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How I built an Apple Watch workout app using Cursor and Xcode (with zero mobile-app experience)

Terry Lin is a product manager and developer who built Cooper’s Corner, an AI-powered fitness tracking app that works across iPhone and Apple Watch. Frustrated with traditional fitness apps that require extensive setup and manual logging, Terry created a solution that lets users simply speak their exercises, weights, and reps. The app automatically structures this data and provides analytics on workout consistency and progress. In this episode, Terry shares his vibe-coding process using Cursor and Xcode and explains how he optimizes his codebase for AI collaboration. *What you’ll learn:* 1. How Terry built a voice-powered fitness tracker that works across iPhone and Apple Watch 2. His “dual-wielding” workflow, using Cursor for coding and Xcode for building and debugging 3. Terry’s three-step process for working with AI: create, review, and execute 4. Why optimizing your codebase for AI collaboration can dramatically improve productivity 5. How to use index cards and GPT-4 to rapidly prototype mobile interfaces 6. A technique for “vibe refactoring” that keeps code organized and optimized for both human and AI readability 7. His “rubber duck” technique to better understand generated code and improve your learning process *Brought to you by:* Paragon—Ship every SaaS integration your customers want: https://useparagon.com/HowIAI Miro—A collaborative visual platform where your best work comes to life: http://miro.com/ *Where to find Terry Lin:* LinkedIn: https://www.linkedin.com/in/itsmeterrylin/ GitHub: https://github.com/itsmeterrylin *Where to find Claire Vo:* ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo *In this episode, we cover:* (00:00) Introduction to Terry and his fitness tracker app (02:30) Demo of the voice-powered workout tracking across devices (06:40) Analytics and history views for tracking consistency (07:20) Dual-wielding Cursor and Xcode for mobile development (09:05) Building a v1 using AI tools (11:19) A three-step AI workflow: create, review, execute (19:38) Token conservation and vibe refactoring explained (23:25) Optimizing file sizes for better AI performance (25:28) Using “rubber duck” rules to learn from AI-generated code (28:13) Prototyping with index cards and GPT-4 (31:20) Human creativity and the last 10% (32:29) Lightning round and final thoughts *Tools referenced:* • Cursor: https://cursor.sh/ • Xcode: https://developer.apple.com/xcode/ • GPT-4: https://openai.com/gpt-4 • UX Pilot: https://uxpilot.ai/ • Figma: https://www.figma.com/ • Linear: https://linear.app/ *Other references:* • Apple UI Kit: https://developer.apple.com/design/human-interface-guidelines/ _Production and marketing by https://penname.co/._ _For inquiries about sponsoring the podcast, email jordan@penname.co._

Claire VohostTerry Linguest
Sep 14, 202536mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

Building Apple Watch workout app via Cursor, Xcode, and structured AI workflow

  1. Terry Lin built “Cooper’s Corner,” an Apple Watch + iPhone workout tracker that lets users speak exercises, weight, and reps, then converts the transcript into structured workout logs with history and analytics.
  2. He started with an ultra-simple MVP: recording Voice Memos on Apple Watch, transcribing with GPT, and dumping results into a spreadsheet—then upgraded to a database-backed system and native apps.
  3. Because iOS development requires Xcode for building/debugging, he “dual-wields” Cursor for coding and Xcode for compilation, simulator/device testing, and watch builds.
  4. He developed a repeatable Cursor process—PRD creation, PRD review (rated for executability), PRD execution, plus refactoring and “rubber duck” learning rules—while managing token/context limits with smaller files and frequent Git commits.

IDEAS WORTH REMEMBERING

5 ideas

Start with a “low-effort” MVP before building the full app.

Terry validated the core behavior by narrating workouts into Apple Watch Voice Memos, then using GPT to transcribe/tag and output to a spreadsheet—proving the value before investing in native apps and backend structure.

Voice-to-structured-data is the real product unlock, not just transcription.

The app doesn’t merely convert speech to text; it extracts exercise, weight, reps, timestamp, and other tags into consistent records that power analytics like top exercises and progression charts.

Mobile AI coding still needs platform-native tooling for build/debug.

Cursor can generate and edit Swift/iOS code, but Xcode remains essential for compilation errors, simulator/device behavior, and Watch-specific builds—so he keeps both tools pointed at the same project folder.

Treat the model like a cross-functional team with a real dev process.

His workflow mirrors org structure: write a PRD, have another “role” review it, iterate until it’s executable, then run a phased checklist with pauses—reducing hallucinations and rework.

Use “PRD review scoring” to predict whether execution will succeed.

By asking a model to rate the PRD (0–10) assuming zero context, he finds missing edge cases, unclear file targets, or inconsistent APIs—then iterates until it’s ~9/10 to enable near one-shot implementation.

WORDS WORTH SAVING

5 quotes

Two months later, I now have an Apple Watch and an iPhone app.

— Terry Lin

They could either work with you or work for you.

— Terry Lin

I realized I was just running into a lot of context windows with these same rules.

— Terry Lin

I call it vibe refactoring.

— Terry Lin

Anything that lets me know what's going on in the mobile.

— Terry Lin

Voice-first workout logging (watch/phone)From Voice Memos + spreadsheet to database-backed appCursor + Xcode dual-wield workflowPRD-create / PRD-review / PRD-execute rulesGherkin user stories for requirementsToken/context management via smaller filesVibe refactoring and rubber-duck learning

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