alt Hacker NewsIntroducing System One Models and Jev
Comments
> 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
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.
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.
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.
"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.
I’m not understanding what this is. It’s a faster cheaper LLM?
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.
I could put this to use today.
I think we'll see a bunch of different architectures over the next five years.
um what is going on with the outfit changes in the launch video...
oooooh it can play Doom!
forget LLM benchmaxxing sidequests, I'm sold on the real benchmark
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.
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 :|
Looks like a great model for NLP.
this is interesting, so not an LLM but can be used in these use cases that LLM's have been shoehorned into
If this is true means, AI Stock bubble burst. (For good)
"is this the real thing or is just fantasy"
> 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...
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.
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If this is true, then AI Stock Bubble burst (for Good)
Is there a downloadable technical report somewhere?