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Talk Python To Me

Talk Python To Me

Written by: Michael Kennedy
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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.Copyright 2015-2026
Episodes
  • #548: Event Sourcing Design Pattern

    #548: Event Sourcing Design Pattern
    May 11 2026
    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
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    1 hr and 9 mins
  • #547: Parallel Python at Anyscale with Ray

    #547: Parallel Python at Anyscale with Ray
    May 6 2026
    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
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    59 mins
  • #546: Self hosting apps for Python people

    #546: Self hosting apps for Python people
    Apr 27 2026
    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
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    1 hr and 3 mins
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