On the other hand, I'm wondering if this will help a little with the dependency hell that's caused me to drift away from Julia over the last year or so.
At first I was fairly excited about Julia, and greatly preferred it over R or Python for numerical work, library resources aside. It was fast and I liked the language design itself.
Over the last year or two, though, I've had recurring problems with trying to install and use packages, and them failing at some point due to some dependency not working. Sometimes the target package itself won't install, but most of the time it's some lower-level package.
It's more frustrating than not having packages available, because it creates some sense that packages are available, when they're not. At first I looked at it as some idiosyncratic case with one package, but it's happened over and over and over again.
Basically in this time I've given up on Julia, because there's such a huge discrepancy between what it looks like on the surface and what it looks like in practice, once you get out of certain limited-use cases, or when you're not coding everything yourself from the base language. (Related concerns about misleading speed claims have been raised, although my personal experience in that regard has been mixed, because my experience overall has been pretty good in some critical performance cases, but there have also been some wildly unpredictable exceptions that act as bottlenecks... but it's still much better than R or Python).
When I've tried to figure out what's going on with dependency hell, usually it's because some package that was previously working with a earlier version is no longer working. So maybe stabilizing the language will help that?