Launching today

CodeGate

Launching today

Four specialized AI agents reviewing every PR.

4 followers

Four specialized AI agents reviewing every PR.

4 followers

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CodeGate connects to your GitHub and reviews every pull request with four specialized agents running in parallel — correctness, security, architecture, and performance. High-confidence issues are flagged as blocking; uncertain ones become optional nitpicks, not noise. Security analysis is fail-closed. Findings arrive with suggested fixes, before the first human review. Free to start, no card.

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Launch tags:Developer Tools•Artificial Intelligence•GitHub

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Framer AI AgentsDesign and publish professional sites with AI

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Hey Product Hunt 👋 I'm Marce, solo founder of @CodeGate. The problem: code review is the silent bottleneck on almost every team. Seniors burn hours on trivial PRs, reviews sit waiting across time zones, and the security bugs are exactly the ones that slip through tired eyes. Most "AI code review" is one generic model glancing at a diff. CodeGate is built differently: 🐛 Four specialized agents in parallel — correctness, security, architecture, performance. One specialty each. 🎯 Confidence band, not binary cutoff — high-confidence issues are blocking; uncertain ones are demoted to optional nitpicks. Every comment should be worth reading. 🔒 Fail-closed security — if something can't be analyzed, it says so. Never a fake "all clean". 📋 Your rules, not ours — it reads your project conventions and reviews against them. Everything in the gallery is a real finding from a live review — no mockups. It's in early access: free tier (100 reviews/month, all 4 agents), installs from GitHub Marketplace in under 5 minutes. I'd genuinely love your feedback — especially on where it gets it wrong. AMA in the comments all day.

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7d ago

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6d ago

The trust problem with AI code review

Following up on yesterday's post about fail-closed security today I want to zoom out to the broader thing I think about a lot: what actually makes someone trust an AI reviewer's output, versus just tolerating it until they mute the bot.

I think the trust problem with AI code review isn't accuracy in the abstract. It's variance in what gets flagged. If a tool posts 15 comments on a PR and 12 are trivial style nits dressed up as "issues," the 3 real findings drown with them. The team's response isn't "let's read carefully" it's "let's turn this off."

So the thing that seems to actually build trust over time is consistency in what counts as worth interrupting someone for. Not "did the model find something," but "was this specific thing worth a human's attention right now." That's a different, harder question it means a tool has to be willing to say less, not more, to stay useful.

A few things I think help (built these into CodeGate, but curious if others have found different levers):

p/codegate

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7d ago

Why I made CodeGate fail-closed on security

Hey building CodeGate here, launching August 11. Wanted to share a bit of the "why" behind one specific decision, since it's the one I went back and forth on the most.

Why fail-closed on security, specifically.

Most review tools (AI or static analysis) implicitly treat "no issues found" as "you're clean." But that's only true if the tool actually managed to analyze everything. If it hit a file it couldn't parse, a pattern it wasn't confident about, or context it didn't have silence gets read as a pass.

That's the failure mode I cared most about avoiding. So the security agent is explicitly fail-closed: if it can't analyze something with confidence, it says so, instead of quietly rolling it into "all clean." A false negative disguised as a pass is worse than an honest "couldn't verify this part."

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A few things I think help (built these into CodeGate, but curious if others have found different levers):