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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.
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Трамп высказался о непростом решении по Ирану09:14
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WebAssembly with no kernel at allWebAssembly takes a fundamentally different approach. Instead of running native code and filtering its kernel access, WASM runs code in a memory-safe virtual machine that has no syscall interface at all. All interaction with the host happens through explicitly imported host functions.，详情可参考51吃瓜
Медведев вышел в финал турнира в Дубае17:59