trio

With respect to async, I'm partial to trio [0], which is a spiritual successor to curio [1]. The model is similar to Golang in many ways, e.g. communication using channels [2] and cancellation [3] reminiscent of context.WithTimeout, except that in Golang you need to reify the context passing. The author has written some insightful commentary on designing async runtimes [4] and is actively developing the library, so I'm optimistic about its future. There were plans to use it for requests v3 until the fundraiser fiasco [5]. [0] https://github.com/python-trio/trio [1] https://vorpus.org/blog/announcing-trio/ [2] https://trio.readthedocs.io/en/stable/reference-core.html#us... [3] https://trio.readthedocs.io/en/latest/reference-core.html#ca... [4] https://vorpus.org/blog/notes-on-structured-concurrency-or-g... [5] https://vorpus.org/blog/why-i

I just watched your talk, and found it very interesting, thanks for sharing it! I hope you can do the data flow analysis. That would be so cool and SO useful. Python certainly has the ability to collect the data. A couple existing tools make use of this. For example, MonkeyType and Birdseye observe the values that are passed around by tracing execution during test runs (or even during a production run, but the performance impact can be substantial). https://github.com/Instagram/MonkeyType https://github.com/alexmojaki/birdseye Even more information can be gleaned from the gc module (see https://mg.pov.lt/objgraph/ for a tool using it). These tools make good progress, but I'd be very interested to see what a software-visualization expert would come up with. I'd also love to see how a concurrent execution tree can be visualized. For example