This repository is a high-performance React Native camera library (VisionCamera) that provides photo/video capture, real-time frame processing, barcode scanning, location tagging, and GPU-accelerated frame resizing. It is organized as a monorepo with a core package and several plugin packages (barcode-scanner, location, resizer, skia, worklets). The architecture uses NitroModules to generate C++/Swift/Kotlin hybrid object bridges, enabling zero-copy data flow from platform camera APIs (AVFoundation on iOS, CameraX on Android) to JavaScript. Key entry points include the `<Camera>` React component, hooks like `useCameraSession`, `useFrameOutput`, `useBarcodeScannerOutput`, and factory functions for creating sessions and outputs. External dependencies include platform camera frameworks, ML Kit for barcode detection, CoreLocation for geotagging, and Metal/Vulkan for GPU compute. The library is production-grade, used by major apps (Amazon, Shopify, Tesla, etc.), and features a constraint resolution system for optimal format selection, gesture controllers for zoom and tap-to-focus, and a worklet runtime for offloading frame processing to native threads.
This repository is a high-performance React Native library (llama.rn) that enables on-device Large Language Model (LLM) inference on iOS and Android devices. It wraps the C++ llama.cpp core using JavaScript Interface (JSI) and TurboModules, allowing mobile applications to run GGUF-formatted models locally without cloud dependencies. The architecture consists of a TypeScript/JavaScript layer for the React Native interface, a C++ core for inference and model loading, and native modules for Android (Java/Kotlin) and iOS (Objective-C++). Key capabilities include hardware acceleration via Apple Metal and Qualcomm Hexagon DSP, multimodal support (vision/audio), parallel decoding, and structured output generation. The project includes a comprehensive example app demonstrating chat interfaces, model management, and benchmarking tools.