TouchGrass AI** 🌿 — an AI-powered outdoor activity recommendation system

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

What I Built

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:

• 🧠 Your current mood
• 🎯 Your goal
• ⏱️ How much time you have
• 🌿 Your preferred activity
• 🌳 Your environment
• 👥 Whether you're alone or with someone

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.

Demo

🌿 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.

Code

💻 GitHub Repository:

https://github.com/sanjana-jha-001/touchgrass

The project is open source and built with Python and Streamlit.

How I Built It

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:

• Mood
• Goal
• Available time
• Preferred activity
• Environment
• Social situation

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. 🌱

Why Does Open Innovation Matter?

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:

• Run AI locally
• Experiment with different open-weight models
• Customize the recommendation prompt
• Build without requiring a paid API key
• Keep the AI component flexible and replaceable
• Learn how the AI component actually works

For a project whose goal is to reduce dependence on screens and online services, using local AI felt especially appropriate.

My Agent Session

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.

Prize Categories

• Open-Source AI / AI-powered application
• Open Innovation

Final Thought

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.