Talk Python To Me

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.

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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

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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

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: @[email protected]

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy

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: @[email protected]

X.com: @talkpython

Michael on Bluesky: @mkennedy.codes

Michael on Mastodon: @[email protected]

Michael on X.com: @mkennedy