I didn't download it, but if the animation is exactly like it is on the site, then that is not accurate. I wish I had the design chops to explain what the issue is, but all I can say is on the iphone it looks like the phone moves around the screen, while here it looks like the screen has some sort of 'standing up' effect. Like it kinds skews or something.
That's true! On the Mac there's only one glass pane (the other half is the chassis/keyboard), so my idea was that the lid is just a kind of window into a fourth-dimensional space where the actual screen lives. When you open the lid, you're moving it closer to this "ethereal" screen until the two attach. As you close it, the screen stays fixed in that "virtual" space, so the angled glass distorts the light.
I'm aware it may not make sense physics-wise, eh. :)
I mean, obviously Python 2.7 is newer than Python 3.1
I remember swearing I would never migrate to python3 but here we are, I migrated without even feeling it or realizing when (But I do remember django played a big role).
There are a lot of large projects where X.1 is newer than Y.0. You release a new major version for breaking changes, but you backport security changes to previous major releases as well.
Minors and hotfixes allow you to release a new version for an older release (like if you released 3.7 but you found a bug present in 3.4, you can release 3.4.1 which would be newer than 3.7), making it possible to have multiple supported versions at the same time.
Yeah, my impression is that even the best LLMs are pretty bad at analysing big data directly.
It's not even the hallucinations that are the biggest problem, it's more that they're so bad at managing their context windows that they end up ignoring huge chunks of the data without realising that they're doing it. You end up with a result that looks plausible but is often extremely misleading.
A better approach seems to be getting the LLM to write code to use more traditional analysis techniques (eg. iterative k-means, or whatever). That way you can at least be confident that you've looked at all the data rather than just a small slice of it.
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