When we started developing Plot Engine, a cross-platform tool for writers to manage stories, timelines, and AI-assisted editing, we faced a challenge: how to move fast without sacrificing quality or structure. Enter Kiro, an AI-powered IDE that completely transformed our workflow.
Why Kiro?
Kiro isn’t just an autocomplete tool — it’s a full AI-driven development environment. With features like spec-driven development, vibe coding, agent hooks, steering docs, and MCP integrations, Kiro lets developers prototype, iterate, and maintain complex projects with ease.
We chose Kiro because Plot Engine involves multiple layers — a Flutter frontend, AI-driven analysis backend, and a rich data model. Kiro allowed us to rapidly generate clean, maintainable code while keeping architecture, documentation, and testing standards intact.
How We Used Kiro
Spec-Driven Development
For major features like our enhanced text panel with rich styling, we wrote detailed specs describing functionality, behavior, and UI/UX. Kiro transformed these specs into working code, complete with architecture, event handling, and styling rules. Compared to traditional coding or ad hoc iterations, this approach saved us time, improved consistency, and reduced bugs.
Vibe Coding
For smaller, experimental tweaks — like quick UI adjustments or prototype features — we used Kiro’s vibe coding mode. We’d describe the feature in natural language, get a draft, and iteratively refine it. The most impressive result was the real-time AI suggestion panel, which Kiro generated with clean Flutter code, full state management, and backend integration. What normally would take days was done in hours.
Agent Hooks & Automation
We automated repetitive tasks using agent hooks: generating unit tests, updating documentation, formatting code, and syncing modules whenever key files changed. This kept our codebase consistent, reduced errors, and let us focus on core features instead of boilerplate.
Steering Docs
By maintaining detailed steering docs with architecture context, coding conventions, and AI integration rules, we ensured Kiro generated code that was cohesive across all layers. This centralized knowledge allowed consistent outputs and faster integration.
Results
Thanks to Kiro, we built Plot Engine faster, with fewer errors, and maintained a clean, maintainable codebase. We could switch seamlessly between rapid prototyping and structured spec-driven development depending on the task — giving us both speed and quality.
Kiro isn’t just an AI helper; it’s a partner in coding. For anyone building complex apps, it fundamentally changes how development can happen in 2025 and beyond.
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