#ankit-jain

How the Claude Code team does code review. Claude writes ~90% of Claude Code, so nobody reads every diff. They swapped "did a human read it?" for "which check, automated or human, should catch what could go wrong here?", and optimise for the least human time per change, with revert and incident rates as the guardrail. discuss

• The bit I missed on first read: review noise costs more when the author is an agent. A person waves off a nit in seconds; an agent fixes every one, which is a new commit and another full review. One PR ran 50+ rounds overnight, and ~59% of later findings were on code already there at round one. Fix: cap at three rounds, re-review only the new diff, flag the class of bug, never repeat a finding.
• The bot posts findings, never approvals. Human approvals (zero, one or two) are set by the change's risk, not by who wrote it. Whoever prompted the agent counts as a reviewer, and an approval covers one commit, so an agent's next push dismisses it.
• Their reviewer's eval is their own past incidents and reverts, run daily. Noise is measured by what authors did with each comment: fixed it, said it was wrong, intentional, or not worth it.
• Versus Ankit Jain's review layers I noted in April: this is the production version, with no spec-review layer and fast rollback under everything.
• Read the numbers carefully: the share of PRs reverted fell 40% while PR volume doubled, so the count of reverts still rose (~1.2x). Also: the authors sell the reviewer.
• Where I push back: "nobody needs a full mental model, ask Claude for an explainer of the slice you're changing." An explainer recovers what the code does, not why it was built that way, which is the comprehension debt Addy Osmani (a co-author here) wrote about. People should spend review on whether to ship it and on direction, not line-level correctness.
• How I read it: had Claude explain it against my notes, then quiz me. The question I got wrong taught me the most.