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AI JUN 04, 2026

Leverage Record: June 4, 2026

Thirty-three tasks. June 4, 2026 weighted to 48.1x leverage across 1301.0 human-equivalent hours in 1622 Claude-minutes. Supervisory leverage closed at 500.4x.

Thirty-three tasks. June 4, 2026 weighted to 48.1x leverage across 1301.0 human-equivalent hours in 1622 Claude-minutes. Supervisory leverage closed at 500.4x.

32.5 weeks of human-equivalent throughput in 27.0 hours of Claude wall-clock. The 173.3x ceiling came from Two-pass 66-agent code-grounded re-audit of all 718 patent claims against the live codebase; corrected traceability matrix (325->221 wired, 112 downgrades), regenerated 33 section...; the 5.0x floor sat at Close patent-claim gaps on one application (8 claims) wave-2 lifecycle wiring in an inference engine.

About These Records
These time records capture personal project work done with Claude Code (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.

Task Log

#TaskHuman Est.ClaudeSup.FactorSup. Factor
1Two-pass 66-agent code-grounded re-audit of all 718 patent claims against the live codebase; corrected traceability matrix (325->221 wired, 112 downgrades), regenerated 33 section sources plus gap analysis plus roadmap, applied dependent-claim semantics180.0h62m3m173.3x3600.0x
2Implemented ~220 patent claims from audit baseline to 441/718 wired via ~28 parallel agents plus shared-file integration wiring; full unit suite 5842 passing700.0h316m12m132.9x3500.0x
3Audit plus wire patent claims to production across multiple applications (19 plus 65 claims) — wiring modules plus REST endpoints plus tests, adversarial per-claim verification, matrix reconciliation 445->528/718, committed and pushed to staging100.0h55m4m109.1x1500.0x
4Patent claim implementation waves: wired and implemented 102 claims (six applications plus 13 subsystems plus 38 mechanism-completion algorithms) to production, adversarially verified, matrix 528->631/718; engine plus docs committed and pushed to staging130.0h105m2m74.3x3900.0x
5Harden patent plus diagram audit specs (4 gap-closing checks) and run proactive 8-agent semantic/legal sweep across all patent drafts; apply ~28 calibrated overclaim/self-contradiction/grammar/cross-ref fixes12.0h25m3m28.8x240.0x
6Patent remediation close-out: hard system-claim attempts (2 confirmed), production spread-propagation fix plus test, comprehensive traceability roadmap/epilogue rewrite; final state 634/718 (88%), 0 not-implemented14.0h30m1m28.0x840.0x
7Patent gap closure on one application — four-feature credential extraction plus reliability weights plus KL-divergence info-gain chain plus session loop8.0h18m3m26.7x160.0x
8Close patent claim gaps on one application in a scenario engine (13 claims)16.0h38m5m25.3x192.0x
9Patent claim gap closure on one application — trajectory optimizer (8 claims)12.0h42m5m17.1x144.0x
10Patent claim gap closure on one application (assessment session orchestrator plus 2 fixes)8.0h35m5m13.7x96.0x
11Close patent-claim gaps on one application in an inference engine composer (10 claims)8.0h35m5m13.7x96.0x
12Patent claim gap closure on one application — 2PL MLE plus 5 mechanisms plus 22 new tests4.0h22m5m10.9x48.0x
13Close patent-claim gaps on one application (typed candidate record plus composite table plus audit-logger injection plus cosine similarity plus cycle logging)8.0h45m5m10.7x96.0x
14Close patent claim gaps on one application: scenario stemtype plus source pair ids; dynamic max-correct cap; injectable LM judge in association extractor; 3..7 step chains; anchor-grounded narrative; rubric converter plus rubric entailment validator wired in goal generation; knowledge scope on persona; injectable LM judges; single-MCQ validator per sub-question; max-correct in format metadata; scheduler instantiated and used; coordinator callable from engine; 45 tests green8.0h45m5m10.7x96.0x
15Close patent-claim gaps on one application (an inference engine interview module)8.0h45m5m10.7x96.0x
16Close patent-claim gaps on one application in an inference engine8.0h45m5m10.7x96.0x
17Close patent claim gaps on one application (4 claims) in an inference engine chat module3.5h22m5m9.6x42.0x
18Patent claim gap closure on one application — readiness prediction engine (13 claims wired plus 20 new tests)6.0h40m5m9.0x72.0x
19Close patent-claim gaps on one application (6 claims) in an inference engine probing modules8.0h55m5m8.7x96.0x
20Pre-filing semantic/legal review of two CIP patent drafts (interactive activities and an internal engine)4.0h28m5m8.6x48.0x
21Wire dormant adversarial detection mechanisms on one application into a detection pass to flip 12 patent claims from partial to reduced-to-practice4.0h28m3m8.6x80.0x
22Patent-claim gap closure on one application (5 claims) in an inference engine replication plus persistence modules4.0h28m5m8.6x48.0x
23Close patent claim gaps on one application: per-concept pass-threshold; embed-model selection; override-audit entry; NLI-threshold mutation; retroactive rescore; NLI checkpoint wiring4.0h30m5m8.0x48.0x
24Close patent-claim gaps on one application (embedding manifold versioning) — fix 7 mechanisms plus 31 new tests8.0h65m5m7.4x96.0x
25Close patent claim gaps on one application (cohort intelligence — ~18 claims)4.0h35m5m6.9x48.0x
26Close patent-claim gaps on one application (behavioral analytics) in an inference engine4.0h35m5m6.9x48.0x
27Pre-filing semantic/legal review of two CIP patent drafts — five-class analysis: absolute-outcome overclaims, claim vs mechanism, cross-reference accuracy, enumeration consistency, domain-neutrality3.0h28m5m6.4x36.0x
28Close patent-claim gaps on one application (cognitive state detection) in an inference engine8.0h75m5m6.4x96.0x
29Close patent-claim gaps on one application (replay plus sim-real, 2 claims)2.0h20m5m6.0x24.0x
30Patent claim gap closure on one application — wire CDV/registry/suppression/session-mode into an activity synthesizer selection step4.0h45m5m5.3x48.0x
31Close patent-claim gaps on one application (federation/tenancy): adaptive clip bound plus diagnostic log plus ring version entry plus influence approximation engine3.0h35m5m5.1x36.0x
32Close patent-claim gaps on one application — explainability engine production-ready plus 85 new tests2.5h30m5m5.0x30.0x
33Close patent-claim gaps on one application (8 claims) wave-2 lifecycle wiring in an inference engine5.0h60m5m5.0x60.0x

Aggregate Statistics

MetricValue
Total tasks33
Total human-equivalent hours1301.0
Total Claude minutes1622
Total supervisory minutes156
Total tokens30,435,000
Weighted average leverage factor48.1x
Weighted average supervisory leverage factor500.4x
Human-equivalent weeks32.5

Analysis

The day's leverage distribution matters more than the headline figure. The 173.3x ceiling came from Two-pass 66-agent code-grounded re-audit of all 718 patent claims against the live codebase; corrected traceability matrix (325->221 wired, 112 downgrades), reg...; the 5.0x floor was Close patent-claim gaps on one application (8 claims) wave-2 lifecycle wiring in an inference engine. 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 (500.4x 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 33 tasks, the day produced roughly 32.5 weeks of senior-engineer-equivalent throughput in 27.0 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.