This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built
Run Club is a running app that helps people plan a route and find others to run with. Runners can explore route options, connect with nearby people who have shared their availability, and organize group runs. If no one is available, they can create a public run that other runners can discover and join
Demo
https://run-club-tau.vercel.app/home
Code
https://github.com/Yagya443/Run-Club
How I Built It
The app uses React and Vite for the frontend, Express for the backend, and MongoDB with Mongoose for storing app data. It uses map and routing services to help runners plan routes.
For AI features, I use the open-weight Qwen3:4b model through Ollama running locally. It can help describe route options, explain why runners may be a good match, and generate a post-run reflection or suggestion for the next run. The app’s code handles route generation and matching scores; the AI adds natural-language explanations and suggestions
Why Does Open Innovation Matter?
Using an open-weight model lets me run AI locally and experiment with how it fits into the app without relying on a paid hosted AI API for each request. It also makes it easier to inspect the model setup and try a different model later.
Open tools across the stack helped me build the mapping, backend, database, and AI features as parts I can adapt as the project grows.
My Agent Session
Ollama running Qwen3:4b
Prize Categories
Best Use of Render
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