1) Design in correspondence with AI. Let it criticise your ideas, give you suggestions on tools/libraries/techniques, and have concepts and syntax explained to you. Stay aware that these models are sycophantic yes-machines.
2) Implement yourself.
3) Debug in collaboration with AI. If you ask a question like "I'm getting [error], what are the most likely reasons for this happening?", you can save a lot of time finding the issue. Just make sure to also research why it is happening and how to solve it independently.
4) Let AI criticise your final result and let it offer suggestions on what to improve. Judge these critically yourself.
There is some worth in spending hours trying to fix a bug you don't understand, it builds resilience, helps you get familiar with a lot of language topics, and you probably won't make the same mistake again. But the above approach is a pretty good compromise of letting AI help where it excels, while still keeping enough control to actually learn something yourself.
I believe that Rust is the language benefiting the most from agentic AI, because the compiler is such a strong gate-keeper, and the documentation of almost all aspects of the language is comprehensive and clear. The biggest pain points of Rust are also reduced by AI: Front-loaded learning curve is softened, refactoring is something gen AI is actually decent at, and long compile times can be spent productively by already planning out the next steps.
