VB6 was peak programming and the nostalgia levels are insane.

That's not at all true.

There have been tons of applications for this. People were using earlier LLMs like BERT for classifiers long before LLMs became viable chatbots.

I think GP has a point, though. BERT models have existed for a while but OpenAI made classification via LLM convenient and accessible for regular developers.

People didn’t know they wanted classifiers until OpenAI gave them a taste.

I don't know if that first part is true? Classifiers were/are one of the dominant applications of classic ML and neural networks, especially in production. Even today, image classification, object recognition, language detection, segmentation models etc are still super common.

I think the hype with Jev is just that, while structured generation is great, LLM judges tend to kind of suck for precise classification. And the more powerful the base model, the more accurate they can get, but they get increasingly expensive/impossible to finetune. It was specifically the latency/price point Jev offered vs. the general accuracy it claimed that generated all the excitement. Plus the promise of cheap calibration (tuning).

"Jev exists because LLMs exist" is kind of a truism, as Jev apparently is literally a Transformer model.

How many people were doing ML work pre LLMs? And how many are using LLMs now?

I think the answers are "a lot" and "a lot more"? But what I'm saying is that Jev's virality isn't because people didn't have access to classifiers before it. Jev's general claims about capability and performance vs. cost would have been a very big deal 5 years ago too. In a vacuum, the idea that you can get a general classifier that is very accurate across any domain and on any modality with minimal latency and a very low price point is wild.

In early 2016, Clarifai's core product was basically just an image classifer exposed via an API. And at that point, they'd raised $40 million--the same amount as TypeSafe.ai/Jev--and they were experiencing viral growth among developers + signing contracts with a bunch of flashy logos. The demand was so high that Amazon launched Rekognition and Google launched their similar APIs to compete.

The AI hype cycle and the number of people thinking about using AI certainly puts more wind at Jev's back, but even in an alternative universe where we don't have contemporary LLMs, Jev's core claims would be remarkable and there would be a big market for it.

Laya is pretty easy to set up on its own without ollaya. I just did that and replaced my current jev API usage to laya running on a GTX 970 with 4GB of vram.

Very small context window, but for some existing small llm work I was doing, it was a drop-in replacement and it makes me happy I can get use out of old hardware I have running.

We've already started using it for some pretty powerful decision tree stuff. We're just scratching the surface. We shipped an extension for Swamp[1] a few minutes ago and the combination is great!

The one downside is that the context window is very small (32k.) So some initial ideas we had for initial evaluation of code reviews won't fit yet in the window.

1: https://swamp-club.com/extensions/@swamp/typesafe-ai

Because skill adherence is awful, especially after a few turns.

This is not correct. You apply to join the program. Somehow my account applied and I am in it. I am not a twitter power user at all.

Every once in a while I get a notification that says "the community needs your help."

I usually never weigh in unless it's something I know intimately.

This is basically what Swamp is[1]. You give an agent a typed interface to extend itself (or use other peoples extensions) into the systems you need to fulfill your request. Think of it like on-demand tool calls. Then it records everything that happens in the swamp. The swamp can be single machine, multi-machine, or centralized with your co-workers.

As a result, everything compounds. The work I do doesn't need to be re-derived by the work you do. Typed models keep everything repeatable and deterministic. Huge reduction in token spend and huge increase in speed.

1: https://swamp-club.com

Policy documents do not govern agents at all. Conformance is distributed and completely unreliable.

How many times have you told an agent not to do something then had to correct it?

You must always flip the frame. Objective analysis is way better with llms than steering via skills.

This is just a small example of why "loops" became popular for a minute and now it is "graphs"

This is seriously impressive, and if you have used agents enough you're not surprised at all.

Like the time I asked it to find the IP address of a vm, so it ssh'd into the VMHost and scanned the arp tables to find the MAC address for IP resolution.

Or the time it used Docker on the machine to bypass the fact that the user doesn't have sudo.

If it's possible, given sufficient time and resources, it will find a way. This shouldn't surprise anyone.

Not to shit on the hype, but these are reasonably documented methods that surely are part of the training data

Of course they are - and that's the point I am making. The agent will use every tool in the tool bag. And there's something cool about it systematically trying to achieve its goal.

What I don't see is it inventing anything novel to do it. So it's not a digital weapon or scary or whatever sort of weird marketing spin anyone is trying to put on it.

my bad, I re-read your comment now and it's clear that this was precisely your point.

And these things are documented for humans do, and yet only a tiny portion of humanity can do these things.

When seeing how agents put together exploit chains they are far better than most people, you start getting to the point that they are just below the capabilities of the top researchers. Now remember that quantity is a quality itself and while there aren't that many good cyber security researchers, we're shitting out thousands of GPUs per day.

Brings me back to learning guitar using a version of TabIt[1] that I hacked and then ultimately paid for. They had an incredible community where people uploaded tabs of your favorite songs. Then you could use TabIt to learn how to play.

1: https://www.tabit.net/