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We put two of the most talked-about models head-to-head in a real-world RAG scenario, and the results might surprise you. Hemraj Bedassee , Delivery Excellence Practitioner,
Ever found yourself saying, "But it works on my machine!" when a bug pops up in a microservices environment? It's a common and frustrating problem. Unlike a monolithic application, microservices are a collection of independently deployed services that communicate with each other. This complexity makes it difficult to reproduce real-world issues on your local machine, as you may not have all the necessary services and dependencies running. But what if you could take a snapshot of a running application's behavior and bring it home for debugging?
Multiplayer MCP Server streams full stack session data into your IDE. Give AI tools complete context—frontend, backend, annotations—for accurate fixes.
The promise of agentic AI is huge. But how is it impacting the enterprise and the developers and IT professionals most likely to be working with it right now? To find out, Kong evaluated labor market data and surveyed 550 tech leaders, developers, IT decision-makers, and Kong users.
Every QA engineer has felt the crunch: tighter deadlines, growing complexity, and the same old expectation that everything must work perfectly by release day. It’s not an easy balance. That’s why AI in software testing has become such a hot topic. It promises faster test case generation, smarter insights, and support with tasks that usually eat up hours of time. But let’s be clear: AI isn’t here to replace testers.
IT Professionals Day is our opportunity to celebrate the people who keep the digital world running - the ones who make sure data is secure, reliable, and ready to power innovation. At Qlik, we know IT professionals aren’t just solving problems, they’re enabling possibilities.
Modern enterprises are embracing multi-cloud strategies to avoid vendor lock-in, optimize costs, and ensure resilience. Yet managing API infrastructure (which also happens to be AI infrastructure) across multiple cloud providers while maintaining performance and simplicity remains a significant challenge.
AI agents are only as powerful as the data they can access and share. Confluent’s Sean Falconer explains how when agents can’t communicate effectively, intelligence silos form, limiting their potential and slowing innovation.
All AI problems are data problems—and one of the biggest is getting AI agents to talk to each other. This special episode with Sean Falconer dives into how agents built by different teams often end up stranded in “intelligence silos,” unable to collaborate or share context. The result? Fragmented AI that struggles to deliver real business value.