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.
圖像加註文字，二二八78週年紀念活動資料圖片在新北市長大的李承璦，是少數會表達意見的年輕人。21歲的她來自本省人家庭，從小就聽父親說二二八和台灣歷史，全家會在二二八當天慎重地紀念這段歷史。李承璦回憶，小學時同學開心二二八討論去玩的行程，她會質問同學是否了解放假的原因，她甚至從小就會在新聞社群跟人筆戰，說明二二八跟白恐歷史多重要。。safew官方版本下载是该领域的重要参考
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李 “나와 애들 추억묻은 애착인형 같은 집…돈 때문에 판 것 아냐”
After the work the company had done on AppleWorks 3.0, Simonsen felt ready to jump into the Macintosh market with a “Mac AppleWorks” of their own – they called it Beagle Works. Unfortunately, other companies – giants in the Mac market such as Microsoft, Claris, and Symantec – had the same idea. Their resources were far greater than Beagle Bros had imagined, and the race was costly.