🌿 LeafQuest uses AI-powered nature quests to get people outdoors, explore nature, and spend less time on screens! #Hacktoberfest

A developer built LeafQuest, a full-stack outdoor exploration game that issues real-world nature challenges and uses an open-weight AI image recognition layer to verify photographed discoveries, awarding points recorded in a user's Nature Passport. The app was developed and deployed entirely on Replit with an offline-first, modular architecture that keeps the recognition model swappable and avoids sending every image to a closed third-party API. The project was submitted under a Hacktoberfest open-source AI challenge themed around using open models to push people away from screens and into nature.

LeafQuest is an outdoor exploration game designed around a simple idea: Use technology to help people spend less time using technology. Instead of opening another app to scroll, LeafQuest gives users small real-world nature challenges. For example: The user chooses how long they want to explore, receives a set of challenges, and then puts their phone away and goes outside . When they return, they can photograph their discovery. The AI analyzes the image and helps determine whether it matches the challenge. Completed discoveries earn points and are added to the user's Nature Passport , creating a record of their outdoor exploration. The goal isn't to maximize screen time. The goal is to make the screen useful for a few seconds and the real world interesting for much longer. 🌐 Live Demo: https://leaf-quest-nature-adventure-app--surbhisrivasta7.replit.app https://leaf-quest-nature-adventure-app--surbhisrivasta7.replit.app The complete application was developed and deployed using Replit . I used Replit's development environment to build the frontend, backend, AI integration, and deployment workflow in one place. I built LeafQuest as a full-stack web application with an offline-first mindset and a modular AI recognition system. Replit was used throughout the development process for: This made it possible to go from the initial idea to a working, deployed prototype without maintaining a complicated local development setup. The application provides: The backend manages: LeafQuest uses an open-source/open-weight AI approach for recognizing objects in nature photographs. The AI recognition layer is separated from the rest of the application so that the underlying model can be replaced or upgraded without redesigning the entire application. The basic flow is: Choose a Quest ↓ Go Outside 🌿 ↓ Find Something ↓ Take a Photo 📸 ↓ AI Recognition ↓ Challenge Verification ↓ Earn Points ⭐ ↓ Nature Passport This architecture also leaves room for future contributors to experiment with different open models and improve recognition accuracy. For LeafQuest, open innovation isn't just about making the code public. It gives the project flexibility, transparency, and room for experimentation. Nature photographs can contain contextual or location information. An open/local AI approach can reduce dependence on sending every image to a closed third-party AI service. A closed API can lock a project into one provider. With an open-weight model and a modular AI layer, developers can experiment with different models, fine-tune them, or replace them as better models become available. Open-source technologies make it easier for students and independent developers to experiment without needing access to expensive proprietary AI infrastructure. Because the project is open, contributors can improve different parts of LeafQuest independently: That is what makes open innovation especially interesting for this project: the AI isn't meant to be a black box. It's a component that the community can experiment with and improve. Optional — add your DevRelay agent session here. Agent Session: Add DevRelay session link 🌿 Hacktoberfest Open-Source AI Challenge — Week 1: Touch Grass Theme: Using open-source/open-weight AI to encourage people to spend more time exploring the physical world and less time behind screens. Development & Deployment: Built and deployed using Replit . Most digital products compete for our attention. LeafQuest tries to do the opposite. It uses technology to create a reason to leave the screen, walk outside, look around, and notice the world. The best LeafQuest session isn't one where someone spends an hour inside the app. It's one where they open it, get a challenge, put the phone away, explore, and come back with a discovery. Sometimes the best use of technology is knowing when to put it down. 🌿