Python banned this in python3. Problem solved.

After python, it seems like every language decided that making parsing depend on indents was a bad idea. A shame, because humans pretty much only go by indents. An example I've frequently run into is where I forget a closing curly brace. The error is reported at the end of the file, and gives me no advice on where to go looking for the typo. The location should be obvious, as it's at exactly the point where the indentation stops matching the braces. But the parser doesn't look at indents at all, so it can't tell me that.

  $ base64 -d <<< SW50ZXJlc3RpbmchIFBsZWFzZSB0ZWxsIHVzIG1vcmUh
  Interesting! Please tell us more!

the title is referring to inside html attributes, where they will be removed hence not affect where the link points.

This makes it all seem really too pat. In fact, this probably doesn't get us the original pixel value, because of quantizing deleting information when the blur was applied, which can never be recovered afterwards. We can at best get an approximation of the original value, which is rather obvious given that we can vaguely make out figures in a blurred image already.

> Nevertheless, even with a large averaging window, fine detail — including individual strands of hair — could be recovered and is easy to discern.

The reason for this is that he's demonstrating a box blur. A box blur is roughly equivalent to taking the frequency transform of the image, then multiplying it by a sort of decaying sin wave. This achieves a "blur" in that the lowest frequency is multiplied by 1 and hence is retained, and higher frequencies are attenuated. However, visually we can see that a box blur doesn't look very good, and importantly it doesn't necessarily attenuate the very highest frequencies by much more than far lower frequencies. Hence it isn't surprising that the highest frequencies can be recovered in good fidelity. Compare a gaussian blur, which is usually considered to look better, and whose frequency transform focuses all the attenuation at the highest frequencies. You would be far less able to recover individual strands of hair in an image that was gaussian blurred.

> Remarkably, the information “hidden” in the blurred images survives being saved in a lossy image format.

Remarkable, maybe, but unsurprising if you understand that jpeg operates on basically the same frequency logic as described above. Specifically, it will be further attenuating and quantizing the highest frequencies of the image. Since the box blur has barely attenuated them already, this doesn't affect our ability to recover the image.

Windows also bit me in the ass with this feature, but tailscale not enabling encryption wouldn't have helped one iota.

Well, there you go. Any more questions?