Wednesday, July 22, 2015

A typical day.. Where?

I checked Twitter around noon and saw a local-food food truck I’ve heard good things about was a block away.  I took the elevator down from my 32nd floor IT job writing web services for mobile apps and went outside to a beautiful 78 degree day.  Across the street I saw the old smoker’s sidewalk has newly installed bike rentals with half the bikes out.  I wait a few minutes in a small urban park for my gyro rounder and see not one but two lunch delivery guys on bikes with big insulated backpacks.  Back in the office, I work with a coworker remotely about how to ensure our app’s newest feature is secure.

Just another day of life in downtown Omaha.

Thursday, June 25, 2015

Image analysis help!

Text analysis

Over the past few days I’ve been working on analyzing main causes of some app issues we’ve been having.  With a combination of Splunk, Excel-foo, and some grunt-work, I dug through millions of log statements to find then analyze tens of thousands of error messages.  I eventually came up with some pretty sweet looking charts to summarize my results.
Image analysis

Now I have tens of thousands of images I would like to analyze.  How the heck can I do this?  I have no idea.  How can I evaluate and quantify if an image is blurry, too-low contrast, or off the edge of the image?  Just by looking at some by hand, I can see how some would be rejected but how maybe we could change some parameters to accept more.  How can I try making a change to our processing and run these thousands – and thousands of currently working images – back through to reevaluate the results?

Text is easy, but these images have me stumped.

Tuesday, March 17, 2015

Bad Code

I’ve decided to start documenting some of the more amusing code I find in my project.  Thankfully, so far none of it’s mine.

At least it’s logging the error, right?

And how about these two methods?

Because sometimes, you only kind of need to know.

And not code, but at least we did a good job of stopping a release minutes BEFORE it caused problems rather than minutes (or hours/days) AFTER.