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The Hidden AWS Cost Traps No One Warns You About (and How I Avoid Them)

Calling outfive sneaky AWS cost traps—the kind that creep in through overlooked defaults and quiet misconfigs, then blow up your bill while no one's watching... read more  

The Hidden AWS Cost Traps No One Warns You About (and How I Avoid Them)
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Easy will always trump simple

Rich Hickey’s classic “Simple Made Easy” talk is making the rounds again—as a mirror held up to dev culture under pressure. The punchline: we keep picking solutions that areeasy but tangled, instead ofsimple and sane. The essay draws a sharp line between that habit and a concept from biology: exapt.. read more  

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Subverting code integrity checks to locally backdoor Signal, 1Password, Slack, and more

A fresh CVE (2025-55305) just put Electron apps in the hot seat. The bug? Chromium-based apps fail to treatV8 heap snapshot filesas potential attack vectors. That crack lets unsigned JavaScript slip past code signing and run inside heavyweight targets like Slack, 1Password, and Signal. The heart of.. read more  

Subverting code integrity checks to locally backdoor Signal, 1Password, Slack, and more
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Paused Kubernetes project finds path forward

TheExternal Secrets Operator (ESO)is moving again. After hitting pause from maintainer burnout, it’s back under CNCF incubation—with a rebooted structure in place. New governance, clear contributor paths, and support tracks for CI, core dev, and testing are all in. But don’t expect fresh releases ju.. read more  

Paused Kubernetes project finds path forward
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Pooling Connections with RDS Proxy at Klaviyo

Klaviyo replaced ProxySQL on EC2 and moved toAWS RDS Proxy. Why? Less overhead. Simpler failovers. Smarter pooling. RDS Proxy handlesmultiplexing, packing thousands of client queries into way fewer DB connections. IAM access and built-in failover routing sweeten the deal... read more  

Pooling Connections with RDS Proxy at Klaviyo
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Why "What Happened First?" Is One of the Hardest Questions in Large-Scale Systems

Logical clocks trackevent orderin distributed systems—no need for synced wall clocks. Each node keeps a counter. On every event: tick it. On every message: tack on your counter. When you receive one? Merge and bump. This flips the script. Instead of chasing global time, distributed systems lean int.. read more  

Why "What Happened First?" Is One of the Hardest Questions in Large-Scale Systems
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Kubernetes VPA: Limitations, Best Practices, and the Future of Pod Rightsizing

Kubernetes'Vertical Pod Autoscaler (VPA)tries to be helpful by tweaking CPU and memory requests on the fly. Problem is, it needs to bounce your pods to do it. And if you're also runningHorizontal Pod Autoscaler (HPA)on the same metrics? Now they're fighting over control. VPA sees a narrow slice of .. read more  

Kubernetes VPA: Limitations, Best Practices, and the Future of Pod Rightsizing
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Why I Ditched Docker for Podman (And You Should Too)

Older container technologies like Docker have been prone to security vulnerabilities, such as CVE-2019-5736 and CVE-2022-0847, which allowed for potential host system compromise. Podman changes the game by eliminating the need for a persistent background service like the Docker daemon, enhancing sec.. read more  

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Kubernetes right-sizing with metrics-driven GitOps automation

AWS just dropped a GitOps-native pattern for tuning EKS resources—built to runoutsidethe cluster. It’s wired up withAmazon Managed Service for Prometheus,Argo CD, andBedrockto automate resource recommendations straight into Git. Here’s the play: it maps usage metrics to templated manifests, then sp.. read more  

Kubernetes right-sizing with metrics-driven GitOps automation
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Amazon EKS Enables Ultra-Scale AI/ML Workloads with Support for 100K Nodes per Cluster

Amazon EKS just cranked its Kubernetes cluster limit to100,000 nodes—a 10x jump. The secret sauce? A reworkedetcdwith an internaljournalsystem andin-memorystorage. Toss in tightAPI server tuningand network tweaks, and the result is wild: 500 pods per second, 900K pods, 10M+ objects, no sweat—even un.. read more  

Amazon EKS Enables Ultra-Scale AI/ML Workloads with Support for 100K Nodes per Cluster
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.