Alternatives
Products that do what CodeSolar does
AI code review for GitHub that never nags you twice
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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
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2021 · codeapprove.com
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2015 · chrome.google.com
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Code review that knows what your PR breaks in other repos
11d ago · contextgoblin.com
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Hi Everyone, I made an open-source GitHub bot that automatically submits a detailed line-by-line code review instantly on any new PR. It's just one-click install to get started. You can host your own bot if you'd like. Open for feedback. thanks!
2024 · github.com
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I've created uithub, a tool that allows developers to easily get LLM context for their coding questions and perform AI repo analysis at scale. Here's what it does: - Get Context: Simply change the 'g' in github.com to 'u' to access AI-powered insights on any GitHub repo. - Flexible Querying: Fetch entire repos, specific branches/subfolders, or filter by file type and size. - API for Developers: Power the next generation of development tools with our API. Key features: - Customizable token limits - File type filtering - Multiple response formats - Size-based file exclusion I built this…
2024 · uithub.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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2020 · github.com
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After years of struggling with onboarding to new projects, I got tired of spending weeks just trying to grasp the basics of a codebase. The README rarely tells the whole story, and "just read the code" isn't practical for large repos. I built RepoIQ to create personalized learning paths through any GitHub repository. It analyzes the codebase structure, identifies key components, and creates a step-by-step guide tailored to your learning needs.
2025 · repoiq.be
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Hey again HN. Isaac and I originally released a voice-first code review tool, but have since open-sourced it and turned it into just a better way to review your and your team's Github PRs. Main things we've heard that make code reviews slower are: 1) PRs are too big and overwhelming 2) Need to jump around to find the right context and relevant code 3) Need to spend time thinking (should not be a thing since AI exists), and writing comments What we built to address these: 1) PRs are split up into sub-PRs without needing to actually split them up and stack them yourself -> Automatically done…
2025 · github.com
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Hey HN, Over the last year, I’ve reviewed more than 1000 code changes. Most of the time was spent catching obvious mistakes rather than debating complex design decisions. If we estimate ~10 minutes per review, that’s 160+ hours spent reviewing code in just one year. So I thought: could I get some of that time back using LLMs? That's why I spent the last few weekends building Presubmit.ai, an open-source AI reviewer that runs as a Github Action right when you open a Pull Request. The results so far are promising: I estimate it can reduce the review time by 50%, which in my case would mean I…
2024 · github.com
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Hello all, I'm a software developer. Over the last few months more and more of my work has turned into using coding agents instead of typing the whole code myself. Usually a few claude sessions at once, sometimes codex, one per feature or per revealed bug. I ran them in a split terminal for a few weeks, and quickly spotted two main problems. The first is that I couldn't easily tell which agent was stuck waiting on me and which was still working, so I'd cycle through sessions and checking on them. The second one: agents sharing a single branch step on each other. Two of them could be editing…
Jul 2026 · shikigami.dev
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