Loading…
AWS
Cloud computing platform providing infrastructure, storage, databases, networking, AI, analytics, security, and application services.
- 50 posts summarised
- latest
The Good Stuff
Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.
From their blog
Latest articles
How Dynata scaled AI across the development lifecycle with AI-DLC and Kiro IDE
Dynata, a large first-party data company, sought to accelerate delivery across distributed teams while improving cross-functional alignment. They adopted a staged AI approach: first AI-assisted coding via Amazon Q Developer, then connecting AI to project context from Jira, Confluence, and Lucid, and finally implementing the AI-Driven Development Lifecycle (AI-DLC) methodology using Kiro IDE. AI-DLC, powered by Amazon Bedrock, structures delivery into Inception, Design, and Construction phases with Specs, steering files, and hooks. This process forced all teams to agree on requirements and design during a Day 1 workshop, surfacing gaps before coding began. Consequently, features that once took 3–4 sprints were completed in about one sprint, cutting development phase time by roughly 70% and reducing post-launch defects.
Multi-region disaster recovery for Amazon API Gateway WebSocket APIs
WebSocket connections for real-time applications like live auctions and collaborative editors are vulnerable to regional outages because each connection is tied to a single AWS Region. A regional failure severs all active connections and loses session state. This post presents an active-passive multi-region disaster recovery pattern that uses Amazon API Gateway WebSocket APIs, Route 53 failover routing, DynamoDB Global Tables for cross-Region state replication, a canary health probe, and client-side reconnection logic. The canary Lambda function tests the endpoint every minute and drives a CloudWatch alarm that triggers a Route 53 health check to mark the primary Region unhealthy. Clients detect connection drops via application-level heartbeat messages and reconnect with exponential backoff and full jitter to avoid thundering herd. After reconnection, clients retrieve the latest application state from the secondary Region’s DynamoDB replica and resume their session automatically.
Scope workloads and share resilience policies with AWS Resilience Hub
AWS Resilience Hub’s Next generation, announced September 18, 2026, adds three capabilities to align workload scope and resilience requirements with running environments. Amazon EKS label support selects Kubernetes resources using existing labels with AND logic, establishing precise resource boundaries without reorganizing namespaces. Generative AI-powered Dependency Insights summarizes discovered dependencies and highlights patterns warranting resilience review, such as cross-Region dependencies questioning recovery design. Resilience policy sharing through AWS Organizations lets a central team define requirements once and make them available to member accounts, with policy events tracking adoption. The post walks through scoping a checkout service on EKS using labels, examining dependencies via synthetic traffic, and applying a shared tier-1-critical policy with a 99.9% availability SLO, 5-minute multi-AZ RTO, 1-minute RPO, 15-minute multi-Region RTO, and 60-minute backup intervals.
Stopping business email compromise with AI-driven email security: How VIPRE protects modern enterprises on AWS
Business email compromise (BEC) uses social engineering and impersonation rather than malicious links, bypassing traditional secure email gateways that are blind to internal traffic and lateral movement. VIPRE developed Integrated Email Security (IES), a cloud-native solution on AWS that integrates directly with Microsoft 365 via APIs, eliminating the need for MX record changes. The solution uses multiple machine learning layers, including semantic similarity models, contextual language models, and dynamic behavioral analysis of links and attachments, running on AWS services such as Amazon ECS with NVIDIA GPUs. In production, VIPRE IES processes over half a million messages daily with P95 latency between 1.4 and 78 seconds depending on sandbox analysis, detecting approximately 1% of messages as BEC attempts. Deployment can be completed in minutes rather than days, providing immediate visibility into internal risks that traditional gateways miss.
Bulk Executor for Amazon DynamoDB, Part 2: Lessons we learned the hard way
The post describes design tradeoffs and workarounds encountered while building the open-source Bulk Executor tool for Amazon DynamoDB on AWS Glue. Core requirements included real-time local-script feel, scaling to hundreds of parallel workers without user infrastructure management, and easy command extensibility. AWS Glue was chosen for serverless parallelism and a built-in DynamoDB connector, but the connector simplified data types, losing distinctions like string set versus list of strings. Challenges included estimating costs for provisioned tables, handling job termination with ^C, and streaming output via CloudWatch Live Tail which samples events and delivers out-of-order. Solutions included a two-phase cost estimate for deletes, programmatic interrupt handling, writing large output to S3 with a preview, mandating point-in-time recovery for mutating commands, and using AI for testing and linting; the team also migrated from AWS Glue 4 to 5 and 6 for faster startup and 30 percent lower DPU costs.
Can we change an AI model’s architecture without retraining it from scratch?
The Transformer architecture’s memory cost scales with token count, making long-context AI agents expensive. In controlled comparisons of three state space models - Gated KalmaNet, Gated DeltaNet, and Mamba-2 - Gated KalmaNet performed best, offering variable-length inference without retraining. Priming identifies which attention layers can be replaced by state space models using a sliding-window attention proxy, then reuses pre-trained weights to convert them into hybrid architectures without retraining, reducing pretraining cost by over 99%. A 32-billion-parameter hybrid model matches the original Transformer’s reasoning quality while decoding up to twice as fast or reaching correct answers in two-thirds of wall-clock time. The team extended hybrid context to 256K tokens without additional training and released primed models at 8B and 32B scales along with training code, kernels, and a vLLM plugin under Apache 2.0.
Agentic antimicrobial resistance surveillance with AWS HealthOmics and Amazon Bedrock AgentCore
Antimicrobial resistance, estimated to cause 1.27 million direct deaths in 2019, requires surveillance that normally demands bioinformatics skills to turn raw reads into answers. It ingests public SRA data via a Step Functions workflow and Fargate task, then runs a HealthOmics Nextflow pipeline that trims reads, assembles them, and detects resistance genes with AMRFinderPlus using a baked-in database. Results are normalized into the hAMRonization schema and merged idempotently into Iceberg tables on S3 Tables, so reprocessing an isolate updates rows rather than duplicating them. A Strands agent on Amazon Bedrock AgentCore answers plain-language questions by generating Athena queries, with Guardrails keeping responses grounded. The solution is defined entirely in AWS CDK, deploys with a single command, and avoids manual bioinformatics steps, remaining inexpensive when idle.
Announcing the AWS Digital Sovereignty Well-Architected Lens
The new AWS Digital Sovereignty Well-Architected Lens offers extended guidance for designing, building, and operating sovereign workloads, addressing challenges not fully covered by existing guidance. It includes procedures to deploy Oracle Database on Amazon EVS with FSx for ONTAP, covering provisioning, NFS mounting, and SnapMirror replication. It details multi-day AZ evacuation drills with ARC Zonal Shift across ECS, EKS, RDS for PostgreSQL, and Aurora PostgreSQL, providing CLI commands and observability metrics. MHK built the SmartProminence AI Orchestrator on Amazon Bedrock, a HIPAA-eligible framework that reduced manual medical review by 90% and cut deployment from months to weeks. CSIRO developed Serverless Beacon for genomic variant querying on AWS, implementing the GA4GH standard with S3, Lambda, DynamoDB, and Athena.
Deploy Oracle Database step by step on Amazon EVS with FSx for ONTAP
This post provides step-by-step procedures to deploy Oracle Database on Amazon Elastic VMware Service (Amazon EVS) with Amazon FSx for NetApp ONTAP as NFS datastore storage. It covers provisioning storage volumes, mounting NFS datastores, installing Oracle 19c, and configuring SnapMirror replication for cross-region disaster recovery. The deployment uses FSx for ONTAP volumes pinned to SSD tier and presents them as VMDKs on NFS datastores, with Oracle accessing them as local XFS filesystems. Four migration paths are detailed for moving on-premises Oracle workloads, including SnapMirror-based replication for existing ONTAP environments and RMAN backup/restore for non-ONTAP storage. Day-2 operations such as snapshot backup, point-in-time recovery, and database cloning via SnapCenter FlexClone are also covered.
Architect highly available Oracle Database on Amazon EVS and FSx for ONTAP
Enterprises with existing VMware Cloud Foundation investments can migrate Oracle databases to AWS using Amazon Elastic VMware Service (EVS) and Amazon FSx for NetApp ONTAP without rearchitecting applications or retraining operations teams. The architecture splits storage between vSAN for low-latency transient workloads and FSx for ONTAP NFS datastores for Oracle data files and redo logs, delivering sub-millisecond latency and up to 80,000 IOPS. Cross-region disaster recovery uses SnapMirror to asynchronously replicate FSx for ONTAP volumes, with pre-provisioned standby Oracle VMs that mount replicas only after failover to avoid Oracle double licensing. Recommended EC2 bare metal instance types include i7i.metal-24xl for per-core compute speed and i7i.metal-48xl for vertical scaling, both supporting VCF 9.1. Migration options include VMware HCX for live migration, SnapMirror for ONTAP-to-ONTAP transfer, and RMAN for universal database movement.
Deploy open source Regional availability tools in your VPC
Teams building multi-Region architectures on AWS need to know which services are available per Region, but want that data deployed inside their VPC under their governance. This post introduces Capability Insights for AWS, a self-hosted dashboard that auto-refreshes Regional availability data every 24 hours, and Workload Analysis, which scans CloudTrail and CloudFormation to filter services to only those your account uses. Capability Insights deploys a static website backed by API Gateway, Lambda, and EventBridge within your VPC, pulling data from the centralized AWS Capabilities by Region S3 bucket. Workload Analysis reduces the scope from over 200 services to typically 20-30, attributing each resource type to the specific CloudFormation stacks that deploy it and showing property configurations in use. The dashboard provides a targeted view of service availability, API operations, and CloudFormation resource types per Region, enabling focused gap analysis for Regional expansion planning and helping identify service-availability gaps.
Accelerating airline retailing innovation: how Datalex modernized with AWS Experience-Based Acceleration and agentic AI
Datalex, a leader in airline ecommerce, needed to modernize its mature EJB2/Java 8 system for Modern Airline Retailing while maintaining continuous airline operations. Over three days, 16 Datalex engineers and 6 AWS specialists proved migration feasibility by extracting a Reservation service into a Spring Boot microservice on Java 21. The team used agentic AI tools—Kiro and AWS Transform Custom—to automate code changes, and established repeatable patterns for DevSecOps, observability, and a multi-agent AI prototype via Amazon Bedrock AgentCore. A business service proxy using the Strangler Fig pattern enabled incremental migration with backward compatibility. The board approved significant investment, the prototype team expanded into a full Agile team, and the EBA shortened discovery from an estimated 8–12 weeks to three days.
Graph-centric agentic intelligence
Graphs encode relationships and provide scaffolding for causal inference and compositional reasoning, which is essential for AI agents reasoning over complex networks. The post traces the evolution of graph intelligence from passive topology models to active agentic reasoning, culminating in a three-pillar approach for root cause analysis. The approach combines a continuously synchronized network digital twin graph with a cascaded pipeline of graph algorithms—decomposition, clustering, and centrality ranking—that narrow the search space. An agentic orchestration layer selects and composes these algorithms adaptively based on failure characteristics, performing complexity triage and consulting an incident knowledge base. The system achieved root cause analysis in minutes on commercial networks, demonstrated with NTT DOCOMO at MWC.
Build adaptive AI interfaces with the AG-UI protocol, agent swarms, and Nova Act on AWS
Static interfaces cannot adapt when generative AI produces variable outputs, such as a medical scan showing one fracture versus twenty ambiguous regions requiring expert review. The solution combines three AWS technologies: the AG-UI protocol for standardized agent-to-UI streaming via Server-Sent Events, the Strands Agents SDK Swarm pattern for peer-to-peer agent collaboration, and Amazon Nova Act for browser-based legacy system integration. AG-UI removes custom WebSocket formats by defining typed events that both agent frameworks and frontends implement as a universal contract. Strands swarm agents debate hypotheses and iteratively refine findings, producing explainable results visible in real time, while Nova Act navigates login screens and fills forms on systems without APIs. A complete radiology assistant walkthrough demonstrates reduced interface development time and HIPAA-compliant data handling for medical imaging.
A kernel-centric path to real-time video generation on Trainium
Reactor and the AWS Neuron Science team collaborated to optimize real-time video generation on AWS Trainium. They focused on autoregressive diffusion models, which combine sequential next-token prediction with iterative diffusion to generate video frames that respond to user input. The teams used the Rolling Forcing model as a proxy, addressing challenges such as dynamic shapes, unusual memory access patterns, and heavy cache management that generic compilers struggle with. Optimizations included splitting SDPA heads across four cores and sequences across two, and batching common components of the diffusion and cache update phases to improve hardware utilization. The collaboration achieved real-time video generation above 16 frames per second on a single Trainium2 chip, a result that generalizes across autoregressive diffusion models.
Amazon launches research initiative with Stanford University to advance AI and science
Amazon and Stanford University have launched the Stanford and Amazon Research Initiative, a formalized framework to advance research in AI, energy, and healthcare. The initiative builds on a deep relationship where currently more than 10 teams across Amazon fund active research and PhD fellowships at Stanford, spanning humanoid robotics, post-quantum cryptography, causal measurement science, and AI-driven radiology. It aims to accelerate the path from breakthrough research to real-world solutions that benefit society at large and broaden participation from diverse scholars. Focus areas will leverage strengths in artificial intelligence, machine learning, automated reasoning, and health, supported by Amazon’s cloud computing and AI services. The collaboration includes joint research projects, PhD fellowships, and interdisciplinary symposia, and was announced at a Stanford event with AWS CEO Matt Garman and Vice Provost David Studdert discussing the impact of university-industry collaborations.
Advancing AI for biology: Teaching models to design and characterize antibodies
Monoclonal antibodies are a mainstay of biopharmaceutical development, with over 100 FDA-approved drugs and established manufacturing and regulatory pathways, yet conventional discovery is costly and slow, typically taking six to twelve months from target to lead candidate. AI promises to make antibody development cheaper, faster, and more flexible by designing and characterizing therapeutic antibodies computationally, but scientific questions remain. Development hinges on three factors: the best binding site on the target, which candidates bind most tightly, and whether candidates can survive manufacturing and the clinic. Predictive models for each factor do well on familiar targets and assays but considerably worse on unfamiliar ones, a gap that in-distribution benchmarks have made difficult to measure. Three papers from Amazon Bio Discovery’s science team address research questions about each factor, with methods integrated into the AI-powered application that connects biological AI models with lab services.
ReadyOn’s Four Walls of tenant isolation on Amazon EKS
ReadyOn describes a four-layer tenant-isolation architecture for Harmony, its multi-tenant workforce-intelligence platform on Amazon EKS, which handles payroll data, organizational hierarchies, and operational analytics. The design combines dedicated Kubernetes namespaces, Karpenter-managed tenant node pools, per-tenant Amazon VPC security groups, and dedicated Amazon Aurora databases, with RBAC, admission control, default-deny network policies, IAM Roles for Service Accounts, and tenant-scoped secrets. GitOps through Argo CD ApplicationSets provisions identical controls and continuously reverts drift, while dual taints and admission validation prevent cross-tenant scheduling. ReadyOn says adversarial exercises have blocked all six tested cross-tenant paths to date, and reports defenses across 10 threat-model stages, though shared control-plane dependencies make the layers defense in depth rather than fully independent probabilities.
How CSIRO built scalable, cost-optimized genomic variant querying on AWS
CSIRO built Serverless Beacon (sBeacon), a production-ready implementation of the Global Alliance for Genomics and Health (GA4GH) Beacon protocol for secure genomic variant querying. Its serverless architecture combines Amazon S3, AWS Lambda, Amazon DynamoDB, and Amazon Athena: genomic data remains in its original S3 location, while metadata is indexed in ORC tables and ontology indexes for query processing. Standard VCF files can be queried through API Gateway, with modular Lambda functions resolving ontology terms, querying Athena, and, when needed, issuing HTTP byte-range requests to source buckets so raw VCF bytes do not leave their owning organization. The post reports support for hundreds of millions of individuals and billions of genomic locations, approximately USD 0.40 monthly operation for a 1000 Genomes-scale dataset, roughly five-second real-world queries, and about 18-second generation-to-use onboarding, while noting that whole-genome range queries can time out.
How Equinix cut operational overhead with a shared services architecture on Amazon EKS
Equinix replaced its self-managed Kubernetes environment on Amazon EKS to address operational sprawl caused by independently managed clusters and inconsistent infrastructure practices. The new shared services architecture assigns platform ownership to a cloud operations team while application teams focus on business services. It uses separate Workloads, Platform, and Network accounts: workloads run across two Availability Zones with ALB, Gateway API, Cilium, and Hubble, while shared data services, GitHub Runners, and platform services reside in the Platform account. The Network account provides cross-account and multi-Region connectivity through AWS Transit Gateway, Route 53 Resolver, and Direct Connect. Equinix reports 40% lower operational overhead, 4x higher deployment frequency, and adoption of a unified architecture across multiple business organizations.
Topics they write about
All topics →Weekly digest
The best engineering reads, once a week.