Ah, the other AI: Actual Italian. (w/apologies to Davie504)

lol I saw that video.

Presumably with apologies to Steinbeck: “Everyone was a temporarily embarrassed capitalist.”

Not your question, but 4xxx-series work seamlessly.

This is a fun example, but now you've got me wondering: has anyone checked on folks who might have been in an Amazon Go store during the outage?

Slightly hyperbolic, gpt-image-1 is better on at least a couple of the text metrics.

Claude Code is^W^W^WEvery LLM is a slot machine.

Yes, it's the Firefox version of Python's GIL.

A “dead airspace”, perhaps?

It's not my area of expertise, but I have seen other estimates that American adults, especially men, are likely to have and report numbers of friends such that the median is in the single digits.

So, in reference to the "reasoning" models that the article references, is it possible that the increased error rate of those models vs. non-reasoning models is simply a function of the reasoning process introducing more tokens into context, and that because each such token may itself introduce wrong information, the risk of error is compounded? Or rather, generating more tokens with a fixed error rate must, on average, necessarily produce more errors?

Its a symptom of asking the models to provide answers that are not exactly in the training set, so the internal interpolation that the models do probably runs into edge cases where statistically it goes down the wrong path.

This is exactly it, it’s the result of RLVR, where we force the model to reason about how to get to an answer when that information isn’t in its base training.