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.

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

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 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.

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.

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.

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.

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.

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.

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

In the limitations you say that “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.” - but that’s the whole point, you can obviously fine-tune specialized models but having a model follow you instructions and be promotable and fast makes it massively easier to use.

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.

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.

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.

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.

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.

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.

>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

This project was built on the exact research on jev architecture research one year ago

Am I understanding correctly that the field has gone full circle and we are back to specialized classification models for domain specific tasks ?

This is what I'm talking about. More of the community needs to be like this guy.

"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.

Running against a labeled set (categorize support questions), jev gets 95% answers correct. Layla 48%

been loving hacking on this. just created a vision version of it here https://huggingface.co/thaitea/laya-vision-smolvlm-256m

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.

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.

Landing page full of AI fluff, discussion feels very fake here, I would assume this is some upvote bot, nothing makes sense.

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?

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?

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.

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 }

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?

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

Jev is mostly a cost optimization and some good plumbing, I don't think it's breakthrough of naything

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.

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/

Was this ArXiv pre-print published anywhere (i.e., with proper peer review)?

This sounds cool but it looks like it requires a GPU that I don't have. Is there an API to try it out?

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.

At this size (~400 million parameters), does it become viable running directly on CPU?

I did this a year before OP so how do you think I feel?

This is crying out to become an Excel or LibreOffice Calc add-in

tbh it is very sad though that ripped off the OSS version and played that classic “rewrite this.." with their agent

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

Distribution > Creation

I feel strong Schmidhuber vibes here.

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

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.

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.