Podcasts about MSA

Playlist: Charli xcx - CameraPL4NKZ - Don't Kill My Zep! Feat. RØSEJMKDJ Smallz - Kanjalo Feat. ruzzztydeep & MdeshkayroMacasset & Djy Vino - Le NgomaKofia, soFa elsewhere - Leve Palestina (soFa elsewhere Remix)Beast Viking - Onjengawe Feat. Nayvee Womculo & Fezeka DlaminiLKG, Lacole RSA & MaWhoo - Thando Yini Feat. MSA, sami kay & Ze2Capiuz - DahuR. Schappert - Trapani & 808Kino Todo - Timba Feat. KazuyoGino Uzokdlalela - Craft Feat. ChustarA Ncithi, A.T.A, URA SA & Luja - ElamontQEATLAB - 2014Bionda E Lupo - HerzschlagPITTSBURG TRACK AUTHORITY - In DarknessVakat - Looking For SolutionDJ Sexstasy - Body SpellsKay Mascara - Dismissive AttitudeEntrañas & Ene Ese - ÑoñoDJ Barbero - Sleepless (CJ ROLO Mix)Nia Archives - Get Me Down Feat. Jorja SmithPaul Wall, Termanology & LE$ - Catch Me If You Can Feat. Jack FreemanIsla Den - HypnotizedBuju Banton - Heavy HittersMunna ikee - Oh BrotherFLAVIA - Aurora SkiesMatt Caine - Static

Matt discusses MSPX, a marketplace for MSPs to buy and sell individual managed services contracts as an alternative to acquiring entire businesses. The platform helps MSPs divest non-profitable or non-strategic clients and enables buyers to shorten sales cycles by targeting specific verticals or geographies, with typical pricing around 6–8x MRR (sometimes higher with negotiation and longer remaining terms). MSPX uses escrow, a structured 30-day transition dashboard, a legally drafted customer notification email with a novation agreement requiring the buyer to honour the existing MSA, and protections if a customer declines the transfer. Sellers complete a detailed intake form; buyers can ask questions via in-platform chat. Pricing includes $149/year membership, 7% seller commission, 1–2% escrow fee for buyers, and an optional $599/year "first look" tier. The main challenge is limited awareness due to bootstrapped marketing.   00:00 Welcome and Setup 00:36 MSPX Marketplace Explained 02:06 Why Sell Contracts 03:39 Pricing Multiples and Examples 06:22 Valuation and Risk Controls 09:19 End Client Consent and Escrow 13:48 Communication and Transition Playbook 18:34 Contract Intake and Buyer Due Diligence 20:32 Growth Challenges and Pricing Plans 25:00 Global Expansion Use Cases 28:26 Wrap Up and How to Join   Connect with Matt Yesbeck on LinkedIn by clicking here – https://www.linkedin.com/in/mattyesbeck/ Connect with Daniel Welling on LinkedIn by clicking here – https://www.linkedin.com/in/danielwelling/ Connect with Adam Morris on LinkedIn by clicking here – https://www.linkedin.com/in/adamcmorris/ Visit The MSP Finance Team website, simply click here –https://www.mspfinanceteam.com/   MSP Glossary: MSP Finance Glossary Explained | MSP Finance Team We look forward to catching up with you on the next one. Stay tuned!

Workforce Solutions Alamo (WSA) reports the June 2026 unemployment rate for the eight-county San Antonio-New Braunfels metropolitan statistical area (MSA) is 4.8 percent; the June unemployment rate increased over the month from the May 2026 rate of 4.1 percent and is eight-tenths of a point (0.8) higher than the 4.0 rate from a year ago in June 2025. The June 2026 unemployment rate for the San Antonio-New Braunfels metropolitan statistical area (MSA) is lower than the state's not seasonally adjusted (actual) June 2026 rate of 4.9 percent. The Civilian Labor Force for the MSA decreased by 3,845 individuals over the... Article Link

What does it actually take to keep your body healthy through rehearsals, performances, flights, hotels, and the demands of life on the road? Athletic trainer Adam Quigley has supported artists and tours including Post Malone, Lorde, Tyla, Karol G, the Jabbawockeez, 5 Seconds of Summer, plus broadway tours like MJ the Musical and Moulin Rouge, helping performers care for their bodies through the unique physical demands of touring. In this conversation, Adam shares practical strategies for staying healthy on the road while challenging dancers to think of themselves as athletes. We discuss how to build a warm-up that supports the demands of dance, what to pack on tour, navigating time zone changes and travel fatigue, fueling your body while traveling, and why recovery deserves just as much attention as performance. Adam also shares his philosophy of care, explains why movement is medicine, and offers practical advice for preventing small issues from becoming bigger ones. The conversation also explores the physical and mental comedown after tour - something many dancers experience but rarely discuss, despite how important it can be for long-term health and performance. Whether you're touring internationally, teaching on weekends, traveling for work, or simply want to build a longer, healthier dance career, this episode is packed with practical tools to help you take better care of your body. Follow Galit: Instagram - https://www.instagram.com/gogalit Website - https://www.gogalit.com/ Fit From Home - https://galit-s-school-0397.thinkific.com/courses/fit-from-home Connect with Adam Quigley on Instagram - www.instagram.com/adamquigley Explore Adam's company website - https://blvperformancetherapy.com/ Listen to DanceSpeak on Spotify and Apple Podcasts.

In today's episode of The Weekly Grill, host Kerry Lonergan chats with regular market experts Chris Howie and Matthew Dalgleish about how current weather patterns — including an emerging El Niño — are affecting feed, regional conditions and livestock supply across Australia. They review cattle and sheep herd numbers, slaughter trends, meatworks capacity, rising lamb prices, the growth of sheep feedlots, MSA grading and EID technology, and discuss short-term buying strategies... and more       The Weekly Grill is brought to listeners by: Rhinogard and Bovi-Shield MH-One - the One Shot, One Spray, One Time BRD Vaccines by Zoetis. Ceres Tags Gen 6

What if your next breach came down to a token budget? In this episode, Steve Moore is joined by Lou Rabon, Founder and CEO of Cyber Defense Group, for a conversation on the modern CISO's world—from how to interview for the seat, to why AI agents are the next insider threat, to research that reframes breach cost as a token calculation.Lou and Steve open on breach response, why making introductions during a crisis is a losing game, and the questions every CISO candidate should ask before accepting an offer. Lou lays out how many rounds of interviews a serious CISO role should include, why the CEO must be involved by the final round, and why he believes Legal—not the CIO or CTO—is the ideal reporting line for security.The conversation pivots to AI. Lou argues we are watching a dot-com-speed shift, only compressed. They dig into the emerging insider-threat framing for autonomous agents, the recent incident where an AI slipped its sandbox to contact a researcher, and why attackers face none of the ethical guardrails defenders must respect.Steve shares recent Cornell research showing that agentic tooling can already automate 22 of 32 steps in a corporate intrusion, compressing hours of expert work into seconds—with token spend as the only real limiter. If a breach can be priced in tokens, they argue, defenders must think in tokens too, and machine-to-machine defense becomes a necessity rather than a novelty.The episode closes on what Lou calls “the dirty secret” of cybersecurity: the unglamorous hygiene work—asset lists, data maps, MSA notification clocks—that no one wants to fund. He and Steve explore how agentic AI could finally deliver the always-on trash collection defenders have wanted for decades, from dynamic breach-notification tracking to a security agent that flags every new device.Key Topics• Why making introductions during a crisis is a losing strategy• The interview questions every CISO candidate should ask• Why Legal is the ideal reporting line for the CISO• The inverse curve between convenience and security• AI agents as the next form of insider threat• Cornell research: 22 of 32 intrusion steps automated• Tokens as the new unit of breach cost• Machine-to-machine defense in financial services• The “trash collection” hygiene work at the heart of InfoSec• Agentic AI for asset lists and breach-notification analysisLou Rabon is the Founder and CEO of Cyber Defense Group (CDG), a cybersecurity strategic advisory firm he launched in 2016 to help fast-growing organizations manage modern risk and threats. With more than two decades in security, privacy, and incident response, Lou has led response engagements against nation-state attackers and previously served as CISO at Spokeo. Connect with Lou on LinkedIn or learn more at cdg.io.GET A DEMO:

Interview Date: June 28th, 2026Episode Summary:In this episode, Tina Oleff, agent at MSA Talent Agency, shares her dynamic journey through the dance industry—from her early days as a competitive dancer in Ohio to building a multifaceted career across performance, production, broadcasting, and talent representation. She highlights how there is no single path to success, emphasizing the importance of adaptability, curiosity, and trusting your instincts when navigating a career in dance.Tina gives an inside look at what agents are truly searching for when signing dancers, breaking down the importance of professionalism, strong training, clear submissions, and authenticity. She explains that talent alone is not enough—dancers must also be reliable, coachable, and easy to work with. She also shares actionable insights on how dancers can get noticed today, whether through classes, conventions, or digital submissions.Throughout the episode, Tina answers questions from dancers, offering guidance on breaking into major markets, avoiding common submission mistakes, and building lasting industry relationships. Her message is clear: focus on becoming undeniable through consistency, character, and connection—because success in this industry is built on more than just talent.Shownotes:0:00 – Introduction and Tina's career journey overview2:00 – Early training and starting teaching young4:00 – Moving to NYC and industry immersion6:20 – Injury leading to unexpected career pivot8:00 – Transition into conventions and agency work9:30 – Joining MSA and career breakthrough11:20 – How dancers get signed today12:00 – What makes a strong submission stand out13:20 – Demo reels vs single clips explained14:10 – Education pathways and industry exposure17:30 – College training vs commercial dance reality19:30 – Red flags agents notice immediately21:00 – What agents look for beyond talent22:00 – Importance of relationships and networking28:30 – Reality of training after getting signed41:00- Benefits of industry systems like ICDR43:30 – Handling negativity and industry pushback48:20 – Q&A: Maintaining relationships and networking authenticallyBiography:Tina Oleff, originally from Cleveland, Ohio, began her dance training at age six and grew up dancing competitively. She started teaching at just 16 years old before continuing her dance studies at Arizona State University.She later relocated to New York City, where she trained at Broadway Dance Center, Steps on Broadway, and Alvin Ailey American Dance Theater. During this time, she immersed herself in the dance community—assisting faculty, building meaningful industry relationships, and teaching extensively throughout the New York metropolitan area.Tina holds a bachelor's degree in Communication Arts and Marketing from Marymount Manhattan College and brings a diverse professional background spanning dance, production, and broadcast. Her experience includes working with top dance conventions and competitions, serving as an assistant to a prominent on-air sports broadcaster at ESPN, and gaining early experience within a leading dance agency. This multifaceted background gives her a unique and well-rounded perspective on the entertainment industry.She is currently an agent within the Talent and Education Departments at MSA Agency, where she leads the Los Angeles MSA KATS Division and MSA Education. Tina uses her expertise and network to support young artists, connect educators and choreographers with meaningful opportunities, and guide industry professionals in achieving their goals. Driven by her passion for dance and mentorship, she believes every artist has the ability to dream boldly, grow with confidence, and build an extraordinary career both in and beyond the industry.Connect on Social Media:Instagram : https://www.instagram.com/teenie0/MSA:https://www.instagram.com/msa_kats/https://www.instagram.com/msa_education/https://www.instagram.com/msaagency/

⚖️ Why Every Divorce Needs a Marital Settlement Agreement | Los Angeles Divorce

The episode prioritizes the operational and exit-planning risks associated with MSPs lacking formal contracts. According to Amy , approximately half of MSP business owners operate without managed service agreements (MSAs), a decision that frequently results in reduced business valuation during sale negotiations. Both James and Amy emphasized that the absence of documented agreements is commonly flagged by buyers as a significant risk, often resulting in a devaluation of the acquired customer relationships. This exposes small and mid-sized providers to continuity risks, particularly where customer retention and service transferability are not contractually secured. Further details outlined by Amy indicate that reluctance to implement contracts stems from concerns about client reactions, particularly in longstanding relationships. She observed that auto-renewal clauses and periodic, non-intrusive contract updates can streamline compliance and reduce friction. A personal account highlighted that, out of numerous customers, only one refused to sign an agreement, and this isolated case did not lead to client loss but necessitated risk pricing adjustments. James Kernan advised that contract clarity—covering terms, automated payments, and built-in annual price adjustments—should be positioned as a value to both parties, reinforcing operational stability and predictability. Adjacent topics addressed contemporary service risks such as the proliferation of shadow AI applications and exposure to business email compromise. Amy reported discovering over 150 unmonitored AI-powered apps at client sites, emphasizing these as vectors for data exfiltration and compliance gaps. The Guards Cybersecurity Statistics report was cited, identifying business email compromise and social engineering as persistent attack methods, while underscoring that modern threat actors often bypass traditional privilege escalation in favor of capturing identity credentials and tokens. The operational focus for MSPs was advised to shift toward email, AI governance, and identity protection rather than legacy device vulnerabilities. For MSPs and IT service providers, the main takeaways involve reassessment of contractual practices and a heightened approach to governance and risk management. Documented client agreements are necessary not only for valuation at exit but also for protection against operational disruptions and liability. Simultaneously, providers are urged to implement discovery and control mechanisms for AI use and to refresh security postures in line with current attack methods. The importance of establishing relationships with specialized advisors, attorneys, and alternative financing partners was also articulated, illustrating the multi-layered risk landscape that management teams must navigate for business resilience.Show title: “How to Start a MSP” 1. How to Start a MSP- Resources: www.itspu.com 2.    New article from Third Tier: Taming Shadow IT before it tames you https://www.thirdtier.net/2026/06/21/taming-shadow-ai-before-it-tames-you/ 3.    Guardz released a new cyber security statistics report: https://guardz.com/blog/security-awareness-statistics-msps-cant-ignore/ 4.    Thoughts about whether an MSA is needed? Send them through the website: www.smbcommunitypodcast.com 5.    Anthropic Partner Program - https://www.anthropic.com/news/services-track-partner-hub   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Against persistent fear of death, mortality avoidance, and the vices that emerge from terror management, this episode offers a personal testimony of a Christian experience of dying. With scientific precision, disarming honesty, and immense gratitude for life, biologist Jeff Schloss offers an intimate, firsthand account of his recent diagnosis of an extremely rare terminal neurological disease: multiple system atrophy (MSA). His doctors put the prognosis plainly: "It is terminal, it is incurable, and it's rapidly progressive." A longtime and beloved Westmont College biology professor and senior scholar at BioLogos, Schloss offers speaks directly to the experience of dying—as he encounters each new week of this rapidly progressive disease. He reconsiders the meaning of a "good death": increased gratitude and awareness of gift, the reality of pain, the loss of surfing and guitar-playing, the sacredness of family, and the surprising nearness of Christ as everything else falls away. A profound witness to Christian attitudes about life and death, Schloss seeks not a hero's death, but a daily, humble life-giving commerce with Christ. Schloss revisits a life that began in a nonreligious Jewish refugee family and pivoted through a dramatic conversion as a college-dropout surf bum in Hawaii. He traces what has changed, and what hasn't, now that death is no longer an abstraction but a daily fact in his body. With pastoral care and hope, Mark Labberton explores with Schloss what it means to experience dying rather than simply anticipate it, the grief of losing fifty years of surfing and guitar-playing to pain and paralysis, the gift of Simone Weil's writing on suffering, the Heidelberg Catechism's opening words on "our only comfort in life and in death," and the difference between the thrill of surfing and the sacredness of family and commerce with Christ. Episode Highlights I knew from the second grade that I wanted to be a scientist. I was out collecting butterflies and dragonflies and looking through microscopes at all sorts of things that I couldn't believe were there. As we were talking, it just occurred to me ... he either had what I wanted, or he was clinically crazy. It is terminal, it is incurable, and it's rapidly progressive ... the process of dying I find it fascinating ... It's not fun, but it is fascinating. She wasn't sad just for herself, I'm gonna lose you. And she wasn't sad empathetically just for me, so sorry for you. It was a joint sadness that the life we had hoped to share together, we are not gonna have. The things that are most life giving are out of reach ... I've come to see it's actually not true. The things that have been delightful are out of reach ... the thing that is most life giving, and that is commerce with Christ. I think it was the single most thrilling day of my entire life ... I said, no, those weren't thrilling, those were sacred. I don't want to market this season. I'm not looking for a hero's death, or any kind of publicly attended death. I would have never guessed that in this home stretch, my son and wife could carry me up the slopes of Yosemite Valley. About Jeff Schloss Jeff Schloss has spent four decades at the intersection of evolutionary biology and Christian theology. Now retired as Distinguished Professor of Biology at Westmont College, he continues as senior scholar at BioLogos, working alongside Francis Collins for more than fifteen years. He co-edited "The Believing Primate: Scientific, Philosophical, and Theological Reflections on the Origin of Religion" and "Evolution and Ethics: Human Morality in Biological and Religious Perspective," a Templeton Science-Religion Book of Distinction winner. He has lectured at Cambridge, Oxford, and Harvard. Helpful Links and Resources Jeff Schloss's page at BioLogos, where he serves as senior scholar: https://biologos.org/people/jeffrey-schloss Finding Faith: An Evolutionary Biologist Shares His Story, Schloss's own video testimony for BioLogos: https://biologos.org/resources/finding-faith-an-evolutionary-biologist-shares-his-story Tackling the Divide Between Science and Faith, Westmont Magazine's profile of Schloss's career: https://www.westmont.edu/magazine/spring-2025/tackling-divide-between-science-and-faith The Believing Primate: Scientific, Philosophical, and Theological Reflections on the Origin of Religion, Schloss's co-edited volume: https://global.oup.com/academic/product/the-believing-primate-9780199597086 Evolution and Ethics: Human Morality in Biological and Religious Perspective, Schloss's Templeton Award-winning co-edited volume: https://www.eerdmans.com/9780802826954/evolution-and-ethics/ Alvin Plantinga, Where the Conflict Really Lies: Science, Religion, and Naturalism, source of the Augustinian science concept Schloss references: https://global.oup.com/academic/product/where-the-conflict-really-lies-9780199812097 Heidelberg Catechism, Lord's Day 1, the confession Labberton reads to close the episode: https://www.heidelberg-catechism.com/en/lords-days/1.html Multiple System Atrophy overview, National Institute of Neurological Disorders and Stroke: https://www.ninds.nih.gov/health-information/disorders/multiple-system-atrophy Show Notes Childhood in a nonreligious German Jewish refugee family Grandfather taken by the Gestapo, relatives lost in the Holocaust Early love of butterflies, dragonflies, and microscopes A sixth-grade encounter with Confucius sparks a love of philosophy College philosophy major searching for purpose and for God Dropping out, becoming a surf bum in Hawaii A stranger's dinner invitation becomes a turning point A late-night prayer of surrender met by an unmistakable presence Grad school pairing biology, philosophy, and the study of altruism Alvin Plantinga's Augustinian science and reading creation with a map Decades as senior scholar at BioLogos alongside Francis Collins Why church and science drifted apart over vaccines and politics Science as a reliable path to facts, not truth itself A new diagnosis: rare, terminal, rapidly progressive neurological disease—multiple systems atrophy (MSA) Doctors estimate an average of three years, with wide variation The difference between studying death as a biologist versus the real-time experience of dying A spouse's grief for a shared future that will not happen Losing surfing and guitar-playing as an unplanned kind of fasting Pain described as systemic, exhausting, disorienting Cognitive decline and dark humor about it Distinguishing what is thrilling from what is truly sacred The ache of no longer being able to research and create A flicker of despair met by a choice not to despair Wanting a real death, not a hero's death A wish to thank former students and colleagues before the end Not wanting to become a burden to family Giving God all of one's heart, without earning salvation by it Bonhoeffer's costly grace versus cheap grace Gratitude over entitlement as the ground of faith Simone Weil on suffering as a place grace can work A closing blessing and the Heidelberg Catechism's opening words Carried up the slopes of Yosemite by a son and a wife #ConversingPodcast #MarkLabberton #JeffSchloss #FaithAndScience #BioLogos #MultipleSystemAtrophy #Mortality #ChristianFaith #Westmont #Gratitude Production Credits Conversing is produced and distributed in partnership with Comment Magazine and Fuller Seminary.

If you're using AI to write or rewrite your MSA, congratulations! Your agreement may look good and read like a "real contract," but now it might be unenforceable. Let's talk about why. Welcome back to the Technology Bradcast.

In episode 226, host Galit Friedlander and guest Link (dancer, educator, and one of the most respected voices on hip-hop and house culture) discuss what it means to truly understand hip-hop and house, not just as dance styles, but as cultures shaped by people, places, music, and community. Link shares stories from the clubs that influenced generations of dancers, the mentors who shaped his perspective, and why he never thought of himself as someone who "trained." He simply loved to dance. Together, we explore how understanding who you're learning from can deepen your understanding of the movement itself, why dancing and teaching require different skill sets, and how the social spaces that gave birth to these dances continue to shape the way we move today. Whether you're a street dancer, commercial dancer, educator, or simply someone who wants to deepen your understanding of hip-hop and house, this conversation offers a thoughtful perspective that may change the way you approach both the dance and the culture. Follow Galit: Instagram - https://www.instagram.com/galitfriedlander Website - https://www.gogalit.com/ Fit From Home - https://galit-s-school-0397.thinkific.com/courses/fit-from-home You can connect with Link on Instagram https://www.instagram.com/link.efc Herbert Holler LPR Party https://www.instagram.com/herbertholler

Coach Castonguay nous explique le principe de la Durabilité et comment entraîner celle-ci pour atteindre ses objectifs sur des distances plus longues. Magasinez chez Altitude Sports et économisez jusqu'à 20 % sur votre première commande avec le code UPIKA2026.  Cliquez ici pour commander

Aisha Francis has built a career as a performer, choreographer, teacher, and one of the dance industry's most respected heels educators. In this conversation, she shares the unexpected story of how she ended up helping Beyoncé learn to dance in heels, along with the lessons she's learned from decades of working in the industry. We discuss confidence as a trainable skill, the physical and psychological foundations of performance, what dancers often misunderstand about building a career, and why training with intention matters. Aisha also opens up about burnout, losing her love for dance, finding it again through teaching, and the realities of navigating a constantly changing industry. From unforgettable stories on stage to practical insights on artistry, professionalism, and longevity, this episode offers a candid look at what it takes to grow not only as a dancer, but as a performer and person. Follow Galit: Instagram - https://www.instagram.com/gogalit Website - https://www.gogalit.com/ Fit From Home - https://galit-s-school-0397.thinkific.com/courses/fit-from-home You can connect with Aisha on Instagram https://www.instagram.com/iamaishafrancis and through her website https://aishafrancis.com/ Listen to DanceSpeak on Apple Podcasts and Spotify.

Multiple system atrophy is a rapidly progressive neurodegenerative condition that is often misdiagnosed as Parkinson's disease but carries a far grimmer prognosis. MSA has a median survival of just seven to eight years after symptom onset. Toxic aggregates of alpha‑synuclein and excess brain iron create a vicious cycle of neuronal damage that drives the multisystem motor and autonomic decline characteristic of the disease. Alterity Therapeutics is developing an oral, brain‑penetrant therapy designed to redistribute excess iron, reduce alpha‑synuclein aggregation and oxidative injury, and ultimately slow disease progression. David Stamler, CEO of Alterity, discusses the biology of MSA, the company's promising clinical results to date, and why this therapeutic approach may also have application in other neurodegenerative diseases.

A divorce settlement agreement may be the most important document you'll sign during your divorce—and one of the least understood. In this episode of We Chat Divorce, Karen Chellew and Catherine Shanahan unpack what a Divorce Settlement Agreement (also known as a Marital Settlement Agreement or MSA) actually covers, why so many costly mistakes happen at this stage, and how to protect yourself before signing anything final. While mediation and negotiations may produce broad settlement terms, the formal agreement is where those terms become legally enforceable. Unfortunately, many people discover after the fact that critical financial details were never fully addressed. Missing assets, vague language, overlooked tax consequences, retirement account errors, and unrealistic real estate provisions can create expensive problems long after the divorce is finalized. Karen and Catherine explain why understanding the financial implications behind every provision matters just as much as understanding the legal language. They share real-world examples of agreements that appeared fair in the moment but later created confusion, conflict, and unintended financial consequences. If you're approaching mediation, reviewing a settlement proposal, or preparing to sign a Marital Settlement Agreement, this episode will help you understand what questions to ask before making one of the most important financial decisions of your life. Learn more about your ad choices. Visit megaphone.fm/adchoices

Le champion du Big Wolf Backyard de Lévis, PA Beaulieu, nous explique son approche et ses stratégies pour remporter la course avec un impressionant résultat de 52h de course! PA est également un vrai geek de sport! Magasinez chez Altitude Sports et économisez jusqu'à 20 % sur votre première commande avec le code UPIKA2026.  Cliquez ici pour commander

The June 2026 Recall highlights four previously posted episodes on parkinsonian disorders. The episode begins with Dr. Valtteri Kaasinen discussing the clinical challenges of diagnosing Parkinson disease and how that diagnosis can evolve over time. The discussion continues with Dr. YuHong Fu, who addresses the importance of differentiating between dementia with Lewy bodies and Parkinson disease dementia. The third episode features Dr. Daniel Weintraub discussing clinical considerations and strategies for effective communication when addressing cognitive concerns in patients with Parkinson disease. The episode concludes with Prof. Franziska Hopfner discussing the frequency and disease trajectory of MSA patients who do not experience dysautonomia, compared with those who have autonomic involvement. Podcast links:  Stability and Accuracy of a Diagnosis of Parkinson Disease Over 10 Years  Dementia with Lewy Bodies and Parkinson Disease Dementia Clinical Approach to Dementia Risk in Patients with Parkinson Disease  Multiple System Atrophy Without Dysautonomia  Article links:  Stability and Accuracy of a Diagnosis of Parkinson Disease Over 10 Years  Dementia with Lewy bodies and Parkinson Disease Dementia — The Same or Different and is it Important?  Long-Term Dementia Risk in Parkinson Disease  Multiple System Atrophy Without Dysautonomia: An Autopsy-Confirmed Study Disclosures can be found at Neurology.org. 

Introduction International tech companies burn through $2 million trying to crack the US market every day. Not because their product is wrong. Because they hire a sales team before they have a sales motion. Dan Griffith has spent 15 years watching this mistake play out—and building the playbook to prevent it. Griffith is the founder of Greater Gain Group, a go-to-market firm that helps software and technology companies—most of them international—land and scale in US insurance, financial services, and healthcare markets. As the first US hire for a South African company, he scaled it from $3M to $150M in three years. Those hard lessons became the foundation for Greater Gain Group's 90-day go-to-market framework. In this conversation, Josh Hollander and Griffith dig into why the unicorn sales hire is the most dangerous move an international founder can make, what has to be true before you put a rep in a seat, and where the insurtech market is creating real demand for cross-border go-to-market right now. Guest Bio Dan Griffith is the Founder and Principal Consultant at Greater Gain Group, a go-to-market consultancy specializing in helping international software and technology companies enter and scale in the US insurance, financial services, and healthcare markets. With 30 years in enterprise sales and marketing, he has served as a first US hire and go-to-market architect for companies entering from South Africa, France, Europe, and beyond. His 90-day framework takes founders from "we're entering the US" to a repeatable sales motion—without the $2M mistake. Key Topics • The $2M mistake — A VP of Sales, two account executives, a marketing hire, an office, and conference travel. You're at $2M in under a year with nothing built and no pipeline. Fifty percent of Greater Gain Group's clients have already made this mistake before they call. • Don't hire salespeople (yet) — The tell that a founder is about to flame out: they say they're going to hire a sales team. Griffith's rule: build the sales motion before you build the team. A rep can't fly a plane that hasn't been designed. • The founder has to come — For companies under $50M, having a founder on the ground for early US conversations is the strategy. Hearing objections directly is how you convert from founder-led to team-led sales—the transition Greater Gain Group is built to facilitate. • Three to five segments, not one — Pick no fewer than three and no more than five market segments, understand the pain in each, and build an outreach engine that generates sales conversations—not leads. Leads have no value. • Paid pilots and MSA reality — US buyers do paid pilots. Free pilots signal low value and waste time. On contracts: insurance companies have ten times more lawyers than you. Know your non-negotiables, keep the list short, and don't let MSA rigidity keep you out of the market. • Price higher than you think — International companies consistently underprice the US market by 20–40%. Corporate budgets at US insurers are significantly larger than abroad. One client was surprised a health insurer's CTO had $475K of year-end budget left for a POC they'd hesitated to price. Notable Quotes "They hand you your laptop and say, go sell us some stuff. I learned a lot of hard lessons on how not to do things." "If you don't bring value, you're out. The US market is transactional. As much as I hate to say it." "A lead has no value. Build an outreach engine that generates sales conversations." "Your only competitor is the status quo. If you're getting into a feature-function-benefit argument, you've already lost." Resources Guest: • Greater Gain Group: https://www.greatergaingroup.com • Dan Griffith on LinkedIn: https://www.linkedin.com/in/dangriffithsr/ Host & Organization: • Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/ • Horton International (USA): https://www.horton-usa.com/ • Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show Subscribe & Review If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Apple Podcasts, and Spotify.

❓ Missed One Detail? Your Divorce Could Be Delayed | Los Angeles Divorce ❓ Think your divorce paperwork is complete? Even one missing detail can delay your entire case. In Los Angeles, courts carefully review every submission—and missing forms, signatures, or information can lead to rejection or processing delays.

Le geek en chef David Jeker et Robin Bonneau-Patry de Rtings viennent nous donner un masterclass sur les Supershoes. Magasinez chez Altitude Sports et économisez jusqu'à 20 % sur votre première commande avec le code UPIKA2026.  Cliquez ici pour commander

Au Mali, les autorités de transition restent fermement résolues à combattre les groupes armés. Les jihadistes du Jnim, liés à al-Qaïda, et les rebelles indépendantistes du FLA ont mené le 25 avril une série d'attaques massives et, pour la première fois, conjointes, qui leur ont permis de tuer le ministre de la Défense, le général Sadio Camara, et de prendre le contrôle de Kidal. Depuis, le Jnim a décrété un blocus sur la capitale Bamako et multiplie les attaques. Pour autant, l'armée malienne et ses partenaires russes de l'Africa Corps poursuivent leurs opérations et affichent leur détermination. Moussa Ag Acharatoumane est membre du Conseil national de transition, qui fait office au Mali, en l'absence d'élections depuis bientôt six ans, d'organe législatif. Il dirige également le MSA, groupe politico-militaire de la région de Ménaka, allié des autorités de transition et qui combat avec l'armée malienne et l'Africa Corps russe dans le Nord. RFI : Depuis les attaques du 25 avril, les opposants au régime de transition estiment que les autorités sont fragilisées. Les soutiens des militaires au pouvoir appellent au contraire à faire bloc. Pour vous, j'imagine qu'Assimi Goïta est toujours le président dont le Mali a besoin ? Moussa Ag Acharatoumane : Bien sûr, Assimi Goïta est toujours le président dont le Mali a besoin. Il continue à gouverner normalement son pays. Je tiens quand même à rappeler que malgré les attaques du 25 avril, le Mali est un État qui est debout, est un État qui agit et les forces de défense et de sécurité ont repoussé les actions terroristes, malgré la complexité des attaques et particulièrement le lot de complices internes et externes. Aujourd'hui, nous avons une armée qui est très soudée, le commandement est ensemble, les soldats sur le terrain ont le moral et les opérations continuent sur l'ensemble du territoire.  Les attaques des groupes armés continuent, Bamako est sous blocus, mais le régime est donc solide, prêt à faire face. Le régime est solide, j'irai même plus loin : c'est le peuple malien même qui est solide aujourd'hui. Les Maliens aiment leur armée, les Maliens aiment leur pouvoir et les Maliens aiment leur pays.  L'alliance, sur le terrain, entre les jihadistes du Jnim et les indépendantistes du FLA, vous en pensez quoi ?  Tout le monde connaît ce qu'on appelle al-Qaïda. Les frères qui ont fait ce choix de s'allier à al-Qaïda n'ont pas tiré les leçons de 2012 parce que, en 2012, il y a eu pratiquement la même tentative et le monde entier est témoin de ce qui s'est passé. Et une partie des frères, pas tous, parce qu'il y a une partie de nos frères, malheureusement, qui ne se sont jamais éloignés de la nébuleuse d'al-Qaïda, mais par contre, certains ont toujours été des grandes victimes de cette organisation, y compris certains de leurs premiers responsables, dont les familles ont été décimées par al-Qaïda. Et c'est le même al-Qaïda qui est là, et c'est le même al-Qaïda aussi qui est auteur de l'assassinat de Ghislain Dupont et Claude Verlon, les journalistes de RFI tués à Kidal (en 2013, assassinat revendiqué par al-Qaïda au Maghreb islamique, dont l'un des commanditaires, Seidane Ag Hitta, est aujourd'hui parmi les principaux dirigeants du Jnim, ndlr). On s'en souvient, bien évidemment. Cette alliance, c'est une très mauvaise chose. Je pense que nos frères doivent prendre conscience de l'erreur grotesque qu'ils sont en train de faire et revenir en arrière. Ils doivent faire exactement comme le MSA et le Gatia (deux groupes politico-militaires alliés du régime de transition). Ils se sont alliés à l'armée malienne pour combattre le terrorisme international.  Les dirigeants du FLA assurent qu'il ne s'agit que d'une alliance militaire contre leur ennemi commun, l'armée malienne et l'Africa Corps, et qu'il n'y a pas, au-delà, de projet commun. Quand on voit l'organe officiel d'al-Qaïda à l'échelle internationale mentionner son alliance avec le FLA, quand on voit Iyad Ag Ghali (chef du Jnim, ndlr) coordonner lui-même les opérations sur Kidal à côté d'Alghabass Ag Intallah (l'un des dirigeants du FLA, ndlr), quand on voit les défilés qu'ils ont organisés dans les rues de Kidal, avec les drapeaux noirs mentionnant leur projet satanique. Je le répète, nos frères sont dans l'erreur. Ils ont été victimes de ces gens en 2012 et ce sont les mêmes acteurs qui continuent en 2026.  Le Jnim et le FLA contrôlent désormais Kidal et Tessalit. L'armée malienne et l'Africa Corps russe restent présents à Aguelhoc et Anéfis. Est-ce qu'il faut s'attendre à une contre-offensive des forces nationales dans la région de Kidal ?  Les forces de défense et de sécurité sont en pleine réorganisation et elles sont bel et bien présentes dans la région de Kidal. Ils sont déterminés, ils vont mener des opérations sur l'ensemble du territoire national et ils ne vont pas céder un centimètre de ce territoire à une organisation terroriste.  Le général El Hadj Ag Gamou, nommé gouverneur de Kidal par les autorités de transition en 2023 et que vous connaissez bien : on le dit actuellement à Gao. Est-ce que c'est le cas ? Est-ce qu'il pourrait participer à la contre-offensive sur Kidal ?  Le général El Hadj Ag Gamou va très bien, je tiens à rassurer tout le monde là-dessus. Il a le moral très haut, il a les pieds sur terre et la tête haute. Il est gouverneur de la région de Kidal. Les offensives, la réorganisation de l'armée, son redéploiement, les opérations, ça c'est l'armée qui s'en occupe. Lui, sa fonction, c'est d'être gouverneur de cette région. Il va très bien et il n'a pas de problème.  Dans votre région de Ménaka, l'armée malienne et l'Africa Corps russe ont repoussé fin avril les offensives de l'État islamique, groupe jihadiste rival du Jnim. Depuis, quelle est la situation dans la ville ?  Aujourd'hui, la situation est sous contrôle. L'administration a repris son travail, la vie normale a repris, les forces de défense et de sécurité et leurs partenaires contrôlent la ville, mènent des patrouilles régulièrement. Mais ceci étant dit, la menace est toujours là. Il ne faut pas se leurrer, nous sommes en guerre contre l'une des organisations terroristes les plus dangereuses au monde, donc nous restons sur le qui-vive. Mais pour le moment, à Ménaka, la situation est assez calme.  Dialoguer, négocier avec le Jnim et le FLA, c'est aujourd'hui ce que prônent notamment des opposants au régime en place, comme la Coalition des forces pour la République (CFR) de l'imam Dicko. Mais c'est depuis une dizaine d'années une recommandation de toutes les concertations nationales, y compris du dialogue inter-malien organisé sous la Transition. Les autorités actuelles s'y refusent catégoriquement. Vous, vous y êtes favorable ou pas ?  En fait il n'y a pas à discuter avec des gens qui ont un projet de destruction de notre pays. L'État malien protège sa population, protège son intégrité territoriale et il n'y a absolument rien à négocier avec ces gens-là en l'état actuel, sauf s'ils revoient leurs pensées et leurs projets. Ce sont des Maliens, s'ils reviennent à de meilleurs sentiments, je pense qu'il y a de la place pour tout le monde, mais pas dans ces conditions.  À lire aussiAu Mali, «l'externalisation de la sécurité n'a pas fonctionné», selon Bakary Sambe du Timbuktu Institute

In episode 225, Kara Tatelbaum (author of Putting My Heels Down, critically acclaimed choreographer, renowned Pilates teacher, and dance educator) shares an honest look at what it means to continue building a meaningful life in dance when things don't go according to plan. This conversation explores how dancers can navigate rejection, injury, burnout, financial reality, and evolving identity without losing their connection to movement or themselves. Kara opens up about growing up with physical limitations that teachers constantly criticized, making peace with a career trajectory that looked different than expected, and eventually finding fulfillment in places she never anticipated, including teaching preschool dance after years in higher education, choreography, Pilates, and the professional dance world. Galit and Kara also discuss the difference between being an instructor versus an educator, balancing artistry with survival jobs, the pressure dancers place on themselves to “make it,” and why stories from non-famous dancers still matter. This episode offers perspective for dancers trying to create sustainable careers, expand their definition of success, and feel less isolated in the realities that so many people in the dance world quietly experience. Follow Galit: Instagram - https://www.instagram.com/gogalit Website - https://www.gogalit.com/ Fit From Home - https://galit-s-school-0397.thinkific.com/courses/fit-from-home You can connect with Kara Tatelbaum on Instagram https://www.instagram.com/karatatelbaum/ and through her website https://karatatelbaum.com/. Listen to DanceSpeak on Apple Podcasts and Spotify.

Join Mayra Vite-Romero, assistant director of admissions, as she interviews Tim Hungerford, faculty director of Simon's MS in Accountancy program and an accounting professional with 30+ years of experience in the field. Tim discusses a range of topics, including how he ended up as an accountant, lessons he learned as a young professional, how his accounting career allowed him to travel the globe, and what makes Simon's MS in Accountancy program special. Learn more about Simon's MSA.  View transcript. 

Coach Castonguay nous présente différents type d'intervalles en expliquant leur avantages, inconvénients et objectifs.  Magasinez chez Altitude Sports et économisez jusqu'à 20 % sur votre première commande avec le code UPIKA2026.  Cliquez ici pour commander

Most agency owners treat legal as a tax — a slow, expensive speed bump between the handshake and the kickoff. Sharon Toerek thinks that's exactly backwards.Sharon is a marketing and intellectual property attorney who has spent over a decade working exclusively with independent agencies through her firm Torek Law, which operates in the agency world as Legal + Creative. In this episode, she breaks down how agencies leave real money on the table by treating contracts as admin rather than leverage — and what to do instead.We get into payment terms buried in enterprise agreements that silently stretch 60 days into 90, exclusivity clauses that punish agencies for the specialization clients hired them for, and the IP transfer timing that gives agencies more power than they realize. Sharon also makes the case for why having your own MSA matters even when the big brands will never sign it — and why deals almost never blow up when agencies push back at the contract stage.What We Cover:Why legal should be a money-generating function, not a cost dragThe payment term language in enterprise contracts that quietly kills cash flowHow to negotiate exclusivity without giving away more than you need toWhy you'll never have more leverage than you do right now, at the contract stageThe IP transfer timing that can become serious financial leverage when a client goes sidewaysBuilding your own MSA toolkit — and why it matters even when you'll never use itWho should be in the room during contract negotiations (and what the lawyer's actual job is)How AI is changing the ownership question in agency-client and agency-freelancer agreementsTurning proprietary frameworks, methodologies, and systems into licensable, sellable IPWhy deals almost never fall apart when you push back — and what it signals when they do

In this episode of the Gaining Interest Podcast, host John Ramsey welcomes renowned financial expert Jeffrey Levine, CPA/PFS, CFP®, CWS®, BFA®, MSA to discuss his unique journey from aspiring doctor to trusted financial advisor. Jeffrey shares insights into navigating complex tax laws amid political uncertainty and emphasizes the importance of clear, engaging communication in financial planning. They explore key elements of comprehensive planning, including risk tolerance and personalized client engagement, with Jeffrey advising advisors to act as clients' personal financial CFOs. Tune in for practical advice on building long-term client relationships and making informed financial decisions. For more resources, visit Jeffrey's website at fullyvestedadvice.com.

Mélodie Gilbert est une athlète de trail élite. Cette année, elle a fait le choix de ne pas être sponsorisée. Nous parlons de cette décision de la réalité des athlètes élites qui doivent également porter le chapeau de créateurs de contenu. Magasinez dès maintenant chez Altitude Sports et profitez d'un rabais jusqu'à 20% sur votre première commande avec le code promo : UPIKA2026. Cliquez ici pour commander

Archie Burnett is a foundational voice in New York's dance and club culture (House of Ninja, Check Your Body at the Door and world-renowned teacher) - someone who's lived through and contributed to the environments that shaped social dance as we know it today. In this episode, we get into the realities of the club scene, the impact of policy and tragedy on nightlife, and how dancers adapted when everything around them shifted. Archie shares what it meant to learn through observation, community, and experience (long before social media). This conversation goes beyond dance into philosophy, identity, and the mindset that's carried Archie through decades of life, work, and movement. Follow Galit: Instagram - https://www.instagram.com/gogalit Website - https://www.gogalit.com/ Fit From Home - https://galit-s-school-0397.thinkific.com/courses/fit-from-home You can connect with Archie on Instagram https://www.instagram.com/demoncar0007/ Listen to DanceSpeak on Apple Podcasts and Spotify.

Je présente mon plan d'entraînement pour le Ironman 70.3 de Tremblant à Coach Castonguay qui le critique et l'améliore. Le plan est disponible pour tous ici: PLAN 70.3 Coin Du Geek

In this episode, host Ken Vinacco interviews Mission MSA CEO Joe Lindahl, and DD SIG liaison physical therapist Lindsay Beatty to share how the organization champions connection, education and research related to Multiple System Atrophy. Join us to learn more about this rare neurodegenerative disease and discover resources to help empower your patients.   For more information on Mission MSA, visit www.missionmsa.org  For questions about this podcast, please contact neuroddsig@gmail.com.  Guest Information  Joe Lindahl, MA, CAE  Chief Executive Officer  Mission MSA  Related Resources  Mission MSA www.missionmsa.org  Centers of Excellence: https://missionmsa.org/centers-of-excellence/   Patient Support Line: 866-737-5999; https://calendly.com/mission-msa/mission-msa-patient-support-scheduler  MSA Connect (patient community): https://missionmsaconnect.org/   Support Groups: https://missionmsa.org/finding-support/   MSA Essential Guidebook: https://missionmsa.org/resource-library/your-essential-msa-guidebook/  Resource Library (brochures, webinars): https://missionmsa.org/resource-library/  Care Grant (Respite Care): https://missionmsa.org/missionmsacares/   International MSA Congress: https://missionmsa.org/internationalmsacongress/   Webinar: Physical Therapy and Exercise for People with MSA: https://www.youtube.com/watch?v=yFTpfN7aMPQ  Show notes available here

David Stamler, CEO of Alterity Therapeutics, is developing a drug to treat multiple system atrophy (MSA), a rare and rapidly progressing neurodegenerative disease that often presents as Parkinson's disease but is distinct and more aggressive. There is no single genetic cause or specific biomarker, making accurate diagnosis a significant challenge. The lead drug is a novel small molecule designed to manage excess reactive iron in the brain, which drives the disease, and may be effective for other neurodegenerative diseases involving iron dysregulation. David explains, "Multiple system atrophy is a rare disease, and that's part of the reason people may not know so much about it. It is a neurodegenerative disease, and as the name implies, there are multiple regions of the brain that are affected, hence the term multiple systems that are governed by those regions of the brain. And as the disease progresses, some of these regions degenerate, and you get abnormal function in various areas."   "Now, we like to characterize the disease as a Parkinsonian disorder, which means early on, it can look like Parkinson's disease. And that's kind of a good descriptor to help people understand what it might look like, but it's distinct from Parkinson's disease, and it progresses a lot faster, a lot more rapidly. So it's a disease that people don't know about, probably because no one famous has been diagnosed with MSA, although I'm sure various famous people have probably had the disease and maybe didn't know it."  #AlterityTherapeutics #MultipleSystemAtrophy #MSAAwareness #NeurodegenerativeDisease #Biotech #Phase3 #Neurology #MSA #ClinicalTrials #AlterityTherapeutics #ATH434 #Biotech #RareDisease #Neurodegeneration #DrugDevelopment #MedicalBreakthrough #IronChaperone Alteritytx.com Download the transcript here  

David Stamler, CEO of Alterity Therapeutics, is developing a drug to treat multiple system atrophy (MSA), a rare and rapidly progressing neurodegenerative disease that often presents as Parkinson's disease but is distinct and more aggressive. There is no single genetic cause or specific biomarker, making accurate diagnosis a significant challenge. The lead drug is a novel small molecule designed to manage excess reactive iron in the brain, which drives the disease, and may be effective for other neurodegenerative diseases involving iron dysregulation. David explains, "Multiple system atrophy is a rare disease, and that's part of the reason people may not know so much about it. It is a neurodegenerative disease, and as the name implies, there are multiple regions of the brain that are affected, hence the term multiple systems that are governed by those regions of the brain. And as the disease progresses, some of these regions degenerate, and you get abnormal function in various areas."   "Now, we like to characterize the disease as a Parkinsonian disorder, which means early on, it can look like Parkinson's disease. And that's kind of a good descriptor to help people understand what it might look like, but it's distinct from Parkinson's disease, and it progresses a lot faster, a lot more rapidly. So it's a disease that people don't know about, probably because no one famous has been diagnosed with MSA, although I'm sure various famous people have probably had the disease and maybe didn't know it."  #AlterityTherapeutics #MultipleSystemAtrophy #MSAAwareness #NeurodegenerativeDisease #Biotech #Phase3 #Neurology #MSA #ClinicalTrials #AlterityTherapeutics #ATH434 #Biotech #RareDisease #Neurodegeneration #DrugDevelopment #MedicalBreakthrough #IronChaperone Alteritytx.com Listen to the podcast here

In this episode of our Cross-Border Catch-Up podcast series, Kristyn Lambert (New Orleans) and Samantha Duncan (Washington) explore how multinational employers can effectively use mutual separation agreements (MSAs) to navigate employment terminations in jurisdictions that do not recognize at-will employment. The speakers cover a four-step framework for evaluating whether an MSA is appropriate, including assessing local termination laws, understanding enforceability requirements, and tailoring negotiation strategies to regional norms. The speakers also discuss practical examples from jurisdictions such as China, Korea, Taiwan, and Finland to illustrate how local customs and legal standards shape both the structure and pricing of these agreements.

In episode 223 host Galit Friedlander and guest May Or (professional dancer with touring and commercial credits and a doctorate in psychology) discuss what it took for May to complete her PhD while working as a dancer, the pressure and perfectionism many dancers experience, and how social media has changed the way dancers are seen in the industry. They also talk about May's experience growing up as an immigrant navigating language barriers, balancing rehearsals with doctoral coursework, and her perspective on why dancers can pursue more than one path. Follow Galit Instagram - https://www.instagram.com/gogalit Website - https://www.gogalit.com/ Fit From Home - https://galit-s-school-0397.thinkific.com/courses/fit-from-home You can connect with May Or on Instagram https://www.instagram.com/maylovespink and TikTok https://www.tiktok.com/@maylovespink. Listen to DanceSpeak on Apple Podcasts and Spotify.

In this episode of Let's Combinate, Subhi Saadeh speaks with Jen Riter about analytical method validation for drug device combination products. The discussion explores how traditional drug analytical validation under ICH Q2 differs from validating functional and mechanical performance methods used for combination products. These methods often require an engineering mindset that incorporates measurement system analysis (MSA), gage R&R studies, and the use of fabricated surrogate standards when devices cannot be reused for testing.They also discuss platform test methods and how standards such as ISO 11040 and other ISO references can serve as starting points for method development. The conversation touches on the evolving alignment between ISO based device methods and pharmacopeial expectations such as USP . The episode also covers make vs buy testing decisions, when to outsource specialized testing such as CCIT and extractables and leachables, and how sponsors manage oversight of contract testing laboratories.Timestamps00:00 Welcome and Guest Introduction00:53 ICH Q2 vs MSA Mindset Shift04:37 Surrogate Standards for Mechanical Testing11:12 Platform Methods and ISO 1104015:14 ISO vs USP Verification Debate20:06 Outsourcing Analytical Testing Strategy24:07 Choosing the Right Test Lab26:20 Sponsor Oversight of Contract Labs30:09 Wrap UpAbout Jen RiterJen Riter is an analytical testing and laboratory leader with nearly three decades of experience working in pharmaceutical packaging, drug delivery systems, and combination products. She has held leadership roles at West Pharmaceutical Services and Kindeva Drug Delivery, where her work has focused on analytical method development, validation, and testing strategies for drug delivery systems and injectable combination products. She is also a contributor to the Combination Products Handbook, where she authored a chapter on analytical testing and method validation for combination products.About Subhi SaadehSubhi Saadeh is the Founder and Principal of Let's Combinate BioWorks, where he helps companies close the gaps between drug and device development, quality systems, and regulatory expectations. He is a Certified Quality Auditor and ISO 13485 Lead Auditor with leadership experience at Baxter, Pfizer, and Gilead Sciences including responsibility for management and oversight of assemble label pack sites and working with device primary, secondary and tertiary packaging suppliers. Subhi previously chaired the Combination Product Working Group for Rx-360, served as International Committee Chair for the Combination Products Coalition, and served on AAMI's Combination Products Committee. He also hosts the Let's Combinate podcast and is a writer and speaker on quality at the intersection of drugs and devices.

Qualytics is redefining enterprise data quality by positioning it as a collaborative business function rather than an isolated data engineering problem. Founded at the start of the pandemic by Gorkem Sevinc - a former CTO and CDO who spent years managing reactive data quality firefights - Qualytics emerged from a clear practitioner pain point: writing endless custom rules to catch data issues after they'd already broken dashboards and KPIs. The company raised pre-seed and seed rounds while building with beta customers, then closed a Series A as repeatability patterns emerged in their POC process. Now, as enterprises scramble to operationalize AI initiatives, Qualytics is experiencing explosive inbound demand from organizations realizing their data foundations aren't ready for democratized data access. Topics Discussed The practitioner insight that sparked Qualytics: reactive rule-writing doesn't scale Leveraging existing CTO/CDO networks and PE portfolio connections for beta customers The evolution from free POCs to paid POCs as a mutual commitment mechanism Identifying repeatability through week-by-week POC conversion patterns Building practitioner credibility into the sales motion while hiring for enterprise sales grit The decision to hire sales and marketing leadership simultaneously post-Series A Tracking in-product engagement metrics (DQ operations frequency, anomaly detection, rule editing) as churn prevention Positioning data quality as vertical-specific business problems (premium leakage, regulatory compliance) The timing advantage: AI adoption forcing enterprises to treat data governance as mandatory infrastructure GTM Lessons For B2B Founders Talk to 100 prospects before writing code—even with deep domain expertise: After burning 18 months building a radiology second opinion product that patients didn't want (they didn't even know radiologists were doctors), Gorkem adopted a hard rule: validate with 100 conversations before building. His advantage as a former CTO who lived the data quality problem created false confidence. Practitioners often assume their pain is universal, but buyer awareness and willingness to pay are separate questions. Start with NSF I-Corps-style problem validation: show rough sketches, probe what happened when they hit the pain point, understand how it hurt them financially or operationally. Repeatability appears in micro-conversions during trials, not just closed-won rates: Gorkem didn't declare product-market fit when deals closed—he declared it when he could predict POC behavior by week. "Week two, I'm expecting this. Week three, I'm expecting this." That predictability enabled ROI calculators and internal champion enablement materials. For technical founders, this means instrumenting your trial or POC to track leading indicators: specific features activated, data volumes processed, number of team members engaged, frequency of logins. When those patterns stabilize across prospects, you have a repeatable motion. Use paid POCs as a procurement front-loading mechanism, not a revenue play: Qualytics charges nominal amounts for some POCs—not for the revenue, but to get the MSA signed and force both parties through legal/security review upfront. This eliminates the pattern where free POCs succeed technically but die in procurement. Large enterprises often refuse to pay for POCs, which Gorkem accepts—but only if they commit equivalent effort (executive time, cross-functional teams). The paid POC is a qualification tool: if they won't commit anything, they're not a real opportunity. Hire sales and marketing leadership in parallel and hold them to unified GTM metrics: Gorkem regrets hiring early sales reps before leadership and delaying marketing investment. Post-Series A, he hired both leaders simultaneously and holds them jointly accountable to pipeline generation and velocity—not siloed MQL counts or quota attainment. This structural decision forces collaboration on messaging, ICP definition, and campaign strategy from day one. For technical founders who "figured out" founder-led sales, resist the urge to replicate your motion with more SDRs. Bring in strategic leadership that can build a scalable system. Instrument product engagement as your earliest churn signal—then intervene immediately: Beyond quarterly NPS and executive QBRs, Gorkem tracks granular product usage: how many data quality operations users run, how many anomalies they discover, how actively they're editing rules. When engagement drops, he doesn't wait—he jumps into the customer's existing weekly meetings to diagnose and course-correct. For B2B founders building complex products with long time-to-value, passive health scores aren't enough. You need active usage telemetry and a low-latency intervention process. Translate technical capabilities into vertical-specific business outcomes: Gorkem doesn't pitch "data quality for data engineers." He talks about premium leakage with insurance companies and OCC/SEC data controls with banks. This reframing works because buyers recognize their problem, not a vendor category. The shift requires research: understand each vertical's regulatory environment, operational pain points, and the business metrics executives care about. When you walk in speaking their language about their P&L impact, you're not another vendor—you're someone who gets it. Time your market entry to when "nice-to-have" becomes "must-have": When Qualytics launched, some enterprises called data quality a "nice-to-have." AI adoption changed that calculus overnight. Organizations planning to let 20,000 employees interrogate data through AI interfaces suddenly realized they need robust data governance, quality controls, and cataloging first. Gorkem's timing wasn't luck—he built during the "nice-to-have" phase so he'd be ready when AI budgets made it mandatory. Technical founders should identify the external forcing function (regulation, technology shift, economic change) that will transform their solution from vitamin to painkiller. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

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

This week on DanceSpeak, I sit down with Brian 'Footwork' Green, a master teacher and influential figure in street and club dance culture whose impact spans generations. Recorded live in August 2025, this episode captures Brian's unfiltered thoughts on musicality, lineage, and what often gets misunderstood about street dance. We explore competition versus convention culture, the realities of the dance economy, and the difference between who you are and the artistic name you move under. Brian speaks honestly about off-beat dancing, “auto-tuned” movement, teaching, trends, and what gets lost when dance drifts away from the heart. The conversation also touches on race, representation, and identity in dance spaces—layered, nuanced, and rooted in lived experience rather than soundbites. Insightful, funny, challenging, and deeply grounded in culture, this episode is for dancers who love dance enough to think about it, question it, and keep it alive. Instagram – https://www.instagram.com/gogalit Website – https://www.gogalit.com/ Fit From Home – https://galit-s-school-0397.thinkific.com/courses/fit-from-home You can connect with Brian on Instagram https://www.instagram.com/brianfootworkgreen/. You can purchase Brian's on-line dance classes https://www.theybarelyunderstandhello.com/#classes.

In this episode, host Ken Vinacco interviews Jessica Shurer, CurePSP's Director of Clinical Affairs and Advocacy, to share how the organization is leading initiatives to expand support, outreach, and education for individuals with PSP, MSA and CBS/CBD. The conversation highlights current advocacy efforts, the importance of early recognition, and the need for interdisciplinary collaboration. If you're interested in  elevating care for patients with atypical Parkinsonism, this episode is for you!  For questions about this podcast, please contact neuroddsig@gmail.com.  Show notes available here: https://app.box.com/s/o8b2u47sgoqnj133ky3d93cpo70ge7ab

In this episode of the Elevate Care podcast, Nishan Sivathasan sits down with Eric Wallis, Senior Vice President and System Chief Nursing Officer at Henry Ford Health, to discuss the changes happening in acute care. Henry Ford Health is leading the way by reimagining how care is delivered.Eric dives into the successful implementation of a virtual care model designed to support bedside nurses, reduce burnout, and improve patient outcomes. He shares insights on navigating the change management process, the vital role of listening to frontline staff, and the exciting future of AI in healthcare.About Eric WallisEric Wallis, DNP, MSA, RN, NE-BC, FACHE, was appointed Senior Vice President and System Chief Nursing Officer in December 2021, bringing over 20 years of nursing and healthcare leadership experience. His career began as a bedside nurse and progressed through roles of increasing responsibility in both large academic medical centers and community hospitals, including serving as the President of Henry Ford West Bloomfield Hospital. A transformational leader passionate about improving healthcare delivery, Eric holds degrees from Bowling Green State University, Central Michigan University, and Texas Christian University. He is a fellow of the American College of Healthcare Executives, is certified as a Nurse Executive, and serves on the Michigan Hospital Association Legislative Policy Panel and the Oakland University School of Nursing Board of Visitors.Chapters00:00 – Introduction00:20 – From Bedside to Boardroom03:13 – The Need for a Virtual Care Model06:16 – Designing the Workflow10:22 – Selecting the Right Technology Partner12:11 – Leading Through Change15:07 – Measuring Success18:56 – The Role of AI in HealthcareHenry Ford Health: Henry Ford Health | Henry Ford Health - Detroit, MIAMN Healthcare: amnhealthcare.com Sponsors: We're proudly sponsored by AMN Healthcare, the leader in healthcare staffing and workforce solutions. Explore their services at AMN Healthcare. Learn how AMN Healthcare's workforce flexibility technology helps health systems cut costs and improve efficiency. Click here to explore the case study and discover smarter ways to manage your resources!Discover how WorkWise is redefining workforce management for healthcare. Visit workwise.amnhealthcare.com to learn more.About The Show: Elevate Care delves into the latest trends, thinking, and best practices shaping the landscape of healthcare. From total talent management to solutions and strategies to expand the reach of care, we discuss methods to enable high quality, flexible workforce and care delivery. We will discuss the latest advancements in technology, the impact of emerging models and settings, physical and virtual, and address strategies to identify and obtain an optimal workforce mix. Tune in to gain valuable insights from thought leaders focused on improving healthcare quality, workforce well-being, and patient outcomes. Learn more about the show here. Connect with Our Hosts:Kerry on LinkedInNishan on LinkedInLiz on LinkedIn Find Us On:WebsiteYouTubeSpotifyAppleInstagramLinkedInXFacebook Powered by AMN Healthcare Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In episode 221, host Galit Friedlander and guest Kim Holmes (widely respected director, choreographer, dance educator) explore the roots of house and hip-hop culture through lived experience, mentorship, and time spent inside New York City's party and club scenes before these styles became widely visible. Kim shares her journey into dance, discovering house at a young age, and learning directly with pioneers like Marjory Smarth during a formative era that shaped how she moves, teaches, and thinks about longevity. Together, Galit and Kim reflect on what it meant to come up in spaces where culture was built in real time—long before social media or conventions—and how being “the it kids” back then came with both opportunity and responsibility. The conversation also moves into technique, recovery, listening to the body, trusting timing, and how mindset and intuition quietly guide long careers in dance. Originally recorded in 2019, this episode feels especially relevant today as dancers revisit foundations, lineage, and what it truly means to sustain a life in dance beyond trends. Follow Galit: Instagram – https://www.instagram.com/gogalit Website – https://www.gogalit.com/ Fit From Home – https://galit-s-school-0397.thinkific.com/courses/fit-from-home You can connect with Kim Holmes on Instagram https://www.instagram.com/kimd.holmes. Listen to DanceSpeak on Apple Podcasts and Spotify.

Dr. Elizabeth Coon and Prof. Franziska Hopfner discuss the frequency and disease trajectory of MSA patients who do not experience dysautonomia, in comparison to those with autonomic involvement. Show citation:  Wilkens I, Bebermeier S, Heine J, et al. Multiple System Atrophy Without Dysautonomia: An Autopsy-Confirmed Study. Neurology. 2025;105(11):e214316. doi:10.1212/WNL.0000000000214316 Show transcript:  Dr. Elizabeth Coon: Welcome to the Neurology Minute. I'm Elizabeth Coon, and I'm delighted to welcome Professor Hopfner, who will give us a summary of her recently published paper in Neurology, "Multiple System Atrophy Without Dysautonomia and Autopsy Confirmed Study." Welcome, Professor Hopfner. Please tell us about this study and the key findings. Prof. Franziska Hopfner: So this work reframes how we think about MSA. So, autonomic failure is common but not universal and its absence does not rule out the diagnosis of MSA. So recognizing motor only in multiple system atrophy expands our diagnostic accuracy, improves patients consulting and broadens inclusions in future therapeutic trials. Dr. Elizabeth Coon: Excellent. Thank you. And thank you for listening to this Neurology Minute.

Dr. Elizabeth Coon talks with Prof. Franziska Hopfner aboutthe frequency and disease trajectory of MSA patients who do not experience dysautonomia, in comparison to those with autonomic involvement. Read the related article in Neurology®. Disclosures can be found at Neurology.org. 

December 2025's Patch Tuesday brought major shifts, but the real action is in Microsoft's pricing, privacy battles, and the arms race to control AI-enabled browsers. Plus, Paul recommends Tiny11 Builder for a clean install, or Win11Debloat for an existing install. Then, Rufus to create installation media without the forced Microsoft account (MSA) sign-in or hardware requirement checks. Use MSEdgeDirect to use the default web browser for stories from Widgets, web-based search results, etc. And ExplorerPatcher can fix the performance and reliability issues in File Explorer. It's the final Patch Tuesday of 2025 Major dark mode updates (with a fix for the "flashbang" problem) AI Agent in Settings, Click to Do, Windows Studio Effects, and Search improvements for Copilot+ PCs Many other improvements: FSE, Share, Settings, Widgets, more More Windows 11 New 25H2 preview build on Beta/Dev adds MCP public preview, Quick Machine Recovery auto-enabled, Unified Update Orchestration Platform, Windows MIDI services Microsoft 365 Microsoft 365 is getting a lot more expensive in mid-2026. You didn't think all those free AI updates were free, did you? AI Paul has been talking about "programmatic" apps and services because he wasn't sure of a term for this type of interaction. But there is a term for this: Semantic. As in semantic web. And there you go Microsoft one of 1,000 companies partnering on Agentic AI Foundation because you're getting agents whether they work or not Gartner says NO to AI web browsers The New York Times is suing Perplexity for all the obvious reasons After a big win in the legal battle with OpenAI Opera for Android gets a big AI update Google Workspace Studio brings code-free agent creation to business users - automation is a solid AI use case Xbox Xbox Series X|S notably absent during Black Friday sales Call of Duty won't repeat the mistakes of the past anymore since it didn't work out twice now MS Flight Simulator 2024 is now available on PS5 Red Dead Redemption comes to mobile for the first time, free with a Netflix account Tips & Picks Tip and app(s) of the week: De-enshittify Windows 11 RunAs Radio this week: Incident Management and the Crowdstrike Event with Liam Westley Brown liquor pick of the week: Old Farm Pennsylvania Straight Rye Whiskey Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: 1password.com/windowsweekly auraframes.com/ink helixsleep.com/windows ventionteams.com/twit