This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass.
What I Built
TrailMate AI is an AI-powered outdoor adventure planner designed to help people spend less time scrolling and more time exploring the real world.
The idea is simple: users choose their available time, fitness level, and interests, and the application helps generate a personalized outdoor activity plan.
Whether it's a short nature walk, exploring a garden, or observing the surroundings in a local park, TrailMate AI aims to make getting outside easier and more enjoyable.
Demo
Live demo: Not available yet.
Demo video: Coming soon.
Code
GitHub Repository: https://github.com/akshat0042005/TrailMate-AI
How I Built It
I used Codex to help build TrailMate AI with HTML, CSS, and JavaScript for the frontend, Python and FastAPI for the backend, and Ollama for local inference with an open-weight language model.
The project includes automated tests, configuration examples, and setup documentation.
The test suite passed 14 tests in the development environment. Running the application with the local model and verifying the complete user experience are still part of my testing process.
Why Does Open Innovation Matter?
Open-weight AI gives developers the opportunity to experiment with models locally instead of depending entirely on proprietary AI APIs.
TrailMate AI is designed around local inference, allowing developers to experiment with prompts, change compatible models, and keep user inputs on their own machine when no external service is involved.
I also wanted to explore how AI can be used for something beyond keeping people on a screen. Instead, it can help people plan activities that encourage them to step outside, observe nature, and reconnect with the world around them.
My goal is to make AI a tool that encourages more real-world experiences, not more screen time.
My Agent Session
A DevRelay session link is not available yet.
Prize Categories
No partner-specific prize category claimed at this stage.
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