Anthropic's original take home assignment open sourced

I consider myself rather smart and good at what I do. It's nice to have a look at problems like these once in a while, to remind myself of how little I know, and how much closer I am to the average than to the top.

I suspect this was released by Anthropic as a DDOS attack on other AI companies. I prompted 'how do we solve this challenge?' into gemini cli in a cloned repo and it's been running non-stop for 20 minutes :)

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-haiku-4-5-20251001    147,734    9m
    gemini-3-pro-preview         147,734    3m
    gpt-5-2-codex-xhigh          147,734    25m
    gpt-5-2-xhigh                147,734    34m

Having done a bunch of take home for big (and small) AI labs during interviews, this is the 2nd most interesting one I have seen so far.

Having recently learned more about SIMD, PTX and optimization techniques, this is a nice little challenge to learn even more.

As a take home assignment though I would have failed as I would have probably taken 2 hours to just sketch out ideas and more on my tablet while reading the code before even changing it.

It's pretty interesting how close this assignment looks to demoscene [1] golf [2].

[1] https://en.wikipedia.org/wiki/Demoscene [2] https://en.wikipedia.org/wiki/Code_golf

It even uses Chrome tracing tools for profiling, which is pretty cool: https://github.com/anthropics/original_performance_takehome/...

The writing was on the wall for about half a year (publicly) now. The oAI 2nd place at the atcoder world championship competition was the first one, and I remember it being dismissed at the time. Sakana also got 1st place in another atcoder competition a few weeks ago. Google also released a blog a few months back on gemini 2.5 netting them 1% reduction in training time on real-world tasks by optimising kernels.

If the models get a good feedback loop + easy (cheap) verification, they get to bang their tokens against the wall until they find a better solution.

> This repo contains a version of Anthropic's original performance take-home, before Claude Opus 4.5 started doing better than humans given only 2 hours.

Was the screening format here that this problem was sent out, and candidates had to reply with a solution within 2 hours?

Or, are they just saying that the latest frontier coding models do better in 2 hours than human candidates have done in the past in multiple days?

“If you optimize below 1487 cycles, beating Claude Opus 4.5's best performance at launch, email us at [email protected] with your code (and ideally a resume) so we can be appropriately impressed and perhaps discuss interviewing.”

>so we can be appropriately impressed and perhaps discuss interviewing.

Something comes across really badly here for me. Some weird mix of bragging, mocking, with a hint of aloof.

I feel these top end companies like the smell of their own farts and would be an insufferable place to work. This does nothing but reinforce it for some reason.

The snarky writing of "if you beat our best solution, send us an email and MAYBE we think about interviewing you" is really something, innit?

I wonder if the Ai is doing anything novel? Or if it's like a brute force search of applying all types of existing optimizations that already exist and have been written about.

What is the actual assignment here?

The README only gives numbers without any information on what you’re supposed to do or how you are rated.

Going through the assignment now. Man it’s really hard to pack the vectors right

I wonder if OpenAI follows suit.

This is a knowledge test of GPU architecture?

It shocks me that anyone supposedly good enough for anthropic would subject themselves to such a one sided waste of time.

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Seems like they’re trying to hire nerds who know a lot about hardware or compiler optimizations. That will only get you so far. I guess hiring for creativity is a lot harder.

And before some smart aleck says you can be creative on these types of optimization problems: not in two hours, it’s far too risky vs regurgitating some standard set of tried and true algos.