Was ready to write something snarky because this is essentially RAG, but I think the author is getting at some subtle details which are seemingly important.

- memory systems are a specific type of knowledge base where you generate all the documents. You might as well generate them to be less than your embedding token limit to obviate the need for chunking.

- embedding models are getting better and are no longer just semantic averaging.

- small models are getting dirt cheap, making parallel reads cost manageable

What they describe is sort of the simplest architecture that takes advantage of these observations. I believe them when they say it works well.

I do suspect though that things like keyword lookup will completely fail if every memory is just a vector. Hence why something like Typesense hybrid search can still be useful.