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:
#!/bin/bash | |
function usage () { | |
echo "$0 [CA section name] [username]" | |
exit 1 | |
} | |
if [ $# -ne 2 ] | |
then | |
usage |
Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.
$ python -m SimpleHTTPServer 8000
1) Load the u3g(4) driver: | |
# kldload u3g | |
# kldstat | |
Id Refs Address Size Name | |
... | |
7 1 0xffffffff81b6e000 5ffb u3g.ko | |
2) Set the driver to load at boot: |
As compiled by Kevin Wright a.k.a @thecoda
(executive producer of the movie, and I didn't even know it... clever huh?)
please, please, please - If you know of any slides/code/whatever not on here, then ping me on twitter or comment this Gist!
This gist will be updated as and when I find new information. So it's probably best not to fork it, or you'll miss the updates!
Monday June 16th
(by @andrestaltz)
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.
My notes from implementing Job Vranish's excellent guide.
Follow along with the guide above, getting rustc from rustup or similar:
rustc 1.0.0-nightly (dcaeb6aa2 2015-01-18 11:28:53 +0000)
binary: rustc
commit-hash: dcaeb6aa23ecba2dc2af870668a9239136d20fa3
commit-date: 2015-01-18 11:28:53 +0000
Every application ever written can be viewed as some sort of transformation on data. Data can come from different sources, such as a network or a file or user input or the Large Hadron Collider. It can come from many sources all at once to be merged and aggregated in interesting ways, and it can be produced into many different output sinks, such as a network or files or graphical user interfaces. You might produce your output all at once, as a big data dump at the end of the world (right before your program shuts down), or you might produce it more incrementally. Every application fits into this model.
The scalaz-stream project is an attempt to make it easy to construct, test and scale programs that fit within this model (which is to say, everything). It does this by providing an abstraction around a "stream" of data, which is really just this notion of some number of data being sequentially pulled out of some unspecified data source. On top of this abstraction, sca
Java 8 introduced lambdas to the Java language. While the design choices differ in many regards from Scala's functions, the underlying mechanics used to represent Java lambdas is flexible enough to be used as a target for the Scala compiler.
Java does not have canonical heirarchy of generic function types (ala scala.FunctionN
), but instead allows a lambda to be used as a shorthand for an anonymous implementation of an Functional Interface
Here's an example of creating a predicate that closes over one value:
Welcome to Scala version 2.10.5 (OpenJDK 64-Bit Server VM, Java 1.6.0_27). | |
Type in expressions to have them evaluated. | |
Type :help for more information. | |
// We're using returnResult mode, so that methods return `Result`s instead of throwing exceptions | |
scala> import rapture.core._, modes.returnResult._ | |
import rapture.core._ | |
import modes.returnResult._ | |
// Our LaTeX backend of choice is xelatex |