[qsort]

This is a valuable contribution, but some of the details make me raise my eyebrow, so forgive me if I'm going to put on my reviewer #2 hat.

- You did this with AI. I'm not going to dock points for this, it's $current_year and it's the normal thing to do, but it would be nice to explain your process a little bit? To put it bluntly, why does this improve upon typing "hey claude, read this paper and go nuts" into the proverbial text box?

- As others are pointing out, the 20,000x figure is quite something. For "normal sized" graphs we'd expect this algorithm to be actually slower, why aren't we seeing that? Could you explain how you tested your code?

- Why aren't you benchmarking against other established libraries? It seems like the Dijkstra implementation you claim to be improving upon is also your own.

[danalec]

- 9 months and I did not find any code about DMMSY's method

- 7950X3D has 96MB L3 cache and the the graph is very sparse and tree-like

- The code is opensource and everyone is welcome to contribute. Thanks for the idea.

[Choco31415]

What makes you know this is AI generated? I’m not seeing any obvious signs at first glance.

[Tiberium]

The OP's comment to the post is clearly Markdown-formatted, real humans don't write like that on HN.

The readme is very obviously Claude-written (or a similar model - certainly not GPT), if you check enough vibecoded projects you'll easily spot those readmes.

The style of the HTML page, as noted by others.

Useless comments in the source code, which humans also do, but LLMs do more often:

// Basic random double

static inline double rand_double() { return (double)rand() / (double)RAND_MAX; }

[Retr0id]

The most obvious thing to me is the style of the HTML graphs

[qsort]

The readme is outright slop, the HTML chart has the classic "tailwind dark theme" layout that models default to absent specific instructions, many of the comments in the code are classic AI.

Didn't have time to read the code more in depth.