Source record

Published by Practical AI (podcast) (T1.5 source). The original is at https://share.transistor.fm/s/451da102.

Pipeline notes

The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.

Practical AI podcast episode featuring Nous Research CTO Jeffrey Quesnelle discussing Hermes Agent, self-improving AI agents, and recursive learning systems. Topics include the distinction between models and harnesses, the changing role of developers, and questions about what remains uniquely human as AI capabilities grow.

Primary-source interview with the Nous Research CTO covers a new open-source agent framework and the models-vs-harnesses framing — relevant for anyone building or evaluating agents.

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