The doom demo is quite cool

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.

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

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

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

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

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

I'm very confused by this.

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

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

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

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

>Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out.

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, probabilities, confidence).

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

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

Either way, really cool and impressive.

> 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...

Where is the 20-200x? misleading title.

Also I don't get it, is that decision tree for automation that can apply to generic problems?