tel

{-# LANGUAGE ConstraintKinds #-}
{-# LANGUAGE GeneralizedNewtypeDeriving #-}

module ParserCombinators where

{-

We'll build a set of parser combinators from scratch demonstrating how
they arise as a monad transformer stack. Actually, how they arise as a
choice between two different monad transformer stacks!

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

jpetazzo

Unionize: network superpowers for your docker containers

Unionize lets you connect together docker containers in arbitrarily complex scenarios.

Note: I recommend to use https://github.com/jpetazzo/pipework instead.

• pipework is a better name than unionize
• it's hosted on a "real" github repo instead of a small gist :-)

Now if you want Unionize, it's still here. Just check those examples.

MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname