Podcasts about Cloud computing

Topics covered in this episode: * ty documentation site and uv migration guide* * uv build backend is now stable + other Astral news* * Refactoring long boolean expressions* * fastapi-ml-skeleton* Extras Joke Watch on YouTube About the show Sponsored by Sentry: pythonbytes.fm/sentry Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: ty documentation site and uv migration guide via Skyler Kasko Astral created a documentation site for ty (PR #744 in release 0.0.1-alpha.13). Astral added a page on migrating from pip to a uv project in the uv documentation. (PR #12382 in release 0.7.19). Talk Python episode on ty. Brian #2: uv build backend is now stable + other Astral news The uv build backend is now stable Tim Hopper via Python Developer Tooling Handbook From Charlie Marsh “The uv build backend is now stable, and considered ready for production use. An alternative to setuptools, hatchling, etc. for pure Python projects, with a focus on good defaults, user-friendly error messages, and performance. When used with uv, it's 10-35x faster.” “(In a future release, we'll make this the default.)” [build-system] requires = ["uv_build>=0.7.19,

Matt “Kix” Kixmoeller, Chief Marketing Officer of Glean, shares how Glean partners with AWS to deploy secure, scalable AI solutions that help companies move from basic productivity tools to transformative business intelligence.Topics Include:Introduction to GleanGlean targets Global 2000 companies for AI transformationEnterprise AI needs company context: data, people, processesBottom-up approach: deploy to all employees firstFocus on business results, not just productivity gainsGlean Assistant provides daily AI tool for employeesGlean Agents platform enables natural language agent buildingOpen APIs export context to enterprise systemsStarted as enterprise search, evolved to knowledge graphsKnowledge graphs map content, people, projects, and processesIndividual knowledge graphs created for each personGlean WorkAI platform includes search, protect, agentsGlean Protect ensures data security and AI governancePlatform integrates with existing enterprise tools nativelyMCP enables connection to various AI systemsStrong growth: $100M ARR, $700M+ funding raisedAWS partnership provides models, security, and deploymentParticipants:Matt “Kix” Kixmoeller – Chief Marketing Officer, GleanFurther Links:Website: https://www.glean.com/Glean on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

In this repeat episode, Nikolas Burk, DevRel at Prisma, talks about Prisma Postgres, its unikernel architecture, and its seamless integration with cloud infrastructure. Discover how Prisma Postgres is revolutionizing database management with features like cold start elimination, real-time event handling and advanced caching strategies! Links X: https://x.com/nikolasburk LinkedIn: https://www.linkedin.com/in/nikolas-burk-1bbb7b8a Github: https://github.com/nikolasburk Resources Prisma Postgres®: Building a Modern PostgreSQL Service Using Unikernels & MicroVMs: https://www.prisma.io/blog/announcing-prisma-postgres-early-access We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Let us know by sending an email to our producer, Em, at emily.kochanek@logrocket.com (mailto:emily.kochanek@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. (https://logrocket.com/signup/?pdr) Special Guest: Nikolas Burk.

Quantum computing in 2025 is rapidly advancing toward commercialization, with breakthroughs in algorithms, scalable hardware, and cloud-based quantum services driving real-world applications across finance, healthcare, logistics, and cybersecurityThis week, Dave, Esmee, and Rob dive into the cutting edge of quantum computing with Catherine Vollgraff Heidweiller, Quantum AI PM at Google, and James Goeders, Head of Product for Google Quantum AI, exploring how far we've come since our June 2023 Quantumania! episode and what to expect from Willow—the bold fusion of quantum, AI, digital integration, deployment, and the broader tech ecosystem.TLDR00:46 Meet Catherine and James – intros and backgrounds02:22 Rob is confused about students using AI09:40 Deep dive with Catherine and James on the current state and future of Quantum48:01 Quantum isn't just tech—it's a whole new way of thinking1:01:37 Seize the moment and bringing external users onto quantum hardwareGuestCatherine Vollgraff Heidweiller: https://www.linkedin.com/in/cmv-vollgraffheidweiller/James Goeders: https://www.linkedin.com/in/james-goeders-8876a7164/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/'Cloud Realities' is an original podcast from Capgemini

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

How has Azure been innovating lately? While at Build Richard chatted with Azure CTO Mark Russinovich about some of the latest innovations in Azure, including some of the new hardware available. Mark talks about working with hardware manufacturers to build servers optimized to the workloads the largest Azure customers need. The conversation also explores the new innovations in AI and how Azure is being optimized to serve those workloads - and be shaped by them! Mark also talks about how material science is evolving with generative AI technologies leading to a discussion about the coming role of quantum computing - and how that will live in the cloud as well!LinksScott and Mark Learn to...Azure F-Family VMsHollow Core FiberMajorana 1 Quantum ProcessorRecorded May 20, 2025

Kui Jia, Sumo Logic's Vice President of Engineering and Head of AI, shares how their AWS-powered AI agents transform chaotic security investigations into streamlined workflows.Topics Include:Kui Jia leads AI Engineering at Sumo LogicSREs and SOC analysts work under chaotic, high-pressure conditionsTeams constantly switch between different vendor tools and platformsInvestigation requires quick hypothesis formation and complex query writingSumo Logic processes petabytes of data daily across enterprisesCompany serves 2,000+ enterprise customers for 15 yearsPlatform focuses on observability and cybersecurity use casesInvestigation journey: discover, diagnose, decide, act, learn phasesData flows from ingestion through analytics to human insightsTraditional workflow relies heavily on tribal domain knowledgeSenior engineers create queries that juniors struggle to understandWar room situations demand immediate answers, not learning curvesContext switching between tools wastes time and creates frictionMultiple AI generations deployed: ML anomaly detection to GenAIAgentic AI enables reasoning, planning, tools, and evaluation capabilitiesMo Copilot launched at AWS re:Invent as AI agent suiteNatural language converts high-level questions into Sumo queriesSystem provides intelligent autocomplete and multi-turn conversationsInsight agents summarize logs and security signals automaticallyKnowledge integration combines foundation models with proprietary metadataAI generates playbooks and remediation scripts for automated actionsThree-tier architecture: Infrastructure, AI Tooling, and Application layersBuilt on AWS Bedrock with Nova models for performanceFocus on reusable infrastructure and AI tooling componentsData differentiation more important than AI model selectionGolden datasets and contextualized metadata are development challengesGuardrails and evaluation frameworks critical for enterprise deploymentAI observability enables debugging and performance monitoringEnterprise agents achievable within one year development timelineFuture vision: multiple AI agents collaborating with human investigatorsParticipants:Kui Jia – Vice President of AI Engineering, Head of AI, Sumo LogicFurther Links:Website: https://www.sumologic.com/Sumo Logic in the AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

The Cloud Gambit is joining the Packet Pushers network! Launched in 2023 as an independent podcast, The Cloud Gambit cuts through the hype to deliver what actually matters in cloud and AI. Hosts William Collins and Eyvonne Sharp decode the strategies, technologies, and market forces reshaping enterprise infrastructure. Built for engineers who lead, leaders who... Read more »

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

Wilson Patton, Solutions Architect for Trellix, demonstrates how their four-pillar Gen-AI framework transforms incident alerts into actionable intelligence.Topics Include:Wilson Patton: Trellix Solutions Architect, 20 years government experienceWitnessed evolution from basic firewalls to zero trust architecturesTrellix combines McAfee and FireEye heritage and capabilitiesAI integration isn't new - machine learning embedded for yearsPartnership with AWS Bedrock accelerates Gen-AI development capabilities2014: Developed Impossible Travel Analytic for anomaly detection2016: Launched Guided Investigations framework for SOC analysts2023: Introduced AI Guided Investigations with contextual understanding64% of public sector exploring AI adoption activelyOnly 21% have requisite data ready for trainingGen-AI won't magically clean up messy, siloed data74% of executives doubt AI information accuracy currentlyMonday morning alert queue: 76 high, 318 medium alertsAdversaries steal credentials 90 days before major incidentsCritical breadcrumbs hidden in low-priority informational alerts1000+ data-driven investigative questions developed over eight yearsSkilled analysts take too long reading all answersAutomate analysis, distill thousands down to ten critical alertsFour foundational pillars for effective, trustworthy Gen-AI implementationCybersecurity expertise essential - Gen-AI is just a toolFrameworks ensure reliability and consistent prompting for productionMultiple LLM models tested through AWS Bedrock platformQuality diverse datasets required for accurate question answeringGood prompts combine evidence, context, and comprehensive informationTesting shows order of magnitude price differences between modelsNova Micro provides cost-effective results for many scenariosPrompt engineering superior to fine-tuning for avoiding biasAgentic AI performs multi-step investigations with live dataStrategic model choice based on specific requirements and costsTransparent audit trails mandatory for government compliance requirements Participants:Wilson Patton – Solutions Architect, TrellixFurther Links:Website: https://www.trellix.comTrellix in the AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Nvidia looms large over the world of artificial intelligence thanks to its supply of chips – a critical component of data centers that power AI models. WSJ Heard on the Street columnist Asa Fitch explains that the chip giant's foray into cloud computing is starting to threaten industry stalwarts. Plus, millions of resumes never make it past bots screening data for potential job candidates. WSJ reporter Lauren Weber profiles one man who has sued for discrimination. He worries an algorithm screened him out. Sign up for the WSJ's free Technology newsletter. Learn more about your ad choices. Visit megaphone.fm/adchoices

This week on Cloud Unplugged: AI goes local, Google Cloud breaks the internet, and the DOJ turns up the heat on Google's $32B Wiz acquisition.We're breaking down the biggest stories in cloud and AI:Context & Qualcomm are teaming up to move AI agents off the cloud and onto your device. What does this mean for the future of local-first AI?A major Google Cloud outage caused chaos across Cloudflare, Shopify, and Discord. We explain what went wrong and what it tells us about the risks of centralised cloud infrastructure.The DOJ is investigating Google's acquisition of Wiz, raising questions about cloud security competition and antitrust concerns.Plus: Andrej Karpathy's Software 3.0 vision, is natural language the new programming interface?Hosted by Lewis and Jon, two cloud-native veterans covering the real stories behind the hype in cloud, AI, and dev infrastructure.

Jeff Chow, Chief Product and Technology Officer at Miro, explores how harnessing AI — in addition to reshaping teams and workflows — accelerates the product development lifecycle. He also shares insight into how Miro is embracing new technology and ways of working to transform its Innovation Workspace.Topics Include:Platform & PartnershipMiro serves 250,000+ customers with 90+ million knowledge workers using their Innovation WorkspacePlatform supports discovery, definition, and delivery phases of innovation processReal-time multiplayer canvas enables team co-creation across multiple formats, including seamless transitions between structured and unstructured work.Three-tier AWS partnership: infrastructure backbone, AI services (Bedrock/Q), and joint customer solutionsInnovation Challenges & FrictionProduct development lifecycle bottlenecks: separate tools per function create process delays and collaborative frictionPain points include stalled product kickoffs, lengthy design ideation cycles, and process delays from engineering architecture discussions.Leadership struggles with project visibility and strategic alignment across initiativesAI TransformationAI fundamentally shifts workflows with universal knowledge access at fingertipsCraft democratization blurs traditional role boundaries (PMs prototyping, developers designing)Agentic workflows and agents collapse traditional development stack layersAI shortcuts enable one-button synthesis of workshops into product briefsProduct development lifecycle compression from 20 steps to 5 key phasesBedrock and Q services create significant business accelerationOrganizational DesignCommon organizational rhythms and rituals create shared working languageDriving maximum impact by aligning on big initiatives vs. distributed prioritiesCollaborating across all functions — product, engineering, design — and at all organizational levelsBottom-up innovation requiring clear problem communication throughout organizationInclusive environments welcoming ideas from junior and introverted team membersWorking backwards planning and PR FAQs adopted from Amazon methodologiesCreating the next big thing with MiroLarge enterprises use Miro for strategic planning, OKR planning, capacity planning, roadmappingVisual proof-of-concepts and live demos make abstract concepts tangibleSame-day product brief delivery improves team collaboration and ownershipVoice of customer integration: automated synthesis of feedback into feature developmentMiro uses Miro internally to build next-generation featuresEnhanced employee engagement alongside improved business outcomesCustomers consistently achieve 2-3x time-to-market improvementsParticipants:Jeff Chow – Chief Product and Technology Officer, MiroJohan Broman – EMEA ISV Head of Solutions Architecture, AWSFurther Links:Website: https://miro.com/page/product-leaders/Miro in the AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

RJJ Software's Software Development Service This episode of The Modern .NET Show is supported, in part, by RJJ Software's Software Development Services, whether your company is looking to elevate its UK operations or reshape its US strategy, we can provide tailored solutions that exceed expectations. Show Notes "So the cloud adoption framework actually has a lot of steps for organizations or IT teams to start assessing their existing environments first and planning the stage before they modernise and migrate to Azure. And then the well-architected framework allows the team, whoever is involved, developers, engineers, or architects, working in that migration project to think how they're going to think about architecting for the cloud in a way that it meets all the pillars in terms of resiliency, performance, architecture, and everything. Security, for example, that they need to think about."— Jonah Andersson Welcome friends to The Modern .NET Show; the premier .NET podcast, focusing entirely on the knowledge, tools, and frameworks that all .NET developers should have in their toolbox. We are the go-to podcast for .NET developers worldwide, and I am your host: Jamie “GaProgMan” Taylor. In this episode, which is the final one of season 7, Jonah Andersson joins us to talk all things Azure, the many pathways involved in migrating and modernising .NET applications, and publishing to the cloud. "So one tool that I actually highly recommend when it comes to .NET, there is a plug-in for Visual Studio, actually, for .NET, and even, I think, with Java. There';s a tool called AppCAT plugin, and it's like a modernization tool that is part of the Azure Migrate that allows .NET developers who are ever working in a migration project with .NET, that they can add a plugin in Visual Studio and they can assess their existing source code, .NET source code, based on the well-architected framework, if it's ready or not, or there are gaps in the code."— Jonah Andersoon Along the way, we talk about Jonah's podcast "Extend Women in Tech Podcast" (which I would highly recommend), and her book "Learning Microsoft Azure: Cloud Computing and Development Fundamentals" and why she chose to write it. Anyway, without further ado, let's sit back, open up a terminal, type in `dotnet new podcast` and we'll dive into the core of Modern .NET. Supporting the Show If you find this episode useful in any way, please consider supporting the show by either leaving a review (check our review page for ways to do that), sharing the episode with a friend or colleague, buying the host a coffee, or considering becoming a Patron of the show. Full Show Notes The full show notes, including links to some of the things we discussed and a full transcription of this episode, can be found at: https://dotnetcore.show/season-7/learning-azure-with-jonah-andersson-a-developers-guide-to-cloud-computing-and-development-fundamentals/ Jonah's Links: Jonah Andersson Azure Usergroup Sweden Extend Women in Tech Podcast Learning Microsoft Azure Jonah on LinkedIn Useful Links: Cloud Adoption Framework (CAF) Well-Architected Framework (WAF) AppCAT Azure for .NET Developers Azure ARC Azure Dev/Test Find an MVP Ollama Supporting the show: Leave a rating or review Buy the show a coffee Become a patron Getting in Touch: Via the contact page Joining the Discord Remember to rate and review the show on Apple Podcasts, Podchaser, or wherever you find your podcasts, this will help the show's audience grow. Or you can just share the show with a friend. And don't forget to reach out via our Contact page. We're very interested in your opinion of the show, so please get in touch. You can support the show by making a monthly donation on the show's Patreon page at: https://www.patreon.com/TheDotNetCorePodcast. Music created by Mono Memory Music, licensed to RJJ Software for use in The Modern .NET Show

“การลงทุนแปลก ๆ” ที่คาดไม่ถึง แต่กำไรไม่น่าเชื่อ | Money Buffalo เวลาเราพูดถึง “การลงทุน” เพื่อน ๆ นึกถึงอะไรกัน ? ก็คงจะหนีไม่พ้นพวกเมะกะเทรนด์ต่าง ๆ เช่น AI, Healthcare, Cloud Computing ฯลฯ อะไรพวกนี้กันใช่มั้ยล่ะฮะ แต่จริง ๆ แล้วการลงทุนมันสามารถกว้างไปมากกว่านั้นได้อีกนะ เราสามารถลงทุนได้ในแทบทุกอย่างเลย ตั้งแต่ เงิน, ข้าว, กาแฟ, ไม้, โกโก้ ฯลฯ และอื่น ๆ อีกมากมาย แต่ ! ที่จะหยิบมาเล่าให้ฟังวันนี้ แปลกกว่านั้นไปอีก 5555 บนโลกนี้มีการลงทุนอะไรแปลก ๆ ที่เราอาจจะนึกไม่ถึงบ้าง ? ทั้งที่มีให้เทรดอยู่ในตลาด มี ETF ให้ซื้อ ไปจนถึงธุรกิจแปลก ๆ ที่หลายคนอาจจะไม่รู้ว่ากำไรส่วนต่างโคตรอิ่ม จะมีอะไรกันบ้าง มากันฟังกันที่รายการเรื่องเงินเรื่องใหญ่ได้เลยนะ #MoneyBuffalo #สนุกง่ายได้ประโยชน์ #เรื่องเงินเรื่องใหญ่ #การลงทุน #ลงทุน #ETF

The telecom industry is undergoing a fundamental transformation. This shift is creating new business opportunities and services but also brings significant challenges in transformation and modernization. In this special bonus episode, building on our Reimagining Telecoms mini-series, we dive into the current opportunities shaping today's dynamic telco landscape.This week, Dave, Esmee and Rob talk to Vivek Badrinath,  Director General of the GSMA about the current opportunities shaping today's dynamic telco landscape and the role of GSMA. TLDR01:38 Introduction to Vivek and the bonus episode03:48 In-depth conversation with Vivek Badrinath42:13 Can empathy become a strategic KPI in telecom?47:20 Event in Uzbekistan and doubling down on the digital ecosystem GuestVivek Badrinath: https://www.linkedin.com/in/vivekbadrinath/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/with Praveen Shankar: https://www.linkedin.com/in/praveen-shankar-capgemini/SoundBen Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:  https://www.linkedin.com/in/louis-corbett-087250264/'Cloud Realities' is an original podcast from Capgemini

It's been three years of Environment Variables! What a landmark year for the Green Software Foundation. From launching behind-the-scenes Backstage episodes, to covering the explosive impact of AI on software emissions, to broadening our audience through beginner-friendly conversations; this retrospective showcases our mission to create a trusted ecosystem for sustainable software. Here's to many more years of EV!

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

Andrea Malagodi, CTO of Sonar, discusses how the company successfully transitioned from on-premise to SaaS, leveraging AWS partnership and maintaining focus on developer-centric code quality and security solutions.Topics Include:Andrea Malagodi is CTO of Sonar, guest on podcastSonar founded 16+ years ago by three software engineersFounders wanted to help developers understand code quality issuesFocus on giving developers precise, actionable insights for improvementProducts include SonarQube Server, Cloud, and IDE versionsRecent acquisitions: ACR, Tidelift, and Structure 101 companiesSaaS journey began seven years ago with SonarQube CloudInitially targeted individual developers, then expanded to enterprisesNow multi-region with comprehensive enterprise features availableSeven million developers rely on Sonar's solutions globally400,000 organizations and 28,000 enterprise customers use SonarStarted SaaS to test market demand, not assumptionsEngaged customers early to understand migration requirements neededRecommends alpha versions with design customers for feedbackFree tier for open-source code enables quick trialEnterprise certifications (ISO 27001, SOC 2) build trustAWS partnership includes enterprise support and technical resourcesUsed CDK for infrastructure-as-code, experienced early adoption challengesMulti-region strategy should be considered from the beginningAWS Learning partnership certified all engineers in cloudCloud enables faster development cycles than traditional infrastructureRecommends avoiding architectural one-way doors during transitionConsider data residency requirements for global customer baseAI-generated code creates productivity gains but needs validationSonar provides deterministic rules for AI-generated code reviewWorking on MCP protocol and AI code quality solutionsSecurity approach is "start left" not "shift left"Advanced Security offering includes dependency scanning and vulnerabilitiesAvailable on sonarsource.com and AWS MarketplaceFree tier offers 50,000 lines of code analysisParticipants:Andrea Malagodi – Chief Technical Officer, SonarFurther Links:Website: www.sonarsource.comSonar in the AWS MarketplaceSee 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: * The Python Language Summit 2025* Fixing Python Properties * complexipy* * juvio* Extras Joke Watch on YouTube About the show Sponsored by Posit: pythonbytes.fm/connect Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: The Python Language Summit 2025 Write up by Seth Michael Larson How can we make breaking changes less painful?: talk by Itamar Oren An Uncontentious Talk about Contention: talk by Mark Shannon State of Free-Threaded Python: talk by Matt Page Fearless Concurrency: talk by Matthew Parkinson, Tobias Wrigstad, and Fridtjof Stoldt Challenges of the Steering Council: talk by Eric Snow Updates from the Python Docs Editorial Board: talk by Mariatta PEP 772 - Packaging Governance Process: talk by Barry Warsaw and Pradyun Gedam Python on Mobile - Next Steps: talk by Russell Keith-Magee What do Python core developers want from Rust?: talk by David Hewitt Upstreaming the Pyodide JS FFI: talk by Hood Chatham Lightning Talks: talks by Martin DeMello, Mark Shannon, Noah Kim, Gregory Smith, Guido van Rossum, Pablo Galindo Salgado, and Lysandros Nikolaou Brian #2: Fixing Python Properties Will McGugan “Python properties work well with type checkers such Mypy and friends. … The type of your property is taken from the getter only. Even if your setter accepts different types, the type checker will complain on assignment.” Will describes a way to get around this and make type checkers happy. He replaces @property with a descriptor. It's a cool technique. I also like the way Will is allowing different ways to use a property such that it's more convenient for the user. This is a cool deverloper usability trick. Brian #3: complexipy Calculates the cognitive complexity of Python files, written in Rust. Based on the cognitive complexity measurement described in a white paper by Sonar Cognitive complexity builds on the idea of cyclomatic complexity. Cyclomatic complexity was intended to measure the “testability and maintainability” of the control flow of a module. Sonar argues that it's fine for testability, but doesn't do well with measuring the “maintainability” part. So they came up with a new measure. Cognitive complexity is intended to reflects the relative difficulty of understanding, and therefore of maintaining methods, classes, and applications. complexipy essentially does that, but also has a really nice color output. Note: at the very least, you should be using “cyclomatic complexity” try with ruff check --select C901 But also try complexipy. Great for understanding which functions might be ripe for refactoring, adding more documentation, surrounding with more tests, etc. Michael #4: juvio uv kernel for Jupyter ⚙️ Automatic Environment Setup: When the notebook is opened, Juvio installs the dependencies automatically in an ephemeral virtual environment (using uv), ensuring that the notebook runs with the correct versions of the packages and Python

Kanaiya Vasani, Chief Product Officer, explains how ExtraHop leverages AWS services and generative AI to help enterprise customers address the growing security challenges of uncontrolled AI adoption.Topics Include:ExtraHop reinventing network detection and response categoryPlatform addresses security, performance, compliance, forensic use casesBehavioral analysis identifies potential security threats in infrastructureNetwork observability and attack surface discovery capabilities includedApplication and network performance assurance built-in featuresTraditional IDS capability with rules and IOCs detectionPacket forensics for investigating threats and wire evidenceCloud-native implementations and compromised credential investigation supportExtraHop partnership with AWS spans 35-40 different servicesAWS handles infrastructure while ExtraHop focuses core competenciesExtraHop early adopter of generative AI in NDRNatural language interface enables rapid data access queriesEnglish questions replace complex query languages for usersAgentic AI experiments focus on SOC automation workflowsL1 and L2 analyst workflow automation improves productivityShadow AI creates major risk concern for customersUncontrolled chatbot usage risks accidental data leakageGovernance structures needed around enterprise gen AI usageVisibility required into LLM usage across infrastructure endpointsAI innovation pace challenges security industry keeping upModels evolved from billion to trillion parameters rapidlyTraditional security tools focus policies, miss real-time activity"Wire doesn't lie" - network traffic reveals actual behaviorExtraHop maps baseline behavior patterns across infrastructure endpointsAnomalous behavioral patterns flagged through network traffic analysisMCP servers enable LLM access through standardized protocolsStolen tokens allow adversaries unauthorized MCP server accessMachine learning identifies anomalous traffic patterns L2-L7 protocolsGen AI automates incident triage, investigation, response workflowsBest practices include clear policies, governance, monitoring, educationParticipants:Kanaiya Vasani – Chief Product Officer, ExtraHop NetworksSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/Notes:

Send us a textIn this conversation, Frank Contrepois, Stephen Old, and Rich Gibbons explore the complexities of Software as a Service (SaaS) along with Financial Operations (FinOps) and IT Asset Management (ITAM). They talk about how software licensing has evolved and the challenges of managing SaaS consumption. Security and data management in a SaaS setting are also discussed. The conversation highlights the different views of ITAM and FinOps on SaaS. They consider how SaaS affects procurement models and the future of collaboration between these areas.

[AAA] In 'Access All Areas' shows we go behind the scenes with the crew and their friends as they dive into complex challenges that organizations face—sometimes getting a little messy along the way.This week, we address the ‘big rocks' that can obstruct or delay successful outcomes in organizational transformations. Dave, Esmee, and Rob are joined by Jasmin Booth, Head of Product Delivery to discuss the transformation to being a (digital) product based organization.TLDR05:22 Access All Areas: This third episode focuses on the products we build that drive outcomes.06:52 Conversation with Jasmin about our digital products37:06 What makes it better to be in a product centric organization? 54:00 Conclusion of the seven Big Rocks and how to smash them59:00 Going on the Blue Bell railway 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/with Jasmin Booth: https://www.linkedin.com/in/jasminbooth15/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/'Cloud Realities' is an original podcast from Capgemini

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

Cribl's Field CISO Ed Bailey discusses how customers can manage the quality and quantity of data by providing intelligent controls between data sources and destinations.Topics Include:Cribl company name originCompany helps organizations screen data to find valuable insightsEd Bailey was Cribl's first customer back in 2018Data growth of 25% yearly created seven-figure cost increasesCEOs and CIOs complained about explosive data storage costsUsers demanded more data while budgets remained constrainedBailey discovered Cribl through a random Facebook advertisementCribl Stream sits between data sources and destinationsNo new agents required, uses existing infrastructure connectionsReduced data growth from 28% to 8% within yearDevelopment cycles shortened from six weeks to two weeksBailey managed global security and telemetry data systemsOperated large Splunk instance across forty different countriesTeam spent time collecting data instead of extracting valueCribl provided consistent data control plane for operationsSmart engineers could focus on machine learning solutionsMigrated from terrible SIEM to better security platformData strategy should focus on business requirements firstNot all data has the same business valueTier one: Critical data goes to expensive platformsTier two: Important data stored in cheaper lakesTier three: Compliance data in low-cost object storageSIEM costs around one dollar per gigabyte storedData lakes cost twelve to eighteen cents per gigabyteObject storage costs fractions of pennies per gigabyteAWS partnership provides scalable infrastructure for rapid growthEC2, EKS, and S3 are heavily utilized servicesCribl Search finds data directly in object storageAvoids costly data movement for search and analysisParticipants:Edward Bailey – Field CISO, CriblSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Artificial Intelligence (AI) – how the Algorithm Connects Us All Revisited AZ TRT 2.0 – S06 EP08 (270) 5-11-2025   What We Learned This Week: AI is inter-connected with so many technologies & you use AI often on a daily basis AI is a part of almost all industries from Healthcare, Finance to Defense Human in the Loop - humans will always be needed to Interpret the Data, but AI will assist Software Teams must be managed so the product is integrated properly in the bigger picture   Moore's Law – Each year computing power grows 2x as fast, but cuts the cost in half   Guests: Naru (Narendran Muraleedharan), CEO / Founder of StaxAI – formerly Inventives & Aptus AI https://stax.ai/   linkedin.com/in/naru95       Bio: At Stax.ai, we empower retirement plan TPAs to streamline operations, enhance productivity, and deliver exceptional client experiences. Our innovative platform automates census data collection, payroll integrations, trust accounting, and client communication, all while providing actionable insights through AI-driven tools. Trusted by TPAs managing thousands of plans, Stax.ai transforms administrative burdens into seamless workflows, enabling you to focus on what matters most—growing your business and strengthening client relationships.   Past Bio: Inventives / Aptus AI is a software solutions and artificial intelligence development and consulting firm. They provide aerospace, mechanical, and software engineering services to companies around the world. The Team is strong, and well-rounded consisting of engineers, developers, and physicists that focus on solving problems at the root. They help take ideas or concepts and build prototypes and deployments in very short periods of time with tight budgets while maintaining the best quality and client satisfaction. The main fields of expertise are RPA, Web App Development, Solutions Architecture, Artificial Intelligence, Computer Vision, and Simulation Systems.   Note**: This interview was recorded in Oct. 2021 (S02 EP 43 (90) 10-24-21), since then Naru has changed the company name. Also, the BRT podcast has changed to AZ Tech Roundtable.   Notes: Seg. 2 - 4 Naru returns to the show talking tech, software, and AI. His team is always developing new applications with AI in healthcare, finance, biotech, & aerospace. Naru shows how so many technologies are connected and what the future of business looks like. Inventives is the new company, with the same core technology team, but focusing the business into a venture studio by creating new AI projects for companies not yet using AI. Inventives new projects can become spinoff companies with this new technology.  They work with B2B clients and also Investors for these types of companies.   Radiology is a current project to enhance the patient experience using AI – called SKIP. This project is on the Operations side to make the patient process of paperwork, payments, etc. to be automated (& efficient) with AI working with the patient. This will be stand alone, but if adopted by other medical offices over the long term can streamline the healthcare industry. Naru and his team are working on document management, where their AI program will be able to read documents and determine what the info is. Rising Cloud is another project they are building that manages a company cloud usage to improve costs.   Moore's Law – Each year computing power grows 2x as fast, but cuts the cost in half Cloud Computing happens in the cloud and internet for your programming vs Edge Computing that happens right on your phone and does not need to go out to the cloud. Bigger the data request or process determines if Cloud or Edge is the best choice. People interact with AI (Artificial Intelligence) daily on their phone, email, internet search and beyond. User Agreements in your phone or websites you use say they can take your search data and use it to enhance your experience.   AI Search uses past searches by you, vs what are the popular other searches by other people on the internet. It happens so fast and has the best / popular search options loading before you are even done typing. This is called a Recommendation Engine, just like Netflix or Amazon find shows or products you may like. These recs are similar to what you have watched or bought previously, or in similar genres. The downside is you may not see different options, just more of the same. AI determines what you see daily on the internet, and can create a silo effect. Inventives uses a common solution, called Human in the Loop to review what the AI is doing. Then the searches or recommendations are reviewed to see how accurate they are.  Healthcare AI is assisting doctors, and even finding issues that humans have missed in a radiology study. AI has no bias, and does not get tired. The computer can repeat processes and be consistent.   Key technologies of the day are constantly overlapping – AI, Big Data, Cloud, SaaS Software, etc. This tech can work with each other, and new software can build on top of these applications. Open AI created a product with AI that builds AI software. Naru's Software Teams have different backgrounds of math, engineering or physics who are self taught on software and think more about the solution vs thinking in terms of coding. There are small groups of 2 to 4 people in a team with a Solutions Architect running the small team and determining how the software interacts in the bigger picture. Often some of the best software involved very small teams. Lot of testing and compliance control goes into to creating big software projects that can involve multiple teams. Same issue if building an upgrade to a software that merges with the previous version.   Rewind Clip (Seg. 1) – Replay of a segment from Naru's last appearance on BRT in 11/2020 He talks AI and the morality of the technology. How can a programmer control their product, and what it will be used for in the real world. Naru chose the name Aptus because it is Latin for Apt or Appropriate – design products that works for the clients Chaos Theory / Jurassic Park – Just because you can do it, doesn't mean you should Aptus gets defense contracts as sub-contractor of companies partnered with military. Aptus studies the end applications for what they are requested to build, to determine if they will take the project. Morality is considered, but not always applicable to see where a product may go until it is built. AI software is programmed to build more software, AI building more AI. Per Naru there are companies trying to develop AI weapons that can kill on their own, and robots being controlled by AI already. Aptus has Liability Insurance to protect their projects.       ‘Best Of' Topic: https://brt-show.libsyn.com/category/Best+of+BRT    Thanks for Listening. Please Subscribe to the AZ TRT Podcast.     AZ Tech Roundtable 2.0 with Matt Battaglia The show where Entrepreneurs, Top Executives, Founders, and Investors come to share insights about the future of business.  AZ TRT 2.0 looks at the new trends in business, & how classic industries are evolving.  Common Topics Discussed: Startups, Founders, Funds & Venture Capital, Business, Entrepreneurship, Biotech, Blockchain / Crypto, Executive Comp, Investing, Stocks, Real Estate + Alternative Investments, and more…    AZ TRT Podcast Home Page: http://aztrtshow.com/ ‘Best Of' AZ TRT Podcast: Click Here Podcast on Google: Click Here Podcast on Spotify: Click Here                    More Info: https://www.economicknight.com/azpodcast/ KFNX Info: https://1100kfnx.com/weekend-featured-shows/   Disclaimer: The views and opinions expressed in this program are those of the Hosts, Guests and Speakers, and do not necessarily reflect the views or positions of any entities they represent (or affiliates, members, managers, employees or partners), or any Station, Podcast Platform, Website or Social Media that this show may air on. All information provided is for educational and entertainment purposes. Nothing said on this program should be considered advice or recommendations in: business, legal, real estate, crypto, tax accounting, investment, etc. Always seek the advice of a professional in all business ventures, including but not limited to: investments, tax, loans, legal, accounting, real estate, crypto, contracts, sales, marketing, other business arrangements, etc.  

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

Saviynt Co-Founder Amit Saha discusses how their AWS partnership has enabled the identity security company to deliver comprehensive identity protection while minimizing organizational friction.Topics Include:Saviynt is leading identity security provider in marketSecures human, non-human, workforce, and privileged access identitiesEliminates friction while automating organizational access management processesBiggest challenge: reducing friction in new access processesSecond challenge: visibility into accumulated technical debt problemsLost business context makes access permissions difficult to unwindSaviynt provides quick visibility to prioritize identity risksShadow IT creates ungoverned workloads and cloud applicationsNeed integration with asset management and cloud providersMust derive intelligence from multiple disconnected information sourcesAWS partnership provides access to prolific customer baseAWS security owners are same buyers for SaviyntEleven-year AWS relationship with early security competencyISV Accelerate program connects with sellers and architectsRising Star program helps stand out in crowded marketplaceFind mutual customers for successful AWS partnership storiesGenAI in bad actors' hands compromises customer securityProduct engineering uses GenAI tools for better qualityAgentic AI creates new paradigm between human/non-human identitiesAgentic AI requires dynamic, fluid access management approachesAI agents can generate their own bots needing accessZero trust principles needed at broader scale for AINext twelve months: getting ahead of GenAI curveNew AWS services launch daily in GenAI spaceContributing to new standards like MCP and A2A protocolsAWS Marketplace simplifies procurement and buyer discovery processesEDP program and migration incentives benefit ISV transactionsAWS developer-friendly startup programs accelerate time to marketCloud-native approach enables predictable scaling and AWS integrationAWS-Saviynt partnership aims for once-in-generation security impactParticipants:Amit Saha – Co-Founder and Chief Growth Officer, SaviyntSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

This week, we delve into the UK government's substantial investment in AI infrastructure and its implications for cloud sovereignty; Is it related to the trump administration, the economy or the AI arms race? We discuss China's unprecedented 631 GB personal data leak and whether it is a honeytrap or negligence. Plus, Wandercraft's latest advancements in robotic exoskeletons and how technology is transforming mobility and rehabilitation.Whether you're deep in tech, cloud services, AI innovation, or market dynamics, this episode delivers sharp analysis, insightful predictions, and essential context to stay ahead in a rapidly evolving technological landscape.Hosts:https://www.linkedin.com/in/jonathanshanks/https://www.linkedin.com/in/lewismarshall/

Send us a textIn this episode of What's New in Cloud FinOps, Stephen Old and Frank Contrepois talk about the latest in cloud computing in May 2025. They focus on Azure, AWS, and GCP. They share their experiences at FinOps X (FOX) and discuss new features and public previews. They also explore topics like database innovations, cost management, AI integration, and sustainability in cloud services. Their conversation shows how cloud technology is evolving and why it's important to stay updated on new developments.Link to some videos from MicrosoftMicrosoft Cost Management—Overview in the Azure portal     Microsoft Cost Management—Cost Analysis in the Azure portal (part 1)Microsoft Cost Management—Cost Analysis in the Azure portal (part 2)Microsoft Cost Management—Creating Budgets and AlertsMicrosoft Cost Management—Exporting your Cost Data with Scheduled ExportsManage Azure OpenAI Service Provisioned Reservations

Mark Ericksen, creator of the Elixir LangChain framework, joins the Elixir Wizards to talk about LLM integration in Elixir apps. He explains how LangChain abstracts away the quirks of different AI providers (OpenAI, Anthropic's Claude, Google's Gemini) so you can work with any LLM in one more consistent API. We dig into core features like conversation chaining, tool execution, automatic retries, and production-grade fallback strategies. Mark shares his experiences maintaining LangChain in a fast-moving AI world: how it shields developers from API drift, manages token budgets, and handles rate limits and outages. He also reveals testing tactics for non-deterministic AI outputs, configuration tips for custom authentication, and the highlights of the new v0.4 release, including “content parts” support for thinking-style models. Key topics discussed in this episode: • Abstracting LLM APIs behind a unified Elixir interface • Building and managing conversation chains across multiple models • Exposing application functionality to LLMs through tool integrations • Automatic retries and fallback chains for production resilience • Supporting a variety of LLM providers • Tracking and optimizing token usage for cost control • Configuring API keys, authentication, and provider-specific settings • Handling rate limits and service outages with degradation • Processing multimodal inputs (text, images) in Langchain workflows • Extracting structured data from unstructured LLM responses • Leveraging “content parts” in v0.4 for advanced thinking-model support • Debugging LLM interactions using verbose logging and telemetry • Kickstarting experiments in LiveBook notebooks and demos • Comparing Elixir LangChain to the original Python implementation • Crafting human-in-the-loop workflows for interactive AI features • Integrating Langchain with the Ash framework for chat-driven interfaces • Contributing to open-source LLM adapters and staying ahead of API changes • Building fallback chains (e.g., OpenAI → Azure) for seamless continuity • Embedding business logic decisions directly into AI-powered tools • Summarization techniques for token efficiency in ongoing conversations • Batch processing tactics to leverage lower-cost API rate tiers • Real-world lessons on maintaining uptime amid LLM service disruptions Links mentioned: https://rubyonrails.org/ https://fly.io/ https://zionnationalpark.com/ https://podcast.thinkingelixir.com/ https://github.com/brainlid/langchain https://openai.com/ https://claude.ai/ https://gemini.google.com/ https://www.anthropic.com/ Vertex AI Studio https://cloud.google.com/generative-ai-studio https://www.perplexity.ai/ https://azure.microsoft.com/ https://hexdocs.pm/ecto/Ecto.html https://oban.pro/ Chris McCord's ElixirConf EU 2025 Talk https://www.youtube.com/watch?v=ojL_VHc4gLk Getting started: https://hexdocs.pm/langchain/gettingstarted.html https://ash-hq.org/ https://hex.pm/packages/langchain https://hexdocs.pm/igniter/readme.html https://www.youtube.com/watch?v=WM9iQlQSFg @brainlid on Twitter and BlueSky Special Guest: Mark Ericksen.

Every organization is built on people, structures, and culture. But culture isn't static—it evolves with every interaction, ambition, and shift in circumstance. As IT drives business transformation, new technologies reshape how people connect and collaborate. In this ever-changing landscape, a strong, adaptive culture is the key to lasting success. This week, Dave, Esmee and Rob talk to Jitske Kramer, Corporate Anthropologist about what technology is doing to cultures and human systems and how AI can mess with the narrative. TLDR00:50  Introduction of Jitske Kramer and her book Navigating Tricky Times02:05  Rob shares his confusion about saying “thank you” to AI07:25  In-depth conversation with Jitske Kramer11:30  Visual communication via tattoos even at AWS re:Invent25:00 Corporate framing and what's going on within organizations today46:22  Exploring the contrast between the natural pace of human transformation and the rapid acceleration of technology54:14  Editing the documentary Patterns of Life55:56  Esmee's 2x Outro speed surprises everyone!Guest:Jitske Kramer: https://www.linkedin.com/in/jitskekramer/https://jitskekramer.substack.com/Tricky Times event: https://tricky-times.com/events/navigating-tricky-times-leading-through-the-messy-middle-of-change/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/ 'Cloud Realities' is an original podcast from Capgemini

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

Chief Architect Russell Leighton discusses how Panther's cloud platform revolutionizes security operations by treating detections as Python code and AI enabled alert vetting turning responses from hours into minutes. Topics Include:Panther is a cloud security monitoring tool (cloud SIEM)Works at massive scale, more cost-effective than legacy systemsKey differentiator: "detections as code" written in PythonBrings software engineering best practices to security operationsEnables unit testing and version control for security detectionsRecently adopted generative AI to improve security workflowsSOC burnout is renowned due to tedious ticket processingAI has intelligence of security engineer, works much fasterExample: Alert shows "Russ Leighton removed branch protection"Old way: Manual log analysis, checking user profiles manuallyTakes hours of squinting at detailed log dataNew AI way: Automatic vetting happens in minutesAI checks user profile in Okta or IDPDetermines engineer status, assesses typical behavior patternsProvides risk assessment based on historical alert dataLow risk for engineers, high risk for unusual usersExample: HR person accessing production code is escalatedCustomer quote: Takes vetting "from hours to seconds"Panther customers get dedicated AWS accounts for securityCompany can't see customer data, only self-reported metricsAI provides summaries, risk assessments, timelines, visualizationsAlso suggests remediations like human security engineer wouldInitial concerns about putting AI in production environmentCustomer feedback exceeded expectations with feature requestsAWS Bedrock integration addresses customer security concernsUses Anthropic Claude as base LLM through BedrockCustomers can enable additional Bedrock guardrails independentlyAI transparency prevents hallucination concerns through explanationsClaude's extended thinking mode shows reasoning processAI visualizes thinking with flowcharts explaining decision processParticipants:Russell Leighton – Chief Architect, PantherFurther Links:Website: Panther.comAWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Security leaders from Anthropic and AWS discuss how agentic AI is transforming cybersecurity functions to autonomously handle everything from code reviews to SOC operations.Topics Include:Agentic AI differs from traditional AI through autonomy and agencyTraditional AI handles single workflow nodes, agents collapse multiple stepsHigher model intelligence enables understanding of broader business contextsAgents make intelligent decisions across complex multi-step workflows processesEnterprise security operations are seeing workflow consolidation through GenAIOrganizations embedding GenAI directly into customer-facing production applicationsSoftware-as-a-service transitioning to service-as-software through AI agentsSecuring AI requires guardrails to prevent hallucinations in applicationsNew vulnerabilities appear at interaction points between system componentsAttackers target RAG systems and identity/authorization layers insteadLLMs hallucinate non-existent packages, attackers create malicious honeypotsGovernance frameworks must be machine-readable for autonomous agent reasoningAmazon investing in automated reasoning to prove software correctnessAnthropic uses Claude to write over 50% of codeAutomated code review systems integrated into CI/CD pipelinesSecurity design reviews use MITRE ATT&CK framework automationLow-risk assessments enable developers to self-approve security reviews40% reduction in application security team review workloadAnthropic eliminated SOC, replaced entirely with Claude-based automationIT support roles transitioning to engineering as automation replaces frontlineCompliance questionnaires fully automated using agentic AI workflowsISO 42001 framework manages AI deployment risks alongside securityExecutive risk councils evaluate AI risks using traditional enterprise processesAWS embeds GenAI into testing, detection, and user experienceFinding summarization helps L1 analysts understand complex AWS environmentsAmazon encourages teams to "live in the future" with AIInterview candidates expected to demonstrate Claude usage during interviewsSecurity remains biggest barrier to enterprise AI adoption beyond POCsVirtual employees predicted to arrive within next 12 monthsModel Context Protocol (MCP) creates new supply chain security risksParticipants:Jason Clinton – Chief Information Security Officer, AnthropicGee Rittenhouse – Vice President, Security Services, AWSHart Rossman – Vice President, Global Services Security, AWSBrian Shadpour – GM of Security and B2B Software Sales, AWSSee 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: platformdirs poethepoet - “Poe the Poet is a batteries included task runner that works well with poetry or with uv.” Python Pandas Ditches NumPy for Speedier PyArrow pointblank: Data validation made beautiful and powerful Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: platformdirs A small Python module for determining appropriate platform-specific dirs, e.g. a "user data dir". Why the community moved on from appdirs to platformdirs At AppDirs: Note: This project has been officially deprecated. You may want to check out pypi.org/project/platformdirs/ which is a more active fork of appdirs. Thanks to everyone who has used appdirs. Shout out to ActiveState for the time they gave their employees to work on this over the years. Better than AppDirs: Works today, works tomorrow – new Python releases sometimes change low-level APIs (win32com, pathlib, Apple sandbox rules). platformdirs tracks those changes so your code keeps running. First-class typing – no more types-appdirs stubs; editors autocomplete paths as Path objects. Richer directory set – if you need a user's Downloads folder or a per-session runtime dir, there's a helper for it. Cleaner internals – rewritten to use pathlib, caching, and extensive test coverage; all platforms are exercised in CI. Community stewardship – the project lives in the PyPA orbit and gets security/compatibility patches quickly. Brian #2: poethepoet - “Poe the Poet is a batteries included task runner that works well with poetry or with uv.” from Bob Belderbos Tasks are easy to define and are defined in pyproject.toml Michael #3: Python Pandas Ditches NumPy for Speedier PyArrow Pandas 3.0 will significantly boost performance by replacing NumPy with PyArrow as its default engine, enabling faster loading and reading of columnar data. Recently talked with Reuven Lerner about this on Talk Python too. In the next version, v3.0, PyArrow will be a required dependency, with pyarrow.string being the default type inferred for string data. PyArrow is 10 times faster. PyArrow offers columnar storage, which eliminates all that computational back and forth that comes with NumPy. PyArrow paves the way for running Pandas, by default, on Copy on Write mode, which improves memory and performance usage. Brian #4: pointblank: Data validation made beautiful and powerful “With its … chainable API, you can … validate your data against comprehensive quality checks …” Extras Brian: Ruff rules Ruff users, what rules are using and what are you ignoring? Python 3.14.0b2 - did we already cover this? Transferring your Mastodon account to another server, in case anyone was thinking about doing that I'm trying out Fathom Analytics for privacy friendly analytics Michael: Polars for Power Users: Transform Your Data Analysis Game Course Joke: Does your dog bite?

Spryker's Chief Product Officer, Elena Leonova, discusses the Spryker Business Intelligence platform and how working with AWS as a strategic advisor unlocked deeper opportunities for transformative growth.Topics Include:Elena Leonova introduces Spryker as digital commerce platformSpryker focuses on sophisticated B2B commerce transactionsTraditional industries: manufacturing, industrial goods, med techCustomers sell complex equipment like MRI machines, tractorsProducts are custom-built to order through procurement processesExtensive negotiation and aftermarket servicing are requiredCompetitors focus on fashion, food - not complex equipmentSpryker exclusively hosted on AWS cloud infrastructureAWS partnership enables new capabilities and customer innovationBusiness intelligence tools and AI capabilities now availableRicoh example: global manufacturer of industrial-grade printersRicoh sells through dealers and distributors worldwideS-Diverse: new automotive software marketplace partnership platformConnects automotive manufacturers with embedded software producersSpryker Business Intelligence powered by Amazon QuickSight launchedCommerce becoming more intelligent than traditional repeat purchasesComplex equipment buyers don't purchase MRI machines weeklyPlatform provides insights into customer portal navigation patternsCombines commerce data with search, CRM, competitive intelligenceHelps merchants identify revenue optimization signals from noiseBusiness intelligence integrated directly within Spryker platformCustomers should evaluate platform's future scalability and flexibilityRevenue optimization requires understanding what metrics to improveEasy-to-use data analysis prevents information overload problemsQuickSight's GenAI capabilities enable faster executive decision-makingAWS partnership provided cost optimization and innovation confidenceElena initially viewed AWS as just hosting providerBuilding shared vision with AWS unlocked deeper collaborationAWS became trusted advisor for strategy and partnershipsGenerative AI enables multi-persona communication across customer typesParticipants:Elena Leonova – Chief Product Officer, SprykerSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

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

In this episode, Jon and Lewis cover four wildly different stories, from Warfare to the impact of Trump conspiracies on Datacentres.First up, Microsoft is investing $400 million to turn Switzerland into the next cloud capital. Is it for neutrality, Trump instability syndrome or just demand? Then, we meet a glow-in-the-dark protein made by an AI called ESM3, because why not let machines start designing life?Next, we detour into wartime sci-fi: Ukraine's drone swarm attack: trucks, remote lids, and enough AI autopilot to cause substantial damage. Finally, we finish with Fi, a smart dog collar that integrates with your Apple Watch. Track your dog's steps, sleep, and GPS location. Why? Who knows!All that, plus plenty of opinions, speculation, and the usual unpacking of what is going on in the Cloud, Tech and AI space. 

The telecom industry is undergoing a fundamental transformation. This shift is creating new business opportunities and services but also brings significant challenges in transformation and modernization. In a new five-part mini-series, Reimagining Telecoms, we will explore these challenges through five distinct lenses: Growth, Networks, Simplification, Data & AI, and Regulation, uncovering lessons and insights relevant to telecom organizations and beyond. This week, in the final episode of the mini-series, Dave, Esmee, and Rob talk to Nik Willetts, CEO of TM Forum, to discuss growth—the telco industry's biggest challenge—and how it intersects with Hyperscalers, innovation, and shaping the industry's future. TLDR01:05 Introduction of Nik and an update on the mini-series03:41 Main conversation with Nik Willetts29:10 Navigating the balance between collaboration and competition34:57 Looking ahead to DTW Ignite, the Dolomites, and Brunello wine, served by sommelier Rob GuestNik Willetts: https://www.linkedin.com/in/nikwilletts/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/with Praveen Shankar: https://www.linkedin.com/in/praveen-shankar-capgemini/SoundBen Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:  https://www.linkedin.com/in/louis-corbett-087250264/'Cloud Realities' is an original podcast from Capgemini

ActiveFence CEO Noam Schwartz discusses how his company evolved from protecting platforms against user-generated harmful content to helping companies deploy public-facing AI safely at scale.Topics Include:Noam Schwartz introduces himself as ActiveFence CEOFormer intelligence officer specializing in open source intelligenceMission: protect online experiences for everyone everywhereOnline platforms constantly hammered by various attacksAttacks include cybersecurity, abuse, hate speech, spamCompanies playing endless whack-a-mole game with violationsNeed scalable solution that works across languages/formatsDeveloped enterprise-grade technology for sophisticated companiesAmazon became customer and great partner early onGenerative AI introduction changed the game completelyLLMs non-deterministic unlike traditional programmed chatbotsSame input produces different outputs each timeAI deployed in customer support, healthcare, airlinesNew risks when models speak on company's behalfOne bad output creates legal and reputational damageCompanies need to deploy public-facing AI safelyTransition affects healthcare, finance, gaming, government sectorsBuilding on years of user-generated content expertiseNo specific ChatGPT moment triggered their AI pivotActiveFence was AI company since day oneModel companies like Amazon, Nvidia asked for helpRealized their expertise perfectly suited for AI safetyStaying on top of AI developments is impossibleFocus on customer adoption, not every new releaseMain enterprise challenge is trusting AI technologyUnrealistic expectations for 100% accuracy from AIMost companies will license existing models, not buildSecurity solutions remain independent like traditional cybersecurityParticipants:Noam Schwartz – CEO and Co-Founder, ActiveFenceOfer Oringher – Software and Technology Account Manager, AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Dive deep into the world of cloud security with Rocky Giglio and special guest Sean Atkinson, CISO at the Center for Internet Security (CIS), on this episode of Cloud and Clear! We examine the crucial role of CIS benchmarks and hardened images in establishing a robust and secure cloud infrastructure. In this insightful discussion, Sean breaks down: ✅ What CIS is and its mission to create a safer connected world. ✅ The evolution of CIS Controls from 20 to 18 for greater efficiency. ✅ Understanding CIS Benchmarks and how they standardize security configurations. ✅ The power of Hardened Images: Start secure from day zero in your cloud environment. ✅ Shifting security left and proactively integrating security into design. ✅ How CIS simplifies compliance with NIST, PCI, HIPAA, and other frameworks. ✅ The importance of community and partnership in cybersecurity. Whether you're a security professional, cloud engineer, or anyone concerned about keeping data safe in the cloud, this episode is packed with valuable knowledge. Learn how to leverage CIS resources to strengthen your security posture and simplify compliance. Tune in to discover how CIS is making cloud security more accessible and effective! Don't forget to subscribe to Cloud and Clear for more expert insights on cloud transformation. #CloudSecurity #CIS #Cybersecurity #CloudComputing #HardenedImages #SecurityBenchmarks #CloudAndClear #GoogleCloud #Compliance #NIST #PCI #HIPAA #CISO #TechPodcast Join us for more content by liking, sharing, and subscribing!

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

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

Jeff Moncrief discusses Sonrai Security's Cloud Permissions Firewall, and the best practices for using AI-powered summaries and orchestration to ensure security at all points.Topics Include:Jeff Moncrief introduces Sonrai Security and Cloud Permissions FirewallFocus on achieving least privilege access in AWS quicklyLightweight orchestration layer secures IAM from inside outEliminates need to write hundreds of individual policiesCustomers struggle with identity risk in CNAP/CSPM toolsGenerative AI adoption driving top security use casesBedrock and AI agents mentioned daily by customersProduct managers should consider underlying platform security risksAI models have control over infrastructure they run onIdentity is fundamental infrastructure enabling AWS AI modelsSonrai uses Bedrock capability inside Cloud Permissions FirewallJust-in-time access provides temporary, time-boxed AWS accessBedrock generates session summaries from audit logs automaticallyPlain English insights show what happened during sessionsSession summaries improve audit compliance and incident responseCustomer with 1000 accounts manually deployed service controlsFriday afternoon deployment caused very bad weekend disasterPolicy inheritance issues broke child accounts and OUsPlanning and orchestration essential for scaling AI securitySonrai platform built 100% cloud-native on AWSCoordinates service control policies and resource control policiesJust-in-time access relies on IAM Identity CenterParticipates in ISV Accelerate and AWS MarketplaceSecurity best practices start with identity as foundation"Hackers don't hack, they just log in" philosophyEliminate standing privileges with just-in-time access patternsRestrict AI services by user, location, and accountReview over-permissioned or inactive third-party vendor accessActionable insights through useful logging and AI summarizationFuture focus on protecting new services and permissionsParticipants:Jeff Moncrief – Field CTO & Director of Sales Engineering, Sonrai SecurityLinks:Website – Sonraisecurity.comAWS Marketplace – Sonrai SecuritySee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

RJJ Software's Software Development Service This episode of The Modern .NET Show is supported, in part, by RJJ Software's Software Development Services, whether your company is looking to elevate its UK operations or reshape its US strategy, we can provide tailored solutions that exceed expectations. Show Notes "If your app has a backend, it's Aspire-able. And so it's tools, templates, and packages for really any type of app… So just being able to walk up to a repo, clone it, and hit F5. When was the last time we were able to do that? Like, ten years ago, maybe?"— Maddy Montaquila Welcome friends to The Modern .NET Show; the premier .NET podcast, focusing entirely on the knowledge, tools, and frameworks that all .NET developers should have in their toolbox. We are the go-to podcast for .NET developers worldwide, and I am your host: Jamie “GaProgMan” Taylor. In this episode, we talk with Maddy Montaquila about .NET Aspire, what it is, how it's not just for .NET developers, and how it can help you to run a repo by simply hitting F5, regardless of what's in there. "To me, it really is just a dev tool in a bunch of different ways. It makes you just hit F5 again, no matter how many containers, or local, or deployed services you have to deal with, or projects, or languages, or if you're in VS, or VS Code, or on a Mac, or on a command line, or on a Linux machine. Like Aspire just makes all that magical without replatforming"— Maddy Montaquila Along the way, we also talk about the importance of reducing the complexity of going from, "I have an idea," to, "my app is running in the cloud." And Maddy drops a wonderful metaphor for .NET Aspire using a Logo-based metaphor. And we address the community invented elephant in the room: that .NET Aspire, somehow, locks you into using one vendor. Spoiler alert: it can deploy to any cloud vendor, and even to on-prem servers. Anyway, without further ado, let's sit back, open up a terminal, type in `dotnet new podcast` and we'll dive into the core of Modern .NET. Supporting the Show If you find this episode useful in any way, please consider supporting the show by either leaving a review (check our review page for ways to do that), sharing the episode with a friend or colleague, buying the host a coffee, or considering becoming a Patron of the show. Full Show Notes The full show notes, including links to some of the things we discussed and a full transcription of this episode, can be found at: https://dotnetcore.show/season-7/net-aspire-how-maddy-montaquila-and-the-net-team-are-revolutionizing-development/ Maddy's Links: Maddy on Bluesky Other Links: CNCF OpenTelemetry Helm Codespaces Podman Devcontainers Vim GDB FreeBSD Jail .NET Aspire Community Toolkit CORS MCP Phi-4 Four stages of competence dot.net Cloud features of .NET Customer Stories: customers.microsoft.com dot.net/customers Ollama Supporting the show: Leave a rating or review Buy the show a coffee Become a patron Getting in Touch: Via the contact page Joining the Discord Remember to rate and review the show on Apple Podcasts, Podchaser, or wherever you find your podcasts, this will help the show's audience grow. Or you can just share the show with a friend. And don't forget to reach out via our Contact page. We're very interested in your opinion of the show, so please get in touch. You can support the show by making a monthly donation on the show's Patreon page at: https://www.patreon.com/TheDotNetCorePodcast. Music created by Mono Memory Music, licensed to RJJ Software for use in The Modern .NET Show

Akanksha Bilani of Intel shares how businesses can successfully adopt generative AI with significant performance gains while saving on costs.Topics Include:Akanksha runs go-to-market team for Amazon at IntelPersonal and business devices transformed how we communicateForrester predicts 500 billion connected devices by 20265,000 billion sensors will be smartly connected online40% of machines will communicate machine-to-machineWe're living in a world of data delugeAI and Gen AI help make data effectiveGoal is making businesses more profitable and effectiveVarious industries need Gen AI and data transformationIntel advises companies as partners with AWSThree factors determine which Gen AI use cases adoptFactor one: availability and ease of use casesHow unique and important are they for business?Does it have enough data for right analytics?Factor two: purchasing power for Gen AI adoption70% of companies target Gen AI but lack clarityLeaders must ensure capability and purchasing power existFactor three: necessary skill sets for implementationNeed access to right partnerships if lacking skillsIntel and AWS partnered for 18 years since inceptionIntel provides latest silicon customized for Amazon servicesEngineer-to-engineer collaboration on each processor generation92% of EC2 runs on Intel processorsIntel powers compute capability for EC2-based servicesIntel ensures access to skillsets making cloud aliveAWS services include Bedrock, SageMaker, DLAMIs, KinesisPerformance is the top three priorities for successNot every use case requires expensive GPU acceleratorsCPUs can power AI inference and training effectivelyEvery GPU has a CPU head node component Participants:Akanksha Bilani – Global Sales Director, IntelSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

This week, we dive into OpenAI's $6.5B acquisition of Jony Ive's ‘io' and what it means for the future of AI-native devices. We explore Google's VEO 3 and the deepfake dilemmas it raises, along with Microsoft's Aurora AI and its ability to predict the weather. Plus, Google's new try-on AI lets you see how clothes fit without leaving your house, and in a more random story, it turns out some plants can hear bees to protect their nectar.Whether you're deep in tech, cloud services, AI innovation, or market dynamics, this episode delivers sharp analysis, insightful predictions, and essential context to stay ahead in a rapidly evolving technological landscape.Hosts:https://www.linkedin.com/in/jonathanshanks/https://www.linkedin.com/in/lewismarshall/

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

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