[rvz]

This is the minimum bar that I expect very elite programmers should be striving for in the age of AI and DeepSeek should be studied as an example and this is the only just the first of many projects from them.

There is an extremely high chance (in fact a 99.9% chance) that an AI did not build this and the ones who are able to build or adapt projects like this which are deep into hardware systems will be the most sort after.

Not the horrendous JS or even TS slop across GitHub that is extremely easy for an AI to generate correctly.

You've got until 2030 to decide. And my advice is to study the codebases of pytorch (backends), DeepSeek, tinygrad and ggml.

[PeterStuer]

Honest question:

Do you feel GenAI coding is substantially different from the lineage of 4GL to 'low code' approaches?

Reason I'm asking is because despite all promises al suffered from what Spolsky coined the 'leaky abstraction' problem.

Once something goes wrong, the user is left without recourse in a sea of additional complexity created by the tooling that was meant to not have to deal with it in the first place.

My own opinion is that GenAI is different because of (a) its recursive reflexive potential (you can use the tool itself to help you past the failure) and (b) it shifts the input out of the necessity for algorithmic/systemic thinking (which may come as a surprise to the audience here but my experience has taught me is alien to dare I say the majority of people).

Now don't get me wrong. We have not reached the point where (a)+(b) make it to where you don't need application layer devs, but we are definitely seeing some progress.

As for going deeper into the stack to "escape" AI, I would venture that is probably a non starter as the deeper you go the more constrained the domain is, so your escape strategy relies on AI reasoning making little progress, where AI reasoning has always been more successful in smaller well defined spaces.

[jbm]

It's an interesting opinion, but I read the exact same opinions about JS developers in 2008 too.

I do agree that if you are "only" a developer, you will have to be in some sort of tightly defined niche, and how long those niches survive is anyone's guess.

[KeplerBoy]

What do you mean with "only" developer? Someone who just knows how to code when given a spec but lacking domain knowledge (in this case ai math and hardware optimization) and larger context?

[jbm]

Personally, I don't think having deep domain knowledge is as important. However, being able to write the spec based on interactions with the client / customer / stakeholders is. (The AI Math and hardware optimization "never" being doable by an AI seems like an arbitrary distinction to justify one's choices.)

Incidentally, I put the word "only" in quotes because I morally and aesthetically appreciate the strength of someone who can write to spec. I have no interest in demeaning the effort it takes to do so. I have worked with supposedly senior developers who ignore specs completely, even when the specs are done by a technical person and include details / unit tests.

[menaerus]

I agree that DeepSeek continues to prove themselves as a great example of engineering but the number of job positions requiring this type of knowledge IME is typically very very low so I am not sure if this would be the right advice to follow. Though I wish it was different.

[rob_c]

Yeah, you're hitting the nail on the head. Low tier coding work can be reduced and the high end developers can now avoid boiler plate type coding problems and get back to high level work at reengineering complex frameworks.

Yes, this unfortunately does mean a reduction in the less skilled workforce, but frankly that's an on the whole good thing. Does anyone really enjoy writing and testing boilerplate day in day out for low pay, it's the same as the old white collar pushing paper around until retirement...

[beernet]

LLM generated comments are so 2024

[BoorishBears]

Nothing about that comment implies it's LLM generated, and it's bizzare how it's being received since it's a pretty reasonable take.

[rnewme]

I don't find it a reasonable take, it's like saying stackoverflow.com is taking developer jobs by making it easy to code, we better develop new stackoverflow.com

[WithinReason]

AI is already writing optimized GPU code:

https://sakana.ai/ai-cuda-engineer/

[mirekrusin]

Comments around that page suggest it's more of a facepalm than anything else.

[CamperBob2]

x2 speed increase for ggml by optimizing SIMD: https://github.com/ggml-org/llama.cpp/pull/11453

"99% written by DeepSeek-R1" according to the author.

[rfoo]

Speaks more about how many low hanging fruits remaining in "NOOOOO I DON'T WANT TO DOWNLOAD 200MiB PYTORCH I'D BETTER REINVENT THE WHEEL"-gang inference stacks.

To be fair torch didn't try very hard optimizing on CPU either.

[badsectoracula]

FWIW as someone who "NOOO DOESN'T WANT TO DOWNLOAD 200MB[0] PYTORCH"s i'm glad for those who make alternative minimal/no-dependency stacks that are based on C/C++, like ggml.

[0] 200MB is actually a very generous number, i tried to download some AI thing via pip3 the other day and it wanted 600MB or so of CUDA stuff. Meanwhile i do not even have an Nvidia GPU.

[rfoo]

The wheel of CPU-only PyTorch 2.6.0 for Python 3.12 is ~170MiB in size.

It is indeed pretty silly that's not the default and you have to go to https://pytorch.org/get-started/locally/, copy the argument `--index-url https://download.pytorch.org/whl/cpu` to install CPU-only torch. But the alternative would be having the worlds scientists wondering why they can't use their GPUs after `pip install torch` so /shrug

[wrsh07]

But as a response to the parent saying "LLMs will be great at ts/js slop but not for infra" it's quite reasonable to say: here's an example of someone applying it to backend optimizations today.

Fwiw, there are always many attempts at optimizing code (assembly etc). This is good! Great to try new techniques. However, you get what you constrain. So I've seen optimized code that drops checks that the compiler authors say are required in the standard. So, if you don't explicitly tell your optimizer "this is a case I care about, this is the desired output" it will ignore that case.

Did we find a faster implementation than the compiler creates? Well, I mean, sure, if you don't know why the compiler is doing what is doing