Cursor AI: Your New Coding Companion
Hello, coding enthusiasts! Have you ever wished coding could be as simple as writing a sentence in English? Well, buckle up because it's becoming a reality with Cursor AI, a game-changing AI-powered coding assistant.
What is Cursor AI?
Cursor AI is an innovative tool built upon Visual Studio Code that uses large language models to help you write code faster, smarter, and with fewer errors. Think of it as a super-powered coding buddy who’s always ready to assist.
Key Features and Benefits:
- Code Completion: Cursor AI suggests code completions as you type, accelerating the coding process and reducing typos.
- Function Generation: Describe what you want to build in simple terms, and Cursor AI can generate entire functions or code snippets to bring your ideas to life quickly.
- Codebase Chat: This feature lets you have a conversation with your code, ask questions, receive explanations, and even identify potential issues intuitively.
- Refactoring: Especially helpful with large, complex projects, Cursor AI can clean up your code, identify areas for improvement, and maintain a consistent coding style across your team.
- Rapid Prototyping: Cursor AI is a secret weapon for rapid prototyping, allowing you to create projects quickly and efficiently.
- Bug Detection and Fixing: Cursor AI excels at finding and fixing bugs, simplifying the debugging process and making your code more robust.
- Contextual Understanding: Cursor AI has a deeper understanding of your project’s context than other tools, going beyond simple suggestions to provide relevant and insightful recommendations.
- Integration with Existing IDEs: Built upon Visual Studio Code, Cursor AI offers a seamless integration with this popular IDE.
Why is Cursor AI Special?
Cursor AI offers a deeper understanding of your project’s context, going beyond simple suggestions to provide truly relevant and insightful recommendations. Its codebase chat feature lets you have a conversation with your code, asking questions, receiving explanations, and even identifying potential issues intuitively. It’s like having a seasoned developer looking over your shoulder and offering expert advice in real time.
Comparison with Other Tools:
While GitHub Copilot is an excellent tool integrated within an existing IDE, Cursor AI takes things further with its standalone approach, deeper contextual understanding, and interactive features. Replit, on the other hand, focuses on collaborative coding in a browser-based environment. Cursor AI truly shines when it comes to boosting individual developer productivity.
You can watch the video tutorial here:
How to Set Up Cursor AI:
First, download Cursor AI from their website. Once downloaded, open the installation file and follow the on-screen instructions. Cursor AI should launch automatically after installation.
You'll be greeted with a brief intro and some settings to configure. Since we’re using it with VS Code, you can simply click “Continue.” Choose your data-sharing preferences and sign up for a free two-week trial. Once your account is set up, you can log in.
Next, create a new project folder within VS Code. Then, create a Python environment. You’ll need to have Python 3.11 or Node.js 18 installed on your device. Open the Command Palette, choose “Python: Create Environment,” then choose “venv,” which creates a virtual environment. Select the version you have installed (Python 3.11). You can verify the environment by looking at your terminal.
Now you can open the Cursor AI chat menu using Ctrl + L. Here you can interact with the LLM. First, create a new folder for your project. When you create a Cursor AI account, you get a two-week trial of their pro plan which includes up to 500 requests with premium models including Sonnet 3.5 and GPT-40. After your trial, you can switch to Cursor small, GPT-40-mini, or upgrade to pro.
Click on the settings gear in the top right corner to navigate to “Model Names." You'll see a list of pre-added models. If the model you want isn’t listed, head over to the API documentation on the provider's website, copy the exact name, and paste it into the “Add Model” field. Add your API keys at the bottom of the page and click "Verify.”
Now you can choose which LLM to use for your session by clicking the down arrow on the bottom left of the message box. You can also utilize files as context by opening those files in VS Code, clicking the plus icon to the left of the message box, and making sure they have a green indicator beside them.
Building a Gemini Chatbot Application:
For this project, the model will prompt you to build a Node.js chatbot that can handle text prompts, allows users to upload PDFs and text documents, and leverages the Gemini API.
Creating a Package.json File:
Create a new file with the file name “package.json.” Then, click “apply” to have the model generate and insert code into the file. Then, click accept and save the file. Create the rest of the files by creating a folder in the left pane and generating the code for the remaining files as prompted by the model.
Testing the Chatbot:
Type 127.0.0.1:3000 into the URL bar. You should now see the Gemini chatbot with the "Attach Files" button in the bottom left corner. Test the app by sending messages and files to the chatbot.
Troubleshooting:
If the attach button isn't working, the model can suggest solutions. Keep iterating, updating the suggested fixes, and testing until the functionality works perfectly.
That’s all for this tutorial. I hope you learned something new. Until next time, happy coding!
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