Hagen Kuehn

AI is helping developers ship faster, and it's helping attackers exploit vulnerabilities faster too. GitLab 19.3 brings automation and security remediation to scale with bulk SAST triage and remediation, restricted visibility for custom agents and flows, Flow Creator Agent, and GitLab Credits usage caps. ✨ What's new: ➡️ GitLab Secrets Manager, now in limited availability, injects each secrets directly into the CI job that needs it, scoped to that job's environment and branch, with no separate permission model to maintain ➡️ Flow Creator Agent lets anyone describe an automation in plain English and get back a complete, runnable flow definition, ready to register from the AI Catalog. ➡️ Bulk SAST False Positive Detection and Agentic SAST Vulnerability Resolution lets teams triage and remediate their entire vulnerability backlog in one action instead of one finding at a time. ➡️ Restricted visibility for custom agents and flows, now generally available, lets teams share agents and flows across a top-level group without making them public. …and more! Read the full 19.3 release notes: https://lnkd.in/e73aYSAv 🚀

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Kubernetes observability shouldn’t feel like a scavenger hunt 🔎 Too many tools. Too many dashboards. Too many tabs. And when prod breaks? You’re still guessing. The truth: data isn’t the problem, fragmented context is. ✅ Correlate metrics, logs & events automatically ✅ Detect issues → root cause in seconds ✅ No tool-hopping or manual stitching ✅ Designed for developers AND platform teams Stop wasting time. Start understanding your clusters. Read how modern teams are fixing Kubernetes observability 👉 https://lnkd.in/eVE2HSP7 #Kubernetes #DevOps #PlatformEngineering #SRE #Observability #CloudNative

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A Complete Guide to Karpenter — What You Really Need to Know in 2025 🚀 Kubernetes is evolving fast, and efficient autoscaling is now mission-critical. If you’re still relying on static node groups or slow, rigid autoscalers, you’re leaving performance and money on the table. In this post, I break down Karpenter from the ground up — how it works, when it shines, where it falls short, and how it compares to alternatives like Cluster Autoscaler, KEDA, GKE Autopilot, and AWS Fargate. Whether you’re an SRE, platform engineer, or cloud architect, this guide gives you a clear, practical blueprint for adopting Karpenter across AWS, Azure, or GKE — including real-world setup steps, NodePool strategies, and cost-saving best practices. If Kubernetes elasticity and cost optimization matter to you, this is worth your time.👇 https://lnkd.in/gWQx4dZe #Kubernetes #Karpenter #CloudNative #EKS #GKE #AKS #DevOps #PlatformEngineering #Autoscaling

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Should Crossplane be in your toolbelt? In his latest article, Blagoja Panovski, Principal Software Architect, shares a practical assessment after building a real Azure platform with Crossplane. When does it make sense? When does it add unnecessary complexity? If Kubernetes is already central to your operations, this is a treasure trove of insights packed in one read.

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Spark on Kubernetes looks great on paper. But in production? That's where things break. Spark assumes static executor sizing. Kubernetes expects dynamic workloads. Add the JVM blind spot, where Kubernetes can’t see heap usage, off-heap allocations, or GC pressure, and you get the classic failure mode: executors look healthy to Spark but get killed by cgroup memory limits. That’s not optimization. That’s firefighting. In our latest blog post, Konstantin Zelmanovich breaks down why this happens and how the ScaleOps platform fixes it. If you’re running Spark in K8s and tired of memory guesswork, this one’s for you. Read the full breakdown 🔗 https://lnkd.in/dkQTvi_W

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Working with Sumo Logic for Log Monitoring and Dashboards: In one of my recent projects, I helped set up log collection and monitoring using Sumo Logic to improve visibility across our applications and infrastructure. We started by configuring Sumo Logic collectors and sources on AWS and application servers to send logs directly to the Sumo Cloud. This included application, system, and CloudWatch logs — all centralized in one place for easier analysis. Once the logs were flowing in, we created custom dashboards to track key metrics such as response times, error counts, and resource utilization. These dashboards helped the team quickly identify performance issues and trends. We also set up real-time alerts through email and Slack so that critical issues were flagged instantly. This made troubleshooting much faster and improved our response time during incidents. Overall, Sumo Logic helped us move from manual log checks to a more organized, data-driven approach for monitoring and performance tracking. #DevOps #SumoLogic #Monitoring #AWS #Logs #Observability #Cloud #Infrastructure #Dashboard

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