Talk Python To Me

• #509: GPU Programming in Pure Python

  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 --- 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

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• #508: Program Your Own Computer with Python

  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: @[email protected] 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 #508 deep-dive: talkpython.fm/508 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

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• #507: Agentic AI Workflows with LangGraph

  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 #507 deep-dive: talkpython.fm/507 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

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• #506: ty: Astral's New Type Checker (Formerly Red-Knot)

  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 Analyzer Salsa Docs: docs.rs Ruff Open Issues (label: red-knot): github.com Ruff Types: types.ruff.rs Ruff Docs (Astral): docs.astral.sh uv Repository: github.com Watch this episode on YouTube: youtube.com Episode #506 deep-dive: talkpython.fm/506 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

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• #505: t-strings in Python (PEP 750)

  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: @[email protected] 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 Strings For Writing Domain-Specific Languages: discuss.python.org How To Teach This: peps.python.org PEP 501 – General purpose template literal strings: peps.python.org Python's new t-strings: davepeck.org PyFormat: Using % and .format() for great good!: pyformat.info flynt: A tool to automatically convert old string literal formatting to f-strings: github.com Examples of using t-strings as defined in PEP 750: github.com htm.py issue: github.com Exploits of a Mom: xkcd.com pyparsing: github.com Watch this episode on YouTube: youtube.com Episode #505 deep-dive: talkpython.fm/505 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

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