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Building a resilient DNS client for web-scale infrastructure

DCLflips DNS on its head withadaptive timeouts,exponential backoff, andreal-time config updates. Result? Downtime hits the floor, fault tolerance flexes its muscles. DNS visibility and client-side metrics accelerate alert sharpness, fine-tune infrastructure tweaks, and kick old-school limits to the .. read more  

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The Road to 1.0: Terragrunt Stacks Feature Complete

Terragrunt Stacksjust leveled up. It's like Marie Kondo hit your Infrastructure as Code, making it pristine withOn-Demand and Recursive Generation. Say goodbye to config clutter... read more  

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Introducing the Amazon EKS Auto Mode workshop

Amazon EKS Auto Modetosses the headaches of Kubernetes cluster management to AWS. It dynamically tunes resources, making life easier for your apps. Feeling lazy? The workshop deploys an app with just one command, all while delivering beefy, scalable solutions. Oh, and you’ll master it in two hours f.. read more  

Introducing the Amazon EKS Auto Mode workshop
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SSL/TLS certificates will last 47 days max by 2029

SSL/TLS Cert Lifespan Crashes from 398 to 47 Days by March 2029!Automation’s your lifeline. Kiss those manual migraine-inducers goodbye... read more  

SSL/TLS certificates will last 47 days max by 2029
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Measure risk probability in IAM

AWS actions don't always pick up list capabilities from resource types automatically. You'll often find yourself manually specifying list actions, which throws a wrench into Attribute-Based Access Control (ABAC) plans. AWS docs on dependency themes like PassRole? Incomplete at best. Cue the unexpect.. read more  

Measure risk probability in IAM
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Linux Detection Engineering - A Sequel on Persistence Mechanisms

PANIX turns the tangled web of Linux persistence and Process Capabilities on its head. It makes them as easy to test as flipping a light switch—and sharpens your detection game along the way... read more  

Linux Detection Engineering - A Sequel on Persistence Mechanisms
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Serverless Is a Lie (But It’s Still Useful)

ECS Fargatedominates 24/7 stateless APIs, dodging those peskyLambdacold starts. Meanwhile,Lambdathrives in event-driven bursts but hits a 15-minute ceiling. For lean, mean APIs with built-in auth, lean towardsAPI Gateway. But if speed matters, marryFargatewith anApp Load Balancer.Step Functionsstrea.. read more  

Serverless Is a Lie (But It’s Still Useful)
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Sending Emails with MCP and Azure Communication Services

MCPstruts onto the scene as the new AI-comms rockstar. Now featured in GitHub Copilot, it turns email automation withAzure Communication Servicesinto a walk in the park... read more  

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Scaling Nextdoor’s Datastores

Nextdoor took on database scalability like a pro. Theydynamically routed queriesto read replicas and keptcache consistencytight, even while yanking the carpet out with schema changes. Multi JOINs blocked their move todistributed SQLlike annoying roadblocks. But Nextdoor, the sly foxes, extended thei.. read more  

Scaling Nextdoor’s Datastores
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AWS Well-Architected Framework: Performance Efficiency Pillar

The AWS Well-Architected Framework's Performance Pillar champions nimble, cloud-native and serverless-first approaches. These tactics help systems pivot like a ballerina, dodge vendor lock-in, and slash costs. WithWardley Mapping, gain clarity. Prioritize flexibility. Ditch the shackles of hard-code.. read more  

AWS Well-Architected Framework: Performance Efficiency Pillar
Gemini 3 is Google’s third-generation large language model family, designed to power advanced reasoning, multimodal understanding, and long-running agent workflows across consumer and enterprise products. It represents a major step forward in factual reliability, long-context comprehension, and tool-driven autonomy.

At its core, Gemini 3 emphasizes low hallucination rates, deep synthesis across large information spaces, and multi-step reasoning. Models in the Gemini 3 family are trained with scaled reinforcement learning for search and planning, enabling them to autonomously formulate queries, evaluate results, identify gaps, and iterate toward higher-quality outputs.

Gemini 3 powers advanced agents such as Gemini Deep Research, where it excels at producing well-structured, citation-rich reports by combining web data, uploaded documents, and proprietary sources. The model supports very large context windows, multimodal inputs (text, images, documents), and structured outputs like JSON, making it suitable for research, finance, science, and enterprise knowledge work.

Gemini 3 is available through Google’s AI platforms and APIs, including the Interactions API, and is being integrated across products such as Google Search, NotebookLM, Google Finance, and the Gemini app. It is positioned as Google’s most factual and research-capable model generation to date.