This is a submission for the "Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass" (https://dev.to/challenges/hacktoberfest-week1-2026-10-05)
What I Built
🌱 TouchGrass AI is an AI-powered web application designed to help students spend less time scrolling on screens and more time enjoying the outdoors.
As students, we often spend hours in front of laptops and phones. Sometimes, we want to take a break but don't know what to do with our free time. I built TouchGrass AI to make that decision easier!
Users can choose a break duration of 10, 20, or 30 minutes and receive AI-generated outdoor activity suggestions. They can start a timer, mark activities as completed, and track their progress.
Key Features
- 🌿 AI-generated outdoor activity suggestions
- ⏱️ Flexible 10, 20, and 30-minute breaks
- ✅ Activity completion tracking
- 📈 Progress tracking saved in the browser
- 🤖 Local AI inference using an open-weight model
The goal is simple: use AI to encourage people to step away from their screens and reconnect with the world around them.
Demo
Local demo: The app currently runs locally on my computer.
To try it, the project needs Node.js, Ollama, and the Qwen 2.5 3B model installed.
A publicly accessible live demo is not available yet.
Code
🔗 GitHub Repository: [https://github.com/priyarani8281/TouchGrassAI]
The repository contains the frontend, Express backend, and setup instructions.
How I Built It
I built TouchGrass AI using a combination of web technologies and open-source AI tools.
Tech stack:
- HTML, CSS, and JavaScript for the frontend
- Node.js and Express for the backend
- Ollama for running AI locally
- Qwen 2.5 3B, an open-weight language model, for generating outdoor activity suggestions
The frontend sends a request to the Express backend with the selected break duration. The backend communicates with the locally running Qwen model through Ollama and returns activity suggestions to the frontend.
I also added a timer, activity completion controls, and browser-based progress tracking.
As a beginner, building this project helped me understand how a frontend, backend API, and local AI model can work together in one application.
Why Does Open Innovation Matter?
Open innovation made it possible for me to experiment with AI without depending on a paid, proprietary AI API.
Using an open-weight model through Ollama allowed me to run inference locally, explore how AI requests work, and build an application while learning about the technology behind it.
It also makes the project easier for other developers to explore, learn from, and improve. I hope to continue developing TouchGrass AI and welcome feedback and contributions from the community.
For me, open innovation is about making technology more accessible and giving beginners the opportunity to learn by building real projects.
My Agent Session
I used OpenCode to help me build and improve the project.
I haven't published an agent session link yet.
Prize Categories
I am submitting TouchGrass AI for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass.
I'll select any additional partner prize categories only if the project meets their requirements.
Thanks for checking out TouchGrass AI! 🌱
I'd love to hear your suggestions: What would you add to help people spend more time outdoors and less time on their screens?
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