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Introducing System One Models and Jev

typesafe.ai

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Re: Introducing System One Models and Jev

#371

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

Re: Introducing System One Models and Jev

#372

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

Re: Introducing System One Models and Jev

#373

Earlier quoted context omitted.

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...

Re: Introducing System One Models and Jev

#376

Earlier quoted context omitted.

> 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?

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…

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

Re: Introducing System One Models and Jev

#378

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.

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

Re: Introducing System One Models and Jev

#379
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, [??]

Re: Introducing System One Models and Jev

#380

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

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