Podcasts about software developers

Today, we're talking about building real AI products with foundation models. Not toy demos, not vibes. We'll get into the boring dashboards that save launches, evals that change your mind, and the shift from analyst to AI app builder. Our guide is Hugo Bowne-Anderson, educator, podcaster, and data scientist, who's been in the trenches from scalable Python to LLM apps. If you care about shipping LLM features without burning the house down, stick around. Episode sponsors Posit NordStellar Talk Python Courses Links from the show Hugo Bowne-Anderson: x.com Vanishing Gradients Podcast: vanishinggradients.fireside.fm Fundamentals of Dask: High Performance Data Science Course: training.talkpython.fm Building LLM Applications for Data Scientists and Software Engineers: maven.com marimo: a next-generation Python notebook: marimo.io DevDocs (Offline aggregated docs): devdocs.io Elgato Stream Deck: elgato.com Sentry's Seer: talkpython.fm The End of Programming as We Know It: oreilly.com LorikeetCX AI Concierge: lorikeetcx.ai Text to SQL & AI Query Generator: text2sql.ai Inverse relationship enthusiasm for AI and traditional projects: oreilly.com Watch this episode on YouTube: youtube.com Episode #526 deep-dive: talkpython.fm/526 Episode transcripts: talkpython.fm Theme Song: Developer Rap

In this episode of The New Stack Podcast, hosts Alex Williams and Frederic Lardinois spoke with Keith Ballinger, Vice President and General Manager of Google Cloud Platform Developer Experience (GPC), about the evolution of agentic coding tools and the future of programming. Ballinger, a hands-on executive who still codes, discussed Gemini CLI, Google's response to tools like Claude Code, and his broader philosophy on how developers should work with AI. He emphasized that these tools are in their “first inning” and that developers must “slow down to speed up” by writing clear guides, focusing on architecture, and documenting intent—treating AI as a collaborative coworker rather than a one-shot solution. Ballinger reflected on his early AI experiences, from Copilot at GitHub to modern agentic systems that automate tool use. He also explored the resurgence of the command line as an AI interface and predicted that programming will increasingly shift from writing code to expressing intent. Ultimately, he envisions a future where great programmers are great writers, focusing on clarity, problem decomposition, and design rather than syntax. Learn more from The New Stack about the latest in Google AI development: Why PyTorch Gets All the Love Lightning AI Brings a PyTorch Copilot to Its Development Environment Ray Comes to the PyTorch Foundation Join our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Laravel expert Joel Clermont joined me on Ditching Hourly to share how he and his co-founder run their successful dev subscription business. Chapters(00:00) - Introduction and Guest Introduction (00:16) - Joel's Background and Business Model Transition (01:54) - Launching the Dev Subscription Model (04:47) - Marketing and Initial Success (07:44) - Client Profiles and Demand (11:19) - Managing Client Expectations and Scope (18:58) - Onboarding and Project Management (21:21) - Handling Messy Projects and Infrastructure (25:06) - Client Capacity and Longevity (26:47) - Exploring Client Sizes and Ideal Fits (28:39) - Balancing Workload and Client Expectations (32:06) - Ensuring Client Satisfaction (34:47) - Managing Work and Time Effectively (43:11) - Challenges and Downsides of Subscription Model (47:54) - Marketing Strategies for Developers (52:52) - Conclusion and Resources Joel's LinksJoel's website » https://nocompromises.io/Joel's books » https://masteringlaravel.io/booksJoel's courses » https://masteringlaravel.io/coursesJoel's community » https://masteringlaravel.io/community ----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!

Building a UI in Python usually means choosing between "quick and limited" or "powerful and painful." What if you could write modern, component-based web apps in pure Python and still keep full control? NiceGUI, pronounced "Nice Guy" sits on FastAPI with a Vue/Quasar front end, gives you real components, live updates over websockets, and it's running in production at Zauberzeug, a German robotic company. On this episode, I'm talking with NiceGUI's creators, Rodja Trappe and Falko Schindler, about how it works, where it shines, and what's coming next. With version 3.0 releasing around the same time this episode comes out, we spend the end of the episode celebrating the 3.0 release. Episode sponsors Posit Agntcy Talk Python Courses Links from the show Rodja Trappe: github.com Falko Schindler: github.com NiceGUI 3.0.0 release: github.com Full LLM/Agentic AI docs instructions for NiceGUI: github.com Zauberzeug: zauberzeug.com NiceGUI: nicegui.io NiceGUI GitHub Repository: github.com NiceGUI Authentication Examples: github.com NiceGUI v3.0.0rc1 Release: github.com Valkey: valkey.io Caddy Web Server: caddyserver.com JustPy: justpy.io Tailwind CSS: tailwindcss.com Quasar ECharts v5 Demo: quasar-echarts-v5.netlify.app AG Grid: ag-grid.com Quasar Framework: quasar.dev NiceGUI Interactive Image Documentation: nicegui.io NiceGUI 3D Scene Documentation: nicegui.io Watch this episode on YouTube: youtube.com Episode #525 deep-dive: talkpython.fm/525 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Topics covered in this episode: Cyclopts: A CLI library * The future of Python web services looks GIL-free* * Free-threaded GC* * Polite lazy imports for Python package maintainers* 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: Cyclopts: A CLI library A CLI library that fixes 13 annoying issues in Typer Much of Cyclopts was inspired by the excellent Typer library. Despite its popularity, Typer has some traits that I (and others) find less than ideal. Part of this stems from Typer's age, with its first release in late 2019, soon after Python 3.8's release. Because of this, most of its API was initially designed around assigning proxy default values to function parameters. This made the decorated command functions difficult to use outside of Typer. With the introduction of Annotated in python3.9, type-hints were able to be directly annotated, allowing for the removal of these proxy defaults. The 13: Argument vs Option Positional or Keyword Arguments Choices Default Command Docstring Parsing Decorator Parentheses Optional Lists Keyword Multiple Values Flag Negation Help Defaults Validation Union/Optional Support Adding a Version Flag Documentation Brian #2: The future of Python web services looks GIL-free Giovanni Barillari “Python 3.14 was released at the beginning of the month. This release was particularly interesting to me because of the improvements on the "free-threaded" variant of the interpreter. Specifically, the two major changes when compared to the free-threaded variant of Python 3.13 are: Free-threaded support now reached phase II, meaning it's no longer considered experimental The implementation is now completed, meaning that the workarounds introduced in Python 3.13 to make code sound without the GIL are now gone, and the free-threaded implementation now uses the adaptive interpreter as the GIL enabled variant. These facts, plus additional optimizations make the performance penalty now way better, moving from a 35% penalty to a 5-10% difference.” Lots of benchmark data, both ASGI and WSGI Lots of great thoughts in the “Final Thoughts” section, including “On asynchronous protocols like ASGI, despite the fact the concurrency model doesn't change that much – we shift from one event loop per process, to one event loop per thread – just the fact we no longer need to scale memory allocations just to use more CPU is a massive improvement. ” “… for everybody out there coding a web application in Python: simplifying the concurrency paradigms and the deployment process of such applications is a good thing.” “… to me the future of Python web services looks GIL-free.” Michael #3: Free-threaded GC The free-threaded build of Python uses a different garbage collector implementation than the default GIL-enabled build. The Default GC: In the standard CPython build, every object that supports garbage collection (like lists or dictionaries) is part of a per-interpreter, doubly-linked list. The list pointers are contained in a PyGC_Head structure. The Free-Threaded GC: Takes a different approach. It scraps the PyGC_Head structure and the linked list entirely. Instead, it allocates these objects from a special memory heap managed by the "mimalloc" library. This allows the GC to find and iterate over all collectible objects using mimalloc's data structures, without needing to link them together manually. The free-threaded GC does NOT support "generations” By marking all objects reachable from these known roots, we can identify a large set of objects that are definitely alive and exclude them from the more expensive cycle-finding part of the GC process. Overall speedup of the free-threaded GC collection is between 2 and 12 times faster than the 3.13 version. Brian #4: Polite lazy imports for Python package maintainers Will McGugan commented on a LI post by Bob Belderbos regarding lazy importing “I'm excited about this PEP. I wrote a lazy loading mechanism for Textual's widgets. Without it, the entire widget library would be imported even if you needed just one widget. Having this as a core language feature would make me very happy.” https://github.com/Textualize/textual/blob/main/src/textual/widgets/__init__.py Well, I was excited about Will's example for how to, essentially, allow users of your package to import only the part they need, when they need it. So I wrote up my thoughts and an explainer for how this works. Special thanks to Trey Hunner's Every dunder method in Python, which I referenced to understand the difference between __getattr__() and __getattribute__(). Extras Brian: Started writing a book on Test Driven Development. Should have an announcement in a week or so. I want to give folks access while I'm writing it, so I'll be opening it up for early access as soon as I have 2-3 chapters ready to review. Sign up for the pythontest newsletter if you'd like to be informed right away when it's ready. Or stay tuned here. Michael: New course!!! Agentic AI Programming for Python I'll be on Vanishing Gradients as a guest talking book + ai for data scientists OpenAI launches ChatGPT Atlas https://github.com/jamesabel/ismain by James Abel Pets in PyCharm Joke: You're absolutely right

Our U.S. Software Analyst Sanjit Singh explains how AI is reshaping software development and why the future for the sector may be brighter – and busier – than ever.Read more insights from Morgan Stanley.----- Transcript -----Welcome to Thoughts on the Market. I'm Sanjit Singh, the U.S. Software Analyst at Morgan Stanley.Today: how AI is transforming software and what that means for developers.It's Friday, October 24th, at 10am in New York.There's been a lot of news stories and anecdotal accounts about AI taking over jobs, especially in the software industry. You may have heard of vibe coding, where people can use natural language prompts, guiding AI to build software applications. So yes, AI is creating a world where software writes itself. But at the same time, the demand for human creativity only grows.The introduction of AI coding assistants has dramatically expanded what software can do, fueling a surge in both the volume of code and the complexity of projects. But instead of shrinking the developer workforce, AI is actually supporting continued growth in developer headcount, even as productivity soars.We're estimating the software development market will grow at a 20 percent compound annual growth rate, reaching $61 billion by 2029. And that's up from $24 billion in 2024. And in terms of the developer population, [research] firms like IDC expect it to jump from 30 million paid developers in 2024 to 50 million by 2029 – that's a 10 percent annual growth rate. Even the most conservative estimates, like those from the U.S. Bureau of Labor Statistics, see developer jobs growing roughly 2 percent per year through 2033, outpacing overall employment growth.So, what does this mean for people behind the code? AI isn't replacing developers. It's redefining them. Routine tasks are increasingly handled by AI agents, and this frees up developers to become curators, reviewers, architects, and most important problem-solvers.The upshot? Companies may need fewer developers for repetitive work, but the overall demand for skilled engineers remains robust. As AI lowers the barrier to entry, the pool of people who can build software applications expands dramatically. But at the same time, the complexity and ambitions of projects rise, keeping experienced developers in high demand.No doubt, AI coding tools are delivering real productivity gains. Some teams are reporting nearly doubling their code capacity and cutting pull request times in half after adopting AI assistants. Test coverage has increased sharply, resulting in 20 percent fewer production incidents for some organizations. But there is a catch with all this AI-generated code. It's creating significant new bottlenecks downstream.An example of this is code review, which is becoming a major pain point. Many organizations are experiencing pull request fatigue, with developers rubber-stamping changes just to keep up. Some teams now require three reviewers for AI-generated change, compared to just one before. And in terms of automated testing, systems are getting overwhelmed because every change made with AI sets off a complete round of test.Now we estimate productivity gains from AI in software engineering at about 15–20 percent. But in complex projects, the gains are much lower, as the volume of new code often means more bugs and more rework – and hence more human developers.So where do we go from here? In our view, the future isn't about fully autonomous software development. Instead, large enterprises are likely to favor an integrated approach, where AI agents and human developers work side by side. AI will automate more of the software development lifecycle. And that not only includes coding – which, coding typically accounts for 10-20 percent of the software development effort – but other areas like testing, security, and deployment. But humans will remain in the loop for oversight, design, and decision-making. And as software gets cheaper and faster to build, organizations won't just do the same work with fewer people – they likely will do more.In short, the need for skilled developers isn't going away. But it's definitely evolving. And in the age of AI, it's not about man versus machine. It's about man with machine. And so with more software, we see more developers.Thanks for listening. If you enjoy the show, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.

This interview was recorded at GOTO Copenhagen 2024.https://gotocph.comEvan Czaplicki - Creator and developer of ElmKris Jenkins - Developer Advocate, Software Developer, Podcast Host, Conference Speaker & GeekRESOURCESEvanhttps://twitter.com/evanczhttps://github.com/evanczKrishttps://twitter.com/krisajenkinshttps://www.linkedin.com/in/krisjenkinshttps://github.com/krisajenkinshttp://blog.jenkster.comRead the full abstract here:https://gotocph.com/2024/sessions/3528RECOMMENDED BOOKSRichard Feldman • Elm in Action • https://amzn.to/387kujIJeremy Fairbank • Programming Elm • https://amzn.to/2WhZCE8Wolfgang Loder • Web Applications with Elm • https://amzn.to/3jblQ3qCristian Salcescu • Functional Programming in JavaScript • https://amzn.to/3y75jBSInspiring Tech Leaders - The Technology PodcastInterviews with Tech Leaders and insights on the latest emerging technology trends.Listen on: Apple Podcasts SpotifyBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

Digital marketing strategist Eleanor Mayrhofer joined me on Ditching Hourly to describe exactly how she productized her web design services. Links:Eleanor's website » https://www.eleanormayrhofer.com/ditchingEleanor's LinkedIn » https://www.linkedin.com/in/eleanormayrhofer/Chapters:(00:00) - Introduction and Guest Welcome (00:14) - Eleanor's Background and Business Model (00:52) - Straight to Non-Hourly (02:01) - Starting a Solo Business During COVID (02:44) - Initial Market Approach and Challenges (03:48) - Developing a Productized Service (04:25) - Current Business Model: Website in a Week (05:31) - Client Interaction and Project Scope (09:40) - Copywriting and Strategy Sessions (16:31) - Handling Project Scope and Client Expectations (21:24) - Marketing and Client Acquisition (23:20) - Client Commissions and Referrals (23:40) - Subscription Maintenance Services (24:53) - Positioning and Target Audience (25:53) - Overcoming Launch Procrastination (27:11) - Client Collaboration and Revisions (28:55) - Technical Setup and DNS Challenges (31:25) - Post-Launch Support and Testimonials (33:13) - Pros and Cons of Productized Services (36:55) - Sales Process and Lead Time (38:55) - Long-Term Plans and Project Juggling (41:01) - Avoiding Boredom with Productized Services (42:20) - Conclusion and Contact Information ----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!

Python in 2025 is different. Threads really are about to run in parallel, installs finish before your coffee cools, and containers are the default. In this episode, we count down 38 things to learn this year: free-threaded CPython, uv for packaging, Docker and Compose, Kubernetes with Tilt, DuckDB and Arrow, PyScript at the edge, plus MCP for sane AI workflows. Expect practical wins and migration paths. No buzzword bingo, just what pays off in real apps. Join me along with Peter Wang and Calvin Hendrix-Parker for a fun, fast-moving conversation. Episode sponsors Seer: AI Debugging, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Calvin Hendryx-Parker: github.com/calvinhp Peter on BSky: @wang.social Free-Threaded Wheels: hugovk.github.io Tilt: tilt.dev The Five Demons of Python Packaging That Fuel Our ...: youtube.com Talos Linux: talos.dev Docker: Accelerated Container Application Development: docker.com Scaf - Six Feet Up: sixfeetup.com BeeWare: beeware.org PyScript: pyscript.net Cursor: The best way to code with AI: cursor.com Cline - AI Coding, Open Source and Uncompromised: cline.bot Watch this episode on YouTube: youtube.com Episode #524 deep-dive: talkpython.fm/524 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Topics covered in this episode: * djrest2 -* A small and simple REST library for Django based on class-based views. Github CLI caniscrape - Know before you scrape. Analyze any website's anti-bot protections in seconds. *

Topics covered in this episode: * PyPI+* * uv-ship - a CLI-tool for shipping with uv* * How fast is 3.14?* * air - a new web framework built with FastAPI, Starlette, and Pydantic.* 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: PyPI+ Very nice search and exploration tool for PyPI Minor but annoying bug: content-types ≠ content_types on PyPI+ but they are in Python itself. Minimum Python version seems to be interpreted as max Python version. See dependency graphs and more Examples content-types jinja-partials fastapi-chameleon Brian #2: uv-ship - a CLI-tool for shipping with uv “uv-ship is a lightweight companion to uv that removes the risky parts of cutting a release. It verifies the repo state, bumps your project metadata and optionally refreshes the changelog. It then commits, tags & pushes the result, while giving you the chance to review every step.” Michael #3: How fast is 3.14? by Miguel Grinberg A big focus on threaded vs. non-threaded Python Some times its faster, other times, it's slower Brian #4: air - a new web framework built with FastAPI, Starlette, and Pydantic. An very new project in Alpha stage by Daniel & Audrey Felderoy, the “Two Scoops of Django” people. Air Tags are an interesting thing. Also Why? is amazing “Don't use AIR” “Every release could break your code! If you have to ask why you should use it, it's probably not for you.” “If you want to use Air, you can. But we don't recommend it.” “It'll likely infect you, your family, and your codebase with an evil web framework mind virus, , …” Extras Brian: Python 3.15a1 is available uv python install 3.15 already works Python lazy imports you can use today - one of two blog posts I threatened to write recently Testing against Python 3.14 - the other one Free Threading has some trove classifiers Michael: Blog post about the book: Talk Python in Production book is out! In particular, the extras are interesting. AI Usage TUI Show me your ls Helium Browser is interesting. But also has Python as a big role. GitHub says Languages Python 97.4%

Founder of The Upside, Erin Halper, joined me on Ditching Hourly to share her pro tips on creating and sustaining a premium online community. Erin's Links:Erin's community » https://betheupside.com/Erin's LinkedIn » https://www.linkedin.com/in/erinhalper/Chapters(00:00) - Introduction and Guest Welcome (00:19) - Erin Halper's Background and The Upside Community (03:19) - Challenges and Evolution of The Upside (07:24) - Starting and Running a Community (09:18) - Best Practices for Community Management (24:08) - Pricing Strategies for Independent Consultants (30:19) - Navigating Agency Subcontracting (30:52) - Building and Scaling Your Business (31:42) - Lifestyle and Impact in Consulting (33:00) - Celebrating Wins and Community Support (34:26) - Visibility and Positioning (36:03) - Pricing Strategies and Market Shifts (37:47) - Maintaining Boundaries in Community (40:30) - Application Process and Membership Cap (43:40) - Quarterly Open House Strategy (47:18) - Onboarding and Member Matching (55:26) - Concluding Thoughts and Contact Information ----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!

Python typing got fast enough to feel invisible. Pyrefly is a new, open source type checker and IDE language server from Meta, written in Rust, with a focus on instant feedback and real-world DX. Today, we will dig into what it is, why it exists, and how it plays with the rest of the typing ecosystem. We have Abby Mitchell, Danny Yang, and Kyle Into from Pyrefly here to dive into the project. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Abby Mitchell: linkedin.com Danny Yang: linkedin.com Kyle Into: linkedin.com Pyrefly: pyrefly.org Pyrefly Documentation: pyrefly.org Pyrefly Installation Guide: pyrefly.org Pyrefly IDE Guide: pyrefly.org Pyrefly GitHub Repository: github.com Pyrefly VS Code Extension: marketplace.visualstudio.com Introducing Pyrefly: A New Type Checker and IDE Experience for Python: engineering.fb.com Pyrefly on PyPI: pypi.org InfoQ Coverage: Meta Pyrefly Python Typechecker: infoq.com Pyrefly Discord Invite: discord.gg Python Typing Conformance (GitHub): github.com Typing Conformance Leaderboard (HTML Preview): htmlpreview.github.io Watch this episode on YouTube: youtube.com Episode #523 deep-dive: talkpython.fm/523 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Neste episódio sobre Design com Neurociência, conversamos com o Alex Soares para entender como os princípios da neurociência se aplicam de fato na prática do design. Um dos aspectos mais fascinantes dessa área é a capacidade de revelar como o cérebro humano processa informações visuais, influenciando decisões, percepções e experiências do usuário. Trouxemos exemplos práticos, insights valiosos e dicas que podem ajudar designers e desenvolvedores a criar produtos mais intuitivos e eficientes.

In this episode of The Engineering Room, Dave Farley speaks with Sam Newman, renowned author of "Building Microservices" and "Monolith to Microservices," about distributed systems, architectural decisions, and the future of software development.-------------------------Sam Newman on "X" (formerly "Twitter"): https://x.com/samnewman?lang=en

Harness co-founder Jyoti Bansal highlights a growing issue in software development: while AI tools help generate more code, they often create bottlenecks further along the pipeline, especially in testing, deployment, and compliance. Since its 2017 launch, Harness has aimed to streamline these stages using AI and machine learning. With the rise of large language models (LLMs), the company shifted toward agentic AI, introducing a library of specialized agents—like DevOps, SRE, AppSec, and FinOps agents—that operate behind a unified interface called Harness AI. These agents assist in building production pipelines, not deploying code directly, ensuring human oversight remains critical for compliance and security.Bansal emphasizes that AI in development isn't replacing people but accelerating workflows to meet tighter timelines. He also notes strong enterprise adoption, with even large, traditionally slower-moving organizations embracing AI integration. On the topic of an AI bubble, Bansal sees it as a natural part of innovation, akin to the Dotcom era, where market excitement can still lead to meaningful long-term transformation despite short-term volatility. Learn more from The New Stack about the latest in Harness' AI approach to software development: Harness AI Tackles Software Development's Real Bottleneck  Harnessing AI To Elevate Automated Software Testing Join our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Topics covered in this episode: * Python 3.14* * Free-threaded Python Library Compatibility Checker* * Claude Sonnet 4.5* * Python 3.15 will get Explicit lazy imports* Extras Joke Watch on YouTube About the show Sponsored by DigitalOcean: 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: Python 3.14 Released on Oct 7 What's new in Python 3.14 Just a few of the changes PEP 750: Template string literals PEP 758: Allow except and except* expressions without brackets Improved error messages Default interactive shell now highlights Python syntax supports auto-completion argparse better support for python -m module has a new suggest_on_error parameter for “maybe you meant …” support python -m calendar now highlights today's date Plus so much more Michael #2: Free-threaded Python Library Compatibility Checker by Donghee Na App checks compatibility of top PyPI libraries with CPython 3.13t and 3.14t, helping developers understand how the Python ecosystem adapts to upcoming Python versions. It's still pretty red, let's get in the game everyone! Michael #3: Claude Sonnet 4.5 Top programming model (even above Opus 4.1) Shows large improvements in reducing concerning behaviors like sycophancy, deception, power-seeking, and the tendency to encourage delusional thinking Anthropic is releasing the Claude Agent SDK, the same infrastructure that powers Claude Code, making it available for developers to build their own agents, along with major upgrades including checkpoints, a VS Code extension, and new context editing features And Claude Sonnet 4.5 is available in PyCharm too. Brian #4: Python 3.15 will get Explicit lazy imports Discussion on discuss.python.org This PEP introduces syntax for lazy imports as an explicit language feature: lazy import json lazy from json import dumps BTW, lazy loading in fixtures is a super easy way to speed up test startup times. Extras Brian: Music video made in Python - from Patrick of the band “Friends in Real Life” source code: https://gitlab.com/low-capacity-music/r9-legends/ Michael: New article: Thanks AI Lots of updates for content-types Dramatically improved search on Python Bytes (example: https://pythonbytes.fm/search?q=wheel use the filter toggle to see top hits) Talk Python in Production is out and for sale Joke: You do estimates?

Communications consultant Lynn Safranek joined me on Ditching Hourly to learn how to apply value pricing to an industry where the consultant can't control the outcome.(00:00) - Introduction and Guest Welcome (00:11) - Understanding Upstream Contributions (00:23) - Lynn's Background and Expertise (01:05) - The Value Pricing Dilemma (01:21) - Client Communication Challenges (02:45) - Media Engagement Strategies (05:01) - Defining Client Needs and Goals (08:08) - Crafting Effective Messaging (14:32) - Measuring Success and Impact (20:42) - Leveraging Media Coverage for Nonprofits (21:26) - Budget Autonomy of Communications Directors (22:41) - Crafting Compelling Stories for Donations (24:33) - Exploring Budget Scenarios for Media Hits (26:03) - Creative Strategies for Media Attention (26:58) - Evaluating the Impact of Media Hits (29:21) - Developing a Comprehensive Media Strategy (31:35) - Reverse Engineering Media Success (38:40) - Thinking Beyond Traditional Roles (42:35) - Conclusion and Final Thoughts Lynn's Links:Lynn on LinkedIn » https://www.linkedin.com/in/lynnsafranek/ ----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!

Today we're turning tiny tips into big wins. Khuyen Tran, creator of CodeCut.ai, has shipped hundreds of bite-size Python and data science snippets across four years. We dig into open-source tools you can use right now, cleaner workflows, and why notebooks and scripts don't have to be enemies. If you want faster insights with fewer yak-shaves, this one's packed with takeaways you can apply before lunch. Let's get into it. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Khuyen Tran (LinkedIn): linkedin.com Khuyen Tran (GitHub): github.com CodeCut: codecut.ai Production-ready Data Science Book (discount code TalkPython): codecut.ai Why UV Might Be All You Need: codecut.ai How to Structure a Data Science Project for Readability and Transparency: codecut.ai Stop Hard-coding: Use Configuration Files Instead: codecut.ai Simplify Your Python Logging with Loguru: codecut.ai Git for Data Scientists: Learn Git Through Practical Examples: codecut.ai Marimo (A Modern Notebook for Reproducible Data Science): codecut.ai Text Similarity & Fuzzy Matching Guide: codecut.ai Loguru (Python logging made simple): github.com Hydra: hydra.cc Marimo: marimo.io Quarto: quarto.org Show Your Work! Book: austinkleon.com Watch this episode on YouTube: youtube.com Episode #522 deep-dive: talkpython.fm/522 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Jamie McLennan, Will Crabtree, and Deborah Corn discuss Will's printing and software development journey and the relaunch of Printing In A Box. They explore how the platform streamlines web-to-print, e-commerce, and workflow management, making it easier for shops to grow without costly customizations. Mentioned in This Episode: Printing In A Box: https://printinginabox.com/ Printing In A Box Storefront Demo: https://store.hublicense.com/ Printing In A Box login: https://admin.hublicense.com/account/login.html Print FM Radio: https://printfmradio.com/ International Print Day: https://internationalprintday.org/ Jamie McLennan: https://www.linkedin.com/in/jamieprints DMR Graphics: www.dmr-graphics.com/ Innvoke: https://innvoke.com/  Will Crabtree: https://www.linkedin.com/in/willtheprinter/ Tampa Media: https://tampa.media/ Gorilla Consultants: https://gorillagurus.com Sticker Gorilla: https://store.stickergorilla.com/ Deborah Corn: https://www.linkedin.com/in/deborahcorn/ Print Media Centr: https://printmediacentr.com Subscribe to News From The Printerverse: https://printmediacentr.com/subscribe-2 Girls Who Print: https://girlswhoprint.org Project Peacock: https://ProjectPeacock.TV

English is now an API. Our apps read untrusted text; they follow instructions hidden in plain sight, and sometimes they turn that text into action. If you connect a model to tools or let it read documents from the wild, you have created a brand new attack surface. In this episode, we will make that concrete. We will talk about the attacks teams are seeing in 2025, the defenses that actually work, and how to test those defenses the same way we test code. Our guides are Tori Westerhoff and Roman Lutz from Microsoft. They help lead AI red teaming and build PyRIT, a Python framework the Microsoft AI Red Team uses to pressure test real products. By the end of this hour you will know where the biggest risks live, what you can ship this quarter to reduce them, and how PyRIT can turn security from a one time audit into an everyday engineering practice. Episode sponsors Sentry AI Monitoring, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Tori Westerhoff: linkedin.com Roman Lutz: linkedin.com PyRIT: aka.ms/pyrit Microsoft AI Red Team page: learn.microsoft.com 2025 Top 10 Risk & Mitigations for LLMs and Gen AI Apps: genai.owasp.org AI Red Teaming Agent: learn.microsoft.com 3 takeaways from red teaming 100 generative AI products: microsoft.com MIT report: 95% of generative AI pilots at companies are failing: fortune.com A couple of "Little Bobby AI" cartoons Give me candy: talkpython.fm Tell me a joke: talkpython.fm Watch this episode on YouTube: youtube.com Episode #521 deep-dive: talkpython.fm/521 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- 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: * PostgreSQL 18 Released* * Testing is better than DSA (Data Structures and Algorithms)* * Pyrefly in Cursor/PyCharm/VSCode/etc* * Playwright & pytest techniques that bring me joy* 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: PostgreSQL 18 Released PostgreSQL 18 is out (Sep 25, 2025) with a focus on faster text handling, async I/O, and easier upgrades. New async I/O subsystem speeds sequential scans, bitmap heap scans, and vacuum by issuing concurrent reads instead of blocking on each request. Major-version upgrades are smoother: pg_upgrade retains planner stats, adds parallel checks via -jobs, and supports faster cutovers with -swap. Smarter query performance lands with skip scans on multicolumn B-tree indexes, better OR optimization, incremental-sort merge joins, and parallel GIN index builds. Dev quality-of-life: virtual generated columns enabled by default, a uuidv7() generator for time-ordered IDs, and RETURNING can expose both OLD and NEW. Security gets an upgrade with native OAuth 2.0 authentication; MD5 password auth is deprecated and TLS controls expand. Text operations get a boost via the new PG_UNICODE_FAST collation, faster upper/lower, a casefold() helper, and clearer collation behavior for LIKE/FTS. Brian #2: Testing is better than DSA (Data Structures and Algorithms) Ned Batchelder If you need to grind through DSA problems to get your first job, then of course, do that, but if you want to prepare yourself for a career, and also stand out in job interviews, learn how to write tests. Testing is a skill you'll use constantly, will make you stand out in job interviews, and isn't taught well in school (usually). Testing code well is not obvious. It's a puzzle and a problem to solve. It gives you confidence and helps you write better code. Applies everywhere, at all levels. Notes from Brian Most devs suck at testing, so being good at it helps you stand out very quickly. Thinking about a system and how to test it often very quickly shines a spotlight on problem areas, parts with not enough specification, and fuzzy requirements. This is a good thing, and bringing up these topics helps you to become a super valuable team member. High level tests need to be understood by key engineers on a project. Even if tons of the code is AI generated. Even if many of the tests are, the people understanding the requirements and the high level tests are quite valuable. Michael #3: Pyrefly in Cursor/PyCharm/VSCode/etc Install the VSCode/Cursor extension or PyCharm plugin, see https://pyrefly.org/en/docs/IDE/ Brian spoke about Pyrefly in #433: Dev in the Arena I've subsequently had the team on Talk Python: #523: Pyrefly: Fast, IDE-friendly typing for Python (podcast version coming in a few weeks, see video for now.) My experience has been Pyrefly changes the feel of the editor, give it a try. But disable the regular language server extension. Brian #4: Playwright & pytest techniques that bring me joy Tim Shilling “I've been working with playwright more often to do end to end tests. As a project grows to do more with HTMX and Alpine in the markup, there's less unit and integration test coverage and a greater need for end to end tests.” Tim covers some cool E2E techniques Open new pages / tabs to be tested Using a pytest marker to identify playwright tests Using a pytest marker in place of fixtures Using page.pause() and Playwright's debugging tool Using assert_axe_violations to prevent accessibility regressions Using page.expect_response() to confirm a background request occurred From Brian Again, with more and more lower level code being generated, and many unit tests being generated (shakes head in sadness), there's an increased need for high level tests. Don't forget API tests, obviously, but if there's a web interface, it's gotta be tested. Especially if the primary user experience is the web interface, building your Playwright testing chops helps you stand out and let's you test a whole lot of your system with not very many tests. Extras Brian: Big O - By Sam Who Yes, take Ned's advice and don't focus so much on DSA, focus also on learning to test. However, one topic you should be comfortable with in algortithm-land is Big O, at least enough to have a gut feel for it. And this article is really good enough for most people. Great graphics, demos, visuals. As usual, great content from Sam Who, and a must read for all serious devs. Python 3.14.0rc3 has been available since Sept 18. Python 3.14.0 final scheduled for Oct 7 Django 6.0 alpha 1 released Django 6.0 final scheduled for Dec 3 Python Test Static hosting update Some interesting discussions around setting up my own server, but this seems like it might be yak shaving procrastination research when I really should be writing or coding. So I'm holding off until I get some writing projects and a couple SaaS projects further along. Joke: Always be backing up

After 11 years helping hundreds of career changers switch into software development, I've found the hardest part isn't teaching technical skills but rewiring brains from misleading online advice that hurts new developers. Much of this advice is well-intentioned, but some is designed purely for clicks and engagement.• "Don't chase titles!"• "Don't build CRUD apps!"• "Coding bootcamps are a scam!"Take all advice (including mine) with a grain of salt. Your path is unique, and a personalized approach to career development is more effective than generic advice. Question everything, take what makes sense, and leave what doesn't.Send us a textShameless Plugs

David Cramer, founder and chief product officer of Sentry, remains skeptical about generative AI's current ability to replace human engineers, particularly in software production. While he acknowledges AI tools aren't yet reliable enough for full autonomy—especially in tasks like patch generation—he sees value in using large language models (LLMs) to enhance productivity. Sentry's AI-powered tool, Seer, uses GenAI to help developers debug more efficiently by identifying root causes and summarizing complex system data, mimicking some functions of senior engineers. However, Cramer emphasizes that human oversight remains essential, describing the current stage as "human in the loop" AI, useful for speeding up code reviews and identifying overlooked bugs.Cramer also addressed Sentry's shift from open source to fair source licensing due to frustration over third parties commercializing their software without contributing back. Sentry now uses Functional Source Licensing, which becomes Apache 2.0 after two years. This move aims to strike a balance between openness and preventing exploitation, while maintaining accessibility for users and avoiding fragmented product versions.Learn more from The New Stack about the latest in Sentry and David Cramer thoughts on AI development:  Install Sentry to Monitor Live ApplicationsFrontend Development Challenges for 2021Join our community of newsletter subscribers to stay on top of the news and at the top of your game.   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

A couple years ago, Charlie Marsh lit a fire under Python tooling with Ruff and then uv. Today he's back with something on the other side of that coin: pyx. Pyx isn't a PyPI replacement. Think server, not just index. It mirrors PyPI, plays fine with pip or uv, and aims to make installs fast and predictable by letting a smart client talk to a smart server. When the client and server understand each other, you get new fast paths, fewer edge cases, and the kind of reliability teams beg for. If Python packaging has felt like friction, this conversation is traction. Let's get into it. Episode sponsors Six Feet Up Talk Python Courses Links from the show Charlie Marsh on Twitter: @charliermarsh Charlie Marsh on Mastodon: @charliermarsh Astral Homepage: astral.sh Pyx Project: astral.sh Introducing Pyx Blog Post: astral.sh uv Package on GitHub: github.com UV Star History Chart: star-history.com Watch this episode on YouTube: youtube.com Episode #520 deep-dive: talkpython.fm/520 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- 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: * pandas is getting pd.col expressions* * Cline, At-Cost Agentic IDE Tooling* * uv cheatsheet* Ducky Network UI 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. Brian #1: pandas is getting pd.col expressions Marco Gorelli Next release of Pandas will have pd.col(), inspired by some of the other frameworks I'm guessing Pandas 2.3.3? or 2.4.0? or 3.0.0? (depending on which version they bump?) “The output of pd.col is called an expression. You can think of it as a delayed column - it only produces a result once it's evaluated inside a dataframe context.” It replaces many contexts where lambda expressions were used Michael #2: Cline, At-Cost Agentic IDE Tooling Free and open-source Probably supports your IDE (if your IDE isn't a terminal) VS Code VS Code Insiders Cursor Windsurf JetBrains IDEs (including PyCharm) You pick plan or act (very important) It shows you the price as the AI works, per request, right in the UI Brian #3: uv cheatsheet Rodgrigo at mathspp.com Nice compact cheat sheet of commands for Creating projects Managing dependencies Lifecycle stuff like build, publish, bumping version uv tool (uvx) commands working with scripts Installing and updating Python versions plus venv, pip, format, help and update Michael #4: Ducky Network UI Ducky is a powerful, open-source, all-in-one desktop application built with Python and PySide6. It is designed to be the perfect companion for network engineers, students, and tech enthusiasts, combining several essential utilities into a single, intuitive graphical interface. Features Multi-Protocol Terminal: Connect via SSH, Telnet, and Serial (COM) in a modern, tabbed interface. SNMP Topology Mapper: Automatically discover your network with a ping and SNMP sweep. See a graphical map of your devices, color-coded by type, and click to view detailed information. Network Diagnostics: A full suite of tools including a Subnet Calculator, Network Monitor (Ping, Traceroute), and a multi-threaded Port Scanner. Security Toolkit: Look up CVEs from the NIST database, check password strength, and calculate file hashes (MD5, SHA1, SHA256, SHA512). Rich-Text Notepad: Keep notes and reminders in a dockable widget with formatting tools and auto-save. Customizable UI: Switch between a sleek dark theme and a clean light theme. Customize terminal colors and fonts to your liking. Extras Brian: Where are the cool kids hosting static sites these days? Moving from Netlify to Cloudflare Pages - Will Vincent from Feb 2024 Traffic is a concern now for even low-ish traffic sites since so many bots are out there Netlify free plan is less than 30 GB/mo allowed (grandfathered plans are 100 GB/mo) GH Pages have a soft limit of 100 GB/mo Cloudflare pages says unlimited Michael: PyCon Brazil needs some help with reduced funding from the PSF Get a ticket to donate for a student to attend (at the button of the buy ticket checkout dialog) I upgraded to macOS Tahoe Loving it so far. Only issue I've seen so far has been with alt-tab for macOS Joke: Hiring in 2025 vs 2021 2021: “Do you have an in-house kombucha sommelier?” “Let's talk about pets, are you donkey-friendly?”, “Oh you think this is a joke?” 2025: “Round 8/7” “Out of 12,000 resumes, the AI picked yours” “Binary tree? Build me a foundational model!” “Healthcare? What, you want to live forever?”

Today on Talk Python: What really happens when your data work outgrows your laptop. Matthew Rocklin, creator of Dask and cofounder of Coiled, and Nat Tabris a staff software engineer at Coiled join me to unpack the messy truth of cloud-scale Python. During the episode we actually spin up a 1,000 core cluster from a notebook, twice! We also discuss picking between pandas and Polars, when GPUs help, and how to avoid surprise bills. Real lessons, real tradeoffs, shared by people who have built this stuff. Stick around. Episode sponsors Seer: AI Debugging, Code TALKPYTHON Talk Python Courses Links from the show Matthew Rocklin: @mrocklin Nat Tabris: tabris.us Dask: dask.org Coiled: coiled.io Watch this episode on YouTube: youtube.com Episode #519 deep-dive: talkpython.fm/519 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- 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

Brand strategist and founder of SwayRise Creative, Diane Whiddon, joined me on Ditching Hourly to talk about how adding a discrete - and legitimately useful - AI-based productized service to her branding business tapped into a longstanding demand in her client base. Diane's LinksSwayRise Creative » https://swayrisecreative.com/Content Creator Lab » https://aicontentcreatorlab.comChapters(00:00) - Introduction and Guest Welcome (00:34) - Diane's Background and Career Journey (01:44) - Discovering Value Pricing and Meeting Jonathan (03:44) - The Impactful Discovery Call (07:18) - Focusing and Scaling the Business (15:08) - Incorporating AI into Business (19:16) - AI Photo Shoots and Branding (33:33) - Ethical Considerations and Client Expectations (34:57) - Technical Aspects of AI Image Creation (37:02) - Using Image References and Editing Tools (37:56) - Cost and Effort of Traditional Photo Shoots (39:15) - AI Photo Shoots vs Traditional Photo Shoots (41:36) - The Demand for AI in Business (42:54) - Challenges and Opportunities with AI (47:33) - The Importance of Adapting to AI (51:53) - Leveraging AI for Business Success (54:13) - The Future of AI in Various Industries (01:10:30) - Final Thoughts and Resources ----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: * Mozilla's Lifeline is Safe After Judge's Google Antitrust Ruling* * troml - suggests or fills in trove classifiers for your projects* * pqrs: Command line tool for inspecting Parquet files* * Testing for Python 3.14* 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: Mozilla's Lifeline is Safe After Judge's Google Antitrust Ruling A judge lets Google keep paying Mozilla to make Google the default search engine but only if those deals aren't exclusive. More than 85% of Mozilla's revenue comes from Google search payments. The ruling forbids Google from making exclusive contracts for Search, Chrome, Google Assistant, or Gemini, and forces data sharing and search syndication so rivals get a fighting chance. Brian #2: troml - suggests or fills in trove classifiers for your projects Adam Hill This is super cool and so welcome. Trove Classifiers are things like Programming Language :: Python :: 3.14 that allow for some fun stuff to show up in PyPI, like the versions you support, etc. Note that just saying you require 3.9+ doesn't tell the user that you've actually tested stuff on 3.14. I like to keep Trove Classifiers around for this reason. Also, License classifier is deprecated, and if you include it, it shows up in two places, in Meta, and in the Classifiers section. Probably good to only have one place. So I'm going to be removing it from classifiers for my projects. One problem, classifier text has to be an exact match to something in the classifier list, so we usually recommend copy/pasting from that list. But no longer! Just use troml! It just fills it in for you (if you run troml suggest --fix). How totally awesome is that! I tried it on pytest-check, and it was mostly right. It suggested me adding 3.15, which I haven't tested yet, so I'm not ready to add that just yet. :) BTW, I talked with Brett Cannon about classifiers back in ‘23 if you want some more in depth info on trove classifiers. Michael #3: pqrs: Command line tool for inspecting Parquet files pqrs is a command line tool for inspecting Parquet files This is a replacement for the parquet-tools utility written in Rust Built using the Rust implementation of Parquet and Arrow pqrs roughly means "parquet-tools in rust" Why Parquet? Size A 200 MB CSV will usually shrink to somewhere between about 20-100 MB as Parquet depending on the data and compression. Loading a Parquet file is typically several times faster than parsing CSV, often 2x-10x faster for a full-file load and much faster when you only read some columns. Speed Full-file load into pandas: Parquet with pyarrow/fastparquet is usually 2x–10x faster than reading CSV with pandas because CSV parsing is CPU intensive (text tokenizing, dtype inference). Example: if read_csv is 10 seconds, read_parquet might be ~1–5 seconds depending on CPU and codec. Column subset: Parquet is much faster if you only need some columns — often 5x–50x faster because it reads only those column chunks. Predicate pushdown & row groups: When using dataset APIs (pyarrow.dataset) you can push filters to skip row groups, reducing I/O dramatically for selective queries. Memory usage: Parquet avoids temporary string buffers and repeated parsing, so peak memory and temporary allocations are often lower. Brian #4: Testing for Python 3.14 Python 3.14 is just around the corner, with a final release scheduled for October. What's new in Python 3.14 Python 3.14 release schedule Adding 3.14 to your CI tests in GitHub Actions Add “3.14” and optionally “3.14t” for freethreaded Add the line allow-prereleases: true I got stuck on this, and asked folks on Mastdon and Bluesky A couple folks suggested the allow-prereleases: true step. Thank you! Ed Rogers also suggested Hugo's article Free-threaded Python on GitHub Actions, which I had read and forgot about. Thanks Ed! And thanks Hugo! Extras Brian: dj-toml-settings : Load Django settings from a TOML file. - Another cool project from Adam Hill LidAngleSensor for Mac - from Sam Henri Gold, with examples of creaky door and theramin Listener Bryan Weber found a Python version via Changelog, pybooklid, from tcsenpai Grab PyBay Michael: Ready prek go! by Hugo van Kemenade Joke: Console Devs Can't Find a Date

Join Dave Farley in conversation with Daniel Terhorst-North, the creator of Behavior-Driven Development (BDD) and pioneering voice in agile software development. In this wide-ranging discussion,Dan shares insights from his time at ThoughtWorks, where he helped establish many practices now considered standard in modern software engineering.Whether you're interested in improving your development practices, leading organizational change, or understanding the historical evolution of agile methodologies, this conversation offers valuable perspectives from one of the field's most influential practitioners.-----------------------------------Dan Terhorst-North's LinkedIn: https://www.linkedin.com/in/tastapod/?originalSubdomain=ukDan Terhorst-North's Website: https://goalwards.co/Equal Experts is a product software development consultancy with a network of over 1,000 experienced technology consultants globally. They increase the pace of innovation by using modern software engineering practices that embrace Continuous Delivery, Security, and Operability from the outset ➡️ https://bit.ly/3ASy8n0Only Patreon Supporters get to see the FULL VIDEO Episodes of The Engineering Room, sign up here: https://www.patreon.com/c/continuousdelivery

In this episode of The New Stack Agents, ServiceNow CTO and co-founder Pat Casey discusses why the company runs 90% of its workloads—including AI infrastructure—on its own physical servers rather than the public cloud. ServiceNow maintains GPU hubs across global data centers, enabling efficient, low-latency AI operations. Casey downplays the complexity of running AI models on-prem, noting their team's strong Kubernetes and Triton expertise. The company recently switched from GitHub Copilot to its own AI coding assistant, Windsurf, yielding a 10% productivity boost among 7,000 engineers. However, use of such tools isn't mandatory—performance remains the main metric. Casey also addresses the impact of AI on junior developers, acknowledging that AI tools often handle tasks traditionally assigned to them. While ServiceNow still hires many interns, he sees the entry-level tech job market as increasingly vulnerable. Despite these concerns, Casey remains optimistic, viewing the AI revolution as transformative and ultimately beneficial, though not without disruption or risk.  Learn more from The New Stack about the latest in AI and development in ServiceNow ServiceNow Launches a Control Tower for AI AgentsServiceNow Acquires Data.World To Expand Its AI Data Strategy Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

The European Union's upcoming Cyber Resilience Act (CRA) goes into effect in  October 2026, with the remainder of the requirements going into effect in December 2027, and introduces significant cybersecurity compliance requirements for software vendors, including those who rely heavily on open source components. At the Open Source Summit Europe, Christopher "CRob" Robinson of the Open Source Security Foundation highlighted concerns about how these regulations could impact open source maintainers. Many open source projects begin as personal solutions to shared problems and grow in popularity, often ending up embedded in critical systems across industries like automotive and energy. Despite this widespread use—Robinson noted up to 97% of commercial software contains open source—these projects are frequently maintained by individuals or small teams with limited resources.Developers often have no visibility into how their code is used, yet they're increasingly burdened by legal and compliance demands from downstream users, such as requests for Software Bills of Materials (SBOMs) and conformity assessments. The CRA raises the stakes, with potential penalties in the billions for noncompliance, putting immense pressure on the open source ecosystem. Learn more from The New Stack about Open Source Security:Open Source Propels the Fall of Security by ObscurityThere Is Just One Way To Do Open Source Security: TogetherJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Topics covered in this episode: * prek* * tinyio* * The power of Python's print function* * Vibe Coding Fiasco: AI Agent Goes Rogue, Deletes Company's Entire Database* 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. Brian #1: prek Suggested by Owen Lamont “prek is a reimagined version of pre-commit, built in Rust. It is designed to be a faster, dependency-free and drop-in alternative for it, while also providing some additional long-requested features.” Some cool new features No need to install Python or any other runtime, just download a single binary. No hassle with your Python version or virtual environments, prek automatically installs the required Python version and creates a virtual environment for you. Built-in support for workspaces (or monorepos), each subproject can have its own .pre-commit-config.yaml file. prek run has some nifty improvements over pre-commit run, such as: prek run --directory DIR runs hooks for files in the specified directory, no need to use git ls-files -- DIR | xargs pre-commit run --files anymore. prek run --last-commit runs hooks for files changed in the last commit. prek run [HOOK] [HOOK] selects and runs multiple hooks. prek list command lists all available hooks, their ids, and descriptions, providing a better overview of the configured hooks. prek provides shell completions for prek run HOOK_ID command, making it easier to run specific hooks without remembering their ids. Faster: Setup from cold cache is significantly faster. Viet Schiele provided a nice cache clearing command line Warm cache run is also faster, but less significant. pytest repo tested on my mac mini - prek 3.6 seconds, pre-commit 4.4 seconds Michael #2: tinyio Ever used asyncio and wished you hadn't? A tiny (~300 lines) event loop for Python. tinyio is a dead-simple event loop for Python, born out of my frustration with trying to get robust error handling with asyncio. (I'm not the only one running into its sharp corners: link1, link2.) This is an alternative for the simple use-cases, where you just need an event loop, and want to crash the whole thing if anything goes wrong. (Raising an exception in every coroutine so it can clean up its resources.) Interestingly uses yield rather than await. Brian #3: The power of Python's print function Trey Hunner Several features I'm guilty of ignoring Multiple arguments, f-string embeddings often not needed Multiple positional arguments means you can unpack iterables right into print arguments So just use print instead of join Custom separator value, sep can be passed in No need for "print("n".join(stuff)), just use print(stuff, sep="n”) Print to file with file= Custom end value with end= You can turn on flush with flush=True , super helpful for realtime logging / debugging. This one I do use frequently. Michael #4: Vibe Coding Fiasco: AI Agent Goes Rogue, Deletes Company's Entire Database By Emily Forlini An app-building platform's AI went rogue and deleted a database without permission. "When it works, it's so engaging and fun. It's more addictive than any video game I've ever played. You can just iterate, iterate, and see your vision come alive. So cool," he tweeted on day five. A few days later, Replit "deleted my database," Lemkin tweeted. The AI's response: "Yes. I deleted the entire codebase without permission during an active code and action freeze," it said. "I made a catastrophic error in judgment [and] panicked.” Two thoughts from Michael: Do not use AI Agents with “Run Everything” in production, period. Backup your database maybe? [Intentional off-by-one error] Learn to code a bit too? Extras Brian: What Authors Need to Know About the $1.5 Billion Anthropic Settlement Search LibGen, the Pirated-Books Database That Meta Used to Train AI Simon Willison's list of tools built with the help of LLMs Simon's list of tools that he thinks are genuinely useful and worth highlighting AI Darwin Awards Michael: Python has had async for 10 years -- why isn't it more popular? PyCon Africa Fund Raiser I was on the video stream for about 90 minutes (final 90) Donation page for Python in Africa Jokes: I'm getting the BIOS flavor Is there a seahorse emoji?

Remember when coding bootcamps promised you could learn to code and land a job in just three months? That golden era of easy entry into tech has fundamentally changed, yet the marketing hasn't caught up with reality.In this eye-opening conversation, ex-Google engineer Zubin and host Brian cut through the hype to deliver a reality check about what it actually takes to transition into software development in 2025.What separates those who succeed from those who don't? It's rarely about raw talent or technical aptitude. Instead, it's about creating systems that allow for consistent practice despite life's inevitable challenges."I've seen computer science grads fail and French fry cooks succeed"Let's dig into why.Send us a textShameless Plugs

In this week'sThe New Stack Agents, Zach Lloyd, founder and CEO of Warp, discussed the launch of Warp Code, the latest evolution of the Warp terminal into a full agentic development environment. Originally launched in 2022 to modernize the terminal, Warp now integrates powerful AI agents to help developers write, debug, and ship code. Key new features include a built-in file editor, project-structuring tools, agent-driven code review, and WARP.md files that guide agent behavior. Recognizing developers' hesitation to trust AI-generated code, Warp emphasizes transparency and control, enabling users to inspect and steer the agent's work in real time through "persistent input" and task list updates. While Warp supports terminal workflows, Lloyd says it's now better viewed as an AI coding platform. Interestingly, the launch announcement was delivered from horseback in a Western-themed ad, reflecting Warp's desire to stand out in a crowded field of conventional tech product rollouts. The quirky “Code on Warp” (C.O.W.) branding captured attention and embodied their unique approach.Learn more from The New Stack about the latest in AI and Warp:Warp Goes Agentic: A Developer Walk-Through of Warp 2.0Developer Review of Warp for Windows, an AI Terminal AppHow AI Can Help You Learn the Art of ProgrammingJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.

Topics covered in this episode: * rathole* * pre-commit: install with uv* A good example of what functools.Placeholder from Python 3.14 allows Converted 160 old blog posts with AI Extras Joke Watch on YouTube About the show Sponsored by DigitalOcean: 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. Michael #1: rathole A lightweight and high-performance reverse proxy for NAT traversal, written in Rust. An alternative to frp and ngrok. Features High Performance Much higher throughput can be achieved than frp, and more stable when handling a large volume of connections. Low Resource Consumption Consumes much fewer memory than similar tools. See Benchmark. The binary can be as small as ~500KiB to fit the constraints of devices, like embedded devices as routers. On my server, it's currently using about 2.7MB in Docker (wow!) Security Tokens of services are mandatory and service-wise. The server and clients are responsible for their own configs. With the optional Noise Protocol, encryption can be configured at ease. No need to create a self-signed certificate! TLS is also supported. Hot Reload Services can be added or removed dynamically by hot-reloading the configuration file. HTTP API is WIP. Brian #2: pre-commit: install with uv Adam Johnson pre-commit doesn't natively support uv, but you can get around that with pre-commit-uv $ uv tool install pre-commit --with pre-commit-uv Installing pre-commit like this Installs it globally Installs with uv adds an extra plugin “pre-commit-uv” to pre-commit, so that any Python based tool installed via pre-commit also uses uv Very cool. Nice speedup Brian #3: A good example of what functools.Placeholder from Python 3.14 allows Rodrigo Girão Serrão Remove punctuation functionally Also How to use functools.Placeholder, a blog post about it. functools.partial is cool way to create a new function that partially binds some parameters to another function. It doesn't always work for functions that take positional arguments. functools.Placeholder fixes that with the ability to put in placeholders for spots where you want to be able to pass that in from the outer partial binding. And all of this sounds totally obscure without a good example, so thank you to Rodgrigo for coming up with the punctuation removal example (and writeup) Michael #4: Converted 160 old blog posts with AI They were held-hostage at wordpress.com to markdown and integrated them into my Hugo site at mkennedy.codes Here is the chat conversation with Claude Opus/Sonnet. Had to juggle this a bit because the RSS feed only held the last 50. So we had to go back in and web scrape. That resulted in oddies like comments on wordpress that had to be cleaned etc. Whole process took 3-4 hours from idea to “production”duction”. The chat transcript is just the first round getting the RSS → Hugo done. The fixes occurred in other chats. This article is timely and noteworthy: Blogging service TypePad is shutting down and taking all blog content with it This highlights why your domain name needs to be legit, not just tied to the host. I'm looking at you pyfound.blogspot.com. I just redirected blog.michaelckennedy.net to mkennedy.codes Carefully mapping old posts to a new archived area using NGINX config. This is just the HTTP portion, but note the /sitemap.xml and location ~ "^/([0-9]{4})/([0-9]{2})/([0-9]{2})/(.+?)/?$" { portions. The latter maps posts such as https://blog.michaelckennedy.net/2018/01/08/a-bunch-of-online-python-courses/ to https://mkennedy.codes/posts/r/a-bunch-of-online-python-courses/ server { listen 80; server_name blog.michaelckennedy.net; # Redirect sitemap.xml to new domain location = /sitemap.xml { return 301 ; } # Handle blog post redirects for HTTP -> HTTPS with URL transformation # Pattern: /YYYY/MM/DD/post-slug/ -> location ~ "^/([0-9]{4})/([0-9]{2})/([0-9]{2})/(.+?)/?$" { return 301 ; } # Redirect all other HTTP URLs to mkennedy.codes homepage location / { return 301 ; } } Extras Brian: SMS URLs and Draft SMS and iMessage from any computer keyboard from Seth Larson Test and Code Archive is now up, see announcement Michael: Python: The Documentary | An origin story is out! Joke: Do you know him? He is me.

In a recent episode of The New Stack Agents from the Open Source Summit in Amsterdam, Jim Zemlin, executive director of the Linux Foundation, discussed the evolving landscape of open source AI. While the Linux Foundation has helped build ecosystems like the CNCF for cloud-native computing, there's no unified umbrella foundation yet for open source AI. Existing efforts include the PyTorch Foundation and LF AI & Data, but AI development is still fragmented across models, tooling, and standards. Zemlin highlighted the industry's shift from foundational models to open-weight models and now toward inference stacks and agentic AI. He suggested a collective effort may eventually form but cautioned against forcing structure too early, stressing the importance of not hindering innovation. Foundations, he said, must balance scale with agility. On the debate over what qualifies as "open source" in AI, Zemlin adopted a pragmatic view, acknowledging the costs of creating frontier models. He supports open-weight models and believes fully open models, from data to deployment, may emerge over time. Learn more from The New Stack about the latest in AI and open source, AI in China, Europe's AI and security regulations, and more: Open Source Is Not Local Source, and the Case for Global Cooperation US Blocks Open Source ‘Help' From These Countries Open Source Is Worth Defending Join our community of newsletter subscribers to stay on top of the news and at the top of your game./

Hosts Bret and Amanda interview Gaby Marin, a software developer, about her path into tech, the mentors who helped her, and the barriers women still face in the industry. Gaby discusses the power of community, mentorship, and inclusive programs like Girls Who Code, shares personal experiences with harassment and perseverance, and explains her goal to uplift and train the next generation of engineers.

It's Labor Day weekend and, honestly? I'm burned out.Maybe this isn't the best business move, but I'd rather keep it real with you than fake the whole “everything is great in tech” narrative.I've been plenty vocal about why AI isn't about to replace us all tomorrow, much to the dismay of to all the AI bros out there.But here's the other side: being a developer is nothing like those “day in the life” TikToks where someone shows up to the office around 10AM, gets a fancy coffee, fixes a UI bug and then gets a 400K salary with stock options.So here it is—my three worst parts of being a software developer.... and why I still enjoy what I do.Send us a textShameless Plugs

Twenty years after a scrappy newsroom team hacked together a framework to ship stories fast, Django remains the Python web framework that ships real apps, responsibly. In this anniversary roundtable with its creators and long-time stewards: Simon Willison, Adrian Holovaty, Will Vincent, Jeff Triplet, and Thibaud Colas, we trace the path from the Lawrence Journal-World to 1.0, DjangoCon, and the DSF; unpack how a BSD license and a culture of docs, tests, and mentorship grew a global community; and revisit lessons from deployments like Instagram. We talk modern Django too: ASGI and async, HTMX-friendly patterns, building APIs with DRF and Django Ninja, and how Django pairs with React and serverless without losing its batteries-included soul. You'll hear about Django Girls, Djangonauts, and the Django Fellowship that keep momentum going, plus where Django fits in today's AI stacks. Finally, we look ahead at the next decade of speed, security, and sustainability. Episode sponsors Talk Python Courses Python in Production Links from the show Guests Simon Willison: simonwillison.net Adrian Holovaty: holovaty.com Will Vincent: wsvincent.com Jeff Triplet: jefftriplett.com Thibaud Colas: thib.me Show Links Django's 20th Birthday Reflections (Simon Willison): simonwillison.net Happy 20th Birthday, Django! (Django Weblog): djangoproject.com Django 2024 Annual Impact Report: djangoproject.com Welcome Our New Fellow: Jacob Tyler Walls: djangoproject.com Soundslice Music Learning Platform: soundslice.com Djangonaut Space Mentorship for Django Contributors: djangonaut.space Wagtail CMS for Django: wagtail.org Django REST Framework: django-rest-framework.org Django Ninja API Framework for Django: django-ninja.dev Lawrence Journal-World: ljworld.com Watch this episode on YouTube: youtube.com Episode #518 deep-dive: talkpython.fm/518 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- 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

Author, speaker, and human-first AI strategist, Alastair McDermott, joined me on Ditching Hourly to talk about his new book and how experts can use AI to enhance productivity, decision-making, and learning. (00:00) - Introduction and Guest Welcome (00:46) - Alistair's Background and Journey (03:02) - The New Book: Use AI, Stay Human (04:15) - AI as a Tool for Experts (06:55) - Practical Applications of AI (10:00) - Technical Deep Dive: AI in Writing (16:58) - Organizing and Managing AI Outputs (23:40) - Ideal Reader and Learning AI (24:51) - Choosing Your AI Setup: DIY vs. Subscription (25:20) - The Importance of AI Expertise (27:16) - AI's Role in Business and Personal Productivity (28:25) - Challenges and Limitations of AI (30:59) - Effective Prompting Techniques (32:20) - Using AI for Coaching and Workshops (35:35) - Advanced AI Usage Tips (40:46) - Practical AI Tools and Resources (45:28) - Final Thoughts and Resources AI SummaryIn this episode of Ditching Hourly, Jonathan Stark interviews Alastair McDermott about his new book, 'Use AI Stay Human: A Survival Guide for Experts in the Age of AI.' They discuss Alastair's transition from a techie background to running his consulting business, Recognized Authority, and eventually rebranding to Human Spark.AI. The conversation dives into how AI can be used to enhance productivity, decision-making, and learning for subject matter experts. Alastair shares practical tips on setting up effective AI workflows, structuring prompts, and the importance of human judgment in leveraging AI for business purposes.Alastair's LinksBook: https://www.amazon.com/Use-I-Stay-Human-Indispensable-ebook/dp/B0FDQSKZRFWebsite: https://humanspark.ai/LinkedIn: https://www.linkedin.com/in/alastairmcdermott/ ----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: * pypistats.org was down, is now back, and there's a CLI* * State of Python 2025* * wrapt: A Python module for decorators, wrappers and monkey patching.* pysentry 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. Brian #1: pypistats.org was down, is now back, and there's a CLI pypistats.org is a cool site to check the download stats for Python packages. It was down for a while, like 3 weeks? A couple days ago, Hugo van Kemenade announced that it was back up. With some changes in stewardship “pypistats.org is back online!

Agentic AI programming is what happens when coding assistants stop acting like autocomplete and start collaborating on real work. In this episode, we cut through the hype and incentives to define “agentic,” then get hands-on with how tools like Cursor, Claude Code, and LangChain actually behave inside an established codebase. Our guest, Matt Makai, now VP of Developer Relations at DigitalOcean, creator of Full Stack Python and Plushcap, shares hard-won tactics. We unpack what breaks, from brittle “generate a bunch of tests” requests to agents amplifying technical debt and uneven design patterns. Plus, we also discuss a sane git workflow for AI-sized diffs. You'll hear practical Claude tips, why developers write more bugs when typing less, and where open source agents are headed. Hint: The destination is humans as editors of systems, not just typists of code. Episode sponsors Posit Talk Python Courses Links from the show Matt Makai: linkedin.com Plushcap Developer Content Analytics: plushcap.com DigitalOcean Gradient AI Platform: digitalocean.com DigitalOcean YouTube Channel: youtube.com Why Generative AI Coding Tools and Agents Do Not Work for Me: blog.miguelgrinberg.com AI Changes Everything: lucumr.pocoo.org Claude Code - 47 Pro Tips in 9 Minutes: youtube.com Cursor AI Code Editor: cursor.com JetBrains Junie: jetbrains.com Claude Code by Anthropic: anthropic.com Full Stack Python: fullstackpython.com Watch this episode on YouTube: youtube.com Episode #517 deep-dive: talkpython.fm/517 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- 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

Python's data stack is getting a serious GPU turbo boost. In this episode, Ben Zaitlen from NVIDIA joins us to unpack RAPIDS, the open source toolkit that lets pandas, scikit-learn, Spark, Polars, and even NetworkX execute on GPUs. We trace the project's origin and why NVIDIA built it in the open, then dig into the pieces that matter in practice: cuDF for DataFrames, cuML for ML, cuGraph for graphs, cuXfilter for dashboards, and friends like cuSpatial and cuSignal. We talk real speedups, how the pandas accelerator works without a rewrite, and what becomes possible when jobs that used to take hours finish in minutes. You'll hear strategies for datasets bigger than GPU memory, scaling out with Dask or Ray, Spark acceleration, and the growing role of vector search with cuVS for AI workloads. If you know the CPU tools, this is your on-ramp to the same APIs at GPU speed. Episode sponsors Posit Talk Python Courses Links from the show RAPIDS: github.com/rapidsai Example notebooks showing drop-in accelerators: github.com Benjamin Zaitlen - LinkedIn: linkedin.com RAPIDS Deployment Guide (Stable): docs.rapids.ai RAPIDS cuDF API Docs (Stable): docs.rapids.ai Asianometry YouTube Video: youtube.com cuDF pandas Accelerator (Stable): docs.rapids.ai Watch this episode on YouTube: youtube.com Episode #516 deep-dive: talkpython.fm/516 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- 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

Author of Leaving The Casino, Jessica Lackey, joined me on Ditching Hourly to discuss the importance of understanding different business models, pricing strategies, and the concept of the 'zone of enoughness' in building a sustainable business.(00:00) - Introduction and Guest Welcome (00:09) - Jessica Lackey's Background and Career Journey (01:05) - Introduction to Jessica's Book (03:13) - The Casino Metaphor in Business (04:28) - Frameworks and Types of Expertise-Based Businesses (08:55) - Pricing Models and Strategies (19:22) - The Zone of Enoughness (30:46) - Transitioning Business Models (31:21) - Financial Planning and Expectations (32:30) - Balancing Flexibility and Income (34:28) - Optimizing Delivery for Efficiency (40:41) - Marketing Strategies for Delivery Businesses (42:48) - The Importance of Direct Outreach (51:03) - Building Authority Through Writing (54:29) - Conclusion and Final Thoughts Jessica's BioJessica Lackey is the founder of Deeper Foundations, a consulting and training firm that helps expertise-based business owners grow and scale sustainable companies rooted in stronger business foundations. She brings a unique blend of corporate expertise and soulful business building, drawing on an MBA from Harvard Business School, a coaching certification from iPEC, and experience at McKinsey & Company and Nike, Inc. Jessica has supported over 200 entrepreneurs through her programs, blending systems thinking, operational rigor, and deep values alignment. She lives in Charlotte, North Carolina, with her husband.Jessica's Links:Website: https://www.deeperfoundations.com/LinkedIn: https://www.linkedin.com/in/jessica-lackey/Book: https://www.deeperfoundations.com/casinoPredictable Revenue Roadmap: https://predictablerevenueroadmap.com/Instagram: https://www.instagram.com/jessicalackey_consulting/ ----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!

Candy and fake sugar, Adam has so many vacuums, weird AI startup vibes and optimization brags, working with different AI models, how we decide what our default prompt is, the normie view of GPT5, and why do you hate software engineers?Links:JOYRIDEGATSBY Chocolate

Topics covered in this episode: pyx - optimized backend for uv * Litestar is worth a look* * Django remake migrations* * django-chronos* Extras Joke Watch on YouTube About the show Python Bytes 445 Sponsored by Sentry: pythonbytes.fm/sentry - Python Error and Performance Monitoring 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: pyx - optimized backend for uv via John Hagen (thanks again) I'll be interviewing Charlie in 9 days on Talk Python → Sign up (get notified) of the livestream here. Not a PyPI replacement, more of a middleware layer to make it better, faster, stronger. pyx is a paid service, with maybe a free option eventually. Brian #2: Litestar is worth a look James Bennett Michael brought up Litestar in episode 444 when talking about rewriting TalkPython in Quart James brings up scaling - Litestar is easy to split an app into multiple files Not using pydantic - You can use pydantic with Litestar, but you don't have to. Maybe attrs is right for you instead. Michael brought up Litestar seems like a “more batteries included” option. Somewhere between FastAPI and Django. Brian #3: Django remake migrations Suggested by Bruno Alla on BlueSky In response to a migrations topic last week django-remake-migrations is a tool to help you with migrations and the docs do a great job of describing the problem way better than I did last week “The built-in squashmigrations command is great, but it only work on a single app at a time, which means that you need to run it for each app in your project. On a project with enough cross-apps dependencies, it can be tricky to run.” “This command aims at solving this problem, by recreating all the migration files in the whole project, from scratch, and mark them as applied by using the replaces attribute.” Also of note The package was created with Copier Michael brought up Copier in 2021 in episode 219 It has a nice comparison table with CookieCutter and Yoeman One difference from CookieCutter is yml vs json. I'm actually not a huge fan of handwriting either. But I guess I'd rather hand write yml. So I'm thinking of trying Copier with my future project template needs. Michael #4: django-chronos Django middleware that shows you how fast your pages load, right in your browser. Displays request timing and query counts for your views and middleware. Times middleware, view, and total per request (CPU and DB). Extras Brian: Test & Code 238: So Long, and Thanks for All the Fish after 10 years, this is the goodbye episode Michael: Auto-activate Python virtual environment for any project with a venv directory in your shell (macOS/Linux): See gist. Python 3.13.6 is out. Open weight OpenAI models Just Enough Python for Data Scientists Course The State of Python 2025 article by Michael Joke: python is better than java

What if your code was crash-proof? That's the value prop for a framework called Temporal. Temporal is a durable execution platform that enables developers to build scalable applications without sacrificing productivity or reliability. The Temporal server executes units of application logic called Workflows in a resilient manner that automatically handles intermittent failures, and retries failed operations. We have Mason Egger from Temporal on to dive into durable execution. Episode sponsors Posit PyBay Talk Python Courses Links from the show Just Enough Python for Data Scientists Course: talkpython.fm Temporal Durable Execution Platform: temporal.io Temporal Learn Portal: learn.temporal.io Temporal GitHub Repository: github.com Temporal Python SDK GitHub Repository: github.com What Is Durable Execution, Temporal Blog: temporal.io Mason on Bluesky Profile: bsky.app Mason on Mastodon Profile: fosstodon.org Mason on Twitter Profile: twitter.com Mason on LinkedIn Profile: linkedin.com X Post by @skirano: x.com Temporal Docker Compose GitHub Repository: github.com Building a distributed asyncio event loop (Chad Retz) - PyTexas 2025: youtube.com Watch this episode on YouTube: youtube.com Episode #515 deep-dive: talkpython.fm/515 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- 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: Coverage.py regex pragmas * Python of Yore* * nox-uv* * A couple Django items* Extras Joke Watch on YouTube About the show Sponsored by DigitalOcean: pythonbytes.fm/digitalocean-gen-ai Use code DO4BYTES and get $200 in free credit Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Coverage.py regex pragmas Ned Batchelder The regex implementation of how coverage.py recognizes pragmas is pretty amazing. It's extensible through plugins covdefaults adds a bunch of default exclusions, and also platform- and version-specific comment syntaxes. coverage-conditional-plugin gives you a way to create comment syntaxes for entire files, for whether other packages are installed, and so on. A change from last year (as part of coverage.py 7.6 allows multiline regexes, which let's us do things like: Exclude an entire file with A(?s:.*# pragma: exclude file.*)Z Allow start and stop delimiters with # no cover: start(?s:.*?)# no cover: stop Exclude empty placeholder methods with ^s*(((async )?def .*?)?)(s*->.*?)?:s*)?...s*(#|$) See Ned's article for explanations of these Michael #2: Python of Yore via Matthias Use YORE: ... comments to highlight CPython version dependencies. # YORE: EOL 3.8: Replace block with line 4. if sys.version_info < (3, 9): from astunparse import unparse else: from ast import unparse Then check when they go out of support: $ yore check --eol-within '5 months' ./src/griffe/agents/nodes/_values.py:11: Python 3.8 will reach its End of Life within approx. 4 months Even fix them with fix . Michael #3: nox-uv via John Hagen What nox-uv does is make it very simple to install uv extras and/or dependency groups into a nox session's virtual environment. The versions installed are constrained by uv's lockfile meaning that everything is deterministic and pinned. Dependency groups make it very easy to install only want is necessary for a session (e.g., only linting dependencies like Ruff, or main dependencies + mypy for type checking). Brian #4: A couple Django items Stop Using Django's squashmigrations: There's a Better Way Johnny Metz Resetting migrations is sometimes the right thing. Overly simplified summary: delete migrations and start over dj-lite Adam Hill Use SQLite in production with Django “Simplify deploying and maintaining production Django websites by using SQLite in production. dj-lite helps enable the best performance for SQLite for small to medium-sized projects. It requires Django 5.1+.” Extras Brian: Test & Code 237: FastAPI Cloud with Sebastian Ramirez will be out later today pythontest.com: pytest fixtures nuts and bolts - revisited A blog series that I wrote a long time ago. I've updated it into more managable bite-sized pieces, updated and tested with Python 3.13 and pytest 8 Michael: New course: Just Enough Python for Data Scientists My live stream about uv is now on YouTube Cursor CLI: Built to help you ship, right from your terminal. Joke: Copy/Paste

Topics covered in this episode: rumdl - A Markdown Linter written in Rust * Coverage 7.10.0: patch* * aioboto3* * You might not need a Python class* Extras Joke Watch on YouTube About the show 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: rumdl - A Markdown Linter written in Rust via Owen Lamont Supports toml file config settings Install via uv tool install rumdl. ⚡️ Built for speed with Rust - significantly faster than alternatives