Blizzard: Diablo 5 kommt 2029 – und diesmal hat das Böse gewonnen
Blizzard hat Diablo 5 offiziell angekündigt, und zwar mit einer Besonderheit. Außerdem bekommt die Reihe eine Netflix-Serie. (Diablo, Rollenspiel)
Quelle: Golem
Toyplosion in Deutschland: Action-Figuren-Messe verzeichnet neuen Besucherrekord
Im Ruhrgebiet entwickelt sich ein Stück Nerd-Kultur zu etwas sehr Großem. Über 4.000 Menschen kommen zur Retro-Messe Toyplosion. (BigJim, Star Wars)
Quelle: Golem
AWS DevOps Agent adds support for bidirectional Slack communication
AWS DevOps Agent now enables engineers to manage production operations across AWS, multicloud, and on-premises environments with the complete investigation lifecycle directly within Slack. Previously, on-call engineers and DevOps teams faced fragmented workflows during high-severity incidents, having to context-switch between communication and investigation platforms. This update consolidates both surfaces into a single, unified location where incident response already happens.
Engineering teams can now initiate and steer investigations by simply @mentioning AWS DevOps Agent in any connected Slack private channel. All investigation activity, including team-contributed context, agent findings, and recommended actions, is captured within a single thread. Ask questions about AWS resources, system metrics, alarm status, deployment history, and incident patterns, reducing cognitive load during critical moments and making production operations significantly more efficient.
This capability is available in all commercial AWS Regions where AWS DevOps Agent is currently supported.
To get started, read the user guide. See all the latest AWS DevOps Agent features on the release history page.
Quelle: aws.amazon.com
AWS Lambda now supports direct read configuration for Amazon S3 Files
AWS Lambda now supports direct read configuration for Amazon S3 Files, letting you configure which storage your functions read from: S3 Files high-performance storage or your S3 bucket. With this launch, you can optimize the throughput and latency of file reads for your Lambda functions based on your application requirements.
Customers use S3 Files with Lambda functions to build scalable data processing pipelines and stateful agentic workloads with the performance and simplicity of a file system, while benefiting from the scalability, durability, and cost-effectiveness of S3. S3 Files serves data from high-performance storage for low latency or directly from your S3 bucket for high throughput on large reads, automatically routing each operation to the storage best suited for it. By default, Lambda supports direct reads from your S3 bucket only for functions configured with 512 MB of memory or higher. However, without control over the direct read configuration, you cannot optimize read performance for your specific application requirements. With this launch, you can explicitly enable or disable direct read for S3 Files on your Lambda functions, independent of function memory size. When you enable direct read, your function reads files 1 MB or larger directly from your S3 bucket for maximum throughput, and smaller files are served through the high-performance storage. When you disable it, all reads are served through the high-performance storage for the lowest latency.
This capability is available in all AWS commercial Regions, AWS GovCloud (US-East), and AWS GovCloud (US-West) Regions, except Asia Pacific (New Zealand), Middle East (Bahrain), and Middle East (UAE). You can configure direct read for S3 Files using the AWS Management Console, AWS CLI, AWS SDKs, and AWS CloudFormation. There is no additional charge beyond standard Lambda and S3 Files pricing. To learn more about how to use S3 Files with your Lambda function, visit the AWS Lambda developer guide.
Quelle: aws.amazon.com
So war euer Informatikunterricht: "Die Schüler hielten den Unterricht"
Während eurer Schulzeit trafen oft planlose Lehrer auf engagierte Schüler. Dennoch erinnert ihr euch gern zurück, an den Informatikunterricht. Von Mike Faust (Schulen, Studium)
Quelle: Golem
(g+) Sidecar-Angriffsfläche: Die Dienste, die kein Portscan sieht
Ein Postgresql-Helfer riss Splunk auf. Solche Dienste laufen überall mit, ungezählt. Worauf Admins achten müssen. Ein Ratgebertext von Steffen Zahn (Security, PostgreSQL)
Quelle: Golem
Gamedev: Roblox-Spiele sollen ohne App im Browser laufen
Die Spieleplattform von Roblox öffnet sich: Spiele sollen direkt im Browser, offline und sogar als eigenständige Apps laufen. (Roblox, Spiele)
Quelle: Golem
AWS HealthOmics now publishes real-time run metrics to Amazon CloudWatch
AWS HealthOmics now publishes real-time run metrics to Amazon CloudWatch, giving you visibility into workflow resource utilization as runs execute. The 14 new run metrics span CPU and GPU usage, memory usage, file system usage and I/O metrics, network throughput, and ephemeral storage usage. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows.
Real-time run metrics can help you identify CPU or GPU bottlenecks, detect memory or storage exhaustion before a task fails, and track file system throughput, all without opening a support case. By comparing actual usage against allocated resources, you can right-size the compute and storage configurations for your workflows. These metrics are emitted using the Amazon CloudWatch OpenTelemetry standard, so you can integrate them with third-party observability tools in addition to native CloudWatch dashboards and alarms.
Real-time run metrics are now available in the following AWS HealthOmics Regions: US East (N. Virginia, Ohio), US West (Oregon), Europe (Frankfurt, Ireland, London), and Asia Pacific (Singapore, Seoul, Tokyo). To learn more, visit the Monitoring run metrics with CloudWatch documentation. Amazon CloudWatch charges for these run metrics based on the volume of metric data ingested. For more information on pricing, visit Amazon CloudWatch pricing.
Quelle: aws.amazon.com
AWS DevOps Agent adds support for bidirectional Slack communication
AWS DevOps Agent now enables engineers to manage production operations across AWS, multicloud, and on-premises environments with the complete investigation lifecycle directly within Slack. Previously, on-call engineers and DevOps teams faced fragmented workflows during high-severity incidents, having to context-switch between communication and investigation platforms. This update consolidates both surfaces into a single, unified location where incident response already happens.
Engineering teams can now initiate and steer investigations by simply @mentioning AWS DevOps Agent in any connected Slack private channel. All investigation activity, including team-contributed context, agent findings, and recommended actions, is captured within a single thread. Ask questions about AWS resources, system metrics, alarm status, deployment history, and incident patterns, reducing cognitive load during critical moments and making production operations significantly more efficient.
This capability is available in all commercial AWS Regions where AWS DevOps Agent is currently supported.
To get started, read the user guide. See all the latest AWS DevOps Agent features on the release history page.
Quelle: aws.amazon.com
Amazon Bedrock Managed Knowledge Base now supports multimodal embeddings for video, audio, and image content with TwelveLabs Marengo 3.0
AWS announces the availability of TwelveLabs Marengo 3.0 as an embedding model in Amazon Bedrock Managed Knowledge Base, enabling customers to create multimodal embeddings for video, audio, and image content. Amazon Bedrock Managed Knowledge Base already supports media search by transcribing audio and video to text and generating text-based embeddings—Marengo 3.0 goes further by encoding visual scenes, speech, and video cues directly into multimodal embeddings, capturing meaning that transcription alone cannot. Simply upload your media assets from data sources such as Amazon S3, sync, and search using natural language—with no infrastructure to manage. Marengo 3.0 produces compact 512-dimensional vectors, delivering state-of-the-art retrieval accuracy. Results include segment start and end times, enabling applications to jump directly to the relevant moment in a video. This unlocks use cases across sports analytics, media and entertainment, security, education, and retail—from finding specific plays across seasons of game footage to locating lecture segments by concept rather than keywords. The model offers configurable segmentation options to match your content structure. To learn more, see TwelveLabs Marengo 3.0 embedding model integration in the Amazon Bedrock Knowledge Base User Guide. For more information, visit the Amazon Bedrock Knowledge Bases product page.
Quelle: aws.amazon.com
