The UK’s In-Orbit Servicing, Assembly and Manufacturing (ISAM) Conference for 2025 will take place in Belfast. Following the success of the inaugural conference in 2024, next year’s iteration will be …

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  STMicroelectronics has designed a pair of rail-to-rail comparators that can be power cycled without dragging down circuits connected to their inputs. Both industrial and AEC-Q100 qualified automotive versions are …

The post Comparators can be powered-down without loading source appeared first on Electronics Weekly .

Ubitium, a five month-old Dusseldorf startup, has developed a universal RISC-V processor that handles all computing workloads on a single, monolithic IC. Ubitium has just completed a $3.7 million in …

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Today, AWS announces support for predictive scaling for Amazon Elastic Container Service (Amazon ECS). Predictive scaling leverages advanced machine learning algorithms to proactively scale your Amazon ECS services ahead of demand surges, reducing overprovisioning costs while improving application responsiveness and availability.

Amazon ECS offers a rich set of service auto scaling options, including target tracking and step scaling policies, that automatically adjust task counts in response to observed load, as well as scheduled scaling to manually define rules to adjust capacity for routine demand patterns. Many applications observe recurring patterns of steep demand changes, such as early morning spikes when business resumes, wherein a reactive scaling policy can be slow to respond. Predictive scaling is a new capability that harnesses advanced machine learning algorithms, pre-trained on millions of data points, to proactively scale out ECS services ahead of anticipated demand surges. You can use predictive scaling alongside your existing auto scaling policies, such as target tracking or step scaling, so that your applications scale based on both real-time and historic patterns. You can also choose a “forecast only” mode to evaluate its accuracy and suitability, before enabling it to “forecast and scale“. Predictive scaling enhances responsiveness and availability for applications with recurring demand patterns, while also reducing the operational effort of manually configuring scaling policies and the costs from overprovisioning.

You can use AWS management console, SDK, CLI, CloudFormation, and CDK to configure predictive auto scaling for your ECS services. For a list of supported AWS Regions, see documentation . To learn more, visit this blog post and documentation .

AWS Backup introduces a new restore parameter for Amazon S3 backups, offering you the ability to choose how many versions of an object to restore.

By default, AWS Backup restores only the latest version of objects from the version stack at any point in time. The new parameter will now allow you to recover all versions of your data by restoring the entire version stack. You can also recover just the latest version(s) of an object without the overhead of restoring all older versions. With this feature, you now have more flexibility to control the data recovery process of Amazon S3 buckets/prefixes from your Amazon S3 backups, tailoring restore jobs to your requirements.

This feature is available in all Regions where AWS Backup for Amazon S3 is available. For more information on Regional availability and pricing, see the AWS Backup pricing page .

To learn more about AWS Backup for Amazon S3, visit the product page and technical documentation . To get started, visit the AWS Backup console .
 

AWS AppSync, a fully managed API management service that connects applications to events, data, and AI models. Today, customers use AppSync as an AI gateway to trigger generative AI workflows and use subscriptions, powered by WebSockets, to return progressive updates from long-running invocations. This allows them to implement asynchronous patterns. However, in some cases, customers need to make short synchronous invocations to their models. AWS AppSync now supports Amazon Bedrock runtime as a data source for GraphQL APIs, enabling seamless integration of generative AI capabilities. This new feature allows developers to make short synchronous invocations (10 seconds or less) to foundation models and inference profiles in Amazon Bedrock directly from their AppSync GraphQL APIs.

The integration supports calling the converse and invokeModel APIs. Developers can interact with Anthropic models like Claude 3.5 Haiku and Claude 3.5 Sonnet for data analysis and structured object generation tasks. They can also use Amazon Titan models to generate embeddings, create summaries, or extract action items from meeting minutes.

For longer-running invocations, customers can continue using AWS Lambda functions in event mode to interact with Bedrock models and send progressive updates to clients via subscriptions.

This new data source is available in all AWS Regions where AWS AppSync is available. To get started, customers can visit the AWS AppSync console and refer to the AWS AppSync documentation for more information.
 

Amazon CloudFront now supports origin modification within CloudFront Functions, enabling you to conditionally change or update origin servers on each request. You can now write custom logic in CloudFront Functions to overwrite origin properties, use another origin in your CloudFront distribution, or forward requests to any public HTTP endpoint.

Origin modification allows you to create custom routing policies for how traffic should be forwarded to your application servers on cache misses. For example, you can use origin modification to determine the geographic location of a viewer and then forward the request, on cache misses, to the closest AWS Region running your application. This ensures the lowest possible latency for your application. Previously, you had to use AWS Lambda@Edge to modify origins, but now this same capability is available in CloudFront Functions with better performance and lower costs. Origin modification supports updating all existing origin capabilities such as setting custom headers, adjusting timeouts, setting Origin Shield, or changing the primary origin in origin groups.

Origin modification is now available within CloudFront Functions at no additional charge. For more information, see the CloudFront Developer Guide . For examples of how to use origin modification, see our GitHub examples repository .

AWS announces two significant enhancements to CloudTrail Lake, a managed data lake that enables you to aggregate, immutably store, and analyze your activity logs at scale:

• Comprehensive dashboard capabilities: A new “Highlights” dashboard provides an at-a-glance overview of your AWS activity logs including AI-powered insights (AI-powered insights is in preview). Additionally, we have added 14 new pre-built dashboards catering to various use cases such as security and operational monitoring. These dashboards provide a starting point to analyze trends, detect anomalies, and conduct efficient investigations across your AWS environments. For example, the security dashboard displays top access denied events, failed console login attempts, and more. You can also create custom dashboards with scheduled refreshes, tailoring your monitoring to specific needs.
• Cross-account sharing of event data stores: This feature allows you to securely share your event data stores with select IAM identities using Resource-Based Policies (RBP). These identities can then query the shared event data store within the same AWS Region where the event data store was created, facilitating more comprehensive analysis across your organization while maintaining security.

These features are available in all AWS Regions where AWS CloudTrail Lake is supported, except AI-powered insights on the “Highlights” dashboard, which is in preview in N. Virginia, Oregon, and Tokyo Regions. While these enhancements are available at no additional cost, standard CloudTrail Lake query charges apply when running queries to generate results or create visualizations for the CloudTrail Lake dashboards. To learn more, visit the AWS CloudTrail documentation or read our News Blog .

Today, AWS IoT Device Management announced the general availability of the Commands feature, a managed capability that allows developers to build innovative applications where users can perform remote command and control actions on targeted devices and track the status of those executions. With this feature, you can send instructions, trigger device actions, or modify device configuration settings on-demand, simplifying the development of consumer facing applications.

Using the Commands feature, you can set fine-grained access controls, timeout settings, and receive real-time updates and notifications for each command execution, without having to manually create and manage MQTT topics, payload formats, Rules, Lambda functions, and status tracking. In addition, the feature supports custom payload formats, allowing you to define and store command entities as AWS resources for recurring use.

The AWS IoT Device Management commands feature is available in all AWS Regions where AWS IoT Device Management is offered. To learn more, see technical documentation . To get started, log in to the AWS IoT Management Console or use the CLI.
 

Amazon API Gateway (APIGW) now gives you the ability to manage your private REST APIs using custom user-friendly private DNS name like private.example.com, simplifying API discovery. This feature enhances your security posture by continuing to encrypt your private API traffic with Transport Layer Security (TLS), while providing full control over managing the lifecycle of the TLS certificate associated with your domain.

API providers can get started with this feature in four simple steps using APIGW console and/or API(s). First, create a private custom domain. Second, configure an Amazon Certificate Manager (ACM) provided or imported certificate for the domain. Third, map multiple private APIs using base path mappings. Fourth, control invokes to the domain using resource policies. API providers can optionally share the domain across accounts using Amazon Resource Access Manager (RAM) to provide consumers the ability to access APIs from different accounts. Once a domain is shared using RAM, a consumer can use VPC endpoint(s) to invoke multiple private custom domains across accounts.

Custom domain name for private REST APIs is now available on API Gateway in all AWS Regions, including the AWS GovCloud (US) Regions. Please visit the API Gateway documentation and AWS blog post to learn more.