91–100 of 104 posts
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For those wondering what Chris is talking about, I just tried this: using Plots plot(sin) from fresh start, and it's about 2 seconds on my Dell Latitude 7400 (Core i7).
Julia 1.10 takes 1.00 seconds on my laptop, including loading Julia itself: time julia -e "using Plots; plot(sin)"
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What's wrong with JS/TS or Lua as a fast dynamic language?
I mean, JS, TS, and Python all have at least one major problem: threading. I don’t recall how Lua threads work but I have a feeling it too suffers.
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You don't need Julia. Julia was trying to be a better python. We will have better python in form of Mojo.
It should be fairly clear from studying the structure of Julia that it was never meant to be simply a better Python. Mojo also owes part of its design from the lessons it took from Julia (as per Chris Lattner [1]). [1]: https://news.ycombinator.com/item?id=35791125
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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…
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.
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I mean, JS, TS, and Python all have at least one major problem: threading. I don’t recall how Lua threads work but I have a feeling it too suffers.
There's threading in lua https://github.com/torch/threads
Each thread has its own lua_State. However, we provide a serialization scheme which allows automatic sharing for several Torch objects (storages, tensors and tds types). Sharing of vanilla lua objects is not possible, but instances of classes that support serialization (eg. classic objects with using require 'classic.torch' or those created with torch.class) can be shared, but remember that only the memory in tensor storages and tds objects will be shared by the instances, other fields will be copies. Also if synchronization is required that must be implemented by the user (ie. with mutex).
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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.
Is the "optimal Julia workflow" written down anywhere?
I like R and used it two ways. 1) Scheme-like functionalish 2) Tiddyverse and found Julia to be a lot of talk but seemed clunky to me.
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> Julia was ahead of the game with automatic differentiation which took a few years for Python to get support. In what way is this true? Looks like Julia didn't exist until 2012. If I remember correctly, theano was the big AD thing in python at that point.
Theano existed, but it didn't use autodiff at the time, most loss functions had preprogrammed derivative functions. Python first had native autodiff in June of 2013 with the ad package, but previously had bindings to Fortran and C++ libraries that supported autodiff in different use cases. Julia first had native autodiff in April of 2013 with ForwardDiff.jl.
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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.
Is the "optimal Julia workflow" written down anywhere?
are my main concerns resolved: • Hello World 200 MB ? • discoverability of functions: object.fun => fun(object) in REPL / IDE? object. => List of applicable functions?