That was the moment I decided to build something. I have no engineering background, so "build an iOS app" seemed overly ambitious. Today CouchRot is live on the App Store. AI does NOT make you a developer. It makes a non-technical person dangerously more capable, but there's an enormous gap between "the AI wrote me some code" and "I have a product in production that users actually trust." That gap is hours of debugging errors I didn't understand. Late nights learning what row-level security meant after I realized users could briefly see each other's data. More than one 2am moment. Stack: React Native + Expo, TypeScript, Supabase (Postgres, Auth, RLS across ~30 tables, 11 edge functions), OpenRouter for AI model routing, TMDB for movie data, RevenueCat for subscriptions, Sentry for errors, offline sync, push notifications. Built with a lot of Cursor and Claude/Cowork help. What it does: you tell it what you're in the mood for, rate some titles 0-100 to build a taste profile, and it gives you 5 picks tuned to what you actually like.
⚙️ 自动化 Reddit
零编程背景用 Cowork 构建并上架 iOS 应用 CouchRot
无工程背景的用户用 Claude/Cowork 构建了完整的 iOS 应用 CouchRot（电影推荐）并成功上架 App Store。技术栈：React Native + Expo + TypeScript + Supabase（30 张表的 RLS）+ 11 个 Edge Functions + OpenRouter + RevenueCat
★★★ 高级 100+ hours 2026年6月13日
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使用场景
非技术用户因为每晚在 Netflix/Hulu/Max 上花大量时间选片而决定自己开发一个电影推荐 App。没有编程背景，「做一个 iOS 应用」看起来极其雄心勃勃，但决定用 Claude/Cowork 来弥补技术差距。
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提示词
Help me build a movie recommendation iOS app from scratch. I have no coding experience.
Requirements:
- Users describe their mood ("date night", "need a laugh", "something mind-bending")
- Rate movies 0-100 to build a taste profile
- Get 5 personalized picks based on their actual preferences
- Stack: React Native + Expo, TypeScript, Supabase for backend
- Include subscriptions, error tracking, offline sync, push notifications
- Design philosophy: get users OFF the app fast and watching something
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预期结果
从零开始构建并成功上架的完整 iOS 应用： **技术栈：** - React Native + Expo + TypeScript - Supabase（Postgres + Auth + 约 30 张表的行级安全 + 11 个 Edge Functions） - OpenRouter（AI 模型路由）+ TMDB（电影数据） - RevenueCat（订阅）+ Sentry（错误追踪） - 离线同步 + 推送通知 **真实经历：** - 总共 100+ 小时开发 - 发现用户数据泄露（行级安全问题）后连夜学习修复 - 多个凌晨 2 点的调试时刻 - AI 不能让你成为开发者，但能让非技术人员「危险地」更有能力 **结论：** CouchRot 已上架 App Store，是一个从「AI 写代码」到「用户信任的生产环境产品」的完整旅程。