iomonad

--- backend/cfg-obj.c
+++ backend/cfg-obj.c
@@ -1026,7 +1026,7 @@
       char *vendor = NULL;
       char *model  = NULL;
 
-      sscanf (string, "%*s %as %as", &vendor, &model);
+      sscanf (string, "%*s %ms %ms", &vendor, &model);
 
       if (list_append (_cfg->seen[CFG_KEY_SCSI], info))

iomonad

#define _GNU_SOURCE
#include <errno.h>
#include <fcntl.h>
#include <limits.h>
#include <stdio.h>
#include <stdlib.h>
#include <sys/types.h>
#include <sys/stat.h>
#include <sys/fanotify.h>
#include <unistd.h>

iomonad

#define _GNU_SOURCE

#include <sched.h>
#include <stdio.h>
#include <unistd.h>
#include <stdlib.h>
#include <unistd.h>

int main (int argc, char *argv[]) {
    if (argc != 2) {

iomonad

global _start

section .text
_start:
    mov eax, 0x04   ; write(
    mov ebx, 1      ;   STDOUT,
    mov ecx, m      ;   "Salut",
    mov edx, ml     ;   sizeof("Salut")
    int 0x80        ; );
    mov eax, 0x01   ; exit(

iomonad

!!!!!!! Muh(fia
!!!! X
! Resources
!!!! Brah
!WE WANT DA FONT
URxvt.font:             -beastie-gohufontmod11-*-*-*-*-11-*-100-*-*-*-iso8859-1
URxvt.boldFont:         -beastie-gohufontmod11-*-*-*-*-11-*-100-*-*-*-iso8859-1

! cause shell to be started as login shell
URxvt*loginShell: true

iomonad

while (x --\
            \
             \
              \
               > 0)
     printf("%d ", x);

iomonad

{-# LANGUAGE OverloadedStrings #-}
module Main where

import Pipes
import Network.AMQP
import Control.Monad
import qualified Data.ByteString.Lazy.Char8 as BL

main :: IO ()
main = do

iomonad

1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

iomonad

fib←{r÷⍨-/⍵*⍨2÷⍨1+1 ¯1×r←5*0.5}
fib¨⍳10 
⍝ 0 1 1 2 3 5 8 13 21 34

iomonad

import os
import sys
import glob

from qgis.core import (
    QgsProject, 
    QgsRasterLayer, 
    QgsCoordinateReferenceSystem
)