Oh, so that's how it explodes. Nice. The answer had so many digits 😄

I saw a great speedup when I added tracking of already seen states. No use coming back to them again and again 😀.
(the rest of my approach was pretty similar to yours)

Full input looks like this by the end (and the counts do get astronomical)

And here's the full input!

Wanted to read it but all I get is a
NGINX 502 Error :/

Did you have prior experience with C++ before deciding to use it for the Advent? =)

Why are some points going blue? Are those part 2 obstacle locations? The animation makes me think there is some trick to finding them that I'm not aware of)).

One other thing I tried before was to represent a sequence of changes as a single large number (like 4 digits in base 19 because there are 19 possible changes in -9..9)
Then the 5d array can be a 2d array, where the other dimension is 19**4. But timings were about the same as 5d approach.

I just tried line profiling and indeed the last line with `sequences.clip().sum().max()` takes 50% of total part2 time. I tried putting the buyers dimension last - no change in performance.
It's just a heavy operation however you slice it I think =).

I was wrong here, numpy is also very good for day20 (Race Condition). If distances from starting point are a 2d numpy array then for every cheating offset you can get the gains for the whole grid at once. My solution time went from 1.7s to 100ms when I switched to this approach.

I thought light grey was a background color, was pretty puzzled how the dark grey stuff could mean locks 😅. Almost asked you about that))

Fast solutions aren't general, they use some properties of the input that aren't in the task description. I studied the input program's flow on paper to figure out a shortcut for part 2. A direct "black box" approach does take forever.

I want to debug that circuit with real wires 😄

Pretty proud of solution times this year - all days run in just ~2.5s.
Day 20 Race Condition used to take almost 2s before I gave it some numpy love.

#python solutions and animations code here: github.com/gereleth/aoc...

They are clumped together rather nicely!

Well to be honest this is the first time I've imported numpy in this year's advent :)

I made a couple of manual adjustments to avoid white edges intersecting).
And 0.5px stroke weight is great for hiding the outer hairyness.
I couldn't untangle the crowded third layer, some connections through the center are unavoidable.

I was randomly throwing networkx layouts at the problem, and spectral clustering split nodes into sort of growing layers:
..
|\
....
|\\\\
.......
Where the max clique was the tight group on top. So I made these concentric circles going in bfs layers from max clique.