This is a fascinating mathematical framework, but the post title might be a bit of an overreach. I often wonder if "a theory of deep learning" could exist that could be stated succinctly and that could predict (1) scaling laws and (2) the surprising reliability of gradient descent.
Note that I said "predict" not "describe". It feels like we're still in the era of Kepler, not Newton.
There's less ageism in embedded systems because older folks are more likely to be experienced with a lean & mean tech stack (for example vanilla C programming).
Currently we simply copy a slice of the heap's ArrayBuffer from WASM to JS. In the past we exposed the heap slice directly but it was technically "unsafe" (perhaps because the heap can grow), and doing a copy did not hurt performance in any measurable way.
The fact that triangles have proliferated is not due to half-assery. Hardware can rasterize them very quickly, and a triangle can have only one normal vector. Quads can be non-planar. It's true that quads are nice for humans and artists though!
As an aside, Catmull-Clark subdivision has been around since 1978, which, as a first step, breaks an arbitrary polyhedron into a mesh of quadrilaterals.
I lost interest fairly quickly because the entire article seems to rely on a certain definition of "intelligent" that is not made clear in the beginning.