Regan Bell
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- Y Combinator1h 0m
Self-Improving Harnesses, Local Personal AI And YC's Agent For Work | YC Paper Club
Seth Karten, Jon Saad-Falcon, Josh France, Regan Bell
YC Harness Club researchers demonstrate that "self-improving harnesses" capable of modifying their own code and memory have driven Arc AGI success rates from 30% to 100%, surpassing standard prompt engineering by leveraging recursive learning architectures. Presentations by Seth and John Sad Falcone detail specific implementations like Prime Agent's multi-tier context management and OpenJarvis's local on-device stack, which collectively achieve frontier-level performance while reducing costs by 800x and enabling autonomous long-horizon research. Furthermore, the QM project establishes a centralized cloud-hosted framework with dynamic sandboxing and fine-grained permissions, addressing critical challenges in system-wide coordination and data security for scalable multi-agent deployment.