Good code is not “elegant” code. It’s code that is simple and unsurprising and can be easily understood by a hungover fresh graduate new hire.

I wouldnt go that far, both elegance are simplicity are important. Sure using obvious and well known language feaures is a plus, but give me three lines that solve the problem as a graph search over 200 lines of object oriented boilerplate any day. Like most things it’s a trade-off, going too far in either direction is bad.

Legislators are there to directly reflect the opinions and interests of their constituents, judges are there to have expert knowledge of the law and how it applies to each case uniquely. The first needs some form of democratic mechanism to ensure that they represent people’s current opinions, the later needs a meritocratic mechanism to ensure they are experts in the correct fields.

If judges were the only element of a court I would agree that it would be problematic to have no democratic input, but in common law systems at least that element is represented by juries who are the most powerful element of a court case as they are unchallengable arbiters of fact and drawn through sortition which is even more democratic than election.

Judges shouldnt be elected for the same reasons surgeons shouldnt be elected.

What do you use as a vm on an arm mac? I was looking into this a while back to run linux on my work m3 macbook but i couldnt find any good options

Umatrix is great, you can configure it to automatically allow first party javascript, and if sites still dont work eneable bits until they do them lock those settings so the same bits will be enabled next time you’re on that site.

Why is everyone in this thread constantly putting meaningless @ mentions at the top of their replies?

I was responding to your general statement that python is slow and so there is no point in making it faster, I agree that removing the GIL wont do much to improve the execution speed for programs making heavy use of numpy or things calling outside it.

That’s a bit suss too tbh. Did the C++ version use an existing library like Eigen too or did they implement everything from scratch?

It was written entirely from scratch which is kind of my point, a well writen python program can outperform a naive c implementation and is vastly simpler to create.

If you have the expertise and are willing to put in the effort you likely can squeze that extra bit of performance out by dropping to a lower level language, but for certain workloads you can get good performance out of python if you know what you are doing so calling it extremely slow and saying you have to move to another language if you care about performance is missleading.

Numpy is written in C.

Python is written in C too, what’s your point? I’ve seen this argument a few times and I find it bizarre that “easily able to incorporate highly optimised Fortran and C numerical routines” is somehow portrayed as a point against python.

Numpy is a defacto extension to the python standard that adds first class support for single type multi-dimensional arrays and functions for working on them. It is implemented in a mixture of python and c (about 60% python according to github) , interfaces with python’s c-api and links in specialist libraries for operations. You could write the same statement for parts of the python std-lib, is that also not python?

Its hard to not understate just how much simpler development is in numpy compared to c++, in this example here the new python version was less than 50 lines and was developed in an afternoon, the c++ version was closing in on 1000 lines over 6 files.

Nope, if you’re working on large arrays of data you can get significant speed ups using well optimised BLAS functions that are vectorised (numpy) which beats out simply written c++ operating on each array element in turn. There’s also Numba which uses LLVM to jit compile a subset of python to get compiled performance, though I didnt go to that in this case.

You could link the BLAS libraries to c++ but its significantly more work than just importing numpy from python.

Python can be extremely slow, it doesn’t have to be. I recently re-wrote a stats program at work and got a ~500x speedup over the original python and a 10x speed up over the c++ rewrite of that. If you know how python works and avoid the performance foot-guns like nested loops you can often (though not always) get good performance.

Local models WOULD form the basis of FOSS AI. Supposition on my part but entirely supportable given there is already a open source model movement focus on producing local models and open source software is generally privacy focused.

Local models ARE inherently private due to the way that no information leaves the device it is processed on.

I know you dont want to engage with arguments and instead just wail at the latest daemon for internet points, but you can have more than one statement in a sentence without being incoherent.

So its settled that neurons are the only way to create inteligence? Again you need to get your work published, it’s clearly groundbreaking that you’ve solved these long standing disputes.

It doesnt though, local models would be at the core of FOSS AI, and they dont require you to trust anyone with your data.

It takes only an incredibly basic knowledge of computers and brains to know that we cannot make an actual intelligent program using the Von Neumann style of computer.

Nice to hear that it only takes a very basic knowledge of computers to settle one of the most hotly disputed issues in philosophy and computing. You should let them know you’ve decided it.

If storage space is important using uncompressed json is a bad choice, if you’re compressing the json it doesnt really matter if you have lots of exceptionCase: False fields as they will compress very well.

The point you seem to be missing is that it isnt that linux is too hard for average people, it isnt id even say its easier than windows to use for people who arent deep into using windows already.

Its that installing any os is too much for regular people and Microsoft have been fighting dirty and abusing market dominance to make sure they kill pre-installed linux machines.

I mean gnome and kde both have it so that doesnt feel correct for why macos doesnt.

Huh, i have the complete opposite reaction. Having to move to macos for work finder is probably my least favourite bit. It feels like it is deliberately trying to hide the file system and my files from me and just give me the files it thinks i want, id have nautilus or thunar installed in a heartbeat.

Fwiw i have almost exactly the same feeling going from gnome to macos, sure its polished but it goes out of its way to make anything even slightly complicated incredibly difficult. So yeah im pretty sure its mostly familiarity.

The counterpoint is since 16:9 became the de facto standard for monitors, vertical resolution is at much more of a premium than horizontal resolution is.