This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
🌿 WildQuest: AI Missions That Get You Outside
What if the best AI interaction was the one that convinced you to close the app?
Most AI applications compete for our attention through endless conversations, notifications, and scrolling. I wanted to build something different: an application that uses AI to get people away from their screens and into the real world.
That's why I built WildQuest — an AI-powered outdoor exploration companion that turns a simple walk into a mission of discovery.
What I Built
WildQuest generates personalized outdoor missions that encourage people to observe nature, explore their surroundings, and become more curious about the world around them.
Instead of giving users another chatbot to spend hours talking to, WildQuest gives them a reason to put their phone away.
🌱 How it works
- Generate a Quest: Choose your available time, environment, difficulty, and area of interest to receive an outdoor mission.
- Go Outside: Enter Outdoor Mode, featuring a large countdown timer, minimal controls, and nature-inspired audio cues.
- Observe the World: Look for biodiversity, investigate natural patterns, explore unfamiliar paths, or practice mindful observation.
- Record Discoveries: Return to add field notes or photos about what you observed.
- Reflect and Learn: Receive a reflection on your experience and follow your curiosity into future missions.
- Build Your Field Passport: Track completed quests, time spent outdoors, discoveries, and exploration progress.
WildQuest also includes a Mystery Quest experience and the ability to turn a real-world discovery into a new mission.
The application is designed around a simple principle:
The best interaction with WildQuest is closing the app and stepping into the wind.
Demo
🌐 Live Application: https://wildquest-81wr.onrender.com/
Try generating a quest, entering Outdoor Mode, and exploring the complete experience.
Code
💻 GitHub Repository: https://github.com/saivardhan1245/WildQuest
The project is built with React, TypeScript, Vite, and Tailwind CSS, with an Express server for backend functionality.
Contributions, feedback, and ideas for making outdoor exploration more engaging are welcome!
How I Built It
I built WildQuest around a provider-based AI architecture so that the application isn't tied to a single model or inference service.
🧠 Open-Weight AI with Gemma
WildQuest integrates Google's open-weight Gemma 2 models through Ollama, targeting gemma2:2b by default, with support for other local models.
The local provider generates outdoor missions and reflections using structured prompts. This makes it possible to run inference on a user's own machine rather than requiring every interaction to go through a hosted AI API.
🌿 An Adaptive Offline Engine
Local models aren't always available, especially when users are outside or working with limited connectivity.
To handle this, I implemented an offline adaptive engine that generates missions using predefined ecological patterns and contextual information. The provider registry can fall back to this engine when the preferred provider fails.
This keeps the core quest experience usable without depending entirely on remote inference.
🔌 Swappable AI Providers
The application includes three provider paths:
- Local Gemma: Open-weight model inference through Ollama.
- Offline Adaptive Engine: Algorithmic mission generation and reflections.
- Gemini Development Provider: An optional server-side provider for comparison and testing.
This architecture lets me experiment with different inference approaches while keeping the user experience consistent.
🛠️ The Tech Stack
- Frontend: React, TypeScript, Vite, Tailwind CSS
- Backend: Node.js and Express
- AI: Google Gemma 2, Ollama, optional Gemini integration
- Storage: Browser-based local persistence for user progress and discoveries
- Deployment: Render
- Development: AI-assisted coding and iterative testing
Why Does Open Innovation Matter?
For a project like WildQuest, open innovation isn't just about having access to a model. It's about having control over how intelligence is used.
Open-weight models make local inference possible. Users can experiment with models on their own hardware, and developers can inspect, replace, and adapt the model layer to suit their applications.
This matters particularly for an outdoor companion.
A user shouldn't need a constant cloud connection just to receive a nature observation mission. By combining local model support with an offline adaptive engine, WildQuest can keep its core experience available even when connectivity is limited.
The provider architecture also makes experimentation easier. I can compare models, adjust prompts, and explore alternative open-weight models without rebuilding the entire application around one proprietary API.
My goal is to make AI a bridge to real-world experiences, not another reason to stay online.
My Agent Session
I used AI-assisted development to help build and iterate on WildQuest's React components, AI provider architecture, and user experience.
Agent session: [Add your DevRelay session link here if you saved one.]
Prize Categories
Primary focus: Open-Source AI / Touch Grass.
Potential additional category: Best Use of Gemma, provided the submission meets the category's requirements and Gemma is meaningfully demonstrated in the project.
Final Thoughts
Building WildQuest challenged me to think about AI product design differently.
Instead of asking, "How can I keep users engaged for longer?", I asked, "How can I help users do something meaningful away from the screen?"
That question shaped the mission-generation flow, the distraction-free Outdoor Mode, the offline fallback, and the focus on genuine user discoveries rather than artificial engagement metrics.
WildQuest is a step toward the kind of AI experience I want to build: useful, adaptable, and designed around life beyond the interface.
Generate a quest. Step outside. Discover something. Come back with a story. 🌿
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