In this episode, Python Developer Advocate and author Will Vincent joins the hosts to discuss the lasting appeal of Django, changes in how people learn web development, and the ways AI is reshaping software engineering. While modern AI tools can generate working code in seconds, Django's opinionated design and emphasis on maintainability help developers avoid many of the security and architectural problems that often emerge as projects grow.

Drawing on his background as an educator, author, and Developer Advocate at JetBrains, Will shares his perspective on the challenges facing today's developers and computer science students. The conversation touches on "vibe coding," the misconception that a successful prototype automatically translates into a production-ready application, and the increasing burden AI-generated content is placing on open-source maintainers. Will also discusses the rise of specialized AI models, the importance of human trust in technical communities, and why foundational software engineering skills remain valuable despite rapid advances in AI tooling.

Key Topics Covered

Why Django Still Matters
A look at why Django continues to be a strong choice for building production applications, even if it doesn't receive the same level of attention as newer frameworks.

The Reality Behind "Vibe Coding"
Exploring the gap between generating code with AI and understanding the systems, tradeoffs, and architecture required to build reliable software.

Learning to Program as an Adult
Will reflects on his path from book editing and startup leadership to becoming a self-taught programmer, educator, and author.

AI and Programming Education
A discussion about how AI changes the learning process, why fundamentals still matter, and how concepts like music theory can help explain the value of understanding code beneath the surface.

The Growing Burden on Open Source
How maintainers are dealing with an influx of low-quality AI-generated issues, pull requests, and content, and what that means for community-driven projects.

Local and Specialized AI Models
Why privacy concerns, lower inference costs, and better hardware may drive adoption of smaller, task-focused models rather than ever-larger general systems.

Developer Concerns in the AI Era
How engineers are responding to growing pressure from leadership teams eager to adopt AI, and what trends JetBrains is seeing across the developer ecosystem.

Resources Mentioned
LearnDjango, Will Vincent's platform for learning Django and web development.
Hello World 5 Different Ways, a Django tutorial that introduces key concepts through practical examples.
Django Chat, the podcast Will co-hosts covering the Django ecosystem and web development.
Django News, a weekly newsletter highlighting updates from the Django community.
JetBrains, the software development company behind tools such as PyCharm and IntelliJ IDEA.

Special Guest: Will Vincent.

Kelly talks with Philip Guo, creator of Python Tutor, about how the tool helps students trace code and understand programming basics. They also discuss the challenges AI-generated code creates in the classroom and possible ways to support student learning.

*Wins of the Week
*
Philip: Hiring a second undergraduate student for Python Tutor, including one focused on user experience research with K-12 teachers
Kelly: Finishing a year of in-person teacher trainings and reflecting on how far the teachers have come

*AI, Coding, and Classroom Understanding
*
Much of the conversation focuses on how AI-generated code affects student learning. Kelly describes using AI code with eighth graders and how difficult it can be for them to understand functions, parameters, returns, and other fundamentals when the code is generated all at once. Philip suggests that tools like Python Tutor may be useful for helping students trace code and understand what is happening behind the scenes.

Python Tutor and Possible AI Features

Philip explains that Python Tutor currently visualizes execution and has an AI chat feature that can answer questions about code and errors. They discuss possible future features, including simplified AI-generated examples, alternative execution views that show only the lines actually run, and more guided inline help tied to specific code or variables.

Oral Explanations and Assessment

Kelly describes using a Socratic-style code review with students, where they discuss code aloud in groups. They also talk about using spoken explanations or short oral assessments to check whether students can really explain what code is doing, rather than just copying or prompting AI-generated answers.

Broader Research and “Beyond the Desk”

Philip briefly discusses a new research direction with a PhD student focused on AI support for work beyond the desk, including physical and embodied tasks in science labs and fieldwork. He says this differs from desk-based AI work and involves activities that are harder for current AI systems to support.

**Chapters
**0:25 Python Tutor and AI Learning
1:55 Hiring Help for Python Tutor
4:07 Classroom Wins and AI Reflections
6:11 Teaching Code Through Python Tutor
9:03 AI Code and Student Confusion
14:11 Simplifying Execution Traces
17:19 Functions Are the Hard Part
20:25 Keeping Fundamentals in AI Era
24:25 Socratic Seminars for Code
26:27 Voice-Based Code Thinking
29:27 Learning Beyond Lockdown
36:10 Prompting as a New Skill
36:25 Hardware Troubles and NeoPixels
40:15 Beyond the Code Editor
45:01 New Research on Embodied AI
49:12 PyCon and Community Plans
50:42 Teacher Call to Action

Special Guest: Philip Guo.

In this episode of Teaching Python, Kelly Schuster-Paredes and Julian Sequeira are joined by engineer and maker Todd Kurt to discuss what happens when code leaves the screen and starts interacting with the physical world. The conversation centers on CircuitPython, MicroPython, and physical computing, with a focus on how these tools are used in classrooms and maker projects.

Todd explains his background in engineering, web development, and open source hardware, including his work on LED devices and his recent focus on CircuitPython. He describes the differences between CircuitPython and MicroPython, emphasizing that CircuitPython is designed to feel closer to desktop Python and to support teaching, while MicroPython makes more efficiency-focused tradeoffs.

The discussion also covers the practical challenges of hardware-based learning. Todd and the hosts talk about bootloaders, UF2 files, board compatibility, library management, and common mistakes such as using the wrong cable, the wrong board file, or wiring power and ground incorrectly. They note that these issues can make hardware feel frustrating, especially for beginners and teachers preparing classroom kits.

Kelly and Julian share their classroom experiences, including using preloaded boards, NeoPixels, sensors, and simple student-designed projects. They discuss how hardware can support troubleshooting skills, file-system awareness, and persistence, and why students often engage more when they are building something tangible, such as a sensor-based wearable or a small robot.

The episode also includes Todd’s stories about early embedded work, including a costly lab mistake, and his involvement in hardware that contributed to space missions. He closes by describing a compact synthesizer project built around a Raspberry Pi Pico and by noting that he shares work through his website and online accounts.

Special Guest: Tod Kurt.