Code 100x Faster with AI: Master Your Workflow 🚀

In the rapidly evolving world of software development, leveraging AI coding assistants is not just an advantage; it’s a necessity. This cheatsheet covers essential ideas and best practices to maximize productivity when coding with AI, illustrated through a real-world example of building a Supabase MCP server.

🌟 Key Strategies for Coding with AI

1. Golden Rules of AI Coding

The success of AI coding hinges on a few fundamental principles. Adhering to these guidelines will significantly improve your interactions with AI assistants:

• Keep Context Concise: Avoid overwhelming the AI with long prompts. Keep your code files under 500 lines and avoid lengthy conversations. 📝
• Be Specific: Clearly outline your requests with precise detail about technologies, libraries, and desired outputs.
• Iterate Step-by-Step: Only ask the AI to implement one feature at a time to get better results.
• Integrate Documentation: Constantly maintain and update documentation and comments to ensure that the AI has context for the code it’s working on.
• Prioritize Security: Always manage environment variables and sensitive data manually to secure your projects. 🔒

Tip: Create high-level markdown documents outlining installation instructions and project planning to guide the AI effectively.

2. Planning & Task Management 📅

Before jumping into coding, establish a solid foundation through careful planning:

• Create Planning Documents: Designate a planning.md file to define project scope and architecture. Use markdown files to give the AI comprehensive context. ✍️
• Maintain a Task List: A task.md keeps track of completed and pending tasks, allowing for systematic project management.

Example method: Use tools like Claw Desktop to interact with AI when setting up these documents.

A structured planning phase ensures clarity for both human and AI participants throughout the development journey.

3. Utilizing Multiple AI Resources 🤖

Leverage different AI coding assistants for comprehensive planning and implementation:

• Explore Platforms: Use services like Global GPT to access multiple advanced language models (LLMs) like Claude and DeepSeek under a single subscription.
• Combine Strengths: Ask each AI for different perspectives or solutions on the same prompt, then synthesize their inputs.

Tip: Always ensure your setup process isn’t overly complicated to maintain efficiency!

4. Implementing Global Rules 🛠️

Setting global rules for your AI assistant can drastically streamline repetitive tasks:

• Establish High-Level Instructions: For example, instruct the AI always to refer to your planning file at the start of every new conversation.
• Utilize Workspace-Specific Rules: Set rules tailored to each project that guide how the AI should process requests to ensure consistency.

Applying specific global rules helps to standardize responses and tasks across different coding sessions.

5. Effective Testing & Deployment 🧪

After your project reaches a stable state, focus on testing and deployment:

• Create Automated Tests: Instruct the AI to generate unit tests for each component of your code using best practices to validate functionality continuously.
• Utilize Git for Version Control: As you build, create commits regularly to allow easy reversion to a stable state if something goes awry.

When moving to deployment, using tools like Docker can simplify packaging and running your application in various environments.

🔧 Resource Toolbox

Here are some essential tools and resources referenced for optimizing your AI coding workflow:

1. Global GPT – Cost-effective access to various language models: Global GPT
2. Dynamous AI Mastery – Community and courses for mastering AI: Dynamous AI
3. Google Docs Template – Full AI coding process documentation: AI Coding Process
4. Github Repository of the Project – Access the Supabase MCP server example: Supabase MCP Server
5. Claw Desktop – An AI desktop app designed to aid in coding processes.

🚀 Bringing It All Together

The journey from ideation to deployment using AI coding assistants involves a well-defined process. Understanding how to plan, set global rules, and test your implementations ensures top-quality results and faster development cycles.

By mastering these practices, you not only enhance your coding efficiency but also stay relevant in an increasingly competitive landscape of tech-driven solutions. Embrace AI as your coding ally; when used effectively, it can radically transform your development workflow!