Podcasts about software developers

Dan HafnerSoftware Developer, Creator, No-Code ExpertI hear app ideas every single day, and I've learned how to spot the ones with real potential and steer clear of the rest.I've seen firsthand how powerful an app can be as a marketing, fulfillment, and lead-generation tool. There's both an art and a science to bringing these elements together for success.On shows, I can bring value by breaking down this process and explaining how it works in straightforward, simple terms anyone can understand. Hosted on Acast. See acast.com/privacy for more information.

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

Enterprise AI is still in its infancy, with less than 1% of enterprise data currently used to fuel AI, according to Raj Verma, CEO of SingleStore. While consumer AI is slightly more advanced, most organizations are only beginning to understand the scale of infrastructure needed for true AI adoption. Verma predicts AI will evolve in three phases: first, the easy tasks will be automated; next, complex tasks will become easier; and finally, the seemingly impossible will become achievable—likely within three years. However, to reach that point, enterprises must align their data strategies with their AI ambitions. Many have rushed into AI fearing obsolescence, but without preparing their data infrastructure, they're at risk of failure. Current legacy systems are not designed for the massive concurrency demands of agentic AI, potentially leading to underperformance. Verma emphasizes the need to move beyond siloed or "swim lane" databases toward unified, high-performance data platforms tailored for the scale and complexity of the AI era.Learn more from The New Stack about the latest evolution in AI infrastructure: How To Use AI To Design Intelligent, Adaptable InfrastructureHow to Support Developers in Building AI Workloads Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Author, speaker, and human-first AI strategist, Alastair McDermott, joined me on Ditching Hourly to talk about his new book and how experts can use AI to enhance productivity, decision-making, and learning. (00:00) - Introduction and Guest Welcome (00:46) - Alistair's Background and Journey (03:02) - The New Book: Use AI, Stay Human (04:15) - AI as a Tool for Experts (06:55) - Practical Applications of AI (10:00) - Technical Deep Dive: AI in Writing (16:58) - Organizing and Managing AI Outputs (23:40) - Ideal Reader and Learning AI (24:51) - Choosing Your AI Setup: DIY vs. Subscription (25:20) - The Importance of AI Expertise (27:16) - AI's Role in Business and Personal Productivity (28:25) - Challenges and Limitations of AI (30:59) - Effective Prompting Techniques (32:20) - Using AI for Coaching and Workshops (35:35) - Advanced AI Usage Tips (40:46) - Practical AI Tools and Resources (45:28) - Final Thoughts and Resources AI SummaryIn this episode of Ditching Hourly, Jonathan Stark interviews Alastair McDermott about his new book, 'Use AI Stay Human: A Survival Guide for Experts in the Age of AI.' They discuss Alastair's transition from a techie background to running his consulting business, Recognized Authority, and eventually rebranding to Human Spark.AI. The conversation dives into how AI can be used to enhance productivity, decision-making, and learning for subject matter experts. Alastair shares practical tips on setting up effective AI workflows, structuring prompts, and the importance of human judgment in leveraging AI for business purposes.Alastair's LinksBook: https://www.amazon.com/Use-I-Stay-Human-Indispensable-ebook/dp/B0FDQSKZRFWebsite: https://humanspark.ai/LinkedIn: https://www.linkedin.com/in/alastairmcdermott/ ----Before you go!The next time someone asks you for your hourly rate, I want you to stop what you're doing and head on over to valuepricingbootcamp.com to sign up for my free value pricing email course.Hope to see you there!

Topics covered in this episode: * pypistats.org was down, is now back, and there's a CLI* * State of Python 2025* * wrapt: A Python module for decorators, wrappers and monkey patching.* pysentry Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: pypistats.org was down, is now back, and there's a CLI pypistats.org is a cool site to check the download stats for Python packages. It was down for a while, like 3 weeks? A couple days ago, Hugo van Kemenade announced that it was back up. With some changes in stewardship “pypistats.org is back online!

Neste episódio, recebemos Reginaldo Barros e Tiago Aguiar, para discutir como a inteligência artificial está transformando a forma como desenvolvedores aprendem e evoluem na carreira.Partimos da ideia de vibe coding e dos artigos The Junior Developer Extinction e Senior project legacy para refletir sobre questões importantes: até que ponto a IA pode acelerar o aprendizado de um júnior e em que momento ela pode atrapalhar?

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

Anthropic's Model Context Protocol (MCP) has become the standard for connecting AI agents to tools and data, but its security has lagged behind. In The New Stack Agents podcast, Tzvika Shneider, CEO of API security startup Pynt, discussed the growing risks MCP introduces. Shneider sees MCP as a natural evolution from traditional APIs to LLMs and now to AI agents. However, MCP adds complexity and vulnerability, especially as agents interact across multiple servers. Pynt's research found that 72% of MCP plugins expose high-risk operations, like code execution or accessing privileged APIs, often without proper approval or validation. The danger compounds when untrusted inputs from one agent influence another with elevated permissions. Unlike traditional APIs, MCP calls are made by non-deterministic agents, making it harder to enforce security guardrails. While MCP exploits remain rare for now, most companies lack mature security strategies for it. Shneider believes MCP merely highlights existing API vulnerabilities, and organizations are only beginning to address these risks. Learn more from The New Stack about the latest in Model Context Protocol: Model Context Protocol: A Primer for the Developers Building With MCP? Mind the Security Gaps MCP-UI Creators on Why AI Agents Need Rich User InterfacesJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Google's big product announcement was... unique? It was more like a talk show crossed with the shopping channel. US Late Night host Jimmy Fallon hosted it, and it had celebrity cameos from athletes like Stephen Curry, podcaster Alex Cooper, and the Jonas Brothers even made an appearance. Reddit users dubbed it as “unwatchable". They launched new phones, enhanced their folding phones, but of course AI was everywhere: Gemini Live will gain a new audio model that will detect your tone — like whether you're excited or concerned The Pixel phone camera will have a photo coach built in to help with composition. They're trying to make these AI suggestions more contextual and push relevant things to you rather than you needing to seek them out, e.g. you're at the airport, here's your boarding pass. Or you're in a car, here's the directions to your next appointment, etc. A software developer has been sent to prison sabotaging his former employer This wasn't a hack of systems after he was fired, or a mass deletion of data before he was walked out the door, this was pre-planned. The 55 year old developer had created a "kill switch" designed to be used if he was ever fired, and he designed it in such a way it was smart enough to know when he was fired. The software was tracking to see if his work email account was active, and then when it was deactivated the "kill switch" was automatically triggered, crashing the servers. The incident locked out thousands of employees from accessing the company's systems and cost the company hundreds of thousands of dollars in damage. He was discovered in part because of his search history, looking up things like “methods to escalate privileges, hide processes, and rapidly delete files”. He's been sentenced to four years behind bars. LISTEN ABOVE See omnystudio.com/listener for privacy information.

Rahul Auradkar, executive VP and GM at Salesforce, grew up in India with a deep passion for cricket, where his love for the game sparked an early interest in data. This fascination with statistics laid the foundation for his current work leading Salesforce's Data Cloud and Einstein (Unified Data Services) team. Auradkar reflects on how structured data has evolved—from relational databases in enterprise applications to data warehouses, data lakes, and lakehouses. He explains how initial efforts focused on analyzing structured data, which later fed back into business processes. Eventually, businesses realized that the byproducts of data—what he calls "data exhaust"—were themselves valuable. The rise of "old AI," or predictive AI, shifted perceptions, showing that data exhaust could define the application itself. As varied systems emerged with distinct protocols and SQL variants, data silos formed, trapping valuable insights. Auradkar emphasizes that the ongoing challenge is unifying these silos to enable seamless, meaningful business interactions—something Salesforce aims to solve with its Data Cloud and agentic AI platform.Learn more from The New Stack about the evolution of structured data and agent AI: How Enterprises and Startups Can Master AI With Smarter Data Practices Enterprise AI Success Demands Real-Time Data PlatformsJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.

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

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

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

Topics covered in this episode: pyx - optimized backend for uv * Litestar is worth a look* * Django remake migrations* * django-chronos* Extras Joke Watch on YouTube About the show Python Bytes 445 Sponsored by Sentry: pythonbytes.fm/sentry - Python Error and Performance Monitoring Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: pyx - optimized backend for uv via John Hagen (thanks again) I'll be interviewing Charlie in 9 days on Talk Python → Sign up (get notified) of the livestream here. Not a PyPI replacement, more of a middleware layer to make it better, faster, stronger. pyx is a paid service, with maybe a free option eventually. Brian #2: Litestar is worth a look James Bennett Michael brought up Litestar in episode 444 when talking about rewriting TalkPython in Quart James brings up scaling - Litestar is easy to split an app into multiple files Not using pydantic - You can use pydantic with Litestar, but you don't have to. Maybe attrs is right for you instead. Michael brought up Litestar seems like a “more batteries included” option. Somewhere between FastAPI and Django. Brian #3: Django remake migrations Suggested by Bruno Alla on BlueSky In response to a migrations topic last week django-remake-migrations is a tool to help you with migrations and the docs do a great job of describing the problem way better than I did last week “The built-in squashmigrations command is great, but it only work on a single app at a time, which means that you need to run it for each app in your project. On a project with enough cross-apps dependencies, it can be tricky to run.” “This command aims at solving this problem, by recreating all the migration files in the whole project, from scratch, and mark them as applied by using the replaces attribute.” Also of note The package was created with Copier Michael brought up Copier in 2021 in episode 219 It has a nice comparison table with CookieCutter and Yoeman One difference from CookieCutter is yml vs json. I'm actually not a huge fan of handwriting either. But I guess I'd rather hand write yml. So I'm thinking of trying Copier with my future project template needs. Michael #4: django-chronos Django middleware that shows you how fast your pages load, right in your browser. Displays request timing and query counts for your views and middleware. Times middleware, view, and total per request (CPU and DB). Extras Brian: Test & Code 238: So Long, and Thanks for All the Fish after 10 years, this is the goodbye episode Michael: Auto-activate Python virtual environment for any project with a venv directory in your shell (macOS/Linux): See gist. Python 3.13.6 is out. Open weight OpenAI models Just Enough Python for Data Scientists Course The State of Python 2025 article by Michael Joke: python is better than java

In this week's episode ofThe New Stack Agents, Scott Carey, editor-in-chief of LeadDev, discussed their first AI Impact Report, which explores how engineering teams are adopting AI tools. The report shows that two-thirds of developers are actively using AI, with another 20% in pilot stages and only 2% having no plans to use AI — a group Carey finds particularly intriguing. Popular tools include Cursor (43%) and GitHub Copilot (37%), with others like OpenAI, Gemini, and Claude following, while Amazon Q and Replit lag behind.Most developers use AI for code generation, documentation, and research, but usage for DevOps tasks like testing, deployment, and IT automation remains low. Carey finds this underutilization frustrating, given AI's potential impact in these areas. The report also highlights concern for junior developers, with 54% of respondents expecting fewer future hires at that level. While many believe AI boosts productivity, some remain unsure — a sign that organizations still struggle to measure developer performance effectively.Learn more from The New Stack about the latest insights about the AI tool adoption: AI Adoption: Why Businesses Struggle to Move from Development to Production3 Strategies for Speeding Up AI Adoption Among DevelopersAI Everywhere: Overcoming Barriers to AdoptionJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

What if your code was crash-proof? That's the value prop for a framework called Temporal. Temporal is a durable execution platform that enables developers to build scalable applications without sacrificing productivity or reliability. The Temporal server executes units of application logic called Workflows in a resilient manner that automatically handles intermittent failures, and retries failed operations. We have Mason Egger from Temporal on to dive into durable execution. Episode sponsors Posit PyBay Talk Python Courses Links from the show Just Enough Python for Data Scientists Course: talkpython.fm Temporal Durable Execution Platform: temporal.io Temporal Learn Portal: learn.temporal.io Temporal GitHub Repository: github.com Temporal Python SDK GitHub Repository: github.com What Is Durable Execution, Temporal Blog: temporal.io Mason on Bluesky Profile: bsky.app Mason on Mastodon Profile: fosstodon.org Mason on Twitter Profile: twitter.com Mason on LinkedIn Profile: linkedin.com X Post by @skirano: x.com Temporal Docker Compose GitHub Repository: github.com Building a distributed asyncio event loop (Chad Retz) - PyTexas 2025: youtube.com Watch this episode on YouTube: youtube.com Episode #515 deep-dive: talkpython.fm/515 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

Topics covered in this episode: Coverage.py regex pragmas * Python of Yore* * nox-uv* * A couple Django items* Extras Joke Watch on YouTube About the show Sponsored by DigitalOcean: pythonbytes.fm/digitalocean-gen-ai Use code DO4BYTES and get $200 in free credit Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Coverage.py regex pragmas Ned Batchelder The regex implementation of how coverage.py recognizes pragmas is pretty amazing. It's extensible through plugins covdefaults adds a bunch of default exclusions, and also platform- and version-specific comment syntaxes. coverage-conditional-plugin gives you a way to create comment syntaxes for entire files, for whether other packages are installed, and so on. A change from last year (as part of coverage.py 7.6 allows multiline regexes, which let's us do things like: Exclude an entire file with A(?s:.*# pragma: exclude file.*)Z Allow start and stop delimiters with # no cover: start(?s:.*?)# no cover: stop Exclude empty placeholder methods with ^s*(((async )?def .*?)?)(s*->.*?)?:s*)?...s*(#|$) See Ned's article for explanations of these Michael #2: Python of Yore via Matthias Use YORE: ... comments to highlight CPython version dependencies. # YORE: EOL 3.8: Replace block with line 4. if sys.version_info < (3, 9): from astunparse import unparse else: from ast import unparse Then check when they go out of support: $ yore check --eol-within '5 months' ./src/griffe/agents/nodes/_values.py:11: Python 3.8 will reach its End of Life within approx. 4 months Even fix them with fix . Michael #3: nox-uv via John Hagen What nox-uv does is make it very simple to install uv extras and/or dependency groups into a nox session's virtual environment. The versions installed are constrained by uv's lockfile meaning that everything is deterministic and pinned. Dependency groups make it very easy to install only want is necessary for a session (e.g., only linting dependencies like Ruff, or main dependencies + mypy for type checking). Brian #4: A couple Django items Stop Using Django's squashmigrations: There's a Better Way Johnny Metz Resetting migrations is sometimes the right thing. Overly simplified summary: delete migrations and start over dj-lite Adam Hill Use SQLite in production with Django “Simplify deploying and maintaining production Django websites by using SQLite in production. dj-lite helps enable the best performance for SQLite for small to medium-sized projects. It requires Django 5.1+.” Extras Brian: Test & Code 237: FastAPI Cloud with Sebastian Ramirez will be out later today pythontest.com: pytest fixtures nuts and bolts - revisited A blog series that I wrote a long time ago. I've updated it into more managable bite-sized pieces, updated and tested with Python 3.13 and pytest 8 Michael: New course: Just Enough Python for Data Scientists My live stream about uv is now on YouTube Cursor CLI: Built to help you ship, right from your terminal. Joke: Copy/Paste

Neste episódio, conversamos com Flávio Lopes sobre protocolos MCP e A2A, a evolução das APIs no cenário corporativo e como a Arquitetura de Soluções se adapta às demandas modernas de integração entre sistemas.

While AI training garners most of the spotlight — and investment — the demands ofAI inferenceare shaping up to be an even bigger challenge. In this episode ofThe New Stack Makers, Sid Sheth, founder and CEO of d-Matrix, argues that inference is anything but one-size-fits-all. Different use cases — from low-cost to high-interactivity or throughput-optimized — require tailored hardware, and existing GPU architectures aren't built to address all these needs simultaneously.“The world of inference is going to be truly heterogeneous,” Sheth said, meaning specialized hardware will be required to meet diverse performance profiles. A major bottleneck? The distance between memory and compute. Inference, especially in generative AI and agentic workflows, requires constant memory access, so minimizing the distance data must travel is key to improving performance and reducing cost.To address this, d-Matrix developed Corsair, a modular platform where memory and compute are vertically stacked — “like pancakes” — enabling faster, more efficient inference. The result is scalable, flexible AI infrastructure purpose-built for inference at scale.Learn more from The New Stack about inference compute and AIScaling AI Inference at the Edge with Distributed PostgreSQLDeep Infra Is Building an AI Inference Cloud for DevelopersJoin our community of newsletter subscribers to stay on top of the news and at the top of your game  

Author Robin Bonn joined me on Ditching Hourly to discuss the importance of differentiation as described in his book Market of One. About RobinRobin Bonn is the CEO of Co:definery, one of the world's leading consultancies specialising in agency positioning. From renowned global networks to the world's top independents, he's repositioned close to 150 agencies and coached dozens of senior leaders. He's the author of Market of One, the host of The Immortal Life of Agencies podcast, and a columnist for Marketing Week. ----Before you go!The next time someone asks you for your hourly rate, I want you to stop what you're doing and head on over to valuepricingbootcamp.com to sign up for my free value pricing email course.Hope to see you there!

Topics covered in this episode: rumdl - A Markdown Linter written in Rust * Coverage 7.10.0: patch* * aioboto3* * You might not need a Python class* Extras Joke Watch on YouTube About the show Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: rumdl - A Markdown Linter written in Rust via Owen Lamont Supports toml file config settings Install via uv tool install rumdl. ⚡️ Built for speed with Rust - significantly faster than alternatives

If I lost everything today—no job, no network, no portfolio—and had to start over in 2025 as a software developer, I wouldn't be looking for a 3-month miracle. I'd be planning for 12 months. That's how long it really takes now.We cover:what tech stack you should learnnetworking as a developer using BFSa capstone project that is actually impressivean interviewing strategy3 books I'd read as a new developerAt Parsity, we cover all this and much more. Apply here. Send us a textShameless Plugs

Topics covered in this episode: * Open Source Security work isn't “Special”* * uv v0.8* * Extra, Extra, Extra* Announcing Toad - a universal UI for agentic coding in the terminal 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: Open Source Security work isn't “Special” Seth Larson It seems like security is special in a sense that we don't want just anyone working on the security aspect of a project. We just want the trusted maintainers, right? Seth is arguing that this is the wrong mindset It makes more sense that we maybe have security experts contribute to many projects, and that someone working on security for just one project doesn't benefit from scale. “Maintainers don't see how other projects are triaging vulnerabilities and can't learn from each other. They can't compare notes on what they are seeing and whether they are doing the right thing. Isolation in security work breeds a culture of fear. Fear of doing the wrong thing and making your users unsafe.” “These “security contributors” could be maintainers or contributors of other open source projects that know about security, they could be foundations offering up resources to their ecosystem, or engineers at companies helping their dependency graph.” But how do we build trust in these individuals? Meeting in person works. But there are other ways as well. I'd personally love to have someone contact me about a project of mine regarding a security problem or process that the project could/should follow. Especially if I could see other projects I trust already trusting this individual to work on the other projects. Michael #2: uv v0.8 Changes Install Python executables into a directory on the PATH Register Python versions with the Windows Registry Prompt before removing an existing directory in uv venv Bump --python-platform linux to manylinux_2_28 Make uv_build the default build backend in uv init And many more And uv v0.8.1 Lots of enhancements. And uv v0.8.2 And uv v0.8.3 Adds Add CPython 3.14.0rc1 Brian #3: Extra, Extra, Extra fstrings.wtf - Armin Ronacher Python 3.14 release candidate 1 is go! Django turns 20, with parties mkdocs-redirects I'm Tired of Talking About AI - Paddy Carver Michael #4: Announcing Toad - a universal UI for agentic coding in the terminal by Will McGugan A universal front-end for AI in the terminal. Watch the video. Joke: Heaviest objects in the universe And … Cloud Architects 2025 “They send us our cloud bills in scientific notation… “

Fal.ai, once focused on machine learning infrastructure, has evolved into a major player in generative media. In this episode of The New Stack Agents, hosts speak with Fal.ai CEO Burkay Gur and investor Glenn Solomon of Notable Capital. Originally aiming to optimize Python runtimes, Fal.ai shifted direction as generative AI exploded, driven by tools like DALL·E and ChatGPT. Today, Fal.ai hosts hundreds of models—from image to audio and video—and emphasizes fast, optimized inference to meet growing demand.Speed became Fal.ai's competitive edge, especially as newer generative models require GPU power not just for training but also for inference. Solomon noted that while optimization alone isn't a sustainable business model, Fal's value lies in speed and developer experience. Fal.ai offers both an easy-to-use web interface and developer-focused APIs, appealing to both technical and non-technical users.Gur also addressed generative AI's impact on creatives, arguing that while the cost of creation has plummeted, the cost of creativity remains—and may even increase as content becomes easier to produce.Learn more from The New Stack about AI's impact on creatives:AI Will Steal Developer Jobs (But Not How You Think) How AI Agents Will Change the Web for Users and Developers Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Topics covered in this episode: * Distributed sqlite follow up: Turso and Litestream* * PEP 792 – Project status markers in the simple index* Run coverage on tests docker2exe: Convert a Docker image to an executable Extras Joke Watch on YouTube About the show Sponsored by Digital Ocean: pythonbytes.fm/digitalocean-gen-ai Use code DO4BYTES and get $200 in free credit Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Distributed sqlite follow up: Turso and Litestream Michael Booth: Turso marries the familiarity and simplicity of SQLite with modern, scalable, and distributed features. Seems to me that Turso is to SQLite what MotherDuck is to DuckDB. Mike Fiedler Continue to use the SQLite you love and care about (even the one inside Python runtime) and launch a daemon that watches the db for changes and replicates changes to an S3-type object store. Deeper dive: Litestream: Revamped Brian #2: PEP 792 – Project status markers in the simple index Currently 3 status markers for packages Trove Classifier status Indices can be yanked PyPI projects - admins can quarantine a project, owners can archive a project Proposal is to have something that can have only one state active archived quarantined deprecated This has been Approved, but not Implemented yet. Brian #3: Run coverage on tests Hugo van Kemenade And apparently, run Ruff with at least F811 turned on Helps with copy/paste/modify mistakes, but also subtler bugs like consumed generators being reused. Michael #4: docker2exe: Convert a Docker image to an executable This tool can be used to convert a Docker image to an executable that you can send to your friends. Build with a simple command: $ docker2exe --name alpine --image alpine:3.9 Requires docker on the client device Probably doesn't map volumes/ports/etc, though could potentially be exposed in the dockerfile. Extras Brian: Back catalog of Test & Code is now on YouTube under @TestAndCodePodcast So far 106 of 234 episodes are up. The rest are going up according to daily limits. Ordering is rather chaotic, according to upload time, not release ordering. There will be a new episode this week pytest-django with Adam Johnson Joke: If programmers were doctors

Every year the core developers of Python convene in person to focus on high priority topics for CPython and beyond. This year they met at PyCon US 2025. Those meetings are closed door to keep focused and productive. But we're lucky that Seth Michael Larson was in attendance and wrote up each topic presented and the reactions and feedback to each. We'll be exploring this year's Language Summit with Seth. It's quite insightful to where Python is going and the pressing matters. Episode sponsors Seer: AI Debugging, Code TALKPYTHON Sentry AI Monitoring, Code TALKPYTHON Talk Python Courses Links from the show Seth on Mastodon: @sethmlarson@fosstodon.org Seth on Twitter: @sethmlarson Seth on Github: github.com Python Language Summit 2025: pyfound.blogspot.com WheelNext: wheelnext.dev Free-Threaded Wheels: hugovk.github.io Free-Threaded Python Compatibility Tracking: py-free-threading.github.io PEP 779: Criteria for supported status for free-threaded Python: discuss.python.org PyPI Data: py-code.org Senior Engineer tries Vibe Coding: youtube.com Watch this episode on YouTube: youtube.com Episode #514 deep-dive: talkpython.fm/514 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

Traditional headless browsers weren't built for AI agents, often breaking when web elements shift even slightly. Paul Klein IV, founder of Browserbase and its open-source tool Stagehand, is tackling this by creating a browser infrastructure designed specifically for AI control. On The New Stack Agents podcast, Klein explained that Stagehand enables AI agents to interpret vague, natural-language instructions and still function reliably—even when web pages change. This flexibility contrasts with brittle legacy tools built for deterministic testing. Instead of writing 100 scripts for 100 websites, one AI-powered script can now handle thousands.Klein's broader vision is a world where AI can fully operate the web on behalf of users—automating tasks like filing taxes without human input. He acknowledges the technical challenges, from running browsers on servers to handling edge cases like time zones and emojis. The episode also touches on Klein's concerns with AWS, which he says held a “partnership” meeting that felt more like corporate espionage. Still, Klein remains confident in Browserbase's community-driven edge.Learn more from The New Stack about the latest insights in AI browser based tools: Why Headless Browsers Are a Key Technology for AI Agents Ladybird: That Rare Breed of Browser Based on Web Standards Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

Topics covered in this episode: * Switching to direnv, Starship, and uv* * rqlite - Distributed SQLite DB* * Some Markdown Stuff* Extras Joke Watch on YouTube About the show Sponsored by PropelAuth: pythonbytes.fm/propelauth77 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: Switching to direnv, Starship, and uv Last week I mentioned that I'm ready to try direnv again, but secretly, I still had some worries about the process. Thankfully, Trey has a tutorial to walk me past the troublesome parts. direnv - an extension for your shell. It augments existing shells with a new feature that can load and unload environment variables depending on the current directory. Switching from virtualenvwrapper to direnv, Starship, and uv - Trey Hunner** Trey has solved a bunch of the problems I had when I tried direnv before Show the virtual environment name in the prompt Place new virtual environments in local .venv instead of in .direnv/python3.12 Silence all of the “loading”, “unloading” statements every time you enter a directory Have a script called venv to create an environment, activate it, create a .envrc file I'm more used to a create script, so I'll stick with that name and Trey's contents A workon script to be able to switch around to different projects. This is a carry over from “virtualenvwrapper', but seems cool. I'll take it. Adding uv to the mix for creating virtual environments. Interestingly including --seed which, for one, installs pip in the new environment. (Some tools need it, even if you don't) Starship Trey also has some setup for Starship. But I'll get through the above first, then MAYBE try Starship again. Some motivation Trey's setup is pretty simple. Maybe I was trying to get too fancy before Starship config in toml files that can be loaded with direnv and be different for different projects. Neato Also, Trey mentions his dotfiles repo. This is a cool idea that I've been meaning to do for a long time. See also: It's Terminal - Bootstrapping With Starship, Just, Direnv, and UV - Mario Munoz Michael #2: rqlite - Distributed SQLite DB via themlu, thanks! rqlite is a lightweight, user-friendly, distributed relational database built on SQLite. Built on SQLite, the world's most popular database Supports full-text search, Vector Search, and JSON documents Access controls and encryption for secure deployments Michael #3: A Python dict that can report which keys you did not use by Peter Bengtsson Very cool for testing that a dictionary has been used as expected (e.g. all data has been sent out via an API or report). Note: It does NOT track d.get(), but it's easy to just add it to the class in the post. Maybe someone should polish it up and put it on pypi (that person is not me :) ). Brian #4: Some Markdown Stuff Textual 4.0.0 adds Markdown.append which can be used to efficiently stream markdown content The reason for the major bump is due to an interface change to Widget.anchor Refreshing to see a symantic change cause a major version bump. html-to-markdown Converts html to markdown A complete rewrite fork of markdownify Lots of fun features like “streaming support” Curious if it can stream to Textual's Markdown.append method. hmmm. Joke: Vibecon is hard to attend

Why do people list to this podcast? Sure, they're looking for technical explorations of new libraries and ideas. But often it's to hear the story behind them. If that speaks to you, then I have the perfect episode lined up. I have Barry Warsaw, Paul Everitt, Carol Willing, and Brett Cannon all back on the show to share stories from the history of Python. You'll hear about how import this came to be and how the first PyCon had around 30 attendees (two of whom are guests on this episode!). Sit back and enjoy the humorous stories from Python's past. Episode sponsors Posit Agntcy Talk Python Courses Links from the show Barry's Zen of Python song: youtube.com Jake Vanderplas - Keynote - PyCon 2017: youtube.com Why it's called “Python” (Monty Python fan-reference): geeksforgeeks.org import antigravity: python-history.blogspot.com NIST Python Workshop Attendees: legacy.python.org Paul Everitt open-sources Zope: old.zope.dev Carol Willing wins ACM Software System Award: awards.acm.org Watch this episode on YouTube: youtube.com Episode #513 deep-dive: talkpython.fm/513 Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

In a recent episode of The New Stack Agents livestream, Antje Barth, AWS Developer Advocate for Generative AI, discussed the growing developer interest in building agentic and multi-agent systems. While foundational model knowledge is now common, Barth noted that developers are increasingly focused on tools, frameworks, and protocols for scaling agent-based applications. She emphasized the complexity of deploying such systems, particularly around navigating human-centric interfaces and minimizing latency in multi-agent communication.Barth highlighted AWS's support for developers through tools like Amazon Q CLI and the newly launched open-source Strands SDK, which AWS used internally to accelerate development cycles. Strands enables faster, flexible agentic system development, while services like Bedrock Agents offer a managed, enterprise-ready solution.Security was another key theme. Barth stressed that safety must be a “day one” priority, with built-in support for authentication, secure communication, and observability. She encouraged developers to leverage AWS's GenAI Innovation Center and active open-source communities to build robust, scalable, and secure agentic systems.Learn more from The New Stack about AWS' support for developers through tools that support multiple agents: Code in Your Native Tongue: Amazon Q Developer Goes Global AWS Launches Its Take on an Open Source AI Agents SDKAmazon's Bedrock Can Now 'Check' AI for Hallucinations Join our community of newsletter subscribers to stay on top of the news and at the top of your game.     

Topics covered in this episode: * ty documentation site and uv migration guide* * uv build backend is now stable + other Astral news* * Refactoring long boolean expressions* * fastapi-ml-skeleton* Extras Joke Watch on YouTube About the show Sponsored by Sentry: pythonbytes.fm/sentry 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: ty documentation site and uv migration guide via Skyler Kasko Astral created a documentation site for ty (PR #744 in release 0.0.1-alpha.13). Astral added a page on migrating from pip to a uv project in the uv documentation. (PR #12382 in release 0.7.19). Talk Python episode on ty. Brian #2: uv build backend is now stable + other Astral news The uv build backend is now stable Tim Hopper via Python Developer Tooling Handbook From Charlie Marsh “The uv build backend is now stable, and considered ready for production use. An alternative to setuptools, hatchling, etc. for pure Python projects, with a focus on good defaults, user-friendly error messages, and performance. When used with uv, it's 10-35x faster.” “(In a future release, we'll make this the default.)” [build-system] requires = ["uv_build>=0.7.19,

In Episode 9 of Building Better Developers with AI, Rob Broadhead and Michael Meloche explore how cultivating essential habits for software developers, alongside AI tools and consistent routines, can unlock productivity, sharpen your skills, and accelerate long-term growth. Why Essential Habits for Software Developers Matter for Career Growth Rob and Michael emphasize that technical skills alone won't set you apart. Instead, it's your daily discipline and consistent habits that fuel success. As shared in the episode: “Success as a developer isn't just about what you know—it's about what you consistently do.” Essential habits for software developers, from daily practice to continuous learning, create momentum that turns average developers into high performers. How AI Supports the Best Habits for Software Developers AI can accelerate your growth, but only when used wisely. Rob and Michael stress that: AI-generated code requires critical review AI tools like CodeSignal and Codacy help improve coding habits Building AI chatbots sharpens your understanding of prompts and system behavior By incorporating AI tools into your daily routines, you strengthen the essential habits of modern software development. Mastering Time Management: A Core Habit for Productive Developers Effective time management is one of the most essential habits for software developers aiming to maximize output. Rob recommends the Pomodoro technique, supported by focus tools like Brain.fm, to create distraction-free work sessions. Michael offers a practical addition: Maintain daily task lists Document roadblocks and scope changes Prioritize meaningful work over busy tasks Time management habits like these reduce burnout and keep developers focused on what matters most. Continuous Learning: A Vital Habit for Software Developer Success One of the most powerful essential habits for software developers is embracing continuous learning. Rob and Michael suggest: Reading technical blogs, books, or documentation weekly Watching educational videos or listening to podcasts Staying up to date with frameworks, languages, and soft skills Rob explains: “Learning equals leverage—it's how you move from junior to lead.” They also invite listeners to request a free copy of their developer career roadmap book by emailing info@developerneur.com by the end of July. Tools and Techniques to Strengthen Developer Habits Rob and Michael recommend practical resources to help cultivate essential habits for software developers: Free AI tools and cloud credits from AWS, Azure, and GCP Static code analysis tools like SonarQube can be used to improve code quality Daily self-review of your code to identify and correct issues Experimenting with AI chatbots to boost prompt engineering skills By combining these tools with consistent habits, software developers can stay competitive and continuously improve. Final Thoughts: Start Building Essential Habits for Software Developers Today Whether it's time management, AI tools, daily practice, or continuous learning, success in software development comes from building better habits and sticking to them. If you want to boost productivity, sharpen your skills, and accelerate your career, focus on developing the essential habits that top software developers rely on. Stay Connected: Join the Developreneur Community We invite you to join our community and share your coding journey with us. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at info@develpreneur.com with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development. Additional Resources Productivity Habits To Start Your Day Right The 21-Day Habit Building Challenge 3 Habits For Every Day and a Happier Life Code Reviews – Build Habits And Best Practices Building Better Developers With AI Podcast Videos – With Bonus Content

Do you like to dive into the details and intricacies of how Python executes and how we can optimize it? Well, do I have an episode for you. We welcome back Brandt Bucher to give us an update on the upcoming JIT compiler for Python and why it differs from JITs for languages such as C# and Java. Episode sponsors Posit Talk Python Courses Links from the show Brandt Bucher: github.com/brandtbucher PyCon Talk: What they don't tell you about building a JIT compiler for CPython: youtube.com Specializing, Adaptive Interpreter Episode: talkpython.fm Watch this episode on YouTube: youtube.com Episode #512 deep-dive: talkpython.fm/512 Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

Topics covered in this episode: * Python Cheat Sheets from Trey Hunner* * Automatisch* * mureq-typed* * My CLI World* Extras Joke Watch on YouTube About the show Sponsored by Posit: pythonbytes.fm/connect Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Python Cheat Sheets from Trey Hunner Some fun sheets Python f-string tips & cheat sheets Python's pathlib module Python's many command-line utilities Michael #2: Automatisch Open source Zapier alternative Automatisch helps you to automate your business processes without coding. Use their affordable cloud solution or self-host on your own servers. Automatisch allows you to store your data on your own servers, good for companies dealing with sensitive user data, particularly in industries like healthcare and finance, or those based in Europe bound by General Data Protection Regulation (GDPR). Michael #3: mureq-typed Single file, zero-dependency alternative to requests. Fully typed. Modern Python tooling. Typed version of mureq (covered in 2022 on episode 268) Intended to be vendored in-tree by Linux systems software and other lightweight applications. mureq-typed is a drop-in, fully API compatible replacement for mureq updated with modern Python tooling: Type checked with mypy, ty, and pyrefly. Formatted with black, no ignore rules necessary. Linted with ruff (add these rules for mureq.py to your per-file-ignores). Brian #4: My CLI World Frank Wiles Encouragement to modify your command line environment Some of Franks tools direnv, zoxide, fd, ack, atuin, just Also some aliases, like gitpulllog Notes We covered poethepoet recently, if just just isn't cutting it for you. I tried to ilke starship, bit for some reason with my setup, it slows down the shell too much. Extras Brian: Interesting read of the week: New theory proposes time has three dimensions, with space as a secondary effect Michael's: New quantum theory of gravity brings long-sought 'theory of everything' a crucial step closer Joke: Brian read a few quotes from the book Disappointing Affirmations, by Dave Tarnowski “You are always just a moment away from your next worst day ever. Or your next best day ever, but let's be realistic.” “You can be anything you want. And yet you keep choosing to be you. I admire your dedication to the role.” “Today I am letting go of the things that are holding me back from the life that I want to live. Then I'm picking them all up again because I have separation anxiety.”

The software development market didn't die, it's just unrecognizable.Let's go over 8 harsh pieces of advice that will help you as a career changer to make a successful switch into a career as a software developer.PS. If you're a front-end developer looking to expand your skills, grab the Node Express Starter Kit here.Have a question for the Friday Q&A show? Submit it through the form in the show notes, and I'll shout you out or keep you anonymous.Send us a textShameless Plugs

If you're doing data science and have mostly spent your time doing exploratory or just local development, this could be the episode for you. We are joined by Catherine Nelson to discuss techniques and tools to move your data science game from local notebooks to full-on production workflows. Episode sponsors Agntcy Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show New Course: LLM Building Blocks for Python: training.talkpython.fm Catherine Nelson LinkedIn Profile: linkedin.com Catherine Nelson Bluesky Profile: bsky.app Enter to win the book: forms.google.com Going From Notebooks to Scalable Systems - PyCon US 2025: us.pycon.org Going From Notebooks to Scalable Systems - Catherine Nelson – YouTube: youtube.com From Notebooks to Scalable Systems Code Repository: github.com Building Machine Learning Pipelines Book: oreilly.com Software Engineering for Data Scientists Book: oreilly.com Jupytext - Jupyter Notebooks as Markdown Documents: github.com Jupyter nbconvert - Notebook Conversion Tool: github.com Awesome MLOps - Curated List: github.com Watch this episode on YouTube: youtube.com Episode #511 deep-dive: talkpython.fm/511 Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

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

Are you using Polars for your data science work? Maybe you've been sticking with the tried-and-true Pandas? There are many benefits to Polars directly of course. But you might not be aware of all the excellent tools and libraries that make Polars even better. Examples include Patito which combines Pydantic and Polars for data validation and polars_encryption which adds AES encryption to selected columns. We have Christopher Trudeau back on Talk Python To Me to tell us about his list of excellent libraries to power up your Polars game and we also talk a bit about his new Polars course. Episode sponsors Agntcy Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show New Theme Song (Full-Length Download and backstory): talkpython.fm/blog Polars for Power Users Course: training.talkpython.fm Awesome Polars: github.com Polars Visualization with Plotly: docs.pola.rs Dataframely: github.com Patito: github.com polars_iptools: github.com polars-fuzzy-match: github.com Nucleo Fuzzy Matcher: github.com polars-strsim: github.com polars_encryption: github.com polars-xdt: github.com polars_ols: github.com Least Mean Squares Filter in Signal Processing: www.geeksforgeeks.org polars-pairing: github.com Pairing Function: en.wikipedia.org polars_list_utils: github.com Harley Schema Helpers: tomburdge.github.io Marimo Reactive Notebooks Episode: talkpython.fm Marimo: marimo.io Ahoy Narwhals Podcast Episode Links: talkpython.fm Watch this episode on YouTube: youtube.com Episode #510 deep-dive: talkpython.fm/510 Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

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

Topics covered in this episode: * Free-threaded Python no longer “experimental” as of Python 3.14* typed-ffmpeg pyleak * Optimizing Test Execution: Running live_server Tests Last with pytest* Extras Joke Watch on YouTube About the show Sponsored by PropelAuth: pythonbytes.fm/propelauth66 Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Free-threaded Python no longer “experimental” as of Python 3.14 “PEP 779 ("Criteria for supported status for free-threaded Python") has been accepted, which means free-threaded Python is now a supported build!” - Hugo van Kemenade PEP 779 – Criteria for supported status for free-threaded Python As noted in the discussion of PEP 779, “The Steering Council (SC) approves PEP 779, with the effect of removing the “experimental” tag from the free-threaded build of Python 3.14.” We are in Phase II then. “We are confident that the project is on the right path, and we appreciate the continued dedication from everyone working to make free-threading ready for broader adoption across the Python community.” “Keep in mind that any decision to transition to Phase III, with free-threading as the default or sole build of Python is still undecided, and dependent on many factors both within CPython itself and the community. We leave that decision for the future.” How long will all this take? According to Thomas Wouters, a few years, at least: “In other words: it'll be a few years at least. It can't happen before 3.16 (because we won't have Stable ABI support until 15) and may well take longer.” Michael #2: typed-ffmpeg typed-ffmpeg offers a modern, Pythonic interface to FFmpeg, providing extensive support for complex filters with detailed typing and documentation. Inspired by ffmpeg-python, this package enhances functionality by addressing common limitations, such as lack of IDE integration and comprehensive typing, while also introducing new features like JSON serialization of filter graphs and automatic FFmpeg validation. Features : Zero Dependencies: Built purely with the Python standard library, ensuring maximum compatibility and security. User-Friendly: Simplifies the construction of filter graphs with an intuitive Pythonic interface. Comprehensive FFmpeg Filter Support: Out-of-the-box support for most FFmpeg filters, with IDE auto-completion. Integrated Documentation: In-line docstrings provide immediate reference for filter usage, reducing the need to consult external documentation. Robust Typing: Offers static and dynamic type checking, enhancing code reliability and development experience. Filter Graph Serialization: Enables saving and reloading of filter graphs in JSON format for ease of use and repeatability. Graph Visualization: Leverages graphviz for visual representation, aiding in understanding and debugging. Validation and Auto-correction: Assists in identifying and fixing errors within filter graphs. Input and Output Options Support: Provide a more comprehensive interface for input and output options, including support for additional codecs and formats. Partial Evaluation: Enhance the flexibility of filter graphs by enabling partial evaluation, allowing for modular construction and reuse. Media File Analysis: Built-in support for analyzing media files using FFmpeg's ffprobe utility, providing detailed metadata extraction with both dictionary and dataclass interfaces. Michael #3: pyleak Detect leaked asyncio tasks, threads, and event loop blocking with stack trace in Python. Inspired by goleak. Use as context managers or function dectorators When using no_task_leaks, you get detailed stack trace information showing exactly where leaked tasks are executing and where they were created. Even has great examples and a pytest plugin. Brian #4: Optimizing Test Execution: Running live_server Tests Last with pytest Tim Kamanin “When working with Django applications, it's common to have a mix of fast unit tests and slower end-to-end (E2E) tests that use pytest's live_server fixture and browser automation tools like Playwright or Selenium. ” Tim is running E2E tests last for Faster feedback from quick tests To not tie up resources early in the test suite. He did this with custom “e2e” marker Implementing a pytest_collection_modifyitems hook function to look for tests using the live_server fixture, and for them automatically add the e2e marker to those tests move those tests to the end The reason for the marker is to be able to Just run e2e tests with -m e2e Avoid running them sometimes with -m "not e2e" Cool small writeup. The technique works for any system that has some tests that are slower or resource bound based on a particular fixture or set of fixtures. Extras Brian: Is Free-Threading Our Only Option? - Interesting discussion started by Eric Snow and recommended by John Hagen Free-threaded Python on GitHub Actions - How to add FT tests to your projects, by Hugo van Kemenade Michael: New course! LLM Building Blocks in Python Talk Python Deep Dives Complete: 600K Words of Talk Python Insights .folders on Linux Write up on XDG for Python devs. They keep pulling me back - ChatGPT Pro with o3-pro Python Bytes is the #1 Python news podcast and #17 of all tech news podcasts. Python 3.13.4, 3.12.11, 3.11.13, 3.10.18 and 3.9.23 are now available Python 3.13.5 is now available! Joke: Naming is hard

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

If you're looking to leverage the insane power of modern GPUs for data science and ML, you might think you'll need to use some low-level programming language such as C++. But the folks over at NVIDIA have been hard at work building Python SDKs which provide nearly native level of performance when doing Pythonic GPU programming. Bryce Adelstein Lelbach is here to tell us about programming your GPU in pure Python. Episode sponsors Posit Agntcy Talk Python Courses Links from the show Bryce Adelstein Lelbach on Twitter: @blelbach Episode Deep Dive write up: talkpython.fm/blog NVIDIA CUDA Python API: github.com Numba (JIT Compiler for Python): numba.pydata.org Applied Data Science Podcast: adspthepodcast.com NVIDIA Accelerated Computing Hub: github.com NVIDIA CUDA Python Math API Documentation: docs.nvidia.com CUDA Cooperative Groups (CCCL): nvidia.github.io Numba CUDA User Guide: nvidia.github.io CUDA Python Core API: nvidia.github.io Numba (JIT Compiler for Python): numba.pydata.org NVIDIA's First Desktop AI PC ($3,000): arstechnica.com Google Colab: colab.research.google.com Compiler Explorer (“Godbolt”): godbolt.org CuPy: github.com RAPIDS User Guide: docs.rapids.ai Watch this episode on YouTube: youtube.com Episode #509 deep-dive: talkpython.fm/509 Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

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

If you've heard the phrase "Automate the boring things" for Python, this episode starts with that idea and takes it to another level. We have Glyph back on the podcast to talk about "Programming YOUR computer with Python." We dive into a bunch of tools and frameworks and especially spend some time on integrating with existing platform APIs (e.g. macOS's BrowserKit and Window's COM APIs) to build desktop apps in Python that make you happier and more productive. Let's dive in! Episode sponsors Posit Agntcy Talk Python Courses Links from the show Glyph on Mastodon: @glyph@mastodon.social Glyph on GitHub: github.com/glyph Glyph's Conference Talk: LceLUPdIzRs: youtube.com Notify Py: ms7m.github.io Rumps: github.com QuickMacHotkey: pypi.org QuickMacApp: pypi.org LM Studio: lmstudio.ai Coolify: coolify.io PyWin32: pypi.org WinRT: pypi.org PyObjC: pypi.org PyObjC Documentation: pyobjc.readthedocs.io Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

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

If you want to leverage the power of LLMs in your Python apps, you would be wise to consider an agentic framework. Agentic empowers the LLMs to use tools and take further action based on what it has learned at that point. And frameworks provide all the necessary building blocks to weave these into your apps with features like long-term memory and durable resumability. I'm excited to have Sydney Runkle back on the podcast to dive into building Python apps with LangChain and LangGraph. Episode sponsors Posit Auth0 Talk Python Courses Links from the show Sydney Runkle: linkedin.com LangGraph: github.com LangChain: langchain.com LangGraph Studio: github.com LangGraph (Web): langchain.com LangGraph Tutorials Introduction: langchain-ai.github.io How to Think About Agent Frameworks: blog.langchain.dev Human in the Loop Concept: langchain-ai.github.io GPT-4 Prompting Guide: cookbook.openai.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

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

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

The folks over at Astral have made some big-time impacts in the Python space with uv and ruff. They are back with another amazing project named ty. You may have known it as Red-Knot. But it's coming up on release time for the first version and with the release it comes with a new official name: ty. We have Charlie Marsh and Carl Meyer on the show to tell us all about this new project. Episode sponsors Posit Auth0 Talk Python Courses Links from the show Talk Python's Rock Solid Python: Type Hints & Modern Tools (Pydantic, FastAPI, and More) Course: training.talkpython.fm Charlie Marsh on Twitter: @charliermarsh Charlie Marsh on Mastodon: @charliermarsh Carl Meyer: @carljm ty on Github: github.com/astral-sh/ty A Very Early Play with Astral's Red Knot Static Type Checker: app.daily.dev Will Red Knot be a drop-in replacement for mypy or pyright?: github.com Hacker News Announcement: news.ycombinator.com Early Explorations of Astral's Red Knot Type Checker: pydevtools.com Astral's Blog: astral.sh Rust Analyzer Salsa Docs: docs.rs Ruff Open Issues (label: red-knot): github.com Ruff Types: types.ruff.rs Ruff Docs (Astral): docs.astral.sh uv Repository: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

Python has many string formatting styles which have been added to the language over the years. Early Python used the % operator to injected formatted values into strings. And we have string.format() which offers several powerful styles. Both were verbose and indirect, so f-strings were added in Python 3.6. But these f-strings lacked security features (think little bobby tables) and they manifested as fully-formed strings to runtime code. Today we talk about the next evolution of Python string formatting for advanced use-cases (SQL, HTML, DSLs, etc): t-strings. We have Paul Everitt, David Peck, and Jim Baker on the show to introduce this upcoming new language feature. Episode sponsors Posit Auth0 Talk Python Courses Links from the show Guests: Paul on X: @paulweveritt Paul on Mastodon: @pauleveritt@fosstodon.org Dave Peck on Github: github.com Jim Baker: github.com PEP 750 – Template Strings: peps.python.org tdom - Placeholder for future library on PyPI using PEP 750 t-strings: github.com PEP 750: Tag Strings For Writing Domain-Specific Languages: discuss.python.org How To Teach This: peps.python.org PEP 501 – General purpose template literal strings: peps.python.org Python's new t-strings: davepeck.org PyFormat: Using % and .format() for great good!: pyformat.info flynt: A tool to automatically convert old string literal formatting to f-strings: github.com Examples of using t-strings as defined in PEP 750: github.com htm.py issue: github.com Exploits of a Mom: xkcd.com pyparsing: github.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy