I find there are some concepts used to qualify complexity and data-model fit in information theory and machine learning that can also be used to think about pro

I find there are some concepts used to qualify complexity and data-model fit in information theory and machine learning that can also be used to think about product-market fit or software-domain fit. I tried to describe them here. I'm not sure I did a good job: https://medium.com/@b.essiambre/product-market-crossfit-c09b019188f3?source=friends_link&sk=5a57eddd18dd948ebb512afb40a21667 https://medium.com/@b.essiambre/product-market-crossfit-c09b... The gist: Low resolution fit: The product vaguely and simply fits its market or domain. High resolution fit: The product is complex and is tightly tailored to the market or domain. Overfit: The product is so tightly tailored to some users that it only fits a small part of the market. Underfit: The product is so generic that it’s missing important features and corner cases. And then another qualifier 'crossfit' which is a bit harder to grasp, inspired by cross-entropy, that has to do with whether the design language, which can be seen in some ways as 'encoding' the problem, fits the domain or market well.