It’s Not AI Psychosis If It Works#Before I wrote my blog post about how I use LLMs, I wrote a tongue-in-cheek blog post titled Can LLMs write better code if you keep asking them to “write better code”? which is exactly as the name suggests. It was an experiment to determine how LLMs interpret the ambiguous command “write better code”: in this case, it was to prioritize making the code more convoluted with more helpful features, but if instead given commands to optimize the code, it did make the code faster successfully albeit at the cost of significant readability. In software engineering, one of the greatest sins is premature optimization, where you sacrifice code readability and thus maintainability to chase performance gains that slow down development time and may not be worth it. Buuuuuuut with agentic coding, we implicitly accept that our interpretation of the code is fuzzy: could agents iteratively applying optimizations for the sole purpose of minimizing benchmark runtime — and therefore faster code in typical use cases if said benchmarks are representative — now actually be a good idea? People complain about how AI-generated code is slow, but if AI can now reliably generate fast code, that changes the debate.
Захват Кубы может быть «дружеским»Не смог оставить без комментария Дональд Трамп и тему Кубы, которая, по его мнению, стала «страной-неудачницей», где все люди всегда хотели и хотят перемен. Чтобы разрешить все проблемы, кубинское правительство, по словам Трампа, ведет с Америкой переговоры.
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Implementers shouldn't need to jump through these hoops. When you find yourself needing to relax or bypass spec semantics just to achieve reasonable performance, that's a sign something is wrong with the spec itself. A well-designed streaming API should be efficient by default, not require each runtime to invent its own escape hatches.
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sa_nl.nl_groups = MONITOR_GROUP_UDEV;。关于这个话题,搜狗输入法2026提供了深入分析
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