TrailMate AI: An Open-Source AI Companion That Gets You Outside

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

I built TrailMate AI, a small outdoor companion designed to turn a little curiosity into time spent outside.

Users can choose an activity such as a nature walk, birdwatching, gardening, or a mindful outdoor break. They can specify how much time they have and where they plan to go, and TrailMate generates a simple outdoor micro-adventure with practical steps and observation prompts.

The idea is simple: use AI to help someone decide what to do outdoors, then encourage them to put their phone away and enjoy the experience.

Demo

Screenshot:

Code

GitHub repository: https://github.com/Satyam-Godara/Trailmate-Ai.git

The project includes a React frontend, a Node.js/Express API, and instructions for running the AI locally.

How I Built It

I built TrailMate AI using React, Vite, Node.js, and Express. For AI inference, it connects to Ollama and uses the open-weight qwen2.5:3b model by default.

Instead of sending prompts to a hosted commercial AI API, the backend sends them to the local Ollama service. The model generates a short outdoor activity plan based on the user's selected activity, available time, location description, and interests.

The app is designed as a small, self-hostable project that developers can modify to use another locally available model.

Why Does Open Innovation Matter?

Open innovation makes TrailMate AI easier to inspect, experiment with, and adapt.

Running an open-weight model locally gives users more control over where their prompts are processed. Developers can experiment with different models and prompts without being locked into one hosted AI provider or paying per-request API charges.

It also makes the project a learning opportunity: anyone can inspect the code, run the model on their own machine, and build on the idea.

The initial model download requires internet access, but inference can run locally once the model is installed. The goal is to make AI a tool for real-world exploration rather than another reason to stay on a screen.