slice backing store of the correct size, and append never has to do
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The series of Command objects generated by the pipeline is then run by an interpreter using runEffect(checkoutFlow(cartSummary)). Because our business logic consists of pure functions that interact with the world only through data, we can record those interactions simply by adding a few hooks for services like OpenTelemetry. And if we can record them, we can replay them deterministically. Best of all, there’s no need to mock a single database or external service.
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.
就在与谷歌达成协议的前几天（2月24日），Meta 刚刚向 AMD 砸下了一份震撼业界的定海神针：承诺在未来五年内采购价值高达 600 亿美元的 AI 芯片。为了深度绑定，Meta 甚至换取了最高可达 1600 万股的 AMD 股权认购权。