The Q2 enterprise WLAN market grew 13.2% YoY to reach $2.6 billion, says  IDC. A significant driver of growth was the adoption of Wi-Fi 6E and Wi-Fi 7 which enable …

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Apple’s entry into the folding phone market is the long awaited catalyst which smartphone makers hope will spark consumer acceptance of the format. At the moment folding phones only make …

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AWS is showing how to implement Amazon EKS (Elastic Kubernetes Service) Hybrid Nodes using the Raspberry Pi 5. In the cloud, an Amazon Virtual Private Cloud (Amazon VPC)  hosts the …

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AWS Step Functions adds supports for IPv6. You can now send IPV6 traffic to AWS Step Functions via new dual-stack IPv4 and IPv6 endpoints. AWS Step Functions is a visual workflow service that enables customers to build distributed applications, automate IT and business processes, and build data and machine learning pipelines using AWS services. This enhancement addresses the growing need for IP addresses as the internet continues to expand, providing a larger address space than the traditional IPv4 format.

With IPv6 support, organizations modernizing their applications can now build serverless workflows without being constrained by limited IPv4 address space. The new dual-stack endpoints support both IPv4 and IPv6 protocols while maintaining backwards compatibility with existing IPv4 endpoints. Step Functions also supports IPv6 connectivity through PrivateLink interface Virtual Private Cloud (VPC) endpoints, enabling you to access the service privately without traversing the public internet. This enables organizations operating in IPv6 environments to natively integrate with Step Functions without requiring complex translation mechanisms between IPv6 and IPv4.

IPv6 support for AWS Step Functions is now generally available in US East (Ohio), US East (N. Virginia), US West (Oregon), US West (N. California) as well as AWS GovCloud (US-East), and AWS GovCloud (US-West) Regions, where AWS Step Functions is available.

To learn more about IPv6 support on AWS, visit the documentation page.

We are excited to announce the launch of disk-optimized vector support for Amazon OpenSearch Serverless, offering customers a cost-effective solution for vector search operations without compromising on accuracy and recall rates. This new feature enables organizations to implement high-quality vector search capabilities while significantly reducing operational costs.

With the introduction of Disk Optimized Vectors, customers can now choose between memory-optimized and disk-optimized vector storage options. The disk-optimized option delivers the same high accuracy and recall rates as memory-optimized vectors at lower cost. While this option may introduce slightly higher latency, it’s ideal for use cases where sub-millisecond response times aren’t critical such as semantic search applications, recommendation systems, and other AI-powered search scenarios.

Amazon OpenSearch Serverless, our fully managed deployment option, eliminates the complexities of infrastructure management for search and analytics workloads. The service automatically scales compute capacity, measured in OpenSearch Compute Units (OCUs), based on your workload demands.

Please refer to the AWS Regional Services List for more information about Amazon OpenSearch Service availability. To learn more about OpenSearch Serverless, see the documentation.

Amazon SageMaker HyperPod now supports managed node autoscaling using Karpenter, enabling customers to automatically scale their clusters to meet dynamic inference and training demands. Real-time inference workloads require automatic scaling to address unpredictable traffic patterns and maintain service level agreements, while optimizing costs. However, organizations often struggle with the operational overhead of installing, configuring, and maintaining complex autoscaling solutions. HyperPod-managed node autoscaling eliminates the undifferentiated heavy lifting of Karpenter setup and maintenance, while providing integrated resilience and fault tolerance capabilities.

Autoscaling on HyperPod with Karpenter enables customers to achieve just-in-time provisioning that rapidly adapts GPU compute for inference traffic spikes. Customers can scale to zero nodes during low-demand periods without maintaining dedicated controller infrastructure and benefit from workload-aware node selection that optimizes instance types and costs. For inference workloads, this provides automatic capacity scaling to handle production traffic bursts, cost reduction through intelligent node consolidation during idle periods, and seamless integration with event-driven pod autoscalers like KEDA. Training workloads also benefit from automatic resource optimization during model development cycles. You can enable autoscaling on HyperPod using the UpdateCluster API with AutoScaling mode set to “Enable” and AutoScalerType set to “Karpenter”.

This feature is available in all AWS Regions where Amazon SageMaker HyperPod EKS clusters are supported. To learn more about autoscaling on SageMaker HyperPod with Karpenter, see the user guide and blog .

Amazon Elastic VMware Service (Amazon EVS) now allows you to securely migrate and stretch your layer 2 networks from your on-premises data centers to Amazon EVS environments over the public internet. This launch adds to the existing capability of migrating workloads to Amazon EVS through dedicated private connectivity such as AWS Direct Connect or Virtual Private Networks (VPNs).

By enabling internet connectivity, Amazon EVS uses Elastic IP Addresses (EIPs) to provide a stable endpoint and a faster setup for performing workload migrations using VMware HCX. You now have the option to use internet connectivity for your migration projects when you might not have access to private connectivity options or are looking for a cost-effective alternative for the type of applications or projects that do not require the high network performance characteristics of a private connection during the migration.

Public HCX connectivity is available in all AWS Regions where Amazon EVS is available. To learn more about EVS migration options with HCX visit the AWS User Guide . To learn more about Amazon EVS, visit the product detail page .

AWS Step Functions now supports additional data sources and new observability metrics for Distributed Map. AWS Step Functions is a visual workflow service capable of orchestrating over 14,000+ API actions from over 220 AWS services to build distributed applications and data processing workloads. Distributed Map is a task state of Step Functions that runs the same process for multiple entries in a data set.

With this update, Distributed Map now supports additional data inputs, so you can orchestrate large-scale analytics and ETL workflows. You can now process AWS Athena data manifest and Parquet files directly, iterate over S3 objects under a specified prefix using S3ListObjectsV2, read from JSON objects and natively extract array data from JSON object from Amazon S3 or state input, eliminating the need for custom pre-processing. You also now get visibility into your Distributed Map usage with the following metrics, such as Approximate Open Map Runs Count, Open Map Run Limit, and Approximate Map Runs Backlog Size.

New input sources and improved observability for Distributed Map are available in all commercial AWS Regions where AWS Step Functions is available. To get started, you can use the Distributed Map mode today in the AWS Step Functions console . To learn more, visit the Step Functions developer guide.

Amazon Lex now provides support for confirmation and currency slot types in 10 additional languages: Portuguese, Catalan, French, Italian, German, Spanish, Mandarin, Cantonese, Japanese, and Korean. Built-in slots help you build more natural and efficient conversations by understanding synonyms of what you user says and resolving those inputs to a standard format. The confirmation slot helps understand various expressions of user acknowledgement and converts them into ‘Yes’, ‘No’, “Don’t know’‘, or ‘Maybe’. The currency slot helps identify currency and represents the input in a structured way. For example, when a user says “nope” or “absolutely not”, the confirmation slot resolves to ‘No’ or when the user says “1 dollar’, the currency slot resolves it to ”USD 1.00“. These built-in slots help you build more natural and efficient conversational experiences.

This feature is available in all commercial AWS Regions where Amazon Lex operates. To learn more about these features, visit Amazon Lex documentation or to learn how Amazon Connect and Amazon Lex deliver cloud-based conversational AI experiences for contact centers, please visit the Amazon Connect website .

DeepSeek-V3.1 is now available as a fully managed foundation model in Amazon Bedrock. This advanced open weight model allows you to switch between thinking mode for detailed step-by-step analysis and non-thinking mode for quicker responses. With comprehensive multilingual support, it delivers enhanced accuracy and reduced hallucinations compared to previous DeepSeek models, while maintaining visibility into its decision-making process.

You can use DeepSeek-V3.1’s enterprise-grade capabilities across critical business functions, from state-of-the-art software development to complex mathematical reasoning and data analysis. The model excels at sophisticated problem-solving tasks, demonstrating strong performance in coding benchmarks and technical challenges. Its enhanced tool-calling capabilities and seamless workflow integration make it ideal for building AI agents and automating enterprise processes, while its transparent reasoning approach helps teams understand and trust its outputs.

DeepSeek-V3.1 is now available in the US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Mumbai), Europe (London), and Europe (Stockholm) AWS Regions. To learn more, read the blog , product page , Amazon Bedrock pricing , and documentation . To get started with DeepSeek in Amazon Bedrock, visit the Amazon Bedrock console .