pmav99

Tuning Intel Skylake and beyond for optimal performance and feature level support on Linux:

Note that on Skylake, Kabylake (and the now cancelled "Broxton") SKUs, functionality such as power saving, GPU scheduling and HDMI audio have been moved onto binary-only firmware, and as such, the GuC and the HuC blobs must be loaded at run-time to access this functionality.

Enabling GuC and HuC on Skylake and above requires a few extra parameters be passed to the kernel before boot.

Instructions provided for both Fedora and Ubuntu (including Debian):

Note that the firmware for these GPUs is often packaged by your distributor, and as such, you can confirm the firmware blob's availability by running:

This gist will contain all the exercises from the book

Kinesis Freestyle (Terrible key switches. Mushy and un-lovable)
Kinesis Freestyle Edge (Traditional layout with too many keys, mech switches, proably too big to be tented easily/properly)
Matias Ergo Pro (Looks pretty great. Have not tried.)
ErgoDox Kit (Currently, my everyday keyboard. Can buy pre-assembled on eBay.)
ErgoDox EZ (Prolly the best option for most people.)

A Python Popen that does not suck.

Function

import os
import subprocess

def popen(*args, **kwargs):
    output      = kwargs.get('output', False)
    directory   = kwargs.get('dir')

	#!/usr/bin/env python3

	import os
	import logging
	import psycopg2 as pg

	from decimal import Decimal
	from functools import wraps
	from psycopg2.pool import ThreadedConnectionPool

	#!/bin/bash
	iatest=$(expr index "$-" i)

	#######################################################
	# SOURCED ALIAS'S AND SCRIPTS BY zachbrowne.me
	#######################################################

	# Source global definitions
	if [ -f /etc/bashrc ]; then
	. /etc/bashrc

	#!/usr/bin/env python3
	# -- coding: utf-8 --
	#author: Panagiotis Mavrogiorgos
	#email: gmail, pmav99

	"""
	A wrapper around ``rsync``.

	If the ``source`` or the ``destination`` directories are on a remote host
	then you must specify the user and ip. E.g.

	import numpy as np
	import pandas as pd
	from flask import Flask, render_template, request, jsonify

	app = Flask(__name__)

	MY_DATA = pd.DataFrame(np.random.random((10000, 3)), columns=["a", "b", "c"])

	@app.route('/index')
	@app.route('/')

	#!/usr/bin/env python
	# -- coding: utf-8 --

	from collections import OrderedDict


	def find_index_of_first_unique_char(string):
	seen_once = OrderedDict() # LinkedHashMap
	seen_many = set() # HashSet
	for index, char in enumerate(string): # O(N)