In fact, many of the failures you see write-ups on HN I've been able to model and solve in a few minutes with MMA. In particular the rap genius Heroku queue issue.
Can you elaborate and/or provide a thorough example of this? I'm curious.
So with a line of code you could represent heroku's queues and determine exactly how many dyne's you needed. Or, you could try different queueing methods and determine how much money you could have been saving.
In some sense Mathematica is further down the What vs How line of language power. Even in ruby, Clojure or Haskell you're still left specifying HOW to do optimization, integration, etc. Not what you'd like to optimize, integrate or manipulate.
[1] http://reference.wolfram.com/mathematica/guide/QueueingProce... [2] http://reference.wolfram.com/mathematica/guide/Optimization....
If you wanted to be ambitious, you could simulate different servicing distributions, and times. But that might add a whole 2-3 lines of code to the solution.
Manipulate[ ListLinePlot[ RandomFunction[QueueingProcess[arrivals, 60/8] , {0, 50}][ "Path"]], {arrivals, 1, 30}]
On heroku's side, you could model their network with a few lines of code and experiment with different types of queues to find things that work well and are affordable.
As PG said in beating the averages, it's one o those things that's easy to dismiss when you're looking up the power curve.