What is Dirty Code. But Works.?
Over 20% of code is now AI-generated. Clean Code is obsolete. This manifesto shows why architecture matters more than syntax, how to judge AI output, and why dirty code that works beats perfect code that doesn't ship.
Problem
Users are relying on traditional clean code practices in an era where over 20% of code is AI-generated, leading to inefficiencies and prioritizing perfect syntax over functional, deployable code.
Solution
A manifesto (ebook/guide) outlining principles for evaluating AI-generated code and prioritizing architecture over syntax. Examples include frameworks to assess AI output quality and strategies to ship functional code faster.
Customers
Software engineers, developers, and engineering managers working with AI-generated code, particularly in startups or teams adopting AI coding tools.
Unique Features
Challenges the 'clean code' dogma by advocating for pragmatic, architecture-first approaches and embracing 'dirty code' that ships, backed by actionable guidelines for AI code evaluation.
User Comments
Validates the shift toward practical AI code integration
Relieves pressure to achieve code perfection
Offers frameworks for teams using GitHub Copilot
Critiques AI-generated code quality concerns
Recommended for devs embracing AI tools
Traction
Launched 2 days ago on ProductHunt with 120+ upvotes and 40+ comments, positioning it as a timely resource amid rising AI code adoption.
Market Size
The AI code generation market is projected to reach $4.3 billion by 2028 (MarketsandMarkets, 2023), driven by tools like GitHub Copilot used by 1.2M+ developers.
