Reading the article, I definitely agreed with the author, but I also found myself agreeing with the counter arguments in the comments. What I find conflicting personally about AI coding practices, is that I completely agree that AI is incredibly impressive at completing even complicated tasks, and I can at the very least say it is much much better than I am at writing code.

My issue with it, is that it gives you a "lazy" option every time that doesn't require the same level of thinking. I understand that this is completely on me as the developer, and the simple solution is that I need to make sure I'm taking my time to learn and understand what exactly the LLM is producing. I try this and have set up separate skills to make sure I'm building my understanding as I go.

Regardless, if I sit down today and implement something without the use of LLM, it takes me a lot longer, but once I get into it, I find a state of flow that I can never get from the back and forth reading of LLM output. Then when I finish, even if my solution is not perfect, I have learned so much more and my own context of problem is so much better, where usually then I can review with an LLM. This usually leaves me with a better implementation and more importantly one I can stand over. I think for a newer dev like me (~2 years experience), since I haven't built up years and years of problem solving experience, if I don't carve out time in my day to put down the AI tools and improve on my problem solving, I'll plateau and that's my biggest push against all this LLM use. I don't necessarily disagree that 'coding is solved', to be honest, I think it largely is, but it's still the foundation for me to be a good Software Engineer and I definitely haven't solved it.

I totally agree and think this is The New Skill of software engineering: can you steer agents well enough to get work done at the speed they will allow, while still keeping enough context/understanding to step in when it matters?