> we were talking about implementing an actual language/library
No, we're talking about language-level syntactic standardization of the most fundamental computational concept in linear algebra (and I would argue all of mathematics). My list just gives evidence to how unifying the concept is.
If you want supercomputing-grade efficiency you won't stop using numpy/scipy just because of this update, or you've already rewritten your Python prototype in C/Fortran anyway.
> If you want supercomputing-grade efficiency you won't stop using numpy/scipy just because of this update
Especially since the update is just creating a language feature for numpy and similar libraries to leverage -- there is still no matrix implementation (and thus no matrix multiplication) in the standard library, just an overloadable operator so that numerical libraries can consistently use it for matrix multiplication and use * for elementwise multiplication, providing a standard API for those libraries.
No, we're talking about language-level syntactic standardization of the most fundamental computational concept in linear algebra (and I would argue all of mathematics). My list just gives evidence to how unifying the concept is.
If you want supercomputing-grade efficiency you won't stop using numpy/scipy just because of this update, or you've already rewritten your Python prototype in C/Fortran anyway.