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docs(readme): add 7 comprehensive example sections
Added collapsed sections with badges, feature tables, and tutorials for: - Agentic-Jujutsu: Quantum-resistant version control (23x faster commits) - SciPix: Scientific document OCR (50ms text, 80ms math) - Meta-Cognition SNN: Spiking neural networks (5-54x SIMD speedup) - RuvLLM: Self-learning LLM orchestration (SONA 3-tier learning) - REFRAG: Compress-Sense-Expand RAG (~30x latency reduction) - 7sense: Bioacoustic bird call analysis (150x HNSW speedup) - EXO-AI: Cognitive substrate with IIT consciousness (8-54x SIMD) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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@ -1726,6 +1726,557 @@ npm install @neural-trader/core @neural-trader/strategies @neural-trader/mcp
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</details>
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<details>
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<summary><strong>🥋 Agentic-Jujutsu - Quantum-Resistant Version Control</strong></summary>
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[](https://www.npmjs.com/package/agentic-jujutsu)
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[](https://opensource.org/licenses/MIT)
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### What is Agentic-Jujutsu?
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Agentic-Jujutsu is a **quantum-resistant, self-learning version control system** designed for AI agents. It combines lock-free concurrent operations with ReasoningBank trajectory learning for continuous improvement.
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| Traditional Git | Agentic-Jujutsu |
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|----------------|-----------------|
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| Lock-based commits | Lock-free (23x faster) |
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| Manual conflict resolution | 87% automatic resolution |
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| Static operations | Self-learning from patterns |
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| No quantum protection | SHA3-512 + HQC-128 |
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| Sequential agents | Concurrent multi-agent |
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### Key Features
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| Feature | Performance | Description |
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|---------|-------------|-------------|
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| **Concurrent Commits** | 350 ops/s | 23x faster than Git (15 ops/s) |
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| **Context Switching** | <100ms | 10x faster than Git (500-1000ms) |
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| **Conflict Resolution** | 87% auto | AI-powered pattern matching |
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| **Quantum Security** | <1ms verify | SHA3-512 fingerprints, HQC-128 encryption |
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| **ReasoningBank** | Continuous | Trajectory learning with verdicts |
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### Quick Start
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```bash
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# Install
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npm install agentic-jujutsu
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# Basic usage
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npx agentic-jujutsu
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```
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```typescript
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import { JjWrapper } from 'agentic-jujutsu';
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const jj = new JjWrapper();
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// Start learning trajectory
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jj.startTrajectory('Implement feature X');
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// Make changes and commit
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await jj.newCommit('Add authentication module');
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jj.addToTrajectory();
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// Finalize with success score
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jj.finalizeTrajectory(0.9, 'Feature implemented successfully');
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// Get AI-powered suggestions
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const suggestions = await jj.getSuggestions();
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```
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### Multi-Agent Coordination
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```typescript
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// Concurrent commits without locks
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const agents = ['agent-1', 'agent-2', 'agent-3'];
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await Promise.all(agents.map(agent =>
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jj.newCommit(`Changes from ${agent}`)
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));
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// All commits succeed - no lock waiting!
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```
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> **Full Documentation**: [agentic-jujutsu README](./examples/agentic-jujutsu/README.md)
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</details>
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<details>
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<summary><strong>🔬 SciPix - Scientific Document OCR</strong></summary>
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[](https://crates.io/crates/ruvector-scipix)
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[](https://docs.rs/ruvector-scipix)
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[](https://opensource.org/licenses/MIT)
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### What is SciPix?
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SciPix is a **blazing-fast, memory-safe OCR engine** written in pure Rust, purpose-built for scientific documents, mathematical equations, and technical diagrams.
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| Feature | SciPix | Tesseract | Mathpix |
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|---------|--------|-----------|---------|
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| Simple Text OCR | **50ms** | 120ms | 200ms* |
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| Math Equation | **80ms** | N/A | 150ms* |
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| Batch (100 images) | **2.1s** | 8.5s | N/A |
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| Memory Usage | **45MB** | 180MB | Cloud |
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| LaTeX Output | Yes | No | Yes |
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*API latency, not processing time
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### Key Features
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| Feature | Description |
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|---------|-------------|
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| **ONNX Runtime** | GPU-accelerated with CUDA, TensorRT, CoreML |
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| **LaTeX Output** | Mathematical equation recognition with LaTeX, MathML, AsciiMath |
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| **SIMD Optimized** | 4x faster image preprocessing with AVX2, SSE4, NEON |
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| **REST API** | Production-ready HTTP server with rate limiting |
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| **MCP Server** | Integrate with Claude, ChatGPT via Model Context Protocol |
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| **WebAssembly** | Run OCR directly in browsers |
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### Quick Start
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```bash
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# Add to Cargo.toml
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cargo add ruvector-scipix
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# With features
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ruvector-scipix = { version = "0.1.16", features = ["ocr", "math", "optimize"] }
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```
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```rust
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use ruvector_scipix::{SciPixOcr, OcrConfig};
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// Initialize OCR engine
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let ocr = SciPixOcr::new(OcrConfig::default())?;
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// Process scientific image
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let result = ocr.process_image("equation.png")?;
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println!("LaTeX: {}", result.latex);
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println!("Confidence: {:.2}%", result.confidence * 100.0);
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```
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### Use Cases
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- **Academic Paper Digitization** - Extract text and equations from scanned research papers
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- **Math Homework Assistance** - Convert handwritten equations to LaTeX for AI tutoring
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- **Technical Documentation** - Process engineering diagrams and scientific charts
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- **AI/LLM Integration** - Feed scientific content to language models via MCP
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> **Full Documentation**: [scipix README](./examples/scipix/README.md)
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</details>
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<details>
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<summary><strong>🧠 Meta-Cognition SNN - Spiking Neural Networks</strong></summary>
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### What is Meta-Cognition SNN?
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A hybrid AI architecture combining **Spiking Neural Networks (SNN)**, **SIMD-optimized vector operations**, and **5 attention mechanisms** with meta-cognitive self-discovery capabilities.
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| Capability | Performance | Description |
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|------------|-------------|-------------|
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| **Spiking Neural Networks** | 10-50x faster | LIF neurons + STDP learning with N-API SIMD |
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| **SIMD Vector Operations** | 5-54x faster | Loop-unrolled distance/dot product calculations |
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| **5 Attention Mechanisms** | Sub-millisecond | Multi-Head, Flash, Linear, Hyperbolic, MoE |
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| **Vector Search** | 150x faster | RuVector-powered semantic search |
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| **Meta-Cognition** | Autonomous | Self-discovering emergent capabilities |
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### SIMD Performance
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| Operation | Speedup | Notes |
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|-----------|---------|-------|
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| LIF Updates | **16.7x** | Leaky integrate-and-fire neurons |
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| Synaptic Forward | **14.9x** | Forward propagation |
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| STDP Learning | **26.3x** | Spike-timing dependent plasticity |
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| Distance (128d) | **54x** | Euclidean distance calculation |
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| Full Simulation | **18.4x** | End-to-end SNN simulation |
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### 5 Attention Mechanisms
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| Mechanism | Best For | Latency |
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|-----------|----------|---------|
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| **Flash** | Long sequences | 0.023ms |
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| **MoE** | Specialized domains | 0.021ms |
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| **Multi-Head** | Complex patterns | 0.047ms |
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| **Linear** | Real-time processing | 0.075ms |
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| **Hyperbolic** | Hierarchical data | 0.222ms |
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### Quick Start
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```bash
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# Install and run demos
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cd examples/meta-cognition-spiking-neural-network
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npm install
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node demos/run-all.js
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```
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```javascript
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const { createFeedforwardSNN, rateEncoding } = require('./demos/snn/lib/SpikingNeuralNetwork');
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// Create SNN with SIMD optimization
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const snn = createFeedforwardSNN([100, 50, 10], {
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dt: 1.0,
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tau: 20.0,
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a_plus: 0.005,
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lateral_inhibition: true
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});
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// Train with STDP
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const input = rateEncoding(pattern, snn.dt, 100);
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snn.step(input);
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```
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### 6 Emergent Discoveries
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1. Multi-Scale Attention Hierarchy (Novelty: 5/5)
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2. Spike Synchronization Patterns
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3. Attention-Gated Spike Propagation
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4. Temporal Coherence Emergence
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5. Emergent Sparsity (80% fewer active neurons)
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6. Meta-Plasticity (faster learning on later tasks)
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> **Full Documentation**: [meta-cognition-snn README](./examples/meta-cognition-spiking-neural-network/README.md)
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</details>
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<details>
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<summary><strong>🤖 RuvLLM - Self-Learning LLM Orchestration</strong></summary>
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[](https://www.rust-lang.org/)
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[](LICENSE)
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[](#)
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### What is RuvLLM?
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RuvLLM is a **self-learning language model orchestration system** that combines frozen foundation models with adaptive memory and intelligent routing. Unlike traditional LLMs that rely solely on static parameters, RuvLLM continuously improves from every interaction.
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> *"The intelligence is not in one model anymore. It is in the loop."*
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### SONA: 3-Tier Temporal Learning
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| Loop | Frequency | Latency | Description |
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|------|-----------|---------|-------------|
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| **A: Instant** | Per-request | <100μs | MicroLoRA adaptation (rank 1-2) |
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| **B: Background** | Hourly | ~1.3ms | K-means++ clustering, base LoRA (rank 4-16) |
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| **C: Deep** | Weekly | N/A | EWC++ consolidation, concept hierarchies |
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### Core Components
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| Component | Description |
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|-----------|-------------|
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| **LFM2 Cortex** | Frozen reasoning engine (135M-2.6B params) |
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| **Ruvector Memory** | Adaptive synaptic mesh with HNSW indexing |
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| **FastGRNN Router** | Intelligent model selection circuit |
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| **Graph Attention** | 8-head attention with edge features |
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| **SONA Engine** | LoRA + EWC++ + ReasoningBank |
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### Performance (CPU-Only)
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| Metric | Value |
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|--------|-------|
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| **Initialization** | 3.71ms |
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| **Average Query** | 0.09ms |
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| **Session Query** | 0.04ms |
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| **Throughput** | ~38,000 q/s |
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| **Memory** | ~50MB |
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### Quick Start
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```rust
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use ruvllm::{RuvLLMOrchestrator, OrchestratorConfig};
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// Initialize orchestrator
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let config = OrchestratorConfig::default();
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let orchestrator = RuvLLMOrchestrator::new(config)?;
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// Query with automatic learning
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let response = orchestrator.query("Explain quantum entanglement").await?;
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println!("{}", response.text);
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// Response improves over time as SONA learns patterns
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```
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### Federated Learning
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```rust
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// Ephemeral agents collect trajectories
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let agent = EphemeralAgent::new("task-specific-agent");
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agent.process_task(&task).await?;
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let export = agent.export();
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// Central coordinator aggregates learning
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coordinator.accept_export(export)?;
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coordinator.consolidate(); // Share patterns with new agents
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```
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### Advanced Features
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- **SIMD Inference**: AVX2/AVX512/SSE4.1 optimization
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- **Q4 Quantization**: 4-bit weights for memory efficiency
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- **HuggingFace Export**: Export LoRA weights and preference pairs
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- **Multi-Model Routing**: SmolLM, Qwen2, TinyLlama selection
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- **WASM Support**: Run SONA in browsers and edge devices
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> **Full Documentation**: [ruvLLM README](./examples/ruvLLM/README.md)
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</details>
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<details>
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<summary><strong>🗜️ REFRAG - Compress-Sense-Expand RAG</strong></summary>
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### What is REFRAG?
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REFRAG implements the **Compress-Sense-Expand architecture** from [arXiv:2509.01092](https://arxiv.org/abs/2509.01092), achieving **~30x RAG latency reduction** by storing pre-computed representation tensors instead of raw text.
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### Architecture
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```
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┌────────────────┐ ┌────────────────┐ ┌────────────────┐
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│ COMPRESS │───▶│ SENSE │───▶│ EXPAND │
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│ Layer │ │ Layer │ │ Layer │
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└────────────────┘ └────────────────┘ └────────────────┘
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Binary tensor Policy network Dimension projection
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storage with decides COMPRESS (768 → 4096 dims)
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zero-copy access vs EXPAND
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```
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### Compression Strategies
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| Strategy | Compression | Use Case |
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|----------|-------------|----------|
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| `None` | 1x | Maximum precision |
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| `Float16` | 2x | Good balance |
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| `Int8` | 4x | Memory constrained |
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| `Binary` | 32x | Extreme compression |
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### Policy Networks
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| Policy | Latency | Description |
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|--------|---------|-------------|
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| `ThresholdPolicy` | ~2μs | Cosine similarity threshold |
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| `LinearPolicy` | ~5μs | Single layer classifier |
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| `MLPPolicy` | ~15μs | Two-layer neural network |
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### Quick Start
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```bash
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# Run demo
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cargo run --bin refrag-demo
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# Run benchmarks
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cargo run --bin refrag-benchmark --release
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```
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```rust
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use refrag_pipeline_example::{RefragStore, RefragEntry};
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// Create REFRAG-enabled store
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let store = RefragStore::new(384, 768)?;
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// Insert with representation tensor
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let entry = RefragEntry::new("doc_1", search_vector, "The quick brown fox...")
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.with_tensor(tensor_bytes, "llama3-8b");
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store.insert(entry)?;
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// Hybrid search (policy-based COMPRESS/EXPAND)
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let results = store.search_hybrid(&query, 10, Some(0.85))?;
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for result in results {
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match result.response_type {
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RefragResponseType::Compress => {
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// Tensor directly injectable into LLM context
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println!("Tensor: {} dims", result.tensor_dims.unwrap());
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}
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RefragResponseType::Expand => {
|
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// Original text when full context needed
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println!("Text: {}", result.content.unwrap());
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}
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}
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}
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```
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### Target LLM Dimensions
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| Source | Target | LLM |
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|--------|--------|-----|
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| 768 | 4096 | LLaMA-3 8B |
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| 768 | 8192 | LLaMA-3 70B |
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| 1536 | 8192 | GPT-4 |
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> **Full Documentation**: [refrag-pipeline README](./examples/refrag-pipeline/README.md)
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</details>
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<details>
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<summary><strong>🐦 7sense - Bioacoustic Intelligence Platform</strong></summary>
|
||||
|
||||
[](https://www.rust-lang.org)
|
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[]()
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[]()
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|
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### What is 7sense?
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|
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7sense transforms **bird calls into navigable geometric space** using cutting-edge AI and vector search. It converts audio recordings of bird songs into rich, searchable embeddings using Perch 2.0 neural networks and ultra-fast HNSW indexing.
|
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|
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| Traditional Monitoring | 7sense |
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|----------------------|--------|
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| Expert human listeners | Instant AI species ID |
|
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| Basic spectrogram analysis | Neural embeddings (1536-dim) |
|
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| Limited scale | Millions of recordings |
|
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| Manual pattern finding | Automated discovery |
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|
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### Performance Targets
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|
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| Metric | Target | Status |
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|--------|--------|--------|
|
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| HNSW Search Speedup | 150x vs brute force | Achieved |
|
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| Query Latency (p99) | < 50ms | Achieved |
|
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| Recall@10 | >= 0.95 | Achieved |
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| Embedding Throughput | > 100 segments/sec | Achieved |
|
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| Memory per 1M vectors | < 6 GB | Achieved |
|
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|
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### 9 Rust Crates
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|
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| Crate | Description |
|
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|-------|-------------|
|
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| `sevensense-core` | Species taxonomy, temporal types |
|
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| `sevensense-audio` | WAV/MP3/FLAC, Mel spectrograms |
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| `sevensense-embedding` | Perch 2.0 ONNX, 1536-dim vectors |
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| `sevensense-vector` | HNSW with 150x speedup |
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| `sevensense-learning` | GNN training, EWC regularization |
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| `sevensense-analysis` | HDBSCAN clustering, Markov models |
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| `sevensense-interpretation` | Evidence packs, species narratives |
|
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| `sevensense-api` | GraphQL, REST, WebSocket streaming |
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| `sevensense-benches` | Criterion.rs performance suites |
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|
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### Quick Start
|
||||
|
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```bash
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# Build and run
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cd examples/vibecast-7sense
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cargo build --release
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cargo run -p sevensense-api --release
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```
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|
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```rust
|
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use sevensense_audio::AudioProcessor;
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use sevensense_embedding::EmbeddingPipeline;
|
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use sevensense_vector::HnswIndex;
|
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|
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// Load and process audio
|
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let processor = AudioProcessor::new(Default::default());
|
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let segments = processor.process_file("recording.wav").await?;
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|
||||
// Generate Perch 2.0 embeddings
|
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let pipeline = EmbeddingPipeline::new(Default::default()).await?;
|
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let embeddings = pipeline.embed_segments(&segments).await?;
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|
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// Search for similar calls (150x faster)
|
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let index = HnswIndex::new(Default::default());
|
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index.add_batch(&embeddings)?;
|
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let neighbors = index.search(&embeddings[0], 10)?;
|
||||
|
||||
println!("Found {} similar bird calls", neighbors.len());
|
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```
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||||
|
||||
### Use Cases
|
||||
|
||||
- **Species Identification** - Instant predictions with confidence scores
|
||||
- **Pattern Discovery** - Find similar calls across millions of recordings
|
||||
- **Behavioral Insights** - Detect singing patterns, dialects, anomalies
|
||||
- **Conservation Monitoring** - Track biodiversity at scale
|
||||
|
||||
> **Full Documentation**: [7sense README](./examples/vibecast-7sense/README.md)
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>🧬 EXO-AI - Advanced Cognitive Substrate</strong></summary>
|
||||
|
||||
[](https://crates.io/crates/exo-core)
|
||||
[](https://docs.rs/exo-core)
|
||||
[](LICENSE)
|
||||
|
||||
### What is EXO-AI?
|
||||
|
||||
EXO-AI 2025 is a comprehensive **cognitive substrate** implementing cutting-edge theories from neuroscience, physics, and consciousness research. It provides 9 interconnected Rust crates totaling ~15,800+ lines of research-grade code.
|
||||
|
||||
> Traditional AI systems process information. EXO-AI aims to understand it.
|
||||
|
||||
### SIMD-Accelerated Performance
|
||||
|
||||
| Operation | Speedup | Notes |
|
||||
|-----------|---------|-------|
|
||||
| Distance (128d) | **54x** | AVX2/NEON optimized |
|
||||
| Cosine Similarity | **2.73x** | Batch operations |
|
||||
| Pattern Matching | **8-54x** | Loop-unrolled |
|
||||
| Meta-Simulation | **13+ quadrillion/s** | From ultra-low-latency-sim |
|
||||
|
||||
### 9 Rust Crates
|
||||
|
||||
| Crate | Description |
|
||||
|-------|-------------|
|
||||
| `exo-core` | IIT consciousness (Φ) measurement & Landauer thermodynamics |
|
||||
| `exo-temporal` | Temporal memory with causal tracking & consolidation |
|
||||
| `exo-hypergraph` | Topological analysis with persistent homology |
|
||||
| `exo-manifold` | SIREN networks + SIMD-accelerated retrieval |
|
||||
| `exo-exotic` | 10 cutting-edge cognitive experiments |
|
||||
| `exo-federation` | Post-quantum federated cognitive mesh |
|
||||
| `exo-backend-classical` | SIMD-accelerated compute backend |
|
||||
| `exo-wasm` | Browser & edge WASM deployment |
|
||||
| `exo-node` | Node.js bindings via NAPI-RS |
|
||||
|
||||
### Key Theories Implemented
|
||||
|
||||
| Theory | Implementation |
|
||||
|--------|---------------|
|
||||
| **IIT (Integrated Information Theory)** | Consciousness level (Φ) measurement |
|
||||
| **Landauer's Principle** | Computational thermodynamics |
|
||||
| **Free Energy Principle** | Friston's predictive processing |
|
||||
| **Strange Loops** | Hofstadter's self-referential patterns |
|
||||
| **Morphogenesis** | Pattern formation emergence |
|
||||
|
||||
### Quick Start
|
||||
|
||||
```toml
|
||||
[dependencies]
|
||||
exo-core = "0.1"
|
||||
exo-temporal = "0.1"
|
||||
exo-exotic = "0.1"
|
||||
exo-manifold = "0.1" # SIMD acceleration!
|
||||
```
|
||||
|
||||
```rust
|
||||
use exo_core::consciousness::{ConsciousnessSubstrate, IITConfig};
|
||||
use exo_core::thermodynamics::CognitiveThermometer;
|
||||
|
||||
// Measure integrated information (Φ)
|
||||
let substrate = ConsciousnessSubstrate::new(IITConfig::default());
|
||||
substrate.add_pattern(pattern);
|
||||
let phi = substrate.compute_phi();
|
||||
println!("Consciousness level (Φ): {:.4}", phi);
|
||||
|
||||
// Track computational thermodynamics
|
||||
let thermo = CognitiveThermometer::new(300.0); // Kelvin
|
||||
let cost = thermo.landauer_cost_bits(1024);
|
||||
println!("Landauer cost: {:.2e} J", cost);
|
||||
```
|
||||
|
||||
### SIMD Pattern Retrieval
|
||||
|
||||
```rust
|
||||
use exo_manifold::{ManifoldEngine, cosine_similarity_simd, batch_distances};
|
||||
|
||||
// 54x faster similarity search
|
||||
let query = vec![0.5; 768];
|
||||
let results = engine.retrieve(&query, 10)?;
|
||||
|
||||
// Batch distance computation
|
||||
let distances = batch_distances(&query, &database); // 8-54x speedup
|
||||
```
|
||||
|
||||
> **Full Documentation**: [exo-ai README](./examples/exo-ai-2025/README.md)
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>🐘 PostgreSQL Extension</strong></summary>
|
||||
|
||||
|
|
|
|||
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