Developer Playbook

Mind-Mix: Generative Stem Morphing

1. Sparse VAE Training: During firmware creation, a Sparse Variational Autoencoder (VAE) is trained on the album's master stems. It learns a compressed, disentangled latent space where each dimension corresponds to a meaningful musical feature (e.g., vocal timbre, drum intensity).
2. Biometric Mapping: The on-device Crypto-OS reads biometric data (HRV, GSR). This data is mapped to a vector that navigates the VAE's latent space.
3. Real-time Decoding: The Audio-OS decoder takes the new latent space vector and generates a morphed version of the stems in real-time, resulting in a mix that reflects the user's physiological state.

Pleasure Prior: Multimodal RLHF

This builds on the Mind-Mix engine, allowing the device to learn what the user *likes*.

1. Interaction as Signal: A lightweight, on-device multimodal model (e.g., a highly quantized Qwen variant) interprets user interactions—like thumb gestures on a capacitive surface—as reward signals.
2. Reward-Guided Exploration: This reward signal biases the biometric mapping. If a user positively reacts to a certain sound, the device learns to explore that region of the latent space more often. This is conceptually similar to techniques like Gumbel-Softmax for differentiable sampling.
3. Federated Gradient Sharing: The resulting model adjustments (gradients) are what can be shared via the differentially private federated loop, improving the global model's "Pleasure Prior" without sharing any personal data.