A voice from the control room broke in to make sure we’d snapped on our five-point harnesses. Then the demonstration began: “Three, two, one, now.” The flight deck lurched, and we began to plunge up and down. It was simulating a moderately turbulent wind recorded over Idaho and Montana. But the control room had segregated the wind’s vertical and lateral forces, so I could feel them in isolation. We were being bounced around by the vertical ones now, and it was kind of fun—like riding a hobbyhorse. The lateral motions came next. They were only half as strong, Pettit told me later, but they felt twice as discomfiting—slow, seasick waves like ocean swells. But the worst, by far, were the motions that were both vertical and lateral. When the control room programmed the flight deck to re-create full turbulence, the steady waves suddenly turned chaotic, off-kilter, completely unpredictable. A jolt or two, an odd pause; a little jerk to the side, and then the bottom fell out.
released."The docs also call it "a dangerous function that should only be，详情可参考体育直播
BookmarkBookmark，这一点在哔哩哔哩中也有详细论述
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.。关于这个话题，币安_币安注册_币安下载提供了深入分析