Ever dealt with a misbehaving consumer group? Imbalanced broker load? This could be due to your consumer group and partitioning strategy! Once, on a dark and stormy night, I set myself up for this error. I was creating an application to demonstrate how you can use Apache Kafka® to decouple microservices. The function of my “microservices” was to create latte objects for a restaurant ordering service.

There are now nearly 9 million apps worldwide, and practically all of them run on an internal clock. Financial apps are built on timestamped data. Office apps run on calendars. Social apps are defined by their feeds, timelines and anniversaries. So, as devs, we need to know how to compute date and time. JavaScript, the world’s most popular programming language, has plenty of helpful APIs, but they can throw up complications if you’ve never used them before.

Generative artificial intelligence (AI) is a game-changer, bringing with it the promise of unparalleled efficiency and the potential for entrée into new markets. As generative AI continues to soar in popularity, organizations are eager to tap into its transformative power. However, this enthusiasm should come with a side of caution. It’s critical that organizations develop a strong generative AI policy so the allure of new technology doesn’t lead to devastating mishaps.

Have you heard the phrase “AI won't replace humans - but humans with AI will replace humans without AI”? I personally love this quote because it perfectly encapsulates the nature of the anticipated workforce shift from the rise of generative AI. As I wrote back in 2017, the power of AI is not about machines supplanting human abilities, but rather about a symbiotic relationship between humans and AI. I think the Star Trek analogy I used then is standing the test of time…