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.
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'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.
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.
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.
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.
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
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.
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.
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?
I think the interesting part is less about whether the architecture is completely new, and more about whether it makes specialized models easy enough to actually use.
LLMs won partly because they removed a lot of friction. You can call an API, give it a prompt, and get something useful without building a whole training pipeline.
If something like this can keep that simplicity while being cheaper, faster, and more consistent for narrow tasks, that is still pretty valuable even if the underlying ideas are not new.
So yeah, maybe it is “back to basics,” but with a much better developer experience.
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'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
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.
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.
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?
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?
This is crying out to become an Excel or LibreOffice Calc add-in
I think the interesting part is less about whether the architecture is completely new, and more about whether it makes specialized models easy enough to actually use.
LLMs won partly because they removed a lot of friction. You can call an API, give it a prompt, and get something useful without building a whole training pipeline.
If something like this can keep that simplicity while being cheaper, faster, and more consistent for narrow tasks, that is still pretty valuable even if the underlying ideas are not new.
So yeah, maybe it is “back to basics,” but with a much better developer experience.