Wearable technology is advancing health monitoring by enabling real-time, privacy-preserving physiological analysis. However, traditional devices often rely on cloud processing, restricting access to raw sensor data and limiting the progress of health-related research. To overcome these limitations, we introduce OmniBuds, a programmable earable research platform that enables multimodal bio-sensing and on-device learning while providing direct access to raw physiological data, fostering advancements in health perception and wearable intelligence. It integrates PPG, temperature, IMUs, and multiple microphones, leveraging an embedded ML accelerator for efficient real-time processing. This paper presents its design, architecture, and applications, demonstrating its potential to shape the future of health-aware wearables.