ANSAI Video Tutorial Scripts¶
Ready-to-record scripts for demos and tutorials.
Perfect for: - Your Friday talk - YouTube tutorials - Launch marketing - Community contributions
🎬 Video 1: ANSAI Quick Start (5 minutes)¶
Goal: Show how easy it is to get started with ANSAI
Script¶
[0:00 - 0:30] Introduction
"Hi! I'm going to show you ANSAI - AI-powered automation infrastructure.
ANSAI is Ansible plus AI. It transforms traditional automation into intelligent automation.
Without AI, it's just scripts. With ANSAI, your infrastructure actually thinks.
Let me show you how easy it is to get started."
[0:30 - 1:00] Installation
"First, installation is a single command."
"The installer checks prerequisites, sets up your PATH, and offers to install AI backends like LiteLLM or Fabric.
I'll choose LiteLLM for multi-model support."
[Show installer output, select option 1 for LiteLLM]
[1:00 - 2:00] AI Backend Setup
"Now I need to set up the AI backend. I'll use OpenAI, but ANSAI also supports Claude, Groq, or local models like Ollama."
"LiteLLM acts as a proxy. It can route to the cheapest model, implement fallbacks, and track costs. Pretty cool."
[Show health check]
[2:00 - 3:30] First AI Analysis
"Let's test it with log analysis. I'll create a simple error log."
echo "ERROR: Connection refused on port 5432
ERROR: Database connection timeout
WARNING: High memory usage
ERROR: Cannot connect to database" > test.log
"Now let's see what the AI thinks."
[Show AI analysis output]
"Look at that! The AI identified: - Root cause: Database connection issues - Pattern: Multiple connection errors - Recommendation: Check if PostgreSQL is running on port 5432
It's not just grepping for 'ERROR'. It's actually analyzing and making connections."
[3:30 - 4:30] Deploy to Server
"Now let's deploy AI-powered monitoring to a real server. First, I configure my inventory."
cat > inventory/hosts.yml << 'EOF'
all:
children:
servers:
hosts:
my-server:
ansible_host: 192.168.1.100
ansible_user: jbyrd
EOF
"Now I deploy with one command."
[Show playbook running, highlight key steps]
"This deploys: - Universal self-healing for services - AI-powered root cause analysis - Email alerts with diagnostics - Automatic remediation"
[4:30 - 5:00] Wrap Up
"That's it! In 5 minutes we: - Installed ANSAI - Set up AI - Analyzed logs intelligently - Deployed to a real server
Your infrastructure is now thinking, not just executing.
Check out ansai.dev for more examples. Thanks for watching!"
🎬 Video 2: AI Root Cause Analysis Demo (8 minutes)¶
Goal: Show AI identifying root causes in a real incident
Script¶
[0:00 - 0:45] Setup the Scenario
"I'm going to simulate a production incident and show you how ANSAI's AI finds the root cause.
The scenario: Our web API is slow. Users are complaining. Let's investigate."
[Show a web app with slow response times]
[0:45 - 2:00] Collect Logs
"First, I grab the logs from our API service."
"Let's take a quick look."
[cat the logs, scroll through - show it's overwhelming]
"Over 5,000 lines. Lots of noise. This would take me hours to analyze manually.
Let's see what the AI finds."
[2:00 - 4:00] AI Analysis
[Show AI analyzing]
"The AI is reading all the logs, identifying patterns, and correlating events. Let's see what it found."
[Show AI output]
"Wow. Look at this:
Root Cause: Database connection pool exhausted
Contributing Factors: - Traffic spike: 300% above baseline - Connection pool: 100/100 (maxed out) - No connection timeout configured
Trigger: Marketing campaign launched at 2:30 PM
The AI not only found the immediate cause - connection pool exhaustion - but it traced it back to the marketing campaign and identified configuration gaps.
This is what I mean by intelligent automation. It's not just showing errors. It's explaining WHY."
[4:00 - 5:30] AI Recommendations
"But it doesn't stop there. Look at the recommendations:
Immediate: 1. Increase connection pool from 100 to 200 2. Add connection timeout (30 seconds) 3. Implement rate limiting
Long-term: 1. Add connection pool monitoring 2. Auto-scaling based on utilization 3. Coordinate with marketing for high-traffic events
This is actionable. I can fix this right now."
[5:30 - 6:30] Apply Fix
"Let me apply the immediate fix."
[Edit database config, increase pool size]
[Show service restart, check metrics]
[6:30 - 7:30] Generate Incident Report
"Now let's document this incident. I'll use ANSAI's AI incident reporter."
[Fill in the prompts]
"Service: web-api Start time: 2:30 PM Severity: high Impact: API latency increased 7x Root cause: Connection pool exhausted Template: postmortem"
[Show AI generating the report]
"And just like that, I have a complete post-mortem with timeline, root cause analysis, action items, and lessons learned.
This would normally take me an hour to write. AI did it in 30 seconds."
[7:30 - 8:00] Wrap Up
"So that's AI root cause analysis with ANSAI: - Found the root cause in seconds (not hours) - Explained WHY it happened - Provided actionable recommendations - Generated a complete post-mortem
This is what intelligent infrastructure looks like.
Links in the description. Thanks for watching!"
🎬 Video 3: Safe Deployments with AI (6 minutes)¶
Goal: Show AI catching deployment issues before production
Script¶
[0:00 - 0:30] Introduction
"Ever deployed something to production and immediately regretted it?
Let me show you how ANSAI uses AI to catch issues BEFORE they hit production.
This is ansai-deploy-safe - your AI safety reviewer."
[0:30 - 1:30] The Deployment
"Here's a Kubernetes deployment I want to push to production."
[Show deployment.yaml]
apiVersion: apps/v1
kind: Deployment
metadata:
name: my-app
spec:
replicas: 3
template:
spec:
containers:
- name: app
image: myapp:latest
ports:
- containerPort: 8080
"Looks fine, right? Let's see what the AI thinks."
[1:30 - 3:30] AI Safety Analysis
[Show AI analyzing]
"The AI is checking: - Security vulnerabilities - Resource limits - Configuration risks - Production readiness - Best practices
And here are the results..."
[Show AI output]
"Whoa. The AI found some serious issues:
CRITICAL: - No resource limits (can crash the node) - Missing readiness probe (traffic to unhealthy pods) - Using 'latest' tag (breaks rollbacks) - No monitoring configured (blind deployment)
Overall Assessment: HIGH RISK - NO GO
If I had deployed this, I would have had problems. The AI just saved me from a production incident."
[3:30 - 5:00] Fix the Issues
"Let me fix these issues based on the AI's recommendations."
[Edit deployment.yaml]
apiVersion: apps/v1
kind: Deployment
metadata:
name: my-app
annotations:
prometheus.io/scrape: "true" # AI recommended
spec:
replicas: 3
template:
spec:
containers:
- name: app
image: myapp:v1.2.3 # Specific tag
ports:
- containerPort: 8080
resources: # AI recommended
requests:
memory: "512Mi"
cpu: "250m"
limits:
memory: "1Gi"
cpu: "500m"
readinessProbe: # AI required
httpGet:
path: /health/ready
port: 8080
initialDelaySeconds: 5
periodSeconds: 10
"Now let's check again."
[5:00 - 5:30] Recheck
[Show new output]
"Overall Assessment: LOW RISK - GO
All checks passed! This is safe to deploy."
[5:30 - 6:00] Wrap Up
"That's AI-powered deployment safety: - Caught 4 critical issues before production - Provided specific fixes - Validated the corrected deployment
This is like having an SRE reviewing every deployment. But it's instant, consistent, and never gets tired.
Check out ansai.dev. Thanks for watching!"
🎬 Bonus: Your Friday Talk - "ANSAI: Ansible + AI" (15 minutes)¶
Goal: Comprehensive demo for technical audience
Structure¶
[0-2 min] The Problem - Traditional automation: Static, reactive, dumb - You write the rules, it follows blindly - When things break, you read logs manually - Configuration is guesswork - "We've had intelligent humans for thousands of years. Intelligent infrastructure? That's new."
[2-4 min] The Solution: AI Layer - ANSAI = Ansible + AI - Same proven patterns (Ansible Galaxy) - New intelligence layer (LLMs) - Not replacing humans, augmenting automation - "If Ansible is the hands, AI is the brain."
[4-7 min] Demo 1: AI Log Analysis - Show real logs from testserver - Traditional: grep, find, manual correlation - ANSAI: AI finds root cause in seconds - Explain the "why", not just "what"
[7-10 min] Demo 2: Galaxy + ANSAI - Show popular Ansible Galaxy role (nginx, postgres) - "This works great. But what if it was smart?" - Add ANSAI AI enhancement - Show AI optimization, predictions, recommendations
[10-13 min] Demo 3: Production Safety - Show deployment manifest - AI safety check catches issues - Fix issues - "This just prevented a production incident"
[13-15 min] The Future - Infrastructure that thinks - Proactive, not reactive - Learning from experience - Community-driven patterns + AI intelligence - "This is what the next 10 years of ops looks like."
Q&A Tips: - "Isn't this expensive?" → Show Groq/local models - "What about hallucinations?" → Validation layers, confidence scores - "How is this different from X?" → AI as intelligence layer, not replacement - "Can I use my own models?" → Yes! LiteLLM supports any OpenAI-compatible API
📝 Recording Tips¶
Before Recording¶
Environment Setup: - ✅ Clean terminal (clear history) - ✅ Increase font size (24pt minimum) - ✅ Hide sensitive info (API keys, IPs) - ✅ Test all commands work - ✅ Prepare "worst case" backups
Audio: - ✅ Use external microphone (not laptop mic) - ✅ Record in quiet room - ✅ Test audio levels - ✅ Have water nearby
Screen: - ✅ 1920x1080 minimum resolution - ✅ Hide notifications - ✅ Clean desktop - ✅ Close unnecessary apps
During Recording¶
Pacing: - Speak clearly and slowly - Pause between commands - Let AI output fully display - Don't rush through errors
Engagement: - Make eye contact with camera - Use hand gestures (if on camera) - Emphasize key points - Show enthusiasm!
Demo Flow: - Show the problem first - Then show the solution - Explain what's happening - Highlight impressive results
After Recording¶
Editing: - Cut long waits - Add text overlays for commands - Zoom in on important output - Add chapter markers
Publishing: - Catchy title with keywords - Detailed description - Links to ansai.dev - Timestamps in description - Tags: ANSAI, AI, Ansible, DevOps, SRE
🎯 Key Messages to Emphasize¶
1. AI Makes Automation Intelligent
"Traditional automation follows rules. ANSAI understands context."
2. Built on Proven Patterns
"ANSAI doesn't replace Ansible Galaxy. It enhances it with AI."
3. Real Results, Not Hype
"62% cost savings. 71% latency reduction. 95% faster query performance. These are real numbers from real infrastructure."
4. Easy to Get Started
"One-line install. 5-minute quick start. Deploy AI-powered automation today."
5. Community-Driven
"ANSAI is what you build with it. Share your workflows. Learn from others."
📚 Resources to Mention¶
- Website: ansai.dev
- GitHub: github.com/thebyrdman-git/ansai
- Docs: ansai.dev/GETTING_STARTED
- Examples: ansai.dev/examples
- Community: github.com/thebyrdman-git/ansai/discussions
Good luck with your recording! 🎬 These scripts are templates - make them your own! 🚀
Part of the ANSAI Framework Learn more: https://ansai.dev