This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass TouchGrass AI is a lightweight, offline-first web application that generates hyper-local, 3-step micro-adventures. The goal is simple: make the screen the shortest part of the experience.
You select your current environment (e.g., a local park, city center, or quiet neighborhood) and the time you have available. The app instantly generates a unique real-world observation quest. Instead of scrolling, you get specific physical tasks-like finding a distinct architectural feature, observing specific colors in nature, or completing a walking challenge. You lock your device, put it in your pocket, and actually interact with the physical world around you.
gemma2:2b running locally via The core challenge was taming a lightweight 2B model to generate realistic, physical tasks instead of fictional role-playing narratives (initially, it tried to send me on a detective quest to investigate a fictional neighbor). By enforcing a rigid JSON schema in the C# backend and explicitly restricting the prompt from inventing fictional characters, the API reliably outputs highly contextual, real-world tasks formatted as clean JSON arrays.
Building this with an open-source model was mandatory for the core concept to work: