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We’ll always remember 2025 as the year AI code assistants went big. Copilot, Cursor, Claude, Windsurf, whatever. Developers went from mistrusting these tools, to being expected to turn over much of their coding labor to them. Even if, according to an extensive Stack Overflow survey, only 3 percent of professional developers say they ‘highly trust’ AI coding tools.

In Alloy Navigator, AI moves beyond insights to drive real action. Discover how our enhanced intelligence helps you optimize workflows, reduce resolution times, and make smarter decisions faster than ever before.

Josh Mesout (Chief Innovation Officer at Civo) took the main stage at Civo Navigate London 2025 to deliver a critical message: The AI revolution isn't just coming, it's here, and the way companies are built is changing faster than ever before. His session cut through the hype, delivering hard data on what separates the companies that scale AI from the ones that sink money into failed prototypes. The takeaway is blunt: The biggest threat to your AI ambition isn't the model; it’s your infrastructure.

As companies race to ship AI features into production, FinOps teams everywhere are scrambling to keep up with the variability of their engineers’ new favorite toys. The rapid pace of AI experimentation is creating a new wave of unpredictable usage patterns and costs.

AI is generating more code than ever, but delivery hasn’t kept pace. The Harness IDP Knowledge Agent helps teams close that gap by turning their internal developer portal into an intelligent platform for faster, safer software delivery. Joining a new engineering team can be exciting, but it can also be overwhelming. You spend the first few days figuring out what each service does, where documentation lives, and who owns what.

This is the next installment of the series on building a database and an application called dbRosetta using AI/LLM. Part 1 introduces the concept. THE AI PICKED DATABASE FIRST! Look, I talk databases at this thing a lot, so it probably knows my own preference, but when I asked it, it chose to build a database separate from the code. Let’s get into it.

When you're shipping LLM features, a lot of the work goes into keeping the model's behavior predictable. You deal with questions like: These are everyday concerns when you integrate LLMs into production systems. Guardrails AI provides a Python framework that helps you enforce those expectations. You define the schema or constraints you need, and the framework validates both the inputs going into the model and the outputs coming back.