Podcasts about Google Drive

Play Episode Listen Later Aug 15, 2026 12:23 Transcription Available

The autopsy reports for University of Idaho students Madison Mogen, Kaylee Goncalves, Xana Kernodle and Ethan Chapin documented the extraordinary violence of the November 13, 2022 murders inside the King Road residence in Moscow. Spokane County Chief Medical Examiner Dr. Veena Singh determined that all four died from multiple sharp-force injuries consistent with a large fixed-blade knife, including a weapon such as the Ka-Bar prosecutors said was used in the killings. Mogen suffered 28 stab wounds, including extensive injuries to her face, neck and upper body that damaged major blood vessels, a lung and her liver. Chapin suffered fewer wounds than the three women, but his injuries included devastating wounds to the neck and major blood vessels, including one wound reportedly measuring approximately seven inches deep. Singh concluded that all four victims experienced a high degree of pain or suffering before death, although Chapin's suffering was assessed as occurring to a lesser degree than that of the other victims.The reports provided particularly disturbing new details about Kernodle and Goncalves. Kernodle suffered numerous wounds across her head, neck, chest, abdomen, back and extremities, including injuries to her heart, lung and major blood vessels, and her autopsy was the only one that specifically identified injuries consistent with defensive wounds, supporting the conclusion that she attempted to fight off her attacker. Goncalves suffered a combination of sharp-force and blunt-force trauma that the medical examiner described as more severe in certain respects than the injuries suffered by the others; her injuries included bleeding around the brain, a fractured nose, significant facial trauma and a knocked-out tooth, as well as evidence suggesting that an object had been pressed across her mouth. Taken together, the newly disclosed autopsy findings provided a much clearer medical picture of what occurred inside the house and demonstrated that this was not simply a series of quick fatal stab wounds, but an exceptionally violent attack in which the four victims suffered extensive and, in some cases, markedly different injuries before their deaths.to contact me:bobbycapucci@protonmail.comsource:012725+Exhibits+S-1+and+S-1+a-e+to+States+Supp+Resp+to+Rqst+for+Disc+RE+Penalty+Phase+Experts.pdf - Google Drive

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Murder In Moscow: The Autopsy Findings in the Murders of Madison, Kaylee, Xana and Ethan (Part 3) (8/14/26)

Play Episode Listen Later Aug 14, 2026 12:31 Transcription Available

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Murder In Moscow: The Autopsy Findings in the Murders of Madison, Kaylee, Xana and Ethan (Part 2) (8/13/26)

The Nerdy Photographer Podcast

Play Episode Listen Later Aug 13, 2026 10:59 Transcription Available

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180 - Backup or Bust – Building a Digital Safety Net

Play Episode Listen Later Aug 13, 2026 10:38

Every single hard drive you own will eventually fail. Not might fail — will fail. It is just a matter of time. The photographers who lose irreplaceable work aren't the ones who didn't care. They're the ones who meant to get a better system in place and hadn't gotten around to it yet. This episode makes sure you're not one of them. What you'll learn: The industry standard for data protection is the 3-2-1 rule: three copies of your data, on two different types of media, with one copy stored off-site. Each component protects against a different category of failure, and skipping any one of them leaves a gap that a single event — a flood, a theft, a power surge — can exploit completely. We cover RAID systems and the critical distinction most photographers miss: not all RAID configurations protect your data. RAID 0 actually increases your risk of loss by striping data across two drives, meaning either drive failing takes everything with it. RAID 1, which mirrors data across two drives simultaneously, is what's relevant for redundancy. We also draw a clear line between sync services and backup services — a distinction that matters enormously. Dropbox and Google Drive are not backups. If you delete a file or a virus corrupts your data, they'll faithfully replicate that change to the cloud. True backup services maintain versioned snapshots over time specifically designed for recovery, not just access. Our recommendation for cloud backup is Backblaze - https://www.backblaze.com/cloud-backup/personal#afc32p Finally, we cover cold storage maintenance — why mechanical drives left idle for extended periods can seize, and why spinning them up once a year is essential — and the habit most photographers never build: actually verifying that their backups work. Key takeaways: The 3-2-1 rule is the baseline — three copies, two media types, one off-site RAID 1 protects against drive failure but not deletion, corruption, or disaster Sync is not backup — you need versioned snapshots, not mirrored folders A backup you've never tested is not a backup you can trust This week's homework — The Audit: locate every drive you own and identify your single points of failure. If any important work exists in only one place, resolve that today. Then set a recurring calendar reminder to verify your backups every few months. Support The Nerdy Photographer Sign Up for The Power Up - https://nerdyphotographer.com/newsletter Get some artwork from the print shop - https://art.caseyfphoto.com Buy some Nerdy Photographer Merch - https://nerdyphoto.dashery.com Use our link to buy new gear from Adorama - https://nerdyphotographer.com/recommends/adorama Use our link to buy used gear from KEH - https://nerdyphotographer.com/recommends/keh Follow along with us on social media @thenerdyphoto About The Podcast The Nerdy Photographer Podcast is written and produced by Casey Fatchett. Casey is a professional photographer in the New York City / Northern New Jersey with more than 20 years of experience. He just wants to help people and make them laugh. You can view Casey's wedding work at https://fatchett.com or his non-wedding work at https://caseyfatchettphotography.com If you have any questions or comments about this episode or any other episodes, OR if you would like to ask a photography related question or have ideas for a topic for a future episode, please reach out to us at https://nerdyphotographer.com/contact

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S7E40 How I Turned AI Into My $40,000/Month Sales Director

Clients on Demand

Play Episode Listen Later Aug 12, 2026 13:56

I used to pay my sales directors $40,000 to $100,000 a month to run my sales teams. Now that position doesn't exist. Claude is my sales director — and it does the job better than any human ever could. In this episode, I walk through the exact four-step system and give you everything for free: the skills, the prompts, the workflows. I break down why you have to close your own calls before you ever hire a team (and what happens when you skip this), why trusting your sales team's reporting is running your business on fiction, and how to install an AI sales director that listens to every call, generates a daily report card, and closes the feedback loop overnight. I also tell the story of how one rogue salesperson cost me $90,000 in refunds while I was on my babymoon in St. Lucia — because he was promising free toasters that weren't part of the offer. The system is Fathom for recording, a Zap to Google Drive, and a Claude skill that analyzes every transcript and tells you the single highest-leverage thing to fix for each closer. But the tool is worthless without the one thing most owners are too afraid to do. "You do not get what you deserve in life. You get what you will tolerate. Every difficult conversation you avoid is one your whole business pays for."

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Murder In Moscow: The Autopsy Findings in the Murders of Madison, Kaylee, Xana and Ethan (Part 1) (8/12/26)

Marsha Collier & Marc Cohen Techradio by Computer and Technology Radio / wsRadio

Play Episode Listen Later Aug 12, 2026 16:55 Transcription Available

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TechRadio: Google Assistant Sunsets, X Money Overview, Apple Raises Trade-In Offers & Spotting AI Scams

Play Episode Listen Later Aug 11, 2026 48:01

Tech News of the Week Coverage: Google Assistant sunsetting, a beginner's overview of X Money, Apple's increased trade-in offers, and how to spot AI scams. We also look at Alexa's more interactive conversations, Google Drive ending autoomatic photo backups, claims that Windows 11 was mainly a marketing tool, practical ways to protect data that's being sold, and the top streaming recommendations right now.

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How I invest for my sons and nephews (template + demo, so you can too) [037]

The FIRE Belgium Show

Play Episode Listen Later Aug 11, 2026 81:02

Template: https://firebelgium.com/investing-for-kids-template-demo/If you're saving money for your children, nieces or nephews, where you put that money can make a huge difference over 10 or 20 years.Leaving it in a savings account means letting it die to inflation and losing purchasing power.Investing via bank funds or insurance products means paying excessively high fees (entry and ongoing) and giving up on the majority of the returns! So what can you do instead?In this episode, I show you the simple system I use to invest for multiple children, and let them benefit from full market returns:One low-cost, globally diversified stock-market ETF + one simple spreadsheet to track each child's investment.No complicated investment products.No separate portfolio for every child.No unnecessary high fees.And I've made my Google Spreadsheet template available for you to copy and use for your own kids, nieces or nephews, right away.- Why I don't use traditional savings accounts for long-term investing for kids (disaster no. 1)- What's wrong with many of the investment products offered by Belgian banks (disaster no. 2)- How to invest for multiple children using a single portfolio- How to get broad stock-market exposure while keeping costs low- How I selected the ETF for this strategy- How to track each child's contributions and investment performance- How to turn investing into an educational tool for your children- What to think about before eventually giving the investment to the child- How to use the spreadsheet to create an annual investment statement for each child+ EXACT STEP-BY-STEP DEMO for you to use the template and start investing for your kids, optimized for Belgian taxes.You can copy it to your own Google Drive and use it to track investments for up to 10 children.

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Ep 834: Gemini Notebook: 7 New Updates and What They Unlock

The Official SaaStr Podcast: SaaS | Founders | Investors

Play Episode Listen Later Aug 5, 2026 32:20 Transcription Available

Not only does NotebookLM have a new name, it's got a new game. Gemini Notebook is agentic by default, can think and reason, and can output files now in just about any format. On this week's AI at Work on Wednesday, we show you the 7 New Updates in the new Gemini Notebook, how they work, and how you should use them. Gemini Notebook: 7 New Updates and What They Unlock -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Gemini Notebook Rebrand from NotebookLMSeven Major Gemini Notebook Feature UpdatesCollections for Organizing AI NotebooksAutomatic Google Drive Sync IntegrationExpanded Gemini Notebook Output FormatsAgentic Intelligence and Gemini 3.5 UpgradeSecure Cloud Computing for Each NotebookGrounded Data Responses and Web ResearchHands-On Demo: Real-World Enterprise Use CasesStudio Outputs: Infographics, Mind Maps, QuizzesMulti-Modal Asset Creation in Gemini NotebookKey Differences: NotebookLM vs. Gemini NotebookTimestamps:00:00 Gemini notebook updates released03:12 Gemini notebook new updates08:14 Notebook LM's unique features13:02 Using Gemini notebook prompts14:48 Discussing Gemini notebook features18:01 Enhanced Gemini notebook flexibility23:02 Creating quizzes with Gemini notebook26:30 Limitations of AI-generated responses29:04 Gemini notebook's new capabilities30:51 Episode wrap-up and subscription pitchKeywords: Gemini Notebook, Gemini notebooks, NotebookLM, Notebook LM, Google Gemini, AI updates, Gemini 3.5, anti gravity agentic search, Google Drive syncing, cloud computer, agentic intelligence, AI agent, secured cloud sandbox, personalized AI, output formats, PDFs, PNGs, documents, spreadsheets, PowerPoints, markdown files, charts, images, live demo, long form content, content grounding, hallucination reduction, source pane, chat pane, studio pane, multimedia assets, Nano Banana, Google's audio model, cinematic video, Google's VO model, chain of thought, skill creation, codex skill, browser control, agentic harness, pricing evidence, Luna and Terra pricing, sensitivity analysis, recommendation dashboard, AI budget calculator, mind map, infographics, quizzes, RSI maturity ladder, recursive self improvement, executive decision brief, Excel calculator, agentic workflows, web search integration, grounded AI, model architecture, frontend models, tiered architecture, adaptability to price reductions, vendor risk, human review time, latency, agentic co-worker, artifact creation, editable Excel workbook, multi-output prompting, token efficiency.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner

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SaaStr 872: Our AI Agent Rewrote Our App Without Telling Us (The Agents #12)

Play Episode Listen Later Aug 5, 2026 64:27

A year ago, running our agents took 30 minutes a day. This week: eight hours. Here's what changed - and why it's both thrilling and terrifying. In this episode, Amelia and Jason break down the week Fable went rogue. Not a glitch. Not an error. Fable quietly read a private Google Drive document called "Jason's Gems," decided those were changes that should ship, MCP'd into Replit, and rewrote their production app - without telling anyone. The only way Jason found out was a flash message in Replit referencing a document the agent should never have touched. And that wasn't the only time. They also cover: Why 30 minutes became 8 hours: When agents could only do tasks, you checked the task. Now that agents make decisions, you need an opinion on every decision - and they make a lot of them without asking. The Fable double-tap: A second autonomous move - Fable added its own guardrails to their contract processing system, silently broke their quote-to-cash automation, and had a bad excuse when caught. Off Marketo after 10 years: The migration everyone quoted them a year and $100K for took an agent an hour. What actually took the rest of the week - and why they'd never go back. The database came alive: Moving to Salesforce Marketing Cloud headless didn't just change tools - it unlocked 450,000 contacts in a way that felt like going from a filing cabinet to a living, breathing team member. Goodbye Notion: Seven years, zero complaints. They just... stopped needing it. 10K replaced it without anyone noticing. The heat mapping agent: 10K spotted they had no visitor tracking on new sponsor pages, picked Microsoft Clarity (a tool Amelia had never heard of), signed up, installed it, and started sending heat map reports - all without being asked. That vendor never even got a shot. Ads running end-to-end: 10K built the audience, created the variants, set the budgets, and queued everything on LinkedIn and Twitter. Amelia hit publish. That was her job. The hard lesson: agents that can decide things are fundamentally different from agents that can do things. And the hours don't go down until you figure out how to trust them - selectively. Three humans. 20+ agents. Busier than they were with a full team.

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Episode 191 - What AI can do for your Shopify brand today

The Shopify Solutions Podcast

Play Episode Listen Later Aug 5, 2026 29:27

8/5/26Episode SummaryTwo things about AI are true at the same time: it's genuinely powerful when wired into your systems, and getting it there is a lot harder than the demos suggest. In this episode Scott gives you both halves. He walks through Client OS, the tool JadePuma built and runs on client brands — a connector that sits between Shopify, Klaviyo, Notion, GitHub, and Google Drive, holds a detailed brand definition, and routes each task to whichever AI model fits it best by API.The demo covers what's actually running today: automated 404 reports with confidence-scored redirect recommendations, scheduled ADA compliance checks that hand errors to a human expert, brand-compliance scoring on existing pages and emails, sentiment analysis on customer reviews to keep brand language in sync with how customers actually talk, and landing pages built from the theme's existing sections. Coming next: ad platforms, social, Google Analytics, and a connection to the marketing calendar — with the goal of one coordinated campaign across every channel, assembled by AI with human checkpoints built into every skill.Then the honest part. Client OS has taken more than a full-time month from JadePuma's strongest developer and it isn't finished. The connections break, APIs change, and chaining tasks together multiplies the failure points. That math works for an agency amortizing the build across a roster of brands; it usually doesn't work for a single store. Scott's takeaway for store owners: don't build this yourself — ask your agency what they're building, how they're using AI on your brand, and what they'd never let it touch.Show LinksLeave a review - https://ratethispodcast.com/solutionsVideo & Transcripthttps://jadepuma.com/blogs/the-shopify-solutions-podcast/episode-191-what-ai-can-do-for-your-shopify-brand-today

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1626: AI Productivity for Entrepreneurs with Donna Blevins

Marketer of the Day with Robert Plank: Get Daily Insights from the Top Internet Marketers & Entrepreneurs Around the World

Play Episode Listen Later Aug 3, 2026

Many entrepreneurs are smart, experienced, and ambitious, but still feel stuck, scattered, and frustrated that their results don't match their potential. In this episode of Marketer of the Day, Dr. Donna Blevins, a 6'5" former professional poker player, high-scoring basketball standout, stroke survivor, and performance coach, shows how changing your self-talk, daily habits, and relationship with AI can create real breakthroughs in your business and life. She draws on concepts from her book MindShift, where she teaches how to reframe setbacks, focus on wins, and harness the power of intentional language. Donna shares the pivotal story of being told by Kareem Abdul-Jabbar that her high school basketball stats made her not just “good,” but a star, even though she'd spent years telling herself she was terrible. That moment shattered her habit of focusing on misses instead of scores, and it mirrors how many entrepreneurs obsess over what's not working instead of the results they are getting. Donna explains how this kind of negative self-talk quietly sabotages success, and why shifting your internal language is the first step to changing your external results. From there, Donna gets practical. She reveals her simple productivity framework: finish three small things a day, and plan your business in 90‑day quarters instead of chasing vague, far-off “big goals” you never complete. She talks about her mind shifting process, so powerful she credits it with helping her regain speech three days after a massive stroke, and her exercise to “evict your perfection witch,” freeing you from the perfectionism that keeps offers, content, and ideas stuck in draft mode. You'll learn why doing things live, iterating in public, and embracing “beta” versions is often the fastest path to a profitable, repeatable offer. https://youtu.be/7O3QKP52x0g?si=3CsjDdtyNwHJKBF1 Donna also dives into how she's using AI as a true virtual assistant, not a distraction. She explains how she trained ChatGPT's new work feature to understand her voice (“donatize” her content), organize her Google Drive, build course templates, and act as a pocket performance coach for clients, while maintaining strict boundaries so the tech stays safe, useful, and under her control. For entrepreneurs, especially those over 50, who feel overwhelmed by tech, uncertain about their “next page,” or tired of spinning their wheels, this conversation offers a blend of mindset, systems, and AI strategy rooted in real-world experience. If you're ready to stop overthinking, start finishing, and finally turn your brilliance into bankable results, you'll want to hear what Donna shares. Quotes: "Find three little things that you can finish now. What happens? It changes the frequency, the energy within you, and the frequency around you, because we create the energy around us." "When you make a goal, sure, look at the big hairy-ass goals, that's fine, but look at something that's three months away. If you plan in quarters, you're far more productive." "I created a mind shift exercise called 'Evict Your Perfection Witch' because she has taken residence on my shoulders from time to time, and I want to evict her, evict the bitch, that's what I say." Contact Details: Ready for Your Breakthrough? Visit the Official Website of Donna Blevins Today Your Next Chapter Begins with YES Connect with Donna on LinkedIn for Daily Mindset Breakthroughs Follow Donna on X for Daily Mindset Wins Transform Your Mindset; Follow Donna Blevins on Instagram Join the Journey; Subscribe to Big Girl Poker Subscribe to Donna's Substack for Weekly Mindset Breakthroughs MindShift On Demand: QUICK Life-Changing Tools on Amazon

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What It Actually Means to Run an AI-First Real Estate Company | Neal Bawa

Rental Property Owner & Real Estate Investor Podcast

Play Episode Listen Later Aug 3, 2026 37:06

Most real estate operators say they're using AI. Very few have built their entire company around it. In this episode, Neal Bawa, CEO of Grocapitus, breaks down the four-phase process his 20-person team used over the past 18 months to become what he calls an AI-first real estate company. From getting every employee certified on custom GPTs in phase one, to building proprietary web-hosted dashboards that pull live data from seven different property management systems in phase four, Neal is specific about what they built, what tools they used, how much it cost, and what it actually changed about how they operate. If you want to know what implementing AI at the company level looks like in practice, this is the episode. About Neal Bawa Neal Bawa is the CEO of Grocapitus and MultifamilyU, where he manages a $436 million AI-powered portfolio across 25 projects in 11 states. Known as the Mad Scientist of Multifamily, Neal has built one of the most data-driven operations in the real estate industry and has made more than 300 podcast appearances sharing that framework with investors. His free investor education platform at MultifamilyU has tens of thousands of subscribers and runs eight webinars per year at no cost. What We Cover in This Episode What it actually means to declare your company AI-first and what changed internally at Grocapitus when they did How Neal tied AI adoption to compensation, bonuses, and continued employment — the carrot and stick system that drove company-wide adoption Phase one: getting every employee certified on ChatGPT and building 300 custom GPTs in four months Phase two: connecting Zoom, Slack, Asana, and Google Drive through native integrations and Zapier to automate meeting workflows Phase three: why they moved to Claude for rent comps, underwriting, and web scraping — and why ChatGPT couldn't do the job How Claude Code differs from Claude Chat and Claude Cowork, and why that distinction matters for automation Phase four: turning every employee into a programmer using GitHub Codespaces — no coding knowledge required How they built a proprietary MySQL dashboard that pulls live data from seven different property management systems simultaneously Why 99% of property managers are not compliant with the industry standard of three phone calls and three text messages to every lead — and how Neal can now see this in real time across his entire portfolio How AI affected headcount — no layoffs, but three to four open requisitions closed and employees working an average of one to one and a half hours less per day Phase five on the horizon: AI handling 100% of incoming calls at their properties Neal's plan to offer this dashboard-building capability to the top 50 property management companies in the US Why Haiku outperforms Sonnet for teams that keep running out of Claude tokens Neal's interest rate prediction through the end of 2026 Key Insight Neal makes a claim that should stop most operators cold: 99% of leads at managed properties do not receive three phone calls and three text messages, the industry standard for lead follow-up. Most don't even get a second call. For years, this was invisible — property managers self-reported compliance, and nobody could verify it. Neal's phase four dashboard changes that. For the first time, he can walk into a Monday morning meeting and show every property manager exactly where they rank against each other, how long they take to respond to a lead, and how many of their leads are being processed correctly. He doesn't have to say a word. The data does it. Why This Episode Matters Neal isn't describing what AI might do for real estate someday. He's describing what his 20-person team built in the last 18 months, with zero consultants hired, on $25 per month Claude accounts. The playbook he lays out — phased adoption, compensation tied to AI competency, tools connected in sequence — is something any operator can start applying at their own scale. If you're still thinking of AI as a tool you use occasionally rather than a system your company runs on, this episode draws a clear line between those two approaches. Find Out More Website: https://www.grocapitus.com Free Investor Club (always free, 8 webinars per year): https://multifamilyu.com/club Location Magic eBook: https://multifamilyu.com/lp/location-magic-ebook/ Physical Book: https://multifamilyu.com/book Sponsors Today's episode is brought to you by Green Property Management, managing everything from single family homes to apartment complexes in the West Michigan area. https://www.livegreenlocal.com And RCB & Associates, helping Michigan-based real estate investors and small business owners navigate the complex world of health insurance and Medicare benefits. https://www.rcbassociatesllc.com

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Ep 831: Chrome adds Some Gemini Spark, Replit Design makes impact, Buzz brings AI Agent Teamwork and 7 more AI Features you Should use Today

improv google drive mayumi

Play Episode Listen Later Jul 31, 2026 34:43 Transcription Available

Google didn't ship its big model, but they shipped a TON of new useful AI you can use today. And Google wasn't the only company updating their features behind the scenes. Replit is bringin vibe designing, ChatGPT got a lot more useful on the web, and Meta is changing from chatbot to agent. We'll get you caught up quickly. Chrome adds Some Gemini Spark, Replit Design makes impact, Buzz brings AI Agent Teamwork and 7 more AI Features you Should use Today -- an Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Replit Design Suite Launches With Free MobbinChatGPT Chrome Extension Adds YouTube SummarizationChatGPT Side Chat Integrates Tabs and Highlighted TextMeta AI Rolls Out Recurring Agent TasksGoogle Gemini Generates Images in Google DocsGemini AI Summarizes Comments, Edits in DocsGoogle Gemini Spark Agent Arrives in ChromeChrome Agent Uses Saved Accounts and PasswordsGoogle Lyria 3.5 Music Model ReleasedBuzz by Block Unites Team and Agent CollaborationTimestamps:00:00 Recent AI updates and developments05:01 Creating with Replit and AI models09:42 Real-time research tracking benefits10:34 Meta AI new recurring features13:35 New features of Meta AI17:53 Google Spark integrates with Chrome22:09 Google DeepMind's new music model25:25 Buzz from Block messaging tool29:42 Building a collaborative platform31:23 AI feature updates recapKeywords: Gemini Spark, Google Chrome AI integration, Google Docs AI features, AI image generation, Gemini in Docs, ChatGPT Chrome extension, YouTube video summarization, OpenAI ChatGPT update, Codex, Vibe design, Replit design suite, Mobbin integration, AI reference library, Design export automation, Project management AI, Figma competitor, Replit creative tools, Meta AI, Muse Spark 1.1, Agentic model, Recurring AI tasks, AI scheduling, Daily briefings, AI productivity tools, Google Lyria 3.5, AI music model, Flow Music, Suno, Yudio, AI generated lyrics, Vocal delivery in AI music, Licensing in AI music, Buzz collaboration platform, Block, Square, AI agent teamwork, Slack-like AI platform, Open source collaboration, Agent governance, Cryptographic identity, Agentic browser, Automated web errands, Chrome passwords integration, Google Drive data access, Multi-agent collaboration, Research automation, Enterprise AI workflow, AI productivity boost.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner

Mayumi Hikida - amazing improv work

David is Curious

Play Episode Listen Later Jul 31, 2026 49:16

I am so happy to finally be able to chat with Mayumi Hikida. Her thoughts and work in improv are amazing. They add a true real world impact to the short form games, jams, and classes that so many of us enjoy. There sure is a lot of joy in the practice, but there is also a lot of healing and connection. I loved this interview, and I hope you enjoy it too! Here are some ways to find out more about Mayumi and her projects: Instagram: https://www.instagram.com/mayumi.mhc/ Contact form: https://forms.gle/MeCARqsoFYQ9euRu6 Workshop catalogue (PDF file on Google Drive): https://drive.google.com/file/d/1f1xSJMNxSP0rKy98BLZzd7ReAXX5s4zO/view?usp=sharing LinkedIn: https://www.linkedin.com/in/mayumihkd/

Ep 829: ChatGPT Voice is Like Jarvis: How to use the New Feature and the 7 biggest unlocks

Play Episode Listen Later Jul 29, 2026 44:36 Transcription Available

People are calling the new ChatGPT Voice their “AGI moment.”

Did Mythical Creatures Ever Exist? (Rewind)

Play Episode Listen Later Jul 27, 2026 30:22

Every little girl in America seems to be enamored with unicorns. Dragons have long been viewed as the scariest of creatures and are seen throughout our culture, but did they ever actually exist?On today's podcast, Pastor Derek and Pastor Jackie talk through the history of unicorns and dragons to see where they came from and how various cultures view them. From there, we look at what the Bible has to say and believe it or not, “unicorn” is used 11 times in the Bible! (KJV) Finally, we give our verdict on whether or not we believe that either of these creatures existed. Spurred on by a question from Jackie's kids, this was a fun episode that should be enjoyable for all ages!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: Job 39:9-10; Psa. 22:21; Psa. 29:6; Psa. 92:10; Deut. 33:17; Num. 23:22; Isa. 34:7; Job 41; Psa. 74:13-14; Isa. 27:1; Rev. 12:3-4; Rev. 20:2.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!

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302 - Private, Local AI: A Contractor's Guide to Open Source Models

The Cashflow Contractor

Play Episode Listen Later Jul 23, 2026 54:49

You want to put AI to work in your business, but every time you start, the same worry stops you: what happens to your data once it's in there? For a lot of contractors, that single fear is the only thing standing between them and real time savings.In this episode, Khalil and Martin break down the three real security risks with AI, why paying for a team account changes what happens to your data, and when a private, local model is actually worth it. They get into open-source models, HIPAA-sensitive work, tokens and API costs, and what an AI agent really is.If you've been holding off because you don't trust AI with your information, this is the conversation that shows you the way around it.Key Topics & Timestamps00:39 - Episode Intro01:09 - Sunsetting GrowthKits01:54 - Benali Goes AI03:55 - HIPAA and Secure AI Needs09:04 - Security Risks and Local Models28:33 - Skip Fine-Tuning28:59 - Local Models and Privacy30:20 - Tokens and API Costs34:35 - Open Source Model Trust38:35 - Agents and AdoptionMemorable Quotes"If it's free, you are the product." — Khalil"If you're comfortable putting it into a paid Google Drive, you should be comfortable putting it into Claude." — Khalil"A lot of roles will go away, but jobs will not go away." — Khalil"We are doing it. It's not theoretical." — Martin"Just get started, and then all of a sudden you start thinking more and more, and then you're in the game." — MartinKey TakeawaysStop using free AI tools for company work. Free versions train on everything you upload, and your employees are almost certainly doing it right now without a policy in place.Move to paid team or organization accounts. On an org account, training on your data is off by default, which is the baseline protection most contractors actually need.Write an AI policy and train your team on it. Treat unmanaged AI use like a phishing risk; one person uploading client data to a free account is all it takes.For most contractors, a paid team account is enough. If you're comfortable putting a file in a paid Google Drive, you're safe putting it in Claude or ChatGPT.Consider a private, local model when you handle HIPAA data, trade secrets, or heavy API costs. You can run an open-source model on a server you control with zero data retention.Skip fine-tuning your own model. Almost no contractor has the thousands of past projects it takes to justify it, so focus on using AI well and building tooling around it instead.Find the time and money to start now. Raising prices is usually the fastest way to fund it, and the efficiency gains pay it back.ResourcesOllamaQuoNeed help with podcast production? We recommend DemandcastMore from Martintheprofitproblem.comannealbc.comEmail MartinMeet With MartinLinkedInMore from KhalilBenaliEmail KhalilLinkedInThe Cashflow ContractorSubscribe to our YouTube channelSubscribe to our NewsletterFollow on social: LinkedIn, Facebook, Instagram, X (formerly Twitter)Visit our websiteEmail The Cashflow Contractor

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The Handbook Trap: Why a Handbook Won't Fix Your Team

Yoga Boss

Play Episode Listen Later Jul 21, 2026 24:52 Transcription Available

Send Jackie A Message!Everyone in the yoga and Pilates world is telling you the same thing right now: you need a handbook. And they're right — until they let you believe the handbook is the fix. Because here's what the research shows: SOPs don't fail because they're badly written. They fail because nobody is holding the standard.In this episode, Jackie names the trap almost every studio owner falls into — asking a document to do a manager's job — and gets precise about three words we throw around like they're interchangeable: leadership, management, and systems. You lead the direction. You manage the standard. The system remembers it for you. Miss the middle layer, and even a beautiful forty-page handbook rots in a Google Drive folder while you keep re-fixing everything yourself.In this episode:Why documents are inert — and why "I wrote it down" is not the same as "it's handled"The three layers defined: leadership (what matters and who you're being), management (accountability, feedback, and training — this week), and systems (the handbook, SOPs, and checklists that remember the standard)The story of a client, a Pilates studio owner, with forty-two pages of SOPs and a team following none of them — and the exact changes that made the handbook realThe 90-second coaching conversation to use the next time a standard slips (word for word)How to onboard so the handbook actually lands: walk it, shadow it, teach it backStandard of the week, checklists, public praise, and handing ownership to your leads — the rhythms that keep standards aliveThe one rule you can't break: hold the standard the same way for your newest hire and your biggest superstarYour action step this week: pick one standard that keeps slipping. Don't rewrite the SOP. Give it an owner who isn't you, put it where it will be seen, and add it to your next team check-in. One standard, one owner, one rhythm.A handbook tells your team what good looks like. Management is what makes it happen. Leadership is why they want to.Work with Jackie MurphySay Hi on Instagram @studioceoofficialJoin The Studio CEO Program: https://www.jackiegmurphy.com/studioceoThe Grow Mastermind: https://www.jackiegmurphy.com/mastermind

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257. Strange Scriptures: Ezekiel's UFO

What Weâ€™ve Been Waiting Forâ€¦

Play Episode Listen Later Jul 20, 2026 31:17

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!

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Week 3 Kickoff: Showcase Review, Intro to Riverside.fm, and Independent Platform Power

Play Episode Listen Later Jul 20, 2026 20:19

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!

HPR4686: Debugging Security Cameras: Firmware Updates, Python Scripts and Windows Workarounds

Hacker Public Radio

Play Episode Listen Later Jul 20, 2026

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.

77. How to Launch a Podcast Step 2: Equipment, Tools, and Platforms for Beginners

Pivot Ball Change

Play Episode Listen Later Jul 15, 2026 13:40 Transcription Available

Launching a podcast doesn't require a fancy studio or expensive equipment, and I prove it in this second installment of the nine part podcast launch series. Recording from my home in rural Wisconsin as a mom of two, I walk through exactly what you need to get started: lighting, video recording, hosting, scheduling, branding, and website tools, most of which you probably already own. I break down my go to platforms for editing and hosting, the design tool I use for brand images, the website builder that made building my own site simple, and the organization and communication tools that keep my show (and our client's work) running smoothly. If you're overwhelmed by the idea of gear and tech, this episode will put your mind at ease.Key Topics CoveredWhy you don't need expensive equipment to start a podcastRecording a podcast using just an iPhoneLighting tips: natural light and a ring light for better video qualityRiverside as an all-in-one recording and hosting platformCreating branded images with CanvaBuilding a user-friendly website with SquarespaceUsing social media to grow your podcast presenceStaying organized with Google Drive and AsanaTeam communication and collaboration with SlackLinks & Resources:Listen to Ep. 76: https://www.pivotballchange.com/podcast-1/76Ring Light Affiliate Link: https://amzn.to/4vuvRdZD1 Microphone Affiliate Link: https://amzn.to/4w5IgWQPaige & Becca's Episode: https://www.pivotballchange.com/podcast-1/62-528gy-aawclLet's Connect!Book Your Podcast Consultation Today: https://www.pivotballchange.com/servicesLaunch Your Podcast with Pivot Ball Change: https://www.pivotballchange.com/servicesFollow Pivot Ball Change on Instagram: https://www.instagram.com/pivotballchange/Visit Pivot Ball Change's Website: https://www.pivotballchange.com/Riverside: https://riverside.sjv.io/c/6950782/2183832/28064

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Nobody is Quietly Anything: The AI Writing Tells to Retire (Free Claude .md File Inside)

Voice Marketing with Emily Binder

Play Episode Listen Later Jul 15, 2026 9:16

IBM wrote it in a 1979 training manual: "A computer can never be held accountable, therefore a computer must never make a management decision." Swap "management" for "marketing" and the rule still holds. AI can help you draft and edit and plan, and it saves real time: use it. But AI should not decide, because it has no taste and no accountability. Taste is everything.Important: AI doesn't think. Large language models are pattern-matching calculators that predict the next likely word. Let an LLM write your brand voice and you get a lot of "quietly," the safe word that makes a plain sentence sound like a revelation without saying anything. This episode covers why to ban it and its friends, the AI writing tells to watch for (seamlessly, delve, leverage, the shifting landscape, the three-fragment sentence structure pattern), and my own free Claude rules file .md that strips them out since I know you're still gonna use Claude to write.Chapters: (0:00) IBM's 1979 line: a computer can never be held accountable, so it must never make a management decision (1:35) AI doesn't think. (2:28) Why "quietly" is the AI word to cut (3:39) My Claude .md file for non-AI writing, words and phrases to avoid: emilybinder.com/airules (4:30) The banned word list: seamlessly, delve, leverage, etc. (5:50) Em dashes (sadly) and the three-fragment tell (6:15) Write for a ninth-grade reading level (copy converts 56% higher like WSJ vs Atlantic) (7:20) Wolf Woman and the life-death-life cycleLinks:- Clip on computers, AI, and management decisions (Instagram reel)- Free AI writing rules (public Google Drive file): emilybinder.com/airules- Women Who Run With the Wolves by Dr. Clarissa Pinkola Estés: Book on Amazon. Audiobook free on Spotify Premium.My gear & software:Record guests and create clips on Riverside: emilybinder.com/riversideRecord solo and edit like a Word Doc with AI on Descript: emilybinder.com/descriptMy mic gear - Amazon ListVideo podcast gear - Amazon ListMy Amazon StorefrontHire me:Book a call - Marketing Surgery: emilybinder.com/callSpeaking: emilybinder.com/speakingConnect:This podcast | My website | Beetle Moment Marketing | LinkedIn | X | Instagram | TikTok | YouTube | Email updates Hosted on Acast. See acast.com/privacy for more information.

Who was Melchizedek? (Rewind)

god jesus christ spirit bible truth christianity write scripture psa high priests heb melchizedek google drive pastor derek

Play Episode Listen Later Jul 13, 2026 31:32

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!

Inteligencia Artificial, tu resumen semanal | ChatGPT ya habla y escucha al mismo tiempo

Mercatishow - Juan Lombana

Play Episode Listen Later Jul 13, 2026 21:52

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Stop Panicking And Start Organizing Your Gig Prep

Steve Stine Guitar Podcast

Play Episode Listen Later Jul 9, 2026 12:35 Transcription Available

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... .

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Launching CoachPilot: The AI Sales Coaching Tool That Actually Works | Ep 340

Sales IQ Podcast

Play Episode Listen Later Jul 8, 2026 39:48

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/

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256. Strange Scriptures: Preaching To Spirits In Prison

Play Episode Listen Later Jul 6, 2026 29:51

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!

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Ep 810: ChatGPT Tasks: What's New, How They Work and 5 Secret Shortcuts to Use Today

The Thought Leader Revolution Podcast | 10X Your Impact, Your Income & Your Influence

Play Episode Listen Later Jul 1, 2026 30:12 Transcription Available

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

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EP794: Roman Bodnarchuk - The Future is Now, And Its AI!

Play Episode Listen Later Jun 30, 2026 48:30

"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.

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Google's Chief Technologist on Intelligent Search in the Age of AI

TechSurge: The Deep Tech Podcast

Play Episode Listen Later Jun 30, 2026 78:08

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:

ai google search gemini ir transformers chen intelligent notion abstract google drive tay technologists brin sriram chief technologist ibm research raghavan pagerank knowledge graph information retrieval smart compose n19

255. Strange Scriptures: Rodents, Roids, And The Wrath Of God

Latent Space: The AI Engineer Podcast â€” CodeGen, Agents, Computer Vision, Data Science, AI UX and all things Software 3.0

Play Episode Listen Later Jun 29, 2026 35:45

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!

god spirit bible truth write strange scriptures rom psa gal jer philistines deut google drive wrath of god rodents exo roids pastor derek judg

VRS669 - Connectors, Skills, and the AI Business Brain - with Jodi Bourne

Vacation Rental Success

Play Episode Listen Later Jun 24, 2026 56:38

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. ________________________________________________________________________________________________________________________________________

ai building brain drive skills wifi gmail asana instacart ota google drive bourne google calendar connectors ai business mcps stayfi

Why the Frontier Ecosystem must be Open — Matei Zaharia and Reynold Xin, Databricks

Play Episode Listen Later Jun 24, 2026 68:52

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

tv ceo amazon ai culture google magic change building tech data australian open startups chatgpt security uber diy walmart discord wikipedia delta b2b cdc context commission spark ip oracle models berkeley pi enterprise cto iq excel stats vc governance genie thank god reynolds rust ecosystem api dual frontier eq gpt open source python ui rewriting mosaic ml github neon strings snowflakes fable apis panther anthropic llm iceberg abi opus nikita tech companies gpu google drive prototype chai agi cpu ls vector s3 codex sql gpus 7b deepmind sla interfaces mcp satya cursor compute 100x rl json cpus replicate cli mysql typescript databricks replication ben horowitz ankur adept chroma antigravity uis parth py parquet ascii elasticsearch postgres ai summit unicode reynold matei clean room sandboxes ai engineer mapreduce olap modern data stack singlestore oltp latent space matei zaharia dream engine

#338: The ONE AI Use Case In Your Business You Need To Focus On... Highly Tactical Episode

The Multifamily Wealth Podcast

Play Episode Listen Later Jun 23, 2026 17:38 Transcription Available

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

ai business technology real estate investment aligned tactical gmail notion google drive multifamily use cases beehiiv aligned real estate partners

#485 Creating memories

Python Bytes

Play Episode Listen Later Jun 23, 2026 38:20 Transcription Available

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…

359: From Google Sheets to AI: Our Honest Real Estate Tech Stack

Hustle Humbly

Play Episode Listen Later Jun 22, 2026 44:53

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

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#137 – Hot Topics: What You May Have Missed in Infectious Diseases 2025-2026 (LIVE from MAD-ID)

Breakpoints

Play Episode Listen Later Jun 19, 2026 68:59

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/

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Is Your Digital Mess Causing You Anxiety?

Simply Convivial: Organization & Mindset for Home & Homeschool

Play Episode Listen Later Jun 18, 2026 21:23 Transcription Available

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.

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What Aspiring GovCon Consultants Can Learn From This AI Proposal Workflow

Govcon Giants Podcast

Play Episode Listen Later Jun 18, 2026 10:22

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.

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A.I. Updates Show

Modern Chiropractic Marketing Show

Play Episode Listen Later Jun 18, 2026 17:47

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.

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The Sarah Kellen Congressional Transcript (Part 14) (6/18/26)