What can small language models teach us that the largest AI models cannot?

Kelly and Julian are joined by Microsoft Cloud Advocate Gwyneth Peña-Sigüenza to explore why working with small language models (SLMs) may be one of the best ways to understand AI. Rather than relying on increasingly capable models that hide complexity, Gwyneth argues that constraints build stronger fundamentals. From prompt engineering and context management to deployment and security, SLMs force learners to think more carefully about how AI actually works.

The conversation extends beyond AI models into learning itself. Gwyneth shares her self-taught journey from growing up on a remote farm in Ecuador with limited internet access to becoming a Microsoft Cloud Advocate and creator of the Learn to Cloud platform. Along the way, the group discusses productive struggle, mentorship, cloud engineering, Python, security, and what educators should prioritize as AI becomes part of every student's learning experience.

The episode closes with a thoughtful discussion about AI dependency, judgment, and whether we would actually flip the switch and turn AI off if given the choice.

• Gwyneth celebrates the New York Knicks reaching the NBA Finals after more than 50 years.
• Julian shares that he has accepted a new role as a Fractional CTO.
• Kelly reflects on taking her first real vacation in over a year—and how stepping away from work sparked unexpected ideas.

• Why SLMs are valuable teaching tools
• Learning prompt engineering through constraints
• Running models locally on everyday hardware
• When local AI makes sense for classrooms
• Understanding tokens, context windows, and model limitations
• Why bigger models can sometimes hide important lessons

• Learning to drive in an old manual pickup truck as a metaphor for learning AI fundamentals
• Why difficult learning experiences often create lasting understanding
• Building strong habits before relying on more capable tools
• Consistency versus constantly chasing the newest resource

• Growing up without reliable internet in rural Ecuador
• Downloading YouTube playlists to learn programming offline
• Developing discipline through limited access
• The value of repetition and focused practice
• Why mentorship accelerates learning

• Transitioning from cloud engineering to Python advocacy
• Learning Python beyond scripting
• Discovering what "Pythonic" really means
• Wrestling with list comprehensions and other advanced syntax
• Favorite learning resources:
  • Fluent Python
  • Effective Python

• Building an open-source cloud engineering curriculum
• Hands-on labs and automated verification
• AI-assisted assessment
• Supporting self-taught learners around the world
• Creating accessible technical education

• Deploying AI applications to the cloud
• Containers, virtual machines, and serverless deployments
• Why operations and security deserve more classroom attention
• Introducing secure development practices early
• The importance of authentication, secrets management, and responsible deployment

• Helping students understand how AI works instead of simply using it
• Why productive struggle still matters
• The changing role of educators
• Balancing AI assistance with independent thinking
• Preparing students for a future where AI is always available

• AI dependency versus capability
• Judgment as the skill that matters most
• Human connection in an AI-driven world
• Would we actually turn AI off?
• Finding balance between technological progress and intentional learning

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.

In this episode, Sean, Kelly, and Julian tackle a provocative question: is the traditional "Hello, World" first program dead? What was once a thrilling moment of agency — telling a computer to do something and watching it respond — now competes with AI assistants, voice interfaces, and tools that can build entire applications from a single prompt.

The conversation dives into the different types of learners Kelly encounters in her classroom: the students who want AI to do everything, the ones who light up when they catch AI writing unused functions, and the old-school coders who just want to write it themselves. Sean shares how he turned a massive org design challenge at work into a Python project with a SQLite database, proving that the best way to learn is still to find a real problem and solve it with code.

Kelly describes her fourth-quarter experiment to create a new "Hello, World" moment for her 8th graders using school-approved AI tools, while Julian raises the important question of whether the real challenge is just showing people that code can solve their problems in the first place. The trio also explores whether AI can strip away the administrative clutter in teaching to let educators focus on what matters: engagement, personalization, and good pedagogy.

The episode wraps with two pieces of news: the PyCon US Education Summit is confirmed for Thursday, May 14th, and Julian Sequeira is officially joining the show as a regular co-host — complete with a live, slightly fumbled first sign-off.

• Why "Hello, World" no longer delivers the same dopamine hit for new learners
• The three types of student responses to AI-assisted coding
• Using AI to write deterministic code vs. using generative AI for repetitive tasks
• Sean's Python + SQLite org design tool as a real-world "solve a problem with code" example
• Kelly's classroom experiments with AI-generated Python apps for 8th graders
• EarSketch and making music with Python as a reliable engagement tool
• Whether AI can remove administrative clutter and let teachers focus on pedagogy
• The concept of "desirable difficulty" in learning
• Bridging the knowledge gap: helping non-coders see code as a problem-solving option
• PyCon US Education Summit — May 14, 2026
• Julian Sequeira joining as a regular co-host

Kelly: Bringing two Pine Crest colleagues to PyCon US this year — Chris and Kayla, an aspiring data scientist who is excited to dive into Python and attend the Education Summit.

Julian: His 10-year-old son scored his first basketball basket after multiple seasons of showing up, practicing, and persisting — a nothing-but-net shot that had the entire gym erupting.

Sean: Used Claude to create a comprehensive, interactive study guide from his daughter's 11-page science PDF on water quality — complete with clickable concept maps, pH level visualizations, and chain-of-events diagrams that made 7th-grade science genuinely engaging.

• PyCon US Education Summit — Thursday, May 14, 2026 in Pittsburgh. Kelly is chairing the summit with 150–200 seats available. Proposals are open and encouraged.
• Julian Sequeira joins Teaching Python — After almost 8 years as a duo, Sean and Kelly have invited Julian to be a regular co-host, bringing fresh perspective, energy, and an Australian accent to the show.

• Teaching Python — Podcast website
• PyBites — Julian Sequeira's Python coaching platform
• EarSketch — Making music with Python (Georgia Tech)
• PyCon US 2026 — May 14–22, 2026 in Pittsburgh, PA
• Claude Code — AI coding assistant mentioned by Kelly

What does it really mean to be "techie"? Sean, Kelly, and guest Amelia Hough-Ross dig into the labels we put on ourselves and others — and why curiosity and persistence matter more than credentials. From imposter syndrome to productive struggle, this episode redefines what it means to be technical in a rapidly changing world.

• Amelia: Getting both kids to all their activities this week — taekwondo, Chinese language classes, and a piano competition where her oldest did very well
• Kelly: Running a series of well-attended trainings at school, including a Canva AI session that drew 60 attendees across two campuses, with new audiences (kindergarten and first grade teachers) showing up for the first time
• Sean: Finally getting fiber internet installed at his house after over a decade of waiting — a major upgrade from cable with latency dropping from 20-30ms to 3ms, at half the cost

• vBrownBag — Tech community show that Amelia is preparing to present at and Sean is scheduled for later in the year
• PyCon US 2025 — Pittsburgh, May 2025; Education Summit on Thursday, May 14
• LEGO Mindstorms — Referenced in Amelia's story about building a vending machine in 4th grade
• Architects of Intelligence — Book Kelly is currently reading (dense but informative, structured as short stories/interviews)
• How to Winter by Kari Leibowitz — Book Amelia is reading about mindset and how people approach difficult things
• Lars von Trier / Bjork / Catherine Deneuve film — Referenced in Amelia's story about visiting a film set in Denmark at age 18 (the film Dancer in the Dark, 2000)
• Chris Williams / vBrownBag — Mutual connection who introduced Sean and Amelia at AWS re:Invent

• PyCon US 2025 — Pittsburgh, PA. Education Summit is Thursday, May 14. Proposals still open at time of recording.
• Kelly will be attending PyCon with her youngest son, who will spend the weekend with family at Disneyland
• Sean will be supporting from home this year as his wife has a conflicting travel commitment

"It's hard to think outside of the box when you don't know what's inside of the box." — Kelly, quoting a conference in Tampa

"The difference between viewing yourself as technical and not technical is getting those successes... even just once, where something really cool happens that you weren't expecting to work." — Sean

"It's much harder to believe that someone has that greatness in them and help them achieve it... It's easy to say someone's hopeless. The harder part is figuring out how to support them to get to that next level." — Amelia

Special Guest: Amelia Hough-Ross.

In this episode, we sit down with Quincy Tennyson, who teaches an impressive four-year computer science pathway at Fern Creek High School. Quincy's background in the Marine Corps and as a network engineer brings a unique perspective to CS education. He discusses his curriculum progression from introductory courses through AP Computer Science Principles (heavily inspired by UC Berkeley's CS61A), AP Computer Science A (Java), and a culminating Project-Based Programming course. We dive deep into his philosophy of being a "warm demander" - setting high expectations while providing intensive coaching and support.

The conversation touches on several compelling topics including teaching agile methodology to high school students, the importance of transparency about failure, and how behavioral economics concepts (from thinkers like Daniel Kahneman) inform his approach to helping students understand their own thinking processes. Quincy also shares insights on supporting underserved students, running a successful Girls Who Code chapter, and navigating the integration of AI tools in the classroom. His students' enthusiasm at PyCon 2024 was infectious, and this episode reveals the thoughtful pedagogy behind their success.

Key resources mentioned include CS61A from UC Berkeley, CodeHS, Code.org, Sandra McGuire's book "Teach Students How to Learn," Eric Matthes' Python Crash Course, and Al Sweigart's educational resources including his new Buttonpad library for Tkinter.

Special Guest: Quincy Tennyson.