#variadic FUNctions

[20mice]

Godot 4.0 hack for easy vaargs/variadic functions

(had to make a correction, original code was broken) I wanted to make a script to print out all my nonsense signals. I couldn't figure out a way to do that sensibly, so here's a kind of Faustian hack. I couldn't get the code in here formatted nicely, so here it is in a big heap: func _ready(): connect_signals()

#this is of course an extremely silly way to do this

func print_signal(a='~',b='~',c='~',d='~',e='~',f='~',g='~',h='~',i='~',j='~'): var output = '' var last = '' for x in [a,b,c,d,e,f,g,h,i,j]: if not x is String or x != '~': # hopefully we're not using '~' much output += last last = str(x) set_text('%s : %s\n%s' % [last, output, text])

func connect_signals(): # we could use unbind if print_signal had less args, but that would lose information #if make_storage_connection: Events.make_storage_connection.connect(print_signal.unbind(3)) if make_storage_connection: Events.make_storage_connection.connect(print_signal.bind('mk_strg_con'))

[mentalisttraceur-software]

Wrote another rendition of my nicer type-safe variadic functions in C post, as a StackOverflow answer. Maybe I should roll this idea into a nicer-variadics.h and throw it up on GitHub?

[souhaillaghchimdev]

C++ for Advanced Developers

C++ is a high-performance programming language known for its speed, flexibility, and depth. While beginners focus on syntax and basic constructs, advanced developers harness C++'s full potential by leveraging its powerful features like templates, memory management, and concurrency. In this post, we’ll explore the key areas that elevate your C++ skills to an expert level.

1. Mastering Templates and Meta-Programming

Templates are one of the most powerful features in C++. They allow you to write generic and reusable code. Advanced template usage includes:

Template Specialization

Variadic Templates (C++11 and beyond)

Template Metaprogramming using constexpr and if constexpr

template<typename T> void print(T value) { std::cout << value << std::endl; } // Variadic template example template<typename T, typename... Args> void printAll(T first, Args... args) { std::cout << first << " "; printAll(args...); }

2. Smart Pointers and RAII

Manual memory management is error-prone. Modern C++ introduces smart pointers such as:

std::unique_ptr: Exclusive ownership

std::shared_ptr: Shared ownership

std::weak_ptr: Non-owning references

RAII (Resource Acquisition Is Initialization) ensures that resources are released when objects go out of scope.#include <memory> void example() { std::unique_ptr<int> ptr = std::make_unique<int>(10); std::cout << *ptr << std::endl; }

3. Move Semantics and Rvalue References

To avoid unnecessary copying, modern C++ allows you to move resources using move semantics and rvalue references.

Use T&& to denote rvalue references

Implement move constructors and move assignment operators

class MyClass { std::vector<int> data; public: MyClass(std::vector<int>&& d) : data(std::move(d)) {} };

4. Concurrency and Multithreading

Modern C++ supports multithreading with the `<thread>` library. Key features include:

std::thread for spawning threads

std::mutex and std::lock_guard for synchronization

std::async and std::future for asynchronous tasks

#include <thread> void task() { std::cout << "Running in a thread" << std::endl; } int main() { std::thread t(task); t.join(); }

5. Lambda Expressions and Functional Programming

Lambdas make your code more concise and functional. You can capture variables by value or reference, and pass lambdas to algorithms.std::vector<int> nums = {1, 2, 3, 4}; std::for_each(nums.begin(), nums.end(), [](int x) { std::cout << x * x << " "; });

6. STL Mastery

The Standard Template Library (STL) provides containers and algorithms. Advanced usage includes:

Custom comparators with std::set or std::priority_queue

Using std::map, std::unordered_map, std::deque, etc.

Range-based algorithms (C++20: ranges::)

7. Best Practices for Large Codebases

Use header files responsibly to avoid multiple inclusions.

Follow the Rule of Five (or Zero) when implementing constructors and destructors.

Enable warnings and use static analysis tools.

Write unit tests and benchmarks to maintain code quality and performance.

Conclusion

Advanced C++ programming unlocks powerful capabilities for performance-critical applications. By mastering templates, smart pointers, multithreading, move semantics, and STL, you can write cleaner, faster, and more maintainable code. Keep experimenting with modern features from C++11 to C++20 and beyond — and never stop exploring!

[j0ecool]

When you're making a game engine, there's a tradeoff when it comes to working on dev quality-of-life features vs game-facing features.

A factor I don't usually see discussed is the project's PITA Budget. It may take more swe-hours to implement a shortcut that saves 5 seconds at a time than it will ever pay back, but the qualitative difference in working without that 5s annoyance reduces the mental-strain cost of continuing to work on the project at all. As a motivation-bottlenecked solo dev, having good tools not only makes asset creation more efficient, but effectively gives you more hours in the month to work with.

today's small thing is interpreting a UI Label of just "\n" to be a shortcut for a linefeed, which lets us change this from

to

cutting lines-of-code in half

It's the sort of thing that makes actually* zero difference in the finished product, but it makes the daily life of being a game developer fractionally less frustrating, so that's satisfying.

*: If compilers can optimize across strcmp, which I strongly suspect they can, then they would literally compile into the same code

For funsies (because I'm stoned) let's pretend we're compiler engineers again. Why should this be optimizable? ui.labels() is a variadic template function that emits one call to ui.label() per argument, which has overloads for each type. the const char* overload has a clause at the top of it:

With inlining, we would get if (strcmp("\n", "\n") == 0), which a human programmer can clearly see is always true. Can compilers? Well, given that strcmp is a C Standard Library function, compilers are free to make special assumptions about how they work that may not be obvious from the type signature. We also would not be able to inline strcmp, because it's compiled separately (unless you're building your own libc from source, or if your libc ships bitcode which would be weird)

Anyway! So it stands to reason that some enterprising young lad may have realized that they can save 0.2ms on some benchmark by constant-folding strcmps, or by precomputing string manipulations, or whatever. How can we check? Godbolt.

Sure enough, even at -O1 clang is able to constant-fold the strcmps and turn test("foo") into just doSomethingElse()

This ramble didn't really have much of a point. C++ is a language built around zero-cost abstractions. There's something really satisfying about knowing enough about how the optimizer works, and what the language's semantics are, including its cost model, and thus being able to reason about what will actually get optimized away in practice.

[mentalisttraceur-incomplete]

One of the reasons for why I wrote a separate "compose" package for Python was that it would've served as a good reference implementation for a PEP.

I figured - here's an implemenation of "compose" that strongly parallels "functools.partial" and pays attention to following all the little Python details (pickling works, method-binding works, signature introspection works, ...).

The thing is, I have yet to have the inclination or the time for the very social acts of drafting a PEP tuned for others, or convincing anyone. And maybe I never will.

In the meantime, Python is enough of a moving target that now, just a few years later, the Best design includes type hints. Type hints are everywhere now. Runtime type-checking, static type-checking, both, some of each.

I'd love it if the type system were any good for stuff like this. It isn't. It really really isn't. It's terrible, actually. Those who remember my type stubs for compose already know this. You've seen it first-hand. It can be brute-forced to support variadic "compose" up to some arity, but then since that requires describing the type signature as functions, you lose the ability to type-hint things like the ".functions" attribute. I'd like to think that there's some way to type-hint a class with overloads to achieve the same thing that my type-hint function overloads achieve, but when I last tried it, I spent like half a day on it and got nowhere.

But still, even with the existing type system limitations, we can do better:

If we embrace a little more introspection at runtime, we can implement automatic unpacking of tuple return values. I resisted this for a while, for a mix of reasons, but it's definitely a "for humans" thing to do - even without type hints, the number of times when it would be useful to compose a "return foo, bar" into a "def f(foo, bar)" is higher than the number of times when the composition would unpack a tuple into

The author of Beartype drew my attention to "if typing.TYPE_CHECKING:", which is worth investigating - it might solve the problems I saw w/ MyPy when trying to keep the type hint overloads inline. It does kinda suck to make people install a separate type stubs package.

[thealgorist]

C Functions

When it comes to programming in C, functions are an essential building block. They allow you to break down your code into manageable pieces, promote code reusability, and make your programs more organized. In this article, we will delve into the world of C functions, covering topics like return types, parameters, passing arguments by value and by reference, variadic functions, and function prototypes.

[cousedrive]

Programming, Problem Solving, Project Building, and Design Skills. 7X Quizzes, Practice, Hmwk & REAL Projects than others

What you’ll learn

Mastering 4 critical SKILLS using C++ 17

Deep Dive with C++ 11/14/17 Modern Syntax from basic to advanced

EXTENSIVE practice and homework sets to master the key concepts

MANY Projects from easy to hard with their solutions for projects-building skills

MANY Quizzes to master the concepts

FOUR critical skills to master not just one

A proven curriculum: Many of my thousands of students highly recommend it

Short lectures, to the point, comprehensive and easy to get in an iterative style

Learn from Ph.D. holder in AI: Teaching, Training & Coaching for many years

Requirements

Passion for building things!

Passion with problem-solving!

Access to a computer.

Description

Almost all other courses focus on knowledge. In this course, we focus on 4 critical skills.

Overall:

The course covers basic to advanced modern C++ syntax. Beginners in C++ will learn a lot!

The course helps you master the 4 most important skills for a programmer

7+ times practice & homework compare to other courses + 6 projects

Special Teaching style: iterative, easy, and short

This is an English Course only.  1/3 Course now has Manual English subtitles. Remaining under-progress.

Programming skills

Problem-solving skills: rarely covered by other courses

Project building skills: partially covered by other courses

Design skills: rarely covered by other courses

Content

Basic to advanced modern C++.

A huge set of Quizzes, Practice, Homework, and Projects

Fundamentals: Variables, Loops, Control Flow, Functions, Recursive Functions, Arrays

Advanced Topics: Pointers, STL, Templates, Headers, and Includes, Exception Handling, Compilation Process, Debugging

Object-Oriented Programming (OOP): Classes, Objects, Inheritance, Polymorphism, Operator Overloading

Modern Topics in C++11/C++14/C++17:

Design: Several principles and patterns are embedded in the homework & projects

Move Semantics, Perfect Forwarding, Variadic Template, Folding expressions, Smart Pointers, Lambda Expressions, Wrappers (Optional, Variant, Any), Uniform initialization, except, Structured Binding, Nested namespaces, misc.

OOP: Member initializer, Defaulted and Deleted Functions, Delegating constructors, Inheriting Constructors

STL: Forward list, Initializer list, Array, Unordered containers

2 styles of homework: Algorithmic (problem-solving) and applications

Several software design concerns are embedded in the homework.

So you are getting introduced to Software Engineering & Design Patterns

Several quizzes to master the concepts

Building Skills: Practice, Homework, and Projects

One unique feature in this course is my education strategy:

Each video smoothly explains a simple concept(s)

Typically followed by easy to medium practice to administrate the concept

Then typically followed by an easy-medium-hard set of homework questions to challenge you

Extensive homework from easy to medium to hard to help to build the skills.

Most of the practice/homework questions are unique in this course

Small-to-large set of projects to build up project building and design skills

Solutions to all of them.

Explain, Administrate & Challenge

Programming questions are mainly from my competitive programming experience

OOP questions are mostly invented to achieve several goals:

Mastering the OOP concepts

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Preparing you for design principles and patterns

By the end of the journey

Solid understanding of programming concepts using C++

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[jacob-cs]

Swift Functions

Functions

Swift’s unified function syntax is flexible enough to express anything from a function with no parameter names to method with names and argument labels for each parameter. Parameters can provide default values.

Every function in Swift has a type, consisting of the function’s parameter types and return type. Functions can also be written within other functions to encapsulate useful functionality within a nested function scope.

Defining and Calling Functions

When you define a function, you can optionally define one or more named, typed values that the function takes as input, known as parameters. You can also optionally define a type of value that the function will pass back as output when it is done, known as its return type.

To use a function, you “call” that function with its name and pass it input values. A function’s arguments must always be provided in the same order as the function’s parameter list.

func greet(person: String) -> String {    let greeting = "Hello, " + person + "!"    return greeting }

NOTE

The print(_:separator:terminator:) function doesn’t have a label for its first argument, and its other arguments are optional because they have a default value.

Function Parameters and Return Values

Functions Without Parameters

func sayHelloWorld() -> String {    return "hello, world" } print(sayHelloWorld()) // Prints "hello, world"

Functions With Multiple Parameters

func greet(person: String, alreadyGreeted: Bool) -> String {    if alreadyGreeted {        return greetAgain(person: person)    } else {        return greet(person: person)    } } print(greet(person: "Tim", alreadyGreeted: true)) // Prints "Hello again, Tim!"

greet(person:alreadyGreeted:) 함수와  greet(person:) 는 이름은 같으나 받아들이는 parameters 가 다르므로 다른 함수이다.

Functions Without Return Values

func greet(person: String) {    print("Hello, \(person)!") } greet(person: "Dave") // Prints "Hello, Dave!"

Because it does not need to return a value, the function’s definition does not include the return arrow (->) or a return type.

NOTE

Strictly speaking, this version of the greet(person:) function does still return a value, even though no return value is defined. Functions without a defined return type return a special value of type Void. This is simply an empty tuple, which is written as ()

.The return value of a function can be ignored when it is called:

func printAndCount(string: String) -> Int {    print(string)    return string.count } func printWithoutCounting(string: String) {    let _ = printAndCount(string: string) } printAndCount(string: "hello, world") // prints "hello, world" and returns a value of 12 printWithoutCounting(string: "hello, world") // prints "hello, world" but does not return a value

NOTE

Return values can be ignored, but a function that says it will return a value must always do so. A function with a defined return type cannot allow control to fall out of the bottom of the function without returning a value, and attempting to do so will result in a compile-time error.

Functions with Multiple Return Values

func minMax(array: [Int]) -> (min: Int, max: Int) {    var currentMin = array[0]    var currentMax = array[0]    for value in array[1..<array.count] {        if value < currentMin {            currentMin = value        } else if value > currentMax {            currentMax = value        }    }    return (currentMin, currentMax) }

let bounds = minMax(array: [8, -6, 2, 109, 3, 71]) print("min is \(bounds.min) and max is \(bounds.max)") // Prints "min is -6 and max is 109"

Note 

that the tuple’s members do not need to be named at the point that the tuple is returned from the function, because their names are already specified as part of the function’s return type.

Optional Tuple Return Types

you can use an optional tuple return type to reflect the fact that the entire tuple can be nil. You write an optional tuple return type by placing a question mark after the tuple type’s closing parenthesis, such as (Int, Int)?or (String, Int, Bool)?.

NOTE

An optional tuple type such as (Int, Int)? is different from a tuple that contains optional types such as (Int?, Int?). With an optional tuple type, the entire tuple is optional, not just each individual value within the tuple.

func minMax(array: [Int]) -> (min: Int, max: Int)? {    if array.isEmpty { return nil }    var currentMin = array[0]    var currentMax = array[0]    for value in array[1..<array.count] {        if value < currentMin {            currentMin = value        } else if value > currentMax {            currentMax = value        }    }    return (currentMin, currentMax) }

You can use optional binding to check whether this version of the minMax(array:) function returns an actual tuple value or nil:

if let bounds = minMax(array: [8, -6, 2, 109, 3, 71]) {    print("min is \(bounds.min) and max is \(bounds.max)") } // Prints "min is -6 and max is 109"

Function Argument Labels and Parameter Names

Each function parameter has both an argument label and a parameter name. The argument label is used when calling the function; each argument is written in the function call with its argument label before it. The parameter name is used in the implementation of the function. By default, parameters use their parameter name as their argument label.

func someFunction(firstParameterName: Int, secondParameterName: Int) {    // In the function body, firstParameterName and secondParameterName    // refer to the argument values for the first and second parameters. } someFunction(firstParameterName: 1, secondParameterName: 2)

You write an argument label before the parameter name, separated by a space:

func someFunction(argumentLabel parameterName: Int) {    // In the function body, parameterName refers to the argument value    // for that parameter. }

argument label 과  parameter name 를 구분해서 사용하는 예시

func greet(person: String, from hometown: String) -> String {    return "Hello \(person)!  Glad you could visit from \(hometown)." } print(greet(person: "Bill", from: "Cupertino")) // Prints "Hello Bill!  Glad you could visit from Cupertino."

Omitting Argument Labels

If you don’t want an argument label for a parameter, write an underscore (_) instead of an explicit argument label for that parameter.

func someFunction(_ firstParameterName: Int, secondParameterName: Int) {    // In the function body, firstParameterName and secondParameterName    // refer to the argument values for the first and second parameters. } someFunction(1, secondParameterName: 2)

Default Parameter Values

func someFunction(parameterWithoutDefault: Int, parameterWithDefault: Int = 12) {    // If you omit the second argument when calling this function, then    // the value of parameterWithDefault is 12 inside the function body. } someFunction(parameterWithoutDefault: 3, parameterWithDefault: 6) // parameterWithDefault is 6 someFunction(parameterWithoutDefault: 4) // parameterWithDefault is 12

Variadic Parameters

A variadic parameter accepts zero or more values of a specified type. You use a variadic parameter to specify that the parameter can be passed a varying number of input values when the function is called. Write variadic parameters by inserting three period characters (...) after the parameter’s type name.

The values passed to a variadic parameter are made available within the function’s body as an array of the appropriate type.

func arithmeticMean(_ numbers: Double...) -> Double {    var total: Double = 0    for number in numbers {        total += number    }    return total / Double(numbers.count) } arithmeticMean(1, 2, 3, 4, 5) // returns 3.0, which is the arithmetic mean of these five numbers arithmeticMean(3, 8.25, 18.75) // returns 10.0, which is the arithmetic mean of these three numbers

NOTE

A function may have at most one variadic parameter.

In-Out Parameters

Function parameters are constants by default. Trying to change the value of a function parameter from within the body of that function results in a compile-time error. If you want a function to modify a parameter’s value(전달된 variable형식의 argument값을 함수내에서 처리한후 외부에서도 variable의 변화값이 유지되게 하는 경우 ), and you want those changes to persist after the function call has ended, define that parameter as an in-out parameter instead.

You write an in-out parameter by placing the inout keyword right before a parameter’s type. 

You can only pass a variable as the argument for an in-out parameter. You cannot pass a constant or a literal value as the argument, because constants and literals cannot be modified. You place an ampersand (&) directly before a variable’s name when you pass it as an argument to an in-out parameter, to indicate that it can be modified by the function.

NOTE

In-out parameters cannot have default values, and variadic parameters cannot be marked as inout.

func swapTwoInts(_ a: inout Int, _ b: inout Int) {    let temporaryA = a    a = b    b = temporaryA }

var someInt = 3 var anotherInt = 107 swapTwoInts(&someInt, &anotherInt) print("someInt is now \(someInt), and anotherInt is now \(anotherInt)") // Prints "someInt is now 107, and anotherInt is now 3"

NOTE

In-out parameters are not the same as returning a value from a function. The swapTwoInts example above does not define a return type or return a value, but it still modifies the values of someInt and anotherInt. In-out parameters are an alternative way for a function to have an effect outside of the scope of its function body.

Function Types

Every function has a specific function type, made up of the parameter types and the return type of the function.

func addTwoInts(_ a: Int, _ b: Int) -> Int {    return a + b } func multiplyTwoInts(_ a: Int, _ b: Int) -> Int {    return a * b }

The type of both of these functions is (Int, Int) -> Int. This can be read as:

“A function that has two parameters, both of type Int, and that returns a value of type Int.”

Here’s another example, for a function with no parameters or return value:

func printHelloWorld() {    print("hello, world") }

The type of this function is () -> Void, or “a function that has no parameters, and returns Void.”

Using Function Types

You use function types just like any other types in Swift. For example, you can define a constant or variable to be of a function type and assign an appropriate function to that variable:

var mathFunction: (Int, Int) -> Int = addTwoInts

This can be read as:

“Define a variable called mathFunction, which has a type of ‘a function that takes two Int values, and returns an Int value.’ Set this new variable to refer to the function called addTwoInts.”

The addTwoInts(_:_:) function has the same type as the mathFunction variable, and so this assignment is allowed by Swift’s type-checker.

You can now call the assigned function with the name mathFunction:

print("Result: \(mathFunction(2, 3))") // Prints "Result: 5"

A different function with the same matching type can be assigned to the same variable, in the same way as for nonfunction types:

mathFunction = multiplyTwoInts print("Result: \(mathFunction(2, 3))") // Prints "Result: 6"

As with any other type, you can leave it to Swift to infer the function type when you assign a function to a constant or variable:

let anotherMathFunction = addTwoInts // anotherMathFunction is inferred to be of type (Int, Int) -> Int

Function Types as Parameter Types

You can use a function type such as (Int, Int) -> Int as a parameter type for another function. This enables you to leave some aspects of a function’s implementation for the function’s caller to provide when the function is called.

Here’s an example to print the results of the math functions from above:

func printMathResult(_ mathFunction: (Int, Int) -> Int, _ a: Int, _ b: Int) {    print("Result: \(mathFunction(a, b))") } printMathResult(addTwoInts, 3, 5) // Prints "Result: 8"

This example defines a function called printMathResult(_:_:_:), which has three parameters. The first parameter is called mathFunction, and is of type (Int, Int) -> Int. You can pass any function of that type as the argument for this first parameter. The second and third parameters are called a and b, and are both of type Int. These are used as the two input values for the provided math function.

When printMathResult(_:_:_:) is called, it is passed the addTwoInts(_:_:) function, and the integer values 3and 5. It calls the provided function with the values 3 and 5, and prints the result of 8.

The role of printMathResult(_:_:_:) is to print the result of a call to a math function of an appropriate type. It doesn’t matter what that function’s implementation actually does—it matters only that the function is of the correct type. This enables printMathResult(_:_:_:) to hand off some of its functionality to the caller of the function in a type-safe way.

(printMathResult 라는 wrapper 함수를 만들고 함수의 타입만 맞는다면 다양한 함수와 변수를 받아드리고 가변적인 결과를 만들어 낼수 있다는 것을 보여주는 예시 )

Function Types as Return Types

You can use a function type as the return type of another function. You do this by writing a complete function type immediately after the return arrow (->) of the returning function.

The next example defines two simple functions called stepForward(_:) and stepBackward(_:). The stepForward(_:) function returns a value one more than its input value, and the stepBackward(_:) function returns a value one less than its input value. Both functions have a type of (Int) -> Int:

func stepForward(_ input: Int) -> Int {    return input + 1 } func stepBackward(_ input: Int) -> Int {    return input - 1 }

Here’s a function called chooseStepFunction(backward:), whose return type is (Int) -> Int. The chooseStepFunction(backward:) function returns the stepForward(_:) function or the stepBackward(_:) function based on a Boolean parameter called backward:

func chooseStepFunction(backward: Bool) -> (Int) -> Int {    return backward ? stepBackward : stepForward }

You can now use chooseStepFunction(backward:) to obtain a function that will step in one direction or the other:

var currentValue = 3 let moveNearerToZero = chooseStepFunction(backward: currentValue > 0) // moveNearerToZero now refers to the stepBackward() function

The preceding example determines whether a positive or negative step is needed to move a variable called currentValue progressively closer to zero. currentValue has an initial value of 3, which means that currentValue > 0 returns true, causing chooseStepFunction(backward:) to return the stepBackward(_:)function. A reference to the returned function is stored in a constant called moveNearerToZero.

Now that moveNearerToZero refers to the correct function, it can be used to count to zero:

print("Counting to zero:") // Counting to zero: while currentValue != 0 {    print("\(currentValue)... ")    currentValue = moveNearerToZero(currentValue) } print("zero!") // 3... // 2... // 1... // zero!

(상황에 따라 리턴하는 함수가 달라지는 wrapper함수를 만드는 과정을 보여준다. )

Nested Functions

All of the functions you have encountered so far in this chapter have been examples of global functions, which are defined at a global scope. You can also define functions inside the bodies of other functions, known as nested functions.

Nested functions are hidden from the outside world by default, but can still be called and used by their enclosing function. An enclosing function can also return one of its nested functions to allow the nested function to be used in another scope.

You can rewrite the chooseStepFunction(backward:) example above to use and return nested functions:

func chooseStepFunction(backward: Bool) -> (Int) -> Int {    func stepForward(input: Int) -> Int { return input + 1 }    func stepBackward(input: Int) -> Int { return input - 1 }    return backward ? stepBackward : stepForward } var currentValue = -4 let moveNearerToZero = chooseStepFunction(backward: currentValue > 0) // moveNearerToZero now refers to the nested stepForward() function while currentValue != 0 {    print("\(currentValue)... ")    currentValue = moveNearerToZero(currentValue) } print("zero!") // -4... // -3... // -2... // -1... // zero!

[mobappdevelopmentcompany]

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As recorded in January 2023, the average Bitcoin transactions executed in one single day were between 291015 and 205314.

By the year 2024, the global expenditure on Blockchain apps and solutions will reach $19 billion

Developing a disruptive Blockchain application or solution is a lucrative option for entrepreneurs and investors. However, businesses planning to build a Blockchain solution should understand the basics of Blockchain programming before jump-starting their project or proceeding to hire Blockchain app development services. This post discusses the offerings of the top Blockchain programming languages. After reading this post you will be able to figure out which language would be the best fit for your Blockchain use case.

Top Programming Languages For Blockchain Development

Solidity

Solidity is an object-oriented Blockchain programming language specifically designed to create smart contracts and decentralized applications that run on the EVM (Ethereum Virtual Machine). Ethereum is a massive computing platform based on Blockchain; its ecosystem is one of the most crucial components of Blockchain app development. The creators of Ethereum have developed Solidity and provide active support to this high-level programming language for fulfilling their in-platform requirements. Influenced by other programming languages like Java, JavaScript, Python, and C++; Solidity has proved its worth as one of the best languages for writing smart contracts.

Features

Solidity is flexible, stable, and promises a good accuracy rate. It comes with numerous disruptive features like variadic return and static typing. It supports concepts such as user-defined functions, inheritance properties, and libraries. Solidity comes with an easy learning curve and enables access to tools like debuggers, JS infrastructures, etc. Solidity has several type-safe functions due to the presence of ABI (Application Binary Interface).

Use Cases

Solidity is used for developing Ethereum smart contracts and Chainlink smart contracts. Chainlink is a decentralized Oracle network used for on-chain as well as off-chain Blockchain computations. Another use case of Solidity is the compound protocol on Ethereum Blockchain. This is an autonomous interest rate protocol involving algorithms. Solidity is also used for developing Uniswap. Uniswap is a decentralized crypto trading platform involving a network of decentralized finance apps governed by a community.

Python

Python is one of the most popular Blockchain programming languages. Its robust nature and versatility speed up the development time. Python has a simple English-like syntax that reduces the lines of coding and so, is a perfect pick for newbie coders. Python programming suits both approaches – scripting, and base. It is a high-level language that can be effortlessly integrated with other programming languages like Java, C++, etc. It functions on various platforms including Mac, Linux, Windows, and Raspberry.

Features

Python is object-oriented, easy to code, and extensively portable. It offers strong open-source language support, OOP support, rapid prototyping, access to a dynamic architecture, and dynamic memory allocation. The availability of multiple online resources like libraries, plugins, and development manuals facilitates Blockchain app development; developers get the solution to almost every issue faced during Blockchain projects. Libraries like Numba speeds up coding without compromising on crucial factors like security and performance. Python fares better in performing complicated mathematical operations and handling Big data as compared to most other programming languages.

Use Cases

It is used to write smart context for Hyperledger Fabric, NEO contracts, Steemit, and develop cryptocurrencies like Ethereum and Bitcoin.

Java

This is a popular platform-independent Blockchain programming language that is widely used for developing decentralized applications and smart contracts. The language is derived from C-Syntax and functions on the WORA (Write Once Run Anywhere) concept. Its ubiquitous nature allows one to use Java for almost every web system. As such, the code written by programmers is highly portable and can be run on every device that has JVM (Java Virtual Machine).

Features

Java’s offerings are manifold. Its Portability makes it an apt choice for Blockchain development projects. Java comes with an extensive API (Application Programming Interface), that includes multiple Java classes, packages, and interfaces. Owing to its multi-threaded nature, you utilize the CPU to the fullest. It’s a developer-friendly language and can support heavy APIs like object-oriented programming based on its class. Java offers adequate libraries and simplifies the process of memory cleaning. Using Java’s security manager, you can define access rules for a specific class; this minimizes the chances of security vulnerabilities. Java’s programming is based on Java Virtual Machine and is not dependent on any specific system-based infrastructure. Hence, its capabilities are not limited by a device’s architecture and it can handle a huge number of users on a Blockchain network simultaneously.

Use Cases

The use case examples include creating Blockchains on platforms like Hyperledger Fabric, Ethereum, NEO, IOTA, etc.

JavaScript

JavaScript is a popular web language and is pre-installed in most PCs and so, it becomes easy to build Blockchain solutions with JS.

Features

JavaScript is a lightweight, object-oriented, and prototype-based scripting language that provides support for functional programming. JS can easily handle asynchronous actions and the communications taking place between nodes. It comes with a wide range of tools and libraries that facilitate Blockchain app development.

Use Cases

Using JavaScript, Blockchain app developers can connect an app’s front end to Ethereum’s network and smart contracts. JS is has also been used in Hyperledger Fabric.

PHP

PHP (Hypertext Preprocessor) is an open-source and object-oriented programming language that can be used to develop Blockchain solutions of various complexity levels. The language is straightforward and simple and offers an easy learning curve.

Features

PHP is platform-independent and powered by the Zend Engine; so, it can be written on a wide variety of OSs. It offers a highly configurable library that comes in handy for developers. Its interactive pages enable one to sail through complex requirements. PHP has a built-in database connection module; this reduces the hassles and speeds up the development time during web development projects.

Use Cases

PHP is used for smart contract development.

Go

This Google-developed language has gained traction as one of the top Blockchain programming languages. Go is an open-source and statically typed language. It offers benefits like speed, user-friendliness, flexibility, and scalability that make it suitable for Blockchain development.

Features

Go comes with a powerful library containing functions & packages. It provides organized syntaxes. It enables you to run multiple processes simultaneously without compromising on memory resources. Despite being a static language, Go gives developers a feel of being dynamic.

Use Cases

Examples of its use cases are Go-Ethereum (Ethereum-based project written in Go) and GO-Hyperledger Fabric.

Ruby

Ruby is a high-level and general-purpose programming language that comes with cross-platform compatibility. This open-source language is developer-friendly and focuses on simplicity and high productivity. It can be installed in Windows & POSIX and can be connected to Oracle, MySQL, Sybase, and DB2.

Features

Ruby is a multi-paradigm language that has exceptional memory allocation abilities. It is an interpreted and scripting language. The feature of multiple-language adaptability makes Ruby a good choice for Blockchain app development.

Use Cases

Ruby allows developers to program Blockchain solutions using third-party plugins and APIs.

Rholang

Rholang is newer as compared to other Blockchain programming languages. It comes with an easy-to-understand syntax. It is reliable, speedy, and user-friendly and provides high accuracy levels. Rholang employs a functional programming approach instead of an object-oriented programming approach.

Use Cases

Rholang is used for developing smart contracts and other high-end Blockchain-based projects.

Simplicity

This language was designed to minimize the low-level understanding of cryptocurrencies. Simplicity is reliable and offers the security of funds, an offering that provides it an edge over many other Blockchain programming languages. Simplicity is in harmony with the “Elements platform” of Blockstream. Simplicity is a viable option for creating sophisticated and secured smart contracts in Blockchain environments.

Use Cases

Simplicity is compatible with the Blockchain-based platform Ethereum. It reduces the complex functionality of the Bitcoin Script by enabling a low-level understanding of the Bitcoin Script. It’s a good option for coding smart contracts.

SQL

SQL (Structured Query Language) is one of the most recent Blockchain programming languages that can be used for creating secure and effective solutions. This is an IBM-created language meant for facilitating communication with databases like SQL Server, MySQL, Oracle, and PostgreSQL. With SQL, users can store data queries and also manipulate and raise those queries.

Use Cases

Aergo is an important use case of Blockchain development in SQL. It is a Blockchain project that offers ready-to-use solutions to companies that work with technologies like Coinstack or Blocko. SQL can also be used for developing robust business-centric smart contracts.

CX

CX is one of the most sought-after Blockchain programming languages that can function as a contractual digital intermediary.

Features

Its features include a simplified error control process and access to pointers, arrays, and propelled cuts. CX enables developers to effortlessly manipulate programs and apply vectors, pointers, forced reductions, etc.

Use Cases

CX when integrated with the programming language Go, gives businesses an escape from critical issues like discretionary code execution steps. CX integrates well with Open Graphics Library (OpenGL). This integration is leveraged by Blockchain developers to gain advantages regarding GPU capacity.

C++

C++ is a general-purpose programming language that can be used for creating a wide variety of applications like finance solutions, AR/VR apps, 3D gaming apps, etc. It is a robust, flexible, & object-oriented programming language that is capable of managing resource-intensive apps smoothly. It’s a multi-paradigm language and follows the OOPs technique. This language is developer-friendly and offers ease of usage. C++ is one of those Blockchain programming languages that promises a faster execution time.

Features

Its offerings include efficient memory control, function overloading, and effective CPU management. C++ can effortlessly run parallel and non-parallel threads. It can isolate the code for different data structures as well. The capability of run-time polymorphism results in improved app performance. Its data-hiding capability strengthens the security quotient. There’s also the option for moving semantics to copy data productively.

Use Cases

C++ is used for developing smart contracts on the EOS Blockchain and developing cryptocurrencies such as Stellar, Litecoin, Bitcoin, EOS, and Ripple.

C#

C# is an open-source and object-oriented programming language created by Microsoft. It happens to be one of the most popular Blockchain programming languages. It can be used to build scalable applications with .NET compatibility. C# is a great pick for crafting powerful codes with cross-platform compatibility.

Features

C# is an Extensible Markup language and can function as a support for distributed systems. With this language, programmers can create highly portable codes that run on a wide variety of hardware and OSs including Windows, Mac, Android, Linux, etc. The assembly control feature makes it easier for developers to handle issues like version control. The OOPs feature in C# helps in optimizing the performance of Blockchain solutions and apps.

Use Cases

C# has been used in NEO in combination with other programming languages such as Python, JavaScript, Java, & Go. C# has been used Stratis. This is a Blockchain-as-a-service providing platform powered by Microsoft. IOTA, an open-source distributed ledger and cryptocurrency, is another use case example.

Vyper

Vyper is one of the newest Blockchain programming languages. It is compatible with EVM and its syntax is similar to that of Python 3’s. Vyper can be used as an alternative to the popular Blockchain programming language Solidity.

Features

Vyper comes with an exceptional control structure that enables handling security challenges more effectively. Its other offerings include modifiers, recursive calling, etc.

Use Cases

Vyper is used for the Ethereum Virtual Machine (EVM) and for developing smart contracts.

Concluding Views:

All the aforementioned Blockchain programming languages come with distinct offerings and are suitable for specific use cases. You need to pick the language based on your use case requirements. A thorough knowledge of the offerings of these languages will help you to make the right decision while choosing the tech stacks for your Blockchain project. A good understanding of these programming languages will also prove beneficial when you’re discussing tech stack selection, with the Blockchain App Development Company to whom you’ve outsourced your project.

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The complete guide to the PHP variadic functions

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Blend art text with swift publisher 3

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This variation of prepend takes a variadic list of values using the variadic. In other words, you’ll use them to add values that emit before any values from your original publisher.Īt fbeg fehxaiv, wia’tv roatx ojuom lramuqr(Iehgep.), qbusobw(Lubiacta) ufn bcepupz(Vagpaqqax). You’ll start slowly here with a group of operators that are all about prepending values at the beginning of your publisher.

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Throughout this chapter, you’ll add code to your playground and run it to see how various operators create different combinations of publishers and their events. You can find the starter playground for this chapter in the projects/ayground folder.

Blend art text with swift publisher 3 how to#

You’ll need to combine this data to compose a single publisher with all of the information you need.Īs you learn more about how each operator functions and how to select the right one for your needs, your code will become more coherent. Why is combining useful? Think about a form with multiple inputs from the user - a username, a password and a checkbox. This set of operators lets you combine events emitted by different publishers and create meaningful combinations of data in your Combine code. In this chapter, you’ll learn about one of the more complex, yet useful, categories of operators: Combining operators. You’ve learned how operators work, how they manipulate the upstream and how to use them to construct logical publisher chains from your data. Now that the transforming and filtering operator categories are in your tool belt, you have a substantial amount of knowledge. Custom Publishers & Handling Backpressure

15.7 Subscribing to an external publisher.

15.5 Using ObservableObject for model types.

15.3 A first taste of managing view state.

Section IV: Advanced Combine Section 4: 5 chapters

14.3 Multiple stories via merging publishers.

14.1 Getting started with the Hacker News API.

12.2 Preparing and subscribing to your own KVO-compliant properties.

12.1 Introducing publisher(for:options:).

10.3 Using the debugger as a last resort.

10.2 Acting on events - performing side effects.

9.3 Publishing network data to multiple subscribers.

Section III: Combine in Action Section 3: 6 chapters

8.6 Publishing properties with 8.7 Operators in practice.

8.4 Presenting a view controller as a future.

8.3 Wrapping a callback function as a future.

Section II: Operators Section 2: 6 chapters

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[inextures]

The Python 3.11 release: everything you need to know

In Python 3.11, numerous new features will be available that can bring efficiency in your programming skills. Here are the things you can anticipate when using the alpha version prior to the release on October 22. Check out the blog for more info.

Python 3.11 Introduces New Features and Functionality

General alterations

Improved Speed – The usual benchmark suite runs around 25% quicker than in 3.10, which is the first notable change that will thrill data scientists. According to the Python documentation, 3.11 can occasionally be up to 60% quicker.

Smooth Error Locations in Tracebacks for Easier Debugging – In the past, lines would be used to identify errors.

Using Exception Groups and except, error handling is made easier – Previously, if a task produced numerous errors, you had to address each one separately.

Standard Library support for TOML parsing – A customization file format comparable to YAML is called TOML, or Tom’s Obvious Minimal Language. It includes reliable and consistent build information for your project.

Typing and Language Changes

Self Type – The annotation of self returns has been awkward for a long time, resulting in confusing results from your analysis tools.

Variadic Generics – Python supports type hints, which now include include TypeVarTuple. When you are anticipating a particular array structure, this lets you define a placeholder for tuples, which is useful.

Independent Literal String Type – Before, there was no method for type annotations to specify that a specific variable had to be a string defined in the source code.

TypedDict Required vs Missing Items – The declaration of some keys as needed and others as potentially absent is not supported by TypedDict at this time. 3.11 adds Required() and notrequired() to give you a mechanism to take these into account.

Conclusion

Additionally, Python 3.12 is already in development. Stay tuned with us if you want to be aware of all the most recent language developments.

This blog originally was published by https://www.inexture.com/python-3-11-release/