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Surviving Code Reviews in the era of AI

My coworkers are all fully AI pilled. As a consequence, every PR that comes my way for review is on average ~6k lines of diff. It's well established that even PRs an order of magnitude smaller than this are too big to be effectively reviewed. I'm still clinging to the idea that us humans should understand how the systems we are building work, so I'm hesitant to use AI for reviews aside from more…

We haven't written up this one. Lobsters has the full story — the link below goes straight to it.

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More in AI

Fair Queue for a Shared Free AI Server: 5-Dev Postmortem

Five independent clients on one free AI server will produce 429s and a thundering herd unless you add a fair queue. We fixed it with a client-side asyncio queue that capped concurrency at two…

  • Five developers collaborated on a single free AI server.
  • Lack of coordination caused high latency and 429 responses.
  • Queue implementation fixed concurrency issues and ensured fair access.

Opinion: AI Patch Acceptance Is a Vanity Metric — Revert Rate Is the Truth

Every AI code review metric you track measures the hour before merge, and that is precisely the hour when the least information exists.

  • Pre-merge metrics like AI patch acceptance are misleading indicators of patch quality.
  • Revert rate accurately reflects a patch's performance after deployment.
  • Classifying reverts into categories guides appropriate response actions.

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