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Hi. Great talk. Basically introduced me to audio development! Given you're experience and the future you're talking about with Soul or a similar language being

Hi. Great talk. Basically introduced me to audio development! Given you're experience and the future you're talking about with Soul or a similar language being how audio development is done... what resources and approaches would you recommend someone completely new to the domain study?

julesrms 7y ago
Audio dev has always been insanely hard - that's part of the motivation behind creating this! It's hard because you need some serious mathematical skills to understand the DSP itself (that's the bit I'm lacking in..). Then if you're building a "real" product, you're going to have to write in C++ and have a rock-solid understanding of concurrency, real-time coding and many other very tricky subjects which take huge amounts of experience to get good at. I've been doing this for over 20 years so have lost sight of how beginners should learn it.. But most people seem to just dive in with an idea they want to build, and start trying to swim! It can be painful to see people using JUCE/C++ who don't really have any interest in C++ for its own sake, but who are struggling to get anywhere without putting the effort in to learn the language properly.
- gugagore 7y ago
  I wonder if the SOUL team has considered looking at Julia, which aims to solve the two-language problem, in which programming languages are either fast or easy. It's in the context of scientific computing, which is usually almost entirely about throughput and not about latency. But I think there's a lot in common.
  - julesrms 7y ago
    At a high level it might seem like that, but there's a big difference between "fast" and "realtime". We need to go fast, but the main problem the language needs to solve for us is to break the work down into a steady, realtime stream. Overall throughput is Julia's strong point, but that's not the same thing at all.
    - gugagore 7y ago
      I would argue that Julia's strong point is generating specialized code, and facilitating generic programming. Throughput is definitely the driver, but I think several aspects of Julia's design could benefit real-time programming as well. Currently, there are many blockers to using Julia in real-time application, such as dynamic memory allocation and lack of thread safety. But I find it promising that a subset of Julia could be used for real-time programming.

julesrms 7y ago

Audio dev has always been insanely hard - that's part of the motivation behind creating this! It's hard because you need some serious mathematical skills to understand the DSP itself (that's the bit I'm lacking in..). Then if you're building a "real" product, you're going to have to write in C++ and have a rock-solid understanding of concurrency, real-time coding and many other very tricky subjects which take huge amounts of experience to get good at. I've been doing this for over 20 years so have lost sight of how beginners should learn it.. But most people seem to just dive in with an idea they want to build, and start trying to swim! It can be painful to see people using JUCE/C++ who don't really have any interest in C++ for its own sake, but who are struggling to get anywhere without putting the effort in to learn the language properly.

gugagore 7y ago
I wonder if the SOUL team has considered looking at Julia, which aims to solve the two-language problem, in which programming languages are either fast or easy. It's in the context of scientific computing, which is usually almost entirely about throughput and not about latency. But I think there's a lot in common.
- julesrms 7y ago
  At a high level it might seem like that, but there's a big difference between "fast" and "realtime". We need to go fast, but the main problem the language needs to solve for us is to break the work down into a steady, realtime stream. Overall throughput is Julia's strong point, but that's not the same thing at all.
  - gugagore 7y ago
    I would argue that Julia's strong point is generating specialized code, and facilitating generic programming. Throughput is definitely the driver, but I think several aspects of Julia's design could benefit real-time programming as well. Currently, there are many blockers to using Julia in real-time application, such as dynamic memory allocation and lack of thread safety. But I find it promising that a subset of Julia could be used for real-time programming.