D vs Julia

With few exceptions, D will either compile C code to work exactly as when compiled as C, or it won't compile - it won't quietly change the semantics of C code and give unexpected results. This means that D contains an improved C, as it fails compilation where type safety is missing in C.

This allows learning the same machine operations available in C and other low-level languages.

When you're just starting out, dealing with manual memory management and its bugs is a huge pain! D is garbage collected by default, which removes a huge class of things that could go wrong. And later on, if you decide you don't want or need the GC, you can turn it off.

D is usually up to 10 times faster than C++. Having a language that compiles this fast means that you are free to write highly optimized code because of the relatively low cost of experimentation.

D provides unittest blocks to insert code that verifies functions preform their expected behavior and document edge cases. Since these are enabled with a compiler switch, there is no need to teach new programmers how to install new libraries and import modules, instead training on test driven design can start from the very first function.

D provides a structure that builds on C's arrays called slices. A slice is a segment of an array that tracks the pointer and the length of the segment.

Slices are extremely powerful because they combine the protection of knowing the length of the data with the garbage collector that manages the memory backing that data, thus avoiding most memory corruption issues.

For a new user adding types can feel tedious, and takes focus off the meaning of the code, but they are also important for checking logic. D provides static types, and a good system to infer types, so types are checked when calling functions, but do not need to be specified everywhere, making it feel more dynamic.

The compiler can check that functions don't have side effects, extremely important for functional programming in concurrent scenarios, and can check immutability.

Therefore, the compiler will prove that your programs are functionally pure and respect immutable data, if you want it to.

Because all types can be treated as objects, all files can call functions in the same manner -- even stdin and stdout. stdout.writeln(); stdin.readln(); file.writeln(); file.readln();

D practically has the same memory structure as C and C++; all D does it build a structure around that. The entire C standard library is accessible at no cost (syntactic or speed) and it's being worked on allowing the same for the C++ standard library.

Supports first-class functionality for both concurrency and parallelism, offered as part of the standard library.

if (exists(file)) {} may be written as if (file.exists) {}.

writeln(file); may be written as file.writeln();

isDivisibleBy(10, 2); may be written as 10.isDivisibleBy(2);

writeln(isEven(add(5, 5))); may be written as 5.add(5).isEven().writeln();

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.