zeitlupe

From time to time I am peeking over from VS Code to PyCharm and recently I have been surprised how much it seems to fall behind. No (official) ruff integration, you have to use 'External Tools', which is not part of the backup&sync feature. Seriously?

q3dm6

hunter2 still gives me a smile

It reminds me of long-gone times where the web was about sharing easy fun like this instead of viral marketing and influencers.

To me, the most difficult part about quitting youtube, twitter or reddit is that these websites are incredibly rewarding and an incredible waste of time at the same time. Reddit has great discussions on careers, hobbies, whatever you do not stumble upon elsewhere, but the rewarding content unfortunately always comes in packs with some mindless scrolling and mediocre memes – I've never managed to have one without the other. Quitting them means to forfeit a huge, valuable chunk of input and I am yet not sure what's better.

The problem with multiple monitors for me is that you have to turn your head very wide to look onto the adjacent screens' center, which is definitely not ergonomic and healthy.

That being said, it is still my current setup as this wide turn holds me off from looking at the second screen, where slack and other distracting stuff is placed.

I thought Jupyter Notebook has been superseded by Jupyter Lab. What reason is there to prefer Jupyter Notebook over Jupyter Lab?

Mind sharing what the diagnosis entailed for you to have such an impact? Medication, strategies, cognitive therapy? Relieve in the sense of "everything makes (more) sense now"?

Imho the problem with LaTeX CVs is that they all look bleak and the same because it is (on purpose) tedious to create anything except the standard format.

Spark is my favorite tool to deal with jsons. It can read as many jsons – in any format located in any even nested folder structure – as you want, offers parallelization, and is great to flatten structs. I've never run into memory issues (or never ran out of workarounds) so far.

> I have noticed some differences in engineers who have gone through bootcamps

How do they differ?

I do not fully get the speed argument. If the dataset is small'ish, it does not significantly matter (given you only use the vectorized functions, of course). And if it's big data, I do not use pandas but Spark/a cloud data warehouse solution. For this reason I also do not get the use case for duckdb.

Beyond this, a dataframe api/syntax, compared to SQL syntax, is to me way easier to follow and to debug.