Opus 5.5 writes 187 lines of Python to simulate agent success rates
To show how small daily gains could feel large, the author had Opus 5.5 write a 187-line Python simulation of 2,000 agent workflows over 14 days, which renders in 40 seconds.
dot "my favorite openai product so far" and says it feels noticeably better every day as it learns more of the workflow and style. To see why small daily gains could feel that big, I simulated 2,000 agent workflows a day for 14 days, 20 steps each, with per-step accuracy rising from 90% to 97.7%. With no checks, a task succeeds only if all 20 steps do: 11% on day 1, 65% on day 14. Add retries when a tool error or a critic catches a wrong step, and it goes from 57% to 89%. Runs where a wrong step slipped through fall from 40% to 11%. The accuracies, the critic and the learning curve are made up. This is not how dot works inside. Claude Opus 5.5 wrote it in 187 lines of Python. It renders in 40 seconds.
This post quotes another post: x.com/yumzlef/status/2095517043634126889