Outside.exe - The Real World Is the Game Map 🌿🎮

Sneha Choudhary — Fri, 09 Oct 2026 17:06:44 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

Outside.exe — The Real World Is the Game Map 🌿🎮

What if going outside felt like playing a game?

That question inspired me to build Outside.exe, an AI-powered real-world adventure game that turns ordinary surroundings into opportunities for exploration.

Instead of giving people another reason to stay on their phones, Outside.exe encourages them to step away from their screens and complete physical-world quests.

Users choose their available time, energy level, mood, and environment. Based on this context, the application generates personalized adventure missions.

For example:

Mission 1: Walk for five minutes without headphones.

Mission 2: Find something you've walked past many times but never actually noticed.

Mission 3: Find something that is moving, something making a sound, and something that looks out of place.

Once an adventure starts, users are encouraged to put their phones away and explore the real world.

When they return, they can describe what they encountered. These observations can then inform the next mission, turning the adventure into an evolving experience rather than a fixed checklist.

The core idea is simple:

Use AI to make the real world more interesting, not the screen more addictive.

Outside.exe is designed for people who want to break their routines, explore their surroundings, spend less time on their phones, and rediscover the little things they usually overlook.

The goal isn't to maximize screen time. It's to make the screen the shortest part of the experience.

Demo

🌐 Live Demo: https://outside-exe-4.onrender.com/

Try the application and start your own real-world adventure.

The project is designed around a simple loop:

Configure → Explore → Observe → Reflect → Explore Again

Code

💻 GitHub Repository: https://github.com/snehach03/outside.exe

The source code for Outside.exe is available in the repository.

How I Built It

I built Outside.exe as a web application using Next.js, React, TypeScript, and Node.js.

The application follows an adventure flow that connects user preferences with real-world missions and subsequent check-ins.

The Adventure Loop

1. Adventure Setup

Users provide information such as their available time, energy level, mood, and environment.

2. Mission Generation

The AI uses the available context to create an adventure with physical-world challenges.

3. Real-World Exploration

Users put their phones away and complete their missions in the world around them.

4. Check-in and Adaptation

After returning, users describe what they experienced. Their observations can inform the next mission, making the experience more personal and dynamic.

AI Integration

I used Ollama as the local runtime for an open-weight AI model.

The goal of this integration is to use an open-weight model to generate adventure missions and adapt challenges to the user's context and observations, rather than making a closed cloud API the only possible foundation.

Ollama provides a way to run models locally, giving developers greater control over the inference environment and the freedom to experiment with different supported models.

I used Claude Code as my AI coding agent during development. It helped me implement features, debug Next.js and TypeScript build errors, and prepare the application for deployment.

The web application is deployed on Render.

Why Does Open Innovation Matter?

AI doesn't always have to keep people staring at screens.

For Outside.exe, I wanted to explore the opposite: using AI to encourage curiosity, movement, and interaction with the physical world.

Open-weight models and tools such as Ollama make it possible to experiment with local inference, explore different models, and have more control over how an AI-powered application behaves.

This matters because developers should have the freedom to understand, adapt, and experiment with the technology behind their applications.

For a project like Outside.exe, local inference also offers the possibility of keeping user interactions within a locally controlled environment when the application is configured to run that way.

The larger idea behind the project is to change how we think about AI-powered experiences.

Instead of:

AI → More Screen Time

Outside.exe explores:

AI → Physical Challenge → Exploration → Observation → New Challenge

I want AI to become a bridge between the digital and physical worlds, helping people notice more, explore more, and spend less time passively consuming content.

The best outcome is not a user spending hours inside Outside.exe.

It's a user closing the app, stepping outside, and discovering something new.

Prize Categories

Hacktoberfest Open-Source AI Challenge — Week 1: Touch Grass

devchallenge #hf26challenge

DEV Community: Sneha Choudhary