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Topics covered in this episode: * The Python Language Summit 2025* Fixing Python Properties * complexipy* * juvio* Extras Joke Watch on YouTube About the show Sponsored by Posit: pythonbytes.fm/connect Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: The Python Language Summit 2025 Write up by Seth Michael Larson How can we make breaking changes less painful?: talk by Itamar Oren An Uncontentious Talk about Contention: talk by Mark Shannon State of Free-Threaded Python: talk by Matt Page Fearless Concurrency: talk by Matthew Parkinson, Tobias Wrigstad, and Fridtjof Stoldt Challenges of the Steering Council: talk by Eric Snow Updates from the Python Docs Editorial Board: talk by Mariatta PEP 772 - Packaging Governance Process: talk by Barry Warsaw and Pradyun Gedam Python on Mobile - Next Steps: talk by Russell Keith-Magee What do Python core developers want from Rust?: talk by David Hewitt Upstreaming the Pyodide JS FFI: talk by Hood Chatham Lightning Talks: talks by Martin DeMello, Mark Shannon, Noah Kim, Gregory Smith, Guido van Rossum, Pablo Galindo Salgado, and Lysandros Nikolaou Brian #2: Fixing Python Properties Will McGugan “Python properties work well with type checkers such Mypy and friends. … The type of your property is taken from the getter only. Even if your setter accepts different types, the type checker will complain on assignment.” Will describes a way to get around this and make type checkers happy. He replaces @property with a descriptor. It's a cool technique. I also like the way Will is allowing different ways to use a property such that it's more convenient for the user. This is a cool deverloper usability trick. Brian #3: complexipy Calculates the cognitive complexity of Python files, written in Rust. Based on the cognitive complexity measurement described in a white paper by Sonar Cognitive complexity builds on the idea of cyclomatic complexity. Cyclomatic complexity was intended to measure the “testability and maintainability” of the control flow of a module. Sonar argues that it's fine for testability, but doesn't do well with measuring the “maintainability” part. So they came up with a new measure. Cognitive complexity is intended to reflects the relative difficulty of understanding, and therefore of maintaining methods, classes, and applications. complexipy essentially does that, but also has a really nice color output. Note: at the very least, you should be using “cyclomatic complexity” try with ruff check --select C901 But also try complexipy. Great for understanding which functions might be ripe for refactoring, adding more documentation, surrounding with more tests, etc. Michael #4: juvio uv kernel for Jupyter ⚙️ Automatic Environment Setup: When the notebook is opened, Juvio installs the dependencies automatically in an ephemeral virtual environment (using uv), ensuring that the notebook runs with the correct versions of the packages and Python

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 --- 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

Ed Kless joined me on Ditching Hourly to talk about what he and his Threshold co-founder Ron Baker think is the next big thing (spoiler alert: transformations).Talking PointsThe Concept of the Experience EconomyProgression of Economic ValueTransformations and Their ImpactThreshold: Guiding TransformationsTransitioning from Service to Transformation EconomySkipping the Experience StageCharging for TransformationSubscription Model as a SolutionValue-Based Pricing vs. Hourly BillingThe Revelation DocumentThe Concept of Moral InjuryAnd here's the link to the document we discussed on the show: THRESHOLD - A REVELATION for the Transformation EconomyAbout EdEd Kless believes entrepreneurs continue the work of creation and that business exists to promote human flourishing. To advance this vision, he cofounded THRESHOLD, where he guides professional leaders and teams through edifying transformation. Previously, Ed served as Sage's senior director of partner development and strategy, hosted the Sage Thought Leadership Podcast, and designed curricula for Sage Partners and Customers. He also co-hosts The Soul of Enterprise with Ron Baker, his friend and fellow THRESHOLD co-founder. ----Before you go!The next time someone asks you for your hourly rate, I want you to stop what you're doing and head on over to valuepricingbootcamp.com to sign up for my free value pricing email course.Hope to see you there!

Topics covered in this episode: * Free-threaded Python no longer “experimental” as of Python 3.14* typed-ffmpeg pyleak * Optimizing Test Execution: Running live_server Tests Last with pytest* Extras Joke Watch on YouTube About the show Sponsored by PropelAuth: pythonbytes.fm/propelauth66 Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. 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: Is Free-Threading Our Only Option? - Interesting discussion started by Eric Snow and recommended by John Hagen Free-threaded Python on GitHub Actions - How to add FT tests to your projects, by Hugo van Kemenade Michael: New course! LLM Building Blocks in Python Talk Python Deep Dives Complete: 600K Words of Talk Python Insights .folders on Linux Write up on XDG for Python devs. They keep pulling me back - ChatGPT Pro with o3-pro Python Bytes is the #1 Python news podcast and #17 of all tech news podcasts. Python 3.13.4, 3.12.11, 3.11.13, 3.10.18 and 3.9.23 are now available Python 3.13.5 is now available! Joke: Naming is hard

My day-to-day coding looks very different from what it was a few years ago. Today, you'll learn about my voice-to-code workflow and how I leverage smart tools to have so much free time that I feel guilty for "not working enough." Seriously.The blog post: https://thebootstrappedfounder.com/from-code-writer-to-code-editor-my-ai-assisted-development-workflow/The podcast episode:  https://tbf.fm/episodes/395-from-code-writer-to-code-editor-my-ai-assisted-development-workflowCheck out Podscan, the Podcast database that transcribes every podcast episode out there minutes after it gets released: https://podscan.fmSend me a voicemail on Podline: https://podline.fm/arvidYou'll find my weekly article on my blog: https://thebootstrappedfounder.comPodcast: https://thebootstrappedfounder.com/podcastNewsletter: https://thebootstrappedfounder.com/newsletterMy book Zero to Sold: https://zerotosold.com/My book The Embedded Entrepreneur: https://embeddedentrepreneur.com/My course Find Your Following: https://findyourfollowing.comHere are a few tools I use. Using my affiliate links will support my work at no additional cost to you.- Notion (which I use to organize, write, coordinate, and archive my podcast + newsletter): https://affiliate.notion.so/465mv1536drx- Riverside.fm (that's what I recorded this episode with): https://riverside.fm/?via=arvid- TweetHunter (for speedy scheduling and writing Tweets): http://tweethunter.io/?via=arvid- HypeFury (for massive Twitter analytics and scheduling): https://hypefury.com/?via=arvid60- AudioPen (for taking voice notes and getting amazing summaries): https://audiopen.ai/?aff=PXErZ- Descript (for word-based video editing, subtitles, and clips): https://www.descript.com/?lmref=3cf39Q- ConvertKit (for email lists, newsletters, even finding sponsors): https://convertkit.com?lmref=bN9CZw

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

Topics covered in this episode: platformdirs poethepoet - “Poe the Poet is a batteries included task runner that works well with poetry or with uv.” Python Pandas Ditches NumPy for Speedier PyArrow pointblank: Data validation made beautiful and powerful Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: platformdirs A small Python module for determining appropriate platform-specific dirs, e.g. a "user data dir". Why the community moved on from appdirs to platformdirs At AppDirs: Note: This project has been officially deprecated. You may want to check out pypi.org/project/platformdirs/ which is a more active fork of appdirs. Thanks to everyone who has used appdirs. Shout out to ActiveState for the time they gave their employees to work on this over the years. Better than AppDirs: Works today, works tomorrow – new Python releases sometimes change low-level APIs (win32com, pathlib, Apple sandbox rules). platformdirs tracks those changes so your code keeps running. First-class typing – no more types-appdirs stubs; editors autocomplete paths as Path objects. Richer directory set – if you need a user's Downloads folder or a per-session runtime dir, there's a helper for it. Cleaner internals – rewritten to use pathlib, caching, and extensive test coverage; all platforms are exercised in CI. Community stewardship – the project lives in the PyPA orbit and gets security/compatibility patches quickly. Brian #2: poethepoet - “Poe the Poet is a batteries included task runner that works well with poetry or with uv.” from Bob Belderbos Tasks are easy to define and are defined in pyproject.toml Michael #3: Python Pandas Ditches NumPy for Speedier PyArrow Pandas 3.0 will significantly boost performance by replacing NumPy with PyArrow as its default engine, enabling faster loading and reading of columnar data. Recently talked with Reuven Lerner about this on Talk Python too. In the next version, v3.0, PyArrow will be a required dependency, with pyarrow.string being the default type inferred for string data. PyArrow is 10 times faster. PyArrow offers columnar storage, which eliminates all that computational back and forth that comes with NumPy. PyArrow paves the way for running Pandas, by default, on Copy on Write mode, which improves memory and performance usage. Brian #4: pointblank: Data validation made beautiful and powerful “With its … chainable API, you can … validate your data against comprehensive quality checks …” Extras Brian: Ruff rules Ruff users, what rules are using and what are you ignoring? Python 3.14.0b2 - did we already cover this? Transferring your Mastodon account to another server, in case anyone was thinking about doing that I'm trying out Fathom Analytics for privacy friendly analytics Michael: Polars for Power Users: Transform Your Data Analysis Game Course Joke: Does your dog bite?

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 --- 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

Ryan Quakenbush, Human Resources Specialist at Rocket Communications, emphasizes that authenticity is key when applying for a job. Rocket is a UX design company deeply involved in the space community, with a fun, curious, and supportive culture. Many roles are remote or hybrid-remote, located in Colorado. The company uses structured interviews for consistency and to reduce bias, and offers remote work perks to help employees thrive.3:08 Rocket Communications is a user-experience design company. Everything one experiences when using technology. In the space domain for 10 years.5:41 Associate to Senior level UX Designers and Software Developers. Looking to hire Secret, Top Secret, and TS/SCI.11:48 Rocket offers a remote stipend when an employee starts, as well as a monthly remote work allowance.Find show notes and additional links at: https://clearedjobs.net/rocket-communications-ux-design-podcast/_ This show is brought to you by ClearedJobs.Net. Have feedback or questions for us? Email us at rriggins@clearedjobs.net. Sign up for our cleared job seeker newsletter. Create a cleared job seeker profile on ClearedJobs.Net. Engage with us on LinkedIn, Facebook, Instagram, X, or YouTube. _

Geraldine Carter joined me on Ditching Hourly to give us a behind-the-scenes recap of what went into her pivot from selling one-time $9,500 mastermind programs to selling a $995/mo ongoing subscription model.(00:00) - Introduction and Guest Welcome (00:34) - Defining 'Melting the Pyramid' (04:38) - Transitioning Business Models (08:54) - Challenges and Key Changes (14:00) - New Program Structure and Benefits (24:41) - Launch Phase and Simplification (27:52) - Navigating the Launch Transition (28:36) - Managing High-Touch Programs (29:33) - Prioritizing VIP Clients (31:10) - Finalizing the Launch (33:06) - Subscription Model Insights (34:50) - Building the Community (37:50) - Efficiency and Scalability (42:46) - Handling Uncertainty and Risk (50:04) - Adapting to Subscription Growth (52:54) - Conclusion and Takeaways Geraline's BioGeraldine Carter helps overworked CPAs create the accounting firm and the life they have always wanted.Geraldine's LinksThe website » https://geraldinecarter.com/The podcast » https://www.businessstrategyforcpas.com/The book » https://geraldinecarter.com/bookThe community » https://geraldinecarter.com/peak-freedom ----Before you go!The next time someone asks you for your hourly rate, I want you to stop what you're doing and head on over to valuepricingbootcamp.com to sign up for my free value pricing email course.Hope to see you there!

AI agents hold the promise of continuously testing, scanning, and fixing code for security vulnerabilities, but we're still progressing toward that vision. Startups like Aptori are helping bridge the gap by building AI-powered security engineers for enterprises. Aptori maps an organization's codebase, APIs, and cloud infrastructure in real time to understand data flows and authorization logic, allowing it to detect and eventually remediate security issues. At Google Cloud Next, Aptori CEO Sumeet Singh discussed how earlier tools merely alerted developers to issues—often overwhelming them—but newer models like Gemini 2.5 Flash and Claude Sonnet 4 are improving automated code fixes, making them more practical. Singh and co-founder Travis Newhouse previously built AppFormix, which automated OpenStack cloud operations before being acquired by Juniper Networks. Their experiences with slow release cycles due to security bottlenecks inspired Aptori's focus. While the goal is autonomous agents, Singh emphasizes the need for transparency and deterministic elements in AI tools to ensure trust and reliability in enterprise security workflows.Learn more from The New Stack about the latest insights in AI application security: AI Is Changing Cybersecurity Fast and Most Analysts Aren't ReadyAI Security Agents Combat AI-Generated Code RisksDevelopers Are Embracing AI To Streamline Threat Detection and Stay AheadJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.

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 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

Topics covered in this episode: Making PyPI's test suite 81% faster People aren't talking enough about how most of OpenAI's tech stack runs on Python PyCon Talks on YouTube Optimizing Python Import Performance Extras Joke Watch on YouTube About the show Sponsored by Digital Ocean: pythonbytes.fm/digitalocean-gen-ai Use code DO4BYTES and get $200 in free credit Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Making PyPI's test suite 81% faster Alexis Challande The PyPI backend is a project called Warehouse It's tested with pytest, and it's a large project, thousands of tests. Steps for speedup Parallelizing test execution with pytest-xdist 67% time reduction --numprocesses=auto allows for using all cores DB isolation - cool example of how to config postgress to give each test worker it's on db They used pytest-sugar to help with visualization, as xdist defaults to quite terse output Use Python 3.12's sys.monitoring to speed up coverage instrumentation 53% time reduction Nice example of using COVERAGE_CORE=sysmon Optimize test discovery Always use testpaths Sped up collection time. 66% reduction (collection was 10% of time) Not a huge savings, but it's 1 line of config Eliminate unnecessary imports Use python -X importtime Examine dependencies not used in testing. Their example: ddtrace A tool they use in production, but it also has a couple pytest plugins included Those plugins caused ddtrace to get imported Using -p:no ddtrace turns off the plugin bits Notes from Brian: I often get questions about if pytest is useful for large projects. Short answer: Yes! Longer answer: But you'll probably want to speed it up I need to extend this article with a general purpose “speeding up pytest” post or series. -p:no can also be used to turn off any plugin, even builtin ones. Examples include nice to have developer focused pytest plugins that may not be necessary in CI CI reporting plugins that aren't needed by devs running tests locally Michael #2: People aren't talking enough about how most of OpenAI's tech stack runs on Python Original article: Building, launching, and scaling ChatGPT Images Tech stack: The technology choices behind the product are surprisingly simple; dare I say, pragmatic! Python: most of the product's code is written in this language. FastAPI: the Python framework used for building APIs quickly, using standard Python type hints. As the name suggests, FastAPI's strength is that it takes less effort to create functional, production-ready APIs to be consumed by other services. C: for parts of the code that need to be highly optimized, the team uses the lower-level C programming language Temporal: used for asynchronous workflows and operations inside OpenAI. Temporal is a neat workflow solution that makes multi-step workflows reliable even when individual steps crash, without much effort by developers. It's particularly useful for longer-running workflows like image generation at scale Michael #3: PyCon Talks on YouTube Some talks that jumped out to me: Keynote by Cory Doctorow 503 days working full-time on FOSS: lessons learned Going From Notebooks to Scalable Systems And my Talk Python conversation around it. (edited episode pending) Unlearning SQL The Most Bizarre Software Bugs in History The PyArrow revolution in Pandas And my Talk Python episode about it. What they don't tell you about building a JIT compiler for CPython And my Talk Python conversation around it (edited episode pending) Design Pressure: The Invisible Hand That Shapes Your Code Marimo: A Notebook that "Compiles" Python for Reproducibility and Reusability And my Talk Python episode about it. GPU Programming in Pure Python And my Talk Python conversation around it (edited episode pending) Scaling the Mountain: A Framework for Tackling Large-Scale Tech Debt Brian #4: Optimizing Python Import Performance Mostly pay attention to #'s 1-3 This is related to speeding up a test suite, speeding up necessary imports. Finding what's slow Use python -X importtime

In this episode ofThe New Stack Makers, Nitric CEO Steve Demchuk discusses how the frustration of building frontend apps within rigid FinTech environments led to the creation of the Nitric framework — a tool designed to eliminate the friction between developers and cloud infrastructure. Unlike traditional Infrastructure as Code (IaC), where developers must manage both app logic and infrastructure definitions separately, Nitric introduces “Infrastructure from Code.” This approach allows developers to focus solely on application logic while the platform infers and automates infrastructure needs using SDKs and CLI tools across multiple languages and cloud providers.Demchuk emphasizes that Nitric doesn't remove platform team control but enforces it consistently. Guardrails defined by platform teams guide infrastructure provisioning, ensuring security and compliance — even as developers use AI tools to rapidly generate code. The result is a streamlined workflow where developers move faster, AI enhances productivity, and platform teams retain oversight. This episode offers engineering leaders insight into a paradigm shift in how cloud infrastructure is managed in the AI era.Learn more from The New Stack about the latest insights about Nitric:  Building a Serverless Meme Generator With Nitric and OpenAIWhy Most Companies Are Struggling With Infrastructure as Code Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

CodeRabbit, led by founder Harjot Gill, is tackling one of software development's biggest bottlenecks: the human code review process. While AI coding tools like GitHub Copilot have sped up code generation, they've inadvertently slowed down shipping due to increased complexity in code reviews. Developers now often review AI-generated code they didn't write, leading to misunderstandings, bugs, and security risks. In an episode of The New Stack Makers, Gill discusses how Code Rabbit leverages advanced reasoning models—OpenAI's o1, o3 mini, and Anthropic's Claude series—to automate and enhance code reviews. Unlike rigid, rule-based static analysis tools, Code Rabbit builds rich context at scale by spinning up sandbox environments for pull requests and allowing AI agents to navigate codebases like human reviewers. These agents can run CLI commands, analyze syntax trees, and pull in external context from Jira or vulnerability databases. Gill envisions a hybrid future where AI handles the grunt work of code review, empowering humans to focus on architecture and intent—ultimately reducing bugs, delays, and development costs.Learn more from The New Stack about the latest insights about AI code reviews: CodeRabbit's AI Code Reviews Now Live Free in VS Code, Cursor AI Coding Agents Level Up from Helpers to Team Players Augment Code: An AI Coding Tool for 'Real' Development WorkJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Topics covered in this episode: git-flight-rules Uravelling t-strings neohtop Introducing Pyrefly: A new type checker and IDE experience for Python Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: git-flight-rules What are "flight rules"? A guide for astronauts (now, programmers using Git) about what to do when things go wrong. Flight Rules are the hard-earned body of knowledge recorded in manuals that list, step-by-step, what to do if X occurs, and why. Essentially, they are extremely detailed, scenario-specific standard operating procedures. [...] NASA has been capturing our missteps, disasters and solutions since the early 1960s, when Mercury-era ground teams first started gathering "lessons learned" into a compendium that now lists thousands of problematic situations, from engine failure to busted hatch handles to computer glitches, and their solutions. Steps for common operations and actions I want to start a local repository What did I just commit? I want to discard specific unstaged changes Restore a deleted file Brian #2: Uravelling t-strings Brett Cannon Article walks through Evaluating the Python expression Applying specified conversions Applying format specs Using an Interpolation class to hold details of replacement fields Using Template class to hold parsed data Plus, you don't have to have Python 3.14.0b1 to try this out. The end result is very close to an example used in PEP 750, which you do need 3.14.0b1 to try out. See also: I've written a pytest version, Unravelling t-strings with pytest, if you want to run all the examples with one file. Michael #3: neohtop Blazing-fast system monitoring for your desktop Features Real-time process monitoring CPU and Memory usage tracking Beautiful, modern UI with dark/light themes Advanced process search and filtering Pin important processes Process management (kill processes) Sort by any column Auto-refresh system stats Brian #4: Introducing Pyrefly: A new type checker and IDE experience for Python From Facebook / Meta Another Python type checker written in Rust Built with IDE integration in mind from the beginning Principles Performance IDE first Inference (inferring types in untyped code) Open source I mistakenly tried this on the project I support with the most horrible abuses of the dynamic nature of Python, pytest-check. It didn't go well. But perhaps the project is ready for some refactoring. I'd like to try it soon on a more well behaved project. Extras Brian: Python: The Documentary Official Trailer Tim Hopper added Setting up testing with ptyest and uv to his “Python Developer Tooling Handbook” For a more thorough intro on pytest, check out courses.pythontest.com pocket is closing, I'm switching to Raindrop I got one question about code formatting. It's not highlighted, but otherwise not bad. Michael: New course! Polars for Power Users: Transform Your Data Analysis Game Apache Airflow 3.0 Released Paste 5 Joke: Theodore Roosevelt's Man in the Arena, but for programming

At the close of this year's Google Cloud Next, The New Stack's Alex Williams, AI editor Frederic Lardinois, and analyst Janakiram MSV discussed the event's dominant theme: AI agents. The conversation focused heavily on agent frameworks, noting a shift from last year's third-party tools like Langchain, CrewAI, and Microsoft's Autogen, to first-party offerings from model providers themselves. Google's newly announced Agent Development Kit (ADK) highlights this trend, following closely on the heels of OpenAI's agent SDK. MSV emphasized the significance of this shift, calling it a major milestone as Google joins the race alongside Microsoft and OpenAI. Despite the buzz, Lardinois pointed out that many companies are still exploring how AI agents can fit into real-world workflows. The panel also highlighted how Google now delivers a full-stack AI development experience — from models to deployment platforms like Vertex AI. New enterprise tools like Agent Space and Agent Garden further signal Google's commitment to making agents a core part of modern software development. Learn more from The New Stack about the latest in AI agents: How AI Agents Will Change the Web for Users and Developers AI Agents: A Comprehensive Introduction for Developers AI Agents Are Coming for Your SaaS Stack Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Join me as I chat with Ben Tossell to discuss how non-technical users can leverage OpenAI's Codex to build and modify websites without writing code. The video showcases a practical example of adding a new tab to a personal website through simple text commands, with Codex handling all the coding work. Ben explains GitHub basics, pull requests, and how to manage code changes while emphasizing that this approach makes coding more accessible to beginners. Timestamps: 00:00 - Intro 01:28 - What is Codex 02:38 - Explanation of GitHub and repositories 05:54 - Demonstration of adding a new tab to a website 10:53 - Why use Codex if you are non-technical 15:26 - Explanation of merging and closing pull requests 18:18 - Best practices for non-technical people using Codex 21:54 - Tips for Debugging using ChatGPT 23:47 - More advanced use cases beyond personal websites 25:30 - Cloning a Repo and using Codex 30:08 - Conclusion and Final Thoughts on Codex Key Points: • Codex is an OpenAI terminal product that allows users to type in tasks, generate code, and push it to GitHub without writing code themselves • The workflow involves creating tasks, reviewing generated code, creating pull requests, and merging changes • For non-technical people, Codex offers a more accessible entry point to coding than traditional development • Best practice is to start with simple personal website projects before attempting more complex applications: LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ BoringMarketing — Vibe Marketing for Sale: http://boringmarketing.com/ Startup Empire - a membership for builders who want to build cash-flowing businesses https://www.startupempire.co FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND BEN ON SOCIAL X/Twitter: https://x.com/bentossell Ben's Bites: https://www.bensbites.com

Craig Hewitt, founder of Castos, joined me on Ditching Hourly to discuss the current state and potential future of podcasting as a medium for building trust with your audience.AI SummaryIn this episode of Ditching Hourly, Jonathan Stark speaks with Craig Hewitt, founder of Castos, about the evolving landscape of podcasting. They discuss the shifting definitions and formats of podcasts, the importance of different content strategies, including private and hybrid podcasting, and the impact of new technologies like AI. Craig also shares insights on leveraging platforms such as YouTube and podcasting for trust-building and discovery, what makes podcasts unique in the AI era, and tips for solo business owners looking to optimize their content. The conversation is packed with actionable advice on how to effectively use podcasting to build authority and nurture audiences.Chapters(00:00) - Introduction and Guest Welcome (01:14) - The Evolution of Podcasting (02:24) - Private and Hybrid Podcasting Explained (03:27) - The Role of Video in Podcasting (05:21) - Discovery and Monetization Strategies (06:44) - Business Models for Podcasters (09:34) - Content Strategy and Audience Engagement (16:14) - The Future of Podcasting and Platforms (29:09) - Content Strategy and Funnel Optimization (30:36) - The Power of Long-Form Audio (31:46) - Podcasting for Authority Building (34:24) - YouTube as a Discovery Platform (34:48) - Shorts, Segments, and Long-Form Content (51:18) - The Role of AI in Content Creation (55:44) - Private and Hybrid Podcasting (59:12) - Conclusion and Future of Podcasting Craig's LinksCastos » https://castos.com/Craig's Website » https://craighewitt.com/Craig's YouTube » https://www.youtube.com/@thecraighewittCraig's LinkedIn » https://www.linkedin.com/in/craig-hewitt-78386a66/ The next time someone asks you for your hourly rate, I want you to stop what you're doing and head on over to valuepricingbootcamp.com to sign up for my free value pricing email course.Hope to see you there!

Agentic AI represents the next phase beyond generative AI, promising systems that not only generate content but also take autonomous actions within business processes. In a conversation recorded at Google Cloud Next, Kevin Laughridge of Deloitte explains that businesses are moving from AI pilots to production-scale deployments. Agentic AI enables decision-making, reasoning, and action across complex enterprise environments, reducing the need for constant human input. A key enabler is Google's newly announced open Agent2Agent (A2A) protocol, which allows AI agents from different vendors to communicate and collaborate securely across platforms. Over 50 companies, including PayPal, Salesforce, and Atlassian, are already adopting it. However, deploying agentic AI at scale requires more than individual tools—it demands an AI platform with runtime frameworks, UIs, and connectors. These platforms allow enterprises to integrate agents across clouds and systems, paving the way for AI that is collaborative, adaptive, and embedded in core operations. As AI becomes foundational, developers are transitioning from coding to architecting dynamic, learning systems.Learn more from The New Stack about the latest insights about Agent2Agent Protocol: Google's Agent2Agent Protocol Helps AI Agents Talk to Each OtherA2A, MCP, Kafka and Flink: The New Stack for AI AgentsJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.

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 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

Topics covered in this episode: pre-commit: install with uv PEP 773: A Python Installation Manager for Windows (Accepted) Changes for Textual The Best Programmers I Know Extras Joke Watch on YouTube About the show Sponsored by NordLayer: pythonbytes.fm/nordlayer Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: pre-commit: install with uv Adam Johnson uv tool works great at keeping tools you use on lots of projects up to date quickly, why not use it for pre-commit. The extension of pre-commit-uv will use uv to create virtual environments and install packages fore pre-commit. This speeds up initial pre-commit cache creation. However, Adam is recommending this flavor of using pre-commit because it's just plain easier to install pre-commit and dependencies than the official pre-commit install guide. Win-win. Side note: No Adam, I'm not going to pronounce uv “uhv”, I'll stick with “you vee”, even Astral tells me I'm wrong Michael #2: PEP 773: A Python Installation Manager for Windows (Accepted) via pycoders newsletter One manager to rule them all – PyManager. PEP 773 replaces all existing Windows installers (.exe “traditional” bundle, per-version Windows Store apps, and the separate py.exe launcher) with a single MSIX app called Python Install Manager (nick-named PyManager). PyManager should be mainstream by CPython 3.15, and the traditional installer disappears no earlier than 3.16 (≈ mid-2027). Simple, predictable commands. python → launches “the best” runtime already present or auto-installs the latest CPython if none is found. py → same launcher as today plus management sub-commands: py install, py uninstall, py list, py exec, py help. Optional python3 and python3.x aliases can be enabled by adding one extra PATH entry. Michael #3: Changes for Textual Bittersweet news: the business experiment ends, but the code lives on. Textual began as a hobby project layered on top of Rich, but it has grown into a mature, “makes-the-terminal-do-the-impossible” TUI framework with an active community and standout documentation. Despite Textual's technical success, the team couldn't pinpoint a single pain-point big enough to sustain a business model, so the company will wind down in the coming weeks. The projects themselves aren't going anywhere: they're stable, battle-tested, and will continue under the stewardship of the original author and the broader community. Brian #4: The Best Programmers I Know Matthias Endler “I have met a lot of developers in my life. Lately, I asked myself: “What does it take to be one of the best? What do they all have in common?”” The list Read the reference Know your tools really well Read the error message Break down problems Don't be afraid to get your hands dirty Always help others Write Never stop learning Status doesn't matter Build a reputation Have patience Never blame the computer Don't be afraid to say “I don't know” Don't guess Keep it simple Each topic has a short discussion. So don't just ready the bullet points, check out the article. Extras Brian: I had a great time in Munich last week. I a talk at a company event, met with tons of people, and had a great time. The best part was connecting with people from different divisions working on similar problems. I love the idea of internal conferences to get people to self organize by topic and meet people they wouldn't otherwise, to share ideas. Also got started working on a second book on the plane trip back. Michael: Talk Python Clips (e.g. mullet) Embrace your cloud firewall (example). Python 3.14.0 beta 1 is here Congrats to the new PSF Fellows. Cancelled faster CPython https://bsky.app/profile/snarky.ca/post/3lp5w5j5tws2i Joke: How To Fix Your Computer

Aja Hammerly, director of developer relations at Google, sees AI as the always-available coding partner developers have long wished for—especially in those late-night bursts of inspiration. In a conversation with Alex Williams at Google Cloud Next, she described AI-assisted coding as akin to having a virtual pair programmer who can fill in gaps and offer real-time support. Hammerly urges developers to start their AI journey with tools that assist in code writing and explanation before moving into more complex AI agents. She distinguishes two types of DevEx AI: using AI to build apps and using it to eliminate developer toil. For Hammerly, this includes letting AI handle frontend work while she focuses on backend logic. The newly launched Firebase Studio exemplifies this dual approach, offering an AI-enhanced IDE with flexible tools like prototyping, code completion, and automation. Her advice? Developers should explore how AI fits into their unique workflow—because development, at its core, is deeply personal and individual.Learn more from The New Stack about the latest AI insights with Google Cloud:Google AI Coding Tool Now Free, With 90x Copilot's OutputGemini 2.5 Pro: Google's Coding Genius Gets an UpgradeQ&A: How Google Itself Uses Its Gemini Large Language ModelJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

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 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 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

At Google Cloud Next '25, the company introduced Ironwood, its most advanced custom Tensor Processing Unit (TPU) to date. With 9,216 chips per pod delivering 42.5 exaflops of compute power, Ironwood doubles the performance per watt compared to its predecessor. Senior product manager Chelsie Czop explained that designing TPUs involves balancing power, thermal constraints, and interconnectivity. Google's long-term investment in liquid cooling, now in its fourth generation, plays a key role in managing the heat generated by these powerful chips. Czop highlighted the incremental design improvements made visible through changes in the data center setup, such as liquid cooling pipe placements. Customers often ask whether to use TPUs or GPUs, but the answer depends on their specific workloads and infrastructure. Some, like Moloco, have seen a 10x performance boost by moving directly from CPUs to TPUs. However, many still use both TPUs and GPUs. As models evolve faster than hardware, Google relies on collaborations with teams like DeepMind to anticipate future needs.Learn more from The New Stack about the latest AI infrastructure insights from Google Cloud:Google Cloud Therapist on Bringing AI to Cloud Native InfrastructureA2A, MCP, Kafka and Flink: The New Stack for AI AgentsJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Join Kamyabi Network: https://kamyabinetwork.com/Guest Introduction: Joining us today is Zeeshan Sikander, the Founder & CEO of Zenkoders, a cutting-edge software company he's been passionately building since 2019. With over 10 years of experience in Software Development and Project Management, Zeeshan has grown Zenkoders from a solo venture into a team of 80+ talented individuals. His background also includes experience as a Product Development Engineer at Habib Bank Limited, where he focused on designing and developing HBL's mobile apps. At Zenkoders, they specialize in turning ideas into tangible success, offering services ranging from Mobile Apps and Web Development to Cloud Services and E-commerce. Zeeshan's vision is to lead Zenkoders to the forefront of the global software landscape, and he's always open to innovative collaborations.Do not forget to subscribe and press the bell icon to catch on to some amazing conversations coming your way!Socials:TBT's Official Instagram: https://www.instagram.com/thoughtbehindthings      Muzamil's Instagram: https://www.instagram.com/muzamilhasan  Muzamil's LinkedIn: https://www.linkedin.com/in/muzamilhasan    Zeeshan's LinkedIn: https://www.linkedin.com/in/mzeeshansikander/Podcast Links:Spotify: https://spoti.fi/3z1cE7F       Google Podcast: https://bit.ly/2S84VEd        Apple Podcast: https://apple.co/3cgIkf 

At Google Cloud Next, Bobby Allen, Group Product Manager for Google Kubernetes Engine (GKE), emphasized GKE's foundational role in supporting AI platforms. While AI dominates current tech conversations, Allen highlighted that cloud-native infrastructure like Kubernetes is what enables AI workloads to function efficiently. GKE powers key Google services like Vertex AI and is trusted by organizations including DeepMind, gaming companies, and healthcare providers for AI model training and inference. Allen explained that GKE offers scalability, elasticity, and support for AI-specific hardware like GPUs and TPUs, making it ideal for modern workloads. He noted that Kubernetes was built with capabilities—like high availability and secure orchestration—that are now essential for AI deployment. Looking forward, GKE aims to evolve into a model router, allowing developers to access the right AI model based on function, not vendor, streamlining the development experience. Allen described GKE as offering maximum control with minimal technical debt, future-proofed by Google's continued investment in open source and scalable architecture.Learn more from The New Stack about the latest insights with Google Cloud: Google Kubernetes Engine Customized for Faster AI WorkKubeCon Europe: How Google Will Evolve Kubernetes in the AI EraApache Ray Finds a Home on the Google Kubernetes EngineJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Jason Lengstorf, founder of Code TV, joined me on Ditching Hourly to share how he is successfully navigating the transformation of the video production business. Jason's Links: https://jason.energy/https://codetv.dev/AI Summary:In this episode of Ditching Hourly, Jonathan Stark is joined by Jason Lengstorf, founder of Code TV, to explore the current landscape of the video production industry, especially within the tech sector. Jason shares insights into his career journey from being a web engineer to running a successful video production business for tech companies. The discussion delves into industry transitions, the significance of genuine community engagement, the crucial role of video in marketing campaigns, and strategies for delivering measurable ROI. Jason also offers practical advice for video production professionals on pricing their services, specializing in niches, and maintaining consistency to build a strong client base.Chapters:(00:00) - Introduction and Guest Welcome (00:50) - Jason Langsdorf's Background (01:29) - The State of the Video Production Industry (03:34) - Strategic Video Production (10:20) - Top of Funnel Growth Strategies (17:08) - Jason's Approach to Video Production (24:29) - Specialization and Niche Market (29:26) - Measuring Campaign Effectiveness (30:19) - The Importance of Patience and Follow-Through (30:55) - Qualitative Metrics and Social Listening (31:18) - The Value of Organic Mentions (31:56) - Case Study: Microsoft's Image Transformation (34:36) - Challenges in Sales Processes (40:16) - The Power of Consistency in Content Creation (43:41) - Outsourcing Video Production (45:31) - Finding Your Niche and Adding Value (53:57) - The Journey to Success (56:22) - Conclusion and Resources ----Have you ever thought about starting a podcast but gave up because it seemed too hard?I've got good news for you:If you can run a Zoom call, you can host a podcast.In my 5-Day Podcast Challenge, you will learn exactly what to do (and, more importantly, NOT do) to get your podcast off the ground in as little as five days.Stop thinking and start doing. You could be inviting guests to your new show in less than two weeks.ENROLL IN 5DPC NOW »I hope to see (and hear) you there!

Without this, developers waste time managing infrastructure instead of focusing on code. VMware addresses this with VCF, a pre-integrated Kubernetes solution that includes components like Harbor, Valero, and Istio, all managed by VMware. While some worry about added complexity from abstraction, Turner dismissed concerns about virtualization overhead, pointing to benchmarks showing 98.3% of bare metal performance for virtualized AI workloads. He emphasized that AI is driving nearly half of Kubernetes deployments, prompting VMware's partnership with Nvidia to support GPU virtualization. Turner also highlighted VMware's open source leadership, contributing to major projects and ensuring Kubernetes remains cloud-independent and standards-based. VMware aims to simplify Kubernetes and AI workload management while staying committed to the open ecosystem.Learn more from The New Stack about the latest insights with VMware Has VMware Finally Caught Up With Kubernetes?VMware's Golden PathJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  

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 ChatGPT: chat.openai.com Claude: anthropic.com LM Studio: lmstudio.ai Hetzner: hetzner.com Docker: docker.com Aider Chat: github.com Goose AI: goose.ai IndyPy: indypy.org OctoPrint Community Forum: community.octoprint.org spaCy GitHub: github.com Hugging Face: huggingface.co 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 Mastodon: mkennedy

Topics covered in this episode: pirel: Python release cycle in your terminal FastAPI Cloud Python's new t-strings Extras Joke Watch on YouTube About the show Sponsored by NordLayer: pythonbytes.fm/nordlayer Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: pirel: Python release cycle in your terminal pirel check shows release information about your active Python interpreter. If the active version is end-of-life, the program exits with code 1. If no active Python interpreter is found, the program exits with code 2. pirel list lists all Python releases in a table. Your active Python interpreter is highlighted. A picture is worth many words Brian #2: FastAPI Cloud Sebastián Ramírez, creator of FastAPI, announced today the formation of a new Company, FastAPI Cloud. Here's the announcement blog post: FastAPI Cloud - By The Same Team Behind FastAPI There's a wait list to try it out. Promises to turns deployment into fastapi login; fastapi deploy Side note: announcement includes quote from Daft Punk: Build Harder, Better, Faster, Stronger I just included this in a talk I'm gave last week (and will again next week), where I modify this to “Build Easier, Better, Faster, Stronger” Sebastian and I are both fans of the rocket emoji. BTW, we first covered FastAPI on episode 123 in 2019 Brian #3: Python's new t-strings Dave Peck, one of the authors of PEP 750, which will go into Python 3.14 We covered t-strings in ep 428 In article t-strings security benefits over f-strings How to work with t-strings A Pig Latin example Also, I think I have always done this wrong Is it the first consonant to the end? or the first consonant cluster? So… Brian → Rianbay? or Ianbray? BTW, this is an example of nerdgassing What's next once t-strings ship? On thing that's next (in Python 3.15, maybe, is using t-strings in shlex and subprocess) PEP 787 – Safer subprocess usage using t-strings deferred to 3.15 Michael #4: zev A simple CLI tool to help you remember terminal commands. Examples: # Find running processes zev 'show all running python processes' # File operations zev 'find all .py files modified in the last 24 hours' # System information zev 'show disk usage for current directory' # Network commands zev 'check if google.com is reachable' # Git operations zev 'show uncommitted changes in git' Again, picture worth many words: Extras Brian: Holy Grail turns 50 nerdgassing Michael: Transcripts are a bit better now. Zen is better now Joke: Can my friend come in?

Prequel is launching a new developer-focused service aimed at democratizing software error detection—an area typically dominated by large cloud providers. Co-founded by Lyndon Brown and Tony Meehan, both former NSA engineers, Prequel introduces a community-driven observability approach centered on Common Reliability Enumerations (CREs). CREs categorize recurring production issues, helping engineers detect, understand, and communicate problems without reinventing solutions or working in isolation. Their open-source tools, cre and prereq, allow teams to build and share detectors that catch bugs and anti-patterns in real time—without exposing sensitive data, thanks to edge processing using WebAssembly.The urgency behind Prequel's mission stems from the rapid pace of AI-driven development, increased third-party code usage, and rising infrastructure costs. Traditional observability tools may surface symptoms, but Prequel aims to provide precise problem definitions and actionable insights. While observability giants like Datadog and Splunk dominate the market, Brown and Meehan argue that engineers still feel overwhelmed by data and underpowered in diagnostics—something they believe CREs can finally change.Learn more from The New Stack about the latest Observability insights Why Consolidating Observability Tools Is a Smart MoveBuilding an Observability Culture: Getting Everyone Onboard Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

At Arm, open source is the default approach, with proprietary software requiring justification, says Andrew Wafaa, fellow and senior director of software communities. Speaking at KubeCon + CloudNativeCon Europe, Wafaa emphasized Arm's decade-long commitment to open source, highlighting its investment in key projects like the Linux kernel, GCC, and LLVM. This investment is strategic, ensuring strong support for Arm's architecture through vital tools and system software.Wafaa also challenged the hype around GPUs in AI, asserting that CPUs—especially those enhanced with Arm's Scalable Matrix Extension (SME2) and Scalable Vector Extension (SVE2)—are often more suitable for inference workloads. CPUs offer greater flexibility, and Arm's innovations aim to reduce dependency on expensive GPU fleets.On the AI framework front, Wafaa pointed to PyTorch as the emerging hub, likening its ecosystem-building potential to Kubernetes. As a PyTorch Foundation board member, he sees PyTorch becoming the central open source platform in AI development, with broad community and industry backing.Learn more from The New Stack about the latest insights about Arm: Edge Wars Heat Up as Arm Aims to Outflank Intel, Qualcomm Arm: See a Demo About Migrating a x86-Based App to ARM64Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Neha Sharma is a Product Manager at Grantek, she has 10+ years of experience in defining technology solutions to solve business/IT problems for manufacturers. Over the years, Neha has worked across various positions such as Software Developer, Module Lead, Techno-Functional Consultant and Manufacturing Engineer. Neha has worked for world class clients across the globe. Neha has experience in the automotive industry, packaging industry, agricultural industry, building materials, food & beverage and pharmaceuticals. The Industry 4.0 Podcast with Grantek delivers a look into the world of manufacturing, with a focus on stories and trends that lead to better solutions.   Our guests will share tips and outcomes that will help improve your productivity. You will hear from leading providers of Industrial Control System hardware and software, Grantek experts and leaders at best-in-class industry associations that serve Life Sciences and Food & Beverage manufactures.

Author/creator of Sales for Nice People, Martin Stellar, joined me on Ditching Hourly to talk about sticking to your guns in a sales meeting and how to build the confidence to do so. Martin's Links:https://martinstellar.com/https://www.linkedin.com/in/martinstellar/https://salesfornicepeople.com/academy ----Do you have questions about how to improve your business? Things like:Value pricing your work instead of billing for your time?Positioning yourself as the go-to person in your space?Productizing your services so you never have to have another awkward sales call or spend hours writing another custom proposal?Book a one-on-one coaching call with me and get answers to these questions and others in the time it takes to get ready for work in the morning.Best of all, you're covered by my 100% satisfaction guarantee. If at the end of the call, you don't feel like it was worth it, just say the word, and I'll refund your purchase in full.To book your one-on-one coaching call, go to: https://jonathanstark.com/callI hope to see you there!

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.org Pandas user guide: pandas.pydata.org Pandas GitHub issues: github.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: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

Topics covered in this episode: pip 25.1 has dependency groups, pylock.toml, plus more aiohttp goes free threaded uv 0.6.15 supports pylock.toml Whenever Extras Joke Watch on YouTube About the show Sponsored by Porkbun! Use our link pythonbytes.fm/porkbun and get a .app or .dev domain for $5.99 at Porkbun. Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: pip 25.1 has dependency groups, pylock.toml, plus more post What's new in pip 25.1 - Dependency groups! Richard Si Discovered this through Hugo van Kemenade Dependency groups, PEP 735, supported # pyproject.toml [dependency-groups] test = ["pytest", "pytest-xdist"] lint = ["mypy", "isort"] # Dependency Groups can include other groups! ✨ dev = [ {include-group = "test"}, {include-group = "lint"} ] Package installation progress bar Resumable downloads Experimental lockfile generation, PEP 751, with pip lock so cool pip index versions is stable, no longer experimental use this to get a list of available versions ex: python3 -m pip index versions pytest-check combine with --json to get a nice script readable output Michael #2: aiohttp goes free threaded Thanks to months of consistent contributions by Lysandros Nikolaou, all of the mandatory dependencies of #aiohttp now ship free-threaded variants of #wheels! This unlocks the same in aiohttp! Brian #3: uv 0.6.15 supports pylock.toml Discovered through Brett Cannon So far, these projects support pylock.toml pip pip-audit pdm uv With uv To export a uv.lock to the pylock.toml format, run: uv export -o pylock.toml To generate a pylock.toml file from a set of requirements, run: uv pip compile -o pylock.toml -r requirements.in To install from a pylock.toml file, run: uv pip sync pylock.toml or uv pip install -r pylock.toml Michael #4: Whenever via Pat Decker Typed and DST-safe datetimes for Python, available in Rust or pure Python. Whenever helps you write correct and type checked datetime code. It's also way faster than other third-party libraries—and usually the standard library as well. Extras Brian: Every UUID Michael: New Vulnerability in GitHub Copilot and Cursor: How Hackers Can Weaponize Code Agents via Brian Skinn And typosquatting in the AI age Firefox Send alternatives file.pizza via @rafaelwo bitwarden send Joke: Can you Vibe? Interview with Vibe Coder in 2025 Senior Engineer tries Vibe Coding

GoldBridge AI: https://goldbridge.ai GoldBridge on LinkedIn: https://www.linkedin.com/posts/goldbridge-ai_goldbridgeai-demo-activity-7306070534235033600-rSWO?utm_source=share&utm_medium=member_desktop&rcm=ACoAADGbBvgBMTPIFZ7W6zGKUx8RuMivec4RXUI GoldBridge on Instagram: https://www.instagram.com/thegoldbridge/ Contact product manager Siddarth (Sid) Srivastava here: https://www.linkedin.com/in/sid-srivastava-4488721b2/ The post 298 Senior Software Developer talks about Job Pivots first appeared on Agile Noir.

"We all want to help, but without the right tools and resources, TNR can feel impossible. That's why we need technology to bridge the gaps." This episode is sponsored-in-part by Maddie's Fund. Trapping and caring for community cats comes with numerous logistical challenges—finding vet appointments, securing transportation, and funding medical care. Alexa Kyler, software developer and TNR advocate, is tackling these obstacles with the Trapper Aid Initiative, an app designed to connect trappers with critical resources. In this episode, Alexa shares how her own experience with TNR inspired this innovative solution, the hurdles of building tech for animal rescue, and her vision for the future of community cat support. Press Play Now For: How Alexa's husband introduced her to the world of TNR The biggest challenges facing community cat trappers today Why resource-sharing is critical for successful TNVR programs The impact of veterinary appointment shortages on community cats How technology can help streamline TNR efforts The complexities of funding and structuring tech-based nonprofit solutions Resources & Links: Trapper Aid Initiative (http://catcrisispossiblesolution.com) Sponsor Links: Maddie's Fund (https://www.communitycatspodcast.com/maddies609) Follow & Review We'd love for you to follow us if you haven't yet. Click that purple '+' in the top right corner of your Apple Podcasts app. We'd love it even more if you could drop a review or 5-star rating over on Apple Podcasts(https://podcasts.apple.com/us/podcast/the-community-cats-podcast/id1125752101?mt=2). Select “Ratings and Reviews” and “Write a Review” then share a quick line with your favorite part of the episode. It only takes a second and it helps spread the word about the podcast.

Tracing mysterious errors to their source, jousting with product managers, and rolling out new features (without breaking the old ones) with Taylor Hughes, a software engineer. How did one failed software fix ruin Christmas for kids around the country? And what is "spaghetti code"?Taylor is currently a co-founder and the CTO at Hypernatural.ai.NEW BOOK ALERT!You may be aware that I've written or co-written five business books, including The Power of Moments and Made to Stick. I've got a sixth book out now called RESET: How to Change What's Not Working. It's a book intended to help you and your team get unstuck, to overcome the gravity of the way things have always worked. Learn more about the book and order it here. You can also listen to it on Audible and at Apple Books.Got a comment or suggestion for us? You can reach us via email at jobs@whatitslike.comWant to be on the show? Leave a message on our voice mailbox at (919) 213-0456. We'll ask you to answer two questions: What do people think your job is like and what is it actually like? What's a word or phrase that only someone from your profession would be likely to know and what does it mean?

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 Mastodon: mkennedy

Topics covered in this episode: Huly CVE Foundation formed to take over CVE program from MITRE drawdb 14 Advanced Python Features Extras Joke Watch on YouTube About the show Sponsored by Posit Workbench: pythonbytes.fm/workbench Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Huly All-in-One Project Management Platform (alternative to Linear, Jira, Slack, Notion, Motion) If you're primarily interested in self-hosting Huly without the intention to modify or contribute to its development, please use huly-selfhost. Manage your tasks efficiently with Huly's bidirectional GitHub synchronization. Use Huly as an advanced front-end for GitHub Issues and GitHub Projects. Connect every element of your workflow to build a dynamic knowledge base. Everything you need for productive team work: Team Planner • Project Management • Virtual Office • Chat • Documents • Inbox Self hosting as a service: elest.io Brian #2: CVE Foundation formed to take over CVE program from MITRE Back story: CVE, global source of cybersecurity info, was hours from being cut by DHS The 25-year-old CVE program, an essential part of global cybersecurity, is cited in nearly any discussion or response to a computer security issue. CVE was at real risk of closure after its contract was set to expire on April 16. The nonprofit MITRE runs CVE on a contract with the DHS. A letter last Tuesday sent Tuesday by Yosry Barsoum, vice president of MITRE, gave notice of the potential halt to operations. Another possible victim of the current administration. CVE Foundation Launched to Secure the Future of the CVE Program CVE Board members have spent the past year developing a strategy to transition CVE to a dedicated, non-profit foundation. The new CVE Foundation will focus solely on continuing the mission of delivering high-quality vulnerability identification and maintaining the integrity and availability of CVE data for defenders worldwide. Over the coming days, the Foundation will release more information about its structure, transition planning, and opportunities for involvement from the broader community. Michael #3: drawdb Free and open source, simple, and intuitive database design editor, data-modeler, and SQL generator. Great drag-drop relationship manager Define your DB visually, export as SQL create scripts Or import existing SQL to kickstart the diagramming. Brian #4: 14 Advanced Python Features Edward Li Picking some favorites 1. Typing Overloads 2. Keyword-only and Positional-only Arguments 9. Python Nitpicks For-else statements Walrus operator Short Circuit Evaluation Operator Chaining Extras Michael: Thunderbird send / other firefox things. Joke: Python Tariffs Thanks wagenrace Thanks Campfire Tales

Yuval Yeret, founder of Yeret Agility and OG Agile expert, joined me on Ditching Hourly to discuss the current state of Agile as a platform, how it has evolved over the years, and what practitioners should consider when pivoting their careers as the platform matures.About YuvalYuval Yeret is a Product/Scaling/Agility Coach focused on helping product/tech leaders scale their organizations without slowing down, improving outcomes by leveraging flow, agility, and product orientation. (while avoiding the dogma and process BS of Agile Theater). Yuval is a globally recognized expert on scaling w/ agility, a SAFe Fellow, a Professional Scrum Trainer, and a co-author of the Kanban Guide for Scrum Teams. These days Yuval is focused on helping organizations evolve from Feature Factories to Empowered Product Organizations, as well as helping deeper tech organizations develop a pragmatic agility strategy. Yuval shares his insights on scaling w/ agility at https://yuvalyeret.com/scaling-with-agility-newsletter/Chapters(00:00) - Introduction and Guest Welcome (00:17) - Yuval's Background and Journey into Agile (01:35) - Early Days of Agile (03:56) - Transition to Consulting and Coaching (07:21) - Agile's Evolution and Current State (09:46) - Challenges and Criticisms of Agile (17:30) - Future of Agile and Role Adaptation (22:18) - Advice for Agile Practitioners (30:22) - Reflecting on Agile Leadership (31:24) - Anecdote: Transition from FileMaker to Web Development (34:57) - The Future of Agile and Product Operating Models (39:20) - Adapting Skills for New Opportunities (41:48) - Navigating Organizational Change (44:47) - Strategies for Career Pivoting (48:01) - The Role of Scrum Masters in Modern Organizations (52:00) - Consulting and Value Proposition (57:55) - Closing Thoughts and Resources Notable Quotes"What happened over the years is... agile has become mainstream for most of corporate America, technology organizations and product companies. And this created the reality where the people that are, the organizations that are currently adopting agile are the late adopters.""[Late adopters] are slapping names like Scrum Master and Sprint and User Story and Daily Scrum... on the way that they've been doing things already. And it's like lipstick on a pig. It's not really creating any impact other than a bad name for Agile and a bad name for people in these roles.""The biggest issue with Agile... is the over-reliance on specific roles in organizations.""We will have a significantly smaller number of people that dedicate their career to something like agile, whatever it's called. You will need to specialize. You will need to start to think like consultants need to start to think and build your content solar system."Yuval's Links and Other ResourcesYuval's article on "The Future of Agile Roles and Agility"Yuval's private podcast on navigating the landscape of Agile theater, feature factories, and product operating models"Crossing the Chasm" by Geoffrey Moore (book on technology adoption)Netflix culture book (featuring the "Netflix question")The career mini-course that Jonathan mentioned: Unblock Your Career by Shachar Meir ----Do you have questions about how to improve your business? Things like:Value pricing your work instead of billing for your time?Positioning yourself as the go-to person in your space?Productizing your services so you never have to have another awkward sales call or spend hours writing another custom proposal?Book a one-on-one coaching call with me and get answers to these questions and others in the time it takes to get ready for work in the morning.Best of all, you're covered by my 100% satisfaction guarantee. If at the end of the call, you don't feel like it was worth it, just say the word, and I'll refund your purchase in full.To book your one-on-one coaching call, go to: https://jonathanstark.com/callI hope to see you there!

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.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

Topics covered in this episode: How to Write a Git Commit Message Caddy Web Server Some new PEPs approved juv Extras Joke Watch on YouTube About the show Sponsored by Posit Connect: pythonbytes.fm/connect Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: How to Write a Git Commit Message Chris Beams 7 rules of a great commit message Separate subject from body with a blank line Limit the subject line to 50 characters Capitalize the subject line Do not end the subject line with a period Use the imperative mood in the subject line Wrap the body at 72 characters Use the body to explain what and why vs. how Article also includes Why a good commit message matters Discussion about each of the 7 rules Cool hat tips to other articles on the subject “Keep in mind: This has all been said before.” Each word is a different link. Michael #2: Caddy Web Server via Fredrik Mellström Like a more modern NGINX Caddy automatically obtains and renews TLS certificates for all your sites. Caddy's native configuration is a JSON document. Even localhost and internal IPs are served with TLS using the intermediate of a fully-automated, self-managed CA that is automatically installed into most local trust stores. Configure multiple Caddy instances with the same storage, and they will automatically coordinate certificate management as a fleet. Production-grade static file server. Brian #3: Some new PEPs approved PEP 770 – Improving measurability of Python packages with Software Bill-of-Materials Accepted for packaging Author: Seth Larson, Sponsor Brett Cannon “This PEP proposes using SBOM documents included in Python packages as a means to improve automated software measurability for Python packages.” PEP 750 – Template Strings Accepted for Python 3.14 Author: Jim Baker, Guido van Rossum, Paul Everitt, Kaudai Aono, Lysandros Nikolaou, Dave Peck “Templates provide developers with access to the string and its interpolated values before they are combined. This brings native flexible string processing to the Python language and enables safety checks, web templating, domain-specific languages, and more.” Michael #4: juv A toolkit for reproducible Jupyter notebooks, powered by uv. Create, manage, and run Jupyter notebooks with their dependencies Pin dependencies with PEP 723 - inline script metadata Launch ephemeral sessions for multiple front ends (e.g., JupyterLab, Notebook, NbClassic) Powered by uv for fast dependency management Use uvx to run jupyterlab with ephemeral virtual environments and tracked dependencies. Extras Brian: Status of Python versions new-ish format Use this all the time. Can't remember if we've covered the new format yet. See also Python endoflife.date Same dates, very visible encouragement to move on to Python 3.13 if you haven't already. Michael: Python 3.13.3 is out. .git-blame-ignore-revs follow up Joke: BGPT (thanks Doug Farrell)

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 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

Topics covered in this episode: Git Town solves the problem that using the Git CLI correctly PEP 751 – A file format to record Python dependencies for installation reproducibility git-who and watchgha Share Python Scripts Like a Pro: uv and PEP 723 for Easy Deployment Extras Joke Watch on YouTube About the show Sponsored by Posit Package Manager: pythonbytes.fm/ppm Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Git Town solves the problem that using the Git CLI correctly Git Town is a reusable implementation of Git workflows for common usage scenarios like contributing to a centralized code repository on platforms like GitHub, GitLab, or Gitea. Think of Git Town as your Bash scripts for Git, but fully engineered with rock-solid support for many use cases, edge cases, and error conditions. Keep using Git the way you do now, but with extra commands to create various branch types, keep them in sync, compress, review, and ship them efficiently. Basic workflow Commands to create, work on, and ship features. git town hack - create a new feature branch git town sync - update the current branch with all ongoing changes git town switch - switch between branches visually git town propose - propose to ship a branch git town ship - deliver a completed feature branch Additional workflow commands Commands to deal with edge cases. git town delete - delete a feature branch git town rename - rename a branch git town repo - view the Git repository in the browser Brian #2: PEP 751 – A file format to record Python dependencies for installation reproducibility Accepted From Brett Cannon “PEP 751 has been accepted! This means Python now has a lock file standard that can act as an export target for tools that can create some sort of lock file. And for some tools the format can act as their primary lock file format as well instead of some proprietary format.” File name: pylock.toml or at least something that starts with pylock and ends with .toml It's exciting to see the start of a standardized lock file Michael #3: git-who and watchgha git-who is a command-line tool for answering that eternal question: Who wrote this code?! Unlike git blame, which can tell you who wrote a line of code, git-who tells you the people responsible for entire components or subsystems in a codebase. You can think of git-who sort of like git blame but for file trees rather than individual files. And watchgha - Live display of current GitHub action runs by Ned Batchelder Brian #4: Share Python Scripts Like a Pro: uv and PEP 723 for Easy Deployment Dave Johnson Nice full tutorial discussing single file Python scripts using uv with external dependencies Starting with a script with dependencies. Using uv add --script [HTML_REMOVED] [HTML_REMOVED] to add a /// script block to the top Using uv run Adding #!/usr/bin/env -S uv run --script shebang Even some Windows advice Extras Brian: April 1 pranks done well BREAKING: Guido van Rossum Returns as Python's BDFL including Brett Cannon noted as “Famous Python Quotationist” Guido taking credit for “I came for the language but I stayed for the community” which was from Brett then Brett's title of “Famous Python Quotationist” is crossed out. Barry Warsaw asking Guido about releasing Python 2.8 Barry is the FLUFL, “Friendly Language Uncle For Life “ Mariatta can't get Guido to respond in chat until she addresses him as “my lord”. “… becoming one with whitespace.” “Indentation is Enlightenment” Upcoming new keyword: maybe Like “if” but more Pythonic as in Maybe: print("Python The Documentary - Coming This Summer!") I'm really hoping there is a documentary April 1 pranks done poorly Note: pytest-repeat works fine with Python 3.14, and never had any problems If you have to explain the joke, maybe it's not funny. The explanation pi, an irrational number, as in it cannot be expressed by a ratio of two integers, starts with 3.14159 and then keeps going, and never repeats. Python 3.14 is in alpha and people could be testing with it for packages Test & Code is doing a series on pytest plugins pytest-repeat is a pytest plugin, and it happened to not have any tests for 3.14 yet. Now the “joke”. I pretended that I had tried pytest-repeat with Python 3.14 and it didn't work. Test & Code: Python 3.14 won't repeat with pytest-repeat Thus, Python 3.14 won't repeat. Also I mentioned that there was no “rational” explanation. And pi is an irrational number. Michael: pysqlscribe v0.5.0 has the “parse create scripts” feature I suggested! Markdown follow up Prettier to format Markdown via Hugo Been using mdformat on some upcoming projects including the almost done Talk Python in Production book. Command I like is mdformat --number --wrap no ./ uv tool install --with is indeed the pipx inject equivalent, but requires multiple --with's: pipx inject mdformat mdformat-gfm mdformat-frontmatter mdformat-footnote mdformat-gfm-alerts uv tool install mdformat --with mdformat-gfm --with mdformat-frontmatter --with mdformat-footnote --with mdformat-gfm-alerts uv follow up From James Falcon As a fellow uv enthusiast, I was still holding out for a use case that uv hasn't solved. However, after last week's episode, you guys finally convinced me to switch over fully, so I figured I'd explain the use case and how I'm working around uv's limitations. I maintain a python library supported across multiple python versions and occasionally need to deal with bugs specific to a python version. Because of that, I have multiple virtualenvs for one project. E.g., mylib38 (for python 3.8), mylib313 (for python 3.13), etc. I don't want a bunch of .venv directories littering my project dir. For this, pyenv was fantastic. You could create the venv with pyenv virtualenv 3.13.2 mylib313, then either activate the venv with pyenv activate mylib313 and create a .python-version file containing mylib313 so I never had to manually activate the env I want to use by default on that project. uv doesn't have a great solution for this use case, but I switched to a workflow that works well enough for me: Define my own central location for venvs. For me that's ~/v Create venvs with something like uv venv --python 3.13 ~/v/mylib313 Add a simple function to my bashrc: `workon() { source ~/v/$1/bin/activate } so now I can run workon mylib313orworkon mylib38when I need to work in a specific environment. uv's.python-version` support works much differently than pyenv's, and that lack of support is my biggest frustration with this approach, but I am willing to live without it. Do you Firefox but not Zen? You can now make pure Firefox more like Zen's / Arc's layout. Joke: So here it will stay See the follow up thread too! Also: Guido as Lord Python via Nick Muoh

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: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

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: 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 Mastodon: mkennedy