OutLoot: Turn Screen Time Into Real Loot with Local Gemma AI & Verified Outdoor Trails

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

What I Built

Most fitness or location-based games commit one of two fundamental mistakes:

1. They demand screen addiction: They force you to stare directly at your glass display while navigating busy sidewalks and nature paths, turning an outdoor walk into another session of visual fatigue.
2. They are trivially spoofed: They rely on easily faked client timers or step counts that can be simulated by shaking a phone from a desk chair or riding in a car.

OutLoot flips this entirely. It is a screen-light, server-authoritative exploration game designed for developers, students, and knowledge workers who spend 8+ hours a day staring at code editors and need a compelling, gameful reason to step away, disconnect, and touch grass.

Key Highlights:

• Server-Authoritative Anti-Spoofing Movement Engine: The client collects raw GPS breadcrumbs, but never validates its own progress. The server evaluates coordinates against physics constraints (speed between 0.5–3.5 m/s, GPS horizontal accuracy ≤ 30m, sample interval ≤ 30s, and a minimum displacement threshold of 75m) to ensure only genuine outdoor foot travel earns rewards.
• Local Gemma AI Storytelling & Quests: A local open-weight Gemma model (gemma2:2b via Ollama) serves as the game master. It generates dynamic outdoor scavenger quests, walking tips, and mythic relic lore (like Clover Sprouts, Dewdrop Crystals, and Ancient Acorns) without sending any location data to third-party cloud APIs.
• Active Screen Time Heartbeats & Grass Debt: A background engagement tracker monitors active mouse, keyboard, and touch interactions. When you exceed your threshold (e.g., 30 minutes of continuous screen time), a frosted-glass Touch Grass Alert modal nudges you to take a walk. You can also import daily screen hours from Google Digital Wellbeing to calculate your Grass Debt (15 outdoor minutes required per hour of screen time).
• Social Leaderboard, Follow System & Trail Inspector: Compete on a global leaderboard ranked by Grass Score (points + outdoor_mins * 2). Follow teammates, send tactile 🌱 Nudge alerts to get friends outside, and click 🗺️ Trail on any player to inspect their verified outdoor walking path directly on the retro dark-mode map!
• Double-Entry Points Ledger & Real Perks: All points are recorded in an append-only ledger with cryptographic idempotency keys. Redeem points for promotional partner vouchers with unique redemption codes.
• Zero Commercial API Keys & 100% Open Data: Built with OpenStreetMap (OSM) and Leaflet using custom CSS dark styling. No Mapbox/Google Maps billing, no watermarks, and no third-party telemetry.

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Demo

• Live Deployed App (Render): https://outloot-tgtrails.onrender.com
• Local Run: Run uvicorn server.main:app --reload and visit http://127.0.0.1:8000/.
• Zero-Setup PWA: Fully responsive across mobile browsers and desktops with a retro cyber/pixel aesthetic.

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Code

devanshu-puri / OutLoot

TG Trails (OutLoot) 🌿✦

Hacktoberfest Open-Source AI Challenge (Week 1: "Touch Grass")
An outdoor walking PWA where genuine physical movement unlocks virtual drops and sponsor vouchers, accompanied by private local Gemma AI missions, real-time screen time tracking, and social leaderboards.

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🌟 What is TG Trails?

Most apps optimize to maximize your screen time. TG Trails is built to minimize it.

1. Walk outside: Put your phone in your pocket. The app uses server-authoritative physics checks to qualify genuine pedestrian movement (not GPS jumps or vehicle speeds).
2. Audio & Haptic Discovery: When your accumulated movement unlocks a nearby drop, your phone rings with a dual-tone chime and vibration cue.
3. Collect & Earn: Step within proximity of the drop to collect items across 5 rarities, backed by an append-only points ledger.
4. Local AI Missions & Lore: A locally-hosted open-weight model (Google Gemma via Ollama) generates tailored outdoor walking missions and immersive…

View on GitHub

• GitHub Repository: https://github.com/devanshu-puri/OutLoot
• License: MIT Open Source
• Test Suite: 61 automated unit and integration tests passing (python -m pytest) covering physics filters, ledger accounting, authentication, trail rendering, and local Gemma fallbacks.

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How I Built It

OutLoot is built with a focus on simplicity, offline resilience, and zero cloud API dependencies:

1. Architecture Overview

2. Technologies Used:

• AI Engine: Google's Gemma open-weight model (gemma2:2b / gemma:7b) running locally via Ollama. Responses are strictly constrained to JSON schemas with prompt sanitization, an 8-second circuit breaker, and an offline fallback pack of 12 hand-crafted missions ensuring 100% uptime even on remote trails without internet.
• Server & Logic: Python 3.12, FastAPI, and Pydantic v2 schemas. SQLite in WAL mode with strict foreign key constraints and ACID transaction managers.
• Mapping: Leaflet 1.9.4 and OpenStreetMap standard tiles enhanced with CSS inversion filters (filter: invert(100%) hue-rotate(180deg) brightness(82%) contrast(108%)), delivering a clean dark cyber aesthetic without commercial API keys or tile watermarks.

* Screen-Light UX: Screen Wake Lock API and audio/haptic chimes allow players to keep the app active in their pocket at minimum screen brightness.

Why Does Open Innovation Matter?

1. Absolute Location Privacy: Geolocation breadcrumbs and daily movement patterns are deeply personal. Sending real-time coordinates to proprietary cloud AI providers risks leaking sensitive physical routines. By running open-weight Gemma models locally on the device or self-hosted server, your physical whereabouts never leave your custody.
2. Separation of Determinism and Creativity: Core game economics (points, anti-spoofing verification, vouchers) must remain 100% deterministic and auditable. Open-source architecture ensures open-weight AI is dedicated solely to creative tasks (quests, lore, and wellbeing coaching), while deterministic code guarantees fairness.
3. No Hidden Costs, Rate Limits, or Paywalls: Proprietary APIs inevitably deprecate endpoints, introduce per-token costs, or enforce usage caps. Open-source AI and community OpenStreetMap data make OutLoot completely free to self-host, fork, and operate forever.

4. Resilience in Nature: True outdoor exploration often happens in dead zones with zero cellular connectivity. Open-weight models and offline fallback systems ensure the game keeps working whether you are in a downtown park or a secluded mountain ridge.

My Agent Session

OutLoot was designed, implemented, tested, and deployed end-to-end with the assistance of Antigravity (Google DeepMind's agentic AI coding assistant). The session coordinated:

• Drafting Pydantic schemas, database migrations, and geodesic haversine algorithms.
• Developing the 61-test verification suite (pytest).
• Integrating the local Gemma Ollama interface and fallback pipelines.
• Automating the deployment to Render via infrastructure-as-code (render.yaml).
• Interactive trail inspection and social follow mechanics. You can view the full source code and commit history directly on GitHub: 👉 https://github.com/devanshu-puri/OutLoot --- ## Prize Categories
• Open-Source AI Challenge Week 1: Touch Grass (Primary Track)
• Best Use of Gemma (Local open-weight Gemma model generating structured JSON quests, seasonal walking tips, and biome relic lore with offline fallback guarantees)
• Best Use of Render (Automated infrastructure-as-code deployment on Render serving FastAPI backend and PWA frontend with zero external dependencies)

2. Technologies Used:

• AI Engine: Google's Gemma open-weight model (gemma2:2b / gemma:7b) running locally via Ollama. Responses are strictly constrained to JSON schemas with prompt sanitization, an 8-second circuit breaker, and an offline fallback pack of 12 hand-crafted missions ensuring 100% uptime even on remote trails without internet.
• Server & Logic: Python 3.12, FastAPI, and Pydantic v2 schemas. SQLite in WAL mode with strict foreign key constraints and ACID transaction managers.
• Mapping: Leaflet 1.9.4 and OpenStreetMap standard tiles enhanced with CSS inversion filters (filter: invert(100%) hue-rotate(180deg) brightness(82%) contrast(108%)), delivering a clean dark cyber aesthetic without commercial API keys or tile watermarks.

* Screen-Light UX: Screen Wake Lock API and audio/haptic chimes allow players to keep the app active in their pocket at minimum screen brightness.

Why Does Open Innovation Matter?

1. Absolute Location Privacy: Geolocation breadcrumbs and daily movement patterns are deeply personal. Sending real-time coordinates to proprietary cloud AI providers risks leaking sensitive physical routines. By running open-weight Gemma models locally on the device or self-hosted server, your physical whereabouts never leave your custody.
2. Separation of Determinism and Creativity: Core game economics (points, anti-spoofing verification, vouchers) must remain 100% deterministic and auditable. Open-source architecture ensures open-weight AI is dedicated solely to creative tasks (quests, lore, and wellbeing coaching), while deterministic code guarantees fairness.
3. No Hidden Costs, Rate Limits, or Paywalls: Proprietary APIs inevitably deprecate endpoints, introduce per-token costs, or enforce usage caps. Open-source AI and community OpenStreetMap data make OutLoot completely free to self-host, fork, and operate forever.

4. Resilience in Nature: True outdoor exploration often happens in dead zones with zero cellular connectivity. Open-weight models and offline fallback systems ensure the game keeps working whether you are in a downtown park or a secluded mountain ridge.

My Agent Session

OutLoot was designed, implemented, tested, and deployed end-to-end with the assistance of Antigravity (Google DeepMind's agentic AI coding assistant). The session coordinated:

• Drafting Pydantic schemas, database migrations, and geodesic haversine algorithms.
• Developing the 61-test verification suite (pytest).
• Integrating the local Gemma Ollama interface and fallback pipelines.
• Automating the deployment to Render via infrastructure-as-code (render.yaml).
• Interactive trail inspection and social follow mechanics. You can view the full source code and commit history directly on GitHub: 👉 https://github.com/devanshu-puri/OutLoot --- ## Prize Categories
• Open-Source AI Challenge Week 1: Touch Grass (Primary Track)
• Best Use of Gemma (Local open-weight Gemma model generating structured JSON quests, seasonal walking tips, and biome relic lore with offline fallback guarantees)
• Best Use of Render (Automated infrastructure-as-code deployment on Render serving FastAPI backend and PWA frontend with zero external dependencies)