Systems | Development | Analytics | API | Testing

If activation feels fuzzy in your company, you’re not alone. In fact, Rodrigo Fernandez has seen the same pattern across hundreds of SaaS businesses: growth teams get handed “increase activation,” but no one actually owns what activation means, how it’s defined, or how it’s measured. And when activation isn’t owned, it becomes a committee decision. It turns into noise. And your product data stops being useful.

The Model Context Protocol (MCP) is reshaping business intelligence. It provides the technical standard for a new class of generative BI tools that let you talk to your data. The engine behind this revolution is the MCP server—the essential component that connects AI models (like Claude or Cursor) to a company’s data. This article examines Tableau’s official MCP server vs. Databox MCP to help you decide between a traditional BI add-on and an AI-native headless platform.

Software testing is critical in assessing the quality of apps, testers oftentimes have to deal with limited resources when it comes to creating tests, as well as repetitively creating tests for all feature coverage. These factors lead to a significant reduction in both the speed of development and efficiency in the testing process.

Modern software systems are exposed to a constant stream of disclosed vulnerabilities. Thousands of new issues are published every year across operating systems, runtimes, libraries, and frameworks. Treating all of them as equally urgent is not realistic, and trying to do so often leads to ineffective security work. To manage this volume, the security community relies on two foundational mechanisms: CVE and CVSS.

If you’re an SAP customer, you’ve probably heard the same pitch more than once: just wait a little longer. Your migration to S/4HANA will unlock the agility you need. Your SAP Business Technology Platform (BTP) investments will catch up soon. Your workflows will get faster. Your data will become more accessible. Just a little more time. But what if you can’t wait? What if your supply chain teams are buried under manual work?

Your first data integration took two weeks. The second took three weeks, because you had to refactor some code. Your third one is now in week five, and you’re realizing the architecture you built for two sources doesn’t scale to three. If this sounds familiar, you’re not alone. This isn’t because your team isn’t capable. It’s because data integration complexity doesn’t scale linearly. It compounds. It doesn’t have to be this way.

DreamFactory 2.5 now supports Apple Push Notification Service (APNs). This blog post will show you how easy it is configure DreamFactory as your iOS app’s push notification provider. Note that APNs support is a premium feature in DreamFactory's Silver and Gold products, not an open source feature. APNs is a native DreamFactory service that supports role-service-access, live API documentation, script access, etc.

If you want to spin up a fast API solution, DreamFactory is a great way to do that with a Bitnami install. Within minutes you can have a fully documented and secure REST API to utilize. Just like any program bundle, there are lots of features to learn and interact with. Outside of a Docker Swarm or AWS ELB setup, it is pretty hard to find a way to spin up a DreamFactory instance faster. We are going to dive in a bit further to find out how to interact with the system database.

KAi, the AI assistant inside Kong Konnect, just got significantly more capable. Today, we're announcing an enhanced beta version powered by the new Kong Konnect MCP Server — a shared infrastructure layer that also opens up your API platform to IDE copilots and custom agents. The result? KAi can now do things it couldn't before, and those same capabilities are available wherever you work. If you've used KAi before, you'll notice the difference immediately.

AI agents are spreading across organizations rapidly. Each agent needs secure access to different Model Context Protocol (MCP) servers. Authentication becomes complex. Scaling creates bottlenecks. The dreaded "too many endpoints" problem emerges. You face a classic AI infrastructure headache. The numbers tell the story. Organizations using AI in at least one business function jumped from 55% to 78% in just one year. Generative AI usage specifically rose from 33% in 2023 to 71% in 2024.