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Beyond the Build: Integrating Security into CI/CD Pipelines

In today’s fast-paced software development landscape, Continuous Integration and Continuous Deployment (CI/CD) pipelines are essential for delivering applications efficiently. However, the speed and automation they offer can inadvertently introduce security vulnerabilities if not properly managed. Integrating security into CI/CD pipelines, often referred to as DevSecOps, is no longer optional; it’s a necessity.​ The Importance of Security in CI/CD Traditional security practices often occur late in the development cycle, leading to delays and increased costs when vulnerabilities are discovered. By embedding security checks into the CI/CD pipeline, teams can identify and address issues early, reducing risk and maintaining development velocity.​ Key Strategies for Integrating Security Automated Security Testing Incorporate tools that automatically scan code for vulnerabilities during the build process. Static Application Security Testing (SAST) and Dynamic Application ...
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The Messy Reality of Vibe Coding

The default reaction to vibe coding has been alarm — a default assumption that letting AI write large chunks of an application is going to flood production with vulnerabilities and undocumented behavior. That fear is doing as much damage as the bad code people are afraid of. Teams that freeze, ban the tools or push the work into the shadows end up with less visibility into how AI is actually showing up in their codebase, not more. Tyler Merritt, CTO at UneeQ, joins Mike Vizard to push back on the panic and reframe the problem. Merritt’s argument is that AI-assisted development is a construction site, not a finished building — and construction sites are inherently messy. The job for engineering leaders isn’t to keep the site spotless, it’s to make sure the right safety systems, inspections and review steps are wrapped around the work that’s happening anyway. They get into the practical mechanics of doing that. Instead of trusting any single model, Merritt makes the case for using mu...

Survey Sees AI Driving DevOps Productivity Gains Despite Challenges

A global survey of 636 software development professionals published today finds nearly two-thirds (64%) believe they are achieving at least a 25% increase in developer velocity and productivity using artificial intelligence (AI). Conducted by Jellyfish, a provider of a software engineering intelligence platform, just under a quarter (24%) report there has been a 50% to 100% increase in developer velocity and productivity, while another 6% have seen an increase of 100% or more. The top use cases for AI are code writing (53%), code review (49%) and code explanation (43%), with Claude Code (39%), Gemini Code Assist (35%) and GitHub Copilot (31%) being the top three tools adopted. However, only slightly more than half (53%) said AI is improving the quality of the code being developed. Other challenges include increasing cost of AI tools (42%), reluctance in adoption from senior engineers (36%) and a proliferation of tools making it difficult to select the best one (31%). Despite these ...

Bridging the IT Divide Without Breaking What Already Works

Let’s be honest for a second. If you walk into most enterprise IT environments and ask whether they should modernize their SQL Server infrastructure, you’re not going to get alignment. You’re going to get a debate. Sometimes a polite one. Sometimes not. And that’s not dysfunction. That’s reality. Because the people in that room are optimizing for completely different things. You’ve got DBAs who have spent years building systems that don’t go down. Not theoretically. Not “in a lab.” Actually stable. Predictable. Recoverable. The idea of introducing new platforms, new operating systems, or containers into that equation feels like you’re poking at something that already works. Then you’ve got platform engineers trying to bring consistency to everything . Kubernetes. Automation. No matter where it runs, infrastructure that behaves the same way. A Windows-bound SQL Server setup looks like the last holdout in an otherwise modern stack, from their perspective. Stuck in the middle is DevO...

Atlassian Underpins Code Creation With New Agentic Insight Channels

Atlassian used its Team ’26 user conference this month in Anaheim to explain how its platform has now further evolved to underpin the reality of what the company defines as the AI‑native organization. This still-emerging entity is a company (or indeed a department, an individual team or working group) where human teams are co‑creating alongside agents.  User Base Spread & Reach While many of the automation advancements coming out of Atlassian will be directed at businesspeople and non-technical staff, an equal and opposite number (give or take) are aligned to serve software engineering teams with agentic automations. The company champions various tools and functions at this level, not least of which is Rovo. Atlassian Rovo is an AI-powered knowledge discovery tool that connects fragmented data across enterprise apps. It uses a specialized search engine, interactive chat, and autonomous agents to surface insights and automate complex workflows. Inside modern code workshops, ...

The Great Decoupling: Scaling the Outer Loop for the Agentic Era

The “Inner Loop” of software development—the iterative cycle of writing, building, and debugging code—has just broken the sound barrier. With the emergence of agentic coding tools like Claude Code and GitHub Copilot Workspace, the developer experience has undergone a fundamental shift. Developers are no longer merely tab-completing snippets; they are orchestrating agents that generate entire features, refactor monolithic modules, and manage complex terminal commands in real-time. However, this unprecedented acceleration has exposed a critical structural flaw: the “Outer Loop” of the traditional Software Development Life Cycle (SDLC) is anchored in legacy speeds. While the Inner Loop now operates at the speed of thought, the Outer Loop—comprising manual PR reviews, security scans, and compliance audits—is still stuck in a pre-agentic mindset. This creates a massive bottleneck in the delivery pipeline, where AI can generate a thousand lines of code in seconds, bu...

Mistral Moves Coding Agents to the Cloud — and Gets Out of Your Way

For the past year or so, AI coding agents have been tethered to your local machine. You kick off a task, watch the terminal, and babysit every step. It works — but it’s not exactly hands-free. Mistral just changed that. On April 29, the Paris-based AI company announced remote coding agents for its Vibe platform, powered by a new model called Mistral Medium 3.5. The idea is simple: Instead of running coding sessions on your laptop, they now run in the cloud — asynchronously, in parallel, and without you watching over them. What’s Actually New Coding sessions can now work through long tasks while you’re away. Many can run in parallel, and you no longer become the bottleneck at every step the agent takes. That’s the core pitch. You start a task from the Mistral Vibe CLI or directly from Le Chat — Mistral’s AI assistant — and the agent handles the rest. When it’s done, it opens a pull request on GitHub and notifies you, so you review the result inste...