My new project

A developer built TrailSpark AI, a static HTML/CSS/JavaScript web app that runs the open-weight flan-t5-small model locally in the browser via Transformers.js to turn user preferences into short outdoor activity missions. The project, submitted to the Hacktoberfest Open-Source AI Challenge, aims to use AI for one minute before users close the screen and go outside.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass https://dev.to/challenges/hacktoberfest-week1-2026-10-05 TrailSpark AI is a small outdoor activity planner designed to use AI for the opposite of what we usually expect from AI: helping people leave the screen . You tell TrailSpark: It then turns those preferences into a short outdoor mission with a simple goal, something to notice, a playful challenge, and a stopping point. The idea is simple: Use AI for one minute → close the screen → go outside. The project is aimed at students, remote workers, and anyone who wants a small push to spend more time outdoors without turning it into another complicated planning session. 🌐 Live Demo: https://trailspark-lprbvtdsa-adityaraj3532.vercel.app/ https://trailspark-lprbvtdsa-adityaraj3532.vercel.app/ Try entering your preferences and clicking Generate my mission . The first generation can take a little longer because the open-weight model is downloaded and cached by the browser. 💻 GitHub Repository: https://github.com/adityaraj3532/trailspark-ai https://github.com/adityaraj3532/trailspark-ai The project is a static web application built with HTML, CSS, and JavaScript. The most important part of TrailSpark is that the AI runs locally in the browser . I used: The basic flow is: text User preferences ↓ Prompt template ↓ Xenova/flan-t5-small ↓ Local browser inference ↓ Outdoor mission ↓ 📵 Close the screen ↓ 🌿 Go outside