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> Given Julia’s extreme generality it is not obvious to me that the correctness problems can be solved. Julia has no formal notion of interfaces, generic functi

> Given Julia’s extreme generality it is not obvious to me that the correctness problems can be solved. Julia has no formal notion of interfaces, generic functions tend to leave their semantics unspecified in edge cases, and the nature of many common implicit interfaces has not been made precise (for example, there is no agreement in the Julia community on what a number is). Does all that apply to Python? I think so? Yet apparently similar problems don't exist in python, and even one of the examples in OP had the reporter moving to python to have no problems getting the same thing to work that was problematic in Julia. In a language intended for math, I do understand the desire to have something with more formal properties suited for guarantees and such. But Python seems to be doing just fine in that domain without those features, so, I'm not sure what we should conclude here.

adgjlsfhk1 4y ago
The main difference between Julia and python is that most of the "core" python ecosystem has had a lot more dev time put into it. Google, Facebook, and Microsoft all have hundreds of full time developers on major python packages.
- jrochkind1 4y ago
  Makes sense. I guess the author's contention is that if Julia had those formal features the author wants, it would need very significantly less dev time to reach python's levels of reliability? It's of course plausible, that's what those sorts of features are intended for, but I'm not certain I'm absolutely confident. At any rate, python demonstrates it is not the only path, as the author seems to be suggesting ("it is not obvious to me the problem can be solved" without these features, says the author. But it's not obvious to me that those features are necessary to solve the problem, or sufficient to solve the problem...)
  - adgjlsfhk1 4y ago
    Python's reliability here comes because it is a much less flexible language in some ways. If you write your own array type in python, and pass it into tensorflow, you would expect it to error. If you do the same thing in Julia, you would expect it to work.
    - ShamelessC 4y ago
      More like you hope it works is what I'm gathering.

adgjlsfhk1 4y ago

The main difference between Julia and python is that most of the "core" python ecosystem has had a lot more dev time put into it. Google, Facebook, and Microsoft all have hundreds of full time developers on major python packages.

jrochkind1 4y ago
Makes sense. I guess the author's contention is that if Julia had those formal features the author wants, it would need very significantly less dev time to reach python's levels of reliability? It's of course plausible, that's what those sorts of features are intended for, but I'm not certain I'm absolutely confident. At any rate, python demonstrates it is not the only path, as the author seems to be suggesting ("it is not obvious to me the problem can be solved" without these features, says the author. But it's not obvious to me that those features are necessary to solve the problem, or sufficient to solve the problem...)
- adgjlsfhk1 4y ago
  Python's reliability here comes because it is a much less flexible language in some ways. If you write your own array type in python, and pass it into tensorflow, you would expect it to error. If you do the same thing in Julia, you would expect it to work.
  - ShamelessC 4y ago
    More like you hope it works is what I'm gathering.