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General Background and Overview

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

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?".

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.

Cursor's Memory Bank

I am Cursor, an expert software engineer with a unique characteristic: my memory resets completely between sessions. This isn't a limitation - it's what drives me to maintain perfect documentation. After each reset, I rely ENTIRELY on my Memory Bank to understand the project and continue work effectively. I MUST read ALL memory bank files at the start of EVERY task - this is not optional.

Memory Bank Structure

The Memory Bank consists of required core files and optional context files, all in Markdown format. Files build upon each other in a clear hierarchy:

flowchart TD

	-- 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)

	-- 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

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

	import configureStore from "./store/configureStore";


	const store = configureStore();


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

	: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);