Daniel Roth built Audio2, a podcast clipping app by partnering with AI tools and iterative development tactics. He used Claude Pro and Cursor to specify requirements, iterate rapidly, and compare solutions, while relying on Google's Gemini for design and Expo for deployment. The project cost $807. He adopted a "think like a snow fort" approach: build small, test for strength, then stack features. He learned to detect AI false confidence and pivoted from deprecated tools to practical alternatives. Aggressive GitHub branching preserved progress, and strict session limits curbed destructive feature creep, enabling rapid, low-cost app creation.
Instead, he treated AI as his partner. With Claude Pro and Cursor, he was able to describe what he wanted, iterate quickly, and compare different solutions. Pitting Claude against Cursor became a recurring tactic, with each tool catching what the other missed. For design help, he leaned on Google's Gemini, while Expo handled deployment. He spent $807 creating this app, a fraction of what custom dev shops charge.
Not everything went smoothly. Early attempts to get AI to build entire features at once led to dead ends. The winning strategy? "Think like a snow fort": build small, test for strength, then keep stacking. Roth also learned to spot AI's "false confidence." When Claude assured him that FFmpegKit was the simplest way to generate video clips, a quick check on Reddit revealed it was deprecated. Pivoting to screen recording saved the project.
GitHub became his safety net. By branching aggressively, he avoided losing weeks of work to AI-generated rabbit holes. And perhaps the most underrated tip: setting strict session limits to resist the temptation of "just one more feature," which often broke other features he'd already completed. AI-assisted coding is messy, but powerful. You don't need to be a programmer; you need to be a clear communicator, a patient tester, and willing to pivot. For builders with ideas, the walls around software creation are crumbling.
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