Podcasts about testing

Today, I'm joined by the thoughtful Dr. Clement Lee, a naturopathic doctor blending ancient wisdom from Chinese medicine with cutting-edge regenerative therapies. In this episode, Dr. Lee gets real about why people can feel deeply unwell long before anything shows up on standard labs—and how subtle issues like limbic system stress, toxins, and chronic inflammation quietly undermine our vitality.   Episode Timestamps: Welcome and episode overview ... 00:00:00 Prolotherapy, Chinese medicine, and complex healing cases ... 00:05:16 Common misconceptions in chronic illness treatment ... 00:13:15 Why mindset and the limbic system affect healing ... 00:18:02 Early warning signs of chronic illness ... 00:22:50 Evaluating toxins, hormones, and inflammation in root cause medicine ... 00:26:03 Toxic overload concerns in children ... 00:27:09 First steps for chronic, hard-to-treat cases ... 00:31:56 Top interventions for longevity now ... 00:33:43 Testing for heavy metals and toxins ... 00:35:12 Inflammation loops and surprising causes like mold ... 00:38:12 The role of cellular healing and the nervous system ... 00:47:09 Advanced therapies: peptides, IV nutrients, and more ... 00:52:01 Mast cell activation and immune overdrive signals ... 01:08:23 Key takeaways and easy strategies for stress ... 01:26:40   Our Amazing Sponsors: Mitopure Longevity Gummies by Timeline — Clinically backed Urolithin A supports mitochondrial health to boost energy, recovery, and healthy aging, all in an easy daily gummy instead of another pill; go to timeline.com/nat20 for 20% off Mitopure Gummies.   BEAM Minerals - Low energy often starts in the mitochondria. Support cellular energy with a bioavailable liquid mineral supplement — and get 20% off at beamminerals.com with code NAT20.   Youth Daily by Young Goose — An all-in-one moisturizer powered by NAD+ nano precursors to boost elasticity, smooth wrinkles, and keep your skin looking fresh, dewy, and full of life; grab yours at younggoose.com and use code Nat10 for first orders or 5NAT for returning customers.   Nat's Links:  YouTube Channel Join My Membership Community Sign up for My Newsletter  Instagram  Dr. Bill Lawrence Episode

In this conversation, Tarun Agarwal draws parallels between Starbucks' business strategies and the challenges faced by dental practices. He emphasizes the importance of expanding service offerings to break through revenue ceilings and enhance patient care. By introducing new procedures, such as dental implants, practitioners can leverage existing resources and improve their practice's profitability. The discussion highlights the need for dentists to embrace growth and adapt to changing patient needs to avoid stagnation. Takeaways Starbucks' near failure teaches valuable lessons for dental practices.Many dentists feel stuck despite working harder and adding team members.Efficiency improvements alone do not lead to significant growth.Diversifying services is crucial for breaking revenue ceilings.Patients may leave for specialists offering broader menus.Testing new procedures can lead to substantial revenue increases.Committing to new categories can transform a practice's success.Overhead costs remain constant, making high-value procedures more profitable.Practices can plateau and fade if they don't adapt and grow.Growth in dentistry can be exponential with the right strategies. Titles From Coffee to Crowns: Lessons from StarbucksBreaking the Revenue Ceiling in Dental Practices sound bites "You've hit a menus ceiling.""TRT is your sandwich test.""Growth is exponential." Chapters 00:00 The Starbucks Connection: Lessons for Dental Practices02:36 Breaking Through the Revenue Ceiling05:46 The Sandwich Test: Expanding Your Offerings09:36 Going All In: Committing to New Categories13:43 Your Next Move: Embracing Growth in Dentistry Chapters (00:00:00) - How to Get Out of Trap Your Salary(00:01:19) - Dental Restorative and Preventative Procedures(00:02:34) - Why Starbucks didn't expand in 2003(00:08:40) - What Happened to Starbucks When They Stopped Testing and Went All(00:13:04) - Plastic Surgery

Stuart Young, Program Manager, Tactical Technology Office, DARPA joined Grayson Brulte on The Road to Autonomy podcast to discuss DARPA's RACER (Robotic Autonomy in Complex Environments with Resiliency) Program and the development of high-speed autonomous vehicles capable of navigating unstructured off-road terrain without maps or GPS.The operational backbone of this program is a departure from the breadcrumb approach of the Grand Challenge, challenging robots to navigate complex, unstructured environments at speeds faster than manned formations. By removing the dependency on pre-existing maps and GPS, DARPA is forcing the autonomous systems to generalize across environments.In the field, RACER has rigorously tested platforms ranging from modified Polaris RZRs to Textron M5 tracked vehicles across diverse landscapes, including the Mojave Desert, Camp Roberts, and Fort Hood. This ecosystem has not only spurred the creation of companies such as Overland AI and Field AI but also demonstrated tactical relevance, as seen when the 11th Armored Cavalry Regiment utilized RACER technology as an opposition force at the National Training Center.Looking ahead, Stuart envisions a future where autonomy shifts from simple movement to strategic maneuver, enabling a single operator to command platoons of vehicles. This evolution aims to fundamentally change the risk calculus for soldiers while opening new opportunities for dual-use applications in mining, agriculture and search and rescue.Episode Chapters0:00 The History of Autonomy at DARPA: From the Grand Challenge to Today6:54 How RACER Differs from The Grand Challenge11:59 Operating Without Maps or GPS14:00 Managing Heat, Acoustic, and Visual Signatures in Autonomy19:43 Testing in the Mojave, Central California, and Texas25:11 Building the RACER Brain and Spawning New Companies (Overland AI, Field AI)27:12 The Rules of RACER: Speed Metrics and “No Maps” Constraints33:36 The Hardware: Modifying Polaris RZRs and Textron M5 Tanks37:37 Requirements vs. Possibilities40:01 Field Testing with the 11th Armored Cavalry Regiment at the National Training Center44:43 Deploying RACER in the Field46:12 The Legacy of RACER: Dual-Use Applications and Saving Lives--------About The Road to AutonomyThe Road to Autonomy provides market intelligence and strategic advisory services to institutional investors and companies, delivering insights needed to stay ahead of emerging trends in the autonomy economy™. To learn more, say hello (at) roadtoautonomy.com.Sign up for This Week in The Autonomy Economy newsletter: https://www.roadtoautonomy.com/ae/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

"This man is constantly DJ-ing..." This week, Ann and Amanda discuss the Winter Olympics (confessions of a Norwegian biathlete), Summer House (Amanda and Kyle's relationship), and more!We release two types of episodes -- pop culture/reality TV chats (that's this one!) and interviews. If you missed our recent interviews with Kate Riccio and Kevin Grossman, we HIGHLY recommend you check those out!WSANDA SUBMISSIONS: wsandasubmissions@gmail.comFollow us on instagram @wesignedannda @mikiannmaddox @liffordthebigreddog so you can slither in our DMs with constructive feedback, but please, for the love of god, don't cyberbully us. We're fragile :-/If you're picking up what we're putting down and want even more Ann and Amanda comedy content, support us on Patreon. You have no idea how many times we've said "Wait, this is too batshit.....we'll put it on Patreon." Our cover art was made by America's sweetheart, producer Maddy, and our theme song features parts of "Kawaii Til I Die" by Starjunk 95 Hosted on Acast. See acast.com/privacy for more information.

Testing is supposed to give us clarity. Instead, it has given us anxiety.This week we break down our increasingly skeptical takes on the 2026 pecking order after a chaotic round of pre-season running. Between odd new starting procedures, Aston Martin's gearbox headaches, and a paddock full of “it's just testing” coping mechanisms, we're trying to separate real red flags from strategic sandbagging.We'll talk about the early bad news stories, who looks genuinely comfortable with the new regulations, and who might already be scrambling behind the scenes. And yes… we have to address the elephant in the room:Are we staring down a George Russell championship season?Can anything be done to stop it? Should anything be done to stop it? And are we emotionally prepared for what that would mean?It's testing overreactions, early-season paranoia, and the usual Fast Ones detours as we inch closer to lights out in 2026.

Tune in for our conversation with Dr. Stephen Cabral where he breaks down why even health-conscious biohackers can struggle with chronic symptoms, pointing to hidden imbalances that standard approaches often miss. He emphasizes starting with gut and immune health and explains why comprehensive lab testing, especially Hair Tissue Mineral Analysis (HTMA), offers insights that blood work alone can't capture. The conversation explores how stress, electrolyte imbalances, and heavy metal exposure quietly drain resilience, making proper detoxification and mineral balance essential. Dr. Cabral also shares practical guidance on magnesium, zinc, copper, iron, and adrenal support, tailoring advice for high performers under constant pressure. We also dive into our personal HTMA results so you can get a front row seat on what a typical HTMA consultation looks like. Throughout the episode, he underscores that optimal health isn't just about supplements and protocols, but also mindset—building “stress calluses” and taking a truly holistic, systems-based approach.Dr. Stephen Cabral is a Board-Certified Doctor of Naturopathy and founder of EquiLife, the global integrative health organization providing at-home lab testing, personalized wellness protocols, and concierge health coaching to over 300,000 clients over the past 25 years.He also founded the Integrative Health Practitioner Institute (IHPI), which has certified over 5,000 practitioners globally, and hosts The Cabral Concept podcast with 3,500+ episodes and 100 million+ downloads.After overcoming a life-threatening illness at 17, Dr. Cabral went on to get his doctoral degree and completed over 2,200 internship hours all over the world including India, Sri Lanka, China, Europe, and the U.S.SHOW NOTES:0:42 Welcome to the podcast!3:13 About Dr. Stephen Cabral4:12 Welcome him to the show!4:48 Why do healthy people get sick?7:48 Addressing gut & immune system first10:36 Starting with HTMA Testing12:21 Electrolytes in hair vs blood work15:12 Validity of HTMA16:05 Overview of our results interpretation18:09 Lauren's HTMA results22:38 Supporting the HPA axis with electrolytes24:33 Magnesium recommendations25:29 Iron, Copper & Zinc31:51 Advice for high performers & high achievers33:44 *CALOCURB*34:56 Lauren's heavy metals41:17 Renee's HTMA results44:32 Creating stress “calluses”50:21 Renee's heavy metals52:50 Finding a Biological Dentist54:47 Where else metals are stored56:42 Adrenal support ingredients57:25 Zinc & Copper balancing1:03:45 Minerals & Metals Test1:05:32 His final piece of advice1:06:50 Thanks for tuning in!RESOURCES:Calocurb - discount code: RENEE10Website: www.drstephencabral.comIG: @stephencabralYouTube50% off the Minerals & Metals TestBook: PersonomicsCoaching: sign up for the Personomics waitlist at:Personomics.ioThe Julian Center for Comprehensive DentistryTo find a Biological Dentist: iaomt.orgSupport this podcast at — https://redcircle.com/biohacker-babes-podcast/donationsAdvertising Inquiries: https://redcircle.com/brands

What's the worst that could happen if you just start taking a supplement, right? You already don't feel great, so something has to help… right? In this episode, I explain why that mindset often backfires, especially when it comes to hormones. I see this all the time: women taking "hormone balance" supplements, DIM, progesterone support, adrenal formulas, or detox products without testing first. The intention is good, but without knowing what your body actually needs, you can easily make symptoms worse instead of better. In this episode, I break down: Why supplements are not all the same (foundational vs targeted vs phase-specific) How taking the wrong supplement at the wrong time can worsen fatigue, anxiety, PMS, cycle issues, and sleep Why hormones require timing, balance, and sequencing, not random support How cortisol, estrogen, progesterone, and neurotransmitters all interact and why treating one without the others often fails Why DIM is one of the most commonly misused hormone supplements (and how it can actually drive estrogen too low) How adrenal dysfunction can block progesterone support from ever "landing" Why liver detox, gut health, and hormone clearance matter just as much as hormone production Why hormones work on a negative feedback loop and why real change takes longer than 30 days I also explain why we use DUTCH testing to look at the full picture: Hormone production Hormone metabolism and pathways Cortisol rhythm Neurotransmitters Nutrient markers Testing allows us to answer the real questions: Is the issue production, processing, or clearance? Are adrenals driving hormone imbalance? Is your system overstimulated or suppressed? What phase of healing do you actually need right now? This is exactly why I created the Hormone Foundations Program. Inside the program, we: Test first with the DUTCH Complete Build a personalized, phase-based supplement and lifestyle plan Adjust support as your body moves through healing stages Retest to confirm progress (because numbers don't lie) Guide you for a full six months so changes actually stick If you've tried supplements before and felt worse, stalled out, or burned out, this episode is for you. Learn more about the Hormone Foundations Program: https://drbethwestie.com/hormone-foundations-program/ Not sure? Book a Discovery call: https://calendly.com/dr-beth-westie/program-discovery-call

The Bible gives us a standard by which we may discern truth from falsehood, especially when someone proclaims themselves to be a prophet (Deuteronomy 13; 18; Matthew 24:24; 1 John 4:1; 1 Peter 5:8; Ephesians 6). Watchman Fellowship has published free articles on our blog related to this topic. Visit and subscribe to our new blog today! Be sure to check out these articles. Testing Joseph SmithDiscerning False ChristsThis week and next on Apologetics Profile, we were privileged once again to have been able to interview former Latter-day Saint and the great, great granddaughter of Brigham Young, Sandra Tanner in Utah this past fall. Sandra shares her wisdom and experience from her decades-long research of the LDS Church. Her insights will help equip you to be better prepared to engage your Mormon friends, neighbors or missionaries who come to your door. If you are enjoying Apologetics Profile, be sure to leave us a nice review on your favorite podcast platform! ⭐️⭐️⭐️⭐️⭐️

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In this episode, Etienne Nichols sits down with Edwin Lindsay, a seasoned MedTech operator and QARA leader, to discuss the systemic challenges facing the pediatric medical device market. Following a personal experience in a neonatal ward, Edwin highlights the stark reality that many pediatric treatments rely on adult devices adapted off-label, often leading to safety risks and clinical inefficiencies.The conversation delves into the "mismatch" of the pediatric market: these devices require the same rigorous regulatory and quality standards as adult products but offer significantly lower financial upside due to smaller patient populations. This creates a barrier for investors and manufacturers, leaving clinicians and nurses to "work miracles" with tools that aren't always fit for purpose.Despite these hurdles, Edwin shares an optimistic vision for the future. He discusses his initiative to build a collaborative network of experts—including regulatory consultants, testing houses, and grant writers—willing to provide pro-bono or at-cost support for pediatric startups. The goal is to create a streamlined regulatory roadmap that prioritizes patient safety without the prohibitive costs that currently stall innovation.Key Timestamps00:45 – The "Pediatric Gap": Why pediatric devices have adult-level requirements but lower ROI.03:12 – Personal Insight: Edwin's experience in the hospital and the "Guinness philosophy" of giving back.05:30 – The danger of adhesives and adapting adult materials for newborn skin.08:15 – Building a pediatric volunteer network: Testing houses and consultancies stepping up.11:40 – Regulatory Roadmaps: Navigating the age variability from premature infants to adolescents.14:50 – Off-label usage risks and the "mindset shift" required for manufacturers.18:25 – Micro-timestamp: The FDA's Humanitarian Device Exemption (HDE) and P-Sub programs.21:10 – Real-world clinical friction: Alarm fatigue and sensor sensitivity in NICU settings.25:40 – The hidden costs: Manufacturing complexity, multiple SKUs, and low-volume production.Quotes"We need to give clinicians the correct tools to work their miracles. They don't want to use products off-label; they want devices actually designed for the children they are saving." - Edwin Lindsay"If you have a pediatric project, there is a community behind you. We are breaking down the barriers of risk and cost because these babies deserve a chance." - Edwin LindsayTakeawaysRegulatory Flexibility: Utilize specific FDA pathways like the Humanitarian Device Exemption (HDE) and the Pediatric Submissions (P-Sub) program to gain early feedback and specialized guidance.Collaborative Cost-Sharing: Seek out "altruistic" partners; many testing houses and manufacturers are willing to work at-cost or under different financial models for pediatric-specific innovations.Design for Sensitivity: Pediatric innovation isn't just about miniaturizing adult tech—it requires solving unique issues like alarm fatigue and skin sensitivity (e.g., non-damaging adhesives).Workflow Integration: Engage the "head nurse" early in R&D to ensure the device fits into the high-stress environment of a pediatric ward without adding to clinical fatigue.ReferencesFDA HDE Program: A regulatory pathway for devices intended for diseases or conditions that affect small populations.Greenlight Guru: The industry-leading platform for QMS & EDC solutions, helping MedTech companies maintain...

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  • Crew-12 Docks at ISS — The SpaceX Crew-12 mission docked at the International Space Station on Valentine's Day, restoring the station to full strength after over a month with a skeleton crew. Astronauts Jessica Meir, Jack Hathaway, Sophie Adenot, and Andrey Fedyaev join Expedition 74 for an eight-month mission.   • Artemis 2 Hydrogen Leak Update — NASA's “confidence test” on the SLS rocket's repaired hydrogen fueling seals showed mixed but cautiously encouraging results. March remains the earliest potential launch window for humanity's first crewed mission to the Moon in over 50 years.   • Enceladus: Electromagnetic Powerhouse — A major new study of 13 years of Cassini data reveals Saturn's tiny moon Enceladus generates Alfvén waves extending over 504,000 km, transforming our understanding of how small moons influence giant planetary magnetospheres.   • Catching 3I/ATLAS — Researchers from the Initiative for Interstellar Studies propose a Solar Oberth Manoeuvre mission launching in 2035 that could intercept the interstellar comet, currently heading toward Jupiter for its closest pass on March 16.   • Geomagnetic Storm Watch — G1 minor storming is likely today as a coronal mass ejection arrives alongside fast solar wind from a returning transequatorial coronal hole. Aurora possible at higher latitudes tonight.   • SpaceX Starlink 6-103 — 29 Starlink V2 Mini satellites launched to orbit in the early hours of today, the 10th orbital flight from Cape Canaveral in 2026.   LINKS & RESOURCES: • NASA Crew-12 Docking: https://www.nasa.gov/blogs/spacestation/2026/02/14/spacex-crew-12-docks-to-station-beginning-long-duration-mission/ • Artemis 2 Confidence Test Update: https://www.nasa.gov/blogs/missions/2026/02/13/following-confidence-test-nasa-continues-artemis-ii-data-review/ • Enceladus Alfvén Wings Study: https://phys.org/news/2026-02-tiny-enceladus-giant-electromagnetic-saturn.html • 3I/ATLAS Solar Oberth Paper: https://www.universetoday.com/articles/a-new-concept-for-catching-up-with-3iatlas • Space Weather Updates: https://earthsky.org/sun/sun-news-activity-solar-flare-cme-aurora-updates/ • Spaceflight Now Launch Schedule: https://spaceflightnow.com/launch-schedule/   Astronomy Daily is part of the Bitesz.com Podcast Network Website: https://astronomydaily.io Social: @AstroDailyPodBecome a supporter of this podcast: https://www.spreaker.com/podcast/astronomy-daily-space-news-updates--5648921/support.Sponsor Details:Ensure your online privacy by using NordVPN. To get our special listener deal and save a lot of money, visit www.bitesz.com/nordvpn. You'll be glad you did!Become a supporter of Astronomy Daily by joining our Supporters Club. Commercial free episodes daily are only a click way... Click HereThis episode includes AI-generated content.

Spanners, Trumpets and Stuffeyy survey the damage from the first week of the F1 Test in Bahrain and sort the perils from the politics in this the latest episode of Missed Apex Podcast!Give Spanners Insta a go!!!https://www.instagram.com/spannersreadyCheck out the awesome Missed Apex MotoGP podhttps://open.spotify.com/show/3IEB1Q2STelYNP7nda3gxd⭐Missed Apex Tik Tokhttps://www.tiktok.com/@missedapexf1⭐ Spanners https://x.com/SpannersReadyhttps://bsky.app/profile/spannersready.bsky.social⭐ Matt Trumpets https://x.com/mattpt55https://bsky.app/profile/mattpt55.bsky.socialWays To Support Missed Apex:✅ Join our Patreon to gain access to our exclusive Patreon Only Discord Chat + Bonus ContentWe Only Exist Due to Our Patron Support https://www.patreon.com/MissedApex✅ Leave a tip https://missedapexpodcast.com/tipjarOn Tonight's Show:⭐Missed Apex Tik Tokhttps://www.tiktok.com/@missedapexf1⭐ Spanners https://x.com/SpannersReadyhttps://bsky.app/profile/spannersready.bsky.social⭐ Matt Trumpets https://x.com/mattpt55https://bsky.app/profile/mattpt55.bsky.social⭐ Stuffeyy https://www.youtube.com/@stuffeyyCheck out Stuffey's F1 watchalongs!!! https://www.youtube.com/@stuffeyyGive Spanners Insta a go!!!https://www.instagram.com/spannersreadyKeep an eye (or ear) out for Stevens on comms!!! Season begins 19/04/2026 on YouTube!!!https://youtube.com/@gtopenseries?si=YNS0AidFc364XX1qGive Stevens show reel a spin!!! https://loudspeakeragency.com/talent/chris-stevens/Show some love to SomersF1 Substack! Learn from the best tech F1 analyst in the biz! Hit Like and Subscribe!https://somersf1.substack.com/Give Spanners Insta a go!!!https://www.instagram.com/spannersreadyCome watch our iRacing Series with Spanners on comms!https://youtube.com/live/U4qkMR_GLuE?feature=shareCheck out Missed Apex Tik Tok!!!! https://www.tiktok.com/@missedapexf1Give us a shout on WhatsApp! Save +44 79 4747 1840 if you are interested in calling into a show or sending us things you reckon Hosted on Acast. See acast.com/privacy for more information.

Ben and Sam break down the key takeaways from the first week of pre-season testing in Bahrain, analysing how each team appears to be shaping up and the biggest questions still hanging over the paddock. They also discuss growing talk of a surprise return for a much-loved circuit, and explore the career highs and lows for a selection of F1 drivers... Want more Late Braking? Support the show on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Patreon⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and get: Ad-free listening Full-length bonus episodes Power Rankings after every race Historical race reviews & more exclusive extras! Don't forget! You can also gift a Late Braking Patreon subscription—perfect for loved ones or your own wish list. Choose anything from 1 month up to a full year of top-notch F1 content: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/latebrakingf1/gift⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Connect with Late Braking: You can find us on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X (Twitter)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Come hang out with us and thousands of fellow F1 fans in our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Discord⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ server and get involved in lively everyday & race weekend chats! Get in touch any time at podcast@latebraking.co.uk Learn more about your ad choices. Visit podcastchoices.com/adchoices

MMM is sponsored by 321 - a new online introduction to Christianity, presented by former MMM guest Glen Scrivener. Check it out for free at 321course.com/MMM. Just enter your email, choose a password and you're in — there's no spam and no fees. Give the gift of everyday luxury and make every moment comfortable. Head to cozyearth.com and use my code COZYMMM for 20% off sitewide. And if you get a Post-Purchase Survey, be sure to mention you heard about Cozy Earth at the Maiden Mother Matriarch podcast.Testing a foetus or an embryo for some medical conditions is now a routine part of the modern pregnancy experience. Prenatal Down's Syndrome tests, for instance, are now so widespread that in some Scandinavian countries almost 100 per cent of women choose to abort a foetus diagnosed with the condition, or – if using IVF – not implant the affected embryo. The result is a visible change to these populations: there are simply no more people with Down's to be seen on the streets of Iceland and Denmark.New technology is now available – at a high price – for those who want to go further. So-called polygenic embryo screening can give a very full picture of the adult that the embryo could become, including his or her vulnerability to an enormous number of diseases – heart disease, diabetes, cancer – and also the physical and psychological traits that he or she would likely possess: height, hair colour, athletic ability, conscientiousness, altruism, intelligence. Is this a good thing? Should we welcome a world in which parents are routinely selecting their embryos in this way? I'm joined today by two guests who take a very different view. Emma Waters is a policy analyst at the Center for Technology and the Human Person at the Heritage Foundation. Her work focuses on family, biotechnology, and reproductive medicine.Jonathan Anomaly is a philosopher, author of the book 'Creating Future People: The Science and Ethics of Genetic Enhancement', and is also the director of scientific research and communication for Herasight, a genetics startup that offers polygenic embryo screening. Hosted on Acast. See acast.com/privacy for more information.

“Send us a Hey Now!”This week saw the F1 teams head to Bahrain for the first round of pre-season testing. We saw all the new liveries on display and got a first look at them on track as portions of the testing were televised.We pick apart the various theories coming out of testing and also rank the liveries to create our combined leaderboard.Episode running order as always is...1) News & SocialAll the best bits from both the sports news out there as well as what caught our eye on the various social channels 2) Brian's Video Vault             https://www.youtube.com/watch?v=P7qAimzZ5YY Cadillac 2026 Super Bowl Commercial The Mission Begins Cadillac Formula 1 Racing. 1 min 6 seconds.https://www.instagram.com/reel/DUiGK79Dxl5/?utm_source=ig_web_copy_link&igsh=NTc4MTIwNjQ2YQ== extremecarsofficial_ Instagram. The moment when the Cadillac Formula 1 team unveiled their 2026 car in Times Square, New York City!https://www.youtube.com/watch?v=8-rzpNM21zk. 10 Tales from F1 Testing. Formula 1 channel. 9 mins. https://www.youtube.com/watch?v=tyw8etDrQ-c. Formula 1: Drive To Survive Season 8 Official Trailer | Netflix. Formula 1 channel. 2 mins. https://www.youtube.com/watch?v=_HWVT-fzlmY. Asking F1 drivers the REAL questions | Get to know Liam Lawson and Arvid Lindblad. Visa cash app RB F1 channel. nearly 8 mins. https://www.youtube.com/watch?v=WeBRmSFcRxs&t=9s. Valtteri Bottas REVEALS why he chose Cadillac! Kym Illman channel. 13 mins.3) Cadillac CornerUpdates from the team we saw this week in terms of the livery launch at the Super Bowl and Times Square4) Livery LeaderboardWe rank the liveries from 1-11 and use our combined scores to create the Dirty Side Livery Leaderboard 5) Testing TribulationsWe take a look at all the key data coming out from testing and try to see if you can really use it to understand wherSupport the showWe would love you to join our Discord server so use this invite link to join us https://discord.gg/XCyemDdzGB To sign up to our newsletter then follow this link https://dirty-side-digest.beehiiv.com/subscribeIf you would like to sign up for the 100 Seconds of DRS then drop us an email stating your time zone to dirtysideofthetrack@gmail.comAlso please like, follow, and share our content on Threads, X, BlueSky, Facebook, & Instagram, links to which can be found on our website.One last call to arms is that if you do listen along and like us then first of all thanks, but secondly could we ask that you leave a review and a 5 star rating - please & thanks!If you would like to help the Dirty Side promote the show then we are now on Buy me a coffee where 100% of anything we get will get pumped into advertising the show https://www.buymeacoffee.com/dirtysideofthetrackDirty Side of the Track is hosted on Buzzsprout https://www.buzzsprout.com/

Jesus made it back to Jerusalem!. After years of traveling and teaching, Jesus finally enters the familiar streets for the last week of his life on earth. It's a monumental moment: crowds ran out to meet him and to accompany him back into the city. It's a provocative moment, too, for the crowds are waving palm branches, rejoicing, shouting and saying “Blessed is the King who comes in the name of the Lord. Peace in heaven and glory in the highest!” The words are from Psalm 118 and would announce the Messiah! Do the crowds know that? The Pharisees did, and tried to stop it but they couldn't. So did the Sadducees. But they couldn't either. Come find out why.

Dr. Gabriel Blass shares a fascinating case of an infant with a rare genetic bone disorder and how homeopathy helped support the child's health. He also used AI to explore potential remedies and even a homeopathic preparation made from a pharmaceutical medicine, which produced impressive results. The episode reveals how curiosity, creativity, and careful listening can open doors in even the most challenging medical cases. Episode Highlights: 03:34 - Tips for using AI responsibly 05:52 - Malignant Osteopetrosis Case in an Infant 09:35 - Different types of Osteopetrosis 11:29 - Purpose of Bone Remodelling 14:10 - Repertorization + Muscle Testing 16:32 - First Prescription: Silica + Hecla Lava and the Once-Weekly Dosing Choice 18:36 - Genetics Confirm Malignant Form 20:09 - Treatment Pivot 23:35 - Third Follow-Up: Strength Gains, Stability, and Early Mobility Improvements 26:02 - Switching From Analysis to Intuition 30:04 - Shortlisting Homeopathic Drug Remedies 32:43 - Testing & Prescribing: Atorvastatin Emerges as the Key Remedy 35:32 - Potency Changes, Hecla Lava/Sulfur Added, Child Thrives 39:27 - DNA Hardware vs Vital Force Software 43:23 - Single-Gene Defects, COV!D/mRNA Speculation About my Guests: Dr. Gabriel Blass is a distinguished homeopathic physician and educator based in Glasgow, Scotland, with decades of experience in both conventional and complementary medicine. He earned a B.Sc. Honours degree in Pathology in 1986 and completed his M.B. Ch.B. with Commendation from the Faculty of Medicine at the University of Glasgow in 1988. Dr. Blass has been actively involved in homeopathic medicine since 1987 and currently practises as a homeopathic doctor in Glasgow. Alongside his clinical work, he is deeply committed to advancing the field of homeopathy through education and professional development. He regularly lectures to doctors, homeopaths, and other health-care professionals, delivers public presentations, and trains students pursuing studies in homeopathy. In addition to his clinical and teaching roles, Dr. Blass contributes to the academic community by translating scientific papers for Homeopathy (formerly The British Homeopathic Journal), helping make research more accessible to practitioners worldwide. He is a Registered Member of the Society of Homeopaths, adhering to its Code of Ethics and high academic and clinical standards. Dedicated to excellence in patient care and professional growth, Dr. Blass maintains an ongoing programme of Continuing Professional Development, reflecting his belief that learning is a lifelong journey. Find out more about Gabriel Website: https://www.homeopathy-glasgow.co.uk/ If you would like to support the Homeopathy Hangout Podcast, please consider making a donation by visiting www.EugenieKruger.com and click the DONATE button at the top of the site. Every donation about $10 will receive a shout-out on a future episode. Join my Homeopathy Hangout Podcast Facebook community here: https://www.facebook.com/groups/HelloHomies Follow me on Instagram https://www.instagram.com/eugeniekrugerhomeopathy/ Here is the link to my free 30-minute Homeopathy@Home online course: https://www.youtube.com/watch?v=vqBUpxO4pZQ&t=438s Upon completion of the course - and if you live in Australia - you can join my Facebook group for free acute advice (you'll need to answer a couple of questions about the course upon request to join): www.facebook.com/groups/eughom

Ben and Sam break down the key takeaways from the first week of pre-season testing in Bahrain, analysing how each team appears to be shaping up and the biggest questions still hanging over the paddock. They also discuss growing talk of a surprise return for a much-loved circuit, and explore the career highs and lows for a selection of F1 drivers... Want more Late Braking? Support the show on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Patreon⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and get: Ad-free listening Full-length bonus episodes Power Rankings after every race Historical race reviews & more exclusive extras! Don't forget! You can also gift a Late Braking Patreon subscription—perfect for loved ones or your own wish list. Choose anything from 1 month up to a full year of top-notch F1 content: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/latebrakingf1/gift⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Connect with Late Braking: You can find us on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X (Twitter)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Come hang out with us and thousands of fellow F1 fans in our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Discord⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ server and get involved in lively everyday & race weekend chats! Get in touch any time at podcast@latebraking.co.uk Learn more about your ad choices. Visit podcastchoices.com/adchoices

Exodus 17:1-16

The first part of testing is over and the vibes are iffy. F1's new regulations have given drivers and racers a bit of annoyance to say the least. Max Verstappen has some choice words. They also look at the new results for Cadillac, Audi and much more. Meg and Spanners also ask the big questons coming out of testing and what we can expect in round two.(00:00) Intro(05:13) Max Hates it Here(16:06) Expectations for Red Bull(24:12) Aston Martin is having a tough time(35:26) Audi and cadillac are back of the pack(45:22) Will the vibes improve for new regulations?(51:06) Will Mercedes have any consequences?(59:13) Will the starts be weird?Host: Megan SchusterGuest: Spanners ReadySenior Producer: Steve Ahlman Learn more about your ad choices. Visit podcastchoices.com/adchoices

Preseason testing in Bahrain is done — and the 2026 F1 season is already throwing up surprises

Time to practice your Spanish out loud! Today we'll learn about the number one way to solidify a new language in your brain — and we'll get to put it into practice right away. If you follow this one tip, you'll be fluent in Spanish faster than you ever thought possible. Let's put everything we've learned so far into Spanish practice, out loud. Practice all of today's Spanish for free at LCSPodcast.com/5

#883 Show Notes: https://wetflyswing.com/883   Presented by: Patagonia If you've ever wondered why some anglers seem to always be in the right spot at the right time, this episode digs straight into that idea. In this conversation with Simon Chu, we talk about New Zealand fly fishing, spring creeks in Montana, and why slowing down and walking often reveal what boat fishing hides. Simon spends his seasons split between hemispheres, guiding and testing gear in some of the most demanding conditions on the planet. We get into Patagonia waders, sight fishing big browns, and the mindset shift that comes from hunting individual fish instead of covering water. Show Notes: https://wetflyswing.com/883    

Scott talks with Mark Gebert from Verizon about something that sits at the heart of every reliable enterprise network: testing. Automation is moving fast in the telco world, but automation without testing is just an accident waiting to happen. They unpack what makes enterprise service provisioning so complex—multi-vendor networks, optical and IP gear, security functions,... Read more »

In today's episode, I'm sharing a major shift I see happening in the digital space and how it completely changed the way I think about offers, coaching, and scalability. Information alone isn't enough anymore. People want speed, simplicity, and implementation. I walk you through the realizations that led us to overhaul our entire offer suite, why done-for-you is becoming the future, and how this shift can help you grow faster without burning out.

Scott talks with Mark Gebert from Verizon about something that sits at the heart of every reliable enterprise network: testing. Automation is moving fast in the telco world, but automation without testing is just an accident waiting to happen. They unpack what makes enterprise service provisioning so complex—multi-vendor networks, optical and IP gear, security functions,... Read more »

In this installment of our Payroll Brass Tax podcast series, Mike Mahoney (shareholder, Morristown/New York) is joined by Stephen Kenney (associate, Dallas) and Stephen Riga (counsel, Minneapolis/Indianapolis) to discuss how nondiscrimination testing rules under Internal Revenue Code Sections 125, 105(h), and 129 affect payroll and tax reporting for cafeteria plans, self-insured health plans, and dependent care assistance programs. Mike, who is the chair of the Employment Tax Group, Stephen, and Stephen review who qualifies as a highly compensated or key employee, the consequences of testing failures, and critical timing considerations, as well as offer practical guidance on prevention strategies, coordinating with benefits administration, and ensuring accurate Form W-2 reporting.

Ashley & Jacqui Derrick   Ashley Derrick & Jacqui Derrick/Workright, LLC The Drug Lady is an important team member for any business wishing to create or maintain a Drug Free Workplace. Our “Drug Lady” is powered by two  amazing ladies- a mother-daughter team who have been working together for almost 30 years. Jacqui Derrick developed […]

What if chronic inflammation—not aging itself—is the real reason energy fades, disease risk rises, and resilience declines as the years go by? Today's conversation challenges the idea that inflammation is just something that "comes with age." Instead, we explore how targeted science, smart biology, and informed choices can dramatically reshape how you age. Chronic inflammation is the silent driver behind many of the diseases we associate with aging—cardiovascular disease, neurodegeneration, metabolic dysfunction, autoimmune disorders, and even cancer. Yet most people don't understand why inflammation persists, how it accelerates biological aging, or what actually works to control it at the cellular level. Samuel Shepherd is an award-winning physicist, inventor, and biomedical innovator with multiple patents in applied health science. After surviving a rare and aggressive bone-marrow cancer, Sam redirected his scientific expertise toward understanding inflammation at its molecular roots. He is the inventor behind ValAsta, the only patented glycosidic form of astaxanthin designed to improve cellular uptake and therapeutic impact. In this episode, you'll learn why inflammation is a root cause of aging, how bioavailability determines whether supplements help or fail,  what makes targeted antioxidants fundamentally different and practical steps to reduce inflammatory load and promote healthspan extension. Episode TimelIne 00:00 — Welcome & Episode Framing Why chronic inflammation sits at the root of cardiovascular disease, diabetes, neurodegeneration, cancer, and autoimmunity 03:20 — Introducing Samuel Shepherd & the Oxidative Stress Problem Sam's scientific background and focus on cellular-level antioxidant interventions 05:00 — A Personal Turning Point: Polycythemia Vera A rare bone-marrow cancer diagnosis and the limitations of conventional treatment 07:40 — Cancer Resistance in Nature Why some species rarely develop cancer and how this led to astaxanthin research 10:30 — From Algae Stress to Astaxanthin Production How Haematococcus pluvialis produces powerful antioxidants under stress 14:10 — How Inflammation Really Works Reactive oxygen species, antioxidant balance, and why some antioxidants backfire 17:30 — Bioavailability Breakthroughs Why molecular structure and glycosidic attachment determine effectiveness 23:30 — Aging, Micronutrients & Enzyme Decline The role of minerals and antioxidant enzymes as we age 29:50 — Practical Steps for Reducing Inflammation as we age Testing, targeted supplementation, and proactive longevity strategies Connect with Dr. Shepherd Facebook: https://www.facebook.com/ValastaHome   YouTube: https://www.youtube.com/@ValAsta Website: www.ValAsta.net Gift for Listeners: Title: 10% Off ValAsta – Take Control of Your Health URL: https://www.valasta.net Promo Code: PODCASTS-VALASTA Connect with Dr. Lockitch Download your Guide to Nature's Colouful Antioxidants for Eye, Heart, Brain and Skin Wellness  Connect with Dr. Gillian Lockitch at askdrgill@gmail.com to request a phone conversation or zoom call   Join the Growing Older Living Younger Facebook Community here    Share the Growing Older Living Younger podcast link for anyone you care about and invite them to subscribe to the podcast  

The state is clawing back more than $3-million dollars from a nearly $5 million grant it awarded Cleveland for a program that would help rid old houses of lead. The Ohio Department of Development administered the grant as part of the Lead Safe Ohio Program. It would pay up to $15,000 to remove old windows and doors, a major source of lead paint that chips and flakes and exposes occupants to lead poisoning. Lead can cause permanent neurological damage in children, and Cleveland has been working for years to remove lead from its older housing stock and the city's health director says there is actually promising news: Testing has shown for the second straight year a reduction in lead levels for kids. The story begins our discussion of the week's top news on the Friday “Sound of Ideas Reporters Roundtable.” Cuyahoga County Executive Chris Ronayne wants to take control of finances for the county sheriff's department as overtime costs there soar. The sheriff has said he'll sue if that happens. Cuyahoga County prosecutors argued before the Ohio Supreme Court on Feb.11 that a murder conviction is appropriate for the man who struck and killed Cleveland Johnny Tetrick as the firefighter was responding to an accident on I-90. Leander Bissell was convicted of murder, but an appeals court reduced it to involuntary manslaughter. Bissell struck Tetrick as he drove around stopped traffic at an accident scene. A federal judge yesterday denied the Trump administration's request to pause a ruling that allows Haitians in the U.S. under Temporary Protected Status to maintain that status. Thousands of Haitians with such protection live in Springfield. The administration's appeal continues. The Trump administration announced yesterday it was ending its immigration surge in Minneapolis. Border czar Tom Homan called Operation Metro Surge a success. Two U.S. citizens were killed, and widespread protests gripped the city. Homan credited coordination with local law enforcement as a factor in the operation's success. Protests continue across the country, including locally, where Thursday students at Cleveland Heights High School staged a long-planned walk out to show solidarity with immigrant families impacted by Immigration and Customs Enforcement. Many of those participating have direct ties to immigrant communities and want schools to be safe spaces. This week, Akron became the latest city to oppose proposed bills in the Ohio legislature that would require local police to help with federal immigration enforcement. The College of Wooster is cutting staff in response to shrinking enrollment. President Anne McCall announced that the school is laying off 22 non-faculty staff. It's almost time for public schools to submit their budget forecasts to the state for approval and the districts in Cleveland and Akron say they'll need to make significant cuts over the next several years, despite already going through consolidation and collecting more money from taxpayers with levies. More than half of the public school districts in Ohio, part of a coalition called Vouchers Hurt Ohio, are suing the state over how it funds schools, diverting money to vouchers for private schools. Lawmakers who approve of the vouchers say they allow families to have education choice. A new bill introduced in Columbus would allow the state to yank funding from districts that sue. Guests: -Abigail Bottar, Reporter, Ideastream Public Media -Conor Morris, Education Reporter, Ideastream Public Media -Karen Kasler, Statehouse News Bureau Chief, Ohio Public Radio/TV

On this episode Lane talks about driving 2 vintage 911s, Art says Negra Modelo with a strong accent, and we are looking forward to Seabright Motors this coming weekend. All this and more on this episode of the DWA! Podcast.

Have you been told you have histamine intolerance… so now you're cutting out everything—leftovers, fermented foods, bone broth, even avocado—because you read they're histamine high foods… and you're still reacting? Or maybe you don't have an “official” diagnosis… but you've got classic histamine intolerance symptoms like anxiety, reflux, bloating, eczema, dizziness, insomnia, or weird food reactions… and you're wondering if histamine is the problem? Or you've tried all the things to treat high histamine - low histamine diet, histamine intolerance supplements like DAO, even a full-on histamine intolerance treatment plan from another practitione - but you still have histamine symptoms, so you're asking: why won't my histamine calm down? If you said “yes” to any of these questions, then this episode is for you. Today we're continuing the Real Root Cause Series—where I take conditions that get treated like the root cause… and I show you what's actually driving them. Because knowing a fake root cause vs. a real root cause is the difference between managing symptoms forever… or finally getting your life back. Today's topic is: histamine. In this episode, I'm breaking down:What histamine intolerance really is The most common histamine intolerance symptoms you're probably not connecting to histamineWhy a low histamine diet can become a cyclical trap that never heals youThe 6 real root causes that keep histamine high (even when you “eat perfectly”)And how to choose the right testing and support to find and reverse those root causes And one more thing—because I'm doing something fun for this whole series: visuals. If you're a visual learner and want the flow chart that goes with this episode, go to betterbellytherapies.com/root and download the graphics. Because when you stop trying to avoid high histamine food like they're your enemy… and start fixing what's making your body unable to handle histamine - that's when you get your life back. TIMESTAMPS:00:00 - Introduction to Histamine Intolerance 00:55 - Real Root Cause Series Overview 01:16 - Understanding Histamine Intolerance 05:40 - Common Symptoms of Histamine Intolerance 08:43 - Challenges with Low Histamine Diets 09:51 - Six Root Causes of High Histamine 23:22 - Testing and Diagnosis 25:32 - Better Belly Blueprint Program 27:36 - Conclusion and Next Steps EPISODES MENTIONED:232// Is Sodium Deficiency Causing Your Bloating and Constipation?267// The Best Food Sensitivity Test for You, with Vibrant Wellness

Russia Blocks WhatsApp, Promotes Surveillance-Prone ‘National Messenger’ MAX, Soaring Memory Prices Accelerate Corporate PC Purchases, and Anthropic Significantly Upgrades Free Tier of Claude Chatbot. MP3 Please SUBSCRIBE HERE for free or get DTNS Live ad-free. A special thanks to all our supporters–without you, none of this would be possible. If you enjoy what you seeContinue reading "Apple Delays Major AI Siri Redesign Due to Testing Issues – DTH"

Welcome to Nerd Alert, a series of special episodes bridging the gap between marketing academia and practitioners. We're breaking down highly involved, complex research into plain language and takeaways any marketer can use. In this episode, Elena and Rob explore what makes ads memorable over time, not just minutes after viewing. They reveal how emotion, brand relevance, and AI are reshaping how marketers should think about ad recall and creative testing. Topics covered: [01:00] "Long-Term Ad Memorability: Understanding and Generating Memorable Ads"[02:00] Why short-term recall is a poor proxy for advertising effectiveness[04:00] Emotion as the strongest driver of long-term memory[05:00] How brand relevance affects ad memorability[06:00] AI model Henry predicts and generates more memorable ads[07:00] Practical takeaways for marketers on creative testing  To learn more, visit marketingarchitects.com/podcast or subscribe to our newsletter at marketingarchitects.com/newsletter.  Resources: Khosla, A., Ranjan, A., Torralba, A., Oliva, A., & colleagues. (2024). Long-term ad memorability: Understanding and generating memorable ads. Adobe Research and collaborating universities. Get more research-backed marketing strategies by subscribing to The Marketing Architects on Apple Podcasts, Spotify, or wherever you listen to podcasts. 

“I do not believe we should be testing to test. We have to know, is this test going to change management and is it going to make a difference,” says pediatric allergist-immunologist Dr. Zachary Rubin. His knack for providing that sort of straightforward guidance explains why Dr. Rubin has become a trusted voice on allergies, asthma, and vaccines for his millions of followers on social media platforms. It's also why we couldn't ask for a better guide for our discussion on the rise in allergies, asthma, and immune-related conditions in children, and how families can navigate the quickly evolving science and rampant misinformation in the space. On this episode of Raise the Line, we also preview Dr. Rubin's new book, All About Allergies, in which he breaks down dozens of conditions and diseases, offering clear explanations and practical treatment options for families. Join host Lindsey Smith for this super informative conversation in which Dr. Rubin shares his thoughts on a wide range of topics including: What's behind the rise in allergic and immune-related conditions.Tips for managing misinformation, myths and misunderstandings. How digital platforms can be leveraged to strengthen public health.How to build back public trust in medicine.Mentioned in this episode:All About Allergies bookBench to Bedside PodcastInstagramTikTokYouTube Channel If you like this podcast, please share it on your social channels. You can also subscribe to the series and check out all of our episodes at www.osmosis.org/podcast

Welcome to the Indian Summer Sessions. The world's greatest podcast has placed its feet upon the warpath of joy, disclosure, and insight. Not ours, of course. That would be unseemly. We will instead place the Indian war bonnet upon our oddly-shaped heads and chat to interesting people. And ride Indian motorcycles. And summer the shit out of ourselves. ​ Also, if you're as hooked on MotoGP as Borrie check out his new friend Connor and the unique packages he offers at On Track Experiences.

This podcast features Gabriele Corso and Jeremy Wohlwend, co-founders of Boltz and authors of the Boltz Manifesto, discussing the rapid evolution of structural biology models from AlphaFold to their own open-source suite, Boltz-1 and Boltz-2. The central thesis is that while single-chain protein structure prediction is largely “solved” through evolutionary hints, the next frontier lies in modeling complex interactions (protein-ligand, protein-protein) and generative protein design, which Boltz aims to democratize via open-source foundations and scalable infrastructure.Full Video PodOn YouTube!Timestamps* 00:00 Introduction to Benchmarking and the “Solved” Protein Problem* 06:48 Evolutionary Hints and Co-evolution in Structure Prediction* 10:00 The Importance of Protein Function and Disease States* 15:31 Transitioning from AlphaFold 2 to AlphaFold 3 Capabilities* 19:48 Generative Modeling vs. Regression in Structural Biology* 25:00 The “Bitter Lesson” and Specialized AI Architectures* 29:14 Development Anecdotes: Training Boltz-1 on a Budget* 32:00 Validation Strategies and the Protein Data Bank (PDB)* 37:26 The Mission of Boltz: Democratizing Access and Open Source* 41:43 Building a Self-Sustaining Research Community* 44:40 Boltz-2 Advancements: Affinity Prediction and Design* 51:03 BoltzGen: Merging Structure and Sequence Prediction* 55:18 Large-Scale Wet Lab Validation Results* 01:02:44 Boltz Lab Product Launch: Agents and Infrastructure* 01:13:06 Future Directions: Developpability and the “Virtual Cell”* 01:17:35 Interacting with Skeptical Medicinal ChemistsKey SummaryEvolution of Structure Prediction & Evolutionary Hints* Co-evolutionary Landscapes: The speakers explain that breakthrough progress in single-chain protein prediction relied on decoding evolutionary correlations where mutations in one position necessitate mutations in another to conserve 3D structure.* Structure vs. Folding: They differentiate between structure prediction (getting the final answer) and folding (the kinetic process of reaching that state), noting that the field is still quite poor at modeling the latter.* Physics vs. Statistics: RJ posits that while models use evolutionary statistics to find the right “valley” in the energy landscape, they likely possess a “light understanding” of physics to refine the local minimum.The Shift to Generative Architectures* Generative Modeling: A key leap in AlphaFold 3 and Boltz-1 was moving from regression (predicting one static coordinate) to a generative diffusion approach that samples from a posterior distribution.* Handling Uncertainty: This shift allows models to represent multiple conformational states and avoid the “averaging” effect seen in regression models when the ground truth is ambiguous.* Specialized Architectures: Despite the “bitter lesson” of general-purpose transformers, the speakers argue that equivariant architectures remain vastly superior for biological data due to the inherent 3D geometric constraints of molecules.Boltz-2 and Generative Protein Design* Unified Encoding: Boltz-2 (and BoltzGen) treats structure and sequence prediction as a single task by encoding amino acid identities into the atomic composition of the predicted structure.* Design Specifics: Instead of a sequence, users feed the model blank tokens and a high-level “spec” (e.g., an antibody framework), and the model decodes both the 3D structure and the corresponding amino acids.* Affinity Prediction: While model confidence is a common metric, Boltz-2 focuses on affinity prediction—quantifying exactly how tightly a designed binder will stick to its target.Real-World Validation and Productization* Generalized Validation: To prove the model isn't just “regurgitating” known data, Boltz tested its designs on 9 targets with zero known interactions in the PDB, achieving nanomolar binders for two-thirds of them.* Boltz Lab Infrastructure: The newly launched Boltz Lab platform provides “agents” for protein and small molecule design, optimized to run 10x faster than open-source versions through proprietary GPU kernels.* Human-in-the-Loop: The platform is designed to convert skeptical medicinal chemists by allowing them to run parallel screens and use their intuition to filter model outputs.TranscriptRJ [00:05:35]: But the goal remains to, like, you know, really challenge the models, like, how well do these models generalize? And, you know, we've seen in some of the latest CASP competitions, like, while we've become really, really good at proteins, especially monomeric proteins, you know, other modalities still remain pretty difficult. So it's really essential, you know, in the field that there are, like, these efforts to gather, you know, benchmarks that are challenging. So it keeps us in line, you know, about what the models can do or not.Gabriel [00:06:26]: Yeah, it's interesting you say that, like, in some sense, CASP, you know, at CASP 14, a problem was solved and, like, pretty comprehensively, right? But at the same time, it was really only the beginning. So you can say, like, what was the specific problem you would argue was solved? And then, like, you know, what is remaining, which is probably quite open.RJ [00:06:48]: I think we'll steer away from the term solved, because we have many friends in the community who get pretty upset at that word. And I think, you know, fairly so. But the problem that was, you know, that a lot of progress was made on was the ability to predict the structure of single chain proteins. So proteins can, like, be composed of many chains. And single chain proteins are, you know, just a single sequence of amino acids. And one of the reasons that we've been able to make such progress is also because we take a lot of hints from evolution. So the way the models work is that, you know, they sort of decode a lot of hints. That comes from evolutionary landscapes. So if you have, like, you know, some protein in an animal, and you go find the similar protein across, like, you know, different organisms, you might find different mutations in them. And as it turns out, if you take a lot of the sequences together, and you analyze them, you see that some positions in the sequence tend to evolve at the same time as other positions in the sequence, sort of this, like, correlation between different positions. And it turns out that that is typically a hint that these two positions are close in three dimension. So part of the, you know, part of the breakthrough has been, like, our ability to also decode that very, very effectively. But what it implies also is that in absence of that co-evolutionary landscape, the models don't quite perform as well. And so, you know, I think when that information is available, maybe one could say, you know, the problem is, like, somewhat solved. From the perspective of structure prediction, when it isn't, it's much more challenging. And I think it's also worth also differentiating the, sometimes we confound a little bit, structure prediction and folding. Folding is the more complex process of actually understanding, like, how it goes from, like, this disordered state into, like, a structured, like, state. And that I don't think we've made that much progress on. But the idea of, like, yeah, going straight to the answer, we've become pretty good at.Brandon [00:08:49]: So there's this protein that is, like, just a long chain and it folds up. Yeah. And so we're good at getting from that long chain in whatever form it was originally to the thing. But we don't know how it necessarily gets to that state. And there might be intermediate states that it's in sometimes that we're not aware of.RJ [00:09:10]: That's right. And that relates also to, like, you know, our general ability to model, like, the different, you know, proteins are not static. They move, they take different shapes based on their energy states. And I think we are, also not that good at understanding the different states that the protein can be in and at what frequency, what probability. So I think the two problems are quite related in some ways. Still a lot to solve. But I think it was very surprising at the time, you know, that even with these evolutionary hints that we were able to, you know, to make such dramatic progress.Brandon [00:09:45]: So I want to ask, why does the intermediate states matter? But first, I kind of want to understand, why do we care? What proteins are shaped like?Gabriel [00:09:54]: Yeah, I mean, the proteins are kind of the machines of our body. You know, the way that all the processes that we have in our cells, you know, work is typically through proteins, sometimes other molecules, sort of intermediate interactions. And through that interactions, we have all sorts of cell functions. And so when we try to understand, you know, a lot of biology, how our body works, how disease work. So we often try to boil it down to, okay, what is going right in case of, you know, our normal biological function and what is going wrong in case of the disease state. And we boil it down to kind of, you know, proteins and kind of other molecules and their interaction. And so when we try predicting the structure of proteins, it's critical to, you know, have an understanding of kind of those interactions. It's a bit like seeing the difference between... Having kind of a list of parts that you would put it in a car and seeing kind of the car in its final form, you know, seeing the car really helps you understand what it does. On the other hand, kind of going to your question of, you know, why do we care about, you know, how the protein falls or, you know, how the car is made to some extent is that, you know, sometimes when something goes wrong, you know, there are, you know, cases of, you know, proteins misfolding. In some diseases and so on, if we don't understand this folding process, we don't really know how to intervene.RJ [00:11:30]: There's this nice line in the, I think it's in the Alpha Fold 2 manuscript, where they sort of discuss also like why we even hopeful that we can target the problem in the first place. And then there's this notion that like, well, four proteins that fold. The folding process is almost instantaneous, which is a strong, like, you know, signal that like, yeah, like we should, we might be... able to predict that this very like constrained thing that, that the protein does so quickly. And of course that's not the case for, you know, for, for all proteins. And there's a lot of like really interesting mechanisms in the cells, but yeah, I remember reading that and thought, yeah, that's somewhat of an insightful point.Gabriel [00:12:10]: I think one of the interesting things about the protein folding problem is that it used to be actually studied. And part of the reason why people thought it was impossible, it used to be studied as kind of like a classical example. Of like an MP problem. Uh, like there are so many different, you know, type of, you know, shapes that, you know, this amino acid could take. And so, this grows combinatorially with the size of the sequence. And so there used to be kind of a lot of actually kind of more theoretical computer science thinking about and studying protein folding as an MP problem. And so it was very surprising also from that perspective, kind of seeing. Machine learning so clear, there is some, you know, signal in those sequences, through evolution, but also through kind of other things that, you know, us as humans, we're probably not really able to, uh, to understand, but that is, models I've, I've learned.Brandon [00:13:07]: And so Andrew White, we were talking to him a few weeks ago and he said that he was following the development of this and that there were actually ASICs that were developed just to solve this problem. So, again, that there were. There were many, many, many millions of computational hours spent trying to solve this problem before AlphaFold. And just to be clear, one thing that you mentioned was that there's this kind of co-evolution of mutations and that you see this again and again in different species. So explain why does that give us a good hint that they're close by to each other? Yeah.RJ [00:13:41]: Um, like think of it this way that, you know, if I have, you know, some amino acid that mutates, it's going to impact everything around it. Right. In three dimensions. And so it's almost like the protein through several, probably random mutations and evolution, like, you know, ends up sort of figuring out that this other amino acid needs to change as well for the structure to be conserved. Uh, so this whole principle is that the structure is probably largely conserved, you know, because there's this function associated with it. And so it's really sort of like different positions compensating for, for each other. I see.Brandon [00:14:17]: Those hints in aggregate give us a lot. Yeah. So you can start to look at what kinds of information about what is close to each other, and then you can start to look at what kinds of folds are possible given the structure and then what is the end state.RJ [00:14:30]: And therefore you can make a lot of inferences about what the actual total shape is. Yeah, that's right. It's almost like, you know, you have this big, like three dimensional Valley, you know, where you're sort of trying to find like these like low energy states and there's so much to search through. That's almost overwhelming. But these hints, they sort of maybe put you in. An area of the space that's already like, kind of close to the solution, maybe not quite there yet. And, and there's always this question of like, how much physics are these models learning, you know, versus like, just pure like statistics. And like, I think one of the thing, at least I believe is that once you're in that sort of approximate area of the solution space, then the models have like some understanding, you know, of how to get you to like, you know, the lower energy, uh, low energy state. And so maybe you have some, some light understanding. Of physics, but maybe not quite enough, you know, to know how to like navigate the whole space. Right. Okay.Brandon [00:15:25]: So we need to give it these hints to kind of get into the right Valley and then it finds the, the minimum or something. Yeah.Gabriel [00:15:31]: One interesting explanation about our awful free works that I think it's quite insightful, of course, doesn't cover kind of the entirety of, of what awful does that is, um, they're going to borrow from, uh, Sergio Chinico for MIT. So he sees kind of awful. Then the interesting thing about awful is God. This very peculiar architecture that we have seen, you know, used, and this architecture operates on this, you know, pairwise context between amino acids. And so the idea is that probably the MSA gives you this first hint about what potential amino acids are close to each other. MSA is most multiple sequence alignment. Exactly. Yeah. Exactly. This evolutionary information. Yeah. And, you know, from this evolutionary information about potential contacts, then is almost as if the model is. of running some kind of, you know, diastro algorithm where it's sort of decoding, okay, these have to be closed. Okay. Then if these are closed and this is connected to this, then this has to be somewhat closed. And so you decode this, that becomes basically a pairwise kind of distance matrix. And then from this rough pairwise distance matrix, you decode kind of theBrandon [00:16:42]: actual potential structure. Interesting. So there's kind of two different things going on in the kind of coarse grain and then the fine grain optimizations. Interesting. Yeah. Very cool.Gabriel [00:16:53]: Yeah. You mentioned AlphaFold3. So maybe we have a good time to move on to that. So yeah, AlphaFold2 came out and it was like, I think fairly groundbreaking for this field. Everyone got very excited. A few years later, AlphaFold3 came out and maybe for some more history, like what were the advancements in AlphaFold3? And then I think maybe we'll, after that, we'll talk a bit about the sort of how it connects to Bolt. But anyway. Yeah. So after AlphaFold2 came out, you know, Jeremy and I got into the field and with many others, you know, the clear problem that, you know, was, you know, obvious after that was, okay, now we can do individual chains. Can we do interactions, interaction, different proteins, proteins with small molecules, proteins with other molecules. And so. So why are interactions important? Interactions are important because to some extent that's kind of the way that, you know, these machines, you know, these proteins have a function, you know, the function comes by the way that they interact with other proteins and other molecules. Actually, in the first place, you know, the individual machines are often, as Jeremy was mentioning, not made of a single chain, but they're made of the multiple chains. And then these multiple chains interact with other molecules to give the function to those. And on the other hand, you know, when we try to intervene of these interactions, think about like a disease, think about like a, a biosensor or many other ways we are trying to design the molecules or proteins that interact in a particular way with what we would call a target protein or target. You know, this problem after AlphaVol2, you know, became clear, kind of one of the biggest problems in the field to, to solve many groups, including kind of ours and others, you know, started making some kind of contributions to this problem of trying to model these interactions. And AlphaVol3 was, you know, was a significant advancement on the problem of modeling interactions. And one of the interesting thing that they were able to do while, you know, some of the rest of the field that really tried to try to model different interactions separately, you know, how protein interacts with small molecules, how protein interacts with other proteins, how RNA or DNA have their structure, they put everything together and, you know, train very large models with a lot of advances, including kind of changing kind of systems. Some of the key architectural choices and managed to get a single model that was able to set this new state-of-the-art performance across all of these different kind of modalities, whether that was protein, small molecules is critical to developing kind of new drugs, protein, protein, understanding, you know, interactions of, you know, proteins with RNA and DNAs and so on.Brandon [00:19:39]: Just to satisfy the AI engineers in the audience, what were some of the key architectural and data, data changes that made that possible?Gabriel [00:19:48]: Yeah, so one critical one that was not necessarily just unique to AlphaFold3, but there were actually a few other teams, including ours in the field that proposed this, was moving from, you know, modeling structure prediction as a regression problem. So where there is a single answer and you're trying to shoot for that answer to a generative modeling problem where you have a posterior distribution of possible structures and you're trying to sample this distribution. And this achieves two things. One is it starts to allow us to try to model more dynamic systems. As we said, you know, some of these structures can actually take multiple structures. And so, you know, you can now model that, you know, through kind of modeling the entire distribution. But on the second hand, from more kind of core modeling questions, when you move from a regression problem to a generative modeling problem, you are really tackling the way that you think about uncertainty in the model in a different way. So if you think about, you know, I'm undecided between different answers, what's going to happen in a regression model is that, you know, I'm going to try to make an average of those different kind of answers that I had in mind. When you have a generative model, what you're going to do is, you know, sample all these different answers and then maybe use separate models to analyze those different answers and pick out the best. So that was kind of one of the critical improvement. The other improvement is that they significantly simplified, to some extent, the architecture, especially of the final model that takes kind of those pairwise representations and turns them into an actual structure. And that now looks a lot more like a more traditional transformer than, you know, like a very specialized equivariant architecture that it was in AlphaFold3.Brandon [00:21:41]: So this is a bitter lesson, a little bit.Gabriel [00:21:45]: There is some aspect of a bitter lesson, but the interesting thing is that it's very far from, you know, being like a simple transformer. This field is one of the, I argue, very few fields in applied machine learning where we still have kind of architecture that are very specialized. And, you know, there are many people that have tried to replace these architectures with, you know, simple transformers. And, you know, there is a lot of debate in the field, but I think kind of that most of the consensus is that, you know, the performance... that we get from the specialized architecture is vastly superior than what we get through a single transformer. Another interesting thing that I think on the staying on the modeling machine learning side, which I think it's somewhat counterintuitive seeing some of the other kind of fields and applications is that scaling hasn't really worked kind of the same in this field. Now, you know, models like AlphaFold2 and AlphaFold3 are, you know, still very large models.RJ [00:29:14]: in a place, I think, where we had, you know, some experience working in, you know, with the data and working with this type of models. And I think that put us already in like a good place to, you know, to produce it quickly. And, you know, and I would even say, like, I think we could have done it quicker. The problem was like, for a while, we didn't really have the compute. And so we couldn't really train the model. And actually, we only trained the big model once. That's how much compute we had. We could only train it once. And so like, while the model was training, we were like, finding bugs left and right. A lot of them that I wrote. And like, I remember like, I was like, sort of like, you know, doing like, surgery in the middle, like stopping the run, making the fix, like relaunching. And yeah, we never actually went back to the start. We just like kept training it with like the bug fixes along the way, which was impossible to reproduce now. Yeah, yeah, no, that model is like, has gone through such a curriculum that, you know, learned some weird stuff. But yeah, somehow by miracle, it worked out.Gabriel [00:30:13]: The other funny thing is that the way that we were training, most of that model was through a cluster from the Department of Energy. But that's sort of like a shared cluster that many groups use. And so we were basically training the model for two days, and then it would go back to the queue and stay a week in the queue. Oh, yeah. And so it was pretty painful. And so we actually kind of towards the end with Evan, the CEO of Genesis, and basically, you know, I was telling him a bit about the project and, you know, kind of telling him about this frustration with the compute. And so luckily, you know, he offered to kind of help. And so we, we got the help from Genesis to, you know, finish up the model. Otherwise, it probably would have taken a couple of extra weeks.Brandon [00:30:57]: Yeah, yeah.Brandon [00:31:02]: And then, and then there's some progression from there.Gabriel [00:31:06]: Yeah, so I would say kind of that, both one, but also kind of these other kind of set of models that came around the same time, were kind of approaching were a big leap from, you know, kind of the previous kind of open source models, and, you know, kind of really kind of approaching the level of AlphaVault 3. But I would still say that, you know, even to this day, there are, you know, some... specific instances where AlphaVault 3 works better. I think one common example is antibody antigen prediction, where, you know, AlphaVault 3 still seems to have an edge in many situations. Obviously, these are somewhat different models. They are, you know, you run them, you obtain different results. So it's, it's not always the case that one model is better than the other, but kind of in aggregate, we still, especially at the time.Brandon [00:32:00]: So AlphaVault 3 is, you know, still having a bit of an edge. We should talk about this more when we talk about Boltzgen, but like, how do you know one is, one model is better than the other? Like you, so you, I make a prediction, you make a prediction, like, how do you know?Gabriel [00:32:11]: Yeah, so easily, you know, the, the great thing about kind of structural prediction and, you know, once we're going to go into the design space of designing new small molecule, new proteins, this becomes a lot more complex. But a great thing about structural prediction is that a bit like, you know, CASP was doing, basically the way that you can evaluate them is that, you know, you train... You know, you train a model on a structure that was, you know, released across the field up until a certain time. And, you know, one of the things that we didn't talk about that was really critical in all this development is the PDB, which is the Protein Data Bank. It's this common resources, basically common database where every biologist publishes their structures. And so we can, you know, train on, you know, all the structures that were put in the PDB until a certain date. And then... And then we basically look for recent structures, okay, which structures look pretty different from anything that was published before, because we really want to try to understand generalization.Brandon [00:33:13]: And then on this new structure, we evaluate all these different models. And so you just know when AlphaFold3 was trained, you know, when you're, you intentionally trained to the same date or something like that. Exactly. Right. Yeah.Gabriel [00:33:24]: And so this is kind of the way that you can somewhat easily kind of compare these models, obviously, that assumes that, you know, the training. You've always been very passionate about validation. I remember like DiffDoc, and then there was like DiffDocL and DocGen. You've thought very carefully about this in the past. Like, actually, I think DocGen is like a really funny story that I think, I don't know if you want to talk about that. It's an interesting like... Yeah, I think one of the amazing things about putting things open source is that we get a ton of feedback from the field. And, you know, sometimes we get kind of great feedback of people. Really like... But honestly, most of the times, you know, to be honest, that's also maybe the most useful feedback is, you know, people sharing about where it doesn't work. And so, you know, at the end of the day, it's critical. And this is also something, you know, across other fields of machine learning. It's always critical to set, to do progress in machine learning, set clear benchmarks. And as, you know, you start doing progress of certain benchmarks, then, you know, you need to improve the benchmarks and make them harder and harder. And this is kind of the progression of, you know, how the field operates. And so, you know, the example of DocGen was, you know, we published this initial model called DiffDoc in my first year of PhD, which was sort of like, you know, one of the early models to try to predict kind of interactions between proteins, small molecules, that we bought a year after AlphaFold2 was published. And now, on the one hand, you know, on these benchmarks that we were using at the time, DiffDoc was doing really well, kind of, you know, outperforming kind of some of the traditional physics-based methods. But on the other hand, you know, when we started, you know, kind of giving these tools to kind of many biologists, and one example was that we collaborated with was the group of Nick Polizzi at Harvard. We noticed, started noticing that there was this clear, pattern where four proteins that were very different from the ones that we're trained on, the models was, was struggling. And so, you know, that seemed clear that, you know, this is probably kind of where we should, you know, put our focus on. And so we first developed, you know, with Nick and his group, a new benchmark, and then, you know, went after and said, okay, what can we change? And kind of about the current architecture to improve this pattern and generalization. And this is the same that, you know, we're still doing today, you know, kind of, where does the model not work, you know, and then, you know, once we have that benchmark, you know, let's try to, through everything we, any ideas that we have of the problem.RJ [00:36:15]: And there's a lot of like healthy skepticism in the field, which I think, you know, is, is, is great. And I think, you know, it's very clear that there's a ton of things, the models don't really work well on, but I think one thing that's probably, you know, undeniable is just like the pace of, pace of progress, you know, and how, how much better we're getting, you know, every year. And so I think if you, you know, if you assume, you know, any constant, you know, rate of progress moving forward, I think things are going to look pretty cool at some point in the future.Gabriel [00:36:42]: ChatGPT was only three years ago. Yeah, I mean, it's wild, right?RJ [00:36:45]: Like, yeah, yeah, yeah, it's one of those things. Like, you've been doing this. Being in the field, you don't see it coming, you know? And like, I think, yeah, hopefully we'll, you know, we'll, we'll continue to have as much progress we've had the past few years.Brandon [00:36:55]: So this is maybe an aside, but I'm really curious, you get this great feedback from the, from the community, right? By being open source. My question is partly like, okay, yeah, if you open source and everyone can copy what you did, but it's also maybe balancing priorities, right? Where you, like all my customers are saying. I want this, there's all these problems with the model. Yeah, yeah. But my customers don't care, right? So like, how do you, how do you think about that? Yeah.Gabriel [00:37:26]: So I would say a couple of things. One is, you know, part of our goal with Bolts and, you know, this is also kind of established as kind of the mission of the public benefit company that we started is to democratize the access to these tools. But one of the reasons why we realized that Bolts needed to be a company, it couldn't just be an academic project is that putting a model on GitHub is definitely not enough to get, you know, chemists and biologists, you know, across, you know, both academia, biotech and pharma to use your model to, in their therapeutic programs. And so a lot of what we think about, you know, at Bolts beyond kind of the, just the models is thinking about all the layers. The layers that come on top of the models to get, you know, from, you know, those models to something that can really enable scientists in the industry. And so that goes, you know, into building kind of the right kind of workflows that take in kind of, for example, the data and try to answer kind of directly that those problems that, you know, the chemists and the biologists are asking, and then also kind of building the infrastructure. And so this to say that, you know, even with models fully open. You know, we see a ton of potential for, you know, products in the space and the critical part about a product is that even, you know, for example, with an open source model, you know, running the model is not free, you know, as we were saying, these are pretty expensive model and especially, and maybe we'll get into this, you know, these days we're seeing kind of pretty dramatic inference time scaling of these models where, you know, the more you run them, the better the results are. But there, you know, you see. You start getting into a point that compute and compute costs becomes a critical factor. And so putting a lot of work into building the right kind of infrastructure, building the optimizations and so on really allows us to provide, you know, a much better service potentially to the open source models. That to say, you know, even though, you know, with a product, we can provide a much better service. I do still think, and we will continue to put a lot of our models open source because the critical kind of role. I think of open source. Models is, you know, helping kind of the community progress on the research and, you know, from which we, we all benefit. And so, you know, we'll continue to on the one hand, you know, put some of our kind of base models open source so that the field can, can be on top of it. And, you know, as we discussed earlier, we learn a ton from, you know, the way that the field uses and builds on top of our models, but then, you know, try to build a product that gives the best experience possible to scientists. So that, you know, like a chemist or a biologist doesn't need to, you know, spin off a GPU and, you know, set up, you know, our open source model in a particular way, but can just, you know, a bit like, you know, I, even though I am a computer scientist, machine learning scientist, I don't necessarily, you know, take a open source LLM and try to kind of spin it off. But, you know, I just maybe open a GPT app or a cloud code and just use it as an amazing product. We kind of want to give the same experience. So this front world.Brandon [00:40:40]: I heard a good analogy yesterday that a surgeon doesn't want the hospital to design a scalpel, right?Brandon [00:40:48]: So just buy the scalpel.RJ [00:40:50]: You wouldn't believe like the number of people, even like in my short time, you know, between AlphaFold3 coming out and the end of the PhD, like the number of people that would like reach out just for like us to like run AlphaFold3 for them, you know, or things like that. Just because like, you know, bolts in our case, you know, just because it's like. It's like not that easy, you know, to do that, you know, if you're not a computational person. And I think like part of the goal here is also that, you know, we continue to obviously build the interface with computational folks, but that, you know, the models are also accessible to like a larger, broader audience. And then that comes from like, you know, good interfaces and stuff like that.Gabriel [00:41:27]: I think one like really interesting thing about bolts is that with the release of it, you didn't just release a model, but you created a community. Yeah. Did that community, it grew very quickly. Did that surprise you? And like, what is the evolution of that community and how is that fed into bolts?RJ [00:41:43]: If you look at its growth, it's like very much like when we release a new model, it's like, there's a big, big jump, but yeah, it's, I mean, it's been great. You know, we have a Slack community that has like thousands of people on it. And it's actually like self-sustaining now, which is like the really nice part because, you know, it's, it's almost overwhelming, I think, you know, to be able to like answer everyone's questions and help. It's really difficult, you know. The, the few people that we were, but it ended up that like, you know, people would answer each other's questions and like, sort of like, you know, help one another. And so the Slack, you know, has been like kind of, yeah, self, self-sustaining and that's been, it's been really cool to see.RJ [00:42:21]: And, you know, that's, that's for like the Slack part, but then also obviously on GitHub as well. We've had like a nice, nice community. You know, I think we also aspire to be even more active on it, you know, than we've been in the past six months, which has been like a bit challenging, you know, for us. But. Yeah, the community has been, has been really great and, you know, there's a lot of papers also that have come out with like new evolutions on top of bolts and it's surprised us to some degree because like there's a lot of models out there. And I think like, you know, sort of people converging on that was, was really cool. And, you know, I think it speaks also, I think, to the importance of like, you know, when, when you put code out, like to try to put a lot of emphasis and like making it like as easy to use as possible and something we thought a lot about when we released the code base. You know, it's far from perfect, but, you know.Brandon [00:43:07]: Do you think that that was one of the factors that caused your community to grow is just the focus on easy to use, make it accessible? I think so.RJ [00:43:14]: Yeah. And we've, we've heard it from a few people over the, over the, over the years now. And, you know, and some people still think it should be a lot nicer and they're, and they're right. And they're right. But yeah, I think it was, you know, at the time, maybe a little bit easier than, than other things.Gabriel [00:43:29]: The other thing part, I think led to, to the community and to some extent, I think, you know, like the somewhat the trust in the community. Kind of what we, what we put out is the fact that, you know, it's not really been kind of, you know, one model, but, and maybe we'll talk about it, you know, after Boltz 1, you know, there were maybe another couple of models kind of released, you know, or open source kind of soon after. We kind of continued kind of that open source journey or at least Boltz 2, where we are not only improving kind of structure prediction, but also starting to do affinity predictions, understanding kind of the strength of the interactions between these different models, which is this critical component. critical property that you often want to optimize in discovery programs. And then, you know, more recently also kind of protein design model. And so we've sort of been building this suite of, of models that come together, interact with one another, where, you know, kind of, there is almost an expectation that, you know, we, we take very at heart of, you know, always having kind of, you know, across kind of the entire suite of different tasks, the best or across the best. model out there so that it's sort of like our open source tool can be kind of the go-to model for everybody in the, in the industry. I really want to talk about Boltz 2, but before that, one last question in this direction, was there anything about the community which surprised you? Were there any, like, someone was doing something and you're like, why would you do that? That's crazy. Or that's actually genius. And I never would have thought about that.RJ [00:45:01]: I mean, we've had many contributions. I think like some of the. Interesting ones, like, I mean, we had, you know, this one individual who like wrote like a complex GPU kernel, you know, for part of the architecture on a piece of, the funny thing is like that piece of the architecture had been there since AlphaFold 2, and I don't know why it took Boltz for this, you know, for this person to, you know, to decide to do it, but that was like a really great contribution. We've had a bunch of others, like, you know, people figuring out like ways to, you know, hack the model to do something. They click peptides, like, you know, there's, I don't know if there's any other interesting ones come to mind.Gabriel [00:45:41]: One cool one, and this was, you know, something that initially was proposed as, you know, as a message in the Slack channel by Tim O'Donnell was basically, he was, you know, there are some cases, especially, for example, we discussed, you know, antibody-antigen interactions where the models don't necessarily kind of get the right answer. What he noticed is that, you know, the models were somewhat stuck into predicting kind of the antibodies. And so he basically ran the experiments in this model, you can condition, basically, you can give hints. And so he basically gave, you know, random hints to the model, basically, okay, you should bind to this residue, you should bind to the first residue, or you should bind to the 11th residue, or you should bind to the 21st residue, you know, basically every 10 residues scanning the entire antigen.Brandon [00:46:33]: Residues are the...Gabriel [00:46:34]: The amino acids. The amino acids, yeah. So the first amino acids. The 11 amino acids, and so on. So it's sort of like doing a scan, and then, you know, conditioning the model to predict all of them, and then looking at the confidence of the model in each of those cases and taking the top. And so it's sort of like a very somewhat crude way of doing kind of inference time search. But surprisingly, you know, for antibody-antigen prediction, it actually kind of helped quite a bit. And so there's some, you know, interesting ideas that, you know, obviously, as kind of developing the model, you say kind of, you know, wow. This is why would the model, you know, be so dumb. But, you know, it's very interesting. And that, you know, leads you to also kind of, you know, start thinking about, okay, how do I, can I do this, you know, not with this brute force, but, you know, in a smarter way.RJ [00:47:22]: And so we've also done a lot of work on that direction. And that speaks to, like, the, you know, the power of scoring. We're seeing that a lot. I'm sure we'll talk about it more when we talk about BullsGen. But, you know, our ability to, like, take a structure and determine that that structure is, like... Good. You know, like, somewhat accurate. Whether that's a single chain or, like, an interaction is a really powerful way of improving, you know, the models. Like, sort of like, you know, if you can sample a ton and you assume that, like, you know, if you sample enough, you're likely to have, like, you know, the good structure. Then it really just becomes a ranking problem. And, you know, now we're, you know, part of the inference time scaling that Gabby was talking about is very much that. It's like, you know, the more we sample, the more we, like, you know, the ranking model. The ranking model ends up finding something it really likes. And so I think our ability to get better at ranking, I think, is also what's going to enable sort of the next, you know, next big, big breakthroughs. Interesting.Brandon [00:48:17]: But I guess there's a, my understanding, there's a diffusion model and you generate some stuff and then you, I guess, it's just what you said, right? Then you rank it using a score and then you finally... And so, like, can you talk about those different parts? Yeah.Gabriel [00:48:34]: So, first of all, like, the... One of the critical kind of, you know, beliefs that we had, you know, also when we started working on Boltz 1 was sort of like the structure prediction models are somewhat, you know, our field version of some foundation models, you know, learning about kind of how proteins and other molecules interact. And then we can leverage that learning to do all sorts of other things. And so with Boltz 2, we leverage that learning to do affinity predictions. So understanding kind of, you know, if I give you this protein, this molecule. How tightly is that interaction? For Boltz 1, what we did was taking kind of that kind of foundation models and then fine tune it to predict kind of entire new proteins. And so the way basically that that works is sort of like instead of for the protein that you're designing, instead of fitting in an actual sequence, you fit in a set of blank tokens. And you train the models to, you know, predict both the structure of kind of that protein. The structure also, what the different amino acids of that proteins are. And so basically the way that Boltz 1 operates is that you feed a target protein that you may want to kind of bind to or, you know, another DNA, RNA. And then you feed the high level kind of design specification of, you know, what you want your new protein to be. For example, it could be like an antibody with a particular framework. It could be a peptide. It could be many other things. And that's with natural language or? And that's, you know, basically, you know, prompting. And we have kind of this sort of like spec that you specify. And, you know, you feed kind of this spec to the model. And then the model translates this into, you know, a set of, you know, tokens, a set of conditioning to the model, a set of, you know, blank tokens. And then, you know, basically the codes as part of the diffusion models, the codes. It's a new structure and a new sequence for your protein. And, you know, basically, then we take that. And as Jeremy was saying, we are trying to score it and, you know, how good of a binder it is to that original target.Brandon [00:50:51]: You're using basically Boltz to predict the folding and the affinity to that molecule. So and then that kind of gives you a score? Exactly.Gabriel [00:51:03]: So you use this model to predict the folding. And then you do two things. One is that you predict the structure and with something like Boltz2, and then you basically compare that structure with what the model predicted, what Boltz2 predicted. And this is sort of like in the field called consistency. It's basically you want to make sure that, you know, the structure that you're predicting is actually what you're trying to design. And that gives you a much better confidence that, you know, that's a good design. And so that's the first filtering. And the second filtering that we did as part of kind of the Boltz2 pipeline that was released is that we look at the confidence that the model has in the structure. Now, unfortunately, kind of going to your question of, you know, predicting affinity, unfortunately, confidence is not a very good predictor of affinity. And so one of the things that we've actually done a ton of progress, you know, since we released Boltz2.Brandon [00:52:03]: And kind of we have some new results that we are going to kind of announce soon is kind of, you know, the ability to get much better hit rates when instead of, you know, trying to rely on confidence of the model, we are actually directly trying to predict the affinity of that interaction. Okay. Just backing up a minute. So your diffusion model actually predicts not only the protein sequence, but also the folding of it. Exactly.Gabriel [00:52:32]: And actually, you can... One of the big different things that we did compared to other models in the space, and, you know, there were some papers that had already kind of done this before, but we really scaled it up was, you know, basically somewhat merging kind of the structure prediction and the sequence prediction into almost the same task. And so the way that Boltz2 works is that you are basically the only thing that you're doing is predicting the structure. So the only sort of... Supervision is we give you a supervision on the structure, but because the structure is atomic and, you know, the different amino acids have a different atomic composition, basically from the way that you place the atoms, we also understand not only kind of the structure that you wanted, but also the identity of the amino acid that, you know, the models believed was there. And so we've basically, instead of, you know, having these two supervision signals, you know, one discrete, one continuous. That somewhat, you know, don't interact well together. We sort of like build kind of like an encoding of, you know, sequences in structures that allows us to basically use exactly the same supervision signal that we were using to Boltz2 that, you know, you know, largely similar to what AlphaVol3 proposed, which is very scalable. And we can use that to design new proteins. Oh, interesting.RJ [00:53:58]: Maybe a quick shout out to Hannes Stark on our team who like did all this work. Yeah.Gabriel [00:54:04]: Yeah, that was a really cool idea. I mean, like looking at the paper and there's this is like encoding or you just add a bunch of, I guess, kind of atoms, which can be anything, and then they get sort of rearranged and then basically plopped on top of each other so that and then that encodes what the amino acid is. And there's sort of like a unique way of doing this. It was that was like such a really such a cool, fun idea.RJ [00:54:29]: I think that idea was had existed before. Yeah, there were a couple of papers.Gabriel [00:54:33]: Yeah, I had proposed this and and Hannes really took it to the large scale.Brandon [00:54:39]: In the paper, a lot of the paper for Boltz2Gen is dedicated to actually the validation of the model. In my opinion, all the people we basically talk about feel that this sort of like in the wet lab or whatever the appropriate, you know, sort of like in real world validation is the whole problem or not the whole problem, but a big giant part of the problem. So can you talk a little bit about the highlights? From there, that really because to me, the results are impressive, both from the perspective of the, you know, the model and also just the effort that went into the validation by a large team.Gabriel [00:55:18]: First of all, I think I should start saying is that both when we were at MIT and Thomas Yacolas and Regina Barzillai's lab, as well as at Boltz, you know, we are not a we're not a biolab and, you know, we are not a therapeutic company. And so to some extent, you know, we were first forced to, you know, look outside of, you know, our group, our team to do the experimental validation. One of the things that really, Hannes, in the team pioneer was the idea, OK, can we go not only to, you know, maybe a specific group and, you know, trying to find a specific system and, you know, maybe overfit a bit to that system and trying to validate. But how can we test this model? So. Across a very wide variety of different settings so that, you know, anyone in the field and, you know, printing design is, you know, such a kind of wide task with all sorts of different applications from therapeutic to, you know, biosensors and many others that, you know, so can we get a validation that is kind of goes across many different tasks? And so he basically put together, you know, I think it was something like, you know, 25 different. You know, academic and industry labs that committed to, you know, testing some of the designs from the model and some of this testing is still ongoing and, you know, giving results kind of back to us in exchange for, you know, hopefully getting some, you know, new great sequences for their task. And he was able to, you know, coordinate this, you know, very wide set of, you know, scientists and already in the paper, I think we. Shared results from, I think, eight to 10 different labs kind of showing results from, you know, designing peptides, designing to target, you know, ordered proteins, peptides targeting disordered proteins, which are results, you know, of designing proteins that bind to small molecules, which are results of, you know, designing nanobodies and across a wide variety of different targets. And so that's sort of like. That gave to the paper a lot of, you know, validation to the model, a lot of validation that was kind of wide.Brandon [00:57:39]: And so those would be therapeutics for those animals or are they relevant to humans as well? They're relevant to humans as well.Gabriel [00:57:45]: Obviously, you need to do some work into, quote unquote, humanizing them, making sure that, you know, they have the right characteristics to so they're not toxic to humans and so on.RJ [00:57:57]: There are some approved medicine in the market that are nanobodies. There's a general. General pattern, I think, in like in trying to design things that are smaller, you know, like it's easier to manufacture at the same time, like that comes with like potentially other challenges, like maybe a little bit less selectivity than like if you have something that has like more hands, you know, but the yeah, there's this big desire to, you know, try to design many proteins, nanobodies, small peptides, you know, that just are just great drug modalities.Brandon [00:58:27]: Okay. I think we were left off. We were talking about validation. Validation in the lab. And I was very excited about seeing like all the diverse validations that you've done. Can you go into some more detail about them? Yeah. Specific ones. Yeah.RJ [00:58:43]: The nanobody one. I think we did. What was it? 15 targets. Is that correct? 14. 14 targets. Testing. So we typically the way this works is like we make a lot of designs. All right. On the order of like tens of thousands. And then we like rank them and we pick like the top. And in this case, and was 15 right for each target and then we like measure sort of like the success rates, both like how many targets we were able to get a binder for and then also like more generally, like out of all of the binders that we designed, how many actually proved to be good binders. Some of the other ones I think involved like, yeah, like we had a cool one where there was a small molecule or design a protein that binds to it. That has a lot of like interesting applications, you know, for example. Like Gabri mentioned, like biosensing and things like that, which is pretty cool. We had a disordered protein, I think you mentioned also. And yeah, I think some of those were some of the highlights. Yeah.Gabriel [00:59:44]: So I would say that the way that we structure kind of some of those validations was on the one end, we have validations across a whole set of different problems that, you know, the biologists that we were working with came to us with. So we were trying to. For example, in some of the experiments, design peptides that would target the RACC, which is a target that is involved in metabolism. And we had, you know, a number of other applications where we were trying to design, you know, peptides or other modalities against some other therapeutic relevant targets. We designed some proteins to bind small molecules. And then some of the other testing that we did was really trying to get like a more broader sense. So how does the model work, especially when tested, you know, on somewhat generalization? So one of the things that, you know, we found with the field was that a lot of the validation, especially outside of the validation that was on specific problems, was done on targets that have a lot of, you know, known interactions in the training data. And so it's always a bit hard to understand, you know, how much are these models really just regurgitating kind of what they've seen or trying to imitate. What they've seen in the training data versus, you know, really be able to design new proteins. And so one of the experiments that we did was to take nine targets from the PDB, filtering to things where there is no known interaction in the PDB. So basically the model has never seen kind of this particular protein bound or a similar protein bound to another protein. So there is no way that. The model from its training set can sort of like say, okay, I'm just going to kind of tweak something and just imitate this particular kind of interaction. And so we took those nine proteins. We worked with adaptive CRO and basically tested, you know, 15 mini proteins and 15 nanobodies against each one of them. And the very cool thing that we saw was that on two thirds of those targets, we were able to, from this 15 design, get nanomolar binders, nanomolar, roughly speaking, just a measure of, you know, how strongly kind of the interaction is, roughly speaking, kind of like a nanomolar binder is approximately the kind of binding strength or binding that you need for a therapeutic. Yeah. So maybe switching directions a bit. Bolt's lab was just announced this week or was it last week? Yeah. This is like your. First, I guess, product, if that's if you want to call it that. Can you talk about what Bolt's lab is and yeah, you know, what you hope that people take away from this? Yeah.RJ [01:02:44]: You know, as we mentioned, like I think at the very beginning is the goal with the product has been to, you know, address what the models don't on their own. And there's largely sort of two categories there. I'll split it in three. The first one. It's one thing to predict, you know, a single interaction, for example, like a single structure. It's another to like, you know, very effectively search a space, a design space to produce something of value. What we found, like sort of building on this product is that there's a lot of steps involved, you know, in that there's certainly need to like, you know, accompany the user through, you know, one of those steps, for example, is like, you know, the creation of the target itself. You know, how do we make sure that the model has like a good enough understanding of the target? So we can like design something and there's all sorts of tricks, you know, that you can do to improve like a particular, you know, structure prediction. And so that's sort of like, you know, the first stage. And then there's like this stage of like, you know, designing and searching the space efficiently. You know, for something like BullsGen, for example, like you, you know, you design many things and then you rank them, for example, for small molecule process, a little bit more complicated. We actually need to also make sure that the molecules are synthesizable. And so the way we do that is that, you know, we have a generative model that learns. To use like appropriate building blocks such that, you know, it can design within a space that we know is like synthesizable. And so there's like, you know, this whole pipeline really of different models involved in being able to design a molecule. And so that's been sort of like the first thing we call them agents. We have a protein agent and we have a small molecule design agents. And that's really like at the core of like what powers, you know, the BullsLab platform.Brandon [01:04:22]: So these agents, are they like a language model wrapper or they're just like your models and you're just calling them agents? A lot. Yeah. Because they, they, they sort of perform a function on behalf of.RJ [01:04:33]: They're more of like a, you know, a recipe, if you wish. And I think we use that term sort of because of, you know, sort of the complex pipelining and automation, you know, that goes into like all this plumbing. So that's the first part of the product. The second part is the infrastructure. You know, we need to be able to do this at very large scale for any one, you know, group that's doing a design campaign. Let's say you're designing, you know, I'd say a hundred thousand possible candidates. Right. To find the good one that is, you know, a very large amount of compute, you know, for small molecules, it's on the order of like a few seconds per designs for proteins can be a bit longer. And so, you know, ideally you want to do that in parallel, otherwise it's going to take you weeks. And so, you know, we've put a lot of effort into like, you know, our ability to have a GPU fleet that allows any one user, you know, to be able to do this kind of like large parallel search.Brandon [01:05:23]: So you're amortizing the cost over your users. Exactly. Exactly.RJ [01:05:27]: And, you know, to some degree, like it's whether you. Use 10,000 GPUs for like, you know, a minute is the same cost as using, you know, one GPUs for God knows how long. Right. So you might as well try to parallelize if you can. So, you know, a lot of work has gone, has gone into that, making it very robust, you know, so that we can have like a lot of people on the platform doing that at the same time. And the third one is, is the interface and the interface comes in, in two shapes. One is in form of an API and that's, you know, really suited for companies that want to integrate, you know, these pipelines, these agents.RJ [01:06:01]: So we're already partnering with, you know, a few distributors, you know, that are gonna integrate our API. And then the second part is the user interface. And, you know, we, we've put a lot of thoughts also into that. And this is when I, I mentioned earlier, you know, this idea of like broadening the audience. That's kind of what the, the user interface is about. And we've built a lot of interesting features in it, you know, for example, for collaboration, you know, when you have like potentially multiple medicinal chemists or. We're going through the results and trying to pick out, okay, like what are the molecules that we're going to go and test in the lab? It's powerful for them to be able to, you know, for example, each provide their own ranking and then do consensus building. And so there's a lot of features around launching these large jobs, but also around like collaborating on analyzing the results that we try to solve, you know, with that part of the platform. So Bolt's lab is sort of a combination of these three objectives into like one, you know, sort of cohesive platform. Who is this accessible to? Everyone. You do need to request access today. We're still like, you know, sort of ramping up the usage, but anyone can request access. If you are an academic in particular, we, you know, we provide a fair amount of free credit so you can play with the platform. If you are a startup or biotech, you may also, you know, reach out and we'll typically like actually hop on a call just to like understand what you're trying to do and also provide a lot of free credit to get started. And of course, also with larger companies, we can deploy this platform in a more like secure environment. And so that's like more like customizing. You know, deals that we make, you know, with the partners, you know, and that's sort of the ethos of Bolt. I think this idea of like servicing everyone and not necessarily like going after just, you know, the really large enterprises. And that starts from the open source, but it's also, you know, a key design principle of the product itself.Gabriel [01:07:48]: One thing I was thinking about with regards to infrastructure, like in the LLM space, you know, the cost of a token has gone down by I think a factor of a thousand or so over the last three years, right? Yeah. And is it possible that like essentially you can exploit economies of scale and infrastructure that you can make it cheaper to run these things yourself than for any person to roll their own system? A hundred percent. Yeah.RJ [01:08:08]: I mean, we're already there, you know, like running Bolts on our platform, especially on a large screen is like considerably cheaper than it would probably take anyone to put the open source model out there and run it. And on top of the infrastructure, like one of the things that we've been working on is accelerating the models. So, you know. Our small molecule screening pipeline is 10x faster on Bolts Lab than it is in the open source, you know, and that's also part of like, you know, building a product, you know, of something that scales really well. And we really wanted to get to a point where like, you know, we could keep prices very low in a way that it would be a no-brainer, you know, to use Bolts through our platform.Gabriel [01:08:52]: How do you think about validation of your like agentic systems? Because, you know, as you were saying earlier. Like we're AlphaFold style models are really good at, let's say, monomeric, you know, proteins where you have, you know, co-evolution data. But now suddenly the whole point of this is to design something which doesn't have, you know, co-evolution data, something which is really novel. So now you're basically leaving the domain that you thought was, you know, that you know you are good at. So like, how do you validate that?RJ [01:09:22]: Yeah, I like every complete, but there's obviously, you know, a ton of computational metrics. That we rely on, but those are only take you so far. You really got to go to the lab, you know, and test, you know, okay, with this method A and this method B, how much better are we? You know, how much better is my, my hit rate? How stronger are my binders? Also, it's not just about hit rate. It's also about how good the binders are. And there's really like no way, nowhere around that. I think we're, you know, we've really ramped up the amount of experimental validation that we do so that we like really track progress, you know, as scientifically sound, you know. Yeah. As, as possible out of this, I think.Gabriel [01:10:00]: Yeah, no, I think, you know, one thing that is unique about us and maybe companies like us is that because we're not working on like maybe a couple of therapeutic pipelines where, you know, our validation would be focused on those. We, when we do an experimental validation, we try to test it across tens of targets. And so that on the one end, we can get a much more statistically significant result and, and really allows us to make progress. From the methodological side without being, you know, steered by, you know, overfitting on any one particular system. And of course we choose, you know, w

Final liveries are launched, cars are now OFFICIALLY testing and we are seeing actual coverage of it all too. F1 is finally back! Join our Patreon for exclusive content and access to the Discord Back of the Grid on Patreon Enter the prediction league on our site; BackofTheGrid.com Join our F1 Fantasy League now! Join our F1 fantasy league on Grid Rival F1 today! Join our IndyCar fantasy league on Grid Rival IndyCar today! Follow us on X or Facebook for the latest news; X | Facebook Back of the Grid is a Formula 1 podcast hosted by 3 passionate F1 fans. Tom , Chris & Stu discuss the weekly goings on of the sport, review and preview races and offer their thoughts up on all the talking points. New episodes released each Tuesday during the season! F1 | Formula 1 | F12026 | 2026 | Mercedes | Lewis Hamilton | McLaren | Charles Leclerc | Ferrari | Red Bull | Max Verstappen | Honda | Motorsport | Alexander Albon | Carlos Sainz | Lando Norris | Williams | George Russell | RB | Pierre Gasly | Aston Martin | Alpine | Fernando Alonso | Haas | Nico Hulkenberg | Oscar Piastri | Liam Lawson | Arvid Lindblad | Esteban Ocon | Oliver Bearman | Gabriel Bortoleto | Kimi Antonelli | Isack Hadjar | Franco Colapinto | Valtteri Bottas | Sergio Perez | Cadillac | Audi | Bahrain | Testing

In a podcast recorded at ITEXPO / MSP EXPO, Doug Green, Publisher of Technology Reseller News, spoke with Gregory Tellone, CEO of Cloud IBR, about simplifying disaster recovery (DR) testing and turning recoverability into a practical, recurring revenue opportunity for MSPs. Cloud IBR is a SaaS platform designed for organizations using Veeam backups. With a single click, the system provisions dedicated bare-metal cloud servers, installs operating systems, restores encrypted backup repositories, configures networking, VPN access, firewalls, and hands off a fully operational environment for either a live disaster or a scheduled recovery test. “Most backup products are great at backup,” Tellone explained. “The problem is knowing whether your backups are actually good and being able to test recovery easily.” The platform addresses a longstanding gap in the SMB market: the complexity and cost of maintaining secondary DR sites and conducting realistic recovery testing. Traditional DR requires duplicate infrastructure, bandwidth, replication management, and ongoing maintenance—often making full testing impractical. Cloud IBR automates that entire process in approximately 20 minutes of onboarding time, enabling monthly recovery testing by default and generating detailed PDF reports documenting every recovered server and recovery time objective (RTO). For MSPs, the opportunity is strategic. Starting at $299 per month, the service provides a low-barrier entry point into customer accounts while strengthening trust and expanding monthly recurring revenue. Tellone described it as a relationship builder: “It's always easier to sell to a customer than to a prospect. You start with something simple that works, and from there you grow.” With automated reporting suitable for cyber insurance applications and RFP responses, Cloud IBR transforms disaster recovery from a checkbox exercise into a demonstrable operational advantage. Visit https://cloudibr.com/

Send a textThe Apostle John gives an urgent warning to the Christians he has pastored and to us. Many false prophets and teachers have gone out into the world but we must not gullibly believe them but we must test them. The purpose of the test is to determine if they are from God or the spirit of antichrist. He gives two major theological test and some related social test. In this episode of Bible Insights, Wayne Conrad teaches on “Testing the Spirits” from 1 John 4:1–6. He explains that Christians live in an age of many persuasive voices—religious, cultural, and spiritual—and therefore must not be gullible nor cynical, but discerning. John's command is clear: “Do not believe every spirit, but test the spirits to see whether they are from God.”The reason for testing is that many false prophets have gone out into the world. False teaching is not rare, and it often sounds spiritual and convincing. Discernment, however, is not suspicion; it is love guarding the truth.Conrad highlights several key tests drawn from the text:1. The Christological Test The primary test concerns the identity of Jesus Christ. True teaching confesses that Jesus Christ has come in the flesh—fully God and fully man. Any teaching that denies or distorts Christ's incarnation, deity, humanity, or saving work reflects the spirit of the Antichrist. Redefining Jesus while using religious language is a mark of error.2. The Apostolic Test True doctrine aligns with the apostolic witness preserved in Scripture. Those who know God listen to the apostles' teaching; those shaped by the world reject it. Scripture—Old and New Testaments together—is the authoritative standard for testing all spiritual claims.3. The Reception Test The world often embraces false teaching and resists biblical truth. Popularity is not evidence of divine approval. Fidelity to apostolic truth, not success or charisma, is the true measure.4. The Goal and Fruit Test The Spirit of truth exalts Christ, produces worship of God, nurtures godliness, and brings assurance through the indwelling Holy Spirit. The spirit of error promotes self-centered independence, false ideas, and deception—even if it appears impressive or spectacular.The episode concludes by urging believers to:Examine doctrine carefully.Compare all teaching with Scripture.Evaluate whether a message reflects worldly thinking or biblical truth.The ultimate focus must remain on Jesus Christ—the incarnate Son of God and Savior. By grounding themselves in Scripture and holding firmly to the true confession of Christ, believers can walk in truth and avoid deception.Bible Insights with Wayne ConradContact: 8441 Hunnicut Rd Dallas, Texas 75228email: Att. Bible Insights Wayne Conradgsccdallas@gmail.com (Good Shepherd Church) Donation https://gsccdallas.orghttps://www.youtube.com/channel/UCJTZX6qasIrPmC1wQpben9ghttps://www.facebook.com/waconrad or gscchttps://www.sermonaudio.com/gsccSpirit, Truth and Grace MinistriesPhone # 214-324-9915 leave message with number for call backPsalms 119:105 Your word is a lamp for my feet, a light on my path.

Today, Josh is joined by Mythical Crew member Chase to taste test Doritos...NAKED (and blind). Leave us a voicemail at (833) DOG-POD1 Check out the video version of this podcast: youtube.com/@ahotdogisasandwich To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

We have IndyCars on track this week! Conor Daly checks into Speed Street to follow the results coming in from the two day Sebring test. He also weighs in on the latest news regarding Prema and the official announcement of the Grand Prix of Washington DC. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

What if wealth isn't about the product you choose, but the mindset you bring before you choose it?   In this Live Counterflow interview, Brandon sits down with Bob Regnerus, a coach, strategist, and long-time contributor in the Perry Marshall ecosystem, to explore why most financial and business strategies fail without alignment, conviction, and self-awareness.   Bob shares insights from over 30 years of coaching high-performing entrepreneurs, athletes, and leaders, revealing how purpose, natural advantage, and 80/20 leverage shape results long before tactics ever matter.   This conversation goes deep into: Why copying successful people often backfires How "clarity" can become a trap without action The difference between hard work and aligned hard work What your "financial nervous system" reveals about money decisions Why systems pressure people into default thinking, and how to step out How AI can amplify your strengths without replacing human intuition   Bob also explains the Purpose Factor framework, a tool that helps people remember who they are, identify what lights them up, and recognize blind spots that quietly sabotage progress.   If you're a business owner, entrepreneur, or leader who's tired of chasing tactics that don't fit, this episode will challenge how you think about money, growth, and leverage   00:00 Welcome to Wealth Wisdom Financial Podcast 00:05 Introducing Live Counterflow 00:50 Mindset Shifts and Financial Nervous System 01:13 Interview with Bob Ris: Coach, Mentor, Entrepreneur 01:29 Bob's Background and Achievements 05:49 The Importance of Purpose and Natural Advantage 13:26 Financial Systems and Personal Purpose 18:24 Purpose Factor and Self-Discovery 22:09 Applying 80/20 Principle in Life and Business 27:28 Discovering Your Marketing DNA 28:03 Embracing Your Natural Strengths 29:48 The Power of Experimentation 31:00 Understanding Your Purpose Factor 32:09 Commitment and Testing in Business 34:03 The 80/20 Principle in Action 35:30 How You Do One Thing is How You Do Everything 37:52 Aligning Business with Personal Values 45:12 Leveraging AI for Creativity and Efficiency 49:17 Purpose Factor Assessment and Its Benefits 52:51 Closing Remarks and Appreciations]   Watch on YouTube: https://youtu.be/EQS5mXEXyPQ 

Shannon Sharpe and Chad “Ochocinco” Johnson react to the Miami Dolphins and the Arizona Cardinals are both looking for trades for their quarterbacks, David Njoku is leaving the Cleveland Browns to become a free agent, and Will Campbell declined to speak to the media after losing the Super Bowl and much more! Subscribe to Nightcap presented by PrizePicks so you don’t miss out on any new drops! Download the PrizePicks app today and use code SHANNON to get $50 in lineups after you play your first $5 lineup! Visit https://prizepicks.onelink.me/LME0/NI...0:00 - Dolphins and Cardinals looking to trade Tua and Kyler Murray22:30 - Browns’ Njoku heading for free agency26:06 - Will Campbell declined to speak with media after brutal Super Bowl performance40:47 - 49ers Keon White shot early this morning47:01 - Fight broke out between Hornets and Pistons tonight48:32 - Mark Cuban and private group looking to buy back the Dallas Mavs51:12 - Jayson Tatum’s road to recovery54:59 - Winter Olympics officials address claims of Penis fillers1:02:26- Q & Aaayyy (Timestamps may vary based on advertisements.) #ClubSee omnystudio.com/listener for privacy information.

Healing after miscarriage requires compassion, nourishment and a deeper understanding of what the body goes through during and after loss. In this heartfelt episode, Needed co-founder, Julie Sawaya, opens up about her personal miscarriage journey and shares the nutrient support, functional testing and emotional tools that helped her rebuild strength and trust in her body. You will learn what to test, which nutrients matter most after loss, how partners can support recovery and how to begin preparing for a healthy future pregnancy. Julie's vulnerability and wisdom offer comfort and clarity for any mom navigating this tender season. Topics Covered In This Episode: Nutrient deficiencies after miscarriage Testing for iron and micronutrient levels How to replenish nutrient stores Preparing for conception after loss Emotional healing and self-compassion Show Notes: Follow @needed and @juliesawaya on Instagram Visit Needed's website to learn more Sign up for FREE perinatal nutrition consultation, sponsored by Needed. Eligible for those who have experienced a pregnancy loss and looking for added support. Click here to learn more about Dr. Elana Roumell's Doctor Mom Membership, a membership designed for moms who want to be their child's number one health advocate! Click here to learn more about Steph Greunke, RD's online nutrition program and community, Postpartum Reset, an intimate private community and online roadmap for any mama (or mama-to-be) who feels stuck, alone, and depleted and wants to learn how to thrive in motherhood. Listen to today's episode on our website Julie Sawaya is the co-founder and co-CEO of Needed, a perinatal nutrition company focused on optimal nourishment before, during, and after pregnancy. Needed delivers products, education, and nutrient testing backed by the latest clinical research and insights from their collective of perinatal health practitioners that regularly test nutrient and hormone levels to know what's optimal. Julie is a mama to a 1-year-old girl, and she is preparing to conceive again after a recent pregnancy loss. INTRODUCE YOURSELF to Steph and Dr. Elana on Instagram. They can't wait to meet you! @stephgreunke @drelanaroumell Please remember that the views and ideas presented on this podcast are for informational purposes only.  All information presented on this podcast is for informational purposes and not intended to serve as a substitute for the consultation, diagnosis, and/or medical treatment of a healthcare provider. Consult with your healthcare provider before starting any diet, supplement regimen, or to determine the appropriateness of the information shared on this podcast, or if you have any questions regarding your treatment plan.