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Conclusion: my next car will be an affordable Chinese car.

Excel is never ever going to break backwards compatability. In fact, quite some "features" in Excel are just there to stay bug-for-bug compatible with existing systems.

Example: Excel stores dates internally as a float - called the serial date, you can view it by running DATEVALUE on any cell that contains a date. It is supposed to be the number of days since 1 January 1900. However, since early Excel versions had to be compatible with Lotus1-2-3, Excel had to be compatible with a bug in Lotus123: they had erroneously assumed 1900 to be a leap year. In addition, the indexing is off by one. So the actual 0 epoch of an Excel serial date is 30 December 1899 for all dates starting 1 March 1900.

This was a seriously good shitpost. Had me for a second.

De FNV betaalt haar eigen werknemers overigens ook bar weinig.

Oh this one is absolutely golden!

And nothing of value was lost.

Good riddance.

A literal shitpost.

I'm from a country where the main focus of the judicial system is rehabilitation. Mental health facilities are in fact part of it.

If our experience is anything to go by, you will absolutely still need to lock up certain people. For their own safety and that of society at large.

Some people cannot be cured, or resist treatment to such an extend that it is effectively impossible.

It's an uncomfortable truth.

My country has psychiatric treatment facilities as part of the judicial systems (ours is mostly based on rehabilitation, not punitive). The problem with them is that being send to one of them is indefinite, until one is deemed cured. Is that ethical?

Ironically, this means that bona fide gangsters will often try to prove they do not have a mental health problem, because ordinary prison at least has a well-defined end date.

My country tried to privatize mail. It failed. Why: it's very hard to operate profitably.

Chatgpt flat out hallucinates quite frequently in my experience. It never says "I don't know / that is impossible / no one knows" to queries that simply don't have an answer. Instead, it opts to give a plausible-sounding but completely made-up answer.

A good AI system wouldn't do this. It would be honest, and give no results when the information simply doesn't exist. However, that is quite hard to do for LLMs as they are essentially glorified next-word predictors. The cost metric isn't on accuracy of information, it's on plausible-sounding conversation.