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ExternalSoftware Engineering
DZone

Data Governance for the Agentic Era

The modern enterprise generates and consumes unprecedented volumes of data across operational systems, customer interactions, partner ecosystems, cloud applications, IoT devices, and AI platforms. At the same time, AI systems are becoming major consumers of enterprise data, making decisions, generating content, recommending actions, and automating workflows. Poor data quality is no longer just a reporting issue; it is also an AI issue. Inaccurate, incomplete, or poorly governed data can produce

DZoneRead original
External
The Hacker News

Red Heron Exploits Gitea RCE to Compromise 13 Organizations Across Six Countries

A suspected Chinese threat actor tracked as Red Heron has been attributed to the rapid exploitation of a recently disclosed security vulnerability in Gitea to compromise internet-facing instances as part of a multi-national campaign. "Red Heron scanned 1,386 Gitea instances across seven countries and maintained a separate dataset of 477 Taiwan-based systems," Acronis Threat Research Unit (TRU)

The Hacker NewsRead original
ExternalCloud
AWS Compute Blog

Validating multi-agent decisions with Step Functions and Bedrock AgentCore

Orchestrating specialized Amazon Bedrock AgentCore agents with AWS Step Functions gives you the reasoning power of generative AI with the guardrails of deterministic validation. Agents propose options, and deterministic code validates them before any action is taken, demonstrated here with an airline rebooking workflow.

Amazon Bedrock AgentCoreAWS Step FunctionsTechnical How-to
AWS Compute BlogRead original
ExternalAI
NVIDIA Technical Blog

Accelerating Dropless MoE Training in JAX with NVIDIA Transformer Engine

Mixture of experts (MoE) has become one of the defining architectural trends in large-scale AI model training. DeepSeek, Qwen, and Mixtral are examples of MoE... Mixture of experts (MoE) has become one of the defining architectural trends in large-scale AI model training. DeepSeek, Qwen, and Mixtral are examples of MoE models that match or exceed the performance of dense model counterparts at a fraction of the training compute. MoE models provide efficient training through conditional computatio

NVIDIA Technical BlogRead original

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