Businesses “Going Green” is so commonplace now it’s more than just an encouraging a trend; it’s become expected of companies big and small. In fact, consumers frequently place more of an obligation on companies to go green than they do themselves. The last few years—the infancy of what may turn out to be a green revolution—have really proven that there are many ways to go green, and that some of these ways are much more financially sound than others. But rather than providing a cut and dry list of green options and their pros and cons, I’d like to take a look at the bigger picture incentives for businesses going green and how consumers are responding.

What does it mean to be green?

The original article was posted by Michael Veksler on Quora

A very well known fact is that code is written once, but it is read many times. This means that a good developer, in any language, writes understandable code. Writing understandable code is not always easy, and takes practice. The difficult part, is that you read what you have just written and it makes perfect sense to you, but a year later you curse the idiot who wrote that code, without realizing it was you.

The best way to learn how to write readable code, is to collaborate with others. Other people will spot badly written code, faster than the author. There are plenty of open source projects, which you can start working on and learn from more experienced programmers.

Readability is a tricky thing, and involves several aspects:

1. Never surprise the reader of your code, even if it will be you a year from now. For example, don’t call a function max() when sometimes it returns the minimum().
2. Be consistent, and use the same conventions throughout your code. Not only the same naming conventions, and the same indentation, but also the same semantics. If, for example, most of your functions return a negative value for failure and a positive for success, then avoid writing functions that return false on failure.
3. Write short functions, so that they fit your screen. I hate strict rules, since there are always exceptions, but from my experience you can almost always write functions short enough to fit your screen. Throughout my carrier I had only a few cases when writing short function was either impossible, or resulted in much worse code.
4. Use descriptive names, unless this is one of those standard names, such as i or it in a loop. Don’t make the name too long, on one hand, but don’t make it cryptic on the other.
5. Define function names by what they do, not by what they are used for or how they are implemented. If you name functions by what they do, then code will be much more readable, and much more reusable.
6. Avoid global state as much as you can. Global variables, and sometimes attributes in an object, are difficult to reason about. It is difficult to understand why such global state changes, when it does, and requires a lot of debugging.
7. As Donald Knuth wrote in one of his papers: “Early optimization is the root of all evil”. Meaning, write for readability first, optimize later.
8. The opposite of the previous rule: if you have an alternative which has similar readability, but lower complexity, use it. Also, if you have a polynomial alternative to your exponential algorithm (when N > 10), you should use that.

Use standard library whenever it makes your code shorter; don’t implement everything yourself. External libraries are more problematic, and are both good and bad. With external libraries, such as boost, you can save a lot of work. You should really learn boost, with the added benefit that the c++ standard gets more and more form boost. The negative with boost is that it changes over time, and code that works today may break tomorrow. Also, if you try to combine a third-party library, which uses a specific version of boost, it may break with your current version of boost. This does not happen often, but it may.

Don’t blindly use C++ standard library without understanding what it does - learn it. You look at std::vector::push_back() documentation at it tells you that its complexity is O(1), amortized. What does that mean? How does it work? What are benefits and what are the costs? Same with std::map, and with std::unordered_map. Knowing the difference between these two maps, you’d know when to use each one of them.

Never call new or delete directly, use std::make_unique and [cost c++]std::make_shared[/code] instead. Try to implement usique_ptr, shared_ptr, weak_ptr yourself, in order to understand what they actually do. People do dumb things with these types, since they don’t understand what these pointers are.

Every time you look at a new class or function, in boost or in std, ask yourself “why is it done this way and not another?”. It will help you understand trade-offs in software development, and will help you use the right tool for your job. Don’t be afraid to peek into the source of boost and the std, and try to understand how it works. It will not be easy, at first, but you will learn a lot.

Know what complexity is, and how to calculate it. Avoid exponential and cubic complexity, unless you know your N is very low, and will always stay low.

Learn data-structures and algorithms, and know them. Many people think that it is simply a wasted time, since all data-structures are implemented in standard libraries, but this is not as simple as that. By understanding data-structures, you’d find it easier to pick the right library. Also, believe it or now, after 25 years since I learned data-structures, I still use this knowledge. Half a year ago I had to implemented a hash table, since I needed fast serialization capability which the available libraries did not provide. Now I am writing some sort of interval-btree, since using std::map, for the same purpose, turned up to be very very slow, and the performance bottleneck of my code.

Notice that you can’t just find interval-btree on Wikipedia, or stack-overflow. The closest thing you can find is Interval tree, but it has some performance drawbacks. So how can you implement an interval-btree, unless you know what a btree is and what an interval-tree is? I strongly suggest, again, that you learn and remember data-structures.

These are the most important things, which will make you a better programmer. The other things will follow.

Being treated like a twelve year old at work by a Tasmanian-devil-manager and not sure what to do about it? It is simply a well-known fact that no one likes to be micro managed. Not only do they not like to be micro managed, but tend to quit for this very reason. Unfortunately the percentage of people leaving their jobs for this reason is higher that you would imagine. Recently, an employee retention report conducted by TINYpulse, an employee engagement firm, surveyed 400 full-time U.S. employees concluded that, "supervisors can make or break employee retention."

As companies mature, their ability to manage can be significant to their bottom line as employee morale, high staff turnover and the cost of training new employees can easily reduce productivity and consequently client satisfaction.  In many cases, there is a thin line between effective managing and micro managing practices. Most managers avoid micro managing their employees. However, a decent percentage of them have yet to find effective ways to get the most of their co-workers.  They trap themselves by disempowering people's ability to do their work when they hover over them and create an unpleasant working environment. This behavior may come in the form of incessant emailing, everything having to be done a certain way (their way), desk hovering, and a need to control every part of an enterprise, no matter how small.

Superimpose the micro manager into the popular practice of Agile-SCRUM methodology and you can imagine the creative ways they can monitor everything in a team, situation, or place. Although, not always a bad thing, excessive control, can lead to burnout of managers and teams alike.  As predicted, agile project management has become increasingly popular in the last couple of decades in project planning, particularly in software development.  Agile methodology when put into practice, especially in IT, can mean releasing faster functional software than with the traditional development methods. When done right, it enables users to get some of the business benefits of the new software faster as well as enabling the software team to get rapid feedback on the software's scope and direction.

Despite its advantages, most organizations have not been able to go “all agile” at once. Rather, some experiment with their own interpretation of agile when transitioning.  A purist approach for instance, can lead to an unnecessarily high agile project failure, especially for those that rely on tight controls, rigid structures and cost-benefit analysis.  As an example, a premature and rather rapid replacement of traditional development without fully understating the implications of the changeover process or job roles within the project results in failure for many organizations.  

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