Systems | Development | Analytics | API | Testing

Behind every smooth user experience is a maze of APIs quietly handling requests, responses, and data flows. This makes APIs critical connectors that enable applications to communicate and share data seamlessly. When these vital conduits fail, the consequences can be severe—system outages, data breaches, and frustrated users. API testing is the unsung hero ensuring your digital world runs smoothly and securely.

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?

Ever been confused about OpenAPI and Swagger? You’re definitely not alone. I were on the same line. These two terms get thrown around a lot in the API world, and honestly, it’s pretty easy to mix them up. It was driving me crazy! In this blog, we are going to talk about what is OpenAPI and Swagger Schema, how they are different from each other, their advantages and disadvantages and lots more. Trust me, by the end of this, you’ll be the one helping other people understand this mess.

Visual regression tests have effectively enhanced the consistency of the UI maintenance by teams. It improves the quality of the user experience, lowers the possibility of UI bugs in production, saves hours of manual quality assurance activities by automatically detecting unintentional visual changes in web applications.

In this day and age of the world wide web, users don’t wait around – neither should your app. Imagine your site going viral overnight, or a flash sale that generates thousands of users overnight. Will your server crash or expand with confidence? Welcome to Spike Testing. In this blog, we’ll explore what spike testing is, why it matters, how to do it right, and how tools like Keploy can help make your tests more stable and meaningful.

Industries like manufacturing and smart cities rely on connected devices to generate data streams for predictive maintenance, automation, and efficiency. But moving this data between systems can be slow, insecure, and inefficient. Here's the solution: smart data flows powered by edge computing and automated APIs.

Modern innovation demands fast, secure, and flexible access to data. But when organizations deal with scattered databases and strict security policies, manual API development slows everything down. The solution? Automate how APIs are built, secured, and managed—using AI and open-source tools like DreamFactory.