# Perfection Thought Experiment - Status
## What "Perfection" Means
A system where services are managed automatically from one config file. No manual setup, no hardcoded lists, everything just works.
---
## Status Checklist
### ✓ DONE: One manifest as source of truth
**Location**: `manifest/services.example.json`
One file lists all services. Each service has:
- Name (like "compute" or "auth")
- Kind (is it a "service" or "worker"?)
- Base URL (where it lives)
- Health path (to check if it's alive)
- Routes (what endpoints it has)
**Why this matters**: Change one file, everything updates everywhere.
---
### ✓ DONE: Two enforced templates
**Locations**:
- `templates/go-service/main.go` - For backend services
- `templates/worker/index.ts` - For edge workers
Both templates:
- Read from the manifest (no hardcoding)
- Have /health endpoint
- Have logging (JSON format)
- Have /metrics endpoint
**Why this matters**: Every service looks the same, behaves the same, easy to understand.
---
### ✓ DONE: Generators
**Location**: `scripts/generate-config.js`
Generates code in 6 languages:
- TypeScript (for frontends)
- Go (for backends)
- Rust (for fast stuff)
- Python (for scripts)
- JSON (for anything)
- Markdown (for docs)
**How it works**:
```bash
node scripts/generate-config.js manifest/services.example.json
```
Creates files in `output/` folder with helper functions like:
- "Get me the compute service for dev environment"
- "Get me all edge workers"
- "Get me all services tagged 'database'"
**Why this matters**: Developers import these files. No copy-paste, no mistakes.
---
### ✗ NOT DONE: Single gateway
**What it would be**: One entry point that routes requests to the right service
**How it would work**:
- User makes request → Gateway checks manifest → Sends to right service
- Knows which services are at the edge (fast, close to user)
- Knows which are on servers (powerful, but slower)
- Automatically picks based on what's faster
**Why it matters**: Users don't need to know about your architecture. They just make a request.
---
### ✓ DONE: Dashboard/control center
**Location**: `frontend/dashboard.html`
Run it: `node frontend/server.js`
Shows:
- All services from manifest
- Health status (green/yellow/red)
- Filter by environment (dev/staging/prod)
- Filter by kind (service/worker)
- View routes for each service
**Why this matters**: Visual understanding. No need to read code or config files.
---
### ✗ NOT DONE: Policy/templates + connectors
**What it would be**:
- Pre-built "data connectors" (connect to Postgres, Redis, S3, etc.)
- Pre-built "policy bundles" (auth rules, rate limiting, etc.)
- Stored as JSON/YAML files
- Selectable in dashboard UI
- Applied to services automatically
**Why it matters**: "Click to add database" instead of writing code.
---
### ✗ NOT DONE: Ops baked in
**What it would be**:
- Logging config generated from manifest
- Metrics config generated from manifest
- Deployment scripts generated from manifest
- Alert rules generated from manifest
**Why it matters**: Add a service to manifest → deployment, logging, monitoring all set up automatically.
---
## Summary for Non-Developers
**Think of it like building with LEGO**:
- ✓ We have the instruction manual (manifest)
- ✓ We have the brick molds (templates)
- ✓ We have machines that read the manual and make bricks (generators)
- ✓ We have a display case showing all the bricks (dashboard)
- ✗ We DON'T have the automatic builder (gateway)
- ✗ We DON'T have pre-made kits (policy/connectors)
- ✗ We DON'T have the factory automation (ops)
**Current State**: 60% done. The foundation is solid.
**What's Left**: The automation layer that makes it truly hands-off.
DONE (60%):
- ✓ Manifest - One file controls everything
- ✓ Templates - Go service + Worker templates (both read from manifest)
- ✓ Generators - Creates code in 6 languages automatically
- ✓ Dashboard - Visual control center (the HTML you liked)
NOT DONE (40%): 5. ✗ Gateway - Smart router that picks fastest path 6. ✗ Policies/Connectors - Click-to-add features (databases, auth, etc.) 7. ✗ Ops automation - Auto-setup deployment/logging/monitoring
1. **Hub** - What's in neuais.com/hub.neuais.com/ - what are the components?
2. **Services** - What's in neuais.com/services/ - list all services and their purposes
3. **Infrastructure** - Find any infra-related code/configs
4. **Libraries** - What's in lib/ if it exists
5. **ML/AI** - Find ml/ or ai-related code
6. **SMO and RIC** - Find references to smo, ric, or related AI networking
7. **nix-ide** - Find the IDE for AI app building
8. **Config** - Where are configs stored
NeuAIs Platform - Complete Architecture Map
Overview
NeuAIs is a revolutionary platform where thousands of specialized micro-AI agents autonomously manage entire SaaS infrastructures, achieving 70% cost reduction vs traditional
cloud through intelligent orchestration and self-healing mesh networking.
---
1. HUB - User-Facing Applications
Location: /home/adminator/projects/neuais.com/neuais.com/hub.neuais.com/
1.1 admin.neuais.com - Administrative Interface
Path: hub.neuais.com/admin.neuais.com/
Components:
- Frontend (Next.js)
- Main admin interface
- Network topology visualization (D3.js)
- Agent management UI
- Real-time monitoring dashboards
- Server (Go/Python/TypeScript hybrid)
- network-orchestrator/ - Coordinates mesh network operations
- expert_agents/ - Specialized AI agents
- mesh-network/ - Mesh network management
- mesh-network-worker/ - Cloudflare Worker for mesh API
- ide-backend/ - IDE backend service
- compute-worker/ - Compute orchestration worker
- storage-worker/ - Storage API worker
- monitoring/ - Monitoring stack
- nix/ - Nix container management
- IDE App Builder (ide-app-builder-online/)
- nix-templates/ - 60+ Nix templates for AI app building
- Astro, React, Go, Flask, Django, Remix, SvelteKit, Flutter, etc.
- ide-backend/ - Backend for browser-based IDE
- ide-backend-preview/ - IDE preview server
- inlineCompletions/ - AI code completion
Purpose: Primary administrative control center for the entire NeuAIs platform
Technologies: Next.js, Go, Python, Nix containers, Monaco Editor, D3.js
1.2 dashboard.neuais.com - SvelteKit Dashboard
Path: hub.neuais.com/dashboard.neuais.com/
Components:
- Frontend (SvelteKit + TypeScript)
- Agent management interface
- Real-time monitoring
- Logs viewer
- Task management
- Settings & billing
- Dark/light themes
- Backend (Rust + Axum)
- HTTP REST API
- WebSocket/SSE for real-time updates
- Authentication (JWT)
- PostgreSQL integration
- Redis caching
Purpose: Beautiful, modern dashboard for micro-AI orchestration
Technologies: SvelteKit, Rust, Axum, PostgreSQL, Redis, Tailwind CSS
1.3 dev.neuais.com - Developer Tools
Path: hub.neuais.com/dev.neuais.com/
Components:
- micro_ai/ - Python CLI tool (Calliope framework)
- commands/ - Agent, model, deployment commands
- core/ - RIC client, agent generator
- templates/ - Agent templates (Rust, Python, Go)
Purpose: Command-line tools for developers building micro-AI agents
Technologies: Python, Calliope (Google Cloud SDK-style framework)
1.4 docs.neuais.com - Documentation
Path: hub.neuais.com/docs.neuais.com/
Components:
- src/ - Markdown documentation source
- platform/ - Platform overview
- services/ - Service documentation
- api/ - API reference
- guides/ - How-to guides
- agents/ - Agent development docs
- book/ - Built documentation (mdBook)
Purpose: Complete platform documentation
Technologies: mdBook, Markdown
1.5 observatory.neuais.com - Network Visualization
Path: hub.neuais.com/observatory.neuais.com/
Components:
- Rust + wgpu GPU-accelerated visualization
- src/
- graph/ - Graph data structures
- render/ - GPU rendering pipeline
- network/ - Network data ingestion
- ui/ - User interface
- shaders/ - WGSL shaders for rendering
- config/ - Agent and render configurations
Purpose: Living, breathing constellation visualization of micro-AI agents (60 FPS, real-time)
Technologies: Rust, wgpu, winit, WebSocket
1.6 Mobile Apps
- android.neuais.com/ - Android application (Kotlin/Java)
- ios.neuais.com/ - iOS application (Swift)
1.7 Assets
Path: hub.neuais.com/assets/
- models/ - 3D models and scenes
- svgs/ - Brand assets, icons, illustrations
- 3d-models/ - 3D assets
- colors/ - Color schemes
---
2. SERVICES - Backend Microservices
Location: /home/adminator/projects/neuais.com/neuais.com/services/
Service Registry
All services register with Consul for discovery and health monitoring.
2.1 ai-network - AI Network Intelligence Layer
Path: services/ai-network/
Major Components:
SMO (Service Management & Orchestration)
Path: ai-network/smo/
- server.go - HTTP REST API (port 8080)
- rapp_manager.go - rApp lifecycle management
- policy_engine.go - Policy enforcement
- orchestrator.go - Resource orchestration
- events.go - Event bus (Kafka/Redis)
- mesh_client.go - Mesh network integration
Purpose: Centralized orchestration for the AI network layer
RIC (RAN Intelligent Controller)
Path: ai-network/ric/
- server.go - HTTP REST API (port 8081)
- ml_engine.go - ML model registry and inference
- models/interface.go - ML model interface
- models/anomaly.go - Isolation Forest anomaly detection (426 lines)
- features/extractor.go - Feature extraction (473 lines)
- registry/ - Model registry
Purpose: AI-powered network intelligence with real-time ML inference
rApps (Network Applications)
Path: ai-network/rapps/
- framework/ - Base rApp framework
- rapp.go - RApp interface and BaseRApp
- registry.go - RApp registration
- anomaly-detector/ - Real-time anomaly detection (790+ lines)
- traffic-optimizer/ - AI-powered route optimization (606+ lines)
- auto-remediation/ - Automatic issue remediation (design complete)
Purpose: Network automation applications powered by RIC ML models
Key Features:
- ML-powered anomaly detection
- Traffic optimization with multi-objective scoring
- Policy-based automation
- Real-time event processing
2.2 auth - Authentication Service
Port: 8080 | Language: Go
- JWT authentication
- OAuth 2.0 / OIDC support
- API key management
- Session management
2.3 iam - Identity & Access Management
Port: 8081 | Language: Go
- Role-based access control (RBAC)
- Permission management
- User/group management
2.4 kms - Key Management Service
Port: 8082 | Language: Go
- Encryption key management
- Secret storage
- Certificate management
2.5 billing - Billing & Metering
Port: 8083 | Language: Go
- Usage tracking
- Invoice generation
- Payment processing integration
- Cost optimization analytics
2.6 compute - Compute Orchestration
Port: 8084 | Language: Go
- Nix container management
- Instance lifecycle management
- Resource allocation
2.7 storage - Object Storage
Port: 8085 | Language: Go
- S3-compatible API
- File management
- CDN integration
2.8 database - Database Service
Port: 8086 | Language: Go
- Database provisioning
- Backup/restore
- Migration management
- Schemas: data/schemas/
- Migrations: data/migrations/
- Seeds: data/seeds/
2.9 mesh-network - Service Mesh
Path: services/mesh-network/
Port: 8089 | Language: Go
Components:
- protocols/quic/ - QUIC protocol implementation
- protocols/yggdrasil/ - Yggdrasil mesh networking
- protocols/frp/ - Fast Reverse Proxy tunneling
- netif/ - Network interface management
- exitnodes/ - Exit node configuration
Purpose: Self-healing IPv6 mesh network with CGNAT bypass
2.10 edge - Edge Services
Path: services/edge/
- api-gateway/ - API gateway routing
- ui/ - Edge UI components
2.11 registry - Service Registry
Port: 8500 (Consul) | Language: Go
- Service discovery
- Health checking
- Configuration management
2.12 file-service - File Operations
Language: Go
- File upload/download
- Format conversion
- Processing pipelines
2.13 staging-api - Staging Environment API
Path: services/staging-api/
Language: TypeScript/Cloudflare Workers
- Edge API for staging deployments
2.14 perfection - Service Generator
Path: services/perfection/
- Service scaffolding templates
- Code generation
- templates/worker/ - Worker templates
- templates/go-service/ - Go service templates
---
3. INFRASTRUCTURE - Deployment & Configuration
Location: /home/adminator/projects/neuais.com/neuais.com/infra/
3.1 agents/ - Micro-AI Agents
Path: infra/agents/
37+ specialized agents including:
- auth-validator/ - JWT validation + anomaly detection
- cache-optimizer/ - Cache optimization with ML prediction
- code-generator/ - AI code generation
- code-improver/ - Code quality improvement
- framework/ - Base agent framework
- ansible-agent/ - Infrastructure automation
- blockchain-dev-agent/ - Blockchain development
- docker-agent/ - Docker operations
- git-agent/ - Git operations
- kubernetes-agent/ - K8s orchestration
- network-agent/ - Network management
- ide-agent/ - IDE operations
- Plus many more specialized agents
Key Files:
- CODEX_AGENT.md - Codex AI agent documentation
- GROK_AGENT.md - Grok AI agent documentation
- LOCAL_MODEL_AGENT.md - Local learning system (24/7 learning, 120B params)
- example-agent-layers.md - Agent architecture patterns
3.2 nix-agenic-coding/ - Nix Templates
Path: infra/nix-agenic-coding/
60+ Nix templates organized by category:
- backend/ - 14 backend frameworks
- frontend/ - 20 frontend frameworks
- fullstack/ - 32 fullstack templates
- templates/ - 62 total templates including:
- Astro, React, Go, Flask, Django, Remix, SvelteKit
- Flutter, Ionic, React Native (mobile)
- Solid, Qwik, HTMX (modern web)
- CMS templates (Directus)
- And many more
Purpose: Pre-configured Nix development environments for AI-powered app building
3.3 workers/ - Edge Workers
Path: infra/workers/
- compute/ - Compute worker (Cloudflare)
- mesh-network/ - Mesh API worker
- storage/ - Storage API worker
Purpose: Cloudflare Workers for low-latency edge processing
3.4 monitoring/ - Observability Stack
Path: infra/monitoring/
- Prometheus - Metrics collection
- Grafana - Dashboards
- Loki - Log aggregation
- Jaeger - Distributed tracing
3.5 docker/ - Container Orchestration
Path: infra/docker/
- Docker Compose configurations
- Service definitions
- Volume mappings
3.6 cloudflare/ - Cloudflare Configuration
Path: infra/cloudflare/
- Workers configurations
- Pages deployments
- DNS settings
3.7 fly.io/ - Fly.io Deployment
Path: infra/fly.io/
- Fly.io app configurations
- Multi-region deployment
3.8 mesh/ - Mesh Network Infrastructure
Path: infra/mesh/
- Yggdrasil configuration
- FRP tunneling setup
- Network status scripts
- Cloudflare DNS integration
3.9 infra/infra/ - Meta Infrastructure
Path: infra/infra/
- Infrastructure as Code
- Terraform configurations
- Ansible playbooks
---
4. LIBRARIES - Shared Code
Location: /home/adminator/projects/neuais.com/neuais.com/libs/
4.1 Rust Libraries
Path: libs/rust/
Components:
- ric/ - RIC client library
- Rust bindings for RIC API
- Model inference client
- smo/ - SMO client library
- SMO API client
- Event subscription
- rapp-adapter/ - rApp adapter framework
- Base adapter for building rApps
- Examples included
- orchestrator/ - Orchestration library
- Coordination logic
- Task scheduling
- common/ - Shared utilities
- Common types
- Helper functions
Purpose: Rust libraries for building agents and network applications
4.2 SDKs - Client Libraries
Path: libs/sdks/
Supported Languages:
- Python - sdks/python/
- Full-featured SDK with async support
- Examples and tests included
- Node.js - sdks/nodejs/
- TypeScript-first SDK
- ESM/CommonJS support
- Go - sdks/go/cloudnix/
- Idiomatic Go SDK
- Context support
- Java - sdks/java/
- Java 11+ SDK
- Spring Boot integration
- .NET - sdks/dotnet/
- .NET 6+ SDK
- Async/await patterns
- Ruby - sdks/ruby/
- Ruby 2.7+ SDK
- Rails integration
Features:
- Full API coverage (Compute, Storage, Functions, Mesh)
- Automatic retry with exponential backoff
- Built-in pagination
- Type safety
- Multi-region support
- Streaming support
---
5. ML/AI - Machine Learning
Location: /home/adminator/projects/neuais.com/neuais.com/ml/
5.1 models/ - Model Storage
- image-models/ - ComfyUI models (149GB)
- Stable Diffusion variants
- ControlNet models
- VAE encoders
- Upscalers
- text-models/ - Llama.cpp models (122GB)
- Llama models (7B, 13B, 70B)
- Code generation models
- Fine-tuned variants
- agent-models/ - Micro-agent models
- Tiny BERT (auth validation, 50MB)
- DistilBERT (sentiment, 250MB)
- FastText (classification, 100MB)
- Custom micro-models
5.2 specialized/ - Specialized ML Systems
- vision-system/ - Computer vision
- Image analysis
- Object detection
- OCR processing
- arthur/ - Arthur ML system
- Specialized ML tasks
- Code repository indexes
- Operations management
5.3 training/ - Training Systems
- local-learning-system/ - 24/7 learning system
- 120B parameter capability
- Knowledge base accumulation
- Offline inference
- retraining-pipeline/ - Automated retraining
- Daily retraining jobs
- Model deployment
5.4 tools/ - ML Utilities
- model-armour/ - Model security
- Integrity checking
- Security scanning
- Performance benchmarking
- Format validation
5.5 experiments/ - ML Experiments
- translate/ - Translation experiments
- language/ - NLP experiments
- vision/ - Vision experiments
- video/ - Video processing
- speech/ - Speech recognition
5.6 mlflow/ - MLflow Model Registry
- Model versioning
- Experiment tracking
- Artifact storage
5.7 datasets/ - Training Data
- Training datasets
- Validation sets
- Benchmarks
---
6. SMO and RIC - AI Network Intelligence
6.1 SMO (Service Management & Orchestration)
Location: services/ai-network/smo/
Port: 8080
Architecture:
SMO Server
├── rApp Manager - Deploy/manage network applications
├── Policy Engine - Enforce network policies
├── Orchestrator - Coordinate actions across mesh
└── Event Bus - Kafka/Redis pub/sub
Key Capabilities:
- rApp lifecycle management (deploy, start, stop, monitor)
- Policy definition and enforcement based on conditions
- Resource orchestration across mesh network
- Event streaming and processing
- Mesh network integration via REST API
API Endpoints:
- /api/v1/rapps - rApp management
- /api/v1/policies - Policy management
- /api/v1/events - Event publishing
- /health - Health check
- /api/v1/metrics - System metrics
6.2 RIC (RAN Intelligent Controller)
Location: services/ai-network/ric/
Port: 8081
Architecture:
RIC Server
├── ML Engine - Model registry & inference
├── Model Manager - Lifecycle management
└── Analyzer - Network analysis
ML Models
├── Anomaly Detection - Isolation Forest (426 lines)
├── Prediction - LSTM traffic forecasting
└── Optimization - Reinforcement learning routing
Feature Extraction
└── Network Metrics → ML Features (473 lines)
Key Capabilities:
- Real-time ML inference (<15ms latency)
- Anomaly detection (92% accuracy)
- Traffic optimization (88% accuracy)
- Model training and validation
- Feature extraction from network metrics
- Redis caching for performance
API Endpoints:
- /api/v1/models - Model management
- /api/v1/infer - Single inference
- /api/v1/infer/batch - Batch inference
- /api/v1/train - Start training
- /api/v1/metrics - RIC metrics
ML Models:
1. Anomaly Detector (anomaly-detector-v1)
- Algorithm: Isolation Forest (100 trees)
- Features: latency, packet loss, bandwidth, CPU, memory
- Output: anomaly score, severity (critical/high/medium/low)
- Accuracy: 92%
- Latency: ~15ms
2. Traffic Optimizer (traffic-optimizer-v1)
- Multi-objective scoring (latency, throughput, cost)
- Weighted optimization
- Route recommendations
- Accuracy: 88%
- Latency: ~20ms
Integration Flow:
Mesh Network → SMO → RIC Feature Extraction → ML Models → Predictions
↓ ↓
Policy Engine ←────────────────────── Actions/Insights
6.3 rApps (Network Applications)
Location: services/ai-network/rapps/
Framework: rapps/framework/
- rapp.go - Base interfaces and BaseRApp
- registry.go - rApp registration and management
- errors.go - Error definitions
Production rApps:
1. Anomaly Detection rApp (790+ lines)
- Real-time monitoring with RIC integration
- Configurable threshold (default: 0.7)
- Alert management with webhooks
- Automatic remediation suggestions
- Severity-based alerts
2. Traffic Optimization rApp (606+ lines)
- AI-powered route optimization
- Multi-objective scoring
- SMO integration for route updates
- Local fallback when RIC unavailable
- Automatic application when score > 0.7
3. Auto-Remediation rApp (design complete)
- Automatic issue detection
- Safety mechanisms (validation, rollback, rate limiting)
- Integration with other rApps
---
7. nix-ide - IDE for AI App Building
7.1 IDE App Builder
Location: hub.neuais.com/admin.neuais.com/ide-app-builder-online/
Components:
- ide-backend/ - Go backend for IDE
- WebSocket support for terminals
- File operations
- Nix container orchestration
- ide-backend-preview/ - Preview server
- HTTP proxy for web app previews
- Hot reload support
- nix-templates/ - 60+ templates
- Full-stack frameworks
- Backend frameworks
- Frontend frameworks
- Mobile frameworks
- CMS templates
- inlineCompletions/ - AI code completion
- Context-aware suggestions
- Multi-language support
Features:
- Browser-based Monaco Editor
- Terminal with PTY over WebSocket
- Nix container isolation
- AI-powered code completion
- Template-based project creation
- Real-time preview
- File tree navigation
- Git integration
7.2 Nix Templates Library
Location: infra/nix-agenic-coding/templates/
62 Templates organized by category:
Backend (14):
- Go, Python (Flask/Django), Ruby (Rails), Node.js, Rust, Java, PHP, Elixir
Frontend (20):
- React, Vue, Angular, Svelte, Solid, Qwik, HTMX, HTML, Astro, Alpine.js
Fullstack (32):
- Next.js, Nuxt, SvelteKit, Remix, Astro (React/Solid), Redwood, Blitz
Mobile (3):
- React Native, Flutter, Ionic
CMS (1):
- Directus
Each template includes:
- dev.nix - Nix development environment
- Pre-configured dependencies
- Development server setup
- Build tooling
- Testing framework
- Linting/formatting
Purpose: Instant, reproducible development environments for any stack
---
8. CONFIG - Configuration Management
Location: /home/adminator/projects/neuais.com/neuais.com/config/
Configuration Files
- dev.toml - Development environment
- Local LLM endpoints (GPT-OSS:120b, BERT/ONNX)
- Debug mode enabled
- Verbose logging
- No TLS required
- staging.toml - Staging environment
- Info-level logging
- TLS recommended
- Environment variables for secrets
- prod.toml - Production environment
- Warn-level logging
- TLS required
- Cloud fallback disabled
- API key authentication
- requirements.txt - Python dependencies
Secrets Management
- .secrets/ - API keys and tokens (gitignored)
- .grok/ - Grok AI configuration
Key Configuration Sections
LLM Configuration:
- local_endpoint - GPT-OSS:120b server (http://localhost:18088/v1)
- fast_endpoint - BERT/ONNX (http://localhost:8082)
- fallback_enabled - Cloud LLM fallback
RIC/SMO:
- ric.url - Agent registration endpoint
- smo.url - Service orchestration endpoint
Database:
- Development: Direct connection strings
- Production: Environment variables
Models:
- model_dir - Model storage location
- cache_dir - Model cache
- max_cache_size_gb - Cache limits
Environment Variables
# Environment
NEUAIS_ENV=dev|staging|prod
# Database
DATABASE_URL=postgresql://user:pass@host/neuais
# Redis
REDIS_URL=redis://host:6379/0
# API Keys
OPENAI_API_KEY=sk-...
# TLS (production)
NEUAIS_TLS_CERT=/etc/neuais/certs/agent.crt
NEUAIS_TLS_KEY=/etc/neuais/certs/agent.key
---
Technology Stack Summary
Frontend
- Frameworks: Next.js, SvelteKit, React, Svelte
- Languages: TypeScript, JavaScript
- Styling: Tailwind CSS, CSS Variables
- Visualization: D3.js, wgpu (Rust)
- Editors: Monaco Editor
- State: Zustand, Svelte stores
Backend
- Primary: Go (services, SMO, RIC, mesh)
- Performance: Rust (agents, dashboard backend, observatory)
- Scripting: Python (CLI, ML, agents)
- Edge: TypeScript/Cloudflare Workers
Databases & Storage
- Relational: PostgreSQL
- Cache: Redis, Upstash
- Object Storage: S3-compatible
- Time-series: Prometheus
Infrastructure
- Containers: Docker, Nix
- Orchestration: Docker Compose, Consul
- Networking: Yggdrasil (IPv6 mesh), QUIC, FRP
- Monitoring: Prometheus, Grafana, Loki, Jaeger
ML/AI
- Frameworks: Isolation Forest, LSTM, Reinforcement Learning
- Inference: Llama.cpp, ComfyUI, ONNX
- Models: Llama (7B-70B), Stable Diffusion, BERT variants
- MLOps: MLflow, custom training pipelines
Deployment
- Edge: Cloudflare Pages, Cloudflare Workers
- Compute: Fly.io (multi-region)
- Local: Docker Compose
- CI/CD: GitHub Actions
---
Key Architectural Patterns
1. Micro-AI Agent Architecture
Client → API Gateway → Service Mesh → Microservices
↘ Edge Workers (Cloudflare)
↘ Micro-AI Agents (Rust)
↓
SMO (Orchestration)
↓
RIC (ML Intelligence)
↓
Mesh Network (Self-healing)
2. Network Intelligence Flow
Mesh Metrics → SMO Collection → RIC Feature Extraction
↓
ML Models (Anomaly, Optimization)
↓
Predictions/Insights
↓
SMO Policy Engine → Actions
↓
Mesh Network Updates
3. Service Discovery
Services → Consul Registry → Health Checks
↓
Load Balancer → API Gateway → Clients
4. Real-time Updates
Events → Redis/Kafka → SMO Event Bus
↓
rApps (Subscribers)
↓
Actions → Mesh
---
How Components Connect
Hub ↔ Services
- admin.neuais.com → All services via API Gateway
- dashboard.neuais.com → Backend API (Rust) → Services
- dev.neuais.com → RIC API for agent management
- docs.neuais.com → Static documentation
- observatory.neuais.com → SMO/RIC APIs for real-time data
Services ↔ Infrastructure
- All services register with Consul (registry)
- Services use mesh-network for inter-service communication
- SMO orchestrates service interactions
- RIC provides ML intelligence to services
- Edge Workers provide low-latency API access
AI Network ↔ Mesh
- SMO integrates with mesh-network via REST API
- rApps subscribe to mesh network events
- RIC analyzes mesh metrics for optimization
- Policies trigger mesh network configuration changes
ML ↔ Services
- RIC serves ML models via HTTP API
- Agents use local models (BERT, FastText) for fast inference
- Training pipeline feeds updated models to RIC
- MLflow manages model versions and experiments
Nix IDE ↔ Platform
- IDE backend uses compute service for Nix containers
- Templates integrate with storage service
- AI completions use RIC or local models
- Preview uses edge workers for hosting
---
Deployment Architecture
Production URLs
- Frontend: https://intranetice.pages.dev (Cloudflare Pages)
- Mesh API: https://intranetice-mesh.dicerollerdnd.workers.dev
- Compute API: https://intranetice-compute.dicerollerdnd.workers.dev
- Storage API: https://intranetice-storage.dicerollerdnd.workers.dev
Local Development Ports
| Service | Port | Protocol |
|--------------------|------|-----------|
| SMO | 8080 | HTTP |
| RIC | 8081 | HTTP |
| Auth | 8080 | HTTP |
| IAM | 8081 | HTTP |
| KMS | 8082 | HTTP |
| Billing | 8083 | HTTP |
| Compute | 8084 | HTTP |
| Storage | 8085 | HTTP |
| Database | 8086 | HTTP |
| AI Network | 8087 | HTTP |
| IDE Backend | 8088 | HTTP + WS |
| Mesh Network | 8089 | HTTP |
| Prometheus | 9090 | HTTP |
| Grafana | 3001 | HTTP |
| Dashboard Frontend | 5173 | HTTP |
| Admin Frontend | 3000 | HTTP |
| Consul | 8500 | HTTP |
---
Cost Breakdown
Traditional SaaS: $4,000/mo
- Auth0: $800/mo
- Stripe: $300/mo
- SendGrid: $200/mo
- Datadog: $500/mo
- Redis Cloud: $250/mo
- AWS Lambda: $400/mo
- Plus more...
NeuAIs Platform: $1,200/mo
- 10 agents (Fly.io): $400/mo
- RIC (Cloudflare): $0/mo (free tier)
- Upstash Redis: $200/mo
- Database: $100/mo
- Domain/SSL: $20/mo
- Additional services: $480/mo
Total Savings: 70% ($2,800/mo)
---
Current Status
Deployed & Operational
- SMO server with rApp framework
- RIC with ML models (anomaly detection, traffic optimization)
- 3 production rApps (anomaly detector, traffic optimizer, auto-remediation design)
- Mesh network (Yggdrasil, QUIC, FRP)
- Dashboard UI (Phase 1 complete)
- Observatory visualization (Phase 1 complete)
- 37+ micro-AI agents
- 60+ Nix templates
- Multi-language SDKs
In Development
- Dashboard Phase 2 (real-time integration)
- Observatory Phase 2B (text rendering)
- Additional rApps
- Mobile applications
Planned
- 50+ deployed agents
- Continuous learning pipeline
- Public beta
- Production launch
---
Your Understanding vs Reality - CONFIRMATION
You said: “the hub is the user-facing area and their support systems”
Reality: ✓ CORRECT
- hub.neuais.com/ contains:
- admin.neuais.com (admin interface)
- dashboard.neuais.com (main dashboard)
- docs.neuais.com (documentation)
- observatory.neuais.com (3D visualization of AI agents)
- Plus mobile apps
You said: “The infra is for stuff that supports apps”
Reality: ✓ CORRECT
- infra/ contains:
- 37+ micro-AI agents
- 60+ Nix templates for app building
- Monitoring (Prometheus, Grafana)
- Docker, Cloudflare configs
- Mesh network infrastructure
You said: “services are stuff that supports the stuff that supports apps”
Reality: ✓ CORRECT
- services/ contains backend services:
- auth, iam, kms, billing, compute, storage, database
- ai-network (SMO + RIC)
- mesh-network
- registry (Consul)
You said: “the config is passwords”
Reality: ✓ PARTIALLY CORRECT
- config/ has environment configs (dev.toml, staging.toml, prod.toml)
- .secrets/ has actual passwords/API keys (gitignored)
You said: “the ml is for the ‘smart ai’ system to keep learning and growing”
Reality: ✓ CORRECT
- ml/ contains:
- 149GB of image models (Stable Diffusion)
- 122GB of text models (Llama 7B-70B)
- Training pipelines
- 24/7 local learning system (120B params)
- Model registry (MLflow)
You said: “the whole system is meant to have many little ais who each do a tiny task”
Reality: ✓ 100% CORRECT - This is the CORE concept
- 37+ micro-AI agents already built
- Each agent: 20-50MB
- Examples:
- auth-validator (JWT validation)
- cache-optimizer (cache prediction)
- code-generator (AI code gen)
- docker-agent, git-agent, kubernetes-agent, etc.
You said: “so any business could have 1000 mini ai - at like 20mb each = a whole business intelligence run on a few gb”
Reality: ✓ EXACTLY RIGHT
- Current: 37 agents
- Goal: 1000+ agents
- Each agent: 20-50MB
- Total: ~20-50GB for entire business intelligence
- Saves 70% cost vs traditional SaaS
You said: “they are controllable predictable infinite loggable”
Reality: ✓ YES
- SMO = orchestrates all agents
- RIC = provides ML intelligence
- Observable = Prometheus + Grafana + Loki logs everything
- Predictable = Each agent has defined input/output
You said: “the smo is … i cant remember…”
Reality: SMO = Service Management & Orchestration
- Port 8080
- Manages rApps (network applications)
- Enforces policies
- Coordinates actions across mesh
- Event bus for communication
You said: “maybe ric is for testing the network using a yang ai model and it finds the fastest path”
Reality: ✓ CLOSE! RIC = RAN Intelligent Controller
- Port 8081
- ML Engine with 2 production models: a. Anomaly Detection (92% accuracy) - finds problems b. Traffic Optimizer (88% accuracy) - finds fastest routes ← YOU WERE RIGHT!
- Provides ML intelligence to SMO and rApps
- Real-time inference (<15ms)
You said: “the nix-ide is for users to do ‘ai app building’”
Reality: ✓ EXACTLY RIGHT
- Browser-based IDE (Monaco Editor)
- 60+ Nix templates (React, Go, Python, etc.)
- AI code completion
- Instant dev environments
- No local setup needed
The “Perfection” Project - What It Is
Perfection is a mini thought experiment showing how services SHOULD work:
- ✓ One manifest - Single config file lists all services
- ✓ Two templates - Go service + Cloudflare Worker templates
- ✓ Generators - Auto-generate code for TS/Go/Rust/Python
- ✗ Single gateway - NOT DONE (smart router)
- ✓ Dashboard - Visual control center (the HTML I made)
- ✗ Policy/connectors - NOT DONE (click-to-add features)
- ✗ Ops automation - NOT DONE (auto-deploy/logging)
Foundation is solid, automation layer missing