I am kind of amazed how negative the comments are here, especially on HN.
Learning to hack something together in high school using the latest technology (vacuum tubes, radios, microprocessors, web/javascript) has been a common theme in the tech world for generations. With LLMs and online tutorials, this isn't even a difficult suggestion. Do people think learning new tech is somehow wasted effort?
> I am kind of amazed how negative the comments are here, especially on HN.
I can't recall or point out exactly when, but there is a stark before/after moment where the opinions of anything pg went from "Interesting and maybe true in some ways" to what we see today, lots of knee-jerk reactions and hardly any comments about the actual content.
Hazarding a guess, I think the moment Altman became the CEO and later during COVID, the sentiment seemed to have been shifting towards what we see today. But this is all based on hazy memory, rather than looking at the data. I'm sure there is a blog post waiting to be written about analyzing the sentiment of comments to PGs articles on HN, and you'll see a shift somewhere.
Because at some point in life everyone gets tired of fairytales. He started mending the anecdotes to his content which always rubs people the wrong way.
So, this comment of yours obviously isn't in the "knee-jerk reaction" category of comments, I suppose? What exactly from the linked tweet(s) are fairytales here? There is hardly any text at all, so strikes me as a comment about previous pg content, but then this would be one of those comments I talk about? Very confusing.
Your hand waving doesn't make it knee-jerk. It's just what happened to his writing since COVID. He goes for more of a shock and awe style and not everybody likes it. He's been writing for over 20 years now, hasn't he? His style has clearly changed, and an changing style attracts a different audience so it's no surprise his original readers might not connect with his newer work...
> I can't recall or point out exactly when, but there is a stark before/after moment where the opinions of anything pg went from "Interesting and maybe true in some ways" to what we see today, lots of knee-jerk reactions and hardly any comments about the actual content.
Hard disagree. This submission is still being highly upvoted, while another recent post[1] on the harms caused by Graham’s fellows[2], with a fairly tame comment section, has been flagged. That is a constant on HN. It’s not a fluke, it’s as predictable as the sunrise and getting more pronounced.
I’m sure we’re both biased in our perceptions. Mine is that HN in general (certainly more than any other website) used to worship[3] everything he wrote, together with others like Musk, until things started to really go to shit and many eyes have been opened to the effects of the unfettered greed of rich tech guys out of touch with reality.[4]
[3]: That word I choose hyperbolically but deliberately. It definitely was not “interesting and maybe true in some ways”, it was much more hardcore than that.
[4]: That is not “knee-jerk” but a slow realisation still ongoing.
Imo it's a breakdown of trust of the startup ecosystem as a whole. Repeatedly startups have enshittified and it's become undeniable that the investment apparatus around startups is partly responsible. We have seen a great driver of uncreative destruction, industries undermined, small businesses undermined just to drive masses of money into few pockets - less fairness for the people working in what is now the gig economy and ultimately prices and other costs that end up as high or higher than they were before for consumers. Not to mention the whole AI/OpenAI situation which many perceive as threatening their skillset per se, essentially tearing up the social contract that existed on this site.
The sycophancy on HN is starting to break down because there is a higher proportion of users sceptical towards the outputs of the VC and wider investment world than ones who believe they're potential beneficiaries of it.
Tech industry people are becoming less interested in HN as a warm handshake into the startup world because, frequently, they're disgusted by it. And this reflects on the sentiments people post on PG's articles.
Increasingly if those at YC want the same kind of low-bar praise they got before, they will need to get it from machines.
If you did a data analysis, I expect you would see an uptick with the 2023 Reddit API controversy. HN got a big influx of redditors with different sociopolitical perspectives and discussion norms.
This might be hard to disentangle from the tech correction layoffs of 2023 and 2024, but my perception is that most of the spite comes from Tech Outsiders opposed to jaded Developers.
> With LLMs and online tutorials, this isn't even a difficult suggestion.
Don't many of the commercial ones prevent you from using them to build LLMs?
I would say the reason for the negativity is not because it's a bad idea for a project, or that doing projects in general is a bad idea (it's not!), it's because it's a very specific thing that is not for everyone. The best thing about computing is the low barriers to entry. You can basically work on anything that takes your fancy. So those who are interested in ML will be drawn to learn about LLMs. They don't need anyone to tell them to do it. Telling everyone to do it reminds me of the "just learn to code" stuff of a decade ago. No, please don't, please find something you enjoy.
Completely agreed. The point is the knowledge, the learning and the journey. If a kid has a passion for building or toying with LLMs, then of course, by all means, please start tearing them apart or even build and train your own model. You'll learn a ton, even if you won't necessarily end up using it here and now. The learning experience will compound and of course that will be useful.
The above is, after all, the whole genesis of the word 'hacker'. We should celebrate that.
How exactly does one go about "tinkering" with an LLM? Any architectural change you introduce needs fine tuning. That needs data and compute
I tried to modify the embedding output of bert to make it generate box embeddings instead of point ones. At the time I had access to university provided A100 gpus but even with all that a training run took half a day. Models these days I don't think I can train it in any reasonable time with that much compute.
(As a TML person, I'm obviously biased, but I couldn't resist because of "tinkering").
TBF it's hard to imagine a real architecture change that wouldn't require a ton of compute, but you could certainly fine tune and play with different recipes, loss functions, etc. And Claude can carry you a lot of the way through doing this.
One fun task is to invent a tool and then train a small model to use it. You could export that small model and run it locally for free forever to do your thing. I think this is what a lot of Software Engineering will look like later.
There are a lot of other high level abstractions here to look at. Prime Intellect has one.
The other thing to play with is self-hosting small models, but IMO most of the interesting stuff is actually related to multi-gpu or multi-node inference so there's not necessarily a ton to learn here.
This is one question that is not just for kids but for all "gpu poor" adults as well. One concept that I found useful is "toy models": small compute models that replicate a certain, not very well explained phenomenon that the large models have. Of course, you can't have a toy model for everything, some behaviors are only visible at scale. But, there is a bunch of things like in-context learning that you can play with without burning a ton of compute.
The best analogy is strip mining (big labs) vs cave exploration (solitary/small teams). I think this is how science progresses at the boundaries by smart/curious/hardworking individuals because depth is a requisite for finding the right questions and then the answer. It is not for everyone and it does not always work. But you learn a ton even if it doesn't pan out to be a big breakthrough.
This guidebook covers pretraining, post training (SFT, RL) and a couple other topics. And others authors have also written books that fit on single node reasonable hardware.
If you want to start with a pretrained base I built Gemma 270m and released it last year. This fits on a raspberry pi.
The fundamentals of AI don't require industrial amounts of large scale. Think of it like this, when I was learning how a plane worked when I was a kid I didn't build a 747 at home, I started with scale sized model planes. Same idea here.
And FWIW I'm a staff researcher at Deepmind (opinions here are my own) so I want to specifically encourage all people out there, you can learn a lot about how these LLMs work at home, for (mostly free), using resources like colabs or spot pricing on accelerator providers. There's many great resources out there and I encourage anyone willing to learn to go for it!
> Do people think learning new tech is somehow wasted effort?
No. But funnily enough that is a promise by some of the AI cretins and their boosters. Oh yeah best case scenario you learn how to build LLMs for us. We’ll employ you. And then ultimately that just becomes training data for the LLMs to do it themselves.
Hacker News is a Y Combinator (which PG co-founded) sponsored website whose original code was created by PG and whose guidelines were originally formed by PG. For those who are "sick"(sic) of rich people, VCs, or capitalism in general, you've come to the wrong place.
It is indeed puzzling. Back in the day, I built myself a music playing device using TTL gates, eagerly studied what CPU's looked like inside and in general wanted to know how everything worked - even though it had no hope of replicating most of it.
Looking back, it was an ideal start to a 40 year career as a software engineer. And of course currently I'm reading "Why Machines Learn: The Elegant Maths Behind Modern AI" because the urge to understand the machines I want to master and control hasn't gone away.
I like the hacking spirit suggestion. But my disagreement is the specificity of it. The suggestion here seems more inline with not tinkering and hacking but first principles learning typical of science and math curriculum. What made hacking radios and JS cool was the short feedback loops to see the progress you made. Unfortunately for LLMs starting from scratch doesnt give a good feedback loop on improvement, although starting from pre-trained model to quantising, fine-tuning might be more aligned with the hacking culture
The hacker ethos and "information wants to be free" of the '80s-'90s was drowned out by a flood of coders in the '10s-early '20s whose only creed was FYPM and "what's the minimum career effort for the fattest paycheck?"
No, but there's a WHOLE ENTIRE WORLD of tech that isn't LLMs. LLMs aren't the future or anything. Sure they'll keep existing. Right now though all the opportunities exist for people looking literally anywhere else
Let's be very, very generous and say that an LLM is intelligent. Does intelligence eliminated the need for tools? If you have an executive assistant and your executive assistant has ten executive assistants, does that mean that Microsoft Excel is obsolete?
Why the everliving fuck has everyone stopped building tools?
This is essentially most discussion here. I close the HN tab so often after seeing the people that just came to disagree with someone on the internet. There are too few discussions here that come from a place of "That's a great perspective and I can see it being useful when applied in X, just trying to solicit a discussion here, I wonder how this could work or break down in condition Y?"
I have also noticed a general drop in fawning over and deification of 'made a billion, must be a visionary' figures, and a receding tolerance for every sundry kind of hot take and proclamation. Mustn't be related.
A lot of people here are responding to the message but not to the meaning.
It would be a good idea for young people to deeply know how these programs work. Not so that they can spend their career building them, but so that they can approach the next class of problems we'll all start trying to solve, with intuition all the way down to the weights and underlying mathematics. And also, to develop a healthy intuition of when "Just LLM it" will not be the right choice.
"Build an OS" wasn't a common university project because we were all expected to go out and work on Windows, but because understanding the bare-metal firmware for a computer helps you deeply understand how to intuit building for a whole class of problems.
> "Build an OS" wasn't a common university project because we were all expected to go […] helps you deeply understand how to intuit building for a whole class of problems.
Sure, but it was for a specific degree with a syllabus that taught you the foundational knowledge. It was not expected from the law students to learn how to build one.
> understanding the bare-metal firmware for a computer
IMO this is still relevant, everything surrounding the LLMs needs such a vast infrastructure that I don't know if I would find it more useful to learn the maths behind ML than CS
As a 17 year old, I agree with this. Ofc I'm against all the hate directed at PG, I believe that all knowledge has value regardless of its economic utility, but I understand where the hate is coming from. Personally, I find LLMs boring for now, and I'm more focused on CS and electrical engineering.
> It would be a good idea for young people to deeply know how these programs work.
It would be a good idea for _everyone in the industry_ to deeply know how LLM training, inference and "agents" work, not least because it removes the ability of shysters to bamboozle with bullshit.
But, as much as a good idea it is for the young to understand this, it's the elderly who will be really taken advantage of if they do not keep up - just look at Facebook for good examples of why.
I'm not sure it's possible to have intuition about systems that work in thousands of orthogonal dimensions. In fact I'm pretty sure most of the research is people trying fairly arbitrary things and testing them and then post rationalising implied understanding of what is really happening on top of good outcomes.
I'd have to push back, though not on the part you'd expect. Your description of human researchers is roughly right: a lot of the field is try-things-and-narrativize-after.
But the load-bearing assumption is that intuition has to be human-shaped intuition. Humans can't intuit thousands of orthogonal directions because we project everything down into a 3D metaphor and hope it holds. That's a fact about our hardware, not about the systems.
And the reason why is the most interesting part: nothing requires the compression step. A model or an agent can operate over the actual objects, holding thousands of runs and ablations in context and noticing regularities in the native dimensionality, without translating them into a picture of a ball rolling down a hill. No bottleneck at "can you visualize it."
So the narrower claim: it's not that intuition here is impossible full-stop, it's that human intuition is unreliable. Your post-hoc rationalization point is evidence for that, not against it. The story exists because a person needs something to hold in their head. Drop that requirement and the failure mode goes with it.
I disagree. It's absolutely possible to develop an intuition about extremely complex mathematical ideas, including llms or high dimensional systems. Learning to build an llm is a great way to start building that intuition.
What provable conclusions have you intuited around how LLMs work? Give me some examples to prove your point? I'm absolutely happy to change my view with enough data.
I'm not looking to change your view. But for other readers who are curious, here is a link to an interesting task to gain intuition. Ahmad is a good data point for someone who tinkered, built intuition, then started his own ai company.
https://twitter.com/TheAhmadOsman/status/2087742080793620593...
I think we are talking about different things to be honest. Understanding LLMs is a fine thing to do but to say you understand what the network is actually doing, even in toy examples like MNIST is extremely tricky. Figuring out what a ~3 trillion parameter network is doing in thousands of dimensions seems intractable to me.
Linking to a Twitter thread about training a small LLM to be good at Wordle is not an example of what I'm talking about. It might well be a useful task but it doesn't allow us to understand deeply what's happening.
I think provable conclusions and building an intuition are different things to be fair. In my opinion it is entirely possible to build intuition about seemingly impossible to understand topics like infinitesimal areas, infinite limits, function mappings, and high dimensional spaces. For example, 3D spaces are easy to intuit if you can visualize vectors in your head. Some people claim it is impossible to understand dimensions higher than 3 but that's obviously not true because you could assign a color to each vector to visualize the next dimension. You could then assign a width to visualize a 5th dimension, and an arrow shape to signify the next, and so on and so on. That's more of a creative act that can start to build at least a visual understanding in you head of these higher dimension spaces. That's a way to start to intuit about those things.
Now with llms we need way more that 6 dimensions so we can start thinking of assigning matrices to each 3d point for example. That allows us to increase the dimension from 3 to 3 + whatever the matrix dimension is.
We can visualize the matrices instead of having numbers as having colors for each entry, so they can be a sort of cube with each vowel being a different color.
Now you can start to visually intuit about how these massively high dimensional spaces can be formed of these colored matrices that can react to some input training data.
That's a start of an idea for intuiting things that might seem impossible to have an intuition about. I think visualization is a great way to start.
Visualizing large dimensional matrices is a way of starting to develop intuition about llms. If you can't visualize high dimensional spaces, and you haven't studied llms, you're not going to have any intuition about how they work.
Depending on your knowledge of math, I recommend starting with linear algebra, building an understanding of the equations and try to visualize more and more complex systems, then study llms to see how you can apply your linear algebra intuition to your understanding of llms.
VTK is a great toolkit for visualizing complex systems. 3 blue one brown on YouTube has other visuals that might help you.
Yes, if it cannot be measured it doesn’t exist no matter how much it would make everyone feel more comfortable if we could intuitively understand these systems.
Like a cat who knows how to teleport so rapidly around the room that he becames a blur. All the while taking into account stuff falling around he knocked down.
Developing an intuition about high dimensional systems is pretty different from understanding the character of some specific point on a 1e9+ dimensional manifold of parameters, in my professional opinion (setting aside all the degrees of freedom that come from the structure of the thing). Sure one can understand generic principles like the curse of dimensionality, but truly groking how an LLM works is basically an open problem as far as I'm aware. I'm not saying there's no benefit for amateurs to study how LLMs work, but let's be realistic about how far mere intuition can truly take anyone in this space.
I think it’s reasonable to have a shallow understanding of most parts and a deep understanding of a small number of parts. That’s how most engineers are.
Most software engineers do not have a deep understanding of CPU architectures. In fact they probably don’t even have a shallow understanding and get around just fine. How many of them are looking up the instruction set for the CPUs they deploy their CRUD app to in EC2?
But in the case of CPU architecture there are SOME people who understand how things work 100%, and they've built and vetted abstractions/mental models that enable other engineers and scientists to have that kind of mixed shallow/deep understanding in a way that works. On the side of LLMs we're still lacking an expertise which could flawlessly explain how these things operate; the abstractions that we're using are instead derived inductively and are totally unvetted.
You are right that the field doesn’t have a theoretically sound explanation for the architectural choices aside from “A works better than B”. However, I would argue this is an ideal opportunity for the “gentleman scientist” or eager 17 year old.
Basically every part of the original transformer was replaced with something more efficient or better:
LayerNorm -> RMSNorm
Sinusoidal position encoding -> RoPE
MHA -> GQA
ReLU -> GELU
What this means is that there is ample opportunity to improve on what we’ve done thus far.
Oh yeah I absolutely agree that there's lots of greenfield for relative newcomers to find some big improvements! I'm just saying that reasoning about 'best practices' in terms of what works for something like a CPU is missing some important and relevant differences.
The original RoPE paper is a great example of the authors not actually understanding how their own discovery was functioning fully which is a fun example of what I’m talking about I guess…
Yes you might progress the field, but will you really understand why? You can make up an explanation and anthropomorphise it with a few contrived diagrams and everyone will cheer!
In fact, one of the jobs of an engineer is to make sure that other engineers who don't work in his or her area do not need to understand that area deeply, yet build something reliable with it. They need just the summary that he or she writes up into the datasheet for the part. Ensure these conditions are met for safe/reliable operation, give it these inputs, expect these outputs, these timings, this energy consumption, this heat generation, frequency response, tensile strength, whatever.
You may be right. Nonetheless, it's possible that the act of trying to get your brain to understand a system that works in more than a few orthogonal dimensions will help develop useful intuitions about how LLMs, and the many systems/products/robots that will be built on top of them, work.
Agreed. During comp sci we got to re-implement various algos of networks, OS, database, firmware.. and it all gave complimentary intuitions that were useful when tackling practical implementations and bottlenecks.
The funny thing about LLM is, you can build one from scratch and yet you still won't understand how it works. You would understand what kind of matrix multiplications the neural network performs (in fact that's not that hard. An OS is orders of magnitudes more complex), but you would still have no idea why it does what it does.
This is a great point. The complexity sources are entirely different. An OS is complex because it’s solving a difficult problem and we’ve seen decades of evolution both in knowledge and-how and, at the frontier, in performance standards.
The abilities of LLMs are emergent. You can experiment with LLMs and know as much about their observable behavior as the experts. But there’s no way to “crack open” an LLM and see precisely where each skill or tendency lives; as far as we currently understand, it’s all mashed together.
Learning how to build emergent systems is also a skill kids should learn these days. The closest I got was coding up game of life for CSE 142 (intro programming).
If stochastic gradient descent isn't taught in whatever CS Theory 101 is now, it really should be these days.
The spirit of your point stands, though. This kind of research is interesting to read about, but it's very hard, more like neuroscience or biology than computer science ("LLMs are grown, not made"). You're dealing with a lot of extremely _messy_ complexity, for which organic life is really the only good point of comparison. Most of us here are't really equipped for that kind of work; it's not at all like, say, reverse-engineering a piece of software written by humans. And of course the only people who can do it on frontier models from Anthropic and OpenAI are people within the labs themselves. (But I'm optimistic we'll see more of this work on open weights models...)
Yah, "building" it is not sufficient. But a lot of times when I build I want to know the why. "Why does gradient Descent have some clever tricks that easily translate to matrix math"?
This is a silly point. Just because the size of these things is too large for our tiny brains, it doesn't mean we have no idea why it does what it does. If you run a physics simulation of a weather system, you have a situation that is unpredictable for a human - but it's not fair to say "we have no idea why the outputs are what they are!!"
That is not a silly point at all. You confuse understanding the mechanics of it with having a theory of why it works. For the physics simulation, physics provides us with the theories which give us the equations underlying the physics simulation. For neural networks, why is next word prediction giving us AI that can do math? We don't really know!
There is no "theory" of tomorrow's weather. We understand the math of every single individual equation of an LLM (for example), just like we understand every single equation in the physics simulation. It's the entirety of the system that we don't understand (and hence can't predict) in our heads.
From a biology perspective - we have a good understanding of the carbon atom and how forces influence it. We really don't know much about a cell.
The next class of problems to solve for future generations is how to survive on a hot planet and how to recover from the damage done by reckless lunatic profiteering assholes.
Maybe people want to take it as cynicism but I think you are factually correct that personal cooling, infectious disease, atypical weather and fire breaks are all more realistic topics for people too young to already have patents, connections or their own oil well.
35 years ago, the enormously popular animated series Captain Planet taught kids about protecting the ozone layer, using biodegradable materials and renewable energy.
Absolutely none of that mattered in the long run because capital markets think environmental destruction is a small price to pay as long as some people get rich.
What kids learn about personal cooling tech and what not won't matter when the supply of the raw materials required to make them is reserved for some VC-funded trillion-dollar 'startup' trying to synthesize an undetectable chemical weapon to get past the bureaucratic red tape of international treaties.
What corpus would one be advised to use for training? Should one limit oneself to legally downloadable documents or should one follow the examples of Altman, Amodei, and Zuckerberg and use LibGen and Anna's Archive?
I'm (more than) twice that age, but I've spent time learning this exactly this from videos by Andrej Karparthy and from books by Sebastian Raschka.
I didn't do it because it was useful to me in a practical sense. It's because LLMs are fascinating and I want to know how they work. From that perspective it's been a great experience. I have afirm grasp of the basics. This makes it much easier to understand frontier concepts like compressed latent attention. I can follow the field and understand it.
Not sure I would have got as much out of it at seventeen. I have a lot of background and experience which made it much easier to learn. I wasn't struggling with the linear algebra or with python. I already knew pytorch and neural networks. That helped a lot and I covered these tutorials fast and could skip over large sections. A few evenings and the odd weekend day over a couple of months was enough for me.
For seventeen year olds the tutorials are good enough to make it possible to learn this but it would have taken a lot longer to understand. On the other hand I would have learned a lot more. I think I would have learned a lot of valuable stuff.
However I also think 17 year old me was studying for his A levels and probably this was right choice in terms of maximising future opportunities. I'm not sure I think learning about LLMs instead is sensible. Indeed it might be bad advice. But I can absolutely agree with the sentiment.I think 17 year old me would have wanted to do this too.
I've read Raschka's "Build Your Own LLM from Scratch" book and really enjoyed it. I haven't tried the code yet, but the code from his previous Python ML book worked great.
This video is excellent. I'm a huge fan. Also the video is zero commitment and instantly available which makes it a good way to check you are interested.
The book by Sebastian Raschka is slightly less accessible but very reasonably priced and the experience of working through a book is a lot nicer than skipping back and forth in a video (for me). Sebastian's blog posts on recent architectures are absolutely great too.
I'll use this post as a shameless opportunity to tell more people about a little side project, I made:
http://languagemodelbuilder.com teaches you (in a few hours to days) how to build an LLM from scratch. It's entirely free, without accounts, and without data collection.
I wish this was available for young folk in Romania: As it happens: macOS: 2.70% of desktop operating-system usage in Romania; OS X: 2.55%; Combined Apple desktop share: approximately 5.25%; Windows: 90.65%; Linux: 4.02% or so AI tells me.
There are GitHub repos with the code from Sebastian Raschka's books, which build an LLM from scratch in PyTorch (some examples run fine even on CPU-only setups) using a GPT-2-like architecture, as well as his new book on reasoning models that came out recently.
[Off-topic] Your name sounds familiar...
As someone who's at a similar age and was interested in learning how to do this, there just aren't enough resources to do so. Most LLM research is in the form of academic papers, and there isn't any 'popular' way to learn these things, and besides that all said research assumes you have a B200 cluster ready to go. If you have weaker hardware (say an 8GB nVidia GPU, which is what I have) you're going to be limited to fine tuning small models or torturing yourself working the GPU for days per iteration trying to run things like https://github.com/karpathy/nanochat, which is hardly an educational experience. Renting cloud GPUs is expensive, and at this age the most I could muster up for experimentation is probably $100 or so, which only gets me 25 or so hours on a B200 which just isn't enough. So why would I bother myself with this if I'm already at a disadvantage because of not having access to the right hardware and when surely there are better ways to spend my time? I concluded the only way to learn and be competitive is by finding work at an AI lab somehow (not happening at 17), or studying ML at the right university.
Putting this in contrast with programming, I learned coding when I was 8, and it was incredibly stimulating to learn because you can quickly iterate and there were thousands of books and YouTube tutorials that dumb everything down and teach you fundamentals. All you needed was a $300 computer, and you can learn nearly anything you want, without being gatekept from this or that because you don't have enough vRAM / an sm_100 GPU.
I agree more with Yann LeCunn's salty reply. Over long run, knowing how autoregressive language models work from scratch will be just one step in having foundational understanding, and they might become dated... like knowing how a CRT monitor work. Something of historical interest and good for learning, but not crucial to being well-rounded.
There are other types of models like diffusion models right now that are showing more efficiency and have a higher ceiling for improvement. Understanding math and fundamentals are more important.
He has, but I think he's right in the sense that there will be better models. It would be premature to think that the current frontier model architectures will last into the future. It would be like making DVD engineering a standard part of high school curricula
> It would be like making DVD engineering a standard part of high school curricula
I fail to see how PG suggesting that a 17 year old, today, should study something... is in any way a parallel to formalizing it as part of the curriculum.
I would, in fact, expect PG to recommend something completely different in 10 years.
The whole point of hacking is to understand the world around you, today
Horrible advice. This may have been good advice 10 years ago, but not today. There are no positions for people who "kind of understand how toy LLMs work" because so many engineers do these days. Most of the real LLM optimization work is at the edge of research and highly proprietary and not something you could ever do without infra that costs millions.
Yeah he's a moron with a lot of money, that's about it. I'm sure he's said the same thing about various other bags he had bets on throughout the years.
I think the answer then is to get a Phd? I mean, one should learn it to satisfy their curiosity and to build things but I don't think it's going to help you career in any significant way.
Throw the computer and the smartphone out of the window.
Or more reasonably, the same old thing : use Linux, hack a little, why not learn programming basics. But learn to own your technology, fight against centralization of technology. The same old RMS story.
IDK where the tech industry is going, if there will be jobs anymore or not, but what I'm sure (and what have been the case for the last 10-15 years anyway) is that for most tech jobs, having good technical knowledge beyond the basics is pretty useless and will probably not be recognized.
If you can, stay a computer geek if that's your thing, but don't make it your career choice, the Eldorado is behind us.
As someone who very much enjoys self learning, I can't comprehend what you wrote. It doesn't make any sense. In fact, I don't believe you've played in the same sandbox at all.
> why not learn programming basics
Do you think implementing an LLM from scratch would not require programming!?
A generic project based goal like this is the best approach to self learning, because you fight your way to it, picking up what you need to know on the way. And, once it's all working, and you get your first meaningful sign of success (in this case, some tokens out), after your long path, you fucking celebrate!
"it doesn't matter what you choose to study because no job is safe from being outsourced overseas, handed over to an indentured servant, or made redundant by a machine. you will spend your whole life surviving while the cannibalistic pedophiles who own everything invent new ways to make you own nothing. be frugal, don't get married, don't have children, do everything you can to stay healthy and independent, and you just might live a reasonably comfortable life."
Most reasonable advice I've seen under this post. Having graduated in 2025 I've experienced the horror of competing with infinitely many third worlders in my small country, who will gladly take 1/3 my pay and be serfs.
I really hope we get another hiring boom like in 2020 when I decided to study CS. Otherwise my career will be very rough. I love it and can't imagine doing anything else.
who would have guessed when first world were enjoying their heyday as they were colonizing the world, and teaching everyone and their mother to learn English.
Kinda sad now you have to compete with serfs from thirls world. chu chu chu. so cute.
shouldn’t have robbed others then. a richer world for you comes at poorer world for someone. we are playing infinite game in finite world.
say that to visa laws that were designed by your ancestors that carry around the world. Say the same to currency arbitrage, freedom of mobility and everything that you enjoy for granted in protected walls.
The reason remote worker can out compete you is because of this currency arbitrage you designed. Your pennies goes a long way in this so called third world. Your one day dinner at nice place is a months salary for family of 4.
Seriously, if you can’t compete globally, then you don’t deserve your privilege of being born in rich world.
Stop labelling other humans as serf when your entire wealth was built on shaky foundations.
You == not you the person, but the government and laws.
very compelling. do the nations that never participated in colonization -- such as Switzerland, for example -- get exemption, or is the blood guilt shared among all wh*te people?
> Having graduated in 2025 I've experienced the horror of competing with infinitely many third worlders in my small country, who will gladly take 1/3 my pay and be serfs
You should really read up on disruptive innovation. Many of those who you call "serfs" who'll "take 1/3rd of my pay" will tomorrow establish themselves, justify their presence, and move up the wage/income scale, while you'll be left complaining.
I am from and live in a third-world country and have spent some time in a first-world one. I've seen the ETH Zurich tag open doors that I didn't even know existed. If you aren't leveraging your passport, your education and your network to reach a place where you don't need to worry about "serfs", then you're doing something seriously wrong. What you have, compared to the competition you denigrate, is invaluable, making best use of it is your job.
Bad advice. You’re going to need family connections and loyal people you can bet your life on to survive if the rest of what you’re saying is remotely true.
Very silly reply. If you learn how toy LLMs work at 17 don't you think that would help you at university and grad school? There's plenty of time to go into the private sector workforce later.
I appreciate the sentiment and I'm pretty curious how I could train an LLM, even are really basic one from 5yrs ago without nuts hardware.
That said, I a good starting point for a 17yo is reading about perceptrons[0], then the basics of neural networks[1] (ex. 3-layer perceptron) then writing a program to train a 3-layer perceptron and classifying the MNIST dataset[2] - a dataset of characters.
This can anywhere between a day and a week and you will demystify the basics of neural networks and work your way forward with more advanced contemporary concepts.
Fun fact: any multi-layer perceptron neural net can be reduced to a 3-layer perceptron network.
I think a problem a lot of people are grappling with here is that due to LLMs and AI generally, it’s basically impossible to predict what the future will look like or what jobs will still be around.
I’d probably say something like: do something you enjoy and seems like it might be useful, but accept that the pace of change may mean that whatever you study ends up being irrelevant.
Whatever solution there ends up being to this, it’s not going to be one that an individual 17 year old can implement. We’re past the point where individual good and bad choices matter that much to economic outcomes.
I would advise any somewhat ambitious 17 year old to avoid tech and get into healthcare if they can stomach human interactions and bodily fluids. Sure, it is not all sunshine and rainbows, but there will still be plenty of work helping people who are ill or elderly. Even in the worst-case economic scenario, medicine will be a more socially rewarding and stable life path.
This seems like a much more interesting question to me.
Telling other people's children what to do is easy and basically doesn't have any downside to being wrong. With your own children things are a bit different.
So: what are people here with school age children telling their own kids about the future? If their kids ask, what kind of careers would they encourage them to pursue, assuming they have the skills and interest?
When I was last in the Bay Area, maybe about a decade ago the bookshops were full of titles like "Python for Preschoolers" (I exaggerate, but only slightly). Clearly at the time a lot of people working in tech thought that cultivating an interest in programming was going to be the path to being a successful (by some metric) adult. Is that still the case?
"learn to code because it will get you a good career" perhaps, but "learn to code at at least a high school level, because it will give you a very good mental model for how computers do things" is still very useful.
Or maybe generalized to "get good at problem solving." I've found building software systems has a lot of parallels to working on physical things such as carpentry/DIY and working on small engines. The concepts of breaking down a problem into a bunch of smaller problems, and identifying what can be independently tested/built remain the same.
the problem solving aspect is great too, but I mean specifically "this is what it means for a computer to know something, and this is what it means for it to do something" expressed in terms of data stored in memory, and algorithms that read to and write from that memory, and "this is how computers think" in terms of control flow and if tests, is a form of literacy that everyone should at least be exposed to.
The real reason to recommend this is that they have an excellent chance of avoiding the "nerd-to-incel" pipeline that tech all-but-guarantees for its best nerds. Post GenAI boom, the social cost of working in tech combined with the coming collapse in high paying jobs, means that unironically coders should be learning a bit about coal mines.
In this regard, Healthcare is a polar opposite. It's pretty hard as a male nurse to not "accidentally" become a home wrecker.
If I were 17 again I'd prepare to go for volunteering overseas after high school for 1-2 years (plenty of free options in the EU where you might only need to cover the plane ticket). See the world, you learn a new language, help others and then think about what you want to do.
I'd say, if you were 17, you'd probably be better off learning how to socialize or at least gracefully approach and relate to people your own age. That's probably the greatest challenge of the near future, sadly.
I get this is basically advice for young founders and entrepreneurs, but i would ignore that request and encourage 17 year olds to spend time trying to find a happy medium between work and life.
Being a super rich and an unhappy workaholic, or a super-impressive engineer who wakes up one day at 45 and realizes they regret wasting half their life (I ran into way too many of these) is a much worse fate than "not being rich from your startup" and working a relatively regular job while feeling fulfilled and happy by more than just work.
Especially in the US, which is uniquely bad at this and encourages people to work themselves to death, mental health and work life balance are much more valuable things for 17 year olds to focus on than finding good startup ideas.
In case you think i'm being a bit dramatic, let's look at the state of 17 year old mental health in the heart of Silicon Valley:
"The City of Palo Alto and the Palo Alto Unified School District approved a funded contract to place 24/7 human security guards and monitors at all four local Caltrain grade crossings, including the Churchill Avenue crossing directly adjacent to Palo Alto High School."
(in case it's not obvious, it's because of suicides by high school students)
The 17 year olds do not need advice on better startups, and this situation will never get better if we focus our advice on how to be better at work instead of how to be better at life.
This will require redirecting the conversations.
Thank you. They need human contact, not more "sit in a room alone and get stressed as fuck for little ROI" tech bullshit. Unless the kid has a genuine, self-motivated interest in learning these things (a great, positive thing that should be nurtured), they should file pg's advice under "ok boomer."
I'd move to the middle of nowhere and work multiple jobs on a farm and in construction. Learn how to grow food, and build things. Meet the farmer's daughter, and marry her. Then, buy my own land, grow my own food, and build my own things.
I already do this. I live in a small village where there isn't even a wired Internet connection (wireless only). I work full-time with LLMs, on own product ideas and client projects (all LLM led).
I started investing in farms, have 50 pigs and 100+ chickens now. We are planning to grow to 100 pigs and 2000 chickens in a year. We will start growing Shiitake mushrooms in a few months too.
Most 17 year olds I know don’t want anything to do with AI and see the entire industry as an existential threat.
Nothing wrong with learning the theory and understanding the papers. Getting to that point you’ll have to get your fundamentals down. Might be an interesting exercise.
But as a future? I guess we’ll see. I suspect the next financial apocalypse will determine if there is one. Another AI Winter that may outlast all others so far.
Don't really understand why people are saying this is terrible advice. If you're 17, you should be learning everything. At that age, your brain is a sponge and your energy levels are the highest they will ever be.
None of us know what the future of work, education, or AI is going to look like. But your best bet is to become a life-long learner. Be it LLMs, musical instruments, physics, or business.
Oddly, I just remembered I did the nearest possible thing to this when I was 17... back in 1991.
On an Amiga, I took various public domain text documents from cover disks and counted the probability of the next word given the previous word. Then spat out random sequences of words from it and printed them out. It was called "Splurge". Basically a very very simple single layer statistical language model.
Some of the sentences were randomly not bad sentences, which seemed amazing at the time!
That kind of thing (and Core Wars and Tierra etc) did lead me to getting a job at an artificial life startup at the end of the decade. But that was in turn about 10/15 years too early (no GPUs).
There's some lesson from this about timing, but honestly I've gained the most as a person when I did something that was fun, ethical and gained an audience. A tricky combination.
Yann LeCun's response is interesting. I've heard that the transformer architecture underlying LLMs is actually really effective for robotics, but progress is a lot slower here because instead of just scraping the web and Anna's Archive, you have to outfit workers with motion-capture suits that record their every move, and then have them physically do enough jobs that you can gather good training data on how the job is done. This is a little challenging when no blue-collar worker wants to be replaced by a robot.
Long-term there's probably a lot more potential, and much bigger markets, in robotics than in LLMs, but it will really take the long term to get there.
While knowledge is always great, I would encourage people not to seek advice from successful people like this (survivorship bias).
Moreover I am not sure it is even good advice? Would you advise a 17 y.o. to learn how transistors work or how to code (i.e. is LLM training the right level in the stack)? LLM training, a discipline where relevant work is already out of reach for 99.999% of budgets really as essential as this post implies?
> I would encourage people not to seek advice from successful people like this (survivorship bias).
Personally I don't see the problem, as long as you're aware there is survivorship bias involved here.
What's the alternative really, seek advice from unsuccessful people? That seems worse :)
Personally I do both, read about what worked for people, also read about what didn't work for people, then ignore both and do whatever the fuck I want.
I don't know; I think people in general are just not great at this. Successful people tend to underrate luck and overrate the brilliance of their own decisions, but the rest of us are prone to either reversing that and blaming everyone but ourselves, or being so determined to take accountability (or just depressed) that we become overly self-critical, or simply not understanding why things happened the way they did and reaching for any explanation that resolves the chaos into something narratively satisfying.
If there's a single unifying fault to the unsuccessful people I know (for any definition of unsuccesful, including my own failures) is that they're precisely bad at working out why they failed - if they even think to ask the question. The successful people are generally much clearer that it was studying hard or networking or natural talent that contributed to their success. The latter group may have people who don't realise luck played a part, but they massively increased their chances of good luck by the aforementioned behaviours.
I would say you should seek advice from someone has lucky and successful as Paul Graham but I wouldn’t take his public Twitter as advice it’s entertainment period.
It's best not to take advice on direction of careers from anyone. It's better to find and work on things that interest you, and then take advice from people that are amazing in that specific field. In mid 2000s in Australia all the "top people" were telling me not to get into a software engineering career because it was dead. It's certainly challenged right now, but it took off during those 15+ years.
“It's best not to take advice on direction of careers from anyone. It's better to find and work on things that interest you, and then take advice from people that are amazing in that specific field.”
Understandable irony. The way I see it, there are no buckets, only context surrounding the advice and your awareness thereof. If the context is unknown, best to take it with a big chunk of salt... Or spend time familiarizing yourself with the context if you think it's worth the time/effort; working on things that interest you is a nice way of doing that.
But some advice is less specific than other advice though. Some things are always stupid, and some things are always smart, if you look at the context of our world and society. I find myself pursuing these "fundamental truths" with great interest lately, especially now that the world is changing so quickly.
That’s what you get from listening to “successful people”. You get to learn about all the things they tried that failed, then the things that did work on that 24th try, which was successful.
The “survivorship bias” people always seem to assume that the “survivor” lucked into his fortune on his first try ever, so he can’t have learned anything, so we don’t have to listen to him. But that’s seldom the case.
very good read. I get into the same discussion sometimes with people. If you're successful you're automatically labelled as "lucky".
There are successful people that failed many times before being successful. Somehow these days, previous failures and persistence seems to be ignored and they focus just on the luck you got on the 20th try.
The unsuccessful person is much more likely to be actually connected to the daily needs and struggles of a normal person. Paul Graham hasn't seen the inside of a grocery store in the past twenty years.
I just don't think he realizes how saturated it got over the years. Or maybe he knows at a conscious level, but not subconsciously.
In 2000 (his era), it would have been really smart to study the source of Linux or Apache. Would have paid dividends over decades. Cuz that knowledge was so rare. The number of people hacking on LLMs now dwarfs the number of people hacking on web servers 30 years ago, by several orders of magnitude.
And if you turn back the clock even more, I mean just even having access to a computer, let alone owning one, would have put you at a massive advantage.
I don't know what to call it. The pioneers should be respected obviously, but at the same time you need to understand that for them, the game wasn't nearly as played out as it is now.
I just don't think you can afford to be dicking around with LLMs like you could afford to dick around with random Linux distros 20 years ago. Too many people willing to do it for free these days.
You don't wanna end up being the 2030 equivalent of a certain SNES emulator developer, or maintainer of a package manager for jailbroken iPhones, I mean the list goes on and on. Being a hacker doesn't automatically give you a path to being rich, or even making a decent living. It hasn't been that way for a while.
I would encourage young people to be born rich. It's the best time in 100 years to be advantaged. Why waste your potential by having your labor stolen?
With the hindsight of experience, the remnants of my 18-year old energy go “woah, that’s cool!” at plenty of engineering feats… and my decades-older second brain goes “well d’oh, I could’ve just learned a trade to work on that!”
I think the last one was seeing a skilled electronics repairman do surgery on a CT machine controller.
I'm not sure what 17 year old me would have done with YouTube tutorials for everything under the sun available.
I'm much older and less wise now, but I still afforded myself the opportunity to follow karpathy's tutorials to build a LLM from scratch. Got to play with a few ideas. Seen similar ideas turn up in frontier model work, which is quite gratifying.
There are so many ideas to try.
Currently playing with autoencoders that takes A and B and produce latents A', B', and C'. Reconstruction of A is from A' and C', B is from B' and C'
The idea is if C' can be made to improve both outputs, it must store as much information as it can about what is common to both inputs.
The only realistic approach is to train on a limited data set which is probably less usable than the comibnation of a custom RAG + one of the many available LLMs.
Also many people/kids don't have access to proper "productive" systems anymore, since the whole computing and electronics industry shifted to make "consumer"-devices like smartphones or laptops made for netflix, gaming and spotify.
Breaking the barrier to build a custom system, install linux (or developer tools for Windows, MacOS) is already a complex AND costly task. It was just way simpler in the late 90s and 00s to get something working.
Writing, supervising and training LLMs are now the purview of... even larger LLMs. Optimising CUDA kernels; hand-writing SIMD assembly to speed up data loading; tinkering with your particular brand of DRAM to see if there's anything to gain from optimising for its memory topology and NUMA --- these are now the job of AI.
There is very little reason for humans to get all too engrossed in this type of work now, today, with the hope of being good enough at it to command a high salary in 3-5 years. AI can already do it incredibly well, and they can do it persistently and doggedly 24 hours a day.
telling a 17 year old to get into tech right now is horrible advice, literally telling them to get at the back of a line with a better part of a million more experienced people in it.
Telling them not to get into tech is also terribly reactionary advice. The truth of the matter is that we don’t yet know whether tech roles will be eliminated or if they’re just going to follow previous innovation breakthroughs where “one person producing way more work” makes software even more of a desirable industry to be involved in.
There really isn’t a very strong correlation between tech industry hiring strength and AI as of yet. Various studies that are out there haven’t even witnessed AI workflows contributing more than modest gains in software engineering efficiency. I.e., being able to write code 20-40% faster isn’t a seismic shift in the industry where everyone is getting laid off tomorrow and we’re all replaced by software.
Even with the questions surrounding the current job market, it’s still an incredibly good ROI career compared to so many other jobs out there.
For example, in my local area you can get a job as a registered nurse working nights in the emergency room and only make ~$115k.
I make almost double that telling an LLM what to do from my house in my pajamas during the day with less time spent in university.
Even if tech roles lose half their salary to automation pressure it’s still a really good gig.
I just want to make it clear that I’m not assigning some kind of moral superiority to my financial situation and the amount that the economy values my labor per hour.
I don’t make the rules for how much each profession is able to make in compensation.
The world will always need nurses, but that doesn’t mean that it’s a fantastic career to get into if you have a neutral career preference and your primary consideration is university tuition ROI, expected compensation, work schedule, and day-to-day physical exertion.
My point isn’t to debate the virtues of each career, I am intending to stick to objective aspects of them.
In that sense, telling today’s kids that there’s no future in tech careers just because there’s a short term hiring slump is extremely premature. I certainly wouldn’t tell a kid who is passionate about tech to avoid the field just because the unemployment rate is currently 7%.
Mr. McGuire: I just want to say one word to you. Just one word.
Benjamin: Yes, sir.
Mr. McGuire: Are you listening?
Benjamin: Yes, I am.
Mr. McGuire: Plastics.
Benjamin: Exactly how do you mean?
Mr. McGuire: There's a great future in plastics. Think about it. Will you think about it?
---
I love this scene because it so perfectly captures what it's like to be young and given advice, however well-meaning, by an older generation living in a world that no longer exists for the young. And it's ambiguous and trite enough to be essentially useless even if the underlying idea isn't terrible.
What about doing abliteration, weight pruning, representation engineering, etc, directly to open LLMs instead?
Building an LLM from scratch has a hard split between a tutorial project you can complete in a weekend (that's useless for actual usage) and then a solid 1km high brick wall if you want to create anything actually useful from scratch.
Modified open models have a very active community around them, without the need to look much further than Hugging Face.
We should remember the fact that LLMs had emergence doing one mundane thing. Now think what we would have if we do our mundane thing of learning something for the sake of just learning… Emergence.
There's this dilemma where in theory there's a ton of demand for engineers that can do real LLM machine-learning, but in practice there are very few available positions and entrepreneurship opportunities.
The reality is that an incredibly small minority of companies in the world do any real training or optimisation. It's unnecessary and inefficient for most purposes unless you are fully dedicated to being an LLM company, and still then it's a struggle. Those few that do train, they spend most of their budget on compute and have relatively small teams.
Getting experience in this field requires having access to very expensive hardware to begin with. And the skills will be quite hard to convert into any real value for someone, leading to a decent income, unless you have a ton of funding from patient investors, or you have decent contacts in Bay Area networks to get hired at the right place.
With all due respect, paulg is in somewhat of a bubble, this is not congruent with the global situation.
That's not true, because everyone, everyone, everyone seems to want to do training. Which results in a 50 person company training, say, a voice model that then fails, because it's just not good enough.
In reality the problem is that it gets blasted out of the water by a much worse architecture trained on 10000x the infrastructure. And while I'm sure the freshly brought in ML student came up with a 10%, even 30% better architecture, it just doesn't matter. (and never mind that even OpenAI hasn't really solved a voice model yet. Try it. It can probably match 2026-quality call centers, but it's no substitute for an actually empowered human)
... and yet, if you look at what hyperscalers are getting paid for ... comfortably more than half the income is training. Which makes no sense on so many levels.
Yes. It's like looking at the (Apollo) moon rocket launch and then suggesting teenagers should learn to build rockets in their garages for the coming space age.
It is viable as a toy project, but there are vanishingly few career opportunities.
Early internet let me create the best personal web page in my city that I knew of with 2 weeks experience as a 12 year old. I imagine that same 12 year old could be more knowledgeable about LLMs than 99% of people in the same period.
I doubt it. Creating a website can be done by copy pasting a few snippets together and checking if it visually looks like expected.
Good luck with that approach when trying to toy around with models and their training/inference.
There is also a lot of math basics missing that a 12 year old may be able to grasp, but I would bet they are at least 13 by the time the knowledge is deep enough to understand what operations are happening.
They'll learn so much more that won't transfer to as many job opportunities. For ex, say more about C++ and less about cutting edge CSS (because modern browser tech is an ocean). I suppose they might luck into other adjacent or unrelated roles with the same skills.
I have a hard time imagining anything where writing a browser wouldn't be an excellent transferable foundation. Even your example: writing css will never teach you as much as writing a css engine.
Sorry if I was unclear. I mean they'll never have enough time to learn a significant amount of modern web tech because they'll be working up to it from lower layers and basics.
Browsers from scratch are multi-year projects for multiple people. Even just skinning and minor tweaks to modern browsers is a deep well for one person.
Building a browser in the early web was actually a very achievable goal for exactly the reasons it isn’t now. There was not JS. No CSS. No SVG. In fact very few widely supported image formats (and graphical browsers weren’t around in the earliest days of the web anyway). TLS didn’t exist. HTML only had a subset of methods. And even POST was usually just managed by CGI/BIN calling an external process, often written in C++ or Perl.
They will also know the fundamental underpinnings of the web, you know - the thing that those html devs are actually using.
That’s like saying someone who fabricates cars doesn’t have the skills to drive them. Perhaps not, but they’re very well placed to pick it up quickly. They’ve also shown they can do something far more challenging, which is actually better than hiring for narrow immediate skills.
In other words: I’d hire that candidate in a heartbeat.
You'd hire an auto designer to be a limo driver? I think it's just two different skill sets. The auto designer is clearly smart but a limo driver also needs to have customer service skills, etc.
That may have worked at the time, but no companies are interested in learning projects today. If you didn’t do the reqs list for the last 5-10 years with the same title, forget about it. Because there are dozens of folks who have, lined up. No one is indulging career changers (and most fresh grads) for the time being.
That seems like a perfectly good use of time for a kid in the 70’s! You’d learn a lot of engineering skills and demonstrate a tenaciousness that most people don’t have. I don’t think the goal is to predict what will be the important technology in 10 years. The goal is to challenge oneself with hard tasks and learn interesting stuff. A lot of the “AI” people today were compilers people yesterday
When I was 17 and still witnessing Apollo Moon launches, I wanted to build an AI that would handily outperform LLMs as we know them today.
But that was way back in the early 1970's and all I had to work with was a mainframe.
Well the mainframe itself wasn't bad, the real show-stopper was that I didn't own the computer outright, no strings attached, no debt, etc.
>I'd probably try to make an LLM that I could use on some specific problem.
I thought so too back then, still do so I guess this is one of those things that could stand the test of time. I always wanted to start with something a lot simpler than a Moon mission myself. At 17 I already had a significant breakthrough in the chem labs and it was from alternatives to a single processing step plus everything that descended from that, rather than trying to tackle a much more complex detailed multi-step synthesis. I was only 17 but I was not trying to be a slouch, I don't think pg was either at that age but his advice is not for just anybody. I couldn't have done it if I hadn't made major progress since being 16, and it really emphasized at the time how much maturity can make a difference. My imagination ran wild as I extrapolated :)
In a reply from LeCun to pg:
>>I'll figure out a set of methods and architectures beyond LLMs that can quickly learn to perform physical tasks as efficiently as humans and animals.
That last item is also what I would if I were 30, 40, 50, or 66 years old
I see no reason to stop at 66 either ;)
But I figured that people owning more computer power than I could ever afford were going to be doing something like this as soon as they could, without having to wait for something like an LLM to arrive before getting peoples' attention.
It did seem like things were going to take longer than you expect, so it's pretty good to have a lifetime of concentrating on the specialized natural science domain expertise, focused now for 50 full years on how it would combine if AI ever got good enough.
Both the natural science and the AI need to be a major cut above, I still see dramatic room for improvement in my own work. If I'm going to have to rely on "other peoples' AI" then that natural science component is going to have to pull a lot of weight to keep up with the kind of computers that only rich-as-hell high-rollers have access to.
I agree with mostly all of this, but personally I wrote a toy LLM almost 5 years ago and while it never saw much use outside of boring my wife with a shitty command line demo with glee it did help me understand how they worked and how to apply them, played a lot with JAX and pytorch, ended up building a ghetto version of MCP and an LLM-Pool to proxy requests to my baby local models and so I didn't struggle to see the evolution of openrouter and MCP agentic workflows. The same way i'm really glad when I was younger I built a bad webserver by myself, a really painful SQLx type database, etc etc etc - none of these things led me to developing for Nginx or Oracle nor will knowing JAX get me a job at an AI research lab, but I do have a lot of depth in understanding how the technology works so that the flavors on top of them are easy to digest and make more use of immediately, and I think the same can be said for engineers coming into the field - if it's a spooky LLM box you aren't going to be squeezing the same amount of juice as the guy that knows how they work inside and out so having at least the understanding of a _babys first LLM_ is going to get you miles ahead of people who don't.
For anyone who wants to dork around there is https://github.com/rasbt/LLMs-from-scratch which is something amazing that I think anyone who wants to engineer things around LLMs should at least blast through and read.
And so I think the idea is more to understand tomorrow ... from first principles.
In the late 80's, as a teenager, I learned x86 assembly and C because that was the only way to squeeze out enough juice from my shitty CGA (and later VGA) card to programm the games/graphics that interested me.
I haven't written assembly in years.
But whatever I did in my career: it helped me and gave me an edge over my peers to have a foundation that is very close to the metal.
Current AI can automate significant amounts of grunt work in programming and math. It's good at running web searches and writing summaries. There are a few other niches where it is currently successful. But other than that, many corporate AI projects are spectacular failures.
So just given what we have in hand, assuming no further breakthroughs, then we're maybe looking at AI being somewhat bigger than the Internet. Which would make it a revolutionary technology, sure.
But to get from "a revolutionary technology" to "the substrate the future runs on", then you need to assume more breakthroughs: long-context operation over weeks or months, displacing human workers 100% instead of 75%, and the ability to directly economically compete with actual humans. And people are investing literal trillions of dollars to make that future come true, without really thinking through what truly competitive-with-human AI would actually mean. We might be looking at massive job loss, centralization of power, fully automated "companies" with no humans dominating markets, and other dystopian scenarios.
And in those worlds, it's unclear that being good at CUDA and matrix math will be all that helpful, careerwise. The AIs are already pretty good at that stuff. Data scientists get paid OK when they actually get hired, but it's not everything college students were promised in the 2010s, either.
We can't yet build a fully-general competitor for the human mind. But we're getting closer. And if we ever do build one, the consequences will be really weird in any number of ways. So I worry about visions of the future that assume AI keeps improving significantly, but that also assume it still somehow remains a "normal" technology that doesn't, for example, render most humans fundamentally uncompetitive.
We don't know this. So many people are simply claiming this confidently, and a lot of them are betting their careers on it, but nobody has a crystal ball. Whenever someone tells you confidently, and without any doubt or qualifications, that something "is the future," be skeptical.
I remember when the Segway was definitely going to change urban planning worldwide.
In that regard, it's not too different from mobile telephony. Mobile phones drove the electronics industry 20 years ago, but there is limited demand for people who really know how to build a phone (ie. build the hardware and write all the signal processing from scratch), as there aren't that many companies that do phones at the lowest level. A few of the engineers got rich (eg. Viterbi), but most 'just' made a good living. Most people who got rich off phones didn't do it by knowing how phones work.
Incidentally, the skills for the lowest levels of LLMs aren't that far removed from those needed for mobile telephony, in that both are based on maths, computation and information theory.
Good points, but I think we can expect the AI space to be more tumultuous.
What most people want from mobile technology is for it to work, not too expensively, and for it to get out of their way.
What most people want out of AI is for no leader to emerge and wield supremacy against the rest of us. People are afraid of it in ways they weren't afraid of mobile, so they're more willing to work together against whoever is in the lead.
Its more like an arms race and less like a utility. The disadvantage I face when my competition has better mobile coverage and bandwidth is minor. The disadvantage I face when my competition has better intelligence on tap is much more significant.
>* What most people want out of AI is for no leader to emerge and wield supremacy against the rest of us. People are afraid of it in ways they weren't afraid of mobile, so they're more willing to work together against whoever is in the lead.*
No.
That’s what people like us on HN want. The people out in “Greater Userland” just want the black box to answer their questions. They could care less who is behind it. They don’t yet attach their black box to Amazon or Microsoft etc. And most won’t care enough to be inconvenienced even when they do make the connection. (As your competition argument implies.)
Heck, a lot haven’t even made the connection between the black box that gives them answers and data centers. They think, “ ChatGPT good” and at the same time think “data centers bad”.
I was there at the start of the smartphone boom. I built a demo Android device that was capable of telephony/data, 3D rendering, etc. all before Google open-sourced the OS, for a SoC vendor that wasn't in Google's inner circle. Yet the industry was not interested in my junior profile during the subprime crisis.
> The reality is that an incredibly small minority of companies in the world do any real training or optimisation. It's unnecessary and inefficient for most purposes unless you are fully dedicated to being an LLM company, and still then it's a struggle. Those few that do train, they spend most of their budget on compute and have relatively small teams.
And the job postings are often ridiculous. I recently was an AMD job advert in Germany for an ML Kernel Engineer, not Senior mind you. The requirements went something like
> Masters Degree required with strong preference for a PhD with peer reviewed articles in {journals_list}
> 10+ years of experience in C/C++
> GPU programming experience required
> 10 more ridiculous lines
No idea how a teenager self teaching himself LLMs is supposed to even get a shot...
This is a wildly incorrect and myopic view on the world.
Finetuning model is cheap and incredibly useful for deployment. You don't need to pre-train a frontier llm from scratch to make useful models.
There is tons of domains where you and fine-tune llms and deploy them for value in companies and for your own entrepreneurship ambitions. I have made this a big part of my career for the last few years and now I'm working on finetuning models for starting my own companies.
Y'all are missing the point: It's probably less than 100 hours to learn the foundations of one of humanity's most-current breakthroughs. It's a disservice to any young hacker to not learn it. Here's your curriculum. Watch these:
This is a silly take. You can learn to build an LLM, there are great resources to do so (there are books about building them from scratch), you can use older model GPUs or rent them by the hour. The value of understanding them is really high for anyone building any application that uses an LLM at any point.
It similar to understanding how a very basic CPU works. Just because I'm not going to work at intel or nvidia or whatever optimizing the hell out of a chip, it doesn't mean I just throw my hands up and think "magic" - the basic architecture isn't that difficult, and the value of knowing it is astronomical for anyone writing software.
To build LLMs from scratch, one must first learn how to build a computer from scratch because microchip prices are crazy. Oh and before that, Sagan says you need to make a universe. :)
LLMs are just one small part of computing. it is fine to learn how they work but they are nothing magical. if you want to build a real working system using LLMs that actually makes money you still have to learn everything else. but i think his point here is that learning LLMs at 17 means hacking together some home brew rig on shitty consumer hardware and if you learn all of that you will be in a much better place than learning how to use chatgpt to automate a spam campaign for your b2b saas startup.
If i were 17, I'd try to distinguish who to take advice from, and would definitely learn that VCs have interest to spread a specific agenda in their message. Also, I would get drunk and have as much fun as could, as the misery of working under the treat of being replaced by AI, would simply kill any desire to live past 25.
I would (and am) going into MLOps. Not just the general infrastructure/systems administration but how to do inference optimization, caching, quantization, memory pinning, vfio passthrough of gpus etc.
I mean first that is already what plenty of 17yo are actually doing, because that is what they do at school or in parascholar activities. There are already countless of such tutorials where you can do that in an afternoon.
The pointless part though is precisely why Amazon and others are hunting for rare books, all the low hanging fruits have been picked already so just training a bigger model will simply mean burning more energy and money. Sure training a small one for the basic principle is a great pedagogical thing, training another one, medium, then maybe a large one, is also good in term of learning the process and architecture, but one should not expect it to be useful out of that context.
Pure players are precisely doing everything they can to corner the market by making their own scale unreachable by others. Smaller players with access to lesser infrastructure are thus betting on different market, e.g. embedded systems.
17yos should definitely build their (L)LMs from scratch and whatever bigger model they can train for free, or for cheap, but they should not expect that to bring them any riches.
> Someone asked what I'd do if I were 17. I'd learn how to build LLMs from scratch, and then train ones as powerful as I could with whatever hardware I could get access to.
He's seeing a future for models running on everyday hardware just capable enough to do what the use case requires.
i was trying to build llms from scratch at 17. failed miserably because i did not know linear algebra.
ended up in a different but adjacent field. when chatgtp got big suddenly there were so many people doing llms and i didnt want to compete like that. so much of what was happening was hype, and that really turned me off.
am back to building llms from scratch, but like, its a journey teaching myself all the theory on top of my job. i have decent fundamentals, but i need a better grasp of all the advancements in the field in the past 5 years before i would feel comfortable designing anything. baby steps, essentially. am working on better understanding all the layers of an ai while implementing a rag on my local model as an experiment.
17 year olds should learn whatever theyre interested in but need fundamentals in order to do anything advanced.
When I was 17, I made bad cartoons and was in a band. If I were 17, I'd spend more time learning music and design theory. I also learned PHP at this time, but that was low on the list, friendships came first.
The core point here is that AI is a massive thing (at the moment) so it's probably a good idea to understand it deeply. Not sure why people are so worked up about it.
That's why. That future is uncertain. So why gamble your future on something that's popular at the moment for something that could change completely a year later?
Had to reread this a few times to properly parse what the hypothetical was.
My first read was "if you could redo your life from age 17 what would you do". To which "figure out how to make an LLM" would be an insane answer.
I think his advice here is maybe... a year or two too late to be good advice. I can't foresee the job market for machine learning experts being better than it is now in 5-6 years time when said hypothetical 17 year old would be most ready to start career hunting. Either the bubble is gonna pop and the market will be flooded with laid off AI talent.
Even if I'm wrong about there being a bubble at all, I still think that in 5 years time the tech will just have matured to the point of diminishing returns on refining existing architectures. Plateauing until some PHD comes up with something as ground breaking as attention.
I think pg answered the question as “what I’d do as a project” and not “what I’d do as a career.” So the critical comments are kind of missing the point, IMO.
I don’t see why learning how LLMs work is a bad project for a 17 year old.
Optimizing your entire career and the next decade+ of your life on LLMs? Yeah, probably not ideal. It’s almost always a bad idea to make long term decisions based on current trendy things.
And since everyone is using this topic to give their ideal advice to 17 year olds, my advice as a mid-30s guy: seriously consider becoming highly skilled at a specific thing, and don’t be scared off by the idea that it’ll take 5-10-15 years to get there.
When you’re 17-25, the timescale of a decade seems infinite. But it’s really not, and a decade spent “exploring and keeping your options open” sometimes just ends up with you being pretty decent but not amazing at a lot of random things.
Sometimes I wish I had just become a carpenter, chef, electrician, etc. – a specific skill set that leads to mastery over time, rather than the endless exciting-new-thing hamster wheel of working in tech.
> And since everyone is using this topic to give their ideal advice to 17 year olds, my advice as a mid-30s guy: seriously consider becoming highly skilled at a specific thing, and don’t be scared off by the idea that it’ll take 5-10-15 years to get there.
And this is a problem with modern society today: expecting 17 year-olds to know what they want to do professionally for the rest of their lives, and to focus heavily on professional development aligned to that.
By that age, I think it's not uncommon for individuals to have interests and perhaps even dreams, but a well-defined career focus that serves as the foundation of an actionable skills development plan? Nah. That just isn't common and I'd argue not desirable. 17 year-olds should be exploring their interests, enjoying early adulthood, learning valuable lessons in the social realm, etc. Not training themselves to become compliant little worker bees.
When I was 17, I wrote poetry and learned how to play guitar. I’m really happy that I did. That’s what I would do now, were I 17. I feel slightly bad for people who didn’t.
Sure, but isn't this a bit of a sneer? “I feel slightly bad for people who didn’t” has an air of superiority about it, and seems like a veiled swipe at those who lacked whatever qualities your 17-year-old self had. The guidelines have several different ways of asking us to avoid commenting like this.
As to the substance of your comment, there's nothing stopping a 17-year-old from writing poetry, learning guitar, and also learning about how LLMs work, if they're so inspired. Indeed I'm sure pg would encourage it, and that he did the equivalent of all those things himself when he was young. There's plenty of evidence of that in his essays: he wrote short stories, published a scandalous school newspaper, played soccer, and studied fine art and philosophy. See:
> there's nothing stopping a 17-year-old from writing poetry, learning guitar, and also learning about how LLMs work, if they're so inspired.
I assume the people at HN are intelligent enough to understand this without me having to explain the most obvious banalities to them. The point is why you would do anything at 17:
> if they're so inspired
You said it yourself. The Twitter post, to me, reads like career advice or something in that vein. One has plenty of time to run the wheel like a lab rat later, but at that very point in life you have possibility to find the thing that truly inspires you, that is its own motivation.
Funnily enough, I recently started on a series of demos and documentation to explain to my 10 year old daughter how LLMs work - in this case in the context of "don't use LLMs for looking stuff up if you figure out how to circumvent the school blocks yet again, or if you do, at least verify its output", but also figured it can be useful in general. Plus, if I get a 10 year old to understand that there's a slim chance I can get customers to understand that as well.
Sorry but Paul is just so out of touch with reality. I’m tired of seeing everyone on HN evangelize this man. He is very intelligent and has done some really great things but times have changed and we need to let the next generation think for themselves. They are increasingly anti-AI. Maybe that pans out, maybe it doesn’t. If I were 17, I’d pursue my dreams — whatever those may be — and learn how to connect meaningfully with my peers. Perhaps that’s naive of me or perhaps that’s what humanity needs most right now. I’d rather let the 17 year old version of me make that choice for themselves.
I’m sure I’ll get torn to pieces for this but it’s frustrating to continually witness people treat a single person’s prose as the Word of God.
What corpus would one be advised to use for training? Should one limit oneself to legally downloadable documents or should one follow the examples of Altman, Amodei, and Zuckerberg and use LibGen and Anna's Archive?
If I were 17, I’d learn how to invest and build financial literacy, and plot potential growth of my networth throughout my life, before even thinking about a career. Then smoke a bowl.
Paul has the same problem just about every tech obsessed engineer has (including myself), he thinks everyone else loves computers too. They don't. At all. I''m a self-taught ex-bartender and I can't tell you how many grown adults in the service industry I've tried to get into computer stuff and they had 0 interest.
And younger people I meet don't even own laptops. I had a genz/millennial cusp friend who wrote all her college papers on her iphone.
This is about as intelligent as say "If I were 17, I'd learn digital electroinics". You would waste your time. Sure, in theory it's useful, in reality it's not that useful.
17 is such a fantastic age to be free and experience the world, you won't get that much of an advantage as these FOMO groomers are selling you into if you start now versus later.
If you are 17, go be yourself, whatever that is, in whatever way you want that to be, but do it so authentically and fully. Be unapologetic about what you love and what motivates you, and pursue that with passion and commitment.
I remember when I caught PG on reddit arguing with some guy who'd said something mean about him. He didn't reveal who he was. But you could tell from his history - his first post was from before reddit opened to the public.
Good times.
I wonder if you can dig that out of the historical reddit database. I'd like to see that again. I love how everything is recorded now.
I get the sense things have changed a bit since I graduated and there are lot more jobs in AI outside of academia these days, but it's still a very different field from other SWE pursuits, and it's not really accessible to hacker-minded people.
Learning AI isn't like learning HTML in the 90s then expecting to get a job at a tech company building websites. You can't just "learn how to build LLMs" and expect a frontier lab to hire you so I'd argue this is rather bad advise.
Additionally, unlike web development in the 90s you cant really do anything interesting yourself... All of the interesting/useful stuff will require huge amounts of compute and data so there isn't even much point in learning to start your own thing either.
As someone whose built many of NNs from scratch (hand written code, long before the days of LLMs), it's more or less useless knowledge if I wanted to work in a frontier lab or do anything interesting in the field.
I also think anyone thinking about going into a field which is basically a crossover of CompSci and Maths is absolutely insane right now. Even if you think there is a place for CompSci and Maths post LLMs, there's almost no chance anything you learn today will be relevant to the skills required in say 5-10 years.
As someone who doesn't work in this field (I'm in boring old Accounting/Finance), but would like to learn this field (both to future-proof my skillset, but also because I have an interest in it), would it be wise for me to learn math?
Is it something that's going to be fundamental for future technologies?
I always plan to learn, but end up in a death spiral feeling like I'll invest so much time and energy only for the puck to have moved somewhere completely different.
Somewhere in rural America is a 17 year old that doesn't even have working plumbing in their house still.
I'm sure they'll get right on powering up their computer from the hamster wheel, Paul.
My heart goes to all the kids out there that didn't get the fair shake let alone fair access to tech that gets these condescending "learn to code/learn to LLM" bootstrappy talks from rich pricks that don't know what life really can be like for a lot of American kids out there.
I genuinely don't think telling young people to do anything tech-related is good career advice. We don't even know if entry level roles will ever come back. The situation couldn't be worse for these roles. PG thinks that some random teenager will build a startup and get rich from it, or some shit. Like get real, man. Completely out of touch, tech bro who hasn't worked a real tech job for the last like 20 years... Remind me how many startups succeed again, Paul? What about the market dynamics for LLMs and how one might "secure compute"?
These guys (VCs) entire livelihoods rely on tens of thousands of young people throwing away their early years of lives trying to get super rich. Only .01% even see moderate success but PG and co don't care and continue to pump the dream and urge folks to waste their lives to try and make them rich.
What the fuck does this guy know about? I'm sure if we went back through similar statements he's said over the years he's said the same thing about various technologies that are no longer relevant. The guy is a talentless hack who larps as a blogger and his only "redeeming" quality is having lots of money.
Owner of Golf Club Company says I should dedicate my life to golf lmfao.
I think collectively we should all stop listening to Mr Graham..
He capitalizes on greed and hype but with a soft, sober and thoughtful voice so as to lull you with rationalism and now 20 years of his “disruption” has mostly ruined modern society and a whole generation of techies have been led astray into trying to “change the world” is the world of today (minus the magic technology really any better than 20 years ago?)
- Good for him and his Tech Bros, bad for the rest of society
So I am 15. Is it worth trying to build my own archive (s-1.site) of strategy ideas? Do I have any real differentiation? Or am I just wasting time? I figure that I can use it as proof that I have some know-how?
hackers and painters and kids and ROI and startups and capitalism and the destruction of nature and old guys with money talking like they know better in fascist social networks
I do not think it is a proper thing to do for 17 y.o., unless they are exceptionally mathematically gifted, as proper understanding of how LLMs are trained requires a good grasp of calculus, understanding modern OS and SDE tools for proper implementation of pipeline etc.
I'd rather simply write another mnist implementation and check if I really like all that AI stuff at first place. Even then, before going into mature-on-the-way-to-dying tech (LLMs) I'd rather focus on fundamentals - good ols linear models, regressions, stat etc.
If I were 17, I'd be going to parties, music festivals, chasing girls, and enjoying my youth.
But sure, make the kids even more depressed by telling them they need to learn how to build an LLM so they can get a job working themselves to death to make Paul and friends rich.
This community is filled with people who were obsessed with microcomputers or (depending on which generation) websites during their teenage years. HN was created by and is managed by people of this type. No doubt we're a minority, and the type you describe is more common, but if you're implying there's something wrong with teenagers being intellectually curious about technology, I can't help but think of the way "nerds" used to get put down and shamed in the past.
This is the ultimate builder’s mindset. Tinkering with the hardest technical problems—even for frivolous things like games—always yields the highest return on curiosity. Time to go back to the fundamentals.
He bases this decision on all of the experience he has amassed, as a 61 year old man in the tech industry. An actual 17 year old, with 17 years of experience, would not think like this, nor should they.
Naturally 17 year olds don't think long-term like this which is why PG's advice is so useful. It gives them a pathway to follow that they likely wouldn't have reasoned otherwise.
I told my much younger brother when he was 12 what programming was and it'd be a great career. He looked into it and within months was writing CLI games. Eventually releasing his own unity 3d game on steam as a teen.
Eventually he got into CS and did really well because none of it was scary and new. He parlayed that into role at Meta out of university.
My point being, 17 year olds have time to learn new skills and guidance can go a long way.
I don't think you can really call yourself a developer unless you at least have an idea how to build a more complex software project like a compiler, and maybe have built a toy one either at uni or for fun.
It's not clear how long this LLM age of AI will last (to be replaced by something better), but nowadays any developer should at least understand the basics of ANNs, and more than just the "hello world" of a cat vs dog CNN. An LLM/Transformer is maybe the equivalent of a compiler in that regard - something that we all use and is complex enough to present a bit of a challenge. You should at least understand the basics of how an LLM is built, and maybe building a toy LLM will/should become the new Comp. Sci. degree toy compiler replacement.
I am kind of amazed how negative the comments are here, especially on HN.
Learning to hack something together in high school using the latest technology (vacuum tubes, radios, microprocessors, web/javascript) has been a common theme in the tech world for generations. With LLMs and online tutorials, this isn't even a difficult suggestion. Do people think learning new tech is somehow wasted effort?
> I am kind of amazed how negative the comments are here, especially on HN.
I can't recall or point out exactly when, but there is a stark before/after moment where the opinions of anything pg went from "Interesting and maybe true in some ways" to what we see today, lots of knee-jerk reactions and hardly any comments about the actual content.
Hazarding a guess, I think the moment Altman became the CEO and later during COVID, the sentiment seemed to have been shifting towards what we see today. But this is all based on hazy memory, rather than looking at the data. I'm sure there is a blog post waiting to be written about analyzing the sentiment of comments to PGs articles on HN, and you'll see a shift somewhere.
Because at some point in life everyone gets tired of fairytales. He started mending the anecdotes to his content which always rubs people the wrong way.
So, this comment of yours obviously isn't in the "knee-jerk reaction" category of comments, I suppose? What exactly from the linked tweet(s) are fairytales here? There is hardly any text at all, so strikes me as a comment about previous pg content, but then this would be one of those comments I talk about? Very confusing.
Your hand waving doesn't make it knee-jerk. It's just what happened to his writing since COVID. He goes for more of a shock and awe style and not everybody likes it. He's been writing for over 20 years now, hasn't he? His style has clearly changed, and an changing style attracts a different audience so it's no surprise his original readers might not connect with his newer work...
> I can't recall or point out exactly when, but there is a stark before/after moment where the opinions of anything pg went from "Interesting and maybe true in some ways" to what we see today, lots of knee-jerk reactions and hardly any comments about the actual content.
Hard disagree. This submission is still being highly upvoted, while another recent post[1] on the harms caused by Graham’s fellows[2], with a fairly tame comment section, has been flagged. That is a constant on HN. It’s not a fluke, it’s as predictable as the sunrise and getting more pronounced.
I’m sure we’re both biased in our perceptions. Mine is that HN in general (certainly more than any other website) used to worship[3] everything he wrote, together with others like Musk, until things started to really go to shit and many eyes have been opened to the effects of the unfettered greed of rich tech guys out of touch with reality.[4]
[1]: https://news.ycombinator.com/item?id=49411762
[2]: A better English word is escaping me.
[3]: That word I choose hyperbolically but deliberately. It definitely was not “interesting and maybe true in some ways”, it was much more hardcore than that.
[4]: That is not “knee-jerk” but a slow realisation still ongoing.
Imo it's a breakdown of trust of the startup ecosystem as a whole. Repeatedly startups have enshittified and it's become undeniable that the investment apparatus around startups is partly responsible. We have seen a great driver of uncreative destruction, industries undermined, small businesses undermined just to drive masses of money into few pockets - less fairness for the people working in what is now the gig economy and ultimately prices and other costs that end up as high or higher than they were before for consumers. Not to mention the whole AI/OpenAI situation which many perceive as threatening their skillset per se, essentially tearing up the social contract that existed on this site.
The sycophancy on HN is starting to break down because there is a higher proportion of users sceptical towards the outputs of the VC and wider investment world than ones who believe they're potential beneficiaries of it.
Tech industry people are becoming less interested in HN as a warm handshake into the startup world because, frequently, they're disgusted by it. And this reflects on the sentiments people post on PG's articles.
Increasingly if those at YC want the same kind of low-bar praise they got before, they will need to get it from machines.
If you did a data analysis, I expect you would see an uptick with the 2023 Reddit API controversy. HN got a big influx of redditors with different sociopolitical perspectives and discussion norms.
This might be hard to disentangle from the tech correction layoffs of 2023 and 2024, but my perception is that most of the spite comes from Tech Outsiders opposed to jaded Developers.
If he has said "to learn the math and programming skills needed to understand how to build LLMs" it'd have been much more positively received.
He did say that. That's what "from scratch" means, along with actually going ahead and building one.
> With LLMs and online tutorials, this isn't even a difficult suggestion.
Don't many of the commercial ones prevent you from using them to build LLMs?
I would say the reason for the negativity is not because it's a bad idea for a project, or that doing projects in general is a bad idea (it's not!), it's because it's a very specific thing that is not for everyone. The best thing about computing is the low barriers to entry. You can basically work on anything that takes your fancy. So those who are interested in ML will be drawn to learn about LLMs. They don't need anyone to tell them to do it. Telling everyone to do it reminds me of the "just learn to code" stuff of a decade ago. No, please don't, please find something you enjoy.
Completely agreed. The point is the knowledge, the learning and the journey. If a kid has a passion for building or toying with LLMs, then of course, by all means, please start tearing them apart or even build and train your own model. You'll learn a ton, even if you won't necessarily end up using it here and now. The learning experience will compound and of course that will be useful.
The above is, after all, the whole genesis of the word 'hacker'. We should celebrate that.
How exactly does one go about "tinkering" with an LLM? Any architectural change you introduce needs fine tuning. That needs data and compute
I tried to modify the embedding output of bert to make it generate box embeddings instead of point ones. At the time I had access to university provided A100 gpus but even with all that a training run took half a day. Models these days I don't think I can train it in any reasonable time with that much compute.
https://thinkingmachines.ai/tinker/ https://github.com/thinking-machines-lab/tinker-cookbook
(As a TML person, I'm obviously biased, but I couldn't resist because of "tinkering").
TBF it's hard to imagine a real architecture change that wouldn't require a ton of compute, but you could certainly fine tune and play with different recipes, loss functions, etc. And Claude can carry you a lot of the way through doing this.
One fun task is to invent a tool and then train a small model to use it. You could export that small model and run it locally for free forever to do your thing. I think this is what a lot of Software Engineering will look like later.
There are a lot of other high level abstractions here to look at. Prime Intellect has one.
The other thing to play with is self-hosting small models, but IMO most of the interesting stuff is actually related to multi-gpu or multi-node inference so there's not necessarily a ton to learn here.
You can build a model with 100m parameters..... the first L in LLM is not meaningful anymore.
So, you build one from scratch.
This is one question that is not just for kids but for all "gpu poor" adults as well. One concept that I found useful is "toy models": small compute models that replicate a certain, not very well explained phenomenon that the large models have. Of course, you can't have a toy model for everything, some behaviors are only visible at scale. But, there is a bunch of things like in-context learning that you can play with without burning a ton of compute.
The best analogy is strip mining (big labs) vs cave exploration (solitary/small teams). I think this is how science progresses at the boundaries by smart/curious/hardworking individuals because depth is a requisite for finding the right questions and then the answer. It is not for everyone and it does not always work. But you learn a ton even if it doesn't pan out to be a big breakthrough.
A lot of different ways. Every guide on this guidebook runs on a ~$500 gpu or a couple of dollars on a cloud instance with a bigger GPU
https://ravinkumar.com/GenAiGuidebook/book_intro.html
This guidebook covers pretraining, post training (SFT, RL) and a couple other topics. And others authors have also written books that fit on single node reasonable hardware.
If you want to start with a pretrained base I built Gemma 270m and released it last year. This fits on a raspberry pi.
https://developers.googleblog.com/en/introducing-gemma-3-270...
The fundamentals of AI don't require industrial amounts of large scale. Think of it like this, when I was learning how a plane worked when I was a kid I didn't build a 747 at home, I started with scale sized model planes. Same idea here.
And FWIW I'm a staff researcher at Deepmind (opinions here are my own) so I want to specifically encourage all people out there, you can learn a lot about how these LLMs work at home, for (mostly free), using resources like colabs or spot pricing on accelerator providers. There's many great resources out there and I encourage anyone willing to learn to go for it!
> Do people think learning new tech is somehow wasted effort?
No. But funnily enough that is a promise by some of the AI cretins and their boosters. Oh yeah best case scenario you learn how to build LLMs for us. We’ll employ you. And then ultimately that just becomes training data for the LLMs to do it themselves.
But why are people cynical? they ask.
His writing was overrated: It's that simple. People now see his blog posts for what they are: decent blog posts.
I think it is more about people are a bit sick of filthy rich people giving this kind of advice. I would also not read anything he preaches.
Hacker News is a Y Combinator (which PG co-founded) sponsored website whose original code was created by PG and whose guidelines were originally formed by PG. For those who are "sick"(sic) of rich people, VCs, or capitalism in general, you've come to the wrong place.
I stand by what I said, are you gatekeeping HN now? This is a tech news site, I don't care if he once was a co founder.
It is indeed puzzling. Back in the day, I built myself a music playing device using TTL gates, eagerly studied what CPU's looked like inside and in general wanted to know how everything worked - even though it had no hope of replicating most of it.
Looking back, it was an ideal start to a 40 year career as a software engineer. And of course currently I'm reading "Why Machines Learn: The Elegant Maths Behind Modern AI" because the urge to understand the machines I want to master and control hasn't gone away.
I like the hacking spirit suggestion. But my disagreement is the specificity of it. The suggestion here seems more inline with not tinkering and hacking but first principles learning typical of science and math curriculum. What made hacking radios and JS cool was the short feedback loops to see the progress you made. Unfortunately for LLMs starting from scratch doesnt give a good feedback loop on improvement, although starting from pre-trained model to quantising, fine-tuning might be more aligned with the hacking culture
The hacker ethos and "information wants to be free" of the '80s-'90s was drowned out by a flood of coders in the '10s-early '20s whose only creed was FYPM and "what's the minimum career effort for the fattest paycheck?"
FYPM — Fuck You Pay Me
// whose only creed was FYPM
Good. It took a long while for ICs to be paid more than 'management' in the US. It's still a hard-fought battle here.
// a flood of coders in the '10s-early '20s whose only creed was FYPM and "what's the minimum
I wonder what possibly could have happened in that timeframe?
https://www.theguardian.com/technology/2014/apr/24/apple-goo... https://en.wikipedia.org/wiki/High-Tech_Employee_Antitrust_L...
No, but there's a WHOLE ENTIRE WORLD of tech that isn't LLMs. LLMs aren't the future or anything. Sure they'll keep existing. Right now though all the opportunities exist for people looking literally anywhere else
So you're saying that LLMs are a dead end technology? Do you have anything to back up that claim?
Let's be very, very generous and say that an LLM is intelligent. Does intelligence eliminated the need for tools? If you have an executive assistant and your executive assistant has ten executive assistants, does that mean that Microsoft Excel is obsolete?
Why the everliving fuck has everyone stopped building tools?
This is essentially most discussion here. I close the HN tab so often after seeing the people that just came to disagree with someone on the internet. There are too few discussions here that come from a place of "That's a great perspective and I can see it being useful when applied in X, just trying to solicit a discussion here, I wonder how this could work or break down in condition Y?"
I have also noticed a general drop in fawning over and deification of 'made a billion, must be a visionary' figures, and a receding tolerance for every sundry kind of hot take and proclamation. Mustn't be related.
> I am kind of amazed how negative the comments are here, especially on HN.
Sadly, I attribute this to a decline in having “good will” towards others, and an increase in cynicism, prejudice, and meanness.
A lot of people here are responding to the message but not to the meaning.
It would be a good idea for young people to deeply know how these programs work. Not so that they can spend their career building them, but so that they can approach the next class of problems we'll all start trying to solve, with intuition all the way down to the weights and underlying mathematics. And also, to develop a healthy intuition of when "Just LLM it" will not be the right choice.
"Build an OS" wasn't a common university project because we were all expected to go out and work on Windows, but because understanding the bare-metal firmware for a computer helps you deeply understand how to intuit building for a whole class of problems.
> "Build an OS" wasn't a common university project because we were all expected to go […] helps you deeply understand how to intuit building for a whole class of problems.
Sure, but it was for a specific degree with a syllabus that taught you the foundational knowledge. It was not expected from the law students to learn how to build one.
> understanding the bare-metal firmware for a computer
IMO this is still relevant, everything surrounding the LLMs needs such a vast infrastructure that I don't know if I would find it more useful to learn the maths behind ML than CS
As a 17 year old, I agree with this. Ofc I'm against all the hate directed at PG, I believe that all knowledge has value regardless of its economic utility, but I understand where the hate is coming from. Personally, I find LLMs boring for now, and I'm more focused on CS and electrical engineering.
> A lot of people here are responding to the message but not to the meaning.
Well it is framed as quite specific advice.
(I'm done with mining PG tweets for meaning)
> It would be a good idea for young people to deeply know how these programs work.
It would be a good idea for _everyone in the industry_ to deeply know how LLM training, inference and "agents" work, not least because it removes the ability of shysters to bamboozle with bullshit.
But, as much as a good idea it is for the young to understand this, it's the elderly who will be really taken advantage of if they do not keep up - just look at Facebook for good examples of why.
I'm not sure it's possible to have intuition about systems that work in thousands of orthogonal dimensions. In fact I'm pretty sure most of the research is people trying fairly arbitrary things and testing them and then post rationalising implied understanding of what is really happening on top of good outcomes.
I'd have to push back, though not on the part you'd expect. Your description of human researchers is roughly right: a lot of the field is try-things-and-narrativize-after.
But the load-bearing assumption is that intuition has to be human-shaped intuition. Humans can't intuit thousands of orthogonal directions because we project everything down into a 3D metaphor and hope it holds. That's a fact about our hardware, not about the systems.
And the reason why is the most interesting part: nothing requires the compression step. A model or an agent can operate over the actual objects, holding thousands of runs and ablations in context and noticing regularities in the native dimensionality, without translating them into a picture of a ball rolling down a hill. No bottleneck at "can you visualize it."
So the narrower claim: it's not that intuition here is impossible full-stop, it's that human intuition is unreliable. Your post-hoc rationalization point is evidence for that, not against it. The story exists because a person needs something to hold in their head. Drop that requirement and the failure mode goes with it.
This is no longer funny.
You apparently have no intuition on what the intuition is
I disagree. It's absolutely possible to develop an intuition about extremely complex mathematical ideas, including llms or high dimensional systems. Learning to build an llm is a great way to start building that intuition.
What provable conclusions have you intuited around how LLMs work? Give me some examples to prove your point? I'm absolutely happy to change my view with enough data.
I'm not looking to change your view. But for other readers who are curious, here is a link to an interesting task to gain intuition. Ahmad is a good data point for someone who tinkered, built intuition, then started his own ai company. https://twitter.com/TheAhmadOsman/status/2087742080793620593...
I think we are talking about different things to be honest. Understanding LLMs is a fine thing to do but to say you understand what the network is actually doing, even in toy examples like MNIST is extremely tricky. Figuring out what a ~3 trillion parameter network is doing in thousands of dimensions seems intractable to me.
Linking to a Twitter thread about training a small LLM to be good at Wordle is not an example of what I'm talking about. It might well be a useful task but it doesn't allow us to understand deeply what's happening.
I think provable conclusions and building an intuition are different things to be fair. In my opinion it is entirely possible to build intuition about seemingly impossible to understand topics like infinitesimal areas, infinite limits, function mappings, and high dimensional spaces. For example, 3D spaces are easy to intuit if you can visualize vectors in your head. Some people claim it is impossible to understand dimensions higher than 3 but that's obviously not true because you could assign a color to each vector to visualize the next dimension. You could then assign a width to visualize a 5th dimension, and an arrow shape to signify the next, and so on and so on. That's more of a creative act that can start to build at least a visual understanding in you head of these higher dimension spaces. That's a way to start to intuit about those things.
Now with llms we need way more that 6 dimensions so we can start thinking of assigning matrices to each 3d point for example. That allows us to increase the dimension from 3 to 3 + whatever the matrix dimension is.
We can visualize the matrices instead of having numbers as having colors for each entry, so they can be a sort of cube with each vowel being a different color.
Now you can start to visually intuit about how these massively high dimensional spaces can be formed of these colored matrices that can react to some input training data.
That's a start of an idea for intuiting things that might seem impossible to have an intuition about. I think visualization is a great way to start.
i read all that i still dont see what your intuition is about how llm work.
visualizign 100 dim matrix will not tell you how llm work. so what you even talking about.
Visualizing large dimensional matrices is a way of starting to develop intuition about llms. If you can't visualize high dimensional spaces, and you haven't studied llms, you're not going to have any intuition about how they work.
Depending on your knowledge of math, I recommend starting with linear algebra, building an understanding of the equations and try to visualize more and more complex systems, then study llms to see how you can apply your linear algebra intuition to your understanding of llms.
VTK is a great toolkit for visualizing complex systems. 3 blue one brown on YouTube has other visuals that might help you.
It takes time but it's possible. Good luck.
you are just dancing around the question without addressing it directly.
> Visualizing large dimensional matrices is a way of starting to develop intuition about llms.
one last time before i disengage. how do you know this and what intuitions have you personally developed.
Yes, if it cannot be measured it doesn’t exist no matter how much it would make everyone feel more comfortable if we could intuitively understand these systems.
It's like saying that horse can understand physics, because he knows when to jump using his intuition.
Horse, ha.
Like a cat who knows how to teleport so rapidly around the room that he becames a blur. All the while taking into account stuff falling around he knocked down.
Developing an intuition about high dimensional systems is pretty different from understanding the character of some specific point on a 1e9+ dimensional manifold of parameters, in my professional opinion (setting aside all the degrees of freedom that come from the structure of the thing). Sure one can understand generic principles like the curse of dimensionality, but truly groking how an LLM works is basically an open problem as far as I'm aware. I'm not saying there's no benefit for amateurs to study how LLMs work, but let's be realistic about how far mere intuition can truly take anyone in this space.
Seems like a silly goalpost.
As you say, there's a lot of value to unlock with understanding the generic principles, and I would add specific application.
Lots of people are building fantastic Tools or pulling down million dollar salaries without groking the precise representation of a single weight.
> Lots of people are building fantastic Tools or pulling down million dollar salaries without groking the precise representation of a single weight.
I'll borrow my response from a far greater applied mathematician than myself: https://www.youtube.com/watch?v=_oNgyUAEv0Q
I think it’s reasonable to have a shallow understanding of most parts and a deep understanding of a small number of parts. That’s how most engineers are.
Most software engineers do not have a deep understanding of CPU architectures. In fact they probably don’t even have a shallow understanding and get around just fine. How many of them are looking up the instruction set for the CPUs they deploy their CRUD app to in EC2?
But in the case of CPU architecture there are SOME people who understand how things work 100%, and they've built and vetted abstractions/mental models that enable other engineers and scientists to have that kind of mixed shallow/deep understanding in a way that works. On the side of LLMs we're still lacking an expertise which could flawlessly explain how these things operate; the abstractions that we're using are instead derived inductively and are totally unvetted.
You are right that the field doesn’t have a theoretically sound explanation for the architectural choices aside from “A works better than B”. However, I would argue this is an ideal opportunity for the “gentleman scientist” or eager 17 year old.
Basically every part of the original transformer was replaced with something more efficient or better:
LayerNorm -> RMSNorm
Sinusoidal position encoding -> RoPE
MHA -> GQA
ReLU -> GELU
What this means is that there is ample opportunity to improve on what we’ve done thus far.
Oh yeah I absolutely agree that there's lots of greenfield for relative newcomers to find some big improvements! I'm just saying that reasoning about 'best practices' in terms of what works for something like a CPU is missing some important and relevant differences.
The original RoPE paper is a great example of the authors not actually understanding how their own discovery was functioning fully which is a fun example of what I’m talking about I guess…
Yes you might progress the field, but will you really understand why? You can make up an explanation and anthropomorphise it with a few contrived diagrams and everyone will cheer!
In fact, one of the jobs of an engineer is to make sure that other engineers who don't work in his or her area do not need to understand that area deeply, yet build something reliable with it. They need just the summary that he or she writes up into the datasheet for the part. Ensure these conditions are met for safe/reliable operation, give it these inputs, expect these outputs, these timings, this energy consumption, this heat generation, frequency response, tensile strength, whatever.
You may be right. Nonetheless, it's possible that the act of trying to get your brain to understand a system that works in more than a few orthogonal dimensions will help develop useful intuitions about how LLMs, and the many systems/products/robots that will be built on top of them, work.
So computer science actually involves science now.
This is true of most large complex systems though. We run molecular dynamics simulations as an example.
Couldn't have said it better
Agreed. During comp sci we got to re-implement various algos of networks, OS, database, firmware.. and it all gave complimentary intuitions that were useful when tackling practical implementations and bottlenecks.
The funny thing about LLM is, you can build one from scratch and yet you still won't understand how it works. You would understand what kind of matrix multiplications the neural network performs (in fact that's not that hard. An OS is orders of magnitudes more complex), but you would still have no idea why it does what it does.
This is a great point. The complexity sources are entirely different. An OS is complex because it’s solving a difficult problem and we’ve seen decades of evolution both in knowledge and-how and, at the frontier, in performance standards.
The abilities of LLMs are emergent. You can experiment with LLMs and know as much about their observable behavior as the experts. But there’s no way to “crack open” an LLM and see precisely where each skill or tendency lives; as far as we currently understand, it’s all mashed together.
Learning how to build emergent systems is also a skill kids should learn these days. The closest I got was coding up game of life for CSE 142 (intro programming).
If stochastic gradient descent isn't taught in whatever CS Theory 101 is now, it really should be these days.
> there’s no way to “crack open” an LLM and see precisely where each skill or tendency lives
Mechanistic Interpretability has entered the chat.
For a classic example, see https://www.anthropic.com/research/tracing-thoughts-language...
The spirit of your point stands, though. This kind of research is interesting to read about, but it's very hard, more like neuroscience or biology than computer science ("LLMs are grown, not made"). You're dealing with a lot of extremely _messy_ complexity, for which organic life is really the only good point of comparison. Most of us here are't really equipped for that kind of work; it's not at all like, say, reverse-engineering a piece of software written by humans. And of course the only people who can do it on frontier models from Anthropic and OpenAI are people within the labs themselves. (But I'm optimistic we'll see more of this work on open weights models...)
Yah, "building" it is not sufficient. But a lot of times when I build I want to know the why. "Why does gradient Descent have some clever tricks that easily translate to matrix math"?
Lot's of neat stuff to learn.
This is a silly point. Just because the size of these things is too large for our tiny brains, it doesn't mean we have no idea why it does what it does. If you run a physics simulation of a weather system, you have a situation that is unpredictable for a human - but it's not fair to say "we have no idea why the outputs are what they are!!"
That is not a silly point at all. You confuse understanding the mechanics of it with having a theory of why it works. For the physics simulation, physics provides us with the theories which give us the equations underlying the physics simulation. For neural networks, why is next word prediction giving us AI that can do math? We don't really know!
There is no "theory" of tomorrow's weather. We understand the math of every single individual equation of an LLM (for example), just like we understand every single equation in the physics simulation. It's the entirety of the system that we don't understand (and hence can't predict) in our heads.
From a biology perspective - we have a good understanding of the carbon atom and how forces influence it. We really don't know much about a cell.
The next class of problems to solve for future generations is how to survive on a hot planet and how to recover from the damage done by reckless lunatic profiteering assholes.
Maybe people want to take it as cynicism but I think you are factually correct that personal cooling, infectious disease, atypical weather and fire breaks are all more realistic topics for people too young to already have patents, connections or their own oil well.
35 years ago, the enormously popular animated series Captain Planet taught kids about protecting the ozone layer, using biodegradable materials and renewable energy.
Absolutely none of that mattered in the long run because capital markets think environmental destruction is a small price to pay as long as some people get rich.
What kids learn about personal cooling tech and what not won't matter when the supply of the raw materials required to make them is reserved for some VC-funded trillion-dollar 'startup' trying to synthesize an undetectable chemical weapon to get past the bureaucratic red tape of international treaties.
The next wave is already in view and it's going to surprise and horrify the survivors in equal amounts.
Some people will be delighted. Most are going to have a very hard time adjusting.
YC and adjacent isn't it.
What corpus would one be advised to use for training? Should one limit oneself to legally downloadable documents or should one follow the examples of Altman, Amodei, and Zuckerberg and use LibGen and Anna's Archive?
That depth requires math and traditional computing depth. Where is the time to be young and have that depth?
I think you're conflating complex and complicated. An OS is very complicated but is predictable. An LLM is complex and not necessarily understandable.
I'm (more than) twice that age, but I've spent time learning this exactly this from videos by Andrej Karparthy and from books by Sebastian Raschka.
I didn't do it because it was useful to me in a practical sense. It's because LLMs are fascinating and I want to know how they work. From that perspective it's been a great experience. I have afirm grasp of the basics. This makes it much easier to understand frontier concepts like compressed latent attention. I can follow the field and understand it.
Not sure I would have got as much out of it at seventeen. I have a lot of background and experience which made it much easier to learn. I wasn't struggling with the linear algebra or with python. I already knew pytorch and neural networks. That helped a lot and I covered these tutorials fast and could skip over large sections. A few evenings and the odd weekend day over a couple of months was enough for me.
For seventeen year olds the tutorials are good enough to make it possible to learn this but it would have taken a lot longer to understand. On the other hand I would have learned a lot more. I think I would have learned a lot of valuable stuff.
However I also think 17 year old me was studying for his A levels and probably this was right choice in terms of maximising future opportunities. I'm not sure I think learning about LLMs instead is sensible. Indeed it might be bad advice. But I can absolutely agree with the sentiment.I think 17 year old me would have wanted to do this too.
Which resources from these two would you recommend? Or just blanket-recommend all their videos/books?
I've read Raschka's "Build Your Own LLM from Scratch" book and really enjoyed it. I haven't tried the code yet, but the code from his previous Python ML book worked great.
I kind of want to blanket recommend but that's not very helpful
I would suggest starting with Andrej Karpathy's YouTube video: https://youtu.be/kCc8FmEb1nY?is=oiDsrBYJg_MUUmoD
This video is excellent. I'm a huge fan. Also the video is zero commitment and instantly available which makes it a good way to check you are interested.
The book by Sebastian Raschka is slightly less accessible but very reasonably priced and the experience of working through a book is a lot nicer than skipping back and forth in a video (for me). Sebastian's blog posts on recent architectures are absolutely great too.
Thanks! Will definitely look into it. I'm currently grinding Math Academy to fix my math deficiencies so I can understand LLMs.
so can u explain to me how they are works?
How much time are willing to spend and money are you willing to pay?
Yes
I'll use this post as a shameless opportunity to tell more people about a little side project, I made:
http://languagemodelbuilder.com teaches you (in a few hours to days) how to build an LLM from scratch. It's entirely free, without accounts, and without data collection.
I wish this was available for young folk in Romania: As it happens: macOS: 2.70% of desktop operating-system usage in Romania; OS X: 2.55%; Combined Apple desktop share: approximately 5.25%; Windows: 90.65%; Linux: 4.02% or so AI tells me.
There are GitHub repos with the code from Sebastian Raschka's books, which build an LLM from scratch in PyTorch (some examples run fine even on CPU-only setups) using a GPT-2-like architecture, as well as his new book on reasoning models that came out recently. [Off-topic] Your name sounds familiar...
Beautiful work! What prompted you to build this? Pun not intended.
Man, this is excellent! thank you so much for sharing this for free
As someone who's at a similar age and was interested in learning how to do this, there just aren't enough resources to do so. Most LLM research is in the form of academic papers, and there isn't any 'popular' way to learn these things, and besides that all said research assumes you have a B200 cluster ready to go. If you have weaker hardware (say an 8GB nVidia GPU, which is what I have) you're going to be limited to fine tuning small models or torturing yourself working the GPU for days per iteration trying to run things like https://github.com/karpathy/nanochat, which is hardly an educational experience. Renting cloud GPUs is expensive, and at this age the most I could muster up for experimentation is probably $100 or so, which only gets me 25 or so hours on a B200 which just isn't enough. So why would I bother myself with this if I'm already at a disadvantage because of not having access to the right hardware and when surely there are better ways to spend my time? I concluded the only way to learn and be competitive is by finding work at an AI lab somehow (not happening at 17), or studying ML at the right university.
Putting this in contrast with programming, I learned coding when I was 8, and it was incredibly stimulating to learn because you can quickly iterate and there were thousands of books and YouTube tutorials that dumb everything down and teach you fundamentals. All you needed was a $300 computer, and you can learn nearly anything you want, without being gatekept from this or that because you don't have enough vRAM / an sm_100 GPU.
I agree more with Yann LeCunn's salty reply. Over long run, knowing how autoregressive language models work from scratch will be just one step in having foundational understanding, and they might become dated... like knowing how a CRT monitor work. Something of historical interest and good for learning, but not crucial to being well-rounded.
There are other types of models like diffusion models right now that are showing more efficiency and have a higher ceiling for improvement. Understanding math and fundamentals are more important.
Yann has been consistently wrong about the limits of LLMs.
He has, but I think he's right in the sense that there will be better models. It would be premature to think that the current frontier model architectures will last into the future. It would be like making DVD engineering a standard part of high school curricula
> It would be like making DVD engineering a standard part of high school curricula
I fail to see how PG suggesting that a 17 year old, today, should study something... is in any way a parallel to formalizing it as part of the curriculum.
I would, in fact, expect PG to recommend something completely different in 10 years.
The whole point of hacking is to understand the world around you, today
Well good for them, I think their comment is heavily suggesting they're dissatisfied if said limits, and want to see them broken.
Horrible advice. This may have been good advice 10 years ago, but not today. There are no positions for people who "kind of understand how toy LLMs work" because so many engineers do these days. Most of the real LLM optimization work is at the edge of research and highly proprietary and not something you could ever do without infra that costs millions.
But of course, 10 years ago this wasn't obvious.
Yeah he's a moron with a lot of money, that's about it. I'm sure he's said the same thing about various other bags he had bets on throughout the years.
you don't reach cutting edge immediately. you start with the basics
I think the answer then is to get a Phd? I mean, one should learn it to satisfy their curiosity and to build things but I don't think it's going to help you career in any significant way.
What would be better advice for a 17 year old?
Stay in school, get good grades.
Learn math
Throw the computer and the smartphone out of the window.
Or more reasonably, the same old thing : use Linux, hack a little, why not learn programming basics. But learn to own your technology, fight against centralization of technology. The same old RMS story.
IDK where the tech industry is going, if there will be jobs anymore or not, but what I'm sure (and what have been the case for the last 10-15 years anyway) is that for most tech jobs, having good technical knowledge beyond the basics is pretty useless and will probably not be recognized.
If you can, stay a computer geek if that's your thing, but don't make it your career choice, the Eldorado is behind us.
As someone who very much enjoys self learning, I can't comprehend what you wrote. It doesn't make any sense. In fact, I don't believe you've played in the same sandbox at all.
> why not learn programming basics
Do you think implementing an LLM from scratch would not require programming!?
A generic project based goal like this is the best approach to self learning, because you fight your way to it, picking up what you need to know on the way. And, once it's all working, and you get your first meaningful sign of success (in this case, some tokens out), after your long path, you fucking celebrate!
"it doesn't matter what you choose to study because no job is safe from being outsourced overseas, handed over to an indentured servant, or made redundant by a machine. you will spend your whole life surviving while the cannibalistic pedophiles who own everything invent new ways to make you own nothing. be frugal, don't get married, don't have children, do everything you can to stay healthy and independent, and you just might live a reasonably comfortable life."
Most reasonable advice I've seen under this post. Having graduated in 2025 I've experienced the horror of competing with infinitely many third worlders in my small country, who will gladly take 1/3 my pay and be serfs.
I really hope we get another hiring boom like in 2020 when I decided to study CS. Otherwise my career will be very rough. I love it and can't imagine doing anything else.
Horrible. Which country is this? Canada maybe?
Switzerland. I graduated from ETH Zürich.
awwww…… so cute……
who would have guessed when first world were enjoying their heyday as they were colonizing the world, and teaching everyone and their mother to learn English.
Kinda sad now you have to compete with serfs from thirls world. chu chu chu. so cute.
shouldn’t have robbed others then. a richer world for you comes at poorer world for someone. we are playing infinite game in finite world.
> shouldn’t have robbed others then. a richer world for you comes at poorer world for someone. we are playing infinite game in finite world.
Young people are not responsible for the crimes of their ancestors centuries ago.
say that to visa laws that were designed by your ancestors that carry around the world. Say the same to currency arbitrage, freedom of mobility and everything that you enjoy for granted in protected walls.
The reason remote worker can out compete you is because of this currency arbitrage you designed. Your pennies goes a long way in this so called third world. Your one day dinner at nice place is a months salary for family of 4.
Seriously, if you can’t compete globally, then you don’t deserve your privilege of being born in rich world.
Stop labelling other humans as serf when your entire wealth was built on shaky foundations.
You == not you the person, but the government and laws.
very compelling. do the nations that never participated in colonization -- such as Switzerland, for example -- get exemption, or is the blood guilt shared among all wh*te people?
> Having graduated in 2025 I've experienced the horror of competing with infinitely many third worlders in my small country, who will gladly take 1/3 my pay and be serfs
You should really read up on disruptive innovation. Many of those who you call "serfs" who'll "take 1/3rd of my pay" will tomorrow establish themselves, justify their presence, and move up the wage/income scale, while you'll be left complaining.
I am from and live in a third-world country and have spent some time in a first-world one. I've seen the ETH Zurich tag open doors that I didn't even know existed. If you aren't leveraging your passport, your education and your network to reach a place where you don't need to worry about "serfs", then you're doing something seriously wrong. What you have, compared to the competition you denigrate, is invaluable, making best use of it is your job.
> don't get married, don't have children
Bad advice. You’re going to need family connections and loyal people you can bet your life on to survive if the rest of what you’re saying is remotely true.
the likelihood of finding a partner you can bet your life on is nonexistent. the likelihood of the opposite outcome is well-known at this point.
We always hoped that we wouldn't live to see the dystopia in Snow Crash become reality.
We never imagined that we'd live to see Snow Crash depict a utopia by comparison to what we got.
Stay away from the internet and learn how to build a local business instead.
Implement a linear regression or Bayes' theorem.
Be first to invent and patent the "$15 dollar, battery-less, flying carbon capture drone" and save the world from global warning.
What? You want me to tell kids not to be ambitious?
Very silly reply. If you learn how toy LLMs work at 17 don't you think that would help you at university and grad school? There's plenty of time to go into the private sector workforce later.
The point of courses like "Nand to Tetris" is not to become CPU designer. It is not useless just because you can't build a chip factory.
Watching Karpathy's "Zero to Hero" won't make you AI specialist. It will improve your understanding of the basics.
I appreciate the sentiment and I'm pretty curious how I could train an LLM, even are really basic one from 5yrs ago without nuts hardware.
That said, I a good starting point for a 17yo is reading about perceptrons[0], then the basics of neural networks[1] (ex. 3-layer perceptron) then writing a program to train a 3-layer perceptron and classifying the MNIST dataset[2] - a dataset of characters.
This can anywhere between a day and a week and you will demystify the basics of neural networks and work your way forward with more advanced contemporary concepts.
Fun fact: any multi-layer perceptron neural net can be reduced to a 3-layer perceptron network.
[0] https://en.wikipedia.org/wiki/Perceptron [1] http://geeksforgeeks.org/deep-learning/neural-networks-a-beg... [2] https://www.kaggle.com/datasets/hojjatk/mnist-dataset/data
I think a problem a lot of people are grappling with here is that due to LLMs and AI generally, it’s basically impossible to predict what the future will look like or what jobs will still be around.
I’d probably say something like: do something you enjoy and seems like it might be useful, but accept that the pace of change may mean that whatever you study ends up being irrelevant.
Whatever solution there ends up being to this, it’s not going to be one that an individual 17 year old can implement. We’re past the point where individual good and bad choices matter that much to economic outcomes.
I would advise any somewhat ambitious 17 year old to avoid tech and get into healthcare if they can stomach human interactions and bodily fluids. Sure, it is not all sunshine and rainbows, but there will still be plenty of work helping people who are ill or elderly. Even in the worst-case economic scenario, medicine will be a more socially rewarding and stable life path.
This seems like a much more interesting question to me.
Telling other people's children what to do is easy and basically doesn't have any downside to being wrong. With your own children things are a bit different.
So: what are people here with school age children telling their own kids about the future? If their kids ask, what kind of careers would they encourage them to pursue, assuming they have the skills and interest?
When I was last in the Bay Area, maybe about a decade ago the bookshops were full of titles like "Python for Preschoolers" (I exaggerate, but only slightly). Clearly at the time a lot of people working in tech thought that cultivating an interest in programming was going to be the path to being a successful (by some metric) adult. Is that still the case?
We've wronged an entire generation by pushing "learn to code".
"learn to code because it will get you a good career" perhaps, but "learn to code at at least a high school level, because it will give you a very good mental model for how computers do things" is still very useful.
Or maybe generalized to "get good at problem solving." I've found building software systems has a lot of parallels to working on physical things such as carpentry/DIY and working on small engines. The concepts of breaking down a problem into a bunch of smaller problems, and identifying what can be independently tested/built remain the same.
the problem solving aspect is great too, but I mean specifically "this is what it means for a computer to know something, and this is what it means for it to do something" expressed in terms of data stored in memory, and algorithms that read to and write from that memory, and "this is how computers think" in terms of control flow and if tests, is a form of literacy that everyone should at least be exposed to.
The real reason to recommend this is that they have an excellent chance of avoiding the "nerd-to-incel" pipeline that tech all-but-guarantees for its best nerds. Post GenAI boom, the social cost of working in tech combined with the coming collapse in high paying jobs, means that unironically coders should be learning a bit about coal mines.
In this regard, Healthcare is a polar opposite. It's pretty hard as a male nurse to not "accidentally" become a home wrecker.
If I were 17 again I'd prepare to go for volunteering overseas after high school for 1-2 years (plenty of free options in the EU where you might only need to cover the plane ticket). See the world, you learn a new language, help others and then think about what you want to do.
I'd say, if you were 17, you'd probably be better off learning how to socialize or at least gracefully approach and relate to people your own age. That's probably the greatest challenge of the near future, sadly.
I get this is basically advice for young founders and entrepreneurs, but i would ignore that request and encourage 17 year olds to spend time trying to find a happy medium between work and life.
Being a super rich and an unhappy workaholic, or a super-impressive engineer who wakes up one day at 45 and realizes they regret wasting half their life (I ran into way too many of these) is a much worse fate than "not being rich from your startup" and working a relatively regular job while feeling fulfilled and happy by more than just work.
Especially in the US, which is uniquely bad at this and encourages people to work themselves to death, mental health and work life balance are much more valuable things for 17 year olds to focus on than finding good startup ideas.
In case you think i'm being a bit dramatic, let's look at the state of 17 year old mental health in the heart of Silicon Valley:
"The City of Palo Alto and the Palo Alto Unified School District approved a funded contract to place 24/7 human security guards and monitors at all four local Caltrain grade crossings, including the Churchill Avenue crossing directly adjacent to Palo Alto High School."
(in case it's not obvious, it's because of suicides by high school students)
The 17 year olds do not need advice on better startups, and this situation will never get better if we focus our advice on how to be better at work instead of how to be better at life. This will require redirecting the conversations.
Thank you. They need human contact, not more "sit in a room alone and get stressed as fuck for little ROI" tech bullshit. Unless the kid has a genuine, self-motivated interest in learning these things (a great, positive thing that should be nurtured), they should file pg's advice under "ok boomer."
Yeah, no way.
I'd move to the middle of nowhere and work multiple jobs on a farm and in construction. Learn how to grow food, and build things. Meet the farmer's daughter, and marry her. Then, buy my own land, grow my own food, and build my own things.
Slight problem with step “buy my own land”. Your working 2 blue collar jobs and paying rent are incompatible with this plan.
> Meet the farmer's daughter, and marry her.
I already do this. I live in a small village where there isn't even a wired Internet connection (wireless only). I work full-time with LLMs, on own product ideas and client projects (all LLM led).
I started investing in farms, have 50 pigs and 100+ chickens now. We are planning to grow to 100 pigs and 2000 chickens in a year. We will start growing Shiitake mushrooms in a few months too.
Most 17 year olds I know don’t want anything to do with AI and see the entire industry as an existential threat.
Nothing wrong with learning the theory and understanding the papers. Getting to that point you’ll have to get your fundamentals down. Might be an interesting exercise.
But as a future? I guess we’ll see. I suspect the next financial apocalypse will determine if there is one. Another AI Winter that may outlast all others so far.
> Most 17 year olds I know don’t want anything to do with AI
The few exceptional individuals will innovate what the millions will benefit from.
At 17, the mother of Isaac Newton removed Isaac from school and tried to make him a farmer. We all know that wasn't his destiny.
There’s no such thing as destiny.
You could also walk out your front door and get hit be a meteor tomorrow.
Don't really understand why people are saying this is terrible advice. If you're 17, you should be learning everything. At that age, your brain is a sponge and your energy levels are the highest they will ever be.
None of us know what the future of work, education, or AI is going to look like. But your best bet is to become a life-long learner. Be it LLMs, musical instruments, physics, or business.
Oddly, I just remembered I did the nearest possible thing to this when I was 17... back in 1991.
On an Amiga, I took various public domain text documents from cover disks and counted the probability of the next word given the previous word. Then spat out random sequences of words from it and printed them out. It was called "Splurge". Basically a very very simple single layer statistical language model.
Some of the sentences were randomly not bad sentences, which seemed amazing at the time!
That kind of thing (and Core Wars and Tierra etc) did lead me to getting a job at an artificial life startup at the end of the decade. But that was in turn about 10/15 years too early (no GPUs).
There's some lesson from this about timing, but honestly I've gained the most as a person when I did something that was fun, ethical and gained an audience. A tricky combination.
Yann LeCun's response is interesting. I've heard that the transformer architecture underlying LLMs is actually really effective for robotics, but progress is a lot slower here because instead of just scraping the web and Anna's Archive, you have to outfit workers with motion-capture suits that record their every move, and then have them physically do enough jobs that you can gather good training data on how the job is done. This is a little challenging when no blue-collar worker wants to be replaced by a robot.
Long-term there's probably a lot more potential, and much bigger markets, in robotics than in LLMs, but it will really take the long term to get there.
If I were 17 again, I would probably be single-mindedly focused on getting laid adequately and getting into something non-STEM. Or at least I hope so.
While knowledge is always great, I would encourage people not to seek advice from successful people like this (survivorship bias).
Moreover I am not sure it is even good advice? Would you advise a 17 y.o. to learn how transistors work or how to code (i.e. is LLM training the right level in the stack)? LLM training, a discipline where relevant work is already out of reach for 99.999% of budgets really as essential as this post implies?
> I would encourage people not to seek advice from successful people like this (survivorship bias).
Personally I don't see the problem, as long as you're aware there is survivorship bias involved here.
What's the alternative really, seek advice from unsuccessful people? That seems worse :)
Personally I do both, read about what worked for people, also read about what didn't work for people, then ignore both and do whatever the fuck I want.
> seek advice from unsuccessful people
Intuitively, I would guess that they have a better grasp of what made them fail than successful people have of what made them succeed.
I don't know; I think people in general are just not great at this. Successful people tend to underrate luck and overrate the brilliance of their own decisions, but the rest of us are prone to either reversing that and blaming everyone but ourselves, or being so determined to take accountability (or just depressed) that we become overly self-critical, or simply not understanding why things happened the way they did and reaching for any explanation that resolves the chaos into something narratively satisfying.
If there's a single unifying fault to the unsuccessful people I know (for any definition of unsuccesful, including my own failures) is that they're precisely bad at working out why they failed - if they even think to ask the question. The successful people are generally much clearer that it was studying hard or networking or natural talent that contributed to their success. The latter group may have people who don't realise luck played a part, but they massively increased their chances of good luck by the aforementioned behaviours.
Seek advice from people who have had a normal level of success. Not a one-in-a-million level.
I would say you should seek advice from someone has lucky and successful as Paul Graham but I wouldn’t take his public Twitter as advice it’s entertainment period.
It's best not to take advice on direction of careers from anyone. It's better to find and work on things that interest you, and then take advice from people that are amazing in that specific field. In mid 2000s in Australia all the "top people" were telling me not to get into a software engineering career because it was dead. It's certainly challenged right now, but it took off during those 15+ years.
“It's best not to take advice on direction of careers from anyone. It's better to find and work on things that interest you, and then take advice from people that are amazing in that specific field.”
What bucket should I put this advice in?
Understandable irony. The way I see it, there are no buckets, only context surrounding the advice and your awareness thereof. If the context is unknown, best to take it with a big chunk of salt... Or spend time familiarizing yourself with the context if you think it's worth the time/effort; working on things that interest you is a nice way of doing that.
But some advice is less specific than other advice though. Some things are always stupid, and some things are always smart, if you look at the context of our world and society. I find myself pursuing these "fundamental truths" with great interest lately, especially now that the world is changing so quickly.
A lot of the advice that is relevant for top performers in a field has no relevance to an average person interested in that field.
> Personally I do both
That’s what you get from listening to “successful people”. You get to learn about all the things they tried that failed, then the things that did work on that 24th try, which was successful.
The “survivorship bias” people always seem to assume that the “survivor” lucked into his fortune on his first try ever, so he can’t have learned anything, so we don’t have to listen to him. But that’s seldom the case.
I’ve written about this before:
https://expatsoftware.com/Articles/survivorship-bias.html
very good read. I get into the same discussion sometimes with people. If you're successful you're automatically labelled as "lucky".
There are successful people that failed many times before being successful. Somehow these days, previous failures and persistence seems to be ignored and they focus just on the luck you got on the 20th try.
>What's the alternative really, seek advice from unsuccessful people?
Seek advice from the averagely successful people, since that is statistically what you're most likely to be.
The unsuccessful person is much more likely to be actually connected to the daily needs and struggles of a normal person. Paul Graham hasn't seen the inside of a grocery store in the past twenty years.
I know who the 17 years old is closest to.
Just play the lottery once, pick the correct numbers and win $100mil.
If you can't do that - not sure anyone can help you.
I just don't think he realizes how saturated it got over the years. Or maybe he knows at a conscious level, but not subconsciously.
In 2000 (his era), it would have been really smart to study the source of Linux or Apache. Would have paid dividends over decades. Cuz that knowledge was so rare. The number of people hacking on LLMs now dwarfs the number of people hacking on web servers 30 years ago, by several orders of magnitude.
And if you turn back the clock even more, I mean just even having access to a computer, let alone owning one, would have put you at a massive advantage.
I don't know what to call it. The pioneers should be respected obviously, but at the same time you need to understand that for them, the game wasn't nearly as played out as it is now.
I just don't think you can afford to be dicking around with LLMs like you could afford to dick around with random Linux distros 20 years ago. Too many people willing to do it for free these days.
You don't wanna end up being the 2030 equivalent of a certain SNES emulator developer, or maintainer of a package manager for jailbroken iPhones, I mean the list goes on and on. Being a hacker doesn't automatically give you a path to being rich, or even making a decent living. It hasn't been that way for a while.
I would encourage young people to be born rich. It's the best time in 100 years to be advantaged. Why waste your potential by having your labor stolen?
I'd learn a trade in all seriousness.
(Edit: And learn how honest business works)
With the hindsight of experience, the remnants of my 18-year old energy go “woah, that’s cool!” at plenty of engineering feats… and my decades-older second brain goes “well d’oh, I could’ve just learned a trade to work on that!”
I think the last one was seeing a skilled electronics repairman do surgery on a CT machine controller.
I'm not sure what 17 year old me would have done with YouTube tutorials for everything under the sun available.
I'm much older and less wise now, but I still afforded myself the opportunity to follow karpathy's tutorials to build a LLM from scratch. Got to play with a few ideas. Seen similar ideas turn up in frontier model work, which is quite gratifying.
There are so many ideas to try.
Currently playing with autoencoders that takes A and B and produce latents A', B', and C'. Reconstruction of A is from A' and C', B is from B' and C'
The idea is if C' can be made to improve both outputs, it must store as much information as it can about what is common to both inputs.
LLMs are incredibly boring to me as a technology..Not in terms of what it can do, but how it works.
Same for me. Neural networks in general.
I first studied them in 2011, and I was like what? Just a bunch of partial derivatives?
I keep looking at AI to check if now it's something else but it keeps being gradient descent.
Okay, it's great that you can perform miracles using gradient descent but that doesn't make it captivating in any way.
The only realistic approach is to train on a limited data set which is probably less usable than the comibnation of a custom RAG + one of the many available LLMs.
Also many people/kids don't have access to proper "productive" systems anymore, since the whole computing and electronics industry shifted to make "consumer"-devices like smartphones or laptops made for netflix, gaming and spotify.
Breaking the barrier to build a custom system, install linux (or developer tools for Windows, MacOS) is already a complex AND costly task. It was just way simpler in the late 90s and 00s to get something working.
Writing, supervising and training LLMs are now the purview of... even larger LLMs. Optimising CUDA kernels; hand-writing SIMD assembly to speed up data loading; tinkering with your particular brand of DRAM to see if there's anything to gain from optimising for its memory topology and NUMA --- these are now the job of AI.
There is very little reason for humans to get all too engrossed in this type of work now, today, with the hope of being good enough at it to command a high salary in 3-5 years. AI can already do it incredibly well, and they can do it persistently and doggedly 24 hours a day.
telling a 17 year old to get into tech right now is horrible advice, literally telling them to get at the back of a line with a better part of a million more experienced people in it.
Telling them not to get into tech is also terribly reactionary advice. The truth of the matter is that we don’t yet know whether tech roles will be eliminated or if they’re just going to follow previous innovation breakthroughs where “one person producing way more work” makes software even more of a desirable industry to be involved in.
There really isn’t a very strong correlation between tech industry hiring strength and AI as of yet. Various studies that are out there haven’t even witnessed AI workflows contributing more than modest gains in software engineering efficiency. I.e., being able to write code 20-40% faster isn’t a seismic shift in the industry where everyone is getting laid off tomorrow and we’re all replaced by software.
Even with the questions surrounding the current job market, it’s still an incredibly good ROI career compared to so many other jobs out there.
For example, in my local area you can get a job as a registered nurse working nights in the emergency room and only make ~$115k.
I make almost double that telling an LLM what to do from my house in my pajamas during the day with less time spent in university.
Even if tech roles lose half their salary to automation pressure it’s still a really good gig.
“only make $115k”
This right here is why nobody is shedding tears for the massive employment crisis in tech.
You make double that shilling ai slopware while they work nights saving lives.
I would tell a 17 year old that the world will always need nurses, same can’t be said for guys sitting in their pajamas burning tokens.
What is happening now in tech has been a long time coming, and it can’t happen fast enough.
I just want to make it clear that I’m not assigning some kind of moral superiority to my financial situation and the amount that the economy values my labor per hour.
I don’t make the rules for how much each profession is able to make in compensation.
The world will always need nurses, but that doesn’t mean that it’s a fantastic career to get into if you have a neutral career preference and your primary consideration is university tuition ROI, expected compensation, work schedule, and day-to-day physical exertion.
My point isn’t to debate the virtues of each career, I am intending to stick to objective aspects of them.
In that sense, telling today’s kids that there’s no future in tech careers just because there’s a short term hiring slump is extremely premature. I certainly wouldn’t tell a kid who is passionate about tech to avoid the field just because the unemployment rate is currently 7%.
https://www.investopedia.com/bachelor-s-degrees-with-the-bes...
Mr. McGuire: I just want to say one word to you. Just one word.
Benjamin: Yes, sir.
Mr. McGuire: Are you listening?
Benjamin: Yes, I am.
Mr. McGuire: Plastics.
Benjamin: Exactly how do you mean?
Mr. McGuire: There's a great future in plastics. Think about it. Will you think about it?
---
I love this scene because it so perfectly captures what it's like to be young and given advice, however well-meaning, by an older generation living in a world that no longer exists for the young. And it's ambiguous and trite enough to be essentially useless even if the underlying idea isn't terrible.
What about doing abliteration, weight pruning, representation engineering, etc, directly to open LLMs instead?
Building an LLM from scratch has a hard split between a tutorial project you can complete in a weekend (that's useless for actual usage) and then a solid 1km high brick wall if you want to create anything actually useful from scratch.
Modified open models have a very active community around them, without the need to look much further than Hugging Face.
I get it the sentiment behind the post…but it has some “let them eat cake” vibes though.
We should remember the fact that LLMs had emergence doing one mundane thing. Now think what we would have if we do our mundane thing of learning something for the sake of just learning… Emergence.
There's this dilemma where in theory there's a ton of demand for engineers that can do real LLM machine-learning, but in practice there are very few available positions and entrepreneurship opportunities.
The reality is that an incredibly small minority of companies in the world do any real training or optimisation. It's unnecessary and inefficient for most purposes unless you are fully dedicated to being an LLM company, and still then it's a struggle. Those few that do train, they spend most of their budget on compute and have relatively small teams.
Getting experience in this field requires having access to very expensive hardware to begin with. And the skills will be quite hard to convert into any real value for someone, leading to a decent income, unless you have a ton of funding from patient investors, or you have decent contacts in Bay Area networks to get hired at the right place.
With all due respect, paulg is in somewhat of a bubble, this is not congruent with the global situation.
That's not true, because everyone, everyone, everyone seems to want to do training. Which results in a 50 person company training, say, a voice model that then fails, because it's just not good enough.
In reality the problem is that it gets blasted out of the water by a much worse architecture trained on 10000x the infrastructure. And while I'm sure the freshly brought in ML student came up with a 10%, even 30% better architecture, it just doesn't matter. (and never mind that even OpenAI hasn't really solved a voice model yet. Try it. It can probably match 2026-quality call centers, but it's no substitute for an actually empowered human)
... and yet, if you look at what hyperscalers are getting paid for ... comfortably more than half the income is training. Which makes no sense on so many levels.
e.g. https://valueaddvc.com/blog/inference-chips-vs-training-chip... (I get it, not great first source, but st
We finetune LLMs. Small ones like Gemma 4 for semantic tasks.
There are plenty of areas were we need people to do this for insurances, banks etc.
AI/ML exists on many levels.
+1, I have a friend, math PhD that's been working on ML research 5+ years in London yet he has not been able to find any position.
The only jobs that he found he was highly over qualified or paid very little.
In any case, it doesn't look like there's this crazy rush to hire all ML talent, even the one that understand the math and technology deeply.
This is like saying, “teens shouldn’t learn how to make their own game engine because no one is hiring for that.”
You’re missing the point. Understanding how Unity works fundamentally makes you a better Unity dev.
Yes. It's like looking at the (Apollo) moon rocket launch and then suggesting teenagers should learn to build rockets in their garages for the coming space age.
It is viable as a toy project, but there are vanishingly few career opportunities.
It's like looking at the early internet and then suggesting teenagers should write browsers as their projects instead of webpages.
Early internet let me create the best personal web page in my city that I knew of with 2 weeks experience as a 12 year old. I imagine that same 12 year old could be more knowledgeable about LLMs than 99% of people in the same period.
I doubt it. Creating a website can be done by copy pasting a few snippets together and checking if it visually looks like expected.
Good luck with that approach when trying to toy around with models and their training/inference.
There is also a lot of math basics missing that a 12 year old may be able to grasp, but I would bet they are at least 13 by the time the knowledge is deep enough to understand what operations are happening.
You're vastly over-estimating the difficulty of making a web page and even more vastly under-estimating the difficulty of LLMs.
If someone writes a browser in their teens, they will probably learn more about the web than if they were just writing web pages
They'll learn so much more that won't transfer to as many job opportunities. For ex, say more about C++ and less about cutting edge CSS (because modern browser tech is an ocean). I suppose they might luck into other adjacent or unrelated roles with the same skills.
I have a hard time imagining anything where writing a browser wouldn't be an excellent transferable foundation. Even your example: writing css will never teach you as much as writing a css engine.
Sorry if I was unclear. I mean they'll never have enough time to learn a significant amount of modern web tech because they'll be working up to it from lower layers and basics.
Browsers from scratch are multi-year projects for multiple people. Even just skinning and minor tweaks to modern browsers is a deep well for one person.
CSS wasn’t around in the early web.
Building a browser in the early web was actually a very achievable goal for exactly the reasons it isn’t now. There was not JS. No CSS. No SVG. In fact very few widely supported image formats (and graphical browsers weren’t around in the earliest days of the web anyway). TLS didn’t exist. HTML only had a subset of methods. And even POST was usually just managed by CGI/BIN calling an external process, often written in C++ or Perl.
It was a simpler time.
They will also know the fundamental underpinnings of the web, you know - the thing that those html devs are actually using.
That’s like saying someone who fabricates cars doesn’t have the skills to drive them. Perhaps not, but they’re very well placed to pick it up quickly. They’ve also shown they can do something far more challenging, which is actually better than hiring for narrow immediate skills.
In other words: I’d hire that candidate in a heartbeat.
You'd hire an auto designer to be a limo driver? I think it's just two different skill sets. The auto designer is clearly smart but a limo driver also needs to have customer service skills, etc.
That may have worked at the time, but no companies are interested in learning projects today. If you didn’t do the reqs list for the last 5-10 years with the same title, forget about it. Because there are dozens of folks who have, lined up. No one is indulging career changers (and most fresh grads) for the time being.
> No one is indulging career changers (and most fresh grads) for the time being.
Oh hey thank you for that. It really helps. Hope you have your rug pulled from under you today too.
— signed, a career changer trying his best.
it's just a fact of the market, it's not an attack on you.
Indeed, I speak from experience. Generalists like myself are unfavored as well, it seems.
That seems like a perfectly good use of time for a kid in the 70’s! You’d learn a lot of engineering skills and demonstrate a tenaciousness that most people don’t have. I don’t think the goal is to predict what will be the important technology in 10 years. The goal is to challenge oneself with hard tasks and learn interesting stuff. A lot of the “AI” people today were compilers people yesterday
When I was 17 and still witnessing Apollo Moon launches, I wanted to build an AI that would handily outperform LLMs as we know them today.
But that was way back in the early 1970's and all I had to work with was a mainframe.
Well the mainframe itself wasn't bad, the real show-stopper was that I didn't own the computer outright, no strings attached, no debt, etc.
>I'd probably try to make an LLM that I could use on some specific problem.
I thought so too back then, still do so I guess this is one of those things that could stand the test of time. I always wanted to start with something a lot simpler than a Moon mission myself. At 17 I already had a significant breakthrough in the chem labs and it was from alternatives to a single processing step plus everything that descended from that, rather than trying to tackle a much more complex detailed multi-step synthesis. I was only 17 but I was not trying to be a slouch, I don't think pg was either at that age but his advice is not for just anybody. I couldn't have done it if I hadn't made major progress since being 16, and it really emphasized at the time how much maturity can make a difference. My imagination ran wild as I extrapolated :)
In a reply from LeCun to pg:
>>I'll figure out a set of methods and architectures beyond LLMs that can quickly learn to perform physical tasks as efficiently as humans and animals. That last item is also what I would if I were 30, 40, 50, or 66 years old
I see no reason to stop at 66 either ;)
But I figured that people owning more computer power than I could ever afford were going to be doing something like this as soon as they could, without having to wait for something like an LLM to arrive before getting peoples' attention.
It did seem like things were going to take longer than you expect, so it's pretty good to have a lifetime of concentrating on the specialized natural science domain expertise, focused now for 50 full years on how it would combine if AI ever got good enough.
Both the natural science and the AI need to be a major cut above, I still see dramatic room for improvement in my own work. If I'm going to have to rely on "other peoples' AI" then that natural science component is going to have to pull a lot of weight to keep up with the kind of computers that only rich-as-hell high-rollers have access to.
I agree with mostly all of this, but personally I wrote a toy LLM almost 5 years ago and while it never saw much use outside of boring my wife with a shitty command line demo with glee it did help me understand how they worked and how to apply them, played a lot with JAX and pytorch, ended up building a ghetto version of MCP and an LLM-Pool to proxy requests to my baby local models and so I didn't struggle to see the evolution of openrouter and MCP agentic workflows. The same way i'm really glad when I was younger I built a bad webserver by myself, a really painful SQLx type database, etc etc etc - none of these things led me to developing for Nginx or Oracle nor will knowing JAX get me a job at an AI research lab, but I do have a lot of depth in understanding how the technology works so that the flavors on top of them are easy to digest and make more use of immediately, and I think the same can be said for engineers coming into the field - if it's a spooky LLM box you aren't going to be squeezing the same amount of juice as the guy that knows how they work inside and out so having at least the understanding of a _babys first LLM_ is going to get you miles ahead of people who don't.
For anyone who wants to dork around there is https://github.com/rasbt/LLMs-from-scratch which is something amazing that I think anyone who wants to engineer things around LLMs should at least blast through and read.
AI is the subtrate the future runs on.
And so I think the idea is more to understand tomorrow ... from first principles.
In the late 80's, as a teenager, I learned x86 assembly and C because that was the only way to squeeze out enough juice from my shitty CGA (and later VGA) card to programm the games/graphics that interested me.
I haven't written assembly in years.
But whatever I did in my career: it helped me and gave me an edge over my peers to have a foundation that is very close to the metal.
> AI is the subtrate the future runs on.
Current AI can automate significant amounts of grunt work in programming and math. It's good at running web searches and writing summaries. There are a few other niches where it is currently successful. But other than that, many corporate AI projects are spectacular failures.
So just given what we have in hand, assuming no further breakthroughs, then we're maybe looking at AI being somewhat bigger than the Internet. Which would make it a revolutionary technology, sure.
But to get from "a revolutionary technology" to "the substrate the future runs on", then you need to assume more breakthroughs: long-context operation over weeks or months, displacing human workers 100% instead of 75%, and the ability to directly economically compete with actual humans. And people are investing literal trillions of dollars to make that future come true, without really thinking through what truly competitive-with-human AI would actually mean. We might be looking at massive job loss, centralization of power, fully automated "companies" with no humans dominating markets, and other dystopian scenarios.
And in those worlds, it's unclear that being good at CUDA and matrix math will be all that helpful, careerwise. The AIs are already pretty good at that stuff. Data scientists get paid OK when they actually get hired, but it's not everything college students were promised in the 2010s, either.
We can't yet build a fully-general competitor for the human mind. But we're getting closer. And if we ever do build one, the consequences will be really weird in any number of ways. So I worry about visions of the future that assume AI keeps improving significantly, but that also assume it still somehow remains a "normal" technology that doesn't, for example, render most humans fundamentally uncompetitive.
I keep saying AI is going to prove more impactful than cloud computing but less impactful than the sewing machine and a bunch of people get mad at me.
> AI is the subtrate the future runs on.
We don't know this. So many people are simply claiming this confidently, and a lot of them are betting their careers on it, but nobody has a crystal ball. Whenever someone tells you confidently, and without any doubt or qualifications, that something "is the future," be skeptical.
I remember when the Segway was definitely going to change urban planning worldwide.
Segways failed because they were a solution looking for a problem. You can't say that about current-gen LLMs, much less AI in general.
I don't really agree, I think the future should run on humans.
AI is a great solution in niche areas but generally doesn't make much money. All the large companies are in the negative.
The steam engine was less of a bubble and was much more revolutionary and had a greater impact.
In that regard, it's not too different from mobile telephony. Mobile phones drove the electronics industry 20 years ago, but there is limited demand for people who really know how to build a phone (ie. build the hardware and write all the signal processing from scratch), as there aren't that many companies that do phones at the lowest level. A few of the engineers got rich (eg. Viterbi), but most 'just' made a good living. Most people who got rich off phones didn't do it by knowing how phones work.
Incidentally, the skills for the lowest levels of LLMs aren't that far removed from those needed for mobile telephony, in that both are based on maths, computation and information theory.
Also, in a few years, LLMs will be building the next generation of LLMs anyway, probably autonomously to a high degree.
Good points, but I think we can expect the AI space to be more tumultuous.
What most people want from mobile technology is for it to work, not too expensively, and for it to get out of their way.
What most people want out of AI is for no leader to emerge and wield supremacy against the rest of us. People are afraid of it in ways they weren't afraid of mobile, so they're more willing to work together against whoever is in the lead.
Its more like an arms race and less like a utility. The disadvantage I face when my competition has better mobile coverage and bandwidth is minor. The disadvantage I face when my competition has better intelligence on tap is much more significant.
>* What most people want out of AI is for no leader to emerge and wield supremacy against the rest of us. People are afraid of it in ways they weren't afraid of mobile, so they're more willing to work together against whoever is in the lead.*
No.
That’s what people like us on HN want. The people out in “Greater Userland” just want the black box to answer their questions. They could care less who is behind it. They don’t yet attach their black box to Amazon or Microsoft etc. And most won’t care enough to be inconvenienced even when they do make the connection. (As your competition argument implies.)
Heck, a lot haven’t even made the connection between the black box that gives them answers and data centers. They think, “ ChatGPT good” and at the same time think “data centers bad”.
I was there at the start of the smartphone boom. I built a demo Android device that was capable of telephony/data, 3D rendering, etc. all before Google open-sourced the OS, for a SoC vendor that wasn't in Google's inner circle. Yet the industry was not interested in my junior profile during the subprime crisis.
Ridiculous frankly.
More than sad...
> The reality is that an incredibly small minority of companies in the world do any real training or optimisation. It's unnecessary and inefficient for most purposes unless you are fully dedicated to being an LLM company, and still then it's a struggle. Those few that do train, they spend most of their budget on compute and have relatively small teams.
And the job postings are often ridiculous. I recently was an AMD job advert in Germany for an ML Kernel Engineer, not Senior mind you. The requirements went something like
> Masters Degree required with strong preference for a PhD with peer reviewed articles in {journals_list} > 10+ years of experience in C/C++ > GPU programming experience required > 10 more ridiculous lines
No idea how a teenager self teaching himself LLMs is supposed to even get a shot...
It reminds me of that "stone soup" story.
1. I can make turn a stone and water into a delicious soup
"17 year olds, learn to build an LLM from scratch"
2. This soup would be more delicious if we add a few carrots. Does anyone have carrots
"Increase your chance of success by getting a Masters degree"
3. How about potatoes?
"And get a PHD"
4. What about some salt?
"And publish some peer reviewed articles in {journals_list}
5. We should also add beef
"Now work in the industry for 10 years"
6. See, this soup is delicious, and I made it all with a stone
"See, you're rich, and it's all because you learned LLMs as a 17 year old"
In Sweden we tell that same story but with a nail instead of a rock. "Cooking soup on a nail" is a somewhat common expression here.
It is written in the first person, I suppose.
This is a wildly incorrect and myopic view on the world.
Finetuning model is cheap and incredibly useful for deployment. You don't need to pre-train a frontier llm from scratch to make useful models.
There is tons of domains where you and fine-tune llms and deploy them for value in companies and for your own entrepreneurship ambitions. I have made this a big part of my career for the last few years and now I'm working on finetuning models for starting my own companies.
> With all due respect, paulg is in somewhat of a bubble
I feel like the "ALWAYS HAS BEEN" meme is apropos here.
Y'all are missing the point: It's probably less than 100 hours to learn the foundations of one of humanity's most-current breakthroughs. It's a disservice to any young hacker to not learn it. Here's your curriculum. Watch these:
-- 3Blue 1Brown's Neural Network Series: https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_6700...
-- Karpathy on LLMs: https://www.youtube.com/watch?v=7xTGNNLPyMI
-- Stanford CS336: https://www.youtube.com/watch?v=JuoVZkPBiKk
Then do this hands-on:
-- Karpathy's zero-to-hero: https://karpathy.ai/zero-to-hero.html
This is a silly take. You can learn to build an LLM, there are great resources to do so (there are books about building them from scratch), you can use older model GPUs or rent them by the hour. The value of understanding them is really high for anyone building any application that uses an LLM at any point.
It similar to understanding how a very basic CPU works. Just because I'm not going to work at intel or nvidia or whatever optimizing the hell out of a chip, it doesn't mean I just throw my hands up and think "magic" - the basic architecture isn't that difficult, and the value of knowing it is astronomical for anyone writing software.
Yes. There's a lot of demand for elite talent, and no demand for slightly sub-elite talent.
I thought pg was trying to live forever. Has he learned LLMs from scratch?
To build LLMs from scratch, one must first learn how to build a computer from scratch because microchip prices are crazy. Oh and before that, Sagan says you need to make a universe. :)
LLMs are just one small part of computing. it is fine to learn how they work but they are nothing magical. if you want to build a real working system using LLMs that actually makes money you still have to learn everything else. but i think his point here is that learning LLMs at 17 means hacking together some home brew rig on shitty consumer hardware and if you learn all of that you will be in a much better place than learning how to use chatgpt to automate a spam campaign for your b2b saas startup.
If i were 17, I'd try to distinguish who to take advice from, and would definitely learn that VCs have interest to spread a specific agenda in their message. Also, I would get drunk and have as much fun as could, as the misery of working under the treat of being replaced by AI, would simply kill any desire to live past 25.
> Notice that what I would not do is try to start a startup. Instead I'd build the foundation of knowledge to base a startup on later.
There's also this little-known concept called learning things for learning's sake and not always trying to capitalize on it.
I would (and am) going into MLOps. Not just the general infrastructure/systems administration but how to do inference optimization, caching, quantization, memory pinning, vfio passthrough of gpus etc.
... and it would be totally pointless.
I mean first that is already what plenty of 17yo are actually doing, because that is what they do at school or in parascholar activities. There are already countless of such tutorials where you can do that in an afternoon.
The pointless part though is precisely why Amazon and others are hunting for rare books, all the low hanging fruits have been picked already so just training a bigger model will simply mean burning more energy and money. Sure training a small one for the basic principle is a great pedagogical thing, training another one, medium, then maybe a large one, is also good in term of learning the process and architecture, but one should not expect it to be useful out of that context.
Pure players are precisely doing everything they can to corner the market by making their own scale unreachable by others. Smaller players with access to lesser infrastructure are thus betting on different market, e.g. embedded systems.
17yos should definitely build their (L)LMs from scratch and whatever bigger model they can train for free, or for cheap, but they should not expect that to bring them any riches.
I had the same inspiration!
I spent a days reviewing the lecture notes for CS336: Language Modeling from Scratch - and then trained a nanoGPT-esque model in PyTorch.
I'd recommend trying it for those who are curious. Computational bottlenecks become much more intuitive when you've looked at the overall process.
> Someone asked what I'd do if I were 17. I'd learn how to build LLMs from scratch, and then train ones as powerful as I could with whatever hardware I could get access to.
He's seeing a future for models running on everyday hardware just capable enough to do what the use case requires.
That is the idea of ai Not agi A specific model for each problem.
Agi is the academia solution Software is the practical solution
i was trying to build llms from scratch at 17. failed miserably because i did not know linear algebra. ended up in a different but adjacent field. when chatgtp got big suddenly there were so many people doing llms and i didnt want to compete like that. so much of what was happening was hype, and that really turned me off. am back to building llms from scratch, but like, its a journey teaching myself all the theory on top of my job. i have decent fundamentals, but i need a better grasp of all the advancements in the field in the past 5 years before i would feel comfortable designing anything. baby steps, essentially. am working on better understanding all the layers of an ai while implementing a rag on my local model as an experiment. 17 year olds should learn whatever theyre interested in but need fundamentals in order to do anything advanced.
https://github.com/huggingface/tau
If I were 61, I'd learn to not be so out of touch with the younger generation
When I was 17, I made bad cartoons and was in a band. If I were 17, I'd spend more time learning music and design theory. I also learned PHP at this time, but that was low on the list, friendships came first.
Seek advice from nice people that you actually know instead of rich people on the internet.
If I'd were 17 I'd choose a different career.
And what resources would you use for that?
(Besides the obvious nano gpt)
"...and then train..." and those dummies in openai and google wasting billions, while all they need is some 17 year old and few pizzas
One of the replies:
"Why would you reinvent the wheel rather than making it better?"
"To learn how wheels are made"
Banger reply.
The core point here is that AI is a massive thing (at the moment) so it's probably a good idea to understand it deeply. Not sure why people are so worked up about it.
> (at the moment)
That's why. That future is uncertain. So why gamble your future on something that's popular at the moment for something that could change completely a year later?
This is what I did, tho I’m 41
Had to reread this a few times to properly parse what the hypothetical was.
My first read was "if you could redo your life from age 17 what would you do". To which "figure out how to make an LLM" would be an insane answer.
I think his advice here is maybe... a year or two too late to be good advice. I can't foresee the job market for machine learning experts being better than it is now in 5-6 years time when said hypothetical 17 year old would be most ready to start career hunting. Either the bubble is gonna pop and the market will be flooded with laid off AI talent.
Even if I'm wrong about there being a bubble at all, I still think that in 5 years time the tech will just have matured to the point of diminishing returns on refining existing architectures. Plateauing until some PHD comes up with something as ground breaking as attention.
I think pg answered the question as “what I’d do as a project” and not “what I’d do as a career.” So the critical comments are kind of missing the point, IMO.
I don’t see why learning how LLMs work is a bad project for a 17 year old.
Optimizing your entire career and the next decade+ of your life on LLMs? Yeah, probably not ideal. It’s almost always a bad idea to make long term decisions based on current trendy things.
And since everyone is using this topic to give their ideal advice to 17 year olds, my advice as a mid-30s guy: seriously consider becoming highly skilled at a specific thing, and don’t be scared off by the idea that it’ll take 5-10-15 years to get there.
When you’re 17-25, the timescale of a decade seems infinite. But it’s really not, and a decade spent “exploring and keeping your options open” sometimes just ends up with you being pretty decent but not amazing at a lot of random things.
Sometimes I wish I had just become a carpenter, chef, electrician, etc. – a specific skill set that leads to mastery over time, rather than the endless exciting-new-thing hamster wheel of working in tech.
> And since everyone is using this topic to give their ideal advice to 17 year olds, my advice as a mid-30s guy: seriously consider becoming highly skilled at a specific thing, and don’t be scared off by the idea that it’ll take 5-10-15 years to get there.
And this is a problem with modern society today: expecting 17 year-olds to know what they want to do professionally for the rest of their lives, and to focus heavily on professional development aligned to that.
By that age, I think it's not uncommon for individuals to have interests and perhaps even dreams, but a well-defined career focus that serves as the foundation of an actionable skills development plan? Nah. That just isn't common and I'd argue not desirable. 17 year-olds should be exploring their interests, enjoying early adulthood, learning valuable lessons in the social realm, etc. Not training themselves to become compliant little worker bees.
When I was 17, I wrote poetry and learned how to play guitar. I’m really happy that I did. That’s what I would do now, were I 17. I feel slightly bad for people who didn’t.
Same. But if I could go back, I would add skating as well. I slept on that one and spent too much time trying to be a hacker.
Sure, but isn't this a bit of a sneer? “I feel slightly bad for people who didn’t” has an air of superiority about it, and seems like a veiled swipe at those who lacked whatever qualities your 17-year-old self had. The guidelines have several different ways of asking us to avoid commenting like this.
As to the substance of your comment, there's nothing stopping a 17-year-old from writing poetry, learning guitar, and also learning about how LLMs work, if they're so inspired. Indeed I'm sure pg would encourage it, and that he did the equivalent of all those things himself when he was young. There's plenty of evidence of that in his essays: he wrote short stories, published a scandalous school newspaper, played soccer, and studied fine art and philosophy. See:
https://www.paulgraham.com/worked.html
https://www.paulgraham.com/nerds.html
> there's nothing stopping a 17-year-old from writing poetry, learning guitar, and also learning about how LLMs work, if they're so inspired.
I assume the people at HN are intelligent enough to understand this without me having to explain the most obvious banalities to them. The point is why you would do anything at 17:
> if they're so inspired
You said it yourself. The Twitter post, to me, reads like career advice or something in that vein. One has plenty of time to run the wheel like a lab rat later, but at that very point in life you have possibility to find the thing that truly inspires you, that is its own motivation.
Interesting advice I will be interested in how this advice holds up in the future
It’s really disheartening to see how many people don’t know shit about LLMs, by reading the comments… ironic given what OP is trying to say
Hot take - but I’d learn to build CNNs from scratch. We are only just now scratching the surface of convolutional neural networks capabilities
The problem is tech moves so fast that by time kid is adult llm are outdated
Ah nostalgia. At 17, I learned how to write programs in BASIC on a mainframe.
Funnily enough, I recently started on a series of demos and documentation to explain to my 10 year old daughter how LLMs work - in this case in the context of "don't use LLMs for looking stuff up if you figure out how to circumvent the school blocks yet again, or if you do, at least verify its output", but also figured it can be useful in general. Plus, if I get a 10 year old to understand that there's a slim chance I can get customers to understand that as well.
Sorry but Paul is just so out of touch with reality. I’m tired of seeing everyone on HN evangelize this man. He is very intelligent and has done some really great things but times have changed and we need to let the next generation think for themselves. They are increasingly anti-AI. Maybe that pans out, maybe it doesn’t. If I were 17, I’d pursue my dreams — whatever those may be — and learn how to connect meaningfully with my peers. Perhaps that’s naive of me or perhaps that’s what humanity needs most right now. I’d rather let the 17 year old version of me make that choice for themselves.
I’m sure I’ll get torn to pieces for this but it’s frustrating to continually witness people treat a single person’s prose as the Word of God.
What corpus would one be advised to use for training? Should one limit oneself to legally downloadable documents or should one follow the examples of Altman, Amodei, and Zuckerberg and use LibGen and Anna's Archive?
If I were 17, I’d learn how to invest and build financial literacy, and plot potential growth of my networth throughout my life, before even thinking about a career. Then smoke a bowl.
When I was 17 I was building Windows Phone apps, bad decision on my part.
You're not 18 so you can steal as much of media as you want for training and not face real legal consequences
If I were 77 I'd do the same
Does anyone have a roadmap ?
Paul has the same problem just about every tech obsessed engineer has (including myself), he thinks everyone else loves computers too. They don't. At all. I''m a self-taught ex-bartender and I can't tell you how many grown adults in the service industry I've tried to get into computer stuff and they had 0 interest.
And younger people I meet don't even own laptops. I had a genz/millennial cusp friend who wrote all her college papers on her iphone.
Paul Graham:
>"Someone asked what I'd do if I were 17. I'd learn how to build LLMs from scratch..."
That's funny, Paul Graham, because if I were 17 again,
I'd learn how to program in LISP.
https://www.paulgraham.com/rootsoflisp.html
https://www.paulgraham.com/iflisp.html
https://www.paulgraham.com/hundred.html
(And/or other LISP derived languages... Clojure, Scheme, Racket, TinyScheme, etc.)
I guess "the grass is always greener..." as that old expression, that old "chestnut", goes... :-)
This is about as intelligent as say "If I were 17, I'd learn digital electroinics". You would waste your time. Sure, in theory it's useful, in reality it's not that useful.
17 is such a fantastic age to be free and experience the world, you won't get that much of an advantage as these FOMO groomers are selling you into if you start now versus later.
If you are 17, go be yourself, whatever that is, in whatever way you want that to be, but do it so authentically and fully. Be unapologetic about what you love and what motivates you, and pursue that with passion and commitment.
I remember when I caught PG on reddit arguing with some guy who'd said something mean about him. He didn't reveal who he was. But you could tell from his history - his first post was from before reddit opened to the public.
Good times.
I wonder if you can dig that out of the historical reddit database. I'd like to see that again. I love how everything is recorded now.
Let’s make the xcancel link the actual link, please!
I get the sense things have changed a bit since I graduated and there are lot more jobs in AI outside of academia these days, but it's still a very different field from other SWE pursuits, and it's not really accessible to hacker-minded people.
Learning AI isn't like learning HTML in the 90s then expecting to get a job at a tech company building websites. You can't just "learn how to build LLMs" and expect a frontier lab to hire you so I'd argue this is rather bad advise.
Additionally, unlike web development in the 90s you cant really do anything interesting yourself... All of the interesting/useful stuff will require huge amounts of compute and data so there isn't even much point in learning to start your own thing either.
As someone whose built many of NNs from scratch (hand written code, long before the days of LLMs), it's more or less useless knowledge if I wanted to work in a frontier lab or do anything interesting in the field.
I also think anyone thinking about going into a field which is basically a crossover of CompSci and Maths is absolutely insane right now. Even if you think there is a place for CompSci and Maths post LLMs, there's almost no chance anything you learn today will be relevant to the skills required in say 5-10 years.
if i were 17, i'd learn a trade.
As someone who doesn't work in this field (I'm in boring old Accounting/Finance), but would like to learn this field (both to future-proof my skillset, but also because I have an interest in it), would it be wise for me to learn math?
Is it something that's going to be fundamental for future technologies? I always plan to learn, but end up in a death spiral feeling like I'll invest so much time and energy only for the puck to have moved somewhere completely different.
Would gladly accept any advice :)
I assume in this hypothetical Cornell just gives away degrees to passersby.
Paul Graham wants you to work for him, not be him.
plug: studymode.ca/ml is an AI-generated cirricculum + chapters/lessons that teaches ML by building practical projects
the lessons have svg diagrams, ai chat inline (google docs), etc
apologies if this comes off as slop, but its been working great for me.
[flagged]
Somewhere in rural America is a 17 year old that doesn't even have working plumbing in their house still.
I'm sure they'll get right on powering up their computer from the hamster wheel, Paul.
My heart goes to all the kids out there that didn't get the fair shake let alone fair access to tech that gets these condescending "learn to code/learn to LLM" bootstrappy talks from rich pricks that don't know what life really can be like for a lot of American kids out there.
This is always such a nonsense question-answer thing, asking a person who already succeeded what they would do if they were young.
Even worse when they ask themselves.
Support your idea with arguments.
Edit: not "support", I meant: give it a justification. For example, to give the "succeed" part some sense, instead of suggesting assumptions.
Can't this guy enjoy being rich in silence? His takes get worse with every passing year.
He did get rich by being pretty much the opposite of silent, so I'm guessing you can't just turn off that part, kind of comes with the package ;)
Venture capitalist suggests everyone to become his future employee, just as he has been (successfully) doing for his whole life.
it is once again time to balance out paulg sycophancy https://idlewords.com/2005/04/dabblers_and_blowhards.htm
If I were a billionaire, I'd learn how to give away a lot more of my wealth.
If I were 17, but have the money I have now he means.
better priorities: save democracy; counter rise of disinfo & fraud; cure cancer; carbon climate crisis
I genuinely don't think telling young people to do anything tech-related is good career advice. We don't even know if entry level roles will ever come back. The situation couldn't be worse for these roles. PG thinks that some random teenager will build a startup and get rich from it, or some shit. Like get real, man. Completely out of touch, tech bro who hasn't worked a real tech job for the last like 20 years... Remind me how many startups succeed again, Paul? What about the market dynamics for LLMs and how one might "secure compute"?
These guys (VCs) entire livelihoods rely on tens of thousands of young people throwing away their early years of lives trying to get super rich. Only .01% even see moderate success but PG and co don't care and continue to pump the dream and urge folks to waste their lives to try and make them rich.
What the fuck does this guy know about? I'm sure if we went back through similar statements he's said over the years he's said the same thing about various technologies that are no longer relevant. The guy is a talentless hack who larps as a blogger and his only "redeeming" quality is having lots of money.
Owner of Golf Club Company says I should dedicate my life to golf lmfao.
I think collectively we should all stop listening to Mr Graham..
He capitalizes on greed and hype but with a soft, sober and thoughtful voice so as to lull you with rationalism and now 20 years of his “disruption” has mostly ruined modern society and a whole generation of techies have been led astray into trying to “change the world” is the world of today (minus the magic technology really any better than 20 years ago?)
- Good for him and his Tech Bros, bad for the rest of society
So I am 15. Is it worth trying to build my own archive (s-1.site) of strategy ideas? Do I have any real differentiation? Or am I just wasting time? I figure that I can use it as proof that I have some know-how?
hackers and painters and kids and ROI and startups and capitalism and the destruction of nature and old guys with money talking like they know better in fascist social networks
I do not think it is a proper thing to do for 17 y.o., unless they are exceptionally mathematically gifted, as proper understanding of how LLMs are trained requires a good grasp of calculus, understanding modern OS and SDE tools for proper implementation of pipeline etc.
I'd rather simply write another mnist implementation and check if I really like all that AI stuff at first place. Even then, before going into mature-on-the-way-to-dying tech (LLMs) I'd rather focus on fundamentals - good ols linear models, regressions, stat etc.
If I were 17, I'd be going to parties, music festivals, chasing girls, and enjoying my youth.
But sure, make the kids even more depressed by telling them they need to learn how to build an LLM so they can get a job working themselves to death to make Paul and friends rich.
This community is filled with people who were obsessed with microcomputers or (depending on which generation) websites during their teenage years. HN was created by and is managed by people of this type. No doubt we're a minority, and the type you describe is more common, but if you're implying there's something wrong with teenagers being intellectually curious about technology, I can't help but think of the way "nerds" used to get put down and shamed in the past.
If I were 17, I wouldn't be using a social media plattform run by racist neo-fascists...
This is the ultimate builder’s mindset. Tinkering with the hardest technical problems—even for frivolous things like games—always yields the highest return on curiosity. Time to go back to the fundamentals.
He bases this decision on all of the experience he has amassed, as a 61 year old man in the tech industry. An actual 17 year old, with 17 years of experience, would not think like this, nor should they.
Naturally 17 year olds don't think long-term like this which is why PG's advice is so useful. It gives them a pathway to follow that they likely wouldn't have reasoned otherwise.
I told my much younger brother when he was 12 what programming was and it'd be a great career. He looked into it and within months was writing CLI games. Eventually releasing his own unity 3d game on steam as a teen.
Eventually he got into CS and did really well because none of it was scary and new. He parlayed that into role at Meta out of university.
My point being, 17 year olds have time to learn new skills and guidance can go a long way.
LLMs are the new compilers.
I don't think you can really call yourself a developer unless you at least have an idea how to build a more complex software project like a compiler, and maybe have built a toy one either at uni or for fun.
It's not clear how long this LLM age of AI will last (to be replaced by something better), but nowadays any developer should at least understand the basics of ANNs, and more than just the "hello world" of a cat vs dog CNN. An LLM/Transformer is maybe the equivalent of a compiler in that regard - something that we all use and is complex enough to present a bit of a challenge. You should at least understand the basics of how an LLM is built, and maybe building a toy LLM will/should become the new Comp. Sci. degree toy compiler replacement.