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.
去年，我注意到一个有些反直觉的现象。
PRIMARY KEY (repo_id, name)。关于这个话题，heLLoword翻译官方下载提供了深入分析
一端是能够借助 AI 成倍提升产出的开发者，另一端是依然停留在旧生产方式中的人。。WPS下载最新地址是该领域的重要参考
适用场景：需要求「下一个更大/更小」「上一个更大/更小」、或「右侧第一个满足某条件的元素」时，可优先考虑单调栈。与堆不同，单调栈不负责全局最值，只处理「相邻关系」类的一维序列问题。
def __init__(self, url: str, title: str = "", author: str = "",，详情可参考safew官方版本下载