The Boundaries of Cursor AI: What I Learned from a Coding Experiment

The landscape of software development is continually evolving, with artificial intelligence promising to revolutionize how code is written and projects are built. Among the latest entrants in this domain is Cursor AI, an AI-powered code editor built upon the familiar foundation of Visual Studio Code. This integration immediately sparks interest, suggesting a blend of a well-established development environment with the cutting-edge capabilities of AI, aiming to boost productivity through intelligent code suggestions and interactive chat functionalities¹. This report delves into a personal, hands-on experience utilizing the free trial of Cursor AI on real-world projects, moving beyond the theoretical promises to explore its practical effectiveness in everyday development scenarios.

Exploring Cursor AI in Real-World Projects

Initial curiosity surrounding Cursor AI stemmed from its close relationship with VS Code, a tool widely adopted by developers for its versatility and extensive ecosystem². The allure of enhanced coding efficiency through AI within a comfortable interface was a compelling draw. The free trial offered a 15-day window to explore the premium features, granting 150 requests to its advanced models alongside unlimited access to more basic options like gpt-4o-mini and cursor-small. For a thorough evaluation, the gpt-4o model was selected as the primary engine for experimentation. This structure of the trial, with its defined limits on premium model usage, is an important consideration for developers assessing the tool's potential and whether its benefits justify a subscription.

The first project undertaken was the modification of an existing portfolio website, a project that had been started four years prior but remained incomplete and featured a somewhat outdated design. This provided an opportunity to test Cursor AI's ability to handle common front-end development tasks. The initial results were encouraging. The AI successfully changed the website's background to a lighter theme from its previous darker aesthetic. Furthermore, it effectively modified fonts and improved the site's responsiveness across different mobile devices. The AI also demonstrated an ability to adjust the positioning of various HTML elements (divs) based on its own suggestions, indicating a degree of understanding of layout and design principles. These positive initial experiences align with the capabilities highlighted in research, suggesting that Cursor AI can indeed assist with basic HTML and CSS modifications through natural language prompts⁵. This indicates that the tool can offer immediate value for developers needing to make quick user interface adjustments and enhancements⁵.

Testing Cursor AI on a Portfolio Website

However, the attempt to integrate more advanced front-end functionality revealed limitations. The goal was to incorporate Particle.js, a popular JavaScript library for creating dynamic particle backgrounds. Despite generating code intended to implement this feature, the resulting implementation did not function as expected. Frustration mounted after an hour of dedicated effort, involving rephrasing commands and even providing visual context through screenshots, yet the AI could not resolve the issue. Notably, there were no syntax errors reported in the terminal or browser's console, pointing towards a deeper semantic error that remained elusive to both the user and the AI⁷. This experience touches upon reported issues where AI editing can be slow for larger code files and may face challenges with complex, multi-file projects or ambiguous instructions⁸. There are also instances where AI-generated code is not applied correctly or doesn't adhere to the expected format, further complicating the debugging process¹⁰. In the context of Particle.js, a known challenge can be the correct placement and configuration of HTML elements to ensure the particle effect renders properly¹¹. The AI's inability to address this suggests a potential gap in its understanding of the intricate logic and contextual dependencies that certain libraries require. The user's difficulty in resolving the problem, even with detailed prompts, implies a lack of advanced debugging capabilities or the AI's struggle with the nuances of third-party library integrations¹⁰. This suggests that while Cursor AI can handle simpler styling and layout tasks, more complex integrations expose limitations in its ability to grasp and implement intricate front-end logic and effectively troubleshoot underlying semantic errors⁷.

Cursor AI on a Production Project

The second project focused on improving the user-friendliness of an email list page within an official project, which involved significant backend functionality. In this endeavor, Cursor AI demonstrated a mixed bag of results. It successfully improved the front-end functionality and modified the backend according to the user's specifications, indicating a degree of competence in handling both aspects of web development. However, other functionalities proved problematic, potentially stemming from issues with the prompts provided. A notable challenge arose with file management. Despite the existence of file A intended for task A and the entire project directory being accessible to the AI, it inexplicably created file B to perform the same task¹². This highlights potential inaccuracies in the AI's codebase awareness⁶. Furthermore, the AI generated models that were already present and imported in the project's controllers, leading to naming conflicts and errors. This could be indicative of challenges in maintaining context across multiple files¹⁴. The AI also seemed to get stuck in its own logic, possibly related to the camelCase naming convention used in the project. Perhaps most perplexing was the AI's tendency to generate a multitude of unnecessary files containing excessive lines of code for tasks that should have only required around 20 lines of code¹⁵. These experiences echo user reports of Cursor AI making superfluous changes, reordering code unnecessarily, creating duplicate files, and struggling with context retention¹². Specifically, issues with the AI not recognizing pasted code or working with outdated file versions have been documented¹⁷. While Cursor AI offers features like codebase awareness, the user's experience suggests that this functionality is not always reliable⁶. The tendency to over-generate code for simple tasks suggests a lack of efficiency and a preference for creating new code rather than intelligently modifying existing structures¹⁵.

Reflecting on AI Code Generation Challenges

Reflecting on the overall experience, Cursor AI did not appear to offer a significant advantage over using a general-purpose language model like ChatGPT for performing coding tasks. While a perceived time saving of approximately 10-15% was noted compared to interacting with ChatGPT directly, this marginal improvement might be attributed to Cursor AI's integration within the IDE and its partial awareness of the codebase². The experience felt akin to granting ChatGPT full access to the project directory, which presents both benefits in terms of contextual understanding and drawbacks such as the potential for errors to propagate across multiple files due to misinterpreted context. Comparisons between Cursor AI and ChatGPT highlight Cursor's VS Code integration, multi-file support, and codebase awareness as key differentiators, leading some users to find it significantly better for coding-specific tasks¹⁸. The potential for time savings due to Cursor AI's specialized coding features is also a noted advantage¹⁸. However, the fundamental limitations of the underlying language models appear to result in comparable challenges when tackling complex problems and ensuring code generation accuracy²¹.

Based on the experiences with both the portfolio and makerspace projects, an overall rating of approximately 6 out of 10 seems appropriate. It is evident that AI tools like Cursor AI can contribute to reducing the time developers spend on certain tasks that traditionally require hours of manual work¹⁴. However, the current state of the technology does not yet enable a non-technical individual with no prior coding knowledge to independently build a project from scratch using such tools⁵. While AI coding assistants offer valuable support for developers, they are not a replacement for fundamental coding knowledge and problem-solving skills⁵.

Comparison with Copilot

I had a very positive experience using Copilot, primarily because it allowed me to have control over where and how it made changes within my code. This level of control is particularly important when working on complex projects, as it ensures that the AI's suggestions align with my intentions. The main distinction between Copilot and Cursor lies in their scope of operation. Copilot, unless you have access to Microsoft Insider features, is restricted to making changes within a single file at a time. This can be limiting when working on larger projects that span multiple files. On the other hand, Cursor has the capability to operate across the entire project, allowing it to make more comprehensive changes and understand the context of the entire codebase. In this regard, Copilot offers a more focused approach, which can be advantageous for precise modifications. However, for this particular experiment, I chose not to use Copilot. My goal was to experience coding from the perspective of a non-technical person, which meant avoiding the use of advanced features like changing file names or utilizing the editor's full capabilities. This approach allowed me to better understand the challenges faced by beginners and evaluate the effectiveness of AI tools in assisting those without extensive coding knowledge.

Feature Comparison: Cursor AI vs. ChatGPT for Coding Tasks

In conclusion, artificial intelligence is poised to play an increasingly significant role in the future of software development, with the potential to automate more routine tasks and enhance developer productivity¹. However, based on this hands-on experience, tools like Cursor AI are currently best viewed as powerful assistants that can augment a developer's capabilities rather than completely supplant them, particularly for complex projects that demand deep technical understanding and creative problem-solving. The most effective utilization of these AI tools likely lies in a collaborative approach, where AI handles the more repetitive or straightforward aspects of development, allowing human developers to concentrate on higher-level design, architecture, and innovation²³. These thoughts are personal and not meant as a political critique. Looking at the current situation, I think about the challenges and opportunities ahead. While there are clear obstacles, I am hopeful about the potential for positive change. The resilience and innovation shown by individuals and communities make me believe that improvements are possible. I believe that through working together and understanding each other, we can tackle these challenges and create a better future. I am hopeful that the days ahead will bring meaningful progress and a brighter outlook for everyone.

Works cited

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