# Intermediate (200) Published articles for Intermediate (200). This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Improving HCLS AI reasoning with open-source agent skills DevFeed: [Improving HCLS AI reasoning with open-source agent skills]() Original publisher: [Read original article]() Author: Michael Hsieh Published: 2026-09-16T19:00:00Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Agent Skills](), [Artificial Intelligence](), [foundation-models](), [Open Source](), [Bioinformatics](), [Medical imaging]() Tags: [agent-skills](), [agents](), [ai](), [ai-agents](), [amazon-bedrock](), [amazon-quick-suite](), [announcements](), [artificial-intelligence](), [foundation-models](), [generative-ai](), [healthcare](), [healthcare-and-life-sciences](), [intermediate-200](), [kiro](), [life-sciences](), [medical-imaging](), [open-source](), [strands-agents]() ### AI overview This post presents 38 open-source agent skills spanning 11 healthcare and life sciences domains. The skills encode domain decision procedures for AI agents, and the reported evaluation found a 70-86% head-to-head win rate over agents without the skills. ### Source excerpt AI agents on foundation models often misapply healthcare and life sciences decision frameworks, citing the right guideline but applying it incorrectly. This post shares 38 open-source agent skills across 11 HCLS domains that close this gap, with installation steps, three worked use cases, and a 410-prompt evaluation showing a 70-86% win rate. ## Optimizing agent system prompts with Amazon Bedrock AgentCore DevFeed: [Optimizing agent system prompts with Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Han Ding Published: 2026-09-16T15:47:39Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [Prompt optimization](), [Prompt Engineering](), [Traces](), [Benchmark](), [A/B Testing](), [Automated prompt engineering]() Tags: [a-b-testing](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [benchmark](), [best-practices](), [evaluation](), [intermediate-200](), [open-source](), [prompt-optimization](), [system-prompts](), [traces]() ### AI overview This technical companion explains how Amazon Bedrock AgentCore's system prompt optimizer uses production agent traces and reward signals to propose revised system prompts. It describes the reflector engine, recommendation explanations, offline batch evaluation, online A/B testing, and promotion workflow, and reports benchmark results for Single Agent Reflector and the experimental open source Sub-Agent Reflector. ### Source excerpt AgentCore optimization turns production traces into proposed configuration changes, then validates them before promotion. This technical companion to the launch post explains how the system prompt optimizer's reflector engine works and shares benchmark results for the Single Agent and Sub-Agent Reflectors. ## Announcing instance preference lists for Amazon SageMaker AI training jobs DevFeed: [Announcing instance preference lists for Amazon SageMaker AI training jobs]() Original publisher: [Read original article]() Author: Kanwaljit Khurmi Published: 2026-09-15T16:01:47Z Content type: release Language: en Sources: [Artificial Intelligence]() Topics: [Amazon SageMaker AI](), [GPU](), [Model Development](), [data-processing]() Tags: [amazon-sagemaker-ai](), [announcements](), [gpu](), [intermediate-200](), [model-development](), [pipelines](), [scheduled](), [training]() ### AI overview Amazon SageMaker AI introduces instance preference lists for training and processing jobs. Users can specify up to five instance types in priority order, and SageMaker AI launches the job on the first option with available capacity, reducing manual retries and capacity monitoring. ### Source excerpt Amazon SageMaker AI now offers instance preference lists for training and processing jobs. Specify an ordered list of up to five instance types, and SageMaker AI automatically launches on the first type with available capacity, eliminating manual retry loops and capacity-watching scripts. ## Manage end-user OAuth consent for AI agents with Amazon Bedrock AgentCore DevFeed: [Manage end-user OAuth consent for AI agents with Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Swara Gandhi Published: 2026-09-14T20:35:45Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [OAuth](), [Amazon Web Services](), [AWS CloudTrail](), [GitHub](), [Slack](), [Model Context Protocol](), [ide]() Tags: [amazon-bedrock](), [amazon-bedrock-agentcore](), [announcements](), [aws](), [aws-cloudtrail](), [claude-code](), [cursor](), [github](), [intermediate-200](), [kiro](), [model-context-protocol](), [oauth](), [slack](), [visual-studio-code]() ### AI overview This tutorial explains how Amazon Bedrock AgentCore Identity's Consent portal manages end-user OAuth consent and session binding for AI agents. It covers configuring GitHub and Slack targets through an AgentCore Gateway, storing user tokens, supporting IDE and MCP clients, and reviewing activity in AWS CloudTrail. ### Source excerpt Amazon Bedrock AgentCore Identity now offers a Consent portal, a managed web experience and session binding endpoint for AgentCore Gateway. This post walks through provisioning a portal, configuring GitHub and Slack 3LO targets, and the end-user consent flow, and shows how to review activity in AWS CloudTrail. ## Build interactive MCP Apps using Amazon Bedrock AgentCore DevFeed: [Build interactive MCP Apps using Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Dantis Stephen Published: 2026-09-11T18:23:17Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [AI Chat](), [Web Development]() Tags: [amazon-bedrock-agentcore](), [chatgpt](), [claude](), [how-to](), [html](), [intermediate-200](), [mcp](), [model-context-protocol](), [serverless](), [technical-how-to](), [ui]() ### AI overview Tutorial on building and deploying an MCP App with interactive HTML widgets on Amazon Bedrock AgentCore for use across supported AI hosts. ### Source excerpt Learn how to build and deploy an MCP App with interactive HTML widgets on Amazon Bedrock AgentCore. Because MCP Apps is a host-agnostic standard, the same server delivers the same rich experience across AI hosts like ChatGPT and Claude that support the extension. ## Video and image search in Amazon Bedrock Knowledge Base using Marengo 3.0 DevFeed: [Video and image search in Amazon Bedrock Knowledge Base using Marengo 3.0]() Original publisher: [Read original article]() Author: Eric Kim Published: 2026-09-10T21:15:39Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [AI search](), [Retrieval Augmented Generation (RAG)](), [Amazon S3](), [AWS IAM]() Tags: [amazon-bedrock](), [amazon-bedrock-knowledge-bases](), [announcements](), [audio](), [embedding](), [how-to](), [images](), [intermediate-200](), [multimodal](), [rag](), [s3](), [search](), [video]() ### AI overview A walkthrough for building an Amazon Bedrock Knowledge Base with TwelveLabs Marengo Embed 3.0 to perform natural-language semantic search across video, images, and audio. ### Source excerpt TwelveLabs Marengo Embed 3.0 is now generally available as an embedding model in Amazon Bedrock Knowledge Bases, bringing fully managed natural language search to video, image, and audio content. This walkthrough shows how to build a knowledge base powered by Marengo 3.0 and run semantic queries against your media. ## Customize Amazon API Gateway destinations for execution logs DevFeed: [Customize Amazon API Gateway destinations for execution logs]() Original publisher: [Read original article]() Author: Giedrius Praspaliauskas Published: 2026-09-09T22:35:42Z Content type: tutorial Language: en Sources: [AWS Compute Blog]() Topics: [Amazon API Gateway](), [Amazon CloudWatch Logs](), [log management](), [Amazon S3](), [SIEM, Security, Observability]() Tags: [amazon-api-gateway](), [amazon-cloudwatch-logs](), [amazon-s3](), [announcements](), [intermediate-200](), [logs](), [observability](), [rest-api]() ### AI overview This tutorial explains how to route Amazon API Gateway REST API execution logs to CloudWatch Logs, Amazon S3, or Amazon Data Firehose. It contrasts execution logs with access logs and describes their use for diagnosing authorization, validation, integration, mapping, and error-handling behavior. ### Source excerpt Amazon API Gateway execution logs help you trace request processing step by step through your REST API stages. They capture authorization results, integration latency, mapping template output, and error details that are otherwise invisible at the API surface. When a production request fails in a way the access log cannot explain, the execution log is [...] ## ICYMI: What landed for AI builders in August 2026 DevFeed: [ICYMI: What landed for AI builders in August 2026]() Original publisher: [Read original article]() Author: Tanvi Girinath Published: 2026-09-09T20:01:03Z Content type: news Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock](), [Inference](), [Caching](), [SDKs](), [Deployment]() Tags: [agents](), [ai](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [announcements](), [caching](), [deployment](), [govcloud](), [inference](), [intermediate-200](), [latency](), [open](), [robots](), [updates]() ### AI overview A recap of August 2026 Amazon Bedrock, AgentCore, and Strands updates for AI builders, including expanded context windows, inference, long-running agents, GovCloud availability, and robot deployment. ### Source excerpt A recap of August 2026 launches for AI builders across Amazon Bedrock, Amazon Bedrock AgentCore, and Strands: million-token context for OpenAI models, cross-Region inference, agents that run for up to 14 days on dedicated compute, expanded AWS GovCloud availability, and Strands Robots for physical deployment. ## Simplify and support your TorchServe workloads using Ray Serve Deep Learning Containers DevFeed: [Simplify and support your TorchServe workloads using Ray Serve Deep Learning Containers]() Original publisher: [Read original article]() Author: Ananth Raghavendra Published: 2026-09-09T15:51:29Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Inference](), [model-deployment](), [Amazon Elastic Kubernetes Service](), [Containers](), [vlm](), [GPU](), [PyTorch]() Tags: [amazon-eks](), [containers](), [gpu](), [inference](), [intermediate-200](), [pytorch](), [technical-how-to](), [vulnerabilities]() ### AI overview The article introduces AWS Ray Serve Deep Learning Containers as a maintained, pre-tested inference option for teams moving from unmaintained TorchServe workloads. It demonstrates deploying a vision-language model on Amazon EKS using a single GPU node. ### Source excerpt TorchServe is no longer maintained, leaving teams to own the entire GPU inference stack. The AWS Ray Serve Deep Learning Container is a supported, pre-tested container with the framework, GPU drivers, and serving layer already assembled. This post walks through deploying a vision-language model on Amazon EKS using the Ray Serve DLC on a single GPU node. ## Automate user-level custom permissions for Amazon Quick DevFeed: [Automate user-level custom permissions for Amazon Quick]() Original publisher: [Read original article]() Author: Ashok Dasineni Published: 2026-09-09T15:45:24Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Authorization](), [AWS Lambda](), [IAM Identity Center](), [Script]() Tags: [amazon-eventbridge](), [amazon-quick-suite](), [api](), [automation](), [aws-iam-identity-center](), [aws-lambda](), [batch](), [cli](), [intermediate-200](), [python](), [technical-how-to]() ### AI overview This tutorial presents four ways to automate user-level custom permissions in Amazon Quick: setting permissions during user registration, applying account or role defaults, using EventBridge and Lambda for group-based logic, and running retroactive batch updates. ### Source excerpt Amazon Quick custom permissions let you enforce least-privilege access by toggling features per user. This post walks through four patterns to automate custom permissions across the user lifecycle: a RegisterUser API parameter, account and role defaults, event-driven Amazon EventBridge and AWS Lambda automation, and a retroactive batch update script. ## Take on your most ambitious work with GPT-6 Astra on Amazon Bedrock DevFeed: [Take on your most ambitious work with GPT-6 Astra on Amazon Bedrock]() Original publisher: [Read original article]() Author: Tanvi Girinath Published: 2026-09-08T22:06:58Z Content type: release Language: en Sources: [Artificial Intelligence]() Topics: [AI, ML & Data Engineering](), [AI-assisted coding](), [AI Chat]() Tags: [agents](), [amazon-bedrock](), [announcements](), [api](), [aws](), [cache](), [chatgpt](), [codex](), [gpt](), [intermediate-200](), [openai](), [scalability](), [security]() ### AI overview GPT-6 Astra is generally available on Amazon Bedrock. The article describes its reasoning, codebase work, browser use, prompt caching, and enterprise controls for production workloads. ### Source excerpt GPT-6 Astra from OpenAI is now generally available on Amazon Bedrock. It brings deeper reasoning and sharper judgment to your most demanding tasks, running on the Amazon Bedrock inference engine built for high performance, security, and scale. ## Bring your own client certificate for backend mTLS in Amazon API Gateway DevFeed: [Bring your own client certificate for backend mTLS in Amazon API Gateway]() Original publisher: [Read original article]() Author: Biswanath Mukherjee Published: 2026-09-08T21:46:39Z Content type: tutorial Language: en Sources: [AWS Compute Blog]() Topics: [Amazon API Gateway](), [AWS Certificate Manager](), [Amazon Elastic Container Service](), [nginx]() Tags: [amazon-api-gateway](), [amazon-elastic-container-service](), [announcements](), [architecture](), [authentication](), [aws-certificate-manager](), [aws-fargate](), [intermediate-200](), [rest-api](), [tls]() ### AI overview This tutorial shows how to configure Amazon API Gateway to present a third-party or AWS Private CA-issued client certificate for outbound mTLS to an Amazon ECS backend. ### Source excerpt Enterprises that use Amazon API Gateway often want to bring their own client certificate for backend mutual TLS (mTLS) authentication. With API Gateway, you can now use a third-party or AWS Private CA-issued client certificate for the outbound mTLS handshake. In this post, you build a REST API with an outbound mTLS connection to an Amazon ECS backend. ## Pathway's brain-inspired architecture development on Amazon SageMaker HyperPod DevFeed: [Pathway's brain-inspired architecture development on Amazon SageMaker HyperPod]() Original publisher: [Read original article]() Author: Paulo Aragão Published: 2026-09-08T19:12:51Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Language models](), [Training AI Models](), [compression and generalization](), [LLM evaluation / benchmarking]() Tags: [amazon](), [amazon-sagemaker](), [amazon-sagemaker-hyperpod](), [announcements](), [architecture](), [benchmark](), [chain-of-thought](), [distributed-training](), [intermediate-200](), [pytorch](), [transformer-architecture]() ### AI overview Pathway describes BDH, a brain-inspired architecture that performs reasoning in latent space rather than producing chain-of-thought token traces. The article covers its recurrent internal memory, its contrast with transformer limitations, and scaling training with Amazon SageMaker HyperPod. ### Source excerpt Pathway's Baby Dragon Hatchling (BDH) is a brain-inspired, post-transformer architecture that reasons in latent space instead of emitting chain-of-thought tokens. See how Pathway develops and scales BDH on Amazon SageMaker HyperPod, and how BDH-CQ set a new cost-efficiency mark on the ARC-AGI-1 benchmark. ## Amazon SageMaker Feature Store introduces UpdateRecord for feature-level writes DevFeed: [Amazon SageMaker Feature Store introduces UpdateRecord for feature-level writes]() Original publisher: [Read original article]() Author: Mona Mona Published: 2026-09-08T18:29:15Z Content type: release Language: en Sources: [Artificial Intelligence]() Topics: [AI, ML & Data Engineering](), [Training AI Models](), [AWS IAM]() Tags: [amazon-dynamodb](), [amazon-elasticache](), [amazon-machine-learning](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [announcements](), [api](), [artificial-intelligence](), [aws-identity-and-access-management-iam](), [intermediate-200](), [technical-how-to]() ### AI overview Amazon SageMaker Feature Store adds the UpdateRecord API for atomic feature-level updates without reading or rewriting an entire record. ### Source excerpt Amazon SageMaker Feature Store now supports feature-level writes. With the new UpdateRecord API, you can update one or more feature values in a single call without reading or rewriting the entire record. It is available for both the Standard (Amazon DynamoDB) and In-Memory (Amazon ElastiCache) online store tiers. ## Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 2 DevFeed: [Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 2]() Original publisher: [Read original article]() Author: Melanie Li Published: 2026-09-08T17:03:50Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon SageMaker AI](), [AWS IAM](), [CI/CD](), [Command-line interface]() Tags: [amazon-sagemaker-ai](), [announcements](), [aws](), [aws-identity-and-access-management-iam](), [ci-cd](), [intermediate-200](), [model](), [sync]() ### AI overview This article describes cross-account model-governance patterns for managed MLflow and Amazon SageMaker AI Model Registry sync. It covers hub-and-spoke and hybrid topologies, IAM-based roles, and CI/CD deployment of approved models. ### Source excerpt Governing models across accounts is the next step after automatic model registration. This post extends managed MLflow and Amazon SageMaker AI Model Registry sync to two cross-account governance topologies: a hub-and-spoke pattern that centralizes governance with AWS RAM, and a hybrid pattern that keeps development accounts isolated. ## Getting started with Oracle Database@AWS: A complete onboarding guide DevFeed: [Getting started with Oracle Database@AWS: A complete onboarding guide]() Original publisher: [Read original article]() Author: Raghu Soma Published: 2026-09-08T16:59:29Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Oracle Database](), [cloud-infrastructure](), [AWS IAM]() Tags: [aws](), [aws-marketplace](), [best-practices](), [database](), [guide](), [iam](), [infrastructure](), [intermediate-200](), [onboarding](), [oracle](), [oracle-database-aws]() ### AI overview A step-by-step guide to onboarding Oracle Database@AWS, covering service selection, AWS Marketplace offers, prerequisites, OCI tenancy linking, and IAM configuration before provisioning. ### Source excerpt A practical, step-by-step guide to getting Oracle Database@AWS up and running. It covers the five procurement and onboarding steps, from securing your AWS Marketplace offer through validating prerequisites, linking your OCI tenancy, and configuring IAM, so you can move from purchase to a provisioning-ready environment. ## Monitor self-managed databases with Amazon CloudWatch Database Insights DevFeed: [Monitor self-managed databases with Amazon CloudWatch Database Insights]() Original publisher: [Read original article]() Author: Nirupam Datta Published: 2026-09-03T18:10:43Z Content type: article Language: en Sources: [AWS Database Blog]() Topics: [Databases](), [Amazon EC2](), [dashboards]() Tags: [amazon-aurora](), [amazon-cloudwatch](), [amazon-ec2](), [amazon-rds](), [announcements](), [dashboards](), [database](), [intermediate-200](), [monitoring](), [postgresql](), [sql]() ### AI overview Amazon CloudWatch Database Insights now monitors self-managed PostgreSQL on Amazon EC2 alongside Aurora and Amazon RDS databases from one console. ### Source excerpt Amazon CloudWatch Database Insights now extends to self-managed databases. Monitor self-managed PostgreSQL on Amazon EC2 alongside your Amazon Aurora and Amazon RDS fleet from a single console, with the same DB Load, Top SQL, and wait event analysis you use for managed databases. ## Fix circular role dependencies before upgrading Amazon RDS and Amazon Aurora PostgreSQL DevFeed: [Fix circular role dependencies before upgrading Amazon RDS and Amazon Aurora PostgreSQL]() Original publisher: [Read original article]() Author: Ravi Teja Adabala Published: 2026-08-31T23:42:55Z Content type: article Language: en Sources: [AWS Database Blog]() Topics: [PostgreSQL](), [RDS for PostgreSQL](), [Amazon Aurora](), [Amazon RDS](), [SQL](), [postgresql clusters](), [Graphs]() Tags: [amazon-aurora](), [amazon-rds](), [database](), [how-to](), [intermediate-200](), [postgresql](), [rds-for-postgresql](), [sql](), [technical-how-to]() ### AI overview This article explains how circular PostgreSQL role dependencies can cause major version upgrades of Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL to stall or roll back when upgrading from PostgreSQL 14 or earlier to PostgreSQL 15 or later. It describes a diagnostic query and steps to resolve the dependencies before the upgrade. ### Source excerpt Circular role dependencies can stall or roll back a major version upgrade of Amazon RDS for PostgreSQL or Amazon Aurora PostgreSQL when you move from PostgreSQL 14 or earlier to 15 or later. Learn why this happens, how to detect it with a single pre-upgrade query, and how to clear it before you upgrade. ## AWS Network Firewall now supports rule hit count DevFeed: [AWS Network Firewall now supports rule hit count]() Original publisher: [Read original article]() Author: Preetkumar Shah Published: 2026-08-20T18:40:20Z Content type: article Language: en Sources: [AWS Security Blog]() Topics: [Firewall](), [Amazon Web Services](), [Amazon CloudWatch Logs](), [Network](), [Security](), [Amazon S3](), [log management](), [Incident response]() Tags: [amazon-cloudwatch](), [amazon-cloudwatch-logs](), [amazon-s3](), [announcements](), [aws](), [aws-network-firewall](), [compliance](), [firewall](), [incident](), [incident-response](), [intermediate-200](), [logs](), [network](), [s3](), [security](), [security-blog](), [security-identity-compliance](), [storage]() ### AI overview AWS Network Firewall now provides rule hit counts for stateful rules, using alert-log data to show how often rules match network traffic. The feature helps teams identify unused rules, support incident response, and demonstrate security-control effectiveness for compliance. ### Source excerpt As firewall rule sets grow in complexity, security teams face a common challenge: manual log analysis is used to determine which rules are actively matching traffic and which are consuming capacity without being triggered. This lack of visibility creates operational and compliance gaps. Organizations with governance policies that require removal of dormant rules after a [...] ## Set up your AI coding agent to build with AWS Step Functions DevFeed: [Set up your AI coding agent to build with AWS Step Functions]() Original publisher: [Read original article]() Author: D Surya Sai Published: 2026-08-19T11:41:31Z Content type: release Language: en Sources: [AWS Compute Blog]() Topics: [AWS Step Functions](), [ai-coding](), [Model Context Protocol](), [Amazon Web Services](), [Serverless]() Tags: [ai-coding](), [announcements](), [aws](), [aws-step-functions](), [intermediate-200](), [mcp](), [serverless]() ### AI overview AWS Step Functions added a console button that provides a prompt for configuring compatible AI coding agents with Step Functions skills and an MCP server. The setup enables natural-language workflow development and AWS state-machine operations. ### Source excerpt AWS Step Functions has added a Copy agent prompt button to the console that configures your AI coding agent with Step Functions skills and an MCP server in one step. Paste the prompt into Claude Code, Kiro CLI, Cursor, or any MCP-compatible agent and start building workflows with natural language. ## Integrate your Spring Boot application with Amazon ElastiCache using Spring Data Valkey DevFeed: [Integrate your Spring Boot application with Amazon ElastiCache using Spring Data Valkey]() Original publisher: [Read original article]() Author: Chris Gillespie Published: 2026-08-18T16:38:30Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [App](), [AWS IAM](), [telemetry]() Tags: [amazon-elasticache](), [applications](), [aws-iam](), [cache](), [cli](), [encryption](), [how-to](), [integration](), [intermediate-200](), [observability](), [opentelemetry](), [performance](), [redis](), [serverless](), [spring-boot](), [technical-how-to]() ### AI overview A tutorial for adding Amazon ElastiCache caching to a Spring Boot application with Spring Data Valkey. It covers dependencies, Valkey configuration, TTL settings, encryption, and migration compatibility with Spring Data Redis. ### Source excerpt Learn how to integrate a Spring Boot application with Amazon ElastiCache using Spring Data Valkey for caching. This walkthrough covers adding caching to a serverless cache, plus the advantages of Spring Data Valkey over Spring Data Redis: native AWS IAM authentication, Availability Zone affinity, and OpenTelemetry observability. ## Amazon Aurora DSQL observability concepts and usage with Amazon CloudWatch DevFeed: [Amazon Aurora DSQL observability concepts and usage with Amazon CloudWatch]() Original publisher: [Read original article]() Author: James Morle Published: 2026-08-17T23:37:48Z Content type: article Language: en Sources: [AWS Database Blog]() Topics: [DSQL](), [telemetry]() Tags: [amazon](), [amazon-aurora](), [amazon-cloudwatch](), [cost](), [dash](), [database](), [diagnostics](), [dsql](), [insights](), [intermediate-200](), [metrics](), [monitoring](), [mysql](), [observability](), [performance](), [postgresql](), [sql](), [technical-how-to]() ### AI overview This article explains Amazon Aurora DSQL's time-based observability model and its link to performance and DPU-based cost. It introduces DASH session sampling and using Amazon CloudWatch Database Insights, PromQL, and system diagnostics to identify bottlenecks. ### Source excerpt Amazon Aurora DSQL offers time-based observability through Amazon CloudWatch Database Insights. Learn how the DSQL observability model, DASH, Database Insights, PromQL, and the system diagnostics AI skill help you find performance bottlenecks and connect session time directly to cost. ## Updates to your AWS Sign-In experience DevFeed: [Updates to your AWS Sign-In experience]() Original publisher: [Read original article]() Author: Vaibhav Chowla Published: 2026-08-17T17:22:33Z Content type: release Language: en Sources: [AWS Security Blog]() Topics: [Amazon Web Services (AWS)](), [AWS IAM](), [identity and access management](), [AWS IAM Identity Center](), [browser]() Tags: [amazon-web-services-aws](), [aws](), [aws-iam](), [aws-iam-identity-center](), [aws-identity-and-access-management-iam](), [browser](), [identity-and-access-management](), [intermediate-200](), [scripted](), [security-blog](), [security-identity-compliance](), [uncategorized](), [updates]() ### AI overview AWS is gradually introducing a redesigned sign-in and sign-up experience to a limited number of customers. The changes add new account access options, unify the initial email entry point, and refresh the session selection page while existing customers continue using their current credentials and sign-in methods. ### Source excerpt Amazon Web Services (AWS) is gradually introducing updates to the AWS Sign-In and sign-up experience to a limited number of customers. We're sharing these changes so you will know what to expect as we gradually make the updated experience available to more customers. These updates include new options for creating and accessing AWS accounts. To [...] ## Serverless vehicle tracking at scale: Bosch L.OS on AWS DevFeed: [Serverless vehicle tracking at scale: Bosch L.OS on AWS]() Original publisher: [Read original article]() Author: Yogish Kutkunje Pai Published: 2026-08-14T10:02:13Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [Amazon Web Services](), [Serverless](), [real-time](), [Amazon Elastic Container Service](), [AWS Lambda](), [cloud-infrastructure](), [data](), [Networks](), [Amazon EC2]() Tags: [amazon-elastic-container-service](), [architecture](), [aws](), [complexity](), [compliance](), [cost](), [customer-solutions](), [data](), [india](), [integration](), [intermediate-200](), [networks](), [operations](), [performance](), [platform](), [real-time](), [scale](), [serverless](), [supply-chain](), [workflows]() ### AI overview Bosch Mobility Platform Solutions built L.OS, a serverless platform on AWS that unifies fragmented vehicle-tracking data across India's spot logistics market. The article describes how the platform standardizes data from multiple providers and supports scalable, real-time visibility through workflows for discovery, tracking, and termination. ### Source excerpt Learn how Bosch Mobility Platform Solutions built L.OS, a serverless vehicle tracking platform on AWS that unifies India's fragmented spot logistics market into a single real-time visibility layer using Amazon ECS, AWS Lambda, and Amazon MSK. [Next page]()