Speak English to me: The secret world of programmers

There are three variants:

#1. They are just stupid.

#2. They don't care about work other than bare minimum to not get fired. They have better things in their life to care about.

#3. They are as smart as you are, but they put their efforts elsewhere that you don't directly observe. May be they don't know how to operate Linux environment, but they spent days reading about DSP math and they can apply this knowledge to optimise some signal filter. May be they don't know HTML, but they spend that time to learn Spring internals and can compose really beautiful code out of it or solve very hard problems.

I think that specialisation is not that bad. It's everywhere. Some people in IT are generalists, they know a bit of everything. I consider myself one. I'm trying to build an embedded Linux distro right now, few days ago I was writing parser for PDF to extract some information (real parser, byte after byte), few weeks ago I tinkered with STM32 program, few months ago I wrote Java service, next week I'l continue to work on React web app which is going to run on embedded device. I do everything, my knowledge helps me immensely to quickly dive into new area. But i'm absolutely far from expert and sometimes I'd spend lots of time when I lack a knowledge. For example I spent two weeks trying to start graphics on i.MX8MM board and I failed. I need to debug kernel but I just don't have that kind of skill in Linux internals. But there are people who prefer to specialise and that's fine and they would probably solve that problem in few days.

Man learns outside world exists, is annoyed by it.

I am the first to graduate college and to go into software. I learned a lot on my own before studying Computer Science, and still do after.

I can agree that people tend to quickly get condescending ("just google it") instead of giving keywords ("I think you are looking for X, Y, Z, try to read about them and come back when you have specific questions"). IMO the latter is constructive, the former is discouraging. The point here is: don't be condescending, try to give advice/keywords.

This said, many beginners think that their first project can be a complex application even though they don't know how to write code. The constructive comment for them is "start small, learn the language first", but of course many beginners don't take that as an answer. Beginners need to accept that learning software engineering takes time and dedication.

Finally, some devs tend to think that they are so smart that if they don't grasp something in 2 seconds, then that something is bad. I disagree with that. An experienced developer should know how to learn a new tool. If they don't like it, they can try another one, write their own (and see if a community follows them), or just accept that they don't have a choice. That's how it works.

Literally everything I write is in Markdown because it’s so simple to me. I use it for blogposts, presentations, documentation, todos, internet comments.

But this article doesn’t strike a chord with me. The author fails to understand that it seems easy to us because we’re familiar with it. Of course we know how to transform it into PDF or HTML. But this knowledge isn’t basic, and it’s understandable that people want to stick to what they know.

Here’s an example. Say you had 10000 rows of CSV to slice and dice and extract insights and visualisations out of. All of us would go to the tools we’re familiar with - pandas, R, duckDB etc. But I think the median Excel user probably completes this task quicker and better than the median pandas user. And then if the pandas person is told “oh, just pick up Excel, it’s easy to learn and so useful” they won’t listen. And for good reason! Excel is powerful, but it’s not easy to learn. It only seems that way to people who already know how it works.

Don’t confuse the familiar with easy. Just because it’s easy for you doesn’t mean it’s objectively easy.

As a person with an extensive background in ML, I feel similarly about CS grads attaching themselves to "AI" enterprises until they convince themselves they're an "expert" without putting in the due diligence to understand the unifying math under the hood. They end up doing stupid things with their models, completely ruining their business proposition through their lack of understanding in how the ML theory makes the implementation make sense. And then they don't even know how to appropriately judge the quality of the outputs from the reported metrics.

It's normies all the way down.