I have a very high opinion of Julia's dynamic type system. Some static type systems are not very expressive, e.g., Elm, which encourages hacky workarounds. Julia's type system encourages specificity, which exposes problems early.
That's because it's a strong type system (as opposed to python's weak one where objects can change their shape anytime). Even calling it dynamic is a sort of lie since the compiler is always able to reason about the types it is given due to the way Julia's JIT compiler works. It's "dynamic" in the same way passing around `void*` (or `object`) is "static".
That being said Julia's type system is definitely the way of the future in my opinion.
In a dynamic language "strong" vs "weak" typing really depends on the standard library. All the examples in the given link - for example - focus on the addition function in the standard library. So here are some counter examples:
Everything can be used as a bool. This was often used to check for None, but had some issues when used with - for example - datetimes which evaluated midnight as false. In part due to the fact the integer 0 evaluates to false.
Changing type unexpectedly is the key example given in your link (`"foo" + 3` is `"foo3"`). Meanwhile in python `foo.method()` can change the type of the variable foo. Which is a level of fuckery commonly found in javascript.
Let alone the fact that dynamic duck typing encourages weak typing over performing explicit conversions. This is embodied by "easier to ask forgiveness than permission" which says it's better to catch the type conversion exception than check if the type is an integer ahead of time. Which then leads to implementing javascript-esque add functions anyway.
I'll grant you python is stronger than some other dynamic languages, but it is still at least half an order of magnitude weaker than Julia, which is strong in ways approaching Haskell.
