baobabKoodaa

Maybe they're not trying to be great at the tax avoidance game.

Do you think you're some wise oracle speaking in riddles? You first vagueposted, and someone very nicely asked you to explain what the post is about, and you reply with some variation of a rickroll? You're an asshole.

You didn't answer the question. Quoting from upthread:

A: "In particular, we show that the formalised Lean proof does not correspond to the NL proof of blow-up of solutions to the Navier-Stokes equations."

B: "No, they're not claiming that [...] The authors are claiming that the Lean proof is not the same proof as the NL one"

The question was: what's the difference between A and B?

Your answer to that question was "This paper says the NL proof does not match does not match the lean proof".

It seems to me like A is the exact same thing as B, and you are here claiming that they're different things while merely repeating the same claim that was already claimed in both A and B.

That's not what this is about. The person I was responding to said "if a problem I was working on had an AI also working on it, I would want to know as early as possible - even with flaws or gaps". That's what I was responding to. They were basically suggesting that the amount of mathematical slop in the world should be maximized, and then it would be up to humans to choose what they would want to read. You know, a needle in a haystack type of thing, where you're maximizing the size of the haystack.

Not a great sign when nobody wants to attach their own name to words thrown into the world.

Not sure why you're being downvoted, because this is exactly what I think is happening.

I can prompt ChatGPT right now and ask for mountains of more "mathematical work"; thousands and thousands of pages of nonsense for you to review. So you can "be informed".

Also an outsider to the field and wondering the same thing. I can't see the difference. Can someone explain please?

This doesn't answer the question. The question was: what was the difference in A and B. Your answer looks like this:

A: The English language description doesn't do the same thing as the code the AI wrote.

B: The English language description doesn't do the same thing as the code the AI wrote.

A and B are the same; there is no difference. Is this what you meant? Or did you mean the opposite of this? If you meant the opposite of this, what exactly is the difference?

Yep. I hate everything about this. Just pure slop. Fancy visuals that mean nothing. Text that sounds impressive but has no purpose in educating the reader even though it's supposed to be "an interactive explainer". Just slop, slop, slop.

The user needs to be dazzled with slop... is what they must be thinking here

Can we just ban Fortune and any other sources which trick the reader by giving the impression that the article is not paywalled, only to blur the text halfway through? The archive.ph link is not working either. We shouldn't have this type of deceptive moneygrabs advertised on HN.

In one of my client projects we've had to do forced upgrades of LLM models because of deprecations (I believe three or four of them over the course of 2 years). Each time our internal benchmarks have shown REDUCED performance after upgrading to "better" models.

You don't seem to understand what the point of Jev is when you say "you can fine tune it for your use case". Building your own classifiers for your specific business problems is the type of work we all used to do back in 2016 or so. It costs very much. Jev is a cheap and fast general purpose classifier.

If you have an easy problem that can be solved by a classifier from pre LLM era, then sure, go ahead. But we have LLMs now and we can use those to expensively and slowly classify harder problems using general purpose models, without needing to spend a huge amount of time fine tuning a model for the specific task. Jev offers to do the same fast and cheap.

For clarity: no, a 0.8B model is not gonna do that.

The claim in OP is that "ASML currently sells no chipmaking machines in Europe". A single chipmaking machine purchase into a fab in Europe would be enough to prove that claim wrong. The number of fabs in Taiwan is irrelevant to this claim.

Is there any particular area of employment for algo geeks that you would approve of? Maybe we should just live off unemployment benefits instead of working?

I've seen enough proof to convince myself, but unfortunately I don't have enough to convince you. Maybe others will publish proper evals.

Yes, all of those are using small models (not this particular model, but small models nonetheless). Small models are not frontier intelligence.

Don't cut the quote mid-sentence. Here's the full quote:

> note: this is a parody blog post, see these links for better/more complete open implementations of Jev: OpenJev, openjev-sglang, and OpenJev on DiffusionGemma.

Is Jev built on top of 8-bit quantized 0.6B param Qwen model? No.

Fast and accurate general purpose classifiers DID NOT EXIST before Jev. You could either use an LLM to get a slow and accurate general purpose classifier, or you could use a smaller model to get a fast and inaccurate general purpose classifier, or you could fine tune your own model that would be both fast and accurate, but it wouldn't be general purpose.

Except in this case it's not technically correct. Jev's claim is that it's frontier intelligence and these guys are pretending that a 8-bit quantized 0.6B param Qwen model is that. There's no universe in which that claim is technically correct.

Yeah it says it's a parody, but then in the same sentence it refers to the other "OpenJev" implementations, which are basically the same thing with marginally more effort. And it doesn't imply that those things are parodies too (and I don't think they are parodies).

Somehow the HN crowd has a bunch of "professionals" who don't care about error rates and think that a Qwen model running on a potato is frontier intelligence.

I'm so sick of seeing these people who "made Jev in 25 lines of Python" or whatever the flavor of the day is. Do you people seriously think that Qwen3-0.6B-Q8_0.gguf is frontier intelligence? If you want to argue that Jev is NOT frontier intelligence, then go make that argument. Don't try to pretend that Qwen3-0.6B-Q8_0.gguf is frontier intelligence. That's retarded.

You might be right. I don't know. But regardless if Jev is frontier intelligence or not, Laya most definitely is NOT.

The point is that it's silly that the law is specific to onions, and the explanation "it's because onion market was small enough to be cornered" is unsatisfactory because there are other small markets too

There are many other small markets as well, and yet, this law is not called the "Don't Corner Small Markets Act".

Jev claims to be frontier intelligence. Laya, while claiming to be "the open source version of Jev", is using a tiny open weight model with a tiny context window. Anyone who has experimented with tiny models knows that they are far from "frontier intelligence". It's not plausible that Laya could be "the open source version of Jev", with "frontier intelligence", when it is using these tiny models.

Also, the paper that OP is referring, is not describing anything that sounds like a generalist classifier (which is what Jev is). Their paper describes a tailored solution to one specific business problem. I'm sure it has some similarities with Jev, but it's still a completely different thing, and I'm confused why OP is claiming it to be the same thing.

If you don't believe me, just open the PDF and read the abstract.

If you need a reasoning model, then that is a System 2 decision, not a System 1 decision. This thread is about "Laya", a "Jev" competitor/precursor, which is a System 1 thing.