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#! /bin/sh

readonly LIGHT_IMAGE=alpine:latest

generate() {
  docker run ${LIGHT_IMAGE} sleep 0
  container_id=$(docker ps -a -l -q)
  echo "$(docker inspect --format='{{ .Name  }}' ${container_id} | tr '_/' ' ')"
  docker rm ${container_id} &> /dev/null
}

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RT Data Platform

• Pusher - Real time apps

Pas de request/response mais une solution élégante en PUB/SUB

• Pubnub - The global data stream network for IoT, Mobile, and Web applications

Ultra overkill mais apparemment très puissant. Fait à peu près tout.

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# You don't need Fog in Ruby or some other library to upload to S3 -- shell works perfectly fine
# This is how I upload my new Sol Trader builds (http://soltrader.net)
# Based on a modified script from here: http://tmont.com/blargh/2014/1/uploading-to-s3-in-bash

# export S3KEY="my aws key"
# export S3SECRET="my aws secret" # pass these in

BUCKET="apps-crawler-prod"

putS3 () {

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stream
    // Select just the cpu_usage_idle measurement from our example database.
    .from().measurement('cpu_usage_idle')
    .alert()
        .crit(lambda: "value" <  70)
        // Whenever we get an alert write it to a file.
        .log('/tmp/alerts.log')

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#!/usr/bin/env bash

# abort quietly if already setup
[ -z "${__PROJECT__}" ] || return

# generic project management 
export __PROJECT__="$(basename $PWD)"

# go specific settings
export __GO_VERSION__="1.5.1"

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# Makefile
# vim:ft=make

PROJECT := $(shell basename $(PWD))
SOURCEDIR=.
SOURCES := $(shell find $(SOURCEDIR) -path './vendor' -prune -o -type f -name '*.go' -print)
PACKAGES=$(shell go list ./... | grep -v /vendor/)

GIT_COMMIT=`git rev-parse HEAD`
GIT_USER=`git config --get user.name`

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# TODO Remove archives
FROM java:7

# Constants
#ENV JAVA_VERSION 7
ENV INSTALL_WORKSPACE /opt
#ENV ANDROID_SDK_VERSION r23.0.2-linux
ENV ANDROID_SDK_VERSION r24.2-linux
ENV ANDROID_BUILDER ant
ENV ANDROID_SDK_TOOLS android-21,build-tools-22.0.0,platform-tools,extra-android-support

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# The Brain

Compilation de services permettant de mettre en place des algorithmes de recommendation et, plus largement, de machine learning et d'analyse de data.

L'idée principale est de pouvoir considérer la partie machine learning comme un service brique lego : des requêtes pour trainer les modèles et récupérer les predictions. Une plateforme/des outils pour manipuler les données ou développer les modèles serait un + pour anticiper les besoins à venir.

## [Prediciton.io](https://prediction.io/)

> Build and Deploy Machine Intelligence in a fraction of the time

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Split the fat application

As articles state everywhere, we're living in a fast pace digital age. Project complexity, or business growth, challenges existing development patterns. That's why many developers are evolving from [the monolithic application][16] toward [micro-services][17]. Facebook is moving away from its [big blue app][1]. Soundcloud is [embracing microservices][2].

Yet this can be a [daunting process][3], so what for ?

• Scale. Better plugging new components than digging into an ocean of code.
• Split a complex problem into smaller ones, easier to solve and maintain.
• Distribute work through independent teams.

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#!/bin/bash

DEFAULT_NAME="lab"
DEFAULT_IMAGE="ubuntu"
WORKDIR="/root/app"
REPL="bash"

usage() {
  printf "hack --help\t\tprint help\n"
  printf "hack <image> <name>\trun a lab container\n"