Once AI started “hands-on” code generation, the easiest pitfall for engineers became obvious: chasing the thrill of making AI write & refactor—without building a controllable engineering process. LINE Taiwan’s enterprise solution team shared a blunt lesson: early wins came from small office automations (AI operating vending machines, grabbing meeting-room availability, turning repeats into tiny tools). But as everyone became an AI user, they realized the real challenge was product development. Their pilot, “Mini Home,” used Vibe Coding: prompts → AI code → tweak while coding. It looked great—~70% of code from AI, and cycle time dropped to 2–3 weeks. Then the specs got fuzzier. Output drifted, rework multiplied. It was like commissioning a chair: the first version is “close,” but once requirements get complex, you end up with the wrong product. So they switched to Spec Kit: write clear constraints first (stack, naming, scope, data/API design), then let AI implement + test in steps. And for security: bake security rules into specs, require vetted dependencies, add AI-driven CI scans, and link to internal docs via MCP. Speed is fun—governance is how it ships. #AICoding #SoftwareEngineering #SpecDriven #DevSecOps #MLOps #GitHub
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