Microgpt explained interactively

[politelemon]

> By the end of training, the model produces names like "kamon", "karai", "anna", and "anton". None of them are copies from the dataset.

Hey, I am able to see kamon, karai, anna, and anton in the dataset, it'd be worth using some other names: https://raw.githubusercontent.com/karpathy/makemore/988aa59/...

[ayhanfuat]

You are absolutely right. The whole post reads like AI generated.

[growingswe]

Thanks, will fix

[jmkd]

It says its tailored for beginners, but I don't know what kind of beginner can parse multiple paragraphs like this:

"How wrong was the prediction? We need a single number that captures "the model thought the correct answer was unlikely." If the model assigns probability 0.9 to the correct next token, the loss is low (0.1). If it assigns probability 0.01, the loss is high (4.6). The formula is − log ⁡ ( � ) −log(p) where � p is the probability the model assigned to the correct token. This is called cross-entropy loss."

[growingswe]

I see. The problem with me writing these is even though I'm not an expert, I do have a bit of knowledge on certain things so I'm prone to say things that make sense to me but not to beginners. I'll rethink it

[windowshopping]

The part that eludes me is how you get from this to the capability to debug arbitrary coding problems. How does statistical inference become reasoning?

For a long time, it seemed the answer was it doesn't. But now, using Claude code daily, it seems it does.

[ferris-booler]

IMO your question is the largest unknown in the ML research field (neural net interpretability is a related area), but the most basic explanation is "if we can always accurately guess the next 'correct' word, then we will always answer questions correctly".

An enormous amount of research+eng work (most of the work of frontier labs) is being poured into making that 'correct' modifier happen, rather than just predicting the next token from 'the internet' (naive original training corpus). This work takes the form of improved training data (e.g. expert annotations), human-feedback finetuning (e.g. RLHF), and most recently reinforcement learning (e.g. RLVR, meaning RL with verifiable rewards), where the model is trained to find the correct answer to a problem without 'token-level guidance'. RL for LLMs is a very hot research area and very tricky to solve correctly.

[fc417fc802]

Because it's not statistical inference on words or characters but rather stacked layers of statistical inference on ~arbitrarily complex semantic concepts which is then performed recursively.

[mike_hearn]

DNNs aren't really "statistical" inference in the way most people would understand the term statistics. The underlying maths owes much more to calculus than statistics. The model isn't just encoding statistics about the text it was trained on, it's attempting to optimize a solution to the problem of picking the next token with all the complexity that goes into that.

[antonvs]

One problem is that "statistical inference" is overly reductive. Sure, there's a statistical aspect to the computations in a neural network, but there's more to it than that. As there is in the human brain.

[malnourish]

I read through this entire article. There was some value in it, but I found it to be very "draw the rest of the owl". It read like introductions to conceptual elements or even proper segues had been edited out. That said, I appreciated the interactive components.

[davidw]

It started off nicely but before long you get

"The MLP (multilayer perceptron) is a two-layer feed-forward network: project up to 64 dimensions, apply ReLU (zero out negatives), project back to 16"

Which starts to feel pretty owly indeed.

I think the whole thing could be expanded to cover some more of it in greater depth.

[love2read]

Is it becoming a thing to misspell and add grammatical mistakes on purpose to show that an LLM didn't write the blog post? I noticed several spelling mistakes in Karpathy's blog post that this article is based on and in this article.

[klysm]

I expect this kind of counter signaling to become more common in the coming years.

[efilife]

You just started to notice it

[refulgentis]

People aren't gonna be happy I spell this out, but, Karpathy's not The Dude.

He's got a big Twitter following so people assume somethings going on or important, but he just isn't.

Biggest thing he did in his career was feed Elon's Full Self Driving delusion for years and years and years.

Note, then, how long he lasted at OpenAI, and how much time he spends on code golf.

If you're angry to read this, please, take a minute and let me know the last time you saw something from him that didn't involve A) code golf B) coining phrases.

[grey-area]

The original article from Karpathy: https://karpathy.github.io/2026/02/12/microgpt/

[thebiblelover7]

I know many comments mentioned that it was too introductory, or too deep. But as someone that does not have much experience understanding how these models work, I found this overview to be pretty great.

There were some concepts I didn't quite understand but I think this is a good starting point to learning more about the topic.

[kinnth]

That was one of the most helpful walkthroughs i've read. Thanks for explaining so well with all of the steps.

I wasn't a coder but with AI I am actually writing code. The more i familiarise myself with everything the easier it becomes to learn. I find AI fascinating. By making it so simple and clear it helps when i think what i need to feed it.

[dreamking]

It seems that Tmobile is originally block this website that I can't open this blog page...

https://www.t-mobile.com/home-internet/http-warning?url=http...

[danhergir]

I went through the article, and it makes sense to me that we're getting names as an output, but why doing so with names?

[growingswe]

Names is just a random problem to demonstrate the model. It could be anything, I believe

[ChrisArchitect]

Related:

Microgpt

https://news.ycombinator.com/item?id=47202708