Not all streaming workloads involve I/O. When your source is in-memory and your transforms are pure functions, async machinery adds overhead without benefit. You're paying for coordination of "waiting" that adds no benefit.
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。同城约会对此有专业解读
const output = Stream.pull(source, compress, encrypt);
但 Lambert 的判断是，这些能力恰恰也是最难通过蒸馏获得的。。业内人士推荐服务器推荐作为进阶阅读
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.