wlonkly

I wrote this down after I responded to a page today (a holiday) because it would've been a decent pairing opportunity for a couple of new people on my team. Second best is that people can read what I did afterwards and ask me any questions. And then I realized that there's nothing PagerDuty-specific or confidential in here, so I may as well share it wider. It's hardly an epic incident, but it's a good example of "doing the work", I think. I borrowed the "write down what you learned" approach from Julia "b0rk" Evans. It's a fantastic practice.

The PagerDuty incident: "Disk will be full in 12 hours. device:/dev/nvme0n1p1, host:stg-nomadusw2-client-..."

(Note for non-PD readers: We run Nomad where others might run Kubernetes.)

Here's the process I went through.

• Noticed that the usual docker system prune -a -f didn't resolve it
• Tried docker system prune -a -f and it cleared up 0B

nicm

#	Reconstructed via infocmp from file: /usr/share/terminfo/t/tmux-256color
tmux-256color|tmux with 256 colors,
	OTbs, OTpt, am, hs, km, mir, msgr, xenl, AX, G0,
	colors#256, cols#80, it#8, lines#24, pairs#32767, U8#1,
	acsc=++\,\,--..00``aaffgghhiijjkkllmmnnooppqqrrssttuuvvwwxxyyzz{{||}}~~,
	bel=^G, blink=\E[5m, bold=\E[1m, cbt=\E[Z, civis=\E[?25l,
	clear=\E[H\E[J, cnorm=\E[34h\E[?25h, cr=^M,
	csr=\E[%i%p1%d;%p2%dr, cub=\E[%p1%dD, cub1=^H,
	cud=\E[%p1%dB, cud1=^J, cuf=\E[%p1%dC, cuf1=\E[C,
	cup=\E[%i%p1%d;%p2%dH, cuu=\E[%p1%dA, cuu1=\EM,

jj1bdx

#!/bin/sh

# search mount point based on the device name
search_devicename () {
  diskutil list -plist | \
    plutil -convert json -r -o - - | \
    jq -c '[.AllDisksAndPartitions[].APFSVolumes, .AllDisksAndPartitions[].Partitions] | flatten | map(select(has("VolumeName")))[] | {volume: .VolumeName?, id: .DeviceIdentifier?}' | \
    jq -r --arg volname "$1" 'select(.volume == $volname) | .id?'
}

aparrish

gcr

NIPS ML in the Law symposium 2016 notes

Including notes for the second session and the first panel.

My three takeaways

• Tech folks have a tendency to "fly in and fix everything." That feels like a dangerous approach here. It's far better to stand on the shoulders of existing legal precedent, which has studied fairness, discrimination, and bias for decades, even if that slows down progress.
• Machine learning systems mirror and amplify bias by default. We cannot simply ignore sensitive attributes because the system averages loss over the majority. (Disparate mistreatment). Pithy corollary: this problem will only go away if we devote resources into making it go away.
• Providing explanations for decisions is the only humane way to build automatic classification systems. Why? If I can't test a result, I can't contest it. If the decisions must be testable and explainable, they will be much more reliable as a result.

Aaron Roth: Quantitative tradeoffs between fairness and accuracy in machine learning

crlane

from collections import defaultdict

def node():
  return defaultdict(node)
  
def word_exists(word, node):
    if not word:
        return None in node
    return word_exists(word[1:], node[word[0]])

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

chrismdp

# You don't need Fog in Ruby or some other library to upload to S3 -- shell works perfectly fine
# This is how I upload my new Sol Trader builds (http://soltrader.net)
# Based on a modified script from here: http://tmont.com/blargh/2014/1/uploading-to-s3-in-bash

S3KEY="my aws key"
S3SECRET="my aws secret" # pass these in

function putS3
{
  path=$1

burke

Local (OS X) Side

~/Library/LaunchAgents/pbcopy.plist

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple Computer//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
     <key>Label</key>
     <string>localhost.pbcopy</string>

darcyparker