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.
而在加入 Meta 之前，庞若鸣曾领导苹果基础模型团队（AFM），带领约 100 人研发大型语言模型，为 Apple Intelligence 等功能提供底层能力。
，详情可参考51吃瓜
更关键的是，如果行业尚未稳定，平台仍处于补贴扩张阶段，那么合伙人的收益结构可能高度依赖平台流量分发。一旦补贴停止、规则变化，收益模型可能迅速改变。
It's worth noting that these benchmarks compare a pure TypeScript/JavaScript implementation of the new API against the native (JavaScript/C++/Rust) implementations of Web streams in each runtime. The new API's reference implementation has had no performance optimization work; the gains come entirely from the design. A native implementation would likely show further improvement.