While reading my fairly forgotten SocialNet screen (yes, I have nonchalantly moved to graphbeams and I think you should too), I’ve noticed that a fellow member of our Knowledge Constructing Laboratory has spoken on the matter of movie sequels. I wish to set forth an encoded algorithm to exemplify the matter.

# This script iterates over the collection
# of all highly-rated Hollywood movies
# and produces sequels based on 
# random factors. 

import fileinput

import WallStreet
import HollywoodJunk

class Sequel:
     # This class finds financing and a producer for a Sequel.
     # If it is succesful, a sequel will be made in the next 
     # year or two.

    def __init__(self, movie):
        eager_producer = finance()
        if eager_producer:
            producer.execute_event()
       else:
           # No Sequel produced...

    def finance(self):
        producer = HollywoodJunk.Producer()
        investor = WallStreet.Investor()
        if investor.hasMoneyAvailable() and WallStreet.isBullish():
            producer.produce_sequel(movie)
        else:
            # Write a comic, fan fic, homebrew game, etc.
            # Careful!! You may lose all your DollarRep!!
            if hasattr(movie, 'nichemarket'):
                 investor = WallStreet.PrivateInvestor()
                 if investor.hasMoneyAvailable() and WallStreet.isNeutral():
                       producer.produce_multimedia(movie)
            else:
                 investor = SocialCapital()
                 if investor.hasMoneyAvailable() and WallStreet.isBearish():
                     producer.produce_social_event(movie)
        return producer

if __name__ == "__main__":
    obj = {}
    while True:
        for movie in HollywoodJunk:
            obj[movie] = Sequel(movie)
 
            ## If you're a member of the Bilderberg Group,
            ## insert your brainwash code here.

May it help you in your own individual ThoughtProcesses. I really wish graphbeams were more popular… but of course, my screen will always lend its pixels to SocialNet. May you rest in peace tonight.

timescrypt:        091410
validitycert:      d41d8cd98f00b204e9800998ecf8427e
origindiv:         alej_@BioComplabs