Guest: Jordan Davis, solo founder of TensorPunk with 15 years of music industry experience spanning performance, recording, and sync licensing. Musician-technologist building AI tools from a producer's perspective.

Topics: AI music tool development, Mace generative sampler, Anvil model training platform, DAW integration, dataset curation, performance-focused AI instruments, hacker ethos in music tech

About

Jordan Davis represents the emerging wave of musician-technologists who are building AI tools from the perspective of actual producers rather than data scientists. His company Tensor Punk has created Mace, a generative sampler that runs natively in DAWs, and Anvil, a tool for training custom models. This conversation reveals hard-won insights about product development, model training challenges, and the philosophical tensions shaping AI music tools.

Key Insights

Building for Real Workflows: Jordan emphasizes that most AI music tools fail because they live in web browsers, disconnected from producers' actual creative environments. Mace runs as a VST plugin directly in DAWs, allowing seamless integration into existing workflows. This seemingly simple decision reflects deeper understanding of how musicians actually work.

The Art of Model Training: Jordan shares fascinating technical details about his journey from TensorFlow to PyTorch, discovering that high-frequency content like hi-hats trains faster and produces better quality than low-end samples. He deliberately chose "grittier, grainier" results over pristine fidelity to create genuinely experimental sounds rather than polished imitations.

Accessibility Without Compromise: TensorPunk tools run on CPU when GPU isn't available, supporting everything from Windows 7 to modern systems. Jordan developed much of the initial code on an old Windows 7 machine, demonstrating commitment to democratizing access to AI music tools.

The Performance Future: Rather than focusing on one-click generation, Jordan envisions AI tools as performance instruments. He describes frustration with traditional DJ equipment and imagines knobs controlling "live neural samplers" that morph genres in real-time, freeing performers to explore "radically different sonic territory."

Notable Perspectives

On AI Music's Identity Crisis: "There's going to be really cool and interesting instruments that haven't even been invented yet... I see the future of there being really, really cool and interesting instruments."

On the Hacker Ethos: "The branding behind Tensor Punk is that maybe there can be a company that has that hacker ethos and spirit, and is there supporting the creative and the artists. It's creative first, hacker and punk mentality first."

On Controllability: "A lot of producers that might be hesitant with AI tools... want more control of the outcome and less black box results. I think there's a lot right now that just needs to be re-engineered and can have amazing results."

Practical Takeaways

* Dataset Quality Over Quantity: Focus on curated, labeled datasets using producer vocabulary rather than generic metadata

* Platform Integration: Build tools that live inside existing creative workflows rather than isolated web applications

* Community-Driven Development: Engage with actual users to discover unexpected use cases and workflows

* Performance-First Design: Consider AI tools as instruments for live manipulation rather than just generation engines

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Music

Memory States - ȶʀǟɨռɨռɢ ɖǟȶǟ [12] - mini_ep

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