Tech Bros make a panopticon and call it a novel approach

Unfortunately it requires vulkan (it says 1.3, but because vulkan is based on extensions so it probably doesn’t require the full 1.3). So if you have the Intel GMA 950 that’s in the motherboard for your Pentium 4 HT is not supported. But I’m confident that an AMD HD 6000 from 2010 with the Mesa driver “terakan” is enough to run it. And theoretically one could implement vulkan even for an HD 2000 from 2007, but it’s an unreasonable effort.

If they made an opengl backend, you would be golden, as the Mesa driver i915 implements opengl 2.1 for the GMA 950, and it’s definitely enough to run an editor

P.s.: and I sure did not spend the last 30 minutes looking up vulkan hardware

You forgot the part where TeX was created by a CS professor because he didn’t like how his editor printed the formulas in his book

It allows too many attempts. Even in hard you can just run 6 binary searches simultaneously and you will always win.

Only expert mode is un exploitable.

It’s cotton, actually

It looks like my ass is very knowledgeable. Definitely a good source

I feel like this argument is way too imprecise, to the point of being basically untrue. That’s probably based on the average emissions or something like that, but people are not the same and “emission responsibility” is wildly different.

Imagine killing 34k exploited African people, the world’s climate won’t even notice that. On the other hand, killing 34k middle class Americans or Europeans would probably be a little more effective, but still won’t fix anything. Now, killing 34k high-profile megacorp executives would definitely be much more effective, but would also collapse some economies, leading to various climate unfriendly events (like riots, war and shit).

But the simplest empirical evidence is: COVID killed 6 million people and the climate is still shit.

Source: I made it the fuck up, I’m talking out of my ass

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This will affect the productivity goals of the company

This research challenges the standard argument that the mere existence of a product implies positive welfare for its users.

I know this probably means something I don’t understand, but it feels so stupid… Like, what about asbestos?

It’s not a dumb question. The answer is “it depends”, it’s mostly a choice. The general rule is that when yo start a project, you choose the language that you think will help you the most, whether that is because you already know the language, or because you have to work with stuff that already use that language, or because the language is better at doing that.

Regarding whether to stick to a language or learn a new one, in general your CS teachers will tell you (and they are correct) that you should not “learn to program in a language” but just “learn to program” and then apply that knowledge to whatever language you need. So, you should always learn languages that are different from the ones you already know in order to learn new paradigms, and then when you need a specific language, just learn the details about it. BUT, even if this way you will be able to use most languages, you will not be “good” at most, so you should also have some languages that you know really well and are experienced in. And for that you should choose the ones that are more useful to you (or maybe useful for your job) or the ones that you like.

For various reasons.

Different languages take different approaches and models to do stuff. Thus making each one better or worse for different challenges.

For example, python is easy and quick to get up and running. Bug it’s slow and unsuited for low level programming (like OSes, drivers, and embedded stuff). On the other hand, C is very fast and very low level, allowing you to make both high performance programs and low level stuff, but it’s a nightmare to use and you will run into all sorts of problems that an easier language, like python would avoid. All of this is mostly due to python being interpreted (instead of compiled) and c having manual memory management.

But they might also take a different approach at computation, like for functional languages (opposed to imperative languages) which try to solve problems in a more “mathematical” and declarative way, instead of defining a sequence of instructions. Or, even more, logic programming languages, in which you define a series of logical statements and the language tries to find solution and draw conclusions from them.

And some languages are designed around some particular concept that they think is beneficial and worth exploring, maybe taken from various other languages and put together, like rust which is designed around the RAII design pattern and takes large inspiration from functional languages, while still being and imperative procedural language (like C, so not object oriented). Or java which was designed to be a portanle but performante object oriented language, with support for low powered embedded system, full of features but simpler than c++, etc… all stuff that now is nothing special, but remember that java us very old. Or kotlin which tries to be java (they are interoperable), but modern and better and more “pure” or “consistent”. Or c++ that started as an object oriented superset of c, but then started adding every single feature of every other imperative language, and never giving up on older stuff, to the point of becoming very hard to use correctly while making it very easy to screw yourself in the most intricate ways.

And of course there are domain specific languages (opposed to general purpose languages). Which aren’t even necessarily Turing complete. They are designed for one purpose only, and they do that better than general purpose languages. For example AWK for text processing, or SQL for databases, or matlab for scientific calculations, or TeX for typesetting, or GLSL for shaders, or coq for theorem proving. And here imagination truly is the limit.

So, the point is, every language is more suited then other for some kind of work. And when people stumble across some problem that is hard to tackle and come up with some approach to solve it, or when they grow fed up with the issues of some older languages, they often like to make a new language around that. And some times this leads to entirely new paradigms which are worth exploring.

P.s.: I like languages:)