Project: Velocity_Nova_Prime (v0.8.0) is a system designed to ingest 134 synchronous data streams (from biometrics to network entropy) to create a unified world-model. It doesn't just predict the next token; it predicts the next consequence.
Source Code: https://github.com/AI-Sovereign/Multimodal-AGI-Architecture-...
Technical Highlights:
1. TCS-25 Plasticity: A custom learning rule where weight updates are driven by Surprisal (S) and Causal Risk (C). It’s optimized for real-time implementation, not just backprop simulation.
2. HTSP (Hierarchical Temporal Synthetic Plasticity): A dual-stream architecture using a Fast Stream (GRU) for reflexes and a Slow Stream (LSTM) for episodic memory fusion.
3. 134 Modalities: The system maps a 134-dimensional vector in real-time. This includes everything from biometric data (HRV, GSR) to environmental noise and network packet entropy via Scapy.
The Stack: Python 3.10+, PyTorch, Polars (for high-entropy data handling), Scapy (for network ingestion), and FastAPI for the WebSocket interface.
The complexity depth is currently theoretical at 1.1 quintillion operations, and I'm pushing for local-only inference to maintain sovereignty. I'm looking for feedback from builders who have actually scaled multimodal ingestion or worked with non-Euclidean data in spiking neural networks.
The code is live. Let me know where the implementation breaks.