Not too long ago, Apple added something phenomenal to the iPhone OS: a dashboard screen. If you have a Macintosh computer, you may be familiar with the dashboard that is available (regularly) by pressing F4. Otherwise, you can draw similarities to your Windows 7 Dashboard on the right hand side of your desktop, that shows you updates on your applications and widgets you add to it. Finding your dashboard on your iPhone is just as easy: just put your finger on the top of your iPhone screen, and drag down.

Here, in your dashboard, you will see all of the updates that has been pushed into such by your applications that desire to send you messages: things like new text messages, new updates to your subscribed magazines, your messages on payment applications. If you have reviewed a message set by an application by tapping on it, that message will automatically become deleted. However, if you don’t desire to go into the application to delete it, simply tap in the top right on the bar that categorizes that particular application, and tap again to clear all of the messages set by that application, and clear up your dashboard.

But, your dashboard isn’t all about your application. You not only get your messages, but you get important information set by default applications, such as the weather. If you don’t feel like scouting out your weather application amidst all your applications you have downloaded, simply go into your dashboard, and find out the forecast for the whole week, just by a simple swipe. Not only that, tickers for your stocks are displayed near the bottom of the dashboard.

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.

Back in the late 90's, there were a number of computer scienctists claiming to know java in hopes of landing a job for $80k+/year.  In fact, I know a woman you did just that:  land a project management position with a large telecom and have no experience whatsoever.  I guess the company figured that some talent was better than no talent and that, with some time and training, she would be productive.  Like all gravey train stories, that one, too, had an end.  After only a year, she was given a pink slip.

Not only are those days over, job prospects for the IT professional have become considerably more demanding.  Saying you know java today is like saying you know that you have expertise with the computer mouse; that's nice, but what else can you do.   This demand can be attributed to an increase in global competition along with the introduction of a number of varied technologies.   Take .NET, Python, Ruby, Spring, Hibernate ... as an example;  most of them, along with many others, are the backbone of the IT infrastructure of most mid-to-large scale US corporations.  Imagine the difficulty in finding the right mix of experience, knowledge and talent to support, maintain and devlop with such desparate technologies.

Well imagine no more.  According to the IT Hiring Index and Skills Report, seventy percent of CIO’s said it's challenging to find skilled professionals today.  If we add the rapid rate of technological innovation into the mix of factors affecting more businesses now than ever before, it’s understandable that the skill gap is widening.  Consider this as well:  the economic downturn has forced many potential retires to remain in the workforce.  This is detailed in MetLife's annual Study of Employee Benefits which states that“more than one-third of surveyed Baby Boomers (35%) say that as a result of economic conditions they plan to postpone their retirement.”  How then does the corporation hire new, more informed/better educated talent?    Indeed, the IT skills gap is ever widening.

In order to compensate for these skill discrepencies, many firms have resorted to hire the ideal candidates by demanding they possess a christmas wish list of expertise in a variety of different IT disciplines.  It would not be uncommon that such individuals have a strong programming background and are brilliant DBA's.  What about training?  That is certainly a way to diminish the skills gap.

I suspect that many of you are familiar with the term "hard coding a value" whereby the age of an individual or their location is written into the condition (or action) of a business rule (in this case) as shown below:

if customer.age > 21 and customer.city == 'denver'

then ...

Such coding practices are perfectly expectable provided that the conditional values, age and city, never change. They become entirely unacceptable if a need for different values could be anticipated. A classic example of where this practice occurred that caused considerable heartache in the IT industry was the Y2K issue where dates were updated using only the last 2 digits of a four digit number because the first 2 digits were hard-coded to 19 i.e. 1998, 1999. All was well provided that the date did not advance to a time beyond the 1900’s since no one could be certain of what would happen when the millennia arrived (2000). A considerably amount of work (albeit boring) and money, approximately $200 billion, went into revising systems by way of software rewrites and computer chip replacements in order to thwart any detrimental outcomes. It is obvious how a simple change or an assumption can have sweeping consequences.

You may wonder what Y2K has to do with Business Rule Management Systems (BRMS). Well, what if we considered rules themselves to be hard-coded. If we were to write 100s of rules in Java, .NET or whatever language that only worked for a given scenario or assumption, would that not constitute hard-coded logic? By hard-coded, we obviously mean compiled. For example, if a credit card company has a variety of bonus campaigns, each with their own unique list of rules that may change within a week’s time, what would be the most effective way of writing software to deal with these responsibilities?