🌿 EcoQuest AI: Touch Less Screen. Explore More Earth.
This is a submission for the Hacktoberfest Open-Source AI Challenge. Week 1: Touch Grass
What I Built
EcoQuest AI is an open-source, AI-powered outdoor exploration app designed to help people spend less time scrolling and more time experiencing the world around them.
Instead of using AI to keep people glued to their screens, EcoQuest uses it to encourage real-world exploration through small, achievable outdoor missions.
With EcoQuest AI, users can:
- 🌱 Discover Nature Quests: Explore outdoor activities such as identifying plants, observing birds, mapping natural sounds, and cleaning up litter.
- 📓 Maintain a Field Journal: Record outdoor observations and document experiences.
- 🏆 Track Progress: Earn points by completing missions and monitor exploration progress.
- 🤖 Explore with AI: Use a locally running Gemma model to generate personalized missions and ask questions about nature.
- 🌍 Make a Difference: Build awareness of the environment through simple, actionable outdoor activities.
EcoQuest AI is designed for students, nature enthusiasts, and anyone who wants to build healthier digital habits while reconnecting with the natural world.
The core idea is simple: AI should encourage us to experience more of the real world, not just spend more time online.
Demo
The application currently runs locally using Streamlit.
To try it, follow the setup instructions in the repository README.
A deployed demo or video walkthrough will be added when available.
Code
GitHub Repository: https://github.com/Jitin2102/HactoberFest-Challenges-Week-1
The project includes the Streamlit application, outdoor quest system, field journal, progress tracking, and optional local Gemma integration.
How I Built It
EcoQuest AI combines a lightweight Python application with open-weight AI.
Technology stack:
- **Python—Application logic
- **Streamlit—Interactive web interface
- **Gemma—Locally running AI model for mission generation and nature-related questions
- **Ollama—Local model inference
- python-dotenv — Environment-based configuration
The application provides ready-made outdoor missions without requiring an AI model. For AI-powered features, users can run Gemma locally through Ollama.
This approach keeps the basic experience accessible while making AI features available without depending entirely on a hosted, proprietary API.
Why Does Open Innovation Matter?
AI should not be limited to centralized platforms or expensive API subscriptions.
Open-weight models make it possible for developers to experiment, build locally running applications, and adapt AI-powered experiences to specific needs.
For EcoQuest AI, local Gemma inference provides an opportunity to explore AI-assisted nature activities without making a proprietary cloud API the foundation of the application.
Open innovation also makes experimentation more accessible to students and independent developers. Developers can inspect their implementation, learn from existing tools, contribute improvements, and build on shared technology.
Most importantly, open AI gives us the freedom to explore different ways of using technology—including ways that encourage people to step away from it.
My Agent Session
I don't have a published agent session to share yet.
Prize Categories
- Gemma: Local AI integration using an open-weight Gemma model.
What's Next?
EcoQuest AI is an early MVP, and there is plenty of room to grow.
Future improvements could include:
- Persistent user profiles and mission history
- Nature identification from uploaded photographs
- Voice-based outdoor guidance
- Location-aware missions
- Offline-first mobile support
- Community challenges and collaborative environmental activities
I'd love to see contributors help turn this initial concept into a practical tool for reconnecting people with nature.
Less scrolling. More exploring. More Earth. 🌿
Top comments (1)
One thing I'd pay attention to is how Gemma handles structured quest generation. A model can return valid-looking JSON while still producing an invalid difficulty level, unsupported activity type, or a mission that doesn't match the user's location or available conditions. Schema validation catches the structural issues, but the app still needs semantic checks before presenting a quest as actionable. Keeping those checks separate from the prompt would make the system much more reliable as the number of quest types grows.