Messi and Inter Miami were wallpaper for Trump’s whims in their White House visit

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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.

But it also sees the measure as highly pragmatic for a country whose unemployment rate is at an 18-year low, and whose economy grew by nearly 3% last year, a growth rate equal to that of the UK, Germany, France and Italy combined.

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There are trivial cases, like a screw driver of varying length or the width of a table. Traditional geometric constraints can suffice here. But what if we want a key based of a list of numbers describing the heights of a lock's pins? Now we need programming.

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