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Anthropic's original take home assignment open sourced

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Re: Anthropic's original take home assignment open sourced

#191

Earlier quoted context omitted.

Why do you assume it’s cheating?

Because it's a well know failure mode of neural networks & scalar valued optimization problems in general: https://www.nature.com/articles/s42256-020-00257-z

And? Anthropic is not aware of this 2020 paper? The problem is not solvable?

Re: Anthropic's original take home assignment open sourced

#192
post #96

Earlier quoted context omitted.

I'm 30 years in, and literally don't understand the question.

The question isn't clearly written down anywhere, that's why. Presumably actual candidates would have been given more info over the phone or email. Part of the "challenge" is reverse engineering their Python; unclear if that's intentional. If you look at the top of perf_takehome.py then there is a brief comment saying the challenge is to optimize a kernel. Kernel in GPU land means a program that computes on data in p…

> but which requires all parallelism to be statically declared ahead of time

this is what all specialized chips like TPU/Cerebras require today, and it allows for better optimization than a generic CPU since you can "waste" 30 min figuring out the perfect routing/sequencing of operations, instead of doing it in the CPU in nanoseconds/cycles

another benefit is you can throw away all the CPU out-of-order/branch prediction logic and put useful matrix multipliers in it's place

Re: Anthropic's original take home assignment open sourced

#194

Naively tested a set of agents on this task. Each ran the same spec headlessly in their native harness (one shot). Results: Agent Cycles Time ───────────────────────────────────────────── gpt-5-2 2,124 16m claude-opus-4-5-20251101 4,973 1h 2m gpt-5-1-codex-max-xhigh 5,402 34m gpt-5-codex 5,486 7m gpt-5-1-codex 12,453 8m gpt-5-2-codex 12,905 6m gpt-5-1-codex-mini 17,480 7m claude-sonnet-4-5-20250929 21,054 10m claude-…

codex cli + gpt-5-2-codex-xhigh got to 1606 with the prompt "beat 1487 cycles. go." ~53 minutes.

Serious prompt engineering right here

Re: Anthropic's original take home assignment open sourced

#195

Earlier quoted context omitted.

Why do you assume it’s cheating?

Because it's a well know failure mode of neural networks & scalar valued optimization problems in general: https://www.nature.com/articles/s42256-020-00257-z

Again, you can just read the code

Re: Anthropic's original take home assignment open sourced

#196

Earlier quoted context omitted.

It's a test of polyhedral layout algebra, what NVIDIA calls CuTe and the forthcoming C++ standard calls std::mdspan. This is the general framework for reasoning about correct memory addressing in the presence of arbitrary constraints like those of hardware.

You can get pretty far without needing to care about this fwiw

Not far enough if you're turning cash into waste heat with GPUs :)

Re: Anthropic's original take home assignment open sourced

#197

Earlier quoted context omitted.

You make it faster

Fewer instructions doesn't mean it's faster. It can be faster but it's not guaranteed in general. Obvious counterexample is single threaded vs multi-threaded code. Single threaded code will have fewer instructions but won't necessarily be faster.

It does in this case; you can read the assignment to see that it is all single-threaded

Re: Anthropic's original take home assignment open sourced

#198

Are you allowed to change the instruction sequence? I see some optimization opportunities - it'd be obviously the correct thing to do an optimizing compiler, but considering the time allotted, Id guess you could hand-optimize it, but that feels like cheating.

Yes, in fact this will be one of the first things you will want to do.
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