Touch Grass AI: Offline AI That Turns Screen Time into Real-World Missions

#devchallenge #hf26challenge

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

What I Built

Touch Grass AI is an offline-first application designed to help people step away from their screens and engage with the real world.

Instead of recommending more content to consume, it generates personalized outdoor missions based on the user's mood, available time, energy level, budget, and surroundings.

For example, a user might say:

"I'm bored, have 30 minutes, and don't want to spend any money."

Touch Grass AI can generate a mission such as Mini Explorer, encouraging the user to explore a safe nearby area, discover different types of leaves, and notice things they usually overlook.

The goal is simple: use AI to encourage real-world experiences rather than more screen time.

Demo

🚧 Demo coming soon.

I'll add a screen recording or a live demo once the application is working.

Code

🚧 GitHub repository link coming soon.

How I Built It

The project is being developed using:

Python — application logic
Streamlit — user interface
Ollama — local model inference
Gemma — open-weight language model for generating personalized missions

The application is designed to run AI inference locally, without relying on a paid cloud AI API.
The mission-generation process takes the user's preferences and turns them into practical, time-bounded outdoor activities.

Why Does Open Innovation Matter?

Open innovation makes AI more accessible by allowing developers to experiment with open-weight models, local inference, and customizable tools.

For this project, running a model locally means that mission generation does not need to depend on a paid cloud AI API. It also provides an opportunity to explore more private, offline-first AI experiences.

I want to demonstrate that AI doesn't always need to keep us connected to a screen. It can help us disconnect and engage with the world around us.