Kirill infloop

Running nightmare on Amazon Linux

You may have thought of running nightmare on AWS Lambda. But before we can run it on Lambda, we need first to make it run on Amazon Linux.

Provision instance which replicates Lambda environment

According to AWS Documentation on Lambda Execution Environment and available Libraries we would need this AMI image with this alias amzn-ami-hvm-2016.03.3.x86_64-gp2. Keep in mind that AMI-image-id for this instance would be different in different regions (eg):

In eu-west-1 - ami-f9dd458a
In us-east-1 - ami-6869aa05

Running nightmare on Amazon Linux

You may have thought of running nightmare on AWS Lambda. But before we can run it on Lambda, we need first to make it run on Amazon Linux.

Provision instance which replicates Lambda environment

According to AWS Documentation on Lambda Execution Environment and available Libraries we would need this AMI image with this alias amzn-ami-hvm-2016.03.3.x86_64-gp2. Keep in mind that AMI-image-id for this instance would be different in different regions (eg):

In eu-west-1 - ami-f9dd458a
In us-east-1 - ami-6869aa05

	// stub for typescript
	declare function BigInt(n: number): number

	abstract class Trampoline<A> {
	abstract map<B>(ab: (a: A) => B): Trampoline<B>
	abstract flatMap<B>(ab: (a: A) => Trampoline<B>): Trampoline<B>

	zip<B>(tb: Trampoline<B>): Trampoline<[A, B]> {
	const ta = this
	return ta.flatMap(a => tb.map(b => [a, b] as [A, B]))

	# MikroTik (RouterOS) script for automatically setting DNS records
	# for clients when they obtain a DHCP lease.
	#
	# author SmartFinn <https://gist.github.com/SmartFinn>
	# based on https://github.com/karrots/ROS-DDNS

	:local domain;
	:local fqdn;
	:local hostname;
	:local safeHostname;

	// 1.1 Вывести на экран сумму переменных a + b
	let a = 1;
	let b = 20;

	console.log(?);

	// 1.2 Вывести на экран разницу переменных b - a
	// Пример: 4
	console.log(?);

	const {Builder, By, until} = require('selenium-webdriver');

	(async function yandexTest() {
	let driver = await new Builder().forBrowser('chrome').build();
	try {
	await driver.get('http://yandex.ru');

	// индекс текущей ссылки
	let currentLinkIndex = 0;

	import {isImplements} from '../index';

	export type InterfaceTag<T> = ({ new (...args: any[]): T, readonly id: symbol });

	export function createInterface<I>(name: string): InterfaceTag<I>{
	let id = Symbol.for(name);
	let interfaceTag = (class {
	private static readonly identifier: symbol = id;

	static [Symbol.hasInstance](instance) {

	type NonFunctionPropertyNames<T> = { [K in keyof T]: T[K] extends Function ? never : K }[keyof T];
	type NonFunctionProperties<T> = Pick<T, NonFunctionPropertyNames<T>>;

	type Omit<T, K extends keyof T> = Pick<T, ({ [P in keyof T]: P } & { [P in K]: never } & { [x: string]: never })[keyof T]>;

	export function assign<T extends NonFunctionProperties<Omit<S, K>>, S extends Object, K extends keyof S>(target: T, source: S, exclude?: K[]): void {
	Object.keys(source).forEach(key => {

	// exclude Object methods
	if (typeof source[key] === 'function') {

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats