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 like saying the only way to do real engineering is with Google-scale Borg deployments. You can do quite a lot on very little hardware, r/StableDiffusion is a prime example.
You can do plenty of "real engineering" under normal conditions. But specifically when it comes to LLM engineering, no there's really not much you can do, they are called "large" for a reason. You can play around at small scale, but those lessons you learn will not be very relevant to the real problems in the market.
Sure you can gradually climb the ladder by demonstrating your skills bit by bit and getting access to more resources. It has very good prospects if you do manage to push through. But it's a hard and risky path, and you will not be able to get any interesting results for the longest time.
For a young middle-class student, it just doesn't make much sense. You can do much more impressive and impactful things with your time without getting into that black hole.
I know how to build an LLM, I know plenty of fellow young engineers that do too. It's really not that complex. But they can't do much with it without capital or access.
Good engineering has never been a bottleneck in this field, it's been all about having access to capital and taking smart but dangerous risks burning it on compute, without much idea of how long you need to keep burning for. There's still no end in sight, some are still managing to convince investors and keep burning, and we are seeing progress, but the business case is still unclear. If you want to get in that game, go ahead, but it's not something I would advice the average young engineer.
> . But specifically when it comes to LLM engineering, no there's really not much you can do, they are called "large"
The “large” qualifier dates back to pre-transformer language models, where even training a multi-million model was hard due to how poorly it scaled. GPT-2 was a large language model, despite being only 124 millions parameters.
Due to how much high quality data is readily available, anyone can now train a sub-billion (L?)LM on commodity hardware.
And I'm personally convinced that pretty much any enterprise use-case of an LLM (except coding) is better served by a fine-tuned small (<2B) model that is trained specifically on the task, rather than a generalist frontier model, so learning the engineering around fine-tuning is a key skill that companies will realize they need sooner than later.
> I know how to build an LLM, I know plenty of fellow young engineers that do too. It's really not that complex.
I’m 40, and I don’t.I took that abstraction for granted and “left it to the big labs”. However I want to build my own LLM for learning purposes.
On needing big expensive hardware.. necessity is the mother of great innovation. Perhaps 18year olds trying to build their own LLMs in constrained resources environments will result in ground breaking ideas of achieving better intelligence than the one we currently have….
The world needs pragmatic folks who work at a higher abstraction and make LLMs useful, AND also folks who think why not “this other way”? And build newer ways to do fundamental things.
Given the usefulness of current LLMs, I would certainly encourage anybody to try and build their own LLMs, and see what they come up with…
Heck if they build a rack full of old laptops and run something with it that could be done “better” with modern servers, I’d still appreciate the learning running things on those little machines bring.
Agreed. It's hard to learn unless you have access to quite high end hardware, and even paying by the hour is expensive. There's a low ceiling on what you can learn without doing training runs.
You can however learn everything you need to know to get on the career ladder as a software engineer on a regular home PC.
While the topic here is narrow, the concept is broader.
Do you take the first step or rule it out because you don’t yet see the complete picture.
As a teenager I never hesitated to try things out. As a young adult I wanted the whole picture. Now I’m back to playing / trying things out. I kinda wish I’d not given it up. PG being a bit older and reminiscing - I bet he’s in that bucket too, whereas someone trying to establish themselves professionally probably (aka me early 20s) wants to see the path.
Interesting/capable diffusion models are much smaller than similarly interesting language models. But yes you could always scale things down to learn the fundamentals.
> +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.
Maybe people simply don't want math PhDs but something else? Since 1-2 years ago I started doing consulting/freelancing in the ML space, but more on the infrastructure, deployments and similar stuff, as a general purpose developer, and I have a waiting list of clients interested in more work, some of them even trying to recruit me to work for them full-time as well. I'm based in continental Europe, fwiw.
How've you gone about getting into this btw? I have extensive experience in infra and pipeline rollout but have struggled to find freelance clients for this kind of thing. Would be great to tie it into ML as a learning opportunity there
This only proves the original point which is that there is not much demand for actual machine learning expertise because that is only carried out in a small number of places and what demands there is is for the more basic software carpentry like infrastructure and operations rather than the actual technology and Engineering side of things
I think companies of all sizes will want their own models, or at least customised ones, for their own specific use cases or competition and security issues.
1. Both training and optimisation will get significantly cheaper and easier quickly.
2. Politics will probably get even more insane before a potential reprieve on the 20th of Jan 2029.
3. The big AI firms will become part of the surveillance capitalism network, if they're not already.
So I think for self-protection a lot of companies will be looking near to medium term AI independence.
The argument is sound, but the maths don't math for now, and it's unclear when/if they will.
For the time being, unless you truly have millions, the outcome from training will be very net negative, while focusing on building on top of existing AI will yield amazing things if you apply the same talent and effort.
When it does get cheaper, then it will be easier to acquire the skills and experience too, and the struggle you went through by trying to do it now will be somewhat wasted.
Besides, I am well versed in this field, and it is not rocket science. There are plenty of software engineering domains that are a lot more challenging, like high-end graphics, large-scale data engineering or kernel programming. People will learn to train LLMs when people want them to.
The cost of training a model from scratch is going to be cost prohibitive for the vast majority of companies (even if renting the hardware needed for the 1-2 month training time). It's an interesting learning exercise, and some of the things learned can be applied to other parts of the process. There's also the issue of needing a huge amount of data needed to get decent weights.
Fine-tuning a model or LoRA based on the companies data set is more feasible but you're likely going to need several runs as you test/try out different base models, parameters, etc. This is why there are a lot of fine-tuned models on huggingface based on base or instruction-trained models from the larger AI companies that have released open weight models (Microsoft, Google, IBM, Mistral, DeepSeek, Qwen, etc.).
Training is limited on memory first (storing training data and weights) and computation second. Realistically you need to own or rent 2-8 H100/B100 devices or Google's TPUs.
The majority of workflows for a company providing AI capabilities are likely best solved by tailoring a system prompt for the chosen model, evaluating the prompt and model with tools like promptfoo, and then running it on a compute cloud provider (including AWS Bedrock). If the company is big/financially well off enough they could look at buying the hardware needed to run it on their own servers.
For other uses like agentic software development you'd need to spin up a suitable model on a compute cloud provider (or local hardware if the model is small enough) and then tell your IDE/editor to use that model. You would need some way of benchmarking and evaluating the models to see if they are capable of doing the tasks you need. -- There have been some tests done by people on YouTube that suggests that Qwen 3.8 27B is a decent model, but your needs may vary.
In reality, enterprises are happy to offload even risky tasks to others as long as they get some contractual guarantees about their data. Would they like more choice in who to buy from? Yes, but not enough to in-house such a specific discipline.
Most companies that build physical goods don't care for one second about their IT department other than how much money they can save per month, starting by outsourcing whole of it, thus they have little use for internal LLMs.
And it's across the industry, thinking banks, private banks, insurance, pharamcy etc don't outsource their IT, including development...
I believe US outsource even more than Europe on this matter.
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.
The big question is whether companies hold enough proprietary data to do useful things that for e.g. Anthropic, etc. can't easily replicate.
For some very niche cases I think this is probably the case but for the vast majority, the company's data isn't as useful as they think it is or anywhere near the size needed.
Everyone says they want to do training, because it's sexy and an easy way to justify raising mad funding rounds. Some manage, most don't.
I don't know where you are located, but in EU, in China, and yes even in Silicon Valley, the vast majority of companies do not do any real AI engineering. There's nothing wrong with it, it's just not a smart path for most purposes. You can do amazing things without training, and if you try to train, you cannot get anything amazing unless you burn millions.
Very few people can afford to play the long game and cross that dessert. And, sure, you will not get far without good engineering, but good engineering is definitely not sufficient and is not the primary bottleneck.
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.
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.
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,
Agreed, my previous stated "ignore both and do whatever the fuck I want" approach has worked out very well for me in life, people should probably focus on identifying better what their gut tells them, rather than what randoms on the internet thinks and writes.
Kids don’t even know what it is, even less actually feel what it is. At 17 you think that it won’t hit you, that you will be the one to survive until you don’t.
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.
Yep, many successful people greatly underappreciate the effect of simple dumb luck in their lives. And often they just make up complex reasoning chains, even wholly believing them, that more of it was in their control/talent, etc.
Nonetheless, there are many successful people I would gladly listen to for advice, though they are often successful in a different meaning than what venture capitalists would use (e.g. parents with great kids, managing to keep a healthy work-life balance, happiness, and maybe even having time to spend on some cool hobby project -- you are heros!)
Intuitively, that'd make sense if that unsuccessful person eventually found success, otherwise who knows if they actually picked up what made them unsuccessful in the first place?
I would seek advice from people who have a theory of why or why not they were successful.
A lot of those results were happening in very specific contexts and usually should not be regarded as a blueprint, but as inspiration to whatever I do.
> seek advice from unsuccessful people? That seems worse :)
Not sure why would you think so.
Inverse reasoning is very powerful, and unsuccessful people can tell you teach you plenty of "don't do this mistake", which the survivors would not even think about.
> unsuccessful people can tell you teach you plenty of "don't do this mistake",
But how can I know for sure that that particular mistake is actually why they were unsuccessful? Has exactly the same issue as listening only to successful people as they hardly know what actually made them successful most of the time, but they still compose large blog posts with their reasoning for why.
Again, I still think my approach of reading both but then regardless go my own way is the preferable approach, at least for me, ymmv.
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.
An advantage that is not "advisable", like being born in january, in a rich country, in an above average family, or just having luck, might have more influence on the outcome than any conscious action. It is almost sure that one-in-a-million level people only edge over the other 999,999 they competed with is just "have more luck".
> Would you advise a 17 y.o. to learn how transistors work or how to code
how many of us out here are doing work directly in what we got a degree in? I majored in economics and now I'm a CTO.
I would absolutely advise a 17 yo to learn how to code, understand how transitors work and how to code an llm. even if he never works on llms, you basically end up with a kid with applied knowlege of statistics, math, physics hardware, logic and a whole lot of practice in critical thinking.
I think it is. He isn't saying to learn how to train a LLM so that you can go on to train LLMs. He's saying to learn it so that you gain a deep understanding of how LLMs work. Ordinary startups can still benefit from things like training or fine tuning highly specialised smaller models, knowing how to select and configure an appropriate model for the task at hand, knowing what software to use and why, understanding what's going on behind the scenes instead of treating everything like a black box, having a higher level of intuition about LLMs generally, etc.
Most computer science courses do in fact teach things which are lower level than coding, such as how transistors work.
As a 17 y.o (way back in the last century) I didn’t need to be advised to learn about transistors. I just had a thirst for the knowledge.
I would encourage everyone to learn something about transistors. They are one of mankind’s most useful discoveries.
If I were 61 and wealthy, I wouldn’t write a stupid shit like this.
I’m no paulg, but if you’re reading this - and you’re 17 - just focus on getting into a university and having a good time that you won’t regret later. Play games/sports, make relationships, fall in love, explore.
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 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?
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.
> 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...
Now I'm curious, do people actually tried to hack vacuum tubes or other big servers that's barely 1MB RAM? It seems like another thing that needs big investment to work properly, unlike those other techs where results can be shown even with little materials.
Yeah, there are retrocomputing hobbyists who mess around with sometimes-physically-large computers that were important many decades ago. I don't know if anyone is hacking on vacuum tubes of the kind that you could in principle build a computer with - there's a reason they became obsolete for digital computation almost as soon as the transistor was invented. On the other hand, I personally think it would be neat to try to build a CRT in a garage, which is of course a type of vacuum tube. I don't think this would be an easy garage project, but it does seem like might be tractable for someone who understand physical manufacturing and electronics well, has access to glassblowing equipment, etc.
> 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.
> 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.
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, help others and then think about what you want to do.
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.
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.
When I was not 17 at the times of GPT2, I decided to not bother with learning how to build LLMs because it’s too expensive for an individual. This escalated quickly.
As an aside, for someone interested and who's an absolute beginner, can someone please recommend good resources on how to build LLMs from scratch? Thank you in advance.
I learned HTML when I was 17 in about 1995 and it's certainly taken me on a pretty fun career path. Less technical than LLMs for sure, but 'figure out where the industry is going and move what you're learning to there' is solid advice.
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.
This only changes whether you are naive enough to believe “honest” business means anything in today’s age. If anything, I worry being honest is holding back smart people who try to compete in a rigged game.
> you are naive enough to believe “honest” business means anything in today’s age
Might be that these people are from outside the US as well, where things like "honest business" is very much possible today, probably most businesses I interact with AFK on a daily business are "honest businesses".
2 years before everyone was doing custom training. What happened to all those today when frontier models itself become more powerful than custom trained ones?
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.
Why would 17 year old do something that only brings them money? I do not think Mr Graham here is advocating for the path that makes most money as a result of learning how to train a model. I assume that tinkering and learning about LLMs is what enterprising 17 year olds will do to discover ways they can get a competitive edge or further the SotA with their insights further down the line.
It seems this is poor advice in that it’s suggesting young people should focus on the current problem as opposed to future problems. Focus on the current problem can result in making some money but it will result in making the incumbents more money, which is not disruptive. Isn’t the goal of radical software startups to maximize disruption?
If the two current bottlenecks, for this LLM madness that could very well be a bubble, are processing capacity and accuracy (a second processing problem) then what comes next? Isn’t that where young people should be looking or are we just giving up on innovation?
Andrej Karpathy has a great Youtube series on how to build LLMs from scratch. Perfect for somebody who just learned a lot of high school math. Would start there and get busy with some handson python coding.
I don't think individuals have the resources to build an interesting llm. The l stands for large. You need a dataset too. Llms are only interesting because theyre large
And it's basically a weekend project to put transformers together in a ML library and train it.
The follow up comment,train it to play a game also doesn't make sense? Llms Sony really play games and there are better ml approaches to do that?
Incredible counterexample, but oddly relatable.
I'd probably have achieved techbro 'post-economic' status earlier if I focused on Android dev instead of the shiny (and new at that time) Xamarin for Windows phones.
God I almost invested in Xamarin after Windows Phone got aborted, I did spend a lil time on UWP, but thank god Flutter came out not so long after that. After all these years I learned to stay away from Microsoft tech stack.
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.
Yes. And they almost certainly have a better understanding of their own situation that him. This is not a dig at Paul Graham, the closer anyone is in age, the better they understand what they have to deal with. I'm roughly in the middle between Paul G and the 17 year old, and even though I'm really quite fascinated with zoomer culture and probably come more in touch with it than most (due to relatives in the age range etc.) I realize I have very little idea what it's like to grow up in the world they grow up in.
>Whoa. I’m 19 and I trained a 100M language model from scratch. Did a v2 now with a new SFT experiment to see if I can get better results on same size.
The amount of people who missed the point here is absurd. He's advocating for learning about how LLMs work. For the sake of learning. Because no one's going to invent the next thing without at least some understanding of the current thing.
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 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.
> I do not think it is a proper thing to do for 17 y.o
If I'd get a buck every time someone said something like this to me when I was in the 13-18 range, I wouldn't have a ton of money, but it's so very annoying when people tell you this.
Regardless if they're "gifted" or not, regardless if you believe in myths like that or not, let children explore what they want to explore, even if you don't understand what it is or why they want to explore that, just let people explore, regardless of age.
It was such a terrible experience being a young kid growing up, with so many adults spending hours trying to convince me to stop sitting in front of the computer so much doing whatever; "why are you even trying to learn that stuff, you have to go to school to understand anything of this" and so much other similar trash.
Sorry, not your fault and I'm borderline trauma-dumping now, but really sad to see this sort of gatekeeping on HN of all places, age is irrelevant to learning ANYTHING, in my humble opinion at least.
Kids, find anything interesting? Jump into it, ignore what adults tell you, and do whatever you feel like, you'll find your place eventually.
Agree with this - started programming through learning scripting in ROBLOX when I was like 12 (this was back like 17 or 18 years ago) and it developed into a life-long passion for software engineering. I am thankful I had people around me (my parents), who were aware enough to realize I wasn't just playing video games and gave me the time I needed on the computer to learn and experiment with programming...
This also meant that by the time I was actually offered to take a programming class in school (junior year of HS), I had already been able to self-teach myself well beyond what that class was covering, thanks to just working on random projects that scratched an itch I had at the time, looking up anything I didn't know or understand, and internalizing those concepts over time.
In short though, I definitely agree, young kids and teens (and also, frankly, adults too!) should be encouraged to explore things that they have a passion for, without being told 'you need to go to school for this' or 'you cant understand this at your age'
I just voiced my opinion. I just think buiding an LLM from the scratch for 17 y.o. is pointless exercise, advising a teenager to do so is borderline irresponsible, and frankly PG is simply virtue signalling here, as LLMs are still trendy, esp. in his circles.
There still will be varyy small number of outliers among youngsters who'd be able to extract tremensous value from such an excercise, but for most that'd be _IMO_ waste of of time, with illusion of understanding w/o actually having any.
Same! I just happened to disagree with your opinion, and frankly, I'd say trying to gatekeep what people learn is closer to "borderline irresponsible" compared to asking people to build/learn/do X.
> youngsters who'd be able to extract tremensous value from such an excercise
But they're youngsters, who are about "extracting value"? Life is about fun, not extraction, not value, not avoiding waste of time but literally enjoy what you do, nothing is more important (IMO).
Then who knows, doing fun stuff sometimes lead to useful stuff, like in my life. But if you only think about "extracting most value for time spent" or similar "optimization strategies", then you'd never discover this part of life.
> Life is about fun, not extraction, not value, not avoiding waste of time but literally enjoy what you do, nothing is more important (IMO).
This is, pardon, demagoguery. There is always "future fun" and "present fun" which a normal person would assign different nonzero weights (https://en.wikipedia.org/wiki/Discounted_utility). Besides, building a LLM _truly_ from the scratch, just using the famous 2017 paper and numpy manuals is not fun at all, esp. for a high schooler.
> Besides, building a LLM _truly_ from the scratch, just using the famous 2017 paper and numpy manuals is not fun at all, esp. for a high schooler.
To you it isn't, is my entire point here. But why extrapolate what you think is fun, to others? Sure, I don't find that fun either (although useful), but who am I to say it isn't fun for others?
Why would you tell people that the correct order is to build foundational knowledge before exploring a subject? For some (many?) people, a 'proper' understanding develops _after_ the exploration.
> Why would you tell people that the correct order
Because I can?. JK. Because that was my experience, of someone who is 2.5 older than 17?
> For some (many?) people, a 'proper' understanding develops _after_ the exploration.
I am afraid you have a too confrontational attitude here, but I'll answer anyway: because I do not believe you can simply "explore" such complex topics like building an LLMs. You'd simply be unable to build LLM drom scratch, unless you'd call cargo-cult chaining magic numpy incantations you've taken from Karpathy's tutorials "exploring".
If I were in "exploratory" state of mins, I'd rather go from entirely different side - I'd try playing with LoRA-ing existing small LLMs, such as venerable 2 y.o. Mistral Nemo, to get "feeling" for what training is and how hyperameters influence the process.
> I do not think it is a proper thing to do for 17 y.o., unless they are exceptionally mathematically gifted
I attempted many projects at a young age that I was absolutely not equipped for. The result of the attempts more often than not left me equipped, every time it left me better off. This is terrible advice.
That'd would be a terrible advice if there weren't a plenty of other things "you are not equipped for", but far less daunting both theoretically and practically. Such as, say, convolutional neural networks, or some older ML tech. Or even something totally unrelated to ML.
Transformers are difficult to understand even to people with strong ML background, let alone a teenager.
How would you filter out garbage from your training data, for example? If you are trying to use someone elses corpus, would it be "from the scratch" then?
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 like saying the only way to do real engineering is with Google-scale Borg deployments. You can do quite a lot on very little hardware, r/StableDiffusion is a prime example.
You can do plenty of "real engineering" under normal conditions. But specifically when it comes to LLM engineering, no there's really not much you can do, they are called "large" for a reason. You can play around at small scale, but those lessons you learn will not be very relevant to the real problems in the market.
Sure you can gradually climb the ladder by demonstrating your skills bit by bit and getting access to more resources. It has very good prospects if you do manage to push through. But it's a hard and risky path, and you will not be able to get any interesting results for the longest time.
For a young middle-class student, it just doesn't make much sense. You can do much more impressive and impactful things with your time without getting into that black hole.
I know how to build an LLM, I know plenty of fellow young engineers that do too. It's really not that complex. But they can't do much with it without capital or access.
Good engineering has never been a bottleneck in this field, it's been all about having access to capital and taking smart but dangerous risks burning it on compute, without much idea of how long you need to keep burning for. There's still no end in sight, some are still managing to convince investors and keep burning, and we are seeing progress, but the business case is still unclear. If you want to get in that game, go ahead, but it's not something I would advice the average young engineer.
> . But specifically when it comes to LLM engineering, no there's really not much you can do, they are called "large"
The “large” qualifier dates back to pre-transformer language models, where even training a multi-million model was hard due to how poorly it scaled. GPT-2 was a large language model, despite being only 124 millions parameters.
Due to how much high quality data is readily available, anyone can now train a sub-billion (L?)LM on commodity hardware.
And I'm personally convinced that pretty much any enterprise use-case of an LLM (except coding) is better served by a fine-tuned small (<2B) model that is trained specifically on the task, rather than a generalist frontier model, so learning the engineering around fine-tuning is a key skill that companies will realize they need sooner than later.
> I know how to build an LLM, I know plenty of fellow young engineers that do too. It's really not that complex.
I’m 40, and I don’t.I took that abstraction for granted and “left it to the big labs”. However I want to build my own LLM for learning purposes.
On needing big expensive hardware.. necessity is the mother of great innovation. Perhaps 18year olds trying to build their own LLMs in constrained resources environments will result in ground breaking ideas of achieving better intelligence than the one we currently have….
The world needs pragmatic folks who work at a higher abstraction and make LLMs useful, AND also folks who think why not “this other way”? And build newer ways to do fundamental things.
Given the usefulness of current LLMs, I would certainly encourage anybody to try and build their own LLMs, and see what they come up with…
Heck if they build a rack full of old laptops and run something with it that could be done “better” with modern servers, I’d still appreciate the learning running things on those little machines bring.
Agreed. It's hard to learn unless you have access to quite high end hardware, and even paying by the hour is expensive. There's a low ceiling on what you can learn without doing training runs.
You can however learn everything you need to know to get on the career ladder as a software engineer on a regular home PC.
While the topic here is narrow, the concept is broader.
Do you take the first step or rule it out because you don’t yet see the complete picture.
As a teenager I never hesitated to try things out. As a young adult I wanted the whole picture. Now I’m back to playing / trying things out. I kinda wish I’d not given it up. PG being a bit older and reminiscing - I bet he’s in that bucket too, whereas someone trying to establish themselves professionally probably (aka me early 20s) wants to see the path.
But can you _sell_ that? If you can't you can't get a job doing it.
Interesting/capable diffusion models are much smaller than similarly interesting language models. But yes you could always scale things down to learn the fundamentals.
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.
+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 and they 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.
> +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.
Maybe people simply don't want math PhDs but something else? Since 1-2 years ago I started doing consulting/freelancing in the ML space, but more on the infrastructure, deployments and similar stuff, as a general purpose developer, and I have a waiting list of clients interested in more work, some of them even trying to recruit me to work for them full-time as well. I'm based in continental Europe, fwiw.
How've you gone about getting into this btw? I have extensive experience in infra and pipeline rollout but have struggled to find freelance clients for this kind of thing. Would be great to tie it into ML as a learning opportunity there
[delayed]
This only proves the original point which is that there is not much demand for actual machine learning expertise because that is only carried out in a small number of places and what demands there is is for the more basic software carpentry like infrastructure and operations rather than the actual technology and Engineering side of things
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.
I think companies of all sizes will want their own models, or at least customised ones, for their own specific use cases or competition and security issues.
1. Both training and optimisation will get significantly cheaper and easier quickly.
2. Politics will probably get even more insane before a potential reprieve on the 20th of Jan 2029.
3. The big AI firms will become part of the surveillance capitalism network, if they're not already.
So I think for self-protection a lot of companies will be looking near to medium term AI independence.
The argument is sound, but the maths don't math for now, and it's unclear when/if they will.
For the time being, unless you truly have millions, the outcome from training will be very net negative, while focusing on building on top of existing AI will yield amazing things if you apply the same talent and effort.
When it does get cheaper, then it will be easier to acquire the skills and experience too, and the struggle you went through by trying to do it now will be somewhat wasted.
Besides, I am well versed in this field, and it is not rocket science. There are plenty of software engineering domains that are a lot more challenging, like high-end graphics, large-scale data engineering or kernel programming. People will learn to train LLMs when people want them to.
The cost of training a model from scratch is going to be cost prohibitive for the vast majority of companies (even if renting the hardware needed for the 1-2 month training time). It's an interesting learning exercise, and some of the things learned can be applied to other parts of the process. There's also the issue of needing a huge amount of data needed to get decent weights.
Fine-tuning a model or LoRA based on the companies data set is more feasible but you're likely going to need several runs as you test/try out different base models, parameters, etc. This is why there are a lot of fine-tuned models on huggingface based on base or instruction-trained models from the larger AI companies that have released open weight models (Microsoft, Google, IBM, Mistral, DeepSeek, Qwen, etc.).
Training is limited on memory first (storing training data and weights) and computation second. Realistically you need to own or rent 2-8 H100/B100 devices or Google's TPUs.
The majority of workflows for a company providing AI capabilities are likely best solved by tailoring a system prompt for the chosen model, evaluating the prompt and model with tools like promptfoo, and then running it on a compute cloud provider (including AWS Bedrock). If the company is big/financially well off enough they could look at buying the hardware needed to run it on their own servers.
For other uses like agentic software development you'd need to spin up a suitable model on a compute cloud provider (or local hardware if the model is small enough) and then tell your IDE/editor to use that model. You would need some way of benchmarking and evaluating the models to see if they are capable of doing the tasks you need. -- There have been some tests done by people on YouTube that suggests that Qwen 3.8 27B is a decent model, but your needs may vary.
Even for most organizations, testing AI systems is too cost prohibitive, so they YOLO in production, including public facing systems.
Right, just like companies don't use SAAS.
In reality, enterprises are happy to offload even risky tasks to others as long as they get some contractual guarantees about their data. Would they like more choice in who to buy from? Yes, but not enough to in-house such a specific discipline.
Most companies that build physical goods don't care for one second about their IT department other than how much money they can save per month, starting by outsourcing whole of it, thus they have little use for internal LLMs.
And it's across the industry, thinking banks, private banks, insurance, pharamcy etc don't outsource their IT, including development... I believe US outsource even more than Europe on this matter.
Did you read his comments on this? It's not to actually do LLM research stuff, it's to trigger and unlock ideas.
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
The big question is whether companies hold enough proprietary data to do useful things that for e.g. Anthropic, etc. can't easily replicate.
For some very niche cases I think this is probably the case but for the vast majority, the company's data isn't as useful as they think it is or anywhere near the size needed.
Everyone says they want to do training, because it's sexy and an easy way to justify raising mad funding rounds. Some manage, most don't.
I don't know where you are located, but in EU, in China, and yes even in Silicon Valley, the vast majority of companies do not do any real AI engineering. There's nothing wrong with it, it's just not a smart path for most purposes. You can do amazing things without training, and if you try to train, you cannot get anything amazing unless you burn millions.
Very few people can afford to play the long game and cross that dessert. And, sure, you will not get far without good engineering, but good engineering is definitely not sufficient and is not the primary bottleneck.
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.
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.
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,
Agreed, my previous stated "ignore both and do whatever the fuck I want" approach has worked out very well for me in life, people should probably focus on identifying better what their gut tells them, rather than what randoms on the internet thinks and writes.
Kids don’t even know what it is, even less actually feel what it is. At 17 you think that it won’t hit you, that you will be the one to survive until you don’t.
> 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.
Yep, many successful people greatly underappreciate the effect of simple dumb luck in their lives. And often they just make up complex reasoning chains, even wholly believing them, that more of it was in their control/talent, etc.
Nonetheless, there are many successful people I would gladly listen to for advice, though they are often successful in a different meaning than what venture capitalists would use (e.g. parents with great kids, managing to keep a healthy work-life balance, happiness, and maybe even having time to spend on some cool hobby project -- you are heros!)
Intuitively, that'd make sense if that unsuccessful person eventually found success, otherwise who knows if they actually picked up what made them unsuccessful in the first place?
I would seek advice from people who have a theory of why or why not they were successful. A lot of those results were happening in very specific contexts and usually should not be regarded as a blueprint, but as inspiration to whatever I do.
> seek advice from unsuccessful people? That seems worse :)
Not sure why would you think so.
Inverse reasoning is very powerful, and unsuccessful people can tell you teach you plenty of "don't do this mistake", which the survivors would not even think about.
> unsuccessful people can tell you teach you plenty of "don't do this mistake",
But how can I know for sure that that particular mistake is actually why they were unsuccessful? Has exactly the same issue as listening only to successful people as they hardly know what actually made them successful most of the time, but they still compose large blog posts with their reasoning for why.
Again, I still think my approach of reading both but then regardless go my own way is the preferable approach, at least for me, ymmv.
> 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
Seek advice from people who have had a normal level of success. Not a one-in-a-million level.
Veritasium has a good piece for this kind of bias:
https://www.youtube.com/watch?v=3LopI4YeC4I
An advantage that is not "advisable", like being born in january, in a rich country, in an above average family, or just having luck, might have more influence on the outcome than any conscious action. It is almost sure that one-in-a-million level people only edge over the other 999,999 they competed with is just "have more luck".
> Would you advise a 17 y.o. to learn how transistors work or how to code
how many of us out here are doing work directly in what we got a degree in? I majored in economics and now I'm a CTO.
I would absolutely advise a 17 yo to learn how to code, understand how transitors work and how to code an llm. even if he never works on llms, you basically end up with a kid with applied knowlege of statistics, math, physics hardware, logic and a whole lot of practice in critical thinking.
> Moreover I am not sure it is even good advice?
I think it is. He isn't saying to learn how to train a LLM so that you can go on to train LLMs. He's saying to learn it so that you gain a deep understanding of how LLMs work. Ordinary startups can still benefit from things like training or fine tuning highly specialised smaller models, knowing how to select and configure an appropriate model for the task at hand, knowing what software to use and why, understanding what's going on behind the scenes instead of treating everything like a black box, having a higher level of intuition about LLMs generally, etc.
Most computer science courses do in fact teach things which are lower level than coding, such as how transistors work.
As a 17 y.o (way back in the last century) I didn’t need to be advised to learn about transistors. I just had a thirst for the knowledge. I would encourage everyone to learn something about transistors. They are one of mankind’s most useful discoveries.
If I were 61 and wealthy, I wouldn’t write a stupid shit like this.
I’m no paulg, but if you’re reading this - and you’re 17 - just focus on getting into a university and having a good time that you won’t regret later. Play games/sports, make relationships, fall in love, explore.
Is going to university really that good of advice nowadays?
Everybody goes to college nowadays and the average white collar has lots of debt and relatively minor financial benefits over a skilled trade worker.
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.
you don't reach cutting edge immediately. you start with the basics
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 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?
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.
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.
> 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...
Now I'm curious, do people actually tried to hack vacuum tubes or other big servers that's barely 1MB RAM? It seems like another thing that needs big investment to work properly, unlike those other techs where results can be shown even with little materials.
Yeah, there are retrocomputing hobbyists who mess around with sometimes-physically-large computers that were important many decades ago. I don't know if anyone is hacking on vacuum tubes of the kind that you could in principle build a computer with - there's a reason they became obsolete for digital computation almost as soon as the transistor was invented. On the other hand, I personally think it would be neat to try to build a CRT in a garage, which is of course a type of vacuum tube. I don't think this would be an easy garage project, but it does seem like might be tractable for someone who understand physical manufacturing and electronics well, has access to glassblowing equipment, etc.
> 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.
> 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.
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, help others and then think about what you want to do.
This might be a helpful resource: https://laurentiugabriel.github.io/token-town/
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.
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.
This might be of use
https://github.com/raiyanyahya/how-to-train-your-gpt
When I was not 17 at the times of GPT2, I decided to not bother with learning how to build LLMs because it’s too expensive for an individual. This escalated quickly.
As an aside, for someone interested and who's an absolute beginner, can someone please recommend good resources on how to build LLMs from scratch? Thank you in advance.
1. Build an LLM from Scratch by Sebastian Raschka (https://sebastianraschka.com/llms-from-scratch/)
2. LLM from 0 to Hero, and nanoGPT by Andrej Karpathy
Check the front page of this god forsaken website a few times a day and you'll get about 10 different posts a day about it.
I learned HTML when I was 17 in about 1995 and it's certainly taken me on a pretty fun career path. Less technical than LLMs for sure, but 'figure out where the industry is going and move what you're learning to there' is solid advice.
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.
Depends if you’re 17 with rich parents or not.
This only changes whether you are naive enough to believe “honest” business means anything in today’s age. If anything, I worry being honest is holding back smart people who try to compete in a rigged game.
> you are naive enough to believe “honest” business means anything in today’s age
Might be that these people are from outside the US as well, where things like "honest business" is very much possible today, probably most businesses I interact with AFK on a daily business are "honest businesses".
2 years before everyone was doing custom training. What happened to all those today when frontier models itself become more powerful than custom trained ones?
yeah i loved tech so started learning circuity and soldering. but it was a waste of time i made my living learning how to program web applications.
... 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.
Why would 17 year old do something that only brings them money? I do not think Mr Graham here is advocating for the path that makes most money as a result of learning how to train a model. I assume that tinkering and learning about LLMs is what enterprising 17 year olds will do to discover ways they can get a competitive edge or further the SotA with their insights further down the line.
he does mention that it would later on be about building a startup, which is about making money
If I were 17, I wouldn't be using a social media plattform run by racist neo-fascists...
It seems this is poor advice in that it’s suggesting young people should focus on the current problem as opposed to future problems. Focus on the current problem can result in making some money but it will result in making the incumbents more money, which is not disruptive. Isn’t the goal of radical software startups to maximize disruption?
If the two current bottlenecks, for this LLM madness that could very well be a bubble, are processing capacity and accuracy (a second processing problem) then what comes next? Isn’t that where young people should be looking or are we just giving up on innovation?
What are the best resources to learn how to build LLMs from scratch for 17 year olds?
I have my opinion on this but I'd like to hear the HN opinion, I will just say one thing:
If you are starting with little knowledge, like a 17 year old would, letting an LLM explain it to you is a terrible idea.
Andrej Karpathy has a great Youtube series on how to build LLMs from scratch. Perfect for somebody who just learned a lot of high school math. Would start there and get busy with some handson python coding.
It’s crazy how much survivorship bias gets repackaged as generic advice.
Wait no it’s not, that was always happening.
What’s crazy is that people still believe in it.
If I were 17, but have the money I have now he means.
I don't think individuals have the resources to build an interesting llm. The l stands for large. You need a dataset too. Llms are only interesting because theyre large
And it's basically a weekend project to put transformers together in a ML library and train it.
The follow up comment,train it to play a game also doesn't make sense? Llms Sony really play games and there are better ml approaches to do that?
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.
When I was 17 I was building Windows Phone apps, bad decision on my part.
Incredible counterexample, but oddly relatable. I'd probably have achieved techbro 'post-economic' status earlier if I focused on Android dev instead of the shiny (and new at that time) Xamarin for Windows phones.
God I almost invested in Xamarin after Windows Phone got aborted, I did spend a lil time on UWP, but thank god Flutter came out not so long after that. After all these years I learned to stay away from Microsoft tech stack.
YC Combinator guy says that with a time machine he would learn to build the currently trillions-valued or whatever technology. Okay.
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.
Yes. And they almost certainly have a better understanding of their own situation that him. This is not a dig at Paul Graham, the closer anyone is in age, the better they understand what they have to deal with. I'm roughly in the middle between Paul G and the 17 year old, and even though I'm really quite fascinated with zoomer culture and probably come more in touch with it than most (due to relatives in the age range etc.) I realize I have very little idea what it's like to grow up in the world they grow up in.
Most. But, that isn't the point.
Either way, this isn't really advice for 17 year olds. Pg is thinking out loud about the pathways for founders.
What about a 19 year old?
>Whoa. I’m 19 and I trained a 100M language model from scratch. Did a v2 now with a new SFT experiment to see if I can get better results on same size.
The amount of people who missed the point here is absurd. He's advocating for learning about how LLMs work. For the sake of learning. Because no one's going to invent the next thing without at least some understanding of the current thing.
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 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.
> I do not think it is a proper thing to do for 17 y.o
If I'd get a buck every time someone said something like this to me when I was in the 13-18 range, I wouldn't have a ton of money, but it's so very annoying when people tell you this.
Regardless if they're "gifted" or not, regardless if you believe in myths like that or not, let children explore what they want to explore, even if you don't understand what it is or why they want to explore that, just let people explore, regardless of age.
It was such a terrible experience being a young kid growing up, with so many adults spending hours trying to convince me to stop sitting in front of the computer so much doing whatever; "why are you even trying to learn that stuff, you have to go to school to understand anything of this" and so much other similar trash.
Sorry, not your fault and I'm borderline trauma-dumping now, but really sad to see this sort of gatekeeping on HN of all places, age is irrelevant to learning ANYTHING, in my humble opinion at least.
Kids, find anything interesting? Jump into it, ignore what adults tell you, and do whatever you feel like, you'll find your place eventually.
Agree with this - started programming through learning scripting in ROBLOX when I was like 12 (this was back like 17 or 18 years ago) and it developed into a life-long passion for software engineering. I am thankful I had people around me (my parents), who were aware enough to realize I wasn't just playing video games and gave me the time I needed on the computer to learn and experiment with programming...
This also meant that by the time I was actually offered to take a programming class in school (junior year of HS), I had already been able to self-teach myself well beyond what that class was covering, thanks to just working on random projects that scratched an itch I had at the time, looking up anything I didn't know or understand, and internalizing those concepts over time.
In short though, I definitely agree, young kids and teens (and also, frankly, adults too!) should be encouraged to explore things that they have a passion for, without being told 'you need to go to school for this' or 'you cant understand this at your age'
I just voiced my opinion. I just think buiding an LLM from the scratch for 17 y.o. is pointless exercise, advising a teenager to do so is borderline irresponsible, and frankly PG is simply virtue signalling here, as LLMs are still trendy, esp. in his circles.
There still will be varyy small number of outliers among youngsters who'd be able to extract tremensous value from such an excercise, but for most that'd be _IMO_ waste of of time, with illusion of understanding w/o actually having any.
> I just voiced my opinion.
Same! I just happened to disagree with your opinion, and frankly, I'd say trying to gatekeep what people learn is closer to "borderline irresponsible" compared to asking people to build/learn/do X.
> youngsters who'd be able to extract tremensous value from such an excercise
But they're youngsters, who are about "extracting value"? Life is about fun, not extraction, not value, not avoiding waste of time but literally enjoy what you do, nothing is more important (IMO).
Then who knows, doing fun stuff sometimes lead to useful stuff, like in my life. But if you only think about "extracting most value for time spent" or similar "optimization strategies", then you'd never discover this part of life.
> Life is about fun, not extraction, not value, not avoiding waste of time but literally enjoy what you do, nothing is more important (IMO).
This is, pardon, demagoguery. There is always "future fun" and "present fun" which a normal person would assign different nonzero weights (https://en.wikipedia.org/wiki/Discounted_utility). Besides, building a LLM _truly_ from the scratch, just using the famous 2017 paper and numpy manuals is not fun at all, esp. for a high schooler.
> Besides, building a LLM _truly_ from the scratch, just using the famous 2017 paper and numpy manuals is not fun at all, esp. for a high schooler.
To you it isn't, is my entire point here. But why extrapolate what you think is fun, to others? Sure, I don't find that fun either (although useful), but who am I to say it isn't fun for others?
I think he is basically saying YC has enough startups that are just making API calls.
Why would you tell people that the correct order is to build foundational knowledge before exploring a subject? For some (many?) people, a 'proper' understanding develops _after_ the exploration.
> Why would you tell people that the correct order
Because I can?. JK. Because that was my experience, of someone who is 2.5 older than 17?
> For some (many?) people, a 'proper' understanding develops _after_ the exploration.
I am afraid you have a too confrontational attitude here, but I'll answer anyway: because I do not believe you can simply "explore" such complex topics like building an LLMs. You'd simply be unable to build LLM drom scratch, unless you'd call cargo-cult chaining magic numpy incantations you've taken from Karpathy's tutorials "exploring".
If I were in "exploratory" state of mins, I'd rather go from entirely different side - I'd try playing with LoRA-ing existing small LLMs, such as venerable 2 y.o. Mistral Nemo, to get "feeling" for what training is and how hyperameters influence the process.
> unless you'd call cargo-cult chaining magic numpy incantations you've taken from Karpathy's tutorials "exploring"
That's what I would call exploring.
I too started exploring programming as a teen by cargo culting. Fooling around and getting results is what made it fun. Understanding came later.
> I do not think it is a proper thing to do for 17 y.o., unless they are exceptionally mathematically gifted
I attempted many projects at a young age that I was absolutely not equipped for. The result of the attempts more often than not left me equipped, every time it left me better off. This is terrible advice.
That'd would be a terrible advice if there weren't a plenty of other things "you are not equipped for", but far less daunting both theoretically and practically. Such as, say, convolutional neural networks, or some older ML tech. Or even something totally unrelated to ML.
Transformers are difficult to understand even to people with strong ML background, let alone a teenager.
>understanding modern OS and SDE tools for proper implementation of pipeline etc.
Can you provide an example?
How would you filter out garbage from your training data, for example? If you are trying to use someone elses corpus, would it be "from the scratch" then?
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 ;)
Twitter mirror: https://nitter.net/paulg/status/2091544343589060625
Another great quote by PG. I've been really enjoying his essays recently - truly a great and curious mind.