Building ÉLAN

Miaoya Camera hit 600K DAU and lost 94% within a year. Most AI photo apps sell novelty — but novelty decays. I wanted to try something different with ÉLAN: curated 'Muse Cards' that encode a complete visual formula for effortless luxury, so users get photos and social captions in 3 taps. Here's what I've learned so far.

A deep dive into ÉLAN's 10-section prompt system, the VANITY_DESIGN_INSTRUCTIONS that make luxury look incidental, SSE streaming architecture, caption generation with 3 style templates, and the real cost breakdown — output image tokens dominate virtually all of the per-session cost.

I sent 5 research agents out simultaneously to study Xiaohongshu photo trends, pose libraries, caption formulas, luxury aesthetics, and competitor UX. 42 data points came back. From that research, I designed a catalog of Muse Cards — curated photoshoot packages that encode everything from scene and pose to color grading and caption. Here's the full design process, one card dissected to its atoms, and honest notes on what works and what doesn't.

ÉLAN's target user takes photos on her phone and posts from her phone. A web-only product misses the core use case. Here's what actually happened when I brought a Next.js app to React Native via Expo — what transferred, what didn't, and the SSE streaming hack that saved the project.

Adding 37 travel landmark cards to ÉLAN in a single session revealed that the real problem isn't content creation — it's discovery. Region-first tabs don't work when users think in vibes, not geography. Here's how we rebuilt the browse experience around a two-axis vibe + region filter, and what it took to orchestrate 37 card definitions, 37 covers, and 444 sample images with parallel AI agents.