Podcasts about 3B

In June 1988, 24-year-old truck driver David Churchill Jackson walked out of his Pembroke Pines, Florida apartment and completely vanished. He left behind a loving mother, a complicated past, and a young son who would grow up wondering what happened to his father. For fifteen years, David's disappearance remained a frozen mystery—until a cold case detective's vision board caught the eye of an unexpected visitor. ​In this chapter of The Book of the Dead, I explored the life of David Jackson, the devastating silence left in the wake of his disappearance, and the jaw-dropping twist that finally brought a hidden killer to justice decades later. This isn't just a story about how David died; it is about who he was, the family that never stopped looking for him, and why his memory matters.Connect with us on Social Media!You can find us at:Instagram: @bookofthedeadpodX: @bkofthedeadpodFacebook: The Book of the Dead PodcastTikTok: BookofthedeadpodOr visit our website at www.botdpod.comAFTER 7 YEARS, DISAPPEARANCE STILL MYSTERY. (2021, September 24). Sun Sentinel. https://www.sun-sentinel.com/1995/08/13/after-7-years-disappearance-still-mystery/Ambushed: The murder of David Jackson. (2014, May 11). CBS News. https://www.cbsnews.com/news/ambushed-the-murder-of-david-jackson/David Churchill Jackson (1963-1988). (2013, March 16). FInd a Grave. https://www.findagrave.com/memorial/106814812/david_churchill-jacksonDeutsch, K. (2005, January 22). Ohioian linked to 1988 murder. The Miami Herald, 6B.Elmore, C. (1994, September 14). Missing Pines man topic of TV talk show. Sun Sentinel, 2B.Ex-wife charged with murder after 19 years. (2021, September 26). Sun Sentinel. https://www.sun-sentinel.com/2007/12/15/ex-wife-charged-with-murder-after-19-years/?clearUserState=trueGuilty plea closes 24-year-old murder case. (2021, September 28). Sun Sentinel. https://www.sun-sentinel.com/2012/04/17/guilty-plea-closes-24-year-old-murder-case-2/James, S. (1990, June 25). Disappearance baffles police. Sun Sentinel, 1B.Kamph, S. (2011, June 23). My Father's Bones. Broward Palm Beach New Times, 34, 15–20.Pazdera, D. (1992, July 4). Mom still can't find her son. Sun Sentinel, 13B.Santana, S. (2001, November 3). Man convicted of Miramar murder. Sun Sentinel, 3B.Santana, S., & Marino, J. (2007, December 15). Ex-wife hit with murder charge years after crime. Sun Sentinel, 1B-6B.SUSPECT HELD IN '88 DEATH OF PINES MAN. (2021, September 27). Sun Sentinel. https://www.sun-sentinel.com/2004/10/13/suspect-held-in-88-death-of-pines-man/WOLFE v. STATE, No. 4D07-4555. | Fla. Dist. Ct. App., Judgment, Law, casemine.com. (n.d.). https://www.casemine.com. https://www.casemine.com/judgement/us/59146407add7b04934271346Woman implicated in ex-husband's murder to be released on bail. (2021, September 28). Sun Sentinel. https://www.sun-sentinel.com/2010/09/16/woman-implicated-in-ex-husbands-murder-to-be-released-on-bail/If you enjoyed the episode, consider leaving a review or rating! It helps more than you know! If you have a case suggestion, or want attention brought to a loved one's case, email me at bookofthedeadpod@gmail.com with Case Suggestion in the subject line.Stay safe, stay curious, and stay vigilant.

Reading by David Anderson ---   2 Samuel 24; 1 Chronicles 21-22; Psalm 30   https://www.biblegateway.com/passage/?search=2%20Samuel%2024%3B%201%20Chronicles%2021-22%3B%20Psalm%2030&version=ESV&interface=print

You're an accredited investor. You've got capital to deploy. But before you wire $100K, $250K, or $500K into a syndication — do you actually know what to look for?   In this episode of The Vinney & Beau Show, Beau Eckstein asks Vinney Chopra — 4x Amazon bestselling author with $300M+ raised, 42 deals, 5,000+ units, and 500+ accredited investors — the questions every sophisticated LP wishes they had asked before their first wire transfer.   A rare, candid behind-the-curtain conversation about how veteran syndicators actually structure deals, vet operators, manage K-1s, and protect investor capital.  

JAKE PEAVY (MLB Network) Jake was in town last weekend and saw this M's team firsthand- what are his thoughts on this team so far this season? Which of the Mariners pitchers reminds Jake most of himself? What are his thoughts on the Mariners starting rotation and the piggyback idea? JP has offered to move from SS to 3B; what kind of an impact can that have on the clubhouse? Are we in good hands with Dan Wilson as our manager? :30- BERKLY CATTON (Seattle Kraken) joins the show to give us the latest from Saskatoon and his thoughts on this Kraken offseason thus far. Did he have a favorite NHL team growing up? What about a favorite player? Is he watching the NHL playoffs? Who does he think looks the best right now? :45- We wrap up the show and the week with Booze News!See omnystudio.com/listener for privacy information.

In this talk, the speaker will explore the future of health care through the lens of Tissue Engineering, Regenerative Medicine, and Precision Medicine. These approaches offer promising therapies for a wide range of diseases and traumas, while also enabling the development of advanced 3D disease models—including for cancer—that can reduce reliance on animal testing. The speaker will discuss the role of innovative materials, stem cell integration, and cutting-edge processing techniques in creating bioresponsive therapies, implants, and research models. Drawing on groundbreaking work by the 3B's Research Group, the talk highlights key breakthroughs at the intersection of biology and engineering. Speaker Rui L. Reis, Professor of Tissue Engineering, Regenerative Medicine and Stem Cells, University of Minho

This week on Swimming with Allocators, growth-investor-turned-LP Yuri Lee (TMRS) joins Earnest and Alexa and explains how her global upbringing and love of technology shaped her investing philosophy and belief that talent can come from anywhere. She walks through TMRS's $3B and growing venture/growth mandate, how they split exposure between early-stage and multi-stage funds, and why they are building an aggressive 50/50 funds and co-investment program. Yuri shares what she looks for in emerging managers in clear, differentiated edge in sourcing, picking, or winning; true product–market fit between a manager's edge and fund strategy; and non-consensus, outlier ideas. Throughout, she offers candid advice on how GPs can better pitch institutional LPs, why most decks sound the same, and what it really takes to stand out in a consensus-heavy, AI-dominated market. Also, Chuck Daly of Sidley talks about how emerging VC managers should think proactively about compliance, conflicts of interest, disclosure, and performance/marketing practices under (and aligned with) the Advisers Act and SEC's marketing rule principles. Highlights from this week's conversation include: How a Global Upbringing Shapes an Investor's Worldview (0:13) Consumer Investing and Game Development Experience (2:22) Market Cycles in SaaS and Consumer Narratives (4:19) TMRS Mandate and Building a New Venture Program (6:16) Early Stage Managers and Differentiated, Non-Consensus Portfolios (9:20) What Matters Most at Early Stage vs Growth Stage (12:08) What LPs Really Want to Hear About: Companies and Decisions (14:11) How Pensions and Institutional LPs Run Diligence (17:04) Managing Portfolio Company Synergies and Conflicts (23:56) Marketing Rule Principles, Performance, and Case Studies (26:21) Why TMRS Uses Co-Invests and Target Mix With Funds (30:15) Barbell Approach: Early Stage Funds and Later Stage Co Invests (32:02) Information Gaps for LPs vs GPs and Founder Access (35:55) Consensus Rounds, Party Rounds, and Manager Profiles (38:56) Role of Non-Consensus Managers and Unique Edges (42:09) Rethinking AI Thesis and Value Capture by Model Labs (43:22) Advice For GPs Moving to LP Roles and Building Empathy (45:20) Final Thoughts and Takeaways (47:18) The Texas Municipal Retirement System is a $48+ billion public pension plan serving employees of participating Texas cities. TMRS invests across a diversified portfolio including public equities, fixed income, real assets, and private equity, with venture and growth investments forming an important component of its private markets strategy. Sidley Austin LLP is a premier global law firm with a dedicated Venture Funds practice, advising top venture capital firms, institutional investors, and private equity sponsors on fund formation, investment structuring, and regulatory compliance. With deep expertise across private markets, Sidley provides strategic legal counsel to help funds scale effectively. Learn more at sidley.com. Swimming with Allocators is a podcast that dives into the intriguing world of Venture Capital from an LP (Limited Partner) perspective. Hosts Alexa Binns and Earnest Sweat are seasoned professionals who have donned various hats in the VC ecosystem. Each episode, we explore where the future opportunities lie in the VC landscape with insights from top LPs on their investment strategies and industry experts shedding light on emerging trends and technologies.  The information provided on this podcast does not, and is not intended to, constitute legal advice; instead, all information, content, and materials available on this podcast are for general informational purposes only. Learn more about your ad choices. Visit megaphone.fm/adchoices

The insurance industry is shifting fast, and this episode of RiskCellar doesn't let a single headline slide. Hosts Brandon Schuh and Nick Hartmann are back in the cellar breaking down three of the most talked-about stories in the industry right now. The Rad Power Bikes wrongful death lawsuit and what it means for micro-mobility product liability, the escalating legal fallout from Howden's alleged broker raid on Brown & Brown, and the accelerating softening of the reinsurance market that's reshaping valuations across the board. Plus a deep dive on the collapse of litigation finance as an asset class, and why that might matter more than anyone's admitting.Brandon brings his front-row seat at the Christensen Group specialty desk to every story, and Nick matches him stride for stride with sharp market observations from the field. The episode covers the wrongful death suit filed by Shannon Stephens against Rad Power Bikes after an e-bike lithium-ion battery fire in an Alabama garage on January 3, 2025, killed her husband, Dr. Keith Stephens. Brandon unpacks the legal complexity of successor liability after Rad Power's bankruptcy, notes that the company's assets were reacquired for roughly $13 million, and questions whether the evidence standard will meet the plaintiff's burden given expert testimony that focused on design containment, not outright defect. The CPSC's intervention requiring UL certification for e-bike batteries is also examined in the context of legacy stock.The second half of the episode is equally dense. Brandon and Nick dissect the Brown & Brown vs. Howden broker raid litigation, including a new Minnesota temporary restraining order barring 16 former Brown & Brown employees now at Howden from soliciting clients or recruiting staff. With $31 million in business allegedly lost and Howden carrying roughly $5 billion in total debt against a $3 billion revenue base, the guys ask tough questions about runway and financial viability. They also explore the quiet implosion of litigation finance as an asset class, Burford Capital's 47% stock drop on a single day after a $16.1 billion Argentina judgment was overturned in a US appeals court being the centerpiece moment, alongside JP Morgan's latest analysis calling for continued reinsurance price softening into 2027. It's a dense, entertaining, and genuinely insightful look at where the market stands right now.Key Takeaways:Rad Power Bikes faces a wrongful death lawsuit tied to a lithium-ion battery thermal runaway event in January 2025Successor liability questions are unresolved, old entity is defunct; assets were acquired for ~$13M in bankruptcyBrown & Brown obtained a Minnesota TRO barring 16 former employees at Howden from soliciting clients or staffBrown & Brown disclosed ~$31M in lost business from the Howden broker raid on a recent earnings callHowden carries approximately $5B in debt against a ~$3B broker revenue base, a tight leverage positionChapters:00:00 Introduction01:55 Banter, Travel & Industry Events09:45 Rad Power Bikes Wrongful Death Lawsuit14:05 E-Bike Battery Safety, CPSC & UL Certification17:25 Brown & Brown vs. Howden, Broker Raid Update20:55 Howden's Debt Load & Financial Runway24:05 Property Rate Softening, Real Numbers from the Field26:40 Deductible Buy-Down Policies & Adverse Selection Risk29:45 Litigation Finance Collapse, Burford Capital & Argentina37:40 JP Morgan: Reinsurance Softening Into 202739:15 Three Truths and a Lie: Space Edition47:00 AI at Insurance Innovators Conference51:25 Data Centers in Space? Cooling Logistics Debate52:45 Wrap-Up & CheersConnect with Risk Cellar:Website: https://www.riskcellar.comBrandon SchuhLinkedIn: https://www.linkedin.com/in/brandon-stephen-schuhInstagram: https://www.instagram.com/schuhpapaFacebook: https://www.facebook.com/profile.php?id=61552710523314Nick HartmannLinkedIn: https://www.linkedin.com/in/nickjhartmann

– What the heck are we supposed to make of that win for the Cardinals? Plus, What's your level of concern at this point with Riley O'Brien's status as the closer?– Nolan Gorman is showing signs of life at 3B. Plus, is it time for the Cardinals to send down Thomas Saggese?– Which of these Snuggy contracts would you be most likely to sign up for?– Questions and Answers– Matthew Liberatore showed the signs of a step in the right direction right before he fell apart.– Who is the pro athlete most synonymous with playoff failures in the past 20 years?– Voice of the Blues Chris Kerber– The Junk Drawer– The Cardinals did it!– It took all of 2 off days for Wetherholt to get back on track.– More Likely to Happen– BK & Ferrario rewindSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

– What the heck are we supposed to make of that win for the Cardinals? Plus, What's your level of concern at this point with Riley O'Brien's status as the closer?– Nolan Gorman is showing signs of life at 3B. Plus, is it time for the Cardinals to send down Thomas Saggese?– Which of these Snuggy contracts would you be most likely to sign up for?– Questions and AnswersSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

The Musk v. Altman jury unanimously rejected Musk's claims on statute of limitations grounds. Andrej Karpathy joined Anthropic's pre-training team. Polymarket partners with Nasdaq on private company markets, Blackstone and Google form a TPU venture, and KPMG embeds Claude into tax advisory. Musk v. Altman: the jury unanimously rejects Elon Musk's claims against OpenAI and Sam Altman, as he filed them outside of a three-year statute of limitations (CNBC) Andrej Karpathy joins Anthropic to help launch a team focused on using Claude to accelerate pre-training research; he helped found OpenAI and worked at Tesla (Axios) Polymarket partners with Nasdaq to launch markets tied to private company milestones, including IPO timing, valuations, earnings, and secondary market activity (The Block) Blackstone announces a joint venture with Google to create a US company that will offer customers Google TPU access, and makes a $5B initial equity commitment (WSJ) KPMG partners with Anthropic to embed Claude into its tax and advisory platforms; KPMG's tax and legal services unit saw revenue grow ~8% YoY to $9.3B in 2025 (WSJ) Learn more about your ad choices. Visit megaphone.fm/adchoices

This Episode Chris sits down with Dwight Dunton, Founder of Bonaventure (launched in 1999), to talk market cycles, risk resilience, and the real-world tax playbook that helps active landlords transition into passive investing without writing a giant check to the IRS on the way out. Dwight shares the origin story: how a family “mailbox money” apartment investment turned into Bonaventure, and how a 25-year-old with no formal real estate background convinced Fannie Mae to finance a $16M buyout and kickstart a vertically integrated multifamily platform. Today, Bonaventure manages roughly $3B in assets, focused entirely on multifamily (with a meaningful senior housing sleeve). Dwight breaks down we he refuses to anchor to a single market forecast, how Bonaventure evaluates “lift-off” in overheated Sunbelt markets, and why B/C assets in strong submarkets can outperform when rent growth is muted because you can create NOI instead of waiting for the market to hand it to you. If you're sitting on a low-basis portfolio and want to go more passive without detonating your tax bill, this one is packed with frameworks and decision points. Disclaimer The content of this podcast is for informational purposes only. All host and participant opinions are their own. Investment in any asset, real estate included, involves risk, so use your best judgment and consult with qualified advisors before investing. You should only risk capital you can afford to lose. Past performance is not indicative of future results. This podcast may contain paid advertisements or other promotional materials for real estate investment advisers, investment funds, and investment opportunities, which should not be interpreted as a recommendation, endorsement, or testimonial by PassivePockets, LLC or any of its affiliates. Viewers must conduct their own due diligence and consider their own financial situations before engaging with any advertised offerings, products, or services. PassivePockets, LLC disclaims all liability for direct, indirect, consequential, or other damages arising out of reliance on information and advertisements presented in this podcast.

AI compute futures are now live on the CME, and IREN has raised $3B in a new convertible note offering. Welcome back to The Blockspace Podcast! Today for news, we cover IREN's new $3B convertible note – the largest convert ever for a public bitcoin miner – Trump's Q1 bitcoin equity buys, and the 90-day pause on zoning discussions for Hut 8's proposed 500 MW data center in Logan County, Illinois. Plus, Mike Alfred of Alpine Fox Hedge Fund joins us to discuss his top stock picks for AI, and Kush Bavaria of Ornn jumps on to discuss how Ornn is providing an H100 index for the CME's new AI compute futures – and his thoughts on the future of these incipient compute futures markets. Mike San Miguel of Luxor also joins us to discuss the latest in GPU markets and AI ASICs, and pseudonymous user Soup explains how he used Claude and $15 in tokens to spin up 3.5 trillion passwords to crack his long-lost bitcoin wallet.  

It's Casual Friday on The Majority Report On today's program: During a dinner with Donald Trump in Beijing, President Xi Jinping references the "Thucydides Trap" — the idea that when a rising superpower threatens to overtake an existing global power, the resulting tension often leads to war. Afterwards, Trump takes to Truth Social to spin Jinping's comments as a critique on the Biden Administration. Ryan Grim, journalist, co-host of Breaking Points and co-founder of Drop Site News, joins the program to recap the week's biggest news stories. Topics include upcoming primaries, the unreleased DNC autopsy, Majorie Taylor-Greene/AOC and more. In the Fun Half: JD Vance and head of CMS Dr. Oz announce that the federal government is deferring $1.3B in Medicaid reimbursements on grounds of alleged fraud. Dummy Nick Shirley gets sketchy dating advice from Patrick Bet-David. Stephen Crowder fails at trying to critique Zohran Mamdani's administration balancing of the budget. A man in Marblehead, MA calls out his city council for using golf courses as way to avoid complying with multi-family housing zoning laws. Canadian Billionaire Kevin O'Leary doxxes two women who are organizing against his proposed data center in Utah. All that and more. To connect and organize with your local ICE rapid response team visit ICERRT.com The Congress switchboard number is (202) 224-3121. You can use this number to connect with either the U.S. Senate or the House of Representatives. Follow us on TikTok here: https://www.tiktok.com/@majorityreportfm Check us out on Twitch here: https://www.twitch.tv/themajorityreport Find our Rumble stream here: https://rumble.com/user/majorityreport Check out our alt YouTube channel here: https://www.youtube.com/majorityreportlive Gift a Majority Report subscription here: https://fans.fm/majority/gift Subscribe to the AMQuickie newsletter here: https://am-quickie.ghost.io/ Join the Majority Report Discord! https://majoritydiscord.com/ Get all your MR merch at our store: https://shop.majorityreportradio.com/ Get the free Majority Report App!: https://majority.fm/app Go to https://JustCoffee.coop and use coupon code majority to get 10% off your purchase Check out today's sponsors: SELECT QUOTE: Get the right life insurance for you and save more than 50% on term life insurance at SelectQuote.com/MAJORITY RIDGE WALLET: Upgrade your wallet today! Get up to 40% off @Ridge with code MAJORITYREPORT at Ridge.com/MAJORITYREPORT SUNSET LAKE CBD: Use coupon code "Left Is Best" (all one word) for 20% off of your entire order at SunsetLakeCBD.com Follow the Majority Report crew on Twitter: @SamSeder @EmmaVigeland @MattLech On Instagram: @MrBryanVokey Check out Matt's show, Left Reckoning, on YouTube, and subscribe on Patreon! https://www.patreon.com/leftreckoning Check out Matt Binder's YouTube channel: https://www.youtube.com/mattbinder Subscribe to Brandon's show The Discourse on Patreon! https://www.patreon.com/ExpandTheDiscourse Check out Ava Raiza's music here! https://avaraiza.bandcamp.

A deep dive into 10 Canadian secondary markets worth serious investor attention in 2026. With Toronto condo sales at a 35-year low and Vancouver projects struggling to hit presale thresholds, capital is flowing into cities where the fundamentals actually pencil.The episode covers Moncton (2.9% population growth, $386K avg price), Halifax (#1 nationally for investor interest, lowest office vacancy in Canada), Quebec City (13% YoY price growth), Ottawa (Ontario's highest industrial rents at $17.33/sq ft, 130K+ federal employees), Hamilton ($2.3B in building permits, LRT in final phases), Kitchener-Waterloo (200K+ tech workers, 46% job growth), Winnipeg (6% multifamily cap rates), Regina (2.9 months of supply, $343K benchmark), Saskatoon (100%+ construction growth, HQ to Nutrien and Cameco), Edmonton (most affordable of Canada's six largest cities), Victoria ($3.15B tech sector), and Kelowna (contrarian buyer's market play).Each market analyzed for population, employers, housing prices, rental data, and the investor thesis. EDMONTON MULTIPLEX EVENT Try it NordVPN risk-free now with a 30-day money-back guarantee! Use our code "realestate" to get 4 extras months from a 2 years plan Exchange-Traded Funds (ETFs) | BMO Global Asset Management LISTEN AD FREESee omnystudio.com/listener for privacy information.

From the NFL schedule release to Netflix’s football takeover, Matt Chernoff and Hadley Engelhardt break down the biggest business stories in sports.

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

From the NFL schedule release to Netflix’s football takeover, Matt Chernoff and Hadley Engelhardt break down the biggest business stories in sports.

Reading by Robyn Byus ---   2 Samuel 11-12; 1 Chronicles 20   https://www.biblegateway.com/passage/?search=2%20Samuel%2011-12%3B%201%20Chronicles%2020&version=ESV&interface=print

Thank you everyone for bearing with us whilst we've been hard at work on season 3B of Haunted: The Audio Drama. We wanted to share with you a preview of the first episode of the season "Between The Static" written by Benton Hodges.HAUNTED returns at the end of the month releasing 3 mini-episodes week beginning 25th May with Between The Static kicking off the remainder of the season on 1st June. Hosted on Acast. See acast.com/privacy for more information.

IREN is planning a $2.3B convertible note offering to scale its AI line and DeFi degens are starting to trade STRC. Welcome back to The Blockspace Podcast! Today, Jay Patel, CEO of Lygos Finance, joins us to talk about the emerging trend of Strategy's STRC derivatives in DeFi. We also break down IREN's new $2.3B convertible notes offering, BitGo CEO Mike Belshe's take on quantum computing threats and Project 11's new research paper on the topic, KEEL's Q1 2026 results, and why Morgan Stanley's Bitcoin ETF saw zero outflows in its first month.

The Mass. Senate's $63.3B budget adds $53M in local aid via a new per-capita formula, and the state's highest court weighs if a rent control ballot bid illegally exempts religious groups.

Gerónimo De Abreu pasó de CTO de DealMaker (160 personas, $2.3B+ facilitados, backing de Google Cloud) a Founder & CEO de Stoda.ai. Oz y Tito Neira lo entrevistan sobre el salto al otro lado de la mesa: levantar capital, vender cuando vienes del código, manejar inversionistas y aprender growth. 21% de los founders de unicornios vienen de engineering, pero 52% de los founder-CEOs son reemplazados antes de cerrar la Serie C.00:00 Intro y bienvenida01:11 De CTO de DealMaker a Founder CEO03:51 El cambio de mindset CTO → CEO06:38 Por qué dio el salto09:34 Síndrome del impostor pitcheando como CEO12:42 Cómo transferir skills de CTO a CEO16:14 La AI te da superpoderes (pero te puede confundir)22:36 Por qué Stoda y no Cursor/Lovable/Cloud27:57 Cómo se cierra el primer cliente: Design Partnership33:13 Mensaje para el CTO de hace 5 años39:19 La parte humana: golden handcuffs y familia45:01 La soledad del CEO47:41 Consejo final: convicción + riesgo medido50:11 Cierre✩ CURSOS DISPONIBLES

In this Crypto Town Hall, the crew breaks down Circle's big $222M ARC token raise at a $3B valuation from BlackRock, Apollo, and a16z. They debate whether ARC actually has real utility (governance, staking & security) now that USDC will be the gas token, how the token value stacks up against Circle's equity, and if this is legitimate infrastructure play or just smart financial engineering. They also discuss tokenomics, the flaws with most governance tokens, network effects, XRP/Ripple comparisons, meme coins & community value, and the future of tokenized infrastructure. Learn more about your ad choices. Visit megaphone.fm/adchoices

Patrick Moorhead and Daniel Newman dig into the week's biggest moves in enterprise AI: Anthropic and OpenAI launching PE-backed enterprise JVs on the same day, Anthropic filling its compute gap with SpaceX's Colossus, Cerebris filing for a $3.5 billion IPO, NVIDIA going deep on co-packaged optics with Corning, and a full IBM Think and ServiceNow recap. Plus, for The Flip, hosts debate whether Anthropic, at $1.2 trillion, is the most important company in enterprise tech. The handpicked topics for this week are: 1. Anthropic and OpenAI Launch PE-Backed Enterprise JVs on the Same Day — Both companies announced private equity joint ventures, with OpenAI backed by Bain, Brookfield, and Advent, and Anthropic partnering with Blackstone, Goldman Sachs, Apollo, and General Atlantic. Daniel's read is that this is fundamentally a distribution play, using private equity portfolio companies as a deployment channel for AI at scale. Pat sees it as the clearest admission yet that enterprise AI cannot be self-implemented at scale without specialized consulting support, and flags that mid-tier systems integrators (SIs) could get cut out of the middle. (The Decode) 2. Anthropic Signs Massive Compute Deal with SpaceX Colossus — Anthropic urgently needed compute and SpaceX had 300 megawatts and 220,000 GPUs sitting at Colossus One in Memphis without enough business to fill them. Pat's take is blunt: this move is pragmatic. Anthropic needs it, xAI has it. Daniel adds that Dario himself said they planned for 10x growth and got 80x, and this deal is the fast backfill that reality demanded. The side note both hosts flag: Anthropic is running on H100s, H200s, and B200s, which puts the whole "Anthropic only runs on Trainium and TPUs" narrative to rest. (The Decode) 3. Cerebris Files for a $3.5 Billion IPO at $26.6 Billion Valuation — This marks their second attempt at an IPO after pulling the first filing. The architecture is genuinely unique, a complete wafer with massive on-chip SRAM and interconnects built directly onto the wafer rather than copper or photonics. Pat calls it the first credible Western alternative for AI inference. Daniel's framing cuts through: you do not have to beat NVIDIA to sell right now. You just need to have availability. The more interesting headline, both hosts agree, is that Sam Altman and Greg Brockman are angel investors, which adds fuel to the ongoing OpenAI lawsuit. (The Decode) 4. NVIDIA and Corning Announce $500 Million Optical Partnership — Three new US factories, co-packaged optics for Vera Rubin, and a supply chain strategy that mirrors what NVIDIA did with Coherent. Pat's context: this is vertical integration through investment rather than acquisition. Daniel's observation is that the pace of movement toward co-packaged optics is accelerating faster than anyone expected, and his "rule of and" applies here too. Copper is not going away. Optics are being added on top because the data volumes moving across these racks are outrunning what copper alone can handle. US manufacturing in North Carolina and Texas is a strategic bonus. (The Decode) 5. IBM Think 2026: Day Zero, Sovereign Core, and the Quantum Plus AI Bet — Pat moderated on stage with CEO Arvind Krishna and calls this IBM's best showing in five years. Arvind opened with the AI divide, the gap between companies still running POCs and companies already in production, and framed where IBM sits as day zero, not because nothing has happened, but because enterprise AI deployment at scale is still so early. Daniel's biggest takeaways: watsonX Orchestrate updates, Sovereign Core going GA with policy at runtime, and the Confluent acquisition potentially being IBM's most important asset since Red Hat, given that 40% of Fortune 500 companies run on it and real-time streaming data is foundational to agentic systems. Both hosts land on quantum plus AI as IBM's next inflection moment. (The Decode) 6. ServiceNow Knowledge 2026: Enterprise SaaS 2.0 is Emerging — Daniel got there on day three of the event and noted the conference was densely packed. His observation: enterprises have not gotten the memo from Wall Street that SaaS is supposedly dead. His emerging thesis is that middleware could make a comeback for AI, with companies needing a layer that lets agents work across any infrastructure, any app, and within the rules of their specific business. Pat agrees and adds that the growth question is about mix, not survival. (The Decode) 7. The Flip: Is Anthropic at $1.2 Trillion the Most Important Company in Enterprise Tech? — Daniel took the affirmative citing that Claude Code is deeply entrenched in developer workflows. Anthropic went from $9 billion to $45 billion ARR in months. Every major hyperscaler is both a customer and an investor. The PE JVs are turning verticals into Anthropic engines. Dario said they planned for 10x and got 80x. Pat's counter: the enterprise trust gap is real after what Anthropic pulled on pricing and performance. Microsoft has 2 billion users across 365, Azure, and Copilot. NVIDIA is the infrastructure Anthropic runs on. And workforce replacement, which is how Anthropic extracts its terminal value, is not arriving as fast as the valuation suggests. In reality, both hosts admit their notes looked almost identical. (The Flip) 8. AMD — Lisa Su guided AI data center growth up from 60% to 80%. With OpEx growing 83%, net income up 95%, free cash flow ripping, and CPUs growing at nearly 40% without price increases, Pat reads this as unit market share gains coming soon. Daniel's framing: AMD is now a two-headed juggernaut with CPUs and GPUs for the data center. And Helios has not even started shipping yet. Both hosts take a victory lap for previously calling this one. (Bulls and Bears) 9. Palantir — Triple beat on revenue, EPS, and forward guidance. Rule of 40 at 145%. Government revenue up 84%, 47 deals over $10 million, and the largest guidance raise in the company's history. Daniel's take: Palantir is redefining the category entirely. It's not a software company in the Salesforce or ServiceNow sense. It's technology, plus ontology, plus people, deployed at the deepest layers inside governments and enterprises. Pat adds that the four deployed FTE model lets them stand up AIP POCs within a week, which is why they are winning business at this pace. (Bulls and Bears) 10. ARM — AGI processor demand doubled from $1 billion to $2 billion within 45 days. Record revenue, strong pipeline, royalty growth at 21% for the full year. The stock ripped after hours, then sold the next day when management confirmed only enough supply for $1 billion of that $2 billion demand. Pat's read: 50% CPU market share with hyperscalers at the core level is the most underdiscussed signal on the call. Daniel adds that the worry about ARM competing with its own customer base in custom silicon has been quietly swept away by the sheer volume of compute demand. (Bulls and Bears) 11. Supermicro — A board member allegedly used a hairdryer to remove labels from GPU boxes being shipped to China. Approximately 20% of their revenue has reportedly been illegally shipped to China. They beat on EPS and Q4 guide but missed Q3 revenue versus consensus. Stock still ripped 18%. Daniel's take: if you are selling picks and shovels during a gold rush and you are this messed up, he cannot imagine owning it with the overhang that is building. (Bulls and Bears) 12. Lattice Semi and Coherent — Lattice revenue up 42%, back into growth, guiding to 50% year-on-year at midpoint. The AMI acquisition at $1.65 billion doubles their serviceable market from $6 billion to $12 billion and puts them inside every AI server on the planet at the BIOS and platform firmware layer. Pat calls the timing right: core financials crushing it, time to make a move. Coherent printed 21% year-on-year growth, 55% EPS growth, margins expanding, debt coming down, entered the S&P 500, and sits at the center of the co-packaged optics trend that is accelerating. Pat's choke point note: Indium phosphide capacity is the constraint. Six-inch fabs are doubling capacity in 2026, a quarter ahead of plan, and competitors are still ramping their transitions. (Bulls and Bears) Want the full breakdown from IBM Think and ServiceNow Knowledge, and check out our on-the-ground coverage linked in the show notes. Be part of our community. Hit that subscribe button and let us know what you want us to cover next week in the comments. Intro Pat on Stage at IBM Think https://x.com/PatrickMoorhead/status/2051381046537601101?s=20 The Decode OpenAI and Anthropic Both Launch PE-Backed Enterprise Services JVs on the Same Day — The Palantir FDE Model Goes Mainstream https://www.bloomberg.com/news/articles/2026-05-04/openai-finalizes-10-billion-joint-venture-with-pe-firms-to-deploy-ai https://techcrunch.com/2026/05/04/anthropic-and-openai-are-both-launching-joint-ventures-for-enterprise-ai-services/ https://www.semafor.com/article/05/04/2026/openai-anthropic-ramp-up-enterprise-push Anthropic and SpaceX Sign Massive Compute Deal — Full 300MW / 220,000 GPU Colossus 1 Memphis Data Center Plus Exploration of Multi-Gigawatt Orbital AI Compute https://www.cnbc.com/2026/05/06/anthropic-spacex-data-center-capacity.html https://www.bloomberg.com/news/articles/2026-05-06/anthropic-inks-computing-deal-with-spacex-to-meet-ai-demand https://www.tomshardware.com/tech-industry/artificial-intelligence/musks-spacex-has-rented-out-access-to-its-supercomputers-220-000-nvidia-gpus-and-300-megawatts-of-ai-compute-power-to-rival-anthropic Cerebras Files for $3.5B IPO at $26.6B Valuation — The First Major AI Chip IPO of 2026 https://www.cnbc.com/2026/05/04/cerebras-ipo-ai-chipmaker.html https://theaiinsider.tech/2026/05/06/cerebras-systems-eyes-3-5b-in-largest-tech-ipo-of-2026-on-strength-of-ai-chip-demand/ https://www.briefs.co/news/ai-chipmaker-cerebras-just-filed-for-a-3-5-billion-ipo/ NVIDIA and Corning Announce Game-Changing Optical Partnership — $500M Investment, 3 New U.S. Factories, and Co-Packaged Optics for Vera Rubin and Beyond https://www.corning.com/worldwide/en/about-us/news-events/news-releases/2026/05/nvidia-and-corning-announce-long-term-partnership-to-strengthen-us-manufacturing-for-ai-infrastructure.html https://www.cnbc.com/2026/05/06/nvidia-corning-optical-factories-nc-texas-ai.html https://www.wsj.com/tech/nvidia-corning-form-partnership-to-expand-fiber-optic-manufacturing-17f525de https://kfgo.com/2026/05/06/corning-partners-with-nvidia-to-expand-us-fiber-optic-output-for-ai-growth/ IBM Think 2026 Boston — Watsonx Orchestrate Next-Gen, Confluent Real-Time Data, IBM Concert, and Sovereign Core Define IBM's Agentic Operating Model https://newsroom.ibm.com/2026-05-05-think-2026-ibm-delivers-the-blueprint-for-the-ai-operating-model-as-the-ai-divide-widens https://www.ibm.com/new/announcements/ibm-announcements-at-think-2026 https://www.instagram.com/reel/DX42DlrglOs/ ServiceNow Knowledge 2026 Las Vegas https://www.servicenow.com/events/knowledge.html https://newsroom.servicenow.com/press-releases/details/2026/Cohesity-and-ServiceNow-Deliver-Real-Time-Recovery-for-Enterprise-AI-Agents/default.aspx https://www.cnbc.com/2025/09/04/nvidia-backed-cohesity-eyes-2026-ipo-with-valuation-rivaling-17-billion-rubrik.html   The Flip: Anthropic at $1.2T Now the Most Important Company in Enterprise Tech — More Important Than NVIDIA, Microsoft, or OpenAI FOR: Dual-hyperscaler compute anchor (Amazon $33B + Google $40B = $73B) is structural — unmatched https://futurumgroup.com/insights/anthropics-gigawatt-scale-tpu-deal-with-broadcom-creates-a-structural-advantage/ Constitutional AI safety positioning wins regulated industries https://www.anthropic.com/news/anthropic-nec-japan-ai-engineering-workforce $900B valuation surpasses OpenAI ($852B) at faster revenue growth and lower burn rate https://techcrunch.com/2026/04/30/anthropic-potential-900b-valuation-round-could-happen-within-two-weeks/   AGAINST: NVIDIA still controls the substrate — every Anthropic dollar of revenue requires NVIDIA inference at some layer https://www.cnbc.com/2026/04/27/nvidia-just-hit-an-all-time-high-why-some-think-a-rally-is-just-getting-started.html Microsoft has the enterprise distribution — 365 + Azure + Copilot reach >2 billion users https://www.marketbeat.com/originals/microsofts-maia-200-the-profit-engine-ai-needs/ $900B valuation is venture marketing — the IPO will reset the number https://www.semafor.com/article/05/04/2026/openai-anthropic-ramp-up-enterprise-push   Bulls & Bears: AMD Q1 2026 — Revenue $10.3B (+38% YoY), MI300X Data Center GPU Demand Drives Stock +20% on the Print https://ir.amd.com/news-events/press-releases/detail/1284/amd-reports-first-quarter-2026-financial-results https://www.cnbc.com/2026/05/05/amd-q1-2026-earnings-report.html https://finance.yahoo.com/markets/stocks/articles/amd-q1-2026-earnings-revenue-203331768.html Palantir Q1 2026 — Revenue +85% YoY, US Commercial +133%, Rule of 40 Score Hits 145%; Largest Guidance Raise in Company History https://investors.palantir.com/files/Palantir%20-%20Q1%202026%20Business%20Update.pdf https://www.reddit.com/r/PLTR/comments/1t3t0me/palantir_reports_q1_2026_us_revenue_growth_of_104/ https://finance.yahoo.com/markets/stocks/articles/palantir-technologies-inc-q1-2026-002218719.html https://semiconalpha.substack.com/p/palantir-q1-2026-rewriting-the-rule Arm Holdings Q4 FY2026 — Record $1.49B Quarter, Full-Year Revenue Crosses $4.92B, $2B AGI CPU Pipeline; Stock +16% After Hours https://finance.yahoo.com/markets/stocks/articles/arm-q4-earnings-call-highlights-225942093.html https://www.stocktitan.net/sec-filings/ARM/6-k-arm-holdings-plc-uk-current-report-foreign-issuer-7e9ca9ac7dda.html https://semiconalpha.substack.com/p/arm-q4-fy2026-record-quarter-2-billion Super Micro Computer Q3 FY2026 — Revenue $10.2B (+123% YoY), Strong Q4 Guide; Stock +18% AH on First Earnings Call Since Co-Founder Indictment Drama https://www.cnbc.com/2026/05/05/super-micro-smci-q3-earnings-report-2026.html https://www.stocktitan.net/sec-filings/SMCI/8-k-super-micro-computer-inc-reports-material-event-e70b2f8b3cb7.html https://www.instagram.com/reel/DX42DlrglOs/ Lattice Semiconductor Q1 2026 — Beat-and-Raise Quarter ($170.9M, +42% YoY) Paired With $1.65B AMI Acquisition That Doubles Lattice's SAM to $12B https://www.stocktitan.net/sec-filings/LSCC/8-k-lattice-semiconductor-corp-reports-material-event-642a862b2bf9.html https://www.ami.com/resources/ami-announces-agreement-to-be-acquired-by-lattice-semiconductor/ https://www.linkedin.com/posts/patmoorhead_lattice-semiconductor-posts-beat-and-raise-activity-7457411226944425984-xA8T Coherent Q3 2026 Earnings https://www.msn.com/en-us/money/companies/coherent-cohr-tops-revenue-expectations-in-q3-as-ai-demand-accelerates-shares-decline/ar-AA22Bz24?ocid=finance-verthp-feeds  

Do you think putting Holliday at 3B is good for the O's infield defense? full 643 Mon, 11 May 2026 10:26:13 +0000 TZvEfHYDBz9C0GENX5Y3oMeqEgCsUr2M mlb,baltimore orioles,jackson holliday,sports The Big Bad Morning Show mlb,baltimore orioles,jackson holliday,sports Do you think putting Holliday at 3B is good for the O's infield defense? 5:30a-10a weekdays on 105.7 The FAN 2024 © 2021 Audacy, Inc. Sports False https://player.amper

Reading by David Anderson ---   2 Samuel 8-9; 1 Chronicles 19; Psalm 20   https://www.biblegateway.com/passage/?search=2%20Samuel%208-9%3B%201%20Chronicles%2019%3B%20Psalm%2020&version=ESV&interface=print

Today's Post - https://bahnsen.co/3QMkXSl David Bahnsen analyzes Rupert Murdoch's 2019 sale of major 21st Century Fox entertainment assets to Disney for $71.3B, emphasizing not the politics of the parties but the business logic and investing takeaways. He contrasts Disney's struggles since the deal with Fox's stronger stock performance, arguing the outcome reflects capital intensity and duration risk: Disney bought scale and IP to compete in streaming, requiring heavy reinvestment amid intense competition and limited margin of safety, while Murdoch kept Fox's news and sports assets (Fox News, Fox Business, broadcast and sports rights) as more durable, real-time, less disrupted businesses with higher margins. Bahnsen connects this to dividend growth investing as a shorter-duration equity profile that “gets paid now,” helping de-risk unknowns versus long-duration, capital-heavy bets like streaming content. 00:00 Welcome and Setup 01:10 Polarization Disclaimers 03:32 The 2019 Fox Disney Deal 05:13 Stock Performance Aftermath 06:48 Disney's IP Playbook 08:25 Murdoch Keeps News Sports 10:59 Streaming Wars and Capital Risk 12:52 Capital Light Durability Lesson 15:17 Duration Risk and Dividends 18:16 Dividend Growth Takeaways 19:30 Closing Thoughts Links mentioned in this episode: DividendCafe.com TheBahnsenGroup.com

Is there a better option for the Birds at 3B other than Mayo? full 759 Fri, 08 May 2026 10:43:23 +0000 ZGcXSTYaBB2awz33TYT3oXx7WC0YR0fI mlb,baltimore orioles,sports The Big Bad Morning Show mlb,baltimore orioles,sports Is there a better option for the Birds at 3B other than Mayo? 5:30a-10a weekdays on 105.7 The FAN 2024 © 2021 Audacy, Inc. Sports False https://player.amperwavepodcast

May 7, 2026: Your daily rundown of health and wellness news, in under 5 minutes. Today's top stories: Deloitte projects global women's sports revenue will surpass $3B in 2026 up 25% year-over-year, with WNBA franchise values averaging $460M and Golden State Valkyries hitting $1B valuation MOSH co-founded by Maria Shriver and Patrick Schwarzenegger raises $13M to scale brain health protein bars into Target and national retail RFK Jr. announces initiatives reducing reliance on antidepressants including deprescribing reimbursement support, as one in six US adults currently use SSRIs Today's episode is brought to you by AIIR — a modern communications and experiential agency for health, wellness, fitness, and performance brands. From earned media to events and creator-led campaigns, AIIR helps companies sharpen their story, earn attention, and build trust that compounds. Visit https://aiir.agency to learn more. More from Fitt: Fitt Insider breaks down the convergence of fitness, wellness, and healthcare — and what it means for business, culture, and capital. Subscribe to our newsletter → insider.fitt.co/subscribe Work with our recruiting firm → https://talent.fitt.co/ Follow us on Instagram → https://www.instagram.com/fittinsider/ Follow us on LinkedIn → linkedin.com/company/fittinsider Reach out → insider@fitt.co

Reading by Heidi Wilson ---   2 Samuel 6-7; 1 Chronicles 17   https://www.biblegateway.com/passage/?search=2%20Samuel%206-7%3B%201%20Chronicles%2017&version=ESV&interface=print

A transformation and growth leader at heart, Paul Idziak is a CEO who thrives in complexity and turns bold vision into disciplined execution and scalable results. Like a catalyst for momentum, he does not just grow businesses; he engineers ecosystems where people, process, and performance move in sync. He leads from the front, combining grit with clarity to transform underperforming operations into high-impact, multi-location enterprises. What he brings to the table is a rare blend of private equity acumen, operational rigor, and commercial instinct. He builds strong leadership teams, installs KPI-driven cultures, and creates structures that scale with precision. From due diligence to exit readiness, he aligns strategy with execution, driving profitability, expanding markets, and reducing risk. He operates with urgency, accountability, and a relentless focus on value creation. Over the years, Paul has scaled businesses from the ground up, launching new divisions, expanding across the U.S., Canada, and international markets, and building distributed workforces of 300+ technicians. He has driven 35% revenue CAGR and 110% EBITDA growth, transforming operational performance and positioning companies for successful exits. He has secured tier-1 OEM partnerships, negotiated MSAs, and led high-value projects exceeding $20M while building diversified, resilient customer portfolios. From sourcing more than 100 acquisition targets and supporting approximately $3B in transaction value to executing value creation plans targeting 4X returns, his experience spans the full investment lifecycle. He has improved margins, reduced the cost of poor quality, implemented Lean 6S practices, and built safety cultures, achieving 0 recordables, consistently delivering measurable, repeatable results. His previous experience across Johnson Controls, Siemens, and AWC has further sharpened his leadership approach, strengthening his ability to scale operations, build high-performing teams, and drive consistent enterprise-level impact. What matters most to Paul is building businesses that endure and teams that win long after the strategy is set. He measures success not just by growth, but by the legacy of performance, discipline, and leadership he leaves behind.

Shoot us a Text.Episode #1333: Used-car consolidation accelerates as the biggest dealer groups tighten their grip, Ford opens up employee pricing to drive volume, and Audible tests a bold physical retail concept. It's all about scale, strategy, and finding new ways to connect with today's customer.Show Notes with links:Automotive News dropped its 2026 Top 100 used dealer group ranking, and the big headline is scale: the top 100 retailed 3.45 million used vehicles in 2025, up 1.4%, while the top 10 alone moved 52% of the total. Big stores got bigger, and the used-car chessboard got sharper. Lithia stayed on top with 435,070 used units, followed by AutoNation at 269,558, Group 1 at 234,906, Penske at 226,301, and Sonic at 171,838.Group 1 had a monster year, jumping from 209,687 to 234,906 used units, while Hudson Automotive cracked the top 10 with 72,044 units after a big gain from 2024.The list also shows plenty of movement below the giants, with new names including DriveChoice, Price Family Dealerships, Sam Pack, Bob Moore, Young Automotive, Bayway, ZT, Hiley, and American Motors Group.Takeaway for dealers: used-car scale is still a weapon, but so is execution. The groups climbing fastest are proving inventory discipline, acquisition strategy, and turn speed still matter more than just rooftop count.Ford is opening up employee pricing to the public on most 2025 and 2026 models, leaning into affordability concerns while trying to keep volume strong. The move comes as competition tightens and buyers remain cautious on big purchases.Ford extends employee pricing broadly to attract cost-conscious buyers sitting on the sidelines.The strategy aims to boost showroom traffic and maintain plant utilization in a competitive market.Q1 2026 results show solid footing with $43B revenue and $2.5B net income, helped by a $1.3B tariff refund.The big question: will increased volume offset thinner margins or create a discounting habit?Audible is stepping into the physical world with a limited-time NYC pop-up designed to bring audio storytelling to life. The 6,000-square-foot “Story House” blends retail, community, and immersive tech into a new kind of media experience.Audible opened a three-story listening lounge in Manhattan, running through May 31.Features include Dolby Atmos rooms, live events, and interactive “story tiles” for browsing content.The space blends digital and physical, letting users sample, stream, and engage with content on-site.Community-driven programming includes creator panels, fan events, and themed experiences.James Finn, Audible's global head of brand and content marketing:“What does a bookstore look like without any books? A place where audio storytelling comes alive.”Join Paul J Daly and Kyle Mountsier every morning for the Automotive State of the Union podcast  as they connect the dots across car dealerships, retail trends, emerging tech like AI, and cultural shifts—bringing clarity, speed, and people-first insight to automotive leaders navigating a rapidly changing industry.Get the Daily Push Back email at https://www.asotu.com/JOIN the conversation on LinkedIn at: https://www.linkedin.com/company/asotu/

Episode 426 of The VentureFizz Podcast features Natan Linder, CEO & Co-Founder of Tulip. There are very few entrepreneurs in Boston who have built multiple tech companies to a billion+ valuation (Tulip & Formlabs), but Natan is one of them. He is a “builder” in the truest sense of the word, focusing on building what he calls “important things” rather than just the latest “shiny object.” In our conversation, we have a discussion about Physical AI, meaning AI that lives outside of the digital domain in things like robotics and self-driving cars. Natan believes we are hitting a “ChatGPT moment” for the physical world, so it was interesting to hear his perspective on this trend. Tulip is the leader in frontline operations. They help companies of all sizes and industries equip their workforces with connected, composable, and intelligent tools. With Tulip's no-code platform, manufacturers can digitize processes, collect real-time data, and drive continuous improvement by using AI and without writing a line of code. The company recently announced a $120M Series D round of funding at a $1.3B valuation. In this episode of our podcast, we also cover: Chapters: 00:00 Intro - Natan Linder, Co-Founder & CEO, Tulip 03:30 The Physical-AI Moment 10:56 Natan Linder's Early Life and Curiosity 14:58 Early leadership role with Samsung 19:07 Getting involved with Rethink Robotics 25:34 Natan's thoughts on humanoid robots 28:55 Revolutionizing 3D Printing at Formlabs 33:49 Landing Mitch Kapor as an Investor in Formlabs & The "Elevator" Pitch Story 39:38 The Genesis of Tulip & Details of the Platform 50:31 Customer Use Cases 57:11 Tulip's Growth and Future Direction 01:04:30 Why Now Is a Good Time to Join Tulip 01:07:28 Boston is an Epicenter of Advanced Technology This podcast is brought to you by one of the strongest longtime supporters of the local startup ecosystem, Silicon Valley Bank, a division of First Citizens Bank. With more than 1,500 bankers and relationship advisors and $44B in loans as of Q4 2025 – SVB delivers expert guidance, specialized products and a team that knows the innovation economy inside and out. Learn more at SVB.com.

Summary: where we're at in Romans and where we're going in this passage. Relevance: why does this matter to me? Thesis: Because God has justified us in Christ, we can rejoice even in suffering, knowing he uses it to deepen our hope and assure us of his love by the Holy Spirit. THE SURPRISE: WE REJOICE IN SUFFERING (V. 3A) THE PROCESS: SUFFERING PRODUCES HOPE (VV. 3B–4) THE ASSURANCE: HOPE WILL NOT SHAME US (V. 5)

Reading by Pastor John Dunn ---   2 Samuel 5; 1 Chronicles 11-12   https://www.biblegateway.com/passage/?search=2%20Samuel%205%3B%201%20Chronicles%2011-12&version=ESV&interface=print

American farmers are being crushed by fertilizer price spikes tied to the Strait of Hormuz, rising regulations, and razor‑thin margins, forcing $44.3B in subsidies while threatening higher food prices, global instability, and a deeper debate over real free‑market capitalism.

Shoot us a Text.Episode #1330: Carvana keeps its growth streak alive with record sales and profits, automakers book billions in tariff refunds (on paper), and AI is forcing retail media to evolve from impressions to real, measurable outcomes.Carvana is back in the fast lane, posting another record-breaking quarter with massive sales growth and strong profits. The online retailer continues to scale, signaling confidence in its long-term used car dominance.Carvana sold 187,393 vehicles in Q1 2026, up 40% year-over-year and marking its sixth straight quarter of 40%+ growth.Revenue jumped to $6.43B with net income hitting $405M, beating analyst expectations across the board.The company is expanding capacity, integrating ADESA sites, and building toward 1.5M annual unit capability—with room to reach 3M.Carvana expects continued growth in Q2, assuming stable market conditions and momentum holds.“We are continuing to hit records… and scale a business of Carvana's complexity at high speed,” said CEO Ernie Garcia.Automakers are seeing a short-term earnings lift from expected U.S. tariff refunds—but the cash isn't in hand yet, and the optics could get tricky. As billions in reimbursements loom, companies are balancing accounting wins with political uncertainty.Ford, GM, Mercedes, and Stellantis booked roughly $2.3B in expected tariff refunds, boosting Q1 profits on paper.Ford alone expects $1.3B back, GM about $500M, tied to overturned tariffs under IEEPA.Automakers stress the cash hasn't arrived yet—so it's not counted as free cash flow.The refund process could take months, adding uncertainty to already complex financial planning.The overturned IEEPA tariffs are just one piece—automakers still face ongoing import taxes on steel, aluminum, and vehicles and parts from Mexico, Canada, and beyond.As AI agents begin browsing, buying, and acting on behalf of users, Cloudflare says the internet isn't fully prepared. A new push for “agent readiness” could reshape how businesses structure sites, data, and digital experiences.Cloudflare warns most websites are built for humans—not AI agents that search, decide, and transact automatically.“Agent readiness” means structuring sites so AI can easily access, interpret, and act on information.This includes better APIs, structured data, and permissions for what agents can or can't do.Businesses may need to rethink UX entirely—designing for machines as much as for people.“The web is being rebuilt for agents,” Cloudflare suggests, signaling a major shift in how digital commerce will operate.Join Paul J Daly and Kyle Mountsier every morning for the Automotive State of the Union podcast  as they connect the dots across car dealerships, retail trends, emerging tech like AI, and cultural shifts—bringing clarity, speed, and people-first insight to automotive leaders navigating a rapidly changing industry.Get the Daily Push Back email at https://www.asotu.com/JOIN the conversation on LinkedIn at: https://www.linkedin.com/company/asotu/

Wines We're Drinking Jessica: Para Wines, Vino Blanco 2023 - lemongrass, apple, citrus finish. Fresh, light, and bright. Perfect for a warm day. Erika: Sitting this one out (allergies have her hostage), but had her heart set on a Prosecco with Chambord brunch cocktail. Noted for next time. It's our April chisme session, and mi gente, we had a LOT to unpack this month. From Coachella controversies to Celia Cruz making history, Olympic ticket sticker shock, and some deeply personal highs from Jessica, including two years of marriage and a home loan approval. Pull up a glass and let's get into it. In This Episode We Cover [00:00] Welcome & Wine Check-In: Jessica's dealing with spring allergies but still showing up with a glass of Para Wines Vino Blanco 2023. Erika gives us her imaginary Prosecco-Chambord brunch cocktail. [05:00] Karol G at Coachella: The controversy unpacked. The all-female mariachi group that wasn't actually the first, the Trump-supporting ownership, vetting failures by her team, contract logistics, and why Becky G's cameo felt intentional and powerful. [17:00] Coachella Then vs. Now: How the festival shifted from music-first to influencer activations and fashion moments. The economics behind it: $29M+ paid to 150+ artists, $25M in production, and why it's only going to keep escalating. [26:00] LA 2028 Olympics Ticket Sale: Jessica secured swimming event tickets for her nephew's graduation gift. The breakdown: opening ceremony seats at $5K to $10K, the lottery system, and the plan to use only existing venues and public transportation. [34:00] The White House Correspondents' Dinner Incident: Jessica and Erika break down why they're cynical, the questions about security failures, the Stephen Miller photo moment, and a broader conversation about political theater and manufactured narratives. [43:00] The Onion Buys InfoWars: What it means that a parody site now owns Alex Jones's platform, the $1.3B judgment, and why Tucker Carlson, Candace Owens, and Megyn Kelly distancing themselves from Trump might not be what it looks like. [54:00] Celia Cruz & Rock and Roll Hall of Fame: She's a 2026 inductee and the first Spanish-language artist ever inducted. Jessica and Erika break down what her induction through the "Early Influence" category really says about how the Hall handles Latin music history. [59:00] Billboard Mujeres Latinas de la Música: Rosalía named Mujer del Año, Becky G receiving Global Impact honors. Jessica celebrates Becky G's pivot to Spanish-language music as a first-gen Mexican-American artist and what her journey means for the comunidad. [01:06:00] On a Personal Note: Jessica and Antonio celebrate two years of marriage, just got approved for a home loan, and are starting to look at houses. A beautiful reflection on life not going to plan and why that's okay at 48. [01:19:00] Wrapping Up + Listener Shoutout: The podcast is ending at Episode 300! Jessica wants to hear from you. Call or text: 858-304-0266. DM on Instagram: @thewineandchisme. Connect with Wine & Chisme

Foreign powers are spending billions to “buy ears” in America through universities, youth culture, and podcasters. PBD breaks down Qatar's $6.3B push, China's long game, and how media, philanthropy, and elites quietly shape propaganda and public opinion.

Send us Fan MailJoin the gentlemen of 3B (all 3 of them!) for a show of Sun Belt baseball coverage!Weekly Pick ReviewPower RankingsStandingsWeekly RecapUpcoming Games & SeriesPlus, our usual comedy and antics! Support the showLike this content? Follow us on our socials;https://www.facebook.com/Sunbeltsyndicatehttps://www.instagram.com/sunbeltsyndicate/https://x.com/SunbeltSyndicatCovering the Sun Belt conference from the first kick(off) to the last pitch.#sunbelt #college #sportsBe sure to check out Don't Sleep Energy at www.dontsleepenergy.com or at their Amazon shop. Go to Amazon and search 'Don't Sleep Energy'.Check out all Phenom has to offer at www.phenomelitebrand.com. Whether you need cleats, gloves, or accessories, Phenom's got you covered! Use code SBSYNDICATE at checkout for 10% off!

A new documentary claims to have solved the case of Satoshi' identity, and Justin Sun is bringing World Liberty Financial to court. Welcome back to Block Space Live! Today, William Cohan and Tyler Maroney of the Finding Satoshi documentary join us to talk about their years-long search for Bitcoin's creator. We also dive into the wild crypto extortion scheme that scammers are waging against unwitting tanker crews in the Strait of Hormuz, and we break down the explosive  lawsuit between Justin Sun and the Trump-linked World Liberty Financial. Plus, Core Scientific's massive $3.3 billion raise, and how Bitget and Republic are bringing SpaceX investment to crypto rails before Musk's company even goes public.  Subscribe to the newsletter! https://newsletter.blockspacemedia.com Notes: • Core Scientific to raise $3.3 billion via senior notes. • Justin Sun's $45M WLFI investment allegedly worth $766M before it was frozen. • Hormuz toll trolls: scammers trick Indian oil tanker crew into paying fake crypto toll • Reuters: 20,000 seafarers currently in the Gulf. • Finding Satoshi documentary 6 years in the making • Bitget, Republic partner to list “mirror token” for pre-IPO SpaceX stock Timestamps: 00:00 Start 02:22 Softwar Thesis is back baby! 09:16 Hormuz crypto scam 15:57 Justin Sun Sues WLFI 24:11 Finding Satoshi 41:44 CORZ $3.3B note 48:11 Bitget SpaceX IPO

Bitcoin slips from near $80K on oil spikes and profit-taking despite ceasefire progress, while Iran's Nobitex exchange moved $2.3B+ through networks tied to Trump-linked ventures. BlackRock led a 7-day Bitcoin ETF inflow streak of $1.9B, and Strategy continues aggressive accumulation. Justin Sun sues World Liberty Financial over frozen tokens, New York/Illinois ban state employees from prediction markets, and the UK raids illegal P2P trading sites. April hacks top $600M with Kelp DAO laundered via THORchain, Russia passes its crypto bill in first reading, and Ethereum logs its busiest quarter ever—markets cautious with focus on geopolitics, security, and institutional resilience. Hosted on Acast. See acast.com/privacy for more information.

Start your morning with Buzzcast with Abe Madkour:  Pittsburgh's coming out party; Royals target 85-acre, $3B complex in downtown KC as sport hub and buzz on 24-team playoff builds Sign up for SBJ 360, our free, daily newsletter. SBJ 360 delivers a concise, high-level overview of the most important stories shaping the sports industry. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, we sit down with Dev Motwani to dig into one of South Florida's most unique real estate journeys - from growing up working the front desk of a struggling beach motel to leading over $3B in development across the region.Dev shares how his family survived the collapse of Fort Lauderdale's spring break era, pivoted into distressed hotel acquisitions, and ultimately helped reshape the city into a luxury destination. We get into the evolution of Merrimac Ventures, the lessons learned during the 2008 financial crisis, and the strategy behind landmark projects like the Four Seasons Fort Lauderdale and beyondIt's a conversation about resilience, long-term vision, and what it actually takes to assemble, entitle, and execute transformative projects; told by someone who's been in the trenches since childhood.Connect with usWant to dive deeper into Miami's commercial real estate scene? It's our favorite topic and we're always up for a good conversation. Whether you're just exploring or already making big moves, feel free to reach out at info@builtworldadvisors.com or give us a call at 305.498.9410.Prefer to connect online? Find us on LinkedIn or Instagram - we're always open to expanding the conversation.Ben Hoffman: LinkedIn Felipe Azenha: LinkedIn  We extend our sincere gratitude to Büro coworking space for generously granting us the opportunity to record all our podcasts at any of their 8 convenient locations across South Florida.

In this episode, Chris sits down with Matthew Ogle, Co-founder & CEO of Legacy Knight, a $2.8B multi-family office in Dallas, TX that he co-founded in 2019. We dig into how you build a world-class multi-family office from scratch - and why so many wealthy families out there don't actually have one yet. Matthew's path into wealth management didn't start in a boardroom - it started on a tennis court. A summer teaching tennis to a CIO's family at Cape Cod opened the first door, which led him to Credit Suisse's private bank through the GFC and then five years at the Crow family office, helping transform it into one of the first true multi-family offices in Dallas. He opened Legacy Knight's doors in October 2019 with $2.5M of operating capital, 14 seed families, and a contrarian bet - that the new generation of sub-50-year-old entrepreneurs hitting their first liquidity event needed something the bulge brackets couldn't offer. Six years later, Legacy Knight manages over $3B and was named the fastest-growing RIA in Texas. Chris and Matthew go deep on what it actually takes to build a multi-family office the right way - the technology, the hiring, the legacy conversations with families, and why Matthew refuses to grow by acquiring other books of business. They discuss: Why every hire at Legacy Knight comes out of the family office world, not from the bulge brackets How most $100M+ families are still running their wealth on a Google Doc and a handshake with their accountant Why "do nothing in the year after a liquidity event" is half good advice and half terrible advice The most creative things Matthew has seen ultra-wealthy families do with their capital How Matthew thinks about his own kids, legacy, and when to start the wealth conversation Links: Legacy Knight - https://legacyknight.com/ Matthew on LinkedIn - https://www.linkedin.com/in/matthew-ogle-ab11873/ Topics: (02:01) Matthew's First Exposure to Wealth Management (07:58) Joining Credit Suisse (Pre-GFC): Why the "Bulge Bracket" Mattered, How the Private Banking Associate Model Works (13:08) Why Credit Suisse Failed to Serve Ultra-High-Net-Worth Families (20:07) The First Client Meeting: Soft-Tissue Questions (28:57) Tax Timing and Mitigation Strategies (37:57) The Founding Thesis: People and Platform (Building Legacy Knight) (44:46) The Decision to Launch Legacy Knight Independently (54:43) Fundraising Lessons: Managing Expectations and The Importance of Pitch Order (01:01:18) The Full-Service Family Office Model (01:06:24) What a Vertically Integrated Family Office Actually Includes (01:09:07) Proactive Investment Sourcing (01:13:02) Next-Gen Engagement and Family Legacy Planning: How to Involve Children Appropriately (01:21:46) Matthew's Hiring Philosophy (01:30:05) Time as the Hidden Cost of Unstructured Wealth Support our Sponsors: Collateral Partners: https://collateral.com/fort Chris on Social Media: X: https://x.com/fortworthchris Instagram: https://www.instagram.com/thepowerspodcast LinkedIn: https://www.linkedin.com/in/chrispowersjr/ Visit our website: https://www.powerspod.com/ Leave a review on Apple: https://bit.ly/45crFD0 Leave a review on Spotify: https://bit.ly/3Krl9jO

Spencer "SB Mowing" has built a media empire with 50M social media followers, 3M YouTube subscribers, and 3B+ annual views. 4.5 years ago he started mowing one overgrown lawn for free each week and uploading it.If you enjoyed this episode please share it with a friend. It helps me out a lot.https://podcasts.apple.com/vg/podcast/real-conversations/id1594231832Jacob's Instagram: https://www.instagram.com/jacoboconnor/Spencer's Instagram: https://www.instagram.com/sbmowing/YouTube Channel: https://www.youtube.com/@jacob-oconnorSB Mowing's YouTube Channel: https://www.youtube.com/@SBMowingSB Mowing's Website: https://sbmowing.com/

WHY AM I SAVED BUT STRUGGLING WITH HURTS, HABITS & HANGUPS? 2 Corinthians 5:17 Therefore, if anyone is in Christ, he is a new creation. The old has passed away; behold, the new has come. (ESV) 1 Thessalonians 5:23 May God himself, the God of peace, sanctify you through and through. May your whole spirit, soul and body be kept blameless at the coming of our Lord Jesus Christ. (NIV) Romans 7:14–24 14 We know that the law is spiritual; but I am unspiritual, sold as a slave to sin. 15 I do not understand what I do. FOR WHAT I WANT TO DO I DO NOT DO, BUT WHAT I HATE I DO.  16 And if I do what I do not want to do, I agree that the law is good. 17 As it is, it is no longer I myself who do it, BUT IT IS SIN LIVING IN ME. 18 For I know that good itself does not dwell in me, that is, in my sinful nature.  For I have the desire to do what is good, BUT I CANNOT CARRY IT OUT. 19 For I do not do the good I want to do, but the evil I do not want to do—THIS I KEEP ON DOING. 20 Now if I do what I do not want to do, it is no longer I who do it, but it is sin living in me that does it. 21 So I find this law at work: Although I want to do good, EVIL IS RIGHT THERE WITH ME. 22 For in my inner being I delight in God’s law; 23 but I see another law at work in me, WAGING WAR AGAINST THE LAW OF MY MIND and making me a prisoner of the law of sin at work within me. 24 What a wretched man I am! Who will rescue me from this BODY that is subject to death? (NIV) Colossians 3:5 5 So put to death the sinful, earthly things LURKING within you. Have nothing to do with sexual immorality, impurity, lust, and evil desires. Don’t be greedy, for a greedy person is an idolater, worshiping the things of this world. (NLT) 1. THERE’S A WAR IN YOUR MIND 1A. YOUR OLD THINKING PATTERNS REMAIN   A. YOUR THINKING IS SHAPED BY THE ENVIRONMENT YOU GREW UP IN   B. YOUR THINKING IS SHAPED BY YOUR EXPERIENCES IN LIFE   C. YOUR THINKING IS SHAPED BY CULTURE  D. YOUR THINKING IS SHAPED BY THE WORDS SPOKEN OVER YOU  Ephesians 4:23 23 to be made new in the attitude of your minds; (NIV) Romans 12:2 2 Do not conform to the pattern of this world, but be transformed by the renewing of your mind. Then you will be able to test and approve what God’s will is—his good, pleasing and perfect will. (NIV)  1B. YOU’RE SAVED BUT MENTAL STRONGHOLDS STILL EXIST  2 Corinthians 10:4–5 4 The weapons we fight with are not the weapons of the world. On the contrary, they have divine power to demolish STRONGHOLDS.  5 We demolish arguments and every pretension that sets itself up against the knowledge of God, and we take captive every thought to make it obedient to Christ. (NIV) 2. THERE’S A WAR IN YOUR EMOTIONS 2A. YOU’RE SAVED BUT YOU STILL CARRY OLD EMOTIONAL PATTERNS  2B. YOU’RE SAVED BUT STILL STRUGGLE CONTROLLING YOUR EMOTIONS  2C. YOU’RE SAVED BUT STILL STRUGGLE WITH EMOTIONAL ATTACHMENTS  2D. YOU’RE SAVED BUT STILL STRUGGLE WITH EMOTIONAL STRONGHOLDS  2E. YOU’RE SAVED BUT STILL STRUGGLE WITH EMOTIONAL TRAUMA AND WOUNDS 2F. YOU’RE SAVED BUT STILL STRUGGLE WITH EMOTIONAL TRIGGERS  3. THERE’S A WAR IN YOUR WILL AND YOUR FLESH 3A. YOUR FLESH AND WILL HAVE BEEN TRAINED FOR YEARS  3B. YOU’RE SAVED BUT YOUR FLESH CRAVES IMMEDIATE GRATIFICATION   3C. YOU’RE SAVED BUT YOUR FLESH STRUGGLES WITH GENERATIONAL PATTERNS  Exodus 34:7 7 I lavish unfailing love to a thousand generations. I forgive iniquity, rebellion, and sin. But I do not excuse the guilty. I lay the sins of the parents upon their children and grandchildren; the entire family is affected— even children in the third and fourth generations.” (NLT) 3D. YOU’RE SAVED BUT YOUR FLESH RESISTS SURRENDERING TO GOD  Galatians 5:17 17 For the flesh desires what is contrary to the Spirit, and the Spirit what is contrary to the flesh. They are in conflict with each other, so that you are not to do whatever you want. (NIV) Romans 7:25 25 Thanks be to God, who DELIVERS ME through Jesus Christ our Lord! So then, I myself in my mind am a slave to God’s law, but in my sinful nature a slave to the law of sin. (NIV)

Bleav Host Robert Land pays tribute to Jackie Robinson on the anniversary of the day he integrated baseball. You'll hear stories from our archives with Astros 3B Bob Aspromonte & Brooklyn Dodgers 3B Randy Jackson who both played with Jackie. Plus, Houston Buff Larry Miggins played against Jackie in his very 1st Dodgers game and Astros longtime Broadcaster Greg Lucas remembers Monte Irvin - who nearly broke baseball's color barrier himself. We conclude with a conversation with Robinson who tells true stories about what he went thru. Today's Show is Presented by FanDuel! (2:34) Did Robinson give his glove to a young Astro? (7:45) Did Randy Jackson replace Robinson at 3B for the Dodgers? (12:59) Did ex-Houston Buff Larry Miggins get worked by Robinson in Jackie's 1st Dodgers Game? (15:43) Was MLB HOF & Houstonian Monte Irvin supposed to be REAL Jackie? (18:41) Jackie Robinson Tells True Story of his Integration Subscribe ️ Youtube, Spotify, Apple & iHeart X ️ https://x.com/HSTPodcast Facebook ️ https://www.facebook.com/HoustonSportsTalkPod Classic Houston Memories & History Playlist ️ https://www.youtube.com/playlist?list=PLP6kjM8cv81ruXBBvH-vfCxXPO0npG_OS Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Today's Post - https://bahnsen.co/41gK4Pa Markets rose for a fourth straight day despite rising Iran tensions and higher oil, with modest gains across the Dow, S&P, and Nasdaq. David Bahnsen underscores ongoing market rotation: all Mag 7 names are in bear‑market or double‑digit declines while the S&P is down just 9%, showing strength elsewhere. Oil spikes offer no predictive value after ~10% pullbacks. Private‑credit defaults remain low at 1.27%. AI/tech sentiment has cooled, though valuations remain a risk. Policy uncertainty includes potential NATO withdrawal. Economic data shows 178,000 March jobs (boosted by a strike reversal), delayed data‑center projects, a $57.3B trade deficit, softer ISM services, mortgage rates near 6.5%, and steep oil backwardation amid sharply reduced Strait of Hormuz shipping. 00:00 Welcome and Setup 00:46 Markets and War Headlines 02:45 Rotation Beyond Mag Seven 04:46 Oil Spike History and Sectors 06:02 Private Credit Defaults 06:53 AI Sentiment Reset 08:13 Politics and Big News 09:22 NATO Exit Threat 10:49 Jobs and Data Centers 12:54 Trade ISM Housing Fed 15:03 Energy Futures and Shipping 17:36 Wrap Up and Next Reports Links mentioned in this episode: DividendCafe.com TheBahnsenGroup.com

Émile Coué genuinely seems to have wanted to help people by teaching them how to plant helpful directives in their subconscious minds. Whether he was effective is something that's still debated. Research: Baldwin, J. Mark, et al. “A Disclaimer.” Science, vol. 12, no. 309, 1900, pp. 850–850. JSTOR, http://www.jstor.org/stable/1629542 Baudouin, Charles. “Émile Coué and His Life’s Work.” American Library Service. New York. 1923. https://digirepo.nlm.nih.gov/ext/dw/55330740R/PDF/55330740R.pdf Baudouin, Charles. “Suggestion and Autosuggestion.” New York. Dodd, Mead and Company, 1921. https://dn720207.ca.archive.org/0/items/suggestionauto00bauduoft/suggestionauto00bauduoft.pdf Britannica Editors. "Émile Coué". Encyclopedia Britannica, 22 Feb. 2026, https://www.britannica.com/biography/Emile-Coué “Coue, After Goodby Lecture, Flees City.” Boston Globe. January 31, 1923. https://www.newspapers.com/image/430295545/ “Coue Explains How to Use Auto-Suggestion.” Boston Globe. January 7, 1923. https://www.newspapers.com/image/430953338/?match=1&terms=Coue COUÉ, EMILE. “SELF MASTERY THROUGH CONSCIOUS AUTOSUGGESTION.” AMERICAN LIBRARY SERVICE PUBLISHERS. NEW YORK. 1922. https://www.gutenberg.org/files/27203/27203-h/27203-h.htm “Delirium Tremens.” Cleveland Clinic. June 5, 2023. https://my.clevelandclinic.org/health/diseases/25052-delirium-tremens “EMILE COUÉ DEAD; A MENTAL HEALER; Many Made Well by Saying ‘Every Day, in Every Way, I'm Growing Better and Better.’” New York Times. July 3, 1926. https://www.nytimes.com/1926/07/03/archives/emile-Coué-dead-a-mental-healer-many-made-well-by-saying-every-day.html Heid, Markham. “Is Hypnosis Real? Here’s What Science Says.” Time. March 2, 2023. https://time.com/5380312/is-hypnosis-real-science/ Myga, Kasia A et al. “Autosuggestion: a cognitive process that empowers your brain?.” Experimental brain research 240,2 (2022): 381-394. doi:10.1007/s00221-021-06265-8 Neal, E. Virgil, ed. “Hypnotism and hypnotic suggestion. A scientific treatise on the uses and possibilities of hypnotism, suggestion and allied phenomena.” New York State Publishing Company. Rochester, NY. 1906. https://archive.org/details/hypnotismhypnoti00roch/page/n9/mode/1up “Pliny 1813 Years Ahead of Coue … “ Boston Globe. January 30, 1923. https://www.newspapers.com/image/430295455/?match=1&terms=Coue Rapp, Dean R. “‘Better and Better—’ Couéism as a Psychological Craze of the Twenties in England.” Studies in Popular Culture, vol. 10, no. 2, 1987, pp. 17–36. JSTOR, http://www.jstor.org/stable/23413989 Sage, X. Lamotte. “Hypnotism As It Is: A Book for Everybody.” New York State Publishing Company. Rochester, NY. 1900. Accessed online: https://upload.wikimedia.org/wikipedia/commons/5/5b/Hypnotism_as_it_is%3B_a_book_for_everybody_%28IA_hypnotismasitisb00sage%29.pdf Sari, N. K. et al.“The role of autosuggestion in geriatric patients’ quality of life: a study on psycho-neuro-endocrine-immunology pathway.” Social Neuroscience, 12(5), pp. 551–559. 2017. doi: 10.1080/17470919.2016.1196243 Schlamann, Marc et al. “Autogenic training alters cerebral activation patterns in fMRI.” The International journal of clinical and experimental hypnosis 58,4 (2010): 444-56. doi:10.1080/00207144.2010.499347 Whiteside, Thomas. “Better and Better.” The New Yorker. May 9, 1953. https://www.newyorker.com/magazine/1953/05/16/better-and-better Yeates, Lindsay B. “Émile Coué and his Method (I): The Chemist of Thought and Human Action.” Australian Journal of Clinical Hypnotherapy & Hypnosis, Volume 38, No.1, (Autumn 2016), pp.3-27. https://www.researchgate.net/publication/374753633_Emile_Coue_and_his_Method_I_The_Chemist_of_Thought_and_Human_Action See omnystudio.com/listener for privacy information.