Slight pushback on this. The web has been spammed with subpar tutorials for ages now. The kind of medium "articles" that are nothing more than "getting started" steps + slop that got popular circa 2017-2019 is imo worse than the listy-boldy-emojy-filled articles that the LLMs come up with. So nothing gained, nothing lost imo. You still have to learn how to skim and get signals quickly.
I'd actually argue that now it's easier to winnow the slop. I can point my cc running in a devcontainer to a "tutorial" or lib / git repo and say something like "implement this as an example covering x and y, success condition is this and that, I want it to work like this, etc.", and come back and see if it works. It's like a litmus test of a tutorial/approach/repo. Can my cc understand it? Then it'll be worth my time looking into it. If it can't, well, find a different one.
I think we're seeing the "low hanging fruit" of slop right now, and there's an overcorrection of attitude against "AI". But I also see that I get more and more workflows working for me, more or less tailored, more or less adapted for me and my uses. That's cool. And it's powered by the same underlying tech.
The problem is that AI makes all of that far, far easier.
Even using tooling to filter articles doesn't scale as slop grows to be a larger and larger percentage of content, and it means I'm going to have to consider prompt injections and running arbitrary code. All of this is a race to the bottom of suck.
The term I like is that AI has _industrialised_ those behaviours. While native hunted buffalo, it wasn't destructive until it was industrialised [1] it that it became truly destructive.