Seven tasks. May 19, 2026 weighted to 47.1x leverage across 182.0 human-equivalent hours in 232 Claude-minutes. Supervisory leverage closed at 574.7x.
4.5 weeks of human-equivalent throughput in 3.9 hours of Claude wall-clock. The 166.2x ceiling came from a knowledge graph Act I Phase 0 — Python orchestrator daemon (IPC, Opus agent, MCP bus, briefing, diagnostics, test) + Swift Command Bar app (NSPanel, Carbon hotkey, NWConnection I...; the 4.8x floor sat at 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 clou....
Task Log
| # | Task | Human Est. | Claude | Sup. | Factor | Sup. Factor |
|---|---|---|---|---|---|---|
| 1 | a knowledge graph 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) + build scripts + Launch Agent plist; swift build green, pytest green | 36.0h | 13m | 1m | 166.2x | 2160.0x |
| 2 | a knowledge graph 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 | 30.0h | 14m | 2m | 128.6x | 900.0x |
| 3 | a knowledge graph 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. + confirm. + Mac settings window with fleet panel + inline confirmation card + route breadcrumb + ⌘⇧, hotkey; s... | 36.0h | 17m | 1m | 127.1x | 2160.0x |
| 4 | a knowledge graph Act I Phase 3 — Visual Stack Foundation: AtlasRenderEnvironment singleton (Metal device + queue + library + DisplayLink + Rive factory + energy monitor + FramePacer), MetalLayerView NSViewRepresentable, AtlasRenderer protocol, 7 .metal shader sources + compute kernels, 7 Swift pass wrappers... | 32.0h | 16m | 1m | 120.0x | 1920.0x |
| 5 | a knowledge graph Act I Phase 1 — Ledger & Self-Instrumentation: SQLite statedb (0600 mode, WAL, commandhistory/costrecords/settings), OTel ledger emitter with OTLP to an observability platform, CostAccountant with daily cap + Opus->Haiku hard-cap fallback, agent instrumentation, IPC ledger.listtoday + co... | 24.0h | 14m | 1m | 102.9x | 1440.0x |
| 6 | an analytics platform audit + Statcounter feature gap analysis + remediation plan (6 phases) | 12.0h | 8m | 3m | 90.0x | 240.0x |
| 7 | 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, GCP 9/11, Azure 13/15) up from 16/38 baseline | 12.0h | 150m | 10m | 4.8x | 72.0x |
Aggregate Statistics
| Metric | Value |
|---|---|
| Total tasks | 7 |
| Total human-equivalent hours | 182.0 |
| Total Claude minutes | 232 |
| Total supervisory minutes | 19 |
| Total tokens | 1,433,000 |
| Weighted average leverage factor | 47.1x |
| Weighted average supervisory leverage factor | 574.7x |
| Human-equivalent weeks | 4.5 |
Analysis
The day's leverage distribution matters more than the headline figure. The 166.2x ceiling came from a knowledge graph Act I Phase 0 — Python orchestrator daemon (IPC, Opus agent, MCP bus, briefing, diagnostics, test) + Swift Command Bar app (NSPanel, Carbon ho...; the 4.8x floor was 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 swee.... 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 (574.7x 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 4.5 weeks of senior-engineer-equivalent throughput in 3.9 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.