I couldn't write engaging tweets or code. Instead of learning either skill traditionally, I used AI tools (Claude, Cursor, V0) to build a tweet improvement tool while learning programming fundamentals.
The process revealed interesting insights about AI-assisted development:
1 ) AI excels at explaining concepts in context. When encountering issues with async/await or state management, Claude would explain the concept while helping implement it.
2)Real-time feedback loops accelerate learning. Cursor's suggestions helped identify patterns in bad code, teaching proper structure through immediate corrections.
3) Building for your own limitation provides unexpected clarity. The tool initially fixed my poor writing (English isn't my first language), which guided product decisions through direct experience.
Technical implementation focused on pragmatic choices:
- Used boilerplate to avoid configuration complexity
- Started with localStorage before moving to Supabase
- Leveraged Vercel to eliminate DevOps learning curve
After 14 days of building and learning simultaneously, the tool launched with paying customers and reach $266 MRR in 7 days.
More interesting than the revenue is how AI tools are changing the learning curve for non-technical founders.
The code isn't elegant, but it works. Happy to share specific examples of AI interactions that proved most educational during development.
The process revealed interesting insights about AI-assisted development:
1 ) AI excels at explaining concepts in context. When encountering issues with async/await or state management, Claude would explain the concept while helping implement it.
2)Real-time feedback loops accelerate learning. Cursor's suggestions helped identify patterns in bad code, teaching proper structure through immediate corrections.
3) Building for your own limitation provides unexpected clarity. The tool initially fixed my poor writing (English isn't my first language), which guided product decisions through direct experience.
Technical implementation focused on pragmatic choices:
- Used boilerplate to avoid configuration complexity - Started with localStorage before moving to Supabase - Leveraged Vercel to eliminate DevOps learning curve
After 14 days of building and learning simultaneously, the tool launched with paying customers and reach $266 MRR in 7 days.
More interesting than the revenue is how AI tools are changing the learning curve for non-technical founders.
The code isn't elegant, but it works. Happy to share specific examples of AI interactions that proved most educational during development.