???? GreenBuddy AI: Your Local AI Companion for Getting Outdoors

What I Built

GreenBuddy AI is a small AI-powered outdoor activity assistant built to encourage people to spend less time staring at screens and more time exploring the world around them.

Sometimes, we want to go outside but don't know what to do or where to start. GreenBuddy helps solve that problem by suggesting simple activities based on our available time, surroundings, interests, and energy levels.

With GreenBuddy AI, users can:

???? Get outdoor activity suggestions for 10, 20, or 30 minutes, or an hour.

???? Discover simple gardening activities.

???? Explore nature observation and birdwatching.

???? Find ideas for a short walk or mindful outdoor break.

???? Follow practical, step-by-step instructions for their chosen activity.

The goal is simple: use AI to help people spend less time on their screens and more time in the real world.

Demo

The project currently runs locally.

To try it, clone the repository, install the dependencies, start Ollama with the required Gemma model, and launch the Streamlit app.

Code

Code

???? GreenBuddy AI — GitHub Repository

Explore the source code, setup instructions, and implementation here:

View GreenBuddy AI on GitHub

The repository contains the Python application, dependency list, and setup instructions.

How I Built It

I built GreenBuddy AI using Python, Streamlit, Ollama, and Google's open-weight Gemma model.

Technology stack:

Python: Application logic.

Streamlit: Interactive web interface.

Gemma: Generates personalized outdoor activity suggestions.

Ollama: Runs the model locally on the user's computer.

The user selects their available time, surroundings, preferred activity, and energy level. These details are included in a prompt sent to the local Gemma model, which generates a suggested activity with actionable steps and a nature-observation prompt.

Instead of depending on a hosted AI API, the application uses Ollama's local API endpoint to communicate with the model.

Why Does Open Innovation Matter?

Open innovation makes projects like GreenBuddy AI more accessible to developers and users.

By using an open-weight model locally, I can experiment with AI without requiring a paid hosted-model API for the core functionality.

This approach offers several benefits:

More control: I can modify prompts and experiment with different compatible models.

Local processing: The user's activity preferences can be processed on their own computer rather than sent to a third-party hosted AI API.

Accessibility: Developers can build and experiment without paying per-request model API fees.

Learning by building: Running an AI model locally helps me understand how model inference and application integration work.

For this project, open-weight AI isn't just an extra feature. It is the core technology that generates the activity recommendations.

My Agent Session

I don't have a DevRelay agent session to share yet.

Prize Categories

Potential category: Best Use of Gemma.

GreenBuddy AI uses Google's open-weight Gemma model through Ollama to generate outdoor activity recommendations.

What's Next?

I'd like to improve GreenBuddy AI by adding an outdoor activity journal, a daily nature streak, and seasonal gardening suggestions.

Ultimately, I want GreenBuddy to do more than generate recommendations. I want it to help people take small, meaningful steps toward spending more time outdoors.

Thanks for checking out my project! ????