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The Problem Breaking into open-source is overwhelming. For many student developers and beginners, finding a manageable "good first issue" involves sifting through thousands of repositories, reading complex documentation, and trying to understand codebases with zero context. The barrier to entry is often just figuring out what the issue actually requires.
What It Does & Who It Helps AI GitHub Issue Radar is a web application designed to help beginner developers confidently make their first open-source contributions. The user enters their strongest programming skill, and the app fetches the most recent open-source tickets labeled "good first issue" for that specific technology. It then uses AI to summarize the issue and generate a clear, two-step beginner action plan so the contributor knows exactly where to start.
How I Built It I built the frontend and backend routing using Next.js and styled the interface with Tailwind CSS to create a dark, minimalistic "cyber" aesthetic with a custom cursor. The app queries the GitHub REST API to retrieve relevant issues, and then passes the issue data to Google's Gemini API (gemini-3.8-flash) to generate the beginner-friendly summaries and action steps. The entire project is deployed on Vercel.
Challenges Faced One of the main challenges was managing API reliability. I had to implement robust error handling when the Gemini model experienced high demand spikes or when model versions were deprecated. I also had to refine the GitHub search logic to force a single-skill search, as allowing multiple comma-separated skills caused the GitHub API to return zero results due to its strict AND filtering logic.
What I Learned I learned how to seamlessly connect multiple external APIs (GitHub and Gemini) within a Next.js serverless route. I also gained practical experience in UI/UX design by learning how to constrain user inputs to ensure the backend logic functions correctly, and how to deploy a full-stack application to Vercel with protected environment variables.
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