Podcasts about software developers

For years, building interactive widgets in Python notebooks meant wrestling with toolchains, platform quirks, and a mountain of JavaScript machinery. Most developers took one look and backed away slowly. Trevor Manz decided that barrier did not need to exist. His idea was simple: give Python users just enough JavaScript to unlock the web's interactivity, without dragging along the rest of the web ecosystem. That idea became anywidget, and it is quickly becoming the quiet connective tissue of modern interactive computing. Today we dig into how it works, why it has taken off, and how it might change the way we explore data. Episode sponsors Seer: AI Debugging, Code TALKPYTHON PyCharm, code STRONGER PYTHON Talk Python Courses Links from the show Trevor on GitHub: github.com anywidget GitHub: github.com Trevor's SciPy 2024 Talk: www.youtube.com Marimo GitHub: github.com Myst (Markdown docs): mystmd.org Altair: altair-viz.github.io DuckDB: duckdb.org Mosaic: uwdata.github.io ipywidgets: ipywidgets.readthedocs.io Tension between Web and Data Sci Graphic: blobs.talkpython.fm Quak: github.com Walk through building a widget: anywidget.dev Widget Gallery: anywidget.dev Video: How do I anywidget?: www.youtube.com PyCharm + PSF Fundraiser: pycharm-psf-2025 code STRONGER PYTHON Watch this episode on YouTube: youtube.com Episode #530 deep-dive: talkpython.fm/530 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Nvidia Distinguished Engineer Kevin Klues noted that low-level systems work is invisible when done well and highly visible when it fails — a dynamic that frames current Kubernetes innovations for AI. At KubeCon + CloudNativeCon North America 2025, Klues and AWS product manager Jesse Butler discussed two emerging capabilities: dynamic resource allocation (DRA) and a new workload abstraction designed for sophisticated AI scheduling.DRA, now generally available in Kubernetes 1.34, fixes long-standing limitations in GPU requests. Instead of simply asking for a number of GPUs, users can specify types and configurations. Modeled after persistent volumes, DRA allows any specialized hardware to be exposed through standardized interfaces, enabling vendors to deliver custom device drivers cleanly. Butler called it one of the most elegant designs in Kubernetes.Yet complex AI workloads require more coordination. A forthcoming workload abstraction, debuting in Kubernetes 1.35, will let users define pod groups with strict scheduling and topology rules — ensuring multi-node jobs start fully or not at all. Klues emphasized that this abstraction will shape Kubernetes' AI trajectory for the next decade and encouraged community involvement.Learn more from The New Stack about dynamic resource allocation: Kubernetes Primer: Dynamic Resource Allocation (DRA) for GPU WorkloadsKubernetes v1.34 Introduces Benefits but Also New Blind SpotsJoin 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.

At KubeCon North America 2025, GitLab's Emilio Salvador outlined how developers are shifting from individual coders to leaders of hybrid human–AI teams. He envisions developers evolving into “cognitive architects,” responsible for breaking down large, complex problems and distributing work across both AI agents and humans. Complementing this is the emerging role of the “AI guardian,” reflecting growing skepticism around AI-generated code. Even as AI produces more code, humans remain accountable for reviewing quality, security, and compliance.Salvador also described GitLab's “AI paradox”: developers may code faster with AI, but overall productivity stalls because testing, security, and compliance processes haven't kept pace. To fix this, he argues organizations must apply AI across the entire development lifecycle, not just in coding. GitLab's Duo Agent Platform aims to support that end-to-end transformation.Looking ahead, Salvador predicts the rise of a proactive “meta agent” that functions like a full team member. Still, he warns that enterprise adoption remains slow and advises organizations to start small, build skills, and scale gradually.Learn more from The New Stack about the evolving role of "cognitive architects":The Engineer in the AI Age: The Orchestrator and ArchitectThe New Role of Enterprise Architecture in the AI EraThe Architect's Guide to Understanding Agentic AIJoin 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: PEP 798: Unpacking in Comprehensions Pandas 3.0.0rc0 typos A couple testing topics 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: PEP 798: Unpacking in Comprehensions After careful deliberation, the Python Steering Council is pleased to accept PEP 798 – Unpacking in Comprehensions. Examples [*it for it in its] # list with the concatenation of iterables in 'its' {*it for it in its} # set with the union of iterables in 'its' {**d for d in dicts} # dict with the combination of dicts in 'dicts' (*it for it in its) # generator of the concatenation of iterables in 'its' Also: The Steering Council is happy to unanimously accept “PEP 810, Explicit lazy imports” Brian #2: Pandas 3.0.0rc0 Pandas 3.0.0 will be released soon, and we're on Release candidate 0 Here's What's new in Pands 3.0.0 Dedicated string data type by default Inferred by default for string data (instead of object dtype) The str dtype can only hold strings (or missing values), in contrast to object dtype. (setitem with non string fails) The missing value sentinel is always NaN (np.nan) and follows the same missing value semantics as the other default dtypes. Copy-on-Write The result of any indexing operation (subsetting a DataFrame or Series in any way, i.e. including accessing a DataFrame column as a Series) or any method returning a new DataFrame or Series, always behaves as if it were a copy in terms of user API. As a consequence, if you want to modify an object (DataFrame or Series), the only way to do this is to directly modify that object itself. pd.col syntax can now be used in DataFrame.assign() and DataFrame.loc() You can now do this: df.assign(c = pd.col('a') + pd.col('b')) New Deprecation Policy Plus more - Michael #3: typos You've heard about codespell … what about typos? VSCode extension and OpenVSX extension. From Sky Kasko: Like codespell, typos checks for known misspellings instead of only allowing words from a dictionary. But typos has some extra features I really appreciate, like finding spelling mistakes inside snake_case or camelCase words. For example, if you have the line: *connecton_string = "sqlite:///my.db"* codespell won't find the misspelling, but typos will. It gave me the output: *error: `connecton` should be `connection`, `connector` ╭▸ ./main.py:1:1 │1 │ connecton_string = "sqlite:///my.db" ╰╴━━━━━━━━━* But the main advantage for me is that typos has an LSP that supports editor integrations like a VS Code extension. As far as I can tell, codespell doesn't support editor integration. (Note that the popular Code Spell Checker VS Code extension is an unrelated project that uses a traditional dictionary approach.) For more on the differences between codespell and typos, here's a comparison table I found in the typos repo: https://github.com/crate-ci/typos/blob/master/docs/comparison.md By the way, though it's not mentioned in the installation instructions, typos is published on PyPI and can be installed with uv tool install typos, for example. That said, I don't bother installing it, I just use the VS Code extension and run it as a pre-commit hook. (By the way, I'm using prek instead of pre-commit now; thanks for the tip on episode #448!) It looks like typos also publishes a GitHub action, though I haven't used it. Brian #4: A couple testing topics slowlify suggested by Brian Skinn Simulate slow, overloaded, or resource-constrained machines to reproduce CI failures and hunt flaky tests. Requires Linux with cgroups v2 Why your mock breaks later Ned Badthelder Ned's taught us before to “Mock where the object is used, not where it's defined.” To be more explicit, but probably more confusing to mock-newbies, “don't mock things that get imported, mock the object in the file it got imported to.” See? That's probably worse. Anyway, read Ned's post. If my project myproduct has user.py that uses the system builtin open() and we want to patch it: DONT DO THIS: @patch("builtins.open") This patches open() for the whole system DO THIS: @patch("myproduct.user.open") This patches open() for just the user.py file, which is what we want Apparently this issue is common and is mucking up using coverage.py Extras Brian: The Rise and Rise of FastAPI - mini documentary “Building on Lean” chapter of LeanTDD is out The next chapter I'm working on is “Finding Waste in TDD” Notes to delete before end of show: I'm not on track for an end of year completion of the first pass, so pushing goal to 1/31/26 As requested by a reader, I'm releasing both the full-so-far versions and most-recent-chapter Michael: My Vanishing Gradient's episode is out Django 6 is out Joke: tabloid - A minimal programming language inspired by clickbait headlines

Author of No BS Strategy, Alex M H Smith, rejoined me on Ditching Hourly to help define a very important word that few business people understand correctly.Chapters(00:00) - Introduction and Welcome (00:11) - Guest Introduction: Alex Smith (01:30) - Understanding Business Strategy (02:02) - The Misunderstood Concept of Value (04:15) - Creating Value in Business (07:55) - Innovating Beyond Traditional Value (13:01) - Practical Examples and Market Research (18:14) - Unique Value Proposition (29:23) - Understanding Differentiation in Business (29:56) - The Importance of Unique Positioning (30:36) - Consulting Strategies and Unique Differences (31:32) - Examples of Effective Differentiation (33:23) - The Role of Specialization in Strategy (38:30) - Embracing Weaknesses for Strategic Advantage (40:53) - Balancing Specialization and Market Reach (41:51) - The Pitfalls of Over-Niching (48:28) - Rooting in Recognizable Categories (53:20) - Conclusion and Resources Guest LinksAlex's free resources » https://basicarts.org/welcome/Alex's book » https://basicarts.org/book/Alex's mailing list » https://basicarts.org/articles/Alex's LinkedIn » https://www.linkedin.com/in/alex-m-h-smith/ ----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!

Kasey Endsley, a graduate of Nashville Software School's full-time, full-stack Web Development Cohort 76, shares her journey from working as a vet technician and manager to becoming a software developer. She discusses finding a career that finally offers the intellectual challenge she was seeking. Kasey dives into the power of the NSS community and the impact of continued training through our ProTech classes. Most importantly, Kasey offers her best advice for anyone asking: Is now the right time to do a bootcamp?. Spoiler: She says you should believe in yourself and take the chance. Hear her successful job search strategy, including why she focused on networking with NSS alumni and applying to companies that hire local graduates. 01:04 Before NSS 01:40 The Motivation to Change Careers 02:37 From Neopets and MySpace 03:28 The Aha Moment in Bootcamp 05:28 The Power of the NSS Community 06:18 Continuing Education with ProTech 09:31 Confidence Boost for the Job Search 10:24 Networking for Success 13:47 The Importance of Genuine Connections 15:32 Why Start a Software Development Bootcamp Now Software Development Bootcamps: Full-time, Full-stack: https://nashvillesoftwareschool.com/programs/web-developer-full-time/ Part-time, Full-stack: https://nashvillesoftwareschool.com/programs/web-developer-part-time/ Continuing Education Classes: Cloud Deployment Fundamentals: https://nashvillesoftwareschool.com/programs/cloud-deployment-fundamentals Introduction to Agentic AI for Developers: https://nashvillesoftwareschool.com/programs/agentic-ai-tools-for-developers

Jonathan Bryce, the new CNCF executive director, argues that inference—not model training—will define the next decade of computing. Speaking at KubeCon North America 2025, he emphasized that while the industry obsesses over massive LLM training runs, the real opportunity lies in efficiently serving these models at scale. Cloud-native infrastructure, he says, is uniquely suited to this shift because inference requires real-time deployment, security, scaling, and observability—strengths of the CNCF ecosystem. Bryce believes Kubernetes is already central to modern inference stacks, with projects like Ray, KServe, and emerging GPU-oriented tooling enabling teams to deploy and operationalize models. To bring consistency to this fast-moving space, the CNCF launched a Kubernetes AI Conformance Program, ensuring environments support GPU workloads and Dynamic Resource Allocation. With AI agents poised to multiply inference demand by executing parallel, multi-step tasks, efficiency becomes essential. Bryce predicts that smaller, task-specific models and cloud-native routing optimizations will drive major performance gains. Ultimately, he sees CNCF technologies forming the foundation for what he calls “the biggest workload mankind will ever have.” Learn more from The New Stack about inference: Confronting AI's Next Big Challenge: Inference Compute Deep Infra Is Building an AI Inference Cloud for Developers 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.

I lay out the project with tons of resources right here

A lot of people building software today never took the traditional CS path. They arrived through curiosity, a job that needed automating, or a late-night itch to make something work. This week, David Kopec joins me to talk about rebuilding computer science for exactly those folks, the ones who learned to program first and are now ready to understand the deeper ideas that power the tools they use every day. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON NordStellar Talk Python Courses Links from the show David Kopec: davekopec.com Classic Computer Science Book: amazon.com Computer Science from Scratch Book: computersciencefromscratch.com Computer Science from Scratch at NoStartch (CSFS30 for 30% off): nostarch.com Watch this episode on YouTube: youtube.com Episode #529 deep-dive: talkpython.fm/529 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Helm — originally a hackathon project called Kate's Place — turned 10 in 2025, marking the milestone with the release of Helm 4, its first major update in six years. Created by Matt Butcher and colleagues as a playful take on “K8s,” the early project won a small prize but quickly grew into a serious effort when Deus leadership recognized the need for a Kubernetes package manager. Renamed Helm, it rapidly expanded with community contributors and became one of the first CNCF graduating projects.Helm 4 reflects years of accumulated design debt and evolving use cases. After the rapid iterations of Helm 1, 2, and 3, the latest version modernizes logging, improves dependency management, and introduces WebAssembly-based plugins for cross-platform portability—addressing the growing diversity of operating systems and architectures. Beyond headline features, maintainers emphasize that mature projects increasingly deliver “boring” but essential improvements, such as better logging, which simplify workflows and integrate more cleanly with other tools. Helm's re-architected internals also lay the foundation for new chart and package capabilities in upcoming 4.x releases. Learn more from The New Stack about Helm: The Super Helm Chart: To Deploy or Not To Deploy?Kubernetes Gets a New Resource Orchestrator in the Form of KroJoin 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.

Kubernetes has relied on role-based access control (RBAC) since 2017, but its simplicity limits what developers can express, said Micah Hausler, principal engineer at AWS, on The New Stack Makers. RBAC only allows actions; it can't enforce conditions, denials, or attribute-based rules. Seeking a more expressive authorization model for Kubernetes, Hausler explored Cedar, an authorization engine and policy language created at AWS in 2022 and later open-sourced. Although not designed specifically for Kubernetes, Cedar proved capable of modeling its authorization needs in a concise, readable way. Hausler highlighted Cedar's clarity—nontechnical users can often understand policies at a glance—as well as its schema validation, autocomplete support, and formal verification, which ensures policies are correct and produce only allow or deny outcomes.Now onboarding to the CNCF sandbox, Cedar is used by companies like Cloudflare and MongoDB and offers language-agnostic tooling, including a Go implementation donated by StrongDM. The project is actively seeking contributors, especially to expand bindings for languages like TypeScript, JavaScript, and Python.Learn more from The New Stack about Cedar:Ceph: 20 Years of Cutting-Edge Storage at the Edge The Cedar Programming Language: Authorization SimplifiedJoin 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: Advent of Code starts today Django 6 is coming Advanced, Overlooked Python Typing codespell 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: Advent of Code starts today A few changes, like 12 days this year, which honestly, I'm grateful for. See also: elf: Advent of Code CLI helper for Python Michael #2: Django 6 is coming Expected December 2025 Django 6.0 supports Python 3.12, 3.13, and 3.14 Built-in support for the Content Security Policy (CSP) standard is now available, making it easier to protect web applications against content injection attacks such as cross-site scripting (XSS). The Django Template Language now supports template partials, making it easier to encapsulate and reuse small named fragments within a template file. Django now includes a built-in Tasks framework for running code outside the HTTP request–response cycle. This enables offloading work, such as sending emails or processing data, to background workers. Email handling in Django now uses Python's modern email API, introduced in Python 3.6. This API, centered around the email.message.EmailMessage class Brian #3: Advanced, Overlooked Python Typing get_args, TypeGuard, TypeIs, and more goodies Michael #4: codespell Learned from this PR for the Talk Python book. Fix common misspellings in text files. It's designed primarily for checking misspelled words in source code (backslash escapes are skipped), but it can be used with other files as well. It does not check for word membership in a complete dictionary, but instead looks for a set of common misspellings. Therefore it should catch errors like "adn", but it will not catch "adnasdfasdf". It shouldn't generate false-positives when you use a niche term it doesn't know about. Extras Brian: Is mkdocs maintained? Hatch 1.16 Michael: Follow up on tach from Gerben Dekker: tach has been unmaintained for a bit but is not anymore. It was the main product from Gauge which is a Y combinator startup that pivoted to something unrelated and abandoned tach. However, https://github.com/DetachHead forked it but now got access to the main repo and has committed to maintaining it. ruff analyze graph is fully independent of tach - we actually started to look into alternatives for tach when it became unmaintained and then found ruff analyze graph. For our use case, with just a bit of manipulation on top of ruff analyze graph we replaced our use of deptry (which was slower - and I try to be careful depending on one-man projects). A Review of Michael Kennedy's book, “Talk Python in Production” - Thanks Doug Joke: NoaaS

Já se imaginou trabalhando em um dos maiores e-commerces do planeta? Neste episódio, conversamos com Bruno Tofollo, Principal Software Engineer na Amazon, que compartilhou insights valiosos sobre como se preparar para entrar na empresa, como funciona o processo seletivo, as tecnologias utilizadas no dia a dia e como a cultura Amazônica molda o trabalho dos times.

In this episode, I'm talking with Vincent Warmerdam about treating LLMs as just another API in your Python app, with clear boundaries, small focused endpoints, and good monitoring. We'll dig into patterns for wrapping these calls, caching and inspecting responses, and deciding where an LLM API actually earns its keep in your architecture. Episode sponsors Seer: AI Debugging, Code TALKPYTHON NordStellar Talk Python Courses Links from the show Vincent on X: @fishnets88 Vincent on Mastodon: @koaning LLM Building Blocks for Python Co-urse: training.talkpython.fm Top Talk Python Episodes of 2024: talkpython.fm LLM Usage - Datasette: llm.datasette.io DiskCache - Disk Backed Cache (Documentation): grantjenks.com smartfunc - Turn docstrings into LLM-functions: github.com Ollama: ollama.com LM Studio - Local AI: lmstudio.ai marimo - A Next-Generation Python Notebook: marimo.io Pydantic: pydantic.dev Instructor - Complex Schemas & Validation (Python): python.useinstructor.com Diving into PydanticAI with marimo: youtube.com Cline - AI Coding Agent: cline.bot OpenRouter - The Unified Interface For LLMs: openrouter.ai Leafcloud: leaf.cloud OpenAI looks for its "Google Chrome" moment with new Atlas web browser: arstechnica.com Watch this episode on YouTube: youtube.com Episode #528 deep-dive: talkpython.fm/528 Episode transcripts: talkpython.fm Theme Song: Developer Rap

AWS's approach to Elastic Kubernetes Service has evolved significantly since its 2018 launch. According to Mike Stefanik, Senior Manager of Product Management for EKS and ECR, today's users increasingly represent the late majority—teams that want Kubernetes without managing every component themselves. In a conversation onThe New Stack Makers, Stefanik described how AI workloads are reshaping Kubernetes operations and why AWS open-sourced an MCP server for EKS. Early feedback showed that meaningful, task-oriented tool names—not simple API mirrors—made MCP servers more effective for LLMs, prompting AWS to design tools focused on troubleshooting, runbooks, and full application workflows. AWS also introduced a hosted knowledge base built from years of support cases to power more capable agents.While “agentic AI” gets plenty of buzz, most customers still rely on human-in-the-loop workflows. Stefanik expects that to shift, predicting 2026 as the year agentic workloads move into production. For experimentation, he recommends the open-source Strands SDK. Internally, he has already seen major productivity gains from BI agents that automate complex data analysis tasks.Learn more from The New Stack about Amazon Web Services' approach to Elastic Kubernetes ServiceHow Amazon EKS Auto Mode Simplifies Kubernetes Cluster Management (Part 1)A Deep Dive Into Amazon EKS Auto (Part 2)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.

Professor-in-residence at StoryBrand and host of the new podcast Badass Softie, Dr. J.J. Peterson, joined me on Ditching Hourly to discuss how to strike a balance between authority and empathy. And be sure to stick around to the end to hear J.J.'s take on AI's impact on professional services and how to avoid creating ‘louder garbage' :-)Chapters(00:00) - Introduction and Guest Welcome (00:17) - JJ Peterson's Current Ventures (01:11) - The Concept of 'Badass Softie' (03:26) - StoryBrand Framework Explained (06:20) - Empathy and Authority in Leadership (08:42) - Balancing Empathy and Authority in Coaching (12:38) - Personal Experiences and Coaching Styles (16:05) - Communicating Empathy and Authority Effectively (24:44) - Engaging Your Audience with Empathy and Authority (28:06) - Controlling the Narrative in Marketing (29:07) - Embracing Your Authentic Self as a Guide (31:41) - Overcoming Imposter Syndrome (40:52) - The Importance of Niching Down (47:35) - Leveraging AI in Professional Services (51:39) - Conclusion and Final Thoughts LinksJ.J.'s website » https://www.drjjpeterson.com/J.J.'s podcast » https://www.badasssoftie.com/ ----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!

AWS re:Invent has long featured CTO Werner Vogels' closing keynote, but this year he signaled it may be his last, emphasizing it's time for “younger voices” at Amazon. After 21 years with the company, Vogels reflected on arriving as an academic and being stunned by Amazon's technical scale—an energy that still drives him today. He released his annual predictions ahead of re:Invent, with this year's five themes focused heavily on AI and broader societal impacts.Vogels highlights technology's growing role in addressing loneliness, noting how devices like Alexa can offer comfort to those who feel isolated. He foresees a “Renaissance developer,” where engineers must pair deep expertise with broad business and creative awareness. He warns quantum-safe encryption is becoming urgent as data harvested today may be decrypted within five years. Military innovations, he notes, continue to influence civilian tech, for better and worse. Finally, he argues personalized learning can preserve children's curiosity and better support teachers, which he views as essential for future education.Learn more from The New Stack about evolving role of technology systems from past to future: Werner Vogels' 6 Lessons for Keeping Systems Simple50 Years Later: Remembering How the Future Looked in 1974Join 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: PEP 814 – Add frozendict built-in type From Material for MkDocs to Zensical Tach Some Python Speedups in 3.15 and 3.16 Extras Joke 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 #0: Black Friday is on at Talk Python What's on offer: An AI course mini bundle (22% off) 20% off our entire library via the Everything Bundle (what's that? ;) ) The new Talk Python in Production book (25% off) Brian: This is peer pressure in action 20% off The Complete pytest Course bundle (use code BLACKFRIDAY) through November or use save50 for 50% off, your choice. Python Testing with pytest, 2nd edition, eBook (50% off with code save50) also through November I would have picked 20%, but it's a PragProg wide thing Michael #1: PEP 814 – Add frozendict built-in type by Victor Stinner & Donghee Na A new public immutable type frozendict is added to the builtins module. We expect frozendict to be safe by design, as it prevents any unintended modifications. This addition benefits not only CPython's standard library, but also third-party maintainers who can take advantage of a reliable, immutable dictionary type. To add to existing frozen types in Python. Brian #2: From Material for MkDocs to Zensical Suggested by John Hagen A lot of people, me included, use Material for MkDocs as our MkDocs theme for both personal and professional projects, and in-house docs. This plugin for MkDocs is now in maintenance mode The development team is switching to working on Zensical, a static site generator to overcome some technical limitations with MkDocs. There's a series of posts about the transition and reasoning Transforming Material for MkDocs Zensical – A modern static site generator built by the creators of Material for MkDocs Material for MkDocs Insiders – Now free for everyone Goodbye, GitHub Discussions Material for MkDocs still around, but in maintenance mode all insider features now available to everyone Zensical is / will be compatible with Material for Mkdocs, can natively read mkdocs.yml, to assist with the transition Open Source, MIT license funded by an offering for professional users: Zensical Spark Michael #3: Tach Keep the streak: pip deps with uv + tach From Gerben Decker We needed some more control over linting our dependency structure, both internal and external. We use tach (which you covered before IIRC), but also some home built linting rules for our specific structure. These are extremely easy to build using an underused feature of ruff: "uv run ruff analyze graph --python python_exe_path .". Example from an app I'm working on (shhhhh not yet announced!) Brian #4: Some Python Speedups in 3.15 and 3.16 A Plan for 5-10%* Faster Free-Threaded JIT by Python 3.16 5% faster by 3.15 and 10% faster by 3.16 Decompression is up to 30% faster in CPython 3.15 Extras Brian: LeanTDD book issue tracker Michael: No. 4 for dependencies: Inverted dep trees from Bob Belderbos Joke: git pull inception

Você sabe a diferença entre Clean Architecture e Vertical Slice? Sabe quando utilizar cada arquitetura? No programa de hoje trouxemos o Tiago Aguiar e o Luiz Motta, pra conversar e discutir alguns pontos de ambas as arquiteturas com a gente, qual e quando é a melhor decisão a escolher, e como isso impacta positivamente os desenvolvedores e a manutenção das aplicações.

DevOps practitioners — whether developers, operators, SREs or business stakeholders — increasingly rely on telemetry to guide decisions, yet face growing complexity, siloed teams and rising observability costs. In a conversation at KubeCon + CloudNativeCon North America, IBM's Jacob Yackenovich emphasized the importance of collecting high-granularity, full-capture data to avoid missing critical performance signals across hybrid application stacks that blend legacy and cloud-native components. He argued that observability must evolve to serve both technical and nontechnical users, enabling teams to focus on issues based on real business impact rather than subjective judgment.AI's rapid integration into applications introduces new observability challenges. Yackenovich described two patterns: add-on AI services, such as chatbots, whose failures don't disrupt core workflows, and blocking-style AI components embedded in essential processes like fraud detection, where errors directly affect application function.Rising cloud and ingestion costs further complicate telemetry strategies. Yackenovich cautioned against limiting visibility for budget reasons, advocating instead for predictable, fixed-price observability models that let organizations innovate without financial uncertainty.Learn more from The New Stack about the latest in observability: Introduction to ObservabilityObservability 2.0? Or Just Logs All Over Again?Building an Observability Culture: Getting Everyone OnboardJoin 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.

Solo data advisor Shachar Meir (ex-Meta, ex-PayPal) joined me on Ditching hourly to share the details of his transition from being a manager in massive corporate environments to becoming a successful solo consultant.Chapters (00:00) - Introduction and Guest Welcome (01:05) - Shachar's Professional Background (03:47) - Transition to Solo Entrepreneurship (13:51) - First Steps as a Solopreneur (15:59) - Landing the First Project (20:25) - Facing the Challenges of Solopreneurship (25:11) - Navigating the Steep Learning Curve (26:17) - The Importance of Networking and Mentorship (29:52) - Leveraging LinkedIn for Business Growth (33:25) - The Art of Content Creation (43:18) - Financial Stability and Client Acquisition (51:47) - Final Thoughts and Advice Shachar's LinksLinkedIn Profile » https://www.linkedin.com/in/shacharmeir/YouTube Channel » https://www.youtube.com/@shacharmeir ----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!

Major banks once built their own Linux kernels because no distributions existed, but today commercial distros — and Kubernetes — are universal. At KubeCon + CloudNativeCon North America, AWS's Jesse Butler noted that Kubernetes has reached the same maturity Linux once did: organizations no longer build bespoke control planes but rely on shared standards. That shift influences how AWS contributes to open source, emphasizing community-wide solutions rather than AWS-specific products.Butler highlighted two AWS EKS projects donated to Kubernetes SIGs: KRO and Karpenter. KRO addresses the proliferation of custom controllers that emerged once CRDs made everything representable as Kubernetes resources. By generating CRDs and microcontrollers from simple YAML schemas, KRO transforms “glue code” into an automated service within Kubernetes itself. Karpenter tackles the limits of traditional autoscaling by delivering just-in-time, cost-optimized node provisioning with a flexible, intuitive API. Both projects embody AWS's evolving philosophy: building features that serve the entire Kubernetes ecosystem as it matures into a true enterprise standard.Learn more from The New Stack about the latest in Kube Resource Orchestrator and Karpenter:  Migrating From Cluster Autoscaler to Karpenter v0.32How Amazon EKS Auto Mode Simplifies Kubernetes Cluster Management (Part 1) Kubernetes Gets a New Resource Orchestrator in the Form of KroJoin 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: Possibility of a new website for Django aiosqlitepool deptry browsr 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: Possibility of a new website for Django Current Django site: djangoproject.com Adam Hill's in progress redesign idea: django-homepage.adamghill.com Commentary in the Want to work on a homepage site redesign? discussion Michael #2: aiosqlitepool

Clockwork began with a narrow goal—keeping clocks synchronized across servers—but soon realized that its precise latency measurements could reveal deeper data center networking issues. This insight led the company to build a hardware-agnostic monitoring and remediation platform capable of automatically routing around faults. Today, Clockwork's technology is especially valuable for large GPU clusters used in training LLMs, where communication efficiency and reliability are critical. CEO Suresh Vasudevan explains that AI workloads are among the most demanding distributed applications ever, and Clockwork provides building blocks that improve visibility, performance and fault tolerance. Its flagship feature, FleetIQ, can reroute traffic around failing switches, preventing costly interruptions that might otherwise force teams to restart training from hours-old checkpoints. Although the company originated from Stanford research focused on clock synchronization for financial institutions, the team eventually recognized that packet-timing data could underpin powerful network telemetry and dynamic traffic control. By integrating with NVIDIA NCCL, TCP and RDMA libraries, Clockwork can not only measure congestion but also actively manage GPU communication to enhance both uptime and training efficiency. Learn more from The New Stack about the latest in Clockwork: Clockwork's FleetIQ Aims To Fix AI's Costly Network Bottleneck What Happens When 116 Makers Reimagine the Clock? 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.

At JupyterCon 2025, Jupyter Deploy was introduced as an open source command-line tool designed to make cloud-based Jupyter deployments quick and accessible for small teams, educators, and researchers who lack cloud engineering expertise. As described by AWS engineer Jonathan Guinegagne, these users often struggle in an “in-between” space—needing more computing power and collaboration features than a laptop offers, but without the resources for complex cloud setups. Jupyter Deploy simplifies this by orchestrating an entire encrypted stack—using Docker, Terraform, OAuth2, and Let's Encrypt—with minimal setup, removing the need to manually manage 15–20 cloud components. While it offers an easy on-ramp, Guinegagne notes that long-term use still requires some cloud understanding. Built by AWS's AI Open Source team but deliberately vendor-neutral, it uses a template-based approach, enabling community-contributed deployment recipes for any cloud. Led by Brian Granger, the project aims to join the official Jupyter ecosystem, with future plans including Kubernetes integration for enterprise scalability. Learn more from The New Stack about the latest in Jupyter AI development: Introduction to Jupyter Notebooks for DevelopersDisplay AI-Generated Images in a Jupyter Notebook 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.

In an interview at JupyterCon, Brian Granger — co-creator of Project Jupyter and senior principal technologist at AWS — reflected on Jupyter's evolution and how AI is redefining open source sustainability. Originally inspired by physics' modular principles, Granger and co-founder Fernando Pérez designed Jupyter with flexible, extensible components like the notebook format and kernel message protocol. This architecture has endured as the ecosystem expanded from data science into AI and machine learning. Now, AI is accelerating development itself: Granger described rewriting Jupyter Server in Go, complete with tests, in just 30 minutes using an AI coding agent — a task once considered impossible. This shift challenges traditional notions of technical debt and could reshape how large open source projects evolve. Jupyter's 2017 ACM Software System Award placed it among computing's greats, but also underscored its global responsibility. Granger emphasized that sustaining Jupyter's mission — empowering human reasoning, collaboration, and innovation — remains the team's top priority in the AI era. Learn more from The New Stack about the latest in Jupyter AI development: Introduction to Jupyter Notebooks for Developers Display AI-Generated Images in a Jupyter Notebook  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: httptap 10 Smart Performance Hacks For Faster Python Code FastRTC Explore Python dependencies with pipdeptree and uv pip tree 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: httptap Rich-powered CLI that breaks each HTTP request into DNS, connect, TLS, wait, and transfer phases with waterfall timelines, compact summaries, or metrics-only output. Features Phase-by-phase timing – precise measurements built from httpcore trace hooks (with sane fallbacks when metal-level data is unavailable). All HTTP methods – GET, POST, PUT, PATCH, DELETE, HEAD, OPTIONS with request body support. Request body support – send JSON, XML, or any data inline or from file with automatic Content-Type detection. IPv4/IPv6 aware – the resolver and TLS inspector report both the address and its family. TLS insights – certificate CN, expiry countdown, cipher suite, and protocol version are captured automatically. Multiple output modes – rich waterfall view, compact single-line summaries, or -metrics-only for scripting. JSON export – persist full step data (including redirect chains) for later processing. Extensible – clean Protocol interfaces for DNS, TLS, timing, visualization, and export so you can plug in custom behavior. Example: Brian #2: 10 Smart Performance Hacks For Faster Python Code Dido Grigorov A few from the list Use math functions instead of operators Avoid exception handling in hot loops Use itertools for combinatorial operations - huge speedup Use bisect for sorted list operations - huge speedup Michael #3: FastRTC The Real-Time Communication Library for Python: Turn any python function into a real-time audio and video stream over WebRTC or WebSockets. Features

Today we're digging into the Model Context Protocol, or MCP. Think LSP for AI: build a small Python service once and your tools and data show up across editors and agents like VS Code, Claude Code, and more. My guest, Den Delimarsky from Microsoft, helps build this space and will keep us honest about what's solid versus what's just shiny. We'll keep it practical: transports that actually work, guardrails you can trust, and a tiny server you could ship this week. By the end, you'll have a clear mental model and a path to plug Python into the internet of agents. Episode sponsors Sentry AI Monitoring, Code TALKPYTHON NordStellar Talk Python Courses Links from the show Den Delimarsky: den.dev Agentic AI Programming for Python Course: training.talkpython.fm Model Context Protocol: modelcontextprotocol.io Model Context Protocol Specification (2025-03-26): modelcontextprotocol.io MCP Python Package (PyPI): pypi.org Awesome MCP Servers (punkpeye) GitHub Repo: github.com Visual Studio Code Docs: Copilot MCP Servers: code.visualstudio.com GitHub MCP Server (GitHub repo): github.com GitHub Blog: Meet the GitHub MCP Registry: github.blog MultiViewer App: multiviewer.app GitHub Blog: Spec-driven development with AI (open source toolkit): github.blog Model Context Protocol Registry (GitHub): github.com mcp (GitHub organization): github.com Tailscale: tailscale.com Watch this episode on YouTube: youtube.com Episode #527 deep-dive: talkpython.fm/527 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Russell McOrmond recently retired from being a Systems Administrator and Software Developer and was granted the title of “Surreal Systems Analyst.” He now spends his time thinking about the meaning of life. We discuss race, gender, culture, Autism, and life. Be sure to visit his amazing Substack! This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit mikeyopp.substack.com/subscribe

Big Idea archaeologist Pranav Kale joined me on Ditching Hourly for a reverse interview about my journey to the center of the solar system.Chapters(00:00) - Introduction and Guest Welcome (00:39) - Pranav's Evolution and Current Focus (03:01) - Jonathan's Epiphany on Hourly Billing (06:26) - The Trust Fracture in Hourly Billing (07:45) - Jonathan's Problem-Solving Journey (16:59) - The Move to Rhode Island and Finding the Solution (22:38) - Implementing Value-Based Pricing (26:30) - Content Creation and Teaching Others (30:35) - Understanding Business Personality Types (31:08) - The Journey to Writing a Book (31:56) - Transitioning to Advisory Roles (32:37) - The Impact of the iPhone Announcement (33:37) - Responsive Web Design and Consulting (35:46) - Facing Criticism and Adjusting Messaging (39:50) - Finding the Central Theme (53:06) - The Importance of Differentiation (59:53) - Concluding Thoughts and Advice ----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!

Topics covered in this episode: The PSF has withdrawn a $1.5 million proposal to US government grant program A Binary Serializer for Pydantic Models T-strings: Python's Fifth String Formatting Technique? Cronboard 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: The PSF has withdrawn a $1.5 million proposal to US government grant program Related post from Simon Willison ARS Technica: Python plan to boost software security foiled by Trump admin's anti-DEI rules The Register: Python Foundation goes ride or DEI, rejects government grant with strings attached In Jan 2025, the PSF submitted a proposal for a US NSF grant under the Safety, Security, and Privacy of Open Source Ecosystems program. After months of work by the PSF, the proposal was recommended for funding. If the PSF accepted it, however, they would need to agree to the some terms and conditions, including, affirming that the PSF doesn't support diversity. The restriction wouldn't just be around the security work, but around all activity of the PSF as a whole. And further, that any deemed violation would give the NSF the right to ask for the money back. That just won't work, as the PSF would have already spent the money. The PSF mission statement includes "The mission of the Python Software Foundation is to promote, protect, and advance the Python programming language, and to support and facilitate the growth of a diverse and international community of Python programmers." The money would have obviously been very valuable, but the restrictions are just too unacceptable. The PSF withdrew the proposal. This couldn't have been an easy decision, that was a lot of money, but I think the PSF did the right thing. Michael #2: A Binary Serializer for Pydantic Models 7× Smaller Than JSON A compact binary serializer for Pydantic models that dramatically reduces RAM usage compared to JSON. The library is designed for high-load systems (e.g., Redis caching), where millions of models are stored in memory and every byte matters. It serializes Pydantic models into a minimal binary format and deserializes them back with zero extra metadata overhead. Target Audience: This project is intended for developers working with: high-load APIs in-memory caches (Redis, Memcached) message queues cost-sensitive environments where object size matters Brian #3: T-strings: Python's Fifth String Formatting Technique? Trey Hunner Python 3.14 has t-strings. How do they fit in with the rest of the string story? History percent-style (%) strings - been around for a very long time string.Template - and t.substitute() - from Python 2.4, but I don't think I've ever used them bracket variables and .format() - Since Python 2.6 f-strings - Python 3.6 - Now I feel old. These still seem new to me t-strings - Python 3.14, but a totally different beast. These don't return strings. Trey then covers a problem with f-strings in that the substitution happens at definition time. t-strings have substitution happen later. this is essentially “lazy string interpolation” This still takes a bit to get your head around, but I appreciate Trey taking a whack at the explanation. Michael #4: Cronboard Cronboard is a terminal application that allows you to manage and schedule cronjobs on local and remote servers. With Cronboard, you can easily add, edit, and delete cronjobs, as well as view their status. ✨ Features ✔️ Check cron jobs ✔️ Create cron jobs with validation and human-readable feedback ✔️ Pause and resume cron jobs ✔️ Edit existing cron jobs ✔️ Delete cron jobs ✔️ View formatted last and next run times ✔️ Accepts special expressions like @daily, @yearly, @monthly, etc. ✔️ Connect to servers using SSH, using password or SSH keys ✔️ Choose another user to manage cron jobs if you have the permissions to do so (sudo) Extras Brian: PEP 810: Explicit lazy imports, has been unanimously accepted by steering council Lean TDD book will be written in the open. TOC, some details, and a 10 page introduction are now available. Hoping for the first pass to be complete by the end of the year. I'd love feedback to help make it a great book, and keep it small-ish, on a very limited budget. Joke: You are so wrong!

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.

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

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

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

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

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