated by a deterministic check; regressions can&#39;t merge</td>
      <td>Engineering, scripts, codemods</td>
    </tr>
  </tbody>
</table>
<p class="mb-4 font-light font-serif">We were aspirationally accessible. We&#39;re now continuously accessible. That transition required three things: a script that catches regressions deterministically, a codebase pattern library that makes the right thing easy, and a set of codemods that did one-time mass fixes without humans touching three thousand files by hand.</p>
<h2 id="the-shape-of-the-problem">The shape of the problem</h2>
<p class="mb-4 font-light font-serif">AccelaStudy AI is one product in a fleet. The fleet shares a design system, a console-simulator library, an activities library, an authentication shell, and a layer of UI primitives. Around that core sit the consumer apps (<code>avian-app-web</code>, <code>avian-app-electron</code>), the recruiter and enterprise surfaces, the internal tools (twenty-one of them, from a Kanban board to a calendar to a unified observability dashboard), and the marketing websites. Fifty-six repositories, all in one monorepo, all needing to pass the same accessibility bar.</p>
<p class="mb-4 font-light font-serif">After running the first deterministic audit pass against fresh <code>main</code>, the script reported:</p>
<table>
  <thead>
    <tr>
      <th>Severity</th>
      <th style="text-align: right">Findings</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>CRITICAL</td>
      <td style="text-align: right">0</td>
    </tr>
    <tr>
      <td>HIGH</td>
      <td style="text-align: right">414</td>
    </tr>
    <tr>
      <td>MEDIUM</td>
      <td style="text-align: right">312</td>
    </tr>
    <tr>
      <td>LOW</td>
      <td style="text-align: right">0</td>
    </tr>
    <tr>
      <td><strong>Total</strong></td>
      <td style="text-align: right"><strong>726</strong></td>
    </tr>
  </tbody>
</table>
<p class="mb-4 font-light font-serif">Six hundred and twenty-six issues clustered into a small number of patterns. Seventy-one percent of the HIGH findings were in two repositories: the cloud-console simulator (<code>avian-console-sim-react</code>, used by labs across forty-five certifications) and a static-site <code>dist/</code> folder that hadn&#39;t been rebuilt in a month. Once I clustered the findings by rule and repository, the path to zero became obvious.</p>
<h2 id="the-audit-script">The audit script</h2>
<p class="mb-4 font-light font-serif">The first thing I built was the auditor itself. The premise: anything mechanical should be deterministic. Color contrast and focus-trap correctness need eyes; missing <code>alt</code> attributes do not.</p>
<p class="mb-4 font-light font-serif">The script is <code>avian-audits/scripts/accessibility_audit.py</code>, stdlib-only Python, about seven hundred lines. It implements fifteen rules covering the mechanical phases of WCAG 2.1 AA:</p>
<table>
  <thead>
    <tr>
      <th>Rule</th>
      <th>Severity</th>
      <th>WCAG</th>
      <th>What it catches</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td><code>img-missing-alt</code></td>
      <td>HIGH</td>
      <td>1.1.1</td>
      <td><code>&lt;img&gt;</code> without <code>alt=</code></td>
    </tr>
    <tr>
      <td><code>svg-info-no-aria</code></td>
      <td>HIGH</td>
      <td>1.1.1</td>
      <td>Standalone informational <code>&lt;svg&gt;</code> lacking <code>role=&quot;img&quot;</code> + <code>aria-label</code> (and not inside a labelled control)</td>
    </tr>
    <tr>
      <td><code>canvas-no-alt</code></td>
      <td>HIGH</td>
      <td>1.1.1</td>
      <td><code>&lt;canvas&gt;</code> without <code>aria-label</code>/<code>aria-labelledby</code>/<code>aria-hidden</code></td>
    </tr>
    <tr>
      <td><code>input-no-accessible-name</code></td>
      <td>HIGH</td>
      <td>1.3.1</td>
      <td>Form input with no label / aria-label / aria-labelledby / wrapping label</td>
    </tr>
    <tr>
      <td><code>input-adjacent-label-no-htmlfor</code></td>
      <td>MEDIUM</td>
      <td>1.3.1</td>
      <td>Sibling <code>&lt;label&gt;</code> exists but no <code>htmlFor</code>/<code>id</code> link</td>
    </tr>
    <tr>
      <td><code>multiple-h1</code></td>
      <td>MEDIUM</td>
      <td>1.3.1</td>
      <td>More than one <code>&lt;h1&gt;</code> per page-tree component</td>
    </tr>
    <tr>
      <td><code>missing-main-landmark</code></td>
      <td>HIGH</td>
      <td>1.3.1</td>
      <td>Repo with no <code>&lt;main&gt;</code> anywhere</td>
    </tr>
    <tr>
      <td><code>missing-skip-link</code></td>
      <td>HIGH</td>
      <td>2.4.1</td>
      <td>Repo with a layout but no skip-to-main-content link</td>
    </tr>
    <tr>
      <td><code>html-no-lang</code></td>
      <td>HIGH</td>
      <td>3.1.1</td>
      <td>HTML file with no <code>lang</code> on <code>&lt;html&gt;</code></td>
    </tr>
    <tr>
      <td><code>generic-link-text</code></td>
      <td>MEDIUM</td>
      <td>2.4.4</td>
      <td>&quot;click here&quot; / &quot;learn more&quot; inside <code>&lt;a&gt;</code> / <code>&lt;Link&gt;</code></td>
    </tr>
    <tr>
      <td><code>clickable-non-interactive</code></td>
      <td>HIGH</td>
      <td>2.1.1</td>
      <td><code>&lt;div&gt;</code>/<code>&lt;span&gt;</code>/<code>&lt;li&gt;</code> with <code>onClick</code> lacking <code>role</code> + <code>tabIndex</code> + <code>onKeyDown</code></td>
    </tr>
    <tr>
      <td><code>aria-hidden-focusable</code></td>
      <td>HIGH</td>
      <td>4.1.2</td>
      <td>Focusable element with <code>aria-hidden=&quot;true&quot;</code> and no <code>tabIndex={-1}</code></td>
    </tr>
    <tr>
      <td><code>positive-tabindex</code></td>
      <td>MEDIUM</td>
      <td>2.4.3</td>
      <td><code>tabIndex={N&gt;0}</code></td>
    </tr>
    <tr>
      <td><code>outline-none-no-replacement</code></td>
      <td>HIGH</td>
      <td>2.4.7</td>
      <td>CSS <code>outline:none</code> on <code>:focus</code> with no replacement; Tailwind <code>focus(-visible):outline-none</code> without a ring/outline/border replacement</td>
    </tr>
    <tr>
      <td><code>no-reduced-motion-guard</code></td>
      <td>MEDIUM</td>
      <td>2.3.3</td>
      <td>Repo defines <code>@keyframes</code> but no CSS file references <code>prefers-reduced-motion</code></td>
    </tr>
    <tr>
      <td><code>missing-sr-only-utility</code></td>
      <td>MEDIUM</td>
      <td>1.3.1</td>
      <td>Repo never references a <code>sr-only</code> / <code>visually-hidden</code> utility</td>
    </tr>
  </tbody>
</table>
<p class="mb-4 font-light font-serif">The script auto-discovers UI-bearing repositories by walking the conventional monorepo parents (<code>clients/</code>, <code>libs/</code>, <code>tools/</code>, <code>websites/</code>, <code>services/</code>, <code>automations/</code>) and identifying any directory that contains TSX, JSX, or HTML files. It honors a small exemption list for repos with no UI surface (server-only services, layout-engine libraries, design archives). Test files, <code>node_modules/</code>, <code>dist/</code>, <code>build/</code>, <code>coverage/</code>, <code>htmlcov/</code>, and per-repo <code>docs/</code> and <code>examples/</code> paths are skipped.</p>
<p class="mb-4 font-light font-serif">The clever part is the JSX tokenizer. Every JSX-aware tool I tried (regex, tree-sitter, Babel) had a tradeoff. Regex is fast but breaks on <code>onClick={() =&gt; x &gt; 0}</code> because the inner <code>&gt;</code> is read as a tag close. Babel is correct but slow and brings a parser dependency I didn&#39;t want in an audit script. So I wrote a small character-by-character walker that tracks brace depth and string state: when it sees <code>&lt;</code>, it walks forward, ignoring <code>&gt;</code> inside <code>{...}</code> and string literals, until it finds the real closing <code>&gt;</code> of the opening tag. About eighty lines. Every rule checks attributes via this walker, which means the script reads JSX correctly the first time on every run.</p>
<p class="mb-4 font-light font-serif">The script writes two artifacts:</p>
<ul class="my-6 lg:mb-0 space-y-4">
<li><code>avian-audits/reports/accessibility-audit-report-YYYY-MM-DD.md</code> — Markdown summary with by-repo and by-rule tables</li>
<li><code>avian-audits/reports/accessibility-audit-findings-YYYY-MM-DD.json</code> — machine-readable findings for CI, dashboards, diffing across runs</li>
</ul>
<p class="mb-4 font-light font-serif">In Mode 1 (the default), it exits with code <code>2</code> if any HIGH or CRITICAL findings remain. That&#39;s the gate.</p>
<h2 id="heuristics-worth-their-weight">Heuristics worth their weight</h2>
<p class="mb-4 font-light font-serif">Determinism is great until your script flags fifty true positives and twelve false ones, and the false-positive review burns more time than the fixes. Three heuristic refinements made the audit usable in practice:</p>
<ol class="my-6 lg:mb-0 space-y-4">
<li><strong>Skip aria-hidden inputs and divs.</strong> An input with <code>aria-hidden=&quot;true&quot;</code> is by definition not in the accessibility tree. Honeypot inputs, autofill catchers, hidden file pickers triggered by a styled button — all of these legitimately omit <code>aria-label</code>. Same logic for <code>&lt;div onClick aria-hidden=&quot;true&quot;&gt;</code> modal backdrops.</li>
<li><strong>Skip spread-prop forwarders.</strong> A generic component like <code>&lt;Input ref={ref} {...props} /&gt;</code> delegates <code>aria-label</code> to the consumer. The wrapper itself can&#39;t statically declare a label. The script checks for <code>{...spread}</code> syntax and exempts the element.</li>
<li><strong>Recognize the conditional-attribute pattern.</strong> A common React idiom is <code>&lt;div role={cond ? &#39;button&#39; : undefined} tabIndex={cond ? 0 : undefined} onKeyDown={cond ? handler : undefined}&gt;</code>. Statically, we can&#39;t verify the conditions match, but the developer is clearly aware of the requirement. When all three attributes appear as JSX expressions, the script treats the element as authored-correctly. (The original implementation had a bug: <code>role</code> was already brace-stripped before this check ran. Took me a few minutes to notice the heuristic was a no-op.)</li>
</ol>
<p class="mb-4 font-light font-serif">These three suppressions cleared dozens of confirmed false positives without weakening real-defect detection. Every suppression is documented inline in the script alongside the rule it modifies, so a future engineer reading <code>accessibility_audit.py</code> understands not just what&#39;s checked but what&#39;s deliberately not checked.</p>
<h2 id="the-fix-waves">The fix waves</h2>
<p class="mb-4 font-light font-serif">I ran the audit, looked at the clustering, and built a wave plan. Each wave targeted a class of fix, not a repository. That ordering matters: fixing the design-system primitives first means downstream consumers inherit the fixes, and a good codemod beats a hundred manual edits.</p>
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  <figcaption class="mermaid-caption text-center text-sm mt-2 italic">The nine-wave fix sequence: 414 HIGH findings to zero across all severities</figcaption>
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<p class="mb-4 font-light font-serif">The biggest single intervention was a Python codemod. The console-sim repository had a stereotypical pattern across 257 dashboard files: <code>&lt;label&gt;Field name&lt;/label&gt; &lt;input ... /&gt;</code>, with no <code>htmlFor</code>/<code>id</code> linkage. Visually, the labels lined up with the inputs. Programmatically, no screen reader knew the input&#39;s name. A codemod that walks the JSX, finds adjacent <code>&lt;label&gt;</code> and input pairs, generates a stable id from the input&#39;s <code>data-testid</code> (or a slug of the label text if no testid), and rewrites both the label and the input to carry the linkage took about two hundred lines and fixed 2,235 inputs in eight seconds. That&#39;s three orders of magnitude faster than the equivalent human pass and one order of magnitude more reliable.</p>
<p class="mb-4 font-light font-serif">A second codemod handled <code>&lt;h1&gt;</code> proliferation. Pages in the simulator render multiple panels, each with its own page-level heading. Source-level, that means multiple <code>&lt;h1&gt;</code> per file; runtime, only one panel renders at a time. The codemod kept the first <code>&lt;h1&gt;</code> per file and demoted the rest to <code>&lt;h2&gt;</code>. 594 demotions across 241 files.</p>
<p class="mb-4 font-light font-serif">A third codemod added <code>aria-label</code> to inputs that had a <code>data-testid</code> but no preceding sibling label, deriving the label from the testid (e.g., <code>net-add-device-name</code> becomes <code>&quot;Device name&quot;</code>). I had to rewrite this one twice; the first version&#39;s regex broke on JSX attribute values containing arrow functions. Brace-aware tokenizers earn their keep.</p>
<p class="mb-4 font-light font-serif">The waves outside the codemods went to subagents. A subagent is just an LLM session with a specific task, scoped to a list of files, with the audit findings JSON as input. I dispatched two in parallel (one for client apps, one for the tools fleet) and they came back with 195 fixes between them. The agents apply the fixes; I review the patterns and the typecheck output. The pattern review caught three confirmed false positives in client apps that the script&#39;s heuristics didn&#39;t yet suppress, which became the basis for Wave 5&#39;s heuristic refinements.</p>
<h2 id="whats-actually-in-the-codebase-now">What&#39;s actually in the codebase now</h2>
<p class="mb-4 font-light font-serif">A snapshot of the AccelaStudy AI surface area, post-audit:</p>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th style="text-align: right">Count</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>UI-bearing repositories</td>
      <td style="text-align: right">56</td>
    </tr>
    <tr>
      <td>TSX/JSX source files (production)</td>
      <td style="text-align: right">2,185</td>
    </tr>
    <tr>
      <td>Native <code>&lt;button&gt;</code> elements</td>
      <td style="text-align: right">2,527</td>
    </tr>
    <tr>
      <td>Form inputs (input / select / textarea)</td>
      <td style="text-align: right">3,486</td>
    </tr>
    <tr>
      <td><code>&lt;a href&gt;</code> links</td>
      <td style="text-align: right">thousands (uncounted; per-repo)</td>
    </tr>
    <tr>
      <td>Total ARIA attribute uses</td>
      <td style="text-align: right">3,019</td>
    </tr>
    <tr>
      <td>Explicit <code>aria-label</code> uses</td>
      <td style="text-align: right">1,530</td>
    </tr>
    <tr>
      <td><code>aria-hidden</code> uses (decorative)</td>
      <td style="text-align: right">752</td>
    </tr>
    <tr>
      <td>Total <code>role=</code> uses</td>
      <td style="text-align: right">1,001</td>
    </tr>
    <tr>
      <td><code>role=&quot;button&quot;</code> (custom clickable elements)</td>
      <td style="text-align: right">101</td>
    </tr>
    <tr>
      <td><code>role=&quot;img&quot;</code> (canvas/SVG with text alternative)</td>
      <td style="text-align: right">112</td>
    </tr>
    <tr>
      <td><code>role=&quot;dialog&quot;</code> (modal containers)</td>
      <td style="text-align: right">109</td>
    </tr>
    <tr>
      <td><code>tabIndex</code> uses</td>
      <td style="text-align: right">150</td>
    </tr>
    <tr>
      <td><code>onKeyDown</code> handlers (custom keyboard support)</td>
      <td style="text-align: right">208</td>
    </tr>
    <tr>
      <td>Tailwind <code>focus-visible:</code> ring classes</td>
      <td style="text-align: right">102</td>
    </tr>
    <tr>
      <td>Skip-to-main-content references</td>
      <td style="text-align: right">200+</td>
    </tr>
    <tr>
      <td>Total fixes shipped in one day</td>
      <td style="text-align: right"><strong>3,317</strong></td>
    </tr>
    <tr>
      <td>HIGH findings before / after</td>
      <td style="text-align: right"><strong>414 / 0</strong></td>
    </tr>
    <tr>
      <td>Findings at every severity, after</td>
      <td style="text-align: right"><strong>0</strong></td>
    </tr>
  </tbody>
</table>
<p class="mb-4 font-light font-serif">Three thousand nineteen ARIA attributes is not a vanity number. It&#39;s the count of places we&#39;ve explicitly chosen to extend or refine the accessibility tree beyond what the native HTML provides. Every one of those is a design decision that the audit will catch the regression on.</p>
<p class="mb-4 font-light font-serif">Two thousand five hundred twenty-seven native <code>&lt;button&gt;</code> elements is the more important metric, because it&#39;s the count of things we <em>didn&#39;t</em> have to make accessible by hand. Native semantics are the foundation; ARIA is the extension. The codebase leans heavily on native semantics: buttons, anchors, fieldsets, labels, headings. The ARIA layer covers the visualizations (the Knowledge Map canvas, the Behavioral Rings SVG, the Ring Forge), the custom widgets (the lab console toolbar, the segmented billing-cadence selector, the keyboard-driven drag-and-drop in activities), and the live regions (toasts, exam timers, chat output, narration logs).</p>
<h2 id="patterns-that-did-the-heavy-lifting">Patterns that did the heavy lifting</h2>
<p class="mb-4 font-light font-serif">A few patterns recur across the codebase. They&#39;re worth naming because they encode the &quot;shape&quot; of an accessible component once, and every consumer inherits the shape.</p>
<h3 id="the-interactive-checkbox-pattern">The interactive-checkbox pattern</h3>
<div class="highlight"><pre><span></span><span class="p">&lt;</span><span class="nt">div</span>
<span class="w">  </span><span class="na">role</span><span class="o">=</span><span class="s">&quot;checkbox&quot;</span>
<span class="w">  </span><span class="na">tabIndex</span><span class="o">=</span><span class="p">{</span><span class="mf">0</span><span class="p">}</span>
<span class="w">  </span><span class="na">aria-checked</span><span class="o">=</span><span class="p">{</span><span class="nx">isComplete</span><span class="p">}</span>
<span class="w">  </span><span class="na">onClick</span><span class="o">=</span><span class="p">{()</span><span class="w"> </span><span class="p">=&gt;</span><span class="w"> </span><span class="nx">toggle</span><span class="p">(</span><span class="nx">id</span><span class="p">)}</span>
<span class="w">  </span><span class="na">onKeyDown</span><span class="o">=</span><span class="p">{(</span><span class="nx">e</span><span class="p">)</span><span class="w"> </span><span class="p">=&gt;</span><span class="w"> </span><span class="p">{</span>
<span class="w">    </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="nx">e</span><span class="p">.</span><span class="nx">key</span><span class="w"> </span><span class="o">===</span><span class="w"> </span><span class="s1">&#39; &#39;</span><span class="w"> </span><span class="o">||</span><span class="w"> </span><span class="nx">e</span><span class="p">.</span><span class="nx">key</span><span class="w"> </span><span class="o">===</span><span class="w"> </span><span class="s1">&#39;Enter&#39;</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w">      </span><span class="nx">e</span><span class="p">.</span><span class="nx">preventDefault</span><span class="p">();</span>
<span class="w">      </span><span class="nx">toggle</span><span class="p">(</span><span class="nx">id</span><span class="p">);</span>
<span class="w">    </span><span class="p">}</span>
<span class="w">  </span><span class="p">}}</span>
<span class="p">&gt;</span>
<span class="w">  </span><span class="p">{</span><span class="nx">label</span><span class="p">}</span>
<span class="p">&lt;/</span><span class="nt">div</span><span class="p">&gt;</span>
</pre></div>

<p class="mb-4 font-light font-serif">Used wherever a styled checkbox replaces the native control. The four ingredients (role, tabIndex, click handler, key handler) are non-negotiable; the audit script enforces all four together.</p>
<h3 id="the-dialog-backdrop">The dialog backdrop</h3>
<div class="highlight"><pre><span></span><span class="p">&lt;</span><span class="nt">div</span><span class="w"> </span><span class="na">className</span><span class="o">=</span><span class="p">{</span><span class="nx">styles</span><span class="p">.</span><span class="nx">overlay</span><span class="p">}</span><span class="w"> </span><span class="na">role</span><span class="o">=</span><span class="s">&quot;presentation&quot;</span><span class="w"> </span><span class="na">onClick</span><span class="o">=</span><span class="p">{</span><span class="nx">onClose</span><span class="p">}&gt;</span>
<span class="w">  </span><span class="p">&lt;</span><span class="nt">div</span><span class="w"> </span><span class="na">role</span><span class="o">=</span><span class="s">&quot;dialog&quot;</span><span class="w"> </span><span class="na">aria-modal</span><span class="o">=</span><span class="s">&quot;true&quot;</span><span class="w"> </span><span class="na">aria-labelledby</span><span class="o">=</span><span class="s">&quot;title&quot;</span><span class="p">&gt;</span>
<span class="w">    </span><span class="p">&lt;</span><span class="nt">h2</span><span class="w"> </span><span class="na">id</span><span class="o">=</span><span class="s">&quot;title&quot;</span><span class="p">&gt;</span><span class="nx">Confirm</span><span class="p">&lt;/</span><span class="nt">h2</span><span class="p">&gt;</span>
<span class="w">    </span><span class="p">{</span><span class="cm">/* content */</span><span class="p">}</span>
<span class="w">  </span><span class="p">&lt;/</span><span class="nt">div</span><span class="p">&gt;</span>
<span class="p">&lt;/</span><span class="nt">div</span><span class="p">&gt;</span>
</pre></div>

<p class="mb-4 font-light font-serif"><code>role=&quot;presentation&quot;</code> removes the backdrop from the accessibility tree. The inner <code>&lt;div role=&quot;dialog&quot;&gt;</code> carries the focus trap, the labelled-by reference, and the Escape-key handler. The audit catches backdrops that pretend to be buttons (and would pollute the keyboard tab order) and silences the rule for this pattern.</p>
<h3 id="the-decorative-svg-inside-a-labelled-control">The decorative SVG inside a labelled control</h3>
<div class="highlight"><pre><span></span><span class="p">&lt;</span><span class="nt">button</span><span class="w"> </span><span class="na">aria-label</span><span class="o">=</span><span class="s">&quot;Close&quot;</span><span class="p">&gt;</span>
<span class="w">  </span><span class="p">&lt;</span><span class="nt">svg</span><span class="w"> </span><span class="na">aria-hidden</span><span class="o">=</span><span class="s">&quot;true&quot;</span><span class="w"> </span><span class="na">focusable</span><span class="o">=</span><span class="s">&quot;false&quot;</span><span class="p">&gt;</span><span class="err">…</span><span class="p">&lt;/</span><span class="nt">svg</span><span class="p">&gt;</span>
<span class="p">&lt;/</span><span class="nt">button</span><span class="p">&gt;</span>
</pre></div>

<p class="mb-4 font-light font-serif">Every icon button. Every Lucide / Phosphor / Heroicons reference. The button carries the name; the SVG is a glyph, not content. Marking the SVG <code>aria-hidden</code> plus <code>focusable=&quot;false&quot;</code> keeps it out of the accessibility tree and out of the tab order on legacy browsers.</p>
<h3 id="the-progress-bar-wrapper">The progress bar wrapper</h3>
<div class="highlight"><pre><span></span><span class="p">&lt;</span><span class="nt">div</span>
<span class="w">  </span><span class="na">role</span><span class="o">=</span><span class="s">&quot;progressbar&quot;</span>
<span class="w">  </span><span class="na">aria-valuenow</span><span class="o">=</span><span class="p">{</span><span class="nb">Math</span><span class="p">.</span><span class="nx">round</span><span class="p">(</span><span class="nx">percent</span><span class="p">)}</span>
<span class="w">  </span><span class="na">aria-valuemin</span><span class="o">=</span><span class="p">{</span><span class="mf">0</span><span class="p">}</span>
<span class="w">  </span><span class="na">aria-valuemax</span><span class="o">=</span><span class="p">{</span><span class="mf">100</span><span class="p">}</span>
<span class="w">  </span><span class="na">aria-label</span><span class="o">=</span><span class="p">{</span><span class="sb">`</span><span class="si">${</span><span class="nx">mastered</span><span class="si">}</span><span class="sb"> of </span><span class="si">${</span><span class="nx">total</span><span class="si">}</span><span class="sb"> concepts mastered`</span><span class="p">}</span>
<span class="p">&gt;</span>
<span class="w">  </span><span class="p">&lt;</span><span class="nt">div</span><span class="w"> </span><span class="na">className</span><span class="o">=</span><span class="p">{</span><span class="nx">styles</span><span class="p">.</span><span class="nx">fill</span><span class="p">}</span><span class="w"> </span><span class="na">style</span><span class="o">=</span><span class="p">{{</span><span class="w"> </span><span class="nx">width</span><span class="o">:</span><span class="w"> </span><span class="sb">`</span><span class="si">${</span><span class="nx">percent</span><span class="si">}</span><span class="sb">%`</span><span class="w"> </span><span class="p">}}</span><span class="w"> </span><span class="na">aria-hidden</span><span class="o">=</span><span class="s">&quot;true&quot;</span><span class="w"> </span><span class="p">/&gt;</span>
<span class="p">&lt;/</span><span class="nt">div</span><span class="p">&gt;</span>
</pre></div>

<p class="mb-4 font-light font-serif">Used for the radial progress on the certifications page, the per-domain bars on the exam score report, and the mastery bar on the activity sidebar. The wrapper carries the role and the values; the inner fill is decorative.</p>
<h3 id="the-radio-group-with-arrow-keys">The radio-group with arrow keys</h3>
<div class="highlight"><pre><span></span><span class="p">&lt;</span><span class="nt">div</span>
<span class="w">  </span><span class="na">role</span><span class="o">=</span><span class="s">&quot;radiogroup&quot;</span>
<span class="w">  </span><span class="na">aria-label</span><span class="o">=</span><span class="s">&quot;Billing cadence&quot;</span>
<span class="w">  </span><span class="na">onKeyDown</span><span class="o">=</span><span class="p">{(</span><span class="nx">e</span><span class="p">)</span><span class="w"> </span><span class="p">=&gt;</span><span class="w"> </span><span class="p">{</span>
<span class="w">    </span><span class="k">if</span><span class="w"> </span><span class="p">([</span><span class="s1">&#39;ArrowRight&#39;</span><span class="p">,</span><span class="w"> </span><span class="s1">&#39;ArrowDown&#39;</span><span class="p">,</span><span class="w"> </span><span class="s1">&#39;ArrowLeft&#39;</span><span class="p">,</span><span class="w"> </span><span class="s1">&#39;ArrowUp&#39;</span><span class="p">].</span><span class="nx">includes</span><span class="p">(</span><span class="nx">e</span><span class="p">.</span><span class="nx">key</span><span class="p">))</span><span class="w"> </span><span class="p">{</span>
<span class="w">      </span><span class="nx">e</span><span class="p">.</span><span class="nx">preventDefault</span><span class="p">();</span>
<span class="w">      </span><span class="nx">onChange</span><span class="p">(</span><span class="nx">billing</span><span class="w"> </span><span class="o">===</span><span class="w"> </span><span class="s1">&#39;monthly&#39;</span><span class="w"> </span><span class="o">?</span><span class="w"> </span><span class="s1">&#39;annual&#39;</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="s1">&#39;monthly&#39;</span><span class="p">);</span>
<span class="w">    </span><span class="p">}</span>
<span class="w">  </span><span class="p">}}</span>
<span class="p">&gt;</span>
<span class="w">  </span><span class="p">&lt;</span><span class="nt">IntervalButton</span><span class="w"> </span><span class="na">role</span><span class="o">=</span><span class="s">&quot;radio&quot;</span><span class="w"> </span><span class="na">aria-checked</span><span class="o">=</span><span class="p">{</span><span class="nx">isMonthly</span><span class="p">}</span><span class="w"> </span><span class="na">tabIndex</span><span class="o">=</span><span class="p">{</span><span class="nx">isMonthly</span><span class="w"> </span><span class="o">?</span><span class="w"> </span><span class="mf">0</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="o">-</span><span class="mf">1</span><span class="p">}</span><span class="w"> </span><span class="err">…</span><span class="w"> </span><span class="p">/&gt;</span>
<span class="w">  </span><span class="p">&lt;</span><span class="nt">IntervalButton</span><span class="w"> </span><span class="na">role</span><span class="o">=</span><span class="s">&quot;radio&quot;</span><span class="w"> </span><span class="na">aria-checked</span><span class="o">=</span><span class="p">{</span><span class="nx">isAnnual</span><span class="p">}</span><span class="w">  </span><span class="na">tabIndex</span><span class="o">=</span><span class="p">{</span><span class="nx">isAnnual</span><span class="w">  </span><span class="o">?</span><span class="w"> </span><span class="mf">0</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="o">-</span><span class="mf">1</span><span class="p">}</span><span class="w"> </span><span class="err">…</span><span class="w"> </span><span class="p">/&gt;</span>
<span class="p">&lt;/</span><span class="nt">div</span><span class="p">&gt;</span>
</pre></div>

<p class="mb-4 font-light font-serif">This is the WAI-ARIA radio pattern: only the selected option is in the tab order; arrow keys cycle through the rest. The subscribe flow&#39;s monthly/annual toggle uses it. Without the arrow-key handler, keyboard users couldn&#39;t discover the second option; the audit catches that omission.</p>
<h2 id="what-the-script-does-not-check">What the script does <em>not</em> check</h2>
<p class="mb-4 font-light font-serif">The script catches the mechanical eighty percent. It does not catch:</p>
<ul class="my-6 lg:mb-0 space-y-4">
<li><strong>Color contrast.</strong> Computed colors per theme, against per-component backgrounds, with consideration for state (hover, disabled, focus). This needs axe-core&#39;s color-contrast rule running against rendered DOM in a real browser.</li>
<li><strong>Touch target size.</strong> A <code>&lt;button&gt;</code> that&#39;s correctly labelled but only 24 pixels tall fails WCAG 2.5.5 on mobile. Computing the box model needs a layout engine.</li>
<li><strong>Modal focus-trap correctness.</strong> Catching whether Tab loops within the modal and Escape closes it requires actual interaction, not static analysis.</li>
<li><strong>Custom ARIA widget pattern correctness.</strong> A <code>&lt;tablist&gt;</code> + <code>&lt;tab&gt;</code> + <code>&lt;tabpanel&gt;</code> triple needs <code>aria-controls</code> on each tab pointing to a panel id and <code>aria-labelledby</code> on each panel pointing back. The script catches missing roles; it doesn&#39;t catch wiring errors between the three.</li>
<li><strong>Screen-reader narrative quality.</strong> &quot;Knowledge map showing 400 concepts: 60% mastered, 25% in progress, 15% not started&quot; is a meaningful text alternative for a canvas. &quot;Knowledge map&quot; is not. The script verifies the attribute exists; it doesn&#39;t verify the words inside it convey the data.</li>
<li><strong>Activity-format keyboard semantics.</strong> A drag-and-drop activity needs Space-to-grab, arrow-keys-to-move, Enter-to-drop, Escape-to-cancel, and live-region announcements of position. The script verifies &quot;an <code>onKeyDown</code> exists&quot;; it doesn&#39;t verify the full pattern.</li>
</ul>
<p class="mb-4 font-light font-serif">These are the LLM-driven phases of the spec. They run after the script passes, on a slower cadence, and they need a human to confirm the result. We have an axe-core Playwright sweep across thirty-four routes for color contrast and a manual screen-reader pass that goes into a release readiness checklist.</p>
<h2 id="what-it-actually-takes">What it actually takes</h2>
<p class="mb-4 font-light font-serif">After three thousand-plus fixes in a day, here&#39;s what I think is non-negotiable for a fully accessible product, and what&#39;s nice-to-have:</p>
<table>
  <thead>
    <tr>
      <th>Area</th>
      <th>Non-negotiable</th>
      <th>Nice-to-have</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Audit</td>
      <td>Deterministic script in CI; exit non-zero on HIGH/CRITICAL</td>
      <td>Coverage dashboard; per-rule trend charts</td>
    </tr>
    <tr>
      <td>Spec</td>
      <td>Single source-of-truth doc; modes formalized</td>
      <td>Rendered as a website page</td>
    </tr>
    <tr>
      <td>Codemods</td>
      <td>Reusable for the most-common bulk fixes</td>
      <td>Plugin into a pre-commit hook</td>
    </tr>
    <tr>
      <td>Patterns</td>
      <td>Documented and exemplified in the design system</td>
      <td>Storybook stories for each pattern</td>
    </tr>
    <tr>
      <td>Native HTML</td>
      <td>Buttons, anchors, fieldsets, labels — used over div+role wherever possible</td>
      <td>—</td>
    </tr>
    <tr>
      <td>ARIA</td>
      <td>Used to extend, never replace, native semantics</td>
      <td>—</td>
    </tr>
    <tr>
      <td>Focus</td>
      <td>Visible indicator on every focusable element; <code>:focus-visible</code> not <code>:focus</code></td>
      <td>High-contrast mode tested</td>
    </tr>
    <tr>
      <td>Keyboard</td>
      <td>Every interactive control reachable; arrow-key patterns where applicable</td>
      <td>Tab-order Playwright tests</td>
    </tr>
    <tr>
      <td>Skip-link</td>
      <td>Present at the top of every shell layout</td>
      <td>Multiple targets (e.g., to nav, to main)</td>
    </tr>
    <tr>
      <td><code>lang</code></td>
      <td>Set on <code>&lt;html&gt;</code> for every page</td>
      <td>Per-section overrides for non-English content</td>
    </tr>
    <tr>
      <td>Reduced motion</td>
      <td><code>@media (prefers-reduced-motion: reduce)</code> guard wherever animations exist</td>
      <td>Per-component opt-outs</td>
    </tr>
    <tr>
      <td>Screen-reader testing</td>
      <td>Manual pass with at least one of NVDA / VoiceOver / JAWS before each release</td>
      <td>Recorded passes for regression comparison</td>
    </tr>
    <tr>
      <td>Color contrast</td>
      <td>Verified per theme</td>
      <td>Computed in CI</td>
    </tr>
  </tbody>
</table>
<p class="mb-4 font-light font-serif">The first row is what gates a release. The rest gets there over time. The script catches the &quot;broken at all&quot; cases. Manual review catches the &quot;could be better&quot; cases. Both have a place; neither is sufficient on its own.</p>
<h2 id="continuous-not-episodic">Continuous, not episodic</h2>
<p class="mb-4 font-light font-serif">What I care most about is what happens in three months, when we ship a new activity format, a new lab dashboard, a new tool. The work this week was finite; the discipline is continuous.</p>
<p class="mb-4 font-light font-serif">The discipline lives in three places:</p>
<ol class="my-6 lg:mb-0 space-y-4">
<li><strong><code>avian-audits/accessibility-audit.md</code></strong> is the spec. It defines the target standard (WCAG 2.1 AA), the rules, the severities, the audit modes, and the fixes-section patterns. It updates in the same commit as the script. Treat it like an ADR.</li>
<li><strong><code>avian-audits/scripts/accessibility_audit.py</code></strong> is the executable. CI runs it. Mode 1 exits non-zero on HIGH/CRITICAL. Pull requests that introduce regressions get blocked at the review gate.</li>
<li><strong>The fix sections of the spec</strong> are the codemod inventory. When a new mechanical pattern surfaces, the rule and the codemod ship together.</li>
</ol>
<p class="mb-4 font-light font-serif">Every six weeks, someone runs Mode 2 (which enforces the MEDIUM gate too). MEDIUMs accumulate slowly in a healthy codebase; the slower cadence is appropriate. The really judgment-heavy phases — color contrast, touch targets, modal focus traps, screen-reader quality — run on release boundaries, not on every commit.</p>
<p class="mb-4 font-light font-serif">If you&#39;re starting from where we were a week ago, my advice is: write the script first. Don&#39;t write the report; don&#39;t make the slide deck; don&#39;t even fix anything. Write the script. The script gives you a baseline number, the baseline tells you the size of the problem, and the size of the problem tells you whether to fix by hand, by codemod, or by subagent. Once the script is in place, every fix is cheap and every regression is impossible. That&#39;s the difference between aspirationally accessible and continuously accessible, and it&#39;s a one-week investment for a permanent payoff.</p>
<h2 id="numbers-i-want-you-to-take-away">Numbers I want you to take away</h2>
<ul class="my-6 lg:mb-0 space-y-4">
<li><strong>Fifty-six</strong> UI-bearing repositories audited. None excluded.</li>
<li><strong>Two thousand one hundred eighty-five</strong> TSX and JSX source files scanned in production code paths.</li>
<li><strong>Three thousand nineteen</strong> explicit ARIA attribute uses across the codebase. Every one of them is a deliberate design decision the audit catches if regressed.</li>
<li><strong>Three thousand four hundred eighty-six</strong> form inputs, every one with an accessible name (label, aria-label, or wrapping label).</li>
<li><strong>Three thousand three hundred seventeen</strong> fixes shipped in a single day across nine fix waves and seven codemods.</li>
<li><strong>Two thousand two hundred thirty-five</strong> of those fixes came from a single 200-line Python codemod.</li>
<li><strong>Four hundred fourteen</strong> HIGH-severity findings became zero. Zero CRITICAL throughout. Zero MEDIUM and zero LOW after the cleanup wave.</li>
</ul>
<p class="mb-4 font-light font-serif">A learner using a screen reader, a keyboard, switch device, voice control, magnification, or reduced-motion settings can now use AccelaStudy AI without hitting a barrier any of the rest of us would notice. That&#39;s not a finishing line; that&#39;s a starting point. It&#39;s also the bar every product in our fleet, and every team I work with, should be willing to clear.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 8, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-08-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-08-leverage-record.html</guid>
      <pubDate>Fri, 08 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">10 tasks. May 8, 2026 closed at 22.4x weighted leverage across 108.5 human-equivalent hours in 291 minutes of wall-clock time. Supervisory leverage came in at 323.9x.</p>
<p class="mb-4 font-light font-serif">That is 2.7 weeks of human-equivalent throughput in 4.8 hours. The ceiling was 53.3x; the floor was 4.7x. 10 of the 10 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Browse-before-auth web client implementation: all 5 phases (router public/gated split, pendingIntent + resumeAfterAuth + AuthCallback dispatcher, anonymous CourseDetail with auth-aware Enroll, AppShell…</td>
      <td>40.0h</td>
      <td>45m</td>
      <td>1m</td>
      <td>53.3x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>ADR-0003 Phase 1 engine: PosteriorWarmStarter module, BetaPosterior trust_flagged field, mastery trust gate, CreateAutopilotRequest/Response field expansion, 5 CrossDomainConfig fields, cloud.toml section,…</td>
      <td>7.0h</td>
      <td>16m</td>
      <td>0m</td>
      <td>26.2x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Pair-to-node ref repair across 247 broken domains via embedding cosine match (146,762 pairs re-anchored, mean cosine 0.91). Bulk readiness-gate stamp across 178 manifests derived from exam metadata.…</td>
      <td>12.0h</td>
      <td>30m</td>
      <td>1m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Documentation</td>
      <td>7.0h</td>
      <td>18m</td>
      <td>2m</td>
      <td>23.3x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>ADR-0003 Phase 2 client wiring (web + Electron): API types, env flag, autopilot store extensions, CrossDomain fast-track buttons, CourseDetail savings callouts, SkillsCarryingOverPanel warm-start data, i18n…</td>
      <td>5.0h</td>
      <td>14m</td>
      <td>0m</td>
      <td>21.4x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>iOS cross-domain fast-track parity (EngineClient types, AppState TransferContext, CrossDomainView fast-track button, AutopilotView pre/post-activation callouts, env flag), invite-code gate removal…</td>
      <td>5.5h</td>
      <td>18m</td>
      <td>0m</td>
      <td>18.3x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Domain pair-to-node integrity audit (323 domains, 76% degraded), EB leaf catastrophic-regression fix (gate on domain_obs_total instead of raw pair_stats — acc92 crashed 1.0→0.001 on broken-pair domains),…</td>
      <td>18.0h</td>
      <td>65m</td>
      <td>4m</td>
      <td>16.6x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>ADR-0003 Phase 3 artifacts: 5 reference profile YAMLs (CLF→SAA, SAA→SAP, AZ900→AZ104, CKA→CKAD, CISSP→CISM), run_warmstart_validation.py synthetic A/B harness (~500 lines, parses clean),…</td>
      <td>4.0h</td>
      <td>15m</td>
      <td>0m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Built shared [engine subsystem] server (FastAPI/MPS) + [model] embeddings client + engine wiring so [engine subsystem] can run 10-way concurrent without OOM</td>
      <td>6.5h</td>
      <td>25m</td>
      <td>4m</td>
      <td>15.6x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Resume ADR-0003 cross-domain warmstart work after crash: bundle drift into 4 focused engine commits + 1 web a11y commit, add Phase 11 to avian-audits content audit (md spec + py implementation) catching…</td>
      <td>3.5h</td>
      <td>45m</td>
      <td>7m</td>
      <td>4.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>10</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>108.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>291</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>20</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>1,425,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>22.4x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>323.9x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>2.7</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 53.3x and the lowest at 4.7x, a spread of 11.4 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 40.0 of the 108.5 human-equivalent hours, or 37 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 20 minutes against 291 minutes of execution, a ratio of about 1 to 14. Supervisory leverage of 323.9x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 7, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-07-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-07-leverage-record.html</guid>
      <pubDate>Thu, 07 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">36 tasks. May 7, 2026 closed at 17.3x weighted leverage across 684.5 human-equivalent hours in 2,368 minutes of wall-clock time. Supervisory leverage came in at 244.5x.</p>
<p class="mb-4 font-light font-serif">That is 17.1 weeks of human-equivalent throughput in 39.5 hours. The ceiling was 165.0x; the floor was 0.7x. 31 of the 36 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Drove avian-app-electron + avian-app-ios from 35 drifted features to full parity with avian-app-web in five waves via 25+ parallel sub-agents. Includes: Convoy, Editable Resume Review, Voice Picker, Web Push…</td>
      <td>220.0h</td>
      <td>80m</td>
      <td>8m</td>
      <td>165.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Phase-2 round 3: Packet Inspector NEW simulator (Wireshark-lite filter language, 10 tests), SQL multi-statement script tabs, Project Board time-advance simulator with blockTask, Policy Editor drag-to-position…</td>
      <td>64.0h</td>
      <td>55m</td>
      <td>1m</td>
      <td>69.8x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Pre-launch burndown: fixed 3 holdout partial labs (git-lab-02, az-700-lab-16, dop-c02-lab-14), shipped Phase-2 polish for 5 simulators (notebook markdown preview, SQL chart panel, project-board drag-and-drop…</td>
      <td>40.0h</td>
      <td>35m</td>
      <td>1m</td>
      <td>68.6x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Phase-2 round 2 across all 7 simulators: Project Board (visual Gantt + burndown SVGs), SQL Workbench (schema browser sidebar + describeSchema SDK), Policy Editor (SVG diagram canvas with arrows), Device…</td>
      <td>56.0h</td>
      <td>50m</td>
      <td>1m</td>
      <td>67.2x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Built Top-3 parity catch-up via parallel sub-agents: Electron SSE event-bus client (port from web), Electron embedded Stripe subscribe flow + useRequireSubscription gate (CSP allowlist, SSE-driven completion,…</td>
      <td>10.0h</td>
      <td>15m</td>
      <td>1m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>avian.renkara.com: generate 5 top-level hero images via Flux 1.1 Pro (home, about, applications, contact, portfolio), wire 7 heroes total into all top-level page templates including index.jinja behind…</td>
      <td>10.0h</td>
      <td>16m</td>
      <td>1m</td>
      <td>37.5x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Fixed 32 pre-existing electron test failures (vitest path alias, native module rebuild, lost data-testids, brittle inner-span assertion); implemented iOS per-question explanations (engine already returned…</td>
      <td>6.0h</td>
      <td>10m</td>
      <td>1m</td>
      <td>36.0x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Wave 5c iOS: port 6 new activity views (CaseStudy, ErrorDetection, MinimalPairContrast, Procedural, RecallSprint, ServiceMatch) to avian-app-ios with SwiftUI, xcodeproj wiring, JSON bundled resources,…</td>
      <td>40.0h</td>
      <td>90m</td>
      <td>10m</td>
      <td>26.7x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Audited avian-app-web vs electron + iOS; expanded parity script (+22 features, 2 false-positive fixes, console-sim reclassification), regenerated FEATURE_PARITY_MATRIX.md, wrote…</td>
      <td>5.0h</td>
      <td>15m</td>
      <td>2m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Deployment</td>
      <td>16.0h</td>
      <td>60m</td>
      <td>5m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Deployment</td>
      <td>24.0h</td>
      <td>95m</td>
      <td>12m</td>
      <td>15.2x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Add Voice Picker + Speed Control to avian-app-ios (Wave 5 parity)</td>
      <td>6.0h</td>
      <td>25m</td>
      <td>5m</td>
      <td>14.4x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>avian-app-ios: Add Editable Resume Review section (Wave 5b parity) — StudentProfile models, AuthClient getStudentProfile/patchStudentProfile, ResumeReviewSectionView, 20 i18n keys (en/es/fr/de), xcodeproj…</td>
      <td>5.0h</td>
      <td>22m</td>
      <td>5m</td>
      <td>13.6x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Reordered Phase E queue to prioritize CompTIA after PMI for launch credibility. Wrote Phase E2 orchestrator (PMI→CompTIA→ScrumAlliance→ISACA→ISC2 at 4-way) and a race-free swap handler that polls for active…</td>
      <td>5.0h</td>
      <td>22m</td>
      <td>4m</td>
      <td>13.6x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Press release rewrite (live vs shipping, Autopilot/behavioral, strip jargon, anchor originating [ip] + perf), add deferred-content launch placeholders, correct HQ city/dateline, build pre-commit canon…</td>
      <td>4.0h</td>
      <td>18m</td>
      <td>6m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Add Study Session Mode to avian-app-ios — StudySessionView.swift, Phase.studySession, per-item review data, streak tracking, 20 localization keys en/es/fr/de, xcodeproj wiring</td>
      <td>8.0h</td>
      <td>38m</td>
      <td>5m</td>
      <td>12.6x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Port embedded subscribe flow from avian-app-web to avian-app-electron (SubscribeModal, SubscribeScreen, SubscribeCompleteScreen, useRequireSubscription, subscription API client, CSP update, TTS gate wiring)</td>
      <td>8.0h</td>
      <td>40m</td>
      <td>5m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Wave 5b: Add ConvoyCardView to avian-app-ios (SwiftUI card, CompositePlan model, getEntityCompositeAutopilot endpoint, DashboardView mount, 16 localization keys en/es/fr/de, xcodeproj wiring)</td>
      <td>4.0h</td>
      <td>20m</td>
      <td>3m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Port ForecastAreaChart (Full-Horizon Forecast) from avian-app-web to avian-app-electron; add recharts dep, wire IPC (pre-existing), create component, replace primitive bar chart in AnalyticsPanel, write 10…</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Add CoachingRadarView to avian-app-ios: custom SwiftUI Path-based radar polygon, CoachingRadarCard with live fingerprint fetch, xcodeproj wired, 5 localization keys in 4 languages</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Add Pending-Deletion Banner to avian-app-ios (Wave 2 parity)</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>AccelaStudy launch teaser end-to-end production pipeline: 5 protagonist refs (Flux 1.1 Pro Ultra), 16+ character-locked stills (Flux Kontext Max) with multiple iterations per shot, 16 video shots (Kling 3…</td>
      <td>80.0h</td>
      <td>540m</td>
      <td>12m</td>
      <td>8.9x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Port SSE event-bus client from avian-app-web to avian-app-electron</td>
      <td>2.0h</td>
      <td>14m</td>
      <td>3m</td>
      <td>8.6x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Add ExamReviewView.swift to avian-app-ios — per-question post-exam review screen with NavigationStack push from ExamResultsView</td>
      <td>4.0h</td>
      <td>28m</td>
      <td>5m</td>
      <td>8.6x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Add Electron native push notifications (Wave 5 parity) - main process notification module, IPC handlers, renderer hook, Settings tile, 4-locale i18n, 11 tests</td>
      <td>3.0h</td>
      <td>22m</td>
      <td>5m</td>
      <td>8.2x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Port WrongAnswerLessonReview from avian-app-web to avian-app-electron</td>
      <td>3.0h</td>
      <td>28m</td>
      <td>5m</td>
      <td>6.4x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>iOS Subscription Gate (StoreKit) wave 5b - SubscriptionGate.swift + SubscriptionGateView.swift + wire TTS/voice-picker callsites + 19 i18n keys + xcodeproj</td>
      <td>3.0h</td>
      <td>28m</td>
      <td>5m</td>
      <td>6.4x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Infrastructure</td>
      <td>18.0h</td>
      <td>180m</td>
      <td>8m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>AVIAN ADR-0002 follow-ups Thread 1+3+4: autopilot-driven harness mode in headless_runner (StudentProfile.harness_mode + _load_pairs_by_goal helper + _grade_one_pair goal_id parameter), clarifying comment…</td>
      <td>4.0h</td>
      <td>60m</td>
      <td>2m</td>
      <td>4.0x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>AVIAN multi-cohort calibration sweep proving predictor handles heterogeneous learners (Charles-style 10/10 pass at predicted 0.975 actual 0.824 ECE 0.025) — MoE design exploration deferred since single-model…</td>
      <td>4.0h</td>
      <td>70m</td>
      <td>3m</td>
      <td>3.4x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>AVIAN predictor mixture-of-experts design exploration + Phase F (heterogeneous goal_target_accuracies in StudentProfile + per-question lookup in headless_runner) + Charles Sieg resume-modeled ANS-C01 profile…</td>
      <td>5.0h</td>
      <td>90m</td>
      <td>8m</td>
      <td>3.3x</td>
    </tr>
    <tr>
      <td>32</td>
      <td>AVIAN ADR-0002 Thread 2: empirical Bayes leaf prior implementation + 225-journey calibration sweep + hotfix for competence floor over-triggering on untouched goals (acc92 cohort calibration crashed from 0.965…</td>
      <td>5.0h</td>
      <td>90m</td>
      <td>1m</td>
      <td>3.3x</td>
    </tr>
    <tr>
      <td>33</td>
      <td>Testing</td>
      <td>7.0h</td>
      <td>130m</td>
      <td>10m</td>
      <td>3.2x</td>
    </tr>
    <tr>
      <td>34</td>
      <td>AVIAN ADR-0002 Phase H v7 calibration sweep (225 journeys, all flags on): EB leaf + gap-focus + observed-only competence floor delivered acc80 underconfidence reduction from 12pp to 6pp, acc80 verdict…</td>
      <td>4.0h</td>
      <td>130m</td>
      <td>2m</td>
      <td>1.8x</td>
    </tr>
    <tr>
      <td>35</td>
      <td>accelastudy.ai title cleanup: hide redundant total pill on provider pages, factor coursetitle macro into _tm_macros, preserve Certified-in-X (ISC2/ISACA) carve-outs, strip trailing Certificate (ISACA…</td>
      <td>1.5h</td>
      <td>110m</td>
      <td>5m</td>
      <td>0.8x</td>
    </tr>
    <tr>
      <td>36</td>
      <td>accelastudy.ai courses page: tighten card cap from 20 to 15, strip Certified word from 99 course titles via template filter (cards + course pages), reorder VMware after Cisco in Networking and…</td>
      <td>1.0h</td>
      <td>88m</td>
      <td>3m</td>
      <td>0.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>36</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>684.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>2,368</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>168</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>12,241,500</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>17.3x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>244.5x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>17.1</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 165.0x and the lowest at 0.7x, a spread of 242.0 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 220.0 of the 684.5 human-equivalent hours, or 32 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 168 minutes against 2,368 minutes of execution, a ratio of about 1 to 14. Supervisory leverage of 244.5x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 6, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-06-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-06-leverage-record.html</guid>
      <pubDate>Wed, 06 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">11 tasks. May 6, 2026 closed at 12.0x weighted leverage across 189.5 human-equivalent hours in 951 minutes of wall-clock time. Supervisory leverage came in at 258.4x.</p>
<p class="mb-4 font-light font-serif">That is 4.7 weeks of human-equivalent throughput in 15.8 hours. The ceiling was 160.0x; the floor was 0.8x. 9 of the 11 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>avian.renkara.com: generate 11 application-domain hero images via Flux 1.1 Pro, wire into application.jinja hero + applications.jinja card grid, WebP optimization (13MB→1.2MB)</td>
      <td>16.0h</td>
      <td>6m</td>
      <td>3m</td>
      <td>160.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Open-items batch 2: mass terminal-wait-output pattern fix (2158 patterns / 454 labs broken by escape mismatch — 13 labs recovered to full-score), 124 unsupported terminal-runs stripped, content cleanup, 3…</td>
      <td>80.0h</td>
      <td>95m</td>
      <td>1m</td>
      <td>50.5x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Three more Phase-1 simulators: Network Topology Sandbox (BFS reachability + static routes + ping with simulated latency, 8 tests), Device Manager Panel (default A+ fleet + Settings + BIOS, 7 tests),…</td>
      <td>24.0h</td>
      <td>30m</td>
      <td>1m</td>
      <td>48.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Custom AccelaStudy AI sound effect library: 28 ElevenLabs-generated sounds (incl. Apple-style branded startup), SoundProvider+useSound hook, volume/preview settings UI, design-system event dispatches…</td>
      <td>16.0h</td>
      <td>30m</td>
      <td>3m</td>
      <td>32.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Designed Phase E launch sprint orchestrator (75 specs across ISC2/ISACA/PMI/ScrumAlliance/Cisco/CompTIA-backfill), auto-chained from Phase D, ramped parallelism 2→3→4-way as labs session freed memory.…</td>
      <td>12.0h</td>
      <td>45m</td>
      <td>12m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Open-items burn-down: VFS reset across labs (memory leak fix), QuickJS node resolver (CDN-loaded, ~3MB lazy), shell stdout redirection (echo &gt; file), Monaco editor listener leak fix, multi-editor-create-file…</td>
      <td>16.0h</td>
      <td>90m</td>
      <td>1m</td>
      <td>10.7x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>AVIAN [engine subsystem] memory fix: opt-in fake-embedder + thread caps cuts worker RSS ~10x (168 GB calibration sweep blow-up reduced to ~15 GB). Tests for fake-embedder contract +…</td>
      <td>4.0h</td>
      <td>30m</td>
      <td>3m</td>
      <td>8.0x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>AVIAN engine CISSP cold-start 500 fixes: UnboundLocalError on avg_per_q (lifted assignment to function scope) + null exam_structure coercion (.get default does not fire on explicit null). AST-based regression…</td>
      <td>2.0h</td>
      <td>25m</td>
      <td>2m</td>
      <td>4.8x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>AVIAN predictor calibration: harness RNG decouple (separate observation/exam streams), [PREDICT/COLD] log, calibration-only answer_key endpoint, n-aware verdict bands, multi-select-bug-unmask. Five sweep…</td>
      <td>16.0h</td>
      <td>360m</td>
      <td>12m</td>
      <td>2.7x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>accelastudy.ai courses page: 5 provider reorders + CNCF hero generation (Flux 2 Pro) + template refactor to honor slug order over live-first split, deployed across 2 prod + 2 staging build cycles</td>
      <td>2.5h</td>
      <td>165m</td>
      <td>4m</td>
      <td>0.9x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>accelastudy.ai courses page: cap provider card course list at 20 items + N more arrow row across all 4 card variants (live+heroed, live+plain, soon+heroed, soon+plain), deployed to Production + Staging with…</td>
      <td>1.0h</td>
      <td>75m</td>
      <td>2m</td>
      <td>0.8x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>11</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>189.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>951</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>44</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>3,453,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>258.4x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>4.7</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 160.0x and the lowest at 0.8x, a spread of 200.0 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 16.0 of the 189.5 human-equivalent hours, or 8 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 44 minutes against 951 minutes of execution, a ratio of about 1 to 22. Supervisory leverage of 258.4x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 5, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-05-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-05-leverage-record.html</guid>
      <pubDate>Tue, 05 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">6 tasks. May 5, 2026 closed at 27.3x weighted leverage across 130.5 human-equivalent hours in 287 minutes of wall-clock time. Supervisory leverage came in at 652.5x.</p>
<p class="mb-4 font-light font-serif">That is 3.3 weeks of human-equivalent throughput in 4.8 hours. The ceiling was 144.0x; the floor was 8.0x. 6 of the 6 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Bulk-migrated 568 legacy lab step-issues: wrote scripts/migrate_free_labs.py extracting code blocks + filenames into editor-create-file uiSteps; flagged 341 templated-stub labs as shipping:false. Audit now…</td>
      <td>60.0h</td>
      <td>25m</td>
      <td>1m</td>
      <td>144.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Five follow-ups: fixed 522 mismatched/wrong-type checkpoints across 245 labs (auto-detector); split 66 &amp;&amp; commands across 44 labs; flagged gql-lab-01 as shipping:false; investigated vitest hang (it is just…</td>
      <td>32.0h</td>
      <td>75m</td>
      <td>1m</td>
      <td>25.6x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Lab audit cleanup (32 issues→0) + per-cert lab-examples-by-simulator doc + simulator-inventory doc covering 8 shipped + 8 planned simulators</td>
      <td>14.0h</td>
      <td>35m</td>
      <td>4m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Reschedule AccelaStudy launch May 5 -&gt; May 11 + cascade dates across plan, canonical-values.yaml, press kit, launch-content drafts, and CLAUDE.md</td>
      <td>2.5h</td>
      <td>12m</td>
      <td>4m</td>
      <td>12.5x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Deleted legacy action-dispatch.ts (20K LOC) + bridge from generic-executor; made expectedActions optional in lab-types; fixed pre-existing lab-loader test failures; ran Watch sweep on 65+ migrated labs (~92%…</td>
      <td>14.0h</td>
      <td>80m</td>
      <td>1m</td>
      <td>10.5x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Watch sweep complete (2.6h, 1700 labs, 36 partial). Triaged: removed 103 unwired propertiesPresent in 69 cloud-cert labs (recovered 8 partial-&gt;full); fixed az-204 slots-&gt;deploymentSlots property name; flagged…</td>
      <td>8.0h</td>
      <td>60m</td>
      <td>1m</td>
      <td>8.0x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>6</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>130.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>287</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>12</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>1,134,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>27.3x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>652.5x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>3.3</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 144.0x and the lowest at 8.0x, a spread of 18.0 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 60.0 of the 130.5 human-equivalent hours, or 46 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 12 minutes against 287 minutes of execution, a ratio of about 1 to 24. Supervisory leverage of 652.5x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 4, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-04-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-04-leverage-record.html</guid>
      <pubDate>Mon, 04 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">45 tasks. May 4, 2026 closed at 13.3x weighted leverage across 479.0 human-equivalent hours in 2,164 minutes of wall-clock time. Supervisory leverage came in at 142.3x.</p>
<p class="mb-4 font-light font-serif">That is 12.0 weeks of human-equivalent throughput in 36.1 hours. The ceiling was 51.4x; the floor was 1.3x. 35 of the 45 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Run full [ip] portfolio audit (3 CIP drafts Z/AA/BB, ~50 supporting docs, 5 gen scripts, 24 diagrams) and write findings report to .audits/</td>
      <td>6.0h</td>
      <td>7m</td>
      <td>1m</td>
      <td>51.4x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>[engine subsystem] hardening: pass_probability vs expected_score_pct field split, binned exam-pass calibration analyzer (Brier+ECE), audit chain, credentials, per-source decay; 48 new tests + 6 pre-existing…</td>
      <td>16.0h</td>
      <td>22m</td>
      <td>8m</td>
      <td>43.6x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Wire avian-admin scaffolds: 4 student-detail tabs, Session Inspector, Domain Detail, viz demo gate, dead Dashboard cleanup, help text refresh, fixed pre-existing banner test</td>
      <td>18.0h</td>
      <td>26m</td>
      <td>4m</td>
      <td>41.5x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Deployment</td>
      <td>24.0h</td>
      <td>35m</td>
      <td>5m</td>
      <td>41.1x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Switch beta_certs ([cost]/[cost]) coupons to early_adopter_certs ([cost]/[cost]); rename BETA_DISCOUNT_<em> → EARLY_ADOPTER_DISCOUNT_</em> env vars; update purchase-service backend (config, coupon service, dev mock,…</td>
      <td>8.0h</td>
      <td>12m</td>
      <td>4m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Extended free-tier adjacency catalog from 92 to 662 certs (84% of paid catalog). 22 new clusters covering…</td>
      <td>16.0h</td>
      <td>25m</td>
      <td>4m</td>
      <td>38.4x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Documentation</td>
      <td>12.0h</td>
      <td>19m</td>
      <td>2m</td>
      <td>37.9x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Documentation</td>
      <td>24.0h</td>
      <td>40m</td>
      <td>1m</td>
      <td>36.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Engine: 4 new admin endpoints (per-session interactions, per-entity sessions, exam attempts, per-domain analytics) + exam_attempts migration/model/repo + 7 unit tests. Admin-service: 4 RPC proxies.…</td>
      <td>36.0h</td>
      <td>73m</td>
      <td>5m</td>
      <td>29.6x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Documentation</td>
      <td>16.0h</td>
      <td>35m</td>
      <td>8m</td>
      <td>27.4x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Plan-only Fargate migration extension: 3 more TF stacks (admin-service, avian-[unreleased product]-backend, avian-enterprise-backend) + per-service docs (migration-concerns, cutover-runbook, cost-analysis,…</td>
      <td>8.0h</td>
      <td>18m</td>
      <td>2m</td>
      <td>26.7x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Bug-fix bundle: AvianMapper noise floor (945 false matches → 38 real ones via stricter substring rules + 0.10 → 0.15 relevance threshold), cap recommended courses at 3 (was 50), onboarding-at-first-login gate…</td>
      <td>8.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>26.7x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Run AVIAN content audit + write 2026-05-04 report</td>
      <td>1.5h</td>
      <td>4m</td>
      <td>1m</td>
      <td>22.5x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Write pytest unit tests for avian-admin backend to push coverage from 37% to 87%</td>
      <td>8.0h</td>
      <td>22m</td>
      <td>5m</td>
      <td>21.8x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Plan-only Fargate migration: 5 new TF stacks (avian-services-cluster + auth/purchase/notification/onboarding) + per-service docs (migration-concerns, cutover-runbook, cost-analysis,…</td>
      <td>12.0h</td>
      <td>35m</td>
      <td>3m</td>
      <td>20.6x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Create CIP BB [ip] application skeleton (Posterior-Bayesian Adaptive Probing - AVIAN Sextant) with 20 claims, 8 Mermaid figures, and full portfolio cross-document updates (CLAUDE.md, AGENTS.md, README.md,…</td>
      <td>8.0h</td>
      <td>28m</td>
      <td>3m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Create english.accelastudy.ai (IELTS &amp; TOEFL coming-soon site, Summer 2026 launch). Cloned test-prep skeleton, customized site.yml/index.jinja/content for English-language testing audience, generated 10…</td>
      <td>8.0h</td>
      <td>28m</td>
      <td>3m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Threaded solo-founder + Claude in 74 days angle through press kit (rewrote lede, added Built by One Person and Claude section, updated bio/FAQ/fact sheets) with verified LoC, test, and repo counts</td>
      <td>4.0h</td>
      <td>14m</td>
      <td>3m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Documentation</td>
      <td>2.5h</td>
      <td>9m</td>
      <td>2m</td>
      <td>16.7x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Refactoring</td>
      <td>6.0h</td>
      <td>22m</td>
      <td>4m</td>
      <td>16.4x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Reorder tiers on avian.renkara.com (5 jinja templates): renumber so Adaptation is Tier 6 between Planning and Delivery, Validation is Tier 12; update 13 tiers -&gt; 12 and 31 -&gt; 32 branded clusters; physically…</td>
      <td>2.0h</td>
      <td>8m</td>
      <td>1m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>avian-app-web: fix /subscribe paywall on already-subscribed accounts (entitlements gate + dashboard toast); axe a11y sweep — fix PageNotFound color-contrast and prune dead routes</td>
      <td>3.0h</td>
      <td>12m</td>
      <td>3m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Content audit follow-ups: backfill 4 manifests, explain manifest &lt;100% jump, identify dropped packages</td>
      <td>1.0h</td>
      <td>4m</td>
      <td>1m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Greenfield Terraform module for avian-app-web in infra/avian-terraform/ — S3+OAC+CloudFront+Stripe-friendly RHP+/.well-known carve-out+pipeline; deployed parallel-run at app-v2.accelastudy.ai</td>
      <td>8.0h</td>
      <td>35m</td>
      <td>3m</td>
      <td>13.7x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Resume avian-domains [content generation] chain after crash: triaged state across phase D synth + CompTIA [engine subsystem], wrote run_comptia_[engine subsystem]_resume + run_phase_d_certs_resume…</td>
      <td>8.0h</td>
      <td>35m</td>
      <td>6m</td>
      <td>13.7x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Reorder [ip portfolio] 1-12, insert Adaptation (BB) between Planning and Delivery; rewrite Platform_Architecture_Tiers.md (was 10 tiers without Validation/Adaptation, now 12 with full Inputs/Outputs and…</td>
      <td>4.0h</td>
      <td>18m</td>
      <td>1m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>Write vitest unit tests for avian-admin REST+SSE migration: restClient, SseClient, hooks, useEngineSSE, useAdminRealtime — 152 tests, 90%+ coverage</td>
      <td>6.0h</td>
      <td>28m</td>
      <td>5m</td>
      <td>12.9x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Built tokenization machinery (canonical_values.yaml + render_kit.sh), shifted launch to Tue May 5 with same-day Product Hunt, dropped wire spend per bootstrapped marketing plan, rewrote launch execution plan,…</td>
      <td>8.0h</td>
      <td>38m</td>
      <td>5m</td>
      <td>12.6x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>Diagnose &amp; fix Stripe Basil API breaking change in purchase-service (Invoice.payment_intent → confirmation_secret)</td>
      <td>3.0h</td>
      <td>15m</td>
      <td>3m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>@avian/subscribe-react 0.1.5: 1Password / browser-autofill compatibility — <form> wrapper + visually-hidden cc-* anchor inputs + type=submit Pay button</td>
      <td>2.0h</td>
      <td>10m</td>
      <td>2m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>Coding</td>
      <td>2.0h</td>
      <td>11m</td>
      <td>3m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>32</td>
      <td>accelastudy.ai full hero corpus regen (813 per-course + 55 provider pairs) + mobile theme/menu/scroll-lock + tier-dupe fix + card layout + nav cleanup</td>
      <td>80.0h</td>
      <td>450m</td>
      <td>25m</td>
      <td>10.7x</td>
    </tr>
    <tr>
      <td>33</td>
      <td>Migrate avian-admin from WebSockets to REST + SSE — backend SSE infra, RPC-over-REST gateway, Redis viz publishers, frontend RestClient/SseClient/hooks, 44 page migrations, viz SSE wiring, full WS code…</td>
      <td>16.0h</td>
      <td>90m</td>
      <td>6m</td>
      <td>10.7x</td>
    </tr>
    <tr>
      <td>34</td>
      <td>Deployment</td>
      <td>4.0h</td>
      <td>25m</td>
      <td>4m</td>
      <td>9.6x</td>
    </tr>
    <tr>
      <td>35</td>
      <td>Debugging</td>
      <td>8.0h</td>
      <td>50m</td>
      <td>4m</td>
      <td>9.6x</td>
    </tr>
    <tr>
      <td>36</td>
      <td>Infrastructure</td>
      <td>5.0h</td>
      <td>32m</td>
      <td>4m</td>
      <td>9.4x</td>
    </tr>
    <tr>
      <td>37</td>
      <td>Verified manifest regen for +60 free labs, ran watch sweep at 6 workers, fixed md-lab-15 + yj-lab-15 (50-&gt;100% score), capped Playwright workers for memory safety</td>
      <td>4.0h</td>
      <td>26m</td>
      <td>5m</td>
      <td>9.2x</td>
    </tr>
    <tr>
      <td>38</td>
      <td>Cut over avian-app-web from legacy terraform/client/ to new avian-terraform/avian-app-web/ — destroyed legacy stack (10 resources), claimed app.accelastudy.ai alias, flipped DNS, renamed pipeline</td>
      <td>4.0h</td>
      <td>30m</td>
      <td>2m</td>
      <td>8.0x</td>
    </tr>
    <tr>
      <td>39</td>
      <td>Engine readiness calibration gap — root-cause investigation, hierarchical prior fix in /autopilot/next, [engine subsystem] omniscient + target_accuracy debug modes, per-question telemetry, calibration sweep +…</td>
      <td>24.0h</td>
      <td>180m</td>
      <td>15m</td>
      <td>8.0x</td>
    </tr>
    <tr>
      <td>40</td>
      <td>@avian/subscribe-react: theme-aware Stripe Elements appearance, host-font in iframe, color-mix loading skeleton, centered Preparing label, US billing default — published 0.1.2 → 0.1.4</td>
      <td>3.0h</td>
      <td>25m</td>
      <td>4m</td>
      <td>7.2x</td>
    </tr>
    <tr>
      <td>41</td>
      <td>Diagnose + fix prod errors in daily study session: CSP blob media-src (terraform applied), 422 fractional minutes (autopilot.ts rounding), 404 cognitive-state (full route impl: engine…</td>
      <td>12.0h</td>
      <td>105m</td>
      <td>4m</td>
      <td>6.9x</td>
    </tr>
    <tr>
      <td>42</td>
      <td>Resume adaptive engine optimization: regression-test full probing protocol, flip cloud flag, apply [engine subsystem] zero-sweep backend diffs, bring up service stack, launch pilot</td>
      <td>6.0h</td>
      <td>60m</td>
      <td>6m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>43</td>
      <td>Per-site full-bleed hero rotation across 7 AccelaStudy sites: 70 unique compositions, 280 images (10 desktop pairs at 16:9 + 10 mobile pairs at 3:2 per site), dark/light locked via shared seed + Flux 2 Pro…</td>
      <td>16.0h</td>
      <td>207m</td>
      <td>8m</td>
      <td>4.6x</td>
    </tr>
    <tr>
      <td>44</td>
      <td>Fixed 4 lab-runner UX bugs (guided auto-advance, Try Again reset to guided, loading spinner with cancel, TTS subscription/settings gate); replaced lab-frame TTS monkey-patches with setTtsConfig API; deployed…</td>
      <td>6.0h</td>
      <td>85m</td>
      <td>8m</td>
      <td>4.2x</td>
    </tr>
    <tr>
      <td>45</td>
      <td>accelastudy.ai hero deployment: found stale heroed_providers template allowlist (22/56 providers), expanded to include all 34 missing (EC-Council, Cisco, IBM, Oracle, SAP, VMware, ISC2, etc.), deployed to…</td>
      <td>2.0h</td>
      <td>93m</td>
      <td>3m</td>
      <td>1.3x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>45</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>479.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>2,164</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>202</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>11,026,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>142.3x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>12.0</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 51.4x and the lowest at 1.3x, a spread of 39.9 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 6.0 of the 479.0 human-equivalent hours, or 1 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 202 minutes against 2,164 minutes of execution, a ratio of about 1 to 11. Supervisory leverage of 142.3x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 3, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-03-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-03-leverage-record.html</guid>
      <pubDate>Sun, 03 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">44 tasks. May 3, 2026 closed at 35.1x weighted leverage across 1,767.5 human-equivalent hours in 3,019 minutes of wall-clock time. Supervisory leverage came in at 380.1x.</p>
<p class="mb-4 font-light font-serif">That is 44.2 weeks of human-equivalent throughput in 50.3 hours. The ceiling was 171.4x; the floor was 1.8x. 43 of the 44 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Code Sandbox migration Waves 2-6 — 758 labs migrated to sandbox schema and lifted to strict-pass via service-cluster fixes (echo injection, CODE/SQL/EDITOR resource type repairs, name filter corrections).…</td>
      <td>600.0h</td>
      <td>210m</td>
      <td>15m</td>
      <td>171.4x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Innovation ideation: 20 features for avian-app-web + avian-admin via /invent skill, then detailed multi-phase implementation plans (with Description + ELIF + files + data flow + acceptance criteria) written…</td>
      <td>16.0h</td>
      <td>8m</td>
      <td>4m</td>
      <td>120.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Wave 2-5 sandbox lab migration: 315 labs (CloudFormation 30, Pulumi 30, Programming 150, DevOps 105) + pre-existing TS error fix</td>
      <td>80.0h</td>
      <td>45m</td>
      <td>10m</td>
      <td>106.7x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Infrastructure</td>
      <td>168.0h</td>
      <td>95m</td>
      <td>8m</td>
      <td>106.1x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Deployment</td>
      <td>14.0h</td>
      <td>8m</td>
      <td>3m</td>
      <td>105.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Documentation</td>
      <td>65.0h</td>
      <td>38m</td>
      <td>3m</td>
      <td>102.6x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Adaptive Probing Protocol full implementation: ADR-0001 Phases 0-8. New probing/ package with BetaPosterior+PosteriorStore, telemetry-weighted observation strength, stepwise difficulty escalation k-index,…</td>
      <td>130.0h</td>
      <td>90m</td>
      <td>12m</td>
      <td>86.7x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>avian-app-web: implemented Phases 0/1/2/3/5/6 from May feature plan — cognitive-state steering POST + indicator (Session.tsx + cognitiveDetect.ts + tests), full-horizon ForecastAreaChart on CourseDetail +…</td>
      <td>32.0h</td>
      <td>26m</td>
      <td>3m</td>
      <td>73.8x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Cross-repo audit of study session reminder feature: engine, notification-service, avian-api gateway, avian-app-web — what is real, stubbed, absent. Produced prioritized tier 1-5 implementation plan including…</td>
      <td>6.0h</td>
      <td>7m</td>
      <td>3m</td>
      <td>51.4x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>avian-app-web lint triage: 4187 problems → 0. Excluded vendored reference-build/<em></em> bundles (root cause of ~3900 errors), relaxed no-console to allow warn/error globally, added e2e/scripts override for…</td>
      <td>6.0h</td>
      <td>8m</td>
      <td>1m</td>
      <td>45.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>avian-console-sim Phase 5: validators, pseudo-executors (Terraform/CFN/Pulumi), AWS/gcloud/az CLI emulators, ProblemsPanel, Monaco markers wiring, 54 new tests, provider catalog (760 entries)</td>
      <td>40.0h</td>
      <td>55m</td>
      <td>5m</td>
      <td>43.6x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Full [ip] audit on CIP applications Z and AA — 97 checks across 7 phases, 25 findings</td>
      <td>6.0h</td>
      <td>10m</td>
      <td>5m</td>
      <td>36.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Gate TTS behind subscription: useRequireSubscription hook + paid-access selector, TTSProvider.speak() chokepoint gate (manual=paywall, auto=silent), SpeakButton lock badge, Settings premium pill + toggle…</td>
      <td>10.0h</td>
      <td>18m</td>
      <td>4m</td>
      <td>33.3x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>avian-admin: PageHelpBanner across every page+tab, viz WSS scheme fix, engine session realtime publisher (events.engine.session.*), four canonical docs sync (requirements, design, testing-strategy,…</td>
      <td>16.0h</td>
      <td>30m</td>
      <td>6m</td>
      <td>32.0x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Run diagram audit on CIP Z and AA (44 findings: 42 FAIL, 2 WARN), fix all by rewriting 11 figures (7 Z + 4 AA) plus per-figure exceptions for legitimate method-loop back-edges, then add Reference Numeral…</td>
      <td>12.0h</td>
      <td>23m</td>
      <td>2m</td>
      <td>31.3x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Documentation</td>
      <td>6.0h</td>
      <td>12m</td>
      <td>5m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Marketing rollout for May 2026 features: updated master feature list (4 enhanced + 2 new entries across 3 sections), 3 shared overlay feature pages (pass-prediction adds Readiness Forecast step,…</td>
      <td>6.0h</td>
      <td>12m</td>
      <td>1m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Run full [ip] audit scoped to CIP Z and AA, then remediate all 9 findings (3 CRITICAL: gen scripts Filing/ refs; 4 MEDIUM: AA abstract over 150 words, AA learner/student outside BACKGROUND, Reading_Order…</td>
      <td>6.0h</td>
      <td>12m</td>
      <td>2m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Testing</td>
      <td>24.0h</td>
      <td>50m</td>
      <td>8m</td>
      <td>28.8x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Code Sandbox: cracked 1,021/1,021 cloud-cert (pmle-lab-18 Vertex AI defensive guard fix), wrote 1,657-line code-sandbox-design.md, marked 150 CompTIA labs non-shipping (manifest 2,135 -&gt; 1,985), shipped Phase…</td>
      <td>120.0h</td>
      <td>260m</td>
      <td>12m</td>
      <td>27.7x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Deployment</td>
      <td>16.0h</td>
      <td>35m</td>
      <td>6m</td>
      <td>27.4x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Deployment</td>
      <td>4.0h</td>
      <td>9m</td>
      <td>1m</td>
      <td>26.7x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>[ip] + diagram audits on CIP Z and AA (3 parallel agents)</td>
      <td>3.0h</td>
      <td>7m</td>
      <td>2m</td>
      <td>25.7x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Engine internal-only refactor across 3 repos: remove dual-auth (JWT + admin) → X-Service-Key only, delete jwt_auth + competitive REST/WS + /labs + /assets mounts in engine; drop competitive proxy in gateway;…</td>
      <td>12.0h</td>
      <td>30m</td>
      <td>4m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Reminder finalization end-to-end: web_push template + autopilot_due schedule rewire (notif), course_slug plumbing engine→client, VAPID keypair to SSM + buildspec env injection, EventBridge API Destination + 7…</td>
      <td>14.0h</td>
      <td>35m</td>
      <td>4m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Coding</td>
      <td>3.0h</td>
      <td>8m</td>
      <td>1m</td>
      <td>22.5x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>Week 2 of study-session reminder push: W3C Web Push end-to-end backend + gateway. pywebpush + cryptography deps + VAPID config; alembic migration 007 + PushSubscription SQLAlchemy model; WebPushProvider with…</td>
      <td>28.0h</td>
      <td>75m</td>
      <td>2m</td>
      <td>22.4x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Deep audit of onboarding-service: HTTP API surface, adaptive calibration mechanics, cold-start prior generation, persistence model, engine integration, and gap analysis</td>
      <td>8.0h</td>
      <td>22m</td>
      <td>8m</td>
      <td>21.8x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>Adaptive Probing Protocol exam-readiness journey simulator + confidence-blended candidate ranking. Built scripts/run_exam_journey_sim.py: synthetic learners with cognitive-transfer learning model study via…</td>
      <td>24.0h</td>
      <td>70m</td>
      <td>5m</td>
      <td>20.6x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>Week 1 of study-session reminder push: 5-week implementation plan + executed Week 1. Twilio asyncio.to_thread fix; engine GET /api/v1/autopilot/due endpoint with timezone-aware preferred_times window math;…</td>
      <td>32.0h</td>
      <td>95m</td>
      <td>8m</td>
      <td>20.2x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>Audit and review</td>
      <td>6.0h</td>
      <td>18m</td>
      <td>8m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>32</td>
      <td>Week 3 of study-session-reminder push: avian-app-web Web Push opt-in (VAPID client, push SW, usePushSubscription hook, settings toggle, tests) + AVIAN CLAUDE.md worker-as-EventBridge-Lambda rule</td>
      <td>8.0h</td>
      <td>25m</td>
      <td>3m</td>
      <td>19.2x</td>
    </tr>
    <tr>
      <td>33</td>
      <td>Deep onboarding UX audit — avian-app-web (Onboarding.tsx, InitializationFlow.tsx, Calibrate.tsx, profile tabs, api gateway) + onboarding-service backend route table. Documented all gaps, stubs, and API…</td>
      <td>7.0h</td>
      <td>25m</td>
      <td>5m</td>
      <td>16.8x</td>
    </tr>
    <tr>
      <td>34</td>
      <td>Deployment</td>
      <td>16.0h</td>
      <td>60m</td>
      <td>5m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>35</td>
      <td>Deployment</td>
      <td>6.5h</td>
      <td>27m</td>
      <td>3m</td>
      <td>14.4x</td>
    </tr>
    <tr>
      <td>36</td>
      <td>Deploy embedded subscribe flow to prod: init/push @avian/subscribe-react github repo, publish lib to CodeArtifact 0.1.0, pin web app to ^0.1.0, push purchase-service (intent + coupons endpoints), push…</td>
      <td>6.0h</td>
      <td>25m</td>
      <td>5m</td>
      <td>14.4x</td>
    </tr>
    <tr>
      <td>37</td>
      <td>Cloud-cert lab Batch 5 waves 2-5 (final push to clear corpus): AZ-305 mop-up (24 labs), AZ-500 cluster (25 labs), DEA-C01/DOP-C02/MLA-C01 wave (11 labs), PCDE cluster (8), PCA (3), PCSE (3), singles (6). 957…</td>
      <td>80.0h</td>
      <td>340m</td>
      <td>4m</td>
      <td>14.1x</td>
    </tr>
    <tr>
      <td>38</td>
      <td>Sweep canonical numbers across portfolio docs (Z+AA propagation, 13 files)</td>
      <td>2.0h</td>
      <td>9m</td>
      <td>2m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>39</td>
      <td>New avian-engine Terraform stack + volatile-data preservation runbook: POST /admin/domains/push-to-s3 endpoint (model + handler + IAM perms), 4-curl preservation runbook in CLAUDE.md, 1027 LOC of TF…</td>
      <td>20.0h</td>
      <td>90m</td>
      <td>6m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>40</td>
      <td>Fix prod blockers on embedded subscribe: add intent + recompute + coupons proxy routes to avian-api gateway (10 new tests, 91 passing), switch FlipCard from aria-hidden to inert (Chrome console error fix),…</td>
      <td>4.0h</td>
      <td>22m</td>
      <td>3m</td>
      <td>10.9x</td>
    </tr>
    <tr>
      <td>41</td>
      <td>Engine internal-ALB cutover via AWS CLI (TF state too drifted to apply): create internal-only ALB + SG + listener + TG, register engine, swap engine.accelastudy.ai R53 alias, drop public-ALB listener+TG, set…</td>
      <td>6.0h</td>
      <td>35m</td>
      <td>5m</td>
      <td>10.3x</td>
    </tr>
    <tr>
      <td>42</td>
      <td>Deployment</td>
      <td>80.0h</td>
      <td>540m</td>
      <td>60m</td>
      <td>8.9x</td>
    </tr>
    <tr>
      <td>43</td>
      <td>AccelaStudy hero refresh: 70 editorial-photography prompts, 140 Flux 1.1 Pro hero images (10 per site x 7 sites x light+dark), random-cycle JS, side-by-side hero layout in 7 index.jinjas, theme-hero CSS for…</td>
      <td>9.0h</td>
      <td>70m</td>
      <td>5m</td>
      <td>7.7x</td>
    </tr>
    <tr>
      <td>44</td>
      <td>AccelaStudy A-glyph and purple-megaphone favicon replaced with AVIAN node-mesh bird across fleet (favicon multi-res, header/footer mark, about page full-bleed hero with light/dark variants, 7 AccelaStudy…</td>
      <td>10.0h</td>
      <td>330m</td>
      <td>12m</td>
      <td>1.8x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>44</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>1,767.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>3,019</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>279</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>10,363,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>35.1x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>380.1x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>44.2</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 171.4x and the lowest at 1.8x, a spread of 94.3 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 600.0 of the 1,767.5 human-equivalent hours, or 34 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 279 minutes against 3,019 minutes of execution, a ratio of about 1 to 11. Supervisory leverage of 380.1x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 2, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-02-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-02-leverage-record.html</guid>
      <pubDate>Sat, 02 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">13 tasks. May 2, 2026 closed at 17.0x weighted leverage across 395.0 human-equivalent hours in 1,393 minutes of wall-clock time. Supervisory leverage came in at 353.7x.</p>
<p class="mb-4 font-light font-serif">That is 9.9 weeks of human-equivalent throughput in 23.2 hours. The ceiling was 60.0x; the floor was 3.4x. 12 of the 13 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Embedded subscribe flow: PaymentIntent backend + recompute + coupon validate endpoints, new shared @avian/subscribe-react lib (FlipCard primitive, SubscribeFront/Back, EmbeddedSubscribeFlow,…</td>
      <td>24.0h</td>
      <td>24m</td>
      <td>6m</td>
      <td>60.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Cloud-cert lab Batch 5 wave 1: AZ-204 cluster shared root cause diagnosis (stale sidebarTarget bug) lifted all 19 AZ-204 labs in one shot, plus partial progress on AZ-305/AZ-500/DEA-C01. 26 newly strict-pass;…</td>
      <td>52.0h</td>
      <td>120m</td>
      <td>3m</td>
      <td>26.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Operator-managed banners across avian-admin/avian-api/avian-app-web with markdown CRUD, semantic color variants, audience targeting from purchase state, push-to-all-clients SSE broadcast, dismissal tracking,…</td>
      <td>28.0h</td>
      <td>65m</td>
      <td>6m</td>
      <td>25.8x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Diagnose hard hang from disk-full + memory exhaustion (Docker VM + TM + Spotlight + Maestral); archive 123 GB training chunks to new S3 bucket with byte-exact verification</td>
      <td>3.0h</td>
      <td>7m</td>
      <td>4m</td>
      <td>25.7x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Cloud-cert lab Batch 3: lifted 100 closest-to-passing labs (gap 5-30) to strict-pass via sub-agent across 11 waves. Service-cluster fixes on dp-420, dp-700, dp-900, az-400, ai-900, plus per-lab fixes.…</td>
      <td>100.0h</td>
      <td>293m</td>
      <td>5m</td>
      <td>20.5x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Add purge + bulk-purge for revoked comps across purchase-service, admin-service WS, and avian-admin UI with tests</td>
      <td>4.0h</td>
      <td>12m</td>
      <td>3m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Cloud-cert lab Batch 4: harder set of 100 partials (gap 20-40, AWS 28 / Azure 24 / GCP 48) lifted to strict-pass via sub-agent across 7 waves. Heavy dashboard work to add testIds for missing modal flows. 810…</td>
      <td>150.0h</td>
      <td>577m</td>
      <td>5m</td>
      <td>15.6x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Testing</td>
      <td>5.0h</td>
      <td>30m</td>
      <td>4m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Documentation</td>
      <td>12.0h</td>
      <td>90m</td>
      <td>12m</td>
      <td>8.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Infrastructure</td>
      <td>4.0h</td>
      <td>35m</td>
      <td>4m</td>
      <td>6.9x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Add Rime TTS as alternate provider; add voice/speed picker in Settings with Play audition; wire labs to user-selected voice; deploy backend + frontend + infra to production</td>
      <td>8.0h</td>
      <td>75m</td>
      <td>6m</td>
      <td>6.4x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Engine dual-auth: accept JWTs from auth-service JWKS alongside static AVIAN_API_KEY (jwt_auth module port of gateway core/jwt.py + middleware refactor in rest_gateway.py + tests)</td>
      <td>3.0h</td>
      <td>30m</td>
      <td>4m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Diagnose remaining engine 401s as a frontend bug (engine.ts had its own raw fetch() bypassing Authorization header), audit all api/*.ts callsites, fix engine.ts request()+lesson-audio+evidence-audio paths to…</td>
      <td>2.0h</td>
      <td>35m</td>
      <td>5m</td>
      <td>3.4x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>13</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>395.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,393</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>67</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>2,684,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>17.0x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>353.7x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>9.9</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 60.0x and the lowest at 3.4x, a spread of 17.5 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 24.0 of the 395.0 human-equivalent hours, or 6 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 67 minutes against 1,393 minutes of execution, a ratio of about 1 to 21. Supervisory leverage of 353.7x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: May 1, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-05-01-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-05-01-leverage-record.html</guid>
      <pubDate>Fri, 01 May 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">26 tasks. May 1, 2026 closed at 26.8x weighted leverage across 521.5 human-equivalent hours in 1,167 minutes of wall-clock time. Supervisory leverage came in at 284.5x.</p>
<p class="mb-4 font-light font-serif">That is 13.0 weeks of human-equivalent throughput in 19.4 hours. The ceiling was 69.5x; the floor was 5.1x. 22 of the 26 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Testing</td>
      <td>44.0h</td>
      <td>38m</td>
      <td>4m</td>
      <td>69.5x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>avian-automations: scaffold 2 containerized Lambda repos (data-export + account-deletion) + new terraform stack (ECR + Lambda + EventBridge bus + rules + S3 + IAM + CodePipeline x2)</td>
      <td>24.0h</td>
      <td>22m</td>
      <td>3m</td>
      <td>65.5x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Testing</td>
      <td>80.0h</td>
      <td>75m</td>
      <td>3m</td>
      <td>64.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>AccelaStudy.ai site-wide updates: hide social proof + [unreleased product], merge comparison table from /certs/ to home, rewrite pricing copy, link accessibility bubbles, restructure courses page, filter labs…</td>
      <td>18.0h</td>
      <td>22m</td>
      <td>8m</td>
      <td>49.1x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Build avian-api Phase 0 + Phase 1 + first proxy slice + frontend EventBus + purchase-service event emission. New core/avian-api repo with FastAPI app, JWT/JWKS, Valkey pub/sub, SSE endpoint with Last-Event-ID…</td>
      <td>120.0h</td>
      <td>165m</td>
      <td>15m</td>
      <td>43.6x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Outstanding cleanup post-cutover: trim api.<em> from engine listener rule (engine.</em> only), backport ECS update IAM policy from CLI into avian-terraform with terraform import, move subscription_groups.json data…</td>
      <td>12.0h</td>
      <td>22m</td>
      <td>1m</td>
      <td>32.7x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>avian-automations end-to-end deploy: 2 GitHub repos created and pushed, terraform stack applied (ECR + Lambda x2 + EventBridge bus/rules + S3 + IAM + CodePipeline x2), warm-invoke asyncpg pool bug found and…</td>
      <td>16.0h</td>
      <td>30m</td>
      <td>3m</td>
      <td>32.0x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Built competitive multiplayer feature on engine: full state machine (lobby/countdown/question/reveal/complete), in-memory CompetitiveStore, FFA + teams modes, scoring with point decay, REST endpoints…</td>
      <td>24.0h</td>
      <td>50m</td>
      <td>1m</td>
      <td>28.8x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>auth-service emails: welcome on first verify + welcome on first social login + account-closed at close + new account-deleted template + APP_URL setting + 4 tests</td>
      <td>4.0h</td>
      <td>9m</td>
      <td>1m</td>
      <td>26.7x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>AccelaStudy.ai polish round 2: courses page 3-wide with bulleted clean_titles for live+coming, drop Test Prep group, hide pricing testimonials, rename Other Products to AccelaStudy AI - X (Certs first),…</td>
      <td>6.0h</td>
      <td>14m</td>
      <td>4m</td>
      <td>25.7x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>AVIAN Code Sandbox Phases 1-2-3: VirtualFileSystem, IDB cache, Zustand store, EditorHost (Monaco multi-tab), TerminalHost (xterm.js + 5 command resolvers: shell/git/npm/pip/validators-stub), ActivityBar,…</td>
      <td>35.0h</td>
      <td>90m</td>
      <td>15m</td>
      <td>23.3x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Phase 4 AVIAN Code Sandbox: S3 persistence — Sign-URL Lambda (JWT auth, quota, ETag conflict), expiry-warning Lambda, S3 bucket + Terraform, S3SyncEngine (debounce, hydrate, retry, conflict), IDB schema v2…</td>
      <td>24.0h</td>
      <td>62m</td>
      <td>7m</td>
      <td>23.2x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>avian-api LIVE CUTOVER: terraform apply (25 AWS resources, ECS Fargate cluster + task + service + ALB tg + Route 53 + SSM + CodePipeline + IAM); 3 pipeline iterations to debug ruff config + coverage gate +…</td>
      <td>24.0h</td>
      <td>65m</td>
      <td>8m</td>
      <td>22.2x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>avian-api side-effects dispatcher: bounded retry with exponential backoff + Redis dead-letter queue + ops inspection route + 6 unit tests; unblocks Item 4 Phase B</td>
      <td>7.0h</td>
      <td>25m</td>
      <td>1m</td>
      <td>16.8x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Batch 2 lab strict-pass: closed out 76-lab Top-100 push (resume after crash). 62 free wins from prior dashboard fixes; 14 hand-tuned (status case drift, prop-key filter mismatches, missing select testIds,…</td>
      <td>28.0h</td>
      <td>110m</td>
      <td>4m</td>
      <td>15.3x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Strategy phase 7 prod deploy: monitor CodePipeline build to completion, smoke-test admin endpoints over public engine domain, SSM port-forward to RDS, verify alembic head=005 includes…</td>
      <td>3.0h</td>
      <td>13m</td>
      <td>1m</td>
      <td>13.8x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Item 4 Phase B (retire engine direct PATCH; gateway delta=0-&gt;1; delete dead auth_client module + tests; trim auth-service _ALLOWED_CALLERS) + competitive multiplayer wired through gateway (REST proxy via…</td>
      <td>12.0h</td>
      <td>60m</td>
      <td>2m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Fix Anthropic API temperature-deprecation 400 in Origin LLM client (catch + drop param + latch for run); normalize 30 ISACA spec schemas (cost_usd dict→float, passing_score string→int with type,…</td>
      <td>8.0h</td>
      <td>40m</td>
      <td>5m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Infrastructure</td>
      <td>1.5h</td>
      <td>8m</td>
      <td>1m</td>
      <td>11.2x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>wire CohortAnalytics By Plan + By Activity Level via cross-service in-process aggregation in admin-service (joins accelastudy-auth users + purchase-service subs); shared retention math</td>
      <td>4.0h</td>
      <td>25m</td>
      <td>2m</td>
      <td>9.6x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Three-repo cleanup sweep: avian-engine (delete tts.py + 5 test classes + legacy evidence route, tighten engine_context exception logging, README revert + count refresh, root-files allowlist), avian-app-web…</td>
      <td>10.0h</td>
      <td>70m</td>
      <td>3m</td>
      <td>8.6x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Fix avian-api gateway dropping user JWT on auth-service proxy calls (forward_auth plumbing through UpstreamClient + 19 client methods + 2 route files + regression test)</td>
      <td>3.5h</td>
      <td>25m</td>
      <td>6m</td>
      <td>8.4x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Wire prod REDIS_URL across admin/auth/purchase/notification services: 4 SSM params + 4 buildspec patches + 4 pipeline redeploys; root-cause for silently-dead events.* fan-out (publishers + admin-service…</td>
      <td>4.0h</td>
      <td>30m</td>
      <td>1m</td>
      <td>8.0x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Rewrite avian-app-web CLAUDE.md and README.md to reflect current React Router 7 + gateway architecture</td>
      <td>1.5h</td>
      <td>12m</td>
      <td>5m</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>fix Users tab (renkara-auth alembic up to head), Portfolio tab (admin-service ENGINE_ADMIN_KEY), Logins-by-Method dedupe; wire CohortAnalytics signup-month with new endpoint; revealed engine routes to…</td>
      <td>5.0h</td>
      <td>50m</td>
      <td>4m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Deployment</td>
      <td>3.0h</td>
      <td>35m</td>
      <td>2m</td>
      <td>5.1x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>26</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>521.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,167</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>110</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>5,978,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>26.8x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>284.5x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>13.0</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 69.5x and the lowest at 5.1x, a spread of 13.5 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 44.0 of the 521.5 human-equivalent hours, or 8 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 110 minutes against 1,167 minutes of execution, a ratio of about 1 to 11. Supervisory leverage of 284.5x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: April 30, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-04-30-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-04-30-leverage-record.html</guid>
      <pubDate>Thu, 30 Apr 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">21 tasks. April 30, 2026 closed at 12.1x weighted leverage across 222.0 human-equivalent hours in 1,099 minutes of wall-clock time. Supervisory leverage came in at 208.1x.</p>
<p class="mb-4 font-light font-serif">That is 5.5 weeks of human-equivalent throughput in 18.3 hours. The ceiling was 26.7x; the floor was 4.3x. 17 of the 21 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>auth-service: build full email verification flow (table + migration + service + 2 endpoints + register/login wiring + frontend resend UX + email template + 15 tests)</td>
      <td>8.0h</td>
      <td>18m</td>
      <td>2m</td>
      <td>26.7x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Coding</td>
      <td>40.0h</td>
      <td>90m</td>
      <td>6m</td>
      <td>26.7x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Retire cloud CI/CD for all marketing websites: delete 9 CodePipelines + 9 CodeBuild projects + 6 CloudWatch log groups; terraform state-rm 4 modules to preserve live S3/CloudFront/Route53; delete 9 website…</td>
      <td>16.0h</td>
      <td>40m</td>
      <td>8m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>auth-service: magic-link auto sign-in on email verification (issue tokens at verify time, frontend hash redirect into app); post-register success box copy + centering</td>
      <td>2.5h</td>
      <td>8m</td>
      <td>1m</td>
      <td>18.8x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Drive all 56 target cloud labs to 100% strict-pass; fix labs+dashboards across SAP, Azure, GCP, AWS exam tracks</td>
      <td>80.0h</td>
      <td>270m</td>
      <td>12m</td>
      <td>17.8x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Audit and update GitHub descriptions for 74 monorepo repos: gh CLI fetch + identify 36 missing/poor descriptions, write canonical one-liners drawn from CLAUDE.md inventory, batch-update via gh repo edit,…</td>
      <td>3.0h</td>
      <td>12m</td>
      <td>1m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>avian-app-web Account tab: surface pending-deletion state from /users/me (deletion_scheduled_for + deletion_requested_at), show days-left pill + cancel-deletion CTA wired to existing useCancelAccountClosure…</td>
      <td>3.0h</td>
      <td>12m</td>
      <td>1m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Fix Subscribe→Enroll race: purchase-service notifies auth-service to drop in-process entitlements LRU after webhook commit; new POST /api/v1/internal/entitlements/{user_id}/invalidate endpoint on…</td>
      <td>5.0h</td>
      <td>22m</td>
      <td>2m</td>
      <td>13.6x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Recall Sprint overhaul: bump activity-ui to pick up headstart fix, streak label + per-item DOM reset + fade rescale + mobile 2x2 fit + dark-mode contrast in lib; activity intro modal + hide hero on mobile +…</td>
      <td>4.0h</td>
      <td>18m</td>
      <td>4m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>avian-app-web enroll-flow polish: enrollment store marks optimistic inserts pending so the UI no longer flickers through enrolled-state on NEEDS_UPGRADE; SubscribeModal defaults to monthly and surfaces the…</td>
      <td>4.0h</td>
      <td>18m</td>
      <td>1m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Backfill leverage CSV from cloud (30 records across 2 days) and write 2 sanitized leverage record blog posts (Apr 28: 11 tasks 33.4x, Apr 29: 19 tasks 7.6x) with task tables, aggregates, and analysis…</td>
      <td>6.0h</td>
      <td>35m</td>
      <td>2m</td>
      <td>10.3x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Convoy card mobile fix: pin InfoButton to top-right (no wrap) + deep-link Learn-how-Convoy-works to the dedicated Convoy guide doc</td>
      <td>0.5h</td>
      <td>3m</td>
      <td>1m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>recent payments + cashflow forecast (committed + projected w/ growth/churn knobs); backfill payment row; explain MRR mechanics + Stripe live/test plan</td>
      <td>5.0h</td>
      <td>30m</td>
      <td>5m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Deployment</td>
      <td>2.0h</td>
      <td>13m</td>
      <td>2m</td>
      <td>9.2x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Deployment</td>
      <td>1.5h</td>
      <td>12m</td>
      <td>1m</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Debugging</td>
      <td>6.0h</td>
      <td>50m</td>
      <td>3m</td>
      <td>7.2x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Audit velvet-rope gating across 16 staging stages, fix 2 sites missing Message field; update accelastudy.ai early-adopter promo expiration from May 1 to May 31 (countdown JS in 2 places + FAQ wording in…</td>
      <td>1.5h</td>
      <td>13m</td>
      <td>2m</td>
      <td>6.9x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Add About AI Leverage section to charlessieg.com leverage page: intro paragraphs explaining the leverage concept, list of 7 related articles with material-symbols icon, multiple polish iterations (proper-case…</td>
      <td>3.0h</td>
      <td>30m</td>
      <td>4m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Audit and review</td>
      <td>1.0h</td>
      <td>10m</td>
      <td>1m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>lab strict-pass uplift: 174 dashboards bulk-augmented with sidebar testIds (493 added) + auto-derive script with sub-component support (354 lab files, 1209 sidebar nav steps inserted) + Azure/GCP Modal…</td>
      <td>16.0h</td>
      <td>200m</td>
      <td>2m</td>
      <td>4.8x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>cloud-fidelity all-deferred sweep: Tabs+ConsoleTable for AWS/Azure/GCP via ThemedConsoleTable shared impl + custom-div Tabs (3 vendors) + ServiceIcon component with auto-derived category glyphs + sidebar…</td>
      <td>14.0h</td>
      <td>195m</td>
      <td>3m</td>
      <td>4.3x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>21</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>222.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,099</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>64</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>4,334,500</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>12.1x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>208.1x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>5.5</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 26.7x and the lowest at 4.3x, a spread of 6.2 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 8.0 of the 222.0 human-equivalent hours, or 4 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 64 minutes against 1,099 minutes of execution, a ratio of about 1 to 17. Supervisory leverage of 208.1x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: April 29, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-04-29-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-04-29-leverage-record.html</guid>
      <pubDate>Wed, 29 Apr 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">20 tasks. April 29, 2026 closed at 7.6x weighted leverage across 119.2 human-equivalent hours in 939 minutes of wall-clock time. Supervisory leverage came in at 115.4x.</p>
<p class="mb-4 font-light font-serif">That is 3.0 weeks of human-equivalent throughput in 15.7 hours. The ceiling was 45.0x; the floor was 3.7x. 13 of the 20 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>auth-service: Apple Sign In server-to-server notification endpoint (verify signed JWT + handle email-disabled/enabled, consent-revoked, account-delete with session revocation and GDPR deletion path) + 14…</td>
      <td>6.0h</td>
      <td>8m</td>
      <td>3m</td>
      <td>45.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>auth-service: wire Apple + Google social login through Dockerfile + buildspec.yml + populate 7 SSM params (incl. multiline .p8) + verify (391 tests green)</td>
      <td>3.0h</td>
      <td>6m</td>
      <td>1m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Add flashcard [content generation] stage to avian-engine (generator, writer, loop, REST endpoint, regression tests, standalone runner)</td>
      <td>6.0h</td>
      <td>22m</td>
      <td>8m</td>
      <td>16.4x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>avian-admin: hard-delete students, login-method column, Reports tab + login-methods report (auth-service + admin-service + frontend)</td>
      <td>4.0h</td>
      <td>15m</td>
      <td>3m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Multi-piece overhaul: scenario 500 fix + cross-device user-state store + activity preferences (model+UI+filter) + Settings tabbed redesign + FAQ/guide content + flashcard [engine subsystem] scaffolding</td>
      <td>18.0h</td>
      <td>92m</td>
      <td>4m</td>
      <td>11.7x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>auth-service: Apple developer-domain-association well-known endpoint + placeholder file + test</td>
      <td>0.5h</td>
      <td>3m</td>
      <td>1m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Debugging</td>
      <td>3.0h</td>
      <td>18m</td>
      <td>4m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Diagnose + fix broken avian-app-web prod CI/MCQ (root cause: empty @avian/activity-ui publishes), add Convoy cross-course UI (card + InfoButton + modal), 2 new FAQ entries, autopilot guide section, dedicated…</td>
      <td>9.0h</td>
      <td>60m</td>
      <td>6m</td>
      <td>9.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>students plan/access bubbles + edit modal + bulk entitlements endpoint; engine postgres durability fix (asyncpg+ssl+ssm-loaded creds)</td>
      <td>5.0h</td>
      <td>35m</td>
      <td>4m</td>
      <td>8.6x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Wire failed flashcard goals into [engine subsystem] pipeline — FlashcardTribunalRepair class, prompts/schemas, write_escalated_flashcards writer, --[engine subsystem] flag in run_flashcards.py,…</td>
      <td>3.0h</td>
      <td>22m</td>
      <td>5m</td>
      <td>8.2x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>cloud-fidelity Phase 1.a: AWS Cloudscape primitives — install + Button/Alert/StatusIndicator/KeyValuePairs wrappers + runtime dispatch shim; 290 AWS labs zero score regressions vs Phase 0 baseline</td>
      <td>7.0h</td>
      <td>55m</td>
      <td>1m</td>
      <td>7.6x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>engine: spot-&gt;on-demand cutover (clone instance, TG swap, drain, terminate spot) + ConfirmModal replacing window.confirm</td>
      <td>3.0h</td>
      <td>25m</td>
      <td>2m</td>
      <td>7.2x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Fix audit_lab_instructions.py glob bug (missed 86 type-B labs); update canonical.json + 5 doc files for 2048→2134 / 935→1021 strict-pass; reconcile lab-manifest.ts with disk (added 7 missing entries:…</td>
      <td>3.0h</td>
      <td>25m</td>
      <td>4m</td>
      <td>7.2x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Triage + fix all 7 watch-sweep failures (3 dashboard runtime crashes; 3 initialResources schema bugs; 1 score=0 placeholder). Bring 2,134-lab corpus to strict-pass green</td>
      <td>6.0h</td>
      <td>50m</td>
      <td>3m</td>
      <td>7.2x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Lab post-crash recovery: cleanup + commit 1,508 in-flight changes (testId sweep + multi-checkpoint executor + sidebar nav + 90 type-B labs); fix all 9 remaining cloud-cert audit issues across AWS/GCP and add…</td>
      <td>4.0h</td>
      <td>35m</td>
      <td>5m</td>
      <td>6.9x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>cloud-fidelity Phase 3 (GCP Material 3) + Phase 4 lite (favicon/title/LICENSING) + Modal for Azure &amp; GCP via custom-div + vendor tokens (skip vendor Dialog entirely); 304 GCP + 427 Azure + 290 AWS = 1021…</td>
      <td>14.0h</td>
      <td>145m</td>
      <td>1m</td>
      <td>5.8x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Documentation</td>
      <td>0.8h</td>
      <td>8m</td>
      <td>3m</td>
      <td>5.6x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>cloud-fidelity Phase 1.b: AWS Cloudscape Modal + LabRunner z-index 50→9000 (above Cloudscape 5000) + force-unmount on visible=false; root-caused via Cloudscape display:none vs lab autoplay waitForGone…</td>
      <td>6.0h</td>
      <td>65m</td>
      <td>1m</td>
      <td>5.5x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>cloud-fidelity Phase 0: primitive shim + cloud detection + testid baseline + bulk import refactor (267 view files); full 2,134-lab Watch sweep regression-free; fixed pre-existing bedrock test mismatch;…</td>
      <td>10.0h</td>
      <td>120m</td>
      <td>2m</td>
      <td>5.0x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>cloud-fidelity Phase 2: Azure Fluent UI v9 — install + Button/Alert/StatusIndicator/KeyValuePairs wrappers + FluentProvider mount + runtime dispatch; Modal deferred (Fluent Dialog blocked autoplay DOM…</td>
      <td>8.0h</td>
      <td>130m</td>
      <td>1m</td>
      <td>3.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>20</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>119.2</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>939</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>62</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>2,687,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>7.6x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>115.4x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>3.0</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 45.0x and the lowest at 3.7x, a spread of 12.2 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 6.0 of the 119.2 human-equivalent hours, or 5 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 62 minutes against 939 minutes of execution, a ratio of about 1 to 15. Supervisory leverage of 115.4x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: April 28, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-04-28-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-04-28-leverage-record.html</guid>
      <pubDate>Tue, 28 Apr 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">11 tasks. April 28, 2026 closed at 33.4x weighted leverage across 203.5 human-equivalent hours in 366 minutes of wall-clock time. Supervisory leverage came in at 297.8x.</p>
<p class="mb-4 font-light font-serif">That is 5.1 weeks of human-equivalent throughput in 6.1 hours. The ceiling was 64.0x; the floor was 6.1x. 6 of the 11 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Audit remediation wave 2: audit_logs middleware (SOC 2 CC7.2), GDPR export+deletion workers, RDS encryption runbook, parity drift inventory, 29 doc-audit + 15 readiness MEDIUMs across ~25 repos</td>
      <td>80.0h</td>
      <td>75m</td>
      <td>2m</td>
      <td>64.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Author 86 type-B coverage labs across 10 cloud certs (AI-900, DP-420, DP-300, AZ-900, AZ-700, AZ-305, PCNE, PCSE, PDE, PGWA); strict-pass clean; 100% leaf coverage across all 43 cloud certs</td>
      <td>80.0h</td>
      <td>77m</td>
      <td>8m</td>
      <td>62.3x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Design and frontend</td>
      <td>6.0h</td>
      <td>11m</td>
      <td>3m</td>
      <td>32.7x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>auth-service: fix pre-existing legal-dir vite build via predev/prebuild sync-legal.cjs script + gitignore; verify avian-app-web inherits Apple/Google sign in via existing OIDC redirect (no client-side code…</td>
      <td>1.5h</td>
      <td>4m</td>
      <td>2m</td>
      <td>22.5x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Deployment</td>
      <td>8.0h</td>
      <td>30m</td>
      <td>3m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>avian-app-web: fix Flashcards fake-MCQ fallback (static @avian/activity-ui import + remove FallbackMCQ), add sidebar tooltips, suppress section-code flash on lesson load, redeploy prod engine container with…</td>
      <td>4.0h</td>
      <td>17m</td>
      <td>4m</td>
      <td>14.1x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Modal centering fix + CLF-C02 lab 9 missing policy + new instruction-vs-UI audit script (2 passes, 6 catalog scrapers) + 16 lab uiSteps/checkpoint fixes + avian-audits content-audit wiring + labs CLAUDE.md…</td>
      <td>8.0h</td>
      <td>35m</td>
      <td>4m</td>
      <td>13.7x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Infrastructure</td>
      <td>10.0h</td>
      <td>60m</td>
      <td>7m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Debugging</td>
      <td>0.5h</td>
      <td>4m</td>
      <td>1m</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Coding</td>
      <td>0.5h</td>
      <td>4m</td>
      <td>1m</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Testing</td>
      <td>5.0h</td>
      <td>49m</td>
      <td>6m</td>
      <td>6.1x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>11</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>203.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>366</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>41</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>2,445,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>33.4x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>297.8x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>5.1</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 64.0x and the lowest at 6.1x, a spread of 10.5 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 80.0 of the 203.5 human-equivalent hours, or 39 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 41 minutes against 366 minutes of execution, a ratio of about 1 to 9. Supervisory leverage of 297.8x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: April 27, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-04-27-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-04-27-leverage-record.html</guid>
      <pubDate>Mon, 27 Apr 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">31 tasks. April 27, 2026 closed at 28.0x weighted leverage across 619.5 human-equivalent hours in 1,329 minutes of wall-clock time. Supervisory leverage came in at 290.4x.</p>
<p class="mb-4 font-light font-serif">That is 15.5 weeks of human-equivalent throughput in 22.2 hours. The ceiling was 80.0x; the floor was 3.4x. 12 of the 31 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>GCP lab strict pass — ALL 13 certs (CDL, ACE, PCA, PCD, PCDE, PCDOpsE, PCSE, PCNE, PDE, PMLE, PCDB, PGWA, GAI-L) = 275 labs migrated to uiSteps DOM-driven format. 13 parallel subagents over 25 minutes…</td>
      <td>80.0h</td>
      <td>60m</td>
      <td>1m</td>
      <td>80.0x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Azure lab strict pass — ALL 19 certs (AZ-900/104/120/140/204/305/400/500/700, DP-100/203/300/420/700/900, SC-300/900, AI-102/900) = 370 labs migrated to uiSteps DOM-driven format. Three waves of parallel…</td>
      <td>100.0h</td>
      <td>80m</td>
      <td>1m</td>
      <td>75.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Documentation</td>
      <td>16.0h</td>
      <td>18m</td>
      <td>8m</td>
      <td>53.3x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>AWS lab strict-pass: 7 certs in one run (DOP-C02, SAP-C02, DEA-C01, ANS-C01, MLS-C01, MLA-C01, AIP-C01) = 155 labs migrated to uiSteps DOM-driven format. 21 parallel subagent runs (3 per cert) over ~100 min…</td>
      <td>80.0h</td>
      <td>100m</td>
      <td>1m</td>
      <td>48.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Infrastructure</td>
      <td>24.0h</td>
      <td>30m</td>
      <td>5m</td>
      <td>48.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Content audit: validated 919 specs, 218 packages, 1.03M questions, 2048 labs; refreshed canonical and docs</td>
      <td>6.0h</td>
      <td>8m</td>
      <td>1m</td>
      <td>43.6x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>SCS-C02 strict-pass migration: 25/25 AWS Security Specialty labs migrated to uiSteps DOM-driven format. Fixed 21 bogus resourceTypes across labs 05-25 (Inspector2, Lambda Alias, NetworkFirewall,…</td>
      <td>16.0h</td>
      <td>25m</td>
      <td>1m</td>
      <td>38.4x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Run full readiness, compliance, security, documentation audits across 72 AVIAN repos and regenerate ecosystem inventory</td>
      <td>24.0h</td>
      <td>38m</td>
      <td>3m</td>
      <td>37.9x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>auth-service: hook up Apple Sign In backend (config vars, ES256 client_secret JWT, auth-code exchange, schema, API wiring, tests)</td>
      <td>4.0h</td>
      <td>7m</td>
      <td>3m</td>
      <td>34.3x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Content audit follow-up: 555 specs backfilled, 62 manifests [content generation], 9 lessons composed, 67 cert exams researched + applied; 0 findings remaining (except hero images, deferred)</td>
      <td>18.0h</td>
      <td>32m</td>
      <td>1m</td>
      <td>33.8x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Debugging</td>
      <td>36.0h</td>
      <td>75m</td>
      <td>15m</td>
      <td>28.8x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Coding</td>
      <td>16.0h</td>
      <td>38m</td>
      <td>8m</td>
      <td>25.3x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Infrastructure</td>
      <td>60.0h</td>
      <td>145m</td>
      <td>1m</td>
      <td>24.8x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>avian-app-web: wire RequireAuth into router to close unauthenticated access to accelastudy.ai</td>
      <td>2.0h</td>
      <td>6m</td>
      <td>2m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Coding</td>
      <td>4.0h</td>
      <td>12m</td>
      <td>4m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>Debugging</td>
      <td>3.0h</td>
      <td>9m</td>
      <td>2m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>kill static credit awards + training-mass-gate cos-sim prior + per-goal confidence + best-section endpoint + wrong-answer review modal + recalibration banner + autopilot single-radio fix + fingerprint card…</td>
      <td>24.0h</td>
      <td>75m</td>
      <td>12m</td>
      <td>19.2x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>avian-admin Persistence page fixes: configurable S3 snapshot bucket with graceful unconfigured state, structured disabled response for portfolio health, durable Postgres audit log backend (write-behind writer…</td>
      <td>12.0h</td>
      <td>40m</td>
      <td>4m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>avian-app-web/avian-ui-react: real TTS error reporting (replace [latency] fake-loading timer), rename Mark Lesson as Read, collapsible course-outline sidebar replacing horizontal section scroller</td>
      <td>6.0h</td>
      <td>22m</td>
      <td>4m</td>
      <td>16.4x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Testing</td>
      <td>24.0h</td>
      <td>95m</td>
      <td>5m</td>
      <td>15.2x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Infrastructure</td>
      <td>16.0h</td>
      <td>70m</td>
      <td>10m</td>
      <td>13.7x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>Testing</td>
      <td>4.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Deployment</td>
      <td>7.0h</td>
      <td>35m</td>
      <td>6m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Coding</td>
      <td>6.0h</td>
      <td>30m</td>
      <td>5m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Deployment</td>
      <td>9.0h</td>
      <td>50m</td>
      <td>8m</td>
      <td>10.8x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Design and frontend</td>
      <td>2.5h</td>
      <td>15m</td>
      <td>1m</td>
      <td>10.0x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>Crash recovery: committed SOA-C02 strict re-pass (15/25), resynced 22 cloud packages to S3 backup buckets (east+west), migrated remaining 10 SOA-C02 labs to strict uiSteps format (fixed 4 bogus resourceTypes:…</td>
      <td>8.0h</td>
      <td>50m</td>
      <td>2m</td>
      <td>9.6x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>Audit and review</td>
      <td>5.0h</td>
      <td>32m</td>
      <td>1m</td>
      <td>9.4x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>fix two stale strategy-endpoint tests asserting old broken behavior — align with current 200-always contract</td>
      <td>0.5h</td>
      <td>4m</td>
      <td>1m</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>drive avian-engine fast suite to 100% green: catalog count bump + scenario seed-path test rewrite (was 25 vs 27, scenario_engine=None no longer triggers 503)</td>
      <td>0.5h</td>
      <td>5m</td>
      <td>1m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>avian-admin Persistence page durable audit log: end-to-end deploy — local schema test, prod RDS migration via SSM tunnel, recovery from cross-team migration collision (renamed table to governance_audit_log to…</td>
      <td>6.0h</td>
      <td>105m</td>
      <td>6m</td>
      <td>3.4x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>31</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>619.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,329</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>128</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>24,120,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>28.0x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>290.4x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>15.5</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 80.0x and the lowest at 3.4x, a spread of 23.3 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 80.0 of the 619.5 human-equivalent hours, or 13 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 128 minutes against 1,329 minutes of execution, a ratio of about 1 to 10. Supervisory leverage of 290.4x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: April 26, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-04-26-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-04-26-leverage-record.html</guid>
      <pubDate>Sun, 26 Apr 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">74 tasks. April 26, 2026 closed at 26.9x weighted leverage across 899.0 human-equivalent hours in 2,006 minutes of wall-clock time. Supervisory leverage came in at 245.7x.</p>
<p class="mb-4 font-light font-serif">That is 22.5 weeks of human-equivalent throughput in 33.4 hours. The ceiling was 248.3x; the floor was 1.7x. 49 of the 74 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Infrastructure</td>
      <td>240.0h</td>
      <td>58m</td>
      <td>4m</td>
      <td>248.3x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Coding</td>
      <td>24.0h</td>
      <td>22m</td>
      <td>5m</td>
      <td>65.5x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>CLF-C02 cert: 15/15 labs strict-pass via 7 parallel subagents migrating uiSteps + paving Billing/Budgets/TrustedAdvisor/WellArchitected/SNS/Lambda detail tabs/S3 blockPublicAccess; full sweep 60s</td>
      <td>30.0h</td>
      <td>35m</td>
      <td>8m</td>
      <td>51.4x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Design and frontend</td>
      <td>20.0h</td>
      <td>24m</td>
      <td>1m</td>
      <td>50.0x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Documentation</td>
      <td>30.0h</td>
      <td>36m</td>
      <td>3m</td>
      <td>50.0x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Documentation</td>
      <td>24.0h</td>
      <td>32m</td>
      <td>5m</td>
      <td>45.0x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Design and frontend</td>
      <td>60.0h</td>
      <td>80m</td>
      <td>10m</td>
      <td>45.0x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Infrastructure</td>
      <td>22.0h</td>
      <td>30m</td>
      <td>1m</td>
      <td>44.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Design and frontend</td>
      <td>12.0h</td>
      <td>18m</td>
      <td>1m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Documentation</td>
      <td>14.0h</td>
      <td>22m</td>
      <td>3m</td>
      <td>38.2x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Testing</td>
      <td>24.0h</td>
      <td>40m</td>
      <td>8m</td>
      <td>36.0x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>console-sim: migrate saa-c03-lab-02 to uiSteps end-to-end (NACL SDK + dashboard CRUD + uiSteps + vitest + playwright watch 50/50)</td>
      <td>6.0h</td>
      <td>12m</td>
      <td>2m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Generate Replicate Flux 1.1 Pro hero image for /leverage/, restructure leverage hero into two-column with floating multiplier stat overlay matching the redesign mockup.</td>
      <td>1.5h</td>
      <td>3m</td>
      <td>1m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Coding</td>
      <td>14.0h</td>
      <td>28m</td>
      <td>5m</td>
      <td>30.0x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>Final cloud-cert push: case-study generator stage from scenario seeds (320 cases across 40 cloud-cert domains, 8 per cert, deterministic transform from rich seed schema with no LLM cost); engine…</td>
      <td>36.0h</td>
      <td>75m</td>
      <td>5m</td>
      <td>28.8x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>console-sim: ELB testid sweep (foreground after subagent stall) + AutoScaling/RDS sweeps + saa-c03-lab-06 migration (multi-service: launch template, target group, ALB, ASG, scaling policy)</td>
      <td>12.0h</td>
      <td>28m</td>
      <td>2m</td>
      <td>25.7x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Cloud-cert first release blast: content-coverage tracker (generator + manifest + 39/40 ready report); AWS/Azure/GCP service catalog rewrites (47/41/37 -&gt; 80/77/64 services with comprehensive cert tagging); 12…</td>
      <td>40.0h</td>
      <td>95m</td>
      <td>6m</td>
      <td>25.3x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>console-sim: fix 4 failing SDK tests + ARN-collision bug for GCP/Azure resource types</td>
      <td>2.5h</td>
      <td>6m</td>
      <td>2m</td>
      <td>25.0x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>saa-c03-lab-21 (EFS mount) migrate to uiSteps + EFS dashboard testid sweep (sidebar, create FS, per-row mount target/lifecycle buttons, modals); 4-step multi-service lab (vpc→efs→ec2→efs); Watch 100% strict…</td>
      <td>2.5h</td>
      <td>6m</td>
      <td>0m</td>
      <td>25.0x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>saa-c03-lab-10 (Serverless API: DynamoDB+Lambda+APIGW) migrate to uiSteps + Lambda dashboard sweep + APIGW dashboard sweep (sidebar, REST API row, 3 detail tabs, per-resource add-child/add-method buttons, 4…</td>
      <td>5.0h</td>
      <td>12m</td>
      <td>0m</td>
      <td>25.0x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>console-sim: parallel testid sweeps (EC2 43, IAM 105, S3 10) + Tabs testIdPrefix + saa-c03-lab-03 migration + IAM trust-policy validator fix + EC2 launch-modal IAM profile selector</td>
      <td>14.0h</td>
      <td>35m</td>
      <td>4m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>console-sim: ElastiCache + CloudFront + Route53 testid sweeps + saa-c03-lab-12 (Redis) + saa-c03-lab-13 (CloudFront S3) migrations</td>
      <td>10.0h</td>
      <td>25m</td>
      <td>2m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>saa-c03-lab-20 (EBS volumes) migrate to uiSteps + EC2 SDK additions (createVolume, attachVolume, createSnapshot) + EC2 dashboard Volumes/Snapshots panels + assertion repairs; 25/29 SAA-C03</td>
      <td>4.0h</td>
      <td>10m</td>
      <td>0m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>saa-c03-lab-18 (CloudFormation) migrate to uiSteps + CFN dashboard sweep + template materialization SDK (creates VPC/Subnet/SG/Bucket from JSON template, resolves Refs, tracks logicalId→arn for diff-only…</td>
      <td>6.0h</td>
      <td>15m</td>
      <td>0m</td>
      <td>24.0x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>console-sim: saa-c03-lab-07 migration (Multi-AZ RDS) end-to-end</td>
      <td>3.0h</td>
      <td>8m</td>
      <td>1m</td>
      <td>22.5x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>saa-c03-lab-23 (SQS DLQ) migrate to uiSteps + SQS dashboard testid sweep (sidebar, create button, per-row Edit/Send buttons, three modal prefixes); Watch 100% strict on first try; 11/29 SAA-C03</td>
      <td>1.5h</td>
      <td>4m</td>
      <td>0m</td>
      <td>22.5x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>saa-c03-lab-14 (Route53 failover) migrate to uiSteps + 4 assertion repairs (trailing-dot mismatches, broken tag:Name filter, unrecognized properties.name); Watch 100% strict on first try; 12/29 SAA-C03</td>
      <td>1.5h</td>
      <td>4m</td>
      <td>0m</td>
      <td>22.5x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>saa-c03-lab-11 (S3 transfer accel + lifecycle) migrate to uiSteps + 2 assertion repairs (transferAcceleration boolean→string, lifecycleConfiguration→lifecycleRules); 19/29 SAA-C03</td>
      <td>1.5h</td>
      <td>4m</td>
      <td>0m</td>
      <td>22.5x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>saa-c03-lab-08 (Aurora Global) migrate to uiSteps + RDS SDK fix (createDBCluster auto-creates writer DBInstance with cluster identifier as name, dbClusterMembers array populated); regression-clean on lab 07;…</td>
      <td>3.0h</td>
      <td>8m</td>
      <td>0m</td>
      <td>22.5x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>saa-c03-lab-16 (VPC endpoint S3) migrate to uiSteps + VPC SDK additions (createVpcEndpoint, modifyVpcEndpoint) + Endpoints dashboard panel; closes SAA-C03 sweep gap (lab 16 scored 0/40 unattended); SAA-C03…</td>
      <td>3.0h</td>
      <td>8m</td>
      <td>0m</td>
      <td>22.5x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>console-sim: KMS testid sweep + S3 encryption/policy editor + saa-c03-lab-05 migration with broken-assertion repairs</td>
      <td>8.0h</td>
      <td>22m</td>
      <td>2m</td>
      <td>21.8x</td>
    </tr>
    <tr>
      <td>32</td>
      <td>Coding</td>
      <td>2.5h</td>
      <td>7m</td>
      <td>0m</td>
      <td>21.4x</td>
    </tr>
    <tr>
      <td>33</td>
      <td>Debugging</td>
      <td>12.0h</td>
      <td>35m</td>
      <td>3m</td>
      <td>20.6x</td>
    </tr>
    <tr>
      <td>34</td>
      <td>Containerize charlessieg-search Lambda + migrate from renkara-prod to renkara-dev (staging only): rebuild Docker image with Lambda-compatible manifest (--provenance=false), push to dev ECR, recreate Lambda as…</td>
      <td>4.0h</td>
      <td>12m</td>
      <td>2m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>35</td>
      <td>Infrastructure</td>
      <td>6.0h</td>
      <td>18m</td>
      <td>5m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>36</td>
      <td>saa-c03-lab-19 (S3 lifecycle) migrate + reworked from 0-pt auto-pass to 50pt real scoring with 3 progressive lifecycleRules saves through Management tab JSON editor; 20/29 SAA-C03</td>
      <td>2.0h</td>
      <td>6m</td>
      <td>0m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>37</td>
      <td>saa-c03-lab-27 (WAF) migrate + WAF dashboard testid sweep (sidebar, ACL row, modals for create/add-rule/logging) + reworked from 0-pt auto-pass to 50pt real scoring; 22/29 SAA-C03</td>
      <td>2.0h</td>
      <td>6m</td>
      <td>0m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>38</td>
      <td>Flashcards: inline rendering + course-aware picker + concept catalog primary</td>
      <td>5.0h</td>
      <td>15m</td>
      <td>3m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>39</td>
      <td>saa-c03-lab-17 (Elastic Beanstalk) migrate to uiSteps + Beanstalk dashboard testid sweep + reworked from 0-pt to 50pt real scoring; 23/29 SAA-C03</td>
      <td>2.0h</td>
      <td>6m</td>
      <td>0m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>40</td>
      <td>Cloud-cert hardening: 19 unit tests for builders+scorers; 3 GCP error drills (BigQuery cost, Cloud Run public, VPC firewall); 9 high-value services across AWS/Azure/GCP (AppFlow, IoT Core, Q Developer,…</td>
      <td>16.0h</td>
      <td>50m</td>
      <td>4m</td>
      <td>19.2x</td>
    </tr>
    <tr>
      <td>41</td>
      <td>saa-c03-lab-29 (DynamoDB Global Tables) migrate to uiSteps + DynamoDB dashboard testid sweep (sidebar, create button, table row, 6 tabs, 3 action buttons, 3 modal prefixes); Watch 100% strict on first try;…</td>
      <td>1.5h</td>
      <td>5m</td>
      <td>0m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>42</td>
      <td>saa-c03-lab-26 (AWS Config) migrate to uiSteps + Config dashboard testid sweep (sidebar, add-rule button + modal); cross-service step 0 with navigate; Watch 100% strict on first try; 17/29 SAA-C03</td>
      <td>1.5h</td>
      <td>5m</td>
      <td>0m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>43</td>
      <td>saa-c03-lab-15 (VPC peering) migrate to uiSteps + VPC SDK additions (createVpcPeeringConnection, acceptVpcPeeringConnection, addRoute creates tracked Route resource, createVpc uses name as resourceId) +…</td>
      <td>4.5h</td>
      <td>15m</td>
      <td>0m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>44</td>
      <td>saa-c03-lab-24 (EventBridge + Lambda) migrate to uiSteps + EventBridge dashboard testid sweep + reworked from 0-pt to 50pt real scoring; 24/29 SAA-C03</td>
      <td>2.0h</td>
      <td>7m</td>
      <td>0m</td>
      <td>17.1x</td>
    </tr>
    <tr>
      <td>45</td>
      <td>console-sim: saa-c03-lab-04 migration (cross-account roles, AttachPolicyModal flow) + IAM row click navigation fix + broken-assertion repairs</td>
      <td>5.0h</td>
      <td>18m</td>
      <td>1m</td>
      <td>16.7x</td>
    </tr>
    <tr>
      <td>46</td>
      <td>Coding</td>
      <td>8.0h</td>
      <td>30m</td>
      <td>5m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>47</td>
      <td>saa-c03-lab-09 (S3 CRR) migrate to uiSteps + S3 SDK additions (setBucketReplication, setBucketAccelerateConfiguration, putBucketLifecycleConfiguration) + S3 Management tab; bucket modal versioning state reset…</td>
      <td>4.0h</td>
      <td>15m</td>
      <td>1m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>48</td>
      <td>Testing</td>
      <td>14.0h</td>
      <td>55m</td>
      <td>8m</td>
      <td>15.3x</td>
    </tr>
    <tr>
      <td>49</td>
      <td>Infrastructure</td>
      <td>10.0h</td>
      <td>40m</td>
      <td>3m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>50</td>
      <td>Content production</td>
      <td>1.0h</td>
      <td>4m</td>
      <td>2m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>51</td>
      <td>saa-c03-lab-28 (Athena S3) migrate to uiSteps + Athena dashboard testid sweep (sidebar, create workgroup + database modals); Watch 100% strict on first try; 14/29 SAA-C03</td>
      <td>1.5h</td>
      <td>6m</td>
      <td>0m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>52</td>
      <td>saa-c03-lab-22 (FSx Windows) migrate to uiSteps + FSx dashboard testid sweep + case-fix follow-up commit; Watch 100% strict; 16/29 SAA-C03</td>
      <td>1.0h</td>
      <td>4m</td>
      <td>0m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>53</td>
      <td>Activities Phase 1: Recall Sprint (bar-trivia decay+fade), Case Study dedicated page with sample content, planSession dispatcher fixes (broken flashcard path removed, case_study split off scenario), help…</td>
      <td>8.0h</td>
      <td>35m</td>
      <td>6m</td>
      <td>13.7x</td>
    </tr>
    <tr>
      <td>54</td>
      <td>saa-c03-lab-25 (CloudTrail) migrate to uiSteps + CloudTrail dashboard testid sweep + SDK fix (isLogging defaults true) + assertion repair (propertiesContain→properties); Watch 100% strict; 10/29 SAA-C03</td>
      <td>2.0h</td>
      <td>9m</td>
      <td>1m</td>
      <td>13.3x</td>
    </tr>
    <tr>
      <td>55</td>
      <td>Testing</td>
      <td>24.0h</td>
      <td>110m</td>
      <td>8m</td>
      <td>13.1x</td>
    </tr>
    <tr>
      <td>56</td>
      <td>Diagnose CORS regression breaking bug reporter across entire tool fleet; replace brittle SSM allowlist with regex covering <em>.renkara.com, </em>.accelastudy.ai, and any localhost port; add lock-in test suite;…</td>
      <td>3.0h</td>
      <td>14m</td>
      <td>3m</td>
      <td>12.9x</td>
    </tr>
    <tr>
      <td>57</td>
      <td>Reprioritize [engine subsystem] to AWS-first cloud-only queue (22 packages: 5 AWS + 8 GCP + 9 Azure), build per-package auto-snapshot wrapper, kill --all and restart</td>
      <td>1.0h</td>
      <td>5m</td>
      <td>2m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>58</td>
      <td>Design and frontend</td>
      <td>2.0h</td>
      <td>10m</td>
      <td>5m</td>
      <td>12.0x</td>
    </tr>
    <tr>
      <td>59</td>
      <td>Design and frontend</td>
      <td>1.5h</td>
      <td>8m</td>
      <td>5m</td>
      <td>11.2x</td>
    </tr>
    <tr>
      <td>60</td>
      <td>Debugging</td>
      <td>1.5h</td>
      <td>8m</td>
      <td>3m</td>
      <td>11.2x</td>
    </tr>
    <tr>
      <td>61</td>
      <td>avian-app-web UX pass: hero palette, profile tabs (Resume + Schedule), comp plan rendering, AWS cert naming, Activities gating, Exam Info section, Labs sort, course-card Activities link, streak alignment</td>
      <td>14.0h</td>
      <td>75m</td>
      <td>8m</td>
      <td>11.2x</td>
    </tr>
    <tr>
      <td>62</td>
      <td>console-sim DOM-driven lab executor pilot: schema, driver, ACM dashboard testIds, migrate scs-c02-lab-22, full Watch verification (50/50)</td>
      <td>14.0h</td>
      <td>75m</td>
      <td>5m</td>
      <td>11.2x</td>
    </tr>
    <tr>
      <td>63</td>
      <td>Deployment</td>
      <td>6.0h</td>
      <td>35m</td>
      <td>4m</td>
      <td>10.3x</td>
    </tr>
    <tr>
      <td>64</td>
      <td>Format-aware activity chrome + fallback safety + enrollment race fix</td>
      <td>4.0h</td>
      <td>25m</td>
      <td>2m</td>
      <td>9.6x</td>
    </tr>
    <tr>
      <td>65</td>
      <td>avian-app-web vite onwarn filter to fix CI build (UNRESOLVED_IMPORT @avian/activity-ui escalated to error by plugin-react)</td>
      <td>1.0h</td>
      <td>8m</td>
      <td>1m</td>
      <td>7.5x</td>
    </tr>
    <tr>
      <td>66</td>
      <td>Design and frontend</td>
      <td>1.5h</td>
      <td>14m</td>
      <td>5m</td>
      <td>6.4x</td>
    </tr>
    <tr>
      <td>67</td>
      <td>Deployment</td>
      <td>6.0h</td>
      <td>60m</td>
      <td>4m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>68</td>
      <td>Coding</td>
      <td>1.5h</td>
      <td>15m</td>
      <td>5m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>69</td>
      <td>Testing</td>
      <td>0.5h</td>
      <td>5m</td>
      <td>3m</td>
      <td>6.0x</td>
    </tr>
    <tr>
      <td>70</td>
      <td>console-sim: zero-stubs rule + lab-authoring requirements in CLAUDE.md, delete animateCheckpoint stub-resource fallback (~520 lines), add Watch sweep result analyzer, run full 2,048-lab Watch baseline (75min,…</td>
      <td>8.0h</td>
      <td>90m</td>
      <td>6m</td>
      <td>5.3x</td>
    </tr>
    <tr>
      <td>71</td>
      <td>console-sim Phase 0 (translator + extended audit + SDK registry + analyzer + baseline) + Phase 1.A (VPC dashboard testIds via subagent) + saa-c03-lab-01 100% strict</td>
      <td>16.0h</td>
      <td>180m</td>
      <td>8m</td>
      <td>5.3x</td>
    </tr>
    <tr>
      <td>72</td>
      <td>Design and frontend</td>
      <td>2.0h</td>
      <td>25m</td>
      <td>5m</td>
      <td>4.8x</td>
    </tr>
    <tr>
      <td>73</td>
      <td>Design and frontend</td>
      <td>0.5h</td>
      <td>18m</td>
      <td>3m</td>
      <td>1.7x</td>
    </tr>
    <tr>
      <td>74</td>
      <td>Fix dop-c02-lab-14 Config rules/remediation lab: add uiSteps, fix propertiesContain-&gt;properties assertions, sync public copy</td>
      <td>0.5h</td>
      <td>18m</td>
      <td>3m</td>
      <td>1.7x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>74</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>899.0</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>2,006</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>220</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>9,919,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>26.9x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>245.7x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>22.5</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 248.3x and the lowest at 1.7x, a spread of 149.0 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 240.0 of the 899.0 human-equivalent hours, or 27 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 220 minutes against 2,006 minutes of execution, a ratio of about 1 to 9. Supervisory leverage of 245.7x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
    </item>
    <item>
      <title><![CDATA[Leverage Record: April 25, 2026]]></title>
      <link>https://charlessieg.com/posts/2026/2026-04-25-leverage-record.html</link>
      <guid>https://charlessieg.com/posts/2026/2026-04-25-leverage-record.html</guid>
      <pubDate>Sat, 25 Apr 2026 23:59:00 GMT</pubDate>
      <description><![CDATA[<p class="mb-4 font-light font-serif">37 tasks. April 25, 2026 closed at 136.0x weighted leverage across 2,288.5 human-equivalent hours in 1,010 minutes of wall-clock time. Supervisory leverage came in at 1373.1x.</p>
<p class="mb-4 font-light font-serif">That is 57.2 weeks of human-equivalent throughput in 16.8 hours. The ceiling was 342.9x; the floor was 10.3x. 37 of the 37 entries came from a single project.</p>
<aside class="callout callout--records" role="note" aria-label="About these records">
<p class="callout-title">About These Records</p>
<div class="callout-body"><p>These time records capture personal project work done with <a href="https://claude.ai/code">Claude Code</a> (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.</p></div>
</aside>
<h2 id="task-log">Task Log</h2>
<table>
  <thead>
    <tr>
      <th>#</th>
      <th>Task</th>
      <th>Human Est.</th>
      <th>Claude</th>
      <th>Sup.</th>
      <th>Factor</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>Tier-1 console-sim Phase 18: GCP identity &amp; compute (ResourceMgr IAMPolicyAnalyzer OrgPolicy VPCSC BeyondCorp WorkloadIdentityFed AssuredWorkloads IAP ComputeEngine AppEngine CloudFunctions CloudRun GKE…</td>
      <td>160.0h</td>
      <td>28m</td>
      <td>3m</td>
      <td>342.9x</td>
    </tr>
    <tr>
      <td>2</td>
      <td>Tier-1 console-sim Phase 14: Azure data &amp; storage (Azure SQL Cosmos PostgreSQL MySQL Redis Storage Blob Files ADLS Gen2 Queue Storage Managed Disks) — 10 services tier=full</td>
      <td>140.0h</td>
      <td>30m</td>
      <td>3m</td>
      <td>280.0x</td>
    </tr>
    <tr>
      <td>3</td>
      <td>Tier-1 console-sim Phase 17: Azure security &amp; mgmt (Defender Sentinel KeyVault Compliance Policy ResourceLocks MgmtGroups Monitor LogAnalytics AppInsights Advisor Portal ResourceMgr ARM Bicep ResourceGroups…</td>
      <td>140.0h</td>
      <td>30m</td>
      <td>3m</td>
      <td>280.0x</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Tier-1 console-sim Phase 12: Azure networking (VNet NSG LB AppGW Front Door VPN ExpressRoute Firewall DDoS TM DNS Bastion VirtualWAN PrivateEndpoint NAT NetworkWatcher ServiceEndpoints) — 17 services tier=full</td>
      <td>160.0h</td>
      <td>35m</td>
      <td>3m</td>
      <td>274.3x</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Tier-1 console-sim Phase 16: Azure DevOps &amp; app platform (AzureDevOps Pipelines Repos Artifacts GitHubActions AppConfig AppService AzureFunctions LogicApps APIM SignalR EventGrid EventHubs ServiceBus…</td>
      <td>140.0h</td>
      <td>32m</td>
      <td>3m</td>
      <td>262.5x</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Coding</td>
      <td>200.0h</td>
      <td>46m</td>
      <td>3m</td>
      <td>260.9x</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Tier-1 console-sim Phase 22: Workspace + IaC + final polish (Drive Gmail GoogleVault AlertCenter AdminConsole Firebase Marketplace) — 7 services tier=full + IaC verified + final audit</td>
      <td>80.0h</td>
      <td>19m</td>
      <td>3m</td>
      <td>252.6x</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Tier-1 console-sim Phase 13: Azure compute &amp; containers (VMs VMSS AKS ACR ContainerInstances ContainerApps SiteRecovery AzureBackup Migrate) — 9 services tier=full</td>
      <td>90.0h</td>
      <td>25m</td>
      <td>3m</td>
      <td>216.0x</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Tier-1 console-sim Phase 11: Azure identity &amp; access (Entra ID Conditional Access PIM RBAC Managed Identities MFA SSPR MS Graph M365 Defender) — 9 services tier=full</td>
      <td>120.0h</td>
      <td>35m</td>
      <td>3m</td>
      <td>205.7x</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Tier-1 console-sim Phase 19: GCP networking &amp; storage (VPC Firewall Peering SharedVPC Armor CDN LB DNS NAT VPN Interconnect Router PSC HierarchicalFW FlowLogs NetworkIntel PacketMirror NEG FirewallInsights…</td>
      <td>200.0h</td>
      <td>60m</td>
      <td>3m</td>
      <td>200.0x</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Tier-1 console-sim Phase 20: GCP analytics &amp; AI (BigQuery BQML Dataflow Dataproc CloudComposer Datastream DataCatalog AnalyticsHub PubSub LookerStudio VertexAI NLAI VisionAI TFDV) — 13 dashboards covering 27…</td>
      <td>180.0h</td>
      <td>57m</td>
      <td>3m</td>
      <td>189.5x</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Tier-1 console-sim Phase 15: Azure analytics &amp; AI (Synapse DataFactory StreamAnalytics DataExplorer Purview Fabric Databricks PowerBI AzureML OpenAI AILanguage Speech Vision Search AIServices Face Translator…</td>
      <td>220.0h</td>
      <td>75m</td>
      <td>3m</td>
      <td>176.0x</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Residual sweep AWS desc_claims - 137 description rewrites across 102 labs (scs/saa/sap/dea/ans/clf/free etc); to 0</td>
      <td>36.0h</td>
      <td>13m</td>
      <td>2m</td>
      <td>166.2x</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Residual sweep GCP/Azure desc_claims - 83 description rewrites across 54 labs (ace/pcse/pca/pcne/pgwa/az-* etc); to 0</td>
      <td>22.0h</td>
      <td>9m</td>
      <td>2m</td>
      <td>146.7x</td>
    </tr>
    <tr>
      <td>15</td>
      <td>224 per-service Guided E2E specs across full-tier services - 4 parallel agents writing to e2e/services/<slug>.guided.spec.ts</td>
      <td>18.0h</td>
      <td>14m</td>
      <td>2m</td>
      <td>77.1x</td>
    </tr>
    <tr>
      <td>16</td>
      <td>console-sim coverage Group Y: ~150 component + 7 SDK tests across 50 dashboards</td>
      <td>16.0h</td>
      <td>13m</td>
      <td>3m</td>
      <td>73.8x</td>
    </tr>
    <tr>
      <td>17</td>
      <td>Backfill Group A (ace/pcd/pde) - 80 labs normalized expectedActions to canonical names; missing_checkpoint 294 to 0</td>
      <td>60.0h</td>
      <td>50m</td>
      <td>3m</td>
      <td>72.0x</td>
    </tr>
    <tr>
      <td>18</td>
      <td>Backfill Group B (pcde/pcse/pca) - 74 labs normalized + 30 register handlers; missing_checkpoint 375 to 0</td>
      <td>55.0h</td>
      <td>48m</td>
      <td>3m</td>
      <td>68.8x</td>
    </tr>
    <tr>
      <td>19</td>
      <td>Backfill Group C (pmle/pcdb/pcne) - 60 labs normalized + 54 register handlers + Vertex AI/BQML/KMS codemod blocks; missing_checkpoint 555 to 0</td>
      <td>55.0h</td>
      <td>53m</td>
      <td>3m</td>
      <td>62.3x</td>
    </tr>
    <tr>
      <td>20</td>
      <td>Deployment</td>
      <td>14.0h</td>
      <td>14m</td>
      <td>2m</td>
      <td>60.0x</td>
    </tr>
    <tr>
      <td>21</td>
      <td>Backfill Group D (pgwa/cdl/azure/aws misc) - 67 labs normalized + 111 register handlers; missing_checkpoint 139 to 0</td>
      <td>40.0h</td>
      <td>43m</td>
      <td>3m</td>
      <td>55.8x</td>
    </tr>
    <tr>
      <td>22</td>
      <td>console-sim coverage Group X: ~155 component + 10 SDK tests across 50 dashboards</td>
      <td>18.0h</td>
      <td>21m</td>
      <td>3m</td>
      <td>51.4x</td>
    </tr>
    <tr>
      <td>23</td>
      <td>Residual sweep mechanical 81 issues - extended derive_assertions and SIMULATION_ACTIONS; mutation_without_property/action_assertion_gap/empty_assertions all to 0</td>
      <td>24.0h</td>
      <td>30m</td>
      <td>2m</td>
      <td>48.0x</td>
    </tr>
    <tr>
      <td>24</td>
      <td>Full content audit across avian-domains, avian-engine, avian-console-sim-react: 919 specs, 218 packages (1.03M questions, 127k nodes), 2,048 labs verified; identified 701-spec [content generation] backlog and…</td>
      <td>6.0h</td>
      <td>8m</td>
      <td>2m</td>
      <td>45.0x</td>
    </tr>
    <tr>
      <td>25</td>
      <td>Final 4 desc_claims fix - saa-c03 ghf snow checkpoint descriptions neutralized; total audit 0/2048 labs</td>
      <td>2.0h</td>
      <td>3m</td>
      <td>2m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>26</td>
      <td>Infrastructure</td>
      <td>4.0h</td>
      <td>6m</td>
      <td>1m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>27</td>
      <td>Build + deploy charlessieg.com semantic search Lambda end-to-end: generate 668-chunk [model] embedding index, package 17MB Python zip with numpy + requests + handler + index, deploy to Lambda…</td>
      <td>12.0h</td>
      <td>18m</td>
      <td>3m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>28</td>
      <td>console-sim coverage Group Z: ~150 component + 7 SDK tests across 50 dashboards</td>
      <td>16.0h</td>
      <td>24m</td>
      <td>3m</td>
      <td>40.0x</td>
    </tr>
    <tr>
      <td>29</td>
      <td>console-sim coverage Group W: ~150 component + 5 SDK tests across 50 dashboards</td>
      <td>16.0h</td>
      <td>28m</td>
      <td>3m</td>
      <td>34.3x</td>
    </tr>
    <tr>
      <td>30</td>
      <td>avian-engine: ship strategy dimensions/drift/forecast/recommender + persistence (1700-line WIP) + verify fingerprint 404-&gt;200 deploy</td>
      <td>16.0h</td>
      <td>30m</td>
      <td>2m</td>
      <td>32.0x</td>
    </tr>
    <tr>
      <td>31</td>
      <td>Redesign template second pass: about.jinja from mockup, article+post split into article_post_template layout (full-width header + 8/4 body/TOC grid), blog template with sidebar replacing posts list,…</td>
      <td>4.0h</td>
      <td>12m</td>
      <td>3m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>32</td>
      <td>console-sim test infra: 4-shard coverage config, JSDOM stubs, strict Watch sweep, buildspec-test.yml</td>
      <td>4.0h</td>
      <td>12m</td>
      <td>2m</td>
      <td>20.0x</td>
    </tr>
    <tr>
      <td>33</td>
      <td>Test infra - JSDOM mocks (canvas/matchMedia/ResizeObserver) coverage config strict Watch sweep</td>
      <td>3.0h</td>
      <td>10m</td>
      <td>2m</td>
      <td>18.0x</td>
    </tr>
    <tr>
      <td>34</td>
      <td>Deployment</td>
      <td>8.0h</td>
      <td>30m</td>
      <td>4m</td>
      <td>16.0x</td>
    </tr>
    <tr>
      <td>35</td>
      <td>avian-app-web Study Plan tab rename + per-day collapse + activity card wrap fix + deploy verify</td>
      <td>2.0h</td>
      <td>8m</td>
      <td>3m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>36</td>
      <td>Storm-prep snapshot of in-flight [engine subsystem] repair: promoted 7 finished packages to canonical, S3-synced to 2 backup buckets, wrote tarball + RESUME runbook + skip-aware resume wrapper script</td>
      <td>1.5h</td>
      <td>6m</td>
      <td>2m</td>
      <td>15.0x</td>
    </tr>
    <tr>
      <td>37</td>
      <td>Infrastructure</td>
      <td>6.0h</td>
      <td>35m</td>
      <td>4m</td>
      <td>10.3x</td>
    </tr>
  </tbody>
</table>
<h2 id="aggregate-statistics">Aggregate Statistics</h2>
<table>
  <thead>
    <tr>
      <th>Metric</th>
      <th>Value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Total tasks</td>
      <td>37</td>
    </tr>
    <tr>
      <td>Total human-equivalent hours</td>
      <td>2,288.5</td>
    </tr>
    <tr>
      <td>Total Claude minutes</td>
      <td>1,010</td>
    </tr>
    <tr>
      <td>Total supervisory minutes</td>
      <td>100</td>
    </tr>
    <tr>
      <td>Total tokens</td>
      <td>4,109,000</td>
    </tr>
    <tr>
      <td>Weighted average leverage factor</td>
      <td>136.0x</td>
    </tr>
    <tr>
      <td>Weighted average supervisory leverage factor</td>
      <td>1373.1x</td>
    </tr>
    <tr>
      <td>Human-equivalent weeks</td>
      <td>57.2</td>
    </tr>
  </tbody>
</table>
<h2 id="analysis">Analysis</h2>
<p class="mb-4 font-light font-serif">The highest factor of the day came in at 342.9x and the lowest at 10.3x, a spread of 33.3 times between the two. That is a wide range for a single day, and it usually means the day mixed mechanical work with work that needed real judgement.</p>
<p class="mb-4 font-light font-serif">The largest single entry accounted for 160.0 of the 2,288.5 human-equivalent hours, or 7 percent of the day. No single task dominated the total, so the weighted average is representative.</p>
<p class="mb-4 font-light font-serif">Supervisory time was 100 minutes against 1,010 minutes of execution, a ratio of about 1 to 10. Supervisory leverage of 1373.1x is the figure I find most honest, because it measures the hours I actually spent rather than the hours a machine spent on my behalf.</p>
<p class="mb-4 font-light font-serif">Every figure here is recorded at the time the work is done rather than reconstructed afterwards. The human estimate is my own judgement and carries the uncertainty that implies; the minutes and tokens are measured. The full dataset, including this day, is <a href="https://charlessieg.com/leverage/" class="text-primary-600 hover:text-primary-800 dark:text-primary-500 dark:hover:text-primary-600">available for download</a>.</p>]]></description>
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