Podcasts about Cloud computing

We speak with Siva Dharmaraj, CEO and Founder of Cloud Destinations, a Silicon Valley-based IT leader with partners in Malaysia. Cloud Destinations has expanded its global footprint, strengthening its presence in the Southeast Asian market and fostering international collaboration in the fields of Digital Transformation, Cloud Computing, Data Engineering, IT security & Software Services.Cloud Destinations were sponsor to Cyber Security Asia 2025, Kuala Lumpur 21 - 22 April

How is Infrastructure-as-Code (IaC) evolving? How does user experience fit in? Today on The Cloud Gambit, Cory O'Daniel, Co-Founder & CEO of Massdriver, lends his experience as a coder, architect, and founder to help us answer these questions. Cory also discusses what it was like building and funding a startup in the 2021-2022 market, the... Read more »

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

Founder and CTO Alex Rice discusses how HackerOne uses generative AI to automate security workflows and prioritizing accuracy over efficiency to achieve end-to-end outcomes.Topics Include:HackerOne uses ethical hackers and AI to find vulnerabilities before criminalsWhite hat hackers stress test systems to identify security weaknesses proactivelyGenerative AI plays a huge role in HackerOne's security operationsSecurity teams struggle with constant toil of finding and fixing vulnerabilitiesAI helps minimize toil through natural language interfaces and automationBoth good and bad actors have access to generative AI toolsSuccess requires measuring individual task inputs and outputs, not just aggregatesBreaking down workflows into granular tasks reveals measurable AI improvementsHackerOne deployed "Hive," their AI security agent to reduce customer toilInitial focus was on tasks where AI clearly outperformed humansStarted with low-hanging fruit before tackling more complex strategic workflowsAccuracy is the primary success metric, not just efficiency or speedSecurity requires precision; wrong fixes create bigger problems than inefficiencyCustomer acceptance and reduced time to remediation are north star metricsHumans remain the source of truth for validation and feedback loopsBreak down human jobs into granular AI tasks using systems thinkingBuild specific agents for individual tasks rather than entire job rolesKeep humans accountable for end-to-end outcomes to maintain customer trustAWS Bedrock chosen for security, confidentiality, and data separation requirementsMoving from efficiency improvements to entirely new AI-enabled capabilitiesParticipants:Alex Rice – Founder & CTO/CISO, HackerOneFurther Links:HackerOne WebsiteHackerOne 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/

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

Why are we still talking about virtualization? This week, Technology Now is returning to a classic topic in computing: Virtualization. So, what's changed in the landscape that's bought virtualization back into the limelight, and how is it being used in our current technological landscape? Brad Parks, Chief Product & Go To Market Officer at HPE's recently acquired Morpheus Data, tells us more.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week, hosts Michael Bird and Aubrey Lovell look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations.About Brad Parks: https://www.linkedin.com/in/brad-parks-b190464/Sources:https://www.techtarget.com/searchitoperations/feature/The-history-of-virtualization-and-its-mark-on-data-center-managementhttps://inventivehq.com/history-of-virtualization/

Spencer Herrick, Principal AI Product Manager of Asana and Oliver Myers of AWS demonstrate how their integration allows Asana's AI workflows to access enterprise data from Amazon Q Business, enabling seamless cross-application automation and insights.Topics Include:Oliver Myers leads Amazon Q Business go-to-market, Spencer Herrick manages Asana AI products.Session focuses on end user productivity challenges with generative AI technology implementations.End users face technology overload with doubled workplace application usage over five years.Data silos prevent getting maximum value from generative AI across fragmented enterprise systems.Workers spend 53% of time on "work about work" instead of strategic contributions.Ideal experience needs single pane of glass with cross-application insights and actions.Amazon Q Business launched as managed service with 40+ enterprise data connectors.Connectors maintain end-user permissions from source systems for enterprise security compliance.QIndex feature enables ISVs to access Q Business data via API calls.End users get answers enriched with multiple data sources without switching applications.Asana's work graph connects all tasks, projects, and portfolios to company goals.Phase 1 AI focused on narrow solutions like smart status updates.Phase 2 aimed for AI teammate capabilities requiring extensive contextual knowledge.AI Studio launched as no-code workflow automation builder within Asana platform.Q integration allows AI Studio to access cross-application context beyond Asana boundaries.SmartChat enhanced with Q can answer "what should I work on today?" holistically.Users returning from PTO can quickly understand goal risks across data sources.AI Studio workflows automate feature request processing across Asana, Drive, Slack, email.Partnership eliminates silos while maintaining enterprise security and permission controls.Integration creates connected ecosystem enabling true cross-application AI automation and insights.Participants:Spencer Herrick - Principal AI Product Manager, AsanaOliver Myers - Worldwide Head of Business Development, Amazon Web ServicesFurther Links:Asana.comAsana 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/

The rise of structure software fueled globalization by streamlining operations across borders. Now, Cloud and AI are accelerating this momentum, enabling faster innovation, smarter decision-making, and scalable growth. By modernizing ERP with intelligent technologies, organizations can stay agile, competitive, and ready for the next wave of global transformation.This week, Dave, Esmee and Rob talk to Timo Elliott, Innovation Evangelist at SAP, to explore how SAP is driving globalization—and how organizations can accelerate innovation through the power of Cloud and AI. TLDR00:55 Introduction of Timo Elliott02:40 Rob shares his confusion about misleading online ads08:06 In-depth conversation with Timo46:32 Rethinking control in enterprise systems1:00:00 Brunch at a Paris café or joining an event?GuestTimo Elliott: https://www.linkedin.com/in/timoelliott/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

Chief Product Officer Dan Brown explains how Celonis creates digital twins of business processes to power AI agents that automate operational improvements.Topics Include:Dan Brown introduces Celonis as the thought leader in process mining for over a decade.Celonis serves largest global companies across all industries seeking operational improvements.Companies have process diagrams but actual operations differ significantly from documentation.Celonis creates digital twins of business processes by analyzing system data flows.Process intelligence reveals how work actually happens versus how companies think it happens.Platform enables process normalization, improvement assessment, and automated corrective actions.Celonis vision: making processes work better for people, companies, and the planet.Process intelligence provides visibility into current operations and improvement strategies.Great AI requires great data, but most companies only have static views.Process intelligence graph shows real-time flow of orders, invoices, and opportunities.Agentic AI requires four capabilities: sensing, planning, executing, and governing operations.Process intelligence enables real-time detection of conformance problems and deviations.AWS partnership leverages Bedrock for agentic AI and infrastructure for data processing.Data ingestion, organization, and enrichment are core to process intelligence value.AI agents now handle process deviations with increasing autonomy and sophistication.Heavy equipment manufacturer uses agents to coordinate with third-party vendors automatically.Agents text and email vendors to confirm delivery dates, reducing manual work.Implementation challenges include data quality, conservative adoption, and governance concerns.Companies should start with achievable use cases and expand gradually across domains.Future involves enterprise-wide process visibility powering automated applications and continuous improvement.Participants:Dan Brown – Chief Product Officer, CelonisFurther Links:Celonis WebsiteCelonis 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/

Topics covered in this episode: * Switching to direnv, Starship, and uv* * rqlite - Distributed SQLite DB* * Some Markdown Stuff* Extras Joke Watch on YouTube About the show Sponsored by PropelAuth: pythonbytes.fm/propelauth77 Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Switching to direnv, Starship, and uv Last week I mentioned that I'm ready to try direnv again, but secretly, I still had some worries about the process. Thankfully, Trey has a tutorial to walk me past the troublesome parts. direnv - an extension for your shell. It augments existing shells with a new feature that can load and unload environment variables depending on the current directory. Switching from virtualenvwrapper to direnv, Starship, and uv - Trey Hunner** Trey has solved a bunch of the problems I had when I tried direnv before Show the virtual environment name in the prompt Place new virtual environments in local .venv instead of in .direnv/python3.12 Silence all of the “loading”, “unloading” statements every time you enter a directory Have a script called venv to create an environment, activate it, create a .envrc file I'm more used to a create script, so I'll stick with that name and Trey's contents A workon script to be able to switch around to different projects. This is a carry over from “virtualenvwrapper', but seems cool. I'll take it. Adding uv to the mix for creating virtual environments. Interestingly including --seed which, for one, installs pip in the new environment. (Some tools need it, even if you don't) Starship Trey also has some setup for Starship. But I'll get through the above first, then MAYBE try Starship again. Some motivation Trey's setup is pretty simple. Maybe I was trying to get too fancy before Starship config in toml files that can be loaded with direnv and be different for different projects. Neato Also, Trey mentions his dotfiles repo. This is a cool idea that I've been meaning to do for a long time. See also: It's Terminal - Bootstrapping With Starship, Just, Direnv, and UV - Mario Munoz Michael #2: rqlite - Distributed SQLite DB via themlu, thanks! rqlite is a lightweight, user-friendly, distributed relational database built on SQLite. Built on SQLite, the world's most popular database Supports full-text search, Vector Search, and JSON documents Access controls and encryption for secure deployments Michael #3: A Python dict that can report which keys you did not use by Peter Bengtsson Very cool for testing that a dictionary has been used as expected (e.g. all data has been sent out via an API or report). Note: It does NOT track d.get(), but it's easy to just add it to the class in the post. Maybe someone should polish it up and put it on pypi (that person is not me :) ). Brian #4: Some Markdown Stuff Textual 4.0.0 adds Markdown.append which can be used to efficiently stream markdown content The reason for the major bump is due to an interface change to Widget.anchor Refreshing to see a symantic change cause a major version bump. html-to-markdown Converts html to markdown A complete rewrite fork of markdownify Lots of fun features like “streaming support” Curious if it can stream to Textual's Markdown.append method. hmmm. Joke: Vibecon is hard to attend

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

AWS partners Automation Anywhere, Qlik, and Vectra.ai discuss revolutionizing customer experience through generative AI, sharing real-world implementations in automation, analytics, and cybersecurity applications. Topics Include:AWS Technology Partnerships panel on agentic AI implementationThree AWS partners share real-world AI deployment experiencesAutomation Anywhere automates end-to-end business processes with agentsVectra.ai uses autonomous agents for cybersecurity threat detectionQlik applies generative AI across their data platform portfolioCustomer service automation handles L1 support requests efficientlyUtility company processes 144,000 complaints annually using agentsRegulatory compliance improved through faster complaint resolution workflowsCybersecurity agents reduce threat detection time by 50-60%Triage, correlation, and prioritization handled by autonomous agentsSignal fatigue reduced through intelligent alert filtering systemsNatural language queries enable faster business decision makingSales AI agent provides competitive information during callsAWS Marketplace reduced 7,000 weekly tickets to zero2023 was proof-of-concept year, 2024 focuses production deploymentAWS Bedrock integration seamless with existing data repositoriesModel optionality crucial for different use case requirementsAgility most important capability in generative AI journeyCode abandonment becomes acceptable due to rapid innovationMaximum team size of 10 people maintains development agilityTargeted solutions outperform broad platform capabilities in adoptionImplementation expertise becomes bottleneck for customer scaling effortsNatural language interaction patterns completely shifted user behaviorKeywords searches replaced by conversational query approachesResponsible AI committees review decisions and establish principlesSecurity considerations balance speed with responsible deployment practicesBad actors adopt generative AI faster than defendersExplainability requirements slow feature rollout but ensure auditabilityMulti-modal deployments use different models for specific casesFuture trends include AI-powered business process outsourcingParticipants:Peter White – SVP, Emerging Products, Automation AnywhereRyan Welsh – Field CTO - Generative AI, QlikJohn Skinner – Vice President Corporate/Business Development, Vectra.aiChris Grusz – Managing Director for Technology Partnerships, AWSFurther Links:Automation Anywhere in AWS MarketplaceQlik in AWS MarketplaceVectra.ai in AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Zak Krider, Trellix's Director of Strategy and AI, shares how Trellix has successfully integrated generative AI into their security operations and democratized access to AI models across the organization.Topics Include:Trellix formed from McAfee Enterprise and FireEye mergerProvides full security stack visibility in single platformServes SMBs to Fortune 500 and government customersUsed machine learning for two decades with 30 modelsRecently pivoted to generative AI with Wwise platformAI finds critical events among thousands daily alertsIncorporates threat hunting knowledge into AI prompt structuresAWS Bedrock central to AI strategy for model flexibilityFormed small tiger team to investigate generative AIAnthropic Claude provided breakthrough "aha moments" for capabilitiesAdopted "fail fast, learn fast" innovation culture approachEnabled employee access to models through Bedrock APIConducted innovation jam sessions with VC-style pitchesAI decoded Base64 without prompting, identified benign activityJunior analysts elevated to level two with AICommon misconception: models train on customer data falselyEarly challenge: providing too much data overwhelmed modelsSmaller models hallucinated more with plausible-sounding responsesDesign partner programs help prioritize product developmentDemocratize AI access beyond just technical teamsTest multiple models for specific use casesLarge models work better than small ones initiallyPrompt engineering crucial for effective model communicationModel Context Protocol will gain traction next yearBackend data security remains largely unsolved challengeFederal customers require on-premises, air-gapped AI solutionsParticipants:Zak Krider – Director of AI and Innovation, TrellixFurther Links:Website: https://www.trellix.comTrellix 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 episode, we explore how TileDB, an innovative data system originally developed at MIT and Intel Labs, is revolutionizing healthcare and life sciences data management. We'll dive deep into how TileDB's universal data engine efficiently handles complex scientific data - from genomic sequences to medical imaging - in a unified format, and learn how healthcare organizations can leverage TileDB on AWS to accelerate research, improve collaboration, and reduce infrastructure costs. Join host AWS Solutions Architect for HCLS Gokhul Srinivasan and guest Devika Garg form TileDB as they discuss real-world applications in precision medicine, genomic analysis, and how TileDB is transforming the way healthcare providers manage and analyze patient data.To Learn More:https://www.tiledb.com/AWS Hosts: Nolan Chen & Gokhul SrinivasanEmail Your Feedback: rethinkpodcast@amazon.com

GreenOps is a cultural transformation that empowers developers to turn emissions data into meaningful action, bridging the communication gap with ESG teams and exposing the critical truth that cloud cost and carbon cost are not the same, which fundamentally reshapes how we approach sustainable IT.This week, Dave, Esmee and Rob talk to James Hall, Head of GreenOps at Green Pixie, to unpack the real state of GreenOps today—and why we've only just scratched the surface.  TLDR 01:57 Rob is confused about AGI 06:11 Cloud conversation with James Hall 22:10 Esmee as media archeologist, found GreenOps is 50 years old 30:46 Having some drinks in the summer Guest James Hall: https://www.linkedin.com/in/james-f-hall/ Hosts Dave 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/Production Marcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/ Dave Chapman: https://www.linkedin.com/in/chapmandr/ Sound Ben 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

NVIDIA's Global Head of Partnerships & Cloud for Startups, Jen Hoskins, details their collaboration with AWS to support over 25,000 startups through their Inception program.Topics Include:AI transformation happening across all industries and verticalsNVIDIA evolved from GPU company to full-stack AI solutionsAccelerated computing requires complete stack re-engineering from chip upTraditional CPU scaling has reached its fundamental performance limitsNVIDIA-AWS partnership spans over 13 years of co-developmentDGX Cloud integrates seamlessly with AWS SageMaker and BedrockOver 26 NVIDIA solutions available in AWS MarketplaceNVIDIA AI Enterprise accelerates data science and deployment pipelinesNIM microservices streamline AI model development like Docker containersCodeway gaming startup saved 48% on compute costs using NVIDIAEternal improved marketing ROI by 30X with generative AIQuoto achieved 10X content length and 3X throughput improvementNOATech biotech scaled cancer research with small team efficientlyNVIDIA Inception program supports over 25,000 startups globallyProgram covers 100+ countries across all verticals and stagesStartups get AWS credits up to $100,000 through ActivateDeveloper program offers free access to hundreds of SDKsThree program pillars: Innovate, Build, and Grow stagesVC Alliance connects startups with over 1,000 investorsVenture Capital Connect directly links startups to funding opportunitiesParticipants:Jen Hoskins – Startups, Global Head of Cloud, Partnerships & Go to Market, NVIDIAFurther Links:Website: https://www.nvidia.comNVIDIA Inception ProgramNVIDIA 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/

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

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/

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/

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.

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/

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

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

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