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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.
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为了在相对公平的环境下对比，我决定将人工干预降到最低：只提供基础内容和最简单的指令，以此测试各家软件生成能力的「下限」。这不仅是因为（囊中羞涩）测试积分有限，更为了模拟真实的「开箱即用」场景——毕竟，作为普通用户，大多数人只想要一个能用的 PPT，而不是被强迫系统学习提示词工程。
“该拦的拦不住，不该拦的乱拦。”令仪对此表示困惑，“作为用户，我们并不清楚过滤系统的具体运作机制，难道它只能识别明确的关键词？”，推荐阅读91视频获取更多信息