Took me 2 hours to find out why the final output of a neural network was a bunch of NaN. This is always very annoying but I can't really complain, it make sense. Just sucks.

this is just like in regular math too. not being a number is just so fun that nobody wants to go back to being a number once they get a taste of it

Fucking over-dramatic divisions by 0, sigh.

The funniest thing about NaNs is that they're actually coded so you can see what caused it if you look at the binary. Only problem is; due to the nature of NaNs, that code is almost always going to resolve to "tried to perform arithmetic on a NaN"

There are also coded NaNs which are defined and sometimes useful, such as +/-INF, MAX, MIN (epsilon), and Imaginary

Thanks. This is great

Also applies to nulls in SQL queries.

It's not fun tracing where nulls are coming from when dealing with a 1500 line data warehouse pipeline query that aggregates 20 different tables.

"Bounds checking, mobof--ker! Do you speak it?"

Consider IEEE754 arithmetic as monadic, simple!

NaN is such a fun floating point virus. Some really wonky gameplay after we hit NaN in a few spots.

As I was coding in C++ my own Engine with OpenGL. I forgot something to do. Maybe forgot to assign a pointer or forgot to pass a variable. At the end I had copied a NaN value to a vertieces of my Model as the Model should be a wrapper for Data I wanted to read and visualize.

Printing the entire Model into the terminal confused me why everything is NaN suddenly when it started nicely.

Nanananana! Batman!

This gave me some real Agent Smith vibes