Discover gists

Pasos para configurar Jest con TypeScript, en Node

npm install -D jest @types/jest ts-jest supertest

jq is useful to slice, filter, map and transform structured json data.

brew install jq

Необходимо создать ssh ключ и добавить его в настройки профиля GitHub, которому пренадлежит нужный репозиторий, если это не было сделано ранее.

Tip: вы можете использовать данный скрипт для создания и добавления SSH ключа.

"repositories": [

this is the runtime context of a function.
this is determined by the call site
the same function can be executed with different this runtime contexts. You can think of this as another arguement to the function
Comparison: scopes are generally defined at compile time (exception: eval)

const person = {
 name: 'Alex',

	## Where to find info and how to report on system extensions in macOS Catalina+

	# Staged system extensions location in folder based on unique ID
	/Library/SystemExtensions/

	# Info on each
	/Library/SystemExtensions/db.plist
	Includes...
	- state (enabled, activated, etc.)
	- associated launchd plists

	options = { :body =>
	{ :username => 'my',
	:password => 'password'
	}
	}
	results = HTTParty.post("http://api.topcoder.com/v2/auth", options)

	##
	## example for post with papertrail and basic auth
	##

	# CLI
	sudo apt update -y
	sudo apt install -y \
	git curl \
	docker.io docker-buildx \
	build-essential pkg-config autoconf bison rustc cargo clang \
	libssl-dev libreadline-dev zlib1g-dev libyaml-dev libreadline-dev libncurses5-dev libffi-dev libgdbm-dev libjemalloc2 \
	libvips imagemagick libmagickwand-dev mupdf mupdf-tools \
	redis-tools sqlite3 libsqlite3-0 libmysqlclient-dev \
	rbenv apache2-utils

	IFOMITTED = LAMBDA(arg,then,IF(ISOMITTED(arg),then,arg));
	SUMPRODUCT2 = LAMBDA(array, [axis],
	SUM(IF(IFOMITTED(axis,0)=0, BYROW, BYCOL)(array, PRODUCT))
	);

	#!/usr/bin/env python3
	from cryptography.fernet import Fernet

	from kdf import derive_key

	passphrase = b"hunter2"
	f = Fernet(derive_key(passphrase))
	with open('encrypted.txt', 'rb') as file:
	encrypted = file.read() # binary read

	import argparse
	import numpy as np
	import torch
	import torch.nn as nn
	import coremltools as ct
	from transformers import AutoTokenizer, AutoModelForCausalLM

	# When using float16, all predicted logits are 0. To be debugged.
	compute_precision = ct.precision.FLOAT32
	compute_units = ct.ComputeUnit.CPU_ONLY