8 tasks. April 3, 2026 closed at 30.9x weighted leverage across 143.0 human-equivalent hours in 278 minutes of wall-clock time. Supervisory leverage came in at 260.0x.
That is 3.6 weeks of human-equivalent throughput in 4.6 hours. The ceiling was 60.0x; the floor was 21.3x. 5 of the 8 entries came from a single project.
Task Log
| # | Task | Human Est. | Claude | Sup. | Factor |
|---|---|---|---|---|---|
| 1 | Testing | 12.0h | 12m | 5m | 60.0x |
| 2 | Terraform + CI/CD for purchase-service and notification-service: 20 TF files + 2 buildspecs + ALB/DNS/ECR/SG + website deploys + readiness audit updates | 20.0h | 25m | 3m | 48.0x |
| 3 | CodeArtifact private npm registry + source map audit + persistence timeout fix + pipeline debugging: Terraform + 4 library buildspecs + 2 client buildspec updates + 3 sourcemap fixes + regression tests +… | 24.0h | 35m | 5m | 41.1x |
| 4 | Delete accelastudy-ai-blog repo, add blog section to accelastudy-ai-website (listing page, 4 articles, CSS, nav, sitemap, dark mode) | 12.0h | 18m | 2m | 40.0x |
| 5 | Deployment fixes + Terraform reconciliation + DNS cleanup + README + rename plan: 3 dep fixes + 9 TF reconciled + 4 stale DNS deleted + README rewrite + migration plan | 16.0h | 30m | 5m | 32.0x |
| 6 | Infrastructure | 24.0h | 60m | 5m | 24.0x |
| 7 | Create infra CLAUDE.md + websites README.md + deployment log for static-sites terraform stack | 3.0h | 8m | 3m | 22.5x |
| 8 | Coding | 32.0h | 90m | 5m | 21.3x |
Aggregate Statistics
| Metric | Value |
|---|---|
| Total tasks | 8 |
| Total human-equivalent hours | 143.0 |
| Total Claude minutes | 278 |
| Total supervisory minutes | 33 |
| Total tokens | 1,815,000 |
| Weighted average leverage factor | 30.9x |
| Weighted average supervisory leverage factor | 260.0x |
| Human-equivalent weeks | 3.6 |
Analysis
The highest factor of the day came in at 60.0x and the lowest at 21.3x, a spread of 2.8 times between the two. That is a narrow range, which tends to happen when a day stays inside one kind of work.
The largest single entry accounted for 12.0 of the 143.0 human-equivalent hours, or 8 percent of the day. No single task dominated the total, so the weighted average is representative.
Supervisory time was 33 minutes against 278 minutes of execution, a ratio of about 1 to 8. Supervisory leverage of 260.0x 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.