Quote: "Coming very soon: a version of DifferentialEquations.jl that fully precompiles the solvers on Vector{Float64}, virtually eliminating the any #julialang #sciml JIT lag."
And agreed that the folks at SciML (and the rest of Julia) have put amazing efforts into reducing the compilation lag from where it used to be :) I'm optimistic that things will improve--it'll just take some time.
Also, if you take a look at a tutorial, say the tutorial video from 2018, https://youtu.be/KPEqYtEd-zY, you'll see that the code is still exactly the same an unbroken over the half decade. So no, compile times have only been worked on for about a year and code from half a decade ago still runs just fine.
I think passerbys should be made aware of the state of things in the language without spin from people making a living selling it. No personal offence to you, just please consider not overselling, it's damaging to people who jump in expecting a good experience.
> I'm not going to waste anymore time with digging into this to file an issue or prove a point. > No personal offence to you, just please consider not overselling, it's damaging to people who jump in expecting a good experience.
I'm sorry, but non-concrete information isn't helpful to anyone. It's not helpful to the devs (what tutorial needs to be updated where?) and it's not helpful to passerbys (something changed according to somebody, what does that even mean?). I would be happy to add a backwards compatibility patch if there was some more clear clue.
> I think passerbys should be made aware of the state of things in the language without spin from people making a living selling it.
The DiffEq/SciML ecosystem is free and open source software. There is nobody making a living from selling it.