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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Последние новости，这一点在搜狗输入法下载中也有详细论述
The new partnership with NVIDIA evolves the long-standing collaboration between the two companies. OpenAI has pledged to consume 2 gigawatts of training capacity on NVIDIA's Vera Rubin systems and an additional 3 gigawatts of computing resources, likely in the form of GPUs, to run specific AI inference tasks. In other words, NVIDIA is spending a lot of money on OpenAI and then OpenAI will turn around and spend a lot of money with NVIDIA. The ouroboros must feed.，详情可参考谷歌浏览器【最新下载地址】
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