This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
IssuePilot AI is a beginner-friendly tool that helps aspiring open-source contributors understand GitHub issues before they start coding.
Getting started with open source can feel overwhelming. Issues often contain technical descriptions, unfamiliar terminology, long discussions, and requirements that beginners may struggle to understand.
IssuePilot AI aims to make this process easier. Users paste a public GitHub issue URL, and the application fetches the issue details, including its description, labels, and comments. It then uses AI to generate a beginner-friendly breakdown of the issue.
The goal is to help beginners understand:
- What the issue is about.
- What problem needs to be solved.
- Which parts of the issue deserve attention.
- How to approach the issue as a potential contributor.
Who would pay for it?
The initial target audience is students, coding bootcamp learners, and aspiring open-source contributors. In the future, this could evolve into a freemium product for developer communities, coding academies, and organizations that want to help newcomers contribute to open-source projects.
Demo
🚀 Live application: https://issue-pilot-ai-six.vercel.app
The frontend is deployed on Vercel, and the backend is hosted on Render.
Code
💻 GitHub repository: https://github.com/Architap18/IssuePilot-AI
The project is organized into two main parts:
- Frontend: React and Vite, with a dark interface designed to make issue exploration straightforward.
- Backend: Node.js and Express, responsible for fetching GitHub issue data and handling AI analysis requests.
How I Built It
I built IssuePilot AI using React, Vite, Node.js, and Express.
The application follows a simple workflow:
- A user submits a public GitHub issue URL.
- The Express backend retrieves the issue information using the GitHub REST API.
- The issue data is passed to an AI analysis endpoint.
- The AI generates a beginner-friendly explanation, estimates the issue's difficulty, and suggests investigation steps.
- The frontend displays the issue information and AI-generated analysis.
For AI, I use GPT-OSS 20B (openai/gpt-oss-20b) through the Groq API. The model provides the AI analysis, while Groq handles inference.
The project is deployed using Vercel for the frontend and Render for the backend. Environment variables keep server-side credentials separate from the frontend code.
Why Does Open Innovation Matter?
Open innovation matters because it makes AI development more accessible to individual developers and students.
I wanted to build something useful without training a model from scratch or requiring expensive infrastructure. Using an open-weight model gave me access to a model that I could integrate into a practical application, while Groq provided a convenient way to run inference.
Open-source tools such as React, Express, and Vite also made it possible to build and deploy the complete application with a relatively simple development setup.
For me, this project demonstrates how existing open technologies can be combined to address a real barrier to entry: helping beginners understand open-source projects and find a starting point for contributing.
What I Learned
Building IssuePilot AI helped me work through several parts of a full-stack AI application:
- Connecting a React frontend to a Node.js backend.
- Retrieving and processing data from the GitHub API.
- Integrating an AI inference API.
- Managing environment variables and protecting API credentials.
- Deploying the frontend and backend separately.
- Configuring cross-origin requests between deployed services.
- Updating model configurations when an API model becomes unavailable.
This project is an early step toward making open-source contribution more approachable for beginners, and I plan to keep improving it as I learn more.
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