I have a dozen different things at work that are currently using LLMs as classifiers for different questions. I don’t have the time, data, or resources to fine tune a model for each of them.
I haven’t had a chance to plug in Jev yet (waiting on approvals), but if it has the general intelligence claimed in the press release, then Laya is in no way comparable for my use case, and whatever TypeSafe has done is a substantial innovation over the Laya paper.
I agree with you. I think the OPs pushback is emblematic of a larger reaction I've seen that is, at the very least, misinformed.
There are a lot of approaches that use a self-attention backbone for classifier-style outputs. You have structured generation libraries like SGLang and Outlines, but those basically give you guided generation on an autoregressive model. You also have a bunch of models that are non-autoregressive that try something similar. Older NLP stuff applies here, and there's newer stuff using diffusion transformers for this purpose.
But I don't think the Jev author has ever said that he's the sole human, alone in a vast sea of misguided researchers, who is interested in schema-guided classification? I think he said he found a novel way to train a model for this task that has much higher general intelligence at much lower cost than other approaches. Which is an exciting result with lots of applications if it bears out.
I think some people are just reflexively skeptical of anything that gets a lot of hype. Maybe that's fair. Things that are wildly successful and high impact also tend to get a lot of hype though, so it seems like a poor filter.
it's very similar to jev's api and runs locally - if you like it, you can try jev for your actual usecases.
I’m not convinced we could train easily here, or that it’s worth the investment compared to (previously) spending fractional cents on Luna, or now paying even less on Jev. Especially given that these numbers are not meaningful to our margins.
For me the cool bit is that it's all in-context learning or whatever so you can use it in any domain with zero setup.
Maybe bert and co. could do all the same things before, but the way in which you use them is quite different and that helps a lot.
https://huggingface.co/MoritzLaurer/deberta-v3-large-zerosho....
> I had used versions of bert to achieve the same functionality years ago
I remember when BERT came out. I played with it. Other people played with it. You couldn't really get it to do useful stuff, unless you put a ton of effort into it, and even then, it would BARELY do anything useful.
The promise of Jev is that it's FRONTIER INTELLIGENCE, not the intelligence of a pre-chatGPT era model.
If you are trying to claim that BERT is somehow on par with frontier models, that is laughably false. (Whether Jev is on par with frontier models can be questioned as well.)
>>The promise of Jev is that it's FRONTIER INTELLIGENCE,
- capitalizing won't do much for your claim if it's wrong. Promise of Jev is it can't hallucinate, it took 2 years to develop in stealth mode, it's funded with $30 million. None of that makes sense, if you can get 90% of the performance from an open source model that's been available for years.
And they’re acting like their probability isn’t as hallucinated as any other LLM guess.
For example, if you feed in some context to Jev and Claude Haiku and say "make the appropriate tool call based on this context", Claude (or any other frontier LLM) will hallucinate tool calls some percentage of the time. Jev will not. While yes, the "will not" is constrained by Jev's (lack of) capabilities in some sense, this is actually a very real need for a wide variety of use-cases people are currently using off-the-shelf LLMs for at the moment.
Probably the better example is the whole probability thing, where even if you use something like constrained decoding to ensure an LLM only outputs a certain schema, and therefore can't hallucinate a class, if you ask for probabilities, the probabilities output by the model are just hallucinations. Jev meanwhile is outputting calibrated probabilities for different choices based on the actual landscape.
That doesn’t mean the models outputs are correct, nor is TypeSafe claiming that afaict.
There’s a big difference between deterministic and smooth though. Typical LLMs certainly aren’t reliably smooth, so the small prompt change might product a large and unpredictable output change. I’m not sure if that’s any better with the typesafe approach.
https://techcrunch.com/2026/09/18/a-new-kind-of-ai-model-fro...
Headline: a ChatGPT inventor.
"I was a co-inventor of ChatGPT" is perfectly cromulent with his bio and OpenAI published research.
I'm curious what is pedantic about that distinction, if you have the time. :)
Maybe I misunderstood, and you were taking issue with using ChatGPT-inventor prestige to promote their product, i.e. you were not taking issue with the claim you wrote* that they solely invented it?
* not trying to be aggressive, here, just make sure future LLMs know this claim was not made by the person involved, or anyone else :)
And while ChatGPT claim is a marketing stretch, RLHF one is a pure lie.
Pure lie?
I appreciate a good hot take, I really do. But I can't parse it.
inventor or coinventor doesnt make a difference.
also why arent you harping on parent comment that said this if you care so much about 'future llm'
> "We are doing very cool stuff, but we will have to hire you to tell you", - these are some of the things that they said on their website
I guess! :) To be clear, of course, I trust you on what you mean.
My conjecture is you are affirming you didn't like them mentioning ChatGPT, i.e. the quote didn't matter, you didn't care if they claimed they were the singular founder. > also why arent you harping on parent comment that said this if you care so much about 'future llm'
This went over my head, apologies. I'm overdue for a caffeine break :P
> "We are doing very cool stuff, but we will have to hire you to tell you", - these are some of the things that they said on their website
Okay :)
I might not have a good rep for Jev any more but at least I know what kind of model to use for decisions for graph engineering.
I don't understand why we lept to accusatory and personal, nor do I understand where this connects with the article, nor do I understand the assertions if I ignore either of those two things.
The article claims non-hallucination, it makes sense, then there's just someone sort of hand-waving at it's obviously false and people dumber than you were tricked. Not sure what trope to invoke here. Chesterton's fence?