Posted by nandakishor_ml 10 hours ago
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.
Statistical modeling, from simple classical stuff up to modern deep learning, just has this dynamic where the theory is rich and bottomless, but the actual components of implementation are pretty neat and compact. So for any given idea, there are probably 20,000 other people who have had the same intuition, just with subtly different application or implementation. Add in that depending on what your particular flavor of research is, you might name an almost identical implementation something completely different. And it leads to a huge amount of sour grapes whenever anyone's idea really garners attention.
If you listen to any podcast with a founder in the ML space who has been in it for long enough, they will invariably say at some point "We actually developed xyz over a year before OpenAI"
I can understand it, and it wouldn't excite me at all.
Jev has a beautiful API and is advertised as something much more general.
(the project before it was rehashed into Laya since Jev was released)
It is arrogant and entitled for the author to take credit for the concept of RL over sequence embeddings, and none of the work that went into pretraining, not to mention the egregious target leakage [1]
[0]: Author fails to grasp the concept of virtual environments https://www.reddit.com/r/LocalLLaMA/comments/1kl0uvv/comment...
[1]: his `train.py` has `outcome` as a model input (conversation_metrics built from _parse_conversation which includes outcome): https://huggingface.co/DeepMostInnovations/sales-conversion-... https://huggingface.co/DeepMostInnovations/sales-conversion-...
[2]: 100% of this post is AI-generated https://www.pangram.com/history/97e0be84-391d-46b8-9c16-2d8f...
So while the initial post was not good, the author is currently succeeding to some extent at what you're describing
Also, the way highly empirical fields like ML work is that it could very well be the case that typesafe had to do a _lot_ of work to improve this one, and in this field it ends up different enough that they feel they are doing something entirely novel[1]. I am not endorsing that 100%, but that happens a lot even between academics. In many cases it is valid.
[1] For example, this guys implementation seems to have atleast one serious issue, as {solution to OLS} points out in a sibling comment: https://news.ycombinator.com/item?id=49770027
No one cares if you are "first". They only care if your product is known by as many people as possible and is better than all the other alternatives at solving a problem that is worth paying for.
If you don't market, then no-one will care that you exist even if you solved a problem decades ago. Someone else will use your solution and take inspiration (and credit) off of your discovery because you didn't bother to tell anyone about it.
This is exactly what happened here.
But that is NOT the point AT ALL. The point is, the same point that comes up on hacker news 1000 times a year - ideas alone are near worthless and execution matters.
Execution includes marketing that gets you enough attention. Because theres 10,000 other similar ideas of varying quality and marketing that others will point go saying "I wAS tHErE fiRsT"
All of this stems from the human bias of both (a) wishful thinking and (b)thinking people value what what we produce. These are natural human biases and are often dangerously wrong.
Programmers always think its just the idea and a prototype that is valuable, because they can produce and idea and a prototype (people what what I have) which causes them to massively overvalue ideas and the importance of "who was first" and all of that because they are sanctifying the small thing they produce.
It doesnt have to be malicious - the plain truth is theres 10,000 other ideas that are close enough that could be considered stealing even if they were truly independently developed, ideas are virtually worthless, get rid of your human biases that are clouding your judgment and focus on what matters if your idea is truly great : execution
"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 have a dozen different things at work that are currently using LLMs as classifiers for different questions. I don’t have the time, data, or resources to fine tune a model for each of them.
I haven’t had a chance to plug in Jev yet (waiting on approvals), but if it has the general intelligence claimed in the press release, then Laya is in no way comparable for my use case, and whatever TypeSafe has done is a substantial innovation over the Laya paper.
it's very similar to jev's api and runs locally - if you like it, you can try jev for your actual usecases.
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.
https://huggingface.co/MoritzLaurer/deberta-v3-large-zerosho....
> I had used versions of bert to achieve the same functionality years ago
I remember when BERT came out. I played with it. Other people played with it. You couldn't really get it to do useful stuff, unless you put a ton of effort into it, and even then, it would BARELY do anything useful.
The promise of Jev is that it's FRONTIER INTELLIGENCE, not the intelligence of a pre-chatGPT era model.
If you are trying to claim that BERT is somehow on par with frontier models, that is laughably false. (Whether Jev is on par with frontier models can be questioned as well.)
>>The promise of Jev is that it's FRONTIER INTELLIGENCE,
- capitalizing won't do much for your claim if it's wrong. Promise of Jev is it can't hallucinate, it took 2 years to develop in stealth mode, it's funded with $30 million. None of that makes sense, if you can get 90% of the performance from an open source model that's been available for years.
And they’re acting like their probability isn’t as hallucinated as any other LLM guess.
For example, if you feed in some context to Jev and Claude Haiku and say "make the appropriate tool call based on this context", Claude (or any other frontier LLM) will hallucinate tool calls some percentage of the time. Jev will not. While yes, the "will not" is constrained by Jev's (lack of) capabilities in some sense, this is actually a very real need for a wide variety of use-cases people are currently using off-the-shelf LLMs for at the moment.
Probably the better example is the whole probability thing, where even if you use something like constrained decoding to ensure an LLM only outputs a certain schema, and therefore can't hallucinate a class, if you ask for probabilities, the probabilities output by the model are just hallucinations. Jev meanwhile is outputting calibrated probabilities for different choices based on the actual landscape.
That doesn’t mean the models outputs are correct, nor is TypeSafe claiming that afaict.
There’s a big difference between deterministic and smooth though. Typical LLMs certainly aren’t reliably smooth, so the small prompt change might product a large and unpredictable output change. I’m not sure if that’s any better with the typesafe approach.
https://techcrunch.com/2026/09/18/a-new-kind-of-ai-model-fro...
Headline: a ChatGPT inventor.
I don't understand why we lept to accusatory and personal, nor do I understand where this connects with the article, nor do I understand the assertions if I ignore either of those two things.
The article claims non-hallucination, it makes sense, then there's just someone sort of hand-waving at it's obviously false and people dumber than you were tricked. Not sure what trope to invoke here. Chesterton's fence?
I might not have a good rep for Jev any more but at least I know what kind of model to use for decisions for graph engineering.
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.
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.
Today, we're extremely spoiled by trillion parameter-scale models. Our conceptualization of vibe coding relies on wasteful tool-calling paradigms, the one-size-fits-all mentality of LLMs is part of the marketing blitz to make people buy more tokens. It's lazy on the part of frontier labs, but also wastes electricity, time and money.
You guys dont understand that the Lowest common denominator ALWAYS wins - its why excel is the linga franca for most companies
LLMS and AI coding are the new javascript easy way to build amazing things and that trumps the tool specializers
Years of Big Data and Data Engineers building fit for purpose ML pipelines expensively working in a shadowy corner of the company have been replaced by the PM vibe coding a tool to categorize his emails by relevance
But also, frontier LLMs are enormously expensive and slow. Using Astra for things like simple text classification is not going to scale, and you're likely to end up in the same boat as those people who saw their Vercel bill shoot up to $96k/week when their site got traction, if not worse.
See https://www.eetimes.com/fpga-market-to-pass-2-7-billion-by-1...
For those who need to dive really deep into each specific avenue and squeeze maximal quality out, the photographers will be packing DSLRs and intense gamers will wait til they get home to strap into a PS5 or a gaming rig or VR or whatever.
But "can get 90% of anyone's needs met in this field, and can do the same in dozens or hundreds of other fields simultaneously" will remain the killer solution for anyone with lots needs that each have bounded depth.
I work with LLMs daily. 5 of my specialized tasks are outperformed by a custom model than a general purpose frontier model. The performance of my custom models not only beat them but are orders of magnitude low in costs and thus are able to be used by more customers.
Either, please correct me if I'm misinterpreting
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.
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.
- 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.
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.
Mode switching has a cost. Usually std::sort is good enough compared to picking the prime optimal algorithm for your expected shape. Just call the function and get on with your day.
Once we get out of this hypergriwth phase the very same AI companies that now are giving you llms will provide a service that employed a rich mixture of optimized models that will reduce the operational costs to achieve the required results
That's an interesting choice. One question I had when looking at the jev copy on their blog is if one "line" in their output looks / attends to other lines. I think not, since they say it's parallel and not autoregressive. In that regard, it would be interesting to play with diffusion, and see if you'd get better results by playing with types, locking some, and so on.
I don't understand the connection between the lack of autoregression and options attending to each other.
Non autoregressive models can attend to all the inputs simultanously.
An autogregressive model can can attend to all the options in the context of each other by simply writing the options out twice. Autoregressive models actually requires this, since one of them will come later, and the earlier prefill inputs can't attend to the later ones.
> I see it as a wake up call for the tech community to go back to basics for most tasks instead of relying solely on generic LLMs.
I always say the cheapest LLM request is no request at all.
Their generality also comes with a latency/computation costs.
My company specializes in statistical long document text classification, but nowadays we mainly work with audit trail requirements because we got tired of hearing complaints about our 5 example learning curve. Seems like the industry standard is telling an llm to label and telling an llm to eval, and crossing your fingers that it’s correct.
Let's take that as a given. Is BERT with more data not useful?
> I can see why people would want ready made one shot classifier, and I can see the value of sending multiple classifier in one call, but I wouldn’t call it breakthrough
Are those things that people want less useful because of what someone else calls it?
> I see it as a wake up call for the tech community to go back to basics for most tasks instead of relying solely on generic LLMs.
Maybe, or maybe to use Jev, which is useful?
Whether something is overmarketed or undermarketed, novel or derivative, it does not change its function.
The OP acknowledged they needed to fine tune their model to the training data of the task vs. zero-shot Jev
Gemini 2.5 Flash Lite is $500/Gt, Jev is $42/Gt. AKA an order of magnitude cheaper.
> BERT with more data
It is specifically not just that, in the same way that models which have been chat/task-optimized via RLHF (which made these models much more useful for a huge variety of tasks) are not just "the base transformer model with more data".
probably a prompt injection can still affect the output though, in unforeseeable ways.
I really doubt this actually. To me, Jev is a great example ofcounter positioning. When you consider just how hyper optimized the labs are around auto regressive LLMs, and just how much money they have already invested and are pre committed to investing in an entire stack for auto regressive transformers... then responding to Jev becomes nearly impossible actually. They would just be giving up too much.
Just think, everything from their current sources of revenue, the sales use cases they tout, the marketing on the websites, the messaging to customers, then technically to the APIs, their internal batching and scheduling algos, their GPU configs, the chips themselves. ALL OF IT is designed with generative text models in mind. Jev breaks all of it.
I think basically no chance of a response any time soon.
How could Jev have possibly built something out of reach of a frontier lab providing the same or 5x as much resourcing to one of their teams to achieve? Which they can do because Jev has only received $40M of funding recently, so a round that is approximately what OpenAI is spending per math problem they try cracking.
In addition to that, these frontier labs have got extremely good at generating synthetic data and running generalised training pipelines. I can only imagine how easy it would be for them to build this internally vs Jev building it from scratch.
And then the final thing: one of the best places you might apply Jev is within a harness, behind layers that customers increasingly have abstracted from them. Frontier labs have huge incentives to do this as it could make their offering much better and cheaper. And whoever gets this first wins another big attraction for users.
My take on this is Jev is either acquired almost immediately for the benefit of the next 1-3 months head start for whichever lab acquires them or we get a similar model offered from all labs in 3-6 months or sooner.
First, I think comprehensibility is a major part of why certain products grab the interest of the mainstream portions of the market. The 75% of posts in your feed are not from people who evaluate products based on underlying technology. They typically value signal from social reinforcement higher than anything else. This is the same reason why we see people mentioning products instead of technologies, i.e. PlanetScale versus Postgres & Tailscale versus WireGuard. The consumers understand the value proposition, but would have never discovered it without relatable messaging. This isn't a new phenomenon in computer software either; jQuery is probably one of the first examples that I can remember with this sort of texture.
The other side is a perception of expertise in a specialty. Software development, especially in AI, has become an incredibly desirable profession, and there are more people than ever racing to be included in it. In my own professional experience I find an excessive amount of entry level talent leveraging the same comprehension of product, but not comprehension of technology to get their foot in the door. A vast majority of the "thought leaders" occupying our feeds are not as well practiced as they claim to be, they're just trying to get a job or raise funding.
And finally, AI has brought out a certain amount of desperation in practitioners, for lack of a better term, materializing as an anecdotal, but certainly observable need to remain on the very tip of the news cycle in order to feel well informed. And so, using the dynamics above and many other human social dynamics, we find certain concepts spreading across cohorts that would not normally have a need or a want for these particular techniques, or products, or solutions, but because they feel pressured to remain relevant.
I'd like to remind us all that there is a reason Joseph Liouville took the time to painstakingly review Galois’s chaotic manuscripts to credit him. It matters who did what before everyone else - if you do want to say "ideas are cheap" - we'd need to control for other variables before drawing conclusions.
To me, there is a meaningful difference and I'd add a third category, but I can also see the contract angle
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.
When you market a product you make exciting claims relative to the audience you’re engaging with. When was the last time you saw a product marketing page reverently lost all the academic research and prior art that came together to make a product possible?
If Layla’s functionality was available in a SaaS form in a way that could be used by all the people who are excited about and using Jev, wouldn’t this research have won hearts and minds last year when it landed? I would have a lot more empathy for the author if they’d taken a product to market and nobody cared. But even then maybe the market wasn’t ready. There are still reasonable explanations why sometimes ideas take off. We’re on a venture capital forum this shouldn’t need an explanation.
Good models take time and effort. There wasn't a good option for satisficers until a few days ago.
The work is very amateurish, the "paper" would be a strong reject if I were still peer reviewing.
https://www.reddit.com/r/LocalLLaMA/comments/1wijo3e/i_liter...
“I personally found that this sequential approach captured sales dynamics much more effectively than traditional classification models.”
that first person phrase stuck out to me, especially given it had plural versions on either side, the author never edited for clarity or consistency
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.
It reminds. Me of Devin. Took a while to debunk. Not saying Jev is a fraud , but the gap between structuring typed output and playing a game involving logical interpretation of frames made of pixels, screams unstructured interpretation they made and forgot to mention.
[1] https://arxiv.org/abs/2507.18546
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.
No matter how much we pretend, that's how a lot of abstractions work. Things that touch the real world can change; there's a risk that the change could be as something as simple as a bugfix to changing the underlying implementation but preserving a higher level goal; you generally want a human in the loop to make sure the semantics work out and everybody's agreeing.
The relationship aspect of a business has a lot to do with how effective it is at continuing to justify its core value in an easy and relatable way; especially so when the decision makers that front the bill may not be as engaged with the underlying machinery behind the why it works how it does.
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.
It's also why Meta can make Muse and get a lot of users even though there's 10,000 personal agent startups
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.