Systems | Development | Analytics | API | Testing

January didn’t bring radical changes to Node.js, and that’s precisely why it was important. Instead of headline features, the first month of the year reinforced a clear direction for the ecosystem. Stability over novelty. Signal over noise. Security handled with context rather than urgency. For teams running Node.js in production, January delivered clarity. Here’s what actually mattered.

In the rapidly evolving landscape of AI-driven development, the Model Context Protocol (MCP) has emerged as the critical standard for connecting AI applications to the data and tools they need. We are excited to announce the Technical Preview (TP) of Kong MCP Registry, a major milestone in our mission to provide the most comprehensive platform for modern API and AI management.

In the semiconductor world, “software" is more than just application code. It is a complex stack of firmware, bootloaders, microcode, drivers, and Board Support Packages (BSPs) that are intricately linked to the hardware being designed. To secure the supply chain, meet customer expectations, and maintain market access, semiconductor leaders need a dynamic, "living" SBOM strategy that assesses risk in real-time and provides a single source of truth for all teams to work from.

While every organization races to deploy AI agents faster, a quieter crisis is compounding in the background, and it will play a large part in determining who survives the agentic era. The numbers are stark. Too many executives see AI governance as a brake on innovation or something to figure out later, after the speed problem is solved. With agentic AI, that's backwards.

While every organization races to deploy AI agents faster, finance departments are watching something alarming unfold—and it will play a large part in determining who survives the agentic era. The numbers are stark: 84% of companies report more than 6% gross margin erosion from AI costs. Within that, 26% report erosion of 16% or more. And only 15% of companies can forecast AI costs within ±10% accuracy—the majority miss by 11-25%, and nearly one in four miss by more than 50%.

Artificial Intelligence in real estate is no longer a future concept or a conference buzzword. It’s already reshaping how properties are leased, managed, valued, and invested in — often quietly, behind the scenes, inside operational workflows. Over the past months, ORIL has been hosting conversations with founders, CEOs, operators, and technology leaders on the Innovation Blueprint podcast, discussing how AI is actually being used in PropTech today. Not hypotheticals. Not hype.

Have you ever noticed how your favorite apps just know what you want? Whether it’s a curated playlist that suits your mood, a movie recommendation that hits the spot, or ads that seem oddly relevant, none of it feels surprising anymore. These experiences have become so routine that we barely pause to think, “How does this even work?” But maybe we should.

Complex user tasks often need multiple AI agents working together, not just a single assistant. That’s what agent collaboration enables. Each agent has its own specialism - planning, fetching, checking, summarising - and they work in tandem to get the job done. The experience feels intelligent and joined-up, not monolithic or linear. But making that work means more than prompt chaining or orchestration logic.

Executive Summary: As enterprises integrate AI and large language models (LLMs) into their data workflows, the need for predictable, secure, and auditable database interactions has never been greater. Deterministic queries—particularly those encapsulated in stored procedures—provide the guardrails necessary for both human analysts and AI systems to access sensitive data safely.

Coding with artificial intelligence is not just a nice-to-have; AI applications in computer programming are becoming integral to modern computer programming workflows. Presently, two primary applications dominate the discussions in this area: GitHub Copilot and Cursor AI. While both applications provide faster coding times and fewer bugs, fewer bugs, and smarter code, they offer such features in extremely different ways.