defunkt

# unicorn_rails -c /data/github/current/config/unicorn.rb -E production -D

rails_env = ENV['RAILS_ENV'] || 'production'

# 16 workers and 1 master
worker_processes (rails_env == 'production' ? 16 : 4)

# Load rails+github.git into the master before forking workers
# for super-fast worker spawn times
preload_app true

jimeh

Rails Setting model

This is a semi-quick key/value store I put together for a quick way to store temporary data related to what's in my database, in a reliable way. For example, I'm using it to keep track of where a hourly and a daily crontask that processes statistics left off, so it can resume only processing and summarizing new data on the next run.

Code quality most likely is not great, but it works. And I will update this gist as I update my project.

How It Works

The settings table has two columns, a key and a value column. The value column is serialized, so you can technically store almost anything in there. Memcache is also used as a caching layer to minimize database calls.

icyleaf

#!/bin/sh
#
# git autodeploy script when it matches the string "[deploy]"
#
# @author    icyleaf <icyleaf.cn@gmail.com>
# @link      http://icyleaf.com
# @version   0.1
#
# Usage:
#       1. put this into the post-receive hook file itself below

chuangbo

替换上你的 API Token，域名ID，记录ID等参数，就可以运行了。 会在后台一直运行，每隔30秒检查一遍IP，如果修改了就更新IP。

获取 API Token 的方式

获得 domain_id 可以用 curl

curl -k https://dnsapi.cn/Domain.List -d "login_token=TOKEN"`

radar

edge-ticketee (master)♐ bundle install
Fetching source index for http://rubygems.org/
Bundler could not find compatible versions for gem "bcrypt-ruby":
  In Gemfile:
    devise (~> 1.4) depends on
      bcrypt-ruby (~> 2.1.2)

    rails (= 3.1.0.rc8) depends on
      bcrypt-ruby (3.0.0)

huacnlee

# 在本地服务器建立 rubygems.org 的镜像缓存，以提高 gem 的安装速度
# 此配置设置缓存过期为1天，也就是说，新上的 gem 无法马上安装
# 做这个起什么作用？
# rubygems 的很多资源文件是存放到 Amazon S3 上面的，由于 GFW 对某些 S3 服务器又连接重置或丢包，导致 gem 安装异常缓慢或有时候根本无法连接安装。
# 而通过这种跳板的方式可以很好的解决这个问题，当然前提是 Nginx反向代理 服务器需要在国外

proxy_cache_path  /var/cache/rubygems  levels=1:2    keys_zone=RUBYGEMS:10m
                                       inactive=24h  max_size=1g;
server {
  listen 80;

gregd

[Default Applications]
x-scheme-handler/txmt=openInMine.desktop

johnjohndoe

// FFmpeg settings for vimeo.com
// =============================
// Trying to find the best settings for encoding videos as described here: http://vimeo.com/help/compression
//
// Input file: MTS
// Video: H264, 1920x1080, 50fps
// Audio: A52 Audio (aka AC3), Stereo, 48kHz, 256kbps

ffmpeg -i input.mts -vcodec libx264 -acodec aac -strict experimental -vpre hq -s hd720 -b 5000k -ab 320k -r 25 -g 25 -threads 0 output.mp4

emk

-- This is a Hive program. Hive is an SQL-like language that compiles
-- into Hadoop Map/Reduce jobs. It's very popular among analysts at
-- Facebook, because it allows them to query enormous Hadoop data
-- stores using a language much like SQL.

-- Our logs are stored on the Hadoop Distributed File System, in the
-- directory /logs/randomhacks.net/access.  They're ordinary Apache
-- logs in *.gz format.
--
-- We want to pretend that these gzipped log files are a database table,

andyferra

body {
  font-family: Helvetica, arial, sans-serif;
  font-size: 14px;
  line-height: 1.6;
  padding-top: 10px;
  padding-bottom: 10px;
  background-color: white;
  padding: 30px; }

body > *:first-child {