🌿 GreenQuest AI: Turn AI Time Into Outside Time
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built
I built GreenQuest AI, an open-source AI-powered web application designed to help people spend less time on screens and more time outdoors.
The idea is simple:
AI plans the adventure → You close the screen → You go outside. 🌱
GreenQuest AI asks the user for:
- ⏱️ Available time
- 😊 Mood
- 🌳 Outdoor location
It then uses a local open-weight AI model through Ollama to generate a personalized outdoor quest with 5 simple activities.
For example, a user can choose:
30 minutes + Relaxing + Park
GreenQuest AI might create a short nature exploration quest involving observing the surroundings, walking, noticing sounds, and finding interesting natural details.
The project is designed for students, developers, remote workers, and anyone who feels they are spending too much time in front of a screen.
The goal isn't to keep users inside the app.
The goal is to get them out of it.
Demo
🌐 Live Website:
https://himanshugupta4105.github.io/GreenQuest-AI/
You can try GreenQuest AI directly in your browser.
Note: The AI-powered generation works with Ollama running locally. The project also includes an offline fallback so users can still experience the core functionality.
Code
💻 GitHub Repository:
https://github.com/Himanshugupta4105/GreenQuest-AI
The project is built using:
- HTML
- CSS
- JavaScript
- Ollama
- Open-weight AI model
- Local browser storage
There is no traditional backend or database required.
How I Built It
The main idea behind GreenQuest AI was to use open-source AI as a bridge between digital technology and real-world experiences.
Instead of building another AI chatbot that keeps users talking to the screen, I wanted AI to do the planning and then encourage the user to leave the screen.
AI Stack
GreenQuest AI uses:
Ollama for local AI inference.
The default model is:
llama3.2
The application sends a structured prompt to the local Ollama API:
http://localhost:11434/api/generate
The AI generates a structured outdoor quest containing:
- Quest title
- Introduction
- 5 outdoor tasks
- Duration
- Nature/safety tip
The application then displays the generated quest as an interactive checklist.
Project Architecture
User
↓
GreenQuest AI Web Interface
↓
JavaScript
↓
Ollama Local API
↓
Open-Weight AI Model
↓
Personalized Outdoor Quest
↓
User Leaves the Screen
↓
Real-World Experience 🌱
I also added an offline fallback system. If Ollama is unavailable, GreenQuest AI can still generate predefined outdoor quests.
The application stores progress such as completed quests, outdoor minutes, and streaks using browser localStorage.
Why Does Open Innovation Matter?
Open innovation was important for GreenQuest AI because the project is built around the idea of local and accessible AI.
Using an open-weight model with Ollama provides several advantages.
🔒 Privacy
The AI can run locally on the user's computer.
Personal inputs don't have to be sent to a third-party cloud AI service.
🧠 Model Freedom
Because the application uses Ollama, developers can experiment with different open-weight models instead of being locked into one closed API.
💰 Lower Cost
Local inference means there is no per-request cloud AI API cost for the application.
🛠️ Customization
Developers can modify the prompts, change the model, experiment with different AI behaviors, and improve the project according to their needs.
🌍 Open Collaboration
Anyone can inspect the code, understand how the AI integration works, suggest improvements, and contribute new ideas.
For GreenQuest AI, open innovation isn't just a technical choice.
It supports the project's philosophy:
AI should help us experience more of the real world, not keep us trapped inside a screen.
My Agent Session
I used AI-assisted development while building GreenQuest AI to help with project planning, UI ideas, JavaScript logic, and AI integration.
Agent session:
Prize Categories
I am submitting GreenQuest AI for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass.
The project focuses on:
- 🌿 Getting people outdoors
- 🤖 Open-source/local AI
- 🔓 Open innovation
- 💻 Developer experimentation
- 🌎 Real-world impact
What Makes GreenQuest AI Different?
There are already many AI applications designed to keep people engaged with screens.
GreenQuest AI takes the opposite approach.
The best outcome isn't another minute spent using the application.
The best outcome is:
The user closes the browser and goes outside. 🌱
The AI is intentionally only a small part of the experience.
It plans the activity, and then the real experience happens away from the screen.
Future Improvements
Some features I would like to add in future versions include:
- 🗺️ Outdoor location suggestions
- 🌤️ Weather-aware quests
- 🐦 Nature and wildlife recognition
- 🏆 More gamification
- 📱 Progressive Web App support
- 📊 More detailed outdoor statistics
- 🌱 Community-created quests
- 🤖 Support for more open-weight AI models
- 🌍 Multi-language support
Safety & Nature Guidelines
GreenQuest AI is designed around simple and responsible outdoor activities.
Generated quests should:
- Avoid dangerous activities
- Avoid disturbing wildlife
- Avoid damaging plants or nature
- Avoid requiring expensive equipment
- Encourage users to stay in familiar and appropriate outdoor areas
- Keep activities realistic and accessible
🤝 Open Source
GreenQuest AI is open to improvements and contributions.
Developers can contribute by:
- Improving the UI
- Adding new quest types
- Improving AI prompts
- Supporting additional models
- Adding accessibility features
- Improving offline functionality
- Adding new languages
- Fixing bugs
- Suggesting new outdoor experiences
🌿 Final Thought
Technology doesn't always have to keep us online.
Sometimes the best thing AI can do is help us put the phone down and experience the world around us.
AI plans it.
You experience it.
Less screen. More world. 🌱
🔗 Links
Live Demo:
https://himanshugupta4105.github.io/GreenQuest-AI/
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