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Application integrity: The new standard for AI-era software quality

Over the past few years, we’ve watched coding velocity accelerate at an extraordinary pace. AI has completely disrupted how developers build software. Agentic tools can now generate clean code faster than ever before. While AI has turbocharged code generation, code review, and code-level testing, it’s created a massive strain on the rest of the software development lifecycle.

JavaScript debugger Statement: How to Use It and When

The JavaScript debugger statement is a built-in keyword that tells the JavaScript engine to pause execution at a specific line of code. When execution stops, you can inspect variables, function scope, and the call stack using developer tools. It is commonly used during development to analyze how values change and where logic breaks, without relying on repeated logging or assumptions. No more guesswork. No more partial truths.
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Unlocking Innovation with the API Economy

As the technology stacks utilised by modern businesses grow increasingly complex, so does the number of integrated applications that are required to work together. The key enablers of this collaboration are Application Programming Interfaces (APIs), which act as the "glue" between applications, machines and databases, and let the different elements of an organisation's system work together as one cohesive whole.

Kafka Migrations Need More Than a Replicator

Jonas Best & Patrick Polster Kafka migrations are one of the riskiest infrastructure projects a platform team can take on. Miss a dependency and a downstream app starts reprocessing events it already handled leading to breaking SLAs and eroding trust with application teams. Migrate without visibility and you risk a major production issue. The instinct is to reach for a replication tool and call it done. But replication is only one piece of the puzzle.

Why does AI native development require AI native testing?

AI native development requires AI native testing because testing teams now face code generated not just by developers, but by AI agents as well. To keep pace and maintain quality, testers need comparable AI-powered capabilities that can generate, assist, and scale testing alongside AI-driven development, helping level the playing field and support faster, more efficient delivery — Coty Rosenblath, Chief Technology Officer at Katalon.

Lenses 6.2 - Trusting Agents to build & operate event-driven applications

At Lenses, our goal has always been to help organizations get the most out of their streaming data. We started with visibility into the Apache Kafka, moving up to the part that drives value, the application layer and now the Agentic layer. Lenses 6 moved us into a multi-Kafka world, as increasing, our clients aren’t just running on one type of Kafka anymore, and as sovereign cloud becomes increasingly topical (no pun intended) this is only increasing.

Your AI agent is one misconfigured MCP server away from leaking production data.

2025 was vibe coding. 2026 is Agentic Engineering - and the security rules haven't caught up. AI agents now have direct access to your databases, your APIs, your Kafka clusters. The protocol giving them that access is MCP. And most teams have no idea how exposed they are. We are fixing this problem with OAuth 2.1.

Legacy VM Footprints are Holding Back Digital Transformation

Enterprises in 2026 are under increasing pressure to modernize applications, adopt hybrid cloud architectures, and streamline operations—but their expanding and aging VMware footprints have become a major obstacle. As VMware licensing models evolve and operational costs climb, reducing or restructuring this footprint has become just as critical as adopting new platforms.

JavaScript Is Evolving Faster Than Ever - And JSConf Spain Made It Impossible to Ignore

There’s something powerful about stepping away from your day-to-day work and being surrounded by people asking the same questions you’ve been thinking about: At JSConf Spain, those answers don’t come from a single talk. They emerge from patterns — ideas that repeat across different speakers, different companies, and different perspectives.

What is an AI Data Gateway? | DreamFactory

An AI Data Gateway is a secure intermediary that connects enterprise data sources (like databases and file systems) with AI systems. It simplifies how AI accesses data while enforcing strict security, compliance, and governance measures. Instead of allowing direct access to sensitive data, the gateway uses secure REST APIs to control and monitor all interactions.