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.
开辟新赛道，主体是企业，却不只是企业自己的事，营商环境同样至关重要。“便利店摊蛋饼打盒饭”“网订柜取早餐”“预包装零食店制售咖啡面包”……类似的案例中，面对棘手诉求，监管部门没有简单地说“不行”，而是一起研究“怎样能行”，帮助新业态健康规范发展。这样一种“有解思维”，呵护的是创新创造活力，激发的是企业发展潜力，也带来不少启示。
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We see a divide in society between people who want AI to do impressive things with their photos and videos, and those who don't want AI to do anything with photos and videos because it's eroding our ability to believe that what we have seen is real, destroying the concept of photographic evidence.
Jan Oberhauser Founder & CEO, n8n