Systems | Development | Analytics | API | Testing

At Ably's February fan engagement event, Sam Renouf, CEO of the Professional Triathletes Organisation (PTO), captured the essence of impactful fan engagement with an observation about motorsports: Sam's insight points to a transformation across sports, and fan experiences more generally: that effective engagement now depends on meaningful realtime data storytelling.

QA automation means turning manual test steps into code so they can run faster, more consistently, and at scale. Instead of executing the same test cases over and over, testers write scripts that do the work for them. These scripts validate core functionality, trigger workflows, and report pass/fail status automatically. We'll show you how to do it in this article, in-depth.

Agile and DevOps changed how we build software. Fast feedback, continuous delivery, and constant iteration are the new normal. But the faster you move, the more you risk breaking things—especially if your testing can’t keep up. That’s where test automation comes in. Not as a silver bullet, but as a strategic foundation for scaling quality at speed.

Choosing the right storage format is crucial for optimizing performance, cost, and flexibility when working with cloud data. While file formats like Apache Parquet and Avro have been popular choices for storing data in data lakes, in recent years a new category called table formats has emerged to provide more management capabilities on top of these files. Among these, Apache Iceberg has been gaining significant adoption and momentum. So what exactly is Iceberg and why does it matter? Let’s dive in.

Large language models are under threat from a tactic called LLM grooming, where bad actors flood public data sources with biased or misleading content to influence AI training behind the scenes. Hemraj Bedassee , Senior Solutions Manager, AI Testing,

Not long ago, I wrote about a growing problem in enterprise AI: agents that don’t talk to each other. You’ve got a customer relationship management (CRM) agent doing its thing, a data warehouse agent crunching numbers, a knowledge bot quietly surfacing documents—but none of them are sharing what they know. Instead of a smart, connected ecosystem, we’re stuck with isolated pockets of intelligence: an island of agents.

A recent study by PwC suggests that AI could contribute up to $15.7 trillion to the global economy by 2030, with automation playing a key role in boosting efficiency and innovation. AI agents are central to this transformation, streamlining workflows, handling repetitive tasks, and enabling data-driven decision-making. From virtual assistants in customer service to intelligent fraud detection in finance, these agents are reshaping industries and driving business growth.

AI code generators are revolutionizing the way software is developed. By leveraging the power of machine learning, these tools automate repetitive tasks, minimize human error, and accelerate the coding process. From GitHub Copilot to Keploy VS Code Extension, AI code generators are becoming essential for modern developers – whether they’re writing new features, debugging, or generating tests.

Generative AI is quickly becoming ubiquitous in the software development space, with tools like Anthropic’s Claude offering rapid methodologies for code iteration, testing, and deployment. As new solutions, such as MCP (Model Context Protocol), are created to make integration more seamless, enterprises are adopting these AI solutions to optimize their development processes, a familiar challenge repeatedly arises: cost.