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.
В Германии назвали Мерца предателемНемецкий политик Нимайер назвал Мерца предателем за отказ от газа России
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Resolved Disney+ playback issue.
スズキ・鈴木俊宏社長「社員の主体性引き出す組織づくりとは」
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在那個空間裡，她和女性朋友可以用一種更自主的方式接觸男男愛情作品。透過「讓人感到愉悅的寫作」，她們可以創造自己想看到但「現實中難以擁有」的男性角色與關係。
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.，推荐阅读safew官方版本下载获取更多信息