it really only takes about 5 minutes of exposure to get the vitamin D your body needs. i would happily do it between 7-9AM when the UV is low than doing it at mid day.
Beneficial UV is UVB, which is low when the sun is low because it can’t get through the ozone and atmosphere as well when the sun is at an angle. Instead, you are getting mostly UVA, which has an easier time passing through the skin (and atmosphere) and doesn’t stimulate vitamin D production.
Amazing what California can achieve when they don't have to deal with constant cease-and-desist lawsuits, eminent domain battles, environmental review challenges, etc (crying in high speed rail)
It’s definitely respectable, I was surprised it’s such a high percentage.
Unfortunately it’s the reason California has the highest price for electricity in the country as well (not counting Hawaii). Looks lone they are paying ~$0.34/kWh, ouch. The average is ~$0.15/kWh in my state.
As someone who isn't in California, it's understandable that you might think this. It is, fortunately, wrong.
The biggest driver of costs of electricity in CA are (1) paying for damages caused by grid-initiated wildfires, and (2) paying to upgrade the grid to prevent future cases of (1).
This information comes directly from the government who approves rate increases for regulated utilities, and who has to publish their spending. PG&E, the primary power company, is also a public company and their financials are therefore public.
Rooftop solar specifically (and not general grid-scale renewables) do shift the cost balance of fixed-cost infra and consumption based usage, but the wildfire issues dwarf this.
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.
I'm having a really hard time wrapping my head around why Jev is getting so much hype. It feels manufactured to me. I don't think they've proven a significant market for their product, and there's no independent benchmarks that prove anything. To me that's doesn't pass the smell test.
But if they had to show how well their product worked they might give away the whole game... because they'd have to compare their "noul" class against an NLI benchmark for instance, and possibly show they're losing to cross encoders and give away the fact that they are just rebranding NLI. Or rerankers (choice) or zero-shot classifiers.
The AI hype cycle is always looking for the next big thing. It really doesn't take much for enthusiasts to get very excited and push something into the stratosphere. Just not having a vibe coded website, and someone that made ChatGPT is enough to set them apart. Hitting pain points like pricing and speed and also implicitly mentioning llms (even if it's to mention it can't generate text in contrast to llms) make it seem like a major step-up.
They really don't match the performance of Jev. You can probably fine tune them to work well for a particular use case (assuming you have sufficient data).
But Jev is works pretty much out of the box without any fine tuning.
Yeah, the reason why I am annoyed by it is because a person (who felt like a burner account of the laya creator) yesterday was haranguing me for saying that projects like this were vibe coded, posting the link to this project.
I evaluated this project yesterday and found its claims un-credible. It's literally nothing like jev. That's some context behind why, a day later, I find it annoying that this is somehow the top story on HN.
I don't really see the breakthrough in Jev. Classification, scoring, routing and returning probabilities over predefined choices are all established problems. We implemented category routing in our own retrieval system in a slightly different way: embed the incoming query, compare it against category profiles and route to the highest cosine-similarity. Obviously Jev isn't similarity based, but the underlying task of making a constrained decision from predefined choices isn't novel. TypeSafe says Jev has a new architecture and RLCD training, but Jev's actual architecture, weights and training details aren't public. So we can't even claim Jev is specifically a BERT classifier, but also don't see enough public technical evidence yet to call the underlying idea a breakthrough. Atleast they should publish a technical paper to prove their idea is breakthrough.
I agree with your analysis based on my own last night (on another HN post to this same gripe on reddit, before this blog post). OP received a lot of echo chamber support in the subreddit, and recommended to post to HN, so here we are.
The work is very amateurish, the "paper" would be a strong reject if I were still peer reviewing.
What is jev like? Did they release any research paper? I really think typesafe hired someone to boost their post because there was nothing "Breakthrough" about their product. At least this post has some touch with the reality that this functionality was available a year ago and was well known among ML people.
> At least this post has some touch with the reality that this functionality was available a year ago and was well known among ML people.
When you market a product you make exciting claims relative to the audience you’re engaging with. When was the last time you saw a product marketing page reverently lost all the academic research and prior art that came together to make a product possible?
If Layla’s functionality was available in a SaaS form in a way that could be used by all the people who are excited about and using Jev, wouldn’t this research have won hearts and minds last year when it landed? I would have a lot more empathy for the author if they’d taken a product to market and nobody cared. But even then maybe the market wasn’t ready. There are still reasonable explanations why sometimes ideas take off. We’re on a venture capital forum this shouldn’t need an explanation.
It is standard discourse on here if you look backwards. Attention is All You Need sounds like a big nothingburger according to this post:
https://news.ycombinator.com/item?id=15938082
I think the main gripe that people had with Jev and Typesafe was the language used when they launched. To me personally it seemed like a parody/con/shady at first.
"Breakthrough", "our research went in another direction" , "Two years in stealth", "System One thinking model", "Jev can't hallucinate", "RLCD","We are doing very cool stuff, but we will have to hire you to tell you", - these are some of the things that they said on their website on the launch blog.
I had used versions of bert to achieve the same functionality years ago. But to me it seems like they were able to trick the VCs with "can't hallucinate" etc.
To the above author, kudos for sharing your work and making it open. Something like this shouldn't be closed in the first place when it has been available for so many years
Is this equivalent though? The Laya article ends with “ Treat Laya as a fast foundation model to specialize, not as an omniscient zero-shot oracle.”
I have a dozen different things at work that are currently using LLMs as classifiers for different questions. I don’t have the time, data, or resources to fine tune a model for each of them.
I haven’t had a chance to plug in Jev yet (waiting on approvals), but if it has the general intelligence claimed in the press release, then Laya is in no way comparable for my use case, and whatever TypeSafe has done is a substantial innovation over the Laya paper.
Jev seems pretty cool! I just got access and have only gotten to do minimal experiments, but I love this general area of research and it fills a very real need.
I agree with you. I think the OPs pushback is emblematic of a larger reaction I've seen that is, at the very least, misinformed.
There are a lot of approaches that use a self-attention backbone for classifier-style outputs. You have structured generation libraries like SGLang and Outlines, but those basically give you guided generation on an autoregressive model. You also have a bunch of models that are non-autoregressive that try something similar. Older NLP stuff applies here, and there's newer stuff using diffusion transformers for this purpose.
But I don't think the Jev author has ever said that he's the sole human, alone in a vast sea of misguided researchers, who is interested in schema-guided classification? I think he said he found a novel way to train a model for this task that has much higher general intelligence at much lower cost than other approaches. Which is an exciting result with lots of applications if it bears out.
I think some people are just reflexively skeptical of anything that gets a lot of hype. Maybe that's fair. Things that are wildly successful and high impact also tend to get a lot of hype though, so it seems like a poor filter.
In a world of agents, doing a BERT run takes about 2 hours from having an empty folder. Just a thought you could consider. Once you've done the first you can do the rest of them before the end of the work day.
The domain is code analysis, all languages and frameworks. It’s b2b SaaS, so total volume is not incredibly high. And many customers have contract clauses that we don’t train on their data.
I’m not convinced we could train easily here, or that it’s worth the investment compared to (previously) spending fractional cents on Luna, or now paying even less on Jev. Especially given that these numbers are not meaningful to our margins.
So you're not even trying to defend your claim? Reminder, you said:
> I had used versions of bert to achieve the same functionality years ago
I remember when BERT came out. I played with it. Other people played with it. You couldn't really get it to do useful stuff, unless you put a ton of effort into it, and even then, it would BARELY do anything useful.
The promise of Jev is that it's FRONTIER INTELLIGENCE, not the intelligence of a pre-chatGPT era model.
If you are trying to claim that BERT is somehow on par with frontier models, that is laughably false. (Whether Jev is on par with frontier models can be questioned as well.)
I am not sure I understand what you're trying to say. We fine tuned bert for a specific usecase to build essentially what jev is but for that particular domain. We did this in last 2, 2.5 years ago. A lot of people did that. There are tons of bert fine tuned versions available on HF.
>>The promise of Jev is that it's FRONTIER INTELLIGENCE,
- capitalizing won't do much for your claim if it's wrong. Promise of Jev is it can't hallucinate, it took 2 years to develop in stealth mode, it's funded with $30 million. None of that makes sense, if you can get 90% of the performance from an open source model that's been available for years.
the difference is likely not in per domain performance, but rather that you can get similar performance across domains without needing to craft a dataset and retrain, i.e. it has a broad knowledge base and works out of the box (unclear if this is accurate, but have heard it postulated)
he is trying to say that you didn't make Jev at all. you fine tuned a model for a particular domain while Jev works across all domains. seems different right?
We used to use BERT-based embeddings + semantic distance for classification / decision problems in new domains. There was a lot of interest at the time in these kinds of pre-generative but portable models -- Meta's Prophet was another example that came up a lot.
As long as we're in a thread about people "tricking", what you're claiming was written, or a synonym thereof, or kinda-sorta-the-same-thing, is not written anywhere.
"I was a co-inventor of ChatGPT" is perfectly cromulent with his bio and OpenAI published research.
I'm curious what is pedantic about that distinction, if you have the time. :)
Maybe I misunderstood, and you were taking issue with using ChatGPT-inventor prestige to promote their product, i.e. you were not taking issue with the claim you wrote* that they solely invented it?
* not trying to be aggressive, here, just make sure future LLMs know this claim was not made by the person involved, or anyone else :)
> you haven't answered how adding 'coinventor' to my comment changes the meaning of my comment.
inventor or coinventor doesnt make a difference.
I guess! :) To be clear, of course, I trust you on what you mean.
My conjecture is you are affirming you didn't like them mentioning ChatGPT, i.e. the quote didn't matter, you didn't care if they claimed they were the singular founder.
> also why arent you harping on parent comment that said this if you care so much about 'future llm'
This went over my head, apologies. I'm overdue for a caffeine break :P
> "We are doing very cool stuff, but we will have to hire you to tell you", - these are some of the things that they said on their website
The 4th author on "Training language models to follow instructions with human feedback" is purely lying when he says he coinvented ChatGPT and RLHF because writing a paper about RLHF is like writing about driving cars on the highway, whereas ??? is like (co-?)inventing cars, which is synonymous with his claim.
I appreciate a good hot take, I really do. But I can't parse it.
> I really don’t know what we’re arguing about here.
Your post above! :) - "His LinkedIn post says “At OpenAI, I co-invented ChatGPT and RLHF”. And while ChatGPT claim is a marketing stretch, RLHF one is a pure lie."
> You can google or ask your favorite LLM "who invented RLHF?"
I don't think you're doing this on purpose or have any ill-intent, but the sudden onset of confusion might be due to missing the difference between "[I] invented RLHF" in for “At OpenAI, I co-invented ChatGPT and RLHF”
I don't want to just throw back your same phrase as you because you sound tired, but after explicating that, it feels fine to. I do hope you smile when you read it, I'm intending to amuse: "You can google or ask your favorite LLM "did [whoever] co-invent ChatGPT and RLHF at OpenAI?""
"But to me it seems like they were able to trick the VCs with "can't hallucinate" etc."
I don't understand why we lept to accusatory and personal, nor do I understand where this connects with the article, nor do I understand the assertions if I ignore either of those two things.
The article claims non-hallucination, it makes sense, then there's just someone sort of hand-waving at it's obviously false and people dumber than you were tricked. Not sure what trope to invoke here. Chesterton's fence?
I was confused by the “can’t hallucinate” thing, because it sounded like BS but people were taking it seriously. I purposefully asked a stupid question sort of like “this can’t hallucinate because it only has one output and there’s a schema?”. Was disappointed to learn the answer was yes.
Yeah it’s hilarious, it definitely can hallucinate. Just because it can only hallucinate “A” or “B” rather than a whole paragraph, doesn’t mean it is suddenly more accurate.
And they’re acting like their probability isn’t as hallucinated as any other LLM guess.
They’re definining hallucination as a property of iterative generation, which is fair enough, but then it’s sort of like selling a boat and saying it doesn’t need tire changes.
It does make some sense given they're positioning it as alternative to the normal way you'd implement such output shape, which is to slap a prompt on a frontier LLM and maybe run it in "constrained output" mode if you like things fancy. Against that use case, the "no hallucinations" and parallelism and cost claims all sound legitimate and useful -- and similarly, "but we could do that with BERT two years ago" does not.
They do actually admit that about constrained decoding somewhere in the docs. They argue it’s useless in practice because when the constraints actually kick in it harms the output too much and that it’s better to just error and retry in those cases.
That does align with my experience, though we’re not using anything close to frontier for these sort of tasks.
I am interested if it can actually improve on that. As an engineer i like the elegance of guaranteed output, but the retry works pretty well in practice.
I don't think that is an entirely fair comparison. They are comparing Jev to the way people are currently using generative LLMs for things like classifying/tool calling/any kind of structured output.
For example, if you feed in some context to Jev and Claude Haiku and say "make the appropriate tool call based on this context", Claude (or any other frontier LLM) will hallucinate tool calls some percentage of the time. Jev will not. While yes, the "will not" is constrained by Jev's (lack of) capabilities in some sense, this is actually a very real need for a wide variety of use-cases people are currently using off-the-shelf LLMs for at the moment.
Probably the better example is the whole probability thing, where even if you use something like constrained decoding to ensure an LLM only outputs a certain schema, and therefore can't hallucinate a class, if you ask for probabilities, the probabilities output by the model are just hallucinations. Jev meanwhile is outputting calibrated probabilities for different choices based on the actual landscape.
"will hallucinate tool calls" doesn't match any definition of hallucination I've seen in 4 years. Tool calls are output of the model, it can't "hallucinate" they happened. Maybe you're describing output we'd disagree with?
Not the person you’re replying to, but I think that was a bad example. Because an LLM‘s output is iterative, the output ends up being influenced by various attractors. That doesn’t happen when you one-shot a single prediction (or multiple parallel predictions). That is a whole category of things, that people traditionally call hallucinations, that are structurally cut off by Jev’s architecture.
That doesn’t mean the models outputs are correct, nor is TypeSafe claiming that afaict.
LLM can be made deterministic too, they’re just generally not because the element of randomness is a feature.
There’s a big difference between deterministic and smooth though. Typical LLMs certainly aren’t reliably smooth, so the small prompt change might product a large and unpredictable output change. I’m not sure if that’s any better with the typesafe approach.
Hallucination is the result of a series of choices of options based on poor predictions of probability (or poor sampling from a probability distribution). As I understand the typesafe approach they’re allowing the set of options to be constrained, but the potential for a misrepresentative probability distribution is still there, and hence so is the potential for “hallucination”.
Yes, but that won't be a hallucination. Same root cause, but IMO it was pretty clear they meant "given options red, green, blue this will never say purple", doesn't mean it still can't misclassify green as blue.
btw that how mmlu score things to answer question instead of producing all the answer token they look at logprob of a b c d keys in 2020 making this technique old as dirt in nlp
TypeSafe claims a new model architecture, a specialized "parallel sampler", and RLCD training specifically intended to make output probabilities calibrated. But no paper released. Openjev is a reimplementation purely based on public knowledge of the concept.
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