j-keck

Keybase proof

I hereby claim:

• I am j-keck on github.
• I am jkeck (https://keybase.io/jkeck) on keybase.
• I have a public key ASCJGDzJpcMANJxwx6mzY2ac_APwI_PCEdQriQD0BFNATgo

To claim this, I am signing this object:

j-keck

with import <nixpkgs> {}; {
  pyEnv = stdenv.mkDerivation {
    name = "octave-kernel";

    buildInputs = [ python27Packages.pip python27Packages.virtualenv python27Packages.jupyter python27Packages.notebook octave gnuplot];

    shellHook = ''
      if [ ! -d venv ] ; then
        virtualenv venv
        ./venv/bin/pip install octave_kernel

j-keck

#!/usr/bin/env perl
#
# save systemd journal logs to a postgres instance
#
#
#  * first run with '-init-db' to intialize the database
#  * second run to load all logs
#    (maybe with '-journalctl-args "-n 1000000"')
#  * the following runs inserts only new log entries
#

j-keck

{-# LANGUAGE OverloadedStrings #-}
module Main where

import           Control.Exception       (SomeException, try)
import           Control.Lens
import qualified Data.ByteString.Lazy    as BL
import           Network.Connection      (TLSSettings (..))
import           Network.HTTP.Client.TLS (mkManagerSettings)
import           Network.Wreq

j-keck

/*
The MIT License (MIT)

Copyright (c) 2016 j-keck <jhyphenkeck@gmail.com>

Permission is hereby granted, free of charge, to any person obtaining a copy of this 
software and associated documentation files (the "Software"), to deal in the Software 
without restriction, including without limitation the rights to use, copy, modify, 
merge, publish, distribute, sublicense, and/or sell copies of the Software, and to 
permit persons to whom the Software is furnished to do so, subject to the following conditions:

j-keck

1. 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

j-keck

# tcpdump

* HTTP GET request

        tcpdump -s 0 -A 'tcp[((tcp[12:1] & 0xf0) >> 2):4] = 0x47455420'
        
* HTTP POST

        tcpdump -s 0 -A 'tcp dst port 80 and (tcp[((tcp[12:1] & 0xf0) >> 2):4] = 0x504f5354)'
        

j-keck

PROGRAMMING WITH EFFECTS
Graham Hutton, January 2014

Shall we be pure or impure?

The functional programming community divides into two camps:

 o "Pure" languages, such as Haskell, are based directly
   upon the mathematical notion of a function as a
   mapping from arguments to results.

j-keck

from: http://play.golang.org/p/P5DDZrcXZB

package main

import "fmt"

type Monad interface {
	Bind(func(interface{}, Monad) Monad) Monad
	Return(interface{}) Monad
}

j-keck

* print generated code: scala -Xprint:typer <scala file>

* compiler phases: scalac -Xshow-phases
* show generated code: scala -Xprint:<phases> (-Xprint:typer or -Xprint:1-14 or -Xprint:all)
* show generated code in gui: scala -Ybrowse:<phases>



scalac options in sbt [source](http://tpolecat.github.io/2014/04/11/scalac-flags.html):