We benchmarked native WebStream pipeThrough at 630 MB/s for 1KB chunks. Node.js pipeline() with the same passthrough transform: ~7,900 MB/s. That is a 12x gap, and the difference is almost entirely Promise and object allocation overhead."
ВсеГосэкономикаБизнесРынкиКапиталСоциальная сфераАвтоНедвижимостьГородская средаКлимат и экологияДеловой климат
，这一点在搜狗输入法2026中也有详细论述
（四）收购国家禁止收购的其他物品的。。搜狗输入法2026对此有专业解读
Even though my dataset is very small, I think it's sufficient to conclude that LLMs can't consistently reason. Also their reasoning performance gets worse as the SAT instance grows, which may be due to the context window becoming too large as the model reasoning progresses, and it gets harder to remember original clauses at the top of the context. A friend of mine made an observation that how complex SAT instances are similar to working with many rules in large codebases. As we add more rules, it gets more and more likely for LLMs to forget some of them, which can be insidious. Of course that doesn't mean LLMs are useless. They can be definitely useful without being able to reason, but due to lack of reasoning, we can't just write down the rules and expect that LLMs will always follow them. For critical requirements there needs to be some other process in place to ensure that these are met.
另有媒体报道称，从2026年4月起，松下就会将北美及欧洲的电视销售业务移交给创维集团，未来松下只会专注于日本本土市场销售及高端机型生产，此举也是为了降低松下自身在人事、物流等方面的成本。