Revolutionizing AI Coding with Claude Code’s New Sub Agents Feature

There’s a new era dawning for AI-assisted software development, thanks to the latest breakthrough from Claude Code. In this post, we’ll explore the powerful sub agents feature recently introduced by Claude Code—a game-changer that transforms how developers build, collaborate, and automate complex workflows within AI coding environments.

From Custom Commands to Specialized Sub Agents

Claude Code has long been celebrated for its custom commands, which enabled developers to tailor AI agents to perform specific roles or tasks. These commands allowed for multi-agent coding teams where each “member” had unique capabilities, such as project management, UI design, development, or quality assurance.

However, the new sub agents feature takes this concept to an entirely new level. Instead of merely running commands within one shared context, sub agents operate as independent, specialized agents with their own context windows and toolsets. This separation allows each sub agent to:

• Maintain a focused, uncluttered context related only to its task
• Use dedicated tools and resources without interfering with the main session
• Operate autonomously and report concise summaries back to the primary agent

This architectural shift means developers can now build complete, efficient AI-driven development teams inside Claude Code, with each sub agent excelling in its domain.

How Sub Agents Work: A Practical Walkthrough

Setting Up a Sub Agent

Getting started with sub agents is straightforward:

1. Use the /agents slash command in Claude Code to manage agents.
2. Create a new agent, choosing between:
3. A personal agent that works globally across sessions
4. A project agent scoped to the current project
5. Opt for auto-generation with Claude by providing a clear, descriptive prompt defining the agent’s purpose.
6. Select the necessary tools (e.g., code editing, MCP servers). Avoid unnecessary tools to optimize security and performance.
7. Name and customize the agent’s appearance.
8. Save and review the generated configuration, which includes a detailed system prompt and specifications.

For example, a UI design expert agent might be created with instructions emphasizing best practices for visual appeal, color palettes, layout, and platform-specific guidelines for mobile prototypes.

Real-World Example: Building a Next.js App with Shad CN Components

One compelling demonstration involved a Shad CN UI builder sub agent tasked with creating a Next.js application using Shad CN components. Here’s what made it stand out:

• The sub agent operated in a fresh context window, consuming over 70,000 tokens independently, without cluttering the main conversation.
• It followed a comprehensive prompt detailing all rules, workflows, and MCP server usage.
• The agent generated a fully functional front-end app with correct page navigation, API key management, and smooth animations.
• Styling and theme customization were applied afterward using an external tool (Tweak CN), which automatically generated themes based on reference images.
• The agent successfully integrated the new themes, enhancing the app’s visual appeal with minimal manual intervention.

This example highlights how sub agents can autonomously handle complex, multi-step development tasks with precision and efficiency.

Why Sub Agents Outperform Traditional Custom Commands

The key differentiator is context isolation:

• Traditional custom commands share the main agent’s conversation context, which can become cluttered and inefficient as workflows grow.
• Sub agents launch in their own isolated environments, preserving relevant context and discarding irrelevant data.
• This allows for better performance, scalability, and clarity when managing multiple specialized roles.
• Sub agents can also be precisely controlled regarding which tools they can access, improving security and task focus.

Advanced Usage: Chaining and Collaboration

Sub agents unlock advanced possibilities such as:

• Automatic agent selection: Claude Code intelligently activates the appropriate sub agent based on task descriptions.
• Explicit agent invocation: Developers can directly instruct Claude Code to use a specific sub agent for a given job.
• Chaining sub agents: Multiple sub agents can be linked in sequence to perform complex workflows. For instance, a code analyzer sub agent can detect performance issues, then trigger an optimizer sub agent to fix them—all orchestrated through a single custom command.

This flexibility enables developers to create powerful, modular AI workflows that save time and improve consistency.

Boosting Your Development Workflow with Test Sprite MCP

While exploring sub agents, it’s worth mentioning Test Sprite MCP, a cutting-edge AI testing platform that seamlessly integrates with IDEs and coding agents like Claude Code. Key features include:

• Automated test plan creation by interpreting product goals and repo contents.
• Generation of test cases and code without manual input.
• Cloud-based front-end and back-end testing with detailed failure diagnostics.
• Accuracy improvements from typical 40% to over 90%.

Test Sprite MCP complements Claude Code sub agents perfectly by ensuring high-quality, reliable AI-generated code through continuous validation and feedback loops.

Final Thoughts

Claude Code’s sub agents feature marks a major milestone in AI-assisted software development. By enabling specialized, context-isolated agents that work independently yet collaboratively, developers gain:

• Greater workflow efficiency
• Enhanced task specialization
• Cleaner context management
• Improved security and performance
• The ability to build sophisticated AI-driven development teams

If you’re working with Claude Code or exploring AI-assisted coding tools, embracing sub agents will undoubtedly transform your productivity and code quality.

Explore Further

• Check Claude Code’s documentation and examples for sub agents to get started.
• Visit GitHub repositories offering collections of pre-built sub agents.
• Experiment with chaining sub agents and custom commands to automate complex workflows.
• Try Test Sprite MCP for automated AI testing and quality assurance.

Ready to revolutionize your AI coding experience? Dive into Claude Code’s sub agents today and build smarter, faster, and more reliable software with AI-powered teams!

If you found this post helpful, consider supporting the creator by using the “Super Thanks” feature on their channel. Stay tuned for more insights and tutorials on AI-driven development.

Revolutionizing AI-Powered Development: Autonomous Multi-Agent Coding with T-Mox Orchestrator and Claude Code

AI-assisted coding continues to evolve at a rapid pace, transforming how developers build software. From the early days of Cursor enhancing VS Code with AI capabilities to AI-powered terminals, each innovation has brought us closer to fully autonomous development workflows. Today, we explore a groundbreaking system that takes AI coding to the next level—introducing a multi-agent, autonomous coding environment powered by Claude Code and orchestrated through T-Mox, a terminal multiplexer.

What Makes This Workflow Truly Revolutionary?

Unlike traditional AI coding tools that operate on single tasks or require constant user supervision, this system creates an entire team of AI agents working simultaneously and independently. The main agent manages multiple subordinate Claude Code instances running in separate terminal sessions, all coordinated without manual intervention. This is made possible by harnessing two key concepts:

1. Terminal Multiplexer (T-Mox)

T-Mox allows multiple terminal sessions to run inside a single window, enabling one Claude Code agent to spawn and control many others within the same environment. These sessions persist in memory, so you can pause and resume work without losing context. This setup simulates a multi-engineer environment where different agents handle various parts of the project in parallel.

2. Terminal Scheduling

Scheduling empowers users to assign tasks to AI agents with precise timing instructions. Agents follow the schedule autonomously, executing commands at designated intervals and moving on without needing user approvals. This transforms your terminal into a self-running system, eliminating bottlenecks caused by waiting for manual inputs.

How to Set Up the Autonomous AI Coding Environment

Getting started involves cloning a specialized GitHub repository called T-Max Orchestrator and running setup scripts that enable this agentic workflow. Here’s a simplified overview:

• Clone the repository to your working directory.
• Run the setup scripts to make files executable and ready for use.
• Start a new T-Mox session to initialize a fresh multi-terminal environment.
• Apply two essential fixes to the repository files:
• Correct hard-coded file paths to match your system.
• Modify commands to include a “dangerously skip permissions” flag, allowing agents to execute commands without manual permission prompts.

These minor edits ensure smooth, uninterrupted operation.

How the Multi-Agent Workflow Operates

Once set up, you interact with the system by providing a detailed project specification, including:

• Full path to your spec folder (absolute paths only).
• Definitions of teams to create, such as front-end and back-end teams, each with project managers and developers.
• Development phases with strict timing requirements to keep progress on track.

The orchestrator then spins up multiple Claude Code agents in separate T-Mox terminals, briefing each team with their tasks and schedules. Agents work in parallel—front-end and back-end teams develop independently but coordinated, running tests, committing code regularly, and updating to-do lists. Every 15 minutes, the system performs automated check-ins, reporting progress and moving through phases either automatically or with your approval depending on your configuration preference.

Real-World Benefits and Use Cases

• Parallel Development: Multiple agents coding simultaneously reduce total development time.
• Autonomy: Minimal human intervention required, freeing developers for higher-level oversight.
• Version Control Built-In: Regular commits create restore points, safeguarding against errors.
• Flexible Team Structures: Easily add specialized teams (e.g., authentication) by modifying specs.
• Context Preservation: Persistent terminal sessions keep all agents’ states intact, even when paused.

Hands-On Demonstration and Resources

Although live coding demonstrations may be limited by usage caps, the workflow has been successfully tested with complex full-stack web app builds. The orchestrator understands detailed UI implementation plans and backend requirements, executing them autonomously while providing clear status updates.

To explore and customize the workflow yourself:

• Visit the T-Max Orchestrator GitHub repository (link in description).
• Use Git Ingest to convert repository contents into AI-readable summaries—perfect for Claude or ChatGPT to explain installation and usage step-by-step.
• Find ready-to-use command scripts and templates in the video description for a smooth setup experience.

Join the AI Labs Community Hackathon!

From July 22nd to July 28th, AI Labs Discord is hosting its first-ever hackathon. Submit your most innovative AI builds and projects for a chance to be featured in upcoming videos. Join the community by clicking the link pinned in the comments and stay tuned for exciting developments.

Final Thoughts

This autonomous multi-agent coding system powered by Claude Code and orchestrated with T-Mox represents a significant leap in AI-driven software development. By enabling simultaneous, independent AI agents to collaborate without constant supervision, it redefines productivity and workflow efficiency in programming.

If you’re passionate about the future of AI coding, this is a project worth diving into. Subscribe for updates, try out the setup yourself, and join the community pushing the boundaries of what AI can achieve in software development.

Thanks for reading! If you found this post helpful, consider supporting the channel via the Super Thanks button, and stay tuned for more deep dives into cutting-edge AI tools.

Unlocking the Full Potential of Claude Code: How Semantic Search and MCP Servers Can Supercharge Your Coding Workflow

If you’re using Claude Code to assist with editing components or fixing bugs, you might be unknowingly limiting its effectiveness. What if I told you that your Claude Code is probably operating at only 30% of its true potential right now? The culprit? An overloaded context window filled with irrelevant information, causing Claude to wade through thousands of lines of unnecessary code every time you ask it to perform a task.

In this blog post, I’ll walk you through why this happens, how it affects Claude’s performance and accuracy, and most importantly, how you can optimize your Claude Code experience for faster, smarter, and more precise coding assistance — all for free.

The Problem: Overloaded Context Windows

When you start a new session with Claude Code and request an improvement or bug fix in your codebase, Claude has no prior context. It tries to read through everything in your project—every file, every line—regardless of whether it’s relevant to your current task. This exhaustive reading:

• Wastes tokens (which count against usage limits)
• Clutters the context window with irrelevant data
• Slows down Claude’s ability to provide accurate edits or suggestions
• Increases the likelihood of mistakes or missed connections

Imagine working on a small HTML file—that’s manageable. But now imagine a complex project using Next.js with dozens or hundreds of files. If Claude blindly searches through every file to find a login component or debug a random error, it will struggle to maintain meaningful context and performance will suffer dramatically.

Traditional Workarounds Aren’t Enough

You might think that documenting your entire codebase in a markdown file (claude.md) would solve the problem. While this reduces file reading, Claude still has to process a potentially large markdown file filled with both relevant and irrelevant information. This still clutters the context window and impacts performance.

The Solution: Semantic Search and RAG (Retrieval-Augmented Generation)

The key to optimizing Claude’s performance lies in how it searches for and retrieves context:

• Textual Search: A basic approach where Claude searches through raw text (like claude.md) to find relevant information.
• Semantic Search: A smarter, faster, and more accurate method that understands the meaning behind the text and retrieves only the most relevant pieces of information.

Tools like Context 7 MCP leverage semantic search to give Claude up-to-date and focused documentation on libraries or code segments. For example, if you ask about a Next.js feature, the tool finds only the exact library docs you need instead of the entire documentation.

This focused retrieval dramatically improves Claude’s speed and accuracy by keeping the context window lean and relevant.

Meet Serena MCP: Making Semantic Search Work for Your Codebase

What if Claude didn’t have to guess where to look? What if it already knew everything about your codebase and could pull exactly what it needed using semantic search?

Serena MCP is a tool that does exactly that. It knows your code intimately and uses semantic search to keep Claude’s context window relevant and uncluttered. This results in:

• Faster, more efficient code editing and bug fixing
• Reduced token consumption
• More accurate suggestions and fewer errors

Because Serena MCP is an MCP (Model-Connected Plugin) server, it isn’t limited to just Claude Code—you can also use it with other MCP-compatible clients like Cursor or Windsurf.

How to Set Up Serena MCP for Claude Code

Getting started with Serena MCP is straightforward:

1. Install Serena MCP in your project directory (it’s directory-specific, so install it where you plan to use Claude Code).
2. Initialize the MCP server using the provided commands.
3. Connect Serena MCP to Claude Code via the MCP settings—once connected, you’ll see a valid connection checkmark.
4. Use the Serena dashboard to monitor logs and manage the server, including the ability to gracefully shut it down when you’re done.

This setup ensures that when you ask Claude to work on your code, it uses Serena MCP to fetch only relevant code snippets and context, significantly improving performance.

Bonus: Monitor and Optimize Your Claude Code Usage

Alongside Serena MCP, I recommend using the Claude Code Usage Monitor, a handy tool that lets you track:

• Your message usage and limit resets (especially useful if you’re on the Pro plan with 5-hour usage windows)
• Token consumption
• Cost usage
• Model distribution

The monitor runs in your terminal, avoiding the need for additional UI tools, and helps you avoid hitting message limits unexpectedly by providing alerts and usage stats in real time.

Practical Tips for Maximizing Claude Code Efficiency

• Index your project with Serena MCP to enable semantic search. Note that indexing currently supports languages like TypeScript and Python but may not work for simple HTML-only projects.
• Provide clear instructions to Claude on how to use the MCP tools. This helps Claude understand how to fetch context efficiently.
• Use the focused context window approach to save tokens and get better results within your usage limits.
• Leverage the usage monitor to track and optimize your session usage and avoid surprises.

Join the Community and Keep Learning

We’re hosting our first-ever AIS Discord hackathon from July 22nd to July 28th. Submit your coolest builds and projects for a chance to be featured in upcoming YouTube videos. Join us through the link in the pinned comment of the original video.

Conclusion

By switching from a cluttered textual search model to a focused semantic search powered by Serena MCP, you can unlock the full potential of Claude Code. This simple, free optimization leads to faster, more accurate code assistance and smarter token usage. Combined with usage monitoring tools, you’ll become far more efficient and effective in your coding workflow.

Give Serena MCP a try today, and watch Claude Code transform from a slow, overwhelmed assistant into a laser-focused coding partner!

If you found this guide helpful, consider supporting the creator by using the super thanks button on the original video page. Thanks for reading, and happy coding!

How to Build Successful AI-Powered Apps That Users Love: A Step-by-Step Framework

With the rise of AI, many believe that building apps has become easy and accessible to everyone. However, the reality is quite different. Most AI-built apps never get downloaded, and those that do often get deleted within days. So, what separates the few apps that succeed from the countless that fail? It’s not about flashy features or sleek designs—it’s about understanding the psychology behind user engagement and crafting an experience that makes users open your app repeatedly throughout the day.

In this post, I’m going to share a practical framework for building AI-powered apps that truly resonate with users, turning casual visitors into daily active users who can’t imagine life without your app.

Step 1: Choose the Right AI Tools for Building Your App

There are many AI builders available, each with pros and cons:

• Lovable: Builds entire apps automatically but offers less control.
• Cursor AI: Provides a balance—more control than web-based builders and easier to use than advanced AI coding tools like Claude Code or Gemini CLI.
• AI Agents: Tools like Claude Code and Gemini CLI are powerful but less beginner-friendly.

Why Cursor AI?
Cursor AI offers simple installation, an intuitive interface with a chat box for coding, and a file explorer, making it ideal for beginners who want control over the app-building process.

Step 2: Start with a Clear User Journey

Before writing any code, clearly define your app’s user journey:

• Describe your app in simple terms to AI models like Claude or ChatGPT (free versions are sufficient).
• Request a detailed user journey that maps out how users will interact with your app.
• Review and customize the journey—for example, group photos by month instead of source to give users a sense of achievement when cleaning photos.

Save this user journey in a dedicated file (e.g., ux.md) because AI tools like Cursor don’t retain memory between sessions. This practice is called context engineering, the modern evolution of prompt engineering, and is vital for keeping your project coherent.

Step 3: Build a Prototype in HTML

Begin by creating a simple HTML prototype of your app:

• HTML is easier to modify than native iOS app code.
• Use your saved user journey to guide the prototype’s structure and user experience.
• Example: A photo cleaner app where users swipe left to delete and right to save photos.

This prototype lets you iterate quickly on design and functionality before moving to platform-specific development.

Step 4: Improve Design with Color Palettes and Fonts

A consistent and appealing look is crucial:

• Use tools like colors to generate and export color palettes.
• Map your chosen palette to UI elements (cards, buttons, backgrounds) and save this mapping (e.g., color-scheme.md).
• Implement the color scheme into your HTML prototype for a cohesive visual experience.

Next, select fonts from Google Fonts to enhance readability and style:

• Pick fonts that reflect your app’s personality (modern, playful, etc.).
• Embed font links into your HTML and refresh to see the updated typography.

Step 5: Create an App Icon with AI

A recognizable app icon matters:

• Use AI image generation (like GPT-4’s image model) to create a cartoonish or thematic icon.
• Provide the color palette and style guidelines to ensure the icon matches your app’s look.
• Refine the icon with additional input if needed.

Step 6: Convert HTML Prototype to a Native iOS Swift App

To launch on iOS, convert your HTML prototype to Swift:

• Use Cursor AI to generate conversion rules (saved as conversion.md).
• Leverage Context 7 MCP, a powerful helper that provides AI with documentation and context about Swift and other frameworks, improving code accuracy.
• Open a fresh chat session in Cursor and instruct it to convert HTML into Swift using the conversion guidelines.
• Cursor will generate a to-do list of tasks, implement code changes step-by-step, and iteratively fix errors during test runs.
• End result: a functional iOS app running in the simulator, ready for further refinement.

Step 7: Add Polished Animations

Animations make the app feel alive and engaging:

• Return to your user journey file and ask Claude to describe visual animations for each app screen in simple terms.
• Create an animations-guide.md file to store these descriptions.
• Use Cursor AI with Context 7 MCP to translate animation descriptions into Swift UI code, referencing official documentation for best practices.
• Implement animations one screen at a time to avoid overwhelming the AI or losing context.
• Test and fix animation bugs with AI assistance, using screenshots and error logs for context-aware debugging.

Step 8: Iterate and Polish

Building a polished app takes time—even with AI:

• Fix UI glitches like overlapping views gradually.
• Refine animations and interactions to feel smooth and natural.
• Continuously test user flows and update based on feedback.

Remember, what used to take six months can now be done in about a month, but careful iteration is key.

Bonus: Join the AI Labs Community Hackathon

Get involved with a vibrant community by joining the AI Labs Discord hackathon from July 22nd to July 28th. Submit your projects for a chance to be featured and gain valuable feedback.

Final Thoughts

The secret to building successful AI-powered apps is not just leveraging AI for coding but deeply understanding user psychology and experience design. By combining AI tools thoughtfully with human creativity and iterative refinement, you can build apps that users love and keep coming back to.

If you found this guide helpful, consider supporting the creator’s work and subscribing for more insights on AI app development.

Ready to build your own AI-powered app? Start with the user journey, pick your tools wisely, and remember—execution beats ideas every time!

Unlocking the Power of Programmable AI Coding with Claude Code

In the rapidly evolving landscape of AI-powered coding assistants, Claude Code stands out as a truly unique and powerful tool. What sets Claude Code apart is not just its AI capabilities but its fundamentally programmable architecture, enabling deep customization and adaptability to fit any developer’s workflow. In this post, we’ll explore what makes Claude Code revolutionary, how it integrates with various tools, and some exciting projects pushing its capabilities even further.

What Makes Claude Code Different?

Unlike typical AI coding assistants, Claude Code is designed to be programmable at its core. This means you can tailor it precisely to your needs—whether that’s automating specific coding tasks, adapting to complex workflows, or integrating it with other tools in innovative ways. This level of customization is not available in most competing products, including popular tools like Cursor.

The secret sauce? The Claude Code SDK, which transforms Claude Code into a programmable agent capable of executing a wide variety of tasks based on your instructions.

Real-World Implementation: Claude Code as an MCP Server

One of the most impressive demonstrations of Claude Code’s flexibility is its use as a fully functional MCP (Multi-Component Prototype) server. Here’s how it works in practice:

• Imagine you have a taxi app prototype with a yellow-themed interface.
• You can send a prompt to the Claude MCP to edit the app, for example, changing the main color from yellow to blue.
• Claude Code processes this request, applying the changes seamlessly.
• You can continue issuing commands to update the UI, and the system will keep adapting and evolving your project.

This interaction is smooth because Claude Code supports an extended context window (around 200k tokens), significantly larger than many models, including Claude 3’s 120k tokens. This allows it to handle more complex projects without losing track of the context.

Why Use Claude Code Alongside Cursor?

While Cursor is a great coding assistant, it sometimes struggles with context limits or complex UI changes. Claude Code complements Cursor perfectly because:

• When Cursor hits its limits, you can offload tasks to Claude Code, which handles larger contexts and more intricate workflows.
• Unlike using Claude Code’s IDE feature alone, integrating it with Cursor maintains contextual continuity across both tools, avoiding mismatches and workflow disruptions.

Enhancing Workflow with Claude Desktop App and Canvas

Another fantastic use case is combining Claude Code with the Claude Desktop app, especially its canvas feature. Developers can brainstorm and design components visually in the desktop app, then ask Claude Code to implement those components in code. This integration creates a powerful feedback loop where ideas flow seamlessly from concept to implementation.

Using Multiple AI Models with Claude Code Router

What if you want to leverage other AI models with Claude Code’s powerful architecture? Enter the Claude Code Router tool.

• It enables you to route coding tasks to different AI models, including Gemini, OpenAI models, and Kimmy K2—a model offering Claude Code-level performance at a fraction of the cost.
• Setting this up involves installing the router tool, configuring your API keys in a config.json file, and specifying which model to use.
• This flexibility means you can balance cost, speed, and performance based on your project needs.

For example, using Kimmy K2 through Open Router might be slower than Claude Code’s default model but offers significant cost savings without sacrificing quality.

Advanced Multi-Agent Systems with Claude Flow and Hivemind

Taking programmability even further, Claude Flow is a project that allows Claude Code to spawn multiple agent instances working in parallel on a single task, dramatically speeding up complex operations like implementing a REST API.

An exciting feature here is Hivemind, a queen-led multi-agent system where:

• A “queen” agent coordinates multiple “worker” agents.
• Each agent works on a specific feature or subtask, all sharing the same context.
• Memory and task coordination are handled efficiently, and sessions can be saved and resumed, enabling long-term project continuity.

While still in development, this architecture promises powerful, scalable AI-assisted coding workflows for complex projects.

Resources to Supercharge Your Claude Code Experience

• Awesome Claude Code Repository: A curated collection of command files, workflows, and tools to extend Claude Code’s capabilities. It includes community workflows, custom commands, and hooks that you can integrate directly into your projects.
• Claude Code Chat Extension: For those using Cursor or VS Code, this extension transforms Claude Code into a dedicated chat interface integrated with your IDE. It supports multiple models, “thinking mode” adjustments, and seamless workflow integration, providing a superior user experience compared to basic chat panels.

Final Thoughts

Claude Code represents a new paradigm in AI-assisted coding—one where programmability, adaptability, and extensibility are baked into the core. Whether you’re enhancing UI workflows, experimenting with multi-agent architectures, or optimizing costs with model routing, Claude Code offers unparalleled flexibility and power.

If you’re a developer looking to push the boundaries of what AI coding assistants can do, Claude Code is definitely worth exploring.

Interested in Trying Claude Code?

• Install Claude Code and the Claude Code Router tool.
• Explore the Awesome Claude Code repository for pre-built workflows and commands.
• Try the Claude Code Chat extension for a seamless coding assistant experience in your favorite IDE.
• Experiment with multi-agent setups using Claude Flow and Hivemind (with caution).

Unlock the future of coding assistants today with Claude Code!

If you enjoyed this deep dive, consider subscribing to our channel for more insights and tutorials on AI development tools. Happy coding!

How to Build Professional and Unique UI with Shad CN Components Using the MCP Server and Tweak CN

Your website’s user interface (UI) is the cornerstone of user experience. It’s how visitors interact with your project and form their first impressions. Even with powerful AI tools like Cursor, understanding how to build and style proper UI components is essential to create a polished, professional look. Shad CN components are among the most popular React UI components available — but many developers, including myself, have been using them incorrectly, leading to broken components and styling headaches.

In this post, I’ll walk you through a proven workflow that leverages the Shad CN UI MCP server and Tweak CN customizer to build flawless, unique UI quickly and efficiently.

The Common Problem with Shad CN Components

Many developers simply instruct AI tools to “build this with Shad CN,” hoping for the best. However, this often results in:

• Broken components
• Styling issues
• UI that looks generic and indistinguishable from other Shad CN-based sites

This happens because Shad CN components require specific implementation patterns and context-aware usage to function correctly.

The Game-Changer: Shad CN UI MCP Server

To solve these problems, I turned to an MCP (Model-Centric Programming) server built specifically for Shad CN UI components. This server has completely transformed my development process by:

• Providing the correct context and implementation patterns for every component
• Offering tools that let you list available components and blocks (complex combinations of components)
• Delivering component demos that show exactly how to use each component in real code, eliminating guesswork and errors

How the MCP Server Works in Practice

1. Planning Phase:
   Using AI (Claude in my case), I brainstorm UI/UX designs and generate a detailed implementation plan that lists which Shad CN components and blocks to use and where.

2. Rule Files:
   I set up “rule files” to program my AI agent. These rules tell it to:

3. Automatically use the MCP server when working with Shad CN UI components
4. Prioritize using pre-made blocks (combinations of components) where possible

5. Always consult component demos before implementation to follow correct usage patterns

6. Implementation Phase:
   Cursor follows the plan and uses the MCP server’s demo tool to build each component step-by-step, ensuring everything is implemented perfectly on the first try.

This workflow leads to professional-looking sites with fewer bugs and no repeated trial-and-error.

Customizing Your UI with Tweak CN

After building a solid UI structure with the MCP server, the next step is to make your site visually unique. That’s where Tweak CN comes in.

Tweak CN is an amazing online customizer for Shad CN components that lets you:

• Modify base component designs extensively
• Adjust everything from colors to styles to create a unified, unique look
• Export your custom theme code

You can then give this code to Cursor to install your custom theme. If automatic installation fails, you can manually apply the small changes shown in the export.

This two-step approach — build structure first with MCP, then style with Tweak CN — ensures your site looks great and stands out from the crowd.

Quick Installation Guide for the MCP Server

To get started with the Shad CN UI MCP server:

1. Visit the GitHub repository for the MCP server.
2. Choose the appropriate MCP configuration (desktop config recommended for Cursor on Windinsurf).
3. Copy the config and add it to your Cursor settings under Tools & Integrations.
4. For increased request limits (5,000 requests per hour), get and add your GitHub Personal Access Token (PAT).
5. It’s easy to generate and remember to copy it as you won’t see it again.
6. Save your MCP file and you’re ready to build with the optimized workflow.

Why This Workflow Works So Well

• Context Engineering: The MCP server provides the AI with proper context around Shad CN components, preventing common mistakes.
• Component Demos: Real code examples eliminate guesswork during implementation.
• Pre-made Blocks: Using blocks simplifies building complex UI elements.
• Custom Styling: Tweak CN ensures your site looks unique and polished rather than “just another Shad CN site.”
• Efficiency: Most of the UI can be built correctly on the first try, saving time and frustration.

Final Thoughts

If you’re working with Shad CN UI components, using the MCP server combined with Tweak CN customizer will elevate your development process and final product. This approach ensures clean, professional UI that’s easy to build and customize.

If you want to dive deeper and follow along with actual code, check out the Shad CN UI MCP server GitHub repo and Tweak CN site. Also, consider subscribing to channels or communities focusing on these tools for ongoing tips and support.

Happy coding and designing!

If you found this guide helpful, please subscribe and consider supporting the content creators to keep these tutorials coming. Thanks for reading!

Revolutionizing Software Development with the BMAD Method: Agile AI-Driven Workflow Inside Your IDE

Since the advent of AI, creating websites and apps has become more accessible than ever. However, many developers and hobbyists often skip the critical steps of a proper software development process, opting instead for quick AI-generated demos that aren’t production-ready. To bridge this gap, the industry has evolved beyond simple “vibe coding” into sophisticated context engineering. Today, we explore a groundbreaking workflow that integrates an entire AI-driven software development team directly inside your IDE—ushering in a new era of agile, scalable, and manageable software creation.

Introducing the BMAD Method: Breakthrough Method for Agile AI-Driven Development

The BMAD method is a revolutionary workflow designed to emulate a real software development team by leveraging AI agents working in concert. Developed by BMAD Code, this approach aligns with standard agile principles—building software in incremental, testable chunks—while harnessing AI’s power to automate and streamline the entire process.

If you’re familiar with traditional agile development, you know it starts with a Product Requirement Document (PRD) outlining the features to build, followed by breaking these into smaller tasks, iterative building, testing, and shipping. The BMAD method mirrors this process but supercharges it with AI agents that execute roles such as Product Owner, Scrum Master, Developer, and Tester—all orchestrated inside your favorite IDE like Cursor AI, Claude Code, or Windsurf.

Why BMAD Stands Out

• Complete Workflow Integration: Combines context engineering with step-by-step AI commands to manage the full software lifecycle.
• Multi-IDE Support: Works seamlessly across multiple environments including Cursor, Windsurf, and Claude Code.
• Clear Documentation and Usability: Unlike previous frameworks, BMAD comes with solid, built-in user documentation, making onboarding straightforward.
• Orchestrator Agent: A dedicated AI agent guides you through the workflow, ensuring smooth progress and system understanding.
• Accessible to Everyone: Not just for developers—anyone interested in building software can leverage this AI-driven process.

How the BMAD Workflow Works: Step-by-Step

1. Start with the Foundation: PRD and Architecture Files

The process begins with two critical files:
- Product Requirement Document (PRD): Captures the feature list without technical details.
- Architecture File: Defines the app’s architecture, tech stack, and component connections.

These files can be prepared using AI brainstorming tools like ChatGPT, Claude, or Gemini. For example, you might brainstorm features for an iOS productivity app, iteratively refining ideas and finalizing a roadmap.

2. Initialize Your AI Agents in the IDE

Once your documentation is ready, open your IDE and run the BMAD installation script. This sets up folders and predefined AI agent roles representing different software team members.

• Create a docs folder and place your architecture.md and prd.md files there.
• Initialize the Product Owner (PO) agent to shard (break down) the documentation into manageable tasks.
• Launch the Scrum Master agent to generate epics and user stories based on the PRD.
• Approve stories manually to signal readiness for development.
• Start the Developer agent to build features story by story.
• Use the Tester agent to review and approve completed stories, marking them done only after successful testing.

3. Agile-Driven, Modular, and Iterative

The workflow strictly follows agile principles:
- Focus on one story at a time.
- Build, test, and approve incrementally.
- Agents work independently in separate chats to avoid context issues.
- The Scrum Master ensures only approved stories proceed to development.
- The system enforces discipline through status updates (draft → approved → ready for review → done).

4. Installation Made Simple

Installing BMAD locally inside your project is easy:
- Run the NPX command from the BMAD GitHub repository in your terminal.
- Provide your project path.
- Choose to shard PRD and architecture files.
- Select your IDE(s) for integration.
- Confirm pre-built web bundle inclusion if needed.
- Restart your IDE to load the new agent rule files.

Once installed, commands like PM (Product Manager) become available to guide you through the workflow smoothly.

Unlocking the Future of Software Development

The BMAD method is a game-changer that democratizes software development by embedding AI-powered teams inside your everyday tools. Whether you’re a solo developer, part of a small startup, or managing a large project, BMAD offers a structured, scalable, and efficient way to build real software—not just demos.

By combining agile methodologies with AI’s capabilities, BMAD ensures every piece of your product is carefully designed, implemented, tested, and approved—minimizing bugs and maximizing productivity.

Ready to Try BMAD?

1. Visit the BMAD GitHub repository to explore the full framework and documentation.
2. Follow the installation steps to integrate BMAD into your project.
3. Start your AI agents inside your IDE and experience the future of software development firsthand.

Final Thoughts

With AI evolving rapidly, frameworks like BMAD are redefining how software is built. This workflow not only boosts productivity but enforces the discipline and rigor required for production-quality software. If you want to stay ahead in software development, BMAD is a must-try.

If you found this post useful, be sure to subscribe to our channel for more insights and tutorials on AI-driven development workflows. Join our community, ask questions, and start building smarter today!

Note: This blog post is based on a detailed walkthrough of the BMAD method and its underlying concepts. For a hands-on tutorial and installation guide, watch the full video linked in the description.

How to Clone Any Website Design in Minutes: 3 Powerful Methods for Developers

Have you ever come across a stunning website design and wished you could replicate it perfectly for your own project? Maybe you tried screenshotting the site, pasted it into AI tools like ChatGPT or Cursor, and asked them to recreate it — only to end up with a messy, inaccurate version. The spacing is off, colors are wrong, elements are missing, and it feels more frustrating than helpful.

The problem? You’re expecting AI to “guess” the design from images or vague inputs instead of providing it with the right data and tools.

In this post, I’ll walk you through three amazing methods to clone any website design efficiently — from existing websites to Figma files — and get a working, clean codebase in minutes, not hours. These approaches leverage cutting-edge AI tools and clever workflows that automate the heavy lifting while maintaining accuracy.

Method 1: Clone Websites by Screenshot with Gemini and Cursor

This method is great for when you want to replicate the structure and look of an existing website or web app, like a wallpaper site or portfolio.

Step 1: Capture a Full-Page Screenshot

• Use your browser’s Inspect tool.
• Press Cmd + Shift + P (Mac) or Ctrl + Shift + P (Windows) to open the command palette.
• Type “screenshot” and select “Capture full-size screenshot.”
• This saves the entire webpage as a single image.

Step 2: Use Gemini’s Canvas Feature to Analyze the Screenshot

• Upload the screenshot to Gemini with the canvas feature enabled.
• Prompt Gemini to recreate the design exactly as shown in React.
• Gemini will analyze the layout, text, and interface elements and generate React code accordingly.

Step 3: Convert the Generated Code into a Next.js App with Cursor and Gemini

• Ask Cursor to create a Next.js app and provide the file structure.
• Paste the file structure back into Gemini with a prompt to split the code into components and generate a bash script (integrated.sh) that assembles the app automatically.
• Run the script to integrate the components into your source folder.
• Fix any minor errors or missing dependencies.
• Run the app locally to see the cloned website in action.

Why This Method Works

• Gemini can maintain context across the design and split code intelligently.
• The integration script saves you from manually dividing components.
• The result is a near-perfect replica of the original site’s interface, ready for backend implementation.

Gemini vs. Claude Artifacts

A comparison showed Gemini produced a much cleaner, more accurate recreation, even correctly placing images, while Claude’s output missed elements and had color inaccuracies.

Method 2: Convert Figma Designs to Code with the Figma MCP Server

If you have a Figma design file and want pixel-perfect code, this method is a game-changer.

What Is the Figma MCP?

• A custom SSE server that connects directly to Cursor.
• It fetches Figma design data and images, downloading assets like avatars and icons into your project folder.
• The server automates implementing designs into code, greatly reducing guesswork.

How It Works

• Select frames or components in Figma and copy their links.
• Provide these links to Cursor.
• The MCP server fetches design data and images.
• Cursor generates the corresponding code, integrating images and styling exactly as designed.

Benefits

• Images and icons are downloaded and organized automatically — no more guessing or blurry placeholders.
• You can clone entire pages or individual components.
• The output is highly accurate visually.
• Great for mobile prototypes and apps.

Limitations

• Does not automatically implement navigation or interactive behaviors (e.g., clickable cards).
• Some minor formatting tweaks may be needed depending on device or screen size.

For a detailed setup guide, check out the linked video walkthrough (see description).

Method 3: Clone Entire Websites Using Firecrawl MCP

Want to clone a live website’s entire structure, text, and images? Firecrawl MCP offers a powerful scraping toolset for this purpose.

How Firecrawl MCP Works

• Provide the website URL to Cursor using the Firecrawl MCP integration.
• Firecrawl scrapes the entire site, capturing HTML structure, div classes, metadata, text, and embedded images.
• It downloads assets and recreates the site’s layout and content in code.

Results and Limitations

• The cloned site is remarkably accurate in layout and visuals.
• Images and icons are perfectly placed.
• Some animations and interactive graphs may not be fully replicated.
• Slight UI jittering can occur, especially if implemented in plain HTML instead of React/Next.js.
• Using frameworks improves animation smoothness and optimization.

Pricing and Setup

• Firecrawl is a paid service with a free tier allowing scraping up to 500 pages.
• Monthly plans start at $16 for heavier use.
• A dedicated video covers installation and usage in detail.

Bonus Tip: Use git2doc to Leverage GitHub Repositories in Your Projects

Instead of cloning entire GitHub projects blindly, you can use git2doc to convert any public repo into documentation that Cursor can understand and reference.

How to Use git2doc

• Go to the git2doc website.
• Input the GitHub repo URL.
• Copy the generated documentation link.
• Add this as a doc source in Cursor.
• Ask Cursor to build features or MVPs with reference to that codebase.

This approach helps you understand architectural decisions, reuse code patterns, and build on top of existing projects intelligently.

Final Thoughts

Whether you want to:

• Clone a website from a screenshot (Method 1),
• Convert detailed Figma designs into code (Method 2),
• Or scrape and recreate a live website (Method 3),

these methods provide fast, accurate ways to get production-ready code with minimal manual effort.

They combine the power of AI tools like Gemini, Cursor, and specialized MCP servers to automate the tedious parts of frontend development, letting you focus on customization and backend logic.

If you found these techniques helpful, consider subscribing for more tutorials and tips on modern web development. You can also support the channel via memberships or super thanks to get priority feedback and exclusive content.

Happy coding and design cloning!

Useful Links & Resources

• Gemini Canvas & Integration Script Tutorial
• Figma MCP Server Setup & Demo
• Firecrawl MCP Setup & Usage Guide
• git2doc Website
• Related YouTube Videos (linked in original description)

Have you tried any of these methods? Share your experiences or questions in the comments!

Supercharging Claude Code: Two Free Tools That Will Revolutionize Your Workflow

If you’re a developer or AI enthusiast who loves pushing the boundaries of what AI coding agents can do, you’ve probably heard of Claude Code by Anthropic. This powerful agent offers an extraordinary level of customization, allowing you to create custom commands, tailor workflows, and even modify configuration files to fit your specific development needs. But what if I told you there are two free tools that can supercharge Claude Code and transform how you build projects with it? Let’s dive in!

What Makes Claude Code So Powerful?

Claude Code is already a beast for developers who want granular control over their AI coding workflows. You can:

• Create custom commands tailored to your tasks
• Modify the claw.md file to specialize Claude for different development phases
• Build workflows that automate multi-step coding processes

However, building effective workflows from scratch can be daunting, especially if you’re a Vibe coder or someone without deep development experience. Testing and refining workflows alone can take hours or days.

Tool #1: Super Cloud — A Configuration Framework for Claude Code

The first game-changer is a configuration framework called Super Cloud (yes, that’s really the name!). It adds a structured system of commands, flags, and personas that elevate Claude Code’s capabilities significantly.

Key Features:

• 18 Structured Commands: Each command is focused on a specific development task or workflow.
• Persona Flags: Choose from 9 personas representing various stages of the development lifecycle—front-end, back-end, architecture planning, security, problem analysis, and more.
• Multi-Context Plugins (MCPs): Integrates powerful MCPs like Context 7 (external documentation), Sequential MCP (multi-step reasoning), Magic UI, and Puppeteer for enhanced automation.
• YAML-Based Workflows: The backend uses YAML files to define workflows and rules, effectively “prompt programming” Claude to behave like real developers during each phase.

How It Works in Practice

For example, to build a React-based UI component, you’d use a /build command with flags like react, magic (for Magic MCP), watch (to monitor changes), and the persona front-end. Claude then follows a developer-like workflow, pulling in context, analyzing, building, and iterating.

The Learning Curve Problem

Despite its power, Super Cloud lacks proper user-friendly documentation. Many demos show what it can do but don’t teach you how to use it effectively. This can leave new users overwhelmed by the variety of commands, flags, and MCP configurations.

Overcoming the Learning Curve with Cursor

Here’s where the second free tool comes into play — Cursor, an advanced code editor with a powerful AI search and documentation generation capability.

How Cursor Helps:

• Load the entire Super Cloud configuration folder into Cursor.
• Ask Cursor to read all files and generate a comprehensive, easy-to-understand user guide.
• Use Cursor’s intelligent search to quickly find commands, flags, and workflow details.
• Export the generated documentation into a docs.md file.

With this documentation, you can then feed it back into Claude Code or Cursor itself to create tailored workflows for your projects without memorizing every command. For example, when building a Next.js app with 11 Labs API and OpenAI API integration, Cursor helped generate a full multi-phase project plan with commands, flags, and prompts structured for Claude Code to execute step-by-step.

Tool #2: Web-Based GUI for Claude Code — Access Claude Anywhere

Imagine running Claude Code on your powerful desktop but accessing it from your phone, tablet, or any other device on the same network. The second tool is a simple web-based GUI that makes this possible.

Highlights:

• Lightweight, easy installation with a simple script.
• Runs on localhost or your network, accessible via IP address and port.
• Allows you to create/select projects and chat with Claude Code through your browser.
• Perfect for cross-device accessibility — work on your project from anywhere in your house or office.

While this GUI is basic and doesn’t support all advanced commands (like Super Cloud commands), its convenience is unmatched for remote access and casual use.

How to Get Started

Installing Super Cloud

1. Visit the Super Cloud GitHub repository.
2. Clone the repo to your system using the provided command.
3. Run the install script inside the cloned folder.
4. Choose global installation (makes Super Cloud available to all Claude Code instances) or project-specific installation.
5. For project-specific installs, ensure your target folder ends with .claude, as all configuration files go inside that directory.

Installing the Web-Based GUI

1. Download the executable and place it in your preferred folder.
2. Run the installation script.
3. Launch the app locally or on your network using the appropriate command.
4. Find your current IP address to connect from other devices.
5. Open a browser on any device in your network and navigate to http://<your-ip>:<port>.

Why These Tools Matter

• Democratizing Development: Even non-expert coders can follow structured workflows embedded in frameworks like Super Cloud.
• Enhanced Productivity: Pre-built personas and commands help you skip the trial-and-error of workflow creation.
• Remote Access: The web-based GUI breaks hardware boundaries, letting you code and manage projects from anywhere.
• Community Sharing: The generated documentation and workflows can be shared, improving collaboration and knowledge transfer.

Final Thoughts

Claude Code combined with the Super Cloud framework and Cursor’s documentation powers is a must-try combo that can revolutionize your AI-assisted development workflow. Add the web-based GUI, and you have a flexible, accessible coding environment that adapts to your style and devices.

If you want to jump straight into these tools, I’ve included links and the complete documentation generated by Cursor in the description below. Don’t forget to subscribe for more tutorials and updates on AI-driven coding!

Happy coding with Claude Code!

Resources:

• Super Cloud GitHub Repository
• Cursor AI Editor
• Complete Super Cloud Documentation (Generated)

If you found this post helpful, please share and subscribe to support the channel. Your feedback and questions are always welcome!

Mastering Context Engineering: The Future of Coding with AI

You might have heard of "vibe coding," a term popularized by Andrej Karpathy, a founding member of OpenAI. Interestingly, Karpathy didn’t invent vibe coding but merely gave a name to a practice many had been doing for months. Now, he’s introduced another term that’s gaining traction in the AI development community: context engineering. Like vibe coding, context engineering isn’t brand new, but it is essential—and it’s shaping the way we code with AI.

In this post, we’ll dive deep into what context engineering is, why it matters, and how to implement it effectively in your AI-assisted development projects.

What is Context Engineering?

At its core, context engineering is about managing the information you provide to large language models (LLMs) so they can perform tasks accurately and efficiently. All LLMs have a context window—a limited amount of text they can "remember" or consider at once. Traditional prompt engineering focused on crafting a single prompt to get the best answer. In contrast, context engineering involves:

• Providing all relevant facts, rules, tools, and information within the model’s context window.
• Structuring this information so that the model does not hallucinate (make up false information) and understands exactly what it is supposed to do.
• Efficiently managing this context to avoid overwhelming the model and reducing accuracy.

In essence, context engineering is a broader practice that subsumes prompt engineering and includes strategies like retrieval-augmented generation (RAG) and memory management.

Why Context Engineering is Crucial

The shift from prompt engineering to context engineering reflects a deeper understanding of how AI models work in practical applications. Karpathy points out that:

• The AI app you use matters. It’s no longer just a ChatGPT wrapper but an ecosystem that includes tools and workflows designed for context engineering.
• Apps like Cursor and Claude Code are more than frontends; they are integral parts of effective context engineering.
• Efficient context management reduces hallucinations and improves your model’s output quality.

Practical Context Engineering Workflow

Here’s a step-by-step workflow based on insights and personal experience that you can adapt for your projects:

1. Start with a Project Requirement Document (PRD)

• The PRD lists all features and requirements for your project.
• It helps the AI model understand the scope and constraints (e.g., tech stack preferences like Next.js for frontend and FastAPI for backend).
• Even if you don’t know the specifics, the workflow can automatically configure a ready-made app.

2. Build a Documentation Folder

This folder contains key files that provide structured context:

• Implementation Plan: The blueprint for the entire development process.
• Project Structure: A live document that evolves as the project takes shape.
• UI/UX Documentation: Guidelines and details about the user interface and experience.
• Bug Tracking: A log of issues to avoid redundant troubleshooting.

3. Define Two Crucial Rules for the Model

• Generate Rule: Converts the PRD into all other documentation files, effectively “filling” the context window with relevant info.
• Work Rule: Guides the model on how to use each file during development (e.g., referring to bug tracking when errors arise).

These rules ensure the model can work step-by-step without losing track or hallucinating due to context overflow.

4. Manage the Context Window Efficiently

• Don’t dump everything into one file; break context into manageable pieces.
• Provide context only when necessary to keep the model focused.
• Use your app’s task list features to break down large tasks into subtasks intelligently.

Tools for Context Engineering: Cursor vs Claude Code

Both tools support context engineering workflows but have distinct strengths:

• Claude Code: Currently more powerful as an agent, supporting multiple agents working simultaneously, which is great for parallel tasks (e.g., generating UI variations).
• Cursor: Recently enhanced with features like to-do lists, making it easier to follow step-by-step workflows.

The workflow described works well with either tool, so choose based on your preferences and budget.

Important Tips for Successful Context Engineering

• Review Everything: AI models follow instructions literally. If your documentation has contradictions or ambiguous instructions, the model might not behave as expected.
• Customize Your Workflow: Don’t rely solely on generic implementation files. Build workflows tailored to your project and tech preferences.
• Decide Your Tech Stack: Automating tech stack selection can lead to mismatches. Make this decision yourself to ensure compatibility and satisfaction.
• Use Documentation Files to Preserve Context: When you start a new chat or session, the model can reload the entire project context from files, avoiding loss of information.

Context Engineering in Action

To illustrate, here’s how a project gets built using this approach:

• The AI app reads the implementation plan and starts with stage one: setting up the foundations.
• It creates folders and installs dependencies based on the specified tech stack.
• Task lists guide the model to complete one step thoroughly before moving on.
• The model cross-references the project structure and bug tracking files to ensure consistency and avoid errors.
• Progress is incremental and controlled, preventing errors caused by skipping essential setup steps.

Conclusion

Context engineering isn’t just a buzzword—it’s a necessary evolution in how we collaborate with AI models for software development. By managing the model’s context window carefully and structuring information effectively, you can dramatically improve the quality, reliability, and efficiency of AI-assisted coding.

Remember, the key isn’t just having the right files or tools, but understanding how to craft and manage context so the AI can truly become your coding partner.

If you’re excited to try this out, check out tools like Cursor and Claude Code, and start experimenting with your own context engineering workflows. And if you want to dive deeper, there are plenty of resources and community discussions around this emerging practice.

Enjoyed this post? Subscribe for more insights on AI development workflows, and feel free to share your experiences or questions about context engineering in the comments below!

Note: Some implementation plans and example files mentioned here are available in the description or linked resources to help you get started.