🐉 Roost

Grow your dragon by spending wisely. An offline, on-device budgeting game powered by Qualcomm Snapdragon NPU + Google LiteRT-LM.

Track 1: LLM Based Consumer Use Journeys Google AI Edge x Qualcomm Hackathon 2026

Earlier name: DragonBudget. The app, the package, and most code identifiers still use that name; Roost is the user-facing brand only.

📲 Download

⬇️ Download Roost v1.0 APK (106 MB)

Requires an Android device with a Snapdragon 8 Elite NPU (e.g. Galaxy S25 Ultra) for the on-device Gemma 4 model. After installing, push the model file:

adb push gemma-4-E2B-it.litertlm /sdcard/Android/data/com.example.dragonbudget/files/

The model file (~2.4 GB) is downloadable from HuggingFace. All other releases are listed here.

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🛠️ Building from source

This repo deliberately does not ship the Qualcomm Snapdragon NPU native libraries — they're 80+ MB each and come from the Qualcomm QNN SDK. To build the APK locally:

1. Install the Qualcomm QNN SDK and the LiteRT-LM AAR.
2. Drop the following .so files into app/src/main/jniLibs/arm64-v8a/:
   • libLiteRt.so
   • libLiteRtDispatch_Qualcomm.so (must be patched — see below)
   • libLiteRtGpuAccelerator.so
   • libLiteRtOpenClAccelerator.so
   • libLiteRtTopKOpenClSampler.so, libLiteRtTopKWebGpuSampler.so, libLiteRtWebGpuAccelerator.so
   • libGemmaModelConstraintProvider.so
   • libQnnHtp.so, libQnnHtpPrepare.so, libQnnHtpV79Skel.so, libQnnHtpV79Stub.so, libQnnSystem.so
3. The dispatch lib needs a one-time patchelf patch so it can find symbols from libLiteRt.so at dlopen time:

   brew install patchelf  # or apt install patchelf
   patchelf --add-needed libLiteRt.so app/src/main/jniLibs/arm64-v8a/libLiteRtDispatch_Qualcomm.so

4. Download the Gemma 4 E2B universal LiteRT-LM model from HuggingFace and push it to the device:

   curl -L -o gemma-4-E2B-it.litertlm \
     "https://huggingface.co/litert-community/gemma-4-E2B-it-litert-lm/resolve/main/gemma-4-E2B-it.litertlm"
   adb push gemma-4-E2B-it.litertlm /sdcard/Android/data/com.example.dragonbudget/files/

5. Build with JDK 17 (JAVA_HOME pointing to a real JDK, not a JRE):

   ./gradlew :app:assembleDebug
   adb install -r app/build/outputs/apk/debug/app-debug.apk

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🎯 What is DragonBudget?

DragonBudget turns personal budgeting into a game. Users care for a virtual dragon called SnapDragon whose health, mood, and level directly reflect their spending habits. Log purchases, track budgets, and get personalized AI advice — all 100% offline on the Snapdragon 8 Elite NPU.

No cloud. No bank login. No tracking. Just you, your budget, and your dragon.

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🔥 Core Features

Feature	Description
Dragon Companion	Visual health bar, XP, level, mood, and streak tracking
Purchase Logging	Manual entry or AI-powered receipt scanning
Budget Tracking	8 categories with weekly limits and progress bars
Ask Dragon	Natural language budgeting Q&A powered by on-device Gemma
Purchase History	Full history with category filtering and spending totals
100% Offline	All data stored locally via Room. Zero network calls.

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🏗️ Architecture

┌─────────────────────────────────────────────────────┐
│                    Jetpack Compose UI                │
│  HomeScreen │ AddPurchase │ Budget │ AskDragon │ History │
├─────────────────────────────────────────────────────┤
│                    ViewModels (MVVM)                 │
├─────────────────────────────────────────────────────┤
│              DragonBudgetRepository                  │
├───────────────┬───────────────┬──────────────────────┤
│  Room Database│ DragonEngine  │   AI Engines          │
│  (SQLite)     │ (Deterministic│   LocalLLMEngine      │
│  - Purchases  │  health/XP    │   ReceiptVisionEngine │
│  - Budgets    │  mood logic)  │   PromptBuilder       │
│  - DragonState│               │                       │
│  - AIAdvice   │               │                       │
├───────────────┴───────────────┴──────────────────────┤
│           LiteRT-LM / Qualcomm QNN NPU              │
│  Gemma 4 E2B → Budget Advice Generation              │
│  FastVLM     → Receipt/Item Scan Understanding       │
│  EmbeddingGemma → Spending History Search (planned)  │
└─────────────────────────────────────────────────────┘

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🤖 Model Stack

Model	Source	Role
Gemma 4 E2B	litert-community/gemma-4-E2B-it-litert-lm	Generates personalized budgeting advice via "Ask Dragon"
FastVLM 0.5B	litert-community SM8750	Receipt scan → extracts merchant, amount, category
EmbeddingGemma	litert-community	(Planned) Vector search over spending history

Why This Architecture?

• Rule-based engine controls the dragon. Gemma explains decisions but never controls financial logic. This ensures deterministic, reliable behavior.
• AI inference is triggered only on user action (scan receipt or ask question). No continuous drain.
• LiteRT-LM compiled models run on the Snapdragon 8 Elite NPU for maximum speed and energy efficiency.
• Memory scheduling: Only one model is loaded at a time to stay within NPU memory limits.

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🐉 Dragon Health Logic

Condition	Effect
Purchase keeps category under 50% budget	+5 XP
Category at 50–80%	Mood → "Alert"
Category at 80–100%	Health −5, Mood → "Worried"
Category exceeds 100%	Health −15, Mood → "Tired"
3-day logging streak	Health +10, XP +20
Every 100 XP	Level up

Health Range	Mood
≥ 85	🐉 Energized
60–84	🐲 Stable
35–59	⚠️ Worried
< 35	😴 Exhausted

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📂 Project Structure

app/src/main/java/com/example/dragonbudget/
├── AppContainer.kt          # Simple DI container
├── MainActivity.kt          # Compose entry point
├── data/
│   ├── Entities.kt          # Room entities + data classes
│   ├── Daos.kt              # Room DAOs
│   ├── DragonBudgetDatabase.kt
│   └── DragonBudgetRepository.kt
├── engine/
│   ├── DragonStateEngine.kt # Deterministic health/XP logic
│   ├── AIEngines.kt         # LLM + Vision interfaces + mocks
│   └── PromptBuilder.kt     # Gemma prompt templates
├── ui/
│   ├── Navigation.kt        # NavHost setup
│   ├── theme/Theme.kt       # Snapdragon-inspired dark theme
│   └── screens/
│       ├── HomeScreen.kt
│       ├── AddPurchaseScreen.kt
│       ├── BudgetScreen.kt
│       ├── AskDragonScreen.kt
│       └── HistoryScreen.kt
└── viewmodel/
    ├── HomeViewModel.kt
    ├── AddPurchaseViewModel.kt
    ├── BudgetViewModel.kt
    ├── AskDragonViewModel.kt
    └── HistoryViewModel.kt

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🚀 Setup & Run

Prerequisites

• Android Studio Ladybug or newer
• JDK 11+
• Qualcomm device with Snapdragon 8 Elite (for NPU inference)

Build

git clone <repo>
cd hackk
./gradlew assembleDebug

Run

Open in Android Studio → Select device → ▶ Run

(Optional) Push Gemma model for real AI

adb push gemma-4-E2B-it_qualcomm_sm8750.litertlm /sdcard/Download/

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📊 Judging Criteria Fit

Criteria	How DragonBudget Addresses It
Uses LiteRT / LiteRT-LM	Gemma 4 via LiteRT-LM for advice; FastVLM for receipt scanning. Clean LocalLLMEngine interface with TODO-annotated LiteRtGemmaEngine placeholder.
Runs fully offline	Room database, no INTERNET permission needed for core functionality. All AI inference on-device.
Uses provided HuggingFace models	litert-community/gemma-4-E2B-it-litert-lm SM8750 variant
Resource utilization	Rule-based engine handles 95% of logic; AI only fires on explicit user request. Single model loaded at a time.
Latency & performance	NPU-accelerated. Rule-based dragon updates < 1ms.
Energy efficiency	No background processing, no network polling, no always-on inference.
Easy to install and demo	Standard Android Studio project. Mock engines work without model files.
Clear code & documentation	MVVM + Repository + clean interfaces. This README.

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🔧 LiteRT-LM Integration Points

The codebase is structured for easy model integration:

1. AIEngines.kt — LiteRtGemmaEngine class with TODO comments showing exact integration steps
2. AIEngines.kt — LiteRtVisionEngine class with TODO for FastVLM receipt scanning
3. LiteRTLMManager.kt — Existing engine manager with NPU/GPU/CPU backend fallback
4. PromptBuilder.kt — Production-ready prompt templates optimized for low TTFT

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🛡️ Privacy

• No cloud APIs. No Firebase. No Supabase. No OpenAI. No Anthropic.
• No Plaid. No bank login. No online banking connections.
• Financial data never leaves the device.
• All AI inference runs locally on the Snapdragon NPU.

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Built with ❤️ for the Google AI Edge x Qualcomm Hackathon 2026