I'm more okay with never eating from what are, by my use anyway, public restrooms than installing some app.

The sort of application I'd much rather self-host.

Flagship? No, H20 was their cut down chip they were allowed to sell to China.

Isn't 3 actionable under employment law?

If you could discover an email where an executive admitted their RTO strategy was a layoff then maybe, in some jurisdictions. But it's hard to prove, most of the time they could just say their motivation was "culture" or similarly vague and unfalsifiable like that.

I think it depends how far you are. There are laws covering you against forced relocation, treating it essentially as a layoff if it’s >50 miles (not sure exact numbers, and it may be state specific)

> rather than something nicer.

Python was the something nicer. A lot of the other options were so much worse.

If sideloading goes, so does their OS.

What are you planning to use instead?

> Similar to the no-free-lunch theorem, no lossy compression method is universally better than another,

No lunch theorem only works, because they assume you care about every single value of noise. Nobody does. There's a free lunch to be had, and it's order. You don't care about a single pixel difference between two cat pictures, NFL does.

Lossy compression is precisely where NFL does not apply.

Just similar in theorem style, I try to emphasise that no lossy representation is universally (i.e. for all downstream tasks) better than another.

Okay, but the reality of society becoming shittier is society becoming shittier.

Unfortunately for those companies, their APIs are a commodity, and are very fungible. So they'll need to keep training or be replaced with whichever competitor will. This is an exercise in attrition.

I wonder if we’re reaching a point of diminishing returns with training, at least, just by scaling the data set. I mean, there’s a finite amount of information (that can be obtained reasonably) to be trained on. I think we’re already at a sizable chunk of that, not to mention the cost of naively scaling up. My guess is that the ultimate winner will be the one that figures out how to improve without massive training costs, through better algorithms, or maybe even just better hardware (i.e. neuristors). I mean, we know that at worst case, we should be able to build something with human level intelligence that takes about 20 watts to run, and is about the size of a human head, and you only need to ingest a small slice of all available information to do that. And training should only use about 3.5 MWh, total, and can be done with the same hardware that runs the model.