EVs and ICE cars can both catch on fire in a bad enough accident, but this is true regardless of the age of the vehicle, and tends to be more sudden and violent with gasoline explosions vs battery fires.

People have plenty of other ways of finding out about useful products and services. You can talk to your friends and family, or go to a store and talk to a salesperson, or look up product reviews online, or even pay for something like a Consumer Reports subscription.

Yes, there are times when hard work feels great, and it's absolutely worth seeking out this kind of work.

But any serious endeavor is going to have times that are a slog, and your ability to stick with it through the bad times will very directly dictate your ability to get back to the good times.

Many people supposed that it’s just the acne making people depressed because it’s a nice plausible explanation, but it’s verifiably wrong.

Not trying to defend PE here, but this narrative doesn't make sense to me.

In a lot of businesses you get praise and look important if you’re responsible for leading a large group of highly paid employees, more so then if you have a smaller team.

Thus the motivation is frequently to spend as much money as possible, not to improve efficiency.

If you improve efficiency then maybe you just get your team size cut and people ask hard questions about why you needed all those resources in the first place.

Slow Productivity by Cal Newport talks about this trade-off extensively and provides interesting points of reference where real famous historical figures achieved incredible things in ways that would seem slow and lazy by modern standards.

This has nothing to do with whether a human or AI created the art, and I don't think it's controversial to say that AI-generated art is derivative; the models are literally trained to mimic existing artwork.

Just as an LLM may be good at spitting out code that looks plausible but fails to work, diffusion models are good at spitting out art that looks shiny but is lacking in any real creativity or artistic expression.

The conclusion of the study was that linguistic aptitude seemed to be more correlated with programming aptitude than mathematical aptitude, which seems fairly interesting, and also fairly unconcerned with which specific physical regions in the brain might happen to be involved.

I don't know if the Cybertruck will follow the same pattern, or if the whole company has jumped the shark, but if we're looking for non-political opinions I would not necessarily write them off on quality issues alone.

Most diagrams of our cone frequency responses are subtly wrong. Diagrams typically show three separate smooth, overlapping peaks, centered around red, green, and blue. What they leave out is that our L-cones (the "red" cones) also have a separate little sensitivity bump way off in the violet end of the spectrum. So when you see violet light, it's actually stimulating both the cones that are most sensitive to red light and the ones that are sensitive to blue light. This is pretty much the same stimulation pattern you get if you send both pure red and blue light into your eyes together, which is why purple and violet look so similar.

If you Google "cone sensitivity diagram" you'll mostly find the misleading versions of the diagrams, but you can see one that includes the extra bit of high-frequency L-cone sensitivity in this paper, for example: https://hal.science/hal-01565649/file/Vienot_ConeFundamental...

Well, yes. And his approval rating has been steadily declining in tandem with the stock market declines he's caused. If/when prices suddenly skyrocket because of tariffs, you can bet his approval ratings will decline further.

But this isn't a problem specific to CRDTs, it's a limitation with any database that favors availability over consistency. And there are use cases that don't require these kinds of constraints where these limitations are more manageable.

Yeah, exactly right. This is why CRDTs are popular: they give you well-defined semantics for automatic conflict resolution, and save you from having to implement all that stuff from scratch yourself.

The author writes that CRDTs "don’t generalize to arbitrary data." This is true, and sometimes it may be easier to your own custom app-specific conflict resolution logic than massaging your data to fit within preexisting CRDTs, but doing that is extremely tricky to get right.

It seems like the implied tradeoff being made by Graft is "you can just keep using the same data formats you're already using, and everything just works!" But the real tradeoff is that you're going to have to write a lot of tricky, error-prone conflict resolution logic. There's no such thing as a free lunch, unfortunately.

It might not even have anything to do with the drug itself: mental health issues tend to wax and wane on their own over time, so if someone happens to feel better right after starting a new medication, it's easy to think "oh hey this one must be working" and then that can trigger the placebo effect and turn into a positive feedback cycle.

Obviously the effect feels extremely real to you, but we wouldn't see a strong placebo effect in the numbers if people on placebos didn't genuinely feel much better.

I get that it feels like the second drug worked much better, but expectancy effects and internal narratives are extremely strong, and they're impossible to untangle at the level of an individual.

Many double blind studies are completely broken due to side effects triggering a stronger placebo response, and this is an especially huge problem for drugs like SSRIs where a placebo gets you about 80% of the benefit of the actual drug.

Similar to the study you linked, there was a more recent study where they found that for the SSRI escitalopram (aka Lexapro), the benefits disappear when you lie and tell people that they're receiving an active placebo that mimics the side effects of an SSRI. That is, if people don't actually think they're taking an SSRI, they don't get any benefit.

https://app.dimensions.ai/details/publication/pub.1142338190