Wow, BIND, though? Do a quick search on "history of BIND vulnerabilities." Sure, people run all these tests on it now, and in the past, and the history has been there's been a lot of causes of concern.

Yeah, that's pretty old-fashioned. In my experience, it's barely done now. Kinda like how nobody writes JavaScript like it's Pascal anymore.

"Important for our species" ... how? Trust me, our species ain't going anywhere. There's nowhere to go.

That's why it is using it to look over drafts. You take the AI output and then go through its suggested edits or concerns. Essentially it functions as a very fast, half-way decent proof-reader. For example, my spouse looks over a lot of drafts of things I write, but if I give her a 15 page paper, there's good work on the first 8 or 9 pages, probably a joke on page 10 about my notation, one or two comments on the next page, and then a few comments on the bibliography. Claude is not as good a proof-reader as she is, but Claude doesn't poop out after a few pages. And Claude is much faster. So reliability goes up without having to rely on the AI being reliable itself.

And sometimes it catches subtle things. For example, not too long ago, I gave a paper draft to Claude, and it noted in the bibliography that the journal volume number seemed unlikely given the year; it turned out that the volume was correct, but I had actually written the wrong journal down in the entry. Another time, Claude noted that one entry in a bibliography was from a "borderline predatory" (its words but it wasn't wrong) journal, and since I wasn't using it for much other than as part of a list of "hey, here's prior literature" and the paper in question didn't prove anything interesting, I discussed just dropping it from the citation list. Claude then without me asking, pulled up preprint I already had on the arXiv and noted it saw that paper cited there and suggested we remove the paper there also. I didn't do so, but this is the sort of thing that it can suggest. One still has the human make the decisions, but the input itself is useful. And for the most part, verification by a human for most issues is much faster than finding the issues one self.

Not really. It turns out that specialized models do not do as well as large general models. This is essentially a variant of the Bitter Lesson https://en.wikipedia.org/wiki/Bitter_lesson. In this case, there's other reason for it also. There's a lot of reasoning science and math discussions in non-specialized training data. For example, one paper I wrote with two coauthors started as a Reddit discussion. But there's also just a lot of subtle interaction that specialized models don't necessarily get. Math is easier to understand if one has a good command of physics. Engineering is better if one has math and physics. Etc. And sometimes a technique gets directly pulled over. For example, in the large OpenAI paper drop, one of the graph theory problems relied on a trick involving a weighing on the graph that involved complex numbers designed to cancel, where the trick came from a physics context that did something similar. A math specialized model would have been unlikely to find and import that trick.

Right, and the line here isn't clear. One take on NFTs is that they are nominally a tech but functioned purely as a commodity bubble. Or that NFT is a tech but it was used to make commodities which had a bubble. And the line between tech and commodity was blurry historically. When Spain entered the "platinum age," that was based in part on how to make nearly pure platinum was a tech, but the platinum itself was a commodity.

Since per a token output seems to be getting better also for open-weight models, this suggests this is more than just a business decision. And some of it is specific changes; we know that larger models with more training data for example will often produce higher quality output. But also aside from scaling, there have been architectural improvements. For example, one insight that occurred was that having lower precision weights which allowed more weights was often a positive tradeoff. That lead to changes in both the software and hardware ends, as the GPUs were modified as a result. Similar, there's been changes in what functions are used for the feedforward function.

The business case for LLMs is a bit broader than that. They don't need to replace that many workers if they make enough workers more efficient or more reliable (such as simply having LLMs look over drafts). There's also at this point, clear usage cases for scientific and mathematical research but that's using the very high end models, and admittedly is a pretty small market, not enough to justify massive expenditure at this level. The tulip example is interesting; what you are touching on their is the distinction between a "tech bubble" (like railroads or internet) and a "commodities bubble" (tulips, housing, Beanie Babies). Note that the degree to which the tulip bubble was real is questionable. See https://theconversation.com/tulip-mania-the-classic-story-of-a-dutch-financial-bubble-is-mostly-wrong-91413, and the line between a tech bubble and a commodity bubble itself can be blurry (see for example Bitcoin and NFTs). One of the difficulties here is that the LLMs keep improving, and the cost per token is going down, and the amount one can do per a token is going up. But that's happening in part due to massive investment in both infrastructure and research. How sustainable any of that is is unclear.

There are at least four problems with this. First, linear algebra is from a math standpoint insanely powerful. For example, all of representation theory https://en.wikipedia.org/wiki/Representation_theory is using linear algebra to understand groups. Much of the study of modular forms https://en.wikipedia.org/wiki/Modular_form involves understanding the behavior of modular forms over SL_2(Z) which has heavy linear algebra elements to it. People who see linear algebra as somehow a weak thing are often people who took a single semester of it and didn't do much else with it.

Second, the idea that these AI systems are just linear algebra isn't accurate anyhow. While there's a lot of linear involved, a major part of how transforms like LLMs work is the intervening non-linear layers, which use ReLu https://en.wikipedia.org/wiki/Rectified_linear_unit, Softplus, or a similar function. So, summarizing this as just linear algebra isn't accurate. It is linear algebra with some spice.

Third, even if this were somehow just linear algebra, that shouldn't therefore be a reason to think that there's any limits to what is going on. At the end of the day, the empirical success is what matter, and systems which can do things like prove the quasi-Riemann Hypothesis or solve other major math problems are pretty impressive regardless of what is under the hood. So, if one does think this is just linear algebra, that's not a reason to dismiss the systems but a reason to increase your estimate of what can be done with mere linear algebra.

Fourth, slowing down connections like this may make crashes *less likely.* In particular, the main way we could have a serious bubble here is over-investment. Delays including power grid delays force bottlenecks on the investment level, and make that sort of problem less likely.

We may be in a bubble here. Certainly some elements look very bubble-like. In the late 1990s, during the dot-com boom, one had people making perfectly normal businesses but just tacking on "on the internet" to it and it would get millions of dollars of funding automatically. See for example pets.com . And we're seeing some of that now; we've had a whole bunch of businesses see their stock price shootup just because they mention AI. So we may be in a bubble, and it may crash now, or might crash never or decades from now. (Arguably the US had an 80 year automobile bubble that didn't crash until the late 2000s which then forced the auto bailout.) But the other thing to keep in mind is that a crash does not mean the tech goes away. In 1873, the US had a devastating collapse of the railroad bubble, and yet railroads continued to grow, with the amount of track not peaking until around 1910 https://transportgeography.org/contents/chapter5/rail-transportation-pipelines/rail-track-mileage-united-states/. Similarly, although the internet bubble burst, by 2015, one had more internet use than even the most internet hyping person in 1998 would have likely dreamed of. A bubble bursting does not mean the tech itself goes away.

The set of problems that only a human can solve is getting rapidly smaller. The set of puzzles that a human can solve, that an AI cannot, and which take a small amount of time for a human and aren't highly inconvenient may already be empty.

So while everyone seems to agree that building server-side applications with things like Node and Python is wrong, foolish, and that "there are much better options" ... does anyone care to tell me what those options are?

I mean ... aren't a lot of server-side apps basically just playing catch ("I'm tossing you a ball, now you toss me a ball back") and in between there, it interacts with a database? You really don't think you can do that with Node or Python? Does e.g. Java really give you so much more reliability, maintainability, performance, etc?

Well, it wasn't Unix. But it was inspired by Minix, a widely-taught teaching operating system that Torvalds decided to make into a thing you could use every day.

2001: Windows XP ran on as little as 64mb with 128mb being the recommended.

2021: Windows 11 minum is 4gb is listed as the minimum. I've never even touched Windows 11, so I don't know if that is realistic.

The minimum specs for each system is a 64x multiple, as you said. That's a good observation. I'm pretty sure in the late 90s I was running Windows95 with 96mb and my motherboard was capped at 128mb. Negligible ram on the graphics card. It also could have only been 64mb. I don't have a clear memory of what I was running when I first got XP.

Today my PC is a partially upgraded (old) i7-4790k with 32gb ram and 10gb of ram on the video card, so approximately 448x increase in memory in ~25 years.

My Mac is 256gb unified, so ~2,730x multiple.

Still not to the Moore's Law rate!

Jumping a car with compressed air isn't a technological miracle. It is a useless stunt, and one which if went into production cars would be dangerous to both those in the car and people around them.

This is probably the best sentence (and post) that I have read on Slashdot in some time. Amen.

Another thing that I have found Claude is really good at doing is taking an error from a log and doing a deep dive analysis of what happened. I give it read-only database access, access to www logs, access to application logs, access to a checked out source tree, etc. I paste in the error message (or in some cases paste in a tech support email from a user) and have it write up an analysis, a proposed response to the customer, and any recommendations (if there is a bug or actual problem).

I don't know what field you are in, but in my area, math, this is wildly not the case. They are much, much better. And the next line of the public frontier models are obviously even better judging from everything that we've seen drop from OpenAI and Anthropic. The large OpenAI release would by itself would have four or five results which would arguably be a Fields Medal if a human had done that, and quasi-RH which would if a human did it be almost definitely a Fields Medal result.

On the contrary. The people trying to make this sort of claim at this point are overwhelmingly the non-mathematicians. To the mathematicians the number of new proofs of genuinely difficult problems and the use of novel techniques like those in the Unique Games Conjecture proof and Hadwiger-Nelson make us generally have the exact position you are claiming is something that is believed by the non-mathematicians. Even the most anti-AI mathematicians like Tasmin Chu agree that the AIs are producing novel, major work at this point.