Earlier quoted context omitted.
Programming before AI was always variable reward. It was a gamble against your own time and patience. Maybe I'd waste hours down the wrong rabbit holes trying to find a library that worked for my use case. Maybe I'd waste a day trying to get an API to do something it turned out it couldn't do. Maybe I'd have to redo my entire approach because of some factor I hadn't considered. Something I wrote could have worked on…
>Maybe I'd waste a day trying to get an API to do something it turned out it couldn't do. I was working on a side project recently. I had spent months designing the data model in my spare time, thinking through how to make it as elegant and durable to change as possible in the long term, since (if I launched it) the repercussions for getting it wrong would be significant. Once I had a working design, it probably woul…
Also, my coming from being a non-coder, I have a lot of appreciation for the possibilities for project failure one way or another due to a data model or project schema being wrong, even though I still only have a superficial understanding of what either of those concepts even are. . My question is, does your conception of data models in the abstract come from a formal academic course, like an algo’s & data structures course, or from trade-knowledge acquired through the practitioner grapevine?