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.
Build first, raise later, keep control always
，这一点在heLLoword翻译官方下载中也有详细论述
나무 돌보는 ‘자연인’이 월300만원…나무의사 자격증 관심 커지는 이유는[은퇴 레시피]
For implementers, this promise-heavy design constrains optimization opportunities. The spec mandates specific promise resolution ordering, making it difficult to batch operations or skip unnecessary async boundaries without risking subtle compliance failures. There are many hidden internal optimizations that implementers do make but these can be complicated and difficult to get right.