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

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/

This is your brain on ai influencer twitter

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.

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?

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.

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.

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.

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.

And on Vercel AI gateway

I just got my invite so the waitlist doesn't seem to be particularly long

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.

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.

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/

This post is either majorly astroturfed or nobody upvoting this has read past a couple sentences.

Thee author of this post has a specialized model that does routing for his sales pipeline.

After jev came out, the author adapted his model to produce jev-shaped output: choice, noul, and score primitives.

Then, author writes a post about how jev copied him. (even though this was released in the past couple days, after jev was released)

If I were to download this model, I would get nothing like jev. I would get a model that is able to beat jev in a very small domain that this model has been fine tuned for.

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

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.

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.

Excuse me, but calibrating language models to accurately reflect probabilities did not start with you.

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.

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???

I'm just glad to see focus being shifted (albeit slowly) to conventional ML. Enough with LLM guys

paper the reddit OP "published" to arxiv (before they put the vouching process in place, it's what you'll expect if you click through)

https://arxiv.org/pdf/2503.23303

Does not appear to be like what Jev is doing