I think people often underestimate (or just plain don't know about) the degree to which a multiple-dispatch-based programming language like Julia effectively implies its whole own dispatch-oriented programming paradigm, with both some amazing advantages (composability [1], and an IMO excellent balance of speed and interactivity when combined with JAOT compilation), but also some entirely new pitfalls to watch out for (particularly, type-instability [2,3]). Meanwhile, some habits and code patterns that may be seen as "best practices" in Python, Matlab can be detrimental and lead to excess allocations in Julia [4], so it may almost be easier to switch to Julia (and get good performance from day 1) if you are coming from a language like C where you are used to thinking about allocations, in-place methods, and loops being fast.

Things are definitely stabilizing a bit post-1.0, but it's still a young language, so it'll take a while for documentation to fully catch up; in the meanwhile, the best option in my experience has been to lurk the various chat forums (slack/zulip/etc. [5]) and pick up best-practices from the folks on the cutting edge by osmosis.

[1] https://www.youtube.com/watch?v=kc9HwsxE1OY

[2] https://www.johnmyleswhite.com/notebook/2013/12/06/writing-t...

[3] https://docs.julialang.org/en/v1.5/manual/performance-tips/#...

[4] https://github.com/brenhinkeller/JuliaAdviceForMatlabProgram...

[5] https://julialang.org/community/#official_channels