IRL Quest — AI That Gives You a Reason to Put Your Phone Down 🌿

A developer built IRL Quest, a local-first Next.js web app that uses open-weight language models via LM Studio to generate real-world quests, deliberately inverting the engagement-maximizing pattern of typical conversational AI. The app routes prompts through Zod validation and a deterministic safety filter before hitting a local OpenAI-compatible API, then sends users into a distraction-free "Touch Grass Mode" countdown with a reflection journal stored in browser storage. Field tests documented in the repository cover an urban sidewalk, a public park, and a domestic balcony.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass https://dev.to/challenges/hacktoberfest-week1-2026-10-05 AI that gives you a reason to put your phone down. Built for the Hacktoberfest Open-Source AI Challenge — Week 1: Touch Grass. Most modern consumer applications are designed to maximize digital engagement: longer sessions, deeper scroll depth, and habitual screen time. Conversational AI can fall into the same pattern, encouraging endless back-and-forth interactions. IRL Quest turns that paradigm upside down. The successful user spends less time using the application, not more. Traditional AI Apps: User → Prompt → Endless Chat & Scroll → More Screen Time IRL Quest: User → Preferences → Local AI Generation → Put Phone Down → Explore the Real World → Return → Brief Reflection The goal is simple: use AI to facilitate real-world experiences rather than keeping people glued to a screen. Cover banner 1. Quest Configurator / Configure quest preferences and check the local model connection. 2. Touch Grass Mode /active A distraction-free interface with a countdown and the instruction to put the phone away. 3. Completion & Reflection /complete Capture sensory observations and reflect on the experience. 4. Local Journal & Statistics /journal Review completed quests, track time spent outdoors, and export journal data. Real-world field testing The project includes documentation of outdoor tests in docs/FIELD TESTS.md https://github.com/Ani0811/irl-quest/blob/main/docs/FIELD TESTS.md , covering an urban sidewalk, a public park, and a domestic balcony. Note: Screenshots and test documentation supplement a demo; if you have a working deployed demo or video walkthrough, add its direct link here. google/gemma-3-4b and Meta-Llama-3.1-8B-Instruct , subject to availability in your LM Studio setup. The source code and project documentation are available in the repository. IRL Quest is designed as a local-first web application, emphasizing user control, minimal distractions, and reduced dependence on cloud services. php flowchart TD User "User" -- UI "Next.js / React Frontend" UI -- Setup "Quest Setup" Setup -- API "Next.js API Route" API -- Schema "Zod Validation" Schema -- Safety "Deterministic Safety Filter" Safety -- Provider "AI Provider Abstraction" Provider -- LM "LM Studio Local Server" LM -- Model "Open-Weight Language Model" Model -- Safety Safety -- Active "Touch Grass Mode" Active -- Complete "Reflection" Complete -- Journal "Local Journal" Journal -- Storage "Browser Storage" AIProvider interface communicates with LM Studio through its local OpenAI-compatible API. Math.max 0, targetEndTimestamp - Date.now , helping the timer recover from backgrounding and screen-lock interruptions. git clone https://github.com/Ani0811/irl-quest.git cd irl-quest npm install Start LM Studio, load a supported model, and enable its local server on port 1234 . Then run: npm run dev Open http://localhost:3000 and configure a quest. Exact setup requirements may depend on your local environment and the model you choose. Open innovation makes it possible to experiment with AI in ways that prioritize user autonomy rather than maximizing engagement. Real-world habits and personal reflections can be sensitive. A local-first design can keep journal data in browser storage and use local inference, reducing the need to transmit that information to third-party services. Cloud-based AI requires network connectivity. Running inference locally can make quest generation available without an internet connection, provided the application, model, and required assets are already available on the device. Open-weight models give developers more control over experimentation and deployment. Local inference can also avoid per-request API charges, although hardware, electricity, and setup still have costs. A model-agnostic provider abstraction makes it easier to experiment with different compatible models without redesigning the entire application. IRL Quest challenges the assumption that successful technology must maximize time spent inside an app. Here, success means helping someone leave the interface and engage with the world around them. Open innovation makes this kind of experimentation more accessible to independent developers and the wider community. This project was developed with AI-assisted programming support using Google's Antigravity, including assistance with implementation, testing, and debugging. IRL Quest supports Google's open-weight Gemma model through LM Studio for locally generating real-world quests. This allows users to generate personalized offline activities using local inference rather than relying on a cloud-hosted AI API. The project combines open-weight AI with a local-first architecture to encourage real-world exploration while reducing dependence on external AI services. Thank you to DEV, MLH, Google, and the Hacktoberfest 2026 team for championing open AI innovation