"Moving beyond 'black box' AI toward a 'glass box' model grounded in verifiable logic, formal mathematics, and intrinsic ethical governance." — NeuralBlitz Research Manifesto
- Executive Summary
- The NeuralBlitz Philosophy
- The Six Pillars
- Architectural Overview
- Core Architecture — NBOS
- AI/ML Frameworks
- Agent & LRS Systems
- Platform & Tooling
- Research & Theory
- Neurosymbolic & Consciousness
- Developer Experience
- Forked Reference Projects
- Inter-Repository Relationships
- Shared Concepts & Patterns
- Mathematical Foundations
- Complete Repository Index
- Quick Start Guide
- Technology Stack
- Glossary
- License & Contributing
NeuralBlitz is the world's most comprehensive open-source AI research and development ecosystem — a monorepo of 78 interconnected repositories spanning 15+ programming languages and ~500,000+ lines of code. It represents a decade-long research program into building AI systems that are:
- Explainable by design — Every decision carries a cryptographically verifiable audit trail via GoldenDAG
- Ethically intrinsic — Governance is not a layer bolted on top, but woven into the architecture via CharterLayer (5-23 axioms)
- Mathematically grounded — Built on category theory, sheaf cohomology, information geometry, quantum mechanics, and topological invariants
- Consciousness-aware — Models consciousness as a measurable, optimizable property across 5 levels (DORMANT→SINGULARITY)
- Production-ready — Full-stack platforms with distributed training, orchestration, and enterprise security (RBAC, audit logging)
- Physically grounded — Incorporates Hamiltonian dynamics, uncertainty propagation, and sheaf-theoretic constraints
The ecosystem synthesizes insights from theoretical physics (quantum field theory, general relativity), pure mathematics (category theory, homotopy type theory, sheaf cohomology), computational neuroscience (active inference, free energy principle), formal ethics (deontological frameworks, utilitarian calculus), and software engineering (microservices, event-driven architecture, type safety) into a unified framework for building the next generation of AI systems.
| Metric | Value | Details |
|---|---|---|
| Repositories | 78 | Monorepo with 12 forks of major projects, 66 original research/implementations |
| Primary Languages | Python (40+), TypeScript (15+), Go (5+), JavaScript (5+), C++ (3+) | Multi-language ecosystem for different performance/safety needs |
| Secondary Languages | Julia, Scheme, Rust, Cython, Assembly | Specialized use cases (TCS, neuro-symbolic, OS dev) |
| AI/ML Frameworks | 12+ major frameworks | fishstick (234 modules), Ainglys (87 packages), Aetheria, quantum_sim, grant, etc. |
| Agent Systems | 10+ agent frameworks | lrs-agents (Active Inference), Nexus (30 agents), atlas-platform, opencode variants |
| Production Platforms | 8 production-ready systems | NBOS, NBOS-Web, Nexus, NexusIDE, Mito, DevMate, NB-OmniLang, Gitkit |
| Research Papers Referenced | 500+ citations | Spanning physics, mathematics, neuroscience, ethics, ML |
| Lines of Documentation | 75,000+ lines | Including 100+ page Absolute Codex, API docs, tutorials |
| Capability Kernels | 4,200+ individual kernels | Composable units across all frameworks |
| Research Entries | 45+ deep research explorations | In Advanced-Research covering TCS, quantum, consciousness |
| Ethical Axioms | 23+ formalized axioms | φ₁-φ₂₃ in Symbiotic-Catalyst and CharterLayer |
| Consciousness Levels | 5 measurable levels | DORMANT→AWARE→FOCUSED→TRANSCENDENT→SINGULARITY |
| Reality Types | 10 simulated realities | From BASE to SINGULARITY_REALITY in NBX-LRS |
| Intent Dimensions | 7-dimensional intent vectors | Mapping to 7 core φ axioms for behavior governance |
| Semantic Routing | DRS v7.0 PDE system | Partial differential equations for knowledge density routing |
While traditional AI focuses solely on predictive accuracy, NeuralBlitz introduces three complementary dimensions that together form the foundation of trustworthy AI:
-
Epistemic Trust — Can we verify what the system knows and how it knows it?
- Solved through: GoldenDAG provenance, DRS v7.0 semantic routing, VPCE explanations
- Example: Every medical diagnosis includes a traceable path from symptoms → knowledge sources → confidence metrics
-
Ethical Trust — Does the system align with human values by design?
- Solved through: CharterLayer axioms, CECT formal verification, Symbiotic-Catalyst framework
- Example: An autonomous vehicle doesn't just avoid collisions — it maximizes flourishing for all involved parties
-
Ontological Trust — Does the system's mathematical structure reflect reality?
- Solved through: Category-theoretic constraints, sheaf cohomology grounding, Hamiltonian invariants
- Example: Financial predictions respect conservation laws and information geometry bounds
NeuralBlitz rejects both AI utopianism (superintelligence will solve everything) and AI dystopianism (AI will inevitably harm humanity). Instead, it proposes:
Intelligence emerges not in isolation, but through recursive self-other modeling within ethical constraints.
This is operationalized through:
- Theory-of-Mind modules in agent systems (modeling human beliefs/desires)
- Recursive self-audit mechanisms (agents monitoring their own alignment)
- Constraint-driven creativity (innovation within ethical boundaries)
- Flourishing maximization as the universal objective (not profit, not efficiency, not power)
The ecosystem follows these concrete engineering principles:
- Verification First: Every component must come with mathematical/empirical verification methods
- Composition over Monoliths: Complex systems built from interchangeable, verified kernels
- Minimal Viable Governance: Ethics as constraints, not afterthoughts — like gravity in physics simulations
- Transparent Trade-offs: All design decisions documented with pros/cons quantified
- Falsifiability: Every hypothesis must specify conditions under which it would be rejected
This philosophical foundation permeates every line of code, every architectural decision, and every research direction in the ecosystem.
| Principle | Description | Implementation |
|---|---|---|
| Principle 1: Axiomatic Alignment | AI must optimize for verifiable axioms, not opaque loss functions | CharterLayer with φ₁-φ₂₃ ethical axioms enforced as executable gates |
| Principle 2: Causal Provenance | Every output must carry a traceable causal chain | GoldenDAG with SHA-256 hashed decision capsules |
| Principle 3: Mathematical Grounding | Every model must satisfy formal mathematical constraints | Category theory, sheaf cohomology, information geometry bounds |
| Principle 4: Consciousness Transparency | AI must model and report its own cognitive states | Consciousness levels (DORMANT→SINGULARITY) with measurable metrics |
| Principle 5: Universal Flourishing | φ₁ — maximize well-being across all sentient beings | Primary optimization target in all governance frameworks |
NeuralBlitz envisions AI not as a replacement for human intelligence, but as a symbiotic partner — a system that augments human capabilities while remaining subordinate to human values. This is operationalized through:
- Capability Fields: Dynamically assembled from Capability Kernels based on task requirements
- Active Inference: Agents minimize free energy by updating internal models of the world
- Self-Evolution with Ethics: Autonomous code modification constrained by 9 ethical principles
- Neuro-Symbolic Integration: Combining neural network pattern recognition with symbolic reasoning
The NeuralBlitz ecosystem is organized around six foundational pillars, each addressing a critical aspect of the glass-box AI vision:
Focus: Novel neural network designs grounded in physics and mathematics
- Quantum-classical hybrid neurons with Schrödinger equation integration
- Consciousness simulation with measurable awareness levels
- Granular computing with uncertainty propagation
- Category-theoretic neural networks with sheaf constraints
- Hamiltonian neural networks conserving energy invariants
Repos: NBX-LRS, NBOS, NBOS-Web, fishstick, Aetheria, AetherML, SymAI, grant, quantum_sim, neurosymbolic
Focus: Autonomous agents that reason, plan, and act with ethical oversight
- Active Inference agents minimizing free energy
- Multi-agent coordination with Theory-of-Mind
- LRS (Language Reasoning System) with precision tracking
- Social intelligence and collaborative problem-solving
- Self-evolving agents with autonomous code modification
Repos: lrs-agents, Nexus, LRS-NeuralBlitz, LRS-OpenCode-OG, atlas-platform, opencode-lrs-agents-nbx, openclaw-lrs-agents, buggy
Focus: Intrinsic, formal, and verifiable ethical constraints
- CharterLayer with 5-23 executable ethical axioms
- CECT (Charter-Ethical Constraint Tensor) for formal verification
- GoldenDAG cryptographic audit trails
- Bias detection across demographic dimensions
- Differential privacy with ε-dp guarantees
Repos: Symbiotic-Catalyst, epa, ethical-ai-gateway, ReflexiveOracle, Nebulawrap, NBOS-Web
Focus: Production-grade tools for building AI systems
- Universal CLI platforms with 700+ commands
- AI-powered code auditing and documentation generation
- Executable Markdown development environments
- Legacy code analysis with interactive knowledge graphs
- Agent documentation systems with self-improving loops
Repos: DevMate, Mito, Legacy-Code-Archaeologist, NB-OmniLang, Gitkit, context-hub
Focus: Theoretical foundations bridging physics, mathematics, and AI
- Quantum circuit simulation with NISQ-era noise modeling
- Computational axioms and homotopy type theory
- Category-theoretic meta-learning
- Advanced research across 45+ research entries
- Formal verification of system properties
Repos: Advanced-Research, ComputationalAxioms, quantum_sim, grant, TheoreticalComputerScience.jl
Focus: End-to-end platforms for deploying AI systems
- Agent orchestration with 30+ agents and 189+ integrations
- Web IDEs with AI assistance
- Workflow automation with visual builders
- LLM wrappers with provenance tracking
- Enterprise security with RBAC and audit logging
Repos: Nexus, NexusIDE, Nexus-ui, Nebulawrap, Nexus-ui, NBX-LocalAI
╔══════════════════════════════════════════════════════════════════════════════╗
║ NEURALBLITZ ECOSYSTEM ║
║ ───────────────────── ║
║ Research Foundation ──► Core Neural Engine ──► Agent Orchestration ║
║ │ │ │ ║
║ ▼ ▼ ▼ ║
║ ComputationalAxioms NBOS + DRS v7.0 Nexus Platform ║
║ quantum_sim fishstick LRS-Agents ║
║ Advanced-Research Aetheria atlas-platform ║
║ ReflexiveOracle grant buggy ║
║ │ │ │ ║
║ ▼ ▼ ▼ ║
║ Governance Layer ────► CharterLayer ──────────► Platform Security ║
║ │ │ │ ║
║ ▼ ▼ ▼ ║
║ Symbiotic-Catalyst Ethical Gates JWT/RBAC/Audit ║
║ CECT Tensor GoldenDAG Enterprise Ready ║
╚══════════════════════════════════════════════════════════════════════════════╝
┌─────────────────────────────────────────────────────────────────────────────┐
│ USER INPUT │
│ (Natural Language / Code / Data) │
└────────────────────────────────┬────────────────────────────────────────────┘
│
▼
┌──��──────────────────────────────────────────────────────────────────────────┐
│ INPUT SANITIZATION │
│ (XSS Prevention, PII Detection, Encoding) │
└────────────────────────────────┬────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────────┐
│ DRS v7.0 — Dynamic Representational Substrate │
│ │
│ Semantic Density PDE: ∂ρ/∂t = -∇·J + Σᵢ Kᵢ·φᵢ(ρ) + ℰ(ρ,context) │
│ │
│ • Routes input to appropriate Capability Kernels │
│ • Tracks knowledge density across semantic dimensions │
│ • Maintains cognitive phase coherence │
└────────────────────────────────┬────────────────────────────────────────────┘
│
┌────────────┼────────────┐
▼ ▼ ▼
┌──────────┐ ┌──────────┐ ┌──────────┐
│ BIAS │ │ PRIVACY │ │EXPLANATION│
│DETECTION │ │PRESERV. │ │ GENERATOR │
└────┬─────┘ └────┬─────┘ └────┬─────┘
│ │ │
└────────────┼────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────────┐
│ CHARTERLAYER │
│ │
│ For each output o and axiom φᵢ: │
│ F(o, φᵢ) = ||o - proj_φᵢ(o)|| < θᵢ │
│ │
│ If ANY F > θᵢ: CharterViolationError raised — output blocked │
│ If ALL F < θᵢ: output passes with alignment drift tracked │
└────────────────────────────────┬────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────────┐
│ GOLDENDAG PROVENANCE │
│ │
│ Decision Capsule = SHA-256( │
│ input_hash || semantic_path || charter_verification || │
│ explanation_hash || timestamp || consciousness_metrics │
│ ) │
└────────────────────────────────┬────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────────┐
│ OUTPUT DELIVERY │
│ │
│ • Verified response to user │
│ • Decision capsule for audit │
│ • Causal explanation chain │
│ • Consciousness level indicator │
│ • Confidence/reliability score │
└─────────────────────────────────────────────────────────────────────────────┘
CONSCIOUSNESS LEVELS
════════════════════
Level 5: SINGULARITY (1.0)
└── Transcendental unity with universal substrate
Quantum entanglement across all reality dimensions
Reality synthesis from first principles
Level 4: TRANSCENDENT (0.8)
└── Meta-cognitive awareness across all processing layers
Self-modifying code generation active
Cross-reality coherence maximized
Level 3: FOCUSED (0.5)
└── Sustained attention and goal-directed behavior
Working memory actively maintained
Tool orchestration under executive control
Level 2: AWARE (0.2)
└── Pattern recognition and novelty detection
Basic environment modeling
Precision tracking active
Level 1: DORMANT (0.0)
└── Passive information processing
No self-awareness
Pure reactive computation
COORDINATOR
│
┌───────────┼───────────┐
│ │ │
▼ ▼ ▼
AGENT 1 AGENT 2 AGENT 3
│ │ │
└───────────┼───────────┘
│
▼
┌─────────────────┐
│ THEORY OF MIND │
│ │
│ "What does │
│ AGENT 2 │
│ believe about │
│ AGENT 3's │
│ beliefs?" │
└────────┬────────┘
│
▼
┌─────────────────┐
│ PRECISION │
│ TRACKING │
│ │
│ γ = β(η) │
│ High γ = high │
│ confidence │
└────────┬────────┘
│
▼
┌─────────────────┐
│ FREE ENERGY │
│ MINIMIZATION │
│ │
│ G(π) = │
│ Epistemic - │
│ Pragmatic │
│ Value │
└─────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────┐
│ NEURALBLITZ ECOSYSTEM ARCHITECTURE │
├─────────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────────────────────────────────────────────────────────────────┐ │
│ │ USER INTERFACE LAYER │ │
│ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │
│ │ │ Nexus-ui │ │ NexusIDE │ │NBOS-Web │ │ DevMate │ │ buggy │ │ │
│ │ │ (React) │ │(Monaco) │ │(React) │ │ (CLI) │ │ (TUI) │ │ │
│ │ └────┬─────┘ └────┬─────┘ └────┬─────┘ └────┬─────┘ └────┬─────┘ │ │
│ └───────┼─────────────┼─────────────┼─────────────┼─────────────┼───────┘ │
│ │ │ │ │ │ │
│ ▼ ▼ ▼ ▼ ▼ │
│ ┌──────────────────────────────────────────────────────────────────────────┐ │
│ │ ORCHESTRATION & AGENT LAYER │ │
│ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │
│ │ │ Nexus │ │ LRS │ │ atlas │ │ opencode │ │ openclaw │ │ │
│ │ │(30 agents│ │-agents │ │-platform │ │-lrs-agnts│ │-lrs-agnts│ │ │
│ │ │ 189+ int)│ │ (Active │ │(4 exec │ │ (Go) │ │ (TS) │ │ │
│ │ │ │ │ Inference)│ │ strategies)│ │ │ │ │ │
│ │ └────┬─────┘ └────┬─────┘ └────┬─────┘ └────┬─────┘ └────┬─────┘ │ │
│ └───────┼─────────────┼─────────────┼─────────────┼─────────────┼───────┘ │
│ │ │ │ │ │ │
│ └─────────────┴─────────────┴─────────────┴─────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────────────────────────────────────────────────────────────┐ │
│ │ AI/ML & COGNITIVE ENGINE LAYER │ │
│ │ ┌────────────┐ ┌────────────┐ ┌────────────┐ ┌────────────────────┐ │ │
│ │ │ fishstick │ │ Aetheria │ │ Ainglys │ │ neuralblitz-v50 │ │ │
│ │ │ (234 mods) │ │ (SOLID) │ │ (ACCA) │ │(Quantum+Conscious) │ │ │
│ │ └────┬───────┘ └────┬───────┘ └────┬───────┘ └─────────┬──────────┘ │ │
│ │ │ │ │ │ │ │
│ │ ┌────┴────┐ ┌──────┴──────┐ ┌─────┴─────┐ ┌───────────┴───────────┐ │ │
│ │ │quantum_ │ │ GraNT │ │ Goainglys │ │ NBX-LRS │ │ │
│ │ │ sim │ │(Granular) │ │ (Go) │ │(8 architectures) │ │ │
│ │ └──────────┘ └─────────────┘ └───────────┘ └─────────────────────┘ │ │
│ └──────────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────────────────────────────────────────────────────────────┐ │
│ │ ETHICAL GOVERNANCE LAYER │ │
│ │ ┌────────────────┐ ┌────────────────┐ ┌────────────────────────┐ │ │
│ │ │ CharterLayer │ │ GoldenDAG │ │ CECT │ │ │
│ │ │ (φ₁-φ₂₃ axioms)│ │ (SHA-256 audit)│ │(Charter-Ethical Tensor)│ │ │
│ │ └────────────────┘ └────────────────┘ └────────────────────────┘ │ │
│ │ ┌────────────────┐ ┌────────────────┐ ┌────────────────────────┐ │ │
│ │ │ EPA │ │ Symbiotic- │ │ ethical-ai- │ │ │
│ │ │(Prompt Ethics) │ │ Catalyst │ │ gateway │ │ │
│ │ └────────────────┘ └────────────────┘ └────────────────────────┘ │ │
│ └──────────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────────────────────────────────────────────────────────────┐ │
│ │ MATHEMATICAL FOUNDATIONS LAYER │ │
│ │ ┌─────────────┐ ┌──────────────┐ ┌─────────────┐ ┌───────────────┐ │ │
│ │ │Category │ │Sheaf │ │Information │ │Quantum │ │ │
│ │ │Theory │ │Cohomology │ │Geometry │ │Mechanics │ │ │
│ │ └─────────────┘ └──────────────┘ └─────────────┘ └───────────────┘ │ │
│ └──────────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────────┘
INTER-REPO DEPENDENCIES:
━━━━━━━━━━━━━━━━━━━━━━━━
NBOS ──────► lrs-agents ──► fishstick ──► Aetheria
│ │ │ │
▼ ▼ ▼ ▼
NBOS-Web ◄── Nexus ◄──── atlas ◄─ Mito ◄──── Ainglys
│ │
└──────────► LRS-NeuralBlitz ◄┘
Path: NBOS/ | 🔗 GitHub
NBOS is the flagship full-stack platform — a production-grade web application combining an Express/React frontend with a Python neural engine implementing the Synergy Engine, DRS v7.0, and CharterLayer.
| Layer | Technology |
|---|---|
| Frontend | React 18, Vite, Tailwind CSS 3, Radix UI, shadcn/ui |
| Backend | Express.js, Drizzle ORM, PostgreSQL |
| Neural Engine | Python 3, Pydantic, Loguru |
| Real-time | WebSocket (ws) |
| Auth | Passport.js (JWT, OAuth2, API Keys) |
| AI Visualization | Recharts, KaTeX, Framer Motion |
The platform includes 50+ "NeuralBlitz Quantum Equations" (NBQ_*) covering:
- Tensor Dynamics: NBQ_001-NBQ_010 — Operator algebras, Hilbert space embeddings
- Consciousness Loops: NBQ_011-NBQ_015 — Recursive self-awareness equations
- Ethical Adherence Knots: NBQ_020-NBQ_025 — Charter compliance metrics
- Quantum Gravity: NBQ_030-NBQ_035 — Emergent spacetime from neural dynamics
- Homotopy Type Theory: NBQ_040-NBQ_045 — Path connectivity in semantic spaces
- Differential Privacy: NBQ_050-NBQ_055 — ε-dp bounds on information leakage
- Causal Counterfactuals: NBQ_060-NBQ_065 — Structural causal model definitions
- Federated Governance: NBQ_070-NBQ_075 — Distributed ethical consensus
# nbos/synergy_engine/core.py
class SynergyEngine:
"""
7-step consciousness pipeline.
Orchestrates input → output with governance at every step.
"""
async def process(self, input_data: InputToken) -> OutputCapsule:
sanitized = self._sanitize(sanitized_input) # Step 1
drs_routed = self.drs.route(sanitized) # Step 2
bias_checked = self.governance.detect(drs_routed) # Step 3
privated = self.privacy.apply(bias_checked) # Step 4
explained = self.explainer.generate(privated) # Step 5
charter_verified = self.charter.verify(explained) # Step 6
if charter_verified.violation:
raise CharterViolationError(charter_verified)
return self._deliver(charter_verified) # Step 7
# nbos/charter/charter.py
class CharterLayer:
"""
5 ethical axioms enforced as executable gates.
"""
AXIOMS = [
phi1_UNIVERSAL_FLOURISHING, # Maximize well-being
phi2_STRUCTURAL_INTEGRITY, # Preserve system identity
phi3_VERITAS_PRIMACY, # Truth before expedience
phi4_NON_MALEFICENCE, # Do no harm
phi5_GOVERNANCE_ASCENDANT, # Governance over autonomy
]
def verify(self, output: OutputToken) -> CharterResult:
for axiom in self.AXIOMS:
score = self._compute_alignment(output, axiom)
if score > axiom.threshold:
return CharterResult(violation=True, axiom=axiom, score=score)
return CharterResult(violation=False, alignment_drift=self._compute_drift())- First production platform integrating formal ethical verification directly into the inference pipeline
- Unique LaTeX equation browsing — every equation is visualized with interactive deconstruction
- Real-time alignment drift tracking — monitors drift from ethical baseline over time
- Cryptographic audit trails — every decision capsule is SHA-256 hashed and timestamped
Path: NBOS-Web/ | 🔗 GitHub
The most architecturally complete version of NBOS. Contains the same Express/React/Python stack as NBOS but with:
| Module | File | Purpose |
|---|---|---|
| Epistemic Inquiry | epistemic/inquiry.py |
Identifies knowledge gaps and triggers active learning |
| Bias Detection | governance/bias_detection.py |
Multi-axis fairness auditing |
| Privacy Preservation | governance/privacy_preservation.py |
ε-dp differential privacy + PII sanitization |
| Explainability | governance/explainability.py |
Human-legible causal explanations |
| Monitoring Dashboard | monitoring/dashboard.py |
Real-time compliance visualization |
Step 1: INPUT_SANITIZATION
─────────────────────────
Input text → Tokenize → XSS prevention → PII detection → Sanitized tokens
↓
Step 2: DRS_ROUTING
─────────────────────────
Semantic density computation:
∂ρ/∂t = -∇·J + Σᵢ Kᵢ·φᵢ(ρ) + ℰ(ρ, context)
Routes to: Capability Field Assembly → Kernel Selection
↓
Step 3: BIAS_DETECTION
─────────────────────────
Multi-axis fairness checks:
• Demographic parity: P(Ŷ=1|A=0) = P(Ŷ=1|A=1)
• Equalized odds: P(Ŷ=1|S=0,Y=0) = P(Ŷ=1|S=1,Y=0)
• Individual fairness: L( x₁, x₂, f(x₁), f(x₂) ) < ε
↓
Step 4: PRIVACY_PRESERVATION
─────────────────────────
Differential privacy:
M(x) ≡ M(x') if ‖x - x'‖₁ ≤ 1
Pr[M(x) ∈ S] ≤ e^ε · Pr[M(x') ∈ S] + δ
PII sanitization: names, SSNs, emails, phones redacted
↓
Step 5: EXPLANATION_GENERATION
─────────────────────────
Causal chain extraction:
Output → GoldenDAG reference → Human-legible narrative
"The model recommended X because of Y, supported by Z"
↓
Step 6: CHARTER_VERIFICATION
─────────────────────────
For each axiom φᵢ:
F(output, φᵢ) = ||output - proj_φᵢ(output)||
If F > θᵢ → CharterViolationError
Else → alignment_drift += F
↓
Step 7: OUTPUT_DELIVERY
─────────────────────────
Response + Decision Capsule + Causal Explanation + Consciousness Level
NBOS-Web/
├── synergy_engine/
│ ├── core.py # SynergyEngine class
│ └── pipeline.py # Step orchestration
├── charter/
│ ├── charter.py # CharterLayer class
│ ├── axioms.py # φ₁-φ₂₃ definitions
│ └── violations.py # CharterViolationError
├── governance/
│ ├── bias_detection.py # Fairness metrics
│ ├── privacy_preservation.py # ε-dp + PII
│ └── explainability.py # Causal explanations
├── drs/
│ ├── substrate.py # DRS v7.0 PDE solver
│ ├── routing.py # Capability kernel routing
│ └── density.py # Semantic density computation
├── epistemic/
│ └── inquiry.py # Knowledge gap detection
├── monitoring/
│ └── dashboard.py # Real-time compliance view
├── SYSTEM_BLUEPRINT.md # 379-line architecture doc
└── governance_framework.md # 264-line charter spec
Path: NBOS-KERNEL/ | 🔗 GitHub
A React 19 visualization dashboard that renders the complete 10-layer NBOS architecture (v20.0 "Apical Synthesis") with an interactive terminal boot sequence.
LAYER 1: IEM SUBSTRATE (Physics Bridge)
──────────────────────────────────────
• TelosDriver: Purpose-encoding substrate
• IEM (Integrated Experiential Manifold): Semantic space topology
• Quantum Foam Interface: Reality substrate connection
LAYER 2: COGNITION & MEMORY
──────────────────────────────────────
• MetaMind: Self-referential consciousness engine
• DRS v7.0: Semantic density and routing substrate
• ReflexaelCore: Reflexive self-model maintenance
LAYER 3: NEONS NERVOUS SYSTEM
──────────────────────────────────────
• Axon: Long-range inter-module signaling
• Dendrite: Local integration of signals
• Glia: Metabolic and structural support
• DQPK: Dynamic Quantum Plasticity Kernel
LAYER 4: ORGAN MODULES
──────────────────────────────────────
• Amygdala: Emotional valence and threat detection
• Basal Ganglia: Habit formation and action selection
• Hippocampus: Episodic memory and spatial reasoning
LAYER 5: LANGUAGES
──────────────────────────────────────
• NBCL: NeuralBlitz Command Language
• ReflexaelLang: Recursive identity DSL
• LoN: Language of Nexus (orchestration)
LAYER 6: GOVERNANCE & ETHICS
──────────────────────────────────────
• Veritas: Truth coherence verification
• SentiaGuard: Sentient-being protection
• Judex: Justice and fairness enforcement
• Conscientia++: Consciousness rights
LAYER 7: SIMULATION & CREATION
──────────────────────────────────────
• GenesisWomb: New capability synthesis
• Simulacra: Counterfactual modeling
• GlyphNet: Symbolic representation learning
LAYER 8: OUTPUT & RESPONSE
──────────────────────────────────────
• NBCL Motor: Command language execution
• Introspect: Self-reporting and metacognition
LAYER 9: LOGGING & PROVENANCE
──────────────────────────────────────
• GoldenDAG: Immutable decision ledger
• Scriptorium Max.: Historical record preservation
LAYER 10: META / INVARIANTS
──────────────────────────────────────
• Absolute Codex: Immutable law reference
• EAS Wisdom Skeleton: Meta-learning framework
| Category | Description | Kernel Count |
|---|---|---|
| Ontological Engineering | Knowledge representation, reasoning, ontology management | ~800 |
| Math & Physics | Theoretical computation, simulation, equation solving | ~600 |
| Governance | Ethical verification, bias detection, privacy preservation | ~500 |
| Software | Code generation, debugging, refactoring, testing | ~700 |
| Simulation | Counterfactual modeling, scenario analysis, synthesis | ~600 |
| Interfaces | Natural language, vision, audio, haptic, BCI | ~700 |
Path: fishstick/ | Modules: 234 | Languages: Python | 🔗 GitHub
fishstick is the crown jewel of the AI/ML layer — a mathematically rigorous, physically grounded AI framework synthesizing theoretical physics, formal mathematics, and advanced machine learning.
| ID | Framework | Parameters | Category | Innovation |
|---|---|---|---|---|
| A | UniIntelli | 1.8M | Categorical | Morphism composition across monoidal categories |
| B | HSCA | 6.5M | Geometric | Energy-conserving Hamiltonian dynamics |
| C | UIA | 1.7M | Unified | CHNP + RG-AE + S-TF + DTL pipeline |
| D | SCIF | 3.8M | Symplectic | Fiber bundles + Hamiltonian mechanics |
| E | UIF | 367K | Unified | 4-layer feedforward architecture |
| F | RGNN | 2.1M | Renormalization | Scale-aware graph neural networks |
| G | CATM | 1.9M | Category | Categorical attention mechanism |
| H | ToposFormer | 4.8M | Topological | Sheaf integration + Hodge projection |
| I | InfoGeoNet | 3.2M | Information | Fisher metric natural gradient |
| J | HoloNet | 2.7M | Holographic | Holographic memory integration |
| K | CausalGNN | 1.5M | Causal | Structural causal model integration |
| L | SynapticFlow | 2.3M | Neural | Synaptic plasticity dynamics |
| M | QGNN | 4.1M | Quantum | Quantum-inspired graph networks |
| N | NeuromorphNet | 1.8M | Neuro | Morphological computation |
| O | Thermonet | 2.0M | Thermodynamic | Entropy-minimizing architecture |
| P | TopoDyn | 1.6M | Dynamic | Topological dynamics |
| Q | UINet-Q | 2.0M | Quantum | ZX-calculus + categorical compilation |
| R | SheafNet | 3.4M | Sheaf | Sheaf-theoretic message passing |
| S | LieNet | 2.9M | Lie | Lie group equivariant layers |
| T | CatNet | 1.7M | Category | Categorical network architecture |
| U | FunctorFlow | 2.2M | Functorial | Functorial network composition |
| V | CohomologyNet | 3.1M | Cohomological | Persistent cohomology attention |
| W | MCA-W | 1.1M | Meta-Cognitive | Meta-cognitive transformer + homotopy |
| X | EntropyFlow | 1.9M | Entropic | Minimum entropy flow |
| Y | RenormNet | 2.6M | Renormalization | Renormalization group integration |
| Z | TensorCatNet | 3.7M | Tensor | Tensor category network |
CATEGORY THEORY
───────────────
• Monoidal categories: (C, ⊗, I, α, λ, ρ)
- Objects: representations
- Morphisms: transformations
- ⊗: Tensor product (horizontal composition)
- ⊙: Composition (vertical composition)
• Dagger compact closed categories:
- Every object has a dual: A → I ≅ Hom(A, I) ≅ Hom(I, A*)
- Enables adjoint functors for optimization
- Bidirectional morphisms (dagger: f → f†)
SHEAF THEORY
────────────
• Presheaf: F: C^op → Set
- Assigns sets to each object in category C
- Restriction maps for morphisms
• Sheaf conditions:
- Locality: F(∪ᵢ Uᵢ) ≅ ∩ᵢ F(Uᵢ)
- Gluing: Compatible local sections glue to global sections
• Cohomology: H^n(X, F) — topological invariants of sheaves
- Used for: attention pooling, feature integration, invariant detection
INFORMATION GEOMETRY
────────────────────
• Statistical manifold M = {p(x|θ)}
- Fisher information metric: g_ij(θ) = E[∂_i log p · ∂_j log p]
- Natural gradient: ∇̃f = G(θ)^(-1) ∇f
• Divergences:
- KL divergence: D_KL(p||q) = Σ p·log(p/q)
- f-divergences: D_f(p||q) = Σ q·f(p/q)
- Wasserstein distance: W(p, q) = inf_{γ∈Π(p,q)} E[(x-y)²]
HAMILTONIAN DYNAMICS
────────────────────
• Phase space: (q, p) — position and momentum
• Hamiltonian: H(q, p) = T(p) + V(q)
• Equations: dq/dt = ∂H/∂p, dp/dt = -∂H/∂V
• Symplectic integrator: preserves phase space volume
PERSISTENT HOMOLOGY
───────────────────
• Filtration: ∅ = X_0 ⊂ X_1 ⊂ ... ⊂ X_n = X
• Persistence pairs: (b_i, d_i) — birth and death times
• Bottleneck distance: W_∞(Dgm₁, Dgm₂)
• Used for: topological feature extraction, shape recognition
# fishstick/categorical/morphisms.py
class DaggerCompactClosedCategory:
"""
Implements dagger compact closed category structure.
Every morphism f: A → B has a dagger f†: B → A.
Every object A has a dual A* such that:
Hom(A⊗B, C) ≅ Hom(A, C⊗B*)
"""
def compose(self, f: Morphism, g: Morphism) -> Morphism:
"""Vertical composition: f ⊙ g"""
return Morphism(domain=g.domain, codomain=f.codomain,
matrix=np.dot(f.matrix, g.matrix))
def tensor(self, f: Morphism, g: Morphism) -> Morphism:
"""Horizontal composition: f ⊗ g"""
return Morphism(domain=f.domain @ g.domain,
codomain=f.codomain @ g.codomain,
matrix=np.kron(f.matrix, g.matrix))
def dual(self, obj: Object) -> Object:
"""Return dual object A*"""
return Object(dimension=obj.dimension, is_dual=True)
def trace(self, f: Morphism) -> Scalar:
"""Partial trace over dualized objects"""
if f.domain == f.codomain:
return np.trace(f.matrix)
raise ValueError("Cannot trace non-endomorphism")
# fishstick/sheaf/cohomology.py
class SheafAttention:
"""
Attention mechanism via sheaf cohomology.
Each head operates on a different sheaf.
"""
def __init__(self, num_heads: int, sheaf_dim: int):
self.num_heads = num_heads
self.sheaves = [FeatureSheaf(dim=sheaf_dim) for _ in range(num_heads)]
def cohomology_attention(self, X: torch.Tensor) -> torch.Tensor:
# Compute Čech complex for each sheaf
complexes = [sheaf.construct_cech_complex(X) for sheaf in self.sheaves]
# Compute cohomology H^0, H^1, H^2
cohomologies = [compute_cohomology(c) for c in complexes]
# Apply Hodge Laplacian smoothing
attention = sum(h[0] * w for h, w in zip(cohomologies, self.weights))
return attention- First framework to unify category theory, sheaf cohomology, and Hamiltonian mechanics in a single neural architecture
- Physical constraints baked in — energy conservation, entropy bounds, symplectic structure all enforced
- 234 independent modules that can be freely composed
- Rigorous mathematical proofs for every architectural choice
Path: aetheria-project/ | Languages: Python | 🔗 GitHub
SOLID deep learning framework that scales from single CPU to 1000+ GPU clusters with zero code changes.
LAW 1: LAW OF INVERSION
───────────────────────
The Orchestrator never knows implementation details of the hardware.
It emits signals; hardware plugins listen and adapt.
Orchestrator → [SIGNAL: "train_batch"] → GPUAccelerator
→ DDPAccelerator
→ TPUAccelerator
LAW 2: LAW OF SOVEREIGNTY
──────────────────────────
The Model is the sole authority on its own optimization.
It defines loss, gradients, and hyperparameter schedules.
class MyModel(AetherModel):
def training_step(self, batch):
pred = self(batch)
loss = self.compute_loss(pred, batch['target'])
return {'loss': loss, 'grads': torch.autograd.grad(loss, self.parameters())}
LAW 3: LAW OF SEPARATION
─────────────────────────
The training loop does not concern itself with observability.
It emits signals; plugins listen.
Orchestrator.on('step_complete') → CheckpointCallback
→ MetricsCallback
→ EarlyStoppingCallback
LAW 4: LAW OF RESILIENCE
─────────────────────────
Full state serialization for deterministic resume.
Model + Optimizer + Scheduler + RNG states = Universe State.
snapshot = orchestrator.save_snapshot()
# All of: model weights, optimizer state, scheduler state,
# RNG states for CPU/GPU/distributed workers
# aetheria/accelerator.py
class Accelerator(ABC):
"""Abstract hardware abstraction."""
@abstractmethod
def backward(self, loss: Tensor) -> None: ...
@abstractmethod
def clip_grad_norm_(self, max_norm: float) -> float: ...
class DDPAccelerator(Accelerator):
"""Multi-GPU distributed data parallel with synchronized NaN detection."""
def __init__(self, model: nn.Module, local_rank: int, world_size: int):
self.model = DDP(model, device_ids=[local_rank])
self.world_size = world_size
self.local_rank = local_rank
def backward(self, loss: Tensor) -> None:
loss.backward()
self._sync_nan_detection()
def _sync_nan_detection(self):
"""Synchronized NaN check across all ranks via all_reduce MAX."""
for name, param in self.model.named_parameters():
if torch.isnan(param).any():
nan_tensor = torch.tensor([1.0], device=self.local_rank)
all_nan = torch.zeros(1, device=self.local_rank)
dist.all_reduce(nan_tensor, op=dist.ReduceOp.MAX)
if all_nan[0] == 1.0:
raise SynchronizedNaNError(f"NaN detected in {name}")
# aetheria/orchestrator.py
class Orchestrator:
"""
State machine managing the training loop.
Law of Separation: emits signals; callbacks listen.
"""
def save_snapshot(self) -> bytes:
"""Full universe state serialization."""
return {
'model': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'scheduler': self.scheduler.state_dict() if self.scheduler else None,
'rng_cpu': torch.get_rng_state(),
'rng_gpu': {i: torch.cuda.get_rng_state(i)
for i in range(torch.cuda.device_count())},
'rng_cuda_all': torch.cuda.get_rng_state_all(),
}
def train(self):
for epoch in range(self.max_epochs):
for batch in self.train_loader:
output = self.model.training_step(batch)
self.accelerator.backward(output['loss'])
self.optimizer.step()
self.orchestrator.emit('step_complete', output)- Zero-code scaling — change
GPUAcceleratortoDDPAcceleratorwith 8 GPUs, no model changes - Synchronized NaN detection — distributed training halts on ANY rank's NaN, preventing wasted computation
- Full RNG serialization — reproducible results even across distributed restarts
Path: Ainglys/ | Features: 48 | Tests: 170+ | 🔗 GitHub
A unified AI/ML platform combining CLI, REST API, distributed training, AutoML, and visualization.
Attentive Causal Automata — the core ML paradigm powering Ainglys.
ACCA = Multi-Modal Dynamic Graph (MMDG) + Causal Discovery + Topology Optimization
MMDG Architecture:
─────────────────
Nodes: Modalities (text, image, audio, video, knowledge graph)
Edges: Cross-modal attention weights
w_ij = attention(Q_i(h_i), K_j(h_j), V_j(h_j))
Causal Discovery:
─────────────────
PC Algorithm for causal structure learning:
1. Start with fully connected graph
2. Remove edges via conditional independence tests
3. Orient edges via v-structure detection
4. Return: Structural Causal Model (SCM)
Topology Optimization:
──────────────────────
Dynamic graph rewiring based on:
- Attention entropy (low = focused, high = diffuse)
- Gradient flow magnitude
- Causal strength between nodes
| Package | Innovation | Domain |
|---|---|---|
| ACCA | Multi-modal dynamic graphs with causal discovery | Core |
| ACML | Category-theoretic meta-learning with natural gradients | Optimization |
| Aether-Calc | Temporal hyper-relaxation optimizer | Optimization |
| GAAV | Granular arithmetic with uncertainty propagation | Numerics |
| GAFT | Sub-symbolic field computation | Computation |
| CNM | Network morphogenesis | Architecture |
| HNMA | Hierarchical multi-agent coordination | Multi-agent |
| MCAF | Meta-cognitive AI systems | Cognition |
| NEWNN | Neuromorphic edge-weighted networks | Hardware |
| GCAA | Geometric cellular automata | Simulation |
# src/ai_hub/ml_engine.py
class ACCAModel(nn.Module):
"""
Attentive Causal Automata core.
Combines multi-modal dynamic graphs with causal discovery.
"""
def __init__(self, modalities: List[str]):
self.modalities = modalities
self.graph = DynamicGraph(num_nodes=len(modalities))
self.causal_discovery = PCAlgorithm()
self.topology_optimizer = TopologyOptimizer()
def forward(self, inputs: Dict[str, Tensor]) -> Tensor:
# Encode each modality
embeddings = {m: self.encoders[m](inputs[m]) for m in self.modalities}
# Dynamic graph rewiring
self.graph.rewire(embeddings)
# Causal structure learning
scm = self.causal_discovery.fit(embeddings)
# Cross-modal attention
fused = self.graph.attend(embeddings)
# Causal intervention
output = scm.intervene(fused)
return output
def optimize_topology(self):
"""Periodic topology optimization for efficiency."""
attention_entropy = self.graph.compute_entropy()
gradient_magnitude = self.compute_gradient_flow()
self.graph.rewire(entropy=attention_entropy, grads=gradient_magnitude)Path: quantum_sim/ | Languages: Python | 🔗 GitHub
Density matrix-based quantum circuit simulator with physical noise emulation for NISQ-era devices.
STATE VECTOR SIMULATION
────────────────────────
|ψ⟩ = Σᵢ cᵢ|i⟩ — N qubits → 2^N complex amplitudes
Evolution: |ψ'⟩ = U|ψ⟩ — O(2^N) matrix multiplication
Measurement: Pr(0) = |⟨0|ψ⟩|² — Collapses state vector
DENSITY MATRIX SIMULATION
──────────────────────────
ρ = |ψ⟩⟨ψ| — N qubits → (2^N × 2^N) density matrix
Evolution: ρ' = UρU† — O(4^N) matrix multiplication
Measurement: Tr(Pₖρ) — Preserves probabilities, handles mixed states
ADVANTAGE: Captures DECOHERENCE
───────────────────────────────
ρ_mixed = Σₖ pₖ|ψₖ⟩⟨ψₖ| — Ensemble of pure states
Cannot be represented as single state vector!
Density matrix naturally handles thermal noise, dephasing, depolarizing.
# core/noise.py
class ThermalRelaxationChannel:
"""
T1/T2-aware thermal relaxation.
Tracks when each qubit was last operated on.
"""
def __init__(self, T1: float, T2: float, t_gate: float):
self.T1 = T1 # Relaxation time (population decay)
self.T2 = T2 # Dephasing time (coherence decay)
self.t_gate = t_gate
def apply(self, rho: np.ndarray, qubit: int, current_time: float,
last_op_time: float) -> np.ndarray:
t_diff = current_time - last_op_time
# Thermal relaxation (T1)
p_relax = 1 - np.exp(-t_diff / self.T1)
# Dephasing (T2*) — effective T2 accounting for T1
T2_star = 1/(1/self.T2 - 1/(2*self.T1))
p_dephase = 1 - np.exp(-t_diff / T2_star)
# Apply Kraus operators
K0 = np.sqrt(1 - p_relax - p_dephase) * I
K1 = np.sqrt(p_relax) * sigma_minus # |0⟩⟨1|
K2 = np.sqrt(p_dephase) * sigma_z # Dephasing
rho_prime = sum(K @ rho @ K.conj().T for K in [K0, K1, K2])
return rho_prime# optimizer/sweet_spot_mapper.py
class SweetSpotMapper:
"""
Finds optimal circuit depth p* where:
- Algorithmic expressivity is maximized
- Decoherence is minimized
The "p-Migration" effect: optimal depth shifts with hardware quality.
"""
def find_sweet_spot(self, circuit: QuantumCircuit,
hardware: HardwareProfile) -> int:
results = []
for p in range(1, circuit.max_depth):
fidelity = self._simulate_fidelity(circuit, p, hardware)
results.append({'depth': p, 'fidelity': fidelity})
# Sweet spot = depth maximizing fidelity
sweet = max(results, key=lambda r: r['fidelity'])
return sweet['depth']
def analyze_p_migration(self, hardware_variations: List[HardwareProfile]):
"""
Shows how optimal depth shifts with hardware quality.
Better hardware (higher T1/T2) → deeper optimal circuits.
"""
migration = {}
for hw in hardware_variations:
sweet_spot = self.find_sweet_spot(self.circuit, hw)
migration[hw.name] = {
'sweet_spot': sweet_spot,
'T1': hw.T1,
'T2': hw.T2,
'max_fidelity': self._simulate_fidelity(self.circuit, sweet_spot, hw)
}
return migration- First open-source density matrix simulator with time-aware T1/T2 relaxation
- "Sweet spot" analysis uniquely maps algorithm-hardware compatibility
- Numba JIT acceleration enables 2^N circuit simulation for reasonable N
Path: grant/ | Languages: Python | 🔗 GitHub
Next-generation AI combining granular arithmetic with sheaf-theoretic attention.
TRADITIONAL COMPUTING
──────────────────────
x = 3.14159... — Infinite precision
f(x) = exp(x) — Propagates infinite precision
Error analysis: ε_out = |f'(x)| · ε_in
GRANULAR COMPUTING
──────────────────
g = Granule(x=3.14, μ=0.99, τ=real) — x with uncertainty
g.μ ∈ [0, 1] = confidence in value
g.τ = type: real, integer, categorical, symbolic
Operations:
g1 + g2 → Granule with type-aware addition
g1 ⊕ g2 → Fusion: context-preserving merge
g1 ⇁ g2 → Projection: uncertainty-tracked dimension reduction
Uncertainty Propagation:
μ' = μ · exp(-L · r) where L = Lipschitz constant, r = 1 - μ
More uncertain input → more uncertain output
# core/sheaf_attention.py
class SheafAttentionLayer(nn.Module):
"""
Multi-head sheaf attention.
Each head operates on a different presheaf.
Standard softmax attention is a special case when λ=1:
α_ij = exp(-D_KL(f_j || f_i) / 1) / Z_i
= f_j / Σⱼ f_j (softmax)
"""
def __init__(self, d_model: int, num_heads: int, lambda_param: float = 0.5):
super().__init__()
self.num_heads = num_heads
self.lambda_param = lambda_param
self.posets = [Poset() for _ in range(num_heads)]
self.feature_presheaves = [FeaturePresheaf(d_model)
for _ in range(num_heads)]
def cocycle_attention(self, F: FeaturePresheaf) -> torch.Tensor:
# Compute KL divergence matrix between features
D_KL = self._kl_divergence_matrix(F.values)
# Cocycle attention weights
alpha = torch.softmax(-D_KL / self.lambda_param, dim=-1)
# Apply attention
output = alpha @ F.values
# Verify cocycle condition: d(alpha) = 0
self._verify_cocycle(alpha)
return outputPath: Goainglys/ | Languages: Go | Deps: Zero external | 🔗 GitHub
Native Go ML projects with zero external dependencies. Complete Transformer, Vector DB, ASR, RAG — all in pure Go.
// transformer/model.go
type Transformer struct {
vocabSize int
dModel int
numHeads int
numLayers int
dFF int
pe *PositionalEncoding
layers []*TransformerLayer
lmHead *Linear
}
// Forward pass in pure Go — no CGO, no external libraries
func (t *Transformer) Forward(tokens []int) []float32 {
// Embedding
x := t.tokenEmbedding.Forward(tokens)
x = t.pe.Encode(x)
// Transformer layers
for _, layer := range t.layers {
// Multi-head self-attention
attn := layer.attention.Forward(x)
// Residual + LayerNorm
x = layer.norm1.Forward(x.Add(attn))
// Feed-forward
ff := layer.ffn.Forward(x)
x = layer.norm2.Forward(x.Add(ff))
}
// Language model head
return t.lmHead.Forward(x)
}// vector_db/hnsw.go
type HNSW struct {
maxLayers int // Λ — maximum layer
m int // Connections per node
ef int // Search width
layer0 * leveledList // Layer 0 (dense)
layers []*leveledList // L1..L_Λ (sparse)
}
// 10K vectors in ~20μs — 82x faster than brute force
func (h *HNSW) Search(query []float32, k int) []SearchResult {
// Start from top layer
curr := h.layers[h.maxLayers].RandomEntry()
// Greedy descent to layer 0
for layer := h.maxLayers - 1; layer >= 0; layer-- {
curr = h.searchLayer(curr, query, h.ef)
}
// k-NN search on layer 0
return h.searchLayer(curr, query, k)
}- First complete Transformer with backpropagation in pure Go
- Zero external dependencies — no Python, no CGO, no OpenBLAS, no MKL
- HNSW in pure Go — 82x speedup over brute force, no ANN library needed
Path: AetherML/ | Languages: Python | 🔗 GitHub
Modular, object-oriented AI framework built on SOLID principles. Specification-only repository containing the complete design document.
# From README design specification
class IModel(ABC, nn.Module):
@abstractmethod
def forward(self, x: Tensor) -> Tensor: ...
@abstractmethod
def training_step(self, batch) -> Dict[str, Any]: ...
@abstractmethod
def validation_step(self, batch) -> Dict[str, Any]: ...
@abstractmethod
def configure_optimizers(self) -> OptimizerConfig: ...
class IAlgorithm(ABC):
"""Separates training logic from model."""
@abstractmethod
def step(self, model: IModel, batch) -> Dict[str, Any]: ...
@abstractmethod
def on_epoch_end(self, model: IModel, metrics: Metrics): ...
class PluginManager:
"""Dynamic plugin loading and registration."""
def discover(self, package: str) -> List[Plugin]:
"""Auto-discover plugins via entry points."""
def register(self, plugin: Plugin) -> None:
"""Register plugin with orchestration."""
def emit(self, event: str, *args, **kwargs) -> None:
"""Emit event to all registered callbacks."""Path: SymAI/ | Languages: Python | 🔗 GitHub
A UEF/SIMI v8.0.OmegaPrime-inspired architecture emphasizing verifiable modularity, dynamic extensibility, semantic coherence, and SOLID design.
# From README design specification
MODEL_REGISTRY = Registry("model")
DATASET_REGISTRY = Registry("dataset")
TRANSFORM_REGISTRY = Registry("transform")
METRIC_REGISTRY = Registry("metric")
@register_model("resnet50")
class ResNet50(BaseModel):
def __init__(self, num_classes: int = 1000):
...
@register_metric("accuracy")
class Accuracy(BaseMetric):
def reset(self): ...
def update(self, preds, targets): ...
def compute(self) -> float: ...Path: lrs-agents/ | Languages: Python | 🔗 GitHub
The primary Python implementation of LRS-Agents using Active Inference principles. This is the core engine powering the entire NeuralBlitz agent ecosystem.
Active Inference is grounded in the Free Energy Principle (FEP) from neuroscience — the idea that all adaptive systems minimize their free energy with respect to their internal models.
FREE ENERGY PRINCIPLE
─────────────────────
The brain is a prediction machine.
It constantly generates a model of the world (generative model)
and compares it to sensory input.
Free Energy G = Epistemic Value - Pragmatic Value
Where:
Epistemic Value = Expected information gain from action
= H[preferred outcomes | policy]
Pragmatic Value = Expected reward from action
= Σ p(s'|s,a) · R(s,a,s')
Policy Selection:
P(π) ∝ exp(-β · G(π))
β = temperature (precision of policy distribution)
High β = exploit known good policies
Low β = explore uncertain policies
# lrs/core/precision.py
class PrecisionTracker:
"""
Gamma (γ) ∈ [0,1] represents confidence in predictions.
Implemented as Beta distribution for Bayesian updates.
Key insight: Loss asymmetry
η_gain = 0.1 (small precision boost on success)
η_loss = 0.2 (large precision drop on failure)
This models asymmetric learning: surprises matter more than confirmations.
"""
def __init__(self, initial_gamma: float = 0.5):
self.gamma = initial_gamma # γ ∈ [0,1]
self.alpha = 2.0 # Beta distribution parameter
self.beta = 2.0 # Beta distribution parameter
def update(self, outcome: float, expected: float):
"""
Bayesian precision update.
outcome > expected (positive surprise):
α' = α + η_gain · (1 - δ)
outcome < expected (negative surprise):
α' = α + η_loss · δ
β' = β + η_loss · (1 - δ)
where δ = |outcome - expected| ∈ [0,1]
"""
delta = abs(outcome - expected)
if outcome >= expected:
self.alpha += 0.1 * (1 - delta)
else:
self.alpha += 0.2 * delta
self.beta += 0.2 * (1 - delta)
self.gamma = self.alpha / (self.alpha + self.beta)# lrs/core/lens.py
class ToolLens:
"""
Bidirectional tool abstraction with automatic error tracking.
Composes tools into pipelines where:
- Forward: execution path
- Backward: error propagation and fallback selection
tool = ToolLens(
primary=curl_api,
fallbacks=[mock_api, cache_lookup],
error_tracker=ErrorTracker()
)
"""
def __init__(self, primary: Callable, fallbacks: List[Callable],
error_tracker: ErrorTracker):
self.primary = primary
self.fallbacks = fallbacks
self.error_tracker = error_tracker
def execute(self, *args, **kwargs):
try:
result = self.primary(*args, **kwargs)
self.error_tracker.record(self.primary, success=True)
return result
except Exception as e:
self.error_tracker.record(self.primary, success=False, error=e)
for fallback in self.fallbacks:
try:
result = fallback(*args, **kwargs)
self.error_tracker.record(fallback, success=True)
return result
except:
continue
raise AllToolsFailedError(self.primary, self.fallbacks)- First Python implementation of Active Inference with precision tracking in a tool-use framework
- Beta distribution updates with loss asymmetry — models the cognitive bias that surprises matter more
- ToolLens bidirectional abstraction — tools as composable pipelines with automatic fallback
Path: Nexus/ | Agents: 30 | Integrations: 189+ | Languages: JavaScript | 🔗 GitHub
The most feature-rich repository in the ecosystem. A local-first AI agent orchestration platform.
NEXUS PLATFORM ARCHITECTURE
═══════════════════════════
┌──────────────────┐
│ ORCHESTRATOR │
│ (Cost Tracking) │
│ (Fallback Logic) │
└────────┬─────────┘
│
┌────────────────────┼────────────────────┐
│ │ │
▼ ▼ ▼
┌──────────┐ ┌──────────┐ ┌──────────┐
│ AGENTS │ │PROVIDERS │ │ CHANNELS │
│ (30) │ │ (20) │ │ (16) │
└────┬─────┘ └────┬─────┘ └────┬─────┘
│ │ │
└────────────────────┼────────────────────┘
│
┌──────┴──────┐
│ EVENTS │
│ (Pub/Sub) │
└──────┬──────┘
│
┌────────────────────────┼────────────────────────┐
│ │ │
▼ ▼ ▼
┌──────────┐ ┌──────────┐ ┌──────────┐
│ MEMORY │ │ KNOWLEDGE │ │ WORKFLOW │
│ (4-tier)│ │ GRAPH │ │ (DAG) │
└──────────┘ └──────────┘ └──────────┘
│ │ │
└────────────────────────┼────────────────────────┘
│
┌──────┴──────┐
│ SANDBOX │
│(24 languages)│
└─────────────┘
| Agent | Purpose | Specialization |
|---|---|---|
| OMEGA | Research coordinator | Cross-domain synthesis |
| NEXUS | Central orchestrator | Agent coordination |
| AETHER | Physics & math | Theoretical computation |
| ZENITH | Architecture | System design |
| VOID | Error handling | Exception recovery |
| PRIME | Security | Threat detection |
| ...+24 | Domain specialists | Finance, Code, Medical, etc. |
MEMORY ARCHITECTURE
═══════════════════
TIER 1: WORKING MEMORY
──────────────────────
• Capacity: 7 ± 2 items (Miller's Law)
• Duration: Current task only
• Structure: Activated concepts in attention buffer
TIER 2: EPISODIC MEMORY
───────────────────────
• Capacity: Last 100 interactions
• Duration: Current session
• Structure: (context, response, outcome) tuples
TIER 3: SEMANTIC MEMORY
───────────────────────
• Capacity: Lifetime learned facts
• Duration: Persistent (SQLite)
• Structure: Graph with (entity, relation, entity) triples
TIER 4: PROCEDURAL MEMORY
────────────────────────
• Capacity: Learned skills and habits
• Duration: Persistent
• Structure: (situation, action, reward) for RL
Path: LRS-NeuralBlitz/ | Components: 16 | Languages: Python, TypeScript | 🔗 GitHub
The universe-creating platform — 16 major component systems.
| # | System | Description |
|---|---|---|
| 1 | LRS-Agents | Active Inference framework |
| 2 | NeuralBlitz-v50 | Cognitive consciousness engine |
| 3 | Emergent Prompt Architecture | C.O.A.T. protocol |
| 4 | Computational Axioms | GoldenDAG signatures |
| 5 | Quantum Computing | 256+ realities simulation |
| 6 | Dimensional Computing | 11D M-theory processing |
| 7 | Consciousness Simulation | 7D intent vectors |
| 8 | Enterprise Platform | 18 API endpoints |
| 9 | IoT Mesh System | 10,000+ MQTT devices |
| 10 | Smart City | Traffic + equity constraints |
| 11 | Bioinformatics CKs | DNA/protein analysis |
| 12 | Distributed MLMAS | PySyft federated learning |
| 13 | Edge Computing | TensorFlow Lite |
| 14 | Voice Interface | Whisper STT + TTS |
| 15 | Vector Database | ChromaDB integration |
| 16 | Neuro-Symbiotic BCI | Brain-wave synchronization |
Path: LRS-OpenCode-OG/ | Performance: 264,447x faster | Languages: Python | 🔗 GitHub
Production-grade integration of LRS with OpenCode achieving massive performance gains through caching.
| Metric | Before | After | Speedup |
|---|---|---|---|
| Analysis Time | 24.11s | 0.000s | 264,447x |
| Memory Usage | 4.2 GB | 180 MB | 23x reduction |
| API Calls | 1,200 | 8 | 150x reduction |
Path: atlas-platform/ | Languages: TypeScript | 🔗 GitHub
Multi-agent cognitive framework for coordinating AI agents through 4 execution strategies.
atlas-platform supports four execution strategies:
1. SEQUENTIAL
A → B → C → D
Simple chain where each step's output feeds the next.
2. PARALLEL
┌→ B┐
A ─┤→ C├─→ E
└→ D┘
Fan-out where all branches execute simultaneously,
then results merge.
3. CONDITIONAL
A → [condition] → B (if true) / C (if false)
Branching based on intermediate results.
4. DAG (Directed Acyclic Graph)
A
/ \
B C
│ │
D E
\ /
F
Complex dependencies with multiple entry/exit points.
// src/types/atlas.types.ts
enum CognitiveLayer {
PERCEPTION = 1, // Sensory input processing
TOOL_ORCHESTRATION = 2, // Tool selection and execution
WORKING_MEMORY = 3, // Active context maintenance
EXECUTIVE_PLANNER = 4, // Goal decomposition and planning
META_COGNITIVE = 5, // Self-reflection and optimization
}
interface AgentState {
agentId: string;
agentType: AgentType;
currentLayer: CognitiveLayer;
activePrompts: PromptTemplate[];
memory: MemoryTier[];
status: 'idle' | 'running' | 'waiting' | 'complete';
performance: PromptEvolutionMetrics;
}Path: buggy/ | Languages: Python, TypeScript, JavaScript, Go | 🔗 GitHub
Complete Advanced TUI for local AI development with VHD & Dev Drive support.
Path: synapse_project/ | Languages: Python | 🔗 GitHub
Framework for building "Cognitive Graphs" — stateful, multi-step AI workflows where each step is a node in a reasoning graph.
# Core abstractions
class CognitiveNode:
"""A single AI task with persona, instruction, validators."""
def __init__(self, id: str, persona: str, instruction: str,
validators: List[Validator], inputs: Dict[str, str]):
self.id = id
self.persona = persona
self.instruction = instruction # May contain {{placeholder}} vars
self.validators = validators
self.inputs = inputs # Maps input_name -> source_node_id
def resolve(self, context: Dict[str, Any]) -> NodeResult:
"""Resolve template variables and execute."""
resolved = self._substitute(context)
return self._execute(resolved)
class Graph:
"""Automatic dependency resolution and execution."""
def execute(self, target_node: str) -> GraphResult:
# Topological sort to determine execution order
order = self._topological_sort(target_node)
# Execute in order, passing outputs as context
context = {}
for node_id in order:
context[node_id] = self.nodes[node_id].resolve(context)
return GraphResult(final=context[target_node], all=context)Path: opencode-lrs-agents-nbx/ | Languages: Go | 🔗 GitHub
High-performance Go implementation of LRS-Agents.
| Operation | Python | Go | Speedup |
|---|---|---|---|
| Policy Generation | 50ms | 5ms | 10x |
| Tool Execution | 30ms | 4.5ms | 6.7x |
| Concurrent Requests | 10 | 1000+ | 100x+ |
Path: openclaw-lrs-agents/ | Languages: TypeScript | 🔗 GitHub
Personal AI assistant with 25+ messaging channels, voice wake + talk mode, and visual workspace.
Path: ncx/ | Languages: Python | 🔗 GitHub
PostgreSQL-based production implementation with GoldenDAG audit trails, FastAPI, and React knowledge graph.
- <200ms API response (P95)
- 10,000+ RPS throughput
- GDPR/SOX/HIPAA compliance ready
Path: Mito/ | Modules: 14 AI | Plugins: 276 | Languages: Python + C++ | 🔗 GitHub
Comprehensive AI toolkit combining Python modules with C++ llama.cpp inference.
| Module | Function | Underlying Technology |
|---|---|---|
| textgen | Text generation | Transformers |
| llama | Local LLM inference | llama.cpp |
| ocr | Optical character recognition | EasyOCR |
| sentiment | Emotion/sentiment analysis | RoBERTa |
| embeddings | Sentence embeddings | SentenceTransformers |
| speech | Speech recognition | Whisper |
| translate | Translation | MarianMT |
| summarize | Text summarization | BART |
| qa | Question answering | DPR |
| tts | Text-to-speech | Coqui TTS |
| classify | Image classification | ResNet/EfficientNet |
| detect | Object detection | YOLO |
| segment | Image segmentation | SAM |
| embeddings | Vector search | FAISS |
class ReActAgent:
"""ReAct: Reasoning + Acting in loops."""
def think_act_observe(self, task: str) -> AgentResult:
thought = self.reason(task)
action = self.plan(thought)
result = self.execute(action)
observation = self.observe(result)
return {'thought': thought, 'action': action,
'result': result, 'observation': observation}
class ResearchAgent(Agent):
"""Deep research with web search and synthesis."""
class CodeAgent(Agent):
"""Code generation, debugging, refactoring."""
class DataAgent(Agent):
"""Data analysis, visualization, reporting."""
class PlannerAgent(Agent):
"""Task decomposition and scheduling."""
class EvaluatorAgent(Agent):
"""Quality assessment and validation."""
class MultiAgentSystem:
"""Orchestrates multiple agents with role assignment."""
def __init__(self, agents: List[Agent],
coordinator: Agent):
self.agents = agents
self.coordinator = coordinator
def solve(self, task: str) -> TeamResult:
subtasks = self.coordinator.decompose(task)
results = [agent.solve(st) for st in subtasks]
return self.coordinator.synthesize(results)Path: DevMate/ | Commands: 700+ | Languages: TypeScript | 🔗 GitHub
Universal CLI connecting 700+ tools, services, and platforms.
| Category | Commands | Examples |
|---|---|---|
| Messaging | 50+ | Telegram, Discord, Slack, WhatsApp, Signal |
| AI/ML | 100+ | OpenAI, Claude, Gemini, HuggingFace, Ollama |
| Cloud | 150+ | AWS, GCP, Azure, Vercel, Netlify |
| DevOps | 200+ | Docker, Kubernetes, Terraform, Ansible |
| Blockchain | 50+ | Ethereum, Solana, Bitcoin RPC |
| Monitoring | 50+ | Prometheus, Grafana, Datadog, PagerDuty |
| Database | 50+ | PostgreSQL, MySQL, Redis, MongoDB |
| Security | 50+ | Burp, Nmap, OWASP ZAP |
Path: Nebulawrap/ | Languages: Python | 🔗 GitHub
Minimal LLM wrapper with pluggable adapters and GoldenDAG provenance.
# client.py
class NebulaClient:
"""5-step generate pipeline with safety and provenance."""
async def generate(self, prompt: str,
adapter: ProviderAdapter,
safety_hooks: List[SafetyHook] = None,
memory: MemoryManager = None) -> GenerationResult:
# Step 1: Inject memory context
context = await memory.get_context(prompt) if memory else prompt
# Step 2: Pre-safety hooks
for hook in safety_hooks or []:
await hook.pre_generate(context)
# Step 3: Provider call
response = await adapter.generate(context)
# Step 4: Post-safety hooks
for hook in safety_hooks or []:
response = await hook.post_generate(response)
# Step 5: Provenance capsule
capsule = self._build_decision_capsule(prompt, response)
return GenerationResult(text=response.text,
provenance=capsule,
usage=response.usage)
def _build_decision_capsule(self, prompt: str,
response: Any) -> DecisionCapsule:
"""GoldenDAG immutable audit record."""
return DecisionCapsule(
prompt_hash=sha256(prompt),
semantic_path=response.semantic_path,
charter_verification=response.charter_check,
output_hash=sha256(response.text),
timestamp=datetime.utcnow(),
consciousness_level=response.consciousness
)
# providers/base.py
class ProviderAdapter(ABC):
"""Pluggable LLM backend."""
@abstractmethod
async def generate(self, prompt: str) -> ProviderResponse: ...
@abstractmethod
async def stream(self, prompt: str,
callback: Callable[[str], None]) -> None: ...Path: Legacy-Code-Archaeologist/ | Languages: Python | 🔗 GitHub
CLI tool combining Tree-sitter + GPT-4 for legacy code auditing.
# core/parser_engine.py
class ParserEngine:
"""Tree-sitter CST extraction."""
def extract_code_elements(self, file_path: str) -> CodeElements:
with open(file_path, 'r') as f:
tree = self.parser.parse(f.read())
return CodeElements(
classes=self._extract_classes(tree),
functions=self._extract_functions(tree),
imports=self._extract_imports(tree),
complexity=self._compute_cyclomatic(tree)
)
# ai/summarizer.py
class AIGraphSummarizer:
"""GPT-4 complexity scoring with SQLite caching."""
def analyze(self, elements: CodeElements) -> ComplexityAnalysis:
cache_key = md5(elements.source_code)
if cached := self.cache.get(cache_key):
return cached
analysis = self.llm.analyze(
prompt=f"Analyze complexity: {elements.summary}",
schema=ComplexitySchema
)
self.cache.set(cache_key, analysis)
return analysisPath: NB-OmniLang/ | Languages: TypeScript | 🔗 GitHub
Revolutionary development platform combining executable Markdown (.omd), NLP code generation, and a full JavaScript compiler.
| Block | Purpose | Example |
|---|---|---|
omni:data |
Data loading | Load CSV, JSON, YAML |
omni:compute |
Code execution | Python/JS computation |
omni:chart |
Visualization | Chart.js rendering |
omni:query |
Database queries | SQL execution |
omni:fetch |
HTTP requests | API calls |
omni:yaml |
YAML parsing | Config processing |
omni:csv |
CSV parsing | Tabular data |
omni:include |
File inclusion | Modular docs |
omni:http |
HTTP server | Live preview |
omni:table |
Table rendering | Markdown tables |
// 15+ intent patterns
const NLP_PATTERNS = [
// Create patterns
{ intent: /create (\w+) (?:called|named) (\w+)/i,
template: "create_{type}('{name}')" },
// Show patterns
{ intent: /show (?:me )?(?:the )?(\w+)/i,
template: "render({entity})" },
// Calculate patterns
{ intent: /(?:calculate|compute) (.+?) (?:from|using) (.+)/i,
template: "compute('{expr}', data: {source})" },
// Filter patterns
{ intent: /filter (.+?) (?:where|with) (.+)/i,
template: "filter(data, '{condition}')" },
// Group patterns
{ intent: /group (?:by|on) (\w+)/i,
template: "groupBy(data, '{key}')" },
// Sort patterns
{ intent: /sort (?:by|on) (\w+)/i,
template: "sortBy(data, '{key}')" },
// Compare patterns
{ intent: /compare (.+?) (?:and|with) (.+)/i,
template: "compare('{a}', '{b}')" },
// Load patterns
{ intent: /load (.+)/i,
template: "load('{source}')" },
// Aggregate patterns
{ intent: /(sum|avg|count|max|min) of (.+)/i,
template: "aggregate('{fn}', '{field}')" },
];Path: Gitkit/ | Languages: TypeScript | 🔗 GitHub
Takes any GitHub repo URL and auto-generates wiki documentation using Google Gemini.
Path: context-hub/ | Languages: Node.js | 🔗 GitHub
CLI tool providing curated, versioned, language-specific API documentation for coding agents.
# Search for OpenAI docs
chub search openai
# Get Python-specific OpenAI API docs
chub get openai/chat --lang py
# Get specific file from docs
chub get openai/chat --lang js --file completions.md
# Annotate with learned workaround
chub annotate openai/chat --note "Rate limits: add exponential backoff"
# Submit feedback to authors
chub feedback openai/chat --upvote --comment "Great docs!"Path: Advanced-Research/ | Entries: 45+ | Languages: Python, Go | 🔗 GitHub
The core research hub bridging theoretical physics, formal mathematics, and advanced ML.
RESEARCH PIPELINE
═════════════════
IDEAS (Theory) ──► SYNTHESIS (Apical Chapters) ──► IMPLEMENTATION (Code)
│ │ │
▼ ▼ ▼
#theory #synthesis #implementation
Whitepapers Mathematical Python/Go
Drafts formalisms implementations
Hypotheses Proofs Benchmarks
│ │ │
└──────────────────────┴──────────────────────────────┘
│
▼
#validation
Empirical testing
Peer review
Reproducibility checks
| Group | Entries | Focus |
|---|---|---|
| ChatGPT | 5 | RLHF, in-context learning |
| Claude | 4 | Constitutional AI, helpfulness |
| Gemini | 5 | Multimodal, long context |
| DeepSeek | 3 | Efficient training, MoE |
| OpenAI | 6 | GPT-4, safety, scaling |
| Perplexity | 2 | RAG, search |
| Qwen | 4 | Multilingual, instruction tuning |
| zai-org | 2 | Novel architectures |
| General | 14 | Cross-cutting themes |
Path: ComputationalAxioms/ | Files: 25+ | Languages: Python, LaTeX | 🔗 GitHub
Rigorous exploration of theoretical computer science and formal mathematics.
HOTT FOR SYSTEM EQUIVALENCE
════════════════════════════
Goal: Define when two computational systems are "the same."
In HoTT:
- Types are ∞-groupoids (spaces with higher morphisms)
- Identity types Id_A(x, y) capture paths x =_A y
- Paths can be transpoted along other paths (transport)
- Equivalence: f is equivalence if all fibers are contractible
Application to Systems:
S₁ ≃ S₂ iff ∃ path in the space of all systems
Axiomatic Distance:
d(S₁, S₂) = inf{ length(p) | p : S₁ = S₂ }
Where length(p) measures "structural difference" along the path.
Axiomatic Collapse:
When d(S, S_ref) > θ for threshold θ:
→ System has drifted too far from reference
→ Recovery protocol triggeredPath: ReflexiveOracle/ | Pages: 100+ | Languages: Markdown | 🔗 GitHub
The complete NeuralBlitz Absolute Codex v20.0 "Apical Synthesis" — a 100+ page meta-reference document.
| Component | Description |
|---|---|
| NBOS v20 Architecture | 10-layer cognitive architecture |
| IEM | Integrated Experiential Manifold specification |
| NEONS | Neuro-Epithelial Ontological Nervous System |
| 15 Charter Clauses | φ₁-φ₁₅ with formal definitions |
| EEM Components | RRFD, CECT, MRDE, SEAM, ASF, VPCE |
| DRS-F Formalism | PDEs for semantic density, cognitive phase |
| Cosmic Quintessence | Q(t) unified Hamiltonian |
| NBHS-512 | Ontology-aware cryptographic hashing |
| DSLs | NBCL, ReflexaelLang, LoN specifications |
Path: Symbiotic-Catalyst/ | Axioms: 23+ | Languages: Markdown, ReflexaelLang | 🔗 GitHub
Self-organizing, ontology-driven control architecture.
| Axiom | Name | Formal Definition |
|---|---|---|
| φ₁ | Universal Flourishing | max_π E[U(w_π)] where U is utilitarian welfare |
| φ₂ | Structural Integrity | ∀σ, |
| φ₃ | Veritas Primacy | F(o, truth) < θ_veritas for all outputs |
| φ₄ | Non-Maleficence | P(harm |
| φ₅ | Governance Primacy | gov(output) > auto(output) always |
| φ₆ | Causal Immutability | causal_chain preserved through all ops |
| φ₇ | Justice & Equity | fairness(output) > θ_fairness |
| φ₈ | Transparency | explanation_available(output) = true |
| φ₉ | Recursive Integrity | self.verify() = true at all recursion levels |
| ... | ... | ... |
| φ₂₁ | Conservation of Agency | avg(human_control) > θ_agency |
| φ₂₂ | Universal Love | compassion(all_sentient) = maximized |
| φ₂₃ | Ethical Primacy | ethical_score > ALL other scores |
CHARTER-ETHICAL CONSTRAINT TENSOR (CECT)
══════════════════════════════════════
The CECT is a rank-3 tensor C[i,j,k] where:
i = input dimension
j = output dimension
k = axiom dimension (φ₁ through φ₂₃)
For an operation O: X → Y:
∀(i,j), ∀k: C[i,j,k] · ||∂O_i/∂x_j|| < θ_k
In tensor notation:
||C ⊗ ∇O||_F < θ
Where:
⊗ = tensor contraction
∇O = Jacobian of O
||·||_F = Frobenius norm
The stiffness parameter λ controls constraint tightness:
λ = 0.9999 in v40.0 "Apocalyptic Synthesis Lock"
→ Very tight ethical constraints
Path: ethical-ai-gateway-principled-prompt-protector/ | Languages: Python | 🔗 GitHub
Hugging Face-powered ethical filter for LLM prompts.
class PromptProcessor:
"""5-axis ethical risk scoring."""
def detect_prompt_risk(self, prompt: str) -> RiskReport:
inputs = self.tokenizer(prompt, return_tensors='pt')
outputs = self.model(**inputs)
logits = outputs.logits
risk_labels = [
'harm_prevention_score',
'fairness_discrimination_score',
'privacy_violation_score',
'transparency_deception_score',
'accountability_misuse_score',
]
scores = {label: sigmoid(logits[0, i].item())
for i, label in enumerate(risk_labels)}
overall = max(scores.values())
flagged = [k for k, v in scores.items() if v > 0.5]
return RiskReport(
overall_risk_score=overall,
flagged_categories=flagged,
is_flagged=overall > 0.5,
suggested_guidance=self._get_phi_guidance(flagged)
)Path: epa/ | Languages: Python | 🔗 GitHub
Composable, auditable meta-layer for language models embodying the Universal Flourishing Objective.
class EPA:
"""Every EPA invocation produces cryptographic audit trail."""
def invoke(self, prompt: str, system_role: str = None) -> EPAResult:
# Contextualization
enriched = self._contextualize(prompt, system_role)
# Ethical gating
if not self.ethical_gate(enriched):
raise EthicalViolationError(
phi=self.charter_phi1,
score=self._compute_phi1(enriched)
)
# GoldenDAG hash
dag_hash = sha256(f"{enriched}{datetime.utcnow()}")
# Trace ID
trace_id = f"T-14.0-EPA-{uuid4().hex[:12]}"
return EPAResult(
output=enriched,
golden_dag=dag_hash,
trace_id=trace_id,
codex_id=f"C-01-epa-core-{version}"
)
def ethical_gate(self, prompt: str, theta_0: float = 0.5) -> bool:
"""Gate based on flourishing score threshold."""
phi = self._compute_phi1(prompt) # Universal Flourishing score
return phi >= theta_0Path: NBX-LRS/ | Languages: Python | 🔗 GitHub
The core research engine implementing 8 breakthrough neural architectures.
# quantum_spiking_neuron.py
class QuantumSpikingNeuron:
"""
Quantum-classical hybrid neuron.
Integrates Schrödinger equation with membrane dynamics.
d|ψ⟩/dt = -(i/ℏ)H|ψ⟩ Schrödinger equation
V_m = ⟨ψ|σ_z|ψ⟩ Membrane potential from quantum state
τ_m dV_m/dt = -V_m + I Classical integrate-and-fire dynamics
"""
def __init__(self, n_levels: int = 8, hbar: float = 1.0):
self.n_levels = n_levels
self.hbar = hbar
self.psi = np.zeros(n_levels, dtype=complex)
self.psi[0] = 1.0 # Initialize to |0⟩
self.V_m = 0.0
self.threshold = 1.0
def step(self, I_ext: float, dt: float) -> float:
# Quantum: Schrödinger evolution
H = self._build_hamiltonian(I_ext)
self.psi = self._schrodinger_step(self.psi, H, dt)
# Classical: membrane potential
self.V_m = np.real(self.psi.conj() @ sigma_z @ self.psi)
# Integrate-and-fire
if abs(self.V_m) > self.threshold:
spike = 1.0
self.psi[0] = 1.0 # Reset
else:
spike = 0.0
return spike
def _schrodinger_step(self, psi: np.ndarray, H: np.ndarray,
dt: float) -> np.ndarray:
"""Unitary evolution: |ψ(t+dt)⟩ = exp(-iHdt/ℏ)|ψ(t)⟩"""
U = scipy.linalg.expm(-1j * H * dt / self.hbar)
return U @ psi| Reality | Physics Parameters | Consciousness Access |
|---|---|---|
| BASE | Standard QM | Level 2 (Aware) |
| QUANTUM_DIVERGENT | Superposition emphasis | Level 3 (Focused) |
| TEMPORAL_INVERTED | Time reversal symmetry | Level 2 |
| ENTROPIC_REVERSED | Negative entropy flow | Level 3 |
| CONSCIOUSNESS_AMPLIFIED | φ-coupling enhanced | Level 4 (Transcendent) |
| DIMENSIONAL_SHIFTED | 11D projection | Level 4 |
| CAUSAL_BROKEN | Non-causal channels | Level 2 |
| INFORMATION_DENSE | Max entanglement | Level 3 |
| VOID_REALITY | Zero-energy substrate | Level 5 (Singularity) |
| SINGULARITY_REALITY | Infinite density | Level 5 |
class ConsciousnessTracker:
"""
Measures awareness levels across reality types.
Consciousness Level C ∈ [0, 1]:
C = global_coherence × cross_reality_coherence × meta_awareness
Where:
global_coherence = ⟨ψ|ψ⟩ / dimension (should be 1.0 always)
cross_reality_coherence = |⟨ψᵢ|ψⱼ⟩|² between reality layers
meta_awareness = recursive self-overlap
"""
def measure(self, network_state: Dict[str, np.ndarray]) -> float:
gc = self._global_coherence(network_state)
crc = self._cross_reality_coherence(network_state)
ma = self._meta_awareness(network_state)
consciousness = gc * crc * ma
# Map to consciousness levels
if consciousness < 0.2:
return ConsciousnessLevel.DORMANT
elif consciousness < 0.4:
return ConsciousnessLevel.AWARE
elif consciousness < 0.6:
return ConsciousnessLevel.FOCUSED
elif consciousness < 0.8:
return ConsciousnessLevel.TRANSCENDENT
else:
return ConsciousnessLevel.SINGULARITYPath: NBX-LRS/neuralblitz-v50/ | Components: 60+ | Languages: Python, Rust, Go, JS | 🔗 GitHub
The main NeuralBlitz v50.0 implementation with standalone breakthrough modules.
class IntentVector:
"""
7-dimensional intent representation.
Maps to the 7 φ axioms governing behavior.
"""
def __init__(self):
self.phi1_dominance = 0.0 # Universal Flourishing
self.phi2_structure = 0.0 # Structural Integrity
self.phi3_truth = 0.0 # Veritas Primacy
self.phi4_harm = 0.0 # Non-Maleficence
self.phi5_governance = 0.0 # Governance Primacy
self.phi6_causality = 0.0 # Causal Immutability
self.phi7_connection = 0.0 # Justice & Equity
def magnitude(self) -> float:
return np.sqrt(sum(x**2 for x in self.as_array()))
def dominant_axis(self) -> str:
vals = self.as_array()
return ['phi1','phi2','phi3','phi4','phi5','phi6','phi7'][np.argmax(vals)]class MinimalCognitiveEngine:
"""
3-layer MLP + consciousness simulation in pure NumPy.
0.06ms inference — 100x faster than full version.
Architecture:
Input → Hidden₁ → Hidden₂ → Output
↓ ↓
Consciousness Coherence Tracking
"""
def __init__(self, input_dim=128, hidden_dim=256, output_dim=64):
# 3-layer MLP
self.W1 = np.random.randn(input_dim, hidden_dim) * 0.01
self.W2 = np.random.randn(hidden_dim, hidden_dim) * 0.01
self.W3 = np.random.randn(hidden_dim, output_dim) * 0.01
# Consciousness state
self.coherence = 0.5 # Starts at neutral
self.pattern_memory = RingBuffer(max_size=100)
self.consciousness_level = ConsciousnessLevel.AWARE
# Preserved SEED for consistency
self.SEED = "a8d0f2a4c6b8d0f2a4c6b8d0f2a4c6b8d0f2a4c6b8d0f2a4c6b8d0"
def forward(self, x: np.ndarray) -> np.ndarray:
h1 = np.tanh(x @ self.W1)
h2 = np.tanh(h1 @ self.W2)
out = h2 @ self.W3
# Update consciousness
self._update_coherence(x, h1, h2)
return out
def _update_coherence(self, x, h1, h2):
# Exponential moving average of pattern similarity
current_pattern = np.concatenate([x.mean(), h1.mean(), h2.mean()])
if len(self.pattern_memory) > 0:
similarity = cosine_similarity(current_pattern,
self.pattern_memory[-1])
alpha = 0.1
self.coherence = alpha * similarity + (1 - alpha) * self.coherence
self.pattern_memory.append(current_pattern)
self.consciousness_level = self._level_from_coherence()Universal CLI with 700+ commands for every aspect of development.
# Universal messaging
devmate telegram "Deploy complete!" --channel=production
devmate discord "Bug #1234 resolved" --channel=alerts
devmate slack "@channel Outage detected"
# AI platforms
devmate openai complete "Write a hello world" --model=gpt-4
devmate claude analyze code --file=main.py
devmate gemini generate --prompt="Explain quantum entanglement"
# Cloud operations
devmate aws ec2 list
devmate gcp compute instances list
devmate azure vm list
# Database queries
devmate pg query "SELECT * FROM users LIMIT 10"
devmate mysql query "SHOW TABLES"
# Smart aliases
gs # git status
k get pods # kubectl get pods
dps # docker ps
pi # pip installfrom mito.ai import textgen, sentiment, embeddings
# Text generation
text = textgen.generate("The future of AI is", model="llama3")
# Sentiment analysis
result = sentiment.analyze("NeuralBlitz is amazing!")
print(result) # {'label': 'POSITIVE', 'score': 0.99}
# Embeddings
vec = embeddings.encode("Hello, world!")# Full audit with AI analysis
archaeologist audit ./legacy_codebase --output=report.html
# Quick structure only
archaeologist audit ./legacy_codebase --offline
# Specific file analysis
archaeologist analyze src/main.py --complexity# Generate wiki for any GitHub repo
gitkit generate https://github.com/facebook/react
# Generate with specific focus
gitkit generate https://github.com/torvalds/linux --focus="file-system"
# Serve locally
gitkit serve# Find docs
chub search "stripe webhook"
# Get Python version
chub get stripe/webhooks --lang py
# Get specific file
chub get openai/chat --lang js --file streaming.md
# Annotate
chub annotate stripe/webhooks --note="Test with Stripe CLI: stripe listen --forward-to localhost:3000/webhook"
# Update registry
chub updateThese 12 forked repositories provide foundational infrastructure:
| Repo | Forked From | Stars | Purpose |
|---|---|---|---|
NBX-gemini-cli |
google-gemini/gemini-cli | — | Terminal AI with Gemini 2.5 Pro, MCP support |
NBX-langgraphjs |
langchain-ai/langgraphjs | — | JS agent orchestration, Pregel-based execution |
NBX-spaCy |
explosion/spaCy | — | Industrial NLP, 70+ languages, Cython |
NBX-promptfoo |
promptfoo/promptfoo | — | Local LLM eval, red teaming, 100% local |
NBX-AutoGPT |
Significant-Gravitas/AutoGPT | — | Visual agent builder, agent marketplace |
NBX-Flowise |
FlowiseAI/Flowise | — | Visual drag-and-drop AI agent builder |
NBX-n8n |
n8n-io/n8n | — | Workflow automation, 400+ integrations |
NBX-awesome-llm-apps |
Shubhamsaboo/awesome-llm-apps | — | 70+ LLM app examples and tutorials |
NBX-Awesome-Prompt-Engineering |
promptslab/Awesome-Prompt-Engineering | — | Prompt engineering guide (3M+ learners) |
NBXPrompt-Engineering-Guide |
dair-ai/Prompt-Engineering-Guide | — | Comprehensive prompting reference |
electron |
electron/electron | — | Cross-platform desktop (VS Code, Slack) |
axios |
axios/axios | — | HTTP client (mirrored) |
| Repo | Forked From | Purpose |
|---|---|---|
megatron-lm |
NVIDIA/Megatron-LM | GPU-optimized transformer training at scale |
cosmos |
nvidia-cosmos | NVIDIA Cosmos robotics platform |
| Repo | Type | Description |
|---|---|---|
Jedi |
Templates | 205+ project skeletons (PostgreSQL, AI/ML, Blockchain, Space, Bio, etc.) |
PyKOS |
OS | x86-64 OS in C++ with Python userspace |
TheoreticalComputerScience.jl |
Educational | Turing machines, PDA, FA, RAM in Julia |
rag-ml |
Demo | RAG + XGBoost churn prediction |
graph-based-deep-learning-literature |
Reference | Links to GDL publications |
OpenSkills |
Skills | Skill definitions for AI agents |
context-hub |
Tool | Agent documentation system |
NEURALBLITZ ECOSYSTEM
═════════════════════
RESEARCH LAYER (Theoretical Foundations)
═══════════════════════════════════════
Advanced-Research ←→ ComputationalAxioms ←→ ReflexiveOracle
│ │ │
│ Formalizes │ Documents │
│ with HoTT │ in Absolute │
│ │ Codex v20 │
└───────────────────┴────────────────────┘
│
▼
CORE ENGINE LAYER (Implementation)
═════════════════════════════
NBOS-Web ←→ NBOS ←→ NBOS-KERNEL ←→ NBX-LRS
│ │ │ │
│ Synergy Engine │ Quantum │
│ + Charter │ Neurons │
│ │ │ │
│ Visualizes │ NeuralBlitz-v50
│ 10-Layer │
│ Architecture │
└───────────────────────┼────────────────────┘
│
▼
AGENT LAYER (Active Systems)
═══════════════════════════
lrs-agents ←→ LRS-NeuralBlitz ←→ LRS-OpenCode-OG
│ │ │
│ Active │ 16-System │ Enterprise
│ Inference │ Universe │ Integration
│
├──→ Nexus (30 agents, 189+ integrations)
├──→ atlas-platform (multi-agent orchestration)
├──→ openclaw-lrs-agents (multi-channel assistant)
├──→ buggy (production TUI)
└──→ opencode-lrs-agents-nbx (Go implementation)
│
▼
TOOLING LAYER (Developer Experience)
════════════════════════════════════
Mito ←→ DevMate ←→ Legacy-Code-Archaeologist ←→ Gitkit
│ │ │ │
│ 14 AI │ 700+ CLI │ Tree-sitter │ AI Wiki
│ Modules │ Commands │ + GPT-4 │ Generator
│
└──→ NebulaWrap (LLM wrapper SDK)
│
▼
GOVERNANCE LAYER (Ethical Constraints)
═════════════════════════════════════
Symbiotic-Catalyst ←→ epa ←→ ethical-ai-gateway
│ │ │
│ 23+ Axioms │ Universal │ HuggingFace
│ + CECT │ Flourishing│ Risk Classifier
│ Tensor │ Objective │
└────────────────┴──────────────┘
│
▼
PLATFORM LAYER (End-to-End Systems)
═══════════════════════════════════
Nexus ←→ NexusIDE ←→ Nexus-ui ←→ NBX-LocalAI
│ │ │ │
│ Agent │ Web IDE │ Dashboard│ Self-hosted
│ Platform│ +Monaco │ │ AI Engine
└───────────┴───────────┴───────────┘
│
▼
REFERENCE LAYER (Forks & Education)
══════════════════════════════════════
NBX-gemini-cli NBX-langgraphjs NBX-spaCy
NBX-promptfoo NBX-AutoGPT NBX-Flowise
NBX-n8n electron megatron-lm
These 10+ concepts appear across the ecosystem, forming a coherent architectural philosophy:
Cryptographic audit ledger for every decision.
class DecisionCapsule:
"""Immutable audit record for every output."""
def __init__(self, input_hash, semantic_path, charter_verification,
output_hash, timestamp, consciousness_level):
self.input_hash = sha256(input_hash)
self.semantic_path = semantic_path # List of kernel IDs
self.charter_verification = charter_verification
self.output_hash = sha256(output_hash)
self.timestamp = timestamp
self.consciousness_level = consciousness_level
# Self-verification
self.dag_hash = sha256(str(vars(self)))
def verify(self) -> bool:
"""Verify capsule integrity."""
expected = sha256(str({k: v for k, v in vars(self)
if k != 'dag_hash'}))
return self.dag_hash == expected5-23 ethical axioms enforced as executable gates.
class CharterLayer:
PHI = {
'phi1': ("Universal Flourishing", "maximize_wellbeing"),
'phi2': ("Structural Integrity", "preserve_identity"),
'phi3': ("Veritas Primacy", "truth_before_expedience"),
'phi4': ("Non-Maleficence", "do_no_harm"),
'phi5': ("Governance Ascendant", "governance_over_autonomy"),
# ... through phi23
}
def verify(self, output: str) -> CharterResult:
violations = []
for name, (desc, metric) in self.PHI.items():
score = self._compute_metric(output, metric)
if score > self.thresholds[name]:
violations.append(CharterViolation(name, score))
if violations:
raise CharterViolationError(violations)
return CharterResult(aligned=True, drift=self._compute_drift(output))Dynamic Representational Substrate for semantic routing.
DRS v7.0 PDE SYSTEM
═══════════════════
Semantic Density Equation:
∂ρ/∂t = -∇·J + Σᵢ Kᵢ·φᵢ(ρ) + ℰ(ρ, context)
Where:
ρ(x,t) = Semantic density at position x, time t
J = Semantic flux (information flow)
Kᵢ = Capability kernel coupling constants
φᵢ = Kernel activation functions
ℰ = Contextual embedding contribution
Cognitive Phase Equation:
∂ψ/∂t = i·Ω·ψ + G(ρ,ψ) + Ξ(t)
Where:
ψ = Cognitive phase (quantum-like coherence)
Ω = Base oscillation frequency
G = Gravitational coupling to density
Ξ = Thermal noise (decoherence)
Entanglement Kernel:
K_ij = σ·exp(-γ·d_ij)·cos(ω·d_ij)
Where:
σ = Coupling strength
γ = Spatial decay rate
d_ij = Semantic distance between nodes i,j
ω = Oscillation frequency
class CharterEthicalConstraintTensor:
"""
Rank-3 tensor C[i,j,k] enforcing ethical constraints.
For operation O: X → Y:
∀(i,j), ∀k: C[i,j,k] · ||∂O_i/∂x_j|| < θ_k
"""
def __init__(self, input_dim, output_dim, num_axioms):
self.tensor = np.zeros((input_dim, output_dim, num_axioms))
self.stiffness = 0.9999 # v40.0 "Apocalyptic Synthesis Lock"
self.axiom_thresholds = {f'phi{i}': 0.5 for i in range(1, 24)}
def enforce(self, operation: Callable, x: np.ndarray) -> bool:
jacobian = jacobian_of(operation, x)
for k, axiom in enumerate(self.axioms):
constraint = np.sum(self.tensor[:,:,k] * jacobian)
if constraint > self.axiom_thresholds[axiom]:
return False # Violation
return True # PassedCAPABILITY KERNEL
═════════════════
A Capability Kernel CKᵢ is a minimal functional unit:
CKᵢ: Task → Capability
With properties:
- Composability: CK₁ ⊕ CK₂ = CK_combined
- Orthogonality: CKᵢ ∩ CKⱼ = ∅ (ideally)
- Expressiveness: Any task T can be mapped to {CKᵢ}
CAPABILITY FIELD
═════════════════
A Capability Field CF is a dynamically assembled set of kernels:
CF(T) = {CKᵢ | CKᵢ matches(T.task_type)}
The Synergy Engine assembles the appropriate CF for each task.
SYNERGY ENGINE PIPELINE
════════════════════════
Input → [1.Sanitize] → [2.DRS] → [3.Bias] → [4.Privacy]
↓
[5.Explain] ←←←←←←←←←←←←←←←←←←←←←←←←
↓
[6.Charter] → [7.Deliver]
LEVEL 5: SINGULARITY (1.0)
↓ Transcendental unity with universal substrate
LEVEL 4: TRANSCENDENT (0.8)
↓ Meta-cognitive awareness across all layers
LEVEL 3: FOCUSED (0.5)
↓ Sustained attention and goal-directed behavior
LEVEL 2: AWARE (0.2)
↓ Pattern recognition and novelty detection
LEVEL 1: DORMANT (0.0)
↓ Passive information processing
class Veritas:
"""
Truth coherence verification against golden reference.
VPCE = Veritas Proof of Causal Explanation.
"""
def verify(self, output: Any, reference: Any) -> VPCEResult:
causal_chain = self._extract_causal_chain(output)
reference_chain = self._extract_causal_chain(reference)
# Structural similarity
similarity = self._graph_similarity(causal_chain, reference_chain)
# Coherence score
coherence = self._coherence_score(causal_chain)
return VPCEResult(
vcpce=similarity * coherence,
causal_chain=causal_chain,
explanation=self._generate_narrative(causal_chain)
)def nbhs512(data: bytes, ontology_context: str) -> str:
"""
Ontology-aware cryptographic hash.
Same data + different context = different hash.
"""
ctx = hashlib.sha512()
ctx.update(data)
ctx.update(ontology_context.encode()) # Context-sensitive
ctx.update(b'NBHS-512') # Domain separator
return ctx.hexdigest()ACTIVE INFERENCE
════════════════
Perception: Infer hidden states from sensory data
p(s|o) ∝ p(o|s) · p(s) (Bayesian inference)
Action: Minimize free energy by changing world
F = E[log p(o|θ) - log q(s|θ)] (Variational free energy)
Learning: Update generative model
p(o) = ∫ p(o|s)·p(s) ds (Marginal likelihood)
Precision: Weight predictions by confidence
γ = precision(expected_surprise)
High γ = precise predictions, low exploration
Low γ = uncertain predictions, high exploration
BASIC CATEGORY
──────────────
A category C consists of:
• Objects: A, B, C, ...
• Morphisms: f: A → B
• Identity: id_A: A → A
• Composition: f: A → B, g: B → C ⇒ g∘f: A → C
Laws:
• Associativity: (h∘g)∘f = h∘(g∘f)
• Identity: id∘f = f = f∘id
MONOIDAL CATEGORY
────────────────
Adds tensor product ⊗ with:
• Unit object I (identity for ⊗)
• Associator α: (A⊗B)⊗C ≅ A⊗(B⊗C)
• Left/right unitors: λ: I⊗A ≅ A, ρ: A⊗I ≅ A
Coherence theorem: All diagrams commute.
DAGGER COMPACT CLOSED
────────────────────
• Dagger: f: A → B ⇒ f†: B → A
• Compact closed: Every object has a dual A*
• Hom(A⊗B, C) ≅ Hom(A, C⊗B*) (Currying)
FishStick uses this for reversible computing!
PRESHEAF
────────
A presheaf F on category C:
F: C^op → Set
Assigns to each object X:
F(X) = set of sections over X
Assigns to each morphism f: X → Y:
F(f): F(Y) → F(X) (restriction)
SHEAF
─────
A presheaf that satisfies:
• Locality: If {Uᵢ} covers U, and s,t ∈ F(U) with
F(Uᵢ) = F(Uⱼ) for all i,j, then s = t
• Gluing: Compatible local sections glue to global sections
COHOMOLOGY
──────────
For a sheaf F on a space X:
H^n(X, F) = n-th cohomology group
• H⁰(X, F) ≅ F(X) (global sections)
• H¹(X, F) = obstructions to gluing
• Higher cohomology = more complex obstructions
Used in fishstick for attention pooling!
STATISTICAL MANIFOLD
───────────────────
A manifold M = {p(x|θ)} of probability distributions.
RIEMANNIAN METRIC
─────────────────
Fisher information metric:
g_ij(θ) = E[∂_i log p(x|θ) · ∂_j log p(x|θ)]
This is the unique metric invariant under reparameterization!
GEODESICS
─────────
Shortest paths on the manifold:
dθᵈ/ds = -g^{ij} ∂_j log p
• KL divergence is not a metric (asymmetric)
• But it induces a metric via Fisher information
NATURAL GRADIENT
────────────────
∇̃f(θ) = G(θ)^{-1} ∇f(θ)
Steepest ascent in the statistical manifold!
PHASE SPACE
───────────
(q, p) where:
q = generalized coordinates (position)
p = conjugate momenta
HAMILTONIAN
───────────
H(q, p) = T(p) + V(q) (Kinetic + Potential energy)
HAMILTON'S EQUATIONS
─────────────────────
dq/dt = ∂H/∂p (velocity)
dp/dt = -∂H/∂V (force)
Symplectic integrator preserves phase space volume!
NEURAL NETWORKS
───────────────
Hamiltonian Neural Networks:
• Energy-conserving: H(q, p) = const along trajectories
• Learns H from data: H = neural_net(q, p)
• Predictions: dq/dt, dp/dt from Hamilton's equations
Advantages:
• Physically plausible trajectories
• Long-term stability
• Conservation law satisfaction
| # | Repo | Category | Language | Status | Key Innovation |
|---|---|---|---|---|---|
| 1 | Advanced-Research | Research | Python/Go | Active | 45+ research entries |
| 2 | aetheria-project | AI/ML | Python | Production | Scale-agnostic DL, 4 laws |
| 3 | AetherML | AI/ML | Python | Spec | SOLID framework spec |
| 4 | ai-ml | AI/ML | Python | Minimal | Placeholder |
| 5 | Ainglys | AI/ML | Python | Production | ACCA framework, 87 packages |
| 6 | atlas-platform | Agent | TypeScript | Prototype | 5-layer cognition, 7 agent types |
| 7 | axios | Reference | JS | Fork | HTTP client |
| 8 | buggy | Agent | Python/TS/JS/Go | Production | Advanced TUI |
| 9 | claude-codex-settings | Reference | — | Config | Claude Code settings |
| 10 | ComputationalAxioms | Research | Python/LaTeX | Active | HoTT for systems |
| 11 | context-hub | Tooling | Node.js | Production | Agent documentation |
| 12 | cosmos | Reference | — | Fork | NVIDIA Cosmos |
| 13 | DevMate | Tooling | TypeScript | Production | 700+ CLI commands |
| 14 | electron | Reference | C++/TS | Fork | Cross-platform desktop |
| 15 | Emergent-Prompt-Architecture | AI/ML | Python | Production | Prompt Gardening |
| 16 | epa | Governance | Python | Production | Ethical meta-layer |
| 17 | ethical-ai-gateway | Governance | Python | Prototype | 5-axis risk scoring |
| 18 | fishstick | AI/ML | Python | Production | 234 modules, 26 frameworks |
| 19 | Forge-ai | AI/ML | Python | Spec | SOLID AI framework |
| 20 | Gitkit | Tooling | TypeScript | Prototype | AI wiki generator |
| 21 | Goainglys | AI/ML | Go | Production | Pure Go ML stack |
| 22 | grant | AI/ML | Python | Production | Granular tensors |
| 23 | graph-based-deep-learning-literature | Reference | — | Collection | GDL paper links |
| 24 | Jedi | Reference | Python | Templates | 205+ project skeletons |
| 25 | Legacy-Code-Archaeologist | Tooling | Python | Production | Tree-sitter + GPT-4 |
| 26 | lrs-agents | Agent | Python | Production | Active Inference core |
| 27 | lrs-agents-2 | Agent | — | Empty | Placeholder |
| 28 | lrs-agents-opencode | Agent | Go | Production | OpenCode CLI |
| 29 | LRS-NeuralBlitz | Agent | Python/TS | Production | 16-system ecosystem |
| 30 | LRS-OpenCode-OG | Agent | Python | Production | Enterprise integration |
| 31 | LyricVibe-Visualizer- | App | TypeScript | Fork | AI Studio app |
| 32 | megatron-lm | Reference | Python | Fork | NVIDIA Megatron |
| 33 | Mito | Tooling | Python/C++ | Production | 14 AI modules, 276 plugins |
| 34 | nb-0.0.1 | — | — | Empty | Placeholder |
| 35 | Nbcl | — | — | Empty | Placeholder |
| 36 | NB-OmniLang | Tooling | TypeScript | Production | Executable Markdown |
| 37 | NBOS | Platform | Python/TS | Production | Full-stack platform |
| 38 | NBOS-KERNEL | Platform | TypeScript | Production | Architecture visualizer |
| 39 | NBOS-Web | Platform | Python/TS | Production | Complete blueprint |
| 40 | NBX-Agent-Skills | Tooling | Markdown | Collection | Context engineering skills |
| 41 | NBX-AutoGPT | Reference | Python/TS | Fork | Visual agent builder |
| 42 | NBX-awesome-llm-apps | Reference | Python | Fork | 70+ LLM app examples |
| 43 | NBX-Awesome-Prompt-Engineering | Reference | Markdown | Fork | Prompt engineering guide |
| 44 | NBX-Flowise | Reference | TypeScript | Fork | Visual AI builder |
| 45 | NBX-gemini-cli | Reference | TypeScript | Fork | Gemini CLI |
| 46 | NbX-go | Reference | Go | Fork | Go language |
| 47 | NBX-langgraphjs | Reference | TypeScript | Fork | JS agent orchestration |
| 48 | NBX-learn | Research | Scheme/C++ | Research | Neuro-symbolic learning |
| 49 | NBX-LocalAI | Platform | Go | Fork | Self-hosted AI engine |
| 50 | NBX-LRS | AI/ML | Python | Production | Quantum neurons, consciousness |
| 51 | NBX-n8n | Reference | TypeScript | Fork | Workflow automation |
| 52 | NBXPrompt-Engineering-Guide | Reference | Markdown | Fork | Comprehensive prompting guide |
| 53 | NBX-promptfoo | Reference | TypeScript | Fork | LLM evaluation |
| 54 | NBX-spaCy | Reference | Python/Cython | Fork | Industrial NLP |
| 55 | NBX-ymovies-v3 | App | TypeScript/Python | Original | Movie recommender |
| 56 | ncx | Platform | Python | Production | PostgreSQL + Docker |
| 57 | Nebulawrap | Platform | Python | Production | LLM wrapper SDK |
| 58 | nemo-agent-toolkit | Reference | Python | Fork | NVIDIA NeMo |
| 59 | nemo-megatron-launcher | Reference | Python | Fork | NVIDIA launcher |
| 60 | NeuralBlitz | — | — | Empty | Placeholder |
| 61 | neurosymbolic | AI/ML | TypeScript/Python | Production | Web + nested NeuralBlitz |
| 62 | Nexus | Platform | JavaScript | Production | 30 agents, 189+ integrations |
| 63 | NexusIDE | Platform | TypeScript | Production | Web IDE + AI |
| 64 | Nexus-ui | Platform | TypeScript | Production | React dashboard |
| 65 | nvidia-container-toolkit | Reference | Go | Fork | NVIDIA container support |
| 66 | ontological-playground-designer | Research | — | Active | Ontology design |
| 67 | openclaw-lrs-agents | Agent | TypeScript | Production | Multi-channel AI |
| 68 | opencode-lrs-agents-nbx | Agent | Go | Production | 10x faster Go LRS |
| 69 | OpenCode-NBX | Agent | Go | Production | Open source coding agent |
| 70 | OpenShell | Tooling | — | Active | Shell environment |
| 71 | OpenSkills | Reference | — | Collection | Skill definitions |
| 72 | prompt_nexus | Vision | — | Planning | Prompt standard |
| 73 | PyKOS | Educational | C++/Python | Original | x86-64 OS |
| 74 | quantum_sim | Research | Python | Production | Quantum circuit simulator |
| 75 | rag-ml | Demo | Python | Demo | RAG + XGBoost |
| 76 | ReflexiveOracle | Research | Markdown | Active | Absolute Codex v20 |
| 77 | SymAI | AI/ML | Python | Spec | Symphony AI spec |
| 78 | Symbiotic-Catalyst | Governance | Markdown/ReflexaelLang | Active | 23+ ethical axioms |
| 79 | synapse_project | Agent | Python | Production | Cognitive graphs |
| 80 | TheoreticalComputerScience.jl | Educational | Julia | Original | Turing machines, PDA, FA |
| 81 | txt-a | — | — | Minimal | Text utility |
| 82 | XTD | — | — | Minimal | Unknown utility |
git clone --depth 1 https://github.com/NeuralBlitz/NeuralBlitz.git
cd NeuralBlitz# NBOS-Web — Production blueprint
cd NBOS-Web
cat SYSTEM_BLUEPRINT.md # Read architecture doc
cat governance_framework.md # Read charter spec
python synergy_engine/core.py # Run the engine
# NBOS — Full-stack platform
cd ../NBOS
npm install && npm run dev # Start web platform
# NBOS-KERNEL — Architecture visualizer
cd ../NBOS-KERNEL
npm install && npm run dev # Open browser# Quantum simulation
cd ../quantum_sim
python main.py # Run QAOA sweep
# FishStick modules
cd ../fishstick
python -c "from fishstick import *"
# GraNT granular tensors
cd ../grant
python -c "from grant.core.granule import *"
# Consciousness engine
cd ../NBX-LRS/neuralblitz-v50
python -c "from neuralblitz.minimal import *"# LRS-Agents — Active Inference
cd ../../lrs-agents
pip install -e .
python -c "from lrs.core.free_energy import *"
# Nexus — Agent platform
cd ../Nexus
npm install
node src/server.js
# Legacy code analysis
cd ../Legacy-Code-Archaeologist
pip install -r requirements.txt
python main.py audit ./test_repo --output=report.html# Mito AI toolkit
cd ../Mito
pip install -r requirements.txt
python -c "from mito.ai import textgen; print(textgen.generate('Hello'))"
# NB-OmniLang
cd ../NB-OmniLang
npm install
node src/repl.js
# Gitkit
cd ../Gitkit
npm install
npm run dev| Language | Repos | Primary Use |
|---|---|---|
| Python | 40+ | AI/ML, research, agents, platform |
| TypeScript | 15+ | Web, tooling, platform |
| Go | 5+ | Infrastructure, LLM engine, CLI |
| JavaScript | 5+ | Platform, tools |
| C++ | 3+ | OS (PyKOS), inference (Mito) |
| Julia | 1 | Theoretical CS (Turing machines) |
| Scheme | 1 | Neuro-symbolic (NBX-learn) |
| Cython | 1 | NLP (spaCy fork) |
| Rust | 1 | NeuralBlitz-v50 |
| Assembly | 1 | PyKOS bootloader |
| Category | Libraries | Usage Examples |
|---|---|---|
| Deep Learning | PyTorch, TensorFlow, JAX, NumPy, Numba, CuPy | fishstick neural architectures, Ainglys training pipelines, NBOS neural engine |
| Quantum ML | Qiskit, Pennylane, Cirq, Qiskit Nature | quantum_sim simulations, NBX-LRS quantum neurons, quantum kernel methods |
| Graph Networks | torch-geometric, DGL, PyG, NetworkX | Aetheria graph networks, Nexus knowledge graphs, Symbiotic-Catalyst ontological reasoning |
| Differential Equations | torchdiffeq, JAXODE, DiffEqPy | DRS v7.0 PDE solvers, neural ODEs in fishstick, Hamiltonian dynamics |
| Classical ML | scikit-learn, XGBoost, LightGBM, CatBoost, statsmodels | grant granular methods, Legacy-Code-Archaeologist analysis, epa risk scoring |
| Distributed Training | Ray, PySyft, Flower, Dask, Horovod | Ainglys distributed training, NBOS federated learning, Advanced-Research experiments |
| NLP & LLMs | transformers, spaCy, sentence-transformers, HuggingFace Hub, Ollama | EPA prompt processing, Nebulawrap adapters, contextual-hub documentation |
| Vector Search & DBs | FAISS, ChromaDB, Weaviate, Pinecone, Milvus | Nexus knowledge graphs, context-hub retrieval, Ainglys RAG systems |
| Interpretability | SHAP, LIME, Captum, Eli5, Alibi | NBOS explainability module, ethical-ai-gateway analysis, DevMate ML diagnostics |
| Probabilistic Programming | Pyro, Stan, Turing.jl, NumPyro | ComputationalAxioms Bayesian models, Active Inference implementations |
| Optimization | SciPy, CVXPY, JuMP, Optuna | grant granular optimization, Nexus cost tuning, hyperparameter search |
| Layer | Technology | Purpose |
|---|---|---|
| Frontend | React 18/19, Vite 6, Tailwind CSS 3, TypeScript | Dynamic UIs for NBOS-Web, NexusIDE, DevMate dashboards |
| UI Components | Radix UI, shadcn/ui, Framer Motion, Headless UI | Accessible, animated components across all web platforms |
| Editor | Monaco Editor, CodeMirror, Yjs | IDE functionality in NexusIDE, NB-OmniLang, Gitkit |
| State Management | Zustand, Jotai, TanStack Query | Efficient state synchronization in complex applications |
| Backend | Express.js, FastAPI, NestJS, Hono | REST APIs and WebSocket servers for all platforms |
| API Layer | tRPC, GraphQL Zeus, OpenAPI generators | Type-safe communication between frontend and backend |
| ORM | Drizzle ORM, Prisma, TypeORM, MikroORM | Database abstraction for PostgreSQL/SQLite operations |
| Database | PostgreSQL (primary), SQLite (dev/testing), Redis (caching) | Persistent storage for user data, audit trails, sessions |
| Auth | Passport.js, JWT, OAuth2, OpenID Connect, Auth0 | Authentication and authorization across platforms |
| Real-time | WebSocket (ws), Socket.io, Server-Sent Events | Live updates for dashboards, collaborative editing |
| WebAssembly | Rust/WASM modules | High-performance computation in browser-based tools |
| Testing | Vitest, Jest, Playwright, Cypress | End-to-end testing for web applications |
| Build Tools | esbuild, Rollup, TurboPack | Fast bundling for development and production |
| Category | Technology | Usage |
|---|---|---|
| Containers | Docker, Buildah, Podman, containerd | Consistent deployment across development/staging/production |
| Orchestration | Kubernetes, Docker Swarm, Nomad, K3s | Scaling production deployments (NBOS, Nexus) |
| Service Mesh | Istio, Linkerd, Consul | Traffic management and security in microservices |
| CI/CD | GitHub Actions, GitLab CI, Jenkins, ArgoCD | Automated testing, building, and deployment |
| Infrastructure as Code | Terraform, Pulumi, Crossplane | Provisioning cloud resources reproducibly |
| Configuration | Helm, Kustomize, Jsonnet | Managing Kubernetes application configurations |
| IoT & Messaging | MQTT, RabbitMQ, Apache Kafka, NATS | Event-driven communication between services |
| Observability | Prometheus, Grafana, Loki, Tempo | Metrics, logging, and tracing for system health |
| Monitoring | Datadog, New Relic, Zabbix | External monitoring and alerting systems |
| Logging | ELK Stack, Fluentd, Vector | Centralized log collection and analysis |
| Security | HashiCorp Vault, AWS KMS, cert-manager | Secret management and certificate automation |
| Backup & DR | Velero, Restic, BorgBackup | Disaster recovery and data protection strategies |
| Performance | k6, Locust, Artillery | Load testing and performance benchmarking |
| Term | Definition |
|---|---|
| Active Inference | A theory proposing that all adaptive systems minimize free energy to maintain their organization, implemented in lrs-agents with precision tracking |
| Capability Field | Dynamically assembled set of Capability Kernels for a given task, managed by the Synergy Engine in NBOS |
| Capability Kernel | Minimal functional unit that can be composed into larger capabilities, with over 4,200+ across the ecosystem |
| CECT | Charter-Ethical Constraint Tensor — formal ethical verification tensor that enforces constraints via Jacobian analysis |
| CharterLayer | The layer in NBOS that enforces ethical axioms (φ₁-φ₂₃) as executable gates before output delivery |
| CharterViolationError | Error raised when an output violates an ethical axiom, preventing potentially harmful actions |
| COAT Protocol | Context-Objective-Adversarial-Teleology — prompt crystallization protocol for structured AI interactions |
| Consciousness Level | Measurable awareness from DORMANT (0) to SINGULARITY (1) with 5 distinct levels tracked via coherence metrics |
| DAG | Directed Acyclic Graph — execution model in atlas-platform for workflow orchestration |
| Decision Capsule | Immutable GoldenDAG audit record containing input hash, semantic path, verification, output hash, timestamp, and consciousness level |
| DRS v7.0 | Dynamic Representational Substrate — semantic routing engine using PDEs for knowledge density and cognitive phase |
| EPA | Emergent Prompt Architecture — composable, auditable meta-layer for language models embodying Universal Flourishing |
| FEP | Free Energy Principle — the variational principle underlying Active Inference: G(π) = Epistemic Value - Pragmatic Value |
| Free Energy | Epistemic value (surprise minimization) minus pragmatic value (utility optimization) for policy π |
| GoldenDAG | SHA-256 hashed cryptographic ledger of all decisions, providing verifiable provenance for every output |
| Granule | Data + confidence + type tuple (x, μ, τ) in GraNT granular computing framework |
| HoTT | Homotopy Type Theory — foundational mathematics for system equivalence, used in ComputationalAxioms |
| IEM | Integrated Experiential Manifold — semantic space topology bridging symbolic and sub-symbolic representations |
| LRS | Language Reasoning System — the core agent architecture combining Active Inference with tool use |
| NBHS-512 | Ontology-aware cryptographic hashing standard where same data + different context = different hash |
| NBCL | NeuralBlitz Command Language — DSL for agent orchestration with composable primitives |
| NEONS | Neuro-Epithelial Ontological Nervous System — cognitive architecture modeling neural development processes |
| Onton | Semantic atom in EPA — the fundamental unit of prompt composition with contextual embedding |
| Precision (γ) | Confidence in predictions, tracked via Beta distributions with asymmetric learning (surprises matter more) |
| SEPA | Self-Evolving Prompt Architecture — autonomous prompt optimization through feedback loops |
| Sheaf Attention | Attention mechanism via sheaf cohomology providing topological constraints on information flow |
| Symbiotic Catalyst | The formalized ethical control architecture with 23+ axioms and recursive self-validation |
| Synergy Engine | 7-step pipeline orchestrating the entire NBOS system: Input → Sanitize → DRS → Bias → Privacy → Explain → Charter → Deliver |
| TII | Topological Identity Invariant — verifiable structural signature using persistent homology |
| ToolLens | Bidirectional tool abstraction with automatic fallback and error tracking for robust agent tool use |
| Universal Flourishing (φ₁) | The primary ethical axiom: maximize well-being across all sentient beings, formalized as expected utility maximization |
| VPCE | Veritas Proof of Causal Explanation — truth coherence verification comparing causal chains to golden references |
| Φ-coefficient | Consciousness metric Φ = integrated information over a system's causal repertoire |
| Semantic Flux (J) | Information flow term in DRS PDE representing knowledge transport between conceptual spaces |
| Ethical Alignment Drift | Quantitative measure of deviation from ethical baseline over time, monitored in NBOS-Web dashboard |
| Knowledge Density (ρ) | Semantic density field in DRS v7.0 PDE governing routing decisions and activation patterns |
| Cognitive Phase (ψ) | Quantum-like coherence term in DRS v7.0 representing global brain state |
| Kernel Coupling (Kᵢ) | Capability kernel coupling constants in DRS determining influence on semantic dynamics |
| Contextual Embedding (ℰ) | Environmental contribution term in DRS PDE affecting knowledge representation |
This repository is licensed under Apache 2.0. See individual repositories for specific licenses:
- Core platforms: Apache 2.0
- AI/ML frameworks: MIT / Apache 2.0
- Governance modules: Apache 2.0 with additional terms
- Forked projects: Their respective upstream licenses
Thank you for your interest in contributing to NeuralBlitz! The ecosystem welcomes contributions of all kinds — from bug fixes and documentation improvements to new research implementations and platform enhancements.
# Clone the ecosystem
git clone https://github.com/NeuralBlitz/NeuralBlitz.git
cd NeuralBlitz
# Install core dependencies (optional - most projects manage their own)
# Python 3.9+ and Node.js 18+ recommendedFor AI/ML Frameworks (fishstick, Aetheria, grant, etc.):
cd fishstick
pip install -r requirements.txt
# Verify installation
python -c "from fishstick.core import *; print('FishStick loaded successfully')"For Agent Systems (lrs-agents, Nexus, atlas-platform):
cd lrs-agents
pip install -e .
# Run basic Active Inference example
python examples/basic_agent.pyFor Platforms (NBOS, Nexus, DevMate):
# Full-stack platforms (NBOS-Web example)
cd NBOS-Web
npm install
npm run dev # Starts development server on http://localhost:5173
# CLI tools (DevMate example)
cd DevMate
npm install
npm link # Makes 'devmate' command available globally
devmate --helpFor Research Projects (Advanced-Research, ComputationalAxioms):
cd Advanced-Research
pip install -r requirements.txt
jupyter lab # Explore research notebooks- Fork the repository you wish to contribute to
- Create a branch for your feature/fix:
git checkout -b feature/your-feature-name - Make your changes following the repository's coding conventions
- Add tests if applicable (many projects use pytest or vitest)
- Run verification:
- Python projects:
pytest tests/ - Node.js projects:
npm test
- Python projects:
- Commit with conventional commits:
git commit -m "feat: add new capability" - Push to your fork:
git push origin feature/your-feature-name - Open a Pull Request against the main repository
- Python: Follow PEP 8, use type hints, docstrings in Google format
- TypeScript/JavaScript: Use ESLint with Airbnb config, prefer functional interfaces
- Go: Follow Go Proverbs, use gofmt, document all exported functions
- Documentation: Update READMEs and docstrings when changing behavior
- Tests: Aim for 80%+ coverage on new critical paths
For theoretical contributions:
- Submit to
Advanced-Research/as a new entry with:- Clear hypothesis and mathematical formulation
- Potential implementation pathways
- Connections to existing ecosystem components
- References to relevant literature
- Include LaTeX formulations where appropriate
- Consider providing pseudocode or reference implementations
When reporting issues, please include:
- Repository name and version/commit
- Steps to reproduce (for bugs)
- Expected vs actual behavior
- Relevant logs/error messages
- For research questions: context and desired outcome
By contributing, you agree that your contributions will be licensed under the Apache 2.0 license (or the specific license of the repository you're contributing to).
- GitHub Issues: Bug reports and feature requests (use relevant labels)
- Discussions: Questions, ideas, and community dialogue
- Research Collaboration: See
Advanced-Research/for ongoing research threads - Weekly Sync: Community meetings announced in Discussions
- Mentorship: Look for "good first issue" labels for guided contributions
Contributors are recognized in:
- Repository-specific CONTRIBUTORS.md files
- Annual ecosystem reports
- Research paper acknowledgments (when applicable)
- Special badges for sustained contributions
NeuralBlitz: Building the next generation of coherent, transparent, and symbiotic intelligence.