[LudwigNagasena]

I used Julia to build a macroeconomic model (DSGE-VAR) during my econ studies. I liked the conceptual decisions and the language per se (ie as a spec), but DX was quite bad: low discoverability of features and proper typings, clunky metaprogramming, long compilation times, impossibility of struct redefinitions in REPL. My interest died pretty fast because of it.

[huitzitziltzin]

At the very least Tom Sargent and the New York Fed see it differently so you may by the odd man out. If you haven’t checked out the quant Econ project you are missing a great resource for exactly the problems you are working on.

[smabie]

My experience was exactly the same. This is probably unfair, but I got the impression that the people who made Julia never actually.. used it? But of course that can't be true so maybe my work flow was just significantly different than theirs?

Not a fan of Python at all but now I just stick with that for my quant analysis. Tons of issues with Python too but atleast they are all known / well documented problems (also chatgpt knows pandas / matplotlib / python very well).

[markkitti]

Apparently ChatGPT does pretty well with Julia. https://www.stochasticlifestyle.com/chatgpt-performs-better-...

It does take some asking around to discover the optimal Julia workflow with Revise.jl, PkgTemplates.jl, VSCode settings/debugger, Pluto.jl, but now it's probably my best development experience. Julia 1.10 improves much of this as well.

[spekcular]

Is the "optimal Julia workflow" written down anywhere?

[xgdgsc]

https://modernjuliaworkflows.github.io/

[pdeffebach]

Maybe [this](https://m3g.github.io/JuliaNotes.jl/stable/workflow/)? The key is to work with modules, use Revise.jl, and Infiltrator.jl.