I’m kind of dubious its effective in any term whatsoever, unless the term is “nothing works but we got a lot of it”.

I think if people are citing in another 3 months time, they’ll be making a mistake

In 3 months they’ll think they’re 40% faster while being 38% slower. And sometime in 2026 they will be exactly 100% slower - the moment referred to as “technological singularity”.

Yeah, the glorious future where every half-as-good-as-expert developer is now only 25% as good as an expert (a level of performance also known as being “completely shit at it”), but he’s writing 10x the amount of unusable shitcode.

I suspect that the kind of people who would “know how to use it” don’t use it right now since it has not yet reached “useful if you know how to use it” status.

Software work is dominated by the fat tail distribution of time it takes to figure out and fix a bug. Not by typing code. LLMs, much like any other form of cutting and pasting code without having any clue what it does, gives that distribution a longer, fatter tail, hence its detrimental effect on productivity.

And the other “nuanced” take, common on my linkedin feed, is that people who learn how to use (useless) AI are gonna replace everyone with their much increased productive output.

Even if AI becomes not so useless, the only people whose productivity will actually improve are the people who aren’t using it now (because they correctly notice that its a waste of time).

That philosophy always ends in stepping into dogshit to try to boost stock prices.

When they tested on bugs not in SWE-Bench, the success rate dropped to 57‑71% on random items, and 50‑68% on fresh issues created after the benchmark snapshot. I’m surprised they did that well.

After the benchmark snapshot. Could still be before LLM training data cut off, or available via RAG.

edit: For a fair test you have to use git issues that had not been resolved yet by a human.

This is how these fuckers talk, all of the time. Also see Sam Altman’s not-quite-denials of training on Scarlett Johansson’s voice: they just asserted that they had hired a voice actor, but didn’t deny training on actual Scarlett Johansson’s voice. edit: because anyone with half a brain knows that not only did they train on her actual voice, they probably gave it and their other pirated movie soundtracks massively higher weighting, just as they did for books and NYT articles.

Anyhow, I fully expect that by now they just use everything they can to cheat benchmarks, up to and including RAG from solutions past the training dataset cut off date. With two of the paper authors being from Microsoft itself, expect that their “fresh issues” are gamed too.

Yeah I’m thinking that people who think their brains work like LLM may be somewhat correct. Still wrong in some ways as even their brains learn from several orders of magnitude less data than LLMs do, but close enough.

You can film with an actual camera then use video to video to make it look very AI. If you’re just grifting, that would be the way to go I think.

They’re also very gleeful about finally having one upped the experts with one weird trick.

Up until AI they were the people who were inept and late at adopting new technology, and now they get to feel that they’re ahead (because this time the new half-assed technology was pushed onto them and they didn’t figure out they needed to opt out).

I was writing some math code, and not being an idiot I’m using an open source math library for doing something called “QR decomposition”, and its efficient, and it supports sparse matrices (matrices where many numbers are 0), etc.

Just out of curiosity I checked where some idiot vibecoder would end up. AI simply plagiarizes from some shit sample snippets which exist purely to teach people what QR decomposition is. It’s actually unusable, due to being numerically unstable.

Who in the fuck even needs this shit to be plagiarized, anyway?

It can’t plagiarize a production quality implementation, because you can count those on the fingers of one hand, they’re complex as fuck and you can’t just blend a few together to try to pretend you didn’t plagiarize.

The answer is, people who are peddling the AI. They are the ones who ordered plagiarism with extra plagiarism on top. These are not coding tools, these are demos to convince the investors to buy the actual product, which is company’s stock. There’s a little bit of tool functionality (you can ask them to refactor the code), but it’s just you misusing a demo to try to get some value out of it.

And to that end, the demos take every opportunity to plagiarize something, and to talk about how the “AI” wrote the code from scratch based on its supposed understanding of fairly advanced math.

And in coding, it is counter productive to plagiarize. Many of the open source libraries can be used in commercial projects. You get upstream fixes for free. You don’t end up with some bugs or worse yet security exploits that may have been fixed since the training cut-off date.

No fucking one in the right mind would willingly want their product to contain copy pasted snippets from stale open source libraries, passed through some sort of variable-renaming copyright laundering machine.

Except of course the business idiots who are in charge of software at major companies, who don’t understand software. Who just failed upwards.

They look at plagiarized lines and count them as improved productivity.

Indistinguishable from a business idiot.

If it was a basement dweller with a chatbot that could be mistaken for a criminal co-conspirator, he would’ve gotten arrested and his computer seized as evidence, and then it would be a crapshoot if he would even be able to convince a jury that it was an accident. Especially if he was getting paid for his chatbot. Now, I’m not saying that this is right, just stating how it is for normal human beings.

It may not be explicitly illegal for a computer to do something, but you are liable for what your shit does. You can’t just make a robot lawnmower and run over a neighbor’s kid. If you are using random numbers to steer your lawnmower… yeah.

But because it’s OpenAI with 300 billion dollar “valuation”, absolutely nothing can happen whatsoever.

In theory, at least, criminal justice’s purpose is prevention of crimes. And if it would serve that purpose to arrest a person, it would serve that same purpose to court-order a shutdown of a chatbot.

There’s no 1st amendment right to enter into criminal conspiracies to kill people. Not even if “people” is Sam Altman.

It’s curious how if ChatGPT was a person - saying exactly the same words - he would’ve gotten charged with a criminal conspiracy, or even shot, as its human co-conspirator in Florida did.

And had it been a foreign human in the middle east, radicalizing random people, he would’ve gotten a drone strike.

“AI” - and the companies building them - enjoy the kind of universal legal immunity that is never granted to humans. That needs to end.

I appreciate the sentiment but I also hate the whole “AI is a power loom for coding”.

The power loom for coding is called “git clone”.

What “AI” (LLM) tools provide is just English as a programming language with plagiarized sum total of all open source as the standard library. English is a shit programming language. LLMs are shit at compiling it. Open source is awesome. Plagiarized open source is “meh” - you can not apply upstream patches.

So, the judge says:

In cases involving uses like Meta’s, it seems like the plaintiffs will often win, at least where those cases have better-developed records on the market effects of the defendant’s use.

And what is that supposed to ever look like? Do authors need a better developed record of effects of movies on book sales, to get paid for movie adaptations, too?

It’s called sarcasm.

One thing that I couldn’t easily figure out is what is the constant factor. If the constant factor is significantly worse than for Strassen, then it would be much slower than Strassen except for very large matrices.

Let’s say the constant factor is k.

N should be large enough that N^((log(49)-log(48))/log(4)) > k where k is the constant factor. Let’s say the difference in exponents is x, then

N^x > k

log(N)*x > log(k)

N > exp(log(k)/x)

N > k^(1/x)

So lets say x is 0.01487367169 , then we’re talking [constant factor]^67 for how big the matrix has to be?

So, 2^67 sized matrix (2^134 entries in it) if Google’s is 2x greater constant than Strassen.

That don’t even sound right, but I double checked, (k^67) ^ 0.01487367169 is approximately k.

edit: I’m not sure what the cross over points would be if you use Google’s then Strassen’s then O( n^3 )

Also, Strassen’s algorithm works on reals (and of course, on complex numbers), while the new “improvement” reduces by 1 the number of real multiplications required for a product of two 4x4 complex-valued matrices.

Old McDonald had a startup, iyo io o[4-mini].

It’s funny how just today in a completely unrelated context a generative ai enthusiast used an example of OpenAI getting sued by NYT as a reason why they wouldn’t commit some other malfeasance because they’d get caught if they did.