Although the index is an important itemized guide of what people are searching for on the internet, it’s arguable that certain languages getting recognition is a direct result of early adopters posting tutorials and filling up discussion boards on current trends. Additionally, popular tech blogs pick up on technological shifts and broadcast related versions of the same themes.

When does the popularity of a software language matter?

1. If you want marketable skills, knowing what employers are looking for is beneficial. As an example, languages such as Java and Objective C are highly coveted in the smart-phone apps businesses.
2. A consistently shrinking language in usage is an indicator not only that employers are apt to pass on those skills but fall in danger of being obsolete.
3. Focusing on languages that are compatible with other developers increases your chances to participate on projects that companies are working on.

It is said that spoken languages shape thoughts by their inclusion and exclusion of concepts, and by structuring them in different ways. Similarly, programming languages shape solutions by making some tasks easier and others less aesthetic. Using F# instead of C# reshapes software projects in ways that prefer certain development styles and outcomes, changing what is possible and how it is achieved.

F# is a functional language from Microsoft's research division. While once relegated to the land of impractical academia, the principles espoused by functional programming are beginning to garner mainstream appeal.

As its name implies, functions are first-class citizens in functional programming. Blocks of code can be stored in variables, passed to other functions, and infinitely composed into higher-order functions, encouraging cleaner abstractions and easier testing. While it has long been possible to store and pass code, F#'s clean syntax for higher-order functions encourages them as a solution to any problem seeking an abstraction.

F# also encourages immutability. Instead of maintaining state in variables, functional programming with F# models programs as a series of functions converting inputs to outputs. While this introduces complications for those used to imperative styles, the benefits of immutability mesh well with many current developments best practices.

For instance, if functions are pure, handling only immutable data and exhibiting no side effects, then testing is vastly simplified. It is very easy to test that a specific block of code always returns the same value given the same inputs, and by modeling code as a series of immutable functions, it becomes possible to gain a deep and highly precise set of guarantees that software will behave exactly as written.

Further, if execution flow is exclusively a matter of routing function inputs to outputs, then concurrency is vastly simplified. By shifting away from mutable state to immutable functions, the need for locks and semaphores is vastly reduced if not entirely eliminated, and multi-processor development is almost effortless in many cases.

Type inference is another powerful feature of many functional languages. It is often unnecessary to specify argument and return types, since any modern compiler can infer them automatically. F# brings this feature to most areas of the language, making F# feel less like a statically-typed language and more like Ruby or Python. F# also eliminates noise like braces, explicit returns, and other bits of ceremony that make languages feel cumbersome.

Functional programming with F# makes it possible to write concise, easily testable code that is simpler to parallelize and reason about. However, strict functional styles often require imperative developers to learn new ways of thinking that are not as intuitive. Fortunately, F# makes it possible to incrementally change habits over time. Thanks to its hybrid object-oriented and functional nature, and its clean interoperability with the .net platform, F# developers can gradually shift to a more functional mindset while still using the algorithms and libraries with which they are most familiar.

Related F# Resources:

F# Programming Essentials Training

Programmers often tend to be sedentary people. Sitting in a chair and pressing keys, testing code, and planning out one logical step-wise strategy after another to get the computer to process data the way you want it to is just what life as a programmer is all about. But, is being too sedentary hindering a programmers max potential? In other words, will getting up, moving around, and getting the blood pumping make us better programmers? To answer this question more efficiently, we will need to consider the impact of exercise on various aspects of programming.

Alertness And Focus

It is no surprise that working up a sweat makes the mind wake up and become more alert. As the blood starts pumping, the body physically reacts in ways that helps the mind to better focus. And improving our focus might make us better programmers in the sense that we are more able to wrap our mind around a problem and deal with it more efficiently than if we feel sluggish and not so alert. However, improving one's focus with exercise can be augmented by taking such vitamins as B6, Coleen, and eating more saturated fats rather than so many sugars. Exercise alone may be a good start, but it is important to realize that the impact of exercise on overall focus can be enhanced when combined with other dietary practices. However, it never hurts to begin a day of programming with fifteen minutes of rigorous workout to give the mind a little extra push.

Increase In Intellect

Does exercise cause a programmer to become a smarter programmer? This is perhaps a trickier question. In some sense, it might seem as if exercise makes us more intelligent. But, this may be more because our focus is sharper than because of any increase in actual knowledge. For example, if you don't know how to program in Python, it is highly doubtful that exercising harder will all of a sudden transfer such insights directly to your brain. However, exercise might have another indirect impact on a programmer’s intellect that will help them to become a better programmer. The more a person exercises, the more stamina and energy they will tend to have, as compared to programmers who never exercise all that much. That additional energy and stamina might help a programmer to be able to push themselves to learn things more efficiently, simply because they aren't getting tired as much as they study new languages or coding techniques. If you have more energy and stamina throughout the day, you will likely be more productive as a programmer as well. Greater productivity can often make one program better simply because they actually push themselves to finish projects. Other programmers who do not exercise on a regular basis may simply lack the energy, stamina, and motivation to follow through and bring their programming projects to completion.

Memory

The ability to remember things and recall them quickly is key to being an efficient programmer. Getting up and getting real exercise may be central to making sure that one does not lose control of these cognitive abilities. According to the New York Times, article, Getting a Brain Boost Through Exercise, recent research studies on mice and humans have shown that, in both cases, exercise does in fact appear to promote better memory function as well as other cognitive factors like spacial sense. (1) Consequently, if a person intends to be a programmer for a long time and wants their mind to be able to remember things and recall them more easily, then exercise may need to become an essential part of such a programmer's daily routine.

As much as one might want to resist the need for exercise and be sedentary programmers, the simple fact is that exercise very well could improve our ability to program in numerous ways. More importantly, exercise is critical to improving and maintaining good health overall. Even if a person does not have much time to get up and move around during the day, there are exercises that one can do while sitting, which would be better to do than no exercise at all.

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Python and Ruby, each with roots going back into the 1990s, are two of the most popular interpreted programming languages today. Ruby is most widely known as the language in which the ubiquitous Ruby on Rails web application framework is written, but it also has legions of fans that use it for things that have nothing to do with the web. Python is a big hit in the numerical and scientific computing communities at the present time, rapidly displacing such longtime stalwarts as R when it comes to these applications. It too, however, is also put to a myriad of other uses, and the two languages probably vie for the title when it comes to how flexible their users find them.

A Matter of Personality...

That isn't to say that there aren't some major, immediately noticeable, differences between the two programming tongues. Ruby is famous for its flexibility and eagerness to please; it is seen by many as a cleaned-up continuation of Perl's "Do What I Mean" philosophy, whereby the interpreter does its best to figure out the meaning of evening non-canonical syntactic constructs. In fact, the language's creator, Yukihiro Matsumoto, chose his brainchild's name in homage to that earlier language's gemstone-inspired moniker.

Python, on the other hand, takes a very different tact. In a famous Python Enhancement Proposal called "The Zen of Python," longtime Pythonista Tim Peters declared it to be preferable that there should only be a single obvious way to do anything. Python enthusiasts and programmers, then, generally prize unanimity of style over syntactic flexibility compared to those who choose Ruby, and this shows in the code they create. Even Python's whitespace-sensitive parsing has a feel of lending clarity through syntactical enforcement that is very much at odds with the much fuzzier style of typical Ruby code.

For example, Python's much-admired list comprehension feature serves as the most obvious way to build up certain kinds of lists according to initial conditions:

a = [x**3 for x in range(10,20)]
b = [y for y in a if y % 2 == 0]

first builds up a list of the cubes of all of the numbers between 10 and 19 (yes, 19), assigning the result to 'a'. A second list of those elements in 'a' which are even is then stored in 'b'. One natural way to do this in Ruby is probably:

a = (10..19).map {|x| x ** 3}
b = a.select {|y| y.even?}

but there are a number of obvious alternatives, such as:

a = (10..19).collect do |x|
x ** 3
end

b = a.find_all do |y|
y % 2 == 0
end

It tends to be a little easier to come up with equally viable, but syntactically distinct, solutions in Ruby compared to Python, even for relatively simple tasks like the above. That is not to say that Ruby is a messy language, either; it is merely that it is somewhat freer and more forgiving than Python is, and many consider Python's relative purity in this regard a real advantage when it comes to writing clear, easily understandable code.

And Somewhat One of Performance