Glad you got fired. Vaccines should always be mandatory save for legitimate, doctor-validated medical exemptions.

Anti-vaxxers are fucking stupid and should either be educated properly or, if they still refuse to do their civic duty after being de-programmed of misinformation, punished. You are only allowed to participate in society if you take the necessary steps that you are morally and ethically obligated to do in order to protect it from preventable, transmissible disease. We had eradicated polio until stupid motherfuckers like yourself decided that it would be a good idea to forgo the standard polio vaccine schedule that we’ve had for decades. Now, we saw the first case in 30 years in 2022 because someone selfishly thought that their personal beliefs were more important than the health and livelihood of everyone else.

Yep. Postgres is fantastic and there’s no justification to use proprietary bullshit like that.

The issues are primarily with Azure, I believe.

From what I recall, particularly the younger generations that exclusively use mobile devices (though of course this is not limited to them) actually have terrible tech literacy across the board, primarily related to spending all of their time in apps that basically spoon-feed functionality in a closed ecosystem. In particular, these groups are particularly vulnerable to very basic scams and phishing attacks.

VMware workstation supports using a GPU. You can even use it in “pass-through” mode to give the VM full, exclusive access to the GPU.

Common Lisp isn’t a functional programming language. Guile being based on Scheme is closer, but I’d still argue that opting into OOP is diverging from the essence of FP.

Yeah that’s what I was referring to by “archaic”. Pretty much anything using the LAMP stack falls in that category. I don’t generally see new things using it.

Databases aren’t related to VMs or containers.

https://en.m.wikipedia.org/wiki/Database does a good job of describing what a database is. That page also has a lot of examples of uses of databases.

To answer your question about MySQL: in my experience it’s rarely used outside of classrooms or archaic systems. Postgres is a much better general-purpose option for SQL. Sqlite is also nice for different use cases (such as a database on a mobile device).

Just remember that if you aren’t actually concatenating files, cat is always unnecessary.

I used to work for a company that made software built on VMware. The biggest customer was using hundreds of thousands of VMs. Pretty sure they’re working on moving off VMware now because of all this bullshit.

But yeah, it’s gonna take a long time to move off.

https://porkmail.org/era/unix/award#cat

jq < file.json

cat is for concatenating multiple files, not redirecting single files.

Meanwhile, I can open a 1GB file in (stock) vim without any trouble at all.

Formatting is what xmllint is for.

:syntax off and it works just fine.

I understand what you’re saying—I’m saying that data validation is precisely the purpose of parsers (or deserialization) in statically-typed languages. Type-checking is data validation, and parsing is the process of turning untyped, unvalidated data into typed, validated data. And, what’s more, is that you can often get this functionality for free without having to write any code other than your type (if the validation is simple enough, anyway). Pydantic exists to solve a problem of Python’s own making and to reproduce what’s standard in statically-typed languages.

In the case of config files, it’s even possible to do this at compile time, depending on the language. Or in other words, you can statically guarantee that a config file exists at a particular location and deserialize it/validate it into a native data structure all without ever running your actual program. At my day job, all of our app’s configuration lives in Dhall files which get imported and validated into our codebase as a compile-time step, meaning that misconfiguration is a compiler error.

You’re just describing parsing in statically-typed languages, to be honest. Adding all of this stuff to Python is just (poorly) reinventing the wheel.

Python’s a great language for writing small scripts (one of my favorite for the task, in fact), but it’s not really suitable for serious, large scale production usage.

I know it’s a joke, but just wanted to say that Uranium used for fuel is not something you can actually use for weaponry directly. It requires enrichment to increase the concentration of U-235 to weapons-grade levels.

Sure yeah, but like, I work remote and will always work remote (I live in a city with a pretty mediocre tech scene). On top of that, I work in a non-mainstream programming language (Haskell). So it’s hard to envision what I could actually do.

I’m very pro-union btw, it just seems like there are certain things that can sometimes make it more difficult to make happen

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.