This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built
🌱 Touch Grass AI — An AI companion that helps people spend less time on screens and more time outdoors.
Touch Grass AI is a full-stack application that recommends outdoor activities based on a user's check-in and encourages them to take meaningful breaks from their screens.
Instead of using AI to keep people glued to their devices, this project uses AI to help them step away from technology and reconnect with the real world.
Key features:
- 🌿 Personalized outdoor activity recommendations.
- 🤖 Local AI inference using the Gemma model through Ollama.
- 📍 Outdoor place discovery using OpenStreetMap.
- ⏱️ Screen-free sessions to encourage real-world activities.
- 📊 An impact dashboard to track completed sessions.
- 🛡️ Deterministic safety rules for recommendations.
- 🔒 A privacy-conscious design using local AI inference.
The project is designed for anyone who wants to reduce screen time, discover outdoor activities, and build healthier digital habits.
Demo
🎥 Demo Video: Watch Touch Grass AI
The demo showcases the application interface, activity recommendations, screen-free sessions, and the impact dashboard.
Code
💻 GitHub Repository: https://github.com/msairitvik/touch-grass-ai
The repository includes the React frontend, FastAPI backend, AI integration, configuration files, tests, and setup instructions.
How I Built It
I built Touch Grass AI using React and Vite for the frontend and Python with FastAPI for the backend.
The core AI component uses Gemma, an open-weight model running locally through Ollama. The backend integrates the model into the recommendation workflow, while deterministic safety rules provide additional control over application behavior.
I used OpenStreetMap to discover outdoor places and built screen-free session functionality and an impact dashboard to help users track their outdoor activities.
The main technologies are:
- Frontend: React, Vite, CSS
- Backend: Python, FastAPI
- AI: Gemma through Ollama
- Location data: OpenStreetMap
- Testing: Pytest
The project brings these components together to create an AI experience whose purpose is to encourage users to spend more time away from screens.
Why Does Open Innovation Matter?
Open innovation made it possible for me to explore AI beyond a conventional chatbot.
By using an open-weight model that can run locally, Touch Grass AI can generate recommendations without depending entirely on a closed, hosted AI API. Local inference can also reduce the need to send personal check-in information to an external AI provider.
Open-source tools make the project easier for other developers to inspect, learn from, modify, and extend.
I especially like the idea that AI can be used not just to increase digital engagement, but to encourage people to disconnect, explore their surroundings, and experience the world outside their screens.
For me, this project demonstrates how open-source AI can support a practical, human-centered use case.
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