Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Proactive control through AI: NKT saves millions
NKT makes the “electrical superhighways” that bring renewable energy to city consumers. Its 24/7 site in Karlskrona, Sweden is the world’s largest producer of high voltage undersea cables, making operational stability vital. However, the plant faced hurdles. Data was trapped in silos, leading to intuition-based decisions with no single source of truth.
LIVE Build: Claude Code + Spotter | Agentic AI Meets Your Analytics Stack
Where agentic AI meets your analytics stack to drive action at scale. The shift is here. As the industry moves from Generative AI (Chat) to Agentic AI (Action), the pressure is on for developers and data practitioners to design intelligent apps that don't just talk: they perform. The real challenge? Bridging the gap between sophisticated developer tooling and your enterprise analytics stack. That’s exactly what this session solves.
SAP's latest API policy raises the stakes for your AI strategy
What the new policy means, what it doesn't, and why Open Data Infrastructure matters more than ever.
Securing AI Agents with MCP
Is your AI agent one misconfigured server away from a production data leak? In this deep dive, Jeremy from Lenses explores the critical security architecture of the Model Context Protocol (MCP) and how it’s evolving to protect the future of Agentic Engineering.
The 5 Pillars of AI Ready Data
Most AI failures aren’t model problems. They’re data pipeline problems. Disconnected systems. Inconsistent preparation. No governance at query time. This short animation walks through the 5 Pillars of AI-Ready Data and shows how data needs to move through a structured pipeline before it can power reliable AI. 5 Pillars of AI-Ready Data Access → Prep → Context → Governance → Monitoring Five stages. One connected flow.
Ep 71 | AI Adoption: The Data Readiness Problem Holding Enterprises Back
AI ambition is everywhere. The models are ready, the investment is flowing, yet the outcomes aren’t keeping up. Cloudera’s Data Readiness Index 2026 survey identifies a widening gap between what enterprises want from AI and what they can actually deliver. In this episode of The AI Forecast, Paul Muller sits down with Cloudera CTO Sergio Gago to bring a practitioner’s lens to the problem, drawing on experience across the full spectrum from startups to global enterprises.
The Great Disconnect: Why 77% Confidence in AI Results Is a Major Business Risk
According to the Perforce 2026 State of DevOps report, 77% of organizations express high confidence in the outputs generated by their artificial intelligence systems. Yet, this widespread optimism masks a critical vulnerability. While executive confidence in AI results remains high, only 38% of organizations have embedded AI deeply across their delivery stages. Plus, only 39% maintain the fully automated audit trails required to verify these results.
Application integrity in the AI era | From the Bear Cave Ep. 3
The tsunami of AI-generated code creates downstream bottlenecks for QA teams, and shift-left or traditional test automation aren't enough in the AI era. In this From the Bear Cave session, SmartBear CEO Dan Faulkner and CMO Kelly Wenzel unpack how AI code generation impacts software quality and why traditional testing struggles to keep up.