This repository is a mobile SDK for real-time interactive AI avatars, primarily targeting Android and iOS platforms. It provides on-device inference for speech recognition (via WeNet) and face/body segmentation (via ncnn-based U-Net models), enabling low-latency lip-sync and avatar animation driven by audio input. The SDK is structured as a C++ core with platform-specific wrappers (Java for Android, Objective-C for iOS), and includes bundled third-party libraries (ncnn, OpenCV, FFmpeg, ONNX Runtime) for computer vision, audio processing, and neural network inference. In production, the system ingests 16kHz PCM audio from a microphone, extracts MFCC features, runs streaming speech recognition, and simultaneously processes the audio through a U-Net model to generate facial animation frames. These frames are rendered using OpenGL ES (Android) or Metal (iOS) with alpha blending for compositing onto a background. The SDK also includes model download, encryption (AES-128-CBC), and resource management utilities. The architecture is monolithic: all core logic lives in a single C++ module (duix-sdk), with platform-specific integration code in the app directories. The demo app in Android provides a fully functional test harness. While the SDK is feature-complete and demonstrates significant engineering effort in mobile AI inference, the codebase exhibits several quality issues: duplicated third-party headers across architectures, hardcoded encryption keys, outdated library versions (OpenCV 4.6.0, ncnn 2022/2023), and minimal inline documentation. The high star count and fork activity suggest a strong community, but the code itself is functional rather than exemplar. The most reusable components are the reference-counted memory allocator (dh_mem.h) and the AES stream decryption wrapper (gaes_stream.h), which can be extracted for general-purpose use.