Weex layout engine powered by FlexBox algorithm
在上篇文章里面谈了Weex在iOS客户端工作的基本流程。这篇文章将会详细的分析Weex是如何高性能的布局原生界面的，之后还会与现有的布局方法进行对比，看看Weex的布局性能究竟如何。
打开Weex的源码的Layout文件夹，就会看到两个c的文件，这两个文件就是今天要谈的Weex的布局引擎。
Layout.h和Layout.c最开始是来自于React-Native里面的代码。也就是说Weex和React-Native的布局引擎都是同一套代码。
当前React-Native的代码里面已经没有这两个文件了，而是换成了Yoga。
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I am a research-oriented machine learning systems engineer working on foundation model infrastructure, closed-loop evaluation and optimization systems, and scalable AI platforms. My work focuses on building reliable Model-as-a-Service and Harness-as-a-Service platforms that connect data, training, inference, evaluation, and feedback loops into measurable, continuously improving AI products.
My recent work centers on Model-as-a-Service platforms and high-performance LLM inference. I develop serving infrastructure with vLLM and SGLang across model runtime integration, scheduling and continuous batching, KV-cache and memory management, distributed execution, observability, and reliability. This systems work is closely connected to my research on distributed disaggregated inference, preference optimization, instruction-tuning data selection, multimodal evaluation.
My broader research centers on reinforcement learning infrastructure and reinforcement learning optimization algorithms for scalable AI systems. I am interested in how policy optimization, reward modeling, preference learning, offline RL, simulation environments, distributed rollout systems, and automated evaluation harnesses can be engineered together to improve model behavior. My goal is to build frontier AI systems that learn from feedback efficiently, evaluate progress rigorously, and remain dependable when deployed at scale.