Example with JSONDecoder

struct Test<T>: Codable where T: Codable {
    enum CodingKeys: String, CodingKey {
        case value
    }
    
    let value: T
    let info: String
}

extension Test {
    init(from decoder: Decoder) throws {
        let container = try decoder.container(keyedBy: CodingKeys.self)
        self.value = try container.decode(T.self, forKey: .value)
        self.info = "Default init(from decoder:)"
    }
}

extension Test where T == String {
    init(from decoder: Decoder) throws {
        let container = try decoder.container(keyedBy: CodingKeys.self)
        self.value = try container.decode(T.self, forKey: .value)
        self.info = "Custom init(from decoder:)"
    }
}

let data = #"{"value":"Hello, World!"}"#.data(using: .utf8)!
let object = try? JSONDecoder().decode(Test<String>.self, from: data)
print(object.debugDescription)

Optional(
    Test<String>(
        value: "Hello, World!", 
        info: "Default init(from decoder:)"
    )
)

Recently Epic Games’ Fortnite was removed from the Apple App Store and Google Play Store. The main reason being Epics bypassing of in-app purchases instead of using the officially sanctioned system for both platforms. While it is still possible for you to download Fortnite directly, this large scale case brings to light the duopoly of Apple and Google in the mobile market.

For most developers, these two stores account for almost all of their revenue and userbase. While Epic Games will be fine to go without, for the time being, what about the other 99% of developers who rely on these two stores for distributing and monetizing their apps. In this article, we’ll provide some of the alternative stores available for both developers and consumers for finding or distributing apps.

For one reason or another, you may have found yourself wondering, ‘where are some other places I can go to find and download new apps and games for my mobile device’? Or ‘are there any other marketplaces for me to share my apps’?

Major players in the Chinese app market are joining forces to take on the almighty Google Play store. Xiaomi, Oppo and Vivo are reported to launch the Global Developer Service Alliance (GDSA), a platform allowing Android developers to publish their apps in the partnering stores from one upload.

The GDSA is expected to launch in nine countries—including India, Indonesia, Malaysia, Russia, Spain, Thailand, the Philippines, and Vietnam—although paid app support may vary across the regions. Canalys’ Nicole Peng explains the wide reach of this alliance:

By forming this alliance each company will be looking to leverage the others’ advantages in different regions, with Xiaomi’s strong user base in India, Vivo and Oppo in Southeast Asia, and Huawei in Europe. 

The Chinese Mobile RPG: the Genre of Giants.

Chinese RPGs make up 56% of the top 500 grossing iOS games in China. According to data gathered in 2019 by Game Refinery’s Joel Julkunen. Let’s take a look at the genre and some important points for game developers and RPG enthusiasts to consider Chinese Mobile RPGs.

RPG or role-playing game is a game where the player plays as a character, often the main character, in a fictional game world.

This article presents generalized approaches for using value converters into writing of XAML code.

>> Read in Russian

Repository

concurrencpp allows applications to write asynchronous code easily and safely by using executors and coroutines.

By using concurrencpp applications can break down big procedures that need to be processed asynchronously into smaller tasks that run concurrently and work in a co-operative manner to achieve the wanted result.

concurrencpp also allows applications to write parallel algorithms more easily by using parallel coroutines.

concurrencpp main advantages are:

• Being able to write non-blocking, asynchronous code easily by using the C++20 coroutines and the co_await keyword.
• Being able to write modern concurrent code without having to rely on low-level concurrency primitives like locks and condition variables.
• The concurrency runtime manages all low-level resources such as threads automatically.
• Reducing the possibility of race conditions, data races and deadlocks by using high-level objects with built-in synchronization.
• concurrencpp provides various types of commonly used executors with a complete coroutine integration.
• Applications can extend the library by using their own provided executors.
• Applications automatically scale-up to use all hardware processors (cores).

(originally published on Medium)

I have talked to many Android developers, and most of them are excited about Kotlin. So am I. When I just started learning Koltin, I was solving Kotlin Koans, and along with other great features, I was impressed with the power of functions for performing operations on collections. Since then, I spent three years writing Koltin code but rarely utilised all the potential of the language.

During this year, I did more than a hundred coding problems on Leetcode in Java. I didn’t switch to Kotlin because I know the syntax of Java 6 so well, that I could effortlessly write code without autocompletion and syntax highlighting. But I didn’t keep track of new Java features, as Android support of Java SDK lacked many versions behind. I didn’t switch to Kotlin for solving problems right away. Although I was writing Kotlin code for several years, I felt that I need to make an extra cognitive effort to get the syntax and the language constructions right. Solving algorithmic problems, especially under the time pressure, is very different from Android app development. Still, the more I learned about Kotlin, the more I realised how many powerful features I’m missing, and how much boilerplate code I need to write.

One day, I have decided that I need to move on, so I started a new session in Leetcode and switched the compiler to Kotlin. I solved just a few easy problems, but I already feel that I have something to share.

Intro

So you are writing some CPU-intensive code in Python and really trying to find ways out of its single-threaded prison. You might be looking towards Numba's "nopython parallel" mode, you might be using forked processes with multiprocessing, you might be writing microservices with database-like coordinators, or even writing your own multithreaded programs in C/C++ just like creators of TensorFlow did.

In this article I'm describing a rationale for my pet project where I try to implement facilities for general purpose multitasking to be used in a form of simple python code, employing a database-like approach for interpreters communication, while keeping the GIL (Global Interpreter Lock) and trying to be as pythonic as possible.

It could also become handy in the light of upcoming multiple interpreters support in CPython.

As far as I know, nobody came that far in trying to provide Python program with native shareable storage. The last closest attempt was Python Object Sharing which is pretty much dead by now. I hope my project won't meet the same fate.