Python Bytes - #436 Slow tests go last

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Brian #1: Free-threaded Python no longer “experimental” as of Python 3.14

  • “PEP 779 ("Criteria for supported status for free-threaded Python") has been accepted, which means free-threaded Python is now a supported build!” - Hugo van Kemenade
  • PEP 779 – Criteria for supported status for free-threaded Python
  • As noted in the discussion of PEP 779, “The Steering Council (SC) approves PEP 779, with the effect of removing the “experimental” tag from the free-threaded build of Python 3.14.”
  • We are in Phase II then.
  • “We are confident that the project is on the right path, and we appreciate the continued dedication from everyone working to make free-threading ready for broader adoption across the Python community.”
  • “Keep in mind that any decision to transition to Phase III, with free-threading as the default or sole build of Python is still undecided, and dependent on many factors both within CPython itself and the community. We leave that decision for the future.”
  • How long will all this take? According to Thomas Wouters, a few years, at least: “In other words: it'll be a few years at least. It can't happen before 3.16 (because we won't have Stable ABI support until 15) and may well take longer.”

Michael #2: typed-ffmpeg

  • typed-ffmpeg offers a modern, Pythonic interface to FFmpeg, providing extensive support for complex filters with detailed typing and documentation.

  • Inspired by ffmpeg-python, this package enhances functionality by addressing common limitations, such as lack of IDE integration and comprehensive typing, while also introducing new features like JSON serialization of filter graphs and automatic FFmpeg validation.

  • Features :

    • Zero Dependencies: Built purely with the Python standard library, ensuring maximum compatibility and security.
    • User-Friendly: Simplifies the construction of filter graphs with an intuitive Pythonic interface.
    • Comprehensive FFmpeg Filter Support: Out-of-the-box support for most FFmpeg filters, with IDE auto-completion.
    • Integrated Documentation: In-line docstrings provide immediate reference for filter usage, reducing the need to consult external documentation.
    • Robust Typing: Offers static and dynamic type checking, enhancing code reliability and development experience.
    • Filter Graph Serialization: Enables saving and reloading of filter graphs in JSON format for ease of use and repeatability.
    • Graph Visualization: Leverages graphviz for visual representation, aiding in understanding and debugging.
    • Validation and Auto-correction: Assists in identifying and fixing errors within filter graphs.
    • Input and Output Options Support: Provide a more comprehensive interface for input and output options, including support for additional codecs and formats.
    • Partial Evaluation: Enhance the flexibility of filter graphs by enabling partial evaluation, allowing for modular construction and reuse.
    • Media File Analysis: Built-in support for analyzing media files using FFmpeg's ffprobe utility, providing detailed metadata extraction with both dictionary and dataclass interfaces.

Michael #3: pyleak

  • Detect leaked asyncio tasks, threads, and event loop blocking with stack trace in Python. Inspired by goleak.
  • Use as context managers or function dectorators
  • When using no_task_leaks, you get detailed stack trace information showing exactly where leaked tasks are executing and where they were created.
  • Even has great examples and a pytest plugin.

Brian #4: Optimizing Test Execution: Running live_server Tests Last with pytest

  • Tim Kamanin

  • “When working with Django applications, it's common to have a mix of fast unit tests and slower end-to-end (E2E) tests that use pytest's live_server fixture and browser automation tools like Playwright or Selenium. ”

  • Tim is running E2E tests last for

    • Faster feedback from quick tests
    • To not tie up resources early in the test suite.
  • He did this with

    • custom “e2e” marker

    • Implementing a

      pytest_collection_modifyitems
      

      hook function to look for tests using the

      live_server
      

      fixture, and for them

      • automatically add the e2e marker to those tests
      • move those tests to the end
  • The reason for the marker is to be able to

    • Just run e2e tests with -m e2e
    • Avoid running them sometimes with -m "not e2e"
  • Cool small writeup.

    • The technique works for any system that has some tests that are slower or resource bound based on a particular fixture or set of fixtures.

Extras

Brian:

Michael:

Joke: Naming is hard

The Government Huddle with Brian Chidester - 186: The One with the Global National Security Experts

Dr. David Bray, Former Chief Information Officer of the Federal Communications Commission and CEO at Lead Do Adapt Ventures, and the Honorable Ellen McCarthy, former Assistant Secretary of State for the Bureau of Intelligence and Research join the show for a candid, wide-ranging conversation about the evolving landscape of data, digital trust, and national security. We unpack how the explosion of connected devices, AI-generated content, and synthetic data is reshaping decision-making, security, and public trust at every level of government and society and explore challenges from authenticating digital content to decentralizing emergency response, and the urgent need to empower individuals and local communities in the face of complex, rapidly changing information ecosystems.

array(3) { [0]=> string(77) "https://mcdn.podbean.com/mf/web/e9ub9gy6kndnrmhz/Final_Bray_McCarthyb0pg4.m4a" [1]=> string(0) "" [2]=> string(8) "42155343" }

Lex Fridman Podcast - #472 – Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

Terence Tao is widely considered to be one of the greatest mathematicians in history. He won the Fields Medal and the Breakthrough Prize in Mathematics, and has contributed to a wide range of fields from fluid dynamics with Navier-Stokes equations to mathematical physics & quantum mechanics, prime numbers & analytics number theory, harmonic analysis, compressed sensing, random matrix theory, combinatorics, and progress on many of the hardest problems in the history of mathematics.
Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep472-sc
See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.

Transcript:
https://lexfridman.com/terence-tao-transcript

CONTACT LEX:
Feedback – give feedback to Lex: https://lexfridman.com/survey
AMA – submit questions, videos or call-in: https://lexfridman.com/ama
Hiring – join our team: https://lexfridman.com/hiring
Other – other ways to get in touch: https://lexfridman.com/contact

EPISODE LINKS:
Terence’s Blog: https://terrytao.wordpress.com/
Terence’s YouTube: https://www.youtube.com/@TerenceTao27
Terence’s Books: https://amzn.to/43H9Aiq

SPONSORS:
To support this podcast, check out our sponsors & get discounts:
Notion: Note-taking and team collaboration.
Go to https://notion.com/lex
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AG1: All-in-one daily nutrition drink.
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OUTLINE:
(00:00) – Introduction
(00:36) – Sponsors, Comments, and Reflections
(09:49) – First hard problem
(15:16) – Navier–Stokes singularity
(35:25) – Game of life
(42:00) – Infinity
(47:07) – Math vs Physics
(53:26) – Nature of reality
(1:16:08) – Theory of everything
(1:22:09) – General relativity
(1:25:37) – Solving difficult problems
(1:29:00) – AI-assisted theorem proving
(1:41:50) – Lean programming language
(1:51:50) – DeepMind’s AlphaProof
(1:56:45) – Human mathematicians vs AI
(2:06:37) – AI winning the Fields Medal
(2:13:47) – Grigori Perelman
(2:26:29) – Twin Prime Conjecture
(2:43:04) – Collatz conjecture
(2:49:50) – P = NP
(2:52:43) – Fields Medal
(3:00:18) – Andrew Wiles and Fermat’s Last Theorem
(3:04:15) – Productivity
(3:06:54) – Advice for young people
(3:15:17) – The greatest mathematician of all time

PODCAST LINKS:
– Podcast Website: https://lexfridman.com/podcast
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Big Technology Podcast - Sam Altman’s Gentle Singularity, Zuck’s AI Power Play, Burning Of The Waymos

Ranjan Roy from Margins is back for our weekly discussion of the latest tech news. We cover: 1) Sam Altman's 'Gentle Singularity' essay 2) Is Altman overhyping the technology's current capabilities 3) Why the next few years may see crazy AI development 4) The case for and against humanoid robots 5) OpenAI's o3 pro model and the value of tool use 6) Meta's acquihire-zition of ScaleAI and founder Alexandr Wang 7) The case for the move and the rationale behind Zuck's aggressiveness 8) MetaAI posts 'private' conversations 9) Google traffic to web publishers falls off a cliff -- here's the data. 10) The burning of the Waymos 11) Alex fight robots

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Code Story: Insights from Startup Tech Leaders - S11 Bonus: Yaroslav & Sergiy, Railsware – Part 2

Yaroslav Lazor started writing code when he was 10 years old. It took him a while to understand that making a difference in the world wasn't just about writing code - but he has arrived there these days. He is the father of 4 daughters, 2 of which are artists and a couple who are digging into their entrepreneurial roots with running their own lemonade stand. He lives in Los Angeles, and has learned to be a better person through pushing himself as a founder.

Sergiy Korolov also started coding when he was young, though 4 years later than Yaroslav. He went to a technical university, and as his career in leadership grew, the number of lines of code he contributed to decreased. He is located in Poland, and loves to snowboard in the winter, and bike in the summer. He has 3 kids, and recently started teaching coding to his oldest son.

Yaroslav & Sergiy were building software for clients, the typical Ukrainian software route. Over time, they realized that building their own products was the best way to make an impact - so much so, that they decided to start building their own.

This is the creation story of Railsware.

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Big Technology Podcast - What Apple’s Failures Say About AI’s Limitations —  With M.G. Siegler

M.G. Siegler is the author of Spyglass. He joins Big Technology to discuss Apple’s subdued WWDC and whether it signals deeper trouble with AI's capabilities. Tune in to hear a breakdown of Apple’s “UI over AI” approach, whether the company can pull off the Apple Intelligence vision at all, and if Liquid Glass is a redesign destined to fail. We also cover the vibes at the conference and the state of Apple today. Hit play for clear-eyed, no-nonsense analysis of the tech world’s biggest stories.

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Questions? Feedback? Write to: bigtechnologypodcast@gmail.com


Code Story: Insights from Startup Tech Leaders - S11 Bonus: Yaroslav & Sergiy, Railsware – Part 1

Yaroslav Lazor started writing code when he was 10 years old. It took him a while to understand that making a difference in the world wasn't just about writing code - but he has arrived there these days. He is the father of 4 daughters, 2 of which are artists and a couple who are digging into their entrepreneurial roots with running their own lemonade stand. He lives in Los Angeles, and has learned to be a better person through pushing himself as a founder.

Sergiy Korolov also started coding when he was young, though 4 years later than Yaroslav. He went to a technical university, and as his career in leadership grew, the number of lines of code he contributed to decreased. He is located in Poland, and loves to snowboard in the winter, and bike in the summer. He has 3 kids, and recently started teaching coding to his oldest son.

Yaroslav & Sergiy were building software for clients, the typical Ukrainian software route. Over time, they realized that building their own products was the best way to make an impact - so much so, that they decided to start building their own.

This is the creation story of Railsware.

Sponsors


Links



Our Sponsors:
* Check out Vanta: https://vanta.com/CODESTORY


Support this podcast at — https://redcircle.com/code-story/donations

Advertising Inquiries: https://redcircle.com/brands

Privacy & Opt-Out: https://redcircle.com/privacy

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

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Talk Python Courses

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 ---
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Security Unlocked - Ignore Ram Shankar Siva Kumar’s Previous Directions

In this episode of The BlueHat Podcast, host Nic Fillingham and Wendy Zenone share Ram Shankar Siva Kumar’s dynamic keynote from BlueHat India 2025, where he explores the evolving threat landscape of AI through the lens of the Microsoft AI Red Team. From adversarial machine learning to psychosocial harms and persuasive AI, Ram highlights real-world case studies, including prompt injection, content safety violations, and memory poisoning in AI agents. Ram underscores the urgent need for robust red teaming practices to secure AI systems against traditional security flaws and emerging threats across images, text, audio, and autonomous agents. 

 


In This Episode You Will Learn:  

 

  • Why old-school security flaws still break modern AI systems 
  • Real-world AI red teaming in action, from scams to memory hacks 
  • How small input tweaks can fool AI across images, audio, and text 

 

Some Questions We Ask: 

 

  • Can attackers fool AI using just slight image changes? 
  • Are generative AI systems vulnerable to prompt manipulation? 
  • Do you need to be an expert to break an AI model? 

   

Resources:      

View Ram Shankar Siva Kumar on LinkedIn    

View Wendy Zenone on LinkedIn   

View Nic Fillingham on LinkedIn  


Watch Ram’s BlueHat India 2025 Keynote: BlueHat India 2025 Day 2 Keynote - Ram Shankar Siva Kumar 

Listen to Ram’s Previous Appearance on The BlueHat Podcast: Not with a Bug but with a Sticker 

 


Related Microsoft Podcasts:   

  

  

  

Discover and follow other Microsoft podcasts at microsoft.com/podcasts   


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