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Discussion (179 Comments)Read Original on HackerNews
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. So, I am back to books and mostly try to use LLMs to clarify certain questions or ideas I have.
For example having an LLM summarize a dense topic and to find books so that you can filter faster and spend time reading those books works way better than having the LLM summarize the books or the topic (or even relying on second hand information). Another one is having the LLM quiz you on your topics of interest. With questions tailored to attack specific areas that you struggle with. Its wonderful at this, nothing I've used comes close to what an LLM can do here.
You define for yourself what your goals are, slowly refining them as you learn more, and use LLM as a tool. This ,I find works best for learning.
It's long been the case that the best way to learn something is to teach something.
And you notice when it's a topic you know well or something like software where you can immediately tell the options it's giving you don't exist on the page. Leading to the amusing statement "LLMs are bad at what I do but great at everything else".
https://adaptive.bounded.cc
Trying to diagrams/animations didn't yield good results even with frontier models. But pure text, any model does a decent job.
i run into context window limits, or practical limitations of digitizing the book
I even wonder if this behavior is due to next-token prediction architectures, somehow.
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 a personal theory: LLMs are *fundamentally* handicapped at perceiving what's going on in the mind of the human (this can't be "innovated away") and that's at the root of what makes them suck at conversation.
Next time you're chatting with someone, notice how much understanding is shared without anything being said. E.g. the other person might share something deeply disappointing, and they can tell without you even saying anything whether you get what they're going through. This unspoken-yet-communicated information guides the conversation. Or as another example: humans can read the room -- you walk into a room and immediately adjust your demeanor based on what you see and sense.
LLMs are totally blind to things like this, and this adds an inescapable awkwardness to interacting with them. I don't believe they'll ever grow out of this. Which thankfully implies more long term demand for humans instead of robots. :)
I think this is one reason why LLM text is pretty exhausting to read for long stretches.
It's possible that this quality you describe stems from the extensive training corpora utilized by the major AI labs. These almost certainly include work from the esteemed economist Jacob Silj:
https://www.youtube.com/watch?v=Poc1upTejD8
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.
And yes, it is not that it is just presenting the facts. By me taking control of the direction the questions and answers go, I can flesh out my mental model. I won't retain every little thing it tells me. But I am much farther ahead than before.
That's speculative, isn't it
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’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”.
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
I've stopped using CC because of it. I find it insufferable.
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.
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.
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.
Try it out, fairly sure that if you out in 100 random words for 30 of them it will just refuse to translate them (it will copy paste the original word into the target language) or it will do silly things like use the target 4th dictionary definition instead of the primary one).
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.
Much more likely that people will believe themselves to be an expert in a subject after having had a conversation with Claude about it.
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.
Personally I think this is a bad characterization of using LLMs to fix up LLMs because while you can never guarantee results this way (as the quoted line claims here, which is worthy of criticism), it is, in practice, useful to use LLMs on top of LLMs. And there's no infinite regress. Auto-mode in Claude Code, for example, seems to me like it's been successful at making the system more safe than --dangerously-bypass-permissions without prompting the user for permissions constantly.
People don’t even have to be lying to be wrong about this stuff. Someone can learn enough about a topic to be halfway up Mt. Stupid in no time flat, and in doing so, think they really understand the topic at hand. People that know less are impressed. People that know what they’re talking about cringe at the overconfidence: It’s incredible how many times I’ve had people in tech confidently, even smugly “explain” design concepts and strategies to me that they did not actually understand, knowing I was an experienced, degree-holding designer… and they didn’t even have a chatbot’s lips on their ass telling them how smart and insightful they were.
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.
i can't even get agents to remember core instructions like "use jq instead of writing a python script to parse some json"..
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.
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.
“asking the right questions” is also a moving target with each model release
People simply underestimate the value of doing the work and think that the end result is all that matters
https://en.wiktionary.org/wiki/eat_one%27s_seed_corn#English
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 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 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'm not 'wasting time' but I'm also not really learning.
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.
In particular it might be valuable to be in the habit of learning things that one is bad at doing.
Or not.
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.
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 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.
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
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.
It’s true of most things. Running, dieting, weightlifting being uncomfortable is a sign of progress.
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
That's actually a fun way to learn processes!
Totally agree, unfortunately careful simulation games are very rare
Otherwise you probably get more confused as you have mentioned.
On the other side, Peter Diamandis describes a situation where a bunch of kids were given a internet-connected computer and they had no teacher. Instead of it there was a “grandma” that checked kids from time to time.
After that there was a knowledge test that revealed “no teacher” approach was more efficient.
But it was a group, not an individual activity…
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.
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
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.
I assume this will become less of an issue in the future as there is more trust between the AI tools and me.
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. Now I'm aggressively working with it, almost like I'm addicted to a gamification, of getting through our learning timeline, and I'm excited to move forward as quickly as possible and pass its tests.
It's like a fake teacher, because I can also ask it to drill into a topic or re-explain itself if it made no sense.
The only thing that worries me is, sometimes I'll say something like, "Um, are you sure about that?", and it'll apologize and correct itself. I barely challenged it!
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 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.
As for how useful it is to understand thins, I believe it's still useful and hope it will continue to be.
LLMs and systems intersection - https://kernelspace.naigap.com
Distributed systems - https://byzantine.play.naigap.com
What is your process for creating these resources?
But
> What you get is a beautiful animation that is 100% accurate and free of hallucinations
100% free of hallucinations when you're not an expert that can check it is impossible. LLM hallucinations are an unsolved problem.
I think that would be a way more natural way to explore than being stuck on the classic linear output of a LLM.
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'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.
> What you get is a beautiful animation that is 100% accurate and free of hallucinations.
How do you make that leap?
> [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.
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.
Also worth mentioning that Matt Pocock has a /teach skill that creates interactive, learning sites for learning a new skill.
This is my biggest societal issue with LLMs: they allow you to think that you’ve “learned” a topic because you read a lot of technical terms. I don’t think it bodes well for the future.
0: https://en.wikipedia.org/wiki/Semiconductor_device_fabricati...
Inthink this was always the best way to learn. But it used to require immense work for a teacher.
My compiler course is a great example - program in plug in a stage of a compiler.
These exercises can be made on demand and incredibly easy now.
I've been working on a similar process of pushing to github pages, but focused more on having "practice sessions" with coding blocks to test content. Using webassembly and mock servers to mock backend endpoints Here's one I built to build a full stack llm chat system in the browser.
https://model-systems-labs.github.io/latent/llm-systems/less...
How do you know if you're learning this for the first time? Very risky to learn from LLMs. I've done it, but you have to keep your wits about you. Lots of "oh of course you're right - what I just told you was completely wrong".
It reduces friction a ton, but at the end of the day I’m not skipping anything.
Last month, I read The Prince and had it make a text adventure campaign for me.
For a lot of other topics, I often just ask it to create a simple python example that I can run.
Then I went back to a book written by humans (Eric Nikitin's "Realm of Oberon").
You're just memorizing a nonsensical recipe. What are the constraints? Why do we do X rather than Y? How does a particular thing scale? etc.
All you're doing is fooling yourself into thinking that you've acquired some knowledge. When in reality you haven't even learned the basic mental model to reason about this stuff.
You've learned something when you have a mental model that makes correct predictions. Until then you've memorized it at best, and as with most memorized things it will decay exponentially and will be gone from your memory soon enough.
God does everything have to be productized and glorified as if you’ve invented a new way of learning. Read some books!
Surprisingly effective.
(Yes I confirmed it was ok to use AI with the material)
I also struggle with LLMs explaining things, but for the opposite reason.
I consistently have problems to get short, precise but plain/simple answers.
Instead I'm overwhelmed with walls of texts, often filled with jargon that is a mixture of imprecise and unneeded.
The style at which I learn better is by asking about stuff interactively. I ask you what something is, you give me a 3-4 sentences top answer. Then I explore and dig into the topic from your answer on the things I want to know better.
Do you know it is free of hallucinations because you crossed checked it with the source material or because you told the LLM "don't hallucinate"