На шее Трампа заметили странное пятно во время выступления в Белом доме23:05
Anthropic’s prompt suggestions are simple, but you can’t give an LLM an open-ended question like that and expect the results you want! You, the user, are likely subconsciously picky, and there are always functional requirements that the agent won’t magically apply because it cannot read minds and behaves as a literal genie. My approach to prompting is to write the potentially-very-large individual prompt in its own Markdown file (which can be tracked in git), then tag the agent with that prompt and tell it to implement that Markdown file. Once the work is completed and manually reviewed, I manually commit the work to git, with the message referencing the specific prompt file so I have good internal tracking.
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Key finding: The ANE supports a queue depth of 127 — you can have up to 127 evaluation requests in-flight simultaneously. This is far deeper than most accelerator queues and suggests the hardware is designed for high-throughput streaming inference.
一个连锁反应是，高算力需求推高能耗，而高能耗产生的巨大热量，又对散热系统构成了极限挑战，传统的风冷技术已触达瓶颈，散热本身成为制约算力密度提升的“热墙”。