Enterprise organizations collect massive volumes of unstructured data, such as images, handwritten text, documents, and more. They also still capture much of this data through manual processes. The way to leverage this for business insight is to digitize that data. One of the biggest challenges with digitizing the output of these manual processes is transforming this unstructured data into something that can actually deliver actionable insights.

Today, cyber defenders face an unprecedented set of challenges as they work to secure and protect their organizations. In fact, according to the Identity Theft Resource Center (ITRC) Annual Data Breach Report, there were 2,365 cyber attacks in 2023 with more than 300 million victims, and a 72% increase in data breaches since 2021. The constant barrage of increasingly sophisticated cyberattacks has left many professionals feeling overwhelmed and burned out.

In modern software development, clear code documentation and commenting are not only just a part of good practices, but also they are essential for effective collaboration, maintainability, and scaling projects over time. However, they are often neglected due to time constraints or lack of clarity on how much detail is necessary. But, with the advent of artificial intelligence, developers now have tools to streamline this process, allowing them to focus on coding without compromising on clarity.

Over the years, CI/CD has become such an integral part of DevOps that it is difficult to think of one exclusive of the other. However, DevOps’ much broader set of utilities might often undermine the role and scope of CI/CD. The vast DevOps toolchain for configuration management, IaC, monitoring, security, and other such use cases confines the CI/CD strategies to mere code handling.

A web development company needs to determine the right tools and frameworks required for streamlining processes and testing efficiency, hence proper consideration is needed. The global automation testing market size was estimated at USD 15.39 billion in 2023 and is projected to reach USD 17.71 billion in 2024. Notably, in 2023, North America dominated the global market with a leading share of 24.37%.

In recent years, the serverless paradigm has become known as a transformative approach to application development, allowing us to focus more on writing code and less on managing infrastructure. Laravel Vapor, a serverless deployment platform for Laravel applications, brings the power of the Laravel framework into the world of serverless architecture.

We are thrilled to introduce our newest feature to Moesif - AI Explain - a powerful conversational tool that lets you interact with your API analytics data like never before. Instead of spending time digging through numerous charts or building complex queries, you can simply ask questions and get meaningful answers in just a few seconds. AI Explain is designed to make analytics accessible to everyone, whether you’re new to working with data or simply want a more efficient way to gain insights.

In today's hyperconnected world, where negative reviews on social media can wreak havoc on a company’s reputation, delivering an exceptional customer experience isn't just a luxury—it's a business imperative. Companies are locked in a fierce battle for customers that is primarily based on their ability to deliver outstanding customer experiences (CX). According to research by The Conference Board, 65% of CEOs globally prioritize investing in strategies to improve CX.

In Kong Gateway 2.0, we released Hybrid Mode, also known as Control Plane/Data Plane separation. With it, our customers could efficiently and securely deploy clusters of Kong Gateway Dataplanes on their on-prem, private, and public clouds in any combination they wanted, and they could control the entire cluster from a single point, the Control Plane. Hybrid Mode became instantly popular with all our customers with large and varied deployments, due to the increased flexibility and ease of management.

Artificial Intelligence (AI) is revolutionizing industries globally, and wealth management is no exception. As firms explore innovative ways to enhance customer experience and streamline operations, a critical hurdle looms – trust. The complex algorithms driving AI decisions can be a black box for stakeholders, particularly in a heavily regulated and trust-driven industry like wealth management. This is where Explainable AI (XAI) steps in as a game changer.