lacker

Different incident that AFAIK did not become public. But yeah, I bet things like this happened a lot. In 2005 people were still getting used to the idea of a robot crawling their website regularly.

To me, it's easy to imagine them going more all in. For example, they could let their employees use ChatGPT and Claude!

They love to say "AI" but they don't seem to want to accept the reality of what AI is right now.

I remember once at Google someone complained that GoogleBot hacked them and deleted their data, and it turned out that GoogleBot was just crawling the pages, and they had unfortunately designed their website so that there was no authentication, page URLs were generally secret, and GET requests to certain URLs were treated as requests to delete data. So once one URL leaked the site got crawled and a lot of data was deleted....

Yes, I worked for a bit on site at a datacenter in Fort Worth and was surprised by how many ex-nuclear-submarine engineers there were. I guess it's similar, you have a lot of high tech equipment in an environment where the climate is closely controlled.

Offering Luna for cheap is like restaurants giving you free bread and water. They're pretty sure that you're going to end up eating the expensive stuff on the menu.

The increased revenue is in startups, like the recent couple YC batches.

Well you mention Android, iPhone, Nintendo, and Microsoft. Those are the biggest companies, and many of them don't even use the best coding tools. So they are not going to be the ones improving.

On the other hand, look at the YC companies. I think the last batch or two is growing revenue faster than any batches ever before. Those are the companies that are being sped up a huge amount by AI.

I have seen two worries recently:

1. People will publish so much frontier mathematics, humans won't be able to understand it all

2. Frontier mathematics will all be kept secret

Fortunately, these seem like they can't both happen at once.

Yes, quite well in fact. This lecture was in 1939. In the 50's, Lie groups (previously just a pure, beautiful mathematics) were used in particle physics. Gell-Mann used SU(3) to predict some new particles before anyone observed them.

And then similar stuff happened later in quantum mechanics, with gauge theory, but I understand that only at a handwavy level. I think overall the "standard model" is a perfect example of what Dirac predicted.

Whether this still holds up in the past 40 years is another question. I don't have a great example from my lifetime.

Recently I showed my 7-yr-old son and a friend of his how they could build their own little video games with the Claude app. Simple stuff, make me a maze game, make the walls move sometimes, make it bigger, make there be a score.

The delight they have at, video games aren't just a thing to play, it's a thing they can design and change, share with each other... I think that dream of a truly malleable experience is still there, I think this "explosion of new forms" you talk about is happening right now with AI, and I think this time it is going to be bigger than ever.

I think the tech companies would be supporters of new rules that would let builders create more housing all throughout San Francisco. We need a big tent, with all pro-housing groups coming together, to change the rules, and start allowing new housing again.

If you own a lot in San Francisco you should be able to build an apartment building on it.

This is like giving you three outputs from md5sum and asking you to guess for which one the input ended in a "q". There's no way to tell unless you break the RNG.

Maybe some sort of blockchain could make it so that you don't have to verify the whole proof yourself, to know that it's true. Because some of these proofs could take a long time to verify. Similar to a Merkle tree, but you'd have to make it respect the laws of mathematical proof in some way.

edit: Yes I think this should be possible, using a "recursive SNARK".

I don't think the mathematicians are going to be able to make that work, because journals are already struggling to keep up with their review load, and AI seems like it will make that harder. So a solution that involves "journals will do a lot more effort to review each paper" doesn't seem practical.

It would work better as a bar for hiring, rather than as a bar for publishing.

Yeah, totally. But... the problem is that I don't have time to test every single model that comes out. So I rely on reports like this to decide, should I even bother testing out Muse?

My conclusion is the opposite. If benchmarks were meaningless, surely Meta would be able to find some benchmark that shows they are better than Sol and Fable. The fact that they can't do that tells me that benchmarks still do mean something.

Personally, I end up throwing away a decent amount of books that the book donation people won't take. Especially old technical books. Textbooks from 2001. These headlines just don't tell me anything useful. "Rare" doesn't mean anything.

Please, give me one example of an interesting and unique book that the AI companies have destroyed.

I don't think people working at Amazon "know that it is a part of a larger bad", it's one of the most trusted American institutions.

https://www.theargumentmag.com/p/why-everyone-loves-amazon

Yes there are studies, for example last year Pangram's false positives were measured to be under 0.5%.

https://www.pangram.com/blog/third-party-pangram-evals

Personally, at first I thought these sorts of tools were dumb and wouldn't really work, but I think it works because it just isn't designed to be "adversarial". If you want your AI to trick Pangram, you can make an AI to trick Pangram. It just catches people who are cutting and pasting from the AIs without putting any more effort into hiding it.

It is a smell. But it's the EU that smells bad, when it comes to tech regulation. It's the smell of cookie popup warnings.

Think of it this way, how would you turn a set into a vector in the first place? We solve this in programming a lot, for example, the "one-hot" encoding for neural networks. Here, a set turns into a vector that has a zero for every item that isn't in the set, a one for every item that is.

Now, there are a lot of things that |v| for a vector can mean. In the L1 distance you just add up the absolute value of each dimension. You could argue that that's a simpler sort of |v| than L2.

And there you go! |S| on a set actually means exactly the same thing as |x| on a vector, if you interpret sets as vectors in the right way.

"Falling behind schedule" doesn't really seem like the right term, for a sector of the economy that has been accelerating for the past few years.

You could easily describe this trend positively rather than negatively, like:

"Google has built an incredible amount of datacenters in the past few years, which makes sense since Google Cloud revenue has tripled since 2021. But they are trying to grow even faster and add more revenue."

The next step in my fight against screen addiction is to have my children not watch Toy Story 5.

I don't think it's nonsensical, it's just another name for the same thing. E.g. in the Haskell wiki it says, "the Error monad, also called the Exception monad".

https://wiki.haskell.org/index.php?title=All_About_Monads

Yeah, and rate-limiting is only one of the things a PaaS needs to handle, to avoid looking like a bad actor to the underlying platform. The trickiest thing to handle is people using your PaaS to host malware, because:

1. There may be no simple rule of thumb like "suddenly using tons of bandwidth"

2. Bad actors can open up so many accounts, you have to close them automatically

3. Malware can infect a good actor, who is unaware or struggling to deal with it

Perfectly demonstrating the truth of the "Microsoft org chart" cartoon.

https://bonkersworld.net/organizational-charts

The specific code I was working on, I had a general idea of the sort of performance improvement that would be possible. I just thought that it would be too hard for the models to figure out without a lot of hand-holding.

But it ended up being not "too hard ever", but more like, in 1 out of every 5 tries, the model did in fact manage to get a large refactoring to the point where it improved performance. So once I set it up to try something, use the perf test, see if it worked, if not, throw it away, repeat. Then it started, slowly, finding some useful things.

It's an especially awkward situation because Railway is a competitor of Google Cloud, with many third parties involved. So, I just think it will take them a little more time to figure out how to message things.

To me, what it sounds like is that a Google Cloud system identified Railway as a misbehaving customer. Spam, hackers, that sort of thing. Often this happens for "platform as a service" companies, because Railway themselves probably do host some spammers and hackers, and they have their own systems for dealing with it.

So, it's quite possible that according to the Google team, Railway violated the terms of something or other, and according to the Railway team, they did not, and now everyone has to argue about it.

But who knows, this is just me guessing based on some experience running a PaaS that itself was running on top of AWS.

I didn't dig into what the actual repository was doing, but personally, I took some inspiration from the idea after reading about it and realizing that I might have been underestimating the ability of LLMs. I put a bit more work into a performance harness I was using locally and just set some agents to brainstorming and they did seem to find some great stuff. So I don't really have a stance one way or another on this specific repo, but the general idea seems like a really good one.

Reminds me of:

“In his presence, reality is malleable. He can convince anyone of practically anything. It wears off when he’s not around, but it makes it hard to have realistic schedules.”