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

demisx

belongs_to:

1. Assigning an object to a belongs_to association does not automatically save the object. It does not save the associated object either.

has_one:

1. When you assign an object to a has_one association, that object is automatically saved (in order to update its foreign key).
2. In addition, any object being replaced is also automatically saved, because its foreign key will change too
3. If either of these saves fails due to validation errors, then the assignment statement returns false and the assignment itself is cancelled.
4. If the parent object (the one declaring the has_one association) is unsaved (that is, new_record? returns true) then the child objects are not saved. They will automatically when the parent object is saved.

joepie91

Not all random values are created equal - for security-related code, you need a specific kind of random value.

A summary of this article, if you don't want to read the entire thing:

• Don't use Math.random(). There are extremely few cases where Math.random() is the right answer. Don't use it, unless you've read this entire article, and determined that it's necessary for your case.
• Don't use crypto.getRandomBytes directly. While it's a CSPRNG, it's easy to bias the result when 'transforming' it, such that the output becomes more predictable.
• If you want to generate random tokens or API keys: Use uuid, specifically the uuid.v4() method. Avoid node-uuid - it's not the same package, and doesn't produce reliably secure random values.
• If you want to generate random numbers in a range: Use random-number-csprng.

You should seriously consider reading the entire article, though - it's

ptarjan

Scaling your API with rate limiters

The following are examples of the four types rate limiters discussed in the accompanying blog post. In the examples below I've used pseudocode-like Ruby, so if you're unfamiliar with Ruby you should be able to easily translate this approach to other languages. Complete examples in Ruby are also provided later in this gist.

In most cases you'll want all these examples to be classes, but I've used simple functions here to keep the code samples brief.

Request rate limiter

This uses a basic token bucket algorithm and relies on the fact that Redis scripts execute atomically. No other operations can run between fetching the count and writing the new count.

XVilka

THIS GIST WAS MOVED TO TERMSTANDARD/COLORS REPOSITORY.

PLEASE ASK YOUR QUESTIONS OR ADD ANY SUGGESTIONS AS A REPOSITORY ISSUES OR PULL REQUESTS INSTEAD!

enricofoltran

package main

import (
	"context"
	"flag"
	"fmt"
	"log"
	"net/http"
	"os"
	"os/signal"

mziwisky

OAUTH2

The Problem

I’m a web app that wants to allow other web apps access to my users’ information, but I want to ensure that the user says it’s ok.

The Solution

I can’t trust the other web apps, so I must interact with my users directly. I’ll let them know that the other app is trying to get their info, and ask whether they want to grant that permission. Oauth defines a way to initiate that permission verification from the other app’s site so that the user experience is smooth. If the user grants permission, I issue an AuthToken to the other app which it can use to make requests for that user's info.

Note on encryption

Oauth2 has nothing to do with encryption -- it relies upon SSL to keep things (like the client app’s shared_secret) secure.

graceavery

alias accio=wget
alias avadaKedavra='rm -f'
alias imperio=sudo
alias priorIncantato='echo `history |tail -n2 |head -n1` | sed "s/[0-9]* //"'
alias stupefy='sleep 5'
alias wingardiumLeviosa=mv

alias sonorus='set -v'
alias quietus='set +v'

steveklabnik

#![feature(lang_items)]
#![no_std]

#[no_mangle]
pub fn add_one(x: i32) -> i32 {
    x + 1
}


// needed for no_std

rui314

A C compiler for Brainfuck

I want to share my friend's crazy project because it demonstrates how a simple Turing-machine-like programming language is actually equivalent to usual real-world computers.

I think we all know the theory that all Turing complete programming languages are equivalent in terms of their powers. If languages A and B are Turing complete, A can emulate B and vice versa. But that's not obvious at all. How can a simple programming model like Turing Machine can run "real" programs such as ones that run on a general-purpose PC? If it is possible in theory, it can be demonstrated.