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圖像加註文字，預算管理局局長羅素・沃特（Russell Vought）於七月在美國國會大廈接受記者訪問多名「2025計劃」撰稿人如今位居特朗普政府要職，包括中情局（CIA）局長拉特克利夫（John Ratcliffe）、聯邦通訊委員會（FCC）主席布蘭登·卡爾（Brendan Carr）、特朗普的「邊境沙皇」湯姆·霍曼（Tom Homan）、證券交易委員會主席保羅·阿特金斯（Paul Atkins），以及主張關稅政策的貿易顧問彼得·納瓦羅（Peter Navarro）。
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Ольга опубликовала видео c празднования своего дня рождения, во время которого она объявила родным и друзьям о беременности. Также на мероприятии раскрыли пол будущего ребенка — у пары родится мальчик.。旺商聊官方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.