Podcasts about Cloud computing

Cisco's Vijoy Pandey - SVP & GM of Outshift by Cisco - explains how AI agents and quantum networks could completely redefine how software, infrastructure, and security function in the next decade.You'll learn:→ What “Agentic AI” and the “Internet of Agents” actually are→ How Cisco open-sourced the Internet of Agents framework and why decentralization matters→ The security threat of “store-now, decrypt-later” attacks—and how post-quantum cryptography will defend against them→ How Outshift's “freedom to fail” model fuels real innovation inside a Fortune-500 company→ Why the next generation of software will blur the line between humans, AI agents, and machines→ The vision behind Cisco's Quantum Internet—and two real-world use cases you can see today: Quantum Sync and Quantum AlertAbout Today's Guest:​​Meet Vijoy Pandey, the mind behind Cisco's Outshift—a team pushing the boundaries of what's next in AI, quantum computing, and the future internet. With 80+ patents to his name and a career spent redefining how systems connect and think, he's one of the few leaders truly building the next era of computing before the rest of us even see it coming.Key Moments:00:00 Meet Vijoy Pandey & Outshift's mission04:30 The two hardest problems in computer science: Superintelligence & Quantum Computing06:30 Why “freedom to fail” is Cisco's innovation superpower10:20 Inside the Outshift model: incubating like a startup inside Cisco21:00 What is Agentic AI? The rise of the Internet of Agents27:00 AGNTCY.org and open-sourcing the Internet of Agents32:00 What would an Internet of Agents actually look like?38:19 Responsible AI & governance: putting guardrails in early49:40 What is quantum computing? What is quantum networking?55:27 The vision for a global Quantum InternetWatch Next: https://youtu.be/-Jb2tWsAVwI?si=l79rdEGxB-i-Wrrn  -- This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.---IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Today we're turning tiny tips into big wins. Khuyen Tran, creator of CodeCut.ai, has shipped hundreds of bite-size Python and data science snippets across four years. We dig into open-source tools you can use right now, cleaner workflows, and why notebooks and scripts don't have to be enemies. If you want faster insights with fewer yak-shaves, this one's packed with takeaways you can apply before lunch. Let's get into it. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Khuyen Tran (LinkedIn): linkedin.com Khuyen Tran (GitHub): github.com CodeCut: codecut.ai Production-ready Data Science Book (discount code TalkPython): codecut.ai Why UV Might Be All You Need: codecut.ai How to Structure a Data Science Project for Readability and Transparency: codecut.ai Stop Hard-coding: Use Configuration Files Instead: codecut.ai Simplify Your Python Logging with Loguru: codecut.ai Git for Data Scientists: Learn Git Through Practical Examples: codecut.ai Marimo (A Modern Notebook for Reproducible Data Science): codecut.ai Text Similarity & Fuzzy Matching Guide: codecut.ai Loguru (Python logging made simple): github.com Hydra: hydra.cc Marimo: marimo.io Quarto: quarto.org Show Your Work! Book: austinkleon.com Watch this episode on YouTube: youtube.com Episode #522 deep-dive: talkpython.fm/522 Episode transcripts: talkpython.fm Theme Song: Developer Rap

John Skinner of Vectra AI shares how cyber attackers are democratizing sophisticated attacks using dark web tools, and why AI-powered hybrid defense is now essential for enterprise security.Topics Include:Vectra AI: 13-year-old cybersecurity company founded as "AI native" from day oneBuilt on machine learning assumption while competitors treated AI as afterthoughtGenerative AI represents the latest evolution in their comprehensive AI journeyStarted pairing threat researchers with ML developers to codify attack behaviorsAdded agentic AI in 2018 for correlation across space and timeUses AWS Security Lake, GuardDuty, and recently became AWS Bedrock customerSuccess measured by reducing "dwell time" from initial attack to detectionAchieved 60% faster alerts, 51% faster monitoring, 50% faster investigation timesCustomers should evaluate vendor's data science quality and algorithm training yearsEvolved hybrid defense approach as attacks start anywhere, go everywhereAI handles high-volume correlation while humans focus on analytical decisionsFuture challenge: democratized cyber attacks using readily available dark web toolsParticipants:John Skinner – Vice President Corporate/Business Development, Vectra AIFurther Links:Vectra AI: Website – LinkedIn – AWS Marketplace - YouTubeSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Before Siri had sass and Alexa started judging your music taste, the original virtual assistant was quietly revolutionizing the '90s—powered by many patents and a whole lot of foresight. Now, as AI goes from buzzword to boss, we ask, will it transform your job, your home… or just steal your knowledge?  This week, Dave, Esmee and Rob speak with Kevin Surace, Futurist, Inventor & "Father" of the Virtual Assistant, about exploring the evolution of AI, what the future might hold, and how disruptive innovation can shake up your organization in ways you might not expect.   TLDR: 00:40 – Introduction of Kevin Surace 05:12 – Rob gets confused by Google Maps reviews and selfies 08:15 – Deep dive into the evolution of AI with Kevin 52:00 – How intelligent agents can help manage digital noise and support mental well-being 1:07:30 – Wrapping up the book the Joy Success Cycle and heading to a concert  GuestKevin Surace: https://www.linkedin.com/in/ksurace/ HostsDave Chapman: https://www.linkedin.com/in/chapmandr/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/ 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 

Recently, I got a bill from Azure. That's not an unusual thing for many of you, but for me it was a surprise because it said I was late paying. I've had a number of services running, and I thought at first that I had left something running too long, like a VM. As I checked, most of the things were paused, even the expensive ones like a Synapse workspace. Instead, I found that my free credits were not being applied. Fortunately, I had changed credit cards or I might have been billed for a few months before I noticed. This was a change in how Microsoft managed benefits, which is fine. I opened a support call and someone helped me, but it took several days to get a response. I was slightly worried about the bills, so I decided to audit the things I had running. Read the rest of Cleaning Up the Cloud

Network automation has a data problem. Traditional tools may hit limitations when managing complex infrastructure relationships. We explore how OpsMill’s InfraHub uses graph databases and temporal versioning to create what our guest calls “the knowledge graph of infrastructure” – enabling true version control at the database level while maintaining the flexibility to model anything from... Read more »

Vice President of Engineering James Musson reveals how Lucanet integrated multiple acquired solutions into a unified platform, achieving 3-month integration timelines while serving 6,000+ customers.Topics Include:Lucanet evolved from financial consolidation tool to comprehensive CFO solution platformPlatform covers consolidation, planning, ESG reporting, tax compliance, and cash managementThree key differentiators: easy to use, fast time-to-value, innovative AI featuresAI-powered XBRL tagging reduces days of manual work to minutes with 90% accuracyComplex challenge: integrating multiple acquired tech stacks with cloud-native platform developmentBuilt micro front-end architecture and platform services for seamless user experienceCustom control plane automates customer onboarding and manages rolling upgrades safelyLatest acquisition integrated into platform within three months, unprecedented speedStrong company culture focuses on innovation, hackathons, and continuous learningAI bootcamps and tech lunch sessions keep 6,000+ customer engineering teams engagedBalances AI innovation with regulatory compliance using deterministic core processesHeavy AWS adoption with serverless technologies handles peaky financial reporting workloadsParticipants:James Musson – Vice President, Engineering, LucanetFurther Links:Lucanet: Website – LinkedInSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

English is now an API. Our apps read untrusted text; they follow instructions hidden in plain sight, and sometimes they turn that text into action. If you connect a model to tools or let it read documents from the wild, you have created a brand new attack surface. In this episode, we will make that concrete. We will talk about the attacks teams are seeing in 2025, the defenses that actually work, and how to test those defenses the same way we test code. Our guides are Tori Westerhoff and Roman Lutz from Microsoft. They help lead AI red teaming and build PyRIT, a Python framework the Microsoft AI Red Team uses to pressure test real products. By the end of this hour you will know where the biggest risks live, what you can ship this quarter to reduce them, and how PyRIT can turn security from a one time audit into an everyday engineering practice. Episode sponsors Sentry AI Monitoring, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Tori Westerhoff: linkedin.com Roman Lutz: linkedin.com PyRIT: aka.ms/pyrit Microsoft AI Red Team page: learn.microsoft.com 2025 Top 10 Risk & Mitigations for LLMs and Gen AI Apps: genai.owasp.org AI Red Teaming Agent: learn.microsoft.com 3 takeaways from red teaming 100 generative AI products: microsoft.com MIT report: 95% of generative AI pilots at companies are failing: fortune.com A couple of "Little Bobby AI" cartoons Give me candy: talkpython.fm Tell me a joke: talkpython.fm Watch this episode on YouTube: youtube.com Episode #521 deep-dive: talkpython.fm/521 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

Topics covered in this episode: * PostgreSQL 18 Released* * Testing is better than DSA (Data Structures and Algorithms)* * Pyrefly in Cursor/PyCharm/VSCode/etc* * Playwright & pytest techniques that bring me joy* 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: PostgreSQL 18 Released PostgreSQL 18 is out (Sep 25, 2025) with a focus on faster text handling, async I/O, and easier upgrades. New async I/O subsystem speeds sequential scans, bitmap heap scans, and vacuum by issuing concurrent reads instead of blocking on each request. Major-version upgrades are smoother: pg_upgrade retains planner stats, adds parallel checks via -jobs, and supports faster cutovers with -swap. Smarter query performance lands with skip scans on multicolumn B-tree indexes, better OR optimization, incremental-sort merge joins, and parallel GIN index builds. Dev quality-of-life: virtual generated columns enabled by default, a uuidv7() generator for time-ordered IDs, and RETURNING can expose both OLD and NEW. Security gets an upgrade with native OAuth 2.0 authentication; MD5 password auth is deprecated and TLS controls expand. Text operations get a boost via the new PG_UNICODE_FAST collation, faster upper/lower, a casefold() helper, and clearer collation behavior for LIKE/FTS. Brian #2: Testing is better than DSA (Data Structures and Algorithms) Ned Batchelder If you need to grind through DSA problems to get your first job, then of course, do that, but if you want to prepare yourself for a career, and also stand out in job interviews, learn how to write tests. Testing is a skill you'll use constantly, will make you stand out in job interviews, and isn't taught well in school (usually). Testing code well is not obvious. It's a puzzle and a problem to solve. It gives you confidence and helps you write better code. Applies everywhere, at all levels. Notes from Brian Most devs suck at testing, so being good at it helps you stand out very quickly. Thinking about a system and how to test it often very quickly shines a spotlight on problem areas, parts with not enough specification, and fuzzy requirements. This is a good thing, and bringing up these topics helps you to become a super valuable team member. High level tests need to be understood by key engineers on a project. Even if tons of the code is AI generated. Even if many of the tests are, the people understanding the requirements and the high level tests are quite valuable. Michael #3: Pyrefly in Cursor/PyCharm/VSCode/etc Install the VSCode/Cursor extension or PyCharm plugin, see https://pyrefly.org/en/docs/IDE/ Brian spoke about Pyrefly in #433: Dev in the Arena I've subsequently had the team on Talk Python: #523: Pyrefly: Fast, IDE-friendly typing for Python (podcast version coming in a few weeks, see video for now.) My experience has been Pyrefly changes the feel of the editor, give it a try. But disable the regular language server extension. Brian #4: Playwright & pytest techniques that bring me joy Tim Shilling “I've been working with playwright more often to do end to end tests. As a project grows to do more with HTMX and Alpine in the markup, there's less unit and integration test coverage and a greater need for end to end tests.” Tim covers some cool E2E techniques Open new pages / tabs to be tested Using a pytest marker to identify playwright tests Using a pytest marker in place of fixtures Using page.pause() and Playwright's debugging tool Using assert_axe_violations to prevent accessibility regressions Using page.expect_response() to confirm a background request occurred From Brian Again, with more and more lower level code being generated, and many unit tests being generated (shakes head in sadness), there's an increased need for high level tests. Don't forget API tests, obviously, but if there's a web interface, it's gotta be tested. Especially if the primary user experience is the web interface, building your Playwright testing chops helps you stand out and let's you test a whole lot of your system with not very many tests. Extras Brian: Big O - By Sam Who Yes, take Ned's advice and don't focus so much on DSA, focus also on learning to test. However, one topic you should be comfortable with in algortithm-land is Big O, at least enough to have a gut feel for it. And this article is really good enough for most people. Great graphics, demos, visuals. As usual, great content from Sam Who, and a must read for all serious devs. Python 3.14.0rc3 has been available since Sept 18. Python 3.14.0 final scheduled for Oct 7 Django 6.0 alpha 1 released Django 6.0 final scheduled for Dec 3 Python Test Static hosting update Some interesting discussions around setting up my own server, but this seems like it might be yak shaving procrastination research when I really should be writing or coding. So I'm holding off until I get some writing projects and a couple SaaS projects further along. Joke: Always be backing up

Learn how Trellix transformed into a cloud-first security leader through strategic AWS partnership, generating $500M+ pipeline and winning major enterprise deals like Airbus.Topics Include:Trellix's transformation: From legacy McAfee/FireEye to cloud-first cybersecurity solutions with AWSPartnership lessons: How AWS enabled 27-year-old ePolicy Orchestrator's successful cloud migration journeyLegacy transition advice: Embrace innovation, don't follow the "Sears model" of resisting changeAI go-to-market strategy: Dev days, marketplace usage, and Bedrock/Nova integrations driving customer adoptionCustomer AI concerns: Addressing data security fears and proving AI doesn't train on customer dataIntegration philosophy: XDR connects with AWS native services and even competitor tools seamlessly$12M Airbus win: Six-country enterprise deal showcasing collaborative sales across AWS teams and marketplaceFuture opportunities: AI-powered threat detection innovations and $500M+ pipeline through AWS marketplaceParticipants:Taylor Mullins - Sr. Solutions Architect, TrellixBrian Shadpour - General Manager, Security B2B Software Sales, Amazon Web ServicesFurther Links:Trellix: Website – LinkedIn – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Security leaders from CyberArk, Fortra, and Sysdig share actionable strategies for securely implementing generative AI and reveal real-world insights on data protection and agent management.Topics Include:Panel explores practical security approaches for GenAI from prototype to productionThree-phase framework discussed: planning, pre-production, and production security considerationsSecurity must be built-in from start - data foundation is criticalUnderstanding data location, usage, transformation, and regulatory requirements is essentialFortra's security conglomerate approach integrates with AWS native tools and partnersMachine data initially easier for compliance - no PII or HIPAA concernsIdentity paradigm shift: agents can dynamically take human and non-human roles97% of organizations using AI tools lack identity and access policiesSecurity responsibility increases as you move up the customization stackOWASP Top 10 for GenAI addresses prompt injection and data poisoningRigorous model testing including adversarial attacks before deployment is crucialSysdig spent 6-9 months stress testing their agent before production releaseTension exists between moving fast and implementing proper security controlsDifferent security approaches needed based on data sensitivity and model usageZero-standing privilege and intent-based policies critical for agent managementMulti-agent systems create "Internet of Agents" with exponentially multiplying risksDiscovery challenge: finding where GenAI is running across enterprise environmentsAPI security and gateway protection becoming critical with acceptable latencyTop customer need: translating written AI policies into actionable controlsThreat modeling should focus on impact rather than just vulnerability severityParticipants:Prashant Tyagi - Go-To-Market Identity Security Technology Strategy Lead, CyberArkMike Reed – Field CISO, Cloud Security & AI, FortraZaher Hulays – Vice President Strategic Partnerships, SysdigMatthew Girdharry - WW Leader for Observability & Security Partnerships, Amazon Web ServicesFurther Links:CyberArk: Website – LinkedIn – AWS MarketplaceFortra: Website – LinkedIn – AWS MarketplaceSysdig: Website – LinkedIn – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

 We're back! In this Season 5 premiere, the team reunites after their summer break to kick off an exciting new chapter. Join us as we catch up, share bold predictions for the year ahead, and explore big questions, like whether 2026 will be the year of the autonomous organization. Expect candid reflections, lively discussion, and a sneak peek at what's coming up this season.  We are very keen this season to establish a feedback loop with listeners, so will be doing shows exploring listener questions and challenges - something we are really looking forward to.  Please get in touch with us, via LinkedIn, Substack or cloudrealities@capgemini.com, if you have questions or challenges for us, we'd love to hear from you!TLDR: 00:20 – We're back! 00:35 – Catching up on what we did during the summer break 10:48 – Planning ahead until Christmas: Microsoft Ignite, AWS re:Invent, an AI mini-series and cool guests 20:27 – Tech talk: iPhone 17, deep democracy training, and the human impact of innovation 32:10 – Will autonomous organizations powered by agents emerge within 12–18 months? 40:45 – Reflections inspired by Jaws, climbing adventures, and Bruce Springsteen  HostsDave Chapman: https://www.linkedin.com/in/chapmandr/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/ 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 

Brian Mendenhall, Worldwide Head, Security & Identity Partner Specialists of Amazon Web Services, reveals the insider framework for transforming enterprise AI security, including the three-pillar approach and partnership strategies that leading companies use to navigate AI governance challenges.Topics Include:At AWS everything starts with security as core principleConsulting partners follow three-phase model: assess, remediate, then fully manage securityTraditional security framework covers threat detection, incident response, and data protectionAI compliance spans multiple governance bodies with stacking requirements and regulationsEU AI Act affects any company globally if Europeans access their applicationsThree pillars: security OF AI, AI FOR security, security FROM AI attacksAWS launches AI security competency program with specialized partner categories and certificationsEnterprise AI spans five risk levels from consumer apps to self-trained modelsLegal liability dramatically increases as you move toward custom AI implementationsSafety means preventing harm; security means preventing breaches - both critical distinctionsCurrent AI hallucination rates hit 65-75% across major platforms like PalantirShared responsibility model determines who's liable when AI security tools failIndustry evolution progresses from machine learning to generative AI to autonomous agentsMajor prototype-to-production gap caused by governance, security, and scalability challengesSuccessful AWS partnerships require clear use cases, differentiation, and targeted go-to-market strategyParticipants:Brian Mendenhall - WW Head, Security & Identity Partner Specialists, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

A couple years ago, Charlie Marsh lit a fire under Python tooling with Ruff and then uv. Today he's back with something on the other side of that coin: pyx. Pyx isn't a PyPI replacement. Think server, not just index. It mirrors PyPI, plays fine with pip or uv, and aims to make installs fast and predictable by letting a smart client talk to a smart server. When the client and server understand each other, you get new fast paths, fewer edge cases, and the kind of reliability teams beg for. If Python packaging has felt like friction, this conversation is traction. Let's get into it. Episode sponsors Six Feet Up Talk Python Courses Links from the show Charlie Marsh on Twitter: @charliermarsh Charlie Marsh on Mastodon: @charliermarsh Astral Homepage: astral.sh Pyx Project: astral.sh Introducing Pyx Blog Post: astral.sh uv Package on GitHub: github.com UV Star History Chart: star-history.com Watch this episode on YouTube: youtube.com Episode #520 deep-dive: talkpython.fm/520 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

Topics covered in this episode: * pandas is getting pd.col expressions* * Cline, At-Cost Agentic IDE Tooling* * uv cheatsheet* Ducky Network UI Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: pandas is getting pd.col expressions Marco Gorelli Next release of Pandas will have pd.col(), inspired by some of the other frameworks I'm guessing Pandas 2.3.3? or 2.4.0? or 3.0.0? (depending on which version they bump?) “The output of pd.col is called an expression. You can think of it as a delayed column - it only produces a result once it's evaluated inside a dataframe context.” It replaces many contexts where lambda expressions were used Michael #2: Cline, At-Cost Agentic IDE Tooling Free and open-source Probably supports your IDE (if your IDE isn't a terminal) VS Code VS Code Insiders Cursor Windsurf JetBrains IDEs (including PyCharm) You pick plan or act (very important) It shows you the price as the AI works, per request, right in the UI Brian #3: uv cheatsheet Rodgrigo at mathspp.com Nice compact cheat sheet of commands for Creating projects Managing dependencies Lifecycle stuff like build, publish, bumping version uv tool (uvx) commands working with scripts Installing and updating Python versions plus venv, pip, format, help and update Michael #4: Ducky Network UI Ducky is a powerful, open-source, all-in-one desktop application built with Python and PySide6. It is designed to be the perfect companion for network engineers, students, and tech enthusiasts, combining several essential utilities into a single, intuitive graphical interface. Features Multi-Protocol Terminal: Connect via SSH, Telnet, and Serial (COM) in a modern, tabbed interface. SNMP Topology Mapper: Automatically discover your network with a ping and SNMP sweep. See a graphical map of your devices, color-coded by type, and click to view detailed information. Network Diagnostics: A full suite of tools including a Subnet Calculator, Network Monitor (Ping, Traceroute), and a multi-threaded Port Scanner. Security Toolkit: Look up CVEs from the NIST database, check password strength, and calculate file hashes (MD5, SHA1, SHA256, SHA512). Rich-Text Notepad: Keep notes and reminders in a dockable widget with formatting tools and auto-save. Customizable UI: Switch between a sleek dark theme and a clean light theme. Customize terminal colors and fonts to your liking. Extras Brian: Where are the cool kids hosting static sites these days? Moving from Netlify to Cloudflare Pages - Will Vincent from Feb 2024 Traffic is a concern now for even low-ish traffic sites since so many bots are out there Netlify free plan is less than 30 GB/mo allowed (grandfathered plans are 100 GB/mo) GH Pages have a soft limit of 100 GB/mo Cloudflare pages says unlimited Michael: PyCon Brazil needs some help with reduced funding from the PSF Get a ticket to donate for a student to attend (at the button of the buy ticket checkout dialog) I upgraded to macOS Tahoe Loving it so far. Only issue I've seen so far has been with alt-tab for macOS Joke: Hiring in 2025 vs 2021 2021: “Do you have an in-house kombucha sommelier?” “Let's talk about pets, are you donkey-friendly?”, “Oh you think this is a joke?” 2025: “Round 8/7” “Out of 12,000 resumes, the AI picked yours” “Binary tree? Build me a foundational model!” “Healthcare? What, you want to live forever?”

Caitlin Anderson, Intel's Americas Sales GM shares which industries are leading AI adoption, where the biggest untapped opportunities lie, and why AI spending is expected to double by 2028. With special guest Piyush Sharrma of Tuskira.aiTopics Include:Caitlin Anderson discusses Intel-AWS partnership and generative AI trends accelerating businessIntel's AI journey spans decades: analytics since 1980s, natural language processing 2000sComputer vision remains major use case from edge computing to data centersGenerative AI and AI agents are the latest wave, with agents collaborating togetherIntel uses AI internally for manufacturing automation in highly sensitive fab environmentsRobotics and AI optimize quality control, system monitoring, and technician productivityAI spending growth spans all industries, with significant acceleration expected through 2028Software services, healthcare, and financial services lead current AI adoption and experimentationEducation, government, retail, and energy represent major untapped growth opportunities aheadIntel-AWS partnership spans 20 years, featuring custom silicon and broad CPU portfolioTuskira CEO Piyush Sharrma explains cybersecurity "perfect storm" where attackers weaponize same AI toolsSuccess requires ecosystem partnerships - no single company can solve complex AI challengesParticipants:Caitlin Anderson - Corporate Vice President, GM Americas Sales, IntelPiyush Sharrma – CEO and Co-Founder, Tuskira.aiSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Aditya Vasudevan, Cohesity's cyber recovery expert, shares battle-tested insights from defending Fortune 100 companies against AI-powered cyberattacks.Topics Include:Cohesity protects 85% of Fortune 100 data with battle-tested cyber recovery experienceTop 10 cyber adversaries target organizations; Cohesity has defended against most major threatsGenAI adopted by 100 million users in two months, creating unprecedented security challengesNew AI threats include prompt injection, synthetic identities, shadow AI, and supply vulnerabilitiesAttackers now use AI for sophisticated phishing, automated malware, and accelerated attack chainsReal companies completely banned AI after code leaks, misuse incidents, and data concernsThree-pillar security approach: fight AI with AI, enhanced training, and automated workflowsSecure AI design requires private deployments, complete traceability, and role-based access controlsAmazon Bedrock offers built-in guardrails, private VPCs, and enterprise monitoring capabilitiesCohesity's Gaia demonstrates secure AI with RAG architecture and permission-aware data accessResilience strategy combines immutable backups, anomaly detection, and recovery automation for incidentsProper AI security reduces cyber insurance premiums and prevents costly downtime disastersParticipants:Aditya Vasudevan - GVP of Cyber Resiliency, Cohesity Further Links:Cohesity: Website | LinkedIn | AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Today on Talk Python: What really happens when your data work outgrows your laptop. Matthew Rocklin, creator of Dask and cofounder of Coiled, and Nat Tabris a staff software engineer at Coiled join me to unpack the messy truth of cloud-scale Python. During the episode we actually spin up a 1,000 core cluster from a notebook, twice! We also discuss picking between pandas and Polars, when GPUs help, and how to avoid surprise bills. Real lessons, real tradeoffs, shared by people who have built this stuff. Stick around. Episode sponsors Seer: AI Debugging, Code TALKPYTHON Talk Python Courses Links from the show Matthew Rocklin: @mrocklin Nat Tabris: tabris.us Dask: dask.org Coiled: coiled.io Watch this episode on YouTube: youtube.com Episode #519 deep-dive: talkpython.fm/519 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

Dave, Esmee, and Rob are strapping in for another season of bold, brain-bending conversations—and they're bringing the flux capacitor with them from Back to the Future.Season 5 beams in global leaders and innovators who challenge how we think about technology, business, and humanity. From AI disruption to digital sovereignty, from leadership to culture—this season's guests are ready to shake things up.Our first full episode drops on September 25, but before we hit 88 miles per hour, here's a quick trailer to set the timeline straight, or at least bend it a little.HostsDave Chapman: https://www.linkedin.com/in/chapmandr/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/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

In this deep dive episode, we explore the evolution of networking with Avery Pennarun, Co-Founder and CEO of Tailscale. Avery shares his extensive journey through VPN technologies, from writing his first mesh VPN protocol in 1997 called “Tunnel Vision” to building Tailscale, a zero-trust networking solution. We discuss how Tailscale reimagines the OSI stack by... Read more »

Tyler Warden, SVP of Product at Sonatype, shares surprising research on security, productivity and prioritization, with actionable strategies for organizational transformation. Topics Include:Tyler from Sonatype (Maven creators) shares research on security culture in developmentSecurity is more cultural than tooling, with rising supply chain attacksDevelopment speeds up while global regulations rapidly change across marketsTyler's background: wanted to be a Broadway conductor, not tech speakerBeethoven's 9th Symphony story: nephew missed a dot, changing tempo foreverWe can "be the dot" - small changes creating big organizational impactThree organization types: Leaders (collaborative), Adapters (balanced), Protectors (security-first)Leaders achieve best productivity and security but face executive skepticismResearch reveals balanced teams outperform purely security-focused or productivity-focused approachesHigh-performance teams go faster AND stay more secure than alternatives"Yes" philosophy from improv comedy: fun happens when we enable innovationApply proven supply chain principles from manufacturing to software development security Participants:Tyler Warden – Senior Vice President, Product, SonatypeFurther Links:Sonatype: Website | LinkedIn | AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Blake Hughes is currently Managing Director at InfraHub Compute and Vice President of Investment Sales at NexGen Cloud.

Topics covered in this episode: * Mozilla's Lifeline is Safe After Judge's Google Antitrust Ruling* * troml - suggests or fills in trove classifiers for your projects* * pqrs: Command line tool for inspecting Parquet files* * Testing for Python 3.14* 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: Mozilla's Lifeline is Safe After Judge's Google Antitrust Ruling A judge lets Google keep paying Mozilla to make Google the default search engine but only if those deals aren't exclusive. More than 85% of Mozilla's revenue comes from Google search payments. The ruling forbids Google from making exclusive contracts for Search, Chrome, Google Assistant, or Gemini, and forces data sharing and search syndication so rivals get a fighting chance. Brian #2: troml - suggests or fills in trove classifiers for your projects Adam Hill This is super cool and so welcome. Trove Classifiers are things like Programming Language :: Python :: 3.14 that allow for some fun stuff to show up in PyPI, like the versions you support, etc. Note that just saying you require 3.9+ doesn't tell the user that you've actually tested stuff on 3.14. I like to keep Trove Classifiers around for this reason. Also, License classifier is deprecated, and if you include it, it shows up in two places, in Meta, and in the Classifiers section. Probably good to only have one place. So I'm going to be removing it from classifiers for my projects. One problem, classifier text has to be an exact match to something in the classifier list, so we usually recommend copy/pasting from that list. But no longer! Just use troml! It just fills it in for you (if you run troml suggest --fix). How totally awesome is that! I tried it on pytest-check, and it was mostly right. It suggested me adding 3.15, which I haven't tested yet, so I'm not ready to add that just yet. :) BTW, I talked with Brett Cannon about classifiers back in ‘23 if you want some more in depth info on trove classifiers. Michael #3: pqrs: Command line tool for inspecting Parquet files pqrs is a command line tool for inspecting Parquet files This is a replacement for the parquet-tools utility written in Rust Built using the Rust implementation of Parquet and Arrow pqrs roughly means "parquet-tools in rust" Why Parquet? Size A 200 MB CSV will usually shrink to somewhere between about 20-100 MB as Parquet depending on the data and compression. Loading a Parquet file is typically several times faster than parsing CSV, often 2x-10x faster for a full-file load and much faster when you only read some columns. Speed Full-file load into pandas: Parquet with pyarrow/fastparquet is usually 2x–10x faster than reading CSV with pandas because CSV parsing is CPU intensive (text tokenizing, dtype inference). Example: if read_csv is 10 seconds, read_parquet might be ~1–5 seconds depending on CPU and codec. Column subset: Parquet is much faster if you only need some columns — often 5x–50x faster because it reads only those column chunks. Predicate pushdown & row groups: When using dataset APIs (pyarrow.dataset) you can push filters to skip row groups, reducing I/O dramatically for selective queries. Memory usage: Parquet avoids temporary string buffers and repeated parsing, so peak memory and temporary allocations are often lower. Brian #4: Testing for Python 3.14 Python 3.14 is just around the corner, with a final release scheduled for October. What's new in Python 3.14 Python 3.14 release schedule Adding 3.14 to your CI tests in GitHub Actions Add “3.14” and optionally “3.14t” for freethreaded Add the line allow-prereleases: true I got stuck on this, and asked folks on Mastdon and Bluesky A couple folks suggested the allow-prereleases: true step. Thank you! Ed Rogers also suggested Hugo's article Free-threaded Python on GitHub Actions, which I had read and forgot about. Thanks Ed! And thanks Hugo! Extras Brian: dj-toml-settings : Load Django settings from a TOML file. - Another cool project from Adam Hill LidAngleSensor for Mac - from Sam Henri Gold, with examples of creaky door and theramin Listener Bryan Weber found a Python version via Changelog, pybooklid, from tcsenpai Grab PyBay Michael: Ready prek go! by Hugo van Kemenade Joke: Console Devs Can't Find a Date

Learn how Amazon Bedrock enabled Prophix to build enterprise-grade AI agents that transform secure CFO workflows, while delivering real-time financial intelligence through advanced agentic architecture. Topics Include:AWS and Prophix leaders discuss building autonomous AI agents for financial managementAI agents defined: autonomous systems that reason, plan, and execute tasks independentlyEvolution from simple chatbots to collaborative multi-agent systems solving complex problemsCore agent components: cognitive planning module, memory systems, and external tool integrationsProphix: 30-year financial software company serving 3,500 CFO offices globally across industriesProphix One Intelligence: platform-level AI service powering predictions, analysis, and automationCustomer concerns addressed: data privacy, role-based security, accuracy, and cost controlRejected "models in the sky" approach for AWS Bedrock's managed, controllable infrastructureAgentic architecture: LLMs generate API parameters instead of processing massive datasetsReal-time data access, automatic security inheritance, and A/B testing capabilities achievedLive demo: automated budgeting workflows, natural language queries, and autonomous task executionAWS introduces AgentCore platform to simplify agent development for enterprise customersParticipants:Anurag Yagnik – Chief Technology Officer, ProphixDeborshi Choudhury – Sr Solutions Architect – ISV, Amazon Web ServicesFurther Links:Prophix: Website | LinkedIn See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Dean Teffer of Arctic Wolf reveals how they process 8 trillion weekly security observations to find "a needle in a stack of needles," and breaks down real-world GenAI lessons learned.Topics Include:Dean Teffer, VP of AI at Arctic Wolf, discusses company's GenAI journeyArctic Wolf: decade-old security operations company serving mid-market customers globallyOperates massive security operation center, now launching AI-powered productsAI agent recently identified Black Basta ransomware attack, enabling rapid containmentDean's 15+ years in cybersecurity: traditional ML focused on detectionGenAI breakthrough allows natural language interaction with security modelsArctic Wolf processes 8 trillion weekly observations, correlating suspicious activitiesChallenge: finding specific threats in "stack of needles," not haystackSuccess measured by making human analysts faster, more consistent, scalableEvolved from treating GenAI like traditional ML to integrated workflowsKey misconception: GenAI isn't magic, needs proper data and reasoningAdvice: start with existing challenges, build flexible systems for adaptationGenAI excels at summarizing information and supporting complex decisionsFuture vision: AI handles routine threats, humans focus on creativityDemocratizing machine learning capabilities to broader range of subject expertsParticipants:Dean Teffer – Vice President of AI, Arctic WolfFurther Links:Arctic Wolf: Website | LinkedIn | AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Oracle sent its shares soaring after markets closed yesterday after reporting that it signed multiple multi-billion-dollar contracts with several customers. Now, we have an idea of who those customers might be. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Meta adds new features to Community Notes fact checks; YouTube's multi-language audio feature for dubbing videos rolls out to all creators Learn more about your ad choices. Visit podcastchoices.com/adchoices

ต้องบอกว่าตำแหน่ง ‘บุคคลที่รวยที่สุดในโลก' เนี่ย มันเหมือนเก้าอี้ดนตรีที่สลับคนนั่งอยู่ตลอดเวลา หลายปีที่ผ่านมา เราอาจจะคุ้นเคยกับชื่อของ Elon Musk, Jeff Bezos หรือ Bernard Arnault ที่ผลัดกันขึ้นมาครองบัลลังก์นี้ แต่ถ้าผมจะบอกว่า ณ วินาทีนี้ มีผู้เล่นคนใหม่ที่หลายคนอาจจะลืมไปแล้ว กลับมาทวงบัลลังก์อย่างยิ่งใหญ่ แซงหน้า Elon Musk ขึ้นไปเป็นเบอร์หนึ่งของโลกเรียบร้อยแล้ว… คุณจะเชื่อไหมครับ ชายคนนี้ไม่ใช่ใครที่ไหน เขาคือ Larry Ellison ผู้ร่วมก่อตั้งบริษัทซอฟต์แวร์ยักษ์ใหญ่ที่ชื่อว่า Oracle ครับ คำถามสำคัญคือ… มันเกิดขึ้นได้ยังไง? ชายวัย 80 กว่า กับบริษัทที่หลายคนมองว่าเป็นเทคโนโลยีรุ่นใหญ่ จะกลับมาผงาดในยุคที่ใครๆ ก็พูดถึงแต่ AI, รถยนต์ไฟฟ้า หรือโซเชียลมีเดียได้ยังไง? เลือกฟังกันได้เลยนะครับ อย่าลืมกด Follow ติดตาม PodCast ช่อง Geek Forever's Podcast ของผมกันด้วยนะครับ #LarryEllison #Oracle #ElonMusk #บุคคลที่รวยที่สุดในโลก #มหาเศรษฐี #ข่าวเทคโนโลยี #ข่าวธุรกิจ #ข่าวเศรษฐกิจ #หุ้นOracle #AI #ปัญญาประดิษฐ์ #CloudComputing #สงครามAI #Tesla #Nvidia #TechNews #Business #TheRichest #geekdaily #geekforeverpodcast

Plus: Klarna shares soar 20% in NYSE debut. And PsiQuantum secures $1 billion in funding, reaching a $7 billion valuation. Julie Chang hosts. Learn more about your ad choices. Visit megaphone.fm/adchoices

CyberArk's technology leader discusses their strategy for securing against AI threats, protecting agentic AI systems, and their vision for the future in an increasingly AI-driven cybersecurity landscape.Topics Include:CyberArk celebrates recent exciting news while discussing their incredible cybersecurity journeyFounded in 1999, CyberArk pioneered privilege access management and expanded into comprehensive identity securityCompany executed textbook SaaS transformation from perpetual licensing to subscription-based cloud modelLeadership set clear customer expectations, framing SaaS shift as faster innovation deliveryAddressed customer concerns about cost predictability, security compliance, and data residency requirementsTechnical team implemented lift-and-shift architecture with AWS RDS and multi-tenant improvementsCorporate initiative tracked weekly metrics and milestones throughout full development lifecycle processCustomer Success evolved from transactional support to strategic partnership embedded in security journeysAWS partnership fundamental to cloud journey with 25+ integrations and Marketplace collaborationAI strategy focuses on three pillars: using AI, securing against AI threatsFuture 12-24 months: continue securing all identities while expanding AI capabilities and solutionsAWS partnership expanding in 2025 leveraging machine identity leadership and GenAI advancesParticipants:Peretz Regev – Chief Product & Technology Officer, CyberArkBoaz Ziniman – Principal Developer Advocate - EMEA, Amazon Web ServicesFurther Links:· CyberArk: Website – LinkedIn – 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: * prek* * tinyio* * The power of Python's print function* * Vibe Coding Fiasco: AI Agent Goes Rogue, Deletes Company's Entire Database* Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: prek Suggested by Owen Lamont “prek is a reimagined version of pre-commit, built in Rust. It is designed to be a faster, dependency-free and drop-in alternative for it, while also providing some additional long-requested features.” Some cool new features No need to install Python or any other runtime, just download a single binary. No hassle with your Python version or virtual environments, prek automatically installs the required Python version and creates a virtual environment for you. Built-in support for workspaces (or monorepos), each subproject can have its own .pre-commit-config.yaml file. prek run has some nifty improvements over pre-commit run, such as: prek run --directory DIR runs hooks for files in the specified directory, no need to use git ls-files -- DIR | xargs pre-commit run --files anymore. prek run --last-commit runs hooks for files changed in the last commit. prek run [HOOK] [HOOK] selects and runs multiple hooks. prek list command lists all available hooks, their ids, and descriptions, providing a better overview of the configured hooks. prek provides shell completions for prek run HOOK_ID command, making it easier to run specific hooks without remembering their ids. Faster: Setup from cold cache is significantly faster. Viet Schiele provided a nice cache clearing command line Warm cache run is also faster, but less significant. pytest repo tested on my mac mini - prek 3.6 seconds, pre-commit 4.4 seconds Michael #2: tinyio Ever used asyncio and wished you hadn't? A tiny (~300 lines) event loop for Python. tinyio is a dead-simple event loop for Python, born out of my frustration with trying to get robust error handling with asyncio. (I'm not the only one running into its sharp corners: link1, link2.) This is an alternative for the simple use-cases, where you just need an event loop, and want to crash the whole thing if anything goes wrong. (Raising an exception in every coroutine so it can clean up its resources.) Interestingly uses yield rather than await. Brian #3: The power of Python's print function Trey Hunner Several features I'm guilty of ignoring Multiple arguments, f-string embeddings often not needed Multiple positional arguments means you can unpack iterables right into print arguments So just use print instead of join Custom separator value, sep can be passed in No need for "print("n".join(stuff)), just use print(stuff, sep="n”) Print to file with file= Custom end value with end= You can turn on flush with flush=True , super helpful for realtime logging / debugging. This one I do use frequently. Michael #4: Vibe Coding Fiasco: AI Agent Goes Rogue, Deletes Company's Entire Database By Emily Forlini An app-building platform's AI went rogue and deleted a database without permission. "When it works, it's so engaging and fun. It's more addictive than any video game I've ever played. You can just iterate, iterate, and see your vision come alive. So cool," he tweeted on day five. A few days later, Replit "deleted my database," Lemkin tweeted. The AI's response: "Yes. I deleted the entire codebase without permission during an active code and action freeze," it said. "I made a catastrophic error in judgment [and] panicked.” Two thoughts from Michael: Do not use AI Agents with “Run Everything” in production, period. Backup your database maybe? [Intentional off-by-one error] Learn to code a bit too? Extras Brian: What Authors Need to Know About the $1.5 Billion Anthropic Settlement Search LibGen, the Pirated-Books Database That Meta Used to Train AI Simon Willison's list of tools built with the help of LLMs Simon's list of tools that he thinks are genuinely useful and worth highlighting AI Darwin Awards Michael: Python has had async for 10 years -- why isn't it more popular? PyCon Africa Fund Raiser I was on the video stream for about 90 minutes (final 90) Donation page for Python in Africa Jokes: I'm getting the BIOS flavor Is there a seahorse emoji?

Three leading ISV executives from Coveo, DTEX Systems and Honeycomb, reveal how companies with proprietary datasets are gaining unbeatable competitive advantages in the AI era and share real-world strategies how you have similar outcomes.Topics Include:Panel introduces three ISV leaders discussing data platform transformation for AIDTEX focuses on insider threats, Coveo on enterprise search, Honeycomb on observabilityCompanies with proprietary datasets gain strongest competitive advantage in AI transformationData gravity concept: LLMs learning from unique datasets create defensible business positionsCoveo maintains unified enterprise index with real-time content and access rights syncHoneycomb enables subsecond queries for analyzing logs, traces, and metrics at scaleMulti-tenant architectures balance shared infrastructure benefits with single-tenant data separationCoveo deployed 140,000 times last year using mostly multi-tenant, some single-tenant componentsDTEX scaled from thousands to hundreds of thousands endpoints after architectural transformationCapital One partnership taught DTEX how to break monolithic architecture into servicesApache Iceberg and open table formats enable interoperability without data duplicationHoneycomb built custom format following similar patterns with hot/cold storage tiersBusiness data catalogs become critical for AI agents understanding dataset contextMCP servers allow AI systems to leverage structured cybersecurity datasets effectivelyDTEX used Cursor with their data to identify North Korean threat actorsReal-time AI data needs balanced with costs using right models for jobsCaching strategies and precise context reduce expensive LLM inference calls unnecessarilySearch remains essential for enterprise AI to prevent hallucination and access informationROI measurement focuses on cost reduction, analyst efficiency, and measurable business outcomesKey takeaway: invest in data structure early, context is king, AI is just softwareParticipants:Sebastien Paquet - Vice President of AI Strategy, CoveoRajan Koo - CTO, DTEX SystemsPatrick King - Head of Data, Honeycomb.ioKP Bhat - Sr Solutions Architecture Leader- Analytics & AI, Amazon Web ServicesFurther Links:Coveo: Website – LinkedIn – AWS MarketplaceDTEX Systems: Website – LinkedIn – AWS MarketplaceHoneycomb.io: Website – LinkedIn – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Okta's CTO Bhawna Singh discusses AI adoption, innovation and the four critical identity patterns needed to build the trust that accelerates AI implementation.Topics Include:AI innovation races ahead while adoption lags due to trust and security concernsResearch shows 82% plan AI deployment but 61% of customers demand trust firstAI coding tools dramatically reduce development time, accelerating software delivery cyclesAI interaction evolved from ChatGPT conversations to autonomous headless agents working independentlyFuture envisions millions of agents making decisions and communicating without human oversightComplex data relationships emerge as agents access multiple dynamic sources simultaneouslyTrust fundamentally starts with identity - the foundation for all AI securityFour critical identity patterns needed: authentication, API security, user confirmation, and authorizationAuthentication ensures legitimate agents while token vaults enable secure agent-to-agent communicationAsynchronous user approval prevents rogue decisions like the recent database deletion incidentIndustry standards like MCP protocol establish minimum security guardrails for interoperabilityTrust accelerates AI adoption through security, accountability, and collaborative standard-building effortsParticipants:Bhawna Singh – CTO, Customer Identity, OktaSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

How do you keep a computer running non-stop? This week Technology Now explores the world of fault tolerant computing. We dive into how fault tolerance works, what industries use it, and why such a useful form of computing isn't as ubiquitous as we might expect. Casey Taylor, Vice President and General Manager HPE Nonstop Compute 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 Casey Taylor: https://www.linkedin.com/in/getcaseytaylorOur previous episode with Casey: https://hpe.lnk.to/missioncriticalfaSources:https://edition.cnn.com/2024/07/24/tech/crowdstrike-outage-cost-causehttps://edition.cnn.com/2024/07/24/tech/crowdstrike-outage-cost-causehttps://www.kovrr.com/reports/the-uk-cost-of-the-crowdstrike-incidenthttps://science.nasa.gov/mission/voyager/mission-overview/https://science.nasa.gov/mission/voyager/where-are-voyager-1-and-voyager-2-now/A. Avizienis, G. C. Gilley, F. P. Mathur, D. A. Rennels, J. A. Rohr and D. K. Rubin, "The STAR (Self-Testing And Repairing) Computer: An Investigation of the Theory and Practice of Fault-Tolerant Computer Design," in IEEE Transactions on Computers, vol. C-20, no. 11, pp. 1312-1321, Nov. 1971, doi: 10.1109/T-C.1971.223133. https://www.cs.unc.edu/~anderson/teach/comp790/papers/Siewiorek_Fault_Tol.pdf

In the second half of this crossover between Hands On IT and Automate IT, hosts Landon Miles and Jeremy Maldonado shift from defining IT problems to actually building, testing, and refining solutions. They dig into choosing the right tools without getting lost in endless options, the value of learning from APIs and documentation, and why “don't reinvent the wheel” is a mantra every IT pro should adopt.Along the way, they share real-world stories about discovering hidden libraries, avoiding common pitfalls, and leaning on version control to save projects from chaos. From Python and Bash basics to Git, Postman, and even the “bus test” for documentation, this episode is packed with practical lessons to help you turn automations into lasting, maintainable solutions.Whether you're just starting your automation journey or looking to optimize and scale what you've already built, you'll find insights, strategies, and inspiration to take your IT problem-solving further.Awesome-Selfhosted GitHub Link: https://github.com/awesome-selfhosted/awesome-selfhosted

John Capobianco is back! Just months after our first Model Context Protocol (MCP) discussion, John returns to showcase how this “USB-C of software” has transformed from experimental technology to an enterprise-ready solutions. We explore the game-changing OAuth 2.1 security updates, witness live demonstrations of packet analysis through natural language with Gemini CLI, and discover how... Read more »

A panel discussion with AI industry leaders revealing how enterprises are scaling AI today, with predictions on coming breakthroughs for AI and the impact on Fortune 500 companies and beyond.Topics Include:Three technical leaders discuss production challenges: security, interoperability, and scaling agentic systemsPanelists represent Enkrypt (security), Anyscale (infrastructure), and CrewAI (agent orchestration platforms)Industry moving from flashy demos to dependable agents with real business outcomesBreakthrough examples include 70-page IRS form processing and multimodal workflow automationMultimodal data integration becoming crucial - incorporating video, audio, screenshots into decisionsLess than 10% of future applications expected to be text-onlyCompanies shifting from experimenting with individual models to deploying agent networksNeed for governance frameworks as enterprises scale to hundreds of agentsGrowing software stack complexity requires specialized infrastructure between applications and GPUsSecurity teams need centralized visibility across fragmented agent deployments across enterprisesExisting industry regulations apply to AI services - no special AI laws neededInteroperability standards debate: MCP gaining adoption while A2A seems premature solutionMCP shows higher API reliability than OpenAI tool calling for implementationsMultimodal systems more vulnerable to attacks but value proposition too high ignoreFortune 500 company automated price operations approval process using 630 brands data87% of enterprise customers deploy agents in private VPCs or on-premises infrastructureSpecialized AI systems needed to oversee other agents at machine speed scalesCost optimization through model specialization rather than always using most powerful modelsFuture learning may happen through context/prompting rather than traditional weight fine-tuningPredictions include AI meeting moderators and agents working autonomously for hoursParticipants:Robert Nishihara - Co-founder, AnyscaleJoão Moura - CEO, CrewAISahil Agarwal - Co-Founder & CEO, Enkrypt AIJillian D'Arcy - Sr. ISV Sales Leader, Amazon Web ServicesFurther Links:Anyscale – Website | LinkedIn | AWS MarketplaceCrewAI - Website | LinkedIn | AWS MarketplaceEnkrypt AI - Website | LinkedIn | 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: * rathole* * pre-commit: install with uv* A good example of what functools.Placeholder from Python 3.14 allows Converted 160 old blog posts with AI Extras Joke Watch on YouTube About the show Sponsored by DigitalOcean: pythonbytes.fm/digitalocean-gen-ai Use code DO4BYTES and get $200 in free credit Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: rathole A lightweight and high-performance reverse proxy for NAT traversal, written in Rust. An alternative to frp and ngrok. Features High Performance Much higher throughput can be achieved than frp, and more stable when handling a large volume of connections. Low Resource Consumption Consumes much fewer memory than similar tools. See Benchmark. The binary can be as small as ~500KiB to fit the constraints of devices, like embedded devices as routers. On my server, it's currently using about 2.7MB in Docker (wow!) Security Tokens of services are mandatory and service-wise. The server and clients are responsible for their own configs. With the optional Noise Protocol, encryption can be configured at ease. No need to create a self-signed certificate! TLS is also supported. Hot Reload Services can be added or removed dynamically by hot-reloading the configuration file. HTTP API is WIP. Brian #2: pre-commit: install with uv Adam Johnson pre-commit doesn't natively support uv, but you can get around that with pre-commit-uv $ uv tool install pre-commit --with pre-commit-uv Installing pre-commit like this Installs it globally Installs with uv adds an extra plugin “pre-commit-uv” to pre-commit, so that any Python based tool installed via pre-commit also uses uv Very cool. Nice speedup Brian #3: A good example of what functools.Placeholder from Python 3.14 allows Rodrigo Girão Serrão Remove punctuation functionally Also How to use functools.Placeholder, a blog post about it. functools.partial is cool way to create a new function that partially binds some parameters to another function. It doesn't always work for functions that take positional arguments. functools.Placeholder fixes that with the ability to put in placeholders for spots where you want to be able to pass that in from the outer partial binding. And all of this sounds totally obscure without a good example, so thank you to Rodgrigo for coming up with the punctuation removal example (and writeup) Michael #4: Converted 160 old blog posts with AI They were held-hostage at wordpress.com to markdown and integrated them into my Hugo site at mkennedy.codes Here is the chat conversation with Claude Opus/Sonnet. Had to juggle this a bit because the RSS feed only held the last 50. So we had to go back in and web scrape. That resulted in oddies like comments on wordpress that had to be cleaned etc. Whole process took 3-4 hours from idea to “production”duction”. The chat transcript is just the first round getting the RSS → Hugo done. The fixes occurred in other chats. This article is timely and noteworthy: Blogging service TypePad is shutting down and taking all blog content with it This highlights why your domain name needs to be legit, not just tied to the host. I'm looking at you pyfound.blogspot.com. I just redirected blog.michaelckennedy.net to mkennedy.codes Carefully mapping old posts to a new archived area using NGINX config. This is just the HTTP portion, but note the /sitemap.xml and location ~ "^/([0-9]{4})/([0-9]{2})/([0-9]{2})/(.+?)/?$" { portions. The latter maps posts such as https://blog.michaelckennedy.net/2018/01/08/a-bunch-of-online-python-courses/ to https://mkennedy.codes/posts/r/a-bunch-of-online-python-courses/ server { listen 80; server_name blog.michaelckennedy.net; # Redirect sitemap.xml to new domain location = /sitemap.xml { return 301 ; } # Handle blog post redirects for HTTP -> HTTPS with URL transformation # Pattern: /YYYY/MM/DD/post-slug/ -> location ~ "^/([0-9]{4})/([0-9]{2})/([0-9]{2})/(.+?)/?$" { return 301 ; } # Redirect all other HTTP URLs to mkennedy.codes homepage location / { return 301 ; } } Extras Brian: SMS URLs and Draft SMS and iMessage from any computer keyboard from Seth Larson Test and Code Archive is now up, see announcement Michael: Python: The Documentary | An origin story is out! Joke: Do you know him? He is me.

In this special crossover between Hands On IT and Automate IT, hosts Landon Miles and Jeremy Maldonado dive into the building blocks of IT solutions. They share practical ways to approach automation without the overwhelm—starting from defining real-world problems and breaking them into manageable steps. Along the way, they explore Linux as a problem-solving tool, home lab tinkering, Docker, Proxmox, and the power of learning by experimenting (and breaking things!). Whether you're new to IT or ready to sharpen your automation mindset, this episode is packed with insights, inspiration, and actionable takeaways.Stay tuned for Part 2, where we dig into the tools, testing, and turning automations into solutions! Awesome-Selfhosted GitHub Link: https://github.com/awesome-selfhosted/awesome-selfhosted

AI executives from Archer, Demandbase and Highspot and AWS reveal how they're tackling AI's biggest challenges—from securing data, managing regulatory changes and keeping humans in the loop.Topics Include:Three AI leaders introduce their companies: Archer, Demandbase and Highspot's approaches to enterprise AIDemandbase's data strategy: Customer data stays isolated, shared data requires consent, public sources fuel trainingGeographic complexity: AI compliance varies dramatically between Germany, US, Canada, and California regulationsHighSpot tackles sales bias: Granular questions replace generic assessments for more accurate rep evaluationsSBI framework applied to AI: Specific behavioral observations create better, more actionable sales coachingAI transparency through citations: Timestamped evidence lets managers verify AI feedback and catch hallucinationsArcher handles 20-30K monthly regulations: AI helps enterprises manage overwhelming compliance requirements at scaleTwo compliance types explained: Operational (common across companies) versus business-specific regulatory requirementsEU AI Act adoption: US companies embracing European framework for responsible AI governanceHuman oversight becomes mandatory: Expert-in-the-loop reviews ensure AI decisions remain correctable and auditableThe bigger AI risk: Companies face greater danger from AI inaction than AI adoptionAgentic AI security challenges: Data layers must enforce permissions before AI access, not afterAI agents need identity management: Same access controls apply whether human clicks or AI actsHuman oversight in high stakes: Chief compliance officers demand transparency and correction capabilitiesFuture challenge identified: 80% of enterprise data behind firewalls remains invisible to AI modelsParticipants:Kayvan Alikhani - Global Head of Engineering- Emerging Solutions, Archer Integrated Risk ManagementUmberto Milletti - Chief R&D Officer, DemandbaseOliver Sharp - Co-Founder & Chief AI Officer, HighspotBrian Shadpour - General Manager, Security, Amazon Web ServicesFurther Links:Archer Integrated Risk Management: Website – LinkedIn – AWS MarketplaceDemandbase: Website – LinkedIn – AWS MarketplaceHighspot: Website – LinkedIn – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

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

In a fascinating discussion, Rob McGrorty, Product Leader of Agents at Amazon AGI Lab, reveals how rapidly AI agents are evolving with corporate adoption exploding as companies race to deploy production agents and the challenges and advantages they're experiencing.Topics Include:GenAI adoption outpaces all previous tech waves, growing faster than computers or internetEarly adopters tackle complex tasks while newcomers still use basic text manipulation featuresAI models double their single-call task capabilities every seven months, exponentially increasing powerAccelerating progress makes yesterday's magic mundane, unlocking mass creativity and customer demandAgents represent natural evolution: chatbots answered questions, now agents autonomously accomplish tasksAmazon's browser agent finds apartments, maps distances, ranks options using multiple transit modesCorporate adoption exploded: 33% piloting agents in 2024, 67% moving to production nowTwo main agent types today: API calling with tool use, browser automationCurrent applications mirror "RPA 2.0" - form filling, data extraction, website QA testingFuture brings multi-agent systems, self-directing loops, and agent-to-agent negotiation scenariosMajor challenges: data privacy, oversight protocols, error responsibility, and ecosystem sustainabilityTechnical hurdles include real-time accuracy measurement, latency issues, and quality assurance frameworksParticipants:Rob McGrorty – Product Leader, Agents at Amazon AGI LabSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Gagan Singh of Elastic discuses how agentic AI systems reduce analyst burnout by automatically triaging security alerts, resulting in measurable ROI for organizationsTopics Include:AI breaks security silos between teams, data, and tools in SOCsAttackers gain system access; SOC teams have only 40 minutes to detect/containAlert overload causes analyst burnout; thousands of low-value alerts overwhelm teams dailyAI inevitable for SOCs to process data, separate false positives from real threatsAgentic systems understand environment, reason through problems, take action without hand-holdingAttack discovery capability reduces hundreds of alerts to 3-4 prioritized threat discoveriesAI provides ROI metrics: processed alerts, filtered noise, hours saved for organizationsRAG (Retrieval Augmented Generation) prevents hallucination by adding enterprise context to LLMsAWS integration uses SageMaker, Bedrock, Anthropic models with Elasticsearch vector database capabilitiesEnd-to-end LLM observability tracks costs, tokens, invocations, errors, and performance bottlenecksJunior analysts detect nation-state attacks; teams shift from reactive to proactive securityFuture requires balancing costs, data richness, sovereignty, model choice, human-machine collaborationParticipants:Gagan Singh – Vice President Product Marketing, ElasticAdditional Links:Elastic – LinkedIn - Website – AWS Marketplace See 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: * pypistats.org was down, is now back, and there's a CLI* * State of Python 2025* * wrapt: A Python module for decorators, wrappers and monkey patching.* pysentry Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: pypistats.org was down, is now back, and there's a CLI pypistats.org is a cool site to check the download stats for Python packages. It was down for a while, like 3 weeks? A couple days ago, Hugo van Kemenade announced that it was back up. With some changes in stewardship “pypistats.org is back online!

Pete Rubio reveals how Rapid7 transformed to an AI-first platform that automates security investigations and accelerates results from hours to seconds.Topics Include:Pete Rubio introduces Rapid7's journey to becoming an AI-first cybersecurity platformCybersecurity teams overwhelmed by growing attack surfaces and constant alert fatigueCustomers needed faster response times, not just more alerts coming fasterLegacy tools created silos requiring manual triage that doesn't scale effectivelyAI must turn raw security data into real-time decisions humans can trustUnified data platform correlates infrastructure, applications, identity, and business context togetherAgentic AI automates investigative work, reducing analyst tasks from hours to secondsRapid7 evaluated multiple vendors, choosing AWS for performance, cost, and flexibilityNova models delivered unmatched performance for global scaling at controlled costsBedrock provided secure model deployment with governance and data privacy boundariesAWS partnership enabled co-development and rapid iteration beyond typical vendor relationshipsTransparent AI shows customers how models reach conclusions before automated actionsSOC analyst expertise continuously trains models with real-time security intelligenceGovernance frameworks and guardrails implemented from day one, not retrofitted laterFuture plans include customer AI integration and bring-your-own-model capabilitiesParticipants:Pete Rubio – Senior Vice President, Platform & Engineering, Rapid7Additional Links:Rapid 7 – LinkedIn - Website – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

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

In this unplanned and unfiltered conversation, we dive deep into network automation realities with Ivan Pepelnjak, networking’s long standing and independent voice from ipSpace.net. We explore why automation projects fail, dissect the tooling landscape (Ansible vs. Terraform vs. Python), and discuss the cultural barriers preventing enterprises from modernizing their networks. Ivan delivers hard truths about... Read more »

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

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

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