Just to give an example why a "domain specific" language like Julia is more appealing then a "general purpose" language like Swift: I would like to demonstrate this on the old and classy Fortran vs C++ discussion in numerical computing.
In Fortran, you can write linear algebra on n-dimensional arrays (similar as in numpy and julia) very compactly, i.e.
d(i) = TRANSPOSE(MATMUL(B(i,:),c))
Writing something like this in C++ is absolutely possible and elegant with modern templates libraries such as `eigen`. However, the compilation will be slower, the compiler errors will be hard to read and it is hard to beat Fortrans runtime efficiency of such code.
But it get's more interesting. Think of tensor contractions. This is something where you probably want to implement your own algebra (say for relativistic quantum mechanics or for general relativity) -- or you just stick to the n-dimensional array again and use index-wise loops:
DO i=1,4
DO j=1,4
DO k=1,4
DO l=1,4
A(i,j) = B(k,l)*C(i,k)*D(l,j) ! note: compe up with better examples
END DO
END DO
END DO
END DO
I maintain a templated C++ library to write such expressions in one line instead of 4 loops. But contrary to this Fortran code, in order to understand my code, you first have to learn this library. Means you need to learn C++, then the library. In Fortran, it is just Fortran. Nothing more.
Believe it or not: Many scientists are no good programmers. Cut-down domain specific languages are perfect to avoid them to loose time on weird compiler features such as "const", templates and all that overhead which is hard to regain in time.