julia the language is really good. but a lot of core infrastructure julia libraries are maintained by some overworked grad student.
sometimes that grad student is a brilliantly productive programmer + the libraries reach escape velocity and build a community, and then you get areas where Julia is state of the art like in differential equation solving, or generally other areas of "classical" scientific computing.
in other cases the grad student is merely a very good programmer, and they just sort of float along being "almost but not quite there" for a long time, maybe abandoned depending on the maintainer's career path.
the latter case is pretty common in the machine learning ecosystem. a lot of people get excited about using a fast language for ML, see that Julia can do what they want in a really cool way, and then run into some breaking problem or missing feature ("will be fixed eventually") after investing some time in a project.
sometimes that grad student is a brilliantly productive programmer + the libraries reach escape velocity and build a community, and then you get areas where Julia is state of the art like in differential equation solving, or generally other areas of "classical" scientific computing.
in other cases the grad student is merely a very good programmer, and they just sort of float along being "almost but not quite there" for a long time, maybe abandoned depending on the maintainer's career path.
the latter case is pretty common in the machine learning ecosystem. a lot of people get excited about using a fast language for ML, see that Julia can do what they want in a really cool way, and then run into some breaking problem or missing feature ("will be fixed eventually") after investing some time in a project.