Neue Daten zum Ausbautempo von Windkraft, Solarenergie und Biogas zeigen Fortschritte, aber auch große Lücken. Die Schieflage ist gefährlich. (Energiewende, Solarenergie)
Quelle: Golem
Im Streit um eine Böhmermann-Sendung erhält Arne Schönbohm ein weiteres Mal Recht. Allerdings rüffelt ihn das Gericht in zwei Punkten. (Arne Schönbohm, Internet)
Quelle: Golem
Schwachstellenmanagement erfordert belastbare Priorisierung statt CVSS-Autopilot. Ein Live-Online-Workshop vermittelt Methoden, um Risiken einzuordnen und Maßnahmen abzuleiten. (Golem Karrierewelt, Server-Applikationen)
Quelle: Golem
Bei Amazon sind die beliebten Eneloop-Akkus von Panasonic befristet auf den besten Vergleichspreis gefallen. (Akku, Technik/Hardware)
Quelle: Golem
Tim Sweeney skizziert die Zukunft seiner Engine. Gleichzeitig erscheint Unreal Engine 5.8 mit neuen Rendering- und KI-Funktionen. (Unreal-Engine, Epic Games)
Quelle: Golem
AWS Transform for mainframe now delivers a connected, traceable reimagine experience from assessment through code generation. Previously, modernizing mainframe applications required months of analysis across multiple tools for discovery, reverse engineering, and code generation with manual handoffs between phases. With this launch, enterprises running z/OS COBOL and PL/I workloads can assess their portfolio to identify the discrete business functions, extract business rules, generate development-ready requirements, and produce traceable cloud-native code in a single connected workflow.
The experience starts with a portfolio assessment, where AWS Transform systematically identifies and catalogs discrete business functions. Selected business functions flow directly into the reimagine workflow, creating a connected path from portfolio analysis through code generation. For each business function, AWS Transform generates development-ready requirements with full traceability, flowing directly into Kiro and other IDEs through MCP-based integrations. Teams can generate interactive documentation for any requirement or code directly in the IDE. Every requirement traces back to the source code, so teams can audit any transformation decision back to its origin. This end-to-end approach compresses what previously took years of manual effort into months of automated, evidence-based modernization.
These capabilities are available in all AWS Regions where AWS Transform for mainframe is available. For more information, see the AWS Region table.
To learn more, visit AWS Transform for mainframe or see the AWS Transform for mainframe documentation.
Quelle: aws.amazon.com
Amazon S3 Vectors can now return up to 10,000 similarity search results per query, a 100x increase from the previous limit. The higher result limit helps you retrieve a larger, more comprehensive set of candidates during similarity queries. This is especially valuable for applications with multi-stage retrieval pipelines that need to apply additional processing such as reranking, aggregations, or deduplication to produce a more relevant final result set.
To get started with the higher limit, use the latest AWS SDK and update your application code to specify up to 10,000 relevant results (topK nearest neighbors) when making a QueryVectors API request. Query results are now returned across multiple pages, and you can start processing the first page immediately while retrieving additional pages as needed. For queries that return larger result sets, you pay a small data-returned fee based on the total size of results returned. The first 512 KB of data returned per query is free. For full pricing details, visit the S3 pricing page.
S3 Vectors supports retrieving up to 10,000 results per query in all AWS Regions where it is available. To learn more about S3 Vectors, visit the product page and S3 User Guide.
Quelle: aws.amazon.com
AWS Transform now offers a model-to-model migration custom transformation that assesses your generative AI workloads and produces a comprehensive migration plan for moving from third-party providers to Amazon Bedrock. The AI-powered agent scans your codebase, identifies every AI SDK and model in use, gathers your migration requirements through interactive questions, and maps models to Bedrock equivalents with transparent cost comparisons and production-ready code changes. This managed custom transformation helps organizations consolidate their AI workloads on AWS to gain IAM-based security, VPC endpoint isolation, prompt caching, Amazon Bedrock Guardrails, and unified operational tooling through Amazon CloudWatch. The transformation supports migrations from OpenAI, Google Gemini, direct Anthropic SDK usage, and open-source models via LiteLLM or Ollama. It handles direct SDK integrations, framework-wrapped patterns such as LangChain and LlamaIndex, agentic architectures including CrewAI and LangGraph, and multi-provider routing layers — preserving your application architecture while swapping only the model layer. The agent includes intelligent cost optimization with tiered model routing recommendations, prompt caching analysis, and model lifecycle awareness that excludes models within 90 days of end-of-life from all recommendations. For some workloads, it recommends Amazon Bedrock’s OpenAI-compatible endpoints as a zero-code-change migration path.
AWS Transform model-to-model migration is available in all AWS Regions where AWS Transform is offered, at no additional charge beyond standard AWS Transform pricing. To get started, install the ATX CLI and run the mke-genai-model-migration custom transformation against your codebase. To learn more, see the AWS Transform Custom Transformations documentation and the announcement blog.
Quelle: aws.amazon.com
Amazon Bedrock Guardrails now offers the InvokeGuardrailChecks API, a new resourceless API that lets you apply individual safeguards at any point in your agentic AI applications without creating guardrail resources. The API provides granular, per-request control over which safeguards to run at each step of your agent loop, returning numeric severity and confidence scores so you can implement custom thresholds and actions, whether to block, pass, retry, or log based on your specific requirements.
Agentic AI applications operate through iterative loops; planning tasks, calling tools, processing outputs, and iterating again while often executing dozens of steps for a single request. Each step carries a different risk profile, making a one-size-fits-all guardrail difficult to scale. The InvokeGuardrailChecks API addresses this by operating in detect-only mode with no guardrail IDs to track and no versions to manage. You specify which safeguards to run directly in each request, making it straightforward to add, remove, or adjust checks as your workflows evolve.
The API supports content filters (detecting harmful content across categories including hate, violence, sexual, insults, and misconduct), prompt attack detection (identifying jailbreak, prompt injection, and prompt leakage as independent standalone checks), and sensitive information filters (detecting supported PII entity types). Prompt attack detection is exposed as a separate safeguard, giving you the granularity to invoke each supported attack vector independently.
The InvokeGuardrailChecks API is available today in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (London), Europe (Stockholm), Asia Pacific (Tokyo), and Asia Pacific (Sydney).
To learn more, visit the Amazon Bedrock Guardrails technical documentation.
Quelle: aws.amazon.com
Die US-Regierung lehnt Ausnahmen ab. Wegen Exportkontrollen hat Anthropic seine neuesten KI-Modelle weltweit abgeschaltet. (Anthropic, KI)
Quelle: Golem
