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The same first impression for me. It's great people with more faith than me have pushed forward and actually I'm still waiting to make the jump. It was very sim

by plafl 6y ago

The same first impression for me. It's great people with more faith than me have pushed forward and actually I'm still waiting to make the jump. It was very similar with python for scientific computing in the beginning. When I wrote my master thesis in python in 2006 python was completely dwarfed by Matlab. I was more enthusiastic on those days trying new things and also had more time. It is yet to be seen if Julia displaces python but at least it has non zero chance of doing it.

peatmoss 6y ago
I’ve wanted Scheme / Racket to make a showing in places where R and Python have dominated the data science and numerical computing spaces (for those of us not writing C/C++/Fortran). But, I think Julia is the closest thing to an honest-to-goodness lisp that has a prayer of mainstream adoption. I quite like Julia and think the community has leveraged the meta programming strengths of the language well enough to convince me to relinquish my love of minimalist lisp syntax. I do wonder if the biggest risk to Julia isn’t that it’s not extreme / superlative in any dimension. If you want raw performance, Rust seems to be capturing the lion’s share of excitement among “new” languages. If you want meta programming, Julia still feels like a compromise compared to Scheme/Racket/Clojure/Common Lisp. If you want libraries, Python is still king. If you want stats libraries, R is still king. Julia is impressively good at all of those things... but it’s not good enough to be the most compelling of any of them. Still, it makes me think that it may be time to dive in head first rather than dabble my toes as I've been doing for the past few years. I guess my main barrier at the moment is compiler / deployment story—it still seems hard to build a deployable binary that fits in, say, a Lambda layer as far as I can tell.
- improbable22 6y ago
  > it’s not extreme / superlative in any dimension. If you want raw performance ... good at all of those things... but I guess the selling point is that being good at many things means you don't have to keep switching between all these languages, or glueing them together. Others have mentioned the composability benefits of this. But it also has a learning advantage, I think. You can incrementally learn to speed up the crucial piece of something. You can learn just enough metaprogramming to solve some problem you have.
- ddragon 6y ago
  Even if Julia never ends up being the very best at anything particular, Python has shown that being "generally the second best language for everything" has even more value for most people. Leveraging 80% of the flexibility and composability of Lisp, 80% of the "code that looks like pseudo-code" aspect and overall easy to use from Python, 80% of the speed of the fastest static languages and eventually the library and tooling maturity to support all use cases, it can definitely become an even better second best language for everything.

by plafl 6y ago

peatmoss 6y ago

I’ve wanted Scheme / Racket to make a showing in places where R and Python have dominated the data science and numerical computing spaces (for those of us not writing C/C++/Fortran). But, I think Julia is the closest thing to an honest-to-goodness lisp that has a prayer of mainstream adoption. I quite like Julia and think the community has leveraged the meta programming strengths of the language well enough to convince me to relinquish my love of minimalist lisp syntax. I do wonder if the biggest risk to Julia isn’t that it’s not extreme / superlative in any dimension. If you want raw performance, Rust seems to be capturing the lion’s share of excitement among “new” languages. If you want meta programming, Julia still feels like a compromise compared to Scheme/Racket/Clojure/Common Lisp. If you want libraries, Python is still king. If you want stats libraries, R is still king. Julia is impressively good at all of those things... but it’s not good enough to be the most compelling of any of them. Still, it makes me think that it may be time to dive in head first rather than dabble my toes as I've been doing for the past few years. I guess my main barrier at the moment is compiler / deployment story—it still seems hard to build a deployable binary that fits in, say, a Lambda layer as far as I can tell.

improbable22 6y ago
> it’s not extreme / superlative in any dimension. If you want raw performance ... good at all of those things... but I guess the selling point is that being good at many things means you don't have to keep switching between all these languages, or glueing them together. Others have mentioned the composability benefits of this. But it also has a learning advantage, I think. You can incrementally learn to speed up the crucial piece of something. You can learn just enough metaprogramming to solve some problem you have.
ddragon 6y ago
Even if Julia never ends up being the very best at anything particular, Python has shown that being "generally the second best language for everything" has even more value for most people. Leveraging 80% of the flexibility and composability of Lisp, 80% of the "code that looks like pseudo-code" aspect and overall easy to use from Python, 80% of the speed of the fastest static languages and eventually the library and tooling maturity to support all use cases, it can definitely become an even better second best language for everything.