AWS EKS engineers published the real root cause of slow CUDA/PyTorch image pulls on Aug 10, 2026: it's serialization, not bandwidth. Two containerd fixes now shipping in EKS Auto Mode — chunked range-request downloads and concurrent layer unpacking.
All four major clouds shipped agent code sandboxes in the same quarter. Four isolation architectures, four trade-off profiles. Here's the technical comparison and decision framework for Tech Leads picking the right sandbox for their AI stack.
A deep technical dive into Atlassian's cloud migration journey — stateless compute, Tenant Context Service, CQRS, and why they never rewrote Jira.
A practical guide to building an AI-powered monitoring dashboard that shows EC2 health, web platform status, CI/CD quality checks, and uses Claude to interpret anomalies in plain English.
Stripe's autonomous agents write 1,300 PRs per week. OpenAI extended its Responses API with shell tools. AWS launched Strands Labs. The agentic shift is no longer theoretical — here's what it looks like in the real world.
Production scaling patterns for multi-agent AI systems. Covers AWS Step Functions orchestration, Lambda auto-scaling, Bedrock provisioned throughput, cost optimization strategies, and operational runbooks.
Production implementation of state management for multi-agent systems. Covers DynamoDB checkpointing with LangGraph, S3 for large payloads, Athena query patterns, conversation memory, and state recovery strategies.
Production-tested architecture for multi-agent AI systems using LangGraph on AWS. Brain/Worker cost optimization, Step Functions orchestration, DynamoDB checkpointing, CI/CD pipelines, and real-world cost modeling that achieves 5-10x savings.
Deploying MarketingOS three ways: self-hosted Ubuntu with Docker Compose and Nginx, AWS with Fargate/S3/CloudFront, and Azure with App Service/Blob Storage/CDN. Infrastructure as Code with Terraform, monitoring, and cost comparison.
Build AI-powered educational content with Amazon Bedrock, train custom models on SageMaker, implement RAG pipelines with Knowledge Bases for Bedrock, and create personalized learning paths with Amazon Personalize.
Build the Kids Learn backend with API Gateway HTTP APIs, Lambda functions for CRUD operations, and ECS Fargate for long-running AI inference. Includes VPC networking, cold start optimization, and container deployment.
Aurora Serverless v2 with pgvector for vector search, DynamoDB for session events, ElastiCache Redis for caching, RDS Proxy for connection pooling, and data modeling patterns for educational SaaS.
Build a complete CI/CD pipeline with CodePipeline, run tests and builds with CodeBuild, manage Docker images with ECR, implement blue/green deployments for ECS Fargate, and automate infrastructure changes with CDK Pipelines.
Deploy Next.js with SSR on AWS Amplify Gen 2, serve static assets from S3 with CloudFront CDN, configure custom domains with ACM certificates, and implement cache invalidation strategies.
Define your entire AWS infrastructure in TypeScript with CDK v2. VPC design, multi-stack architecture, reusable constructs, and deploying to staging vs production with a single command.
Build comprehensive observability: custom CloudWatch metrics and dashboards, X-Ray distributed tracing across Lambda and Fargate, structured logging with Lambda Powertools, alerting strategies, cost dashboards, and anomaly detection.
Take Kids Learn production-ready: multi-region Active-Passive with Aurora Global Database, Route 53 health-checked failover, auto-scaling strategies, load testing with Artillery, chaos engineering, runbooks, and a comprehensive launch checklist.
Implement production security for a children's platform: least-privilege IAM policies, Cognito user pools with COPPA-compliant parental consent flows, WAF rule groups, KMS encryption, Secrets Manager auto-rotation, and GuardDuty threat detection.
Why we chose AWS to build the Kids Learn platform from the ground up. AWS Well-Architected Framework, account setup, cost planning, and the complete service map for a production SaaS.
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