[ChrisRackauckas]

Oh definitely agreed. A lot of this is because Julia always loads BLAS and LLVM, so it's like doing `import numpy; import numba; print("Hello World")` and then noticing most of the startup time is loading numpy and numba. But it's a hard question because if BLAS isn't always loaded with Julia, its previously core scientific computing case is impacted. As it has been growing to be more widely used in a general purpose case, these kinds of assumptions are having to be revisited.

One can of course remove this in the compilation stage of PackageCompiler because PackageCompiler builds a new system image, and where BLAS is loaded is in the system image, so you can create a new from-scratch system image that is more lean. However, the tooling isn't quite there yet: right now the main way that's documented is something that extends the default system image, hence the large binaries. There's StaticCompiler.jl which does tree shaking so it makes small binaries, but it doesn't support most of the Julia runtime right now so it's limited in the codes it can handle. So right now the foundation all exists and it's at a usable state but definitely needs to improve.