Inspiration
I've spent a lot of time inside the course creator world, between building Persist and watching educators try to hold onto their students' attention past week one. The ones who kept people around had one thing in common: their emails sounded like them. Not polished. Not templated. Them.
Most creators don't have time to write like that consistently. So they either burn out trying, or they send something generic and watch open rates tank.
What it does
Pigeon is an AI email tool built specifically for course creators. It learns how you write, and then writes emails that actually sound like you sent them. Launch sequences, re-engagement emails, weekly newsletters — all of it, in your voice, not a template.
How I built it
Claude API as the core voice engine, Next.js on the frontend, Neon for the database, Vercel for deployment, and Clerk for auth. The voice modeling layer took the most iteration, getting the AI to pick up on someone's actual patterns rather than just their topic.
Challenges I ran into
Voice is hard. Generic is easy. Getting Pigeon to produce an email that felt like a specific person wrote it, not just an AI that read their previous emails, required a lot of prompt engineering and a lot of test outputs that were close but not quite right. "Sounds like you" is a high bar.
Accomplishments that I'm proud of
The email preview experience. When you see a draft that actually sounds like you, something clicks. We got that feeling consistently, and that was the hardest part to get right.
What I learned
I went into this thinking the hardest part would be the tech. It wasn't. The hardest part was figuring out what "voice" actually means at a structural level and how to teach a model to replicate it without flattening it.
What's next for Pigeon
Deeper voice calibration, more email types, and eventually a way for creators to build a full launch sequence in one session. The goal is for Pigeon to feel less like a tool and more like the writing partner your favorite course creator wishes they had.
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