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The latest News and Information on DevOps, CI/CD, Automation and related technologies.

Modernizing Communications For Mission-Critical Networks

Mission-critical networks are changing fast. Utilities, transport operators, and critical infrastructure providers are under pressure to deliver more data, more automation, and more resilience—without ever compromising reliability. The challenge is simple: legacy SDH/SONET networks were built for a different era. They still deliver reliability. But they can’t support what comes next.

Shipped: What did the feature cost to ship? What does this customer cost to serve?

You can already split AI spend by team and by model. But that’s not what your CEO asks in the QBR. The question is what you got for it: what did it cost to ship that feature, to launch that campaign, to serve that customer. And is the AI bet behind it paying off? Now you can allocate AI spend to the outcomes you own: customer, product, feature, the strategic bet on the P&L. Not just the team that spent it.

The next era of telco clouds: get open infrastructure choice with Sylva and Canonical Kubernetes

The telco industry is undergoing a fundamental change. Over the past few years, the increasing maturity of cloud-native infrastructure has accelerated the movement from manually operated and hardware-centric systems to automated, software-defined platforms. Underpinning this change are open source initiatives such as the Sylva project. Sylva is hosted by Linux Foundation Europe and heavily backed by major telecom operators and vendors.

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.

Turning down grad school, self-learning Power BI, and Lego! (Kristyna Ferris) | Simple Talk Podcast

Kristyna Ferris turned down grad school, learned Power BI, moved into the data world - and never looked back. In this chat with Steve Jones, Kristyna explains why she did it, what she’s learned, and even why her first DBA changed her password! Plus: being a Microsoft MVP, the importance of self-learning, being inspired to get involved with the community, and Kristyna’s passion for Lego, movies, and more!

Why Small Business IT Disasters Are Almost Always Preventable

A server goes down on a Tuesday morning. A ransomware file starts encrypting documents at 2 a.m. A key employee clicks a link in what looked like a vendor invoice, and by the time anyone notices, credentials have been sitting in the wrong hands for six hours.

We won't train on your data is not a security architecture

Every enterprise contract I’ve signed in the last two years has the same clause. “Vendor will not use Customer Data to train machine learning models.” Sometimes it’s a paragraph. Sometimes it’s a whole section. The language varies but the intent is identical: don’t feed our production data into your AI. I get it. I sign the same clause as a vendor. But here’s what’s been bothering me: that clause is a promise, not an architecture.

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.