First of all, I think this sort of aggressive tone is unwarranted.
Secondly, I think it's on you to clarify that you were talking specifically and exclusively about static compilation to standalone binaries. Re-reading your first post strongly gives the impression that you were talking about the compilation strategy in general.
I would also remind you that Julia always does does-ahead-of-time compilation.
Furthermore, my limited understanding of the static compiler (--trim feature), based on hearsay, is that it does pretty much what you are suggesting, supporting dynamic dispatch as long as one can enumerate all the types in advance (though requiring special implementation tricks). Open-ended type sets are not at all supported.
I'm not exactly sure what you don't believe, your comment is hard to follow, or relies on premises I haven't detected. What you are describing in your first paragraph is somewhat reminiscent of dynamic dispatch, which Julia does use, but generally hampers performance. It is something to avoid in most cases.
Anyway, performance in Julia relies heavily on statically inferring types and aggressive type specialization at compile time. Triggering the compiler later, during actual runtime, can happen, but is certainly not beneficial for performance, and it's quite unusual to claim that it's central to the performance model of Julia.
If you are asking why Julia allows recompiling code and has dynamic types, it's not for performance, but to allow an interactive workflow and user friendly dynamism. It is the central tradeoff in Julia to enable this while retaining performance. If performance was the only concern, the language would be very different.
This is not how I understand the performance model. Allowing invokation of the compiler at runtime is definitely not something that is done for performance, but for dynamism, to allow some code to run that could not otherwise be run.
In performant Julia code, the compiler is not invoked, because types are statically inferred. In some cases you can have dynamic dispatch, but that doesn't necessarily mean that the compiler needs to run. Instead you can get runtime lookup of previously compiled methods. Dynamic dispatch does not necessitate running the compiler.
> Julia is fastest with immutable structures--why provide a built-in syntax for complex assignment to mutable types, but then relegate lenses to a library that only FP aficionados will use?
This is not really accurate. Performance in Julia is heavily organized around mutability, in particular for arrays. The main reason Julia does not fully embrace immutability for everything is, simply, performance.
Interesting. I have not experienced that, except when trying out the pre-release version of tinymist, and did some messy multiple view+cropping into a big pdf (testing out the new pdf-image stuff.) I chalked it up to it being new and beta.
Admittedly, I have still not created large documents in Typst.
> 2. (minor compared to Overleaf) typst compiles faster.
I would argue that this isn't minor. At least in my opinion, it makes a big difference.
Overleaf, already 3 pages into a document, with a couple of TikZ figures, was getting slow, as in multiple seconds wait for each save.
Typst, on the other hand (Tinymist in VS Code) is really realtime. Text updating within some tens of milliseconds, and figures included in far below a second. It really _feels_ instant, and to me that changes the experience a lot.
That is not really correct. Type instabilities tend to disappear at function boundaries, which is one of the reasons why using functions is so heavily promoted in Julia, it helps keep type instabilites 'localized'.
Then you are back to the "two language problem". I'm sure that's not a problem for you and for many others, but there is a reason it has its own, widely known name. It really is a problem for people who are mostly not software developers, but instead engineers or researchers.
"Clanky"? That is a word I would use when comparing Julia and Python, but I would reverse the roles. I mean, python works well, and has almost everything, but it really feels, well, clanky.
That article was about handling of "Missing" values (which Julia now had natively), and a wish for inclusion of an 80-bit float type. I don't know what languages has that, but you can have 128bit, 256bit and even larger floats now, if that is relevant.
I didn't even mention the dot operator syntax (.*,.^,./) used in Matlab, while numpy uses only implicit broadcasting. On the other hand, numpy can partially leverage map, filter, comprehension (though with performance loss). Julia has both much expanded dot-syntax and (multidimensional) comprehensions/map/filter with full performance.
What else? Matlab indexes with end, while numpy just leaves it open (e.g. 3:). Numpy allows negative indices, not Matlab. n:m is both a standalone range and an indexing expression in Matlab, not in numpy. Also numpy uses open ranges, Matlab closed ranges. And A[2:2:10] has dramatically different meaning in Matlab and numpy.
I forgot to mention the difference in function passing, the fact that Matlab passes arguments by value (unless it's a `handle` class) makes it really hard to do in-place transformations, as in passing an array to a function and modifying it, since the modifications are not visible outside the function.
In some cases, applying a limited set of basic operations might make up a significant part of development time, but in my experience most of the time is spent designing algorithms, parsing function input and managing the flow of vectorized expressions across function boundaries. And that is generally more efficient in the context of multiple dispatch with efficient loops.
For example, writing a function that works on scalars, then mapping or broadcasting it over array input is very good for productivity (and is efficient in Julia). On the other hand, designing an inherently array-based algorithm is often a headache that one must contend with for the sake of performance in numpy.
So, if you design somewhat complex or novel algorithms, I don't think object-dot notation, or the lack thereof, has a significant impact on productivity.
Yes, python exposes a limited list of names that map to operators, like __add__, __sub__, __ne__, __or__, etc. The list is large enough that it covers most normal usage, but the semantic meaning of the operators is normally fixed, so creating your own more specialized operators with new meanings is not very convenient (I mean, in principle, you can overload __xor__ to do something inventive, but it's probably a bad idea to veer from the established meaning of 'exclusive or'.)
In contrast, julia provides a nearly endless list of operators to use for whatever you like, allowing you to adopt mathematical operators from your field of study, for example.
Also, I am not aware of any type promotion mechanism in python, which, in Julia, makes e.g. binary operators on different types convenient to implement. The inherent multiple dispatch also seems more natural than the __roperator__ stuff you need to do in python.
Secondly, I think it's on you to clarify that you were talking specifically and exclusively about static compilation to standalone binaries. Re-reading your first post strongly gives the impression that you were talking about the compilation strategy in general.
I would also remind you that Julia always does does-ahead-of-time compilation.
Furthermore, my limited understanding of the static compiler (--trim feature), based on hearsay, is that it does pretty much what you are suggesting, supporting dynamic dispatch as long as one can enumerate all the types in advance (though requiring special implementation tricks). Open-ended type sets are not at all supported.