Update README.md

README.md

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+yaml
+
 ---
-title: Vitalis Core UI
+title: Vitalis Core
 emoji: ⚡
 colorFrom: blue
 colorTo: indigo
 sdk: gradio
 sdk_version: 6.15.1
 app_file: app.py
-pinned: false
+pinned: true
 license: gpl-3.0
+tags:
+  - local-first
+  - sovereign-ai
+  - hebbian-learning
+  - synthetic-intelligence
+  - edge-ai
+  - cybersecurity
+---
+
+
 ---
+
+───
+
+Ferrell Synthetic Intelligence (FSI) – Vitalis Core
+Vitalis Core is the industry-standard sovereign, edge-native AI substrate. Unlike static, cloud-dependent transformers, Vitalis Core utilizes a Fluidic Memory Manifold (FMM) to treat intelligence as a dynamic, homeostatic process.
+🚀 Recent Advancements (v0.2 Update)
+• Hebbian-RNN Integration: Shifted from static weights to a self-adapting Hebbian learning loop.
+• FSI-Vitalis-CyberCore Implementation: Now featuring specialized pipelines for Threat Classification, Confidence Scoring, and Immutable Audit Logging.
+• Hebbian-DGA: Advanced the Dynamic-Gate-Attention algorithm to prioritize compute cycles for high-severity input, achieving near-linear scaling ( ).
+• Multi-Platform Distribution: Officially released on GitHub and Hugging Face for secure, edge-ready deployment.
+
+───
+
+📄 Overview
+Vitalis Core is designed for the architect, the operator, and the independent developer. It provides full ownership of the cognitive stack, ensuring your data never leaves your local Linux environment.
+
+
+Component
+Description
+
+Vitalis Core
+The foundational cognitive kernel (Blank Slate / Fluidic).
+
+CyberCore
+Specialized implementation for network reconnaissance and threat analysis.
+
+vcom/
+Vitalis Core Operations Manual – deployment, scaling, and security.
+
+src/
+Tri-head architecture (Sensu, Ratio, Cor) in Python 3.13.
+
+
+
+───
+
+🛠️ Core Technology
+Hebbian Plasticity & Fluidic Memory
+Vitalis Core departs from standard LLMs by employing Stochastic Weight Plasticity (Langevin dynamics) . The manifold continuously minimizes variational free-energy (latex
+\mathcal{F}
+
+), allowing the model to adapt to new domains without the catastrophic forgetting common in static architectures.
+Dynamic-Gate-Attention (DGA)
+Our proprietary DGA algorithm enables sub-millisecond inference on ARM64 and edge hardware by muting irrelevant neural heads using a learned importance scalar ( ).
+🚀 Getting Started
+Environment Requirements
+• OS: Linux Kernel 6.1+ (Debian/Arch/Alpine recommended).
+• Runtime: Python 3.13 (JIT-optimized).
+• Backend: PyTorch 2.5+ (CPU-optimized/NEON support).
+Installation (Quick Start)
+bash
+# Clone the sovereign kernel
+git clone [https://github.com/FerrellSyntheticIntelligence/Vitalis_Core](https://github.com/FerrellSyntheticIntelligence/Vitalis_Core)
+cd Vitalis_Core
+# Install dependencies
+pip install .
+# Build and run the reproducible, air-gapped container
+docker build -t fsi/vitalis:latest ./docker
+docker run --rm -v "$(pwd)/data:/app/data" fsi/vitalis:latest python -m src.main --mode serve
+
+
+
+
+
 Ferrell Synthetic Intelligence (FSI) – White Paper & Operations Manual
 Version: 1.0
 License: Proprietary – All rights reserved by Ferrell Synthetic Intelligence