debasishg

General Background and Overview

1. 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.
2. Models and Issues in Data Stream Systems
3. 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
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

iangreenleaf

#!/bin/bash

# Monitors for signals from gnome-screensaver and activates or deactivates
# bitcoin miners accordingly.

# Params to the mining programs are all hardcoded. Change 'em manually. Leave the "&"s.

dbus-monitor --session "type='signal',interface='org.gnome.ScreenSaver',member='ActiveChanged'" | \
while read line; do
  case "$line" in

brandonb927

#!/bin/sh

###
# SOME COMMANDS WILL NOT WORK ON macOS (Sierra or newer)
# For Sierra or newer, see https://github.com/mathiasbynens/dotfiles/blob/master/.macos
###

# Alot of these configs have been taken from the various places
# on the web, most from here
# https://github.com/mathiasbynens/dotfiles/blob/5b3c8418ed42d93af2e647dc9d122f25cc034871/.osx

gavinandresen

# Raw transaction API example work-through
# Send coins to a 2-of-3 multisig, then spend them.
#
# For this example, I'm using these three keypairs (public/private)
# 0491bba2510912a5bd37da1fb5b1673010e43d2c6d812c514e91bfa9f2eb129e1c183329db55bd868e209aac2fbc02cb33d98fe74bf23f0c235d6126b1d8334f86 / 5JaTXbAUmfPYZFRwrYaALK48fN6sFJp4rHqq2QSXs8ucfpE4yQU
# 04865c40293a680cb9c020e7b1e106d8c1916d3cef99aa431a56d253e69256dac09ef122b1a986818a7cb624532f062c1d1f8722084861c5c3291ccffef4ec6874 / 5Jb7fCeh1Wtm4yBBg3q3XbT6B525i17kVhy3vMC9AqfR6FH2qGk
# 048d2455d2403e08708fc1f556002f1b6cd83f992d085097f9974ab08a28838f07896fbab08f39495e15fa6fad6edbfb1e754e35fa1c7844c41f322a1863d46213 / 5JFjmGo5Fww9p8gvx48qBYDJNAzR9pmH5S389axMtDyPT8ddqmw

# First: combine the three keys into a multisig address:
./bitcoind createmultisig 2 '["0491bba2510912a5bd37da1fb5b1673010e43d2c6d812c514e91bfa9f2eb129e1c183329db55bd868e209aac2fbc02cb33d98fe74bf23f0c235d6126b1d8334f86","04865c40293a680cb9c020e7b1e106d8c1916d3cef99aa431a56d253e69256dac09ef122b1a9

matthewmueller

# OSX for Hackers (Mavericks/Yosemite)
#
# Source: https://gist.github.com/brandonb927/3195465

#!/bin/sh

# Some things taken from here
# https://github.com/mathiasbynens/dotfiles/blob/master/.osx

# Ask for the administrator password upfront

rondy

FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.

---

Effective Engineer - Notes

• By Edmond Lau
• Highly Recommended 👍
• http://www.theeffectiveengineer.com/

What's an Effective Engineer?

wojteklu

Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.

---

General rules

1. Follow standard conventions.
2. Keep it simple stupid. Simpler is always better. Reduce complexity as much as possible.
3. Boy scout rule. Leave the campground cleaner than you found it.
4. Always find root cause. Always look for the root cause of a problem.

Design rules