SK Hynix is looking for a site in Japan to build a memory fab to be operated as a joint venture. “We are looking all over Japan,”  SK Hynix chairman […]

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Optoelectronics form the backbone of modern technologies such as fibre-optic networks, solar power and digital displays, writes Anthony Coia. Production of a lens for the aerospace sector Optoelectronics – the […]

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Novaspace, the space consulting and market intelligence firm, has released the 19th edition of its Earth Observation Satellite Systems report. This covers Earth observation (EO) satellite supply and demand, and […]

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Amazon Bedrock in AWS GovCloud (US) now supports SpaceXAI Grok 4.6, a frontier model built for coding, agentic tasks, and knowledge work. Grok 4.6 is SpaceXAI’s latest flagship model, built for long-running agents and ambitious interactive and visual work. It offers 500k context window and configurable reasoning efforts (low, medium, high, xhigh).

 

The model runs on the bedrock-runtime endpoint with support for the Responses, Chat Completions, and Converse APIs, and customers can access Grok 4.6 at scale with cross-Region inference routing requests across both AWS GovCloud (US) Regions. Grok 4.6 is also avialable via the bedrock-mantle endpoint in AWS GovCloud (US-East).  

 

To get started, review the model card for Grok 4.6   in the Amazon Bedrock User Guide.

Starting today, Amazon Aurora MySQL-Compatible Edition 3 (compatible with MySQL 8.0) supports MySQL 8.0.45, which includes community MySQL fixes and Aurora-specific improvements. For detailed information on this release, refer to Aurora MySQL 3 and MySQL 8.0.45 release notes.

You can upgrade your databases during scheduled maintenance windows using automatic minor version upgrades. To simplify operations at scale, enable automatic minor version upgrades and use the AWS Organizations Upgrade Rollout Policy to orchestrate upgrades across your clusters in phases. You can perform minor version upgrades in-place or via snapshot restore. This release is supported in all AWS Regions where Aurora MySQL is available.

Amazon Aurora is designed for high performance and availability at global scale with full MySQL compatibility. It provides scale-to-zero serverless compute, Aurora Global Database for multi-Region resilience, Aurora I/O-Optimized for improved price performance on I/O-intensive workloads, and built-in security and continuous backups. To get started, take a look at Aurora’s getting started page . 

AWS announces support for collecting systemd journal (journald) logs with the Amazon CloudWatch agent. You can now configure the CloudWatch agent to read log entries directly from the systemd journal on Linux instances and send them to Amazon CloudWatch Logs, without first writing those logs to files on disk.

Many modern Linux distributions, including Amazon Linux 2023, use systemd journal as the primary logging system and no longer write traditional text log files such as /var/log/messages by default. Previously, collecting these logs with the CloudWatch agent required additional configuration to export the journal to files on disk. With this launch, the CloudWatch agent reads journald entries natively, preserving the structured metadata that journald captures, such as the systemd unit, priority, and process information. You can filter log entries using systemd units, journal priority levels, and journal field matches, and you can apply regular expression filters before logs are published to CloudWatch Logs. This helps you reduce noise and control log volume and costs.

Support for journald in the CloudWatch agent is available in all AWS Commercial Regions and GovCloud(US) regions. Standard Amazon CloudWatch Logs pricing applies for ingested logs. For more information, see Amazon CloudWatch Pricing page .

To get started, update to the latest version of the CloudWatch agent and add a journald section to your agent configuration file. To learn more, see Manually create or edit the CloudWatch agent configuration file in the Amazon CloudWatch User Guide.

Starting today, Amazon Elastic Cloud Compute (Amazon EC2) P6-B300 instances are available in Asia Pacific (Hyderabad) and South America (Sao Paulo) Regions. P6-B300 instances provide 8xNVIDIA Blackwell Ultra GPUs with 2.1 TB high bandwidth GPU memory, 6.4 Tbps EFA networking, 300 Gbps dedicated ENA throughput, and 4 TB of system memory.

P6-B300 instances deliver 2x networking bandwidth, 1.5x GPU memory size, and 1.5x GPU TFLOPS (at FP4, without sparsity) compared to P6-B200 instances, making them well suited to train and deploy large trillion-parameter foundation models (FMs) and large language models (LLMs) with sophisticated techniques. The higher networking and larger memory deliver faster training times and more token throughput for AI workloads.  

P6-B300 instances are now available in p6-b300.48xlarge size in the following AWS Regions: US West (Oregon), AWS GovCloud (US-East), US East (N. Virginia), Asia Pacific (Hyderabad, Seoul), and South America (Sao Paulo). To learn more about P6-B300 instances, visit Amazon EC2 P6 instances.

The AWS Transform service is now in scope for FedRAMP Class C (formerly Moderate baseline) in the US East (N. Virginia) Region. You can use AWS Transform from this region to migrate and modernize resources that are or will be located in any of the US East/US West Regions in order to satisfy FedRAMP Class C compliance requirements. Additionally, AWS Transform MGN is in scope for FedRAMP Class D, and can be used independently to migrate resources that require FedRAMP Class D compliance.

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. AWS Transform is an agentic migration and modernization service designed to compress enterprise transformation timelines from years to months — handling everything from large-scale infrastructure migrations to continuous tech debt reduction, without the manual handoffs and lost context that commonly stall these programs.

To learn more about FedRAMP , visit the AWS services in scope page . To learn more about AWS Transform, visit the webpage and documentation .

Amazon Bedrock AgentCore Memory now supports fine-grained access control (FGAC), enabling you to enforce per-user and per-tenant memory isolation through AgentCore Gateway without building custom authorization logic.

With FGAC, you can front your Memory resource with an AgentCore Gateway configured for OAuth (JWT) authentication and attach Cedar policies that restrict access based on the authenticated caller’s identity. You can enforce that each user only accesses their own actor’s data, restrict memory records to namespaces derived from the user’s token claims, and allow or deny specific Memory operations per caller. This lets you move access control enforcement from application code to the infrastructure layer using cryptographic proof of identity. FGAC for Memory is built on the AgentCore Memory connector, a managed gateway connector that wires a gateway target to the Memory data plane and exposes 12 Memory operations as Cedar actions with their request attributes available for policy conditions.

To get started, see Fine-grained access control for Memory in the Amazon Bedrock AgentCore Developer Guide.

Amazon Bedrock AgentCore Memory now lets developers define flexible namespace variables to scope long-term memories along any application-specific dimension – such as organization, tenant, team, or environment – without creating duplicate strategies or overloading built-in variables. This gives multi-tenant and complex-hierarchy applications fine-grained control over how memories are organized, isolated, and accessed.

Define keys on the memory resource, reference them in a strategy’s namespace template, and supply values at runtime through the CreateEvent API. The service substitutes them into namespace templates during long-term memory extraction. Up to five keys can be defined per memory resource, each referenceable across multiple strategies.

Flexible namespace variables are available today in all AWS Regions where Amazon Bedrock AgentCore Memory is generally available, at no additional cost. To get started, see Specify long-term memory organization with namespaces in the Amazon Bedrock AgentCore Developer Guide.