Submission for Hacktoberfest Open-Source AI Challenge — Week 1: Touch Grass
What I Built
TouchGrass is a web app concept designed to help people spend less time scrolling and more time exploring the world around them.
The idea is simple: instead of spending more time deciding what to do, users should be able to get a practical outdoor activity plan and go try it.
TouchGrass aims to help users discover short outdoor activities based on factors such as their available time, energy, mood, and interests. These could include taking a walk, exploring a nearby area, spending time in nature, or trying a simple outdoor challenge.
The core philosophy is:
Use AI → Get a plan → Leave the screen.
The current implementation includes a rule-based fallback. Real inference from an AI model is not yet verified, so the project is still a work in progress.
Demo
- Repository: https://github.com/Titus-Dsouza/Hacktober_fest_week_2
- Live demo: A public deployment URL still needs to be confirmed.
The Vercel link currently available is the project dashboard: https://vercel.com/titus-dsouza/hacktober_fest_week_2
How I Built It
The project uses the following planned technology stack:
- Frontend: HTML, CSS, and JavaScript
- Backend: Python with Django and Django REST Framework
- Authentication: Supabase Auth
-
AI integration: Ollama with
qwen2.5:3bplanned for local, open-weight inference - Fallback: Rule-based responses when model inference is unavailable
The current working fallback helps establish the basic experience while AI integration remains a work in progress.
Why I Chose This Idea
Technology is useful, but it can also keep us indoors and glued to our screens for longer than we intend.
I wanted to explore a project that uses technology to encourage people to step away from it. Rather than building another chatbot that encourages endless conversation, TouchGrass is intended to help users find an activity and then get off the screen.
My goal is to develop the project further by making outdoor suggestions more useful, personal, and accessible.
Challenges and What I Learned
One of the main challenges is building a useful experience before the AI integration is fully verified. A rule-based fallback provides a foundation, but connecting and testing a real open-weight model is an important next step.
This project is also helping me learn how frontend interfaces, backend APIs, authentication, and AI components fit together in a web application.
What's Next?
- Verify real inference with the open-weight model.
- Test the complete user journey in a browser.
- Improve personalization and the quality of outdoor missions.
- Publish a working public demo.
- Add screenshots and improve setup documentation so others can try the project.
Prize Category
Open Innovation
Final Thought
TouchGrass is a work in progress, but the mission behind it is straightforward: use technology to help people experience more of the world beyond their screens.
Build something useful. Get outside. Touch grass. 🌿
Top comments (0)