README.md
June 18, 2026 · View on GitHub
Genesis 2 — Cascade MoE Neural Network
The World's First Patented Neural Architecture That Runs on CPU
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Academic \$299 • Professional \$1,499 • Enterprise \$4,999 • Source + Patent Bundle \$5,000 • Interactive Reference Guide
Try the Live Demo — click the link in the Gist for the current demo URL. Model hosted on Kaggle. If the demo is unavailable, email avlarionov@hotmail.com to request a restart.
What is Genesis 2?
Genesis 2 is a fundamentally new neural network architecture that eliminates the need for GPU, external LLMs, and massive compute resources. It uses Cascade Activation of a Shared Neuron Pool — a patented approach where experts share neurons instead of duplicating parameters.
No GPU. No Cloud. No API costs. No token limits. Runs on your laptop.
Traditional MoE: Expert₁[500MB] + Expert₂[500MB] + ... = 50GB+, GPU required
Genesis 2: Expert₁[route] + Expert₂[route] + ... = 3.64 GB total, CPU only
↑ shared neuron pool, each expert is just a list of neuron IDs
Why Genesis 2?
| Traditional AI (GPT, LLaMA, etc.) | Genesis 2 |
|---|---|
| $2,000+/mo GPU costs | $0 — runs on CPU |
| API rate limits & downtime | Unlimited — self-hosted |
| Data leaves your network | 100% on-premise |
| Catastrophic forgetting | Zero forgetting — mathematically guaranteed |
| Minutes to fine-tune | 130ms to learn a new fact |
| Token window limits (4K-128K) | Infinite context — no limits |
| Vendor lock-in | You own the code |
Quick Start
# Install dependencies
pip install torch numpy requests
# Start the web server
python genesis2_web.py
# Open in browser
open http://localhost:8765
API
import requests
API = "http://localhost:8765"
# Ask a question (returns answer + executable commands)
r = requests.post(f"{API}/api/query", json={"question": "configure nginx reverse proxy"})
print(r.json()["answer"])
print(r.json()["commands"])
# Teach new knowledge (learns in 130-550ms)
requests.post(f"{API}/api/learn", json={
"question": "how to restart Apache",
"answer": "Restart Apache web server",
"exec": "systemctl restart apache2"
})
# Save state
requests.post(f"{API}/api/save")
What's New in v1.1
Released: June 2026
| Feature | Description |
|---|---|
| 🧠 Neuron Splitting (Patent п.5) | Overloaded neurons auto-split via 2-means clustering. Coherence threshold 0.40 triggers split → two child neurons inherit parent weights |
| 💬 Dialogue Context | Model tracks conversation state: "no thanks", "nothing needed", "пока ничего" → correct conversational replies instead of technical routing |
| 🔧 Command Substitution | Auto-fills IP/port/subnet from user's question into exec commands: ping 10.0.0.1 → ping -c 4 10.0.0.1 |
| 🔤 Typo Normalization | Repeated Cyrillic letters collapsed: "ппривет" → "привет", "приввет" → "привет" (Latin preserved: "need" stays "need") |
| 📊 111/111 Test Suite | Extended benchmark from 30 to 111 queries across 43 topics: networking, security, Docker, Cisco, VPN, DNS, databases, monitoring, SCADA, VoIP and more |
| 🌐 Bilingual 100% | Both RU and EN at 100% accuracy simultaneously — verified across all 43 topic categories |
Benchmarks
| Metric | v1.0 | v1.1 |
|---|---|---|
| Shared Neurons | 12,100+ | 12,651 |
| Trained Experts | 10,800+ | 10,800+ |
| Test accuracy | 100% (30/30) | 100% (111/111) |
| Topics covered | 15 | 43 |
| Inference latency | 18-27ms | 18-27ms |
| Learning speed | 130-550ms | 130ms per fact |
| Zero forgetting (cosine) | 1.000000 | 1.000000 |
| Neuron splitting | ✗ | ✓ (auto) |
| Dialogue context | ✗ | ✓ |
| Command substitution | ✗ | ✓ |
| RAM usage | 3.5GB | 3.64 GB |
| GPU required | No | No |
Test Results v1.1 — 111/111 across 43 topics
networking RU/EN ✅✅✅✅✅✅✅✅✅✅✅ (11/11)
linux RU/EN ✅✅✅✅✅✅✅✅✅✅✅ (11/11)
security RU/EN ✅✅✅✅✅✅✅✅✅ (9/9)
vpn RU/EN ✅✅✅✅✅ (5/5)
docker/k8s RU/EN ✅✅✅✅✅✅✅✅ (8/8)
cisco RU/EN ✅✅✅✅✅ (5/5)
dns/dhcp RU/EN ✅✅✅✅✅✅ (6/6)
monitoring RU/EN ✅✅✅✅✅ (5/5)
databases ✅✅✅✅ (4/4)
nginx/web ✅✅✅✅✅ (5/5)
windows ✅✅ (2/2)
mikrotik ✅✅ (2/2)
voip/sip ✅✅ (2/2)
scada/iot ✅✅ (2/2)
backup ✅✅ (2/2)
devops ✅✅✅✅ (4/4)
troubleshooting ✅✅✅✅ (4/4)
cloud/virt ✅✅✅ (3/3)
macos ✅✅✅ (3/3)
traffic ✅✅✅ (3/3)
greetings/typos ✅✅✅✅✅✅✅ (7/7)
slang/infra ✅✅✅✅ (4/4)
───────
TOTAL: ✅ 111/111 = 100%
Architecture
Genesis 2 is built on 8 patented innovations:
1. Shared Neuron Pool
All neurons live in a single shared pool. Experts don't have their own parameters — they reference neurons by ID. One neuron can serve 50+ experts simultaneously. This makes the model 100x smaller than traditional MoE.
2. Expert as Route
Each expert is just a list of neuron IDs — a "route" through the shared pool. Adding a new expert costs bytes, not megabytes. 10,800+ expert routes fit in 3.64 GB.
3. Cascade Activation (No Router)
Traditional MoE uses a trained router to pick experts. Genesis 2 uses a reverse index (neuron → experts) to find relevant experts in 0.14ms. No router training, no routing errors.
4. One-Step Learning
To learn a new fact: freeze all shared neurons, create a new expert with a micro-head. Takes 130-550ms. The new knowledge never interferes with existing knowledge.
5. Zero Catastrophic Forgetting
Each expert has its own micro-head (output layer). New experts can't modify existing ones. Mathematically guaranteed — cosine similarity = 1.000000 before/after learning.
6. Hash Neuron Embedding
Custom embedding system with 9,761 tokens across 72 types. No dependency on external models (MiniLM, BERT, etc.). Fully self-contained.
7. Infinite Context
Every learned fact becomes a permanent expert. No token window limits. 10,000 facts = 10,000 experts, all accessible instantly.
8. Native Generation via Concept Chains
Output is generated through a composer that chains related concepts from activated experts. Not template matching — actual generation.
Input → Hash Embedding (512d) → ANN Search → Seed Experts
→ Cascade Activation → Shared Neuron Pool → Composer → Output
Knowledge Domains (35)
The model is fully bilingual (RU + EN). Trained on 35 domains with 100% accuracy in both languages. Genesis 2 learns new facts in 130ms — you can train your own model on any language and any domain in minutes, not days.
| Networking (Cisco, MikroTik) | Linux Administration | Docker & Kubernetes |
| Security & Hardening | WiFi Configuration | DNS/DHCP/BIND |
| VPN (WireGuard, OpenVPN) | Databases (PostgreSQL, MySQL) | Web Servers (Nginx, Apache) |
| Monitoring (Zabbix, Prometheus) | DevOps (Ansible, Terraform) | Python Scripting |
| Bash Automation | Packet Analysis | VoIP (Asterisk) |
| Windows Active Directory | macOS Administration | Virtualization |
| SCADA/ICS | Cloud (AWS/GCP/Azure) | Server Configuration |
| Mobile Protocols | ||
System Requirements
| Component | Minimum | Recommended |
|---|---|---|
| CPU | Any modern (ARM or x86) | 4+ cores |
| RAM | 6 GB | 16 GB |
| Disk | 4 GB | 10 GB |
| Python | 3.9+ | 3.11+ |
| PyTorch | 2.0+ | 2.3+ |
| OS | macOS / Linux / Windows | Any |
| GPU | Not required | Not required |
Patent
Status: Filed at FIPS Russia, 31.05.2026 Type: Utility Model, IPC G06N 3/04 Claims: 2 independent + 6 dependent (8 total) RCIS Blockchain Certificate: #1823-376-572
The Cascade MoE architecture is protected by a pending patent. The patent covers all 8 architectural innovations listed above.
OS-Aware Execution
Genesis 2 detects the host operating system and adapts:
- macOS: Strips
sudo, warns about Linux-only commands, uses macOS equivalents - Linux: Full command execution with
sudosupport - Windows: Suggests PowerShell alternatives
- Safety: Blocks dangerous commands (
rm -rf,mkfs,dd,shutdown)
Editions
| Edition | Price | License | Includes |
|---|---|---|---|
| Academic | $299 | 1 person, research only | Source + model + docs |
| Professional | $1,499 | 5 users, commercial | + 30 datasets + 12mo updates |
| Enterprise | $4,999 | Unlimited, commercial | + patent docs + book + lifetime updates |
| Source + Patent Bundle | $5,000 | White-label rights | + patent license + 5h consultation |
Project Structure
genesis2-cascade-moe/
├── genesis2_core.py # Core: neurons, cascade, shared pool, training
├── genesis2_gen.py # Generation: concept chains, composer, boost
├── genesis2_agent.py # Agent: learn/reason/plan/chat/self-learn
├── genesis2_web.py # Web UI + REST API + OS detection
├── genesis2_repl.py # Interactive terminal REPL
├── embedding/
│ └── train_embedding.py # Custom hash embedding training
├── datasets/ # 30 training datasets (Professional+)
├── PATENT/ # Patent materials (Enterprise+)
└── requirements.txt
Author
Larionov Alexander Viktorovich (Ларионов Александр Викторович)
- SCADA/ICS Engineer with 10+ years of industrial automation experience
- AI Researcher specializing in novel neural architectures
- Patent holder (Cascade MoE, FIPS Russia 2026)
Contact: avlarionov@hotmail.com GitHub: larionovavi-stack Products: avlarion.gumroad.com
Also by Author
- atwSCADA — Free SCADA system in a single HTML file (IEC 61850, OPC UA, Modbus TCP)
- Network Automation with AI — 132-page practical guide ($29)
Affiliate Program
Earn 40% commission on every sale by promoting Genesis 2.
Payouts via Gumroad. No approval required — instant access.
License
This repository contains the documentation, architecture description, and demo materials. The full source code and trained model are available through Gumroad.
Patent pending. All rights reserved. (c) 2026 Larionov Alexander Viktorovich.
No GPU. No Cloud. No Limits.
Get Genesis 2 Academic — \$299