The Python 3 breaking changes were a big mess for them, because by that time people had built lots of code around Python 2. There seems to be a similar problem happening now with updates to TensorFlow.
What areas might lead to similar issues for Julia? What is being done, or can be done, to prevent or mitigate this?
The biggest potential issue I see with community scalability is our approach to “reserved names”. By discouraging type piracy, freely
using packages, and needing to be careful not to use a name that’s used differently in an existing package (especially a popular one), I’d expect Julia programming to gradually become more and more constrained. Eventually something may need to shake this up, so it seems a possible pain point on the horizon.
Are there other things like this with some potential benefit to considering well ahead of any big problem? How can we avoid an eventual breaking-changes dumpster fire?