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

> Source: <https://dev.to/parthverma2005/what-if-we-used-ai-to-spend-less-time-on-screens-556e>
> Published: 2026-10-09 14:14:15+00:00

*This is a submission for the [Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass](https://dev.to/challenges/hacktoberfest-week1-2026-10-05)*

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.

Watch the project demo:

**GitHub repository:** [https://github.com/parthverma2005/Touchgrass_AI.git](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.

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

**Tech stack**

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.

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

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.

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

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.

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.

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.

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.**