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Introducing System One Models and Jev

typesafe.ai

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Re: Introducing System One Models and Jev

#211

Post by zenlikethat »

threecheese wrote: post #207
zenlikethat wrote: post #198

Earlier quoted context omitted.

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

Re: Introducing System One Models and Jev

#212

Post by Invictus0 »

bqsile wrote: post #197

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.

“Crime against humanity” buddy please

Re: Introducing System One Models and Jev

#213

Post by cooljoseph »

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?

Re: Introducing System One Models and Jev

#214

Post by davideg »

Flere-Imsaho wrote: post #102
CompleteSkeptic wrote: post #65

Earlier quoted context omitted.

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)

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 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).

Re: Introducing System One Models and Jev

#215

Post by zenlikethat »

seizethecheese wrote: post #123
CompleteSkeptic wrote: post #110

Earlier quoted context omitted.

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 coul…

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.

Re: Introducing System One Models and Jev

#216

Post by zenlikethat »

big_toast wrote: post #103
zenlikethat wrote: post #66

Earlier quoted context omitted.

> 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 corr…

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

Re: Introducing System One Models and Jev

#217

Post by bjconlan »

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.

Re: Introducing System One Models and Jev

#218

Post by 8note »

dbbk wrote: post #100
jacobgold wrote: post #81

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 out…

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

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

Re: Introducing System One Models and Jev

#220

Post by riknos314 »

sroussey wrote: post #168
WhitneyLand wrote: post #149

Earlier quoted context omitted.

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.
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