This is my submission for the Hacktoberfest Open-Source AI Challenge — Week 1: Touch Grass.
What I Built
I built TouchGrass AI, a web application that encourages people to take a break from their screens and spend more time outdoors.
The idea behind the project is simple: sometimes, we want to go outside but don't know what to do or where to start. TouchGrass AI helps solve this by generating personalized outdoor missions based on a user's preferences, available time, and difficulty level.
Users can generate a mission, follow its steps, mark tasks as completed, and track their progress.
I built this project to combine AI with a practical idea that encourages people to spend more time away from their screens.
Demo
Here is a short video demonstration of TouchGrass AI.
Demo Video: https://youtu.be/ALmuc2VEnUU
The video shows the application and how it generates outdoor missions.
Code
GitHub Repository: https://github.com/ChiragKumarChouhan/touchgrass-ai
The repository contains the frontend, backend, and setup instructions for running the project locally.
How I Built It
I built TouchGrass AI using React for the frontend and Node.js with Express for the backend.
For the AI functionality, I used Gemma 3 1B, an open-weight language model, running locally through Ollama.
The application sends requests from the frontend to the Express backend, which communicates with the local Ollama API to generate outdoor missions. The generated results are then displayed in the interface.
The main technologies I used are:
React and Vite: Frontend development
Node.js and Express: Backend API
Gemma 3 1B: AI-powered mission generation
Ollama: Running the language model locally
Browser localStorage: Saving progress locally
Working on this project helped me understand how a local language model can be integrated into a full-stack web application.
Why Does Open Innovation Matter?
Open innovation matters because it gives developers the opportunity to experiment, learn, and build useful applications without depending entirely on closed AI APIs.
Using an open-weight model through Ollama allowed me to explore local AI inference and understand how a language model can become part of a real application.
It also gave me more control over the development environment and helped me learn about the connection between AI models, backend APIs, and frontend interfaces.
For me, this challenge is an opportunity to learn by building something practical and share it with the open-source community.
My Agent Session
I don't have a DevRelay agent session to share for this submission, so I'm leaving this section out.
Prize Categories
I'll update this section based on the eligible partner prize categories listed on the official challenge page.
Final Thoughts
TouchGrass AI started with a simple idea: use AI to help people spend a little less time in front of a screen and a little more time outdoors.
Building it has been a learning experience in full-stack development and local AI integration. I'm looking forward to improving the project and learning from the open-source community.
Thanks for checking out my submission!
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