Cursor编程初体验，搭载GPT-4大模型，你的AI助手，自然语言编程来了

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背景

这两天体验了下最新生产力工具Cursor，基于最新的 GPT-4 大模型，目前免费，国内可访问，不限次数，跨平台，你确定不来体验一把？官方的 Slogan ：

Build Software. Fast. Write, edit, and chat about your code with GPT-4
In partnership with OpenAI

  • 在进行初步体验时，以下几点需要了解：
  1. Cursor编程的上下文是你所在的代码文件，即如果你的文件后缀名为.py，那么在具体沟通时，就无需再出现“使用Python实现……”这种多余的前提，可以用更具体的限制；
  2. 代码生成之后还会提示合并还是丢弃：Accept或者Reject；
  3. 支持回滚：如果想撤销某次的Prompt，Ctrl+Z, Reject；
  4. 偶尔会出现生产的代码不完整的问题，只需问一句：没写完吧？
  5. Ctrl+K，生成代码或者对选定的代码段进行优化；
  6. Ctrl+L，对聊模式，对编程问题或者选定的代码段直接提问；
  • 支持的语言

  • 聊天模式

  • 提示合并还是丢弃


以下通过12个简单的问题，从语言支持、语法支持、业务场景、代码解释、代码优化等方面来体验一把这个编程生产力利器。

理论上，对于复杂的任务，只要分解到GPT能够理解的那一步，都可以胜任。程序员的AI助手（它竟然可以帮我们找Bug~~），自然语言编程真的来了。至于下面生成的代码能不能用，还需要进行实际验证，但目测从语言支持、语义理解以及逻辑推理等方面来看，可用率至少80%吧。

语言支持

编程语言支持： TypeScript , html , CSS , Python , C , Rust , Go , CSharp , Java , php , SQL 等数十种。

Node.js

// Welcome to Cursor

// 1. Try generating with command K on a new line. Ask for a new react component of an error popup.
// 2. Then, select the outputted code and hit chat. Ask if there's a bug. Ask how to improve.
// 3. Try selecting some code and hitting edit. Ask the bot to add a button that updates a statefield.
// 4. To try out cursor on your own projects, go to file menu (top left) and open a folder.
const bcrypt = require('bcryptjs');

const password = 'myPassword123';
const salt = bcrypt.genSaltSync(10);
const hash = bcrypt.hashSync(password, salt);

console.log(hash);

const isMatch = bcrypt.compareSync('myPassword123', hash);
console.log(isMatch); // true

Python

  • 第二问：
    • 编写一个聚类算法，并提供实例
# Welcome to Cursor

# 1. Try generating with command K on a new line. Ask for a pytorch script of a feedforward neural network
# 2. Then, select the outputted code and hit chat. Ask if there's a bug. Ask how to improve.
# 3. Try selecting some code and hitting edit. Ask the bot to add residual layers.
# 4. To try out cursor on your own projects, go to the file menu (top left) and open a folder.
# K-Means Clustering Algorithm

import numpy as np
import matplotlib.pyplot as plt

class KMeans:
    def __init__(self, k=2, max_iter=100):
        self.k = k
        self.max_iter = max_iter
        
    def fit(self, X):
        self.centroids = 
        
        # Initialize centroids randomly
        for i in range(self.k):
            self.centroids[i] = X[np.random.choice(len(X))]
        
        # Iterate until convergence or max iterations reached
        for i in range(self.max_iter):
            self.clusters = 
            for j in range(self.k):
                self.clusters[j] = []
            
            # Assign each data point to the closest centroid
            for x in X:
                distances = [np.linalg.norm(x - self.centroids[c]) for c in self.centroids]
                closest_centroid = distances.index(min(distances))
                self.clusters[closest_centroid].append(x)
            
            # Update centroids to be the mean of their assigned data points
            for c in self.clusters:
                self.centroids[c] = np.mean(self.clusters[c], axis=0)
    
    def predict(self, X):
        predictions = []
        for x in X:
            distances = [np.linalg.norm(x - self.centroids[c]) for c in self.centroids]
            closest_centroid = distances.index(min(distances))
            predictions.append(closest_centroid)
        return predictions

# Example usage
X = np.array([[1, 2], [1.5, 1.8], [5, 8], [8, 8], [1, 0.6], [9, 11]])
kmeans = KMeans(k=2)
kmeans.fit(X)
predictions = kmeans.predict(X)
colors = ['r', 'g', 'b', 'c', 'm', 'y']
for i in range(len(X)):
    plt.scatter(X[i][0], X[i][1], color=colors[predictions[i]])
plt.show()

Java

  • 第三问（源自于一道面试题或者日常工作中遇到的一个小需求）：
    • 请简单封装一个函数，实现友好时间的功能，要求该函数可输入一个参数【时间戳】，输出一个字符串【三天前、昨天、今天、明天、刚刚（10分钟内）】。
public static String friendlyTime(long timestamp) 
    long current = System.currentTimeMillis();
    long diff = current - timestamp;
    if (diff < 10 * 60 * 1000) 
        return "刚刚";
     else if (diff < 24 * 60 * 60 * 1000) 
        return "今天";
     else if (diff < 2 * 24 * 60 * 60 * 1000) 
        return "昨天";
     else if (diff < 3 * 24 * 60 * 60 * 1000) 
        return "前天";
     else 
        return "更早之前";

Java

  • 第四问（源自于一道面试题或者日常工作中遇到的一个小需求）：
    • 请将一维数组的内容进行正向排序，并进行反向输出。[1，7，8，4，3，6，2，5，9]
public class Array 
    public static void main(String[] args) 
        int[] arr = 1, 7, 8, 4, 3, 6, 2, 5, 9;
        Arrays.sort(arr); // sort the array in ascending order
        for (int i = 0; i < arr.length; i++) 
            System.out.print(arr[i] + " ");
        
        System.out.println();
        for (int i = arr.length - 1; i >= 0; i--) 
            System.out.print(arr[i] + " ");

Java

  • 第五问（源自于一道面试题或者日常工作中遇到的一个小需求）：
    • 请将两个数组的交集与并集取出，存放于新的数组中。[q,w,e,r,t],[a,w,d,s,r,g]
public static void main(String[] args) 
    String[] arr1 = "q", "w", "e", "r", "t";
    String[] arr2 = "a", "w", "d", "s", "r", "g";

    Set<String> set1 = new HashSet<>(Arrays.asList(arr1));
    Set<String> set2 = new HashSet<>(Arrays.asList(arr2));

    // Intersection
    Set<String> intersection = new HashSet<>(set1);
    intersection.retainAll(set2);
    System.out.println("Intersection: " + intersection);

    // Union
    Set<String> union = new HashSet<>(set1);
    union.addAll(set2);
    System.out.println("Union: " + union);

SQL

  • 数据表结构

  • 第六问：
    • 连接本地数据库：localhost:3306，用户名：root，密码：root，从数据库poetry中的poems读取前10条数据并打引，通过author_id关联poems_author表。
USE poetry;
SELECT poems.title, poems.content, poems_author.name 
FROM poems 
JOIN poems_author 
ON poems.author_id = poems_author.id 
LIMIT 10;

语法支持

  • 第七问：
    • 编码实现从数组中找出最小值：[8, 1, 4, 0, -3]
    • 采用lambda方式实现
public class Min 
  public static void main(String[] args) 
    int[] arr = 8, 1, 4, 0, -3;
    int min = Arrays.stream(arr).min().getAsInt();
    System.out.println("The minimum value in the array is: " + min);

业务场景

  • 第八问（源自于实际的物联网开发场景）：
    • 使用YoLoV5编码实现识别烟雾火焰
import torch
from PIL import Image
import requests
from io import BytesIO

# Load model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)

# Define class names
class_names = ['smoke', 'fire']

# Define function to predict image
def predict_image(image_url):
    # Load image from URL
    response = requests.get(image_url)
    img = Image.open(BytesIO(response.content))
    
    # Predict image
    results = model(img)
    
    # Get class predictions
    class_predictions = results.pred[0].softmax(-1)
    
    # Get class indices
    class_indices = class_predictions.argmax(-1).tolist()
    
    # Get class names
    class_names_predicted = [class_names[i] for i in class_indices]
    
    return class_names_predicted

  • 第九问：
    • 连接本地数据库：localhost:3306，用户名：root，密码：root，从数据库poetry中的poems读取前5条数据并打印。
    • 把这5条数据导出到Excel
    • 发送到钉钉
import java.sql.*;

public class Database 
    public static void main(String[] args) 
        try 
            // Connect to local database
            String url = "jdbc:mysql://localhost:3306/poetry";
            String user = "root";
            String password = "root";
            Connection connection = DriverManager.getConnection(url, user, password);

            // Read first 5 rows from poems table
            Statement statement = connection.createStatement();
            ResultSet resultSet = statement.executeQuery("SELECT * FROM poems LIMIT 5");

            // Print results
            while (resultSet.next()) 
                System.out.println(resultSet.getString("title") + " by " + resultSet.getString("author") + " " + resultSet.getString("content") + " " + resultSet.getString("style") + " " + resultSet.getString("dynasty") + " " + resultSet.getString("notes"));
            
			// Export results to Excel
			ResultSetToExcelConverter converter = new ResultSetToExcelConverter(resultSet);
			converter.export("poems.xlsx");

			DingTalkClient client = new DefaultDingTalkClient("https://oapi.dingtalk.com/robot/send?access_token=ACCESS_TOKEN");
            OapiRobotSendRequest request = new OapiRobotSendRequest();
            request.setMsgtype("text");
            OapiRobotSendRequest.Text text = new OapiRobotSendRequest.Text();
            text.setContent("Results from database query: " + resultSet.toString());
            request.setText(text);
            client.execute(request);

            // Close connection
            resultSet.close();
            statement.close();
            connection.close();
         catch (SQLException e) 
            System.out.println("Error connecting to database: " + e.getMessage());

  • 第十问（源自于实际的物联网开发场景）：
    • 编码实现一个OPC UA客户端
import org.eclipse.milo.opcua.sdk.client.OpcUaClient;
import org.eclipse.milo.opcua.stack.core.types.builtin.NodeId;
import org.eclipse.milo.opcua.stack.core.types.builtin.QualifiedName;
import org.eclipse.milo.opcua.stack.core.types.builtin.Variant;
import org.eclipse.milo.opcua.stack.core.types.structured.ReadValueId;
import org.eclipse.milo.opcua.stack.core.types.structured.ReadValueId;
import org.eclipse.milo.opcua.stack.core.types.structured.ReadResponse;
import org.eclipse.milo.opcua.stack.core.types.structured.WriteValue;
import org.eclipse.milo.opcua.stack.core.types.structured.WriteResponse;

import java.util.concurrent.CompletableFuture;

public class OPCClient 

    public static void main(String[] args) throws Exception 
        // Create a new OPC UA client
        OpcUaClient client = OpcUaClient.create("opc.tcp://localhost:12686/example");

        // Connect to the server
        client.connect().get();

        // Read a value from the server
        NodeId nodeId = new NodeId(2, "/HelloWorld/ScalarTypes/Int32");
        ReadValueId readValueId = new ReadValueId(nodeId, QualifiedName.DEFAULT, null, null);
        CompletableFuture<ReadResponse> readResponse = client.read(readValueId);
        Variant value = readResponse.get().getResults()[0].getValue().getValue();

        System.out.println("Value: " + value);

        // Write a value to the server
        WriteValue writeValue = new WriteValue(nodeId, QualifiedName.DEFAULT, new Variant(42));
        CompletableFuture<WriteResponse> writeResponse = client.write(writeValue使用GPT-4生成QT代码


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