Best Python Resources to Check Before 2024 (AI Version)

Prepare to leave the year empowered with cutting-edge Python insights, trends, and open-source tools, ensuring you’re well-equipped for the coming era of AGI.

Python stands as a continuously evolving programming language to meet the demands of modern technology. As we approach the end of 2023, the insights embedded within Python’s framework have become indispensable for developers aiming to push the boundaries of their coding prowess.

Notably, Silicon Valley, synonymous with technological innovation, has witnessed a transformative union between Python and Artificial Intelligence (AI). This convergence has redefined the landscape, introducing novel possibilities and propelling Python into uncharted territories.

In today’s article, we will talk about 5 of the best open-source Python and AI projects that have marked a paradigm shift in the tech epicentre of Silicon Valley.

Note: This article will discuss some of the not-so-famous but really good Python (AI) Resources you can use to make your next projects. I recommend following the link along the project to read more about each.

https://unsplash.com/photos/a-close-up-of-a-small-animal-with-a-blurry-background-RtMeaovyPTM

As an additional resource, I’m including links to several courses that significantly contributed to my learning journey in data science, machine learning, and artificial general intelligence.

Personally, I’ve been an avid supporter of DataCamp since the beginning of my learning path, and I continue to expand my skills through their platform, engaging in new and intriguing courses.

Their offerings include a variety of captivating classes that I highly recommend exploring to enhance your understanding.

Feel free to check them out and delve into the world of data science, AI and ML education.

Introduction to ChatGPT

Working with the OpenAI API

ChatGPT Prompt Engineering for Developers

Introduction to Embeddings with the OpenAI API

Large Language Models (LLMs) Concepts

Large Language Models for Business

AI Ethics

Data Scientist with Python

Data Scientist with R

Machine Learning Scientist with R

Machine Learning Scientist with Python

Coming back to the topic -

1. AutoGPT

GitHub Link: https://github.com/Significant-Gravitas/AutoGPT

Official Documentation: https://news.agpt.co/

Stars: 154K

Forks: 37.5K

Keywords: Python, ai, artificial-intelligence, openai, autonomous-agents, gpt-4

https://news.agpt.co/

AutoGPT emerges as a visionary platform, democratizing access to AI for everyone fostering both utilization and creation. Striving to simplify the AI landscape, AutoGPT empowers users with tools designed to streamline the process of building, testing, and delegating AI agents in Python.

Pros

• Accessibility: AutoGPT opens the doors to AI for all, making it user-friendly and inclusive.
• Comprehensive Testing: Fine-tune your agent with the agbenchmark, ensuring optimal performance in real-world scenarios.
• Open Source Triumph: Noteworthy achievements like evo.ninja showcase the power of open-source collaboration within the AutoGPT community.

Cons

• Learning Curve: Beginners may face a learning curve, particularly in navigating the diverse functionalities.
• Limited Agent Diversity: While evo.ninja shines, there’s room for expanding the repertoire of specialized agents.
• Resource Intensive: The agbenchmark, though robust, may require considerable computing resources for optimal use.

Key Features and Benefits

• A ready-to-use template facilitating quick agent application development.
• Integration with the project’s CLI for seamless use with AutoGPT and forge-based agents.
• Submit benchmark runs to claim a spot on the AutoGPT Arena Leaderboard.
• Provides a user-friendly interface to control and monitor agents.
• Simple setup with ‘./run setup’ after cloning the repository.

2. gpt4free

GitHub Link: https://github.com/xtekky/gpt4free#-whats-new

Official Discord Community: https://discord.com/invite/XfybzPXPH5

Stars: 49.8K

Forks: 12.3K

Keywords: Python, chatbot, reverse-engineering, openai, chatbots, gpt, language-model, gpt-3, gpt3, openai-api, gpt-4, gpt4, chatgpt, chatgpt-api, openai-chatgpt, chatgpt-free, chatgpt-4, chatgpt4, gpt4-api, free-gpt

https://github.com/xtekky/gpt4free/tree/main

G4F is a revolutionary software package abbreviated for gpt4free. The driving force behind G4F lies in its commitment to pushing the boundaries of AI-powered conversations. Anchored by cutting-edge models like GPT-3.5 and GPT-4, G4F is a beacon for those seeking unparalleled advancements in interactive AI experiences.

Tailored for AI enthusiasts, G4F, with its robust features, including flawless chat generation and completion capabilities, G4F establishes an ecosystem that nurtures collaboration and innovation.

Benefits and Cool Features

• Diverse Provider Ecosystem: Access a wide range of providers such as Aichat, ChatgptAi, and OpenaiChat, tailoring your AI experiences to specific preferences.
• Docker Compatibility: Effortlessly set up and run the project using Docker, streamlining the installation process and ensuring consistency across different environments.
• Async Support: Leverage asynchronous execution for enhanced speed and performance, which is especially crucial when dealing with multiple providers.
• Proxy and Timeout Customization: Enjoy flexibility with proxy and timeout configurations, allowing users to adapt the project to diverse network conditions.

Key Functions and Use Cases

1. Seamlessly integrate the g4f package for comprehensive AI-powered Python experiences.
2. Explore a variety of providers such as AItianhu, ChatgptAi, and Vercel, each offering unique AI capabilities.
3. Dive into models like GPT-4, GPT-3.5, and others, understanding their strengths and potential use cases.

3. gpt-engineer

GitHub Link: https://github.com/AntonOsika/gpt-engineer

Official Documentation: https://gpt-engineer.readthedocs.io/en/latest/

Stars: 46.1K

Forks: 7.6K

Keywords: Python, ai, openai, coding-assistant, autonomous-agent, gpt-4, codebase-generation

https://medium.com/geekculture/dawn-ai-powered-software-development-gpt-engineer-artificial-intelligence-code-openai-bard-d7fe530ac1d

GPT Engineer empowers users to articulate their coding requirements, initiating a seamless dialogue with the AI.

Developed for adaptability and extensibility, GPT Engineer transforms prompts into entire codebases, simplifying the coding process. The library offers a fast and iterative interaction between AI and human developers.

With a straightforward setup and various usage options, including Docker support, GPT Engineer provides a dynamic platform for crafting and enhancing code effortlessly.

Benefits and Use Cases

• Effortless Code Generation: GPT Engineer streamlines code creation by translating user prompts into complete codebases, offering a novel and efficient approach to software development.
• Flexible AI Integration: The library allows users to define their AI agent’s identity, fostering a personalized coding experience in Python and enabling the agent to remember and learn from past interactions.
• Iterative Improvement of Code: GPT Engineer facilitates code enhancement by enabling users to specify improvements in existing codebases, promoting an iterative and collaborative coding process.

4. minGPT

GitHub Link: https://github.com/karpathy/minGPT

Stars: 16.8K

Forks: 2K

Keywords: Python, ai, openai, gpt-4, PyTorch, Transformers

https://github.com/karpathy/minGPT

MinGPT, a PyTorch re-implementation of GPT for training and inference, embodies simplicity, cleanliness, interpretability, and educational value.

Unlike sprawling implementations, minGPT prides itself on a concise codebase comprising around 300 lines (see mingpt/model.py).

The essence lies in a straightforward concept — a sequence of indices enters a Transformer, yielding a probability distribution for the next index. The library excels in clever batching techniques, optimizing efficiency across examples and over sequence length.

Benefits and Use Cases

1. minGPT is an invaluable educational resource, offering a transparent and compact implementation of the GPT model. Developers, students, and AI enthusiasts can leverage it to effortlessly comprehend the intricacies of Transformer-based language models.
2. The library provides an efficient platform for training GPT models, emphasizing runtime efficiency. Developers can benefit from minGPT’s streamlined codebase to experiment with training configurations, making it ideal for research and development in natural language processing.
3. With included projects such as “adder” for training GPT to perform mathematical operations and “chargpt” for character-level language modelling, minGPT demonstrates versatility. This library is suitable for various applications, including custom language models, creative text generation, and experimentation with AI-driven tasks.

Tip: minGPT’s versatility, simplicity, and educational focus make it a compelling choice for those eager to delve into advanced language models, fostering learning and innovation.

5. openai-Python

GitHub Link: https://github.com/openai/openai-python

Official Documentation: https://pypi.org/project/openai/

Stars: 16.3K

Forks: 2.2K

Keywords: Python, ai, openai

https://pypi.org/project/openai/

The OpenAI Python API library, available on PyPI, serves as a gateway to the OpenAI REST API for Python 3.7 and above.

This versatile library, powered by httpx, seamlessly integrates synchronous and asynchronous clients, making it adaptable to various application needs. It encompasses type definitions for both request parameters and response fields, enhancing ease of use.

Key Benefits and Use Cases

• Effortless Integration: The library streamlines access to the OpenAI API, enabling developers to integrate powerful AI capabilities into their Python applications, whether synchronous or asynchronous.
• Real-time Interaction: With support for streaming responses via SSE, the library facilitates real-time, dynamic interactions, making it suitable for applications requiring instantaneous AI-generated content.
• Enhanced Developer Experience: Typed request parameters and Pydantic response models provide a robust development environment, offering autocomplete and documentation features within editors. This ensures a smoother development process and aids in catching potential bugs early.

If you enjoyed reading this article, I am sure we share similar interests and are/will be in similar industries. So, let’s connect via LinkedIn and Github. Please do not hesitate to send a contact request!

If you liked this article, then my article on Long Short Term Memory (LSTM) and how to implement LSTM using Python is a good resource for you.

Long Short Term Memory (LSTM) and how to implement LSTM using Python