Systems | Development | Analytics | API | Testing

By Erez Schnaider, Technical Product Marketing Manager, ClearML The number of AI applications are rapidly increasing, and it can be difficult to keep up. Every month brings a new protocol, LLM, or tool. In this environment, the true strength of a platform is measured not only by its core features but also by its extensibility and adaptability to change. Many platforms address this challenge by hosting OSS tools or exposing API connections.

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,

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.

In addition to its existing text embedding models, you can now use both Google’s Gemini and open-source (OSS) models directly within BigQuery.

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.

When AI projects derail, it’s rarely because the model was weak. More often, failure comes from using MCP in the wrong way—forcing it to act as a universal API, data pipeline, or real-time engine instead of what it truly is: an orchestration and intelligence layer. Recently, Nate B.

The AI mandate is real. Boards and executives are demanding that software organizations move faster, embrace AI, and deliver without breaking trust. Development velocity is accelerating at machine speed, but testing has not kept up. The question every QA leader faces today is simple: will quality keep pace, or will it become the bottleneck? This is where the shift from automation to AI-native testing comes in.