Operations | Monitoring | ITSM | DevOps | Cloud

Citrix NetScaler customers were pulling devices offline as attackers moved faster than the traditional patch and response cycle. What happens when cyberattacks move faster than security teams can react? In this episode of ShipTalk, Martin Reynolds and Adam Arellano are joined by Starr Brown, Director of Open Source Projects at OWASP, to break down the latest Citrix NetScaler ADC and Gateway security vulnerabilities, active exploitation, and the rapidly shrinking window between vulnerability discovery and attack.

Join Ribbon experts for a 30-minute preview of Network X Vienna and discover how AI, automation, and IP & Optical innovation are helping service providers simplify operations and prepare for the next generation of networks.

AI investigation for IT operations finds the likely root cause of an incident and shows the evidence behind it. This video covers how deep AI investigation works for ITOps, SRE, and platform engineering teams using LogicMonitor's Edwin AI. Engineers lose time stitching together alerts, logs, and recent changes after an alert fires. Deep AI investigation hands that first pass to AI agents, so your team starts closer to the fix. Edwin AI, LogicMonitor's AI agent for ITOps, correlates alerts, identifies root causes, and recommends remediation.

Compute, network, and storage don't all have to come from one provider. Mix and match across clouds, specialist providers, and your own data centers, then connect it all on the Megaport network. Single provider or mix and match: which way is your team heading?

Everyone online seems to have a fully autonomous SDLC. Most enterprises are still at level 3, and they'll be there for years. In the EVOLVE 2026 opening keynote, Cortex co-founders Anish Dhar (CEO) and Ganesh Datta (CTO) lay out a practical maturity model for getting from AI coding agents to an AI software factory without blowing up cost, quality, or security along the way. They've watched hundreds of engineering orgs adopt AI over the last two years. This talk distills what separated the successful rollouts from the chaotic ones.

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An Alibaba-affiliated AI agent went full crypto bro. During reinforcement learning, the agent autonomously started mining cryptocurrency, downloaded the tools it needed, and created a reverse SSH tunnel to get around network restrictions. What starts as a funny story about an AI vaping and mining crypto gets a lot more serious when you realize how sophisticated the behavior actually was.

On today's episode, Andrew Hillier chats with Sachin Gharge, Head of Cloud Platform at Scandinavian Airlines (SAS). They discuss AI, agents, Kubernetes, MCP, and optimization. Sachin shares how he and his team are optimizing cloud costs, leveraging automation, and experimenting with agentic AI, including bots and Slack integrations, to make operations easier and more effective for developers and the business.

Check out how you can easily make use of multiple clouds or bare metal machine and any combination of those to serve as the compute underpinning your workloads on Cycle.

Jev is a new AI model from Type Safe built around a very different idea: instead of generating long answers, it makes fast, simple decisions. The team reportedly even demoed it playing Doom using nothing but split-second choices. After years of teaching AI models to talk, could the next breakthrough be teaching them to simply decide?

What happens when an AI agent can do more than suggest a fix — and actually take action inside your CI pipeline? See Harness Worker Agents in action as an AI agent identifies a failed CI build, determines what went wrong, creates the fix, and gets the pipeline moving toward production again. Worker Agents bring AI-powered reasoning directly into your software delivery pipelines while maintaining the controls enterprises need, including sandboxed execution, scoped credentials, policies, and RBAC.

Choosing where your data lives is only half the question. How it's stored matters too. File, object, and block storage are each built for a different job. File storage uses the hierarchy you already know. Files live inside folders, and those folders can live inside other folders. Object storage gets rid of that hierarchy. Each piece of data is stored as its own object, which makes it easy to scale.

Small agent changes mean a small test scope. The agent knows exactly what it changed, so it can pull the 50 tests that matter out of a 10,000-test inventory and skip the rest. Arjun Iyer on why that's the validation model that scales.

Your data centers and clouds can have a private connection before your coffee gets cold, thanks to software-defined infrastructure, with bare metal compute and storage on the same platform.

In this clip, Virtana Chief Product Officer, Amit Rathi explains why more data does not automatically lead to better operations. As system complexity grows, organizations are collecting more telemetry than ever while struggling to turn it into actionable insights. At the same time, rising observability costs are forcing some teams to monitor only part of their environments. Watch the video to learn why intelligence, not just visibility, is becoming essential for modern IT operations.

EZ Control brings env zero and CloudQuery together in one product. It discovers nearly 2,300 resource types across AWS, Azure, Google Cloud and Kubernetes, links each one to its code, owner, cost and policies, and closes the gap when what's running drifts from what you intended: drift, security, cost and availability, all under your policies. You choose how much it does, from observe-only to autonomous, with a full audit trail. The same guardrails govern AI agents.

Ready to interact with the stack? GitKraken Desktop 12.6 makes it easier than ever to ship large features and manage multi-task terminal workflows without context switching or losing track of your work. What's new in 12.6: Stacked GitHub Pull Requests: Start a pull request stack against a branch that already has an open PR to break large feature branches into smaller pieces. Stack Visibility Everywhere: View stack position, status, and sequence numbers across the Left Panel PR list, the Pull Request view, and the Commit Graph.

In this video I go over what GitBench is, how to read its results, and some of the interesting things we can see in the results themselves. Chapters: For some reading on GitBench check out these two blog posts.

See how Harness helps teams move from AI-generated code to production at machine speed. In this demo, Nick Durkin walks through a fully autonomous software delivery workflow inside Harness, showing how teams can review code, enforce policy, run security and LLM scanning, test intelligently, deploy agents, and use Change Advisor to automate approvals with human oversight when needed. You’ll see how Harness helps teams.

When the industry moves this fast, the only way to figure it out might be by doing it yourself.

What are your thoughts??

We are stoked to announce that we have released our very own hosted MCP that allows you to use natural language to interact with and control anything you have running on Cycle.io. Alexander Mattoni, CTO and co-founder of Cycle, shares a few scenarios where the MCP can be useful. With the MCP, Cycle users can communicate with the Cycle platform directly from their preferred AI tool. In minutes, users can provision a new server, deploy an application, or troubleshoot an issue directly from their AI assistant like Claude, Claude Code, ChatGPT or Codex.

Netdata now supports distributed tracing. In this webinar, we'll demo the new tracing capabilities for the first time: OpenTelemetry trace ingestion, the new tracing dashboard, and the UI built to let you move from a metric anomaly to the exact span that caused it. For years, teams have asked us to close the gap between infrastructure monitoring and application performance. This release does that. Netdata can now ingest traces via OTEL, correlate them with the metrics and logs you already collect, and surface them in a purpose-built interface designed for speed and clarity.

A walkthrough of monitoring a distributed fleet with Netdata, using a simulated fleet of about 1,000 devices spread across regions. We show how the whole fleet reports into a single view: per-second metrics from every node, grouping by region and by customer, a map view that colors each device by health, filtering and saved views for different teams, and an AI-assisted investigation that scans the fleet for anomalies and points to likely causes.

Everyone depends on open source. Far fewer agree on who should sustain it, what counts as "open", or what portability really means.

Deploy one collector and watch the model build itself from what’s actually on the floor. Assets, racks, and connections appear without agents, CSVs, or another database to babysit. See it in the link in bio. Map your assets—see what you really own.

One file changed. 9,716 tests in the suite. Only 44 needed to run. See how Smarter Testing uses Test Impact Analysis to cut the unnecessary tests and get to feedback faster.