Hum.. I'm not sure there's that much hype. It's just saying that Clojure can also be used as a high level layer for fast linear algebra. I do see some claims that as a high level layer, Clojure works even better, it gives you a full REPL, and the power of macros and all that.
It also goes to show you that there's barely any overhead added by the Clojure layer, which is something you want from a higher level layer.
Maybe you need some additional context, but in the Clojure world, there are many offerings for that layering, core.matrix, neanderthal, NumPy, CuPy and others can all be used. So I guess a lot of what draganj is trying to show is he believes neanderthal is the layer with the least overhead, which everyone can agree to disagree on.
You're never going to beat an assembly level implementation, or 50 year's worth of optimization done to a Fortran implementation, or straight openCL/cuda implementation in a higher level language, especially not in Clojure. So in the market of high level languages like Julia, Clojure, Python, R, Swift, etc. and their corresponding libraries, they all are just competing on being more user friendly, having better more useful/flexible abstractions, a better development flow, and the least amount of overhead. So that's what you'll be hearing them all fight over.