Touchgrass.local — An AI Coach That Gets You Outside

A developer built Touchgrass.local, a browser-based AI app that uses WebLLM by MLC AI to run open-weight models — Gemma 2 2B, Qwen 2.5 1.5B, and Llama 3.2 1B — entirely on-device via WebGPU. Users describe their day and receive a small outdoor mission from the locally running model, with completed missions tracked through localStorage and visualized as growing CSS grass. The project requires no backend or API key, caches the model in the browser for offline use, and keeps user input on the device.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass Touchgrass.local is a small AI-powered browser app that encourages people to step away from their screens and spend some time outdoors. Users describe how their day has gone, and an open-weight language model running directly in the browser gives them one small outdoor mission along with a friendly nudge. Once the mission is completed, the user can mark it as done and watch a field of CSS grass grow. The app also tracks completed missions and streaks using localStorage. The project is designed to be simple, private, lightweight, and useful for students and anyone who spends too much time in front of a screen. 🚀 Live Demo: Add your deployed GitHub Pages link here 🤖 Claude Artifact: https://claude.ai/artifact/RaTCVWFjQi3FrfYGTA213Z https://claude.ai/artifact/RaTCVWFjQi3FrfYGTA213Z Code 💻 GitHub Repository: https://github.com/aniruddhapandey01/Touchgrass.local https://github.com/aniruddhapandey01/Touchgrass.local The repository contains the complete source code and is built using plain HTML, CSS, and JavaScript. How I Built It Touchgrass.local uses WebLLM by MLC AI to run an open-weight language model directly inside the browser using WebGPU. The project supports models including: Gemma 2 2B Qwen 2.5 1.5B Llama 3.2 1B The application is built with: HTML — page structure CSS — UI, themes, animations, and growing grass JavaScript — model loading, prompting, mission generation, streaks, and local storage WebLLM — in-browser AI inference There is no backend and no API key. After the model is downloaded, it is cached by the browser and can work offline. User input stays on the device instead of being sent to a server. Why Does Open Innovation Matter? Open innovation makes it possible to build AI applications without depending entirely on closed APIs. For Touchgrass.local, using open-weight models and WebLLM makes the project more private, accessible, customizable, and transparent. The model runs locally in the browser, so there is no API bill or backend required. It also makes experimentation easier. Users can switch between different supported models and compare their behaviour. Most importantly, the project can be shared openly so other developers can learn from it, modify it, add new missions, improve the UI, or contribute new ideas. My Agent Session 🤖 Claude Artifact / Agent Session: https://claude.ai/artifact/RaTCVWFjQi3FrfYGTA213Z https://claude.ai/artifact/RaTCVWFjQi3FrfYGTA213Z Prize Categories Hacktoberfest Open-Source AI Challenge — Week 1: Touch Grass Open-Source AI Local / In-Browser AI Open-Weight Models Final Thoughts Touchgrass.local started with a simple idea: What if AI could encourage us to spend less time using technology? Instead of making another app that keeps users on their screens, I wanted to build something that uses AI to give people a reason to step away from the screen and do something in the real world.