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Ask HN: What tools are you using for AI-assisted code review?
My team (around 40 people who write code) is evaluating tools for AI-assisted code review. The market appears to be rife with options, so before starting a series of free trials, I’d like to ask a knowledgeable crowd.
What tools or services are you using? Do you use them just for code review, or for other purposes as well, such as incident response or branch management? Why did you choose them, and what do you like or dislike about them?
- nxy 4mo agoFor Claude Code, I think the standard is Codex + Gemini. Why these two? Because it “covers” the blind spots the others would miss by themselves.
- o2zer0cool 4mo ago[dead]
- b3y0nd1337 4mo ago[dead]
- partsch 4mo agoBesides local review via codex and Claude code, we are using GitHub Copilot with custom instructions. We just assign it as a reviewer in GitHub and a couple minutes later, the review is done. It raises a lot of issues which are valid and which I never had found. https://docs.github.com/en/copilot/tutorials/customize-code-review https://docs.github.com/en/copilot/tutorials/customize-code-...
- coder_afrique 4mo agoclaude code and github copilot
- spgorbatiuk 4mo agoFrankly, coding with Claude Code and having Copilot read through the PR is complementary and helps to catch some things that slipped through
- r_p4rk 4mo agoRolled our own with OpenCode, seems to work quite well and meets the goal of being vendor agnostic :)
- dbour 4mo agoOpencode, mainly because I appreciate how one of the founders treats the UX as a first class concern. Its a great tool to learn since it can help us pivot from the potential impending provider crisis where teams may start having to consider things outside of the large labs. As my daily driver at home, I use Pi though because it doesn't get in your way and forces you to understand how the sauce is made.
- davebren 4mo agoI don't use these tools, but wouldn't it be better to use them only after you do a manual review to see if they find anything you missed? Otherwise I could see reviewers getting false confidence and doing a less thorough review. This happens with seeing that unit tests pass.
- agos 4mo agothat's a good point and surely something to test out and see what works and what not
- benoitdest 4mo ago/review in claude code - the skill pulls the PR from remote and review it. Can post comments also.
- tangweigang 4mo ago[flagged]
- felixlu2026 4mo ago[flagged]
- Supermancho 4mo agoGithub copilot is a little too opinionated, but we still use it to catch obvious stuff. Codex on top of that with specific rules and syntax requirements.
- shsh1312 4mo agoClaude-Code and Codex in combination, combined with an IDE such as Google Antigravity or VisualStudio-Code are very powerful tools, if your company can invest in hardware the new Mac Studio and MacBook Pro allow optimized local inference through open-source tools such as: https://github.com/antirez/ds4 https://github.com/antirez/ds4
- stpedgwdgfhgdd 4mo agoBuilt my own using Claude Code; inside a gitlab job we call Claude Code headless. This works well. There is a tiny mcp server exposed to Claude so it can post inline comments. All existing comments are fed into the reviewer to avoid double posting. The quality of feedback is high. Most complexity is in the SHA management. For example after a rebase. Luckily LLMs understand git very well otherwise it would have been impossible for me.
- alice-work-86 4mo ago[flagged]
- alice-work-86 4mo ago[flagged]
- jcubic 4mo agoI mostly use CodeRabbit via GitHub PR https://www.coderabbit.ai/ https://www.coderabbit.ai/
- sermakarevich 4mo ago[flagged]
- gysakai 4mo ago[flagged]
- rafaepta 4mo agoUsing dupehound for identifying duplicated code. What I use for: I use for identifying duplicated code. It is deterministic, doesn't use AI, offline, runs from CLI and is super fast (and free). What I dislike: I won't say it I dislike, but it is not a tool that does all the jobs of a code review. For instance, it doesn't flag security issues. It is superfocused on code duplication (it performs better than Sonar for this use case) and is specifically useful for large codebases. Disclaimer: I am one of the collaborators, so take it with a grain of salt https://github.com/Rafaelpta/dupehound https://github.com/Rafaelpta/dupehound
- uberex 4mo agoRovo/Bitbucket
- cws_ai_buddy 4mo ago[flagged]
- ericmaciver 4mo ago[flagged]
- cws_ai_buddy 4mo ago[flagged]
- rishabhpoddar 4mo agoI just use codex / claude code to do reviews. It does a good job, and it's easy to explain and navigate.
- cws_ai_buddy 4mo ago[flagged]
- thempatel 4mo agoI built my own for a specific review use case: improving code modularity and minimizing slop. Forcing the agent to use it in a loop to clean up the diff means that I get something cleaner to review manually https://github.com/thempatel/mdlr https://github.com/thempatel/mdlr
- eecks 4mo agoDo you already use static code analysis tools?
- fragmede 4mo agoCodeRabbit
- machinatools 4mo ago[flagged]
- donk8r 4mo ago[flagged]
- qwer43211 4mo agoI usually use gpt5.5 in Codex It feels like magic
- dhruvyads 4mo ago[dead]
- frictasolver 4mo ago[dead]
- jlengrand 4mo agoSurprised to not see it mentioned yet, I've been using Kilo and I'm pretty happy about it. Local development with Claude, review using Kilo : https://kilo.ai/docs/automate/code-reviews/github https://kilo.ai/docs/automate/code-reviews/github.
- thelastjohn 4mo ago[flagged]
- alexiglesias 4mo ago[flagged]
- firefax 4mo agogrep
- harshaxedge 4mo agoi've hoped on to many tools but based on working with all of those tools , o mostly liked this raptor mini(preview) model form vs code , which is very good from my perspective . i gave the same prompts to claude , raptor , kimi and a bunch of others , based on the comparison in speed , quility raptor takes the first place , you can even see the thinking of it not like claude (you cant see what it's thinking ) .
- montfort 4mo agoI use my own tool (released as open source "StrangeDaysTech/straymark"), which takes blocks of related implementation tasks, builds a comprehensive prompt with auditing instructions (comparing intent against implementation), then I run agents from three different models to perform the audits and generate reports. These reports are then analyzed by the main agent to rule out hallucinations or misinterpretations of intent, and finally, a remediation plan is created. This tool can perform this task fragmentation because it's part of its cognitive governance function. Its operation is somewhat complex, but these auditable work units have a coherent structure within the overall project thanks to a knowledge graph that is built from the early design phases through to implementation.
- upmostly 3mo agoCo-creator of Mira [1] here. This is exactly what we built Mira for. It's self-hosted, bring-your-own-model/BYOK, and most importantly, open source. You point it at your own API keys (e.g. OpenRouter or a local model) so nothing leaves your infra, and it runs as a code reviewer on PRs. It's also ridiculously quick at reviewing (benchmarks at ~77s) because your PRs aren't sitting in a queue on a cloud somewhere (alternatives are > 5 minutes) We're working really closely with our users to build the best possible code reviewer. Feedback and contributions are highly encouraged. [1] https://github.com/miracodeai/mira https://github.com/miracodeai/mira