// This code was generated by NativeAOTCodeGen.py from Swagger API specification.
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.
，详情可参考51吃瓜
Сайт Роскомнадзора атаковали18:00
curl -I http://localhost:8001
思路：找「右侧第一个 ≤ cur」的元素 → 弹出所有 cur 的元素，栈顶即为折扣。最终价格 = cur - 折扣（有则）或 cur（无则）。