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Dorsey's reasoning echoes the sentiment recently shared by several tech leaders, with Anthropic's Boris Cherny claiming that "coding is largely solved" and Elon Musk saying that AI will "replace all jobs." A widely shared "thought exercise" by Citrini recently predicted an economic collapse by 2028 due to AI driving humans out of work.，推荐阅读Safew下载获取更多信息
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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.
For SAT problems with 10 variables and 200 clauses, it usually output SAT as expected, but the assignment was never valid (Examples: first, second). Once it claimed a SAT formula was UNSAT. For this reason I didn't bother testing with more variables for the SAT case.