Also potentially interesting to this crowd are the underlying editor, which I split out from the online Dusa editor and called "sketchzone" (https://github.com/robsimmons/sketchzone). Some of my motivations and hopes for sketchzone are blogged here: https://typesafety.net/rob/blog/endless-sketchzone
Also, I more-or-less did Advent of Code 2024 in Dusa: journal entries and links to solutions are at https://typesafety.net/rob/blog/advent-of-dusa-2024
Zig was also apparently partly developed whilst Andrew Kelley was there. Fun place.
Logic systems will be a key part of solving problems of hybrid data analysis (e.g. involving both social graphs, embedding spaces, and traditional relational data) - Cozo[1] sticks out as a great example.
[0] https://codeql.github.com/docs/ql-language-reference/about-t...
The Dusa implementation has a couple of ways to reason at a high level about the performance of programs. The academic paper that febin linked to elsethread spends a fair amount of time talking about this, and if you really want to dig in, I recommend searching the paper for the phrases "deduce, then choose" and "cost semantics".
There's work to do in helping non-academics who just want to use Dusa reason about program performance, so I appreciate your comment as encouragement to prioritize that work when I have the chance.
(The ArXiV preprint has the exact same content)
https://popl25.sigplan.org/attending/live-streams
I want to say that the cultural changes inside of the ACM to make historical research open access and to have excellent live streams of the conferences is just so damn wholesome and wonderful. Thank you ACM and the people inside the ACM that made this happen.
And in case someone from the ACM is reading this, the live streams are very useful for physical attendees. I was attending Splash! and there were a ton of talks where I would have needed to change rooms, wanted lots of desk space for notes and research. It was somewhat ironic attending half a day from a vacation rental. :)
The linked page suggests one intro if you have experience with Datalog, another intro if you have experience with Answer Set Programming (ASP), and a third for everyone else. That's because Datalog and ASP are the two logic programming things that are most like finite-choice logic programming. Finite-choice logic programming gives a completely new way of understanding what answer set programs mean. The Dusa implementation is able to solve some problems vastly more efficiently than state-of-the-art ASP solvers, and is able to easily solve other problems that mainstream ASP solvers are simply unable to handle because of something called the "grounding bottleneck." Right now it's not a strict win by any means: there are many problems that Dusa chokes on that state-of-the-art ASP solvers can easily handle, but we know how to solve at least some of those problems for implementations of finite-choice logic programming.
> Answer set programming is a way of writing Datalog-like programs to compute acceptable models (answer sets) that meet certain constraints. Whereas traditional datalog aims to compute just one solution, answer set programming introduces choices that let multiple possible solutions diverge.
Fascinating! I could see useful applications in litigation (e.g., narrowing potential claims; developing the theory of the case; finding impeaching lines of questioning).
Why not write it like it’s written in English? It could be one less thing to learn for people trying to adopt the language.
From a principled point of view, the rule "a :- b, c" helps define what "a" means, and it seems, in practice, most helpful to be able to quickly visually identify the rules that define a certain relation. The list of premises tends to be of secondary importance (in addition to often being longer and more complicated).
From a practical point of view, we wrote Dusa as people familiar with existing Datalog and Answer Set Programming languages, which use the backwards ordering and ":-" notation, and some of the core target audience we hope to get interested in this project is people familiar with those languages, so we made some syntax choices to make things familiar to a specific target audience. (Same reason Java uses C-like syntax.)
You can also think the same way about functions in typical languages: we don't write the body of the function first and then assign it to an identifier.
f(X) :- g(X), h(X).
f(a).
It's a reverse of the implication arrow and is meant to be read that way:
f(X) :- g(X), h(X).
if (want: edge Y X) { search for: edge X Y }
This is searching in reverse compared to a different if statement:
if (have: edge X Y) { assert: edge Y X }
No, what I want is a code example, front and center.
Even a month ago, I'd have asked "Where's the parallelism?" looking at any new language. AI has upended my world. My subscriptions are getting out of hand, they're starting to look like some peoples' sports channel cable bills. I'll be experimenting with the right specification prompt to get AI to write correct programs in three languages side by side, in either Cursor or Windsurf. Then ask it to write a better prompt, and go test that in the other editor. I'm not sleeping much, it's like buying my first Mac.
One constant debate I have with Claude is how much the choice of language affects AI reasoning ability. There's training corpus, but AI is even more appreciative of high level reasoning constructs than we are. AI doesn't need our idioms; when it taught itself the game Go it came up with its own.
So human documentation is nice, but who programs that way anymore? Where's the specification prompt that suffices for Claude to code whatever we want in Dusa?