This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built
TouchGrass AI — Less scrolling. More exploring.
TouchGrass AI is a nature-inspired web application designed to help people take a break from their screens and explore the world around them.
Users can choose an activity, such as nature walking, birdwatching, gardening, or nature discovery, and receive personalized outdoor missions generated using open-source AI.
The goal is simple: spend less time looking at a screen and more time experiencing the real world.
Key features:
Personalized outdoor mission generation.
Simple, achievable activities for different time limits.
A nature journal to record observations and completed missions.
A clean, responsive interface designed around nature and well-being.
Demo
Screenshots: Add screenshots of the homepage, mission generator, and nature journal
Code
How I Built It
I built TouchGrass AI with React and Vite for the frontend and Python with FastAPI for the backend. The application is designed to use an open-weight language model through Ollama for local AI inference.
The model generates outdoor missions based on the user's selected activity, available time, and interests. The nature journal stores entries locally in the browser.
My focus was to keep the application lightweight, easy to use, and centered on a real-world activity rather than prolonged screen engagement.
Why Does Open Innovation Matter?
Open innovation makes it possible to experiment with AI models that users can run and control on their own devices.
With a suitable local model, TouchGrass AI can generate activities without depending on a paid proprietary AI API. The model can also be replaced or customized as the project evolves.
This approach offers opportunities for greater user control, experimentation, and privacy. Local inference can also work without an internet connection once the model and required dependencies are installed.
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