When I would code by hand I often found I was concerned with the house of cards in my head, am I remembering the right variable names, did I forget something that would lead to an exploit, etc. Now I can direct the LLM to do the work and the skill is split between having an understanding of the interactions of the technologies, choosing tech that will be less of a pain to update/migrate. You can even put some LLM-bow grease into finding more efficient approaches catching corner cases etc. And just by asking I can have test frameworks setup, I can drill into some pet peeve and it takes less time. Now we still have lots of ops moments, like the radicel distributed git forge forgetting to use encryption for private repos, and I run into this class of problem in my own casual LLM use, but that's where our taste and attention to detail powers need to be spent in this modern developer world.

I've always been either entirely self-directed, choosing my own goals, or almost entirely self-directed, being given high-level goals, and getting to choose my own means to accomplish them. So my opinions are probably different from somebody who was a smaller part in a chain.

You say that you liked learning how things worked inside out, whether it was Postgres or whatever. You can still do that. You can learn it at a high level, and then it becomes like a magic word in your arsenal. You don't have to know or remember the details of implementation, but only the nature of its costs, benefits, and synergies with other systems. You just have to remember that high-level abstraction, maybe just a single vocabulary word, of a low-level reality - the exact syntax is really no longer a concern.

The better the agents get, the less there is playing manager. Really your job now is to have and execute vision. Execution is largely a function of having a map of the high level vision and low-level realities in your head, or just having in-the-moment intuition about these things... then just speaking it.

Also, there is developing personal libraries. You may find that in your business you repeatedly tackle similar problems. You develop and refine a library that your future agents can utilize. You develop prompts that make your work more automatic and more robust. And when I say you, I mean, you direct your agents to do it.

It's not so different from what it always was, except that years get compressed into months. What was slow and tedious is now... less so.

I don't know how much advantage I have in this new world, having programmed (sometimes more, sometimes less) for 30 years. My brother has never written a line of code in his life. He's been heavy into AI for a while, and he has developed some really incredible stuff. I think he may have already outpaced me. His $1500/mo+ in subscriptions definitely help.

How do LLMs work? Yes there's a model and there's inference, but dig deeper. How can you, today, with < $50, train a small GPT-2 class model, how do you fine tune it, what training data do you give it, how do you give it training data, what algorithms do you use for reinforcement learning, what even is RLHF? You don't have to know any of those details to use ChatGPT, just like you don't need to know how a D flip-flop works in a CPU to write some code that runs on one, but if you're looking for feeling like you know what's going on, at their heart, LLMs are still next word prediction engines, so knowing, deeply, how that engine is created and works, would be my idea of mastery of it. Karpathy has a good set of tutorials on this.

It's funny though, llms are quite terrible at most things really.

They can build things, but it's all terrible really. Their "ideas" are terrible.