Friendly Code. How to Program Easily and Simply

A guide on how to survive in a new future where communication with technology becomes as natural as reading.
Michael Littman shares unique ideas on how to learn to program and manage devices in an interesting and accessible way, so that... they serve faithfully instead of watching you. Forget the fear of artificial intelligence that can take your job! Master the basic programming skills and improve your relationships with technology.
Littman offers simple and comprehensible examples of how programming can be used in everyday life, giving practical advice on experimenting with these ideas. You will learn how machine learning can speed up and simplify the coding process, making it accessible to everyone.

Life hacks for effective communication with AI:
• Be thoughtful: while using the computer, ask yourself what you are trying to achieve, and aim to stay focused on your goal.
• Insist on support: if you want the computer to perform certain actions but the existing software doesn’t allow it, switch to another software.
• Learn to program: try to tailor the system to your needs. Develop the ability to express ideas in a form understandable to machines.
• Even more useful and interesting insights are inside.

“Michael Littman's 'Friendly Code' is an engaging guide that reveals programming as a life skill accessible to everyone. Littman demonstrates how even basic coding knowledge helps adapt technology to personal needs, making tech a true ally. The book is full of practical examples that teach a critical approach to tasks, implement machine learning, and automate everyday processes. This edition inspires bold mastery of digital skills and will be an excellent companion for anyone striving to become a confident user in the world of technology.” — Alexey Didenko, Team Lead of the Wildberries payment gateway division

Michael Littman is a scientist, researcher, educator, and writer. His scientific interests lie in machine learning, game theory, and the development of computer networks. He is currently a professor of computer science at Brown University, where he has been teaching since 2012.