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Ardit Sulce

In today's fast-paced technological landscape, staying ahead of the competition is paramount for any programmer. The integration of AI into the coding process dramatically increases efficiency, enhance code quality, and reduce time spent on debugging and routine tasks. This course is designed for forward-thinking developers who recognize the necessity of adapting to these changes to maintain a competitive edge.

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In today's fast-paced technological landscape, staying ahead of the competition is paramount for any programmer. The integration of AI into the coding process dramatically increases efficiency, enhance code quality, and reduce time spent on debugging and routine tasks. This course is designed for forward-thinking developers who recognize the necessity of adapting to these changes to maintain a competitive edge.

In this course you will learn different AI code assistants like ChatGPT, GitHub Copilot, Tabnine, and Cursor AI Editor which add value to your programming skillset. We will compare these tools and you will learn how to use each of them. You will also learn best practices how programmers use AI in 2024 using the latest technology. These tools are not just about speeding up the coding process; they represent a new way of thinking about problem-solving and code generation. By learning to effectively integrate AI into your workflow, you can free up valuable time to focus on more complex and creative aspects of your projects, leading to faster development cycles and more innovative solutions.

You will learn through real-world use cases and hands-on tutorials designed to give you practical experience and confidence in using AI as part of your coding toolkit. The course includes cheatsheets for each of the tools we will use during the course so you will be equipped with the list of shortcuts you can use in your daily programming.

Whether you're a junior developer looking to quickly move up the ranks or a senior programmer aiming to stay relevant and efficient, mastering AI code assistants is crucial. This course is your gateway to not just keeping up with the industry but setting the pace. Equip yourself with the skills needed to stay ahead in the ever-evolving world of software development and ensure you remain at the forefront of innovation and productivity.

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What's inside

Learning objectives

  • Learn and compare various ai code assistants
  • Master chatgpt, google gemini, github copilot, tabnine, and cursor ai
  • Generate, fix, refactor, and test any code (python, html, css, and other programming languages)
  • Learn how to integrate the ai tools into existing projects
  • Apply ai tools to solve practical programming challenges across various domains

Syllabus

Introduction
Using ChatGPT as an Assistant
ChatGPT Acting as a Junior Developer
ChatGPT Acting as a Senior Advisor
Read more
ChatGPT Acting as a Tutor
Resource of Python Projects
Comparison of Different AIs
Comparing ChatGPT 3.5 with ChatGPT 4
Comparing Github Copilot vs. Tabnine vs. Cursor AI
Comparing Google Gemini vs. ChatGPT 3.5
AI Code Assistants in a Real Use-Case
Generating an Initial Codebase with ChatGPT
Fixing a bug with AI
Adding a New App Feature with Different AIs
Refactoring code with AI
AI Code Assistant Tutorials
Github Copilot Tutorial
Github Copilot Cheatsheet
Tabnine Tutorial
Tabnine Cheatsheet
Cursor AI Editor Tutorial
Cursor AI Editor Cheatsheet
Conclusion

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Emphasizes practical implementation and integration of AI code assistants in real-world projects
Designed for both junior developers seeking rapid advancement and senior programmers aiming to maintain relevance and efficiency, catering to a broad range of experience levels
Provides curated resources, including cheatsheets for each AI tool, to enhance learning and productivity
Covers a comprehensive range of AI code assistants, including ChatGPT, GitHub Copilot, Tabnine, and Cursor AI, offering a holistic perspective on the latest advancements
Focuses on problem-solving and code generation using AI tools, addressing the evolving needs of software development in the face of technological advancements
Provides hands-on tutorials and case studies to facilitate hands-on experience and boost confidence in applying AI tools to coding

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Code Faster with AI: ChatGPT, GitHub Copilot, Tabnine & More with these activities:
Review Python basics and data structures
Solidify your understanding of Python basics and data structures to build a stronger foundation for using AI code assistants.
Browse courses on Data Structures
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  • Go through Python tutorials and refresh your understanding of variables, data types, operators, and control flow.
  • Review common data structures such as lists, dictionaries, and sets, and their operations.
  • Practice solving simple coding problems using Python and basic data structures.
Follow tutorials on ChatGPT and GitHub Copilot
Gain hands-on experience with popular AI code assistants to enhance your understanding of their capabilities and limitations.
Browse courses on ChatGPT
Show steps
  • Find beginner-friendly tutorials on using ChatGPT to generate code, debug, and answer programming questions.
  • Explore tutorials on GitHub Copilot and learn how to use it for autocompletion, code suggestions, and refactoring.
  • Practice using both tools on small coding exercises to get a feel for their strengths and weaknesses.
Solve coding challenges with AI assistance
Apply your knowledge of AI code assistants to solve real-world coding problems, improving your problem-solving skills and confidence.
Browse courses on Coding Challenges
Show steps
  • Find coding challenges online or in textbooks that align with the course topics.
  • Attempt to solve the challenges on your own first, then use AI code assistants to assist with debugging or generating alternative solutions.
  • Compare and analyze the solutions generated by AI assistants to enhance your understanding of different approaches.
Two other activities
Expand to see all activities and additional details
Show all five activities
Create a blog post or video tutorial on your learnings
Reflect on your experiences using AI code assistants and share your insights with others, reinforcing your understanding and potentially helping others.
Show steps
  • Summarize the key concepts you learned about AI code assistants and their applications in programming.
  • Share your experiences and tips on using specific AI tools, including their strengths and limitations.
  • Publish your blog post or video tutorial on a platform where other learners can access it.
Contribute to open-source projects using AI assistants
Gain practical experience working on real-world projects while leveraging AI code assistants to enhance your collaboration and productivity.
Browse courses on Open Source
Show steps
  • Find open-source projects on platforms like GitHub that align with your interests and skill level.
  • Identify areas where AI code assistants can be used to improve the project, such as code generation, refactoring, or testing.
  • Make contributions to the project using AI assistance, ensuring code quality and following project guidelines.

Career center

Learners who complete Code Faster with AI: ChatGPT, GitHub Copilot, Tabnine & More will develop knowledge and skills that may be useful to these careers:
Programmer
This course can help Programmers learn how to use AI tools to automate and improve their work. They will learn how to use AI to generate, fix, refactor, and test code, which can increase efficiency and lead to faster development cycles.
AI Engineer
This course can help AI Engineers learn how to use AI tools to automate and improve their work. They will learn how to use AI to generate, fix, refactor, and test code, which can increase efficiency and lead to faster development cycles. This course may also be useful for AI Engineers who want to learn how to use AI to solve practical programming challenges across various domains.
Computer Scientist
This course can help Computer Scientists learn how to use AI tools to automate and improve their work. They will learn how to use AI to generate, fix, refactor, and test code, which can increase efficiency and lead to faster development cycles. This course may also be useful for Computer Scientists who want to learn how to use AI to solve practical programming challenges across various domains.
Full-Stack Developer
This course can help Full Stack Developers learn how to use AI tools to automate and improve their work. They will learn how to use AI to generate, fix, refactor, and test code, which can increase efficiency and lead to faster development cycles. This course may also be useful for Full Stack Developers who want to learn how to use AI to solve practical programming challenges across various domains.
Data Scientist
This course can help Data Scientists learn how to use AI tools to automate and improve their work. They will learn how to use AI to generate, fix, refactor, and test code, which can increase efficiency and lead to faster development cycles. This course may also be useful for Data Scientists who want to learn how to use AI to solve practical programming challenges across various domains.
Machine Learning Engineer
This course can help Machine Learning Engineers learn how to use AI tools to automate and improve their work. They will learn how to use AI to generate, fix, refactor, and test code, which can increase efficiency. This course may also be useful for Machine Learning Engineers who want to learn how to use AI to solve practical programming challenges across various domains.
Web Developer
Web Developers can use the skills they learn in this course to automate and improve their work. They will learn how to use AI tools to generate, fix, refactor, and test code, which can increase efficiency and lead to faster development cycles. This course may also be useful for Web Developers who want to learn how to use AI to solve practical programming challenges across various domains.
Software Developer
Software Developers can use the skills they learn in this course to automate and improve their work. They will learn how to use AI tools to generate, fix, refactor, and test code, which can increase efficiency and lead to faster development cycles. This course may also be useful for Software Developers who want to learn how to use AI to solve practical programming challenges across various domains.
Software Architect
This course can help Software Architects learn how to use AI tools to automate and improve their work. They will learn how to use AI to generate, fix, refactor, and test code, which can increase efficiency and lead to faster development cycles.
Data Analyst
This course may be useful for Data Analysts who want to learn how to use AI tools to improve their work. They can use the skills they learn in this course to generate, fix, refactor, and test code, which can increase efficiency and speed up development cycles.
Researcher
This course may be useful for Researchers who want to learn how to use AI tools to improve their work. They can use the skills they learn in this course to generate, fix, refactor, and test code, which can increase efficiency and speed up development cycles.
User Experience Designer
This course may be useful for User Experience Designers who want to learn how to use AI tools to improve their work. They can use the skills they learn in this course to generate, fix, refactor, and test code, which can increase efficiency and speed up the development of user interfaces and experiences.
Product Manager
This course may be useful for Product Managers who want to learn how to use AI tools to improve their work. They can use the skills they learn in this course to generate, fix, refactor, and test code, which can increase efficiency and speed up development cycles.
Technical Writer
This course may be useful for Technical Writers who want to learn how to use AI tools to improve their work. They can use the skills they learn in this course to generate, fix, refactor, and test code, which can increase efficiency and speed up development cycles.
Engineering Manager
This course may be useful for Engineering Managers who want to learn how to use AI tools to improve their work. They can use the skills they learn in this course to generate, fix, refactor, and test code, which can increase efficiency and speed up development cycles.

Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Code Faster with AI: ChatGPT, GitHub Copilot, Tabnine & More.
Provides a comprehensive overview of generative adversarial networks, covering the fundamental concepts and algorithms. It valuable resource for learners who want to gain a solid foundation in generative adversarial networks.
Provides a comprehensive overview of deep learning, covering the fundamental concepts and algorithms. It valuable resource for learners who want to gain a solid foundation in deep learning.
Provides a comprehensive overview of reinforcement learning with Tensorflow, covering the fundamental concepts and algorithms. It valuable resource for learners who want to gain a solid foundation in reinforcement learning with Tensorflow.
Provides a comprehensive overview of machine learning with Python, covering a wide range of topics from data preprocessing to model evaluation. It valuable resource for learners who want to gain a solid foundation in machine learning concepts, using Python.
Provides a comprehensive introduction to machine learning, covering a wide range of topics from data preprocessing to model evaluation. It valuable resource for learners who want to gain a solid foundation in machine learning concepts.
Provides a practical introduction to natural language processing, covering the key concepts and algorithms. It good starting point for learners who want to get started with natural language processing and apply it to their own projects.
Provides a comprehensive overview of natural language processing, covering topics such as text classification, sentiment analysis, and machine translation. It would be a useful resource for learners who want to understand the use of AI in natural language processing.
Provides a comprehensive overview of reinforcement learning, covering the fundamental concepts and algorithms. It would be a useful resource for learners who want to understand the use of AI in reinforcement learning.
While this book focuses on deep learning with Python, it nevertheless provides a more detailed look at the underlying algorithms and concepts that are employed by many AI code assistants. Learners who want to develop a deeper understanding of the technical underpinnings of AI will benefit from reading this book.
Provides a concise overview of machine learning, covering the key concepts and algorithms. It good starting point for learners who are new to machine learning and want to get a quick overview of the field.

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