This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass I built GrassQuest 🌿, an app that gives you a reason to step away from your screen and do something outside.
The idea came from a simple thought: we use AI for so many things on our phones, but what if we used it to help us spend less time on them?
With GrassQuest, you choose your mood, how much time you have, and the kind of activity you want to try. The AI generates an outdoor quest based on your choices. It could be exploring a new route, noticing things around your neighbourhood, or going on a small nature hunt.
It also has ElevenLabs voice guidance, GPS-based distance tracking, and a completion screen to wrap up the quest.
The part I like most is that once you start a quest, you're encouraged to put your phone away and actually do it.
“AI that gives you a mission, then gets out of your way.”
Powered by GPT-OSS 20B (Groq) • ElevenLabs • Flask • Render
Modern AI products are almost universally designed to maximize engagement: chat boxes that lure you into infinite conversation, recommendation algorithms that keep your eyes glued to glass, and synthetic companions that replace outdoor living.
GrassQuest is the antithesis of the screen-addiction trap.
Instead of keeping you talking to an AI on your phone or laptop, GrassQuest uses an open-weight AI model (GPT-OSS 20B) to synthesize your current emotional state, time availability, and physical environment into a realistic, playful outdoor mission. Once your mission is generated, ElevenLabs speaks a concise starting instruction into your ears:
“Your GrassQuest has started. You have 20 minutes. Walk outside and find three things you normally overlook. Put your phone in your pocket and start walking.”
…
I used GPT-OSS 20B through Groq to generate quests based on the user's mood, available time, and activity preference.
The app is built with Flask on the backend and HTML, CSS, and vanilla JavaScript on the frontend. I used ElevenLabs for voice instructions and the browser's Geolocation API to track walking distance. The Haversine formula calculates the distance between GPS coordinates.
I deployed the app on Render. I kept the stack relatively simple because I wanted to focus on making the actual experience work rather than adding unnecessary complexity.
The AI returns structured quest data, which the backend validates before sending it to the frontend. I also added fallback handling for cases where voice generation or location access isn't available.
For this project, I wanted to experiment with an open-weight model rather than build everything around a closed model. GPT-OSS 20B gives me the flexibility to work with the model's prompts, shape its output, and explore different models as the project develops. Using Groq for inference also meant I could build the app without needing to run the model on my own laptop or server.
What matters to me is that the AI is being used for something beyond generating answers on a screen. It creates a small mission that someone can actually go out and complete.
GrassQuest is still a small project, but I'd like to explore how open-weight AI can be used to encourage more real-world experiences instead of more screen time.
I built GrassQuest with Antigravity and Gemini. I haven't included a saved DevRelay agent session link for this build.
Team Submissions: @rozoa @akash_70 @sycojoker