Talk Python To Me - Python Conversations For Passionate Developers

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 DigitalOce

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: git

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.c

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: @sethmlarson@fosstodon.org 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 Engine

Why do people list 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

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 --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: 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 Con

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:

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 A

If you've heard the phrase "Automate the boring things" for Python, this episode starts with that idea and takes it to another level. We have Glyph back on the podcast to talk about "Programming YOUR computer with Python." We dive into a bunch of tools and frameworks and especially spend some time on integrating with existing platform APIs (e.g. macOS's BrowserKit and Window's COM APIs) to build desktop apps in Python that make you happier and more productive. Let's dive in! Episode sponsors Posit Agntcy Talk Python Courses Links from the show Glyph on Mastodon: @glyph@mastodon.social Glyph on GitHub: github.com/glyph Glyph's Conference Talk: LceLUPdIzRs: youtube.com Notify Py: ms7m.github.io Rumps: github.com QuickMacHotkey: pypi.org QuickMacApp: pypi.org LM Studio: lmstudio.ai Coolify: coolify.io PyWin32: pypi.org WinRT: pypi.org PyObjC: pypi.org PyObjC Documentation: pyobjc.readthedocs.io Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subsc

If you want to leverage the power of LLMs in your Python apps, you would be wise to consider an agentic framework. Agentic empowers the LLMs to use tools and take further action based on what it has learned at that point. And frameworks provide all the necessary building blocks to weave these into your apps with features like long-term memory and durable resumability. I'm excited to have Sydney Runkle back on the podcast to dive into building Python apps with LangChain and LangGraph. Episode sponsors Posit Auth0 Talk Python Courses Links from the show Sydney Runkle: linkedin.com LangGraph: github.com LangChain: langchain.com LangGraph Studio: github.com LangGraph (Web): langchain.com LangGraph Tutorials Introduction: langchain-ai.github.io How to Think About Agent Frameworks: blog.langchain.dev Human in the Loop Concept: langchain-ai.github.io GPT-4 Prompting Guide: cookbook.openai.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to

The folks over at Astral have made some big-time impacts in the Python space with uv and ruff. They are back with another amazing project named ty. You may have known it as Red-Knot. But it's coming up on release time for the first version and with the release it comes with a new official name: ty. We have Charlie Marsh and Carl Meyer on the show to tell us all about this new project. Episode sponsors Posit Auth0 Talk Python Courses Links from the show Talk Python's Rock Solid Python: Type Hints & Modern Tools (Pydantic, FastAPI, and More) Course: training.talkpython.fm Charlie Marsh on Twitter: @charliermarsh Charlie Marsh on Mastodon: @charliermarsh Carl Meyer: @carljm ty on Github: github.com/astral-sh/ty A Very Early Play with Astral’s Red Knot Static Type Checker: app.daily.dev Will Red Knot be a drop-in replacement for mypy or pyright?: github.com Hacker News Announcement: news.ycombinator.com Early Explorations of Astral’s Red Knot Type Checker: pydevtools.com Astral's Blog: astral.sh Rust An

Python has many string formatting styles which have been added to the language over the years. Early Python used the % operator to injected formatted values into strings. And we have string.format() which offers several powerful styles. Both were verbose and indirect, so f-strings were added in Python 3.6. But these f-strings lacked security features (think little bobby tables) and they manifested as fully-formed strings to runtime code. Today we talk about the next evolution of Python string formatting for advanced use-cases (SQL, HTML, DSLs, etc): t-strings. We have Paul Everitt, David Peck, and Jim Baker on the show to introduce this upcoming new language feature. Episode sponsors Posit Auth0 Talk Python Courses Links from the show Guests: Paul on X: @paulweveritt Paul on Mastodon: @pauleveritt@fosstodon.org Dave Peck on Github: github.com Jim Baker: github.com PEP 750 – Template Strings: peps.python.org tdom - Placeholder for future library on PyPI using PEP 750 t-strings: github.com PEP 750: Tag Stri

What trends and technologies should you be paying attention to today? Are there hot new database servers you should check out? Or will that just be a flash in the pan? I love these forward looking episodes and this one is super fun. I've put together an amazing panel: Gina Häußge, Ines Montani, Richard Campbell, and Calvin Hendryx-Parker. We dive into the recent Stack Overflow Developer survey results as a sounding board for our thoughts on rising and falling trends in the Python and broader developer space. Episode sponsors NordLayer Auth0 Talk Python Courses Links from the show The Stack Overflow Survey Results: survey.stackoverflow.co/2024 Panelists Gina Häußge: chaos.social/@foosel Ines Montani: ines.io Richard Campbell: about.me/richard.campbell Calvin Hendryx-Parker: github.com/calvinhp Explosion: explosion.ai spaCy: spacy.io OctoPrint: octoprint.org .NET Rocks: dotnetrocks.com Six Feet Up: sixfeetup.com Stack Overflow: stackoverflow.com Python.org: python.org GitHub Copilot: github.com OpenAI Chat

Pandas is at a the core of virtually all data science done in Python, that is virtually all data science. Since it's beginning, Pandas has been based upon numpy. But changes are afoot to update those internals and you can now optionally use PyArrow. PyArrow comes with a ton of benefits including it's columnar format which makes answering analytical questions faster, support for a range of high performance file formats, inter-machine data streaming, faster file IO and more. Reuven Lerner is here to give us the low-down on the PyArrow revolution. Episode sponsors NordLayer Auth0 Talk Python Courses Links from the show Reuven: github.com/reuven Apache Arrow: github.com Parquet: parquet.apache.org Feather format: arrow.apache.org Python Workout Book: manning.com Pandas Workout Book: manning.com Pandas: pandas.pydata.org PyArrow CSV docs: arrow.apache.org Future string inference in Pandas: pandas.pydata.org Pandas NA/nullable dtypes: pandas.pydata.org Pandas `.iloc` indexing: pandas.pydata.org DuckDB: duckdb.or

Do you or your company need accounting software? Well, there are plenty of SaaS products out there that you can give your data to. but maybe you also really like Django and would rather have a foundation to build your own accounting system exactly as you need for your company or your product. On this episode, we're diving into Django Ledger, created by Miguel Sanda, which can do just that. Episode sponsors Auth0 Talk Python Courses Links from the show Miguel Sanda on Twitter: @elarroba Miguel on Mastodon: @elarroba@fosstodon.org Miguel on GitHub: github.com Django Ledger on Github: github.com Django Ledger Discord: discord.gg Get Started with Django MongoDB Backend: mongodb.com Wagtail CMS: wagtail.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mas

Have you ever spent an afternoon wrestling with a Jupyter notebook, hoping that you ran the cells in just the right order, only to realize your outputs were completely out of sync? Today's guest has a fresh take on solving that exact problem. Akshay Agrawal is here to introduce Marimo, a reactive Python notebook that ensures your code and outputs always stay in lockstep. And that's just the start! We'll also dig into Akshay's background at Google Brain and Stanford, what it's like to work on the cutting edge of AI, and how Marimo is uniting the best of data science exploration and real software engineering. Episode sponsors Worth Search Talk Python Courses Links from the show Akshay Agrawal: akshayagrawal.com YouTube: youtube.com Source: github.com Docs: marimo.io Marimo: marimo.io Discord: marimo.io WASM playground: marimo.new Experimental generate notebooks with AI: marimo.app Pluto.jl: plutojl.org Observable JS: observablehq.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython

We're sitting down with Eric Matthes, the educator, author, and developer behind Django Simple Deploy. If you've ever struggled with taking that final step of getting your Django app onto a live server (without spending days wrestling with DevOps complexities), then give Django Simple Deploy a look. Eric shares how Django Simple Deploy automates away the boilerplate parts of deployment, so you can focus on building features instead of deciphering endless configs. We'll talk about this new project's journey to 1.0, the range of hosting platforms it supports, and why it's not just for beginners. Episode sponsors Worth Search Talk Python Courses Links from the show django-simple-deploy documentation: readthedocs.io django-simple-deploy repository: github.com Python Crash Course book: ehmatthes.github.io Code Red: codered.cloud Docker: docker.com Caddy: caddyserver.com Bunny.net CDN: bunny.net Platform.sh: platform.sh fly.io: fly.io Heroku: heroku.com Watch this episode on YouTube: youtube.com Episode transcri

This episode is all about Beeware, the project that working towards true native apps built on Python, especially for iOS and Android. Russell's been at this for more than a decade, and the progress is now hitting critical mass. We'll talk about the Toga GUI toolkit, building and shipping your apps with Briefcase, the newly official support for iOS and Android in CPython, and so much more. I can't wait to explore how BeeWare opens up the entire mobile ecosystem for Python developers, let's jump right in. Episode sponsors Posit Python in Production Talk Python Courses Links from the show Anaconda open source team: anaconda.com PEP 730 – Adding iOS: peps.python.org PEP 738 – Adding Android: peps.python.org Toga: beeware.org Briefcase: beeware.org emscripten: emscripten.org Russell Keith-Magee - Keynote - PyCon 2019: youtube.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @t

In this episode, we welcome back Will McGugan, the creator of the wildly popular Rich library and founder of Textualize. We'll dive into Will's latest article on "Algorithms for High Performance Terminal Apps" and explore how he's quietly revolutionizing what's possible in the terminal, from smooth animations and dynamic widgets to full-on TUI (or should we say GUI?) frameworks. Whether you're looking to supercharge your command-line tools or just curious how Python can push the limits of text-based UIs, you'll love hearing how Will's taking a modern, web-inspired approach to old-school terminals. Episode sponsors Posit Python in Production Talk Python Courses Links from the show Algorithms for high performance terminal apps post: textual.textualize.io Textual Demo: github.com Textual: textualize.io Zero ver: 0ver.org memray: github.com Posting app: posting.sh Bulma CSS framewokr: bulma.io JP Term: davidbrochart.github.io Rich: github.com btop: github.com starship: starship.rs Watch this episode on YouTube