🌱 TouchGrass AI: Stop Scrolling, Start Exploring

A developer built TouchGrass AI, a local Streamlit and Ollama application that uses the open-weight Llama 3.2 3B model to generate short, practical outdoor missions from a user's available time, preferred activity, and company. The project, submitted to the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass, sends prompts to Ollama's local API at localhost:11434 and displays the resulting 3–5 step challenge, with source code available on GitHub. Its stated goal is to make the screen the shortest part of the experience rather than another app that keeps users chatting with AI.

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 AI-powered outdoor activity companion designed to help people spend less time on screens and more time in the real world. The idea is simple: Use AI for a few seconds → get an outdoor mission → put the phone away → go outside. 🌱 The user chooses: TouchGrass AI then uses a locally running AI model to generate a short, practical outdoor mission. For example, a user with 30 minutes who chooses Nature can receive a simple nature exploration challenge with 3–5 steps. The project is designed for anyone who wants a quick reason to walk, explore, observe nature, or spend time outdoors with friends and family. The goal isn't to create another app that keeps users chatting with AI. The goal is to make the screen the shortest part of the experience. The project currently runs locally using Streamlit and Ollama. I will include a short screen recording showing: The complete source code is available on GitHub: https://github.com/Samruddhipathrikar/TouchGrassAI https://github.com/Samruddhipathrikar/TouchGrassAI The repository contains the Streamlit application and its dependencies. I built TouchGrass AI using Python, Streamlit, Ollama, and the open-weight Llama 3.2 3B model. The user interacts with a simple Streamlit interface. The application collects the user's: These inputs are converted into a prompt for the local Llama 3.2 model. The application sends the prompt to Ollama through its local API: http://localhost:11434/api/chat The generated response is then displayed as the user's outdoor mission. text User ↓ Streamlit UI ↓ Time + Activity + Company ↓ Prompt ↓ Ollama Local API ↓ Llama 3.2 3B ↓ Outdoor Mission ↓ 🌱 Go Outside