不是因为算力不重要，而是模型和模型之间的差距，正在以肉眼可见的速度收窄。大模型之间当然有差异，但对于绝大多数企业的实际需求来说，它们已经"够用了"。当"够用"成为基准线，比拼谁的模型更聪明就变成了一场没有终点的消耗战，边际的改善却极为有限。
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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.
Article InformationAuthor, 鄭佩珊，推荐阅读搜狗输入法下载获取更多信息
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