Revisiting phonon thermal transport in penta-graphene via a machine-learning potential-driven large-scale molecular dynamics simulation

· · 来源:plus资讯

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公开信息显示,他于1996年加入LVMH集团,开启奢侈品行业职业生涯。于2018年加入Rimowa(日默瓦),担任高级管理职务,发起并监督品牌分销渠道重新定位、商业模式重组及电子商务渠道建设等多项战略工作。。夫子对此有专业解读

智利与美国关系紧张

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iFi's new

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.