Julia vs ATS

Julia runs almost as fast as (and in fact in some cases faster than) C code.

Dynamic and high level, but does not isolate the user from properly thinking about types. Can do explicit type signatures which is great for teaching structured thinking.

Out of the box Julia has a very good Read-Eval-Print-Loop, which both completes functions and types, as well as completion based on history of previous statements. It integrates well with the shell and has an excellent online help system.

Julia code is easy to read and avoid a lot of unnecessary special symbols and fluff. It uses newline to end statements and "end" to end blocks so there is no need for lots of semicolons and curly braces. It is regular in that unless it is a variable assignment, function name always comes first. No need to be confused about whether something is a method on an object or a free function.

Unlike Python and Ruby, since you can annotate the types a function operates on, you can overload function names, so that you can use the same function name for many data types. So you can keep simple descriptive function names and not have to invent artificial function names to separate them from the type they operate on.

The Julia language is written in itself to a much larger extent than most other languages, so a budding programmer can read through the depths of the standard library and learn exactly how things work all the way down to the low-level bit-twiddling details, which can be englightening.

Julia has built in n-dimensional arrays similar in functionality as Python's numpy.

You can have multiple functions with the same name, but doing different things depending on function arguments and argument types.

Julia requires no boilerplate code – a beginner needs to write only the parts they care about. This combined with the REPL provides the best learning experience available.

Julia provides a high level of abstraction, making platform-independent programming trivial and easing the learning curve.

You can perform operations on scalars, for example 2^2 or [1, 2, 3].^2.

Julia allows you to edit Julia code in the language itself and write powerful macros. It is a great introduction to metaprogramming features

The Julia REPL allows quickly testing how some code behaves and gives access to documentation and package management immediately in the REPL.

ATS has a theorem-proving type system powerful enough to prove that its functions meet their specifications. This happens at compile time with no performance impact at runtime. This can be used to prove that an ATS program doesn't have bugs commonly found in C++ programs, like "this function never leaks memory" or "this program never attempts to divide by zero" or "this buffer never overflows" or to verify pointer arithmetic, etc.

The compiler (ATS/Postiats) is GPLv3.

The syntax is ML-like with the usual functional language features like pattern matching and tail-call optimization.

ATS works as a low-level systems language. ATS programs have performance and footprint comparable to programs written in C/C++.

Similar to Modula-3. This makes ATS a viable choice even for large-scale projects.

ATS can prove its concurrent programs have no deadlocks or race conditions.