> many of the popular educational videos on YouTube are fun to watch and don't really teach you anything.

As a tangent, I think the concept of pop science has wasted so much time of what could be considered brilliant minds. I can't believe how much YouTube people I consider really smart consume under the guise of "learning stuff." And the videos are always designed to be addicting and to entice you to watch other of their stuff, which makes sense, because theyre a business, not a school.

I'm guilty of wasting time on YouTube as much as anyone else (I like watching stand-up routines and Red Bull extreme sports) but I am never under the guise that I'm doing anything productive with my time. Its okay to have fun learning, but I always felt that entertainment and education should be kept separate. You gotta learn something intentionally, not just get it served to you via algorithm.

Note im talking about the educational "shorts" not the 60+ minute deep dives that are basically a college level lecture.

I like to use them to generate me tutorial series for technical topics I want to learn or improve. So I start a prompt like this:

> I want you to create a tutorial series about X for me. The prime objective is that I improve in topic X so never provide a solution but guide and teach. (for programming never write code). First create a question catalog to assess my current level.

Then I would ask it to structure the tutorial challenges in the following way: - Goal - Concept - Instructions

I figured that if I don't need to read any additional material on the topic the LLM is giving me too much information and I need to change the prompt. Works for me and I used this too learn topics I feel now comfortable with, like nushell, opencyper, elisp, boot loaders etc. But maybe you don't consider this "complex"

> don't really teach you anything

Dunno, I've been learning a lot of Rust in the past few days. Just dove right into a project and asked AI to teach me stuff on a need to know basis. I'm actually getting used to Rust by now.

I think it depends on what you want to learn and what your definition of "learnt" is.

The other day I realised I had no idea how DNA and life works. I guess I studied it at high school (25 years ago), but maybe it didn't go into much detail or it just didn't click.

So I asked ChatGPT to explain it to me, I came up with my own mental model from it's explanation, told it that, then it corrected me where I misunderstood things. We went backwards and forwards for an hour, me asking questions, it correcting me, until I felt like I understood the whole picture.

Am I going to become a biologist and study the origins of life from that? Definatley not! But if my kids need help on their biology homework, I now understand the basics of it.

> many of the popular educational videos on YouTube are fun to watch and don't really teach you anything.

I really enjoyed and learn a lot of things from Karpathy's and Andrew Ng's video. sure many don't really teach you anything, but I could say many others are useful too. Maybe it depends on the way we're prompting as well? it seems useful for some like Terence's message that was shared few weeks ago

https://news.ycombinator.com/item?id=49010345

The test I'd like to see: take a problem set or task you couldn't solve beforehand, learn the topic this way, then try to solve it without the LLM in the loop

I would tend to agree in the case that someone is using LLMs as their primary source for learning. But I've found a lot of use in having a claude project containing the PDF of a textbook I'm working through so that I can ask it to clarify or help me through parts I find confusing. I would definitely say that's greatly accelerated my learning - or at least greatly accelerated the speed at which I integrate information from textbooks.

LLMs can't "read the room" and infer how much context the audience already has, so they try include everything.

human conceptual thinking is very much a multi-dimensional graph, which relies on light "approximate" concepts that are "good enough". LLM AR token generation is extremely one dimensional and doesnt care about the "weight" of the concept behind a token.

LLMs hold billions of parameters in "mind" at once. humans hold like four "concepts".

This is the essential mismatch and the primary reason LLM conversation can be so painful and exhausting.

Explaining this and limiting "concepts" to four at a time tops is one of the very few AGENTS.md / system prompts I always use, and it has proven invaluable time and again.

Thinking traces show how effective this is at forcing the LLM to simplify its thinking.

[edit] Also, myself and nearly all of my peers are struggling to choke down the flaws of LLM tooling along with the benefits. the speed at which LLM adoption is being forced, without truly crafting them into quality tools first, is not ok, and not normal.

LLMs have stirred an inhumane hunger and fear. the tech is fine, but the way tech companies (creators and consumers) are behaving should be deeply questioned.

it's NOT normal. it's not ok.

It's much better to feed the book to the LLM and ask questions as you read along, instead of asking the LLM to basically write a custom book for you from scratch.

>I really don't want to read anything generated by something like Opus 5 at this point.

Personally, I find that its generated prose tends to have an undue weight to it, almost as if every topic I ask about somehow bears a heavy burden, or is otherwise load-bearing, to use its parlance.

Quite puzzling, really.

I have had very similar experience! I wanted to learn Probablistic ML, checked out a couple of MOOCs, but didn't find any that were at my level - some were too advanced, some too beginner level. Claude was unable to one-shot a course, so I am now asking it to generate it module by module. But even here, it is not doing a very good job. I muddle through the concepts that it has written, do a whole bunch of back-and-forth, which tbh is exhausting, and then rewrite everything in my words so it actually makes sense to another human being.

> I get exhausted reading LLM prose

So much this! If I see one more sentence with the words "genuinely" juxtaposed with "load bearing" my head is going to explode!

btw, I am building the tutorial here for anybody interested in this topic: https://github.com/avilay/learn-probml

I never used office hours as a student, which I later regretted because it made me work longer and harder to perhaps achieve somewhat better understanding in some classes, but also I dropped every proof-based math course I ever took. Overall I think my education would have been stronger by attending office hours.

I view LLMs in education similarly to office hours. Some people abuse it to get homework answers without grappling with the material, but the optimal amount is not zero.

LLM certainly not a replacement for a book, where you get someone’s extended personal approach to a topic, thoughtfully organized, reviewed and edited, often times actual courses taught based on it, with answers checked and errata available online.

I've decided that the main thing I'm building is my own mental model. You can take notes, create docs, put graphs and websites together, but unless I'm just trying to generate some reference material the only real objective is to develop the understanding and intuitions inside my own brain.

So I have the LLM offer a very short explanation of something, and from there's it's just me asking questions. Anything that feels fuzzy or not fully internalized is something I poke at until I'm satisfied.

It really has helped me develop a sensitivity to what I understand vs what I don't, and the ability to drill into any part of it is amazing.

Good to know other people who get migrane reading LLMs dense prose. I started reading books again recently, since everything online is polluted by LLM prose. What i realise, is that a human author, especially a teacher understands the learning pathways of new learners, they motivate the learning, and start from simplest concepts (a spherical cow), and then building all the complexities. This helps us to emphasize on most important concepts, while throwing away unnecessary complexities. While reading LLM prose is like reading a research article, that is written to an expert in the area, that talks about bleeding edge, with full of jargons, caveats, that just is not conducive to the learning process for a new learner.

I have the exact same experience, so reassuring to know I'm not the only person who feels this way.

I will say, opus 5 is an egregiously bad case of this, but other LLMs have this too, just less bad.

The sycophancy is also a concern, it’s not really an impartial teacher, all its training is to suck up and maximize engagement rather than learning. The incentives are wrong.

I have been using LLMs to help me turn my journals into interconnected notes and sometimes it is so confusing to read the notes that it doesn't resemble any human would write. Its like the models are getting stronger while also losing its touch to write human sounding sentences on complex topics.

> First, I get exhausted reading LLM prose. I really don't want to read anything generated by something like Opus 5 at this point.

Agreed.

I find Opus 5, and even Fable, to be overly wordy in eg PR descriptions and code comments.

However, I suspect that's more to do with what they are trained to do by default than LLMs in general. I have a little setup where I tell Claude to work together with Codex to tighten up prose and comments, and for me that produces much more palatable text that needs less human editing afterwards.

I think there's an underrated difference between information generation and pedagogy here. LLMs are very good at producing more explanation, yet "more explanation" is often exactly what you don't need when learning something difficult

> I really don't want to read anything generated by something like Opus 5 at this point.

Recently switched to OpenAI and I've gotta say Sol is so much better at writing than Claude. Opus has a distinctive sentence structure and Fable somehow manages to be even more obtuse. The personality of these models really does come through...

I think one problem is that books are not customizable, and many books are aimed at people with some certain knowledge. With LLMs, you can tell it what your knowledge level is and ask it to customize the answer for you. This is difficult to achieve with books.

Claude code has a „fork“ feature where you can fork an existing conversation and keep talking in the fork and then you can go back to the original of the fork. You can fork as many times as you want and let LLMs write to a markdown file to keep important facts and learnings - also good for agents to do research without expanding the context window

I use the LLM to point me at relevant books and papers. But there is still a trust problem: I am trusting the LLM to point me at reliable, trustworthy sources.

I'm not sure I'm better off with humans though -- I'm not qualified to judge whether a source is a proper authority, not an I qualified to judge whether someone knows enough to point me to a reliable source.

It seems this is a fundamental epistemological problem to which there may never be an answer.

> I get exhausted reading LLM prose.

While my advice is specific to learning about codebases, the way I do it is to have it generate mock data and put it in the local development environment, and give me some exploratory commands, and then ask away. It's a machine after all, so I don't have to read its preceding prose to understand whether it did tell me something, it can just repeat it however many times I ask it, and the hands on commands etc. give me something to actually try and implement.

> LLM prose is annoyingly dense, and the useful information gets lost in a bunch of noise

This is my biggest gripe with reading AI-generated text as well (ignoring the meta issue of whether it's worth taking the time to read something that an author didn't think was worth the time to write). It's gotten to the point that weird AI-style analogies just take me completely out of the text and kill my interest.

And I can usually tolerate a lot of purple prose.

You can make something like the link below. I'd say it's a more than marginal improvement. It's still tiring, but it's much better according to my taste. I imagine everyone would have their own version of this for their own preferences.

It's a loop that uses adversarial review to check several dimensions of the writing:

https://github.com/Vibecodelicious/llm-conductor/blob/main/w...

Have you tried using the caveman skill ? :D Might be worth a try if you dont like long prose https://github.com/JuliusBrussee/caveman

Even if you tell the LLM not to use LLM pros they (still) do it. If you feed the Wikipedia article on signs of AI writing and tell them to use none of those signs they will also (still) do it. I have tried (many times) to get an LLM to explain a concept to me, or a process, or an algorithm or what have you, and every time they cannot help themselves. Either they use LLM pros, or they get so verbose that it all just becomes noise and I spend more time filtering out unnecessary jargon than I do reading let alone learning anything.

I've found it helpfull to ask the LLM to generate a sylybus for the topic. treat the sylybus as a design doc for a price of software. i find they do much better when they have subtasks to focus on. they can do big picture and small picture, but they can't do both at the same time.

Same. I usually just read the book along and ask questions on a specific part, rather than trying to get the LLM to produce an entire study guide for me. It seems to work better as a Q&A than a "teach me" advisor

> First, I get exhausted reading LLM prose. I really don't want to read anything generated by something like Opus 5 at this point.

I’ve found the tone of Kimi K3 to be less obnoxious. Unfortunately it doesn’t wholly solve the issue, I don’t think any LLMs out there have a truly pleasant writing style, but at least not every assumption is “load bearing”.

Yeah, I am currently trying to work with Opus 5 to refresh myself on deep learning fundamentals, and... it's a mixed bag. I'm glad I already am familiar with the subject matter, as I can prompt for refinement and improvement. It is kinda following the Karpathy videos so far (a couple lessons in) but adding more math/derivations, which was what I asked for. It has trouble staying on topic, presenting information in a coherent/meaningful order, and providing all the context necessary to move through steps in its "course notes".

Like I said, I'm essentially continually prompting to refine the material. LLMs certainly continue to append, and never cut back. It just keeps spitting out additional content at me. So that's a bit annoying too. But I can basically get figure out what's going on with a few extra promps.

If youre curious what i've got so far... just be warned it is quite literally AI slop plus me continually prompting for clarification/cleanup etc. : https://github.com/cmoscardi/ai-for-ai

To get rid of the llm prose issue you can take a representative sample of its prose (say a question and its answer), rewrite the answer in the way you'd prefer, and add that to the system prompt; I did this to get my LLMs to compress down what they say, and now everything they say is very dense and to the point

I have been using this tool for the past few months that was posted on here: https://github.com/devenjarvis/lathe

It generates tutorials for you, and serves a webpage that lets you complete them. It does a remarkable job.

It still has a bit of the LLM prose problem, but it does help you fine tune the ‘voice’ it uses.

So you want it to teach you and take notes for you?

anybody adds some prologue about what style of answers are prefered ? i know i often try to change the linguistic patterns because i too (unsurprisingly) am tired of llm prose.

> want to read anything generated by something like Opus 5 at this point

I've stopped using CC because of it. I find it insufferable.

"hey LLM, I'm trying to learn about ___. I already know ___. My favorite authors are ___. Suggest some reading material."

It annoys me that the default AI mode is so tedious and longwinded. I read a lot of nonfiction - the house style of AI is basically marketing copy.

>LLM prose is annoyingly dense, and the useful information gets lost in a bunch of noise.

This problem doesn't get talked about enough and is second only to the hallucination problem IMO.

AI produces so much noise to wade through in order to find signal, and the more expertise you have in a field the more that costs. That noise directly subtracts signifcantly from productivity gains.

And, I think the problem is directly related to the hallucination problem. It feels very much like an effort to kitchen sink the response in order to provide some value among possible hallucinations.

It also seems to be a byproduct of Gen AI operation. It just fundamentally doesn't understand what it's outputting, so doesn't know how to narrow down to the most salient bits.

> LLM prose is annoyingly dense

It's just long. It just doesn't shut up. It's overly verbose. And you can't tell it to be concise or you degrade its quality.

If I ask what an integral is, the correct answer is that it is the continuos analog of a sum, generally used to calculate areas and volumes.

It should really be a single sentence, and then let me ask more about the terms I don't understand, and here's the beauty, in the previous one there can be only 5 terms I cannot know.

An LLM will vomit an entire page or more of explanation which isn't bad per se, but is an answer to something different: "give me a short introductory explanation to integrals". And that's not what I asked.

Its aligned in getting you interested and frustrated about a topic enough to go to a primary source you would have never looked at

LLMs help me refine my search. If I want to dive deeper into any topic, I can yield a strong list of primary sources relatively quickly.

something to try that works really well is to put "explain this as if you're talking to a 5th grader" at the end of your request.... it just breaks down the text into more manageable sentences that can be understood by general audiences

You can instruct it to be terse and even take on a specific voice if the standard prose bothers you.

I think AI is surprisingly good at this. I use voice mode while working out to learn complex topics, follow up with reading, and then go back to ask the LLM more questions. They excel at simplifying complex ideas and have endless patience. One hack I found is telling the LLM to test my knowledge by asking me questions—that gives me a clear idea of what to read next. Overall, they’re a great tool to use alongside traditional learning methods like reading books and working through practice problems.

This is on top of the issue of LLMs being a moron. like, I'm sure dumb people can learn stuff from them, but I'm sticking to books written by people who know what they're talking ahout and not vibed slop.

AI hater here. Too me this story is all to predictable. This is exactly how I thought using LLMs to learn would go. Or in simpler terms: well duh.

I'm actually going to make a prediction here as well. I think you will soon realize that using LLMs to clarify certain questions or ideas you have will turn out to have frustrations as well. And that you will soon direct those questions to either peers you know in real life or internet forums which are very likely to have a non-AI policy.

> First, I get exhausted reading LLM prose.

Often "be concise, to the point." is enough, but you can also paste it some stuff you like as an example text and ask to do style transfer.

You can change the prose that it outputs to anything you like. ask the llm how.

This feels like the weirdest complaint to me. Just tell it to shorten its response.

Literally saying "one sentence response" solves most of this problem.

All these LLM-as-review hype pieces don’t acknowledge that it’s turtles all the way down

Agreed. Given how many significant errors LLMs make in my topic of expertise, despite my taking multiple error checking steps, the idea of catching 100% of hallucinations because you told the LLM to check itself is hilarious. It’s just a wild lack of insight: “I’m using the LLM to teach me something I don’t know about, I definitely have the knowledge base to spot any errors that might remain!”

Completely agree. While I didn't set things up to have AI review its output in a loop, my experience trying to build a specific acoustic testing rig with Opus 5 also aligns with the other "it's turtles all the way down" comment.

Opus 5 first built me a detailed plan, but a couple important details were either obviously wrong or felt unnecessary. I went back and forth asking for sources and more information probably like 4 times and every time it did the "in looking at things in more detail it appears my previous advice was incorrect" spiel. It just became exhausting at some point because it feels like it really lays bare how LLMs are just minimizing that loss function but don't actually "understand" anything. It was really useful as a search engine (it correlated some highly relevant source docs), but I just couldn't trust it to believe it was actually done at any step.

The "100% accurate and free of hallucinations" claim should probably be replaced with something much weaker

I could certainly envision a scenario whereby review would increase reliability but not how it would every guarantee 100%, there is a pretty big logical gap there.

even if you say use RAG or something to a source you can trust, there's no guarantee the agent will still use exactly what the source has.

i can't even get agents to remember core instructions like "use jq instead of writing a python script to parse some json"..

I don't do animations, but I have an answer. You research a topic well enough to be able to understand if the result is OK or not. Usually it means figuring out some sort of testing.

I'm researching causal inference right now, and my main goal was to make sure I understand how to test estimation on synthetic data.

Basically, it's the same way it works with people. If you delegate a task that you don't understand, and you can't have a credibility proof (i.e. doctors, lawyers), then you research a topic well enough to be able to (1) define the task and (2) verify the end result.

You can add "make no mistakes" to the end of the prompt and achieve the same result while burning less tokens.

Yep, that's impossible. The hard truth is that most people this lost to LLM psychosis cannot understand that fact. It's better to treat it like someone in a cult, arguing the facts isn't going to help if they refuse to accept them.

I thought after reading the title that the text was about learning something, yet the actual text seems to be about having a system do something for me.

can you share ane example of one of your prompts? I have tried to convert some text into the socratic method and haven't succeeded yet.

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 longer operating at the lowest depth, your knowledge of that layer will be helping.

If nothing else, the experience to know when something is actually slow is a skill in itself. If a function takes 200ms, sometimes that's as quick as it can realistically go, and sometimes that's literally a million times slower than it could be, and there's actual skill and experience wrapped up in knowing what "slow" looks like.

Generally speaking, the pattern is that people are overestimating how much "work replacement" will happen, and underestimating how much "work shifting" will happen.

What is fascinating is how you can witness it at so many levels of organization. One example: Employer executive get enamored with moving from labor to capital. They believe that by using LLMs, they can replace a lot of workers. At my place of employment, we have people that are surprised they can't file a Jira ticket describing a product ask, and have it kick off an implementation. You can build the skill to attempt that, but invariably you'll get back questions like "what do you mean by <x>" and "what do you want to do in this case, a, b, or c?"; questions that a product person or an exec are not well suited to answer.

In the past, programmers did that kind of interpretation and judgment call. So then you're in a quandary; who should do that work? Work that previously, you never imagined was an inherent part of what the replaceable code monkeys do at your beck and call?

And then, how do you hire for that? How do you find the training for the people that are experienced enough with... something... to know what a cohesive error response is, or what kind of telemetry strategy is best for that particular product and organization, what collection of product asks are incredibly complicated for what they're asking and can deliver 95% of the benefits at 5% of the work if we just do this instead, and whether you want to aim more towards thick or thin clients?

Who are those people? Wait, those are programmers? Wait, there's this whole collection of inherently human skills that we devalued, by not appreciating they were always quietly doing that for us in the past?

That's just one example. There's a repeating pattern of discovering where the work truly is, work that was embedded in manual patterns we might not have to involve ourselves with anymore, but is yet still essential. So the nature of our jobs changes massively, but the overall level of employment does not.

At least, not in the medium to long term. There is a lot of painful churn we have to suffer through first.

I can only speak for myself, so I hope this resonates with you.

I wasn’t even really concerned with optimizing low level code before LLMs and that wasn’t why I was hired either.

However following that low level thread: We can look at the reasonable options and immediately know if they’re reasonable or nonsense. Why? We know the code. Now zoom a level out, where I think our expertise really lies.

Building a complex system isn’t easy. There are customers with requirements, there are budgets, SLAs etc. Sometimes one customer needs X and one needs Y. Our expertise is taking all of this in, and producing something that balances all the different variables. It’s knowing that we’ll expect X events a second so we’ll need Y to ensure we can tolerate failure.

Is it possible LLMs will be able to do all of that too? Maybe. But then why would our customers need the enterprises they pay for?

> I still learn new stuff, but I’m afraid it won’t have any value in a year or so.

I have stuff to do now, the value of the knowledge in a year or two isn't important if it solves the issues I have today.

I sometimes get vague ideas for solving maths/science problems. They never pan out but i can talk in detail on group theory and advanced maths and science topics due to investigating such vague ideas over the years. These days the LLM shoots the ideas down instantly and honestly correctly, i know enough to know "yeah that's right, oh well" and move on. Which actually takes away a huge avenue of learning. I'm pretty torn on the outcome of this honestly.

I'm not 'wasting time' but I'm also not really learning.

There's a big gap between being able to ask questions about something and understanding something.

The more things you understand, the higher the chance you'll spot a situation to use them in the future.

I think the best innovations come from times when someone is uniquely able to combine two of their previous experiences together. The more experiences you have in your back pocket the more combinations you have access to and the more likely you'll have a unique combination when the right problem comes along.

There’s still an immense value in training the brain to learn and be able to approach new problems with the sort of procedural thinking that LLMs enable. We can explore topics that we are curious about and develop that sort of “muscle” to continue asking questions when we have them. I have no fear that when those bigger (and existential) problems arise we’ll be well equipped to keep asking questions and figuring out ways to solve them.

Not good at subjects you have no previously learnt knowledge.

They tell you have "hit the nail on the head" when you really haven't.

They tell you have had a "great insight" when you are really haven't.

They give you the illusion of learning and progress but essentially give you faulty preconceptions will trip you up further down the road.

You can ask the LLM to be more critical and less sycophantic but that only gets you so far:

They want you to continue using, being dependent on and feeding data into the LLM--your independence isn't a priority.

I'm choosing to (mostly) switch off the news and continue learning things I find interesting anyway. Maybe the world will punish me for it at some point, but I guess I'll have to deal with that when it happens. The alternative is too depressing otherwise.

One possible value in learning new things is developing a habit of learning.

In particular it might be valuable to be in the habit of learning things that one is bad at doing.

Or not.

So you think building a warp drive is pointless because 99% of work, per your judgement, will be done by AI? Is your contribution meaningless and artifact useless? I wouldn't think so.

It depends strongly on the prompt. Like recently I was also doing some performance optimizations, and if I did not mention profiling none of the LLMs even profiled the code; they merely read the code and assumed, based on their own analysis of big-O time complexity. It is after I explicitly asked for profiling that the LLM started actually profiling.

> I still learn new stuff, but I’m afraid it won’t have any value in a year or so.

This is silly. This would be like arguing that encyclopedias made knowing things pointless. I learn new stuff for me.

Professionally, it's important to know enough to know if you're going in the correct direction. Practically, tokens are going to continue to cost money and knowledge can save you tokens.

I mean, part of the reason the LLM can do that is because you know enough to direct the LLM to do so and verify the results to some degree, right? It's good to learn new things because:

1. It satisfies you curiosity (and curiosity is always valuable)

2. You can better utilize the LLM to expedite something you now have knowledge about

3. You still improve as an engineer/programmer/prompter/whatever

I still think it's very important not to outsource everything to AI because there is a lot of value in learning and doing things yourself which is an important part of life.

I don’t agree with the implication that there has to be a practical reason to learn new things. I enjoy learning new technologies because it’s fun.

The way learning new stuff rewires your brain cannot really be predicted, but the effects are positive.

I decided to learn watchmaking instead

I have staff ranging from 10 years of IR experience to right out of college.

I can tell you that there is an enormous gap in ability between them despite them both using LLMs for daily IR work.

The reasons aren’t complicated. The senior responders have tacit knowledge of how breaches evolve and what to look for which gives them a much better framework for where to employ the LLM.

The juniors will normally start from “here are some logs, look for weird” which is fine but leads to tunnel vision and a lack of confidence in their reporting.

I don’t mandate that anyone do work with or without an LLM. I hire seniors based on experience and juniors based on interest. But my experience has so far been that our best up and comers focusing more on learning the technologies instead of leaving those details to the LLM are developing their intuition and understanding faster and in a more robust manner.

Author likes this kind of animation. I have something similar like this, but without the animation step.

The main idea is you can do any style you want or like to learn.

It's such a surprising and delightful turn, I love it!

Having an initial higher level understanding across the domain is extremely useful to contextualize the deeper stuff. I think boring details is a very leaky characterization, but I'll continue with it.

In my experience it's infinitely easier and faster to learn deep, "boring" things when you understand how they relate to your shallow and wide understanding of all of the related components.

The LLM is merely a tool. And you can use it for domain discovery that enables efficient deep learning at an unprecedented rate or you can develop a cursory understanding of a topic and think yourself an expert.

I’ve found that if I’m not struggling I’m not learning. If stuff is coming fast and easy that’s a sign that what I’m doing is not stretching existing skills enough.

It’s true of most things. Running, dieting, weightlifting being uncomfortable is a sign of progress.

> 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.

Quite - you still have to do the work yourself. I think LLMs are best placed to act as an eager tutor that doesn't mind discussing a topic ad nauseam until you're certain you understand it.

yes i've found that there are a few topics that i've really been able probably 10x my understanding of using LLMs, in particular in getting me over hoops that are hard to navigate when solo, BUT I have to be really careful for it to not just show me the answer all the time.

Its fun but is it really effective ? I mean I checked the LLM one and I came out more confused about a topic I already know about, I find the best way to to learn using LLMs is to just generate an example try to somewhat get a mental model of how it works and then ground my understanding with traditional documentation and resources, its an iteration of a technique I used to do in college where I would read the textbook questions first to understand what is important and then read the chapter

Thanks! The original title was "How I use LLMs to learn...", but somehow HN removed the "How" part. I even removed the initial post thinking it was a typo on my end and tried to post again, but I stumbled upon the same behavior.

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.

You can ask the LLM to connect the dots to the sources before displaying it to you so you can see the proof in line.

You can ask the LLM how to do this. Start with a topic you know well to get the mechanism working and trust it well.

100% agreed.

Wrote about this awhile ago, and it hasn’t changed:

> Time and time again, when talking to people who rely on ChatGPT, Claude, Perplexity, and other general AI tools, I hear them say, “AI is incredible. It handles nearly everything I throw at them.”

> “What does it fumble with?” I’ll ask.

> “Well, it still gets things wrong when it comes to my line of work.”

https://www.dbreunig.com/2025/04/08/on-ai-observational-comi...

It's the kind of overstated unjustified claim an LLM would make.

I thought of the "does he know" meme when I saw this.

Gell-Mann Amnesia. Recognises the failings in their own field of discourse, assumes all true about anything else, same source.

Imo the podcast and the video are better served as background material for some other task. The low bandwidth becomes an advantage because it's often ok if you miss out on some parts due to lack of attention.

Indeed it's often a waste of time to just focus on talking people fully if you want to learn fast, reading and especially deliberate practice are better for that. But if you don't have the time, energy or focus, then listening to interviews in the background can be useful supplementally

I’ve started doing a similar thing after reading a post on hn about manually applying the code so that you actually understand it.

I have done this for all my work this week and it works quite well.

For one it lets you actually query the LLM as to why, their plans give a high level not every single change and it allows you to correct it as you go and the plan will change.

It's not close to zero. https://artificialanalysis.ai/?omniscience=omniscience-hallu....

Hallucinations bring to question what you think you've learned. That's going to cost long-term if you labor under mis-apprehensions until you maybe figure out you learned something wrong.

Sorta. If its response is grounded in actual material, and you're thorough, it's not so risky. As with all learning, trusting one source is a risk in itself. Hell, I didn't even trust my physics textbooks in college. Physics.

These are high quality at first glance. the visualizations and exercises are helpful.

What is your process for creating these resources?

I'm also not sure you can really learn chip production from widely available public information. It's a hugely complex industry where the details tend to shape larger strategies. For example, you can't really understand the relationship between Micron and TSMC without some awareness of the trade-offs of memory processes for peripheral transistors.

YouTube is just a big dump of information. Having a structured way to learning along with interacting helps you learn. Otherwise you're just binging information

I had an internal company assessment I needed to pass before end of our fiscal year. The study material consisted of 10 ppt decks about 80 slides each (so around 800 total). I had an AI read all the decks and compose a study guide with quizzes along the way. It came up with a 100page word doc that I used in place of the decks to prepare. It worked very well for this including, like you said, quizzing me over various sections.

(Yes I confirmed it was ok to use AI with the material)

Or is it just an unproven attempt? Seems like a pretty straightforward value proposition. Caveat emptor.

What does this have to do with the topic?

Rejoice for your chance to brag