A lot of AI stuff is like that, actually. GPT 2 and 3 were mainly curiosities that would get articles on tech news sites but didn’t attract much attention from the general public. But after RLHF the UX improved dramatically and ChatGPT became mainstream. A lot of MLEs underestimate how much UX matters, and the same is true for Jev.

> how do these models calculate probabilities?

The short answer is: more and better data.

The long answer is: probably the same way any other classifier does. Take your ground truth probabilities and your predicted probabilities. The gap between those is called cross entropy, which is used to push the model towards the correct probabilities.

I think its more like how LLMs caught everyone by surprise. A reasonable person would not think such a simple system would have been this effective, and yet here we are. Distilling that system down even further into a decision model again does not seem like it should work. But it seems to.

The whole system is foreign to us, and makes us rethink what "intelligence" actually is.

You can do it with LLMs by looking at the logprobs of ('yes', 'Yes', 'YES'...) vs. ('no'...). This works for bool or whatever they call it in jev space nool I think?

You can do it manually or use ready made libs. I know there's one in python on top of vllm that can do something like this.

We were doing zero and few-shot classification with LLMs since before they could reliably complete whole sentences. It’s an easier task than generating coherent text. The model needed to be tuned for classification (not generation) and for the domain, but it would often succeed at new classification tasks with a simple description.

I guess given the huge advances in LLM since then, these models probably achieve much better accuracy on a wide range of domains with lower effort compared to what we were doing, which makes it compelling as a product. And yeah, the models probably were already good enough a few years ago too. Other factors include how easy it is to release products at high speed now, and the desire in the industry to reduce token spend.

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Image input is a primary reason I've never moved off my "home rolled" version of Jev (that Claude wrote for me over a year ago) for my file organizer/indexer. Takes images resizes them to 1k, then asks a handful of questions about the image for tagging purposes. It is able to process around 3 images a second based on Qwen 3 VL, but distilled and optimized for 1k images and up to 2048 token inputs (after the system prompt, then the questions are asked one after each other reusing the cache). Documents that are longer than that end up going through a summarizer that runs on my other GPU (Qwen 3.6, I think). And all of that gets fed into a vector search engine, for easy lookup later.

I'm not even sure what all is going on inside it anymore, because I just add new features when they popup, and tell Claude to do it, and it let's me know when it is done, and every new "input feature" can run against the entire catalogue of files.

Routing, classification, replacement for LLM-as-judge

What got me into Claude Code started with Google Antigravity. Somewhere along the way Antigravity took a dive. The rollout was great, then it started feeling like other G-products, the dreaded tail of lackluster developer energy after a peak.

Startup / Founder focus vs Large Organizations with no vision. With AI, we know distribution moat really doesn't matter and Google doesn't have a great Developer distribution moat anyway

They just got a huge round, almost a billion in financing - that was after OAI Decisions API was released. I really wonder what their next move is.

Because they model this on Jev's API instead of OpenAI's version.

Was my question too. Apparently it is derived from Bernoulli (Can't find the source now).

It was a hot topic 2 months ago.

Antigravity is far superior to Jules.

I say plant a thousand flowers and cull the ones that don't bloom.

That doesn't make any sense, this is a Jev competitor and a model release, like their Carbon and Argon models. It won't simply be 'killed' like apps might without something to succeed it.

Barring Search, Gmail, and YouTube I am really wary of using anything from Google now.

Looking at their benchmarks, it's actually a better model than OpenAI and Jev (which in the same evals is worst than Jev?).