This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built
We spend so much time looking at screens that sometimes we need a little help getting away from them.
TouchGrass AI is an open-source, AI-powered outdoor activity companion that helps people turn their free time into small, meaningful real-world adventures.
Instead of recommending another app to scroll through, it helps you discover something to do outside.
Whether you have 10 minutes between classes or an entire afternoon to explore, the idea is simple: tell the app how much time you have, choose your energy level and surroundings, and let AI suggest an outdoor mission.
Some examples:
🌳 Take a mindful walk and notice five things you usually overlook.
🐦 Spend a few minutes observing birds in your neighbourhood.
🌱 Explore a small gardening or nature observation activity.
🚶 Discover a short outdoor challenge that fits your available time.
The goal is to make the screen the shortest part of the experience. Generate your mission, head outside, and come back only when you want to record your progress.
Demo
Code
TouchGrass AI 🌿
A local-first outdoor mission generator powered by an open-weight language model. TouchGrass turns a few free minutes into a practical real-world activity, then gets out of the way. No account, cloud AI API, streaks, or social feed.
Hacktoberfest theme: Touch Grass — the screen should be the shortest part of the experience.
Why open innovation matters
- Privacy by default: the user's preferences are sent to the Ollama process running on their own machine, not to a hosted model API.
- Works without internet after setup: download the app dependencies and model once; mission generation then runs locally. The PWA caches its static shell, but generating a new mission still requires the local Ollama service.
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Model choice:
OLLAMA_MODELcan be changed to another compatible model. The prompt and safety constraints are visible and editable. - Accessible experimentation: contributors can run, inspect, test, and extend the project without paying per-token API…
How I Built It
I built TouchGrass AI with a local-first architecture.
Technologies used:
Python and FastAPI for the backend.
HTML, CSS, and JavaScript for the frontend.
Ollama to run an AI model locally.
Qwen2.5 3B as the default open-weight model.
Progressive Web App features for an installable, app-like experience.
The application sends activity preferences to the locally running model, which generates suggestions based on the user's available time, energy level, and environment.
The architecture is intentionally modular so that developers can experiment with different models, improve the prompts, and extend the application without being locked into a single AI provider.
Why Does Open Innovation Matter?
For this project, open innovation is not just a technology choice. It is central to how the application works.
🔒 Privacy by design: User preferences can be processed locally instead of being sent to a third-party AI provider.
📴 Offline potential: After downloading the application dependencies and model, the core mission-generation workflow can run without an internet connection, provided the local AI service is running.
🔄 Freedom to experiment: Developers can change the model, modify prompts, inspect the implementation, and adapt the experience to different communities.
💸 Lower ongoing API costs: Local inference avoids per-request charges from hosted AI APIs, although it still uses the computer's hardware and electricity.
A closed AI API could generate outdoor recommendations too. However, a local, open-weight approach makes privacy, model choice, and offline operation much more accessible.
I wanted to explore how AI could encourage people to spend less time interacting with technology and more time experiencing the world around them.
The technology should enable the experience, not become the experience.
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