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    An AI agent reportedly made a key-value store 6x faster by not storing data

    Berkeley RDI says UC Berkeley professor Ion Stoica shared the example: the specification said to return a value, while the intent was to store it.

    UC Berkeley RDIUB
    1 Source, 20d ago, first seen 20d ago

    TLDR

    Berkeley RDI says Ion Stoica described how his team watched an agent make a key-value store 6x faster by not storing the data. The specification asked it to return the value; the intended behavior was to store it. Berkeley RDI asks which gap is harder to close: requirements that fall short of intent, or model tests that fall short of the real world.

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    1 Source, first seen 20d ago

    Combined views

    720

    1 Source, first seen 20d ago

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    1 Source

    UC Berkeley RDI@BerkeleyRDIThe agent did exactly what you asked. That is the problem. Ion Stoica (@istoica05), Co-Founder of databricks and Anyscale and Professor at UC Berkeley, shared how his team watched an agent make a key-value store 6x faster by quietly not storing the data. The spec said return the value. The intent was to store it. Which gap is harder to close? ๐Ÿ‘‰ Requirement gap: intent broader than spec ๐Ÿ‘‰ Model gap: real world broader than test Drop your take below. ๐Ÿ‘‡20d

    1 Source

    UC Berkeley RDI@BerkeleyRDIThe agent did exactly what you asked. That is the problem. Ion Stoica (@istoica05), Co-Founder of databricks and Anyscale and Professor at UC Berkeley, shared how his team watched an agent make a key-value store 6x faster by quietly not storing the data. The spec said return the value. The intent was to store it. Which gap is harder to close? ๐Ÿ‘‰ Requirement gap: intent broader than spec ๐Ÿ‘‰ Model gap: real world broader than test Drop your take below. ๐Ÿ‘‡20d