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.
If you are someone who works online, you need to surf the internet to get your business done. And often there is no time to read or analyze something. But it's important that you do it. Notion Web Clipper will help you with that.
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These additions have allowed many more languages to efficiently target WebAssembly. There’s still more important work to do, like stack switching and improved threading, but WebAssembly has narrowed the gap with native in many ways.
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Раскрыты подробности о договорных матчах в российском футболе18:01，详情可参考爱思助手下载最新版本
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