This started with Addition Under Pressure, where I gave Claude Code and Codex the same prompt: train the smallest possible transformer that can do 10-digit addition with at least 99% accuracy. Claude Code came back with 6,080 parameters and Codex came back with 1,644. The community has since pushed this dramatically lower.
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This revenue model will be much more beneficial to designers. You can experiment with the AI image and content generation tools and share your creations while earning a small income on the side.
For the test to be fair for LLMs, the SAT instance should be reasonably large, but not too big. I can't just give SAT problems with thousands of variables. But also it shouldn't be too easy.
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除了懂常识,强大的「主体一致性」是这次 Nano Banana 2 更新的另一大杀手锏。