Let me add some nuance to the hype. When someone says “for ML/data science” (just as an example) the common confusion is whether we are referring to researchers

Let me add some nuance to the hype. When someone says “for ML/data science” (just as an example) the common confusion is whether we are referring to researchers & library authors (those who create exciting new capabilities) or library users (most of whom will never release a package of algorithms, but just use available packages). For the former, the Julia experience is absolutely delight in many ways. For the latter, there will typically be a lag in usability (because the former group needs to create the libraries). Julia is somewhere along that curve of transition (remember that Python is older by almost 30 years!), and completeness of libraries might depend on the specific nature of your work. A rough thumb rule: the more algorithmic your work, the more likely the Julia ecosystem already generates value for you. The more plumbing your work is, it might still be a hit or miss, but is improving steadily (but could take up to a decade, to match the maturity of something like Python for the last few use cases).