I actually tend to agree. In the article, I didn't see the strong argument highlighting what powerful feature exactly people were missing in relation to embeddings. Those who work in ML they probably know these basics.
It is a nice read though - explaining the basics of vector spaces, similarity and how it is used in modern ML applications.
> Hopefully it's clear from the domain name and intro that I'm suggesting technical writers are underrating how useful embeddings can be in our work. I know ML practitioners do not underrate them.
> I didn't see the strong argument highlighting what powerful feature exactly people were missing in relation to embeddings
I had to leave out specific applications as "an exercise for the reader" for various reasons. Long story short, embeddings provide a path to make progress on some of the fundamental problems of technical writing.
It is a nice read though - explaining the basics of vector spaces, similarity and how it is used in modern ML applications.