Repurpose AI

Content creators spend 10+ hours weekly repurposing one video into platform-specific posts. Manual reformatting for LinkedIn, Twitter, YouTube, and newsletters kills productivity and delays content distribution.

1. The Founder's Problem

2. Why Traditional Solutions Failed

✗Manual repurposing requires understanding each platform's format, tone, and character limits
✗Existing tools produce generic output that needs heavy editing before publishing
✗No single tool handles YouTube transcript extraction + multi-platform content generation
✗Content creators waste time context-switching between 5+ tools for one video

3. What We Built

✓YouTube transcript extraction via URL (supports watch, shorts, live, youtu.be formats)
✓Manual transcript input for podcasts, webinars, and interviews
✓6 AI-powered content formats: LinkedIn long-form (1200-1500 words), LinkedIn hook (<700 chars), Twitter/X thread (7 tweets), email newsletter, YouTube description with timestamps, 3 short-form video scripts (60s each)
✓Tone control: professional, casual, punchy writing styles
✓Project dashboard with full history and search
✓Usage tracking with tier-based limits (Free/Premium)
✓Supabase Auth with email/password + Google OAuth
✓Dark-mode-first UI with glassmorphism design

4. The Process

WEEK 1

YouTube API integration (2 days), transcript parser (2 days), Supabase schema + auth (3 days)

WEEK 2

Groq AI prompt engineering for 6 formats (4 days), tone control system (2 days), project dashboard (1 day)

WEEK 3

Usage tracking + limits (2 days), export functionality (2 days), UI polish + deploy (1 day)

5. Results & Metrics

Users

38 signups

Revenue

$0 (free tool)

GitHub

12 stars

Performance

<5s for 6 formats, 95%+ AI accuracy

Code

96.7% TypeScript, React 18 + Vite

6. Tech Stack & Why

React 18 + Vite:Faster dev server than CRA, better HMR performance

Supabase:Edge Functions for YouTube API + AI generation, PostgreSQL for project storage

Groq Llama 3.3 70B:10x cheaper than GPT-4, 800 tokens/s, perfect for bulk content generation

shadcn/ui:Accessible component library, faster than building from scratch

TanStack Query:Better caching than useState, automatic refetch on window focus

7. What We Cut to Ship Fast

–Real-time collaboration (would add 5+ days)
–Bulk processing (queue system adds complexity)
–Custom AI model training (overkill for MVP)
–Mobile app (PWA sufficient for MVP)

→YouTube transcript API has rate limits. Implemented caching to reduce API calls by 60%.
→Platform-specific content requires different prompt strategies. LinkedIn needs storytelling, Twitter needs hooks.
→Users want control over tone. Added 3 preset tones instead of forcing one style.
→Export functionality is critical. Users want to copy/paste, not manually transcribe AI output.
→Dark mode isn't optional for creator tools. 80% of users prefer dark UI.

8. Lessons Learned

→YouTube transcript API has rate limits. Implemented caching to reduce API calls by 60%.
→Platform-specific content requires different prompt strategies. LinkedIn needs storytelling, Twitter needs hooks.
→Users want control over tone. Added 3 preset tones instead of forcing one style.
→Export functionality is critical. Users want to copy/paste, not manually transcribe AI output.
→Dark mode isn't optional for creator tools. 80% of users prefer dark UI.

Screenshots

What This Actually Proves

✓I ship fast: 6 MVPs in 14-21 days each. Not prototypes — actual working products with auth, payments, databases.
✓I'm honest about metrics: 10-50 users, $0 revenue (test mode), 7-10 GitHub stars. No fake numbers.
✓I know what to cut: Real-time features, team collaboration, mobile apps. Ship core value first.
✓I learn from mistakes: RLS policies are hard. Prompt engineering takes 15+ iterations. Financial calculations need edge case testing.
✓I justify tech choices: Supabase over Firebase (cheaper, better for relational data). Groq over OpenAI (10x cheaper, 800 tokens/s).

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Same quality, same speed. 14–21 days from idea to live product with auth, payments, and analytics.

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