Cursor Practice Project - 19-40 Hours, 100+ Prompts, 5 Languages: Building a Complete AI Prompt Platform with Cursor

Impressive Results: How Capable Is This Website?

Before the formal introduction, let the data speak. With 40 hours of development using Cursor, I built a fully functional AI prompt library platform, Nano Banana Studio, from scratch, with the following achievements:

• 100+ professionally validated prompts covering 6 core scenarios including product design, image editing, portrait photography, etc.
• 30+ real Before/After case studies, each with detailed usage tips
• Full support for 2 language versions (Chinese, English), over 200 pages
• Pure frontend Before/After comparison tool, with user images processed entirely locally for privacy protection
• Lighthouse performance score 95+, first screen load time under 1.5 seconds
• 180+ pages indexed by Google, with steadily improving keyword rankings

More importantly, all of this runs on Vercel's free plan, with no backend servers, no databases, and zero deployment costs.

Online Access: nanoprompts.org

Website Core Features Showcase

This is not a simple collection of static pages, but a fully functional prompt learning platform. The website is designed around practicality, with each feature carefully considered.

First is the organization of the prompt library. I categorized the content into six major categories: product design visualization, image editing and retouching, portrait art photography, character creative transformation, e-commerce marketing materials, and spatial design. Each category contains multiple practical scenarios. For example, under the product design category, there are sub-scenarios like "concept sketches to industrial design drawings" and "product photos to 3D modeling effects." Each scenario includes complete prompt examples, bilingual comparisons (Chinese and English), usage tips, and Before/After comparison images.

Case studies are the core value of the website. I meticulously compiled over 30 real usage cases, each demonstrating the actual effects of AI tools. Users can directly see what kind of results can be achieved with specific prompts. All prompts support one-click copying, allowing users to use them directly in their own projects.

The website also integrates a Before/After comparison tool. This is a completely browser-based image processing tool where users can upload two images, choose between left-right or top-bottom comparison layouts, add titles and prompt descriptions, and then export composite images suitable for social media sharing with one click. The entire process requires no data upload to servers, fully protecting user privacy.

Multilingual support was planned from the beginning of the project. The website supports full versions in 5 languages, not just simple machine translations, but content optimized for the habits of users in different languages. The English version is placed as the default language at the root path, while other languages are distinguished by path prefixes, such as /zh/ for Chinese and /ja/ for Japanese. Each page includes complete hreflang tags to inform search engines about the relationships between different language versions.

How Cursor Helped Me Achieve All This

Without Cursor, the development cycle of this project would have at least doubled. Cursor played an extremely important role throughout the development process; it's not just a code generation tool but more like a development assistant that understands the entire project.

The starting point of the entire project was a clear project rules file. In the .cursorrules file, I defined the project's technical stack, code standards, SEO requirements, performance goals, and other core principles. With this rules file, Cursor could understand the entire project's architecture and requirements. When I asked it to generate new pages, it automatically followed these specifications, producing code that met the project's standards. For example, each page would automatically include complete SEO meta tags, Open Graph tags, multilingual hreflang tags, etc.

During the content creation phase, Cursor demonstrated powerful batch generation capabilities. I first manually wrote a complete case document as an example, including YAML Front Matter metadata, feature descriptions, prompt examples, usage tips, and a complete structure. Then, using Composer mode, I had Cursor generate frameworks for the other 30+ cases based on this example. This approach ensured content structure consistency while significantly improving work efficiency.

Multilingual implementation was a technical challenge, but Cursor helped solve it easily. When I described the requirements, Cursor not only generated the vercel.json configuration file to implement Clean URLs and route rewriting but also automatically added correct hreflang tags to each page. The entire multilingual architecture implementation took less than 2 hours.

SEO optimization was another highlight. I had Cursor write automated scripts that could scan all HTML files, automatically generate sitemap.xml including URLs for all language versions, set reasonable priorities and update frequencies. The scripts also supported the IndexNow protocol, automatically submitting updates to search engines like Bing. These tasks, which originally required manual maintenance, are now fully automated.

Key Conversations: How Cursor Understands Requirements

Let me share a few key conversations with Cursor to demonstrate how it helped solve specific problems.

The first important conversation was about the overall project architecture design. My question was straightforward:

"I need to create an AI prompt library website, with the main content being Nano Banana AI prompts and usage cases. Please help me design the file directory structure, route planning, and content organization. It needs to support 5 languages (Chinese, English, Japanese, Korean, Spanish), be SEO-friendly, easy to maintain and expand, simple to deploy, and have fast page loading speeds."

Cursor's response was very professional. It recommended using a static website architecture, managing content with Markdown, distinguishing language versions through directory paths, and leveraging Vercel's CDN for global acceleration. It provided a complete directory structure design, explained the purpose of each directory, and also gave examples of vercel.json configuration. This response set the technical tone for the entire project.

The second key conversation was about developing the Before/After tool. I described the functional requirements: upload two images, choose layout options, add text descriptions, export composite images, all processed entirely in the browser. Cursor immediately understood the requirements, using HTML5 Canvas API for image composition, FileReader API for reading local files, and the toBlob method for exporting high-quality images. The generated code included complete drag-and-drop upload support, real-time preview, responsive design, and special attention to Chinese font rendering issues.

The third impressive conversation was about performance optimization. When I proposed achieving a Lighthouse performance score of 90+, Cursor provided a systematic optimization plan: using WebP format for images with lazy loading, inlining critical CSS into HTML to reduce render blocking, asynchronously loading JavaScript files, and configuring long-term caching for static resources. It also helped me configure Vercel's cache headers to ensure optimal caching policies for resources on the CDN.

Core Experience: Lessons from 40 Hours of Development

After practicing with this complete project, I've summarized several key experiences for using Cursor in web development.

First, the project rules file is the foundation of success. Don't rush to start coding; spending time writing a good .cursorrules file is the most worthwhile investment. This file is like a project manual for Cursor, helping it understand your coding style, architectural principles, and quality standards. With clear rules, the quality of code generated by Cursor improves significantly, and the workload for later modifications is greatly reduced.

Second, make full use of Cursor's contextual understanding capabilities. Use the @ symbol to reference existing files, allowing Cursor to generate new content based on existing content, which helps maintain style and structure consistency. For example, when I needed to create new case pages, I would @ reference the first complete case and tell Cursor "generate new pages in this format," which worked much better than simply describing the requirements.

Third, for complex features, having step-by-step conversations with Cursor yields better results. Don't present all requirements at once; instead, have Cursor implement the core functionality first, then gradually add details and optimizations. For example, when developing the Before/After tool, I first had it implement basic image composition, confirmed the effect, then added text rendering, layout selection, export settings, and other features. This iterative development approach makes it easier to control quality.

Fourth, static website architecture is indeed the best choice for content-driven projects. Without the complexity of backends, no database maintenance costs, simple deployment, excellent performance, and SEO-friendly. Vercel's free plan is completely sufficient and provides global CDN acceleration. For content-driven projects like prompt libraries, tutorial websites, and documentation sites, static architecture is the optimal solution.

Finally, SEO optimization should be planned from the beginning of the project. Don't wait until the website is built to consider SEO, as that will lead to a lot of rework. From directory structure design, URL planning, and page template creation, SEO factors should be considered. Using automated scripts to generate sitemaps and submit to search engines can save a lot of manual maintenance time.

Conclusion: Cursor Makes Focusing on Content Possible

The biggest takeaway from this project is not learning a specific technology, but validating an idea: AI tools can truly allow developers to focus on creating value rather than getting bogged down in technical details.

I am a content creator with in-depth research on AI prompts, but frontend development is not my specialty. Without Cursor, implementing such a complete multilingual website would have been almost impossible. Even if勉强 made, the quality would be hard to guarantee, and maintenance would be a nightmare.

With Cursor, the situation is completely different. I can focus my energy on prompt research, case organization, and usage tip summarization—these are my strengths and the real value of the website. For technical implementation, I only need to clearly describe the requirements, and Cursor can generate high-quality code. It understands SEO best practices, knows how to implement responsive design, and grasps performance optimization techniques.

This is the true value of AI tools: not to replace people, but to enable people to focus on what they do best. My professional knowledge combined with Cursor's technical capabilities created this complete product.

If you also have ideas you want to realize, don't be intimidated by technical barriers. Cursor has significantly lowered the difficulty of web development. The key is to have clear goals, be willing to learn and iterate, and then let Cursor handle the technical details. From idea to product is no longer out of reach.

Related Resources

• Website Address: nanoprompts.org
• Cursor Practice Community: cursorpractice.com
• GitHub: GitHub