Performance testing applications requires a set of skill that are build and gathered over many years of studying and using the various techniques and tools that are required to make sure the application you are testing is fit for production. Now we have all heard of Artificial Intelligence (AI) and the many tools and companies that now exist in the AI space. Based on a quick look on the internet there are around 15,000 AI startups in the United States alone.

The past year has seen an unprecedented AI hype wave triggered by the launch of OpenAI’s ChatGPT. Analysis abounds on whether the hype is real, where value will accrue and whether generative AI-first product builders have a real shot at category disruption or creation. As frenzied R&D and market activity continue unabated, market maps and take after take continue to drop hot. But what about revenue?

In a previous blog post (How To Survive an Apache Kafka® Outage) I outlined the effects on applications during partial or total Kafka cluster outages and proposed some architectural strategies to handle these types of service interruptions. The applications most heavily impacted by this type of outage are external interfaces that receive data, do not control request flow, and possibly perform some form of business transaction with the outside world before producing to Kafka.

An array is like a box with compartments, where you can store a set number of items of the same kind. Arrays play a crucial role in Kotlin, helping us hold many items together. They allow us to send multiple values to a function easily, or make various changes to the data. There are various different forms of arrays in Kotlin, including the object-type array, represented by something called the array class.

2024 is going to be an important transition year for artificial intelligence. 2023 was the public debut of generative AI and large language models (LLMs), a year of amazement, excitement, occasional panic and, yes, more than a little bit of hype. The year ahead is when businesses begin to make the promise of advanced artificial intelligence real, and we’ll begin seeing the effects on how we work and live.

In one of our recent blog posts, about six key predictions for Enterprise AI in 2024, we noted that while businesses will know which use cases they want to test, they likely won’t know which ones will deliver ROI against their AI and ML investments. That’s problematic, because in our first survey this year, we found that 57% of respondents’ boards expect a double-digit increase in revenue from AI/ML investments in the coming fiscal year, while 37% expect a single-digit increase.

Stepping into the world of Apache Kafka® can feel a bit daunting at first. I know this firsthand—while I have a background in real-time messaging systems, shifting into Kafka’s terminology and concepts seemed dense and complex. There’s a wealth of information out there, and it’s sometimes difficult to find the best (and, ideally, free) resources.