“As software engineers, only a fraction of your time is spent coding. A lot of your time is spent thinking. And I’m not seeing LLMs taking that away from us anytime soon, at least, for now."
Can AI help you learn to code? Will AI take your developer job? Join me discussing these topics with Leo Porter and Daniel Zingaro, the co-authors of “Learning AI-Assisted Python Programming”.
In this episode, we discuss the impact of AI assistants on how we learn and approach programming, particularly for students and educators. We examine the shifting skillset of developers, emphasizing the importance of code reading, specification, testing, and problem decomposition over syntax and library semantics.
We also confront critical questions like the ethical implications of AI, the potential impact on developers’ job, and whether it can help lead us to a more equitable society.
Listen out for:
- Career Journey - [00:01:11]
- AI Assistant - [00:07:55]
- How AI Assistant Affect Student - [00:11:04]
- Problem Decomposition Skill - [00:16:46]
- How LLM Works - [00:19:47]
- Prompt Engineering - [00:23:36]
- Automating Tedious Tasks - [00:29:29]
- AI Ethical Issues - [00:33:30]
- AI Replacing Developers - [00:40:08]
- A More Equitable Society - [00:47:34]
- 3 Tech Lead Wisdom - [00:55:58]
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Leo Porter’s Bio
Leo Porter is a Teaching Professor in the Computer Science and Engineering Department at UC San Diego. He is best known for his award-winning research on the impact of Peer Instruction in computing courses, the use of clicker data to predict student outcomes, and the development of the Basic Data Structures Concept Inventory. He co-wrote the first book on integrating LLMs into the instruction of programming with Daniel Zingaro, entitled “Learn AI-Assisted Python Programming: With GitHub Copilot and ChatGPT”. He also co-teaches popular Coursera and edX courses with over 500,000 enrolled learners. He is a Distinguished Member of the ACM.
Daniel Zingaro’s Bio
Dr. Daniel Zingaro is an award-winning Associate Teaching Professor of Mathematical and Computational Sciences at the University of Toronto Mississauga. He is well known for his uniquely interactive approach to teaching and internationally recognized for his expertise in active learning. He is the co-author of “Learn AI-Assisted Python Programming” (Manning Publications, 2023), author of “Algorithmic Thinking” 2/e (No Starch Press, 2024), co-author of “Start Competitive Programming!” (self-published, 2024), and author of Learn to Code by Solving Problems (No Starch Press, 2021).
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