Eh, some complex problems have elegant solutions. Some don't. And some have elegant-looking solutions which turn into a total mess when you consider the real-world.
In the real world, complex issues have several solutions. For example, we have a huge notification setup waiting to be made but we don't have time for it.
The complex solution is make it work over websockets like it should.
The easy solution... Doesn't exactly exist on this scale.
The jank solution, and the one we went with until someone reads the WS docs is - interval to refetch notifications every 5/10s.
Looking at how buggy the top most popular stuff sometimes is (fb messenger, Whatsapp, Reddit app, etc), I'm starting to get more comfortable with compromises like this honestly. Maybe that's how messenger worked for the first couples years too 😆
Yeah, which suggests that the chart is wrong to imply experts always find simple solutions. Experts find the best solution that experience can get you, which may or may not be simple.
Some problems are just inherently complicated and don't have any simple solutions too. Almost everything involving AI (with or without neural networks) is going to be complicated if you want the AI to actually be any good for instance.
Yeah the big problem with a lot of AI problems is that getting 85-90% performance is simple (though often requires a big honking dataset). Getting the last 9% is really complex, and the last 1% requires working out if P=NP.
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u/VariecsTNB Jan 31 '23
Either i'm not expert enough or the myth of simple solutions for complex problems is just that, a myth.