The most common piece of feedback I received in the first week after launching my Rork-built app was: "it sometimes crashes on startup."
The code Rork generated worked. Functionally, it did what it was supposed to do. But placed into the real world — users tapping in unexpected sequences, older devices, unstable network connections — it was fragile in places I hadn't anticipated.
AI code generation tools are genuinely fast at the "building" phase. The "maintaining" phase — what happens after launch — is a different kind of challenge. This article is about that difference.
What Rork Does Well (and Where You'll Need to Supplement)
To be clear upfront: building with Rork was a good experience. The speed of going from "I want a screen that does X" to a working prototype is genuinely impressive, and for an independent developer, that speed matters a lot.
That said, extended use reveals patterns in what AI code generation tends to handle well versus where human judgment needs to fill in gaps.
Rork handles well:
- UI component generation (scaffold a screen's structure extremely fast)
- Standard CRUD operations (fetch, display, create, update, delete flows)
- Basic third-party library integration
Where you'll need to add:
- Error handling coverage (happy path is solid; error paths tend to be thin)
- Accessibility (VoiceOver support, dynamic text sizes)
- Performance optimization (lazy loading in lists, image caching)
- Device-specific edge cases (older iOS versions, low-memory conditions)
This isn't a critique of Rork specifically — it's a fair description of where AI code generation tools sit right now. They're excellent at producing the average case quickly. The edge cases need a human in the loop.
The Error Handling Problem You'll Hit After Launch
The most consistent pattern in Rork-generated code is happy-path focus. When the API responds correctly and the user does exactly what you designed for, everything works. When a request times out, returns an unexpected status code, or comes back in a changed format — that's where gaps appear.
// ❌ What Rork typically generates (happy path only)
func fetchUserData(userId: String) async {
let response = try await apiClient.get("/users/\(userId)")
let user = try JSONDecoder().decode(User.self, from: response.data)
self.user = user
}
// ✅ What production usage actually requires
func fetchUserData(userId: String) async {
isLoading = true
defer { isLoading = false }
do {
let response = try await apiClient.get("/users/\(userId)")
guard response.statusCode == 200 else {
errorMessage = "Failed to load data (\(response.statusCode)). Please try again."
return
}
let user = try JSONDecoder().decode(User.self, from: response.data)
self.user = user
} catch URLError.timedOut {
errorMessage = "Connection timed out. Please try again."
} catch URLError.notConnectedToInternet {
errorMessage = "No internet connection. Please check your network."
} catch DecodingError.keyNotFound(let key, _) {
// API changed — prompt user to update
errorMessage = "Please update the app to the latest version."
print("Missing key: \(key)")
} catch {
errorMessage = "An unexpected error occurred."
print("Unexpected error: \(error)")
}
}The DecodingError case is especially important. When your backend API changes structure, old app versions won't crash — they'll hit a decoding error. Whether you surface that as "update the app" or as an opaque failure determines how many confused users contact you for support.
Reading Crash Reports After Launch
Once you've shipped, checking App Store Connect's Crashes tab daily is worth building into your routine. Every app has some crashes on launch — the goal is catching them early, before they affect too many users.
The two numbers to look at first: occurrence count and affected users. High occurrences but low unique users suggests a device-specific issue. Low occurrences but high unique users means something affects a broad population — higher priority.
The crashes I encountered most often in my Rork-built app:
- Launch crashes: Initialization code failing under specific conditions on first run
- Background resume crashes: State management not handling the pause-and-resume lifecycle correctly
- Post-memory-warning crashes: Not releasing image caches when the OS requested it, eventually running out of memory
None of these were covered in the code Rork generated. Rork builds code that works in normal conditions — defensive code for abnormal conditions needs to be added deliberately.
Maintaining What Rork Built
Rork saves real time in the building phase. The question is where you reinvest that time — because some of it needs to go toward the maintenance work that the AI doesn't handle.
After a few release cycles, the post-launch activities I now prioritize:
Weekly crash report review. Addressing issues when the occurrence count is small is much cheaper than dealing with a growing backlog. One crash at 50 occurrences is a different problem than the same crash at 5,000.
Responding to reviews. "I wish this could do X" is design input for your next version. Users rarely give detailed feedback through support channels; reviews are often the clearest signal you get.
Error logging inside the app. Knowing that your API error handler fired 40 times yesterday — versus knowing that users are having problems — is the difference between debugging and guessing.
The quality of Rork-generated code improves substantially based on what you add around it. The right mental model isn't "Rork built this app" — it's "Rork built the structure, I'm responsible for what ships." That distinction shapes how you treat the output and how much you invest in making it robust.
When my first app hit the App Store and real users started using it, that moment was genuinely exciting. Keeping it working well for them is slower, less glamorous work — but it's what makes the exciting part last.