Operations | Monitoring | ITSM | DevOps | Cloud

AI at the edge: simplifying infrastructure with Cisco and Canonical

Legacy infrastructure was not designed for the requirements of the AI era. While large-scale model training remains centralized in data centers, test-time inference is rapidly shifting to the edge to reduce latency and bandwidth consumption. This shift creates a new frontier for enterprise AI, but deploying at the edge introduces significant manual complexity, interoperability issues, and security vulnerabilities.

How Agentic AI is Transforming Infrastructure and Operations

Infrastructure and Operations (I&O) teams have long operated under a familiar paradox: the faster the business scales, the more pressure I&O absorbs. Every new application deployment, every endpoint added, and every cloud workload spun up generates more complexity, more risk and more tickets. The traditional responses to this pressure — more headcount, more tooling, more scripts, more APIs — have delivered incremental relief at best.

Introducing the Rootly Agent

During an incident, ask the Rootly Agent anything and it'll respond (and act) based on context and your data. Use the Rootly Agent to: The Rootly Agent performs actions on your behalf, so it is bound by the permissions assigned to your user. It will also ask for confirmation before taking significant actions. Rootly admins can turn it on for their workplaces and start running incidents even more efficiently.

Atlassian's HR team leads AI transformation

AI transformation doesn’t succeed without people at the center. At Atlassian, HR is leading the way. Our People team believes that the best AI culture isn’t mandated from the top. It’s built by meeting employees where they are, partnering with leaders across the business, and making AI part of how work gets done from day one. See how Atlassian’s HR team is building a culture of experimentation where everyone builds, and what that looks like in practice.

AI Made Infrastructure Weird Again | Ubuntu Summit 26.04

For years, we were told we were escaping hardware. Virtualization, containers, and Kubernetes made the underlying servers practically invisible to the average application developer. Then came the AI boom and infrastructure got incredibly weird again. In this fast-paced lightning talk, Billy Olson from Canonical breaks down why the modern AI server is no longer just a machine, but a volatile distributed system packed inside a single chassis.

OpenAI's o1-preview Highlights a New Phase in AI Infrastructure Economics, Says iFrame®

OpenAI's release of the o1-preview reasoning model in September 2024 sparked widespread discussion about advances in artificial intelligence performance. While many observers focused on benchmark results and reasoning capabilities, iFrame founder Vlad Panin examined the launch from a different perspective, emphasizing its implications for the economics and architecture of AI delivery.

Top AI App Makers Transforming Software Development in 2026

Software development has never moved faster than it does today. Just a few years ago, building a functional app required a team of engineers, months of planning, and a significant budget. Now, thanks to the rise of the AI app maker, that process has been compressed into days or even hours. These tools are reshaping how developers, entrepreneurs, and businesses think about creating software, and the shift is happening across industries at a pace that is hard to ignore.

Agent Hooks + Chunk sidecars: Stop Broken AI Code Before It Hits CI

AI agents write code fast, but the feedback loop usually can't keep up. In this tutorial, you'll see how to wire Chunk sidecars into your agent's hooks so basic failures get caught before they ever reach your CI pipeline. We'll walk through the two hooks that chunk init writes automatically: Both hooks return exit 2 on failure, blocking the commit or keeping the turn open so the agent can fix its own mistakes with no manual prompting required.