Show HN: Stargazers Reloaded – LLM-Powered Analyses of Your GitHub Community

The article discusses a new app that uses large language models (LLMs) to analyze GitHub profiles of users who have starred a repository, providing insights about their interests. The app, inspired by the original Stargazers app, uses EvaDB and extracts information from the stargazers' webpages, unlike the original which only used the GitHub API. The app's analysis of the GPT4All community revealed that most stargazers are proficient in Python and JavaScript, with 43% interested in Web Development.

To reduce the high cost of using GPT-4 for generating data, the developers set up an LLM model cascade in a SQL query, running GPT-3.5 before GPT-4. This reduced the cost to $5.5 for analyzing 1000 GitHub stargazers from the original $60. After a month of development, the app has been open-sourced.

Key takeaways

An app has been developed to provide insights about GitHub communities using large language models (LLMs).
The app analyzes the GitHub profiles of users who have starred the repository, capturing details like their interests, by using OCR models and LLMs.
The app was inspired by the original Stargazers app but uses EvaDB and LLMs to extract insights from unstructured data from stargazers' webpages.
To reduce the cost of using GPT-4, an LLM model cascade was set up in a SQL query, running GPT-3.5 before GPT-4, lowering the cost to $5.5 for analyzing 1000 GitHub stargazers.

Show HN: Stargazers Reloaded – LLM-Powered Analyses of Your GitHub Community

Key takeaways

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