app_store_button

google_play_button

Device Banner Block-copy 894x1036

Astu lugude lõputusse maailma

Loe ja kuula

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

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

The cloud is convenient until it isn't. You upload your photos, sync your contacts, click through the cookie banners. Then prices go up again or you read about a family that lost their entire Google account over a medical photo sent to a doctor. At some point, the question shifts from "why would I run this myself?" to "why aren't I?"

My guest this week is Alex Kretzschmar, head of DevRel at Tailscale, longtime host of the Self-Hosted podcast, and co-founder of Linuxserver.io. We cover what self-hosting really means in 2026, the apps worth running yourself like Immich and Home Assistant, why Docker Compose ties it all together, and how Tailscale lets you reach any of it from anywhere, without opening a single port. If you've been thinking about pulling your digital life back behind your own walls, this is your roadmap.

Episode sponsors

Temporal

Talk Python Courses

Links from the show
Guest

Alex Kretzschmar: alex.ktz.me

Bitflip podcast: bitflip.show

Self-Hosted podcast (Alex's previous show): selfhosted.show

Perfect Media Server: perfectmediaserver.com

KTZ Systems on YouTube: youtube.com/@ktzsystems

Linuxserver.io (co-founded by Alex): linuxserver.io

"How Tailscale Works" blog post: tailscale.com/blog/how-tailscale-works

https://tailscale.com/: tailscale.com

Self-hosted apps discussed

Awesome Self-Hosted (GitHub list): github.com

Immich (Google Photos alternative): immich.app

Home Assistant: home-assistant.io

Open Home Foundation: openhomefoundation.org

Plausible Analytics: plausible.io

Umami Analytics: umami.is

Python integration for umami: pypi.org

Pi-hole: pi-hole.net

AdGuard Home: adguard.com

NextDNS: nextdns.io

Coolify: coolify.io

Docker + ufw: docs.docker.com

Storage, backup & filesystem

OpenZFS: openzfs.org

ZFS.rent (offsite ZFS replication): zfs.rent

Backblaze: backblaze.com

Hetzner Storage Box: hetzner.com

DigitalOcean: digitalocean.com

Secrets management mentioned

OpenBao (open-source Vault fork): openbao.org

HashiCorp Vault: hashicorp.com

Bitwarden: bitwarden.com

1Password: 1password.com

Hardware mentioned

Proxmox VE: proxmox.com

Minisforum MS01: minisforum.com

Zima Board / Zima OS: zimaspace.com

Other references

Cory Doctorow on "enshittification" (Cory's blog where he coined the term): pluralistic.net

Linus Tech Tips' WAN Show (Linus mentioned NAS-building going mainstream): linustechtips.com

Watch this episode on YouTube: youtube.com

Episode #546 deep-dive: talkpython.fm/546

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

#546: Self hosting apps for Python people

The OWASP Top 10 just got a fresh update, and there are some big changes: supply chain attacks, exceptional condition handling, and more. Tanya Janca is back on Talk Python to walk us through every single one of them. And we're not just talking theory, we're going to turn Claude Code loose on a real open source project and see what it finds. Let's do it.

Episode sponsors

Temporal

Talk Python Courses

Links from the show
DevSec Station Podcast: www.devsecstation.com

SheHacksPurple Newsletter: newsletter.shehackspurple.ca

owasp.org: owasp.org

owasp.org/Top10/2025: owasp.org

from here: github.com

Kinto: github.com

A01:2025 - Broken Access Control: owasp.org

A02:2025 - SecuA02 Security Misconfiguration: owasp.org

ASP.NET: ASP.NET

A03:2025 - Software Supply Chain Failures: owasp.org

A04:2025 - Cryptographic Failures: owasp.org

A05:2025 - Injection: owasp.org

A06:2025 - Insecure Design: owasp.org

A07:2025 - Authentication Failures: owasp.org

A08:2025 - Software or Data Integrity Failures: owasp.org

A09:2025 - Security Logging and Alerting Failures: owasp.org

A10 Mishandling of Exceptional Conditions: owasp.org

https://github.com/KeygraphHQ/shannon: github.com

anthropic.com/news/mozilla-firefox-security: www.anthropic.com

generalpurpose.com/the-distillation/claude-mythos-what-it-means-for-your-business: www.generalpurpose.com

Python Example Concepts: blobs.talkpython.fm

Watch this episode on YouTube: youtube.com

Episode #545 deep-dive: talkpython.fm/545

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

#545: OWASP Top 10 (2025 List) for Python Devs

When you pip install a package with compiled code, the wheel you get is built for CPU features from 2009. Want newer optimizations like AVX2? Your installer has no way to ask for them. GPU support? You're on your own configuring special index URLs. The result is fat binaries, nearly gigabyte-sized wheels, and install pages that read like puzzle books. A coalition from NVIDIA, Astral, and QuanSight has been working on Wheel Next: A set of PEPs that let packages declare what hardware they need and let installers like uv pick the right build automatically. Just uv pip install torch and it works. I sit down with Jonathan Dekhtiar from NVIDIA, Ralf Gommers from Quansight and the NumPy and SciPy teams, and Charlie Marsh, founder of Astral and creator of uv, to dig into all of it.

Episode sponsors

Sentry Error Monitoring, Code talkpython26

Temporal

Talk Python Courses

Links from the show
Guests

Charlie Marsh: github.com

Ralf Gommers: github.com

Jonathan Dekhtiar: github.com

CPU dispatcher: numpy.org

build options: numpy.org

Red Hat RHEL: www.redhat.com

Red Hat RHEL AI: www.redhat.com

RedHats presentation: wheelnext.dev

CUDA release: developer.nvidia.com

requires a PEP: discuss.python.org

WheelNext: wheelnext.dev

Github repo: github.com

PEP 817: peps.python.org

PEP 825: discuss.python.org

uv: docs.astral.sh

A variant-enabled build of uv: astral.sh

pyx: astral.sh

pypackaging-native: pypackaging-native.github.io

PEP 784: peps.python.org

Watch this episode on YouTube: youtube.com

Episode #544 deep-dive: talkpython.fm/544

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

#544: Wheel Next + Packaging PEPs

When you type a question into ChatGPT, the model only has what you typed to work with. But tools like Claude Code can plan, iterate, test, and recover from mistakes. They work more like we do. The difference is the agent harness: Planning tools, file system access, sub-agents, and carefully crafted system prompts that turn a raw LLM into something genuinely capable.

Sydney Runkle is back on Talk Python representing LangChain and their new open source library, Deep Agents: A framework for building your own deep agents with plain Python functions, middleware hooks, and MCP support. This is how the magic works under the hood.

Episode sponsors

Sentry Error Monitoring, Code talkpython26

Agentic AI Course

Talk Python Courses

Links from the show
Guest

Sydney Runkle: github.com

Claude Code uses: x.com

Deep Research: openai.com

Manus: manus.im

Blog post announcement: blog.langchain.com

Claudes system prompt: github.com

sub agents: docs.anthropic.com

the quick start: docs.langchain.com

CLIs: github.com

Talk Python's CLI: talkpython.fm

custom tools: docs.langchain.com

DeepAgents Examples: github.com

Custom Middleware: docs.langchain.com

Built in middleware: docs.langchain.com

Improving Deep Agents with harness engineering: blog.langchain.com

Prebuilt middleware: docs.langchain.com

Watch this episode on YouTube: youtube.com

Episode #543 deep-dive: talkpython.fm/543

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

#543: Deep Agents: LangChain's SDK for Agents That Plan and Delegate

If you've built documentation in the Python ecosystem, chances are you've used Martin Donath's work. His Material for MKDocs powers docs for FastAPI, uv, AWS, OpenAI, and tens of thousands of other projects. But when MKDocs 2.0 took a direction that would break Material and 300 ecosystem plugins, Martin went back to the drawing board. The result is Zensical: A new static site generator with a Rust core, differential builds in milliseconds instead of minutes, and a migration path designed to bring the whole community along.

Episode sponsors

Sentry Error Monitoring, Code talkpython26

Talk Python Courses

Links from the show
Guest

Martin Donath: github.com

Zensical: zensical.org

Material for MkDocs: squidfunk.github.io

Getting Started: zensical.org

Github pages: docs.github.com

Cloudflare pages: pages.cloudflare.com

Michaels Example: gist.github.com

Material for MkDocs: zensical.org

gohugo.io/content-management/shortcodes: gohugo.io

a sense of size of the project: blobs.talkpython.fm

Zensical Spark: zensical.org

Watch this episode on YouTube: youtube.com

Episode #542 deep-dive: talkpython.fm/542

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

#541: Monty - Python in Rust for AI

See the full show notes for this episode on the website at talkpython.fm/264

#264: 10 tips every Flask developer should know

Loe ja kuula

Muud podcastid, mis võivad sulle meeldida ...