vasanthk

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

brandonb927

#!/bin/sh

###
# SOME COMMANDS WILL NOT WORK ON macOS (Sierra or newer)
# For Sierra or newer, see https://github.com/mathiasbynens/dotfiles/blob/master/.macos
###

# Alot of these configs have been taken from the various places
# on the web, most from here
# https://github.com/mathiasbynens/dotfiles/blob/5b3c8418ed42d93af2e647dc9d122f25cc034871/.osx

jFransham

Achieving warp speed with Rust

Contents:

• Number one optimization tip: don't
• Never optimize blindly
• Don't bother optimizing one-time costs
• Improve your algorithms
• CPU architecture primer
• Keep as much as possible in cache
• Keep as much as possible in registers

aparrish

marktheunissen

This playbook has been removed as it is now very outdated.

lelandbatey

Description

This simple script will take a picture of a whiteboard and use parts of the ImageMagick library with sane defaults to clean it up tremendously.

The script is here:

#!/bin/bash
convert "$1" -morphology Convolve DoG:15,100,0 -negate -normalize -blur 0x1 -channel RBG -level 60%,91%,0.1 "$2"

Results

jakevdp

seanjensengrey

This is a small demo of how to create a library in Rust and call it from Python (both CPython and PyPy) using the CFFI instead of ctypes.

Based on http://harkablog.com/calling-rust-from-c-and-python.html (dead) which used ctypes

CFFI is nice because:

• Reads C declarations (parses headers)
• Works in both CPython and PyPy (included with PyPy)
• Lower call overhead than ctypes

pbugnion

This gist lets you keep IPython notebooks in git repositories. It tells git to ignore prompt numbers and program outputs when checking that a file has changed.

To use the script, follow the instructions given in the script's docstring.

For further details, read this blogpost.

The procedure outlined here is inspired by this answer on Stack Overflow.

llimllib

import json
from pprint import pprint as pp

def jenks_matrices_init(data, n_classes):
    #fill the matrices with data+1 arrays of n_classes 0s
    lower_class_limits = []
    variance_combinations = []
    for i in xrange(0, len(data)+1):
        temp1 = []
        temp2 = []