luqmana

fn main() {
    unsafe {
        do str::as_c_str(~"The answer is %d.\n") |c| {
            let a = 42;
            asm!("mov $0, %rdi\n\t\
                  mov $1, %rsi\n\t\
                  xorl %eax, %eax\n\t\
                  call _printf"
                 :

MikeInnes

# Repeat an operation n times, e.g.
# @dotimes 100 println("hi")

macro dotimes(n, body)
  quote
    for i = 1:$(esc(n))
      $(esc(body))
    end
  end
end

paulcuth

Slimdown

This is a Lua port of of jbroadway's PHP project of the same name.

A very basic pattern-based Markdown parser. Supports the following elements (and can be extended via slimdown.addRule()):

• Headers
• Links
• Bold

skariel

module GeometricalPredicates

# GeometricalPredicates_v0.2.9
#
# Fast, robust 2D and 3D geometrical predicates on generic point types.
# Implementation follows algorithms described in http://arxiv.org/abs/0901.4107
# and used (for e.g.) in the Illustris Simulation
# http://www.illustris-project.org/
#
# Author: Ariel Keselman ([email protected])

carnaval

# master: 3b2269d8
# incgc:  2aeaa59d

# micro
test name            old       new     % speedup  % st. dev
-----------------------------------------------------------
fib                   0.055     0.053     3.64%     3.64%
mandel                0.163     0.152     6.75%     4.91%
micro.mem           255.609   178.449    30.19%     0.00%
parse_int             0.216     0.172    20.37%   354.17%

skariel

[JULIA] Faster than CGAL Delaunay

TL;DR

DelaunayJL is an incremental 2D Delaunay triangulation algorithm implemented in Julia, it is robust and ~20% faster than CGAL, the C++ de-facto industry standard. And it's MIT Licensed! all links to code are below

Show me the data!

The figure below shows how much time it takes to run a benchmark on my computer, an Intel Core i7-4800MQ CPU @ 2.7Ghz, 16GB RAM, Win8 64bit. The benchmark consists of inserting a number of points uniformly distributed. The benchmark is run 5 times for each number of points once for CGAL and once for Julia. The numbers of points used are 10K, 100K, 1M, and 10M. CGAL v4.4 was compiled with VS2013 64bit release mode, Julia is of version 0.3.0 Commit 7681878 (2014-08-20 20:43 UTC) x86_64-w64-mingw32 the delaunay code is here (see other gists of mine for complementing files... I'll compile this all into a library when I have the time)

Veedrac

#include <iostream>
#include <iterator>
#include <numeric>
#include <unordered_set>
#include <vector>

int32_t some_calculations(int32_t number) {
    std::vector<int32_t> a;
    std::unordered_set<int32_t> s;

dtheodor

import select
import datetime

import psycopg2
import psycopg2.extensions

conn = psycopg2.connect(database="postgres", user="vagrant")
#conn.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)

curs = conn.cursor()

Oxyrus

type CacheInterface interface {
  Get(key string) ([]byte, error)
  Set(key string, value interface{}) error
}

// Cache implements CacheInterface.
type Cache struct {
  // wrap redis pool connection
  // or whatever you need
}

nicksam112

#Evolution Strategies with Keras
#Based off of: https://blog.openai.com/evolution-strategies/
#Implementation by: Nicholas Samoray

#README
#Meant to be run on a single machine
#APPLY_BIAS is currently not working, keep to False
#Solves Cartpole as-is in about 50 episodes
#Solves BipedalWalker-v2 in about 1000