some things are easier to change than others. it’s easy to slop on new features, rewrite them etc. changing core models after you’ve built a ton on them isn’t easy, even with ai. the odds it comes up with the perfect data model aren’t great, but for isolated features that doesn’t matter since it’s easy to throw them away and rewrite
all of their examples are pre ai anyway. it’s almost impossible to change a core data model thing without ai too.
all the legacy codebases I’ve worked in have very much been shaped by the original abstractions they decided on. as much as I wanted to change them, there wasn’t really a way to do so because of the scale and backwards compatibility requirements
some things are easier to change than others. it’s easy to slop on new features, rewrite them etc. changing core models after you’ve built a ton on them isn’t easy, even with ai. the odds it comes up with the perfect data model aren’t great, but for isolated features that doesn’t matter since it’s easy to throw them away and rewrite
Cause programmers never had to fix legacy code or anything.
all of their examples are pre ai anyway. it’s almost impossible to change a core data model thing without ai too.
all the legacy codebases I’ve worked in have very much been shaped by the original abstractions they decided on. as much as I wanted to change them, there wasn’t really a way to do so because of the scale and backwards compatibility requirements