Their definition is clearly stated in the first paragraph:
Using data compiled by the federal government’s Bureau of Labor Statistics, the True Rate of Unemployment tracks the percentage of the U.S. labor force that does not have a full-time job (35+ hours a week) but wants one, has no job, or does not earn a living wage, conservatively pegged at $26,000 (in 2025 dollars) annually before taxes.
FWIW quitting any SSRI cold turkey is highly disrecommended vs gradual tapering over ~2–6 weeks but even that current guidance is probably too aggressive.
Something like one step down every 2–4 weeks or ideally hyperbolic tapering is better for neurobiology but that's also a PITA.
That's why I clarified that the current clinical guidance is probably too aggressive. Severity of symptoms, length of time on the drug, etc seem to be important factors but still the side effects are very personal.
I think it is more about tapering at a tolerable rate than any particular extended duration.
It will be interesting to see how the guidelines change once they're updated.
By playing Wordle well, you are essentially eliminating as much unknown words as possible each turn, e.g., SALET alone takes 12974 words down to 34 here today.
But this is like Wordle hard mode with the forced letters, so the optimal strategy is probably to invert and eliminate as little words as possible each turn.
I got pretty far here using weird words, but PIZZA turned out to be a terrible first guess for Don't Wordle (eliminating ~75% of the words):
Wordle hard mode (which I play) generally limits you to a similar degree as this game - it's less strict programmatically, but anyone willingly opting into hard mode is likely following the same rules as are enforced here.
So it should be the same.
(assuming here that you're deliberately attempting to lose this game).
No, the strategy in Wordle of solving hard mode by guessing the target word vs in Not Wordle preserving along as possible in Not Wordle by not guessing the target word are not the same thing at all.
The post is just full of half-baked conjectures masquerading as facts... combined with "things they read" by unspecified authors and sources... the author seems to prefer engaging in AI doomerism as opposing to actually understanding.
> Diseconomies of Scale. Every new technology I can think of thrived, in part, due to economies of scale, where the larger the industry grew, the more efficient it got. AI is going in the opposite direction, where every new AI model consumes more resources than its predecessors. This may turn out to be the fatal flaw – the bigger the industry gets, the more its operating costs increase.
[Agreed] Newer gens of models are more power-efficient per task, not less.
It's possible that the demands on the model are growing faster than it gets efficient though. A lot of the improvement in output quality/metrics come from more churning and passes.
You could argue that a RTX5090 needs less power than a 1996 3Dfx Voodoo 1 to render GLQuake at 320x200/60fps, but it's rarely being used for that.
In the end I'm happy it turned out the way it did. I've had some great experiences since and stayed in tech. Plus that freed up a slot for one of the many early 20s kids that were trying to get in.
Their definition is clearly stated in the first paragraph: