checker659

OS kernels? Everything from numpy to CUDA to NCCL is using C/C++ (doing all the behind the scene heavy lifting), never mind the classic systems software like web browsers, web servers, networking control plane (the list goes on).

I think the burden of proof is on the people making the original claim (that LLMs are indeed spitting out original code).

I've always wondered. If you have fuck you money, wouldn't it be possible to build GPUs to do LLM matmul with 2008 technology. Again, assuming energy costs / cooling costs don't matter.

> The fundamentals actually haven't changed that much in the last 3 years

Even said fundamentals don't have much in the way to foundations. It's just brute forcing your way using a O(n^3) algorithm using a lot of data and compute.

Great tool. Thank you.

I think the current grads are going to be shafted either way. In 5 years, there might be more opening for "fresh" young grads and the companies will prefer them over the young people who're just graduating.

Without open source there would be no code writing LLMs. It is charity of the highest order (to say the least).

You're right. But I easily round robin between Claude, ChatGPT and Gemini every day without noticing any friction whatsoever.

This is the most creative comment I've read on HN as of late.

The structure in the README.md (not the actual structure).

That project structure is reminding me of claude.

But isn't that their "moat"? If the moat is the source of losses, then yes, they're operating at a huge loss.

Are the companies making a profit on every prompt I give it (factoring in the cost of upgrading GPUs every year or two)?

The economics of it is what's the problem, not the power of LLMs.

Ray Kurzweil

We need to start a company that is like a cooperative that trains juniors to become seniors.

I think you'd get better answers from GLP1 subreddits and /r/science than from HN readers.

> ~7× faster than NumPy

Is that on the CPU (not sure if NumPy has a GPU backend)

I meant that IFF the author wrote it themself. IFF.

I was skeptical at first, but this does stink of auto-generated code. I want to believe it is not.

It could be a vehicle to learn about traceroute / ICMP.

> making it quite unappealing from an engineering standpoint

The other option being rewriting everything under the sun from scratch.

Latency Numbers Every Programmer Should Know (originally by Jeff Dean / Peter Norvig)

https://gist.github.com/jboner/2841832

The concept of pages is baked deep into the memory hierarchy (cache, TLBs). What exactly do you mean by precursor? I imagine you're thinking of pages as an abstraction over something more fundamental (?).

I mean, sandboxing AI agents once they get smarter is a valid concern (if not a major future concern).

They are working with tiny models. Not sure how well it'd scale to bigger models (if at all).

Isn't it the DMA engine that's responsible for the data movement? How does the size of the BAR even factor into that?

> Even when looking at something as barebones as perceptrons

I was thinking something like "it is trying to approximate a non-linear function" (which is what it is in the case of MLPs).

I have done a little bit of DL stuff (with keras) before this. I'm currently in the attention chapter. The book gives you the code, but I feel like there is very little in the way of building intuition. Thankfully, there are tons of videos online to help with that.

I think it is a great guide. An extended tutorial if you will (at least until this point in my reading). Also having the code right in front of you helps a lot. For example, I was under the impression that embedding vectors were static like in word2vec. Turns out, they are learnable parameters too. I wouldn't have been able to tell for sure if I didn't have the code right in front of me.

So, they were all blind? They couldn't see each other? (Not to mention reflection in water).