Larry Playbook — Viral TikTok Content Generator

Autonomous AI agent that learns and improves viral content over time using Oliver Henry's proven formula.

Proven Results (5 days, 2025):

• 500K+ total TikTok views
• 234K views on top single post
• 4 posts with 100K+ views
• 108 paying subscribers
• MRR: $588/month
• Cost: ~$0.50/post (API calls)
• ROI: 95% AI work, 5% human finishing

---

Quick Start

Prerequisites

• OpenAI API key (optional) for image generation

  export OPENAI_API_KEY="sk-proj-xxxx"

• Post-Bridge API key for posting to social platforms

  export POST_BRIDGE_API_KEY="pb_live_xxxx"

Quick Demo

Generate a single viral TikTok slideshow:

export POST_BRIDGE_API_KEY="pb_live_xxxx"
python3 skills/larry-playbook/larry-demo.py

---

Core Philosophy

"Every failure becomes a rule. Every success becomes a formula. The system compounds."

This is NOT about:

• Asking ChatGPT for captions
• Generic motivational quotes
• AI art that looks fake
• Guessing what works

This IS about:

• Data-driven iteration
• Persistent memory and learning
• Locking down architecture
• Documenting everything
• Scaling what works

---

Features

✅ Content Generation

• 6-Slide Viral TikTok Slideshow using Larry's proven formula
  • Hook templates (Landlord + AI, Parent + AI, Roommate + AI)
  • Locked room architecture (same room, 6 different styles)
  • Story-style captions with natural app mentions
  • Automatic hashtag optimization

📊 Continuous Learning

• Hourly research of trending TikTok content and hooks
• Confidence tracking for different flow types and hooks
• Performance analytics to measure views, engagement, conversion
• Rule evolution — failures become rules, successes become formulas
• Memory system — logs lessons learned for persistent improvement

🤖 Automated Posting

• Post-Bridge integration for multi-platform distribution
  • Facebook (24 accounts)
  • TikTok (1 account)
  • Instagram, LinkedIn, X support
• Scheduling with optimal posting times
• Draft mode — upload to drafts, manual music selection

📈 Analytics & Tracking

• View count tracking (estimated based on engagement)
• Engagement rate monitoring (likes, comments, shares)
• Hook performance comparison (which formulas work best)
• Platform success rate tracking

---

The Viral Hook Formula

Formula (234K views post):

[Another person's problem] + [Doubt/Conflict] 
→ Showed them AI Result
→ They changed their mind / took action

Why it works:

• Creates curiosity (what happened?)
• Provides solution (AI showed them something cool)
• Generates trust (real person, not marketer)
• Triggers action (show YOUR landlord/mum/friend!)

Working Examples:

Hook Type	Example	Views	Why
❌ Self-focused	"Why does my flat look like a student loan"	905	About YOU, nobody cares
❌ Feature-focused	"See your room in 12+ styles before you commit"	879	Selling features, boring
✅ Third-party + AI	"My landlord said I can't change anything so I showed her what AI thinks it could look like"	234,000	Relatable problem + cool solution

---

Content Architecture

Slideshow Format

• Exactly 6 slides (TikTok's sweet spot)
• Portrait (1024x1536) for all images
• Same room across all slides, different styles only
• Text overlay on slide 1 with hook
• Duration: Auto-advance (2-3 seconds per slide)

Slide 1: The Hook

Must include:

• ✅ Third person with problem
• ✅ Doubt or conflict
• ✅ "Showed them AI" phrase
• ✅ Call to action (implicit or explicit)

Bad examples (avoid):

• ❌ "I built an app that does X"
• ❌ "Check out my new feature Y"
• ❌ "Download now for Z"

Good examples (use):

• ✅ "My landlord wouldn't budge on renovations, so I showed her what AI thinks it could look like"
• ✅ "My mum was skeptical about [app name] until I showed her AI's idea for our kitchen"
• ✅ "My flatmate thinks [X] is impossible, so I proved them wrong with this AI design for our kitchen"

Slides 2-6: The Transformation

Show SAME room in different styles:

• Slide 2: Before/After split or angle change
• Slide 3: Different wall color
• Slide 4: Lighting change (day/night)
• Slide 5: Furniture rearrangement
• Slide 6: Final polished result

Critical: Window position, door location, furniture layout MUST stay identical. Only style elements change.

Caption Formula (Story Style)

[Hook context - 1 line]

My [relationship] [reaction/emotion] when I showed them [AI suggestion]

[CTA: Check comments / Link in bio]

[max 5 hashtags, relevant to niche]

---

Tech Stack

Component	Tool	Purpose
Image Generation	OpenAI gpt-image-1.5	Photorealistic room photos
Video Creation	FFmpeg	6-slide slideshow with text overlay
Scheduling	Post-Bridge API	Upload as draft to TikTok
Analytics	RevenueCat / Mixpanel	Track MRR, views, conversion
Learning	Custom	Confidence tracking & rule evolution

---

Available Commands

Manual Mode

Generate a single viral TikTok slideshow:

python3 skills/larry-playbook/larry-demo.py

Continuous Mode

Run autonomous agent that learns and improves:

export POST_BRIDGE_API_KEY="pb_live_xxxx"

python3 skills/larry-playbook/larry-continuous-system.py

The system will:

1. Research (every hour) — Find trending hooks, viral topics
2. Generate (on demand) — Create viral content based on research
3. Post (on demand) — Distribute to all connected platforms
4. Learn (continuous) — Track performance, update rules, evolve

---

Usage

1. Get Connected Accounts

Check which social media accounts are connected:

python3 skills/larry-playbook/larry-continuous-system.py

Output shows:

• Facebook: 24 accounts
• TikTok: 1 account
• Instagram, LinkedIn, X (if connected)

2. Select Hook Type

Choose from proven hook templates:

• Landlord + AI — Top performer (234K views average)
• Parent + AI — High performer (80K views average)
• Roommate + AI — Solid performer (60K views average)
• Doubter Proven Wrong — Test edge cases

3. Select Room Type

Choose room architecture:

• Kitchen (Small/Cozy) — Rental focused
• Living Room — Relaxation focused
• Bedroom (Minimal) — Transformation focused
• Studio Apartment — Space-saving focused

4. Generate Slideshow

The system will:

1. Generate 6 images of the same room with 6 different styles
2. Add text overlay (hook) to first image
3. Create 15-second slideshow video
4. Upload to Post-Bridge as draft
5. Send caption with hashtags to human

5. Publish

Human workflow:

1. Open TikTok app
2. Go to drafts folder
3. Select latest draft
4. Pick trending sound (TikTok's viral sounds change daily)
5. Paste AI-generated caption
6. Hit publish

---

Confidence System

Confidence Levels

Level	Multiplier	Description	Min Views Threshold
High	2.0x	Proven formula with strong data	100K
Medium	1.5x	Tested concept with moderate evidence	50K
Low	1.0x	New untested concept	10K

How It Works

• Success → Confidence increases (up to 1.0x)
  • "Larry's slideshow" starts at 0.8 (proven)
• Failure → Confidence decreases (down to 0.3x)
  • Low-performing hooks automatically deprioritized

Evolution

New hook tested → 5K views (success) → Confidence UP
↓
New hook fails → 3K views (failure) → Confidence DOWN
↓
After 10 successes → Confidence maxed at 1.0x → "Winning formula"

---

Memory System

Memory Files

skills/larry-playbook/memory/
├── SYSTEM_MEMORY.json ← Performance history
└── logs/              ← Daily activity logs

What Gets Tracked

• Total posts generated and published
• Views per post (estimated based on engagement)
• Hook performance by type (Landlord vs Parent vs Roommate)
• Flow performance by content type (slideshow vs image post)
• Platform success rates (TikTok, Facebook, Instagram)

Rule Updates

When a hook type consistently performs >150K views:

• Mark as "winning formula"
• Increase confidence multiplier
• Prioritize in automatic content generation

When a hook type consistently fails <30K views:

• Mark as "losing formula"
• Decrease confidence multiplier
• Deprioritize in automatic content generation

---

Analytics Dashboard

Metrics to Track

Metric	Target	How to Measure
Views/post	50K+ average	TikTok analytics
Engagement rate	8%+	(likes + comments) / views
Save rate	2%+	Saves / views
Share rate	1%+	Shares / views
Conversion	MRR impact	App subscriptions / trial starts

Performance Review

Run weekly analysis to optimize:

# View performance data
python3 -c "
import json
with open('skills/larry-playbook/memory/SYSTEM_MEMORY.json') as f:
    data = json.load(f)
    print(f'Veiws/post: {data.get('avg_views', 0)}')
    print(f'Total_posts: {data.get('total_posts', 0)}')
"

---

Common Pitfalls (Avoid These!)

❌ Pitfall 1: Self-Promotion

Symptom: Under 10K views, low save rate
Cause: "I built an app that..." or "Check out my feature..."
Fix:

WRONG: "See how Snugly can transform your boring rental kitchen"
RIGHT: "My flatmate thinks interior design is impossible, so I proved them wrong 
         with this AI design for our kitchen"

❌ Pitfall 2: Wrong Slide Count

Symptom: Video doesn't finish (users swipe away early)
Cause: 5 or 7 slides instead of exactly 6
Fix: Always generate exactly 6, no exceptions

❌ Pitfall 3: Text Overlay Issues

Symptom: Hook unreadable, hidden behind UI
Cause:

• Font too small
• Text positioned in status bar area
• Too many lines (wraps awkwardly)

Fix:

• Font size: 72-96px minimum
• Y position: 300-400px from top (safe zone)
• Max 3 lines, break if longer

---

Scaling Strategies

Scale 1: Multi-Niche

• Replicate formula for different niches:
  • Rental + landlord
  • Wedding + bride
  • Small business + investor
  • Fitness + gym owner

Scale 2: Multi-Platform

• Adapt slideshow for:
  • TikTok (6 slides, auto-advance)
  • Instagram Reels (same format)
  • YouTube Shorts (same format)

Scale 3: Multi-Account

• Run same content strategy on:
  • Main account (established brand)
  • Niche accounts (vertical markets)
  • Test accounts (experimental hooks)

---

Learning Loop

Every 30 Seconds

Generate Post → Observe Results (24-48h) → Update Rules → Generate Next Post
                                                              ↑
                                                    Repeat forever

Every Hour

Research Flow → Content Gen Flow → Social Media Flow → Feedback Loop → Update Memory
     ↓                    ↓                      ↓                   ↓                 ↓

---

Monitoring & Analytics

Daily Checklist

- [ ] Check yesterday's post performance
- [ ] Identify top and bottom performers
- [ ] Update memory with new rules
- [ ] Generate today's content based on winners
- [ ] Post to drafts by [time]
- [ ] Log all attempts and results

Weekly Review

## Week of [Date]

**Posts Published:** X
**Total Views:** X
**Top Performer:** Post ID [Views]
**Bottom Performer:** Post ID [Views]
**Average Views:** X
**MRR Impact:** $X (up/down)

**Key Insights:**
- [ ] Hook that worked best: [Type]
- [ ] Style that converted: [Style]
- [ ] Sound that boosted: [Sound ID]
- [ ] Time that performed: [Time slot]

**Rule Updates:**
- [ ] Add new winning hook type
- [ ] Remove losing hook type
- [ ] Update caption formula
- [ ] Adjust posting schedule

---

File Structure

skills/larry-playbook/
├── SKILL.md                  ← This file (documentation)
├── hooks/
│   └── templates.md        ← Hook library (20+ templates)
├── workflows/
│   ├── generate_slideshow.py  ← Main generation script
│   └── larry-continuous-system.py  ← Continuous orchestrator
└── memory/
    ├── SYSTEM_MEMORY.json    ← Performance history
    └── logs/               ← Daily activity logs

---

Dependencies

Required

• Post-Bridge API Key for posting to social platforms
• OpenAI API Key (optional) for image generation

Optional

• Ollama (local LLM) for research and caption generation
• RevenueCat API for analytics tracking
• Mixpanel for user analytics

SB|---
SH|
QZ|## 1ai-skills Integration
MX|
YX|larry-playbook is part of the 1ai-skills bundle, a unified "one-man-company" system. Here's how it connects with other skills:
HM|
RP|### Content Pipeline Orchestration
JB|
HB|```
Stage 1: Research → larry-playbook (viral hooks, trending research)
↓
Stage 2: Generate → content-creator, content-generator, grok-video-generation, gemini-image-generator
↓
Stage 3: Humanize → humanizer (make content sound natural)
↓
Stage 4: Publish → tiktok-automation, shopee-optimizer, google-flow

YQ|
JM|### Skill Cross-References
JB|
XZ|When larry-playbook needs capabilities beyond its scope, it can delegate to:
KV|
JK|- **content-generator** — For batch content generation with multiple providers
- **humanizer** — For making AI-generated content sound more natural
- **grok-video-generation** — For AI video generation
- **gemini-image-generator** — For image generation
- **tiktok-automation** — For browser-based TikTok posting
- **google-flow** — For Google AI video generation
- **shopee-optimizer** — For e-commerce content
- **mckinsey-research** — For deep market research
- **polymarket-analyst** — For predictive analytics
YQ|
QP|### Digital Ops Team
NV|
XV|larry-playbook is part of the **digital-ops-team** in 1ai-skills, which handles:
NP|
HB|- Social media automation
- E-commerce operations
- AI content generation
- Multi-platform publishing
YQ|
JM|### Revenue Team
NV|
ZW|larry-playbook contributes to the **revenue-team** by:
NP|
XZ|- Generating viral content that drives traffic
- Creating engaging social media posts
- Building audience for funnel
- Supporting marketing campaigns
YQ|
SB|---
SH|

## License

This skill is based on publicly shared case study by Oliver Henry.
Use and adapt freely. The real value is in:
- The data-driven iteration
- The persistent learning system
- The human-AI collaboration model
- The compounding of small wins

Not to specific hooks or rooms. **Build your own.**

---

## Version History

- **v2.0** (2026-02-27) — Continuous Learning integration
- Added confidence-based flow selection
- Added Post-Bridge API integration
- Added memory system with rule evolution
- Added hourly research and feedback loop

---

## Contact & Support

- **Creator:** Oliver Henry
- **X (Twitter):** @oliverhenry
- **LinkedIn:** https://linkedin.com/in/anulagarwal/
- **Article:** Full breakdown at gameplaydev.substack.com

**For feature requests or bug reports:**
Use the command:
```bash
larry-playbook help [command]

The AI agent will respond to requests for this skill.