Blog posts like this make me think model adoption and appropriate use case for the model is...lumpy at best. Every time I read something like it I wonder what tools they are using and how? Modern systems are not raw transformers. A raw transformer will “always output something,” they're right, but nobody deploys naked transformers. This is like claiming CPUs can’t do long division because the ALU doesn’t natively understand decimals. Also, a model is stat aprox trained on the empirical distribution of human knowledge work. It is not trying to compute the exact solution to NP complete problems? Nature does not require worst case complexity, real world cognitive tasks are not worst case NP hardness instances...