Chris Aniszczyk CTO, Linux Foundation
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.
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Жители Санкт-Петербурга устроили «крысогон»17:52
It helps users to create entirely original content
，更多细节参见同城约会
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That gives me the math for the title of this post. Each test user had a playfield with ~2,200 characters, and each character contains 2 pixels. The game runs at 10 FPS. 2500 * 2200 * 2 * 10 is a little over 100 million! Maybe that’s not a fair measurement, but it’s the one I chose.，这一点在雷电模拟器官方版本下载中也有详细论述