TouchGrass AI — An Open-Weight AI That Gets You Off the Screen and Into the Real World

A developer built TouchGrass AI, an open-source web app that uses open-weight models via Hugging Face Inference Providers to generate personalized outdoor missions and analyze user-uploaded nature photos, aiming to push people off their screens and outside. The project pairs a JavaScript frontend with a Node.js/Express backend that proxies the Hugging Face API token, using openai/gpt-oss-120b for text and Qwen/Qwen2.5-VL-3B-Instruct for vision. Its stated philosophy is that "the best AI interaction is the one that gets you to stop using the AI.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass https://dev.to/challenges/hacktoberfest-week1-2026-10-05 I built TouchGrass AI , an AI-powered outdoor adventure companion designed to turn screen time into real-world activity. The idea is simple: instead of keeping users inside an AI chat, the AI gives them a reason to put the phone down and go outside . Users choose how much time they have, their environment, their mood, and what kind of experience they want. TouchGrass AI then generates a personalized outdoor mission, such as exploring nature, taking a short walk, observing plants or birds, or completing a small outdoor challenge. The website also includes: The target audience is anyone who spends too much time on screens and needs a simple push to get outside. The core philosophy is: The best AI interaction is the one that gets you to stop using the AI. Live Website: https://ayushkasaudhan957-eng.github.io/TouchGrass-AI/ https://ayushkasaudhan957-eng.github.io/TouchGrass-AI/ The website is deployed and can be used directly in the browser. GitHub Repository: https://github.com/ayushkasaudhan957-eng/TouchGrass-AI https://github.com/ayushkasaudhan957-eng/TouchGrass-AI The complete source code is open and available in the repository. TouchGrass AI is built around open-weight AI models accessed through Hugging Face Inference Providers. For text generation, I used: openai/gpt-oss-120b Qwen/Qwen2.5-VL-3B-Instruct for vision-based image analysis Hugging Face Inference Providers provides access to open models through an OpenAI-compatible chat-completion endpoint, allowing the application to use hosted inference without requiring users to download a local model. User ↓ TouchGrass AI Website ↓ HTML + CSS + JavaScript ↓ Node.js / Express Backend ↓ Hugging Face Inference Providers ↓ Open-Weight AI Models ↓ Personalized Outdoor Mission / Nature Analysis The frontend handles the user experience, mission builder, timer, XP, journal and image upload. The backend acts as a secure AI proxy so that the Hugging Face API token is not exposed in the browser. For mission generation, the user's preferences are converted into a structured prompt and sent to the open-weight language model. For the nature discovery feature, an uploaded image is sent to a vision-language model which analyzes what is visible and provides an AI-generated observation. I intentionally chose hosted inference instead of requiring users to install Ollama, LM Studio, or download large models. This makes the project much easier to try while still keeping open-weight models at the core of the AI functionality. Open innovation makes this project possible because the AI layer is not loc