GreenQuest 🌿 — Turning Open-Weight AI into Real-World Outdoor Adventures

Tanmay Dalvi — Sun, 11 Oct 2026 11:31:57 +0000

🌿 GreenQuest: Turning Open-Weight AI into Real-World Outdoor Adventures

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

What if AI could help us spend less time looking at screens and more time experiencing the world around us?

That's the idea behind GreenQuest, a local-first, offline-capable outdoor companion that turns 15–60 minutes of free time into personalized nature micro-adventures.

Instead of endlessly scrolling through a phone, users can choose their environment, available time, mindset, physical energy, sensory interests, and companions. GreenQuest turns those preferences into a structured outdoor quest with practical activities, progress tracking, and safety guidance.

Whether you're visiting a forest, walking through a city park, exploring a garden, or spending time near a waterfront, GreenQuest encourages you to slow down and notice things you might otherwise overlook.

A typical quest might invite you to listen to the sounds beneath a tree canopy, observe the textures of tree bark, or discover the miniature world growing on a fallen log.

Each quest includes:

Personalized outdoor activities with step-by-step instructions.
A duration suited to the user's available time.
Sensory prompts for sight, sound, touch, scent, and spatial awareness.
Progress tracking and completion feedback.
Outdoor safety guidance and Leave-No-Trace principles.

GreenQuest also keeps a local history of saved quests, allowing users to revisit their experiences without creating an account.

Built for the real world, including offline situations

GreenQuest is designed to remain useful when the internet isn't available. Its Progressive Web App (PWA) caches essential assets, quest history is stored locally, and a browser-side deterministic generator can create activities when the backend cannot be reached.

The application also includes safeguards against risky activities such as consuming wild plants, trespassing, disturbing wildlife, and unsafe exploration.

The objective is simple: use technology to help people disconnect from technology.

Demo

🌐 Live application: https://greenquest-pwa.onrender.com

Try creating a quest, completing its steps, and revisiting it through the saved history panel.

The hosted demo uses a deterministic nature-quest engine on the backend. The local version supports open-weight AI inference through Gemma 3 1B and Ollama.

Code

💻 GitHub repository: https://github.com/Tanmay-Dalvi/greenquest

GreenQuest is open source under the MIT License.

The repository contains the React/TypeScript frontend, FastAPI backend, AI orchestration logic, safety validation, tests, and deployment configuration.

How I Built It

GreenQuest uses a modular architecture so the application doesn't depend on a single proprietary AI provider.

Technology stack

Frontend: React, TypeScript, Vite, and Tailwind CSS.
Progressive Web App: Service Worker caching and local browser storage.
Backend: Python, FastAPI, and Pydantic.
Local AI: Gemma 3 1B running through Ollama.
Orchestration: Lightweight Python state machine coordinating generation and validation.
Testing: Pytest for the backend and Node's built-in test runner for API URL normalization.
Deployment: Render for the hosted frontend and backend.
Version control: Git and GitHub.

System architecture

The frontend collects user preferences and sends a request to the backend. The backend coordinates mission generation, validates the result, and returns a structured quest. Local storage preserves progress and history, while the browser-side fallback provides an alternative when the backend is unavailable.

The AI quest-generation pipeline

The local AI implementation uses Gemma 3 1B through Ollama. A lightweight orchestration flow separates the work into three responsibilities:

Planner Agent: Organizes a quest around the user's preferences and available time.
Nature Activity Agent: Produces concrete sensory activities and instructions.
Safety Validation: Checks the generated content against deterministic application rules before it is presented.

The application uses structured JSON output, response repair, and bounded retries to handle malformed model responses. If generation cannot be completed reliably, a deterministic fallback keeps the experience usable.

Local AI, hosted demo, and offline browser mode

These are three distinct execution modes:

Local mode: Ollama runs Gemma 3 1B on the user's machine. Inference does not require a paid cloud AI API, and the model can run without an internet connection once installed.

Hosted demo: Render hosts the frontend and backend. The public demo uses the deterministic backend rule engine by default; visitors are not downloading or running Gemma model weights in their browsers.

Browser offline mode: When the backend is unreachable, the PWA can use its browser-side deterministic generator and locally saved data. This is separate from running Gemma locally.

Safety and reliability

Generated content is treated as untrusted until it passes application-level validation. GreenQuest includes deterministic safeguards against:

Eating or tasting wild plants, mushrooms, berries, or unpurified water.
Trespassing or entering restricted areas.
Dangerous climbing or unsafe water crossings.
Touching, chasing, or feeding wildlife.
Activities that conflict with Leave-No-Trace principles.

The backend test suite has 31 passing tests, covering API behavior, schemas, safety rules, provider behavior, orchestration, and model-output repair. The frontend production build also passes, and API URL normalization has dedicated unit tests.

Why Does Open Innovation Matter?

Open innovation makes GreenQuest more accessible, inspectable, and adaptable.

1. Local inference and user control

Gemma 3 1B and Ollama give users a way to run the AI locally instead of sending every prompt to a proprietary cloud service. This offers greater control over data and a path toward reducing recurring inference costs.

2. Freedom to experiment

The provider abstraction makes it possible to inspect the orchestration and change the inference provider without rebuilding the entire application around one vendor.

3. Accessibility without mandatory paid APIs

The deterministic fallback keeps the basic experience usable without a paid API key or a cloud GPU. The hosted demo can also operate without a hosted AI provider.

4. Transparent safety logic

The safety rules are part of the source code. Developers can inspect them, test them, and propose improvements rather than relying entirely on an opaque model response.

5. AI with a purpose beyond the screen

The most important part of GreenQuest isn't generating more text. It's turning a simple intention—“I have 30 minutes and want a mindful break”—into a real-world activity.

Open models let me experiment with this idea on ordinary hardware, while open-source code lets others learn from it, adapt it, and build on it.

My Agent Session

I used AI-assisted development to build and debug the application, improve structured-output handling, test safety behavior, and resolve deployment configuration issues.

I don't have a verified public DevRelay session link to include here. If I publish one, I'll add it to this section.

Prize Categories

GreenQuest uses Gemma 3 1B locally through Ollama and is deployed on Render. I'll select the partner prize categories only after verifying their official eligibility requirements.

GreenQuest is a reminder that AI doesn't always need to make us more productive, more connected, or more engaged with a screen.

Sometimes, it can help us notice the world just outside the door.

🌲 Explore less digitally. Experience more naturally.

DEV Community: Tanmay Dalvi