This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built
🌿 NatureQuest AI — Less Scrolling, More Exploring!
NatureQuest AI is an AI-powered outdoor adventure web app designed to encourage people to spend less time scrolling on their screens and more time exploring the real world.
The idea is simple: instead of spending free time endlessly browsing social media, users can generate fun outdoor quests and complete small activities in nature.
✨ Key Features
- 🤖 AI Quest Generator: Creates personalized outdoor adventures based on users' interests.
- 🌱 Nature Challenges: Explore plants, observe birds, take nature photographs, and discover the environment.
- ⭐ XP and Progress Tracking: Earn experience points by completing quests.
- 🏆 Achievement Badges: Unlock milestones as you complete more challenges.
- 📓 Nature Journal: Record observations and memorable moments from outdoor adventures.
- 📱 Responsive Interface: A simple, nature-inspired design for desktop and mobile users.
NatureQuest AI is designed for students, nature lovers, and anyone who wants to build healthier digital habits while reconnecting with the environment.
The goal isn't to eliminate technology. It's to use technology as a starting point for real-world experiences.
Demo
🌐 Live Website:
https://mightybeaman95-lgtm.github.io/Naturequest-AI/
Try the interface, explore the quest system, and discover how gamification can make outdoor activities more engaging.
Note: The deployed GitHub Pages version is static. AI generation through the local Ollama backend requires running the backend separately.
Code
💻 GitHub Repository:
https://github.com/mightybeaman95-lgtm/Naturequest-AI
The project is built with HTML, CSS, and JavaScript, with a Node.js and Express backend for AI integration.
The repository includes the frontend interface, quest interactions, progress tracking, nature journal, and backend integration with a locally running AI model.
How I Built It
I built NatureQuest AI using familiar web technologies and an open-weight AI model that can run locally.
Technology stack:
- HTML5: Structure of the application.
- CSS3: Nature-inspired styling and responsive layouts.
- JavaScript: Quest interactions, XP tracking, badges, and journal functionality.
- Node.js and Express: Backend API and application server.
- Ollama: Runs the AI model locally.
- Qwen2.5 3B: Open-weight language model used to generate outdoor quests.
The backend sends the user's selected interests, difficulty level, and location type to the locally running Qwen2.5 3B model through Ollama. The model generates a structured quest that can be displayed in the web interface.
For example, a user interested in nature photography can receive a challenge to photograph different natural patterns, while someone interested in wildlife can try observing birds from a safe distance.
The application also includes demo quests as a fallback when the AI backend is unavailable.
Building this project helped me explore how open-weight models can be integrated into a practical web application instead of relying entirely on a hosted AI API.
Why Does Open Innovation Matter?
Open innovation makes AI development more accessible, flexible, and transparent.
For NatureQuest AI, using an open-weight model through Ollama offers several advantages:
- Local inference: Quest generation can run on a user's own machine without sending prompts to an external hosted AI API.
- More control: Developers can experiment with different compatible models and customize the experience.
- Accessibility: Developers can explore AI integration without requiring a paid hosted AI API, although local hardware resources are still needed.
- Privacy-conscious design: With the local setup, prompts can remain on the machine running the model.
- Learning and experimentation: Students and independent developers can understand how AI systems work by building and modifying their own applications.
A closed API can be convenient, but open-weight models make it possible to experiment with how AI runs, where it runs, and how it can be adapted.
For a project about reconnecting people with the real world, I wanted AI to be a useful tool behind the experience—not another reason to stay glued to a screen.
My Agent Session
I developed this project through an iterative AI-assisted coding workflow, from planning the features and designing the interface to implementing quest generation and local AI integration.
Agent session: Not linked yet.
Prize Categories
Primary challenge: Touch Grass — Hacktoberfest Open-Source AI Challenge, Week 1.
The project explores local inference with an open-weight model using Ollama and Qwen2.5 3B.
Note: Additional partner prize categories should be listed only if the project meets their specific eligibility requirements.
🌍 My vision: Use AI to inspire people to explore more, notice the natural world, and spend meaningful time away from their screens.
Step outside. Complete a quest. Touch grass. 🌿
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