today at 9:22 AM
If Jev is fundamentally trained using RLCD while you’re building on a Qwen model that was trained using RLHF, how can the resulting model be considered Jev-like?
today at 10:20 AM
I can't find it but saw that if you give Jev English alphabet as choices and ask it in a loop what model it is, it would say Qwen
today at 9:28 AM
Also my question.
today at 10:17 AM
All the people that are just writing an Jev-like API on top of a normal LLM are missing the point. What makes Jev special is the training data; it's how it's trained. The architecture is probably nothing special. Just a text encoder with parallel prediction branches.
I have tried many of these open-source Jev-like models on some linguistic tasks and they are so bad compared to Jev.
today at 7:57 AM
I think a great use case for these will be when they have large context windows and are able to enforce styling rules for frontend development, and component creation rules for react. You can then ditch the styles guides and styling skills and create a decision tree for enforcing styling, so that you can't run into drift issues or duplication issues. That's where I'm wasting most of my time right now, constantly correcting all of the UX/UI issues that are created for every single feature.
today at 8:54 AM
Back in the good old days we would prevent these ux/ui issues by rigorously enforcing the use of our own stylesheets and classes. Later that grew to only using the company ux components. This was very successful in keeping everything neat and tidy. The only drawback was creating and curating new elements and getting consensus. But otherwise it works wonders.
Try constructing reusable components out of what you are doing instead of building everything up from basic building blocks. This also allows more concrete testing of individual parts and then if you want to change the look you can change it in one place and have it apply everywhere.
Agentic development doesn’t mean “throw all what we learned out of the window”, the same practices that helped speed up and improve quality of work of humans also helps agents. In fact, the multiplier is even bigger. You will notice it in development speed and reduced cost due to avoiding churn.
today at 9:34 AM
Hi, I'm currently planning on launching a product like this "Grammarly for Design" in a few weeks. Would you be interested in being part of the alpha group ?
today at 7:51 AM
Bit of a Jev explosion going on. Is it because it's taking us back to a simpler time we understand better? Classification models have been around for a while.
today at 8:57 AM
It reminds me a bit of what Ansible got right: user communication. The underlying tech may have existed for a long time, but the genius is presenting it to a regular developer in a way that reads "yes, even you can understand ML, just using a little JSON". The contribution of that should not be understated, as has been clearly evident recently.
today at 10:16 AM
Yeah except it doesn't really work. It constantly breaks underneath you. The whole system has to be managed, e.g NixOS, or else it's a house of cards.
today at 9:16 AM
The way I see this (I havent played around with Jev or layla the OSS version) is that classifiers have always existed and a recognised tool in the ML world. But, the norm is that one needs to not only know what to classify as, but determine what weights to use to classify the input.
Jev came in, and added that magic of "you dont need to train your classifier or determine the weights" if you dont want to, and just get the classified answer out. I think that's what is making people see this with a glitter in their eyes.
today at 10:09 AM
I would be curious to see comparisons of jev and similar things with problem specific classifiers. I think layla suggested making problem specific versions anyway? There is a lot of demand for magic don't do any work solutions, which is kind of weird in an era where agents can really help you build a customised solution effectively.
today at 8:27 AM
It’s because it’s practically useful and enabled things that were impractical previously.
today at 8:59 AM
> and enabled things that were impractical previously
I think that there are not _that_ many use-cases that have been opened up by this that tool-calling on other models didn't solve already. Really depends what benchmark you're looking at. This one against BANKING77[0] has many issues, but suggests it's really not far off DeepSeek 4.1 Flash. This one against BoolQ[1] shows marginal improvement over Qwen3.6. This one against MMLU-Pro[2] (same author as the previous) shows significant improvements over two Qwen models.
So there's definitely _some_ alpha there, but I don't think it's the sea-change that the hype would suggest; that is to say, yes, some things that weren't practical before are now, but many things were already very practical with the existing tools.
0: https://sanand0.github.io/llmevals/jev/
1: https://github.com/ekzhang/openjev-sglang/blob/a3554ed9e9c26...
2: https://github.com/ekzhang/openjev-sglang/blob/a3554ed9e9c26...
today at 9:30 AM
For a while is the keyword. It’s just vibe coders have just discovered the classifiers
today at 8:38 AM
It's appealing not having to fine-tune separate model for each use case
So you have more flexibility to get on with building, evolve your business logic etc
today at 9:26 AM
Jev is creating a sort of identity crisis for me, because the number of absolutely clueless folks parroting the classifier thing is the first time I've seen this sort of mass psychosis in CS upfront.
Like even 5 minutes of tinkering captures why this isn't anymore like BERT or any past classification model than ChatGPT is like those old Markov Chain generators, yet folks cannot shut up about how this is nothing new.
Absolutely scary and makes me wonder how much of the field is just people super confidently discrediting otherwise promising/interesting directions for development for a cheap dunk!
today at 9:40 AM
Hey I am clueless, how do I learn more?
Why is Jev fundamentally better than classification models like BERT or traditional ML?
Happy to read a written response or if you suggest a prompt to put into my LLM to get it to research and explain the relevant details.
today at 10:13 AM
You already wrote the prompt, no? What I'd do, if I were you, is run the question through a LLM and then come back with targeted questions that it didn't answer.
I did the first part yesterday, jumped down the rabbit hole, and have 3 product ideas in my head now.
"Why is Jev fundamentally better than classification models like BERT or traditional ML?"
today at 7:57 AM
[flagged]
today at 8:36 AM
Thanks to these projects, what was an innovative-but-closed piece of technology one week ago is now much more accessible. Whether they're in it for fame or not, I couldn't care less!
today at 9:33 AM
There already was an alternative a year ago, with a published paper and open weight lmao... all the other projects are literal slop shat out by script kiddies 2 hours after the release of jev, it reminds me of the flappy bird era, depressing
today at 7:45 AM
Quite impressed by the energy people are putting into making OSS Jev-like models.
I understand the hype but I wonder: what are the use cases for this kind of model? Could it be used in the context of coding agents, or is it more relevant in totally different situations?
today at 8:17 AM
> what are the use cases for this kind of model? Could it be used in the context of coding agents
Yeah, it could. The most obvious usage would be to have local fast cheap "feedback" / "control" over a slower more expensive agent (i.e. cc / codex / opencode). Things like "goals" could now be split from a long prompt into "actions" and "verifiers". Where for each action you also produce a verifier. Then after each action you run the verifier w/ this kind of "universal classifier" and decide if the step was done correctly, if it needs follow-up and so on.
Example: implement auth in this repo -> llm_plan() -> for item in plan generate_verifier() -> for item in plan implement() ; verify() ; accept() / followup().
Verifiers could be something like this. take a plan item as input, generate classification questions that might verify the task "is this following project conventions?" | "is this touching files from other tasks?", etc.
You can do that with LLMs, but some things might become cheaper / faster. And you can pretty much use it to check against an ever growing list of conventions. Yours or project specific.
today at 8:00 AM
Consider every situation where you "force" an LLM to output only a choice / category, or a set of them. If you have workflows like that, you're now being promised significant cost- and latency reduction.
For coding agents it'd only be useful in a subset of situations. E.g. you could imagine using one to classify bash tool calls into safe and unsafe for example.
today at 8:00 AM
To develop a smart ai system for my 2d roguelike platformer? game has way too many moving system for classic state-machine ai + i cant spare the time to develop it. its low latency entices me.
today at 7:52 AM
You should call Jev-like models when you give it a JSON-like structure to produce, it is useful when you need _some_ intelligence in your code.
Edit: I want to add that you can see Jev like a smart if-statement.
today at 7:47 AM
Yeah same. Got access to their API and then realised I don’t really have an immediate use case
today at 8:07 AM
Because these decision models do not have tool calling, the knowledge cutoff might become a problem. We'll either have to keep training continuously if we run locally or switch to the newer version every month or so when using a closed one like Jev
today at 8:30 AM
even with knowledge cutoff set a second from now, you still want to provide as much info as you can if you’re using such tools for delegating decisions
today at 8:28 AM
Can someone tell me what is the difference between Jev and a normal neural network that does classification ?
My understanding is: it takes text input and it does one shot classification (no training data)
today at 8:30 AM
Yes, this is essentially it.
As a corollary, the output classes can be any set, rather than needing to be set before training.
today at 9:02 AM
Can someone do a ELI5A of how they achieve classification over any user defined list of items? Normal neural networks do a softmax over a known output set to get probabilities
today at 9:34 AM
I can think of two possible approaches 1. Jev limits to 255 distinct options. So they can preprocess your set of options and “tell” the LLM via input tokens 1 = red, 2 = blue, etc then jev need only output softmax over 255 states while benefiting from pretrain of other LLMs 2. You allow the forward pass to output over the total token state but mask over the logits to limit to the user options. Less plausible? bc tricky when input is multi token which they clearly support.
My guess would be option 1. Didn’t read the kev repo here which would also explain
today at 8:11 AM
I wonder how these would do filtering my spam. I have been using 27B-class models for a while now, and they are nearly perfect at determining what is spam and what isn't. The only disadvantage is computational cost.
today at 8:33 AM
Take a look at Thomson 1.0-small, which is a variant of qwen 3.6 35b post trained by Thomson Reuters for text analysis. It classifies text content very well.
today at 9:02 AM
The bright side of Jev being so popular could be that many companies and individuals realize that their applications might work well with a System One model, and they decide to run an open-source (or fine-tuned) version on their own
today at 8:18 AM
On Gemma 4 12B, I am getting 220 ms per move or QS. I used it to play the Snake game locally:
prompt_eval=244 ms wall=245 ms schema_cache=hit generated=0
Move limit reached after 200 moves: score=16, length=19.
So, if a 12B dense model can offer this latency on a local old PC, then definitely you can scale it up with more powerful machines and get even lower latency.
today at 7:39 AM
Been hoping for something in this space. Jev-like decision models on Qwen3.5 could really simplify some of our internal routing logic.
today at 7:57 AM
- https://github.com/logan-markewich/jeff - https://github.com/bespokelabsai/nimble
today at 7:59 AM
Why does nobody ever ship these as a docker image?
today at 8:02 AM
I guess you have AI to write your docker files and push your images now.
today at 9:09 AM
Why not name it Qev?
today at 8:32 AM
Interesting approach with Qwen3.5 for decision models. Curious how "tiny" they've made them while keeping LLM reliability for critical paths.
today at 8:36 AM
Interesting to see a Jev-like approach applied to Qwen3.5. Always appreciated Jev's simplicity for quick decisions.
today at 9:43 AM
Always?
today at 9:19 AM
Oh Jared is cool - he made After and Razzle - nice
today at 9:08 AM
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