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Michael Kennedy

Talk Python to Me is a weekly podcast hosted by developer and entrepreneur Michael Kennedy. We dive
 deep into the popular packages and software developers, data scientists, and incredible hobbyists doing
 amazing things with Python. If you're new to Python, you'll quickly learn the ins and outs of the community
 by hearing from the leaders. And if you've been Pythoning for years, you'll learn about your favorite
 packages and the hot new ones coming out of open source.

Talk Python To Me

Twenty years after a scrappy newsroom team hacked together a framework to ship stories fast, Django remains the Python web framework that ships real apps, responsibly. In this anniversary roundtable with its creators and long-time stewards: Simon Willison, Adrian Holovaty, Will Vincent, Jeff Triplett, and Thibaud Colas, we trace the path from the Lawrence Journal-World to 1.0, DjangoCon, and the DSF; unpack how a BSD license and a culture of docs, tests, and mentorship grew a global community; and revisit lessons from deployments like Instagram. We talk modern Django too: ASGI and async, HTMX-friendly patterns, building APIs with DRF and Django Ninja, and how Django pairs with React and serverless without losing its batteries-included soul. You’ll hear about Django Girls, Djangonauts, and the Django Fellowship that keep momentum going, plus where Django fits in today’s AI stacks. Finally, we look ahead at the next decade of speed, security, and sustainability.

Episode sponsors

Talk Python Courses

Python in Production

Links from the show
Guests

Simon Willison: simonwillison.net

Adrian Holovaty: holovaty.com

Will Vincent: wsvincent.com

Jeff Triplett: jefftriplett.com

Thibaud Colas: thib.me

Show Links

Django's 20th Birthday Reflections (Simon Willison): simonwillison.net

Happy 20th Birthday, Django! (Django Weblog): djangoproject.com

Django 2024 Annual Impact Report: djangoproject.com

Welcome Our New Fellow: Jacob Tyler Walls: djangoproject.com

Soundslice Music Learning Platform: soundslice.com

Djangonaut Space Mentorship for Django Contributors: djangonaut.space

Wagtail CMS for Django: wagtail.org

Django REST Framework: django-rest-framework.org

Django Ninja API Framework for Django: django-ninja.dev

Lawrence Journal-World: ljworld.com

Watch this episode on YouTube: youtube.com

Episode #518 deep-dive: talkpython.fm/518

Episode transcripts: talkpython.fm

Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong

--- Stay in touch with us ---

Subscribe to Talk Python on YouTube: youtube.com

Talk Python on Bluesky: @talkpython.fm at bsky.app

Talk Python on Mastodon: talkpython

Michael on Bluesky: @mkennedy.codes at bsky.app

Michael on Mastodon: mkennedy

#518: Celebrating Django's 20th Birthday With Its Creators

Agentic AI programming is what happens when coding assistants stop acting like autocomplete and start collaborating on real work. In this episode, we cut through the hype and incentives to define “agentic,” then get hands-on with how tools like Cursor, Claude Code, and LangChain actually behave inside an established codebase. Our guest, Matt Makai, now VP of Developer Relations at DigitalOcean, creator of Full Stack Python and Plushcap, shares hard-won tactics. We unpack what breaks, from brittle “generate a bunch of tests” requests to agents amplifying technical debt and uneven design patterns. Plus, we also discuss a sane git workflow for AI-sized diffs. You’ll hear practical Claude tips, why developers write more bugs when typing less, and where open source agents are headed. Hint: The destination is humans as editors of systems, not just typists of code.

Episode sponsors

Posit

Talk Python Courses

Links from the show
Matt Makai: linkedin.com

Plushcap Developer Content Analytics: plushcap.com

DigitalOcean Gradient AI Platform: digitalocean.com

DigitalOcean YouTube Channel: youtube.com

Why Generative AI Coding Tools and Agents Do Not Work for Me: blog.miguelgrinberg.com

AI Changes Everything: lucumr.pocoo.org

Claude Code - 47 Pro Tips in 9 Minutes: youtube.com

Cursor AI Code Editor: cursor.com

JetBrains Junie: jetbrains.com

Claude Code by Anthropic: anthropic.com

Full Stack Python: fullstackpython.com

Watch this episode on YouTube: youtube.com

Episode #517 deep-dive: talkpython.fm/517

Episode transcripts: talkpython.fm

Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong

--- Stay in touch with us ---

Subscribe to Talk Python on YouTube: youtube.com

Talk Python on Bluesky: @talkpython.fm at bsky.app

Talk Python on Mastodon: talkpython

Michael on Bluesky: @mkennedy.codes at bsky.app

Michael on Mastodon: mkennedy

#517: Agentic Al Programming with Python

Python’s data stack is getting a serious GPU turbo boost. In this episode, Ben Zaitlen from NVIDIA joins us to unpack RAPIDS, the open source toolkit that lets pandas, scikit-learn, Spark, Polars, and even NetworkX execute on GPUs. We trace the project’s origin and why NVIDIA built it in the open, then dig into the pieces that matter in practice: cuDF for DataFrames, cuML for ML, cuGraph for graphs, cuXfilter for dashboards, and friends like cuSpatial and cuSignal. We talk real speedups, how the pandas accelerator works without a rewrite, and what becomes possible when jobs that used to take hours finish in minutes. You’ll hear strategies for datasets bigger than GPU memory, scaling out with Dask or Ray, Spark acceleration, and the growing role of vector search with cuVS for AI workloads. If you know the CPU tools, this is your on-ramp to the same APIs at GPU speed.

Episode sponsors

Posit

Talk Python Courses

Links from the show
RAPIDS: github.com/rapidsai

Example notebooks showing drop-in accelerators: github.com

Benjamin Zaitlen - LinkedIn: linkedin.com

RAPIDS Deployment Guide (Stable): docs.rapids.ai

RAPIDS cuDF API Docs (Stable): docs.rapids.ai

Asianometry YouTube Video: youtube.com

cuDF pandas Accelerator (Stable): docs.rapids.ai

Watch this episode on YouTube: youtube.com

Episode #516 deep-dive: talkpython.fm/516

Episode transcripts: talkpython.fm

Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong

--- Stay in touch with us ---

Subscribe to Talk Python on YouTube: youtube.com

Talk Python on Bluesky: @talkpython.fm at bsky.app

Talk Python on Mastodon: talkpython

Michael on Bluesky: @mkennedy.codes at bsky.app

Michael on Mastodon: mkennedy

#516: Accelerating Python Data Science at NVIDIA

What if your code was crash-proof? That's the value prop for a framework called Temporal. Temporal is a durable execution platform that enables developers to build scalable applications without sacrificing productivity or reliability. The Temporal server executes units of application logic called Workflows in a resilient manner that automatically handles intermittent failures, and retries failed operations. We have Mason Egger from Temporal on to dive into durable execution.

Episode sponsors

Posit

PyBay

Talk Python Courses

Links from the show
Just Enough Python for Data Scientists Course: talkpython.fm

Temporal Durable Execution Platform: temporal.io

Temporal Learn Portal: learn.temporal.io

Temporal GitHub Repository: github.com

Temporal Python SDK GitHub Repository: github.com

What Is Durable Execution, Temporal Blog: temporal.io

Mason on Bluesky Profile: bsky.app

Mason on Mastodon Profile: fosstodon.org

Mason on Twitter Profile: twitter.com

Mason on LinkedIn Profile: linkedin.com

X Post by @skirano: x.com

Temporal Docker Compose GitHub Repository: github.com

Building a distributed asyncio event loop (Chad Retz) - PyTexas 2025: youtube.com

Watch this episode on YouTube: youtube.com

Episode #515 deep-dive: talkpython.fm/515

Episode transcripts: talkpython.fm

Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong

--- Stay in touch with us ---

Subscribe to Talk Python on YouTube: youtube.com

Talk Python on Bluesky: @talkpython.fm at bsky.app

Talk Python on Mastodon: talkpython

Michael on Bluesky: @mkennedy.codes at bsky.app

Michael on Mastodon: mkennedy

#515: Durable Python Execution with Temporal

Every year the core developers of Python convene in person to focus on high priority topics for CPython and beyond. This year they met at PyCon US 2025. Those meetings are closed door to keep focused and productive. But we're lucky that Seth Michael Larson was in attendance and wrote up each topic presented and the reactions and feedback to each. We'll be exploring this year's Language Summit with Seth. It's quite insightful to where Python is going and the pressing matters.

Episode sponsors

Seer: AI Debugging, Code TALKPYTHON

Sentry AI Monitoring, Code TALKPYTHON

Talk Python Courses

Links from the show
Seth on Mastodon: @sethmlarson@fosstodon.org

Seth on Twitter: @sethmlarson

Seth on Github: github.com

Python Language Summit 2025: pyfound.blogspot.com

WheelNext: wheelnext.dev

Free-Threaded Wheels: hugovk.github.io

Free-Threaded Python Compatibility Tracking: py-free-threading.github.io

PEP 779: Criteria for supported status for free-threaded Python: discuss.python.org

PyPI Data: py-code.org

Senior Engineer tries Vibe Coding: youtube.com

Watch this episode on YouTube: youtube.com

Episode #514 deep-dive: talkpython.fm/514

Episode transcripts: talkpython.fm

Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong

--- Stay in touch with us ---

Subscribe to Talk Python on YouTube: youtube.com

Talk Python on Bluesky: @talkpython.fm at bsky.app

Talk Python on Mastodon: talkpython

Michael on Bluesky: @mkennedy.codes at bsky.app

Michael on Mastodon: mkennedy

#514: Python Language Summit 2025

Why do people listen to this podcast? Sure, they're looking for technical explorations of new libraries and ideas. But often it's to hear the story behind them. If that speaks to you, then I have the perfect episode lined up. I have Barry Warsaw, Paul Everitt, Carol Willing, and Brett Cannon all back on the show to share stories from the history of Python. You'll hear about how import this came to be and how the first PyCon had around 30 attendees (two of whom are guests on this episode!). Sit back and enjoy the humorous stories from Python's past.

Episode sponsors

Posit

Agntcy

Talk Python Courses

Links from the show
Barry's Zen of Python song: youtube.com

Jake Vanderplas - Keynote - PyCon 2017: youtube.com

Why it’s called “Python” (Monty Python fan-reference): geeksforgeeks.org

import antigravity: python-history.blogspot.com

NIST Python Workshop Attendees: legacy.python.org

Paul Everitt open-sources Zope: old.zope.dev

Carol Willing wins ACM Software System Award: awards.acm.org

Watch this episode on YouTube: youtube.com

Episode #513 deep-dive: talkpython.fm/513

Episode transcripts: talkpython.fm

Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong

--- Stay in touch with us ---

Subscribe to Talk Python on YouTube: youtube.com

Talk Python on Bluesky: @talkpython.fm at bsky.app

Talk Python on Mastodon: talkpython

Michael on Bluesky: @mkennedy.codes at bsky.app

Michael on Mastodon: mkennedy

#513: Stories from Python History

Do you like to dive into the details and intricacies of how Python executes and how we can optimize it? Well, do I have an episode for you. We welcome back Brandt Bucher to give us an update on the upcoming JIT compiler for Python and why it differs from JITs for languages such as C# and Java.

Episode sponsors

Posit

Talk Python Courses

Links from the show
Brandt Bucher: github.com/brandtbucher

PyCon Talk: What they don't tell you about building a JIT compiler for CPython: youtube.com

Specializing, Adaptive Interpreter Episode: talkpython.fm

Watch this episode on YouTube: youtube.com

Episode #512 deep-dive: talkpython.fm/512

Episode transcripts: talkpython.fm

Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong

--- Stay in touch with us ---

Subscribe to Talk Python on YouTube: youtube.com

Talk Python on Bluesky: @talkpython.fm at bsky.app

Talk Python on Mastodon: talkpython

Michael on Bluesky: @mkennedy.codes at bsky.app

Michael on Mastodon: mkennedy

#512: Building a JIT Compiler for CPython

If you're doing data science and have mostly spent your time doing exploratory or just local development, this could be the episode for you. We are joined by Catherine Nelson to discuss techniques and tools to move your data science game from local notebooks to full-on production workflows.

Episode sponsors

Agntcy

Sentry Error Monitoring, Code TALKPYTHON

Talk Python Courses

Links from the show
New Course: LLM Building Blocks for Python: training.talkpython.fm

Catherine Nelson LinkedIn Profile: linkedin.com

Catherine Nelson Bluesky Profile: bsky.app

Enter to win the book: forms.google.com

Going From Notebooks to Scalable Systems - PyCon US 2025: us.pycon.org

Going From Notebooks to Scalable Systems - Catherine Nelson – YouTube: youtube.com

From Notebooks to Scalable Systems Code Repository: github.com

Building Machine Learning Pipelines Book: oreilly.com

Software Engineering for Data Scientists Book: oreilly.com

Jupytext - Jupyter Notebooks as Markdown Documents: github.com

Jupyter nbconvert - Notebook Conversion Tool: github.com

Awesome MLOps - Curated List: github.com

Watch this episode on YouTube: youtube.com

Episode #511 deep-dive: talkpython.fm/511

Episode transcripts: talkpython.fm

Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong

--- Stay in touch with us ---

Subscribe to Talk Python on YouTube: youtube.com

Talk Python on Bluesky: @talkpython.fm at bsky.app

Talk Python on Mastodon: talkpython

Michael on Bluesky: @mkennedy.codes at bsky.app

Michael on Mastodon: mkennedy

#511: From Notebooks to Production Data Science Systems

Are you using Polars for your data science work? Maybe you've been sticking with the tried-and-true Pandas? There are many benefits to Polars directly of course. But you might not be aware of all the excellent tools and libraries that make Polars even better. Examples include Patito which combines Pydantic and Polars for data validation and polars_encryption which adds AES encryption to selected columns. We have Christopher Trudeau back on Talk Python To Me to tell us about his list of excellent libraries to power up your Polars game and we also talk a bit about his new Polars course.

Episode sponsors

Agntcy

Sentry Error Monitoring, Code TALKPYTHON

Talk Python Courses

Links from the show
New Theme Song (Full-Length Download and backstory): talkpython.fm/blog

Polars for Power Users Course: training.talkpython.fm

Awesome Polars: github.com

Polars Visualization with Plotly: docs.pola.rs

Dataframely: github.com

Patito: github.com

polars_iptools: github.com

polars-fuzzy-match: github.com

Nucleo Fuzzy Matcher: github.com

polars-strsim: github.com

polars_encryption: github.com

polars-xdt: github.com

polars_ols: github.com

Least Mean Squares Filter in Signal Processing: www.geeksforgeeks.org

polars-pairing: github.com

Pairing Function: en.wikipedia.org

polars_list_utils: github.com

Harley Schema Helpers: tomburdge.github.io

Marimo Reactive Notebooks Episode: talkpython.fm

Marimo: marimo.io

Ahoy Narwhals Podcast Episode Links: talkpython.fm

Watch this episode on YouTube: youtube.com

Episode #510 deep-dive: talkpython.fm/510

Episode transcripts: talkpython.fm

Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong

--- Stay in touch with us ---

Subscribe to Talk Python on YouTube: youtube.com

Talk Python on Bluesky: @talkpython.fm at bsky.app

Talk Python on Mastodon: talkpython

Michael on Bluesky: @mkennedy.codes at bsky.app

Michael on Mastodon: mkennedy

#510: 10 Polars Tools and Techniques To Level Up Your Data Science

If you're looking to leverage the insane power of modern GPUs for data science and ML, you might think you'll need to use some low-level programming language such as C++. But the folks over at NVIDIA have been hard at work building Python SDKs which provide nearly native level of performance when doing Pythonic GPU programming. Bryce Adelstein Lelbach is here to tell us about programming your GPU in pure Python.

Episode sponsors

Posit

Agntcy

Talk Python Courses

Links from the show
Bryce Adelstein Lelbach on Twitter: @blelbach

Episode Deep Dive write up: talkpython.fm/blog

NVIDIA CUDA Python API: github.com

Numba (JIT Compiler for Python): numba.pydata.org

Applied Data Science Podcast: adspthepodcast.com

NVIDIA Accelerated Computing Hub: github.com

NVIDIA CUDA Python Math API Documentation: docs.nvidia.com

CUDA Cooperative Groups (CCCL): nvidia.github.io

Numba CUDA User Guide: nvidia.github.io

CUDA Python Core API: nvidia.github.io

Numba (JIT Compiler for Python): numba.pydata.org

NVIDIA’s First Desktop AI PC ($3,000): arstechnica.com

Google Colab: colab.research.google.com

Compiler Explorer (“Godbolt”): godbolt.org

CuPy: github.com

RAPIDS User Guide: docs.rapids.ai

Watch this episode on YouTube: youtube.com

Episode #509 deep-dive: talkpython.fm/509

Episode transcripts: talkpython.fm

--- Stay in touch with us ---

Subscribe to Talk Python on YouTube: youtube.com

Talk Python on Bluesky: @talkpython.fm at bsky.app

Talk Python on Mastodon: talkpython

Michael on Bluesky: @mkennedy.codes at bsky.app

Michael on Mastodon: mkennedy

#509: GPU Programming in Pure Python

See the full show notes for this episode on the website at talkpython.fm/335

#335: Gene Editing with Python

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