Yes, LLMs are okay-ish at javascript and python, but if I ask it to generate me some VHDL, it often just produces plain garbage. And without a basic understanding of hardware design you wouldn't even know what to look out for. It's just not possible to learn anything from it.
I don't know why, but I find it easy to imagine a future where real programmers who understand both the craft and the art from its lowest fundamentals to the highest levels of abstraction are the equivalent of https://en.wikipedia.org/wiki/Low-background_steel.
Often overlooked point. The only reason I even consider LLMs a useful tool for my work is because for the things I request from them I do have the prerequisites to validate their output.
Still useless for trying to understand subtle complex concepts not previously well-defined in the data sets.
The numerous slop articles posted on YC that obviously were partially written with LLM help often makes the alleged authors sound like they had a stroke. lol =3
I remain skeptical the current well-formatted LLM nonsense will help students, and have concerns it may cause hindrance with long-term information recall.
We shall soon see I guess... Best of luck, =3
The learning outcomes for students are often terrible for this path, but perhaps good if you charge for the tutorial service I guess. Attrition rates are not something institutions proudly promote in public.
YC seems to down-vote anything that deals with a data-driven reality. lol =3
In introductory and undergraduate academics, the departments will usually offer unstructured TA lab hours for those who seek additional help understanding material. However, the reality is "A" level students do not require good teachers, as their grades are usually not correlated with instructor proficiency (often due to a 3 year head start from out-of-band after-school tutoring services prior to the conclusion of grade school.)
The danger from LLM contaminating long-term memory recall from an erroneous primacy effect is a concern.
Have a nice day, =3
That requires some basic knowledge, a specific mental model for interacting with LLM’s, and a modicum of good taste in code to be able to correct for the code smells that LLM’s fall victim to.
You can never let the LLM take the wheel, you need to be the driver knowing what you want. This works if for example you know how to something in language x but need it in language Y, but it doesn’t work if you have never been able to solve the problem before.
For something like leetcode however which is far from novel, there are that many correct answers in the training data that the risk is low.