Alternatives
Products that do what Stargazers Reloaded – LLM-Powered Analyses of Your GitHub Community does
Hey friends! We have built an app for getting insights about your favorite GitHub community using large language models. The app uses LLMs to analyze the GitHub profiles of users who have starred the repository, capturing key details like the topics they are interested in. It takes screenshots of the stargazer's GitHub webpage, extracts text using an OCR model, and extracts insights embedded in the extracted text using LLMs. This app is inspired by the “original” Stargazers app written by Spencer Kimball (CEO of CockroachDB). While the original app exclusively used the GitHub API, this…
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When I came across a study that traced 4.5 million fake GitHub stars, it confirmed a suspicion I’d had for a while: stars are noisy. The issue is they’re visible, they’re persuasive, and they still shape hiring decisions, VC term sheets, and dependency choices—but they say very little about actual quality. I wrote StarGuard to put that number in perspective based on my own methodology inspired with what they did and to fold a broader supply-chain check into one command-line run. It starts with the simplest raw input: every starred_at timestamp GitHub will give. It applies a…
2025 · github.com
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Gitingest is a open-source micro dev-tool that I made over the last week. It turns any public Github repository into a text extract that you can give to your favourite LLM easily. Today I added this url trick to make it even easier to use! How I use it myself: - Quickly generate a README.md boilerplate for a project - Ask LLMs questions about an undocumented codebase It is still very much work in progress and I plan to add many more options (file size limits, exclude patterns..) and a public API I hope this tool can help you Your feedback is very valuable to help me prioritize And…
2024 · gitingest.com
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Hi HN, we’re Jamie and Matti, co-founders of Twigg. During our master’s we continually found the same pain points cropping up when using LLMs. The linear nature of typical LLMs interfaces - like ChatGPT and Claude - made it really easy to get lost without any easy way to visualise or navigate your project. Worst of all, none of them are well suited for long term projects. We found ourselves spending days using the same chat, only for it to eventually break. Transferring context from one chat to another is also cumbersome. We decided to build something more intuitive to the ways humans think.…
Oct 2025 · twigg.ai
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GitHub profile analysis - Build your embedding from your Stars - Compare and discover popular people with similar interests and share yours - Generate a Skill Radar - Recommend repositories you might like
Jan 2026 · puzer.github.io
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2024 · starlens.aisprint.dev
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Since everyone seems to be making a github application, I thought I'd share mine. It's an aggregated view of all of a given person's github projects, as well as easyish lookups for linkedin/stackoverflow/hackernews. It attempts to determine both productivity (how much code did this person write?) as well as impact (did anyone care?) I'd love any feedback, but go easy on the number of simultaneous searches as I'm trying to play nice by github's api limits. check it -- http://www.gitalytics.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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For the past month, we've been monitoring GitHub's public event stream to catch spam comments and fake stars. Today, the spammers are back. Go check out the damage! You can see the spam comments and which users are posting the spam in real time. Also, here's a post we wrote about how we set it up: https://www.trytrench.com/blog/preventing-spam-and-fake-star...
2024 · play.trytrench.com
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Simon(sfarshid) and I spend a lot of time on GitHub. As data nerds we put together a quick tool to explore your repository’s data. How it works: - Data Loading: We use dlt to pull data (issues, PRs, commits, stars) from GitHub - Semantic Layer: Relta wraps the underlying dataset into a semantic layer so the LLM doesn’t hallucinate. - Text-to-SQL: A text-to-SQL agent transforms your plain-English question into a query using the semantic layer - Generative Charts: assistant-ui dynamically generates a chart based on the SQL query - Refinements: If the semantic layer can’t handle your question,…
2024 · github.com
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I made this as a fun tiny project to experiment with Remotion [1] to generate videos. You can find the source code on GitHub [2]. [1] https://www.remotion.dev/ [2] https://github.com/scastiel/github-stars-video
2023 · scastiel.dev
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Hey! I wanted to share a tool I've been working on. It's still very early and a work in progress, but I've found it incredibly helpful when working with Claude and OpenAI's models. What it does: I created a Python script that dumps your entire Git repository into a single file. This makes it much easier to use with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. Key Features: - Respects .gitignore patterns - Generates a tree-like directory structure - Includes file contents for all non-excluded files - Customizable file type filtering Why I find it useful for…
2024
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Hi HN, I built this open-source LLM red teaming tool based on my experience scaling LLMs at a big co to millions of users... and seeing all the bad things people did. How it works: - Uses an unaligned model to create toxic inputs - Runs these inputs through your app using different techniques: raw, prompt injection, and a chain-of-thought jailbreak that tries to re-frame the request to trick the LLM. - Probes a bunch of other failure cases (e.g. will your customer support bot recommend a competitor? Does it think it can process a refund when it can't? Will it leak your user's address?) -…
2024 · promptfoo.dev
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When working on data science projects I often want to just save my work, without having to think deeply about what’s changed. I found the intentionality of Git to feel like friction in these times, and I built GitEase to simplify my workflow. Instead of having to think about add/commit/push/pull with GitEase I just think about `ge load, ge save, ge share, ge undo`. And for even less thinking, I use an LLM (text-da-vinci-003) to generate commit messages by summarizing the diff. Take a look, try it out, and I’d welcome any contributions & feedback. pip install gitease $ ge —help…
2023 · medium.com
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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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