Because of the nature of this work, it is very labor intensive. There are too many factory trips, it is physically exhausting, and the pay is not great relative to the amount of effort involved. That is why I want to become more of a product oriented programmer. To move in that direction, I have been trying to use AI as actively as possible. In my current job, though, that is difficult because factory security is usually closed off and AI tools cannot really be connected there.

I have been reading about prompt engineering, harness style documentation, and similar topics. I also use OpenClaw, and honestly I feel like I am already trying almost everything I can get my hands on. I connect it with Obsidian and write down the knowledge I think my agents need while I work.

Still, the idea of ten times productivity feels somewhat exaggerated to me. I want to know how people actually get better at using AI, and how they learn the underlying methods. There is so much hype around AI that it is hard to tell what is real, and learning this stuff has been more difficult than I expected.

How should I study this properly?

Right now I especially want to learn how to manage multi agent systems. Every AI only multi agent framework I have built or tried so far has failed. At first I tried to control it in a TDD like way, but people who have used TDD seriously probably know what I mean when I say that tests can become too locally focused. Sometimes the architecture starts to fall apart, and then the agents keep fixing only those small areas over and over while the token cost keeps rising.

At the same time, in Korea there is a huge amount of talk that if you are not using AI, you will fall behind. Because of that, I have been trying hard to learn it so I do not get left behind. And to be honest, programming has become much more enjoyable for me since I started using AI.

One reason is that programming feels deeply tied to English ways of thinking, and that has always felt awkward with Korean. Even the act of writing code used to feel mentally heavy for me. But with AI, I can think through things in Korean and still code effectively.

You know that huge fatigue you feel when you first start writing code from scratch?

When I write a single interface, I immediately start seeing the number of implementations. When I see the implementations, I start seeing lifecycle conflicts. When I see lifecycle issues, I start thinking about ownership and disposal. When I think about ownership, I start thinking about pooling possibilities and reset contracts. When I think about DI, I start seeing the composition root and the test seams. When I think about the contract itself, I start worrying about future extension costs.

All of that used to make coding feel painfully heavy for me.

But AI just writes a draft without getting stuck in all of that, and I genuinely enjoy taking that draft and reshaping it around my own thinking.

I want to get much better at using AI this way.

What are the best ways to improve at it, and how do you keep up with useful trends without getting buried in the hype?