Systems | Development | Analytics | API | Testing

Financial institutions and other heavily regulated industries can use generative AI in their testing strategy today. Read on to see the use cases.

Many people don’t test their WordPress websites, and it’s a problem. While the core of WordPress is fairly well-tested by its creators, users, and the open-source community, the same cannot be said for every plugin and theme. There’s an infinite combination of versions, hosting environments, plugins, themes, and configurations. You can’t trust the community to catch every bug that could affect your website.

AI is everywhere. Depending on who you ask, it’s either making developers obsolete, or it’s just hype. But for those of us who’ve actually used AI tools in real-world engineering workflows, especially in complex distributed systems, the truth lies somewhere in between. At Multiplayer, we’ve spent the past few years exploring how AI can—and can’t—help solve two of the most persistent challenges in distributed systems: documentation and debugging.

In many enterprise organizations, particularly across public sector and regulated industries, identity and access management (IAM) are not just a best practice; it’s a mandate. From federal agencies and financial institutions to healthcare and utilities, internal IT policies often require every application to integrate with a centralized identity provider (IdP) such as Microsoft Entra ID or Okta.

Manual testing gets old fast. You end up clicking through the same workflows over and over, and it’s easy to miss bugs when you’re going through dozens of test cases. Automated testing tools handle the repetitive stuff for you, running tests in the background while you work on actual development. This guide looks at some solid automated testing tools for 2025 that can help streamline your testing process.

Modern companies generate large volumes of data, but often the internal users who need that data to do their jobs—data engineers, managers, business analysts, and developers—can find it challenging to quickly figure out answers to their questions. Apache Kafka is a powerhouse for real-time data processing of high-throughput workloads, and many organizations use Kafka to enable self-service access to data streams.

As your business grows, so does your data. And if you’re running Microsoft SQL Server, you’ve likely faced the challenge of managing storage in a way that doesn’t break the bank but keeps your applications running smoothly. It’s a balancing act that many IT professionals and business leaders struggle with daily. We hear from numerous mid-sized companies that hit a wall with their Microsoft SQL Server database.

Regression testing is a software testing practice that ensures recent code changes do not negatively impact the existing functionality of an application. It involves re-running previously executed test cases to verify that new updates - such as bug fixes, enhancements, or feature additions - do not introduce defects into previously working software components. The primary goal of regression testing is to maintain software stability while allowing continuous improvements.

At ThoughtSpot, we’re on a mission to empower every business user to become a data champion. Over the past year, I’ve witnessed firsthand how organizations across Australia and New Zealand are embracing this vision, transforming the way they work, make decisions, and serve their customers. Today, I’m excited to share some of the incredible momentum we’re seeing in the region and to celebrate the forward-thinking organizations leading the charge.

Gen AI call center co-pilots can provide enterprises with operational visibility and insights while automating repetitive tasks, to improve the customer experience. In this session, we’ll show how a large health insurance provider implemented an agentic co-pilot designed scale across multiple call centers and environments. To dive deep into the architecture and see a demo of the co-pilot, you can watch the webinar this blog is based on.