DeepReinforce AI’s Ornith-1.0-9B and Ornith-1.0-35B models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models are part of the Ornith 1.0 family of open-source, self-scaffolding agentic coding models released under the MIT license, achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo, and OpenClaw.

These models address different agentic coding challenges with specialized capabilities:

Ornith-1.0-9B is a lightweight 9B-parameter dense model post-trained on Qwen 3.5 9B, designed for efficient single-GPU deployment. With a 256K-token context window and 32 decoder layers, it is well-suited for individual developers and small teams running agentic coding workflows on a single GPU — including laptops with consumer-grade hardware. Its self-improving training framework uses reinforcement learning to jointly optimize scaffold generation and solution rollouts, enabling the model to discover better search trajectories and generate higher-quality code solutions.

Ornith-1.0-35B is a 35B-parameter Mixture-of-Experts (MoE) model post-trained on Qwen 3.5, delivering stronger agentic coding performance while remaining deployable on a single 24GB consumer GPU. Like its smaller sibling, it employs reinforcement learning to jointly optimize the scaffolds that drive coding tasks and the resulting solution rollouts, discovering superior code trajectories. Its MoE architecture activates only a subset of parameters per token, providing a strong balance of quality and efficiency for production agentic coding workloads.

With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.

To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.

​DeepReinforce AI’s Ornith-1.0-9B and Ornith-1.0-35B models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models are part of the Ornith 1.0 family of open-source, self-scaffolding agentic coding models released under the MIT license, achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo, and OpenClaw.
These models address different agentic coding challenges with specialized capabilities:
Ornith-1.0-9B is a lightweight 9B-parameter dense model post-trained on Qwen 3.5 9B, designed for efficient single-GPU deployment. With a 256K-token context window and 32 decoder layers, it is well-suited for individual developers and small teams running agentic coding workflows on a single GPU — including laptops with consumer-grade hardware. Its self-improving training framework uses reinforcement learning to jointly optimize scaffold generation and solution rollouts, enabling the model to discover better search trajectories and generate higher-quality code solutions.
Ornith-1.0-35B is a 35B-parameter Mixture-of-Experts (MoE) model post-trained on Qwen 3.5, delivering stronger agentic coding performance while remaining deployable on a single 24GB consumer GPU. Like its smaller sibling, it employs reinforcement learning to jointly optimize the scaffolds that drive coding tasks and the resulting solution rollouts, discovering superior code trajectories. Its MoE architecture activates only a subset of parameters per token, providing a strong balance of quality and efficiency for production agentic coding workloads.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.  

DeepSeek’s DeepSeek-V4-Pro, DeepSeek-V4-Flash, and DeepSeek-V4.1-Flash models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These three open-weight Mixture-of-Experts (MoE) language models, released under the MIT license, deliver frontier-class performance across reasoning, coding, and agentic workloads while activating only a fraction of their total parameters per token, enabling high efficiency at scale.

These models address different AI challenges with specialized capabilities:

DeepSeek-V4-Pro is designed for complex reasoning and high-quality text generation at flagship scale. This 1.6T-parameter MoE model activates 49B parameters per token and supports a 1M-token context window, making it well-suited for long-document analysis, multi-step reasoning, and demanding generation tasks that require maximum model capacity.

DeepSeek-V4-Flash delivers strong performance at significantly lower compute cost as an efficient 284B-parameter MoE model activating 13B parameters per token. It supports the same 1M-token context window as V4-Pro, making it ideal for latency-sensitive and cost-conscious production deployments across a wide range of language tasks.

DeepSeek-V4.1-Flash provides next-generation agentic efficiency with native multimodal support. Built on a novel Causal Encoder-Decoder (CED) architecture with a 552B-parameter backbone, it activates just 8B parameters during prefill and 16B during decode, supporting a 1M-token context window and 384K max output tokens. Its Compressed Sparse Attention 2 (CSA2) and FP4 KV caching reduce the global KV cache footprint to roughly 1/4 of V4-Flash, delivering substantially improved cost efficiency for agentic workloads, coding, and vision tasks.

With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.

To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.

​DeepSeek’s DeepSeek-V4-Pro, DeepSeek-V4-Flash, and DeepSeek-V4.1-Flash models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These three open-weight Mixture-of-Experts (MoE) language models, released under the MIT license, deliver frontier-class performance across reasoning, coding, and agentic workloads while activating only a fraction of their total parameters per token, enabling high efficiency at scale.
These models address different AI challenges with specialized capabilities:
DeepSeek-V4-Pro is designed for complex reasoning and high-quality text generation at flagship scale. This 1.6T-parameter MoE model activates 49B parameters per token and supports a 1M-token context window, making it well-suited for long-document analysis, multi-step reasoning, and demanding generation tasks that require maximum model capacity.
DeepSeek-V4-Flash delivers strong performance at significantly lower compute cost as an efficient 284B-parameter MoE model activating 13B parameters per token. It supports the same 1M-token context window as V4-Pro, making it ideal for latency-sensitive and cost-conscious production deployments across a wide range of language tasks.
DeepSeek-V4.1-Flash provides next-generation agentic efficiency with native multimodal support. Built on a novel Causal Encoder-Decoder (CED) architecture with a 552B-parameter backbone, it activates just 8B parameters during prefill and 16B during decode, supporting a 1M-token context window and 384K max output tokens. Its Compressed Sparse Attention 2 (CSA2) and FP4 KV caching reduce the global KV cache footprint to roughly 1/4 of V4-Flash, delivering substantially improved cost efficiency for agentic workloads, coding, and vision tasks.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.  

Amazon Elastic Kubernetes Service (Amazon EKS) now offers a larger 24 GB etcd database, up from 16 GB, for 4XL and 8XL Provisioned Control Plane clusters. etcd is the key-value store in the Kubernetes control plane that holds your cluster state, so its size limits how many Kubernetes objects your cluster can store. With 50% more capacity, you can run bigger clusters with more pods, jobs, events, and custom resources.

The larger database gives more headroom to workloads that create and update many Kubernetes objects, such as large-scale AI/ML training and inference, batch processing, and CI/CD pipelines. When the etcd database fills up, you can’t add or change objects in your cluster, so the extra headroom helps keep these workloads running as they scale.

To get the larger database, choose the 4XL or 8XL tier of Provisioned Control Plane on a cluster running EKS version 1.34 or later. Existing clusters on the 4XL and 8XL tiers running these versions get the larger database automatically, with no action required. The 24 GB etcd database is available in all commercial AWS Regions where Provisioned Control Plane is available. To learn more, see the Amazon EKS Provisioned Control Plane user guide. 

​Amazon Elastic Kubernetes Service (Amazon EKS) now offers a larger 24 GB etcd database, up from 16 GB, for 4XL and 8XL Provisioned Control Plane clusters. etcd is the key-value store in the Kubernetes control plane that holds your cluster state, so its size limits how many Kubernetes objects your cluster can store. With 50% more capacity, you can run bigger clusters with more pods, jobs, events, and custom resources.
The larger database gives more headroom to workloads that create and update many Kubernetes objects, such as large-scale AI/ML training and inference, batch processing, and CI/CD pipelines. When the etcd database fills up, you can’t add or change objects in your cluster, so the extra headroom helps keep these workloads running as they scale.
To get the larger database, choose the 4XL or 8XL tier of Provisioned Control Plane on a cluster running EKS version 1.34 or later. Existing clusters on the 4XL and 8XL tiers running these versions get the larger database automatically, with no action required. The 24 GB etcd database is available in all commercial AWS Regions where Provisioned Control Plane is available. To learn more, see the Amazon EKS Provisioned Control Plane user guide.   

Amazon Cognito now supports session management for user pools, giving you visibility into and control over the sessions your users establish when they sign in to your applications. Single sign-on (SSO) sessions are created when a user signs in through managed login and let them access other app clients without re-authenticating. Token sessions are created when a user signs in to an app client and receives tokens. Session management lets you list a user’s active SSO and token sessions across all devices and app clients and revoke any of them on demand. You can also now configure how long each app client reuses an SSO session before requiring re-authentication, instead of the previous fixed one-hour duration.

Each session includes details such as session type, app client ID, device type, last activity time and location, and initial sign-in time, so you can build your own end-user or admin session experiences. You can revoke an individual session, optionally with its linked sessions, or use global sign-out to end all sessions associated with one user. You can also set a maximum number of active token sessions per user.

Session management is available for user pools in the Essentials or Plus feature plans in all AWS Regions where Amazon Cognito is available. You can manage sessions using the AWS Management Console, API, AWS Command Line Interface, and AWS Software Development Kits, and configure settings with AWS CloudFormation. Visit the Developer Guide and API Reference for details. 

​Amazon Cognito now supports session management for user pools, giving you visibility into and control over the sessions your users establish when they sign in to your applications. Single sign-on (SSO) sessions are created when a user signs in through managed login and let them access other app clients without re-authenticating. Token sessions are created when a user signs in to an app client and receives tokens. Session management lets you list a user’s active SSO and token sessions across all devices and app clients and revoke any of them on demand. You can also now configure how long each app client reuses an SSO session before requiring re-authentication, instead of the previous fixed one-hour duration.
Each session includes details such as session type, app client ID, device type, last activity time and location, and initial sign-in time, so you can build your own end-user or admin session experiences. You can revoke an individual session, optionally with its linked sessions, or use global sign-out to end all sessions associated with one user. You can also set a maximum number of active token sessions per user.
Session management is available for user pools in the Essentials or Plus feature plans in all AWS Regions where Amazon Cognito is available. You can manage sessions using the AWS Management Console, API, AWS Command Line Interface, and AWS Software Development Kits, and configure settings with AWS CloudFormation. Visit the Developer Guide and API Reference for details.   

AWS Cloud WAN now supports VPC Flow Logs, giving you central visibility into IP traffic across your global network. Flow Logs capture detailed information for each flow, so you can understand how traffic moves across your core network without leaving your AWS Cloud WAN account.

Until now, network administrators had to stitch together VPC Flow Logs from many VPCs, accounts, and Regions to see traffic across their global network. They often depended on other teams for access to those logs. Cloud WAN Flow Logs give core network owners one consolidated view of traffic between VPCs, Regions, and on-premises networks connected over AWS Direct Connect, AWS Site-to-Site VPN, or SD-WAN. You can use this data to troubleshoot connectivity and performance problems, plan capacity from historical bandwidth trends, find unusual traffic patterns, and keep records for security and compliance. Flow log records include IP addresses, ports, protocol, traffic volume, and packet-loss counters, along with Cloud WAN metadata such as segment, Region, service insertion hops and attachment details. You can turn on Flow Logs for an entire core network, a single core network edge, or individual attachments. You can publish them to Amazon CloudWatch Logs, Amazon Simple Storage Service (Amazon S3), or Amazon Data Firehose. Cloud WAN adds no charge for Flow Logs. Standard charges apply for each destination.

Cloud WAN Flow Logs are available in all AWS Regions where AWS Cloud WAN is available. You can get started with the AWS Management Console, AWS CLI, or API. To learn more, see AWS Cloud WAN Flow Logs user guide.

​AWS Cloud WAN now supports VPC Flow Logs, giving you central visibility into IP traffic across your global network. Flow Logs capture detailed information for each flow, so you can understand how traffic moves across your core network without leaving your AWS Cloud WAN account.
Until now, network administrators had to stitch together VPC Flow Logs from many VPCs, accounts, and Regions to see traffic across their global network. They often depended on other teams for access to those logs. Cloud WAN Flow Logs give core network owners one consolidated view of traffic between VPCs, Regions, and on-premises networks connected over AWS Direct Connect, AWS Site-to-Site VPN, or SD-WAN. You can use this data to troubleshoot connectivity and performance problems, plan capacity from historical bandwidth trends, find unusual traffic patterns, and keep records for security and compliance. Flow log records include IP addresses, ports, protocol, traffic volume, and packet-loss counters, along with Cloud WAN metadata such as segment, Region, service insertion hops and attachment details. You can turn on Flow Logs for an entire core network, a single core network edge, or individual attachments. You can publish them to Amazon CloudWatch Logs, Amazon Simple Storage Service (Amazon S3), or Amazon Data Firehose. Cloud WAN adds no charge for Flow Logs. Standard charges apply for each destination.
Cloud WAN Flow Logs are available in all AWS Regions where AWS Cloud WAN is available. You can get started with the AWS Management Console, AWS CLI, or API. To learn more, see AWS Cloud WAN Flow Logs user guide.  

AWS Emulator is now available for building and testing AWS applications locally.

Starting today, you can use AWS Emulator to build and test AWS applications on your computer or in your continuous integration (CI) environment. Your existing AWS SDK and CLI calls go to AWS Emulator and come back in milliseconds, so you and your coding agents can find and fix errors locally, with no deployments or AWS account required.

You create and manage resources with the same AWS SDK and AWS CLI calls you use with AWS, signed with credentials the emulator provides. For the operations it supports, AWS Emulator returns the same result as AWS for valid calls and the same errors for invalid ones. Operations it doesn’t yet support return 501 Not Implemented. AWS Emulator keeps the state of your resources in memory until you stop it. Each copy you start gets its own port, keys and in-memory state, so agents, test suites, and CI jobs never see each other’s resources. For example, a coding agent can create an Amazon DynamoDB table, test against it offline, and fix its code before it deploys.

Services in AWS Emulator are generated from the API models AWS publishes, the same models behind the AWS SDKs. When AWS adds a service or API, it shows up in AWS Emulator, and its behavior is filled in and verified release by release. To see how its responses compare to AWS, review the AWS Emulator fidelity report.

AWS Emulator doesn’t depend on an AWS Region. AWS Emulator has no additional service fee. You pay only for AWS resources when you deploy to AWS. Its source code is published under the CC0 1.0 Universal public domain dedication. To get started, see the AWS Emulator documentation, or view the source in the AWS Emulator GitHub repository.

​AWS Emulator is now available for building and testing AWS applications locally.
Starting today, you can use AWS Emulator to build and test AWS applications on your computer or in your continuous integration (CI) environment. Your existing AWS SDK and CLI calls go to AWS Emulator and come back in milliseconds, so you and your coding agents can find and fix errors locally, with no deployments or AWS account required.
You create and manage resources with the same AWS SDK and AWS CLI calls you use with AWS, signed with credentials the emulator provides. For the operations it supports, AWS Emulator returns the same result as AWS for valid calls and the same errors for invalid ones. Operations it doesn’t yet support return 501 Not Implemented. AWS Emulator keeps the state of your resources in memory until you stop it. Each copy you start gets its own port, keys and in-memory state, so agents, test suites, and CI jobs never see each other’s resources. For example, a coding agent can create an Amazon DynamoDB table, test against it offline, and fix its code before it deploys.
Services in AWS Emulator are generated from the API models AWS publishes, the same models behind the AWS SDKs. When AWS adds a service or API, it shows up in AWS Emulator, and its behavior is filled in and verified release by release. To see how its responses compare to AWS, review the AWS Emulator fidelity report.
AWS Emulator doesn’t depend on an AWS Region. AWS Emulator has no additional service fee. You pay only for AWS resources when you deploy to AWS. Its source code is published under the CC0 1.0 Universal public domain dedication. To get started, see the AWS Emulator documentation, or view the source in the AWS Emulator GitHub repository.  

• Categoría: Noticias de Microsoft

noviembre 11, 2026

Enhorabuena a los ganadores y finalistas de los Microsoft Partner of the Year Awards 2026

4 minuto

Por: NicoleDezen, directora de Socios y CVP, Ventas de Socios de Canal Global.

Pasar de la experimentación con IA a la transformación requiere ingenio, profunda experiencia y una comprensión clara de los objetivos empresariales de los clientes. Cada día, los socios de Microsoft unen estas cualidades para convertir ideas audaces en resultados significativos para clientes y comunidades de todo el mundo.

Los Microsoft Partner of the Year Awards celebran ese trabajo, para reconocer a socios que ofrecen soluciones innovadoras y servicios de alto impacto en áreas de solución, industrias y geografías.

Me enorgullece felicitar a nuestros ganadores y finalistas de 2026. Sus logros demuestran cómo es la Transformación Frontier en la práctica: conectar las capacidades de Microsoft Cloud e IA con el conocimiento del sector y relaciones de confianza con los clientes para ofrecer un valor empresarial medible.

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Celebramos la excelencia en nuestro ecosistema global de socios

Los premios de este año reconocen logros excepcionales en siete categorías globales:

• Plataformas de Nube e IA
• Seguridad
• Soluciones de negocio con IA
• Industria
• Innovación en socios
• Transformación empresarial
• Impacto social

Estas categorías reflejan la variedad de conocimientos en la que los clientes confían a lo largo de sus procesos de transformación. Los logros reconocidos abarcan aplicaciones y agentes de IA, migración y modernización, plataformas de datos, seguridad, soluciones empresariales con agentes, transformación industrial, Microsoft Marketplace e impacto social.

Los premios regionales celebran la excelencia en América, EMEA y Asia. Reconocen las contribuciones de integradores de sistemas, empresas de desarrollo de software, socios de canal y socios de dispositivos que atienden a clientes en estas regiones.

Los premios Country Partner of the Year reconocen además a socios destacados en países y mercados de todo el mundo. Juntos, los premios globales, regionales y nacionales muestran la amplitud de nuestro ecosistema y las múltiples formas en que los socios convierten la tecnología de Microsoft en impacto para el cliente.

En particular, queremos felicitar a los socios ganadores del premio Country Partner of the Year en nuestra región:

• Argentina
  Pi Consulting
• Brasil
  Telefônica Cloud e Tecnologia do Brasil
• Chile
  TC1 Labs
• Colombia
  TD SYNNEX
• Costa Rica
  Noventiq Costa Rica
• Ecuador
  CONTROLES EMPRESARIALES COEMP S.A.S
• El Salvador
  Grupo Sega
• Guatemala
  Intcomex
• Honduras
  Grupo Sega
• Jamaica
  Inova Solutions Jamaica (ISJ) Limited
• México
  GCG
• Nicaragua
  GBM Nicaragua, S.A.
• Panamá
  GBM de Panamá, S.A.
• Perú
  Business IT
• Puerto Rico
  SoftwareONE
• República Dominicana
  GBM Dominicana, S.A.
• Uruguay
  Intcomex

Convertir la innovación en un valor duradero para el cliente

En distintos sectores y geografías, los socios impulsan a los clientes superar escenarios aislados de IA y reimaginar cómo se realiza el trabajo. Conectan tecnología, servicios y experiencia del sector para integrar la IA en los flujos de trabajo cotidianos y en procesos empresariales críticos.

Ese progreso comienza con entender las prioridades de cada cliente. Se requieren relaciones de confianza, innovación y la capacidad de conectar capacidades emergentes con necesidades empresariales prácticas. Los ganadores y finalistas de este año ejemplifican esas cualidades.

Su trabajo abarca los cimientos y la ejecución de la transformación: asegurar entornos de IA, modernizar aplicaciones y conjuntos de datos, mejorar la productividad de la plantilla y rediseñar procesos empresariales con agentes. También aportan la estrategia, las habilidades y la gestión del cambio que los clientes necesitan para sostener el progreso a medida que crece la adopción.

Para los socios de todo nuestro ecosistema, estos logros ofrecen inspiración y ejemplos prácticos de formas de traducir la innovación en un valor duradero para el cliente.

Celebremos juntos en Microsoft Ignite

Esperamos celebrar a nuestros ganadores y finalistas de 2026 en Microsoft Ignite, del 17 al 20 de noviembre de 2026.

Únanse a nosotros para la ponencia principal de Ignite Frontier Transformation Partner el miércoles 18 de noviembre de 9:00 a 10:30 AM PT, donde destacaremos a los ganadores de los premios Partner of the Year de este año y el impacto en el cliente detrás de sus logros.

En esta sesión me acompañarán Judson Althoff, CEO de Microsoft Commercial Business; Mala Anand, vicepresidenta ejecutiva y directora de atención al cliente; Ralph Haupter, vicepresidente ejecutivo y CRO, Pequeñas y Medianas Empresas y Canal (PIME&C); y Stephen Boyle, CVP, Ventas de Socios Empresariales. Juntos, exploraremos cómo las organizaciones rediseñan el trabajo, escalan la adopción de la IA y aceleran los resultados para los clientes, y qué significan estos cambios para las capacidades y servicios que nuestros socios desarrollan y ofrecen a clientes de todo el mundo.

La ponencia principal forma parte de una experiencia más amplia de Ignite que conecta la estrategia de producto con la implementación mediante el acceso a líderes, ingenieros y equipos de producto de Microsoft, así como a sesiones técnicas, demostraciones, laboratorios y experiencias de habilidades. Regístrense para asistir de manera presencial o únanse a la experiencia digital.

También pueden consultar la lista completa de ganadores y finalistas y conocer más sobre el trabajo que ha respaldado el reconocimiento de este año.

A cada ganador, finalista y nominado: gracias por su dedicación a nuestros clientes y por las ideas, experiencia y cuidado que aportan a su trabajo. Enhorabuena por sus logros y el impacto que tienen en todo el mundo.

Etiquetas:

• Anuncios
• Eventos
• Microsoft Ignite 2026
• Socios de Negocio

The post Enhorabuena a los ganadores y finalistas de los Microsoft Partner of the Year Awards 2026 appeared first on Source LATAM.

​The post Enhorabuena a los ganadores y finalistas de los Microsoft Partner of the Year Awards 2026 appeared first on Source LATAM.  

AWS Lambda MicroVMs now supports the x86 CPU architecture, so you can run your existing x86 applications, tools, and software dependencies without recompiling or porting them. When you create a MicroVM image, you can now choose between x86 and Arm (AWS Graviton) to give each user or job an isolated MicroVM on the architecture your workload uses.

Lambda MicroVMs is a serverless compute primitive that provides VM-level isolation, near-instant launch and resume, and state preservation for running user or AI-generated code, in applications such as AI coding assistants, CI/CD runners, interactive development environments, and vulnerability scanners. Today’s launch enables you to run x86-based workloads on Lambda MicroVMs as-is. For workloads that run on Arm, you can continue using AWS Graviton processors which is the default CPU architecture option.

To start building x86 workloads on Lambda MicroVMs, choose the x86 architecture when you create a MicroVM image from your Dockerfile, then launch MicroVMs from that image. You can use MicroVMs through the AWS Lambda console, AWS CloudFormation, AWS Cloud Development Kit, or with your preferred agentic development tools using the Agent Toolkit for AWS.

Support for the x86 CPU architecture is available in all AWS Regions where Lambda MicroVMs is available, except Asia Pacific (Hong Kong), Asia Pacific (Malaysia), Asia Pacific (Thailand), and Europe (Spain). To learn more, visit the AWS Lambda MicroVMs developer guide. For information on pricing, see Lambda MicroVMs pricing.

​AWS Lambda MicroVMs now supports the x86 CPU architecture, so you can run your existing x86 applications, tools, and software dependencies without recompiling or porting them. When you create a MicroVM image, you can now choose between x86 and Arm (AWS Graviton) to give each user or job an isolated MicroVM on the architecture your workload uses.
Lambda MicroVMs is a serverless compute primitive that provides VM-level isolation, near-instant launch and resume, and state preservation for running user or AI-generated code, in applications such as AI coding assistants, CI/CD runners, interactive development environments, and vulnerability scanners. Today’s launch enables you to run x86-based workloads on Lambda MicroVMs as-is. For workloads that run on Arm, you can continue using AWS Graviton processors which is the default CPU architecture option.
To start building x86 workloads on Lambda MicroVMs, choose the x86 architecture when you create a MicroVM image from your Dockerfile, then launch MicroVMs from that image. You can use MicroVMs through the AWS Lambda console, AWS CloudFormation, AWS Cloud Development Kit, or with your preferred agentic development tools using the Agent Toolkit for AWS.
Support for the x86 CPU architecture is available in all AWS Regions where Lambda MicroVMs is available, except Asia Pacific (Hong Kong), Asia Pacific (Malaysia), Asia Pacific (Thailand), and Europe (Spain). To learn more, visit the AWS Lambda MicroVMs developer guide. For information on pricing, see Lambda MicroVMs pricing.  

Amazon EKS Auto Mode now supports security groups for Pods, letting you apply dedicated Amazon EC2 security group rules to individual Pods. Different workloads on the same node can carry different security groups, giving your Pods network-level segmentation without creating separate node pools for each application.

Security groups for Pods extends your existing AWS network controls to the Pod level, so your Pods can reach resources such as Amazon RDS and Amazon ElastiCache databases that authorize connections by security group under the same rules the rest of your VPC uses. You define which Pods receive which security groups with a SecurityGroupPolicy resource, and Pods not matched by a policy keep using their node’s security groups.

Security groups for Pods is included as part of EKS Auto Mode at no additional charge and is available on clusters running Kubernetes version 1.36 or later, in all AWS Regions where EKS Auto Mode is supported. To learn more, see Security groups for Pods in the Amazon EKS User Guide and the Amazon EKS Auto Mode product page.

​Amazon EKS Auto Mode now supports security groups for Pods, letting you apply dedicated Amazon EC2 security group rules to individual Pods. Different workloads on the same node can carry different security groups, giving your Pods network-level segmentation without creating separate node pools for each application.
Security groups for Pods extends your existing AWS network controls to the Pod level, so your Pods can reach resources such as Amazon RDS and Amazon ElastiCache databases that authorize connections by security group under the same rules the rest of your VPC uses. You define which Pods receive which security groups with a SecurityGroupPolicy resource, and Pods not matched by a policy keep using their node’s security groups.
Security groups for Pods is included as part of EKS Auto Mode at no additional charge and is available on clusters running Kubernetes version 1.36 or later, in all AWS Regions where EKS Auto Mode is supported. To learn more, see Security groups for Pods in the Amazon EKS User Guide and the Amazon EKS Auto Mode product page.  

The AWS Quick desktop app for macOS and Windows and the Quick mobile app for iOS and Android are now available in the Asia Pacific (Singapore) Region. This launch builds on the recent availability of Quick in Singapore.

The desktop app works with your local files and connects to your calendar, email, and business apps. The mobile app lets you chat with Quick, review your activity feed, and open dashboards. Your conversations, context, and agents stay in sync across web, desktop, and mobile, so you can start an agent on your desktop, check on it from your phone, and let it keep running after you close your computer. 

To get started, download the desktop app from the AWS Quick download page or the mobile app from the Apple App Store or Google Play. To learn more, see AWS Quick on mobile in the AWS Quick User Guide and the AWS Quick product page.

​The AWS Quick desktop app for macOS and Windows and the Quick mobile app for iOS and Android are now available in the Asia Pacific (Singapore) Region. This launch builds on the recent availability of Quick in Singapore.
The desktop app works with your local files and connects to your calendar, email, and business apps. The mobile app lets you chat with Quick, review your activity feed, and open dashboards. Your conversations, context, and agents stay in sync across web, desktop, and mobile, so you can start an agent on your desktop, check on it from your phone, and let it keep running after you close your computer. 
To get started, download the desktop app from the AWS Quick download page or the mobile app from the Apple App Store or Google Play. To learn more, see AWS Quick on mobile in the AWS Quick User Guide and the AWS Quick product page.