So You Want to be a Programmer

So You Want to be a Programmer

so_you_want_to_be_a_programmer

Here's my recipe for programming success:

• Get interested in programming, and do some because it is fun. Make sure that it keeps being enough fun so that you will be willing to put in your ten years/10,000 hours.
• Program. The best kind of learning is learning by doing. To put it more technically, "the maximal level of performance for individuals in a given domain is not attained automatically as a function of extended experience, but the level of performance can be increased even by highly experienced individuals as a result of deliberate efforts to improve and the most effective learning requires a well-defined task with an appropriate difficulty level for the particular individual, informative feedback, and opportunities for repetition and corrections of errors." (p. 20-21) The book Cognition in Practice: Mind, Mathematics, and Culture in Everyday Life is an interesting reference for this viewpoint.
• Talk with other programmers; read other programs. This is more important than any book or training course.
• If you want, put in four years at a college (or more at a graduate school). This will give you access to some jobs that require credentials, and it will give you a deeper understanding of the field, but if you don't enjoy school, you can (with some dedication) get similar experience on your own or on the job. In any case, book learning alone won't be enough. "Computer science education cannot make anybody an expert programmer any more than studying brushes and pigment can make somebody an expert painter" says Eric Raymond, author of The New Hacker's Dictionary. One of the best programmers I ever hired had only a High School degree; he's produced a lot of great software, has his own news group, and made enough in stock options to buy his own nightclub.
• Work on projects with other programmers. Be the best programmer on some projects; be the worst on some others. When you're the best, you get to test your abilities to lead a project, and to inspire others with your vision. When you're the worst, you learn what the masters do, and you learn what they don't like to do (because they make you do it for them).
• Work on projects after other programmers. Understand a program written by someone else. See what it takes to understand and fix it when the original programmers are not around. Think about how to design your programs to make it easier for those who will maintain them after you.
• Learn at least a half dozen programming languages. Include one language that supports class abstractions (like Java or C++), one that supports functional abstraction (like Lisp or ML), one that supports syntactic abstraction (like Lisp), one that supports declarative specifications (like Prolog or C++ templates), one that supports coroutines (like Icon or Scheme), and one that supports parallelism (like Sisal).
• Remember that there is a "computer" in "computer science". Know how long it takes your computer to execute an instruction, fetch a word from memory (with and without a cache miss), read consecutive words from disk, and seek to a new location on disk. (Answers here below.)

• Answers

  Approximate timing for various operations on a typical PC:

  execute typical instruction	1/1,000,000,000 sec = 1 nanosec
  fetch from L1 cache memory	0.5 nanosec
  branch misprediction	5 nanosec
  fetch from L2 cache memory	7 nanosec
  Mutex lock/unlock	25 nanosec
  fetch from main memory	100 nanosec
  send 2K bytes over 1Gbps network	20,000 nanosec
  read 1MB sequentially from memory	250,000 nanosec
  fetch from new disk location (seek)	8,000,000 nanosec
  read 1MB sequentially from disk	20,000,000 nanosec
  send packet US to Europe and back	150 milliseconds = 150,000,000 nanosec

• Get involved in a language standardization effort. It could be the ANSI C++ committee, or it could be deciding if your local coding style will have 2 or 4 space indentation levels. Either way, you learn about what other people like in a language, how deeply they feel so, and perhaps even a little about why they feel so.
• Have the good sense to get off the language standardization effort as quickly as possible.

With all that in mind, its questionable how far you can get just by book learning. Before my first child was born, I read all the How To books, and still felt like a clueless novice. 30 Months later, when my second child was due, did I go back to the books for a refresher? No. Instead, I relied on my personal experience, which turned out to be far more useful and reassuring to me than the thousands of pages written by experts.

Source:- http://norvig.com/21-days.html
I want to thank him for this useful information to us

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See also->> learn-how-to-program

See also->> what-is-linux-brief-history

See also->> the-early-hackers

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Learn How To Program

This, of course, is the fundamental hacking skill. If you don't know any computer languages, I recommend starting with Python. It is cleanly designed, well documented, and relatively kind to beginners. Despite being a good first language, it is not just a toy; it is very powerful and flexible and well suited for large projects. I have written a more detailed evaluation of Python. Good tutorials are available at the Python web site.
I used to recommend Java as a good language to learn early, but this critique has changed my mind (search for “The Pitfalls of Java as a First Programming Language” within it). A hacker cannot, as they devastatingly put it“approach problem-solving like a plumber in a hardware store”; you have to know what the components actually do. Now I think it is probably best to learn C and Lisp first, then Java.
There is perhaps a more general point here. If a language does too much for you, it may be simultaneously a good tool for production and a bad one for learning. It's not only languages that have this problem; web application frameworks like RubyOnRails, CakePHP, Django may make it too easy to reach a superficial sort of understanding that will leave you without resources when you have to tackle a hard problem, or even just debug the solution to an easy one.
If you get into serious programming, you will have to learn C, the core language of Unix. C++ is very closely related to C; if you know one, learning the other will not be difficult. Neither language is a good one to try learning as your first, however. And, actually, the more you can avoid programming in C the more productive you will be.
C is very efficient, and very sparing of your machine's resources. Unfortunately, C gets that efficiency by requiring you to do a lot of low-level management of resources (like memory) by hand. All that low-level code is complex and bug-prone, and will soak up huge amounts of your time on debugging. With today's machines as powerful as they are, this is usually a bad tradeoff — it's smarter to use a language that uses the machine's time less efficiently, but your time much more efficiently. Thus, Python.
Other languages of particular importance to hackers include Perl and LISP. Perl is worth learning for practical reasons; it's very widely used for active web pages and system administration, so that even if you never write Perl you should learn to read it. Many people use Perl in the way I suggest you should use Python, to avoid C programming on jobs that don't require C's machine efficiency. You will need to be able to understand their code.
LISP is worth learning for a different reason — the profound enlightenment experience you will have when you finally get it. That experience will make you a better programmer for the rest of your days, even if you never actually use LISP itself a lot. (You can get some beginning experience with LISP fairly easily by writing and modifying editing modes for the Emacs text editor, or Script-Fu plugins for the GIMP.)
It's best, actually, to learn all five of Python, C/C++, Java, Perl, and LISP. Besides being the most important hacking languages, they represent very different approaches to programming, and each will educate you in valuable ways.
But be aware that you won't reach the skill level of a hacker or even merely a programmer simply by accumulating languages — you need to learn how to think about programming problems in a general way, independent of any one language. To be a real hacker, you need to get to the point where you can learn a new language in days by relating what's in the manual to what you already know. This means you should learn several very different languages.
I can't give complete instructions on how to learn to program here — it's a complex skill. But I can tell you that books and courses won't do it — many, maybe most of the best hackers are self-taught. You can learn language features — bits of knowledge — from books, but the mind-set that makes that knowledge into living skill can be learned only by practice and apprenticeship. What will do it is (a) reading code and (b) writing code.
Peter Norvig, who is one of Google's top hackers and the co-author of the most widely used textbook on AI, has written an excellent essay called Teach Yourself Programming in Ten Years. His "recipe for programming success" is worth careful attention.
Learning to program is like learning to write good natural language. The best way to do it is to read some stuff written by masters of the form, write some things yourself, read a lot more, write a little more, read a lot more, write some more ... and repeat until your writing begins to develop the kind of strength and economy you see in your models.
Finding good code to read used to be hard, because there were few large programs available in source for fledgeling hackers to read and tinker with. This has changed dramatically; open-source software, programming tools, and operating systems (all built by hackers) are now widely available

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Also look at this->> The-Early-Hackers
Also look at this->> the-original-hackers-dictionary
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The Early Hackers

The beginnings of the hacker culture as we know it today can be conveniently dated to 1961, the year MIT acquired the first PDP-1. The Signals and Power Committee of MIT's Tech Model Railroad Club adopted the machine as their favorite tech-toy and invented programming tools, slang, and an entire surrounding culture that is still recognizably with us today. These early years have been examined in the first part of Steven Levy's book Hackers [Levy].

MIT's computer culture seems to have been the first to adopt the term `hacker'. The Tech Model Railroad Club's hackers became the nucleus of MIT's Artificial Intelligence Laboratory, the world's leading center of AI research into the early 1980s. Their influence was spread far wider after 1969, the first year of the ARPAnet.

The ARPAnet was the first transcontinental, high-speed computer network. It was built by the Defense Department as an experiment in digital communications, but grew to link together hundreds of universities and defense contractors and research laboratories. It enabled researchers everywhere to exchange information with unprecedented speed and flexibility, giving a huge boost to collaborative work and tremendously increasing both the pace and intensity of technological advance.

But the ARPAnet did something else as well. Its electronic highways brought together hackers all over the U.S. in a critical mass; instead of remaining in isolated small groups each developing their own ephemeral local cultures, they discovered (or re-invented) themselves as a networked tribe.

The first intentional artifacts of the hacker culture—the first slang lists, the first satires, the first self-conscious discussions of the hacker ethic—all propagated on the ARPAnet in its early years. In particular, the first version of the Jargon File developed as a cross-net collaboration during 1973–1975. This slang dictionary became one of the culture's defining documents. It was eventually published as "The Hacker's Dictionary" in 1983; that first version is out of print, but a revised and expanded version is New Hacker's Dictionary [Raymond].

Hackerdom flowered at the universities connected to the net, especially (though not exclusively) in their computer science departments. MIT's AI and LCS labs made it first among equals from the late 1960s. But Stanford University's Artificial Intelligence Laboratory (SAIL) and Carnegie-Mellon University (CMU) became nearly as important. All were thriving centers of computer science and AI research. All attracted bright people who contributed great things to the hacker culture, on both the technical and folkloric levels.

To understand what came later, though, we need to take another look at the computers themselves; because the AI Lab's rise and its eventual fall were both driven by waves of change in computing technology.

Since the days of the PDP-1, hackerdom's fortunes had been woven together with Digital Equipment Corporation's PDP series of minicomputers. DEC pioneered commercial interactive computing and time-sharing operating systems. Because their machines were flexible, powerful, and relatively cheap for the era, lots of universities bought them.

Cheap time-sharing was the medium the hacker culture grew in, and for most of its lifespan the ARPAnet was primarily a network of DEC machines. The most important of these was the PDP-10, first released in 1967. The 10 remained hackerdom's favorite machine for almost fifteen years; TOPS-10 (DEC's operating system for the machine) and MACRO-10 (its assembler) are still remembered with nostalgic fondness in a great deal of slang and folklore.

MIT, though it used the same PDP-10s as everyone else, took a slightly different path; they rejected DEC's software for the PDP-10 entirely and built their own operating system, the fabled ITS.

ITS stood for `Incompatible Time-sharing System' which gives one a pretty good fix on the MIT hackers' attitude (technically, the name was a play on its predecessor, the Compatible Time-Sharing System CTSS). They wanted it their way. Fortunately for all, MIT's people had the intelligence to match their arrogance. ITS, quirky and eccentric and occasionally buggy though it always was, hosted a brilliant series of technical innovations and still arguably holds the record as the single time-sharing system in longest continuous use.

ITS itself was written in assembler, but many ITS projects were written in the AI language LISP. LISP was far more powerful and flexible than any other language of its day; in fact, it is still a better design than most languages of today, twenty-five years later. LISP freed ITS's hackers to think in unusual and creative ways. It was a major factor in their successes, and remains one of hackerdom's favorite languages.

Many of the ITS culture's technical creations are still alive today; the EMACS program editor is perhaps the best-known. And much of ITS's folklore is still `live' to hackers, as one can see in the Jargon File.

SAIL and CMU weren't asleep, either. Many of the cadre of hackers that grew up around SAIL's PDP-10 later became key figures in the development of the personal computer and today's window/icon/mouse software interfaces. Meanwhile hackers at CMU were doing the work that would lead to the first practical large-scale applications of expert systems and industrial robotics.

Another important node of the culture was XEROX PARC, the famed Palo Alto Research Center. For more than a decade, from the early 1970s into the mid-1980s, PARC yielded an astonishing volume of groundbreaking hardware and software innovations. The modern mice, windows, and icons style of software interface was invented there. So was the laser printer, and the local-area network; and PARC's series of D machines anticipated the powerful personal computers of the 1980s by a decade. Sadly, these prophets were without honor in their own company; so much so that it became a standard joke to describe PARC as a place characterized by developing brilliant ideas for everyone else. Their influence on hackerdom was pervasive.

The ARPAnet and the PDP-10 cultures grew in strength and variety throughout the 1970s. The facilities for electronic mailing lists that had been used to foster cooperation among continent-wide special-interest groups were increasingly also used for more social and recreational purposes. DARPA deliberately turned a blind eye to all the technically `unauthorized' activity; it understood that the extra overhead was a small price to pay for attracting an entire generation of bright young people into the computing field.

Perhaps the best-known of the `social' ARPAnet mailing lists was the SF-LOVERS list for science-fiction fans; it is still very much alive today, in fact, on the larger `Internet' that ARPAnet evolved into. But there were many others, pioneering a style of communication that would later be commercialized by for-profit time-sharing services like CompuServe, GEnie and Prodigy (and later still dominated by AOL).

Your historian first became involved with the hacker culture in 1977 through the early ARPAnet and science-fiction fandom. From then onward, I personally witnessed and participated in many of the changes described here.

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