The loop that learns — a five-part series on AI dev loops that remember, verify, and enforce, what happened when the method was turned on its own tool, and what other agent setups and recent research say about keeping a setup true. Every measured claim traces to a committed receipt in the public repo it describes, or to a linked source.
How one developer's AI dev loop remembers: lessons recorded once, retrieved per task, across repos, with A/B receipts. Part 1 of the loop-that-learns series.
The verifier fails too: exit codes, green builds, and judges that lie. Four rungs of independence for the verify step. Part 2 of the loop-that-learns series.
A checklist nobody is forced to run is documentation, not control: hooks, gates, and installers that wire instead of instruct. Part 3 of the series.
The method, pointed at the tool that implements it: four defects in surfaces nobody had run, and one carefully argued change that only a measurement caught. Part 4 of the series.
Notes from about 25 coding-agent tools and published setups and six recent papers: agents follow what is in front of them, skip what they have to fetch, and almost nothing checks whether a rule is still true. Part 5 of the loop-that-learns series.