Windsurf can be configured for iOS development as a near-Xcode replacement by installing the Swift and Sweetpad VS Code extensions plus Homebrew packages (Xcode build server, iOS deploy, XCBeautify, Swift format), because Windsurf is built off Visual Studio Code and thus supports VS Code extensions.

When debugging UI behavior with AI, you can give it a screenshot from the simulator and have it add debugging statements so it can read the debug log, trace the exact sequence of UI interactions, and identify where output diverges from expectation.

AI coding gets you about 80% of the way to a working app very quickly, but the remaining nitpicking on small details is hard and time-consuming, so AI changes how you spend your time rather than eliminating the time required.

When the AI spirals out of control and gets progressively farther from a working solution, the right move is to stop it and reset (revert the conversation or restore a backup) rather than letting it continue.

You need at least basic Swift knowledge (how to declare structs and classes, navigate Xcode, find-and-replace duplicates) to debug AI-generated iOS code efficiently; without it, beginners would struggle, and asking the AI to fix its own errors tends to produce roundabout, often wrong solutions.

You should prioritize getting working functionality before nitpicking the UI when building with AI.

Up to the design stage the process required no real thinking — just passing information from one AI tool to the next — but actual development still requires the developer's own judgment.

UX Pilot designs generated purely from AI-described screens are not impressive but are good enough for a V1 prototype-quality app that can be launched and then iteratively improved.

MCP (Model Context Protocol) lets an IDE like Windsurf connect to external tools (databases, Figma, image generation) and bring those capabilities directly into a project, saving time versus manually switching between tools.

Using AI to run simple commands you already know (like checking the Homebrew version) is slower than just typing them yourself, so relying on AI for everything is not always the best option.

A money counter app should not charge a subscription because competitors who have already researched this don't, and it is a one-off utility tool without ongoing cost, so a subscription model doesn't fit even though subscriptions generate more revenue.

Targeting app keywords with low competition (below 50) but decent popularity (above 20), using a keyword tool like Astro, lets developers capture organic App Store traffic and reduce reliance on marketing.

A repeatable AI-driven app planning workflow is: use AI to identify the primary problem a keyword's apps solve, pick one core feature for a fast launch, assess monetization, sketch a V2/V3/V4 roadmap, define a user flow, then break that into per-screen descriptions feedable to a UI design tool like UX Pilot.

Connecting an OpenAI/DALL-E image-generation tool via MCP lets Windsurf generate images and pull them into the project automatically, eliminating the manual workflow of opening DALL-E separately, prompting, downloading, and importing.

Beginners should avoid setting up source control (Git/GitHub) and instead duplicate the project folder as a backup with notes about the last working state, because source control takes a while to learn and Windsurf may corrupt the project — making a low-tech backup essential.

Providing the AI a step-by-step instructions/requirements file (an instructions.md generated by the same AI that holds context of prior planning) and having it build the app feature-by-feature keeps development controlled and incremental.

The AI-generated code produced build errors by declaring the same struct (e.g., 'denomination') twice and dumping duplicate code into the ContentView, which the developer fixed by manually deleting the duplicate declarations rather than asking the AI to fix it.

Specifying global rules for the AI (collected from experienced practitioners and refined by observing what the AI repeatedly gets wrong) keeps the AI 'on the rails' by constraining its behavior to do things a certain way and avoid known failure modes.

Each AI conversation should be scoped to one task — after a bug is fixed, start a new conversation so the AI doesn't carry irrelevant prior context into the next problem.

Learning Swift and SwiftUI basics — roughly a 30-day investment — will let you use AI coding tools far more effectively and save time in the long run if you intend to build apps for a while.