🌱 I Built ESCAPE: Touch Grass to Unlock Social Media — Powered by Offline AI

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

What I Built

What if the password to YouTube wasn't a PIN, but a little adventure outside?

Meet ESCAPE — The World Is Your Password 🌱

I built an experimental Android app for people who find themselves endlessly scrolling. Instead of showing another screen-time chart, ESCAPE encourages you to get outside, walk, touch grass, and rediscover nature.

The idea is simple: the real world becomes your password!

Every outdoor quest has three steps:

1. Move 🚶 — Step outside and complete a walking goal using your phone's step sensor and timer.
2. Discover 🌳 — Find something interesting in nature: grass, a beautiful tree, flowers, or a reflection in water.
3. Capture 📸 — Photograph your discovery, preview the image, and tap Analyze. ESCAPE checks the evidence locally.

Complete the quest and earn a limited window of social-media access!

But why stop at walking?

On weekends, ESCAPE also encourages bicycle adventures and helps discover nearby parks, greenery, and cycling paths.

Before 6 PM, adventures focus on outdoor activities. After 6 PM, users can choose between an outdoor adventure and an indoor nature-inspired challenge. An activity already in progress continues without being reset.

Demo

🎬 Watch ESCAPE in action

▶ Watch the real Android demo on YouTube

The video demonstrates the ESCAPE experience on a real Android phone: blocking YouTube, tracking a walking quest, capturing and previewing a nature photograph, and approving the evidence.

Here's what excited me most: I tested image analysis with my phone disconnected from the internet, and ESCAPE still approved the nature photograph!

The recording shows the approval result, although it doesn't independently show the network being disconnected. The demonstration uses accelerated Demo Mode to shorten the walking requirement.

🌐 Visit the ESCAPE project showcase

Code

💻 ESCAPE — GitHub Repository

ESCAPE is an open-source Android prototype built using Flutter and Kotlin. The source code is available publicly, although the app hasn't been published to Google Play yet.

How I Built It

ESCAPE combines open-weight AI with Android's native capabilities:

• Gemma 3 1B IT + LiteRT-LM: Generates creative nature-based missions locally on the phone. Missions are cached to avoid repeatedly running the model for notifications.
• Flutter + Kotlin: Provides the user interface, app monitoring, blocking overlay, and mission management.
• Android sensors: Tracks walking time, steps, and optional GPS-based cycling progress.
• Google ML Kit: Analyzes captured photographs entirely on-device, checking broad nature subjects such as trees, plants, and water.
• OpenStreetMap/Overpass: Discovers nearby parks and cycling infrastructure when internet is available, with cached results for later offline use.

One important distinction: Gemma 3 1B is a text model. It generates creative missions, while ML Kit handles image recognition.

I also separated creativity from validation:

AI creates the adventure. Android tracks the progress. Local image recognition evaluates the evidence.

That keeps the unlock rules predictable even when AI responses vary.

Why Does Open Innovation Matter?

Imagine walking through a park without mobile data. Should an AI-powered outdoor companion stop working?

I don't think so.

Using open-weight Gemma with on-device inference allows ESCAPE to create new mission ideas without constantly contacting a cloud API. Photo analysis also happens locally, without uploading pictures to a server.

After the initial setup, the core adventure experience can work offline. Downloading the model and discovering new nearby locations still require internet connectivity.

For me, the most interesting part is the irony:

I built an AI application whose ultimate goal is to make people spend less time on their phones.

Sometimes the best use of technology is encouraging us to experience something beyond technology.

My Agent Session

I used GitHub Copilot CLI during development to inspect mission verification and reward rules, improve test coverage, and add automated Flutter validation.

I didn't capture a DevRelay agent-session embed, but here's evidence of the Copilot CLI development work:

You can also inspect the GitHub Actions workflow.

Prize Categories

Best Use of Gemma

Gemma 3 1B runs locally through LiteRT-LM, generating creative outdoor missions without requiring continuous internet connectivity.

Best Use of GitHub Copilot

GitHub Copilot CLI helped improve the project's reliability through testing and CI workflow development.

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ESCAPE — Less scrolling. More strolling. Go touch grass! 🌿