bendc

:root {
  --ease-in-quad: cubic-bezier(.55, .085, .68, .53);
  --ease-in-cubic: cubic-bezier(.550, .055, .675, .19);
  --ease-in-quart: cubic-bezier(.895, .03, .685, .22);
  --ease-in-quint: cubic-bezier(.755, .05, .855, .06);
  --ease-in-expo: cubic-bezier(.95, .05, .795, .035);
  --ease-in-circ: cubic-bezier(.6, .04, .98, .335);

  --ease-out-quad: cubic-bezier(.25, .46, .45, .94);
  --ease-out-cubic: cubic-bezier(.215, .61, .355, 1);

markerikson

import React from "react";
import ReactDOM from "react-dom";

import configureStore from "./store/configureStore";


const store = configureStore();


const rootEl = document.getElementById("root");

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

---

jvns

A lot of these are outright stolen from Edward O'Campo-Gooding's list of questions. I really like his list.

I'm having some trouble paring this down to a manageable list of questions -- I realistically want to know all of these things before starting to work at a company, but it's a lot to ask all at once. My current game plan is to pick 6 before an interview and ask those.

I'd love comments and suggestions about any of these.

I've found questions like "do you have smart people? Can I learn a lot at your company?" to be basically totally useless -- everybody will say "yeah, definitely!" and it's hard to learn anything from them. So I'm trying to make all of these questions pretty concrete -- if a team doesn't have an issue tracker, they don't have an issue tracker.

I'm also mostly not asking about principles, but the way things are -- not "do you think code review is important?", but "Does all code get reviewed?".

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

lessmost

-- This script is used to analyze tcp stream from the input
-- libcap file.
-- This scirpt current only works well with tshark
-- Usage:
-- 	tshark -X lua_script:TcpStreamAnalyze.lua -r inputfile -R display_filter

do
   filter = "ip.addr == 192.168.201.53 && ip.addr == 192.168.201.46 && tcp.port == 80"
   local streams_table = {}                 -- table to store all tcp streams

lessmost

-- A simple script to analyze Tcp Sequence Numbers

-- Usega: wireshark -X lua_script:TcpSeqRetrans.lua
-- open a pcap file in wireshark, and then select
-- the 'TOOLS/Tcp Sequence Analyze' menu.

-- menu function
function tsa_menu_func()
   -- tsa analyze function
   function tsa_do(ip1, ip2)