TLDR DevOps 2026-01-26
2026 Kubernetes Predictions ๐ฎ, Docker MCP ๐ข, AI Observability ๐
Users spike. Your stress level doesn't have to. (Sponsor)
Traffic surges are supposed to be good news. But when your infrastructure can't keep up, that "we're going viral ๐" moment can easily turn into โeverything is down and needs fixing right now.โ
๐ Ditch the stress-coding with Microsoft Azure.
Azure autoscales and optimizes performance behind the scenes, so you can roll out features without worrying about downtime or slowdowns.
๐ With more global regions than any other cloud provider, Azure gives you the headroom to grow โ without the growing pains.
Take the stress out of scaling with Azure ๐
2026 Kubernetes and Cilium Networking Predictions (4 minute read)
Kubernetes networking is evolving to support AI workloads and VMware migrations through eBPF and Cilium, driving VM convergence, tougher KubeVirt operations, renewed microsegmentation, and the rise of hybrid network operators as networking becomes central to platform strategy.
From AI agent prototype to product: Lessons from building AWS DevOps Agent (9 minute read)
This post describes lessons from building the AWS DevOps Agent, outlining five mechanisms to productionize agentic systems: evals, fast feedback loops, trajectory visualization, intentional changes, and production sampling. It details how these practices improve reliability, accuracy, and cost efficiency in incident response.
Ingress Security for AI Workloads in Kubernetes: Protecting AI Endpoints with WAF (6 minute read)
The transition of AI workloads to production within Kubernetes has exposed critical security vulnerabilities in traditional ingress controllers, necessitating advanced Layer 7 inspection and context-aware gateways with WAF capabilities to protect expensive GPU resources from threats like LLM Jacking and prompt injection.
Scaling maintenance: Rethinking HDFS block placement for exabyte-scale clusters (9 minute read)
To address challenges in maintaining its massive ~5 exabyte Apache Hadoop clusters, LinkedIn re-engineered its Block Placement Policy (BPP) and redistributed over 3 exabytes of existing data, successfully eliminating data replication during maintenance operations. This crucial change significantly improved maintenance velocity, allowing daily upgrades for approximately 4.5% of datanodes while unclogging the network and bolstering HDFS reliability and security.
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