In this conversation with Simon Willison, co-creator of Django, he shares insights on integrating AI tools into Python education. He discusses the balance between using LLMs and maintaining foundational coding skills, warning about the risks of losing critical problem-solving moments. Simon highlights security concerns like prompt injection and advocates for local models to ensure privacy while exploring the transformative potential of LLMs in code review and debugging. His extensive experience offers valuable perspectives on navigating the challenges of AI in learning.
01:36:27
forum Ask episode
web_stories AI Snips
view_agenda Chapters
menu_book Books
auto_awesome Transcript
info_circle Episode notes
insights INSIGHT
Value Of Mature Tools
Django's longevity shows value of 'boring software' where bugs are known and fixes documented.
Simon argues choosing mature tools reduces unexpected problems for developers.
question_answer ANECDOTE
Vibe Coding Backfires
Kelly described trying 'vibe coding' with Django and losing the working app when she couldn't fix issues.
That experience convinced her to learn Django properly instead of relying on magic tooling.
volunteer_activism ADVICE
Bootstrap Projects With Templates
Create cookie-cutter project templates with one passing test to bootstrap new projects.
Include CI, testing, and documentation setup so newcomers can start productive work immediately.
Get the Snipd Podcast app to discover more snips from this episode
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.
Teaching Python 
