Code Revolution: Intro to Cursor.ai

The Cursor.ai AI-powered development tool represents a transformative shift in modern programming workflows. As developers increasingly adopt AI-assisted coding solutions, Cursor.ai stands out by enabling engineers to generate, debug, and optimize code through intuitive natural language commands.

Cursor.ai – AI Tool: Technical Capabilities and Features

Cursor.ai extends the functionality of Visual Studio Code by embedding sophisticated language models (including GPT and Claude) within the IDE. Key features include:

• AI-driven code generation
• Context-aware auto-completion and intelligent suggestions
• Agent mode, enabling autonomous code writing and execution
• Integrated chat interface for real-time codebase interrogation

Unlike conventional AI coding assistants that require manual snippet integration, Cursor.ai operates natively within the development environment, significantly streamlining the coding process.

Testing the Cursor.ai Development Tool: Live Implementation

To assess Cursor.ai’s efficacy, we undertook a live development session, constructing a mood-based quote generator application within a constrained timeframe. The stack consisted of:

• Backend: Express.js
• Frontend: JavaScript, HTML, CSS with Tailwind
• Database: SQLite

Implementation Workflow

1. Project Initialization
   • Directed the AI to instead implement a mood-based quote application with the specified technologies. We used the following prompt: “I want to make an app that is mood based. The user comes to the home page, chooses a mood, and the app will show a quote based on the chosen mood. If the user is not satisfied with the quote, it can choose another quote.
     Make this simple, use express for the backend, sqlite for storing the quotes and use javascript, html, css with tailwind for the frontend”
   • The agent autonomously generated the project scaffolding, installed dependencies, and configured the foundational files.
2. Feature Augmentation
   • Requested implementation of an administrative interface for quote management with CRUD operations.
   • Added user authentication (login/registration).
   • Provided a picture design reference for UI refinement, which Cursor.ai adapted programmatically.
3. Debugging and Optimization
   • Encountered database connectivity issues, which were partially resolved through AI-assisted troubleshooting and manually.
   • Experimented with dynamic feature additions, including test data generation and UI behaviour modifications.

While the AI demonstrated impressive automation capabilities, certain scenarios required manual intervention to resolve persistent errors or suboptimal implementations.

Strengths and Limitations

Key Advantages:

1. Development Acceleration – Dramatically reduces time spent on boilerplate code and configuration
2. Educational Value – Provides explanatory insights for learning new frameworks
3. Context Awareness – Maintains project-wide understanding for relevant suggestions
4. Automation – Capable of autonomous file modifications and command execution

Key Challenges:

1. Error Handling – Struggles with recursive errors and complex debugging
2. Privacy Concerns – Relies on external server processing despite privacy settings
3. Cost Factor – Requires $20/month subscription for full functionality
4. Quality Control – Generated code often needs refinement for production use

The tool presents a powerful productivity boost but requires careful consideration of its limitations, particularly regarding error resolution, data security, cost, and output quality. These factors should be weighed against project requirements and team capabilities when evaluating adoption.

Conclusions and Recommendations

1. Augmentation Rather Than Replacement
   • Cursor.ai serves as a powerful adjunct to developer workflows but does not eliminate the need for human oversight and expertise.
2. Competitive Advantage
   • Developers leveraging AI-assisted tools can achieve substantially higher productivity compared to traditional methods.
3. Optimal Use Cases
   • Particularly effective for prototyping and small-scale projects, though larger codebases may necessitate supplementary manual refinement.

Q&A

Q: As AI accelerates code generation, could it also amplify risks—like security flaws and technical debt—if developers rely on it without enforcing standards? Are we trading speed for robustness?
A: Developers must prioritize effective prompting techniques, incorporate non-functional requirements, and adhere to established best practices.
While current standards are human-defined, AI might eventually develop its own paradigms.
Key takeaway: Principles endure even as practices evolve. We must ensure AI aligns with human ethical and technical principles, with rigorous oversight of its outputs.

Q: As senior developers increasingly use AI tools, are we unintentionally limiting our juniors to superficial ‘glue code’ work? How can we ensure they still get the deep learning experiences needed to grow?
A: This echoes past trends, like junior developers relying solely on frameworks (e.g., believing “MVC” was a Microsoft framework or equating Angular/React expertise with JavaScript mastery). Many eventually realized the need to master fundamentals.
Our role: Proactively mentor juniors and stakeholders about knowledge gaps and the importance of core skills.

Q: Could AI marginalize junior developers if clients prioritize AI-driven efficiency over mentorship?
A: Juniors remain essential—they’re future seniors. Two pathways:
Accelerated growth: Integrate AI into junior training to shorten their path to productivity.
New roles: Hire juniors with AI-focused academic backgrounds, redefining entry-level positions to align with emerging needs.

Future Exploration

Cursor.ai represents a significant advancement in AI-assisted development tools. We recommend that engineering teams:

• Evaluate the tool through its trial offering to assess compatibility with existing workflows.
• Compare performance against alternatives such as GitHub Copilot or Tabnine.
• Contribute to community knowledge sharing by documenting use cases and best practices.

Further research into its application across diverse development scenarios would yield valuable insights into its long-term viability in professional environments.

Written by Dobromir Aleksikj, Vera Grgurcin Najdoska, Stefan Aleksikj and Stojan Peshov.
Edited by Iva Gjamovska.