AI developers react, calling Jev a powerful classifier and starting reverse-engineering
2 Sep 16 · 7d ago · 3 articles · 15 posts · 33 comments · 4 sources · development 2 of 6
Commentators on X and HN reacted with a mix of excitement about the classifier design and skepticism about the extraordinary claims, while some began reverse-engineering a 'jev-like' architecture and posting training code.
“jev is basically a general purpose classifier. and i think that is amazing. super curious what the training set for that looks like.”
@badlogicgamesDiogo Almeida Founder, TypeSafe AInandakishor_ml Creator of Laya (open-source rival)Simon Willison Independent AI commentatorflorianstandhar Creator of JevBench
The whole story articlespostscomments the bright band is this development · numbered dots are the others · click one to jump
Reported in the same hours no headline names this development itself — these 3 claims were published in its stretch
-
1 outlet Reverse-engineered Jev-like model
first by HN Frontpage, 7d ago
-
first by Latent Space, 7d ago
-
first by seangoedecke.com, 8d ago
What people said 24 voices · best of 37 · verbatim
-
Sounds almost too good to be true. Jev is an AI model built for decisions rather than text generation, introduced by Diogo Almeida and trained using a new method called RLCD. Claimed to be 20–200× faster and 40–400× cheaper than comparable LLMs. Costs $0.042 per million input
-
T
TypeSafe AI debuts Jev, a model using "Reinforcement Learning for Calibrated Decisions" to produce typed probabilistic decisions that software can use directly (Thomas Claburn/The Register) https://www. theregister.com/ai-and-ml/2026 /09/16/typesafe-ai-debuts-model-for-machines-that-plays-doom/5296711 http://www. techmeme.com/260916/p1#a260916 p1
-
> Structured outputs slot into ordinary software as fuzzy decision rules: classify, route, score, extract, or branch where hand-written logic is too brittle.Oh, I have one of those use cases, matching people in genealogy trees. You can ask all sorts of questions: do the names match? Do they match within some edit distance? Do they match according…
-
I reverse-engineered a jev-like architecture given its type. You can find the repo here to train your own jevlikes:
-
When we speak about good judgement in models and agents we are talking about humans skills such as critical thinking , judgement and decision making , emotional iq, mindfulness etc. We have been building since 2018 a structured good judgement data lake tied to ten core humans skills and sub skills levelled queaisn and answers against blooms…
-
Cool approach, i think less latency and cost is the way to go.Here's how this would have likely been made.- Tiny transformer or equivalent model (maybe a few bn or so?), explaining latency and cost- Questions are sent in parallel to multiple copies of it (I'm sure they're edge located)- The model is post-trained for calibration in a wide variety…
-
do you all see the use cases being similar to what you might use Fastino's Gliner models for? i see similar differentiation from general purpose LLMs in the sense that they can take natural-language input and return outputs adherent to a user-defined schema.https://fastino.ai/blog/gliner2-5-span-free-information-extr...im thinking about how well…
-
I am confused why they say it is not an LLM and then in the documentation it is shown as being an LLM derivative. The documentation makes it sound like they're taking a pretrained LLM and then giving it their unique post-training. How is that not an LLM?FAQ: Is Jev just a smaller LLM?Jev is neither small nor an LLM, hence being off the…
-
I wonder how many choices you can give this thing in multiple choice response mode. I'm guessing you could give it enough choices to produce Turing complete programs one symbol at a time when running in a loop, or to hold a conversation when given a vocabulary as the choices. It would be particularly hilarious to just let it choose the next ASCII…
-
I think the meaning of can't hallucinate in this model is that the type won't be hallucinated.So if the generated schema is for a tool call for calculator, then the numbers will be valid numbers for sure (and not random words).To me, it sounds similar to BNF schema already introduced and implemented few years ago: generally speaking - it limits…
-
I was previously working on LLMs to extract key info from data rooms for energy assets, and this looks great for that use case."Does this contract contain ____?" is a pretty typical query for many industries, and then you can have follow up questions that nest down into further info about X, Y or Z thing.Looks really good for that use case…
-
> but it's nothing like the code generating models we're all using today for code and automation.Is this true? Code is structured output. At the very least it seems like a question of degree rather than kind.While the LLMs we're using today are limited to sequenced text, it seems that a model like Jev could excel at coding on a more structural…
-
Someone else noticed the base64 encoded block on the launch site[0]? Its the fast inverse square root algorithm q_rsqrt[1]. I guess its meant as a joke to put this algorithm that makes use of type punning on a site called typesafe.ai? Or maybe because of its efficiency?0.
-
Looking at the example Jev use cases, it almost feels like Jev's incredible cost/task can make it competitive as a generalized "poor man's ranking" algorithm that can be useful for lean startups or any fast paced development org.I need to rank 1000 articles and pick the 5 most relevant for the user? Jev.I need to audit and strip out content…
-
I am not an expert in this domain but as an engineer-turned-researcher, this looks a lot like GliNER with a fitting harness.This is something I focus on in a bunch of my experiments - how to get immense value out of tiny models (<1b params). There are lots of different architectures out there and there is so much to optimize if you know what you…
-
Amazing work by the team! Looks like they've traded accuracy for speed and this is most likely going to be the case with the next class of models.This is a valid tradeoff for one-off responses but if we're dealing with a distributed system (eg: Kafka), then only the high-confidence responses (>0.8) should move forward as input to the next service…
-
I'd love to know if Jev is still fundamentally LLM-shaped in architecture. Like is it using a single forward pass with a learned readout over the predefined options (i.e. a discriminative head on a transformer, no decoding), or something else? I did similar things for zero-shot criterion-based classification using a 4B Qwen model but could not…
-
> Assuming there's no funny business, the Doom demo is cool.The Doom demo seems very funny business. They're not feeding it video, they're feeding it a text description of what's going on in the game. It's not reading pixel data.I think LLMs would play a lot better with that input too but Jev does seem to have a huge speed advantage; I don't know…
-
Is this the reverse of LLM? Ie, "what's the capital of France?" LLM picks from "Paris" 99.9% / "London" 0.001%, LLM then with some randomness output you "Paris". For Jev, you ask it to give probability of a set of answers "what's the capital of France?" choose from answers (Paris/London), it then gives you (99.9%/0.001%)
-
One application that sounds pretty interesting would be the creation of wikidata pages for anything. Plug a topic/word/concept/historical event in, take a bunch of wikidata properties, rephrase them as questions with the choices being the existing property values. Then feed it to LLMs or something. Does that make them more reliable? Probably not.
-
I suppose at this point, the "new and disruptive" AI labs have been around for long enough and grown big enough so that most of innovation will come from such small new players like this one that may or may not get acqui-hired back into the big blobs.So I guess one could say that by now, "AI" is an established branch of the industry.
-
I'm trying to understand what difference does this make over LLMs.LLMs are universal simulators, their latents model the world. So I bet if you compare their logprobs with probabilities output by this model, it will be highly correlated.Someone should do this quick experiment. I bet there won't be enough of a meaningful difference.
-
Is this fundamentally different from other text-based LLMs, or is it the same except with special reinforcement learning a safe guards around generating valid types?Surely it’s still generating some kind unstructured data internally? For example, what if I told it to generate a short story, but the short story is output as a JSON string?
-
I was going to ask if the inspiration for System One was from Kahneman/Tversky, research, then I read the FAQ.I listened to "Thinking fast and slow" recently and I was surprised how closely in behavior a LLM approaches the "System One" as defined there.I approve of the clever branding!
All 6 developments of TypeSafe AI's Jev sparks priority fight with open-source… →
Hacker NewsNewswiresMastodonXLobstersBlueskyRedditGoogle News