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Fixed intersection and difference with the cascading changes, understanding how “in” works now.

I think that’s what confuses me about lil’s design is certain concepts are overloaded. I get it’s a design choice to unify things to a smaller language, but it does take a bit of mind bending.

Like, “in” has many overloaded uses and I might have called it “isin” to be more apparent to me that it was a vector. Maybe that’s my observation, is that I might have tended towards vector verbiage that indicate that, and a scalar is a special case of a vector of size 1 that can be broadcast to the right shape.

It never occurred to me that x in y was the boolean vector. Was there an example of this that I missed?

Screwtapello84 days ago(+1)

Lil’s vector-handling made more sense to me when I learned that it was inspired by a language called K, which was inspired by a language called APL, which was designed (among other things) to be a more modern alternative to traditional mathematical notation. Mathematicians will use + to represent addition of integers or complex numbers or matrixes or anything other random thing that obeys the algebraic laws of addition, and APL (and eventually Lil) uses + in a similar way.

If you think about vectors in the way they’re normally handled in languages that weren’t designed around the concept (like Python or Fortran), then yeah, you’re not going to get the full benefit. I also have found Lil to be a bit mind-bending (I came up with 1+0*range 5 for what I’ve now learned is more idiomatically expressed as 5 take 1), but it’s still so much more approachable than APL or even K, and there’s this whole cool retro-themed multimedia sketchpad tool built around it.

woodring84 days ago (2 edits)

Yeah… I’m not too keen on (pure) mathematical abstractions, since I think of what’s happening at the electro-mechanical level. Like for example, Haskell did some important things in my opinion - but it wasn’t type systems (macros are better): the Just-Nothing pattern is one of the best ways to handle errors in my opinion.

So something like Guy Belloch and the CM-5 makes more sense to me, since it tries to model parallel vector primitives: scan, reduce, permute, etc. - there’s still an implicit for all. Actually, it’s very close in terms of the vector abstraction with APL, but APL and friends are like brainfuck to me. It’s too terse. That’s why I gravitate more towards something that is explicit, models the hardware, and maybe a bit more verbose. Readability is important to me and I don’t like terse code.

Though, that’s not to say I want everything as array notation, because it only gets you so far. The relational model for multi-sets is immensely useful, too. But, I try to wedge it around parallel vector primitives - because that’s where the performance is.

That said, this probably doesn’t work to do anything for the performance in lil at all, but it’s the mental model I find most useful to think about vector computations, since it maps really well to the hardware (not necessarily lil’s virtual machine, though). Like Iverson and APL, I don’t necessarily care for the modern math model either (I find right-to-left and inside-out unnatural), but I don’t like APL’s either.

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