# 🌿 I Built TouchGrass AI — An AI That Wants You to Use Your Phone Less

> Source: <https://dev.to/sanjana_jha_bed43d0288644/i-built-touchgrass-ai-an-ai-that-wants-you-to-use-your-phone-less-g6i>
> Published: 2026-10-08 16:11:29+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** 🌿 — an AI-powered outdoor activity recommendation system designed to do something unusual: **help people spend less time with technology.**

Instead of recommending another app, video, or online activity, TouchGrass AI gives you a small real-world mission to complete outside.

You tell it:

It then recommends a personalized outdoor activity that fits your situation.

For example:

**Light Hunt**

Find an interesting patch of sunlight and notice three different shadows.

Or:

**No-Route Walk**

Take a short walk without following your usual route and discover three things you normally overlook.

The idea is simple:

**Use AI for a few seconds → Get a mission → Put your phone down → Go outside.**

TouchGrass AI is designed for anyone who feels like they spend too much time in front of a screen and wants a simple reason to step outside.

🌿 **Try TouchGrass AI:**

[https://touchgrass-mmewgdojhn6dqggh52qkfs.streamlit.app/](https://touchgrass-mmewgdojhn6dqggh52qkfs.streamlit.app/)

The goal of the experience is intentionally short. Once you receive your mission, you're encouraged to stop looking at the screen and actually do it.

💻 **GitHub Repository:**

[https://github.com/sanjana-jha-001/touchgrass](https://github.com/sanjana-jha-001/touchgrass)

The project is open source and built with Python and Streamlit.

TouchGrass AI is built with **Python + Streamlit** and combines a built-in recommendation system with optional local AI inference.

The project can use **Ollama with open-weight models** to generate personalized outdoor missions.

The recommendation prompt gives the model the user's:

The model is specifically instructed to recommend activities that get the user **away from the screen**, rather than suggesting more digital content.

I also included a built-in recommendation system, so the application can still work without a running AI model.

One important design decision was making AI an **enabler rather than the destination**.

The successful outcome isn't another long AI conversation.

The successful outcome is:

**The user closes the app and goes outside. 🌱**

Open innovation is especially important for this project because I wanted the AI component to be transparent, accessible, and replaceable.

Using **open-weight models and local inference through Ollama** means the project doesn't have to depend entirely on a proprietary cloud AI API.

That makes it possible to:

For a project whose goal is to reduce dependence on screens and online services, using local AI felt especially appropriate.

I used AI-assisted development to help design, implement, debug, and improve TouchGrass AI.

The project itself is focused on using AI responsibly: instead of optimizing for more engagement, it uses AI to encourage **less screen time and more real-world activity.**

Most technology is designed around:

**"How can we keep the user engaged?"**

I wanted to experiment with the opposite question:

**"How can AI help the user leave?"**

That's TouchGrass AI. 🌿

Use AI for a few seconds.

Get your mission.

**Touch grass.**