CodeGate
Four specialized AI agents reviewing every PR.
CodeGate launched on August 11, 2026 with 2 votes, #5,788 of 23,274 launches that month.
What it does
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.
Most AI reviewers read your diff. CodeGate investigates the repository around it — and reviews every pull request against your project's own rules, not generic ones.
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Git for AI AgentsMay 2026 · github.com · ▲129hi guys. been working on something i think is fundamentally missing in today's workflow with ai agents. vcs. i find myself struggling with questions that agents can't answer like "why did you do it?", "when did u delete this folder? why?", etc. or trying to /rewind (after a /compact...) or basically `bisect` to find when and why something was done by the agent in the current / previous session. just like git did for code, i think we are the same core capabilities with ai agents so... i developed an open source solution for that (currently supporting claude code) would love to…
Continue – Source-controlled AI checks, enforceable in CIFeb 2026 · docs.continue.dev · ▲44We now write most of our code with agents. For a while, PRs piled up, causing review fatigue, and we had this sinking feeling that standards were slipping. Consistency is tough at this volume. I’m sharing the solution we found, which has become our main product. Continue (https://docs.continue.dev) runs AI checks on every PR. Each check is a source-controlled markdown file in `.continue/checks/` that shows up as a GitHub status check. They run as full agents, not just reading the diff, but able to read/write files, run bash commands, and use a browser. If it finds…
We built an AI to review your pull requests2025 · infinitcode.ai · ▲53Hey HN, We’re two developers (co-founders) with a team of 20 who got tired of spending hours reviewing PRs, so we built Infinitcode.ai, an AI-powered code reviewer that: - *Summarizes PRs in plain English*: No more deciphering 1,000-line diff jungles - *Catches more than bugs*: Security holes, performance pitfalls, code smells, even typos (yes, we’ll flag “vurnerabilities” and vulnerabilities) - *Zero onboarding*: Works instantly—no “let me learn your codebase for weeks” nonsense. Why we’re posting: We’re in alpha and need brutal honesty. Roast our tool, mock our UI, or tell us why AI will…
- IBI built a tool to assist AI agents to know when a PR is good to goJan 2026 · dsifry.github.io · ▲45
I've been using Claude Code heavily, and kept hitting the same issue: the agent would push changes, respond to reviews, wait for CI... but never really know when it was done. It would poll CI in loops. Miss actionable comments buried among 15 CodeRabbit suggestions. Or declare victory while threads were still unresolved. The core problem: no deterministic way for an agent to know a PR is ready to merge. So I built gtg (Good To Go). One command, one answer: $ gtg 123 OK PR #123: READY CI: success (5/5 passed) Threads: 3/3 resolved It aggregates CI status, classifies review comments…
Dashboard tracking all GitHub PRs and analyzing Code Agent activity2025 · github.com · ▲10Hi HN, We are researchers from ETH Zurich interested in the real-world adoption and impact of Code Agents. To measure this, we built a dashboard, scraping all public PRs on GitHub, analyzing which are created by different code agents (Codex, Jules, Copilot, Devin, etc.), and measuring their merge rates, sliced by various repository and PR characteristics. https://insights.logicstar.ai Since mid-May, we've analyzed over 10 million PRs and already found some interesting trends: Usage is high, but shallow. Agents submit ~7% of all PRs overall, but only ~1–2% on popular repos. Most…
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