Why Learning to Code Is So Damn Hard

That's because you, like all of us that learned in the 80s, were very, very lucky. We started in a world where writing good, production code and learning the very simple basics of programming were the same thing. I started with a ZX Spectrum. You could use simple machine code, or simple basic. Libraries? What are those? The one barrier to get good was when you ran out of memory, and had to switch to machine code and learn memory saving techniques, but by then, you were as ready as anyone.

I compare it to wha we do today: My code uses libraries, that use libraries, that use libraries. Languages are huge in comparison. Sure, it's easier to do what we used to do 20 years ago, but nobody expects from us what we did then: Even someone that is just learning wants to do more. This is what builds the despair phase of the article.

It's a well known issue that both affects how we train new people and how we even manage large pieces of software today, so it's well talked about. For instance, the first talk of JSRemoteConf last night was all about this issue. Hopefully they make the recordings openly available soon.

Given that autism is a mental condition characterized by difficulty in communicating and forming relationships with others, involving an atypical brain development for some reason or another, I believe that this is evidence enough that social skills or lack thereof are influenced very much by the brain. We really shouldn't place so much faith in free will :-)

On mathematics and physics - if you'll notice, they share common traits that go beyond the lack of social interaction. Creativity is also required (well, not if you think about math or physics as learned in school), focus and juggling with abstractions as well. In fact mathematics is even more extreme, because with software development one usually gets much faster feedback from normal people. And it's not like you see mathematicians and physicists everywhere.

>Would that be 100% retarded or just about 70% retarded?

I'll go with "less than 70%". Or at least I hope, because that's what I did.

Coming from a Economics background (with a minor in Statistics), I took a job as a "data analyst" at a software company out of University. It was fun, but I relied on working with a programmer to get even basic things done.

Then came the "Data Science" wave. I thought, this is right up my alley! Except I needed to learn to program. I tried doing the tutorials and academies online, but was continuously stuck in the hand-holding stage. So I did the opposite of what is often recommended around here: I quit my job and enrolled in a 2 year Computer Programming course at a college. I'll be finishing up this spring. The enforced, formal structure and discipline has been a boon.

I feel great about having done it. I still feel pretty green, but I'm probably somewhere in the middle of the "desert" now. I credit school with having got me that far.

I don't think it's a good thing either.

I also find the spread of autism terrifying. My wife works at a kindergarten and she has had to deal with children that have problems from the autistic spectrum, healthy enough to be allowed to integrate in a normal collectivity, problematic enough that adjustment is very hard - they simply stand out from everybody else. And she noticed an interesting pattern, those children usually come from families of engineers, mathematicians or software developers.

And maybe the sample is not representative of the general population, but it scares the bejesus out of me, especially since we have a son that is happy, loving, socially apt, smart and healthy, but that was a late talker and at 4 years old he still does mispronunciation for many words - this is another recent trend.

I have read a book at some point, can't remember the title, but especially in boys you have brain parts specialized for certain tasks and this happens when you've got certain parts of the brain underdeveloped, with maybe other parts overdeveloped. It usually goes away, once the child grows. Maybe this is just an evolutionary step, or maybe we are broken, I don't know.

With respect to your CS prof at one of my favorite places (Go Cavaliers!), this is not an issue of discrete vs. continuous. If your structure can have either 9 or 10, but not 9.001, supports, it is discrete, not continuous, regardless of failure mode. And remove one of the 3 legs of your stool, and you probably wouldn't have 2/3rds of its support remaining, but whether you did or didn't would be an issue of proportionality, not continuity.

There are a lot of jumbled concepts here, most of which don't matter anyway, because what you are talking about is the phenomenon of graceful degradation. In the physical world, both natural and man-made, almost nothing at the macro scale is ever perfect, so the best designs tend to be those that remain good enough under the widest range of circumstances vs. a more common software goal of being perfect under perfectly controlled circumstances.

As software gradually moves out from the wall garden of a single mainframe to fill the world with interacting systems spanning diverse machines, sensors, communications channels, data types, etc., design for graceful degradation becomes more and more of a focus for professional software architects.

Coding in the gracefully degrading way is much harder than coding in the "if even one of your ten lines is wrong, you crash" tradition. The fact that even the latter is so hard for us humans means we will need more and more help from machines that learn what to do without being explicitly told by us.