I really wonder where this assessment is coming from? It's not that I use LLM written code for something (in those exercises at least), in fact I don't let the LLM write code (see my prompt example).

It's about guiding me in _doing_ exercises so I learn and I can evaluate if I learned something if I can apply the learning myself.

This feels like an impossible assessment - yes, a model probably can't give you the education that a advanced/expert book on a topic will, but implying that having a verifiable goal is somehow fake learning feels like an intractable problem.

What level of evidence would be sufficient for you to accept that a model may be able to teach a concept?

I'm happy to take on this challenge with a topic of your choosing, but I don't believe there will be an evidence base that satisfies you that the knowledge is earned or deep enough.

> It's like with latest vector of attacks being spamming Github with malware injected in proper looking code in hope of AI to index it.

First of all "malware" which is often technology working as intended happens at the OS level, not the github repo level. You can't say a repo is malware in most cases - it depends on the deployment. Second and more importantly, the vast majority of training data would have to reflect the "injected attack" for it to have any bearing.

There are ~30 keywords in JavaScript, compared to 100s of thousands in spoken/written languages.

You cannot "attack" anyone or anything through an LLM. It's an offline, read-only model of text, that's it.

Hilarious comments though