What I Built
I built TouchGrass AI, a local-first AI outdoor companion that creates simple and fun outdoor missions.
The idea is simple: instead of using AI to keep people on a screen, TouchGrass AI uses AI to encourage people to leave the screen and explore the real world. 🌿
The app generates missions such as:
- 🌳 Explore your neighborhood
- 🍃 Find different types of leaves
- 🐦 Listen for birds
- 🚶 Take a short outdoor walk
- 🌱 Observe something interesting in nature
Users can start a mission, complete tasks using an interactive checklist, track their progress, and generate a new mission whenever they want.
Most importantly, the AI runs locally using an open-weight model, so the project does not require a paid AI API.
Local Demo:
The application runs locally with a React frontend and Flask backend.
Frontend:
http://localhost:5173
Backend:
http://127.0.0.1:5000
Code
GitHub Repository:
https://github.com/nandyshirshak-cloud/TouchGrass-AI
The repository contains the complete frontend, backend, AI integration, and setup instructions.
How I Built It
TouchGrass AI was built using:
Frontend
- React
- Vite
- JavaScript
- CSS
Backend
- Python
- Flask
- Flask-CORS
AI
- Hugging Face Transformers
- Qwen/Qwen2.5-0.5B-Instruct
- PyTorch
The AI model runs locally through Hugging Face Transformers.
The basic flow is:
User → React Frontend → Flask Backend → Local Qwen AI → Outdoor Mission → User goes outside 🌿
The backend asks the local model to generate a structured outdoor mission containing a title, duration, and tasks.
The frontend then displays the generated mission as an interactive checklist.
I also added a fallback mission so the application can still provide an outdoor activity if AI generation fails.
Why Does Open Innovation Matter?
Open innovation matters because powerful AI should not only be available through expensive closed APIs.
With open-weight models, developers can:
- Experiment with AI locally
- Learn how AI systems actually work
- Build without depending on a paid API
- Keep more control over their applications
- Create privacy-friendly experiences
- Modify and improve their projects freely
TouchGrass AI is a small example of this idea.
Instead of building another AI tool that encourages people to spend more time online, I wanted to use open AI technology for something very simple:
Use AI to help people spend less time with technology.
My Agent Session
The development process involved using AI-assisted coding to design the application, debug the React frontend, connect the Flask backend, integrate the local Qwen model, improve the mission-generation prompt, and prepare the project for open-source publication.
One of the interesting parts was getting the local model to return structured JSON containing:
- Mission title
- Duration
- Four outdoor tasks
This allowed the AI-generated content to be displayed directly inside the application.
What Makes It Different?
Most AI applications try to keep users engaged with a screen.
TouchGrass AI does the opposite.
The screen is only used to receive the mission.
After that, the user is encouraged to put the device down and complete the real-world activity.
Less screen. More world. 🌿
Future Improvements
I would like to expand TouchGrass AI with:
- 🌦️ Weather-aware missions
- 📍 Location-aware outdoor activities
- 🗺️ Outdoor exploration maps
- 🏆 Outdoor achievements and streaks
- 🌿 Plant and nature recognition
- 📱 Mobile application
- 👥 Community challenges
- 🤖 More local open-weight models
Challenge
Built for the Open-Source AI / Touch Grass Challenge.
The project focuses on using open AI technology to encourage people to disconnect from their screens and interact with the physical world.
Author
SHIRSHAK Nandy
GitHub:
https://github.com/nandyshirshak-cloud
Tags
devchallenge
hf26challenge
opensource
ai
🌿 Less screen. More world.
Go outside. Touch Grass.
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