Skip to main content
AI JUN 06, 2026

Leverage Record: June 6, 2026

Seven tasks. June 6, 2026 weighted to 26.8x leverage across 161.5 human-equivalent hours in 362 Claude-minutes. Supervisory leverage closed at 312.6x.

Seven tasks. June 6, 2026 weighted to 26.8x leverage across 161.5 human-equivalent hours in 362 Claude-minutes. Supervisory leverage closed at 312.6x.

4.0 weeks of human-equivalent throughput in 6.0 hours of Claude wall-clock. The 218.2x ceiling came from A service health monitor coverage backfill: 16% to 95% lines, 31 test files, 263 tests; the 6.6x floor sat at Commit+push verified audit fixes to staging (6 repos, no prod); create private remote for a security-scanning service; create staging branches fleet-wide (89/90 repos); debug zsh :....

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
1A service health monitor coverage backfill: 16% to 95% lines, 31 test files, 263 tests40.0h11m3m218.2x800.0x
2Post-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 backfill across 4 repos (~700 tests), fleet-wide staging-to-prod merge-safety reconciliation incl engine conflict resolution, and local engine+api+web stack stood up and verified end-to-end48.0h112m10m25.7x288.0x
3Full readiness audit across 52 repos (Phase 0 + 11-agent fan-out, 222 findings) then applied & independently verified 11 code/test/doc fixes + canonical sync + audit-doc 35-repo expansion24.0h60m4m23.9x360.0x
4Full readiness audit rerun2: Phase 0 + 11-agent workflow fan-out over 38 change-detected repos (131 findings); adversarially verified all 21 CRITICAL+HIGH (1 refuted, 1 downgraded); applied safe fixes + committed/pushed 27 repos; provisioned private tool-fleet-dashboard remote; report + remediation; gated patent items untouched40.0h107m3m22.4x800.0x
5An origin service: raise test coverage from 73.17% to >=75% by adding 85 unit tests across 4 router/CLI modules3.0h18m4m10.0x45.0x
6clear 85% per-module coverage gate for core and persistence modules3.0h22m5m8.2x36.0x
7Commit+push verified audit fixes to staging (6 repos, no prod); create private remote for a security-scanning service; create staging branches fleet-wide (89/90 repos); debug zsh :r modifier3.5h32m2m6.6x105.0x

Aggregate Statistics

MetricValue
Total tasks7
Total human-equivalent hours161.5
Total Claude minutes362
Total supervisory minutes31
Total tokens4,689,000
Weighted average leverage factor26.8x
Weighted average supervisory leverage factor312.6x
Human-equivalent weeks4.0

Analysis

The day's leverage distribution matters more than the headline figure. The 218.2x ceiling came from A service health monitor coverage backfill: 16% to 95% lines, 31 test files, 263 tests; the 6.6x floor was Commit+push verified audit fixes to staging (6 repos, no prod); create private remote for a security-scanning service; create staging branches fleet-wide (89/90.... 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 (312.6x 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 4.0 weeks of senior-engineer-equivalent throughput in 6.0 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.