For LLM training I do wonder if they assigned a weight, but I doubt it.

Given my experience with models I think they might actually do assign a weight. Otherwise, I would get a lot more bogus results. It also isn’t as if it is that difficult to implement some basic, naive, weighing based on the amount of stars/forks/etc.

Of course it might differ per model and how they are trained.

Having said that, I wouldn’t trust the output from an LLM to write secure code either. For me it is a very valuable tool on the end of helping me debug issues on the scale of being a slightly more intelligent rubber ducky. But when you ask most models to create anything more than basic functions/methods you damn well make sure it actually does what it needs it to do.

I suppose there is some role there for seniors to train juniors in how to properly use this new set of tooling. In the end it is very similar to having to deal with people who copy paste answers directly from stack overflow expecting it to magically fix their problem as well.

The fact that you not only need your code/tool to work but also understand why and how it works is also something I am constantly trying to teach to juniors at my place. What I often end up asking them is something along the lines of “Do you want to have learned a trick that might be obsolete in a few years? Or do you want to have mastered a set of skills and understanding which allows you to tackle new challenges when they arrive?”.

Most code on GitHub either is unsecure, or it was written without needing to be secure.

That is a bit of a stretch imho. There are myriads of open source projects hosted on github that do need to be secure in the context where they are used. I am curious how you came to that conclusion.

I’m already getting pull requests from juniors trying to sneak in AI generated code without actually reading it.

That is worrysome though. I assume these people have had some background/education in the field before they were hired?