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: @[email protected]

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy

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: @[email protected]

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy

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: @[email protected]

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy

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: @[email protected]

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy

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: @[email protected]

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy