t0chas

using UnityEngine;
using UnityEditor;
using UnityEditorInternal;
using System.Collections.Generic;
using UnityEditor.AnimatedValues;

[CustomEditor(typeof(UnityEngine.Object), true, isFallback = true)]
[CanEditMultipleObjects]
public class CustomEditorBase : Editor
{

chrismdp

# You don't need Fog in Ruby or some other library to upload to S3 -- shell works perfectly fine
# This is how I upload my new Sol Trader builds (http://soltrader.net)
# Based on a modified script from here: http://tmont.com/blargh/2014/1/uploading-to-s3-in-bash

S3KEY="my aws key"
S3SECRET="my aws secret" # pass these in

function putS3
{
  path=$1

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

---

Kartones

PSQL

Magic words:

psql -U postgres

Some interesting flags (to see all, use -h or --help depending on your psql version):

• -E: will describe the underlaying queries of the \ commands (cool for learning!)
• -l: psql will list all databases and then exit (useful if the user you connect with doesn't has a default database, like at AWS RDS)

stuart11n

git branch -m old_branch new_branch         # Rename branch locally    
git push origin :old_branch                 # Delete the old branch    
git push --set-upstream origin new_branch   # Push the new branch, set local branch to track the new remote

denji

Moved to git repository: https://github.com/denji/nginx-tuning

NGINX Tuning For Best Performance

For this configuration you can use web server you like, i decided, because i work mostly with it to use nginx.

Generally, properly configured nginx can handle up to 400K to 500K requests per second (clustered), most what i saw is 50K to 80K (non-clustered) requests per second and 30% CPU load, course, this was 2 x Intel Xeon with HyperThreading enabled, but it can work without problem on slower machines.

You must understand that this config is used in testing environment and not in production so you will need to find a way to implement most of those features best possible for your servers.

mblondel

# Mathieu Blondel, September 2010
# License: BSD 3 clause

import numpy as np
from numpy import linalg
import cvxopt
import cvxopt.solvers

def linear_kernel(x1, x2):
    return np.dot(x1, x2)