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This project was built on the exact research on jev architecture research one year ago
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
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.
It’s the other way around for me. OP has published everything in the open, so I can take him at face value. A PR media release on the other hand, I can accept with some reservations. The objective and non-conspiratorial reading I could offer is, this is most probably two independent discoveries of the same idea, maybe with different implementation. I still think the Jev team should look at prior art before going so hard on the marketing.
Jev is only on people's mouths because they made friends with venture capitalists and used the publicity blowhorns that come with that.
Whereas the other guy went through the unglorious but formerly respectable path of publishing software and papers for other professionals to look at. A year ago.
We're in a bad place where the latter looks less reliable than the former.
(EDIT: I'm not saying the research here is in fact the same as what "Jev" is doing; and Jev is in fact more "product shaped." But I think it's important to temper the hype and back up and focus on the fact that this whole industry is built on research by both academics and enthusiasts ... first ... and gold rushes can often bulldoze over those people who are focused primarily on making-doing-researching instead of fundraising-hyping-promoting. That's not good.)
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 built a pypi for it called hallunox https://pypi.org/project/hallunox/
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.
This is your brain on ai influencer twitter
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
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.
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.
I've come to the same conclusions as you.
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?
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.
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’s a middle option. Once you figure that out, you’d soon understand my point today or tomorrow. I’ve been in this field for 21 years and I use LLMs everyday. I also know when to not use them.
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.
People have been having this same debate in a very similar way on typed languages vs untyped interpreted languages. I think that, in a similar vein, if you look at the trend over time:
- the addition and standardization (with incomplete coverage) of the solution of adding typing to Python
- how much people are re-discovering the value of performance + typing (e.g. Rust)
then I'm going to take a small leap and extrapolate that the trend will be similar here.
The equivalent of the "one off script in python" will be the LLM, and the long term stable and maintainable solution will be something much more structured and focused like Jev.
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.
That's part of the irony here I guess. In specialized fields, think computer vision, there were lots of teams whose innovative state of the art model was essentially just a function of the limitless compute they could throw at the problem. Now there are just people with even bigger sticks.
There are lots of scenarios where specialized models still are the only option for real time, power efficiency, and so on. And transformers and other tech behind LLMs can equally produce better specialized models. But no sympathy for those who confused compute with innovation.
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.
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 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.
That goes all the way back to at least to Stein's Paradox in 1955, sadly too few people get educated about Statistics and keep thinking specialized models will necessarily be better. If you want to estimate the batting averages of 3 MLB baseball players from samples, you are better off building a model to predict all of their batting averages than computing the mean from a sample of each one separately.
https://en.wikipedia.org/wiki/Stein%27s_example
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.
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.
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.
This, intuitively, feels like a "lower level, basal, reflex" layer for the LLM's intelection.-
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.
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
And on Vercel AI gateway
I just got my invite so the waitlist doesn't seem to be particularly long
“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.
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.
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.
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.
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.
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
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.
This is crying out to become an Excel or LibreOffice Calc add-in
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/
Landing page full of AI fluff, discussion feels very fake here, I would assume this is some upvote bot, nothing makes sense.
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
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.
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.
And yet no one cared about this research until it was productized and communicated well. Multitouch existed before the iPhone.
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 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.
And how is laya previous art? The project was vibecoded and posted yesterday.
https://github.com/NandhaKishorM/laya/commits/main/
https://huggingface.co/convaiinnovations/laya/commits/main
The paper is one year old. https://arxiv.org/abs/2510.01237 https://pypi.org/project/hallunox/
Excuse me, but calibrating language models to accurately reflect probabilities did not start with you.
I didn't claimed it bro it was first. Just shared the findings here.
Explain what you feel bitter about?
just not these jev guys not opensourcing their work
By releasing a paper you are specifically telling the world, “hey look I did this thing for no comp, pomp or circumstance and I thought it was interesting enough to share with the world”. You get academic interest and credit when your paper is cited. It’s not “prior art” in any negative use of the term when a company takes the ideas in an academic paper and builds a product around them. This is a standard and well understood cycle where academic research happens and then gets applied to industry. Have you heard of a company called Google? And are you sure Jev is based on your work, or did you just do something similar? Ideas are cheap…
Generally they do credit the papers and/or people who developed the theory behind their product.
Researchers definitely do get paid for research. That Google company you mention funds research which can be integrated into their products.
@prodigycorp - reading your comments here on this post - you seem pretty hurt by this post.
Yeah, the reason why I am annoyed by it is because a person (who felt like a burner account of the laya creator) yesterday was haranguing me for saying that projects like this were vibe coded, posting the link to this project.
I evaluated this project yesterday and found its claims un-credible. It's literally nothing like jev. That's some context behind why, a day later, I find it annoying that this is somehow the top story on HN.
https://news.ycombinator.com/item?id=49752902
What is jev like? Did they release any research paper? I really think typesafe hired someone to boost their post because there was nothing "Breakthrough" about their product. At least this post has some touch with the reality that this functionality was available a year ago and was well known among ML people.
I can't believe you say in another post that you have experience with bert and yet you don't understand the value of a generalist classifier.
Good models take time and effort. There wasn't a good option for satisficers until a few days ago.
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.
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
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???
Maybe it’s the fact that “the number” could refer to both 6 and 3 to this model?
This is not a good faith test of the system.
But it's hallucination-free, isn't it?
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.
I'm just glad to see focus being shifted (albeit slowly) to conventional ML. Enough with LLM guys
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), 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
I just built a server based on Jev API to run Laya here https://github.com/jlt-commons/laya-jolt
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
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....
This sounds cool but it looks like it requires a GPU that I don't have. Is there an API to try it out?
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.
I feel strong Schmidhuber vibes here.