You can build apps with Agentic Coding

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Summary
This video is a live walkthrough of how I use OpenAI Codex to refactor a production iOS app — no cherry-picked demos, just the real workflow with all the trial and error. I take my Settings feature from a two-way binding mess to a clean unidirectional architecture that matches the rest of my codebase, all driven by Codex with TDD.

What You'll Learn:
How to use Codex's plan mode to review architecture before writing any code
The full TDD workflow with an AI coding agent — writing failing tests first, then production code
Why high reasoning effort matters for architectural changes vs. quick fixes
How to bounce between Codex and Xcode to verify changes in real time
The PR workflow — committing to a feature branch, pushing, and reviewing diffs
Where Codex falls short (spoiler: it didn't follow TDD order even when explicitly asked)
How Codex compares to Claude Code for iOS development
Why modular Swift Packages make agentic coding 10x easier with fast test cycles

Chapters
0:00 Introduction — Agentic Coding for Production iOS
0:19 The App — Inventory Tracker Overview
1:01 The Problem — Settings Isn't Unidirectional
1:55 Code Walkthrough — Two-Way Binding vs. Unidirectional Flow
3:04 Architecture Overview — Feature-Based Swift Packages
3:34 View Model Pattern — Public State, Private Mutations
4:02 Running Existing Tests Before Changes
5:02 Codex Prompt — Plan Mode with TDD Instructions
6:45 Waiting on Codex — High Reasoning Effort Tradeoffs
8:08 Codex vs Claude Code — Verbosity and Speed Differences
10:10 Codex Asks Clarifying Questions
12:00 Reviewing the Plan
14:20 Codex Implements — But Doesn't Follow TDD Order
16:30 Calling Out the TDD Miss
17:50 This Is the Trial and Error Phase
20:00 Running Tests — All Passing
20:40 Reviewing Production Code Changes
22:20 Verifying Unidirectional Flow in the View
24:30 Settings View Updates — Sign Out, Delete Account Working
28:45 PR Workflow — Commit, Push, Review
32:30 Final Code Review on GitHub
33:40 Squash and Merge
33:50 Recap — What Worked, What Didn't, What's Next