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For implementers, this promise-heavy design constrains optimization opportunities. The spec mandates specific promise resolution ordering, making it difficult to batch operations or skip unnecessary async boundaries without risking subtle compliance failures. There are many hidden internal optimizations that implementers do make but these can be complicated and difficult to get right.,详情可参考谷歌浏览器【最新下载地址】

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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.