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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

#121
post #12

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/...

I was in the demoscene long ago and that kind of optimisation is definitely in the ballpark of what we did: optimize algorithm down to machine code level (and additionally, cheat like hell to make you believe we ran the algorithm for real :-)).

But to be honest, I wonder what algorithm they implement. I have read the code for 2 minutes, and it sound like random forest prediction. Anyone knows what the code does ?

Re: Anthropic's original take home assignment open sourced

#123
post #96

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

Which part exactly are ypu having trouble with?

- Optimize the kernel (in KernelBuilder.build_kernel) as much as possible in the available time, as measured by test_kernel_cycles on a frozen separate copy of the simulator

Re: Anthropic's original take home assignment open sourced

#125

Earlier quoted context omitted.

Could you make a repo with solutions given by each model inside a dir/branch for comparison?

Are you giving instructions to a stranger on the internet?

Instructions?! Just asked since GP already did it. No need to realize top comment's "DDOS attack on other AI companies" joke.

Re: Anthropic's original take home assignment open sourced

#126

Earlier quoted context omitted.

The person replying was trying to turn the conversation into some sort of IQ pissing contest. Not sure why, that seems like their own problem. I was reminding them that there is always someone smarter.

Your comment history is littered with “nerds”, “elite”, “better” and all sorts of comparisons. > I was reminding them that there is always someone smarter. And even with this comment you literally do not understand that you have some skewed view of the world. Do you have some high school trauma?

Where I come from, nerd is a term of endearment buddy.

> And even with this comment you literally do not understand that you have some skewed view of the world.

I’m well aware I don’t have a perfect view of reality and the map isn’t the territory. Do you?

Re: Anthropic's original take home assignment open sourced

#127

Earlier quoted context omitted.

Which Gemini model did you use? My experience since launch of G3Pro has been that it absolutely sucks dog crap through a coffee straw.

/model: Auto (Gemini 3) Let Gemini CLI decide the best model for the task: gemini-3-pro, gemini-3-flash After ~40 minutes, it got to: The final result is 2799 cycles, a 52x speedup over the baseline. I successfully implemented Register Residency, Loop Unrolling, and optimized Index Updates to achieve this, passing all correctness and baseline speedup tests. While I didn't beat the Opus benchmarks due to the complexit…

we've lost the plot.

you can't compete with an AI on doing an AI performance benchmark?

Re: Anthropic's original take home assignment open sourced

#130
post #96

Earlier quoted context omitted.

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

Generate instructions for their simulator to compute some numbers (hashes) in whatever is considered the memory of their "machine"¹. I didn't see any places where they actually disallow cheating b/c it says they only check the final state of the memory² so seems like if you know the final state you could just "load" the final state into memory. The cycle count is supposedly the LLM figuring out the fewest number of i…

Well, they read your code in the actual hiring loop.
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