> So it definitely isn't leveraging GHC's typechecker for your compositions. Is the TidalCycles runtime doing some kind of runtime typechecking on whatever it parses from these strings?
the runtime is GHC (well GHCi actually). tidal's type system (and thus GHC's typechecker) ensures that only computationally valid pattern transformations can be composed together. if you're interested in the type system here's a good overview from a programmer's perspective https://www.imn.htwk-leipzig.de/~waldmann/etc/untutorial/tc/...
these strings are a special case, they're formatted in "mini-notation" which is parsed into composed functions at runtime. a very expressive kind of syntactic sugar you could say. while they're the most immediately obvious feature of Tidal (and have since been adapted in numerous other livecoding languages), mini-notation is really just the tip of the iceberg.
>The whole paradigm is going to encourage a very specific style of composition where repeating structures and their variations are the primary organizational principle.
but that applies to virtually all music, from bach to coltrane to the beatles! my point is that despite what the average livecoder might stream/perform online, live coding languages are certainly not restricted to or even particularly geared towards repetitive dance music - it just happens that that's a common denominator of the kind of demographic who's interested in livecoding music in the first place.
i'd argue that (assuming sufficient knowledge of the underlying theory) composing a fugue in the style of bach is much easier in tidal than in a DAW or other music software.
on the more experimental end, a composition in which no measure ever repeats fully is trivial to realize in tidalcycles - it takes only a handful of lines of code to build up a stochastic composition based on markov chains, perlin noise and conditional pattern transformations. via the latter you can actually sculpt these generative processes into something that sounds intentional and follows some inner logic rather than just being random.
the text-based interface makes it much easier to use than anything GUI-based. it's all just pure functions that you can compose together, you could almost say that Tidal is like a musical equivalent of shell programs and pipes. equally useful and expressive both for a 10 year old and a CS professor.
>I think Pandoc or Shellcheck would win on this metric.
Quite the opposite actually. certain live coding languages give you the tools to create extremely complex patterns in a very controlled manner, in ways you simply wouldn't be able to do via any other method. the most popular artist exploring these ideas is Kindohm, who is sort of an ambassador figure for the TidalCycles language.
Having used TidalCycles myself, the language lends itself particularly well to this kind of stuff as opposed to more traditional song/track structures. And yet it also constrains and prevents the construction of bad programs in a very strict manner via its type system and compiler.
It's also notable for being probably the only Haskell library used almost exclusively by people with no prior knowledge of Haskell, which is an insane feat in itself.
Had to do the same with my IdeaPad Flex 5, which always felt super infuriating.
I ended up so frustrated with it that i've ultimatly caved and bought a macbook. I guess i'll have to stay on desktop machines for x86 until someone finally figures out how to make laptop that runs linux and stays silent under heavy load.
From my own experience and that of many peers I've talked to it really seems to benefit DJing as a skill. It's an activity where you get to be hyperfocused and freely associating/improvising at the same time. Not really a career-path I'd recommend to anyone though.
regarding those usual objections, i'd argue that a spectrograph representation of a given piece of audio is just a different (lossy) encoding of the same content/information, so any hypothetical objections would still apply here.
great stuff, while it comes with the usual smeary iFFT artifacts that AI-generated sound tends to have the results are surprisingly good.
i especially love the nonsense vocals it generates in the last example, which remind me of what singing along to foreign songs felt like in my childhood.
This instantly reminded me of the paper "pattern recognition in a bucket"[0], which I've seen referenced a lot when I first started reading about AI in general. I only have surface-level knowledge about the field, but how exactly does what's described in the article differ from reservoir computing? (The article doesn't mention that term, so I assume there must be a difference)