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Essay: Programming languages and their uses

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In general, there are 256 of programming languages exist in the programming world. Programming languages are classified in many ways. The most commonly used programming languages are Hypertext Markup Language (HTML), Java and Php. The first most commonly used programming language is Hypertext Markup Language, or commonly known as HTML. HTML is the standard mark-up language used to create web pages. According to Shanon (2007), HTML is a language created for computer to read human command to develop websites. Human can view the websites by connecting to the Internet. HTML started as an easy way to transfer data between computers over the Internet. The earlier objective of designing HTML is for scientists and researches that do not have any experience in publishing articles or journals of their researches. In 1980’s, HTML was proposed and prototyped as ‘ENQUIRE’ by Tim Berners-Lee, a contractor at CERN Inc., for researches to share their research documents over the Internet (Wikipedia, 2015). HTML codes are written in the form of HTML elements consisting of tags enclosed in angle brackets. Some elements that are important in writing codes for HTML is the paragraph tags, the header tags, the image tags, the hypertext reference tags and the bold and italics tags. Each HTML tag describes different document content such as the body of the document, paragraph in the document, the title, links, header and footer, and others. In designing a website, user interface is the most important thing to be considered. This is because user interface is the first thing human sees when they open the website. In complementary of designing the website, HTML is coded along with Cascading Style Sheet (CSS). CSS controls the layout of the interfaces in the website while HTML provides the information displayed in it. In programming world, the line codes are called syntax. The example of HTML syntax is as shown in Table 1. Even though HTML is coded for websites that can be viewed by connecting to the Internet, ‘coding it can be done offline by saving it in the computer and later transfer the files onto the web’ (Shanon, 2007). There are many types of HTML deliverables such as Hypertext Transfer Protocol to send the HTML documents from the web servers to web browsers, HTML e-Mail and HTML Application. Over the time, HTML had gained acceptance through the world of Internet quickly. HTML version 4.0 or HTML4 and HTML version 5 or HTML5 was developed to enhance the websites. Some ‘What You See Is What You Get’ (WYSIWYG) editors (Rohde, n.d.), were developed so that user can get whatever appears in the HTML document using a graphical user interface or GUI. Programmers usually combine HTML, CSS, PHP and JavaScript to create a dynamic websites which are more interesting for users. The second type of most commonly used programming languages is Java language. Java language is currently one of the most popular programming languages being used. Java language is an object-oriented programming language was developed by James Gosling in 1995 at Sun Microsystems that can be run on many different operating systems (Wikipedia, 2015). It is also known as high-level language because it is easier for humans to read and write the command structures. It also helps programmers to write the computer instruction using English commands, rather than write in numeric code. There are a lot of applications and websites that working on Java application, such as to connect a laptop or desktop to data center and from mobile phone to the internet and so on. These applications are called applets. The applets can runs in websites (Arnold, 2005). Java programming is designed to create the functions as C++ programming language but with much simpler understanding and easy to learn and use. There are few things needed to codes Java programming. The Java Runtime Environment (JRE) is the package that consists of the Java Virtual Machine (JVM), Java platform core classes, and supporting Java platform libraries. To run Java in the web browser, the JRE will be needed. Java Virtual Machine or JVM helps Java applications to run by compiling the ‘bytecodes’ into a workable codes as an another option to understand one instruction at a time; However, to run Java applets in browser, the JRE need to be installed along with the Java Plug-in software. According to Rouse (2015), ‘Java applets will run on almost any operating system without requiring recompilation and Java is generally regarded as the most strategic language in which to develop applications for the Web because it has no specific operating system extensions’. There are few major characteristics of Java. One of them is ‘the programs created are portable in a network’ (Rouse, 2015). It means that any programs created using Java programming can be run in the network on a server as long as the server has a Java virtual machine. Another characteristic is ‘the code is robust, which means unlike other languages, the Java objects can contain no references to data external to themselves or other known objects’ (Rouse, 2015). The objects inside the code have the same traits or inherit the traits of other objects by being a part of the object class (Rouse, 2015). Apart from that, java programming has the Java applet that was designed to make the programming run fast when executed at the client or server (Rouse, 2015). Besides that, Java is open source software, which means that this software is free to download. Like any programming language, Java language also has its own structure and syntax rules. Once a program has been written, the high-level instruction will be translated into a numeric code which is the computer can understand the instruction and execute the commands. Table 1 shows the example of differences structure to write the Java syntax compared to other programming languages syntax. However, there is a thing called JavaScript that people always confused with Java. Even though the name consists of the ‘Java’ word, but JavaScript is not Java programming. JavaScript is easier to learn than Java and it requires higher level of understanding but it does not have the Java mobility and ‘bytecode’ speed. The third most commonly used programming language that used in developing a program is Hypertext Preprocessor (PHP). PHP is suitable used in web development and also can be embedded into HTML. Besides, PHP scripts are usually used in three main areas which are server-side scripting, command line scripting and writing desktop applications. Usually, PHP used to read data and information from databases, and to add or update the databases content. A single PHP template can be written to retrieve and display the databases records. PHP is a language developed by Rasmas Lerdorf which originally an abbreviation of ‘Personal Home Page (tools)’ (Motive Glossary, 2004). PHP is a recursive programming where the command can be used over and over again to gain data. Before this, this language has been said that has been widely used in server-side scripting (Motive Glossary, 2004), which is can do anything that are related to any other computer graphic image (CGI) program likes collect data, generate dynamic page content or send and receive cookies. This language can be used in any operating systems, including Linux, Microsoft Windows, Mac OS X, and RISC OS. Each of the programming language has its function and same goes with PHP language which are can output images, form a server-side cache for content and easily output any text such as XHTML and XML file. Example code of PHP language that used in developing program can be seen in Table 1. PHP code can be inserted into the HTML webpage because it is an HTML-embedded web scripting language. When PHP page is opened, the PHP code is read from the located page by the server. The results from the PHP functions on the page usually read as HTML codes that can be read by the browser. This is because PHP codes do not have its own interface and were transformed into HTML codes first before the page is loaded and users cannot view the PHP codes on a page without a user interface from the HTML. The reason is to make the PHP page secure to access databases and other secure information. In conclusion, Hypertext Markup Language, Java and Hypertext Preprocessor are considered as the most commonly used programming language in programming world because it is used in creating websites that are essential to our daily lives nowadays.

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What is Python? Executive Summary

Essays on programming I think about a lot

Every so often I read an essay that I end up thinking about, and citing in conversation, over and over again.

Here’s my index of all the ones of those I can remember! I’ll try to keep it up to date as I think of more.

There's a lot in here! If you'd like, I can email you one essay per week, so you have more time to digest each one:

Nelson Elhage, Computers can be understood . The attitude embodied in this essay is one of the things that has made the biggest difference to my effectiveness as an engineer:

I approach software with a deep-seated belief that computers and software systems can be understood. … In some ways, this belief feels radical today. Modern software and hardware systems contain almost unimaginable complexity amongst many distinct layers, each building atop each other. … In the face of this complexity, it’s easy to assume that there’s just too much to learn, and to adopt the mental shorthand that the systems we work with are best treated as black boxes, not to be understood in any detail. I argue against that approach. You will never understand every detail of the implementation of every level on that stack; but you can understand all of them to some level of abstraction, and any specific layer to essentially any depth necessary for any purpose.

Dan McKinley, Choose Boring Technology . When people ask me how we make technical decisions at Wave, I send them this essay. It’s probably saved me more heartbreak and regret than any other:

Let’s say every company gets about three innovation tokens. You can spend these however you want, but the supply is fixed for a long while. You might get a few more after you achieve a certain level of stability and maturity, but the general tendency is to overestimate the contents of your wallet. Clearly this model is approximate, but I think it helps. If you choose to write your website in NodeJS, you just spent one of your innovation tokens. If you choose to use MongoDB, you just spent one of your innovation tokens. If you choose to use service discovery tech that’s existed for a year or less, you just spent one of your innovation tokens. If you choose to write your own database, oh god, you’re in trouble.

Sandy Metz, The Wrong Abstraction . This essay convinced me that “don’t repeat yourself” (DRY) isn’t a good motto. It’s okay advice, but as Metz points out, if you don’t choose the right interface boundaries when DRYing up, the resulting abstraction can quickly become unmaintainable:

Time passes. A new requirement appears for which the current abstraction is almost perfect. Programmer B gets tasked to implement this requirement. Programmer B feels honor-bound to retain the existing abstraction, but since isn’t exactly the same for every case, they alter the code to take a parameter…. … Loop until code becomes incomprehensible. You appear in the story about here, and your life takes a dramatic turn for the worse.

Patrick McKenzie, Falsehoods Programmers Believe About Names . When programming, it’s helpful to think in terms of “invariants,” i.e., properties that we assume will always be true. I think of this essay as the ultimate reminder that reality has no invariants :

People’s names are assigned at birth. OK, maybe not at birth, but at least pretty close to birth. Alright, alright, within a year or so of birth. Five years? You’re kidding me, right?

Thomas Ptacek, The Hiring Post . This essay inspired me to put a lot of effort into Wave’s work-sample interview, and the payoff was huge—we hired a much stronger team, much more quickly, than I expected to be able to. It’s also a good reminder that most things that most people do make no sense:

Nothing in Alex’s background offered a hint that this would happen. He had Walter White’s resume, but Heisenberg’s aptitude. None of us saw it coming. My name is Thomas Ptacek and I endorse this terrible pun. Alex was the one who nonced. A few years ago, Matasano couldn’t have hired Alex, because we relied on interviews and resumes to hire. Then we made some changes, and became a machine that spotted and recruited people like Alex: line of business .NET developers at insurance companies who pulled Rails core CVEs out of their first hour looking at the code. Sysadmins who hardware-reversed assembly firmware for phone chipsets. Epiphany: the talent is out there, but you can’t find it on a resume. Our field selects engineers using a process that is worse than reading chicken entrails. Like interviews, poultry intestine has little to tell you about whether to hire someone. But they’re a more pleasant eating experience than a lunch interview.

Gergely Orosz, The Product-Minded Engineer . I send this essay to coworkers all the time—it describes extremely well what traits will help you succeed as an engineer at a startup:

Proactive with product ideas/opinions • Interest in the business, user behavior and data on this • Curiosity and a keen interest in “why?” • Strong communicators and great relationships with non-engineers • Offering product/engineering tradeoffs upfront • Pragmatic handling of edge cases • Quick product validation cycles • End-to-end product feature ownership • Strong product instincts through repeated cycles of learning

tef, Write code that is easy to delete, not easy to extend . The Wrong Abstraction argues that reusable code, unless carefully designed, becomes unmaintainable. tef takes the logical next step: design for disposability, not maintainability. This essay gave me lots of useful mental models for evaluating software designs.

If we see ‘lines of code’ as ‘lines spent’, then when we delete lines of code, we are lowering the cost of maintenance. Instead of building re-usable software, we should try to build disposable software.

Business logic is code characterised by a never ending series of edge cases and quick and dirty hacks. This is fine. I am ok with this. Other styles like ‘game code’, or ‘founder code’ are the same thing: cutting corners to save a considerable amount of time. The reason? Sometimes it’s easier to delete one big mistake than try to delete 18 smaller interleaved mistakes. A lot of programming is exploratory, and it’s quicker to get it wrong a few times and iterate than think to get it right first time.

tef also wrote a follow-up, Repeat yourself, do more than one thing, and rewrite everything , that he thinks makes the same points more clearly—though I prefer the original because “easy to delete” is a unifying principle that made the essay hang together really well.

Joel Spolsky, The Law of Leaky Abstractions . Old, but still extremely influential—“where and how does this abstraction leak” is one of the main lenses I use to evaluate designs:

Back to TCP. Earlier for the sake of simplicity I told a little fib, and some of you have steam coming out of your ears by now because this fib is driving you crazy. I said that TCP guarantees that your message will arrive. It doesn’t, actually. If your pet snake has chewed through the network cable leading to your computer, and no IP packets can get through, then TCP can’t do anything about it and your message doesn’t arrive. If you were curt with the system administrators in your company and they punished you by plugging you into an overloaded hub, only some of your IP packets will get through, and TCP will work, but everything will be really slow. This is what I call a leaky abstraction. TCP attempts to provide a complete abstraction of an underlying unreliable network, but sometimes, the network leaks through the abstraction and you feel the things that the abstraction can’t quite protect you from. This is but one example of what I’ve dubbed the Law of Leaky Abstractions: All non-trivial abstractions, to some degree, are leaky. Abstractions fail. Sometimes a little, sometimes a lot. There’s leakage. Things go wrong. It happens all over the place when you have abstractions. Here are some examples.

Reflections on software performance by Nelson Elhage, the only author of two different essays in this list! Nelson’s ideas helped crystallize my philosophy of tool design, and contributed to my views on impatience .

It’s probably fairly intuitive that users prefer faster software, and will have a better experience performing a given task if the tools are faster rather than slower. What is perhaps less apparent is that having faster tools changes how users use a tool or perform a task. Users almost always have multiple strategies available to pursue a goal — including deciding to work on something else entirely — and they will choose to use faster tools more and more frequently. Fast tools don’t just allow users to accomplish tasks faster; they allow users to accomplish entirely new types of tasks, in entirely new ways. I’ve seen this phenomenon clearly while working on both Sorbet and Livegrep…

Brandur Leach’s series on using databases to ensure correct edge-case behavior: Building Robust Systems with ACID and Constraints , Using Atomic Transactions to Power an Idempotent API , Transactionally Staged Job Drains in Postgres , Implementing Stripe-like Idempotency Keys in Postgres .

Normally, article titles ending with “in [technology]” are a bad sign, but not so for Brandur’s. Even if you’ve never used Postgres, the examples showing how to lean on relational databases to enforce correctness will be revelatory.

I want to convince you that ACID databases are one of the most important tools in existence for ensuring maintainability and data correctness in big production systems. Lets start by digging into each of their namesake guarantees.

There’s a surprising symmetry between an HTTP request and a database’s transaction. Just like the transaction, an HTTP request is a transactional unit of work – it’s got a clear beginning, end, and result. The client generally expects a request to execute atomically and will behave as if it will (although that of course varies based on implementation). Here we’ll look at an example service to see how HTTP requests and transactions apply nicely to one another.

In APIs idempotency is a powerful concept. An idempotent endpoint is one that can be called any number of times while guaranteeing that the side effects will occur only once. In a messy world where clients and servers that may occasionally crash or have their connections drop partway through a request, it’s a huge help in making systems more robust to failure. Clients that are uncertain whether a request succeeded or failed can simply keep retrying it until they get a definitive response.

Jeff Hodges, Notes on Distributed Systems for Young Bloods . An amazing set of guardrails for doing reasonable things with distributed systems (and note that, though you might be able to get away with ignoring it for a while, any app that uses the network is a distributed system). Many points would individually qualify for this list if they were their own article—I reread it periodically and always notice new advice that I should have paid more attention to.

Distributed systems are different because they fail often • Implement backpressure throughout your system • Find ways to be partially available • Use percentiles, not averages • Learn to estimate your capacity • Feature flags are how infrastructure is rolled out • Choose id spaces wisely • Writing cached data back to persistent storage is bad • Extract services.

J.H. Saltzer, D.P. Reed and D.D. Clark, End-to-End Arguments in System Design . Another classic. The end-to-end principle has helped me make a lot of designs much simpler.

This paper presents a design principle that helps guide placement of functions among the modules of a distributed computer system. The principle, called the end-to-end argument, suggests that functions placed at low levels of a system may be redundant or of little value when compared with the cost of providing them at that low level. Examples discussed in the paper include bit error recovery, security using encryption, duplicate message suppression, recovery from system crashes, and delivery acknowledgement. Low level mechanisms to support these functions are justified only as performance enhancements.

Bret Victor, Inventing on Principle :

I’ve spent a lot of time over the years making creative tools, using creative tools, thinking about them a lot, and here’s something I’ve come to believe: Creators need an immediate connection to what they’re creating.

I can’t really excerpt any of the actual demos, which are the good part. Instead I’ll just endorse it: this talk dramatically, and productively, raised my bar for what I think programming tools (and tools in general) can be. Watch it and be amazed.

Post the essays you keep returning to in the comments!

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10x (engineer, context) pairs

What i’ve been doing instead of writing, my favorite essays of life advice.

format comments in markdown .

Quite a few of these are on my list, here’s some others that I keep returning to every so often:

https://www.stilldrinking.org/programming-sucks
https://medium.com/@nicolopigna/this-is-not-the-dry-you-are-looking-for-a316ed3f445f
https://sysadvent.blogspot.com/2019/12/day-21-being-kind-to-3am-you.html
https://jeffknupp.com/blog/2014/05/30/you-need-to-start-a-whizbang-project-immediately/

Great list! Some essays I end up returning to are:

https://www.destroyallsoftware.com/compendium/software-structure?share_key=6fb5f711cae5a4e6
https://caseymuratori.com/blog_0015

These are conference talks on youtube, not blog posts, but here’s a few of the ones I often end up sending to collaborators as addenda to discussions:

Don Reinertsen - Second Generation Lean Product Development Flow

Joshua Bloch

The Language of the System - Rich Hickey

Some posts:

https://speakerdeck.com/vjeux/react-css-in-js - diagnosis of problems with CSS (not because of React)

https://zachholman.com/talk/firing-people

Especially for fault-tolerant systems, “why restart helps” really opened my eyes:

https://ferd.ca/the-zen-of-erlang.html

Oh, I forgot: http://web.mit.edu/2.75/resources/random/How%20Complex%20Systems%20Fail.pdf

Oldie but a goodie:

https://www.developerdotstar.com/mag/articles/reeves_design_main.html

+1 for that one

This is a great list. If i could make one addition it would have to be Rich Hickey’s “simple made easy”: https://www.youtube.com/watch?v=oytL881p-nQ

I was once working with a newly formed (4 person) team on a large and complex project under a tight deadline. For a while we weren’t seeing eye to eye on many of the key decisions we made. Watching and reflecting on this talk gave us a shared aim and, perhaps even more importantly, a shared language for making choices that would reduce the complexity of our system. It is a gift that keeps on giving.

Another one that belongs on this list: https://www.kitchensoap.com/2012/10/25/on-being-a-senior-engineer/

A couple of my favorites:

https://nedbatchelder.com/text/deleting-code.html
https://www.joelonsoftware.com/2002/01/23/rub-a-dub-dub/

Out of the Tar Pit. https://github.com/papers-we-love/papers-we-love/blob/master/design/out-of-the-tar-pit.pdf

I’d like to nominate another of Nelson Elhage’s posts:

https://blog.nelhage.com/2016/03/design-for-testability

This has had more direct influence on my day-to-day code writing than anything else. (Also, his other writing on testing is great.)

As another commenter mentioned conference talks, Bryan Cantrill on debugging is important—it meshes well with Nelson’s Computer can be understood . ( https://www.slideshare.net/bcantrill/debugging-microservices-in-production )

A fave of mine: Clojure: Programming with Hand Tools https://www.youtube.com/watch?v=ShEez0JkOFw

Some essays I like:

Science and the compulsive programmer by Joseph Weizenbaum - written in 1976, but the described phenomena of a compulsive programmer still exists and may be relevant to many: https://www.sac.edu/academicprogs/business/computerscience/pages/hester_james/hacker.htm

https://www.mit.edu/~xela/tao.html - Tao of Programming - not sure if you can classify as an essay, but it is classic!

https://norvig.com/21-days.html - Teach Yourself Programming in Ten Years by Peter Novig - a great essay on how to master programming and why reading books like “Learn X in Y days” won’t be of much help. I recommend it to all beginners

Reginald Braithwaite, Golf is a good program spoiled - http://weblog.raganwald.com/2007/12/golf-is-good-program-spoiled.html . Raganwald has more great essays on his weblog, I just like this one the most.

The link of the last one ( https://vimeo.com/36579366 ) is broken. You may want to update it.

Paul Graham, “Maker’s Schedule, Manager’s Schedule " https://paulgraham.com/makersschedule.html

I keep thinking about those too:

https://www.teamten.com/lawrence/programming/write-code-top-down.html

https://rubyonrails.org/doctrine#provide-sharp-knives

Topics for Essays on Programming Languages: Top 7 Options

Java Platform Editions and Their Peculiarities

Python: a favorite of developers, javascript: the backbone of the web, typescript: narrowing down your topic, the present and future of php, how to use c++ for game development, how to have fun when learning swift.

‍ Delving into the realm of programming languages offers a unique lens through which we can explore the evolution of technology and its impact on our world. From the foundational assembly languages to today's sophisticated, high-level languages, each one has shaped the digital landscape.

Whether you're a student seeking a deep dive into this subject or a tech enthusiast eager to articulate your insights, finding the right topic can set the stage for a compelling exploration.

This article aims to guide you through selecting an engaging topic, offering seven top options for essays on programming languages that promise to spark curiosity and provoke thoughtful analysis.

"If you’re a newbie when it comes to exploring Java programming language, it’s best to start with the basics not to overcomplicate your assignment. Of course, the most obvious option is to write a descriptive essay highlighting the features of Java platform editions:

- Java Standard Edition (Java SE). It allows one to develop Java applications and ensures the essential functionality of the programming language;

- Java Enterprise Edition (Java EE). It's an extension of the previous edition for developing and running enterprise applications;

- Java Micro Edition serves for running applications on small and mobile devices.

You can explain the purpose of each edition and the key components to inform and give value to the readers. Or you can go in-depth and opt for a compare and contrast essay to show your understanding of the subject and apply critical thinking skills."

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You probably already know that this programming language is widely used globally.

Python is perfect for beginners who want to master programming because of the simple syntax that resembles English. Besides, look at the opportunities it opens:

- developing web applications, of course;

- building command-line interface (CLI) for routine tasks automation;

- creating graphical user interfaces (GUIs);

- using helpful tools and frameworks to streamline game development;

- facilitating data science and machine learning;

- analyzing and visualizing big data.

All these points can become solid ideas for your essay. For instance, you can use the list above as the basis for argumentation why one should learn Python. After doing your research, you’ll find plenty of evidence to convince your audience.

And if you’d like to spice things up, another option is to add your own perspective to the debate on which language is better: Python or JavaScript.

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"This programming language is no less popular than the previous one. It’s even considered easier to learn for a newbie. If you master it, you’ll gain a valuable skill that can help you start a lucrative career. Just think about it:

- JavaScript is used by almost all websites;

with it, you can develop native apps for iOS and Android;

- it allows you to grasp functional, object-oriented, and imperative programming;

you can create jaw-dropping visual effects for web pages and games;

- it’s also possible to work with AI, analyze data, and find bugs.

So, drawing on the universality of JavaScript and the career opportunities it brings can become a non-trivial topic for your essay.

Hint: look up job descriptions demanding the knowledge of JavaScript. Then, compare salaries to provide helpful up-to-date information. Your professor should be impressed with your approach to writing."

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"Yes, you guessed right - this programming language kind of strengthens the power of JavaScript. It allows developers to handle large-scale projects. TypeScript enables object-oriented programming and static typing; it has a single open-source compiler.

If you want your essay to stand out and show a deeper understanding of the programming basics, the best way is to go for a narrow topic. In other words, niche your writing by focusing on the features of TypeScript.

For example, begin with the types:

- Tuple, etc.

Having elaborated on how they work, proceed to explore the peculiarities, pros, and cons of TypeScript. Explaining when and why one should opt for it as opposed to JavaScript also won't hurt.

Here, you can dive into details as much as you want, but remember to give examples and use logical reasoning to prove your claims."

"This language intended for server-side web development has been around for a really long time: almost 80% of websites still use it.

But there’s a stereotype that PHP can’t compete with other modern programming languages. Thus, the debates on whether PHP is still relevant do not stop. Why not use this fact to compose a top-notch analytical essay?

Here’s how you can do it:

1. research and gather information, especially statistics from credible sources;

2. analyze how popular the programming language is and note the demand for PHP developers;

3. provide an unbiased overview of its perks and drawbacks and support it with examples;

4. identify the trends of using PHP in web development;

5. make predictions about the popularity of PHP over the next few years.

If you put enough effort into crafting your essay, it’ll not only deserve an “A” but will also become a guide for your peers interested in programming.

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C++ is a universal programming language considered most suitable for developing various large-scale applications. Yet, it has gained the most popularity among video game developers as C++ is easier to apply to hardware programming than other languages.

Given that the industry of video games is fast-growing, you can write a paper on C++ programming in this sphere. And the simplest approach to take is offering advice to beginners.

For example, review the tools for C++ game development:

- GameSalad;

- Lumberyard;

- Unreal Engine;

- GDevelop;

- GameMaker Studio;

- Unity, among others.

There are plenty of resources to use while working on your essay, and you can create your top list for new game developers. Be sure to examine the tools’ features and customer feedback to provide truthful information for your readers.

Facing hurdles with your C++ assignments? Click on " C++ homework help " and discover how Studyfy can guide you to success!

"Swift was created for iOS applications development, and people argue that this programming language is the easiest to learn. So, how about checking whether this statement is true or false?

The creators of Swift aimed to make it as convenient and efficient as possible. Let’s see why programmers love it:

- first of all, because it’s compatible with Apple devices;

- the memory management feature helps set priorities for introducing new functionality;

- if an error occurs, recovering is no problem;

- the language boasts a concise code and is pretty fast to learn;

- you can get advice from the dedicated Swift community if necessary.

Thus, knowing all these benefits, you can build your arguments in favor of learning Swift. But we also recommend reflecting on the opposite point of view to present the whole picture in your essay. And if you want to dig deeper, opt for a comparison with other programming languages."

Java is the best programming language Essay

To find inspiration for your paper and overcome writer’s block
As a source of information (ensure proper referencing)
As a template for you assignment

I consider Java as the best programming language due to its small language vocabulary, portability and simplicity. Java has a small and regular vocabulary; a programmer can easily master and grasp .Any computer program written in Java can run and execute on any operating system hence compatibility with all operating systems.

Java was developed and released in 1995, much later after C and C++. As such it tends to solve some of the shortcomings cited in C and C++.For instance, it uses Javadoc, a documenting system that develops a systematic and organized method for documenting codes (Pawlan 1999).

It also uses Byte code verifier which enhances correctness and security of the compiled code hence reducing the number of runtime checks. This implies that with the knowledge of Java, one can comfortably take up programming tasks in C and C++ or any other language. Java is also purely object oriented, easy to interpret and has high execution performance. This makes it simple and easier to work with. For this reason, Java is sometimes referred to as “a powerful yet easy to learn and work with language” (Pawlan 1999).

Java is the most widely used programming language by web developers for both simple complex programming tasks. Due to its much simplified user platform, most people tend to have a general notion that Java cannot be used to create much complicated web applications due to its simple platform.

This is not true since most programmers use Java to write complex programs in a simple way easy that is to follow and interpret. Java was developed to write more complex programs in a simpler language. Even the most complex and sophisticated programs look so simple in Java making it hard to realize their complexity (Lindholm & Yellin 1999).

It is considered flexible; due to incremental development in terms of object- oriented features, coupled with Java’s simplicity, it is possible to rapidly develop applications and easily change them at will. This offers much freedom to the programmer to make changes to the program whenever he deems fit to do so.

It has the ability to develop robust and secure programs. It is also used to break down and portably distribute some complex programs. These features, together with its simple user platform, have made it find extensive application in programming tasks.

Java is the best in compilation and execution speed. Initially, most developers avoided using Java in more complex tasks due to its slow speed. Much effort was put in place to improve its speed and as a result, today, Java speed is comparable to C or C++ for equivalent tasks. With this speed, plus its improved performance characteristics, it is the best programming language (Gosling & Joy 2005).

Consequently, in modern programming, speed does not majorly rely on the language used, but also on the memory card capacity and processor speed. Java therefore, enables a programmer to write codes which compile fast and have few runtime checks using Java than in any other programming language. In this line, Java does not only offer a fast compilation process, but also better programs, with few errors and which can be run on any operating system.

Basing on speed, simplicity and the extent of application; Java no doubt, is the best programming language. It is so simple to use, with a user friendly language vocabulary and so fast in compilation these have made it the widely used language in programming. Its improved features regarding the aforementioned aspects make it more preferable than any other programming language. Hence, Java is the best programming language.

Works Cited

Gosling, James and Joy, Bill . The Java Language Specification . Boston: Addison – Wesley. 2005. Print.

Lindholm, Tim and Yellin, Frank. The Java Virtual Machine Specification . Boston: Addison-Wesley. 1999. Print.

Pawlan, Monica. Essentials of the Java Programming Language .1999. New York: McGraw. Print.

Contribution of the Factors to the Internet Growth
Software Solutions in Web Design
Anthropology Issues: Extinct Hominid Java Man
Methods Used to Develop Java Applications for Government Projects
Combining Programming Languages C++ and Python
Levels of Computer Science and Programming Languages
Hyper Text Markup Language (HTML)
Reasons why developing software for wireless devices is challenging
Ethical analysis of illegal downloading and the effects of it
Technology Impact on Human Civilization
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Learn How to Write a Compelling Essay with Python Programming Language

In today’s digital age, programming languages have extended their reach beyond traditional software development and into various domains. Python, a versatile and powerful programming language, has found its way into the realm of writing essays. This article aims to explore the intersection of Python and essay writing, addressing questions such as whether Python can write an essay, the characteristics of a Python programming language essay, the debate between Java and Python, tips for writing good code in Python, the best AI tools for essay writing, and how to achieve success in Python programming.

Can Python Write an Essay?

Python, being a programming language, is primarily designed to process and manipulate data, automate tasks, and build applications. While Python can assist in automating certain aspects of the essay-writing process, it is important to note that it cannot independently generate an entire essay from scratch. The creativity and critical thinking required for crafting an essay are inherent to human intelligence and are yet to be replicated by machines.

What is a Python Programming Language Essay?

A Python programming language essay refers to an essay that delves into the intricacies and applications of Python programming. It typically covers topics related to Python syntax, libraries, frameworks, and various use cases. Python essays serve as valuable resources for learners, enabling them to understand the language’s concepts and explore its potential.

Why Java is Better than Python?

The debate between Java and Python has long been a topic of discussion among developers. While both languages have their strengths and weaknesses, it is essential to consider the context and purpose of their usage. Java is known for its performance, robustness, and wide range of applications, particularly in enterprise-level software development. On the other hand, Python boasts a simpler syntax, ease of use, and a vast ecosystem of libraries and frameworks, making it an ideal choice for tasks like data analysis, web development, and artificial intelligence.

Writing Good Code in Python

To write good code in Python, it is crucial to follow best practices and adhere to certain principles. Here are a few tips to help you:

1. Maintain code readability: Python emphasizes readability with its clean and concise syntax. Use meaningful variable names, comment your code, and structure it in a logical manner.

2. Follow the PEP 8 style guide: PEP 8 provides guidelines for writing Python code. Adhering to these standards ensures consistency and improves code readability across projects.

3. Utilize modular and reusable code: Break your code into functions or classes that perform specific tasks. This promotes code reusability, readability, and easier maintenance.

4. Handle exceptions gracefully: Python provides robust error handling mechanisms. Utilize try-except blocks to catch and handle exceptions, making your code more resilient.

5. Test and debug your code: Thoroughly test your code to identify and fix any issues. Utilize debugging tools and techniques to streamline the debugging process.

The Best AI for Writing Essays

Artificial intelligence (AI) has made significant strides in natural language processing, including essay writing. Some notable AI tools for generating essays include OpenAI’s GPT-3, ChatGPT, and other language models. These models can assist in generating coherent text, providing ideas, and improving language fluency. However, it is important to remember that AI-generated content should always be used as a supplement and not a replacement for human creativity and critical thinking.

How to Be Successful in Python Programming

Becoming successful in Python programming requires dedication, practice, and continuous learning. Here are some tips to help you on your journey:

1. Start with the fundamentals: Develop a strong foundation by learning the basic syntax, data types, and control structures of Python.

2. Work on projects: Apply your knowledge to real-world projects. Building practical applications helps

reinforce concepts and improves problem-solving skills.

3. Engage with the community: Join online forums, participate in coding communities, and collaborate with other Python enthusiasts. Sharing ideas and experiences can accelerate your learning process.

4. Read code: Analyze and study well-written Python code. Understanding how experienced developers structure their code and solve problems can provide valuable insights.

5. Embrace documentation and resources: Python has extensive documentation and numerous online resources. Make use of them to deepen your understanding of the language and its libraries.

Python, although unable to independently write essays, can significantly aid in the essay-writing process through automation and data processing. Understanding the characteristics of a Python programming language essay can help learners utilize these resources effectively. Additionally, while the Java versus Python debate continues, both languages have their strengths depending on the task at hand. By following best practices, utilizing AI tools wisely, and embracing a growth mindset, you can embark on a successful journey in Python programming. So, dive in, explore, and leverage the power of Python to enhance your essay writing and programming skills.

Programming Languages Essay Examples

Programming languages refer to a set of keywords, phrases, and rules that help you to communicate with a computer system. An introduction to programming languages essay shows how these languages are instructions written to perform specific tasks. The most common examples of programming/coding languages are Ruby, Perl, COBOL, ALGOL, Python, Java, C, C++, C#, JavaScript, R, and PHP.

Every essay about programming languages must also detail the common types of languages and their uses. App developers, video game developers, web designers, and control systems engineers use the knowledge of coding in their daily work. If you are curious and motivated enough, you can learn how to code.

Also, it might prove difficult to find high-quality, simplified content about programming languages as an Art student. When you write your essays in programming languages, you’ll definitely need a solid guide to complete the perfect paper. Instead of whining like other students, avail yourself of the perfect outstanding coding-related paperwork online today.

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It's hard to predict what life will be like in a hundred years. There are only a few things we can say with certainty. We know that everyone will drive flying cars, that zoning laws will be relaxed to allow buildings hundreds of stories tall, that it will be dark most of the time, and that women will all be trained in the martial arts. Here I want to zoom in on one detail of this picture. What kind of programming language will they use to write the software controlling those flying cars?

This is worth thinking about not so much because we'll actually get to use these languages as because, if we're lucky, we'll use languages on the path from this point to that.

I think that, like species, languages will form evolutionary trees, with dead-ends branching off all over. We can see this happening already. Cobol, for all its sometime popularity, does not seem to have any intellectual descendants. It is an evolutionary dead-end-- a Neanderthal language.

I predict a similar fate for Java. People sometimes send me mail saying, "How can you say that Java won't turn out to be a successful language? It's already a successful language." And I admit that it is, if you measure success by shelf space taken up by books on it (particularly individual books on it), or by the number of undergrads who believe they have to learn it to get a job. When I say Java won't turn out to be a successful language, I mean something more specific: that Java will turn out to be an evolutionary dead-end, like Cobol.

This is just a guess. I may be wrong. My point here is not to dis Java, but to raise the issue of evolutionary trees and get people asking, where on the tree is language X? The reason to ask this question isn't just so that our ghosts can say, in a hundred years, I told you so. It's because staying close to the main branches is a useful heuristic for finding languages that will be good to program in now.

At any given time, you're probably happiest on the main branches of an evolutionary tree. Even when there were still plenty of Neanderthals, it must have sucked to be one. The Cro-Magnons would have been constantly coming over and beating you up and stealing your food.

The reason I want to know what languages will be like in a hundred years is so that I know what branch of the tree to bet on now.

The evolution of languages differs from the evolution of species because branches can converge. The Fortran branch, for example, seems to be merging with the descendants of Algol. In theory this is possible for species too, but it's not likely to have happened to any bigger than a cell.

Convergence is more likely for languages partly because the space of possibilities is smaller, and partly because mutations are not random. Language designers deliberately incorporate ideas from other languages.

It's especially useful for language designers to think about where the evolution of programming languages is likely to lead, because they can steer accordingly. In that case, "stay on a main branch" becomes more than a way to choose a good language. It becomes a heuristic for making the right decisions about language design.

Any programming language can be divided into two parts: some set of fundamental operators that play the role of axioms, and the rest of the language, which could in principle be written in terms of these fundamental operators.

I think the fundamental operators are the most important factor in a language's long term survival. The rest you can change. It's like the rule that in buying a house you should consider location first of all. Everything else you can fix later, but you can't fix the location.

I think it's important not just that the axioms be well chosen, but that there be few of them. Mathematicians have always felt this way about axioms-- the fewer, the better-- and I think they're onto something.

At the very least, it has to be a useful exercise to look closely at the core of a language to see if there are any axioms that could be weeded out. I've found in my long career as a slob that cruft breeds cruft, and I've seen this happen in software as well as under beds and in the corners of rooms.

I have a hunch that the main branches of the evolutionary tree pass through the languages that have the smallest, cleanest cores. The more of a language you can write in itself, the better.

Of course, I'm making a big assumption in even asking what programming languages will be like in a hundred years. Will we even be writing programs in a hundred years? Won't we just tell computers what we want them to do?

There hasn't been a lot of progress in that department so far. My guess is that a hundred years from now people will still tell computers what to do using programs we would recognize as such. There may be tasks that we solve now by writing programs and which in a hundred years you won't have to write programs to solve, but I think there will still be a good deal of programming of the type that we do today.

It may seem presumptuous to think anyone can predict what any technology will look like in a hundred years. But remember that we already have almost fifty years of history behind us. Looking forward a hundred years is a graspable idea when we consider how slowly languages have evolved in the past fifty.

Languages evolve slowly because they're not really technologies. Languages are notation. A program is a formal description of the problem you want a computer to solve for you. So the rate of evolution in programming languages is more like the rate of evolution in mathematical notation than, say, transportation or communications. Mathematical notation does evolve, but not with the giant leaps you see in technology.

Whatever computers are made of in a hundred years, it seems safe to predict they will be much faster than they are now. If Moore's Law continues to put out, they will be 74 quintillion (73,786,976,294,838,206,464) times faster. That's kind of hard to imagine. And indeed, the most likely prediction in the speed department may be that Moore's Law will stop working. Anything that is supposed to double every eighteen months seems likely to run up against some kind of fundamental limit eventually. But I have no trouble believing that computers will be very much faster. Even if they only end up being a paltry million times faster, that should change the ground rules for programming languages substantially. Among other things, there will be more room for what would now be considered slow languages, meaning languages that don't yield very efficient code.

And yet some applications will still demand speed. Some of the problems we want to solve with computers are created by computers; for example, the rate at which you have to process video images depends on the rate at which another computer can generate them. And there is another class of problems which inherently have an unlimited capacity to soak up cycles: image rendering, cryptography, simulations.

If some applications can be increasingly inefficient while others continue to demand all the speed the hardware can deliver, faster computers will mean that languages have to cover an ever wider range of efficiencies. We've seen this happening already. Current implementations of some popular new languages are shockingly wasteful by the standards of previous decades.

This isn't just something that happens with programming languages. It's a general historical trend. As technologies improve, each generation can do things that the previous generation would have considered wasteful. People thirty years ago would be astonished at how casually we make long distance phone calls. People a hundred years ago would be even more astonished that a package would one day travel from Boston to New York via Memphis.

I can already tell you what's going to happen to all those extra cycles that faster hardware is going to give us in the next hundred years. They're nearly all going to be wasted.

I learned to program when computer power was scarce. I can remember taking all the spaces out of my Basic programs so they would fit into the memory of a 4K TRS-80. The thought of all this stupendously inefficient software burning up cycles doing the same thing over and over seems kind of gross to me. But I think my intuitions here are wrong. I'm like someone who grew up poor, and can't bear to spend money even for something important, like going to the doctor.

Some kinds of waste really are disgusting. SUVs, for example, would arguably be gross even if they ran on a fuel which would never run out and generated no pollution. SUVs are gross because they're the solution to a gross problem. (How to make minivans look more masculine.) But not all waste is bad. Now that we have the infrastructure to support it, counting the minutes of your long-distance calls starts to seem niggling. If you have the resources, it's more elegant to think of all phone calls as one kind of thing, no matter where the other person is.

There's good waste, and bad waste. I'm interested in good waste-- the kind where, by spending more, we can get simpler designs. How will we take advantage of the opportunities to waste cycles that we'll get from new, faster hardware?

The desire for speed is so deeply engrained in us, with our puny computers, that it will take a conscious effort to overcome it. In language design, we should be consciously seeking out situations where we can trade efficiency for even the smallest increase in convenience.

Most data structures exist because of speed. For example, many languages today have both strings and lists. Semantically, strings are more or less a subset of lists in which the elements are characters. So why do you need a separate data type? You don't, really. Strings only exist for efficiency. But it's lame to clutter up the semantics of the language with hacks to make programs run faster. Having strings in a language seems to be a case of premature optimization.

If we think of the core of a language as a set of axioms, surely it's gross to have additional axioms that add no expressive power, simply for the sake of efficiency. Efficiency is important, but I don't think that's the right way to get it.

The right way to solve that problem, I think, is to separate the meaning of a program from the implementation details. Instead of having both lists and strings, have just lists, with some way to give the compiler optimization advice that will allow it to lay out strings as contiguous bytes if necessary.

Since speed doesn't matter in most of a program, you won't ordinarily need to bother with this sort of micromanagement. This will be more and more true as computers get faster.

Saying less about implementation should also make programs more flexible. Specifications change while a program is being written, and this is not only inevitable, but desirable.

The word "essay" comes from the French verb "essayer", which means "to try". An essay, in the original sense, is something you write to try to figure something out. This happens in software too. I think some of the best programs were essays, in the sense that the authors didn't know when they started exactly what they were trying to write.

Lisp hackers already know about the value of being flexible with data structures. We tend to write the first version of a program so that it does everything with lists. These initial versions can be so shockingly inefficient that it takes a conscious effort not to think about what they're doing, just as, for me at least, eating a steak requires a conscious effort not to think where it came from.

What programmers in a hundred years will be looking for, most of all, is a language where you can throw together an unbelievably inefficient version 1 of a program with the least possible effort. At least, that's how we'd describe it in present-day terms. What they'll say is that they want a language that's easy to program in.

Inefficient software isn't gross. What's gross is a language that makes programmers do needless work. Wasting programmer time is the true inefficiency, not wasting machine time. This will become ever more clear as computers get faster.

I think getting rid of strings is already something we could bear to think about. We did it in , and it seems to be a win; some operations that would be awkward to describe as regular expressions can be described easily as recursive functions.

How far will this flattening of data structures go? I can think of possibilities that shock even me, with my conscientiously broadened mind. Will we get rid of arrays, for example? After all, they're just a subset of hash tables where the keys are vectors of integers. Will we replace hash tables themselves with lists?

There are more shocking prospects even than that. The Lisp that McCarthy described in 1960, for example, didn't have numbers. Logically, you don't need to have a separate notion of numbers, because you can represent them as lists: the integer n could be represented as a list of n elements. You can do math this way. It's just unbearably inefficient.

No one actually proposed implementing numbers as lists in practice. In fact, McCarthy's 1960 paper was not, at the time, intended to be implemented at all. It was a , an attempt to create a more elegant alternative to the Turing Machine. When someone did, unexpectedly, take this paper and translate it into a working Lisp interpreter, numbers certainly weren't represented as lists; they were represented in binary, as in every other language.

Could a programming language go so far as to get rid of numbers as a fundamental data type? I ask this not so much as a serious question as as a way to play chicken with the future. It's like the hypothetical case of an irresistible force meeting an immovable object-- here, an unimaginably inefficient implementation meeting unimaginably great resources. I don't see why not. The future is pretty long. If there's something we can do to decrease the number of axioms in the core language, that would seem to be the side to bet on as t approaches infinity. If the idea still seems unbearable in a hundred years, maybe it won't in a thousand.

Just to be clear about this, I'm not proposing that all numerical calculations would actually be carried out using lists. I'm proposing that the core language, prior to any additional notations about implementation, be defined this way. In practice any program that wanted to do any amount of math would probably represent numbers in binary, but this would be an optimization, not part of the core language semantics.

Another way to burn up cycles is to have many layers of software between the application and the hardware. This too is a trend we see happening already: many recent languages are compiled into byte code. Bill Woods once told me that, as a rule of thumb, each layer of interpretation costs a factor of 10 in speed. This extra cost buys you flexibility.

The very first version of Arc was an extreme case of this sort of multi-level slowness, with corresponding benefits. It was a classic "metacircular" interpreter written on top of Common Lisp, with a definite family resemblance to the eval function defined in McCarthy's original Lisp paper. The whole thing was only a couple hundred lines of code, so it was very easy to understand and change. The Common Lisp we used, CLisp, itself runs on top of a byte code interpreter. So here we had two levels of interpretation, one of them (the top one) shockingly inefficient, and the language was usable. Barely usable, I admit, but usable.

Writing software as multiple layers is a powerful technique even within applications. Bottom-up programming means writing a program as a series of layers, each of which serves as a language for the one above. This approach tends to yield smaller, more flexible programs. It's also the best route to that holy grail, reusability. A language is by definition reusable. The more of your application you can push down into a language for writing that type of application, the more of your software will be reusable.

Somehow the idea of reusability got attached to object-oriented programming in the 1980s, and no amount of evidence to the contrary seems to be able to shake it free. But although some object-oriented software is reusable, what makes it reusable is its bottom-upness, not its object-orientedness. Consider libraries: they're reusable because they're language, whether they're written in an object-oriented style or not.

I don't predict the demise of object-oriented programming, by the way. Though I don't think it has much to offer good programmers, except in certain specialized domains, it is irresistible to large organizations. Object-oriented programming offers a sustainable way to write spaghetti code. It lets you accrete programs as a series of patches. imposes constraining caste restrictions. In any academic field there are topics that are ok to work on and others that aren't. Unfortunately the distinction between acceptable and forbidden topics is usually based on how intellectual the work sounds when described in research papers, rather than how important it is for getting good results. The extreme case is probably literature; people studying literature rarely say anything that would be of the slightest use to those producing it.

Though the situation is better in the sciences, the overlap between the kind of work you're allowed to do and the kind of work that yields good languages is distressingly small. (Olin Shivers has grumbled eloquently about this.) For example, types seem to be an inexhaustible source of research papers, despite the fact that static typing seems to preclude true macros-- without which, in my opinion, no language is worth using.

The trend is not merely toward languages being developed as open-source projects rather than "research", but toward languages being designed by the application programmers who need to use them, rather than by compiler writers. This seems a good trend and I expect it to continue.

Unlike physics in a hundred years, which is almost necessarily impossible to predict, I think it may be possible in principle to design a language now that would appeal to users in a hundred years.

One way to design a language is to just write down the program you'd like to be able to write, regardless of whether there is a compiler that can translate it or hardware that can run it. When you do this you can assume unlimited resources. It seems like we ought to be able to imagine unlimited resources as well today as in a hundred years.

What program would one like to write? Whatever is least work. Except not quite: whatever least work if your ideas about programming weren't already influenced by the languages you're currently used to. Such influence can be so pervasive that it takes a great effort to overcome it. You'd think it would be obvious to creatures as lazy as us how to express a program with the least effort. In fact, our ideas about what's possible tend to be so by whatever language we think in that easier formulations of programs seem very surprising. They're something you have to discover, not something you naturally sink into.

One helpful trick here is to use the of the program as an approximation for how much work it is to write. Not the length in characters, of course, but the length in distinct syntactic elements-- basically, the size of the parse tree. It may not be quite true that the shortest program is the least work to write, but it's close enough that you're better off aiming for the solid target of brevity than the fuzzy, nearby one of least work. Then the algorithm for language design becomes: look at a program and ask, is there any way to write this that's shorter?

In practice, writing programs in an imaginary hundred-year language will work to varying degrees depending on how close you are to the core. Sort routines you can write now. But it would be hard to predict now what kinds of libraries might be needed in a hundred years. Presumably many libraries will be for domains that don't even exist yet. If SETI@home works, for example, we'll need libraries for communicating with aliens. Unless of course they are sufficiently advanced that they already communicate in XML.

At the other extreme, I think you might be able to design the core language today. In fact, some might argue that it was already mostly designed in 1958.

If the hundred year language were available today, would we want to program in it? One way to answer this question is to look back. If present-day programming languages had been available in 1960, would anyone have wanted to use them?

In some ways, the answer is no. Languages today assume infrastructure that didn't exist in 1960. For example, a language in which indentation is significant, like Python, would not work very well on printer terminals. But putting such problems aside-- assuming, for example, that programs were all just written on paper-- would programmers of the 1960s have liked writing programs in the languages we use now?

I think so. Some of the less imaginative ones, who had artifacts of early languages built into their ideas of what a program was, might have had trouble. (How can you manipulate data without doing pointer arithmetic? How can you implement flow charts without gotos?) But I think the smartest programmers would have had no trouble making the most of present-day languages, if they'd had them.

If we had the hundred-year language now, it would at least make a great pseudocode. What about using it to write software? Since the hundred-year language will need to generate fast code for some applications, presumably it could generate code efficient enough to run acceptably well on our hardware. We might have to give more optimization advice than users in a hundred years, but it still might be a net win.

Now we have two ideas that, if you combine them, suggest interesting possibilities: (1) the hundred-year language could, in principle, be designed today, and (2) such a language, if it existed, might be good to program in today. When you see these ideas laid out like that, it's hard not to think, why not try writing the hundred-year language now?

When you're working on language design, I think it is good to have such a target and to keep it consciously in mind. When you learn to drive, one of the principles they teach you is to align the car not by lining up the hood with the stripes painted on the road, but by aiming at some point in the distance. Even if all you care about is what happens in the next ten feet, this is the right answer. I think we can and should do the same thing with programming languages.

I believe Lisp Machine Lisp was the first language to embody the principle that declarations (except those of dynamic variables) were merely optimization advice, and would not change the meaning of a correct program. Common Lisp seems to have been the first to state this explicitly.

to Trevor Blackwell, Robert Morris, and Dan Giffin for reading drafts of this, and to Guido van Rossum, Jeremy Hylton, and the rest of the Python crew for inviting me to speak at PyCon.

The 11 Easiest Programming Languages To Learn According To Developers

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Considering a career as a developer?

The first step is deciding which programming language to learn. Programming languages allow developers to tell computers what to do. Each language comes with its own advantages, and many of their functions overlap. And with over 600 languages to choose from, it can be hard to figure where to start.

The good news is, there are a few languages that stand out amongst developers as go-tos for beginners. So, to make your decision a little easier, let’s explore 11 of the easiest programming languages to learn.

Just about everyone has heard of HTML , yet you may be surprised to learn that it’s known as a controversial programming language. That’s because HTML is technically a markup language — HTML stands for “hypertext markup language.” What’s the difference? Essentially, HTML isn’t capable of the basic functions of other programming languages, such as logic building, conditional statements, or even basic mathematical operations.

But just because you can’t create an IF-ELSE statement doesn’t mean you won’t be glad you dedicated time to learning HTML . As a markup language, HTML is the Internet’s standard language for structuring web pages and displaying text.

HTML is known for its extensive use of tags or labels that define what kind of text should be on the page. For example, the body text in this article would start with a <body> tag and end with a </body> tag. HTML tags define almost everything about the text on a web page, from font size to hyperlinks.

Who uses HTML?

Anyone who works with web pages should know HTML. This includes Front-End Engineers and Full-Stack Engineers . And, if you enjoy fine-tuning websites, then learning HTML will allow the most customization and let you go beyond pre-designed templates.

Why HTML is easy to learn

Because it’s so popular, there’s no shortage of HTML courses to get you started. The language itself is fairly simple, and HTML tags follow consistent rules that make it easy to learn new commands and functions.

If HTML defines the content of your webpage, Cascading Style Sheets (CSS) is used for defining the look of each HTML element . All of the different frames you see on a web page, including text boxes, background images, and menus, are coded in CSS.

Have you ever noticed how the same web page is organized differently when you’re viewing it on your phone versus on your desktop? That’s because CSS also controls which page elements are visible or hidden depending on the screen size and resolution.

CSS is a rule-based language, which means you define how different kinds of text and pages look by applying rules to each type of group defined in HTML. For example, you can use CSS to make all hyperlinks underlined in hot pink, while all level 2 headers are bolded and green. So, while CSS and HTML are used independently, the two languages complement each other to create web pages with customized content and style.

Like HTML, CSS isn’t considered to be a full programming language, but that hasn’t stopped it from becoming part of the unstoppable trio of web page languages.

Who uses CSS?

Because it works so closely with HTML, CSS is a must-know for Front-End Engineers as well as Full-Stack Engineers .

Why CSS is easy to learn

A basic CSS course will teach you the language’s fundamentals as you customize web pages. But if you’re interested in more advanced CSS functions, there are plenty of CSS templates and frameworks available — that is, pre-written CSS code that produces a certain page style and color scheme.

3. JavaScript

Since HTML and CSS can’t directly incorporate conditional statements and other decision-making functions, they aren’t considered complete programming languages. But what happens if you do want an interactive web page? For example, what if you want to add a drop-down menu or a button that changes color and text when your mouse hovers over it? Enter: JavaScript .

As a full programming language, JavaScript is used to handle programming loops and make logical decisions based on input, such as when you hover your mouse over a menu or when you type something into a search box. And because JavaScript can output HTML and CSS code, it’s able to make web pages interactive and dynamic.

But that’s not all JavaScript can do. Through project environments like Node.js , it’s possible to run JavaScript outside of a web browser and on the back end . This allows web applications to run using a single programming language from the screen to the server.

Who uses JavaScript?

As the third of the web page design trio of languages, Front-End Engineers and Full-Stack Engineers should master JavaScript along with HTML and CSS. Also, since it’s functional on the server-side with environments like Node.js, Back-End Engineers can benefit from learning JavaScript too.

Why JavaScript is easy to learn

While it’s more involved than HTML and CSS, JavaScript is one of the easiest true programming languages to learn. It’s an interpreted language and can easily be embedded with languages like HTML. Another thing that makes JavaScript easy to learn is that you can write complex snippets of code and test them in the web browser as you go. Also, if you already know HTML and CSS, then you’ll have a head start in learning JavaScript .

We can’t keep talking about easy programming languages without addressing the giant snake in the room. Python is consistently ranked as one of the most popular programming languages, and for good reason. From its conception in the 1980s, Python was designed to be a highly readable code that could be easily extended with modules well into the future.

People also really like Python because it’s a multi-paradigm programming language. This means that it supports different styles (paradigms) of programming. This includes object-oriented programming , which focuses on manipulating datasets (or objects ), as well as functional programming — which focuses on using functions to perform complex or multi-step operations.

Who uses Python?

Python is a widely used application language, and you’ll find Web Developers using it for websites, applications, and games. At the same time, Data Scientists use Python because the language works well with retrieving and analyzing large datasets.

Why Python is easy to learn

It’s not often that a programming language is invented specifically with readability in mind. As you learn Python , you’ll discover that not only is everything meant to be simple, but complex code is frowned upon. Alex Martelli, a Python Software Foundation Fellow, puts it best: “To describe something as ‘clever’ is not considered a compliment in Python culture.”

Since it first appeared in 1993, R has become the go-to programming language for anyone interested in statistical analysis, data science, or data mining. While R is usually accessed through a command-line prompt, there are plenty of graphical interfaces available. Some of them allow people to use basic R functions without needing to learn any R code, which is one reason why the language is so popular.

R is open source, which means it’s free to use for personal or commercial purposes. This also means that there are thousands of user-created downloadable packages that provide functions well beyond the original code.

Some packages are for general functions, like data visualization. But most are designed for very specific professional functions, which is why R is so widely used . There’s an R package out there to fit your needs, whether you’re interested in general statistics, genetic sequencing, geospatial analysis, or anything in between.

Another strength of R is the knitr engine, which can produce dynamic, publication-ready reports and web pages that integrate R code with LaTeX, HTML, or Markdown.

Who uses R?

R is most popular among Data Scientists, Data Analysts , and Statisticians. But, more and more STEM professionals are drawn to R because of the many packages designed specifically for their fields and, sometimes, specifically for their companies.

Why R is easy to learn

At first glance, learning R might seem like a challenge as the language can take some getting used to, especially if you’re already familiar with other programming languages. But one reason why learning R is easier than other languages is because every R function comes with extensive documentation that includes explanations of each argument as well as example commands.

What do you call a Perl with a Lisp? A Ruby, of course! Yukihiro Matsumoto, the creator of Ruby, set out to create a language that incorporated the best elements of Perl, Lisp, Smalltalk, Ada, and Eiffel. And that’s how Ruby was born.

Compared to Python, which focuses on providing a single, simple solution for every problem, Ruby aims to allow multiple approaches that achieve the same end. This gives Ruby a sort of flexibility that programmers love.

Another reason why Ruby is so popular is that programmers can change even fundamental parts of the language to suit your needs. For example, if you prefer your mathematical operators to be spelled out instead of using symbols (“plus” instead of “+”), you can define that in Ruby.

Who uses Ruby?

Like Python, Ruby is a general-purpose language that’s especially popular with Web Developers, since it’s most commonly used to build web applications. But, you can also use Ruby for web scraping, command-line tools, automation, data processing, and more.

Why Ruby is easy to learn

Once you start learning Ruby , you’ll soon understand why it’s called the “language of careful balance.” And, because so many developers use and love it, you’ll find no shortage of Ruby documentation, community forums, and sample code available online.

One of the biggest advantages of Java is that it was originally designed to run in distributed environments like the Internet. That is, among multiple servers and computers. And, even though the language is old, Java is still relevant and cutting edge due to constant testing and updating.

Java developers can be confident that creating a Java application on one platform means that the application will work on all other major platforms too. The language’s flexibility also means that developers can use it not just on computers and mobile devices, but also in gateways, consumer products, or practically any electronic device.

Finally, Java is known for its reliability and security, which is yet another reason that developers are so attracted to it.

Who uses Java?

Not surprisingly, Java is a favorite among Front-End Engineers and Full-Stack Engineers. It’s also one of the first languages that Computer Scientists learn as an introduction to object-oriented programming.

Why Java is easy to learn

Learning Java is especially easy because its syntax is similar to English. Plus, you can count on a large support community to provide guidance and answer your questions as you learn Java.

We’ve focused so far on programming languages that help with front-end and application development, but Back-End Engineers have their favorite programming languages too — and PHP: Hypertext Preprocessor (PHP) is one of them. This language is widely used within HTML to quickly access and manage server-side content, including databases . In fact, many online forms use PHP to create new database records or update existing ones.

Another advantage to PHP is the built-in security it provides, as it can encrypt data and restrict access to certain parts of your website.

Between the ease of use, wide functionality , and security features, it’s not surprising that major companies like WordPress and Facebook use PHP.

Who uses PHP?

PHP is chiefly used to manage interaction with the server-side of a website, which is why it’s a staple programming language for Back-End Engineers as well as Full-Stack Engineers.

Why PHP is easy to learn

PHP is known for its simplicity and forgiving syntax. As you learn PHP , you’ll never be far from documentation and resources to help you along the way.

Go , or Golang, is a general-purpose programming language that Google originally developed as an alternative to C / C++ . The result was a language that combines the faster performance offered by C/C++ with a simplified syntax.

As an open-source programming language, Go is used on servers, DevOps, web development, and even command line tools, as well as a variety of applications, such as cloud and server-side applications.

Who uses Go?

Computer Scientists and Application Developers who need to quickly develop high-performing applications turn to Go as the best programming language to get the job done.

Why Go is easy to learn

Go was designed with simplicity in mind, making it a beginner-friendly programming language. Check out our Learn Go course , created in partnership with Google, to get started with the language.

In 2014, Apple developed Swift as an alternative to Objective-C to use with macOS (MacBooks and iMacs) and iOS (iPhones and iPads). With its introduction, Swift presented many modern features that made programming significantly easier. Now, it’s the top choice of developers who build apps for Mac OSX, the Apple iPhone, Apple Watch, and Apple TV.

Who uses Swift?

Swift is a must if you’re a Front-End Engineer or Full-Stack Engineer interested in developing apps within the Apple ecosystem.

Why Swift is easy to learn

As with all of its products and services, Apple put a lot of effort into making Swift as intuitive as possible. Apple-centric developers love Swift because it’s easy to read and write. And as you learn Swift , you can even download a free app, Swift Playgrounds , that allows you to develop and test your own Swift programs while you learn.

Just a few years after the first generation of smartphones, app developers realized that they needed a powerful and fast language. Enter JetBrains, the company that first released Kotlin in 2011.

Kotlin is specifically for mobile development on the Android operating system, and has become the preferred language for Android applications. While Kotlin is fully compatible with Java, one of the benefits of Kotlin is that it generally allows developers to write less code than they would have to in Java.

Who uses Kotlin?

Any Front-End Engineer or Full-Stack Engineer who develops Android apps uses Kotlin.

Why Kotlin is easy to learn

In addition to being a beginner-friendly language, Kotlin is especially easy and quick to grasp if you already have knowledge of Java or Python. It’s also straight-forward for iOS developers to learn because it was built on the same modern concepts they already use. Get started learning the basics of Kotlin .

Learn smarter, not harder

Are there a ton of programming languages out there that make developers’ lives easier? You bet. Do you need to learn them all? Absolutely not. Instead, we recommend focusing on a few languages that are most helpful in your chosen career.

Not sure where to start? Our free course Learn to Code with Blockly will introduce you to the basics of programming, and we also take a closer look at some of the best languages for beginners in Choosing a Programming Language . And in Choosing a Career in Tech , you can explore different careers in the field to get a sense of which path might be right for you.

You could also try taking our sorting quiz ! It’ll give you recommendations on which language is right for you. And our career paths include tailored course recommendations that take all the guesswork out of figuring out which programming languages help you be the most prepared to start your new career.

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