A developer built a Go audio library to convert files to telecom-standard PCM WAV, avoiding external binaries. After manual coding stalled, they tested AI tools. GitHub Copilot provided a starting point, but Claude with agent capabilities proved transformative for the project.

The experience revealed key lessons about AI-assisted coding. Developers must provide clear, specific tasks and critique AI suggestions, as agents won't flag flawed designs. They should also enforce modern Go idioms for tests and benchmarks, and write integration tests with real-world files, not just unit tests.

Crucially, the author learned to treat AI as a fast pair programmer, not an architect. They fixed most bugs themselves, prioritized maintainable code, and used AI-generated examples to discover their own library's hidden capabilities. The final takeaway: human oversight and taste remain essential for quality.