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.
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.
Task Log
| # | Task | Human Est. | Claude | Sup. | Factor |
|---|---|---|---|---|---|
| 1 | 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) | 16.0h | 6m | 3m | 160.0x |
| 2 | 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… | 80.0h | 95m | 1m | 50.5x |
| 3 | 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),… | 24.0h | 30m | 1m | 48.0x |
| 4 | 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… | 16.0h | 30m | 3m | 32.0x |
| 5 | 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.… | 12.0h | 45m | 12m | 16.0x |
| 6 | Open-items burn-down: VFS reset across labs (memory leak fix), QuickJS node resolver (CDN-loaded, ~3MB lazy), shell stdout redirection (echo > file), Monaco editor listener leak fix, multi-editor-create-file… | 16.0h | 90m | 1m | 10.7x |
| 7 | 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 +… | 4.0h | 30m | 3m | 8.0x |
| 8 | 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… | 2.0h | 25m | 2m | 4.8x |
| 9 | 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… | 16.0h | 360m | 12m | 2.7x |
| 10 | 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 | 2.5h | 165m | 4m | 0.9x |
| 11 | 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… | 1.0h | 75m | 2m | 0.8x |
Aggregate Statistics
| Metric | Value |
|---|---|
| Total tasks | 11 |
| Total human-equivalent hours | 189.5 |
| Total Claude minutes | 951 |
| Total supervisory minutes | 44 |
| Total tokens | 3,453,000 |
| Weighted average leverage factor | 12.0x |
| Weighted average supervisory leverage factor | 258.4x |
| Human-equivalent weeks | 4.7 |
Analysis
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.
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.
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.
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 available for download.