This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
Built with Gemma by Google · Powered by Hugging Face · Deployed on Render · Open source on GitHub
What if AI didn't try to keep you online, but helped you step away?
Introducing TouchGrass — a lightweight, AI-powered micro-mission generator designed to turn spare time into real-world action.
Instead of another productivity dashboard, endless feed, or chatbot conversation, TouchGrass gives you one simple thing to do offline.
Try it live: https://t0uchgrass.onrender.com/
Source code: https://github.com/SachchitNesarikar/hacktober-26
The problem
We use technology to learn, build, work, and organize our lives. But sometimes, we get stuck consuming more content instead of acting on what matters.
I wanted to experiment with a different kind of AI product: one that helps you spend less time interacting with the product itself.
Meet TouchGrass
Choose one of three focuses:
- Move: Take a walk, stretch gently, or change your surroundings.
- Learn: Solve a problem on paper, read something meaningful, or explain a concept from memory.
- Explore: Discover a new route, notice details around you, or make something by hand.
Choose whether you have 5, 15, or 30 minutes, optionally add a personal goal, and generate a micro-mission tailored to your situation.
Complete it, record a small reflection, and build a history of your wins — without needing to create an account.
The technology behind it
Gemma + Hugging Face
TouchGrass uses Google's Gemma 3 4B Instruct through Hugging Face's inference API to generate personalized, structured missions. The model receives the selected focus, available time, and optional personal goal, then returns a concise mission.
Render
The application is deployed as a Node.js web service on Render, serving the frontend and handling API requests. Model credentials stay in server-side environment variables rather than being exposed in browser code.
GitHub
The project is open source, with the implementation available for others to inspect, learn from, and extend.
Resilient by design
If inference is unavailable or the model returns an unusable response, TouchGrass can fall back to a curated library of missions. The core experience doesn't have to disappear just because an external AI service fails.
What makes it different?
Most AI products encourage you to ask another question, generate another response, or stay for another session.
TouchGrass explores the opposite approach:
Generate one useful idea. Take it into the real world. Close the tab.
It's a small experiment in making AI useful beyond the screen, with lightweight personalization and a deliberately simple interface.
What's next?
I'd love to explore knowledge-graph-based personalization, where interests, learning goals, and completed missions connect to produce more relevant suggestions over time — without requiring a complicated user profile.
Other possibilities include better mission-quality evaluation, more personalized recommendations, and measuring whether people actually complete the activities.
Try it. Build on it. Touch grass.
Live app: https://t0uchgrass.onrender.com/
GitHub repository: https://github.com/SachchitNesarikar/hacktober-26
Built as an open-AI experiment with Gemma, Hugging Face, Render, and GitHub.
The goal isn't to keep you here longer.
It's to help you find something worth doing out there.
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