hbrn
- they have implementation cost.
- they are usually born from arrogance mixed with lack of understanding of existing solutions and their tradeoffs, and typically end up being a net negative.
Before LLMs, the cost is what primarily used to stop us from building them, it used to be a natural filter for bad ideas. Now that the cost is gone, it's nearly impossible for most teams to stay disciplined and not be building those.
Why wouldn't you build a software factory? If you succeed, the ROI is infinite. Who cares about immediate customer needs, we're building a utopia here. Once it's done, all customer needs will be solved. Just one more month, I promise.
Unfortunately this starts to break when there's more than one you on the team.
I see a lot of hand-rolled NIH solutions that create value for one person only, and are a net negative on the team.
This is not a “system one” question.
You’d be much better off using a proper LLM here: more accurate, can provide justification, can be steered when mistakes are made. Jev is going to be a coin flipper with almost no knobs.
Did i hear that correctly? In order for Jev to be accurate you have to give it the answer before asking for the answer?
(btw this is exactly how Jev is playing games).
If they weren't lying about $100M ARR, they had to be at 6.5T/day for a week to hit that number.
They also claimed they were at 1T/day just couple weeks ago. That's $15M ARR.
85% drop in just a couple of weeks?
It's often nearly impossible to know in advance how hard the task will be, so the model has to guess, and will inevitably miss. Just because you know how hard the task is, doesn't mean it's obvious. Often it's only obvious to you, because you have additional context that models are missing.
A bad guess can lead to either context rot (if subagent wasn't used), or slower execution (if subagent was used but it turned out to be a bad idea). Both are bad UX.
Now, we could improve accuracy by having the harness do a research on each task before executing... which leads to even slower execution, also bad UX.
Now, it could be viable if your business has literally hundreds of problems thats require classification. I just haven't seen those.
I treat the fact that almost noone was doing that as evidence that decision models aren't that useful/groundbreaking. That, and the fact that every single demo I saw was either fake (e.g. playing games), contrived, or plain wrong (e.g. using Jev for compaction).
You can create a company with 2B shares and sell one share to your friend for $1000. Lo and behold, you own $2 trillion dollar company, leaving Elon behind.
But confidence value is just a function applied to probabilities. It is not coming from the model, and it carries no additional information.
It is documented btw, and yet you will see plenty of claims that Jev is better than LLM because it returns both.
A business doesn't need Jev for the sake of Jev. Most business are solving specific problems.
And fine-tuning got a lot cheaper these days - I've seen claims here on HN that ~500 examples is enough to beat Jev.
> "nobody thought it's a good tradeoff", which again is false, there was huge demand for a cheap and accurate general purpose classifier
There wasn't. The hope is that there was a latent demand, but we've yet to see if it's truly latent or just manufactured.
Noone is saying "hell yeah, finally we got a general purpose classifier, my business needed it so much". The typical message is "this seems cool, let me see where I can apply it".
The fact that name itself is a play on Jevons Paradox illustrates that there was no demand until Jev was released.
Typesafe claims that Jev is calibrated, but there are plenty of examples where it completely fails (predicting die roll being the most obvious one).
Unfortunately calibration is hard to benchmark.
We've yet to see whether this is true, or is it just manufactured demand. There are dozens of Jev demos, but pretty much all of them are either cool but useless, or simply fake (i.e. harness doing 99% of the work).
You're the one being deceptive here. Jev is trading accuracy, speed, and cost for generality. It's less accurate, slower and more expensive than trained classifiers. So it's still 2 out of 3, but with decimals. Maybe 2.2 out of 3 if I'm being charitable.
And the reason we didn't have that before is because nobody thought it's a good tradeoff.
Here's a hint: confidence is not generated by a model.
Plenty has been said about this claim. If you're still falling for this, I feel sorry for you.
If you remove wheels from your car, your car will get a "no speeding ticket" property, and yet there's nothing exciting about it.
> You can’t get that with a fine tuned LLM
Of course you can. All these claims are nothing but marketing.
But why? If the simplest way to achieve Jev's capabilities (accuracy, cost, latency) is by fine tuning a small model, what makes you think that this isn't exactly what Typesafe did?
And even if they did something different - what makes you think it was a good idea in the first place, given how easy their results were replicated without any "secret sauce"?
Review tool is an interesting one - I find LLMs to be quite useful for code reviews, but people should really stop associating code reviews with Github/Gitlab and running them as a part of CI. Those platforms were built for humans because reviewing patches over email was a terrible experience. Clankers have no problem with raw patches, in fact they prefer them over fancy web UI. Code hosting platforms are not a good place for agentic code reviews, and they never will be.
Most LLM reviews should only run locally on developer's machine, in your regular coding harness.
Google Video was a competitor to YouTube and it's a perfect illustration of what native Google products look like.
They are truly horrible at generating docs, prompts, knowledge bases, memories, etc.
- it feels productive and has illusion of value
- it seems harmless/free, refusing means that at best you'll be at net zero
- if you do refuse, now you actually need to think about what should be documented
So yeah, LLM-generated garbage will inevitably spread.
The good news is that teams that do have the discipline are way more competitive today.
Basically no one is doing that since 2024, have you been living under the rock? Read about constrained decoding.
> I have had failures when I want a field to be specifically formatted Markdown or something.
And Jev has an advantage here because it can’t generate anything?
To be fair, he's just asking how to get customers. And the post is 2 days before Jev's launch date? I don't think he's trying to sell Laya there (though he probably will at this point).
But like you said, at the end of the day he's just a rando.
He's not asking for $40m, not saying "I made ChatGPT, but i hate it, so I built the next big thing". Not claiming to co-invent RLHF.
Laya is just noise. Jev's bullshit affects me today - I see people injecting it into the codebases where it has no place.
That said, Typesafe false marketing caused Laya to fit perfectly into pretty much every advantage that they are claiming: "system one decision model", cheap, fast, no hallucinations, structured, confidence output, parallel, calibrated. Their BS is their own demise.
I think Laya's author genuinely bought their BS and thinks he built the same thing. Unlike Typesafe, I don't think he's intentionally misleading people.
The only unique thing about Jev is that it's a general purpose classifier. Funny enough, they were so busy spreading marketing bullshit that they forgot to mention the only real thing that makes Jev unique.