JitAi's Distinct AI Coding Paradigm Contrasted with Cursor

Without question, Cursor is a highly successful AI coding tool. Developers simply describe their requirements in natural language, and it leverages large language models to generate code and files, significantly boosting daily coding productivity. However, nearly all users encounter some unavoidable pain points during use.

First is the accuracy issue. Generated code often contains errors that require manual correction by developers. What's more challenging is that when Cursor attempts to fix errors itself, it may inadvertently modify other parts of the code that were already correct. Expecting Cursor to complete an entire business system development in one go is nearly impossible. Since code ultimately serves business needs, we naturally need to pursue higher reliability in business application development scenarios.

Second is the barrier to entry. Developers who lack experience and judgment struggle to control Cursor's output, missing the sense of ownership code maintainers should have, and may still create chaotic projects. Another scenario involves developers with sufficient experience and capability but lacking responsibility, who don't rigorously validate AI output quality. Neither of these situations is what we want to see.

Third is the cost issue. The underlying cause is substantial, continuous token consumption. Even as technology advances and large language model inference costs continue to decrease, sustained high token consumption itself represents unnecessary waste—who doesn't want to accomplish more while spending less?

Expecting AI programming tools like Cursor to complete business system development precisely, efficiently, and cost-effectively is unrealistic. The right tool for business application development should possess three characteristics: high accuracy, low barrier to entry, and low cost.

High Accuracy

AI hallucinations are an objective reality. The more content AI needs to understand, the greater the likelihood of errors. Standardized, coarse-grained content is easier to comprehend, while microscopic, fine-grained content is much harder to grasp. Having AI generate coarse-grained, standardized, templated, and modeled content yields far higher accuracy than fine-grained content. While native programming languages are standardized, they are fine-grained, leading to relatively low accuracy in AI code generation.

Business application system development is fundamentally a coarse-grained endeavor. System module composition, module classification, stable technical implementation components, and customizable elements have all accumulated extensive standardized functional patterns and components over the past 30 years of evolution. Enterprise web application systems invariably consist of elements such as portals, pages, components, models, services, approvals, events, tasks, permissions, organizational structures, and authentication methods. Compared to the flexible configuration at the business layer, the technical implementation behind these components is definitive and stable. AI finds it far easier to understand business-layer interfaces and configuration options than to comprehend the complex, microscopic technical layer, and thus generates business system modules with much higher accuracy based on business-layer interfaces and configuration options.

This is fundamentally an application architecture design challenge. JitAi has proposed and implemented a highly extensible Meta/Type/Instance hierarchical module structure. Meta defines domains and categories, Type encapsulates technical implementation while exposing a controlled number of configuration options, and Instance carries application-layer personalized business configurations. A business system comprises many differently classified hierarchical modules of this structure. This structure keeps complexity outside the application layer, making the application layer's complexity extremely low and easy to understand. The complexity AI must handle at the Instance layer is far lower than facing native programming languages, naturally yielding higher generation accuracy.

Low Barrier to Entry

Not every developer is a senior engineer. Can we enable junior engineers to build highly cohesive, loosely coupled, and easily extensible applications? Can we enable business experts with limited programming knowledge to independently complete project deliveries? Can we enable business experts to handle multiple client projects simultaneously?

The answer is yes. Business system development doesn't require relying entirely on professional programmers. In JitAi's visual development platform, business experts only need to understand business logic to deliver projects like assembling building blocks. Combined with AI assistant capabilities, efficiency experiences revolutionary improvements. The AI+GUI development model delivers a fully immersive Vibe Coding experience.

Visual development also produces high-quality native code. Even in extreme cases where professional programmer intervention becomes necessary, simply switching to full-code mode allows continued use of the development tools professional programmers are familiar with, ensuring seamless integration. Leveraging tools like Cursor for local optimization at this stage is perfectly viable.

Low Cost

Templated, coarse-grained module specifications bring higher AI comprehension accuracy. High accuracy means less rework and correction, naturally leading to faster development speed and lower token consumption. This creates a virtuous cycle: sound architecture brings high accuracy, and high accuracy brings low cost.

When it comes to AI-powered business system development, JitAi's approach is pragmatic and viable. The concept of FDE (Forward Deployed Engineers) is rapidly gaining traction—a technical role that combines engineering capabilities with business understanding. Their responsibility is to customize, deploy, and scale complex AI or data-driven systems for enterprise clients. JitAi is clearly well-suited for this model.