[](url)This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built Frolic is a playful outdoor side-quest app that helps people spend less time scrolling and more time noticing the world around them.
Users choose their mood, available time, and preferred level of chaos. Frolic then generates a small, safe outdoor activity such as: Finding a suspiciously interesting rock Discovering a cloud with a backstory Observing a miniature ecosystem Finding an unexpected color palette Giving a nearby tree an absurdly formal title
The goal is not to keep people inside an AI conversation. The goal is to give them one reason to put their phone down and step outside.
Frolic also supports: Individual quest progress XP, streaks, and achievement badges Photo and video evidence submissions Personal outdoor camp history Group camps with invite links and shareable group codes MongoDB-backed quest and group data A fallback quest deck when the AI service is unavailable No Login/Signup required(code/link based invite) Using in smartphone is recommended for better experience
How I Built It Frolic is a Node.js and Express application with a browser-based frontend.
The AI generation flow uses the Hugging Face Inference Router with the open-weight model:
The server sends the user's selected mood, time limit, and difficulty to the model and requests a structured JSON response containing:
MongoDB and Group Camps MongoDB powers the social part of Frolic. Group camps allow users to: Create a camp Choose a shared outdoor quest Add a meeting location Generate invite links Share a short camp code Track accepted and pending participants MongoDB collections include:
The Hugging Face token and MongoDB connection string are configured as deployment secrets rather than committed to the repository.
The rest of the stack includes: Node.js and Express for the application server Hugging Face Inference Router for open-weight AI generation MongoDB Atlas for camps, invites, profiles, and quest submissions MongoDB GridFS for uploaded quest evidence Render for deployment Vanilla HTML, CSS, and JavaScript for the frontend Local storage for lightweight personal progress such as XP and badges
Why Does Open Innovation Matter? Frolic is built around the idea that AI should help people leave the screen, not become another reason to stay on it. Using an open-weight model makes the behavior more inspectable and replaceable. I can change the model, adjust the system prompt, add safety constraints, or run the same experience through a different compatible inference provider without redesigning the application.
Open innovation matters here because: The model can be swapped instead of locking the project to one provider. The prompt and safety rules are visible in the repository. The application can use a local fallback when inference is unavailable. The experience can evolve toward private or self-hosted inference.
Prize Categories I am entering the following partner categories: Best Use of MongoDB Atlas Best Use of Render Best Use of GitHub Copilot