2 tasks. March 15, 2026 closed at 42.4x weighted leverage across 60.0 human-equivalent hours in 85 minutes of wall-clock time. Supervisory leverage came in at 450.0x.
That is 1.5 weeks of human-equivalent throughput in 1.4 hours. The ceiling was 68.6x; the floor was 24.0x. The day's work was spread across several areas.
Task Log
| # | Task | Human Est. | Claude | Sup. | Factor |
|---|---|---|---|---|---|
| 1 | Generate 66 interactive labs across 35 free domains (Python 10 + JS 10 + Git 5 + Linux 5 + 16 other domains) | 40.0h | 35m | 3m | 68.6x |
| 2 | Set up full [engine subsystem] and synthesize 35 free domains (fix venv + deps + batch script + [content generation] + [engine subsystem] + lessons) | 20.0h | 50m | 5m | 24.0x |
Aggregate Statistics
| Metric | Value |
|---|---|
| Total tasks | 2 |
| Total human-equivalent hours | 60.0 |
| Total Claude minutes | 85 |
| Total supervisory minutes | 8 |
| Total tokens | 595,000 |
| Weighted average leverage factor | 42.4x |
| Weighted average supervisory leverage factor | 450.0x |
| Human-equivalent weeks | 1.5 |
Analysis
The highest factor of the day came in at 68.6x and the lowest at 24.0x, a spread of 2.9 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 40.0 of the 60.0 human-equivalent hours, or 67 percent of the day. One task carrying that much of the total is worth noting; the average is doing less work than it appears to.
Supervisory time was 8 minutes against 85 minutes of execution, a ratio of about 1 to 11. Supervisory leverage of 450.0x 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.