Learning Common Lisp to beat Java and Rust on a phone encoding problem

• ulisp

  33 361 2.6 C++

  A version of the Lisp programming language for ATmega-based Arduino boards.

• woo

  15 1,252 5.6 Common Lisp

  A fast non-blocking HTTP server on top of libev

It has great package management with https://www.quicklisp.org/beta/ and some truly great and high quality libraries, especially Fukamachi's suite of web libraries and so many others. Woo, for example, is the fastest web server. https://github.com/fukamachi/woo (Faster then the runner up Go by quite a bit)

• WorkOS

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• bordeaux-threads

  6 220 6.2 Common Lisp

  Portable shared-state concurrency for Common Lisp

common lisp supports type annotations. there is even an ML-type language impletmented in it (see coalton). quick lisp [1] is used for package managment, bordeaux-threads [2] for threading.

1. https://www.quicklisp.org/index.html

2. https://github.com/sionescu/bordeaux-threads

• prechelt-phone-number-encoding

  18 29 2.7 Java

  Comparison between Java and Common Lisp solutions to a phone-encoding problem described by Prechelt

This is a pretty introductory CL article, mostly a commentary on Norvig's solution to the problem. Still, I learned about the #. readmacro from it. The conclusion: "[The Lisp implementation] was the fastest implementation for all input sizes except the largest one, where it performed just slightly worse than my best Java implementation." GH repo at https://github.com/renatoathaydes/prechelt-phone-number-enco.... Sounds like he was mostly measuring the performance of the SBCL bignum implementation.

• prechelt-phone-number-enco

  3 - -

This is a pretty introductory CL article, mostly a commentary on Norvig's solution to the problem. Still, I learned about the #. readmacro from it. The conclusion: "[The Lisp implementation] was the fastest implementation for all input sizes except the largest one, where it performed just slightly worse than my best Java implementation." GH repo at https://github.com/renatoathaydes/prechelt-phone-number-enco.... Sounds like he was mostly measuring the performance of the SBCL bignum implementation.

• cl-cookbook

  50 892 8.8 JavaScript

  The Common Lisp Cookbook

  

It would be cool if you updated the link to the Cookbook in your article from the old one (on Sourceforge) to the newer one (on Github): https://lispcookbook.github.io/cl-cookbook/ Best,

• snabl

  8 0 0.0 Go

  a simple Go scripting language

I've been playing around with designing programming languages in Common Lisp lately.

I'm curious how far it's possible to push performance by generating Lisp code in SBCL compared to the classical C interpreter goto loop.

https://github.com/codr7/snabl

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• coalton

  84 981 8.4 Common Lisp

  Coalton is an efficient, statically typed functional programming language that supercharges Common Lisp.

  

Common lisp has a smaller community than the currently most popular languages, but I'm consistently impressed by the range of and quality of libraries the community has created [1]

Regarding type-checking, common lisp is expressive enough to support an ML dialect (see coalton), and is easily extended across paradigms [2]

1. https://project-awesome.org/CodyReichert/awesome-cl

2. https://coalton-lang.github.io/

• jelm

  3 12 10.0 TeX

  Extreme Learning Machine in J

I have a bunch of links to ML material for either APL or J. I don't know of any particular library for J. J is interpreted, so it is not as fast as other implementations. I am mainly using it to experiment on concepts and teach myself more ML in J because of the iterative nature of the REPL, and the succinct code. I can keep what's going on in my head, and glance at less than 100 lines, usually 15 lines, of code to refresh it.

There is a series of videos of learning neural networks in APL cited by others here on this thread.

Pandas author, Wes McKinney, cited J as an influence in his work on Pandas.

Extreme Learning Machine in J (code and PDF are here too):

https://github.com/peportier/jelm

Convolutional neural networks in APL (PDF and video on page):

https://dl.acm.org/doi/10.1145/3315454.3329960

A DSL to implement MENACE (Matchbox Educable Noughts And Crosses Engine) in APL (Noughts and Crosses or Tic-tac-toe):

https://romilly.github.io/o-x-o/an-introduction.html

Suggest a related project