11 tasks. February 23, 2026 closed at 75.9x weighted leverage across 300.0 human-equivalent hours in 237 minutes of wall-clock time. Supervisory leverage came in at 300.0x.
That is 7.5 weeks of human-equivalent throughput in 4.0 hours. The ceiling was 180.0x; the floor was 16.0x. 7 of the 11 entries came from a single project.
Task Log
| # | Task | Human Est. | Claude | Sup. | Factor |
|---|---|---|---|---|---|
| 1 | Implement 4 Phase 2 engine subsystems + tests | 120.0h | 40m | 12m | 180.0x |
| 2 | 7 new architecture articles (CodeBuild/CodeDeploy/CodePipeline/Lambda images/Lambda CICD/telemetry/Lambda ALB) + writing style revisions across all articles and posts (4198 insertions) | 60.0h | 30m | 8m | 120.0x |
| 3 | Fix 77 diagram reference numerals + placeholder steps + headers across A-K | 40.0h | 25m | 8m | 96.0x |
| 4 | AWS Cognito authentication architecture deep-dive article (~470 lines) | 8.0h | 8m | 4m | 60.0x |
| 5 | Services dark mode + legal pages + auth callbacks + engine integration across auth-service/purchase-service/certprep-app | 16.0h | 20m | 5m | 48.0x |
| 6 | Fix diagram truncation and pseudocode overflow for 11 CIP applications | 16.0h | 25m | 5m | 38.4x |
| 7 | Create AP USH and AP Macro domain specs + README | 16.0h | 25m | 5m | 38.4x |
| 8 | Event-driven messaging article (607 lines) + AI score revisions across 175 files | 12.0h | 25m | 4m | 28.8x |
| 9 | Fix App G Mermaid diagrams (9 figures) with spec-correct numerals and 2-word labels | 4.0h | 12m | 3m | 20.0x |
| 10 | Debugging | 4.0h | 12m | 3m | 20.0x |
| 11 | AI detection scoring via Sapling across 215 files (all articles + ~195 posts) | 4.0h | 15m | 3m | 16.0x |
Aggregate Statistics
| Metric | Value |
|---|---|
| Total tasks | 11 |
| Total human-equivalent hours | 300.0 |
| Total Claude minutes | 237 |
| Total supervisory minutes | 60 |
| Total tokens | 1,679,000 |
| Weighted average leverage factor | 75.9x |
| Weighted average supervisory leverage factor | 300.0x |
| Human-equivalent weeks | 7.5 |
Analysis
The highest factor of the day came in at 180.0x and the lowest at 16.0x, a spread of 11.2 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 120.0 of the 300.0 human-equivalent hours, or 40 percent of the day. No single task dominated the total, so the weighted average is representative.
Supervisory time was 60 minutes against 237 minutes of execution, a ratio of about 1 to 4. Supervisory leverage of 300.0x 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.