Bash script to:
- Iterate all commits made within a Git repository.
Latency Comparison Numbers (~2012) | |
---------------------------------- | |
L1 cache reference 0.5 ns | |
Branch mispredict 5 ns | |
L2 cache reference 7 ns 14x L1 cache | |
Mutex lock/unlock 25 ns | |
Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
Compress 1K bytes with Zippy 3,000 ns 3 us | |
Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
val n = 9 | |
val s = Math.sqrt(n).toInt | |
type Board = IndexedSeq[IndexedSeq[Int]] | |
def solve(board: Board, cell: Int = 0): Option[Board] = (cell%n, cell/n) match { | |
case (r, `n`) => Some(board) | |
case (r, c) if board(r)(c) > 0 => solve(board, cell + 1) | |
case (r, c) => | |
def guess(x: Int) = solve(board.updated(r, board(r).updated(c, x)), cell + 1) | |
val used = board.indices.flatMap(i => Seq(board(r)(i), board(i)(c), board(s*(r/s) + i/s)(s*(c/s) + i%s))) |
import os | |
import time | |
import sched | |
import threading | |
import inquirer | |
class Tamagotchi: | |
age = 0 | |
bored = 0 |
(function() { | |
var CSSCriticalPath = function(w, d, opts) { | |
var opt = opts || {}; | |
var css = {}; | |
var pushCSS = function(r) { | |
if(!!css[r.selectorText] === false) css[r.selectorText] = {}; | |
var styles = r.style.cssText.split(/;(?![A-Za-z0-9])/); | |
for(var i = 0; i < styles.length; i++) { | |
if(!!styles[i] === false) continue; | |
var pair = styles[i].split(": "); |
Makes 12 2 ½” biscuits
This is a recipe for fluffy biscuits, not flakey. They are best straight out of the oven with butter or jam. Always make extra, they are great split, buttered, and griddled for breakfast sandwiches. We use a food processor to mix the butter and flour. You can use your hands or a fork but using the food processor helps to keep the butter cold which will help make tender biscuits (and it is much faster & more uniform).
Equipment
Latency Comparison Numbers (~2012) | |
---------------------------------- | |
L1 cache reference 0.5 ns | |
Branch mispredict 5 ns | |
L2 cache reference 7 ns 14x L1 cache | |
Mutex lock/unlock 25 ns | |
Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
Compress 1K bytes with Zippy 3,000 ns 3 us | |
Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
# You need to install scikit-learn: | |
# sudo pip install scikit-learn | |
# | |
# Dataset: Polarity dataset v2.0 | |
# http://www.cs.cornell.edu/people/pabo/movie-review-data/ | |
# | |
# Full discussion: | |
# https://marcobonzanini.wordpress.com/2015/01/19/sentiment-analysis-with-python-and-scikit-learn | |
/** | |
* A quick example of using Emoji in Java. | |
* compile with: javac HelloWorld.java | |
* run with: java HelloWorld | |
*/ | |
public class HelloWorld { | |
public static void main(String[] args) { | |
String \u0ca2 = "Hello, World 1"; |
This has been moved to a blog post.