robertknight

Compile with:

webpack --config vendor.webpack.config.js
webpack --config app.webpack.config.js

Use with the following index.html

manigandham

Strictly Frameworks

• Mobiledoc - github.com/bustle/mobiledoc-kit - framework to build editors with a standardized JSON structure
• ShareDB - github.com/share/sharedb - framework to sync any JSON document using operational transforms, add real-time collaborative editing to anything else
• Bangle.dev - github.com/bangle-io/bangle.dev - toolkit built for building editors, based on prosemirror

Abstracted Editors

These use separate document structures instead of HTML, some are more modular libraries than full editors

davidfowl

$/
  artifacts/
  build/
  docs/
  lib/
  packages/
  samples/
  src/
 tests/

rodneyrehm

some tools for diagrams in software documentation

Diagrams For Documentation

Obvious Choices

• Graphviz
• Pandoc

ASCII

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

crowdmatt

{
"IAB1": "Arts & Entertainment",
"IAB1-1": "Books & Literature",
"IAB1-2": "Celebrity Fan/Gossip",
"IAB1-3": "Fine Art",
"IAB1-4": "Humor",
"IAB1-5": "Movies",
"IAB1-6": "Music",
"IAB1-7": "Television",
"IAB2": "Automotive",

hellerbarde

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

kfox

# Kernel sysctl configuration file for Red Hat Linux
#
# For binary values, 0 is disabled, 1 is enabled.  See sysctl(8) and
# sysctl.conf(5) for more details.

# Controls source route verification
net.ipv4.conf.default.rp_filter = 1

# Do not accept source routing
net.ipv4.conf.default.accept_source_route = 0

chitchcock

Stevey's Google Platforms Rant

I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.

I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real

duarten

public class Actor<TState>
{
    volatile int executing;
    readonly ConcurrentQueue<Func<TState, Task>> funcs = new ConcurrentQueue<Func<TState, Task>>();

    public TState State { get; set; }

    public void Act(Func<TState, Task> func)
    {
        if (TryAcquire())