I am a builder, not a researcher. I don’t care about theoretical AGI that only exists in white papers—I care about what runs in a kernel. I got tired of seeing "AI" projects that are just wrappers for LLM APIs, so I built a Causal Decision Engine that functions as a Sovereign Life OS.

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.