[kyleperik]

I love the generic function aspect of this, where there aren't necessarily specific inputs or outputs. This will make code reusable by factors. A simple use being not needing to define both `toRadians` and `toDegrees`, just definite the relationship. But I believe this can be taken to the point of getting backpropagation for free once forward propagation is defined in neural networks.

Not to steal any thunder, but I've been prototyping a language very built on this idea.

https://git.kyleperik.com/kyle/judo

[theoh]

I could be jumping to an incorrect conclusion, but I think you are misreading the description of Ko's genericity. It doesn't mean that inputs and outputs are not distinguished. It just means that arguments and return values aren't explicitly typed. The kind of Prolog-style unification you are talking about is something different.

[kyleperik]

You're right. This sentence and the general syntax of the language mislead me

> Genericity means that functions do not have to declare argument and return value types, which makes them highly reusable.

Thanks for being kind about pointing out my massive presupposition