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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

Python typing got fast enough to feel invisible. Pyrefly is a new, open source type checker and IDE language server from Meta, written in Rust, with a focus on instant feedback and real-world DX. Today, we will dig into what it is, why it exists, and how it plays with the rest of the typing ecosystem. We have Abby Mitchell, Danny Yang, and Kyle Into from Pyrefly here to dive into the project.

Episode sponsors

Sentry Error Monitoring, Code TALKPYTHON

Agntcy

Talk Python Courses

Links from the show
Abby Mitchell: linkedin.com

Danny Yang: linkedin.com

Kyle Into: linkedin.com

Pyrefly: pyrefly.org

Pyrefly Documentation: pyrefly.org

Pyrefly Installation Guide: pyrefly.org

Pyrefly IDE Guide: pyrefly.org

Pyrefly GitHub Repository: github.com

Pyrefly VS Code Extension: marketplace.visualstudio.com

Introducing Pyrefly: A New Type Checker and IDE Experience for Python: engineering.fb.com

Pyrefly on PyPI: pypi.org

InfoQ Coverage: Meta Pyrefly Python Typechecker: infoq.com

Pyrefly Discord Invite: discord.gg

Python Typing Conformance (GitHub): github.com

Typing Conformance Leaderboard (HTML Preview): htmlpreview.github.io

Watch this episode on YouTube: youtube.com

Episode #523 deep-dive: talkpython.fm/523

Episode transcripts: talkpython.fm

Theme Song: Developer Rap

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

---== Don't be a stranger ==---

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @talkpython@fosstodon.org

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @mkennedy@fosstodon.org

Michael on X.com: @mkennedy

#523: Pyrefly: Fast, IDE-friendly typing for Python

Today we’re turning tiny tips into big wins. Khuyen Tran, creator of CodeCut.ai, has shipped hundreds of bite-size Python and data science snippets across four years. We dig into open-source tools you can use right now, cleaner workflows, and why notebooks and scripts don’t have to be enemies. If you want faster insights with fewer yak-shaves, this one’s packed with takeaways you can apply before lunch. Let’s get into it.

Episode sponsors

Sentry Error Monitoring, Code TALKPYTHON

Agntcy

Talk Python Courses

Links from the show
Khuyen Tran (LinkedIn): linkedin.com

Khuyen Tran (GitHub): github.com

CodeCut: codecut.ai

Production-ready Data Science Book (discount code TalkPython): codecut.ai

Why UV Might Be All You Need: codecut.ai

How to Structure a Data Science Project for Readability and Transparency: codecut.ai

Stop Hard-coding: Use Configuration Files Instead: codecut.ai

Simplify Your Python Logging with Loguru: codecut.ai

Git for Data Scientists: Learn Git Through Practical Examples: codecut.ai

Marimo (A Modern Notebook for Reproducible Data Science): codecut.ai

Text Similarity & Fuzzy Matching Guide: codecut.ai

Loguru (Python logging made simple): github.com

Hydra: hydra.cc

Marimo: marimo.io

Quarto: quarto.org

Show Your Work! Book: austinkleon.com

Watch this episode on YouTube: youtube.com

Episode #522 deep-dive: talkpython.fm/522

Episode transcripts: talkpython.fm

Theme Song: Developer Rap

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

---== Don't be a stranger ==---

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @talkpython@fosstodon.org

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @mkennedy@fosstodon.org

Michael on X.com: @mkennedy

#522: Data Sci Tips and Tricks from CodeCut.ai

English is now an API. Our apps read untrusted text; they follow instructions hidden in plain sight, and sometimes they turn that text into action. If you connect a model to tools or let it read documents from the wild, you have created a brand new attack surface. In this episode, we will make that concrete. We will talk about the attacks teams are seeing in 2025, the defenses that actually work, and how to test those defenses the same way we test code. Our guides are Tori Westerhoff and Roman Lutz from Microsoft. They help lead AI red teaming and build PyRIT, a Python framework the Microsoft AI Red Team uses to pressure test real products. By the end of this hour you will know where the biggest risks live, what you can ship this quarter to reduce them, and how PyRIT can turn security from a one time audit into an everyday engineering practice.

Episode sponsors

Sentry AI Monitoring, Code TALKPYTHON

Agntcy

Talk Python Courses

Links from the show
Tori Westerhoff: linkedin.com

Roman Lutz: linkedin.com

PyRIT: aka.ms/pyrit

Microsoft AI Red Team page: learn.microsoft.com

2025 Top 10 Risk & Mitigations for LLMs and Gen AI Apps: genai.owasp.org

AI Red Teaming Agent: learn.microsoft.com

3 takeaways from red teaming 100 generative AI products: microsoft.com

MIT report: 95% of generative AI pilots at companies are failing: fortune.com

A couple of "Little Bobby AI" cartoons

Give me candy: talkpython.fm

Tell me a joke: talkpython.fm

Watch this episode on YouTube: youtube.com

Episode #521 deep-dive: talkpython.fm/521

Episode transcripts: talkpython.fm

Theme Song: Developer Rap

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

---== Don't be a stranger ==---

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @talkpython@fosstodon.org

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @mkennedy@fosstodon.org

Michael on X.com: @mkennedy

#521: Red Teaming LLMs and GenAI with PyRIT

A couple years ago, Charlie Marsh lit a fire under Python tooling with Ruff and then uv. Today he’s back with something on the other side of that coin: pyx.

Pyx isn’t a PyPI replacement. Think server, not just index. It mirrors PyPI, plays fine with pip or uv, and aims to make installs fast and predictable by letting a smart client talk to a smart server. When the client and server understand each other, you get new fast paths, fewer edge cases, and the kind of reliability teams beg for. If Python packaging has felt like friction, this conversation is traction. Let’s get into it.

Episode sponsors

Six Feet Up

Talk Python Courses

Links from the show
Charlie Marsh on Twitter: @charliermarsh

Charlie Marsh on Mastodon: @charliermarsh

Astral Homepage: astral.sh

Pyx Project: astral.sh

Introducing Pyx Blog Post: astral.sh

uv Package on GitHub: github.com

UV Star History Chart: star-history.com

Watch this episode on YouTube: youtube.com

Episode #520 deep-dive: talkpython.fm/520

Episode transcripts: talkpython.fm

Theme Song: Developer Rap

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

---== Don't be a stranger ==---

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @talkpython@fosstodon.org

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @mkennedy@fosstodon.org

Michael on X.com: @mkennedy

#520: pyx - the other side of the uv coin (announcing pyx)

Today on Talk Python: What really happens when your data work outgrows your laptop. Matthew Rocklin, creator of Dask and cofounder of Coiled, and Nat Tabris a staff software engineer at Coiled join me to unpack the messy truth of cloud-scale Python. During the episode we actually spin up a 1,000 core cluster from a notebook, twice! We also discuss picking between pandas and Polars, when GPUs help, and how to avoid surprise bills. Real lessons, real tradeoffs, shared by people who have built this stuff. Stick around.

Episode sponsors

Seer: AI Debugging, Code TALKPYTHON

Talk Python Courses

Links from the show
Matthew Rocklin: @mrocklin

Nat Tabris: tabris.us

Dask: dask.org

Coiled: coiled.io

Watch this episode on YouTube: youtube.com

Episode #519 deep-dive: talkpython.fm/519

Episode transcripts: talkpython.fm

Theme Song: Developer Rap

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

---== Don't be a stranger ==---

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @talkpython@fosstodon.org

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @mkennedy@fosstodon.org

Michael on X.com: @mkennedy

#519: Data Science Cloud Lessons at Scale

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

Seer: AI Debugging, Code TALKPYTHON

Sentry AI Monitoring, Code TALKPYTHON

Talk Python Courses

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

Theme Song: Developer Rap

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

---== Don't be a stranger ==---

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @talkpython@fosstodon.org

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @mkennedy@fosstodon.org

Michael on X.com: @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

Theme Song: Developer Rap

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

---== Don't be a stranger ==---

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @talkpython@fosstodon.org

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @mkennedy@fosstodon.org

Michael on X.com: @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

Theme Song: Developer Rap

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

---== Don't be a stranger ==---

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @talkpython@fosstodon.org

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @mkennedy@fosstodon.org

Michael on X.com: @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

Theme Song: Developer Rap

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

---== Don't be a stranger ==---

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @talkpython@fosstodon.org

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @mkennedy@fosstodon.org

Michael on X.com: @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

Theme Song: Developer Rap

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

---== Don't be a stranger ==---

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @talkpython@fosstodon.org

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @mkennedy@fosstodon.org

Michael on X.com: @mkennedy

#514: Python Language Summit 2025

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

Theme Song: Developer Rap

🥁 Served in a Flask 🎸: talkpython.fm/flasksong

---== Don't be a stranger ==---

YouTube: youtube.com/@talkpython

Bluesky: @talkpython.fm

Mastodon: @talkpython@fosstodon.org

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @mkennedy@fosstodon.org

Michael on X.com: @mkennedy

#511: From Notebooks to Production Data Science Systems

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