ChatGPT帮你一起编程

Table of Contents

1. Introduction
2. Creating a Movable Block
3. Adding Rotational Behavior
4. Utilizing ChatGPT for Code Writing
5. Enhancing Workflow with LLMs
6. Verifying ChatGPT Results
7. Implementing a Rotatable Rectangle in React
8. Adjusting Rotation Speed Based on Cursor Distance
9. Slowing Down the Rotation
10. Rotating the Block Towards the Mouse Point
11. Controlling Rotation with Mouse Press
12. Maintaining Rotation Offset
13. Refactoring the Component
14. Conclusion

Introduction

In this episode, we focus on enhancing the capabilities of a block component by adding rotational behavior. Instead of manually coding the rotation functionality, we leverage ChatGPT as a coding assistant to generate the necessary code. We explore the benefits of using language models to enhance workflow and discuss the importance of verifying the generated code. The episode concludes by refactoring the component for improved maintainability and readability.

Creating a Movable Block

In the previous episode, we created a movable block that can be dragged around within a graphics editor. This intuitive behavior emulates the experience found in popular graphics software. By implementing this draggable block, we set the foundation for further enhancements.

Adding Rotational Behavior

To make our block component even more versatile, we want to add rotational behavior. This feature allows users to rotate the block to achieve the desired orientation. Instead of manually writing the code, we opt to utilize ChatGPT as a coding assistant. By providing Prompts, we can generate the necessary code for the rotational behavior.

Utilizing ChatGPT for Code Writing

In this episode, we take AdVantage of ChatGPT's code writing capabilities. We act as an interpreter, running the code generated by ChatGPT. This approach allows us to focus on the task at HAND, saving time and effort by leveraging the insights provided by the language model. While ChatGPT is not infallible, it serves as a valuable tool for enhancing our workflow.

Enhancing Workflow with LLMs

Language models like ChatGPT can significantly improve productivity by assisting with code writing tasks. Rather than getting stuck on roadblocks and debugging issues, we can rely on the model to generate code snippets for us. While not perfect, this workflow allows us to concentrate on the task while learning from the suggestions provided by ChatGPT.

Verifying ChatGPT Results

While ChatGPT provides valuable assistance, it is crucial to verify the generated code. Testing the code within our development environment ensures that the desired behavior is achieved. In case of discrepancies or unexpected results, it is essential to double-check and validate the code manually. ChatGPT serves as a powerful tool but should be used alongside critical thinking and validation.

Implementing a Rotatable Rectangle in React

Firstly, we start by asking ChatGPT to write a rotatable rectangle component in React. By leveraging the useEffect hook and appropriate CSS, we set up the initial code structure. This code is then dumped into CodeSandbox for testing and validation.

Adjusting Rotation Speed Based on Cursor Distance

To Create a more intuitive user experience, we want to adjust the rotation speed of the block depending on the distance of the cursor from the block itself. A common behavior in graphic editors is to rotate objects slower when they are close and faster when they are further away. We Prompt ChatGPT to modify the component to incorporate this behavior.

Slowing Down the Rotation

After incorporating the cursor distance-based rotation speed adjustment, we test the code. However, we Notice that the rotation is still too fast. To fine-tune the rotation speed, we ask ChatGPT to introduce a scaling factor. By slowing down the rotation, we aim to achieve smoother and more precise control over the block's orientation.

Rotating the Block Towards the Mouse Point

To make the user interface more predictable and intuitive, we decide to rotate the block based on the direction of the mouse point. If the cursor moves downward, the block should rotate left. When the cursor moves upward, the block should rotate right. Similarly, left and right cursor movements should invoke corresponding rotations. We inform ChatGPT about this desired behavior and ask it to rewrite the component accordingly.

Controlling Rotation with Mouse Press

To further refine the interaction, we want to limit the rotation of the block when the mouse is pressed. This ensures that rotation is only active when the user actively interacts with the block. By adding event listeners for mouse down and up actions, we can achieve this behavior. We prompt ChatGPT to add the necessary code for this enhancement.

Maintaining Rotation Offset

When we implemented the block rotation, we noticed that there is a jump in rotation when we start rotating the block. To mitigate this jump, we ask ChatGPT to introduce an offset between the Current angle and the new angle of rotation. This offset will be maintained during rotation, resulting in smoother and more natural movement of the block. By updating the component according to ChatGPT's suggestions, we aim to improve the overall user experience.

Refactoring the Component

Currently, the block component's code is contained within a single useEffect. While this is functional, it may not be the most maintainable or readable approach. To improve the code structure, we ask ChatGPT to refactor the component. By breaking it down into smaller functions or suggesting other optimizations, ChatGPT's insights can help make the code more manageable and understandable.

Conclusion

In this episode, we explored the benefits of leveraging ChatGPT as a coding assistant to enhance our workflow. By utilizing natural language prompts, we were able to generate code for rotational behavior in our block component. Although ChatGPT's suggestions were not always perfect, they served as valuable starting points and saved us significant time and effort. We also emphasized the importance of verifying the generated code and manually testing it to ensure the desired behavior. Furthermore, we discussed the possibility of refactoring the component for better maintainability and readability. Overall, using language models like ChatGPT can greatly streamline the software development process and complement human expertise.

Highlights

• Leveraging ChatGPT as a coding assistant
• Enhancing the block component with rotational behavior
• Adjusting rotation speed based on cursor distance
• Refining user interaction with mouse press control
• Maintaining rotation offset for smoother movement

FAQs

Q: Can I rely solely on ChatGPT to write high-quality code? A: While ChatGPT can be a helpful coding assistant, it is essential to verify and test the generated code manually. Human input and expertise are still crucial to ensure the code's correctness and effectiveness.

Q: How can I validate the code generated by ChatGPT? A: Running the code within a development environment and conducting thorough testing can help validate the generated code. It is important to observe the behavior and inspect for any discrepancies or unexpected results.

Q: Can ChatGPT optimize code during the generation process? A: ChatGPT can provide optimizations and suggest improvements for code. However, it is recommended to review and revise the provided code to ensure it aligns with best practices and meets specific project requirements.