While our Bluemix Developer Advocate team continues working on the Logistics Wizard demo, we wanted to take a moment to share some key aspects of our development process. In this post, I'll take a step back from our implementation and talk about how we use GitHub, Travis CI, and our custom Devops Toolchain to streamline the whole development process for continuous integration and delivery.
As a local Austinite, SXSW means one thing and one thing only: Avoid downtown at all costs. That is unless you have a pass to this year’s Interactive event. For those of you fortunate enough to be attending, there are more than enough sessions to keep you occupied during your time in Bat City. The hard part is figuring out which ones will be the most valuable to you as a developer. Lucky for you, we’ve got you covered with our selection of five sessions you’ll want to attend at SXSW Interactive.
Customer feedback contains invaluable insight into client satisfaction, however, we are often presented with the challenge of turning this feedback into business action. The growing amount of unstructured feedback and channels from which we receive it adds to the complexity of creating systems to ingest and aggregate the data in a meaningful, scalable way. Assistant Shop.r is a cloud native app on Bluemix, built to enable department stores to analyze aggregated customer feedback and consumer behavior in order to enable their buyers to make smarter purchasing decisions. In this post, I'll present the user scenario and outline our solution. In Part 2, I will walk you through how we developed this application.
As any journeyman developer has come to realize, a programmer is nothing without their application logs. Since Bluemix went live last year, we have received our fair share of questions regarding best practices for debugging apps in Cloud Foundry. To make accessing logs even easier, we’ve developed and released an update that allows users to view logs for their Cloud Foundry applications directly in the Bluemix console.
If you were in Portland, Oregon this week, you have surely have been affected by the buzz around OSCON. Whether you spotted a few developers with glow sticks jogging down the Willamette riverside for the 5k Glow Run on Monday night or passed by the Oregon Convention Center teeming with badge-equipped conference goers, the vibe in Portland was all about open source technology. For those of you not lucky enough to experience it for yourself, I thought I would recap a bit of the excitement around the event and Bluemix's role in the festivities.
Learn how to build an Node.js app on Bluemix that integrates the Box API's that can use IBM Watson to analyze the personality of the author of your files!
The Watson Developer Cloud has unleashed the potential for developers around the world to tap into the cognitive power that Watson provides. We have seen apps leveraging Watson’s personality extraction capabilities for enhancing social engagement to apps combining speech detection to deliver interactive experiences. Out of all these applications, some of the most unique and innovative use cases have come out of hackathons, where the creative juices are always flowing. I want to highlight some of the great MVPs that have come out of only a few days of hacking with Bluemix and Watson.
One of the intrinsic benefits of cloud platform services is that they drastically simplify the process of scaling your application. Scaling can be done both vertically - by increasing the amount of memory available to each instance - or horizontally - by creating additional instances. This process of scaling your application can be done manually, either through the Cloud Foundry CLI or the application dashboard. However, it is also useful to be able to scale automatically in order to quickly react to spikes in application usage. The Auto-Scaling service provides the ability for users to dynamically scale their application horizontally by creating custom policies that dictate scaling behavior. By binding the service to your application, Auto-Scaling allows you to define rules that control the behavior for scaling in and out.