What You Will Learn

• Understand Windsurf and its "Cascade" AI workflow
• Build full-stack AI apps using Windsurf and Next.js
• Integrate Azure Document Intelligence, Supabase, Stripe, and Anthropic/OpenAI
• Deploy to Railway (and alternatives) and troubleshoot production builds
• Apply Windsurf best practices like instructions.md and feature splitting

Study Guide

Introduction

Welcome to the 'Video Course: Windsurf Masterclass: How to Build & Deploy AI Apps'. In this comprehensive guide, we will delve into the intricacies of using Windsurf, an AI-powered Integrated Development Environment (IDE), to create and deploy full-stack AI applications. Whether you're an agency owner, a developer, or simply interested in AI technologies, this course offers valuable insights into leveraging Windsurf for rapid and efficient AI application development. By the end of this guide, you'll have a solid understanding of Windsurf's capabilities, practical applications, and best practices for effective usage.

Understanding Windsurf: A Revolutionary AI Agnostic IDE

Windsurf as an AI Coder
Windsurf, developed by Codium, is positioned as a groundbreaking AI agnostic IDE, built on top of VS Code. It integrates intelligent agents and co-pilots that collaborate to enhance the development process. Ahmed, the course leader, emphasizes Windsurf's ability to accelerate AI application development significantly, claiming to have spent over 60 hours building with it.

Key Features of Windsurf
Windsurf's core innovation, "Cascade," combines deep code base understanding with advanced tools and real-time awareness. This creates a seamless, collaborative flow akin to working alongside another developer. Key features include:

• Continuous understanding of actions, ensuring real-time updates with every code modification.
• Deep understanding of the entire code base, enhancing code generation accuracy.
• LLM-based code search, which is three times more accurate than traditional methods.
• Integration of tools and APIs, facilitating diverse functionality.
• Image upload capabilities for UI modifications and application building based on images.
• Integration with Anthropic's Model Context Protocol for improved code generation output.

Accelerated Application Development for Agency Owners

Target Audience: Agency Owners
The course is tailored for agency owners developing generative AI solutions for clients. Windsurf's efficiency and speed in building applications make it an ideal tool for this target audience.

Example: Invoice Analyzer
An "invoice analyzer" application is demonstrated as a prime example. This application allows users to upload invoices, extract data fields, and download the data in JSON or CSV format. Ahmed illustrates how Windsurf can reduce development time from weeks to hours.

Step-by-Step Guides to Building AI Applications

1. Invoice Analyzer
This application focuses on document processing using Azure Document Intelligence for PDF analysis and data extraction. Key steps include:

• Setting up an Azure account and integrating the Document Intelligence service.
• Building the frontend with Next.js using Windsurf.

2. Content Repurposing "Social Scribe" Application
This more complex application features:

• User authentication via Superbase.
• Content generation using Anthropic's Claude.
• Social media post creation, token usage tracking, and Stripe integration for payments.

3. Real-time Translation Messenger "Lang Link"
This MVP application showcases:

• Real-time messaging and translation using Superbase for user management and communication.
• OpenAI's chat model for translation.

Deployment Strategies

Deploying with Railway
The course covers deployment using Railway, a platform for hosting applications. Key steps include:

• Building the application for production (npm run build).
• Setting up environment variables and deploying from a GitHub repository.
• Troubleshooting common deployment errors with Windsurf's assistance.

Best Practices and Tips for Effective Windsurf Usage

Ahmed shares ten key best practices for using Windsurf effectively:

• Well-defined instructions file: Create a comprehensive instructions.md file.
• Splitting up features: Break down applications into manageable features.
• Adding documentation snippets: Include relevant documentation in the instructions file.
• Breaking up the chat window: Start new chats for each major feature to maintain context.
• Keeping the app running: Test features in a live development environment.
• Using image upload for debugging and UI guidance.
• Accepting parts of generated code selectively.
• Using specific prompts for minor updates: Avoid unintended code changes.
• Committing changes frequently to GitHub: Establish save points for easier rollback.
• Leveraging ChatGPT's gpt-3.5-turbo-016k model for resolving persistent errors.

Community and Resources

Ahmed emphasizes the value of community support, offering resources, templates, and code for the solutions presented. Weekly Q&A sessions and access to a 50+ page document with step-by-step instructions enhance the learning experience.

Conclusion

By now, you should have a comprehensive understanding of how to build and deploy AI applications using Windsurf. This course has provided you with the tools and knowledge to harness the power of AI-powered IDEs, streamlining the development lifecycle. As you apply these skills, remember the importance of thoughtful application and continuous learning to stay ahead in the ever-evolving field of AI development.

Podcast

There'll soon be a podcast available for this course.

Frequently Asked Questions

Welcome to the FAQ section for the 'Video Course: Windsurf Masterclass: How to Build & Deploy AI Apps'. This resource is designed to answer your questions about using Windsurf for AI application development, from the basics to advanced concepts. Whether you're new to AI or an experienced developer, you'll find valuable insights here.

What is Windsurf and why is it highlighted as a superior AI coder, especially for agency owners?

Windsurf, developed by Codium, is the first AI agentic Integrated Development Environment (IDE). Built on top of VS Code's open-source framework, it features a backend powered by AI agents and co-pilots that collaborate. What makes Windsurf stand out is its "Cascade" system, which combines a deep understanding of the codebase with advanced tools and real-time awareness of actions, creating a seamless and collaborative development experience akin to working alongside another developer. For agency owners building generative AI solutions for clients, Windsurf promises significantly faster development times for complex applications, potentially reducing weeks of work to mere hours, as exemplified by the invoice analyser application. Its features, concepts, and capabilities are presented as revolutionary in how AI applications are built and deployed.

What are some of the key features and capabilities that differentiate Windsurf from other AI coders?

Windsurf boasts several unique features and capabilities. It maintains a continuous understanding of a developer's actions, updating its awareness with every change made. It has a deep understanding of the entire codebase due to its underlying contextual awareness engine. Its LLM-based code search is claimed to be three times more accurate. Recent updates include the ability to upload images, allowing for UI tweaking or building new applications based on UI screenshots. Furthermore, Windsurf is embracing the "Model Context Protocol" from Anthropic, which aims to improve the output of AI models, particularly in code generation, by better connecting AI systems to various content repositories and development environments. These features collectively aim to create a more intuitive, collaborative, and efficient AI-powered development workflow.

Could you describe some of the practical AI applications that can be built using Windsurf, as demonstrated in the source?

The source highlights several practical AI applications built using Windsurf. The first is a simple invoice analyser, allowing users to upload invoice documents, select specific data fields for extraction, and download the extracted information in JSON or CSV format, leveraging Azure Document Intelligence for PDF analysis. The second application is a more complex content repurposing micro-application called "Social Scribe," complete with user authentication, Superbase integration, token usage tracking, and Stripe integration for payments. This application generates articles and social media posts from various content inputs. Finally, a real-time translation messenger for a client is showcased, featuring user authentication, contact management, real-time messaging, and message translation. These examples illustrate Windsurf's versatility in building different types of AI-powered tools, from document processing to content generation and real-time communication.

What is the role of the "instructions.md" file in the Windsurf development process, and what kind of information should it contain?

The "instructions.md" file is central to the Windsurf development workflow. It acts as a comprehensive guide for the AI model, outlining the project's overview, core functionalities, and step-by-step instructions for building each feature. It's recommended to invest significant time in perfecting this document before starting development. For each feature, it should include a detailed description, the steps required for implementation, layout specifications, user actions, and the necessary tech stack. Additionally, the instructions file can be enhanced by including relevant documentation snippets from the services being used (e.g., Azure Document Intelligence, Anthropic API, Stripe), including code examples and schemas. This helps to minimise errors and improve the accuracy of the code generated by Windsurf by providing it with specific and contextual information to reference.

How does Windsurf facilitate integration with external services like Azure Document Intelligence, Superbase, and Stripe?

Windsurf facilitates integration with external services by guiding the user through the setup process and generating the necessary code and configurations. For example, in the invoice analyser application, the user is shown how to set up an Azure account and obtain the API key and endpoint for the Document Intelligence service. These credentials are then used within the Next.js application, with Windsurf generating the API calls to Azure for PDF analysis and data extraction. Similarly, for Superbase in the "Social Scribe" application, Windsurf helps in setting up the database schema, handling user authentication, and storing/retrieving data. For Stripe integration, particularly for handling payments, Windsurf assists in implementing the necessary API calls and webhooks, often leveraging documentation snippets provided in the instructions file to ensure correct implementation of payment processing and token management. Environment variables are used to securely store API keys and other sensitive information.

What are some of the best practices and tips highlighted for effectively working with Windsurf and similar AI coding tools?

Several best practices and tips are emphasised for a smoother development experience with Windsurf: having a well-defined and frequently referenced instructions file; splitting features into manageable, incremental steps; adding relevant documentation snippets to the instructions; breaking up long chat sessions by starting new chats for new features; keeping the application running to test code in real-time; using the image upload feature for UI-related tasks; selectively accepting generated code to avoid unwanted changes; for minor code updates, explicitly instructing the AI not to alter existing functionality; committing changes to a version control system (like Git/GitHub) after each successfully implemented and tested feature; and as a fallback for persistent errors, consulting models like ChatGPT's 3.5 for potential solutions. These practices aim to provide better context, prevent errors, manage the AI's scope, and ensure a more controlled and successful development process.

How can one deploy AI applications built with Windsurf, and what are some of the services mentioned for hosting?

The source demonstrates deployment using Railway (railway.app), a platform that offers a relatively simple and cost-effective way to host applications. The deployment process typically involves building the application locally using a command like npm run build to ensure no production errors. Then, the project repository (e.g., on GitHub) is connected to the hosting service. Environment variables used in the local development are configured on the hosting platform. Once configured, the hosting service builds and deploys the application, often providing a public URL. Other services like Vercel and Render are also mentioned as alternatives for hosting web applications. The process may involve some troubleshooting of build errors on the deployment platform, which can often be resolved by feeding the error logs back into Windsurf or another LLM for analysis and suggested fixes.

What were some of the key challenges or lessons learned during the development of the "Social Scribe" and "Lang Link" applications using Windsurf?

The development of "Social Scribe" and "Lang Link" highlighted several key challenges and lessons. For "Social Scribe," managing the complexity of integrating multiple services like Superbase (for authentication and data), Stripe (for payments), and Anthropic's API (for content generation) required careful planning in the instructions file and accurate implementation of API calls and data handling. Token management and ensuring users were limited based on their token balance also added complexity. For "Lang Link," ensuring real-time communication using Superbase's capabilities and accurate translation using OpenAI's chat model were critical. A recurring lesson across both projects was the importance of a well-structured instructions file, breaking down features logically, and consistently referring back to it. Debugging errors, particularly during deployment, often involved analysing logs and iteratively refining the code with Windsurf's assistance. The need to manage the context window in chat sessions by starting new chats for new features was also crucial to maintain the model's performance and prevent it from making unintended changes or losing track of the project's goals.

How do AI-powered IDEs like Windsurf impact the efficiency and workflow of AI application development?

AI-powered IDEs like Windsurf significantly enhance the efficiency and workflow of AI application development by automating repetitive coding tasks and providing real-time suggestions. These tools can drastically reduce the time required to develop complex applications by providing a collaborative environment where AI agents assist developers in writing, debugging, and optimizing code. For instance, the invoice analyser application, which traditionally might take weeks to develop, can be completed in hours using Windsurf. This efficiency allows developers to focus more on creative problem-solving and less on routine coding tasks, thereby increasing productivity and innovation.

How does the development process with Windsurf compare to traditional coding methods?

The development process with Windsurf differs from traditional coding methods primarily in its use of AI to assist and automate coding tasks. Traditional coding requires manual writing and debugging of code, which can be time-consuming and error-prone. In contrast, Windsurf uses AI agents to understand the codebase and provide real-time suggestions, reducing the chances of errors and speeding up the development process. Additionally, Windsurf's ability to integrate with external services and manage complex workflows through its instructions file allows for a more streamlined and efficient development process compared to traditional methods.

Why is a well-structured instructions.md file crucial in the development process with Windsurf?

A well-structured instructions.md file is crucial because it serves as a comprehensive guide for both the AI and the developer. It outlines the project's objectives, core functionalities, and step-by-step instructions for feature implementation. This document ensures that the AI has a clear understanding of the project requirements, which minimizes errors and enhances the accuracy of the generated code. By providing detailed descriptions and relevant documentation snippets, the instructions.md file acts as a constant reference point, facilitating a more efficient and successful development process.

What strategies are recommended for handling errors when using Windsurf?

When encountering errors while using Windsurf, it's recommended to first consult the error logs and cross-reference them with the instructions.md file to identify potential discrepancies. Starting a new chat session can help reset the context and focus on the specific issue. If the error persists, consulting external AI models like ChatGPT for additional insights can be beneficial. Keeping the application running to test code in real-time and committing changes frequently to a version control system like GitHub can also help manage and resolve errors effectively.

How can businesses leverage Windsurf to build AI-powered solutions efficiently?

Businesses can leverage Windsurf to build AI-powered solutions efficiently by utilizing its AI-driven development capabilities to reduce coding time and improve accuracy. Windsurf's ability to integrate with various external services allows businesses to create comprehensive solutions that cater to specific needs, such as automated invoice processing or content generation. By following best practices, such as maintaining a detailed instructions.md file and using version control, businesses can streamline their development processes, reduce errors, and enhance the overall quality of their AI applications.

What are some common misconceptions about using AI coding tools like Windsurf?

One common misconception is that AI coding tools like Windsurf can completely replace human developers. While these tools significantly enhance productivity by automating routine tasks and providing intelligent suggestions, they still require human oversight for creative problem-solving and decision-making. Another misconception is that AI-generated code is always flawless. In reality, developers must review and test the code to ensure it meets the project's specific requirements. Understanding these tools as collaborative partners rather than replacements is key to leveraging their full potential.

How does Windsurf handle security and privacy concerns, especially regarding sensitive data?

Security and privacy are critical considerations when using AI tools like Windsurf. The platform uses environment variables to securely store API keys and other sensitive information, minimizing the risk of unauthorized access. Additionally, developers are encouraged to follow best practices for data security, such as encrypting sensitive data and implementing robust authentication mechanisms. By adhering to these practices, developers can ensure that their applications comply with security standards and protect user data effectively.

Can you provide real-world examples of businesses successfully using Windsurf for AI development?

Several businesses have successfully used Windsurf to develop AI applications that streamline operations and enhance customer engagement. For instance, a marketing agency used Windsurf to create a content generation tool that automates the production of social media posts, saving time and resources. Another example is a financial services firm that developed an invoice analyser to automate the extraction of key data from invoices, reducing manual processing time and improving accuracy. These examples demonstrate Windsurf's versatility and effectiveness in real-world applications.

What is the learning curve like for new users of Windsurf?

The learning curve for Windsurf is relatively gentle, especially for users familiar with coding environments like VS Code. The platform's intuitive interface and comprehensive documentation make it accessible to both beginners and experienced developers. New users can benefit from following structured tutorials and leveraging the instructions.md file to guide their development process. As they become more familiar with Windsurf's features and capabilities, users can gradually take on more complex projects and fully harness the power of AI-assisted development.

What advanced features does Windsurf offer for experienced developers?

For experienced developers, Windsurf offers advanced features like LLM-based code search, which provides highly accurate search results across the codebase. The platform also supports integration with various external services, enabling developers to build complex applications with ease. Additionally, Windsurf's real-time awareness and contextual understanding capabilities allow developers to collaborate seamlessly with AI agents, enhancing productivity and innovation. These features make Windsurf a powerful tool for developers looking to push the boundaries of AI application development.

What does the future hold for AI-powered development tools like Windsurf?

The future of AI-powered development tools like Windsurf is promising, with continuous advancements in AI technology expected to further enhance their capabilities. As AI models become more sophisticated, these tools will offer even greater accuracy and efficiency in code generation and project management. Developers can look forward to more seamless integrations with a wider range of services, enabling the creation of increasingly complex and innovative applications. The ongoing evolution of AI-powered development tools will continue to transform the software development landscape, making it more accessible and efficient for businesses and developers alike.

What support options are available for Windsurf users?

Windsurf users have access to a variety of support options, including comprehensive documentation, community forums, and direct support channels. The platform's documentation provides detailed guides on using its features and integrating with external services. Community forums offer a space for users to share insights, ask questions, and collaborate on solutions. For more personalized assistance, users can reach out to Windsurf's support team for help with technical issues or specific development challenges. These support resources ensure that users have the guidance they need to successfully leverage Windsurf for AI application development.