This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
What I Built
Your first open-source pull request shouldn't feel like solving a mystery.
When I started exploring open source, I realized that contributing isn't just about writing code. Beginners must understand unfamiliar GitHub issues, navigate large codebases, figure out what to test, and prepare a pull request that follows the project's expectations. That learning curve can be intimidating.
FirstPR AI is a local AI mentor designed to make that journey easier.
Instead of spending hours trying to figure out where to begin, developers can choose a focused skill and get structured, beginner-friendly guidance.
FirstPR AI includes five AI-assisted skills:
🧩 Issue Analyzer: Breaks down an issue into its requirements, unknowns, and investigation steps.
🧭 Codebase Explorer: Helps plan how to navigate an unfamiliar repository.
🧪 Test Planner: Suggests test cases and verification steps.
🔀 PR Mentor: Guides contributors through preparing a clear, reviewable pull request.
🛡️ Contribution Safety Checker: Helps identify things to review before making changes or contributing.
The goal isn't to make developers stop learning. It's to remove the unnecessary friction that keeps beginners from getting started.
Demo
🌐 Project website: https://firstpr-ai.vercel.app/
🖥️ Run it locally: Clone the repository, install the dependencies, start Ollama, and run python app.py.
The local interface runs at http://127.0.0.1:7860.
Code
GitHub repository: https://github.com/prabhtheone/FirstPR-AI
The project is built with Python, Gradio, Ollama, and focused skill instructions. It includes automated tests and a GitHub Actions workflow for basic regression checks.
How I Built It
I built FirstPR AI around a simple idea: AI should make open source more approachable without forcing every contributor to depend on a hosted AI service.
The application uses Python and Gradio for its interface and Ollama for local model inference. Each skill has its own instructions, helping the model follow a specific workflow rather than answering every request in the same generic way.
For the Gemma 3 4B setup, the intended local model is gemma3:4b, which can be downloaded through Ollama. After the model and application dependencies are installed, inference can run on the user's own computer without requiring a cloud AI API key.
The application also includes basic error handling, input validation, and instructions that tell the model not to pretend it has inspected files, executed tests, or modified code when it hasn't.
This matters because AI-generated guidance is only a starting point. Developers must still inspect the real code, verify suggestions, run tests, and follow the repository's contribution guidelines.
Why Does Open Innovation Matter?
For a tool designed to help open-source contributors, using open technology is a natural fit.
With a local-first design, developers can experiment with AI-assisted workflows without sending issue descriptions to a third-party AI API by default. They can inspect the code, change the instructions, experiment with different open-weight models, and adapt the tool to their own workflow.
There is also a practical benefit: once the dependencies and model are installed, the core AI workflow can run locally without ongoing hosted-model API charges.
Open innovation makes the tool more than an interface around a remote service. Developers can understand how it works, improve it, and contribute back.
My Agent Session
The core workflow is available in the repository. I haven't included a separately recorded agent session in this submission.
The Lazy Payoff 🦥
The most useful kind of laziness is removing repetitive work without skipping the important parts.
FirstPR AI brings issue analysis, repository exploration planning, test planning, pull-request preparation, and contribution safety guidance into one interface.
Instead of repeatedly searching for separate tutorials and trying to connect the steps, a beginner can start with one issue and receive a structured plan.
I haven't established a measured time-saving benchmark yet, so I'm not claiming a specific number of minutes saved. The goal is to make the first step into open source less intimidating and easier to repeat.
Prize Categories
Open-Source AI Challenge — overall submission.
Top comments (0)