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distributed caching layer designed to optimize LLM inference. By moving vector embedding generation to the client side and synchronizing a shared semantic cache via AWS S3, it eliminates redundant cloud computation, reduce latency and API costs for repeated queries.

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semantic caching system for LLMs. performs edge-based vectorization in the browser and uses a go backend synchronized with AWS S3 to serve cached AI responses, cutting API costs, latency, and potential carbon emissions.

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