131–140 of 513 posts
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 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…
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.
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
Earlier quoted context omitted.
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.
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.
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…
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…
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.
https://x.com/completeskeptic/status/2099925682726002904?s=4... The doom demo is quite cool
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?
Ouh! Any open weights models that can do this yet?? If not, how much longer? I have a Mac Studio coming soon.
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.
(though ideally everyone gets off the waitlist and can try it out for themselves )