Blog
Writing
Notes on code quality, static analysis, and keeping AI-generated codebases maintainable.
Ruff, mypy, pytest, and then what?
Structural quality in the age of AI-written Python. What the standard toolchain checks, what it does not, and what a real agent-written repository looks like when you measure it.
- 日本語
コードレビューがボトルネックになった
コーディングエージェントを使うと、パッチを書く時間は短くなりますが、レビューする時間は短くなりません。レビューのうち「コードベース全体への影響を見る」部分は機械に任せないと、レビューが回らなくなります。
Review Is the Bottleneck Now
Coding agents made writing a patch cheap and left reading one exactly as expensive as before. The half of code review that guards the shape of the codebase used to be done mostly before review, by the author. Agents don't do it, and reviewers no longer have the time. Here is what I think has to happen to it.
13 of 14 Models Write Messier Code Than the Human Who Fixed the Same Bug
We scored 14 SWE-bench submissions for maintainability against the commit a human wrote to fix the same bug. Thirteen of the fourteen left the code messier.
What Your Coding Agent Can't See
AI agents score perfectly on the code quality metrics they can perceive, and badly on the ones they can't. The fix isn't better prompting — it's giving them a structural feedback loop.