Мощный удар Израиля по Ирану попал на видео09:41
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一路上，几个乘客基本无话，后排我们三个挤得紧紧的，胳膊肘都没法舒展，只能尽量保持不动，生怕不小心蹭到身边人。车主倒是侃侃而谈，说她非专业跑顺风车的，顺路拉几个人只是为了凑个油费。好在快到我们县城的时候，有一位乘客先下车，后排空间才宽松起来。因为是顺风车，需要逐一送每位乘客，而我是最后一位，所以我到家的时间已经是下午4点左右了。粗略算了下路上的时间，坐顺风车与坐火车再倒客车的时间相差无几，只是体验反而更差了些。
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.