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    Julia: to Lisp or not to Lisp?

    European Lisp Symposium

    4 HN recommenders · recommended in 11 months, 2016–2022

    YouTube

    1:04:53 · 24K views · May 30, 2016

    Stefan Karpinski's European Lisp Symposium 2016 talk asks whether Julia is a Lisp. He covers the features it borrows from Lisp, such as macros and closures, and where it departs, including syntax and its type system, with generated machine code shown.

    HN commenters recommend it as a brilliant talk on Julia's design and how it is or isn't a Lisp.

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    13 comments from Hacker News

    1. @FlowMay 2016· in “European Lisp Symposium 2016”

      Ah, the Julia talk ( this video ) was just great. Showing the generated machine code is so hackerish yet so rare these days. :-/

      Strongly recommendsView on HN
    2. @e12eJun 2016· in “Nim Programming Language 0.14.0 released”

      This reminded me of the recent brilliant talk about design of Julia: "Julia: to lisp or not to lisp?" this video

      Strongly recommendsView on HN
    3. @e12eJul 2016· in “Why is Python slow”

      From the linked [lwn] article: "Another example he gave demonstrates the slowness of the C runtime: import itertools sum(itertools.repeat(1.0, 100000000)) That will calculate the sum of 100 million 1.0s. But it is six times slower than the equivalent JavaScript loop. Float addition is fast, as is sum(), but the result is not. Larry Hastings asked what it was that was slowing everything down. Modzelewski replied that it is the boxing of the numbers, which requires allocations for creating objects. Though an audience member did point out with a chuckle that you can increase the number of iterations and Python will still give the right answer, while JavaScript will not." Reminded me about the excellent talk about Julia: "Julia: to Lisp or not to Lisp?" this video One things he points out early is that both the C99 and the R6RS Scheme spec is 20% numerical. Because correct and (reasonably) fast numbers and arithmetic is actually pretty hard to get right on a computer - if you want to abstract away hardware "short-cuts" and allow for precise arithmetic by default. It will be interesting to see how much type hints (eliminating some of the boxing/unboxing) will help python. And if it turns out to really be a good fit for the language -- everyone wants "free" performance, but transitioning to a (even partially) typed language is certainly not "free". Another point with the above loop, is that ideally, even if it can't be optimized/memoized down to a constant - it really shouldn't have t

      RecommendsView on HN
    4. @e12eMar 2017· in “Some fun with π in Julia”

      I'm not sure if "special language" is a particularly apt description of what Julia is. SQL is a "special" language. At any rate - a lisp-like language with llvm backend, package manager, and a (small, but not trivial) group of users writing real software - what's not to like? I (still) recommend the talk: "Julia - to lisp or not to lisp?", that gives a quick overview of some of the design choices: this video For me, I like that it has a real story trying to balance "actual integer math" (not this silly machine constrained twos-compliment hack" and "you could conceivably write a ray tracer that wasn't unusably slow" :)

      RecommendsView on HN
    5. @travisdSep 2022· in “From Common Lisp to Julia”

      Stefan Karpinski (one of the core Julia contributors) gave a talk (in 2016, so rather dated as far as the Julia syntax and whatnot go) about how Julia is secretly more lisp-y than even lisp & Scheme. The core of this argument is that most language specifications dedicate many pages to defining how arithmetic works, whereas Julia is actually just able to define that in Julia code as part of the stdlib (the implementation just calls out to LLVM intrinsics, but nonetheless, super neat!). `Int64` is actually defined in a Julia file. this video

      RecommendsView on HN