Alternatives
Products that do what ANCHR does
Architectural authority at merge time.
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Deep Work Plan▲114Models matter. Context matters more. Give your agent a plan.
Jun 2026 · deepworkplan.com
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Hey 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…
2025 · infinitcode.ai
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I've found it helps PR reviewers when they can look through a set of commits with clear messages and logically organized changes. Typically reviewers prefer a larger quantity of smaller changes versus a smaller quantity of larger changes. Sometimes it gets really messy to break up a change into sufficiently small PRs, so thoughtful commits are a great way of further subdividing changes in PRs. It can be pretty time consuming to do this though, so this tool automates the process with the help of AI. The tool sends the diff of your git branch against a base branch to an LLM provider. The LLM…
2025 · github.com
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We 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…
Feb 2026 · docs.continue.dev
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I have been pushing up to 90 commits a day on a MacBook Air via 4-5 parallel agents. As you can imagine when all the agents try to build, test and run dev servers on an 8GB machine it is the fast lane to a force quit and restart. I also did not want to pay the CI minutes on 90 pushes a day. So I designed a local merge queue to have all commits land one at a time and fully tested. Hopefully this helps other folks with more modest machines. Appreciate any feedback.
Jul 2026 · github.com
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Hey HN! I built Retain as the evolution of claude-reflect (github.com/BayramAnnakov/claude-reflect). The original problem: I use Claude Code/Codex daily for coding, plus claude.ai and ChatGPT occasionally. Every conversation contains decisions, corrections, and patterns I forget existed weeks later. I kept re-explaining the same preferences. claude-reflect was a CLI tool that extracted learnings from Claude Code sessions. Retain takes this further with a native macOS app that: - Aggregates conversations from Claude Code, claude.ai, ChatGPT, and Codex CLI - Instant full-text…
Jan 2026 · github.com
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Mar 2026 · github.com
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Helping Agents and Human Orchesterators read the same notes.
Mar 2026
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Review the Python you changed, not the Python you inherited. Git-aware AST code review that runs in the seconds before git push. - mukundzha/avouch
19d ago · github.com
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I built this because I was tired of creating pull requests in 20 repositories just to change a single line of workflow job version. With Infra as AI, just mention the change. Agents work on all repos in parallel, read the docs, make a bunch of PRs and fill in the description. You can see the demo of the actual dashboard in the landing. Let me know your thoughts :) It means a lot to me!
Sep 2025 · infrastructureas.ai
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Hi 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…
2025 · github.com
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Figured I'd track the popular coding agents to see merge and approval rates. At first I was tracking Opened vs. Approved, but realized most people aren't approving; they just merge. I haven't found a way to reliably track the other agents (Jules, Devin, etc), since they don't have a standardized branch naming convention. Interesting to watch this over time. Open to ideas.
2025 · github.com
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