having llm audit all my work/code, and generate review pages (as a teacher) of before/after with working examples is incredibly useful. Its something no course can do for me, even a teacher wouldn't have enough patience to go through each one of my mistakes.

having things defined/have correct solution to compare for review is useful, and keeps llm on track. Don't think i would trust llm if it were reviewing it all on its own

"Complex topics" in this case means reading 22 AI-generated paragraphs that supposedly cover the entire chip manufacturing process. If the author seriously thinks this level of detail is complex then they have psychosis.

My main issue with using AI as a learning platform is that unlike documentation, books, Youtube videos, there is not really a process of having someone "review the learning material". For example, I can always read the review of some book or ciriculum, the comments under a video, or if it's some for of open source documentation you can check the PRs and verify to some extent it's claim. With AI I can't really say what it has halucinated, because I am learning a new thing, I don't have that benefit of previously reviewed material.

Buildings are a fantastically complicated and interesting complex problem space. I found my architecture studio students using it to ask technical and code questions about the buildings they were tasked with designing. I observed that the inaccurate information it was returning was compounding...

To be clear, I say "inaccurate" rather than "wrong" in this case because even if the information it returns is factually correct to the question being asked, students don't have an understanding of the complexity of the interdependent tectonic, regulatory, and spatial / experiential factors of a building sophisticated enough to ask their questions of the specificity and nuance necessary to get a good output that addresses the entire problem.

Anyway - with the students still learning to ask questions the right way, and the conditionally-incorrect facts making their learning more complicated rather than less, I hit on a strategy for them to use LLM's that seemed to help much better.

I suggested that instead of ask the LLM for the factual answer, or even better for the facts and an explanation, that they ask it to direct them to the proper place in the source material to find the answer themselves. Then, to treat it like a lab partner. IE:

Hey Claude I'm looking for "x."

Claude: "look at foo, bar."

Thank you - chapter (foo) part (bar) table (goo) says "car." However I notice that footnote (hoo) says there's an exception if "dar." Which is what I have. Walk me through this exception...

It seemed to have good results as a guide to understanding the disparate bodies of knowledge that they will eventually have to keep together in their heads and work synthetically and non-linearly through, rather than just as an external source of blindly trusted authority.

> What you get is a beautiful animation that is 100% accurate and free of hallucinations

How does he know?

The author says it's "100% accurate and free of hallucinations" but I am sceptical. Although I don't know much about chip production, when I ask LLMs about advanced topics like memory order LLMs tend to hallucinate more than entry-level questions. But the point is that I didn't found that LLM hallucinated before knowing it deeper. It's possible that LLM hallucinates but you don't find out because you are just learning it.

These animations are great. I have learnt so many things from LLM's, cross checking things is easy enough, but the hallucinations are really not much of a thing any more (in my experience). When you deep dive on things it does seem to get very wordy sometimes (this is claude anyway). Its taught me flutter and dart without opening a book (with the occasional reference page), set me straight on monads finally, refined some linear algebra, various bits of history, and philosophy, I'm learning spinors at the moment. Is some of it wrong - maybe, but it's not like my brain is 100% accurate any way, and when I need accuracy I look up references. It is fantastic getting a broad overview of a subject you don't know or a precis of a current subject, and its so much faster.

I am very interested in figuring out how people use LLMs for learning. I definitely have the knowledge, but it is severely autistic in a way.

OTOH, I am curious if there's a "practical value" to this exercise? If the LLM already contains the information and implementation knowledge to implement the networking stack inside an FPGA by itself, what value do I gain by learning about HDL, TCP, the bespoke Xillinx tooling, reading the documentation, reading papers on the implementation and going through every bit of details and theory. I feel like there's a meta skill that is more worthwhile for "practical value".

The main bottleneck as an engineer is no longer writing or testing code. It is how long it takes to understand complex systems. This is a really nice approach that, if you have the tokens and the patience, feels like I great way to learn something and I think we'll see more and more stuff like this.

I had a similar realization a few months back and am working on a tool that generates "mermaid walkthroughs". It is 1000% less pretty but it is fast and is pretty good at explaining how services work or what a code review does or just as a way for your agent to explain some decision to you.

https://github.com/scottrogowski/ariel

I guess we all learn in different ways. I prefer reading, and can fairly fast find the articles that make sense to me.

Colleagues often suggest podcasts and videos - I very, very rarely listen to them or see them.

The bandwidth is too low. It's not efficient and ultimately I'm bored.

This is a nice project, it looks cute. I watched some of the pages But I want more than that, more information, and faster - still a Wiki fan.

Also, step number 2 in the flow: have the LLM check itself... Naah, I don't believe that.

But you're not the only using gen ai like that. Take care.