Stargazers Reloaded – LLM-Powered Analyses of Your GitHub Community
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…
In plain words
Stargazers Reloaded analyzes GitHub repository stargazers using large language models and OCR technology. It examines stargazer profiles by taking screenshots of their GitHub pages, extracting text, and using LLMs to identify interests and skills. The tool captures details like programming language proficiency and topic interests that the GitHub API alone cannot access. It is designed for GitHub community managers and repository maintainers who want to understand who is following their projects and what skills or interests those users have.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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 LLM-powered app built using EvaDB additionally extracts insights from unstructured data obtained from the stargazers’ webpages. Our analysis of the fast-growing GPT4All community showed that the majority of the stargazers are proficient in Python and JavaScript, and 43% of them are interested in Web Development. Web developers love open-source LLMs! We found that directly using GPT-4 to generate the “golden” table is super expensive — costing $60 to process the information of 1000 stargazers. To maintain accuracy while also reducing cost, we set up an LLM model cascade in a SQL query, running GPT-3.5 before GPT-4, that lowers the cost to $5.5 for analyzing 1000 GitHub stargazers. We’ve been working on this app for a month now and are excited to open source it today :) Some useful links: * Blog Post - https://medium.com/evadb-blog/stargazers-reloaded-llm-powere... * GitHub Repository - https://github.com/pchunduri6/stargazers-reloaded/ * EvaDB - https://github.com/georgia-tech-db/evadb Please let us know what you think!
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