Commenters question the broader framing and whether this is hype or substance
2 Sep 22 12:23 PM · 1d ago · 13 comments · 1 source · development 2 of 2
Several voices expressed skepticism about the entire premise—some dismissing the article as LLM-written and outdated, others questioning whether Jev represents rediscovery of pre-transformer classifier ideas, and one suggesting the viral success is marketing rather than genuine innovation. The conversation shifted from OpenAI risk to whether classifiers are actually the future or a step backward.
“Everyone was obsessed with classification prior to transformers, then we had 5+ years of everyone (rightfully) obsessed with next-token prediction. What's this sudden resurgence of interest in classifiers?”
halyconWaysJohn Berryman Arcturus Labs analystTypeSafe Creator of Jev classifier modelOpenAI Potential fast-follower
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What people said 24 voices · best of 26 · verbatim
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People are desperately trying to cope themselves into thinking that there are alternatives to scaling up transformers to AGI/actual competition to OpenAI or Anthropic. Jev, continual learning, linear attention, local models, non-transformer architectures etc. Imo these are just random technologies that nerdsnipe your average twitter or hackernews…
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The next step is to rediscover Eliza :)I did so a month ago, I developed a deterministic agent framework that works with a set of predefined intents, it is instantaneous and fully deterministic.It works thanks to FlintParser (https://github.com/gioblu/NPC-Forge/blob/main/src/FlintParse...) which can transpile plain English to any Programming…
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People in my lab (sklearn people) developped something that I feel close to jev but focused on tabular data : https://tabicl.readthedocs.io/en/latest/This is a transformer based classifier with massive pretraining on synthetic datasets and it outperforms boosting classifiers on many benchmarks without the need of more gradient descent steps (the…
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"I don't see much substance to this buzz..."Agreed. This isn't new. I led a research team at a Fortune 500 that used a transformer based classifier approach in a commercial product as far back as 2022 and we didn't come up with it. It was already common enough that we found the inspiration for our implementation on some web forum. Models like…
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Every single AI shop has a ton of in-house classifiers already. Some are used in inference pipelines (e.g. safeguards), some are used in data preparation, training, various one-off and intermediate tasks etc. Offering them on a public API doesn't always make business sense. I don't see much substance to this buzz, looks like people that are new to…
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Isn't this more and more likely on all shapes of model evolution? The providers will all copy each other.And in this case I hope its true. I've been classifying a lot of email and while OpenAI `text-embedding-3-small` has been very helpful for fast and cheap embeddings, initial tests with Jev are very promising and much more ergonomic.I put more…
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Everyone was obsessed with classification prior to transformers, then we had 5+ years of everyone (rightfully) obsessed with next-token prediction. What's this sudden resurgence of interest in classifiers? I thought we all agreed that ML tasks generally require something far more advanced than pretrained classifiers. My timeline was also…
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Rather than focusing on OpenAI in particular, let's just say that there are many smart people at other AI labs and if it seems like it will be popular, this technique will probably be copied. What would prevent them from adding another API?Hopefully there will be some decent benchmarks and gateways for switching between providers easily.
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> Every major AI shop has a ton of in-house classifiers already, big, small, generalistI think building generalist classifier is some open ended research task, where frontier labs can contribute: different internal reasoning, instruction tuning, building datasets and benchmarks, building and distilling super large models.
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This. It’s machine learning vs. “AI” for the uninitiated. Soon there will be a new ground breaking model that does k-means clustering and will get a billon dollar funding (but only if you're young and live in SF)The good news is it’s fun to see people discover and get excited about things that I like as well.
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Why would OAI need to follow Jev? I really think this is paid by Jev. Jev itself won't have lunch money in a shortwhile because there are literally 10s of free alternatives available which can be run locally on basic consumer hardware. Terrible utility aside, there is no sensible business proposition in Jev.
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All discussions around Jev and its moat and whether it is new or not all focus on the architecture part, but what Diogo always states is that he thinks their secret sauce is in the data and the post training with RLCD … that is also why I think these clones using RLHF trained LLMs completely miss the point.
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I'd be surprised if they weren't aqui-hired by one of the big labs as a unicorn.The ability to use classifiers under the hood for the larger models has the potential to dramatically improve cost and throughput, allowing them to increase margin on a very similar service.
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If your "master AI" is good enough, it should be able to find and learn about and use specialized tech AI like Jev if it suits your goalsand then whatever tech it is will be absorbed/assimilated/Sherlocked into the leading products anyway
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Correct me if I'm wrong, but a zero-shot classifier like Jev is fundamentally different to a classifier with a fixed task (e.g. for safeguards), unless they trained a general purpose system to complete the safeguard task, which seems unlikely.
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I fed into the hype at first. Testing Jev and Laya, they both suffer from the same issues as LLMs that stop them being useful beyond limited classifications.I can't see any benefits that a typical ML classifier would not be better at.
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Entertainingly, OpenAI had a general purpose zero-shot classifier API built on GPT-3! Just no one ever cared that much about it, so I guess it got dropped somewhere along the way since 2020/2021.
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There are already open-weight versions of Jev that are equal or better performance, and can run on regular hardware. It took people 24 hours to create them. OAI could create Jev in a day if they wanted to.
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Yeah, if your "master AI" is good enough, it should be able to find and learn about and use specialized tech AI like Jev if it suits your goals, before you even know about such stuff.
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the real win with jev isnt beating a trained classifier on accuracy, its that you can change what you're classifying by editing a prompt instead of retraining and redeploying a model.
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For me, I think the big deal is that it promises to be general and broadly applicable and high quality. That's new and special. But we'll wait to see if the claims actually hold.
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Not many people know this but OpenAI even has a FREE separate moderation model and API endpoint that can classify user generated content.
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I think most labs have internal classification systems across functions, some generic, some specialized etc.
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love JEV, but you are right - how much time before a Frontier Lab release the same?
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