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.
chunk[i] = (offset + i) & 0xFF;
Highly Divergent Profiles: For routing configurations that are not pre-calculated as common scenarios and whose costs vary too much from default configurations, the original A* algorithm might still be faster (and is often used as an automatic fallback).，推荐阅读Line官方版本下载获取更多信息
* @param arr 待排序数组
。业内人士推荐搜狗输入法2026作为进阶阅读
回顾历史，早期智能硬件热潮中，租赁与渠道扩张都曾出现过短期红利。但真正持续盈利的公司，往往掌握核心技术或平台规则。
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