What is the Future of Programming Languages?

What is the Future of Programming Languages

Future of Programming

What’s wrong with the current ‘future of technology’ hype is what’s right with it. It’s the same old story you hear repeatedly in today’s news and social media – the more things change, the worse they get, then that’s how you should prepare your mind for its inevitable downfall.

There is a lot to be afraid of when talking about this topic because new technology can often cause a sudden increase in complexity and confusion. That’s why we keep returning to the famous quote by Bill Gates from his book The Road Ahead:

A better approach would be the opposite. Think about the problem from two slightly different perspectives. What if no one understands or can explain something? Or the whole thing from two slightly different viewpoints? … [Then] look at the world from two different points and consider two slightly different solutions. At some point, both could be viable…

The Same Goes With This Question About Future Programming Languages:

There are innumerable articles and conversations on the internet debating future programming languages that will probably dominate the next decade’s IT landscape. However, for programming experts, the core of discussions about futurism is about something other than the best language or framework to develop it. But about the future.

Let’s look closer to understanding where these new choices will lead us. First, discuss some popular programming languages already underway or being developed. Then, we’ll see what makes them really good at their tasks over the coming years and what they must have to thrive in the era where everything becomes more extensive and complex. Finally, only some candidates seem capable of disrupting the established programming systems (or so-called ecosystems) set up over the past several decades.

  1. C# vs. Go vs. Rust vs. Python vs. Scala vs. Javascript vs. TypeScript vs. VueJS vs. React
  2. C# vs. Go vs. Rust vs. Python vs. Scala vs. javascript vs. type-scala vs. Vuejs vs. react
  3. C# vs. Go vs. Rust vs. Python vs. Scala vs. Vuejs vs. react

What is the Future of Programming Languages?

We’ve seen that many developers are eager to learn the latest technologies since they offer excellent benefits and simplify the process from code to code. For instance, JavaScript has always been a dominant programming language among most companies. And while this is true, it doesn’t necessarily translate into an automatic place in the programmer’s heart.

Nowadays, people have started to pay attention to tools such as Node.js instead of C++ or Java, a time-consuming task that used to bring them only frustration but now also opens doors to several modern technologies. As soon as those new options become available, everyone is curious whether we can use all of them.

Unfortunately, very little comes in handy when developing applications in those languages. Not only that but using multiple similar libraries complicates the development process. This leads to more errors and bugs all the time. So, what makes programming faster in Go, Python, Scala, or VueJS than with older languages like C++? The answer lies in these languages themselves.

They are strongly typed languages offering great power of abstraction and simplicity without sacrificing any performance or correctness. In the same way, Python, Scala, and R are all strongly typed, but only Python and Scala offer a rich list of particular types. These types enable a higher degree of developer productivity, making it possible to find answers and create a program in a particular structure.

In addition, Python and Go have been designed to support other frameworks that don’t need to be installed separately. So, as long as you know how to access the framework without writing separate programs, you’re good to go.

Future of Coding:

Future of Coding

On the other hand, Python and Go are strongly typed languages, too. The fact that Go is almost single-threaded, while Django is multi-threaded. Both are full of features that help you build powerful apps. On the contrary, Python can only exist with the Jupyter Notebook, which automatically makes all sorts of notebooks. However, it still needs to be a full-fledged programming language.

The same thing applies to R; R is single-threaded but offers a wide range of capabilities to write Pythonic scripts, which is quite beneficial. While I’m writing this article, I wanted to see if this article is still relevant after the release of Pytorch last year. It works perfectly and looks fantastic! So why do I still feel the urge to return to Python and explore the possibilities of machine learning that Pytorch offers? Because there are much fewer reasons to resist it anymore.

One of the main advantages of dealing with a completely object-oriented language like Python is that you can quickly call predefined functions, data structures, and operations. Also, you can add more layers and modify existing code, so extending it into another application is more accessible. It is easy to adapt existing examples to your needs and use them for your projects. Another advantage is that with Python, it’s straightforward to read the code when you need to figure out how to do what’s inside. If you want to read it at once, you can do it in Python, and if you prefer to see how others write code, you can.

On the other hand, it takes a lot of work to maintain the current state of code in Python. With it, you must constantly check your version manually each time you commit a new line. But, thanks to our community, Python can be modified and extended occasionally. To conclude, why do we still focus on Python when it promises to provide impressive power of usability and a high level of functionality over the whole lifetime of the platform?

Programming Language of the Future:

That may be why we still use it so well. Perhaps the reason is that Python and other prominent languages tend to teach programmers to master basic concepts over time. In contrast, some small or medium-sized languages need help thinking about complex ideas they can’t express well in less time. So, in the end, they’ll always need more advanced features.

Choosing between Rust and Go, which are among the newest paradigms of the programming language industry! Choosing between two great candidates is pretty complicated, and you can think of many factors: language itself and its ecosystem, pros and cons, community, etc.

There are lots of reasons to choose Go and Rust over Python and Scala, which are currently dominating the market; I can name a few: Go uses a functional style, Rust is a concurrent language, and it’s easier to write in it and improve it for your needs and those of your team. Nevertheless, Python will remain a leader in the software industry for a long time. Moreover, it remains a mature environment for much research work. It’s not the case with Rust or Goes.

Rust vs. Go — Comparing Different Paradigms of the Software World

As I said earlier, it’s challenging to state which candidates are the best, but both can be considered equal or superior. After all, the choice doesn’t matter entirely; Rust is a general-purpose programming language, whereas Go doesn’t belong to the narrow domain of computer science. It belongs to another class on this scale: system scripting languages.

A system script is a piece of code that can help you accomplish certain goals while running it in a specific runtime environment. Some classic systems scripting languages are Shell Script, PowerShell, and MSP, whereas Go and Rust are free. Let’s compare Rust and Go and see which seems preferable to all of us.

A Typical Example of a Code:

I’ve chosen the above example that executes commands in Rust. Since this code can execute on various OS installations, we will see the differences between Linux, Mac, and Windows. Firstly, let me tell you this information about Rust and Go:

A Typical Example of a Code

Rust is a standard library of dynamic memory allocators that supports low-latency communication like concurrency.

Go runtime environments comprise various operating system drivers and standard drivers. This can affect the speed of execution due to a combination of different hardware components that aren’t part of CPU cores. Rust and Go are both compiled at different times and platforms. The significant difference is that Rust compiles to a human-friendly binary while Go compiles to garbage by default to keep the platform environment safe.

Rust compiler is distributed and includes three binaries: binaries, source files, and binaries. Source files contain source code for the target OS (OS X and macOS). When creating the source files in Rust, you write a unique file called.RC (or.rs if you want to write some additional boilerplate code for an existing project), which is basically Rust’s syntax.

To compile for Go, you must install go-to-market dependencies via Crates. Go and add go-to-market dependencies through get (and its equivalents of some third-party crates). It’s not so easy to run Go on a regular Windows host, so when coding in Rust, you install Docker in every OS you want to use, and it’s easy to run Python.

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