Podcasts about Google Drive

Are UFO's in the Bible? What kind of vision did Ezekiel see?On today's podcast, Pastor Derek and Pastor Jackie talk through an interesting vision that Ezekiel had and how the meaning of the strange section of Scripture. We look through some of the other passages in the Bible that paint a similar picture, how these manifestations may have been seen throughout history, and the application for us today!The 17:17 podcast is a ministry of Roseville Baptist Church (MN) that seeks to tackle cultural issues and societal questions from a biblical worldview so that listeners discover what the Bible has to say about the key issues they face on a daily basis. The 17:17 podcast seeks to teach the truth of God's Word in a way that is glorifying to God and easy to understand with the hope of furthering God's kingdom in Spirit and in Truth. Scriptures: Ezek. 1:1-21; Ezek. 10:8-17; Ezek. 3:13; Ezek. 11:22; 2 Kings 2:11-12; Isa. 66:15; 1 Chr. 28:18-19; Zech. 4:10; Rev. 4:6-8; Rev. 5:6; Acts 2:1-3; Psa. 139:7-10; Psa. 21:4; Prov. 5:21.If you'd like access to our show notes, please visit www.rosevillebaptist.com/1717podcast to see them in Google Drive!Please listen, subscribe, rate, and review the podcast so that we can reach to larger audiences and share the truth of God's Word with them!Write in your own questions to be answered on the show at 1717pod@gmail.com or tweet at us @1717pod on Twitter.  God bless!

Welcome to Week 3 of The Second Act Executive: Podcast Workshop!In tonight's power packed episode, host Tawnie Wolf officially launches Week 3: Recording & Live Streaming. We bridge the gap between real world executive leadership, parenting in a digital age, macroeconomics, and the studio setups needed to build an independent media platform you own 100%.Whether you are currently navigating corporate America or transitioning out to launch your own private firm, this session is your blueprint for turning your expertise into sovereign media authority. What We Cover in This Episode:Monday Vibe Check & Kitchen Lessons: Why oat milk is non negotiable for crisp onion rings...Wolf Vibrations,LLC YouTube Announcement: A special shoutout for the grown and extremely sexy (ages 55–110)! Discover how Wolf Vibrations, LLC supports corporate leaders and transitioning executives in stepping into their power, asserting their authority, and staying on top.Market Insights: Palantir ($PLTR) & AI Patriotism: Why backing foundational U.S. defense AI infrastructure in 2026 is an act of patriotism, and why long term investors don't sweat short term price fluctuations around $136.Parenting, Social Media Literacy, & Real Value:Reminding our children that social media is a business, not real life.Unpacking the post 2017 foreign capital pullback, desperate monetization models, and why online trolling is just broken business models in disguise.Focusing on big economic success, global competition (BRICS), and real innovation over performative “costume party” clout.Asher the Chief Ranger: A personal story of Tawnie's son finding his voice despite a speech delay, inspired by tech “wizards” like Mark Zuckerberg, Bill Gates, Sundar Pichai, and Satya Nadella.Week 2 Recap (Days 12 & 13): Reviewing the 30 Second Hook Formula (Problem → Promise → Identity) and the non negotiable rule of keeping PDF music licensing certificates stored safely in Google Drive.Day 14 Audit & Showcase: Evaluating cover art.Day 15 Intro: Riverside.fm:Local High Quality Recording: Why local WAV audio and 4K video recording on Riverside eliminates glitchy Zoom streams permanently.Leveraging Riverside's direct partnership with Spotify for Creators and AI generated short form clips.“Our freedom of speech is not for sale. When the public square is compromised by trolls and broken algorithms, you must own your microphone.” Tonight's Homework Assignment (Next 24 Hours) and so much more! Links & Resources:YouTube: Subscribe to Wolf Vibrations for corporate leadership strategies and executive transition guidance.Workshop Schedule: Join us live every night at 8:30 PM EST. Be sure to turn on your notifications so you never miss an episode!

This show has been flagged as Explicit by the host. Show Notes Episode Overview Operator kicks off the episode feeling under the weather but shares a quick tip for making perfect egg drop soup before diving into his main project: diagnosing why his front-door security camera stopped sending alerts and recording events. What follows is a live-debugging session covering network config, script logging, Windows permission hacks, NTP time drift, and firmware flashing. Key Topics & Breakdown Egg Drop Soup Hack: How to get that perfect ribbony texture by creating a boiling swirl before pouring in the eggs, plus broth-to-egg ratio tips. Camera Setup & Network Config: Using static DHCP via MAC address binding on a UniFi Dream Machine (UDM) for local domain resolution instead of hardcoding IPs. Python & Cron Automation: Running a custom Python script every 2 minutes to check for new recordings, parsing logs with grep -v , and navigating massive log files in vi . Windows Troubleshooting Tangent: Deleting the stubborn Windows.old folder using the TrustedInstaller service hack ( ExecTI.exe ) instead of taking ownership manually. Time Sync & Firmware Quirks: Discovering the camera's system clock was stuck in 2011/2026, causing missed events. Downloading firmware via a slow third-party link, renaming .bin to .zip , and extracting with 7-Zip. Pre-Flash Backup Routine: Exporting camera configuration before upgrading, storing it in Google Drive for searchable documentation, and clearing old log/trigger files to reset the event pipeline. ️ Tools & Techniques Mentioned crontab + Python scripts for automated monitoring grep -v , cat , tail , and vi (line navigation with :1000 ) Obsidian for note-taking & AI assistant integration Firefox/Playwright for headless browser testing Turbo Download Manager & Bolt Media Downloader for multi-threaded/sniffing downloads 7-Zip for archive extraction Google Drive for searchable config backups Resources & Links Python API Script: Uniview IPC3628SR Recording Checker Camera Model: IPC3628SR (Uniview Wyze ISP Warm Light Deterrent Network Camera) TrustedInstaller Run-as Tool: ExecTI TrustedInstaller Runner Quick Takeaways Always verify NTP/time sync on IoT cameras before troubleshooting missed events or alerts. Use grep -v "noise" to quickly filter out repetitive log entries when debugging automation scripts. Windows system folders can be stubborn; running commands as TrustedInstaller bypasses hidden file locks without manual ownership changes. Always export and back up device configs before flashing firmware, even if the upgrade seems straightforward. Third-party download links often use temporary tokens or .bin wrappers; renaming to .zip and verifying with 7-Zip can save headaches. Thanks for listening! Stay curious, keep your logs clean, and remember: defense in depth starts at home. Example trusted installer hack # Shhhh I can't IR ... Defender, ForcePoint, SMS Agent Host ...I just can't anymore ... sc config TrustedInstaller binPath= "Reg add "HKEY_LOCAL_MACHINESYSTEMCurrentControlSetServicesSense" /v Start /t reg_dword /d 4 /f" sc start "TrustedInstaller" sc config TrustedInstaller binPath= "Reg add "HKEY_LOCAL_MACHINESYSTEMCurrentControlSetServicesFppsvc" /v Start /t reg_dword /d 4 /f" sc start "TrustedInstaller" sc config TrustedInstaller binPath= "Reg add "HKEY_LOCAL_MACHINESYSTEMCurrentControlSetServicesCcmExec" /v Start /t reg_dword /d 4 /f" sc start "TrustedInstaller" sc config TrustedInstaller binPath= "Reg add "HKEY_LOCAL_MACHINESYSTEMCurrentControlSetServicesWinDefend" /v Start /t reg_dword /d 4 /f" sc config TrustedInstaller binPath= "C:WindowsservicingTrustedInstaller.exe" Provide feedback on this episode.

Melchiza-who?! You may not be familiar with Melchizedek, but this High Priest has an interesting story in Scripture that is quite connected to Jesus and our salvation. On today's podcast, Pastor Derek and Pastor Jackie talk about that Bible character, Melchizedek, who is seldom mentioned but quite important. We talk through the debate in Christianity about who exactly he was. Was he a picture of Jesus? Was he a pre-incarnate Christ? Does it even matter? These are the questions we seek to answer on this podcast.The 17:17 podcast is a ministry of Roseville Baptist Church (MN) that seeks to tackle cultural issues and societal questions from a biblical worldview so that listeners discover what the Bible has to say about the key issues they face on a daily basis. The 17:17 podcast seeks to teach the truth of God's Word in a way that is glorifying to God and easy to understand with the hope of furthering God's kingdom in Spirit and in Truth.Scriptures: Gen. 14:17-20; Psa. 110:1-7; Heb. 5:5-10; Heb. 6:19-20; Heb. 7:1-28.If you'd like access to our show notes, please visit www.rosevillebaptist.com/1717podcast to see them in Google Drive!Please listen, subscribe, rate, and review the podcast so that we can reach to larger audiences and share the truth of God's Word with them!Write in your own questions to be answered on the show at 1717pod@gmail.com or tweet at us @1717pod on Twitter.  God bless!

Email threads, shared drives, and spreadsheets can feel “fine” until you're coordinating 30 firms, thousands of plaintiffs, and a calendar full of court deadlines that can't slip. We sit down with Keao Caindec, CEO and co-founder of Clarra (https://clarra.com), to unpack what complex litigation case management really requires and why the biggest pain is often coordination, not just documents.Keao explains how Clarra grew out of real-world needs from a complex litigation law firm handling antitrust, mass tort, class action, and bankruptcy matters. We compare purpose-built litigation workflows with more intake-oriented or small-firm platforms, then get specific about what breaks at scale: multi-party responsibility tracking, bulk docketing, complex calendaring, discovery flow, and the constant question of who owns the next action.We also go deep on collaboration. Clarra's permissioned workspaces are designed to let co-counsel, local counsel, and clients work from a shared repository without exposing everything a firm keeps private. Add in integrations with tools like NetDocuments, iManage, Google Drive, and OneDrive, and you start to see a realistic path away from the spreadsheet monster.Then we talk AI for lawyers in practical terms: voice-driven time entries, agentic help for docketing and field population, document summarization, and analytics. Subscribe for more legal tech startup stories, share this with a litigator or legal ops leader who lives in spreadsheets, and leave a review with the biggest workflow bottleneck you want software to fix.

Le monde s'accélère à une vitesse folle ! Ce soir dans le CKB SHOW, on décrypte les révolutions technologiques majeures qui bousculent notre quotidien et redessinent l'avenir. De la transformation radicale de nos outils Google à l'avènement massif des robots humanoïdes en Europe et au Japon, découvrez tout ce qui vous attend dans ce nouvel épisode.

Send Steve a Text MessageThirty songs for Saturday. A totally different set for Monday. No rehearsals. If that sounds familiar, you already know the real enemy isn't your technique, it's the scramble to keep everything in your head while you switch styles and expectations from gig to gig. We talk through the exact approach we use to stay organized, reduce panic, and get songs back under our fingers fast when we're playing with multiple bands or rotating church sets. We start with a simple habit that pays off immediately: building dedicated Spotify playlists for each musical situation so we can learn faster through focused listening. We explain what to listen for beyond the guitar part, how to mentally map the song form, and how to flag the moments that usually cause train wrecks on stage like stops, endings, dynamic shifts, and solo sections. If you're working on cover songs and want better guitar memorization, this step alone can make practice feel far more efficient. Then we get practical about cheat sheets. Rather than drowning in full tabs, we show how to make compact charts that capture only what you need: the tricky riff, the odd chord, the key center, the tone change, or the one bar that always derails you. We also share why iPad chart organization (using apps like forScore) and a MIDI page-turn pedal can keep you confident while still letting you watch the band and react to live cues. Finally, we cover a “future you will thank you” move: recording quick videos of your own playthroughs and saving them to Google Drive so you can instantly relearn parts months later. If you want a cleaner gig prep workflow, less stress, and more musical freedom on stage, listen now, then subscribe, share the episode with a gigging friend, and leave a review with your go-to method for remembering songs.Thanks for being here!! I will continue to do my best to bring you the best, most informative guitar discussions to help you along your guitar journey! The more you share this podcast with others, the more I can continue to grow this channel and offer the best information and advice I can to you.Thank you!SteveLinks:Check out the GuitarZoom Academy:https://academy.guitarzoom.com/Steve's Channel → https://www.youtube.com/user/stinemus... GuitarZoom Channel → https://www.youtube.com/user/guitarz0... Songs Channel → https://www.youtube.com/user/GuitarSo... . 

AI for sales is everywhere right now, but most of it is noise. This episode is about what actually works: AI sales coaching that lives inside a rep's daily workflow instead of sitting unused in a Google Drive.After 18 months of building, Dave, Luigi and Regan officially launch CoachPilot on this episode of Revenue Leaders. It's the AI coaching layer that sits on top of your CRM and coaches every rep on every deal, live.The three of them have spent decades in sales, built hundreds of playbooks, and onboarded over 200 SDRs for companies like Stripe. They kept hitting the same wall: even the best playbook fails because it never lives in the rep's daily workflow. CoachPilot is their answer.What you'll learn:→ Why the number one problem in every sales org is adherence to the sales process, not talent→ The playbook problem: why even great playbooks end up sitting unused→ What a day in the life with CoachPilot looks like: morning briefings, live call coaching, post-call CRM updates→ The live coaching sidebar that prompts you on objections, pacing, and talk ratio mid-call→ Why they built their own call recorder instead of integrating with existing tools→ The four IP layers: forecast score, coaching score, deal score, and methodology score→ Gartner's 2.8x revenue stat: why action-based AI paired with humans beats AI alone→ Why AI can't replace sellers in complex, multi-stakeholder deals→ The 50% unapproved AI usage problem inside sales teams and why governance matters→ Who CoachPilot is NOT for: the honest ICP conversation most founders avoidWhether you're a founder, sales leader, or rep, this is a rare inside look at why three sales veterans killed their own consulting business model to build a product.⭐ Learn more about CoachPilot: https://coachpilot.comFollow us:https://www.instagram.com/davidfastuca/https://www.linkedin.com/in/luigiprestinenzi/https://www.linkedin.com/in/reganbarker/https://www.linkedin.com/in/davidfastuca/

What spirits did Jesus preach to in 1 Peter 3? Was it humans or angels? Did He go to Hell after his death?In today's episode, Pastor Derek and Pastor Jackie continue through their Strange Scriptures series and talk through a few verses in 1 Peter 3 that have caused much confusion for theologians over the years. We talk through three primary views on this passage and look at the merits and holes in each belief and use the rest of Scripture to build a case for the meaning of this passage as well as encouragement for believers listening today!The 17:17 podcast is a ministry of Roseville Baptist Church (MN) that seeks to tackle cultural issues and societal questions from a biblical worldview so that listeners discover what the Bible has to say about the key issues they face on a daily basis. The 17:17 podcast seeks to teach the truth of God's Word in a way that is glorifying to God and easy to understand with the hope of furthering God's kingdom in Spirit and in Truth. Scriptures: 1 Pet. 3:18-20; Rev. 1:17-18; Heb. 2:14-17; 1 Pet. 4:6; 1 Pet. 1:10-12; Gen. 6:3; Gen. 6:1-8; 11-13; 2 Pet. 2:4; Jude 6; Col. 2:15; 1 Pet. 3:22; Rev. 18:2; Rev. 20:7; 1 John 1:1-4. If you'd like access to our show notes, please visit www.rosevillebaptist.com/1717podcast to see them in Google Drive!Please listen, subscribe, rate, and review the podcast so that we can reach to larger audiences and share the truth of God's Word with them!Write in your own questions to be answered on the show at 1717pod@gmail.com.  God bless!

Here's a little secret: ChatGPT tasks are the Gateway Agent.

Text me and tell me what you think of this ep. Oula, Em, and Natasha all said the same thing: they didn't expect the business structure side of it, they thought it was going to be more about the AI tools. If you've been on the fence about Studio Build what's the actual question you'd want answered before you'd feel ready to commit?Emily Bere — Musa StudioEmily is the founder of Musa Studio, based in Jindabyne in the Snowy Mountains. She works across residential and commercial projects, with a focus on craftsmanship, spatial design, and environments that feel both considered and warm. musastudioOula — Oulala Interior DesignOula runs Oulala Interior Design, a Sydney-based studio working with homeowners and businesses across residential and commercial projects. Her service suite runs from single-room design plans through to full end-to-end project management, with a focus on spaces that feel warm, refined, and intentional. oulalaNatasha Kennedy — Project NNatasha Kennedy leads Project N, bringing over 20 years of creative direction, interior design, and spatial storytelling to her work. The studio doesn't do signature style — it designs by interpretation, working across brand environments, cultural projects, strategic collaborations, and residential design by application. projectnAI for interior designers, interior design business, interior design studio systems, design business coaching, Studio Build Rhiannon Lee, Studio CEO interior design, AI implementation interior design, Notion for interior designers, Claude AI interior design, Google Drive interior design workflow, interior design systems, AI business coach Australia, Rhiannon Lee, Oleander and Finch, Studio Build testimonials, interior design community Australia, how to use AI in your interior design studio, AI coaching for designers Australia, interior design business systems course, Australian interior designers, interior design Australia Thanks for listening to this episode of "Designing Success: From Study to Studio"! Connect with me on social media for more business tips, and a real look behind the scenes of my own practicing design business. Grab  more insights and updates:Follow me on Instagram: https://instagram.com/oleander_and_finchLike Oleander & Finch on Facebook:https://www.facebook.com/oleanderandfinch For more FREE resources, templates, guides and information, visit the Designer Resource Hub  on my website ; https://oleanderandfinch.com/Ready to take your interior design business to the next level? Check out my online course, "The Framework," designed to provide you with everything they don't teach you in design school and to give you high touch mentorship  essential to having  a successful new business  in the industry. Check it out now and start designing YOUR own successTHE FRAMEWORK  ( now open)  https://www.oleanderandfinch.com/the-framework-for-emerging-designers/Remember to subscribe to the podcast and leave a review. Your feedback helps me continue providing valuable content to aspiring interior designers. Stay tuned for more episodes filled with actionable insights and inspiring conversations...

"The excuses are over. More millionaires and billionaires have been created through AI in the last 40 months than through any other industry we've ever seen — and this is just the beginning." Most people using AI are getting about 2% of what it can actually do. Not because the technology is too complicated, but because they have never set it up properly — and nobody has shown them how. Roman Bodnarczuk has been working in artificial intelligence since 2017, when a conversation with Dr. Geoffrey Hinton at the University of Toronto changed his view of what was coming. He is now one of Canada's leading AI strategists and the founder of N5R.AI, and what he finds everywhere he goes — in boardrooms, at conferences, on executives' phones — is the same gap: people know the buzzwords, but they have not done the setup. They have not personalized their AI. They have not connected it to their data. They have never had the moment where it stops feeling like a search engine and starts feeling like a co-founder. In this conversation, Roman walks through the setup live, in real time, with Nicky working through his own ChatGPT configuration on screen. He explains why personalization is not optional, how connecting your email, calendar, and documents changes everything the AI produces, and why speaking to your AI — rather than typing at it — is the fastest way to close the gap between where you are and what you want to build. He also shares the story of two brothers who took a $20,000 investment, used twelve off-the-shelf AI tools, and built a business to $400 million in revenue in a single year — with no employees and no outside investors. Roman closes with three concrete steps anyone can take today — none of them require technical expertise, and the most important one takes about ten minutes. Expert action steps: 1. Personalize your AI immediately. Go into the settings of whichever AI you use — ChatGPT, Claude, or Gemini — and fill in the custom instructions, occupation, and "more about you" fields. Ask the AI itself to generate optimized versions of all three based on everything it knows about you; each section should be around 1,350 characters. 2. Connect your data. Link your email, calendar, Google Drive, and any other apps you use regularly. The AI is only as useful as the context it has access to; connecting your real data transforms generic responses into genuinely relevant ones. 3. Talk to your AI every day. Speak rather than type — it is three times faster and more natural. Use it as a thinking partner, a coach, a strategist. The more you engage with it and ask it to remember what matters to you, the more useful it becomes over time. Learn more & connect: https://www.N5R.AI Resources mentioned: ChatGPT — https://www.chatgpt.com Claude (Anthropic) — https://www.claude.ai Gemini (Google) — https://gemini.google.com HeyGen (AI video and avatar creation) — https://www.heygen.com Super Whisper (voice-to-text app) — https://superwhisper.com Visit https://www.eCircleAcademy.com and book a success call with Nicky to take your practice to the next level.

TechSurge is sponsored by Notion. From product roadmaps to investor updates, Notion is where modern teams plan, write, and ship together. Get started at http://notion.dev/techsurge.Search began as a way to find pages. AI is turning it into a way to ask, reason, decide, and act.Search has always been more than a technical problem. It is a way of organising knowledge, connecting intent with information, and increasingly, turning questions into actions. In the age of artificial intelligence, that basic function is being redefined.In this episode of TechSurge, host Sriram Vishwanath speaks with Prabhakar Raghavan, Chief Technologist at Google, about the long arc of search: from the early web and link analysis to knowledge graphs, language models, transformers, Gemini, and the unresolved question of how AI will change the way we find, trust, and use information.Prabhakar reflects on his career as a computer scientist, researcher, and technology leader, beginning with his time at IBM Research, where he worked on algorithms, optimization, databases, and early information retrieval. He explains how the explosion of unstructured data on the web created a new class of technical and economic problems. Search was not simply about indexing pages; it was about imposing structure on a chaotic information environment and building mechanisms that could connect supply, demand, relevance, authority, and trust.The conversation traces how early search evolved through link analysis and PageRank, drawing on ideas from scholarly citation analysis, graph theory, and algorithmic ranking. Prabhakar describes why authority and trust became central to search as the web grew, and why users themselves changed alongside the technology. As search engines became more capable, people moved from looking for simple webpages to asking richer, more contextual questions that required intent understanding rather than mere document retrieval.Sriram and Prabhakar then explore the transition from classical search to AI-infused products. Through examples such as Gmail Smart Reply, Smart Compose, Google Drive recommendations, and knowledge graphs, Prabhakar shows how prediction, context, and language modelling were already reshaping user experiences well before the current generative AI wave. These systems were early signals of a broader shift: computers moving from retrieving information to anticipating what users might need next.The episode also offers a technical tour of the major algorithmic milestones that led to today's AI systems, including deep learning, sequence-to-sequence models, attention mechanisms, transformers, and the compute architectures needed to train and serve large models. Prabhakar explains why attention changed the quality of language modelling, why AI systems appear increasingly conversational, and why compute remains one of the central constraints in the field.At the heart of the discussion is the central tension facing search today: if AI systems can generate answers directly, what becomes of search as we know it? Prabhakar does not frame AI as the end of search, but as its next transformation. The future of search may be less about finding a page and more about understanding intent, synthesising knowledge, reasoning through ambiguity, and helping users complete complex tasks.The conversation closes with deeper questions about AI world models, hallucination, test-time compute, diffusion models, recursive self-improvement, theorem proving, and whether AI systems can ever reason with the same grounded understanding as humans. For Prabhakar, the challenge is not only to build more powerful models, but to understand their limits, failure modes, and relationship to truth.This episode is a wide-ranging exploration of how search became one of the defining technologies of the internet age—and how artificial intelligence may now force us to rethink what it means to search at all.Sign up for our newsletter at techsurgepodcast.com for updates on upcoming TechSurge Live Summits and future episodes.Links:Prabhakar Raghavan - Google Research profile: https://research.google/people/prabhakarraghavan/?&type=googlePrabhakar Raghavan - Google blogs and writing: https://blog.google/authors/prabhakar-raghavan/References Mentioned During the DiscussionBrin and Page - The Anatomy of a Large-Scale Hypertextual Web Search Engine: https://research.google/pubs/the-anatomy-of-a-large-scale-hypertextual-web-search-engine/Page, Brin, Motwani and Winograd - The PageRank Citation Ranking: https://ilpubs.stanford.edu:8090/422/1/1999-66.pdfJon Kleinberg - Authoritative Sources in a Hyperlinked Environment: https://www.cs.cornell.edu/info/people/kleinber/auth.pdfManning, Raghavan and Schutze - Introduction to Information Retrieval: https://nlp.stanford.edu/IR-book/ Google - Introducing the Knowledge Graph: things, not strings: https://blog.google/products-and-platforms/products/search/introducing-knowledge-graph-things-not/Google Help - How Google's Knowledge Graph works: https://support.google.com/knowledgepanel/answer/9787176Further ReadingKrizhevsky, Sutskever and Hinton - ImageNet Classification with Deep Convolutional Neural Networks: https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.htmlVaswani et al. - Attention Is All You Need: https://papers.neurips.cc/paper/7181-attention-is-all-you-needDevlin et al. - BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding: https://aclanthology.org/N19-1423/Chen et al. - Gmail Smart Compose: Real-Time Assisted Writing: https://arxiv.org/abs/1906.00080Kannan et al. - Smart Reply: Automated Response Suggestion for Email: https://arxiv.org/abs/1606.04870Hoffmann et al. - Training Compute-Optimal Large Language Models: https://arxiv.org/abs/2203.15556 Tay et al. - Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers:

God's punishment is....hemorrhoids?!In today's episode, Pastor Derek and Pastor Jackie continue through their Strange Scriptures series and talk through an account in 1 Samuel 5 and 6 involving the Philistines, some rats, and tumors. We look into what caused this form of judgment, why God used these exactly, and what we can take from this account and apply to our lives today!The 17:17 podcast is a ministry of Roseville Baptist Church (MN) that seeks to tackle cultural issues and societal questions from a biblical worldview so that listeners discover what the Bible has to say about the key issues they face on a daily basis. The 17:17 podcast seeks to teach the truth of God's Word in a way that is glorifying to God and easy to understand with the hope of furthering God's kingdom in Spirit and in Truth. Scriptures: 1 Sam. 5; 1 Sam. 6; Psa. 78:66; Judg. 16:23-25; Deut. 28:27; Judg. 1:33; Exo. 20:3; Jer. 10:10; Gal. 6:7; Rom. 1:18.If you'd like access to our show notes, please visit www.rosevillebaptist.com/1717podcast to see them in Google Drive!Please listen, subscribe, rate, and review the podcast so that we can reach to larger audiences and share the truth of God's Word with them!Write in your own questions to be answered on the show at 1717pod@gmail.com.  God bless!

Jetzt habt ihr euer NAS, um eure Dateien lokal zu speichern statt bei Google Drive, OneDrive oder Dropbox. Aber wie denn nun konkret? Bindet man den Netzwerkspeicher einfach als SMB-Share ein? Installiere ich umständlich eine Nextcloud? Bringt das NAS vielleicht was mit, beispielsweise Qsync von Qnap? Und was können diese Peer-2-Peer-Lösungen wie Syncthing, Resilio und Seafile? Oder machen Localsend und KDE Connect den ganzen Kram nicht viel einfacher? Im Podcast gehen die c't-Redakteure Niklas Dierking, Stefan Porteck und Jörg Wirtgen diese Fragen durch. Zuerst die einfachste Lösung, ein Netzwerk-Share. Wir diskutieren die Vor- und Nachteile der gängigsten Protokolle SMB, NFS, Webdav und AFP und gehen dann auf die strukturellen Eigenschaften ein: Die Dateien liegen dann nicht lokal vor, sondern man benötigt ständig eine Netzwerkverbindung, und um von außerhalb des eigenen LANs zuzugreifen, muss beispielsweise der Router ein VPN- oder Wireguard-Netz aufspannen und man muss das irgendwie per DynDNS finden. Auch wie gut das auf dem Smartphone geht, erwähnen wir. Eine Alternative wäre, Nextcloud auf dem NAS zu installieren: Die Daten liegen dann lokal vor, allerdings benötigt man DynDNS und VPN weiterhin. Wir sind einerseits zufrieden damit, auch weil die meisten Nextcloud-Clients ganz gut mit virtuellen Dateien umgehen können (die nur auf Wunsch lokal gepuffert vorliegen). Andererseits haben wir alle drei Zuverlässigkeits- und Geschwindigkeitsprobleme mit Nextcloud festgestellt, vielleicht aber auch aufgrund zu schwachbrüstiger NAS-Geräte. Gerade wenn man einen Reverse Proxy benötigt, der den DynDNS-Namen innerhalb des LANs auf eine lokale IP umbiegt, damit Transfers nicht auf die Geschwindigkeit der Internet-Anbindung beschränkt sind, und wenn man das beispielsweise mit einem PiHole im Docker auf dem NAS löst, könnten einige NAS überfordert sein. Einen interessanten Tipp brachte Niklas: OpenCloud ist ein abgespeckter Fork von Nextcloud, der vielleicht performanter läuft. Was auch einen Blick wert ist: Die Software, die das NAS mitbringt. Qnap beispielsweise kommt mit Qsync, was ein ähnlicher Dienst ist: Clients für Windows, macOS, Linux, Android, iOS; die Clients verbinden sich im LAN automatisch direkt per IP; DynDNS über Qnap-Dienste eingebaut; virtuelle Dateien funktionieren einigermaßen. Weitere Alternativen sind Peer-2-Peer-Lösungen. Eigentlich synchronisieren sie Verzeichnisse zwischen zwei Geräten, die dazu eingeschaltet sein müssen. Auf den ersten Blick klingt es unpraktisch, extra seinen PC einschalten zu müssen, damit das Handy auf aktuellen Stand kommt. Aber der Trick beim NAS ist natürlich, dort einen Client permanent laufen zu lassen. Vorteil der Systeme: Sie fummeln sich von selbst durch die Firewall, sodass man keinen DynDNS benötigt, und der lokale Zugriff erfolgt automatisch schnell. Syncthing machte sich als solches Peer-2-Peer-System bei allen drei gut, auch weil es das für einige NAS als native Anwendung gibt. Allerdings fehlen virtuelle Dateien. Zur Sprache kommen auch die Alternativen Resilio Sync und Seafile. Schließlich sprachen wir über ganz andere Lösungen, um explizit nur einzelne Dateien auszutauschen, aber auch beispielsweise Zwischenablagen, und das ganze auch ohne Usernamen und Passwörter einfach mit Leuten im gleichen Raum – so etwas wie Apple AirDrop oder Google Quick Share, nur systemübergreifend. Gute Erfahrungen gabs mit Localsend und mit KDE Connect – das anders als der Name vermuten lässt, auch Clients für Windows, macOS, Android und iOS bereit hält. Mit dabei: Niklas Dierking, Stefan Porteck Moderation: Jörg Wirtgen Produktion: Tobias Reimer ► Der von Niklas erwähnte Newsletter c't Open Source Spotlight: https://www.heise.de/newsletter/anmeldung.html?id=ct-opensource

Jetzt habt ihr euer NAS, um eure Dateien lokal zu speichern statt bei Google Drive, OneDrive oder Dropbox. Aber wie denn nun konkret? Bindet man den Netzwerkspeicher einfach als SMB-Share ein? Installiere ich umständlich eine Nextcloud? Bringt das NAS vielleicht was mit, beispielsweise Qsync von Qnap? Und was können diese Peer-2-Peer-Lösungen wie Syncthing, Resilio und Seafile? Oder machen Localsend und KDE Connect den ganzen Kram nicht viel einfacher? Im Podcast gehen die c't-Redakteure Niklas Dierking, Stefan Porteck und Jörg Wirtgen diese Fragen durch. Zuerst die einfachste Lösung, ein Netzwerk-Share. Wir diskutieren die Vor- und Nachteile der gängigsten Protokolle SMB, NFS, Webdav und AFP und gehen dann auf die strukturellen Eigenschaften ein: Die Dateien liegen dann nicht lokal vor, sondern man benötigt ständig eine Netzwerkverbindung, und um von außerhalb des eigenen LANs zuzugreifen, muss beispielsweise der Router ein VPN- oder Wireguard-Netz aufspannen und man muss das irgendwie per DynDNS finden. Auch wie gut das auf dem Smartphone geht, erwähnen wir. Eine Alternative wäre, Nextcloud auf dem NAS zu installieren: Die Daten liegen dann lokal vor, allerdings benötigt man DynDNS und VPN weiterhin. Wir sind einerseits zufrieden damit, auch weil die meisten Nextcloud-Clients ganz gut mit virtuellen Dateien umgehen können (die nur auf Wunsch lokal gepuffert vorliegen). Andererseits haben wir alle drei Zuverlässigkeits- und Geschwindigkeitsprobleme mit Nextcloud festgestellt, vielleicht aber auch aufgrund zu schwachbrüstiger NAS-Geräte. Gerade wenn man einen Reverse Proxy benötigt, der den DynDNS-Namen innerhalb des LANs auf eine lokale IP umbiegt, damit Transfers nicht auf die Geschwindigkeit der Internet-Anbindung beschränkt sind, und wenn man das beispielsweise mit einem PiHole im Docker auf dem NAS löst, könnten einige NAS überfordert sein. Einen interessanten Tipp brachte Niklas: OpenCloud ist ein abgespeckter Fork von Nextcloud, der vielleicht performanter läuft. Was auch einen Blick wert ist: Die Software, die das NAS mitbringt. Qnap beispielsweise kommt mit Qsync, was ein ähnlicher Dienst ist: Clients für Windows, macOS, Linux, Android, iOS; die Clients verbinden sich im LAN automatisch direkt per IP; DynDNS über Qnap-Dienste eingebaut; virtuelle Dateien funktionieren einigermaßen. Weitere Alternativen sind Peer-2-Peer-Lösungen. Eigentlich synchronisieren sie Verzeichnisse zwischen zwei Geräten, die dazu eingeschaltet sein müssen. Auf den ersten Blick klingt es unpraktisch, extra seinen PC einschalten zu müssen, damit das Handy auf aktuellen Stand kommt. Aber der Trick beim NAS ist natürlich, dort einen Client permanent laufen zu lassen. Vorteil der Systeme: Sie fummeln sich von selbst durch die Firewall, sodass man keinen DynDNS benötigt, und der lokale Zugriff erfolgt automatisch schnell. Syncthing machte sich als solches Peer-2-Peer-System bei allen drei gut, auch weil es das für einige NAS als native Anwendung gibt. Allerdings fehlen virtuelle Dateien. Zur Sprache kommen auch die Alternativen Resilio Sync und Seafile. Schließlich sprachen wir über ganz andere Lösungen, um explizit nur einzelne Dateien auszutauschen, aber auch beispielsweise Zwischenablagen, und das ganze auch ohne Usernamen und Passwörter einfach mit Leuten im gleichen Raum – so etwas wie Apple AirDrop oder Google Quick Share, nur systemübergreifend. Gute Erfahrungen gabs mit Localsend und mit KDE Connect – das anders als der Name vermuten lässt, auch Clients für Windows, macOS, Android und iOS bereit hält.

Jetzt habt ihr euer NAS, um eure Dateien lokal zu speichern statt bei Google Drive, OneDrive oder Dropbox. Aber wie denn nun konkret? Bindet man den Netzwerkspeicher einfach als SMB-Share ein? Installiere ich umständlich eine Nextcloud? Bringt das NAS vielleicht was mit, beispielsweise Qsync von Qnap? Und was können diese Peer-2-Peer-Lösungen wie Syncthing, Resilio und Seafile? Oder machen Localsend und KDE Connect den ganzen Kram nicht viel einfacher? Im Podcast gehen die c't-Redakteure Niklas Dierking, Stefan Porteck und Jörg Wirtgen diese Fragen durch. Zuerst die einfachste Lösung, ein Netzwerk-Share. Wir diskutieren die Vor- und Nachteile der gängigsten Protokolle SMB, NFS, Webdav und AFP und gehen dann auf die strukturellen Eigenschaften ein: Die Dateien liegen dann nicht lokal vor, sondern man benötigt ständig eine Netzwerkverbindung, und um von außerhalb des eigenen LANs zuzugreifen, muss beispielsweise der Router ein VPN- oder Wireguard-Netz aufspannen und man muss das irgendwie per DynDNS finden. Auch wie gut das auf dem Smartphone geht, erwähnen wir. Eine Alternative wäre, Nextcloud auf dem NAS zu installieren: Die Daten liegen dann lokal vor, allerdings benötigt man DynDNS und VPN weiterhin. Wir sind einerseits zufrieden damit, auch weil die meisten Nextcloud-Clients ganz gut mit virtuellen Dateien umgehen können (die nur auf Wunsch lokal gepuffert vorliegen). Andererseits haben wir alle drei Zuverlässigkeits- und Geschwindigkeitsprobleme mit Nextcloud festgestellt, vielleicht aber auch aufgrund zu schwachbrüstiger NAS-Geräte. Gerade wenn man einen Reverse Proxy benötigt, der den DynDNS-Namen innerhalb des LANs auf eine lokale IP umbiegt, damit Transfers nicht auf die Geschwindigkeit der Internet-Anbindung beschränkt sind, und wenn man das beispielsweise mit einem PiHole im Docker auf dem NAS löst, könnten einige NAS überfordert sein. Einen interessanten Tipp brachte Niklas: OpenCloud ist ein abgespeckter Fork von Nextcloud, der vielleicht performanter läuft. Was auch einen Blick wert ist: Die Software, die das NAS mitbringt. Qnap beispielsweise kommt mit Qsync, was ein ähnlicher Dienst ist: Clients für Windows, macOS, Linux, Android, iOS; die Clients verbinden sich im LAN automatisch direkt per IP; DynDNS über Qnap-Dienste eingebaut; virtuelle Dateien funktionieren einigermaßen. Weitere Alternativen sind Peer-2-Peer-Lösungen. Eigentlich synchronisieren sie Verzeichnisse zwischen zwei Geräten, die dazu eingeschaltet sein müssen. Auf den ersten Blick klingt es unpraktisch, extra seinen PC einschalten zu müssen, damit das Handy auf aktuellen Stand kommt. Aber der Trick beim NAS ist natürlich, dort einen Client permanent laufen zu lassen. Vorteil der Systeme: Sie fummeln sich von selbst durch die Firewall, sodass man keinen DynDNS benötigt, und der lokale Zugriff erfolgt automatisch schnell. Syncthing machte sich als solches Peer-2-Peer-System bei allen drei gut, auch weil es das für einige NAS als native Anwendung gibt. Allerdings fehlen virtuelle Dateien. Zur Sprache kommen auch die Alternativen Resilio Sync und Seafile. Schließlich sprachen wir über ganz andere Lösungen, um explizit nur einzelne Dateien auszutauschen, aber auch beispielsweise Zwischenablagen, und das ganze auch ohne Usernamen und Passwörter einfach mit Leuten im gleichen Raum – so etwas wie Apple AirDrop oder Google Quick Share, nur systemübergreifend. Gute Erfahrungen gabs mit Localsend und mit KDE Connect – das anders als der Name vermuten lässt, auch Clients für Windows, macOS, Android und iOS bereit hält. Mit dabei: Niklas Dierking, Stefan Porteck Moderation: Jörg Wirtgen Produktion: Tobias Reimer ► Der von Niklas erwähnte Newsletter c't Open Source Spotlight: https://www.heise.de/newsletter/anmeldung.html?id=ct-opensource

In Part 2 of the Securing Google Workspace for Nonprofits webinar podcast, Carolyn Woodard and Steve Longenecker, Director of IT Consulting at Community IT, move from foundational configurations into the question every nonprofit eventually asks: do we need to pay for a higher tier of Google Workspace to get real security?The short answer is: probably not right away. Steve walks through the third-party tools that should come before a tier upgrade for most nonprofits: formal security awareness training, third-party backups, advanced email protection, and cloud monitoring. He explains when a paid Google Workspace tier does make sense, particularly for organizations handling financial or healthcare data, legal holds, or complex app integrations. The conversation closes with a lively Q&A session drawn from attendee questions and poll results, covering oversharing in Google Drive, data loss prevention, password strength visibility in the admin console, and how to give staff secure, convenient ways to do their jobs without creating unsecured workarounds.Haven't listened to Part 1 yet? Find it in your podcast feed.This episode covers:Why third-party tools for security awareness training, backups, and advanced email protection are the right next layer for most nonprofits, before considering a paid Google Workspace tier.When upgrading to a paid tier does make sense: handling sensitive financial or healthcare data, e-discovery and legal holds via Google Vault, or managing frequent third-party app integrations.Nonprofits still receive significant discounts on paid Google Workspace tiers -  you just won't get them for free.You can find out which staff members have and haven't set up two-step verification before you enforce it so no one gets locked out unexpectedly.Making security convenient matters as much as making it mandatory: if IT makes it too hard for people to do their jobs, staff will find workarounds.Resources Mentioned:Google for Nonprofits Security Checklist — Google — https://support.google.com/nonprofits/answer/9251886Google Workspace Security Checklist for Small Organizations — Google — https://knowledge.workspace.google.com/admin/security/security-checklist-for-small-businesses-1-100-usersGoogle Workspace Security Checklists (all sizes) — Google — https://knowledge.workspace.google.com/admin/security/security-checklistsCybersecurity Readiness for Nonprofits Playbook — Community IT Innovators — https://communityit.com/cybersecurity-readiness-for-nonprofits-playbook/Nonprofit Data Retention with Ian Gottesman — Community IT Innovators Podcast — https://communityit.com/podcast-nonprofit-data-retention-with-ian-gottesman/Cybersecurity Resource Hub — NTEN — https://www.nten.org/learn/resource-hubs/cybersecurityNonprofit IT Management Community — Reddit — https://www.reddit.com/r/nonprofitITmanagementWebinar: AI Maturity Model for Nonprofits - Community IT Innovators - https://communityit.com/webinar-ai-maturity-model-for-nonprofits/ _______________________________Start a conversation :)Register to attend a webinar in real time, and find all past transcripts at https://communityit.com/webinars/email Carolyn at cwoodard@communityit.comon LinkedIn on reddit/r/nonprofitITmanagementon the Community IT websiteThanks for listening. 

This Episode is Sponsored by StayFi Your ultimate tool for Vacation Rental WiFi marketing allowing you to collect guest emails automatically via custom captive WiFi login splash pages. Drive repeat direct bookings and convert your OTA bookings to book direct for their next visit. Visit https://stayfi.com/vrsuccess/ and use code VRSUCCESS for 50% off 3 months of StayFi service. _________________________________________________________________________________________________________ Jodi Bourne is back for the latest installment of the new regular segment with Heather, built around a simple premise: AI is moving fast, and the two of them are going to keep working through it together, out loud, for listeners who want to come along. This conversation goes deeper into the practical mechanics of working with Claude. Heather and Jodi talk through connectors (MCPs that link Claude to tools like Gmail, Google Drive, Asana, and accounting software), skills (saved, reusable instruction sets that replace the old habit of copying and pasting prompts), and what both of them call their AI business brain - a structured foundational document that teaches Claude who you are, what you sell, and how you sound, before you ask it to produce anything. You'll come away with a clear starting point: build the foundation first, connect the tools you already use, and create one simple skill - Jodi's suggestion is a daily "morning coffee" briefing - before trying to do anything more ambitious. Key Takeaways AI output defaults to generic. The fix isn't a better prompt - it's a structured foundation document (Jodi calls hers the Hospitality Brand Bible; Heather calls hers her Business Brain) that teaches the model your business, voice, and audience before you ask it to create anything. Building that foundation properly is not a five-minute job. Heather recommends setting aside the better part of a day and using reverse prompting - asking Claude to interview you, question by question, until it has a full picture of your business. Connectors (MCPs) link Claude directly to the tools already in use - Gmail, Google Drive, Google Calendar, Asana, accounting platforms - so requests can be carried out end to end instead of copying information back and forth manually. Skills replace the old habit of maintaining a library of saved prompts. A skill is a reusable, named instruction set that automatically pulls in the right reference documents and brand voice without being told to every time. The recommended first skill for anyone starting out is a daily briefing - a "morning coffee" routine that summarizes email, flags anything unanswered, and reviews the calendar - because it is simple, immediately useful, and teaches the basics of how skills work. AI will hallucinate and occasionally get things wrong with total confidence. Both hosts were emphatic that nothing goes out the door - a guest bio, an email, an Instacart order - without a human checking it first. ________________________________________________________________________________________________________________________________________

We're excited to have Databricks join us at AIEWF, among hundreds of the top companies in the AI Engineer ecosystem. LS subscribers can use their discount to get past the late bird pricing and access over $50k in sponsor offers! Everyone is still talking about Satya's Frontier Ecosystems post, but few have actually built a (now $175 billion) frontier ecosystem and cloud like our guests today.From open-sourcing the layer above coding agents to rethinking databases for the agent era, Databricks cofounders Matei Zaharia and Reynold Xin are pushing the company beyond the lakehouse into a full data-and-AI operating system. In this episode, Matei and Reynold join swyx at the 2026 Data + AI Summit to unpack Omnigent, LTAP, Lakebase, agent security, open formats, Mosaic, and why databases may matter more than ever once AI agents start doing real work.We go deep on Omnigent: Databricks' open-source meta-harness for combining, controlling, and sharing agents across Claude Code, Codex, Cursor, Pi, custom agents, and internal tools. Matei explains why coding agents and enterprise agents run into the same problems: portability, collaboration, session history, security, spend controls, and the need for a common API above every harness.Then Reynold walks through Databricks' database dream: why CDC is brittle enough to joke that it means “continuous data corruption,” why HTAP has been the holy grail of database engineering, and why Databricks thinks LTAP gets most of the benefits by unifying the storage layer instead of collapsing every query engine. We also cover Databricks' infrastructure scale, the culture behind rapid prototyping, the difference between tech and enterprise customers, Databricks vs Snowflake, whether vector databases should have ever existed, the Mosaic model strategy, Genie, AI Runtime, RL fine-tuning, and the thesis that traditional software gets rewritten once the data is in the right place and agents sit on top.Databricks began as a company for the big data era. The origination of Spark from the Berkeley AMPLab which eventually turned into the product Lakehouse convinced enterprises that they didn't need a separate data lake, warehouse, ML platform, and governance layer. They just needed one open foundation where all of their data could live and be reasoned over.Since then a lot has changed, but data has only become more important. Data is no longer something you keep track of and analyze ad hoc, it's the necessary context agents need in order to act. So the framing has shifted from “where do we put all of our data?” to “how do we expose the right slice of state, history, permissions, and business logic to an AI system at the exact moment it's doing work?”If frontier model performance becomes commoditized, the durable advantage then becomes the company-specific context around them: proprietary data, governed access, operational state, transaction logs, workflows, and feedback loops. Which makes Databricks positioned perfectly.Now coming fresh off the Data + AI Summit 2026, the company is moving just as fast to keep up, announcing Genie One, Omnigent, LTAP, and many more, indicating a central mission in its newer work: Databricks is trying to become the operating system for enterprise agents.Models are getting good enough, but agents are only useful if they have the right context, permissions, memory, state, cost controls, and access to live business data. Fundamentally it appears that significantly better model performance in production is a systems problem, one that data guys like us are remarkably well prepared to solve!We discuss:* Why Databricks built Omnigent as a meta-harness above existing AI agents* Why coding agents and custom enterprise agents need the same infrastructure* The common API for agent sessions, files, streams, tool calls, and cancellation* Why persistent sessions, cloud sandboxes, sharing, search, and collaboration matter* Why Databricks open-sourced Omnigent instead of keeping it proprietary* Databricks' internal agent usage, cloud sandboxes, and coding workflows* The scale of Databricks: 50–60 million virtual machines a day and exabytes before breakfast* Why agent security needs contextual and stateful policies* How an agent could read confidential docs, install a compromised npm package, and leak data* Why spend control matters when an agent can burn $500 reading logs* Startup opportunities around coding-agent analytics, quality, skills, and spend* LTAP, Lakebase, and why Databricks wants to rethink the database stack* OLTP vs OLAP, CDC, and why data pipelines break at 3 a.m.* Why HTAP has historically been the holy grail of database engineering* Why Databricks thinks LTAP is “HTAP done right”* How writing transactional data into column-oriented formats changes analytics* Why agents need live operational context from databases, not just telemetry* How Databricks prototypes strategic systems without endless process* Enterprise vs tech customers, governance, procurement, and DIY culture* The “second system syndrome” risk of rewriting a database engine* Building a database engine from a decade of traces and quadrillions of data points* Why vector databases should never have been a separate category* Why open formats and AI changed the race with Snowflake* The Mosaic story, DBRX, Genie, document parsing models, and specialized model training* Why model customization and RL fine-tuning may become mainstream* Why “get the data there, slap some agent on top” may rewrite traditional softwareMatei Zaharia* LinkedIn: https://www.linkedin.com/in/mateizaharia* X: https://x.com/matei_zahariaReynold Xin* LinkedIn: https://www.linkedin.com/in/rxin* X: https://x.com/rxinDatabricks* Website: https://www.databricks.com* X: https://x.com/databricksTimestamps00:00:00 Introduction00:02:22 Omnigent and the Agent Infrastructure Layer00:08:39 Agent Clouds, Common APIs, and Open Source00:16:52 Databricks Scale and Internal AI Workflows00:18:03 Agent Security, Governance, and Spend Controls00:27:34 LTAP and the Database Dream00:30:30 CDC, HTAP, and Why Data Pipelines Break00:34:05 Lakebase, Parquet, and Live Data for Agents00:36:47 Databricks' Culture of Fast Prototyping00:43:40 The Dream Engine and Rewriting the Database Stack00:51:02 Vector Databases, Query Engines, and LTAP00:52:36 Databricks vs Snowflake00:57:48 Mosaic, DBRX, Genie, and Specialized Models01:03:11 Context, AI Runtime, and RL Fine-Tuning01:06:15 Why Data + Agents May Rewrite Software01:07:09 Closing ThoughtsTranscriptIntroduction: Databricks, Data + AI Summit, and Founder DynamicsSwyx [00:00:00]: Matei and Reynold from Databricks, welcome to Latent Space.Reynold Xin [00:00:06]: Hey, thanks for having us.Swyx [00:00:07]: Yeah.Matei Zaharia [00:00:08]: Yeah, thanks so much.Swyx [00:00:09]: thanks for taking time out. You have your Databricks, Data AI Summit going on. You were just telling me how the first summit that you guys ran was just 50 peopleReynold Xin [00:00:17]: Yeah, it wasSwyx [00:00:17]: in BerkeleyReynold Xin [00:00:18]: little meetup at Berkeley, I thinkMatei Zaharia [00:00:19]: YeahReynold Xin [00:00:19]: put togetherMatei Zaharia [00:00:20]: We were doing these tutorials and, yeah, just teach people Spark.Swyx [00:00:23]: Yeah. obviously now it's like, I think like the headline number's like 100,000 people around the world, 30,000 in person.Swyx [00:00:30]: it's a crazyMatei Zaharia [00:00:31]: AmazingSwyx [00:00:31]: community. Well, I just saw the keynote.Swyx [00:00:35]: Ali's just. Did was it obvious or that back when that Ali would be, like, such a great, like, CEO? LikeReynold Xin [00:00:42]: OhSwyx [00:00:42]: such a great presenter?Reynold Xin [00:00:43]: What do you think?Matei Zaharia [00:00:44]: I think among our group of founders it was clear that, I think he'd be the best at this.Swyx [00:00:50]: Yeah.Matei Zaharia [00:00:50]: And yeah, it turned out great. And he's, he's ramped up on so many topics growing a company. He would just go in and, like, study it and, be talk to all the experts. Like, even if he can't hire the person, learn enough about, like, finance and sales and whatever it was, and, and go from there. Yeah.Swyx [00:01:09]: Yeah.Reynold Xin [00:01:10]: he's obviously very high IQ and a very high EQ, but it wasn't. Like, Ali today is quite different from Ali from, like 10 years ago. I think there's a lot of work that he put in to, get to this point.Swyx [00:01:20]: Yeah. no, to me the most appealing thing about him is that he's funny. And like, it, it's, it'Matei Zaharia [00:01:26]: It's true, yeahSwyx [00:01:26]: it's hard to make jokes about, data warehousesReynold Xin [00:01:30]: About serious topicsSwyx [00:01:31]: securityMatei Zaharia [00:01:32]: YeahSwyx [00:01:32]: what have you.Matei Zaharia [00:01:33]: Oh, yeah. That's for sure.Swyx [00:01:34]: Yeah. So you guys launched a whole bunch of things. I'll, I'll just name check briefly, the stuff because we're not gonna cover everything. Omnigentt, your baby. LTAP, your baby, your dream engine.Swyx [00:01:47]: we're also gonna cover Genie, cover CustomerLake, you acquired PantherMatei Zaharia [00:01:52]: YeahSwyx [00:01:52]: Open Sharing, and there's Unity AI Gateway. A lot of these, I think, like, are things that you would expect a Databricks to do. It's, it's like part of the roadmap. Everyone in your category has similar things. But I think, probably the two of you are leading the two most unique and differentiated initiativesOmnigent and the Agent Infrastructure LayerSwyx [00:02:09]: on, in the landscape. Maybe we'll start with, Omnigentt we'll, we'll, we'll, we'll go into it. I do think that a lot of people are exploring this meta harness concept.Matei Zaharia [00:02:21]: Yeah, totally.Swyx [00:02:21]: What led you to it?Matei Zaharia [00:02:22]: Yeah. There were a couple of, like, converging lines, which I think is a good sign that you need something new. So on the one hand, there's all the coding agent info internally. We have really great, dev infra team. they built something called Isaac, that's like a wrapper on Claude Code and Codex, and, lets you use them either on the web in, like, sandboxes or, just on your dev machine or on your laptop or whatever. And then, they were adding all kinds of stuff there. And we saw all the more advanced engineers like, were building their own workflows with tons of agents, and they were building their own UIs and stuff on top or even on top of that. And then the other one was, like, us building agents. We ship this, like, data science agent called Genie on the research team, which I lead. We also build a lot of internal ones for various things, and then we have all the customer ones. And all of them running into this thing of like, “Oh, I need to switch model and harness and so on,” every few months. Plus the agent is, like, completely useless if you can't share sessions with someone and have history and have search and all this, like, layer on top of it for collaboration. I thought a bit about it from both contexts and, at first people thought it was weird. They're like, “Why are you doing coding agents and custom agents in the same thing?” But I said it's, it's the same problems and, you just wanna build the stuff that lets you deliver the agent, maybe control it if you care about security, and, make it portable across things. And then we prototyped some things as experiments. We saw, yeah, we can make it work, and then we built that for real.Swyx [00:04:06]: I'm wondering if this let's call it architectureMatei Zaharia [00:04:11]: YeahSwyx [00:04:11]: maps to anything in your careers in the past. like I always think about how a lot of things just tie back to operating systems.Swyx [00:04:18]: A lot of operatingMatei Zaharia [00:04:19]: YeahSwyx [00:04:20]: systems tie back to databases,Matei Zaharia [00:04:21]: SoSwyx [00:04:21]: or the other way aroundMatei Zaharia [00:04:22]: so the thing, I do think it ties a lot to, like, network protocols, internet protocol. we alsoSwyx [00:04:29]: Communication between entities.Matei Zaharia [00:04:30]: Yeah. We did stuff with, like, data sharing also, which is probably, most viewers probably won't know unless they'Swyx [00:04:36]: Yeah, open protocol is the term.Matei Zaharia [00:04:37]: Yeah.Swyx [00:04:38]: Open sharing. Open sharing.Matei Zaharia [00:04:38]: Open sharing.Swyx [00:04:39]: Yes.Matei Zaharia [00:04:39]: Yeah. So it's like you have a company, you maintain some table, like let's say like a Walmart or something. They have like the, inventory and what's been sold in each store. And then you also have suppliers, and they would love to produce more things and ship them, like, exactly the moment you need them. So they would love, like, real-time access to your table. So instead of like sending emails around or Excel sheets or phone calls, why can't you share like a view of that table in real time with them? Then they query, they, join it with their data, and they decide what to send. So it's one of these things where you, like you might ask like today since we can vibe code anything so fast, why do we even need to design like protocols or APIs or software? Why can't you just vibe code things on demand? But for this type of interoperability where multiple parties that are moving at different speeds are building stuff and you still want some layer on top to coordinate, you do wanna design it and build it. So it reminds me of that, like agents talking to each other and, users talking to agents and tools.Agent Clouds, Cloud Sandboxes, and Keeping Sessions AliveSwyx [00:05:42]: Reynold, any other comments alternative viewpoints?Reynold Xin [00:05:46]: I think, by the way, we had a debate on exactly which set of benefits would, matter a lot, and I think around the time we decided to do this thing I was telling Matei, “Hey,” it just happened to be there's a particular week that I was coding nonstopSwyx [00:06:00]: from the moment I woke up to, like, the moment I went to bed, I was, like, looking at my Claude sessions, my Codex sessions. And one of the things that was particularly annoying was having to keep my laptop open.Swyx [00:06:12]: I was driving to a doctor's appointment, and I remember because I wanted to make sure the whole thing continues working.Matei Zaharia [00:06:18]: But by the way, it's so comforting to hear you say that because I'm like, “I don't know if I'm a clown and I'm doing this or like.”Swyx [00:06:25]: Yeah. Like honestly, I was driving and I was tethering my laptop to my phone.Matei Zaharia [00:06:29]: huh.Swyx [00:06:29]: Keeping it on the side. Whenever I hit a red light, I started looking at what's going on my laptop.Matei Zaharia [00:06:35]: Yeah.Swyx [00:06:35]: And I just felt that was ridiculous.Matei Zaharia [00:06:37]: Yeah.Swyx [00:06:37]: It felt like we went back to the dark agesMatei Zaharia [00:06:39]: YeahSwyx [00:06:40]: programming. the productivity you gain from all this coding age is amazing, but, yeah.Matei Zaharia [00:06:45]: Have you heard of cloud?Swyx [00:06:47]: Yeah.Swyx [00:06:48]: It was crazy to me.Matei Zaharia [00:06:49]: Oh, the thing you were working on was the sandboxes or was this before that?Swyx [00:06:52]: It was a sandbox.Matei Zaharia [00:06:53]: Okay.Swyx [00:06:54]: I was workMatei Zaharia [00:06:54]: So you were inSwyx [00:06:55]: So I was approaching from a very different angle. I wanted to, “Hey, we're gonna have cloud sandboxes that doesn't shut down. You can get one very quickly,” but not just for running agentic sessions.Matei Zaharia [00:07:06]: Yeah.Swyx [00:07:06]: It's also for running development. So I was personally building that week, and through building that, I ran into all these issues, and then I wroteMatei Zaharia [00:07:15]: YeahSwyx [00:07:15]: a document for Matei, it's like, “Here's my wish list of what the actual environment should do.” And I think he ended up almost implementingMatei Zaharia [00:07:22]: YeahSwyx [00:07:22]: every single one of them.Matei Zaharia [00:07:23]: Yeah, I remember Reynolds saying, ‘cause my first prototype of this had just chats with your agent and he said, “I have to be able to open a shell, like my own shell and like list files and like tail them and stuff.” SoSwyx [00:07:36]: So SSH into a mainframe.Matei Zaharia [00:07:37]: Yeah. it has that now.Swyx [00:07:39]: Tailing my log.Matei Zaharia [00:07:40]: Yeah.Matei Zaharia [00:07:41]: Yeah.Swyx [00:07:41]: And also another thing I think I asked was, I had. I still use cursor for the sole purpose of rendering markdown files.Matei Zaharia [00:07:48]: huh. Yes.Swyx [00:07:49]: So I said, “If you just give me a way to see my markdown files and renderMatei Zaharia [00:07:53]: YeahSwyx [00:07:53]: them properly, I don't need a separate tool anymore.”Matei Zaharia [00:07:55]: Yeah.Swyx [00:07:56]: And I think you also built that in.Matei Zaharia [00:07:57]: Yeah, we, yeah, we did that, yeah. Yeah, we had a lot of engineers building, their own vibe coding setup. But then the other thing they all said is like, “Hey, I built something that's amazing for me, but, like, no one else on the team can use it ‘cause I don't have a server to collaborate.” And this is why we tried to set up, Omnigent, so you can have a server and have the security, set up in there. So, like log in with Google or whatever and, like securely share stuff. which. And that's where we've seen a lot of other agents like hit things. Like people think they prototyped an awesome agent, but it's not allowed to connect to like some really important data or whatever because of the security team.Omnigent Architecture, Open Source, and Common APIsSwyx [00:08:38]: Yeah.Matei Zaharia [00:08:38]: So yeah.Swyx [00:08:39]: Yeah. At this point, so for those watching along on YouTube, we're gonna putting up a image of the structure here, and we can talk a little bit of the architecture. I think I just want to have people understand, ‘cause like when we're talking about software, it can be very abstract and like here is what we're talking about. You've worked out in open source this entire platform and there's a runner component and server component with a uniform API that you've, you've figured out. any other element and obviously you can plug in all this, persistence layers and compute layers. This is a whole cloud. It's an agent cloud.Matei Zaharia [00:09:12]: Yeah. It's, it's got these components to work with it. The, a lot of the action happens like on the machine where you deploy your agent too. So whatever you've got on there, you can run. But yeah, it's, I think it's the minimal thing you want to have hosted, like collaborative agents and to have that server. And one of the reasons we open sourced it is, anyone building agents, this gives them an app they can start with and customize, which we were seeing in Databricks too. Like someone would make a nice, agent app and then other teams would ask, “Oh, can I just use yours for my agent?”Swyx [00:09:45]: Yeah, I think we had like five or six different agentic frameworksMatei Zaharia [00:09:48]: YeahSwyx [00:09:48]: built by every different team. They do all do more or less the same thing. Yeah, you need to. people wanna take something that works in Forkit, and you might as well have something open source. Yeah, which also was another question, which is interesting for Databricks. Like what do you choose to open source? What do you choose to make it proprietary? It's in. this goes back to Spark, right?Matei Zaharia [00:10:05]: Yeah.Matei Zaharia [00:10:06]: One, so one of the reasons to open source something is if you think it's a layer that will there'll be some network effect, it'll benefit from many, people collaborating, on it. So, for example, with Spark, I don't know if when Spark came out, we also focused a lot on letting you have libraries on top. So like there used to be differentSwyx [00:10:28]: EcosystemMatei Zaharia [00:10:28]: distributed computing engines for like machine learning and graph computation. We said they should all be libraries that you can compose. And we made it super easy to add connectors to data sources too. And then we benefit because, we don't have the time to write like connectors to like, 1,000 like different databases and file formats, but we can just use the ones people make, and of course they benefit from joining, this thing. So that's like one of these as it. Another way to think about it is like imagine, we our thing wasn't open. We had some agent hosting thing, but it's not open and then there is an open one. if you're. Which one's gonna win in the long run? So like here, because there is this benefit from like people writing integrations, it'll be, it'll be that. And then there are other things that like you just can't, even deliver as open source that are things the company does. Like for example, how do you make sure you're like streaming, jobs or your Lakebase database doesn't like, lose all your data at night? Well, that requires an operational team that's gonna sit there. There's no way it has to be a service. So like we wanna make sure as a company we're really good at those infra services and then we're as open as we can in terms of like what you build on top.Swyx [00:11:42]: speaking from a benefits, I think we are already seeing pull requestsMatei Zaharia [00:11:45]: YeahSwyx [00:11:45]: of all kinds of ecosystem integration, even though it was only released on Saturday.Matei Zaharia [00:11:50]: Yeah, Saturday. Yeah. So someoneSwyx [00:11:51]: Let's see, let's see what's going on. Yeah, you can look at the merge ones. I asked Sam Nigon this morning aboutMatei Zaharia [00:11:59]: 400 merge already?Matei Zaharia [00:12:00]: Yeah. I think Recent quite, I would guess around half are not from our team. but for example, someone added support for running it on Kubernetesrnetes. people added, many cloud sandboxes, so this can launch a cloud sandbox and run your agent in there, which is great for sharing too, ‘cause it's not, like, on your laptop and someone's, like, running scary code on there. so yeah, many startups have put those in, and, we expect to see more of them. We also have more agent harnesses already. Cursor, CLI, and Antigravity also.The Modern Data Stack and the Emerging AI StackMatei Zaharia [00:12:34]: Yeah. That's all, beautiful. And I, I feel like the last time this happens, there was the rise of the modern data stack.Matei Zaharia [00:12:42]: I don't know if it's that useful. I'm, I'm curious in your postmortem.Matei Zaharia [00:12:46]: I think most peopleSwyx [00:12:47]: AgreeMatei Zaharia [00:12:47]: will agree that it is finally dead. but maybe this arises to a new modern AI stack that, like, does the same thing.Matei Zaharia [00:12:52]: I don't know.Reynold Xin [00:12:54]: I think the modern data stack was a pretty useful thing, probably even up until this day. I think what, maybe for the audience who don't understand the history, I think the modern data stack is effectively decomposed into you need a layer to ingest the data in, you need a layer to transform your data, and then all of this are run, and then you need a layer to maybe visualize your data. And all of this runs on some data warehouse, or later on, as we're doing data warehouse or lakehouse.Reynold Xin [00:13:21]: I think that concepts are all very powerful and very useful. They enable a lot of workloads. What people eventually run into is a question of unification and consolidation is, hey, do you really need to chop all this into different pieces and work with so many different vendors and platforms in order to get, like, a very simple visualization done, right? So I think, like, over time, everybody started realizing that customers are pushing us. We started, we can realize that, so we started building more and more capabilities and trying to consolidate. And at the end of the day now, customers don't have to worry about having me hook up five different systems in orderMatei Zaharia [00:13:55]: YeahReynold Xin [00:13:55]: produce a chart. But the. I think, honestly, something like this is probably happening, in how many different frameworks do you want to hook up together in order to produce, like do a very simple agent.Matei Zaharia [00:14:06]: Just to be clear, I would say the core of this is this common API on top of all the harnesses. So the API is like, you've got an agent session, and you can send in a message or, like, a file. That's what you can send in, and then you get out, these streams as it's streaming text or as it's doing tool calls. And, or the other thing you can send in is you can, like, tell it to cancel a turn. So that's the API. Now, the thing we did is we could get you that on top of, like, cloud code running in a terminal, Codex, Py, OpenAI SDK, all that stuff. We map them all to that same interface. So that is something that you'd have to maintain yourself if you built your own, like, agent orchestrator, and then whenever cloud changes its API, you gotta, tweak your thing or it's gonna lose some messages. So that's the thing that's valuable to maintain. Then on top of that, like, we built a few apps. I think we built a pretty cool UI and stuff, but that's, And we built a security and control piece, which I'm excited about. But it's that common interface, so we don't. We. That doesn't try to be a stack. And in fact, you could plug in your own UI on top of this, server. That, and that's one of the use cases we care a lot about, ‘cause we want to use this in our own products.Compute, Sandboxes, and Databricks ScaleSwyx [00:15:20]: Yeah. It should be everywhere.Matei Zaharia [00:15:22]: Yeah.Swyx [00:15:22]: I think one of those things that is really interesting to me is, like, well, first of all, I'll, I'll endeavor to do everything and not call it the modern AI stack because like it needs a different name.Matei Zaharia [00:15:32]: Yeah.Swyx [00:15:32]: But like, yes, like, so one of the first people that told me about compute, sandboxing was Nikita from Neon.Swyx [00:15:39]: Because a lot of people think about Neon as like, well, it's serverless Postgres with, like, the separation of compute and storage and, instant branching and all those things. But every database company is also a compute company.Matei Zaharia [00:15:51]: Yeah. Yeah.Swyx [00:15:52]: And so he was showing to me his whole, his sandboxing solution. I don't think he have ever launched it.Matei Zaharia [00:15:57]: So our sandbox solution, the reason we could build it so quickly was because we realized if you just take the actual Lakebase architectureSwyx [00:16:05]: YeahMatei Zaharia [00:16:05]: and remove the database from it, by the coming from NeonSwyx [00:16:08]: Exactly, rightMatei Zaharia [00:16:09]: you have this sandboxSwyx [00:16:09]: Every database company has it already, yeah.Matei Zaharia [00:16:11]: Now, there are some differences. For example, in the one to support this particular workflow, it's important to have local persistence,Swyx [00:16:19]: YeahMatei Zaharia [00:16:19]: because you want your state to persist. Your libraries, you don't have to install your library every time, right?Matei Zaharia [00:16:24]: whereas the Neon architecture, because of the separation of storage from compute, you don't need persistent local disk.Swyx [00:16:30]: Yeah.Matei Zaharia [00:16:30]: So there's some differences.Swyx [00:16:32]: Yeah.Matei Zaharia [00:16:32]: But the, at the end of the day, yeah, it's, Yeah, so this is when you run, like, a coding sandbox. Like, if I use it, yeah, we have the dev env internally at Databricks. There's, like, many, like, tens of gigabytes of data just for, like, all the source code and, like, artifacts and stuff that I built, and I want that to come back next time, so.Matei Zaharia [00:16:51]: Yeah.Matei Zaharia [00:16:51]: But yeah.Matei Zaharia [00:16:52]: Before the show, we was talking about some statistics that might be surprising at the adoption.Matei Zaharia [00:16:56]: It could be internal, it could be external, whatever comes to mind, just to impress people the scale this is happening.Swyx [00:17:02]: So we, on the analytics side, I think we launchedReynold Xin [00:17:06]: Maybe 50 or 60 million virtual machines a day across all three clouds, so we're one of the biggest compute orchestrators out there.Reynold Xin [00:17:13]: Stuff for sure for CPU compute.Swyx [00:17:14]: Yeah.Matei Zaharia [00:17:14]: Yeah.Reynold Xin [00:17:15]: the. And all of this process, I think exabytes of data, I joked about depending on which time zone you are, typically before you have breakfast, Databricks would have processed exabytes of data already on that day. and on Neon, it's pretty interesting, too. It's launching, I think, 13 million databasesSwyx [00:17:34]: YeahReynold Xin [00:17:34]: a day now.Swyx [00:17:35]: Yeah, to me that was, like, aReynold Xin [00:17:36]: And that's just likeSwyx [00:17:37]: Like, what do you mean?Matei Zaharia [00:17:38]: Yeah. And that's the point.Reynold Xin [00:17:40]: And a lot of those were thanks to agent- agents and branching experimentationSwyx [00:17:44]: YeahReynold Xin [00:17:44]: because we made it so easy and so quickly, and thanks a lot to Nikita's team, to launch databases. It's, the. So it's changing the way people use databases.Swyx [00:17:54]: Yeah. Okay, we're gonna go into more database talk in a bit, but I wanna make sure we close up anything on Omnigentt. you mentioned, you were excited about the securityOmnigent Security, Contextual Policies, and Spend ControlsSwyx [00:18:03]: control side.Matei Zaharia [00:18:04]: Yeah.Swyx [00:18:04]: a lot of companies are figuring that out right now, as well as the spend side.Matei Zaharia [00:18:08]: Yep.Swyx [00:18:09]: what have you found there?Matei Zaharia [00:18:11]: Yeah, so I spent quite a bit of time talking to internal users, developers, security team, managers, and also lots of customers, and there's a few things. Like, first of all, one thing, that immediately was. became obvious is for security, there's this tension between, like, usability and security. And, the way people do. Like, a lot of coding agents today have very basic things like you can tell me which tool patterns I'll allow or disallow or whatever. It's like yes or no. But that puts you in a very tough spot. So just as an example, like, should my agent be able to read, some confidential documents, or let's say, should it be able to install new packages from npm, which, maybe it's compromised. Yes or no? Like, maybe I wanna allow it. Should my agent be able to publish stuff to the company website? Well, if I'm using it to code on the website, yes. But should it be able to do both, so it can, like grab a confidential document and be prompt injected and leak it? Probably not. So the thing we decided we need is stateful or what we call contextual policies where you keep track of the state of that session. It's not like is it allowed to push to the marketing site or not, but, like, hey, if it did a risky thing, like it installed, a old package from npm, or it read, like, 1,000 confidential docs, then no. Then don't, don't do it. Otherwise, maybe it's okay. That's one example of, like, moving that trade-off so it's both more secure and more useful by having a more powerful engine, essentially. This requires tracking sessions. The other piece that was interesting there is, like, there are these very level events it's doing, and you want some libraries on top that parse them. Like, for example, we have a, MCP server on Google Drive internally. It's got 60 API calls. like, how do I know which of those, like, will share a document with stuff on the internet and which ones won't? It's, it's annoying. So we designed in Omnigentt the policy layer so that it's functions and you can have libraries. Like, someone can make something that maps the level events to high-level ones, and then you write a policy about the high-level things that came out. so and thatSwyx [00:20:25]: This is related to the Panther,Matei Zaharia [00:20:27]: Yeah, Panther is. will help with that. PantherSwyx [00:20:30]: YeahMatei Zaharia [00:20:30]: a similar idea on the event processing side, and it's Python-based versus a weird custom language. this is more, as in realSwyx [00:20:39]: I didn't even know we were good yeah.Matei Zaharia [00:20:41]: Those things are happening, yeah.Swyx [00:20:42]: Yeah.Matei Zaharia [00:20:42]: So yeah, but these are the cool things. I think the contextual or stateful part, and then the way it can be libraries, and that was another reason to make it open source because others will write libraries and, like, we and our customers can use them. And the final thing, because it's stateful, one of the states we track is how much you spent in that session. So I can. I've had, like, I ask an agent to debug something, and it spent $500 because it decided to read a lot of log files and burn a lot of tokens. but I can literally say, “Okay, launch a agent to do this and cap it to spending $5.” Like, ask me for permission if it needs more. And because we're counting that within that session, it'll pop up and tell me, “Okay, you spent five, $5. Do you wanna go on?”Reynold Xin [00:21:27]: So important context here. Matei spent the last five years, a lot of his time was architecting Unity Catalog at DatabricksMatei Zaharia [00:21:34]: YeahReynold Xin [00:21:34]: which is the governance layer for data.Matei Zaharia [00:21:35]: That's right, yeah.Reynold Xin [00:21:36]: And he's combining expertise at that layer together with all the AI governance he knows.Matei Zaharia [00:21:41]: Yeah.Swyx [00:21:41]: DoMatei Zaharia [00:21:41]: But I also spent a lot of time being annoyed by coding agents and getting prompts.Matei Zaharia [00:21:46]: And also as theReynold Xin [00:21:48]: All the aboveMatei Zaharia [00:21:48]: I don't want to end up on the front page as, like, I installed some weird npm package and leakedSwyx [00:21:53]: YeahMatei Zaharia [00:21:53]: all the code, so I'm especially paranoid. But also I have very little time, so I don't want to sit there approving, like, do you want to run a 20-line, bash script, yes or no? so that's why I spend a lot of time figuring out, like, how can I make it as safe as possible and not annoying?Swyx [00:22:10]: Yeah. Is safety and mmm, let's call it security a bigger concern than token maxing or token budgets? which one is, likeMatei Zaharia [00:22:19]: Oh, yeah, they're both there. I don't know. I guess it depends on the type of company you are. So I think, some companies, like, the budget is, limited and, they really care about thatSwyx [00:22:34]: you can be Uber and still be concerned?Matei Zaharia [00:22:36]: Yeah. Oh, yeah, totally. Yeah. If you haveReynold Xin [00:22:38]: for us, securityMatei Zaharia [00:22:39]: YeahReynold Xin [00:22:40]: super paramount.Matei Zaharia [00:22:40]: For us, security is absolutely critical as a, cloud provider. It's, it's the most important thing, and, token maxing, we're not so worried about it yet, but I've seen the Like, for example, I talked to some consulting companies. They have, like, 100,000 employees who are all coding for customers. If those each spend, like, an extra $1,000 a month, that's, that's not fun.Swyx [00:23:04]: YeahMatei Zaharia [00:23:04]: we have, like, only a few thousand engineers.Swyx [00:23:06]: What's the policy in Databricks? Is it just unlimited or what'Matei Zaharia [00:23:08]: It's, it's unlimited, but we do. we use our own product to, like, analyze the traces and stuff, and we have a team that'looking to optimize and to see if anyone's doing something weird. And, we had some really cool insights just from analyzing current traces, like whichSwyx [00:23:24]: YeahMatei Zaharia [00:23:25]: models are better at, say, Rust versus like TypeScript or whatever. So yeah, at least in our code base.Swyx [00:23:31]: Yeah. Amazing. Obviously, I have to ask the token question, obviously.Matei Zaharia [00:23:34]: Yeah.Swyx [00:23:34]: I think it'sReynold Xin [00:23:34]: YeahSwyx [00:23:34]: it's a key thing. But yes, security and control above that, and figuring out a sane layer there you can have some autonomy, but, not too much.Matei Zaharia [00:23:43]: Yeah. Yeah, and we wanna make it super easy. As a engineer, you should set a thing. So in Omnigentt, you can ask your agent, “Set a policy on yourself to do this.” So it can likeSwyx [00:23:52]: But if there's something I should be showingMatei Zaharia [00:23:53]: YeahSwyx [00:23:53]: I don't, I don't see it on the GitHub, but,Matei Zaharia [00:23:55]: Oh, yeahSwyx [00:23:56]: there's justMatei Zaharia [00:23:56]: Well, in the docs there's something.Swyx [00:23:57]: Yeah, this is it.Matei Zaharia [00:23:58]: You can look at it later.Swyx [00:23:59]: Okay. Yeah.Matei Zaharia [00:23:59]: Just look in the docsSwyx [00:24:00]: YeahMatei Zaharia [00:24:00]: contextual policies if you wanna see.Swyx [00:24:04]: I just like to point peopleMatei Zaharia [00:24:05]: look at the built-in policies.Swyx [00:24:06]: Yeah.Reynold Xin [00:24:06]: Yeah.Swyx [00:24:06]: If you want to, follow up on this is exactly where to look, right?Reynold Xin [00:24:10]: Yeah.Matei Zaharia [00:24:10]: Yeah. yeah, and the story of these is, like, I just wrote, like, I wrote a doc with like 10 ideas for things before as you were working on them. Well, that was, like, my wish list of things people asked, and I told the team, like, “Hey, can you do like at least five of these for the launch?” And then they just got back with all of them, so.Swyx [00:24:29]: Oh, wow.Matei Zaharia [00:24:29]: so you can come up with more, but them- some of them are just meant to be examples. really you can intercept, like, any event the agent is making, and you can then either block or force it to ask the user or, like, allow, and you can update state to keepSwyx [00:24:45]: YeahMatei Zaharia [00:24:45]: track stuff.Swyx [00:24:46]: Yeah, ‘cause ultimately you're, I think of you as, like, a systems designer.Swyx [00:24:50]: You let people plug in, right? That's the wholeMatei Zaharia [00:24:51]: YeahSwyx [00:24:52]: modus operandi of what you do.Matei Zaharia [00:24:53]: Yeah.Swyx [00:24:54]: It's likeMatei Zaharia [00:24:54]: And we care a lot about also composab- like, can someone else write a library that others use, whichSwyx [00:24:59]: YeahMatei Zaharia [00:24:59]: this is meant to.Reynold Xin [00:25:00]: There's also a batteries included philosophy hereMatei Zaharia [00:25:03]: YesReynold Xin [00:25:03]: probably very similar to how you did Spark, which is you could just start using.Swyx [00:25:06]: Yeah.Matei Zaharia [00:25:06]: Yeah, that's right. It has to be good out of the box at certain things, and then you can build your own things on top that, like, we don't wanna do. But in Spark, if you just wanna like, I don't know, like read a table or do, like, a aggregation, it should be awesome at that out of the box.Building on Omnigent: Contributions, Startups, and AnalyticsSwyx [00:25:23]: Yeah. People wanna catch up on Omnigentt, they should watch your keynote.Swyx [00:25:26]: they should go through the GitHub and the docs. If they wanted to contribute, or they want to build on this ecosystem what would you call out as the most high-leverage places get involved?Matei Zaharia [00:25:36]: Yeah, do get involved in the Discord and in GitHub. Our team is there, is monitoring, and, some of the things people ask for we just built ourselves. Some of them, we're, we're collaborating with them to build it. and also tell us, likeSwyx [00:25:49]: Yeah, they're gonna be veryMatei Zaharia [00:25:49]: how you would like to use it because I think especially for developers, like, everyone wants it to work their own way, and a really good developer tool, like you have to hear the feedback on all the ways and figure out the abstractions and how to let people customize. So we'd love to hear, like, if you think, “Hey, I, I don't want it to work this way,” tell us. We really just wanna get that compatibility layer across agents and then let you do stuff on top.Swyx [00:26:14]: Yeah. is there any, in terms of like the startup side, I'm, I'm a founder.Swyx [00:26:18]: I wantMatei Zaharia [00:26:18]: YeahSwyx [00:26:18]: I see an opportunity, I wanna get in front of you. What's your request for, like, a startup that, like, I wish someoneMatei Zaharia [00:26:23]: Oh, like you wanna integrate with us?Swyx [00:26:24]: someone was working on this.Matei Zaharia [00:26:26]: Oh, for a startup?Swyx [00:26:27]: Yeah.Swyx [00:26:28]: Like, your, you got your own startup. It's doing well.Matei Zaharia [00:26:30]: Yeah.Swyx [00:26:30]: But like, if you weren't working on your own startup, what is, like, obvious that you should You advise many startups too, obviously.Matei Zaharia [00:26:37]: I do think, just as a company with a lot of engineers, like anything that helps me make sense of how people are usingSwyx [00:26:46]: SpendMatei Zaharia [00:26:46]: coding agents and,Swyx [00:26:48]: Yeah. AnalyticsMatei Zaharia [00:26:48]: spend, but also quality or like you should write, you should add this skill, or you should write this thing, or your agents are really horrible at tasks involving this service, so I go spend time. That would be nice. yeah.Swyx [00:27:00]: Yeah. The closest I've found is, this team, GitAI.Matei Zaharia [00:27:03]: Oh, cool. Yeah.Swyx [00:27:04]: They started with, like, we will just do, code and human attribution, but they're building the analytics layer on top of that.Matei Zaharia [00:27:12]: Yeah.Swyx [00:27:12]: I do think, like, there are a bunch of, like, artificial analysis is obviously,Matei Zaharia [00:27:18]: Yeah, they have their benchmarksSwyx [00:27:18]: doing super wellMatei Zaharia [00:27:19]: YeahSwyx [00:27:19]: with their stuff. so there's, there will be people. I think this is like the domain of consultants first, but then peopleMatei Zaharia [00:27:26]: YeahSwyx [00:27:26]: will build software that, let's say, it's kinda like the management planeMatei Zaharia [00:27:29]: YeahSwyx [00:27:30]: for coding agents.Matei Zaharia [00:27:30]: Yeah, I think there'll be a lot of insights there. You have it in other areas.Swyx [00:27:34]: Okay. Well, and then the other, big thing is your dream engine.LTAP: Lake Transactional/Analytical ProcessingSwyx [00:27:39]: maybe you wanna tell the story of, LTAP.Reynold Xin [00:27:45]: So, and background with. I'm, I'm gonna make people listen to our Ankur Goyal episode where we talked about SingleStore, HTAPMatei Zaharia [00:27:52]: YeahReynold Xin [00:27:52]: and all that history.Matei Zaharia [00:27:52]: Yeah. The LTAP idea is pretty simple. so if people have heard of the, Ankur's, talk about HTAP, it's effectively the world of databases. Sorry, there's like maybe a lot of context needs to be injected here. The world of databasesSwyx [00:28:06]: I am happy to be the database podcast that I'm forcing people to, like, learn your databases, guys.Swyx [00:28:11]: You cannot vibe code with just markdown files.Reynold Xin [00:28:13]: Yeah.Swyx [00:28:13]: Like,Reynold Xin [00:28:14]: It's one of the most important fundamental systems technologies out there. But the world of database effectively split into roughly two halves. There's what we call OLTP databases, which are transactional, and think of your Postgres, your MySQL, your Oracle databases, and the other side is what we call analytics, and sometime might refer to term OLAP. And the difference is on OLTP, you typically have maybe run some transaction on some event that looks up at one specific row. We update that row, right? It's a very oriented data structure. And on analytics, you're trying to reason on the data. You're trying to compute, “Hey, what's my revenue per store? What's my. How's my website doing every day?” And then you, eventually want to probably end up running anal- machine learning on it to predict, “Hey, how will my maybe sales be going in the future?” they are so very different architecture, and everybody start with OLTP databases. Every app, when you become serious enough, that needs more than markdown files, you need to have a database. You want to lose your data, you want to have some transactional consistency. But once you want to reason on the data, if you only have like- A hundred rows, it's probably okay to run it on your Postgres or your own, your MySQL database. But once you have more data and want to run more complicated analysis, the very analysis might crush your Postgres database. So you start doing, getting data out of the OLTP databaseSwyx [00:29:35]: Replication.Reynold Xin [00:29:36]: Replicate them into the analytic systems and just startSwyx [00:29:39]: Yeah, which for people, Elasticsearch is, like, aReynold Xin [00:29:42]: Yeah. So some of them get into Elasticsearch for, like, blocked analysis. A lot of our customers obviously get into Databricks to run more sophisticated things.Swyx [00:29:51]: Yeah.Reynold Xin [00:29:51]: And there's this term called CDC, whichMatei Zaharia [00:29:54]: Change data captureReynold Xin [00:29:55]: change data capture. and what it does, it reads the binlog of the database, and if you don't understand what binlog is, it's fine. The, but it's a little delta of the data, and it reconstructs based on the delta, the state of the database, on the analytics side. But CDC is, like, a very painful thing. It's how standard in the industry, everybody uses it, but, it ends up being. I think many data engineers ends up being waken up at, like, 3:00 a.m, because there's some pipeline thing.Swyx [00:30:22]: my explanation is, like, Airbyte is like a, became a $5 billion company just doing CDC.Reynold Xin [00:30:27]: Yeah, exactly.Reynold Xin [00:30:28]: CDC is, like, a veryMatei Zaharia [00:30:30]: It's hard.Reynold Xin [00:30:30]: It's one of the most boring but one of the most fundamental operations, like, powering modern society.Matei Zaharia [00:30:37]: huh.Reynold Xin [00:30:37]: But it's so brittle that, we joke that it's, should be called continuous data corruption, because you might change your schema on your OLTP database, and then the CDC pipeline fails to handleSwyx [00:30:48]: YeahReynold Xin [00:30:48]: the schema change.Swyx [00:30:49]: Yeah.Reynold Xin [00:30:49]: And then everything goes out.Swyx [00:30:51]: And there's all sorts of tricks that you can do, like, you add in, like, some versioning or whatever, but yeah.Reynold Xin [00:30:55]: Yeah, but it's a very, in general, very complicated. Like, I think at my keynote, I asked the audience put up their hand if they love their CDC pipeline. Only, like, maybe two people put it up. So if single store, like, about maybe a decade ago, I think the industry had this idea, hey, what if I built a single database that can handle both workloads? Now I don't.Swyx [00:31:12]: Which, like, by the way, every database person ever has ever always dreamed about this.Reynold Xin [00:31:15]: Yes. Yes.Reynold Xin [00:31:16]: This is the holy grail of database engineering is why not build a single system that can do both of this? But it ends up just being a lot of compromises. one, I think one of the first issue is that, hey, each. they say Postgres has a massive ecosystem, right? You want to be using the tools that's built for Postgres. And Spark, for example, had a massive ecosystem. There's a lot of libraries you want to use. If you were to create now a new thing, you don't have a ecosystem. You tend to create a new, smaller proprietary API, and you're lacking both, and it's also very difficult to make it performance-wise to be, comparable on either side. So it ends up being sucking on both. And our whole idea of LTAP, it's obviously a wordplay on the term HTAP, is that we think this is HTAP done right. HTAP wants to build a single engine for both. We think you can get 99% of what you need by unifying the storage, and just have a single storage layer. And once you have the single storage layer, if your Postgres databases are writing data in a column-oriented format, everything analytics can just go read that data directly without any delay, right? There's no pipeline in between, so all the data will immediately be available for reasoning analytics. I think I was telling some customers earlier, hey, when we talked about this is gonna be super useful for agents, I at first didn't really believe in it myself, even though we wrote that positioning.Lakebase, Agents, and Live Operational DataMatei Zaharia [00:32:39]: Yeah.Reynold Xin [00:32:40]: But then last night I was having dinner with a Australian customer, and they told me, “Oh, hey, one of the big issue we have is we have all these logs from our services, and we see SLA dips and want to investigate. But then there's no way for those agents to even understand what's going on in the actual databases themselves. All we see is just, like, product telemetry of the database and the services.” It would make those agents 10 times more powerful if understand, for example, who's placing those orders, what is happening, what exactly are they doing. So now I'm sold on our own message.Swyx [00:33:13]: Yeah.Reynold Xin [00:33:14]: I think it's really. It gets you the almost all of the benefits of the HTAP holy grail, which is, hey, make the data available immediately for reasoning analyticsSwyx [00:33:26]: Yeah, I think,Reynold Xin [00:33:27]: without compromiseSwyx [00:33:28]: in the way that humans are generally intelligent and want to have the ability and access to query anythingReynold Xin [00:33:34]: YeahSwyx [00:33:35]: while they do the work, they also need history and need context.Swyx [00:33:38]: And, like, where else does they get context? That's it's an analytical workload.Reynold Xin [00:33:41]: Exactly.Matei Zaharia [00:33:42]: Yeah. Yeah. And I remember when we had incidents with our databases and engineers said, “Well, I can't just run a giant query on it to see what's going on because that's gonna bring down the database and hoard it even more.” Like, that's the stuff that this gets rid of, because you spin up a whole separate fleet of machines that's doing the analytics. You're not overloading, like, the main databaseReynold Xin [00:34:02]: RightMatei Zaharia [00:34:02]: that's still trying to serve stuff.Reynold Xin [00:34:04]: Yeah.Matei Zaharia [00:34:04]: Yeah.Why LTAP Works Now: Parquet, Postgres, and LakebaseSwyx [00:34:05]: So this has been a dream for a while. what had to get done in order to get to today? Like,Reynold Xin [00:34:11]: Yeah.Swyx [00:34:11]: I feel like, you have announced variants of this several times, but it wasn't as clear as LTAP.Reynold Xin [00:34:18]: Yeah.Swyx [00:34:18]: I think LTAP is like Like, okay, we've got it, guys.Matei Zaharia [00:34:21]: This thing, yeah.Reynold Xin [00:34:21]: I was talking to somebody at Meta, and then he was asking me, “Hey, what's the catch? Why is it possible now?” And I think the reality is we took a lot of time to work on the Lakebase architecture. obviously a lot of it came from the Neon team, which is a separation of storage from compute. And it turned out it was just a tiny little step away going from that to this LTAP idea, which is, hey, we just. in the Neon architecture and in Lakebase architecture, we're writing data in oriented format to the open data lake, but in there we're writing in Postgres pages. Ali and I were spending a lot of time debating, hey, can we just change that to write in column-oriented format? And we're just debating, and one day, one of our engineers who's, like, super smart came in, he's like, “Hey, I just prototyped it. It works.”Swyx [00:35:07]: Wait, it's, prototype what?Reynold Xin [00:35:09]: Prototype, instead of storing the data in the data lake in the oriented formatSwyx [00:35:15]: ColumnReynold Xin [00:35:15]: like Postgres pagesSwyx [00:35:15]: YeahReynold Xin [00:35:16]: write them in Parquet.Swyx [00:35:17]: Yeah.Reynold Xin [00:35:18]: and he just made the observation that, hey, our storage fleet has a lot of extra idle CPUs And we could use those CPUs to do the transcoding from row to column, where row is good for OLTP, but column is good for analytics. so let's do that transcoding at that time. And as a matter of fact, once you transcode the data compresses better. So from those services writing to, for example, S3 or other data lake, like object stores, you can write them faster ‘cause now they are now smaller.Matei Zaharia [00:35:49]: Yeah.Reynold Xin [00:35:49]: So there's no overhead, it's no compromise in performanceMatei Zaharia [00:35:52]: Some CPU overhead.Swyx [00:35:54]: Yeah, because,Matei Zaharia [00:35:55]: YeahSwyx [00:35:55]: we had extra CPUs anyway.Matei Zaharia [00:35:56]: We had that fleet anyway, yeah.Swyx [00:35:57]: so the debate ended. it's one of the classics of, tech, issue of a lot of debate, but then somebody went ahead and just tried to prototype it and it worked.Matei Zaharia [00:36:06]: But, like, something this strategicSwyx [00:36:07]: That's rightMatei Zaharia [00:36:07]: and important to the company, I expect there to be, like, a kickoff thing, like a design doc. Nothing like that.Swyx [00:36:13]: Nothing like that.Swyx [00:36:14]: He just. We were debating in many meetingsMatei Zaharia [00:36:17]: Yeah.Swyx [00:36:17]: and then we're just debating whether it's possible or not from first principle.Matei Zaharia [00:36:20]: YeahSwyx [00:36:20]: and then, somebody just did it.Matei Zaharia [00:36:23]: Yeah, if you set yourself up so people do that'll be great. And that happened a bit with Omnigentt too. I think if I just had a doc on, like, we can make these together, everyone would, would think, “Oh, what about this? What about this?” But then you. if you try it out, it helps. And then if you have real users and they bash it and, like, it's still working, or in this case, if you have the workload, what the workload looks like, you can just test the same pattern then.Databricks' Culture of Fast PrototypingSwyx [00:36:47]: Yeah.Matei Zaharia [00:36:47]: Yeah.Swyx [00:36:47]: Tech aside, which is very cool, this is, like, the most important thing, the culture of innovation, and you don't have to ask my permission, you don't have like, do a whole form- formal process, just do it?Matei Zaharia [00:36:59]: Well, especially these days, I think withSwyx [00:37:01]: YeahMatei Zaharia [00:37:01]: AI, it's easier to buildSwyx [00:37:02]: But so, likeMatei Zaharia [00:37:03]: a prototypeSwyx [00:37:03]: I think you are very I made a lot of suite of, like, large companies and, like, I think that at scale, things slow down, and I'm sure you felt it already, but somehow you have this core of people that, like, are exempt. How? I think we hire and we work with really good people, and that's a very important part of it, and empowering them, but also spending a lot of time, maybe us in the trenches matter a lot also.Matei Zaharia [00:37:28]: Yeah, I think, I think first, people can adapt to being in the larger company, so that helps. And we wanna make sure they know that they can try stuff and settle debates and have a lot of examples of how it was done before, or launch a thing in beta or whatever. and then the other thing I do think as a company, like despite the size, we don't launch that many, like, products. We try to keep it pretty coherent. That's, that was the whole, like, theory of the company, was like instead of having, like, 20 Amazon services you need to set up, like a analytics and machine learning stack, you just have one, and it's, like, the same API, the same semantics across all of them, the same copy of the data. So that requires, like, unification. And then we added one more thing at a time. Like, we added storage with Delta Lake. We didn't used to do any storage. Then we added SQL, we added, machine learning platform stuff. So, but yeah, don't, don't do too many, but do those things well and, that also helps, it helps keep it manageable.Reynold Xin [00:38:33]: Yeah. The other thing we encourage a lot is instead of building, boil the ocean for everything, let's figure out how do we do it incrementally, how do we do it very quickly. Like, many of our productsMatei Zaharia [00:38:43]: YeahReynold Xin [00:38:43]: they're built in the span of weeks, and then we go to, hey. Like, usually my first question to whoever team is building is who's the target customer? Who are you working with? Are you on a first-name basis with them? Are you texting with them? I think having that very tight loop,Matei Zaharia [00:38:59]: Can you bring up another launch that comes to mind when, in this thing? I just want to give examples.Reynold Xin [00:39:04]: Omnigentt itself happened that way.Reynold Xin [00:39:05]: Yeah.Matei Zaharia [00:39:06]: Who's the customer? That's a good oneReynold Xin [00:39:34]: storage layer we did. we had, our largest customer at the time said like, “Okay, I need some. I want something in the cloud ‘cause, I. if the rest of our network is compromised, like this thing needs to be separate to store and query the events.” And then, talked to us, he said, “Okay, this is the rate of events per second. This is, like, the freshness I want. Can you do it?” So that was, like, way larger than any workload we had, and we had our, engineer, working on that, Michael Armbrust, and he worked just to make this work. And once it worked for them, it worked for everyone else. Yeah. This was early in the company, probably like four years in or something.Matei Zaharia [00:40:24]: 20- 2018?Swyx [00:40:26]: Yeah, ‘17, ‘18.Matei Zaharia [00:40:28]: Few companiesSwyx [00:40:28]: Do you have other examples?Matei Zaharia [00:40:30]: there'Swyx [00:40:31]: Maybe you have othersMatei Zaharia [00:40:31]: yeah, Clean Room, which is how you share data in a way without sharingSwyx [00:40:35]: YeahMatei Zaharia [00:40:35]: underlying data, but you allow specific operations. Those were done effectively initially just for two customers. I think the industry has a sense of, hey, maybe if you overfit to, like, one or two customers, it's gonna be really bad for you. But I think the, downside of overfitting is much smaller than the upside itself. And if you try to be too ambitious and boil the ocean, it's a much bigger problem.Swyx [00:40:58]: Yeah. Yeah.Matei Zaharia [00:40:58]: ‘Cause you might end up having no customer.Swyx [00:41:00]: Yeah, that's more, that's the more likely outcome.Matei Zaharia [00:41:02]: Yeah.Tech Companies vs. EnterprisesSwyx [00:41:03]: than you can pivot from there. I do think there is such a thing as a bad customer that sometimes you should fire. Yeah.Matei Zaharia [00:41:08]: They could exist sometimes if you drive. well, one of the challenge I think we probably see, and maybe many AI, so newer generation companies are seeing is, so tech companies are very different from tech companies or traditional enterprises.Swyx [00:41:22]: Yeah.Matei Zaharia [00:41:22]: And, if you optimize everything just for tech companies, you might have various challengesSwyx [00:41:27]: OhMatei Zaharia [00:41:27]: scaling them outside of tech companies.Swyx [00:41:28]: Okay, what likeMatei Zaharia [00:41:30]: YeahSwyx [00:41:30]: what like top three differences that you always think about?Reynold Xin [00:41:33]: Governance is a big oneMatei Zaharia [00:41:34]: I think, yeah, a big one is like, yeah, security, data privacy, governance, all that stuff. So usually if you're building some kinda like B2B or developer tool, like your biggest market is gonna be enterprises, but it's just very different. A company that's existed for like, it's had some form of IT for like 30 years, they have so many legacy systems or they operate in a regulated space. whereas a startup or, even like a, like sorta more recent tech company, all the. everything is new and pristine. So yeah, it's just different, and if you've never worked with enterprises or been in one, you just won't know about it.Reynold Xin [00:42:13]: Yeah.Matei Zaharia [00:42:13]: Yeah.Reynold Xin [00:42:13]: And the procurement process is probably quite different. There's far more stakeholders.Matei Zaharia [00:42:17]: Yeah, that is one. Yeah.Matei Zaharia [00:42:18]: Another piece that's interesting is I think some tech companies, people, will say, “Oh, I can build that myself,” right? I'll just build that myself.Matei Zaharia [00:42:27]: So then you go,Reynold Xin [00:42:28]: I don't think people say that about Databricks, butMatei Zaharia [00:42:31]: yeah, it dependsReynold Xin [00:42:32]: They do.Matei Zaharia [00:42:32]: They do?Matei Zaharia [00:42:32]: Yeah, the. Yeah, and it depends on the teams and things. So, but, on the other hand, like many of the enterprises say, “I don't, I never wanna be in the business of building that.” Like, I don't want my, whatever, I'm a retailer or something, I never wannaReynold Xin [00:42:45]: Yeah, sell clothes,Matei Zaharia [00:42:46]: be down because like some weird like nerd like couldn't get streaming pipelines working.Matei Zaharia [00:42:51]: That is not what I'm doing.Reynold Xin [00:42:53]: Yeah.Reynold Xin [00:42:53]: Yeah. This makes them great customers, to be honest, right?Matei Zaharia [00:42:55]: Yeah. But you have to understand that it's hard without having worked there and stuff, like you may not appreciate.Reynold Xin [00:43:01]: Look, I think they're all great. don't get me wrong, they have different challenges. But the, many of the tech companies, for sure there's a lot, far more DIY.Matei Zaharia [00:43:10]: On the flip side, you have people who are. they're very much experts in their domain, like they're building airplanes, they're, designing medicines, whatever, and they just want to bridge the technology, where like they don't wanna learn, databases or whatever. As cool as we think it is, even as interesting as the average software engineer might think it is to read a little bit, like they just never wanna know. They just say, “I have a, giant like, matrix or whatever with my, clinical data, like how do I, how do I like cluster it or whatever?” So yeah.The Dream Engine and Rewriting the Database StackReynold Xin [00:43:40]: Yeah. That's true. Okay, so and then I wanted to build out the dream engine, vision. where does this all lead? So one of the thing we, realized maybe a couple years back is that every single database engine out there, especially on the analytics side, are a decade old. pretty much everything that have reasonable traction are about a decade old. And they all started targeting some very specific narrow use cases, and then over time it's become more and more successful. They have grown in their ambition, and then they try to support more and more use cases. But the fastest way to support those use cases tend to be hacked around the abstractions that were initially created, that were not for those use cases.Matei Zaharia [00:44:23]: Yeah.Reynold Xin [00:44:23]: And then, but you can support them more or less okay. And before it, after 10 years of organic evolution that way, it becomes a gigantic pile of s**t.Reynold Xin [00:44:31]: the. And, but that includes Databricks. And very few company or very few systems, I think, have the gut to say, let's go start from scratch. Let's go back to the drawing board and design, knowing everything we know today after a decade of workloads and probably billions in revenue, let's attempt to rewrite it from scratch and make sure it will work and it can support all of these use cases. So we started doing that, but it's a very ambitious project. by the way, you can search on Wikipedia, there's this thing called second system syndrome.Matei Zaharia [00:45:08]: Yeah, I know that. Yes.Reynold Xin [00:45:09]: Or second system effect.Matei Zaharia [00:45:11]: Every developer must know what a second syndrome is.Reynold Xin [00:45:12]: It's you built your first thing and it works out great, and the second one's bound to fail because you become too ambitious.Reynold Xin [00:45:19]: And then you ask so many requirements.Matei Zaharia [00:45:20]: Or like you think everythingReynold Xin [00:45:21]: YeahMatei Zaharia [00:45:21]: and then you're likeReynold Xin [00:45:22]: You justMatei Zaharia [00:45:22]: you're, “I'm gonna design the perfect system this time.”Reynold Xin [00:45:24]: Yeah. And it turned out it's not perfect, and then it start failing and you're too ambitious, never launch, and you get killed. The, and the engineering team that started this, they were brilliant. I think we hired some of the best database engineers, on the planet into Databricks, and they were brilliant. Thank God it's not their second system. Many of them have built more than two in the past.Matei Zaharia [00:45:44]: Ah, nice.Reynold Xin [00:45:45]: But they were still worried about this, hey, building a database engine from scratch, I think the conventional wisdom is gonna take like five years to mature. This would be a very long-term project. It could fail. I think one of the engineers jokingly said, “Hey, maybe we just call it Reynolds Stream Engine.” If we name after a founder, maybe we then may get canceled or killed. But I think they built something pretty remarkable. they went back to. They changed the way the database engines were built from a paradigm point of view. Usually when y

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the release of Microsoft Copilot Cowork and its hidden financial implications for your business. You’ll learn how to calculate potential costs by categorizing your daily tasks into light, medium, and heavy workloads. You’ll discover how to apply the 5P framework to prevent runaway AI spending in your organization. You’ll identify specific strategies to optimize your workflows by separating planning from execution. You’ll explore how command-line tools can help you maintain efficiency without burning through expensive credits. 00:00 – Introduction 03:15 – Categorizing AI tasks 08:45 – The shock of the credit-based bill 14:20 – Applying the 5P framework for cost control 19:10 – Using planning to save money 25:30 – Call to action Watch this episode now to learn how to keep your enterprise AI costs under control before you start using Microsoft Copilot Cowork. Use the free Trust Insights Microsoft Copilot Cowork Cost Calculator! Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-how-to-manage-microsoft-copilot-cowork-costs.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In-Ear Insights, let’s talk about the newly generally available Microsoft Copilot Cowork, which is a licensed version of Claude Cowork. So Katie, you have spent a lot of time with Claude Cowork. You teach for Smarter X for their AI Academy on all the different uses of Claude Cowork. You’ll be doing an entire workshop at the Marketing AI conference on the Claude ecosystem and stuff like that. So when you hear that now Microsoft, the largest enterprise AI deployment system, has made effectively a copy of Claude Cowork available, what comes to mind? Katie Robbert: Endless opportunities. I have never met someone who is like, “Yay, Microsoft.” And we’ve talked about why a lot of companies are tied into Microsoft and a lot of it comes down to security and privacy. Chris, you have a whole series on enterprise AI, so enterprise AI not being the size of the company, but really more of the security and governance requirements needed. Microsoft as a workforce software, Microsoft 365, tends to check the most of those boxes, which is why so many large companies or companies in general tend to be tied into Microsoft. Which also means what we hear is, “Well, I can’t use Claude or I can’t use OpenAI, I can only use Copilot. I want all the bells and whistles that I’m seeing you guys talking about.” Very quick anecdote. My husband, who I’ve mentioned numerous times, is not a technology person—that is not the nature of his job—was lamenting that the new version of Microsoft is hiding all the replies to his emails from the entry-level user to the expert user. I don’t know anyone who enjoys using Microsoft, but I’m hoping now that this little bell and whistle is something that could bring people around on the users. Because Claude Cowork has been such a literal game changer for the way that I operate. The amount of things that I can get done that I couldn’t get done before because I’m just one person is infinite. Just the other day, I’ve always done the company financial projections—it’s very laborious. I have a spreadsheet, I have to check numbers from four or five different places. That’s something that Cowork can now not only help me with, but build an interactive dashboard for. And it’s like, “Yeah, you got multiple data sets, I got this, I can build that for you.” The amount of time it saves me is immense because it unlocks my time to do things like, “Hey, what’s a new target market we need to go after? What does that look like?” I didn’t have the brain space to do that before because I was so bogged down. So when I hear that Microsoft now has their version of Cowork, I’m like, “Wow, people are going to get so much done if they want to, if they see the opportunities within the software, if they’re curious.” Christopher S. Penn: If they can afford it. So that’s what I want to talk about on today’s show because Microsoft has released an Excel spreadsheet, of course, a calculator for how much Cowork will cost you because it is pay-as-you-go, it is not flat rate. So let’s talk about some of the tasks that you do, Katie. They define tasks in three categories: light, medium, or heavy. A light task is basically prompt and chat, no tool calls, one deliverable. And they classify this by the four different categories: corporate knowledge workers, customer-facing knowledge workers, technical workers, and managers and senior leaders. Now I would say that you are a manager and senior leader—I think that’s who you are, what you do. I am a technical worker. We have Kelsey who is a customer-facing knowledge worker—she’s our account manager—and we have John who is our corporate knowledge worker. John is our head of business development. So we actually check the box on each of these Cowork types of people. Now on a daily basis, Katie, you for sure have at least one Cowork process that calls more than one tool because you send out a daily update. So you have at least one of those that’s a medium-level task that sends up our daily sales report. What other daily tasks do you have Coworks have to do? Katie Robbert: I have Cowork Daily set up to send me a daily writing prompt. All it’s doing is writing to a Word document. I would imagine that’s a lightweight task. Basically, one of the things that I’m doing for my own professional development is I’m trying to make sure I don’t lose that writing muscle. As AI makes it so easy to replicate our voices, I want to make sure I don’t lose it. So I spend a few minutes every morning writing to a randomly generated prompt. So I would imagine that’s a lightweight thing. You mentioned the update that I send to the team. This is calling on our CRM data, and that I would imagine is sort of a medium because that’s only one piece of software. But once a month, I’m calling on our CRM and our financial data and a couple of other sources, so that would be a heavy task. So on a day-to-day basis, the scheduled tasks that I have are fairly lightweight. But then when I get into the real thinking, that’s when—so I was working on something this morning on behalf of the team. I was engaging a plug-in, I was engaging the Google Drive connector, I was engaging the Google Search connector, I was engaging that deep thinking of “put all this information together,” and all of the skills that are involved: the skill of building a Word doc, the skill of building a PDF, the skill of building an HTML interactive page, the skill of building a PowerPoint—all of those in one specific task. So I would say that is a heavy task, even though it looks at the surface like a lightweight task. Christopher S. Penn: I would say, and I think this is a fair characterization, you probably do two heavy projects a day in Cowork because you’re constantly doing deep strategy and things. So I’m going to put two a day—this is a monthly calculus—put down 60 there. Now for Kelsey, I would say Kelsey at least does at least one light and one medium task in Claude per day. I think it’s actually more than that, but I’m going to put that down as a starting point. What do you think? Katie Robbert: I think that’s a fair starting point. Christopher S. Penn: Okay. For me, I work in Claude code, which is slightly different, but since we’re just trying to get a sense of what Cowork will cost, I’m going to do the equivalent. On a day-to-day basis, I probably do five tasks that are light, so that’s going to be 150 of those a month. I probably do 10 tasks that are medium, so that’s going to be 300 a month. And I probably—actually, I know I do over 10 tasks a day that are heavy, that are like pure heavy code lifting. So that’s going to be another 300 there for John. John really doesn’t use Claude much at all, I don’t think. So maybe like 30 at most. Katie Robbert: Yeah, I think so. We have a skill that was built specifically with his role in mind, and he runs it maybe once every couple of weeks. When I look at the weekly tasks—so this is looking at a month at a glance—I would actually bump up the medium tasks for me because I have weekly reports that are run that engagement, the Claude Chrome extension, the connections to our CRM, connections to our project management software. I have eight of those weekly. Christopher S. Penn: Okay, so you’re basically running two mediums a day. Effectively. Katie Robbert: Yeah. Christopher S. Penn: Claude or Microsoft Copilot Cowork bills on what are called credits because why make this easy? Light tasks bill 125 credits, medium tasks bill 500 credits, and heavy tasks bill 1,200 credits. The cost is a penny per credit. So our Microsoft Copilot Cowork cost—are you ready for this, Katie? $1,600 a month. Katie Robbert: Get out. We’re going back to candlelight and whittling pencils. Christopher S. Penn: That is because it’s a penny per credit, which they do to make it sound cheap, not realizing that a single heavy task is 1,200 credits. So a single task is $12. So for me to do one QA run on a piece of software is swipe the credit card for $12. On a monthly basis, we are consuming effectively 657,000 credits, which is $6,570 total, all in. It’s $1,600 per user. So Katie, our Trust Insights Copilot Cowork bill is $6,570. Katie Robbert: I have no words. That is insane. And to be fair, so you and I, Chris, I would say are power users. We are turning to these tools to do all kinds of things all day long. Even with trying to do things and schedule them off-hours to not be during peak usage, we’re still using up usage. And yeah, we are a small team. If we take out the work that Kelsey does just for the sake of this example, you and I are still eating up the majority of the cost. If we take out you, I’m still eating up a majority of the cost. I don’t know how a company or team is supposed to be able to afford to use this. It’s a real bait and switch. Shame on Microsoft. Christopher S. Penn: Well, this is enterprise. They can do this. Katie Robbert: Yeah, they can. It doesn’t mean they should. Christopher S. Penn: So your usage, because a credit is a penny, your usage of Copilot Cowork a month would be $1,057.50. That is how much you consume in equivalent credits in the system. Now granted, we pay for the four of us to share a Claude Max 20 account; we pay $200 a month for it. This at the enterprise level, you’re talking four people, $1,600 for four people, one of whom barely will use it. Realistically, like you said, we’re probably going to average $3,000 an employee is what it will cost to use Cowork. Katie Robbert: Which is an insane amount. For some companies that don’t even blink at that, but that’s a very small handful of companies who would feel that way about $3,000 a month. One of the things that we’re doing with a lot of our clients right now is trying to help them find cost savings in their tech stack—like how many tools can they reduce or licenses they can let go of and replace with things like Claude Code or Claude Cowork. But if they’re like, “Yeah, I want to do that exercise,” and what I have is Microsoft Cowork, I would say, “Cool, we’re not doing that exercise until Microsoft changes the billing,” because it’s going to cost you 10x more than it’s costing you now. It’s not worth it. Which is a real shame because Microsoft users have been waiting for this kind of functionality. Christopher S. Penn: And so what I wanted to talk about on today’s podcast episode, now that we’ve worked out that this thing is going to cost you three grand a month—because one of the things that people have pointed out on LinkedIn is, “Oh great, you fired all these people so you can switch to AI; now AI is going to cost you more than the people did”—is how do we reduce AI costs? How do we use AI more efficiently? Because this is clearly a lot of money. Katie Robbert: If only we had a few things to start with. I’m going to shock and dazzle everyone and say, “Guess what? Start with the 5P framework by Trust Insights.” You can learn more about it at TrustInsights.ai/5P-framework. At a high level, the five Ps are: Purpose—what the heck are you doing? People—who the heck’s involved? Process—how do you do the thing? (These are your SOPs). Platform—what tools are you using? (Not just the AI, but also your external data sources). And Performance—did you do the thing? It sounds really straightforward because it is. However, a lot of people go straight to pushing the buttons and “vibe coding” and, “Hey, build a thing.” “What do you want it to be?” “I don’t know, you pick.” Without doing this work up front, yeah, you’re going to find yourself at $650,000 a month very quickly. There is no tool that allows you to skip over good planning upfront, good governance up front. Microsoft Cowork is no different from any other large language model in that you still need to have good requirements, you still need to have good prompting, you still need to have good governance, even if you’re just using it internally on your own systems. Enterprise companies, any company, has sensitive data somewhere within their SharePoint stack, within their databases, their document repositories. You don’t want to accidentally or carelessly give a large language model access to that because you didn’t plan ahead. So that’s my soapbox. I’m coming down off of it. Chris, what would you add to how to make AI efficient? Christopher S. Penn: So planning, yes, 100% is going to make the most of the tools you have. The other question is, given these outlandish costs, is Microsoft the right system for you to use? Because Claude in Anthropic’s enterprise level is just as expensive. Companies have recently seen their burn through their entire Claude usage for the year, their budget in weeks. I think it’s Uber that burned their 12-month budget in a month and a half in terms of their token budget. So when we look at these prices, Katie, you remember a while back I had said, “Hey, Nvidia’s got this cool little desktop box. It’s $5,000.” You’re like, “You’re not buying $5,000 worth of hardware.” Absolutely not. Now if Microsoft or Anthropic said, “Hey Katie, you need to pay us $6,500 a month,” you’d be like, “You know what, Chris, go and buy one of those boxes; let’s buy one for each of the team and we’re going to drop Anthropic because we are not paying $6,500 a month for AI.” Right? Katie Robbert: You know, and so it’s an interesting question because where we started the conversation was saying there’s a reason why people are wedded to using Microsoft because of the security and privacy. I don’t know that introducing an Nvidia box would comply with the regulations set forth by that company. I mean, that’s a big question. It’s an interesting workaround, but it’s not going to work for everybody, especially the more regulated the industry gets. It just might not be an option. Christopher S. Penn: Yeah, it’s going to very heavily depend on IT. However, because it lives literally in your infrastructure, you do have a lot more governance over it because it’s literally a box that sits on your desk that you control. But more importantly, today’s top local models match a lot of the cloud foundation models and capabilities. GPU AI’s new GLM 5.2 matches Claude Opus 4.8 capabilities. Now you’re going to need a few of those Nvidia boxes to be able to load and run it well for a small cluster of employees. But for the lighter models like Qwen 3.6 or Google’s Gemma 4 if you have to, or Nvidia’s Neotron Ultra if you have to use a US-based model because of regulatory reasons—like you’re not allowed to use anything Chinese, regardless of the fact that it’s on your infrastructure—those are options that you would then use a tool like Open Cowork to handle the inference for it. So my suggestion is that to Katie’s point, use the 5Ps and then drill down and say, “What are the things that we absolutely positively have to use Cowork for?” Or can we make that task as deterministic as possible using command-line tools and stuff that do not require AI? So for example, Katie, when you query HubSpot every day with Claude Cowork, that is using the MCP connector that uses a ton of tokens back and forth. Now we don’t see it because we’re on an individual plan. The moment we’re forced to switch to a team or an enterprise plan, we will say, “Okay, we’re going to use the HubSpot command-line tool which can fetch data in and out.” And then the AI just says, “Hey tool, give me the thing,” and it goes off and does the back and forth and brings the data back and hands it to the AI. That will dramatically cut the amount of AI usage you have because a non-AI tool is getting data for you. Katie Robbert: As you’re describing it, I want to sort of make sure I understand because you’re making it sound like it’s an easy switch from the process that I currently have built in Cowork to, “Okay, just use a command-line tool.” I’m not someone who’s well-versed in command-line tools. You’re someone who is. However, you have your own set of things to do right now. So it’s time. It’s internal resources to make those switches to make the cost savings. I just want to be clear about that; it’s not a, “Oh well, in order to save money, let me just go ahead and use a command-line tool.” Like you still have to set it up. Christopher S. Penn: Yes, and corporate IT will be very busy doing that. However, corporate IT also likes us because they can then govern it. They can say, “Okay, we will ensure that this suite of 10 command-line tools is installed on every computer in the company, and there’s a joint service key that we can maintain programmatically and rotate every 30 days and stuff like that.” So that infrastructure, which corporate IT is very well-versed in, is going to be much happier with that than kind of like the whole shadow IT where people are like, “Oh, I’ll just have Claude make me this thing.” No, they would much rather say, “I would like to have control over the command-line tools that are installed on every machine in the company.” Katie Robbert: So work that out. You’ve worked with IT teams before. How likely is it that they’re going to—if you say, “Hey, I would like to have control over the command-line tools on every machine in the company,” they’re like, “Yeah, sure, Chris, no problem. Let me bump you to the top of the list. You’re a priority now.” I think you’re going to have a hard time. Like, we see the value in it, we know that it’s a useful thing. I just want to be realistic, and I’m trying not to derail the conversation too much, but I just want to be realistic that, like, yes, that’s the thing. If you have the skills to do it and if you don’t have to go through your IT team to do it, absolutely do it. If you have to go through your IT team and they have to set it up, get comfy, get in line; you’re not a top priority right now. Christopher S. Penn: Yeah, well, my perspective is IT would want to do that. It would be like, “We would love to have more control over this to stop the shadow IT that’s happening all over the place because of AI.” So IT in its MDM config would say, “Okay, these are the 10 tools that we’re going to drop on every machine, and we’re going to also programmatically alter your Claude MD files and stuff to tell Claude this is what’s installed. You must use it so that it cuts those costs.” And IT can then say, “We certify these 10 command-line applications are safe to use.” Katie Robbert: Provided it has the time to get skilled up to do that. So yeah, I like to make sure that we’re very clear about caveats because in the 25 to 30 minutes we have for a podcast, we go through things like “do this, do this,” and then it’s, “Well, what do you mean? It said no.” So let’s get back to—Microsoft has started to release Cowork, their version of Cowork, and we’re talking about AI efficiencies. When you think about starting places for someone who’s using Microsoft, someone who’s using their Cowork version, what is the first thing you think somebody should do before they start burning tokens or usage or spending pennies? Christopher S. Penn: The five Ps, the planning, and build all of your prompts and all of your infrastructure for Cowork in regular Copilot, because regular Copilot is very smart. Now in regular Copilot, if you go in the upper right-hand side, there’s a little menu, a little drop-down saying “models,” and you should choose for planning. Choose GPT 5.5, soon to be 5.6—”think deeper,” that’s the smartest model that’s available. And say—and that’s where you have your conversation like, “Oh, I want to do this in Cowork. I don’t want to do this, I want to do this. Help me figure this out. Ask me questions. Let’s plan this out. Here’s the Trust Insights 5P framework. Help me use this to come up with these plans.” So you do all of your planning and all that heavy token usage in regular Copilot to build the skills and the pieces that you can then drop into Cowork, so you don’t have to use Cowork to plan because Cowork is going to chew up your usage. That way, if you can use regular Copilot, it should be a little bit lighter on your budget. Katie Robbert: And I think one of the questions that you should add into your planning is, “Can I do this in Copilot or do I need Cowork for this?” And you know, I want you to use your human judgment, but it would be a good idea to ask the large language model like, “Do you have the capabilities to do this within Copilot or do I need to bring this into Cowork to actually execute it?” Because you may be surprised. You know, to Chris’s point, the models are getting smarter every day. And so you may not need to execute what you think you need to execute in Cowork; you may be fine with using Copilot. Yes, I get it’s not as shiny and as exciting, but you know what’s also not exciting? Being told you owe the company $60,000. That’s not exciting. Christopher S. Penn: Exactly. Even for something like scheduled tasks—Microsoft Copilot tasks are scheduled tasks—so if it’s not something that needs Cowork’s horsepower, that will obviously keep you from chewing up those extra credits over there. Katie Robbert: Yeah, and I think that’s a good best practice for a lot of these tools, you know? So can you do your planning in Claude Chat before bringing it into Cowork? Can you do your planning in Gemini before bringing it into their version of whatever that is? And that’s just a good best practice for efficiency in general. Christopher S. Penn: Yeah, I mean, when I do my planning for even software builds and stuff like that, the first thing I do is I have a master planning prompt. It’s actually a skill that incorporates the 5P framework by Trust Insights. And so I have the model ask me questions from the 5P framework: “What are you doing? Who’s it for? How should it work? What are the additional command-line tools that we should be using? What is the definition of done?” And all of that is stuff that if I don’t dictate it out loud, it knows to ask me for it. So I can plan first, and then the language model rebuilds the prompt into something that meets all of those conditions and produces a really solid output that I can then go use to build requirements documents and all the stuff. You will save so much time and money by investing more heavily in planning up front, and you can then hand off the execution of the plan to a very small, fast model. Katie Robbert: And I think that’s a really good pro tip. And I just want to give a small plug—you can actually download, we have for sale in our academy at Academy.TrustInsights.ai, a “prompt-to-skill.” So basically, as Chris was just describing, he has a specific process for building those requirements. This prompt-to-skill will help you do that and get more efficient at building those requirements. And then what you may find that you have is a reusable template, and it makes that even more efficient. So start with that. Go to Academy.TrustInsights.ai, purchase the prompt-to-skill—it’s very awkward to say that—and then start building out those requirements before you bring it into something like Cowork. And you’re going to save yourself a lot of time and money, and people are going to be like, “Wow, you did that really fast. How did you do that?” And you’ll be like, “I don’t know, I’m just that good.” But in the back of your mind you’re like, “I use the 5P framework by Trust Insights. It got me there faster.” Christopher S. Penn: Exactly. So Copilot Cowork from Microsoft is now generally available. Before you type one character into it, please take the time to use the 5P framework by Trust Insights. Take the time to understand what your company has budgeted. Take the time to understand what tasks fall in each category, and as best as you can, try to reserve it for the things that truly need Cowork’s capabilities. And don’t just make it the default. If you’ve got some thoughts about the new Microsoft Copilot Cowork that you want to share, pop by our free Slack group. Go to TrustInsights.ai/analytics-for-marketers where you and over 4,700 other marketers are asking and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to TrustInsights.ai/TI-Podcast. You can find us in all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

In this solo episode, Axel gets highly tactical on the one thing every real estate investor should be doing right now to get the most out of AI — regardless of which tools they use, how big their portfolio is, or how tech-savvy they are. The answer isn't a new app or a prompt hack. It's building context: the foundational informational backend of your business that allows AI tools like Claude to actually understand your company, your portfolio, and your goals well enough to do meaningful work on your behalf.Axel opens up his own Notion workspace and walks through exactly what Aligned Real Estate Partners has built — from company information and brand voice to portfolio dashboards, transaction coordination, and vendor contacts. He also shares specific real-world use cases: auto-completing loan applications, running weekly email analyses to identify new automation opportunities, and having Claude keep the Notion database updated on its own.This episode is essential listening for any investor or operator who wants to build a real estate business that scales with AI — not one that uses AI as a party trick.Join us as we dive into:Why Axel recommends Notion as the informational backbone of your real estate business — and why it integrates cleanly with Claude, Google Drive, Gmail, and other tools.The new employee analogy: why giving AI context is exactly like training a new hire, and why most people skip this step entirely.A walkthrough of Aligned's Notion workspace: company information, brand voice, mission and values, organizational chart, glossary, lessons learned, software tools, and business history.Why uploading monthly property management statements to Notion creates a living dashboard that Claude can analyze and reference at any time.How Claude is integrated with Axel's Gmail, calendar, Notion, and Beehiiv — and what becomes possible once those connections are live.The 21-day email analysis scheduled task: how Axel uses Claude Cowork to identify workflows that can be automated or removed from his plate entirely.The rent comp use case: how Claude now automatically runs a rent comp search and drafts a renewal offer whenever a lease renewal email appears in the inbox.The weekly vendor discovery task: Claude scans the last seven days of email, flags new contacts worth adding to Notion, and updates the database with one click.Are you looking to invest in real estate, but don't want to deal with the hassle of finding great deals, signing on debt, and managing tenants? Aligned Real Estate Partners provides investment opportunities to passive investors looking for the returns, stability, and tax benefits multifamily real estate offers, but without the work - join our investor club to be notified of future investment opportunities.Connect with Axel:Follow him on InstagramConnect with him on LinkedinSubscribe to our YouTube channelLearn more about Aligned Real Estate Partners

Topics covered in this episode: Backup Docker volumes locally or to any S3 Pyodide 314.0 Release nb-cli: A Command-Line Interface for AI Agents and Notebook Automation Hindsight Agent Memory That Learns Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python AWS Community Day Midwest tomorrow Wednesday the 24th in downtown Indianapolis, Six Feet Up is sponsoring and there are 2 Sixies presenting Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an bonus digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Backup Docker volumes locally or to any S3 Via Bryan Weber (thanks Bryan!), who spotted it over on Virtualization HowTo. Find Bryan at bryanwweber.com. offen/docker-volume-backup is a lightweight companion container that backs up the volumes your apps actually depend on, then ships them somewhere safe. It's tiny: written in Go and about 25MB compressed, roughly 1/20th the size of the shell-based image (jareware/docker-volume-backup) that inspired it. Drop it into your docker compose file as a backup service, mount the volumes you care about as read-only, and you're off. Push backups to a pile of destinations: a local directory, plus any S3, WebDAV, Azure Blob Storage, Dropbox, Google Drive, or SSH-compatible target. Mix and match as many as you want in one run. Recurring cron-style backups in a Compose setup, or one-off backups straight from the Docker CLI. Production-friendly touches worth calling out: Rotates away old backups so you don't quietly fill the disk. GPG encryption for your archives. Notifications on finished and failed runs (so you find out about failures before you need the backup). Stop a container during backup for a consistent snapshot using a simple docker-volume-backup.stop-during-backup=true label, then auto-restart it. Run custom commands during the backup lifecycle (great for a database dump before the file copy). Docker Swarm support, plus arm64 and arm/v7 builds. Hello, Raspberry Pi homelab. Fun aside from Bryan: he searched our back catalog for this tool and the search came back so fast he thought it hadn't run. Love to hear it. Calvin #2: Pyodide 314.0 Release PEP 783 is the real news — Pyodide maintainers used to hand-build 300+ packages. Now anyone can publish Pyodide wheels to PyPI with cibuildwheel. The version jump from 0.29 to 314.0 is intentional — it now tracks the Python version, so 314.x = Python 3.14. Binary compatibility is locked per Python cycle, meaning packages you build today won't break on the next Pyodide release. sqlite3, ssl, and lzma are back in the default stdlib — no more await pyodide.loadPackage("sqlite3"). Bigger download, but a much smoother experience for newcomers. bigint precision bug is fixed — values above 2^53 were silently losing precision when crossing the Python/JS boundary. The new JsBigInt type makes the roundtrip correct. Worth flagging if anyone is doing numeric work in a browser app. Experimental TCP sockets in Node.js — you can now connect Pyodide to a real database (MySQL, PostgreSQL, Redis tested) when running server-side. Blurs the line between "Python in the browser" and "Python runtime anywhere Wasm runs." Michael #3: nb-cli: A Command-Line Interface for AI Agents and Notebook Automation From Piyush Jain (Jupyter and LangChain maintainer) on the Jupyter blog: nb-cli: A Command-Line Interface for AI Agents and Notebook Automation. nb-cli is an experimental, Rust-based CLI to read, write, execute, and search Jupyter notebooks. The premise: agents are great at CLIs but terrible at hand-editing the nested JSON in an .ipynb, so let them operate on the notebook from the outside instead of running inside it. Works with or without a Jupyter server. No server? It reads/writes .ipynb files directly and talks to kernels over ZeroMQ. Connected to a live JupyterLab, your edits show up instantly via Y.js (the same CRDT Jupyter uses). Smart output format: instead of token-heavy JSON or ambiguous plain markdown, it uses @@cell / @@output sentinels with inline metadata. Less wasted context, unambiguous structure, and it degrades gracefully on truncation. The payoff is composability. "Add a summary section and run it" becomes one shell pipeline instead of six agent tool calls. And nb search notebook.ipynb --with-errors returns only the failing cells, so the agent skips the cells that worked. Claude Code tie-in: it ships as an agent skill. npx skills install jupyter-ai-contrib/nb-cli and your agent can drive notebooks via nb. Out of jupyter-ai-contrib, which aims to become an official Jupyter AI subproject. Still early (crates.io is at v0.0.5), so kick the tires before anything load-bearing. See also marimo-pair. Calvin #4: Hindsight Agent Memory That Learns AI agents forget everything between sessions — Hindsight gives them persistent memory that learns over time Simple three-method API: retain(), recall(), reflect() — store, retrieve, and reason over memories TEMPR retrieval runs semantic, keyword, graph, and temporal search in parallel for accurate results Automatically consolidates related facts into durable observations instead of piling up duplicates pip install hindsight-all runs the entire server in-process; integrates with LangChain, LlamaIndex, Pydantic AI, CrewAI, and more Extras Calvin: Clanker: A Word For The Machine **Ponytail — You know him. Long ponytail. Oval glasses. Has been at the company longer than the version control** **Klangk: Multi-User AI Sandboxing, Collaboration and Coding Platform** Cursor announces Origin performative-ui to quick start your new idea Michael: Astral Joins OpenAI: The Interview SpaceX to acquire Cursor And OpenAI renews Open Source support Portuguese subtitles are now available for Talk Python courses DSF is hiring including Six Feet Up support Joke: Oh Babe…

#publi #colaboración

Is your brain cluttered with every new tool, platform, and software someone told you that you absolutely need? Same. This episode started as a simple refresh of our tech stack after a listener wrote in asking what we actually use to run our businesses. Spoiler: the list is still shockingly short. But somewhere between Google Sheets and listing descriptions, we took a turn into AI territory, and y'all, we are not turning back. We walk through our bare-bones tech stack (yes, we still use a spreadsheet), talk honestly about how we are each using AI in our real estate businesses right now, and share why keeping things simple is not laziness, it is a strategy. We also get into SEO vs. AEO vs. GEO (yes, GEO is a real thing and it matters), what AI actually pulled up when Alissa searched herself, and the big announcement: Katy is teaching a monthly AI class inside the Hustle Humbly Community starting now. If you have been quietly panicking every time someone mentions AI, or if you have been using it already and want to go deeper, this episode is for you. Here's what we cover in this episode: The listener email that prompted this episode and our full simple tech stack answer Why simpler systems get used more consistently (and cost less money) Google Sheets, Trello, Canva, email, Google Drive, MLS, and yes, a little AI Transaction management and e-sign tools required by our brokerages How to qualify your tech choices based on the type of business you want to run SEO vs. AEO vs. GEO: what each one means and which one actually matters for your real estate business What happened when Alissa's seller looked her up with AI (hint: reviews matter a lot) How Alissa used ChatGPT to analyze multiple offers and review pre-approval letters Using AI to virtually stage photos, repaint rooms, and show sellers the vision How Katy used Claude to turn listing appointment notes and photos into a beautiful PDF checklist for a seller The Plaud device that listens to your appointments and summarizes them Why shiny tools do not fix broken habits Claude vs. ChatGPT: what is different and why Katy made the switch Projects, skills, and co-work inside Claude explained simply The new monthly AI class inside the Hustle Humbly Community for $25/month Why your Google reviews are your most important AI visibility tool right now Key Quotes & Takeaways: "Shiny objects feel productive, but they can delay real progress." Alissa "New tools do not fix broken habits. If you have systems in place that you're not using, a new system is not going to help you." Alissa "As long as you're running your business like a business and getting reviews from people, the internet and the AI are going to pick up on that." Alissa "You can not ask for more business if you are not taking care of the business you already have." Alissa "Using AI well will probably cut your task in half." Katy Products, People & Previous Episodes Mentioned: Episode 8: Tech Tools for Real Estate (hustlehumblypodcast.com/8) Google Sheets (free) Trello (free) Canva (free and paid) Google Drive (free and paid) ChatGPT (chatgpt.com) paid plan ~$20/month for photo editing Claude / Claude.ai (claude.ai) paid plan ~$22/month MLS e-sign (included with board dues) Dotloop (e-sign & transaction management) SkySlope (transaction management) Hustle Humbly Community (hustlehumblypodcast.com/membership) Email Templates 101 (emailtemplates101.com) Want to toast someone on the show? Send us a voice or video message with your name, who you are toasting, and why! Email it to team@hustlehumblypodcast.com. Leave us a review at http://ratethispodcast.com/hustlehumbly Get your FREE Database Template: http://hustlehumblypodcast.com/starthere Email Templates 101: http://emailtemplates101.com All Resources: http://hustlehumblypodcast.com Submit your topic ideas and toasts to Team@HustleHumblyPodcast.com

What does it mean to be baptized for the dead? Is this a practice Christians are supposed to do since Paul mentioned it?In today's episode, Pastor Derek and Pastor Jackie continue through their Strange Scriptures series and talk through an odd verse in 1 Corinthians 15 that has become the foundation for some problematic doctrines. We look at what it means (and what it doesn't mean) to be baptized for the dead and try to come away with some applications for us today!The 17:17 podcast is a ministry of Roseville Baptist Church (MN) that seeks to tackle cultural issues and societal questions from a biblical worldview so that listeners discover what the Bible has to say about the key issues they face on a daily basis. The 17:17 podcast seeks to teach the truth of God's Word in a way that is glorifying to God and easy to understand with the hope of furthering God's kingdom in Spirit and in Truth. Scriptures: 1 Cor. 15:29; 1 Cor. 15:12-28; Acts 17:32; John 3:16; Eph. 2:8-9; Titus 3:5; Rom. 14:12; Rom. 9:3; 1 Cor. 15:30-32.If you'd like access to our show notes, please visit www.rosevillebaptist.com/1717podcast to see them in Google Drive!Please listen, subscribe, rate, and review the podcast so that we can reach to larger audiences and share the truth of God's Word with them!Write in your own questions to be answered on the show at 1717pod@gmail.com.  God bless!

In this must-listen episode, Dr. Ryan Moenster and Dr. Megan Klatt dive deep into the latest infectious diseases trials, guidelines, and novel therapeutics. Staying current in the field is a challenge, but this podcast episode does the legwork for you. Note: BCIDP credit is available for this episode. Google Drive to Slides from Live Session: [https://drive.google.com/file/d/11uHzZoValckfsjUTIdVyAJ35QDSVRfQu/view?usp=sharing](https://drive.google.com/file/d/11uHzZoValckfsjUTIdVyAJ35QDSVRfQu/view?usp=sharing "https://drive.google.com/file/d/11uHzZoValckfsjUTIdVyAJ35QDSVRfQu/view?usp=sharing") How to Obtain BCIDP Recertification Credit for this Episode: Visit https://sidp.org/BCIDPhttps://sidp.org/BCIDPSIDP - BCIDP Recertification for more information SIDP welcomes pharmacists and non-pharmacist members with an interest in infectious diseases, learn how to join here: https://sidp.org/Become-a-Member Listen to Breakpoints on iTunes, Overcast, Spotify, Listen Notes, Player FM, Pocket Casts, Stitcher, Google Play, TuneIn, Blubrry, RadioPublic, or by using our RSS feed: https://sidp.pinecast.co/

Digital clutter does not take up visible space in your home, but it still takes up head space.In this episode, I talk with Kari Denker about physical memories, photo boxes, old albums, digital files, Dropbox, Google Drive, email clutter, phone photos, and what happens when the next generation has to sort through what we keep. This is not a guilt trip. It is a practical conversation about managing our resources—physical and digital—with small, doable steps.In this episode: Digital clutter becomes overwhelming when we treat it like one huge project we have to solve all at once. Instead, we can manage it little by little by deleting small batches, narrowing down photos, reducing duplicates, and keeping what actually helps tell the story.You'll learn:Why inherited photos and papers can feel sad, confusing, and guilt-ladenHow digital clutter creates mental friction even when it is invisibleA simple weekly method for deleting files and phone photosWhy narrowing an event to seven photos can help you tell the storyHow to stop treating digital decluttering like an emergency projectBest next step:Take the free Smile and Start Challenge: simplyconvivial.com/smileKari's website: ordinarykari.comSusan Allibone memoir: https://amzn.to/4efcYX4Kari shares how sorting through a family estate made her think differently about her own digital clutter. She began deleting 25 files at a time from different storage locations and 50 phone photos during her weekly review. Those small steps help reduce the overwhelm of finding files, managing photos, and leaving behind a more understandable digital legacy.Stop feeling overwhelmed by digital clutter. Learn practical strategies to organize your files and regain control of your workspace today.This discussion focuses on the challenges of managing an ever-growing volume of information. If you struggle with disorganized folders, endless email chains, or general digital overwhelm, these insights offer a clear path forward. We break down actionable steps to improve your digital organization habits and make your daily workflow more manageable.Implementing these methods for digital minimalism helps you clear the noise and focus on what actually matters. By applying these techniques to manage digital files, you can create a sustainable system that keeps your desktop and documents clean over the long term. Many people find that simple adjustments to how they declutter digital life lead to immediate improvements in overall productivity tips and mental clarity.Subscribe for weekly productivity breakdowns, and comment below on which area of your computer gives you the most stress.

If you're searching for how to find government contract opportunities and actually keep up with them, this episode is a real-time, screen-share walkthrough of exactly how it's done. Ryan Atencio shows the entire process of spotting an opportunity, evaluating it, and moving it through a pipeline, no theory, just the actual workflow. Whether you're already bidding or just trying to understand how serious contractors stay organized, this episode breaks down a system you can copy today. In this episode, you'll learn: How a custom Gemini AI gem can instantly "shred" a lengthy statement of work into a condensed summary, saving hours of manual review on opportunities like a 400-square-foot restroom renovation in San Antonio Why high-visibility projects in nationally significant structures (like a National Park restroom facility) come with hidden cost drivers such as toilet trailer rentals, gray water removal, and historic preservation requirements How to email subcontractors using the solicitation's exact naming convention so they can give a fast yes-or-no decision without extra back-and-forth A simple Google Drive folder system for organizing solicitation documents and proposal documents so nothing gets lost over a long weekend How a color-coded pipeline tracker (with linked solicitation folders and SAM.gov references) helps contractors stay on top of SDVOSB opportunities like a Tinker Air Force Base sand repair and roof repair project EPISODE CHAPTERS: 0:00 - Welcome to the Federal Help Center podcast 0:30 - Building a custom AI gem to shred solicitations 1:13 - Breaking down the San Antonio restroom renovation 2:01 - Hidden costs of national park preservation projects 3:04 - Emailing subcontractors using exact solicitation naming 4:24 - Organizing solicitation documents inside Google Drive 6:24 - Building a color-coded opportunity pipeline tracker 8:01 - Linking SAM.gov solicitations to your pipeline sheet 10:03 - Closing thoughts and community recap Mindy gives you the federal opportunities, agency signals, recompete intel, and pursuit briefs that tell you not just what contracts exist, but which ones to chase and how to win them. Sign up for free Daily Alerts and get opportunities delivered to your inbox before the day starts.

Dr. Kevin Christie shares AI updates for chiropractors, outlining tools and practical uses he's testing and recommending. Dr. Christie highlights Perplexity for answering clinical questions and pulling reputable research, then describes using Claude (including Claude Cowork as an AI agent) to turn call notes into written action plans and to convert research into PowerPoint presentations. He explains how Google's paid Gemini integrates with Google Drive and Google Slides, including a “beautify this slide” feature to improve slide design. Dr. Christie also suggests using AI chat tools to research and build community outreach lists (gyms, trainers, yoga studios, attorneys, doctors) based on a clinic's target audience. Finally, he discusses AI phone answering for clinics as something to watch, but remains cautious and prefers a human touch, possibly using AI after hours.

Sarah Kellen told Congress that she was not a willing architect of Jeffrey Epstein's operation but one of his victims, claiming Epstein groomed, abused, isolated, and controlled her for years. She described herself as trapped inside his world through sexual, psychological, and emotional coercion, and said Epstein continued to exert power over her even while he was incarcerated. That testimony matters because Kellen has long been one of the most controversial names in the Epstein case: she was not some distant acquaintance or occasional employee, but a close assistant whose name appeared in the non-prosecution agreement and whose alleged role has been described by survivors as central to the scheduling, travel, and logistics that made Epstein's abuse machine function.The skeptical read is that Kellen's testimony may explain parts of her relationship with Epstein, but it does not automatically erase the serious questions about what she did, what she knew, and how long she remained embedded in his operation. Being abused by Epstein and enabling Epstein's access to other victims are not mutually exclusive possibilities, and that is the uncomfortable center of the issue. Her testimony shifts the frame from co-conspirator to coerced participant, but Congress and the public still have to weigh that against the survivor accounts, the documented logistics, the years of proximity, and the fact that Epstein's criminal enterprise required trusted people to keep the appointments, movements, and access points running. In plain terms, Kellen may have been victimized by Epstein, but that does not settle the question of whether she also helped him victimize others.to contact me:bobbycapucci@protonmail.comsource:2026-05-21 Sarah Kellen - Transcript.pdf - Google Drive

Sarah Kellen told Congress that she was not a willing architect of Jeffrey Epstein's operation but one of his victims, claiming Epstein groomed, abused, isolated, and controlled her for years. She described herself as trapped inside his world through sexual, psychological, and emotional coercion, and said Epstein continued to exert power over her even while he was incarcerated. That testimony matters because Kellen has long been one of the most controversial names in the Epstein case: she was not some distant acquaintance or occasional employee, but a close assistant whose name appeared in the non-prosecution agreement and whose alleged role has been described by survivors as central to the scheduling, travel, and logistics that made Epstein's abuse machine function.The skeptical read is that Kellen's testimony may explain parts of her relationship with Epstein, but it does not automatically erase the serious questions about what she did, what she knew, and how long she remained embedded in his operation. Being abused by Epstein and enabling Epstein's access to other victims are not mutually exclusive possibilities, and that is the uncomfortable center of the issue. Her testimony shifts the frame from co-conspirator to coerced participant, but Congress and the public still have to weigh that against the survivor accounts, the documented logistics, the years of proximity, and the fact that Epstein's criminal enterprise required trusted people to keep the appointments, movements, and access points running. In plain terms, Kellen may have been victimized by Epstein, but that does not settle the question of whether she also helped him victimize others.to contact me:bobbycapucci@protonmail.comsource:2026-05-21 Sarah Kellen - Transcript.pdf - Google Drive

Sarah Kellen told Congress that she was not a willing architect of Jeffrey Epstein's operation but one of his victims, claiming Epstein groomed, abused, isolated, and controlled her for years. She described herself as trapped inside his world through sexual, psychological, and emotional coercion, and said Epstein continued to exert power over her even while he was incarcerated. That testimony matters because Kellen has long been one of the most controversial names in the Epstein case: she was not some distant acquaintance or occasional employee, but a close assistant whose name appeared in the non-prosecution agreement and whose alleged role has been described by survivors as central to the scheduling, travel, and logistics that made Epstein's abuse machine function.The skeptical read is that Kellen's testimony may explain parts of her relationship with Epstein, but it does not automatically erase the serious questions about what she did, what she knew, and how long she remained embedded in his operation. Being abused by Epstein and enabling Epstein's access to other victims are not mutually exclusive possibilities, and that is the uncomfortable center of the issue. Her testimony shifts the frame from co-conspirator to coerced participant, but Congress and the public still have to weigh that against the survivor accounts, the documented logistics, the years of proximity, and the fact that Epstein's criminal enterprise required trusted people to keep the appointments, movements, and access points running. In plain terms, Kellen may have been victimized by Epstein, but that does not settle the question of whether she also helped him victimize others.to contact me:bobbycapucci@protonmail.comsource:2026-05-21 Sarah Kellen - Transcript.pdf - Google Drive

Sarah Kellen told Congress that she was not a willing architect of Jeffrey Epstein's operation but one of his victims, claiming Epstein groomed, abused, isolated, and controlled her for years. She described herself as trapped inside his world through sexual, psychological, and emotional coercion, and said Epstein continued to exert power over her even while he was incarcerated. That testimony matters because Kellen has long been one of the most controversial names in the Epstein case: she was not some distant acquaintance or occasional employee, but a close assistant whose name appeared in the non-prosecution agreement and whose alleged role has been described by survivors as central to the scheduling, travel, and logistics that made Epstein's abuse machine function.The skeptical read is that Kellen's testimony may explain parts of her relationship with Epstein, but it does not automatically erase the serious questions about what she did, what she knew, and how long she remained embedded in his operation. Being abused by Epstein and enabling Epstein's access to other victims are not mutually exclusive possibilities, and that is the uncomfortable center of the issue. Her testimony shifts the frame from co-conspirator to coerced participant, but Congress and the public still have to weigh that against the survivor accounts, the documented logistics, the years of proximity, and the fact that Epstein's criminal enterprise required trusted people to keep the appointments, movements, and access points running. In plain terms, Kellen may have been victimized by Epstein, but that does not settle the question of whether she also helped him victimize others.to contact me:bobbycapucci@protonmail.comsource:2026-05-21 Sarah Kellen - Transcript.pdf - Google Drive

In this episode, Mike and Ben welcome Creators Leverage Guild member Andy Ly for a conversation about scaling an Amazon Influencer business, hiring virtual assistants, and focusing more on the activities that actually drive revenue.If you're interested in learning more from Andy, he currently offers three programs: Hire Up, Scale Up, and the full LyPad Complete Program. His Scale Up course is also currently on sale for a limited time. We'll leave the affiliate link below for anyone who wants to check those out and learn more.Andy shares his story of leaving a 15-year corporate career, discovering the Amazon Influencer Program, and growing from zero to five figures a month in a relatively short period of time. The conversation covers how he used Creator Connections, targeted product research, and comparison videos to grow more efficiently while building systems that supported scale.A big part of this episode focuses on virtual assistants and how creators can start thinking differently about delegation. Andy breaks down when he hired his first VA, what different roles can cost, the difference between general and specialized help, and why workflows, SOPs, and communication systems matter if you want outsourcing to actually work.Mike, Ben, and Andy also talk through the tools he uses to manage a team, including Slack, Dropbox, Frame.io, and Google Drive, along with the importance of staying focused on the highest-value tasks instead of getting stuck in low-return work.Toward the end of the episode, Mike and Ben share an exciting new partnership with Andy Ly and LyPad Academy. Going forward, Creators Leverage Guild will also be the home for Andy's community, creating more opportunities for creators who want support around hiring, scaling, and building a stronger content business.If you are an Amazon Influencer, content creator, or entrepreneur trying to scale more efficiently, build better systems, and learn how virtual assistants can help you grow, this episode is packed with practical insight.____________________Take one of Andy's Virtual Assistant courses:Scale Up on sale for limited time!LyPad AcademySubscribe to Andy's YouTube Channel!LyPad Academy Channel____________________JOIN THE COMMUNITYIf you are looking for deeper strategy, accountability, & honest conversations with other serious content creators, the Creator's Leverage Guild was built for exactly thatLearn more and join here:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Creator's Leverage Guild⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠_____________________CHECK OUT OUR 2 NEW EBOOKS THAT JUST LAUNCHED!⁠⁠⁠⁠⁠⁠⁠⁠⁠The AIP Master Guide⁠⁠⁠⁠⁠⁠⁠⁠⁠ - Stop guessing your way through AIP. The AIP Master Guide is your go-to resource for setup, backend navigation, Store IDs, payments, uploads, & more.⁠⁠⁠⁠⁠⁠⁠⁠⁠Leveraging Brand Deals Playbook⁠⁠⁠⁠⁠⁠⁠⁠⁠ - Stop leaving money on the table. The Leveraging Brand Deals Playbook helps you pitch smarter, negotiate better, & turn free product offers into real paid opportunities._____________________JOIN OUR FREE FACEBOOK COMMUNITYConnect with other Amazon Influencers & content creators, ask questions, & stay up to date on what is working right now.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Amazon Influencer Success Facebook Group⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠_________________________TOOLS AND RESOURCES FOR CREATORSViral VueMake smarter content decisions & grow faster.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Viral Vue here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Use code STRAHL10 for 10% off for lifeOinkTrack earnings & performance across platforms.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Oink here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Use code STRAHL10 for 10% off for lifeGeniuslinks: Our #1 Deeplinking Pick!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Geniuslinks!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠VidiQ: Our #1 pick for YouTube channel Insights!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try VidIQ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Affiliate links. We may earn a small commission at no extra cost to you.__________________________CONTACTHave a question, collaboration opportunity, or topic request?Email: mike@creatorsleverageguild.com

Sarah Kellen told Congress that she was not a willing architect of Jeffrey Epstein's operation but one of his victims, claiming Epstein groomed, abused, isolated, and controlled her for years. She described herself as trapped inside his world through sexual, psychological, and emotional coercion, and said Epstein continued to exert power over her even while he was incarcerated. That testimony matters because Kellen has long been one of the most controversial names in the Epstein case: she was not some distant acquaintance or occasional employee, but a close assistant whose name appeared in the non-prosecution agreement and whose alleged role has been described by survivors as central to the scheduling, travel, and logistics that made Epstein's abuse machine function.The skeptical read is that Kellen's testimony may explain parts of her relationship with Epstein, but it does not automatically erase the serious questions about what she did, what she knew, and how long she remained embedded in his operation. Being abused by Epstein and enabling Epstein's access to other victims are not mutually exclusive possibilities, and that is the uncomfortable center of the issue. Her testimony shifts the frame from co-conspirator to coerced participant, but Congress and the public still have to weigh that against the survivor accounts, the documented logistics, the years of proximity, and the fact that Epstein's criminal enterprise required trusted people to keep the appointments, movements, and access points running. In plain terms, Kellen may have been victimized by Epstein, but that does not settle the question of whether she also helped him victimize others.to contact me:bobbycapucci@protonmail.comsource:2026-05-21 Sarah Kellen - Transcript.pdf - Google Drive

Sarah Kellen told Congress that she was not a willing architect of Jeffrey Epstein's operation but one of his victims, claiming Epstein groomed, abused, isolated, and controlled her for years. She described herself as trapped inside his world through sexual, psychological, and emotional coercion, and said Epstein continued to exert power over her even while he was incarcerated. That testimony matters because Kellen has long been one of the most controversial names in the Epstein case: she was not some distant acquaintance or occasional employee, but a close assistant whose name appeared in the non-prosecution agreement and whose alleged role has been described by survivors as central to the scheduling, travel, and logistics that made Epstein's abuse machine function.The skeptical read is that Kellen's testimony may explain parts of her relationship with Epstein, but it does not automatically erase the serious questions about what she did, what she knew, and how long she remained embedded in his operation. Being abused by Epstein and enabling Epstein's access to other victims are not mutually exclusive possibilities, and that is the uncomfortable center of the issue. Her testimony shifts the frame from co-conspirator to coerced participant, but Congress and the public still have to weigh that against the survivor accounts, the documented logistics, the years of proximity, and the fact that Epstein's criminal enterprise required trusted people to keep the appointments, movements, and access points running. In plain terms, Kellen may have been victimized by Epstein, but that does not settle the question of whether she also helped him victimize others.to contact me:bobbycapucci@protonmail.comsource:2026-05-21 Sarah Kellen - Transcript.pdf - Google Drive

God sends Moses on a mission and immediately threatens to kill him? Why does his wife then call him a "bridegroom of blood?"In today's episode, Pastor Derek and Pastor Jackie continue through their Strange Scriptures series and talk through an account in Exodus 4 where Zipporah has to circumcise her son to prevent Moses from being killed by God. This passage brings a lot of questions, and we seek to answer them!The 17:17 podcast is a ministry of Roseville Baptist Church (MN) that seeks to tackle cultural issues and societal questions from a biblical worldview so that listeners discover what the Bible has to say about the key issues they face on a daily basis. The 17:17 podcast seeks to teach the truth of God's Word in a way that is glorifying to God and easy to understand with the hope of furthering God's kingdom in Spirit and in Truth. Scriptures: Exo. 4:19-30; Exo. 2:15; Exo. 3:1; Gen. 17:7-14, 26-27; Gen. 21:2-4; Lev. 12:1-3; Josh. 5:1-7; James 4:17; 1 Tim. 3:4-5; Prov. 22:6; James 3:1.If you'd like access to our show notes, please visit www.rosevillebaptist.com/1717podcast to see them in Google Drive!Please listen, subscribe, rate, and review the podcast so that we can reach to larger audiences and share the truth of God's Word with them!Write in your own questions to be answered on the show at 1717pod@gmail.com.  God bless!

Sarah Kellen told Congress that she was not a willing architect of Jeffrey Epstein's operation but one of his victims, claiming Epstein groomed, abused, isolated, and controlled her for years. She described herself as trapped inside his world through sexual, psychological, and emotional coercion, and said Epstein continued to exert power over her even while he was incarcerated. That testimony matters because Kellen has long been one of the most controversial names in the Epstein case: she was not some distant acquaintance or occasional employee, but a close assistant whose name appeared in the non-prosecution agreement and whose alleged role has been described by survivors as central to the scheduling, travel, and logistics that made Epstein's abuse machine function.The skeptical read is that Kellen's testimony may explain parts of her relationship with Epstein, but it does not automatically erase the serious questions about what she did, what she knew, and how long she remained embedded in his operation. Being abused by Epstein and enabling Epstein's access to other victims are not mutually exclusive possibilities, and that is the uncomfortable center of the issue. Her testimony shifts the frame from co-conspirator to coerced participant, but Congress and the public still have to weigh that against the survivor accounts, the documented logistics, the years of proximity, and the fact that Epstein's criminal enterprise required trusted people to keep the appointments, movements, and access points running. In plain terms, Kellen may have been victimized by Epstein, but that does not settle the question of whether she also helped him victimize others.to contact me:bobbycapucci@protonmail.comsource:2026-05-21 Sarah Kellen - Transcript.pdf - Google Drive

Sarah Kellen told Congress that she was not a willing architect of Jeffrey Epstein's operation but one of his victims, claiming Epstein groomed, abused, isolated, and controlled her for years. She described herself as trapped inside his world through sexual, psychological, and emotional coercion, and said Epstein continued to exert power over her even while he was incarcerated. That testimony matters because Kellen has long been one of the most controversial names in the Epstein case: she was not some distant acquaintance or occasional employee, but a close assistant whose name appeared in the non-prosecution agreement and whose alleged role has been described by survivors as central to the scheduling, travel, and logistics that made Epstein's abuse machine function.The skeptical read is that Kellen's testimony may explain parts of her relationship with Epstein, but it does not automatically erase the serious questions about what she did, what she knew, and how long she remained embedded in his operation. Being abused by Epstein and enabling Epstein's access to other victims are not mutually exclusive possibilities, and that is the uncomfortable center of the issue. Her testimony shifts the frame from co-conspirator to coerced participant, but Congress and the public still have to weigh that against the survivor accounts, the documented logistics, the years of proximity, and the fact that Epstein's criminal enterprise required trusted people to keep the appointments, movements, and access points running. In plain terms, Kellen may have been victimized by Epstein, but that does not settle the question of whether she also helped him victimize others.to contact me:bobbycapucci@protonmail.comsource:2026-05-21 Sarah Kellen - Transcript.pdf - Google Drive

Sarah Kellen told Congress that she was not a willing architect of Jeffrey Epstein's operation but one of his victims, claiming Epstein groomed, abused, isolated, and controlled her for years. She described herself as trapped inside his world through sexual, psychological, and emotional coercion, and said Epstein continued to exert power over her even while he was incarcerated. That testimony matters because Kellen has long been one of the most controversial names in the Epstein case: she was not some distant acquaintance or occasional employee, but a close assistant whose name appeared in the non-prosecution agreement and whose alleged role has been described by survivors as central to the scheduling, travel, and logistics that made Epstein's abuse machine function.The skeptical read is that Kellen's testimony may explain parts of her relationship with Epstein, but it does not automatically erase the serious questions about what she did, what she knew, and how long she remained embedded in his operation. Being abused by Epstein and enabling Epstein's access to other victims are not mutually exclusive possibilities, and that is the uncomfortable center of the issue. Her testimony shifts the frame from co-conspirator to coerced participant, but Congress and the public still have to weigh that against the survivor accounts, the documented logistics, the years of proximity, and the fact that Epstein's criminal enterprise required trusted people to keep the appointments, movements, and access points running. In plain terms, Kellen may have been victimized by Epstein, but that does not settle the question of whether she also helped him victimize others.to contact me:bobbycapucci@protonmail.comsource:2026-05-21 Sarah Kellen - Transcript.pdf - Google Drive

Sarah Kellen told Congress that she was not a willing architect of Jeffrey Epstein's operation but one of his victims, claiming Epstein groomed, abused, isolated, and controlled her for years. She described herself as trapped inside his world through sexual, psychological, and emotional coercion, and said Epstein continued to exert power over her even while he was incarcerated. That testimony matters because Kellen has long been one of the most controversial names in the Epstein case: she was not some distant acquaintance or occasional employee, but a close assistant whose name appeared in the non-prosecution agreement and whose alleged role has been described by survivors as central to the scheduling, travel, and logistics that made Epstein's abuse machine function.The skeptical read is that Kellen's testimony may explain parts of her relationship with Epstein, but it does not automatically erase the serious questions about what she did, what she knew, and how long she remained embedded in his operation. Being abused by Epstein and enabling Epstein's access to other victims are not mutually exclusive possibilities, and that is the uncomfortable center of the issue. Her testimony shifts the frame from co-conspirator to coerced participant, but Congress and the public still have to weigh that against the survivor accounts, the documented logistics, the years of proximity, and the fact that Epstein's criminal enterprise required trusted people to keep the appointments, movements, and access points running. In plain terms, Kellen may have been victimized by Epstein, but that does not settle the question of whether she also helped him victimize others.to contact me:bobbycapucci@protonmail.comsource:2026-05-21 Sarah Kellen - Transcript.pdf - Google Drive

Sarah Kellen told Congress that she was not a willing architect of Jeffrey Epstein's operation but one of his victims, claiming Epstein groomed, abused, isolated, and controlled her for years. She described herself as trapped inside his world through sexual, psychological, and emotional coercion, and said Epstein continued to exert power over her even while he was incarcerated. That testimony matters because Kellen has long been one of the most controversial names in the Epstein case: she was not some distant acquaintance or occasional employee, but a close assistant whose name appeared in the non-prosecution agreement and whose alleged role has been described by survivors as central to the scheduling, travel, and logistics that made Epstein's abuse machine function.The skeptical read is that Kellen's testimony may explain parts of her relationship with Epstein, but it does not automatically erase the serious questions about what she did, what she knew, and how long she remained embedded in his operation. Being abused by Epstein and enabling Epstein's access to other victims are not mutually exclusive possibilities, and that is the uncomfortable center of the issue. Her testimony shifts the frame from co-conspirator to coerced participant, but Congress and the public still have to weigh that against the survivor accounts, the documented logistics, the years of proximity, and the fact that Epstein's criminal enterprise required trusted people to keep the appointments, movements, and access points running. In plain terms, Kellen may have been victimized by Epstein, but that does not settle the question of whether she also helped him victimize others.to contact me:bobbycapucci@protonmail.comsource:2026-05-21 Sarah Kellen - Transcript.pdf - Google Drive

Sarah Kellen told Congress that she was not a willing architect of Jeffrey Epstein's operation but one of his victims, claiming Epstein groomed, abused, isolated, and controlled her for years. She described herself as trapped inside his world through sexual, psychological, and emotional coercion, and said Epstein continued to exert power over her even while he was incarcerated. That testimony matters because Kellen has long been one of the most controversial names in the Epstein case: she was not some distant acquaintance or occasional employee, but a close assistant whose name appeared in the non-prosecution agreement and whose alleged role has been described by survivors as central to the scheduling, travel, and logistics that made Epstein's abuse machine function.The skeptical read is that Kellen's testimony may explain parts of her relationship with Epstein, but it does not automatically erase the serious questions about what she did, what she knew, and how long she remained embedded in his operation. Being abused by Epstein and enabling Epstein's access to other victims are not mutually exclusive possibilities, and that is the uncomfortable center of the issue. Her testimony shifts the frame from co-conspirator to coerced participant, but Congress and the public still have to weigh that against the survivor accounts, the documented logistics, the years of proximity, and the fact that Epstein's criminal enterprise required trusted people to keep the appointments, movements, and access points running. In plain terms, Kellen may have been victimized by Epstein, but that does not settle the question of whether she also helped him victimize others.to contact me:bobbycapucci@protonmail.comsource:2026-05-21 Sarah Kellen - Transcript.pdf - Google Drive

Sarah Kellen told Congress that she was not a willing architect of Jeffrey Epstein's operation but one of his victims, claiming Epstein groomed, abused, isolated, and controlled her for years. She described herself as trapped inside his world through sexual, psychological, and emotional coercion, and said Epstein continued to exert power over her even while he was incarcerated. That testimony matters because Kellen has long been one of the most controversial names in the Epstein case: she was not some distant acquaintance or occasional employee, but a close assistant whose name appeared in the non-prosecution agreement and whose alleged role has been described by survivors as central to the scheduling, travel, and logistics that made Epstein's abuse machine function.The skeptical read is that Kellen's testimony may explain parts of her relationship with Epstein, but it does not automatically erase the serious questions about what she did, what she knew, and how long she remained embedded in his operation. Being abused by Epstein and enabling Epstein's access to other victims are not mutually exclusive possibilities, and that is the uncomfortable center of the issue. Her testimony shifts the frame from co-conspirator to coerced participant, but Congress and the public still have to weigh that against the survivor accounts, the documented logistics, the years of proximity, and the fact that Epstein's criminal enterprise required trusted people to keep the appointments, movements, and access points running. In plain terms, Kellen may have been victimized by Epstein, but that does not settle the question of whether she also helped him victimize others.to contact me:bobbycapucci@protonmail.comsource:2026-05-21 Sarah Kellen - Transcript.pdf - Google Drive

Sarah Kellen told Congress that she was not a willing architect of Jeffrey Epstein's operation but one of his victims, claiming Epstein groomed, abused, isolated, and controlled her for years. She described herself as trapped inside his world through sexual, psychological, and emotional coercion, and said Epstein continued to exert power over her even while he was incarcerated. That testimony matters because Kellen has long been one of the most controversial names in the Epstein case: she was not some distant acquaintance or occasional employee, but a close assistant whose name appeared in the non-prosecution agreement and whose alleged role has been described by survivors as central to the scheduling, travel, and logistics that made Epstein's abuse machine function.The skeptical read is that Kellen's testimony may explain parts of her relationship with Epstein, but it does not automatically erase the serious questions about what she did, what she knew, and how long she remained embedded in his operation. Being abused by Epstein and enabling Epstein's access to other victims are not mutually exclusive possibilities, and that is the uncomfortable center of the issue. Her testimony shifts the frame from co-conspirator to coerced participant, but Congress and the public still have to weigh that against the survivor accounts, the documented logistics, the years of proximity, and the fact that Epstein's criminal enterprise required trusted people to keep the appointments, movements, and access points running. In plain terms, Kellen may have been victimized by Epstein, but that does not settle the question of whether she also helped him victimize others.to contact me:bobbycapucci@protonmail.comsource:2026-05-21 Sarah Kellen - Transcript.pdf - Google Drive

Whether you're a podcaster working with an editor, a musician collaborating with producers, or a filmmaker managing a team across multiple countries, one question inevitably comes up: What's the best way to share media?I provide the most popular solutions available today—from traditional cloud storage platforms like Google Drive and Dropbox to cloud-native virtual drive systems like LucidLink and Suite Studios that are changing the way creative teams collaborate.I discuss the strengths and limitations of each option, when it makes sense to use them, and how different creative professionals can choose the right workflow based on their needs, budget, and team size.Topics Include: Cloud-native virtual drives vs traditional cloud storage LucidLink, Suite Studios, EditShare, Hedge PostLab Drive, and BeBop Google Drive, Dropbox, and OneDrive Managing large media files remotely Collaborating with editors, producers, and creative teams around the world Using Frame.io for feedback, approvals, and client reviews The workflow I personally use for my own projectsAs creative collaboration becomes increasingly global, understanding how to efficiently share, organize, and access media can save time, money, and countless headaches.What tools are you using to share media with your team? Let me know in the comments.

Sure, you didn't miss Anthropic's BIG Opus 4.8 drop.

Paul has been testing various Linux distributions and other Windows alternatives for months as part of a Switcher series. The zen of Linux can mostly apply to Windows, too: Install and manage software with package managers, and embrace the command line, especially. And if you're going to use a local account, at least be smart about it. Also, Vivaldi 8.0 looks awesome and appears to deliver what Firefox is promising with its Nova UI. Plus, Discord has a native app for Windows 11 on Arm now. Windows Week D arrives with a surprise: 24H2/26H1 are aligned and getting the same new features Shared audio with BT LE, multi-app camera support, many improvements - but the big deal may be the performance and reliability improvements across the board This is the next Patch Tuesday, today Friday builds - new accessibility features in Experimental and Beta, more Microsoft CMO Yusuf Mehdi to leave company after an astonishing 35-year run - started in Windows, but with IE, Bing & MSN, Interactive Entertainment (Xbox), Windows and Devices, and then a SLT position before the end. Incredible run. Paul has three milestones and one throughline to share. Lenovo revenues surge 27 percent to $21.6 billion NVIDIA revenues really surged 85 percent to $81.6 billion AI/dev Google adds Google Drive sync to NotebookLM, and moves preferred sources into AI Mode and AI Overviews Saying no to AI: DuckDuckGo usage surges in the wake of Google I/O's AI tsunami OpenAI releases ChatGPT plugin for PowerPoint .NET MAUI to get Material You support for Android in .NET 10 Follow-up on last week's vibe coding adventures: Paul talked about this last week, but a lot has happened since then. The Android app creation capability in Google AI Studio is live. A few thoughts on vibe coding with Android Studio, Claude Code, and more Xbox and gaming XBOX—and, yes, it's XBOX now—has an official merchandise store to go alongside all its other official merchandise stores The Steam Deck is back in stock! Also, it's 40 percent more expensive Tips & picks Tip of the week: Understanding the zen of Linux can help a Windows user too App pick of the week: A grab-bag of apps for Windows RunAs Radio this week: Team Productivity using Loop with Karinne Bessette Brown liquor pick of the week: John Sleeman & Sons Rye Whisky 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: trustedtech.team/windowsweekly365 webroot.com/twit