Code-centric development leads to maintenance problems, hinders modernization, and causes business misalignment.
Traditional development is code-centric. Requirements get outdated, documentation drifts, and when bugs appear, we dig through code to understand what the system was supposed to do.
AI coding tools make this worse by generating code faster without fixing the underlying process problems.
AI Unified Process flips this around. Requirements stay at the center, and everything else gets generated from them using AI as the consistency engine.
Iterative Improvement: Through short iterations, specifications, code, and tests improve together. Documentation enables sustainable development and modernization.
Test-Driven Consistency: Tests ensure the system behaves the same regardless of code generation changes, enabling safe refactoring and evolution.
Building software with AIUP and Claude Code is genuinely fun. Two Rust projects came out of it: AudioSnip, a cross-platform desktop app built with Tauri 2 to extract audio from video files, and Konzertmeister CLI, a tool for the Konzertmeister API. Thanks to Claude Code, both were packaged as a Homebrew Tap, so I can install them directly with brew install on my Mac. Writing specs, implementing, and testing together with Claude Code — cool stuff. And afterwards you actually understand what the code does.
With AIUP and spec-driven development, I shipped a complete product — deckweaver — in three calendar days, with maybe four to five hours of actual work. From a two-sentence README, Claude Code generated requirements and use cases that matched exactly what I had in mind. It then handled the tedious parts — OAuth, the Google Slides API, the Thymeleaf frontend — without a hitch. Genuinely impressed.
Pick a thread, the methodology, the enterprise story, the videos, or the tools.
Four agile phases, two workflows (Greenfield and Brownfield), six core principles, and the iterative approach that replaces the determinism fallacy.
Governance and traceability, brownfield modernization, parallel team scaling, risk-managed AI evolution, and knowledge that outlives teams.
Conference talks, walkthroughs, and methodology overviews showing AIUP and spec-driven development in practice.
Open-source Claude Code plugins for the AIUP workflow, plus an IntelliJ plugin that links use case specs to their tests.
Curated writing on spec-driven development, requirements engineering, AI-ready architectures, and the AIUP methodology.
AIUP combines the best of proven methodologies with modern AI tooling.
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