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Jeffrey Quesnelle

Founder and CEO of Nous Research (the episode describes him as leading an open-source, user-aligned AI effort); appears as the episode's primary guest to discuss open-source AI, Hermes models and the Psyche training network.

Top 3 podcasts with Jeffrey Quesnelle

Ranked by the Snipd community
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25 snips
Oct 16, 2025 • 3h 4min

IM 841: Dust and Deli Meat - Open Source AI Revolution

Jeffrey Quesnelle, Founder and CEO of Nous Research, champions open-source, user-aligned AI. He argues that truly neutral AI models can empower users against corporate influences. Topics include the Hermes model's transparency and the Psyche training network's innovative use of idle GPUs. Quesnelle emphasizes the ethical importance of user-directed prompts and the necessity of principled, competitive talent attraction. The conversation also touches on decentralized consensus through cryptocurrency and the potential ethical dilemmas posed by AI-generated media of deceased individuals.
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5 snips
Mar 18, 2025 • 2h 6min

#51 – Jeffrey Quesnelle on Nous Research, large language models, and the human mind

In this engaging conversation, Jeffrey Quesnelle, cofounder of Nous Research and an expert on large language models, shares insights into the intersection of AI and human cognition. He discusses the historical evolution of neural networks and how they parallel human reasoning. The dialogue explores the philosophical implications of AI consciousness and the importance of decentralized systems in technology. Quesnelle also emphasizes the balance between computational advancements and human creativity, revealing how meditation and artistic expression relate to AI's development.
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Sep 27, 2024 • 1h 13min

DisTrO and the Quest for Community-Trained AI Models

Bowen Peng and Jeffrey Quesnelle from Nous Research discuss their mission to revive open-source AI, emphasizing the DisTrO project, which enables rapid training of AI models over the internet. They explore the challenges faced by independent builders in AI and the critical role of community collaboration. The conversation dives into impressive innovations like the Hermes models, designed for neutral interactions and enhanced with synthetic data. They reflect on the tension between decentralization and centralization in AI protocols and advocate for community-driven solutions.

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