All the naysayer here have clearly no idea. Your large matrix multiplication implementation is quite impressive! I have set up a benchmark loop and let GPT-5.1-Codex-Max experiment for a bit (not 5.2/Opus/Gemini, because they are broken in Copilot), but it seems to be missing something crucial. With a bit of encouragement, it has implemented:

    - padding from 2000 to 2048 for easier power-of-two splitting
    - two-level Winograd matrix multiplication with tiled matmul for last level
    - unrolled AVX2 kernel for 64x64 submatrices
    - 64 byte aligned memory
    - restrict keyword for pointers
    - better compiler flags (clang -Ofast -march=native -funroll-loops -std=c++17)

But yours is still easily 25 % faster. Would you be willing to write a bit about how you set up your evaluation and which tricks Claude used to solve it?

How are you qualified to judge its performance on real code if you don't know how to write a hello world?

Yes, LLMs are very good at writing code, they are so good at writing code that they often generate reams of unmaintainable spaghetti.

When you submit to an informatics contest you don't have paying customers who depend on your code working every day. You can just throw away yesterday's code and start afresh.

Claude is very useful but it's not yet anywhere near as good as a human software developer. Like an excitable puppy it needs to be kept on a short leash.

If that is true; then all the commentary around software people having jobs still due to "taste" and other nice words is just that. Commentary. In the end the higher level stuff still needs someone to learn it (e.g. learning ASX2 architecture, knowing what tech to work with); but it requires IMO significantly less practice then coding which in itself was a gate. The skill morphs more into a tech expert rather than a coding expert.

I'm not sure what this means for the future of SWE's though yet. I don't see higher levels of staff in big large businesses bothering to do this, and at some scale I don't see founders still wanting to manage all of these agents, and processes (got better things to do at higher levels). But I do see the barrier of learning to code gone; meaning it probably becomes just like any other job.

None of the problems you've shown there are anything close to "very complex computer engineering problems", they're more like "toy problems with widely-known solutions given to students to help them practice for when they encounter actually complex problems".

>I'm currently #1 on 5 different very complex computer engineering problems

Ah yes, well known very complex computer engineering problems such as:

* Parsing JSON objects, summing a single field

* Matrix multiplication

* Parsing and evaluating integer basic arithmetic expressions

And you're telling me all you needed to do to get the best solution in the world to these problems was talk to an LLM?