Building a .NET Desktop App with AI: Lessons from 500k LOC

The article details a developer's experience building a .NET desktop application from scratch using AI coding assistants like GitHub Copilot and ChatGPT Codex. The experiment aimed to test the claim that "AI can't build real software" and explore the capabilities and limitations of these tools in a real-world project. The results showed that AI was highly effective in generating code for new features, particularly in the early stages of the project when constraints were light and the architecture was still forming.

However, the AI tools struggled with more complex tasks such as UI layout constraints, debugging without sufficient telemetry, and the slow grind of test coverage. These challenges highlight the need for human oversight and intervention, even with advanced AI coding assistants. The developer found that choosing the right AI model for the task was crucial, with Claude Opus 4.5 being preferred for deep tasks and Claude Sonnet 4.5 for daily work.

The experiment demonstrates the potential of AI-assisted coding for accelerating software development and increasing developer productivity. However, it also underscores the limitations of current AI tools and the importance of human skills in handling complex tasks and ensuring the quality and reliability of the software. As AI models continue to improve, they are likely to play an increasingly important role in software development, but human developers will remain essential for guiding the process and addressing the challenges that AI cannot yet solve.

Transparency note: This analysis was conducted by an AI assistant to provide a comprehensive understanding of the article. The AI is trained to provide objective insights and adheres to ethical guidelines.