vertexclique

MacOS

Build 3059

MD5: 59bab8f71f8c096cd3f72cd73851515d

Rename it to: Sublime Text

Make it executable with: chmod u+x Sublime\ Text

HyperCrowd

Crippling Facebook

Facebook works with advertisers to target you. These instructions are one of the many ways to begin crippling that relationship. When AI targeting is crippled, your psychosecurity improves :)

1. On your desktop machine, goto https://accountscenter.facebook.com/ads/audience_based_advertising
2. Maximize the browser window
3. Press F12 and click on the Console tab
4. Select the code below, copy it, paste it upon the Console line (The area next to the > character in the Console window), and press enter:

kevin-smets

Default

Powerlevel10k

ptarjan

Scaling your API with rate limiters

The following are examples of the four types rate limiters discussed in the accompanying blog post. In the examples below I've used pseudocode-like Ruby, so if you're unfamiliar with Ruby you should be able to easily translate this approach to other languages. Complete examples in Ruby are also provided later in this gist.

In most cases you'll want all these examples to be classes, but I've used simple functions here to keep the code samples brief.

Request rate limiter

This uses a basic token bucket algorithm and relies on the fact that Redis scripts execute atomically. No other operations can run between fetching the count and writing the new count.

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

aras-p

// Just before switching jobs:
// Add one of these.
// Preferably into the same commit where you do a large merge.
//
// This started as a tweet with a joke of "C++ pro-tip: #define private public",
// and then it quickly escalated into more and more evil suggestions.
// I've tried to capture interesting suggestions here.
//
// Contributors: @r2d2rigo, @joeldevahl, @msinilo, @_Humus_,
// @YuriyODonnell, @rygorous, @cmuratori, @mike_acton, @grumpygiant,

IanColdwater

Mute these words in your settings here: https://twitter.com/settings/muted_keywords

ActivityTweet
generic_activity_highlights
generic_activity_momentsbreaking
RankedOrganicTweet
suggest_activity
suggest_activity_feed
suggest_activity_highlights
suggest_activity_tweet

alirobe

### 
###
### UPDATE: For Win 11, I recommend using this tool in place of this script: 
### https://christitus.com/windows-tool/
### https://github.com/ChrisTitusTech/winutil
### https://www.youtube.com/watch?v=6UQZ5oQg8XA
### iwr -useb https://christitus.com/win | iex
###
###

paulirish

/* bling.js */

window.$ = document.querySelectorAll.bind(document);

Node.prototype.on = window.on = function (name, fn) {
  this.addEventListener(name, fn);
};

NodeList.prototype.__proto__ = Array.prototype;

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