News For Dev-ops

We're excited to announce in-region support for Anthropic's Claude Opus 5.5 and Claude Sonnet 5 on Amazon Bedrock in the UK (London) Region. Customers who need to process data within the United Kingdom, including those in financial services, healthcare, and the public sector, can now use these models at scale while keeping inference in-country. In the UK, Amazon Bedrock supports Claude Opus 5.5 and Claude Sonnet 5 with in-region inference on the bedrock-runtime endpoint in the London (eu-west-2) Region. Amazon Bedrock processes inference requests and data within the Region you call, and the processing never leaves that Region. For the most current information about model availability in each Region, see Regional availability by models in the Amazon Bedrock User Guide. To get started with Claude models on Amazon Bedrock, visit the Amazon Bedrock console. Post Updated on September 30, 2026 at 06:00PM

I have noted in the past that x86-32 and x86-64 versions of Windows are careful to start each function with a patch point. But what about AArch64 (known in Windows as arm64)? Windows also inserts patch points for functions on AArch64, but they are much simpler due to the fixed-length instruction set. You don't have to worry about patching an instruction when the instruction pointer happens to be in the middle of the byte sequence, because the instruction pointer is never in the middle of the byte sequence. The instruction pointer is always on a multiple of 4. Therefore, there is no special restriction on the first instruction of a function. All instructions meet the requirements of being atomically updatable without risk of the instruction pointer being in the middle of the instruction. Before each function is a patch space of 12 bytes, which is exactly enough for a three-instruction trampoline : ; overwrite the patch space with these three instructions adrp xip0, ...

Amazon WorkSpaces Core Managed Instances now supports Graphics G7 instances, powered by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs and Intel Xeon 6 processors. G7 instances deliver up to 2.1X better performance for graphics-intensive workloads compared to previous generation G6 instances. With Graphics G7, customers can run demanding professional applications such as CAD/CAM, 3D rendering, scientific visualization, video editing, and AI-assisted design workflows at higher fidelity and frame rates. G7 instances feature 32 GB of GDDR7 GPU memory per GPU and 2.67X faster memory bandwidth, enabling larger, more complex 3D scenes and models. Six instance sizes are available with 1 to 8 GPUs, vCPUs ranging from 8 to 192, and system memory from 32 GB to 768 GB, with support for Linux and Windows, including bring-your-own-license. Graphics G7 instances for WorkSpaces Core Managed Instances are available in US East (N. Virginia), US East (Ohio), US West (Oregon), and Europe (Spain). Ad...

AWS Parallel Computing Service (AWS PCS) now supports scaling logs, which record how AWS PCS scales the compute node groups in your cluster. Each log entry records one state transition for one compute node—for example, an instance launch, a node registration, a scale-down, or a launch failure with its reason. Using scaling logs, you can more easily troubleshoot scaling issues. For example, you can determine why a compute node group did not reach its target size, which launches failed for capacity reasons, and when a specific node started or stopped. Scaling log delivery is opt-in, and you can configure AWS PCS to emit scaling logs to Amazon CloudWatch Logs, Amazon S3, and Amazon Data Firehose. AWS PCS is a managed service that simplifies running and scaling HPC workloads on AWS using Slurm. You can build complete, elastic environments that integrate compute, storage, networking, and visualization tools, while the service handles cluster operations with managed updates and built-in obs...

We're excited to announce the availability of Anthropic's Claude Opus 5, Claude Sonnet 5 , and Claude Haiku 4.5 on Amazon Bedrock in India, Claude Opus 5 and Claude Sonnet 5 in South Korea, as well as Claude Sonnet 5 in Singapore. Customers who need to process data within a specific geography, including those in financial services, healthcare, and the public sector, can now use these models at scale while keeping inference in-country. In India, the regional endpoint is served through geographic cross-Region inference across the Mumbai and Hyderabad Regions. Customers in India can access these models while processing data in India Regions, in addition to the already supported global cross-Region inference. In South Korea, Amazon Bedrock supports Claude Opus 5 and Claude Sonnet 5 models with in-region inference on the bedrock-runtime endpoint in the Seoul (ap-northeast-2) Region. In Singapore (ap-southeast-1), Amazon Bedrock supports Claude Sonnet 5 with in-region inference o...

Developed jointly by AWS and OpenAI, Bedrock Managed Agents (BMA) is built on a customized version of OpenAI's Agents API engineered to be AWS-native and integrated with AWS resources. You can now build agents optimized for OpenAI models that run entirely inside AWS with the identities, permissions, and governance controls you already use.  BMA manages how the model preserves state, selects and uses tools, executes code, and coordinates work across multiple steps and decisions. Durable sessions retain messages, tool calls, and intermediate results so you can return later, provide new information, and continue from the progress already made. You can add reusable skills for specialized procedures and connect agents to tools, including via Model Context Protocol (MCP) servers. Each agent operates with its own IAM role, supports human approval before consequential actions, and records supported API activity with AWS CloudTrail. During preview, there is no additional charge ...

You can now use AWS Transform agentic migration assessments to evaluate the migration of on-premises Apache Kafka clusters to Amazon Managed Streaming for Apache Kafka (Amazon MSK) . AWS Transform migration assessments use agentic AI to analyze your workloads, recommend the best-fit AWS services, and generate a total cost of ownership (TCO) business case with pricing options and actionable next steps - all in minutes, through natural-language chat or with AWS Transform MCP server . For Apache Kafka workloads, the assessment agent delivers compatibility checks, right-sized Amazon MSK Express broker recommendations, and projected costs - helping you quickly build a migration business case and accelerate your migration decisions. Evaluating a Kafka-to-MSK migration often involves multiple steps - mapping topologies, validating compatibility, right-sizing brokers, and projecting costs - that can take weeks when done manually. With this launch, you can start your migration assessment by...

Amazon ElastiCache Serverless for Valkey now supports public endpoints, letting you connect to your cache directly from a laptop, a serverless function, or any application running outside AWS, without setting up a VPN, a bastion host, or an SSH tunnel. With a public endpoint, ElastiCache Serverless gives you a fully managed cache reachable over the internet, with no VPC to configure and no infrastructure to provision, and you can create one in under a minute. Use it to rapidly prototype, connect AI coding tools and agents straight to your cache, or add caching to workloads that can't reach a VPC. Every connection uses IAM authentication over TLS 1.3, so there's no password to store or rotate. Connect using Valkey GLIDE 2.2 or later, which has built-in IAM support, or another Valkey client paired with the Developer Toolkit for ElastiCache, an open-source library that generates and refreshes IAM authentication tokens for you. Public endpoints for ElastiCache Serverless are avai...

For developers building MongoDB-compatible applications, retryable writes improve application resilience during transient errors such as network interruptions or primary failovers. Amazon DocumentDB (with MongoDB compatibility) now supports retryable writes starting with engine version 8.0.2, eliminating the need for additional code changes when enabling this capability. With retryable writes, the MongoDB driver automatically retries certain write operations one time if a transient error occurs. Most current MongoDB-compatible drivers enable retryable writes by default. With this release, applications connecting to Amazon DocumentDB 8.0.2 no longer need to set retryWrites=false in the connection string to avoid errors. Retryable writes are supported for all write commands except updateMany and deleteMany, as well as transaction commit and abort commands. On engine versions earlier than 8.0.2, retryable writes remain unsupported and must be disabled. Retryable writes are available on ...

AWS Backup logically air-gapped vault now supports Amazon FSx for NetApp ONTAP. Logically air-gapped vaults are a type of AWS Backup vault that allows secure sharing of backups across accounts and AWS Organizations, supporting direct restore to reduce recovery time from a data loss event. You can now protect your Amazon FSx for NetApp ONTAP volumes in logically air-gapped vaults. A logically air-gapped vault stores immutable backups that are locked by default, and isolated with encryption using AWS owned keys or customer-managed keys. You can store your FSx for NetApp ONTAP backups in a logically air-gapped vault in the same account or across other accounts and Regions. This helps reduce the risk of downtime, ensure business continuity, and meet compliance and disaster recovery requirements. You can get started using the AWS Backup console, AWS Command Line Interface (CLI), or AWS SDKs. Target FSx for NetApp ONTAP backups to a logically air-gapped vault by specifying it as the primar...

AI enables natural and powerful ways for people to interact with software through language, voice, and vision. Applications with agents go further by helping users accomplish work. Agents can gather context, use tools, propose plans, update shared artifacts, and carry multi-step tasks forward in collaboration with the user. These capabilities create new possibilities for application design and new challenges. Agent output may arrive incrementally. Users need visibility into what an agent is doing and control over consequential actions. The user, agent, and application may all contribute to shared state. The resulting experience needs to remain understandable and responsive as the work evolves. The new experimental Blazor AI components provide building blocks for these experiences, which we call Agentic UI . They turn agent interactions into content blocks and observable state that Blazor components can render and control. Developers can combine natural interaction with the visual str...

We're pleased to share that SQLAlchemy 2.1.0 is now generally available, with built-in support for mssql-python , Microsoft's Python driver for SQL Server. You can now use it with SQLAlchemy's ORM and Core APIs through the first-party mssql+mssqlpython dialect. If SQLAlchemy is already part of how you build, we want this to feel like a natural next step - not another thing to learn. The goal is simple: help you connect to SQL Server and Azure SQL with less setup, while keeping the tools and patterns you know. This integration is now part of the production release, and SQLAlchemy 2.1 is the version installed by default when you run pip install sqlalchemy . Key benefits For SQLAlchemy users who connect to SQL Server or Azure SQL, the built-in mssql-python dialect provides: Simpler setup and deployment: Install the driver as a Python package without separately installing and configuring an external ODBC driver. A familiar development experience: Continue using SQ...

We are thrilled to announce the public preview of local time zone support in Azure SQL Database . Starting today, you can configure the time zone directly at the database or session level — all from T-SQL, without changing any infrastructure settings. This feature addresses one of the most consistently requested capabilities from the Azure SQL community, and it brings Azure SQL Database into alignment with ANSI SQL standard and competitors such as PostgreSQL, MySQL, and Oracle.   Background SQL Server on Windows and Linux inherits the time zone from the host operating system, giving customers full control over local time behavior at the OS or container level. Azure SQL Managed Instance defaults to UTC but allows a single time zone to be set at creation time , and that setting cannot be changed afterward. Azure SQL Database, on the other hand, has historically been locked to UTC — customers had no way to configure the time zone of their database or session. This gap forced de...

AWS End User Messaging and Amazon SES now publish AI agent skills for the AWS MCP Server, enabling developers to build and send messages by asking their AI coding agent in plain language. Previously, developers had to navigate multiple documentation pages and management console screens to complete messaging tasks. Now, each skill provides your agent with step-by-step, validated guidance for tasks like verifying sending identities, building branded Rich Communication Services (RCS) agents, and sending production emails. These skills integrate with popular AI coding agents including Claude Code, Codex, Cursor, and Kiro. For example, you can ask Amazon SES to verify a sending identity and send your first production email. You can ask AWS End User Messaging to build a branded RCS agent and send yourself a rich message with cards and buttons. The skills eliminate the need to manually search documentation or switch between console screens, allowing you to complete messaging workflows direct...

Amazon ElastiCache now supports resource tagging and tag-based access control (TBAC) for Global Datastore. Previously, ElastiCache supported tagging on all resources except Global Datastore, which prevented customers from applying a single, consistent permission and cost-allocation model across their ElastiCache fleet. With this launch, you can use AddTagsToResource, RemoveTagsFromResource, and ListTagsForResource on a Global Datastore, and reference those tags as conditions in IAM policies and Service Control Policies (SCPs) to grant permissions based on tag attributes rather than enumerating individual resources. Tag changes on a Global Datastore propagate automatically to every Region it spans, so access-control and cost-allocation policies stay consistent without per-Region operations. There is no additional cost for this feature, and no change to how you create or manage Global Datastores. This feature is available in all AWS Regions where ElastiCache Global Datastore is availab...

Some time ago, I was looking at a pull request, and saw that the code was manually calculating the "number of minutes since the last change" by doing annoying math. static const DWORD c_TicksPerSecond = 10000000; static const DWORD c_SecondsPerMinute = 60; uint64_t FileTimeToULongLong(FILETIME time) { ULARGE_INTEGER value; value.LowPart = time.dwLowDateTime; value.HighPart = time.dwHighDateTime; return value.QuadPart; } DWORD GetMinutesSinceLastChange() { FILETIME now; GetSystemTimeAsFileTime(&now); uint64_t now64 = FileTimeToULongLong(now); uint64_t last64 = FileTimeToULongLong(m_lastChange); if (now64 < last64) { return 0; } uint64_t diff = last64 - now64; return static_cast<DWORD>(diff / (static_cast<uint64_t>(c_dwSecondsPerMinute) * static_cast<uint64_t>(c_TicksPerSecond))); } Okay, first of all, we have helpers in the Windows Implementation Library (wil) to save you ...

Today, AWS announces that Amazon EMR on EKS now supports running workloads on IPv6 Amazon Elastic Kubernetes Service (Amazon EKS) clusters. Amazon EMR on EKS enables data platform and analytics teams to run open-source big data frameworks such as Apache Spark and Apache Flink on Amazon EKS. With IPv6 support, teams operating at scale can now run these workloads on IPv6 Amazon EKS clusters, giving them access to the vastly larger address space of IPv6 as their analytics workloads grow. You can now scale large Spark and Flink workloads using the expanded IPv6 address space, which removes the need for IPv4 conservation workarounds such as secondary CIDR ranges or prefix delegation. High-executor jobs, such as 500-executor Spark jobs, can run concurrently without planning around address limits. To submit workloads, use StartJobRun, Spark Connect Interactive Endpoints, and Amazon SageMaker Unified Studio with no additional configuration, while the Flink, Livy, and Spark Operators are also ...

See how GitHub Copilot added completions to its unified inline suggestions model, refined ghost text and editor behavior, and improved user satisfaction. Read the full article Post Updated on September 23, 2026 at 01:00AM Thanks for reading from devamazonaws.blogspot.com

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...