Systems | Development | Analytics | API | Testing

AI is now writing more of your code than ever. But the problem is that your test suite was built to catch errors, not to catch the difference between what an AI agent produced and what your original specification actually required. As AI tools accelerate development velocity, the volume of code moving through pipelines is outpacing traditional quality processes.

Quick summary: AI accelerates code creation, but its inherent confidence pushes structural risks downstream, where they surface as costly, release-blocking problems. As code output scales, performance validation that can’t keep pace becomes a headache and a business risk. Agentic performance testing embeds skepticism and performance awareness into the development process before risk can compound. Software development requires specialized expertise for a reason.

Most core banking systems were never designed to move. They were built to run reliably inside controlled environments, with tightly bound processes, batch cycles, and layers of regulatory logic stitched over time. Now, those same systems are expected to support real-time payments, embedded finance, and API-driven ecosystems, often without a fundamental redesign. That mismatch is forcing a shift.

I spoke at AI Engineer Europe last week, and came away with a clearer picture of where the industry actually is right now. My talk was about why AI user experience breaks at the transport layer. But the bigger takeaway wasn't from my own session. It was from watching what the rest of the room was building, and what problems they were running into.

For decades, software engineers have relied heavily on C and C++ to build embedded systems. These legacy languages offer the deep control and speed required for constrained environments, but they reveal gaps in memory management and concurrency. The Rust programming language has emerged as a solution.

Security expectations for version control infrastructure have evolved dramatically over the years. While Perforce P4 has always empowered administrators with deep configurability, the default configurations shipped with previous versions of P4 are no longer sufficient. With the upcoming P4 2026.1 (scheduled for availability in May), we are implementing a Secure by Default posture designed to enforce best practices when protecting the source code and binary assets stored in P4.

If you want to know which Android frameworks are worth your time, job postings will tell you more than any opinion piece ever will. That’s why I searched Google Jobs for "Android developer", went through every posting the pagination would show me (177 in all), and logged every framework or library mentioned. “Required” frameworks got tagged as required. Anything in a "preferred," "nice to have," or "bonus" section got tagged as nice to have.

The artificial intelligence landscape has crossed a consequential inflection point. Enterprises that approached AI as an efficiency instrument, deploying it to automate discrete tasks, accelerate content generation, or augment human decision-making at the margins, are now confronting a paradigm of an altogether different magnitude.

How runtime intelligence, platform engineering, and AI-native workflows are shifting operations to developer workflows. Highlighted by NodeSource extending its Diagnostics and Security Runtime Observability solution into the IDE.

What the PocketOS incident reveals about AI agents, unscopped API tokens, and why enterprise data needs a gateway in front of it. DreamFactory is a secure, self-hosted enterprise data access platform that provides governed API access to any data source, connecting enterprise applications and on-prem LLMs with role-based access and identity passthrough.