How to learn programming and CS in the AI hype era – interview with prof Mark Mahoney [Podcast #215]

Deskripsi

Today Quincy Larson interviews Mark Mahoney. He worked as a dev before becoming a computer science professor. He's taught computer science for 23 years at Carthage College, a 180-year-old US university. He's also taught thousands of developers through his free programming courses built on top of his own open source course platform, Playback Press. We talk about: - Why learning programming the hard way is still the right way - How to not deskill yourself when programming with LLM tools - And why now is a great time to study computer science Support for this podcast comes from the 10,113 kind folks who donate to our charity each month. Join them and support our mission at https://donate.freecodecamp.org Get a freeCodeCamp tshirt for $20 with free shipping anywhere in the US: https://shop.freecodecamp.org Links from our discussion: - Playback Press, Mark's free interactive courses: https://playbackpress.com/books - Mark's personal website: https://markm208.github.io/ - One of the many vibe-coded projects Mark mentions: https://markm208.github.io/vibeCodingInClassTools/git-workflow-simulator.html - Mark's tutorials on freeCodeCamp: https://www.freecodecamp.org/news/author/markm208/ Community news section: 1. freeCodeCamp just published a new course on AI-assisted software development. You'll learn common terminal workflows and tips for “pair programming” alongside LLM tools. You'll also get exposure to tools like GitHub Copilot, Claude Code, Gemini CLI, and OpenClaw. At the end of the day, the entire goal of using these tools is to build more features without compromising the maintainability of your codebase. (90 minute YouTube course): https://www.freecodecamp.org/news/ai-tools-for-developers/ 2. freeCodeCamp also published a beginner level course on AI literacy for everybody that you can also share with your family. First you'll learn about the two traits that definte artificial intelligence: autonomy and adaptivity. Then you'll build your own image classifier right on your own phone or laptop. This course