Talk Python To Me - Python Conversations For Passionate Developers

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.

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 - Sec

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 QuantSight 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 QuantSight 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 opt

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 Temporal 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:

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.

When LLMs write code to accomplish a task, that code has to actually run somewhere. And right now, the options aren't great. Spin up a sandboxed container and you're paying a full second of cold start overhead plus the complexity of another service. Let the LLM loose on your actual machine and... well, you'd better be watching. On this episode, I sit down with Samuel Colvin, creator of Pydantic, now at 10 billion downloads, to explore Monty, a Python interpreter written from scratch in Rust, purpose-built to run LLM-generated code. It starts in microseconds, is completely sandboxed by design, and can even serialize its entire state to a database and resume later. We dig into why this deliberately limited interpreter might be exactly what the AI agent era needs. Episode sponsors Talk Python Courses Python in Production Links from the show Guest Samuel Colvin: github.com CPython: github.com IronPython: ironpython.net Jython: www.jython.org Pyodide: pyodide.com monty: github.com Pydantic AI: pydantic.dev

Monorepos -- you've heard the talks, you've read the blog posts, maybe you've seen a few tantalizing glimpses into how Google or Meta organize their massive codebases. But it's often in the abstract and behind closed doors. What if you could crack open a real, production monorepo, one with over a million lines of Python and over 100 of sub-packages, and actually see how it's built, step by step, using modern tools and standards? That's exactly what Apache Airflow gives us. On this episode, I sit down with Jarek Potiuk and Amogh Desai, two of Airflow's top contributors, to go inside one of the largest open-source Python monorepos in the world and learn how they manage it with uv, pyproject.toml, and the latest packaging standards, so you can apply those same patterns to your own projects. Episode sponsors Agentic AI Course Python in Production Talk Python Courses Links from the show Guests Amogh Desai: github.com Jarek's GitHub: github.com definition of a monorepo: monorepo.tools airflow: airflow.apach

You're adding type hints to your Python code, your editor is happy, autocomplete is working great. But then you switch tools and suddenly there are red squiggles everywhere. Who decides what a float annotation actually means? Or whether passing None where an int is expected should be an error? It turns out there's a five-person council dedicated to exactly these questions -- and two brand-new Rust-based type checkers are raising the bar. On this episode, I sit down with three members of the Python Typing Council -- Jelle Zijlstra, Rebecca Chen, and Carl Meyer -- to learn how the type system is governed, where the spec and the type checkers agree and disagree, and get the council's official advice on how much typing is just enough. Episode sponsors Sentry Error Monitoring, Code talkpython26 Agentic AI Course Talk Python Courses Links from the show Guests Carl Meyer: github.com Jelle Zijlstra: jellezijlstra.github.io Rebecca Chen: github.com Typing Council: github.com typing.python.org: typing.python.org de

Digital humanities sounds niche, until you realize it can mean a searchable archive of U.S. amendment proposals, Irish folklore, or pigment science in ancient art. Today I’m talking with David Flood from Harvard’s DARTH team about an unglamorous problem: What happens when the grant ends but the website can’t. His answer, static sites, client-side search, and sneaky Python. Let’s dive in. Episode sponsors Sentry Error Monitoring, Code talkpython26 Command Book Talk Python Courses Links from the show Guest David Flood: davidaflood.com DARTH: digitalhumanities.fas.harvard.edu Amendments Project: digitalhumanities.fas.harvard.edu Fionn Folklore Database: fionnfolklore.org Mapping Color in History: iiif.harvard.edu Apatosaurus: apatosaurus.io Criticus: github.com github.com/palewire/django-bakery: github.com sigsim.acm.org/conf/pads/2026/blog/artifact-evaluation: sigsim.acm.org Hugo: gohugo.io Water Stories: waterstories.fas.harvard.edu Tsumeb Mine Notebook: tmn.fas.harvard.edu Dharma and Punya: dharmapunya201

You love building web apps with Python, and HTMX got you excited about the hypermedia approach -- let the server drive the HTML, skip the JavaScript build step, keep things simple. But then you hit that last 10%: You need Alpine.js for interactivity, your state gets out of sync, and suddenly you're juggling two unrelated libraries that weren't designed to work together. What if there was a single 11-kilobyte framework that gave you everything HTMX and Alpine do, and more, with real-time updates, multiplayer collaboration out of the box, and performance so fast you're actually bottlenecked by the monitor's refresh rate? That's Datastar. On this episode, I sit down with its creator Delaney Gillilan, core maintainer Ben Croker, and Datastar convert Chris May to explore how this backend-driven, server-sent-events-first framework is changing the way full-stack developers think about the modern web. Episode sponsors Sentry Error Monitoring, Code talkpython26 Command Book Talk Python Courses Links from th

You've built your FastAPI app, it's running great locally, and now you want to share it with the world. But then reality hits -- containers, load balancers, HTTPS certificates, cloud consoles with 200 options. What if deploying was just one command? That's exactly what Sebastian Ramirez and the FastAPI Cloud team are building. On this episode, I sit down with Sebastian, Patrick Arminio, Savannah Ostrowski, and Jonathan Ehwald to go inside FastAPI Cloud, explore what it means to build a "Pythonic" cloud, and dig into how this commercial venture is actually making FastAPI the open-source project stronger than ever. Episode sponsors Command Book Python in Production Talk Python Courses Links from the show Guests Sebastián Ramírez: github.com Savannah Ostrowski: github.com Patrick Arminio: github.com Jonathan Ehwald: github.com FastAPI labs: fastapilabs.com quickstart: fastapicloud.com an episode on diskcache: talkpython.fm Fastar: github.com FastAPI: The Documentary: www.youtube.com Tailwind CSS Situation: a

Building on the web is like working with the perfect clay. It’s malleable and can become almost anything. But too often, frameworks try to hide the web’s best parts away from us. Today, we’re looking at PyView, a project that brings the real-time power of Phoenix LiveView directly into the Python world. I'm joined by Larry Ogrodnek to dive into PyView. Episode sponsors Talk Python Courses Python in Production Links from the show Guest Larry Ogrodnek: hachyderm.io pyview.rocks: pyview.rocks Phoenix LiveView: github.com this section: pyview.rocks Core Concepts: pyview.rocks Socket and Context: pyview.rocks Event Handling: pyview.rocks LiveComponents: pyview.rocks Routing: pyview.rocks Templating: pyview.rocks HTML Templates: pyview.rocks T-String Templates: pyview.rocks File Uploads: pyview.rocks Streams: pyview.rocks Sessions & Authentication: pyview.rocks Single-File Apps: pyview.rocks starlette: starlette.dev wsproto: github.com apscheduler: github.com t-dom project: github.com Watch this episode on

Your cloud SSD is sitting there, bored, and it would like a job. Today we’re putting it to work with DiskCache, a simple, practical cache built on SQLite that can speed things up without spinning up Redis or extra services. Once you start to see what it can do, a universe of possibilities opens up. We're joined by Vincent Warmerdam to dive into DiskCache. Episode sponsors Talk Python Courses Python in Production Links from the show diskcache docs: grantjenks.com LLM Building Blocks for Python course: training.talkpython.fm JSONDisk: grantjenks.com Git Code Archaeology Charts: koaning.github.io Talk Python Cache Admin UI: blobs.talkpython.fm Litestream SQLite streaming: litestream.io Plash hosting: pla.sh Watch this episode on YouTube: youtube.com Episode #534 deep-dive: talkpython.fm/534 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Today on Talk Python, the creators behind FastAPI, Flask, Django, Quart, and Litestar get practical about running apps based on their framework in production. Deployment patterns, async gotchas, servers, scaling, and the stuff you only learn at 2 a.m. when the pager goes off. For Django, we have Carlton Gibson and Jeff Triplet. For Flask, we have David Lord and Phil Jones, and on team Litestar we have Janek Nouvertné and Cody Fincher, and finally Sebastián Ramírez from FastAPI is here. Let’s jump in. Episode sponsors Talk Python Courses Python in Production Links from the show Carlton Gibson - Django: github.com Sebastian Ramirez - FastAPI: github.com David Lord - Flask: davidism.com Phil Jones - Flask and Quartz(async): pgjones.dev Yanik Nouvertne - LiteStar: github.com Cody Fincher - LiteStar: github.com Jeff Triplett - Django: jefftriplett.com Django: www.djangoproject.com Flask: flask.palletsprojects.com Quart: quart.palletsprojects.com Litestar: litestar.dev FastAPI: fastapi.tiangolo.com Coolify: coo

Python in 2025 is in a delightfully refreshing place: the GIL's days are numbered, packaging is getting sharper tools, and the type checkers are multiplying like gremlins snacking after midnight. On this episode, we have an amazing panel to give us a range of perspectives on what matter in 2025 in Python. We have Barry Warsaw, Brett Cannon, Gregory Kapfhammer, Jodie Burchell, Reuven Lerner, and Thomas Wouters on to give us their thoughts. Episode sponsors Seer: AI Debugging, Code TALKPYTHON Talk Python Courses Links from the show Python Software Foundation (PSF): www.python.org PEP 810: Explicit lazy imports: peps.python.org PEP 779: Free-threaded Python is officially supported: peps.python.org PEP 723: Inline script metadata: peps.python.org PyCharm: www.jetbrains.com JetBrains: www.jetbrains.com Visual Studio Code: code.visualstudio.com pandas: pandas.pydata.org PydanticAI: ai.pydantic.dev OpenAI API docs: platform.openai.com uv: docs.astral.sh Hatch: github.com PDM: pdm-project.org Poetry: python-poetry

Have you ever thought about getting your small product into production, but are worried about the cost of the big cloud providers? Or maybe you think your current cloud service is over-architected and costing you too much? Well, in this episode, we interview Michael Kennedy, author of "Talk Python in Production," a new book that guides you through deploying web apps at scale with right-sized engineering. Episode sponsors Seer: AI Debugging, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Christopher Trudeau - guest host: www.linkedin.com Michael's personal site: mkennedy.codes Talk Python in Production Book: talkpython.fm glances: github.com btop: github.com Uptimekuma: uptimekuma.org Coolify: coolify.io Talk Python Blog: talkpython.fm Hetzner (€20 credit with link): hetzner.cloud OpalStack: www.opalstack.com Bunny.net CDN: bunny.net Galleries from the book: github.com Pandoc: pandoc.org Docker: www.docker.com Watch this episode on YouTube: youtube.com Episode #531 deep-dive: talkpython.fm

For years, building interactive widgets in Python notebooks meant wrestling with toolchains, platform quirks, and a mountain of JavaScript machinery. Most developers took one look and backed away slowly. Trevor Manz decided that barrier did not need to exist. His idea was simple: give Python users just enough JavaScript to unlock the web’s interactivity, without dragging along the rest of the web ecosystem. That idea became anywidget, and it is quickly becoming the quiet connective tissue of modern interactive computing. Today we dig into how it works, why it has taken off, and how it might change the way we explore data. Episode sponsors Seer: AI Debugging, Code TALKPYTHON PyCharm, code STRONGER PYTHON Talk Python Courses Links from the show Trevor on GitHub: github.com anywidget GitHub: github.com Trevor's SciPy 2024 Talk: www.youtube.com Marimo GitHub: github.com Myst (Markdown docs): mystmd.org Altair: altair-viz.github.io DuckDB: duckdb.org Mosaic: uwdata.github.io ipywidgets: ipywidgets.readthedocs.

A lot of people building software today never took the traditional CS path. They arrived through curiosity, a job that needed automating, or a late-night itch to make something work. This week, David Kopec joins me to talk about rebuilding computer science for exactly those folks, the ones who learned to program first and are now ready to understand the deeper ideas that power the tools they use every day. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON NordStellar Talk Python Courses Links from the show David Kopec: davekopec.com Classic Computer Science Book: amazon.com Computer Science from Scratch Book: computersciencefromscratch.com Computer Science from Scratch at NoStartch (CSFS30 for 30% off): nostarch.com Watch this episode on YouTube: youtube.com Episode #529 deep-dive: talkpython.fm/529 Episode transcripts: talkpython.fm Theme Song: Developer Rap

In this episode, I’m talking with Vincent Warmerdam about treating LLMs as just another API in your Python app, with clear boundaries, small focused endpoints, and good monitoring. We’ll dig into patterns for wrapping these calls, caching and inspecting responses, and deciding where an LLM API actually earns its keep in your architecture. Episode sponsors Seer: AI Debugging, Code TALKPYTHON NordStellar Talk Python Courses Links from the show Vincent on X: @fishnets88 Vincent on Mastodon: @koaning LLM Building Blocks for Python Co-urse: training.talkpython.fm Top Talk Python Episodes of 2024: talkpython.fm LLM Usage - Datasette: llm.datasette.io DiskCache - Disk Backed Cache (Documentation): grantjenks.com smartfunc - Turn docstrings into LLM-functions: github.com Ollama: ollama.com LM Studio - Local AI: lmstudio.ai marimo - A Next-Generation Python Notebook: marimo.io Pydantic: pydantic.dev Instructor - Complex Schemas & Validation (Python): python.useinstructor.com Diving into PydanticAI with marimo:

Today we’re digging into the Model Context Protocol, or MCP. Think LSP for AI: build a small Python service once and your tools and data show up across editors and agents like VS Code, Claude Code, and more. My guest, Den Delimarsky from Microsoft, helps build this space and will keep us honest about what’s solid versus what's just shiny. We’ll keep it practical: transports that actually work, guardrails you can trust, and a tiny server you could ship this week. By the end, you’ll have a clear mental model and a path to plug Python into the internet of agents. Episode sponsors Sentry AI Monitoring, Code TALKPYTHON NordStellar Talk Python Courses Links from the show Den Delimarsky: den.dev Agentic AI Programming for Python Course: training.talkpython.fm Model Context Protocol: modelcontextprotocol.io Model Context Protocol Specification (2025-03-26): modelcontextprotocol.io MCP Python Package (PyPI): pypi.org Awesome MCP Servers (punkpeye) GitHub Repo: github.com Visual Studio Code Docs: Copilot MCP Serve