🌿 I Built TouchGrass AI — An AI That Wants You to Use Your Phone Less

A developer built TouchGrass AI, an open-source Python and Streamlit app that uses a recommendation system with optional local inference via Ollama and open-weight models to suggest short real-world outdoor missions instead of more digital content. The project is designed so the AI acts as an enabler rather than a destination, with a built-in fallback recommender that works without a running model, and the intended outcome is that users close the app 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 I built TouchGrass AI 🌿 — an AI-powered outdoor activity recommendation system designed to do something unusual: help people spend less time with technology. Instead of recommending another app, video, or online activity, TouchGrass AI gives you a small real-world mission to complete outside. You tell it: It then recommends a personalized outdoor activity that fits your situation. For example: Light Hunt Find an interesting patch of sunlight and notice three different shadows. Or: No-Route Walk Take a short walk without following your usual route and discover three things you normally overlook. The idea is simple: Use AI for a few seconds → Get a mission → Put your phone down → Go outside. TouchGrass AI is designed for anyone who feels like they spend too much time in front of a screen and wants a simple reason to step outside. 🌿 Try TouchGrass AI: https://touchgrass-mmewgdojhn6dqggh52qkfs.streamlit.app/ https://touchgrass-mmewgdojhn6dqggh52qkfs.streamlit.app/ The goal of the experience is intentionally short. Once you receive your mission, you're encouraged to stop looking at the screen and actually do it. 💻 GitHub Repository: https://github.com/sanjana-jha-001/touchgrass https://github.com/sanjana-jha-001/touchgrass The project is open source and built with Python and Streamlit. TouchGrass AI is built with Python + Streamlit and combines a built-in recommendation system with optional local AI inference. The project can use Ollama with open-weight models to generate personalized outdoor missions. The recommendation prompt gives the model the user's: The model is specifically instructed to recommend activities that get the user away from the screen , rather than suggesting more digital content. I also included a built-in recommendation system, so the application can still work without a running AI model. One important design decision was making AI an enabler rather than the destination . The successful outcome isn't another long AI conversation. The successful outcome is: The user closes the app and goes outside. 🌱 Open innovation is especially important for this project because I wanted the AI component to be transparent, accessible, and replaceable. Using open-weight models and local inference through Ollama means the project doesn't have to depend entirely on a proprietary cloud AI API. That makes it possible to: For a project whose goal is to reduce dependence on screens and online services, using local AI felt especially appropriate. I used AI-assisted development to help design, implement, debug, and improve TouchGrass AI. The project itself is focused on using AI responsibly: instead of optimizing for more engagement, it uses AI to encourage less screen time and more real-world activity. Most technology is designed around: "How can we keep the user engaged?" I wanted to experiment with the opposite question: "How can AI help the user leave?" That's TouchGrass AI. 🌿 Use AI for a few seconds. Get your mission. Touch grass.