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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
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;Accept
或者Reject
;Prompt
,Ctrl+Z
, Reject
;Ctrl+K
,生成代码或者对选定的代码段进行优化; Ctrl+L
,对聊模式,对编程问题或者选定的代码段直接提问;
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。
编程语言支持:
TypeScript
,HTML
,CSS
,Python
,C
,Rust
,Go
,CSharp
,Java
,PHP
,SQL
等数十种。
// 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
# 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()
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";
}
}
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] + " ");
}
}
}
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); }
USE poetry;
SELECT poems.title, poems.content, poems_author.name
FROM poems
JOIN poems_author
ON poems.author_id = poems_author.id
LIMIT 10;
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);
}
}
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
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()); } } }
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); Boolean success = writeResponse.get().getResults()[0].isGood(); System.out.println("Write success: " + success); // Disconnect from the server client.disconnect().get(); } }
public static int test(int n) {
int cnt = 0;
while(0 != n) {
n = n & (n - 1);
++cnt;
}
return cnt;
}
public static void main(String[] args) {
System.out.println(test(15));
}
}
先将上述代码写入一个.java文件,然后 Ctrl+K
输入以下问题。
// 这段代码是一个Java程序,其中定义了一个名为test的静态方法,该方法接受一个整数参数n并返回一个整数。该方法使用while循环计算n的二进制表示中1的个数,并将其存储在cnt变量中。最后,该方法返回cnt的值。在main方法中,该程序调用test方法并将15作为参数传递给它,然后将结果打印到控制台。
public class Improve { public static int test(int n) { int cnt = 0; while(0 != n) { if((n & 1) != 0) { ++cnt; n >>= 1; } else { n >>= 1; } } return cnt; } public static void main(String[] args) { System.out.println(test(15)); } }
先将上述代码写入一个.java文件,然后选择需要优化的代码段或者全选, Ctrl+K
输入以下问题。
public class Improve {
public static int test(int n) {
int cnt = 0;
cnt = Integer.bitCount(n);
return cnt;
}
public static void main(String[] args) {
System.out.println(test(15));
}
}
从结果看, Cursor
将我们自己编写的通过位运算右移计算二进制表示中1的个数,改成了一个内置方法调用。
其实,除了上述的入门体验,还有其他很多事情可以做,eg: 编写测试用例,解决力扣编程问题,网络爬虫,制作网页,小游戏编程,you name it,这一切仅受限于我们的想象力。
以下引用池建强的一句话:
人们需要警惕的是,当年因为汽车的诞生而失业的马车夫,他们并不是转行去干司机了,而是真失业了,或者去干别的苦力活。
当上时代司机的,是另一群人。
If you have any questions or any bugs are found, please feel free to contact me.
Your comments and suggestions are welcome!
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