Prevent cognitive debt by manually retyping LLM-generated code

When I talk about PhD level code, I'm not referring to research code quality. I'm talking about research level algorithms.

Production code is built around reusability, policies, monadic chaining, and various code hygiene practices. Research code isn't. So what I'm saying is: 'Yes, I've collaborated with professors from top Korean universities.'

That said, I'm not writing PhD level code myself. The reason is that I take existing algorithms and apply code hygiene to them. I'm not actually developing new algorithms at a PhD level of understanding

So from what you're saying, it sounds like:

'Are you talking about those horrible research paper codes?'

But what exists in those research papers is: 'The domain modeling itself is difficult.'

I learned about code hygiene in production code—things like using functional approaches, and so on. Research code often lacks those things, so it might look bad. But that means the code quality is bad—not that the modeling itself is bad. And in fact, the core value is in being able to produce modeling at that level of difficulty.

In fact, most programmers, if trained like me, can do these things well enough. But domain modeling is different.

This touches on the distinction Frederick Brooks made in The Mythical Man-Month between 'Essential Complexity' and 'Accidental Complexity.' Code hygiene, functional chaining, monadic structures—these are techniques for reducing accidental complexity. With enough training, you can learn them. But domain modeling deals with essential complexity. It's about how difficult the problem itself is, and it's not easily solved through training alone. That's why the depth of modeling that researchers produce should be evaluated on a different axis from code quality.

This is what we call 'Essential Complexity.' From that perspective, saying code is 'good' usually means that the essential complexity is handled well on average. What I'm calling 'PhD-level' usually refers to modeling problems that are commonly difficult to solve.

I was pretty good at it - mostly remembered to change the name at the top of the paper too.

Struggled at moderated exams; think it must have been the time pressure or something.

You can ask Claude to "sdoc me an annotated code walkthrough of this MR" and it will produce something like this:

https://smalldocs.org/s/Ju9GOmWZ0JXTtzqCVfgt1q#k=6HBrpcCjIu7...

I use this a lot to stay in touch with the code the LLM is producing.

[1] https://smalldocs.org [2] https://github.com/espressoplease/smalldocs

So transcription can definitely be helpful when part of a broader, intentional process, while also being insufficient to do much on its own.

And the next logical question to ask is whether there's a better form of intentional behavior that might be more effective.