The Power of Simplicity: Why Manual Control and Markdown Files Trump Complex AI Tools in Project Management

In the fast-evolving landscape of AI-assisted workflows, many developers are exploring tools designed to streamline tasks and enhance productivity. However, after extensive experimentation with advanced AI solutions like memory-bank, Spec Kit, and Boomerang Tasks, a recurring insight emerges: simple Markdown (.md) files often provide a more effective and controllable approach for managing project context and planning. This method keeps developers firmly in control, safeguards crucial project knowledge, and avoids the “monstrous” setups that can disconnect from actual work requirements.

A Journey Through AI-Assisted Workflows

The journey into AI-assisted development often begins with basic AI chat for code generation, moving towards more integrated solutions. Initially, AI chat proves useful for generating code snippets, refactoring, and providing examples without much project context. This then evolves into using AI autocomplete tools like ‘Continue’, and integrating advanced models such as Claude 4 through interfaces like Windsurf, or console tools like factory.ai and claude code. Some even attempt “semi-free” Windsurf setups with VSCode + RooCode, though these often hit limitations without a robust backend.

Further exploration often leads to tools like MCP and context7, or attempts to preserve project context with solutions like memory-bank. Memory-bank, often touted for its ability to “speed up AI agents 100x by saving context,” indeed offers some benefits in token usage. However, it quickly reveals limitations such as managing a single large bank across diverse projects, leading to confusion, or the inconvenience of separate workspaces for each project.

It’s at this juncture that the simplicity of a .md file shines through. A well-maintained Markdown file can accomplish 80% of the required context management. By simply writing down project context, architectural notes, plans, problems, steps, and key decisions, and instructing the AI model to read it, developers maintain direct control. This approach keeps project-specific information separate from universal .rules files, offering greater flexibility.

Evaluating Advanced Task Management Tools

The allure of sophisticated task management tools like Boomerang Tasks (with RooCode) and Spec Kit is strong. Boomerang Tasks, however, often doesn’t offer significant advantages over a standard .md context/plan file. The direct editability of a Markdown file often surpasses waiting for a plugin to “catch up.” Furthermore, a .md context file boasts greater versatility, working seamlessly across different backends like Droid or Windsurf, a crucial factor for lightweight, cost-efficient setups.

Spec Kit, while promising, also presents its own challenges. When applied to complex tasks, such as refactoring a 2D game, it can quickly escalate the scope and hit daily token limits before completion. This experience highlights that even “serious” tools can face the same practical limitations as homegrown scripts, often failing to replace a more controlled, manual approach to defining refactoring steps.

Key Takeaways for an Efficient Workflow

These extensive trials offer valuable lessons, shaping a more effective workflow:

1. Dual Planning: Leverage AI to analyze project problems and generate two distinct plans: a minimum viable plan and a maximum comprehensive plan.
2. Iterative Execution: Execute the minimum plan first.
3. Strategic Review: Separately review the maximum plan, strategically deciding which steps to split or delay.

For new projects, while Spec Kit can be a starting point, a simple plan generated through chat, followed by manual adjustments and careful revision, often yields superior results. The “chat+hands” approach, without fully automated task slicing, consistently provides better outcomes because:
a) Project context remains firmly with the developer.
b) One or two .md files are far more manageable than complex, “monstrous” setups like memory-bank or Spec Kit.

The Enduring Value of Manual Control

The question often arises: why maintain manual control when an AI agent could technically handle everything? The answer lies in two critical aspects:

1. Preserving Project Knowledge: In large projects, excessive “vibe-coding”—allowing AI to autonomously generate large chunks of code—can quickly detach the developer from the project’s intricacies. While acceptable for simple cases, for complex scenarios involving edge cases, memory allocation trade-offs, algorithmic subtleties, or intricate component dependencies (especially in game development), preserving deep project knowledge is paramount. Manual steps help developers stay synchronized with the project, significantly reducing the need for constant re-reading and re-familiarization. Local .md files are invaluable here: simple, transparent, and offering the best aspects of tools like memory-bank without the overhead.

2. The Genesis of Good Prompts: A truly effective prompt is rarely a first attempt; it often emerges after several less successful ones. Genuine experience and understanding, as many developers attest, are cultivated through the process of struggle and iterative refinement.

Final Thoughts: Ownership Over Automation

Automation is undeniably powerful, but context ownership is superior. When a tool begins to “think” for you, it risks forgetting your original intentions and nuances. A plain .md file, conversely, provides just enough automation support while keeping everything easily manageable. It strikes a crucial balance, fostering a collaborative dynamic between automation and manual effort, ensuring that the developer remains the ultimate steward of project knowledge and direction.