pauladam

ad-account id: id

page id: id
post id: id
message: message

campaign:
  id: id
  objective: objective
  name: name

pauladam

{
  "estimates": {
    "post_impressions": 38760.211670480545
  },
  "id": "135249540516_10153617542060517",
  "predictions": {
    "post_impressions": {
      "series_high": [
        [
          1387211207,

pauladam

#!/bin/sh
user=`git st | grep 'modified' | cut -d ":" -f 2 | tr -d ' ' | while read fn; do
    git --no-pager blame $fn
done | cut -d " " -f 2- | cut -d ')' -f 1  | sed 's/[0-9].*//' | tr -d '(' | sort | uniq -c | sort -n | tail -r | head -n 1 | cut -d ' ' -f 3-`
echo $user
cat $1 > /tmp/commit-msg
echo '\n' > $1
echo "cc: $user" >> $1
cat /tmp/commit-msg >> $1

pauladam

// tree
// .
// ├── bin
// │   └── scribe
// └── src
//    └── scribe
//        └── scribe.go

// $ gb build all; bin/scribe
// Jesse

pauladam

#!/bin/bash
if [ $# -eq 0 ]
then
    echo "Usage: git reviewer filename"
    exit
fi
git blame $1 | cut -d "(" -f 2 | ruby -ne 'puts $_.split(/\d{4}/).first' | sort | uniq -c | sort -n | tail -r  | head -n 1 | cut -d ' ' -f 3-

pauladam

https://beta.socialcode.com/advisor/#audience-profiler?|hvac:act_321764907982593,hvad:6018552247870,hvsc:y,connection_frac:0.8659861889250815-0.88324|

pauladam

// ex
// $ gb build all; bin/scribe
// scribe
// Jesse

package main
import "os"
import "fmt"
import "time"
import "bufio"

pauladam

SC Interview notes

• Repo: git@github.com:SocialCodeInc/interview-exercises.git
• Shared notes: http://tinyurl.com/sc-shared-notes

TODO

• setup-interview.sh
• teardown-interview.sh

pauladam

#!/usr/bin/python
import urllib,urllib2, sys

BASE_URL = 'http://192.168.100.101:5000'
ADMIN_PASS = 'YOUR ADMIN PASS'

# Login to admin interface

parameters = {'username' : 'admin', 'passwd':ADMIN_PASS,'service_type':'1'}
request = urllib2.Request(BASE_URL+'/webman/modules/login.cgi', urllib.urlencode(parameters))

pauladam

# typical to sum over your list of items perhaps 
# performing some calculation on each item
normalizing_constant = \
	sum([calc(item) for item in items if filter(item)])

r = random.random()

for item in items:

  prob = get_probability(item) / normalizing_constant