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// 核心循环：弹出所有≤当前身高的元素 → 这些人都能被当前位置看到（矮个子，无遮挡）
。同城约会是该领域的重要参考
Demo 背后的提示词，我们也放在这里，方便大家复制到 Gemini 内使用。在我们的测试中，如果是将下面的英文提示词翻译成中文输入给模型，Nano Banana 的表现，会在文字的渲染上大打折扣。。业内人士推荐91视频作为进阶阅读
"AI answers and search features can now cite Reddit more often which reinforces discovery and traffic at the margin."
Returning back to the Anthropic compiler attempt: one of the steps that the agent failed was the one that was more strongly related to the idea of memorization of what is in the pretraining set: the assembler. With extensive documentation, I can’t see any way Claude Code (and, even more, GPT5.3-codex, which is in my experience, for complex stuff, more capable) could fail at producing a working assembler, since it is quite a mechanical process. This is, I think, in contradiction with the idea that LLMs are memorizing the whole training set and uncompress what they have seen. LLMs can memorize certain over-represented documents and code, but while they can extract such verbatim parts of the code if prompted to do so, they don’t have a copy of everything they saw during the training set, nor they spontaneously emit copies of already seen code, in their normal operation. We mostly ask LLMs to create work that requires assembling different knowledge they possess, and the result is normally something that uses known techniques and patterns, but that is new code, not constituting a copy of some pre-existing code.