The Augmented Developer: Code Smarter, Not Harder

This repository contains the code used in the book "The Augmented Developer: Code Smarter, Not Harder" by Aymen El Amri.

About the Book

The Augmented Developer is a comprehensive guide to AI-assisted coding. It covers everything from the use of AI in code completion, generation, refactoring, security, and bug detection, to the creation of custom model chains and AI assistants.

The guide dives into advanced prompt engineering techniques and best practices used to maximize the potential of AI in coding. It also explores the ethical and technical implications of using generative AI in coding. Moreover, it looks into the future impacts of AI in coding and its potential to change the developer role as we know it.

Across the chapters of The Augmented Developer, you will:

Discover the Fusion of Pair Programming and AI: Learn how AI-assisted coding is reshaping the dynamics of pair programming and remote work. Understand how these tools not only support but also enhance the overall coding experience.
Unpack GitHub Copilot: Dive into the specifics of GitHub Copilot, from its foundational AI training to its real-world application in coding. This section breaks down its features, pricing, and setup, making it practical for you to start using it in your projects right away.
Learn How to Use GitHub Copilot Effectively: Master the art of using GitHub Copilot to boost your productivity. From writing code, debugging, refactoring, security hardening, unit testing, documentation, code reviews, pull requests, context-aware prompting, and more. You'll gain a deep understanding of how to leverage GitHub Copilot to its full potential.
Explore a Spectrum of AI Coding Assistants and Agents: Get acquainted with the broad landscape of AI coding tools available today: Tabnine, DeepCode, fauxpilot, privy, aider, Devin, SWE-Agent, Codeium, Tabby, CodeGeeX, Amazon CodeWhisperer, AskCodi, Blackbox AI, Bito, and many more.. We'll compare their features, assess their impacts, and discuss their potential applications.
Master Prompt Engineering for Developer Productivity: Learn how to write better prompts for GitHub Copilot, ChatGPT and AI coding assistants. This section will guide you through the process of crafting prompts that yield the best results. From Few-Shot Learning, Chain of Thoughts, Self Consistency, Tree of Thoughts, Autonomous Agents, General Knowledge Prompting, and more, you'll gain a deep understanding of how to maximize the potential of AI in coding using the right prompts.
Understand the Best Practices of Prompt Engineering: Acknowledge the best practices of prompt engineering and how you can apply them with the techniques you've learned. This section will provide you with a clear roadmap to follow when writing prompts for AI coding assistants.
Build Your In-house Developer Productivity Tools: Learn how to use tools like OpenAI APIs, LangChain, Chroma, and others to code your own AI-powered developer productivity tools. This section will guide you through the process of coding your custom AI assistants, step by step.
Understand Advanced Concepts in Generative AI: Gain a clear understanding of useful concepts such as RAG, vector databases, embeddings, tokens, and more.
Discover Advanced LLM Orchestration Tools: Immerse yourself in the world of advanced LLM orchestration and workflow tools. From DiFy and Flowise AI to LLMStack and others, you'll receive an overview of the best tools to help you create your own LLM-based solutions in a matter of minutes.
Understand the Ethical and Technical Implications of AI in Coding: Gain a clear perspective on these implications and answer the most pressing questions about AI in coding. What's controversial about AI in coding? Can we really trust AI to write code? Will AI replace developers? And more.

After reading this book, you'll possess the practical knowledge and tools needed to effectively use AI in coding, thereby enhancing your productivity as a developer.

Who Is This Book For?

This book is for developers, software engineers, data scientists, DevOps engineers, and anyone who wants to leverage AI to boost their productivity. A basic knowledge of Python programming and familiarity with coding concepts are recommended to understand the examples and code snippets provided in this book. The book is also suitable for all levels of expertise, from beginners to advanced developers and software engineers.

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About the Author

Aymen El Amri is an author, entrepreneur, trainer, and software engineer who has excelled in a variety of roles and responsibilities in the field of technology. These include DevOps & Cloud Native, Cloud Architecture, Python, Natural Language Processing, and more. Aymen has trained hundreds of software engineers and has written multiple books and courses, read by thousands of developers and software engineers.

Aymen adopts a practical approach to teaching, simplifying complex concepts into easy-to-understand language and providing real-world examples that resonate with his audience.

He has founded several projects, including FAUN, eralabs.io, and Marketto. You can find him on Twitter and Linkedin.