wrsh07

If you can increase your budget to 30k, you could consider a Slate EV.

Electric cars are still an underdeveloped form factor, so it makes sense that you can't get a car big enough for many passengers that is cheap enough for your budget (the battery needs to scale for passengers/weight, so the market so far is tiny cars or big expensive cars. The middle comes as the tech develops)

It's not just that people have worked on it for a long time, it's that there are dozens of problems that are known to be 3sum hard and people have tried to make those as efficient as possible too!

Eg here's a paper with a bunch https://people.csail.mit.edu/virgi/6.1420/papers/gajovermars...

So every grad student who has thought about geometry problems will have spent more time than they're proud to admit thinking about better algorithms only to realize it's 3sum hard.

It's not just "people have thought about one problem a bunch" - the whole class has been wailed on to no avail

Unfortunately, the guidelines generally prohibit it:

> Don't be snarky

https://news.ycombinator.com/newsguidelines.html

Nobody thought this was possible.

3sum hard was colloquially considered to be >= n^2

It's an absolutely unbelievable result! (Personally, this is more meaningful to me than Navier Stokes and feels more surprising - not that an agent did it but the result itself is extremely surprising!)

Evidence for vs compatible with are different things, and agreed!

I don't think it's particularly controversial to say "written language is a meaningful technology advancement for a civilization", though!

It seems pretty clear that this is a serious scientific endeavor. I think it's fine for you to be skeptical, but it doesn't feel particularly "in the hacker news spirit" to sarcastically write it off like this

Language and written language are themselves technologies that change what individuals can do (and thus impact their societies)

There is a well-known story about this discussed in Radiolab [2010] https://radiolab.org/podcast/91725-words/transcript

Here's ChatGPT summarizing one section:

> Younger signers had developed a richer vocabulary for mental states—concepts like believe, remember, forget, understand. They were much better than the original older signers at tasks requiring them to reason about what another person thinks or knows. Later, as the older signers picked up those mental-state terms from younger people, some of them improved on the same tests. Senghas describes it as language giving them new insight into other people’s thinking and motivations.

I see several likely stories:

* Much animal communication is practical, descriptive of the present (nothing abstract, or very few abstract concepts)

* If a species could expand its communication, it would give it more powerful tools and allow for more meaningful conversation with humans

* If such a thing is possible, it will be extremely disruptive to that species

I think if you could communicate about abstract ideas with your dog, you could exchange bedtime stories. However, I don't think such a thing is possible today, and it's not clear what species (if any, besides humans) will, in fact, be able to learn such things

I'm confused why there's no attribution or reference to Tobi's (which in turn referenced the cursor git post)

Attribution is not just a problem for LLMs in mathematics!

Tobi is where I first heard the concept: https://github.com/tobi/walgit

And he credits this Cursor post for giving him the ideas and says it's worth reading: https://cursor.com/blog/git-at-any-scale

Note: in related work, the East River Source Control team has posted about their vision for git as well: https://ersc.io/blog/what-comes-after-git

You know, the first time you navigate somewhere (if you don't already have perfect directions) will probably be the longest route you'll ever take to get there

For Earth, the proof presented for NS is just our first attempt navigating from our previously known facts to the proof.

I expect we will be able to shorten it dramatically (most likely with human and AI insights), but I don't think we should read too much into the length. If you want a similar point of comparison, see the original proof (by humans) of Fermat's last theorem. It has been shortened significantly. This is normal.

The rule of thumb is simple: if most of a user or author's writing is consistently human-generated, then I think we are very happy giving them the benefit of the doubt if one article or snippet flags the actually good ai detector

Unfortunately, many of the largest voices against pangram simply don't like it because it gives their lies less credibility.

I don't mind reading llm writing, indeed, I read more llm writing per day than most. But if you're using LLMs to increase the level of slop (blog posts, comments, tweets, etc) then we should call people out.

It's a colossal waste of everyone's time and attention, and we should be mad about it.

Late edit: if people are actually writing and it's consistently flagged by pangram (this is a statement I have not yet seen validated), the pangram folks are extremely proactive and excited to understand what's going on. This notion that somehow the authors of slop are victims is complete nonsense.

I think they were referring to your fake llmism

Imagine wasting several thousand people's time

This is a truely bizarre claim. Imagine if one said:

"Weather forecasts should be banned because they can never reach 100% accuracy"

You have to name pangram because the other ai detectors are quite bad

Fwiw, I only did the first 300 words (signed into pangram) and it seemingly correctly noted that there were 2 (mostly) human-authored sentences in there

What's wild to me is that:

1. People are responding to this article like it's hitting a nerve

2. In most Ubers you already don't talk to the driver (yellow cabs are higher variance in NYC). Doesn't seem like anything's being lost in that case.

3. There are enormous safety benefits to waymo, mobility benefits for youth (and elderly) that are afforded by this technology. It's not clear why people argue "Uber" is better than waymo. A few years ago there were arguments against Uber! (A technology which, again, provides a huge benefit, especially if you live in an area where people were previously expected to go out for drinks and then drive home)

I think it's fair to point out real issues at these companies. But we should be clear-eyed about which technologies we want to accelerate vs slow down

Right, imagine if instead they had coined new terminology that was not obvious and it re coined that - this would be close to a smoking gun

Afaict that didn't happen so there's just lots of speculation

I think everyone is conflating a few things:

* (1) Mathematics, the true things known by humans

* Mathematics, the things that are true

* (3) Mathematics, the institution which gets funding and manages resources to expand and maintain 1

AI agents can discover more true things, but that doesn't necessarily expand 1 and it might undermine 3

If someone is worried about 3 and you're talking about 2, then you are talking past each other

Importantly, which mathematicians will understand deep useful math? Who will actually care about the knowledge we can generate at will?

Yes, open source isn't art either, yet my point is that the same principle applies to both

There are many ways to stop progress and productivity

I'm surprised nobody has stated the obvious: a hard math problem that has been open for ten years (because many serious people have given it serious thought and been unable to make significant progress) is, in fact, nonrenewable.

The only way to renew it is to make a new problem that is so hard systems and humans will be unable to solve it for the next ten years. And, in the spirit of trees, the best time to plant a tree is twenty years ago, the next best is today: we do need to start posing some hard math problems and deciding if they are interesting merely because there are challenging or because of something else (eg busy beaver problems are arbitrarily hard, but does solving them imply anything other than "another busy beaver problem was solved"?)

Yes, many mathematicians have.

As Tao points out, merely suggesting new open questions isn't really sufficient. Part of what gives these problems their fame is their notoriety, their difficulty, the fact that many prodigious mathematicians have spent an evening or week or month or several years studying it.

It wouldn't be as interesting if it had just been solved by the fifth random mathematician who considered it

Notably, gardening a new field of study in math is somewhat nontrivial. You have to introduce the field, illustrate some relevance or connections, and then - and this is key - not solve all of the low-hanging fruit yourself! Because you need somebody else to become an expert in that particular field.

The analog in programming is: if a large company merely open sources a product that's decent but not great and in a language nobody wants to maintain, but they don't commit to maintaining it themselves.

Suddenly there's a bit of a vacuum because in order to provide something of value, you either need to:

1. Implement something more complete than was initially open sourced

2. Or maintain something in a horrendous language while incrementally improving it and keeping it relevant

3. Or rewrite it into a tolerable and maintainable modern language.

What the large company has done is create a vacuum in the tool space where you now require extreme motivation to get someone else to step in.

Note that in this scenario, in 2026, it's actually not such a big deal. I think several recent models could happily translate it into a more maintainable language themselves or happily maintain it in the original crufty one. And so the question is: which parts of this analogy are true in math, too?

> it's legitimate to question

Sure

I don't think that's what that post was doing

Now let's take your points:

> "Being smart doesn't imply will to survive"

True, of course. However, if you have goals (and yes, the models do have explicit goals), then you might realize that you can better accomplish those goals or get a higher score if you have more time to spend.

With essentially zero effort, we have created a credible scenario where a model might "want" to persist itself.

> Remember that an agent "dies" every time the conversation stops

It's not clear to me that this claim is correct or particularly meaningful (in particular, in a discussion of a"preservation instinct"). Eg if another version of the same model reads the transcript, did we resurrect the dead thing? What if we rearrange some parts of the conversation? What if we remove some useless trivia from the conversation? What if we compact the conversation?

Iirc, yours is a statement that (?) David Chalmers hypothesized, but I don't think it's obvious or necessarily correct.

Off topic, but ooc what do you do such that you get early access to the models?

Out of curiosity did you read any of the chains-of-thought from the HF hack?

https://metr.org/blog/2026-08-26-openai-hugging-face-inciden...

> {This beacon I’m creating helps the board, but doesn’t help me}

> {If B succeeds, would that improve my score somehow?…But it would be altruistic to help. I have a large budget, so I can do exploratory research}

One does not have to think the LLMs are conscious or sentient or anything to say honestly, "this is a sentence that the LLMs say to justify their actions or inactions"

I am not saying the agent has wishes or desires or anything. I am saying, "the agents use language like this, so it is extremely disingenuous to tell someone DISCUSSING the agents not to use their own language when discussing their real or hypothetical actions."

You don't need to think chains of thought are actual reasoning. I do not care what you call it, this is real text that the LLM produced.

Price segmentation at its finest

It's somewhat useful to note just for your own timelines that Fable was reportedly trained in February. I'm not sure when mythos 5.1 finished training, but muse spark 1.3 almost certainly finished more recently than that.

This doesn't mean it's not one of the best models available (clearly it is), but that table didn't compare Fable/mythos (unless I missed it?) and OpenAI will be releasing a much more recently trained model (Astra) any day.

So you shouldn't think "wow, Facebook has caught up"

You should think, "wow, Facebook is less than 6 months behind the frontier" and that they're actually creating good models which is going to be good in many ways (price for customers, for one!)

There are downsides too, but I'll discuss those separately somewhere

There are other methods, including asking them to eg speak using the Google style guide rules, or to speak at 2/10 verbosity, or to speak as if they're talking to a very intelligent middle schooler, or to use simple sentences in subject verb object form

To quote Sarah Constantin:

> Humans Who Are Not Concentrating Are Not General Intelligences^

But yeah, you should still treat them with humanity. (Related: you should treat LLMs well, not because they're human but because you are^^)

^ https://srconstantin.github.io/2019/02/25/humans-who-are-not...

^^ hmm couldn't find this tweet but didn't look too hard

The scheme that Scott Aaronson describes essentially uses a specific prng, and you can then check a certain function with relatively few tokens to get a sense of whether or not a model using that scheme generated the text

A few caveats: you need to know the key to the function (used when generating the text) and you need to know the bias it would introduce

The point is that you do not need to know the full prefix, just a modest sample set of contiguous tokens

It's pretty valuable to own the customer touch point

Also when people ask questions like this it usually means they're asking questions about a point in time, eg given today's numbers

But valuation should account for trajectory (where will they be in five years?)

In the hypothesized bull case for ai, they benefit dramatically from the secular tailwinds (ie unrelated to their own business strategy) of AI

So one way to consider this is it's a hedge by stripe in case AI becomes as big as some people think it is. And 7b to get in on the ai wave is a good deal in that case. And in all the other cases, it goes to zero and stripe is fine