contrary to what i feel is extreme negativity of the author, to me it seems Jev is one of the only novel releases that lived up to the hype and has staying power. Not only that it jump started a whole new "classifier" war, leading to now things like cloudflare's clairic for extraction.
We use it in production. Tested a bunch of alternatives (openai decision api, llm based approaches) and they are either too slow or not as good. But mostly too slow.
Did Jev take off briefly and stopped getting talked about a few days later? Yes.
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.
They literally raised $870 million this month. It's like people think the hype cycle = success of business for some reason. I'm sure a couple of months later, they're going to release something new, hire a lot of PR, and the same people will be "man, Jev had fallen but this new [xyz] brings them so back! [xyz] industry will now be dead!"
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.
Likewise, I switched over about 20 frustratingly buggy llm judgement calls that were using Luna (should the model use X tool, should it trigger deep research, what topic is the question etc.). I had spent days tuning the prompts to get somewhat decent results - meaning correct maybe 70% of the time.
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
OpenAI has a decisions API now so you didn't even have to migrate to Jev at all. Personally I'm using the open weight D3 decision models which work great and work locally on everything from phones to beefier computers due to the range of models they have.
I already have openrouter wired in so it was about as easy either way. Anyway I'm always happy to switch even a little bit of the stack away from OpenAI.
It's still a pretty new field but Jev handily beats Luna-decisions on the Artificial Analysis Decision Index on every metric (price, latency, and accuracy)
E every 2 weeks we have a new hype cycle so all in according to how stuff already works I don't see something special here. I use jev, but it does not solve all my problems.
Doomerism + Wishful thinking. We are what ~3 years into mass adoption of llm tech? Every couple of months it improves. Jev came out a couple of months ago and there is nothing that makes me believe that it will not improve over time as people fine tune small models to work better over time.
>We are what ~3 years into mass adoption of llm tech? Every couple of months it improves.
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.
Jev feels like what happens when people that have had no serious statistics get a glimpse of what statistics makes possible. I'm not talking about the developers of Jev, I'm talking about the uninformed that went all hype cycle on it. It's well designed and the advanced system a good statistical programmer would write.
I think there seems to be extremes in both ends and in my opinion neither of these extremes are the ICP. If you're using AI to finish your work, you're not the ICP. If you're building a product and want to implement AI in your product, you're the ICP.
The article says that other models are better at performance or cost. But whaz is not mentioned? Models that are _better_. From my testing, most models are much worse. And if you do an analysis on quality/performance/cost no one is beating Jev. Heck, no one is beating it at performance and cost at the same time.
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?
Look up Laya. Also the notion of "best" is flawed because half of that equation depends on the use case. Benchmarks try to answer that by creating artificial use cases and test in isolation but that's not the concrete answer for a given use case either.
In the digital age, the "fall" of anything just means you, and the people in your bubble, stop using it. It could be something that enough people get value out of it that they can afford to pay for the domain name, and a bunch of other stuff, and you'd never even know. So to claim anything falls, well. There's Yahoo, AOL, Kodak, Snapchat, Facebook, Twitter, and Zombo.com. And they ain't going away.
HN has a funny was of bubbling up low value article such as this.
Jev is far from being a fallen software. It works and it works well. Just because no one is talking about it does not make it a failure.
We reached inverse hype: where the folks jaded by hype are so jaded that they start to hype the same low value content they normally deride... as long as it speaks counter to any hyped topic.
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.
I call it “SchadenFraud”: reveling in and playing up every little negative thing that happens in the AI world to make it look like it’s all just hype and will come crashing down any moment now.
At one point the hype truly was overwhelming (maybe it only appears less so these days because a lot of it seems justified now) and needed some counterbalance, but this is not it. We need balanced expert perspectives grounded in evidence.
Instead we get get TFA and other similar content from the usual suspects (you know who they are) largely because they’re building their careers on very loudly telling people what they want to hear, which may not be what they need to hear.
This is not harmless, because it gives folks the hope that things have not irrevocably changed and will soon go back to normal after the bubble pops. Personally, I’d rather get a stark but realistic picture of what’s happening now so I can prepare better for the future.
Yeah but do Openrouter stats represent local classifiers? Unlike LMs decision models are quite likely to run at edge, especially because their utility for a specific task improves with training.
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".
After reading the article I can see that the comments here are a bit off. For example see this quote from the article:
> 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.
> It’s not dead. Some companies use it while I believe the majority of use cases are addressed by open-weight rivals.
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
Typesafe over-hyped their useful product and successfully got it in front of AI devs. So to be assholingly snarkily reductive I can sum this up as
"founder did founder shit well"
Seems to me most of the comments here miss the main point of the article about the pattern of hyped marketing (name dropping and jargon, ambiguity and inflated expectations, drama).
The author is obviously informed, but just as clearly biased.
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.
I've said it before, but the most surprising thing about Jev was that no one thought of doing it before. And second to that, that so many people didn't realise how useful classifiers were...
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.
I'm pretty sure Jev is one of the fastest adapted new technology in production.
The concept is so simple and there are so many use cases which are very useful at the right places.
The fact that OpenAI released a decisions API shortly after Jev release means people are using it for business applications. It's getting talked less because it's a niche product and not something widely applicable like Claude Code/Codex or assistent like Muse.
I heard about Jev by chance by a second degree link. I had heard nothing of it before then. It seemed immediately obvious to me that it wasn't just a good thing, but that it was a _necessary_ thing.
So if you feel people were "hyping" it up, you were likely witnessing genuine enthusiasm, not bandwagon jumping.
>I think Jev is a good example of how great marketing fizzed out in the face of reality.
My read on Jev was totally different, I guess.
I first heard about it here. I found a video demo and thought it looked amazing, I saw a lot of discussion about it because other people probably also thought it looked amazing and imagined the possibilities, and then things quiet down because real-world use-cases for it are going to not happen right away, and definitionally it seems like Jev is more about supporting other stuff than being the thing itself.
All the other stuff about ambiguity and jargon and drama don't really register to me, I dunno. But it was interesting to see the author's take on that stuff, I suppose!
danr4 | 22 hours ago
really early to call it "the fall of jev"
therealalexz | 22 hours ago
eknkc | 21 hours ago
gonzalohm | 21 hours ago
owebmaster | 20 hours ago
4r73m190r0s | 21 hours ago
FinnLobsien | 21 hours ago
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.
altmanaltman | 21 hours ago
Harukadia | 21 hours ago
andyferris | 21 hours ago
ordinaryradical | 21 hours ago
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.
JKCalhoun | 20 hours ago
(They may be right.)
nameless912 | 20 hours ago
esperent | 20 hours ago
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
satvikpendem | 20 hours ago
esperent | 18 hours ago
pants2 | 13 hours ago
zer00eyz | 20 hours ago
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.
alex7o | 20 hours ago
cyanydeez | 19 hours ago
Daviey | 14 hours ago
Semaphor | 14 hours ago
dust-jacket | 21 hours ago
Sure. And yet...
christkv | 21 hours ago
coldtea | 19 hours ago
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.
pmdr | 21 hours ago
bsenftner | 21 hours ago
freecodeio | 21 hours ago
tpetry | 21 hours ago
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?
hanifbbz | 21 hours ago
tonyhart7 | 21 hours ago
jorisw | 21 hours ago
tonyhart7 | 19 hours ago
jorisw | 19 hours ago
owebmaster | 21 hours ago
solyin | 21 hours ago
wateralien | 21 hours ago
handzhiev | 21 hours ago
yitchelle | 21 hours ago
BoorishBears | 21 hours ago
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.
david_shi | 19 hours ago
keeda | 15 hours ago
At one point the hype truly was overwhelming (maybe it only appears less so these days because a lot of it seems justified now) and needed some counterbalance, but this is not it. We need balanced expert perspectives grounded in evidence.
Instead we get get TFA and other similar content from the usual suspects (you know who they are) largely because they’re building their careers on very loudly telling people what they want to hear, which may not be what they need to hear.
This is not harmless, because it gives folks the hope that things have not irrevocably changed and will soon go back to normal after the bubble pops. Personally, I’d rather get a stark but realistic picture of what’s happening now so I can prepare better for the future.
Ozzie_osman | 21 hours ago
It's the top model for classification.
hanifbbz | 20 hours ago
zhisme | 21 hours ago
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".
armcat | 21 hours ago
> 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.
owebmaster | 21 hours ago
senordevnyc | 21 hours ago
alexey-salmin | 20 hours ago
vitorfblima | 19 hours ago
Kuinox | 21 hours ago
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
leemoore | 21 hours ago
hanifbbz | 20 hours ago
owebmaster | 20 hours ago
mikeocool | 20 hours ago
hanifbbz | 20 hours ago
aeon_ai | 20 hours ago
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.
kypro | 20 hours ago
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.
hanifbbz | 20 hours ago
So we're gonna pretend Laya didn't exist 1.5 years prior? https://laya.convaiinnovations.com/
david_shi | 19 hours ago
kissgyorgy | 17 hours ago
lp92 | 16 hours ago
ghusto | 15 hours ago
So if you feel people were "hyping" it up, you were likely witnessing genuine enthusiasm, not bandwagon jumping.
Brendinooo | 14 hours ago
My read on Jev was totally different, I guess.
I first heard about it here. I found a video demo and thought it looked amazing, I saw a lot of discussion about it because other people probably also thought it looked amazing and imagined the possibilities, and then things quiet down because real-world use-cases for it are going to not happen right away, and definitionally it seems like Jev is more about supporting other stuff than being the thing itself.
All the other stuff about ambiguity and jargon and drama don't really register to me, I dunno. But it was interesting to see the author's take on that stuff, I suppose!
Cameri | 12 hours ago