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Generally agree with your points, even though I"m honestly not sure what a union would look like like in practice.

But I just wanted to say that this job is definitely harder than plumbing. I usually do my own plumbing and it's not really that bad. It's not my favorite thing to do and can sometimes be a pain in the ass, but it's way less taxing imo.

Teaching kids is hard as fuck though and good teachers are priceless. Honestly quality caregiving of any sort is massively underrated.

You do not understand how these things actually work. I mean, fair enough, most people don't. But it's a bit foolhardy to propose changes to how something works without understanding how it works now.

There is no "database". That's a fundamental misunderstanding of the technology. It is entirely impossible to query a model to determine if something is "present" or not (the question doesn't even make sense in that context).

A model is, to greatly simplify things, a function (like in math) that will compute a response based on the input given. What this computation does is entirely opaque (including to the creators). It's what we we call a "black box". In order to create said function, we start from a completely random mapping of inputs to outputs (we'll call them weights from now on) as well as training data, iteratively feed training data to this function and measure how close its output is to what we expect, adjusting the weights (which are just numbers) based on how close it is. This is a gross simplification of the complexity involved (and doesn't even touch on the structure of the model's network itself), but it should give you a good idea.

It's applied statistics: we're effectively creating a probability distribution over natural language itself, where we predict the next word based on how frequently we've seen words in a particular arrangement. This is old technology (dates back to the 90s) that has hit the mainstream due to increases in computing power (training models is very computationally expensive) and massive increases in the size of dataset used in training.

Source: senior software engineer with a computer science degree and multiple graduate-level courses on natural language processing and deep learning

Btw, I have serious issues with both capitalism itself and machine learning as it is applied by corporations, so don't take what I'm saying to mean that I'm in any way an apologist for them. But it's important to direct our criticisms of the system as precisely as possible.

It's got nothing to do with capitalism. It's fundamentally a matter of people using it for things it's not actually good at, because ultimately it's just statistics. The words generated are based on a probability distribution derived from its (huge) training dataset. It has no understanding or knowledge. It's mimicry.

It's why it's incredibly stupid to try using it for the things people are trying to use it for, like as a source of information. It's a model of language, yet people act like it has actual insight or understanding.

1 Is pretty standard in the industry for people with experience. I haven't actually applied to any jobs myself in a while. Job hunting for me is sifting through the recruiter messages that hit my inbox.

In my experience, your average software developer has absolutely terrible security hygiene. It's why you see countless instances of private keys copy/pasted into public GitHub repos or the seemingly daily occurrences of massive data breaches.

My undergrad in CS (which I should point out, is still by far the most common major for software engineers) did not require a security course, and I'm fairly confident that this is pretty typical. To be honest, I wouldn't have trusted any of my CS professors to know the first thing about security. It's a completely different field and something that generally requires a lot of practical experience. The closest we ever got was an explanation of asymmetric vs. symmetric encryption. There was certainly no discussion of even basic things like how to properly manage secrets or authn best practices.

Everything I know now as a senior software engineer about software security has come from experience on the job. I've been very fortunate to work at some places that take it very seriously (including a government contractor writing cybersecurity software for the Department of Defense) and learned a lot there. But a lot of shops don't have a culture that promotes good security hygiene, and it shows in the litany of insecure software out in the wild today.

In case you're not familiar, https://en.m.wikipedia.org/wiki/Grok.

It's somewhat common slang in hacker culture, which of course Elon is shitting all over as usual. It's especially ironic since the meaning of the word roughly means "deep or profound understanding", which their AI has anything but.

I mean, you're scapegoating developers right now. Developers don't determine priorities. That's a product/business direction problem.

Also, UX doesn't get to say what is hard to do or not (that's the job of a developer, you really don't have any way of knowing without familiarity with the implementation details), so that's certainly at least part of your problem right there.

This whole situation just emphasizes the fact that rebasing >>>>>>>>>> merge squashing.

If you're working with csv data, https://www.visidata.org/ >>>>> excel (assuming you're comfortable with terminal UIs, anyway). You can very rapidly slice and dice data and for formulas and such, you can just write Python.

Nope, based on the user's comment history, they're a conservative nutjob.

Corporate IT never goes for it, unfortunately.

My experience thus far is that the intersection of IT professionals and people who know how to administrate Linux systems well is a really small set of people. Not enough sysadmins these days.