This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built
I built GreenQuest AI 🌿, an AI-powered outdoor activity companion that encourages people to spend less time scrolling on their phones and more time exploring nature.
Users can select their mood, available time, and interests to generate personalized outdoor quests. Activities include exploring plants and trees, observing birds, walking outdoors, and gardening.
Key Features
- 🤖 AI-powered nature quest generation
- 🌳 Personalized outdoor activities
- 🏆 Points for completing quests
- 💾 Progress saved using browser local storage
- 📱 Responsive design for different screen sizes
- 🌱 Built-in fallback activities if AI generation fails
The goal is to use technology to encourage real-world experiences rather than endless screen time.
Demo
🌐 Live Website: https://aaravyadav192007-crypto.github.io/GreenQuest-AI/
Try GreenQuest AI and generate your own nature quest!
Code
💻 GitHub Repository: https://github.com/aaravyadav192007-crypto/GreenQuest-AI
The project source code is available on GitHub.
How I Built It
I built GreenQuest AI using HTML, CSS, and JavaScript, with Hugging Face Transformers.js for browser-based AI inference.
Technologies used:
- HTML5 for the website structure
- CSS3 for styling, animations, and responsive design
- JavaScript for interactivity and application logic
- Hugging Face Transformers.js for running AI models in the browser
- Qwen2.5-0.5B-Instruct as the intended open-weight language model for generating nature quests
The user selects their mood, available time, and interests. JavaScript creates a prompt, the AI generates a nature quest, and the website displays the result. Users can then complete the quest and earn points.
The application is designed to run AI inference in the browser without requiring a paid AI API key. An internet connection is needed to download the model initially unless the required files have already been cached or hosted locally.
Why Does Open Innovation Matter?
Open innovation makes it easier for students and developers to experiment with AI, customize applications, and learn without depending entirely on proprietary services.
For GreenQuest AI, an open-weight model offers several advantages:
- Flexibility: Developers can experiment with compatible models and customize prompts.
- Cost efficiency: No paid AI API is required for each quest-generation request.
- Privacy: User preferences can be processed locally in the browser.
- Local inference: The model can generate activities on the user's device after its files are available.
- Learning: The project demonstrates how open-weight AI can be integrated into a practical web application.
I believe AI should not only keep people engaged with screens. It can also encourage them to disconnect, explore their surroundings, and reconnect with nature.
My Agent Session
No agent-session recording is included yet. I may add a DevRelay session link after recording one.
Prize Categories
- Touch Grass — Week 1
What's Next?
- 🌿 Plant identification using images
- 🐦 Bird identification and nature challenges
- 🗺️ Outdoor activity recommendations based on location
- 🏅 Daily challenges, achievements, and badges
- 📶 Improved offline functionality
This project helped me explore how open-weight AI can be combined with frontend development to create something useful beyond the screen.
Built by Aarav Yadav 🌿
🔗 Live Demo | GitHub Repository
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