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It is the loss of the sense of wonder. The trick is that computers today are really really really fast versions of computers from 60 years ago. Fundamentally the same box, just faster and with more memory and disk space. That isn't entirely true of course, there are special function architectures like GPUs which have a specialness all their own, but it is true for most people. But the thing that drives people in the beginning, is the sense of wonder. Same thing with relationships, when they are new, everything about them is new and exciting, and then they aren't that much new any more and you don't get any relationship augmentation from the newness. So once you've discovered computers, and made them do your bidding, that is all new and all exciting and the feedback loop is strong. But once you have done all of the 'usual' things once, and due to work obligations are doing them again, but in a slightly different way, you aren't getting the adrenalin rush from the newness. Computer games, same thing, movie franchises, same thing. So what to do. Well when the rush of newness runs out, you have three choices; pick a new field, pick a different area in the field, or take it to the next level. New field or different area choices give you a new thing to be excited about, taking it to the next level requires that you challenge yourself. Taking an example from the article, "... I don’t feel like I really grok the module system. I definitely don’t understand the class system. What the hell is a generator and how does it work the way it does? I am so lost." Decide to take your understanding of Python to the next level, and by that seek out the more advanced texts, read them, read the source code, build Python from scratch, add some change to the module system, analyze the impacts, look at how classes work in Python and compare them to classes in C++, Scala, Java, and Go. If you can get over that hump then you can get engaged in learning things at a deeper level. I really didn't understand Python modules at all well at Google until I took apart the SWIG system to write some C code that was accessible by Python. Port it to a different architecture, benchmark it, change it and benchmark the changes. That is what I mean by "going deep." You will find after a while that there is a lot of similarity at that next level too. Another skill people don't work on until later than they should, reading code. Read a bunch of code, figure out how it works, prove your understanding by changing it. All of these things are the programmatic equivalent of strength training at a professional level. If you can't get any satisfaction from this though, option 1 (try something new) will give you more satisfaction long term. |