Christopher Stevenson pro 1D AGO

Maintaining an AI-built property finance app: proven Codex testing, release and handoff workflows? I’m building a small family property-management app using ChatGPT for requirements and Codex desktop on Linux for implementation, with TypeScript, PostgreSQL/Supabase and Vercel. It covers renovation forecasts, quotes, costs and payments, operating income and expenses, and family loans. The app is already in use. We have automated checks, isolated synthetic data, financial reconciliation and controlled releases. I’m looking for practical experience from people who have maintained an AI-built financial or admin app beyond its prototype: 1. How do you keep requirements, acceptance tests, candidate versions and deployed releases aligned across long Codex threads and workspaces? 2. What browser-testing and release-evidence workflow has caught failures that builds and unit tests missed? 3. Which GitHub skills or agent packages have improved your results without conflicting instructions or excessive process? 4. What works well for translating approved mockup images into maintainable application code, including screenshot comparison and mobile behaviour? 5. What broke after deployment, and what did you change? Links to maintained repositories, runbooks, example acceptance records or fictional-data screen recordings would be especially useful. The aim is to improve the existing application and workflow while keeping everyday tasks understandable for non-technical family users.