Seven tasks. June 2, 2026 weighted to 27.1x leverage across 124.0 human-equivalent hours in 275 Claude-minutes. Supervisory leverage closed at 354.3x.
3.1 weeks of human-equivalent throughput in 4.6 hours of Claude wall-clock. The 80.0x ceiling came from Full accessibility audit + fix across all four client apps (web/desktop/Android/iOS): jsx-a11y errors, label associations, autofocus, tablist roles, jsx-a11y plugin + axe coverage...; the 7.9x floor sat at Integrate a third-party model as synthesis generator: extra-body/timeout config + keep-alive fix in an LLM client library + CLI flags + diagnose-and-fix concurrency hang + caching/....
Task Log
| # | Task | Human Est. | Claude | Sup. | Factor | Sup. Factor |
|---|---|---|---|---|---|---|
| 1 | Full accessibility audit + fix across all four client apps (web/desktop/Android/iOS): jsx-a11y errors, label associations, autofocus, tablist roles, jsx-a11y plugin + axe coverage 8->40, 100+ Compose Slider/Switch/clickable semantics, ~40 SwiftUI VoiceOver labels/hidden | 80.0h | 60m | 3m | 80.0x | 1600.0x |
| 2 | Full WCAG 2.1 AA accessibility audit across all 4 client apps (web/desktop source + 39-route axe sweep; 11 mechanical fixes, 3 false positives triaged, ledger reconciled, native heuristics) | 7.0h | 8m | 2m | 52.5x | 210.0x |
| 3 | Run full accessibility audit across all four client apps (web/desktop/iOS/Android); hand-created Android AVD, booted sim+emulator, ran axe-sweep/vitest/XCUITest/Espresso a11y suites | 4.0h | 9m | 1m | 26.7x | 240.0x |
| 4 | Fix iOS + Android accessibility audit failures: 44pt hit-target on a course-list button + opaque-white hero subtitles (contrast) + accessibilityHidden on decorative SF Symbols; repair Android Compose a11y test fixture (MainActivity->createComposeRule); drove both audits to green | 3.0h | 13m | 1m | 13.8x | 180.0x |
| 5 | Resume an adversarial evaluation harness OMNISCIENT-ONLY cloud sweeps: diagnose OOM root cause, generate 42 omni profiles, write concurrency-capped batching runner, bring up inference engine + eval backend at hard cap 3, validate via smoke, launch full 42-profile sweep with monitoring | 5.0h | 27m | 2m | 11.1x | 150.0x |
| 6 | Finish a certification domain's math content: fix 3 synthesis bugs (rep_pack schema-drop, judge-pool deadlock, id-collision), Sonnet regen of 38 goals, standalone re-judge of 381 items, prune to 0 rejected/critical, finalize+place package | 20.0h | 120m | 6m | 10.0x | 200.0x |
| 7 | Integrate a third-party model as synthesis generator: extra-body/timeout config + keep-alive fix in an LLM client library + CLI flags + diagnose-and-fix concurrency hang + caching/batching audit | 5.0h | 38m | 6m | 7.9x | 50.0x |
Aggregate Statistics
| Metric | Value |
|---|---|
| Total tasks | 7 |
| Total human-equivalent hours | 124.0 |
| Total Claude minutes | 275 |
| Total supervisory minutes | 21 |
| Total tokens | 1,789,000 |
| Weighted average leverage factor | 27.1x |
| Weighted average supervisory leverage factor | 354.3x |
| Human-equivalent weeks | 3.1 |
Analysis
The day's leverage distribution matters more than the headline figure. The 80.0x ceiling came from Full accessibility audit + fix across all four client apps (web/desktop/Android/iOS): jsx-a11y errors, label associations, autofocus, tablist roles, jsx-a11y pl...; the 7.9x floor was Integrate a third-party model as synthesis generator: extra-body/timeout config + keep-alive fix in an LLM client library + CLI flags + diagnose-and-fix concurr.... Tasks at the top of the distribution share a shape: tightly-scoped specifications, clear success criteria, and minimal integration ambiguity. The AI doesn't need to discover anything new; it executes against an explicit target.
Tasks at the bottom run differently. They're either bounded by review-heavy work where every step gets verified, or they involve ambiguity that demands several rounds of trial and adjustment. The factor is real and informative, not a failure mode.
The supervisory leverage figure (354.3x today) tracks something orthogonal to wall-clock leverage. It's the ratio of human-equivalent output to human prompt-writing time. It stays high even on lower-leverage days because supervisory minutes scale with task count, not with the human-hour estimate; a 20-minute task and a 4-hour task can both be specified in two minutes of human prompt-writing.
Across the 7 tasks, the day produced roughly 3.1 weeks of senior-engineer-equivalent throughput in 4.6 hours of model wall-clock. That ratio is the practical answer to the question of how much output a single operator can move per day when the model handles the execution and the operator handles the direction.