For excessively paranoid client authentication.
Updated Apr 5 2019:
because this is a gist from 2011 that people stumble into and maybe you should AES instead of 3DES in the year of our lord 2019.
some other notes:
At Crush + Lovely, we use Railsmachine's Moonshine to automate the configuration of our servers. When writing our deployment recipes, VMWare Fusion's ability to take snapshots and rollback to these snapshots is a huge timesaver because it takes just seconds to roll a server image to it's original state.
When you're just configuring a single server, having a static IP address for your server image isn't too important, but when you're configuring multi-server setups, it can be useful to duplicate a number of server images and give each a static IP address so you can consistently deploy to them. While not documented well at all, it turns out that this is relatively easy to accomplish in four simple steps.
Let's say you have a guest machine with the name ubuntu-lucid-lynx-base
a
I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.
I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real
ssh-srv-wrapper is bash shell script which tries to find a SSH SRV record for the first host and uses what is found rather than what was passed (if a valid record is found).
Run the script directly or feel free to rename or symlink to the name ssh. It will look for another ssh in your path to execute.
import scalaj.collection.Imports._ | |
import org.yaml.snakeyaml.Yaml | |
def parse(str: String) = { | |
new Yaml().load(str) match { | |
case obj: ArrayList[_] => | |
val seqOfMaps = obj.asScala.collect { | |
case hashMap: HashMap[_, _] => | |
hashMap.asScala.toMap.asInstanceOf[Map[String, Any]] | |
}.toSeq |
GitHub supports several lightweight markup languages for documentation; the most popular ones (generally, not just at GitHub) are Markdown and reStructuredText. Markdown is sometimes considered easier to use, and is often preferred when the purpose is simply to generate HTML. On the other hand, reStructuredText is more extensible and powerful, with native support (not just embedded HTML) for tables, as well as things like automatic generation of tables of contents.
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 |
L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns
Compress 1K bytes with Zippy ............. 3,000 ns = 3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns = 20 µs
SSD random read ........................ 150,000 ns = 150 µs
Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs