🌿 TouchGrass AI: Turning Screen Time Into Outdoor Time with Local Gemma

What if AI's job wasn't to keep us on a screen longer, but to help us leave the screen?

That was the idea behind TouchGrass AI, my project for the Hacktoberfest 2026 Week 1 challenge: Touch Grass.

The goal is simple: give AI a small role in planning an outdoor experience, then let the user step away from the screen and actually do it.

🌱 What I Built

TouchGrass AI is a personalized outdoor activity planner.

The user provides:

• ⏰ Available time
• 👥 Number of people
• 😊 Mood
• 🌳 Interest
• 📍 Location

The application then uses Gemma 3, an open-weight AI model running locally through Ollama, to generate a personalized outdoor plan.

The plan includes:

• A suitable outdoor activity
• A time-based schedule
• Things to carry
• A screen-free challenge
• An alternative outdoor activity

The final goal is not another AI conversation.

The goal is to get the user outside. 🌿

🤖 Why Open-Weight AI?

The open part of this project isn't just an extra feature. It is what makes the application work.

I chose Gemma 3 because I wanted the core AI generation to happen locally rather than depending on a closed cloud AI API.

With Ollama, Gemma runs directly on my laptop.

This gives the project a few important advantages:

🔒 Local Processing

The user's preferences can be processed locally on their own computer instead of being sent to a remote AI service for generating the plan.

💰 No Per-Request AI API Cost

Once the model is downloaded, the application can generate plans locally without requiring a paid API request for every interaction.

🔄 Model Flexibility

Because the model is running through Ollama, the architecture can potentially be adapted to use other locally available models in the future.

🌐 Local AI Inference

After the model has been downloaded, the AI inference itself can run without an internet connection.

For a project designed to encourage people to spend less time online, having the AI itself run locally felt especially appropriate.

🏗️ How It Works

The workflow is intentionally simple:
User Preferences
↓
Streamlit Interface
↓
Python Application
↓
Ollama
↓
Gemma 3
↓
Personalized Outdoor Plan
↓
📵 Screen-Free Challenge
↓
🌳 Real-World Activity

🛠️ Tech Stack

• Python
• Streamlit
• Ollama
• Gemma 3 (1B)
• Git
• GitHub ## 📸 What the App Looks Like ### 🏠 User Input

🌳 Generated Outdoor Plan

📵 Screen-Free Challenge

🔗 Project

The complete source code is available on GitHub:

TouchGrass AI — GitHub

Built for the Hacktoberfest 2026 Week 1: Touch Grass challenge.

devchallenge #hf26challenge