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Why Meta's new model is a zero “It is a zero because there is no API. We cannot even use it. It is only for internal use by Meta. This is the most performative eval I have seen to date. I am not saying it is not nominally true,...

132,509 Aufrufe • vor 26 Tagen •via X (Twitter)

19 Kommentare

Profilbild von Alexandr Wang
Alexandr Wangvor 26 Tagen

da fuck you talking about? we got so many models on our API. you want spark, we got spark. you want wonder, we got wonder. you want image, we got image.

Profilbild von Anant Narayanan
Anant Narayananvor 26 Tagen

Not sure what you're referring to, all models (including today's open weights launch) are available via several router products. Muse Wonder is also on and Muse Wonder Full is accessible via the Muse Code subscription.

Profilbild von Andrew 💥♻️
Andrew 💥♻️vor 26 Tagen

Smoking crack. They even released an open weight version.

Profilbild von Jason ✨👾SaaStr.Ai✨ Lemkin
Jason ✨👾SaaStr.Ai✨ Lemkinvor 26 Tagen

Seems since released! :)

Profilbild von Gabriel
Gabrielvor 26 Tagen

Why is this guy so angry? All the time he’s like against stuff, even though the last thing he built was 20 years ago

Profilbild von Conor Bronsdon
Conor Bronsdonvor 26 Tagen

Harry wtf are you talking about? Not only are they open sourcing weights, its literally on their API..

Profilbild von Fireply.ai
Fireply.aivor 26 Tagen

a model you can't call is a slide, not a product

Profilbild von Shardul
Shardulvor 26 Tagen

VCs doing due diligence. lmao

Profilbild von Alex D. 📎
Alex D. 📎vor 25 Tagen

Maybe delete this?

Profilbild von chloe
chloevor 26 Tagen

no api... so it’s basically a very expensive locked sample

Profilbild von 𝔰𝔠𝔬𝔯𝔭𝔦𝔬𝔫🦂
𝔰𝔠𝔬𝔯𝔭𝔦𝔬𝔫🦂vor 26 Tagen

Fair point — no API kills the value. Curious what @mozlyxy thinks on META, he's sharp on this stuff.

Profilbild von colourorange 🥪
colourorange 🥪vor 25 Tagen

loud and wrong 😭

Profilbild von antoine
antoinevor 26 Tagen

??

Profilbild von Ash Jhaveri
Ash Jhaverivor 26 Tagen

Wut?

Profilbild von ShadowAguy
ShadowAguyvor 26 Tagen

i'll take spark

Profilbild von Nepo Capital
Nepo Capitalvor 26 Tagen

What a dumbass take

Profilbild von ShadowAguy
ShadowAguyvor 26 Tagen

no call, no model

Profilbild von Alice BR
Alice BRvor 26 Tagen

Time for a change of guard, some might say…

Profilbild von perper
perpervor 26 Tagen

no api no score, fair rule

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your agent reviewing its own work is not a check. it is a second opinion from the same source. this is the most common gap in agent systems and it hides in plain sight, because the step exists. there is a review. it just cannot do the thing you think it does. here is the mechanism. the model produced an output from a context. you then ask the same model, holding the same context, whether that output is correct. it answers fluently, because that is what it does. and the answer is drawn from the same distribution that produced the thing being judged. same weights, same window, same blind spots. if the reason the output is wrong is something the model does not know, the review does not know it either. if the reason is something the context does not contain, the review has the same context. the failure mode and the detector share a cause. > why it feels like it works because most of the time the output is fine, and the review says fine. agreement is not evidence of detection. a reviewer that says pass on everything agrees with reality most of the time too. what you actually want to measure is what happens on the cases that are wrong. that is the only place a check earns its name, and it is exactly the place where a self-review is weakest. there is research on this. Huang and colleagues at DeepMind showed at ICLR 2024 that intrinsic self-correction, revising without external grounding, does not reliably help and often makes things worse. > what to actually do move the check outside the model. a test that runs, a schema that validates, a file that exists or does not, an exit code from something you did not write. these are not smarter than the model. they are just not correlated with it, and that is the entire value. when the judgement genuinely needs a model, at minimum use a different family. same family means shared blind spots, and frontier judges measurably inflate scores for outputs that look like their own. and split the work by kind. anything objectively checkable goes to code. only the genuinely semantic calls go to a judge, and those get a rubric written as one line. a review inside the loop tells you the model is confident. a check outside it tells you whether the work is done. save this - then read the eval setup below

Hanako

14,325 Aufrufe • vor 3 Monaten