GPTs for Devs

// GitHub

// Problem

Apple's documentation is comprehensive but notoriously difficult to navigate. Developers learning Swift frameworks like SwiftUI, CoreML, or HealthKit had to piece together knowledge from scattered docs, WWDC videos, and Stack Overflow posts. ChatGPT was great for general coding questions, but it hallucinated constantly when asked about Apple-specific APIs—it didn't have deep knowledge of framework-specific patterns, edge cases, or the latest SDK changes. I wanted to build a biometrics app (Map) that needed HealthKit data, but realized I'd need to actually learn Swift properly to access Apple's health APIs. The learning curve felt unnecessarily steep.

// Solution

I built a TypeScript scraper that crawled Apple's official documentation for the most popular frameworks and converted it into structured knowledge bases. Each framework got its own custom GPT on OpenAI's newly launched GPT Store—SwiftUI, UIKit, CoreML, HealthKit, ARKit, and 30+ others. The GPTs used RAG (retrieval-augmented generation) to ground their responses in actual Apple documentation, dramatically reducing hallucinations. Developers could ask framework-specific questions and get accurate, citation-backed answers instead of generic guesses.

// What I Built

A website (gptsfordevs.com) that served as a directory for 38 custom GPTs, each specialized in a different Apple framework. The site featured a grid of framework icons linking to their respective GPTs, a roadmap showing which frameworks were complete vs. in progress, testimonials from users, and a newsletter signup. Behind the scenes, each GPT was powered by JSON knowledge bases I'd scraped and curated from Apple's developer documentation. The project included educational blog content—16 MDX articles covering Swift fundamentals, SwiftUI state management, animations, and framework comparisons. I promoted it on Reddit—first with SwiftData (reddit.com/r/swift/comments/188dci0), then EventKit and Combine (reddit.com/r/swift/comments/18acyyv)—where it gained significant traction, scaling to over 100,000 users in the first week.

// Technologies

// Lessons Learned

• 01Timing matters more than polish. I launched this in December 2023, right after OpenAI announced the GPT Store. Being early to a new platform meant less competition and more visibility. The GPTs themselves weren't technically sophisticated—they were essentially curated prompts with documentation attached—but being first mattered.
• 02Reddit distribution still works if you're solving a real problem. I posted to r/swift and r/SwiftUI with genuine value (free tools that actually helped), not self-promotion spam. The testimonials came organically because developers found it useful. Community-first marketing compounds.
• 03I learned I need Swift knowledge for HealthKit access. This whole project started because I wanted biometric data for Map (another project) but couldn't access Apple's HealthKit without native Swift code. The GPTs helped others, but the real lesson was that some Apple APIs require you to meet them on their terms—no shortcuts around learning Swift for health data.
• 04Meeting the Swift team through this project opened doors I didn't expect. Building something useful for a developer community creates real connections. Several people from Apple's Swift team reached out after seeing the project gain traction on Reddit.
• 05RAG quality depends entirely on source quality. Apple's documentation is actually quite good once you extract and structure it properly. The GPTs worked well because the underlying knowledge base was authoritative. Garbage in, garbage out—but gold in, gold out too.