gdtsitlauri/hermes-sdr

Hermes-SDR sits at the intersection of Deep Learning and Radio Frequency (RF) engineering. While technically sophisticated, the project currently scores a 2 on defensibility due to its extreme infancy (0 stars, 0 forks, 0 days old). It functions as a specialized implementation of 'neural transceivers,' a concept gaining traction in 6G research. Its primary moat would be the specific CADUCEUS-WAVE architecture, likely a State-Space Model (SSM) or Mamba-derivative applied to waveforms. Competitive Landscape: The project faces significant competition from NVIDIA's 'Sionna,' which is the industry standard for link-level simulations with GPU acceleration. It also competes with DeepSig (a commercial leader in AI/ML for wireless). Risk Assessment: Frontier labs (OpenAI/Google) are unlikely to enter the SDR space directly as it requires niche hardware (USRPs, HackRF) and specialized domain expertise in electromagnetics. However, NVIDIA (the platform provider) poses a high domination risk; if NVIDIA incorporates similar neural transceiver templates into Sionna, Hermes-SDR loses its raison d'être. The displacement horizon is 1-2 years, as the academic field of 'Deep Learning for PHY' is moving rapidly, and newer architectures often render older learned modulations obsolete quickly.