The spec on your GitHub says: "Two simultaneously-live resizable values never share a stack."

So I surely can't have a dynamic array of dynamic arrays share a single stack? That's an unbounded number of resizeable values sharing a stack?

You could make it work on a single stack but that would involve having non-contiguous memory for each inner array since you can add to any array in any order.

The spec implies that we need one stack per element of the outer dynamic array which would let you have contiguous memory for each inner array, but you wouldn't know how many stacks you needed at compile time.

I'm a bit confused about how you could decide the number of stacks at compile time. I can see you could see how many arrays are lexically in scope. However if the code puts one dynamic array inside another dynamic array I can get to an unbounded number of total stacks without ever needing more than two in lexical scope at once. Do you also add stacks at runtime? Like a stack of stacks?

How are you deciding how many stacks to use?

Also I guess this is the final nail in the coffin of Tensorflow.

There was a time when CERN would have been establishing partnerships with US labs. The US is not a CERN member but has provided a lot of funding in the past despite it's observer status. These days funding is getting cut, the US is not as trusted as a partner anymore and a European partner is seen as important. This is sad.

That said I'm happy for Mistral and I'm sure they'll be a great partner for CERN.

It's perhaps a face saving thing to pretend you'll charge the same. Let's see if it actually happens.

Sam Altman is much more dangerous because he is occasionally able to come across as human.

The biggest differentiating factor seems to be compute. I know most Chinese labs would really like more compute. Google and xAI too, but largely because they sold compute to their competitors.

Knowledge has been available just by asking Google for decades now. The LLM makes it easier but it's a difference of degree not of kind

Until the models are 100% reliable knowledge will be required in order to quickly spot issues and work efficiently with the model to address them.

You are right that most work gets published in mid tier journals. These are still peer reviewed. The difference is that they have a lower bar for impact. The bar for correctness is still very very high however.

I am pretty sure that journal publication is the norm. I do know mathematics although I am not one myself. The mathematicans I know want/need journal publications as unpublished research isn't counted in their research output. I know Arxiv is typically used for preprints while awaiting review but publication is always the aim.

Don't take it from me though. zbMath did a study finding 60-70 percent of arxiv papers on math end up in peer reviewed journals. https://ems.press/content/serial-article-files/47210.