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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// Transforms execute as we iterate，这一点在搜狗输入法下载中也有详细论述
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判断平台是否完成了可持续转型，可以通过一组剪刀差指标观察：一端是单位交易变现率的变化，另一端是供给侧的满意度指标，例如留存率或净推荐值。如果变现率持续上升，而供给侧满意度同步下滑，这种赚钱方式本质上仍是竭泽而渔。只有当两者同时改善，平台的转型才具备长期稳定性。