dachworker

The abstraction is by necessity. Our puny brains have only a very small working memory. The only way we can reason about many problems is by creating multiple levels of hierarchy. That is actually the essence of what mathematics is.

It's an economic metric, not a political statement.

I find all this gender wars crap competely untrue in real life. I wouldn't be surprised if it was all due to sampling errors.

My parents bought their house cash. No they were not high earners and not even median income earners. Yes the house is now worth close to a million dollars.

Why should I not just port my kernel to Triton? What's the appeal of Scale?

Here's a thought: maybe liberal democracy was never very free.

Corporate taxes don't work and taxing the rich is super hard. Could this be a way for the state to suplement tax revenue?

For the kids, sure. Incidentally, if you post anything political you must now do it with the knowledge that your identity is tied to your comment or opinion.

I'm excited for the opportunities that an abundance of compute brings, even in the absense of AI applications. Big compute is the right direction, if we can afford it. There are always things to simulate or discover or automate in some way shape or form.

My grandparents were all born in Europe between the two world wars. Actually, despite humble origins they all had a fairly prosperous life. Even though I have much more education than they did, I don't think I can ever achieve the same level of prosperity as they had.

Like, I certainly cannot afford a family of 12 children. Nor can I afford to buy the amount of land that they acquired, and certainly not by working the same kind of jobs they did.

Maybe I'm weird, but does anyone else have worries about what future their prospective children would inherit? In particular things that worry me: 1. the growing geopolitcal turmoil which is likely to eventually descend into a great war of sorts, the footage coming out of Ukraine is horrifying, 2. climate change isn't going to be dealt with and again, lots of violence will ensure because of that, almost certainly, 3. not sure what to think about AGI, but I'm not entirely dismissive and at best it seems like a dual use technology, 4. a GATTACA-type future where the super rich figure out a way to birth super humans with perfect genetics and top 0.001% IQs. All of those make the future look so unappealing.

I'm super excited to give this one a spin. It seems like a neat idea, Triton, but simpler and with automatic autotuning. My head is spinning with options right now. I love how everyone was hyping up CUDA this and CUDA that a couple of years ago, and now CUDA is all but irrelevant. There's now so many different and opinionated takes on how you should write high performant accelerator cluster code. I love it.

It's also kinda of ironic that right now in 2025, we have all this diversity in tooling, but at the same time, the ML architecture space has collapsed entirely and everyone is just using transformers.

The bubble will burst when all this agentic AI slop fails to deliver. Might take a while yet, because generative models are very good at aping competence and it's therefore easy to produce compelling demos.

I love how they always style themselves experts on economics, as if there is one global economic policy that benefits everyone, equally. How cute.

Fair point, but here's my counter: consumers won't analyze the data but insurance companies will.

It's the British English equivalent.

Isn't the lesson from the success of TSLA, that you don't compete on price? That's what made Tesla the first successful EV. Because unlike the rest, they didn't try to compete on price and offer a mass market consumer vehicle. Instead they started with a roadster and then a luxury saloon both targeting the upper end of the market. I don't see the point of a budget taxi car. After all even the human driven counterparts tend to be higher end luxury saloons or SUVs.

You constructed a narrative and overlaid it over the facts.

The facts are that for many decades past, it was possible for hopeful economic immigrants to abuse asylum laws, or the back then less protected border, to gain entry to the US. Neither red nor blue administrations handled this properly and lots of people benefited from the status quo, and your focus on, "the left" is quite conspicuous, because one does not tend to think of farm owners, meat processing plants and construction contracting businesses as "the left".

And rightfully past administrations should shoulder the blame for not dealing with immigration in a lawful manner back then. If there was a need for immigrant labor they should have handed temporary VISAs or whatever, instead of ignoring illegal immigration.

If you think of hiring as trying to get the best deal: best candidate for the most discounted price, what you want to do is find the proverbial gem in the rough. But AI does precisely the opposite of that. It will recommend exactly the same profile that appeals to everyone else. The gems will remain undiscovered and you will all be competing for the few individuals that according to their profile, are the safest bet.

He did a lecture. Not sure if you can still find it on Youtube, because IIRC, he published a paper and then redacted it. From what I can tell it was bits of old fashioned differential geometry and a whole lot of hand waving.

ML Research is ripe for such a subculture to emerge, because there are truly so many research directions that are nothing more than a tower of cards ready to be exposed. You need an element of truth to capture your audience. Once you have an audience and you already deconstructed the tower of cards, you start looking for more content. And then you end up like Sabine.

My impression of NVIDIA is that internally the teams are quite independent and there's not much in terms of broad strategy. So it could be these two efforts are not related.

You can also use LLMs to write python code for FreeCAD. It kinda works, if you coach it through the whole process in baby steps.

I mean, trivially true, if you consider that AI-enhanced programming requires 1000% more code written, deleted and rewritten, countless times, by AI itself in it's feeble attempt to "reason" about the problem.

I agree with you that tech stocks are overvalued. However, there is no free cake that I can think of. You can invest in bonds but in the short to medium term they under perform compared to equities by a lot. And most industries have their own risk profiles that if anything are more tangible than the notion that someday, the tech bubble will pop.

Actually you must be thinking of the most reputable banks.

Because most of them will absolutely try to fleece their older customer demographic by trying to make them invest in high expense funds that perform terribly. For them to advice you to put them into a decent index tracking fund is already a good sign.

NVIDIA put a lot of effort into making their hardware and accompanying software useful and usable. CUDA by itself might have never got any attention if not for the effort that NVIDIA puts into helping their customers use their technology, effectively. And they are by no means perfect at it. Most of their products are a horrible mess and they often have 7 different ways to do the same thing. A lot of their libraries are closed source and you're forced to use an API that links to a black box. There's lots to complain about.

Maybe they also do that, but I work with a class of problems* that no other model has managed to crack, except for R1 and that is still the case today.

Remember that DeepSeek is the offshoot of a hedge fund that was already using machine learning extensively, so they probably have troves of high quality datasets and source code repos to throw at it. Plus, they might have higher quality data for the Chinese side of the internet.

* Of course I won't detail my class of problems else my benchmark would quickly stop being useful. I'll just say that it is a task at the undergraduate level of CS, that requires quite a bit of deductive reasoning.

The "I trained a trillion parameter model" club is a very small club.

I'm not at all familiar with this whole field, but why would you publish a trading strategy if it has potential? Why not sell it to a hedgefund, at least? Or is this research formally publishing what industry is already doing?