Practical summary of the latest updates around Amazon SageMaker Inference: 2026 year-to-date launches in review, with implications for engineering teams.

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Practical summary of the latest updates around The new AgentCore runtime: Elastic, optimized, and consistently fast starts, with implications for engineering teams.

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Practical summary of the latest updates around Selecting a vector store for Amazon Bedrock Knowledge Bases, with implications for engineering teams.

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Practical summary of the latest updates around Build an AI-powered product tagging system with Amazon SageMaker serverless model customization, with implications for engineering teams.

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Practical summary of the latest updates around How Fyxer built an AI executive assistant people trust, with implications for engineering teams.

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Practical summary of the latest updates around The generative AI customization spectrum: From prompt engineering to custom models on AWS, with implications for engineering teams.

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Practical summary of the latest updates around Beyond the price per token: Choosing the right OpenAI model on Amazon Bedrock for your workload, with implications for engineering teams.

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Practical summary of the latest updates around Reduce LLM latency with prefix-aware routing on Amazon SageMaker Inference, with implications for engineering teams.

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Practical summary of the latest updates around ICYMI: What landed for AI builders in August 2026, with implications for engineering teams.

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Pathway’s Baby Dragon Hatchling (BDH) is a brain-inspired, post-transformer architecture that reasons in latent space instead of emitting chain-of-thought tokens. This article explores how Pathway develops and scales BDH on Amazon SageMaker HyperPod, setting a new cost-efficiency mark on the ARC-AGI-1 benchmark.

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