d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
444 lines
11 KiB
Markdown
444 lines
11 KiB
Markdown
# 🎥 Agentic-Synth Video Tutorial Script
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**Duration**: 8-10 minutes
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**Target Audience**: Developers, ML engineers, data scientists
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**Format**: Screen recording with voice-over
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---
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## Video Structure
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1. **Introduction** (1 min)
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2. **Installation & Setup** (1 min)
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3. **Basic Usage** (2 mins)
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4. **Advanced Features** (2 mins)
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5. **Real-World Example** (2 mins)
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6. **Performance & Wrap-up** (1 min)
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---
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## Script
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### Scene 1: Introduction (0:00 - 1:00)
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**Visual**: Title card, then switch to terminal
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**Voice-over**:
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> "Hi! Today I'll show you agentic-synth - a high-performance synthetic data generator that makes it incredibly easy to create realistic test data for your AI and ML projects.
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>
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> Whether you're training machine learning models, building RAG systems, or just need to seed your development database, agentic-synth has you covered with AI-powered data generation.
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>
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> Let's dive in!"
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**Screen**: Show README on GitHub with badges
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---
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### Scene 2: Installation (1:00 - 2:00)
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**Visual**: Terminal with command prompts
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**Voice-over**:
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> "Installation is straightforward. You can use it as a global CLI tool or add it to your project."
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**Type in terminal**:
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```bash
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# Global installation
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npm install -g @ruvector/agentic-synth
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# Or use directly with npx
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npx agentic-synth --help
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```
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**Voice-over**:
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> "You'll need an API key from Google Gemini or OpenRouter. Let's set that up quickly."
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**Type**:
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```bash
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export GEMINI_API_KEY="your-key-here"
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```
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**Voice-over**:
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> "And we're ready to go!"
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---
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### Scene 3: Basic Usage - CLI (2:00 - 3:00)
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**Visual**: Terminal showing CLI commands
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**Voice-over**:
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> "Let's start with the CLI. Generating data is as simple as running a single command."
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**Type**:
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```bash
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npx agentic-synth generate \
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--type structured \
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--count 10 \
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--schema '{"name": "string", "email": "email", "age": "number"}' \
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--output users.json
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```
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**Voice-over**:
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> "In just a few seconds, we have 10 realistic user records with names, emails, and ages. Let's look at the output."
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**Type**:
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```bash
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cat users.json | jq '.[0:3]'
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```
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**Visual**: Show JSON output with realistic data
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**Voice-over**:
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> "Notice how the data looks realistic - real names, valid email formats, appropriate ages. This is all powered by AI."
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---
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### Scene 4: SDK Usage (3:00 - 4:00)
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**Visual**: VS Code with TypeScript file
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**Voice-over**:
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> "For more control, you can use the SDK directly in your code. Let me show you how simple that is."
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**Type in editor** (`demo.ts`):
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```typescript
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import { AgenticSynth } from '@ruvector/agentic-synth';
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// Initialize with configuration
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const synth = new AgenticSynth({
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provider: 'gemini',
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apiKey: process.env.GEMINI_API_KEY,
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cacheStrategy: 'memory', // Enable caching for 95%+ speedup
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cacheTTL: 3600
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});
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// Generate structured data
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const users = await synth.generateStructured({
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count: 100,
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schema: {
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user_id: 'UUID',
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name: 'full name',
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email: 'valid email',
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age: 'number (18-80)',
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country: 'country name',
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subscription: 'free | pro | enterprise'
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}
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});
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console.log(`Generated ${users.data.length} users`);
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console.log('Sample:', users.data[0]);
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```
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**Voice-over**:
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> "Run this code..."
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**Type in terminal**:
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```bash
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npx tsx demo.ts
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```
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**Visual**: Show output with generated data
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**Voice-over**:
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> "And we instantly get 100 realistic user profiles. Notice the caching - if we run this again with the same options, it's nearly instant!"
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---
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### Scene 5: Advanced Features - Time Series (4:00 - 5:00)
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**Visual**: Split screen - editor on left, output on right
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**Voice-over**:
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> "agentic-synth isn't just for simple records. It can generate complex time-series data, perfect for financial or IoT applications."
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**Type in editor**:
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```typescript
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const stockData = await synth.generateTimeSeries({
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count: 365,
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startDate: '2024-01-01',
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interval: '1d',
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schema: {
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date: 'ISO date',
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open: 'number (100-200)',
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high: 'number (105-210)',
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low: 'number (95-195)',
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close: 'number (100-200)',
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volume: 'number (1000000-10000000)'
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},
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constraints: [
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'high must be >= open and close',
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'low must be <= open and close',
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'close influences next day open'
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]
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});
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console.log('Generated stock data for 1 year');
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```
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**Voice-over**:
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> "The constraints ensure our data follows real-world patterns - high prices are actually higher than opens and closes, and there's continuity between days."
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**Show output**: Chart visualization of stock data
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---
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### Scene 6: Advanced Features - Streaming (5:00 - 6:00)
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**Visual**: Editor showing streaming code
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**Voice-over**:
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> "Need to generate millions of records? Use streaming to avoid memory issues."
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**Type**:
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```typescript
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let count = 0;
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for await (const record of synth.generateStream('structured', {
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count: 1_000_000,
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schema: {
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id: 'UUID',
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timestamp: 'ISO timestamp',
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value: 'number'
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}
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})) {
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// Process each record individually
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await saveToDatabase(record);
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count++;
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if (count % 10000 === 0) {
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console.log(`Processed ${count.toLocaleString()}...`);
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}
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}
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```
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**Voice-over**:
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> "This streams records one at a time, so you can process a million records without loading everything into memory."
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**Visual**: Show progress counter incrementing
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---
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### Scene 7: Real-World Example - ML Training Data (6:00 - 7:30)
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**Visual**: Complete working example
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**Voice-over**:
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> "Let me show you a real-world use case: generating training data for a machine learning model that predicts customer churn."
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**Type**:
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```typescript
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// Generate training dataset with features
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const trainingData = await synth.generateStructured({
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count: 5000,
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schema: {
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customer_age: 'number (18-80)',
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annual_income: 'number (20000-200000)',
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credit_score: 'number (300-850)',
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account_tenure_months: 'number (1-360)',
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num_products: 'number (1-5)',
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balance: 'number (0-250000)',
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num_transactions_12m: 'number (0-200)',
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// Target variable
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churn: 'boolean (higher likelihood if credit_score < 600, balance < 1000)'
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},
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constraints: [
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'Churn rate should be ~15-20%',
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'Higher income correlates with higher balance',
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'Customers with 1 product more likely to churn'
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]
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});
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// Split into train/test
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const trainSize = Math.floor(trainingData.data.length * 0.8);
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const trainSet = trainingData.data.slice(0, trainSize);
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const testSet = trainingData.data.slice(trainSize);
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console.log(`Training set: ${trainSet.length} samples`);
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console.log(`Test set: ${testSet.length} samples`);
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console.log(`Churn rate: ${(trainSet.filter(d => d.churn).length / trainSet.length * 100).toFixed(1)}%`);
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```
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**Voice-over**:
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> "In minutes, we have a complete ML dataset with realistic distributions and correlations. The AI understands the constraints and generates data that actually makes sense for training models."
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---
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### Scene 8: Performance Highlights (7:30 - 8:30)
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**Visual**: Show benchmark results
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**Voice-over**:
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> "Let's talk performance. agentic-synth is incredibly fast, thanks to intelligent caching."
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**Visual**: Show PERFORMANCE_REPORT.md metrics
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**Voice-over**:
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> "All operations complete in sub-millisecond to low-millisecond latencies. Cache hits are essentially instant. And with an 85% cache hit rate in production, you're looking at 95%+ performance improvement for repeated queries.
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>
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> The package also handles 1000+ requests per second with linear scaling, making it perfect for production workloads."
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---
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### Scene 9: Wrap-up (8:30 - 9:00)
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**Visual**: Return to terminal, show final commands
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**Voice-over**:
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> "That's agentic-synth! To recap:
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> - Simple CLI and SDK interfaces
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> - AI-powered realistic data generation
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> - Time-series, events, and structured data support
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> - Streaming for large datasets
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> - Built-in caching for incredible performance
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> - Perfect for ML training, RAG systems, and testing
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>
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> Check out the documentation for more advanced examples, and give it a try in your next project!"
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**Type**:
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```bash
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npm install @ruvector/agentic-synth
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```
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**Visual**: Show GitHub repo with Star button
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**Voice-over**:
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> "If you found this useful, star the repo on GitHub and let me know what you build with it. Thanks for watching!"
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**Visual**: End card with links
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---
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## Visual Assets Needed
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1. **Title Cards**:
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- Intro card with logo
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- Feature highlights card
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- End card with links
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2. **Code Examples**:
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- Syntax highlighted in VS Code
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- Font: Fira Code or JetBrains Mono
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- Theme: Dark+ or Material Theme
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3. **Terminal**:
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- Oh My Zsh with clean prompt
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- Colors: Nord or Dracula theme
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4. **Data Visualizations**:
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- JSON output formatted with jq
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- Stock chart for time-series example
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- Progress bars for streaming
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5. **Documentation**:
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- README.md rendered
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- Performance metrics table
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- Benchmark results
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---
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## Recording Tips
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1. **Screen Setup**:
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- 1920x1080 resolution
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- Clean desktop, no distractions
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- Close unnecessary applications
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- Disable notifications
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2. **Terminal Settings**:
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- Large font size (16-18pt)
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- High contrast theme
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- Slow down typing with tool like "Keycastr"
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3. **Editor Settings**:
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- Zoom to 150-200%
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- Hide sidebars for cleaner view
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- Use presentation mode
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4. **Audio**:
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- Use quality microphone
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- Record in quiet room
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- Speak clearly and at moderate pace
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- Add background music (subtle, low volume)
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5. **Pacing**:
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- Pause between steps
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- Let output display for 2-3 seconds
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- Don't rush through commands
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- Leave time for viewers to read
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---
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## Post-Production Checklist
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- [ ] Add title cards
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- [ ] Add transitions between scenes
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- [ ] Highlight important commands/output
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- [ ] Add annotations/callouts where helpful
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- [ ] Background music at 10-15% volume
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- [ ] Export at 1080p, 60fps
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- [ ] Generate subtitles/captions
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- [ ] Create thumbnail image
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- [ ] Upload to YouTube
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- [ ] Add to README as embedded video
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---
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## Video Description (for YouTube)
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```markdown
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# Agentic-Synth: High-Performance Synthetic Data Generator
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Generate realistic synthetic data for AI/ML training, RAG systems, and database seeding in minutes!
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🔗 Links:
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- NPM: https://www.npmjs.com/package/@ruvector/agentic-synth
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- GitHub: https://github.com/ruvnet/ruvector/tree/main/packages/agentic-synth
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- Documentation: https://github.com/ruvnet/ruvector/blob/main/packages/agentic-synth/README.md
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⚡ Performance:
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- Sub-millisecond P99 latencies
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- 85% cache hit rate
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- 1000+ req/s throughput
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- 95%+ speedup with caching
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🎯 Use Cases:
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- Machine learning training data
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- RAG system data generation
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- Database seeding
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- API testing
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- Load testing
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📚 Chapters:
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0:00 Introduction
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1:00 Installation & Setup
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2:00 CLI Usage
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3:00 SDK Usage
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4:00 Time-Series Data
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5:00 Streaming Large Datasets
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6:00 ML Training Example
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7:30 Performance Highlights
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8:30 Wrap-up
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#machinelearning #AI #syntheticdata #typescript #nodejs #datascience #RAG
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```
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---
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## Alternative: Live Coding Demo (15 min)
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For a longer, more in-depth tutorial:
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1. **Setup** (3 min): Project initialization, dependencies
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2. **Basic Generation** (3 min): Simple examples
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3. **Complex Schemas** (3 min): Nested structures, constraints
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4. **Integration** (3 min): Database seeding example
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5. **Performance** (2 min): Benchmarks and optimization
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6. **Q&A** (1 min): Common questions
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---
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**Script Version**: 1.0
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**Last Updated**: 2025-11-22
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**Status**: Ready for Recording 🎬
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