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Five Guidelines for Software Entrepreneurs: AT&T Foundry, UT Dallas and Juniper Hackathon

by Juniper Employee ‎08-17-2012 11:55 AM - edited ‎08-17-2012 11:55 AM

Our students at the hackathon are deep in creative mode, slinging code for the demonstrations and judging that concludes our six-day training and entrepreneurship workshop.   Before turning them loose on the keyboards, I offered five guidelines for anyone thinking about the next great application.

  • 1. Governance drives survivability. Governance reduces the anarchy in a project, especially one with teams that coalesced as a function of attendance. It's required for ensuring one person's work doesn't break another's, and that you work hard to remove barriers to exit and entry - instruments of technical debt that are hard to pay off when you have a demo in less than three days and need to change your index engine.
  • 2. Relax your assumed constraints. If you presume that you know the upper bound on packets, devices, or page views, challenge your assumption that put that constraint in place. Inherent in the lateral thinking that's been a cross-current the whole week is breaking down a priori limits that prove rate limiting, profit limiting or market reducing. What works for 1,000 devices may not scale to a million of them; a graph relationship that provides a rich view of 100 users and their content involves increasingly larger amounts of data as you add more "likes" to the equation - don't presume to know all of the costs, sizes and scales up front.
  • 3. Keep the network front and center. You can't get to any interesting piece of data today, from any type of client, without traversing at least one network. While our workshop focused on building network management applications, the criticality of those applications will increase as enterprise and consumer applications fail to account for all of the ways in which the network colors experience. James Gosling, father of Java (it's always fun when you can do a call-by-name to the pantheon of computing) neatly summarizes over-simplifications of the network that end in tears.
  • 4. Watch out for the million dollar challenges. Network problems almost always involve graph relationships and cost optimization, two of the ingredients that show up in many of the open problems in computer science. As you work on balancing traffic over multiple network paths, or determining if two paths are equivalent, and the problem sizes get larger, the fully optimized (perfectly solved) solutions get exponentially more difficult.
  • 5. Approximation is your friend. The corrollary to the fourth guideline when it comes to designing an algorithm, and the rule of the day when dealing with an avalanche of data. Given enough time and tools, it's possible to over-analyze, over-match and over-overlay data to the point where you see things that aren't there, as Randall Munroe points out in xkcd..

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