With jupyter notebooks in a devops perspective you could just build a process to export the notebooks to standard py files and then run them.

There are actually a lot of git hooks that will actually expoet/convert .ipynb to .py files automatically since notebooks don't work great with git.

Is it not the thing being fed to Gimli(?) in the bottom right image?

Another thing I haven't seen mentioned is its one of the last places open late anymore. I live close to a city, but if I need to grab something late (9:30+) fast food is about the only place open.

Did they actually do that with Leisure Suit Larry? Cause that's kinda funny.

Yeah, not sure I would listen to this guy. Setting up a venv for each project is about a bare minimum for all the teams I've worked on.

That being said python env can be GBs in size (especially when doing data science).

I mean that was one of the attacks nazis used against jews, they claimed they had a lot of wealth and were the cause of most Germans problems.

The issue is Trump is wealthy and america is more willing to attack the underprivligied.

They should just release metroid prime 5 at this point. Literally dump you into the game as if 4 actually existed.

You may have used women in the prompt, but what it created definitely looks like a little girl.

Yep, don't be afraid to ask for help. The people at polling places are usually very nice and helpful!

This is the same type of criticism the paper made. The real intent behind the saying is given random output (where all outputs have nonzero probability) eventually you will create anything/everything.

Its a thought experiment around infinity, probability, and art.

And customs should be waiving the fee, given the passengers were basically entrapped by the airline. A simple "hey we need to confiscate those apples, then you're free to go" would have made this a non-issue.

It's great seeing time and time again that no one really does understand these models and that their preconceived notions of what biases exist ends up shooting them in the foot. It truly shows that they don't really understand how systematically problematic the underlying datasets are and the repurcussions of relying on them too heavily.