For reference 50% of spending comes from the top 10% of earners. To be in the top 10% you need to make 250k. So it is definitely true that we have a K shaped economy, but I don’t think that’s a great explanation for why tech workers are spending less on tutorials.
Additionally many tech workers’ companies will subsidize spending on learning materials. So we should expect demand to be more stable than for things like bicycles which are or TVs which aren’t usually directly subsidized.
One person’s helpful metadata is another person’s noise. It’s much better for a door to visually indicate that it should be pushed open than to put up a sign there.
However, doing the former requires a level of empathy with humans that LLMs rarely have.
Human brains have caloric demands. It is possible for humans to process enormous amounts of unrelated facts to make a decision, but it’s tiring. It’s much better to not do that, especially just to get some basic information.
To anthropomorphize a bit, an LLM might find it charming and interesting to read someone’s life story as a preamble before their taco recipe. Humans by and large find that annoying, not because we can’t understand the biography but because processing that information is not free.
So it’s probably possible to design using an LLM. You would probably have to be intentional about it.
Much has been discussed about AI coding but there's been a lack of discussion on the design/UX side as that's also a step being rushed by via prompting + "looks pretty enough".
We're entering an era of hyper dense UIs with plenty of excess and quick workarounds.
But zero-shot classifiers with this level of intelligence, world knowledge, ergonomics, cost profile, and ease of use are new.
I feel like good engineering doesn't just ignore those things, or at least it didn't before recently. Now I guess social media has added a pressure to reduce everything to a hot take.
> Ergonomics, cost profile, and ease of use are new.
Following AI from the academic papers side; jev really feels silly. They one-pass the LLM tranformer stack and tune the output network for a probability value.
(some clever pararellization optimisations to make it viable to offer as an api, since the normal kv cashing no longer works if you oneshot the tranformer)
The largest change is the packaging; An api with a tolken based pricing, and a schema to define the output structure for quick setup.
Previous projects would probably involve installing pytorch, running a converter script on Qwen, and write a fair bit of matrix math to change the output shape.
An LLM is a zero-shot classifier with a large number of classes. All you need to do is establish what the output means and you can fine-tune an LLM final layer for this task if you like (and others have done). A student of mine did this as an exercise two years ago, and it was cool, but not publishable.
I agree with you on the "ease of use" business though. No one thought to make this sort of thing commercially available.
But there is no hot take here. Jev is not some new paradigm; engineering-wise, it is a trivial modification to the existing pipeline. That doesn't mean it isn't commercially viable.
No it wasn't. Those were models trained from scratch, required large scale data, and the nontrivial parts involved training at scale and the autoregressive task which no one expected to work as well as it does.
It is the difference between developing a foundation model, and using one. I believe Jev falls in the latter category, because the task itself is no different, only the output.
No, all they had to do was come up with a quality post-training recipe, production inference stack that wouldn't fall over, GTM, documentation, schemas, etc. etc.
(also most signs point to this being LLaDA 2.0-adjacent so throw in solving some substantial mid-training)
I think it's 100% a hot take to call what they built trivial. Or at least it used to be.
There was a time when that kind of stuff was something between sour grapes and cluelessness about the gap between an idea and an actual commercial product deployed at scale, but now that's just weirdly normalized.
In fact, if anything I'm the weirdo for repeatedly taking issue with the way people are trivializing it ¯\_(ツ)_/¯
Jev actually isn’t anywhere near the top. It even loses to open weight clones. This tells me that whatever their “calibration” dataset is, it doesn’t seem to be anything special.
You linked to some weird subtable that labeled: " Not the default — not the JevBench Score", that can only be reached after you see what I just linked... lmao are you really this hard up about things?
Jeeze, this is getting sad. I guess after all the mass-psychoses where people thought pointless things are going to change the world, we were due for a mass-psychosis where something interesting just has to be pointless?
Because I was specifically responding to your claim that Jev’s training recipe would give it better accuracy than others. It doesn’t have better accuracy than others. You could do as well or better by distilling qwen for example.
Jev is ranked higher than others on the overall benchmark due to speed and/or cost, not accuracy.
Architecturally, it is trivial. That's something the community would have consensus on, so not a hot take.
I see your point, but Jev doesn't exist in a vacuum. When one (like me) says "trivial", they mean it relative to other attempts and developments in the field, all of which require everything you've mentioned at minimum. Commercialising any product, and doing it well, is hard. But the R&D factor here is substantially more straightforward than almost any other product in its category, because there is no architectural breakthrough here.
"all of which require everything you've mentioned at minimum"
Sorry who else did everything I mentioned? I think the guy behind Laya tried after noticing Jev's traction... but the site's auth went down and has stayed down for a day now.
"substantially more straightforward than almost any other product in its category"
More straightforward than the spite projects based on constrained decoding? Or Laya with it's couple of days post-training ModernBERT?
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I have no doubt other teams can build models like this and I've love for a frontier lab to give us an even smarter model with these ergonomics... but in the rush to show Jev what's up, we're mostly getting slop.
PS: I don't know anyone who's done anything of note who uses trivial like that. The commentariat do, and the "I could have done that" crowd do, but I don't pay much attention to them until they actually do the thing.
By category, I meant other language models in general. The point of others putting something up to beat Jev is to show that, to date, no one has bothered to produce something like Jev, because anyone with decent LLM experience can roll their own for purpose with little effort and have been doing so for years. And can beat it on any metric you choose.
Let me put it this way. OpenAI and Anthropic have a slight moat over the Chinese labs because they have strong training data and the most advanced RL strategies. It will take the Chinese labs significant R&D effort to bridge that, especially in math (and there is a good chance they will, provided they want to).
Jev has no moat other than the fact that no one else has bothered to package a model in this way. Another lab could build a strong competitor very quickly if they want to put the effort in. That's the point of this post. There is no uncertainty about what they have done, nothing to figure out. Someone just needs to do it. I'm not sure what to say if you can't see the difference between the two. Jev is worth celebrating because of the idea to package it in this way. But it is not a paradigm shift and that is likely a problem for them.
They almost certainly would perform worse than more specialized classifiers trained with less data. It’s kind of a paradox of generalization. I think there’s an interesting space where you use generalized models to generate ad hoc specialized classifiers.
Depends what you man by "more specialised". You wont train very good language understanding without alot of data. It probably uses the core tranformer stack from an LLM.
Classic classifiers are regularly just tuned general models; Training a CCN on ImageNet and tune it for cats and dogs gives better results than just training it on cats and dogs.
There is likley a small network used to tranform model output vector to probabilities, but that wouldn't be massive. Retraining that small network for specific task may beat jev; but that's bairly considered training by modern standards.
Expecting a strong zero-shot performer to perform worse in a low data regime?
That only makes sense if you try to rope in data previously used to establish the model's priors, but that wouldn't make sense in this context. That same additional data is what enables things like...
> use generalized models to generate ad hoc specialized classifiers.
Slowing is a concrete and clear term and so they would be called out as liars immediately, it's also embarrassing for a business. Pace(ing) is vague and unclear non-word, so they can't be called out on something which can't be defined and also, because it is primarily only used by runners, cyclists or other competitive sports, they are kind of signalling to the VCs that they don't actually slowing, they are in a competitive race instead (wink, wink).
Right, if a model says it is Qwen there is no way to distinguish a ModernBert fine tuned with Qwen completion data from a Qwen model fine tuned with completion data.
It’s also entirely possible that they used completions from a pool of open weight models.
> employers expect health care plan costs to rise by an average of 11 percent per worker in 2027, unless benefits are cut
I am sure most workers would prefer to get that extra 11% as a cash raise, but because healthcare costs are out of control the same care costs 11% more.
Certainly some companies will throw up their arms and hire someone overseas instead.
The cap is generous. If an insurance company is hitting it, competition is failing to keep them in check and lifting the cap would just let them gouge deeper. Of course, that doesn't keep them from constantly yapping this talking point, as if it wasn't that way before, as if we don't have evidence that it wouldn't work. "Just lift our profit cap" is self-serving baloney.
That's not the issue, it's because an insurer's absolute profit only grows if the premium base grows. This removes any reason to compete on reduced costs and an incentive to have the costs go up.
If they aren't at the cap -- which is generous -- they can grow their profit by controlling costs. For once.
If they are at the cap, they wouldn't have passed on the benefits from controlling costs anyway because the cap is generous and reaching it proves that they have enormous pricing power.
There is simply no way that lifting the generous cap results in benefit to the consumer. It's a slimy self-serving talking point through and through.
Except you can grow the premium base by adding customers as well, as you are featured in a direct price comparison on the ACA website. Higher premiums mean less customers, unless you are offering more services that they find valuable.
Imagine meeting a person who could do all of those things.
“I once met a person who could beat any grandmaster in chess, translate any language, and complete international math Olympiad problems. He couldn’t solve any Millenium problems though, so I’d say he was a midwit at best.”
Additionally many tech workers’ companies will subsidize spending on learning materials. So we should expect demand to be more stable than for things like bicycles which are or TVs which aren’t usually directly subsidized.
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