Podcasts about Cloud computing

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/

In this week’s What the Hack!, Arthur Goldstuck speaks to Lester Kiewit about why data sovereignty has become a survival issue for organisations using AI and the cloud, drawing on insights from Cisco Live in Amsterdam and a conversation with Cisco’s EMEA president. He also examines emerging workplace technology that could allow employers to monitor employees’ heart rates via work devices, raising major privacy concerns. The feature wraps up with Spotify’s launch of audiobooks in South Africa, opening up a new era of long-form audio for local listeners. Good Morning Cape Town with Lester Kiewit is a podcast of the CapeTalk breakfast show. This programme is your authentic Cape Town wake-up call. Good Morning Cape Town with Lester Kiewit is informative, enlightening and accessible. The team’s ability to spot & share relevant and unusual stories make the programme inclusive and thought-provoking. Don’t miss the popular World View feature at 7:45am daily. Listen out for #LesterInYourLounge which is an outside broadcast – from the home of a listener in a different part of Cape Town - on the first Wednesday of every month. This show introduces you to interesting Capetonians as well as their favourite communities, habits, local personalities and neighbourhood news. Thank you for listening to a podcast from Good Morning Cape Town with Lester Kiewit. Listen live on Primedia+ weekdays between 06:00 and 09:00 (SA Time) to Good Morning CapeTalk with Lester Kiewit broadcast on CapeTalk https://buff.ly/NnFM3Nk For more from the show go to https://buff.ly/xGkqLbT or find all the catch-up podcasts here https://buff.ly/f9Eeb7i Subscribe to the CapeTalk Daily and Weekly Newsletters https://buff.ly/sbvVZD5 Follow us on social media CapeTalk on Facebook: https://www.facebook.com/CapeTalk CapeTalk on TikTok: https://www.tiktok.com/@capetalk CapeTalk on Instagram: https://www.instagram.com/ CapeTalk on X: https://x.com/CapeTalk CapeTalk on YouTube: https://www.youtube.com/@CapeTalk567See omnystudio.com/listener for privacy information.

Vom Grafikkartenhersteller zur globalen KI-Supermacht – Am US-Giganten Nvidia führt nicht nur in der Autoindustrie mittlerweile kein Weg mehr vorbei. In dieser Folge schauen wir auf den vielleicht wichtigsten, aber auch risikoreichsten Player der neuen Auto und KI Ökonomie: den Konzern, der mit atemberaubenden Umsätzen und einer schwindelerregenden Bewertung die Märkte dominiert – und gleichzeitig die gesamte europäische Autoindustrie in eine doppelte Abhängigkeit treibt: von US Clouds und Nvidias zentralem Fahrzeug Stack. Pascal und Yannick sprechen über den CUDA Lock in, den Mythos der „KI Bewertungsrakete“, warnende Bubble Signale – und über die Frage, was passiert, wenn ein einzelnes Unternehmen darüber entscheidet, wie Autos in Europa künftig denken, lernen und fahren. Nvidias Pläne bei autonomen Fahren: https://www.automotiveit.eu/technology/nvidia-liefert-neue-computingpower-fuers-autonome-fahren/928666 Hintergrund zu Nvidias Aufstieg: https://www.automotiveit.eu/autonomes-fahren/alle-wege-fuehren-zu-nvidia/920965 Mehr zu Pascal und Yannick finden Sie auf LinkedIn: Pascal Nagel: https://www.linkedin.com/in/pascal-nagel/ Yannick Tiedemann: www.linkedin.com/in/yannick-tiedemann Hinweis: Die im Podcast getätigten Aussagen spiegeln die Privatmeinung der Gesprächspartner wider und entsprechen nicht zwingend den Darstellungen des jeweiligen Arbeitgebers

In un'epoca in cui Internet è diventato il sistema nervoso della nostra società, sempre più servizi dipendono da un numero ristretto di provider cloud come Amazon Web Services, Microsoft Azure e Google Cloud. Negli ultimi mesi abbiamo assistito a una serie di disservizi globali che hanno colpito milioni di utenti: dal blackout di AWS che ha reso irraggiungibili innumerevoli siti per 15 ore, ai problemi di Cloudflare, Azure e altri giganti del cloud che hanno paralizzato servizi come ChatGPT, Zoom e Shopify. Questi episodi alimentano la percezione che Internet sia diventato più fragile. Ma è davvero così? O è solo il riflesso di come l'infrastruttura di rete è cambiata negli ultimi decenni? In questa puntata analizziamo come il passaggio da server distribuiti al cloud centralizzato ha trasformato la resilienza di Internet.Nella sezione delle notizie parliamo di NanoIC, il nuovo impianto europeo per la produzione di semiconduttori, del progetto europeo REPper e infine di come la NASA ha autorizzato l'utilizzo di smartphone personali a bordo delle prossime missioni spaziali.--Indice--00:00 - Introduzione01:08 - La strategia UE per la sovranità tecnologica (Europa.eu, Luca Martinelli)02:27 - Il progetto REPper per le riparazioni (AltroConsumo.it, Davide Fasoli)03:29 - NASA autorizza gli smartphone nello spazio (Wired.it, Matteo Gallo)04:53 - Internet è diventato più fragile? (Luca Martinelli)18:06 - Conclusione--Testo--Leggi la trascrizione: https://www.dentrolatecnologia.it/S8E7#testo--Contatti--• www.dentrolatecnologia.it• Instagram (@dentrolatecnologia)• Telegram (@dentrolatecnologia)• YouTube (@dentrolatecnologia)• redazione@dentrolatecnologia.it--Brani--• Ecstasy by Rabbit Theft• Moments by Lost Identities x Robbie Rosen

In dieser Episode sprechen Patrick und Joerg über die Herausforderungen im indirekten Vertrieb von B2B Software, insbesondere im Kontext von AI, Cloud Computing und sich verändernden Geschäftsmodellen. Sie diskutieren, warum Vertriebspartner heute mehr Verantwortung übernehmen müssen und weshalb klassische Partnerstrukturen zunehmend an ihre Grenzen stossen.  Ein Schwerpunkt liegt auf der Komplexität moderner Preismodelle, AI Tools und Handelsplätze, sowie auf der Frage, wie Transparenz und Struktur im Vertrieb geschaffen werden können. Die beiden beleuchten zudem, warum systematische Herangehensweisen und klare Rollenverteilungen entscheidend sind, um Transformationen erfolgreich zu gestalten.  Die Episode zeigt, dass Vertrieb nicht nur eine operative Aufgabe ist, sondern tief im Geschäftsmodell und dessen Struktur verankert sein muss. 

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/

Sprachsteuerung ist im Smart Home und auf dem Smartphone mittlerweile Standard. Doch die Technologie dahinter bleibt für viele eine Blackbox. In Episode 174 öffnen wir diese Box. Statt Zauberei handelt es sich um reine Mathematik und massive Rechenleistung. Wir erklären den Weg deiner Stimme vom Mikrofon im Wohnzimmer bis zu den riesigen Rechenzentren der Tech-Giganten und wieder zurück. Dabei gehen wir darauf ein, wie schwierig es für Computer eigentlich ist, menschliche Sprache mit all ihren Dialekten und Nuancen zu entschlüsseln. Highlights der Folge: Lokale Verarbeitung vs. Cloud-Computing. Warum Sprachassistenten manchmal völlig falsche Dinge verstehen. Der Unterschied zwischen einfacher Befehlserkennung und echter Konversation. Ein kurzer Blick auf den Datenschutz: Was wird eigentlich gespeichert? Eine Folge für alle, die wissen wollen, mit wem (oder was) sie da eigentlich täglich reden.

"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

Abrigo's Chief Product Technology Officer reveals how they're leveling the playing field for 2,400 community banks using AWS-powered AI to compete with billion-dollar financial institutions.Topics Include:Abrigo serves 2,400 community banks and credit unions across the USThey provide risk management, fraud detection, and digital loan origination solutionsConnect platform delivers data analytics for institutions with legacy systemsCommunity banks need instant digital experiences to compete with fintech upstartsCustomers expect Uber-like speed from application to cash within hoursThree technology waves transformed finance: iPhone, cloud computing, then AIChatGPT changed conversational experiences and knowledge search expectations in bankingAI enables instant policy search for new employee onboarding needsEvery minute saved from grunt work gets redeployed into customer relationshipsSimple borrower experiences work across all demographics from boomers to millennialsAbrigo embraced agentic AI early using AWS Bedrock and Agent CoreNew guardrails and evaluations accelerate deterministic workflow reimagination with agentsParticipants:Ravikumar Nemalikanti – Chief Product and Technology Officer, AbrigoSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Aizon.ai provides an AI software platform for the pharmaceutical and biotech industries to optimize manufacturing processes, ensure GxP compliance, and improve product quality. Their top outcomes include financial savings and improved operational efficiency for clients. Aizon's ability to optimize manufacturing production and quality in highly regulated industries by providing real-time visibility and predictive insights while ensuring GxP compliance. The AWS case study details how a pharmaceutical company utilized Aizon's AI platform to achieve double-digit yield improvements through clinical process optimization. AWS Hosts: Nolan Chen & Gokhul Srinivasan https://aws.amazon.com/solutions/case-studies/aizon-case-study/https://aws.amazon.com/marketplace/seller-profile?id=5bb4e9b6-8a87-40d9-aea5-adc6ebcad7c0https://www.aizon.ai/success-storiesEmail Your Feedback: rethinkpodcast@amazon.com

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

Hear how Halo manages market disruption and technology innovation with their unique culture, helping them scale to be a leader in workflow automation software.Topics Include:Halo serves 125,000 teams across 75 countries with enterprise ITSM solutionsPaul Hamilton founded company 21 years ago as freelance IT consultantBuilt ticketing software to track their own freelance client work originallyNo marketing budget so mastered organic SEO without paid advertising spendReached number one Google ranking globally for help desk software 2006Hired first employee in 2011 when co-founder wanted outAWS partnership began years ago recognizing trajectory not current snapshot sizeAWS team proactively delivered 20 percent infrastructure optimization cost savings recentlyHalo reducing prices using savings for customer value creationHires graduates and trains them rather than poaching experienced enterprise talentMonday morning all-company meetings ensure transparency with minimal management hierarchy levelsNo traditional sales teams, culture emphasizes autonomy and employee ownership stakesTechnology completely rebuilt 2017-2018 delivering deployments in one-third typical timeframesTotal cost ownership 70 percent lower than competitors while winning tendersVision transcends software through music festivals, documentaries pioneering fulfilling workplace cultureParticipants:Paul Hamilton – CEO and Founder, HaloAlison Kay – Vice President / Managing Director, AWS UKISee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

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

Neo4j's Ajay Singh discusses future shifts in AI and why knowledge graphs may be the missing layer in your Gen AI strategy.Topics Include:Ajay Singh from Neo4j discusses graph intelligence platform serving 80+ Fortune 100 companies.Financial services firms use Neo4j knowledge graphs to detect fraud rings and accounts.IT companies build digital twins of infrastructure to analyze attack surfaces and vulnerabilities.Knowledge graphs provide richer context for Gen AI agents beyond what vector search offers.Gaming company achieved 10x faster insights and 92% reduction in analyst data gathering.Transportation company improved tariff code workflow from 50% abandonment to 95% completion rate.Neo4j has partnered with AWS since 2013, running on AWS infrastructure and Marketplace.Customers combine Neo4j with AWS Bedrock and SageMaker to build agentic AI applications.Neo4j evolved from late-stage AWS collaboration to early-stage joint customer solution development approach.Success requires business-first mindset over technology-first to avoid POCs that never reach production.Effective Gen AI needs semantic layers and knowledge graphs, not just throwing documents at LLMs.Future agents will tackle outcome-based objectives requiring explainability, security, and proper LLM operations.Participants:Ajay Singh – Global Vice President, Neo4jSee 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: 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:

Executives from March Networks explain how Amazon Bedrock transformed petabytes of surveillance video into economically viable solutions that unlock untapped business value.Topics Include:March Networks CEO and CPO discuss video surveillance for enterprise banks and retailers globallyCompany serves 10,000 customers with 250,000 installations worldwide since 2003 from Ottawa headquartersBanking customer operates across 25 countries centralizing video operations on one standardized platformSingle enterprise customer manages 65 petabytes of distributed data across March Networks recordersAWS partnership enables five-to-ten-year cloud transition starting with economical DeepGlacier storage solutionsAmazon Bedrock powers AI Smart Search analyzing snapshots for searchable business intelligence insightsEnterprise video data remains largely untapped for retail traffic patterns and operational efficiencyOne retailer processes three million daily POS transactions across 6,000 locations synchronized with video2025 brought AI Smart Search launch enabling natural language queries across entire operationsCloud storage became economically viable using Glacier after traditional quotes reached millions annuallyFraudsters exploit 90-180 day retention windows by waiting six months to file lawsuits2026 vision emphasizes consultative selling for efficiency gains supported by strong AWS partnershipParticipants:Peter Strom – President & Chief Executive Officer, March NetworksJeff Corrall – Chief Product Officer, March NetworksSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

In this special encore episode from AWS re:Invent, AWS CEO Matt Garman joins Acquired podcast co-hosts Ben Gilbert and David Rosenthal for an in-depth conversation on AI, agents, and the future of business. Listen in as Garman shares his leadership journey from AWS intern to CEO, discusses why inference is becoming a fundamental building block for developers, and reveals how AI is enabling smaller teams to deliver exponentially more value. He also explores the organizational shifts enterprises must make to stay competitive, the evolution of agentic AI, and why agility and speed remain critical regardless of technological change.To catch the full interview session featuring additional speakers, Max Neukirchen (J.P. Morgan Payments), Greg Peters (Netflix), and Aravind Srivinas (Perplexity), click here to watch on YouTube -> https://www.youtube.com/watch?v=2ExjNvGYDiU.

We sat down with Martyn Wallen, Executive Director & Co-Founder, and Gino Brenzini, Executive Director & COO of Quantum Six. Their team helps banks, credit unions, and fintech's modernize core systems and maximize technology investments through ecosystem strategies—driving agility and long-term value in a landscape shaped by cloud, AI, and composable architectures.We explore key banking trends in the UK versus the US, uncover lessons learned by European banks, and share actionable insights for U.S. financial institutions navigating digital transformation.Send us a textPresented by Remedy ConsultingFor more information on BankTalk:BankTalk WebsiteSubscribe to BankTalk NewsRemedy Consulting WebsiteRemedy LinkedInTo speak on the BankTalk Podcast, please email us.

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

Topics covered in this episode: ty: An extremely fast Python type checker and LSP Python Supply Chain Security Made Easy typing_extensions MI6 chief: We'll be as fluent in Python as we are in Russian 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. Brian #1: ty: An extremely fast Python type checker and LSP Charlie Marsh announced the Beta release of ty on Dec 16 “designed as an alternative to tools like mypy, Pyright, and Pylance.” Extremely fast even from first run Successive runs are incremental, only rerunning necessary computations as a user edits a file or function. This allows live updates. Includes nice visual diagnostics much like color enhanced tracebacks Extensive configuration control Nice for if you want to gradually fix warnings from ty for a project Also released a nice VSCode (or Cursor) extension Check the docs. There are lots of features. Also a note about disabling the default language server (or disabling ty's language server) so you don't have 2 running Michael #2: Python Supply Chain Security Made Easy We know about supply chain security issues, but what can you do? Typosquatting (not great) Github/PyPI account take-overs (very bad) Enter pip-audit. Run it in two ways: Against your installed dependencies in current venv As a proper unit test (so when running pytest or CI/CD). Let others find out first, wait a week on all dependency updates: uv pip compile requirements.piptools --upgrade --output-file requirements.txt --exclude-newer "1 week" Follow up article: DevOps Python Supply Chain Security Create a dedicated Docker image for testing dependencies with pip-audit in isolation before installing them into your venv. Run pip-compile / uv lock --upgrade to generate the new lock file Test in a ephemeral pip-audit optimized Docker container Only then if things pass, uv pip install / uv sync Add a dedicated Docker image build step that fails the docker build step if a vulnerable package is found. Brian #3: typing_extensions Kind of a followup on the deprecation warning topic we were talking about in December. prioinv on Mastodon notified us that the project typing-extensions includes it as part of the backport set. The warnings.deprecated decorator is new to Python 3.13, but with typing-extensions, you can use it in previous versions. But typing_extesions is way cooler than just that. The module serves 2 purposes: Enable use of new type system features on older Python versions. Enable experimentation with type system features proposed in new PEPs before they are accepted and added to the typing module. So cool. There's a lot of features here. I'm hoping it allows someone to use the latest typing syntax across multiple Python versions. I'm “tentatively” excited. But I'm bracing for someone to tell me why it's not a silver bullet. Michael #4: MI6 chief: We'll be as fluent in Python as we are in Russian "Advances in artificial intelligence, biotechnology and quantum computing are not only revolutionizing economies but rewriting the reality of conflict, as they 'converge' to create science fiction-like tools,” said new MI6 chief Blaise Metreweli. She focused mainly on threats from Russia, the country is "testing us in the grey zone with tactics that are just below the threshold of war.” This demands what she called "mastery of technology" across the service, with officers required to become "as comfortable with lines of code as we are with human sources, as fluent in Python as we are in multiple other languages." Recruitment will target linguists, data scientists, engineers, and technologists alike. Extras Brian: Next chapter of Lean TDD being released today, Finding Waste in TDD Still going to attempt a Jan 31 deadline for first draft of book. That really doesn't seem like enough time, but I'm optimistic. SteamDeck is not helping me find time to write But I very much appreciate the gift from my fam Send me game suggestions on Mastodon or Bluesky. I'd love to hear what you all are playing. Michael: Astral has announced the Beta release of ty, which they say they are "ready to recommend to motivated users for production use." Blog post Release page Reuven Lerner has a video series on Pandas 3 Joke: Error Handling in the age of AI Play on the inversion of JavaScript the Good Parts

In this episode of the Crazy Wisdom podcast, host Stewart Alsop interviews Marcin Dymczyk, CPO and co-founder of SevenSense Robotics, exploring the fascinating world of advanced robotics and AI. Their conversation covers the evolution from traditional "standard" robotics with predetermined pathways to advanced robotics that incorporates perception, reasoning, and adaptability - essentially the AGI of physical robotics. Dymczyk explains how his company builds "the eyes and brains of mobile robots" using camera-based autonomy algorithms, drawing parallels between robot sensing systems and human vision, inner ear balance, and proprioception. The discussion ranges from the technical challenges of sensor fusion and world models to broader topics including robotics regulation across different countries, the role of federalism in innovation, and how recent geopolitical changes are driving localized high-tech development, particularly in defense applications. They also touch on the democratization of robotics for small businesses and the philosophical implications of increasingly sophisticated AI systems operating in physical environments. To learn more about SevenSense, visit www.sevensense.ai.Check out this GPT we trained on the conversationTimestamps00:00 Introduction to Robotics and Personal Journey05:27 The Evolution of Robotics: From Standard to Advanced09:56 The Future of Robotics: AI and Automation12:09 The Role of Edge Computing in Robotics17:40 FPGA and AI: The Future of Robotics Processing21:54 Sensing the World: How Robots Perceive Their Environment29:01 Learning from the Physical World: Insights from Robotics33:21 The Intersection of Robotics and Manufacturing35:01 Journey into Robotics: Education and Passion36:41 Practical Robotics Projects for Beginners39:06 Understanding Particle Filters in Robotics40:37 World Models: The Future of AI and Robotics41:51 The Black Box Dilemma in AI and Robotics44:27 Safety and Interpretability in Autonomous Systems49:16 Regulatory Challenges in Robotics and AI51:19 Global Perspectives on Robotics Regulation54:43 The Future of Robotics in Emerging Markets57:38 The Role of Engineers in Modern WarfareKey Insights1. Advanced robotics transcends traditional programming through perception and intelligence. Dymczyk distinguishes between standard robotics that follows rigid, predefined pathways and advanced robotics that incorporates perception and reasoning. This evolution enables robots to make autonomous decisions about navigation and task execution, similar to how humans adapt to unexpected situations rather than following predetermined scripts.2. Camera-based sensing systems mirror human biological navigation. SevenSense Robotics builds "eyes and brains" for mobile robots using multiple cameras (up to eight), IMUs (accelerometers/gyroscopes), and wheel encoders that parallel human vision, inner ear balance, and proprioception. This redundant sensing approach allows robots to navigate even when one system fails, such as operating in dark environments where visual sensors are compromised.3. Edge computing dominates industrial robotics due to connectivity and security constraints. Many industrial applications operate in environments with poor connectivity (like underground grocery stores) or require on-premise solutions for confidentiality. This necessitates powerful local processing capabilities rather than cloud-dependent AI, particularly in automotive factories where data security about new models is paramount.4. Safety regulations create mandatory "kill switches" that bypass AI decision-making. European and US regulatory bodies require deterministic safety systems that can instantly stop robots regardless of AI reasoning. These systems operate like human reflexes, providing immediate responses to obstacles while the main AI brain handles complex navigation and planning tasks.5. Modern robotics development benefits from increasingly affordable optical sensors. The democratization of 3D cameras, laser range finders, and miniature range measurement chips (costing just a few dollars from distributors like DigiKey) enables rapid prototyping and innovation that was previously limited to well-funded research institutions.6. Geopolitical shifts are driving localized high-tech development, particularly in defense applications. The changing role of US global leadership and lessons from Ukraine's drone warfare are motivating countries like Poland to develop indigenous robotics capabilities. Small engineering teams can now create battlefield-effective technology using consumer drones equipped with advanced sensors.7. The future of robotics lies in natural language programming for non-experts. Dymczyk envisions a transformation where small business owners can instruct robots using conversational language rather than complex programming, similar to how AI coding assistants now enable non-programmers to build applications through natural language prompts.

Python in 2025 is in a delightfully refreshing place: the GIL's days are numbered, packaging is getting sharper tools, and the type checkers are multiplying like gremlins snacking after midnight. On this episode, we have an amazing panel to give us a range of perspectives on what matter in 2025 in Python. We have Barry Warsaw, Brett Cannon, Gregory Kapfhammer, Jodie Burchell, Reuven Lerner, and Thomas Wouters on to give us their thoughts. Episode sponsors Seer: AI Debugging, Code TALKPYTHON Talk Python Courses Links from the show Python Software Foundation (PSF): www.python.org PEP 810: Explicit lazy imports: peps.python.org PEP 779: Free-threaded Python is officially supported: peps.python.org PEP 723: Inline script metadata: peps.python.org PyCharm: www.jetbrains.com JetBrains: www.jetbrains.com Visual Studio Code: code.visualstudio.com pandas: pandas.pydata.org PydanticAI: ai.pydantic.dev OpenAI API docs: platform.openai.com uv: docs.astral.sh Hatch: github.com PDM: pdm-project.org Poetry: python-poetry.org Project Jupyter: jupyter.org JupyterLite: jupyterlite.readthedocs.io PEP 690: Lazy Imports: peps.python.org PyTorch: pytorch.org Python concurrent.futures: docs.python.org Python Package Index (PyPI): pypi.org EuroPython: tickets.europython.eu TensorFlow: www.tensorflow.org Keras: keras.io PyCon US: us.pycon.org NumFOCUS: numfocus.org Python discussion forum (discuss.python.org): discuss.python.org Language Server Protocol: microsoft.github.io mypy: mypy-lang.org Pyright: github.com Pylance: marketplace.visualstudio.com Pyrefly: github.com ty: github.com Zuban: docs.zubanls.com Jedi: jedi.readthedocs.io GitHub: github.com PyOhio: www.pyohio.org Watch this episode on YouTube: youtube.com Episode #532 deep-dive: talkpython.fm/532 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Anthropic's Tobias Harrison Noonan shares the enterprise AI playbook: why coding leads to broader AI adoption, practical tips for getting started, and why you shouldn't wait for perfection.Topics Include:Tobias from Anthropic's Applied AI team discusses enterprise AI adoption trends and insights.Anthropic founded four years ago balancing AI safety mission with world's most intelligent models.Remarkable velocity: Claude 3.7 and Claude Code both shipped just in 2025 alone.Three-layer partnership: foundation models, enterprise capabilities, and end-user platforms like Claude Code.Anthropic leads in agentic coding for eighteen months, now number one enterprise AI market share.Claude Opus 4.5 launched last week, again tops software engineering benchmark for complex tasks.Claude Code enables thirty-hour autonomous coding sessions, ships features five times faster than before.Next frontier expands beyond coding into data-heavy knowledge work like financial and legal analysis.AI adoption maturity curve: employee workflows, internal processes, core products, then AI-native products.Thomson Reuters started with Claude Code for development team doing code modernization and refactoring.They expanded to Claude.ai for sales, marketing, and finance teams after seeing tangible ROI.Built Claude into core products including co-counsel legal platform and fraud prevention systems strategically.Today Thomson Reuters has eight different product lines powered by Claude across their portfolio.AWS partnership offers safe, secure, scalable deployment from POC to production in existing environments.Don't wait for perfection: AI today is dumbest it'll ever be, start prototyping now.Participants:Tobias Harrison-Noonan: Member of Technical Staff, AnthropicSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

From all of us at Cloud Realities, MERRY CHRISTMAS!!!! Back in our December 2022 Christmas special, we explored the far reaches of reality, asking whether we live in a simulation and if that even matters. Now, we return to that question with fresh perspectives and new challenges…In this last Cloud Realities podcast of 2025, Dave, Esmee and Rob return to the simulation with Anders Indset, philosopher, author, and long-time friend of the show, revisiting a question that's been quietly running underneath everything we've discussed since 2022: If reality itself is information and what does that mean for being human? TLDR:00:58 – It's Christmas!08:32 – Major announcement and reflections on the Cloud Realities podcast journey15:32 – Celebrating three big wins: B2B Marketing Awards (Best Content, Best Customer Retention) and The Drum (Best Creative Audio)22:55 – Is there a next thing?23:30 – Welcoming Anders Indset, who shares his vision for practical philosophy and the future of human/AI co-evolution32:02 – Exploring the Quantum Economy and the Singularity Paradox58:10 – Deep dive into the Simulation Hypothesis, revisiting the 2022 discussion and Rob is again confused...01:27:45 – Anders enjoying Christmas in the Norwegian wilderness01:29:40 – Edit pointGuestAnders Indset: https://www.linkedin.com/in/andersindset/ or andersindset.comAdditional information: thequantumeconomy.com and tomorrowmensch.comHostsDave Chapmanger: https://www.linkedin.com/in/chapmandr/Esmee van de Gluhwein: https://www.linkedin.com/in/esmeevandegiessen/Rob Snowmananahan: https://www.linkedin.com/in/rob-kernahan/ProductionDr Mike van Der Buabbles: https://www.linkedin.com/in/marcel-vd-burg/Dave Chapmanger: https://www.linkedin.com/in/chapmandr/ SoundBen Jingle: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Snow:  https://www.linkedin.com/in/louis-corbett-087250264/ 'Cloud Realities' is an original podcast from Capgemini

Topics covered in this episode: Has the cost of building software just dropped 90%? More on Deprecation Warnings How FOSS Won and Why It Matters Should I be looking for a GitHub alternative? 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. HEADS UP: We are taking next week off, happy holiday everyone. Michael #1: Has the cost of building software just dropped 90%? by Martin Alderson Agentic coding tools are collapsing “implementation time,” so the cost curve of shipping software may be shifting sharply Recent programming advancements haven't been that great of a true benefit: Cloud, TDD, microservices, complex frontends, Kubernetes, etc. Agentic AI's big savings are not just code generation, but coordination overhead reduction (fewer handoffs, fewer meetings, fewer blocks). Thinking, product clarity, and domain decisions stay hard, while typing and scaffolding get cheap. Is it the end of software dev? Not really, see Jevons paradox: when production gets cheaper, total demand can rise rather than spending simply falling. (Historically: the efficiency of coal use led to the increased consumption of coal) Pushes back on “only good for greenfield” by arguing agents also help with legacy code comprehension and bug-fixing. I 100% agree. #Legacy code for the win. Brian #2: More on Deprecation Warnings How are people ignoring them? yep, it's right in the Python docs: -W ignore::DeprecationWarning Don't do that! Perhaps the docs should give the example of emitting them only once -W once::::DeprecationWarning See also -X dev mode , which sets -W default and some other runtime checks Don't use warn, use the @warnings.deprecated decorator instead Thanks John Hagen for pointing this out Emits a warning It's understood by type checkers, so editors visually warn you You can pass in your own custom UserWarning with category mypy also has a command line option and setting for this --enable-error-code deprecated or in [tool.mypy] enable_error_code = ["deprecated"] My recommendation Use @deprecated with your own custom warning and test with pytest -W error Michael #3: How FOSS Won and Why It Matters by Thomas Depierre Companies are not cheap, companies optimize cost control. They do this by making purchasing slow and painful. FOSS is/was a major unlock hack to skip procurement, legal, etc. Example is months to start using a paid “Add to calendar” widget! It “works both ways”: the same bypass lowers the barrier for maintainers too, no need for a legal entity, lawyers, liability insurance, or sales motion. Proposals that “fix FOSS” by reintroducing supply-chain style controls (he name-checks SBOMs and mandated processes) risk being rejected or gamed, because they restore the very friction FOSS sidesteps. Brian #4: Should I be looking for a GitHub alternative? Pricing changes for GitHub Actions The self-hosted runner pricing change caused a kerfuffle. It's has been postponed But… if you were to look around, maybe pay attention to These 4 GitHub alternatives are just as good—or better Codeburg, BitBucket, GitLab, Gitea And a new-ish entry, Tangled Extras Brian: End of year sale for The Complete pytest Course Use code XMAS2025 for 50% off before Dec 31 Writing work on Lean TDD book on hold for holidays Will pick up again in January Michael: PyCharm has better Ruff support now out of the box, via Daniel Molnar This is from the release notes of 2025.3: "PyCharm 2025.3 expands its LSP integration with support for Ruff, ty, Pyright, and Pyrefly.” If you check out the LSP section it will land you on this page and you can go to Ruff. The Ruff doc site was also updated. Previously it was only available external tools and a third party plugin, this feels like a big step. Fun quote I saw on ExTwitter: May your bug tracker be forever empty. Joke: Try/Catch/Stack Overflow Create a super annoying linkedin profile - From Tim Kellogg, submitted by archtoad

Straight from re:Invent 2025, technology leaders from C3 AI, nCino, New Relic and Vercel reveal learnings, best practices and predictions for the future of Agentic AI.Topics Include:Four technology executives introduce their companies' AI innovations in fintech, cloud, enterprise software, and observability.Vercel built agents for code reviews, infrastructure optimization, and across finance, sales, and support functions.C3.ai deploys enterprise AI applications from scratch to production in six months for Fortune 500s.New Relic provides observability for AI systems and built agents that resolve infrastructure issues in real-time.Vercel's agents improve code quality by incorporating security and framework best practices into AI-generated output.C3.ai partnered with Department of Defense to autonomously produce mission-critical intelligence assessment reports from data.Industry shifted from copilots everywhere to agents that actually own outcomes and land the plane.New Relic moved beyond natural language translation to agents that execute actions and resolve issues autonomously.Panel debates whether Model Context Protocol or broader ecosystem approaches better enable agent interoperability and communication.Autonomy requires accountability: agent decisions must be explainable with traceable steps and replay capabilities built-in.Governance and security should be prerequisites for acceleration, not impediments—a critical mental model shift needed.Many enterprises struggle with process bottlenecks preventing them from harnessing high-quality agents despite having technology.Financial services must carefully balance where human discretion remains essential versus where agent autonomy justified.Will Jung envisions deeply continuous context enabling banks to deliver truly personalized insights without appearing creepy.Suraj Krishnan predicts agents will own outcomes by 2026, coordinating tools and other agents to achieve goals.Participants:Panelist: Merel Witteveen, SVP of Operations, C3.aiPanelist: Will Jung, Chief Technology Officer, nCinoPanelist: Suraj Krishnan, GVP of Engineering, New RelicPanelist: Aparna Sinha, Senior Vice President, Product, VercelModerator: Olawale Oladehin, Managing Director, NAMER Technology Segments (Enterprise, ISV, DNB, and Model Providers), Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Have you ever thought about getting your small product into production, but are worried about the cost of the big cloud providers? Or maybe you think your current cloud service is over-architected and costing you too much? Well, in this episode, we interview Michael Kennedy, author of "Talk Python in Production," a new book that guides you through deploying web apps at scale with right-sized engineering. Episode sponsors Seer: AI Debugging, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Christopher Trudeau - guest host: www.linkedin.com Michael's personal site: mkennedy.codes Talk Python in Production Book: talkpython.fm glances: github.com btop: github.com Uptimekuma: uptimekuma.org Coolify: coolify.io Talk Python Blog: talkpython.fm Hetzner (€20 credit with link): hetzner.cloud OpalStack: www.opalstack.com Bunny.net CDN: bunny.net Galleries from the book: github.com Pandoc: pandoc.org Docker: www.docker.com Watch this episode on YouTube: youtube.com Episode #531 deep-dive: talkpython.fm/531 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Topics covered in this episode: Deprecations via warnings docs PyAtlas: interactive map of the top 10,000 Python packages on PyPI. Buckaroo 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. Brian #1: Deprecations via warnings Deprecations via warnings don't work for Python libraries Seth Larson How to encourage developers to fix Python warnings for deprecated features Ines Panker Michael #2: docs A collaborative note taking, wiki and documentation platform that scales. Built with Django and React. Made for self hosting Docs is the result of a joint effort led by the French

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

In this year-end episode, William and Eyvonne recap their experiences at AutoCon 4 in Austin, Texas. They discuss the conference’s new multi-track format, including Eyvonne’s presentation in the leadership track on why technical projects fail. The conversation dives into how AI tools like Google Gemini can augment – not replace – human creativity, from research... Read more »

Topics covered in this episode: PEP 798: Unpacking in Comprehensions Pandas 3.0.0rc0 typos A couple testing topics Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: PEP 798: Unpacking in Comprehensions After careful deliberation, the Python Steering Council is pleased to accept PEP 798 – Unpacking in Comprehensions. Examples [*it for it in its] # list with the concatenation of iterables in 'its' {*it for it in its} # set with the union of iterables in 'its' {**d for d in dicts} # dict with the combination of dicts in 'dicts' (*it for it in its) # generator of the concatenation of iterables in 'its' Also: The Steering Council is happy to unanimously accept “PEP 810, Explicit lazy imports” Brian #2: Pandas 3.0.0rc0 Pandas 3.0.0 will be released soon, and we're on Release candidate 0 Here's What's new in Pands 3.0.0 Dedicated string data type by default Inferred by default for string data (instead of object dtype) The str dtype can only hold strings (or missing values), in contrast to object dtype. (setitem with non string fails) The missing value sentinel is always NaN (np.nan) and follows the same missing value semantics as the other default dtypes. Copy-on-Write The result of any indexing operation (subsetting a DataFrame or Series in any way, i.e. including accessing a DataFrame column as a Series) or any method returning a new DataFrame or Series, always behaves as if it were a copy in terms of user API. As a consequence, if you want to modify an object (DataFrame or Series), the only way to do this is to directly modify that object itself. pd.col syntax can now be used in DataFrame.assign() and DataFrame.loc() You can now do this: df.assign(c = pd.col('a') + pd.col('b')) New Deprecation Policy Plus more - Michael #3: typos You've heard about codespell … what about typos? VSCode extension and OpenVSX extension. From Sky Kasko: Like codespell, typos checks for known misspellings instead of only allowing words from a dictionary. But typos has some extra features I really appreciate, like finding spelling mistakes inside snake_case or camelCase words. For example, if you have the line: *connecton_string = "sqlite:///my.db"* codespell won't find the misspelling, but typos will. It gave me the output: *error: `connecton` should be `connection`, `connector` ╭▸ ./main.py:1:1 │1 │ connecton_string = "sqlite:///my.db" ╰╴━━━━━━━━━* But the main advantage for me is that typos has an LSP that supports editor integrations like a VS Code extension. As far as I can tell, codespell doesn't support editor integration. (Note that the popular Code Spell Checker VS Code extension is an unrelated project that uses a traditional dictionary approach.) For more on the differences between codespell and typos, here's a comparison table I found in the typos repo: https://github.com/crate-ci/typos/blob/master/docs/comparison.md By the way, though it's not mentioned in the installation instructions, typos is published on PyPI and can be installed with uv tool install typos, for example. That said, I don't bother installing it, I just use the VS Code extension and run it as a pre-commit hook. (By the way, I'm using prek instead of pre-commit now; thanks for the tip on episode #448!) It looks like typos also publishes a GitHub action, though I haven't used it. Brian #4: A couple testing topics slowlify suggested by Brian Skinn Simulate slow, overloaded, or resource-constrained machines to reproduce CI failures and hunt flaky tests. Requires Linux with cgroups v2 Why your mock breaks later Ned Badthelder Ned's taught us before to “Mock where the object is used, not where it's defined.” To be more explicit, but probably more confusing to mock-newbies, “don't mock things that get imported, mock the object in the file it got imported to.” See? That's probably worse. Anyway, read Ned's post. If my project myproduct has user.py that uses the system builtin open() and we want to patch it: DONT DO THIS: @patch("builtins.open") This patches open() for the whole system DO THIS: @patch("myproduct.user.open") This patches open() for just the user.py file, which is what we want Apparently this issue is common and is mucking up using coverage.py Extras Brian: The Rise and Rise of FastAPI - mini documentary “Building on Lean” chapter of LeanTDD is out The next chapter I'm working on is “Finding Waste in TDD” Notes to delete before end of show: I'm not on track for an end of year completion of the first pass, so pushing goal to 1/31/26 As requested by a reader, I'm releasing both the full-so-far versions and most-recent-chapter Michael: My Vanishing Gradient's episode is out Django 6 is out Joke: tabloid - A minimal programming language inspired by clickbait headlines

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

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