Podcasts about desktops

「デスクトップの片隅で自分だけの釣り場を育てる放置系フィッシング箱庭ゲーム「Desktop Fishing Garden」Steamページが公開！」　DCG Entertainmentは、2026年8月下旬にリリースを予定している放置系フィッシング箱庭ゲーム「Desktop Fishing Garden」のSteamページを公開、ウィッシュリスト登録受付を開始した。

Serious gamers, meet ASUS ROG XG. This SoundBytes show dives into how ASUS ROG XG's Thunderbolt dock lets handheld systems tap into big, cutting-edge GPUs, so you can enjoy high-resolution, modern titles far from your desk. The post HANDHELD GAMING WITH DESKTOP POWER! appeared first on sound*bytes.

Aleksi Paavola on reilut 10 vuotta teknologiayrittäjänä toiminut tekoälyrakentaja, joka on kouluttanut kielimalleihin niin Googlen Euroopan tiimiä kuin porukkaa Saudi-Arabiassa. Tässä jaksossa hän avaa avoimen lähdekoodin hengessä rakennetun Laki.ai MCP:n, joka yhdistää Finlexin oikeuslähteet suoraan tekoälyagentin käyttöön ja tekee luotettavasta suomalaisesta juridisesta tiedonhausta ilmaista. Keskustelu kulkee MCP:n perusteista aina siihen kysymykseen, tuoko ihminen enää lisäarvoa kehittyvälle tekoälylle. Sami ja Aleksi vertailevat Clouden, Codexin ja Copilotin työskentelytapoja, ruotivat Legoran ja Harvin kalliita suljettuja tuotteita ja pohtivat, miksi juuri Suomen juridiikassa yleiskäyttöinen malli lyö erikoistuneet kilpailijat. Lopuksi puhutaan Samanthasta, agenttiarmeijoista, lokaalista inferenssistä ja siitä, onko Euroopalla enää sananvaltaa tekoälyn aikakaudella.00:00 Aleksi Paavola kielimallien ja tekoälyn asiantuntija01:33 Gemini 3 ja Googlen panostukset02:27 Miksi Laki.ai MCP on iso parannus03:30 Juridisen dokumentaation haaste ja NDA-vertailu05:06 Finlexin datan vapautuminen ja Laki.ai06:36 Dokumenttien yhdistäminen toisiinsa07:47 Legitin Claude legal skills ja Aku Nikkola09:01 Mikä MCP on: USB-C-vertaus10:18 MCP verrattuna perinteiseen API-rajapintaan12:41 Mikä oikeasti on tekoälyagentti13:54 Kielimallin ja agentin ero15:57 Käytä aina parasta mallia18:17 The Bitter Lesson ja ihmisen kapeneva rooli20:35 Harnessin pienentyminen ohjelmistokehityksessä22:08 Legora ja Harvey eivät ole kovin hyviä26:27 Cloude ylivertainen Suomen juridiikassa28:00 Kauppakamarin välimiesmenettely ja suljettu data30:22 Skillit selitettynä ja Suomen legal skills32:48 Lavern ja kahden mallin väittely35:12 Lovablen fiksut oletusvalinnat40:13 Fable palaa ja mallin älykkyyden arvo45:14 Clouden ja GPT:n persoonaerot46:35 OpenAI:n data: Codex syrjäyttää chatin50:27 Eroon Copilotista, valitse ekosysteemi53:03 Desktop-sovellukset ja selaimen tulevaisuus55:22 Kuka on korvattavissa tekoälyllä58:11 Samantha, WhatsApp-agentti ja muisti1:03:29 Agenttiarmeija ihan peruslisenssillä1:08:00 Omavaraisuus ja lokaali inferenssi1:10:11 Laki.ai suositus ja tietoturvavaroitus1:13:14 EU-serverit, AI Act ja lopetus

Is Kimi K3 the shocker of 2026? Could be. Now, we have a new (soon to be) Open Model that's competing with Fable 5 and GPT-5.6, a feat few would have believed possible. And that was the only new and important drop this week in AI. Claude brought useful browser to the desktop, ChatGPT made a big fix to how ChatGPT Work works and Google rolled out avatars that could change content creation. Don't miss our Friday Features show, where we recap the most important AI updates and features you can use today. Claude Desktop Gets Upgrade, New Open Source Model Shocks, ChatGPT Desktop Gets Better and 7 More AI Features You Can Use Today -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Anthropic Claude Desktop App Browser UpgradeOpenAI ChatGPT Work Desktop App ImprovementsChatGPT Universal Search Feature LaunchSuperhuman Email Auto-Draft with GPT-4Spotify AI Voice/Text Conversation FeatureGemini Omni Personal Avatar Video CreationGoogle Vids Integration with Personal AvatarsMoonshot Kimmy K3 Open Source Model ReleaseKimmy K3 vs Fable 5 and GPT-5.6 BenchmarksTimestamps:00:00 New open source AI model release03:41 Microsoft Copilot and Claude app updates07:22 Improving chat history search12:30 Spotify's data personalization benefits14:52 Launching Google Avatar Feature18:24 Mainstream avatar video tools21:33 Improved ChatGPT project syncing24:15 Introducing Kimmy K Three Model29:30 New Kimmy k three for enterprises30:45 Friday feature show wrap-upKeywords: Claude desktop, Claude desktop upgrade, open source AI model, proprietary AI, open vs closed AI, Anthropic, built-in browser, Claude app, API docs, browser integration, permissions card, security layers, ChatGPT desktop app, OpenAI, universal search, ChatGPT search, chat history, project sync, mobile AI apps, Codex, ChatGPT work, Codex mode, Superhuman mail, auto draft, Anthropic Frontier models, GPT-3.5, Gmail integration, Outlook integration, Spotify, Talk to Spotify, personalized AI conversation, Gemini Omni, Google Gemini, personal avatars, Google Vids, video editing AI, video avatars, L&D AI, content creation with AI, Kimi k3, Moonshot AI, 2.8 trillion parameter model, 1 million token context, vision mode, benchmark leaderboards, Fable 5, GPT 5.6, Opus 4.8, open model weights, self-host AI, enterprise AI solutions, long context AI, front-end design AI, subscription AI tools, API pricing, AI benchmark, arena rankingsSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Join The Full Nerd gang as they offer level-headed takes about the latest PC building news. In this episode the gang debates between having a gaming laptop or building your own desktop, provides an update to how the charity sale at the Bay Area Electronics Flea Market went, and so much more. And of course we answer questions live! Timecodes: 00:00:00 - Intro 00:02:47 - Flea market sales for charity 00:25:22 - Debate: laptop vs desktop vs mini PC 01:13:23 - Q&A Join the PC related discussions and ask us questions on Discord: https://discord.gg/UWhjwg778a Follow the crew on X and Bluesky: @AdamPMurray @BradChacos @MorphingBall Some links may contain affiliate links, which means if you buy something PCWorld may receive a small commission. ============= Read PCWorld! Website: http://www.pcworld.com Newsletter: http://www.pcworld.com/newsletters/signup ============= Learn more about your ad choices. Visit megaphone.fm/adchoices

Habt ihr auch manchmal das Gefühl, dass sich der aktuelle Formfaktor von KI noch ein bisschen so anfühlt wie „Kutschen mit Motor“? Dann geht es euch wie dem Berliner KI-Experten und Pandata-Gründer Marco Szeidenleder – und er hat sich deshalb gefragt: Wie sieht das Arbeiten der Zukunft aus, wenn wir KI nicht mehr nur als nettes Add-on in bestehende Tools klatschen, sondern Software von Grund auf AI-first denken? Also hat er PandaOS gegründet und berichtet im Gespräch mit Christoph, wie die Desktop-Anwendung E-Mails, Browser, Coding-Tools und lokale Datenbanken nativ miteinander verknüpft – und wie sie es schafft, dass US-Sprachmodelle Analysen auf Unternehmensdaten fahren können, ohne jemals die echten sensiblen Daten zu Gesicht zu bekommen. Außerdem verrät er, warum ein Data Warehouse eine „Liability“ sein kann und mit welcher Kombination aus Cloud-Datenbank, DBT und Visualisierungstool Unternehmen die Hoheit über ihre Daten behalten.

Time to ramble about the Steam Machine red line situation, the state of the Linux desktop and the stalling of the Adelaide game developer scene. ==========Support The Channel==========► Patreon: https://www.patreon.com/brodierobertson► Paypal: https://www.paypal.me/BrodieRobertsonVideo► Amazon USA: https://amzn.to/3d5gykF► Other Methods: https://cointr.ee/brodierobertson==========Support The Show==========► Patreon: https://www.patreon.com/brodierobertson► Paypal: https://www.paypal.me/BrodieRobertsonVideo► Amazon USA: https://amzn.to/3d5gykF► Other Methods: https://cointr.ee/brodierobertson=========Video Platforms==========

This leaked video shows Microsoft's AI centric vision for a future OS. No native (legacy) Windows software. All web based, and all AI.NeXTStep, Emacs, & Desqview/X Walls:https://lunduke.substack.com/p/vim-beats-emacsGet on The Wall with a Massively Discounted Lifetime Sub:https://lunduke.substack.com/p/50-off-yearly-and-massively-discountedMore from The Lunduke Journal:https://lunduke.com/ This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit lunduke.substack.com/subscribe

MPC Software DesktopはAbleton Liveで動かすと熱くならなくていい話今日のビートYouTube https://www.youtube.com/@katsuOfficialnote https://note.com/kermit71/TikTok ⁠⁠https://www.tiktok.com/@katsu_beat⁠⁠instagram https://www.instagram.com/kermit71【今日のキーワード】ROLAND SP-404 MKII / SP-404 MK2 / AKAI PROFESSIONAL MPC ONE / ビートメイク / コーヒー / Teenage Engineering EP-133 K.O.II / Ableton Move

Brenda Albeno, GRC Manager at Loeb & Loeb, joined host Heather Morrow for the She Secures podcast to share her journey from IT operations into cybersecurity. Her entry into the field began with an unexpected project: building a physical asset inventory from scratch after the firm had none. What she assumed would be a quick task turned into weeks of detective work, uncovering laptops stashed under beds, cribs, and even in bathrooms. The director of security was so impressed with both the outcome and her approach that he invited her to join the security team. Fifteen years later, she's still in the field. Brenda is candid about the steep learning curve she faced, crediting late nights of self-study, her Security Plus certification, and the mentorship of her director, Mark Ewing Chao, as the pillars that carried her through. She describes him as someone who believed in her before she fully believed in herself. Today, Brenda's passion is governance, risk, and compliance. Her days are a constant juggle of vendor assessments, client security questionnaires, policy reviews, and cross-functional collaboration with IT, legal, and leadership. She sees herself as a translator between technical teams and legal stakeholders, focused on building programs that people will actually follow, not just policies that look good on paper. She closes with a message for women in and around the industry: she wishes more people, especially women, were exposed to cybersecurity earlier in their careers, so they could see just how many different paths the field has to offer.

Talking about prompts and chatbots won't help you talk about AI strategy in 2026. You've gotta know the ins and outs of loops, plans, goals, subagents and more. In this episode of Everyday AI, we're breaking down the agent lingo and how the key terms play out in systems like Codex and Claude Desktop. Desktop Agent Lingo Simplified: Goals, Loops, Plans, Subagents and how it works in Codex and Claude Code -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Desktop Agent Vocabulary PrimerAgent Harnesses: Codex vs. Claude CodeDesktop Agent Plans: Features and WorkflowGoal Setting in Codex and Claude DesktopPlan vs. Goal: Key DifferencesAgent Loops: Automation and VerificationSub Agents: Parallel Task ManagementContext Windows and Task DelegationGuardrails, Verification, and Cost ControlTransition from Chatbots to Autonomous AgentsTimestamps:00:00 Shifting focus to AI agents03:28 Accessing the Start Here series09:31 Using plan mode in clawed desktop12:04 Understanding plan vs. goal mode14:25 Setting project goals and planning19:33 Accessing Start Here series22:03 Building effective training loops26:48 Managing sub agents effectively27:30 Setting up sub-agent system30:47 Closing and subscription reminderKeywords: desktop agent, desktop AI agent, agent lingo, agent vocabulary, long running agent, autonomous agent, codex, Claude Code, Claude desktop, AI harness, agentic harness, agentic tools, super app, Microsoft super app, OpenAI codex, long running desktop agents, plan mode, planning phase, agent plan, goal setting, AI goal, agent goals, loop mode, agent loops, scheduled automations, sub agents, agent subagents, context windows, parallel work, context hygiene, verification steps, approval points, skills, automations, API token usage, project threads, co work tab, code tab, work trees, checkpoints, file access, browser automation, human in the loop, token efficiency, agent delegation, AI supervision, knowledge work automation, AI subagent management, desktop agent mental model, computer control, AI project management, AI workload delegation, remote steering, front end chatbot, proactive AI, AI context sharing.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

Intuit's Q3 fiscal 2026 earnings call was packed with numbers, and with her co-hosts sitting this one out, Alicia goes through them one by one to explain what each actually signals for bookkeepers, accountants, and QuickBooks users. She breaks down the strength of QuickBooks Online and mid-market growth, the slowdown in Desktop and Mailchimp, AI already running at scale, the shift toward assisted tax, and what the August pricing and packaging changes mean for your clients. She also steps off-script to share why she thinks Intuit raised prices before users were ready, and previews the deeper episodes coming on pricing and the ProPartner program.Sponsors:Aqqrue - http://uqb.promo/aqqrueSTR Search - http://uqb.promo/str(00:00) - Welcome and Setup (01:25) - How to Read Earnings (02:49) - Companywide Results (05:09) - QuickBooks Segment Growth (08:15) - QBO Plans and Pricing (09:33) - Services Payments Payroll (12:14) - Desktop and Migration (13:18) - Mid Market Enterprise Push (15:02) - Mailchimp Reality Check (16:53) - AI at Scale (17:40) - August Pricing Shakeup (23:07) - Accountants as Customers (24:51) - Workforce Cuts and Margins (26:41) - TurboTax Trends (30:43) - Credit Karma Monetization (34:13) - Pro Tax and Wrap Up (35:58) - Training Course and Goodbye LINKSJuly 21 through October 8: HANDS-ON QUICKBOOKS TRAINING COURSE, http://royl.ws/HOT2026?affiliate=5393907Alicia's book on Amazon: http://royl.ws/conversion-bookWe want to hear from you!Send your questions and comments to us at unofficialquickbookspodcast@gmail.com.Join our LinkedIn community at https://www.linkedin.com/groups/14630719/Visit our YouTube Channel at https://www.youtube.com/@UnofficialQBOPodcastSign up to Earmark to earn free CPE for listening to this podcasthttps://www.earmark.app/onboarding 

Well well, what do we have here?   Another episode of the PCPer Podcast for your enjoyment!  Commodore is making phones, Steam Machine is starting to make waves, iGPUs are getting dropped, 12vHPWR is making lightning, a very special segment on pricing of olde, a critical Copilot vulnerability (suprise?), and a Bitlocker Zero Day.  Steam hardware survey, brown cooling, and the infamous Podcast sound board.Relax and take it all in.Timestamps:0:00 Intro1:06 Patreon5:12 Josh fails to deliver a food segment6:31 Steam Machine benchmarked8:06 Zen 6 desktop CPUs may lose iGPU, add NPU11:16 Intel and NVIDIA collaborate on upcoming processor12:51 A trip down memory (and storage) lane18:37 AMD giveth, and AMD taketh away22:19 Noctua NL-LC1 liquid cooler reviewed at TPU26:08 Handheld with Arc G3 Extreme pricing28:22 IQUNIX Ghost in the Shell hall effect keyboards30:24 Commodore Callback34:53 Logitech folding mouse38:40 Another melting 12VHPWR connector story42:02 (In)Security Corner53:44 Gaming Quick Hits1:05:52 Picks of the Week1:16:45 Outro ★ Support this podcast on Patreon ★

In Season 2, Episode 1 of The Citrix Session, XenTegra's Andy Whiteside, Bill Sutton, and Randy Price are joined by Citrix's Mathew Varghese to discuss Citrix DaaS Flex and how it's transforming desktop modernization. The conversation explores persona-based computing, simplified cloud adoption, infrastructure management, security responsibilities, and how organizations can optimize costs while delivering the right user experience for every employee. Learn how Citrix is rethinking Desktop-as-a-Service by combining managed infrastructure, intelligent workload sizing, and operational simplicity into a modern platform built for today's digital workspace.

Rob Moffat (Chief Architect at FINOS) maps out the intersection of workspace interoperability, open-source AI deployment, and multi-cloud security frameworks. He compares MCP (Model Context Protocol) with FDC3, tracks the rollout of the Common Cloud Controls (CCC) live validator tool, and reveals how open-source standards prevent multi-vendor lock-in at the desktop and infrastructure layers.

You're using your computer wrong, says comedian Alan Resnick. In an absurdist talk, he offers a simple solution to data leaking from your desktop (and desk's top). Hosted on Acast. See acast.com/privacy for more information.

VDI (Virtual Desktop Infrastructure) and Desktop as a Service (DaaS) have been arriving “real soon now” for the past couple of decades. Will the advent of vendors' AI spyware (as Google is introducing through Chrome) be the accelerant that finally makes it happen? John and Johna discuss why the challenges in this brave new AI-enabled... Read more »

VDI (Virtual Desktop Infrastructure) and Desktop as a Service (DaaS) have been arriving “real soon now” for the past couple of decades. Will the advent of vendors' AI spyware (as Google is introducing through Chrome) be the accelerant that finally makes it happen? John and Johna discuss why the challenges in this brave new AI-enabled... Read more »

VDI (Virtual Desktop Infrastructure) and Desktop as a Service (DaaS) have been arriving “real soon now” for the past couple of decades. Will the advent of vendors' AI spyware (as Google is introducing through Chrome) be the accelerant that finally makes it happen? John and Johna discuss why the challenges in this brave new AI-enabled... Read more »

Welcome to Lords of Limited, the podcast dedicated to getting you better at drafting in Magic: the Gathering. This week, we've got the Marvel Super Heroes Crash Course to get you everything you need to know for your first limited event of the format! We've got big picture format truths, color and archetype identities, top commons and uncommons, as well as sleeper commons, and a live discovery that turns Red from our least favorite color to something we're actively interested in!

On this episode of Data Driven, hosts Frank La Vigne and Candace Gillhoolley are joined by hardware and open source expert Michael Makowski to discuss the shifting landscape of developer workstations and AI hardware. As Windows usage declines among developers and AI engineers, Linux is experiencing a surge in desktop adoption. Michael takes us inside the latest efforts to make Linux not just accessible, but enterprise-grade—sharing how his team is driving advancements in stability, reliability, and user experience for validated Linux hardware.We talk about the dramatic improvements in Linux desktop support, the importance of privacy and avoiding surveillance-driven proprietary systems, and the game-changing features coming to market—like automated system rollback and curated app installs. Plus, we explore the current state of gaming on Linux, the technical edge unified memory brings to AI development, and why companies are increasingly opting for supported, Linux-based workstations. Whether you're Linux-curious, rethinking your hardware choices, or just passionate about the future of developer tools and data engineering, this conversation will equip you for what's next.LinksMike's Company Website - https://kfocus.org/Mike's LinkedIn Profile - https://www.linkedin.com/in/michael-mikowski-7601393/Watch this show on YouTube - https://youtu.be/E03EObEa2lQTime Stamps00:00 Website security concerns and solutions05:03 Supporting KDE for long-term stability09:45 Desktop environment compatibility issues10:46 Conflicts in desktop environments15:07 AMD vs Intel & Nvidia Performance18:58 Showing the production site23:49 Steam's Linux runtime environment25:43 Running Windows games on Linux29:47 Concerns about software privacy issues33:31 Migrating from Windows challenges37:48 Setting up machine learning hardware41:31 Resolving system issues efficiently42:59 Setting up a VPN correctly47:40 Running VMs on alternative OS52:49 Upcoming OS Upgrade Details56:19 Rigorous testing and development process57:25 Tuning BTRFS for performance

Everyone is talking about Mercury-alpha, the mystery model that many believe could be GPT-5.6.In this live discussion, we're separating fact from speculation and unpacking what would actually matter if OpenAI releases a new flagship model this week.We'll cover:

The Head of Instagram announced the teleprompter feature from Edits is now in the Instagram app, then answers questions about when the Edits desktop app is coming, the best times to post, whether or not to have 2 Instagram accounts or just one.   Links: Instagram: Instagram introduces teleprompter tool  (Social Media Today) Links:        Leave a Review of the Podcast: Apple Podcasts   Connect with me on Instagram: @danielhillmedia Connect with me on Threads: @danielhillmedia Connect with me on YouTube: @danielhill_media   Leave a Review of the Podcast: Apple Podcasts     Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Anthropic reicht das Börsenprospekt ein. Pip erklärt, warum Anthropic unbedingt vor OpenAI rausgehen muss. In Lenny Rachitskys Umfrage unter Tech-Profis ist Anthropic mit Abstand der Lieblings-Arbeitgeber. Google macht eine Kapitalerhöhung über $80 Mrd. statt wie üblich Aktien zurückzukaufen. Nvidia-CEO Jensen Huang sagt mit einer einzigen Aussage die SaaSocalypse ab und schickt Software-Aktien auf eine Rally. Nvidia greift mit RTX Spark Intel und AMD im PC-Markt an. Die Chicago Mercantile Exchange launcht AI-Token-Futures wie für Gold und Öl. Short-Seller Andrew Left wird wegen Marktmanipulation verurteilt. Bloomberg deckt auf, wie der SpaceX-IPO die S&P-500-Regeln zur Profitabilität aushebelt. Antonio Gracias wird durch den IPO zum Milliardär, sein Off-Balance-Sheet-Konstrukt mit SpaceX wird zum Streitthema. US Space Force vergibt $4,16 Mrd. an SpaceX für den Golden Dome. Instagram-Accounts werden gehackt, indem Hacker einfach Meta AI fragen. Salesforce kauft das Berliner Startup Contentful und hält selbst inzwischen einen $5-Mrd.-Anteil an Anthropic. Anthropic gibt der EU-Cybersecurity-Agentur ENISA Zugang zu Mythos. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Anthropic reicht IPO-Prospekt ein (00:18:14) Anthropic ist Lieblings-Arbeitgeber (00:20:17) Google macht $80-Mrd.-Kapitalerhöhung (00:27:04) Jensen Huang sagt SaaSocalypse ab (00:33:47) Nvidia RTX Spark gegen Intel/AMD (00:39:46) AI-Token-Futures an der CME (00:43:06) MiniMax M3: China-Modell für 5-10% des Preises (00:46:01) HPE (00:47:13) Peter Thiel zieht nach Argentinien (00:49:32) SpaceX-Skeptiker: Musk vs. eigenes IPO-Filing (00:55:25) SpaceX-IPO biegt S&P-500-Regeln (00:59:29) Antonio Gracias und SpaceX' Off-Balance-Sheet-Konstrukt (01:07:28) US Space Force vergibt $4,16 Mrd. an SpaceX (01:09:15) Instagram-Hack via Meta AI (01:13:24) Salesforce kauft Berliner Contentful (01:18:17) Anthropic gibt EU/ENISA Zugang zu Mythos (01:19:33) Salesforces Anthropic-Stake auf $5 Mrd. Shownotes Anthropic-Ankündigung - xcancel.com Lenny Rachitsky: Ergebnisse einer Umfrage zu AI-Tools - linkedin.com Alphabet - ft.com Nvidia RTX Spark N1/N1X: AI-CPU/GPU für Laptops und Desktops - theverge.com Nvidia-CEO Jensen Huang zerstreut SaaSocalypse-Sorgen - wsj.com AI-Token-Futures kommen wie Gold und Öl - techcrunch.com MiniMax M3: Schlägt GPT-5.5 und Gemini 3.1 Pro bei 5-10% der Kosten - venturebeat.com HPE shares soar 37% - ft.com NYT: Warum Peter Thiel sich auf ein Leben nach Amerika vorbereitet (Argentinien) - nytimes.com SpaceX-Skeptiker: Musks Aussagen weichen vom IPO-Filing ab - cnbc.com Short-Seller Andrew Left wegen Wertpapierbetrug verurteilt - bloomberg.com SpaceX-IPO zwingt Indexfonds und Retail, die Regeln zu ändern - bloomberg.com Hedgeye-Tweet zu SpaceX/Markt - xcancel.com Fortune: SpaceX-IPO macht Musks Freund Antonio Gracias zum Milliardär - fortune.com US Space Force vergibt $4,16-Mrd.-Vertrag an SpaceX - reuters.com Coinbase und Kalshi launchen regulierte Perpetual-Krypto-Futures - reuters.com Hacker bekommen Zugang zu High-Profile-Instagram-Accounts durch Meta AI - 404media.co Gergely Orosz Tweet - xcancel.com Jane Wong Tweet - xcancel.com Salesforce übernimmt Berliner Startup Contentful - manager-magazin.de Salesforce kauft Contentful: Headless CMS für Agentforce - thenextweb.com Anthropic gibt EU-Cybersecurity-Agentur Zugang zu Mythos - bloomberg.com Salesforces Anthropic-Investment auf rund $5 Mrd. bewertet - bloomberg.com

Apple Intelligence translation, taming your Desktop, fixing 4K Mac displays, iCloud email tips, and the Plex price hike.

In healthcare, every second at a workstation matters. But who's actually measuring the time between a badge tap and a clinician being ready to work?This session explores Tap to App — a feature built to solve one of healthcare IT's most frustrating invisible problems: the gap between when a clinician taps their badge at a workstation and when their full workflow is actually ready to go.Whether it's an ER nurse moving between stations or a physician ducking out to grab coffee and returning to a reconnect, the login and reconnect experience directly impacts patient care. The challenge? Desktop, VDI, and identity teams all operate in silos — and measuring an experience that spans all of them has historically been nearly impossible.In this episode, you'll learn:What Tap to App is and how it was born out of a real healthcare customer challengeWhy login and reconnect are two fundamentally different experiences — and why averaging them together is misleadingHow to measure the full clinician journey: from badge tap → identity provider → broker → VDI session → application readyWhat a real-world deployment looks like, with metrics broken down by login vs. reconnect, device, user, and time of dayHow to identify outliers (like a 56-second reconnect on a specific device) and drill down to root causeWhy reconnect speed is the metric you should be optimizing — and how sessions reconnecting in under 6 seconds are achievableIf you support healthcare environments running VDI and want to move beyond anecdotal complaints toward data-driven performance optimization, this one is essential viewing.

Nvidia heeft tijdens de eigen goed-nieuwsshow GTC, gehouden rond techbeurs Computex in Taiwan, nieuwe chips aangekondigd die juist voor apparaten van eindgebruikers zelf bedoeld zijn. Het betreft de RTX Spark, voorzien van zowel een CPU als een GPU, om te concurreren met AMD en Intel. Joe van Burik vertelt erover in deze Tech Update. Verder in deze Tech Update: Softbank, bekend van investeringen in OpenAI, gaat tientallen miljarden in AI-datacenters in Frankrijk steken See omnystudio.com/listener for privacy information.

Este boletim traz um resumo das principais notícias do dia na análise de Samuel Possebon, editor chefe da TELETIME.TELETIME é a publicação de referência para quem acompanha o mercado de telecomunicações, tecnologia e Internet no Brasil. Uma publicação independente dedicada ao debate aprofundado e criterioso das questões econômicas, regulatórias, tecnológicas, operacionais e estratégicas das empresas do setor. Se você ainda não acompanha a newsletter TELETIME, inscreva-se aqui (shorturl.at/juzF1) e fique ligado no dia a dia do mercado de telecom. É simples e é gratuito.Você ainda pode acompanhar TELETIME nas redes sociais:Instagram: https://www.instagram.com/teletimenews/Linkedin: https://www.linkedin.com/company/teletimenews/Facebook: https://www.facebook.com/Teletime/ Ou entre em nosso canal no Telegram: https://t.me/teletimenews Hosted on Acast. See acast.com/privacy for more information.

Pentecost was never meant to produce comfortable, nominal Christianity. In this Pentecost homily, I explore how Acts 2 connects back to Mount Sinai, John the Baptist's warning to Israel, and the danger of presuming we belong to God while remaining unchanged. The fire of Pentecost was not about emotional hype—it was about repentance, surrender, holiness, and the transforming presence of the Holy Spirit in our lives. If you've ever wondered whether modern Christianity has lost the weight and urgency of the early Church, this message is for you. That You May Know Him, Episode 299.

Every wish on your AI wish list?

This episode is a re-air of one of our most popular conversations, featuring insights worth revisiting. This week on The Data Stack Show, Brooks and John chat with Andy MacMillan, CEO of Alteryx. Andy discusses the evolving landscape of data and AI, focusing on empowering business users to solve complex problems. He explores the concept of "citizen developers" and how tools like Alteryx can bridge the gap between IT and business teams by democratizing data access. The conversation also emphasizes the importance of creating controlled environments where business users can leverage cloud data platforms and AI technologies to reimagine workflows, without bypassing governance. Key takeaways include the need for organizations to enable innovation through accessible data tools, the potential of AI-driven agents to transform business processes, the critical role of employees who understand their business functions in driving technological transformation, and so much more. Highlights from this week's conversation include: Andy's Background and Journey in Data (0:54) Early Web Development at General Motors (2:23) AI Challenges in the Enterprise (9:03) What is Alteryx and Its Value Proposition (11:25) The Importance of Empowering Business Users (16:10) Bridging the Gap Between Data Platforms and Business Users (20:04) Evolution from Desktop to Data Cloud (25:28) Access and Governance in the Cloud Era (27:57) The Return of Local Data Work and AI Governance (31:24) AI Data Clearinghouse and Governance (34:11) AI-Enabled Workflows and Business Impact (38:13) The Future: Agents, Data Platforms, and Business Logic (41:05) How to Get Started with Alteryx or Learn More (46:54) Product Management Lessons for Leadership and Parting Thoughts (47:56) The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it's needed to power smarter decisions and better customer experiences. Each week, we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data. RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Some of the most useful new Illustrator features are easy to miss if you aren't actively exploring the latest updates and beta tools. In this episode, Theresa Jackson talks with illustrator and toy designer Thaddeus Coates and digital artist Luke Choice about the new Illustrator features they're genuinely excited to use. Thaddeus shares how Illustrator fits into his character design and toy-making process, including using Turntable to communicate ideas to manufacturers. Luke dives into the practical side of Illustrator updates, from snapping improvements and smoother performance to AI-assisted vector workflows inside Illustrator Beta. Whether you spend your days designing logos, illustrating characters, building client work, or experimenting with new ideas, there's probably a feature in this episode you haven't tried yet. Episode Highlights Prototyping with Turntable: Hear how Thaddeus Coates uses Turntable to prototype toy ideas and communicate designs to manufacturers faster. Live Preview Tools: Learn why Illustrator's Live Preview drawing tools feel more natural and responsive for illustrators working on iPad and desktop. Workflow Efficiency: Hear Luke Choice explain how small workflow improvements, including font browsing and snapping updates can save serious production time. Sketch to Vector: Explore how Sketch to Vector in Illustrator Beta is changing the way artists turn rough sketches and low-resolution artwork into editable vectors. Real-World Application: Hear both guests share examples of using Illustrator's newest features for experimentation, iteration, and client work. Resources CreativePro Week 2026: Nashville, June 29–July 3, 2026. https://creativeproweek.com/ CreativePro Events: https://creativepro.com/events/ Event Savings: Save $100 on any CreativePro event in 2026 with the discount code PODCAST: https://creativepro.com/events/ Membership Discount: Get $15 off one year of CreativePro membership with the discount code PODCAST: https://creativepro.com/become-a-member/ Adobe Help: What's New in Illustrator on the Desktop: https://helpx.adobe.com/illustrator/desktop/new-features/whats-new.html Thaddeus Coates: https://www.hippypotter.com/ Luke Choice: https://www.velvetspectrum.com/

By Doug Green “We're really innovating the modern desktop, and this is just the beginning of the roadmap we've got planned,” said Matt Dargis, CRO of Plugable. In a Technology Reseller News podcast, I spoke with Lynn Murphy, CEO of Plugable, and Matt Dargis, CRO of Plugable, about how the company is expanding from its leadership in docking stations and PC peripherals into a larger channel opportunity built around the modern desktop, hybrid work, fleet refresh, and local AI. Plugable, founded in 2009 in Redmond, Washington, is best known as a leading third-party docking station provider in North America. The company has built its reputation around deep compatibility testing, especially in mixed environments where businesses may be supporting different laptop brands, monitor types, operating conditions, and end-user needs. Murphy said that mixed environments are now the norm. From 4K and 8K monitors to diverse laptop fleets and hybrid workplace setups, businesses need products that simply work. That has become a defining part of Plugable's value proposition: reducing complexity at the desktop and helping partners deliver reliable solutions. The channel opportunity is expanding as organizations refresh aging fleets, prepare for AI-enabled workstations, and rethink the desktop as a productivity platform. Murphy noted that Plugable's recent minority investment from Acer Gadget will help the company scale faster, expand internationally, and accelerate new product categories. One of the most important areas of focus is local AI. Plugable has launched a secure local AI enclosure with a software stack designed to enable plug-and-play AI at the desk. Murphy said this gives partners a way to help customers begin with proofs of concept and move toward broader adoption, especially where repetitive workloads, private data, or compliance concerns make local AI attractive. “There is going to be a portion of the spend that moves to local, and that is repetitive and private data,” Murphy said. For MSPs and channel partners, the opportunity is not only in hardware sales but also in integration, support, managed services, proof-of-concept work, and ongoing customer engagement. Murphy pointed to use cases in law firms, public sector organizations, federal environments, doctor's offices, and distributed enterprises where local AI may offer a practical complement to cloud AI. Dargis said Plugable is a channel-first company and is investing in resources to create demand for partners. That includes evaluation units, public sector and enterprise support, government vehicles, K-12 contracts, and partner selling motions designed to bring opportunities back to the channel. “We view it as our job to embrace and engage with the customers and help the channel versus rely on the channel to do all that work,” Dargis said. The company is also focused on making the category easier for partners to sell. Plugable sees peripherals not as simple accessories, but as part of a broader desktop strategy involving productivity, asset management, compatibility, and support. For partners that may not yet be comfortable selling in this category, Dargis said Plugable is inviting conversations. The company's roots in digital commerce, customer education, and compatibility-driven support give it a foundation for helping partners serve everyone from small offices to global enterprises. As the workplace continues to change, Plugable is positioning the modern desktop as a growth opportunity for the channel. The company's message is that docks, peripherals, fleet refresh, and local AI are converging into a new desktop conversation—one that partners can lead. Learn more at: https://plugable.com

Special discounts up for AIE Melbourne (LS discount) and AIE World's Fair (group discounts up to 25% - CFPs still open for Autoresearch and Vertical AI) Cya there!Abridge did not start as an “GPT wrapper”. It was founded in 2018, years before the Cambrian explosion of AI application layer companies. OpenAI launched ChatGPT publicly on November 30, 2022 and by then, Abridge had already spent years doing the unglamorous work of building trust for one of the highest context, most important workflows in healthcare: the conversation between a patient and a clinician.Abridge's original wedge was clinical documentation. Listen to the visit, generate the note, reduce the clerical burden, and let clinicians spend more time with patients instead of the EHR. By focusing on how doctors actually document, how health systems actually buy, how EHR integration actually works, how clinicians verify outputs, and how missing context during a visit turns into downstream friction across billing, prior authorization, quality, and follow-up, the adoption of LLMs became a force multiplier on a workflow already optimized for sensitive context gathering.The company has scaled fast: Abridge says it is projected to support 80M+ patient-clinician conversations this year across 250 large and complex U.S. health systems, with support for 28+ languages and 50+ specialties. It raised $300M at a $5.3B valuation in June 2025, after a $250M round earlier that year.Today, Janie Lee and Chaitanya “Chai” Asawa of Abridge join us for another crossover pod with Redpoint's Jacob Effron (who is on the board of Abridge) to dive into how Abridge is building the clinical intelligence layer for healthcare starting with ambient documentation, then expanding into clinical decision support, prior authorization, payer/provider/pharma workflows, and eventually real-time agents that act before, during, and after the patient conversation. We go inside the product, data, infra, evals, workflow, privacy, and org design choices behind bringing AI into one of the highest-stakes enterprise environments from 100M+ medical conversations and specialty-specific evals to real-time alerts, EHR integration, de-identification, clinician-scientist teams, and why healthcare may solve some of the hardest AI problems first.We discuss:* Why Abridge started with clinical documentation, “pajama time,” and saving clinicians 10–20 hours a week* The transition from ambient scribe to clinical intelligence layer: save time, save money, and save lives* Why conversations between patients and clinicians may be the most important workflow in healthcare (patient visit summary feature)* Chai's “healthcare-coded Glean” framing: context is king, but healthcare raises the stakes on safety, evals, and rollout* Why Abridge wants AI to feel like “air conditioning”: always in the background, but only interrupting when it truly matters* The prior authorization example: turning a denied MRI weeks later into real-time guidance while the patient is still in the room* Why payer policies, EHR data, medical literature, and hospital-specific guidelines make the problem hard, and also create the moat* How Abridge thinks about ambient form factors: mobile, desktop, in-room devices, nursing workflows, multimodality, and future AR* The multi-sided healthcare customer: CMIOs, CFOs, CIOs, clinicians, patients, payers, and pharma* The hardest AI problem at Abridge: high-quality, low-latency, low-cost real-time support in a high-stakes clinical setting* When Abridge uses frontier models vs proprietary models, and why its unique data from medical conversations matters* Why “every agent is a coding agent underneath,” and how the EHR can be thought of as a filesystem for healthcare agents* How Abridge approaches personalization across individual doctors, specialties, and health systems* Why “AI slop” is AI without context, and how edits, memories, and clinician preferences create a data flywheel* Abridge's eval stack: LFDs, LLM judges, in-house clinicians, third-party evaluators, specialty-specific evals, and progressive rollout* HIPAA, PHI, de-identification, one-way anonymization, customer contracts, and learning from healthcare data safely* What changes when you operate at 100M+ conversations: reliability, cost, post-training, model routing, and infrastructure optimization* Why the same clinical conversation can serve doctors, patients, payers, pharma, and future clinical-trial workflows* How Abridge works with EHRs, and why deep interoperability is table stakes for clinician adoption* Why healthcare AI has regulatory tailwinds, why 80/20 does not work here, and why high-stakes domains may drive AI forward* Why Abridge embeds “clinician scientists” into product and eval teams* What Chai learned from Glean about search, quality, and durable AI infrastructure* Why the future of AI infra may look like context layers, event-driven systems, Kafka, Temporal, sockets, CRDTs, and tools built for humans* Why Janie changed her mind on “PRDs are dead,” and why crisp written clarity matters more in complex AI products* How Abridge uses Claude Code, Cursor, and coding agents internallyAbridge:* Website: https://www.abridge.com/* X: https://x.com/AbridgeHQJanie Lee:* LinkedIn: https://www.linkedin.com/in/janiejleeChaitanya “Chai” Asawa:* LinkedIn: https://www.linkedin.com/in/casawaTimestamps00:00:00 Introduction and what Abridge does00:02:05 From ambient documentation to clinical intelligence00:04:04 Clinical decision support and context as king00:06:57 Alert fatigue, proactive intelligence, and prior authorization00:12:36 Ambient AI form factors and healthcare customers00:16:59 The hardest AI problems in healthcare00:18:26 Frontier models, proprietary data, and model strategy00:21:07 The EHR as a filesystem for agents00:24:03 Personalization, memory, and clinician preferences00:30:40 Evals, LLM judges, and progressive rollout00:36:47 HIPAA, de-identification, and privacy00:39:21 100M conversations and operating at scale00:44:10 EHR integration and the clinical intelligence layer00:46:39 Healthcare regulation, latency, and high-stakes AI00:50:11 Clinician scientists and long-tail quality00:53:04 Lessons from Glean and durable AI infrastructure00:57:03 The future of agentic healthcare workflows00:57:34 PRDs, product clarity, and building serious AI products01:03:11 AI coding tools at Abridge01:04:06 OutroTranscriptIntroduction: Abridge, Clinical Intelligence, and the Latent Space x Unsupervised Learning CrossoverSwyx [00:00:00]: Okay. This is a special crossover Latent Space Unsupervised Learning pod.Jacob [00:00:07]: Very excited to do this.Jacob [00:00:08]: At this point, we get together once a year.Swyx [00:00:10]: Once a yearJacob [00:00:11]: And this is a fun occasion to get to do it on.Swyx [00:00:13]: I really wanted to talk to Abridge but I felt very underqualified because healthcare is not something we cover very intensely. It just so happens that Redpoint's our big investors and supporters of Abridge.Jacob [00:00:27]: Anytime you want to have a portfolio company on your podcastJacob [00:00:29]: Please, by all means.Swyx [00:00:31]: So we'll introduce our guests. Chai and Janie, welcome to the pod.Janie [00:00:34]: Thanks for having us.Chai [00:00:35]: Thank you.Janie [00:00:35]: We're excited to be here.Chai [00:00:36]: Thank you.Swyx [00:00:36]: So for listeners, what do you guys do, just to situate you guys in the company?Janie [00:00:42]: Abridge is a clinical intelligence layer for health systems. We really started with documentation and building for clinicians and as we think about reducing the burden that clinicians have, they're spending 10 to 20 hours a week on documentation. There's a massive doctor shortage in the country. We also think that conversations between patients and clinicians are probably the most important workflow in healthcare. It's where care is given and received but if you think about the 20% of our GDP that goes towards healthcare, almost everything is a derivative of that conversation, whether it's the claim, the payment, the actual diagnosis given, the treatment. And we've started with a conversation to reduce the burden for doctors on documentation but we're really excited about the path ahead as we become this broader clinical intelligence layer.Chai [00:01:34]: I'm Chai. I work on clinical decision support at Abridge.Swyx [00:01:37]: Yes.Chai [00:01:37]: And so as Janie said, we're uniquely situated where we started off with the clinical note. What I'm really excited about and where we're expanding towards is what are all the things you can do before the conversation, during the conversation and after the conversation if you did have access to all the context about patients, payer guidelines, medical literature and put that together and to serve, how healthcare could look fundamentally different.Swyx [00:02:01]: And that's the context engine that you guys have?Chai [00:02:04]: Yes.Swyx [00:02:04]: Is that what it's called? Okay.Swyx [00:02:05]: So historically, as I understand it, the company started in 2018. A lot of people would be familiar with the AI voice notes form factor that doctors would be “Well, do you consent to being recorded?” It replaces handwriting and what have you. But it sounds like more recently there's been a big transition in the company. Tell me about the broader transition.From Documentation to Clinical Intelligence: Save Time, Save Money, Save LivesJanie [00:02:26]: So from a transition perspective, we really think about our journey as The first act was: how do we help save time? And that's where a lot of that original product was.Swyx [00:02:37]: By the way, one of those interesting statsSwyx [00:02:39]: On your landing page was, doctors spend time after hours.Janie [00:02:43]: They call it pajama time.Swyx [00:02:44]: Why is that pajama time?Janie [00:02:46]: Doctors after work in their pajamasSwyx [00:02:48]: In their pajamas. OhJanie [00:02:49]: At home are just writing and catching up on their notes every day.Janie [00:02:53]: Some of our favorite customer love stories, we have a Slack channel called Love Stories. We have clinicians telling us, “Abridge has helped us, from retiring early or we're now finally able toJanie [00:03:06]: go home and eat dinner with our kids for the first time.”Chai [00:03:08]: Save the marriage in some cases.Swyx [00:03:10]: One of the quotes was “We're not divorcing anymore.”Swyx [00:03:12]: I'm asking, “Why?”Swyx [00:03:14]: Because they're working too much.Janie [00:03:16]: But, in terms of where we're going and where we're expanding, we really think about our second and third acts around how do we help health systems save and make more money. Health systems are operating with record-low operating margins. It's getting harder and harder to serve patients and they have regulatory, some tailwinds but also a lot of headwinds coming their way and AI is ripe for helping on the saving and make-more-money piece. And then ultimately, how do we help save lives? The fact that our software and our product is open millions of times a week before, during and after a patient walks in the room, gives us massive opportunity with products like clinical decision support, which Chai is building but so many others to improve patient outcomes and probably one of the most important workflows and problems to be going after right now.From Glean to Healthcare: Context Is KingJacob [00:04:04]: One thing that's interesting, Chai, is you came over to Abridge from Glean and clinical decision support, which for our listeners is, in the context of a visit, helping a doctor figure out the right type of care. It's really a search problem in many ways, going through lots of different data sources. Very analogous to your previous role as one of the earliest engineers over at Glean. I'm sure a lot of our listeners are curious what's similar about the problems that you're going after now and what feels different, now that you're in healthcare.Chai [00:04:33]: Very similar. Taking a step back, with every wave, there's a lot of very similar patterns that happen across different products. A lot of social networking products look the same. A lot of credit-based products look the same. And we're seeing that very similar in the agent era with many companies, of course, in Redpoint's portfolio and so forth. And the key insight between both companies is that you have amazing models but context is king. Context is what puts them to work. So I see it in a lot of ways, a lot of similarities in this is a healthcare-coded version of Glean but the differences are really interesting. A couple things that come to mind. First and foremost, the rigor of the setting we're in. The downside risk is extremely high here in healthcare. It can be fatal in some cases. You prescribe something that the patient is allergic to for example. Whereas at Glean, it's “Oh, you got the question wrong.” It wasn't the end of the world in most cases. And so what does that mean? That shapes our evaluation strategy, both offline evaluation, progressive rollout and there's a lot more we could go into there. Second thing that comes to mind is, vertical versus horizontal. In both cases, there's a large variance but when Glean is, it's a much more horizontal company, there's a variance of personas, companies that you're working with. We also have a variance of personas, different types of specialties, different hospital systems. But the variance is a little more narrow. So from a product perspective, you're able to focus far more, especially when you have a maturing technology and you're building new products that never existed before. It lets you go after them much more easily and especially in healthcare where so many problems were solved with labor and process, that it's extremely ripe for AI to keep helping augment and enable. And the final thing that's really interesting, Abridge specifically compared to many other companies in the AI area, is the modality we started with where we're ambient and we're always listening in the background. And many more AI products will go that way but it's how we started. And that's the greatest form of AI we can create, AI that's seamless. You're not looking at your screen. It's always there. It's always helping you out and being proactive. The Jarvis vision that, every hackathon I went to over the past decade, there was always a Jarvis competitor. But Abridge very much started from the opportunity and continues to go that way.Ambient AI and Alert Fatigue: When Should the Product Interrupt?Jacob [00:06:57]: One thing that is super interesting then from a product perspective is you have this always-on seamless in the background and then you have to decide when you break the wall almost and say, “Hey, clinician, you might not have thought about X,” or whatever it is that you want to do. And in healthcare traditionally there's been this idea of alert fatigue and a million pop-ups and then a doctor just ignores all of them. It's probably a pattern that a lot of builders are thinking through now. How do you think about the right way to intervene or to pop up in a doctor visit?Janie [00:07:26]: It's such a good question. Alerts are notorious in healthcare specifically. Over 90% of alerts are ignored. The first and most important thing is context is everything, as Chai alluded to and I also think about how do we go from being reactive alerting to really proactive intelligence at the point at which it matters most. One thing we like to say is we want our product to feel like air conditioning. It should be in the background just making things better and if there is something that has great clinical risk and we're acutely aware that intervening now and not later is incredibly important, we should decide to act. But if you think about proactive versus reactive, instead of alerting a clinician during a visit when they're with their patient having a pretty serious and sensitive conversation, how do we prep a clinician before they walk into the room with that patient? And so historically, clinicians might have to manually go through charts with a patient that they've had over the course of months or years and they'll try to suss out what are the things they should be doing. You can imagine a world with Abridge. We'll summarize all of the most recent context for you, tell you based on the reason for a visit the patient is coming in for the types of things you should be discussing. And so you're going into that conversation prepped rather than walking in cold to that patient visit and then having this product interrupt you five or 10 times throughout the visit. And there might be times where it's really important to interrupt. We have a product called Prior Authorization and so this is when you may go into a doctor's office with knee pain. They'll prescribe you an MRI and so many of us have had this experience before, where in four weeks you'll get a call saying, “Hey, Sean, that MRI that you were prescribed wasn't approved and why don't you come back in? We'll figure it out.” In a world with Abridge, we might choose to quietly but still alert a doctor in that visit. And alert is probably not even the word we would want to use. Before a patient leaves, we would want to tell the doctor, “Hey, Doctor, before Sean leaves, you should ask him, has he had physical therapy and has his pain lasted for more than six weeks? Because the Aetna plan that he's on in California requires six things. We've already confirmed four of them have been met ‘cause we have all the context. But these two last criteria, if you can address with Sean before he leaves the room, we could guarantee that your MRI is approved before you leave.” And so when you think about clinical usefulness, impact to the patient, there are instances in which if we can catch a doctor while the patient is still in the room, as we think about save time, save money, save lives, we get to check all of those boxes. But when doctors have 15 minutes between visits, we have to be really thoughtful about when it matters.Prior Authorization: Reducing Latency in CareChai [00:10:23]: There's this interesting product opportunity AI has is reducing latency in the world. For example, prior authorization is an example of where care gets delayed and so great AI can reduce that. And the problem with alerts before partially is a technical problem: the quality of your alerts really matters. They're going to get ignored if you get alerts that... Similarly in engineering, where they're noisy alerts that you can't act on. But if you can make really high-quality alerts with both the context, as Janie said, and really high-quality models, then you can create a whole other game.Janie [00:10:53]: And I really like that experience because it starts to tease apart, what makes this so hard and unique. One, to make that prior authorization example possible, think about all the data that you need to have. You need to integrate with the electronic health record to know all of the patient context. Do we have access to your previous labs, previous imaging? And then to match you and to know that you're on Aetna, we have to collect all of the different payer policies and they vary by state. Some of these payer policies live on websites. Some of them live in unstructured 50-page PDF files.Jacob [00:11:31]: I thought this episode wasJacob [00:11:31]: To make sure we didn't scare people from healthcare.Janie [00:11:34]: But when you think about the things that make it hard, it also gives you the moat.Janie [00:11:39]: And then the second is the AI and the model quality we need to be able to hang our hat on. And so the bar, similarly when I worked at Opendoor, I worked on pricing models. Every outlier wiped out the margins of 30 and so similarly here in healthcare, the bar for accuracy is so high. And then I'd say the last is workflow is everything. If insurance companies deploy AI, it typically happens too late and this is when you have the notorious comical examples of AI just fighting each other when it's too late. But if we can pull forward the use of both the AI but also the ability to solve problems when the patient's in the room, you can start to collapse what typically takes weeks or months after your visit, ideally down to minutes or real-time. And it's where healthcare is both very difficult but also extremely rewarding if you can crack it.Product Form Factors: Mobile, Desktop, In-Room Devices, and ARSwyx [00:12:36]: Just to get some baseline on the form factors, because I've seen some videos on your website and stuff. You guys talk a lot about ambient AI. Is it primarily on the phone? Is there any other form factor that people get Abridge in? Is there an Abridge room setup where it's always on? I don't know.Jacob [00:12:55]: An Abridge podcast studio.Janie [00:12:58]: Primary form factor is mobile and desktop. UsuallyJanie [00:13:00]: Clinicians are walking in and out of rooms with mobile but at the end of the day, when they're closing out their notes or wanting to prep for the day ahead, they might use desktop. We have been having a lot of really interesting partnership conversations with a lot of these in-room device companies as you think about the power of multimodality and even more data, as you think about all of what is not captured today. It is fascinating to think about, especially even as we go into building and scaling our nursing product. It's one where nurses constantly, as they're walking in to check in on a patient for two minutes or maybe even 30 seconds,Janie [00:13:43]: Starting an Abridge experience is probably going to take longer than the visit. And so what can we do with in-room devices that are always on starts to raise really interesting and fun product questions.Swyx [00:13:54]: I was thinking, the way in tech companies we have all these Google MeetSwyx [00:13:58]: And other things, we might as well set up entire rooms with just Abridge tech.Chai [00:14:02]: Very much. AR glasses and related form factors are also relevant: how do we bring the information to the clinician in real-time without a screen, while still letting them focus on the patient?Swyx [00:14:18]: Do you think they want that? I'm skeptical of AR, but I'm curious what you've tried.Chai [00:14:26]: Admittedly, it's not a near-term product roadmapChai [00:14:29]: By any means. I'm being far-fetched.Jacob [00:14:31]: There's some sick AR stuff for surgeries.Swyx [00:14:33]: Really?Jacob [00:14:33]: When people are trying to visualize, you're about to make an incision but you want to see, what the cut might look or what the body might look like inside and they can layer in imaging.Swyx [00:14:43]: That's cool.Chai [00:14:45]: At some point in the future.Janie [00:14:46]: But there are a lot of our largest customers and at the largest health systems integrating already and so even as we think about building into it, unlocks a lot of product capabilities.Swyx [00:14:57]: And just to establish the terminology. Sorry, and I know I'm asking basic questions somewhat for myself but also for the audience who might beHealth Systems, Buyers, Clinicians, Patients, and PayersSwyx [00:15:05]: Less integrated. When you say health systems, it's like the Johns Hopkins, the Kaiser Permanentes.Janie [00:15:09]: Mayos, the Kaisers of the world.Swyx [00:15:10]: These are your customers, right? And the outcome that you deliver for them is happier doctors, reduced cost of processing, reduced mistakes. It's weird in a sense that I feel like there's also, a secondary customer, the customer of the customer and I don't know if you — do you think about it that way?Janie [00:15:28]: The other interesting and complex part of building product is we have our buyers, who are the chief medical information officersJanie [00:15:39]: The chief financial officers, the CIOs of these large health systems. Our users today are clinicians but if you think about who downstream is impacted, it's patients. And so as we build, with every product in mind, we think about who we're building for, who the secondary user is and what does that mean either in terms of experience, security compliance, ROI that we have to make tangible. And so like you said, time savings is one of them. But for CFOs, they care a lot more than just time savings. We have to show for every dollar you put into Abridge, because you have more compliant documentation or because you have fewer queries coming from your billing team, we save or add real dollars to your bottom line or top line, are things that we're constantly thinking about because of the dynamic across all three sets of users.Chai [00:16:32]: There's a whole other axis too with the payers and pharmaChai [00:16:35]: as well. Connecting all these three big stakeholders in healthcare isSwyx [00:16:39]: Do the payers ever see your data? Sorry, the payers meaning the insurers, right?Chai [00:16:44]: Yes.Swyx [00:16:44]: They also see Abridge data?Chai [00:16:47]: NoSwyx [00:16:47]: Like the direct integration to you guysChai [00:16:48]: They wouldn't see the raw Abridge data but when you're working together on something like prior authorization, whatever information they need, we'd communicate to them.Jacob [00:16:59]: That's cool. I would love to dig into the AI side. You still have a lot of problems on the AI side. And so maybe to start at the highest level, what's one of the hardest problems you have to solve in AI at Abridge today?The Hardest AI Problems: Quality, Latency, and CostChai [00:17:11]: To make things simple, let's take, building off the prior auth example. So one thing Janie talked about is okay, this data is all over the place and there's this combinatorial explosion of procedures, payer policies and even sometimes different health systems. There can be some cross-product of all of these different considerations you have to take into account. But what's really hard about this problem is doing it real-time in the conversation. So, in any AI product, usually the three KPIs you care about are quality, latency and cost. Now, what we're saying is we want you to do this real-time in the conversation, guiding the clinician. How do we do it in a way that does not break the bank? But we're using — But we also need very intelligent models because you're working with this cross-product of data and this, all this context layer as well. So you need high intelligence and high-quality because you don't want the alert fatigue but you also need to be fast and cost-effective. And so that's where a lot of clever engineering goes. It's okay, without getting into all the details here, can you model these policies in some intermediate representation or other things that you can do that can make this problem tractable? And of course, the Pareto frontier is always changing but we are also trying to do this now.Model Strategy: Third-Party Models, Proprietary Data, and Medical ConversationsJacob [00:18:26]: What implications has that had for what you take off-the-shelf and say, “ what? We don't need to be world-class at X. We'll just take this from the model providers or from some infrastructure player,” and what you're “No, this is where we spend most of our time focused on”?Chai [00:18:38]: This is, the fun challenge in AI?Jacob [00:18:42]: It changes every three months? SoChai [00:18:42]: Of course, with the shifting landscape, we try to be extremely thoughtful on predicting the trends of where third-party models are going and where we can uniquely go. And, sometimes when you talk about AI models, we're the models are just going to get infinitely better. But I don't think... It may be in the grandness of time you could say that but, within every month, every quarter, there's specific ways they're getting better. They're training on a lot more, coding data to be better coding agents, for example. And soChai [00:19:14]: We have to think about where are the things that won't — unique data that we're uniquely training on or to step back a little, where is a proprietary model bringing advantage to us is if it can give higher quality or lower cost and latency for similar quality, very similar to many other companies. And when we can do that is when we have proprietary data. So, for example, we have on the order of eighty million or hundreds of millions now getting close to of medical conversations.Jacob [00:19:44]: It's insane.Chai [00:19:45]: This is a unique data set. And this data set, it's very interesting because this data set is effectively a large part of the trace between the patient and the provider. That's where the quote-unquote debugging happens in healthcare. We have these traces at scale, as in as, our CEOs even called it, an exhaust that comes out of our product. And so when you have these traces, that's how you can train better agents on certain use cases, whether it's your transcription diarization use cases or so on or like note generation models and we can do that much cheaper and faster. But we're always also working with these third-party model providers. We closely collaborate with them and that's how we predict where the trends are going. The thing that I think about a lot is that, I know that the model providers are going to train much more on agentic workflows and so forth, so that's great, so that you have a better agentic harness. But the other thing that's interesting is that the model providers, because a large class of the consumer model providers is healthcare queries, that they might, optimize to train a lot of healthcare data to encode the knowledge in its weights. And this is just a great thing for us as well, where the off-the-shelf models can keep bett-getting better at general healthcare information, such that what our strategy is, we have a constellation of models, we can use something for this, that and, we only care about, at the end of the day, the best product experience.EHR as File System: Agentic Workflows and Real-Time InterfacesJacob [00:21:07]: And, you have, overall capabilities improving. I'm curious, as these models get better, is there something you look at and you're “, three months ago, we really couldn't do that but God, the the latest models really allow us to do it”?Chai [00:21:19]: So here's something interesting that I've, been toying with. So all models are... This wasn't super obvious a year ago but now it's become clear and clear that almost every agent is a coding agent underneath the hood? So you give it whatever file system, it can write its own code and so forth. So when you think about within healthcare and the use case that we have, you can think of the EHR effectively like a file system. It's just — it's a storage of all this information. It's a lot of information there that cannot fit into the context window, at least of today's models and you want to use that context effectively for all these product use cases we're talking about. And so if you have better agents that can, manipulate data, read that data, treat it as a file system as we see they're going and we know model companies are investing this way, then that very directly benefits us.Swyx [00:22:09]: Yeah. Okay, cool. Again, just establishing basic things. But we're going back to the model stuff. I'm really interested in double-clicking more on the real-time, element, which is pretty important for both of you. Is it — Is real-time just batches of every one minute, every five minutes? Is that how we do it? Or is there some more native, genuinely real-time in the sense that OpenAI has a real-time API or Gemini has a real-time API?Chai [00:22:35]: Yeah. Yeah. So today it is more on the on the batch basis but there's interestingChai [00:22:41]: Prototypes that we have that we're still not fully, full time, voice in text out or in that sense. But, can you trigger your models, your agents or agentic workflows, depending on the right times in the conversation?Chai [00:22:58]: And so you can imagine, different techniques to bring this latency down and, you want to bring the feedback loop down as much as you can. And so a lot of clever engineering there without fully... Maybe one day we'll do full voice in and text out, train a model to do something like that.Swyx [00:23:15]: You do — People don't want voice in voice out?Chai [00:23:18]: Now we aren't creating experiences that are, during the conversation, inter — It's almost likeSwyx [00:23:25]: Might be too disruptiveChai [00:23:26]: Too disruptive until, who knows, maybe eventually you could have full voice agents once we — the quality and we improve the comfort of the technology. But right now gra — that change is much more gradual and it's more text focus, text out.Janie [00:23:42]: And so much of currently what our product is trying to do is allow a clinician to focus on their patient and maybe at some point but right now patients, clinicians don't want a third voice, at least in a literal voice in that room. And so how do we be there with all the contacts and information ready at hand when there's the right moment?Personalization: Individual Doctors, Specialties, and Health SystemsJacob [00:24:03]: Jenny, one thing I'm curious about is how you think about, personalization in the product. I imagine, every doctor is a special snowflake in their own way, has their own way they like to do things. There are probably a bunch of different approaches you could take to doing that, both within the model layer itself but then also just with clever prompting or engineering. How do youJacob [00:24:20]: Deliver on that?Janie [00:24:21]: It's such a good question. Personalization is massive for us. We think about personalization at three levels. The first is at the individual, the second is at the specialty level and then the third is at the health system or the organization level. To your point, there are a lot of individual preferences. You-When a note is produced, it almost is a reflection that is so deeply personal of a doctor's work and how they give care. And so do they have preferences on things like style? They might want bullets versus paragraphs, really concise versus comprehensive. They also might have phrases that they really like to use or the templates that they want every note to be structured. And, we see it in our feedback all the time. We want two spaces in between sentences or I refuse to use this tool. And so that's something that we've had to build in. And the tricky part is how do you make sure that stylistic preferences don't interrupt accuracy and quality and that's something that we've really had to refine and hone over time. Second is at the specialty level. A cardiologist note or workflow is going to look very different from a dermatologist workflow.Jacob [00:25:32]: I assume cardiology notes are the highest stakes for you guys, given your CEO is a cardiologist.Jacob [00:25:36]: It's “Oh my God, make sure we get this one.”Janie [00:25:37]: Shiv, our CEO, is still a practicing cardiologist. He rounds once a month. And so, first call when we want just quick and easy user feedback too.Janie [00:25:46]: But, specialties require a lot of personalization, both in terms of what does the product look and so we make sure that as new users onboard, we catch that and the product proportionally reflects that. But also on the back end, evals at the specialty level, they are hard-earned to calibrate and get. What does a really great dermatology note look like? What makes it complete? What makes it compliant and billable is very different than a primary care doctor. And so it's not just about what does the product experience look but on the back end tuning and really deepening our understanding for the specialists. What does great output look like? And that's, a problem that we need to calibrate internally, externally, online, offline but, takes lots of cycles but is necessary in a high-stakes environment. And then at the health system level, for products like clinical decision support, you have health systems who've spent years or decades refining their best practices and they want to know, “Hey, we love your clinical decision support product but how do we embed our own hospital guidelines into them to inform clinicians before, during or after a visit what brest — best practices should look like?” And as you think about, deepening moats as well, when health systems, trust us with that data, allow us to productize it and directly into the clinical workflow, makes us a really great partner to health systems who want to build something that truly meets their needs, their practicing guidelines.AI Slop, Memory, and Product Data FlywheelsChai [00:27:23]: And I want to add onto that. The for the clinical documentation problem, it's very similar to AI writing that doesn't feel like your own and then we call that slop. But the way I describe one framing of slop is like AI without context. But we have all that context and both the clinicians, can have it and can guide it. And so part of the other interesting exhaust for us is, memory is, one of these new systems recordsChai [00:27:49]: Almost.Janie [00:27:50]: And we also have all the edits people make on our product and when you think about a data flywheel and how we get better over time becomes really powerful as a mechanism to just going deeper in personalization.Jacob [00:28:04]: It's interesting. I love this idea of working with systems on the guidelines they built up over a long time. I feel like so many of the best AI app companies today are... The question is: How do you take the expertise that a law firm or a bank has built up over many years and then add that as context and also a special sauce over, a an AI tool? And so seems like y'all are really doing that very effectively.Janie [00:28:24]: We're now starting to have our customers ask, “What are other customers doing?”Janie [00:28:28]: “And how are they doing it?”Janie [00:28:30]: And as we think about having visibility across such a large set of care being delivered right now, a really interesting place we could also partner.Swyx [00:28:40]: I'm just curious. I — This may be a nothing question but, how different are health system guidelines from each other? Don't they all converge to the same thing? And if not, where do they differ?Chai [00:28:52]: At a really high level, they're going to talk about very similar things but the difference is probably in some more of the details. “Oh, you should refer to specialists only when XYZ conditions are met,” or so forth and maybe different organizations have different practices and guidelines around that. But high level, talking about similar things but the details are what, of course, that shapes the context and the decisions you make.Swyx [00:29:15]: And this all goes into the context engine and it might affect the notes but maybe not.Chai [00:29:21]: The — For these local pathways, we're definitely thinking about it a little more for our clinical decision support product.Chai [00:29:26]: So yeah.Swyx [00:29:27]: Which is your stuff, yeah.Swyx [00:29:28]: And then the memory which you raised, let's just tell us more about that. What have you tried in memory? What's the structure of the memory? What works? What doesn't work?Chai [00:29:38]: There's, of course, many different ways you could do memory, where it's okay, can you bake it into the model weights or can you do it in some external store? For us, what's interesting is, of course, when you think the models are rapidly changing, whether it's in-house or third-party, baking into the model weights, sometimes you worry that it could be a little throwaway. And so, how do you... You need to find a way that you decompose the problem, the preferences from the underlying models and so forth. The thing we're right now most both that's easiest to start with and we're excited about is having, a separate store for memory, where you have, for example, a memory sub-agent that's, working in the background, figuring out what are the important parts of the clinician's actions that we want to remember for the long term. And then you can also imagine, other things where in the — you have background jobs that are running that are collating these, memories similar to Sleep, of course and what other pattern, patterns products do as well. Learning over all these action, all the action data we have, again, note edits, the conversations they did and the actual transcripts.Evals: LFD, LLM Judges, and Clinical SafetyJacob [00:30:40]: What about evals? How in the world do you... It is such a complex product surface area. We would love to hear you riff on that and also how has that evolved? I'm sure you've gotten better at it, so any learnings along the way.Janie [00:30:50]: From an evals perspective, we, from day one when we build any new product or feature, we think about, what does good look like? And there are table stakes things like clinical safety but then you start to get deeper into what does good quality look like. And when you go into something like our core product, there's stuff like style and completeness and there's things like does this note become something that can be billable, which is very high stakes for a health system. We have a number of ways in which we get confidence for this. We have, internal in-house clinicians who do what we call an LFD process to give us our very first pass at is this or isn't this a good enough output, look at the effing data.Jacob [00:31:41]: LFD?Chai [00:31:42]: That's why I was smiling. I was “Is Janie going to mention what it stands for?”Jacob [00:31:46]: I was not... There's like a million acronyms.Jacob [00:31:48]: How am I supposed to know that I don't? So “Oh yeah, of course, an LFD.”Swyx [00:31:51]: I've never heard of LFDs.Chai [00:31:53]: It's a bridge for sure.Janie [00:31:55]: I got through three days and then I had to ask someone.Janie [00:31:58]: I thought it was just me that didn't knowJanie [00:32:01]: It's our internal process.Swyx [00:32:02]: But look at the data as a meme in ML, ‘cause you tend to not look at it. You just want to look at number go up.Chai [00:32:06]: Exactly.Swyx [00:32:07]: But yes.Janie [00:32:08]: But so, we make sure we look at the data and then as we think about all of the components of good output, we, one, create LLM judges across all of these and we make sure with annotated data and either internal or external evaluators, we feel like these judges are calibrated. And then depending on the stakes, we also work with in-house and third-party evaluators across all of these before we ship any big change. And the goal is, in terms of evolution, how do you go from this process taking months, down to weeks, down to days? Some of it is, a true science and ML problem. A lot of it's also just, hard operational work. Have you planned ahead in terms of what you need? Have you really optimized the capacity that you need across all of the different specialties you need? Have you gotten a really good sense of which third parties are great to work with for what use cases? This takes a lot of domain, expertise and, lots of mistakes and errors in figuring that out. And so as much of it is an ML problem, so much of it has also been operational gains that are hugely important, where domain-specific expertise is everything.Specialty-Level Evaluation and Progressive RolloutsJacob [00:33:23]: But it's funny, ‘cause I feel like people talk about healthcare like it's one giant market and the reality isJacob [00:33:26]: It's, dozens and dozens of sub-markets. And so it feels like in your evals you have to build that up across the board, probably.Swyx [00:33:34]: And is specialization the primary cardinality at... That's the word that comes to mind.Janie [00:33:40]: Sometimes, depending on the product or the use case. And so if we're making a note improvement or feature for a particular specialty, definitely but we have products that are for nurses. We have products that, are really aimed at making the document or the output a lot more billable. And so we'll want to work with coding teams and not necessary clinicians. And so likeJacob [00:34:05]: Coding meaning healthcare coding.Janie [00:34:06]: Yes. Yes.Jacob [00:34:07]: NotChai [00:34:07]: Yes. I see you.Swyx [00:34:07]: Other kinds.Janie [00:34:09]: But is this output proportional to the work that was delivered? Is there sufficient documentation to justify the amount that a health system may end up charging? And so, specialty sometimes but also domain, very different across all of the different products that we're working for. And building out that network is, not easy and is where a lot of our operational investments have gone into.Chai [00:34:35]: And I view a lot of analogies to self-driving cars here, where, part of it is we really want progressive rollout of features to test in the real world is this useful? Is this going to work? One big difference compared to past lives is before I'd build a product, maybe I'd alpha it and then I'd like GA it the next week, ‘cause I'm “Go, move fast, ship,” and whatnot. But the mentality is like you... I want to make contact with the reality as quick as possible but I want a progressive rollout. Because as much as I get as large of an offline eval set, I want the distribution of that to match real-life distribution. And over time, by rolling out early, similar to Waymo has a tagline, “The world's most experienced driver,” another thing that can, at least linearly increase for us is, both the size of our evaluation offline and online, that and it all feeds back.Janie [00:35:25]: Something that's been earned over time, speaking of evolution, is just the trust we've gotten with customers. Historically, a lot of these health systems, when they bring on new vendors, their release cycles are quarters, sometimes twice a year. We've gotten our customers onto monthly release cycles, which is pretty fast for health systems but what is more exciting over the last, call it, few quarters, has been, a subset of our customers have said, “We want to innovate with you. We trust you,” and we have a pretty, decent chunk of our customers who say, “We'll develop with you outside of these monthly release cycles. We have a higher tolerance. We know that the stakes are very high but we want to be the first ones using these products, giving you feedback.” And so for a pretty substantial set of our customers, we've been able to convince them to be able to ship, in this gradual way before GA. Something we talk about a lot internally is, trust is earned in drops, earned in buckets and so we still can't do what I used to do when I worked at Loom. We had 30 million users. I'd just be, rolling out experiments left and. The bar is still quite high for iterative rollout but because of the trust we've earned, we're able to learn at pretty high volume very quickly.Privacy, HIPAA, and De-IdentificationSwyx [00:36:45]: Your scale is still pretty huge.Swyx [00:36:47]: One thing I want to... We were going to go into scale? In a sec. One thing I wanted to call up, follow up on evals, which, again, just coming from a generalist engineer point of view, just thinking through what would people be scared of in doing this, the privacy and HIPAAJacob [00:37:00]: Elements of this. I have zero experience in that. What do you have to do? What is surprisingly not that bad?Chai [00:37:06]: So one thing that's really important here from a compliance perspective is very much that any of the data we use needs to be de-identified, any real-world data we use as a basis of online eval sets we're learning from. And so you have to — And there's, very clear, government guidelines, what counts as PHI. And so we've even have built models that can take, for example, a clinical transcript and remove all the key PHI indicators and so you have a scrubbed/de-identified version. And then once you... And so one thing that's important is first you've got to get confidence in that model in the first place? And prove that out. Because, now you have, multiple probabilistic systems on top of each other.Chai [00:37:46]: But once you have that, then you can train on it use it for evaluation and so forth, provided one of the cool things also that you can do from a business side is the right data contracting as well with your partners.Jacob [00:37:57]: Is the anonymization one way? Once it's done, you cannot undo it? Or is there someoneChai [00:38:01]: YesJacob [00:38:02]: Who holds the master key that can... Yeah, okay. So it's one way.Chai [00:38:05]: It's one way. Yeah.Jacob [00:38:06]: That's how it works. I just wanted to... Because, there's a lot of this, learning from feedback and everything that, you would want to debug more but you can't because you just physically don't allow yourself to.Janie [00:38:17]: Some of it's also written in our customer contracts in terms of who can or can't access PHI data, how long do we retain it,Jacob [00:38:27]: Very goodJanie [00:38:27]: Before it gets de-identified. And so we have a pretty high bar for who can access that PHI data, just to make sure that we always respect our customer data and privacy. But that's something that we partner with our customers on too, to make sure that as we want full, as close to precision as possible in that qualityJanie [00:38:48]: We can still use it.Jacob [00:38:50]: But it'll be fascinating to see how that space evolves? Because you think about, I used to work at a company that, did a lot of healthcare data in the cancer space and if you asked, the average cancer patient, “Hey, do you want people, do you want other patients to be able to learn-”Chai [00:39:03]: Take it.Jacob [00:39:03]: “... Learn from your experience?”Chai [00:39:04]: Take it all.Jacob [00:39:05]: They're “Please.”Jacob [00:39:06]: “I'd love, nothing more than for other people to be able to learn fromJacob [00:39:10]: The experience that I had.” And so in the past it was a lot harder to do that learning. But with this technology, that might really be practical and so it'll be fascinating to see how that continues to evolve.Chai [00:39:21]: There's so much in our data set of 100 million conversations.Chai [00:39:26]: You can imagine things like insights that you can give to the clinician. How could you, oh, how could you have reacted to this? In coaching or insights around, which treatments are effective or, like... Because you have this, again, this data source that was never captured before but that's, where, intuition or experience is created from, going back to this idea that the conversation is the agent of truth.Operating at Scale: Reliability, Cost, and Token EfficiencyJacob [00:39:46]: Back to the 100 million conversations, I feel like you have this insane scale that maybe only a few other AI app companies have and everyone else dreams of. So not everyone has had to confront this yet but maybe just talk about some of the challenges of operating at that scale and what, our listeners have to look forward to if they ever get to this level of scale.Chai [00:40:05]: At large and larger in scale, so of course there's a general, infrastructure reliability. When you... In any given startup, you're building the plane while it's flying. So there's some notion of that. But what gets interesting on the AI and ML side for sure is this, as you get at more and more scale, so one, you have the data to first and foremost do this. But, you start thinking about costs or infrastructure in a whole different way at scale versus, a prototype.Chai [00:40:34]: You can use the most expensive model, you can burn as many tokens as you want but when you're doing 100 million conversationsJacob [00:40:41]: Token max on leaderboards are less upsetting than that context.Chai [00:40:45]: . When you're doing that and so that comes for we have the data and we also have the team that's able to post-train based on this and you can optimize for efficiency, especially in areas where you believe that maybe a lot of the quality headroom is less so and you don't expect the other off-the-shelf models to go that way, such that you want to do, efficiency maximization, in terms of compute and tokens.Jacob [00:41:08]: I feel like you guys live in the future in some way where most use cases today are really just in use case discovery mode, where it's “God, I really hope I can find something that can get to scale,” and so you're always going to use the most powerful model. And then the few things that do get to this level of scale, you start to do those optimizations.Chai [00:41:22]: It's a natural trajectory where it's like zero-to-one, we're not talking about any of these optimizations.Chai [00:41:26]: But when maybe we're in the one-to-100 or so forth, then we're in optimization mode and, what works out really well is you've got all this data from zero-to-one that lets you do this.What Comes Next: The Conversation as the Shared Healthcare PlatformJacob [00:41:36]: That's fascinating. I feel like one thing that's so interesting about the Abridge footprint is that you're in the doctor-patient visit in real-time. I always like to say, there's like probably 50 years' worth of product you could build on top of that. What gets each of you, I don't know, what are you most excited about building, either in the short term or medium term or even, long down the line?Janie [00:41:53]: Something that I get really excited about is that the same conversation can serve so many stakeholders. If you think about the conversation, a doctor needs to know what is the documentation, how do I make sure that this fully represent the care I gave? A patient needs to know, “What the heck just happened? This was really overwhelming. What are my next steps?” A payer needs to know, was this the proper and appropriate care given? A pharma company might want to know why isn't this drug being properly used or is there a good candidate for this clinical trial that I'm about to run? And where I get excited is that our product and our platform and our infrastructure can be the same product across all of those things and start to what's today, separate, very expensive, complex systems that serve each one of these stakeholders in very different ways, start to collapse all of that into a singular platform that enables not just more efficiency across the board but also better outcomes for everyone. And, all of us experience healthcare in probably very painful ways and knowing that there is a world in which we can simplify a lot is really exciting to me and it all starts with the conversation.Chai [00:43:15]: It's interesting. Of it very similar to going back to the KPIs that any AI product cares about. How do you increase quality of care? How do you reduce latency to care? And how do you reduce costs? Which is a huge, in healthcareJacob [00:43:28]: They call it the triple aim in healthcare.Chai [00:43:30]: But very similar to building AI products and the thing that really excites me is when we talk about that latency piece, we talked about one example earlier of prior authorization, can you reduce the latency to care? But you can imagine so much more. Oh, as soon as the lab value gets updated, do you have like a background agent that, kicks off and uses all the context to be “Oh, hey, the patient should do this next,” for example. And of flagging that to the clinician who's always in the loop but reducing that latency, to care. And then you can imagine this is much further down the road but it's like even connecting that to the direct patient and the consumer. And so how can you, how can you build a bridge to all of these things?EHR Partnerships and the Clinical Intelligence LayerJacob [00:44:10]: Very cool. The connections piece is just an ever-growing thing. And one of the key partners is the EHR and I wonder what that relationship is like. Will they, look at this as, something that is valuable enough that they want to own someday?Janie [00:44:29]: Our partnerships with the EHR is, we know that we have to be extremely close partners with all the EHRs who we partner with. Being able to not only pull and push all of the data into the right places is, not only table stakes, if we can't do that, health systems don't want to use us. The second and the reality of today is clinicians spend a lot of their days in the EHR. So much of what allowed us to win in the largest health systems was pretty direct and, very close partnerships with some of the largest electronic health records that allowed us to pull and push data with APIs that weren't ready out of the box. And clinicians want to save clicks. Anytime we introduce a new product that, adds two clicks for them in their day, they're “We're not going to use it.”Janie [00:45:21]: They have 15-minute back-to-back appointments with their patients. They're spending, hours during pajama time doing documentation. Every second and every minute counts and so we really think about being deeply integrated into the EHR as also table stakes to getting real usage and adoption. And anything that we build or introduce, we really talk about earn the right internally a lot, which is we have to provide so much value or save so much time that people will use us. But those are the two things that are close to us, is we know that the product won't be used unless it is deeply interoperable.Chai [00:46:01]: And strategically, to your point, it's like what does EHR want to own versus us? EHRs are really focused on the clinical workflows and so forth but some of the things that we're talking about here, I do these traditionally are outside of the domain where it's oh, connecting pairs and providers together with provider policies or the clinical trial matching, as Janie brought up. And so these are, entirely — we position ourselves as building this entirely new intelligence, clinical intelligence layer across, again, providers, pharma and, payers.Chai [00:46:33]: And so that's a it's a whole different ballgame that we try to playChai [00:46:36]: In combination with them.Jacob [00:46:37]: But it's like a different layer of scope.Healthcare AI Regulation, Technical Depth, and What Changed Their MindsJacob [00:46:39]: I'm curious, you are both relatively newcomers to healthcare. People have these, there's lots of futuristic healthcare AI takes of “Oh, everything will look different.”, now that you've been in healthcare for a bit, you live at the edge of AI, what have you, changed your mind on around this, as you think about what healthcare looks like in ten, 20 years? Any updates to your mental model from the time being close to the problems?Chai [00:47:02]: One thing that IChai [00:47:04]: Was hesitant about before and it's a common thing when I'm trying to recruit engineers that people ask me around, is definitely oh, healthcare, heavily regulated space. And it is, rightfully so. You want to keep, the patients at the end of the day safe. But one of the interesting things that, is a that surprised me how much it is coming to the company is there's a lot of really favorable regulatory tailwinds as well. Where you think about, government really wants interoperability between all these systems that we talked about and so agents can access this information. The government just in January, the FDA released updated guidance on clinical decision support, what I work on in such a way that they used to have guidance from like 2022 that required you to have, mention all these options and do all these other things but it's a very forward and forward-looking way. And so for me, what's been really cool to work on is this, there's this very special moment both in AI in general, we all know that but there's a special moment also regulatory in healthcare as well.Janie [00:48:05]: One thing I would call out is for the very reasons things are higher stakes or, potentially considered more difficult in healthcare, it's where some of the hardest AI problems will get solved first, just because the bar is so high. When I first joined, I was “Oh, this is where we'll be on the tail end of where, all of the AI innovation will be able to be applied.” But when you think about, zero error evals or multi-step workflows that have really low tolerance, a lot of the innovation will happen here just because we have to or else we can't ship.Jacob [00:48:42]: ‘Cause like in other domains, you'd much rather just solve the 80%-is-good-enough problems firstJanie [00:48:46]: 80/20 doesn't work hereChai [00:48:48]: And building off that, traditionally, there was a bit of stigma that, oh, healthcare companies are not that interesting from a technical perspective or I've seen that or faced that myself. But these are really hard and fun problems from a pure technical perspective beyond just the impact. How do you bring the latency of this thing down and make it really high-quality?Reducing Latency: Clinical Workflows, Agents, and Implementation RealityJacob [00:49:07]: How do you bring the latency of things down?Chai [00:49:10]: Yeah. Yeah. Yeah. So okay, let's answer the latency question. And maybe hopefully not too redundant with some of the things I've said earlier but some part of it is with any latency, you have to like what is, what is really your bottleneck. In a lot of workflows, it's sometimes it's the model itself. And so that's where like our data flywheel, our post-training team and so forth come in so that can you make the models far more efficient. So that's one aspect of latency. But there's whole other aspects of latency where it's okay, on top of that, if you use a constellation of different models, can you use — can you first use like a — it's like thinking fast and slow. Can you use a cheap, fast model that triages and hands it off to a larger model where you get more intelligence and so forth and so all theseChai [00:49:56]: Clever tricks to make it work.Chai [00:49:58]: And by the way, we are totally — we also realize that the parameter frontier is changing and so these tricks will — may not get us to where we want to be in five years but we need to if we want to build a useful product right now.Jacob [00:50:11]: Should we go to the quick-fire or you want to ask more about Abridge? We can stuff everything that's not Abridge into the quick-fireSwyx [00:50:16]: I don't mind. I was — I feel like Janie was on the topic of more long tail stuff, which isSwyx [00:50:21]: Not the eighty/twenty thing and that really matters. And I'll —, if you have any tips or cool stories or just general approaches that have worked for you that's interesting to dig into.Janie [00:50:32]: One of them is even just how we staff our teams looks different than a traditional software engineering team, I'd say.Swyx [00:50:40]: Let's go.Clinician Scientists, Edge Cases, and Evals at ScaleJanie [00:50:41]: We have a bunch of folks with different roles who are clinicians and so we have this role called the clinician scientist and I heard one of our leaders refer to them as mutants recently. But they are people who've had clinical backgrounds, so MDs typically, who are also deeply technical, somewhere, on the spectrum of like a full stack engineer all the way to like extremely scrappy prompter. But having each of these people embedded within our teams instantly raises the bar for everything that we build because not only are they determining, is this product clinically useful but they're deeply embedded in our whole evals process. And so when we talk about LFDs, when we talk about what is our actual evaluation criteria, you don't want Chai or me creating what those are because we don't have clinical background. But is probably unique to Abridge but has been game changing. And when you think about where the puck is going, you have people build with clinical backgrounds who are technical and where AI tools are going, they just becomeJanie [00:51:53]: More and more, critical and like the killers of the team. And so that's one. And then the second is just the scale at which we do evals to catch that long tail up front before anything ever gets into production is something that we've pretty much like really started to fine-tune, both from a scale but when do we know we need to get several hundred versus several thousand offline responses, what helps us make that quick decision and make this less of an art and as much of a science as possible. But that's also been something we've had to tune over time.Swyx [00:52:27]: And you have partners who opted in to give you those evals.Janie [00:52:31]: So we work either internally or with third-party for offline evals and then we have customers who also agree to give us, whether it's like thumbs up, thumbs down to like choose this or that, a lot of data to get us to what is as close to fully confident as possible.Swyx [00:52:51]: The term that comes to mind isSwyx [00:52:53]: Like active learning on things where you're weak. I feel like it's a lost artSwyx [00:52:58]: Is a lot of the polish that comes into doing something like this.Janie [00:53:02]: Really.Chai [00:53:03]: Hundred percent.Lessons from Glean: Technical Foundations and AI App InfrastructureJacob [00:53:04]: Maybe, on a totally unrelated note, Chai, you had a very, storied run at Glean b

In the Riverside booth at NAB in Las Vegas, Danielle Baxter, Business Development Representative, explains how the platform supports podcasting, video interviews, webinars, testimonials, and live streams. She covers options like local recording, separate audio and video tracks, browser-based guest access, built-in editing, branding, captions, producer mode, and flexible plans for solo creators and businesses.  Show Notes: Chapters: 00:02 NAB 2026 introduction00:27 Riverside's expansion beyond podcasting01:03 Bridging the gap between conferencing and production01:43 Built-in editing, branding, captions, and clips02:02 Matching Riverside tools to different creator needs02:44 Desktop, mobile, and browser-based guest workflow03:04 Local recording and separate audio/video tracks04:32 Guest access through links and mobile app options04:47 Live streaming, webinars, and clickable lower thirds05:32 Hardware and browser requirements06:09 Handling storms, power issues, and connectivity interruptions07:01 Producer mode and behind-the-scenes direction07:37 Exporting isolated files for editing08:38 Hobbyist and business pricing options10:00 Where to learn more about Riverside Support:      Become a MacVoices Patron on Patreon     http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:     http://macvoices.com      Twitter:     http://www.twitter.com/chuckjoiner     http://www.twitter.com/macvoices      Mastodon:     https://mastodon.cloud/@chuckjoiner      Facebook:     http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:     http://www.facebook.com/macvoices/      MacVoices Group on Facebook:     http://www.facebook.com/groups/macvoice      LinkedIn:     https://www.linkedin.com/in/chuckjoiner/      Instagram:     https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes     Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

In the Riverside booth at NAB in Las Vegas, Danielle Baxter, Business Development Representative, explains how the platform supports podcasting, video interviews, webinars, testimonials, and live streams. She covers options like local recording, separate audio and video tracks, browser-based guest access, built-in editing, branding, captions, producer mode, and flexible plans for solo creators and businesses.  Show Notes: Chapters: 00:02 NAB 2026 introduction 00:27 Riverside's expansion beyond podcasting 01:03 Bridging the gap between conferencing and production 01:43 Built-in editing, branding, captions, and clips 02:02 Matching Riverside tools to different creator needs 02:44 Desktop, mobile, and browser-based guest workflow 03:04 Local recording and separate audio/video tracks 04:32 Guest access through links and mobile app options 04:47 Live streaming, webinars, and clickable lower thirds 05:32 Hardware and browser requirements 06:09 Handling storms, power issues, and connectivity interruptions 07:01 Producer mode and behind-the-scenes direction 07:37 Exporting isolated files for editing 08:38 Hobbyist and business pricing options 10:00 Where to learn more about Riverside Support:      Become a MacVoices Patron on Patreon      http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:      http://macvoices.com      Twitter:      http://www.twitter.com/chuckjoiner      http://www.twitter.com/macvoices      Mastodon:      https://mastodon.cloud/@chuckjoiner      Facebook:      http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:      http://www.facebook.com/macvoices/      MacVoices Group on Facebook:      http://www.facebook.com/groups/macvoice      LinkedIn:      https://www.linkedin.com/in/chuckjoiner/      Instagram:      https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes      Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

In this episode: Mark throws his cook books in the bin and buys a Kobo Libra Colour. Alan tidies up Mojinav and puts the source on github. Martin builds his own Framework desktop. You can send your feedback via show@linuxmatters.sh or the Contact Form. If you’d like to hang out with other listeners and share your feedback with the community, you can join us on: The Linux Matters Chatters on Telegram. The Linux Matters Subreddit. If you enjoy the show, please consider supporting us.

In this episode: Mark throws his cook books in the bin and buys a Kobo Libra Colour. Alan tidies up Mojinav and puts the source on github. Martin builds his own Framework desktop. You can send your feedback via show@linuxmatters.sh or the Contact Form. If you’d like to hang out with other listeners and share your feedback with the community, you can join us on: The Linux Matters Chatters on Telegram. The Linux Matters Subreddit. If you enjoy the show, please consider supporting us.

What happens when you need to change your plans?PostbagLionel Guerraz: commenting and its effects on topic authority.Judith Rafferty: how do we deal with birthdays on LinkedIn?———The main topic is about changing your schedule on LinkedIn and whether it's OK to post about different topics.About statement previews are now 6 lines long on mobile. Desktop previews are still 4 lines long.UpLift Live Nano Bristol: our conference's little brother event is on Thursday 17 September 2026 at The Watershed in Bristol. Come to learn about LinkedIn and network. You get lunch, and if you're brave I'll review your LinkedIn profile in front of the room. Use code EARLY10 for a £10 discount until the end of May. Standard price is £59.I'm off to Creator Day in Poole this week. See you there?Off-topic food postSolo and small business owners: join the Espresso+ community to improve your LinkedIn and online presence. Find out more at jesp.me/joinSupport the show: Informed is not sponsored, so all production and hosting is self-funded. To make a small donation, go to jesp.me/informed-tip – thanks!

"Not a creative"?

The U.S. Senate Bars Members From Trading On Prediction Markets, Meta Threatens to Withdraw Services From New Mexico, and a Critical Vulnerability is Threatening Most Linux Distributions. MP3 Please SUBSCRIBE HERE for free or get DTNS shows ad-free. A special thanks to all our supporters–without you, none of this would be possible. If you enjoyContinue reading "AI Drives Up Demand for Desktop Macs – DTH"

Whoops!  How did this get missed?!  Is 12vHPWR really that bad? Why aren't you mad enough about DDR and SSD pricing?!  Nvidia is BUYING ... well it's not HP.  All that "Don't track me bro" stuff is for naught, Sony sells you less and you'll buy it, and Linux stops being able to be loaded on just about anything.  You cannot wait for the segment on Ethernet cables, I know.  All that and more!0:00 Intro0:35 Patreon2:10 Food with Josh3:57 Yes, DDR5 and SSD prices are still insane7:30 NVIDIA is not buying Dell or HP9:40 Also, NVIDIA warranty payments up 1000 percent in 202512:23 NVIDIA N1 engineering board leak14:15 Desktop CPU sales tank for some strange reason17:19 Sorry, you are still being tracked21:24 Sony Bravia TVs losing some OTA functionality23:13 Copper Clad Aluminum27:57 Linux drops 486 support!30:20 MacBook Neo almost sold out34:12 (In)Security Corner44:50 Gaming Quick Hits48:50 Picks of the Week1:02:58 Outro ★ Support this podcast on Patreon ★

Cat Wu is Head of Product for Claude Code and Cowork at Anthropic, building one of the most important AI products of this generation. Before joining Anthropic, Cat spent years as an engineer and briefly worked in VC. Today, she's interviewing hundreds of product managers who are trying to break into AI—and seeing firsthand what separates those who thrive from those who fall behind.We discuss:1. How Anthropic's shipping cadence went from months to weeks to days2. The emerging skills PMs need to develop right now3. Why you need to build products that don't yet fully work, so you're ready when the next model closes the gap4. Cat's most underrated AI skill: asking the model to introspect on its own mistakes5. Why Claude's personality is core to its success6. Why Anthropic's mission alignment eliminates the friction that slows most large organizations7. Why “just do things” is the most important principle for working at AI-native companies—Brought to you by:WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUsVanta—Automate compliance, manage risk, and accelerate trust with AI—Episode transcript: https://www.lennysnewsletter.com/p/why-half-of-product-managers-are-in-trouble—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Cat Wu:• X: https://x.com/_catwu• LinkedIn: linkedin.com/in/cat-wu• Newsletter: https://catwu.substack.com—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Cat Wu(01:29) Working with Boris Cherny(04:29) What Anthropic looks for when hiring PMs(06:18) How to help your teams move fast(08:58) How PRDs and roadmaps have evolved at Anthropic(10:28) The Mythos model and Anthropic's shipping velocity(11:54) What happened with the Claude Code source code leak(12:53) Integrating with OpenClaw(14:19) How the PM team is structured at Anthropic(15:42) How engineer and PM roles are merging(17:54) Why product taste is the most valuable skill(20:10) Where human brains will continue to be useful(22:23) How to stay sane in constant chaos(24:16) What gets sacrificed when you ship so fast(27:47) The /powerup command(28:32) Why Anthropic has been so successful(32:28) When to use Claude Code vs. Desktop vs. Cowork(35:58) Tips for getting started with Cowork(38:44) Demo: Using Cowork to build slide decks overnight(41:48) Cat's PM tech stack and internal tools(46:47) Which teams use the most tokens(51:15) The emerging skills PMs need for AI companies(55:00) Why building evals is underappreciated(58:44) Why Claude's character and personality matter so much(1:00:44) How new models force product changes(1:05:11) The vision for Claude Code and Cowork(1:07:22) Advice for thriving in an AI-driven world(1:09:18) Why 95% automation isn't good enough(1:11:58) Build apps you use every day, not prototypes(1:13:41) The divide between AI skeptics and believers(1:15:19) Lightning round—Referenced: https://www.lennysnewsletter.com/p/how-anthropics-product-team-moves—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Google Ads to Phase Out Dynamic Search Ads | Episode 429Visit us at - https://marketingoclock.com/This week on Marketing O'Clock: Google is moving Dynamic Search Ads into AI Max this September. Also, Google is coming after back button hijackers. Plus, AI Mode is testing a split-view desktop experience.

Was this the biggest 24 hours of AI drops... EVER?

Yeah OpenClaw's great.... but did you see the latest updates to Claude Desktop?!

With Microsoft finally doing right by Windows 11 and the Windows Insider Program, it's time to start testing and provide some feedback. And then we'll see if we can really trust these people. Also, Stardock's Connection Explorer 1.0 is here! And if you want one of macOS's dumbest features on Windows 11, you can get it now. Windows Yesterday was Patch Tuesday - Another month in paradise 26H1 - Eh, 24H2/25H2 - Narrator, File Explorer, display, Pen settings, WRE, Remote Desktop improvements Microsoft reveals how it will simplify the Windows Insider Program Two top-level channels, but really three A way to enable all features in new builds, finally, and easy channel switching. But there are complexities, of course New builds for Canary, Beta, and Dev - Two for Canary, but nothing new, Beta and Dev get Storage, networking, Windows Security, and Feedback Hub improvements The first Snapdragon X2-based PC is out, and Paul has that waiting in PA, and two more PCs are coming to Mexico PC sales were somehow up 2.5 percent in Q1, but the rest of 2026 will be a bloodbath Also, smartphone sales are doing even worse NVIDIA reportedly wants to buy Dell or HP ahead of a big PC chipset push. Interesting Surface/Microsoft 365 Microsoft is forced to hike Surface prices dramatically Microsoft reportedly kills Surface Hub Microsoft College Offer: 12 months of Microsoft 365 Premium, 12 months of Xbox Game Pass Ultimate, and a custom Xbox controller when students in the U.S. purchase a PC AI/Dev Microsoft AI releases a faster and more efficient image model Amazon CEO tries to explain the AI spending Google app for Windows rolls out worldwide, but the Mac gets a Gemini app Claude for Microsoft Word arrives in Beta Claude for Desktop gets a major redesign for multiple AI agents Microsoft's reported plans to charge for AI agents .NET 11 Preview 3 arrives right on schedule, but there's nothing to see here Build session catalog is up - joking, but the new Windows native app strategy should just be vibe coding Google I/O registration is open, and you are never going to believe what the main topics will be - number five will shock you Xbox & gaming New Xbox CEO says Game Pass is too expensive, also that the sky is blue Xbox will show off the next Metro game soon Starfield for PS5 is getting a fix Amazon Luna is stripping down to the basics e.g. "pulling a Stadia" Tips & picks Tip of the week: It's time to get involved App pick of the week: Stardock Connection Explorer RunAs Radio this week: Internal Corporate Communications in 2026 with Emily Mancini Brown liquor pick of the week: ScapeGrace Vanguard Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: cyberhoot.com/windows threatlocker.com/twit

With Microsoft finally doing right by Windows 11 and the Windows Insider Program, it's time to start testing and provide some feedback. And then we'll see if we can really trust these people. Also, Stardock's Connection Explorer 1.0 is here! And if you want one of macOS's dumbest features on Windows 11, you can get it now. Windows Yesterday was Patch Tuesday - Another month in paradise 26H1 - Eh, 24H2/25H2 - Narrator, File Explorer, display, Pen settings, WRE, Remote Desktop improvements Microsoft reveals how it will simplify the Windows Insider Program Two top-level channels, but really three A way to enable all features in new builds, finally, and easy channel switching. But there are complexities, of course New builds for Canary, Beta, and Dev - Two for Canary, but nothing new, Beta and Dev get Storage, networking, Windows Security, and Feedback Hub improvements The first Snapdragon X2-based PC is out, and Paul has that waiting in PA, and two more PCs are coming to Mexico PC sales were somehow up 2.5 percent in Q1, but the rest of 2026 will be a bloodbath Also, smartphone sales are doing even worse NVIDIA reportedly wants to buy Dell or HP ahead of a big PC chipset push. Interesting Surface/Microsoft 365 Microsoft is forced to hike Surface prices dramatically Microsoft reportedly kills Surface Hub Microsoft College Offer: 12 months of Microsoft 365 Premium, 12 months of Xbox Game Pass Ultimate, and a custom Xbox controller when students in the U.S. purchase a PC AI/Dev Microsoft AI releases a faster and more efficient image model Amazon CEO tries to explain the AI spending Google app for Windows rolls out worldwide, but the Mac gets a Gemini app Claude for Microsoft Word arrives in Beta Claude for Desktop gets a major redesign for multiple AI agents Microsoft's reported plans to charge for AI agents .NET 11 Preview 3 arrives right on schedule, but there's nothing to see here Build session catalog is up - joking, but the new Windows native app strategy should just be vibe coding Google I/O registration is open, and you are never going to believe what the main topics will be - number five will shock you Xbox & gaming New Xbox CEO says Game Pass is too expensive, also that the sky is blue Xbox will show off the next Metro game soon Starfield for PS5 is getting a fix Amazon Luna is stripping down to the basics e.g. "pulling a Stadia" Tips & picks Tip of the week: It's time to get involved App pick of the week: Stardock Connection Explorer RunAs Radio this week: Internal Corporate Communications in 2026 with Emily Mancini Brown liquor pick of the week: ScapeGrace Vanguard Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: cyberhoot.com/windows threatlocker.com/twit

With Microsoft finally doing right by Windows 11 and the Windows Insider Program, it's time to start testing and provide some feedback. And then we'll see if we can really trust these people. Also, Stardock's Connection Explorer 1.0 is here! And if you want one of macOS's dumbest features on Windows 11, you can get it now. Windows Yesterday was Patch Tuesday - Another month in paradise 26H1 - Eh, 24H2/25H2 - Narrator, File Explorer, display, Pen settings, WRE, Remote Desktop improvements Microsoft reveals how it will simplify the Windows Insider Program Two top-level channels, but really three A way to enable all features in new builds, finally, and easy channel switching. But there are complexities, of course New builds for Canary, Beta, and Dev - Two for Canary, but nothing new, Beta and Dev get Storage, networking, Windows Security, and Feedback Hub improvements The first Snapdragon X2-based PC is out, and Paul has that waiting in PA, and two more PCs are coming to Mexico PC sales were somehow up 2.5 percent in Q1, but the rest of 2026 will be a bloodbath Also, smartphone sales are doing even worse NVIDIA reportedly wants to buy Dell or HP ahead of a big PC chipset push. Interesting Surface/Microsoft 365 Microsoft is forced to hike Surface prices dramatically Microsoft reportedly kills Surface Hub Microsoft College Offer: 12 months of Microsoft 365 Premium, 12 months of Xbox Game Pass Ultimate, and a custom Xbox controller when students in the U.S. purchase a PC AI/Dev Microsoft AI releases a faster and more efficient image model Amazon CEO tries to explain the AI spending Google app for Windows rolls out worldwide, but the Mac gets a Gemini app Claude for Microsoft Word arrives in Beta Claude for Desktop gets a major redesign for multiple AI agents Microsoft's reported plans to charge for AI agents .NET 11 Preview 3 arrives right on schedule, but there's nothing to see here Build session catalog is up - joking, but the new Windows native app strategy should just be vibe coding Google I/O registration is open, and you are never going to believe what the main topics will be - number five will shock you Xbox & gaming New Xbox CEO says Game Pass is too expensive, also that the sky is blue Xbox will show off the next Metro game soon Starfield for PS5 is getting a fix Amazon Luna is stripping down to the basics e.g. "pulling a Stadia" Tips & picks Tip of the week: It's time to get involved App pick of the week: Stardock Connection Explorer RunAs Radio this week: Internal Corporate Communications in 2026 with Emily Mancini Brown liquor pick of the week: ScapeGrace Vanguard Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: cyberhoot.com/windows threatlocker.com/twit

Welcome to Lords of Limited, the podcast dedicated to getting you better at drafting in Magic: the Gathering. This week, we've got the Secrets of Strixhaven Crash Course! That means we're coming to the episode with a review of the new mechanics and all 5 colleges, then dive into our 5 format truths! Afterwards we're looking at the top uncommons for each color, notable commons, and sleeper cards to look out for. This is everything you need to get caught up to speed on this fun-looking new format!