substitution jutsu

TikTok brain be like

A tweet is too long!

YT music does have a problem with reuploaders stealing revenue through "topics", Upper Echelon made a video about it a few weeks ago

You could never officially choose system colors in AOSP. It was always white with teal accent. If you're thinking of Substratum, that was kinda an unintended exploit when Google was working on adding native theming for OEMs.

Volume buttons are being made more customizable in Android 15, which is launching on Pixels soon.

Privacy settings have never been reset for me, maybe you're confusing it with Windows 11.

Goodbye

Goodbye

I have news for you then! They're already doing it

They're from shitjustworks, the threads is just a meme

Huh? Ik heb het over https://www.volkskrant.nl/a-bb607fbc

Kan iemand het artikel in de comments posten? Ik krijg een betaalmuur

yearning.gay

lmao

same

This seems accurate

Cursive big f: "integration", which can be interpreted in two ways. One is "area under the curve" for some part of the curve. Other is "average value of a part of the curve multiplied by the size of that part of the curve". Curve being the function, the graph, f(x), however you wanna call it.

Normal d: "differentiation" (from difference), infinitely small change. Usually used in ratios: df/dx means how much does f(x) change relative to x when you change x a little bit.

Cursive d: "partial", same as normal d but used when working with higher dimensional data like 3D. Can also mean "boundary" of something. Example: boundary of a volume in 3D, like wrapping paper around a box. Or, boundary of such wrapping paper itself, if it's not perfectly connecting.

Omega: just a Greek letter used as a variable, in this case there's a history of it being used as a sort of "density" variable in the field of differential geometry. The college row in the meme is kind of translating the high school row from a function to a 3D volume.

Companies could take and steal as much as they want from smaller artists in that case

December 6th is still coming

Neural networks aren't going anywhere because they can be genuinely useful, just not to solve every problem

That's specifically LLMs. Image recognition like OP has nothing to do with language processing. Then there's generative AI which needs some kind of mapping between prompts and weights, but is also a completely different type of "AI"

That doesn't mean any of these "AI" products can think, but don't conflate LLMs and AI as being the same

extremely relatable

https://d-shoot.net/kagi.html