Sure. You have to solve it from inside out:

• not()…See comment below for this one, I was tricked is a base function that negates what’s inside (turning True to False and vice versa) giving it no parameter returns “True” (because no parameter counts as False)
• str(x) turns x into a string, in this case it turns the boolean True into the text string ‘True’
• min(x) returns the minimal element of an iterable. In this case the character ‘T’ because capital letters come before non-capital letters, otherwise it would return ‘e’ (I’m not entirely sure if it uses unicode, ascii or something else to compare characters, but usually capitals have a lower value than non-capitals and otherwise in alphabetical order ascending)
• ord(x) returns the unicode number of x, in this case turning ‘T’ into the integer 84
• range(x) creates an iterable from 0 to x (non-inclusive), in this case you can think of it as the list [0, 1, 2, …82, 83] (it’s technically an object of type range but details…)
• sum(x) sums up all elements of a list, summing all numbers between 0 and 84 (non-inclusive) is 3486
• chr(x) is the inverse of ord(x) and returns the character at position x, which, you guessed it, is ‘ඞ’ at position 3486.

The huge coincidental part is that ඞ lies at a position that can be reached by a cumulative sum of integers between 0 and a given integer. From there on it’s only a question of finding a way to feed that integer into chr(sum(range(x)))

Those are typcially the ones without any prefix

With the notable exception of the kg…for some inexplicable reason.

If you wanna see a language model (almost) exclusively trained on 4chan, here you go.

Presumably. Wouldn’t take much to fake that though.

Despite all that, there was the Warlock who Ninja’d the Sword dropping in Naxx on the final day. Fun times. Now that we’re slowly approaching Classic MoP we’ll finally see if the masterplan worked out too.

When were talking about teaching kids the alphabet we need to train both individual and applied letters

This is only slightly related but I once met a young (USAmerican) adult who thought the stripy horse animal’s name was pronounced zed-bra in British English and it was really hard to convince her otherwise. In her mind zebra was strongly connected to Z-bra, so of course if someone was to pronounce the letter “zed” it would turn into “zed-bra” and not just into “zeh-bra”.

That data is also publicly available (of course), so a model could be trained on it. I’d love to say I’d doubt Google/YouTube would ever do that, but at this point nothing would surprise me.

Usually, if there’s a scam, someone’s making money off it. This is them. They want to keep making money.

I trained the generative models all from scratch. Pretrained models are not that helpful when it’s important to accurately capture very domain specific features.

One of the classifiers I tried was based on zoobot with a custom head. Assuming the publications around zoobot are truthful, it was trained exclusively on similar data from a multitude of different sky surveys.

Does it? I worked on training a classifier and a generative model on freely available galaxy images taken by Hubble and labelled in a citizen science approach. Where’s the theft?

Daily login bonus…

It’s even simpler. A strictly increasing series will always have element n be higher than the average between any element<n and element n.

Or in other words, if the number of calls is increasing every day, it will always be above average no matter the window used. If you use slightly larger windows you can even have some local decreases and have it still be true, as long as the overall trend is increasing (which you’ve demonstrated the extreme case of).