TouchGrass AI: An Open-Source AI Outdoor Mission Planner
What if AI helped you spend less time looking at a screen?
That was the idea behind TouchGrass AI, an outdoor mission planner built for the Hacktoberfest 2026 Open-Source AI Challenge: Week 1.
What I Built
TouchGrass AI creates short, practical outdoor missions based on:
- Available time: 15, 30, or 60 minutes
- Activity: Walking, Nature, Photography, Exercise, or Social
- Difficulty: Easy, Medium, or Hard
- Mode: Solo or With Friends
Instead of keeping the user inside the app, the goal is to use the screen briefly, get a mission, and then put the phone away.
For example, a 30-minute nature mission can ask you to walk without looking at a screen, observe different plants, notice a natural sound, and spend a few quiet minutes outside.
The Problem
A lot of digital products are designed to keep us on the screen.
For this challenge, I wanted to build the opposite: an AI experience where the screen is only the starting point.
The useful part of the experience happens outside.
The "Touch Grass" Idea
The core interaction is intentionally simple:
Choose → Generate → Go outside → Complete → Come back
The app gives the user an actionable outdoor objective and encourages them to put their phone away once the mission starts.
This makes the AI a short planning tool rather than another reason to keep scrolling.
How It Works
- The user selects their available time.
- They choose an outdoor activity.
- They choose difficulty and solo/friends mode.
- The backend creates a structured mission.
- The user starts the mission.
- The app provides an actionable checklist.
- The user goes outside and completes it.
- The completed mission is saved to history and reflected in statistics.
TouchGrass AI also keeps a history of completed missions and shows outdoor minutes and streak information.
Technical Architecture
The project uses:
- Frontend: React + Vite
- Backend: FastAPI + Python
- Database: SQLite + SQLAlchemy
- AI layer: Open-weight Gemma integration with a deterministic fallback
- HTTP: httpx
- Deployment: Render
The frontend is deployed as a Render Static Site and the FastAPI backend runs as a Render Web Service.
GitHub:
https://github.com/PRAVIN-KUMAR-295/touchgrass-ai
Live Demo:
https://touchgrass-ai-2.onrender.com
How Gemma Is Used
The project was designed around an AI provider abstraction so that an open-weight model can generate outdoor mission content without tightly coupling the application to one provider.
The intended hosted model is:
google/gemma-3-4b-it
The application also includes a deterministic fallback so the core mission experience remains usable when hosted AI credentials are unavailable.
Important disclosure
For the deployed demo, I did not configure a hosted Gemma API key, so I have not claimed that the live deployment is currently performing real Gemma inference.
The deployed application is using the tested deterministic fallback path.
This keeps the demo honest while preserving the open-weight Gemma integration in the architecture.
Why Open Innovation Matters
The open-weight approach makes the AI layer more replaceable and easier to experiment with.
Instead of building the entire application around a closed provider, the AI service is separated from the rest of the product. This makes it possible to change the model or serving infrastructure without redesigning the outdoor mission experience.
For this project, that flexibility is more important than making the AI itself the main attraction.
Taking It Outside
I actually used the deployed application for an outdoor mission.
I generated a mission, started it, put the phone away, went outside, completed the activity, and then returned to mark the mission complete.
The completed mission appeared in History and the Statistics page updated with completed missions, outdoor minutes, and the streak.
This was the part of the project I liked most: the successful outcome was not spending more time inside the app.
Screenshots
Home
Mission Planning
Generated Mission
Mission in Progress
History
Statistics
What I Learned
One of the biggest lessons was that building an AI application does not have to mean building an experience that keeps users interacting with AI.
The best part of TouchGrass AI happens after the AI finishes its job.
I also learned a lot about separating the AI service from the application logic, building a reliable fallback path, testing the complete mission lifecycle, and deploying a React + FastAPI application to production.
AI Disclosure
AI tools were used during development to help with implementation, debugging, documentation, and development workflow.
The project itself was designed, tested, deployed, and verified as a working application.
Final Thoughts
TouchGrass AI started with a simple question:
Can AI help us use our phones less?
Instead of optimizing for another screen session, this project tries to make the screen the shortest part of the experience.
Choose a mission.
Put the phone away.
Go touch grass. 🌱
Built for the Hacktoberfest 2026 Open-Source AI Challenge: Week 1.
Top comments (1)
good