Something i didnt know for a long time (even though its mentioned in the book pretty sure) is that enum discriminants work like functions

#[derive(Debug, PartialEq, Eq)]
enum Foo {
    Bar(i32),
}

let x: Vec<_> = [1, 2, 3]
    .into_iter()
    .map(Foo::Bar)
    .collect();
assert_eq!(
    x,
    vec![Foo::Bar(1), Foo::Bar(2), Foo::Bar(3)]
);

Not too crazy but its something that blew my mind when i first saw it

i’m tricking the nintendo switch into thinking my computer is a bluetooth pro controller. I’m using a crate called bluer which exposes bindings to the BlueZ stack and it’s been great to use.

I got to the point where it pairs the controller and hits B to exit. However it doesnt seem to accept any more button presses after that… :) So I have some ways to go.

I’ve also needed a project where I can challenge myself with the basics of async without it being overwhelming, and I think this hits the sweet spot. It’s my first time using tokio spawn, join, and select in a real project!

Yeah, thinking about it more, the similarities are kind of narrow.

You could make a better comparison with a regular crowd, but then it wouldn’t feel like much of a showerthought at that point because it’s just observing that the crowd has moved online.

Laugh tracks might be used to improve there ratings of a show, but with memes there’s not really a show and no one’s forcing a laugh

I think the essence of what I was thinking of though is that just like a regular crowd, an online crowd can still influence you to think something is funnier or better than you would alone (at least for me)

LMAO, yeah this one didnt seem to hit did it

I noticed it and placed a few pixels :D

Here’s one on the claw

You might be okay with this:

macro_rules! span {
    ($line:expr, $column:expr) => {
        Span {
            line: $line,
            column: $column,
            file_path: None,
        }
    };
    ($line:expr, $column:expr, $file_path:literal) => {
        Span {
            line: $line,
            column: $column,
            file_path: Some($file_path.to_string()),
        }
    };
    ($line:expr, $column:expr, $file_path:expr) => {
        Span {
            line: $line,
            column: $column,
            file_path: $file_path,
        }
    };
}

Playground

However, sometimes I don’t want to pass in the file path directly but through a variable that is Option<String>.

Essentially I took this to mean str literals will be auto wrapped in Some, but anything else is expected to be Option<String>

Another optimization:

• The first index of each index array can be filled by a function. For 257x257 that would remove 8,487,169 checks out of… 2,164,392,321. Not much, but it’s basically a free optimization, so might as well!

More progress on the Finite Projective Plane (incidence matrix) generation from last week. There already exists an algorithm to generate boards of order p+1 where p is prime. It is stateless, so with CUDA we can generate huge boards in seconds since all you need is the x, y position and board size. 258x258 under 3s!

However, p+1 isn’t the only sequence. It seems by our observations that the fermat numbers also generate valid boards, using our “naïve” algorithm.

Unfortunately 3x3, 5x5, and 17x17 might not contain all the nuggets of generality to find a nice algorithm like the p+1, so we’re gonna generate the next up: 257x257. We’ve been improving the naïve algorithm since it is too slow. (The resulting image would be 65793x65793)

• Rather than allocating the 2d boolean grid, we represent where the true elements would be using row and column indexes. This is okay because of the constraint which limits how many true elements can be in a row/column
  • benefit 1 — less memory usage: “O(2n)” vs O(n²) ((for 257x257: 129MiB vs 4GiB))
  • benefit 2 — faster column-major lookups (flamegraph spent a lot of time sitting in iterators)
  • overall speedup: about 2.7x
• Speed up index lookup with binary search
  • The index list is sorted by nature. To exhaustively check a dot is valid, it checks n² spots in 2 lists of size n. Slightly more expensive than the grid given the 2 index lists. Rather than slice::contains, use slice::binary_search(...).is_ok()
  • overall speedup: about 2.1x

Next steps:

• Assume a square grid and exploit its diagonal symmetry to treat row lookups as column lookups
• Use multi threading to gain a partial speedup
  • Essentially if row 1 is 50% completed, row 2 can be up to 50% completed.
  • I think you get different speeds depending whether the threads and symmetry folds are both row/column major or one is row-major and the other is column-major. My gut says both need to be aligned because there’s less waiting involved.

Apparently generating “Finite Projective Planes”. For context on how I got here, I went camping with my family and brought the game Spot It. My brother was analyzing it and came up with the same type of pattern.

When we got home he made a python script to generate these boards, but it was quite slow, so he half joked asking me to rewrite it in Rust.

I kinda struggled a bit since I didn’t fully understand what it was doing. Near the end I even got a segfault using safe code😃! (i was spawning a thread with a large stack size, and allocating huge slices on its stack, rather than you know… boxing the slice Lol.) When I finally got it working, it ended up being in the ballpark of a 23x speedup. Not bad for changing the language choice!

There’s lots of room for improvement left for sure. The algorithm could benefit with some running statistics about cols/rows and the algorithm itself is quite naïve and could maybe be improved too :P