Scala with Futures:
import java.util.UUID
import scala.concurrent.Future
trait User {
def isAdmin: Boolean
def id: UUIDScala with Futures:
import java.util.UUID
import scala.concurrent.Future
trait User {
def isAdmin: Boolean
def id: UUIDRubric: Software Engineering : Factual Claims : Defect Cost Increase : Wolverton Ratios
See previous note on the IBM Systems Sciences Institute
In absolute numbers, the Wolverton are as follows: 139:455:977:7136:14102, claimed dollar costs of fixing an "average" defect. (Itself an absurd claim, see Leprechauns, I should perhaps write more on that.)
| # To use: | |
| # 1. extract the NCAs from the NSP (I used NSCB) | |
| # 2. extract the contents of 5977df9d4848858cbde157c6723dd1de.nca | |
| # 3. inside 1 [romfs]\rom\Stardust_JP\Textures you'll find texture_pack.cpio. Extract it (I uzed 7zip) | |
| # 4. run this python 2.7 script in the directory with all the .rpt files. It'll create a out folder containing all the PNGs | |
| import struct,glob,os,zlib | |
| from PIL import Image | |
| OUTDIR='out' |
| iex(1)> Foo.Correspondence.validate_number("234-555-6789") | |
| {:ok, "Verizon Wireless", "mobile"} |
What do Tensorflow, Apache Airflow, Rule Engines, and Excel have in common?
Under the hood they all use DAGs to model data-flow dependencies of the program. Using graphs to model programs is great because you can modify the program at runtime. Lets talk about doing this in Elixir for great good.