By “them”, I am not sure whether you were referring to the problems or those people who where complaining…
I’d say that your argument in Problem 2 doesn’t quite stand. Intensive development of Python’s computational capability (thanks to the advent of machine learning, of course) largely started around 2010, slightly before the inception of Julia. In a way, one may argue that Julia started with a cleaner slate than Python (e.g. the Python 2 to 3 transition mess), so the difference is not that big.
IMHO, the biggest complaints the HN crowd have about Julia are:
- The language is a bit over-sold. When people tried the language and it failed to deliver all the promises, they feel they were “conned”.
- Some part of the Julia community can be quite dismissive when responding to valid criticisms on Julia. When people complain about Julia’s problems on HN (probably because they were disappointed), you often see replies arguing along the line of “You are doing it wrong; Julia is different than Python/Foo/Bar”, or when it comes to performance comparisons, “Your implementation is not type stable / is allocating too much; you should be apply these optimization to speed it up”. On itself this is not really an issue, but a lot of people perceived Julia as a language as easy to use as Python while as performant as C/C++ — they then found out that they have to choose between ease of use and performance, which reinforces their opinion that the language over-promised.
- There is also the problem of developer experience (e.g. tooling, static analysis, workflow best practices).