Building Windsurf: A Meta Look at AI-Assisted Website Creation

Welcome to the first post on the "Windsurf" project website! It feels fittingly meta to dedicate this initial entry to the process of creating this personal site. Windsurf is my digital playground—a space to showcase my tech experiments, log interesting projects (especially in the AI/LLM space), and share thoughts. The twist? It’s been built almost entirely with the assistance of AI, specifically Google’s Gemini model, facilitated through tools like the Windsurf IDE environment.

The Goal: AI as a Collaborator

The primary goal wasn’t just to build a website, but to explore how current AI tools can act as genuine collaborators in the development process. Could I guide an AI to understand requirements, establish a design language, generate functional code, and iterate based on feedback, ultimately producing a clean, professional, and maintainable static website suitable for hosting on Cloudflare Pages?

Laying the Foundation: Requirements & Design

Like any good project, we started with requirements. Through a series of questions, Gemini helped me articulate the site’s purpose, target audience (hello, tech enthusiasts!), essential features, and technical constraints. This culminated in a formal **Product Requirements Document (PRD)**. You wouldn’t normally expect this level of documentation for a simple personal site, but it was crucial for keeping the AI (and myself) focused.

Similarly, we defined the aesthetic—“modern, tech-focused, creative”—and translated that into concrete choices for colors (blues and pastels) and typography (clean, accessible sans-serif like Inter). This became the basis for an initial **UI Style Guide**. Having these documents provided a clear framework for all subsequent code generation.

Establishing the PRD and UI Guide upfront was key. It gave the AI clear boundaries and objectives, preventing aimless generation and ensuring consistency across different pages.

Generating the Code: From Blank Page to Functional Site

With the groundwork laid, we moved to implementation. Gemini generated the HTML, CSS (using Tailwind CSS for structure and styling), and JavaScript needed for the core pages:

• A Python script to create the basic file/folder structure.
• The "About Me" page, translating a sample professional profile into a web-friendly format.
• The "Projects & Blog" landing page, complete with placeholder content and functional JavaScript for tag-based filtering.
• And, of course, the structure for this very blog post page!

The process involved providing context (like a generic profile or the desired page functionality) and letting the AI generate the initial code, followed by review and refinement prompts. For example, ensuring the generated Tailwind classes matched the UI guide or asking for specific accessibility considerations.

<!-- Example: A snippet from the blog landing page's filtering JS -->
function filterPosts(tag) {
  posts.forEach(post => {
    const postTags = post.dataset.tags ? post.dataset.tags.split(',').map(t => t.trim()) : [];
    const shouldShow = (tag === 'all' || postTags.includes(tag));
    // ... logic to show/hide post with fade effect ...
  });
}

Benefits and Challenges

The benefits were immediately apparent. The speed at which initial structures and code scaffolding could be generated was impressive. It excelled at boilerplate tasks and translating design concepts (like the color scheme) into functional CSS classes. It also served as a great brainstorming partner for content ideas and structure.

However, challenges exist. **Prompt engineering** is crucial; clearly articulating requirements and constraints is vital to getting useful output. There’s also a need for **vigilant code review**. While the AI can generate code quickly, ensuring it meets quality standards—simple, robust, maintainable, and free of subtle bugs (the "senior developer quality" constraint I imposed)—requires careful human oversight. Debugging AI-generated code can sometimes be tricky if the underlying logic isn’t immediately obvious. Maintaining consistency across multiple generated pieces also requires attention.

Conclusion: A Powerful Partnership

Overall, building Windsurf with AI assistance has been a fascinating and productive personal experiment. It’s not about replacing the developer but augmenting their capabilities. When guided effectively, AI can significantly accelerate development, handle repetitive tasks, and even offer creative solutions. The key lies in clear communication (through prompts and documentation), careful review, and understanding the tool’s strengths and limitations.

This site is a living testament to that process, and I look forward to continuing its development and sharing more projects here—many likely created with the ongoing help of my AI collaborator!