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Learn how integrating Elastic Fabric Adapter (EFA) and DeepEP on Amazon EKS optimizes inter-node expert parallelism, boosting rollout throughput by 40% for Mixture-of-Experts RLHF and GRPO training pipelines.

Discover how 100-year-old Dutch retailer HEMA eliminated developer documentation context-switching by building HAL, an enterprise AI assistant powered by Anthropic's Model Context Protocol (MCP), Amazon Bedrock, and Microsoft Entra ID.

Anthropic's seven co-founders are asking shareholders to approve a governance restructuring granting them 50.1% voting control ahead of an impending IPO, aiming to safeguard its safety mission against investor pressure.

A divided federal appeals court has upheld the Defense Department's authority to designate AI developer Anthropic as a supply chain risk. Explore the legal implications, vendor risk management challenges, and enterprise architectural strategies for multi-LLM resilience.

AI agents are costing more not because of model prices, but because of inefficient loops. Learn how to optimize your trajectory and reduce token consumption.

A comprehensive guide to building secure AI applications by implementing multi-layer security checkpoints for RAG, prompt injection, and data leakage.

An analysis of the strategic impact of Jun Kim, creator of oMLX, joining Hugging Face, and what this means for developers building on Apple Silicon.

A deep dive into optimizing multimodal workflows using the LFM2.5-VL-DSpark framework for high-throughput inference.

An in-depth look at Google's 'Call for Me' feature on Pixel 11, powered by Gemini, and how enterprise developers can leverage similar agentic API workflows.

An analysis of Google's Gemini 3.8 Live update, focusing on the new Live Avatar feature, multilingual lip-syncing, and its implications for enterprise AI communication.

Learn how to stop system-prompt exfiltration and injection attacks by implementing a multi-layered screening pipeline before your LLM processes a single token.

A deep dive into the RL Game Agents benchmark, exploring how deterministic evaluation, per-axis rubrics, and public verbatim trails provide a more reliable way to measure LLM performance in coding tasks.