dkua

[alias]
    # Returns name of current branch
    # Usage: git current
    current = rev-parse --abbrev-ref HEAD
    # Makes a full copy of the current branch with `backup.<epoch>` appended before doing an interactive rebase
    # Usage: git safebase HEAD~2
    safebase = !CURRENT=$(git current) && git branch -c $CURRENT $CURRENT.backup.$(date +%s) && git rebase -i

dkua

collapsible markdown?

CLICK ME

yes, even hidden code blocks!

print("hello world!")

dkua

## chef report handler for Consul
## depends on a chef-client check being registered with the agent
## • pings TTL-style check with ok/warning status on chef run pass/fail
## • stores run report and node attributes in chef/reports/<datacenter>/<consul_node_name>

require "chef/handler"
require "rest_client"

class ConsulHandler < Chef::Handler
    attr_reader :config

dkua

<!-- Bibliography data -->
{{ $numParams := len .Params }}
{{ if eq $numParams 0 }}
  {{ if isset .Page.Params "bibliography" }}
    {{ $.Scratch.Set "bib_source" .Page.Params.bibliography }}
  {{ end }}
  {{ if isset .Page.Params "bibliography_ignore" }}
    {{ $.Scratch.Set "bib_ignore" .Page.Params.bibliography_ignore }}
  {{ end }}
{{ else if eq $numParams 1 }}

dkua

def binary_search(L, s):
    '''(list) -> bool
    Return True iff s is an element of L, otherwise False
    REQ: L must be a sorted list
    '''
    # BASE CASE: If L is empty, it's not in the list
    if L == []:
        result = False
    # RECURSIVE DECOMP: Pick the middle element, if we found
    # what we're looking for, great. If not, then we've at

dkua

export ANACONDAPATH=$HOME/anaconda;
anaconda() {
    export PATH=$ANACONDAPATH/bin:$PATH;
}
alias "anaconda"="anaconda"
nanaconda() {
    export PATH=$(echo $PATH | sed -e "s|$ANACONDAPATH/bin:||g");
}
alias "nanaconda"="nanaconda"

dkua

#lang scheme

(define (fact n)
 (fact-tail n 1))

(define (fact-tail n acc)
  (if (= 0 n)
      acc
      (fact-tail (- n 1) (* acc n))))

dkua

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

dkua

#!/bin/bash

## Sample POST request to /scripts/qmd_bench.sh
##
## {
##   "args": ["1", "10"],
##   "callback_url": "http://192.168.88.88:9090"
## }
                                                                                                       
MIN=$1                                                                                                 

dkua

Keybase proof

I hereby claim:

• I am dkua on github.
• I am dkua (https://keybase.io/dkua) on keybase.
• I have a public key whose fingerprint is C0B1 FA59 DF22 4942 E70C D4FC 3884 0595 7325 33A5

To claim this, I am signing this object: