News For Dev-ops

AWS announces the general availability of Amazon CloudWatch Omni, an evolution of Amazon CloudWatch. Omni is an AI-powered observability experience organized around your teams and the applications they run, so that you can observe and troubleshoot your applications and agents in one place. It combines the interoperability of OpenTelemetry with the scale and reliability of CloudWatch. And it meets you wherever you work: a standalone web experience with single sign-on (SSO) for your team, or a local IDE extension for getting hands-on with your agents. With CloudWatch Omni, you create spaces in your central accounts to see telemetry across your AWS accounts and Regions, as well as other clouds, including Azure workloads. Omni automatically discovers services, maps dependencies, and surfaces golden metrics to help streamline your operational workflows. Using Omni, you can interact with telemetry however you prefer: via chat, through a guided point-and-click path in the console, or directl...

Amazon EMR 7.14 is now available with new features across Amazon EMR on EC2, Amazon EMR on EKS, and Amazon EMR Serverless, along with version upgrades for additional applications. This release also upgrades Apache Spark to 3.5.8 and Apache Iceberg to 1.10.1. Apache Iceberg materialized views now refresh faster on tables with updates and deletes, using change data capture to read only the data files affected by a change. On Amazon EMR on EKS, clusters now support Spark Connect endpoints for interactive Spark sessions with token-based authentication, and can run workloads on IPv6 Amazon EKS clusters. On Amazon EMR Serverless, the storage limit for Spark jobs increases from 200 GiB to 1 TiB, giving more room for shuffle data. Amazon EMR 7.14 is available in all AWS Regions where Amazon EMR is available. To learn more, visit the Amazon EMR 7.14 Release Guide , or get started by creating a cluster from the Amazon EMR console . Post Updated on September 22, 2026 at 09:00AM

C++ repositories can contain millions of lines of code spread across deeply connected source files and headers. Without a reusable index, navigating to definitions, finding references, or searching for symbols may require portions of that information to be analyzed again as you move through the repository. C++ code intelligence in GitHub Copilot CLI is now faster with support for Whole Codebase Indexing (WCI) . Powered by the Microsoft C++ Language Server , WCI uses your project’s compilation information to maintain a persistent index of symbols and relationships across source files and headers, allowing Copilot CLI to understand the codebase without repeatedly parsing it from scratch. Because the index runs directly within the CLI experience, an IDE does not need to be open to establish or maintain project understanding. Copilot CLI can use indexed declarations, definitions, references, implementations, includes, and type relationships when tracing call paths, assessing the impact o...

LangChain Deep Agents is an open-source agent harness with built-in capabilities for building LLM-powered agents and applications, including complex, multi-step workflows. A model can reason and generate responses, but it needs a harness to do useful work over time. The harness provides the tools and runtime that let the model retrieve the right context, take actions, and manage work across multiple steps. Deep Agents supplies that structure through planning, context management, a virtual filesystem, memory and skills, specialized subagents, and human approval points. Filesystems give agents a familiar way to organize and work with context. An agent can list and search files, read only what the current task needs, update artifacts, keep notes, and share work with subagents. This makes the filesystem useful as both a workspace and external memory for long-running tasks, a pattern LangChain highlights in its Deep Agents architecture . Deep Agents exposes this pattern through tools fo...

Foundry hosted agent isolation exposes two independent controls. User isolation identifies whose data may be used . At the same time, Foundry hosted session isolation identifies where code and files continue to live . For each control, Foundry can resolve the value or the application can provide it. Therefore, the available choices support direct callers, trusted middle tiers, application-managed sessions, and shared session pools. While Foundry hosted agents are generally available in Microsoft Foundry, the AgentServer SDKs and the Microsoft Agent Framework Foundry hosting packages for .NET and Python are still in pre-release versions. Foundry hosted agent isolation controls Control Represents Available choices User isolation The user represented by the current request The caller's Microsoft Entra identity, or a delegated identity supplied by a trusted middle tier Foundry hosted session isolation The VM-isolated sandbox, represented by agent_session_id A sessio...

The Amazon Elastic VMware Service (EVS) service is now in scope for FedRAMP Class C (formerly Moderate baseline) in all United States Regions.  The   Federal Risk and Authorization Management Program (FedRAMP)  is a US government-wide program that delivers a standard approach to the security assessment, authorization, and continuous monitoring for cloud products and services. Amazon EVS lets you run the latest VMware Cloud Foundation (VCF) software directly within your Amazon Virtual Private Cloud (VPC) on EC2 bare-metal instances. You can set up a complete VCF environment in just a few hours, enabling rapid workload migration to AWS to help you eliminate aging infrastructure, reduce operational risks, and meet critical timelines for exiting your data center. To learn more, visit the FedRAMP  AWS services in scope page . To learn more about Amazon EVS, visit the  product detail page and user guide .  Post Updated on September...

We've been comparing magic statics, std:: call_ once , and std:: async , but one thing we haven't considered is their behavior in the event of an exception. For magic statics, if an exception occurs during initialization of the static, then the static is considered not to have been initialized. The exception propagates, and the next time the function is called, the language will try to initialize the static again. For call_ once , if an exception occurs during execution of the lambda, then the call is considered not to have occurred. The exception propagates, so the next time you call call_ once with the same once_ flag , it will try to call it. But std:: async is different. If an exception occurs during execution of the invocable, then the exception is saved, and when you ask the future or shared future for the result, the exception is rethrown. It does not try to execute the invocable again. Let's summarize this in a table.   Before After success After exc...

A model's knowledge cutoff marks the latest date its training data could cover. But its actual knowledge of a product might end years earlier or be absent entirely. I learned this while trying to answer what seemed like a straightforward question: how much does a model know about a product? If the model's knowledge ended around a particular date, I should have been able to translate that date to a product version. The model knows the product up to roughly version X. Anything introduced after that needs additional context. On paper, that would be useful when building agent extensions. You could establish what the model already knows and supply the missing documentation, saving the context window for the work. In practice, the results showed no consistent boundary. Looking for the line To test it, I used GPT-5.6 Luna with the GitHub Copilot SDK against two developer products: Dev Proxy and SharePoint Framework (SPFx). I fed the changelogs and release notes for both...

AI coding agents can write much of an application's code, but developers still need to review the decisions behind it. For a Cosmos DB application, that includes choosing partition keys, modeling access patterns, and configuring the client. Those decisions affect cost, performance, and reliability long after the code compiles. We've written before about how Azure Cosmos DB supports agents at query time , with tools and guidance for exploring and querying data. Today we're announcing the public preview of the Azure Cosmos DB extension for GitHub Spec Kit , the first database extension in its ecosystem. It brings Cosmos DB guidance into spec-driven development , helping you and your coding agent work through application design before implementation. What is spec-driven development? In spec-driven development (SDD), you work through requirements, design, and tasks before implementation. The agent produces a document at each stage that you can read, correct, and approve...

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) X8i instances are available in the South America (São Paulo) region. These instances are powered by custom Intel Xeon 6 processors available only on AWS. X8i instances are SAP-certified and deliver the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. They deliver up to 43% higher performance, 1.5x more memory capacity (up to 6TB), and 3.3x more memory bandwidth compared to previous generation X2i instances. X8i instances are designed for memory-intensive workloads like SAP HANA, large databases, data analytics, and Electronic Design Automation (EDA). Compared to X2i instances, X8i instances offer up to 50% higher SAPS performance, up to 47% faster PostgreSQL performance, 88% faster Memcached performance, and 46% faster AI inference performance. X8i instances come in 14 sizes, from large to 96xlarge, including two bare metal options. To get started, visit the AWS Management Cons...

AWS RTB Fabric now supports configurable Availability Zone (AZ) affinity for responder gateways. With this capability, you can configure how your partners connect to your responder gateway: either in their own Availability Zone or to any Availability Zone that the gateway spans. This launch helps advertising technology (AdTech) companies use their infrastructure more efficiently—with no extra charges on RTB Fabric. Demand-side platforms (DSPs) and supply-side platforms (SSPs) run their bidding systems across multiple Availability Zones. Previously, AWS RTB Fabric sent each request to available gateway capacity in the requester's own Availability Zone (AZ), so capacity in other AZs could go unused. Now you can set a client routing policy to control this: prefer the requester's own AZ to avoid added latency of crossing a boundary, or use every AZ that the responder gateway spans to give each requester access to more gateway capacity. Configurable Availability Zone affinity is ...

Amazon Simple Email Service (SES) now supports tenant-level deliverability insights in Virtual Deliverability Manager (VDM). Tenant management allows email senders to isolate email sending across customers, business units, or applications. Previously, VDM provided deliverability metrics at the account, ISP, sending identity, and configuration set levels. Now, customers who use tenants and have enabled VDM can also monitor the deliverability of each tenant. The VDM dashboard adds a Tenants view with per-tenant metrics (send volume, deliveries, bounces, complaints, opens, and clicks) and a detail page that breaks those metrics down by mailbox provider and by associated identities and configuration sets. Customers can also search and export a tenant's sent messages, or query tenant metrics programmatically using the BatchGetMetricData API operation's new TENANT_NAME dimension. This helps senders identify the tenant causing a deliverability issue and correct it without impact t...

The Amazon S3 Express One Zone storage class is now available in 7 additional AWS Regions: Asia Pacific (Singapore), South America (Sao Paulo), US West (N. California), Canada (Central), Europe (Paris), Asia Pacific (Sydney), and Asia Pacific (Seoul). Amazon S3 Express One Zone is a high-performance, single-Availability Zone storage class purpose-built to deliver consistent single-digit millisecond data access for your most frequently accessed data and latency-sensitive applications. S3 Express One Zone delivers data access speed up to 10x faster and request costs up to 80% lower than S3 Standard. It enables workloads such as machine learning training, interactive analytics, and key-value caching in AI search engines to achieve fast data access with high durability and availability. With this expansion, S3 Express One Zone is now available in 15 AWS Regions . For pricing details, visit the S3 pricing page . To learn more, visit the product page and documentation . Post Updated on...

We're excited to announce the General Availability of Microsoft Desired State Configuration (DSC) v3.3.0. This release delivers three new built-in Windows resources, a new registry-backed adapter, expanded --what-if support, experimental export filtering, expression function updates, and Linux packages published to PMC. For background on the DSC v3 platform, see: Get Started Enhanced Authoring DSC v3.2.0 Announcement DSC v3.1.0 Announcement DSC v3.0.0 Announcement For information on installing DSC v3.3, see the installation documentation . What's New in DSC v3.3 All these changes are driven by real-world use, partner feedback, and community contributions. Special thanks to the WinGet team and the incredible DSC community. For a complete list of changes, see the release page on GitHub . New built-in Windows resources DSC v3.3 adds three new resources to the Microsoft.Windows namespace. Microsoft.Windows/RegistryList Manages an array of registry ...

Suppose you have some function like bool should_use_widgets() { bool supported = ⟦ complex code to check OS features ⟧; return supported && is_configuration_enabled("widgets"); } Since OS Widget support is not something that changes during the lifetime of the program, you want to calculate it once and cache the result. One way is to use a so-called "magic static": bool should_use_widgets() { static const bool supported = [] { return ⟦ complex code to check OS features ⟧; }(); return supported && is_configuration_enabled("widgets"); } Function-local statics are initialized the first time execution reaches the variable. On subsequent executions, nothing happens. Another way is to use std::call_once . bool is_supported_cached; std::once_flag is_supported_once; bool are_widgets_supported() { std::call_once(is_supported_once, [] { is_supported_cached = ⟦ complex code to check OS features ⟧; });...

Amazon Corretto 27, a Feature Release (FR) version, is now available for download. Amazon Corretto is a no-cost, multi-platform, production-ready distribution of OpenJDK . You can download Corretto 27 for Linux, Windows, and macOS from our downloads page . Corretto 27 will be supported through April 2027. Noticeable features:  G1 as the Default Garbage Collector in All Environments - Makes G1 the default garbage collector across all environments, delivering more consistent performance and predictable pause times regardless of where your application runs (JEP 523) Post-Quantum Hybrid Key Exchange for TLS 1.3 - Adds protection against future quantum computing threats by combining classical and post-quantum key exchange algorithms in TLS 1.3 connections (JEP 527) Compact Object Headers by Default - Reduces the memory footprint of Java objects by enabling smaller object headers by default, improving memory efficiency and cache utilization (JEP 534) JFR In-Process Data...

Amazon SageMaker AI now supports serverless model customization for the NVIDIA Nemotron 3.5 Lightning model using supervised fine-tuning (SFT), Direct Preference Optimization (DPO), and reinforcement fine-tuning (RFT). This is one of the latest open-weight models from NVIDIA and employs a hybrid Mixture-of-Experts architecture with 3B active parameters and 30B parameters in total. In addition to deploying this model on SageMaker AI, you can now adapt it to your specific domains and workflows. Model customization enables you to tailor foundation models with your proprietary data so a smaller, right-sized model can match frontier-model quality on your tasks, reducing cost and latency. You can use labeled data with SFT to improve accuracy on domain-specific tasks, preference data with DPO to align outputs with your organization's tone, or reward signals with RFT to enhance performance on new tasks. With serverless customization, SageMaker AI handles all infrastructure provisioning a...