gtallen1187

"Scar Tissues Make Relationships Wear Out"

04/26/2103. From a lecture by Professor John Ousterhout at Stanford, class CS142.

This is my most touchy-feely thought for the weekend. Here’s the basic idea: It’s really hard to build relationships that last for a long time. If you haven’t discovered this, you will discover this sooner or later. And it's hard both for personal relationships and for business relationships. And to me, it's pretty amazing that two people can stay married for 25 years without killing each other.

[Laughter]

> But honestly, most professional relationships don't last anywhere near that long. The best bands always seem to break up after 2 or 3 years. And business partnerships fall apart, and there's all these problems in these relationships that just don't last. So, why is that? Well, in my view, it’s relationships don't fail because there some single catastrophic event to destroy them, although often there is a single catastrophic event around the the end of the relation

superfell

package main

import (
	"fmt"
	"io"
	"net"
	"os"
	"path/filepath"
	"strconv"
	"time"

qiuwch

int perm[SZ];
for (int i = 0; i < SZ; i++) perm[i] = i;

// Random permutation the order
for (int i = 0; i < SZ; i++) {
	int j, t;
	j = rand() % (SZ-i) + i;
	t = perm[j]; perm[j] = perm[i]; perm[i] = t; // Swap i and j
}

mikepea

Pull Request Etiquette

Why do we use a Pull Request workflow?

PRs are a great way of sharing information, and can help us be aware of the changes that are occuring in our codebase. They are also an excellent way of getting peer review on the work that we do, without the cost of working in direct pairs.

Ultimately though, the primary reason we use PRs is to encourage quality in the commits that are made to our code repositories

Done well, the commits (and their attached messages) contained within tell a story to people examining the code at a later date. If we are not careful to ensure the quality of these commits, we silently lose this ability.

nicoptere

/**
 * converts a XYZ vector3 to longitude latitude (Direct Polar)
 * @param lng longitude
 * @param lat latitude
 * @param vector3 optional output vector3
 * @returns a unit vector of the 3d position
 */
function lonLatToVector3( lng, lat, out )
{
    out = out || new THREE.Vector3();

devdattaT

import math
import os
import urllib
from shapely.geometry import Polygon

def deg2num(lat_deg, lon_deg, zoom):
	lat_rad = math.radians(lat_deg)
	n = 2.0 ** zoom
	xtile = int((lon_deg + 180.0) / 360.0 * n)
	ytile = int((1.0 - math.log(math.tan(lat_rad) + (1 / math.cos(lat_rad))) / math.pi) / 2.0 * n)

dmitshur

WARNING: This gist was created in 2013 and targets the legacy GOPATH mode. If you're reading this in 2021 or later, you're likely better served by reading https://tip.golang.org/cmd/go/#hdr-Configuration_for_downloading_non_public_code and https://golang.org/ref/mod#private-modules.

$ ssh -A vm
$ git config --global url."git@github.com:".insteadOf "https://github.com/"
$ cat ~/.gitconfig
[url "git@github.com:"]
	insteadOf = https://github.com/
$ go get github.com/private/repo && echo Success!
Success!

emre

package main

import (
	"fmt"
)

func BinarySearch(target_map []int, value int) int {

	start_index := 0
	end_index := len(target_map) - 1

grd

// This package implements 128-bit ("double double") floating point using
// a pair of 64-bit hardware floating point values and standard hardware
// floating point operations. It is based directly on libqd by Yozo Hida,
// Xiaoye S. Li, David H. Bailey, Yves Renard and E. Jason Riedy. Source:
// http://crd.lbl.gov/~dhbailey/mpdist/qd-2.3.13.tar.gz
package float128

import (
	"errors"
	"fmt"

jboner

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