Systems | Development | Analytics | API | Testing

Why Your AI Strategy is Breaking: The Power of AI Anywhere with Cloudera

Hey, did you know AI can’t be confined to just one environment? AI is moving faster than ever, but it cannot be confined to a single environment. From the public cloud to on-prem data centers and out to the edge, AI is everywhere. However, when these environments remain siloed, your data strategy breaks—leading to inconsistent governance and scaling roadblocks. In this video, discover the vision of AI Anywhere. To unlock real business value, AI must operate exactly where your data lives, maintaining the same level of control, trust, and security across every platform.

Lenses MCP Server with OAuth 2.1

You can now drive Lenses from Cursor, VS Code, IBM Bob or Claude Code without running any extra piece of infrastructure locally. Lenses MCP offers secure tools across topics, schemas, Kafka Connect, SQL processors, consumer groups, datasets and pod logs: everything an engineer would normally click through in the Lenses UI, now reachable from any MCP-compatible client over HTTP.

Introducing Kafka Skills for AI Engineering Agents

If you've written a line of code in the last 18 months, you already know this. Tools like Claude, Codex, Bob, Kiro and Cursor have made agentic software engineering the default. Most developers today are writing prompts as much as they are writing code. That shift changes what ‘developer experience’ means. Clean UIs, useful tools and good docs are still the foundation but the focus has shifted to ensuring a coding agent actually knows what it is doing, in the hands of a developer.

What one performance engineering leader would tell industry newcomers who are worried about AI

Quick summary: AI is creating anxiety and excitement — teams can get more work done faster, but does all this automation leave the worker behind? Not necessarily, says one performance engineering leader. The AI revolution, he says, is another technological wave. To ride it, performance engineers must embrace the change.

AI in Banking: Use Cases, Architecture & Implementation - The Complete Guide for Financial Institutions (2026)

AI is already embedded in banking systems. The question is whether it’s delivering measurable outcomes or just adding another layer of complexity. Across the industry, investment is not the constraint. Banks spent over $73 billion on AI in 2025, yet most initiatives haven’t translated into production-scale impact. Nearly 95% of generative AI programs remain in pilot mode, and only a small fraction of institutions report clear ROI. The pattern is consistent.

AI code created a new testing problem | From the Bear Cave Ep. 3

SmartBear’s study Closing the AI software quality gap found that 60% of teams have already experienced quality issues tied to AI-generated code, evidence of how increased abstraction is changing how software gets built. When development shifts from well-defined requirements to prompts and generated outputs, it becomes much harder to understand what the system is actually supposed to do, and what you should be testing against.