Co Giang

Something I keep running into while building LineageLens: teams know they should review AI-generated code before it merges, but "reviewed" in practice means "someone clicked Approve." The diff could have been open for 3 seconds or 30 minutes. The record looks the same. I shipped a feature this week that tries to make this distinction real. Three behavioral signals time-per-line on the diff, comment count, lines reviewed get scored into a classification: `shallow`, `adequate`, or `deep`. Anything below 1 second per line gets hard-classified as `shallow` regardless of score, because no one is actually reading at that speed. The classification gets signed with Ed25519 and stored as an attestation. A CI gate endpoint then blocks merges when the review depth is below a configurable minimum. I have real doubts about parts of the formula. The comment signal is the weakest three "looks good" comments score the same as three substantive engagement comments. The 1-second-per-line floor is a judgment call that will occasionally misclassify a genuinely fast reviewer. And any behavioral proxy like this can be gamed if someone is motivated. But "approved" as the only review record for AI-generated auth code is not a governance posture it is an illusion of one. The question is whether a behavioral floor, even an imperfect one, is better than no floor at all. Curious if anyone here has built or worked with review quality measurement systems and what signals you found actually correlated with genuine engagement vs. nominal approval. Project: https://www.lineagelens.dev/

I've been running Claude Code, Cursor, and Codex pretty heavily for the last few months and I keep hitting the same loop:

1. Start a task in one agent

2. Switch to something else (Slack, Twitter, another terminal)