#Tencent

Delve into the technical architectures of major internet companies like WeChat, Tencent, Taobao, Alibaba, and Meituan-Dianping. This repository includes both classic and thematic architecture documents, highlighting open-source projects and fostering community collaboration. Gain essential insights into large-scale and high-concurrency systems, and explore discussions on pivotal technologies and architectural strategies.

EmbedX is a high-performance C++ platform for embedding training and inference, used widely in Tencent applications like WeChat and QQ Music. It supports multiple business operations, offering robust models capable of managing billion-scale nodes and samples efficiently. The platform provides guides for quick setup, both single and distributed deployment, ensuring seamless scalability for embedding high-dimensional data.

TNN is an open-source neural network inference framework devised by Tencent Youtu Lab, known for its high performance and cross-platform capabilities. It supports model compression and code optimization, enhancing performance on mobile devices and extending compatibility with X86 and NV GPUs. Utilized by applications such as QQ, Weishi, and Pitu, TNN functions as a fundamental component for Tencent Cloud AI's acceleration tasks. It facilitates tasks like face detection and object recognition while ensuring efficient deployment through its integration with TensorFlow, PyTorch, and other frameworks via ONNX. The framework is designed to optimize computational performance, support low precision data, and improve memory usage.

Shadow is an Android plugin framework developed by Tencent and tested by millions. It offers the reuse of standalone app source codes and a unique zero-reflection feature, avoiding conflicts with Google’s SDK policies. The framework's dynamic Kotlin architecture eases maintenance and reduces host integration overhead. It supports major Android components, cross-process services, custom themes, and multi-APK loading.

AnimeSR by Tencent ARC Lab offers a robust solution for enhancing animation videos through super-resolution technology. It provides high-quality frame reconstruction with reduced artifacts, supports frames and video inputs, and is compatible with Python and PyTorch. The project includes pre-trained models for quick inference, using command-line interfaces with single and multi-GPU support. Resources such as video demos and training documentation are available to maximize professional and personal use.