A Quick Look Cursor

In this article, we explore Cursor—a standalone application built as a fork of Visual Studio Code. Unlike extensions for Visual Studio Code or JetBrains products, Cursor offers an immersive environment where both chat and code reside within a single window.

When you launch Cursor and press Ctrl+I, you'll see options such as Add Files, Edit Refactor, and Add Code. The tool also allows you to switch between models like Cloud 3.5 Sonnet, GPT-4, 40 Mini, 01 Mini, 01 Preview, and Cursor Small. For illustration, we scaffold a typical Python application, with Cursor automatically generating the necessary files—much like what GitHub Copilot might do.

After accepting the generated files, you can inspect the project structure. The folder includes a source directory (src) with your project files, such as main.py.

The Generated Python Application

The generated main.py file contains a standard Python entry point, featuring a def main function along with an if __name__ == '__main__': check. It also sets up logging automatically:

import logging

# Main application module.
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(message)s'
)
logger = logging.getLogger(__name__)

def main():
    """Main application entry point."""
    logger.info('Application starting...')
    # Your application logic here
    logger.info('Application finished.')

if __name__ == '__main__':
    main()

A configuration file, config.py, is also generated. It uses a dataclass to manage configuration variables such as the API key and debug mode:

import os
from dataclasses import dataclass
from typing import Optional

@dataclass
class Config:
    """Application configuration."""
    app_name: str = "my_project"
    debug: bool = False
    api_key: Optional[str] = os.getenv("API_KEY")

    @classmethod
    def load(cls) -> "Config":
        """Load configuration from environment."""
        return cls(
            debug=os.getenv("DEBUG", "false").lower() == "true",
            api_key=os.getenv("API_KEY")
        )

config = Config.load()

A simple test file is generated to serve as a placeholder for your test cases:

import pytest
from my_project.main import main

def test_main():
    """Test main function execution."""
    # Add your test cases here
    assert True

Additionally, the project includes a .gitignore file for ignoring file patterns (like virtual environments), a pyproject.toml with metadata and dependency details, and a basic requirements.txt listing both core and development dependencies.

The pyproject.toml file looks like this:

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[project]
name = "my_project"
authors = [
    { name = "Your Name", email = "[email protected]" }
]
description = "A short description of your project"
readme = "README.md"
requires-python = ">=3.8"
dependencies = []

[project.optional-dependencies]
dev = [
    "pytest>=7.0",
    "pytest-cov>=4.0",
    "black>=23.0",
    "isort>=5.0",
    "flake8>=6.0",
]

[tool.pytest.ini_options]
testpaths = ["tests"]
python_files = ["test_*.py"]

Setting Up and Running the Application

To set up your Python environment, open the terminal and create a virtual environment:

python -m venv venv
# If "python" is not recognized, try:
python3 -m venv venv

source venv/bin/activate

Once the virtual environment is activated, install the dependencies:

pip install -r requirements.txt

You can then run your application:

python src/main.py

If you encounter an error like "can't open file 'src/main.py'", verify your directory structure. For instance, if your main file resides at src/my_project/main.py, run:

python src/my_project/main.py

Upon execution, you should see log output indicating the application has started and finished, similar to:

2022-11-19 14:59:16,537 __main__ INFO - Application starting...
Hello, World!
2022-11-19 14:59:16,537 __main__ INFO - Application finished.

Modifying the Application

Cursor supports interactive code generation. For example, you can modify your code so that it reads from a text file (demo.txt) and prints its contents line by line. An updated version of main.py might look like this:

import logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

def main():
    """Main application entry point."""
    logger.info('Application starting...')
    
    print("Hello, World!")
    try:
        with open("demo.txt") as file:
            for line in file:
                print(line.rstrip())
    except FileNotFoundError:
        logger.error("demo.txt file not found")
    except Exception as e:
        logger.error(f"Error reading demo.txt: {e}")
    logger.info('Application finished.')

if __name__ == "__main__":
    main()

Later, you might further modify the code to process the file by removing all spaces and writing the cleaned output to demo_no_spaces.txt:

import logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

def main():
    """Main application entry point."""
    logger.info("Application starting...")
    print("Hello, World!")
    # Your application logic here

    try:
        with open('demo.txt') as file:
            for line in file:
                with open('demo_no_spaces.txt', 'w') as outfile:
                    outfile.write(line.rstrip().replace(" ", ""))
    except FileNotFoundError:
        logger.error('demo.txt file not found')
    except IOError as e:
        logger.error('Error reading demo.txt: {0}'.format(e))
    logger.info("Application finished.")

if __name__ == "__main__":
    main()

After running the application, confirm that demo.txt has been processed and that demo_no_spaces.txt is created as expected.

Refactoring into Functions and Writing Tests

For better code organization, it's a good practice to refactor logic into separate functions. In this case, the file processing logic is moved into its own function:

def process_file():
    """Process demo.txt file and write output without spaces."""
    try:
        with open("demo.txt", "r") as infile, open("demo_no_spaces.txt", "w") as outfile:
            for line in infile:
                outfile.write(line.rstrip().replace(" ", "") + "\n")
    except FileNotFoundError:
        logger.error('demo.txt file not found')
    except IOError as e:
        logger.error(f"Error reading demo.txt: {e}")
    logger.info("Application finished")

def main():
    """Main application entry point."""
    logger.info("Application starting...")
    print("Hello, World!")
    # Your application logic here

process_file()

if __name__ == "__main__":
    main()

Next, it's important to write tests for your functions. In the file tests/test_main.py, you might add tests as follows:

import os
import pytest
from my_project.main import main, process_file

def test_main():
    """Test main function execution."""
    # Add your test cases here.
    assert True

def test_process_file(tmp_path):
    """Test process_file function with sample input."""
    # Prepare test input.
    test_input = "sample text\nwith multiple\nlines"
    demo_file = tmp_path / "demo.txt"
    demo_file.write_text(test_input)
    
    # Change the working directory to the temporary path.
    original_cwd = os.getcwd()
    os.chdir(tmp_path)
    
    # Call process_file which will read demo.txt and write demo_no_spaces.txt.
    process_file()
    
    # Read the output from demo_no_spaces.txt.
    output_file = tmp_path / "demo_no_spaces.txt"
    result = output_file.read_text().splitlines()
    
    # Expected output: each line with spaces removed.
    expected_output = [line.replace(" ", "") for line in test_input.splitlines()]
    
    # Restore the original working directory.
    os.chdir(original_cwd)
    
    # Assert the results.
    assert result == expected_output
    assert len(result) == 3
    assert isinstance(result, list)

touch my_project/__init__.py
touch tests/__init__.py

When running tests with pytest, common troubleshooting steps include verifying module import paths and installing your package in development mode with:

pip install -e .

Final Thoughts

Cursor significantly speeds up code generation and prototyping. However, as demonstrated through iterative testing and debugging, a solid foundation in Python and best development practices remains essential. In our next article, we will explore building a larger application combining multiple AI-assisted development tools like ChatGPT, Tabnine, BlackboxAI, GitHub Copilot, and Cursor.

Stay tuned for our next lesson as we delve deeper into advanced application development methodologies.