Ранее мощные лесные пожары вспыхнули в США. О возгораниях сообщили в штатах Оклахома, Техас, Канзас, Нью-Мексико, Колорадо и Миссури. Распространению огня способствовали сильный ветер и аномально низкая влажность воздуха, уточнили в Национальном межведомственном пожарном центре.
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截至2025年末，瑞幸全球门店总数达31048家，全年净增门店8708家，同比增长39.0%，其中中国市场（含香港）门店达30888家，自营门店达20144家，联营门店10744家。。Line官方版本下载是该领域的重要参考
Фото: Александр Манзюк / Коммерсантъ。快连下载-Letsvpn下载是该领域的重要参考
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.