If you run out of (solvable!) problems in your given logic space, just start branching out your space. Until you find yourself in such esoteric spheres, not even your best math co-researcher knows anymore what's happening and vice versa.
You mean, the high interest landscape made corpos and investors alike cry out in a loud panic while coincidentally people figured out they could scale up deep learning and thus we had a new Jesus Christ born for scammers to have a reason to scam stupid investors by the argument we only need 100000x more compute and then we can replace all expensive labour by one tiny box in the cloud?
Nah, surely Nvidia's market cap as the main shovel-seller in the 2022 - 2026(?) gold-rush being bigger than the whole French economy is well-reasoned and has a fundamentally solid basis.
Almost a decade ago I used to be a hyped up HS graduate fully spoon-fed the AI hype bubble (after 2012, the first "deep" learning breakthroughs for image classification started hyping the game up). I studied at a top 5 university for CS and specialised in deep learning. Three years ago I finished, rejected a (some would call "prestigious") PhD offer and was thoroughly let down by how "stupid" AI is.
For the last 2-ish years, companies found a way to throw supercomputers on a preprocessed internet dictionary dataset and the media gulped it up like nothing, because on the surface it looks shiny and fancy, but when you peek it open, it's utterly stupid and flawed, with very limited uses for actual products.
Anything that requires any amount of precision, accountability, reproducibility?
Yeah, good luck trusting a system that inherently just learns statistics out of data and will thus fundamentally always have an unacceptable margin of error. Imagine using a function that gives you several different answers for the same input, in analytical applications that need a single correct answer. I don't know anyone in SWE that uses AI for more than as a glorified autocomplete which needs to be proof-read and corrected more often than not to the point of oftentimes being contraproductive.
Tldr; it is exactly zero surprising that FSD doesn't work, and it will not work with the current underlying basis (deep learning). The irony is, that people with power to allocate billions of dollars have no technical understanding and just trust the obviously fake marketing slides. Right, Devin?
You wouldn't believe how often I have to fight for UUIDs instead of sequencing. UUIDs are great. For all practical purposes 0 possibility of collision; you can use it as an ID in a global system, it just makes so much fucking sense.
But the default is still a number natural number sequence. As if it matters for 99% of all cases that stuff is "ordered" and "easily identifiable" by a natural number.
But then you want to merge something or make double use and suddenly you have a huge problem that it isn't unique anymore and you need more information to identify.
Guess what, an UUID does that job for you, across multiple databases and distributed systems, a UUID is still unique with 99.9999% probability.
The one counter-example every 10 years can be cared for manually.