TrailBuddy: less scrolling, more fresh air

#hf26challenge #ai #opensource #outdoors

I had a pretty simple idea for this challenge: if I have a free hour and want to get outside, why does deciding what to do sometimes take longer than the walk?

So I made TrailBuddy. You put in a starting area, how much time you have, and what you feel like doing. It gives you three ideas, a rough duration, and one thing to bring. The aim is to pick one and close the tab — not spend the afternoon planning.

Why run the AI locally?

TrailBuddy is set up to use Qwen 2.5 3B through Ollama on your own machine. With the model downloaded and Ollama running, your request goes to the local Ollama endpoint rather than a hosted AI API. That means no API key or per-request model bill, and your location and interests stay on your machine with the default setup. The prompt is in the repo, and you can switch to another compatible Ollama model with an environment variable.

There is one important catch: the model and Python packages need to be downloaded first. After that, the app can work without an internet connection. If Ollama isn't running, TrailBuddy falls back to a few built-in ideas and labels them as offline — those aren't AI-generated.

A work in progress

I built the app with FastAPI and plain HTML, CSS, and JavaScript. It doesn't fetch maps, weather, or trail conditions, so the suggestions are ideas, not verified local recommendations. Please check conditions before you go.

I ran seven tests and tried the browser flow. In my test setup, the local Ollama model wasn't available, so I verified the offline fallback — not actual Qwen generation. I also haven't taken it outside yet. That's the next test I want to do, and I'll update this post if I get a chance to try it on a real walk.

Code: https://github.com/prarthanas9917/trailbuddy-ai

Built for the Hacktoberfest Week 1 “Touch Grass” challenge.