Operations | Monitoring | ITSM | DevOps | Cloud

The Model Context Protocol (MCP) is rapidly becoming the connective tissue for agentic AI systems and IDE tooling. Whether you’re building a dev tool that integrates with LLMs or enabling a context-aware API backend, standing up an MCP server is a rite of passage. But MCP is still in its early days and there are some sharp edges. Here are four practical shortcuts to fast-track your MCP server development so you can skip the boilerplate and get to the good stuff: intelligent tooling.

Managing multiple systems that each generate their alerts and logs can quickly become overwhelming. The challenge of scattered logs is a real headache, especially in the fast-paced world of DevOps. Log consolidation is not just a convenience—it's an essential practice that can save you from chaos and improve your operational efficiency. This guide covers everything you need to know about log consolidation, from understanding what it is and why it matters, to practical steps for making it work.

At Google Cloud Next, one statement particularly caught the attention of innovators and cybersecurity professionals alike: Google’s introduction of Agent 2 Agent (A2A) marks a major evolution in AI architecture. It enables autonomous agents to collaborate across services, platforms, and domains—unlocking powerful use cases across virtually every industry.

In the ever-evolving world of artificial intelligence, a new frontier is emerging—Agentic AI. This revolutionary concept goes beyond the traditional models of AI that we’ve grown accustomed to. Instead of simply following explicit instructions, agentic AI systems are designed to act autonomously, make decisions, and adapt dynamically. In other words, they can “think” independently to achieve specific goals.

Signals is FireHydrant’s modern on-call product that is a replacement for legacy tools such as PagerDuty or Opsgenie. When we began planning Signals, we not only wanted to build a kickass on-call alternative, but also to reset the standard on how much on-call should cost.

CI/CD pipelines don’t wait. When traffic surges and your artifact platform can’t keep up, it’s not just a few slow requests: builds fail, deploys become backlogged, and engineers lose confidence. We’ve seen it all: 502s from overloaded VMs, minutes-long pulls, and pipelines grinding to a halt. That’s why we built Cloudsmith to scale by default; no one should have to firefight with their registry at 2 a.m.

Modern application architectures have evolved from simple monoliths to complex distributed systems spanning multiple environments. This evolution has transformed how we approach monitoring and troubleshooting. Traditional monitoring methods that focus solely on uptime and basic health checks are no longer sufficient for understanding system behavior in cloud-native environments.

Amazon Web Services (AWS) is still the largest cloud service provider today. With over 240 cloud products, this Amazon.com subsidiary offers cloud, on-premises, serverless, and edge computing services. But bigger isn’t always better, and we’ll share why, plus explore the top AWS alternatives to consider if you’re thinking about making a switch.

Keeping your Postgres databases healthy is non-negotiable if you care about application performance and reliability. But monitoring Postgres the right way? That’s where things get tricky. Between the sheer volume of metrics and the noise that comes with them, it’s not always obvious what to pay attention to—or when. This guide breaks things down with a focus on what matters in real-world production setups.