typeswitch-dev

#include <stdio.h>
#include <string.h>
#include <assert.h>
FILE *in; long M[1<<24]={0}, *D, *R, H=0x130000, IP=0, T;
long getu() { long t, h =  getc(in);         if (h < 0xC0) return h;
    t = ((h&0x1F) << 6) | (getc(in) & 0x3F); if (h < 0xE0) return t;
    t = ( t       << 6) | (getc(in) & 0x3F); if (h < 0xF0) return t;
    t = ( t       << 6) | (getc(in) & 0x3F); return t & 0x1FFFFF; }
void putu(long c) { if (c < 0x80) { putchar(c); return; }
    if (c < 0x7FF) { putchar(0xC0|(c>>6)); } else {

geraldvillorente

/**
 * Crawl the sitemap.xml for 301 redirections and 404 errors.
 * Source: http://edmondscommerce.github.io/php/crawl-an-xml-sitemap-quality-check-301-and-404.html
 *
 * To use this script you need to allocate a huge amount of time to maximum_execution_time to 
 * avoid Fatal error: Maximum execution time...I suggest to run this script on terminal.
 * Ex: $ php test-xml.php > ~/Desktop/sitemap-curl-result.txt
 *
 * For 3000 links the average time the script consumed is around 45 minutes to 1 hour.
 */

back2dos

package ;

#if macro
import haxe.macro.Context.*;
using haxe.macro.Tools;
#end

class ClassList {
  static var NAME = 'ClassList';//must be the fully qualified name of this class
  macro static function build() {

AndrasKovacs

{-# LANGUAGE 
    LambdaCase, GADTs, TypeOperators, TypeFamilies, DataKinds #-}

data Type = TInt | TBool | Type :=> Type

-- Needs GHC >= 7.8
type family Interp (t :: Type) where
    Interp TInt      = Int
    Interp TBool     = Bool 
    Interp (a :=> b) = Interp a -> Interp b

ghl3

We couldn’t find that file to show.

tpogden

# ! python
# coding: utf-8

import os
import argparse
import glob

import nbformat
from nbconvert.preprocessors import ExecutePreprocessor
from nbconvert.preprocessors.execute import CellExecutionError

kellabyte

History

For a long time I've been really impacted by the ease of use Cassandra and CockroachDB bring to operating a data store at scale. While these systems have very different tradeoffs what they have in common is how easy it is to deploy and operate a cluster. I have experience with them with cluster sizes in the dozens, hundreds, or even thousands of nodes and in comparison to some other clustered technologies they get you far pretty fast. They have sane defaults that provide scale and high availability to people that wouldn't always understand how to achieve it with more complex systems. People can get pretty far before they have to become experts. When you start needing more extreme usage you will need to become an expert of the system just like any other piece of infrastructure. But what I really love about these systems is it makes geo-aware data placement, GDPR concerns potentially simplified and data replication and movement a breeze most of the time.

Several years ago the great [Andy Gross](ht

palewire

# -*- coding: utf-8 -*-
"""
A command-line interface for running Jupyter Notebooks.

Usage: run.py [OPTIONS] [NOTEBOOK_PATHS]...

    Executes Jupyter Notebooks from the command line.

    Expects one or more file paths input as arguments.

antirez

Meltdown fix impact on Redis performances in virtualized environments

UPDATE: apparently kernel difference may have a serious impact, so I'll redo the test from scratch.

Test performed with AOF enabled, fsync policy 1 second, allowing the rewrites to be triggered.

Command lines used:

AndrasKovacs

open import Data.List
open import Data.Nat
open import Relation.Binary.PropositionalEquality
open import Function
open import Data.Product
open import Data.Sum

data _▶_≡_ {A : Set}(x : A) : List A → List A → Set where
  here  : ∀ {xs} → x ▶ xs ≡ (x ∷ xs)