[zeta0134]

Even putting aside the ethical issues, it's rare that I want to copy/paste code that I find into my own project without doing a thorough review of it. Typically if I'm working off some example I've found, I will hand-type it in my project's established coding style and add comments to clarify things that are not obvious to me in that moment. With an LLM's output, I think I would have to adopt a similar workflow, and right now that feels slower than just solving the problem myself. I already have the project's domain in my mental map, and explaining it to the agent is tedious and a time waste.

I think this is often overlooked, because on the one hand it's really impressive what the predictive model can sometimes do. Maybe it's super handy as an autocomplete, or an exploration, or for rapidly building a prototype? But for real codebases, the code itself isn't the important part. What matters is documenting the business logic and setting it up for efficient maintenance by all stakeholders in the project. That's the actual task, right there. I spend more time writing documentation and unit tests to validate that business logic than I do actually writing the code that will pass those tests, and a lot of that time is specifically spent coordinating with my peers to make sure I understand those requirements, that they were specified correctly, that the customer will be satisfied with the solution... all stuff an LLM isn't really able to replace.