Alternatives
Products that do what Diffly does
A review gate for pull requests too big to read
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2014 · diffmatic.com
- 5DIDiff.io▲194
2015 · diff.io
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- 11DG
Pipe the output of a `diff` command to Diffswarm to get back a persistent URL that contains the diff with workflow, comments, search, etc. Basically some of the helpful features that Github offers on PR diffs, just available for any arbitrary diff. I wrote this tool to use at work for documenting snapshot diff triaging, and also used it to help with the same thing on a couple Ty PRs. Example: https://diffswarm.dev/d-01k2gknwyq82f6x17zqf3apjxc
Jan 2026 · diffswarm.dev
- 12IB
I’ve spent the last 2.5 months building a product that runs LLM-powered code reviews on my pull requests — and I just launched it. The tool is built specifically for solo developers. You install it on your repo, trigger a scan by creating a pull request, and it leaves structured review comments using OpenAI under the hood. Funnily enough, I used the dev version of this app to review its own pull requests while building it. It helped me spot bugs, simplify structure, and keep quality high — all with minimal need for another human in the loop. Things I want to try out in the next months : -…
2025 · codii.dev
- 13DA
2024 · github.com
- 14CB
I built a small benchmark to test CLI coding agents on blind bug detection. A challenger agent injects bugs and writes ground truth (`bugs.json`). A different reviewer agent audits the repo without seeing ground truth, and an LLM matcher scores bug-to-finding assignments. Current run: 50 repos, 150 challenges, 450 reviews, 2,603 injected bugs. Weighted detection: Claude 58.05%, Codex 37.84%, Gemini 27.81%. LLM-judge benchmarks are easy to get wrong, so I’d really appreciate critical feedback on benchmark fairness, scoring/matching methodology, and obvious failure modes I’m missing. Full…
Feb 2026 · github.com
- 15AC
This week we released the Kodus CLI. It took a bit longer than we expected to ship. The reason was simple: there are already many ways to run reviews locally today. IDEs, extensions, terminal commands, agents inside the editor. So building “just another AI CLI” didn’t seem like a good idea. The question that guided the project was different: how can we bring the quality of PR reviews to the moment when the code is still being written? Today the CLI does two main things. The first is running local reviews using the same context we use in PRs. The goal was to avoid that shallow review that…
Mar 2026 · github.com
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2021 · codeapprove.com
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Hi HN! We were frustrated with text diffs on GitHub PRs, so we made DiffLens (https://github.com/marketplace/difflens). DiffLens constructs an Abstract Syntax Tree for each file, and uses that semantic information to intelligently diff changes. Checkout the demo on our website (https://www.difflens.com). Getting started with DiffLens is easy! Simply install the app on your repositories. The next time you open a PR, DiffLens will automatically create a diff for your changes. If you review code, especially TypeScript, JavaScript or CSS, give DiffLens a try! We're…
2022 · github.com
- 20AC
2023 · github.com
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A CLI tool for managing and semantically diffing LLM prompts. Goes beyond text diff by detecting meaning-level changes using embeddings (OpenAI or local). Useful for versioning, testing, and CI/CD workflows.
2025 · github.com
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Hey HN, A bit of backstory: I was job hunting for months, applying everywhere and racking up a ton of rejections. It got frustrating, so I decided to flip the script and build something useful instead: an open-source take on Cluely, that popular stealth AI tool from the $15M company. I figured why not recreate something that's already out there and buzzing, but make it free, transparent, and way better for privacy. Plus, it felt like a fun challenge. This is actually my first project ever using Tauri, and I'm hooked. I started out trying Electron, but it just wasn't cutting it for something…
2025 · github.com
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Hi HN, I built *CodeDrift*, a CLI tool that detects bugs commonly introduced by AI coding assistants like Copilot, Cursor and ChatGPT. Over the last year I noticed that AI tools often generate code that compiles correctly, passes linting and looks reasonable in code review but still contains subtle issues. Some common examples I kept seeing: * async `forEach` loops that never await promises * missing authorization checks (IDOR) * hallucinated dependencies that don’t exist * stack traces leaking sensitive information * request data used without validation These bugs often slip past ESLint,…
Mar 2026 · npmjs.com
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