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Seven Years of Firecracker

AWS is puttingFirecracker microVMsto work in two fresh stacks:AgentCore, the new base layer for AI agents, andAurora DSQL, a serverless, PostgreSQL-compatible database it just rolled out. AgentCore gives each agent session its own microVM. More isolation, less cross-talk - solid for multistep LLM wo.. read more  

Seven Years of Firecracker
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Automated GitHub Self-Hosted Runner Cleanup: Lambda Functions and Auto Scaling Lifecycle Hooks

When an EC2 instance in an Auto Scaling Group shuts down, event-driven plumbing kicks in. Alifecycle hookcatches the scale-in, fires off an SNS notification, and triggers aLambda. That Lambda calls the GitHub API to yank the self-hosted runner before the instance dies. No dangling runners. No manual.. read more  

Automated GitHub Self-Hosted Runner Cleanup: Lambda Functions and Auto Scaling Lifecycle Hooks
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How LogSeam Searches 500 Million Logs per second

LogSeam rips through500M log searches/secand pushes1.5+ TB/s throughputusing Tigris’ geo-distributed object storage. It slashes log volume by 100× with Parquet + Zstandard compression. Then it spins up compute on the fly, right where the data lives—no long-running infrastructure, no laggy reads... read more  

How LogSeam Searches 500 Million Logs per second
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Introducing Headlamp Plugin for Karpenter

The newHeadlamp Karpenter Pluginwires real-time autoscaling insight straight into the Headlamp UI. It showsKarpenterresources, live metrics, scaling moves—no kubectl spelunking required. NodePoolsandNodeClaimsget mapped to core Kubernetes objects. You can tweak configs in the UI, get validation on t.. read more  

Introducing Headlamp Plugin for Karpenter
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Kubernetes for agentic apps: A platform engineering perspective

Agentic AI flips the old model. Instead of stateless, event-by-event workloads, we getstateful, self-steering systemsthat observe, reason, plan, and act - on loop. Kubernetes steps up as the OS for this next phase. Boosted by platform engineering, it brings the right mix:ephemeral compute, persisten.. read more  

Kubernetes for agentic apps: A platform engineering perspective
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Internal HTTPS Routing in Istio.

Istio finally bringsinternal HTTPS routingwithSNI-based traffic rules. Services in the mesh can now talk over port 443—TLS fully intact. Just like in prod. TLS terminates at the ingress gateway. Routing pivots on SNI, not headers. Which makes this much closer to real-world mTLS flows. What’s the pla.. read more  

Internal HTTPS Routing in Istio.
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How I Built My Kubernetes Command Toolkit: A Journey from kubectl Chaos to Command Mastery

A dev-built Kubernetes CLI framework reshapeskubectlfor how teams actually work. Commands get grouped by role - dev, SRE, sec, admin - instead of by resource. It bakes in defaults forKyvernopolicies, encourages muscle-memory workflows, and wires up real-time troubleshooting to shrink downtime in pro.. read more  

How I Built My Kubernetes Command Toolkit: A Journey from kubectl Chaos to Command Mastery
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The Myths (and Costs) of Running Node.js on Kubernetes

Kubernetes struggles to scale Node.js efficiently due to a mismatch in resource usage patterns. Autoscaling can be sluggish with bursty traffic, leading to revenue risks and performance issues. Teams must rethink resource allocation and scaling strategies to optimize Node.js efficiency in Kubernetes.. read more  

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Most Cloud-Native Roles are Software Engineers

Software Engineers still own the cloud-native job boards in 2025 - nearly47%of all Kubernetes-tagged listings. DevOps holds onto second. But Platform Engineers just leapfrogged SREs, which have slid 30% since 2023... read more  

Most Cloud-Native Roles are Software Engineers
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Who’s Calling That API? A Detective Story from the Depths of EKS Networking

A production network got hammered by too many Auth0 token requests. The source? EKS workloads tucked behind a shared NAT Gateway. No easy trail. Engineers stitched it together usingVPC Flow Logs,pod-to-node maps, and some sharpIstio ServiceEntry logs. Even with Kubernetes CNI doing its NAT-obscuring.. read more  

Who’s Calling That API? A Detective Story from the Depths of EKS Networking
Vertex AI is Google Cloud’s end-to-end machine learning and generative AI platform, designed to help teams build, deploy, and operate AI systems reliably at scale. It unifies data preparation, model training, evaluation, deployment, and monitoring into a single managed environment, reducing operational complexity while supporting advanced AI workloads.

Vertex AI supports both custom models and foundation models, including Google’s Gemini model family. It enables organizations to fine-tune models, run large-scale inference, orchestrate agentic workflows, and integrate AI into production systems with strong security, governance, and observability controls.

The platform includes tools for AutoML, custom training with TensorFlow and PyTorch, managed pipelines, feature stores, vector search, and online and batch prediction. For generative AI use cases, Vertex AI provides APIs for text, image, code, multimodal generation, embeddings, and agent-based systems, including support for Model Context Protocol (MCP) integrations.

Built for enterprise environments, Vertex AI integrates deeply with Google Cloud services such as BigQuery, Cloud Storage, IAM, and VPC, enabling secure data access and compliance. It is widely used across industries like finance, healthcare, retail, and science for applications ranging from recommendation systems and forecasting to autonomous research agents and AI-powered products.