Nature Mate AI

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

What I Built

I built NatureMate AI, an outdoor adventure companion designed to help people spend less time on screens and more time in nature.

NatureMate AI helps users discover outdoor activities, take daily nature challenges, track their outdoor progress, and generate personalized outdoor adventure plans.

The project includes:

• Outdoor activity suggestions
• A 20-minute screen-free challenge timer
• An outdoor activity progress tracker
• An AI-powered adventure planner
• A responsive interface built with HTML, CSS, and JavaScript

The goal is simple: use technology to encourage people to disconnect from their screens and reconnect with the real world.

Demo

I built and tested the project locally.

Screenshots and a video demonstration can be added here.

Code

The project uses three separate frontend files:

• index.html — Website structure
• style.css — Responsive styling and user interface
• script.js — Timer, activity tracking, and AI integration

GitHub repository: [Add your GitHub repository link here]

How I Built It

I built NatureMate AI using HTML, CSS, and JavaScript.

For the AI component, I chose Ollama to run an open-weight language model locally on my computer.

The AI planner is designed to generate personalized outdoor activity plans based on the user's mood, available time, and preferred location.

The project is designed to use Gemma through Ollama. The AI functionality requires the local model and Ollama service to be installed and running.

The timer and progress tracker work directly in the browser using JavaScript.

Why Does Open Innovation Matter?

Open innovation makes NatureMate AI more accessible, flexible, and privacy-friendly.

By using an open-weight model through Ollama, I can experiment with local AI inference instead of depending on a paid, closed AI API.

This approach offers several advantages:

• Privacy: Prompts can be processed locally instead of being sent to a third-party AI provider.
• Flexibility: I can experiment with different compatible models.
• Accessibility: Local inference can avoid per-request API charges.
• Learning: Working with open AI tools helps me understand how AI applications work beyond simply calling a hosted API.

The project also demonstrates that AI can be used to encourage healthier offline habits instead of keeping people glued to their screens.

My goal is to make the screen the starting point of an outdoor adventure, not the destination.

My Agent Session

Optional: Add a DevRelay agent session if available.

Prize Categories

Hacktoberfest Open-Source AI Challenge — Week 1: Touch Grass.

Thanks for checking out NatureMate AI! 🌿