Bulldozer Method
The Bulldozer Method as popularized by Dan Luu suggests that sometimes you can achieve results that seem superhuman simply by just sitting down, doing the brute force work, and then capitalizing on what you learn by doing this to get a velocity increase. AI coding is the epitome of brute force work: you can just brute force large refactoring problems if you are willing to spend enough tokens, or you can have the LLM build the workflow you will use to brute force the problem. Look for opportunity in places where previously people had written off a problem as “too much work”. Make sure you inspect what the LLM is actually doing though, because it will happily keep doing the same thing over and over, unlike a human who would get bored and look for a better way.
Examples
A historical annoyance people have had with strongly typed languages like Haskell or Rust is when you make a change to some core function, and then you have to refactor half the universe to account for it (fearless refactoring, they say, because the type checker will help you fix it). In many cases, the “read compile error, fix the problem” loop can be completely automated by an agentic LLM.
I had some test cases with hard coded numbers that had wobbled and needed updating. I simply asked the LLM to keep rerunning the test and updating the numbers as necessary.