Build all your code with AI — and keep full control.
AI-assisted coding is fast — but unpredictable. It can deliver working code today, but leave you with fragile systems tomorrow: little understanding, no audit trail, and poor maintainability.
Micromanaged Driven Development (MMDD)
MMDD is an open-source methodology for AI-assisted software development. It enables you to use AI for every line of code while staying in charge of every decision. By breaking work into small, reviewable steps and documenting each one, MMDD turns AI into a reliable partner — producing maintainable, understandable, and predictable results.
MMDD solves that problem by:
- Forcing small, reviewable steps → fewer regressions, easier rollbacks.
- Documenting AI interactions → knowledge that lasts, not just ephemeral prompts.
- Maintaining control → every change is your decision; AI stays on-script.
The result:
For teams: less costly rework, faster onboarding, maintainable codebases. For founders: AI that ships faster without creating chaos. Get Started
Quick Start
# 1. Create a development log folder
mkdir dev_log && cd dev_log
# 2. Download the MMDD principles guide
wget https://mmdd.dev/00_mmdd.md
# 3. Save the bootstrap prompt for your AI
cat > bootstrap-prompt.txt << 'EOF'
Using the template and methodology in dev_log/00_mmdd.md,
generate a 00_main.md file for my project that [describe your project].
Work in small, reviewable steps and clearly document decisions, alternatives, and next actions.
EOF
How It Works
Micromanaged Driven Development (MMDD) turns AI from an unpredictable code generator into a reliable partner by combining granular control with systematic documentation. Instead of starting with a broad, open-ended request to the AI and hoping for the best, MMDD breaks work into small, reviewable units. At every stage, you review and iterate with the AI until the result matches your intent — before moving on.
The MMDD Loop
- Plan – At the start of a project, state clearly what you want to build. For each new unit, work with the AI to define its scope: ask what it’s about, explore possible decisions, choose your direction, and document your choices.
- Define – Ask the AI to write the unit in detail. Review and refine until it’s exactly what you want.
- Implement – Tell the AI: “Implement this unit.” Test the generated code, review it, and make adjustments as needed.
Why It Works
- Predictable AI results – Controlled scope reduces hallucinations.
- Maintainable code – Documentation creates a permanent audit trail.
- Faster onboarding – New developers can trace the “why” behind every change.
- Confidence in changes – Small steps mean safer, reversible decisions.
MMDD works! — MMDD itself was defined using MMDD. Even this website was built with MMDD, with every prompt, change, and decision recorded in its commit history.
See it in action on GitHub and try it on your next AI coding session.
News
August 14, 2025
Micromanaged Driven Development v2.0
We're excited to announce the release of MMDD Version 2! This release focuses on organizational improvements, better consistency, and streamlined adoption.
What's New: Improved file organization with renamed core principles file (00_mdd.md → 00_mmdd.md), enhanced documentation with updated internal references, and project cleanup removing unimplemented units. The methodology now shows 85% completion status with core principles, templates, and real-world validation complete.
Getting Started: New users can download the latest principles with curl -O https://mmdd.dev/00_mmdd.md. Existing users should update references from 00_mdd.md to 00_mmdd.md and review the updated principles.
July 1, 2025
CodeRipple: AWS Lambda Hackathon 2025 Project Delivery
CodeRipple demonstrates MMDD in action - an automated code analysis pipeline built 100% with AI assistance using systematic documentation and controlled orchestration. The project transforms commit events into comprehensive code insights through serverless architecture.
Built using Micromanaged Driven Development, CodeRipple validates the methodology's effectiveness for complex AI-assisted development. Every architectural decision, service integration, and technical challenge was systematically documented and AI-orchestrated through the MMDD methodology.
June 25, 2024
Code with AI: Micromanagement is all you need
An in-depth article exploring how systematic micromanagement transforms AI from an unpredictable assistant into a controlled, reliable development partner.
Roberto Allende shares the story behind MMDD, from the initial frustration with AI's unpredictability to developing a methodology that maintains control through granular oversight, documentation, and iterative refinement of every AI suggestion.
June 25, 2024
MMDD Methodology Version 1 Released
Micromanaged Driven Development (MMDD) version 1 is now available. This development methodology uses systematic documentation to control AI-assisted software development through granular task breakdown and chronological tracking.
The methodology provides structured templates for project organization, unit-based development tracking, and comprehensive AI interaction documentation to maintain control and visibility in AI-assisted development workflows.
Community
Join the conversation about Micromanaged Driven Development.
Share your experiences, ask questions, and help improve MMDD.
About
MMDD was developed while building CodeRipple, a serverless application for the AWS Lambda Hackathon 2025. The goal was to generate 100% of the code using GenAI.
AI gives you working code fast, but modifications often break everything. Each change risked breaking the entire system with no clear path back to working code.
MMDD emerged as the solution - systematic micromanagement through granular control, comprehensive documentation, and iterative validation. CodeRipple became a production-grade serverless application with eight Lambda functions and event-driven architecture, all generated through systematic AI collaboration.
MMDD in Practice
CodeRipple validated MMDD's effectiveness on complex systems. The project included cross-platform dependency management, Lambda Layers optimization, EventBridge orchestration, and AI-powered code analysis. Every architectural decision was systematically documented and AI-orchestrated through MDD methodology.
The methodology's three core principles proved essential when AI models suggested conflicting approaches. MMDD's systematic approach made LLM outputs deterministic and reinforced that documentation is essential for AI context and quality.
The People
Roberto Allende - MMDD Creator and Champion Connect: LinkedIn | Twitter