LaReview is a dev-first code review workbench for complex changes.
You give it a PR (GitHub/GitLab) or a diff, and it builds a structured review plan grouped by flows (auth, API, billing) and ordered by risk. The goal is to make big reviews feel like a plan you can actually follow, not an endless scroll.
It runs locally and is designed to work with your existing AI coding agent (bring your own agent). No bot comment spam. You decide what feedback gets posted back to the PR.
Highlights:
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You give it a PR (GitHub/GitLab) or a diff, and it builds a structured review plan grouped by flows (auth, API, billing) and ordered by risk. The goal is to make big reviews feel like a plan you can actually follow, not an endless scroll.
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We're Charles and Dean, and we're building Stage: a code review tool that guides you through reading a PR step by step, instead of piecing together a giant diff.
Here's a demo video: https://www.tella.tv/video/stage-demo-1pph.\nYou can play around with some example PRs here: https://stagereview.app/explore.
Teams are moving faster than ever with AI these days, but more and more engineers are merging changes that they don't really understand. The bottleneck isn't writing code anymore, it's reviewing it.
We're two engineers who got frustrated with GitHub's UI for code review. As coding agents took off, we saw our PR backlog pile up faster than we could handle. Not only that, the PRs themselves were getting larger and harder to understand, and we found ourselves spending most of our time trying to build a mental model of what a PR was actually doing.
We built Stage to make reviewing a PR feel more like reading chapters of a book, not an unorganized set of paragraphs. We use it every day now, not just to review each other's code but also our own, and at this point we can't really imagine going back to the old GitHub UI.
What Stage does: when a PR is opened, Stage groups the changes into small, logical "chapters". These chapters get ordered in the way that makes most sense to read. For each chapter, Stage tells you what changed and specific things to double check. Once you review all the chapters, you're done reviewing the PR.
You can sign in to Stage with your GitHub account and everything is synced seamlessly (commenting, approving etc.) so it fits into the workflows you're already used to.
What we're not building: a code review bot like CodeRabbit or Greptile. These tools are great for catching bugs (and we use them ourselves!) but at the end of the day humans are responsible for what gets shipped. It's clear that reviewing code hasn't scaled the same way that writing did, and they (we!) need better tooling to keep up with the onslaught of AI generated code, which is only going to grow.
We've had a lot of fun building this and are excited to take it further. If you're like us and are also tired of using GitHub for reviewing PRs, we'd love for you to try it out and tell us what you think!"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["code"],"value":"Show HN: Stage \u2013 Putting humans back in control of code review"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://stagereview.app/"}},"_tags":["story","author_cpan22","story_47796818","show_hn"],"author":"cpan22","children":[47796999,47797132,47799555,47799945,47804322,47806067,47806199,47806213,47806402,47806409,47806476,47806648,47806895,47806971,47807104,47807461,47807516,47807649,47807771,47808445,47808773,47808797,47809102,47809438,47809762,47809770,47810214,47810343,47810733,47810873,47811529,47812258,47812408,47812764,47813475,47815808,47826031,47835842,47903326],"created_at":"2026-04-16T17:36:29Z","created_at_i":1776360989,"num_comments":111,"objectID":"47796818","points":130,"story_id":47796818,"story_text":"Hey HN! We're Charles and Dean, and we're building Stage: a code review tool that guides you through reading a PR step by step, instead of piecing together a giant diff.
Here's a demo video: https://www.tella.tv/video/stage-demo-1pph.\nYou can play around with some example PRs here: https://stagereview.app/explore.
Teams are moving faster than ever with AI these days, but more and more engineers are merging changes that they don't really understand. The bottleneck isn't writing code anymore, it's reviewing it.
We're two engineers who got frustrated with GitHub's UI for code review. As coding agents took off, we saw our PR backlog pile up faster than we could handle. Not only that, the PRs themselves were getting larger and harder to understand, and we found ourselves spending most of our time trying to build a mental model of what a PR was actually doing.
We built Stage to make reviewing a PR feel more like reading chapters of a book, not an unorganized set of paragraphs. We use it every day now, not just to review each other's code but also our own, and at this point we can't really imagine going back to the old GitHub UI.
What Stage does: when a PR is opened, Stage groups the changes into small, logical "chapters". These chapters get ordered in the way that makes most sense to read. For each chapter, Stage tells you what changed and specific things to double check. Once you review all the chapters, you're done reviewing the PR.
You can sign in to Stage with your GitHub account and everything is synced seamlessly (commenting, approving etc.) so it fits into the workflows you're already used to.
What we're not building: a code review bot like CodeRabbit or Greptile. These tools are great for catching bugs (and we use them ourselves!) but at the end of the day humans are responsible for what gets shipped. It's clear that reviewing code hasn't scaled the same way that writing did, and they (we!) need better tooling to keep up with the onslaught of AI generated code, which is only going to grow.
We've had a lot of fun building this and are excited to take it further. If you're like us and are also tired of using GitHub for reviewing PRs, we'd love for you to try it out and tell us what you think!","title":"Show HN: Stage \u2013 Putting humans back in control of code review","updated_at":"2026-08-20T15:20:29Z","url":"https://stagereview.app/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"adamthegoalie"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["code","rabbit"],"value":"I built adamsreview, a Claude Code plugin that runs deeper, multi-stage PR reviews using parallel sub-agents, validation passes, persistent JSON state, and optional ensemble review via Codex CLI and PR bot comments.
On my own PRs, it has been catching dramatically more real bugs than Claude\u2019s built-in /review, /ultrareview, CodeRabbit, Greptile, and Codex\u2019s built-in review, while producing fewer false positives.
adamsreview is six Claude Code slash commands packaged as a plugin: review, codex-review, add, promote, walkthrough, and fix. I modeled it after the built-in /review command and extended it meaningfully.
You can clear context between review stages because state is stored in JSON artifacts on disk, with built-in scripts for keeping it updated.
The walkthrough command uses Claude\u2019s AskUserQuestion feature to walk you through uncertain findings or items needing human review one by one. Then, the fix command dispatches per-fix-group agents and re-reviews the work with Opus, reverting any regressions before committing survivors.
It runs against your regular Claude Code subscription (Max plan recommended), unlike /ultrareview, which charges against your Extra Usage pool.
I would love feedback from Claude Code users, pro devs, and anyone with strong opinions about AI code reviews.
Repo: https://github.com/adamjgmiller/adamsreview
Install:\n/plugin marketplace add adamjgmiller/adamsreview, /plugin install adamsreview@adamsreview"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["code"],"value":"Show HN: adamsreview \u2013 better multi-agent PR reviews for Claude Code"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/adamjgmiller/adamsreview"}},"_tags":["story","author_adamthegoalie","story_48090276","show_hn"],"author":"adamthegoalie","children":[48091157,48091821,48091864,48091931,48091966,48091999,48092006,48092369,48092419,48093110,48093206,48093689,48093716,48093943,48094047,48094508,48094600,48094938,48096284,48097457,48104921,48106950,48107158,48163785],"created_at":"2026-05-11T02:06:29Z","created_at_i":1778465189,"num_comments":53,"objectID":"48090276","points":85,"story_id":48090276,"story_text":"I built adamsreview, a Claude Code plugin that runs deeper, multi-stage PR reviews using parallel sub-agents, validation passes, persistent JSON state, and optional ensemble review via Codex CLI and PR bot comments.
On my own PRs, it has been catching dramatically more real bugs than Claude\u2019s built-in /review, /ultrareview, CodeRabbit, Greptile, and Codex\u2019s built-in review, while producing fewer false positives.
adamsreview is six Claude Code slash commands packaged as a plugin: review, codex-review, add, promote, walkthrough, and fix. I modeled it after the built-in /review command and extended it meaningfully.
You can clear context between review stages because state is stored in JSON artifacts on disk, with built-in scripts for keeping it updated.
The walkthrough command uses Claude\u2019s AskUserQuestion feature to walk you through uncertain findings or items needing human review one by one. Then, the fix command dispatches per-fix-group agents and re-reviews the work with Opus, reverting any regressions before committing survivors.
It runs against your regular Claude Code subscription (Max plan recommended), unlike /ultrareview, which charges against your Extra Usage pool.
I would love feedback from Claude Code users, pro devs, and anyone with strong opinions about AI code reviews.
Repo: https://github.com/adamjgmiller/adamsreview
Install:\n/plugin marketplace add adamjgmiller/adamsreview, /plugin install adamsreview@adamsreview","title":"Show HN: adamsreview \u2013 better multi-agent PR reviews for Claude Code","updated_at":"2026-08-25T05:02:19Z","url":"https://github.com/adamjgmiller/adamsreview"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"dsifry"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["code","rabbit"],"value":"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 \nOK PR #123: READY \n CI: success (5/5 passed) \n Threads: 3/3 resolved
It aggregates CI status, classifies review comments (actionable vs. noise), and tracks thread resolution. Returns JSON for agents or human-readable text.
The comment classification is the interesting part \u2014 it understands CodeRabbit severity markers, Greptile patterns, Claude's blocking/approval language. "Critical: SQL injection" gets flagged; "Nice refactor!" doesn't.
MIT licensed, pure Python. I use this daily in a larger agent orchestration system \u2014 would love feedback from others building similar workflows."},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: I built a tool to assist AI agents to know when a PR is good to go"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://dsifry.github.io/goodtogo/"}},"_tags":["story","author_dsifry","story_46656759","show_hn"],"author":"dsifry","children":[46656970,46659731,46660127,46661775,46662347,46663322,46664278,46688523,46702168,46702688],"created_at":"2026-01-17T09:55:56Z","created_at_i":1768643756,"num_comments":35,"objectID":"46656759","points":45,"story_id":46656759,"story_text":"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 \nOK PR #123: READY \n CI: success (5/5 passed) \n Threads: 3/3 resolved
It aggregates CI status, classifies review comments (actionable vs. noise), and tracks thread resolution. Returns JSON for agents or human-readable text.
The comment classification is the interesting part \u2014 it understands CodeRabbit severity markers, Greptile patterns, Claude's blocking/approval language. "Critical: SQL injection" gets flagged; "Nice refactor!" doesn't.
MIT licensed, pure Python. I use this daily in a larger agent orchestration system \u2014 would love feedback from others building similar workflows.","title":"Show HN: I built a tool to assist AI agents to know when a PR is good to go","updated_at":"2026-03-05T23:26:35Z","url":"https://dsifry.github.io/goodtogo/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"sanketsaurav"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["code","rabbit"],"value":"Hi there, HN! We\u2019re Jai and Sanket from DeepSource (YC W20), and today we\u2019re launching Autofix Bot, a hybrid static analysis + AI agent purpose-built for in-the-loop use with AI coding agents.
AI coding agents have made code generation nearly free, and they\u2019ve shifted the bottleneck to code review. Static-only analysis with a fixed set of checkers isn\u2019t enough. LLM-only review has several limitations: non-deterministic across runs, low recall on security issues, expensive at scale, and a tendency to get \u2018distracted\u2019.
We spent the last 6 years building a deterministic, static-analysis-only code review product. Earlier this year, we started thinking about this problem from the ground up and realized that static analysis solves key blind spots of LLM-only reviews. Over the past six months, we built a new \u2018hybrid\u2019 agent loop that uses static analysis and frontier AI agents together to outperform both static-only and LLM-only tools in finding and fixing code quality and security issues. Today, we\u2019re opening it up publicly.
Here\u2019s how the hybrid architecture works:
- Static pass: 5,000+ deterministic checkers (code quality, security, performance) establish a high-precision baseline. A sub-agent suppresses context-specific false positives.
- AI review: The agent reviews code with static findings as anchors. Has access to AST, data-flow graphs, control-flow, import graphs as tools, not just grep and usual shell commands.
- Remediation: Sub-agents generate fixes. Static harness validates all edits before emitting a clean git patch.
Static solves key LLM problems: non-determinism across runs, low recall on security issues (LLMs get distracted by style), and cost (static narrowing reduces prompt size and tool calls).
On the OpenSSF CVE Benchmark [1] (200+ real JS/TS vulnerabilities), we hit 81.2% accuracy and 80.0% F1; vs Cursor Bugbot (74.5% accuracy, 77.42% F1), Claude Code (71.5% accuracy, 62.99% F1), CodeRabbit (59.4% accuracy, 36.19% F1), and Semgrep CE (56.9% accuracy, 38.26% F1). \nOn secrets detection, 92.8% F1; vs Gitleaks (75.6%), detect-secrets (64.1%), and TruffleHog (41.2%). We use our open-source classification model for this. [2]
Full methodology and how we evaluated each tool: https://autofix.bot/benchmarks
You can use Autofix Bot interactively on any repository using our TUI, as a plugin in Claude Code, or with our MCP on any compatible AI client (like OpenAI Codex).[3] We\u2019re specifically building for AI coding agent-first workflows, so you can ask your agent to run Autofix Bot on every checkpoint autonomously.
Give us a shot today: https://autofix.bot. We\u2019d love to hear any feedback!
---
[1] https://github.com/ossf-cve-benchmark/ossf-cve-benchmark
[2] https://huggingface.co/deepsource/Narada-3.2-3B-v1
[3] https://autofix.bot/manual/#terminal-ui"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["code"],"value":"Show HN: Autofix Bot \u2013 Hybrid static analysis and AI code review agent"}},"_tags":["story","author_sanketsaurav","story_46237358","show_hn"],"author":"sanketsaurav","children":[46244233,46244611,46244909,46245516,46246622,46249600],"created_at":"2025-12-11T21:24:34Z","created_at_i":1765488274,"num_comments":13,"objectID":"46237358","points":37,"story_id":46237358,"story_text":"Hi there, HN! We\u2019re Jai and Sanket from DeepSource (YC W20), and today we\u2019re launching Autofix Bot, a hybrid static analysis + AI agent purpose-built for in-the-loop use with AI coding agents.
AI coding agents have made code generation nearly free, and they\u2019ve shifted the bottleneck to code review. Static-only analysis with a fixed set of checkers isn\u2019t enough. LLM-only review has several limitations: non-deterministic across runs, low recall on security issues, expensive at scale, and a tendency to get \u2018distracted\u2019.
We spent the last 6 years building a deterministic, static-analysis-only code review product. Earlier this year, we started thinking about this problem from the ground up and realized that static analysis solves key blind spots of LLM-only reviews. Over the past six months, we built a new \u2018hybrid\u2019 agent loop that uses static analysis and frontier AI agents together to outperform both static-only and LLM-only tools in finding and fixing code quality and security issues. Today, we\u2019re opening it up publicly.
Here\u2019s how the hybrid architecture works:
- Static pass: 5,000+ deterministic checkers (code quality, security, performance) establish a high-precision baseline. A sub-agent suppresses context-specific false positives.
- AI review: The agent reviews code with static findings as anchors. Has access to AST, data-flow graphs, control-flow, import graphs as tools, not just grep and usual shell commands.
- Remediation: Sub-agents generate fixes. Static harness validates all edits before emitting a clean git patch.
Static solves key LLM problems: non-determinism across runs, low recall on security issues (LLMs get distracted by style), and cost (static narrowing reduces prompt size and tool calls).
On the OpenSSF CVE Benchmark [1] (200+ real JS/TS vulnerabilities), we hit 81.2% accuracy and 80.0% F1; vs Cursor Bugbot (74.5% accuracy, 77.42% F1), Claude Code (71.5% accuracy, 62.99% F1), CodeRabbit (59.4% accuracy, 36.19% F1), and Semgrep CE (56.9% accuracy, 38.26% F1). \nOn secrets detection, 92.8% F1; vs Gitleaks (75.6%), detect-secrets (64.1%), and TruffleHog (41.2%). We use our open-source classification model for this. [2]
Full methodology and how we evaluated each tool: https://autofix.bot/benchmarks
You can use Autofix Bot interactively on any repository using our TUI, as a plugin in Claude Code, or with our MCP on any compatible AI client (like OpenAI Codex).[3] We\u2019re specifically building for AI coding agent-first workflows, so you can ask your agent to run Autofix Bot on every checkpoint autonomously.
Give us a shot today: https://autofix.bot. We\u2019d love to hear any feedback!
---
[1] https://github.com/ossf-cve-benchmark/ossf-cve-benchmark
[2] https://huggingface.co/deepsource/Narada-3.2-3B-v1
[3] https://autofix.bot/manual/#terminal-ui","title":"Show HN: Autofix Bot \u2013 Hybrid static analysis and AI code review agent","updated_at":"2026-05-13T05:21:33Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"dafelst"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["code","rabbit"],"value":"A good proportion of us and our colleagues are now churning out agent-assisted code at an incredible rate, with some of it that is actually good, and a lot that is not so good. I'm personally finding that the real quality gate for our projects is now how thoroughly the generated code was human reviewed to ensure that it is not just correct, but architecturally sensible.
AI code review tools like coderabbit and copilot, or even pointing claude code at a PR are all generally pretty good at finding bugs and style nits, but less good at finding duplicate code, module cross coupling, bad separation of concerns, and so on, even if prompted to do so.
I'm finding that github's PR interface is not really cutting it for me, it was janky even when the reviews were small, but now at the size they're at, it is becoming unmanageable. Add to that the extra noise of mixing in agent reviews, and people "meat-proxying" in copy-pasted agent output, and it's getting pretty noisy and difficult to navigate.
What have you all found that works well for streamlining human review of AI assisted code? Tools and process suggestions are welcome."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["code"],"value":"Ask HN: What tools are you using for human code review of AI-assisted code?"}},"_tags":["story","author_dafelst","story_49321400","ask_hn"],"author":"dafelst","children":[49321995,49326680,49326737,49326866,49328161,49329051,49332386,49334006,49335907,49340321,49343953,49351024,49353756,49431613,49438416],"created_at":"2026-08-16T16:20:45Z","created_at_i":1786897245,"num_comments":11,"objectID":"49321400","points":13,"story_id":49321400,"story_text":"A good proportion of us and our colleagues are now churning out agent-assisted code at an incredible rate, with some of it that is actually good, and a lot that is not so good. I'm personally finding that the real quality gate for our projects is now how thoroughly the generated code was human reviewed to ensure that it is not just correct, but architecturally sensible.
AI code review tools like coderabbit and copilot, or even pointing claude code at a PR are all generally pretty good at finding bugs and style nits, but less good at finding duplicate code, module cross coupling, bad separation of concerns, and so on, even if prompted to do so.
I'm finding that github's PR interface is not really cutting it for me, it was janky even when the reviews were small, but now at the size they're at, it is becoming unmanageable. Add to that the extra noise of mixing in agent reviews, and people "meat-proxying" in copy-pasted agent output, and it's getting pretty noisy and difficult to navigate.
What have you all found that works well for streamlining human review of AI assisted code? Tools and process suggestions are welcome.","title":"Ask HN: What tools are you using for human code review of AI-assisted code?","updated_at":"2026-09-09T18:10:08Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"gillh"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["code","rabbit"],"value":"Generative AI applications pose a unique challenge in production. They are computationally intensive and orders of magnitude slower than traditional data-intensive applications. Scaling these applications is further complicated by expensive hardware requirements and GPU shortages. Consequently, developers are scrambling to implement home-grown caching and rate-limiting solutions, which are error-prone and difficult to get right.
FluxNinja Aperture delivers a production-grade experience with a purpose-built load management platform that provides rate & concurrency limiting, caching, and request prioritization for generative AI applications. Developers can wrap their workloads with Aperture SDKs and define load management policies on business attributes such as user tier, request type, priority, etc.
Features:
- Global Rate Limiting: Prevent abuse by filtering traffic based on user, service, and tier levels, among other granular options.
- Request Prioritization: Boost application performance by prioritizing critical requests while queueing less urgent ones.
- Serverless Caching: Reduce costs and alleviate system load by caching frequently requested data.
- Manage External Limits: Manage API rate limits from third parties (OpenAI, GitHub, Shopify, etc.) with client-side rate limits and prioritization.
SDKs are available in Typescript, Python, Go, etc. The solution also integrates with API gateways and service meshes with an in-cluster deployment option.
We'd love to hear your feedback!
Links:
Sign up for the cloud service: https://www.fluxninja.com
Open-source: https://github.com/fluxninja/aperture
Use-cases:
Manage OpenAI rate limits with request prioritization: https://blog.fluxninja.com/blog/coderabbit-openai-rate-limit...
Building cost-effective generative AI applications with rate limiting and caching: https://blog.fluxninja.com/blog/coderabbit-cost-effective-ge..."},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Rate limiting, caching and request prioritization for AI apps"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://www.fluxninja.com/"}},"_tags":["story","author_gillh","story_39194126","show_hn"],"author":"gillh","created_at":"2024-01-30T18:58:20Z","created_at_i":1706641100,"num_comments":0,"objectID":"39194126","points":10,"story_id":39194126,"story_text":"Generative AI applications pose a unique challenge in production. They are computationally intensive and orders of magnitude slower than traditional data-intensive applications. Scaling these applications is further complicated by expensive hardware requirements and GPU shortages. Consequently, developers are scrambling to implement home-grown caching and rate-limiting solutions, which are error-prone and difficult to get right.
FluxNinja Aperture delivers a production-grade experience with a purpose-built load management platform that provides rate & concurrency limiting, caching, and request prioritization for generative AI applications. Developers can wrap their workloads with Aperture SDKs and define load management policies on business attributes such as user tier, request type, priority, etc.
Features:
- Global Rate Limiting: Prevent abuse by filtering traffic based on user, service, and tier levels, among other granular options.
- Request Prioritization: Boost application performance by prioritizing critical requests while queueing less urgent ones.
- Serverless Caching: Reduce costs and alleviate system load by caching frequently requested data.
- Manage External Limits: Manage API rate limits from third parties (OpenAI, GitHub, Shopify, etc.) with client-side rate limits and prioritization.
SDKs are available in Typescript, Python, Go, etc. The solution also integrates with API gateways and service meshes with an in-cluster deployment option.
We'd love to hear your feedback!
Links:
Sign up for the cloud service: https://www.fluxninja.com
Open-source: https://github.com/fluxninja/aperture
Use-cases:
Manage OpenAI rate limits with request prioritization: https://blog.fluxninja.com/blog/coderabbit-openai-rate-limit...
Building cost-effective generative AI applications with rate limiting and caching: https://blog.fluxninja.com/blog/coderabbit-cost-effective-ge...","title":"Show HN: Rate limiting, caching and request prioritization for AI apps","updated_at":"2024-09-20T16:19:38Z","url":"https://www.fluxninja.com/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"yuvrajangads"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["code","rabbit"],"value":"I built a CLI that detects patterns AI coding tools leave behind: empty catch blocks, hardcoded secrets, as any everywhere, comments that restate the code, god functions, SQL concatenation.
24 rules across JS/TS and Python. Zero config, runs offline, regex-based so it's fast.
npx @yuvrajangadsingh/vibecheck .\nWhy: CodeRabbit found AI-generated PRs have 1.7x more issues than human PRs. Veracode says 45% of AI code samples have security vulnerabilities. "Vibe coding" is everywhere now but nobody's linting for the patterns it produces.
This isn't a replacement for ESLint. It catches things ESLint doesn't look for, like catch blocks that only console.error without rethrowing, bare except: pass in Python, or mutable default arguments."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["code"],"value":"Show HN: Vibecheck \u2013 lint for AI-generated code smells (JS/TS/Python)"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/yuvrajangadsingh/vibecheck"}},"_tags":["story","author_yuvrajangads","story_47398197","show_hn"],"author":"yuvrajangads","children":[47398503,47399650,47419208],"created_at":"2026-03-16T12:42:02Z","created_at_i":1773664922,"num_comments":4,"objectID":"47398197","points":7,"story_id":47398197,"story_text":"I built a CLI that detects patterns AI coding tools leave behind: empty catch blocks, hardcoded secrets, as any everywhere, comments that restate the code, god functions, SQL concatenation.
24 rules across JS/TS and Python. Zero config, runs offline, regex-based so it's fast.
npx @yuvrajangadsingh/vibecheck .\nWhy: CodeRabbit found AI-generated PRs have 1.7x more issues than human PRs. Veracode says 45% of AI code samples have security vulnerabilities. "Vibe coding" is everywhere now but nobody's linting for the patterns it produces.
This isn't a replacement for ESLint. It catches things ESLint doesn't look for, like catch blocks that only console.error without rethrowing, bare except: pass in Python, or mutable default arguments.","title":"Show HN: Vibecheck \u2013 lint for AI-generated code smells (JS/TS/Python)","updated_at":"2026-03-25T22:41:06Z","url":"https://github.com/yuvrajangadsingh/vibecheck"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"paulddraper"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["code","rabbit"],"value":"CodeMind, CodeAnt, CodeRabbit, etc...
Separate the marketing from reality...what are you using, do you find it useful, and why?"},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["code"],"value":"What is the best AI/automated code review tool?"}},"_tags":["story","author_paulddraper","story_41860624","ask_hn"],"author":"paulddraper","children":[41860750,41860932,41867709,41869591],"created_at":"2024-10-16T15:56:49Z","created_at_i":1729094209,"num_comments":2,"objectID":"41860624","points":7,"story_id":41860624,"story_text":"CodeMind, CodeAnt, CodeRabbit, etc...
Separate the marketing from reality...what are you using, do you find it useful, and why?","title":"What is the best AI/automated code review tool?","updated_at":"2024-10-30T19:03:04Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"codeman001"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["code","rabbit"],"value":"Has anyone here using AI-powered code reviews (e.g. coderabbit, greptile , codeant, korbit etc.) to human reviews in big projects?
I'm curious about:
- How useful feedback is from AI reviewers\n- Whether it really catches bugs or just nitpicks\n- How it fits into your workflow (PR comments, suggestions, etc.)\n- If you've stopped asking teammates for reviews because of it
Is it good enough to trust? Or still just a supplement to human eyes?
Would love to hear your experience."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["code"],"value":"Ask HN: How much better are AI code reviews vs. human code reviews?"}},"_tags":["story","author_codeman001","story_43962154","ask_hn"],"author":"codeman001","children":[43963272],"created_at":"2025-05-12T12:22:27Z","created_at_i":1747052547,"num_comments":1,"objectID":"43962154","points":6,"story_id":43962154,"story_text":"Has anyone here using AI-powered code reviews (e.g. coderabbit, greptile , codeant, korbit etc.) to human reviews in big projects?
I'm curious about:
- How useful feedback is from AI reviewers\n- Whether it really catches bugs or just nitpicks\n- How it fits into your workflow (PR comments, suggestions, etc.)\n- If you've stopped asking teammates for reviews because of it
Is it good enough to trust? Or still just a supplement to human eyes?
Would love to hear your experience.","title":"Ask HN: How much better are AI code reviews vs. human code reviews?","updated_at":"2025-10-12T23:43:23Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"changisaac"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["code","rabbit"],"value":"Hi HN, edgy thought, seeing a lot traction and usage of code review bots lately across both startups and medium/larger companies. Whether it's CodeRabbit, Graphite Diamond, etc. (there are plenty more).
If models keep improving, and we eventually inject these code review bots with more and more context to validate even things like business logic, tribal knowledge, etc. do you think we could eventually arrive very soon in a place where we no longer do traditional code reviews?
Just work with your coding agent to develop things, get it reviewed by a code review bot, fix any issues caught, and ship."},"title":{"fullyHighlighted":false,"matchLevel":"partial","matchedWords":["code"],"value":"Ask HN: Will human code review still exist a year from now?"}},"_tags":["story","author_changisaac","story_44844633","ask_hn"],"author":"changisaac","children":[44844773,44844785,44844811,44844846,44844948,44844988,44845228,44845477,44846223,44846608,44847634],"created_at":"2025-08-09T07:04:02Z","created_at_i":1754723042,"num_comments":24,"objectID":"44844633","points":5,"story_id":44844633,"story_text":"Hi HN, edgy thought, seeing a lot traction and usage of code review bots lately across both startups and medium/larger companies. Whether it's CodeRabbit, Graphite Diamond, etc. (there are plenty more).
If models keep improving, and we eventually inject these code review bots with more and more context to validate even things like business logic, tribal knowledge, etc. do you think we could eventually arrive very soon in a place where we no longer do traditional code reviews?
Just work with your coding agent to develop things, get it reviewed by a code review bot, fix any issues caught, and ship.","title":"Ask HN: Will human code review still exist a year from now?","updated_at":"2026-03-05T22:31:08Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"roddylindsay"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["code","rabbit"],"value":"My team is using tools like Copilot, Phind and Coderabbit, but these seem mostly useful for code suggestions and feedback related to individual files.
Is anyone successfully using tools which, via large context windows/RAG/other IR techniques, are able to generate or review code with complex dependencies across many different files and code areas (i.e. db, backend, API, frontend)? Or is this still a ways out?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Ask HN: Is anyone successfully integrating LLMs with their entire codebase?"}},"_tags":["story","author_roddylindsay","story_40970707","ask_hn"],"author":"roddylindsay","children":[40970835],"created_at":"2024-07-15T19:20:42Z","created_at_i":1721071242,"num_comments":3,"objectID":"40970707","points":5,"story_id":40970707,"story_text":"My team is using tools like Copilot, Phind and Coderabbit, but these seem mostly useful for code suggestions and feedback related to individual files.
Is anyone successfully using tools which, via large context windows/RAG/other IR techniques, are able to generate or review code with complex dependencies across many different files and code areas (i.e. db, backend, API, frontend)? Or is this still a ways out?","title":"Ask HN: Is anyone successfully integrating LLMs with their entire codebase?","updated_at":"2024-09-20T17:29:19Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"dsifry"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["code","rabbit"],"value":"A few weeks ago I posted about GoodToGo https://news.ycombinator.com/item?id=46656759 - a tool that gives AI agents a deterministic answer to "is this PR ready to merge?" Several people asked about the larger orchestration system I mentioned. This is that system.
I got tired of being a project manager for Claude Code. It writes code fine, but shipping production code is seven or eight jobs \u2014 research, planning, design review, implementation, code review, security audit, PR creation, CI babysitting. I was doing all the coordination myself. The agent typed fast. I was still the bottleneck. What I really needed was an orchestrator of orchestrators - swarms of swarms of agents with deterministic quality checks.
So I built metaswarm. It breaks work into phases and assigns each to a specialist swarm orchestrator. It manages handoffs and uses BEADS for deterministic gates that persist across /compact, /clear, and even across sessions. Point it at a GitHub issue or brainstorm with it (it uses Superpowers to ask clarifying questions) and it creates epics, tasks, and dependencies, then runs the full pipeline to a merged PR - including outside code review like CodeRabbit, Greptile, and Bugbot.
The thing that surprised me most was the design review gate. Five agents \u2014 PM, Architect, Designer, Security, CTO \u2014 review every plan in parallel before a line of code gets written. All five must approve. Three rounds max, then it escalates to a human. I expected a rubber stamp. It catches real design problems, dependency issues, security gaps.
This weekend I pointed it at my backlog. 127 PRs merged. Every one hit 100% test coverage. No human wrote code, reviewed code, or clicked merge. OK, I guided it a bit, mostly helping with plans for some of the epics.
A few learnings:
Agent checklists are theater. Agents skipped coverage checks, misread thresholds, or decided they didn't apply. Prompts alone weren't enough. The fix was deterministic gates \u2014 BEADS, pre-push hooks, CI jobs all on top of the agent completion check. The gates block bad code whether or not the agent cooperates.
The agents are just markdown files. No custom runtime, no server, and while I built it on TypeScript, the agents are language-agnostic. You can read all of them, edit them, add your own.
It self-reflects too. After every merged PR, the system extracts patterns, gotchas, and decisions into a JSONL knowledge base. Agents only load entries relevant to the files they're touching. The more it ships, the fewer mistakes it makes. It learns as it goes.
metaswarm stands on two projects: https://github.com/steveyegge/beads by Steve Yegge (git-native task tracking and knowledge priming) and https://github.com/obra/superpowers by Jesse Vincent (disciplined agentic workflows \u2014 TDD, brainstorming, systematic debugging). Both were essential.
Background: I founded Technorati, Linuxcare, and Warmstart; tech exec at Lyft and Reddit. I built metaswarm because I needed autonomous agents that could ship to a production codebase with the same standards I'd hold a human team to.
$ cd my-project-name
$ npx metaswarm init
MIT licensed. IANAL. YMMV. Issues/PRs welcome!"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: 127 PRs to Prod this wknd with 18 AI agents: metaswarm. MIT licensed"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/dsifry/metaswarm"}},"_tags":["story","author_dsifry","story_46864977","show_hn"],"author":"dsifry","children":[46866495,46866628,46866907,46867792,46868317,46870080],"created_at":"2026-02-03T01:18:39Z","created_at_i":1770081519,"num_comments":2,"objectID":"46864977","points":5,"story_id":46864977,"story_text":"A few weeks ago I posted about GoodToGo https://news.ycombinator.com/item?id=46656759 - a tool that gives AI agents a deterministic answer to "is this PR ready to merge?" Several people asked about the larger orchestration system I mentioned. This is that system.
I got tired of being a project manager for Claude Code. It writes code fine, but shipping production code is seven or eight jobs \u2014 research, planning, design review, implementation, code review, security audit, PR creation, CI babysitting. I was doing all the coordination myself. The agent typed fast. I was still the bottleneck. What I really needed was an orchestrator of orchestrators - swarms of swarms of agents with deterministic quality checks.
So I built metaswarm. It breaks work into phases and assigns each to a specialist swarm orchestrator. It manages handoffs and uses BEADS for deterministic gates that persist across /compact, /clear, and even across sessions. Point it at a GitHub issue or brainstorm with it (it uses Superpowers to ask clarifying questions) and it creates epics, tasks, and dependencies, then runs the full pipeline to a merged PR - including outside code review like CodeRabbit, Greptile, and Bugbot.
The thing that surprised me most was the design review gate. Five agents \u2014 PM, Architect, Designer, Security, CTO \u2014 review every plan in parallel before a line of code gets written. All five must approve. Three rounds max, then it escalates to a human. I expected a rubber stamp. It catches real design problems, dependency issues, security gaps.
This weekend I pointed it at my backlog. 127 PRs merged. Every one hit 100% test coverage. No human wrote code, reviewed code, or clicked merge. OK, I guided it a bit, mostly helping with plans for some of the epics.
A few learnings:
Agent checklists are theater. Agents skipped coverage checks, misread thresholds, or decided they didn't apply. Prompts alone weren't enough. The fix was deterministic gates \u2014 BEADS, pre-push hooks, CI jobs all on top of the agent completion check. The gates block bad code whether or not the agent cooperates.
The agents are just markdown files. No custom runtime, no server, and while I built it on TypeScript, the agents are language-agnostic. You can read all of them, edit them, add your own.
It self-reflects too. After every merged PR, the system extracts patterns, gotchas, and decisions into a JSONL knowledge base. Agents only load entries relevant to the files they're touching. The more it ships, the fewer mistakes it makes. It learns as it goes.
metaswarm stands on two projects: https://github.com/steveyegge/beads by Steve Yegge (git-native task tracking and knowledge priming) and https://github.com/obra/superpowers by Jesse Vincent (disciplined agentic workflows \u2014 TDD, brainstorming, systematic debugging). Both were essential.
Background: I founded Technorati, Linuxcare, and Warmstart; tech exec at Lyft and Reddit. I built metaswarm because I needed autonomous agents that could ship to a production codebase with the same standards I'd hold a human team to.
$ cd my-project-name
$ npx metaswarm init
MIT licensed. IANAL. YMMV. Issues/PRs welcome!","title":"Show HN: 127 PRs to Prod this wknd with 18 AI agents: metaswarm. MIT licensed","updated_at":"2026-03-05T23:29:47Z","url":"https://github.com/dsifry/metaswarm"}],"hitsPerPage":50,"nbHits":278,"nbPages":6,"page":0,"params":"query=Code+Rabbit&tags=story&hitsPerPage=50&advancedSyntax=true&analyticsTags=backend","processingTimeMS":10,"processingTimingsMS":{"_request":{"roundTrip":19},"afterFetch":{"format":{"highlighting":2,"total":2},"merge":{"mergeLoop":{"prepareNextHit":1,"total":1},"total":2},"total":2},"fetch":{"query":4,"scanning":2,"total":7},"total":10},"query":"Code Rabbit","serverTimeMS":13}