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.

Ok - not wrong at all. Now take that feedback and put it in a prompt back to the LLM.

They’re very good at honing bad code into good code with good feedback. And when you can describe good code faster than you can write it - for instance it uses a library you’re not intimately familiar with - this kind of coding can be enormously productive.

> They’re very good at honing bad code into good code with good feedback.

And they're very bad at keeping other code good across iterations. So you might find that while they might've fixed the specific thing you asked for—in the best case scenario, assuming no hallucinations and such—they inadvertently broke something else. So this quickly becomes a game of whack-a-mole, at which point it's safer, quicker, and easier to fix it yourself. IME the chance of this happening is directly proportional to the length of the context.

This typically happens when you run the chat too long. When it gives you a new codebase, fire up a new chat so the old stuff doesn't poison the context window.