Nuclear reactors use most surface water, not ground water.

Datacentres are no pickier. You can even cool a datacentre with saltwater, you just need a heat exchanger.

Also, closed loop does not evaporate. The loop is not closed if stuff escapes from it.

You're arguing with the actual terminology used in the nuclear industry. "Closed loop" or "closed cycle" designs have the water pumped in a cycle through cooling towers. The towers lose water to evaporation, taking heat with them, but the rest of the water is returned to be reheated again. "Open loop" or "open cycle" designs have no cooling towers. The water is heated and just discharged hot. They consume much more water (over an order of magnitude more), but most of that is returned. Closed loop are more common, but you see open loop in some older designs, and in seawater-cooled reactors.

"How often do you think I print?"

Seemingly not very.

I've printed many hundreds of kg on my P1S, thanks.

I do not consider having to write data out to a card and transport it back and forth between the printer and the computer to be the pinnacle of convenience. That's something that would be considered embarrassingly inconvenient for a 1980s printer, let alone a modern net-connected device. And it's designed to be inconvenient for non-cloud prints for a reason.

to their detriment for about 10 years? The employee ranking system IIRC

Also, anything sounds big when you put it in gallons. Doesn't sound so big when you mention that's 92 acre feet, the amount used by less than 20 acres / 8 hectares of alfalfa per year. Or when you mention that a typical *closed loop* 1GW nuclear reactor uses 6-20 billion gallons of cooling water per year (once-through uses 200-500 billion gallons, though most of that is returned, whereas closed loop evaporates it)

I don't think it has anything to do with that. As soon as I saw the headline, my mind went "cohort study". And sure enough, yeah, it's a cohort study. Remember that big thing about how wine improves your health, and then it turned out to just be that people who drink wine tend to be wealthier and thus have better health outcomes? And also, the "sick quitter" effect, where people who are in worse health would tend to stop drinking, so you ended up with extra sick people in the non-wine group? Same sort of thing. This study says they're controlling for a wide range of factors, but I'd put money on it just being the same sort of spurious correlations.

"I just put my models on a usb drive then plug said drive into the printer."

You must have a lot of spare time on your hands.

"It works great locally" - Um, no it doesn't?

They've made a nice easy-to-use ecosystem. For $400 you can get a P1S that supports adding an AMS, auto bed leveling, enclosed-chamber printing, high precision, high print speeds, and 300/100C nozzle/plate temps, and has an easy cloud print service and a robust ecosystem of models you can just download and print with no extra config straight from the app.

But yeah, their behavior is increasingly entering bad-actor territory. I wonder how long it'll be before they lock entry-level printers into their branded filament?

It would be a nice idea, but with the deluge of AI content being released across...well...everything...how do you keep up and who decides what is, and what isn't, AI-generated?

>>...and their societies haven't fallen prey to dictators

Hungary, Yugoslavia, Czechoslovakia, France (in times past), Germany, Italy might disagree with you on that.

The problem doesn't occur if you have huge pages enabled, which is a good idea for a database machine anyway, as running without huge pages has almost as much of an impact on Postgres performance as this regression does. So no need to way for postfix to ship the spinlock bug fix.

(And yeah, it was a fun degree. Just a BA :) Decided to go back to school for a second degree on a topic that had long been of interest to me)

DNN-based, like nearly all modern AI. Not Transformers, as far as I'm aware.

Explain how this doesn't count as reasoning. Or this. To name just a couple examples.

Yes, they work by fuzzy logical reasoning. That is literally how neural networks, including the FFNs in Transformers, work. Every neuron is a fuzzy classifier that divides a superposition of questions formed by its input field by a fuzzy hyperplane, "answering" the superposition with an answer ranging from yes to no to anything in-between. Since the answers to each layer form the inputs to the next layer, the effective questions form grow with increasing complexity as network depth grows. Transformers works by combining DNNs with latent states (works on processing concepts, not raw data, with each FFN detecting concepts in their input and encoding resultant concepts into their output) and an attention mechanism (the FFNs of a given layer can choose what information they "want to look at" in the next FFN).