Thank you. Normally I wouldn't judge people by their appearance but considering all the other tricks that were used in their announcement (and the fact that very similar idea existed as far as 1 year back but didn't even get a fraction of attention due to lack of publicity), I think that picture adds to the image of WHAT we're dealing with (carefully crafted hype) not necessarily WHO (he's just one face, I could add more but then the post would become an album, and the appearance is just one part of the argument, not the entire argument).
Schema-valid and wrong is the hardest failure to catch in production, because nothing throws and no parse alert fires. You only see it by joining the accepted labels to what happened next, like reopened tickets or reversed refunds, and reading a sample every week.
Exactly. So the total productivity (factoring in the manual approval) might not be as "exponential" as we were led to believe. To their credit, they primarily talked about cost and speed while letting our brains fill in the ambiguity about the quality.
Nice article! I only didn't get the relevance of the Jev's cofounder picture to the point made
Thank you. Normally I wouldn't judge people by their appearance but considering all the other tricks that were used in their announcement (and the fact that very similar idea existed as far as 1 year back but didn't even get a fraction of attention due to lack of publicity), I think that picture adds to the image of WHAT we're dealing with (carefully crafted hype) not necessarily WHO (he's just one face, I could add more but then the post would become an album, and the appearance is just one part of the argument, not the entire argument).
Jev is a copy of the original open source version (Laya) and the author is pissed https://laya.convaiinnovations.com
It also has been replicated in two weeks by all major labs.
Having said that, it’s still a useful version of an LLM for traditional tasks that just want a vector as output of a bunch of input text
It definitely has some use cases. No question about that. But it's not as useful as the initial announcement sold it to be.
It also raised $870M from a16z around the time OpenAI and Microsoft launched their equivalent models, which is 🤯
Schema-valid and wrong is the hardest failure to catch in production, because nothing throws and no parse alert fires. You only see it by joining the accepted labels to what happened next, like reopened tickets or reversed refunds, and reading a sample every week.
Exactly. So the total productivity (factoring in the manual approval) might not be as "exponential" as we were led to believe. To their credit, they primarily talked about cost and speed while letting our brains fill in the ambiguity about the quality.