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

dreikanter

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Diagnostics;

namespace Interpolation
{
    /// <summary>
    /// Spline interpolation class.

naholyr

Sample service script for debianoids

Look at LSB init scripts for more information.

Usage

Copy to /etc/init.d:

# replace "$YOUR_SERVICE_NAME" with your service's name (whenever it's not enough obvious)

themasch

REST Service for LoL spectators

This is an unofficial, uncomplete and (pretty sure) wrong documentation of the RESTful service which powers the League of Legends spectator mode.

This documentation is desgined to be community driven and should be extended by everyone. If you find things missing, add them please!

How it works

Riot's spectator mode works by requesting replay data via HTTP form a service. The data is split in chunks which usually contain about 30 seconds of gameplay. Additionally there are key frames which seem to contain more information then a single chunk. They seem to be used to support

kevin-smets

Default

Powerlevel10k

theotherian

Spring STOMP chat

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

baraldilorenzo

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

unitycoder

/* Code snippets from Unite 2015 - A coder's guide to spline-based procedural geometry */
/* https://www.youtube.com/watch?v=o9RK6O2kOKo */

// Optimized GetPoint
Vector3 GetPoint( Vector3[] pts, float t ) {
	float omt = 1f-t;
	float omt2 = omt * omt;
	float t2 = t * t;
	return	pts[0] * ( omt2 * omt ) +
			pts[1] * ( 3f * omt2 * t ) +

mariotacke

#!/bin/sh
### BEGIN INIT INFO
# Provides:          TeamCity Build Agent
# Required-Start:    $remote_fs $syslog
# Required-Stop:     $remote_fs $syslog
# Default-Start:     2 3 4 5
# Default-Stop:      0 1 6
# Short-Description: Start build agent daemon at boot time
# Description:       Enable service provided by daemon.
### END INIT INFO