DNF2

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.

As long as Typst is on version 0.x,you should probably expect breaking changes. There is talk about changing even part of the parsing rules.

This is the risk of being an early adopter.

Once v1.0 is out, I hope it will stabilize for the long term.

But those are not languages, but frameworks, and are not general enough to solve many problems, especially outside of machine learning.

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.

Could you elaborate on the numerical precision issue regarding Julia?