Sonic Pi VS clerk

I have wondered what grooves it could come with using https://sonic-pi.net/

Something fascinating about seeing a 'score' for generative music written out as a sort of specification like that.

There's enough detail there that you can take those instructions and reimplement your own version of it, and you'll end up with essentially the same 'piece of music', but certainly a different interpretation of it. Because while the score lays out some details precisely, it leaves other choices less clear. What does 'all inversions' really mean when enumerating chords? Does it include open, spread voicings? What durations should we choose from for our random waveforms? How short is 'short' when deciding to repeat? And of course, what wave synths should you use, and how should you modulate them?

All those are similar to the decisions a traditional instrumentalist makes when interpreting a sheet music score for performance - here, a generative music coder can follow this 'score' and produce a program that represents their own interpretation of the piece.

Coding it up in Sonic Pi (https://sonic-pi.net/) was a fun exercise, and I feel like I was able to produce something along the lines of what the composer intended. It carries the same kind of mood that the recording in the video has. But it's my own 'performance' of the work, if that makes sense (even if it's actually Sonic Pi 'performing' it at runtime...)

All of which got me thinking about the relationship more generally between specification, and implementation. Considering different programmers' implementations of algorithms as individual 'performances' of scores from the overall design - and then thinking about developers building elements of a larger system architecture as individual performers working to deliver their part of the performance as part of a band or orchestra. Some groups, maybe they're directed by a conductor-architect; others maybe are improvisers, riffing off one another and occasionally stepping up to deliver a solo. And some are maybe solid session performers, showing up and delivering strong but unflashy performances to a producer's specification.

So overall, a nice meditative coding exercise for a Sunday afternoon, and a shift in perspective. Thanks for sharing it.

On a seriously light-hearted note, Herve Aniglo, talked about teaching children to code with music using Sonic PI, a language agnostic platform that helps you learn recursions, looping, circuit breaking and functional programming by creating simple tunes.

https://www.youtube.com/playlist?list=PLPYzvS8A_rTYEba_4SDvR...

- Sonic Pi is built on-top of SuperCollider, but it's MUCH easier to get started with making bleeps and bloops. Sam Aaron, who originally created Overtone (a Clojure front-end for SuperCollider) created Sonic Pi initially to teach kids computer programming and music, but now it's turning into a pretty nice live-coding setup. The language is basically a DSL extension of Ruby, and although it's very elegant, I feel like it's a little nerfed in terms of a full language when compared to SCLang, so I'm sticking with the latter for now. High recommend checking it out if you're new to making music or code. https://sonic-pi.net/

- This 'Intro To Live Coding' vid from Alex McLean is great. Gives a good overview of a few fun tools out there that I won't mention here for sake of time (check out Gibber and Hydra for web-based coding things. Gibber is really slick). Alex invented Tidal Cycles, which I feel is like god-tier in terms of power and conciseness. Maybe I'll tinker with Tidal someday, but I want to start with SC.

Strange dice that it seems to mostly be c++, sponsored by 3 prominent elixir shops, with an original OSC server implementation by Joe Armstrong.

https://github.com/sonic-pi-net/sonic-pi/tree/dev/app/server...

There is a programming language+IDE called SonicPI. It's designed to create music by writing code. You can install the program from the lin, then ask chatGPT to generate some sonic PI code that produces some nice melody. Then just copy the code and paste it into the sonicPI program, and run it by clicking the run button. Here's a conversation for example

Something I really like in the Clojure data science stack that isn't mentioned is Clerk* — an interesting take on notebooks. I think it's a good gateway into Clojure for those coming from a Python or R background.

*https://clerk.vision/

Clojure's lazy sequences by default are wonderful ergonomically, but it provides many ways to use strict evaluation if you want to. They aren't really a hassle either. I've been doing Clojure for the last few years and have a few grievances, but overall it's the most coherent, well thought out language I've used and I can't recommend it enough.

There is the issue of startup time with the JVM, but you can also do AOT compilation now so that really isn't a problem. Here are some other cool projects to look at if you're interested:

Malli: https://github.com/metosin/malli

Babashka: https://github.com/babashka/babashka

Clerk: https://github.com/nextjournal/clerk

I'm really enjoying using Clojure with Clerk: https://github.com/nextjournal/clerk

It's a bit like a Jupyter notebook, but you get to use your own editor, you still have a normal Clojure REPL, it's stored in git like "normal" code, etc.

Hey all, I'm new to Clojure and would appreciate your help with a few questions I had getting started. I'm using Leiningen to setup my projects and manage my packages as recommended in Brave & True. So far I've been able to add any dependencies I've needed without much issue, Neanderthal, tech.v3.dataset, etc. I'm interested in data science, and was hoping to set up a notebook environment to be able to quickly produce data visualizations on the fly since I'm used to working with Jupyter. I came across Clerk, but I'm having some trouble adding it to my project. Here's what I tried:

Clojure also has Clerk, which is like Jupyter, but more befitting Clojure's overall philosophy: https://clerk.vision/

If I were working long enough with gnarly enough equations I'd look into using Clerk to visualize the equations with MathJax or similar, probably following Sam Ritchie's footsteps with SICMUtils. To me this is the true readability answer: lisp notation for precise implementations, compiling to a rich & familiar visual representation.