doc VS DifferentialEquations.jl

- it is possible to subscribe on the changes using RSS (this is a feature of the 40ANTS-DOC documentation builder).

So, the article is harsh on CL: YMMV. Also, your goal may vary: I want to build and ship (web) applications, and so far Julia doesn't look attractive to me (at all). Super fast incremental development, build a standalone binary and deploy on my VPS or ship an Electron window? done. Problem(s) solved, let's focus on my app please.

The author doesn't mention a few helpful things:

- editor support: https://lispcookbook.github.io/cl-cookbook/editor-support.ht... Emacs is first class, Portacle is an Emacs easy to install (3 clicks), Vim, Atom support is (was?) very good, Sublime Text seems good (it has an interactive debugger with stack frame inspection), VSCode sees good work underway, the Alive extension is new, usable but hard to install yet, LispWorks is proprietary and is more like Smalltalk, with many graphical windows to inspect your running application, Geany has simple and experimental support, Eclipse has basic support, Lem is a general purpose editor written in CL, it is Emacs-like and poorely documented :( we have Jupyter notebooks and simpler terminal-based interactive REPLs: cl-repl is like ipython.

So, one could complain five years ago easily about the lack of editor support, know your complaint should be more evolved than a Emacs/Vim dichotomy.

- package managers: Quicklisp is great, very slick and the ecosystem is very stable. When/if you encounter its limitations, you can use: Ultralisp, a Quicklisp distribution that ships every 5 minutes (but it doesn't check that all packages load correctly together), Qlot is used for project-local dependencies, where you pin each one precisely, CLPM is a new package manager that fixes some (all?) Quicklisp limitations

> [unicode, threading, GC…] All of these features are left to be implemented by third-party libraries

this leads to think that no implementation implements unicode or threading support O_o

> most of the language proper is not generic

mention generic-cl? https://github.com/alex-gutev/generic-cl/ (tried quickly, not intensively)

Documentation: fair points, but improving etc. Example of a new doc generator: https://40ants.com/doc/

Also I'd welcome a discussion about Coalton (Haskell-like type system on top of CL).

Great news! Feedback: I guess it's time to start working on documentation ;) The readme doesn't say what the system does. I guess you could maintain a high overview "manually", and in parallel set up a documentation system (40ants doc is kinda cool). Best,

- and 40ANTS-DOC builder.

That is why I've put about half of this year into the Common Lisp documentation generator for all of my libraries.

If you are interested, please read it's docs and join the effort of making good documentation for CL projects: https://40ants.com/doc/

And the doc is built with the new https://40ants.com/doc 🎉 Really cool.

This lists some of its unique abilities:

https://docs.sciml.ai/DiffEqDocs/stable/

The routines are sufficiently generic, with regard to Julia’s type system, to allow the solvers to automatically compose with other packages and to seamlessly use types other than Numbers. For example, instead of handling just functions Number→Number, you can define your ODE in terms of quantities with physical dimensions, uncertainties, quaternions, etc., and it will just work (for example, propagating uncertainties correctly to the solution¹). Recent developments involve research into the automated selection of solution routines based on the properties of the ODE, something that seems really next-level to me.

[1] https://lwn.net/Articles/834571/

https://github.com/SciML/DifferentialEquations.jl/issues/786. As you could see from the tweet, it's now at 0.1 seconds. That has been within one year.

Also, if you take a look at a tutorial, say the tutorial video from 2018,

It's not faith, and it's not all from Julia itself. https://github.com/SciML/DifferentialEquations.jl/issues/785 should reduce compile times of what OP mentioned for example.

Let's even put raw numbers to it. DifferentialEquations.jl usage has seen compile times drop from 22 seconds to 3 seconds over the last few months.

https://github.com/SciML/DifferentialEquations.jl/issues/786

https://github.com/SciML/DifferentialEquations.jl/issues/737 double posted, with the answer here. Please don't do that.