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

willurd

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000

scy

Opening and closing an SSH tunnel in a shell script the smart way

I recently had the following problem:

• From an unattended shell script (called by Jenkins), run a command-line tool that accesses the MySQL database on another host.
• That tool doesn't know that the database is on another host, plus the MySQL port on that host is firewalled and not accessible from other machines.

We didn't want to open the MySQL port to the network, but it's possible to SSH from the Jenkins machine to the MySQL machine. So, basically you would do something like

ssh -L 3306:localhost:3306 remotehost

kevin-smets

Default

Powerlevel10k

marcelog

{
  "Version": "2012-10-17",
  "Id": "arn:aws:sqs:YOUR-AWS-REGION:YOUR-AWS-ACCOUNT-ID:YOUR-QUEUE-NAME/SQSDefaultPolicy",
  "Statement": [
    {
      "Sid": "example-statement-ID",
      "Effect": "Allow",
      "Principal": {
        "AWS": "*"
      },

mineshpatel1

import cv2
import operator
import numpy as np
from matplotlib import pyplot as plt


def plot_many_images(images, titles, rows=1, columns=2):
	"""Plots each image in a given list as a grid structure. using Matplotlib."""
	for i, image in enumerate(images):
		plt.subplot(rows, columns, i+1)