On "dbreunig/nesformer"

This is a flex disguised as a proof of concept. Yes, you can technically run NES games inside an LLM's weights—the model learned to emulate a 6502 CPU well enough to execute Mario frame by frame. But "very slowly" is doing heavy lifting; what you're actually watching is a parlor trick that burns compute for something a $20 used cartridge already solved. The real takeaway isn't that LLMs can do computation (they can, inefficiently), it's that people keep testing the boundaries of what these models will memorize and execute, which tells you something about their actual learned representations but nothing about their practical value. For practitioners: this confirms LLMs aren't magical computation engines, just very expensive lookup tables that can approximate arbitrary behavior if trained on enough examples.