Talk Python To Me
Talk Python to Me is a weekly podcast hosted by developer and entrepreneur Michael Kennedy. It dives deep into popular Python packages and features interviews with software developers, data scientists, and hobbyists doing amazing things with Python. Newcomers to Python can quickly learn the ins and outs of the community by hearing from leaders, while experienced Python users can learn about their favorite packages and new open-source releases.
Episodes
#556: Updates on Django's Async Story
For years, "Django and async" came with an asterisk. The docs themselves warned you off it. Scary performance notes, a story that felt half-finished. Well, that story just got rewritten, literally, and the person who rewrote it is here to tell you why the old framing was wrong.
Carlton Gibson is a former Django Fellow, sat on the security team for eight years, and he's on the s
#555: Marimo Pair - A Canvas for Agent + Developers Collaboration
Coding agents have gotten really good at one kind of work. You scope a feature, edit some files, run the tests, ship it. It all happens on disk. But that is not how data work feels. You load something, you look at it, you run a cell, you watch how it responds, and you decide the next move from whatever is sitting in memory. And until now, your agent couldn't see any of that. It on
#554: Trustworthy AI in Healthcare and Longevity
You ask an AI a question and it answers with total confidence. Most of the time, a confidently wrong answer is just an annoyance. But what if the question is medical, and there's a real patient on the other end? In that world, a hallucination isn't a bug, it's a patient-safety event. Sumit Gundawar is a London-based software engineer who builds the clinical platform for a UK longe
#553: All of our tools
This episode is a fun crossover from our Python news and tips podcast, Python Bytes. We have had some big changes over there. Brian Okken has moved on and Calvin Hendryx-Parker has joined the show as the new co-host. To kick off this new era, we decided to do a longer and more personal episode called "All Our Tools". The idea is both of us talk about some of our most useful day-to
#552: Astral joins OpenAI
OpenAI just acquired Astral, the company behind uv, Ruff, and ty. And if your first thought was "wait, is uv toast?", you are not alone. But here's the twist Charlie Marsh shared with me: he thinks they may ship more open source at OpenAI than they ever did at Astral. On this episode, we get into the acquisition, the mixed feelings, the future of your favorite Python tools, and wh
#551: Stroll Down Startup Lane - 2026
If you've ever been to PyCon, you know one of the best parts of the expo hall is Startup Row, a stretch of booths where early-stage companies built on Python show off what they're creating. But only attendees get to walk that lane, so let's bring it to everyone. In this episode, we stroll down Startup Row together. We kick things off with the organizers, Jason and Shay, who share
#550: AI Contributions and Maintainer Load in Open Source
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 m
#549: Great Docs
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
#548: Event Sourcing Design Pattern
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,
#547: Parallel Python at Anyscale with Ray
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 b
#546: Self hosting apps for Python people
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
#545: OWASP Top 10 (2025 List) for Python Devs
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
Tempora
#544: Wheel Next + Packaging PEPs
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, Astr
#543: Deep Agents: LangChain's SDK for Agents That Plan and Delegate
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 Runk
#542: Zensical - a modern static site generator
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 gene
#541: Monty - Python in Rust for AI
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, cr
#540: Modern Python monorepo with uv and prek
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
#539: Catching up with the Python Typing Council
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 t
#538: Python in Digital Humanities
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 d
#537: Datastar: Modern web dev, simplified
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 i
#536: Fly inside FastAPI Cloud
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 Ar
#535: PyView: Real-time Python Web Apps
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#534: diskcache: Your secret Python perf weapon
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#533: Web Frameworks in Prod by Their Creators
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#532: 2025 Python Year in Review
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#531: Talk Python in Production
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#530: anywidget: Jupyter Widgets made easy
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#529: Computer Science from Scratch
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#528: Python apps with LLM building blocks
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#527: MCP Servers for Python Devs
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#526: Building Data Science with Foundation LLM Models
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#525: NiceGUI Goes 3.0
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#524: 38 things Python developers should learn in 2025
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#523: Pyrefly: Fast, IDE-friendly typing for Python
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#522: Data Sci Tips and Tricks from CodeCut.ai
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#521: Red Teaming LLMs and GenAI with PyRIT
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#520: pyx - the other side of the uv coin (announcing pyx)
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#519: Data Science Cloud Lessons at Scale
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#518: Celebrating Django's 20th Birthday With Its Creators
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#517: Agentic Al Programming with Python
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#516: Accelerating Python Data Science at NVIDIA
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#515: Durable Python Execution with Temporal
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#514: Python Language Summit 2025
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#513: Stories from Python History
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#512: Building a JIT Compiler for CPython
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#511: From Notebooks to Production Data Science Systems
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#510: 10 Polars Tools and Techniques To Level Up Your Data Science
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#509: GPU Programming in Pure Python
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#508: Program Your Own Computer with Python
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#507: Agentic AI Workflows with LangGraph
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#506: ty: Astral's New Type Checker (Formerly Red-Knot)
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#505: t-strings in Python (PEP 750)
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#504: Developer Trends in 2025
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#503: The PyArrow Revolution
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#502: Django Ledger: Accounting with Python
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#501: Marimo - Reactive Notebooks for Python
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#500: Django Simple Deploy and other DevOps Things
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#499: BeeWare and the State of Python on Mobile
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#498: Algorithms for high performance terminal apps
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#497: Outlier Detection with Python
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#496: Scaf: Complete blueprint for new Python Kubernetes projects
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#495: OSMnx: Python and OpenStreetMap
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#494: Update on Flet: Python + Flutter UIs
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#493: Quarto: Open-source technical publishing
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#492: Great Tables
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#491: DuckDB and Python: Ducks and Snakes living together
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#490: Django Ninja
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#489: Anaconda Toolbox for Excel and more with Peter Wang
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#488: Multimodal data with LanceDB
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#487: Building Rust Extensions for Python
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#486: CSnakes: Embed Python code in .NET
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#485: Secure coding for Python with SheHacksPurple
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#484: From React to a Django+HTMX based stack
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#483: Reflex Framework: Frontend, Backend, Pure Python
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#482: Pre-commit Hooks for Python Devs
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#481: Python Opinions and Zeitgeist with Hynek
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#480: Ahoy, Narwhals are bridging the data science APIs
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#479: Designing Effective Load Tests for Your Python App
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#478: When and how to start coding with kids
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#477: Awesome Text Tricks with NLP and spaCy
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