mholt

// Package main is a sample macOS-app-bundling program to demonstrate how to
// automate the process described in this tutorial:
//
// https://medium.com/@mattholt/packaging-a-go-application-for-macos-f7084b00f6b5
//
// Bundling the .app is the first thing it does, and creating the DMG is the
// second. Making the DMG is optional, and is only done if you provide
// the template DMG file, which you have to create beforehand.
//
// Example use:

UnquietCode

Lava Lamp Random Number Generator

(extracted from the now defunct SGI project at http://lavarand.sgi.com/cgi-bin/how.cgi via the magical Internet Archive Wayback Machine)

Lava Lamps can be used as a source of randomness, which can be used to establish a random number generator. The output of the RNG can then be consumed by various computer applications.

Step 1: Establish a chaotic system

(Set up Lava Lite® lamps in a machine room.)

prakhar1989

Rich Hickey on becoming a better developer

Rich Hickey • 3 years ago

Sorry, I have to disagree with the entire premise here.

A wide variety of experiences might lead to well-roundedness, but not to greatness, nor even goodness. By constantly switching from one thing to another you are always reaching above your comfort zone, yes, but doing so by resetting your skill and knowledge level to zero.

Mastery comes from a combination of at least several of the following:

jpillora

// A small SSH daemon providing bash sessions
//
// Server:
// cd my/new/dir/
// #generate server keypair
// ssh-keygen -t rsa
// go get -v .
// go run sshd.go
//
// Client:

nocturnalgeek

@binkmail.com
@bobmail.info
@chammy.info
@devnullmail.com
@letthemeatspam.com
@mailinater.com
@mailinator.net
@mailinator2.com
@notmailinator.com
@reallymymail.com

kachayev

Channels Are Not Enough

... or Why Pipelining Is Not That Easy

Golang Concurrency Patterns for brave and smart.

By @kachayev

Intro

debasishg

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

olivierlacan

I'm planning on either writing this up in detail or maybe doing a screencast about screencasting, but I'll give a short version here.

On sound quality:

This matters a lot. In decreasing order of importance:

1. Remove echo. You have to hear this to understand. Set up a mic in front of your mouth and record a sentence. Then, put a thick comforter over you and the mic and say it again at the same distance. Listen to

drewolson

package main

import (
	"fmt"
	"reflect"
)

type Foo struct {
	FirstName string `tag_name:"tag 1"`
	LastName  string `tag_name:"tag 2"`

justinbmeyer

JavaScript Code

var str = "hi";

Memory allocation:

Address	Value	Description
......	...