Systems | Development | Analytics | API | Testing

ChatGPT 4 was just released this past March, yet in just a few months, it has already started to shift the landscape of how we develop materials and assets across industries. As CTO at Perforce Software, I am deeply interested in how new and emerging technologies shape and redefine well-established workflows and processes. In my nearly 30 years of software development experience, few technological advancements have been as exciting and as far-reaching as generative AI.

What device should be used to run the tests? What operating system version, manufacturer and model to use? How to get these devices? These are some of the questions we ask ourselves when planning a mobile application testing strategy. If we consider automating these tests, other questions will surely come to mind.

Machine learning watching generative artificial intelligence (AI) take off feels a little bit like an American Girl doll envying the Barbie movie excitement from afar. What is she, chopped liver? But we can’t forget about machine learning, because it’s the giant that generative AI is standing on. How? Well, machine learning is how generative AI learns. Generative AI takes machine learning a step further by leveraging those learnings to produce something new.

I had the chance to finally sit down and find a way to inspect TLS traffic flowing out of an application running on my machine. Although I did not invent anything, I needed to put together a lot of different tricks in order to succeed, and the documentation I could find online regarding this process is scattered, at best. So, here we are with a guide on “how to inspect TLS encrypted traffic without going nuts”. Hope you enjoy!

Since the inception of our cloud journey, we have extensively utilized Let's Encrypt because it has been very reliable, fully automated, open, and free. Today, we’re proud to become an official sponsor of Let’s Encrypt. In this blog post, we’re celebrating this event by explaining our journey with Let’s Encrypt, how we integrate with their service, and why we chose them.

‍ The mobile analytics stack isn't just about collecting data; it's about making sense of user behavior on your app. It's an amalgamation of various tools and techniques that allow businesses to gauge the effectiveness of their mobile applications, measure user engagement, and optimize for better performance.

Artificial intelligence (AI) has become a driving force in the digital transformation of businesses across various industries. As Chief Information Officers (CIOs) strive to stay ahead of the AI hype cycle in today’s competitive landscape, harnessing generative AI in particular can help them achieve their enterprise AI goals – by transforming processes, boosting productivity, and enhancing decision-making.

Google Cloud Platform (GCP) is a large, cloud-based suite that includes tools for computing, storing data, networking, analyzing big data, networking, managing APIs, and exploring artificial intelligence. The suite includes at least three GCP ETL tools (Cloud Data, Fusion, Dataflow, and Dataproc). However, some users might find that they benefit from a third-party, no-code/low-code ETL platform.

We are happy to announce the release of the Koyeb CLI 3.0! This release brings three crucial improvements: Smoother flow for creating and updating services, reworked error messages to ease troubleshooting, and a new foundation to continue building out our CLI. If you want to get started using the Koyeb CLI to deploy your services and applications worldwide directly from your terminal, read the Koyeb CLI documentation and CLI reference.