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#374: PSF Survey in Review
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#157: The Journal of Open Source Software
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#456: Building GPT Actions with FastAPI and Pydantic
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#192: Python Year in Review 2018 Edition
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#348: Dear PyGui: Simple yet Fast Python GUI Apps
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#149: 4 Python Web Frameworks, Compared
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#250: Capture over 400x C02 as trees with AI and Python
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#414: A Stroll Down Startup Lane
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#62: San Diego Technology Immersion Group Learns Python
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#343: Do Excel things, get notebook Python code with Mito
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#272: No IoT things in hand? Simulate them with Device Simulator Express
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#424: Shiny for Python
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#276: Geekout: Life in the solar system and beyond
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#209: Inside Python's new governance model
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#496: Scaf: Complete blueprint for new Python Kubernetes projects
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#120: Python in Finance
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#256: Click to run your notebook with Binder
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#541: Monty - Python in Rust for AI
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#431: Visualizing CPython Release Process
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#265: Why is Python slow?
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#126: Kubernetes for Pythonistas
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#32: PyPy.js - PyPy Python in Your Browser
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#345: 10 Tips and Tools for Developer Productivity
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#512: Building a JIT Compiler for CPython
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#13: Flask web framework and much, much more
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#316: Flask 2.0
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#273: CoCalc: A fully colloborative notebook development environment
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#257: Exploring the galaxy with the fastest supercomputer, Python, and radio astronomy
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#329: Geekout: Renewable Energy
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#332: Robust Python
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#327: Little Automation Tools in Python
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#253: Moon base geekout
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#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, Astral, and QuanSight 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 Quansight 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 options: numpy.org
Red Hat RHEL: www.redhat.com
Red Hat RHEL AI: www.redhat.com
RedHats presentation: wheelnext.dev
CUDA release: developer.nvidia.com
requires a PEP: discuss.python.org
WheelNext: wheelnext.dev
Github repo: github.com
PEP 817: peps.python.org
PEP 825: discuss.python.org
uv: docs.astral.sh
A variant-enabled build of uv: astral.sh
pyx: astral.sh
pypackaging-native: pypackaging-native.github.io
PEP 784: peps.python.org
Watch this episode on YouTube: youtube.com
Episode #544 deep-dive: talkpython.fm/544
Episode transcripts: talkpython.fm
Theme Song: Developer Rap
🥁 Served in a Flask 🎸: talkpython.fm/flasksong
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Michael on Bluesky: @mkennedy.codes
Michael on Mastodon: @mkennedy@fosstodon.org
Michael on X.com: @mkennedytalkpython.fm -
#116: 10 top talks of PyCon 2017 reviewed
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#301: Deploying and running Django web apps in 2021
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#55: How our engineering environments are killing diversity (and how we can fix it)
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#323: Best practices for Docker in production
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#63: Validating Python tests with mutation testing
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#195: Teaching Python at Apple
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#359: Lifecycle of a machine learning project
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#507: Agentic AI Workflows with LangGraph
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#74: Past, Present, and Future of IronPython
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#80: TinyDB: A tiny document db written in Python
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#137: Design concepts and tips for developers
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#395: Tools for README.md Creation and Maintenance
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#21: PyPy - The JIT Compiled Python Implementation
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#423: Solving 10 different simulation problems with Python
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#286: Python and ML at NASA Jet Propulsion Laboratory (JPL)
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#3: Pyramid Web Framework
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#169: Becoming a Python content creator
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