Big fan of CLIPS! Rule based expert systems are so ubiquitous in business and scientific codes, yet a lot of devs are not even familiar with pattern matching let alone powerful rules engines like CLIPS.
One of the really neat things about mojo is, as a superset of python, things like static typing, and ownership/moving can be gradually adopted. It encourages value semantics, and is borrow-by-default. So you can literally just write pythonic looking `def` functions, but then gradually opt in to the more performant features, e.g. with `fn` functions.
In my mind this is kinda similar to what TypeScript does as a super-set of JavaScript- except that was not about performance, it was about purely about typing.
relating to a nation; common to or characteristic of a whole nation: this policy may have been in the national interest | a national newspaper.
• owned, controlled, or financially supported by the federal government: plans for a national art library.
One view from the geospatial, data science, and machine learning world: Python is the most commonly used language, among my peers. However all the heavy lifting is done by C/C++ libraries which Python binds with. NumPy, GDAL, GEOS, Tensorflow, Torch, are all C/C++ libs.
Zig's C-interoperability is actually pretty huge in this context. Not familiar with the other 5 languages the OP listed.
It also doesn't have all the memory leaks as the 3d pipes screen saver.