### Clone and Run VIVIM App (Bash)

Source: https://github.com/owenservera/vivim-app/blob/main/docs/development/guide.md

Clones the VIVIM app repository, installs dependencies using Bun, and starts all services. It also shows how to start individual services like the PWA, API server, network engine, and admin panel.

```bash
# Clone the repository
git clone https://github.com/vivim/vivim-app.git
cd vivim-app

# Install dependencies
bun run setup

# Start all services
bun run dev

# Or start individually
bun run dev:pwa     # Frontend (http://localhost:5173)
bun run dev:server  # API (http://localhost:3000)
bun run dev:network # Network engine
bun run dev:admin  # Admin panel (http://localhost:5174)
```

--------------------------------

### Database Setup SQL Commands

Source: https://github.com/owenservera/vivim-app/blob/main/docs/deployment/guide.md

SQL commands for setting up the PostgreSQL database for VIVIM. Includes database creation, extension setup (uuid-ossp, pg_trgm, vector), and a placeholder for running migrations.

```sql
-- Create database
CREATE DATABASE vivim;

-- Create extensions
CREATE EXTENSION IF NOT EXISTS "uuid-ossp";
CREATE EXTENSION IF NOT EXISTS "pg_trgm";
CREATE EXTENSION IF NOT EXISTS "vector";

-- Run migrations
-- bun run db:migrate
```

--------------------------------

### Compile and Run C Code Guide

Source: https://github.com/owenservera/vivim-app/blob/main/server/debug-chatgpt-1771911885291.html

Provides instructions on how to compile and run C code on various operating systems. It covers installing a C compiler (GCC/Clang), saving the code, compiling using GCC with the math library, and executing the program. It also details the expected output and suggests parameter tuning.

```bash
sudo apt update
sudo apt install build-essential
```

```bash
brew install gcc
```

```bash
gcc -o reasoning_engine reasoning_engine.c -lm
```

```bash
./reasoning_engine
```

```bash
reasoning_engine.exe
```

--------------------------------

### Backend (Server) Development Commands with Bun

Source: https://github.com/owenservera/vivim-app/blob/main/VIVIM.docs/CONTEXT/AI_CONTEXT.md

Details the commands required for the backend server development using Bun. It covers dependency installation, Prisma client generation and database schema management, and starting the development server.

```bash
cd server

# Install dependencies
bun install

# Generate Prisma client
bun run db:generate

# Push schema to database
bun run db:push

# Start development server
bun run dev

```

--------------------------------

### Frontend (PWA) Development Commands with Bun

Source: https://github.com/owenservera/vivim-app/blob/main/VIVIM.docs/CONTEXT/AI_CONTEXT.md

Provides essential commands for managing the frontend PWA using Bun. This includes installing dependencies, starting the development server, building for production, running tests, performing type checking, and linting.

```bash
cd pwa

# Install dependencies
bun install

# Start development server
bun run dev

# Build for production
bun run build

# Run tests
bun run test

# Type check
bun run typecheck

# Lint
bun run lint

```

--------------------------------

### MFA Setup and Enablement

Source: https://github.com/owenservera/vivim-app/blob/main/server/temp_test2.txt

This section covers the Multi-Factor Authentication (MFA) setup and enablement process. It includes API calls for initiating MFA setup, which returns a secret, and for enabling MFA, which provides backup codes. These operations are part of the user security features.

```bash
✅ GET /me/api-keys successful. User has 1 keys.
✅ MFA Setup successful. Secret: GIMCGLD3AUYQOWLE
✅ MFA Enable successful. Received backup codes: [ "bf56e2ee", "7ae2b324", "ddde9618",
  "7be78e76", "6f5f16a5", "bc284603", "808ddc67", "355bf8bd"
]
```

--------------------------------

### MFA Setup and Enablement

Source: https://github.com/owenservera/vivim-app/blob/main/server/temp_test3.txt

This section covers the Multi-Factor Authentication (MFA) setup and enablement process. It includes API calls for initiating MFA setup, which returns a secret, and for enabling MFA, which provides backup codes.

```http
POST /me/mfa/setup
POST /me/mfa/enable
```

--------------------------------

### API Call Debugging (TypeScript)

Source: https://github.com/owenservera/vivim-app/blob/main/VIVIM.docs/FRONTEND_DEBUGGING_GUIDE.md

Provides example code for debugging API calls by logging the initiation of a request, the completion of a request with status and duration, and details of any request failures.

```typescript
// Before API call
debug.debug('API', 'Request initiated', { endpoint, method, data });

// After response
debug.info('API', 'Request completed', { 
  endpoint, 
  status: response.status, 
  duration 
});

// On error
debug.error('API', 'Request failed', error, { 
  endpoint, 
  status: response?.status,
  responseData 
});
```

--------------------------------

### Vivim App Database Setup and Schema Application (SQL)

Source: https://github.com/owenservera/vivim-app/blob/main/VIVIM.docs/PITCH/OPERATIONS/README.md

This snippet details the process of setting up the Vivim App database. It includes commands to create the database and then apply all schema files in the correct order. Dependencies include a PostgreSQL client and the schema SQL files.

```sql
createdb vivim_costs_db

psql -d vivim_costs_db -f schemas/01_people_labor_schema.sql
psql -d vivim_costs_db -f schemas/02_tools_infrastructure_schema.sql
psql -d vivim_costs_db -f schemas/03_users_operations_schema.sql
psql -d vivim_costs_db -f schemas/04_governance_legal_schema.sql
psql -d vivim_costs_db -f schemas/05_dao_governance_schema.sql
psql -d vivim_costs_db -f schemas/06_ai_wiki_schema.sql
psql -d vivim_costs_db -f schemas/07_work_management_schema.sql
psql -d vivim_costs_db -f schemas/08_ai_agents_schema.sql
psql -d vivim_costs_db -f schemas/09_community_schema.sql
psql -d vivim_costs_db -f schemas/10_operations_management_schema.sql
```

--------------------------------

### Get and Log Performance Metrics (TypeScript)

Source: https://github.com/owenservera/vivim-app/blob/main/VIVIM.docs/FRONTEND_DEBUGGING_GUIDE.md

Retrieves current performance metrics like memory usage, network latency, and response time using the unifiedDebugService. It then logs these metrics to the console and includes a conditional warning for slow response times.

```typescript
import { unifiedDebugService } from './lib/unified-debug-service';

// Get current performance metrics
const metrics = unifiedDebugService.getPerformanceMetrics();
console.log('Memory Usage:', metrics.memoryUsage);
console.log('Network Latency:', metrics.networkLatency);
console.log('Response Time:', metrics.responseTime);

// Report performance issues
if (responseTime > 5000) {
  debug.warn('Performance', 'Slow response detected', {
    endpoint,
    responseTime,
    threshold: 5000
  });
}
```

--------------------------------

### Automatic Flow Tracking with Unified Debug Service (TypeScript)

Source: https://github.com/owenservera/vivim-app/blob/main/VIVIM.docs/FRONTEND_DEBUGGING_GUIDE.md

This example showcases automatic tracking of common operations like API requests and database operations using helper methods from the unified debug service. It simplifies the process of instrumenting these flows.

```typescript
import { unifiedDebugService } from './lib/unified-debug-service';

// Track API requests automatically
const users = await unifiedDebugService.trackApiRequest(
  '/api/users',
  'GET',
  async () => {
    const response = await fetch('/api/users');
    return response.json();
  },
  { filters: { status: 'active' } }
);

// Track database operations automatically
const result = await unifiedDebugService.trackDatabaseOperation(
  'INSERT',
  'users',
  async () => {
    return await db.user.create({ data: userData });
  },
  { query: userData }
);
```

--------------------------------

### VIVIM Icon Library CLI Examples

Source: https://github.com/owenservera/vivim-app/blob/main/VIVIM.docs/ICONS/README.md

Illustrates common commands for the VIVIM Icon Library CLI, including listing all icons, searching for icons by term, retrieving icon information, and previewing icon usage in code.

```bash
# List all icons
bun ./cli.mjs list

# Search for home-related icons
bun ./cli.mjs search "home"

# Get information about a specific icon
bun ./cli.mjs info "home-feed"

# Preview how to use an icon in code
bun ./cli.mjs preview "settings-cog"
```

--------------------------------

### Basic Logging with Unified Debug Service (TypeScript)

Source: https://github.com/owenservera/vivim-app/blob/main/VIVIM.docs/FRONTEND_DEBUGGING_GUIDE.md

This example shows how to perform basic logging (trace, debug, info, warn, error) using the unified debug service. It highlights how to specify a source and provide relevant data for each log entry.

```typescript
import { debug } from './lib/unified-debug-service';

// Trace - Very detailed diagnostic info
debug.trace('Router', 'Route matched', { path: '/users', params: { id: 1 } });

// Debug - Debugging information
debug.debug('API', 'Request started', { endpoint: '/api/users', method: 'GET' });

// Info - General operational info
debug.info('Auth', 'User logged in', { userId: user.id, method: 'google' });

// Warn - Potential issues
debug.warn('Storage', 'Storage quota nearly exceeded', { used: 4.8, limit: 5 });

// Error - Actual errors
debug.error('Database', 'Query failed', error, { query: sql, params });
```

--------------------------------

### Project Build and Development Commands

Source: https://github.com/owenservera/vivim-app/blob/main/VIVIM.docs/FRONTEND/FRONTEND.md

Lists essential commands for developing, building, linting, testing, and type-checking the project using Bun. It covers starting development servers, creating production builds, running linters, executing tests with coverage, and performing TypeScript type checks.

```bash
# Development
bun run dev              # Start dev server
bun run dev:vite         # Start Vite dev server

# Build
bun run build            # Production build
bun run build:tsc        # TypeScript compilation only
bun run build:vite       # Vite build

# Linting
bun run lint             # Run ESLint
bun run lint:fix         # Fix ESLint issues

# Testing
bun run test             # Run tests
bun run test:ui          # Run tests with UI
bun run test:coverage    # Run tests with coverage

# Type checking
bun run typecheck        # TypeScript type check
```

--------------------------------

### Set Up VIVIM Database with Prisma (Bash)

Source: https://github.com/owenservera/vivim-app/blob/main/docs/development/guide.md

Commands to set up the VIVIM application's database using Prisma. This includes generating the Prisma client, running database migrations, and seeding the database with sample data.

```bash
# Generate Prisma client
cd server && bun run db:generate

# Run migrations
cd server && bun run db:migrate

# Seed with sample data
cd server && bun run db:seed
```

--------------------------------

### Sync Operation Debugging (TypeScript)

Source: https://github.com/owenservera/vivim-app/blob/main/VIVIM.docs/FRONTEND_DEBUGGING_GUIDE.md

Demonstrates debugging synchronization processes. It includes starting a sync flow with a unique ID and tracking individual steps within that sync operation, marking them as pending or successful.

```typescript
// Start sync flow
const syncId = debug.startFlow();

// Track sync steps
debug.trackStep(syncId, 'sync', 'pending', { direction: 'push' });
// ... sync operation
debug.trackStep(syncId, 'sync', 'success', { changesPushed: 5 });
```

--------------------------------

### State Management Debugging (TypeScript)

Source: https://github.com/owenservera/vivim-app/blob/main/VIVIM.docs/FRONTEND_DEBUGGING_GUIDE.md

Illustrates how to debug state management operations. It shows logging the start of a state update with relevant payload and logging the state change with previous and new state details.

```typescript
// Before state update
debug.trace('Store', 'State update started', { 
  store: 'user', 
  action: 'SET_NAME',
  payload 
});

// After state update
debug.debug('Store', 'State updated', { 
  store: 'user',
  previousState,
  newState 
});
```

--------------------------------

### Data Flow Tracking with Unified Debug Service (TypeScript)

Source: https://github.com/owenservera/vivim-app/blob/main/VIVIM.docs/FRONTEND_DEBUGGING_GUIDE.md

This TypeScript code illustrates how to track the complete journey of data through your application using the unified debug service. It demonstrates starting a flow, tracking individual steps with their status, and handling errors.

```typescript
import { debug } from './lib/unified-debug-service';

async function fetchData(endpoint: string) {
  // Start a new flow
  const requestId = debug.startFlow();
  
  try {
    // Track each step
    debug.trackStep(requestId, 'request', 'pending', { endpoint });
    const response = await fetch(endpoint);
    debug.trackStep(requestId, 'request', 'success', { status: response.status });
    
    debug.trackStep(requestId, 'validation', 'pending');
    const data = await response.json();
    debug.trackStep(requestId, 'validation', 'success', { size: data.length });
    
    debug.trackStep(requestId, 'processing', 'pending');
    const processed = processData(data);
    debug.trackStep(requestId, 'processing', 'success', { resultSize: processed.length });
    
    debug.trackStep(requestId, 'database', 'pending', { operation: 'INSERT' });
    await saveToDatabase(processed);
    debug.trackStep(requestId, 'database', 'success', { rowsAffected: 1 });
    
    debug.trackStep(requestId, 'response', 'success', { duration: Date.now() - startTime });
    
    return processed;
  } catch (error) {
    debug.trackStep(requestId, 'error', 'error', {}, error.message);
    throw error;
  }
}
```

--------------------------------

### Session-Based Authentication Example (Bash)

Source: https://github.com/owenservera/vivim-app/blob/main/docs/api/overview.md

Demonstrates how to initiate session-based authentication using Google OAuth and highlights the structure of the session cookie returned.

```bash
# Login with Google
GET /api/v1/auth/google
# Redirects to Google OAuth

# Session cookie (HttpOnly, Secure)
Set-Cookie: connect.sid=s%3A...; Path=/; HttpOnly; Secure
```

--------------------------------

### Vivim-App Main Execution Function

Source: https://github.com/owenservera/vivim-app/blob/main/server/debug-chatgpt-1771914943820.html

The 'main' function serves as the entry point for the Vivim-App simulation. It initializes the random number generator and the system state, then runs the simulation for a predefined number of steps (STEPS). After the simulation, it prints a snapshot of the final signal values for each node. Dependencies include init_genrand, init_system, step_system, and diagnostics functions, along with the STEPS constant.

```c++
int main() {
 init_genrand(5489UL);
 init_system();

 for (int t = 0; t < STEPS; t++) {
 step_system();
 diagnostics(t);
 }

 printf("\nFinal signal snapshot:\n");
 for (int i = 0; i < NODES; i++) {
 printf("node %02d : %.6f\n", i, signal[i]);
 }

 return 0;
}
```

--------------------------------

### Troubleshoot Port Already in Use (Bash)

Source: https://github.com/owenservera/vivim-app/blob/main/docs/development/guide.md

Provides commands to identify and terminate processes that are using a specific port, which is a common issue when starting development servers.

```bash
# Find process using port
lsof -i :3000

# Kill process
kill -9 <PID>
```

--------------------------------

### Working with Promises

Source: https://github.com/owenservera/vivim-app/blob/main/server/tests/html-dls-cli/deepseek-cli.html

Shows how to create and consume Promises for handling asynchronous operations. Provides a cleaner way to manage callbacks.

```javascript
function delay(ms) {
  return new Promise(resolve => setTimeout(resolve, ms));
}

async function runWithDelay() {
  console.log('Starting...');
  await delay(1000);
  console.log('...Finished after 1 second.');
}

runWithDelay();

```

--------------------------------

### OpenAI API Integration

Source: https://github.com/owenservera/vivim-app/blob/main/VIVIM.docs/AI_API/ai-models-integration-2026.md

This section details the integration of OpenAI's models, including recommended models, API endpoints, and a TypeScript code example for chat completions.

```APIDOC
## OpenAI API Integration

### Description
This section provides details on integrating with OpenAI's models, including recommended models like GPT-5.2 and GPT-5 mini, their use cases, context windows, and pricing. It also includes the API base URL and specific endpoints for Chat Completions and a new Response API.

### Method
POST

### Endpoint
`https://api.openai.com/v1/chat/completions`

### Parameters
#### Query Parameters
None

#### Request Body
- **model** (string) - Required - The model to use for completion.
- **messages** (array) - Required - An array of message objects representing the conversation history.
- **max_tokens** (integer) - Optional - The maximum number of tokens to generate in the completion.
- **temperature** (number) - Optional - Controls randomness. Lower values make the output more deterministic.
- **stream** (boolean) - Optional - Whether to stream back partial progress.

### Request Example
```json
{
  "model": "gpt-5.2",
  "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Who won the world series in 2020?"}
  ],
  "max_tokens": 100,
  "temperature": 0.7,
  "stream": false
}
```

### Response
#### Success Response (200)
- **choices** (array) - An array of completion choices.
  - **message** (object) - The generated message.
    - **role** (string) - The role of the author of the message (e.g., 'assistant').
    - **content** (string) - The content of the message.

#### Response Example
```json
{
  "id": "chatcmpl-123",
  "object": "chat.completion",
  "created": 1700000000,
  "model": "gpt-5.2",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The Los Angeles Dodgers won the World Series in 2020."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 15,
    "total_tokens": 25
  }
}
```

### Key Features
- Function calling v3
- Structured outputs (JSON mode)
- Vision capabilities
- Web search integration
- Code interpreter
- Prompt caching
- Batch processing
```

--------------------------------

### Neural Network Training and Testing Main Function (C)

Source: https://github.com/owenservera/vivim-app/blob/main/server/debug-chatgpt-1771913454730.html

This C code demonstrates the main execution flow for training and testing a neural network. It sets up XOR problem data, initializes weights, and iterates through epochs, applying packet loss simulation, signal correction, feedforward, backpropagation, and SOM. Finally, it tests the trained network.

```c
int main() {
 // XOR problem input data with packet loss simulation
 float inputs[4][INPUT_NEURONS] = {{0, 0, 1, 0}, {0, 1, 1, 0}, {1, 0, 1, 1}, {1, 1, 1, 0}};
 float expected_outputs[4] = {0, 1, 1, 0};

 // Initialize weights for hidden layer and output layer
 float hidden_weights[INPUT_NEURONS * HIDDEN_NEURONS];
 float output_weights[HIDDEN_NEURONS];
 float som_weights[OUTPUT_NEURONS][INPUT_NEURONS]; // Weights for SOM

 initialize_weights(hidden_weights, INPUT_NEURONS * HIDDEN_NEURONS);
 initialize_weights(output_weights, HIDDEN_NEURONS);
 initialize_weights(&som_weights[0][0], OUTPUT_NEURONS * INPUT_NEURONS);

 // Training the network with feedback loop and packet loss simulation
 for (int epoch = 0; epoch < ITERATIONS; epoch++) {
 for (int i = 0; i < 4; i++) {
 float hidden_layer[HIDDEN_NEURONS];
 float output;

 // Simulate packet loss on inputs
 introduce_packet_loss(inputs[i], INPUT_NEURONS);

 // Perform signal correction
 float corrected_data[INPUT_NEURONS];
 signal_correction(inputs[i], corrected_data, INPUT_NEURONS);

 // Feedforward propagation
 feedforward(corrected_data, hidden_weights, output_weights, hidden_layer, &output);

 // Backpropagation (feedback loop)
 backpropagation(corrected_data, hidden_weights, output_weights, hidden_layer, &output, expected_outputs[i]);

 // Apply Self-Organizing Map (SOM) for clustering
 som(corrected_data, som_weights);
 }
 }

 // Test the trained network
 printf("Testing the trained network:\n");
 for (int i = 0; i < 4; i++) {
 float hidden_layer[HIDDEN_NEURONS];
 float output;

 introduce_packet_loss(inputs[i], INPUT_NEURONS); // Packet loss during test
 float corrected_data[INPUT_NEURONS];
 signal_correction(inputs[i], corrected_data, INPUT_NEURONS);

 feedforward(corrected_data, hidden_weights, output_weights, hidden_layer, &output);
 printf("Input: %.0f,\",{\"_140\":863,\"_142\":864,\"_144\":-5,\"_145\":865,\"_151\":12,\"
 }
 return 0;
}
```

--------------------------------

### Create Context Bundle (Context API)

Source: https://context7.com/owenservera/vivim-app/llms.txt

Creates a new context bundle, which can group topics, entities, and ACUs for a specific purpose like a project. Requires a name, description, and relevant IDs. Requires authentication.

```bash
curl -X POST "https://api.vivim.app/api/v2/context/bundles" \
  -H "Content-Type: application/json" \
  -H "x-did: did:key:z6Mk..." \
  -d '{
    "name": "Machine Learning Project",
    "description": "Context for ML development",
    "topicIds": ["topic-1", "topic-2"],
    "entityIds": ["entity-1"],
    "acuIds": ["acu-1", "acu-2"],
    "bundleType": "project"
  }'
```

--------------------------------

### Create and Deploy Prisma Schema Migration (Bash)

Source: https://github.com/owenservera/vivim-app/blob/main/server/prisma/SCHEMA_MIGRATION_GUIDE.md

This snippet demonstrates how to create a new database migration for the extended Prisma schema and then deploy it. It assumes you are in the 'apps/server' directory and have Prisma installed.

```bash
cd apps/server
bunx prisma migrate dev --name add_acu_tables --schema prisma/schema-extended.prisma
bunx prisma migrate deploy
bunx prisma generate --schema prisma/schema-extended.prisma
```

--------------------------------

### Error Statistics API Response (JSON)

Source: https://github.com/owenservera/vivim-app/blob/main/VIVIM.docs/ERROR_REPORTING.md

Provides an example of the JSON response structure for the GET /api/v1/errors/stats endpoint. This response includes aggregated statistics about errors, categorized by level, component, and severity.

```json
{
  "success": true,
  "data": {
    "total": 1000,
    "byLevel": {
      "error": 500,
      "warning": 300,
      "info": 200
    },
    "byComponent": {
      "pwa": 400,
      "server": 300,
      "network": 300
    },
    "bySeverity": {
      "critical": 50,
      "high": 200,
      "medium": 400,
      "low": 350
    },
    "recent": 50,
    "trending": [...],
    "topErrors": [...] 
  }
}
```

--------------------------------

### MFA Setup and Enablement

Source: https://github.com/owenservera/vivim-app/blob/main/server/temp_test.txt

This section details the process of setting up and enabling Multi-Factor Authentication (MFA) for a user. It includes API calls to '/me/mfa/setup' and '/me/mfa/enable'. The output shows the generated MFA secret and backup codes, which are crucial for user account security.

```shell
# Testing POST /me/mfa/setup...
# MFA Setup successful. Secret: IU2AYNJUJ5FBKABA

# Testing POST /me/mfa/enable...
# MFA Enable successful. Received backup codes: [ "01286a38", "fb119225", "5af56753",
#   "0fbdaf88", "295eb812", "b82f51f3", "33ac74e8", "58007b00" ]
```

--------------------------------

### xAI (Grok) API Integration

Source: https://github.com/owenservera/vivim-app/blob/main/VIVIM.docs/AI_API/ai-models-integration-2026.md

This section covers the integration with xAI's Grok models, including recommended models, API endpoints for chat and deferred completions, and a TypeScript code example.

```APIDOC
## xAI (Grok) API Integration

### Description
This section details the integration with xAI's Grok models, recommending Grok 4.1 and Grok 3 for various use cases. It provides the API base URL and endpoints for Chat Completions, Deferred Completions, and the Batch API.

### Method
POST

### Endpoint
`https://api.x.ai/v1/chat/completions`

### Parameters
#### Query Parameters
None

#### Request Body
- **model** (string) - Required - The model to use for completion.
- **messages** (array) - Required - An array of message objects representing the conversation history.
- **max_tokens** (integer) - Optional - The maximum number of tokens to generate in the completion.
- **temperature** (number) - Optional - Controls randomness. Lower values make the output more deterministic.

### Request Example
```json
{
  "model": "grok-4.1",
  "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Explain the concept of quantum entanglement."} 
  ],
  "max_tokens": 500,
  "temperature": 0.7
}
```

### Response
#### Success Response (200)
- **choices** (array) - An array of completion choices.
  - **message** (object) - The generated message.
    - **role** (string) - The role of the author of the message (e.g., 'assistant').
    - **content** (string) - The content of the message.

#### Response Example
```json
{
  "id": "chatcmpl-xyz789",
  "object": "chat.completion",
  "created": 1700000001,
  "model": "grok-4.1",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Quantum entanglement is a phenomenon where two or more quantum particles become linked in such a way that they share the same fate, regardless of the distance separating them..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 20,
    "completion_tokens": 150,
    "total_tokens": 170
  }
}
```

### Key Features
- Reasoning models for scientific problems
- Agent Tools API (server & client-side tool calling)
- Real-time search from X
- Enterprise security (SSO, audit logs)
- OpenAI-compatible endpoints via some proxies
```

--------------------------------

### RAG Neural Network Training Example (C)

Source: https://github.com/owenservera/vivim-app/blob/main/server/debug-chatgpt-1771911565367.html

Demonstrates the training process for a RAG neural network using the XOR problem. It initializes the Mersenne Twister, sets up input and expected output data for the XOR problem, initializes network weights, and then enters a training loop where feedforward and backpropagation are applied iteratively.

```c
int main() {
 init_genrand(5489UL); // Initialize Mersenne Twister with a seed

 // XOR problem input and expected output
 float inputs[4][INPUT_NEURONS] = {{0, 0, 1}, {0, 1, 1}, {1, 0, 1}, {1, 1, 1}};
 float expected_outputs[4][OUTPUT_NEURONS] = {{1, 0}, {0, 1}, {0, 1}, {1, 0}};

 // Initialize weights with random connections
 float weights[INPUT_NEURONS][HIDDEN_NEURONS]; // Weights between input and hidden layer
 float output_weights[HIDDEN_NEURONS][OUTPUT_NEURONS]; // Weights between hidden and output layer
 initialize_weights(weights, INPUT_NEURONS, HIDDEN_NEURONS, CONNECTION_PROB);
 initialize_weights(output_weights, HIDDEN_NEURONS, OUTPUT_NEURONS, CONNECTION_PROB);

 // Variables for storing hidden layer and output layer values
 float hidden_layer[HIDDEN_NEURONS];
 float outputs[OUTPUT_NEURONS];

 // Training the network
 // ... (Training loop would follow here, calling feedforward and backpropagation)
```

--------------------------------

### Minimal C Implementation of Pre-Cognitive Signal Substrate

Source: https://github.com/owenservera/vivim-app/blob/main/server/debug-chatgpt-1771911885291.html

A single-file, from-scratch C program implementing a pre-cognitive signal substrate. It demonstrates emergent phase behavior without external libraries beyond the C standard library. The code does not enforce phases but allows them to emerge from the system's dynamics.

```c
#include <stdio.h>
#include <stdlib.h>
#include <math.h>

/* ============================
 MT19937 (compact inclusion)
 ============================ */

#define N_MT 624
#define M_MT 397
#define MATRIX_A 0x9908b0dfUL
#define UPPER_MASK 0x80000000UL
#define LOWER_MASK 0x7fffffffUL

static unsigned long mt[N_MT];
static int mti = N_MT + 1;

void init_genrand(unsigned long s) {
    mt[0] = s & 0xffffffffUL;
    for (mti = 1; mti < N_MT; mti++) {
        mt[mti] = (1812433253UL *
                   (mt[mti - 1] ^ (mt[mti - 1] >> 30)) + mti);
        mt[mti] &= 0xffffffffUL;
    }
}

unsigned long genrand_int32(void) {
    unsigned long y;
    static unsigned long mag01[2] = {0x0UL, MATRIX_A};

    if (mti >= N_MT) {
        int kk;
        for (kk = 0; kk < N_MT - M_MT; kk++) {
            y = (mt[kk] & UPPER_MASK) | (mt[kk+1] & LOWER_MASK);
            mt[kk] = mt[kk+M_MT] ^ (y >> 1) ^ mag01[y & 1];
        }
        for (; kk < N_MT - 1; kk++) {
            y = (mt[kk] & UPPER_MASK) | (mt[kk+1] & LOWER_MASK);
            mt[kk] = mt[kk+(M_MT-N_MT)] ^ (y >> 1) ^ mag01[y & 1];
        }
        y = (mt[N_MT-1] & UPPER_MASK) | (mt[0] & LOWER_MASK);
        mt[N_MT-1] = mt[M_MT-1] ^ (y >> 1) ^ mag01[y & 1];
        mti = 0;
    }

    y = mt[mti++];
    y ^= (y >> 11);
    y ^= (y << 7) & 0x9d2c5680UL;
    y ^= (y << 15) & 0xefc60000UL;
    y ^= (y >> 18);
    return y;
}

double rnd() {
    return genrand_int32() * (1.0 / 4294967296.0);
}

/* ============================
 System parameters
 ============================ */

#define NODES 64
#define STEPS 5000
#define PACKET_LOSS 0.15
#define LAMBDA 0.96
#define EDGE_DELTA 0.001
#define EDGE_CUTOFF 0.0

/* ============================
 Node state
 ============================ */

double signal[NODES];
double memory[NODES];
double entropy[NODES];
double edges[NODES][NODES];

/* ============================
 Initialization
 ============================ */

void init_system() {
    for (int i = 0; i < NODES; i++) {
        signal[i] = rnd() * 2.0 - 1.0;
        memory[i] = signal[i];
        entropy[i] = 0.0;
        for (int j = 0; j < NODES; j++) {
            edges[i][j] = (rn
```

--------------------------------

### Tracking Data Flows for Important Operations

Source: https://github.com/owenservera/vivim-app/blob/main/VIVIM.docs/UNIFIED_DEBUGGING.md

Reinforces the practice of tracking the complete flow of critical operations. This example shows how to use `startFlow` and `trackStep` to monitor an `importantOperation` from start to finish, including potential errors.

```typescript
// Always track the complete flow of important operations
async function importantOperation() {
  const requestId = debug.startFlow();
  
  try {
    debug.trackStep(requestId, 'request', 'pending');
    // ... operation
    debug.trackStep(requestId, 'request', 'success');
    
    debug.trackStep(requestId, 'validation', 'pending');
    // ... validation
    debug.trackStep(requestId, 'validation', 'success');
    
    debug.trackStep(requestId, 'response', 'success');
  } catch (error) {
    debug.trackStep(requestId, 'error', 'error', {}, error.message);
    throw error;
  }
}
```

--------------------------------

### Pre-Cognitive Signal Substrate Simulation in C

Source: https://github.com/owenservera/vivim-app/blob/main/server/debug-chatgpt-1771914943820.html

A single-file, from-scratch C program implementing a pre-cognitive signal substrate. It evolves through emergent phases (random diffusion, local stabilization, coherent regions, topology pruning, persistent structures) without explicit phase enforcement. No external libraries beyond the C standard library are used. Entropy is structural and packet loss is fundamental.

```c
#include <stdio.h>
#include <stdlib.h>
#include <math.h>

/* ============================
 MT19937 (compact inclusion)
 ============================ */

#define N_MT 624
#define M_MT 397
#define MATRIX_A 0x9908b0dfUL
#define UPPER_MASK 0x80000000UL
#define LOWER_MASK 0x7fffffffUL

static unsigned long mt[N_MT];
static int mti = N_MT + 1;

void init_genrand(unsigned long s) {
    mt[0] = s & 0xffffffffUL;
    for (mti = 1; mti < N_MT; mti++) {
        mt[mti] = (1812433253UL *
                   (mt[mti - 1] ^ (mt[mti - 1] >> 30)) + mti);
        mt[mti] &= 0xffffffffUL;
    }
}

unsigned long genrand_int32(void) {
    unsigned long y;
    static unsigned long mag01[2] = {0x0UL, MATRIX_A};

    if (mti >= N_MT) {
        int kk;
        for (kk = 0; kk < N_MT - M_MT; kk++) {
            y = (mt[kk] & UPPER_MASK) | (mt[kk+1] & LOWER_MASK);
            mt[kk] = mt[kk+M_MT] ^ (y >> 1) ^ mag01[y & 1];
        }
        for (; kk < N_MT - 1; kk++) {
            y = (mt[kk] & UPPER_MASK) | (mt[kk+1] & LOWER_MASK);
            mt[kk] = mt[kk+(M_MT-N_MT)] ^ (y >> 1) ^ mag01[y & 1];
        }
        y = (mt[N_MT-1] & UPPER_MASK) | (mt[0] & LOWER_MASK);
        mt[N_MT-1] = mt[M_MT-1] ^ (y >> 1) ^ mag01[y & 1];
        mti = 0;
    }

    y = mt[mti++];
    y ^= (y >> 11);
    y ^= (y << 7) & 0x9d2c5680UL;
    y ^= (y << 15) & 0xefc60000UL;
    y ^= (y >> 18);
    return y;
}

double rnd() {
    return genrand_int32() * (1.0 / 4294967296.0);
}

/* ============================
 System parameters
 ============================ */

#define NODES 64
#define STEPS 5000
#define PACKET_LOSS 0.15
#define LAMBDA 0.96
#define EDGE_DELTA 0.001
#define EDGE_CUTOFF 0.0

/* ============================
 Node state
 ============================ */

double signal[NODES];
double memory[NODES];
double entropy[NODES];
double edges[NODES][NODES];

/* ============================
 Initialization
 ============================ */

void init_system() {
    for (int i = 0; i < NODES; i++) {
        signal[i] = rnd() * 2.0 - 1.0;
        memory[i] = signal[i];
        entropy[i] = 0.0;
        for (int j = 0; j < NODES; j++) {
            edges[i][j] = (rnd() < 0.5) ? (rnd() * 0.1) : 0.0;
        }
    }
}

/* ============================
 Update step
 ============================ */

void step_system() {
    double new_signal[NODES];

    for (int i = 0; i < NODES; i++) {
        double acc = 0.0;

        for (int j = 0; j < NODES; j++) {

```

--------------------------------

### xAI (Grok) API Integration (TypeScript)

Source: https://github.com/owenservera/vivim-app/blob/main/VIVIM.docs/AI_API/ai-models-integration-2026.md

Provides a TypeScript example for integrating with the xAI (Grok) API. This function allows sending chat completions, specifying the model, messages, and other options. Authentication is handled via an API key.

```typescript
const xAIClient = {
  async complete(model: string, messages: Message[], options: CompletionOptions) {
    const response = await fetch('https://api.x.ai/v1/chat/completions', {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${config.xaiApiKey}`,
        'Content-Type': 'application/json'
      },
      body: JSON.stringify({
        model,
        messages,
        max_tokens: options.maxTokens || 4096,
        temperature: options.temperature || 0.7
      })
    });
    return response.json();
  }
};
```

--------------------------------

### Create Notebook (Context API)

Source: https://context7.com/owenservera/vivim-app/llms.txt

Creates a new notebook for organizing research notes or findings. Allows specifying a name, description, and color. Requires authentication.

```bash
curl -X POST "https://api.vivim.app/api/v2/context/notebooks" \
  -H "Content-Type: application/json" \
  -H "x-did: did:key:z6Mk..." \
  -d '{"name": "Research Notes", "description": "Collection of research findings", "color": "#4A90D9"}'
```

--------------------------------

### Set User and Session Context for Logging (TypeScript)

Source: https://github.com/owenservera/vivim-app/blob/main/VIVIM.docs/FRONTEND_DEBUGGING_GUIDE.md

Sets the user ID and session ID using the debug service. All subsequent log messages will automatically include this context, aiding in tracking user-specific events and sessions.

```typescript
import { debug } from './lib/unified-debug-service';

// Set user context (all subsequent logs will include this)
debug.setUserId(user.id);
debug.setSessionId(session.id);

// Now all logs include user and session context
debug.info('Auth', 'User performed action', { action: 'capture' });
```