I certainly feel like certain AI coding practices I have leave me with nothing learned in a frustrating way. Sometimes the most effective way to getting what I want is just describing a goal really clearly, and then bashing the AI until it gets there. That bashing process can take a lot of energy and yet I don’t gain useful skills or a “better way” that I would if I was still coding. I generate more training data for BigAI corp, but that’s a slow return cycle.

Not sure if this is the right solution, but I would like to learn/understand/grow more as I pursue reckless efficiency.

Learning is the bottleneck for shipping code. Building the model in your head, feeling out the edge cases and system interactions, and understanding the intricacies. Typing out the code is a small part of the process.

So, if you want to speed things up with LLMs, the bulk of the speed comes from reducing learning.

Reason #1 to keep writing code by hand is that you don't ever lose that connection between you and the code.

There was an excellent post here on HN that unfortunately went unnoticed. I highly recommend reading it as it exactly points out this problem: understanding.

Here is the link: https://news.ycombinator.com/item?id=50128468

Imo if you’re reprompting you’re losing. If the agent oneshots the task its good but if it doesn’t manage it on the first try you will get there faster by doing it yourself.

Code by hand? The good old fashioned way? Might be "less efficient", but you will understand what you did and build experience.