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An AWS Machine Learning post describes prompt caching in Amazon Bedrock, saying it can cut input token costs by up to 90% when the same context is repeatedly sent to foundation models. The article walks through six practical prompt caching scenarios for the Converse API: message content, system prompt, tool definition, mixed TTL, tenant isolation, and LangChain integration.
AWS Machine Learning describes using foundation models to simplify catalog-wide demand forecasting and then building a closed detect–decide–act loop on Databricks and Amazon Quick that compares demand surges to live supplier availability and places replenishment orders automatically, escalating to a human only when no supplier can cover a surge.
TechCrunch AI will host an interactive Builders Stage session at TechCrunch Disrupt 2026 titled “What happens when OpenAI ships your roadmap?” aimed at founders building AI companies to discuss how evolving foundation models affect product roadmaps and value creation.
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