Systems | Development | Analytics | API | Testing

APIs have become the backbone of modern software architecture—but building and securing them is often harder than it should be. Developers today are expected to expose dozens of data sources as clean, secure, and compliant APIs—fast. From managing authentication to documenting endpoints and enforcing governance, the traditional approach to API integration comes with friction, delay, and technical debt.

So we have all been hearing a lot about AI lately. Everyone is talking about ChatGPT, OpenAI, Claude, image generation, and now there is a new trend about “Agentic AI.“ I know it is getting confusing with all these fancy terms flying around us. Let me break it down for you in simple terms. You can think of this as your friendly guide to understanding the two biggest players in the AI game right now!

Discover how Cisco is transforming its global data strategy to drive faster decisions, accelerate AI innovation, and scale globally — all while maintaining enterprise-grade security and control.

If you’d like to try Yellowfin for yourself, go ahead and request a free trial. Generative AI has made it easier than ever to analyze data with plain English. You’ve probably seen dozens of videos showing how to use ChatGPT for data analysis — upload a CSV, ask a question, and get a chart. But I wanted to see how that stacks up against Yellowfin, which is designed for analytics from the ground up.

Picture this: You’re a CFO presenting quarterly results to the board. A sharp-eyed member questions a major variance in your forecasting model. You’re ready, you explain that your AI system caught the anomaly early. But then comes the kicker: “How exactly did the AI reach that conclusion?” Suddenly, your confidence wavers. If your best answer is, “I’m not sure, but the algorithm is sophisticated,” you’re not alone, but you’re on thin ice.

As modern software delivery processes accelerate, QA teams inevitably feel pressure to maintain quality at scale. SeaLights addresses this challenge by providing deep visibility into test coverage across every stage of the software development lifecycle. It helps teams identify untested code changes, optimize execution, and deliver confidence at release time.

Artificial intelligence (AI) is widely used today, from voice assistants to Netflix recommendations, but AI models do not always behave as intended. Testing an app before it is released is standard practice, and similarly, AI models should be thoroughly tested. Testing an AI model can verify that the model’s decisions are accurate, fair, and safe.

AI coding tools are everywhere; they have changed the way we used to code. These days, people are doing vibe coding with the help of these tools. From suggesting code snippets to explaining errors in plain English, these assistants are becoming as common as Stack Overflow tabs. There are only two names that keep popping up in everyone’s conversation when it comes to AI coding tools: Cursor and GitHub Copilot. But here’s the real question: Cursor vs.

With vibe coding, you can build complex applications using powerful AI tools that offer a conversational experience, letting AI handle the heavy lifting. But what happens after you've built your app? You might have a three-tier web application connected to a database running locally, but taking it live—ensuring it’s connected, secure, and production-ready—can be a complex process that disrupts your flow. What if deploying your app was just as easy as building it?