Comment by prodigycorp

21 hours ago

And how is laya previous art? The project was vibecoded and posted yesterday.

https://github.com/NandhaKishorM/laya/commits/main/

https://huggingface.co/convaiinnovations/laya/commits/main

The paper is one year old. https://arxiv.org/abs/2510.01237 https://pypi.org/project/hallunox/

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    • Generally they do credit the papers and/or people who developed the theory behind their product.

    • Ideas are cheap. But the ideas are cheap coming from anyone. The reason some ideas (like JEV's) are taking up our attention (as opposed to Laya's) is not because of their execution ability, but because venture funding now is subbing filling in for execution ability. I do understand your argument to this would be - "welcome to the world!" or "that's just how the world works" - but that does not mean we do not recognize the ideas that came well before "venture funding made it happen".

      I'd like to remind us all that there is a reason Joseph Liouville took the time to painstakingly review Galois’s chaotic manuscripts to credit him. It matters who did what before everyone else - if you do want to say "ideas are cheap" - we'd need to control for other variables before drawing conclusions.

@prodigycorp - reading your comments here on this post - you seem pretty hurt by this post.

  • Yeah, the reason why I am annoyed by it is because a person (who felt like a burner account of the laya creator) yesterday was haranguing me for saying that projects like this were vibe coded, posting the link to this project.

    I evaluated this project yesterday and found its claims un-credible. It's literally nothing like jev. That's some context behind why, a day later, I find it annoying that this is somehow the top story on HN.

    https://news.ycombinator.com/item?id=49752902

    • I don't really see the breakthrough in Jev. Classification, scoring, routing and returning probabilities over predefined choices are all established problems. We implemented category routing in our own retrieval system in a slightly different way: embed the incoming query, compare it against category profiles and route to the highest cosine-similarity. Obviously Jev isn't similarity based, but the underlying task of making a constrained decision from predefined choices isn't novel. TypeSafe says Jev has a new architecture and RLCD training, but Jev's actual architecture, weights and training details aren't public. So we can't even claim Jev is specifically a BERT classifier, but also don't see enough public technical evidence yet to call the underlying idea a breakthrough. Atleast they should publish a technical paper to prove their idea is breakthrough.

    • What is jev like? Did they release any research paper? I really think typesafe hired someone to boost their post because there was nothing "Breakthrough" about their product. At least this post has some touch with the reality that this functionality was available a year ago and was well known among ML people.

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