Podcasts about Cloud computing

In this sponsored episode, FluidCloud co-founders Sharad Kumar and Harshit Omar sit down with William and Eyvonne to discuss how FluidCloud tackles multi-cloud portability. They detail how FluidCloud acts as a cloning platform that scans an existing cloud or VMware environment, extracts complex infrastructure configurations (including compute and storage, as well as firewall rules and... Read more »

Topics covered in this episode: chardet ,AI, and licensing refined-github pgdog: PostgreSQL connection pooler, load balancer and database sharder Agentic Engineering Patterns 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: chardet ,AI, and licensing Thanks Ian Lessing Wow, where to start? A bit of legal precedence research. Chardet dispute shows how AI will kill software licensing, argues Bruce Perens on the Register Also see this GitHub issue. Dan Blanchard, maintainer of a Python character encoding detection library called chardet, released a new version of the library under a new software license. (LGPL → MIT) Dan is allowed to make this change because v7 is a complete “clean room” rewrite using AI BTW, v7 is WAY better: The result is a 48x increase in detection speed for a project that lives in the hot loops of many projects. That will lead to noticeable performance increases for literally millions of users (the package gets ~130M downloads per month). It paves a path towards inclusion in the standard library (assuming they don't institute policies against using AI tools). Thread-safe detect() and detect_all() with no measurable overhead; scales on free-threaded Python 3.13t+ An individual claiming to be Mark Pilgrim, the original creator of the library, opened an issue in the project's GitHub repo arguing that Blanchard had no right to change the software license, citing the LPGL requirement that the license remain unchanged. A 'complete rewrite' is irrelevant, since they had ample exposure to the originally licensed code (i.e. this is not a 'clean room' implementation). Blanchard disagreed, citing how version 7.0.0 and 6.0.0 compare when subjected to JPlag, a library for detecting plagiarism. Blanchard told The Register he had wanted to get chardet added to the Python standard library for more than a decade since it's a core dependency to most Python projects. Brian #2: refined-github Suggested by Matthias Schöttle A browser plugin that improves the GitHub experience A sampling Adds a build/CI status icon next to the repo's name. Adds a link back to the PR that ran the workflow. Enables tab and shift tab for indentation in comment fields. Auto-resizes comment fields to fit their content and no longer show scroll bars. Highlights the most useful comment in issues. Changes the default sort order of issues/PRs to Recently updated. But really, it's a huge list of improvements Michael #3: pgdog: PostgreSQL connection pooler, load balancer and database sharder PgDog is a proxy for scaling PostgreSQL. It supports connection pooling, load balancing queries and sharding entire databases. Written in Rust, PgDog is fast, secure and can manage thousands of connections on commodity hardware. Features PgDog is an application layer load balancer for PostgreSQL Health Checks: PgDog maintains a real-time list of healthy hosts. When a database fails a health check, it's removed from the active rotation and queries are re-routed to other replicas Single Endpoint: PgDog can detect writes (e.g. INSERT, UPDATE, CREATE TABLE, etc.) and send them to the primary, leaving the replicas to serve reads Failover: PgDog monitors Postgres replication state and can automatically redirect writes to a different database if a replica is promoted Sharding: PgDog is able to manage databases with multiple shards Brian #4: Agentic Engineering Patterns Simon Willison So much great stuff here, especially Anti-patterns: things to avoid And 3 sections on testing Red/green TDD First run the test Agentic manual testing Extras Brian: uv python upgrade will upgrade all versions of Python installed with uv to latest patch release suggested by John Hagen Coding After Coders: The End of Computer Programming as We Know It NY Times Article Suggested by Christopher Best quote: “Pushing code that fails pytest is unacceptable and embarrassing.” Michael: Talk Python Training users get a better account dashboard Package Managers Need to Cool Down Will AI Kill Open Source, article + video My Always activate the venv is now a zsh-plugin, sorta. Joke: Ergonomic keyboard Also pretty good and related: Claude Code Mandated Links legal precedence research Chardet dispute shows how AI will kill software licensing, argues Bruce Perens this GitHub issue citing JPlag refined-github Agentic Engineering Patterns Anti-patterns: things to avoid Red/green TDD First run the test Agentic manual testing uv python upgrade Coding After Coders: The End of Computer Programming as We Know It Suggested by Christopher a better account dashboard Package Managers Need to Cool Down Will AI Kill Open Source Always activate the venv now a zsh-plugin Ergonomic keyboard Claude Code Mandated claude-mandated.png blobs.pythonbytes.fm/keyboard-joke.jpeg?cache_id=a6026b

Topics covered in this episode: chardet ,AI, and licensing refined-github pgdog: PostgreSQL connection pooler, load balancer and database sharder Agentic Engineering Patterns 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: chardet ,AI, and licensing Thanks Ian Lessing Wow, where to start? A bit of legal precedence research. Chardet dispute shows how AI will kill software licensing, argues Bruce Perens on the Register Also see this GitHub issue. Dan Blanchard, maintainer of a Python character encoding detection library called chardet, released a new version of the library under a new software license. (LGPL → MIT) Dan is allowed to make this change because v7 is a complete “clean room” rewrite using AI BTW, v7 is WAY better: The result is a 48x increase in detection speed for a project that lives in the hot loops of many projects. That will lead to noticeable performance increases for literally millions of users (the package gets ~130M downloads per month). It paves a path towards inclusion in the standard library (assuming they don't institute policies against using AI tools). Thread-safe detect() and detect_all() with no measurable overhead; scales on free-threaded Python 3.13t+ An individual claiming to be Mark Pilgrim, the original creator of the library, opened an issue in the project's GitHub repo arguing that Blanchard had no right to change the software license, citing the LPGL requirement that the license remain unchanged. A 'complete rewrite' is irrelevant, since they had ample exposure to the originally licensed code (i.e. this is not a 'clean room' implementation). Blanchard disagreed, citing how version 7.0.0 and 6.0.0 compare when subjected to JPlag, a library for detecting plagiarism. Blanchard told The Register he had wanted to get chardet added to the Python standard library for more than a decade since it's a core dependency to most Python projects. Brian #2: refined-github Suggested by Matthias Schöttle A browser plugin that improves the GitHub experience A sampling Adds a build/CI status icon next to the repo's name. Adds a link back to the PR that ran the workflow. Enables tab and shift tab for indentation in comment fields. Auto-resizes comment fields to fit their content and no longer show scroll bars. Highlights the most useful comment in issues. Changes the default sort order of issues/PRs to Recently updated. But really, it's a huge list of improvements Michael #3: pgdog: PostgreSQL connection pooler, load balancer and database sharder PgDog is a proxy for scaling PostgreSQL. It supports connection pooling, load balancing queries and sharding entire databases. Written in Rust, PgDog is fast, secure and can manage thousands of connections on commodity hardware. Features PgDog is an application layer load balancer for PostgreSQL Health Checks: PgDog maintains a real-time list of healthy hosts. When a database fails a health check, it's removed from the active rotation and queries are re-routed to other replicas Single Endpoint: PgDog can detect writes (e.g. INSERT, UPDATE, CREATE TABLE, etc.) and send them to the primary, leaving the replicas to serve reads Failover: PgDog monitors Postgres replication state and can automatically redirect writes to a different database if a replica is promoted Sharding: PgDog is able to manage databases with multiple shards Brian #4: Agentic Engineering Patterns Simon Willison So much great stuff here, especially Anti-patterns: things to avoid And 3 sections on testing Red/green TDD First run the test Agentic manual testing Extras Brian: uv python upgrade will upgrade all versions of Python installed with uv to latest patch release suggested by John Hagen Coding After Coders: The End of Computer Programming as We Know It NY Times Article Suggested by Christopher Best quote: “Pushing code that fails pytest is unacceptable and embarrassing.” Michael: Talk Python Training users get a better account dashboard Package Managers Need to Cool Down Will AI Kill Open Source, article + video My Always activate the venv is now a zsh-plugin, sorta. Joke: Ergonomic keyboard Also pretty good and related: Claude Code Mandated Links legal precedence research Chardet dispute shows how AI will kill software licensing, argues Bruce Perens this GitHub issue citing JPlag refined-github Agentic Engineering Patterns Anti-patterns: things to avoid Red/green TDD First run the test Agentic manual testing uv python upgrade Coding After Coders: The End of Computer Programming as We Know It Suggested by Christopher a better account dashboard Package Managers Need to Cool Down Will AI Kill Open Source Always activate the venv now a zsh-plugin Ergonomic keyboard Claude Code Mandated claude-mandated.png blobs.pythonbytes.fm/keyboard-joke.jpeg?cache_id=a6026b

Monorepos -- you've heard the talks, you've read the blog posts, maybe you've seen a few tantalizing glimpses into how Google or Meta organize their massive codebases. But it's often in the abstract and behind closed doors. What if you could crack open a real, production monorepo, one with over a million lines of Python and over 100 of sub-packages, and actually see how it's built, step by step, using modern tools and standards? That's exactly what Apache Airflow gives us. On this episode, I sit down with Jarek Potiuk and Amogh Desai, two of Airflow's top contributors, to go inside one of the largest open-source Python monorepos in the world and learn how they manage it with uv, pyproject.toml, and the latest packaging standards, so you can apply those same patterns to your own projects. Episode sponsors Agentic AI Course Python in Production Talk Python Courses Links from the show Guests Amogh Desai: github.com Jarek's GitHub: github.com definition of a monorepo: monorepo.tools airflow: airflow.apache.org Activity: github.com OpenAI: airflowsummit.org Part 1. Pains of big modular Python projects: medium.com Part 2. Modern Python packaging standards and tools for monorepos: medium.com Part 3. Monorepo on steroids - modular prek hooks: medium.com Part 4. Shared “static” libraries in Airflow monorepo: medium.com PEP-440: peps.python.org PEP-517: peps.python.org PEP-518: peps.python.org PEP-566: peps.python.org PEP-561: peps.python.org PEP-660: peps.python.org PEP-621: peps.python.org PEP-685: peps.python.org PEP-723: peps.python.org PEP-735: peps.python.org uv: docs.astral.sh uv workspaces: blobs.talkpython.fm prek.j178.dev: prek.j178.dev your presentation at FOSDEM26: fosdem.org Tallyman: github.com Watch this episode on YouTube: youtube.com Episode #540 deep-dive: talkpython.fm/540 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Realities Remixed, formerly know as Cloud Realities, launches a new season exploring the intersection of people, culture, industry and tech.After years of remote‑first work built on swift trust, companies are asking a harder question: what does a organization really stand for when people rarely show up together? As AI accelerates change, leaders are rethinking presence, team design, and collaboration to fuel trust, innovation, and growth. This week, Dave, Esmee, and Rob are joined by Dr. Tim Currie, disruptor, author, innovator, and advisor, to examine transformation versus trust, the role of AI, and whether organisations can truly build culture without deeper human connection. TLDR00:42– Introduction01:10 –  Hang out: New film releases07:17 – Dig in: The trust gap in remote work17:57 – Conversation with Dr. Tim Currie54:07 – The Wizard of Oz at the Sphere in Las Vegas and staying connected GuestDr. Tim Currie: https://www.linkedin.com/in/dr-tim-currie-37756a/Book Swift Trust: https://swifttrustbook.com/HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

The tech industry is split between two fantasies  – that AI writes production software while you get coffee, and that everything AI touches is slop. The reality is messier and more interesting: AI tools are force multipliers for people who already know what good looks like, and an expertise amplifier disguised as an easy button. ... Read more »

CTO Sam Pierson explains how Qlik's associative engine and agentic AI are transforming the way businesses uncover insights and what's next on the data frontier.Topics Include:Qlik is a 30-year-old data analytics and AI company with global customers.Qlik's associative engine surfaces insights from data you aren't even examining.A paper manufacturer optimized supply chain routing and navigated tariff complexity.Generative AI can't easily query databases — Qlik's engine bridges that gap.Qlik built an agentic layer enabling natural language conversations with your data.MCP integration lets users access Qlik insights directly from tools like Claude Desktop.Qlik runs entirely on AWS, with global regions built around local compliance requirements.The AWS partnership prioritizes mutual success over transactional service relationships.Agents will mature in 2026; some agentic bets will succeed, others will be refactored.Fine-tuned, smaller language models will grow in importance alongside larger ones.AI adoption requires restructuring workflows end-to-end, from product spec to go-to-market.Qlik is hiring for curiosity and agency — people who experiment without waiting for permission.Participants:Sam Pierson – Chief Technology Officer, QlikSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

In this episode we join AWS Senior GTM Specialists Aidin Khosrowshahi and Rakesh Kumar to unpack how Kiro is transforming software development through a spec-driven approach that goes beyond traditional AI-assisted coding. We cover what sets spec-driven development apart from "vibe coding" and why Kiro's spec-first approach matters. We also tackle the mindset shifts developers must make when adopting Kiro. The episode closes with a forward-looking discussion on whether the "Software Engineer" title could eventually be redefined—and the skills and first steps anyone needs to get started with Kiro today.Try Kiro today:kiro.devAWS Hosts: Nolan Chen & Malini ChatterjeeEmail Your Feedback: rethinkpodcast@amazon.com

Topics covered in this episode: Setting up a Python monorepo with uv workspaces cattrs: Flexible Object Serialization and Validation Learning to program in the AI age VS Code extension for FastAPI and friends 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 11am 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: Setting up a Python monorepo with uv workspaces Dennis Traub The 3 things Give the Root a Distinct Name Use workspace = true for Inter-Package Deps Use importlib Mode for pytest Michael #2: cattrs: Flexible Object Serialization and Validation cattrs is a Swiss Army knife for (un)structuring and validating data in Python. A natural alternative/follow on from DataClass Wizard Converts to ←→ from dictionaries cattrs also focuses on functional composition and not coupling your data model to its serialization and validation rules. When you're handed unstructured data (by your network, file system, database, …), cattrs helps to convert this data into trustworthy structured data. Batteries Included: cattrs comes with pre-configured converters for a number of serialization libraries, including JSON (standard library, orjson, UltraJSON), msgpack, cbor2, bson, PyYAML, tomlkit and msgspec (supports only JSON at this time). Brian #3: Learning to program in the AI age Jose Blanca “I teach a couple of introductory Python courses and I've been thinking about which advice to give to my students, that are studying how to program for the first time. I have collected my ideas in these blog posts” Why learning to program is as useful as ever, even with powerful AI tools available. How to use AI as a tutor rather than a shortcut, and why practice remains the key to real understanding. What the real learning objectives are: mental models, managing complexity, and thinking like a software developer. Michael #4: VS Code extension for FastAPI and friends Enhances the FastAPI development experience in Visual Studio Code Path Operation Explorer: Provides a hierarchical tree view of all FastAPI routes in your application. Search for routes: Use the Command Palette and quickly search for routes by path, method, or name. CodeLens links appear above HTTP client calls like client.get('/items'), letting you jump directly to the matching route definition. Deploy your application directly to FastAPI Cloud from the status bar with zero config. View real-time logs from your FastAPI Cloud deployed applications directly within VS Code. Install from Marketplace. Extras Brian: Guido van Rossum interviews key Python developers from the first 25 years Interview with Brett Cannon Interview with Thomas Wouters Michael: IntelliJ IDEA: The Documentary | An origin story video Cursor Joined the ACP Registry and Is Now Live in Your JetBrains IDE What hyper-personal software looks like I'm doing in-person training again (limited scope): On-site, hands-on AI engineering enablement for software teams with Michael Joke: Saas is dead

You're adding type hints to your Python code, your editor is happy, autocomplete is working great. But then you switch tools and suddenly there are red squiggles everywhere. Who decides what a float annotation actually means? Or whether passing None where an int is expected should be an error? It turns out there's a five-person council dedicated to exactly these questions -- and two brand-new Rust-based type checkers are raising the bar. On this episode, I sit down with three members of the Python Typing Council -- Jelle Zijlstra, Rebecca Chen, and Carl Meyer -- to learn how the type system is governed, where the spec and the type checkers agree and disagree, and get the council's official advice on how much typing is just enough. Episode sponsors Sentry Error Monitoring, Code talkpython26 Agentic AI Course Talk Python Courses Links from the show Guests Carl Meyer: github.com Jelle Zijlstra: jellezijlstra.github.io Rebecca Chen: github.com Typing Council: github.com typing.python.org: typing.python.org details here: github.com ty: docs.astral.sh pyrefly: pyrefly.org conformance test suite project: github.com typeshed: github.com Stub files: mypy.readthedocs.io Pydantic: pydantic.dev Beartype: github.com TOAD AI: github.com PEP 747 – Annotating Type Forms: peps.python.org PEP 724 – Stricter Type Guards: peps.python.org Python Typing Repo (PRs and Issues): github.com Watch this episode on YouTube: youtube.com Episode #539 deep-dive: talkpython.fm/539 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Realities Remixed, formerly know as Cloud Realities, launches a new season exploring the intersection of people, culture, industry and tech.Business messaging is transforming customer engagement by enabling brands to move conversations into familiar, always‑on messaging platforms. The result for customers is greater convenience, quicker resolutions, and more meaningful, personalized interactions. This week, Dave, Esmee, and Rob are joined by Kathleen Tandy, Global Director and Head of Business Messaging Marketing and WhatsApp for Business at Meta , to explore how companies are using messaging platforms to engage customers, what customers expect from these experiences, and the challenges of scaling messaging in tech.TLDR00:35 – Introduction01:00 –  Hang out: The new Remarkable05:25 – Dig in: Using messaging to enhance customer experiences20:49 – Conversation with Kathleen Tandy55:26 – The passion for college football and championship weekend!GuestKathleen Tandy: https://www.linkedin.com/in/kptandy/HostsDave Chapman: https://www.linkedin.com/in/chapmandr/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/ProductionMarcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman: https://www.linkedin.com/in/chapmandr/ SoundBen Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:  https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Plongez au cœur de l'innovation dans ce dernier épisode dédié à la solution eLION pour l'électrification des engins mobiles.Notre expert Philippe Durand, Responsable Applications & Solutions – Hydraulique Mobile, nous partage ses connaissances sur la mise en service de prototypes complexes pour les engins mobiles et sur les prochaines évolutions du secteur : du Cloud computing pour le diagnostic à distance jusqu'à l'intégration de l'Intelligence Artificielle pour l'analyse de données.À travers des cas concrets, apprenez comment l'optimisation des réglages et l'ingénierie de précision garantissent la performance de vos machines. C'est le moment idéal pour comprendre les défis technologiques de demain et propulser votre savoir-faire vers la mobilité durable.Découvrez tous nos podcasts, livres blancs, webinars... sur expertise.boschrexroth.frContenu proposé par Bosch Rexroth France.Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Sumo Logic's VP of Security Strategy reveals how a ground-up agentic framework transformed their platform, and why clean data and autonomous agents are rewriting the rules of cloud security.Topics Include:Sumo Logic is a cloud analytics platform ingesting data from complex IT stacks.Built on AWS from the start, leveraging microservices for scalable solutions.Early AI efforts produced a natural language query co-pilot for security data.Bolting AI onto existing platforms proved brittle and one-dimensional.Customer feedback drove a decision to redesign AI from the ground up.The Dojo AI framework unifies purpose-built agents across the entire platform.New agents include a SOC analyst agent, knowledge agent, and MCP server.New frontier models on Bedrock give the whole platform an instant brain transplant.Autonomous agents require rethinking security controls beyond traditional programmatic guardrails.Federal and global customers demand rigorous, levelled-up security across all regions.Clean, normalized data proved the biggest unlock for reliable AI query results.Agent-to-agent communication and MCP will define the next era of AI platforms.Participants:Chas Clawson – Vice President, Security Strategy, Sumo LogicSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Topics covered in this episode: Raw+DC: The ORM pattern of 2026? pytest-check releases Dataclass Wizard SQLiteo - “native macOS SQLite browser built for normal people” 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 11am 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: Raw+DC: The ORM pattern of 2026? ORMs/ODMs provide great support and abstractions for developers They are not the native language of agentic AI Raw queries are trained 100x+ more than standard ORMs Using raw queries at the data access optimizes for AI coding Returning some sort of object mapped to the data optimizes for type safety and devs Brian #2: pytest-check releases 3 merged pull requests 8 closed issues at one point got to 0 PR's and 1 enhancement request Now back to 2 issues and 1 PR, but activity means it's still alive and being used. so cool Check out changelog for all mods A lot of changes around supporting mypy I've decided to NOT have the examples be fully --strict as I find it reduces readability See tox.ini for explanation But src is --strict clean now, so user tests can be --strict clean. Michael #3: Dataclass Wizard Simple, elegant wizarding tools for Python's dataclasses. Features

Digital humanities sounds niche, until you realize it can mean a searchable archive of U.S. amendment proposals, Irish folklore, or pigment science in ancient art. Today I'm talking with David Flood from Harvard's DARTH team about an unglamorous problem: What happens when the grant ends but the website can't. His answer, static sites, client-side search, and sneaky Python. Let's dive in. Episode sponsors Sentry Error Monitoring, Code talkpython26 Command Book Talk Python Courses Links from the show Guest David Flood: davidaflood.com DARTH: digitalhumanities.fas.harvard.edu Amendments Project: digitalhumanities.fas.harvard.edu Fionn Folklore Database: fionnfolklore.org Mapping Color in History: iiif.harvard.edu Apatosaurus: apatosaurus.io Criticus: github.com github.com/palewire/django-bakery: github.com sigsim.acm.org/conf/pads/2026/blog/artifact-evaluation: sigsim.acm.org Hugo: gohugo.io Water Stories: waterstories.fas.harvard.edu Tsumeb Mine Notebook: tmn.fas.harvard.edu Dharma and Punya: dharmapunya2019.org Pagefind library: pagefind.app django_webassembly: github.com Astro Static Site Generator: astro.build PageFind Python Lib: pypi.org Frozen-Flask: frozen-flask.readthedocs.io Watch this episode on YouTube: youtube.com Episode #538 deep-dive: talkpython.fm/538 Episode transcripts: talkpython.fm Theme Song: Developer Rap

This episode marks the transition from The Cloudcast to The Reasoning Show, focusing more on AI and cloud topics. Brian Gracely (@bgracely) and Brandon Whichard (@bwhichard, @SoftwareDefTalk) discuss recent trends in AI, the evolution of tech teams, and the shifting landscape of enterprise AI tools.SHOW: 1006SHOW TRANSCRIPT: The Cloudcast #1006 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS" SHOW NOTES:Link to February 2026 News and ArticlesFEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

Realities Remixed, formerly know as Cloud Realities, launches a new season exploring the intersection of people, culture, industry, and tech. Energy transportation is a deeply local business, safely delivering gas and electricity, more and more from renewable sources, directly to the communities it serves. Technology and AI help make that possible by strengthening safety, bringing companies closer to customers, and enabling teams to build the future together. This week, Dave, Esmee, and Rob are joined by John Koerwer, CIO of UGI Corporation, to explore explore why “the business” and tech still struggle to speak the same language, nd what helps close the gap.TLDR00:35 – Introduction01:17 – Hang out: new toys and coffee07:55 – Dig in: the business - tech divide21:07 – Conversation with John Koerwer59:40 – The amazing AI technology in The Sphere's version of The Wizard of OzGuestJohn Koerwer: https://www.linkedin.com/in/john-koerwer-46102127/HostsDave Chapman: https://www.linkedin.com/in/chapmandr/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/ProductionMarcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman: https://www.linkedin.com/in/chapmandr/ SoundBen Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:  https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

William and Eyvonne tackle the biggest AI stories of early 2026. They dissect Matt Schumer’s viral “Something Big is Happening” essay – agreeing professionals need to skill up now while pushing back on the doomsday framing with real-world examples from engineering disciplines. The conversation takes a fascinating turn as Eyvonne draws a parallel between AI-assisted... Read more »

Aaron and Brian discuss how The Cloudcast will be changing going forward, signaling an industry shift from Cloud Computing to AI.  SHOW: 1005SHOW TRANSCRIPT: The Cloudcast #1005 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS"SHOW NOTES:Topic 1 - What are we announcing? (“The Reasoning Show”, a.k.a. “Reasoning”)New name, same RSS feeds (same podcast players)New logoSome existing/renamed and some new social media channels (will be listed)Same co-hosts (and maybe some new friends)Topic 2 - Why are we changing The Cloudcast?The majority of our content is AI focused nowCloud has become mostly stable, AI is at the beginning and so much is changingWe're not abandoning Cloud, just giving it the amount of coverage needs (monthly Cloudcast, monthly CNOTM)Social media algorithms have changed audience acquisitionTopic 3 - Hasn't The Cloudcast already been covering AI for a while? Yes, it's been about 50/50 since 2024We've been discussing this change for almost a year. We actually discussed having an OpenAI-specific podcast at one point, but so much as changed in the market (which is a good reason for a diverse podcast)Topic 4 - What can we expect from the new podcast?Still on Wednesday and SundayBoth audio and video formats (Apple, Spotify, YouTube, TikTok/Insta (clips))Trying to make it easier for someone new to follow along - more concentration around core topics, but not exclusivelyAI Technology, AI Economics, AI Trends, AI (Business) Use-Cases, AI Things to Watch, AI Productivity, AI RegulationWe'll still do a “Cloudcast” once a month, as the Cloud underpins almost everything AIWe'll likely do a “Reasoning Basics” spin-off, like Cloudcast Basics. We'll get a newsletter pulled together (hopefully weekly). Topic 5 - Anything else?Listeners don't have to do anything to keep getting their week podcastSubscribe to the social media channels (show notes)Leave a 5-star review on the podcast playersTell a friend to check out the showFEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

Privacy, AI, and Surveillance with Matt Sailor   In this episode of The Secure Family Podcast, host Andy Murphy chats with Matt Sailor, founder and CEO of IC Realtime, about privacy realities in modern home surveillance.   They discuss a key misconception: with many DIY Wi‑Fi cameras, footage and usage data are processed offsite, meaning consumers may not truly control their data. The conversation covers deceptive opt-in/opt-out practices for data sharing, freemium models that charge users who opt out, and the difference between 'not selling' data versus sharing or trading access, including aggregated geographic datasets that can still include individual contributions.   They also touch on public-space camera concerns, including ALPR cameras and neighborhood-sharing features marketed for community benefits like finding lost dogs.   For more from Matt Sailor and IC Realtime: https://icrealtime.com/ Take control of your data with DeleteMe. Because they sponsor the podcast you can get 20% off a privacy plan from DeleteMe with promo code: DAD.  Connect

En este episodio de No Hay Tos, Héctor y Beto entrevistan a Daniel, ingeniero en sistemas, sobre cómo es trabajar en tech en México. Hablan del uso diario del inglés y el spanglish, los tipos de proyectos (backend, frontend, nube, integración), las oportunidades internacionales y el ambiente laboral, desde empresas exigentes hasta startups con mejores condiciones y trabajo remoto. También comentan el estado de la ciberseguridad en México y comparten consejos prácticos para protegerse en línea. If you'd like to listen to our episodes ad-free and get the full word-for-word transcript of this episode — including English explanations and translations of Mexican slang and colloquial expressions — visit us on Patreon. You can also find more content and resources on our website: nohaytospodcast.com If the podcast has been helpful to you, please leave us a review on Apple Podcasts — it really helps! And if you prefer video, check out our YouTube channel. No Hay Tos is a Spanish podcast from Mexico for students who want to improve their listening comprehension, reinforce grammar, and learn about Mexican culture and Mexican Spanish. All rights reserved. No Hay Tos is a Spanish podcast from Mexico for students who want to improve their listening comprehension, reinforce grammar, and learn about Mexican culture and Mexican Spanish. All rights reserved.

Avalara's Chief Architect Tim Diekmann reveals how AI and agentic technology are transforming tax compliance and accuracy across 40,000 jurisdictions leveraging AWS.Topics Include:Avalara provides tax compliance software across North America, Europe, and beyond.They operate between commerce and government, covering 40,000+ jurisdictions.Services span registration, sales tax calculation, and certificate management.Avalara was the only company keeping pace with rapid tariff changes.AI is used to parse unstructured documents like tax notices and publications.Intelligent mapping automates ERP integration across vastly different system configurations.GenAI lets customers query billions of transactions using plain conversational language.Avalara and AWS are now engaged in a promising co-selling motion.Time-to-go-live and transaction accuracy are the key success metrics tracked.Amazon Q was rolled out company-wide, achieving 95% developer adoption.AI literacy is now prioritized across legal, HR, and engineering teams alike.Agentic AI will embed Avalara directly inside customer ERP systems going forward.Participants:Tim Diekmann – SVP of Engineering, Chief Architect, AvalaraSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Topics covered in this episode: Better Python tests with inline-snapshot jolt Battery intelligence for your laptop Markdown code formatting with ruff act - run your GitHub actions locally 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 11am 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: Better Python tests with inline-snapshot Alex Hall, on Pydantic blog Great for testing complex data structures Allows you to write a test like this: from inline_snapshot import snapshot def test_user_creation(): user = create_user(id=123, name="test_user") assert user.dict() == snapshot({}) Then run pytest --inline-snapshot=fix And the library updates the test source code to look like this: def test_user_creation(): user = create_user(id=123, name="test_user") assert user.dict() == snapshot({ "id": 123, "name": "test_user", "status": "active" }) Now, when you run the code without “fix” the collected data is used for comparison Awesome to be able to visually inspect the test data right there in the test code. Projects mentioned inline-snapshot pytest-examples syrupy dirty-equals executing Michael #2: jolt Battery intelligence for your laptop Support for both macOS and Linux Battery Status — Charge percentage, time remaining, health, and cycle count Power Monitoring — System power draw with CPU/GPU breakdown Process Tracking — Processes sorted by energy impact with color-coded severity Historical Graphs — Track battery and power trends over time Themes — 10+ built-in themes with dark/light auto-detection Background Daemon — Collect historical data even when the TUI isn't running Process Management — Kill energy-hungry processes directly Brian #3: Markdown code formatting with ruff Suggested by Matthias Schoettle ruff can now format code within markdown files Will format valid Python code in code blocks marked with python, py, python3 or py3. Also recognizes pyi as Python type stub files. Includes the ability to turn off formatting with comment [HTML_REMOVED] , [HTML_REMOVED] blocks. Requires preview mode [tool.ruff.lint] preview = true Michael #4: act - run your GitHub actions locally Run your GitHub Actions locally! Why would you want to do this? Two reasons: Fast Feedback - Rather than having to commit/push every time you want to test out the changes you are making to your .github/workflows/ files (or for any changes to embedded GitHub actions), you can use act to run the actions locally. The environment variables and filesystem are all configured to match what GitHub provides. Local Task Runner - I love make. However, I also hate repeating myself. With act, you can use the GitHub Actions defined in your .github/workflows/ to replace your Makefile! When you run act it reads in your GitHub Actions from .github/workflows/ and determines the set of actions that need to be run. Uses the Docker API to either pull or build the necessary images, as defined in your workflow files and finally determines the execution path based on the dependencies that were defined. Once it has the execution path, it then uses the Docker API to run containers for each action based on the images prepared earlier. The environment variables and filesystem are all configured to match what GitHub provides. Extras Michael: Winter is coming: Frozendict accepted Django ORM stand-alone Command Book app announcement post Joke: Plug ‘n Paste

This episode explores the evolving economics of AI development, the rising costs associated with AI agents, and the implications for businesses and developers. It highlights the shift from centralized to decentralized computing, the importance of understanding token budgets, and the future of AI project management.SHOW: 1004SHOW TRANSCRIPT: The Cloudcast #1004 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"CHAPTERS00:00 - Celebrating Milestones and AI Insights02:48 - The Cost of AI Agents and Realizations06:05 - Centralization vs. Decentralization in AI08:51 - The Evolution of AI Economics12:03 - Future Trends in AI and Project Management14:52 - Connecting AI to Real-World EconomicsKEY TOPICS:AI agent costs and pricing modelsThe shift from centralized to decentralized computingToken budgets and project economics in AIHistorical transitions in computing infrastructureFuture trends in AI project managementSHOW NOTESWhen AI Tokens cost more than your employees Should you own (or generate) your own tokens? On running a startup of Claude Code agents: 1 Billion tokens a monthCan AI grow corn? WE'VE REACHED A POINT WITH AI WHERE PEOPLE ARE STARTING TO THINK ABOUT THE BUSINESS IMPACTSHow much should an AI project cost?How do we translate an AI token into some unit that a business can understand?How companies be their own AI token factories?FEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

You love building web apps with Python, and HTMX got you excited about the hypermedia approach -- let the server drive the HTML, skip the JavaScript build step, keep things simple. But then you hit that last 10%: You need Alpine.js for interactivity, your state gets out of sync, and suddenly you're juggling two unrelated libraries that weren't designed to work together. What if there was a single 11-kilobyte framework that gave you everything HTMX and Alpine do, and more, with real-time updates, multiplayer collaboration out of the box, and performance so fast you're actually bottlenecked by the monitor's refresh rate? That's Datastar. On this episode, I sit down with its creator Delaney Gillilan, core maintainer Ben Croker, and Datastar convert Chris May to explore how this backend-driven, server-sent-events-first framework is changing the way full-stack developers think about the modern web. Episode sponsors Sentry Error Monitoring, Code talkpython26 Command Book Talk Python Courses Links from the show Guests Delaney Gillilan: linkedin.com Ben Croker: x.com Chris May: everydaysuperpowers.dev Datastar: data-star.dev HTMX: htmx.org AlpineJS: alpinejs.dev Core Attribute Tour: data-star.dev data-star.dev/examples: data-star.dev github.com/starfederation/datastar-python: github.com VSCode: marketplace.visualstudio.com OpenVSX: open-vsx.org PyCharm/Intellij plugin: plugins.jetbrains.com data-star.dev/datastar_pro: data-star.dev gg: discord.gg HTML-ivating your Django web app's experience with HTMX, AlpineJS, and streaming HTML - Chris May: www.youtube.com Senior Engineer tries Vibe Coding: www.youtube.com 1 Billion Checkboxes: checkboxes.andersmurphy.com Game of life example: example.andersmurphy.com Watch this episode on YouTube: youtube.com Episode #537 deep-dive: talkpython.fm/537 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Realities Remixed, formerly know as Cloud Realities, launches a new season exploring the intersection of people, culture, technology, and society. Hosts Dave Chapman, Esmee van de Giessen, and Rob Kernahan unpack 2026's defining trends, from AI and sovereignty to adaptability and automation, offering fresh insight, candid reflections, and forward‑looking conversations shaping the year ahead. TLDR00:20 – Introduction of Realities Remixed02:30 – Why the show evolved?04:50 – Dig in with the team: Predictions for 202606:40 – Macro trends13:00 – Sovereignty 17:40 – Agentic AI22:17 – Human–AI interaction26:06 – Cloud trends30:42 – AI scaling, domain‑specific models35:03 – Adoption lag39:34 – Physical AI43:47 – Quantum computing48:21 – Hardware acceleration50:30 – Cybersecurity52:38 – Season outlook HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Honeycomb Co-founder and CTO Charity Majors explains why measuring the right engineering metrics in the age of AI matters more than chasing numbers.Topics Include:Charity Majors introduces Honeycomb as the original observability company for complex systemsHoneycomb solves high cardinality problems across millions of individual customer experiencesTheir MCP tool ranked top five in Stack Overflow's most-used listCanva lets developers interact with production software directly from their IDEAI acts as an amplifier requiring strong reliability and observability foundationsMeasuring success requires multiple metrics to avoid gaming single numbersHoneycomb adopted Intercom's 2X productivity challenge enlisting employees to identify gainsWriting code was never the hard part even before generative AI arrivedHoneycomb created AI values prioritizing transparency and emotional safety for employeesStaff tested boundaries on resources and environmental impact prompting honest discussionsHoneycomb acquired Grok and shipped Query Assistant Canvas and MCP productsFuture concerns include AI economics shifting and AI-native developers lacking foundational expertiseParticipants:Charity Majors – Co-Founder/CTO, Honeycomb.ioSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

On Cloud Realities, the real insight rarely came from technology alone, it emerged at the intersection of People, Culture, Industry, and Technology. In the remix we bring back familiar voices and topics while going deeper into the wider impacts, influence, and potential of today's tech across society. The 2026 season trailer, arriving a little later than planned, opens with this renewed focus and sets the stage for Episode 1, launching on February 19. Here's a quick trailer to get you ready!TLDR00:11 The emergence of insight from Cloud Realities01:00  Where the magic happens 01:42 The real impact on People, Culture, Industry and Tech HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

A surprisingly strong US jobs report sent yields higher – most notably at the short end of the curve – and weighed on equity index performance. Market expectations have now shifted, with traders anticipating that the Federal Reserve will hold off on further rate cuts until July. Meanwhile, European equities climbed to fresh highs on the back of robust earnings. In today's episode, Manuel Villegas from Next Generation Research joins us to share his detailed perspective on how investors should navigate the current landscape in cloud computing and artificial intelligence.(00:00) - Introduction: Helen Freer, Product & Investment Content (00:31) - Markets wrap-up: Roman Canziani, Head of Product & Investment Content (06:01) - Cloud Computing & AI: Manuel Villegas, Next Generation Research (13:03) - Closing remarks: Helen Freer, Product & Investment Content Would you like to support this show? Please leave us a review and star rating on Apple Podcasts, Spotify or wherever you get your podcasts.

We’ve spent a decade figuring out how to (more or less) securely authenticate humans. Now AI agents are crashing the party, and identity just got a whole lot more complicated. Today we sit down with Dan Moore, Senior Director of CIAM Strategy and Identity Standards at FusionAuth, to explore the collision course between artificial intelligence... Read more »

You've built your FastAPI app, it's running great locally, and now you want to share it with the world. But then reality hits -- containers, load balancers, HTTPS certificates, cloud consoles with 200 options. What if deploying was just one command? That's exactly what Sebastian Ramirez and the FastAPI Cloud team are building. On this episode, I sit down with Sebastian, Patrick Arminio, Savannah Ostrowski, and Jonathan Ehwald to go inside FastAPI Cloud, explore what it means to build a "Pythonic" cloud, and dig into how this commercial venture is actually making FastAPI the open-source project stronger than ever. Episode sponsors Command Book Python in Production Talk Python Courses Links from the show Guests Sebastián Ramírez: github.com Savannah Ostrowski: github.com Patrick Arminio: github.com Jonathan Ehwald: github.com FastAPI labs: fastapilabs.com quickstart: fastapicloud.com an episode on diskcache: talkpython.fm Fastar: github.com FastAPI: The Documentary: www.youtube.com Tailwind CSS Situation: adams-morning-walk.transistor.fm FastAPI Job Meme: fastapi.meme Migrate an Existing Project: fastapicloud.com Join the waitlist: fastapicloud.com Talk Python CLI Talk Python CLI Announcement: talkpython.fm Talk Python CLI GitHub: github.com Command Book Download Command Book: commandbookapp.com Announcement post: mkennedy.codes Watch this episode on YouTube: youtube.com Episode #536 deep-dive: talkpython.fm/536 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Astronomer's Steven Hillion reveals how OpenAI, Anthropic, Uber, and Lyft use Apache Airflow to orchestrate AI and machine learning pipelines at scale on AWS.Topics Include:Steven Hillion leads data and AI at AstronomerApache Airflow surpassed Spark and Kafka in community metricsAstronomer coordinates data flow like conductor orchestrating instrumental platformsOrganizations with data engineering teams use Airflow at scaleCustomers already used Airflow for ML before official promotionUber and Lyft orchestrate pricing models using AirflowAstronomer runs on AWS with close integration partnershipsOpenAI Anthropic and GitHub Copilot use Airflow for operationsInternal data team uses Airflow creating feedback loopsEvolved from constrained AI reports to agentic workflowsPlatform monitors generative AI output quality at user interactionsMetadata and context increasingly critical for AI applicationsLearn more at Astronomer's Data FlowCast podcastParticipants:Steven Hillion – SVP, Data and AI, AstronomerSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Topics covered in this episode: Command Book App uvx.sh: Install Python tools without uv or Python Ending 15 years of subprocess polling monty: A minimal, secure Python interpreter written in Rust for use by AI 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: Command Book App New app from Michael Command Book App is a native macOS app for developers, data scientists, AI enthusiasts and more. This is a tool I've been using lately to help build Talk Python, Python Bytes, Talk Python Training, and many more applications. It's a bit like advanced terminal commands or complex shell aliases, but hosted outside of your terminal. This leaves the terminal there for interactive commands, exploration, short actions. Command Book manages commands like "tail this log while I'm developing the app", "Run the dev web server with true auto-reload", and even "Run MongoDB in Docker with exactly the settings I need" I'd love it if you gave it a look, shared it with your team, and send me feedback. Has a free version and paid version. Build with Swift and Swift UI Check it out at https://commandbookapp.com Brian #2: uvx.sh: Install Python tools without uv or Python Tim Hopper Michael #3: Ending 15 years of subprocess polling by Giampaolo Rodola The standard library's subprocess module has relied on a busy-loop polling approach since the timeout parameter was added to Popen.wait() in Python 3.3, around 15 years ago The problem with busy-polling CPU wake-ups: even with exponential backoff (starting at 0.1ms, capping at 40ms), the system constantly wakes up to check process status, wasting CPU cycles and draining batteries. Latency: there's always a gap between when a process actually terminates and when you detect it. Scalability: monitoring many processes simultaneously magnifies all of the above. + L1/L2 CPU cache invalidations It's interesting to note that waiting via poll() (or kqueue()) puts the process into the exact same sleeping state as a plain time.sleep() call. From the kernel's perspective, both are interruptible sleeps. Here is the merged PR for this change. Brian #4: monty: A minimal, secure Python interpreter written in Rust for use by AI Samuel Colvin and others at Pydantic Still experimental “Monty avoids the cost, latency, complexity and general faff of using a full container based sandbox for running LLM generated code. “ “Instead, it lets you safely run Python code written by an LLM embedded in your agent, with startup times measured in single digit microseconds not hundreds of milliseconds.” Extras Brian: Expertise is the art of ignoring - Kevin Renskers You don't need to master the language. You need to master your slice. Learning everything up front is wasted effort. Experience changes what you pay attention to. I hate fish - Rands (Michael Lopp) Really about productivity systems And a nice process for dealing with email Michael: Talk Python now has a CLI New essay: It's not vibe coding - Agentic engineering GitHub is having a day Python 3.14.3 and 3.13.12 are available Wall Street just lost $285 billion because of 13 markdown files Joke: Silence, current side project!

If someone walked into your office today and asked you to build a framework for how to value software development, what would you think about it? SHOW: 1000SHOW TRANSCRIPT: The Cloudcast #1000 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS" SHOW NOTES:Chainguard introduces Factory 2.0On running a startup of Claude Code agentsAgentic Product Development and the Theory of ConstraintsSoftware AbundanceHOW SHOULD SOMEONE THINK ABOUT THE ECONOMICS OF SW DEV IN 2026?If someone walked into your office today and asked you to build a framework for how to value software development, how would you think about it? FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

Are we ready to move into an era of wild predictions about where the future of Enterprise software is headed in 2026 and beyond? SHOW: 999SHOW TRANSCRIPT: The Cloudcast #999 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW NOTESThe SPAC-king is going to fix legacy software All Enterprise software is dead Microsoft and Software Survival (Stratechery)WHAT HAPPENS TO ENTERPRISE SOFTWARE NEXT?How much do enterprises want to write their own software? How much do enterprises wish they could write more software?How much do enterprises not understand the economics of owning their own software?How much does “big SaaS” or just “big Enterprise software” actually help because people already know it?Is it possible that this new Agentic-driven software could create a type of new software community? Are “open” software communities prepared for the emerging economics of AI-created software? FEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

In this episode, Carlos Gonzalez de Villaumbrosia, CEO & Founder at Product School, interviews Aparna Sinha, SVP of Product at Vercel, the cloud platform recently valued at $9.3 billion following a $300 million Series F. Aparna joins us to discuss how Vercel is powering the next generation of AI-native applications.Drawing from her experience at Google Kubernetes and Pear VC, Aparna reveals how Vercel empowers Teams of One to ship faster than ever. She explores the cultural shift required to build in the AI era—moving from rigid planning to rapid experimentation and iterating to greatness.What you'll learn:How Vercel's Team of One philosophy maximizes developer leverage.Why shipping imperfect products early is crucial for AI strategy.The mechanics of Hybrid Pricing to balance AI costs and value.How to use internal dogfooding to accelerate product quality.Key takeaways:Speed is Survival: In the AI era, waiting for perfection means falling behind.Agency over Hierarchy: Small, autonomous teams outperform large structures.Price for Value: Align AI pricing with user outcomes, not just compute costs.Credits:Host: Carlos Gonzalez de VillaumbrosiaGuest: Aparna SinhaSocial Links: Follow our Podcast on Tik Tok here Follow Product School on LinkedIn here Join Product School's free events here Find out more about Product School here

Topics covered in this episode: django-bolt: Faster than FastAPI, but with Django ORM, Django Admin, and Django packages pyleak More Django (three articles) Datastar 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 11am 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: django-bolt : Faster than FastAPI, but with Django ORM, Django Admin, and Django packages Farhan Ali Raza High-Performance Fully Typed API Framework for Django Inspired by DRF, FastAPI, Litestar, and Robyn Django-Bolt docs Interview with Farhan on Django Chat Podcast And a walkthrough video Michael #2: pyleak Detect leaked asyncio tasks, threads, and event loop blocking with stack trace in Python. Inspired by goleak. Has patterns for Context managers decorators Checks for Unawaited asyncio tasks Threads Blocking of an asyncio loop Includes a pytest plugin so you can do @pytest.mark.no_leaks Brian #3: More Django (three articles) Migrating From Celery to Django Tasks Paul Taylor Nice intro of how easy it is to get started with Django Tasks Some notes on starting to use Django Julia Evans A handful of reasons why Django is a great choice for a web framework less magic than Rails a built-in admin nice ORM automatic migrations nice docs you can use sqlite in production built in email The definitive guide to using Django with SQLite in production I'm gonna have to study this a bit. The conclusion states one of the benefits is “reduced complexity”, but, it still seems like quite a bit to me. Michael #4: Datastar Sent to us by Forrest Lanier Lots of work by Chris May Out on Talk Python soon. Official Datastar Python SDK Datastar is a little like HTMX, but The single source of truth is your server Events can be sent from server automatically (using SSE) e.g yield SSE.patch_elements( f"""{(#HTML#)}{datetime.now().isoformat()}""" ) Why I switched from HTMX to Datastar article Extras Brian: Django Chat: Inverting the Testing Pyramid - Brian Okken Quite a fun interview PEP 686 – Make UTF-8 mode default Now with status “Final” and slated for Python 3.15 Michael: Prayson Daniel's Paper tracker Ice Cubes (open source Mastodon client for macOS) Rumdl for PyCharm, et. al cURL Gets Rid of Its Bug Bounty Program Over AI Slop Overrun Python Developers Survey 2026 Joke: Pushed to prod

Learn how Docupace transformed from cloud-native platform to AI-powered wealth tech leader, leveraging AWS partnerships and customer obsession to accelerate growth.Topics Include:Docupace Technologies has served wealth management firms for twenty years.Three SaaS product lines streamline advisor workflows and back offices.AI transforms both customer operations and Docupace's internal business practices.Trust between advisors and investors drives conservative technology adoption approach.Serving seven top-ten broker dealers demands careful data security strategies.AI shifts financial systems from deterministic certainty to probabilistic outcomes.Industry began AI adoption with simple meeting note-taking applications.Docupace's agentic AI framework enables safe, observable, orchestrated agent deployment.Multiple verification layers and human oversight ensure zero-error financial operations.Internal AI implementation required nine months navigating change management hurdles.Team curiosity and rapid experimentation matter more than traditional skill sets.AWS customer obsession and partnership programs dramatically accelerate business growth.Participants:Michael Pinsker – Founder and President, Docupace TechnologiesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

"Its amazing to see the rate and pace of advancements in technology in 2025" Todd Pond, AWS Director of Strategic Sales, is out in the field with commercial customers every day, helping them leverage technology to solve their biggest problems, accelerate innovation and transform their businesses. He and his team have a deep understanding of these companies - the challenges they're up against, their limited resources, their unique value propositions, and how to enable their growth with maximum ROI. In this episode, we talk to Todd about the learnings from 2025 and get his actionable insights on the biggest opportunities for success in 2026 and beyond.

In this episode, we sit down with Adam Zimman, author and VC advisor, to explore the world of progressive delivery and why shipping software is only the beginning. Adam shares his fascinating journey through tech—from his early days as a fire juggler to leadership roles at EMC, VMware, GitHub, and LaunchDarkly – and how those... Read more »

Boomi CEO Steve Lucas reveals how to flip AI's 95% failure rate to your favour with practical integration strategies, real-world agent deployments, and an AWS partnership.Topics Include:Boomi solves the forever problem of complexity across applications and systemsTwenty-five thousand customers use Boomi to automate anything and connect everythingBoomi moves more data per second than the entire Visa networkAI agents now integrate systems through simple commands, no coding requiredAgentic platform built with AWS creates custom AI agents in real timeUse cases include expense monitoring and heart defibrillator battery checks dailyAutomotive companies use AI agents to assess tariff risks across supply chainsHospitals deploy agents to detect patient falls and alert medical professionals immediatelyControl Tower co-innovated with AWS monitors and manages all AI agents centrallyDeterministic processes like payroll shouldn't use AI, probabilistic challenges shouldNinety-five percent of AI projects fail due to data access problemsAgentic workshops help companies identify high-ROI opportunities and achieve AI successParticipants:Steve Lucas – Chairman & CEO, BoomiSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Topics covered in this episode: GreyNoise IP Check tprof: a targeting profiler TOAD is out 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 11am 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: GreyNoise IP Check GreyNoise watches the internet's background radiation—the constant storm of scanners, bots, and probes hitting every IP address on Earth. Is your computer sending out bot or other bad-actor traffic? What about the myriad of devices and IoT things on your local IP? Heads up: If your IP has recently changed, it might not be you (false positive). Brian #2: tprof: a targeting profiler Adam Johnson Intro blog post: Python: introducing tprof, a targeting profiler Michael #3: TOAD is out Toad is a unified experience for AI in the terminal Front-end for AI tools such as OpenHands, Claude Code, Gemini CLI, and many more. Better TUI experience (e.g. @ for file context uses fuzzy search and dropdowns) Better prompt input (mouse, keyboard, even colored code and markdown blocks) Terminal within terminals (for TUI support) Brian #4: FastAPI adds Contribution Guidelines around AI usage Docs commit: Add contribution instructions about LLM generated code and comments and automated tools for PRs Docs section: Development - Contributing : Automated Code and AI Great inspiration and example of how to deal with this for popular open source projects “If the human effort put in a PR, e.g. writing LLM prompts, is less than the effort we would need to put to review it, please don't submit the PR.” With sections on Closing Automated and AI PRs Human Effort Denial of Service Use Tools Wisely Extras Brian: Apparently Digg is back and there's a Python Community there Why light-weight websites may one day save your life - Marijke LuttekesHome Michael: Blog posts about Talk Python AI Integrations Announcing Talk Python AI Integrations on Talk Python's Blog Blocking AI crawlers might be a bad idea on Michael's Blog Already using the compile flag for faster app startup on the containers: RUN --mount=type=cache,target=/root/.cache uv pip install --compile-bytecode --python /venv/bin/python I think it's speeding startup by about 1s / container. Biggest prompt yet? 72 pages, 11, 000 Joke: A date via From Pat Decker

Fabric CTO Ankush Goyal reveals how AI Search is transforming commerce discovery and why merchants need AI agents to compete effectively.Topics Include:Fabric builds AI agents for commerce, solving merchant visibility challengesCommerce shifting due to AI Search channels and smaller retail teamsProduct Agent monitors and improves product visibility across AI channelsPetMeds uses Fabric to optimize AI Search and automate SKU onboardingFabric evolved from commerce platform company to AI agent solutionsAgentic and generative AI work together to optimize product catalogsFabric uses AWS EKS, Bedrock, S3, and Nova models heavilyAWS partnership connects Fabric with industry leaders and growth opportunitiesAWS services enable reliable, cost-effective, and performant enterprise AI agentsPrototyping agents is easy, but enterprise-grade reliability is extremely challengingFour key learnings: workflow reliability, context engineering, cost effectiveness, feedback loopsCTOs should define agent goals, guardrails, context, and evaluations earlyLong-running workflow durability and snapshots prevent costly repeated work failuresFuture innovations focus on specialized models, retrieval frameworks, automated evaluationsMerchants can evaluate AI Search performance at fabric.inc or LinkedInParticipants:Ankush Goyal – Chief Technology Officer, FabricSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Building on the web is like working with the perfect clay. It's malleable and can become almost anything. But too often, frameworks try to hide the web's best parts away from us. Today, we're looking at PyView, a project that brings the real-time power of Phoenix LiveView directly into the Python world. I'm joined by Larry Ogrodnek to dive into PyView. Episode sponsors Talk Python Courses Python in Production Links from the show Guest Larry Ogrodnek: hachyderm.io pyview.rocks: pyview.rocks Phoenix LiveView: github.com this section: pyview.rocks Core Concepts: pyview.rocks Socket and Context: pyview.rocks Event Handling: pyview.rocks LiveComponents: pyview.rocks Routing: pyview.rocks Templating: pyview.rocks HTML Templates: pyview.rocks T-String Templates: pyview.rocks File Uploads: pyview.rocks Streams: pyview.rocks Sessions & Authentication: pyview.rocks Single-File Apps: pyview.rocks starlette: starlette.dev wsproto: github.com apscheduler: github.com t-dom project: github.com Watch this episode on YouTube: youtube.com Episode #535 deep-dive: talkpython.fm/535 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Topics covered in this episode: Better Django management commands with django-click and django-typer PSF Lands a $1.5 million sponsorship from Anthropic How uv got so fast PyView Web Framework 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 11am 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: Better Django management commands with django-click and django-typer Lacy Henschel Extend Django manage.py commands for your own project, for things like data operations API integrations complex data transformations development and debugging Extending is built into Django, but it looks easier, less code, and more fun with either django-click or django-typer, two projects supported through Django Commons Michael #2: PSF Lands a $1.5 million sponsorship from Anthropic Anthropic is partnering with the Python Software Foundation in a landmark funding commitment to support both security initiatives and the PSF's core work. The funds will enable new automated tools for proactively reviewing all packages uploaded to PyPI, moving beyond the current reactive-only review process. The PSF plans to build a new dataset of known malware for capability analysis The investment will sustain programs like the Developer in Residence initiative, community grants, and infrastructure like PyPI. Brian #3: How uv got so fast Andrew Nesbitt It's not just be cause “it's written in Rust”. Recent-ish standards, PEPs 518 (2016), 517 (2017), 621 (2020), and 658 (2022) made many uv design decisions possible And uv drops many backwards compatible decisions kept by pip. Dropping functionality speeds things up. “Speed comes from elimination. Every code path you don't have is a code path you don't wait for.” Some of what uv does could be implemented in pip. Some cannot. Andrew discusses different speedups, why they could be done in Python also, or why they cannot. I read this article out of interest. But it gives me lots of ideas for tools that could be written faster just with Python by making design and support decisions that eliminate whole workflows. Michael #4: PyView Web Framework PyView brings the Phoenix LiveView paradigm to Python Recently interviewed Larry on Talk Python Build dynamic, real-time web applications using server-rendered HTML Check out the examples. See the Maps demo for some real magic How does this possibly work? See the LiveView Lifecycle. Extras Brian: Upgrade Django, has a great discussion of how to upgrade version by version and why you might want to do that instead of just jumping ahead to the latest version. And also who might want to save time by leapfrogging Also has all the versions and dates of release and end of support. The Lean TDD book 1st draft is done. Now available through both pythontest and LeanPub I set it as 80% done because of future drafts planned. I'm working through a few submitted suggestions. Not much feedback, so the 2nd pass might be fast and mostly my own modifications. It's possible. I'm re-reading it myself and already am disappointed with page 1 of the introduction. I gotta make it pop more. I'll work on that. Trying to decide how many suggestions around using AI I should include. It's not mentioned in the book yet, but I think I need to incorporate some discussion around it. Michael: Python: What's Coming in 2026 Python Bytes rewritten in Quart + async (very similar to Talk Python's journey) Added a proper MCP server at Talk Python To Me (you don't need a formal MCP framework btw) Example one: latest-episodes-mcp.png Example two: which-episodes-mcp.webp Implmented /llms.txt for Talk Python To Me (see talkpython.fm/llms.txt ) Joke: Reverse Superman

The industry has pivoted from scripting to automation to orchestration – and now to systems that can reason. Today we explore what AI agents mean for infrastructure with Chris Wade, Co-Founder and CTO of Itential. We also dive into the brownfield reality, the potential for vendor-specific LLMs trained on proprietary knowledge, and advice for the... Read more »

Your cloud SSD is sitting there, bored, and it would like a job. Today we're putting it to work with DiskCache, a simple, practical cache built on SQLite that can speed things up without spinning up Redis or extra services. Once you start to see what it can do, a universe of possibilities opens up. We're joined by Vincent Warmerdam to dive into DiskCache. Episode sponsors Talk Python Courses Python in Production Links from the show diskcache docs: grantjenks.com LLM Building Blocks for Python course: training.talkpython.fm JSONDisk: grantjenks.com Git Code Archaeology Charts: koaning.github.io Talk Python Cache Admin UI: blobs.talkpython.fm Litestream SQLite streaming: litestream.io Plash hosting: pla.sh Watch this episode on YouTube: youtube.com Episode #534 deep-dive: talkpython.fm/534 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Philip Johnston is co-founder and CEO of Starcloud, a company building data centers in space to solve AI's power crisis. Starcloud has already launched the first NVIDIA H100 GPU into orbit and is partnering with cloud providers like Crusoe to scale orbital computing infrastructure.As AI demand accelerates, data centers are running into a new bottleneck: access to reliable, affordable power. Grid congestion, interconnection delays, and cooling requirements are slowing the deployment of new AI data centers, even as compute demand continues to surge. Traditional data centers face 5-10 year lead times for new power projects due to permitting, interconnection queues, and grid capacity constraints.In this episode, Philip explains why Starcloud is building data centers in orbit, where continuous solar power is available and heat can be rejected directly into the vacuum of space. He walks through Starcloud's first on-orbit GPU deployment, the realities of cooling and radiation in space, and how orbital data centers could relieve pressure on terrestrial power systems as AI infrastructure scales.Episode recorded on Dec 11, 2025 (Published on Jan 13, 2026)In this episode, we cover: [04:59] What Starcloud's orbital data centers look like (and how they differ from terrestrial facilities)[06:37] How SpaceX Starship's reusable launch vehicles change space economics[10:45] The $500/kg breakeven point for space-based solar vs. Earth [14:15] Why space solar panels produce 8x more energy than ground-based arrays [21:19] Thermal management: Cooling NVIDIA GPUs in a vacuum using radiators [25:57] Edge computing in orbit: Real-time inference on satellite imagery [29:22] The Crusoe partnership: Selling power-as-a-service in space [31:21] Starcloud's business model: Power, cooling, and connectivity [34:18] Addressing critics: What could prevent orbital data centers from workingKey Takeaways:Starcloud launched the first NVIDIA H100 GPU into orbit in November 2024 Space solar produces 8x more energy per square meter than terrestrial solar Breakeven launch cost for orbital data centers: $500/kg Current customers: DOD and commercial Earth observation satellites needing real-time inference Target: 10 gigawatts of orbital computing capacity by early 2030s Enjoyed this episode? Please leave us a review! Share feedback or suggest future topics and guests at info@mcj.vc.Connect with MCJ:Cody Simms on LinkedInVisit mcj.vcSubscribe to the MCJ Newsletter*Editing and post-production work for this episode was provided by The Podcast Consultant

Topics covered in this episode: port-killer How we made Python's packaging library 3x faster CodSpeed 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 11am 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: port-killer A powerful cross-platform port management tool for developers. Monitor ports, manage Kubernetes port forwards, integrate Cloudflare Tunnels, and kill processes with one click. Features:

Today on Talk Python, the creators behind FastAPI, Flask, Django, Quart, and Litestar get practical about running apps based on their framework in production. Deployment patterns, async gotchas, servers, scaling, and the stuff you only learn at 2 a.m. when the pager goes off. For Django, we have Carlton Gibson and Jeff Triplet. For Flask, we have David Lord and Phil Jones, and on team Litestar we have Janek Nouvertné and Cody Fincher, and finally Sebastián Ramírez from FastAPI is here. Let's jump in. Episode sponsors Talk Python Courses Python in Production Links from the show Carlton Gibson - Django: github.com Sebastian Ramirez - FastAPI: github.com David Lord - Flask: davidism.com Phil Jones - Flask and Quartz(async): pgjones.dev Yanik Nouvertne - LiteStar: github.com Cody Fincher - LiteStar: github.com Jeff Triplett - Django: jefftriplett.com Django: www.djangoproject.com Flask: flask.palletsprojects.com Quart: quart.palletsprojects.com Litestar: litestar.dev FastAPI: fastapi.tiangolo.com Coolify: coolify.io ASGI: asgi.readthedocs.io WSGI (PEP 3333): peps.python.org Granian: github.com Hypercorn: github.com uvicorn: uvicorn.dev Gunicorn: gunicorn.org Hypercorn: hypercorn.readthedocs.io Daphne: github.com Nginx: nginx.org Docker: www.docker.com Kubernetes: kubernetes.io PostgreSQL: www.postgresql.org SQLite: www.sqlite.org Celery: docs.celeryq.dev SQLAlchemy: www.sqlalchemy.org Django REST framework: www.django-rest-framework.org Jinja: jinja.palletsprojects.com Click: click.palletsprojects.com HTMX: htmx.org Server-Sent Events (SSE): developer.mozilla.org WebSockets (RFC 6455): www.rfc-editor.org HTTP/2 (RFC 9113): www.rfc-editor.org HTTP/3 (RFC 9114): www.rfc-editor.org uv: docs.astral.sh Amazon Web Services (AWS): aws.amazon.com Microsoft Azure: azure.microsoft.com Google Cloud Run: cloud.google.com Amazon ECS: aws.amazon.com AlloyDB for PostgreSQL: cloud.google.com Fly.io: fly.io Render: render.com Cloudflare: www.cloudflare.com Fastly: www.fastly.com Watch this episode on YouTube: youtube.com Episode #533 deep-dive: talkpython.fm/533 Episode transcripts: talkpython.fm Theme Song: Developer Rap