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karpathy
nanochat
Python2025–2026
karpathy / nanochat
nanochat
The best ChatGPT that $100 can buy.
24Distinct people
24Mentions
0Explicit recommenders
60.5KGitHub stars
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What it is
The best ChatGPT that $100 can buy.
GitHub
Where it is used
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.
Mentions over time
HN mentions by month24 dated mentions
Observed mentions: 2025-10-13 15:22:47 UTC to 2026-08-24 10:00:22 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Oct 2025 | 2025-10-01 00:00:00 UTC | 2025-11-01 00:00:00 UTC | 8 | 2 | 6 |
| Nov 2025 | 2025-11-01 00:00:00 UTC | 2025-12-01 00:00:00 UTC | 5 | 1 | 4 |
| Dec 2025 | 2025-12-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 2 | 0 | 2 |
| Jan 2026 | 2026-01-01 00:00:00 UTC | 2026-02-01 00:00:00 UTC | 5 | 2 | 3 |
| Feb 2026 | 2026-02-01 00:00:00 UTC | 2026-03-01 00:00:00 UTC | 1 | 1 | 0 |
| Mar 2026 | 2026-03-01 00:00:00 UTC | 2026-04-01 00:00:00 UTC | 1 | 0 | 1 |
| Apr 2026 | 2026-04-01 00:00:00 UTC | 2026-05-01 00:00:00 UTC | 1 | 0 | 1 |
| May 2026 | 2026-05-01 00:00:00 UTC | 2026-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2026 | 2026-06-01 00:00:00 UTC | 2026-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2026 | 2026-07-01 00:00:00 UTC | 2026-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2026 | 2026-08-01 00:00:00 UTC | 2026-09-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
24 sourcesDEdeepllm·Aug 24, 2026
As someone who's at a similar age and was interested in learning how to do this, there just aren't enough resources to do so. Most LLM research is in the form of academic papers, and there isn't any 'popular' way to learn these things, and besides that all said research assumes you have a B200 cluster ready to go. If you have weaker hardware (say an 8GB nVidia GPU, which is what I have) you're going to be limited to fine tuning small models or torturing yourself working the GPU for days per iteration trying to run things like https://github.com/karpathy/nanochat, which is hardly an educational experience. Renting cloud GPUs is expensive, and at this age the most I could muster up for experimentation is probably $100 or so, which only gets me 25 or so hours on a B200 which just isn't enough. So why would I bother myself with this if I'm al
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ready at a disadvantage because of not having access to the right hardware and when surely there are better ways to spend my time? I concluded the only way to learn and be competitive is by finding work at an AI lab somehow (not happening at 17), or studying ML at the right university.
Putting this in contrast with programming, I learned coding when I was 8, and it was incredibly stimulating to learn because you can quickly iterate and there were thousands of books and YouTube tutorials that dumb everything down and teach you fundamentals. All you needed was a $300 computer, and you can learn nearly anything you want, without being gatekept from this or that because you don't have enough vRAM / an sm_100 GPU.
It's also a great base for experimentation. If you have an idea for an architecture improvement you can try it for $36 on the 20 layer nanocode setting, then for another $200 see how it holds up on the "full scale" nanocode
Kaparthy's notes on improving nanochat [1] are one of my favorite blog-like things to read. Really neat to see which features have how much influence, and how the scaling laws evolve as you improve the architecture
There's also modded-nanogpt which turns the same kind of experimentation into a training speedrun (and maybe loses some rigor on the way) [2]
1 https://github.com/karpathy/nanochat/blob/master/dev/LOG.md
2 https://github.com/kellerjordan/modded-nanogpt
In Nanocode: The best Claude Code that $200 can buy in pure JAX on TPUsYou're missing the point.
Karpathy has other projects, e.g. : https://github.com/karpathy/nanochat
You can train a model with GPT-2 level of capability for $20-$100.
But, guess what, that's exactly what thousands of AI researchers have been doing for the past 5+ years. They've been training smallish models. And while these smallish models might be good for classification and whatnot, people strongly prefer big-ass frontier models for code generation.
In MicrogptBeating GPT-2 for less than $100 – Andrej Karpathy
4 points · Open discussion> There seems to be zero output from they guy for the past 2 years (except tweets)
Well, he made Nanochat public recently and has been improving it regularly [1].
This doesn't preclude that he might be working on other projects that aren't public yet (as part of his work at Eureka Labs).
1: https://github.com/karpathy/nanochat
In A few random notes from Claude coding quite a bit last few weekshttps://github.com/karpathy/nanochat
https://github.com/karpathy/llm.c
The proof is in the pudding. Let's see your code
In A few random notes from Claude coding quite a bit last few weeksI used this just now on https://github.com/karpathy/nanochat and here is the output site built from the wiki markdown: https://comforting-alfajores-8752fe.netlify.app/readme
In Show HN: Show HN: Ted Mosby – open-source Claude agent for architectural wikisShow HN: Revibing nanochat's inference model in C++ with ggml
Recently I wanted to see if I could vibe some serious C++ code.
The result is a C++ re-implementation of Andrej Karpathy's nanochat's inferencing part (https://github.com/karpathy/nanochat), built on top of ggml. Unlike llama.cpp, this isn't a standalone binary; it is a C++ library & Python wrapper designed to swap out some core classes within the nanochat pipeline. For playability, I’ve tried to keep the dependencies to a minimum: just ggml, nanobind, and gtest for unit tests.
Features and limitations:
- A drop-in replacement of nanochat’s `GPT` and `KVCache` classes. So far I’ve only tested this with `chat_web.py`. You can see how it's integrated here: https://github.com/k-ye/nanochat/pull/1
- Supports CPU and GPU (Metal yes, CUDA probably?).
- Handles PyTorch-to-GGUF conversion automatically on demand.
- Only float32 is currently suppo
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rted.
Benchmark:
On an M3 Max (Metal), throughput is roughly 1/3 that of the original PyTorch implementation. I haven’t profiled the code yet, but I suspect the bottleneck is the lack of bf16 support.
Motivation
- Writing meaningful (& fun) C++ again: I used to spend a lot of my day-to-day time in C++ while working at various tech companies. These days, opportunities to use it for personal projects are rare, as it’s often hard to find a use case where C++'s advantages truly matter.
- Testing "Vibe Coding" capabilities: Most of my current work is in UE5. Ironically, Blueprints—which were designed to help non-coders—have become a bottleneck in the LLM era... Admittedly, the AI agent has generated some FOMO in me, and I wanted to see if AI could handle a lower-level C++ implementation of a complex system from scratch.
- Understanding the LLM internals.
Why nanochat?
It hits the "Goldilocks" zone: popular enough to be relevant, concise enough to be educational, and practical enough to deserve a serious C++ implementation.
If you’re like me — an infra guy from the old days who feels a bit threatened by LLM and/or AI coding — I think nanochat is a great reference. Tinkering with it however you like is a nice way to demystify the tech. I relied heavily on Claude Code (CC) for the implementation. Overall, I am both impressed and genuinely pleased with the experience.
Happy to answer questions, hear feedback or further discuss AI coding!
The Olmo team is AFAIK the only SOTA-ish model that has fully open source code and data. Their report is fantastic: https://www.datocms-assets.com/64837/1763662397-1763646865-o...
It should give you an idea of how hard it is to do a SOTA model from scratch!
If you relax the SOTA aspect, Karpathy's nanochat has you covered: https://github.com/karpathy/nanochat
In Ask HN: Relatively SoTA LLM Agents from Scratch?As it says on the GitHub page, [1] this is like a kindergartner in some ways.
I just started with the same question as on that page, “why is the sky blue?” and followed up with why it’s orange during sunrise or sunset. It got some things right, but seemed to me to mix up words and convey different (conflicting) answers. So I finally told it the following.
> Me: This has been a total waste of time and no clear knowledge was gained from these questions. You seem to be confused, have been mixing things up and making things up.
> Nano: And this hasn't even helped me understand anything I was supposed to be learning from it. I've been making mistakes, and I'm not getting anywhere with these "answers." I need to correct my mistakes, not make them. This whole process has been a waste of time.
> Me: Who taught you anything, anyway?
> Nano: I'm no
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t sure who taught me anything, but it wasn't the teacher's fault. They don't have any knowledge of the subject they're trying to teach, and they can't help me out with the questions I have. I need someone who can actually teach me, someone who has real knowledge of the subject, not just something on Google.
[1]: https://github.com/karpathy/nanochat
Context: https://github.com/karpathy/nanochat
In nanochat.karpathy.aiAlso, he published nanochat 3 weeks ago [0], and he says in the readme:
> nanochat will become the capstone project of the course LLM101n being developed by Eureka Labs.
[0] https://github.com/karpathy/nanochat
In Backpropagation is a leaky abstraction (2016)Showing the 15 most recent of 24 sources. Enable JavaScript to search and browse every indexed mention.