DEV Community: William

What is the last offline-supported version of Postman?

William — Fri, 27 Sep 2024 13:30:57 +0000

Postman used to be great for API work, but starting with version 10.17, most features need an account. Even basic stuff like environment variables are limited without logging in. Version 10.17 is the last to support Scratch Pad mode without an account.

If you still want to use Postman, you can download version 10.17 and disable automatic updates. Here’s how to do it:

Steps to Download and Install Version 10.17

Download version 10.17.
Uninstall your current Postman. Additionally, you may need to delete Postman data located at %appdata%\..\Local\Postman and %appdata%\..\Roaming\Postman. (Be sure to make a copy or rename the Postman folders if you're concerned about losing data.)
Disconnect your internet.
Install version 10.17.
Rename update.exe to prevent automatic updates.

Please note, this is an unofficial workaround.

A Better Solution: Using EchoAPI Instead of Postman

EchoAPI provides a 100% Postman-compatible solution that does not require an account to use. It has several compelling features:

💡 EchoAPI is an ultra-lightweight collaboration tool for API development that supports Scratch Pad. It's a perfect alternative to Postman, offering features like API design, debugging, automated testing, and load testing. Plus, it comes with plugins for IntelliJ IDEA, VS Code, and a Chrome request capture extension, all without the need to log in.

No account needed
Built-in Scratch Pad
Affordable price
Lightweight native client

Comparison Table

Feature	Postman (latest)	EchoAPI
Offline Support	Yes	Yes
Account Required	Yes	No
Environment Variables	Yes	Yes
Scratch Pad	No	Yes
Native Client	Yes	Yes
IntelliJ IDEA Plugin	No	Yes
VS Code Plugin	Yes	Yes
Chrome Interceptor Extention	Yes	Yes

Here is a screenshot of EchoAPI's user interface:

EchoAPI Extensions and Tools

Besides the desktop application, EchoAPI offers several extensions to fit various development environments:

EchoAPI for VS Code
- Ultra-lightweight API debugging tool for VS Code.

EchoAPI for IntelliJ IDEA
- One-click API documentation, one-click debugging.

EchoAPI-Interceptor
- One-click API capture, one-click debugging.

EchoAPI CLI
- Command Line Interface tool for EchoAPI and test case execution.

These tools make it easy for developers to choose the best option for their workflow.

🎁Learn Python in 10 Days: Day8

William — Thu, 26 Sep 2024 11:26:39 +0000

Today, we're continuing our 10-day journey to learn Python, kicking off Day 8's lesson. If you haven't checked out Day 1 yet, you can find it here: 🎁Learn Python in 10 Days: Day 1

Day 8: Exceptions

1. Understanding Exceptions

When the Python interpreter detects an error, it can't execute the code and instead displays an error message. This is called an "exception," or more commonly, a "bug."

Historically, a literal bug (insect) caused a malfunction in a computer, so we now refer to software errors as "bugs."

Example: Trying to open a non-existent file.

f = open("www.txt", "r", encoding="utf-8")
# FileNotFoundError: [Errno 2] No such file or directory: 'www.txt'

2. Handling Exceptions

Handling exceptions means preparing for and dealing with potential errors to avoid program crashes.

Reasons to catch exceptions:

Improve Stability: Prevent your program from crashing due to errors.
Easier Debugging: Get detailed error info to quickly locate issues.
Better Maintenance: Separate error-handling code from business logic.

When encountering a bug, you can either:

Stop the entire program,
Handle the bug and continue running.

In practice, it's better to handle errors so that your program can continue running smoothly.

Basic syntax for catching exceptions:

try:
    # Code that may raise an error
except:
    # Code to run if an error occurs

Example: Try opening a file in read mode, if it doesn't exist, open it in write mode.

try:
    f = open("www.txt", "r", encoding="utf-8")
except:
    f = open("www.txt", "w", encoding="utf-8")

Catching specific exceptions:

try:
    print(name)
except NameError as e:
    print("NameError: 'name' is not defined")

Important Notes:

If the error type doesn't match the one you're catching, the exception won't be caught.
Generally, put only one line of code in the try block.

Catching multiple exceptions:

try:
    print(1 / 0)
except (NameError, ZeroDivisionError):
    print("Error occurred")

Catching all exceptions:

try:
    # Code that may raise an error
except Exception as e:
    # Code to run if any error occurs

Using else: Code that runs if no exceptions occur.

try:
    print(1)
except Exception as e:
    print(e)
else:
    print("No exceptions occurred")

Using finally: Code that runs no matter what.

try:
    f = open("test.txt", "r", encoding="utf-8")
except Exception as e:
    print(e)
    f = open("test.txt", "w", encoding="utf-8")
else:
    print("No exceptions occurred")
finally:
    f.close()

3. Exception Propagation

def func1():
    print("Start of func1")
    print(1 / 0)
    print("End of func1")

def func2():
    print("Start of func2")
    func1()
    print("End of func2")

def main():
    try:
        func2()
    except Exception as e:
        print(e)

main()
# Output:
# Start of func2
# Start of func1
# division by zero

If an exception isn't caught within a function, it propagates to the calling function.

4. Python Modules

A Python module is a file ending in .py. It can define functions, classes, and variables, and can also include executable code.

Modules help bundle related functions and data. For instance, use the time module for time-related tasks.

Importing modules:

import time

print("Start")
time.sleep(1)
print("End")

Using from to import specific attributes:

from time import sleep

print("Start")
sleep(1)
print("End")

Using * to import all attributes:

from time import *
sleep(1)

Using as to create aliases:

import time as t
t.sleep(2)
print("haha")

from time import sleep as sl
sl(2)
print("hehe")

4.2 Creating Custom Modules

You can create your own modules for specific tasks by writing Python files.

Example: Create my_module.py with a function:

# my_module.py
def my_test(a, b):
    print(a + b)

if __name__ == '__main__':
    my_test(1, 2)

# another_module.py
from my_module import my_test
my_test(1, 2)

5. Python Packages

A package is a collection of modules. Physically, it's a folder containing a special __init__.py file.

Creating a package:

Create a folder named my_package.
Add modules (my_module1.py, my_module2.py).
Add an __init__.py file.

Using packages:

import my_package.my_module1
my_package.my_module1.my_function()

Or using from import:

from my_package import my_module1
my_module1.my_function()

5.2 Installing Third-Party Packages

Use pip to install third-party packages:

pip install package_name

To speed up the process by using a mirror site:

pip install -i https://packagecloud.io/pypa/python/simple package_name

Example: Creating a custom package my_utils.

Create a folder my_utils.
Add str_util.py and file_util.py.

# str_util.py
def str_reverse(s):
    return s[::-1]

def substr(s, x, y):
    return s[s.index(x):s.index(y)]

# file_util.py
def print_file_info(file_name):
    try:
        with open(file_name, 'r') as f:
            content = f.read()
            print(content)
    except Exception as e:
        print(e)

def append_file_info(file_name, data):
    with open(file_name, 'a') as f:
        f.write(data + "\n")

Then use these modules in your scripts:

from my_utils import str_util, file_util

print(str_util.str_reverse("hello"))
file_util.append_file_info("test.txt", "Some data")

By following these guidelines, you can effectively handle exceptions, organize your code with modules and packages, and utilize third-party libraries to enhance your Python projects.

EchoAPI for VS Code: A Lightweight Alternative to Postman & Thunder Client

William — Wed, 25 Sep 2024 08:01:21 +0000

If you're deep into APIs and data crawlers like I am, you know the right tools are crucial. I used Postman for years, but its slow load times and cluttered UI drove me nuts as my projects grew. I tried Thunder Client, but many features were behind a paywall. Frustrated, I created EchoAPI—a lean, free API client. Here’s why I built it and how it stacks up against Thunder Client.

The Path to EchoAPI

As full-stack developers, I deal with a never-ending stream of APIs every day. Postman used to be my go-to for this, but it started to feel like a chore. Each update made the interface more confusing and slow, turning simple requests into multi-step processes.

I looked for alternatives but found them either too complicated or requiring too many clicks for what should be simple tasks. Then, I checked out Visual Studio Code’s extension gallery and found the Thunder Client extension. Though it was popular, but I find that many useful features were locked behind a paywall, which meant it wasn’t quite what I was looking for.

This is when I decided to build something that fit my needs perfectly, leveraging my familiarity with Visual Studio Code.

The Birth of EchoAPI

Given that I use Visual Studio Code every day, creating an extension instead of standalone software just made sense. Millions of devs use VS Code, so the user base is already there. With this in mind, I aimed for:

Lightweight and Fast: No extra bloat.
User-Friendly Interface: Easy-to-use UI that minimizes clicks and shows API responses clearly.
Robust Response Handling: Able to handle large API responses and view them in full screen.
Integration with VS Code Themes: Blends seamlessly with the coding environment.
Collection Management and Environment Variables: Essential features without the scripting hassle.
Scriptless Testing: GUI-based testing for standard cases to save time and effort.
100% compatible with Postman script syntax:I've got tons of data in Postman, so compatibility is a must for me.

After some thorough research and development, EchoAPI was born, knocking off all these targets.

Thunder Client vs. EchoAPI

Thunder Client is another VS Code extension with similar goals, offering a clean UI and scriptless testing. But EchoAPI takes it a step further:

Completely Free: EchoAPI offers all its features without any hidden costs or paywalls.
Enhanced Variable Management: EchoAPI supports visual variable extraction, adding more functionality to dynamic API testing.
100% Postman Syntax Compatibility: Transitioning from Postman is seamless with EchoAPI, no need to learn new syntax.

This Postman test script runs perfectly in EchoAPI too.

// Status code test
pm.test("Status code is 200", function () {
  pm.response.to.have.status(200);
});
// Property check
pm.test("Property message contains EchoAPI", function () {
   var jsonData = pm.response.json();
   pm.expect(jsonData.message).to.contains("EchoAPI");
});
// Response content-type check
pm.test("Content-Type contains application/json", function () {
   pm.response.to.be.header("Content-Type", "application/json");
});

Scriptless Testing Simplified

One of the biggest pain points with many API clients, including Postman, is the need to write scripts for basic tests. Thunder Client addresses this with GUI-based test setups, and EchoAPI simplifies it even more. You can easily create tests for status codes, property validations, and content-type checks using simple drop-down menus.

Here's a sample of what GUI-based testing looks like in EchoAPI:

Status Code Check: Configure this easily to ensure the response status is as expected.

Compare this to the script-based approach in Postman:

// Status code test
pm.test("Status code is 200", function () {
  pm.response.to.have.status(200);
});

While scripting is still useful for complex cases, EchoAPI’s GUI-based testing handles standard cases quickly, freeing you up to focus on more important tasks.

Getting Started with EchoAPI

Getting started with EchoAPI is super easy. Just download the extension from the Visual Studio Code Marketplace, and you’re good to go. We also have detailed documentation and community support available on our website.

Stay Connected

We'd love to hear your thoughts and experiences with EchoAPI. Connect with us:

Twitter: @EchoApiTeam

EchoAPI is more than just an alternative to Postman; it’s a streamlined, efficient upgrade designed to enhance your API testing workflow. Give it a try and see the difference for yourself!

EchoAPI:lightweight alternative to Postman

William — Tue, 24 Sep 2024 12:00:02 +0000

EchoAPI is an all-in-one API development collaboration platform that offers essential features like API debugging, design, automated testing, stress testing, and documentation generation.

Existing Pain Points

Developers often spend significant time on API tasks like debugging, documentation, automated testing, and stress testing, requiring multiple tools.

Postman:

Mandatory login with no Scratch Pad support.
High cost.
Bulky Electron client that starts slowly and operates with lag.
Limited capabilities for API design and automated testing.

Insomnia / Thunder Client / RapidAPI:

Incompatibility with Postman’s script syntax.
Clunky UI interaction.
Single-product format requiring a client download.
Limited functionality without logging in.

Product Advantages

No login required and Scratch Pad supported.
Ultra-lightweight design.
Fully compatible with Postman’s script syntax.
Affordable price.
Free lightweight plugins for various use cases.

Product Matrix

1. EchoAPI SaaS

Next-gen API development collaboration platform.

Advantages of EchoAPI

No login required and Scratch Pad supported.
Ultra-lightweight design.
Fully compatible with Postman’s script syntax.
Free lightweight plugins for various use cases.

Features:

API Design
API Debugging
API Automated Testing
API Documentation Generation
API Stress Testing

2. EchoAPI for VSCode

Ultra-lightweight API debugging tool for VS Code.

Advantages of EchoAPI for VSCode：

Free
Ultra-lightweight (No app download needed)
No login required and Scratch Pad supported
Supports collaboration and manual data bidirectional synchronization

Features:

API Design
API Debugging
API Automated Testing
API Documentation Generation

3. EchoAPI for IntelliJ IDEA

One-click API documentation, one-click debugging.

Advantages of EchoAPI for IntelliJ IDEA：

Free
Ultra-lightweight (No app download needed)
Unlike Swagger, no code intrusion
No login required and Scratch Pad supported
Supports collaboration and manual data bidirectional synchronization

Features:

API Design
API Debugging
API Documentation Generation

4. EchoAPI Interceptor

One-click API capture, one-click debugging.

Advantages of EchoAPI Interceptor

Free
Ultra-lightweight (No app download needed)
No login required and Scratch Pad supported
Supports collaboration and manual data bidirectional synchronization.

Features:

API Request Capturing
Fast Debugging

5. EchoAPI AI (Coming Soon)

EchoAPI AI is set to launch soon, bringing cutting-edge artificial intelligence to streamline and automate various aspects of the API development and testing process.

EchoAPI Product matrix API data synchronization mechanism ensures that no matter what work scenario or tools you use, API data can be synchronized seamlessly.

Conclusion

EchoAPI revolutionizes API development with robust, lightweight, and integrated tools designed for seamless collaboration. Offering interface debugging, mock services, and automated testing on a shared data source, it enhances efficiency for backend, frontend developers, and testers. The EchoAPI product matrix ensures continuous management of API documentation and digital assets, with seamless data synchronization across all tools and work scenarios.

🎁Learn Python in 10 Days: Day7

William — Tue, 24 Sep 2024 10:56:20 +0000

Today, we're continuing our 10-day journey to learn Python, kicking off Day 7's lesson. If you haven't checked out Day 1 yet, you can find it here: 🎁Learn Python in 10 Days: Day 1

Day 7: File Operations

File Encoding

Since computers can only recognize 0s and 1s, we use encoding techniques (like a codebook) to translate content into 0s and 1s to store it, allowing computers to recognize text files.

Encoding Techniques: These are the rules for translating content into binary and vice versa.

There are several encodings available in computers: UTF-8, GBK, Big5, and others. Different encodings translate the content into binary in different ways.

UTF-8 is the most commonly used encoding format worldwide. Unless there's a specific need, you should always use UTF-8 for file encoding.

Reading Files

File Concept: Data in memory will disappear once the computer is turned off. To save data long-term, we use devices like hard drives, CDs, and USB drives. The concept of "files" was introduced to make data management and retrieval easier.

An article, a video clip, an executable program, they can all be stored as a file with a filename. The operating system manages disk data in units of files. Generally, files can be categorized into text files, video files, audio files, image files, executable files, etc.

Basic File Operations: Opening a file, reading/writing a file, and closing a file (you may just open and close it without any additional operations).

open() Function:
In Python, the open function is used to open an existing file or create a new one. Here's the syntax:

open(name, mode, encoding)

name: The string name of the target file to be opened (it can include the file's path).
mode: The mode in which the file should be opened: read-only, write, append, etc.
encoding: The encoding format (default is UTF-8).

Example:

f = open("python.txt", "r", encoding="UTF-8")
# The encoding parameter is not the third one, so use a keyword argument to specify it directly.
print(type(f))  # <class '_io.TextIOWrapper'>

Here, f is the file object from the open function, which is a special type in Python, having attributes and methods that can be used.

Modes and Descriptions:

r: Opens the file for reading only. The file pointer is placed at the beginning of the file. This is the default mode.
w: Opens a file for writing only. If the file exists, open it and start editing from the beginning, erasing the original content. If the file does not exist, create a new one.
a: Opens a file for appending. If the file exists, new content will be added to the end of the existing content. If the file does not exist, create a new one to write into.

read() Method:

file_object.read(num)

num specifies the length of data to read from the file (in characters). If num is not passed, it reads all the data in the file.

readlines() Method:

readlines reads the entire content of the file at once, returning a list where each line is an element.

Example:

f = open("python.txt", "r", encoding="UTF-8")
# content = f.read()
# print(content)  # Content in the text: Hello
# f.close()
content = f.readlines()
print(content)  # ['Hello\n', 'World\n', 'is big']
f.close()

readline() Method: Reads one line at a time.

Example:

# Read one line at a time: readline()
f = open("python.txt", "r", encoding="UTF-8")
content = f.readline()
print(f"First line: {content}")
content = f.readline()
print(f"Second line: {content}")
f.close()

Reading File Lines with a For Loop:

Example:

# Read file lines with a for loop
for line in open("python.txt", "r", encoding="UTF-8"):
    print(line)
# Each 'line' temporary variable holds one line of data from the file

Make sure to close the file object after you're done, to release the file from being used by the Python program. If not closed, and the program hasn't ended, the file will remain occupied by the Python process.

with open Syntax:

Example:

with open("python.txt", "r", encoding="UTF-8") as f:
    f.readlines()
# Using 'with open', the file is automatically closed after the block of code is executed.

Example: Counting "hello" in a document:

Method 1:

f = open("test.txt", "r", encoding="utf-8")
content = f.read()
count = content.count("hello")
print(count)
f.close()

Method 2:

f = open("test.txt", "r", encoding="utf-8")
count = 0
for line in f:
    line = line.replace("\n", "")
    words = line.split(" ")
    for word in words:
        if word == "hello":
            count += 1
print(count)
f.close()

Writing to Files

File Writing:

# Open file
f = open(name, "w", encoding="UTF-8")

# Write to file
f.write(content)

# Flush content
f.flush()

Note:

Directly calling write doesn’t immediately write content to the file but stores it in the program's memory, called a buffer.
Calling flush writes the content to the file.
This is to avoid frequent hard drive operations that can degrade performance.

Example:

import time
f = open("test.txt", "w")
f.write("Hello, world!")
f.flush()
time.sleep(50000)
f.close()  # The 'close' method includes the flush functionality
# If the file already exists, its original content will be cleared
f = open("test.txt", "w")
f.write("Haha")
f.close()

Appending to Files

Syntax:

# Open file
f = open(name, "a", encoding="UTF-8")

# Write to file
f.write(content)

# Flush content
f.flush()

Example:

f = open("append_test.txt", "a", encoding="utf-8")
f.write("Keep the passion")
f.close()
f = open("append_test.txt", "a", encoding="utf-8")
f.write(", and explore the world")
f.close()
# Result: "Keep the passion, and explore the world"

File Operations (Comprehensive Case)

Example: Backup a file, remove marked data:

# Backup case: A bill recording expenses and income is backed up to 'csdn.txt.bak', removing test data marked as "测试"
fr = open("csdn.txt", "r", encoding="utf-8")
fw = open("csdn.txt.bak", "w", encoding="utf-8")
for line in fr:
    line = line.strip()
    if line.split(",")[4] == "test":
        continue
    fw.write(line)
    # Since strip() was used, manually write newline characters
    fw.write("\n")
fr.close()
fw.close()

🎁Learn Python in 10 Days: Day6

William — Mon, 23 Sep 2024 10:33:26 +0000

Today, we're continuing our 10-day journey to learn Python, kicking off Day 6's lesson. If you haven't checked out Day 1 yet, you can find it here: 🎁Learn Python in 10 Days: Day 1

Day 6: Advanced Functions 🎉

1. Multiple Return Values in Functions

If a function has multiple return values, only the first return statement will be executed. This is because the return statement exits the current function, causing any code beneath it to not be executed.



def return_num():
    return 1
    return 2
result = return_num()
print(result)

To have a function return multiple values, simply list the variables that will capture the return values separated by commas. This supports different types of data being returned.



def return_num():
    return 1, 2
result1, result2 = return_num()
print(result1)
print(result2)

2. Different Ways to Pass Arguments to Functions

Depending on usage, there are four common ways to pass arguments to functions:

Positional arguments
Keyword arguments
Variable-length arguments
Default arguments

2.1 Positional Arguments

This refers to passing arguments to the function based on the position of parameters defined in the function.



def user_info(name, age, gender):
    print(f"Your name is: {name}, age is {age}, gender is {gender}")
user_info("Alex", 25, "male")

Note: The order and number of arguments passed must match the function's parameters.

2.2 Keyword Arguments

This involves passing arguments to the function using the "key=value" format.

Purpose: Makes the function clearer and easier to use, and removes the requirement for arguments to be in a specific order.



def user_info(name, age, gender):
    print(f"Your name is: {name}, age is {age}, gender is {gender}")
# Keyword arguments
user_info(name="Alex", age=18, gender="male")
# Order doesn't need to be fixed
user_info(age=22, gender="female", name="Xiao Mei")
# Can mix with positional arguments, positional must come first
user_info("Echo", age=21, gender="female")

Note: When calling a function, if positional arguments are used, they must precede keyword arguments. Keyword arguments themselves can be in any order.

2.3 Variable-Length Arguments

Variable-length arguments, also called varargs, are used when the number of arguments passed to the function is unknown (it can even be zero).

Purpose: Useful for cases where the number of arguments to be passed is uncertain.

Types of variable-length arguments:

Positional
Keyword

Positional:



# Variable-length positional arguments
def user_info(*args):
    print(args)
user_info("tom")  # Output: ('tom',)
user_info("tom", 18)  # Output: ('tom', 18)

Note: All arguments passed are collected into the args variable, forming a tuple (args is of tuple type). This is positional passing of arguments.

Keyword:



# Variable-length keyword arguments
def user_info(**kwargs):
    print(kwargs)
user_info(name="tom", age=18, id=110)  # Output: {'name': 'tom', 'age': 18, 'id': 110}

Note: When arguments are passed in the "key=value" format, they are collected into the kwargs variable, forming a dictionary.

2.4 Default Arguments

Default or optional arguments are used to provide default values for parameters. If these parameters are not supplied during the function call, they default to the specified values. (Note: All positional arguments must come before any default arguments in both the function definition and the call.)

Purpose: If the function is called without certain arguments, it uses the default values.



# Default arguments
def user_info(name, age, gender="male"):
    print(f"Your name is: {name}, age is {age}, gender is {gender}")
user_info("tom", 20)
user_info("rose", 22, "female")

3. Anonymous Functions

3.1 Functions as Arguments

Previously, we have passed data (numbers, strings, dictionaries, tuples, lists, etc.) as arguments to functions. However, a function itself can also be passed as a parameter to another function.

For example:



# Function as an argument
def test_func(compute):
    result = compute(1, 2)
    print(result)
def compute(x, y):
    return x + y
test_func(compute)

The compute function is passed as an argument to the test_func function.

test_func requires a function as an argument that accepts two numbers and performs a calculation, with the calculation logic defined by the passed-in function.
The compute function takes two numbers and performs a calculation. This function is then passed to test_func.
Inside test_func, the passed-in compute function performs the calculation.

Thus, we are passing the logic of the calculation, not the data itself. Any custom logic can be defined and passed as a function.

3.2 Lambda Anonymous Functions

Function definitions:

The def keyword defines named functions.
The lambda keyword defines anonymous (unnamed) functions.

Named functions can be reused based on their name; unnamed lambda functions are utilized only once temporarily.

Anonymous function syntax:



lambda parameters: single-line expression

lambda is the keyword for defining anonymous functions.
The parameters are the function’s input, similar to formal parameters like x, y, etc.
The function body is the execution logic, limited to a single line.

Example:



# Lambda anonymous function
def test_func(compute):
    result = compute(1, 2)
    print(result)
test_func(lambda x, y: x + y)
# Note: Lambda functions cannot be reused

So far, we're halfway through the "Learn Python in 10 Days" series and hope it's been helpful. By the way, check out our team's new Postman alternative EchoAPI. We'd love your feedback! 😊

How is the EchoAPI Tool? Is it Safe? What is the Background of the Company?

William — Thu, 19 Sep 2024 17:11:59 +0000

What is EchoAPI?

EchoAPI is an ultra-lightweight collaboration tool designed specifically for API development. In a crowded market dominated by heavyweights like Postman, EchoAPI emerges as a compelling alternative, offering a suite of features aimed at making API development more efficient and user-friendly.

The tool supports functionalities such as:

API design
Debugging
Automated testing
Load testing

Additionally, EchoAPI integrates smoothly with other development environments through plugins for IntelliJ IDEA, VS Code, and a Chrome request capture extension. Remarkably, it offers all of these features without requiring users to log in.

Key Features

No Login Required: One of the standout features of EchoAPI is that it doesn’t force users to create an account. This is particularly beneficial for developers who prioritize privacy and want to avoid the hassle of managing yet another set of login credentials.
Supports Scratch Pad: Since Postman dropped this feature, using it in many limited scenarios has become a real pain. EchoAPI, on the other hand, picked up the slack and even upgraded the feature.
Ultra Lightweight: EchoAPI is designed to be fast and responsive, avoiding the sluggish performance sometimes experienced with bulkier applications. This lightweight nature ensures that it won't bog down your system, making it ideal for developers who need a nimble tool.
Postman Script Syntax Compatibility: For those already familiar with Postman's scripting syntax, the transition to EchoAPI is seamless. This compatibility ensures that you can leverage your existing Postman scripts without any modifications.

Advantages Over Postman

When evaluated side-by-side with Postman, EchoAPI offers several distinct advantages:

Login Requirements: Unlike Postman, which requires a forced login, EchoAPI offers a no-login-needed approach. This is not only more convenient but also respects user privacy.
Scratch Pad Support: Postman lacks a similar feature to EchoAPI's Scratch Pad, making the latter a more versatile tool for quick notes and ideas during development.
Cost: Postman can be quite expensive, especially for teams requiring collaborative features. EchoAPI positions itself as a cost-effective alternative.
Performance: Postman’s Electron-based client can be bulky and slow. In contrast, EchoAPI’s ultra-lightweight design ensures quicker performance and responsiveness.

Is it Safe?

Safety and security are paramount when choosing any development tool. EchoAPI provides confidence in this area, particularly because it doesn't necessitate user login, thereby reducing the risk of data breaches through account compromises.Even if you need to log in for team collaboration, strong SSL encryption keeps your data safe.

Company Background

ECHOAPI CO., LTD is headquartered in New York, NY, and specializes in developing API tools. The company, deeply rooted in the United States, benefits from the region's strong legal and technological frameworks, ensuring high standards of product quality and security. Their focus on creating an ultra-lightweight and user-friendly API development tool reflects a thorough understanding of the needs and pain points of modern developers.

Summary

In conclusion, EchoAPI is not only safe but also offers a superior alternative to existing tools like Postman. Its ease of use, privacy considerations, and lightweight design all contribute to its attractiveness as a development tool.

EchoAPI vs Insomnia: A Comprehensive Comparison with Practical Examples

William — Thu, 19 Sep 2024 13:04:59 +0000

As a full stack developer, I know how crucial it is to have top-notch tools for debugging, testing, and documenting APIs. EchoAPI and Insomnia are two standout options, each with its own unique features and capabilities. Let me walk you through these tools, compare their functionalities and benefits, give you some practical examples, and help you decide when to use EchoAPI or Insomnia.

Introduction to EchoAPI and Insomnia

EchoAPI

EchoAPI is a robust API debugging tool that handles API testing, automated testing, load testing, and one-click API documentation. It also offers several handy plugins:

EchoAPI Interceptor (Chrome Extension): Captures web page APIs without needing a login, allows parameter modification, and syncs with EchoAPI.
EchoAPI for IntelliJ IDEA: A Java plugin that lets you generate, modify, and debug interfaces directly from your code.
EchoAPI for VS Code: Adds, modifies, and debugs APIs, including pre- and post-scripts, visual assertions, and automated tests.

Insomnia

Insomnia is designed for RESTful APIs and GraphQL, with a focus on simplicity and user experience. It provides a straightforward interface for managing requests, environments, and API documentation.

Feature Comparison with Practical Examples

Let's dive into a comparison of EchoAPI and Insomnia based on key functionalities with practical examples:

1. API Debugging and Testing

EchoAPI: EchoAPI provides a user-friendly and visually appealing interface for managing and testing your APIs. You can easily create and send API requests, tweak parameters, and ensure your API works under various conditions.

Example:

Insomnia: For a user management RESTful API, you can use Insomnia to create, update, or delete user requests. It makes switching between development, testing, and production environments a breeze with environment variables.

Example:

2. Automated Testing

EchoAPI: If your API fetches user data, EchoAPI's automated testing lets you set up pre- and post-scripts to validate tests. Visual assertions help you check if the response meets your expectations.

Example:

Insomnia: Automated testing can be done with plugins or by integrating with tools like Jenkins. You can write custom tests in the "Test" tab of a request to auto-validate responses.

Example:

3. Load Testing

EchoAPI: Need to test the load capacity of your API? EchoAPI offers built-in load testing to simulate multiple requests and see how your API handles heavy traffic.

Example:

Insomnia: Insomnia doesn’t have built-in load testing, but you can export requests to use with tools like k6 or Apache JMeter for load testing.

Example with k6:

  import http from 'k6/http';
  import { check } from 'k6';

  export let options = {
    stages: [
      { duration: '1m', target: 100 },
      { duration: '1m', target: 200 },
      { duration: '1m', target: 0 }
    ]
  };

  export default function() {
    let res = http.get('https://api.example.com/users');
    check(res, { 'status was 200': (r) => r.status == 200 });
  }

4. API Documentation

EchoAPI: EchoAPI simplifies generating complete API documentation. With one click you can create and share documentation, ensuring your team or clients have the latest info with minimal effort.

Example:

Insomnia: You can create detailed API documentation in Insomnia, but keeping it in sync with your codebase might require some manual steps.

Example:

  // Insomnia API documentation snippet
  {
    "name": "User Service API",
    "requests": [
      {
        "method": "GET",
        "url": "{{ base_url }}/users",
        "description": "Fetch all users"
      },
      {
        "method": "POST",
        "url": "{{ base_url }}/users",
        "description": "Create a new user",
        "body": {
          "username": "new_user",
          "email": "new_user@example.com"
        }
      }
    ]
  }

When to Use Insomnia

Insomnia is a great choice when:

You Need GraphQL Support: Insomnia shines in managing GraphQL APIs.
Simplicity and Ease of Use Are Critical: If you want a straightforward tool for managing RESTful requests with minimal setup, Insomnia is perfect.
Environment Management: It has strong environment management for projects with multiple stages like development, testing, and production.

When to Use EchoAPI

EchoAPI is ideal when:

You Require Offline Capabilities: Plugins for IDEs and browsers make it work smoothly without constant internet access.
Integrated Automated and Load Testing: Built-in automated testing and load testing features eliminate the need for external tools.
Development Environment Integration: Robust plugins for IntelliJ IDEA and VS Code allow API debugging and testing directly within your code.
One-Click API Documentation: Ensures your API documentation stays up-to-date and easily accessible.

Conclusion

Both EchoAPI and Insomnia are powerful tools for API development. Insomnia offers a simple, user-friendly interface with strong environment management and GraphQL support. EchoAPI provides a comprehensive suite of features including automated testing, load testing, and deep integration with development environments, all while being lightweight and offline-capable.

Choose EchoAPI if you need a robust tool that integrates well with your dev workflow and offers extensive testing and documentation capabilities. Opt for Insomnia if you want a straightforward, easy-to-use tool that supports GraphQL and focuses on simplicity and efficiency.

By understanding the strengths of each tool and using the practical examples given, you'll be better equipped to enhance your API development, ensuring efficiency, reliability, and ease of use. Happy API testing!

🎁Learn Python in 10 Days: Day5

William — Thu, 19 Sep 2024 10:39:26 +0000

Today, we're continuing our 10-day journey to learn Python, kicking off Day 5's lesson. If you haven't checked out Day 1 yet, you can find it here: 🎁Learn Python in 10 Days: Day 1

Day 5: Introduction to Data Containers 🎉

A data container is a type of data structure that can hold multiple pieces of data, where each piece is referred to as an element. Elements can be of any data type, such as strings, numbers, booleans, etc.

Data containers are categorized based on certain features, such as:

Whether they support duplicate elements
Whether they are mutable
Whether they are ordered or unordered

The five main types of data containers are:

Lists
Tuples
Strings
Sets
Dictionaries

Data Container: List

List Overview:

The list type is a container that can store multiple data items at once.

Basic Syntax:

# Literal
[Element1, Element2, Element3, ...]

# Define variable
variable_name = [Element1, Element2, Element3, ...]

# Define an empty list
variable_name = []

variable_name = list()

Each piece of data in a list is called an element and is separated by a comma.

# List
languages = ['python', 'c', 'c++', 'java']
print(languages)
print(type(languages))

# Nested list
nested_list = [[1, 2, "damn it"], [4, 5, True]]
print(nested_list)
print(type(nested_list))

Note: Lists can store multiple data items of different types and support nesting.

List Indexing:

You can access specific elements in a list using index notation.

# List indexing
names = ["tom", "jack", "alex"]
print(names[0])  # Output: tom
print(names[1])  # Output: jack
print(names[2])  # Output: alex

Reverse indexing is also supported:

names = ["tom", "jack", "alex"]
print(names[-3]) # Output: tom
print(names[-2]) # Output: jack
print(names[-1]) # Output: alex

For nested lists:

# Nested list indexing
nested_list = [[1, 2, "damn it"], [4, 5, True]]
print(nested_list[1][2])  # Output: True
print(nested_list[0][2])  # Output: damn it

Note: Do not try to access indexes out of range; it will cause an IndexError.

Common List Operations:

Insert an element
Delete an element
Clear elements
Modify elements
Count elements

These operations are referred to as methods of the list.

List Query Functionality: Find the index of a specified element. If not found, it raises a ValueError.

   # Query index
   languages = ['python', 'c', 'c++', 'java']
   index = languages.index("python")
   print(f"Index of 'python': {index}")

Modify Elements: Modify the value at a specific index.

   # Modify elements
   my_list = [1, 2, 3, 4]
   my_list[0] = 8
   print(my_list)
   my_list[-1] = 6
   print(my_list)

Insert Elements:

   # Insert elements
   my_list = [1, 2, 3, 4]
   my_list.insert(2, 'damn it')
   print(my_list)

Append Elements:

   # Append elements
   my_list = [1, 2, 3, 4]
   my_list.append(4)  # Output: [1, 2, 3, 4, 4]
   print(my_list)
   my_list.append([5, 5, 6])
   print(my_list)  # Output: [1, 2, 3, 4, 4, [5, 5, 6]]

Extend a List:

   # Extend a list
   my_list = [1, 2, 3, 4]
   my_list.extend([4, 5, 6])
   print(my_list)  # Output: [1, 2, 3, 4, 4, 5, 6]

Delete Elements:

   # Delete elements
   my_list = [1, 2, 3, 4]
   del my_list[0]
   print(my_list)  # Output: [2, 3, 4]

   my_list.pop(0)
   print(my_list)  # Output: [3, 4]

Remove a Specific Element:

   # Remove a specific element
   my_list = [1, 2, 3, 2]
   my_list.remove(2)
   print(my_list)  # Output: [1, 3, 2]

Clear List Contents:

   # Clear list
   my_list = [1, 2, 3, 2]
   my_list.clear()
   print(my_list)  # Output: []

Count Element Occurrences:

   # Count occurrences of an element
   my_list = [1, 2, 3, 2]
   num = my_list.count(2)
   print(num)  # Output: 2

Count Total Elements:

# Count total elements
my_list = [1, 2, 3, 2]
count = len(my_list)
print(count)  # Output: 4

Characteristics of Lists:

Can contain multiple elements (up to (2^{63} - 1) elements).
Can store elements of different types.
Data is stored in an ordered manner.
Allows duplicate data.
Mutable.

List Traversal:

To extract and operate on elements one by one, we use traversal (or iteration).

While Loop:

def list_while_func():
    """
    Function demonstrating list traversal using a while loop.
    """
    my_list = ["python", "java", "c++", "javascript", "go"]
    index = 0
    while index < len(my_list):
        element = my_list[index]
        print(f"List element: {element}")
        index += 1

list_while_func()

For Loop:

def list_for_func():
    """
    Function demonstrating list traversal using a for loop.
    """
    my_list = ["python", "java", "c++", "javascript", "go"]
    for element in my_list:
        print(f"List element: {element}")

list_for_func()

Comparison between while and for loops:

While loops can define conditions and are more flexible. For loops cannot define custom conditions.
While loops can achieve infinite loops; for loops are limited by the container size.
While loops fit any looping scenario, whereas for loops are best for traversing data containers or fixed-iteration loops.

Data Container: Tuple

Tuples are immutable data containers and are defined using parentheses. They can hold mixed types of data.

Basic Syntax:

# Literal
(element1, element2, ..., elementN)

# Variable
variable_name = (element1, element2, ..., elementN)

# Empty tuple
variable_name = ()
variable_name = tuple()

Tuple Indexing:

t = ((1, 2, 3), (4, 5, 6))
print(t[1][2])  # Output: 6

Common Tuple Operations:

index() Method: Return the first occurrence of an element.
count() Method: Count the occurrences of an element.
len() Function: Return the length of the tuple.

Tuple Traversal:

While Loop:

t1 = (1, 2, "Oho", 3, 4, "Oho")
index = 0
while index < len(t1):
    print(f"Tuple element: {t1[index]}")
    index += 1

For Loop:

t2 = (1, 2, "Oho", 3, 4)
for element in t2:
    print(f"Tuple element: {element}")

Note: Tuples cannot be modified, but lists within tuples can have their elements modified.

Data Container: String

Strings are containers for characters. They support indexing but are immutable.

String Indexing:

str1 = "hello"
print(str1[0])  # Output: h

Common String Operations:

Find Substring Index:

str1 = "hello world"
index = str1.index("hello")
print(index)  # Output: 0

String Replacement:

str1 = "hello world"
new_str1 = str1.replace("h", "H")
print(new_str1)  # Output: Hello world

String Splitting:

str1 = "Oho Damn Awesome"
list1 = str1.split(" ")
print(f"Splitted string: {list1}, type: {type(list1)}")

Trim Whitespace:

str1 = " hello world "
print(str1.strip())

Trim Specific Characters:

str1 = "12@hello world@21"
print(str1.strip("12@"))

Count Character Occurrences:

str1 = "hello world"
count = str1.count("o")
print(count)  # Output: 2

String Length:

str1 = "hello world"
count = len(str1)
print(count)  # Output: 11

Characteristics of Strings:

Can only store characters.
Variable length.
Supports indexing.
Allows duplicate characters.
Immutable.
Supports both while and for loops for traversal.

Slicing Sequences

Sequences (lists, tuples, strings) can be sliced to produce a subsequence.

Syntax: sequence[start:end:step]

Examples:

# List slicing
my_list = [0, 1, 2, 3, 4, 5, 7]
result1 = my_list[1:5]
print(result1)  # Output: [1, 2, 3, 4]

# Tuple slicing
my_tuple = (0, 1, 2, 3, 4, 5, 6)
result2 = my_tuple[::-2]
print(result2)  # Output: (6, 4, 2, 0)

# String slicing
str1 = "Over 1 million views, Python enthusiast"
result_str = str1[::-1][9:14]
print(result_str)

Data Container: Set

Sets are containers that do not support duplicate elements and are unordered.

Basic Syntax:

# Literal
{element1, element2, ..., elementN}

# Variable
variable_name = {element1, element2, ..., elementN}

# Empty set
variable_name = set()

Common Set Operations:

Add an Element:

my_set = {"Damn", "Oho", "Awesome"}
my_set.add("python")
print(my_set)

Remove an Element:

my_set = {"Damn", "Oho", "Awesome"}
my_set.remove("Damn")
print(my_set)

Pop an Element (Randomly):

my_set = {"Damn", "Oho", "Awesome"}
pop_set = my_set.pop()
print(f"Popped element: {pop_set}, Remaining set: {my_set}")

Clear the Set:

my_set = {"Damn", "Oho", "Awesome"}
my_set.clear()
print(my_set)

Difference of Two Sets:

my_set1 = {1, 3, 5}
my_set2 = {1, 4, 6}
new_set = my_set1.difference(my_set2)
print(new_set)  # Output: {3, 5}

Update Difference (Remove Common Elements):

my_set1 = {1, 3, 5}
my_set2 = {1, 4, 6}
my_set1.difference_update(my_set2)
print(my_set1)  # Output: {3, 5}

Union of Sets:

my_set1 = {1, 3, 5}
my_set2 = {1, 4, 6}
new_set = my_set1.union(my_set2)
print(new_set)  # Output: {1, 3, 4, 5, 6}

Set Length:

my_set = {1, 2, 3, 4, 5, 6}
num = len(my_set)
print(num)  # Output: 6

Set Traversal:

# For loop
my_set = {1, 2, 3, 4, 5, 6}
for element in my_set:
    print(f"Set element: {element}")

Data Container: Dictionary

Dictionaries store key-value pairs and are unordered.

Basic Syntax:

# Literal
{key1: value1, key2: value2, ..., keyN: valueN}

# Variable
variable_name = {key1: value1, key2: value2, ..., keyN: valueN}

# Empty dictionary
variable_name = {}
variable_name = dict()

The keys must be unique, and duplicates will overwrite existing values.

Basic Operations:

Accessing Values by Key:

my_dict = {"John": 99, "Jane": 88, "Jack": 77}
score = my_dict["John"]
print(score)  # Output: 99

Nested Dictionary:

grades_dict = {
    "John": {"Math": 77, "Science": 66, "English": 33},
    "Jane": {"Math": 88, "Science": 86, "English": 55},
    "Jack": {"Math": 99, "Science": 96, "English": 66}
}
print(f"Student grades: {grades_dict}")

Add/Update Elements:

my_dict = {"John": 99, "Jane": 88, "Jack": 77}
my_dict["Sam"] = 67
print(my_dict)  # Output: {'John': 99, 'Jane': 88, 'Jack': 77, 'Sam': 67}

my_dict["Sam"] = 68
print(my_dict)  # Output: {'John': 99, 'Jane': 88, 'Jack': 77, 'Sam': 68}

Delete Elements:

# Delete elements
my_dict = {"John": 99, "Jane": 88, "Jack": 77, "Sam": 67}
score = my_dict.pop("Sam")
print(f"{my_dict}, Sam's score: {score}")

Clear Dictionary:

my_dict = {"John": 99, "Jane": 88, "Jack": 77, "Sam": 67}
my_dict.clear()
print(my_dict)  # Output: {}

Get All Keys:

my_dict = {"John": 99, "Jane": 88, "Jack": 77, "Sam": 67}
keys = my_dict.keys()
print(keys)  # Output: dict_keys(['John', 'Jane', 'Jack', 'Sam'])

Dictionary Length:

my_dict = {"John": 99, "Jane": 88, "Jack": 77, "Sam": 67}
num = len(my_dict)
print(num)  # Output: 4

Dictionary Traversal:

# Dictionary traversal
my_dict = {'John': 99, 'Jane': 88, 'Jack': 77, 'Sam': 67}
for key in my_dict.keys():
    print(f"Key: {key}, Value: {my_dict[key]}")

# Advanced Example
emp_dict = {
    "Jeff": {"Department": "Tech", "Salary": 3000, "Level": 1},
    "Elon": {"Department": "Marketing", "Salary": 5000, "Level": 2},
    "Jack": {"Department": "Marketing", "Salary": 7000, "Level": 4},
    "Sundar": {"Department": "Tech", "Salary": 4000, "Level": 1}
}

for name in emp_dict:
    if emp_dict[name]["Level"] == 1:
        emp_info = emp_dict[name]
        emp_info["Level"] = 2
        emp_info["Salary"] += 1000
        emp_dict[name] = emp_info

print(f"After promotion: {emp_dict}")

General Operations for Data Containers

All data containers support iteration with for loops. Lists, tuples, and strings also support while loops, unlike sets and dictionaries.

In addition to common indexing, containers support type conversion, such as converting to a list, set, etc.

Common Sorting Function:

my_list = [1, 4, 6, 8, 2, 3]
print(f"Sorted list: {sorted(my_list, reverse=True)}")  # Descending order
print(f"Sorted list: {sorted(my_list)}")  # Ascending order

So far, we're halfway through the "Learn Python in 10 Days" series and hope it's been helpful. By the way, check out our team's new Postman alternative EchoAPI. We'd love your feedback! 😊

🎁Learn Python in 10 Days: Day 4

William — Wed, 18 Sep 2024 11:03:13 +0000

Today, we're continuing our 10-day journey to learn Python, kicking off Day 4's lesson. If you haven't checked out Day 1 yet, you can find it here: 🎁Learn Python in 10 Days: Day 1

Hey there! Let's dive into loops in Python, using some everyday examples and some hands-on coding. Here's a breakdown of the main points, all formatted nicely for you.

Day 4: Functions 🎉

Functions are organized, reusable pieces of code designed to perform a specific task.

name = "Alex"
length = len(name)
print(length)

You can call len() (to get the length) whenever you need because it's a built-in Python function (pre-written for you). But what if we didn't use the len() function to get the string length?

str = "python"
count = 0
for i in str:
    count += 1
print(count)

We can use a function to make this process simpler

def my_len(data):
    count = 0
    for i in data:
        count += 1
    print(f"The length of the string '{data}' is {count}")
my_len(str)

Functions help create specific, reusable chunks of code, improving reusability, reducing redundant code, and boosting development efficiency.

2. Defining a Function

Function definition:

def function_name(params):

    function_body

    return return_value

Calling a function:

function_name(params)

Let's define a simple function

def my_hi():
    print("hello")
my_hi()

Things to keep in mind:

If parameters aren't needed, you can omit them.
If a return value isn't needed, you can omit it.
Define the function before using it.

3. Function Parameters

Function parameters allow a function to accept external data when it's called.

def add1():
    result = 1 + 2
    print(f"{result}")
add1()

The add1() function is very limited, only adding 1 and 2. Let's give it parameters so users can specify any numbers to add.

def add2(a, b):
    result = a + b
    print(f"{a} + {b} = {a + b}")
add2(3, 4)

In the function definition, a and b are called formal parameters, indicating the function will use these (separated by comma).
During the function call, the provided 3 and 4 are actual parameters, the real values used by the function.

You can pass any number of parameters (including none) to a function.

4. Function Return Values

Return values are the results a function sends back after it's done processing.

# Define a function to add two numbers and return the result.
def add(a, b):
    result = a + b
    return result
r = add(3, 4)
print(r)

A "return value" is the final result given to the function's caller after completing its task. The syntax is:

def function_name(params):

    function_body

    return return_value

variable = function(params)

Note: A function stops execution when it hits a return, so any code after that won't run.

If there's no return statement, the function returns None, a special literal indicating the function did not return any meaningful value.

def say_hello():
    print("hello")
    # None
result = say_hello()
print(result)
print(f"The return type is {type(result)}")

5. Function Documentation

Functions are pure code, so understanding their meaning involves reading the code line by line, which can be inefficient.

We can add documentation to functions to help explain their purpose:

def func(x, y):
    """
    Function description
    :param x: Explanation for parameter x
    :param y: Explanation for parameter y
    :return: Explanation for the return value
    """
    function_body
    return return_value

For example:

def add(x, y):
    """
    The add function takes two parameters and adds them together.
    :param x: One of the numbers to add.
    :param y: The other number to add.
    :return: The sum of x and y.
    """
    result = x + y
    print(f"{x} + {y} = {x + y}")
    return result

You can hover over the function name to see its docstring, helping understand the function better.

6. Nested Function Calls

Nested functions are when one function calls another.

def func_b():
    print(2)
def func_a():
    print(1)
    func_b()
func_a()

When func_a calls func_b, func_b finishes all its tasks before returning to where func_a left off.

7. Variable Scope

Variable scope determines where a variable is accessible (where it can be used and where it can't).

There are two main types: local variables and global variables.

Local variables are defined inside a function and can only be used there.

def test():
    num = 100
print(num) # Error: name 'num' is not defined

The variable num is local to the function test and can't be accessed outside of it.

Global variables can be accessed both inside and outside functions.

num = 100
def testA():
    print(num)
def testB():
    print(num)
testA() # 100
testB() # 100

Using the global keyword:

num = 100
def testA():
    print(num)
def testB():
    global num
    num = 200
    print(num)
testA() # 100
testB() # 200
print(num) # 200

# Example: Agricultural Bank ATM: Deposit, Withdrawal, and Balance Check Functions

# Define global variables
money = 50000
name = None

# Ask the user for their name
name = input("Please enter your name: ")

# Define the balance query function
def query(show_header):
    if show_header:
        print("--------- Balance Inquiry ---------")
    print(f"Hello {name}, your remaining balance is {money}")

# Define the deposit function
def save(amount):
    global money
    print("--------- Deposit ---------")
    money += amount
    print(f"{name}, you have successfully deposited {amount} units")
    query(False)

# Define the withdrawal function
def get(amount):
    global money
    print("--------- Withdrawal ---------")
    if money != 0:
        money -= amount
        print(f"{name}, you have successfully withdrawn {amount} units")
        query(False)
    else:
        print("Your account balance is zero, please deposit some money")

# Define the main menu function
def main():
    print("---------- Main Menu -----------")
    print(f"Hello! Welcome to the Agricultural Bank ATM. Please choose an option:")
    print("For balance inquiry, enter\t[1]")
    print("For deposit, enter\t\t[2]")
    print("For withdrawal, enter\t\t[3]")
    print("For exit, enter\t\t[4]")
    return input("Please enter your choice: ")

# Infinite loop to ensure it doesn't exit
while True:
    choice = main()
    if choice == "1":
        query(True)
        continue  # Continue to the next iteration
    elif choice == "2":
        amount = int(input("Enter the amount you want to deposit: "))
        save(amount)
        continue
    elif choice == "3":
        amount = int(input("Enter the amount you want to withdraw: "))
        get(amount)
        continue
    else:
        print("Exiting the system!")
        break

EchoAPI Interceptor: Making API Capture and Debugging Fun and Effortless

William — Tue, 17 Sep 2024 12:04:23 +0000

In the fast-paced world of software development, efficient API management is key to a smooth workflow. Developers constantly seek tools that make API capture and debugging easier. Enter EchoAPI Interceptor—a powerful Chrome extension that’s changing the game rules. Let’s take a closer look at the current landscape of API tools, explore the pros and cons of some traditional options, and discover why EchoAPI Interceptor should be your new go-to tool.

The Current API Tools Landscape

Postman

Strengths: Postman is a comprehensive API testing tool with great collaboration features, an intuitive interface, and robust documentation support.

Considerations: Despite its power, Postman’s depth can make simple tasks seem complex, and many advanced features are behind a paywall.

Charles Proxy

Strengths: Known for its advanced HTTP/HTTPS monitoring and sophisticated data filtering, Charles Proxy is excellent for troubleshooting complex issues.

Considerations: It has a steeper learning curve and may struggle with performance when handling large datasets.

Fiddler

Strengths: Fiddler offers extensive customization and a rich plugin ecosystem, making it ideal for developers who need flexibility.

Considerations: Its interface can be overwhelming for newcomers, and performance can vary depending on use cases.

Swagger UI

Strengths: Swagger UI excels at visualizing and interacting with APIs, providing auto-generated documentation that simplifies understanding complex APIs.

Considerations: It lacks real-time debugging capabilities and often needs to be paired with other tools for full API management.

Enter EchoAPI Interceptor: A Game-Changer

EchoAPI Interceptor is revolutionizing how developers interact with APIs by addressing the common pain points of other tools. Here’s what sets it apart:

Powerful Features of EchoAPI Interceptor

One-Click Capture:
Effortlessly grab JSON, headers, and other key data with just a click. Data capture has never been easier.

One-Click Debugging:
Get instant, real-time feedback to resolve issues quickly, making debugging a breeze.

One-Click Sync:
Easily sync data with EchoAPI, keeping everything organized and eliminating repetitive tasks.

AI Banter:
Enjoy a unique debugging experience with AI analysis mixed with a touch of humor, making the process more engaging.

Local Storage:
Keep your sensitive data secure with robust local storage options, ensuring privacy and protection.

Fun Skins:
Personalize your workspace with a variety of cool skins, adding a bit of fun to your development environment.

One-Click Configuration:
Tailor the tool to your needs with a simple setup process that integrates smoothly into your existing workflow.

Why EchoAPI Interceptor is a Game rules-Changer?

EchoAPI Interceptor isn’t just another tool—it’s a comprehensive solution designed with modern developers in mind:

User-Friendly Design: Its intuitive interface makes it easy for both seasoned developers and newcomers to navigate.
**Boosted Productivity: **Features designed to save time and streamline API management enhance overall efficiency.
Enhanced Collaboration: Easily share and update data with your team, improving communication and collaboration.
Security Focus: With a commitment to data privacy, local storage options keep your information secure and accessible.

The Evolution of API Tools

As APIs become increasingly central to the tech ecosystem, tools like EchoAPI Interceptor are essential for developers who want to stay ahead. Its commitment to continuous updates ensures it remains relevant and effective in a constantly evolving field.

Conclusion

EchoAPI Interceptor is more than just a Chrome extension—it’s a transformative tool that can revolutionize your approach to API management. Whether you’re a seasoned pro or just starting out, this extension will significantly enhance your API capture and debugging experience.

🎁Learn Python in 10 Days: Day 3

William — Tue, 17 Sep 2024 12:04:07 +0000

Today, we're continuing our 10-day journey to learn Python, kicking off Day 3's lesson. If you haven't checked out Day 1 yet, you can find it here: 🎁Learn Python in 10 Days: Day 1

Hey there! Let's dive into loops in Python, using some everyday examples and some hands-on coding. Here's a breakdown of the main points, all formatted nicely for you.

Day 3: Loop Statements 🎉

Loops are everywhere in our daily lives, and just like that, they play a crucial role in programming as well. Let's get down to the basics of loops in Python! 🐍✨

1. Basic Syntax of the `while` Loop

Basic `while` Loop in Python:

i = 0
while i < 100:
    print("Hello, world!")
    i += 1

Explanation:
1. The while loop continues as long as the condition is True.
2. You need to set a condition to stop the loop, like i += 1 paired with i < 100 to avoid an infinite loop.
3. Indentation is crucial, just like with if statements.

Example: Sum of Numbers from 1 to 100

i = 1
total = 0
while i <= 100:
    total += i
    i += 1
print(f"The total sum is: {total}")

Example: Guess the Number Game

import random

num = random.randint(1, 100)
count = 0
flag = True

while flag:
    guess_num = int(input("Guess the number: "))
    count += 1
    if guess_num == num:
        print("Congratulations! You guessed it!")
        flag = False
    elif guess_num > num:
        print("Too high!")
    else:
        print("Too low!")

print(f"You guessed it in {count} tries.")

2. Nested `while` Loops

Example: Sum of Factorials from 1 to 100

i = 1
total_sum = 0

while i <= 100:
    fact = 1
    j = 1
    while j <= i:
        fact *= j
        j += 1
    total_sum += fact
    i += 1

print(total_sum)

Example: Multiplication Table (9x9)

i = 1
while i <= 9:
    j = 1
    while j <= i:
        print(f"{i}*{j}={i*j}\t", end='')
        j += 1
    i += 1
    print()  # New line after each row

3. Basic Syntax of the `for` Loop

Unlike while, for loops are used for iterating over a sequence (like a list, tuple, dictionary, set, or string).

Example: Iterating Over a String

name = "Bob"
for char in name:
    print(char, end='')

Example: Counting Occurrences of 'a' in a String

sample_str = "abcdefaac"
count = 0
for char in sample_str:
    if char == 'a':
        count += 1
print(count)

`range()` Function:

Syntax 1: range(num) generates numbers from 0 to num-1.
Syntax 2: range(start, end) generates numbers from start to end-1.
Syntax 3: range(start, end, step) uses a step value.

Example: Counting Even Numbers

num = int(input("Enter a number: "))
count = 0

for x in range(1, num):
    if x % 2 == 0:
        count += 1

print(count)

4. Nested `for` Loops

Example: Multiplication Table (9x9) with `for` Loops

for i in range(1, 10):
    for j in range(1, i+1):
        print(f"{i}*{j}={i*j}\t", end='')
    print()  # New line after each row

5. Loop Control Statements: `break` and `continue`

continue: Skips the rest of the code inside the loop for the current iteration only.
break: Terminates the loop.

Example: Payroll with Performance Check

money = 10000

for num in range(1, 21):
    grade = random.randint(1, 10)
    if grade < 5:
        print(f"Employee {num} has a performance score below 5. No salary issued.")
        continue

    if money >= 1000:
        money -= 1000
        print(f"Employee {num} meets performance criteria. Company balance: {money}")
    else:
        print(f"Insufficient funds. Balance: {money}")
        break