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