The real problem is that we forget that there was a lot of bad code in the early days.

When I studied books from 10 to 15 years ago, the patterns that were considered 'common practice' back then would be considered low-quality code today.

Our threshold has risen. People tend to forget the rings of experience embedded in community codebases and only look at the final results.

And they always claim that only the best results represent their community.

But the 'bad results' were also produced by the same community.

From that perspective, I think the floor has risen significantly. You might disagree. People with name recognition in open source usually only see the 'best' code.

But I mostly see the 'worst' code.

This is an issue of accessibility depending on your environment. I work for $15 an hour, as a freelancer doing subcontracting work in Korea, so I mostly see the worst of the worst. In that context, AI code quality feels like a huge improvement.

Because in closed source codebases, there's plenty of low quality code.

In contrast, open source projects tend to curate only the best code, driven by visibility and reputation. I think that difference is significant.

You might not agree with me. And that's fine. We all have our own value systems. I don't agree with you, and you probably don't agree with me. That's a natural consequence of us being different people.

But here's what I think: I respect you, but our views can differ. I think your point is valid in certain contexts, but I stand by mine.

Can you give an example of a pattern that was considered common practice in a book 10 to 15 years ago that would be considered low-quality code today?

My experience is pretty much the reverse: that we seem to just go round and round relearning the lessons that had already been learned in the past.