💰 Expense Reports App

February 06, 2026

By Ted Steinmann

Expense Reports App

Overview

An applied R&D project that produced a production-grade expense reporting application while exploring AI-assisted development practices. Built entirely client-side with modern web APIs and Cloudflare's edge platform.

Live Application: https://expensereports.app

What I Built

A production-grade expense reporting application demonstrating:

  • Zero-infrastructure architecture using browser-native APIs and edge deployment
  • CI/CD pipeline with end-to-end testing blocking failed deployments
  • Preview environments for every commit via Cloudflare Pages
  • AI-augmented development workflow validated through production constraints

Technical Architecture

Progressive Web App: Installable on any device with offline capability using modern web APIs. No backend servers, databases, or ongoing infrastructure costs.

Edge Deployment: Cloudflare Pages with global edge distribution, integrated directly with GitHub.

Quality Gates: GitHub Actions runs Playwright end-to-end tests on every commit, validating complete user workflows and blocking deployments that fail critical paths. Passing builds automatically deploy to production.

R&D Outcomes

This project validated several development practices with real production constraints:

AI-Assisted Development Patterns

  • Requirements co-evolution: Maintaining requirements, ADRs, and code in the same repository reduced translation loss and enabled faster iteration
  • Upstream AI use: Applying AI to requirements and architecture (not just implementation) accelerated learning and decision-making
  • Context preservation: Structured documentation enabled effective AI-assisted implementation without loss of intent

Architecture & Operations Insights

  • Zero-infrastructure viability: Browser-native storage and edge deployment eliminated backend costs entirely
  • Shift-left testing: CI/CD pipeline with E2E browser tests prevented production breaks while maintaining rapid deployment velocity
  • Ephemeral environments: Preview deployments for every change reduced feedback cycles and increased confidence

Product Development Discipline

  • External constraints as forcing functions: Using external requirements (regulatory, platform) created clear MVP boundaries and prevented scope creep
  • Production as testbed: Building real systems (not prototypes) provided authentic constraints for validating development practices
  • Continuous learning loop: Frequent deployments with usage pattern analysis informed iteration priorities

Development Context

This application serves as both a functional tool and an R&D testbed for modern development workflows including upstream AI use in requirements discovery and architectural decision-making.

For insights into the development methodology and process, see: AI-Assisted Product Development, in Practice

Status: Active development | Access: https://expensereports.app

Categories: projects

Tags: technology, product-management, systems