vgoklani

from flask import Flask, render_template

app = Flask(__name__)

@app.route('/')
@app.route('/index')
def index(chartID = 'chart_ID', chart_type = 'bar', chart_height = 350):
	chart = {"renderTo": chartID, "type": chart_type, "height": chart_height,}
	series = [{"name": 'Label1', "data": [1,2,3]}, {"name": 'Label2', "data": [4, 5, 6]}]
	title = {"text": 'My Title'}

XenGi

#/etc/udev/rules.d/99-displaymagic.rules
# enable LVDS on HDMI disconnect
SUBSYSTEM=="drm", ACTION=="change", RUN+="/usr/local/bin/displaymagic.sh"

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

denji

Moved to git repository: https://github.com/denji/golang-tls

Generate private key (.key)

# Key considerations for algorithm "RSA" ≥ 2048-bit
openssl genrsa -out server.key 2048

# Key considerations for algorithm "ECDSA" ≥ secp384r1
# List ECDSA the supported curves (openssl ecparam -list_curves)

mcastilho

package main

import (
	"bufio"
	"encoding/csv"
	"encoding/json"
	"fmt"
	"io"
	"os"
	"path/filepath"

magujs

# i3 move a workspace to another monitor (multi monitor setup)
mode "move_workspace" {
 bindsym Up move workspace to output up
 bindsym Down move workspace to output down
 bindsym Left move workspace to output left
 bindsym Up move workspace to output up

 bindsym Escape mode "default"
}

ummahusla

Modes

Vim has two modes insertion mode and command mode. The editor begins in command mode, where the cursor movement and text deletion and pasting occur. Insertion mode begins upon entering an insertion or change command. [ESC] returns the editor to command mode (where you can quit, for example by typing :q!). Most commands execute as soon as you type them except for "colon" commands which execute when you press the ruturn key.

Quitting

• :x Exit, saving changes
  
• :q Exit as long as there have been no changes
  
• ZZ Exit and save changes if any have been made
  
• :q! Exit and ignore any changes
  

Inserting Text

graphadvantage

##Neo4j GraphGist - Enterprise Architectures: Real-time Graph Updates using Kafka Messaging

Neo4j Use Case: Low Latency Graph Analytics & OLTP - Update 1M Nodes in 90 secs with Kafka and Neo4j Bolt

Introduction

A recent Neo4j whitepaper describes how Monsanto is performing real-time updates on a 600M node Neo4j graph using Kafka to consume data extracted from a large Oracle Exadata instance.

This modern data architecture combines a fast, scalable messaging platform (Kafka) for low latency data provisioning and an enterprise graph database (Neo4j) for high performance, in-memory analytics & OLTP - creating new and powerful real-time graph analytics capabilities for your enterprise applications.

Brainiarc7

Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:

TensorFlow is now distributed under an Apache v2 open source license on GitHub.

On Ubuntu 16.04LTS+:

Step 1. Install NVIDIA CUDA:

To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:

pteich

package main

import (
	"context"
	"errors"
	"fmt"
	"os/signal"
	"syscall"
	"time"