Operations | Monitoring | ITSM | DevOps | Cloud

In the world of large language models (LLMs), two approaches have dominated how we adapt AI to specific use cases: Retrieval-Augmented Generation (RAG) and Fine-Tuning. But the landscape is rapidly evolving with advanced techniques like MoE, LoRA, and GRPO. Let’s explore how these approaches compare and combine to create more powerful AI systems.

Zoom has become a lifeline for remote work, virtual meetings, and online collaboration. But even the best tools can crumble when network performance takes a hit. For remote users, nothing is more frustrating than a Zoom call that freezes, lags, or drops mid-conversation. The truth is, most Zoom (or AWS because Zoom is supported by AWS) performance issues aren’t caused by the platform itself — they’re rooted in network problems.

Take a drink if you hate that term. Tim O’Reilly has written a post about the huge shift AI is making with our relationship to software engineering (read: The End of Programming as we Know It). Scary title. But the content is less so. Why?

With application synthetic monitoring capabilities, DX Operational Observability (DX O2) can monitor websites and other services by probing the target from various globally distributed monitoring stations. These capabilities, which support SaaS and on-premises deployments, help teams shift from reactive to proactive management, elevate user experience for monitoring, and raise observability to a new level.

As microservices-based architectures have taken off, Docker and Kubernetes have risen as two leading platforms for container operations. While Docker helped popularize the container model, Kubernetes has evolved into a versatile solution for orchestrating production container workloads at a massive scale. However, their similarities obscure important distinctions in how each approaches container management. This post sheds light on the functional differences between Docker and Kubernetes.

With the SaaS market valued at approximately $250 billion in 2025 and projected to reach $299 billion by the end of the year, businesses are increasingly relying on SaaS monitoring tools to ensure the optimal performance of their cloud applications. Ensuring the availability, security, and performance of these applications is vital to maintaining business continuity. That’s where SaaS monitoring tools come into play.

Metricbeat is a powerful tool that can transform how you monitor your systems and services. If you're working in DevOps or as an SRE, this guide will help you understand and implement Metricbeat effectively in your environment.