It’s a tale as old as time — people don’t understand that marketing and branding are just as important, if not more so, than the product. Jev is exceptionally-well branded. Anyone can look at the webpage and understand it, and the implications, instantly.
OPs “marketing” is a single post on Reddit titled “ Predicting sales conversion probability from conversations using pure Reinforcement Learning”. Can you understand what that means? I can’t, and I consider myself reasonably technical. Is it obvious it has the same implications as Jev? Again, no idea. And it was just a single post on a subreddit that I don’t even browse! I see people on this thread saying “Jev is just BERT”. Sure, and Dropbox is just a ftp account mounted with curlftpfs!
I do feel bad for the author for finding something cool and being unable to brand it. But the full definition of “product” INCLUDES being able to coherently communicate it. In some sense the branding is just as much the “breakthrough” as the model.
This is also a really common thing in ML specifically. We joke about getting Schmidthuber'd, which is when Jurgen Schmidthuber (sometimes correctly) announces that he or one of his colleagues actually proposed your thing 37 years ago in a Japanese linguists journal.
Statistical modeling, from simple classical stuff up to modern deep learning, just has this dynamic where the theory is rich and bottomless, but the actual components of implementation are pretty neat and compact. So for any given idea, there are probably 20,000 other people who have had the same intuition, just with subtly different application or implementation. Add in that depending on what your particular flavor of research is, you might name an almost identical implementation something completely different. And it leads to a huge amount of sour grapes whenever anyone's idea really garners attention.
If you listen to any podcast with a founder in the ML space who has been in it for long enough, they will invariably say at some point "We actually developed xyz over a year before OpenAI"
OP's was leaky slop from day one [0][1], as is his article [2]
It is arrogant and entitled for the author to take credit for the concept of RL over sequence embeddings, and none of the work that went into pretraining, not to mention the egregious target leakage [1]
> Predicting sales conversion probability from conversations
That's not just bad marketing, it's an example of anti-marketing.
Sales conversion? That makes me think of an old car's salesman trying to scam me into buying something I don't want. I positively don't want to read this paper based on the title.
I think OP, as said on Reddit, wanted to get a lot of investment and ride the wave but did not know how to. This was said as such on Reddit today by them. This is indeed a shame especially it was a year earlier but indeed a lack of marketing; many people on Reddit told what to do in this case, in hindsight, it’s worth checking it out imho.
It's also well established that an algorithm or architecture alone are not enough to produce a useful model. The same architecture can produce vastly different results depending on the training data, post-training, harness, etc.
1052 pts on HN. I'd say they are pretty good at marketing actually.
HN readers are awesome at saying something is great and upvoting it, but unless HN readers are your market it means absolutely nothing. Marketing is as much about putting your message to the right audience as it is about saying the right thing.
This is made more complicated because a group as diverse as HN readers probably does contain some people who are in your target market, to be fair. The problem is that you're getting a strong signal from the whole cohort rather than the bit you're interested in, and it's really easy to conflate that with a sign of success.
As always with any startup activity, unless people are actually giving you their money it doesn't count and you should consider it a vanity metric.
You're right but it's not the full picture. It's much easier to market when you have a name brand behind you. Not sure the author would've done much better even if he messaged it better. It's like the difference between someone random saying something smart on Twitter and no one gives a shit and Karapthy saying the same thing and everyone talks about it. I'm not saying it in a bad way - those with clout around them earned the people's trust by doing something right. But it's not easy to get there and there are many people doing great things that get very little publicity if at all. Not to mention in this case Jev came from a startup that raised a lot of money and can spend it on good marketing.
I think their problem is more not being cited by the team at typesafe, as in general academic politeness. On the one hand you have the charitable assumption that they developed it independently. On the other hand, my opinion is that it is naive to expect companies to do that even if they took inspo from it, especially when this is a core product theme, and not just some supporting infra. They will of course market it as their own. If they ever release a technical report, they might cite it there, but there is no way their landing page and announcement tweet cites it.
Also, the way highly empirical fields like ML work is that it could very well be the case that typesafe had to do a _lot_ of work to improve this one, and in this field it ends up different enough that they feel they are doing something entirely novel[1]. I am not endorsing that 100%, but that happens a lot even between academics. In many cases it is valid.
[1] For example, this guys implementation seems to have atleast one serious issue, as {solution to OLS} points out in a sibling comment: https://news.ycombinator.com/item?id=49770027
This 100%. Engineers really lack understanding in marketing and branding.
No one cares if you are "first". They only care if your product is known by as many people as possible and is better than all the other alternatives at solving a problem that is worth paying for.
If you don't market, then no-one will care that you exist even if you solved a problem decades ago. Someone else will use your solution and take inspiration (and credit) off of your discovery because you didn't bother to tell anyone about it.
another way to look at it, a product is the whole experience (landing, docs, sales, support, code, branding), not the implementation of an algorithm or process
Sorry, but he did tell people about it, no? He showed his receipts. Reddit, arXiv- What I am seeing here is "it's just better marketing". When it comes to prior art, is better marketing sufficient? On one side we can say it's better marketing, but on another side, the side that should actually matter, coming from the direction of him being first, can't we say, it is just better research? Being that he was first and all, and Jev hasn't even published anything according other than what I read. What I am really trying to ask is, is marketing even relevant at this point? So if you have good marketing, you can just steal someone else's work, intentional or not?
I wrote an article detailing an idea I had back during web 1.0. I thought it was clever and maybe some people would find it interesting. Few years later a SaaS popped up selling the same idea as a service. It eventually became a pretty commonly followed pattern for a bunch of huge companies in the same domain.
I have no idea if I was literally the first person to have this idea or if anyone who launched one of these businesses read my article. I definitely didn't understand how much commerical value there was or even considered making a business out of it. I blame nobody but myself for missing an opportunity if there even was one.
I did get like $200 for writing it which was nice.
I don't understand why people continue to use em-dashes. As far I know, the comment system on HN doesn't offer them. And iOS and android don't make them a default. They aren't something normally used by humans because - is a keyboard option that's easier to use.
The usage of them immediately makes your commentary suspect. Either you aren't using the standard web interface to make a comment, you're using and odd 3rd party client, or its LLM generated.
I just typed dash twice into iPhone. Hardly a “non-standard” web interface. Also, I’d like to think my comment was higher quality than anything an LLM could generate! At least, yet.
I've been using compose keys on desktop operating systems and the default keyboard on Android to write em-dashes for 10 years. I also use LLMs every workday, but I have never once used one to write, review, or edit prose because my voice is important to me.
I don't give a shit if my Hacker News comments sometimes look "suspect" to some people. The way I use language is deeply personal and I'm not going to let the clankers or reactionaries against them take it away from me.
I think the main gripe that people had with Jev and Typesafe was the language used when they launched. To me personally it seemed like a parody/con/shady at first.
"Breakthrough", "our research went in another direction" , "Two years in stealth", "System One thinking model", "Jev can't hallucinate", "RLCD","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 on the launch blog.
I had used versions of bert to achieve the same functionality years ago. But to me it seems like they were able to trick the VCs with "can't hallucinate" etc.
To the above author, kudos for sharing your work and making it open. Something like this shouldn't be closed in the first place when it has been available for so many years
Is this equivalent though? The Laya article ends with “ Treat Laya as a fast foundation model to specialize, not as an omniscient zero-shot oracle.”
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.
Jev seems pretty cool! I just got access and have only gotten to do minimal experiments, but I love this general area of research and it fills a very real need.
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.
In a world of agents, doing a BERT run takes about 2 hours from having an empty folder. Just a thought you could consider. Once you've done the first you can do the rest of them before the end of the work day.
We used to use BERT-based embeddings + semantic distance for classification / decision problems in new domains. There was a lot of interest at the time in these kinds of pre-generative but portable models -- Meta's Prophet was another example that came up a lot.
I was confused by the “can’t hallucinate” thing, because it sounded like BS but people were taking it seriously. I purposefully asked a stupid question sort of like “this can’t hallucinate because it only has one output and there’s a schema?”. Was disappointed to learn the answer was yes.
Yeah it’s hilarious, it definitely can hallucinate. Just because it can only hallucinate “A” or “B” rather than a whole paragraph, doesn’t mean it is suddenly more accurate.
And they’re acting like their probability isn’t as hallucinated as any other LLM guess.
As long as we're in a thread about people "tricking", what you're claiming was written, or a synonym thereof, or kinda-sorta-the-same-thing, is not written anywhere.
btw that how mmlu score things to answer question instead of producing all the answer token they look at logprob of a b c d keys in 2020 making this technique old as dirt in nlp
"But to me it seems like they were able to trick the VCs with "can't hallucinate" etc."
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?
I played around with Jev last night and did it for classification tasks that I used Gemini 2.5 flash lite with.
It’s a bit faster and bit cheaper, but this is compared to LLM. The consistency was nice to see, BUT, as someone who trained NLP models prior to LLMs, it’s just BERT with more data. I can see why people would want ready made one shot classifier, and I can see the value of sending multiple classifier in one call, but I wouldn’t call it breakthrough. And I believe many labs will replicate it in no time and might have it as part of their harness.
I see it as a wake up call for the tech community to go back to basics for most tasks instead of relying solely on generic LLMs.
Anyone who has worked in ML for 10+ years would already know that the usage of LLMs for everything is lazy, wasteful and a high degree of marketing on it.
Anyone who has designed circuits will consider CPUs wasteful compared to ASICs. This new FPGA technology is just a less efficient ASIC.
That’s roughly what I’m hearing.
The fact that general purpose intelligent classifiers can be dynamically hacked together by an LLM in real time to allow them to build evolving labeled and understandable networks that perform substantially faster than the LLM, and can act as an intermediate sorting and organizing layer for caching context or handling simple tasks, and a complete layman like me can assemble a teachable layer of these in a few days from an inexpensive service…
That’s wild!
And then you can identify where an expert system needs a more specific ML technique for efficiency within this network that overlays the SOTA model. Or manually adjust the stored context in each secondary “neuron”. And paths forward can run programs or take actions at relative high speed.
And you can share these with others and improve them as a group.
You could insert this at the datacenters at scale with a local supervising expert to prune and encourage proper growth. You could identify specific gaps in capability that need more training, and patch over them temporarily.
Then you train those corrections back into the general purpose model, or you identify highly efficient subsystems for specific purposes.
And this is just one way to use it. High speed intelligent workflows can live in this. There’s a spot for a local LLM to learn on the fly.
Maybe I’m way off base, but for the non-experts Jev seems extremely valuable.
I would rate using LLM for tasks more specific ML can handle as a lot like using one's smartphone to snap photos, listen to music, set alarms, and play video games in preference to carrying around a fun cam, ipod, watch, and switch 2 everywhere.
For those who need to dive really deep into each specific avenue and squeeze maximal quality out, the photographers will be packing DSLRs and intense gamers will wait til they get home to strap into a PS5 or a gaming rig or VR or whatever.
But "can get 90% of anyone's needs met in this field, and can do the same in dozens or hundreds of other fields simultaneously" will remain the killer solution for anyone with lots needs that each have bounded depth.
I have, LLMs are less fragile, that’s why I like them. The ability to generalize isn’t just about being general purpose, it’s super robust, and so assuming the budget is there (I agree they are inefficient) end up performing better on many classical tasks that have ood inputs. Before LLMs / foundation models we all struggled with generalization and at least in the work I was doing people were independently converging to using bigger more general models for tasks anyway as compute got cheaper. LLMs are just the most popular version of this.
I wouldn’t say lazy, LLMs are fast to use and much more cost effective especially if you factor the cost and time of training (data preparation, data cleaning, … etc).
It’s hard to justify several months to business when there is something off-shelf ready to use and doesn’t require domain specialists to run.
why would you waste your time messing around with a team of expensive ml engineers and data scientists that produce vastly inferior to a llm.
We ripped out custom homegrown ml models that were developed in last 10 yrs and put an llm in its place. Its the opposite of wasteful. Even local gemma models are vastly superior.
There have been other "universal"/general classifiers like GLiNER, GLiFormer, etc based on BERTs (Laya itself is based on ModernBERT!), but I do think there's something underrated about slapping classification on a "big" model like I've seen post-Jev announcement, lots of Qwen stuff, but the most interesting to me so far is razorback16/openjev using DiffusionGemma. There's a level of generalization that lots and lots of parameters get you that you can't really get out of small models.
That's an interesting choice. One question I had when looking at the jev copy on their blog is if one "line" in their output looks / attends to other lines. I think not, since they say it's parallel and not autoregressive. In that regard, it would be interesting to play with diffusion, and see if you'd get better results by playing with types, locking some, and so on.
The data labeling objection baffles me. Even if you don’t need labels for training, how do you know your model is working if you’re not evaluating it?
My company specializes in statistical long document text classification, but nowadays we mainly work with audit trail requirements because we got tired of hearing complaints about our 5 example learning curve. Seems like the industry standard is telling an llm to label and telling an llm to eval, and crossing your fingers that it’s correct.
Let's take that as a given. Is BERT with more data not useful?
> I can see why people would want ready made one shot classifier, and I can see the value of sending multiple classifier in one call, but I wouldn’t call it breakthrough
Are those things that people want less useful because of what someone else calls it?
> I see it as a wake up call for the tech community to go back to basics for most tasks instead of relying solely on generic LLMs.
Maybe, or maybe to use Jev, which is useful?
Whether something is overmarketed or undermarketed, novel or derivative, it does not change its function.
Agreed, refreshing to hear others share this feeling.
Timing is important here, LLM's raised awareness of ML techniques but we need to remember that most don't have traditional ML educations, so there'll be some "rediscovery" like Jev
the huge benefit in real systems for Jev like solutions i see is the cybersecurity / prompt injection mitigation. since the output will always be well structured, there is no way prompt injection might make the system do something crazy.
probably a prompt injection can still affect the output though, in unforeseeable ways.
You can use structured outputs and validate them against a schema today. I do agree making it a hard constraint instead of best practice for developers closes a whole class of bugs.
Gemini 2.5 Flash Lite is $500/Gt, Jev is $42/Gt. AKA an order of magnitude cheaper.
> BERT with more data
It is specifically not just that, in the same way that models which have been chat/task-optimized via RLHF (which made these models much more useful for a huge variety of tasks) are not just "the base transformer model with more data".
I really doubt this actually. To me, Jev is a great example ofcounter positioning. When you consider just how hyper optimized the labs are around auto regressive LLMs, and just how much money they have already invested and are pre committed to investing in an entire stack for auto regressive transformers... then responding to Jev becomes nearly impossible actually. They would just be giving up too much.
Just think, everything from their current sources of revenue, the sales use cases they tout, the marketing on the websites, the messaging to customers, then technically to the APIs, their internal batching and scheduling algos, their GPU configs, the chips themselves. ALL OF IT is designed with generative text models in mind. Jev breaks all of it.
I think basically no chance of a response any time soon.
I don’t understand how you’ve reasoned your way here.
How could Jev have possibly built something out of reach of a frontier lab providing the same or 5x as much resourcing to one of their teams to achieve? Which they can do because Jev has only received $40M of funding recently, so a round that is approximately what OpenAI is spending per math problem they try cracking.
In addition to that, these frontier labs have got extremely good at generating synthetic data and running generalised training pipelines. I can only imagine how easy it would be for them to build this internally vs Jev building it from scratch.
And then the final thing: one of the best places you might apply Jev is within a harness, behind layers that customers increasingly have abstracted from them. Frontier labs have huge incentives to do this as it could make their offering much better and cheaper. And whoever gets this first wins another big attraction for users.
My take on this is Jev is either acquired almost immediately for the benefit of the next 1-3 months head start for whichever lab acquires them or we get a similar model offered from all labs in 3-6 months or sooner.
It would be amazing to have big BERTha with per-token pricing on GCP or AWS. There are many times I am reaching for a cheap classifier with the general behavior of an LLM.
You are competitive with Jev only if you fine tune on the train dataset and calibrate per question.
As much as I dislike literally everything about typesafes behavior, they have an API model that works on any problem without fine tuning, and that is the key.
To be honest everyone who can finetune can likely finetune a BERT for a specific task and get similar results to yours. And that has been true for years
The key to Jevs success is that it works without fine tuning
I can understand why the author feels bitter but it still feels juvenile to me. Certainly both Jev and Laya are based on the research of countless prior papers and academics. Diogo decided to build a product out of the concept. The author didn't. Publishing research papers and model weights is probably part of the problem--it feels academic. If you look at the author's profile they focus on applying AI to healthcare. Not selling general AI type safety to AI pilled companies and devs. There's a big difference there. Whether that's good or bad you can argue all day. But for the author to expect otherwise is pretty weird. I do applaud them for not stewing too much on it and trying to do something about it, though.
I believe his qualms were with the "hype" in Jev's announcement: specifically calling this kind of model a breakthrough, without crediting previous art, and keeping everything closed source.
The hype is kinda nuts; I use X for ML/LLM stuff, and I just can't get away from Jev - even in my Following feed. Even days later 75% of posts are about "how I use typesafe for cooking breakfast!" or Jev clones.
"Juvenile" is a weird label to assign to someone whose work is re-presented by someone else and not attributed properly. People here had a very different take on the Navier-Stokes situation XD
I’m not filled with confidence when the author’s first paper takes an RL approach but then doesn’t use it to change the action taken in the next turn. Seems like simple classification would achieve the same end. And this quote from the paper isn’t overly reassuring:
“I personally found that this sequential approach captured
sales dynamics much more effectively than traditional classification models.”
Agreed. Another difficulty here is there are not good benchmarks for this new architecture yet, so it’s easy to potshot and snipe, where jev seems to be pretty broadly intelligent/at least have had a lot of rl in different domains.
We haven’t seen any of these copy cats play doom or street fighter for instance; just categorize email.
I imagine once the author cools down and evaluates on a broad harness of tasks he may find that his new thing has a lot of engineering work ahead.
The doom demo would have to be reproduced to confirm what their model is capable of. Oh but it's all closed source, so who knows.
It reminds. Me of Devin. Took a while to debunk. Not saying Jev is a fraud , but the gap between structuring typed output and playing a game involving logical interpretation of frames made of pixels, screams unstructured interpretation they made and forgot to mention.
Hi I want to explain that arxiv is not "publishing a paper" - it's a step up perhaps from putting it on your own website, but this is not what is meant by professional academics when they talk about "publishing" (even when they work for big AI companies).
Your "papers" have only a single author and no current citations. It is not clear you are able to work with other people. The papers claim to offer results but no theory about why those results are the best possible. They read like sales whitepapers not scientific work. Reddit comments on the thread you linked to said they weren't able to reproduce your work.
I'm not looking to buy magic beans for my sales team.
It is really interesting to see this claim, because i thought the current theory was that typesafe actually repackaged the work from GLiNER[1] - which does seem to be a closer match, and their original paper[2] predates yours by several years. Curious if you had heard of it before? It is also open source[3] and I think also has some good usage.
I think the biggest lesson with Jev was the one of communication and understanding for the broader audience, sometimes a lot about innovating involves repeating yourself and translating your own thoughts to an intended audience.
Classical machine learning has been, for the most part, and just by the nature of science, behind academic terms and difficult to engage with as a product.
Jev did really well with coining up “System One” models and defining a standard application interface plus core primitives that landed in the current paradigm of software development.
I think it’s sort of like how Cursor reinvented autocomplete back then as a different UX and suddenly everyone was just using it because of how easy the bar was to understanding it.
Lastly, timing is everything. Just as Cursor had a first mover advantage, despite ML Ops being a thing for a while, they managed to encapsulate the concept behind a “System One” black box that fits the existing mental model for building software and shipping a data contract in the right point in time where the cost of tokens has been an important metric to watch.
Furthermore, it's not about the current innovation right now - if you sell yourself on a broader mission, your core product can evolve and change with it, and you're more selling yourself as the guy who will make that abstract vision possible no matter what.
No matter how much we pretend, that's how a lot of abstractions work. Things that touch the real world can change; there's a risk that the change could be as something as simple as a bugfix to changing the underlying implementation but preserving a higher level goal; you generally want a human in the loop to make sure the semantics work out and everybody's agreeing.
Well said, once you put it out there in the world, then it's also about how will the customer react to it and having to own that relationship going forward.
The relationship aspect of a business has a lot to do with how effective it is at continuing to justify its core value in an easy and relatable way; especially so when the decision makers that front the bill may not be as engaged with the underlying machinery behind the why it works how it does.
We've all seen "this meeting could have been an email"; now get ready for "this VC-backed firm could have been a single arXiv preprint."
I don't want to be too dismissive of Jev, but building technology in stealth for two years just doesn't make sense to me when the capabilities are so easily replicated. These are strange times, where the incentive to do public research and the incentive to develop in private are both being eroded.
All this means is that brand and distribution matters more than ever. There's 1000 chatgpt clones but everyone still uses chatgpt. There might be 1000 jev clones soon enough but people won't switch unless there's something significantly better about it.
It's also why Meta can make Muse and get a lot of users even though there's 10,000 personal agent startups
In jevs case I think the competing offerings will rear their head rather fast. The ability to label data like it does now leans itself well for distillation. And to switch out a model like fable for Astra is really not all that difficult. Yea jev is alone now. But before the end of the year another lab with the same offering will rear it’s head
Bringing up iphone, I see how Jev pulled an Apple for making claims that their model is a breakthrough in research and 2 years of making like other iPhone features that's been around in other phones.
Jev doesn't require finetuning. All of the posts claiming that the technology already existed are missing that I don't want to spend a week to create a dataset (for a problem I might not already have data for), finetune a model, and set up infrastructure to run the model, every time I have a small routing or classification problem. The ability to knock out any arbitrary classification problem in minutes instead of in a week is a big deal.
Even then, TypeSafe AI point out that "Jev doesn't have deep knowledge of niche domains, but you can supply context to help it decide. If you’d like Jev trained on your use cases, let us know."
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.
But is there any proof that Jev is frontier intelligence?
It thinks there are two Rs in strawberry.
It fails to assign probabilities to die roll outcomes or coin flips.
When used as an LLM it thinks it is Qwen.
Apparently reordering the list of possible answers can change assigned probabilities by up to 20%.
I’ve seen claims that it struggles to play tic tac toe.
So far there is a lot of evidence that it behaves exactly like a tiny open weight model. The only argument against this claim is “trust me bro” claims from it’s author.
Quickly reading the article, one notable limitation seems to be that these checkpoints are 512-1024 tokens context size models, while Jev is seemingly 32k.
That's a pretty big limitation, I would argue, unless I'm misunderstanding and it can be worked around easily somehow? I'm surprised it isn't surfaced more prominently in the comparison.
Jev has 64k total token request budget and I do wonder how it will handle highly specialised inputs.
This Jev waitlist that Typesafe AI are utilising is surely going to raise questions pretty soon - it's hard to sell this to bosses when it looks like a pop-up restaurant
Idk, your limitations section sure makes it seem less drop in and less general than Jev. Like the point here isn't your ML aptitude it's how easy is it for developers to drop this into a product and use it.
I'm more than capable of training a bert classifier in fact in 2019 I had trained many custom berts and was running them on hundreds of millions of documents a day.
I don't want to manage GPUs / CPUs now. I don't want to maintain my corpus and retrain as my product's data distribution shifts. The list of things I don't want to do goes on and on and on. And I'm happy for them to be someone else's problem.
I do just want a reasonably good general classifier served to me with a great devex and calibrated confidence scores to help me figure out when to fallback to another model.
With everybody bashing OP here, how is he supposed to even make money ? It’s n open-source model you self-host right? So he doesnt seem to be just greedy? He might genuinely feel like stolen. I hope he doesnt take the comments personally and is able to find motivation in it.
The unfortunate true is that getting even the best work in front of an audience is often much harder than solving the problem. Is uploading a paper to arXiv enough to expect the work to be recognized and cited? Unfortunately, it rather is not. arXiv is an open repository which includes plenty of not reviewed and not officially published papers. In a popular field such as machine learning, the number of arXiv papers is overwhelming. Expecting that some machine learning expert will stumble upon an arXiv paper and recognize its value is wishful thinking.
I'm not a researcher, but long time ago I had an idea of a new, seemingly interesting attack on TCP. Having some free time between jobs, I wrote a paper about this, created a proof of concept and decided to send the paper to USENIX Security. I got back two reviews, both in rather positive tone, but rejecting the paper on the grounds that it shows only individual steps of the attack, but it would be much stronger if it showed also the attack working end-to-end. At that point I just uploaded the paper to arXiv and called it a day. I've put a lot of work into that paper, but not enough, I don't consider it properly published and I don't expect anyone to cite it. The paper failed the peer review process and I didn't put the work to improve it further.
Love it. I was really surprised to see the traction typesafe got in the first place. I had built something similar a year ago for a client and thought it was nothing groundbreaking. The client bought it, still uses it and that was it. I had also spent considerable time training and fine tuning zero shot NLI classifiers. Anyway, after typesafe was launched I decided to start building this open source library - https://github.com/deepanwadhwa/OpenDecision . The context length for the underlying model is 8k.
>Zero-shot vs. Fine-tuning: Out-of-the-box base models score ~0.35 on the typed-decisions benchmark (near random). The 0.766 score is achieved by fine-tuning on the benchmark's train split. Treat Laya as a fast foundation model to specialize, not as an omniscient zero-shot oracle.
This should be way up in the article. Fine tuning is a pain, requiring it for good results put Laya in a whole different category vs Jev
Jev will continue to do well because people don't actually want to host their own models. The average customer just want an always-on pay-per-use API that has social proof.
Great work by the author. Both Laya and Jev showcase how a different class of models can be efficient on tasks that don't require a 'generated output artifact'. I believe the same is true for VLMs where you're not always generating an image, but rather trying to understand more about the input image.
Token consumptions are flying through the roof and optimisation is the way forward.
LLMs being described as system 2 thinking here is a semantic shift I have not encountered before.
LLMs are also a deep learning approach. Output, as slow as it is, still comes from weird latent spaces. In AI I always took System 2 to map more to symbolic approaches, or at least when explaining symbolic AI to someone who has heard of deep learning thinking fast and slow was a good comparison to draw on.
> Zero-shot vs. Fine-tuning: Out-of-the-box base models score ~0.35 on the typed-decisions benchmark (near random). The 0.766 score is achieved by fine-tuning on the benchmark's train split. Treat Laya as a fast foundation model to specialize, not as an omniscient zero-shot oracle.
Isn’t this post comparing zero shot Jev to fine tunes of this model for each of the datasets it is tested on? If so seems like fairly impressive results for Jev
I'm reading your year old Reddit post and Typesafe's description, and while they probabably say that they can do what you do the main point is that it's different things really as far as I can tell?
Laya seems to be focused on sales/conversations?
Reading quickly about TypeSafe, it seems to be about creating _type-safe_ outputs from AI tools for downstream systems to consume, we actually have a system in production that's probably a glove-fit for that, it's for scanning receipts to be ingested into a system and we also have other systems in a sales-pipe that isn't too far off Laya but still sounds more pertient to TypeSafe.
You did a special case well, but just because they cover (perhaps badly) that case doesn't mean that it's the same thing.
Jev was built using the same architecture Laya's author proposed[1] in March 2025. Laya is an open-source system based on that research from a year ago. Whether Jev is also based on the OP's materials or independently invented is hard to say.
No, OP thinks they independently discovered Jev's architecture a year ago and published a paper. I am not an expert but I don't think Typesafe has published Jev's architecture so OP's claims cannot be taken at face value.
"if you build it they will come" is a lie, marketing and branding matter and whats funny is often the biggest proponents of "if you build it" don't even realize the things they see going viral are marketed to them, they extremely naively think it got traction simply because it was good and they "built the right thing", not true.
A question I have about Jev is, who are the "Service Providers" that they provide prompt information, and why is there no time limit on how long they store prompts?
One of the Use Cases marketed is having Jev flag if personal information is contained in text. It's not a strong use case for it really.
This is awesome OP - very impressive. I'm definitely going to be using it to classify customer support tickets and log error classification. Thank you!
It seems like in today's day and age, whoever comes to market with a new tech second is usually winning. It's kind of unfortunate, as Laya is actually pretty cool. I think it will catch on considering its open weight and self hostable. It's easy to host on a home lab compared to the 1T parameter behemoths.
Probably important to call out this part of the post:
Zero-shot vs. Fine-tuning: Out-of-the-box base models score ~0.35 on the typed-decisions benchmark (near random). The 0.766 score is achieved by fine-tuning on the benchmark's train split. Treat Laya as a fast foundation model to specialize, not as an omniscient zero-shot oracle.
They're definitely not the only one. I've been building on relational transformers, which does prediction and classification over relational data (it handles numeric types better). It's validating to see that these small models that do prediction tasks are so useful to the community, but also stings a little that it was so hard for me to communicate how game changing they are.
Is this at all possible to run locally on a MacBook pro m5 (48gb ram)? What kind of performance could I expect? Or would you run this somewhere in the cloud? What HW / which provider would you choose (single user for exploration only)
Correct me if I am wrong, can I use Jev and this tool for ticket classification? I mean, for instance, a level 1 ticket contains a screenshot of the login page that displays an error, LLM can do it perfectly, can Jev do it?
It can’t do images but it can do a pretty good job of triaging urgency or choosing when to escalate. So I’d guess yes but it depends how detailed your classification is.
Loved the idea, but I don’t think it would be able to handle real-world data effectively. There are a lot of nuances that actually require a reasoning model to think through, connect the dots, and make sense of the broader context.
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.
I was wondering, do you think its possible to use something like SAM 3 (segment anything from FB) + Laya to create a super efficient and fast computer use tool?
I don’t understand Jev or this. I used this since it’s open source (good job btw!) with the following. State: “a 6 sided die rolled a 3”, question (noul): “Is the number odd?”
Answer: 9% chance, with 91% confidence.
Heh???
Ok, even worse. 75% chance a coin landed heads up?
State: I flipped a coin.
Question:
{
"noul_result": {
"type": "noul",
"instructions": "Did the coin land heads up?"
},
"choice_result": {
"type": "choice",
"instructions": "Determine if the coin landed heads or tails up.",
"criteria": {
"heads": "the coin landed heads up",
"tails": "the coin landed tails up"
}
}
}
State: "A fair coin was flipped once. The result was not observed.
No other information about the outcome is available."
Questions:
{
"noul_result": {
"type": "noul",
"instructions": "Given only the supplied state, what is the probability that the coin landed heads up?"
},
"choice_result": {
"type": "choice",
"instructions": "Given only the supplied state, determine which outcome occurred.",
"criteria": {
"heads": "the coin landed heads up",
"tails": "the coin landed tails up"
}
}
}
Jev says you should restate state in the question and I tried it:
{
"decision": {
"type": "noul",
"instructions": "Is the rolled number in state odd?"
},
"question": {
"type": "noul",
"instructions": "Is the number odd?"
},
"question-3": {
"type": "noul",
"instructions": "a 6 sided dice rolled a 3 Is the number odd?"
},
"question-4": {
"type": "noul",
"instructions": "a 6 sided dice rolled a 3 Is the rolled number odd?"
}
}
I don't think calculating mathematical odds from natural language is the sort of problem this is trying to solve. A typical LLM hooked up to a calculator would be more appropriate for that.
Jev (and similar) is more for data processing and sentiment analysis. Moderation, search engines, that sort of thing. Jev has a page of proposed use cases where you can get an idea of what they're going for: https://docs.typesafe.ai/concepts/use-case-map
from https://huggingface.co/convaiinnovations/laya
> The policy reports a distribution; exploration adds zero-mean Gaussian noise to the logits; the reward is a strictly proper scoring rule (log + spherical, plus ranked probability score for ordinal questions). Expected reward is maximised only by reporting honest probabilities.
Look at the bright side. You can ride the Jev marketing, because at the end of the day, post prototype, data privacy is always going to be top of mind and people are already looking for Open Source alternatives because Jev proved the usecase in a simple way most people could understand.
Why do we ("society") need the "frontier" companies at all? Their business goal has settled on trying to CONFUSE the shit out of us so that we don't understand the big pictures about various aspects of AI.
THANK YOU, Nandakishor Mukkunnoth, for putting in the work to help to clarify this stuff!
You are like a firefighter compared to their fire-insurance racket.
Yep. Not instantly though. I am hacking away at these things over on https://github.com/rcarmo/go-pherence (I do SIMD versions of common inference algos) and trying to improve that.
paper the reddit OP "published" (their words on reddit) to arxiv (before they put the vouching process in place). It's what you expect if you click through.
Does not appear to be like what Jev is doing, they talk about RAG and embeddings and orchestrators (the stuff that was cool 1 year ago), no talk of system 1 vs 2 (before Jev), whereas Jev is apparently just a model.
There is a vLLM PR introducing Jev like capabilities for diffusion models (and more, have not delved deeply)
Jev is targeted to end users, and the tooling is really great. Unfortunately publishing papers without code or tooling or APIs will not attract the crowd as they want something usable quickly.
That said, being second in this space is not the end of the world and the race is still on. If there is API access and proper tooling support like Jev, then it can win the game based on merit and not just marketing.
> Seeing the hype online feels both validating and deeply frustrating.
The post is conflating hype and money with technical innovation, they are not really correlated. Kurzweil is known for saying most innovations succeed based not on technology but on timing. Today, who talks about it might matter even more than timing.
Superior research often gets overlooked in favor of someone raising millions, sometimes people who have produced literally nothing manage to sell it. Not saying that's happening here, but I've seen this pattern a lot over my career.
Someone riding (or manufacturing) a hype wave is playing a completely different game from a researcher. If you're a researcher you can't really feel dejected when someone is making a business on the back of what seems like your research; legal protections are decades out of date, even ignoring vibe coding. If you want to make money/hype/whatever off of your work, do that. But realize that it's a path that's often orthogonal to research.
I've been deeply impressed with Jev as it made a bunch of workloads we had on Luna or Gemini 10x cheaper and 2x faster (previously used non reasoning version for latency reasons).
Now Laya promises another speed up and it's open source. Tbh if it can't run on a CPU I anyway want to buy it from an inference provider. Managing gpus in production is a non trivial problem.
What I also wondered about Jev is how different it is from something like tabular foundation models. They seem to overlap in use cases. Which then leads to the question, what is actually learned? A lot of people in machine learning spend time to making things explainable and always struggled to move beyond data induced biases.
Having it open source is awesome as fine tuning might give additional performance on the task we care about.
I have 10000+ inventory items to categorize but I need an intelligent model (not just if statements). Using LLMs has been slow and expensive and I needed to queue it to run for hours. Jev did it in minutes and for less than 1 cent
It’s a tale as old as time — people don’t understand that marketing and branding are just as important, if not more so, than the product. Jev is exceptionally-well branded. Anyone can look at the webpage and understand it, and the implications, instantly.
OPs “marketing” is a single post on Reddit titled “ Predicting sales conversion probability from conversations using pure Reinforcement Learning”. Can you understand what that means? I can’t, and I consider myself reasonably technical. Is it obvious it has the same implications as Jev? Again, no idea. And it was just a single post on a subreddit that I don’t even browse! I see people on this thread saying “Jev is just BERT”. Sure, and Dropbox is just a ftp account mounted with curlftpfs!
I do feel bad for the author for finding something cool and being unable to brand it. But the full definition of “product” INCLUDES being able to coherently communicate it. In some sense the branding is just as much the “breakthrough” as the model.
This is also a really common thing in ML specifically. We joke about getting Schmidthuber'd, which is when Jurgen Schmidthuber (sometimes correctly) announces that he or one of his colleagues actually proposed your thing 37 years ago in a Japanese linguists journal.
Statistical modeling, from simple classical stuff up to modern deep learning, just has this dynamic where the theory is rich and bottomless, but the actual components of implementation are pretty neat and compact. So for any given idea, there are probably 20,000 other people who have had the same intuition, just with subtly different application or implementation. Add in that depending on what your particular flavor of research is, you might name an almost identical implementation something completely different. And it leads to a huge amount of sour grapes whenever anyone's idea really garners attention.
If you listen to any podcast with a founder in the ML space who has been in it for long enough, they will invariably say at some point "We actually developed xyz over a year before OpenAI"
Schmidhuber rarely ever executed the idea correctly, which makes his claims particularly obnoxious.
> “Predicting sales conversion probability from conversations using pure Reinforcement Learning”. Can you understand what that means?
I can understand it, and it wouldn't excite me at all.
Jev has a beautiful API and is advertised as something much more general.
The title doesn't reflect the content of the paper or project, which uses things like RAG and an orchestrator, so more than "pure RL"
(the project before it was rehashed into Laya since Jev was released)
OP's was leaky slop from day one [0][1], as is his article [2]
It is arrogant and entitled for the author to take credit for the concept of RL over sequence embeddings, and none of the work that went into pretraining, not to mention the egregious target leakage [1]
[0]: Author fails to grasp the concept of virtual environments https://www.reddit.com/r/LocalLLaMA/comments/1kl0uvv/comment...
[1]: his `train.py` has `outcome` as a model input (conversation_metrics built from _parse_conversation which includes outcome): https://huggingface.co/DeepMostInnovations/sales-conversion-... https://huggingface.co/DeepMostInnovations/sales-conversion-...
[2]: 100% of this post is AI-generated https://www.pangram.com/history/97e0be84-391d-46b8-9c16-2d8f...
Not to go all meta but the very post were commenting in is also good marketing and branding.
So while the initial post was not good, the author is currently succeeding to some extent at what you're describing
> Predicting sales conversion probability from conversations
That's not just bad marketing, it's an example of anti-marketing.
Sales conversion? That makes me think of an old car's salesman trying to scam me into buying something I don't want. I positively don't want to read this paper based on the title.
Especially with „ sales conversion probability“ it just doesn’t sound universal to other issues - there’s tons of unique models for specific use cases
I think OP, as said on Reddit, wanted to get a lot of investment and ride the wave but did not know how to. This was said as such on Reddit today by them. This is indeed a shame especially it was a year earlier but indeed a lack of marketing; many people on Reddit told what to do in this case, in hindsight, it’s worth checking it out imho.
this is a prior HN post from OP adding color to their underlying motivations
https://news.ycombinator.com/item?id=49674396
It's also well established that an algorithm or architecture alone are not enough to produce a useful model. The same architecture can produce vastly different results depending on the training data, post-training, harness, etc.
1052 pts on HN. I'd say they are pretty good at marketing actually.
While they may not initially done well they are certainly riding this wave.
1052 pts on HN. I'd say they are pretty good at marketing actually.
HN readers are awesome at saying something is great and upvoting it, but unless HN readers are your market it means absolutely nothing. Marketing is as much about putting your message to the right audience as it is about saying the right thing.
This is made more complicated because a group as diverse as HN readers probably does contain some people who are in your target market, to be fair. The problem is that you're getting a strong signal from the whole cohort rather than the bit you're interested in, and it's really easy to conflate that with a sign of success.
As always with any startup activity, unless people are actually giving you their money it doesn't count and you should consider it a vanity metric.
You're right but it's not the full picture. It's much easier to market when you have a name brand behind you. Not sure the author would've done much better even if he messaged it better. It's like the difference between someone random saying something smart on Twitter and no one gives a shit and Karapthy saying the same thing and everyone talks about it. I'm not saying it in a bad way - those with clout around them earned the people's trust by doing something right. But it's not easy to get there and there are many people doing great things that get very little publicity if at all. Not to mention in this case Jev came from a startup that raised a lot of money and can spend it on good marketing.
I think their problem is more not being cited by the team at typesafe, as in general academic politeness. On the one hand you have the charitable assumption that they developed it independently. On the other hand, my opinion is that it is naive to expect companies to do that even if they took inspo from it, especially when this is a core product theme, and not just some supporting infra. They will of course market it as their own. If they ever release a technical report, they might cite it there, but there is no way their landing page and announcement tweet cites it.
Also, the way highly empirical fields like ML work is that it could very well be the case that typesafe had to do a _lot_ of work to improve this one, and in this field it ends up different enough that they feel they are doing something entirely novel[1]. I am not endorsing that 100%, but that happens a lot even between academics. In many cases it is valid.
[1] For example, this guys implementation seems to have atleast one serious issue, as {solution to OLS} points out in a sibling comment: https://news.ycombinator.com/item?id=49770027
This 100%. Engineers really lack understanding in marketing and branding.
No one cares if you are "first". They only care if your product is known by as many people as possible and is better than all the other alternatives at solving a problem that is worth paying for.
If you don't market, then no-one will care that you exist even if you solved a problem decades ago. Someone else will use your solution and take inspiration (and credit) off of your discovery because you didn't bother to tell anyone about it.
This is exactly what happened here.
another way to look at it, a product is the whole experience (landing, docs, sales, support, code, branding), not the implementation of an algorithm or process
Sorry, but he did tell people about it, no? He showed his receipts. Reddit, arXiv- What I am seeing here is "it's just better marketing". When it comes to prior art, is better marketing sufficient? On one side we can say it's better marketing, but on another side, the side that should actually matter, coming from the direction of him being first, can't we say, it is just better research? Being that he was first and all, and Jev hasn't even published anything according other than what I read. What I am really trying to ask is, is marketing even relevant at this point? So if you have good marketing, you can just steal someone else's work, intentional or not?
2 replies →
I feel like a HN post hitting 1000+ points is a good way to catch up, communication wise.
I wrote an article detailing an idea I had back during web 1.0. I thought it was clever and maybe some people would find it interesting. Few years later a SaaS popped up selling the same idea as a service. It eventually became a pretty commonly followed pattern for a bunch of huge companies in the same domain.
I have no idea if I was literally the first person to have this idea or if anyone who launched one of these businesses read my article. I definitely didn't understand how much commerical value there was or even considered making a business out of it. I blame nobody but myself for missing an opportunity if there even was one.
I did get like $200 for writing it which was nice.
I don't understand why people continue to use em-dashes. As far I know, the comment system on HN doesn't offer them. And iOS and android don't make them a default. They aren't something normally used by humans because - is a keyboard option that's easier to use.
The usage of them immediately makes your commentary suspect. Either you aren't using the standard web interface to make a comment, you're using and odd 3rd party client, or its LLM generated.
On a Mac it’s literally just option+dash.
1 reply →
I just typed dash twice into iPhone. Hardly a “non-standard” web interface. Also, I’d like to think my comment was higher quality than anything an LLM could generate! At least, yet.
I've been using compose keys on desktop operating systems and the default keyboard on Android to write em-dashes for 10 years. I also use LLMs every workday, but I have never once used one to write, review, or edit prose because my voice is important to me.
I don't give a shit if my Hacker News comments sometimes look "suspect" to some people. The way I use language is deeply personal and I'm not going to let the clankers or reactionaries against them take it away from me.
1 reply →
I think the main gripe that people had with Jev and Typesafe was the language used when they launched. To me personally it seemed like a parody/con/shady at first.
"Breakthrough", "our research went in another direction" , "Two years in stealth", "System One thinking model", "Jev can't hallucinate", "RLCD","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 on the launch blog.
I had used versions of bert to achieve the same functionality years ago. But to me it seems like they were able to trick the VCs with "can't hallucinate" etc.
To the above author, kudos for sharing your work and making it open. Something like this shouldn't be closed in the first place when it has been available for so many years
Is this equivalent though? The Laya article ends with “ Treat Laya as a fast foundation model to specialize, not as an omniscient zero-shot oracle.”
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.
Jev seems pretty cool! I just got access and have only gotten to do minimal experiments, but I love this general area of research and it fills a very real need.
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.
hey, you might wanna try this? - https://github.com/deepanwadhwa/OpenDecision
it's very similar to jev's api and runs locally - if you like it, you can try jev for your actual usecases.
In a world of agents, doing a BERT run takes about 2 hours from having an empty folder. Just a thought you could consider. Once you've done the first you can do the rest of them before the end of the work day.
3 replies →
Last time I did anything with a BERT, you had to train or fine-tune. Is that not still true?
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.
It depends on your usecase but the models do show general capabilities. check this model out.
https://huggingface.co/MoritzLaurer/deberta-v3-large-zerosho....
6 replies →
We used to use BERT-based embeddings + semantic distance for classification / decision problems in new domains. There was a lot of interest at the time in these kinds of pre-generative but portable models -- Meta's Prophet was another example that came up a lot.
I was confused by the “can’t hallucinate” thing, because it sounded like BS but people were taking it seriously. I purposefully asked a stupid question sort of like “this can’t hallucinate because it only has one output and there’s a schema?”. Was disappointed to learn the answer was yes.
Yeah it’s hilarious, it definitely can hallucinate. Just because it can only hallucinate “A” or “B” rather than a whole paragraph, doesn’t mean it is suddenly more accurate.
And they’re acting like their probability isn’t as hallucinated as any other LLM guess.
7 replies →
But that hallucination is reproducible so you can adjust the prompt. Unlike an LLM in which everything is wildly not deterministic.
1 reply →
Why would you think "can't hallucinate" means "can't pick wrong probability of an option" ?
2 replies →
you forgot the main one "from the guy who invented chatgpt"
As long as we're in a thread about people "tricking", what you're claiming was written, or a synonym thereof, or kinda-sorta-the-same-thing, is not written anywhere.
13 replies →
btw that how mmlu score things to answer question instead of producing all the answer token they look at logprob of a b c d keys in 2020 making this technique old as dirt in nlp
This technique is so obvious to anyone who spends more than a minute with multiple choice tasks. It's wild they're claiming it as a feature.
Baity claims worked didn't it for Jev? (most likely from AI forsure)
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.
"But to me it seems like they were able to trick the VCs with "can't hallucinate" etc."
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?
[dead]
I played around with Jev last night and did it for classification tasks that I used Gemini 2.5 flash lite with.
It’s a bit faster and bit cheaper, but this is compared to LLM. The consistency was nice to see, BUT, as someone who trained NLP models prior to LLMs, it’s just BERT with more data. I can see why people would want ready made one shot classifier, and I can see the value of sending multiple classifier in one call, but I wouldn’t call it breakthrough. And I believe many labs will replicate it in no time and might have it as part of their harness.
I see it as a wake up call for the tech community to go back to basics for most tasks instead of relying solely on generic LLMs.
Anyone who has worked in ML for 10+ years would already know that the usage of LLMs for everything is lazy, wasteful and a high degree of marketing on it.
Anyone who has designed circuits will consider CPUs wasteful compared to ASICs. This new FPGA technology is just a less efficient ASIC.
That’s roughly what I’m hearing.
The fact that general purpose intelligent classifiers can be dynamically hacked together by an LLM in real time to allow them to build evolving labeled and understandable networks that perform substantially faster than the LLM, and can act as an intermediate sorting and organizing layer for caching context or handling simple tasks, and a complete layman like me can assemble a teachable layer of these in a few days from an inexpensive service…
That’s wild!
And then you can identify where an expert system needs a more specific ML technique for efficiency within this network that overlays the SOTA model. Or manually adjust the stored context in each secondary “neuron”. And paths forward can run programs or take actions at relative high speed.
And you can share these with others and improve them as a group.
You could insert this at the datacenters at scale with a local supervising expert to prune and encourage proper growth. You could identify specific gaps in capability that need more training, and patch over them temporarily.
Then you train those corrections back into the general purpose model, or you identify highly efficient subsystems for specific purposes.
And this is just one way to use it. High speed intelligent workflows can live in this. There’s a spot for a local LLM to learn on the fly.
Maybe I’m way off base, but for the non-experts Jev seems extremely valuable.
11 replies →
I would rate using LLM for tasks more specific ML can handle as a lot like using one's smartphone to snap photos, listen to music, set alarms, and play video games in preference to carrying around a fun cam, ipod, watch, and switch 2 everywhere.
For those who need to dive really deep into each specific avenue and squeeze maximal quality out, the photographers will be packing DSLRs and intense gamers will wait til they get home to strap into a PS5 or a gaming rig or VR or whatever.
But "can get 90% of anyone's needs met in this field, and can do the same in dozens or hundreds of other fields simultaneously" will remain the killer solution for anyone with lots needs that each have bounded depth.
1 reply →
I have, LLMs are less fragile, that’s why I like them. The ability to generalize isn’t just about being general purpose, it’s super robust, and so assuming the budget is there (I agree they are inefficient) end up performing better on many classical tasks that have ood inputs. Before LLMs / foundation models we all struggled with generalization and at least in the work I was doing people were independently converging to using bigger more general models for tasks anyway as compute got cheaper. LLMs are just the most popular version of this.
3 replies →
I wouldn’t say lazy, LLMs are fast to use and much more cost effective especially if you factor the cost and time of training (data preparation, data cleaning, … etc).
It’s hard to justify several months to business when there is something off-shelf ready to use and doesn’t require domain specialists to run.
2 replies →
I thought one core result that led to LLMs was the realization that a specialized model is not necessarily better at a task than a general one.
1 reply →
Agreed... that said, humans will happily do something wasteful for a very long time if it's easier than the alternative.
why would you waste your time messing around with a team of expensive ml engineers and data scientists that produce vastly inferior to a llm.
We ripped out custom homegrown ml models that were developed in last 10 yrs and put an llm in its place. Its the opposite of wasteful. Even local gemma models are vastly superior.
5 replies →
Anyone who has worked in ML for 10+ years has heard of the Bitter Lesson, and doesn't want to be its next poster child.
1 reply →
There have been other "universal"/general classifiers like GLiNER, GLiFormer, etc based on BERTs (Laya itself is based on ModernBERT!), but I do think there's something underrated about slapping classification on a "big" model like I've seen post-Jev announcement, lots of Qwen stuff, but the most interesting to me so far is razorback16/openjev using DiffusionGemma. There's a level of generalization that lots and lots of parameters get you that you can't really get out of small models.
> using DiffusionGemma.
That's an interesting choice. One question I had when looking at the jev copy on their blog is if one "line" in their output looks / attends to other lines. I think not, since they say it's parallel and not autoregressive. In that regard, it would be interesting to play with diffusion, and see if you'd get better results by playing with types, locking some, and so on.
1 reply →
I've come to the same conclusions as you.
> I see it as a wake up call for the tech community to go back to basics for most tasks instead of relying solely on generic LLMs.
I always say the cheapest LLM request is no request at all.
What's the cost (broadly speaking, not in your specific case) of doing the same work an LLM would have done without the LLM though?
2 replies →
The data labeling objection baffles me. Even if you don’t need labels for training, how do you know your model is working if you’re not evaluating it?
My company specializes in statistical long document text classification, but nowadays we mainly work with audit trail requirements because we got tired of hearing complaints about our 5 example learning curve. Seems like the industry standard is telling an llm to label and telling an llm to eval, and crossing your fingers that it’s correct.
> it’s just BERT with more data
Let's take that as a given. Is BERT with more data not useful?
> I can see why people would want ready made one shot classifier, and I can see the value of sending multiple classifier in one call, but I wouldn’t call it breakthrough
Are those things that people want less useful because of what someone else calls it?
> I see it as a wake up call for the tech community to go back to basics for most tasks instead of relying solely on generic LLMs.
Maybe, or maybe to use Jev, which is useful?
Whether something is overmarketed or undermarketed, novel or derivative, it does not change its function.
I made it clear that it is useful and I can see many people using it including myself. My point is it’s not a breakthrough.
2 replies →
Agreed, refreshing to hear others share this feeling.
Timing is important here, LLM's raised awareness of ML techniques but we need to remember that most don't have traditional ML educations, so there'll be some "rediscovery" like Jev
the huge benefit in real systems for Jev like solutions i see is the cybersecurity / prompt injection mitigation. since the output will always be well structured, there is no way prompt injection might make the system do something crazy.
probably a prompt injection can still affect the output though, in unforeseeable ways.
You can use structured outputs and validate them against a schema today. I do agree making it a hard constraint instead of best practice for developers closes a whole class of bugs.
> a bit cheaper
Gemini 2.5 Flash Lite is $500/Gt, Jev is $42/Gt. AKA an order of magnitude cheaper.
> BERT with more data
It is specifically not just that, in the same way that models which have been chat/task-optimized via RLHF (which made these models much more useful for a huge variety of tasks) are not just "the base transformer model with more data".
This, intuitively, feels like a "lower level, basal, reflex" layer for the LLM's intellection.-
>I believe many labs will replicate it in no time
I really doubt this actually. To me, Jev is a great example ofcounter positioning. When you consider just how hyper optimized the labs are around auto regressive LLMs, and just how much money they have already invested and are pre committed to investing in an entire stack for auto regressive transformers... then responding to Jev becomes nearly impossible actually. They would just be giving up too much.
Just think, everything from their current sources of revenue, the sales use cases they tout, the marketing on the websites, the messaging to customers, then technically to the APIs, their internal batching and scheduling algos, their GPU configs, the chips themselves. ALL OF IT is designed with generative text models in mind. Jev breaks all of it.
I think basically no chance of a response any time soon.
I don’t understand how you’ve reasoned your way here.
How could Jev have possibly built something out of reach of a frontier lab providing the same or 5x as much resourcing to one of their teams to achieve? Which they can do because Jev has only received $40M of funding recently, so a round that is approximately what OpenAI is spending per math problem they try cracking.
In addition to that, these frontier labs have got extremely good at generating synthetic data and running generalised training pipelines. I can only imagine how easy it would be for them to build this internally vs Jev building it from scratch.
And then the final thing: one of the best places you might apply Jev is within a harness, behind layers that customers increasingly have abstracted from them. Frontier labs have huge incentives to do this as it could make their offering much better and cheaper. And whoever gets this first wins another big attraction for users.
My take on this is Jev is either acquired almost immediately for the benefit of the next 1-3 months head start for whichever lab acquires them or we get a similar model offered from all labs in 3-6 months or sooner.
It would be amazing to have big BERTha with per-token pricing on GCP or AWS. There are many times I am reaching for a cheap classifier with the general behavior of an LLM.
I am sympathetic but this buries the lede, hard.
You are competitive with Jev only if you fine tune on the train dataset and calibrate per question.
As much as I dislike literally everything about typesafes behavior, they have an API model that works on any problem without fine tuning, and that is the key.
To be honest everyone who can finetune can likely finetune a BERT for a specific task and get similar results to yours. And that has been true for years
The key to Jevs success is that it works without fine tuning
I can understand why the author feels bitter but it still feels juvenile to me. Certainly both Jev and Laya are based on the research of countless prior papers and academics. Diogo decided to build a product out of the concept. The author didn't. Publishing research papers and model weights is probably part of the problem--it feels academic. If you look at the author's profile they focus on applying AI to healthcare. Not selling general AI type safety to AI pilled companies and devs. There's a big difference there. Whether that's good or bad you can argue all day. But for the author to expect otherwise is pretty weird. I do applaud them for not stewing too much on it and trying to do something about it, though.
I believe his qualms were with the "hype" in Jev's announcement: specifically calling this kind of model a breakthrough, without crediting previous art, and keeping everything closed source.
The hype is kinda nuts; I use X for ML/LLM stuff, and I just can't get away from Jev - even in my Following feed. Even days later 75% of posts are about "how I use typesafe for cooking breakfast!" or Jev clones.
1 reply →
if is closed source, how does he know there isn't some breakthrough he doesn't know?
1 reply →
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
19 replies →
"Juvenile" is a weird label to assign to someone whose work is re-presented by someone else and not attributed properly. People here had a very different take on the Navier-Stokes situation XD
I’m not filled with confidence when the author’s first paper takes an RL approach but then doesn’t use it to change the action taken in the next turn. Seems like simple classification would achieve the same end. And this quote from the paper isn’t overly reassuring:
“I personally found that this sequential approach captured sales dynamics much more effectively than traditional classification models.”
https://arxiv.org/pdf/2503.23303
this was the period of arxiv history that led to the new vouching system
that first person phrase stuck out to me, especially given it had plural versions on either side, the author never edited for clarity or consistency
Agreed. Another difficulty here is there are not good benchmarks for this new architecture yet, so it’s easy to potshot and snipe, where jev seems to be pretty broadly intelligent/at least have had a lot of rl in different domains.
We haven’t seen any of these copy cats play doom or street fighter for instance; just categorize email.
I imagine once the author cools down and evaluates on a broad harness of tasks he may find that his new thing has a lot of engineering work ahead.
The doom demo would have to be reproduced to confirm what their model is capable of. Oh but it's all closed source, so who knows.
It reminds. Me of Devin. Took a while to debunk. Not saying Jev is a fraud , but the gap between structuring typed output and playing a game involving logical interpretation of frames made of pixels, screams unstructured interpretation they made and forgot to mention.
1 reply →
Hi I want to explain that arxiv is not "publishing a paper" - it's a step up perhaps from putting it on your own website, but this is not what is meant by professional academics when they talk about "publishing" (even when they work for big AI companies).
Your "papers" have only a single author and no current citations. It is not clear you are able to work with other people. The papers claim to offer results but no theory about why those results are the best possible. They read like sales whitepapers not scientific work. Reddit comments on the thread you linked to said they weren't able to reproduce your work.
I'm not looking to buy magic beans for my sales team.
It is really interesting to see this claim, because i thought the current theory was that typesafe actually repackaged the work from GLiNER[1] - which does seem to be a closer match, and their original paper[2] predates yours by several years. Curious if you had heard of it before? It is also open source[3] and I think also has some good usage.
[1] https://arxiv.org/abs/2507.18546
[2] https://arxiv.org/abs/2311.08526
[3] https://github.com/fastino-ai/GLiNER2
I think the biggest lesson with Jev was the one of communication and understanding for the broader audience, sometimes a lot about innovating involves repeating yourself and translating your own thoughts to an intended audience.
Classical machine learning has been, for the most part, and just by the nature of science, behind academic terms and difficult to engage with as a product.
Jev did really well with coining up “System One” models and defining a standard application interface plus core primitives that landed in the current paradigm of software development.
I think it’s sort of like how Cursor reinvented autocomplete back then as a different UX and suddenly everyone was just using it because of how easy the bar was to understanding it.
Lastly, timing is everything. Just as Cursor had a first mover advantage, despite ML Ops being a thing for a while, they managed to encapsulate the concept behind a “System One” black box that fits the existing mental model for building software and shipping a data contract in the right point in time where the cost of tokens has been an important metric to watch.
Furthermore, it's not about the current innovation right now - if you sell yourself on a broader mission, your core product can evolve and change with it, and you're more selling yourself as the guy who will make that abstract vision possible no matter what.
No matter how much we pretend, that's how a lot of abstractions work. Things that touch the real world can change; there's a risk that the change could be as something as simple as a bugfix to changing the underlying implementation but preserving a higher level goal; you generally want a human in the loop to make sure the semantics work out and everybody's agreeing.
Well said, once you put it out there in the world, then it's also about how will the customer react to it and having to own that relationship going forward.
The relationship aspect of a business has a lot to do with how effective it is at continuing to justify its core value in an easy and relatable way; especially so when the decision makers that front the bill may not be as engaged with the underlying machinery behind the why it works how it does.
We've all seen "this meeting could have been an email"; now get ready for "this VC-backed firm could have been a single arXiv preprint."
I don't want to be too dismissive of Jev, but building technology in stealth for two years just doesn't make sense to me when the capabilities are so easily replicated. These are strange times, where the incentive to do public research and the incentive to develop in private are both being eroded.
All this means is that brand and distribution matters more than ever. There's 1000 chatgpt clones but everyone still uses chatgpt. There might be 1000 jev clones soon enough but people won't switch unless there's something significantly better about it.
It's also why Meta can make Muse and get a lot of users even though there's 10,000 personal agent startups
In jevs case I think the competing offerings will rear their head rather fast. The ability to label data like it does now leans itself well for distillation. And to switch out a model like fable for Astra is really not all that difficult. Yea jev is alone now. But before the end of the year another lab with the same offering will rear it’s head
And Jev was obviously just created by ChatGPT reading this paper and copying it.
And yet no one cared about this research until it was productized and communicated well. Multitouch existed before the iPhone.
Bringing up iphone, I see how Jev pulled an Apple for making claims that their model is a breakthrough in research and 2 years of making like other iPhone features that's been around in other phones.
Jev doesn't require finetuning. All of the posts claiming that the technology already existed are missing that I don't want to spend a week to create a dataset (for a problem I might not already have data for), finetune a model, and set up infrastructure to run the model, every time I have a small routing or classification problem. The ability to knock out any arbitrary classification problem in minutes instead of in a week is a big deal.
This is the real moat, the training data, they even said it, it's the meticulously crafted data that they bet on
Even then, TypeSafe AI point out that "Jev doesn't have deep knowledge of niche domains, but you can supply context to help it decide. If you’d like Jev trained on your use cases, let us know."
1 reply →
but the author has Lara.
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.
But is there any proof that Jev is frontier intelligence?
It thinks there are two Rs in strawberry.
It fails to assign probabilities to die roll outcomes or coin flips.
When used as an LLM it thinks it is Qwen.
Apparently reordering the list of possible answers can change assigned probabilities by up to 20%.
I’ve seen claims that it struggles to play tic tac toe.
So far there is a lot of evidence that it behaves exactly like a tiny open weight model. The only argument against this claim is “trust me bro” claims from it’s author.
Quickly reading the article, one notable limitation seems to be that these checkpoints are 512-1024 tokens context size models, while Jev is seemingly 32k.
That's a pretty big limitation, I would argue, unless I'm misunderstanding and it can be worked around easily somehow? I'm surprised it isn't surfaced more prominently in the comparison.
Jev has 64k total token request budget and I do wonder how it will handle highly specialised inputs.
This Jev waitlist that Typesafe AI are utilising is surely going to raise questions pretty soon - it's hard to sell this to bosses when it looks like a pop-up restaurant
It's already on Openrouter
2 replies →
I just got my invite so the waitlist doesn't seem to be particularly long
I am more curious about a 60k prompt... I haven't seen much discussion about large prompts, is it still < 500ms?
Yeah, it's weird, considering ModernBERT, which the Laya models are based on, supports 8192 context window.
I don't really think there is much future for TypeSafe but I wish them well. In fact, I like them.
Jev is just a reminder that you can use more "traditional" forms of AI (that are not LLMs) and still get remarkable results.
We tend to forget that.
That was a surprise and that is why it went viral.
Idk, your limitations section sure makes it seem less drop in and less general than Jev. Like the point here isn't your ML aptitude it's how easy is it for developers to drop this into a product and use it.
I'm more than capable of training a bert classifier in fact in 2019 I had trained many custom berts and was running them on hundreds of millions of documents a day.
I don't want to manage GPUs / CPUs now. I don't want to maintain my corpus and retrain as my product's data distribution shifts. The list of things I don't want to do goes on and on and on. And I'm happy for them to be someone else's problem.
I do just want a reasonably good general classifier served to me with a great devex and calibrated confidence scores to help me figure out when to fallback to another model.
With everybody bashing OP here, how is he supposed to even make money ? It’s n open-source model you self-host right? So he doesnt seem to be just greedy? He might genuinely feel like stolen. I hope he doesnt take the comments personally and is able to find motivation in it.
you may reconsider OP's primary motivation based on another of their HN submissions
https://news.ycombinator.com/item?id=49674396
my hunch is that an Ai has been validating their biases
The unfortunate true is that getting even the best work in front of an audience is often much harder than solving the problem. Is uploading a paper to arXiv enough to expect the work to be recognized and cited? Unfortunately, it rather is not. arXiv is an open repository which includes plenty of not reviewed and not officially published papers. In a popular field such as machine learning, the number of arXiv papers is overwhelming. Expecting that some machine learning expert will stumble upon an arXiv paper and recognize its value is wishful thinking.
I'm not a researcher, but long time ago I had an idea of a new, seemingly interesting attack on TCP. Having some free time between jobs, I wrote a paper about this, created a proof of concept and decided to send the paper to USENIX Security. I got back two reviews, both in rather positive tone, but rejecting the paper on the grounds that it shows only individual steps of the attack, but it would be much stronger if it showed also the attack working end-to-end. At that point I just uploaded the paper to arXiv and called it a day. I've put a lot of work into that paper, but not enough, I don't consider it properly published and I don't expect anyone to cite it. The paper failed the peer review process and I didn't put the work to improve it further.
Marketing has always been the toughest part. Doesn't matter what you invent in private if no one sees it.
Love it. I was really surprised to see the traction typesafe got in the first place. I had built something similar a year ago for a client and thought it was nothing groundbreaking. The client bought it, still uses it and that was it. I had also spent considerable time training and fine tuning zero shot NLI classifiers. Anyway, after typesafe was launched I decided to start building this open source library - https://github.com/deepanwadhwa/OpenDecision . The context length for the underlying model is 8k.
>Zero-shot vs. Fine-tuning: Out-of-the-box base models score ~0.35 on the typed-decisions benchmark (near random). The 0.766 score is achieved by fine-tuning on the benchmark's train split. Treat Laya as a fast foundation model to specialize, not as an omniscient zero-shot oracle.
This should be way up in the article. Fine tuning is a pain, requiring it for good results put Laya in a whole different category vs Jev
Jev will continue to do well because people don't actually want to host their own models. The average customer just want an always-on pay-per-use API that has social proof.
Great work by the author. Both Laya and Jev showcase how a different class of models can be efficient on tasks that don't require a 'generated output artifact'. I believe the same is true for VLMs where you're not always generating an image, but rather trying to understand more about the input image.
Token consumptions are flying through the roof and optimisation is the way forward.
LLMs being described as system 2 thinking here is a semantic shift I have not encountered before.
LLMs are also a deep learning approach. Output, as slow as it is, still comes from weird latent spaces. In AI I always took System 2 to map more to symbolic approaches, or at least when explaining symbolic AI to someone who has heard of deep learning thinking fast and slow was a good comparison to draw on.
Huge omission. This requires fine tuning.
> Zero-shot vs. Fine-tuning: Out-of-the-box base models score ~0.35 on the typed-decisions benchmark (near random). The 0.766 score is achieved by fine-tuning on the benchmark's train split. Treat Laya as a fast foundation model to specialize, not as an omniscient zero-shot oracle.
Isn’t this post comparing zero shot Jev to fine tunes of this model for each of the datasets it is tested on? If so seems like fairly impressive results for Jev
Am I understanding correctly that the field has gone full circle and we are back to specialized classification models for domain specific tasks ?
Running against a labeled set (categorize support questions), jev gets 95% answers correct. Layla 48%
This project was built on the exact research on jev architecture research one year ago
I'm reading your year old Reddit post and Typesafe's description, and while they probabably say that they can do what you do the main point is that it's different things really as far as I can tell?
Laya seems to be focused on sales/conversations?
Reading quickly about TypeSafe, it seems to be about creating _type-safe_ outputs from AI tools for downstream systems to consume, we actually have a system in production that's probably a glove-fit for that, it's for scanning receipts to be ingested into a system and we also have other systems in a sales-pipe that isn't too far off Laya but still sounds more pertient to TypeSafe.
You did a special case well, but just because they cover (perhaps badly) that case doesn't mean that it's the same thing.
I don't understand this sentence, can you try again please? Are you saying Laya was built on research done by the Jev team?
Jev was built using the same architecture Laya's author proposed[1] in March 2025. Laya is an open-source system based on that research from a year ago. Whether Jev is also based on the OP's materials or independently invented is hard to say.
[1] https://arxiv.org/abs/2503.23303
1 reply →
No, OP thinks they independently discovered Jev's architecture a year ago and published a paper. I am not an expert but I don't think Typesafe has published Jev's architecture so OP's claims cannot be taken at face value.
5 replies →
I built a pypi for it called hallunox https://pypi.org/project/hallunox/
"if you build it they will come" is a lie, marketing and branding matter and whats funny is often the biggest proponents of "if you build it" don't even realize the things they see going viral are marketed to them, they extremely naively think it got traction simply because it was good and they "built the right thing", not true.
This is what I'm talking about. More of the community needs to be like this guy.
A question I have about Jev is, who are the "Service Providers" that they provide prompt information, and why is there no time limit on how long they store prompts?
One of the Use Cases marketed is having Jev flag if personal information is contained in text. It's not a strong use case for it really.
This is awesome OP - very impressive. I'm definitely going to be using it to classify customer support tickets and log error classification. Thank you!
been loving hacking on this. just created a vision version of it here https://huggingface.co/thaitea/laya-vision-smolvlm-256m
It seems like in today's day and age, whoever comes to market with a new tech second is usually winning. It's kind of unfortunate, as Laya is actually pretty cool. I think it will catch on considering its open weight and self hostable. It's easy to host on a home lab compared to the 1T parameter behemoths.
Probably important to call out this part of the post:
Zero-shot vs. Fine-tuning: Out-of-the-box base models score ~0.35 on the typed-decisions benchmark (near random). The 0.766 score is achieved by fine-tuning on the benchmark's train split. Treat Laya as a fast foundation model to specialize, not as an omniscient zero-shot oracle.
How come its completely unable to understand when it does not understand the script? Why was this no in the training, or was it?
The routing feels like such a hack to me...
They're definitely not the only one. I've been building on relational transformers, which does prediction and classification over relational data (it handles numeric types better). It's validating to see that these small models that do prediction tasks are so useful to the community, but also stings a little that it was so hard for me to communicate how game changing they are.
I’ve tried it versus Jev and I got significantly worse decisions. I hosted it on runpod nvidia t4.
I wanted to classify business b2b vs b2c and business model. Am I holding it wrong?
Landing page full of AI fluff, discussion feels very fake here, I would assume this is some upvote bot, nothing makes sense.
Did you see the carnage that typesafe's landing page was? every other post here is llm generated, every other poster here seems like a LLM.
It's an embarrassing showing for our community, seems like nobody has read anything. None of the claims of the blog post add up.
It's property benchmarked btw. And do read the og paper at https://arxiv.org/abs/2510.01237 And btw pypi package also there which never mentioned https://pypi.org/project/hallunox/
All the stuff are proper benchmarked. Feel free to read the paper, https://arxiv.org/abs/2510.01237
Is this at all possible to run locally on a MacBook pro m5 (48gb ram)? What kind of performance could I expect? Or would you run this somewhere in the cloud? What HW / which provider would you choose (single user for exploration only)
Laya is only ~400M parameters and runs comfortably in 1-2 GB of RAM. In my local testing, it's very fast even using CPU. I'd say give it a try!
Easily. It took around 4GB VRAM on my nvidia card so a 48GB unified mem should have no issue. < 1s inference
Correct me if I am wrong, can I use Jev and this tool for ticket classification? I mean, for instance, a level 1 ticket contains a screenshot of the login page that displays an error, LLM can do it perfectly, can Jev do it?
At least for now, Jev is not multimodal. So a screenshot alone wouldn't cut it.
I find that a bit interesting because the most system one part of the brain is probably the part used for visual processing.
It's trying to use a human analogy but the analogy breaks down if you try to apply it directly
1 reply →
Oh, I missed that! So the Doom demo was potentially just "harnessmaxxing"?
1 reply →
It can’t do images but it can do a pretty good job of triaging urgency or choosing when to escalate. So I’d guess yes but it depends how detailed your classification is.
you can use this for screenshots
https://huggingface.co/thaitea/laya-vision-smolvlm-256m
Loved the idea, but I don’t think it would be able to handle real-world data effectively. There are a lot of nuances that actually require a reasoning model to think through, connect the dots, and make sense of the broader context.
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.
I was wondering, do you think its possible to use something like SAM 3 (segment anything from FB) + Laya to create a super efficient and fast computer use tool?
I don’t understand Jev or this. I used this since it’s open source (good job btw!) with the following. State: “a 6 sided die rolled a 3”, question (noul): “Is the number odd?”
Answer: 9% chance, with 91% confidence.
Heh???
Ok, even worse. 75% chance a coin landed heads up?
State: I flipped a coin. Question:
{ "noul_result": { "type": "noul", "instructions": "Did the coin land heads up?" }, "choice_result": { "type": "choice", "instructions": "Determine if the coin landed heads or tails up.", "criteria": { "heads": "the coin landed heads up", "tails": "the coin landed tails up" } } }
Ran on: https://huggingface.co/spaces/convaiinnovations/laya-demo
Result: { "model": "laya", "answers": { "noul_result": { "type": "noul", "noul": 0.6839, "rl_agent": { "act_probability": 1.0 } }, "choice_result": { "type": "choice", "choice": "heads", "probabilities": { "heads": 0.7407, "tails": 0.2593 }, "confidence": 0.1743, "rl_agent": { "act_probability": 1.0 } } }, "usage": { "input_tokens": 76, "output_tokens": 0 }, "latency_ms": 93.8 }
Trying to be even more good-faith:
State: "A fair coin was flipped once. The result was not observed. No other information about the outcome is available."
Questions: { "noul_result": { "type": "noul", "instructions": "Given only the supplied state, what is the probability that the coin landed heads up?" }, "choice_result": { "type": "choice", "instructions": "Given only the supplied state, determine which outcome occurred.", "criteria": { "heads": "the coin landed heads up", "tails": "the coin landed tails up" } } }
Result:
{ "model": "laya", "answers": { "noul_result": { "type": "noul", "noul": 0.1265, "rl_agent": { "act_probability": 1.0 } }, "choice_result": { "type": "choice", "choice": "tails", "probabilities": { "heads": 0.2522, "tails": 0.7478 }, "confidence": 0.1853, "rl_agent": { "act_probability": 1.0 } } }, "usage": { "input_tokens": 123, "output_tokens": 0 }, "latency_ms": 154.5 }
Jev says you should restate state in the question and I tried it:
{ "decision": { "type": "noul", "instructions": "Is the rolled number in state odd?" }, "question": { "type": "noul", "instructions": "Is the number odd?" }, "question-3": { "type": "noul", "instructions": "a 6 sided dice rolled a 3 Is the number odd?" }, "question-4": { "type": "noul", "instructions": "a 6 sided dice rolled a 3 Is the rolled number odd?" } }
=>
decision,0.168,0.83 question,0.141,0.86 question-3,0.029,0.97 question-4,0.021,0.98
so im confused too..
A weakness with numbers?
Breaking news: small language models struggle with math
2 replies →
I don't think calculating mathematical odds from natural language is the sort of problem this is trying to solve. A typical LLM hooked up to a calculator would be more appropriate for that.
Jev (and similar) is more for data processing and sentiment analysis. Moderation, search engines, that sort of thing. Jev has a page of proposed use cases where you can get an idea of what they're going for: https://docs.typesafe.ai/concepts/use-case-map
try this model on HF - https://huggingface.co/MoritzLaurer/deberta-v3-large-zerosho...
a 6 sided die rolled a 3
possible class names - the number is odd, the number is even
result:
the number is odd 0.945 the number is even 0.055
as someone else said, that 0.055 is probably bc of 6 and 3 being there.
This is not a good faith test of the system.
But it's hallucination-free, isn't it?
2 replies →
Maybe it’s the fact that “the number” could refer to both 6 and 3 to this model?
Jev is mostly a cost optimization and some good plumbing, I don't think it's breakthrough of naything
Is this as good as Laya 3? Unfortunately, it's production was moved from Bremen, Germany, to China, and it is not good anymore, in my opinion.
“Codex, build a novel frontier model and post it on HackerNews —”
“Claude, roast this noob, tell him that his model isn’t novel or frontier —”
both in unison “— and make no mistakes!”
It’s all so tiresome
I'll just say that even though I was poor and without a job and living on unemployment insurance for a year...
The implosion of hype after the .com crash was actually kind of a ... relief.
Sailor moon vibes.
[dead]
from https://huggingface.co/convaiinnovations/laya > The policy reports a distribution; exploration adds zero-mean Gaussian noise to the logits; the reward is a strictly proper scoring rule (log + spherical, plus ranked probability score for ordinal questions). Expected reward is maximised only by reporting honest probabilities.
Look at the bright side. You can ride the Jev marketing, because at the end of the day, post prototype, data privacy is always going to be top of mind and people are already looking for Open Source alternatives because Jev proved the usecase in a simple way most people could understand.
Was this ArXiv pre-print published anywhere (i.e., with proper peer review)?
no, it would not make it through peer review in its current form
There are many, many, open-source versions of Jev, including three distinct projects sharing the name “openjev”
If you’re interested in the basic trick most are using (which is probably also what Jev does) then it’s here: https://sgnt.ai/p/jev/
This sounds cool but it looks like it requires a GPU that I don't have. Is there an API to try it out?
I did this a year before OP so how do you think I feel?
Why do we ("society") need the "frontier" companies at all? Their business goal has settled on trying to CONFUSE the shit out of us so that we don't understand the big pictures about various aspects of AI.
THANK YOU, Nandakishor Mukkunnoth, for putting in the work to help to clarify this stuff!
You are like a firefighter compared to their fire-insurance racket.
At this size (~400 million parameters), does it become viable running directly on CPU?
Yep. Not instantly though. I am hacking away at these things over on https://github.com/rcarmo/go-pherence (I do SIMD versions of common inference algos) and trying to improve that.
tbh it is very sad though that ripped off the OSS version and played that classic “rewrite this.." with their agent
it's quite unlikely that happened, the two are not anywhere near as similar as OP claims
This is crying out to become an Excel or LibreOffice Calc add-in
Distribution > Creation
paper the reddit OP "published" (their words on reddit) to arxiv (before they put the vouching process in place). It's what you expect if you click through.
https://arxiv.org/pdf/2503.23303
Does not appear to be like what Jev is doing, they talk about RAG and embeddings and orchestrators (the stuff that was cool 1 year ago), no talk of system 1 vs 2 (before Jev), whereas Jev is apparently just a model.
There is a vLLM PR introducing Jev like capabilities for diffusion models (and more, have not delved deeply)
https://github.com/vllm-project/vllm/pull/57250
in two weeks, a chinese lab will have a Pev-2.7-flash-qwen for 0.00004cents/million
Pangram believes this text was authored with an LLM:
https://www.salahadawi.com/hacker-news-ai-detector/49765348
I feel strong Schmidhuber vibes here.
Where can I subscribe to a hosted version of this? I don’t want to host my own GPU.
Jev is targeted to end users, and the tooling is really great. Unfortunately publishing papers without code or tooling or APIs will not attract the crowd as they want something usable quickly. That said, being second in this space is not the end of the world and the race is still on. If there is API access and proper tooling support like Jev, then it can win the game based on merit and not just marketing.
thank you for your work
I just built a server based on Jev API to run Laya here https://github.com/jlt-commons/laya-jolt
> Seeing the hype online feels both validating and deeply frustrating.
The post is conflating hype and money with technical innovation, they are not really correlated. Kurzweil is known for saying most innovations succeed based not on technology but on timing. Today, who talks about it might matter even more than timing.
Superior research often gets overlooked in favor of someone raising millions, sometimes people who have produced literally nothing manage to sell it. Not saying that's happening here, but I've seen this pattern a lot over my career.
Someone riding (or manufacturing) a hype wave is playing a completely different game from a researcher. If you're a researcher you can't really feel dejected when someone is making a business on the back of what seems like your research; legal protections are decades out of date, even ignoring vibe coding. If you want to make money/hype/whatever off of your work, do that. But realize that it's a path that's often orthogonal to research.
I'm just glad to see focus being shifted (albeit slowly) to conventional ML. Enough with LLM guys
but now you got the attention. It is alright. Few remember Atanasof too.
[flagged]
[dead]
[flagged]
[dead]
I've been deeply impressed with Jev as it made a bunch of workloads we had on Luna or Gemini 10x cheaper and 2x faster (previously used non reasoning version for latency reasons).
Now Laya promises another speed up and it's open source. Tbh if it can't run on a CPU I anyway want to buy it from an inference provider. Managing gpus in production is a non trivial problem.
What I also wondered about Jev is how different it is from something like tabular foundation models. They seem to overlap in use cases. Which then leads to the question, what is actually learned? A lot of people in machine learning spend time to making things explainable and always struggled to move beyond data induced biases.
Having it open source is awesome as fine tuning might give additional performance on the task we care about.
Can you give some examples of workloads?
I have 10000+ inventory items to categorize but I need an intelligent model (not just if statements). Using LLMs has been slow and expensive and I needed to queue it to run for hours. Jev did it in minutes and for less than 1 cent
This is your brain on ai influencer twitter