Operations | Monitoring | ITSM | DevOps | Cloud

AI infrastructure cost optimization for scaling teams

This post is also available in German and in French. The 2026 AI landscape has shifted from "Can we build it?" to "How much will it cost to run it?" For CTOs and engineering leaders, the challenge is no longer just model performance: it is the underlying infrastructure sprawl that silently erodes margins. When AI workloads scale, they often inherit the inefficiencies of legacy cloud models: over-provisioned instances, fragmented data pipelines, and a lack of unified context.

How to Implement an AI Governance Framework Using Safe, Ethical and Reliable AI Guardrails

In my time at Ivanti, I've witnessed firsthand how AI acts as a force multiplier across enterprise organizations. When deployed strategically, AI accelerates decision-making and operational execution at scale in a way that teams simply can't sustain manually. However, without clear and enforceable AI guardrails, implementing AI opens organizations up to serious new risks.

Secure by Design : Defend against AI-driven threats

After several zero-day attacks on leading security vendors that left the industry reeling in 2024 and 2025, Ivanti redoubled our commitment to transparency, product development that prioritizes security and community awareness. The attacks galvanized our Secure by Design framework so that we could accelerate our transformation to kernel-level security — compressing a three-year roadmap into just 18 months.

I let Claude investigate a production incident with Honeybadger's MCP server

In this demo, Kevin shows how you can use Honeybadger's MCP server with Claude to investigate a production incident — going from a natural language prompt to a complete incident dashboard in minutes. Honeybadger is an application health monitoring platform that helps developers catch errors, track performance, and stay on top of incidents. The MCP server lets AI assistants like Claude query your Honeybadger data directly, so you can investigate issues conversationally without digging through dashboards manually.

Technology Trends in the Mortgage Industry

The mortgage industry is changing rapidly due to technology. Many people still see homeownership as a key goal, and new tools are making it easier to go from application to closing. This tech advancement is simplifying the process and helping both consumers and businesses have a more seamless experience.

Top 10 ChatGPT SEO Agencies for 2026 (Manually Reviewed)

A funny shift has appeared in our conversations with marketing leaders over the last year. Teams still ask for SEO help. But more often, the question is: "Who can help us appear inside ChatGPT answers, and can they prove it without hand-waving?" People research inside ChatGPT, Perplexity, Gemini, and AI Overviews, then click only when they trust the source. If your brand is not cited, clearly understood as the right entity, and consistent across your site and the wider web, even strong pages can stay invisible when buyers are deciding.

Why Nexthink Intelligence Is a Game-Changer for IT Teams

Nexthink Intelligence transforms digital employee experience (DEX) for modern enterprises. Learn how IT teams can leverage real-time analytics, proactive insights, and automation to improve user productivity, troubleshoot issues fast, and deliver better workplace tech experiences. Learn more at nexthink.com.

A 4-Month Bug Fixed in <10 Minutes with Olly

In today’s highly interconnected systems, the subtle relationships between services are rarely obvious. Modern, complex architectures generate telemetry that functions less as “flashing signs” and more as faint “breadcrumbs” to be followed across a vast network of signals. In 2025, about two-thirds of outages involved third-party systems like cloud platforms and APIs.

The limits of MCP and how Olly surpasses them

Model Context Protocol (MCP) servers act as adapter layers between clients and AI based workloads. MCP installation into an IDE, such as Cursor, brings a wealth of information directly into the developers primary tool, minimizing context switching and, especially in the world of observability, bringing telemetry closer to the code. MCP is not without its limits. These limits initially seem trivial, but in time, some of the inherent limitations to a basic MCP implementation become apparent.

When AI Writes the Code, Who Keeps Production Running?

The production environment has become a minefield of code nobody really understands. Here’s what’s happening: Development teams are using Claude Code, Cursor, and GitHub Copilot to ship features at 10x their previous velocity. Product managers are ecstatic. Business stakeholders are thrilled. And somewhere in a war room at 2:17 AM, an SRE is staring at a stack trace for code that was AI-generated three weeks ago, trying to figure out why the payment service just fell over.