As I’m currently thinking of creating a new app which requires a cloud service (or at least an online service 24/7), I thought about what are the long term constraints I want to have when signing up with a cloud provider. These are the topics to consider IMHO.
On my quest for a good Flask book, I saw this book from Tarek Ziade. We are more or less of the same generation, both from France and he wrote a far better introductory book to Python in French than mine. He also founded the French Python community (AFPY), so I always had a huge respect for the guy. And the book was appetizing.
I’m thinking of writing a Web service for a project of mine. For this purpose, I wanted to learn Flask (and a bunch of other technologies), as Flask seems well established and well documented. This is a book from Packt that agglomerates 3 previously released books. One of the main questions is the relevance of them as the Flask API evolves.
ATK is updated to 3.1.0 with heavy code refactoring. Old C++ standards are now dropped and it requires now a full C++17 compliant compiler.
The main difference for filter support is that explicit SIMD filters using libsimdpp have been dropped while tr2::simd becomes standard and supported by gcc, clang and Visual Studio.
All major cloud providers provide some support for Machine Learning algorithms. They also evolve all the time. There are not many books ont he subject, due to the evolution of these services, so let’s have a look at this one.
Today, I’m presenting at the ADC my work on analog modelling for the past year.
I will make a more detailed post later this year, but I’d like to put some teasers here. SPICE net lists are an efficient way of representing electronics circuits and there are several very good free and paying simulators. Unfortunately, they are not easy to integrate in a VST plugin.
Audio ToolKit now has a sister project around this topic. The lite version is also licensed under the BSD and can generate a dynamic filter of a net list. The full project is now also capable of generating static filter, with a source file (and compiling it in memory) that can be manually tuned.
Future work on this project will include different solvers for the static filter, as well as a tuner that will be able to drop entries in the Jacobian (full entries or component contributions for a given pin) in the Newton Raphson solver.
Audio ToolKit started with only C++11 a long time ago, and now with version 3.1, it’s going to be full C++17.