NailGenie – Edit nail designs conversationally with AI

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NailGenie – Edit nail designs conversationally with AI

Show HN: NailGenie - Edit nail designs conversationally with AI I built NailGenie ( https://nailgenie.org ) to solve the "that's not what I meant" problem in nail design. It's an AI platform that lets you iteratively edit nail art through simple conversation rather than static generation. THE TECHNICAL CHALLENGE The core challenge was building a system that could understand contextual, incremental editing commands for a specific visual domain. Most generative AI solutions focus on one-shot generation, not a continuing dialogue about the same image. We solved this by: 1. Fine-tuning Gemini on a dataset of nail designs with paired editing instructions 2. Building a stateful context management system to track editing history 3. Creating a visual diffing algorithm that preserves nail boundaries during edits 4. Implementing an instruction parser that handles ambiguous editing requests The backend reaches ~98% instruction comprehension on our test set and produces edits in ~2.7 seconds on average. TECH STACK - Frontend: Next.js App Router with TypeScript and React Server Components - UI: Shadcn/UI + TailwindCSS (we chose these for rapid iteration) - Backend: Supabase for authentication, storing edit history, and managing user credits - Deployment: Vercel edge functions for low-latency API responses - AI: Custom-tuned Gemini models with a multi-stage processing pipeline DEVELOPMENT CHALLENGES AND LEARNINGS The biggest challenges were: 1. Instruction ambiguity: "Make it more pink" means different things to different users. We implemented a clarification system that refines ambiguous requests. 2. Edge detection: Early versions struggled with nail boundaries. We built a specialized segmentation model to ensure edits only affected the nail area. 3. Performance: Initial processing was ~8s per edit. We optimized by parallelizing our pipeline and caching intermediate representations, cutting time by ~65%. 4. Cold starts: Edge function cold starts were killing the experience. We implemented background warmers and optimized model loading. THE WHY AND WHAT'S NEXT I'm not a nail expert, but I noticed my girlfriend spending hours browsing examples before salon visits, then being frustrated when the result didn't match her vision. The challenge of creating a system that bridges this communication gap became technically fascinating. Current metrics: - ~450 users in closed beta - Average session: 8.3 edits per design - 82% completion rate (users reaching a final saved design) FUTURE PLANS - Open source our instruction parsing logic next month - Add API access for nail salons to integrate directly - Implement real-time collaborative editing TRY IT YOURSELF NailGenie is live with free starter credits. I'd appreciate any feedback, especially on: - Instruction parsing accuracy - Performance bottlenecks you experience - UI/UX pain points https://nailgenie.org