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.
{"user_content": "make it red", "tool_name": "change_background_color", "tool_arguments": "{\"color\": \"red\"}"}
，这一点在51吃瓜中也有详细论述
目前全世界最好的AI视频大模型是哪家？
三份报告叠在一起，拼出的结论只有一个：AI的上半场打完了。谁的模型更大、算力更强，这场军备竞赛几乎已经没有悬念。真正的战争，是下半场——谁能把AI嵌进真实的行业里，谁能解决那些又脏又难、但价值巨大的落地问题。