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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

For years, "Django and async" came with an asterisk. The docs themselves warned you off it. Scary performance notes, a story that felt half-finished. Well, that story just got rewritten, literally, and the person who rewrote it is here to tell you why the old framing was wrong.

Carlton Gibson is a former Django Fellow, sat on the security team for eight years, and he's on the steering council. On this episode we get into the async topic doc rewrite, what actually remains versus what was just fear, the new Tasks framework in 6.0, DB-level cascades and fetch modes landing in 6.1, and why free-threading is the bet that's about to pay off big for Django. If you've been told Django's async story isn't ready, this is the episode that puts that myth to bed.

Episode sponsors

Sentry Error Monitoring, Code talkpython26

Python in Production

Talk Python Courses

Links from the show
DjangoCon Europe: djangocon.eu

PyCon Italia: pycon.it

Django on the Med: djangomed.eu

Django Mantle: noumenal.es

PyPI: pypi.org

release notes: docs.djangoproject.com

on_delete: docs.djangoproject.com

Fetch modes: docs.djangoproject.com

HttpRequest.multipart_parser_class: docs.djangoproject.com

async topic doc: docs.djangoproject.com

docs: docs.djangoproject.com

DEP 14: github.com

django-tasks: github.com

django-tasks-local: github.com

Celery: docs.celeryq.dev

PEP 703: peps.python.org

free-threading HOWTO: docs.python.org

PEP 779: peps.python.org

ASGI: docs.djangoproject.com

PGBouncer: www.pgbouncer.org

Channels: channels.readthedocs.io

sync_to_async / async_to_sync: docs.djangoproject.com

noumenal.es: noumenal.es

Django Chat: djangochat.com

@carlton@fosstodon.org: fosstodon.org

Article: Cutting Python Web App Memory Over 31%: mkennedy.codes

Watch this episode on YouTube: youtube.com

Episode #556 deep-dive: talkpython.fm/556

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

#556: Updates on Django's Async Story

Coding agents have gotten really good at one kind of work. You scope a feature, edit some files, run the tests, ship it. It all happens on disk. But that is not how data work feels. You load something, you look at it, you run a cell, you watch how it responds, and you decide the next move from whatever is sitting in memory. And until now, your agent couldn't see any of that. It only saw the files. Never the live state.

This episode, that wall comes down. marimo pair drops a coding agent right inside a running notebook, with full access to every variable Python is holding in memory. The notebook becomes a shared canvas. You point, it runs the code. You tell it to zoom in on the Picasso paintings, and the chart just updates. No MCP tools to wire up, no schema to describe. Just Python, and an agent that can finally see what you see. Trevor Manz is back to walk us through it.

Episode sponsors

Sentry Error Monitoring, Code talkpython26

Talk Python Courses

Links from the show
marimo pair: marimo.io/pair

Course transcripts announcement: talkpython.fm/blog

anywidget: Jupyter Widgets made easy: talkpython.fm

marimo: marimo.io

blog: marimo.io

GitHub: github.com

given this: martinalderson.com

llms.txt: talkpython.fm

mcp: talkpython.fm

cli: talkpython.fm

open issues: github.com

Discord: marimo.io

Marimo Pair: marimo.io

OpenCode: opencode.ai

AI Tooling for Software Engineers in 2026: newsletter.pragmaticengineer.com

Watch this episode on YouTube: youtube.com

Episode #555 deep-dive: talkpython.fm/555

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

#555: Marimo Pair - A Canvas for Agent + Developers Collaboration

You ask an AI a question and it answers with total confidence. Most of the time, a confidently wrong answer is just an annoyance. But what if the question is medical, and there's a real patient on the other end? In that world, a hallucination isn't a bug, it's a patient-safety event. Sumit Gundawar is a London-based software engineer who builds the clinical platform for a UK longevity and aesthetic-medicine clinic, and his whole argument is that in high-stakes AI, the model is the easy part. Earning trust is the real engineering. We dig into grounding, refusal logic, human-in-the-loop design, and the messy frontier of longevity and biohacking, plus a live demo of an assistant that refuses to answer when it can't back up the claim. Let's get into it.

Episode sponsors

Six Feet Up

Talk Python Courses

Links from the show
Guest

Sumit Gundawar: linkedin.com

Course transcripts announcement: talkpython.fm/blog

Sumit Gundawar - JAX London Speaker: jaxlondon.com

Anthropic: anthropic.com

OpenAI Platform: platform.openai.com

Anthropic: anthropic.com

LangChain: langchain.com

OWASP: owasp.org

Pydantic: pydantic.dev

EU AI Act - Regulatory Framework: digital-strategy.ec.europa.eu

HIPAA - HHS: www.hhs.gov

NHS: www.nhs.uk

Llama: llama.com

Qwen - QwenLM on GitHub: github.com

OpenAI Platform: platform.openai.com

Hugging Face: huggingface.co

Llama: llama.com

Granola: www.granola.ai

HIPAA - HHS: www.hhs.gov

CodeRabbit: www.coderabbit.ai

Cursor Origin: cursor.com

GitHub Status: www.githubstatus.com

Midjourney Medical: www.midjourney.com

Neko Health: www.nekohealth.com

CERN: home.cern

ATLAS Experiment: atlas.cern

Watch this episode on YouTube: youtube.com

Episode #554 deep-dive: talkpython.fm/554

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

#554: Trustworthy AI in Healthcare and Longevity

This episode is a fun crossover from our Python news and tips podcast, Python Bytes. We have had some big changes over there. Brian Okken has moved on and Calvin Hendryx-Parker has joined the show as the new co-host. To kick off this new era, we decided to do a longer and more personal episode called "All Our Tools". The idea is both of us talk about some of our most useful day-to-day developer and business owner tools that we think you all would find useful. It was so well received, that I'm bringing it to you all as a crossover episode. Enjoy and we hope you find something new and awesome to help you with your software and data science day to day.

Episode sponsors

Sentry Error Monitoring, Code talkpython26

Python in Production

Talk Python Courses

Links from the show
@calvinhp@sixfeetup.social: sixfeetup.social

@calvinhp.com: bsky.app

calvinhp.com: calvinhp.com

Original airing on Python Bytes: pythonbytes.fm

pi: pi.dev

superpowers: github.com

Warp.dev: Warp.dev

OhMyZSH: ohmyz.sh

Commandbookapp.com: Commandbookapp.com

Blink: blink.sh

kitty: sw.kovidgoyal.net

mosh: mosh.org

tmux: github.com

Claude code: www.anthropic.com

Claude.md: Claude.md

MacWhisper: goodsnooze.gumroad.com

Handy: handy.computer

Tailscale: tailscale.com

Talk Python episode with Alex: talkpython.fm

Telescopo: www.telescopo.app

Typora markdown: typora.io

formal documentation for many of my open source packages: mkennedy.codes

Great Docs: posit-dev.github.io

Statement on the US government directive to suspend access to Fable 5 and Mythos 5: www.anthropic.com

No second date: x.com

Watch this episode on YouTube: youtube.com

Episode #553 deep-dive: talkpython.fm/553

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

#553: All of our tools

OpenAI just acquired Astral, the company behind uv, Ruff, and ty. And if your first thought was "wait, is uv toast?", you are not alone. But here's the twist Charlie Marsh shared with me: he thinks they may ship more open source at OpenAI than they ever did at Astral. On this episode, we get into the acquisition, the mixed feelings, the future of your favorite Python tools, and what it's like to build right at the center of the AI universe.

Episode sponsors

Sentry Error Monitoring, Code talkpython26

Talk Python Courses

Links from the show
Guest

Charlie Marsh: github.com

The announcement: astral.sh

OpenAI: openai.com

uv: github.com

ty: github.com

Ruff: github.com

pyx: astral.sh

Codex team: openai.com

Anthropic did something similar by acquiring Bun: www.anthropic.com

Daily Stars Explorer: emanuelef.github.io

Agentic AI Programming for Python: training.talkpython.fm

Python Web Security: OWASP Top 10 with Agentic AI: training.talkpython.fm

Episode #552 deep-dive: talkpython.fm/552

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

#552: Astral joins OpenAI

If you've ever been to PyCon, you know one of the best parts of the expo hall is Startup Row, a stretch of booths where early-stage companies built on Python show off what they're creating. But only attendees get to walk that lane, so let's bring it to everyone. In this episode, we stroll down Startup Row together. We kick things off with the organizers, Jason and Shay, who share the program's origin story going back to Paul Graham and the PSF, plus some surprising stats, including two unicorns among the alumni. Then we meet five startups: Tetrix, bringing AI to institutional investing in private markets. Arcjet, security that lives inside your app as an SDK. Phemeral.dev, serverless hosting built for Python web apps. CapiscIO, an identity and authority layer for AI agents. And Pixeltable, a multimodal database from Marcel Kornacker, co-creator of Apache Parquet. See if you can spot the theme running through them all. Let's go for a walk.

Episode sponsors

AgentField AI

Talk Python Courses

Links from the show
Guests

Naunidh Bhalla: linkedin.com

Grant Gittes: linkedin.com

Marcel Kornacker: linkedin.com

Beon de Nood: linkedin.com

Chinmaya Joshi: linkedin.com

David Mytton: linkedin.com

Shea Tate-Di Donna: linkedin.com

Jason Rowley: linkedin.com

Azul Garza: github.com

Renée Rosillo: linkedin.com

Tetrix: tetrix.co

Tetrix Jobs: tetrix.co

Arcjet: arcjet.com

Pixeltable: pixeltable.com

Phemeral.dev: phemeral.dev

CapiscIO: capisc.io

Episode #551 deep-dive: talkpython.fm/551

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

#551: Stroll Down Startup Lane - 2026

You wake up, brew the coffee, open GitHub, and there it is. Another pull request on your open source project. Thirteen thousand lines added. No issue filed first. No discussion. Just "here, please review this for me."

Over the past year, GitHub activity has spiked roughly twelve times in a few short months, and a huge chunk of that signal is landing on the same small group of maintainers who were already stretched thin. The curl bug bounty got buried under AI-generated noise. Jazzband, the home of Django classics like pip-tools and the Django debug toolbar, hit what its maintainer called an "apocalypse" and started sunsetting. Even CPython just shipped fresh guidelines on AI-assisted contributions this week.

So what does all of this actually look like from the receiving end of the pull request?

On this episode, Paolo Melchiorre joins us to tell that story from inside the maintainer's chair. Paolo is a director of the Django Software Foundation, an organizer of PyCon Italy, a Django Girls coach, and he has spent the past year carefully collecting examples of how AI is reshaping open source contributions. The good, the bad, and the extra fingers.

We dig into his PyCon US talk on AI-assisted contributions and maintainer load, why AI is best understood as an amplifier rather than a new kind of contributor, the wildly different policies across 86 open source foundations, whether projects banning AI today are reacting to last year's models.

Episode sponsors

AgentField AI

Talk Python Courses

Links from the show
Guest

Paolo Melchiorre: github.com

DSF: www.djangoproject.com

djangonaut-space: djangonaut.space

PyCon Italia: 2026.pycon.it

uDjango: github.com

My PyCon US 2026 post: www.paulox.net

AI-Assisted Contributions and Maintainer Load: www.paulox.net

Senior Engineer Tries Vibe Coding: www.youtube.com

Code Rabbit AI PR Reviews: www.coderabbit.ai

GitHub Usage Graphs: github.blog

Update on CPython's AI Policies: fosstodon.org

High-Quality Chaos from Curl: daniel.haxx.se

The Generative AI Policy Landscape in Open Source: redmonk.com

Watch this episode on YouTube: youtube.com

Episode #550 deep-dive: talkpython.fm/550

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

#550: AI Contributions and Maintainer Load in Open Source

Your documentation has two audiences now - humans reading the rendered HTML, and AI agents trying to make sense of your library. Rich Iannone and Michael Chow from Posit are back on Talk Python with a brand new Python documentation tool called Great Docs that takes both seriously. Rich is the creator of Great Tables, and before that the R package GT, the man has a serious eye for design, and he's pointed that energy at the Python docs ecosystem. We'll talk about how Great Docs spins up a polished site in three commands, why every page ships as Markdown for your favorite LLM, how it leans on Quarto for executable code blocks and tabbed install sections, and where it lands against Sphinx, MkDocs, and Zensical. Plus, you'll meet Tablin. Here we go.

Episode sponsors

Sentry Error Monitoring, Code talkpython26

Temporal

Talk Python Courses

Links from the show
Guests

Michael Chow: github.com

Rich lannone: github.com

Python Web Security with OWASP Top 10 and Agentic AI Course: talkpython.fm

Great Docs: posit-dev.github.io/great-docs

Great Tables: posit-dev.github.io

GT Episode: talkpython.fm

Sphinx: www.sphinx-doc.org

mkdocs: www.mkdocs.org

Zensical: zensical.org

Hugo: gohugo.io

Ghost: ghost.org

Rs pkgdown: pkgdown.r-lib.org

Quarto: quarto.org

quickstart: posit-dev.github.io

llms.txt file: llmstxt.org

llms.txt: talkpython.fm

mcp: talkpython.fm

cli: talkpython.fm

Watch this episode on YouTube: youtube.com

Episode #549 deep-dive: talkpython.fm/549

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

#549: Great Docs

What if your database worked more like Git? Every change captured as an immutable event you can replay, instead of a single mutating row that quietly forgets its own history. That's event sourcing, and Chris May is back on Talk Python, fresh off our Datastar panel, to walk us through what it actually looks like in Python. We'll cover the core patterns, the libraries to reach for, when not to use it, and why event sourcing turns out to be a surprisingly good fit for AI-assisted coding.

Episode sponsors

Sentry Error Monitoring, Code talkpython26

Temporal

Talk Python Courses

Links from the show
Guest

Chris May: everydaysuperpowers.dev

Intro to event sourcing e-book: everydaysuperpowers.gumroad.com

Domain-Driven Design: The Power of CQRS and Event Sourcing: How CQRS/ES Redefine Building Scalable System: ricofritzsche.me

DDD: www.amazon.com

Understanding Eventsourcing (Martin Dilger): www.amazon.com

Event Sourcing Explained using Football Video: www.youtube.com

Why I finally embraced event sourcing and why you should too article: everydaysuperpowers.dev

valkey: valkey.io

diskcache: talkpython.fm

eventsourcing package: github.com

eventsourcing docs: eventsourcing.readthedocs.io

John Bywater: github.com

Datastar: data-star.dev

Microconf: microconf.com

Event Modeling & Event Sourcing Podcast: podcast.eventmodeling.org

Python Package Guides for AI Agents: github.com

Iodine tablets AI joke: x.com

KurrentDb: www.kurrent.io

Watch this episode on YouTube: youtube.com

Episode #548 deep-dive: talkpython.fm/548

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

#548: Event Sourcing Design Pattern

When OpenAI trained GPT-3, they didn't roll their own orchestration layer. They used Ray, an open source Python framework born out of the same Berkeley research lab lineage that gave us Apache Spark. And here's the twist: Ray was originally built for reinforcement learning research, then quietly faded as RL hit a wall. Until ChatGPT showed up. Suddenly reinforcement learning was back, as the post-training step that turns a raw language model into something genuinely useful.

Edward Oakes and Richard Liaw, two founding engineers behind Ray and Anyscale, join me on Talk Python to tell that story. We'll trace Ray from its RISE Lab origins at UC Berkeley to powering some of the largest training runs in the world. We'll talk about what Ray actually is, a distributed execution engine for AI workloads, and how a few lines of Python become work running across hundreds of GPUs. We'll cover Ray Data for multimodal pipelines, the dashboard, the VS Code remote debugger, KubRay for Kubernetes, and where Ray fits alongside Dask, multiprocessing, and asyncio.

If you've ever stared at a single-machine Python script and thought, "there has to be a better way to scale this", this one's for you

Episode sponsors

Sentry Error Monitoring, Code talkpython26

AgentField AI

Talk Python Courses

Links from the show
Guests

Richard Liaw: github.com

Edward Oakes: github.com

Ray: www.ray.io

Example code (we used for walk-through): docs.ray.io

Getting Started with Ray: docs.ray.io

Ray Libraries: docs.ray.io

kuberay: github.com

Watch this episode on YouTube: youtube.com

Episode #547 deep-dive: talkpython.fm/547

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

#547: Parallel Python at Anyscale with Ray

See the full show notes for this episode on the website at talkpython.fm/280

#280: Python and AI in Journalism

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