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 through a systematic 5-step workflow cycle. Instead of hoping for the best with broad requests, MMDD breaks work into small, reviewable units where you and the AI collaborate at every stage.
The 5-Step MMDD Cycle
1. Create Context
Establish shared understanding before any planning or code. For the project: discuss goals, constraints, and tools. For each unit: verify the AI understands the objective, how it fits the larger picture, and any dependencies.
Key question: "Do we both understand what we're trying to accomplish and why?"
2. Plan and Define
With context established, formalize the approach in markdown. The AI drafts the unit file with clear objectives, implementation approach, and success criteria. Review and iterate until the plan is solid before proceeding.
Developer action: Approve the plan or refine it together.
3. Implementation
Execute the plan in manageable chunks. For code units, decide whether to combine or separate implementation and tests. Break down into logical subunits as needed. Implement incrementally, validating each piece before moving forward.
Best practice: Small steps, constant validation.
4. Test and Validate
Verify the implementation meets the unit's objectives. The AI helps execute tests and provides a concise summary of what was implemented. You confirm the unit achieves its stated goals and integrates properly.
Developer action: Validate functionality and integration.
5. Commit
Finalize with structured git commits. Title format: "Complete Unit XX: [Unit Name]". Body includes concise description of changes, key files modified, and focuses on "what" and "why" rather than "how".
Before committing: Update unit markdown status to "Complete" and refresh project status.
Why It Works
- Predictable AI results – Context and controlled scope reduce hallucinations
- Maintainable code – Every decision documented in the unit files
- Faster onboarding – New developers trace the "why" behind changes
- Confident iteration – Small steps mean safer, reversible decisions
- Clear collaboration – The 5-step cycle keeps human and AI aligned
MMDD in Action
MMDD works! — The methodology itself was developed using MMDD principles. This website was built entirely with MMDD, with every prompt, decision, and iteration documented across 15 units.
Real-world validation: CodeRipple, a complex serverless application for the AWS Lambda Hackathon 2025, was built 100% with AI assistance using MMDD — proving the methodology works for production systems.
Ready to try it? Download the v3.0 principles and start your first unit:
curl -O https://mmdd.dev/00_mmdd.md
See the methodology in action on GitHub and apply it to your next AI coding session.
News
November 10, 2025
Micromanaged Driven Development v3.0
We're excited to announce the release of MMDD Version 3! This release adds comprehensive workflow guidance that bridges the gap between MMDD's structural principles and practical, day-to-day application.
What's New: 5-Step Workflow Cycle with Create Context, Plan and Define, Implementation, Test and Validate, and Commit phases. Enhanced commit message format with structured body and clear guidance on title vs. body content. Practical workflow tips including "Context is cheap, confusion is expensive" principle and guidance on documenting deviations and keeping iterations visible.
September 18, 2025
Build and ship an entire app using Amazon Q
How the AWS Lambda Hackathon let me stress test an idea that ended up transforming how I work with Q CLI to develop software. This post is based on a presentation at AWS Community Day 2025, New Zealand, Aotearoa.
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