Aqua was garish and in your face, but it needed to be in order to be a symbol of what the system had achieved, which I think was especially important because that came at the cost of being a buggy, immature OS that had to run crucial apps in a hideous compatibility mode while it was getting established.

Edit: and if I can be a cranky old codger for a moment, I think that Liquid Glass unintentionally serves just as well as a symbol for what Apple has become: a company grasping to relive the memory of innovation without remembering how to innovate.

And to be clear, building intuitions that fail at certain scales is still a useful and important thing to do. But you haven't shown a scale where these intuitions fail. That's not insightful, it's just throwing smoke bombs for no reason.

And while the real situation at scale is more complicated, the math is going to come out to the same answer, albeit with extra terms muddying everything up.

If someone says that something true can be illustrated intuitively with a thought experiment, "sure, but what if we take that to a scale where our intuitions fail" is a sort of odd place to take the discussion unless you're genuinely curious how the math is going to shake out.

That is a very odd thing to say in a post that also mentions WebP. I would say that of all the commonly supported lossy image codecs released since 1992, JPEG takes the least advantage of human perception quirks.

KV caching is a super interesting engineering space, especially when you’re talking about local models where compute and memory bandwidth are highly constrained and you’re trying to trim fractions of a second everywhere you can by flipping between different ICL prefixes. But selling caches for specific documents just makes no sense at all.

When your abstract was clearly generated by an LLM and not curated to at least make it sound human, it does not make me want to read your paper.

So first off, I mean, of course you didn't, right? You had already learned most of what you were going to learn from this specific project by doing it by hand.

But yes, if you use AI to do projects that might have been at the edge of your abilities pre-AI, you will learn a lot less. The solution, I've found, is to get more ambitious until you're back to the edge of your abilities even with AI. You should be asking the agent questions like "has anyone tried?" and keep pushing until it isn't sure, and you're not sure you have any idea what you're doing. Then ask questions until you feel like you sort of know what you're doing, and verify your understanding by directing the agent to build.

And that's fine, right? Like people can do what they want. We're just going to get used to it.

IMO it’s probably that. The difference between where this was a a year ago and now is night and day, and not using frontier models is roughly like stepping back in time 6-12 months.

Just get an algae oil based DHA+EPA supplement.

When converting to grayscale, you typically calculate the value of the pixel and then set all color components to that value. The point of this is to keep the luminance the same as it was in the original color pixel. If you’re doing this correctly, the perceived brightness stays the same.

And just as a smell test: have you ever converted an image to grayscale and flinched away because it seemed twice as bright? Of course not; it just loses its color.

The only way you would get more perceived brightness at lower backlight intensity would be if you physically removed the color gels that overlay the LCD matrix. Which is obviously not what they’ve done here.

I’m pretty sure the increase in battery life they observed is simply because they’re using their phone less, which is very much the main upshot of the other benefits they listed. The idea that color pixels drain more energy is just obviously nonsense.