As a developer, I've always been fascinated by browser extensions. They're like little magic wands that can transform our browsing experience in countless ways. Today, I'm excited to share my journey of creating a Chrome extension using AskCodi, an AI-powered coding assistant, right within Visual Studio Code (VS Code). 

Whether you're a seasoned developer or just starting out, this guide will walk you through the process, making it accessible and fun. So, let's roll up our sleeves and dive in!

## Prerequisites

Before we embark on this coding adventure, make sure you have:

1. Google Chrome installed (obviously!)
2. Visual Studio Code as your code editor
3. AskCodi extension installed in VS Code
4. Basic knowledge of HTML, CSS, and JavaScript

## Step 1: Setting up the VS Code Environment with AskCodi

Let’s start by ensuring your environment is ready.

1. Open **VS Code** and make sure you have installed the **AskCodi** extension. If you haven't yet:
    - Head to the Extensions tab (or use `Ctrl+Shift+X`).
    - Search for **AskCodi**.
    - Click **Install**.

2. You’ll also need API key for AskCodi which you can get in your account settings at https://askcodi.com.  You can follow the detailed instrunctions in AskCodi [docs](https://askcodi.com/docs/extensions/vscode/install)
3.  You can choose between a range of models, I'm going with Claude 3.5 sonnet.

Here's a friendly little screenshot of how it looks when you’ve got AskCodi installed:

![AskCodi Installed in VS Code](https://firebasestorage.googleapis.com/v0/b/assistivai-askcodi.appspot.com/o/askcodi-blog%2Faskcodi.png?alt=media&token=858c32ad-e92a-4c92-b042-e8cc5c609439)


## Step 2: Setting Up Your Project

Let's start by creating a cozy home for our extension:

1. Create a new folder on your computer, for example, `my-chrome-extension`. Inside this folder, open VS Code.

2. Alternatively, you can also use the terminal. Open your terminal and create a new directory and open VS Code:

```bash
   mkdir MyChromeExtension
   cd MyChromeExtension
	 code .
```

![Create directory via terminal](https://firebasestorage.googleapis.com/v0/b/assistivai-askcodi.appspot.com/o/askcodi-blog%2Fterminal.png?alt=media&token=e9d9ad8c-fec7-44d4-8f4a-3c2d7600f54b)

3. Now, let's create the essential files for our extension:

- manifest.json
- popup.html
- content.js
- popup.js
- styles.css

![created files](https://firebasestorage.googleapis.com/v0/b/assistivai-askcodi.appspot.com/o/askcodi-blog%2Ffiles.png?alt=media&token=1134743f-f64c-4181-806c-1f057a4acb9b)

You can do this manually or use the VS Code file explorer. I prefer the latter - it's just so satisfying to click that 'New File' button!

## Step 3: Crafting the Manifest
The manifest.json file is like the ID card of your extension. It tells Chrome what your extension is all about.
Let's use AskCodi to help us write this file:

1. Open manifest.json in VS Code.
2. Open AskCodi panel from right sidebar of VS Code to summon our AI assistant.
3. Ask: "Help me write a basic manifest.json for a Chrome extension that changes the background color of a webpage."

![crafting manifest json](https://firebasestorage.googleapis.com/v0/b/assistivai-askcodi.appspot.com/o/askcodi-blog%2Fmanifest.png?alt=media&token=a3bea0d4-28af-42e2-9935-c4031a0ac419)

AskCodi should provide something like below code. Notice AskCodi suggests to add icon images in images directory. So go ahead, create the images directory and add icon pngs.

```json 
{
  "manifest_version": 3,
  "name": "Background Color Changer",
  "version": "1.0",
  "description": "A simple extension to change the background color of webpages.",
  "permissions": ["activeTab"],
  "action": {
    "default_popup": "popup.html",
    "default_icon": {
      "16": "images/icon16.png",
      "48": "images/icon48.png",
      "128": "images/icon128.png"
    }
  },
  "icons": {
    "16": "images/icon16.png",
    "48": "images/icon48.png",
    "128": "images/icon128.png"
  },
  "content_scripts": [
    {
      "matches": ["<all_urls>"],
      "js": ["content.js"]
    }
  ]
}

```

## Step 4: Creating the Popup
Now, let's create a simple popup for our extension:

Open popup.html and ask AskCodi:
"Write HTML for a popup with a color picker input and a 'Change Color' button with styling in styles.css"

```html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Background Color Changer</title>
    <link rel="stylesheet" href="styles.css">
</head>
<body>
    <div class="container">
        <h1>Background Color Changer</h1>
        <div class="color-picker">
            <label for="colorInput">Choose a color:</label>
            <input type="color" id="colorInput" value="#ffffff">
        </div>
        <button id="changeColorBtn">Change Color</button>
    </div>
    <script src="popup.js"></script>
</body>
</html>
```

![popup html](https://firebasestorage.googleapis.com/v0/b/assistivai-askcodi.appspot.com/o/askcodi-blog%2Fpopup-html.png?alt=media&token=60c1e245-c336-45ab-a194-ef947b286bd3)

For styles.css, let's make it look nice:

```css
body {
    font-family: Arial, sans-serif;
    width: 300px;
    padding: 10px;
    background-color: #f0f0f0;
}

.container {
    display: flex;
    flex-direction: column;
    align-items: center;
}

h1 {
    color: #333;
    font-size: 18px;
    margin-bottom: 15px;
}

.color-picker {
    margin-bottom: 15px;
    display: flex;
    flex-direction: column;
    align-items: center;
}

label {
    margin-bottom: 5px;
    color: #555;
}

#colorInput {
    width: 100px;
    height: 40px;
    border: none;
    cursor: pointer;
}

#changeColorBtn {
    background-color: #4CAF50;
    border: none;
    color: white;
    padding: 10px 20px;
    text-align: center;
    text-decoration: none;
    display: inline-block;
    font-size: 16px;
    margin: 4px 2px;
    cursor: pointer;
    border-radius: 4px;
    transition: background-color 0.3s;
}

#changeColorBtn:hover {
    background-color: #45a049;
}
```

In my case, AskCodi also suggested changes in manifest.json to include the styles file. here is updated manifest.json:

```json
{
    "manifest_version": 3,
    "name": "Background Color Changer",
    "version": "1.0",
    "description": "A simple extension to change the background color of webpages.",
    "permissions": [
        "activeTab"
    ],
    "action": {
        "default_popup": "popup.html",
        "default_icon": {
            "16": "images/icon16.png",
            "48": "images/icon48.png",
            "128": "images/icon128.png"
        }
    },
    "web_accessible_resources": [
        {
            "resources": [
                "styles.css"
            ],
            "matches": [
                "<all_urls>"
            ]
        }
    ],
    "icons": {
        "16": "images/icon16.png",
        "48": "images/icon48.png",
        "128": "images/icon128.png"
    },
    "content_scripts": [
        {
            "matches": [
                "<all_urls>"
            ],
            "js": [
                "content.js"
            ]
        }
    ]
}
```

![manifest json changes](https://firebasestorage.googleapis.com/v0/b/assistivai-askcodi.appspot.com/o/askcodi-blog%2Fmanifest-changes.png?alt=media&token=30d88e56-8fd0-4083-b00b-f299a6dcc1c4)

## Step 5: Adding Functionality
Now for the fun part - making our extension do something! Open popup.js and let's ask AskCodi for help:
"Write JavaScript to change the background color of the current tab when the 'Change Color' button is clicked"
AskCodi should provide something like below:

1. For content.js:
```javascript
chrome.runtime.onMessage.addListener(function(request, sender, sendResponse) {
    if (request.action === "changeColor") {
        document.body.style.backgroundColor = request.color;
    }
});
```

2. For popup.js:
```javascript
document.addEventListener('DOMContentLoaded', function() {
    var changeColorBtn = document.getElementById('changeColorBtn');
    var colorInput = document.getElementById('colorInput');

    changeColorBtn.addEventListener('click', function() {
        var color = colorInput.value;
        
        chrome.tabs.query({active: true, currentWindow: true}, function(tabs) {
            chrome.tabs.sendMessage(tabs[0].id, {action: "changeColor", color: color});
        });
    });
});
```

![content js](https://firebasestorage.googleapis.com/v0/b/assistivai-askcodi.appspot.com/o/askcodi-blog%2Faskcodi-js.png?alt=media&token=6b49bb54-1227-40b2-9b62-490ca15aa446)

## Step 6: Testing Your Extension
Now comes the moment of truth! Let's load our extension into Chrome:

1. Open Chrome and navigate to chrome://extensions/
2. Enable "Developer mode" in the top right
3. Click "Load unpacked" and select your extension directory

![loaded extension](https://firebasestorage.googleapis.com/v0/b/assistivai-askcodi.appspot.com/o/askcodi-blog%2Fextension-loaded.png?alt=media&token=d6a9833f-5967-435d-813a-ca76c234ec2b)

You should see your extension icon appear in the Chrome toolbar. Click it, choose a color, and voilà! Watch the magic happen as you transform web pages with a click.

![result](https://firebasestorage.googleapis.com/v0/b/assistivai-askcodi.appspot.com/o/askcodi-blog%2Fresult.png?alt=media&token=ff4a0b69-d75f-4e29-9415-c6f615296375)

## Wrapping Up
And there you have it! Your very own Chrome extension, brought to life with the help of AskCodi. We've only scratched the surface of what's possible with extensions, but I hope this guide has sparked your curiosity to explore further.

Remember, the key to becoming a great developer is experimentation. Don't be afraid to tweak the code, add new features, or even start a completely new project. The possibilities are endless!
Happy coding, and may your browsing experience never be the same again! 🚀🎨

A beginner-friendly guide to building your first Chrome extension using AskCodi AI in Visual Studio Code, with step-by-step instructions and personal insights.

Building Your First Chrome Extension with AskCodi: A Step-by-Step Guide

**Introduction**

In the rapidly evolving landscape of artificial intelligence (AI) and machine learning, tools like AskCodi and ChatGPT have emerged as powerful assistants for developers and professionals alike. These AI-powered platforms aim to simplify coding tasks, enhance productivity, and provide intelligent solutions across various domains. While both offer remarkable capabilities, they cater to different needs and niches within the AI ecosystem. This blog will delve into a detailed comparison of AskCodi and ChatGPT, highlighting their features, strengths, and ideal use cases.

---

## AskCodi: The AI Coding Assistant

**Overview**

AskCodi is an AI-driven coding assistant designed to make coding simpler and more efficient. With general AI capabilities, it is proficient in multiple programming languages and frameworks, offering a suite of tools tailored for developers. AskCodi aims to streamline the coding process, providing features that range from code generation to bug detection.

**Key Features**

1. **Coding Apps**

   - **Code Generator**: Automatically generates code snippets based on user input.
   - **Code Refactor**: Helps in optimizing and improving existing code.
   - **Code Fixer**: Identifies and corrects code errors.
   - **Language Translator**: Converts code from one programming language to another.
   - **Makefile and Dockerfile Writers**: Generates configuration files for build automation and containerization.
   - **Query Writer and Schema Resolver**: Assists in creating database queries and resolving schema-related issues.
   - **Regex Generator and Explainer**: Creates regular expressions and explains their functions.
   - **Bug Detector and Big-O Analyzer**: Identifies bugs and analyzes algorithm efficiency.
   - **Code Explainer, Reviewer, and Documentation**: Provides insights into code functionality, reviews code, and generates documentation.
   - **Commit Generator and Error Explainer**: Simplifies commit messages and explains errors for easier debugging.
   - **Library Finder**: Helps in finding appropriate libraries for specific tasks.

2. **Codi Sandbox**

   - A platform to build and test front-end code in real-time.
   - Supports various languages and frameworks, including React, Svelte, Tailwind CSS, and Material-UI (MUI).
   - Allows experimentation and visualization of UI components.

3. **Codespaces**

   - **Intelligent Code Assistance**: Offers code completions based on project context.
   - **Project-Wide Dialogue**: Enables users to ask questions about the entire project or specific files and folders.
   - **Template and Repository Integration**: Users can start with templates or integrate their existing GitHub repositories.

4. **Integrations**

   - Supports top Integrated Development Environments (IDEs) like VS Code, JetBrains IDEs, and Sublime Text.
   - Brings AI capabilities directly into the development environment.

5. **Autocomplete Plugin**

   - Provides real-time inline suggestions within VS Code.
   - Enhances coding speed and accuracy.

**Additional Highlights**

- **Access to Multiple AI Models**: Utilizes 10 state-of-the-art models from OpenAI, Google, Meta, Anthropic, and Mistral.
- **Optimized for 65+ Programming Languages**: Offers out-of-the-box support for a wide range of languages.
- **Privacy-Focused**: Does not train on data used within the app.
- **Pricing Plans**: Offers multiple plans, including Premium at \$14.99 and Ultimate at \$34.99.

---

## ChatGPT: The Versatile Language Model

**Overview**

Developed by OpenAI, ChatGPT is a generative AI service renowned for its natural language processing capabilities. It leverages deep learning algorithms to understand and generate human-like text, making it versatile across various contexts such as writing assistance, code generation, tutoring, and information retrieval. Since its inception, ChatGPT has evolved to be multimodal, capable of processing and generating outputs based on text, images, and speech.

**Key Features**

- **Natural Language Understanding and Generation**: Provides coherent and contextually relevant responses in conversational settings.
- **Multimodal Functionality**: Processes and analyzes text, images, and speech inputs to deliver appropriate outputs.
- **Diverse Use Cases**: Assists in writing tasks, code generation, math tutoring, and general knowledge inquiries.
- **Advanced Capabilities**:
  - **Data Analysis and File Uploads**: Processes and analyzes user-provided data and files.
  - **Vision and Web Browsing**: Interprets visual inputs and accesses web information for up-to-date responses.
  - **Advanced Voice Mode**: Engages in voice-based interactions.
  - **DALL·E Image Generation**: Creates images based on textual descriptions.
  - **Custom GPTs**: Allows users to create and use custom language models tailored to specific needs.

**Access and Pricing**

- **Free Access**: Offers basic features accessible to all users.
- **Subscription Plans**:
  - Access to advanced models like OpenAI o1-preview, o1-mini, GPT-4, GPT-4o, and GPT-4o mini.
  - Higher message limits for GPT-4 models.
  - Early access to new features and improvements.

---

## Comparison

### **Functionality**

- **AskCodi**: Specializes in coding assistance, offering tools explicitly designed for developers. Its features like code generation, refactoring, bug detection, and integration with IDEs make it a comprehensive assistant for programming tasks.
  
- **ChatGPT**: A general-purpose AI model proficient in understanding and generating human-like text across various domains. While it can assist with code generation and explanations, its scope extends beyond coding to include writing, tutoring, and information retrieval.

### **Specialization**

- **AskCodi**: Tailored for programmers and developers seeking to enhance their coding workflow. Its focus on supporting multiple programming languages and integration with development tools makes it ideal for technical environments.
  
- **ChatGPT**: Versatile and suitable for a broad audience, including writers, students, educators, and professionals needing assistance in tasks involving natural language.

### **Integration and Accessibility**

- **AskCodi**: Offers seamless integration with popular IDEs like VS Code, JetBrains, and Sublime Text, bringing AI assistance directly into the coding environment.
  
- **ChatGPT**: Accessible through web interfaces and APIs, useful in applications requiring conversational AI and language understanding.

### **AI Models and Technology**

- **AskCodi**: Utilizes 10 AI models from leading providers like OpenAI, Google, Meta, Anthropic, and Mistral, focusing on delivering optimized performance for coding tasks.
  
- **ChatGPT**: Powered by OpenAI's advanced language models, including GPT-4, known for their superior language comprehension and generation abilities.

### **Privacy and Data Usage**

- **AskCodi**: Emphasizes privacy by not training on data used within the app, ensuring that user code and information remain confidential.
  
- **ChatGPT**: Processes user inputs to generate responses, and while OpenAI maintains policies to protect user data, the specifics of data handling may vary.

### **Pricing**

- **AskCodi**: Offers tiered pricing plans—Premium at \$14.99 and Ultimate at \$34.99—providing access to advanced features according to the subscription level.
  
- **ChatGPT**: Provides free access to basic features, with subscription plans unlocking advanced models and capabilities.

---

## Which One Should You Choose?

**Choose AskCodi if:**

- You are a developer or programmer seeking tools to streamline coding tasks.
- Integration with IDEs and support for multiple programming languages are important.
- You need specialized features like code refactoring, bug detection, and code documentation.
- Privacy and not having your code used for training are essential considerations.

**Choose ChatGPT if:**

- You require a versatile AI assistant capable of handling a variety of tasks beyond coding.
- Natural language understanding and conversational abilities are important for your use case.
- You are interested in multimodal functionalities like image generation and voice interactions.
- Access to a broad knowledge base and assistance with writing, tutoring, or general inquiries is desired.

---

## Conclusion

Both AskCodi and ChatGPT are powerful AI tools that offer significant benefits in their respective domains. AskCodi shines as a dedicated coding assistant, packed with features that cater specifically to developers' needs. In contrast, ChatGPT stands out as a versatile language model suitable for a wide range of applications involving natural language processing.

Your choice between the two ultimately depends on your specific requirements. If you're looking to enhance your coding workflow with AI-powered tools integrated into your development environment, AskCodi is the way to go. If you need a multifaceted AI assistant capable of engaging in natural conversations and assisting with various tasks, ChatGPT would be more appropriate.

---

**References**

- [AskCodi Official Website](https://www.askcodi.com/)
- [OpenAI ChatGPT](https://www.openai.com/)

Discover the key differences between AskCodi and ChatGPT in this in-depth comparison. Learn which AI tool suits your needs—whether you're a developer seeking specialized coding assistance with AskCodi or require versatile language capabilities offered by ChatGPT.

ChatGPT vs. AskCodi: A Comprehensive Comparison

# Interactive Learning: How Coding Games Are Revolutionizing Education

As the demand for coding skills continues to rise, educational platforms are increasingly turning to gamification to make learning programming languages engaging and effective. Here are five popular platforms that utilize gaming methodologies to teach coding, each offering a unique approach and supporting various programming languages.

## 1. CodeCombat

**Overview**: CodeCombat is designed to teach programming through an immersive fantasy game where players write code to control their characters. This platform is particularly appealing for students and beginners, as it combines storytelling with coding challenges.

**Languages Offered**: Python, JavaScript, CoffeeScript, and Lua.

**How It Works**: Players progress through levels by solving coding puzzles that require them to write actual code. The game starts with basic concepts and gradually introduces more complex programming principles. 

**Why It Stands Out**: CodeCombat allows learners to see the immediate impact of their code in a fun environment, making it easier for them to grasp programming concepts.

**Link**: Start your coding adventure at [CodeCombat](https://codecombat.com).

## 2. CodinGame

**Overview**: CodinGame offers a wide variety of coding challenges in a game-like format where players can compete against each other or work solo. This platform is ideal for those looking to improve their coding skills while having fun.

**Languages Offered**: Over 25 programming languages including JavaScript, Python, Ruby, C++, and Java.

**How It Works**: Users solve puzzles and challenges that become progressively harder. The platform also hosts contests where users can compete globally, adding a competitive edge to learning.

**Why It Stands Out**: CodinGame fosters a community environment where learners can share solutions and strategies, enhancing collaborative learning.

**Link**: Join the fun at [CodinGame](https://www.codingame.com).

## 3. Scratch

**Overview**: Developed by MIT, Scratch is a block-based programming language aimed primarily at children. It allows users to create interactive stories, animations, and games through a visual interface.

**Languages Offered**: Scratch uses its own block-based language but teaches fundamental programming concepts applicable to many languages.

**How It Works**: Users drag and drop code blocks to create programs, making it accessible for beginners who may find text-based coding intimidating.

**Why It Stands Out**: Scratch encourages creativity and logical thinking while providing a supportive community where users can share their creations.

**Link**: Start creating at [Scratch](https://scratch.mit.edu).

## 4. Tynker

**Overview**: Tynker is an educational platform that offers coding courses for kids through games and interactive lessons. It provides a structured learning path with projects that build on each other.

**Languages Offered**: HTML, CSS, JavaScript, Python, and Java.

**How It Works**: Tynker combines game design with coding lessons, allowing users to create their own games while learning programming concepts. The platform also includes guided tutorials for various skill levels.

**Why It Stands Out**: Tynker's project-based approach helps students apply what they learn in real-world scenarios, reinforcing their understanding of coding principles.

**Link**: Explore coding projects at [Tynker](https://www.tynker.com).

## 5. CodeMonkey

**Overview**: CodeMonkey teaches coding using CoffeeScript—an open-source language that compiles into JavaScript. The platform is designed for kids but is engaging enough for adult learners as well.

**Languages Offered**: CoffeeScript (compiles to JavaScript).

**How It Works**: Players navigate through various levels by solving puzzles that require them to write code. Each level introduces new programming concepts in an engaging way.

**Why It Stands Out**: CodeMonkey makes learning fun with its colorful graphics and interactive gameplay while teaching real-world coding skills.

**Link**: Start your adventure at [CodeMonkey](https://www.playcodemonkey.com).

By integrating these gaming methodologies into learning environments, educators can foster a more engaging and effective approach to teaching coding skills. These platforms not only make learning fun but also equip students with essential skills needed in today's digital world.

---

## References

1. [CodeCombat](https://codecombat.com)
2. [CodinGame](https://www.codingame.com)
3. [Scratch](https://scratch.mit.edu)
4. [Tynker](https://www.tynker.com)
5. [CodeMonkey](https://www.playcodemonkey.com)

Discover how interactive coding games are transforming education by making programming fun, engaging, and accessible for learners of all ages.

How Coding Games are Shaping the Future of Education

# **Python Cheatsheet: Comprehensive Overview**

This cheatsheet covers essential Python concepts across 18 categories, providing a quick reference guide for beginners and experienced programmers alike.

---

## **1. Introduction to Python**

### **What is Python?**
- **Python** is a high-level, interpreted programming language known for its readability and simplicity.
- Created by **Guido van Rossum** and first released in 1991.
- Emphasizes code readability with its use of significant indentation.

### **Why Learn Python?**
- Widely used in **web development**, **data science**, **machine learning**, **automation**, and more.
- Large standard library and active community support.
- Platform-independent and open-source.

### **Installing Python**
- **Download from**: [python.org/downloads](https://www.python.org/downloads/)
- **Verify installation**:
  ```bash
  python --version
  ```
  or
  ```bash
  python3 --version
  ```

### **Setting Up the Development Environment**
- **IDEs**: PyCharm, Visual Studio Code, Sublime Text, Atom.
- **Command Line**: Use terminal or command prompt with a text editor.
- **Virtual Environments**:
  ```bash
  python -m venv env
  source env/bin/activate  # On Windows: env\Scripts\activate
  ```

---

## **2. Python Basics**

### **Syntax and Semantics**
- **Indentation**: 4 spaces per indent level.
  ```python
  if condition:
      # indented block
      execute_code()
  ```
- **Comments**:
  ```python
  # This is a single-line comment
  ```

### **Variables and Data Types**
- **Variables**: No explicit declaration required.
  ```python
  x = 10        # Integer
  y = 3.14      # Float
  name = "Alice"  # String
  ```
- **Data Types**: `int`, `float`, `str`, `bool`, `list`, `tuple`, `dict`, `set`, etc.

### **Operators**
- **Arithmetic**: `+`, `-`, `*`, `/`, `%` (modulus), `**` (exponent), `//` (floor division).
- **Assignment**: `=`, `+=`, `-=`, `*=`, `/=`, etc.
- **Comparison**: `==`, `!=`, `>`, `<`, `>=`, `<=`.
- **Logical**: `and`, `or`, `not`.
- **Bitwise**: `&`, `|`, `^`, `~`, `<<`, `>>`.

### **Input and Output**
- **Input**:
  ```python
  user_input = input("Enter something: ")
  ```
- **Output**:
  ```python
  print("Hello, World!")
  ```

---

## **3. Control Flow Statements**

### **Conditional Statements**
```python
if condition:
    # code block
elif another_condition:
    # another code block
else:
    # else code block
```

### **Loops**

- **For Loops**:
  ```python
  for i in range(5):
      print(i)
  ```
- **While Loops**:
  ```python
  while condition:
      # code block
  ```

### **Loop Control Statements**
- **break**: Exits the loop.
  ```python
  for i in range(10):
      if i == 5:
          break
  ```
- **continue**: Skips to the next iteration.
  ```python
  for i in range(10):
      if i % 2 == 0:
          continue
      print(i)
  ```
- **pass**: Null operation; placeholder.
  ```python
  if condition:
      pass
  ```

---

## **4. Functions**

### **Defining Functions**
```python
def function_name(parameters):
    """Docstring"""
    # code block
    return value
```

### **Example**:
```python
def add(a, b):
    return a + b
```

### **Lambda Functions**
```python
square = lambda x: x ** 2
print(square(5))  # Output: 25
```

### **Variable Scope**
- **Local**: Defined within a function.
- **Global**: Defined outside all functions.
  ```python
  global_var = "I am global"

  def foo():
      local_var = "I am local"
  ```

### **Recursion**
```python
def factorial(n):
    if n == 1:
        return 1
    else:
        return n * factorial(n - 1)
```

---

## **5. Data Structures**

### **Lists**
- **Creation**:
  ```python
  numbers = [1, 2, 3, 4, 5]
  ```
- **Indexing/Slicing**:
  ```python
  first = numbers[0]
  slice = numbers[1:3]
  ```
- **Methods**:
  ```python
  numbers.append(6)
  numbers.remove(2)
  numbers.sort()
  ```
- **List Comprehensions**:
  ```python
  squares = [x ** 2 for x in range(10)]
  ```

### **Tuples**
- **Creation**:
  ```python
  point = (1, 2)
  ```
- **Accessing Elements**:
  ```python
  x = point[0]
  ```

### **Dictionaries**
- **Creation**:
  ```python
  person = {'name': 'Alice', 'age': 30}
  ```
- **Accessing Values**:
  ```python
  name = person['name']
  ```
- **Methods**:
  ```python
  person.keys()
  person.values()
  person.items()
  ```

### **Sets**
- **Creation**:
  ```python
  unique_numbers = {1, 2, 3}
  ```
- **Operations**:
  ```python
  set1.union(set2)
  set1.intersection(set2)
  set1.difference(set2)
  ```

---

## **6. Modules and Packages**

### **Importing Modules**
```python
import math
print(math.pi)
```

### **From Import**
```python
from math import sqrt
print(sqrt(16))
```

### **Third-Party Packages**
- **Installing**:
  ```bash
  pip install requests
  ```
- **Using**:
  ```python
  import requests
  response = requests.get('https://api.example.com')
  ```

### **Creating Modules**
- **my_module.py**
  ```python
  def greet(name):
      return f"Hello, {name}!"
  ```
- **Using the Module**:
  ```python
  from my_module import greet
  print(greet("Bob"))
  ```

---

## **7. Object-Oriented Programming (OOP)**

### **Classes and Objects**
```python
class Person:
    def __init__(self, name):
        self.name = name

    def greet(self):
        print(f"Hello, my name is {self.name}")

p = Person("Alice")
p.greet()
```

### **Inheritance**
```python
class Employee(Person):
    def __init__(self, name, employee_id):
        super().__init__(name)
        self.employee_id = employee_id
```

### **Encapsulation**
- **Private Attributes** (conventionally):
  ```python
  class MyClass:
      def __init__(self):
          self.__private_var = 42
  ```

### **Polymorphism**
- **Method Overriding**:
  ```python
  class Animal:
      def speak(self):
          pass

  class Dog(Animal):
      def speak(self):
          print("Woof!")
  ```

### **Abstraction**
- **Abstract Classes** (using `abc` module):
  ```python
  from abc import ABC, abstractmethod

  class Shape(ABC):
      @abstractmethod
      def area(self):
          pass
  ```

---

## **8. File Handling**

### **Opening Files**
```python
file = open('filename.txt', 'r')  # Modes: r, w, a, rb, wb, etc.
```

### **Reading Files**
```python
content = file.read()
lines = file.readlines()
```

### **Writing to Files**
```python
file = open('filename.txt', 'w')
file.write("Hello, World!")
```

### **Closing Files**
```python
file.close()
```

### **Using Context Managers**
```python
with open('filename.txt', 'r') as file:
    content = file.read()
# File is automatically closed here
```

---

## **9. Exception Handling**

### **Try...Except Blocks**
```python
try:
    # code that may raise an exception
    result = 10 / 0
except ZeroDivisionError:
    print("Cannot divide by zero")
```

### **Else and Finally**
```python
try:
    result = 10 / 2
except ZeroDivisionError:
    print("Cannot divide by zero")
else:
    print("Result is", result)
finally:
    print("Execution completed")
```

### **Raising Exceptions**
```python
def check_age(age):
    if age < 0:
        raise ValueError("Age cannot be negative")
```

---

## **10. Advanced Topics**

### **Iterators and Generators**

- **Iterator Protocol**:
  ```python
  iter_obj = iter([1, 2, 3])
  print(next(iter_obj))
  ```

- **Generators**:
  ```python
  def my_generator():
      yield 1
      yield 2
      yield 3

  for value in my_generator():
      print(value)
  ```

### **Decorators**
```python
def decorator_function(original_function):
    def wrapper_function(*args, **kwargs):
        # Do something before
        result = original_function(*args, **kwargs)
        # Do something after
        return result
    return wrapper_function

@decorator_function
def display():
    print("Display function ran")
```

### **Multithreading and Multiprocessing**

- **Multithreading**:
  ```python
  import threading

  def print_numbers():
      for i in range(5):
          print(i)

  thread = threading.Thread(target=print_numbers)
  thread.start()
  ```
- **Multiprocessing**:
  ```python
  from multiprocessing import Process

  def print_numbers():
      for i in range(5):
          print(i)

  process = Process(target=print_numbers)
  process.start()
  ```

### **Regular Expressions**
```python
import re

pattern = r'\d+'  # Matches one or more digits
text = "There are 12 apples"

matches = re.findall(pattern, text)
print(matches)  # Output: ['12']
```

---

## **11. Working with Data**

### **NumPy for Numerical Computing**
```python
import numpy as np

array = np.array([1, 2, 3])
print(array * 2)  # Output: [2, 4, 6]
```

### **Pandas for Data Analysis**
```python
import pandas as pd

data = {'Name': ['Alice', 'Bob'], 'Age': [30, 25]}
df = pd.DataFrame(data)
print(df)
```

### **Matplotlib for Visualization**
```python
import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [4, 9, 16])
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.title('Sample Plot')
plt.show()
```

---

## **12. Web Development**

### **Flask Example**
```python
from flask import Flask

app = Flask(__name__)

@app.route('/')
def home():
    return "Hello, Flask!"

if __name__ == '__main__':
    app.run()
```

### **Django Example**
- **Install Django**:
  ```bash
  pip install django
  ```
- **Create Project**:
  ```bash
  django-admin startproject myproject
  ```
- **Run Server**:
  ```bash
  python manage.py runserver
  ```

---

## **13. Database Interaction**

### **SQLite with sqlite3 Module**
```python
import sqlite3

conn = sqlite3.connect('example.db')
cursor = conn.cursor()

cursor.execute('CREATE TABLE IF NOT EXISTS users (id INTEGER PRIMARY KEY, name TEXT)')
cursor.execute('INSERT INTO users (name) VALUES (?)', ('Alice',))
conn.commit()

cursor.execute('SELECT * FROM users')
print(cursor.fetchall())

conn.close()
```

### **Using SQLAlchemy**
```python
from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

engine = create_engine('sqlite:///users.db')
Base = declarative_base()

class User(Base):
    __tablename__ = 'users'
    id = Column(Integer, primary_key=True)
    name = Column(String)

Base.metadata.create_all(engine)
Session = sessionmaker(bind=engine)
session = Session()

new_user = User(name='Bob')
session.add(new_user)
session.commit()

users = session.query(User).all()
```

---

## **14. Testing and Debugging**

### **Unit Testing with unittest**
```python
import unittest

def add(a, b):
    return a + b

class TestAdd(unittest.TestCase):
    def test_add(self):
        self.assertEqual(add(2, 3), 5)

if __name__ == '__main__':
    unittest.main()
```

### **Debugging with pdb**
```python
import pdb

pdb.set_trace()
# Your code here
```

### **Logging**
```python
import logging

logging.basicConfig(level=logging.INFO)
logging.info('This is an info message')
```

---

## **15. Deployment and Version Control**

### **Using Git**
- **Initialize Repository**:
  ```bash
  git init
  ```
- **Add and Commit**:
  ```bash
  git add .
  git commit -m "Initial commit"
  ```
- **Push to GitHub**:
  ```bash
  git remote add origin https://github.com/username/repo.git
  git push -u origin master
  ```

### **Deploying to Heroku**
- **Install Heroku CLI**: [devcenter.heroku.com/articles/heroku-cli](https://devcenter.heroku.com/articles/heroku-cli)
- **Commands**:
  ```bash
  heroku login
  heroku create appname
  git push heroku master
  ```

---

## **16. Best Practices and Coding Conventions**

### **PEP 8 Guidelines**
- **Indentation**: 4 spaces per level.
- **Maximum Line Length**: 79 characters.
- **Naming Conventions**:
  - Variables and functions: `lower_case_with_underscores`
  - Classes: `CapWords`
  - Constants: `ALL_CAPS`

### **Writing Clean Code**
- **Modular Code**: Break code into reusable functions and classes.
- **Readability**: Use meaningful variable and function names.
- **Documentation**: Include docstrings and comments.

---

## **17. Practical Projects**

### **Project Ideas**
- **Calculator Application**
- **To-Do List Manager**
- **Web Scraper**
- **Simple Web Blog**

### **Getting Started**
- **Plan the Project**: Outline features and functionalities.
- **Set Up Environment**: Create virtual environment and initialize version control.
- **Iterative Development**: Build and test small components.

---

## **18. Additional Resources**

### **Learning Platforms**
- **Official Documentation**: [docs.python.org](https://docs.python.org/3/)
- **Tutorials**: [Real Python](https://realpython.com/), [Programiz](https://www.programiz.com/python-programming)
- **Online Courses**: Coursera, Udemy, edX

### **Community and Support**
- **Forums**: [Stack Overflow](https://stackoverflow.com/questions/tagged/python), [Reddit r/Python](https://www.reddit.com/r/Python/)
- **Local Meetups**: Look for Python user groups in your area.

---

## **Useful Code Snippets**

### **Swap Variables**
```python
a, b = b, a
```

### **List Flattening**
```python
flat_list = [item for sublist in nested_list for item in sublist]
```

### **Check for Even Numbers in a List**
```python
evens = [num for num in numbers if num % 2 == 0]
```

### **Dictionary Comprehension**
```python
squared_numbers = {x: x**2 for x in range(5)}
```

### **Enumerate in Loops**
```python
for index, value in enumerate(my_list):
    print(f"Index: {index}, Value: {value}")
```

---

Remember, this cheatsheet is a quick reference. For in-depth understanding, experiment with the code and refer to official documentation and comprehensive tutorials.

This cheatsheet covers essential Python concepts across 18 categories, providing a quick reference guide for beginners and experienced programmers alike.

Python Cheatsheet: Comprehensive Overview

As a data scientist, reaching the point where I can efficiently tackle complex datasets and extract meaningful insights feels like a significant milestone. Reflecting on this journey, I realize that certain Python libraries have been instrumental in shaping my skills and success in the field. I want to share these essential tools with you, hoping they might enhance your own data science endeavors.

### 1. NumPy

**The foundation of numerical computing in Python.**

NumPy was my gateway into efficient numerical computations. Its powerful N-dimensional array object and broadcasting functions allowed me to perform complex mathematical operations with ease. Understanding NumPy's capabilities was like unlocking a secret weapon for handling large datasets and performing linear algebra computations crucial in data science.

### 2. Pandas

**Your best friend for data manipulation and analysis.**

Pandas revolutionized the way I manipulated and analyzed data. With its intuitive DataFrame objects, I could clean, transform, and merge datasets seamlessly. Pandas became the backbone of my data preprocessing tasks, making me wonder how I ever managed without it. Its ability to handle missing data and perform group operations saved me countless hours.

### 3. Matplotlib

**Turning data into visual stories.**

Visualization is key in data science, and Matplotlib provided me with the tools to create a wide array of static plots. Whether I needed a simple line graph or a complex histogram, Matplotlib's flexibility made it indispensable for exploratory data analysis. Crafting visual representations of data helped me and my stakeholders understand complex concepts more intuitively.

### 4. Seaborn

**Beautiful statistical graphics made easy.**

Building on Matplotlib, Seaborn added an aesthetic flair to my visualizations. Its high-level interface allowed me to create attractive and informative statistical graphics with minimal code. Seaborn made my plots not just functional but also presentation-ready, enhancing the storytelling aspect of my data insights.

### 5. SciPy

**Advanced computations at your fingertips.**

Delving deeper into scientific computing, SciPy extended NumPy's functionality with modules for optimization, integration, interpolation, and statistics. It became crucial when I needed to perform advanced computations or solve differential equations. SciPy's signal processing and image manipulation capabilities opened up new avenues in data analysis.

### 6. Scikit-learn

**A comprehensive machine learning toolkit.**

When I ventured into machine learning, Scikit-learn was the comprehensive library that provided algorithms for classification, regression, clustering, and more. Its user-friendly interface and consistent API made implementing models straightforward and efficient. Scikit-learn's built-in cross-validation and parameter tuning tools helped me build robust predictive models.

### 7. TensorFlow

**Building scalable deep learning models.**

To tackle deep learning projects, TensorFlow became my go-to library. Developed by Google, it allowed me to build and train neural networks at scale. The ability to deploy models across various platforms expanded the horizons of what I could achieve, from image recognition to natural language processing tasks.

### 8. Keras

**Simplifying deep learning with a high-level API.**

Keras, running on top of TensorFlow, simplified the process of constructing complex neural networks. Its user-friendly API made it accessible to experiment with deep learning models without getting bogged down by technical details. This simplicity enabled rapid prototyping and iteration, essential for innovation.

### 9. Statsmodels

**Delving into statistical modeling and testing.**

For statistical modeling and hypothesis testing, Statsmodels offered a wealth of tools. It was particularly useful for time series analysis and econometrics, enabling me to perform in-depth statistical analyses that complemented my machine learning models. Understanding the underlying statistical properties of data enhanced the credibility of my findings.

### 10. Plotly

**Interactive and shareable visualizations.**

To create interactive and shareable visualizations, Plotly became invaluable. It allowed me to build dynamic charts and dashboards that could be easily integrated into web applications. This interactivity enhanced how I presented data insights to stakeholders, making data exploration a collaborative experience.

---

**"The right tools can transform not just your work, but your entire approach to problem-solving."**

Embracing these Python libraries has not only improved my efficiency but also expanded the scope of projects I can tackle. Each library brought a new dimension to my skillset, enabling me to handle data with greater sophistication and depth.

If you're embarking on your data science journey or looking to enhance your existing skills, I highly recommend exploring these tools. They've been game-changers for me, and they have the potential to significantly accelerate your growth in this ever-evolving field.

Here's to continuous learning and the exciting discoveries that lie ahead!

As a data scientist, reaching the point where I can efficiently tackle complex datasets and extract meaningful insights feels like a significant milestone. Reflecting on this journey, I realize that certain Python libraries have been instrumental in shaping my skills and success in the field. I want to share these essential tools with you, hoping they might enhance your own data science endeavors.

The 10 Python Libraries That Transformed My Data Science Journey

# The Top 5 AI Coding Assistants: A Comprehensive Review

## Introduction

In the rapidly evolving world of software development, AI coding assistants have become indispensable tools for developers. They streamline coding processes, enhance productivity, and help in debugging and optimizing code. This blog highlights the top five AI coding assistants, providing an overview of their features, pricing, pros and cons, and ultimately guiding you on which one to choose.

## Overview of AI Coding Assistants

### 1. AskCodi

AskCodi is an AI-driven coding assistant designed to make coding simpler and more efficient. With general AI capabilities, it is proficient in multiple programming languages and frameworks, offering a suite of tools tailored for developers. AskCodi aims to streamline the coding process, providing features that range from code generation to bug detection.

**Best For**: Developers with a comprehensive development needs and a versatile coding environment.

![AskCodi](https://firebasestorage.googleapis.com/v0/b/assistivai-askcodi.appspot.com/o/askcodi-blog%2FScreenshot%202024-09-30%20at%2022.27.09.png?alt=media&token=6fd8129b-441a-4a86-b261-afd297d5be3c)

**Key Features**:
- **Coding Apps**:
  - **Code Generator**: Automatically generates code snippets based on user input.
  - **Code Refactor**: Optimizes existing code for better performance.
  - **Code Fixer**: Identifies and corrects errors in code.
  - **Language Translator**: Converts code between different programming languages.
  - **Makefile and Dockerfile Writers**: Generates configuration files for automation.
  - **Query Writer and Schema Resolver**: Assists in creating database queries.
  - **Regex Generator and Explainer**: Creates and explains regular expressions.
  - **Bug Detector and Big-O Analyzer**: Identifies bugs and analyzes algorithm efficiency.
  - **Code Explainer, Reviewer, and Documentation**: Provides insights into code functionality.

- **Codi Sandbox**: Real-time front-end code testing.

- **Codespaces**: Intelligent code assistance with project-wide dialogue.

- **Integrations**: Works seamlessly with popular IDEs like VS Code.

- **Autocomplete Plugin**: Provides real-time inline suggestions.

- **Available LLMs**:
  - GPT-3.5
  - GPT-4
  - GPT 4o mini
  - Claude
  - Mistral
  - LLaMA
  - Gemini

**Pricing**:
- **Free Plan**: Basic features with limited credits.
- **Premium Plan**: $14.99/month.
- **Ultimate Plan**: $34.99/month.

**Pros**:
- Extensive feature set tailored for various coding needs.
- Supports multiple programming languages.
- Privacy-focused approach with no training on user data.
- Flexible and multimple AI model options.

**Cons**:
- Higher pricing for advanced plans may deter individual users.
- Some features may require a learning curve for new users.

### 2. Tabnine

Tabnine is a powerful AI code assistant designed to enhance developer productivity through intelligent code suggestions and real-time assistance. Below are the key features based on the information from the Tabnine pricing page.

**Best For**: Teams focused on privacy and security.
![Tabnine](https://firebasestorage.googleapis.com/v0/b/assistivai-askcodi.appspot.com/o/askcodi-blog%2Ftabnine.png?alt=media&token=f22b032f-5b78-4be9-9ab8-89112e0622b4)
**Key Features**
- **AI Chat Agents in the IDE**: Provides real-time assistance for generating code, testing, documentation, and more.
- **Integration with Atlassian Jira**: Enhances AI responses and generation based on project management tools (other integrations coming soon).
- **Switchable AI Chat Models**: Offers flexibility to choose between Tabnine's models and third-party providers.
- **Security Vulnerability Filtering**: Helps identify and mitigate potential security risks in code.
- **Admin Tools**: Basic admin tools for user and policy management in the Pro plan. Advanced admin tools in the Enterprise plan, includes tracking usage and productivity metrics, along with SSO integration for easier administration.

**Pricing**:
- **Starter Plan**: Free.
- **Pro Plan**: $12/month per user.
- **Enterprise Plan**: $39/month per user (one year commitment).

**Pros**:
- Flexible AI model options cater to diverse developer preferences.
- Strong integration capabilities with project management tools like Jira.

**Cons**:
- Limited features in the free version may not meet all user needs.
- The Enterprise plan can be costly for smaller teams or startups.

### 3. GitHub Copilot

GitHub Copilot is an advanced AI-powered coding assistant designed to enhance developer productivity. Here are the key features that make it a valuable tool for developers:

**Best For**: Developers heavily integrated into the GitHub ecosystem.

![Copilot](https://firebasestorage.googleapis.com/v0/b/assistivai-askcodi.appspot.com/o/askcodi-blog%2Fcopilot.png?alt=media&token=16bef88b-df9a-4c33-aa8b-2872d41990a6)

**Key Features**:
**Code Completion**: Provides autocomplete-style suggestions as developers type in supported IDEs. Analyzes existing code to offer relevant and context-aware suggestions.
**Copilot Chat**: Allows users to ask coding-related questions and receive instant responses.
**Debugging and Security Remediation**:Helps diagnose and fix errors in code, providing relevant information about exceptions. Alerts users to insecure coding patterns and suggests improvements in real-time.

**Knowledge Integration**:Users can create and manage collections of documentation to provide context for chat interactions.

**Pull Request Management**:Generates AI-driven summaries of changes made in pull requests, highlighting important files and focus areas for reviewers. Automatically suggests commit messages based on changes made.
**Supported Environments**:Available across various platforms including IDEs (Visual Studio Code, JetBrains IDEs), CLI, GitHub Mobile, and GitHub.com.

**Management and Policies (Enterprise Features)**: Administrators can manage user access and permissions within organizations.

**Pricing**:
- **Individual Plan**: $10/month or $100/year.
- **Business Plan**: $19/month per seat.
- **Enterprise Plan**: $39/month per seat.

*Pros**:
- Deep integration with GitHub enhances workflow for collaborative projects.
- Context-aware suggestions improve coding efficiency significantly.

**Cons**:
- Dependency on GitHub may limit its use for developers not using GitHub repositories.
- Pricing can add up quickly for larger teams using the Business or Enterprise plans.
- Some features may require a learning curv for new users

### 4. Sourcegraph Cody

Sourcegraph Cody is an AI-powered coding assistant designed to enhance developer productivity by providing intelligent code suggestions and automating repetitive tasks. It integrates seamlessly with Sourcegraph’s code intelligence platform, enabling users to search, understand, and generate code efficiently across large and complex codebases.

**Best For**: Large teams managing extensive codebases.

![Cody](https://firebasestorage.googleapis.com/v0/b/assistivai-askcodi.appspot.com/o/askcodi-blog%2Fcody.png?alt=media&token=3df58428-9862-410e-b3a7-97c95e23a5bd)

**Key Features**:
- **Unlimited Autocomplete:** Context-based code autocompletions for any programming language.
- **Chat Functionality:** Ask questions about your codebase using natural language commands.
- **Code Context & Personalization:** Tailors responses based on local or enterprise-scale codebases.
- **Multiple LLM Support:** 
  - Free Plan includes Claude 3.5 Sonnet and Gemini Pro/Flash.
  - Pro Plan adds GPT-4o and Claude 3 Opus.
  - Enterprise allows bringing your own LLM key.
- **Compatibility:** Works with VS Code, JetBrains IDEs, Neovim, all popular coding languages, and major code hosts.

**Pricing**:
- **Free Plan (Cody Free)**: $0/month.
- **Pro Plan (Cody Pro)**: $9/month.
- **Enterprise Plan (Cody Enterprise)**: $19/user/month.

**Pros**:
- Excellent support for large teams with extensive customization options.
- Natural language chat functionality simplifies complex queries about the codebase.

**Cons**:
- Some features may be limited in the free version compared to paid plans.
- May require additional setup time for optimal use within large organizations.

### 5. Codeium

Codeium is a powerful AI coding assistant designed to enhance developer productivity through intelligent code suggestions and real-time assistance.

**Best For**: Teams seeking a user-friendly interface with robust features.

![Codeium](https://firebasestorage.googleapis.com/v0/b/assistivai-askcodi.appspot.com/o/askcodi-blog%2Fcodeium.png?alt=media&token=1da0eddf-85ee-41a2-a6bd-a5119fef5d7c)

**Key Features**
- **Unlimited Code Autocomplete:** Provides continuous suggestions as you type.
- **In-editor AI Chat Assistant:** Helps answer queries directly within the IDE.
- **No Training on Non-Permissive Data:** Ensures data privacy by not using non-permissive data for training models.
- **Encryption in Transit:** Keeps your data secure during transmission.
- **Access to GPT-4o and Claude Sonnet (Pro and above):** Offers advanced AI capabilities for enhanced coding assistance.
- **Fast Autocomplete Speed (Pro and above):** Ensures quick response times for suggestions.
- **Admin Usage Dashboard (Teams and Enterprise):** Provides insights into team usage and productivity metrics.

**Pricing**:
- **Individual Plan**: Free forever
- **Pro Plan**: $9/month(1 team member).
- **Teams Plan**: $12/month per user (up to 50 team members).
- **Enterprise Plan**: Contact for pricing details (customized based on needs)

**Pros**:
- User-friendly interface with minimal learning curve for beginners.
  
**Cons**:
- Limited advanced features compared to competitors like AskCodi or GitHub Copilot.
- 

## Final Thoughts
When choosing an AI coding assistant, consider the following:
**Integration Needs**: If you’re already using GitHub extensively, GitHub Copilot might be the best fit. For teams using various IDEs, AskCodi or Tabnine could be more suitable.
**Budget Constraints**: Evaluate the pricing plans against your budget. Tools like Codeium offer free versions that might meet basic needs without incurring costs.
**Feature Requirements**: Identify which features are essential for your workflow. If you need advanced debugging or refactoring capabilities, AskCodi or Tabnine may be preferable.
**Team Size:** Larger teams might benefit from Sourcegraph Cody or Tabnine due to their customization options and management tools.

Ultimately, the best choice will align with your specific development needs, team dynamics, and budget considerations. Take advantage of free trials where available to test out different tools before committing to a subscription. In summary:
- Choose AskCodi if you're looking for a powerful AI assistant for an All-in-One Solution:. AskCodi's Codespaces feature acts like an AI agent with multiple AI model support for GitHub and other projects that allows developers to use AI for context awareness and AI tools at full capabilities.
- Opt for Tabnine if your primary concern is security and you want a tool that integrates well with existing workflows while offering personalized suggestions.
- Use GitHub Copilot  if you are already using GitHub extensively for your projects, Copilot will enhance your workflow significantly, Other option would be to use AskCodi to integrate github repo in COdi Codespaces.
- Select Sourcegraph Cody if you manage large codebases and need advanced search capabilities where understanding complex codebases is crucial..
- Go with Codeium for teams who want a user-friendly experience with fast suggestions. Codeium is an intuitive tool that enhances productivity without overwhelming you with complexity.

---

### References

1. [AskCodi Pricing](https://askcodi.com/pricing)
2. [Tabnine Pricing](https://www.tabnine.com/pricing/)
3. [GitHub Copilot Pricing](https://github.com/pricing)
4. [Sourcegraph Cody Pricing](https://sourcegraph.com/pricing)
5. [Codeium Pricing](https://codeium.com/pricing)

Explore the top AI coding assistants that are revolutionizing the coding landscape. This blog highlights their features, pricing, pros and cons, and which one suits best for you..

ChatGPT vs. AskCodi: A Comprehensive Comparison

Building Your First Chrome Extension with AskCodi: A Step-by-Step Guide

Python Cheatsheet: Comprehensive Overview

The 10 Python Libraries That Transformed My Data Science Journey

Top 5 AI Coding Assistants: A Deep Dive into the Future of Coding