News For Dev-ops

AWS Trusted Advisor has enhanced its detection of unused NAT Gateway checks powered by AWS Compute Optimizer detection capabilities. The enhanced detection analyzes additional CloudWatch metrics over a 32-day lookback period and verifies whether NAT Gateways are associated with route tables, reducing false positives by avoiding flagging critical backup resources. This helps cost optimization teams and DevOps engineers confidently identify and remove unused NAT Gateways that incur unnecessary charges. Each recommendation includes estimated monthly cost savings, enabling you to prioritize cleanup based on monetary impact. With these recommendations, you can run regular cost audits to catch idle NAT Gateways before charges accumulate. This simplifies cleaning up resources left behind after workload migrations or decommissions. You can view and act on these recommendations in the Trusted Advisor console alongside your other cost optimization checks, or through Trusted Advisor APIs. Thi...

Amazon S3 Tables are now available in AWS GovCloud (US-East) and AWS GovCloud (US-West). Amazon S3 Tables deliver the first cloud object store with built-in Apache Iceberg support, offering optimized tabular data storage at scale. S3 Tables are designed to perform continual table maintenance to automatically optimize query efficiency and storage cost over time, even as your data lake scales and evolves. With S3 Tables support for the Apache Iceberg standard, your tabular data can be easily queried by popular AWS and third-party query engines. Additionally, with the Intelligent-Tiering storage class, S3 Tables automatically manage costs based on access patterns, without performance impact or operational overhead. For a full list of AWS Regions where S3 Tables are available, see S3 Tables AWS Regions and endpoints . To learn more, visit the product page , documentation , and the Amazon S3 pricing page . Post Updated on 2026-02-23T05:00:00Z

AWS IAM Policy Autopilot, the open source static code analysis tool launched at re:Invent 2025, is now available as a Kiro power to bring policy expertise to agentic AI development. This tool helps developers quickly create baseline AWS IAM policies that can be refined as applications evolve, eliminating the need for manual IAM policy creation. The Kiro power delivers significant benefits through one-click installation directly from the Kiro IDE and web interface, removing the need for manual MCP server configuration. This streamlined workflow enables faster policy creation and integrates seamlessly into AI-assisted development environments. Key use cases include rapid prototyping of AWS applications requiring IAM policies, baseline policy creation for new AWS projects, and enhanced productivity within IDE environments where developers can generate policies without leaving their coding workflow. To learn more about AWS IAM Policy Autopilot and access the integration, visit the AWS IA...

Amazon Redshift now offers 3-year Serverless Reservations for Amazon Redshift Serverless, a new discounted pricing option that provides up to 45% savings and improved cost predictability for your analytics workloads. With Serverless Reservations, you commit to a specific number of Redshift Processing Units (RPUs) for a 3-year term with a no-upfront payment option. Amazon Redshift Serverless allows you to run and scale analytics without having to provision and manage clusters with a pay-as-you-go pricing model. Serverless Reservations help you further optimize compute costs and improve cost predictability of existing and new workloads on Amazon Redshift Serverless. Managed at the AWS payer account level, Serverless Reservations can be shared between multiple AWS accounts, reducing your compute costs by up to 45% on all Amazon Redshift Serverless workloads in your AWS account. Serverless Reservations are billed hourly and metered per second, offering a consistent billing model (24 hour...

Last time, we saw that Get­Async­Key­State is not the way to detect whether the ESC key was down at the time the current input message was generated . But what about if we switched to Get­Key­State ? Would that allow us to distinguish between an IDCANCEL caused by the ESC and an IDCANCEL that come from the Close button? It helps, in that it tells you whether the ESC key was down when the event occurred, but just because the ESC is down doesn't mean that the ESC key is why you got the message. For example, suppose your policy is to simply ignore the ESC key, but to close the dialog if the user clicks the Close button. If the user holds the ESC key and clicks the Close button, the initial press of the ESC will generate an IDCANCEL , and your call to Get­Key­State will report that the ESC is down, so you will ignore the message. And then the next IDCANCEL comes in due to the Close button, and your call to Get­Key­State will correctly report "The ESC key is stil...

Introduction: The Problem Agentic systems have some discretion in the parameters they send to tooling, but there are cases, such as experimentation, when you need 100% adherence to a set of parameters. In practice, this tension exists because modern LLM-based agents are optimized for semantic correctness and helpfulness, not for strict schema compliance. Even when instructions are explicit, models may “helpfully” adjust parameters based on inferred intent, prior training patterns, or perceived optimization opportunities. For example, imagine your agent can call a search tool providing a query, a top_k parameter, and a threshold. Then imagine you are running an experiment to see how varying top_k impacts your retrieval performance. You might write in your prompt that your agent should always set top_k=10, but how often does it follow that instruction? In our testing across multiple OpenAI models—from GPT-4o-mini through GPT-5-mini—we observed the same class of problem. For example, wit...