defjm

Will it stay that way?

Mistral is ripping through a lot of releases lately. Large4, Timestral, now Voxtral3. Or is it just me being biased?

This is such a pristine pelican. Let me say it here first folks, AGI is here.

This made me giggle! Thanks.

> I learned, when I was a kid, that I can't always get what I want (I think that someone even sang about it, way back then).

https://en.wikipedia.org/wiki/You_Can%27t_Always_Get_What_Yo...

You made me laugh, thanks.

Could you provide an example article from Wikipedia for such bias?

PS: I had to look up „atlanticist“, did this on Wikipedia. (giggle!)

Just found a related post in another HN thread: https://news.ycombinator.com/item?id=44993288

Agree, but given there is such a big distribution in prompt energy usage, wouldn’t it then make sense to break it down further into some meaningful categories?

Also, they say median and mean diverge a lot. So, in my view, google should disclose both and discuss it. Understanding avg/total consumption of AI use is relevant here.

Do they include AI search summaries here? It would be a big no no in my view.

Google has rolled out AI summaries extensively over the time of the study and they likely use more efficient inference than chatbot prompts to their larger models.

They discuss the median in their paper. But I couldn’t find any breakdown about how the prompts they analyze are distributed across their models.

Jeremy Howard has a great video explaining Random Forests: https://youtu.be/blyXCk4sgEg .