But that doesn't make it a failure. In the era where we have:
a) an AI hype cycle where the benchmark of "successful product" is ChatGPT, Claude Code, etc. b) a social media environment that only rewards the most-engaged content
It's easy to declare something "fell off" because it didn't immediately revolutionize how everyone does their daily work.
But by that measure, nearly everything is a failure, even if it creates great businesses. That's especially true for infrastructure that runs in the background.
The author ironically thinks people talking about a product is more indicative of its success than its success – which is hype, the very thing he railed against.
(They may be right.)
I told an agent to test switching those all over to Jev. Came back a couple of hours later, thinking of this as an experiment, no major expectations, to find that they were all giving 100% accurate results in 800ms median time.
So it got adopted in a couple of hours work, it's live now, working very well, and I haven't had to think about it for the past week. That's the definition of a successful product.
EDIT: I should note two things
- there was actually one non English case that didn't have such high accuracy, now that I think about it.
- I assume a big part of why I got such good results was that I'd already spent ages tuning the prompts for Luna
No one was talking about PHP last week either. Till we all got a reminder that a massive chunk of the web is still happily running on it.
Being talked about is a terrible metric for anything other than how much people are talking about something.
> It’s not dead. Some companies use it while I believe the majority of use cases are addressed by open-weight rivals.
The point the article is making is the incredible way Jev specifically caused insane hysteria and hype, and just as suddently faded away. Not that it's dead or that the concept is severely flawed.
I think Jev is a good example of how great marketing fizzed out in the face of reality.
Is it useful? Yes.
Is it relevant? Yes.
Is it dead? Just its hype.
Is it revolutionary? Depends on your cost, quality, speed triangle.
Is it as big as it was claimed? I hope you have the answer by now.really early to call it "the fall of jev"
Ironically, that technique usually pans out since a lot of useless fads get hyped. But every once in a while it causes people to completely and utterly miss the point.
It's the top model for classification.
There‘s a lot of hype with many releasing their decision models the past weeks. But all are worse. So maybe the years they spent in stealth is something you cant replicate in two weeks?
The quality of the answers on my datasets are in another league compared to the benchmarks. I have a mixed language dataset, Jev answers only lack some accuracy, the open weight rivals output just look like random noise
this actually very useful where you can rely your classical programming logic with coefficients provided by jev decision model results if (jevResponseJudge < 0.3) doSomething() else doNothing()
how would u code with LLM in such cases? parse responses for keywords? xd
no, they provide very useful interface. Just try it before writing posts about "hype".
Accused Dario of being part of Ilya’s coup against Sam Altman (now retracted), stated “ It’s an open secret that the training data wasn’t legally obtained anyway” when fair use has very clearly been established, and panders to the anti-AI doomer crowd with talk about how AI won’t be useful economically until we can get to the energy efficiency of the human brain while still invoking the boogeyman of killer drones and cyberterrorism.
Jev was overhyped, and the marketing was clearly looking to coin a few buzzwords, but the author seems to be intentionally ignoring the pace of development, the erosion of traditional intellectual property assumptions, and the realities of the technology. It can be overhyped, but this is very clearly the telos of all technology.
Nowhere as much as 1-1.5 year ago.
And "it's just 3 years" misses the fact that the spending for it (VC money, investment, etc) has been 10 to 100 to 1000 times what other technologies got for many years in their grown stages, just to get where we are.
It seems a lot of the stuff people have using LLMs for in recent years were things that would be better suited to a classifier model.
Classifiers are not particularly difficult to train, but they also not as easy as just throwing an expensive LLM at the problem. That's really only the problem Jev solved. They've not done anything particularly technically impressive (imo), but they noticed the market for lazy/poorly informed people misusing LLMs for classification tasks and built a better solution.
I see no moat in that, but good on them for building something that is clearly useful and solves a real problem. The only thing I personally dislike about Jev is their Reddit-speak cringe posts and all the marketing nonsense about AGI. Although I'd be willing to believe that could be deliberate and they know their target audience are nerds with AI-psychosis.
So we're gonna pretend Laya didn't exist 1.5 years prior? https://laya.convaiinnovations.com/