Showing posts with label Python. Show all posts
Showing posts with label Python. Show all posts

Thursday, September 19, 2024

39 AI Code Tools - The Ultimate Guide in 2024

To See All Articles About Technology: Index of Lessons in Technology

What are the best AI code tools in 2024?

TL;DR - As of September 2024, most programmers achieve the best results by using Cursor with Anthropic Sonnet 3.5 or OpenAI o1.

AI coding tools are becoming standard practice for many developers. And today, you’ll learn which code generators  and tools are the best ones out there for creating high-quality code with the help of artificial intelligence.

Want to learn more? Read on!

Is it possible to code with AI tools?

Yes, it is possible to code with AI tools.  In fact, leveraging AI tools for coding is not only possible, but it can also significantly enhance productivity and accuracy.

AI code is code written by artificial intelligence (AI), often times utilizing large language models (LLMs). These AI programs can write their own programs or translate from one programming language to another. They also perform tasks like offering assistance in auto-generating documentation and finding code snippets faster.

One of the most popular tools is Open AI’s Codex, an AI system that translates natural language to code. Codex powers GitHub Copilot, another popular AI code tool.

OpenAI Codex is capable of interpreting simple commands in natural language and carrying them out for the programmer. This makes it possible to build on top of the existing application with a natural language interface.

As a general-purpose programming model, OpenAI Codex can be applied to almost any programming task. That said, the tool is in beta and so results will vary.

AlphaCode by DeepMind is another tool that is shaking up the industry. Interestingly, this tool outperforms human coders in certain situations. You see, AlphaCode outperformed 45% of programmers in coding competitions with at least 5,000 participants.

However, there are problems with code generators, too. That's why AI coding tools are used to help developers become more productive and efficient, rather than to replace them entirely.

For example, a Stanford-affiliated research team found that engineers who use AI tools are more likely to cause security vulnerabilities in their apps. Plus, questions around copyright are not entirely resolved.

In other words, AI code tools are not yet completely safe to use. That said, the popularity of these tools means that they can’t be overlooked.

What is AI code written in?

AI code is written in languages supported by the AI code generator. For example, OpenAI Codex is most fluent in Python but is also quite capable in several languages, including JavaScript, Ruby, and TypeScript.

Now, let’s take a look at the best code generators out there.

The best AI code generators and AI development tools

What are some effective AI code generators? The most popular ones include OpenAI Codex, Copilot by Github,  ChatGPT by OpenAI as well as open-source models such as Llama 3.

But there are plenty of other tools out there. I’ve listed them here below, including their features, capabilities, and which companies are behind them. Let’s dive in!

Here are the best AI code generators of 2024.

1. OpenAI (ChatGPT, GPT-4, o1)

GPT-4, OpenAI's latest AI model, is a multimodal tool that excels in programming tasks. It understands and explains code, writes new code, and outperforms existing models on Python coding tasks. Despite its ability to handle complex tasks, it has limitations like reasoning errors and potential security vulnerabilities in the code it produces.  

ChatGPT is primarily a user-friendly interface developed by OpenAI that allows you to interact conversationally with advanced language models like GPT-4 and o1-mini. While it's often referred to as a model, ChatGPT is essentially the platform that enables you to generate or debug code and perform other text-based tasks by communicating with these underlying AI models.

Update May 14th: OpenAI just releaded GPT-4o - their new flagship model that’s as smart as GPT-4 Turbo and much more efficient. With 50% reduced pricing and 2x faster latency, it achieves impressive results.

Update September 16th:  o1 is a new series of AI models designed to enhance reasoning by spending more time thinking through problems before responding, excelling in complex tasks in science, coding, and math. OpenAI o1-mini is a faster, more cost-effective model particularly effective at coding, offering an affordable solution for applications that require reasoning but not extensive world knowledge. Both models are now available in ChatGPT and via the API for users to tackle complex problems efficiently.

Price: Free or $20 for GPT Plus

2. Copilot

Copilot uses publicly available code from GitHub repositories so that users can access large datasets and quickly develop accurate code. The tool detects errors in code and recommends changes to it. You can start using GitHub Copilot by installing one of the extensions in your preferred environment.

Price: $10-$19 - GitHub Copilot is free to use for verified students, teachers, and maintainers of popular open source projects.

3. AWS Bedrock

AWS Bedrock is Amazon Web Services' fully managed service that provides developers with access to a variety of powerful foundation models for building and scaling generative AI applications. For programmers, it offers APIs to interact with models like Amazon's Titan and others from leading AI startups, enabling tasks such as code generation, debugging, and text synthesis. While AWS Bedrock simplifies integrating AI into applications, it may have limitations like model accuracy and potential security vulnerabilities in generated code, so developers should exercise caution and perform thorough testing.

Pricing information can be found here

4. AlphaCode

Another AI-based code generator is Google-backed DeepMind’s AlphaCode, which gives developers access to source code from various language libraries. With AlphaCode, developers can leverage thousands of pre-made libraries, helping them connect and use third-party APIs quickly and easily. AlphaCode is not yet available to the public.

Price: No information available

5. Tabnine

Tabnine is an AI code completion tool that utilizes deep learning algorithms to provide the user with intelligent code completion capabilities. Tabnine supports several programming languages such as Java, Python, C++, and more. This tool is open-source and is used by leading tech companies like Facebook and Google.

Price: Paid plans start from $12/month per seat

6. CodeT5

CodeT5 is an open AI code generator that helps developers to create reliable and bug-free code quickly and easily. It is also open-source and provides support for various programming languages such as Java, Python, and JavaScript. CodeT5 also has an online version as well as an offline version for data security.

Price: Free

7. Polycoder

Polycoder is an open-source alternative to OpenAI Codex. It is trained on a 249 GB codebase written in 12 programming languages. With Polycoder, users can generate code for web applications, machine learning, natural language processing and more. It is well-regarded amongst programmers because of its capability of generating code quickly.

Price: Free

8. Deepcode

DeepCode is a cloud-based AI code analysis tool that automatically scans the codebase of a project and identifies potential bugs and vulnerabilities. It offers support for multiple languages such as Java, Python, and JavaScript. DeepCode is well-regarded for its accurate bug detection.

Price: No information available

9. WPCode

WPCode is an AI-driven WordPress code generator created by Isotropic. It supports both developers and non-technical WordPress creators, allowing them to quickly generate high-quality code snippets. CodeWP supports not only HTML and CSS but languages such as Java and Python. It even includes AI assistants to suggest improvements to code snippets.

Price: Starting at $49

10. AskCodi

AskCodi is a code generator that offers a full suite of development tools to help developers build and ship projects faster. With its AI-based code generation, it helps developers write better code and shorter code blocks, with fewer mistakes. AskCodi can be used to develop both web and mobile applications.

Price: Paid plans start from $7.99/month per seat

11. Codiga

Codiga is a static analysis tool that ensures code is secure and efficient. It supports popular languages like JavaScript, Python, Ruby, Kotlin, and more. With Codiga, you can test your code for vulnerabilities and security issues in real time. It also includes an auto-fixer to quickly address any issues in the code.

Price: Paid plans start from $14/month per seat

12. Visual Studio IntelliCode

Visual Studio IntelliCode is an extension of the Visual Studio Code editor created by Microsoft that provides AI-assisted development experiences to improve developer productivity. It offers smarter IntelliSense completions and helps reduce the amount of time developers spend navigating and debugging code.

Price: Starting from $45/month

13. PyCharm

PyCharm is an AI code completion tool from JetBrains which provides developers with intelligent code completion capabilities. This tool supports various programming languages such as Java, Python, and JavaScript. PyCharm is well regarded for its accuracy and can help developers reduce the amount of time spent on coding tasks.

Price: Starting from $24.90/month per seat

14. AIXcoder

AIXcoder is an AI-powered programming pair designed to aid development teams in writing code. It supports languages such as Java, Python, and JavaScript. This tool also offers a range of features such as automated routine tasks, AI-powered code completion, real-time code analysis and error checks while typing.

Price: No information available

15. Ponicode

Ponicode is an AI-powered code assistant designed to help developers optimize their coding workflow. It uses natural language processing and machine learning to generate code from user-defined descriptions. The tool is maintained by CircleCI.

Price: No information available

16. Jedi

Jedi is an open-source option for code completion in AI. It mostly functions as a plugin for editors and IDEs that use Python static analysis tools.

Price: Free

17. Wing Python IDE Pro

Created by Wingware, Wing IDE is a Python-specific software setup that combines the code editing, code navigation, and debugging mechanisms required to Code and Test Software applications. It offers various features such as an intelligent auto-completing Editor, Refactoring, Multi-Selection, and Code Snippets, which make coding much easier and more efficient.

Price: Annual licenses starting at $179/month

18. Smol Developer

Smol is an open-source artificial intelligence agent designed to function as a personal junior developer, capable of generating an entire codebase from your specific product specifications. Unlike traditional, rigid starter templates, Smol can create any kind of application based on your unique requirements. Boasting a codebase that is simple, safe, and small, it offers the perfect blend of ease-of-understanding, customization, and a helpful, harmless, and honest approach to AI development.

Price: Smol is open-source with a MIT License.

19. Cody (Sourcegraph)

Cody (not to be confused with AskCodi), Sourcegraph's AI tool, is a comprehensive coding assistant. It understands your entire codebase, answers queries, and writes code. Beyond guidance, Cody provides detailed code explanations, locates specific components, and identifies potential issues with suggested fixes. Cody works directly in VS code with an extension.

Price: Cody is free for personal use, Sourcegraph starts at $5k/year

20. CodeWhisperer (Amazon)

CodeWhisperer is a tool developed by Amazon. It offers real-time, AI-driven code suggestions and identifies potential open-source code matches for easier review. It even scans for security vulnerabilities, suggesting immediate patches. An added bonus is its commitment to code safety, always aligning with best security practices such as OWASP guidelines.

Price: Free for personal use, $19/month professional use

21. Bard (Google)

Bard can help with programming and software development tasks, including code generation, debugging and code explanation. These capabilities are supported in more than 20 programming languages including C++, Go, Java, Javascript, Python and Typescript. And you can easily export Python code to Google Colab — no copy and paste required. Bard can also assist with writing functions for Google Sheets.

Price: Google Bard is Free

22. Code Llama (Meta)

Code Llama is a set of large language models specialized for coding, built on the Llama 2 platform. It includes different models for various needs: the general-purpose Code Llama, Code Llama - Python for Python-specific tasks, and Code Llama - Instruct for instruction-based coding. These models vary in size (7B, 13B, and 34B parameters) and can handle up to 16k token inputs, with some improvements on up to 100k tokens. The 7B and 13B models also offer content-based infilling.

Code Llama’s training recipes are available on their Github repository - Model weights are also available.

23. Claude 2 & 3, 3.5 (Anthropic)

Claude 3.5 Sonnet is the latest natural language AI model introduced by Anthropic, a firm established by Dario Amodei, formerly of OpenAI. This new iteration is engineered for enhanced input and output lengths and boasts superior performance relative to its earlier version. In an internal agentic coding evaluation, Claude 3.5 Sonnet solved 64% of problems, outperforming Claude 3 Opus which solved 38%. Users can input up to 100K tokens in each prompt, which means that Claude can work over hundreds of pages of technical documentation. The earlier version, Claude 2 scored a 71.2% up from 56.0% on the Codex HumanEval, a Python coding test.

Their evaluation tests the model’s ability to fix a bug or add functionality to an open source codebase, given a natural language description of the desired improvement. When instructed and provided with the relevant tools, Claude 3.5 Sonnet can independently write, edit, and execute code with sophisticated reasoning and troubleshooting capabilities. It handles code translations with ease, making it particularly effective for updating legacy applications and migrating codebases.

A Stability AI Membership is required for commerical application

24. Stable Code 3B

Stability AI's Stable Code 3B, a new 3 billion parameter Large Language Model specialized in code completion, which is 60% smaller yet performs similarly to the larger CodeLLaMA 7b. This model, trained on diverse programming languages and software engineering-specific data, can run in real-time on modern laptops without a GPU. Stable Code 3B is part of Stability AI's Membership program and offers advanced features like Fill in the Middle capabilities and expanded context size, demonstrating state-of-the-art performance in multi-language coding tasks.

A Stability AI Membership (Starting at $20/mo) is required for commercial applications. Free for non-commercial.

25. Replit AI

Replit AI is an innovative code completion tool designed to streamline your coding experience by offering tailored suggestions that align with the context of your current file. As you delve into coding, the tool intuitively presents inline suggestions, enhancing your efficiency and accuracy. Additionally, Replit AI offers advanced features such as the ability to refine suggestions through code comments, the application of prompt engineering for more relevant results, and the flexibility to toggle the code completion feature on or off within the editor settings, ensuring a customized coding environment tailored to your preferences.

Replit AI is available in Replit's Free tier (Limited) and in their Core tier (Advanced Model).  

26. Plandex

Plandex employs persistent agents that tackle extensive tasks spanning numerous files and involving multiple steps. It segments sizable tasks into manageable subtasks, executing each in sequence until the entire task is accomplished. This tool aids in clearing your backlog, navigating new technologies, overcoming obstacles, and reducing the time spent on mundane activities.

Plandex is open-source on Github

27. Meta AI (Meta Lama 3)

Meta has launched Meta AI, powered by the Llama 3 model with 70 billion parameters.  The model positions itself as a powerful asset for improving application functionalities, but it does not match the customization and transparency of more advanced models like GPT-4 Turbo and Claude Opus. The benefits of Meta's approach to open-source AI are multifaceted, including attracting top talent, leveraging community contributions, fostering standardization and lower costs, building goodwill, and aligning with business models that do not rely solely on AI products.  While it is described as "open weight," providing access to the model's weights, it does not include the full toolkit necessary for reproduction. They also co-developed Llama 3 with torchtune, the new PyTorch-native library for easily authoring, fine-tuning, and experimenting with LLMs.

Moreover, Meta is also currently pretraining a 405B parameter model, signaling an ambitious expansion of its AI capabilities. This larger model, set to be released later, promises even more powerful functionalities and potential industry leadership if it surpasses current leaders like GPT-4 and Claude Opus. Such a development could reshape industry standards and perceptions, especially against competitors who guard their models under the guise of safety concerns. This bold move by Meta not only showcases their commitment to advancing AI technology but also challenges the industry's more cautious narratives around the sharing and utilization of AI models, setting new benchmarks for what’s achievable in AI development.

28. MetaGPT

Not to be confused with Meta AI, MetaGPT is a tool that automates the generation of software development outputs such as user stories, competitive analysis, requirements, data structures, APIs, and documents from a single line of input. It integrates roles typically found in a software company—product managers, architects, project managers, and engineers—into its workflow. These roles are executed by large language models (LLMs) following detailed Standard Operating Procedures (SOPs). The core philosophy behind MetaGPT is "Code = SOP(Team)," emphasizing the application of SOPs to organize and direct the work of its LLM teams. This structure aims to mimic the entire process of a software company, simplifying and automating complex tasks.

MetaGPT is MIT licensed and open-source

29. AutoRegex

AutoRegex is my favorite tool to translate natural language to regex. If you're like me, you wiped all traces of regex syntax from your memory the moment ChatGPT released - this helps!

30. llama.cpp

Llama.cpp is designed to facilitate LLM inference with optimal performance and minimal initial setup across various hardware, both locally and in the cloud. It is implemented in plain C/C++ without dependencies and features extensive support for Apple silicon through ARM NEON, Accelerate, and Metal frameworks. It also supports AVX, AVX2, and AVX512 for x86 architectures and offers integer quantization from 1.5 to 8 bits to enhance inference speed and reduce memory consumption. For NVIDIA GPUs, llama.cpp includes custom CUDA kernels, with AMD GPU support through HIP. Additionally, it supports Vulkan, SYCL, and partial OpenCL backends and can perform hybrid CPU+GPU inference to manage models that exceed VRAM capacity.

31. Aider

Aider is a  command line tool  allowing you to pair program with LLMs directly in your terminal. It seamlessly integrates with your local git repository, editing code directly in your source files and crafting smart commit messages for each change.

Aider is open-source on Github

32. Codestral (Mistral)

A model fluent in 80+ programming languages, Codestral, is Mistrral's first-ever code model. Codestral is an open-weight generative AI model explicitly designed for code generation tasks. It helps developers write and interact with code through a shared instruction and completion API endpoint. As it masters code and English, it can be used to design advanced AI applications for software developers.

Codestral is a 22B open-weight model licensed under the new Mistral AI Non-Production License, which means that you can use it for research and testing purposes. Codestral can be downloaded on HuggingFace

Update July 16th: Codestral Mamba release:  For easy testing, they made Codestral Mamba available on la Plateforme (codestral-mamba-2407), alongside its big sister, Codestral 22B. While Codestral Mamba is available under the Apache 2.0 license, Codestral 22B is available under a commercial license for self-deployment or a community license for testing purposes.

33. Cursor

Cursor is an AI-enhanced code editor designed to boost productivity by enabling developers to interact with their codebase through conversational AI and natural language commands. It includes features like Copilot++, which predicts your next code edit, and Cmd-K, which allows code modifications through simple prompts.

You can try Cursor for free

34. Warp

Warp is a modern, Rust-based terminal with AI built in. Type ‘#’ on your command line and start describing the command you want to run using natural language. Warp will load AI Command Suggestions as you type.

Warp AI is free to use up to 40 requests per user per month. You can create a Team and upgrade to a Team plan to unlock higher Warp AI request limits. Visit the pricing page to learn more.

35. CodiumAI

CodiumAI is a trending tool that developers can use to enhance their coding experience with the power of AI. Key features: When compared to the other tools, CodiumAI provides a set of unique features: Precise code suggestions: CodiumAI thoroughly analyzes your code, providing tailored suggestions. These include adding docstrings, refining exception handling, and implementing best practices, directly improving your code’s quality. Code explanation: This tool offers detailed descriptions of your source code or snippets, breaking down each component and offering insights and sample usage scenarios to enhance code comprehension. Automated test generation: Testing is essential in large codebases. CodiumAI simplifies this by swiftly generating accurate and reliable unit tests without manual intervention, saving significant time and effort and ensuring thorough testing of your codebase. Code behavior coverage: Comprehensive testing means covering all possible code behaviors. CodiumAI’s “Behavior Coverage” feature generates test cases covering various code behaviors and seamlessly applies related changes to your source code. Streamlined collaboration: CodiumAI facilitates teamwork by enabling seamless collaboration among developers. Its Git platform integration allows for sharing and reviewing code suggestions and test cases within your development team, promoting efficient workflows and code quality. Seamless implementation: With CodiumAI’s intelligent auto-completion agent, implementation becomes effortless. It seamlessly integrates with your task plans, ensuring smooth execution from concept to completion of your code. Multiple language and IDE support: CodiumAI supports popular programming languages such as Python, JavaScript, and TypeScript while seamlessly integrating with leading IDEs, including VSCode, WebStorm, IntelliJ IDEA, CLion, PyCharm, and JetBrains. Pricing The pricing of CodiumAI offers free code integrity for developers at $0/user per month, while teams can access optimized collaboration for $19/user per month.

36. MutableAI

MutableAI is a tool that revolutionizes the coding experience with features such as AI autocomplete, one-click production code enhancements, prompt-driven development, test generation, and extensive language and IDE integration, empowering developers to write code more efficiently and effectively. Key features Here are the key features of MutableAI: AI Autocomplete: Minimize time spent on boilerplate code and searching for solutions on Stack Overflow with specialized neural networks providing intelligent code suggestions. Production Quality Code: Refactor, document, and add types to your code effortlessly, ensuring high-quality code output. Prompt-driven Development: Interact directly with the AI by giving instructions to modify your code, enabling a more intuitive and interactive coding experience. Test Generation: Automatically generate unit tests using AI and metaprogramming techniques, ensuring comprehensive test coverage for your code. Language and IDE Integration: Supports popular languages like Python, Go, JavaScript, TypeScript, Rust, Solidity, and more, as well as integration with IDEs like JetBrains and Visual Studio (VS) Code. Pricing MutableAI’s basic plan offers $2 per repo per month, while its premium plan offers $15 per repo per month.

37. Figstack

Figstack is an innovative AI tool that provides developers with various features to improve code understanding, translation, documentation, and optimization. Figstack caters to developers at all levels, from beginners looking to understand complex code to experienced professionals aiming to automate tedious tasks like writing documentation or measuring code efficiency. Key features Code explanation in natural language: This feature helps users easily understand the code written in any language by translating it into clear, natural language descriptions. Cross-Language code translation: Developers can easily convert code from one programming language to another. This simplifies the process of porting applications across different technology stacks. Automated function documentation: Figstack automatically generates detailed docstrings that describe the function’s purpose, parameters, and return values, ensuring that your code is always readable, maintainable, and well-documented. Time complexity analysis: The tool helps developers assess the efficiency of their code in Big O notation, pinpoint bottlenecks, and optimize their code for better performance by identifying the time complexity of a program. Pricing Figstack is free to use and includes most of the essential features.

38. CodeGeeX

CodeGeeX is an AI-powered code generation tool designed to assist developers in writing, completing, and optimizing code more efficiently. It leverages deep learning models trained on a wide variety of programming languages and codebases, where it can provide context-aware code suggestions, complete code snippets, and even generate entire functions or modules. Key features Code generation and completion: CodeGeeX offers accurate code generation capabilities based on natural language descriptions. Also, it can complete the current line or multiple lines ahead, making the development process faster. Code translation: Developers can effortlessly convert their code from one programming language to another. Automated comment generation: The tool saves time by automatically generating line-level comments, which helps improve code readability and maintainability. AI chatbot: The AI chatbot in CodeGeeX provides quick answers to technical questions directly within the development environment instead of having developers find solutions on the internet. Wide IDE and language support: CodeGeeX supports various popular IDEs, including Visual Studio Code, JetBrains IDEs and multiple programming languages, such as Python, C++, JavaScript, and Go. Pricing CodeGeeX offers their plugin completely free for individual users. If there are more advanced requirements, they provide an enterprise plan.

39. Codeium

One I personally use. Millions of engineers, including our own, use these features every single day. Autocomplete Autocomplete faster than thought. Codeium's generative code can save you time and help you ship products faster. Command Give instructions in your editor to perform inline refactors, whether it is generating code, adding comments, or something even more complex. Chat Generate boilerplate, refactor code, add documentation, explain code, suggest bug fixes, and so much more. Powered by the largest models, optimized for coding workflows and Codeium's industry-leading reasoning engine. Context All of Codeium's features are powered by an industry-leading context awareness and reasoning engine. With full repository and multi repository codebase awareness, Codeium provides 35% more value purely from providing more grounded results.

References

Tags: Technology,Artificial Intelligence,Generative AI,Large Language Models,Python,JavaScript,

Monday, August 19, 2024

Intro to Classes and Objects in Python (Video and Interview Questions Inside)

To Watch All Python Videos: YouTube Academy For Python


Interview Questions

1. What is the concept behind the 'self' variable?

2. What does self refer to in the __init__ method?

3. When does a class method does not take self as the first argurment?

4. What is the concept behind the __init__ method?

5. Explain the need for Object Oriented Programming when we could do stuff with Functional programing?

6. What is the difference between a function and a method?

7. Apart from attributes and methods, what else can a Python object hold?

In Python, an object can hold several types of data beyond just attributes (data members) and methods (functions). Here’s a breakdown of other things a Python object can hold:

1. Class Variables

  • Definition: Variables that are shared among all instances of a class. These are defined within the class but outside any instance methods.
  • Usage: Often used to maintain shared state or constants across all instances of a class.
python
class MyClass: shared_variable = "I am shared" def __init__(self, value): self.instance_variable = value

2. Static Methods and Class Methods

  • Static Methods: Methods that do not operate on an instance of the class. They are bound to the class and not the object instance.
python
class MyClass: @staticmethod def static_method(): print("This is a static method")
  • Class Methods: Methods that are bound to the class itself and not to any particular object instance. They receive the class as the first argument (cls).
python
class MyClass: @classmethod def class_method(cls): print("This is a class method")

3. Properties (Getter and Setter Methods)

  • Properties: Special methods that allow controlled access to an object’s attributes. They are often used to encapsulate data and add logic to getting and setting attribute values.
python
class MyClass: def __init__(self, value): self._value = value @property def value(self): return self._value @value.setter def value(self, new_value): self._value = new_value

4. Descriptors

  • Definition: Objects that define the behavior of attribute access, specifically through the use of __get__(), __set__(), and __delete__() methods. Descriptors are a way to create reusable and controlled attribute management.
python
class Descriptor: def __get__(self, instance, owner): return "Descriptor get method" class MyClass: attr = Descriptor()

5. Custom __call__ Method

  • Definition: An object can be made callable by defining the __call__() method. This allows the object to be called as if it were a function.
python
class MyCallable: def __call__(self, x): return x * x obj = MyCallable() print(obj(5)) # Output: 25

6. Slots

  • Definition: A special mechanism (__slots__) that restricts the creation of instance attributes to a predefined set, reducing memory usage for instances of the class.
python
class MyClass: __slots__ = ['attr1', 'attr2'] def __init__(self, attr1, attr2): self.attr1 = attr1 self.attr2 = attr2

7. Magic Methods (Dunder Methods)

  • Definition: Special methods that begin and end with double underscores, like __init__, __str__, __repr__, __eq__, etc. These methods allow objects to interact with Python’s built-in functions and operators.
python
class MyClass: def __str__(self): return "String representation of MyClass"

8. Type Annotations

  • Definition: While not held directly by the object, Python objects can have type annotations for attributes, which are hints for developers and tools to understand what type of data the attribute should hold.
python
class MyClass: attr: int def __init__(self, attr: int): self.attr = attr

9. Metaclasses

  • Definition: The class of a class, controlling the creation of classes themselves. Metaclasses define how classes behave.
python
class Meta(type): def __new__(cls, name, bases, attrs): return super(Meta, cls).__new__(cls, name, bases, attrs) class MyClass(metaclass=Meta): pass

10. Annotations (__annotations__)

  • Definition: A dictionary attribute where type annotations are stored. This can be accessed via the __annotations__ attribute on the class or instance.
python
class MyClass: attr: int = 5 print(MyClass.__annotations__) # Output: {'attr': <class 'int'>}

11. Custom Attributes Added Dynamically

  • Definition: Python objects are dynamic, so you can add attributes to instances even after they’ve been created.
python
class MyClass: pass obj = MyClass() obj.new_attr = "Dynamically added attribute"

These additional elements allow Python objects to be highly flexible and powerful, supporting a wide range of programming paradigms and advanced behaviors.

8. Can you explain this with an example?

Certainly! Let's explore the various things a Python object can hold by creating an example class that demonstrates these concepts.

Example Class: Car

We'll create a class called Car that showcases attributes, methods, class variables, static methods, class methods, properties, descriptors, magic methods, and more.

python
# Descriptor example class FuelEfficiency: def __get__(self, instance, owner): return instance._mileage / instance._fuel_used class Car: # Class variable vehicle_type = "Automobile" # __slots__ example to limit attributes __slots__ = ['make', 'model', 'year', '_mileage', '_fuel_used'] # Annotations for type hints make: str model: str year: int # Static variable for descriptor fuel_efficiency = FuelEfficiency() def __init__(self, make, model, year, mileage, fuel_used): self.make = make self.model = model self.year = year self._mileage = mileage self._fuel_used = fuel_used # Instance method def drive(self, distance): self._mileage += distance print(f"Driven {distance} miles. Total mileage: {self._mileage} miles") # Static method @staticmethod def honk(): print("Honk! Honk!") # Class method @classmethod def from_string(cls, car_string): make, model, year = car_string.split('-') return cls(make, model, int(year), 0, 0) # Property for controlled access to mileage @property def mileage(self): return self._mileage @mileage.setter def mileage(self, value): if value < 0: raise ValueError("Mileage cannot be negative") self._mileage = value # Magic method to represent the object as a string def __str__(self): return f"{self.year} {self.make} {self.model}" # Magic method to make the object callable def __call__(self, action): return f"The car is now {action}." # Creating a car object using the class my_car = Car("Toyota", "Corolla", 2020, 5000, 200) # Demonstrating different aspects of the class # Accessing class variable print(f"Vehicle Type: {Car.vehicle_type}") # Using an instance method my_car.drive(150) # Using a static method Car.honk() # Using a class method to create an instance new_car = Car.from_string("Honda-Civic-2022") print(f"New Car: {new_car}") # Using a property print(f"Mileage: {my_car.mileage}") my_car.mileage = 6000 # Setting mileage via the property print(f"Updated Mileage: {my_car.mileage}") # Using the descriptor for fuel efficiency print(f"Fuel Efficiency: {my_car.fuel_efficiency} miles per gallon") # Using a magic method (__str__) to get a string representation print(str(my_car)) # Using a magic method (__call__) to make the object callable print(my_car("accelerating")) # Adding a dynamic attribute (not allowed due to __slots__) # my_car.color = "Blue" # This will raise an AttributeError

Explanation of Each Concept

  1. Class Variable (vehicle_type):

    • This variable is shared by all instances of the class. It is accessible through the class itself (Car.vehicle_type) or any instance (my_car.vehicle_type).
  2. Instance Variables (make, model, year, _mileage, _fuel_used):

    • These are specific to each instance of the Car class, holding the state of the object. For example, my_car.make holds the value "Toyota".
  3. Static Method (honk):

    • This method does not depend on the instance's state and can be called on the class itself. Car.honk() prints a honk sound.
  4. Class Method (from_string):

    • This method is bound to the class, not the instance. It's used here to create a Car object from a string. Car.from_string("Honda-Civic-2022") creates a new Car object.
  5. Properties (mileage):

    • Properties provide a controlled way to access and set instance variables. The mileage property ensures that the mileage cannot be set to a negative value.
  6. Descriptor (fuel_efficiency):

    • The FuelEfficiency descriptor calculates the car's fuel efficiency dynamically based on the mileage and fuel used. The descriptor is accessed like a normal attribute (my_car.fuel_efficiency).
  7. Magic Methods (__str__, __call__):

    • __str__: Provides a readable string representation of the object, used when you print the object or convert it to a string.
    • __call__: Makes the object callable like a function, allowing my_car("accelerating") to work.
  8. Slots (__slots__):

    • The __slots__ attribute limits the attributes that can be added to an instance, reducing memory usage. Attempting to add an attribute not listed in __slots__ will raise an AttributeError.

This example demonstrates how a Python object can hold various types of data and behavior, making it a powerful and flexible construct in object-oriented programming.

9. Can I change a class variable? And what would it be it's impant on the various instances of this class?

Yes, you can change a class variable in Python. The impact of changing a class variable depends on how you modify it, and it can affect all instances of the class in specific ways. Here's a detailed explanation:

1. Changing the Class Variable via the Class

When you change a class variable directly via the class, the change affects all instances of the class that do not have an overridden value for that variable.

python
class Car: # Class variable vehicle_type = "Automobile" def __init__(self, make, model): self.make = make self.model = model # Create two instances car1 = Car("Toyota", "Corolla") car2 = Car("Honda", "Civic") # Access the class variable print(car1.vehicle_type) # Output: Automobile print(car2.vehicle_type) # Output: Automobile # Change the class variable via the class Car.vehicle_type = "Motor Vehicle" # Access the class variable again print(car1.vehicle_type) # Output: Motor Vehicle print(car2.vehicle_type) # Output: Motor Vehicle

Impact:

  • When Car.vehicle_type is modified, all instances (car1, car2, etc.) reflect the new value, "Motor Vehicle", because they are still referencing the class variable.
  • Since vehicle_type is shared across all instances, changing it through the class affects every instance that hasn't overridden this variable.

2. Overriding the Class Variable via an Instance

If you change the class variable via an instance, it doesn't change the class variable itself but instead creates (or overrides) an instance variable with the same name.

python
class Car: # Class variable vehicle_type = "Automobile" def __init__(self, make, model): self.make = make self.model = model # Create two instances car1 = Car("Toyota", "Corolla") car2 = Car("Honda", "Civic") # Override the class variable in car1 car1.vehicle_type = "Electric Vehicle" # Access the class variable print(car1.vehicle_type) # Output: Electric Vehicle print(car2.vehicle_type) # Output: Automobile

Impact:

  • car1.vehicle_type now points to an instance variable, not the class variable. So car1 shows "Electric Vehicle".
  • car2.vehicle_type still references the class variable, so it continues to show "Automobile".
  • Changing car1.vehicle_type does not affect car2 or any other instance.

3. Resetting the Class Variable via the Class

If you reset the class variable after instances have overridden it, only those instances that have not overridden the variable will see the change.

python
class Car: # Class variable vehicle_type = "Automobile" def __init__(self, make, model): self.make = make self.model = model # Create two instances car1 = Car("Toyota", "Corolla") car2 = Car("Honda", "Civic") # Override the class variable in car1 car1.vehicle_type = "Electric Vehicle" # Change the class variable via the class Car.vehicle_type = "Motor Vehicle" # Access the class variable print(car1.vehicle_type) # Output: Electric Vehicle (instance variable, unchanged) print(car2.vehicle_type) # Output: Motor Vehicle (class variable, changed)

Impact:

  • car1 still holds its instance variable ("Electric Vehicle"), so it isn't affected by the change in the class variable.
  • car2, which didn't override the class variable, now shows the updated value "Motor Vehicle".

Summary

  • Class-Level Change: Changing a class variable directly via the class affects all instances that have not overridden that variable.
  • Instance-Level Override: Changing a class variable via an instance creates an instance-specific variable, leaving the class variable unaffected for other instances.
  • Instance Impact: Instances that override the class variable are unaffected by changes to the class variable, while those that don't will reflect any changes made to the class variable.
Tags: Technology,Python,Interview Preparation,

Wednesday, August 14, 2024

YouTube Academy For Python

Tags: Technology,Python,

Questions from Course 'Pair Programming With a Large Language Model'

To See All Python Articles: Python Course (Index)
Course by DeepLearning.AI: Pair Programming with a Large Language Model
1. What are the four parts of a prompt? Ans: 1. Priming of the Bot / System Role 2. Instruction / Query / Problem 3. Decoration 4. Input (Optional: Depends on whether it is Zero Shot Prompting or Few Shot Prompting) 2. What is zero shot prompting? 3. What is few shot prompting? 4. What things should a developer take care of when using an LLM as a Pair Programmer?

When using a Large Language Model (LLM) as a pair programmer, a developer should be mindful of several key considerations to ensure productive collaboration and to mitigate potential risks. Here are some important things to keep in mind:

1. Understanding the Model’s Strengths and Limitations

  • Strengths: LLMs are excellent at generating code snippets, suggesting improvements, providing explanations, and automating repetitive tasks. They can also assist in learning new languages or frameworks by providing quick examples and explanations.
  • Limitations: LLMs lack real-world understanding, may generate incorrect or inefficient code, and can make errors in complex logic or architecture. They might not understand the specific context of a project or the nuances of a development environment.

2. Code Review and Verification

  • Manual Review: Always review and test code suggestions from an LLM. The model can produce code that looks correct but has subtle bugs or security vulnerabilities.
  • Testing: Implement proper unit tests and integration tests to verify the correctness of any generated code before merging it into production.

3. Security and Privacy

  • Sensitive Data: Avoid sharing sensitive or proprietary information with the LLM. Be cautious about inputting code that contains secrets, credentials, or personally identifiable information (PII).
  • Data Handling: Understand how the LLM processes and stores data, especially if using a third-party API. Ensure compliance with data protection regulations and company policies.

4. Bias and Ethical Considerations

  • Bias: LLMs can sometimes reflect biases present in their training data, leading to suggestions that are discriminatory or unethical. Be vigilant about such biases and correct them if they appear in code or comments.
  • Ethical Use: Use the LLM ethically, ensuring it contributes positively to the project and does not introduce unethical practices, such as generating code for malicious purposes.

5. Context Awareness

  • Project Context: The LLM may not be fully aware of the project’s broader context, such as architectural patterns, long-term goals, or existing codebase nuances. Supplement its suggestions with your contextual knowledge.
  • Documentation: Ensure that generated code fits well within the existing codebase, follows project conventions, and is well-documented to maintain consistency.

6. Model Dependency

  • Avoid Over-Reliance: While LLMs are powerful tools, avoid becoming overly reliant on them. Maintain and develop your own problem-solving and coding skills.
  • Critical Thinking: Use the LLM as a tool to enhance your productivity, not as a replacement for critical thinking. Always question and evaluate the model’s suggestions.

7. Collaboration and Communication

  • Human Collaboration: Balance the use of LLMs with collaboration among human team members. Human insight and experience are invaluable, especially in complex or novel situations.
  • Feedback: Provide feedback to the LLM when possible, to improve its suggestions over time (if using a system that allows learning or customization based on feedback).

8. Legal and Compliance Considerations

  • Licensing Issues: Ensure that any code generated by the LLM does not violate software licenses or intellectual property rights.
  • Compliance: Make sure that the code and practices suggested by the LLM comply with industry regulations and standards applicable to your project.

9. Performance and Efficiency

  • Code Quality: Evaluate the performance and efficiency of the code generated by the LLM. It may generate suboptimal solutions that require refinement.
  • Optimization: Be prepared to optimize or refactor the generated code to meet performance requirements or to improve readability and maintainability.

10. Continual Learning

  • Stay Updated: Continuously learn and stay updated on the capabilities and limitations of LLMs as they evolve. This will help you use them more effectively and responsibly.
  • Skill Development: Use the LLM as a learning tool to deepen your understanding of new languages, frameworks, or algorithms by exploring its suggestions and explanations.

By considering these aspects, developers can effectively and responsibly integrate LLMs into their workflow, enhancing productivity while minimizing potential risks.

5. If I ask an LLM to add comments to the code it provides to explain its work: Which prompt engineering technique am I using? Direction Specific Prompting or Chain of Thought

When you ask an LLM to add comments to the code it provides in order to explain its work, you are primarily using Direction Specific Prompting.

Here's why:

  • Direction Specific Prompting: You are guiding the model to produce an output that not only includes the code but also adds explanatory comments. This is a form of directing the model to follow a specific set of instructions or format—namely, to include comments that explain the code. The prompt is specific about the desired format and content, instructing the model to produce code that is annotated with explanations.

Chain of Thought (CoT) Prompting (for contrast):

  • Chain of Thought Prompting involves encouraging the model to explicitly articulate its reasoning process as it arrives at a solution or answer. In the context of coding, this might involve asking the model to describe each step of the problem-solving process before writing the code, rather than just annotating the code after it has been written.

Example Comparison:

  • Direction Specific Prompting:

    • Prompt: "Please write a Python function that sorts a list of numbers and add comments to explain each part of the code."
    • This is direction-specific because you're telling the model to generate the code and include comments in a specific format.
  • Chain of Thought Prompting:

    • Prompt: "Explain your thought process for sorting a list of numbers, then write the Python function based on that explanation."
    • This would be Chain of Thought, as you're asking the model to articulate its reasoning before generating the final output.

So, in your scenario, Direction Specific Prompting is the technique being used.

Tags: Technology,Python,Generative AI,Large Language Models,