Analysis paralysis is a thing. And as the article makes very clear, there are a lot of ways to get stuck doing anything else then the one thing you are supposed to be doing.
The way to break through that is indeed to start doing. Forget about the edge cases. Handle the happy path first. Build something that does enough to deliver most of the value. Then refine it; or rebuild it.
Seriously. The cost of prototyping is very low these days. So try stuff out and learn something. Don't be afraid to fail.
One reason LLMs are so shockingly effective for this is that they don't do analysis paralysis; they start doing right away. The end results aren't always optimal or even good but often still good enough. You can optimize and refine later. If that is actually needed. Worst case you'll fail to get a useful thing but you'll have a lot better understanding of the requirements for the next attempt. With AI the sunk cost is measured in tokens. It's not free. But also not very expensive. You can afford to burn some tokens to learn something.
A good rule is to not build a framework or platform for anything until you've built at least three versions of the type of thing that you would use it for. Anything you build before that is likely to be under and overengineered in exactly the wrong places. These places make themselves clear when you build a real system.
Just don't mistake prototyping for doing the thing.
Good enough is a self limiting fallacy.
A prototype failing to attract fans doesn't prove a lack of a market for the job the prototype attempts to perform. It only proves the prototype, as it stands, lacks something.
The way to break through that is indeed to start doing. Forget about the edge cases. Handle the happy path first. Build something that does enough to deliver most of the value. Then refine it; or rebuild it.
Seriously. The cost of prototyping is very low these days. So try stuff out and learn something. Don't be afraid to fail.
One reason LLMs are so shockingly effective for this is that they don't do analysis paralysis; they start doing right away. The end results aren't always optimal or even good but often still good enough. You can optimize and refine later. If that is actually needed. Worst case you'll fail to get a useful thing but you'll have a lot better understanding of the requirements for the next attempt. With AI the sunk cost is measured in tokens. It's not free. But also not very expensive. You can afford to burn some tokens to learn something.
A good rule is to not build a framework or platform for anything until you've built at least three versions of the type of thing that you would use it for. Anything you build before that is likely to be under and overengineered in exactly the wrong places. These places make themselves clear when you build a real system.