Systems | Development | Analytics | API | Testing

The New Breed: How to Think About Robots

You’ve heard the saying “if you do what you love, you’ll never work a day in your life,” right? Well, I hate to say it, but that’s me. I never dreamed that I would wind up in a field that combined all of my interests, but somehow that happened. Through my research at the MIT Media Lab I get to apply my legal and social sciences background to human-robot interaction. Which yes, does mean that I mostly get to play with robots all day.

Cloud vendor's MLOps or Open source?

If someone had told my 15-years-ago self that I’d become a DevOps engineer, I’d have scratched my head and asked them to repeat that. Back then, of course, applications were either maintained on a dedicated server or (sigh!) installed on end-user machines with little control or flexibility. Today, these paradigms are essentially obsolete; cloud computing is ubiquitous and successful.

The Modern Data Stack Ecosystem: Spring 2022 Edition

Welcome to the Spring 2022 Edition of the Modern Data Stack Ecosystem. In this article, we’ll provide an in-depth look at the Modern Data Stack (MDS) ecosystem, updated from our Fall 2021 edition. We also highly recommended our article, The Future of the Modern Data Stack, to anyone who is new to the MDS and wants to learn about its history.

The World Beyond Test Automation: AI-Powered Intelligent Testing for Modern Applications

Web and mobile apps are now your primary connection with your customers. Staying relevant and winning market share requires that firms can make constant changes to these apps. But how can you deploy many more small changes - often many per day - with confidence and with managed risk? In the company of two software industry leaders, we take a closer look at how a modern testing toolchain combines production safety nets - from canaries, to feature flags, to error reporting - with AI-powered quality insights to engineer quality at speed for both developers and quality engineers.