This is referring to the fact that when oil gets more expensive, everything else does because oil is used to make fuel for transport of most resources in the supply chain. Because of how important is to the global economy, the exact disastrous effects are still unpredictable.

In other words, turning concentration camps into death camps

These people are evil. These AI data centres need to go up in flames.

“Apple cares about your privacy”

Thanks, now I will never unsee this

Blow 'em all up

Massacre

“Mass casualty event”, like it’s some kind of natural disaster.

Nope, still not using AI and hopefully never will. Writing code is actually pretty fun so why would I want to outsource an AI to do that?

Corporate bootlickers: “Capitalism breeds innovation”

The innovation:

Good, burn it all down.

I wasted far too much time on that, holy shit.

Small miscalculation, 1 TB is only 10¹² bytes, not 10¹⁵ bytes, so you would only have 3840 * 10^12 bytes ⇒ 1280 * 10^12 pixels, so the width of a square RGB image with 3 bytes per pixel would only be around 35,777,088 pixels.

The average woman’s height is 1.588 m and the average woman’s shoulder width is 0.367 m.

Assuming that this average woman fits exactly in this photo, the photo’s “area” would be 1.588 m × 0.367 m = 0.583 m².

Assuming the pixel format is RGB and 8 bits per colour channel, each pixel in the photo would consist of 3 bytes. 2 PB is equal to 2 × 10¹⁵ B, which divided by 3 B for each pixel means there could be at least 6.67 × 10¹⁴ in this photo. In reality most of the time images are compressed so in practice you could get even more pixels. How much more depends exactly on the image and the desired image quality.

To calculate the area of each pixel, divide the photo’s area by the number of pixels. This gives 0.583 m² / 6.67 × 10¹⁴ = 8.74 × 10⁻¹⁶ m² for each pixel. To get the side length of each pixel, take its square root to get 2.96 × 10⁻⁸ m = 29.6 nanometres!

Dividing the widths and heights in metres by the length of each pixel gives (width, height) = (1.588, 0.367) m / 2.96 × 10⁻⁸ m = an image resolution of 12,412,583×53,708,941 pixels!

When it comes for feature size, the bottleneck isn’t actually the pixel size. Assuming the image is in visible light, the shortest wavelength visible to the human eye is 380 nm, so increasing the resolution beyond that point is useless.

In such a photo where features as small as 380 nm can be identified. To quantify the resolution you can see these features in, define an “effective pixel” to be a pixel of side length 380 nm. The actual pixels in such an image aren’t relevant at this point.

Individual skin cells can be identified being 30 μm / 380 nm = 79 effective pixels wide. With similar calculations, blood cells can be identified with a width of 18 effective pixels, and you might be able to even identify individual bacteria. An E. coli bacterium has a length of 2 μm, which is 5 effective pixels.

Wait, that’s an actual formula. When I first saw this I thought it was an employee going insane and keysmashing into a spreadsheet as evidenced by the “send help”.

That was probably a release candidate you were thinking of.

Pretty good. Many new features, one of them being non-destructive filtering which is the ability to apply a filter (say a Gaussian blur) and then re-edit the filter parameters or even remove it without having to undo.

However, at least for me, some changes will take some time to adjust to such as “OK” and “Reset” buttons of dialogue boxes now being displayed at the top rather than the bottom.

Also years work of changes without the wider public testing them means that there are a few bugs/annoyances, one of them being the checkerboard pattern for transparency being replaced with a solid colour (equal to the background colour) when you zoom in too much.

Yeah, same. All of the music I listen to is on YouTube so I only need yt-dlp.

Same

Wow that’s beautiful! Like others have already said, this should be the icon of this community.