This is better in that there is far less boilerplate, but it doesn't solve everything. Async iteration was retrofitted onto an API that wasn't designed for it, and it shows. Features like BYOB (bring your own buffer) reads aren't accessible through iteration. The underlying complexity of readers, locks, and controllers are still there, just hidden. When something does go wrong, or when additional features of the API are needed, developers find themselves back in the weeds of the original API, trying to understand why their stream is "locked" or why releaseLock() didn't do what they expected or hunting down bottlenecks in code they don't control.
这本质上是在提醒我们一件事：AI 正在从「辅助回答问题」，走向「直接进入工作流」。当 AI 开始能够调用工具、跨应用执行任务、甚至在后台持续运转，我们原有的工作组织方式，本身就已经在发生变化。
。旺商聊官方下载对此有专业解读
This fragmentation hurts portability. Code that performs well on one runtime may behave differently (or poorly) on another, even though it's using "standard" APIs. The complexity burden on runtime implementers is substantial, and the subtle behavioral differences create friction for developers trying to write cross-runtime code, particularly those maintaining frameworks that must be able to run efficiently across many runtime environments.
The problem is compounded by APIs that implicitly create stream branches. Request.clone() and Response.clone() perform implicit tee() operations on the body stream — a detail that's easy to miss. Code that clones a request for logging or retry logic may unknowingly create branched streams that need independent consumption, multiplying the resource management burden.