Veteran Engineering Leader Shares Hard Lessons From Building an AI-Native Product Solo

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A software engineering leader with 25 years of experience recently built an AI-native procurement tool from scratch to production in roughly four months, relying heavily on AI coding assistants throughout the process. Working primarily alone and using Python more extensively than before, the author leaned on AI tools in areas where their own experience was thinnest. Early struggles included an AI model that repeatedly failed to fix its own unit tests, while switching to a different model resolved the same issues within 30 minutes. To avoid over-reliance on any single model, the author built multi-model validation into the system's architecture from the start, finding that different models catch different errors. The key takeaway was to treat AI less like an autocomplete tool and more like a capable but literal engineer that still requires active human oversight and direction.

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