Introducing System One Models and Jev | Hacker News Reader

Introducing System One Models and Jev

typesafe.ai

1,989 pointsalbelfio520 comments

It seems like the docs[0] are a better explanation? The comparison to llm tokens is kinda confusing.

It looks like the model takes as input a state (structured text? not sure if multi-modal) and a question (as a "Choice", "Score", or "Noul") with some additional augmentations possible. Then outputs the question's answers as appropriate (e.g. a choice, accompanying probabilities, confidence).

Edit: On the AI primer page, it looks like they do the RLCD on a pre-trained base model?

[0]:https://docs.typesafe.ai/concepts/system-one

CEO here - that is right!

I do agree that the comparison to LLM tokens is hard to understand (also because output tokens are not comparable).

But yes, text or structured state (like a JSON with multiple pieces of text in) -> decisions out (e.g. choice maps to "match" statement, "score" maps to sorting, "noul" short for bernoulli maps to if-statements)

I see this super interestingly as the "subconscious" to the llms "conscious" for lack of better terms. I'm super interested in this for broad and rapid decision making in the context of consumer agents so will be signing up for sure.

1. I am extremely on the same page 2. I do think that subconscious is not only much smarter than we give it credit for, but also much more robust than the "jagged frontier" of current LLMs

(shilling my blog post on that jaggedness: https://www.completeskeptic.com/p/lies-damned-lies-and-bench...)

I wonder if a good analogy would be, they are trying to tune the “gut feeling” of a model more directly

Hi - first congratulations, System One looks really promising.

The Doom demo really help me, at least, to understand how System One differs from LLMs. However the first demo (Side-by-side demonstration) - I'm struggling to understand what is going on here!

I think that's to demonstrate its speed

I was confused at first too, but it makes more sense when you read about their primitives. E.g. https://docs.typesafe.ai/primitives/noul

The demo is showing System One producing its output in parallel very quickly and for little cost compared to an LLM generating its answers token-by-token. The "noul" type is used to evaluate a yes/no question and return the probability that the answer is yes.

So this demo is showing System One offering much more nuanced responses and specific probabilities compared to an LLM's more crude responses (e.g. LLM shows "true" or "false" compared to "0.9" or "0.07" probabilities that the answer to some question is true).

here is how I attempted to explain it to my company's AI group chat, is this roughly accurate?

"instead of autoregressive string output it instead outputs structured type-safe 'decisions' with probabilities/confidence scores, each generated in parallel

so sort of more like a Large Classification Model than a Large Language Model? or, maybe better to think of it as a sort of "shift left" in the LLM's transformer architecture, allowing you to replace the predefined token vocabulary of an LLM with a prescribed set of 'decisions' that need to be made based off the input context; and exposing those probabilities directly so they can be integrated into the system logic, instead of just sampling from top-K.

all of this while still being instruction-tuned (!!!)"

It's always been possible to build classification pipelines using LLM embeddings as the input. seems like this is a much more sophisticated / useful application of that concept

very accurate!

the one nuance I'd get into is I'd call it "zero-shot" over "instruction-tuned" (the latter often implies a particular distribution), but very safe for sharing

For many day-to-day computing use cases, Jev seems far better suited than an autoregressive language model, if for no other reason than it is not wasting compute thinking about anything other than how to spit out a decision.

Do you have an architectural explainer yet for Jev or are you holding that close to your chest and letting the magic rip for now?

architecture is close to the chest for now, but we have talked about writing a paper

I don't want to shill my blog too much, but I will say data is probably far most interesting than architecture: https://www.completeskeptic.com/p/the-bitterest-lesson

Well, I'm stoked to try it out. We have about a billion reasons a day to call a model like this to rid the world of spam and phishing.

It's unclear if the context extends as the conversation grows?

Small request, can we get an explanation of the naming of "noul" in the docs[0]. I tried googling, and searching the docs and didn't understand why it was called that.

(I'd also argue something like p_yes or just probability might be a simpler name, but I'm sure there's a better reason behind Bernoulli maps).

[0] https://docs.typesafe.ai/primitives/noul#noul

Im going to guess bernoulli

It is Bernoulli (mentioned in the comment I replied to). I just failed to guess that myself :)

You really, really, really, really, REALLY REALLY REALLY should explain neologisms in your documentation! Really really! Having "here's a weird unexplained difficult-to-guess-impossible-to-Google-for term" in your documentation is aggressively user-hostile. Please don't.

The actual term is fine. Lack of an inline explanation is a blow-it move. Don't do that.

> the model takes as input a state (structured text? not sure if multi-modal)

Input, and criteria/instructions can both be defined as structured input (JSON). This ends up being pretty powerful because the model is trained to understand structure.

e.g.: https://docs.typesafe.ai/primitives/advanced#structured-inst...

> not sure if multi-modal

just JSON... for now :)

> outputs the question's answers as appropriate

correct!

I assume this isn't really for consumers/individuals currently? Kinda feels like an improved magic 8 ball.

I can't really intuit how I should think about when the model will be accurate. Is there somewhere to read more about that? I assume customers would just have some tests or talk to you.

> I assume this isn't really for consumers/individuals currently?

Unless they're hackers, no. It's not really a chat interface, it's meant for consumption by machines and composing into higher level systems (pairs great with LLMs).

> Is there somewhere to read more about that? I assume customers would just have some tests or talk to you.

We're going to release some more info on evaluations over time, and yeah, join the waitlist! We offer faster access in exchange for good memes

First, congrats to the team on launching something genuinely interesting and new.

Seems like a more accurate title would be "Jev: Trading general purpose generation for fast typed inference" or something like that.

This is interesting, but the speed comparison seems misleading? A generative model that can output code in a Turing-complete language can do anything a computer can do.

Jev can only generate structured output, right? This is probably super useful for classification/routing/scoring, but it's nothing like the code generating models we're all using today for code and automation.

Also "can't hallucinate" seems wrong? Sure, it can't emit an invalid type, but it can still emit a completely wrong valid value. You can enforce structured output from an LLM too, with an appropriate harness, etc.

Assuming there's no funny business, the Doom demo is cool.

> Jev can only generate structured output, right? This is probably super useful for classification/routing/scoring,

My first thought was that it would be ideal for robotics? As in control of limbs, general planning, route finding, etc.

Um, Isn't SELF DRIVING the elephant in the room?

Only if you think that everyone cares about self-driving. Lots of niches require structured domains; self-driving is just one that has a lot of capital thrown at it.

That's a great point! It quite looks like the System 1 model of Physical Intelligence

When they say "can't hallucinate" they mean they produce a confidence value for every result, so you could see for example it has 0.1 confidence, and you can disregard the result - that'd be different from hallucinating where it believes it's correct

that's right, but because these models are probabilistic, it's also possible to be confidently wrong (and all future models will be smarter still and still have that possibility)

Correct. Not to say we're getting into the weeds of probability here as well.

"What are the odds a thunder will strike in Paris at 1pm UTC of 2026-09-16" - that could be a 0.001 chance going from blind historical measurements; 0.01 if it's raining; or 1 or 1 after the date has passed.

Technically speaking when you send the prefix “The capital of France is “ into an LLM it will also produce probabilities across its whole vocabulary.

… which they could provide in their APIs but are vehemently opposed to because it makes distillation much easier, and faster.

Yeah. Yet another reason why open-weight models are better. If I want to use the logits, I can.

The probability values don’t really represent confidence in modern LLMs though, especially after RLHF and RLVR.

System One says they use RLCD, Reinforcement Learning for Calibrated Decisions, which presumably has accurate probabilities as an explicit optimisation goal.

How is that different from RLVR?

RLVR generally upweights tokens along the whole thinking trace that led to a correct answer, whether each token was "correct" or not. RLVR doesn't train a model to output an 80% likelihood, it just trains it to produce correct answers, and not to produce incorrect ones.

System One hasn't said how RLCD works, but they do say it is explicitly training models to output "calibrated" probabilities, which makes it distinct from RLVR. This is how they describe it:

> System One models are trained for calibrated decisions: their probabilities are optimized against outcomes to reflect uncertainty.

In RLCD (which is now an RL acronym that has 3 different unrelated expansions!), you basically massively negatively reward a distribution that is {yes: 0.9, no: 0.1} if the answer was no, and less negatively reward a {yes: 0.6, no: 0.4}. Many nuances when designing the details, but that is the rough idea.

It is a known existing thing variously called "calibrated RL" or such.

Implementing it on top of LLMs was difficult to get it to work, they seem to have done it up so its good enough for a polished product that works in a wide variety of usecases at the same time. I got accepted from the waitlist and it's really neat. Edit: it is now on vercel gateway.

One thing to note, the out of distribution behaviour will be different from what we are used to with regular LLMs. Theoretically, it should be worse, but practically, it depends on their method.

Yeah but what stops it from producing confidently incorrect outputs...

Nothing, but imagine using LLMs for a classification task

People out there are so resigned to the models being unreliable that they are really doing things like hallucinating deliberately, and then matching the hallucinations to embeddings -

https://softwaredoug.com/blog/2026/08/10/hypothetical-classi...

You could do that or you could just... use a model that will never produce unreliable outputs in the first place.

But we're going from "Apple" to "Apple: 99% - trust me". It could still be an image of an orange :)

It's pretty darn smart. If you did want to hack on it in earnest and find out for yourself, send me an email - nathan@typesafe.ai

Oh I'm on the waiting list, I was just being a pedantic prick. I can't wait to try it.

I'm certainly not resigned to that, at least for classification.

Even non-frontier models are absurdly good at this in a broad sense.

Which would make it hard to judge "a model that will never produce unreliable outputs in the first place" against something that is already really, really good and exceptional in domain-specific areas with the tiniest amount of elbow grease.

Speed and cost look good though (for now)!

It would be great to see benchmarks for Jev that demonstrate the value of calibrated uncertainty.

For example, one could set a confidence threshold over which we trust the model decision, and otherwise reject. This provides a lever to trade-off accuracy and automation %.

Then we can ask questions like "What % of decisions can we automate to achieve 90% accuracy"?

i ran some interesting experiments on jev today and wanted to share the results. i compared cost and latency of support ticket triage with different arrival rates for jev vs mainstream LLMs: https://suraj-website-eta.vercel.app/blog/what-a-correct-dec...

if it puts a high confidence value on a wrong answer, thats still hallucinating, no?

llm hallucinations are high probability tokens that are incorrect vs the real world

Yes, there is no magic sauce here that makes stochastic output binary if that’s what people are looking for.

Right and so maybe we should stop saying "can't hallucinate" when it can by definition.

It’s not what people are looking for, but what they wrongly claim.

Correct, they have not made a universal all-knowing omniscient oracle, which is what would be required for "can't hallucinate".

Not to be tooo pedantic, but a bot that assigned 0 confidence to everything wouldn’t hallucinate.

A calculator either gets the right answer or doesn’t answer.

It wouldn’t have to be all knowing as long as it knew perfectly what it doesn’t know

A quantum calculator answers in distributions.

In one universe that is true, in another one not.

Copenhagen is a beautiful city.

That seems like a weird standard.

I would be happy enough with: only produces what it can verify with sources.

If you eg try to remember a court case (ie produce the reference via LLM token generation only), it's easy enough to check with your data whether it really exists. Similar for following links and other references.

If your data or sources are wrong, obviously your report about them will be wrong. But I wouldn't call that a hallucination.

There isn’t a single human in this world and hasn’t ever been that meets your happy-enough standard. Make of it what you will.

It's not a binary thing. You can get closer or further away from that standard.

And humans also behave differently in different contexts. A conversation at the pub has more such hallucinations than a formal deposit in court. For the latter, a good lawyer will look at her shoes, when you ask him what colour her laces are.

Why is that at all relevant?

Humans are known to hallucinate a lot. Ask 10 different witnesses at a crime scene what they saw and they'll all report different things.

A good, non-hallucinating LLM would only report things for which it has evidence. It would consult the facts every single time.

It's a pain in the butt for humans to fact-check everything but LLMs can quickly look up all kinds of stuff. That's what makes them useful.

Yes, and for the LLM you can do it in multiple passes.

So you can bolt the fact-check / source-check pass onto whatever other system you have, without having to redesign the underlying system.

That's not true that incorrect sources means incorrect report. Often, LLMs have some sense of what is true, and due to that, they hallucinate plausible sources that appear to back that knowledge up.

No, I don't believe so. Hallucinations are not "high probability" in a real sense. They are an artifact of the random walk the inference algorithm takes, which causes it to latch on to and chase attractors in the noise. This random walk behavior is necessary for chat interfaces to be useful, but are less critical to typed output predictors. I'm guessing they found some optimization that is possible if you give up caring about chat.

What we would want to see if a confidence value that is in line with the actual correctness. If the value is 0.9 for 1000 different answers, then approximately 900 of those answers should be correct.

I read "hallucinations" as "generates novel output with no grounding/source". i.e. "it just made something completely up".

I believe their "accuracy" metric (sonnet 5 level) is where "right/wrong" is measured.

What about the LLM calls though that are done midchain? In the Home Assistant video the multi-intent prompt gets split using what looks like a traditional llm model, which I'm assuming is vulnerable to classical hallucinations.

That's really funny when you consider that generative models also don't hallucinate if you check up on them on every token generated?

I'm biased but I wouldn't call it misleading - generating text is super awesome and flexible, (we describe that in the blog post - and I personally use string models all the time) but it's true you pay a high tax for autoregressive generation

> Also "can't hallucinate" seems wrong? Sure, it can't emit an invalid type, but it can still emit a completely wrong valid value.

that is likely true of all ML! perhaps we could debate semantics, but I don't think it's fair to say a random forest "hallucinates" in the way LLMs do

From a quick look at this it looks like it could easily generate natural language text by following a structured representation like UMR (Uniform Meaning Representation) or the similar representation the Abstract-Wikipedia folks will be working on for generic encyclopedic text (which will be heavily informed by Universal Dependencies). These are basically linguistically principled and frame-based counterparts to a programming language AST, that can be then converted to natural language (in a broadly language-independent way, to the extent that semantics and pragmatics make that feasible) via some sort of NLG rendering.

(To be clear, this one raw model does not support outputing a full AST directly - it wants to output "choice" among fixed options, "score" on a sliding scale, or a true/false answer (all of these with confidence scores attached), so building the AST/structure would be a code-driven (or even perhaps outside LLM-driven in some more challenging cases) multi-step affair where the model would essentially be playing a "game" of building the structured output step by step and getting a revised partial state back. But one could expect this to lead to interesting results.)

Just to be sure that I understand, you're saying that your model "can't hallucinate" because it only outputs a single thing, right? In this way, an LLM can't hallucinate either if I prompt it to do a classification task with a discrete set of possible outputs, right? (Assuming I reject non-conforming output. Actually, maybe what you're saying is that your system can't output non-conforming output?)

Yeah that's precisely correct.

For e.g. classification tasks, even in 2026 people are doing things like hallucinating deliberately, and then matching the hallucinations to embeddings -

https://softwaredoug.com/blog/2026/08/10/hypothetical-classi...

With TypeSafe it just picks the class (actually probabilities across classes), reliably every single time.

404?

https://softwaredoug.com/blog/2026/08/10/hypothetical-classi...

> I'm biased but I wouldn't call it misleading

- @CompleteSkeptic

Very strange.

His claim was that the title is misleading, not sure how it's relevant to that claim that you use "string models" (full LLMs).

The original title before it changed less than an hour ago was:

"Jev: New frontier model 40-400x cheaper and 20-200x faster"

I'm going to agree that was misleading.

And on the second point:

>>Also "can't hallucinate" seems wrong? Sure, it can't emit an invalid type, but it can still emit a completely wrong valid value.

>that is likely true of all ML! perhaps we could debate semantics, but I don't think it's fair to say a random forest "hallucinates" in the way LLMs do"

Also going to disagree here, and I don't think it's semantics.

Type safety is not factual correctness.

> Type safety is not factual correctness.

I very much agree with this and want to hone in on where do actually disagree. Would you say a linear classifier hallucinates?

Hallucinations were defined in the context of text generation models so your question does not really make sense.

IMO your system can make mistakes that are similar in spirit to hallucination (i.e. answering with a false answer instead of abstaining to answer).

And furthermore, because the model is forced to answer in a boolean (if in boolean mode), if the user input is outside of the range of a boolean, it's forced to hallucinate. It can't abstain.

Let's say classifiers don't hallucinate. To make a fair comparison we should constrain LLMs to the same classification task. In that case, no, LLMs also don't hallucinate.

- Give Jev and LLM the same input

- Lock down both to approved/rejected/unknown (LLM restricts on decoding)

- Both can be wrong, but neither can hallucinate (invent an another option).

id say yes. a linear classifier that classifies between red and yellow balls will hallucinate on blue.

linear regressions hallucinate in the simpson's paradox.

the model output can be quite confident and not representative of reality

No. Your launch post puts “0%” on a hallucination chart, then explains that the number comes from guaranteed schema matching.

You’ve already agreed that this doesn’t establish correctness. An approve for an unauthorized action still meets the schema guarantee.

That’s why I find the messaging misleading. You’re acknowledging the limitations in these replies while defending the broader reliability pitch.

Even granting that each answer is calibrated individually, that doesn’t establish calibration of the decision that combines them.

Sure, I can threshold a composite score, but there may be many wrong answers with the same score. An unauthorized action doesn’t become acceptable because it scores highly on the other dimensions.

I still have to define the constraints and test which wrong actions get through the complete workflow on my own data. That’s a substantial part of the work being pushed back onto the developer.

hallucinations are not wrong answers, that's why we use a different term

User input: "Hey, have your human support agent call me, tomorrow at 5pm."

Model input: "Does the user want to speak to a human support agent?"

Output: Yes.

I imagine that your model would produce this, and I think it's fair to say this is a hallucination. A human would caveat it with: "Yes, but not right now.", your model is incapable of that. Yes is technically correct, but within the context of being in a live chat, a human would understand that the caveat is required.

To be fair - you’re crafting a deliberately bad model input for a contrived example.

No, the model has answered correctly. Your question is poorly phrased (possibly deliberately).

Your question would correctly classify the user's input as requesting a human support agent, but at an indeterminate time.

If you wanted to determine whether the user wants to speak to a human support agent immediately, you would have to correctly qualify your question, e.g. "Does the user want to speak to a human support agent now?". You could have another question which is "Is the user requesting a call-back from a human support agent?". Or you could have a multiple choice query which would filter the conversation into one of a number of pre-written possibilities.

This is nothing to do with accuracy or hallucination. It's a different method of interacting with the model where you are relied upon to be precise.

Add:

    "call_back_day": {
      "criteria": {
        "none": "The user does not want a call back.",
        "today": "The user wants a call back today.",
        "tomorrow": "The user wants a call back tomorrow."
      },
      "instructions": "When does the user want a call back?",
      "type": "choice"
    }

And you get: "tomorrow": confidence 1

A hallucination in the context of LLMs is generally understood as an incorrect answer presented as factual. If you claim that "x can't hallucinate" in the context of LLMs, you're saying that x always gives accurate answers. It does not matter whether the answer is type safe. If its value is incorrect, it's a hallucination.

> I very much agree with this

From the intro blog

"Hallucination and type-safety are intrinsically related"

I'm not entirely sure why we're conflating type safety with, I guess, value or output safety.

"Would you say a linear classifier hallucinates?"

No, but it can be (and often is) mathematically correct and functionally incorrect. It doesn't help to say "a linear classifier can't hallucinate" when you get even 99% accuracy. That's 100% a semantic play, and it doesn't help when the picture of a dog is labeled cat and the response is "yeah but that's not a hallucination, only stupid LLMs do that"

I don’t think it’s misleading if you compare on the use cases they suggested. It’s faster and cheaper (no idea if higher quality), so it’s immediately interesting for certain things.

And if you buy their RLCD claims, this might be even better than huge models that know a bunch of irrelevant things.

What was misleading was the original title:

"Jev: New frontier model 40-400x cheaper and 20-200x faster"

I'm not the gatekeeper of who gets to call themselves a frontier model, but I don't think most people would count Jev in that group. It sounds false.

If their specific claims hold up, then it would make more sense to say something like:

"Advanced the speed/cost frontier for structured decisions"

I dunno, I would consider Waymo and Tesla to have frontier models.

I think AlphaFold and related are also frontier models.

Being an LLM does not seem like the qualifier for frontier.

This is likely still an LLM (in the purest definition of a language model with relatively many parameters) since the inputs are natural language, just not a generative LLM as the output is something other than more language.

The inputs aren't natural language. https://docs.typesafe.ai/primitives

The inputs are natural language, they're just also structured into a tree. The first example on that very page shows natural language instructions:

    questions = {
        "refund_requested": Noul(
            instructions="Does the customer request a refund?",
        ),
    }

The hybrid nature of this thing is not a detriment, nor does it make it an LLM.

How is this not a frontier model? It's bleeding edge in its own niche. It's not a frontier LLM; however, applicable to many of the things people use LLMs for.

It's nothing like a traditional LLM and so should not be compared to one. It's a heavily constrained, tiny model that can only produce a probability score or a yes/no answer over pre-defined selections. It has no long-context capacity.

I mean, imagine comparing this thing to Astra, it's hilarious. They don't even tell you what the max input size is, and they only allow 10 possible answers to choose from for the Choice mode. It's probably like a 1billion param model. They say it's "not small", but there's zero reason to believe that.

I suspect someone will be able to recreate this within a week by piecing together open-weight models.

> It's nothing like a traditional LLM and so should not be compared to one.

Frontier LLMs are expensive jack of all trades. You can absolutely compare them to purpose-built tools on any domain they touch. Engineering is all about assessing tradeoffs.

Well, not quite a week: https://x.com/harshagundal/status/2100044305536889015 - apparently it took him 2 hours.

Large language models are not the only type of model.

It is frontier in the sense it is exploring an unexplored domain. I do agree on questioning the comparatives though. Speed/cost is indeed relevant for problems that can be framed as structured decisions only. The question is, would defining a structured decision model be a structured decision model itself? This would significantly increase the application domain.

A calculator is faster and cheaper and higher quality for the use cases I bind my task space to.

It's therefore misleading because they make people believe they're as general purpose as LLMs.

I feel like the power of the approach presented here is that it gives a model a proper "language" to describe computations directly vs moving tape silliness.

I foresee this to be the path moving forward - giving AI models understanding of the computation directly(as well as compositional rules) This feels like a short path towards total software in many areas.

Agreed. It's a wildly dishonest presentation of their product from many perspectives, which is a shame because it might actually have some good use cases.

The comparison between LLM speed and Jev speed is misleading, because they're using autoregression to generate all of the type names, all of the schema, etc. A closer comparison would be if the LLM was purely outputting the raw numbers. Even then, comparisons to LLMs are pointless because you could train a transformer on the same sort of task that Jev is doing and get even better performance yet again, and a smaller model. I suspect this is some form of stripped down diffusion language model.

You really have to do a lot of hand holding here, and map out your problem space manually, and very carefully, to get any sort of accuracy. For example:

> Keep each Score to one dimension. If a description says “punctual and smart and experienced”, the question is measuring three things, and an input that is high on one and low on another can’t be placed. Confidence drops and the score means less. Split it into one Score per thing and combine them in code

If you don't perfectly represent the distributions of possible answers then you'll likely get garbage results. As far as probabilistic state machines are concerned, I'd say creating the distributions of possible answers, and their hierarchy, is the actual hard part.

One of their examples is:

- "state": "I have asked three times now. Can I please just talk to a real person?"

- "Is the customer asking for a human agent?"

Imagine the users request is: "I want your human agent to call me tomorrow at 5pm."

Human conversation is fuzzy, getting useful reliable results out of this is going to be a challenge. Of course, you could add follow up checks like: "Do they want that now, or later?" -> if later -> "Do they want that tomorrow, or the day after?" and so on... But now you're building an LLM out of if statements. I am skeptical of whether this model has much utility for fluid language interpretation - I suspect it'll only be useful for scenarios where you've tightly constrained the answer space but want to use fuzzy language to describe it. Like:

- Question to human: "Would you like a support agent RIGHT NOW?"

- Their response: Yes | Yeah | Mhmm | ye sure (any possible yes signal)

Model input: "Did they ask for a support agent?"

Still... a tiny LLM could accomplish this sort of thing without problem. And that doesn't stop someone from saying: "No, not right now. But tomorrow." - and the tomorrow would get missed. I think this is why people haven't really tried this approach much already.

Also their Doom demo is on structured state, not on images. Meaning, the enemies must be being served to the model as coordinates (or the exact angle of projectiles that hit the player), otherwise it'd have to scan every pixel of the 360 degrees to know whether an enemy is in front of the crosshair or not. You can see from the map below that it's also choosing travel checkpoints/destinations through walls. So they've severely cooked this to make it look far more capable than it is in practice, and any speed advantage that is offered here is not factoring in the shortcuts it is taking, the training on the map, and the fact that it can cheat because the structured state it is using is not bound by obstructions.

Here is their docs by the way: https://docs.typesafe.ai/ - so you can understand how it works.

Has LLM become so synonymous with Generative Transformer that other high-parameter count models that interpret language need a different name?

For all we know this might be a non-language-generative transformer e.g. a transformer where the decoder produces confidence scores rather than language. Please provide more likely architectures if you know them, I'm genuinely curious.

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 looks similar to BNF schema already introduced and implemented few years ago: generally speaking - it limits the next token that is allowed to be generated, probs are drawn from a subset tokens.

(tbh, I'm not sure why it didn't pick up as a more standard interface to LLMs, as it made a lot of sense back then, and now.)

AFAICT it is the same interface as you describe, but the underlying inference algorithm is fundamentally different, hence the speed gains. There is an application I am currently working on right now where this typed output predictor is the performance bottleneck. I'd be very interested to see how this performs.

Yeah, I thought about constrained generation as well. I've actually done something similar with local models before. And you can even get a "confidence" score by looking at the logits (something along the lines of logprob("YES") + logprob("Yes") + logprob("yes") - logprob("NO")...

There's also a cheeky "one of the models hallucinated a link" in the wiki jump example that most likely could have been avoided by properly using grammars. You can setup constrained gen so that only valid options (say from a list) can be outputted. Their own inference lib likely does that. So comparing to one that doesn't is a bit cheeky.

That being said, after a brief look at the site I could see this working. Especially if this can be ran locally, the speed and cost can enable some workflows where you have this as an "overseer" layer over say a cli agent. After each step you run through a list of "questions" ("is the task completed?" -> yes -> "does the edit touch files it shouldn't" / "does the edit follow our code writing policies") etc.

edit: extra points if the "question" rubric is also generated by a higher abstraction model. Say "/goal Build out auth" -> generate_rubrics(goal) -> "Is auth implemented on all endpoints" / "Has code touched anything else than auth" / "is this following the best practices" / ...

It did, no? If you send a JSON schema to an LLM endpoint, at least for OpenAI and Anthropic, they turn it into a grammar behind the scenes and use it for constrained decoding.

> 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 if the other models could do that in real-time.

In a case like this it still seems more appropriate to encode that data in tabular form and use a tabular foundation model

Forgive my ignorance. Tabular foundation model?

Well, the whole point of the demo was showing that transforming from game state to text to action is so quick with Jev that it can play in real time.

So no, the other models cannot play in real time. Hence why this is interesting.

I'be interested to see how they get doom frame json representation...

> 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 level (factoring, controls) by working within the constraints of an actual language specification and supplemental domain model. I don't know, though -- maybe that's too deep and complex.

Jev isn't new. There has already been an open source project for a year, now called Laya: https://laya.convaiinnovations.com/

Wasn't really till seeing this home assistant demo they have (https://www.loom.com/share/18c4dbcf8db546dfb2d7f2ef018e78e4) that the value really clicked for me.

Seems really cool.

Guess I'm a bit less impressed seeing that for some of the more intelligent driven+action work -- splitting requests in the video -- they had to kick out to an anthropic model.

Haiku, to rewrite a sentence as two discreet commands.

I agree that it was notable that they delegated to an existing LLM, but I don't think it detracts much from the value proposition (not yet proven) of their demo.

Agreed but is it much easier to deal with if you need to have all of these sub processes integrated? How does one know when you need to reword a request? What if Anthropic then has a type error, then debugging that just got harder.

That's fair, but it highlights how this would actually be used. It doesn't really seem like a competitor to other models but instead a way to make these real systems more enjoyable to deal with.

I wonder if they could have accomplished this with spaCy

This video makes a better job at explaining what it is about vs the marketing ones. Thanks for sharing.

This is very cool. However I don’t really want to bounce all my home automation commands to the cloud. I hope there will be an open weights approach one day. I’ve spent a lot of time setting up my local only home automation system, it would suck if it didn’t work during an internet outage, and also there are obvious privacy problems.

GLiClass is performant, and its zero-shot classification scores are in the same ballpark as the Terra-level results Jev points to.

https://github.com/knowledgator/gliclass

Thanks!

Rereading some things and because there's no official benchmarks, I misspoke about the ballpark comparison., but the open model's still a useful foundation to work with

thanks, it's definetly relevant

Open weight or even closed with a proprietary license, I'd definitely like to be able to run it locally for all sorts of physical world stuff.

How many connected devices do you have? How often do you add/remove some?

I feel like this whole scenario could be solved by a very ugly switch case generated by deepseek for 0.02ct.

There's already an open weights one mentioned up thread: https://laya.convaiinnovations.com/

That's good.

Side note - just like most people don't need an intelligent personal assistant to manage and respond their emails and book their flights, most people also don't need smart homes. Century old toggle switches are more than enough in a 3 room apartment or 5 room house unless you have a mention.

My primary beef with smart home (having tried it) is that every person that visits your home ends up confused about some element of it. A light switch that goes up and down is universally understood.

My smarthome has regular switches and wifi.

There's no reason to not do both.

Also a quick NFC sticker in each room taking you to a small HTML site containing settings (temp, ventilation, lights, shutters, setting a alarm by the lights) has been golden.

No one wants to: download Shelly app + AC app + look for ventilation IR controller + figure out how casting works for the TV + figure out how to use the Shelly app to turn lights into an alarm. It's too much friction for little gain. But a quick tap? Great.

But tapping your phone on a NFC sticker bringing all those controls together per room in stead of per category (all lights in Shelly app. Person in room #1 has no interests in the lights in room #4 at the same time.).

IF you tap it while not on Wifi yet it just tells you to connect to Wifi. :-)

One "all house" sticker next to the front door allows any last person leaving or first person entering to put the entire house in active / idle mode.

Works wonders. And as soon as local AI is quick enough the stickers will be a microphone!

That's clever and all - solid setup, good work. But I still think you either overestimate the average house guest or have particularly savvy/young house guests.

Most people think it's cool but use it once.

The regular-ceiling-lights-as-alarm service gets positive feedback. Weird how smarthome companies never market that, seems easy win.

Not sure I totally understand what you mean by light alarms. Do you just flash ceiling lights to notify the user of something?

No they gradually turn on from 0% to 60% to simulate sunrise. Accompanied by the shutters slowly opening, if you want.

Then after 20 min an actual alarm goes off, just in case.

I have a hugely integrated smart home. 2 rules; 1. Everything can still be manually operated in the way people expect it to. 2. Local only.

Zigbee, Zwave, and Bluetooth (although a bit to a lesser extent) are easy mode of you want local only. They're not IP-based networks so no Internet access to begin with.

Bluetooth--local only but setup can be a bit trickier since they don't automatically form a mesh. You may need to configure relays if your existing devices have that capability or buy dedicated relays

My primary beef with my home built in 1963 is that half of the switches aren’t wired to what you would expect and I have no attic or basement with access to make it easy to rewire. Home assistant and smart switches are a godsend because I can repurpose the existing switch boxes to actually do what I want them to do with the added bonus of being able to control them remotely from my phone.

My switches go up and down just like they always did. I can also toggle them from the app (ok so sometimes the switch is up and the thing is off but multi-way switches have been like that forever), track their on/off state, and use them as event sources to do other things.

There is a single magic switch that does "extra" stuff and that one's in the master bedroom. All of them except that one fail open and continue working like regular switches if Home Assistant is unavailable.

The livingroom has a Shelly dimmer that replaced an analogue dimmer and it also turns 2 lamps on and off.

The basement switch was wired to a single bulb and it triggers an event that powers all the smart bulbs in lamp holders spliced into arbitrary circuits on and off so all the basement lights work with the existing switch instead of just the 1 physically wired to it.

I think there's plenty of dumb things you can do with smart home tech but there's also tons of subtle and convenient enhancements as well. Phone alarm emits an event that opens the curtains and turns on a lamp is also a nice one.

Having a dashboard that shows whether (many) things are on or off and their power consumption is super handy. Dropping an Emporia circuit power monitor widget in the dashboard shows if the washer and dryer are running (a little bit of power, a lot of power, a lot of power + a little bit more power)

Need is a big word. Convenience is also a factor.

We have 8 light buttons in our living room/dining room/kitchen space. It is very convenient to us that we have 1 button for turning all of them on/off at the door to upstairs (at night turn off all lights and go to bed upstairs, in the morning come downstairs and turn on all lights) - but also have 1 on/off button near our back door for when we leave/come home.

Next to that: on/off toggles a schedule where the lights are bright and cold-ish by day, and low and warm by night without us having to manually adjust each light every hour or something.

Again, need is a big word. But it's very convenient and pleasant.

I personally just find it convenient, even in my 3.5 room apartment. Though I do have switches that work.

I once made the mistake a buying a light bulb that needed a phone app to turn on and off.

That was my light bulb moment.

That really helped figuring out what this thing does, thank you.

That's a really nice demo and way more helpful than their website, thanks for sharing it!

Thanks, much clearer that way. The speaker is unusually eloquent and to the point for a very nerdy demo, they should put that on their home page.

Its all fun and games till it turns on blender instead of shutting down garage door

This, combined with contracts, could make a lot of things so much fun now!

For those who don't know (which is probably everyone but me), I ported the design-by-contract pattern in Python and combined it with LLMs. This was early 2025. I originally wrote about it here: https://leoveanu.com/2025-03-01-dbc/ . Contracts are a core feature of SymbolicAI ever since. The community seems to have loved it too (https://news.ycombinator.com/item?id=44399234).

I think I'm starting to glimpse the implications and it's gonna change agentic workloads if it holds up to scrutiny. It's too early for me to tell anything other than jot down some rough thoughts.

In short, you get blazingly fast semantic branching you can use in control flows. For contracts, I can now directly take the data model that you have to design and convert it into Jev's expected format. Or I can use Jev for semantic branching in postconditions.

If my understanding is correct, that should be doable, but I need to think more about it. It could be that with Jev I can finally “compile contracts” and better chain them into workflows, which is something I always wanted but didn't know how to do properly.

Eager to test. On the waiting list.

love it. send me an email and i'll try to get you moved up on the list? nathan@typesafe.ai

A lot of their screencaps show an interface that screams Lisp Machine/Genera...I can't find any actual references on the site or in the docs, but the visual nod feels intentional. (disclaimer: I am not a developer, just a devops engineer who nerds out on computer history)

Why wait, if there is already a similar implementation a year ago, and it's fully open source: dataset, paper, repo etc ... The idea is not new.

https://news.ycombinator.com/item?id=49736660

This is a very promising idea - a model that takes arbitrary text input (which can be a complex json), plus a set of questions (yes/no, multiple-choice, or score) and quickly (milliseconds) and cheaply ($0.042/MTok) answers those questions.

Unfortunately, none of this is explained in the announcement, but the documentation [0] is pretty good.

[0]: https://docs.typesafe.ai/concepts/how-to-build-with-system-o...

API example[0] makes it clear how it'd be used:

  from typesafe_sdk import Choice, Noul, Score, TypeSafeClient
  
  with TypeSafeClient() as client:
      response = client.system_one(
          state={"document": "I was charged twice. Please fix this ASAP."},
          questions={
              "billing": Noul(instructions="Is this ticket about billing?"),
              "tone": Choice(
                  instructions="What is the customer's tone?",
                  criteria={"calm": None, "frustrated": None, "angry": None},
              ),
              "urgency": Score(
                  instructions="How urgent is this ticket?",
                  criteria=["can wait", "this week", "today"],
              ),
          },
      )
  
  print(response.nouls["billing"].noul)
  print(response.choices["tone"].choice)
  print(response.scores["urgency"].score)

[0]: https://docs.typesafe.ai/sdk/python

An example with manual combinatorial exclusion in “not_for” field made me cry, this is a wild hybrid of code logic, textual definitions, and AI blackbox. It’s a cool idea, but the “glue” layer is too boilerplate-ish

The concept isn't new: zero-shot classification using e.g. Facebook BART has been available for years. And there is already an open source model Laya: https://laya.convaiinnovations.com/

> 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 to soundex/ metaphone rules (which are themselves a ginormous set of rules for letters and letter combinations which may or may not result in the same sounds, hand-coded as a huge if tree by a linguist not a programmer)? What about their relatives, do they match by the same rules? Should we incorporate domain knowledge about local naming customs? Etc etc.

I pointed a coding agent to this problem, and it aggressively started coming up with complex scoring rules and testing them against real datasets. Which led to sort-of acceptable results, but it still missed lots of cases which were obvious to a human, and had false positives which were obvious to a human. Which I could trade off, and slightly improve, with more back and forth with the coding agent.

Pointing a good LLM to all the information about two people, would of course give great results. Maybe even better than human judgment. But I can't do that for 100000^2 people, it would be too expensive in all sorts of ways. I need a fast, reliable scorer. I could maybe train an embedding, but that would be a huge job and where would I get the quality data?

You need blocking!

Fundamentally this is an entity resolution problem. An LLM can score pairwise really well but scoring all the pairs would be insanely computationally difficult.

If you can constrain the set of potential matches up front by querying the dataset for things that could be matches it gets a lot more tractable to use an LLM for this.

Are there any heuristics you can use to reduce the search space? You mentioned soundex transformation and maybe prefixes of last names could work? Even if you get the number of potential matches down by a few orders of magnitude this gets more reasonable!

Check out https://moj-analytical-services.github.io/splink/index.html

The coding agent was pretty good at coming up with heuristics for matching - even more than the dozen I suggested from domain experience. And it used some of them sensibly for blocking, too. I'm sure I could get it to perform a little better and a lot faster with more agent wrangling. I did consider using the heuristics just for blocking, and letting a local LLM do the actual evaluation, but if Jev or Jev-like models work as advertised, maybe we can have the best of both worlds.

Thanks for the link, it is an interesting topic.

There's also two other important limitations to using an LLM and just providing it with pairs of records.

It does not know enough about the records in the context of the overall dataset:

- what is the data quality and to what extent do we expect a errors in some fields - how unusual are certain values such as names in the context of the dataset as a whole, e.g. some names would be very common in some countries but rare in others.

I've written in more detail about this here: https://www.robinlinacre.com/fellegi_sunter_accuracy/

Another great article, thanks. I thought a lot about that too, and basically realized the same thing (this wasn't the coding agent), that frequency in the dataset mattered a lot for how useful a match is, and made frequency lists from my datasets, both on how common the literal names were and the phoneme-reduced names.

But I'm worried about that "bitter lesson" the TypeSafe CEO refers to, and that we're retreading the steps of natural language processing and a lot of other fields, trying to come up with clever rules, when the rule-based approach simply never gets good enough. I need matching to be good, it's absolutely central to genealogy, and I've seen the damage blindly linking by hand-crafted matching formulas can do.

I agree, and I think LLMs can potentially do a better job than more conventional methods, so long as they're provided with enough content.

Another aspect of context that could be relevant to your work may be how people are nested within households. In some record linkage scenarios you can run a linkage on household membership and use this to make person linkages more precise. I imagine an LLM agent could also use this information effectively if it were explicitly provided, but sometimes they miss logical leaps like this

Maybe you need Prolog instead.

So in theory you could feed it incomplete text, and then ask it for the probabilities of what the next character could be?

If you provide it an AST of the english language, yes.

you could, but it the model is not optimized for text

this is complex, but generating text is highly complicated and requires mode dropping to make long cohesive text

I think the joke here is getting missed

Strings trigger us

was wondering same

Or a partially completed song, asking for the next note. I’m not sure if you’re joking, but using it for space constrained next token generation within a grammar sounds like a really neat use case.

Feed the generated note back into the input for the next query and you have ... autoregression?

After much fumbling around with prompts and evals, this is exactly how I am using LLMs in production, to narrowly make choices and return structured data. Any deterministic work gets pulled out of the prompt and my goal is to narrow the model output to be as clearly defined and as minimal as possible.

Jev's focus on structured I/O and confidence scores are game changing. If this does at all what it claims, I think this is going to quickly become the new standard approach for agentic systems.

we hope so! the bigger hope is to not just eat LLM market share, but to allow for people to use AI much more in the inner loop of software

I'm sure you've thought of self-driving. How does the model work in that space?

This smells like a tool a more broadly capable LLM would take advantage of extremely well.

Great question! Yes, this works much like the doom player. Sensor data (LIDAR, velocity, etc.) becomes the state. You use the score primitive to operate the controls ("What level of braking should be applied" 0: None, 1: just slightly slowing down, 2: there's a suspicious cat on the side of the road you don't trust, ...

Full disclosure, I am not they :=)

But the real problem in self driving isn't the decision making but object description. That is, computer vision if with cameras.

Decision making isn't that of a bottleneck I suppose.

99.9% of the time you are right, but in the long tails (construction works, major road damage, temporary road markings that contradict permanent signs) I could see this kind of architecture being useful.

Curious what your use case is if not confidential.

Not confidential, but not super relevant, as this is something I have learned the hard way over the past year across various projects.

A lot of people have become prompt maximalists, asking for complex multi-part solutions or dynamic workflows in a single prompt. You can get this to work sort of reliably with frontier models, but without much confidence or clarity where things might break in practice. My goal is to strip out as much determinism as possible from prompts so the LLM only needs to handle a narrow, well-informed decision, like "Pick one of these three things" and build around the answer. Sometimes you need to fill out a whole JSON payload and LLMs really actually suck at manipulating and adhering to JSON. They do ok now because labs have put in a ton of effort on making harnesses play nice with structured data. But it comes at a high token and context cost because under the hood I suspect the model is churning invalid text repeatedly until it gets around to passing some internal validation.

Example I have worked: Personal delivery app, that tracks packages from various senders using incoming emails.

I am using the single prompt approach with GPT5.4, which is free, but it’s not reliable. Using Jev I’d decompose the prompt into a bunch of smaller questions, then I’d combine the answers in software. I’m super excited to try Jev out.

This is a really cool use case!

Side note: it took me more time than I would like to admit to realize that Diogo Almeida isn’t a satirical version of the name Dario Amodei

It wasn't until the demo videos that I realized the post wasn't satirical.

That would have to default to Wario Amodei.

I feel like should be Cario Amodei. The D to C flip a rotation of the M to W flip

Flip both, Cario Vmodei.

In lower case you’d flip from d to q (or to b).

Well now I’m rooting for them!

thought the same lol

This sounds good but so far all claims just sound like marketing terms. I'd love to see real proof. e.g. "RLCD" and "parallel sampling" have nothing to back it up.

also "70-500ms vs 3-329 seconds" are apples-to-oranges unless the LLM baseline is doing comparable work (e.g., long chain-of-thought). If Jev is skipping generation entirely for a narrow structured task, of course it's faster.

Nonetheless i want this to be true, so I'm looking forward to Jev

Edit: I really have to say that I like their manifesto https://typesafe.ai/manifesto

They have various benchmarks, e.g. how much time it takes them to do wikipedia page -> page games. Jev seems to take the same or fewer hops but in ~10x less time and for ~10x less money.

It's totally reasonable to compare against LLMs doing chain of thought if it gets comparable performance.

It's not an LLM though it's a frontier model on structured data

Did you see the video where it plays Doom, it made it click for me

BTW it was not multi model playing doom, it was passing structured input and getting structured output. Its not what I thought: frames of video passed and real time game play.

so what? put an LLM on Cerebras and get its responses faster, and put Jev on Cerebras and gets its responses even faster

i've seen it play minecraft as well, what im not sure here is what is the thing that produces the JSON and keeps track of the objects

can jev play battlefield six for example

love that you love the manifesto! letting the first batches off the waitlist now, but we do have some early users describing their experience (https://x.com/danshipper/status/2099947471518474522)

Congrats ! Really excited for the team.

> If Jev is skipping generation entirely for a narrow structured task, of course it's faster

I think this is reasonable if people are actually using LLMs to solve this type of narrow structured task, which they are. The evidence is that every LLM provider has some method of forcing the output to conform to a json schema in their documentation.

> I really have to say that I like their manifesto

Their manifesto: "you only build on top of it if it's trustworthy." - the irony of this while putting out the most misleading, dishonest marketing campaign I've seen in months for their first public appearance doesn't exactly scream "trustworthy" to me.

What do you find dishonest?

Comments under the x post.

> Input tokens: $0.042 / MTok ($42 per billion tokens).

> Output tokens: FREE (too cheap to meter).

Insane. The video demos are really compelling, in particular the speed.

> Structured outputs slot into ordinary software as fuzzy decision rules: classify, route, score, extract, or branch where hand-written logic is too brittle. The surrounding code constrains their freedom, making them easier to compose into reliable systems.

I buy this vision. A lot of LLM integration I see these days is ultimately exactly this. OpenAI-style structured outputs works decently but this would be a great improvement in cost, latency.

thanks a ton!

constrained decoding (OpenAI-style structured outputs) make models dumber unfortunately - the short+dense version is that simply masking logits is insufficient because if ever a model was assigning probability to an invalid token, the model is by definition confused. you'd be better off erroring IMO

I would love for things like this to be accessible via hubs like open router or AWS bedrock. It's hard to justify adding new model vendors directly with all the heightened concerns about privacy and security, but if bold new capabilities are added to a centralized already-vendor like AWS, technical people can adopt them without going through a whole compliance/purchasing/vendor review process. And an extra middleman tax is well worth it when the cost savings of the model itself can be one-two orders of magnitude.

The thing is, in this climate it's hard to believe such tech will remain secret for long.

So, assuming this is not vaporware, this would raise the tide for everyone because it shows what's possible.

Isn't openrouter the exact opposite of caring about security and privacy?

I guess you can choose your provider still? But isn't the point that the lowest bidder is doing inference?

You can set privacy requirements and define an allow list. To me the main value prop is that I get one bill for all models and can quickly try new models without signing up anywhere or changing my code. Oh! Also you can pass an array of models and if the first provider is down it automatically falls through to the next provider. More useful than it should be...

That's still ultimately privacy by contract (where you have to trust the inference providers to uphold their end of the deal), rather than privacy by design.

I always setup guard rails so that only zdr providers are used.

They don't like adding stealth startups :(

I think the trouble is that Typesafe APIs don't fit into the normal OpenAI-style API that every other regular LLM provider users. You're not just providing unstructured text and getting unstructured text back. It would take a different request and response format than every other model on Open Router. Though you could shoe-horn it in some way, it'd be hacky.

But agreed it'd be very useful to see it deployed on other hubs, and it seems worth it to provide the bespoke API format. Perhaps Typesafe's API will end up becoming the standard for a new type of structured model, the way OpenAI's API did.

It can be shoehorned to work with OpenAI's newer Responses format.

JSON or XML in with json schema for the response?

https://developers.openai.com/api/docs/guides/structured-out...

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 because my user is affected by regional privacy laws (without hallucinating)? Jev.

I need to surface the 3 funniest media comments that match the user's sense of humour? Jev.

Wonder if this could lead to better recommendation algorithms.

More like:

I need to ...? -> Open-weight model.

I'm sure someones working on this as we speak using an open-weight LLM base (Qwen or something would be a perfect fit).

This sort of task is a perfect fit for a very small model capable of semantic parsing. You can get away with a LOT less parameters without all the autoregressive generation and long-context reasoning.

you don't say - https://huggingface.co/harshatheg/Qwen-2.5-1B-RLCD

Crazy, looks like this was just published a few hours after the TypeSafe post!

https://x.com/harshagundal/status/2100044305536889015 tweet by the author

https://xxcancel.com/harshagundal/status/2100044305536889015

For the privacy use case, there's Stanford NER and wrappers around it. That is likely cheaper and faster than Jev (it'll run on CPU)

I'm trying to parse it down to what we had before vs what is new here.

We already had encoder models that skipped text generation for giving us a numerical output that could be computed as a probability. we also got no hallucinations and faster inference for free there. So we already had

1. "unstructured state in, probabilistic decisions out" 2. "orders of magnitude faster and more efficient"

What was hard there was to train the model head without ML expertise, and considerable amount of data.

This seems like this is a democratization of those encoders? The addition over existing encoders seems to be coming from being able to specify the output shape (up to a cardinality of 255). It is unclear to me if this is possible using Jev without additional labels for fine-tuning.

If so, that is still very impressive, but I think the faster inference and 0 hallucinations might come for free, from it not being generative.

I can't see how this is different from a fine tuned LFM2.5 encoder

>What was hard there was to train the model head without ML expertise, and considerable amount of data

Which has become a lot easier the big, expensive general purpose LLMs

Some of the use cases seem interesting but a lot of them also seem like the "old" stuff can do it faster/cheaper

There's a whole lot of information on this page that doesn't tell me anything about what this actually is. Can anyone spell out what the architecture is here?

They claim it's not an LLM, which I read as "not an auto-regressive token generator". I assume they are still using a transformer, otherwise they would be talking about the thing that's not a transformer, instead of all the fluff on the linked page. But they emphasize parallel generation, so is it like a text diffusion model?

Sounds like its essentially a generalized zero-shot classifier that takes and option set at runtime and works on unstructured inputs.

you pass in your "prompt" and options (described in natural language) that it can respond with, in addition to your input. it gives back that option set with a probability assigned to each one

yes and can do many of those in parallel

I would guess a tiny stripped down text diffusion model. It only has 32k context, and for choice mode it can only select from 10 choices.

> and for choice mode it can only select from 10 choices.

Rip there goes my excitement. I have a task that something like this would be great for but the list of options is a zero or two larger than that xd

you can still chain them

They said that it works with up to 255 options.

I could see this being fantastic for classification tasks. Last year I shifted from using LLMs for bulk data classification tasks (1M transcripts) to generating embeddings and categorizing based on cosine similarity. It saved a ton of costs and time, but wasn't as accurate as LLMs. This seems like it can give me Terra-level classification ability with the cost/speed I need.

yup just joined the waiting list with a very similar use case in mind

This ought to work better than SpamAssassin, I'm sure

I think this is a great direction -- for some kinds of users. And this makes me wonder if the 'vs' framing is misleading.

Yes, I think it's a mistake that many organizations are cramming LLMs inside of automated pipelines where the extreme generality/flexibility of the model is at odds with the fact that you're using it for a very specific task that gets repeated over and over, and needs a very specific structured output to be successful. But specifying your task carefully (as well as deciding what counts as your input state representation etc) seems like a form of programming. Something (a person or a model working in a relatively unrestricted way) will need to produce a configuration/specification for this system.

So rather than Jev vs Claude I imagine that using Claude/ChatGPT/whatever interactively to define / refine your Jev config which then runs in prod might be the happy combination?

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 are asking and have a grammar to constrain with.

Great to see this and I hope this is a lot on top of what is already openly available.

This is basically a zero-shot classifier that can accept raw text (or structured text) as an input, and is able to classify that text as accurately (they claim) as a frontier-level LLM. I have workflows this would be useful for, looking forward to it showing up on OpenRouter.

exactly right!

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 intelligence Pareto curve.

Image in documentation: https://mintcdn.com/ts-docs/aFVnpmCIX68NpsV1/images/ai-prime...

LLM seems to have become synonymous with Generative Transformer architecture.

While this model may share much with GPT-style models on the encoder side, it clearly has a different decoder architecture. So is a high-parameter count language model an LLM even when it doesn't have a GPT-style decoder? The definitions are in flux.

Yeah, it must be an LLM for some definitions of LLM

It seems to take two forms of context input: 'state' and 'questions'

https://docs.typesafe.ai/concepts/state

> State can be as simple as a string

> State can also be a JSON object or array containing related context, examples, and other information that helps the model answer the associated questions.

> The state contains the content and supporting facts.

The state seems to be schemaless, while the questions determine the output schema.

This is a good product but the naming/branding is pretty unfortunate.

Typesafe.AI sounds like some typescript/structured output type of tool…

What even is “system one” ?

IMO the product/tech is really there, just needs better communication.

I mean, it's a structured output model that (apparently) can't hallucinate. I don't mind the name.

It can't hallucinate, but it doesn't mean it can't make wrong decisions. Just because it adheres to a specific output format at all time, while LLMs have the output format at their mercy, then the claim of not hallucinating is made technically true.

I think that this specific part is not super interesting if your harness just recovers from invalid LLM outputs.

The latency and cost - yes, those are super interesting.

You can get rigid output format from "classic" LLMs https://docs.vllm.ai/en/latest/features/structured_outputs/ though model support is limited.

Would like to have something like in the original post but open weights.

The model can't reason comprehensively (e.g., like Sol XHigh would to solve a complicated problem), but it's designed to be able to answer anything a human reasonably could quickly and intuitively, i.e., system one thinking: https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow

I wonder how well it can play chess, or go.

I was wondering the same! So I asked Astra to build me https://jev-chess-master.vercel.app/ where you play against Jev AI as chess player.

You play White, and Jev plays Black. Rather than asking an LLM to generate move strings or JSON, the backend feeds all server-validated legal candidate moves into Vercel AI SDK's experimental_evaluate(). Jev picks Black's move and outputs its probability distribution across all legal candidates in a single forward pass (~300ms, ~$0.00004/move).

Github link: https://github.com/qibinlou/jev-chess

Give a try and let me know your thoughts! I am having lots of fun coming up with different chess strategies for Jev to try out.

Thanks for the demo. It plays very bad and blunders pieces on every move.

I am horrible at chess and easily won. It does play very very badly.

But it is essentially playing bullet chess right? No time to reason and is basically forced to go with it's knee jerk reaction. So maybe it plays reasonably against another average bullet chess player?

This is an instructive demo and makes me pause a bit when thinking about where I would trust using a classifier like this. Also there is no way to fine tune it right? So you are just left to its interpretation of the classification schema?

Building classifiers is hard and forces you into thinking about your problem space and your comfort level with type1 or type2 errors. I fear this will encourage sloppy work because all those decisions are black boxed.

Not like we were in a utopia careful ML applications before this.

I had a different initial confusion - it seems this company has no relation to the company formerly known as Typesafe https://en.wikipedia.org/wiki/Akka.io

> What even is "system one"

I definitely agree it's underexplained in type safe.ai's materials.

I have to assume it's a reference to the fast, heuristic, intuitive "system 1" process in humans, as opposed to the slow, procedural, reasoning "system 2".

This theory is recognized, among others, in Daniel Kahneman 2002 Nobel prize on Economics.

You are correct, they state that on their website.

Struggled to understand the use case till after i came across this https://eu.36kr.com/en/p/3991165197188099 article, which referred to them as the "referee for agents."

https://x.com/completeskeptic/status/2099925682726002904?s=4...

The doom demo is quite cool

Direct link to the Doom video tweet:

https://x.com/completeskeptic/status/2099925687465570372

The doom video is also in the article itself (headline: "Doom").

I suppose this is the same video as the one from the parent comment, but I don't know for sure - I don't have a twitter account and the above link doesn't work for me.

I linked to the tweet that has the video because if you are not signed in you cannot see the whole thread of tweets.

I can see the individual tweets in the browser while not signed in though.

The doom demo is also in the article, for anyone that doesn't want to go to X.com. :)

Link to a raw MP4 of the video, from the parent article: https://framerusercontent.com/assets/rlL7ImEbISFoYt3IJEHHfvj...

It's in the parent article under a section named "Doom" in case that asset URL ever changes.

But when their system is given the instruction "do not fire, simply dodge" - it doesn't "simply dodge", it actually gets close to the fleshy pink demon rather than keeping its distance. Or am I misunderstanding?

i think its just telling the model that it cant output a shoot action

I'm not sure the authors realize this is way more than "just a cool demo": if this holds up, it's going to be huge for game QA work.

Instrument your game to output properties of entities near the player and the output is the various control inputs - moment to moment gameplay gets solved. Maybe augment with a tick-by-tick controlled stepping mode if particularly twitchy - an LLM can take care of the higher level reasoning then.

yes; and it could also be used to spot unexpected behavior such as inputs not registering due to rare bugs

Doesn't this mean Jev can be used to drive a car as well?

lol! they reinvented aim bot for cheaters.

what is the…epistemic status, for lack of a better way to put it, of the probabilities? what do they mean? what (probabilistic) guarantees do we have about, say, the responses to

- is the capital of france paris?

- it is august. is it raining in paris?

(forgive the examples; they're probably not semantically the sort of thing jev is trained to work on. but i figure the point translates to various kinds of questions that come up in "inner loop of agentic pid controller" contexts)

a normal text-generating model if asked to produce a number will also do that just fine. i assume in jev's case it was actually rled to essentially learn to express priors over things using its implicit world model, which definitely ought to help, but can we say more?

It doesn't produce text -> it does not hallucinate statements

This is kind of trivially satisfied and they make this sound more extraordinary than it is. Also I suppose it can still hallucinate in the sense that for out-of-distribution data it will give miscalibrated probabilities.

Anyway a great step in the direction of calibrated AGI

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 of data (the recipe is relatively simple, and likely targeted on distillation of logprobs / confidence of a bigger model)

Notice how cost is ONLY for input tokens as output is merely numbers (few tokens) because input could be huge (questions and options).

At 0.042-per-million price they have, Astra estimates the model to be 3bn parameters.

One could replicate this by post training Qwen 3.5 2Bn. I expect people to do so soon!

How does Jev compare with encoder language models like BERT/RoBERTa, which could also be used for classification?

Woof, that page is hard to read. I don't understand what they've done to the way text is rendering but it's not great for my eyes.

If you zoom in (especially on the large title), you'll see that the text is a semi-transparent gray with a black internal outline. It seems like all the typography is SVG-rendered. Actually insane. I've never seen this before. Not even the most vibeslopped websites have that.

I don't know if they changed it since your comment, but it's all just text to me

It could be used for coding if you gave it an AST.

If you work at TypeSafe please try this.

Side note: This is probably how LLMs would perform with better encoders and next-latent prediction, so eventually those will beat this architecture out. Still amazing though.

I've implemented tree-sitter in pi before, and while it works, I have no real proof it saves me tokens, or is more accurate. I think a better implementation is a model that's trained for AST's, not just "use tool, see what happens".

I'd love to do research on this when I have the time.

Cool project!

That's what I was insinuating through "better encoder"; the model creating more efficient representations of ASTs using something like JEPA

I saw the CEO reply elsewhere in the comments to some other question. Maybe he can shed some light on it. My gut feeling is that this is non-trivial and they did not get this to work (yet?), otherwise I can’t come up with a good reason as to why they would not demo that as I assume half of the crowd here (myself included) would line up as customers.

Yeah it would be quite trivial to try and implement an auto regressive AST generator for STLC with Jev provided that you had bounded variable names and integers.

As you said, if it worked, they would have demoed it haha

the hard part for coding is actually state engineering (e.g. getting your dependencies in context) - we haven't even tried it yet (because my philosophy is we should automate the easy tasks before the hard and we've been working on getting the model smart on the former)

we do think there's a lot of potential though and do want coding themed releases soon

I could see Jev being great at finding key symbols in codebase before a code generation/code review task. I sent you guys an email (to hello@) about using Jev in Code Review for www.ellipsis.dev.

im not seeing it.

youd ask it to pick a location on the ast to add something from the grammar?

i dont see how this stays confined well enough? make a new output space every time? does that end up auto-regressive?

Just trying to validate my understanding - so this is a Large natural language classifier, a general purpose or zero shot classifier ?? it can recognize entities, can classify text into some pre-defined classes ? right? or did i miss something amid all the marketing terms such as system one or RLCD or whatever??

Looks like it.

A general AI classifier that can be set up easily and used to classify anything… but with probably lower quality than a purpose built one.

If you are wait listed and eager to try this, I will save you some time. Try this model - https://huggingface.co/MoritzLaurer/deberta-v3-large-zerosho... . They are using something similar under the hood. The comparison to LLMs on their blog post is definitely shady.

Example to try on this model:

customer complaint - My credit card was charged twice for the same subscription

labels - billing, technical, sales

The model will always return something from the above classes - "so it can't hallucinate".

We've already started using it for some pretty powerful decision tree stuff. We're just scratching the surface. We shipped an extension for Swamp[1] a few minutes ago and the combination is great!

The one downside is that the context window is very small (32k.) So some initial ideas we had for initial evaluation of code reviews won't fit yet in the window.

1: https://swamp-club.com/extensions/@swamp/typesafe-ai

This has the potential to be huge for computer use.

OpenAI has been teasing how fast computer use is with their models running on Cerebras chips but the difference here is a burning hole in your pocket.

Like which elements to select? Similar to the doom and wikipedia runs?

Yeah. Computer use is essentially a model navigating the OS-provided accessibility tree. I imagine a model trained on it would operate the computer exactly as we saw it control Doom.

https://developer.apple.com/library/archive/documentation/Ac...

I can think of many uses for this thing, robotics being one that could really benefit from something like this. A hybrid approach with this and action models and vllms could be really good mix, also agents inside simulated virtual environments, etc... basically anywhere where latency is important but you need some intelligence this will fill those gaps. Weave it with other systems and you have a nervous system as jev with other models like vllm or even text as the slower deeper thinker.

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%)

99.90% ;D It basically gives you the softmax value instead of using it to generate the output token. (said loosely)

Essentially, although LLM token probabilities tend to be miscalibrated (mostly bc of posttraining). Jev is meant to be particularly calibrated

Not an ML guy so maybe missing something important here. It seems like many use cases could be covered by even dumber, simpler models created with the aide of general purpose LLMs.

Like, can't I just tell Claude or GPT to help me create a BERT or <insert old boring ML framework model> that's even cheaper and faster?

For instance, Frigate runs 4.6mb yolov9 image object detector on a $50 USB Coral stick. It takes like 1-2 watts of power, responds in 15ms, and is completely local. A pay-per-token cloud hosted not-really-an-LLM-but-probably-based-on-one that runs in 150+ ms still seems terribly inefficient.

Could you use this to build a proactive memory formation and retrieval system for LLMs that runs lightning fast?

Last 32k of connect + Summary of current task: Did we learn something useful here (true/false)? What is the category to file it under? Then notify the LLM to file it away.

What class of memory might be useful here? Model gives probability to each item in the list. Short description of all memories ordered by tagged class is used in the next round. Are any of these memories useful in the current context, such that they will inform the model and help in its task (yes/no)?

I’m sure there’s some fine tuning to be had, but this sure seems like the basis for a substantially better proactive memory system that works around an existing LLM conversation.

If I’m understanding what this does and how this works (generic input, intelligent classification with probabilities, rapid and cheap), this is absolutely nuts.

This is too much for me. ML playing doom was a thing since before LLMs, decisions tree were always insanely and no one ever used then anyway, i can't see anything new in this yet everyone is treating this as a revolution. This technology was always there and quite easily accessible all along.

Wow, this is really cool. If this holds up to scrutiny, and has a decent context window (+16k), it suddenly changes our project's status from "cool concept, too slow and expensive to release" to "doable", just like that.

Just joined the waitlist, excited to try it out!

afaik Jev's context window is 32k

They never show exactly how they use it? Only a bunch of animations of it 'working'. Would like to see the actual code used for the demos!

The doom demo shows the program state / query.

It's a bit hastily put together, but I made a dspy fork where you can add a decorator to automatically use TypeSafe where possible on Signatures. It shows a fair bit of what actual, hands on usage looks like.

https://github.com/typesafeainate/dspy-typesafeify

DSPy seems like the right comparison and this is the first comment I've seen mentioning it.

Thanks for putting this together. I'm surprised the cost saving is so little though. I expected much more based on the post.

This is actually pretty cool. I think the undertalked about part of this for TypeSafe is that they can always "extract"/distill the frontier of this type of task from the newest LLMs for cheap. Jev seems seems to be GPT-6-Astra/Fable 5.1 but I imagine a bunch of training data is from earlier models?

Then, you can serve it faster/cheaper than the frontier LLMs. It's basically distilling a small but extremely common use-case from LLMs and serving it. Then RLCD comes into play to update weights when a new model comes out, etc.

Any thoughts on what the next potential "cheap" win to be distilled from frontier LLMs is? I'm going to need to play around with this.

Doom demo is beyond impressive, even scary

It's very misleading. If I'm actually playing a game I don't get the coordinates of enemies sent back to me so that I can feed into my mouse to snap my crosshair to. It's looking through walls too, because it's working off structured state in text form. You could re-create this whole demo without using AI. Have an LLM generate the state machine for you and no model is required to run it.

The impressive part is that it is low latency enough to serve high quality answers at game speed through the model instead of a pre generated ad-hoc machine.

A pre-generated machine can serve the answers in <1ms. It's a far better strategy.

> [others] Output tokens: ~5x more expensive than input tokens.

> [them] Output tokens: FREE (too cheap to meter).

I'm very confused by this.

They're not doing autoregression, so all the outputs are computed in one big forward pass. Very cheap.

I think OP is confused about "others" vs "them".

they’re talking about two totally different things, right?

it's our output tokens that are free (under the system one / jev column)

The output tokens are just responses to your inputed questions and their probability. So relatively few output tokens. No unstructured text back in the response.

This is not confusing

We had early access and found it to be pretty useful. Having a second form of verification, where you can ask multiple questions (in the form of Nouls) raised our confidence in the outputs of other models. [0] IMHO This type of model works incredibly well in concert with LLMs, not as a replacement.

[0] https://goodstartlabs.com/research/verification-is-the-bottl...

Don't your numbers suggest DeepSeek V4.1 Flash, for $100 more, gets you to slightly better agreement?

Yes they definitely do - not claiming it's a perfect solution, but as a V1 product it shows a lot of promise.

Can I put it as Air Traffic Controller? With similar error rates as humans?

That would be the litmus test.

"Does not hallucinate" is not the same as "is never wrong".

So the ATC test could be the benchmark.

Not hallucinating is easy when you don't produce strings.

Hallucinating as we use the word really only applies to generative AI. Non generative AIs can't hallucinate, they can just be wrong.

Can HN have a tag for open-weight vs closed-source models please? The progress is nice, but if it is not released at least in papers or open-weight? These are just ads?

Very cool! Can you explain when I would use this vs. training a standard ML model on my data? Suppose I had a fraud dataset with features like customer ID, amount, merchant, online or in-person, etc. - I can't imagine that a general model like Jev would predict this more accurately or cheaply than even a basic XGBoost model trained on my dataset (one that I could build in a few minutes by asking Codex to build it). Where does Jev add value here?

you will need to collect data for every decision/usecase and then train a model. But this can be used for different use cases with just a prompt.

Founders response to a similar question on X: https://x.com/CompleteSkeptic/status/2100067328620896408?s=2...

pasting it here: zero-shot + general == programmable

I would assume any extreme scale narrow task could then be fine-tuned for, but we'll see - I suspect putting it all in shared cognitive core has bit maintainability/generalization benefits

Thanks. I do concede it’s very general but that is a double-edged sword. I don’t need a general fraud identification algorithm. I need an accurate one. If I have another classification task I’ll train another model for that task.

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 taxonomy from a tagged community of experts , outliers , contrarians . Open ended situational questions and answers like how the real world operates in various sectors to capture tacit knowledge . We have a playground with a tiny slice of just 100 of our over 500k base good judgement scenarios that can then be synthesized across sectors and workflows . With just a tiny slice it outperforms Fable and Open Ai models. Exceptional human judgement outperforms consistently models…. Good judgement is just also rare in us humans like common sense ( no pun intended) . Check us out at lovelyhumans.ai. Holler if curious. Sallyann Dellacasa on LinkedIn .

I am not sure for how long the output will stay absolutely free. But apart from the pricing advantage of Jev itself, I just love the simplicity of having only an input price. Input is pretty easy to estimate and calculate upfront, which makes the cost of running something at scale much more predictable.

With LLMs, even with JSON schema constraints and structured output, the actual cost can still be hard to predict because of varying output lengths and, especially, unpredictable reasoning costs. There is something really nice about being able to tokenize and predicting the budget beforehand.

Haven't seen any docs or so. Is this actually a general model, or does it need training on the the data set it answers? Finding it suspicious you never see some kind of prompt.

Edit: never mind, found https://docs.typesafe.ai/introduction/quickstart by now

1. yes a general model 2. no training at all 3. but it is focused on "System 1" tasks (more human judgment, less math reasoning)

It's very generalized. Can't wait until everyone can see it.

The Doom demo looks impressive but was it a fine-tuned model? It's the difference between a cool demo and revolutionary tech.

Shouldn't self-driving be a piece of cake if it works this well for Doom? Or what am I missing?

I think the doom demo uses a text representation of the world and it's basically, "projectile coming your way" -> "Strafe". "Enemy ahead" -> "shoot. So it works well when spawned in a room of enemies (as we see in the video).

If self driving is red means stop, green means go, and stay in your lane - then it would work great, but having to actually think and test which maneuver is optimal for a given situation while weighting safety, road rules, random unexpected actions and getting to your destination, I think it's a much bigger problem. A bigger model specifically trained on that maybe would do great, but then the output is not the constraint anymore.

But I haven't tried the model, so I 'm just ballparking and could be very wrong.

The model doesn't have image input capabilities (yet, it seems from the post), so for the Doom demo, a harness is extracting a bunch of structured information from the game (map layout, enemy locations, player ammo, health, etc) and providing it as a massive JSON blob to the model so it can make its decisions. This model _could_ be hooked up to make the decisions for a self-driving car, but it would need to be fed a structured blob of the situation around it, so all the computer vision problems of self-driving are still there. And that's before you get into the confidence and accuracy of this model.

Driving is more complicated than Doom, and it doesn't look that great at Doom to me.

I’m struggling to understand how this approach is novel.

How is this different from nearest-neighbor within traditional vector search, used by RAG systems? What am I missing?

1. Compute input embeddings 2. Concurrently compute distance-to/logits-of runtime-specified (sic cached) label embeddings 3. Return greatest/closest label

I do appreciate the Kahneman reference.

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?

An LLM takes (text in) -> (text out).

Jev takes (…questions in) -> (…probabilities out)

So Jev won’t write a story or emit arbitrary structured data. But if you ask it the right questions, it can make near-instant “decisions” against those questions, with accuracy and world knowledge on par with LLMs. The economic advantage is that it’s parallelizable and can give back up to 255 answers at once, in milliseconds.

If we are getting general purpose classifier, decision engine, probabilistic inference machine and likely a couple of other use cases for the fraction of latency/time/price of using SOTA LLMs for such tasks or training specialized classifiers this is very interesting project.

Considering in future we would be able to supply it with our own facts knowledge bases and fine-tune for specific domains, this enables a lot of interesting use cases in various software domains.

I was thinking about something similar (maybe) - generally speaking, embeddings for LLMs tend to learn real world concepts - things like 'fruit' or 'France' or 'city' as directions in embeddings.

But in things like programming, most concepts are abstract - 'if hungry eat an apple' in programming terms would look like

'if hunger > 50 {apples--; hunger-=30;}'

and compilers work with 'concept erasure' - to them, tokens (which are like llm tokens) look like

'if var1 > 50 {var2--;var1-=30}'.

They don't care about how these things map to real concepts. So all the embedding directions used to encode real-world concepts are just noise to LLMs when programming. This greatly reduces dimensionality and training costs. So does a token representation tuned for programming constructs, rather than natural language would probably have a more efficient encoding.

Current models go beyond the simple embedding because you start to encode groups of concepts in the context-aware part of the model (attention heads or any other method). So it is never simply words/tokens in isolation anymore.

I think what this shows is how important branding and comms are. They've captured imaginations with their demos and nomenclature, despite the arguably non-novel architecture. One forward pass, read the embedding space, train some regressors on predicate structure, [??]

You know you're too old when you see the company name and think! Oh I wonder what Martin Odeskey , Jonas Bonér and co are up to. Wait, didn't they become lightbend... Altho this comment takes away from what these guys are doing which legitimately sounds interesting.

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 character of output in a loop.

My guess is that due to its design there is no support for prompt caching, as there would normally be no reason for it. So the performance of my idea would probably be appalling as every step in the loop would reevaluate every input and output token. But it would be interesting to see the outcome.

It likely isn't trained on that task so performance would be worse than frontier models specifically trained for coding.

From the person in the video regarding issues with benchmarks in general, and for LLMs. Also their approach. Good article.

https://substack.com/home/post/p-215252866

Congrats on the release!

Finetuning a language model for decision classification (with probabilities) is already well-understood. What specifically changes in the training objective with RLCD? Are its benefits isolated from Jev’s new architecture/parallelism?

Spent a lot of time - but this makes zero sense to me. It can, maybe, return type safe outputs faster than larger llms - but there is little reason to believe that it will be more accurate. It does absolutely hallucinate - and seems to me that the claim is largely misleading.

You architect your systems with typesafe - because it is marginally faster, but inaccurate - to do what ? You can just wait for the next version of LLM's to get more accuracy at the same cost - or just use a faster model right now from a different provider.

For certain tasks, a model like Jev may be intrinsically more efficient than an LLM because it doesn't have to predict a token distribution and can instead focus solely on the probability of a single question/action

It isn't marginally faster, it seems to be approx 100x faster and 10-100x cheaper.

According to https://goodstartlabs.com/research/verification-is-the-bottl... it is only 2.6x cheaper than DeepSeek V4.1 Flash, and they did not test v4.0 Flash, which would have been same price.

Looks cool! Is there any relation to this area: "Type-constrained code generation with language models"? https://news.ycombinator.com/item?id=43978357

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, especially with certainty as part of the output, as you could flag things that didn't have high enough of a certainty to human review.

I'm sure legora and the other legal AI tech softwares are all over this.

This will be insane for tool usage, and probably where the major economics for day-to-day usage will be.

The goal is going to be to use llms to distill operations down to some dsl, and pass it into something like Jev.

Hm, would be good to understand the architecture better. Is this answering just from a world model informed prior? How informed is it by the information in the prompt? I can't see this maintaining calibration across all domains and all types of structured output.

Is there anything published on how it maintains calibration? Or when you say "outputs calibrated probabilities" you mean "as calibrated as frontier LLM models, just cheaper" - which is a different claim; as LLM's aren't particularly well calibrated

How is that different from machine learning 101 "regression"? And why don't they just put a regression or softmax head on top of a trained transformer? (or do they?)

IIRC, Carmack was working on getting AIs to play Amiga games. The Doom demo suggests a very interesting direction to take this research.

I'm curious to hear his take on this approach.

If we could come up with a system to classify the probabilities across a large number of candidate words (or components thereof) then this could actually be good at producing text, one element at a time. We could call these elements 'tokens' and picking the right one could be called something like 'decoding'. Crazy idea but hear me out...

On a more serious note, it will be fascinating to see how this different spin on modelling inference will create new paradigms or slot into existing ones.

Looks promising. I'm building an AI video editor and multi tool calls take >30s using Gemini. This would be a a game changer if Jev can take that down to single digits at p95.

Interesting concept, I can't see a reason to use a generalist classifier over an api rather then just training my own? If it was open weights I would probably mess around with it.

Great to see something new..

However I don't understand how are they claiming zero hallucination, how does giving confidence score fix hallucination? or am I missing something here?

Hasn't there been a lot talk about Astra's opaque reasoning capabilities (being able to think through complex questions without using a chain of thought)?

Given that, can't you just replicate Jev by telling Astra "here is the question, you must make a multiple choice decision / output a score between 1-10, please answer directly in a single word, no reasoning allowed"?

(Edit: Ok, Jev is much cheaper in input tokens so these two aren't directly comparable at all)

the edit is right - jev would be cheaper, faster, and more self-consistent (in general)

we actually use astra (and fable) in this way for our evals: evals.typesafe.ai

someone on the team cooked hard on that and it shows example traces comparing our model to opus/sol

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 reach the level of intelligence they've got here. Tho speed/cheapness was similar.

Super cool! Instantly joined the waitlist.

It might be boring, but I can see exactly how I could use this right now to improve my agentic rag.[0] In two months I am supposed to deal with a giant corpus, while still maintaining responsive chat UX. I have been working my butt off to make our first big client happy. This could really help solve the chunk ranking problem.

[0] assuming the policies are compatible with sensitive production workloads, some time in the near future.

I can see the value in this but looks like there's going to be trouble in communicating the difference between this and a regular LLM, and also proving the potential cost savings in using this to replace existing systems that are using LLMs with frameworks like langgraph, as this can't be a drop in replacement and would require a significant amount of re-architecting/reengineering of systems to get the type system to work

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. If a low confidence output is propagated, then it can break the entire chain.

Would the data streams that Jev is best at evaluating need to be labelled? Or can it do that first before classifying?

A question I have, with the type { output: string }, would the model not become a LLM? And if it does, shouldn’t it cost the same as a LLM for output?

I don’t see this as an option in their website

You could theoretically ask “what is the next appropriate character?” and add the entire ascii charset but i doubt it’d work well and you’d be implementing autoregressive churn across network latency…

strings (and all sequential data structures) are not allowed at all - this is how we make sure all outputs can be computed in parallel (thus no output token cost)

This is amazing. I really could use this.

I like the idea of System one models but all LLMs so far work as system 1 thinking because humans generate speech subconsciously with system 1.

System 2 thinking requires consciousness which AI does not have, so even reasoning models are still system 1 thinking as system 1 in humans has reasoning with heuristics.

Its limited but most people navigate the world with it completely, so it's enough for AI.

While I understand that accelerating development isn't necessarily the target for this, and it's not at all intended to generate code the way many of us are...

I think this could be pretty decent in CI? There's a lot of "flakes" I've mediated that this could have handled much more efficiently. Maybe observability as well, triggering elevated logging and other initial measures?

How does Jev compare with encoder language models like BERT/RoBERTa/DistilBERT, which could also be used for text classification?

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.

> We deliberately chose not to publish performance against public benchmarks. In fact, we plan to only have one-off evals when we make product updates.

lol, I bet they would publish them if their score on those benchmarks were good.

This makes me think of Expressions of Change [1], a project that aimed to make updates to a program a first-class primitive in a programming language. A model like this can't output code directly, but perhaps it would be well suited to select from the small set of discrete operations on code envisioned by the EoC author?

[1]: www.expressionsofchange.org

So is it a structured data-based language model? Or is there a model and a harness? Hopefully they’ll open up and explain more.

it is just a model, no harness yet ;)

it is a structured data model, but technically not a language model (it doesn't generate language)

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.

Signed up for the beta! :) would love to put this through some real-world shootouts against traditional LLMs to see where this type of model really excels.

I’m guessing it might be able to replace maybe 40-70% of LLM calls for a given pipeline depending on the business task, cutting the API costs on those calls by an order of magnitude.

I was going to ask if the inspiration for System One name came from Kahneman's and Tversky's 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!

The fact this isn't open-source is troublesome.

Such large advances shouldn't be locked up away from local hardware.

Honestly all AI research should be open source but that's just a dream

As a zero-shot classifier, I expect that effectiveness is dependent on the data trained upon.

Jev input … > Unstructured data (e.g. text) with an emphasis on structured program state.

What pre-training data/model is Jev based on? Surely result effectiveness is dependent (outside of one’s own input as “state”) on that?

This sort of stuff almost sends shivers down my spine, it's like i'm looking 5 years into the future.

There was no "AI Winter"

join the discord! we love forward thinkers

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 Jev could be used to replace a current LLM-as-Judge evaluation workflows, specifically on chat transcript data (think ~1,500 tokens) i wonder if the reasoning usually required pushes it a bit out of scope. didnt see anything published about constraints on the state size, so would be curious to hear about that.

definitely seems like a modified version of GLiNER2 or 2.5: - encoder-based (no text generation) - multiple tasks in a single forward pass - deterministic outputs - constraint-based classification

Congrats on the launch! What's different between Jev and Microsoft's Guidance package? https://github.com/guidance-ai/guidance Is it a diffusion generator under the hood?

Very cool, I immediately jumped on the waitlist and shared this with my co-founders. Any plans for offering this through a European provider at some point after launching in the US? We work in EdTech, so non-EU-sovereign solutions are a harder sell to our customers.

Wild that it doesn't generate text. I wonder how its technology compares to Tesla's FSD stack.

Parallel inference where you don't want a subagent seems niche. But there is a lot of random things where businesses ultimately want some kind of score instead of generating something.

I think the interesting thing would be seeing if prompt injections still work with this kind of model.

we have played with this! the fascinating thing we've found so far is that adversarial examples for our model are quite different from that of LLMs so that they work even better together

I am positive I know exactly how this works, I made something similar a few months back. But the problem is without generation you are extremely limited in the use cases. And while the model can't hallucinate, it can still be wrong. It just can't make up data.

Are you able to share how it works in that case?

I'll tell you this. Output isn't too cheap to meter, there is no decoder.

So an encoder-only model with a classifier trained on the heads or something? DeepSeek recently switched to an encoder-decoder architecture in an attempt to get the best of both worlds (fast prefill while preserving generation capability), I wonder if that might be the future?

This is basically what they have. https://github.com/fastino-ai/GLiNER2

That was the first thing that come into my head. OK I can train very simple model, that can generate json's for specific tasks, so what? How we can be sure that this "limited use cases" not just overfitting for particular outputs (or even distillation?)

Except this, this thing looks like revolution.

Last year I also had a rather similar idea, but dropped it before I went very far in working on it. I wonder if you and I had similar ideas?

1. Start with an LLM, so that your model understands natural language.

2. Replace RoPE with a tree embedding scheme, and causal attention with a sparse attention on the graph structure. (You could use full attention... but it's cheaper to use graph attention.)

3. Chop off the final unembedding layer, replacing it with a projection down to two scalars, one for logits and one for confidence.

4. Each option of a choice is represented by a number of tokens in leaf position; average these tokens' logit outputs to get the option's logit. Average all of the confidences from all of the options to get the choice's confidence.

5. Train the logits by KL divergence from a true distribution (or NLL on samples from a true distribution).

6. Train the confidences on a subset of the data in which you know the entire true distribution.

The hardest part is getting real world data for workflows, but I wildly speculate that you can get by with only ~50,000 documents if you first adapt domains using synthetic data.

Yeah saying it can't hallucinate is crazy. It can still forward a billing query to the dev department incorrectly. It can still get an obvious yes/no question completely wrong

Funny how the authors are asserting that "doing the right task > data > compute > algorithms" while simultaneously releasing AI model for calibrated decision making, which if they work, would mean that "compute > doing the right task"

I could put this to use today.

I think we'll see a bunch of different architectures over the next five years.

A few questions:

1. Do you provide any kind of largest common subtree caching for cheaper input?

2. Have you tried auto-generating Lisp programs structurally?

3. Have you tried augmenting a Lisp language with a `choice` function that makes choices given a prompt, the environment, and the continuation stack?

(1) Nope, it's always the same input token cost

(2-3) No, but that's kind of a sick cook ... Want to get access and try it? nathan@typesafe.ai

Thanks for the early access! I was testing the Lisp idea out in the playground, but I don't think the model is smart enough right now to generate actual code. I tried having Jev finish generating the code for a Fibonacci number function, but it kept wanting to create a literal number instead of refer to a variable which is a number. This happened both when I gave Jev the current program as a string and when I gave Jev the program as structured data.

Maybe I'm just not doing a very good job at prompting Jev, but I think right now it's not quite capable enough to generate Lisp code.

Link: https://console.typesafe.ai/playground?share=shr_148e1248984...

Interesting. Perhaps I can see this being quickly adopted in LLMs-as-a-judge, where you normally need (a) a structured answer, say, with lots of different fields (metrics) and (b) you want the judge to be fast, not being a bottleneck.

Always exciting to see people working on novel models, rather than the Nth version of the same slightly tweaked LLM.

I'm very curious how much ressources are needed to run such a model. This could be a complete game changer for local applications.

So how do we evaluate how good this is?

Couldn't you hook it up to a multiple choice exam?

It's still LLM like, how smart is it? I'm wary of something that the company states they don't want to benchmark it across public benchmarks.

Is there a bottleneck which would hinder putting this architecture in charge of a humanoid? Would it be able to operate continuously, for example in conjunction with an LLM for long-term reasoning? Doom seemingly works extremely well.

defining the workflow such that the operation is a set of relevant questions

Overall this seems like a classifier that gives weighted scores per custom labels. It's certainly useful, but whether it brings higher quality than an LLM in structured output mode has to be seen in objective benchmarks.

Zero shot classifier indeed. Reminiscent of asking an llm a yes/no question, constraining the output to either yes or no, and looking at the logits directly

And each question is a separate single token model completion done in parallel

It looks like a Transformer encoder post-trained on classification and regression tasks. The encoder-only model is less noticed in recent years, but this product finds a nice application for it.

Will need hands on to truly tell, but the doom demo seems very promising. If it can play that with text descriptions of where stuff is by distance and degrees in a 3D context then many GUI automation tasks should be easily doable

Super cool. Does it, or will it, work with image, audio, or video input?

Would it be fair to say that this is tailored for tool-selection subagents?

Seems that way.

I used it today as browser agent, the "tool" is a menu of ~10–40 clickable refs from the accessibility tree. Jev picks one per step. 21–23 decisions for six benchmark cards, all correct (!), ~$0.001 total. Really good. The part it can't do is write the text for an input field so a nano model does that when Jev picks "type". Numbers and code in my top-level comment in this thread. Working very good.

Seems like LLM can do everything Jev can do (just structured outputs?) but Jev is highly optimized and purpose built for it and thus way faster and cheaper. Is that a fair description?

What are peoples' thoughts on whether a local version of Jev is possible? Having to call an API for something that's main benefit is speed is orthagonal to their ethos.

What is it about the rendering of this page that is so... off? It almost looks like the entire thing is a <canvas> element.

edit: looks like a framer export where there is a text stroke being applied :|

So... a classifier model?

I think I missed why is this faster? What I’m reading here is it’s similar to constrained decoding but I’m not seeing the explanation of why it’s able to get those results.

Jev is the latest hype everyone has to talk about for a few days to show how cutting edge they are.

Wouldn't that be fun for text adventures? As you can get probabilities for finite actions relating to objects that are in the world/room

This puts the human even more out of the loop I'll guess?

That's kinda the goal. Imagine all the automation in the world being able to embed intelligence directly inside it - factories could route based on more complicated questions, hardware could anticipate your needs. Customer support could be done without humans 90% of the time.

Huh this looks fantastic. The Doom demo really sold for me that this could be a great tool for accelerating QA at my gamedev studio. Signed up for early access.

Is the tradeoff of the parallel output that we don't get arbitrary string generation? like output # of tokens is fixed ahead of time?

Either way, really cool and impressive.

Yeah, it doesn't output strings, just decisions/answers.

Non-hallucinated ones at that.

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. https://typesafe.ai/

1. https://en.wikipedia.org/wiki/Fast_inverse_square_root

Oh man, I’m building a system to classify very large streams of data, this should replace several pieces of my workflow!

Does lightbend still own the typesafe trademark in software? Or did they rebrand because they couldn’t get it?

Funny how it can do everything but not chat. Sort of how when I was a kid I thought of a medicine that could cure any disease except the common cold.

insane doom demo i wonder what the limits of its intelligence are? i'm guessing it's not great at reasoning tasks, it seems breaking down the problem helps significantly, but how much does a problem need to be broken down for reliable performance? also this would be huge if it could run locally but it seems like there's no intention to do that at the moment

explanation with example: https://mchromiak.github.io/articles/2026/Sep/17/Jev-Typed-D...

If it is so cheap, why such a limited release?

Is this a Markov/Diffusion model with some sort of external Engram memory? If so, this could be extremely interesting.

this is so cool. i've been obsessing with jev since i discovered it.

I've been collecting what people built with it in the first days: https://madewithjev.com

feel free to add your demo or project.

I don't know if it's just me but comments under Twitter post felt like paid partnerships to me.

The idea sounds cool though

Very cool. LLMs have been borderline unusable as functions for the longest time, very excited for this direction.

What's the difference compared to just taking an embedding and feed forward a simple net trained for the task?

> Extraordinary claims require extraordinary evidence so see below for the receipts.

Yes, that’s the kind of attitude I want to see in these model releases

But the evidence is not there...

Indeed, they talk as skeptics but don’t offer a ton of evidence, other than a couple videos of demos. A live demo would be far more convincing.

They gesture at not using benchmarks for some reason...

https://typesafe.ai/blog/antibenchmaxxing

But also effectively this is a classification model. It excels at specific certain types of workloads, and obviously will fail at others. Not really sure how one benchmarks this tbf. I can see their argument on why this requires a novel specific eval for whatever your usecase is. A consistent "global" benchmark might be hard to do

So it's kind of like BERT but you don't have to train it for each request/response shape ?

If it work as good as they say it does, confidence score + really fast response when you want very fast response, basically.. To me it is a crime against humanity to not open source it. Just get the money from cloud inference and cloud agentic sessions or whatever but open source it. This tech, a good harness, a good model provider, and you have basically a AGI building machine.

Golem, if you read this, add me on battle.net (europe) Sansviande#2540 and let's talk. Give me 1 minute.

“Crime against humanity” buddy please

> AI Map Reduce over Big Data

> Search for relevant information over giant corpuses

Do you mean as an alternative to embeddings?

This is best thing to use for decision making, evaluation, classification. If I'm not wrong.

If I understand correctly, it can play chess and rubic cube better than LLM ? ( may be go too ? )

ok I've searched, it may not, but it can do "driving car" and "trade 0-DTE Option" much better.

I was wondering the same! So I asked Astra to build me https://jev-chess-master.vercel.app/ where you play against Jev AI as chess player.

Github link: https://github.com/qibinlou/jev-chess

Hope this helps!

this is interesting, so not an LLM but can be used in these use cases that LLM's have been shoehorned into

https://docs.typesafe.ai/concepts/use-case-map

Can this be used in conjunction with a text-generating LLM for better quality code generation?

The technology and the results are very handwavy. What is RLCD exactly ? What are scores on benchmarks compared to LLMs ?

This website does not inspire confidence at all, it all sounds like a marketing piece. I wish it was true, some kind of text-prompted classifier with LLM performance would be cool, but I can't trust it with what we are given.

This could be killer for ingame AI for grand strategy games like Victoria 3 or EU5

Really fun model! Thanks for opening up access as quickly as you are.

The whole page reads like it was vibe-written by an AI. If I'd built something as disruptive as this claims to be, I'd have spent at least fifteen minutes writing the announcement myself. Every time I see 'we' in an announcement like this, I picture one guy alone in his basement.

unfortunately all hand-written :( my chief-of-staff does unironically handwrite em dashes though

For what it’s worth, I didn’t get that impression, and even noticed a couple typos ;)

I really appreciated the hand-written release. Thank you.

oooooh it can play Doom!

forget LLM benchmaxxing sidequests, I'm sold on the real benchmark

This is quite the paradigm shift. Can't wait to get my hands on it.

If I’m understanding correctly, this will work well for self driving cars?

"can't hallucinate" feels like some word game Olympics

Why did they pick the name System One? It's not really explained what "System One tasks" and "System One shaped queries" are. Things that need a fast response?

Does this imply it's a very small model? I couldn't find anything about the model itself.

Maybe: https://thedecisionlab.com/reference-guide/philosophy/system...

Bingo. It's a Psychology term for the part of our brain that reacts instinctively rather than thoughtfully and logically

Okay, so it doesn't output text, that much is understood. What are the inputs like? I'm assuming maybe a text input? maybe an AST definition? Really hard to tell how this works at all from the demos, especially since we can't really try it out.

inputs are structured program state. there is an example at around second 30 of the doom demo

(though ideally everyone gets off the waitlist and can try it out for themselves )

This looks and feels a lot like productionized conformal prediction

> Outputs

> LLMS > Strings / generated text. Strings are flexible and can be anything: chat responses, code, hallucinations, refusals, or even type-safe structured values. To be used by software, responses need to be parsed + validated. There is also always some risk that the AI goes off the rails.

> Jev > Type-safe structured values. Possible outputs and structure are defined in advance. The model never makes type errors. All answers are accompanied with calibrated probabilities and confidence scores.

I mean, this isn't even remotely comparable to LLMs so why compare? Also, why are they bringing up AGI given there approach is so restrictive that what they're building literally cannot have the creativity required for AGI? The video is 100% marketing slop...

The bulk of the application of LLMs is that they generate reasonably reliable text which doesn't need to be defined in advanced. I'm sure there is a niche for this and congrats to the team, but please let's not hype this as if it's the next big thing in AI...

Creativity is not required for AGI, that's maybe the only thing that is not required for AGI actually.

What a sad world would you live in if you don't keep creativity for the humans.

Seems like "some" of LLMs tasks are now Jev tasks.

this reminds me of laravel boost, which does something similar.

it can generate classes/models/routers etc via tool calls, doesnt write the actual code.

Super intrigued by this - large scale automation using LLMs is quite annoying due to deprecation cycles of models from frontier labs and cost of running your own being prohibitive when you have a blend of them.

"While Jev gives up string generation, it’s optimized for structured outputs and can’t hallucinate"

Ouh! Any open weights models that can do this yet?? If not, how much longer? I have a Mac Studio coming soon.

The eval is baffling me

> we assume there is a correct compute graph (a “workflow” represented in code) and use the predictions of the largest, smartest, and most expensive external models as reference probabilities. ... Rephrased: every model gets the same workflow. We test how they compare to the average of the smartest models (in this case, Astra and Fable).

They assume there is a correct graph, but they don't compare to that, they compare to the average of the smarts models? So the smartest models are getting it wrong but you compare that anyway as a benchmark? So the outcome is "how much of a Fable am I getting" etc. Why not compare the actually correct thing?

But then even on this hand constructed eval, the first plot is showing Jev at less than Sonnet 5 accuracy. It is barely better than Luna. There are two Opus 5's and two Sonnet 5's without explanation. What is the plot showing?

I gave up.

Is there a downloadable technical report somewhere?

Any relation / inspiration to GLiClass?

Can this be used in practice to write code?

Not in the traditional sense of a coding agent, but we think there's a ton of opportunity in using it for context management ("do we _really_ need to pass all these tokens to the agent?"), semantic linting ("how does this score against this AGENTS.md: <...>"), etc.

um what is going on with the outfit changes in the launch video...

https://x.com/CompleteSkeptic/status/2099925682726002904

The whole video seemed generated to me

Can't tell if they're just having fun or if it is ai-generated. On the verge of not being able to tell. Voice sounds a little synthetic.

definitely not AI-generated - this is my real wardrobe

we also thought the voice at the end was AI-ish, but apparently that's a real voice actor but slightly sped up

Pretty sure that's an intended joke.

Reminds me of this: https://www.reddit.com/r/ITcrowd/comments/tg05j1/i_cant_beli...

Can’t wait to use this. Amazing work.

Looks like a great model for NLP.

This is potentially huge and can crash the Big Two's stock prices or block their IPOs completely.

I like it. What is it?

A little off-topic, I have to admit, I did play Doom back in the 90s and I know it is just a game.

But is it just me or other folks feel uncanny seeing "a machine" playing Doom with low-latency as the first demo.

While I'm optimistic that humanity is good (but sometimes makes bad decisions), My first thought was seeing such models used by armies.

I’m not understanding what this is. It’s a faster cheaper LLM?

this might finally be smart enough and fast enough for jarvis. hard to feel like iron man when your assistant takes 8 seconds to decide to pause your music

a) Is this available on Bedrock? b) Does it support structured outputs? c) What about trying it out?

Am I the only one struggling to parse the distinction System One (the system/harness?) and Jev (the model?)?

System One seems to be more of a "class" of models, as it's a good classifier but can't do what traditional LLMs with chain of thought do.

It looks like a specialized encoder-only(-ish) transformer with scalar and ordinal output heads. Acausal in effect, maybe? Probably not even autoregressive?

I'd use this as a tool an LLM can use for specialized tasks. It's not AI in itself.

If this is true means, AI Stock bubble burst. (For good)

my sons name is also Jev

TLDR: Like an LLM, the input is a string, but the output is not a completion. The output is a ranking of elements from a certain enum (e.g, [Yes/No], [A/B/C/D]). They use a technique called Reinforcement Learning for Calibrated Decisions (RLCD) instead of RLHF. Also, inference is a lot faster.

https://docs.typesafe.ai/primitives has a much better explanation

Finally a fast solution to isOdd / isEven :) /s

"is this the real thing or is just fantasy"

If this is true, then AI Stock Bubble burst (for Good)

Cool I guess. Definitely not worth the 1000+ points though.

This makes me kind of nervous for the whole AI thing now. Are people gonna lose their jobs, etc.? so much of the economy is now built on top of LLMs.

What's different about this particular model that worries you?

it doesn't require nearly as much compute as normal LLMs. anything depending on increased datacenter and compute spending would be threatened.

a world where people eat so they can feed Big Computer sucks. We need Little Computer, driving robots in the fields.

I definitely agree with this, but the transition is gonna suck for some.

Reply on news.ycombinator.com