Systems | Development | Analytics | API | Testing

In today’s digital economy, businesses aren’t just competing on products and services—they’re competing on insights and decisions. The ability to deliver real-time, contextual analytics within applications and portals isn’t just a nice to have; it’s a critical advantage. Your users expect instant access to insights without switching between tools, hunting for reports, or waiting for analysts to provide answers. This is where embedded AI-powered analytics comes in.

Every article I read highlights the need for rapid adoption of AI, with emphasis on agentic and generative AI redefining the boundaries of data architectures. As organizations expand automation, personalization and real-time decision-making, they face a fundamental challenge: data gravity – especially to meet the demands of AI reasoning inference workloads – is pulling AI workloads toward storage, but disaggregated architectures struggle to keep pace with the need for rapid data access.

The era of AI experimentation is over. Organizations want to see ROI. And they will—as long as they understand that the competitive edge isn’t in AI itself. With AI evolving rapidly, businesses need a clear strategy that cuts through the noise and generates ROI. This key strategy is to embed AI into core business processes. This post will cover five enterprise AI trends for the new era of AI and why process is the key to ROI. The most talked-about trend today is agentic AI.

Dialyzer (DIscrepancy AnaLYZer for ERlang programs) is a powerful static analysis tool that helps developers identify potential issues in their Elixir code without executing it. It excels at finding type mismatches, unreachable code, and unnecessary functions through sophisticated flow analysis. In part one of this two-part series, we'll first get to grips with the basics of Dialyzer. In part two, we'll examine more advanced use cases.

We are proud to announce the release of Apache Kafka 4.0. This release contains many new features and improvements. This blog post will highlight some of the more prominent ones. For a full list of changes, be sure to check the release notes.

This is Chapter 2 of our journey of non-functional testing with Otto Perf. You can read Chapter 1 if you have missed it. Authors note The article is in the OctoPerf Blog pages Otto had previously got to the point where he had defined a set of non-functional requirements and risk assessed these with the programme team.

With the release of Node.js v23.6.0, developers can now use TypeScript natively without additional transpilation tools like ts-node or manual compilation steps. This milestone significantly streamlines development workflows, simplifying build processes and improving the overall developer experience by reducing complexity.

We’re excited to announce the general availability of Kong Insomnia 11! This release introduces third-party vault integrations for enhanced security, an all-new Git sync experience for more seamless collaboration, and support for multi-tabs to improve efficiency within the Insomnia application. Keep reading to learn more.

When your company deployed its first large language model last quarter, your CTO probably didn't mention you'd need entirely new tools to understand what's happening under the hood. But here we are. AI Observability isn't just another tech buzzword—it's the difference between confidently leveraging AI and crossing your fingers hoping nothing breaks.

When it comes to ensuring software quality, having a well-defined Test Plan and Test Strategy is crucial. As a software developer or QA professional, understanding the differences between these two concepts and how to write a Test Plan or execute a test strategy effectively can significantly improve your testing process.