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by stusmall2 hours ago |
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upvoteby binlog2 hours ago|
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This entire conversation around Jev seems weird to me. Like... we started from neural nets that could do basic decision making and classifications pretty well, then trained larger and larger language models to get to where we are now. Now suddenly everyone is going crazy because someone trained a smaller model that is adequate at making decisions? We already went through the "look this AI can play pokemon terribly" phase like a decade ago.
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upvoteby c7b1 hours ago|
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A pre-trained universal classifier that can replace specifically-trained ones would have been considered just as much science fiction in the 2010's as the capabilities of modern LLMs. I'm not sure Jev is actually there yet, but at least it sounds theoretically doable today.

That being said, one thing having been unrealistic 10 years ago and just about possible today doesn't mean that it's going to change the world the same way another technically related, previously-impossible thing did. The Jev hype gives me a bit of the "you're still early to crypto" vibes of some later altcoins. I really like the idea, I think it's going to open up possibilities for using classifiers where we wouldn't or couldn't have trained one before. I'm crossing my fingers for an open weights version to drop. But it's still just a classifier, people have built similar things before Jev, the one thing that really stands out about it is their ability to generate hype.

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upvoteby someothherguyy57 minutes ago|
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> but at least it sounds theoretically doable today

why

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upvoteby thornewolf31 minutes ago|
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We have a bad universal classifier now (via Jev). 0->1, one might say.

A bad universal classifier does suggest a good one later. And that is exactly what I would call "theoretically doable"

That said, I don't think that Jev is a magic breakthrough or anything. I think it is just a particularly good narrative with an easy way to try it out.

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upvoteby 27 minutes ago|
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upvoteby solidasparagus22 minutes ago|
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The cheap, fast and smart-enough LLM space has been wildly neglected. Jev is one of the few players truly targeting that space. And for a lot of people it is the first time they are asking "what could I build if llms were interaction-speed fast?". The answers are cool, the problem is that Jev is not, I think, smart-enough yet to have that many applications, but it's smart enough that you can start to see what they will look like.
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upvoteby gchamonlive25 minutes ago|
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For what it does, it classifies, orchestrates, operates and delegates tasks exceedingly well for its size and weight. It's ridiculously cheap and efficient, but if you can only see progress in terms of raw cognitive power then you'll surely miss how interesting this is.
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upvoteby osener45 minutes ago|
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It is impressive, but all the hype and fake demos are selling it as a model that is as smart as frontier reasoning LLMs in the decisions it makes yet much cheaper and much faster, which is not true.
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upvoteby pancomplex37 minutes ago|
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Nothing fake here and fully open source if you wanna take a peek. It does make a bunch of mistakes, often. But it eventually recovers!

https://github.com/christianmat/jev-pokemon

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upvoteby gchamonlive26 minutes ago|
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I think that misses the point of Jev being ridiculously efficient while maintaining adequate intelligence for automation tasks. We have to train our minds to filter out branding and marketing.
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upvoteby ford2 hours ago|
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I agree it's overhyped, but the transition to a general purpose classifier (vs a narrow scope classifier) is new and noteworthy.

Ie the famous "Hotdog" clip from Silicon Valley [0]

https://www.youtube.com/watch?v=ACmydtFDTGs

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upvoteby janalsncm32 minutes ago|
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Maybe noteworthy but definitely not new. The category of zero-shot classification has been around for a while.

Example (2022):

https://developers.openai.com/cookbook/examples/zero-shot_cl...

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upvoteby azan_2 hours ago|
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Making decisions quickly, cheaply and without having to train your own model.
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upvoteby joshuat2 hours ago|
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Math.random can make poor decisions quickly and cheaply
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upvoteby azan_2 hours ago|
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Benchmark it against jev and you'll have your answer.
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upvoteby zahlman44 minutes ago|
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I mean,

> get stuck in strange loops of going in and out of the same door to no end

Math.random is statistically unlikely to do this.

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upvoteby jbjbjbjb20 minutes ago|
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Jev is for single shot classification, not multi-step RL environments with delayed reward and explore/exploit. My guess is it would go through the door with high confidence every time unless you change the input to add the history.
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upvoteby pancomplex6 minutes ago|
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This runs entirely on Jev as the only AI with a typescript harness that feeds it selective context.
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upvoteby ralusek1 hours ago|
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The exact message I sent my friend this morning:

> the most interesting thing about this jev stuff

> is that people are seemingly like

> completely disinterested in how smart it actually is

> I haven't even heard it mentioned a single time how it actually compares to other LLMs coming up with their own classifications. Just: it's fast and cheap

After watching a few minutes of this it makes me think that maybe we should be a little more interested in how smart it is.

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upvoteby stusmall24 minutes ago|
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There is a lot of room for a lot of different models. For many use cases, intelligence beats out all.

For me in my day job, having extremely fast low quality decision makers over noisy inputs is very valuable. I work in security and having something that can help triage alerts, classify items and group things together is extremely valuable. It doesn't need to be perfect. Just being able to take a set of inputs from deterministic tooling and to be make general priority classifications goes a long way on helping humans look at the most important items first.

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upvoteby pancomplex2 hours ago|
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Like others have mentioned in this post, I think a mix of models like Jev for simple stuff + a smarter reasoning model for more strategic thinking is the optimal solution. This experiment however is purely Jev. Which sometimes can be kinda dumb.
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