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Hey Heal Squad! Our friend Zachary Levi is back and honestly, this may be the most grounded and open we've ever heard him. He's a brand-new dad, he's building Wyldwood Studios from the ground up in Texas, and he's going deeper into his faith, healing, and purpose than ever before. Zach takes us all the way back to being three years old and feeling, deep in his soul, that he was put here to make people laugh and help others. From there, we get into the bigger conversation so many people are quietly having right now: Why does it feel so hard to know what's true anymore? Zach breaks down what he calls the two sides of finding truth: empathy and logic and why he believes we've lost the ability to really hear each other. But the heart of this episode is what happens when Zach opens up about his 2017 mental breakdown, the season where he didn't want to live and didn't understand why. He shares how therapy finally taught him something he'd never learned before: how to love himself. We talk about unresolved trauma, how emotional pain can actually live in the body, and why he now believes every hardship can become an invitation to heal, grow, and go deeper with God.And then he says something I honestly haven't stopped thinking about since we recorded this: that we are “infinitely valuable… and entirely unimportant.” Trust us… you're going to want to hear the way he explains that one. Enjoy! HEALERS & HEAL LINERS You can't outrun unhealed trauma. It lives in the body. It makes you sick on every level — physical, emotional, mental, spiritual — and it keeps showing up until you stop saying "I got it" and finally sit with what's actually there. Every hardship is an invitation. The breakdowns, the breakups, the moments that bring you to your knees — Zach believes those aren't punishment. They're God asking you to come closer. You are infinitely valuable, and entirely unimportant. You matter more than you'll ever know, AND you don't have to carry it all alone. Your only job is to take the next step. HEAL SQUAD SOCIALS IG: https://www.instagram.com/healsquad/ TikTok: https://www.tiktok.com/@healsquadxmaria HEAL SQUAD RESOURCES: Heal Squad Website:https://www.healsquad.com/ Heal Squad x Patreon: https://www.patreon.com/HealSquad/membership Maria Menounos Website: https://www.mariamenounos.com My Curated Macy's Page: https://stylecrew.macys.com/@mariamenounos EMR-Tek Red Light: https://emr-tek.com/discount/Maria30 for 30% off Airbnb: https://www.airbnb.com/host GUEST RESOURCES: Follow Zach on IG: https://www.instagram.com/zacharylevi/ Radical Love:  https://www.amazon.com/Go-Love-Yourself-Radical-Acceptance/dp/0785236759 Wyldwood Studios: wyldwood.com  Nerd HQ: nerdhq.org ABOUT MARIA MENOUNOS: Emmy Award-winning journalist, TV personality, actress, 2x NYT best-selling author, former pro-wrestler and brain tumor survivor, Maria Menounos' passion is to see others heal and to get better in all areas of life. ABOUT HEAL SQUAD x MARIA MENOUNOS: A daily digital talk-show that brings you the world's leading healers, experts, and celebrities to share groundbreaking secrets and tips to getting better in all areas of life. DISCLAIMER: This Podcast and all related content (published or distributed by or on behalf of Maria Menounos or http://Mariamenounos.com and http://healsquad.com) is for informational purposes only and may include information that is general in nature and that is not specific to you. Any information or opinions provided by guest experts or hosts featured within website or on Company's Podcast are their own; not those of Maria Menounos or the Company. Accordingly, Maria Menounos and the Company cannot be responsible for any results or consequences or actions you may take based on such information or opinions. This podcast is presented for exploratory purposes only. Published content is not intended to be used for preventing, diagnosing, or treating a specific illness. If you have, or suspect you may have, a health-care emergency, please contact a qualified health care professional for treatment.

False teachers are rarely obvious. In many cases, they may not even be aware of their own role as a false teacher. They may have bought into a misinterpretation of God's love and grace and are seeking to share what they see as true. This is why it's so important that we're actively submitting ourselves to the guidance of the Holy Spirit and digging into God's Word. As Pastor Ken will warn us in today's message, we can't let our guard down as false teachers will increase in the End Times.

Fluent Fiction - Norwegian: Mystery at Fram-Museet: The Day Oslo's History Was Saved Find the full episode transcript, vocabulary words, and more:fluentfiction.com/no/episode/2026-05-15-22-34-02-no Story Transcript:No: Det var en solfylt vårdag i Oslo, og hele byen feiret nasjonaldagen med flagg og bunader.En: It was a sunny spring day in Oslo, and the entire city was celebrating National Day with flags and bunader.No: I skyggen av denne feststemningen, på Fram-museet, gikk Sindre rundt blant de historiske utstillingene.En: In the shadow of this festive atmosphere, at the Fram-museet, Sindre was walking around among the historical exhibits.No: Han var en ung historiker med en stor drøm om å gjøre sitt navn kjent.En: He was a young historian with a big dream of making his name known.No: Men dagen skulle ikke bli som alle andre.En: But the day was not going to be like any other.No: Midt blant de store skipene og de spennende historiene fra polarekspedisjoner, skjedde noe uventet.En: Amidst the large ships and the captivating stories of polar expeditions, something unexpected happened.No: En verdifull historisk gjenstand, som hadde ligget på utstilling i mange år, var forsvunnet.En: A valuable historical artifact, which had been on display for many years, had disappeared.No: Sindre hadde overhørt samtalen mellom vaktene og Astrid, museets kurator.En: Sindre had overheard a conversation between the guards and Astrid, the museum's curator.No: Astrid virket stresset, men også mystisk rolig.En: Astrid seemed stressed but also mysteriously calm.No: Noe var suspekt.En: Something was suspicious.No: Sindre bestemte seg for å finne sannheten.En: Sindre decided to uncover the truth.No: Hans første mistanke falt på Astrid.En: His first suspicion fell on Astrid.No: Hun var alltid så beskyttende overfor museets samlinger.En: She was always so protective of the museum's collections.No: Han merket at hun visste mer enn hun lot som om.En: He noticed that she knew more than she was letting on.No: Hennes hemmelighetsfulle blikk og unnvikende svar gjorde Sindre enda mer nysgjerrig.En: Her secretive glances and evasive answers made Sindre even more curious.No: Sindre ønsket å bryte museets regler for å avdekke mysteriet, men sikkerhetstiltakene var strenge.En: Sindre wanted to break the museum's rules to uncover the mystery, but the security measures were strict.No: Etter museets stengetid bestemte Sindre seg for å følge etter Astrid.En: After the museum's closing hours, Sindre decided to follow Astrid.No: Østrogen hennes kunne knapt høres da hun beveget seg gjennom gangen med de gamle skipene.En: Her footsteps could barely be heard as she moved through the hallway with the old ships.No: Han gjemte seg bak en stor og imponerende modell av Fram, mens han observerte hver bevegelse fra henne.En: He hid behind a large and impressive model of the Fram, while observing her every move.No: Til slutt konfronterte han Astrid.En: Finally, he confronted Astrid.No: De sto i en avsides del av museet, hvor dagslyset knapt rakk inn.En: They stood in a secluded part of the museum, where daylight barely reached.No: Astrid så overasket ut, men gav raskt etter.En: Astrid looked surprised but quickly gave in.No: Hun forklarte at gjenstanden hadde blitt fjernet for å beskytte den.En: She explained that the artifact had been removed to protect it.No: Det var interne trusler i museet, folk som ønsket å stjele og selge den for egen vinning.En: There were internal threats in the museum, people who wanted to steal and sell it for their own gain.No: Sindre forstod alvoret og innså at Astrid hadde et større ansvar å beskytte museets arv.En: Sindre understood the seriousness and realized that Astrid had a greater responsibility to protect the museum's heritage.No: Sammen diskuterte de en plan for å bringe den tilbake, trygt og i skjul.En: Together, they discussed a plan to bring it back safely and secretly.No: De måtte også avsløre de som ønsket å skade museet.En: They also had to expose those who wanted to harm the museum.No: Sindre overtalte Astrid til å stole på ham.En: Sindre persuaded Astrid to trust him.No: Han hadde bevist sin pålitelighet og sunne fornuft, mens Astrid hadde vist seg å være en trofast beskytter av kulturarven.En: He had proven his reliability and common sense, while Astrid had shown herself to be a loyal protector of cultural heritage.No: Da nasjonaldagen gikk mot slutten, gikk Sindre hjemmeover med en nyvunnet selvtillit.En: As National Day drew to a close, Sindre walked home with newfound confidence.No: Han forstod nå verdien av tillit og ærlighet i historien og fikk en dypere respekt for de som vokter vår fortid.En: He now understood the value of trust and honesty in history and gained a deeper respect for those who guard our past.No: Sammen hadde Sindre og Astrid sikret en trygg fremtid for museets dyrebare gjenstander.En: Together, Sindre and Astrid had secured a safe future for the museum's precious artifacts. Vocabulary Words:sunny: solfylthistorian: historikerexhibits: utstillingerartifact: gjenstandcurator: kuratorsuspicious: suspektprotective: beskyttendesecretive: hemmelighetsfulleevasive: unnvikendesecurity measures: sikkerhetstiltakfollow: følgeimpressive: imponerendesecluded: avsidesexplained: forklarteinternal threats: interne truslersteal: stjeleown gain: egen vinningseriousness: alvoretheritage: arvsecretly: i skjulpersuaded: overtaltereliability: pålitelighetcommon sense: sunn fornuftloyal: trofastprotector: beskytterconfidence: selvtillittrust: tillithonesty: ærlighetsecured: sikretprecious: dyrebare

Can a tradition — even a religious one — get in the way of someone receiving salvation? That is the question at the heart of Acts 15:1-35 (KJV), and it is just as relevant today as it was in the early church. In this episode of Words From The Word, Pastor Roderick Webster walks through the Jerusalem Council, where Peter, Paul, Barnabas, and James each stood up to defend the grace of God for all people. You will learn how the apostles handled church conflict with Scripture, why adding requirements to the gospel is dangerous, and how God's plan to include every nation was always His design. A timely reminder to hold tradition loosely and the Word firmly. Subscribe to Words From The Word wherever you listen to podcasts.

She Saved Her Special Cologne for Me Until She Didn'tBecome a supporter of this podcast: https://www.spreaker.com/podcast/true-cheating-wives-and-girlfriends-stories-2026-true-cheating-stories-podcast--5689182/support.

Supporta BLMP genom att bli en patron! Spana in de olika alternativen på http://www.patreon.com/blmetalpodcast Supporta med Swish: 0708-961174 I avsnitt 7 av Saved By The Demon Bell okulärbesiktar B.L. och Heidenhammer Mercyful Fates sjätte platta, "Dead Again", från 1998. Vi får även återbesök av Magnus "Hatpastorn" Ödling, som ger sina 50 öre om albumet. Bjällerklang, bjällerklang! I samarbete med Medborgarskolan.

Audition Template: Voice Over track with side-chain ducking of Music Track with 25dB of gain reduction. 44.1k, 16 bit, stereo.See omnystudio.com/listener for privacy information.

After a rough Bonfire show yesterday, the vibe is totally refreshed as Paul Virzi brings a professional and funny attitude to the room. | Paul has an idea for the wives to make side-cash by running a restaurant. | Bobby created a code word for his son only to be used in extreme emergencies. | Bob needs advice from a real man like Virzi because he doesn't know how to cheer encouragement to his kid during his lacrosse games. When Paul Virzi is not saving radio shows, you can find him performing stand up comedy- go to PAULVIRZI.COM for all his tour dates and info! *To hear the full show to go www.siriusxm.com/bonfire to learn more! FOLLOW THE CREW ON SOCIAL MEDIA: @thebonfiresxm @louisjohnson @christinemevans @bigjayoakerson @robertkellylive @louwitzkee @jjbwolf Subscribe to SiriusXM Podcasts+ to listen to new episodes of The Bonfire ad-free and a whole week early.  Start a free trial now on Apple Podcasts or by visiting siriusxm.com/podcastsplus. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

Caesar Rodney: The Ride That Saved Independence tells the incredible true story of the forgotten Founding Father whose 80-mile midnight ride helped secure Delaware's vote for American independence. Sick, disfigured by cancer, and battling a literal storm, Caesar Rodney rode through the night in July 1776 to cast the deciding vote that helped change the course of the American Revolution. While every American knows George Washington crossing the Delaware, few know about Caesar Rodney's desperate journey to Philadelphia to help unite the colonies behind independence. In this episode of America's Founding Series, host Nick Giordano explores the high-stakes political divisions inside the Continental Congress, the enormous risks facing the Founders, and the timeless lesson that liberty survives only when citizens show up. What You'll Learn: Why Delaware's vote became a critical turning point for American independence  How Caesar Rodney's physical suffering reveals the true meaning of sacrifice and duty  Why George Read feared the colonies were moving too fast toward revolution  The dramatic story behind Rodney's overnight ride through stormy conditions to Philadelphia  What Caesar Rodney's actions teach about citizenship, courage, and self-government today

New research from leading health economist David Cutler explores what's behind a historic slowdown in health spending, even as millions of Americans struggle to afford their care.Guest:David M. Cutler, Dean of Social Science, Professor of Economics, Harvard Universityearn more and read a full transcript on our website.Want more Tradeoffs? Sign up for our free weekly newsletter featuring the latest health policy research and news.Support this type of journalism today, with a gift. Hosted on Acast. See acast.com/privacy for more information.

Yesterday Thor ranted about the San Diego budget crisis and how they were looking to cut December Nights to save some money. Well it seems like the city had heard because they came out with some big news that may make Thor a hero! It is Throwback Thursday so of course we are playing Throwback Trivia! Coming off of a horrible loss, Jaime takes on Thor who has been on a hot streak. A TikTok went viral recently where a girl is IN TEARS begging men to stop wearing something. We break it down seeing if this is a valid complaint or if she is being a little over dramatic...See omnystudio.com/listener for privacy information.

How were people in the Old Testament saved if Jesus is the only way to God? What about people today who have never heard His name? And if the Jewish people rejected Jesus, does that mean God is finished with Israel forever? Join us as we continue through Romans and answer these important questions about salvation, faith, and God's plan for Israel.

How were people in the Old Testament saved if Jesus is the only way to God? What about people today who have never heard His name? And if the Jewish people rejected Jesus, does that mean God is finished with Israel forever? Join us as we continue through Romans and answer these important questions about salvation, faith, and God's plan for Israel.

We often pray for things and God does not answer them in the way we desire. Why is that? Prayer is not a magic formula; it is a petition to God. However, the efficacy of our prayers depends on the disposition of our souls. Prayer must be a priority in our lives rather than a mere afterthought. This is distressing, since not only our salvation, but the salvation of others depends on prayer. Everything that is important in the universe depends on prayer. With this in mind, we must reflect on the place of prayer in our lives and whether we place it before the frivolities of this world.

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

(SHOW OPEN): Juan Soto's injury scare is another example of how the Mets can never have a perfect day and it's too soon to say their season is saved.

Yesterday Thor ranted about the San Diego budget crisis and how they were looking to cut December Nights to save some money. Well it seems like the city had heard because they came out with some big news that may make Thor a hero! It is Throwback Thursday so of course we are playing Throwback Trivia! Coming off of a horrible loss, Jaime takes on Thor who has been on a hot streak. A TikTok went viral recently where a girl is IN TEARS begging men to stop wearing something. We break it down seeing if this is a valid complaint or if she is being a little over dramatic...See omnystudio.com/listener for privacy information.

The Buffalo Sabres evened their second-round series at 2-2 with a hard-fought 3-2 victory over the Montreal Canadiens, fueled by Zach Benson's power-play goal on his 21st birthday. The WGR team breaks down the win, highlighting Ukko-Pekka Luukkonen's 28-save performance and the immediate impact of Lindy Ruff's strategic lineup changes.

Has your heart come to that place? Have you realized your desperate need for a Savior? It's a beautiful, life-changing moment for anyone who experiences it. God desires that all would come to this point of repentance. It doesn't matter what you've done or who you are; He loves you and has extended the invitation through Jesus to become part of His family. As Pastor Ken examines the prophecy contained in Zechariah concerning the End Times, we're reminded of this transformative truth.

Steve Dale, WGN Radio's pet expert and host of Steve Dale's Pet World and Steve Dale's Other World, joins John Landecker to talk about what animal saved Dick Van Dyke’s life, does your dog laugh when you laugh, BARK annual walk to raise money for anti-cruelty, and more!

jesus saves

>Join Jocko Underground Full Episodes< Ben Ingram was headed toward prison or worse. Drugs, street fights, and survival mode defined his youth—until one decision changed everything: joining the Marines. Warriors In Need

What does the Bible actually teach about the future of Israel—and have we been reading Romans 11 wrong? In this episode, we challenge the common assumptions around “all Israel will be saved” and unpack a view deeply rooted in church history that most Christians rarely hear. Instead of a future defined by endless conflict, Scripture points to something far bigger—and far more hopeful. You’ll discover: What Paul really means in Romans 11 Whether “replacement theology” is a misunderstanding If God’s promises to Israel are physical, spiritual, or both How the New Testament defines the true people of God This isn’t about speculation—it’s about reading the Bible on its own terms.

Cody Berman reached financial freedom at 25 while spending just $24K a year. During that same stretch, his income doubled three years in a row from $96K to over $1M.In this episode, Cody breaks down the simple but powerful math behind his journey: 11 rental units producing $3,700/month in cash flow, a digital products business generating $10K/month, and $500K invested in the stock market by his mid-20s.We cover:Why margin matters more than what you invest inPercentage-based spending and why dollar amounts can be misleadingHow to get aligned with your spouse on financial freedomThe 4 types of side hustles and which ones actually scaleMonthly money meetings and annual life reviewsWhy increasing income beats obsessing over tax loopholesHow Cody unlearned the scarcity mindset after becoming financially freeCody also shares lessons from his new book, Retire by 30.Get the book: www.retireby30book.comCheck out our first episode together where we break down Cody's digital products business:https://youtu.be/1uJFGuGQ3Bc?si=I-fsVM1km3vYeT2eFollow Cody:Instagram: @CodyDBermanCurious as to how we've bought multiple businesses and built millions in equity? Give this video a watch for a full breakdown: https://www.youtube.com/watch?v=cviipnGtDWI&feature=youtu.beIf you are serious about building a life on your terms and want to surround yourself with people who are actually doing it, go to: https://actionacademy.com?el=action_academy_podcastIf you want to leave corporate America in the next 6-18 months - you should check out our Action Academy Community

An incredible mystery plays out beneath the waves. Marine biologist Nan Hauser is an expert in the behaviour of whales. One day, in the breathtaking surroundings of the Cook Islands, Nan is shooting footage for a nature documentary when a 45-tonne humpback comes charging towards her. In seconds, she'll find herself picked up and swept along by the enormous creature, balanced on the tip of its vast jaw. What on earth is happening? And how can Nan hope to emerge in one piece? A Noiser podcast production. Hosted by John Hopkins. Written by Joe Viner | Produced by Ed Baranski | Assistant Producer: Luke Lonergan | Exec produced by Joel Duddell | Sound Supervisor: Matt Peaty | Sound design by Jacob Booth | Assembly edit by Rob Plummer | Compositions by Oliver Baines, Dorry Macaulay, Tom Pink | Mix & mastering: Ralph Tittley. For ad-free listening, bonus material and early access to new episodes, join Noiser+. Click the subscription banner at the top of the feed to get started. Or go to noiser.com/subscriptions If you have an amazing survival story of your own that you'd like to put forward for the show, let us know. Drop us an email at support@noiser.com Learn more about your ad choices. Visit podcastchoices.com/adchoices

On this episode of The Big Ben Show, Ben Domenech opens with the fallout from the Virginia Supreme Court's decision on Democrats' redistricting push, the national reaction from party leaders and media figures, and why the fight over maps became a revealing test of political overreach. Next, Adam Carolla joins the show to explain why California has become nearly impossible to rebuild, especially in Malibu and the Palisades. Adam and Ben discuss overregulation, the “safety first” mentality, the Coastal Commission, and whether California voters are ready to reject the politics that keep the state stuck. Later, Rachel Campos-Duffy joins Ben to discuss her new book, "All American Patriotism: Celebrating 250 Years of America's Greatness,” the importance of family road trips, and why love of country should be passed down through stories, travel, and gratitude.

People ask me this all the time. And given that my website is called Save The Marriage, most assume they already know my answer. They're wrong. No. Not every marriage can (or should) be saved. I want to be straight about that. There are situations where saving the marriage is not the goal, and pursuing it would be a mistake. If that's where you are, this episode will tell you clearly. But here's what I also believe: far more marriages could be saved than actually are. And the gap between those two things — what's possible and what actually happens — usually comes down to three specific places where people get stuck. Not effort. Not even willingness. Three very specific places. And once you can see where you're stuck, the path forward gets a lot clearer. This episode also takes on the question I hear constantly from people working on their marriage alone: How do I know if it's too far gone? It's an honest question, and it deserves an honest answer — not false reassurance, but not unnecessary surrender either. There's also something in here about regret. Not as a motivational tactic, but as a real consideration. Because regret is what's left when we don't take action we wish we had. And that's hard to undo, no matter what happens next. This is episode 601. That's a milestone worth noting, and maybe worth listening to if you're standing at your own crossroads right now, trying to figure out whether to keep going or let go. The answer to the question isn't the same for everyone. But there's only one way to find out which answer is yours. RELATED RESOURCES:  The ARC of Saving Your Marriage There IS No Try Save The Marriage System

Parents, download Mr Jim's app Riffio to create your own stories and songs inside Spyology Squad!iOS Download | AndroidAbout Mr. Jim:What started as a dad recording bedtime stories for his kids while traveling for work has become a phenomenon. Jim Jacob never set out to launch a podcast, he just needed his family to hear his stories. Hundreds of thousands of listeners later, that accidental solution has become one of the most beloved children's audio adventures on the internet.Welcome to Spyology Squad — where science becomes a superpower and every mission is a new discovery.Join Jayden, Ava, and Mr. Jim as they face off against the diabolical Dr. Stinkybreath and his Purple Ninja army in a world of spyience-fiction a blend of pulse-pounding spy adventure and real scientific thinking designed to light up young imaginations. Each episode drops kids ages 6–12 into a fast-moving mission where the only way to save the world is to think like a scientist.What makes Spyology Squad special? This isn't science dressed up as entertainment, it's adventure that sneaks in genuine learning. Episodes are crafted to complement what kids are actually exploring in school, turning concepts like physics, chemistry, and biology into plot twists and mission objectives. New episodes release three times a week, so there's always a new mission waiting.Whether it's a car ride, bedtime routine, or a rainy afternoon, Spyology Squad turns screen-free time into something genuinely exciting. Jim and his wife Jocelyn built this for their own three kids — and now they're building it for yours.Subscribe and join the Squad. There's a new adventure around every corner.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

A Brilliant Adaptation: How Dissociative Identity Disorder and the Power of the Therapeutic Bond Saved Me (New Harbinger Publications, 2026) is a searing and profound memoir of one woman's journey through dissociative identity disorder and childhood sexual abuse--and how she found hope, healing, and recovery.  Sally Maslansky is living the perfect life: a beautiful home in Malibu, California, a successful Hollywood producer husband who adores her, and a recently adopted son she treasures. But when Sally begins to remember the trauma she endured as a child, her world begins to unravel. In this gripping and provocative memoir, psychotherapist Maslansky shares how childhood sexual abuse led her to develop dissociative identity disorder (DID), and how, with the help of renowned therapist Daniel J. Siegel, she ultimately recovers. The book reveals the power of therapeutic bond to heal deep attachment wounds, the science of neuroplasticity in healing the traumatized mind, and our capacity as human beings to reconcile unspeakable experiences in order to grow, change, and live vibrant, loving, and joyful lives against all odds. Together with Siegel, Maslansky slowly recovers her childhood memories and reconnects with the forgotten parts of herself--parts that she grows to admire, respect, honor, and love, because they literally saved her young mind from unimaginable horrors. In the book, Siegel describes Maslansky's DID as a brilliant adaptation of the mind--a protective force that kept her mentally safe when the people she should have trusted most were the ones responsible for her abuse. Whether you have struggled with DID yourself, love someone who has DID, or are simply looking to be inspired by the tenacity of the human spirit, this memoir offers a provocative glimpse into an often pathologized and misunderstood condition, and shows the profound and healing possibilities of therapy, human understanding, and the will to survive. Sally Maslansky, LMFT has been in private practice for twenty years in Chapel Hill, NC. She treats families, adoption, trauma, parenting, and adult individuals. Her training is in interpersonal neurobiology (IPNB), mindfulness-based stress reduction (MBSR), eye movement desensitization and reprocessing (EMDR), dialectical behavior therapy (DBT), adult attachment interview (AAI), attachment theory, polyvagal theory practices, mindfulness, and the wheel of awareness practice. For more information on Sally and her work, please visit her website sallymaslansky.com Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

What does the Bible actually teach about the future of Israel—and have we been reading Romans 11 wrong?In this episode, we challenge the common assumptions around “all Israel will be saved” and unpack a view deeply rooted in church history that most Christians rarely hear. Instead of a future defined by endless conflict, Scripture points to something far bigger—and far more hopeful.You'll discover: What Paul really means in Romans 11Whether “replacement theology” is a misunderstandingIf God's promises to Israel are physical, spiritual, or bothHow the New Testament defines the true people of GodThis isn't about speculation—it's about reading the Bible on its own terms.

The Buffalo Sabres evened their second-round series at 2-2 with a hard-fought 3-2 victory over the Montreal Canadiens, fueled by Zach Benson's power-play goal on his 21st birthday. The WGR team breaks down the win, highlighting Ukko-Pekka Luukkonen's 28-save performance and the immediate impact of Lindy Ruff's strategic lineup changes.

What do you say to someone in crisis? Joe Tuia'ana, founder of the I Love You, Bro project, joins Uncomfy to talk about men's mental health, connection, and what it means to show up for someone in crisis. Joe shares the story of the moment when he stopped to help a stranger on an overpass and how that experience led him to create peer support groups for men across Utah. Joe explores why so many men struggle to open up, what actually helps in those moments, and how we can create safer spaces for honest conversation. ABOUT GUEST Joe Tuia'ana is the founder and CEO of the I Love You, Bro project (https://iloveyoubroproject.org). If you or someone you love is struggling, please reach out. Call or text 988 to reach the Suicide & Crisis Lifeline, free & available 24/7. CHAPTERS (0:00) Content Warning and Introduction (1:26) Meet Joe Tuia'ana (2:03) Overpass Rescue Story (6:03) Aftermath and Shame (7:53) Why Joe Stepped In (9:41) From Rescue to Support Groups (12:02) How the Groups Work (13:04) Why Men Don't Open Up (14:58) Data on Reaching Out (17:04) How to Create Space (19:33) Conclusion and Resources

A Brilliant Adaptation: How Dissociative Identity Disorder and the Power of the Therapeutic Bond Saved Me (New Harbinger Publications, 2026) is a searing and profound memoir of one woman's journey through dissociative identity disorder and childhood sexual abuse--and how she found hope, healing, and recovery.  Sally Maslansky is living the perfect life: a beautiful home in Malibu, California, a successful Hollywood producer husband who adores her, and a recently adopted son she treasures. But when Sally begins to remember the trauma she endured as a child, her world begins to unravel. In this gripping and provocative memoir, psychotherapist Maslansky shares how childhood sexual abuse led her to develop dissociative identity disorder (DID), and how, with the help of renowned therapist Daniel J. Siegel, she ultimately recovers. The book reveals the power of therapeutic bond to heal deep attachment wounds, the science of neuroplasticity in healing the traumatized mind, and our capacity as human beings to reconcile unspeakable experiences in order to grow, change, and live vibrant, loving, and joyful lives against all odds. Together with Siegel, Maslansky slowly recovers her childhood memories and reconnects with the forgotten parts of herself--parts that she grows to admire, respect, honor, and love, because they literally saved her young mind from unimaginable horrors. In the book, Siegel describes Maslansky's DID as a brilliant adaptation of the mind--a protective force that kept her mentally safe when the people she should have trusted most were the ones responsible for her abuse. Whether you have struggled with DID yourself, love someone who has DID, or are simply looking to be inspired by the tenacity of the human spirit, this memoir offers a provocative glimpse into an often pathologized and misunderstood condition, and shows the profound and healing possibilities of therapy, human understanding, and the will to survive. Sally Maslansky, LMFT has been in private practice for twenty years in Chapel Hill, NC. She treats families, adoption, trauma, parenting, and adult individuals. Her training is in interpersonal neurobiology (IPNB), mindfulness-based stress reduction (MBSR), eye movement desensitization and reprocessing (EMDR), dialectical behavior therapy (DBT), adult attachment interview (AAI), attachment theory, polyvagal theory practices, mindfulness, and the wheel of awareness practice. For more information on Sally and her work, please visit her website sallymaslansky.com Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/psychology

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Shane Van Gisbergen's road course dominance continues, and Dale Earnhardt Jr. is here to unpack it all on a new episode of Dirty Air. He joins co-host TJ Majors to chat all things Watkins Glen and look ahead to the All-Star race at Dover: - Exactly what SVG is doing differently than all other NASCAR drivers at road courses - The Trans Am Series is training a new generation of great road racers - Catching up on all the calamity from Watkins Glen - The risk of making unnecessary changes to tracks - Race winner Shane Van Gisbergen joins the show - Recapping this year's format for the All-Star race   During the Ask Jr. portion of the episode, listeners sent in questions regarding: - Cole Swindell at the CARS Tour race at Ace Speedway - Kasey Kahne and Boo Weekley are still winning - The new Hell Let Loose game - Dale's 1999 Busch Series win at Watkins Glen - Cage diving with sharks - Hats or die-casts - Greenville-Pickens Speedway updates - Multi-class racing in NASCAR Check out Dirty Mo Media on YouTube: https://www.youtube.com/@DirtyMoMedia Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

So EVERYONE is in an incriminating, horrid situation where their life is in danger except for Cassie?! The right wing conspiring DESPERATE sex worker?! Download the Poshmark app and use code rose when you sign up to get $10 off your first purchase. Or shop now at Poshmark.com/rose and get $10 off your first purchase. Download Hily Dating App from the App Store or Google Play, or visit hily.com. Listen to our PRE-SHOW and watch us on VIDEO only on Patreon. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join the Rose Garden today⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠! CONNECT WITH US: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Merch⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ EMAIL: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠2blackgirls1rose@gmail.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow Natasha's Substack The Nite Owl: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠theniteowl.substack.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Learn more about your ad choices. Visit podcastchoices.com/adchoices

Dr. Anis Khalaf's life was saved by TCM and he doesn't want this to be a rare experience. That's why Dr. Khalaf, a practitioner with a background in both Western Medicine and Chinese Medicine, is on a mission to share as many different holistic health options as he can with families in his local community. So the Williamson County WellFest was born!  Come see the Ancient Health Podcast LIVE, for a day's worth of interviews about physical, mental and spiritual health, this Saturday May 16, 2026 at Will Co WellFest in Franklin, TN. Get your tickets here: https://shorturl.at/dAHaA ------  Want more of The Ancient Health Podcast? Subscribe to the YouTube channel. Follow Dr. Motley! Instagram Twitter Facebook Tik-Tok Website Follow Dr. Anis! https://www.tiktok.com/@acupuncturefit https://www.youtube.com/@AcupunctureFit See the Podcast (and Dr. Motley) LIVE: https://shorturl.at/dAHaA ------  * You can get cell support in gummy form: Mitopure now starts at $79, when you go to timeline.com/DRMOTLEY. *Join Doctor Motley's newsletter for TCM insights and regular podcast updates: https://www.doctormotley.com/ *If you want to hear more on how Chinese Medicine connects the physical and emotional, check out Doctor Motley's membership, complete with courses, a whole library of video-based resources and the chance to pick his brain on weekly live Q+A's. You can try it free for 15 days here: https://www.doctormotley.com/15

Join hosts Bob Ventimiglia, Scott Omer, and Sir Alex on The Designated Pundits for MLS and USMNT analysis.  In this episode, we recap MLS Week 12 with Gideon Hill who covers the Vancouver Whitecaps. Can they be saved? Tune in for two weekly live shows: Thursdays at 8:00 PM ET for expert betting picks and MLS odds breakdowns, and Mondays at 8:30 PM ET for soccer analysis and insider discussions. The Designated Pundits podcast is renowned among MLS and US soccer fans for in-depth preseason previews of every MLS team. We're your go-to for predictions and insights into America's version of the Beautiful Game. Stream us live on YouTube or your favorite podcast platform.  Explore all our content YouTube- YouTube.com/@thedesignatedpundits Audio Links- linktr.ee/thedesignatedpundits or search “The Designated Pundits” on your favorite podcast platform. #MLSseasonPass #MLS #MLSbetting #MLSbets #MLSpicks #TheDesignatedPundits Learn more about your ad choices. Visit megaphone.fm/adchoices

We're back home from our Euro trip and trying to move into our homestead but we're got a huge issue making that impossible. It's time to assess the situation and see, can we fix this? Never a dull moment. Here's 20 minutes of our day, and then we will see you tomorrow. No Kids Hungry

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This clip is from S3 of our BibleLine LIVE show. S4 will begin June 7th 8PM Sunday EST. HOW TO HAVE ETERNAL LIFE : https://www.youtube.com/watch?v=vX6NdGnm_vASUBSCRIBE https://www.youtube.com/c/biblelineLIKE https://www.facebook.com/biblelineminCOMMENT ask us a question!SHARE with all your friends and familyHave a Bible question? The questions@biblelineministries.org email address is not longer in use, but you can:- Explore Pastor Jesse's full teaching library: https://www.youtube.com/@BibleLine/playlists- Watch a clear gospel presentation: https://www.youtube.com/watch?v=vX6NdGnm_vA- Ask your question live on air during our YouTube call-in show:https://www.youtube.com/playlist?list=PLElaVGv3oAZ6Y9q4uV9TOX5PMEYimFXqgSupport Bibleline - https://www.calvaryoftampa.org/donate/Bibleline is a ministry of Calvary Community Church in Tampa, Florida and is hosted by Pastor Jesse Martinez.LIKE THIS? CHECK THESE GUYS OUT:@Northlandchurchstc@YankeeArnoldMinistries@focusevangelisticministriesinc@TheKeesBoerMinistryChannel@FishersWithFaithMinistries@QuentinRoad@NorthsideChurchAthens@C4CApologetics@OnoDiamante#bibleline #salvation #homosexual #homosexuality #condemnation #condemned #heaven #hell #evanglism #gospel #truth #real

► Vote for ACSOM: https://open.spotify.com/playlist/37i9dQZF1DWXN9OmFxQB3e Welcome to our live coverage as we break down the latest from Parkhead. With the Scottish Premiership title race reaching a fever pitch, Martin O'Neill's interim reign has brought the "scaling mountains" mentality back to Glasgow. Can the Hoops reach the summit, or will the pressure of the climb be too much? In this stream, we discuss: ✅ The O'Neill Factor: How Martin O'Neill has transformed Celtic's grit and determination since his return. ✅ Title Race Analysis: A deep dive into the remaining fixtures and what Celtic need to secure the trophy. ✅ Player Focus: Who is stepping up to lead the team up the mountain? ✅ Glasgow Derby Fallout: Reassessing the impact of that massive 3-1 victory over Rangers.

Parents, download Mr Jim's app Riffio to create your own stories and songs inside Spyology Squad!iOS Download | AndroidAbout Mr. Jim:What started as a dad recording bedtime stories for his kids while traveling for work has become a phenomenon. Jim Jacob never set out to launch a podcast, he just needed his family to hear his stories. Hundreds of thousands of listeners later, that accidental solution has become one of the most beloved children's audio adventures on the internet.Welcome to Spyology Squad — where science becomes a superpower and every mission is a new discovery.Join Jayden, Ava, and Mr. Jim as they face off against the diabolical Dr. Stinkybreath and his Purple Ninja army in a world of spyience-fiction a blend of pulse-pounding spy adventure and real scientific thinking designed to light up young imaginations. Each episode drops kids ages 6–12 into a fast-moving mission where the only way to save the world is to think like a scientist.What makes Spyology Squad special? This isn't science dressed up as entertainment, it's adventure that sneaks in genuine learning. Episodes are crafted to complement what kids are actually exploring in school, turning concepts like physics, chemistry, and biology into plot twists and mission objectives. New episodes release three times a week, so there's always a new mission waiting.Whether it's a car ride, bedtime routine, or a rainy afternoon, Spyology Squad turns screen-free time into something genuinely exciting. Jim and his wife Jocelyn built this for their own three kids — and now they're building it for yours.Subscribe and join the Squad. There's a new adventure around every corner.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

-- Who actually was behind the fight to throw out the leaders, replace board members, and redirect the  NRA to its core values?  Buz Mills, owner of the famous gunfighting school Gunsite, pulls back the curtain.--  Sometimes you want to conceal a handgun, and sometimes you don't.  What goes into making a good holster to fit the particular need.   Mike Barham explains the process they use at Galco Holsters.--  What really happens at a multi-day class in gunfighting?Gun Talk 05.10.26 Hour 3Become a supporter of this podcast: https://www.spreaker.com/podcast/gun-talk--6185159/support.

Steve, Todd, and Aaron are joined by comedian Tim Young for the Deace Group roundtable to discuss the rumblings about rebooting the "Star Wars" franchise and whether it's a fool's errand. The panel also discusses what has become of Megyn Kelly. In Hour Two, it's another round of Feedback Friday. TODAY'S SPONSORS: RELIEF FACTOR: VISIT https://www.relieffactor.com/ OR CALL 800-4-RELIEF PATRIOT MOBILE: https://patriotmobile.com/STEVE or call 972-PATRIOT for your FREE MONTH of service GEVITI: https://www.gogeviti.com/deace FAST GROWING TREES: https://www.fast-growing-trees.com/?utm_source=podcast&utm_medium=audio&utm_campaign=Steve+Deace+Show code DEACE POCKET HOSE: Text DEACE to 64000 Learn more about your ad choices. Visit megaphone.fm/adchoices

This week, Jean sits down with Louise, 56, a high-tech executive who's burned out, stretched thin, and seriously asking herself whether it's time to walk away from her career. Louise has done everything right: she and her husband have $3.2M in retirement accounts, over $400k in vested stock, a home worth almost $2 million, and 529s for the kids. But with a family of six to cover, and healthcare costs that could rival a mortgage, the math is murkier than it looks. Jean helps her think through what early retirement would actually cost, where the real risks are, and what she needs to figure out before she makes any moves. In this episode: The Rule of 55 — what it is, how it works, and whether it's the right move Why the 4% rule may not be enough when you're only 56 Healthcare on the open market: what it really costs for a family of six post-subsidy How unvested stock grants could completely change Louise's retirement picture Why your home equity belongs in your net worth calculation The case for finding a middle path between burnout and full retirement What to ask a financial advisor before making any early retirement decisions Never miss a money moment — sign up for the free HerMoney newsletter. Learn more about your ad choices. Visit megaphone.fm/adchoices

The nationwide blue state fraud superscandal grows wider. This time, a top Democrat in Virginia who just helped redraw the state's House map is at the center of an FBI investigation, and even her fellow Dems seem eager to ditch her. Mike Brest talks the latest Iran deal draft. Steve Hilton and Jack Posobiec respond to the LA mayoral debate and whether America's second city can still be salvaged despite its tremendous decline. Watch every episode ad-free on members.charliekirk.com! Get new merch at charliekirkstore.com!Support the show: http://www.charliekirk.com/supportSee omnystudio.com/listener for privacy information.

Sponsor: Inito For Birth Hour listeners, the Insight Reader is available right now for just $89 with the code BIRTHHOUR at inito.com. The Birth Hour Links: Know Your Options Online Childbirth Course (code 100OFF for $100 OFF!) Beyond the First Latch Course (comes free with KYO course) Access archived episodes and a private Facebook group via Patreon! 

Anita Moorjani did everything right. She ate organic, avoided sugar, studied cancer prevention obsessively. She still got lymphoma. That contradiction is the entire point. Growing up as an Indian woman in Hong Kong, caught between British culture and a community that valued sons over daughters, Anita learned one thing above all else: make yourself small and keep everyone happy. That lesson followed her for 40 years, through a canceled arranged marriage, a cancer diagnosis, and a four-year deterioration that ended with her in a coma, 85 pounds, and organs failing. Then she left her body. In that expanded state, she saw clearly for the first time: it wasn't the cancer that had been killing her. It was the fear. When she came back, every trace of cancer was gone within three weeks. Doctors flew in from the US just to study her case. None of them could explain it. What Anita brought back is a message she's been sharing for 20 years: you don't need to earn your place in the world, you already are what you've been spending your life trying to become. Anita's books: Dying to Be Me Sensitive Is the New Strong: The Power of Empaths in an Increasingly Harsh World What If This Is Heaven?: How Our Cultural Myths Prevent Us from Experiencing Heaven on Earth Anita's Sedona Healing Retreat Anita's Website Anita's Facebook Anita's Instagram In this episode you will: Understand why living in constant fear can manifest as serious physical illness - and how shifting from fear-based choices to love-based ones changes everything Discover what Anita experienced during her 30-hour coma, including what she saw, heard, and understood outside her physical body Learn the mirror exercise that helped Anita rebuild self-worth from scratch after coming back from the edge of death Recognize how people-pleasing and self-repression show up in the body, and why your authentic self will keep demanding to be heard Apply Anita's framework for replacing fear of what you don't want with a focused attention on what you actually want to create For more information go to https://lewishowes.com/1924 For more Greatness text PODCAST to +1 (614) 350-3960 Follow The Daily Motivation for essential highlights from The School of Greatness More SOG episodes we think you'll love: Lewis Howes Solo [STOP Letting People Walk All Over You] Muniba Mazari Dr Joe Dispenza Get more from Lewis! Get my New York Times Bestselling book, Make Money Easy!Get The Greatness Mindset audiobook on SpotifyText Lewis AIYouTubeInstagramWebsiteTiktokFacebookX Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.