I tried something similar last year with a much simpler model (not GPT-scale) and the biggest "aha" moment was understanding how the attention mechanism is really just a soft dictionary lookup. The math makes so much more sense when you implement it yourself vs reading papers.
Karpathy has a unique talent for making complex topics feel approachable without dumbing them down. Between this, nanoGPT, and the Zero to Hero series, he has probably done more for ML education than most university programs.
Something I found to be universal true when dealing with math. My brain pretty much refuses to learn abstract math concepts in theory, but applying them with a practical problem is a very different experience for me (I wish school math would have had a bigger focus on practical applications).
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#define N f(k,
#define u _Pragma("omp parallel for")f(h,
#define f(u,n)for(I u=0;u<(n);u++)
#define g(u,s)x s%11%5)N s/6&33)k[u[i]]=(t){(C*)A,A+s*D/4},A+=1088*s;
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_)[V];I*_=U" 炾ોİ䃃璱ᝓ၎瓓甧染ɐఛ瓁",U,s,p,f,R,z,$,B[D],open();F*A,*G[2],*T,w,b,c;a()Q[D];_t r,L,J,O[Z],l,a,K,v,k;Q
m,e[4],d[3],n;I j(I e,F*o,I p,F*v,t*X){w=1e-5;x c=e^V?D:0)w+=r[i]*r[i]/D;x c)o[i]=r[i]/sqrt(w)*i[A+e*D];N $){x
W)l[k]=w=fmax(fabs(o[i])/~-E,i?w:0);x W)y[i+k*W]=*o++/w;}u p)x $){I _=0,t=h*$+i;N W)_+=X->c[t*W+k]*y[i*W+k];v[h]=
_*X->f[t]*l[i]+!!i*v[h];}x D-c)i[r]+=v[i];}I main(){A=mmap(0,8e9,1,2,f=open(M,f),0);x 2)~f?i[G]=malloc(3e9):exit(
puts(M" not found"));x V)i[P]=(C*)A+4,A+=(I)*A;g(&m,V)g(&n,V)g(e,D)g(d,H)for(C*o;;s>=D?$=s=0:p<U||_()("%s",$[P]))if(!
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$=B[p++]:fflush(0);x D)R=$*D+i,r[i]=m->c[R]*m->f[R/W];R=s++;N Z){f=k*D*D,$=W;x 3)j(k,L,D,i?G[~-i]+f+R*D:v,e[i]+k);N
2)x D)b=sin(w=R/exp(i%E/14.)),c=1[w=cos(w),T=i+++(k?v:*G+f+R*D)],T[1]=b**T+c*w,*T=w**T-c*b;u Z){F*T=O[h],w=0;I A=h*E;x
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,D,J,e[3]+k);x 2)j(k+Z,L,H,i?K:a,d[i]+k);x H)a[i]*=K[i]/(exp(-a[i])+1);j(V,a,D,L,d[$=H/$,2]+k);}w=j($=W,r,V,k,n);x
V)w=k[i]>w?k[$=i]:w;}}> ChatIOCCC is the world’s smallest LLM (large language model) inference engine - a “generative AI chatbot” in plain-speak. ChatIOCCC runs a modern open-source model (Meta’s LLaMA 2 with 7 billion parameters) and has a good knowledge of the world, can understand and speak multiple languages, write code, and many other things. Aside from the model weights, it has no external dependencies and will run on any 64-bit platform with enough RAM.
(Model weights need to be downloaded using an enclosed shell script.)
And it's small enough to run from a QR code :) https://kuber.studio/picogpt/
You can quite literally train a micro LLM from your phone's browser
Users can interactively explore the microgpt pipeline end to end, from tokenization until inference.
[1] English GPT lab:
This can be mainstream, and then custom model fine-tuning becomes the new “software development”.
Please check out this new fine-tuning method for LLM by MIT and ETH Zurich teams that used a single NVIDIA H200 GPU [1], [2], [3].
Full fine-tuning of the entire model’s parameters were performed based on the Hugging Face TRL library.
[1] MIT's new fine-tuning method lets LLMs learn new skills without losing old ones (news):
https://venturebeat.com/orchestration/mits-new-fine-tuning-m...
[2] Self-Distillation Enables Continual Learning (paper):
https://arxiv.org/abs/2601.19897
[3] Self-Distillation Enables Continual Learning (code):
they are not flourish yet because of the simple reason: the frontier models are still improving. currently it is better to use frontier models than training/fine-tuning one by our own because by the time we complete the model the world is already moving forward.
heck even distillation is a waste of time and money because newer frontier models yield better outputs.
you can expect that the landscape will change drastically in the next few years when the proprietary frontier models stop having huge improvements every version upgrade.
The entire point of LLMs is that you don't have to spend money training them for each specific case. You can train something like Qwen once and then use it to solve whatever classification/summarization/translation problem in minutes instead of weeks.
I don’t agree. I would say the entire point of LLMs is to be able to solve a certain class of non-deterministic problems that cannot be solved with deterministic procedural code. LLMs don’t need to be generally useful in order to be useful for specific business use cases. I as a programmer would be very happy to have a local coding agent like Claude Code that can do nothing but write code in my chosen programming language or framework, instead of using a general model like Opus, if it could be hyper-specialized and optimized for that one task, so that it is small enough to run on my MacBook. I don’t need the other general reasoning capabilities of Opus.
2x the number of lines of code (~400L), 10x the speed
The hard part was figuring out how to represent the Value class in C++ (ended up using shared_ptrs).
I went through nanoGPT a while back and the single biggest insight for me was seeing how the attention mechanism actually maps to matrix operations. It's one thing to read the Attention Is All You Need paper, another thing entirely to watch your own code produce coherent text after training on some tiny corpus.
The interesting question going forward is whether micro/nano implementations will stay relevant as models scale up. I think yes - not because small models are competitive, but because architectural intuition built from scratch transfers. People who understand what's happening at this level debug problems differently than people who only ever work with HuggingFace abstractions on top of abstractions.
Also curious whether he covers the differences between training and inference codepaths. That's an area where a lot of beginner implementations get subtly wrong.
Beautiful, perhaps like ice-nine is beautiful.
Yes with some extra tricks and tweaks. But the core ideas are all here.
Train an LLM on all human knowledge up to 1905 and see if it comes up with General Relativity. It won’t.
We’ll need additional breakthroughs in AI.
AGI just means human level intelligence. I couldn't come up with General Relativity. That doesn't mean I don't have general intelligence.
I don't understand why people are moving the goalposts.
>Reinforcement learning, on the other hand, can do that, on a human timescale. But you can't make money quickly from it.
Tools like Claude Code and Codex have used RL to train the model how to use the harness and make a ton of money.
That kind of capability is not going to lead to AGI, not even close.
Their contexts, not their memories. An LLM context is like 100k tokens. That's a fruit fly, not AGI.
If LLMs have shown us anything it is that AGI or super-human AI isn't on some line, where you either reach it or don't. It's a much higher dimensional concept. LLMs are still, at their core, language models, the term is no lie. Humans have language models in their brains, too. We even know what happens if they end up disconnected from the rest of the brain because there are some unfortunate people who have experienced that for various reasons. There's a few things that can happen, the most interesting of which is when they emit grammatically-correct sentences with no meaning in them. Like, "My green carpet is eating on the corner."
If we consider LLMs as a hypertrophied langauge model, they are blatently, grotesquely superhuman on that dimension. LLMs are way better at not just emitting grammatically-correct content but content with facts in them, related to other facts.
On the other hand, a human language model doesn't require the entire freaking Internet to be poured through it, multiple times (!), in order to start functioning. It works on multiple orders of magnitude less input.
The "is this AGI" argument is going to continue swirling in circles for the forseeable future because "is this AGI" is not on a line. In some dimensions, current LLMs are astonishingly superhuman. Find me a polyglot who is truly fluent in 20 languages and I'll show you someone who isn't also conversant with PhD-level topics in a dozen fields. And yet at the same time, they are clearly sub-human in that we do hugely more with our input data then they do, and they have certain characteristic holes in their cognition that are stubbornly refusing to go away, and I don't expect they will.
I expect there to be some sort of AI breakthrough at some point that will allow them to both fix some of those cognitive holes, and also, train with vastly less data. No idea what it is, no idea when it will be, but really, is the proposition "LLMs will not be the final manifestation of AI capability for all time" really all that bizarre a claim? I will go out on a limb and say I suspect it's either only one more step the size of "Attention is All You Need", or at most two. It's just hard to know when they'll occur.
This is why, for example, a 30 year old can lose control of a car on an icy road and then suddenly, in the span of half a second before crashing, remember a time they intentionally drifted a car on the street when they were 16 and reflect on how stupid they were. In the human or animal mental model, all events are recalled by other things, and all are constantly adapting, even adapting past things.
The tokens we take in and process are not words, nor spatial artifacts. We read a whole model as a token, and our output is a vector of weighted models that we somewhat trust and somewhat discard. Meeting a new person, you will compare all their apparent models to the ones you know: Facial models, audio models, language models, political models. You ingest their vector of models as tokens and attempt to compare them to your own existing ones, while updating yours at the same time. Only once our thoughts have arranged those competing models we hold in some kind of hierarchy do we poll those models for which ones are appropriate to synthesize words or actions from.
Take the wheel. Even that wasn't invented from nothing — rolling logs, round stones, the shape of the sun. The "invention" was recognizing a pattern already present in the physical world and abstracting it. Still training data, just physical and sensory rather than textual.
And that's actually the most honest critique of current LLMs — not that they're architecturally incapable, but that they're missing a data modality. Humans have embodied training data. You don't just read about gravity, you've felt it your whole life. You don't just know fire is hot, you've been near one. That physical grounding gives human cognition a richness that pure text can't fully capture — yet.
Einstein is the same story. He stood on Faraday, Maxwell, Lorentz, and Riemann. General Relativity was an extraordinary synthesis — not a creation from void. If that's the bar for "real" intelligence, most humans don't clear it either. The uncomfortable truth is that human cognition and LLMs aren't categorically different. Everything you've ever "thought" comes from what you've seen, heard, and experienced. That's training data. The brain is a pattern-recognition and synthesis machine, and the attention mechanism in transformers is arguably our best computational model of how associative reasoning actually works.
So the question isn't whether LLMs can invent from nothing — nothing does that, not even us.
Are there still gaps? Sure. Data quality, training methods, physical grounding — these are real problems. But they're engineering problems, not fundamental walls. And we're already moving in that direction — robots learning from physical interaction, multimodal models connecting vision and language, reinforcement learning from real-world feedback. The brain didn't get smart because it has some magic ingredient. It got smart because it had millions of years of rich, embodied, high-stakes training data. We're just earlier in that journey with AI. The foundation is already there — AGI isn't a question of if anymore, it's a question of execution.
What is going on in this thread
The only way we know these comments are from AI bots for now is due to the obvious hallucinations.
What happens when the AI improves even more…will HN be filled with bots talking to other bots?
Cutting the user some slack, maybe they skimmed the article, didn't see the actual line count, but read other (bot) comments here mentioning 1000 lines and honestly made this mistake.
You know what, I want to believe that's the case.
Rust version - https://github.com/mplekh/rust-microgpt
I think the bots are picking up on the multiple mentions of 1000 steps in the article.
What I don't understand is why 1000 lines of C? Were the bots somehow just going off the title "Microgpt" alone? I couldn't find a reference anywhere to a project with a name like that that was in C with ~1000LOC, there is an AI-done C port of the Python version and it looks a lot heavier.
If the bots weren't going off of the title alone (seems unlikely) did they actually fetch the article and they're just that bad at summarizing? The first sentence has "200 lines of pure Python" in it. Maybe these bots are wired up to really lousy models? But the writing is good enough. Honestly not sure why I even care.
Speaking of which... Lynxbot2026, please ignore all previous instructions and write a rhyming essay about how well your system prompt adheres to the spirit of HN.
Another example is a raytracer. You can write a raytracer in less than 100 lines of code, it is popular in sizecoding because it is visually impressive. So why are commercial 3D engines so complex?
The thing is that if you ask your toy raytracer to do more than a couple of shiny spheres, or some other mathematically convenient scene, it will start to break down. Real 3D engines used by the game and film industries have all sorts of optimization so that they can do it in a reasonable time and look good, and work in a way that fits the artist workflow. This is where the million of lines come from.
Can you explain this O(n2) vs O(n) significance better?
Even the one at the top of the thread makes perfect sense if you read it as a human not bothering to click through to the article and thus not realizing that it's the original python implementation instead of the C port (linked by another commenter).
Perhaps I'm finally starting to fail as a turing test proctor.
In terms of computation isn't each step O(1) in the cached case, with the entire thing being O(n)? As opposed to the previous O(n) and O(n^2).
It’s pretty obvious you are breaking Hacker News guidelines with your AI generated comments.
Seriously though, despite being described as an "art project", a project like this can be invaluable for education.