Operations | Monitoring | ITSM | DevOps | Cloud

Earlier this week, we gathered in San Francisco for our second SEV0—almost a year after our very first event. SEV0 has always been about shining a light on the biggest challenges (and opportunities) in incident response. Last year, we were still talking about the fundamentals: blameless culture, strong processes, and lessons from the best in reliability. This year felt different. AI has moved from background noise to front and center in every conversation, every team, everywhere.

Sometimes Docker behaves in ways that catch you off guard—containers don’t start as expected, images pause during pull, or networking takes longer than usual to respond. In those moments, the Docker daemon logs are your best reference point. These logs capture exactly what the Docker engine is doing at any given time. They give you a running account of system state, performance signals, and events that help you understand what’s happening beneath the surface.

Cloud cost intelligence has moved beyond simple cost-cutting. Now, it’s about creating value. Cloud bills continue to rise, and workloads are becoming increasingly complex. Teams also need to understand what they’re spending, why, and how that spend ties to business results. FinOps has become the framework for bringing finance and engineering together. It’s helping teams manage costs, improve margins, and plan with confidence. But challenges remain.

Harness acquires Qwiet AI to power application security in the AI era, embedding reachability analysis to cut noise and prioritize real risks. By Sanjay Nagaraj, SVP Global Engineering, Harness; Co-founder and CTO, Traceable by Harness Today, I am excited to share that Harness has acquired Qwiet AI (formerly ShiftLeft), a leader in agentic AI-powered vulnerability detection and reachability analysis.

The integration of Amazon Kiro and Harness’s MCP server enables developers to manage, troubleshoot, and optimize CI/CD pipelines directly from their IDE using natural language, dramatically reducing manual effort and accelerating software delivery from code generation to production.

Retrieval-Augmented Generation (RAG) has transformed how applications interact with Large Language Models (LLMs). RAGs ground LLM responses in external knowledge, improves accuracy, and reduces hallucinations. But traditional RAG systems have a significant limitation: they only process text. Multimodal RAG addresses this limitation by processing and understanding multiple data types (text, images, and potentially audio).

As network teams constantly search for new ways to simplify and optimize their networks, it helps to hear from experts who have spent years at the center of these transformations. One of these voices is Fabio D’Avino, a specialist in Network as a Service (NaaS) with more than seven years of experience researching, designing, and building global network services across industries.