Senior Developer Skills in the AI Age

The premise might possibly be true, but as an actually seasoned Python developer, I've taken a look at one file: https://github.com/dx-tooling/platform-problem-monitoring-co...

All of it smells of a (lousy) junior software engineer: from configuring root logger at the top, module level (which relies on module import caching not to be reapplied), over not using a stdlib config file parser and building one themselves, to a raciness in load_json where it's checked for file existence with an if and then carrying on as if the file is certainly there...

In a nutshell, if the rest of it is like this, it simply sucks.

The more I browse through this, the more I agree. I feel like one could delete almost all comments from that project without losing any information – which means, at least the variable naming is (probably?) sensible. Then again, I don't know the application domain.

Also…

  def _save_current_date_time(current_date_time_file: str, current_date_time: str) -> None:
    with Path(current_date_time_file).open("w") as f:
      f.write(current_date_time)

there is a lot of obviously useful abstraction being missed, wasting lines of code that will all need to be maintained.

The scary thing is: I have seen professional human developers write worse code.

> I feel like one could delete almost all comments from that project without losing any information

I far from a heavy LLM coder but I’ve noticed a massive excess of unnecessary comments in most output. I’m always deleting the obvious ones.

But then I started noticing that the comments seem to help the LLM navigate additional code changes. It’s like a big trail of breadcrumbs for the LLM to parse.

I wouldn’t be surprised if vibe coders get trained to leave the excess comments in place.