Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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?

AI And Sustainability: Measuring The Impact Of The Generative AI Boom

Before 2022, Alex Hanna worked on Google’s Ethical AI team. Today, she’s the director of research at the Distributed AI Research Institute, a transition sparked by Google’s handling of a paper exposing AI’s growing environmental footprint. So, how bad is it, really? That depends on who you ask. Take Jesse Dodge, a senior research analyst at the Allen Institute for AI. Jesse told NPR that a single ChatGPT query can use as much electricity as keeping a light bulb on for 20 minutes.

Rovo AI: Create Work Items from Loom | Demo Den | Atlassian

Ever wish you could turn a quick Loom recording into Jira work items without all the manual typing? Now you can! In this Demo Den episode, Pierre walks through a new Rovo AI feature that automatically converts your Loom videos into actionable Jira work items. Whether you're recording bug reports, feature requests, or project updates, Rovo handles the data entry for you. What Pierre covers: Turning Loom videos into work items with Rovo How it works in your AI-enabled Jira instance.

Why AI Coding Assistants Fail (And How to Fix Them)

Why do developers stop using AI coding assistants? According to Carnegie Mellon research, the top reason is unhelpful suggestions. Tabnine's Principal Architect John Feeney explains how context transforms AI coding tools from generic to genuinely useful. Learn the 4 Cs framework for maximizing AI assistant value: Context (workspace indexing), Connection (repo integration), Coaching (rules-based guidance), and Customization (fine-tuning). Discover how Retrieval Augmented Generation (RAG) helps AI understand your codebase, not just open source patterns.

Building dbRosetta Using AI: Part 1 of Many

Like many of you, over the last couple of years, I’ve been using AI, or, well, let’s just name it appropriately, Large Language Models (LLM), as a part of my job. I’ve also used it in my hobby. With it, I’ve generated snippets of code, tested data conversions, even built a small database for a presentation. However, to date, I haven’t tried doing everything through the LLM. Now, I’m going to.