NatureQuest AI: Touch Grass with Local Gemma AI 🌿

Shubham Hagawane — Sun, 11 Oct 2026 11:17:12 +0000

I built NatureQuest AI, a web application that uses a locally running open-weight AI model to encourage people to spend more time outdoors and notice the natural world.

Users can choose an activity such as walking, birdwatching, gardening, cycling, or nature observation, select a duration and difficulty, and generate a personalized outdoor quest.

The application also provides a reflection prompt so users can record their experience after completing a quest.

Demo

The application runs locally on my computer.

Screenshots: Add real screenshots of:

The NatureQuest AI home page.

The quest creation form.

The local Gemma model running through Ollama.

Code

GitHub repository: https://github.com/ShubhamHagawane/NatureQuest-AI

The repository contains the React frontend, Express backend, local AI integration, and setup instructions.

How I Built It

I built the frontend using React, Vite, and CSS. The backend uses Node.js and Express to handle quest-generation requests.

For AI generation, I integrated Gemma 2 2B through Ollama, allowing the model to run locally rather than relying on a paid hosted inference API.

The workflow is:

The user chooses an activity, duration, and difficulty.

The React frontend sends the preferences to the Express API.

The backend sends a prompt to the local Gemma model through Ollama.

The response is validated and returned as a structured quest.

The user can complete the quest and save a reflection in browser storage.

I also included sample quests for situations where the local model is unavailable.

Why Does Open Innovation Matter?

Open innovation makes AI experimentation more accessible to students, independent developers, and communities that may not have access to expensive cloud infrastructure.

Using an open-weight model locally gave me an opportunity to learn how to integrate AI into a real application while keeping inference on my own computer after the model is downloaded.

NatureQuest AI explores a simple idea: AI doesn't always need to keep us staring at a screen. It can help us plan an activity that encourages us to step away from technology and experience the world around us.

My Agent Session

I used ChatGPT as a development assistant while building and debugging the application

Prize Categories

My project is submitted for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass.

The project combines a locally running open-weight AI model with an application designed to encourage outdoor activities and real-world engagement.

devchallenge #hf26challengease pick one member to publish the submission and credit teammates by listing their DEV usernames directly in the body of the post. -->

NatureQuest AI: Touch Grass with Local Gemma AI 🌿

Shubham Hagawane — Sun, 11 Oct 2026 10:57:36 +0000

The idea

We spend a lot of time looking at screens, even when there are interesting things to discover outside. I wanted to build something that encourages people to take a break from their screens and explore the natural world.

That's why I built NatureQuest AI, an AI-powered outdoor adventure companion that generates personalized nature quests based on a user's activity, available time, and difficulty level.

This project is my contribution to the Hacktoberfest 2026 Open-Source AI Challenge — Week 1: Touch Grass.

What can NatureQuest AI do?

Users can choose activities such as:

🚶 Walking

🚲 Cycling

🌱 Gardening

🐦 Birdwatching

🔎 Nature observation

The application generates a short outdoor quest with instructions, a time limit, a difficulty level, and a reflection prompt. Users can then go outside, complete the quest, and record their experience.

The goal is simple: make the screen the starting point for an outdoor experience, not the destination.

How I built it

The project uses the following technologies:

React + Vite: Frontend interface

Tailwind CSS: Styling

Node.js + Express: Backend API

Ollama: Local model runtime

Gemma 2 2B: Open-weight language model

Browser local storage: Saving quest history locally

Why local AI?

I initially explored hosted inference through Hugging Face Inference Providers, but I ran out of available inference credits.

Instead of stopping there, I switched to running Gemma locally through Ollama.

This gave the project several advantages:

Quest generation does not require paid Hugging Face inference credits.

Once the model is downloaded, generation can work without an internet connection.

User prompts can be processed locally rather than sent to a hosted inference service.

I can experiment with the model and change the AI integration as the project evolves.

Local inference still requires a capable device, sufficient memory, and Ollama running. The initial model download also requires an internet connection.

How it works

A user chooses an outdoor activity and preferences.

The React frontend sends the request to the Express backend.

The backend sends the prompt to the locally running Gemma model through Ollama.

Gemma generates a structured nature quest.

The application displays the quest so the user can head outside and complete it.

Screenshots and demo

Homepage

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AI-generated quest

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Local model running

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What I learned

This project helped me understand how to connect an open-weight language model to a full-stack web application.

I also learned that AI integration isn't only about sending a prompt. The application needs structured responses, validation, error handling, and a fallback experience when generation fails.

Most importantly, I learned that switching from a hosted model API to local inference can make a project more independent of external inference credits.

What's next?

I'd like to improve NatureQuest AI with better outdoor activity recommendations, richer quest history, and additional ways to document discoveries while keeping the experience focused on spending time outside.

Try the project

GitHub repository: [https://github.com/ShubhamHagawane/NatureQuest-AI]

If you try NatureQuest AI, I'd love to hear which outdoor quest you completed!

DEV Community: Shubham Hagawane

NatureQuest AI: Touch Grass with Local Gemma AI 🌿

devchallenge #hf26challengease pick one member to publish the submission and credit teammates by listing their DEV usernames directly in the body of the post. -->

NatureQuest AI: Touch Grass with Local Gemma AI 🌿