josephmisiti

<script>
// MULTIPLE CHOICE TEMPLATE v1.2 {{{
// https://gist.github.com/hgiesel/2e8361afccca5713414a6a4ee66b7ece
const query = 'div#thecard'
const colors = ['orange', 'olive', 'maroon', 'aqua', 'fuchsia', 'navy', 'lime']
const fieldPadding = '4px'

const syntax = {
    openDelim: '(^',
    closeDelim: '^)',

josephmisiti

#!/usr/bin/env bash

# Must be run on an Amazon Linux AMI that matches AWS Lambda's runtime which can be found at:
# https://docs.aws.amazon.com/lambda/latest/dg/current-supported-versions.html
#
# As of May 21, 2019, this is:
# Amazon Linux AMI 2018.03.0 (ami-0756fbca465a59a30)
#
# You need to prepend PATH with the folder containing these binaries in your Lambda function 
# to ensure these newer binaries are used.

josephmisiti

-- Create the table
CREATE TABLE IF NOT EXISTS `StateBoundaries` (
  `State` varchar(10) DEFAULT NULL,
  `Name` varchar(255) DEFAULT NULL,
  `MinLat` varchar(50) DEFAULT NULL,
  `MaxLat` varchar(50) DEFAULT NULL,
  `MinLon` varchar(50) DEFAULT NULL,
  `MaxLon` varchar(50) DEFAULT NULL
) ENGINE=InnoDB DEFAULT CHARSET=utf8;

josephmisiti

josephmisiti

josephmisiti

PSQL

Magic words:

psql -U postgres

If run with -E flag, it will describe the underlaying queries of the \ commands (cool for learning!).

Most \d commands support additional param of __schema__.name__ and accept wildcards like *.*

josephmisiti

/**
 * Chunkify
 * Google Chrome Speech Synthesis Chunking Pattern
 * Fixes inconsistencies with speaking long texts in speechUtterance objects 
 * Licensed under the MIT License
 *
 * Peter Woolley and Brett Zamir
 */
 
var speechUtteranceChunker = function (utt, settings, callback) {

josephmisiti

/**
 * Chunkify
 * Google Chrome Speech Synthesis Chunking Pattern
 * Fixes inconsistencies with speaking long texts in speechUtterance objects 
 * Licensed under the MIT License
 *
 * Peter Woolley and Brett Zamir
 */
 
var speechUtteranceChunker = function (utt, settings, callback) {

josephmisiti

#!/usr/bin/env xcrun swift

/*
gen.swift is a direct port of cfdrake's helloevolve.py from Python 2.7 to Swift 3
-------------------- https://gist.github.com/cfdrake/973505 ---------------------

gen.swift implements a genetic algorithm that starts with a base
population of randomly generated strings, iterates over a certain number of
generations while implementing 'natural selection', and prints out the most fit
string.

josephmisiti

"""
helloevolve.py implements a genetic algorithm that starts with a base
population of randomly generated strings, iterates over a certain number of
generations while implementing 'natural selection', and prints out the most fit
string.

The parameters of the simulation can be changed by modifying one of the many
global variables. To change the "most fit" string, modify OPTIMAL. POP_SIZE
controls the size of each generation, and GENERATIONS is the amount of 
generations that the simulation will loop through before returning the fittest