1.21 jigawatts

Sorry to hear about the health reasons. Stay connected with people local, you’ll need help, as that’s super hard. Best wishes and be nice to yourself.

googol sucks and was never any good.

Ha! Kids these days.

renowned expert in Chinese technology and founder of the media company Tech Buzz China, [Rui Ma]

Is the person they’re talking about who is “stunned” at how super double awesome China is at powering AI.

Ffffffffffffffuck this.

I dunno. I mean that’s true, but it’s also the case that Gates stole a lot from Jobs and they knew each other pretty well so it was also like a friendly loan.

Tbf Gates took the boos he got at AppleWorld or whatever it was then pretty well.

Not just”not profitable”, they don’t make any money at all. Loss only.

That capital was ash earlier this year. The latest $40 Billion-with-a-B financing round is just a temporary holdover until they can raise more fuel. And they already burned through Microsoft, who apparently got what they wanted and are all “see ya”.

“You don’t think we got rich by writing checks?!? Ahahahaha”

Thomas is correct.

Shit, I’ve been right about microsoft for thirty-plus years and it doesn’t make a damned bit of difference.

They are. A. MONOPOLY. They have never “fought fair”, and it wouldn’t ever occur to them to do so. Their heart is all BOGU.

Why would anyone not trust Meta to talk to their kids??

/s obvs

Man that is going to make giving up these fascist platforms so much harder.

Photographer1: Sam, could you give us a goofier face?

*click* *click*

Photographer2: Goofier!!

*click* *click* *click* *click*

It does sound cool, though I’ve never heard of some of those things.

Ohhhhhh they reached tape out.

Right. Right.

For non-deterministic solutions. For AI.

I mean, what’s not to love?

Thermodynamic chips are a world apart from traditional computing — closer in practice to the realms of quantum and probabilistic computing. Where noise is the enemy of standard electronics, thermodynamic and probabilistic chips actively use noise to solve problems. 

“We’re focusing on algorithms that are able to leverage noise, stochasticity, and nondeterminism,” said Zachary Belateche, silicon engineering lead at Normal Computing, in a recent interview with IEEE Spectrum. “That algorithm space turns out to be huge, everything from scientific computing to AI to linear algebra."