mjn

I've also started doing this, and it's surprisingly enjoyable to both do and even to read. The end result is often more readable to me than using a 3rd-party JS visualization library, because I only need to know standard HTML/CSS concepts to understand what's going on. And a side benefit is smaller pages with less bitrot due to being able to skip the dependencies.

Besides growth, Nvidia also has much higher profit margins. In Q1 2024, Apple's margins were 42% gross, 26% net, versus Nvidia's 78% gross, 57% net. May or may not be sustainable, but for now Nvidia's basically printing money.

Yes, this article doesn't appear to have any independent sourcing, it just cites the Jacobin one that you link: "de acordo com a revista norte-americana Jacobin" ("according to the North American magazine Jacobin").

"Unfit for deployment" or "not intended for deployment" is semi-standard wording for research models that are just raw language models with none of the safety/bias/offensiveness filtering that is usually desired for product applications. For example, if you deploy it as a customer-service chatbot, it might tell your customers to kill themselves, or call them racial slurs.

It doesn't mean that there's anything technically wrong with the language model per se as a model of language, just that there has been no effort made to ensure it's fit to be deployed as-is for any given generative-AI use case, and the model authors would prefer you didn't do that.