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.

Getting started with Claude Code and CircleCI

AI-powered coding tools are changing how developers work. Tools like Claude Code can write functions, refactor code, and build features through natural conversation, often faster than you could type them yourself. But speed creates its own risks. AI-generated code can contain subtle bugs, reference packages that don’t exist, or misuse APIs in ways that only surface at runtime. That’s where continuous integration comes in. CI is a safety net that lets you move fast confidently.

AI Assistant vs Skylar Advisor

What happens when AI understands your entire environment? With Skylar Advisor, you move beyond prompts and responses and get prioritized guidance based on real operational impact. Skylar Advisor identifies what matters most, explains why it matters, and provides clear next steps so even junior IT professionals can operate with confidence.

Getting started with Gemini and CircleCI

AI coding assistants like Gemini are changing how developers write code. They can generate entire functions, debug tricky issues, and help you move faster than ever before. But with that speed comes a new challenge: how do you make sure AI-generated code actually works? AI assistants are powerful, but they’re not perfect. They can introduce subtle bugs, miss edge cases, or generate code that breaks existing functionality. That’s where CI (continuous integration) comes in.