Ruby vs Haxe

Ruby is one of the most popular languages for developing web sites. As a result, there's an abundant amount of documentation, sample code, and libraries available for learning the language and getting your project up and running. The most popular features are just 'gem install' away. Additionally, it is easier to find Ruby jobs because of this.

Ruby has a very clean syntax that makes code easier to both read and write than more traditional Object Oriented languages, such as Java. For beginning programmers, this means the focus is on the meaning of the program, where it should be, rather than trying to figure out the meaning of obscure characters.

presidents = ["Ford", "Carter", "Reagan", "Bush1", "Clinton", "Bush2"]
for ss in 0...presidents.length
    print ss, ": ", presidents[presidents.length - ss - 1], "\n";
end

Ruby has a large ecosystem of tools and libraries for just about every use. Such as ORMs (Active Record, DatabMapper), Web Application Frameworks(Rails, Sinatra, Volt), Virtualization Orchestration(docker-api, drelict), CLI tools(Thor, Commando), GUI Frameworks(Shoes, FXRuby) and the list goes on. If you can think of it, there is probably a gem for that ( and if not you can create your own and share with the community).

The Ruby language is equipped with the necessary features to learn the essence of algorithms.

In online playground environments like ideone.com, measures have been taken to prevent beginners from going astray by restricting the use of external libraries such as Python's NumPy and SymPy.
Even in such constrained Ruby execution environments, the required features for learning algorithms are fully available.

Many of the algorithms that should be learned are documented in the book "Hello Ruby: Adventures in Coding." For example, the cake serving problem in the book leads to topological sorting, which is a graph theory concept useful in project management for creating Gantt charts.

To evaluate the effectiveness of algorithms with a level of complexity comparable to topological sorting,
it is necessary to be able to solve mathematical computation problems up to the high school level easily.
As shown in the table below, using only Ruby's standard library, it is possible to solve high school-level math problems effortlessly.
However, other programming languages may not be able to perform such computations in online playground environments.

To experience the superior performance of algorithms, it is important to challenge oneself by reimplementing good algorithms. Ruby's standard library includes implementations of excellent algorithms. For instance, the algorithm for solving linear equations, which has been widely known since the era of Fortran, is used within the code of SolvingLinearEquations through the "/" operator. Reimplementing code from Ruby's standard library serves as an excellent learning resource with high reusability and efficiency.

The SolvingLinearEquations function mentioned above demonstrates the benefits of duck typing and forced type conversion between objects of different types in arithmetic operations. While Rust also has features like duck typing, the implementation of "forced type conversion" is still far from being realized.

# Title: "(1) Cake Serving Procedure Problem"
require 'tsort'

class Hash
  include TSort
  alias tsort_each_node each_key
  def tsort_each_child(node, &block)
    fetch(node).each(&block)
  end
end

puts 'Tasks'
task_names = {
  'A' => 'Arrange the plates.',
  'B' => 'Set the spoons.',
  'C' => 'Place the birthday cake on the table.',
  'D' => 'Spread the tablecloth.'
}
p task_names

puts 'Preceding Tasks'
preceding_tasks = {
  'A' => ['D'],
  'B' => ['C', 'A'],
  'C' => ['A', 'D'],
  'D' => []
}

steps = preceding_tasks.strongly_connected_components
puts 'The appropriate steps are as follows:'
steps.each do |task_candidates|
  p task_candidates.map { |task| [task, task_names[task]] }
end

p "#(2) Equation Solving Rule"
def SolvingLinearEquations(y, a, b)
   x = (y - b) / a
end

p "(2-1) Real Solution", SolvingLinearEquations(1.0, 5, 0.5)
# => 0.1

p "(2-2) Fraction Solution", SolvingLinearEquations(Rational(1, 1), Rational(5, 1), Rational(1, 2))
# =>  (1/10)

p "(2-3) Imaginary Solution", SolvingLinearEquations(1 + 1i, 5, 1.0 / (2 + 2i))
# => (0.15+0.25i)

p "(2-4) Complex & Fraction Solution", SolvingLinearEquations(Rational(1 + 1i, 1), Rational(5, 1), Rational(1, 2 + 2i))
# => ((3/20)+(1/4)*i)

p "(2-5) Matrix Solution with Large Integers", 
   SolvingLinearEquations(Matrix[[Rational(1234567890123456789890, 1), Rational(0, 1)]],
                                                Matrix[[Rational(1234567890123456789890, 1), Rational(1234567890123456789890 * 2, 1)],
                                                             [Rational(1234567890123456789890, 1), Rational(1234567890123456789890 * 3, 1)]],
                                                Matrix[[Rational(1234567890, 1), Rational(123456789, 1)]] )
# => Matrix[[(3703703670366790122789/1234567890123456789890), (-2469135780244567900789/1234567890123456789890)]]

p "(2-7) Matrix Solution with Large Integers, Complex Numbers, and Fractions", 
     SolvingLinearEquations(Matrix[[Rational(1234567890123456789890, 1i), Rational(0, 1)]],
                                                 Matrix[[Rational(1234567890123456789890, 1), Rational(1234567890123456789890 * 2, 1i)],
                                                               [Rational(1234567890123456789890, 1), Rational(1234567890123456789890 * 3, 1i)]],
                                                Matrix[[1234567890, 0 + 1i]] )
# => Matrix[[((-3703703671/1234567890123456789890)-(3/1)*i), ((2469135781/1234567890123456789890)+(2/1)*i)]]

Lays out an easy to follow and opinionated MVC pattern that teaches best practices through necessity.

It's the fore-runner and trend setter for TDD.

Object oriented programming is one of the most important concepts in programming.

Meta-programming provides efficiency and freedom.

No indentation increase development efficiency.

Haxe allows you to develop for Web, iOS, Android, Blackberry, Windows, OSX, Linux and others, all at once, without the need to switch languages and maintain separate code bases.

This is possible because Haxe can compile to JavaScript, ActionScript, Flash AVM bytecode, C++, Neko, PHP, C# (.NET) and Java.

Support for even more platforms and languages is under development.

The language was designed to be very expressive with the smallest possible amount of syntactic sugar. There are actually fewer keywords than other languages with similar power.

Haxe can easily compile over 100,000 lines of code to JS in seconds on a mid-spec computer.

The language is very easy to learn for those with background in JavaScript or ActionScript 3.

Haxelib (common library repo) and other sources contain large codebases for anything from cryptography to communications. A lot of these are fully cross platform and work with the JavaScript target.

The JavaScript target can be used for everything from node.js server applications (with code completion) to games using either the Flash-like OpenFL library or direct canvas or WebGL programming.

Haxe has been around for more than 10 years (since 2005) and whilst not the most popular project, has had continuous growth.

Highly unlikely to disappear or for support to stop.

Friendly community

One big advantage over pure javascript, (or some other languages listed here) is that Haxe will pick up a whole range of errors when you compile, saving you the pain of having to try and debug them later. This includes everything from syntax errors ("Unexpected ;") to type errors ("Class user has no field username. Suggestion: username").

Code completion is built into the compiler and available to the IDE allowing for much smarter code completion that can actually parse and understand the syntax tree.

The output that is generated can be trimmed using "dead code elimination" to only include those functions and libraries that are strictly necessary. All code is very readable with only minimal extras for specific functionality.

Small output is good for frontend development as file size is a major concern.

After a type is inferred from its context, it cannot be changed to a new type, and type safety is done at compile time so it stays safe without the extra maintenance required for static typing.

Syntactic macros allow you to extend compiler features at the syntax tree step. Macros come into play after code is parsed into the abstract syntax tree, and macros allow you to transform it before the rest of the compilation completes.

This provides for immense power, while at the same time scoping the extensibility at a level that is powerful, but well constrained.

You can use the same classes on the server as you do on the client where applicable. This saves a lot of time.

Haxe has the ability to use "externs". These are haxe files which describe the usage of existing JS libraries. Get code completion and compile-time-checking for everything from jQuery to Node.js.

Even without externs, native JS code can still be used through untyped code.

Haxe has the power and expression to not need the npm dependancy hell that is common in js and typescript, bit it's still simple.

Using a remoting proxy you can get type safe server to client communications, allowing for remote method execution on the server as if they were part of the client side code.

Package tree is just directory tree, it's wonderful!

Haxe supports conditional compilation, so depending on compiler flags Haxe will include or exlcude sections of your code. Making it easy to have debug and release builds.

You can define constants in an abstract enum and when used in a switch/case statement Haxe checks for exhaustiveness, making sure every constant is covered - with no runtime implication.

Haxe compiler will check types when using externs for existing libraries.

You should use externs when calling non Haxe code, but if you just need to call one or two external JS functions, you can skip type checking by calling untyped code.