How I use LLMs to learn complex topics

I thought LLMs were a great tool for learning new topics - perhaps even complex ones. But overtime, I have had several frustrations with this. First, I get exhausted reading LLM prose. I really don't want to read anything generated by something like Opus 5 at this point. Second, as I dive deeper, I need a way to organize the information in a useful way as I begin to branch out in many different directions. I have tried to use the LLM to fix this by having it generate a web page with diagrams and organized information flow. It's an improvement, but I still run into the issues I described in my first pint - LLM prose is annoyingly dense, and the useful information gets lost in a bunch of noise. You can direct it do something like "use plain English and avoid LLM prose - provide only as much information as necessary to demonstrate the point", but it is once again only a marginal improvement.And then I begin to think to myself that I should just read a book on the topic written by a trusted source who put a lot of effort into teaching the topic properly and presenting the information in a thoughtful way.... reply: I have had very similar experience!...

> What you get is a beautiful animation that is 100% accurate and free of hallucinations.I'm not sure I follow how this is actually guaranteed? The fact-checking process mentioned just seems to involve asking AI to review its own work. reply: All these LLM-as-review hype pieces don’t acknowledge that it’s turtles all the way down

> In plan mode (using CC, or OpenCode) I ask a model to build the foundational knowledge for X topic.Makes sense.> I ask it to review the accuracy of the knowledge base it built in the previous step.Ooookay that sounds good.> I proceed asking it to build a simulation of that topic in a low-poly, Rollercoaster Tycoon-like animation.wat. reply: It's such a surprising and delightful turn, I love it!

> What you get is a beautiful animation that is 100% accurate and free of hallucinationsHow does he know? reply: Gell-Mann Amnesia. Recognises the failings in their own field of discourse, assumes all true about anything else, same source.

What’s everyone’s opinion on learning new tech things in this day and age? My opinion swings between positive and depressing vision of the future.I still learn new stuff, but I’m afraid it won’t have any value in a year or so.For example, I’m pretty good at optimizing low level stuff, but right now you can just ask LLMs to do so and they are pretty good at it. They will profile the code and suggest reasonable options like 90% of the time. reply: They're amazing at it, provided you keep asking the right questions.Trust me when I say that in the hands of someone who doesn't have your experience, the LLMs would not be getting the results you get.You might think what you're doing is trivial, it may be sessions that flow roughly, "Instrument this, okay this part is slow, profile this part, OK read the profile output and suggest a better approach".But your experience will be steering it in the right direction, and you're probably unaware of just how much your experience is doing that guiding, as the LLM shoots off at 100mph, you feel like it's taking you with it, but you will be guiding it a lot more than you realise, and that's where learning and experience comes in, even if you're no l...

The biggest thing I've learned from doing stuff like this is that there are no shortcuts. At some point or another, to truly learn something deeply, you've got to dig in to the boring details and do things the hard way. LLMs can help with this...but I find it's usually tempting to try and just offload the boring stuff to them, which doesn't work. reply: > The biggest thing I've learned from doing stuff like this is that there are no shortcuts. At some point or another, to truly learn something deeply, you've got to dig in to the boring details and do things the hard way.This is the only path to mastery, or understanding if one prefers. There are no shortcuts to a person achieving deep understanding (a.k.a. "Aha!" moments).Can a tool such as GenAI be beneficial to someone who already has done the work to understand? Absolutely. But it cannot infuse mastery into a person simply by its use.Only the time and effort a person devotes can do that.

I’ve been using LLMs to create readable rewrites of RFCs and specs that interest me. It is not precise enough for implementation use, but it has increased my understanding of the underlying RFC.Another useful approach has been asking Codex to implement complex things, like a Kademlia DHT or BitTorrent client in a literate style with the explicit purpose to increase understanding by reviewing the source code.Examples: https://rickcarlino.com/notes/note-dump-and-ai-summaries/ind...https://github.com/RickCarlino/tiny-bt

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. reply: This is (exactly) why I very strongly tell people not to teach themselves with an LLM. Particularly from the ground up. If you do not understand the domain, you cannot learn from the model because you won't know what questions to ask and it certainly isn't going to answer all of them for you.

The title is not representing what the post is about. “Use LLM to learn complex topics” here actually means that the author asks an agent to describe the problem area, and then implement a simple web-based simulation game, and by playing that game, the author actually learns about the topic and its constraints. They use chip making as an example.That's actually a fun way to learn processes!

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

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.

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 use it by telling it my background, giving it a rough timeline and asking it to create a learning timeline, save progress along the way, and git push / pull periodically so I can use the same thing on both Linux and Mac. I tell it for each phase in the learning timeline, present me information, then challenge me on it. If it's code, it challenges me with a coding challenge, where I use it in an IDE plugin. If I'm learning something that isn't strictly code, then I ask it to give me info, then challenge me with questions and grill til I get it right. I ask it to save what it think I struggled with, so that later we can drill it again and I can also review it in an .md file.Does anyone else use Claude like this?It's sped up my learning by 10x. I struggled with 'just reading a book.' Take kubernetes. I hemmed and hawed and spent years periodically reading some dry book or blog or official doc, falling asleep, and forgetting while I got busy....

I’ve written a skill that I basically feed what I’m looking to do, some ideas I had for accomplishing it and any other details like tech stack, etc.The skill then riffs with me, judging my ideas and suggesting alternatives. We go back and forth until something useful comes out of it. This process isn’t unlike how I do normal development.However, once agreed it breaks the work into “steps”. It then creates a tutorial for me, for those steps, explaining each line, why each change happens etc. I can then ask questions, muse about an alternative idea etc. Then I do the steps, and I’ve learned and gotten what I wanted to get done.This has been how I’ve been learning Godot and making a game for the past month or so. I didn’t go in blind, I started with a course from GDQuest so I could feel confident guiding the tutorials. I will say though, having a tutor to bounce ideas off of has been really useful.I still try to figure it out myself, consult the docs, discord etc. But if I’m stumped I’ll run my tutor skill and have some fun.

I've been using them by reading some docs/wiki/tutorial, then when I think I understand something trying to do a rough explanation to the LLM and ask if I'm right. I'm usually making some analogy to something I already understand a little. I'm usually partially right but missing some key bits at the first pass. I go back and forward asking for explanations of various bits or asking for resources around the area I'm not understanding. Often times just discovering the relevant name for the area of study opens lots of doors. I basically use it like I would talk to a knowledgeable and patient teacher.As for how useful it is to understand thins, I believe it's still useful and hope it will continue to be.

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

The little tool it outputted is nice, but click around the stages and the text is not high quality at all. The snippy titles, abbrievated explanations, I wish a few more iterations and thought was put into the actual main textual content. Especially for 'complex' stuff

> In plan mode (using CC, or OpenCode) I ask a model to build the foundational knowledge for X topic. I ask it to review the accuracy of the knowledge base it built in the previous step.> What you get is a beautiful animation that is 100% accurate and free of hallucinations.How do you make that leap?

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’ve been doing something similar (browser only interactive courses served from GitHub pages) to teach me topics from beginner to advancedLLMs and systems intersection - https://kernelspace.naigap.comDistributed systems - https://byzantine.play.naigap.com

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.

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!

I wish there was a LLM tool to explore a topic recursively, as a tree or a mindmap. You would start with some high level concept (say "cryptography") then dig further and further to more specific topics.I think that would be a way more natural way to explore than being stuck on the classic linear output of a LLM.

I'm all for this and I'm keen to try it. I certainly don't want to take away from sharing another neat use case for learning.But> What you get is a beautiful animation that is 100% accurate and free of hallucinations100% free of hallucinations when you're not an expert that can check it is impossible. LLM hallucinations are an unsolved problem.

Something that I have realized recently is that it has become so easy to get an answer to almost any question with the help of chatbots that its almost unnecessary to spend any effort thinking about the problem or the solution. I feel like before when I had to spend time researching a problem to find an answer I learned so many things around the topic itself which helped me understand the problem itself better and gained a deeper understanding. Today it feels like you can have an answer to the most complex questions you might have, yet you gain a superficial understanding of the topic and might forget about it quickly.

> I ask it to review the accuracy of the knowledge base it built in the previous step.> [I ask it to build an interactive thing]> I then push it to a new repo and enable GitHub Pages for it.Congratulations. You are an echo chamber for LLMs. Use it to create, and verify, and post to then be scraped and trained on again.

Personally I'm excited about these sorts of experiments. We all learn in different ways, and these sorts of techniques allow us to create "on-demand" syllabuses and lessons that fit our learning style and learning level.It's not perfect, but I'm optimistic this will be a useful way to teach/learn in the future.And, to be clear, I think this will be best utilized within a group/community setting. I don't think it will replace teachers or classrooms.

This guy is severely milking it now. If learning means building an inaccurate and incomplete understanding of the topic then go hog wild. Otherwise https://news.ycombinator.com/item?id=49209049 sums up my feelings about the author's attitude.