Implementations have found ways to optimize transform pipelines by collapsing identity transforms, short-circuiting non-observable paths, deferring buffer allocation, or falling back to native code that does not run JavaScript at all. Deno, Bun, and Cloudflare Workers have all successfully implemented "native path" optimizations that can help eliminate much of the overhead, and Vercel's recent fast-webstreams research is working on similar optimizations for Node.js. But the optimizations themselves add significant complexity and still can't fully escape the inherently push-oriented model that TransformStream uses.
In recent years, LLMs have shown significant improvements in their overall performance. When they first became mainstream a couple of years before, they were already impressive with their seemingly human-like conversation abilities, but their reasoning always lacked. They were able to describe any sorting algorithm in the style of your favorite author; on the other hand, they weren't able to consistently perform addition. However, they improved significantly, and it's more and more difficult to find examples where they fail to reason. This created the belief that with enough scaling, LLMs will be able to learn general reasoning.，这一点在WPS官方版本下载中也有详细论述
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Now that we have the above interfaces, we can use them when writing a Rust program that compiles to a WebAssembly Component:
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