Frolic: Tiny Outdoor Side Quests Powered by Open-Source AI

Vivek Lokolakar — Sun, 11 Oct 2026 07:28:59 +0000

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

Demo

Live demo

Code

Github Link

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:

Qwen/Qwen2.5-7B-Instruct

The server sends the user's selected mood, time limit, and difficulty to the model and requests a structured JSON response containing:

{
  "emoji": "🍃",
  "kicker": "THE ART OF NOTICING",
  "title": "Become a tiny nature detective.",
  "description": "A short outdoor activity...",
  "xp": 50,
  "difficulty": "Gentle",
  "minutes": 10
}

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:

groupCamps groupCampInvites individualCamps questSubmissions

Render Deployment
Frolic includes a render.yaml configuration with:

Build command: npm ci
Start command: npm start
Health check: /health

The Hugging Face token and MongoDB connection string are configured as deployment secrets rather than committed to the repository.

The application validates and limits the model response before displaying it.

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

Still Updating Some Contents...

DEV Community: Vivek Lokolakar