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How much of what we learn do we actually remember? And what if one of the most effective ways to learn is to regularly challenge ourselves to recall what we already know? In episode 164 of Teaching Python, Kelly Schuster-Paredes and Julian Sequeira welcome Python educator and Python Morsels creator Trey Hunner to explore the testing effect, retrieval practice, spaced repetition, and interleaving. Trey shares how learning the countries of the world through his geography app, Whereabouts.Earth, helped shape Daily Recall, a spaced repetition learning system for Python programmers. The conversation goes beyond remembering the right answer to ask whether we understand what we know. Are we recalling a concept or recognizing the shape of a familiar question? Can we apply what we have practiced when the context changes? From Duolingo to Python exercises, the hosts explore why recall needs to work alongside mental models and practical experience, and why learning that feels harder can be more useful over time. They also examine what this means when AI can generate code for us. Trey, Kelly, and Julian discuss the difference between learning something deeply and getting a task done, how much understanding we need to evaluate AI-generated work, and why foundational knowledge still matters for making informed decisions. Show Links Python Morsels Jev for Beginners Pilots still practice Linus writes code with AI Special Guest: Trey Hunner.

How do you keep learning when there never seems to be enough time? The answer may be less about finding more time and more about building learning into the way you already work, solve problems, use AI, and connect with other people. In Episode 163 of the Teaching Python Podcast, Sean Tibor, Kelly Schuster-Paredes, and Julian Sequeira discuss how they keep learning in a technology landscape shaped by rapid changes in AI, software development, and computing. They compare practical approaches to continuous learning, including building projects, using commutes and downtime, scheduling dedicated learning time, asking ChatGPT and Claude questions as they come up, watching webinars, reading, experimenting with new tools, and learning from professional networks. The conversation also explores what happens when you need to learn something you are not naturally interested in. They discuss adult learning, desirable difficulty, just-in-time learning, recursive learning, and why struggling with unfamiliar ideas can still be valuable even when AI can explain concepts instantly. Along the way, they talk about vibe coding, AI as a learning partner, professional development, technical communities, balancing creation and consumption, and setting boundaries so that continuous learning does not become constant burnout. If you are trying to keep up with AI, learn new technology, or continue developing technical skills without turning every spare moment into work, this episode offers a practical look at how learning can become part of everyday life.

What does teaching Python mean in 2026, when AI can generate code and programming sits inside a much larger computing landscape? Teaching Python started as a podcast about teaching programming in the classroom, but both the hosts and the world around Python have changed. In this episode, Kelly and Julian reflect on how Python education now connects with AI, data, cybersecurity, cloud computing, software engineering, systems thinking, and automation. They discuss how their own roles have evolved from teacher, coder, and technologist into work that crosses disciplines, and why understanding technology now requires more than simply learning how to write code. The conversation explores why learning Python still matters in the age of AI-generated code, why reading, debugging, evaluating, testing, and modifying code may be more important than ever, and how teachers and learners can build the judgment needed to work with increasingly capable AI tools. Python is still here. But teaching Python in 2026 is increasingly about how people learn, build, reason, and make decisions with technology.

In this episode of Teaching Python, Kelly and Julian welcome IBM Distinguished Engineer Jeff Crume to talk about teaching hard things simply. The conversation begins with Jeff’s short, visually driven videos and the studio setup behind them, including the lightboard format, the editing process, and the amount of planning needed to turn a 15-minute explanation into something clear and usable. They then turn to the challenge of explaining complex ideas in a way that fits the audience. Jeff describes how he chooses topics from his work with clients, why he thinks teaching deepens his own understanding, and how he adapts for YouTube, classrooms, and conference talks. He emphasizes brevity, structure, and using visuals so viewers are not faced with a talking head and a blank background. A major part of the discussion focuses on AI, the humanities, and education. Jeff explains why he believes the humanities are essential for understanding meaning, purpose, truth, and context, and why those questions matter when using AI. He argues that AI should be treated as a tool to augment learning rather than something to exclude from classrooms. The conversation also covers cybersecurity and practical AI risks. Jeff discusses passwords versus passkeys, phishing, public chatbots, data privacy, cloud services, and the security concerns around agents and connected tools. He argues for private instances, stronger security practices, and doing security earlier in the process. Near the end, Jeff highlights communication, curiosity, and critical thinking as key skills for students. He also points listeners to IBM SkillsBuild and Coursera for training, and closes by encouraging lifelong learning in a fast-changing field. Special Guest: Jeff Crume.

In this episode, Kelly Schuster-Paredes speaks with Mahmoud Harding about his work in data science education and the way he thinks about teaching Python, R, and statistics. Mahmoud explains that he is the instructional design director at Data Science for Everyone, where the goal is to make data science available to more students and to connect it to meaningful, real-world contexts. A major part of the conversation focuses on how students learn best through curiosity and project-based work. Mahmoud describes the ADAPT model, including its emphasis on project-based learning and common learning elements, and he argues that students should begin working with their own data early in a course. Kelly and Mahmoud discuss how choosing their own datasets helps students become more engaged, notice mistakes, and ask better questions. The discussion also compares R and Python as tools for data science. Mahmoud explains that R was designed by statisticians for statistical analysis, while Python became popular as a general-purpose language that later grew into a strong data science ecosystem through libraries like NumPy and pandas. He also describes Jupyter Everywhere, a browser-based notebook environment designed to reduce barriers for schools and allow students to use R or Python without complicated setup. Later, the conversation turns to judgment, nuance, and the role of data in learning. Mahmoud argues that students need domain knowledge and human judgment to interpret data responsibly, and that data projects can help them develop those skills. Kelly extends this idea to other subjects, suggesting that books, history, and other classroom materials can also be treated as data for analysis and discussion. The episode closes with Mahmoud sharing ways to connect with him through Data Science for Everyone and with mention of an upcoming Data Science Education K–12 event in Atlanta in February. Special Guest: Mahmoud Harding.

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. Show Notes Wins of the Week 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. Small Language Models 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 Through Constraints 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 Self-Taught Learning 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 Python Journey 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 Learn to Cloud 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 Cloud, AI, and Security 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 Teaching in the AI Era 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 Final Thoughts 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. Key Topics 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 Wins of the Week 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. Announcements 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. Resources & Links 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. Show Notes Wins of the Week 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 Links & Resources Mentioned 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 Announcements 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 Key Quotes "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.

Julian Sequeira from PyBites joins Sean and Kelly to share their top holiday gift picks for coders, makers, and educators. This episode features 15+ gift ideas ranging from budget-friendly maker tools to classroom robots—plus book recommendations, coding platforms, and a few surprises. Show Notes Wins of the Week Julian: Staying focused on "the one thing" at PyBites, plus 3D printing a custom cappuccino stencil for his local café Kelly: Surviving a muddy, clay-covered hill in North Carolina while on vacation Sean: Designing and 3D printing a custom bracket for his screen door using Fusion 360 Holiday Gift Ideas Julian's Picks Hoverboard with Go-Kart Attachment (~$299 AUD) - Two-wheeled self-balancing boards that can convert to a go-kart with a third wheel attachment. Available at Hoveroo in Australia. Secret Coders Book Series (~$10-20 USD each) - A six-book graphic novel series that wraps coding puzzles and concepts into mystery stories. Recommended by Faye Shaw from the Boston PyLadies community. Great for ages 8-15. 3D Printer (~$200-300 USD) - Entry-level printers like the Bambu Lab A1 Mini or Elegoo Neptune 4 Pro have dropped significantly in price. Look for auto bed leveling as a key feature. Duolingo Chess (~$13/month with subscription) - A new addition to Duolingo that teaches chess tactics, strategy, and formal terminology through structured lessons. Great for building problem-solving skills. Classic Video Games (Zelda, Pokémon) - Story-driven games that build resilience and problem-solving skills, as an alternative to dopamine-heavy platforms like Roblox. Kelly's Picks Soccer Bot (~$59.99) - An indoor soccer training robot that challenges footwork skills. Works best on hard floors. "The Worlds I See" by Dr. Fei-Fei Li - Memoir of the computer scientist behind ImageNet and modern image recognition, covering her immigrant journey and rise in AI. A must-read for anyone interested in AI. LEGO Retro Radio Building Set (~$99) - A 1970s-style radio that you build, then insert your phone to play music. Features working dials that create authentic radio crackle sounds. Spydroid Loco Hex Robot (classroom investment) - A large spider-shaped robot that codes in Python and block programming. Features LIDAR and AI-based mapping. Seen at ISTE. Richtie Mini from Hugging Face ($299-$449) - An adorable AI desktop companion robot with onboard models. Two versions: one that connects to your computer and one that's self-contained. Sean's Picks LED Pucks (LED 001 Kit) (~$6-13) - Small USB-powered LED discs perfect for 3D printed projects like planet lamps. Available from Bambu Labs or Amazon. RGB versions include remote controls. Daily Desk Calendar (~$15-20) - A throwback gift that provides daily doses of humor, trivia, or inspiration. Suggestions include The Far Side, "They Can Talk," or "How to Win Friends and Influence People." PyBites Coding Platform (subscription) - Bite-sized Python challenges for sharpening coding skills. Great for teachers, students, and professionals looking for practical coding practice. Digital Calipers (~$40-50) - USB-rechargeable precision measuring tools essential for 3D printing and maker projects. Great for teaching geometry and measurement concepts. Deburring Tool (~$10) - A small tool with a curved swiveling blade for cleaning up 3D prints. A quality-of-life improvement for any maker's toolkit. Links Mentioned PyBites - Python coaching and coding challenges Hoveroo - Hoverboards (Australia) Bambu Lab - 3D printers and LED pucks Printables - 3D printing models MakerWorld - 3D printing models Hugging Face Richtie Mini - AI companion robot Duolingo - Language learning app with chess Secret Coders book series - Available on Amazon "The Worlds I See" by Dr. Fei-Fei Li - Available at bookstores Upcoming Events PyCon US 2026 - Long Beach, California Education Summit - Proposals open after the holidays, deadline around March/April Submit proposals when the website opens! Special Guest: Julian Sequeira.

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.

In this episode, Sean and Kelly welcome Pritesh Patel, a computer scientist specializing in AI who brings over 20 years of experience from companies like Turner Broadcasting, Walmart, and GE to his current role at Fisher Phillips law firm. Pritesh shares fascinating insights about implementing AI in knowledge-based industries, from automating his parents' frozen yogurt shop to helping lawyers transform their workflows. The conversation explores crucial concepts like the "Jobs to Be Done" framework, which emphasizes focusing on outcomes rather than getting stuck in existing processes. Pritesh explains how he approaches AI education through playfulness and intuition-building—whether that's getting Batman to sing "Twinkle, Twinkle Little Star" in ChatGPT or creating AI personalities that debate humans. The discussion touches on the importance of maintaining accountability while delegating responsibility to AI tools, the power of curiosity in adoption, and how reinforcement learning might shape the future of AI integration. Key resources mentioned: Strategyn Jobs to Be Done Framework - Tony Ulwick's innovation methodology NotebookLM - Google's AI-powered research tool Suno.ai - AI music generation platform OpenAI's Real-time API for voice interactions Special Guest: Pritesh Patel.

In this milestone 150th episode, hosts Kelly Schuster-Paredes and Sean Tibor sit down with Simon Willison, co-creator of Django and creator of Datasette and LLM tools, for an in-depth conversation about artificial intelligence in Python education. The discussion covers the current landscape of LLMs in coding education, from the benefits of faster iteration cycles to the risks of students losing that crucial "aha moment" when they solve problems independently. Simon shares insights on prompt injection vulnerabilities, the importance of local models for privacy, and why he believes LLMs are much harder to use effectively than most people realize. Key topics include: Educational Strategy: When to introduce AI tools vs. building foundational skills first Security Concerns: Prompt injection attacks and their implications for educational tools Student Engagement: Maintaining motivation and problem-solving skills in an AI world Practical Applications: Using LLMs for code review, debugging, and rapid prototyping Privacy Issues: Understanding data collection and training practices of major AI companies Local Models: Running AI tools privately on personal devices The "Jagged Frontier": Why LLMs excel at some tasks while failing at others Simon brings 20 years of Django experience and deep expertise in both web development and AI tooling to discuss how educators can thoughtfully integrate these powerful but unpredictable tools into their classrooms. The conversation balances excitement about AI's potential with realistic assessments of its limitations and risks. Whether you're a coding educator trying to navigate the AI revolution or a developer interested in the intersection of education and technology, this episode provides practical insights for working with LLMs responsibly and effectively. Resources mentioned: Simon's blog: simonwillison.net Mission Encodable curriculum Datasette and LLM tools GitHub Codespaces for safe AI experimentation Special Guest: Simon Willison.