My high hopes were quickly dashed at the step that involved turning a complex topic into rollercoaster tycoon.

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

If you're using LLMs to learn or for research, and at some point you don't end up engaging with an actual resource (books, papers, lectures, web pages, etc) then you're playing yourself.

I use LLMs to learn deep technical concepts. I really like them because I can spend countless hours a day understanding things and building an investigation file with all my findings. I code examples and test the findings. It has helped me understand basically anything.

I'm using LLMs right now to build a terminal browser, a GUI browser, and a PyTorch/LibTorch replacement. It's really fun to be able to learn and make progress this way. It's like reading multiple interactive books, where every concept can be explained again and again until I understand it.

That's it? It doesnt feel right. This feels good enough for a casual dinner table conversation and not much more.

For everyone who thinks you can’t learn with LLMs, Dr. Cat Hicks, psychological scientist and author of the recent book “The Psychology of Software Teams[1],” who worked at Google and founded the Developer Success Lab at Pluralsight, has written two skills called learning-opportunities[2] and learning-goal[3] that use validated learning science to help you learn while using LLMs.

Cat also has an awesome podcast with her wife, Ashley Juavinett, Phd, called Change, Technically.[4]

I encourage everyone to check out her work! She’s dedicated her life to helping software developers get the support they need inside organizations to be seen as humans, not just robots.

1: https://www.drcathicks.com#book 2: https://github.com/DrCatHicks/learning-opportunities 3: https://github.com/DrCatHicks/learning-goal 4: https://www.changetechnically.fyi

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’ve been doing something similar (browser only interactive courses served from GitHub pages) to teach me topics from beginner to advanced

LLMs and systems intersection - https://kernelspace.naigap.com

Distributed systems - https://byzantine.play.naigap.com

Gamifying the presentation could make topics more accessible to others. For me the overhead wouldn't help with my own learning. Also I've been burned by just learning things mechanistically (e.g., coding, applying algebraic rules), so I'm leery of learning just by making flashcards or models of the topic.

I find LLM's do great for learning when I ask what are the principles, how the main applications work, what are the key drawbacks, where are the growth plates in the field, etc. - the kind of thing a good advisor points to. Sometimes I have to ask it explicitly to use topological order of topics and show relations, which often highlights the gradient changes in the learning curve. For pruning, it's surprisingly good applying philosophical heuristics - Occam's razor, or Derrida's differance (the difference that makes a difference), etc.

And finally, no learning is effective without problem sets, and for those LLM's at times get me over blocking issues.

The degenerate case is memorizing the glib phrases regurgitated back to me; they're helpful and functional enough to get me into real trouble!

But AI will be better than you at those topics as well, and when someone needs an expert in that topic take a guess who will they approach in such scenario.

I don't think we are even that far when the complexity AI can handle surpasses 99.999% of what humans can handle, where AI make e.g. physics discoveries beyond the grasp of most humans and it will have to "dumb it down" when talking with humans -even physicists- but not with other AIs