
As a (bored? topic for another post) startup founder currently between ventures, I've been methodically evaluating AI development tools to understand their practical applications and limitations. After extensive testing, particularly with Cursor and Claude Code, the results vary significantly across different technology stacks.

## The iOS Reality Check

My primary background is iOS development, where I've spent years building native Swift applications. AI tools in this domain remain frustratingly limited. The Apple ecosystem's complexity—from UIKit intricacies to SwiftUI's evolving patterns, Xcode's quirks, and platform-specific constraints—proves challenging for current AI models. While AI can generate basic Swift code, it struggles with the nuanced understanding required for production iOS development. It often writes outdated code, particularly when it comes to Swift 6 and concurrency. It can quickly iterate itself into a downward spirall of overcomplicated code which can be a complete mess. The feedback loops are slow, and the tools lack deep integration with Xcode workflows that iOS engineers rely on daily.

I've experimented with MCP implementations for Xcode, but the integration remains rudimentary compared to what's needed for serious iOS work. Claude often calls the tool wrong, tried a few times, and only then gets it right. This can be somewhat mitigated by having a well written CLAUDE.md file but it's not perfect.

## Web and Backend: A Different Story

The contrast with web development is stark. When working outside my primary domain—web frontend and backend development—AI tools prove remarkably effective. I recently built a complete [web version](https:///www.swimtemps.com) of my iOS app Swim using pretty much exclusively Claude Code. What would have taken weeks of research, boilerplate generation, and stack decisions was completed in a fraction of the time.

For Rust backend development, similar results emerge. The AI handles dependency management, error handling patterns, and API design with competence that approaches what I'd expect from a junior engineer familiar with the stack. However, due to Rust's intricacies it will still get things wrong. Armin Ronacher [recently wrote](https://lucumr.pocoo.org/2025/6/12/agentic-coding/) about LLMs being quite good at Go so I will give that a try next (and coincidentally, we chose Go as the primary backend stack at Specto for similar reasons why it works well with LLMs).

## Interaction Paradigm

The most significant shift isn't the code generation itself, but the interaction model. Rather than wrestling with command-line syntax or documentation, I can communicate with AI as I would with an engineer on my team. This conversational interface for system administration, project setup, and routine development tasks represents a meaningful productivity improvement.

I've been experimenting with bypassing permission prompts to increase iteration speed—a calculated risk that requires robust backup systems but dramatically improves the development flow. The AI maintains context across tasks, enabling complex multi-step operations that would typically require careful orchestration.

## MCP and Automation

Beyond code generation, I've tested MCP implementations for browser automation with Puppeteer. The ability to describe user interface interactions in natural language and have them translated to reliable automation scripts opens interesting possibilities for testing and data collection workflows. This works pretty well most of the time but again, a well written CLAUDE.md file works wonders.

## Assessment

The current state suggests AI development tools are most valuable when working outside your core expertise. For an iOS engineer like myself, they excel at web development tasks I'd otherwise need to research extensively. The tools serve as force multipliers for exploring adjacent technologies rather than replacing deep domain knowledge.

The interaction paradigm represents the more fundamental shift. We're moving toward intent-driven development where you describe desired outcomes rather than specifying implementation details. This abstraction works well for certain problem domains while remaining limited in others.

For now, AI development tools are powerful assistants rather than replacements, with effectiveness varying significantly by technology stack and problem complexity. It is starting to feel like you can start a company with a less employees, particularly around building a website, blog, doing marketing, etc. The non-coding aspect is something I will explore more of next (I just connected Claude to my gmail inbox...).

**Disclaimer:** This post was proofread and modified for structure by Claude.


Jernej Štrasner

Blog

AI Development Tools: A Pragmatic Assessment