Well, neither Linux nor LLVM loudly proclaimed that they would be the next Internet or GUI. So I am inclined to believe that this will not be the case and the person doing the proclamation might be a little full of himself.

> While there may be a legacy Linux running in a VM to manage all your cloud phoning spyware, the core functionality of the lifelike device is boot to neural network.

No, I do not think future devices will be "boot to neural network." Traditional algorithms still have a place. Your robot vacuum cleaner (his example) may still use A* to route plan, and Quicksort to display your cleanings in terms of most energy usage.

> Without CPUs, we can be freed from the tyranny of the halting problem.

Not sure what this means but I think it still makes sense to have a CPU directing things as in current architectures. You don't just have your neural engine, you also have your GPU, Audio system, input devices, etc. and those need a controller. Something needs to coordinate.

He's got the kernel of a good idea. Deterministic data flows are a good thing. We keep almost getting there, with things like data flow architectures, FPGAs, etc. But there's always a premature optimization for the silicon, instead of the whole system. This leads to failure, over, and over.

He's wrong in the idea of using an LLM for general purpose compute. Using math instead of logic isn't a good thing for many use cases. You don't want a database, or an FFT in a Radar System to hallucinate, for example.

My personal focus is on homogeneous, clocked, bit level systolic arrays.[2] I'm starting to get the feeling the idea is really close to being a born secret[1] though, as it might enable anyone to really make high performance chips on any fab node.

[1] https://en.wikipedia.org/wiki/Born_secret

[2] https://github.com/mikewarot/Bitgrid

TinyGrad is GeoHot's system/compiler to map neural networks onto hardware. He consistently points out this one point: Because the exact number of cycles is know in advance, it can be scheduled, there's no need for branch prediction, or that type of thing in a CPU.

Essentially, he wants to be able to express programs, and even an operating system, as a directed acyclic graph of logical binary operations, so that you can have consistent and deterministic runtime behavior.

The bit about LLMs is a distraction, in my opinion.

I don’t understand the LLVM comparison. Is it somehow a compiler backend for conventional programming languages? Can you run C or Rust code?

> Without CPUs, we can be freed from the tyranny of the halting problem.

Can someone please explain to me what this even means in this context?

Serious question.

I don't know why I should switch from PyTorch to Tinygrad as a researcher and practitioner. In terms of kernel fusion, there is torch.compile. Not to say there is a large ecosystem behind PyTorch and almost every paper today is published with a PyTorch implementation. Probably what Tinygrad shines is bare-metal platforms?

> tinygrad has a hardware abstraction layer, a scheduler, and memory management. It's an operating system

Doesn't every ML framework have that?

The only reason neural networks don't have control flow is because they are not very good. They are incredibly inefficient and the only way to properly solve that is to introduce control flow, for example: https://arxiv.org/abs/2311.10770

>Without CPUs, we can be freed from the tyranny of the halting problem.

In the same way that we can be freed of the tyranny of being able to write a for loop.

Isn’t this the guy who joined Twitter as an intern to “fix” search?

no it won't, because while hitting ioctls in python is cute

https://github.com/tinygrad/tinygrad/blob/master/extra/hip_g...

it is definitely not shippable