TrailSense: Less Scrolling, More Exploring with Local AI

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

What I Built

TrailSense — Less Scrolling, More Exploring

TrailSense is a local-AI-powered outdoor quest generator designed to help people spend less time on their screens and more time exploring the world around them.

Users choose how much time they have and what kind of activity they enjoy. TrailSense generates a short outdoor mission with simple activities that encourage nature observation, mindful walking, and curiosity.

The idea is simple: use AI to prepare an outdoor experience, then put the phone away.

Code

GitHub repository: https://github.com/Rakshit-Chauhan-byte/TrailSense

How I Built It

TrailSense is built with Next.js, TypeScript, and Tailwind CSS. It uses Gemma 3 4B through Ollama for local AI inference.

The model generates short outdoor quests based on the user's selected duration and interests. The app includes a simple checklist, a completion flow, and a local journal for recording discoveries.

When the local model is unavailable, the app can display bundled fallback quests so users can still explore.

Why Does Open Innovation Matter?

I wanted AI to help people spend less time using technology, rather than encouraging them to spend more time chatting with it.

Running an open-weight model locally can keep prompts on the user's device and avoid per-request fees from a hosted AI API. It also gives developers more control over the model and the way it behaves.

Local inference does have trade-offs: the model must be downloaded, compatible hardware is required, and generation speed depends on the device.

TrailSense explores how local AI can make technology more private and useful while encouraging people to reconnect with the world outside their screens.

Prize Categories

No partner prize category claimed at this time.

Thanks for checking out TrailSense!