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Tutorials, how-tos and deep dives on AI, .NET, C#, cloud and developer tools, written by the community.
Learn how to route completed AI agent investigations to Jira using Amazon EventBridge, AWS Lambda, structured events, validation, and Jira's REST API.
Learn how to use SdkWarmUp in AWS SDK for Java 2.x to initialize selected clients during startup and reduce first-request latency in Java and Spring Boot services.
Learn how to route AWS DevOps Agent events through EventBridge and Lambda to create Jira issues, with guidance on authentication, event filtering, retries, and duplicate handling.
Learn how AWS Lake Formation can enforce identity-aware, row- and column-level access controls for AI agents querying governed enterprise data.
Explains how AWS DevOps agents can investigate incidents and propose approved automated remediation while enforcing least privilege, policy checks, auditability, and risk-based guardrails.
Learn how AWS's Digital Sovereignty Lens helps teams evaluate locality, access, continuity, portability, and auditability in cloud architectures.
Learn how SageMaker HyperPod Spaces bring JupyterLab and Code Editor workflows closer to managed GPU infrastructure for machine learning development and training.
Learn how AWS Lambda SnapStart supports container images to reduce cold-start latency, and how to safely manage initialization state in serverless functions.
Learn how SageMaker HyperPod Spaces integrate with SageMaker Studio to run JupyterLab and Code Editor directly on HyperPod EKS clusters for ML development.
Learn how Amazon S3 Tables supports Apache Iceberg V3, including nanosecond timestamps, variant data, schema evolution, and implications for data lake design.
Learn how AWS Lambda SnapStart reduces cold-start latency for container image functions, supported runtimes, and the considerations for snapshot-based initialization.
Learn how AWS's Digital Sovereignty Lens helps teams assess data residency, operational autonomy, infrastructure control, and key management in cloud architecture reviews.
Explains how Claude Code on Amazon Bedrock in AWS GovCloud supports agentic coding workflows for organizations with security, compliance, and controlled deployment requirements.
Learn how Amazon Aurora DSQL foreign keys enforce referential integrity, reduce application-level validation, and support relational schema design in distributed SQL databases.
Learn how AWS cross-account deployment supports Amazon Bedrock agents and knowledge bases in multi-account environments, including IAM roles and production isolation.
Explains how Amazon SageMaker's AI inference skill lets coding agents invoke model endpoints, inspect responses, and support AI application development workflows.
How to host a app in AWS with private RDS 1.Create VPC (Virtual Private Cloud) VPC gives an isolated network in AWS where subnets, routing and network can have controlled access 2.Create Subnet A subnet is a smaller network segment inside my VPC. Ex- Public Subnet for ALB Private/Application Subnet
Learn how the AWS Well-Architected Agent can identify security, reliability, performance, and cost risks while leaving architecture decisions to engineers.
Learn how Amazon Bedrock Knowledge Bases supports RAG applications with retrieved source metadata and citations using the Retrieve and RetrieveAndGenerate APIs.
Multi-tenant DynamoDB tables create a common operational problem. Imagine one table contains data for hundreds or thousands of customers: Tenant A Tenant B Tenant C ... Tenant 1000 Now one customer asks for a copy of its data. Or a deployment corrupts records for one tenant and the engineering team
Learn how Aurora PostgreSQL can query Apache Iceberg data in Amazon S3, allowing applications to combine operational and historical lake data without a traditional ETL pipeline.
Learn how to prepare, validate, transfer, and install frozen package sets for secure, reproducible updates on air-gapped AWS servers.
Learn how to audit AI-driven DevOps changes on AWS using CloudTrail, dedicated IAM roles, workflow logs, approvals, and controlled automation.
Introduction Vector search is useful when an application needs to find information by meaning rather than exact keywords. It is commonly used in semantic search, retrieval-augmented generation (RAG), recommendation systems, document search, and AI agents. But real applications rarely search every ve
Modern cloud applications rarely consist of a single service. A typical production system may contain APIs, background workers, databases, notification services, payment systems, analytics pipelines, and AI workloads. When these services communicate directly with one another, the architecture can qu
Large language models can generate powerful responses, but production inference introduces a different challenge: latency. An application may need to process thousands of requests while maintaining predictable response times. As traffic grows, teams often have multiple models, inference endpoints, w
Learn how DynamoDB Multi-Region Strong Consistency handles Regional failures, quorum, failover, write conflicts, recovery, RPO, and production design.
AI applications often depend on inference APIs to generate predictions, embeddings, summaries, classifications, and large language model responses. As traffic increases, inference latency can become one of the biggest challenges in production. A model may be fast when tested with a few requests, but
Learn how to monitor AWS DataSync using CloudWatch metrics, logs, task reports, and execution details without building unnecessary custom metric pipelines.
Learn how Amazon Transcribe works with AWS KMS customer managed keys, including encryption, key policies, IAM, permissions, monitoring, troubleshooting, and lifecycle.
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