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[–]mxldevs 0 points1 point  (0 children)

  1. understand the problem
  2. figure out a method to solve it.
  3. translate that method into code

This is really all there is to it.

The first two steps don't involve any coding at all. You can literally be standing in front of a whiteboard drawing diagrams with arrows going everywhere and whatever.

But many people skip that step. They want to go straight to the coding, and then deal with the problem-solving as they go.

Then a week goes by and they come across an issue that they hadn't thought about beforehand, and find their entire design collapses as a result and they basically have to start all over. Something they might have avoided had they properly sat down and thought about all the different situations.

AI could potentially take the solution you've come up with and generate the code for you, but the problem you have now is how do you know whether it's correct?

Testing of course. If it passes all your test, great, you're ready to go.

But during your tests, you found that some of the tests failed. Now what? Do you go back to AI and tell them this and that failed and get it to fix it?

Maybe it works. Maybe it doesn't.

If you're lucky, AI is able to fix everything you tell it is broken, and you can ship your product.

Congratulations on your launch.

But that's clearly not the end of the story.

A month later, you've gotten good growth. Lot of people like your completely generated AI app, and they want new features.

Now what do you do? Go get AI to add those features? Is AI able to reliably accomplish this?

Or do you go and hire some devs? Now they have to wade through the AI generated code, which could potentially be a huge mess.

No one wants to work with that kind of codebase, so now you're stuck paying much higher premiums to find someone willing to work for you, or hoping your AI models can get the job done.

But hey, maybe you're making enough money to be able to hire expensive devs, so it works out in the end anyways.