The Problem: Product Hunt Launches Are Hard

Every startup founder knows Product Hunt can make or break a launch. But most launches fail—not because the product is bad, but because the marketing execution is wrong.

1. Writing a compelling product description takes hours of iteration
2. Crafting the perfect tagline requires copywriting skills most developers lack
3. Maker comments need to be authentic but strategic
4. Launch timing and community engagement require careful planning
5. Social media amplification across platforms is time-consuming
6. Analyzing what worked in successful launches requires extensive research

Founders spend weeks preparing launches only to end up with mediocre results because they didn't nail the messaging, timing, or engagement strategy.

I built PHLauncher to solve this: AI-powered campaign generation that gives every product the best chance at Product Hunt success.

The Vision: Launch Like the Pros

PHLauncher democratizes Product Hunt success by providing AI-powered tools that generate professional-quality launch campaigns. The core principles:

The Tech Stack: Modern SaaS Architecture

The Architecture: Monorepo for Scalability

The project structure separates concerns into focused packages:

Monorepo Package Structure

phlauncher/
├── apps/
│   └── web/                    # Next.js 15 application
│       ├── src/app/           # App Router pages
│       ├── src/components/    # React components
│       ├── src/lib/           # Utilities
│       └── src/stores/        # Zustand stores
├── packages/
│   ├── ui/                    # Shared shadcn/ui components
│   ├── types/                 # TypeScript definitions
│   ├── stripe/                # Payment integration
│   ├── jobs/                  # Background job system
│   ├── query/                 # React Query wrapper
│   ├── support-chat/          # Support widget
│   └── teams/                 # Team management
├── supabase/                  # Migrations & config
└── turbo.json                 # Build pipeline

Turborepo's smart caching means rebuilding only what changed. Shared packages prevent code duplication across future apps in the monorepo.

Database Schema Design

The database follows a multi-tenant architecture with comprehensive audit trails:

• user_profiles: User metadata and preferences
• organizations: Team/company entities
• organization_members: Team membership with roles
• user_launch_kits: Generated campaigns with JSON content
• user_payments: Payment history and credits
• ai_generated_content: Content generation history
• ai_usage_tracking: Cost and performance analytics
• background_jobs: Job queue with status tracking
• job_steps: Granular progress per operation

Row Level Security (RLS) policies ensure users only see their own data. Every table has created_at and updated_at for audit trails.

The Launch Kit Generator: AI-Powered Campaign Creation

The core feature is a multi-step wizard that collects product information and generates a complete Product Hunt launch kit:

What Gets Generated

The Generation Pipeline

When a user submits their product information:

1. Payment Verification: Stripe webhook confirms payment, credits user account
2. Job Creation: Background job record created with status: PENDING
3. App Metadata Extraction: If URL provided, scrape product info for context
4. Content Generation: Sequential AI calls for each content type
5. Progress Updates: Real-time step completion via Supabase realtime
6. Kit Assembly: JSON structure with all generated content stored in database

// Simplified job step tracking
const steps = [
  { name: 'App Metadata', status: 'completed' },
  { name: 'Product Description', status: 'in-progress' },
  { name: 'Taglines', status: 'pending' },
  { name: 'Maker Comments', status: 'pending' },
  { name: 'Launch Strategy', status: 'pending' }
];

// Real-time updates via Supabase
supabase
  .channel('job-progress')
  .on('postgres_changes', { 
    event: 'UPDATE', 
    schema: 'public', 
    table: 'job_steps' 
  }, (payload) => {
    updateUI(payload.new);
  })
  .subscribe();

Users see a live progress UI showing exactly which step is being processed, with completion percentages and estimated time remaining.

Authentication & Authorization: Multi-Level Security

Security is layered across the entire stack:

Payment Integration: Per-Kit Pricing with Stripe

The pricing model is intentionally simple: $10 per launch kit. No subscriptions, no tiers, no complexity.

Payment Flow

1. User clicks "Generate Kit" → redirect to Stripe Checkout
2. Stripe processes payment → webhook fires to /api/stripe/webhook
3. Webhook verifies signature → creates payment record
4. User account credited → can generate one launch kit
5. Generation starts → credit consumed, kit created

This model aligns incentives: users pay only when they have a product to launch, and PHLauncher earns when it provides value.

Deployment: Self-Hosted on DigitalOcean Kubernetes

PHLauncher runs on DigitalOcean Kubernetes with self-hosted Supabase for full data control:

CI/CD Pipeline

GitHub Actions automates the deployment pipeline:

1. Push to main branch triggers workflow
2. Build Docker image with Next.js standalone output
3. Push to DigitalOcean Container Registry
4. Apply Kubernetes manifests with new image tag
5. Rolling deployment updates pods without downtime

Self-hosting Supabase gives full control over data, eliminates vendor lock-in, and reduces costs at scale. The admin dashboard is accessible at supabase.phlauncher.com.

The Challenges and Trade-Offs

The Results: Launch Prep in Minutes

The Developer Experience Lessons

Conclusion: AI-Augmented Launch Success

PHLauncher demonstrates that AI can genuinely help founders succeed—not by replacing their creativity, but by handling the time-consuming copywriting and strategy work that blocks launches.

The architecture choices—Turborepo monorepo, self-hosted Supabase, background job processing—create a foundation that scales with user growth while maintaining development velocity.

The key insight: AI tools are most valuable when they solve specific, well-defined problems. Product Hunt launches have clear success patterns. AI can learn those patterns and apply them to any product.

Every founder deserves a professional-quality Product Hunt launch. PHLauncher makes that possible.