I think this is sometimes a barrier to getting started for me. I know that I need to explore the data structure design in the context of the code that will interact with it and some of that code will be thrown out as the data structure becomes more clear, but still it can be hard to get off the ground when me gut instinct is that the data design isn't right.

This kind of exploration can be a really positive use case for AI I think, like show me a sketch of this design vs that design and let's compare them together.

AI is terrible for this.

My recommendation is to truly learn a functional language and apply it to a real world product. Then you’ll learn how to think about data, in its pure state, and how it is transformed to get from point A to point B. These lessons will make for much cleaner design that will be applicable to imperative languages as well.

Or learn C where you do not have the luxury of using high-level crutches.

> This kind of exploration can be a really positive use case for AI I think

Not sure if SoTA codegen models are capable of navigating design space and coming up with optimal solutions. Like for cybersecurity, may be specialized models (like DeepMind's Sec-Gemini), if there are any, might?

I reckon, a programmer who already has learnt about / explored the design space, will be able to prompt more pointedly and evaluate the output qualitatively.

> sometimes a barrier to getting started for me

Plenty great books on the topic (:

Algorithms + Data Structures = Programs (1976), https://en.wikipedia.org/wiki/Algorithms_%2B_Data_Structures...