SharePoint Data Classification: Find and Classify Sensitive Content (2026)
How to classify data in SharePoint Online: classifiers, labels, auto-labeling and on-demand scans, where each one stops, and how to reach archived content.
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Multi-Cloud Monitoring: How to Watch AWS, Azure and GCP Together
Multi-cloud monitoring guide: why the same metric differs across AWS, Azure and GCP, and how to normalize it with OpenTelemetry into one view. Sejal Pandey works on content and growth at Last9, writing about observability, reliability, and SRE practices.
Confidential computing in the real world: finding your use case
Confidential computing is essential when an organization needs to process sensitive data on infrastructure where the operators should not have access to it.
Which models are good at git? Understanding GitBench
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.
GitKraken Desktop 12.6 Release: Stacked GitHub PRs, Multiple Terminal Tabs
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.
Your code says one thing. Your cloud says another. Meet EZ Control. #platformengineering
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.
Where do you see organizations hitting their limits?
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.
Intent-driven development: How to guide agents from idea to implementation
Intent-driven development (IDD) is an approach to AI-assisted software development where teams make the desired behavior, constraints, and criteria for success explicit, then give an agent freedom to determine how to achieve the result. As agents take on larger and more autonomous development and maintenance tasks, the implementation itself becomes easier to replace. The important question is whether the software still behaves the way the team intended.
Building Production-ready AI Infrastructure? Start With the Network
AI workloads depend on fast, secure, and scalable access to data across on-premises systems, colocation, cloud platforms, and GPU environments. Here’s how private connectivity can help enterprises move from AI proof of concept to production-ready infrastructure. AI pilots tend to be forgiving. Production isn’t. In the early stages, a team can usually get by with a simple path into a GPU environment, enough bandwidth to test an idea, and a security model that suits a limited group of users.