Operations | Monitoring | ITSM | DevOps | Cloud

Densify Releases New MCP Server to Bring AI-Driven Resource & GPU Optimization to Platform Teams

As excitement builds for KubeCon North America 2025 in Atlanta, Densify has released its latest innovation for Kubernetes and AI-driven infrastructure resource management: the Densify Model Context Protocol (MCP) Server. This new capability enables organizations to securely integrate Densify’s Kubex resource optimization intelligence directly into popular LLM-powered tools — including ChatGPT, Claude, Cursor, and Gemini CLI.

AI Eliminates Pollution Risk: Oxford's Digital Contrast, Powered by Civo.

The future of medicine is here: Oxford's digital contrast AI is powered by Civo! Watch as Regent Lee, Professor at the University of Oxford and moonshot engineer, reveals a revolutionary solution to healthcare’s biggest hidden problem. Radiology currently accounts for 1% of global carbon emissions, with a single PET CT scan generating up to 60 kg of carbon, while forcing patients to endure long waits and chemical injections. Old habits cause slow systems.

The AI Knowledge Agent: Making Internal Developer Portals Smarter

AI is generating more code than ever, but delivery hasn’t kept pace. The Harness IDP Knowledge Agent helps teams close that gap by turning their internal developer portal into an intelligent platform for faster, safer software delivery. Joining a new engineering team can be exciting, but it can also be overwhelming. You spend the first few days figuring out what each service does, where documentation lives, and who owns what.
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Transform your workflow with Raygun's remote MCP

We're happy to announce Raygun's new remote MCP server, giving AI tools direct access to live error data so they can investigate issues, surface root causes, and take action with real context, not guesses. It's been nearly a year since Anthropic released the Model Context Protocol (MCP), and a lot has changed in the AI space. Since then, almost all major players now support MCP, allowing them to tap into the massive and ever-expanding catalogue of MCP servers. When MCP first launched, we shipped our own Raygun MCP within 48 hours of the spec dropping, which was an early step toward giving LLMs visibility into Raygun data.

Orbital Materials: WorldClass AI Models Built on CivoStack

Daniel Miodovnik, COO of Orbital Materials, explains how the CivoStack enables world‑class AI models that outperform the big‑tech giants. He outlines the power‑draw and cooling of megawatt‑scale GPU racks, the water‑ and CO₂‑intensity of today’s data centres, and why a sovereign, Civo‑based solution is the key to speed, and predictable costs.

Breaking down AI adoption barriers feat. Ivanti's Scott Hughes

ivanti.com/itsm-automation Unlock the secrets to successful Agentic AI deployment and widespread AI adoption in your organization with insights from Scott Hughes, SVP of Revenue Operations and Corporate IT at Ivanti. This video explores why IT-business alignment is critical, the importance of high-quality data, and how legacy infrastructure poses challenges for effective AI integration. Key insights.

Building Smarter AI Products #Datadog #DASH #AI

AI capabilities are advancing faster than ever — transforming how teams design, build, and ship intelligent products. In this teaser from Building Successful AI-powered Products at Datadog DASH, experts discuss the rise of agent-based systems, evolving model capabilities, and how to stay ahead in the new era of automation.

Coffee and Claude: How Honeycomb MCP Makes AI Work for You

If you caught our recent Introducing Honeycomb MCP: Your AI Agent’s New Superpower webinar, you know it was a lively mix of big ideas, demos, and a few laughs about the messy, fast-moving world of AI. Hosted by Austin Parker, Morgante Pell, and James Bland from AWS, the conversation explored how Honeycomb’s new Model Context Protocol (MCP) is changing the way developers and AI agents interact with data.

How to Optimize GPU

The Problem: AI workloads are dynamic, unpredictable, and expensive. Data prep can choke your pipeline, training jobs hog GPUs without awareness, and inference, the most latency-sensitive phase, is notoriously hard to scale efficiently. Worse, traditional infrastructure tools treat GPU as a static commodity, ignoring model intent, workload shape, and sharing capabilities.

Bridging the Gap Between AI Writing and Human Expression

Never before has AI dominated the content we read every day as much as today. As each day passes, the online and offline worlds are being filled with AI writing, and soon, it will become difficult to find the human touch in any content. With AI being so prevalent, it has raised an important question: Will the human essence in writing just disappear as we let AI generate more and more writing each day? Does it really have to be an ongoing fight between human creativity and machine algorithms?