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
? 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

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: youtube.com
Cline - AI Coding Agent: cline.bot
OpenRouter - The Unified Interface For LLMs: openrouter.ai
Leafcloud: leaf.cloud
OpenAI looks for its "Google Chrome" moment with new Atlas web browser: arstechnica.com

Watch this episode on YouTube: youtube.com
Episode #528 deep-dive: talkpython.fm/528
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

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 Servers: code.visualstudio.com
GitHub MCP Server (GitHub repo): github.com
GitHub Blog: Meet the GitHub MCP Registry: github.blog
MultiViewer App: multiviewer.app
GitHub Blog: Spec-driven development with AI (open source toolkit): github.blog
Model Context Protocol Registry (GitHub): github.com
mcp (GitHub organization): github.com
Tailscale: tailscale.com

Watch this episode on YouTube: youtube.com
Episode #527 deep-dive: talkpython.fm/527
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

Today, we?re talking about building real AI products with foundation models. Not toy demos, not vibes. We?ll get into the boring dashboards that save launches, evals that change your mind, and the shift from analyst to AI app builder. Our guide is Hugo Bowne-Anderson, educator, podcaster, and data scientist, who?s been in the trenches from scalable Python to LLM apps. If you care about shipping LLM features without burning the house down, stick around.

Episode sponsors

Posit
NordStellar
Talk Python Courses

Links from the show Hugo Bowne-Anderson: x.com
Vanishing Gradients Podcast: vanishinggradients.fireside.fm
Fundamentals of Dask: High Performance Data Science Course: training.talkpython.fm
Building LLM Applications for Data Scientists and Software Engineers: maven.com
marimo: a next-generation Python notebook: marimo.io
DevDocs (Offline aggregated docs): devdocs.io
Elgato Stream Deck: elgato.com
Sentry's Seer: talkpython.fm
The End of Programming as We Know It: oreilly.com
LorikeetCX AI Concierge: lorikeetcx.ai
Text to SQL & AI Query Generator: text2sql.ai
Inverse relationship enthusiasm for AI and traditional projects: oreilly.com

Watch this episode on YouTube: youtube.com
Episode #526 deep-dive: talkpython.fm/526
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

Building a UI in Python usually means choosing between "quick and limited" or "powerful and painful." What if you could write modern, component-based web apps in pure Python and still keep full control? NiceGUI, pronounced "Nice Guy" sits on FastAPI with a Vue/Quasar front end, gives you real components, live updates over websockets, and it?s running in production at Zauberzeug, a German robotic company. On this episode, I?m talking with NiceGUI?s creators, Rodja Trappe and Falko Schindler, about how it works, where it shines, and what?s coming next. With version 3.0 releasing around the same time this episode comes out, we spend the end of the episode celebrating the 3.0 release.

Episode sponsors

Posit
Agntcy
Talk Python Courses

Links from the show Rodja Trappe: github.com
Falko Schindler: github.com

NiceGUI 3.0.0 release: github.com
Full LLM/Agentic AI docs instructions for NiceGUI: github.com

Zauberzeug: zauberzeug.com
NiceGUI: nicegui.io
NiceGUI GitHub Repository: github.com
NiceGUI Authentication Examples: github.com
NiceGUI v3.0.0rc1 Release: github.com
Valkey: valkey.io
Caddy Web Server: caddyserver.com
JustPy: justpy.io
Tailwind CSS: tailwindcss.com
Quasar ECharts v5 Demo: quasar-echarts-v5.netlify.app
AG Grid: ag-grid.com
Quasar Framework: quasar.dev
NiceGUI Interactive Image Documentation: nicegui.io
NiceGUI 3D Scene Documentation: nicegui.io

Watch this episode on YouTube: youtube.com
Episode #525 deep-dive: talkpython.fm/525
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

Python in 2025 is different. Threads really are about to run in parallel, installs finish before your coffee cools, and containers are the default. In this episode, we count down 38 things to learn this year: free-threaded CPython, uv for packaging, Docker and Compose, Kubernetes with Tilt, DuckDB and Arrow, PyScript at the edge, plus MCP for sane AI workflows. Expect practical wins and migration paths. No buzzword bingo, just what pays off in real apps. Join me along with Peter Wang and Calvin Hendrix-Parker for a fun, fast-moving conversation.

Episode sponsors

Seer: AI Debugging, Code TALKPYTHON
Agntcy
Talk Python Courses

Links from the show Calvin Hendryx-Parker: github.com/calvinhp
Peter on BSky: @wang.social

Free-Threaded Wheels: hugovk.github.io
Tilt: tilt.dev
The Five Demons of Python Packaging That Fuel Our ...: youtube.com
Talos Linux: talos.dev
Docker: Accelerated Container Application Development: docker.com
Scaf - Six Feet Up: sixfeetup.com
BeeWare: beeware.org
PyScript: pyscript.net
Cursor: The best way to code with AI: cursor.com
Cline - AI Coding, Open Source and Uncompromised: cline.bot

Watch this episode on YouTube: youtube.com
Episode #524 deep-dive: talkpython.fm/524
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

Python typing got fast enough to feel invisible. Pyrefly is a new, open source type checker and IDE language server from Meta, written in Rust, with a focus on instant feedback and real-world DX. Today, we will dig into what it is, why it exists, and how it plays with the rest of the typing ecosystem. We have Abby Mitchell, Danny Yang, and Kyle Into from Pyrefly here to dive into the project.

Episode sponsors

Sentry Error Monitoring, Code TALKPYTHON
Agntcy
Talk Python Courses

Links from the show Abby Mitchell: linkedin.com
Danny Yang: linkedin.com
Kyle Into: linkedin.com

Pyrefly: pyrefly.org
Pyrefly Documentation: pyrefly.org
Pyrefly Installation Guide: pyrefly.org
Pyrefly IDE Guide: pyrefly.org
Pyrefly GitHub Repository: github.com
Pyrefly VS Code Extension: marketplace.visualstudio.com
Introducing Pyrefly: A New Type Checker and IDE Experience for Python: engineering.fb.com
Pyrefly on PyPI: pypi.org
InfoQ Coverage: Meta Pyrefly Python Typechecker: infoq.com
Pyrefly Discord Invite: discord.gg
Python Typing Conformance (GitHub): github.com
Typing Conformance Leaderboard (HTML Preview): htmlpreview.github.io

Watch this episode on YouTube: youtube.com
Episode #523 deep-dive: talkpython.fm/523
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

Today we?re turning tiny tips into big wins. Khuyen Tran, creator of CodeCut.ai, has shipped hundreds of bite-size Python and data science snippets across four years. We dig into open-source tools you can use right now, cleaner workflows, and why notebooks and scripts don?t have to be enemies. If you want faster insights with fewer yak-shaves, this one?s packed with takeaways you can apply before lunch. Let?s get into it.

Episode sponsors

Sentry Error Monitoring, Code TALKPYTHON
Agntcy
Talk Python Courses

Links from the show Khuyen Tran (LinkedIn): linkedin.com
Khuyen Tran (GitHub): github.com

CodeCut: codecut.ai
Production-ready Data Science Book (discount code TalkPython): codecut.ai

Why UV Might Be All You Need: codecut.ai
How to Structure a Data Science Project for Readability and Transparency: codecut.ai
Stop Hard-coding: Use Configuration Files Instead: codecut.ai
Simplify Your Python Logging with Loguru: codecut.ai
Git for Data Scientists: Learn Git Through Practical Examples: codecut.ai
Marimo (A Modern Notebook for Reproducible Data Science): codecut.ai
Text Similarity & Fuzzy Matching Guide: codecut.ai
Loguru (Python logging made simple): github.com
Hydra: hydra.cc
Marimo: marimo.io
Quarto: quarto.org
Show Your Work! Book: austinkleon.com

Watch this episode on YouTube: youtube.com
Episode #522 deep-dive: talkpython.fm/522
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

English is now an API. Our apps read untrusted text; they follow instructions hidden in plain sight, and sometimes they turn that text into action. If you connect a model to tools or let it read documents from the wild, you have created a brand new attack surface. In this episode, we will make that concrete. We will talk about the attacks teams are seeing in 2025, the defenses that actually work, and how to test those defenses the same way we test code. Our guides are Tori Westerhoff and Roman Lutz from Microsoft. They help lead AI red teaming and build PyRIT, a Python framework the Microsoft AI Red Team uses to pressure test real products. By the end of this hour you will know where the biggest risks live, what you can ship this quarter to reduce them, and how PyRIT can turn security from a one time audit into an everyday engineering practice.

Episode sponsors

Sentry AI Monitoring, Code TALKPYTHON
Agntcy
Talk Python Courses

Links from the show Tori Westerhoff: linkedin.com
Roman Lutz: linkedin.com

PyRIT: aka.ms/pyrit
Microsoft AI Red Team page: learn.microsoft.com
2025 Top 10 Risk & Mitigations for LLMs and Gen AI Apps: genai.owasp.org
AI Red Teaming Agent: learn.microsoft.com
3 takeaways from red teaming 100 generative AI products: microsoft.com
MIT report: 95% of generative AI pilots at companies are failing: fortune.com

A couple of "Little Bobby AI" cartoons
Give me candy: talkpython.fm
Tell me a joke: talkpython.fm

Watch this episode on YouTube: youtube.com
Episode #521 deep-dive: talkpython.fm/521
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

A couple years ago, Charlie Marsh lit a fire under Python tooling with Ruff and then uv. Today he?s back with something on the other side of that coin: pyx.

Pyx isn?t a PyPI replacement. Think server, not just index. It mirrors PyPI, plays fine with pip or uv, and aims to make installs fast and predictable by letting a smart client talk to a smart server. When the client and server understand each other, you get new fast paths, fewer edge cases, and the kind of reliability teams beg for. If Python packaging has felt like friction, this conversation is traction. Let?s get into it.

Episode sponsors

Six Feet Up
Talk Python Courses

Links from the show Charlie Marsh on Twitter: @charliermarsh
Charlie Marsh on Mastodon: @charliermarsh

Astral Homepage: astral.sh
Pyx Project: astral.sh
Introducing Pyx Blog Post: astral.sh
uv Package on GitHub: github.com
UV Star History Chart: star-history.com

Watch this episode on YouTube: youtube.com
Episode #520 deep-dive: talkpython.fm/520
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

Today on Talk Python: What really happens when your data work outgrows your laptop. Matthew Rocklin, creator of Dask and cofounder of Coiled, and Nat Tabris a staff software engineer at Coiled join me to unpack the messy truth of cloud-scale Python. During the episode we actually spin up a 1,000 core cluster from a notebook, twice! We also discuss picking between pandas and Polars, when GPUs help, and how to avoid surprise bills. Real lessons, real tradeoffs, shared by people who have built this stuff. Stick around.

Episode sponsors

Seer: AI Debugging, Code TALKPYTHON
Talk Python Courses

Links from the show Matthew Rocklin: @mrocklin
Nat Tabris: tabris.us

Dask: dask.org
Coiled: coiled.io

Watch this episode on YouTube: youtube.com
Episode #519 deep-dive: talkpython.fm/519
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

Twenty years after a scrappy newsroom team hacked together a framework to ship stories fast, Django remains the Python web framework that ships real apps, responsibly. In this anniversary roundtable with its creators and long-time stewards: Simon Willison, Adrian Holovaty, Will Vincent, Jeff Triplett, and Thibaud Colas, we trace the path from the Lawrence Journal-World to 1.0, DjangoCon, and the DSF; unpack how a BSD license and a culture of docs, tests, and mentorship grew a global community; and revisit lessons from deployments like Instagram. We talk modern Django too: ASGI and async, HTMX-friendly patterns, building APIs with DRF and Django Ninja, and how Django pairs with React and serverless without losing its batteries-included soul. You?ll hear about Django Girls, Djangonauts, and the Django Fellowship that keep momentum going, plus where Django fits in today?s AI stacks. Finally, we look ahead at the next decade of speed, security, and sustainability.

Episode sponsors

Seer: AI Debugging, Code TALKPYTHON
Sentry AI Monitoring, Code TALKPYTHON
Talk Python Courses

Links from the show Guests
Simon Willison: simonwillison.net
Adrian Holovaty: holovaty.com
Will Vincent: wsvincent.com
Jeff Triplett: jefftriplett.com
Thibaud Colas: thib.me

Show Links
Django's 20th Birthday Reflections (Simon Willison): simonwillison.net
Happy 20th Birthday, Django! (Django Weblog): djangoproject.com
Django 2024 Annual Impact Report: djangoproject.com
Welcome Our New Fellow: Jacob Tyler Walls: djangoproject.com
Soundslice Music Learning Platform: soundslice.com
Djangonaut Space Mentorship for Django Contributors: djangonaut.space
Wagtail CMS for Django: wagtail.org
Django REST Framework: django-rest-framework.org
Django Ninja API Framework for Django: django-ninja.dev
Lawrence Journal-World: ljworld.com

Watch this episode on YouTube: youtube.com
Episode #518 deep-dive: talkpython.fm/518
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

Agentic AI programming is what happens when coding assistants stop acting like autocomplete and start collaborating on real work. In this episode, we cut through the hype and incentives to define ?agentic,? then get hands-on with how tools like Cursor, Claude Code, and LangChain actually behave inside an established codebase. Our guest, Matt Makai, now VP of Developer Relations at DigitalOcean, creator of Full Stack Python and Plushcap, shares hard-won tactics. We unpack what breaks, from brittle ?generate a bunch of tests? requests to agents amplifying technical debt and uneven design patterns. Plus, we also discuss a sane git workflow for AI-sized diffs. You?ll hear practical Claude tips, why developers write more bugs when typing less, and where open source agents are headed. Hint: The destination is humans as editors of systems, not just typists of code.

Episode sponsors

Posit
Talk Python Courses

Links from the show Matt Makai: linkedin.com

Plushcap Developer Content Analytics: plushcap.com
DigitalOcean Gradient AI Platform: digitalocean.com
DigitalOcean YouTube Channel: youtube.com
Why Generative AI Coding Tools and Agents Do Not Work for Me: blog.miguelgrinberg.com
AI Changes Everything: lucumr.pocoo.org
Claude Code - 47 Pro Tips in 9 Minutes: youtube.com
Cursor AI Code Editor: cursor.com
JetBrains Junie: jetbrains.com
Claude Code by Anthropic: anthropic.com
Full Stack Python: fullstackpython.com

Watch this episode on YouTube: youtube.com
Episode #517 deep-dive: talkpython.fm/517
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

Python?s data stack is getting a serious GPU turbo boost. In this episode, Ben Zaitlen from NVIDIA joins us to unpack RAPIDS, the open source toolkit that lets pandas, scikit-learn, Spark, Polars, and even NetworkX execute on GPUs. We trace the project?s origin and why NVIDIA built it in the open, then dig into the pieces that matter in practice: cuDF for DataFrames, cuML for ML, cuGraph for graphs, cuXfilter for dashboards, and friends like cuSpatial and cuSignal. We talk real speedups, how the pandas accelerator works without a rewrite, and what becomes possible when jobs that used to take hours finish in minutes. You?ll hear strategies for datasets bigger than GPU memory, scaling out with Dask or Ray, Spark acceleration, and the growing role of vector search with cuVS for AI workloads. If you know the CPU tools, this is your on-ramp to the same APIs at GPU speed.

Episode sponsors

Posit
Talk Python Courses

Links from the show RAPIDS: github.com/rapidsai
Example notebooks showing drop-in accelerators: github.com
Benjamin Zaitlen - LinkedIn: linkedin.com
RAPIDS Deployment Guide (Stable): docs.rapids.ai
RAPIDS cuDF API Docs (Stable): docs.rapids.ai
Asianometry YouTube Video: youtube.com
cuDF pandas Accelerator (Stable): docs.rapids.ai

Watch this episode on YouTube: youtube.com
Episode #516 deep-dive: talkpython.fm/516
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 code was crash-proof? That's the value prop for a framework called Temporal. Temporal is a durable execution platform that enables developers to build scalable applications without sacrificing productivity or reliability. The Temporal server executes units of application logic called Workflows in a resilient manner that automatically handles intermittent failures, and retries failed operations. We have Mason Egger from Temporal on to dive into durable execution.

Episode sponsors

Posit
PyBay
Talk Python Courses

Links from the show Just Enough Python for Data Scientists Course: talkpython.fm

Temporal Durable Execution Platform: temporal.io
Temporal Learn Portal: learn.temporal.io
Temporal GitHub Repository: github.com
Temporal Python SDK GitHub Repository: github.com
What Is Durable Execution, Temporal Blog: temporal.io
Mason on Bluesky Profile: bsky.app
Mason on Mastodon Profile: fosstodon.org
Mason on Twitter Profile: twitter.com
Mason on LinkedIn Profile: linkedin.com
X Post by @skirano: x.com
Temporal Docker Compose GitHub Repository: github.com
Building a distributed asyncio event loop (Chad Retz) - PyTexas 2025: youtube.com

Watch this episode on YouTube: youtube.com
Episode #515 deep-dive: talkpython.fm/515
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

Every year the core developers of Python convene in person to focus on high priority topics for CPython and beyond. This year they met at PyCon US 2025. Those meetings are closed door to keep focused and productive. But we're lucky that Seth Michael Larson was in attendance and wrote up each topic presented and the reactions and feedback to each. We'll be exploring this year's Language Summit with Seth. It's quite insightful to where Python is going and the pressing matters.

Episode sponsors

Seer: AI Debugging, Code TALKPYTHON
Sentry AI Monitoring, Code TALKPYTHON
Talk Python Courses

Links from the show Seth on Mastodon: @[email protected]
Seth on Twitter: @sethmlarson
Seth on Github: github.com

Python Language Summit 2025: pyfound.blogspot.com
WheelNext: wheelnext.dev
Free-Threaded Wheels: hugovk.github.io
Free-Threaded Python Compatibility Tracking: py-free-threading.github.io
PEP 779: Criteria for supported status for free-threaded Python: discuss.python.org
PyPI Data: py-code.org
Senior Engineer tries Vibe Coding: youtube.com

Watch this episode on YouTube: youtube.com
Episode #514 deep-dive: talkpython.fm/514
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

Why do people listen to this podcast? Sure, they're looking for technical explorations of new libraries and ideas. But often it's to hear the story behind them. If that speaks to you, then I have the perfect episode lined up. I have Barry Warsaw, Paul Everitt, Carol Willing, and Brett Cannon all back on the show to share stories from the history of Python. You'll hear about how import this came to be and how the first PyCon had around 30 attendees (two of whom are guests on this episode!). Sit back and enjoy the humorous stories from Python's past.

Episode sponsors

Posit
Agntcy
Talk Python Courses

Links from the show Barry's Zen of Python song: youtube.com
Jake Vanderplas - Keynote - PyCon 2017: youtube.com
Why it?s called ?Python? (Monty Python fan-reference): geeksforgeeks.org
import antigravity: python-history.blogspot.com
NIST Python Workshop Attendees: legacy.python.org
Paul Everitt open-sources Zope: old.zope.dev
Carol Willing wins ACM Software System Award: awards.acm.org

Watch this episode on YouTube: youtube.com
Episode #513 deep-dive: talkpython.fm/513
Episode transcripts: talkpython.fm

Theme Song: Developer Rap
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Michael on Bluesky: @mkennedy.codes
Michael on Mastodon: @[email protected]
Michael on X.com: @mkennedy

Do you like to dive into the details and intricacies of how Python executes and how we can optimize it? Well, do I have an episode for you. We welcome back Brandt Bucher to give us an update on the upcoming JIT compiler for Python and why it differs from JITs for languages such as C# and Java.

Episode sponsors

Posit
Talk Python Courses

Links from the show Brandt Bucher: github.com/brandtbucher

PyCon Talk: What they don't tell you about building a JIT compiler for CPython: youtube.com
Specializing, Adaptive Interpreter Episode: talkpython.fm

Watch this episode on YouTube: youtube.com
Episode #512 deep-dive: talkpython.fm/512
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

If you're doing data science and have mostly spent your time doing exploratory or just local development, this could be the episode for you. We are joined by Catherine Nelson to discuss techniques and tools to move your data science game from local notebooks to full-on production workflows.

Episode sponsors

Agntcy
Sentry Error Monitoring, Code TALKPYTHON
Talk Python Courses

Links from the show New Course: LLM Building Blocks for Python: training.talkpython.fm

Catherine Nelson LinkedIn Profile: linkedin.com
Catherine Nelson Bluesky Profile: bsky.app
Enter to win the book: forms.google.com
Going From Notebooks to Scalable Systems - PyCon US 2025: us.pycon.org
Going From Notebooks to Scalable Systems - Catherine Nelson ? YouTube: youtube.com
From Notebooks to Scalable Systems Code Repository: github.com
Building Machine Learning Pipelines Book: oreilly.com
Software Engineering for Data Scientists Book: oreilly.com
Jupytext - Jupyter Notebooks as Markdown Documents: github.com
Jupyter nbconvert - Notebook Conversion Tool: github.com
Awesome MLOps - Curated List: github.com

Watch this episode on YouTube: youtube.com
Episode #511 deep-dive: talkpython.fm/511
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

Are you using Polars for your data science work? Maybe you've been sticking with the tried-and-true Pandas? There are many benefits to Polars directly of course. But you might not be aware of all the excellent tools and libraries that make Polars even better. Examples include Patito which combines Pydantic and Polars for data validation and polars_encryption which adds AES encryption to selected columns. We have Christopher Trudeau back on Talk Python To Me to tell us about his list of excellent libraries to power up your Polars game and we also talk a bit about his new Polars course.

Episode sponsors

Agntcy
Sentry Error Monitoring, Code TALKPYTHON
Talk Python Courses

Links from the show New Theme Song (Full-Length Download and backstory): talkpython.fm/blog

Polars for Power Users Course: training.talkpython.fm
Awesome Polars: github.com
Polars Visualization with Plotly: docs.pola.rs
Dataframely: github.com
Patito: github.com
polars_iptools: github.com
polars-fuzzy-match: github.com
Nucleo Fuzzy Matcher: github.com
polars-strsim: github.com
polars_encryption: github.com
polars-xdt: github.com
polars_ols: github.com
Least Mean Squares Filter in Signal Processing: www.geeksforgeeks.org
polars-pairing: github.com
Pairing Function: en.wikipedia.org
polars_list_utils: github.com
Harley Schema Helpers: tomburdge.github.io
Marimo Reactive Notebooks Episode: talkpython.fm
Marimo: marimo.io
Ahoy Narwhals Podcast Episode Links: talkpython.fm

Watch this episode on YouTube: youtube.com
Episode #510 deep-dive: talkpython.fm/510
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

If you're looking to leverage the insane power of modern GPUs for data science and ML, you might think you'll need to use some low-level programming language such as C++. But the folks over at NVIDIA have been hard at work building Python SDKs which provide nearly native level of performance when doing Pythonic GPU programming. Bryce Adelstein Lelbach is here to tell us about programming your GPU in pure Python.

Episode sponsors

Posit
Agntcy
Talk Python Courses

Links from the show Bryce Adelstein Lelbach on Twitter: @blelbach

Episode Deep Dive write up: talkpython.fm/blog

NVIDIA CUDA Python API: github.com
Numba (JIT Compiler for Python): numba.pydata.org
Applied Data Science Podcast: adspthepodcast.com
NVIDIA Accelerated Computing Hub: github.com
NVIDIA CUDA Python Math API Documentation: docs.nvidia.com
CUDA Cooperative Groups (CCCL): nvidia.github.io
Numba CUDA User Guide: nvidia.github.io
CUDA Python Core API: nvidia.github.io
Numba (JIT Compiler for Python): numba.pydata.org
NVIDIA?s First Desktop AI PC ($3,000): arstechnica.com
Google Colab: colab.research.google.com
Compiler Explorer (?Godbolt?): godbolt.org
CuPy: github.com
RAPIDS User Guide: docs.rapids.ai

Watch this episode on YouTube: youtube.com
Episode #509 deep-dive: talkpython.fm/509
Episode transcripts: talkpython.fm

---== 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

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