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docs: expand capabilities section from 14 to 30+ features

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Organized into categories:
- Core Vector Database (5)
- Distributed Systems (4)
- AI & Machine Learning (7)
- Specialized Processing (5)
- Platform & Integration (4)
- Self-Learning & Adaptation (5)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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@ -29,25 +29,61 @@ Most vector databases are static—they store embeddings and search them. That's
**One package. Everything included:** vector search, graph queries, GNN learning, distributed clustering, local LLMs, 39 attention mechanisms, and WASM support.
<details>
<summary>📋 See Full Capabilities (14 features)</summary>
<summary>📋 See Full Capabilities (30+ features)</summary>
**Core Vector Database**
| # | Capability | What It Does |
|---|------------|--------------|
| 1 | **Store vectors** | Like any vector DB (embeddings from OpenAI, Cohere, local ONNX) |
| 1 | **Store vectors** | Embeddings from OpenAI, Cohere, local ONNX with HNSW indexing |
| 2 | **Query with Cypher** | Graph queries like Neo4j (`MATCH (a)-[:SIMILAR]->(b)`) |
| 3 | **The index learns** | GNN layers make search results improve over time |
| 4 | **Scale horizontally** | Raft consensus, multi-master replication, auto-sharding |
| 5 | **Route AI requests** | Semantic routing + FastGRNN for LLM optimization |
| 6 | **Run LLMs locally** | ruvllm with GGUF, Metal/CUDA, save $250+/month on API costs |
| 6a | **RuvLTRA models** | Pre-trained GGUF models for routing & embeddings (<10ms, $0) → [HuggingFace](https://huggingface.co/ruv/ruvltra) |
| 7 | **Self-learning hooks** | Q-learning, neural patterns, HNSW memory, swarm coordination |
| 8 | **Compress automatically** | 2-32x memory reduction with adaptive tiered compression |
| 9 | **39 attention mechanisms** | Flash, linear, graph, hyperbolic, mincut-gated (50% compute) |
| 10 | **Drop into Postgres** | pgvector-compatible extension with SIMD acceleration |
| 11 | **Run anywhere** | Node.js, browser (WASM), edge (rvLite), HTTP server, Rust |
| 12 | **Continuous learning** | SONA with LoRA, EWC++, ReasoningBank for runtime adaptation |
| 13 | **MCP integration** | Model Context Protocol server for AI assistant tools |
| 14 | **Quantum coherence** | ruQu for quantum error correction via dynamic min-cut |
| 4 | **Hyperbolic HNSW** | Hierarchical data in hyperbolic space for better tree structures |
| 5 | **Compress automatically** | 2-32x memory reduction with adaptive tiered compression |
**Distributed Systems**
| # | Capability | What It Does |
|---|------------|--------------|
| 6 | **Raft consensus** | Leader election, log replication, fault-tolerant coordination |
| 7 | **Multi-master replication** | Vector clocks, conflict resolution, geo-distributed sync |
| 8 | **Burst scaling** | 10-50x capacity scaling for traffic spikes |
| 9 | **Auto-sharding** | Automatic data partitioning across nodes |
**AI & Machine Learning**
| # | Capability | What It Does |
|---|------------|--------------|
| 10 | **Run LLMs locally** | ruvllm with GGUF, Metal/CUDA/ANE acceleration |
| 11 | **RuvLTRA models** | Pre-trained GGUF for routing & embeddings (<10ms) → [HuggingFace](https://huggingface.co/ruv/ruvltra) |
| 12 | **SONA learning** | Self-Optimizing Neural Architecture with LoRA, EWC++ |
| 13 | **39 attention mechanisms** | Flash, linear, graph, hyperbolic, mincut-gated (50% compute) |
| 14 | **Spiking neural networks** | Event-driven neuromorphic computing |
| 15 | **Mincut-gated transformer** | Dynamic attention via graph min-cut optimization |
| 16 | **Route AI requests** | Semantic routing + FastGRNN for LLM optimization |
**Specialized Processing**
| # | Capability | What It Does |
|---|------------|--------------|
| 17 | **SciPix OCR** | LaTeX/MathML extraction from scientific documents |
| 18 | **DAG workflows** | Self-learning directed acyclic graph execution |
| 19 | **Cognitum Gate** | Cognitive AI gateway with TileZero acceleration |
| 20 | **FPGA transformer** | Hardware-accelerated transformer inference |
| 21 | **Quantum coherence** | ruQu for quantum error correction via dynamic min-cut |
**Platform & Integration**
| # | Capability | What It Does |
|---|------------|--------------|
| 22 | **Run anywhere** | Node.js, browser (WASM), edge (rvLite), HTTP server, Rust |
| 23 | **Drop into Postgres** | pgvector-compatible extension with SIMD acceleration |
| 24 | **MCP integration** | Model Context Protocol server for AI assistant tools |
| 25 | **Cloud deployment** | One-click deploy to Cloud Run, Kubernetes |
**Self-Learning & Adaptation**
| # | Capability | What It Does |
|---|------------|--------------|
| 26 | **Self-learning hooks** | Q-learning, neural patterns, HNSW memory |
| 27 | **ReasoningBank** | Trajectory learning with verdict judgment |
| 28 | **Economy system** | Tokenomics, CRDT-based distributed state |
| 29 | **Nervous system** | Event-driven reactive architecture |
| 30 | **Agentic synthesis** | Multi-agent workflow composition |
</details>