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Nasa plans first crewed Moon mission in 50 years for February 2026
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“自动驾驶行业将跳过L3，直接从L2迈向L4级全自动驾驶”，何小鹏认为，L3的本质是“过渡性技术陷阱”，为规避风险而堆砌的大量规则，使其沦为“看似安全却限制进化”的存在。与其如此，不如集中攻克L4难题，以真正的技术创新来解决技术发展中的问题。
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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.