What If We Used AI to Spend Less Time on Screens?

Parth Verma — Fri, 09 Oct 2026 14:14:15 +0000

TouchGrass AI — Get out. AI stays in.

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

What I Built

I built TouchGrass AI, a local-first AI outdoor companion that encourages people to spend less time on their screens and more time exploring the real world.

The idea behind the project is simple:

AI creates the experience, but the experience exists to get you away from AI.

Users can choose an outdoor activity, such as running, walking, hiking, or cycling, select a duration, and describe their goal. The AI then generates a personalized outdoor mission with a warm-up, main activity, outdoor challenge, cool-down, and motivation.

But generating a mission is only the beginning.

TouchGrass AI includes a Go Outside Mode with a timer so users can put their phones away and focus on their activity. When they return, they can reflect on their energy, mood, and difficulty, then save their experience to My Journey.

The app keeps experience history in the browser using local storage.

I designed it for students, developers, remote workers, and anyone who wants a little encouragement to step outside.

My goal wasn't to build another AI chatbot that keeps people engaged with a screen. I wanted to build something that gives people a reason to leave it.

Demo

Watch the project demo:

Touchgrass_AI.mp4 - Google Drive

drive.google.com

Code

GitHub repository: https://github.com/parthverma2005/Touchgrass_AI.git

TouchGrass AI is open source under the MIT License. The repository includes the frontend, FastAPI backend, setup instructions, and local AI integration.

How I Built It

I built TouchGrass AI using a lightweight web stack and an open-weight language model running locally.

Tech stack

AI model: Qwen 2.5 0.5B
Local inference: Ollama
Backend: Python and FastAPI
Frontend: HTML, CSS, and JavaScript
Experience history: Browser localStorage

How it works

The user chooses an activity, duration, and goal.
The frontend sends the request to the FastAPI backend.
The backend sends a prompt to Qwen 2.5 0.5B through Ollama.
The generated mission is displayed in the frontend.
The user starts Go Outside Mode and completes the activity.
Afterward, the user records a reflection, which is saved locally in the browser.

I chose a small model because I wanted to experiment with local AI without requiring a powerful machine or a paid AI API.

The application does not require a cloud AI API for its core mission-generation feature. The model must be installed locally first, and the backend must be running for mission generation to work.

I also added a Local AI information panel to make the model and inference approach visible to users.

Why Does Open Innovation Matter?

Open innovation made this project possible in a way that fits its purpose.

Privacy by design

Mission generation can happen locally instead of sending each request to a cloud AI provider. The user's saved reflections also remain in their browser rather than requiring a remote database.

Accessibility and cost

Using a small open-weight model means developers can experiment with AI-powered applications without paying per-request API fees.

Freedom to experiment

Local inference gives developers the freedom to inspect the setup, modify prompts, and experiment with other compatible models. It also makes the project easier for others to learn from and extend.

AI that encourages disconnection

One of the interesting contradictions of modern technology is that AI can be used to encourage people to spend less time using technology.

That became the guiding principle for this project:

Use AI briefly. Go outside. Experience the world. Return only when you need to.

Open AI gives me the ability to build a tool where AI supports the experience instead of becoming the experience itself.

My Agent Session

I used AI-assisted development to help design and implement the project, connect FastAPI to Ollama, integrate the local model, and refine the mission-generation prompts.

I also worked through the frontend integration, outdoor timer, reflection form, and local journey history.

Final Thought

AI is becoming increasingly good at keeping us on our screens.

I wanted to experiment with using it for the opposite purpose.

TouchGrass AI gives you a mission, starts the clock, and encourages you to experience something beyond the screen.

Get out. AI stays in.

DEV Community: Parth Verma