Podcasts about NSF

On Monday, Oregon Democratic U.S. Senator Jeff Merkley and Alaska Republican U.S. Sen. Lisa Murkowski led a group of Democratic Senators to urge the National Science Foundation to stop its plans to dismantle a nearly $400 million ocean monitoring network. The Associated Press reported on the letter Sens. Merkley and Murkowski wrote to the NSF, which was signed by nine other U.S. Senators, including Senator Ron Wyden of Oregon and Sens. Patty Murray and Maria Cantwell of Washington. More than two dozen Democratic U.S. Representatives signed onto a separate letter, per the AP’s reporting, to warn against the “illegal decommissioning” of the Ocean Observatories Initiative.    The OOI is a network of 900 sensors anchored off Oregon, Washington, Alaska, North Carolina and in the North Atlantic. For more than a decade, the instruments have transmitted real-time data that has helped detect coastal flooding events, manage sustainable fisheries, track marine heat waves and more.  A memo from the NSF posted last month said the “major descoping” is already underway for the array of instruments managed by Oregon State University, with the removal of most of the rest of the network expected to be completed next summer.   Sen. Merkley joins us to discuss his and other Democratic lawmakers’ efforts to protect the OOI, along with other federal issues affecting his Oregon constituents.    

Click to Text Thoughts on Today's EpisodeWe are living in a supplement era — and it's overwhelming. Between Instagram reels, podcast ads, and influencer recommendations, it can feel like you need an entire shopping cart of pills and powders just to function. But do you?In this episode, I'm zooming out and taking a common-sense approach to supplements: no hype, no magic wands, just practical guidance to help you figure out what your body actually needs.In this episode:Why food always comes first — and what "bioavailability" actually means for youThe questions to ask yourself before buying any supplementWhy blood work is your best friend (and how to advocate for the panels you want)What to look for on the label — and the red flags that should make you pauseThird-party testing explained: NSF, USP, and Informed ChoiceThe 5 supplements most commonly recommended for women in perimenopause and menopause: protein, creatine, vitamin D, magnesium, and omega-3sWhy more is NOT always better (a cautionary tale about B6)The truth about chia seeds and omega-3s (spoiler: it's not apples to apples)How to choose where to start based on your own goalsEpisodes Discussed:5 Things You Need to Know Before You Take SupplementsHow to Choose Seafood and Avoid the Rare Ciguatera Poisoning I ContractedProtein: A Common Sense Guide for Women in Perimenopause & MenopauseCreatine, Brain Fog, and Muscle Loss: What Every Woman in Perimenopause Should KnowMuscle cramps, fatigue, headaches and stressed? This natural mineral may help.My latest recommended ways to nourish and move your body, mind and spirit: Nourished Notes Bi-Weekly Newsletter30+ Non-Gym Ways to Improve Your Health (free download)Connect with Amy: GracedHealth.com Instagram: @GracedHealthYouTube: @AmyConnell

Thursday, June 11th, 2026 Today, inflation spikes to 4.2% as oil prices climb; Maine, South Carolina and Nevada held their state primary elections; the government admits it lied about events at Cities Church in a new filing in the criminal case against Don Lemon and other journalists, a judge refused to issue a restraining order against the $1.8B Slush Fund but gave a stern warning to Justice Department lawyers; the Justice Department's bid to get the courts to release certain Epstein files was a ruse after all; and Allison delivers your Good News. Thank You, Helix 20% Off Sitewide when you go to HelixSleep.com/dailybeans Thank You, Fast Growing Trees Get 20% off your first purchase  FastGrowingTrees.com/dailybeans The Latest Breakdown:Trump DOJ CORNERED by Judge in Jan 6 Cover-Up | The Breakdown Stories5 takeaways from the latest midterm primaries, with Platner's win and mixed results for Trump support | PBS News Inside Trump's White House, the Epstein Files Caused a Freakout | The New York Times Inflation jumps to 4.2%, the highest since early 2023 | NBC News Don Lemon seeks grand jury transcripts in Minnesota civil rights case, citing misconduct | AP News Good Trouble APA Services defends psychological science amidst NSF upheaval →Noah Caldwell-Gervais - YouTube  is doing a 12hr Livestream June 13 → https://riseupsingout.com and http://nokings.org →Triumphal Arch - Section 106 Assessment of Effect and Draft Programmatic Agreement →Regulation for Federal Financial Assistance -  Open For Comments →The Forest Service is accepting public comments until June 7th →Form WTAF-8647 →Recall Gov. Jeff Landry - Louisianadeservesbetter.com →STOP the deportation of Mohsen Mahdawi - Action Network →detentionwatchnetwork.org →FieldTeam6.org →Standwithminnesota.com →Tell Congress Ice out Now | Indivisible, Defund ICE | 5Calls →Congress: Divest From ICE and CBP | ACLU →ICE List  →iceout.org Good NewsDemocrat Annie Andrews to face off against Sen. Lindsey Graham in South Carolina, CBS News projects Indivisible How to build a pollinator garden | U.S. Fish & Wildlife Service →Share your Good News & Good Trouble - The Daily Beans →Beans Talk audio -beans-talk.simplecast.com →Email Dana LGBTQ Owned eating establishments in your area - hello@mswmedia.com Subject: “Dana's Project” Subscribe to the MSW YouTube Channel - MSW Media - YouTube Harry Dunn is running for CongressHarry Dunn for Maryland Our Donation Links Blue Wave California - bluewavecalifornia.org/concert Donate to Public Citizen - https://citizen.org/beans/ The Daily Beans is donating $10,000 and invites you to give what you can to support their life-affirming work - Donate to It Gets Better / The Daily Beans Fundraiser   Pathways to Citizenship link to MATCH Allison's Donationhttps://crm.bloomerang.co/HostedDonation?ApiKey=pub_86ff5236-dd26-11ec-b5ee-066e3d38bc77&WidgetId=6388736 Join Dana and The Daily Beans in support of Human Rights Campaign http://onecau.se/_ekes71 More Donation LinksNational Security Counselors - Donate, ActBlue.com/donate/msw-bwc, WhistleblowerAid.org/beans Dr. Allison Gill - The Breakdown | Allison Gill, Mueller, She Wrote @muellershewrote.com - Bluesky, MSW & The Daily Beans Podcast @muellershewrote - Instagram, MSW Media - YouTube →Federal workers - email AG at fedoath@pm.me and let me know what you're going to do, or just vent. I'm always here to listen.  Dana Goldberg - Dana is on Patreon! At Dana's Dugout, @dgcomedy - Bluesky, @dgcomedy - IG, Dana Goldberg - Facebook,  DanaGoldberg.com More from MSW Media - Shows - MSW Media, Cleanup On Aisle 45 pod, The Breakdown | Allison Gill Reminder - you can see the pod pics if you become a Patron. The good news pics are at the bottom of the show notes of each Patreon episode! That's just one of the perks of subscribing! patreon.com/muellershewrote Listener Survey:http://survey.podtrac.com/start-survey.aspx?pubid=BffJOlI7qQcF&ver=shortFollow the Podcast on Apple:https://apple.co/3XNx7ckWant to support the show and get it ad-free and early?https://patreon.com/thedailybeanshttps://dailybeans.supercast.com/https://apple.co/3UKzKt0 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Industrial Talk is onsite at Penn State and talking to Dr. Mark Rubeo, Associate Professor of Mechanical Engineering with Penn State about "Educating the Future Industrial Leaders". Overview Scott Mackenzie hosts the Industrial Talk podcast, celebrating industry professionals and their innovations. At Penn State University, the ACE (America's Cutting Edge) program, led by Mark Rubeo, addresses the shortage of skilled workers in manufacturing. The program, designed pre-COVID by Tony Schmitz and his team, uses a hub and spoke model to provide training across the US. Rubeo, an assistant professor with a CNC machinist background, emphasizes the importance of manufacturing knowledge for mechanical designers. The ACE program aims to excite and educate future technicians and engineers, fostering a sense of accomplishment and high-tech skills in manufacturing. Outline Introduction to Industrial Talk Podcast Scott welcomes listeners to the number one industrial-related podcast, celebrating industry professionals worldwide.The podcast is broadcasting on-site at Penn State University, specifically at the Baron campus in Erie, Pennsylvania.Scott humorously mentions the OSHA hazard of cables and the presence of snacks and coffee in Mark's class. Mark's Background and Role at Penn State Mark introduces himself as an assistant professor of mechanical engineering at Penn State Behrend.He began his career as a CNC machinist, was laid off during the 2008 recession, and returned to school to earn a mechanical engineering degree.Mark completed his graduate studies in precision manufacturing and measurement science in Charlotte, North Carolina.He worked as a senior mechanical engineer in New Hampshire before returning to academia at Penn State Behrend in 2021. The ACE Program and Its Origins Mark explains the ACE program, which stands for America's Cutting Edge, designed to address the shortage of skilled workers in manufacturing.The program was conceptualized pre-COVID by his former PhD advisor, Tony Schmitz, and his graduate students.A workshop at the NSF involved academia and industry professionals to identify the training needs in machining.The ACE program was piloted in Knoxville, Tennessee, and has since expanded using a hub and spoke model. Challenges and Goals of the ACE Program The ACE program aims to interest and educate people in the manufacturing field, from technicians to engineers.Scott emphasizes the importance of companies investing in training to address the shortage of skilled workers.Mark suggests using the ACE program as a pre-apprenticeship to filter out those not interested in the field before investing in in-house apprenticeships.The program is designed to be a week-long training to gauge interest and aptitude in the manufacturing field. Importance of Apprenticeships and Skilled Trades Scott and Mark discuss the decline of apprenticeship programs and the need to revive them to ensure future success in manufacturing.Mark highlights the benefits of apprenticeships, including the transfer of knowledge and skills from experienced workers to the younger generation.The federal government is recognizing the critical shortage of workers and investing in changing the narrative around manufacturing.Manufacturing is presented as a high-tech field that offers fulfilling and well-paying careers. Future of the ACE Program and Industry Collaboration Mark outlines the goal of the ACE program to excite people about manufacturing and get them into the industry.The program tracks outcomes and feedback to ensure it is effective in achieving its goals.Mark emphasizes the importance of industry collaboration and support in expanding the ACE program.The hub and spoke model allows for efficient training across the US, with Penn State Behrend serving as a central hub. Mark's Role in Expanding the ACE Program Mark is involved in training instructors and setting up ACE programs at other locations, such as Ohio State and LSU.He collaborates with local industry to provide tours and real-world experiences for students.The ACE program aims to highlight the high-tech nature of manufacturing and the camaraderie among professionals.Mark's background and experience in both industry and academia lend credibility and expertise to the program. Contact Information and Final Thoughts Mark provides his contact information for those interested in the ACE program, including his email and LinkedIn profile.Scott encourages listeners to reach out to Mark and other industry professionals to learn more about manufacturing careers.The podcast concludes with a call to support programs like Penn State's ACE program to inspire the next generation of industrial leaders.Scott emphasizes the importance of storytelling in industry to inspire and attract new talent. If interested in being on the Industrial Talk show, simply contact us and let's have a quick conversation. Finally, get your exclusive free access to the Industrial Academy and a series on “Why You Need To Podcast” for Greater Success in 2026. All links designed for keeping you current in this rapidly changing Industrial Market. Learn! Grow! Enjoy! DR. MARK RUBEO'S CONTACT INFORMATION: Email:  mar349@psu.edu ACE Website: https://www.americascuttingedge.org/ LinkedIn Profile: https://www.linkedin.com/in/markrubeo/ Company Website: https://behrend.psu.edu/ PODCAST VIDEO: https://youtu.be/NEKMn3Q4qek THE STRATEGIC REASON "WHY YOU NEED TO PODCAST": OTHER GREAT INDUSTRIAL RESOURCES: NEOM: https://www.neom.com/en-us Hexagon: https://hexagon.com/ Arduino: https://www.arduino.cc/ Fictiv: https://www.fictiv.com/ Hitachi Vantara: https://www.hitachivantara.com/en-us/home.html Industrial Marketing Solutions:  https://industrialtalk.com/industrial-marketing/ Industrial Academy: https://industrialtalk.com/industrial-academy/ Industrial Dojo: https://industrialtalk.com/industrial_dojo/ We the 15: https://www.wethe15.org/ YOUR INDUSTRIAL DIGITAL TOOLBOX: LifterLMS: Get One Month Free for $1 – https://lifterlms.com/ Active Campaign: Active Campaign Link Social Jukebox: https://www.socialjukebox.com/ Industrial Academy (One Month Free Access And One Free License For Future Industrial Leader): Business Beatitude the Book Do you desire a more joy-filled, deeply-enduring sense of accomplishment and success? 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In this episode, we discuss… ● How the brain controls reproductive hormones and communicates with the endocrine system.  ● How endocrine-disrupting chemicals (EDCs) interfere with brain-hormone communication. ● What EDCs are and how they disrupt normal hormonal signaling. The endocrine system explained through a simple "lock-and-key" hormone model. ● How EDCs can mimic or block natural hormones in the body. ● How EDCs disrupt hormone production, regulation, and overall balance in the body. ● The rise of synthetic chemicals after World War II and links to increasing endocrine and neurological disorders. ● The accidental discovery that BPA leaching from plastic caused reproductive harm in laboratory mice. ● Why endocrine disruption challenged the traditional idea that "the dose makes the poison." ● How chemicals like BPA can affect multiple hormone receptors at very low doses.....and so much more! Dr. Andrea Gore is Professor and Vacek Chair in Pharmacology at the University of Texas at Austin. Her research team is investigating fundamental mechanisms of how environmental endocrine-disrupting chemicals (EDCs) perturb the developing brain; sex differences in EDC actions; and transgenerational epigenetic effects. Dr. Gore's research has been funded continuously by the NIH, NSF, and foundations since 1992. She has published 4 books and over 200 scientific papers. She was Editor-in-Chief of Endocrinology from 2013-2017 and was lead author of the Endocrine Society's two Scientific Statements on EDCs, and the Endocrine Society-IPEN Guides to EDCs, most recently in 2024. Dr. Gore is very active in advocacy for, mentorship of, and education of trainees. Over 150 undergraduates, graduate students, and fellows have conducted independent research in her laboratory at the University of Texas at Austin. Dr. Gore feels fortunate to have multiple passions beyond her research in environmental health: playing violin in an orchestra and string quartet; running a turtle and tortoise sanctuary; and her rescue dogs. Andrea C. Gore, PhD Professor and Vacek Distinguished University Chair in Pharmacology The University of Texas at Austin andrea.gore@austin.utexas.edu http://sites.utexas.edu/gore/

In this episode of the podcast, hosts Dom and Jay sit down with Jane and Shell from Autech Canada to discuss the fascinating world of automated sushi machines. Originating from the engineers at Audio-Technical in Japan—yes, the same company famous for headphones and DJ turntables—Autech has brought incredibly precise sushi-making technology to the Canadian market. We dive deep into their flagship model, the ASM-E-95, an unbelievable "rice sheet printer" capable of pumping out a wild 1,300 perfect rice sheets per hour. What we discuss in this episode:The Origin Story: How a special equipment division at an audio company ended up making a manual prototype featured in the 1987 movie Wall Street. Debunking the Automation Myth: Why automated machinery isn't taking away high-end culinary jobs, but rather freeing up chefs to focus on creative menu curation and artistry. Solving the Labor Crunch: How a single machine can save a restaurant "half a person" in labor costs and remove the intense stress of finding specialized, traditionally trained labor. Unexpected Markets: Why non-traditional venues like high-end steakhouses are suddenly installing sushi machines to diversify their appetizer menus. Sanitation & Safety: The hidden logistical victory of temperature-controlled, easily cleanable, NSF-certified machinery in modern kitchens. Whether you operate a traditional Japanese eatery or a steakhouse looking to safely inject fresh, high-margin ideas into your kitchen, this episode breaks down why commercial automation is a game-changer.

In this episode of the podcast, hosts Dom and Jay sit down with Jane and Shell from Autech Canada to discuss the fascinating world of automated sushi machines. Originating from the engineers at Audio-Technical in Japan—yes, the same company famous for headphones and DJ turntables—Autech has brought incredibly precise sushi-making technology to the Canadian market. We dive deep into their flagship model, the ASM-E-95, an unbelievable "rice sheet printer" capable of pumping out a wild 1,300 perfect rice sheets per hour. What we discuss in this episode:The Origin Story: How a special equipment division at an audio company ended up making a manual prototype featured in the 1987 movie Wall Street. Debunking the Automation Myth: Why automated machinery isn't taking away high-end culinary jobs, but rather freeing up chefs to focus on creative menu curation and artistry. Solving the Labor Crunch: How a single machine can save a restaurant "half a person" in labor costs and remove the intense stress of finding specialized, traditionally trained labor. Unexpected Markets: Why non-traditional venues like high-end steakhouses are suddenly installing sushi machines to diversify their appetizer menus. Sanitation & Safety: The hidden logistical victory of temperature-controlled, easily cleanable, NSF-certified machinery in modern kitchens. Whether you operate a traditional Japanese eatery or a steakhouse looking to safely inject fresh, high-margin ideas into your kitchen, this episode breaks down why commercial automation is a game-changer.

Two Clarke County School District teachers were selected as master teaching fellows in the NSF-funded Cultivating Elementary Mathematics Specialists project. Celena McCormick of Gillmore Elementary School and Tiffany Singley of Grove Hill Elementary School were chosen. Article Link

Our guest tonight is Dr. Michael S. Wong, a professor in the Department of Chemical and Biomolecular Engineering at Rice University. He is also professor in the Departments of Chemistry, Civil and Environmental Engineering, and Materials Science and NanoEngineering. He was educated and trained at Caltech, MIT, and UCSB before arriving at Rice in 2001. His research program broadly addresses chemical engineering problems using the tools of materials chemistry, with a particular interest in energy and environmental applications ("catalysis for clean water"). He has received numerous honors, including the MIT TR35 Young Innovator Award, the American Institute of Chemical Engineers (AIChE) Nanoscale Science and Engineering Young Investigator Award, Smithsonian Magazine Young Innovator Award, and the North American Catalysis Society/Southwest Catalysis Society Excellence in Applied Catalysis Award. He is research thrust leader on multifunctional nanomaterials in the NSF-funded NEWT (Nanotechnology Enabled Water Treatment) Engineering Research Center. He is chair of the ACS Division of Catalysis Science and Technology (CATL), and serves on the Applied Catalysis B: Environmental editorial board. Previous experiences include chairmanship of the AIChE Nanoscale Science and Engineering Forum and Chemistry of Materials editorial board membership.The focus of this podcast is recent work led by Dr. Youngkun Chung, one of Dr. Wong's postdoctoral research associates, which describes a new approach to filtering PFAS from water at 1,000 times the efficiency of methods such as activated carbon. Better still, the captured PFAS can be removed from this new filter medium in a process that renders it safe, and the medium ready for reuse.Topics covered include:Description of PFAS chemicals areHow they get into the environmentLimitations of existing filtration approachesDetails of the new technologyHow Dr. Wong's team at Rice University collaborate to develop technlogies that use chemical engineering to make our environment cleaner.Support the showVisit us at climatemoneywatchdog.org!

Today’s headline news for Canadian IT solution providers: The AI supply chain squeeze: Yesterday, we brought you a special mid-day look at the new partner platform and AI Factory announcements from Dell Technologies World. But if you look past the glitz of the main stage, there was a sobering reality check delivered during the partner-specific keynote. Pete Trizzino, president of global sales at Dell Technologies, warned partners that supply constraints are officially back. Driven by voracious hyperscaler demand for AI infrastructure, the squeeze on GPUs, CPUs, and memory is tightening rapidly. In fact, Trizzino warned that the supply chain issues we are starting to see now could be significantly worse in 2027. For Canadian MSPs and VARs, this is the klaxon sounding for hardware lifecycle planning. Partners need to be having capacity conversations with their clients today, locking in orders, and potentially leveraging IT financing to bridge the gap while hardware makes its way through a congested supply chain. CIRA targets the MSP model: Closer to home, the Canadian Internet Registration Authority (CIRA) is preparing to launch a new channel-oriented product platform at the ChannelNEXT conference in Toronto later this month. Led by channel executive Tim Brien, the upcoming platform marks a dedicated pivot toward a managed service provider model. As Canadian organizations face an increasingly complex threat landscape complicated by strict data privacy regulations like Law 25 and PIPEDA, the demand for sovereign, domestic cybersecurity infrastructure is accelerating. By embracing a multi-tenant channel model, CIRA aims to provide Canadian solution providers with a localized alternative for DNS and enterprise security services, removing the administrative friction of scaling broad deployments. PraisonAI zero-day and Operation Ramz: In the cybersecurity space, threat actors are actively exploiting a critical authentication bypass vulnerability in PraisonAI (CVE-2026-44338). The zero-day flaw was targeted within hours of its disclosure, meaning anyone building agentic AI pipelines with the framework needs to apply patches immediately. On a positive note, INTERPOL has announced the results of Operation Ramz, a massive cybercrime crackdown across 13 countries in the Middle East and North Africa that resulted in 201 arrests and the seizure of dozens of malware and phishing servers. In Brief: Lumina emerges from stealth: Cybersecurity startup Lumina has officially launched an AI-native platform designed to reduce alert noise by 87 percent across cloud, identity, and endpoint environments. With security operations centers overwhelmed by false positives, Lumina is using AI to automatically triage and contextualize threats, freeing up analysts to focus on genuine incidents. Nordian and Starlink partner up: Connectivity provider Nordian has signed a reseller agreement with Starlink to embed high-speed satellite internet directly into industrial equipment. Targeted at the agriculture, mining, and transportation sectors, this allows Canadian edge deployments in remote areas to maintain constant connectivity, enabling real-time telemetry and predictive maintenance. Noah Labs builds local AI: Software developer Noah Labs is building Sentinel, an AI-native integrated development environment designed to run 100 percent on-device. As data sovereignty becomes critical, Sentinel allows developers to build and test AI models locally, removing the risk of exposing sensitive proprietary data to public cloud APIs during the development phase. NSF’s deep-tech initiative: The United States National Science Foundation has announced a $1.5 billion X-Labs initiative to fund deep-tech research. The massive influx of capital is expected to heavily influence cross-border commercialization and innovation in North America, focusing on autonomous systems, quantum networking, and advanced materials. Read Full Transcript Welcome to The Buzz from ChannelBuzz.ca, I’m Robert Dutt, today is Tuesday, May 19, 2026, and here’s what’s happening in the channel today. Yesterday, we brought you a special mid-day look at Dell’s new Modern Partner Platform and the massive expansion of the Dell AI Factory. But if you look past the glitz of the main stage, there was a very sobering reality check delivered during the partner-specific keynote. Pete Trizzino, president of global sales at Dell Technologies, took the stage to warn partners that supply constraints are officially back. Driven by the voracious hyperscaler demand for AI infrastructure, the squeeze on GPUs, CPUs, and memory is tightening rapidly. In fact, Trizzino warned that the supply chain issues we are starting to see now could be significantly worse in 2027. For Canadian MSPs and VARs, this is the klaxon sounding for hardware lifecycle planning. If you are waiting until the quarter a client needs a server refresh, you are going to be too late. Partners need to be having these capacity conversations with their clients today, locking in orders, and potentially leveraging IT financing and distribution partners to bridge the gap while hardware makes its way through a congested supply chain. Closer to home, the Canadian Internet Registration Authority, or CIRA, is preparing to launch a new, heavily channel-oriented product platform later this month at the ChannelNEXT conference in Toronto. Led by channel executive Tim Brien, the upcoming platform marks a dedicated pivot toward a true managed service provider model for the national internet registry. For years, Canadian organizations have faced an increasingly complex threat landscape complicated by strict data privacy regulations like Law 25 and PIPEDA. The demand for sovereign, domestic cybersecurity infrastructure is accelerating. By embracing a multi-tenant channel model, CIRA aims to provide Canadian solution providers with a localized alternative for DNS and enterprise security services. The new program is designed to allow channel partners to self-provision services, exert granular control over technical deployments, and scale enterprise-grade security offerings to their small and medium-sized business clients. Ultimately, this move is intended to remove the administrative friction associated with scaling broad deployments, allowing partners to integrate CIRA capabilities directly into their existing recurring revenue security stacks. In the cybersecurity space, it has been a busy 24 hours. First, a major warning for developers and security teams working with autonomous agents: threat actors are actively exploiting a critical authentication bypass vulnerability in PraisonAI, tracked as CVE-2026-44338. The zero-day flaw was targeted within hours of its disclosure, meaning anyone building agentic AI pipelines with the framework needs to apply patches immediately. On a more positive note, INTERPOL has announced the results of Operation Ramz, a massive, coordinated cybercrime crackdown across thirteen countries in the Middle East and North Africa. The first-of-its-kind operation resulted in 201 arrests and the disruption of major cybercrime networks, including the seizure of dozens of malware and phishing servers that have been targeting businesses globally. In Brief: Cybersecurity startup Lumina emerges from stealth today with an AI-native platform designed to reduce alert noise. Connectivity provider Nordian has signed a reseller agreement with Starlink to embed high-speed satellite internet into industrial equipment. Software developer Noah Labs is building Sentinel, an AI-native integrated development environment designed to run entirely on-device. And the United States National Science Foundation has announced a 1.5 billion dollar X-Labs initiative to fund deep-tech research. Full details and expanded stories on all of our In Brief items can be found in the show notes or the blog post at ChannelBuzz.ca. Later today on In The Channel, we have more from Las Vegas. I’ll be sitting down with Alan Ashby, Dell’s senior director of Americas data center presales, to break down the practical realities of the AI infrastructure boom for mid-market partners. And if you haven‘t heard yesterday’s episode yet, that’s probably because there wasn’t one, because outside of Dell Technologies World, it was Victoria Day back home. That’s how we’re seeing the headlines today. I’m Robert Dutt for ChannelBuzz.ca, thanks for listening. Have a great day.

In this insightful episode of TBCY, we sit down with Jyoti Bhasin, Managing Director for India and the Middle East at NSF, a global leader in standards and certification committed to protecting and improving human health and the environment. With over three decades of cross-sector experience, Jyoti Bhasin discusses the evolution of her leadership philosophy, NSF's global mission, how technology and AI are transforming food and water safety, and the growing importance of certification for consumers worldwide.Discover how NSF maintains impartiality while working with regulators, the role of local capabilities in addressing diverse markets, and the unique challenges of ensuring food and water safety in today's complex ecosystem. Jyoti Bhasin also addresses emerging risks such as supply chain disruptions, traceability, microplastics, and policy gaps in India's regulatory landscape.Whether you're a business leader, policymaker, or conscious consumer, this conversation provides valuable perspectives on the present and future of global public health, safety standards, and leadership diversity.Don't forget to like, comment, and subscribe for more leadership insights!

Scientists at NSF–DOE Vera C. Rubin Observatory, jointly funded by the U.S. National Science Foundation and the U.S. Department of Energy's Office of Science, have submitted an unprecedented set of asteroid detections to the IAU Minor Planet Center, including hundreds of distant worlds beyond Neptune and 33 previously unknown near-Earth asteroids. In this podcast, Dr. Mario Juric discusses how these asteroids were discovered and what we can look forward to in the future from the Rubin Observatory.    Bios:  Rob Sparks is in the Communications, Education and Engagement group at NSF's NOIRLab in Tucson, Arizona. Prof. Mario Juric is the P.I. of UW's contribution to the construction of the Rubin Observatory, Senior Fellow at UW's eScience Institute, and director emeritus of UW's Institute for Data-intensive Astrophysics and Cosmology (DiRAC). Once fully operational in 2026, the Rubin Observatory will deliver the largest sky survey in the history of mankind, answering questions from the nature of Dark Energy to discovering potential "killer" asteroids. Prof. Juric led the definition of Rubin data products and oversees the solar system team. Prof. Juric received his PhD in astrophysical sciences from Princeton University and was a postdoctoral fellow at the Institute for Advanced Study and a Hubble Fellow at Harvard University. His research is in the area of data-intensive survey astronomy and AI. He developed a range of astronomical software products and techniques, including software for asteroid detection, mapping the Milky Way, novel astronomical databases, and cloud-based astronomical data analysis systems. Prof. Juric discovered what was at the time the largest known structure in the Universe (the Sloan Great Wall; with J. Richard Gott), a dwarf galaxy colliding with the Milky Way (the Virgo Overdensity; with Z. Ivezic), and over a hundred asteroids (including 22899 Alconrad, the smallest known main-belt binary asteroid; with Korado Korlevic). A Jupiter-family comet 183P/Korlevic-Juric is named after him.   Links: NOIRLab Press Release NOIRLab social media channels can be found at: https://www.facebook.com/NOIRLabAstro https://twitter.com/NOIRLabAstro https://www.instagram.com/noirlabastro/ https://www.youtube.com/noirlabastro   We've added a new way to donate to 365 Days of Astronomy to support editing, hosting, and production costs.  Just visit: https://www.patreon.com/365DaysOfAstronomy and donate as much as you can! Share the podcast with your friends and send the Patreon link to them too!  Every bit helps! Thank you! ------------------------------------ Do go visit http://www.redbubble.com/people/CosmoQuestX/shop for cool Astronomy Cast and CosmoQuest t-shirts, coffee mugs and other awesomeness! http://cosmoquest.org/Donate This show is made possible through your donations.  Thank you! (Haven't donated? It's not too late! Just click!) ------------------------------------ The 365 Days of Astronomy Podcast is produced by the Planetary Science Institute. http://www.psi.edu Visit us on the web at 365DaysOfAstronomy.org or email us at info@365DaysOfAstronomy.org.

Podcast: Bites and Bytes Podcast (LS 26 · TOP 10% what is this?)Episode: Your Food Waste Has a Second Life. Meet Insect Agriculture with Dr. Heather Jordan & Cheryl PreyerPub date: 2026-05-05Get Podcast Transcript →powered by Listen411 - fast audio-to-text and summarizationMost people have never heard of insect agriculture. By the end of this episode, you'll wonder how you missed it.Bites & Bytes Podcast host Kristin King sits down with Dr. Heather Jordan, microbiologist, professor at Mississippi State University, and site director for the NSF-funded Center for Insect Biomanufacturing and Innovation (CIBI), and Cheryl Preyer, the center's industry liaison and former fast food executive, to unpack one of the most quietly consequential shifts happening in the global food system right now.For consumers, this is where your food waste is going next and why that matters for everything from the fish on your plate to the cost of your groceries. Black soldier fly, cricket, and mealworm farming aren't science fiction. They're converting food waste into high-quality livestock feed, fertilizer, and protein at scale. Research is even showing promise in using these insects to remove plastics, antibiotics, and heavy metals from our environment.For professionals in cyber-physical risk, OT security, and food and agriculture cybersecurity, pay attention. Insect agriculture facilities are automated, sensor-dependent production environments with real operational technology vulnerabilities, and this industry is scaling fast with limited security frameworks in place (aka a factory) This is the circular bioeconomy in action. And it already exists.---------------Guest Contact Information:Dr. Heather JordanProfessor of Microbiology and Molecular Biology, Mississippi State University, and Site Director, Center for Insect Biomanufacturing and Innovation (CIBI)Cheryl PreyerIndustry Liaison Officer, Center for Insect Biomanufacturing and InnovationCenter for Insect Biomanufacturing and Innovation ---------------Episode Key Highlights‍ 00:08:01 — "I Traded Fries for Flies" — Cheryl's Origin Line00:11:49 — Insect Farming Is Livestock Farming00:12:37 — "Feed the Food That Feeds Us."00:16:02 — What a Black Soldier Fly Actually Does as an Adult00:23:19 — Why Organic Chickens Need Synthetic Methionine00:23:50 — The Lauric Acid and Coconut Connection00:28:34 — Using Everything But the Oink00:39:51 — The Cricket Densovirus Crisis That Wiped Out Facilities00:50:15 — Heather's West Africa Origin Story---------------

La tertulia semanal en la que repasamos las últimas noticias de la actualidad científica. En el episodio de hoy:Cara A:-Ha fallecido Craig Venter (9:45)-Trump se carga de un plumazo a la cúpula de la NSF (40:30)Este episodio continúa en la Cara B.Contertulios: Luisa Achaerandio, Juan Carlos Gil, Gastón Giribet, Ignacio Crespo, Francis Villatoro, Héctor Socas. Imagen de portada realizada con Midjourney. Todos los comentarios vertidos durante la tertulia representan únicamente la opinión de quien los hace... y a veces ni eso Hosted on Acast. See acast.com/privacy for more information.

Send us Fan Mailinnovationlens.orginfo@innovationlens.orgMost people talk about AI like it's a faster intern. Jonah Lynch is building something closer to an intellectual compass: a system that can “read” the scientific literature at scale, map what we already know, and point toward the empty spaces where the next discoveries are most likely to happen.We unpack Innovation Lens, Jonah's research forecasting platform that uses natural language processing, text embeddings, and geometry in vector space to detect patterns across millions of papers. He explains the core intuition behind prediction in science: some fields are too sparse to pay off, others are so crowded that the easy value is gone, and there's a Goldilocks zone where the research landscape is ready for a breakthrough. We also talk about validation and benchmarking, why this approach can beat random guessing and even the standard “follow the adviser and find a gap” method, and what it changes for PhD topic selection, literature review, and R&D strategy.The conversation gets personal too. Jonah shares how leaving the Catholic priesthood pushed him to rebuild his life around quantitative tools and a search for truth that doesn't rely on authority. From VC decision-making and capital allocation to philanthropy, NSF-style grant impact, and better alternatives to citation metrics, we explore where AI genuinely helps human flourishing instead of just generating content.If you enjoy episodes about scientific discovery, innovation prediction, and practical AI for research, subscribe, share this with a friend who works in science or investing, and leave us a review. What domain would you want a “map of the future” for? Support the showHelp these new solutions spread by ...Subscribing wherever you listen to podcastsLeaving a 5-star review Sharing your favorite solution with your friends and network (this makes a BIG difference)Comments? Feedback? Questions? Solutions? Message us! We will do a mailbag episode.Email: solutionsfromthemultiverse@gmail.comAdam: @ajbraus - braus@hey.comScot: @scotmaupinadambraus.com (Link to Adam's projects and books)The Perfect Show (Scot's solo podcast)Thanks to Jonah Burns for the SFM music.

This Day in Legal History: Rodney KingOn April 29, 1992, a California jury acquitted four Los Angeles police officers charged in the beating of Rodney King, a Black motorist whose assault had been captured on videotape the year before. The beating took place on March 3, 1991, after a police chase, when officers repeatedly struck King while a bystander recorded the incident from nearby. The footage became one of the most important pieces of video evidence in modern American legal history, not because it settled the matter, but because it showed how even seemingly clear evidence can be interpreted differently in a courtroom.To much of the public, the video appeared to show obvious police brutality. To the defense, it became something to be slowed down, segmented, and reframed as a series of split-second decisions by officers claiming fear and loss of control. When the jury acquitted the officers, the verdict landed in Los Angeles as a statement about far more than one criminal prosecution. For many residents, especially Black Angelenos, it confirmed the belief that the legal system was unwilling or unable to hold police accountable for violence against Black citizens.The verdict triggered several days of unrest across Los Angeles, leaving more than 60 people dead, thousands injured, and large portions of the city damaged. The case also forced the country to confront the relationship between race, policing, prosecutorial burden, and jury perception. The state-court acquittals did not end the legal story, because federal prosecutors later brought civil rights charges against the officers.In 1993, two officers, Laurence Powell and Stacey Koon, were convicted in federal court, while two others were acquitted. King also later received a civil damages award from the City of Los Angeles. April 29 remains a major date in legal history because it revealed the limits of video evidence, the difficulty of prosecuting police officers, and the deep public consequences that can follow when a courtroom verdict collides with what millions of people feel they have already seen.Purdue Pharma was sentenced in federal court in New Jersey to $5.5 billion in fines and penalties tied to its 2020 guilty plea over misconduct connected to OxyContin sales. The sentencing helps clear the path for Purdue to wind down through bankruptcy and fund a broader $7.4 billion opioid settlement. Before approving the plea deal, Judge Madeline Cox Arleo heard hours of testimony from people who described addiction, death, and family devastation connected to the opioid crisis. More than 200 victims submitted letters, and more than 40 people spoke in court.Purdue's chairman, Steve Miller, apologized directly to victims after the judge instructed him to do so. Arleo also apologized from the bench, telling victims that the government had failed them by missing opportunities to stop Purdue's conduct earlier. Many speakers said financial punishment was not enough and argued that Purdue's owners, the Sackler family, or company executives should face prison time. The judge said she could not impose jail time because the Justice Department had charged the company, not the individual owners or executives. Although the formal sentence is $5.5 billion, most of that amount will not actually be paid, with the government expected to collect $225 million if Purdue uses its remaining assets to pay creditors.The settlement includes money for governments and an $865 million fund for individuals, but many victims worry they will be excluded because they cannot produce old prescription records. Purdue says it is on track to exit bankruptcy as a new nonprofit company focused on opioid addiction treatment and overdose-reversal medicines.Purdue Pharma receives $5.5 billion sentence, paving way for opioid settlement | ReutersThe Justice Department has indicted former FBI Director James Comey over a 2025 Instagram post showing seashells arranged as “86 47,” which prosecutors say amounted to a threat against President Donald Trump. The case was filed in federal court in North Carolina and charges Comey with threatening the president's life and transmitting a threat across state lines. Comey has said he did not intend violence, explaining that he deleted the post after learning some people interpreted the numbers that way.Trump and his allies had argued the message was a threat, with “47” referring to Trump as the 47th president and “86” being read by them as a call to remove him violently. Acting Attorney General Todd Blanche defended the indictment as a standard threat case, while critics and Comey's lawyers say it looks like a politically motivated prosecution. The Secret Service had previously looked into the post and interviewed Comey, but he was not charged at that time. One should also place the indictment in the broader context of Trump's Justice Department pursuing cases against people and groups seen as political opponents.Comey already faced a separate criminal case over alleged false testimony to Congress, but that case was dismissed after a judge found a problem with the prosecutor's appointment, and the government is appealing. Comey's lawyers are expected to argue that the new case is both retaliatory and protected by the First Amendment. The central legal fight will likely be whether the post was a “true threat” or protected political speech.Trump's DOJ indicts former FBI director James Comey over ‘86 47' post | ReutersThe Trump administration has fired all current members of the National Science Board, according to two former board members who spoke to Reuters. The board, created in 1950, helps oversee the National Science Foundation and advises both the president and Congress on science and engineering policy. It had more than 20 members, who were appointed to six-year terms, and most of them came from academia, with others from national labs, nonprofits, and private industry. Former board members Yolanda Gil and Keivan Stassun said they were told by email that their removals were effective immediately.According to Gil, all 22 current members were terminated and no explanation was given. Stassun said the move was disappointing but not surprising in light of other Trump administration actions affecting scientific research and independent federal bodies. The National Science Foundation referred questions to the White House. A White House official said the NSF's work would continue without interruption and suggested that the board's congressionally created powers may need to be updated. The firings fit into a broader pattern described by political experts as an effort by the administration to reshape independent institutions by replacing existing officials with more loyal leadership.Trump administration fires entire National Science Board | Reuters This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.minimumcomp.com/subscribe

Are We Computing Quantum in the Wrong Base? with Ivan DeutschIvan Deutsch is Distinguished Regents' Professor of Physics and Astronomy at the University of New Mexico and the founding director of CQuIC, the Center for Quantum Information and Control. Along with his longtime collaborator Poul Jessen, Ivan helped lay the theoretical foundations for neutral-atom quantum computing in the 1990s: trapping individual atoms in optical lattices, cooling them to near absolute zero, and shuttling them in parallel to perform quantum logic. The companies commercializing those ideas today — QuEra, Pasqal, Atom Computing, Infleqtion, and the newly announced Aurora out of Caltech — are building on architectural concepts that trace directly to his group's early papers. His 9,600+ citations across quantum information, atomic physics, and quantum control place him among the most-cited theorists in the field.The reason to talk to Ivan now is that he has been making a quietly heterodox argument: every one of those commercial platforms encodes information in two energy levels of an atom that has ten or sixteen, and Ivan thinks the field should be asking whether that's the right choice — not for information density, which is only a logarithmic gain, but for fault tolerance. This conversation goes deep on qudits, spin cat codes, and the co-design philosophy that has shaped Ivan's career at the interface between theory and experiment, ions and neutral atoms, and academia and industry. If you are following neutral-atom hardware, fault-tolerant quantum error correction, or the emergence of regional quantum ecosystems, this episode is essential.What You'll LearnWhy neutral atoms were the "underdog cousins" of trapped ions — and the precise trade-off at the heart of a 30-year rivalry: ions are great and terrible because they're charged; neutral atoms are great and terrible because they're neutralWhat the original neutral-atom quantum computing paper actually got right: the parallel atom-movement architecture now central to QuEra, Atom Computing, and Infleqtion's roadmaps was already there — even if the Rydberg blockade's full power wasn't appreciated until laterWhat qudits are and why fault tolerance, not information density, is the compelling argument: the information gain from base-2 to base-10 is only logarithmic, but co-designing error-correcting codes with the physical structure of the hardware may be transformativeHow spin cat codes work: using the extra energy levels inside a single atom for error redundancy, directly analogous to bosonic cat codes in microwave cavities, with fault-tolerant thresholds that may surpass standard qubit surface codesWhy biased error correction matters: real physical errors in neutral atoms aren't arbitrary, and codes designed around the dominant error channels — including leakage and erasure — can dramatically outperform worst-case generic schemesHow leakage becomes an asset: when population escapes the qubit subspace into other levels, detecting that escape converts it from an unknown error into an erasure error, which is far easier to correctWhy working at interfaces is where the creative work happens: Ivan's career has been built at the boundary between theory and experiment, between ion-trap and neutral-atom communities, and now between research and industryHow New Mexico became a quantum hub: the founding of QNM-I, the partnership with Colorado, and the Elevate Quantum Tech Hub — turning decades of national-lab and university strength into an actual industrial ecosystemResources & LinksGuest LinksIvan Deutsch — CQuIC Faculty Page — Research profile and publication list at the Center for Quantum Information and Control at UNMGoogle Scholar Profile — 9,600+ citations across quantum information, atomic physics, quantum optics, and quantum controlNSF Q-SEnSE Research Profile — Ivan's role in the NSF quantum sensing and engineering centerKey PapersQuantum optimal control of ten-level nuclear spin qudits in Sr-87 (LANL/CQuIC) — The theoretical demonstration of arbitrary SU(10) maps in strontium-87 with average fidelity ~0.9992; the core technical result behind the qudit computing program discussed in the episodeSpin-cat code paper (ResearchGate) — The fault-tolerant encoding proposal that embeds a qubit in a large-spin qudit, analogous to bosonic cat codes; fault-tolerant thresholds that surpass standard qubit-based encodingsTalks & ContextIMSI Talk — "Neutral Atom Quantum Computing with Nuclear Spin Qudits" — Ivan's accessible lecture-format talk on the full qudit computing research program; a good companion to the episodeQuanta Magazine Q&A with Ivan Deutsch (2015) — Still the most accessible public articulation of his philosophy on qudits and computationEcosystemQuantum New Mexico Institute Launch (Jan 2024) — The founding of the joint UNM/Sandia/LANL institute Ivan establishedUNM/QNM-I Ecosystem Update (Feb 2026) — The current state of the New Mexico quantum industrial ecosystem, including Quantinuum, QuEra, and QNet presence in AlbuquerqueElevate Quantum profile — Background on the only quantum-focused EDA Tech Hub in the countryField ContextNature (Jan 2026) — Harvard/QuEra fault-tolerant neutral-atom paper — Up to 448 neutral atoms demonstrating below-threshold fault-tolerant QEC; the hardware milestone Ivan's theoretical work is designed to exploitKey Quotes & Insights"Ions are great because they're charged. You can hold onto them very tightly and manipulate them extremely precisely. Ions are terrible because they're charged — you can't push many together and they all talk to one another."  — Ivan Deutsch, on the fundamental ion/neutral-atom trade-off at the heart of a 30-year platform rivalry "I don't want to be an evangelist, because I don't really feel I've studied this well enough to say we really should do quantum computation base-10 rather than base-two. But I think it's an important question." — Ivan Deutsch, on qudits — a carefully calibrated position from a theorist making a strong technical bet "We just wanted to make the whole thing faster." — Steve Rolston (Ivan's co-author), on the mindset behind the Rydberg blockade paper, which ultimately unlocked the entire commercial neutral-atom industryInsight: The spin cat code ...

This is a recap of the top 10 posts on Hacker News on April 25, 2026. This podcast was generated by wondercraft.ai (00:30): New 10 GbE USB adapters are cooler, smaller, cheaperOriginal post: https://news.ycombinator.com/item?id=47899053&utm_source=wondercraft_ai(01:59): Trump fires NSF's oversight boardOriginal post: https://news.ycombinator.com/item?id=47905283&utm_source=wondercraft_ai(03:29): Firefox Has Integrated Brave's Adblock EngineOriginal post: https://news.ycombinator.com/item?id=47897891&utm_source=wondercraft_ai(04:58): Replace IBM Quantum back end with /dev/urandomOriginal post: https://news.ycombinator.com/item?id=47897647&utm_source=wondercraft_ai(06:28): Plain text has been around for decades and it's here to stayOriginal post: https://news.ycombinator.com/item?id=47897681&utm_source=wondercraft_ai(07:58): Using coding assistance tools to revive projects you never were going to finishOriginal post: https://news.ycombinator.com/item?id=47902525&utm_source=wondercraft_ai(09:27): Show HN: A Karpathy-style LLM wiki your agents maintain (Markdown and Git)Original post: https://news.ycombinator.com/item?id=47899844&utm_source=wondercraft_ai(10:57): Niri 26.04: Scrollable-tiling Wayland compositorOriginal post: https://news.ycombinator.com/item?id=47902416&utm_source=wondercraft_ai(12:26): USB Cheat Sheet (2022)Original post: https://news.ycombinator.com/item?id=47904876&utm_source=wondercraft_ai(13:56): The AI industry is discovering that the public hates itOriginal post: https://news.ycombinator.com/item?id=47904568&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

An interview with Robert Bean and Lance MacNevinIn this episode we unpack the rapidly transforming world of hydronic heating and cooling. We are joined by two seasoned veterans of the industry, Robert Bean and Lance MacNevin. With many decades of real-world experience and hard-earned perspective between them, they offer a thoughtful and engaging look into why hydronics is at the forefront of modern, highly efficient building practices. Robert is (attempting to be) a retired engineering technology professional and ASHRAE Fellow, while Lance brings his extensive background serving as the Director of Engineering at the Plastics Pipe Institute. This episode is packed with sound-bite worthy moments as our guests cut through the noise to discuss the realities of hydronic-based thermal comfort. Whether you are a homeowner, architect, or builder, you will find their independent, expert perspectives well worth listening to and holding on to. This is definitely an episode you will want to bookmark and share with anyone interested in the future of the HVAC industry!Robert BeanRobert Bean is a retired engineering technology professional who specialized in the design of indoor environments and high-performance building systems. Mr. Bean is an ASHRAE Fellow and ASHRAE Distinguished Lecturer, recipient of the Lou Flagg Award, Distinguished Service Award, and instructor for the ASHRAE Learning Institute. He has authored many papers, articles, and course curricula, and has served on numerous technical committees related to indoor environmental quality, building, and energy systems.Lance MacNevinLance MacNevin, P.Eng. is the senior director of engineering for the Plastics Pipe Institute's Building & Construction Division, with expertise on pressure pipes such as CPVC, HDPE, PEX, PE-RT, and PP. Lance has been in the plastic pipe industry since 1993, working as an R&D engineer, codes and standards specialist, and trainer for a major piping manufacturer for over twenty years before joining PPI in 2015. In this role, he focuses on plumbing and mechanical systems, coordinating research, education, and advocacy activities. He is an active member of ASHRAE, ASPE, ASTM, AWWA, CSA, IAPMO, ICC, IGSHPA, NSF, and RPA.TeamHosted by Kristof IrwinEdited by Nico MignardiProduced by M. Walker

The standard story of American innovation features Silicon Valley, venture capital, and the heroic startup founder.When you trace the history of the internet, GPS, mass-produced penicillin, or the COVID vaccine, the starting point is not a term sheet but a government grant. How much does this matter,  and can we measure it?Tim Phillips speaks to Paolo Surico of London Business School and CEPR who, working with Andrea Gazzani, Joseba Martinez, and Filippo Natoli, has built the first systematic empirical account of how government-funded innovation has shaped US productivity since the Second World War. The headline result: government-funded patents account for roughly 2% of all patents filed in the post-war period, but explain around 20% of medium-term fluctuations in total factor productivity and GDP growth. The return on every dollar of public R&D is more than double the return on every dollar of private R&D. The key mechanism is not that government crowds out private investment; it crowds it in. For every dollar of public research, roughly another dollar of private investment follows, as talent from universities and research institutes moves into startups that commercialise what the public sector seeded. The logic is high-risk, high-reward: the government takes on the uncertainty and fixed costs that the private sector will not bear, accepting a large number of failures in order to find the breakthroughs that private capital would never have funded. The model is now under pressure: 2025 brought the largest cuts to US federal science funding in the post-war period. AI adds a further complication: for the first time, a general-purpose technology is being driven primarily by private capital, and that capital is now pulling the best scientific talent out of research institutes and universities and into industry. If that shift becomes permanent, the direction of innovation will be shaped by profitability rather than by broad productivity and living standards. The paper discussed in this episode:Gazzani, Andrea, Joseba Martinez, Filippo Natoli, and Paolo Surico. 2026. "The Public Origins of American Innovation." CEPR Discussion Paper DP20788. Centre for Economic Policy Research. [gated]To cite this episode:Phillips, Tim, and Paolo Surico. 2026. "The Public Origins of American Innovation." VoxTalks Economics (podcast/video).  Assign this as extra viewing. The citation above is formatted and ready for a reading list or VLE.About the guestPaolo Surico is Professor of Economics at London Business School and a Research Fellow of CEPR. [verify URL before publishing] His research focuses on macroeconomics, monetary policy, and the economics of innovation and growth. He has advised central banks and governments on macroeconomic policy and is one of the leading empirical macroeconomists working on the aggregate effects of technology and public investment.Research cited in this episodeScience: The Endless Frontier (Vannevar Bush, 1945) is the report commissioned by President Roosevelt as the Second World War was ending. Bush, Roosevelt's chief scientific advisor, was asked to distil what the wartime mobilisation of research had taught, and how it could be translated into a peacetime innovation ecosystem. The report identified three pillars: government, to set the direction of innovation by funding areas of strategic importance; research institutes and universities, to push the frontier of knowledge without the constraint of commercial goals; and the private sector, to transform new knowledge into new products. The framework became the organisational blueprint for post-war American science and, Surico argues, is the institutional foundation of American technological and economic leadership. The report is in the public domain and available online.The NIH and NSF are the two federal agencies whose funded innovations show the strongest subsequent links to productivity growth in the paper's results. The NIH (National Institutes of Health) funds health and biomedical research; the NSF (National Science Foundation) funds basic research across science and engineering. Both are predominantly funders of university and research-institute work — which is, Surico argues, precisely why their output generates larger productivity gains than defence-funded innovation. The result is not that health research is inherently more productive than defence research; it is that both the NIH and NSF fund more basic, frontier-pushing work, and that basic research generates the largest spillovers regardless of the department that pays for it.Crowding in versus crowding out is the central empirical question in the public R&D literature. Crowding out would mean that government spending on research displaces private spending that would have happened anyway, leaving total innovation roughly unchanged. Crowding in means the opposite: public research creates opportunities and trains talent that then attracts additional private investment. The paper finds consistent evidence of crowding in, particularly when government funds flow to universities and research institutes. For every dollar of public R&D, roughly another dollar of private investment follows, typically as researchers from publicly funded institutions move into startups to commercialise what they developed. This is why the aggregate return on public R&D is more than double the return on private R&D, even though government-funded patents are only two percent of the total.The Solyndra and Tesla parallel is used to illustrate why anecdote-based arguments about public R&D are unreliable. Solyndra — a solar energy company that received a US government loan guarantee and then failed spectacularly — is a frequently cited example of government waste in innovation funding. Tesla received a loan guarantee in the same round of funding and became one of the most valuable companies in history. Surico's broader point is that the government's logic for innovation investment is high-risk, high-reward: it should expect and accept a large number of failures, because the gains from the successes — when they are large enough — more than compensate for the losses. Evaluating public R&D by its failures misses this; evaluating it by its headline successes also misses it. Systematic analysis across the whole portfolio is required.Philippe Aghion's Nobel Prize lecture is cited by Surico on the relationship between innovation, competition, and market structure. Aghion, who shared the Nobel Prize in Economics in 2018, developed Schumpeterian growth theory — the idea that economic growth is driven by creative destruction, with new entrants displacing incumbents through innovation. The key implication Surico draws on is that incumbents have a structural incentive not to innovate disruptively, because doing so would destroy the market position they already hold. Startups, which have no existing position to protect, are the natural vehicle for disruptive innovation. This is why the paper finds that government-funded startups generate larger macroeconomic impacts than government-funded incumbents: startups have both the mandate from public funding and the commercial incentive to take market share.DARPA (the Defense Advanced Research Projects Agency) is the US defence department's high-risk research arm, responsible for funding some of the most consequential technologies of the post-war era, including early internet infrastructure. Surico mentions a less celebrated DARPA project — an attempt to embed microchips into bags for tracking, before drone technology made the approach obsolete — as an example of a genuine failure. It illustrates the high failure rate that comes with high-risk public R&D, and the importance of evaluating the portfolio rather than individual projects.The Draghi report on European competitiveness is cited by Surico as a potential catalyst for a different model of European public investment in innovation. Europe's problem, in his analysis, is not the level of public spending but its composition: too much goes to procurement and too little to basic research and later-stage startup support. Europe has the talent, the research institutes, and the early-stage startups. What it consistently lacks is the capacity to fund the scaling-up phase, which causes European innovations and innovators to be commercialised in the United States. A reallocation of spending toward public R&D that acts as a venture catalyst for later-stage startups — analogous to what Vannevar Bush's framework did for the US after 1945 — is what Surico believes the Draghi report could enable, if acted on.

Every public company's R&D number is a lie hiding in plain sight. Not because anyone falsified it. Because the number was never built to tell the truth. It was built to satisfy an accounting standard written in 1974. And for fifty years, boards, analysts, and CEOs have been making billion-dollar innovation decisions based on a number designed by accountants to solve a different problem entirely. Here's what makes this genuinely strange. The real number exists. The government has been collecting it from every major US company for decades. It would answer the question every innovation leader and investor actually needs answered. And it is locked away by federal law. Confidential. Never published. Never seen by the people who need it most. It's sitting in a federal database right now. And there's a way to estimate it for any public company, without asking anyone's permission. I know it exists because I spent years building it from the inside. Why the R&D Signal Was Blurry When I was running innovation at HP, we discovered this problem firsthand. We had a connection between R&D investment and gross margin that held up across decades of HP history. Better than anything Wall Street was using. But the signal was blurry. None of us could figure out why. The answer came from a question someone on the team asked almost as an aside. What if R&D isn't one thing? Research and Development Are Not the Same Thing Think about what actually lives inside a typical R&D budget. There's a team somewhere investigating whether a new approach could enable a capability that doesn't exist yet. No product defined. No spec written. Asking whether something is even possible. And there's a team building the next version of a product that ships in eighteen months. Spec locked. Timeline set. Engineering executing against a defined target. Both show up on the same line in the budget. Both get called R&D. Both count equally toward the number that gets reviewed every quarter. They are not the same thing. One is Research. The other is Development. Research is the work you do when you don't yet know what you're building. The output is understanding. New knowledge that might enable future products nobody has designed yet. You can't know exactly what you'll find. If you already knew, it wouldn't be research. Development is the work you do when you know exactly what you're building. The spec exists. The product is defined. The question isn't what to make. It's whether it can be made, on time, at cost, at quality. One creates the future. The other delivers the present. And for fifty years, every public company in America has been required to report them as one indistinguishable number. When we split the HP data along that line, Research on one side and Development on the other, the signal sharpened immediately. Research spend, measured against gross margin three to five years later, was a meaningfully stronger predictor than the combined number had ever been. The blur hadn't been in the gross margin data. It had been in the R&D number itself. Two fundamentally different things, averaged together, producing a number that looked precise and predicted almost nothing. But splitting R from D at the company level was only the beginning. The model was still lying to us. Just more quietly. Why Company-Level R&D Splits Still Mislead Even with the split, something was still soft. HP wasn't one business. It was dozens. Printers, PCs, servers, software, each running on different timelines, different technology cycles, different competitive dynamics. What if the R/D split meant something different depending on where it was applied? We pushed it to the product line level. Then further, to the platform level within product lines. Printers were the clearest example. HP's printer business wasn't one story. There were platforms built on established technology. Mature ink systems, proven print head chemistry, products that had been shipping for years. And there were platforms built on genuinely new core technology. New chemistry. New mechanisms. New approaches to fundamental problems that nobody had solved yet. Research investment by platform told a completely different story than Research investment by product category. The Research going into new technology platforms had a completely different relationship to future margin than Research going into mature platforms. Different time horizons. Different risk profiles. Different margin implications years down the road. Laptops told the same story. A traditional consumer laptop line and a high-performance portable workstation weren't the same investment. One was Development-heavy. Defined product, known market, engineering executing against spec. The other had genuine Research behind it. Unsolved thermal problems, new form factor constraints, and materials questions that hadn't been answered yet. When a single R&D assumption is applied across all of that, treating every dollar the same regardless of what it actually does, the signal disappears into the average. Peanut butter across the portfolio. The model only got honest when it got specific. Research by platform and Development by platform, matched against the margin performance of those specific platforms years later. Which platforms were building future margin? Which ones were running on margin that past Research had already bought? We could see it because we were inside the company. The question is whether anyone on the outside could ever see the same thing. The R&D Data the Government Collects and Won't Release Outside the internal budget process, everyone sees the same thing: a single line on the income statement. The US government recognized decades ago that the combined R&D number was analytically useless. So they built a system to collect the real one. The National Science Foundation runs a survey called the Business Enterprise Research and Development survey. The BERD survey. Every year, roughly 47,500 US companies are required to report their R&D spending broken into three categories: basic research, applied research, and experimental development. The split that every board and every investor needs to see. Mandatory. Collected. Verified. And then locked away. The firm-level data is confidential under federal law. The NSF publishes only industry-level aggregates. So every company fills out this survey and reports its real R/D split to the government. That data sits in a federal database. And the boards, investors, and analysts who need it most cannot access it. Researchers at Northwestern and Boston University were given rare access to that confidential data. What they found is striking. When companies face financial pressure and cut R&D, they don't cut Development. They cut Research. Almost entirely. Development barely moves. Every earnings squeeze. Every activist campaign. Every cost optimization program. Systematically targeting the one part of R&D that builds future margin. And because the combined number barely moves, nobody on the outside sees it happening. That's not a coincidence. That's the accounting standard doing exactly what it was designed to do: produce one clean number for the income statement. It was never asked to protect the future. How to Estimate the Research-to-Development Split Without Inside Access So what can actually be done without access to the locked data? More than most people realize. Step 1. Find the industry baseline. The aggregate BERD data is public at the sector level. Ask an AI tool for the Research-to-Development ratio for the relevant industry. That's the benchmark. Everything else gets measured against it. A company spending 8% of its R&D on Research in an industry where the average is 25% is telling you something the combined number never would. Step 2. Look at the gross margin trend compared to peers. Gross margin over time is the most honest external signal of Research health. A company with a declining margin relative to peers, while reporting flat or growing R&D spend, is almost certainly shifting the mix toward Development. The math works in the other direction, too. An AI tool can pull this comparison for any public company in minutes. This is exactly the signal that was invisible at HP until it was too late. Step 3. Look at patent trends compared to peers over time. Patents are an imperfect but useful directional indicator. Not because more patents always means more Research. It doesn't. But a sustained decline in patent output relative to peers, alongside flat R&D spend, suggests the investment is maintaining existing products rather than creating new knowledge. Combined with the gross margin trend, it starts to triangulate where the split actually sits. None of these three steps requires access to an internal budget. All of them can be done in an afternoon with public data and an AI tool. Together, they produce a working picture of the R/D split that the income statement was never designed to reveal. What the R&D Split Revealed at HP That No One Outside Could See When Hurd took over in 2005, HP was spending $3.5 billion on R&D. Roughly 4% of revenue. By 2009, his last full year as CEO, that had dropped to $2.8 billion. Revenue had grown significantly over that period, so the percentage had fallen further still, to under 2.5%. Both the dollar amount and the ratio were declining simultaneously while the company got larger. Wall Street tracked the combined number. The board reviewed it. Nobody raised a structural alarm. The Research component within that total was well below the industry average for comparable technology companies. Not slightly. Significantly. The margin consequences arrived years later. They always do. What Happens When the Definition of Research Doesn't Exist The R/D split gave us a real predictive signal. We ran with it. The conversations were sharper. But the team kept pulling on a thread that nobody expected. When we looked closely at what was actually being called Research, project by project and budget line by budget line, things that didn't feel the same kept appearing. Work aimed at fundamental discovery. Work aimed at solving a specific defined problem using entirely new methods. Both labeled Research. Up close, they behaved differently, predicted different things, and when budgets got tight, got treated very differently. So we went looking for the agreed definition. The official standard that would tell exactly where to draw the lines inside Research. It didn't exist. Not the way we needed it to. And without it, everything we'd built was sitting on sand. How do you build a predictive model on a definition that doesn't exist? That's the next episode. If this helped you see something you might have missed, subscribe wherever you listen to podcasts. On YouTube, hit subscribe and the bell so you don't miss the next episode. And if you want to go deeper every Monday, join us at Studio Notes — free, at philmckinney.com. Until next time. See the pattern. Make the call.  

The Space Show Presents David Eicher, 4525, 4-3-26Quick Summary:This Space Show discussion featuring David Eicher, editor emeritus of Astronomy Magazine, who shared insights about the current state and future of astronomy, space exploration, and scientific discovery. The conversation covered the rapid pace of astronomical discoveries in recent decades, challenges posed by satellite light pollution for both amateur and professional astronomers, and the philosophical aspects of science education. Eicher discussed the likelihood of extraterrestrial life and the technical challenges of detecting it, while also addressing the prospects of human space exploration and settlement. The panel explored topics including the impact of AI on scientific work, the potential for large space telescopes, and the role of private citizens in funding space missions, with Eicher noting that future discoveries about dark energy and dark matter could revolutionize our understanding of the universe.Detailed Summary:David Eicher discussed the current state of public knowledge about astronomy and space exploration. They noted that many people, including healthcare professionals, lack basic understanding of space topics and recent events like rocket launches. The conversation highlighted concerns about the adequacy of science education in the country, with particular emphasis on the low awareness of NASA's activities among younger generations who weren't alive during the Apollo missions. The discussion also touched on the challenges of scientific literacy and critical thinking in society.Mr. Eicher, editor emeritus of Astronomy Magazine, discussed the complementary nature of Astronomy Magazine and Sky & Telescope, explaining that they served different markets with Astronomy focusing on beginners to intermediates while Sky & Telescope targeted more advanced readers. Eicher attributed the current rapid pace of astronomical discoveries to a combination of factors including more people working on finer details, improved instruments and telescopes, both in space and on the ground. The discussion highlighted significant advances made in recent generations, including better understanding of the universe's age, the Big Bang theory, and the number of galaxies, though mysteries remain about dark energy and dark matter.We discussed the decline in science education through media, particularly television, since the 1960s. and explored how people increasingly rely on authority rather than independent thinking or scientific methods to understand the world. The conversation then shifted to the impact of satellite proliferation in space on astronomy, with Eicher expressing concern about how satellite trails affect professional astronomical research and wide-field imaging. While acknowledging that orbital telescopes might become necessary to avoid light pollution issues, Eicher noted that this would not help amateur astronomers on Earth.The discussion focused on challenges for radio astronomy due to increasing orbital assets and satellite traffic, particularly in the context of a proposed cislunar economy. David Eicher noted that while moving radio telescopes to the far side of the moon or deep space remains a viable long-term solution, these approaches would be extremely expensive and require significant government and private sector investment. The group also discussed current funding challenges for science, with Bill and David Eicher acknowledging that while Congress maintained NASA and NSF science funding despite proposed cuts, the overall climate for scientific investment remains difficult. Marshall presented calculations showing how a large telescope in orbit using Starship technology could significantly enhance light-gathering capacity compared to current telescopes, though Eicher emphasized that such ambitious projects would require substantial financial commitment from governments interested in science.Marshall and Eicher discussed the potential for Elon to fund a large space telescope, estimating a cost of 2-3 billion dollars, which they noted would be manageable for Elon given his resources.Our guest emphasized the revolutionary impact such a telescope could have on understanding dark matter, dark energy, and the composition of the universe. The discussion also touched on the evolving role of citizen science and astronomy, with Eicher noting how amateur contributions have become more valuable and integrated into professional research over the past few decades. Dr. Kothari commented on the public excitement generated by the recent Artemis launch and expressed hope that this interest would help drive astronomy engagement among students.Next, we focused on how space exploration and astronomy interest has evolved over time. David Eicher shared that while the Apollo program in the 1960s and 1970s significantly increased public interest in astronomy, modern space programs like Artemis are likely to generate similar interest. The conversation then shifted to challenges in astrophotography, particularly the impact of satellite trails on images, with David explaining that while software can remove these trails from amateur photos, it doesn't solve the problem for professional astronomers who need accurate data. The discussion concluded with concerns about asteroid detection and planetary defense, with David noting that while no civilization-threatening asteroids are currently known to be in near-Earth space, it's only a matter of time before another major impact occurs.We also discussed asteroid detection and planetary defense, noting that while large civilization-threatening asteroids are well-cataloged, smaller city-killer asteroids pose a detection challenge. They explored potential defense mechanisms, including nuclear detonation to nudge threatening objects, though time constraints could be a significant obstacle. John Jossy mentioned Eric Schmidt's funding of a space telescope that would rival Hubble, expected to begin operations in four years. The discussion concluded with our guest reflecting on how public reactions to comet sightings, like during the Hale-Bop phenomenon, often led to irrational fear and cult behavior, emphasizing the ongoing challenge of promoting rational thinking about astronomical phenomena.Mr. Eicher discussed the prevalence of life in the universe, explaining that chemistry is uniform throughout the cosmos and that stars with planetary systems are common. He argued that the vast distances between stars make physical travel between solar systems extremely unlikely, citing the example that even the closest star system to Earth is four times more distant than the edge of our solar system on a scale where Earth-Sun distance equals 1 centimeter. When asked about the odds of discovering new propulsion methods that could minimize these distances, Eicher indicated the odds are very low, explaining that current physics laws, particularly relativity theory, make it impossible for mass to travel at significant fractions of the speed of light.We discussed the odds of discovering extraterrestrial life, explaining that while the probability of encountering advanced civilizations physically is very low, the chances of detecting them through radio signals using SETI methods are significantly higher. John Hunt raised questions about dark energy, suggesting it might be driven by an inflation field rather than a constant, though Eicher noted that the scientific community still lacks a definitive answer. Ajay asked about progress in identifying terms in the Drake Equation versus addressing the Fermi Paradox, with Eicher explaining that SETI research is in its early stages due to the technical challenges of detecting signals over vast distances.The discussion focused on the Drake Equation and its application to the Milky Way galaxy, with Eicher noting that while astronomers are finding more planetary systems, they haven't yet detected Earth-sized planets and the equation's accuracy remains uncertain. The conversation then shifted to space colonization, where Eicher explained that while building space stations and colonies like those depicted in science fiction is technically possible, it would require significant resources and time, and is not likely to happen soon. The discussion concluded with Bill raising questions about SETI and narrowcasting technology, acknowledging that while narrowcasting makes detection more challenging, the search for extraterrestrial intelligence remains a complex problem despite recent technological advances.Eicher expressed skepticism about AI replacing humans entirely, noting that AI's capabilities are limited by the quality of information fed into it. The group discussed active SETI, with Eicher suggesting that humans have already been broadcasting signals since radio and TV days, and emphasizing the vast distances involved in space travel. John Hunt contributed insights about the physical requirements for advanced life forms to develop technology, while Bill mentioned Project Hail Mary's treatment of alien life in fiction.As the program drew to a close, Eicher discussed human expansion beyond Earth, emphasizing the challenges and risks involved, particularly regarding Mars missions due to extreme temperatures and radiation exposure. He expressed support for space exploration, citing potential resource benefits and the long-term survival of humanity on Earth. Eicher also shared updates on his current projects, including his involvement with the Starmus Festival and writing for astronomy publications. The discussion touched on the limitations of relativistic dynamics in achieving high velocities and the importance of distinguishing science fiction from real science.Special thanks to our sponsors:American Institute of Aeronautics and Astronautics, Helix Space in Luxembourg, Celestis Memorial Spaceflights, Astrox Corporation, Dr. Haym Benaroya of Rutgers University, The Space Settlement Progress Blog by John Jossy, The Atlantis Project, and Artless EntertainmentOur Toll Free Line for Live Broadcasts: 1-866-687-7223 (Not in service at this time)For real time program participation, email Dr. Space at: drspace@thespaceshow.com for instructions and access.The Space Show is a non-profit 501C3 through its parent, One Giant Leap Foundation, Inc. To donate via Pay Pal, use:To donate with Zelle, use the email address: david@onegiantleapfoundation.org.If you prefer donating with a check, please make the check payable to One Giant Leap Foundation and mail to:One Giant Leap Foundation, 11035 Lavender Hill Drive Ste. 160-306 Las Vegas, NV 89135Upcoming Programs:Broadcast 4530 Zoom: James Van Laak, ISS author/Artemis | Tuesday 14 Apr 2026 700PM PTGuests: James Van LaakZoom: Our guest discusses his new ISS book “Too See Far: Conflicts & Cooperation on the Space Frontier” plus he has been part of the Artemis project.Broadcast 4531 Hotel Mars TBD | Wednesday 15 Apr 2026 930AM PTGuests: John Batchelor, Dr. David LivingstonHotel Mars TBDBroadcast 4532: Zoom: Paul Warley | Friday 17 Apr 2026 930AM PTGuests: Paul WarleyZoom: Mr. Warley I work with Paul Warley, CEO of Ascent Solar Technologies, a thin-film solar provider that has applied its tech to major space projects with NASA & JAXA.Broadcast 4533: Zoom: Shubber Ali | Sunday 19 Apr 2026 1200PM PTGuests: Shubber AliZoom: Shubber Ali, Founder of Space Cynics, is back with us on several key space topics such data centers in space & More. Check out https://spacecynic.wordpress.com. Get full access to The Space Show-One Giant Leap Foundation at doctorspace.substack.com/subscribe

Astronomers have discovered one of the most chemically primitive stars ever identified — an ancient stellar relic that preserves the chemical imprint of the very first stars in the Universe. In this podcast, Dr. Ani Chiti discusses the discovery of this ancient star and what it tells us about star formation in the early Universe.    Bios:  - Rob Sparks is in the Communications, Education and Engagement group at NSF's NOIRLab in Tucson, Arizona. - Dr. Anirudh Chiti is a Brinson Prize Fellow at Stanford University, interested in the formation of the first stars and galaxies, the early production of heavy elements, the early Milky Way, and local tracers of dark matter. He observes and characterizes nearby stars and galaxies that formed at early times to understand these topics, in an approach known as "Near-field Cosmology" or "Galactic Archaeology".   We've added a new way to donate to 365 Days of Astronomy to support editing, hosting, and production costs.  Just visit: https://www.patreon.com/365DaysOfAstronomy and donate as much as you can! Share the podcast with your friends and send the Patreon link to them too!  Every bit helps! Thank you! ------------------------------------ Do go visit http://www.redbubble.com/people/CosmoQuestX/shop for cool Astronomy Cast and CosmoQuest t-shirts, coffee mugs and other awesomeness! http://cosmoquest.org/Donate This show is made possible through your donations.  Thank you! (Haven't donated? It's not too late! Just click!) ------------------------------------ The 365 Days of Astronomy Podcast is produced by the Planetary Science Institute. http://www.psi.edu Visit us on the web at 365DaysOfAstronomy.org or email us at info@365DaysOfAstronomy.org.

O mercado global de suplementos já ultrapassa US$ 200 bilhões — e está a caminho de dobrar. Com tanto dinheiro em jogo, proliferam produtos sérios, mas também piratas, adulterados e fora das regras do antidoping.Como um atleta — amador ou profissional — pode ter certeza do que está colocando no corpo?Neste episódio do GREGARIO, conversamos com a Fabiane Zanoti, diretora-executiva da NSF no Brasil, organização americana fundada em 1944 e referência mundial em certificação de qualidade.Ela explica como funciona a norma específica que a NSF acaba de lançar para suplementos esportivos — e o que essa certificação garante na prática.Se você usa suplementação como parte do treino, esse episódio é essencial.

Send us Fan MailIs engineering really a meritocracy?We're taught that hard work, strong performance, and clear metrics determine who advances. But what if the system isn't as objective as it seems?In this episode of ENGINEERING CH∆NGE®, I break down how “merit” is often interpreted, or even manufactured, not measured and how the systems we trust to evaluate performance can actully distort it.In this episode: Why performance without context is incomplete and often misinterpreted.How shifting standards and uneven scrutiny reshape who advances.What happens when metrics become targets and start driving behavior instead of reflecting impact.Through real-world examples - from internship decisions to NSF review panels - this episode reveals how evaluation systems can manufacture merit instead of measure it.If you've ever questioned how decisions really get made in academia, engineering, or leadership, this conversation will change how you see performance, potential, and fairness.Ask yourself: Are we rewarding true impact, or just what's easiest to measure?Grab a latte and listen.If this conversation resonates with you, follow ENGINEERING CH∆NGE® and leave a five-star review to help more engineers and leaders join the conversation.Visit the ENGINEERING CH∆NGE® podcast website to learn more and to request a free copy of my new brief, Engineering for Society.Support the showENGINEERING CHΔNGE® is a registered trademark held by Dr. Yvette E. Pearson for producing and providing podcasts.

Is your water filter giving you something even more contaminated than what's flowing from the tap? Jessica Wynn pours us some facts on Skeptical Sunday.Welcome to Skeptical Sunday, a special edition of The Jordan Harbinger Show where Jordan and a guest break down a topic that you may have never thought about, open things up, and debunk common misconceptions. This time around, we're joined by writer and researcher Jessica Wynn!Full show notes and resources can be found here: jordanharbinger.com/1307On This Week's Skeptical Sunday:Most US tap water meets or exceeds federal safety standards, but those regulations were written decades ago — meaning your water may be "safe" by outdated benchmarks that don't account for emerging threats like PFAS forever chemicals, agricultural runoff, and lead from aging infrastructure.A neglected water filter can become a breeding ground for bacteria, making your water dirtier than what comes straight from the tap — if you're not replacing your Brita, Pur, or other cartridge on schedule, you're essentially cultivating a miniature ecosystem in your fridge.The bottled water industry is largely repackaged tap water sold at a massive markup — brands like Dasani and Aquafina source from municipal supplies, and the FDA regulates bottled water less strictly than the EPA regulates what flows from your faucet.Scammers and pseudoscience have infiltrated the water filter market — from dishonest salespeople using chemical reagents to fake contamination in your home, to "alkaline" and "structured water" claims that have zero scientific backing, fear is the industry's most profitable product.You can take control of your water quality with a few simple, empowering steps — request your free Consumer Confidence Report from your local utility, test your water independently, match any filter you buy to the specific contaminants found, and look for NSF or WQA certification to ensure it actually does what it claims.Connect with Jordan on Twitter, Instagram, and YouTube. If you have something you'd like us to tackle here on Skeptical Sunday, drop Jordan a line at jordan@jordanharbinger.com and let him know!Connect with Jessica Wynn at Instagram and Threads, and subscribe to her newsletters: Between the Lines and Where the Shadows Linger!And if you're still game to support us, please leave a review here — even one sentence helps! Sign up for Six-Minute Networking — our free networking and relationship development mini course — at jordanharbinger.com/course!Subscribe to our once-a-week Wee Bit Wiser newsletter today and start filling your Wednesdays with wisdom!Do you even Reddit, bro? Join us at r/JordanHarbinger!This Episode Is Brought To You By Our Fine Sponsors: Momentous: 35% off first order: livemomentous.com, code JHSMarathon Rewards: Sign up today: marathonrewards.comQuiltmind: Email jordanaudience@quiltmind.com to get started or visit quiltmind.com for more infoMental Illness Happy Hour: Listen here or wherever you find fine podcasts!Castbox: Find, organize, and subscribe to the world's best podcasts: castbox.fmSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Cristina Gomez reviews the latest UFO / UAP news and covers Tennessee Congressman Tim Burchett's Newsmax interview revealing the president is kept on a need-to-know basis on UAP programs, and former NSF program director Dr. Anna Brady-Estevez placing 95% odds on nonhuman intelligence engaging with Earth.To see the VIDEO of this episode, click or copy link - https://youtu.be/oKDNdzST07IVisit my website with International UFO News, Articles, Videos, and Podcast direct links -www.ufonews.co00:00 - UFO Officials Are Hiding It00:33 - The Need-To-Know UFO Secret02:04 - UFO Cover-Up Confirmed02:43 - UFO Witnesses Disappearing04:07 - 95% Sure UFOs Are Real04:54 - UFOs Inside Government Science06:54 - NASA Veteran's UFO Confession08:42 - The UFO Cost Of WaitingBecome a supporter of this podcast: https://www.spreaker.com/podcast/strange-and-unexplained--5235662/support.

Fresh off raising a monster $15B, Marc Andreessen has lived through multiple computing platform shifts firsthand, from Mosaic and Netscape to cofounding A16z. In this episode, Marc joins swyx and Alessio in a16z's legendary Sand Hill Road office to argue that AI is not just another hype cycle, but the payoff of an “80-year overnight success”: from neural nets and expert systems to transformers, reasoning models, coding, agents, and recursive self-improvement. He lays out why he thinks this moment is different, why AI is finally escaping the old boom-bust pattern, and why the real bottleneck may be less about models than about the messy institutions, incentives, and social systems that struggle to absorb technological change.This episode was a dream come true for us, and many thanks to Erik Torenberg for the assist in setting this up. Full episode on YouTube!We discuss:* Marc's long view on AI: from the 1980s AI boom and expert systems to AlexNet, transformers, and why he sees today's moment as the culmination of decades of compounding technical progress* Why “this time is different”: the jump from LLMs to reasoning, coding, agents, and recursive self-improvement, and why Marc thinks these breakthroughs make AI real in a way prior cycles were not* AI winters vs. “80-year overnight success”: why the field repeatedly swings between utopianism and doom, and why Marc thinks the underlying researchers were mostly right even when the timelines were wrong* Scaling laws, Moore's Law, and what to build: why he believes AI scaling laws will continue, why the outside world is messier than lab purists assume, and how startups can still create durable value on top of rapidly improving models* The dot-com crash and AI infrastructure risk: Marc's comparison between today's AI capex boom and the fiber/data-center overbuild of 2000, plus why he thinks this cycle is different because the buyers are huge cash-rich incumbents and demand is already here* Why old NVIDIA chips may be getting more valuable: the pace of software progress, chronic capacity shortages, and the idea that even current models are “sandbagged” by supply constraints* Open source, edge inference, and the chip bottleneck: why Marc thinks local models, Apple Silicon, privacy, trust, and economics all point toward a major role for edge AI* American vs. Chinese open source AI: DeepSeek as a “gift to the world,” why open models matter not just because they're free but because they teach the world how things work, and how open source strategies may shift as the market consolidates* Why Pi and OpenClaw matter so much: Marc's claim that the combination of LLM + shell + filesystem + markdown + cron loop is one of the biggest software architecture breakthroughs in decades* Agents as the new “Unix”: how agent state living in files allows portability across models and runtimes, and why self-modifying agents that can extend themselves may redefine what software even is* The future of coding and programming languages: why Marc thinks software becomes abundant, why bots may translate freely across languages, and why “programming language” itself may stop being a salient concept* Browsers, protocols, and human readability: lessons from Mosaic and the web, why text protocols and “view source” mattered, and how similar principles may shape AI-native systems* Real-world OpenClaw use: health dashboards, sleep monitoring, smart homes, rewriting firmware on robot dogs, and why the most aggressive users are discovering both the power and danger of agents first* Proof of human vs. proof of bot: why Marc thinks the internet's bot problem is now unsolvable via detection alone, and why biometric + cryptographic proof of human becomes necessaryTimestamps* 00:00 Marc on AI's “80-Year Overnight Success”* 00:01 A Quick Message From swyx* 01:44 Inside a16z With Marc Andreessen* 02:13 The Truth About a16z's AI Pivot* 03:29 Why This AI Boom Is Not Like 2016* 06:33 Marc on AI Winters, Hype Cycles, and What's Different Now* 10:09 Reasoning, Coding, Agents, and the New AI Breakthroughs* 12:13 What Founders Should Build as Models Keep Improving* 16:33 AI Capex, GPU Shortages, and the Dot-Com Crash Analogy* 24:54 Open Source AI, Edge Inference, and Why It Matters* 33:03 Why OpenClaw and PI Could Change Software Forever* 41:37 Agents, the End of Interfaces, and Software for Bots* 46:47 Do Programming Languages Even Have a Future?* 54:19 AI Agents Need Money: Payments, Crypto, and Stablecoins* 56:59 Proof of Human, Internet Bots, and the Drone Problem* 01:06:12 AI, Management, and the Return of Founder-Led Companies* 01:12:23 Why the Real Economy May Resist AI Longer Than Expected* 01:15:53 Closing ThoughtsTranscriptMarc: Something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic. Having said that, I think what's actually happened is an enormous amount of technical progress that built up over time. And like for, for example, we now know that neural network is the correct architecture.And I, I will tell you like there was a 60 year run where that was like a, you know, or even 70 years where that was controversial. And so, so the way I think about what's happening is basically, I think, I think about basically the, the, the period we're in right now is it's, I call it 80 year overnight success, right?Which is like, it's an overnight success ‘cause it's like bam, you know, chat GPT hits and then, and then oh one hits, and then, you know, open claw hits and like, you know, these are open, these are, these are like overnight, like radical, overnight transformative successes, but they're drawing on an 80 year sort of wellspring backlog, you know, of, of, of, of ideas and thinking it's not just that it's all brand new, it's that it's an unlock of all of these decades of like very serious, hardcore research.If I were 18, like this is a hundred, this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough.swyx: Before we get into today's episode, I just have a small message for listeners. Thank you. We will not be able to bring you the ai, engineering, science, and entertainment contents that you so clearly want if you didn't choose to also click in and tune into our content.We've been approached by sponsors on an almost daily basis, but fortunately enough of you actually subscribed to us to keep all this sustainable without ads, and we wanna keep it that way. But I just have one favor to ask all of you. The single, most powerful, completely free thing you can do is to click that subscribe button.It's the only thing I'll ever ask of you, and it means absolutely everything to me and my team that works so hard to bring the in space to you each and every week. If you do it, I promise you will never stop working to make the show even better. Now, let's get into it.Alessio: Hey everyone, welcome to the Lidian Space Pockets. This is CIO, founder Kernel Labs, and I'm joined by s Swix, editor of Lidian Space.swyx: Hello. And we're in a 16 Z with a, uh, mark G and welcome.Marc: Yes, yes. A and what, half of 16? Something like that. A one. Exactly,swyx: exactly. Uh, apparently this is the, the final few days in your, your current office.You're moving across the road.Marc: Uh, we're, yeah. We have a, we have some, we have some projects underway, but yeah, this is actually, oh, this is the original. We're in actually the original office. We're in the, we're in the, we're, we're in the whole thing.swyx: It's beautiful. Yeah. Great.Marc: Thank you.swyx: So I have to come out, uh, this is a, you know, I wanted to pick a spicy start in October, 2022.I just made friends with Roone and, uh, I wanted to give him something to sort of be spicy about. And I said, uh. Uh, it'll never not be funny. The A 16 Z was constantly going. The future is where the smart people choose to spend their time and then going deep into crypto and not in ai. And that was in October 22nd, 2022.And Ruen says there was an internal meeting in a 16 Z to reorient around Gen ai. Obviously you have, but was there a meeting? What, what was that?Marc: I mean, I don't, look, I've been doing AI since the late eighties.swyx: Yeah.Marc: So I, I don't know, like all that, as far as I'm concerned, this stuff is all Johnny cum lately.Yeah. You, I mean, look, we've been doing ar entire existence. I mean, we've been doing AI machine learning deep, you know, deeply. We've been doing this stuff way from the beginning. Obviously a AI is just core to computer science. I, I, I actually view them as like quite, uh, quite continuous. Um, you know, Ben and I both have computer science degrees.Um, you know, we, we both, Ben, Ben and I actually both are world enough to remember the actual AI boom in the 1980s. Yeah. There was like a, there was a big AI boom at the time. Um, and there was a, was names like expert systems. Um, and they of like lisp and lisp machines. Uh, I, I coded in lisp. I was coding a lisp in 1989.When that was the, the language of the AI future. Um, yeah. So this is something that we're like completely, you completely comfortable with. I've been doing the whole time and are very enthusiastic aboutswyx: is there a strong, like this time is different because, uh, my closest analog was 20 16 17. It was an AI boom.Mm-hmm. And it petered out very, very quickly. Um, we, it just, it just in terms of investingMarc: sort of, sort of,swyx: yeah. Investment, investment excitement.Marc: Although that's really when the, the, the Nvidia phenomenon really, it was, I would say it was in that period when it was very clear that at, at the time it, the vocabulary was more machine learning, but it, it was very clear at that time that machine learning was hitting some sort of takeoff point.Alessio: Yeah.Marc: Well, and as you guys, you guys have talked about this at length on, on your thing, but, you know, if you really track what happened, I think the real story is, it was, it was the Alex net, uh, basically breakthrough in like 2013. That was the, that was the real knee in the curve. Um, and then it was obviously the transformer breakthrough in 17.Alessio: Yeah.Marc: Um, and then everything that followed. But, but, you know, look, machine learning, you know, there were, you know, look, uh, I mean look, I've been working, you know, I've been working with, uh, one of my, you know, kind of projects working with Facebook since 2004. Um, and on the board since 2007, and of course, you know, they, they started using machine learning very early, um, and, you know, have used it basically, you know, for like 20 years for, you know, content, you know, feed optimization and advertising optimization.And obviously many, you know, financial services. You know, many, many, many companies, many different sectors have been doing this. And so it's like one of these things, it's like, it's not a, it's not a single thing. Like it's, it's like, it's like layers, right? Yeah. Um, and, and the layers arrive at different paces and, but they kind of build up.swyx: Yeah.Marc: Uh, they kind of build up over time and then, and then, yeah. And then look, in retrospect, it was 2017 was kind of the, you know, the key, the key point with the trans transformer and then. And then as you guys know, there was this really weird like four year period where it's like the, the transformer existed and then it was just like,swyx: let's go.Yeah.Marc: Well, but, but it was just, but, but between 2020, but between 2017 and 2021, I mean, that was the era of which like companies like Google had internal chat Botts, but they weren't letting anybody use them.swyx: Yeah.Marc: Right. And then, you know, and then OpenAI developed Chat GT or GPT two, and then they told everybody, this is way too dangerous to deploy.Right. Yeah. You know, we can't possibly let normal people, normal people use this thing. And then you, you guys, I'm sure remember AI Dungeon, um mm-hmm. So the o for, there was like a year where like the only way for a normal person to use GP T three was in, in AI dungeon.Alessio: Yeah.Marc: And so you, you, we would do this, you'd go in there and you'd pretend to play Dungeons and Dragons.In reality, you're just trying to talk to talk to GPT. And so there was this, you know, there was this long, you know, and I, you know, the big, big companies, you know, big companies are cautious and, you know, the big companies were cautious. It, it, by the way, it took open ai. You know, they, they, they talk about this, it took open AI time to actually adjust, you know, kind of re redirect their researchswyx: path.I, I think, uh, let say Rosewood, right? Uh, the, the dinner that founded OpenAI was right there.Marc: Right, right. But that, that dinner would've taken place in 20swyx: 18Marc: 19. The formation of OpenAI Uhhuh as late as 2018.swyx: Uh, uh, sorry. Uh, no, I'm, I'm, I'm, I'm wrong. Probably It should be 20. Yeah. They just celebrated a 10 year anniversary, so it it is 2025.Yeah, so, so 2015?Marc: Yeah. 2015. Yeah. 2015. But then, uh, um, Alec Radford did G PT one in what, probablyswyx: mm-hmm. 17, 18,Marc: yeah. 17, 18. So it, yeah. For, and then, and then they didn't really, and then GPT three was what? 2020? 2020.swyx: 2020.Marc: Because that became copilot immediately. Even open ai, which has been, you know, the leader of, of this thing in the last decade, you know, e even they had to adapt and, and, and lean into the new thing.And so. Um, yeah, I, I think it's just this process of basically sort of wave after wave layer after layer, you know, building on itself. And then you kind of get these catalytic moments where, where the whole thing pops and, and obviously that's what's happening now.swyx: Is it useful to think about will there be any ai, winter?‘cause there's always these patterns. Like, is this, in the summer is something I constantly think about because do I get, do I just like. Just get endlessly hyped and just trust that I will only be early and never wrong or right. Well, are we, will there be a winter?Marc: So there's something about, say the following.There's something about AI that has led to this repeated pattern. Um, and, and, and you guys know this,swyx: it's summer, winter, summer,Marc: winter, summer, winter, summer, winter. And it goes back 80 years. Yeah. 80 years. Uh, so the original neural network paper was 1943. Right. Which is, which is amazing. Uh, that it was, it was far back that long.And then there was you, if you guys have ever talked about this on your show, but there was this, uh, there was a big, uh, there was an a GI conference at Dartmouth University in 1950. 55. 55, yeah. And they got a NSF grant to, uh, for the, all the AI experts at the time to spend the summer together. And they figured if they had 10 weeks together, they could get a GI, uh, at the other end.And they got their, by the way, they got the grant, they got the 10 weeks and then, you know, 1955, you know. No, no. A GI. And like I said, I, I lived through the eighties version of this where there was a big, a big boom and a crash. And so, so there is this thing, and there, there is something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic.Um, and, and it's probably on both sides of like the, the, the boom bus cycle. You, you kind of see that play out. Having said that, I think what's actually happened is like just, and you know, and we now know in retrospect like an enormous amount of technical progress that built up over time. And like for, for example, we now know that neural network is the correct architecture.And I, I will tell you like there was a 60 year run where that was like a, you know, or even 70 years or that was controversial. And, and we now know that that's the case. And so we, we now, you know, everything we're building on today just sort of derives from the original idea in 1943. And so, so in retrospect, we, we now know that like, these, these guys are right.They, they, you know, they would get the timing wrong and they thought, you know, capabilities would arrive faster, or they were, it could be turned into businesses sooner or whatever, but like, they were fundamentally, the, the scientists who worked on this over the course of decades were fundamentally correct about what they were doing.And, and the, and the payoff from, from, from all their work is happening now. And so, so the way I think about what's happening is basically, I think, I think about basically the, the, the period we're in right now is it's, I call it 80 year overnight success, right? Which is like, it's an overnight success.‘cause it's like bam, you know, chat, GPT hits and then, and then oh one hits, and then, you know, open claw hits and like, you know, these are open, these are, these are like overnight, like radical, overnight transformative successes, but they're drawing on an 80 year sort of wellspring backlog, you know, of, of, of, of ideas and thinking it's not just that it's all brand new, it's that it's an unlock of all of these decades of like very serious, hardcore research.Um, and thinking, and look, there were AI researchers who spent their entire lives. They got their PhD. They, they worked for, they've researched for 40 years. They retired in a lot of cases, they passed away and they never actually saw it work.swyx: Yeah. It's all sad.Marc: It is. It is sad. It's sad. Knewswyx: Jeff Hinton was like the last guy.Marc: Yeah. Yeah. Well, there were the guys, uh, was a guy, Alan Newell. I mean, there's tons of John McCarthy. You know, John McCarthy was like one of the inventors in the field. He's one of the guys who organized the Dartmouth Conference and you know, he taught at Stanford for 40 years. Wow. And passed, you know, passed away, I don't know, whatever, 10, 10 years ago or something.Never, never actually go. Got to see it happen. But like, it is amazing in retrospect, like, these guys were incredibly smart and they worked really hard and they were correct. So anyway, so then it's like, okay, you know, say history doesn't repeat, but it rhymes. It's like, okay, does that mean that there's gonna be another, like, you know, basically boom buzz cycle.And I, I will tell you, like, let, like in a sense, like yes, everything goes through cycles and, you know, people get overly enthusiastic and overly depressed and there's, there's a time, there's a timelessness to that. Having said that, there's just no question. Um, so the form, the foremost dangerous words in investing this time are, this time is different.Do you know the 12 most dangerous words investing? No. The four most d foremost dangerous words in investing are this time is different. Yeah. Um, the 12 most dangerous words. And so like, I'll tell you what's different. Like now it's working like, like there's just no, I mean, look, there's just no question.And by the way, I, I'll just give you guys my take. Like L LLMs, like from, from basically the Chad G PT moment through to spring of 25. I think you could still, I think well intention, well, and of. Form skeptics could still say, oh, this is just pattern completion. And oh, these things don't really understand what they're doing.And you know, the hall hallucination rates are way too high. And, you know, this is gonna be great for creative writing and creating, you know, Shakespeare and so sonnets and, you know, as, as rap lyrics or whatever, like, it's gonna be great and all that stuff, but we're not gonna be able to harness this to make this relevant in, you know, coding or in medicine or in law or in, you know, you know, kind of feels that, you know, kind of really, really matter.And I think basically it was the reasoning breakthrough. It, it was oh one and then R one that basically answered that question basically said, oh no, we're gonna be able to actually turn this into something that's gonna work in the real world. And, and then obviously the coding breakthrough over the, over basically the coding breakthrough that kind of catalyzed over the holiday break was kind of the third step in that.Mm-hmm. Where you're just like, alright, if, if, you know, if Linus Tova is saying that the AI coding is no better than he is like. Like, that's, that's never happened before. That's theswyx: benchmark.Marc: Yeah. That's never happened before. And so now we know that it's, it's gonna sweep through coding and, and then, and then we, we know, you know, we know that if it's gonna work in coding, it's gonna work in everything else.Right. It's just then, because that's, that's like, that's like, that's like the hardest in many ways. That's the hardest example. And how everything else is gonna be a, a derivative of that. And then on top of that, we just got the agent breakthrough, you know, with Open Claw, which is fantastic. Which is amazing and incredibly powerful.And then we just got the, the, um, the auto research, uh, you know, the, the self-improvement. You know, we're now into the self-improvement breakthrough. And so the, so the way I think about it is we've had four fundamental breakthroughs in functionality, l OMS reasoning, uh, agents, um, and then, uh, and, and then now RSI, um, and, and they're all actually working.Um, and so I'm, I'm just, as you like, you can tell I'm jumping outta my shoes. Like, like this is, like this is it like this, this is the culmination of 80 years worth of worth of work, and this is the time it's becoming real.Alessio: Yeah.Marc: I, I'm completely convinced.Alessio: I think the anxiety that people feel is like during the transistor era, yet Mors law, and it's like, all right, we understand why these things are getting better.We understand the physics of it. Yeah. With ai, it's. It's so jagged in like the jumps where like, like you said, it's like in three months you have like this huge jump like, and people are like, well this can keep happening. Right? But then it keeps happening,Marc: it'll keep happening.Alessio: And so like how do you think about also timelines of like what's we're building?I think we always have this question with guests, which is like, you know, should you spend time building harness for a model versus like the next model just gonna do it one shot in the lead space. Right. And how does that inform, like how you think about the shape of the technology? You know, you talk about how it's a new computing platform.If you have a computing platform, then like every six months it like drastically changes in what it looks like. It's hard to build companies on top of it.Marc: Yeah. So, so a couple things. So one is like, look, the, the Moore's law was what we now call a scaling law. Like Moore's Law was a scaling law and for your younger viewers, more Moore's Law was every chip chip chips either get twice as powerful or twice as cheap every, every 18 months.And that, and that and that, you know, that it's gotten more complicated in the last few years. But like that, that was like the 50 year trajectory of, of, of the computer industry. And then, and then by the way, and that's what took the mainframe computer from a $25 million current dollar thing into, you know, the phone in your pocket being, you know, a million times more powerful than that.Like that, you know, for, for 500 bucks. And so that, that was a scaling law. And then, and then, and then key to any scaling law, including Moore's Law and the AI scaling laws is, you know, they're not really laws, right? They're, they're, they're, they're predictions, but when they work, they become self-fulfilling predictions because they, they, they, they, they set a benchmark and, and then the entire industry, right?All the smart people in the industry kind of work to make sure that, that, that actually happens. And so they, they kind of motivate the breakthroughs that are required to, to keep that going. And, and in and in chips, that was a 50 year, that was a 50 year run. Right. And it, it was amazing. And it's still happening in, in some areas of, of chips.I think the same thing is happening with the, the core scaling laws. The core scaling laws. In, in, in ai, you know, they're, they're not really laws, but like they, they are basically. There are predictions and then they're motivating catalysts for the research work that is required to be. And, and, and, and by the way, also the investment, uh, dollars, um, uh, you know, required to basically keep, you know, keep the curves going and, and look, it, it is, it's gonna be complicated and it's gonna be variable and they're, you know, there're gonna be walls that are gonna look like they're fast approaching, and then they're gonna be, you know, engineers are gonna get to work and they're gonna figure out a way to punch through the walls.And obviously that's, you know, that's been happening a lot, you know, and then look, there's gonna be times when it looks like the walls have, you know, the, the, the laws have petered out and then they're gonna, they're gonna pick up again and surge and then, and then, and then it, it appears what's happening to the eyes is there's not multiple, you know, multiple scaling laws.Um, there's multiple areas of improvement. And, and I think, you know, I don't know how many more there are already yet to be discovered, but there are probably some more that we don't know about yet. You know, they, like, for example, there's probably some scaling law around, um, world models and robotics that we don't fully understand, you know, kind of acquisition of data at scale in the real world that we don't fully understand yet.So that, that, that one will probably kick in at some point here. There's a bunch of really smart people working on that. Um, and so, yeah, I, I think the expectation is that, that, you know, the, the scaling laws generally are gonna continue. Yeah. The, the pace of improvement will continue to move really fast.Um. To your question on like what to build. So, uh, I'm a complete believer the scaling laws are gonna continue. I'm a complete believer the capabilities are gonna keep getting amazing, um, you know, leaps and bounds. Uh, the part where I kind of part ways a little bit with how, what I would describe as the AI purists, um, you know, which is, which I would characterize as like the people who are.In many ways, the smartest people in the field, but also the people who spend their entire life, like at a lab, um, and have, have, I would say, have very little experience in the outside world. Um, the, the, the nuance I would offer is the outside world of 8 billion people and institutions and governments and companies and economic systems and social systems is really complicated.Um, and, um, and doesn't, you know, it it 8 billion people making collective decisions on planet Earth is not a simple process of like, just like you see this happening now. It's like a bunch of AI CEOs have this thing, which is just like, well, there's just this, they just all have this kind of thing when they talk in public where they're just like, well, there's these, these obvious set of things that so society to do.Alessio: Mm-hmm.Marc: And then they're like, society's not doing any of those things. Right. And it's like, how can society not, you know, what, whatever their theory is, how can society not see x, y, Z? Mm-hmm. And the answer is, well, society is number one. There's no single society, it's like 8 billion people. And they like all have a voice, and they all have a vote, like at the end of the day of how they, they react to change.And then, you know, it just like, it's just human reality is just really complicated and messy. Um, and, and, and so the specific answer to your question is like, as usual, it depends. Um, you know, it, it depends. Look, pe there's no question people are gonna, like, there's no question they're gonna be companies.It's already happening. There are companies that think that they're building value on top of the models and then they're just gonna get blissed by the, by the next model. There's no question that's happening. But I think there's no question also that just the process of adaptation of any technology into the real and into the real messy world of humanity is, is just going to be messy and complicated.It's, it's not going to be simple and straightforward. It's gonna be messy and complicated. And there are gonna be a lot of companies and a lot of products, um, uh, and in, in fact entire industries that are gonna get built to, to, to basically actually help all of this technology actually reach real people.Alessio: The amount of capital going into these companies, I mean, Dario talked about it on the Door Cash podcast and Door Cash was like, why don't you just buy 10 x more GPUs? And he is like, because I'm gonna go bankrupt if the model doesn't exactly hit the, the performance level. How do you think about that?Also as a risk on, you know, you guys are investors, open AI and thinking machines and world apps. It seems like we're leveraging the scaling loss at a pretty high rate, right? Like how comfortable, I guess, do you feel with the downside scenario, like, and say like things Peter out, you think you can kind of like restructure uh, these build outs and uh, you know, capital investments.Marc: Yeah. So should start by saying, so I live through the.com crash, um, and I can tell you stories for hours about the.com crash and it was horrible. No, it was awful. It was, it was, it was apocalyptic by the way. The, a lot of the.com crash was actually at the time, it was actually a telecom crash. It was a bandwidth crash.Like the, the thing that actually crashed, that wiped out all the money with the tele, the telecom companies.swyx: GlobalMarc: crossing. Global, global, yeah.swyx: I'm from Singapore and they, they laid so much cable o over over our oceans.Marc: Actually there was a scaling law in the.com. Era. And it was literally the, the US Commerce Department put out a report in 1996 and they said internet traffic was doubling every quarter.Um, and, and actually in 1995 and 1996, internet traffic actually did double every quarter. And so that became the scaling law. And so what all these telecom entrepreneurs did was they went out and they raised money to build fiber, anticipating that the demand for bandwidth is gonna keep doubling every quarter.Doubling every quarter though is like, you know, grains of chess and the chessboard, like at some point the numbers become extremely large. Right. And, and, and it really, and really what happened was the internet. The internet by the way, continuously kept growing basically since inception. And it's, you know, it's, it's continuously grown.It's never shrunk. And it's grown really fast compared to anything else. Mm-hmm. You know, in, in, in human history. But it wasn't doubling every quarter as of 19 98, 19 99. And so there was this gap in the expectation of what they thought was a scaling law versus reality. And that's actually what caused the.com crash, which was the, it they, they way over companies like global crossing way overbuilt fiber, which is sort of the, and by the way, fiber, telecom equipment, you know, so all the, all the networking gear, you know, and then, and then by the way, the actual physical data centers, like that was the beginning of the, of the, of the data center build and then, and the data center overbuild.And so you had that, but it was, it was literally, I think it was like $2 trillion got wiped out, right? It was like Jesus, it was like a big, it was. And by the way, the other, the other subtlety in it was the internet companies themselves never really had any debt. ‘cause tech, tech companies generally don't run on debt, but the telecom companies run on debt.Physical infrastructure companies run on debt. And so the companies like Global Crossing not just raise a lot of equity, they also raise a lot of debt. So they're highly levered. And so then you just do the thing. It's just like, okay, you have a highly levered thing where you're, you're just over, you're overbuilding capacity.Demand is growing, but not as fast as you hoped. And then boom, bankrupt. Right. And, and then it, and then it's like they say about the hotel industry, which is, it's always the third owner of a hotel that makes money. It has to go bankrupt twice, right? You have to wash out all of the over optimistic exuberance before it gets to actually a stable state.And then it makes money. So by the way, all of those data centers and all of those, all the fiber that they're in use, it's all in use today. Yeah. But 25 years later. But it, it, it took, and actually the elapsed time was, it took 15 years. It took 15 years from 2000 to 2015 to actually fill, fill up all that capacity.The cautionary warning is the, the overbuild can happen. Um, and, and, and, and, you know, you, you get into this thing where basically everybody, everybody who basically has any sort of institutional capital, it's like, wow. It's just, I, I don't know how to invest in these crazy software things. For sure I can put build data centers and for sure I can buy GPUs that I can deploy, you know, compute grids and, and all these things.Um, and so, you know, if you're a pessimist, you could look at this and you could say, wow, this is like really set up to be able to basically replicate, you know, what we went through, what we went through in 2000. Obviously that would be bad. The counter argument, which is the one I I agree with, which is the counter on, on the other side is a couple things.One is the companies that are investing all the, the companies that are investing the money are like the bluest chip of companies. And so back, back, back in the, in the do, like Global Crossing was like a, it was like an entrepreneur. It was like a, a new venture, but like the money that's being deployed now at scale is Microsoft, and, you know, and Amazon and Google, Facebook and Facebook and Nvidia and, you know, these, these, these, and, and now you know, by the way, open ai philanthropic, which are now at like, you know, really serious size, um, you know, as companies with, you know, very serious revenue.These are very large scale companies with like, lots, lots of cash, lots of debt capacity that they've, they've never used. And so th this is institutional in a way that, that really wasn't at the time. And then the other is, at least for now, every dollar that's being put into anything that results in a running GPU is being turned into revenue right away.Like so, and you guys know this, like everybody's starved for capacity, everybody's starved for compute capacity and then, you know, all the associated things, memory and, and, and interconnected and everything else. Um, data center space. And so e every dollar right now that's being put into the ground is turning into revenue.And, and it, and in fact, I actually think there's an interesting thing happening, which is because everybody starve for capacity, the models that we actually have that we can use today are inferior versions of what we would have if not for the supply constraints. That's true. Um, if Right pose a hypothetical universe in which GPUs were 10 times cheaper and 10 times more plentiful mm-hmm.The models would be much better. ‘cause you would just allocate a lot more money to training and you'd just build better models and they would be better. Um, and so we're, we're actually getting the sandbag version of the technology.swyx: Yeah. No. Everything we use is quantized because the, the labs have to keep the, the full versions,Marc: right?swyx: LikeMarc: we're not even getting the good stuff.swyx: Yeah.Marc: But, but getting the good stuff, it's, it's just, even if technical progress stops. Once there's like a much bigger build of like GPU manufacturing capacity and memory, you know, all, all the things that have to happen in the course of the next five or 10 years.Once it happens, even the current technology is gonna get, gonna get much better. And then as you know, like there's just like a million ways to use this stuff. Like there's just like a million use cases for this. Mm-hmm. Like, it, it, you know, this isn't just sending packets across a, a thing, whatever, and hoping that people find something to do with it.This is just like, oh, we apply intelligence into every domain of human activity. And then it works like incredibly well. Yeah. Um. Here's what I know, here's what I know. Um, in the next three or four year, it's like somewhere between three or four years out, basically everything is selling out. So like the, the entire supply chain is, is, is, is sold out or, or, or selling out.And so there, there's no, like, we're just gonna have like chronic supply shortage for, you know, for years to come. Um, there's going to be a response from the market that's gonna result in an enormous, you know, it's happening now. An enormous flood of investment in a new fab capacity and ev you know, every, everything else to be able to do that, at some point the supply chain constraints will unlock, you know, at least to some degree that will be another accelerant to industry growth when that happens.‘cause the products will get better and everything will get cheaper. Um, and so, so I know that's gonna happen. I know that, you know, the deployments, you know, the, the actual use cases are like really compelling. And then, like I said, you know, with reasoning and agents and so forth, like, I know they're just gonna get like much, much better from here.And so I, I, I know the capabilities are like really real and serious. I also know that the technical progress is not going to stop. It. It, it is excel. It is, is accelerating. Like the, the breakthroughs are are tremendous. I mean, even just month over month, the breakthroughs are really dramatic. And so, you know, I think if you were a cynic and there, there are cynics, you can look at 2000, you can find echoes.But I can't even imagine betting it that this is gonna like somehow disappoint and, you know, at least for years to come, I think it would be essentially suicidal to make that bet. Yeah. Um, it was that Michael Burry, uh, uh, that'sswyx: anMarc: interesting guy, huh? We'll pick on a guy. We'll pick, let's pick on one guy.We'll pick. Well ‘cause he did, he he came out with, it was, it was the, heswyx: doesn't mind.Marc: It was the Nvidia short. Right. He came with the Nvidia short. And then if you guys probably talked about this, which is the, the analysis now that like the current models are getting better faster at such a rate that if you are running an Nvidia, if you're running an Nvidia inference chip today, that's three years old, you're making more money on it today than you did three years ago because the pace of improvement of the software is, is faster than the, the, the depreciation cycle, the chip.And then my understanding is Google is running. I don't if they've, I don't know exactly what, uh, these are rumors that I've heard or maybe it's public, but, um, I think Google's running very old TPUs, very profitably. Ference. Yeah. And very profit and very profitably. Yeah. Um, and so, so it actually turns out, as far as I can tell, it's actually the opposite of the Beery thesis is actually.He was actually 180 degrees wrong. It's actually the, the, the, the old Nvidia chips are getting more valuable, which is something that's like literally never happened before. Like it's never been the case that you have an older model chip that becomes more valuable, not less valuable. And that, and again, that's an expression of the just ferocious pace of software progress.Ferocious pace of capability payoff. Yeah. Uh, that you're getting on the other side of this. And so I just, the idea of betting against that, like.swyx: Yeah. Yeah. Well, one ofMarc: my, it seems like an invitation to get your face ripped up.swyx: One of my early hits was like modeling the lifespan of the H 100 and h two hundreds and, and going like, you know, usually they advise like four to seven years and it was, you know, maybe you sort of realistically haircut cut it down to two to three.Yeah. But actually it's going up and not down. Yeah. And, and uh, that's, I mean that's, I think that's the dream. Uh, we are finding utilization and I think utilization solves all problems. Like, you can, you can find use, use cases for even like the poor, like even memory, we're having a shortage. Right. And, and even like the, the shittier versions of, of memory that we do have, we are finding use cases for it.So like That's great.Marc: Yeah.Alessio: How, how important is open source AI and kinda like edge inference in a world in which you have three years of supply crunch. Like, do you think in the, like, you know, if you fast forward like five years, like how do you think about inference, uh, in the data center versus at the edge?Marc: Well, so just to start, yeah. So I think, I think open source is very important for a bunch of reasons. I think edge, edge inference is very important for a bunch of reasons. I, I think just practically speaking, if we're just gonna have fundamental construc, supply crunches for the next, I mean, you, you guys know if you just project forward demand over the next three years, right?Yeah. Relative to supply, one of the, its main predictions you can do is what's gonna, what, what's gonna happen to the cost of, of inference in the core, uh, over the next three years? And like, it may rise dramatically, right? Like, so, so what is, and then is, is, you know, like the, the, the big model competition are subsidizing heavily right now.Right? Right. And so, so what's the, what will be the average person's, you know, per day, per month token cost, you know, three years from now to do all the things that they want to do. And I, I don't know, it's gonna. I mean, I have, you guys probably have friends, I have friends today who are paying a thousand dollars a day for open claw, for claw tokens to run open claw.Right? And so, okay. $30,000 a month. Right? And, and by the way, those, those friends have like a thousand more ideas of the things that they want their claw to do, right? Yeah. And so you, you could imagine there, there's like latent demand of up to, I don't know, five or $10,000 a day of, of, of tokens for a fully deployed, you know, per personal agent.Uh, and obviously consumers can't pay that, right? And so, so, but it gives you a sense of the fu of the fu of the future scope of demand, right? And so, so even, even if there's a 10 x improvement in price performance, that still, you know, goes to a hundred dollars a day, which is still way beyond what people can pay.Mm-hmm. So there's just gonna be like. Ferocious to me, by the way. The agent thing, the other interesting thing is I think the agent thing, so up until now, a lot of the constraints of GGPU constraints, I think the agent thing now also translates into CPU constraints. Mm-hmm. Right?swyx: CPU memory.Marc: Yes. CPU memory, right?And so, like the entire chip ecosystem is just gonna get wait,swyx: wait for network constraints, that that will be the killer.Marc: It's all bottleneck potentially for years. And so, so I, I think that Brad, and, and I think it's actually possible, I mean, generally inference costs are gonna keep coming down, but I think the, let's put it this way, the rate of decline, I think may level out here for a bit because of these supply constraints.And then at some point, maybe the lab stops subsidizing so much and that, that, that again, will be, be an issue. And so there's just gonna be so much more demand for inference than, than can be satisfied. Um, you know, kind of with the centralized model. And then, and then, you know, you guys know this, but like all the, just the dramatic, I mean just the dramatic innovations that have happened in the Apple silicon to be able to do, uh, inferences, it's quite amazing the level of effort being put.Like the open source guys are putting incredible effort into getting, you know, this recurring pattern where the big model will never run on a pc, and then six months later mm-hmm. Oh, it runs in a pc, right? It's like amazing. And there's very smart people working on that. So there's all that. And then look, there's also, you know.There's also like other, there's other motivators. There's other motivators which is just like, okay, how much trust are the big centralized model providers? You know, how much trust are they building in the market versus, you know, how much are, you know, at least for, in certain cases with some people, for certain use cases, people being like, well, I'm not willing to just like, turn everything over.So there, there, there's all the trust issues. Um, by the way, there's also just like straight up price optimization. There's many uses of AI where you don't need Einstein in the cloud. You just need like a, a a, a smart local model. There's also performance issues where you want, you know, you want, you know, you're gonna want your doorknob to have an AI model in it.Right. You know, to be able to, you know, do, um, you know, to be able to do access control. Um, obviously like everything with a chip is gonna have an AI model in it. Mm-hmm. And it, a lot of those are gonna be local. Um, and so, yeah. No, like I think, I think you're gonna have ti and then you're gonna, by the way, also wearable devices, you know, you don't wanna do a complete round trip.You want, you know, you, whatever your smart devices are, you want it to be like super low latency. Yeah.swyx: The question, do we care who makes it? Yeah. One of the biggest news this week was the collapse of AI two, the Allen Institute. Mm-hmm. One of the actual American open source model labs. Yeah. Um, and, uh, I'm not that optimistic on, on American open source.Yeah. Like you, you guys invested in MIS trial and MIS trial's doing extremely well outside of China. That's about it.Marc: Yeah. We'll see. We'll see. I look, I, number one, I do think we care. Uh, I do think we, I do think we care who makes it. Um, I would say this, the, the, the, the previous presidential administration wanted to kill it in the us Oh yeah.They wanted to drown in the bathtub. Um, and so they wanted to kill it. So at least we have a government now that actually like, actually wants it wants it to happen. And youswyx: earned to councilMarc: and Yeah. And the new and the P pcast. Yeah. So the, the, you know, this admin for whatever other political issues people have, which are many, you know, this administration has, I think a very enlightened view and in particular an enlightened view on AI and in particular on open source ai.Uh, and so they're very supportive. Um, my read is the Chi. The Chinese have a very, the various Chinese companies have a very specific reason to do open source, which is, they, they, they don't fundamentally, they don't think they can sell commercial, uh, AI outside of China right now. And or at least specifically not, not in the US for a combination of reasons.And so they, they kind of view, I think, open source AI as a bit of a loss leader against basically domestic, uh, you know, paid, paid services. And then kind of an, you know, kind of an ancillary products. You know, they're, they're very excited about it, by the way. I think it's great. I think it's great that they're doing it.Um, you know, I think Deeps seek was like a gift to the world. Um, I think. The great thing about open source, open source, the, the, the impact of open source is felt two ways. One is you, you get the software for free, but the other is you get to learn how it works, right? And so like the paper, the paper, the paper and, and the code, right?And the code. And so, like, for example, I thought this was amazing. So open comes out with L one and it's an amazing technical breakthrough, and it's just like, absolutely fantastic. But of course they don't explain how it works in detail. And then of course they hide the, they hide the reasoning traces, right?And, and then, and then, and then everybody's like, okay, this is great, but like, who's gonna be able to replicate this? Are other people gonna be able to do this? You know, is their secret sauce in there? And then our one comes out and it's just like, there's the code and there's the paper, and now the whole world knows how to do it.And then, you know, three months later, every other AI model is, is adding reasoning. And so, so you get this kind of double, like even if the Chinese models themselves are not the models that get used, the education that's taken place to the rest of the world, the information diffusion, you know, is incredibly powerful.So that happens and then, I don't know. We'll, we'll see. You know, there are a bunch of American, you know, open source, you know, ai, uh, model companies. I mean, look, there's gonna be tremendous, you know, there already is. There's, you know, there's gonna be tre there's tremendous competition, uh, among the primary model companies.You know, there's, depending on how you count, there's like four or five, you know, big co model companies now that are, you know, kind of neck and neck, uh, in different ways. Um, uh, you know, and, and, and, um, you know, and then obviously Bo Bo both X and then MetAware involved are, you know, both have huge, you know, huge attempts to, you know, kind of, to kind of leapfrog underway.And then you've got, you know, a whole fleet of startups, new companies, including a whole bunch that we're backing, that are, you know, trying to come out with different approaches. And then you've got whatever it is. I don't know how, how many, how many, like main line foundation model companies are there in China at this point?It's probably six. It'sswyx: five Tigers is what they call it. Yeah. Uh, Quinn is in questionable because there's change in leadership,Marc: right?swyx: Yeah.Marc: But that, does that include, that includes like Moonshot,swyx: yes. Can deep seek, uh, uh, ZI, um, Quinn oh one is in there.Marc: Right. And then, um, and by dance and, and then you see,swyx: ance would be like the next tier ance.They weren't as prominent. They weren't, didn't haveMarc: a leading. Yeah. But they, you at least, you know, ance is very inspiring and presumably they have more stuff coming and Tencent probably has more stuff coming and, and so forth. And so, so, so like, look, here, here would be a thing you can anticipate, which is there are not these markets, there are not going to be between the US and China right now, there's like a dozen primary foundation model companies that are like at scale, at, at some level of a critical mass.It's not gonna be a dozen in three years, right? Like, it just because these industries don't bear a dozen, it's, it's gonna be three or you know, there's gonna be three or four big winners or maybe one or two big winners. And so there's gonna be like a whole bunch of those guys that are gonna have to figure out alternate strategies.Um, and I think like open source is one of those strategies. And so I, I think you could see like a whole, i, I, I think the questions like, who's gonna do open source? I think that could change really fast. I, I think that, that, that's a very dynamic thing. I think it's very hard to predict what happens. And, and I think it's very important.swyx: NVIDIA's doing a lot.Marc: Well, I was gonna say. Well, exactly. And then you're got Nvidia and then, and then, you know, just to, again, indu, there's an old thing in business strategy, which is called, uh, commoditize Compliments. Commoditize the compliment. That's right. And so if your Jensen is just kind of obvious, of course, you wanna commoditize the software.Yeah. And he's, and to his enormous credit, he's putting enormous resources behind that. And so maybe it, maybe it's literally Nvidia and I think that would be great.Alessio: Yeah. Uh, narrative violation to European projects, uh, in the, uh, damn.swyx: I'm hosting my, uh, Europe, uh, conference soon. And I got both of them.Alessio: They got us.They got us. MarkMarc: finished. They got us, us. Well, wait a minute. Where was Peter? So where was Steinberger when he did? In AustriaAlessio: was, yeah, yeah, yeah.Marc: He was in what? He was in Vienna. Oh, he was in Vienna. And then where is he now?swyx: Uh, he's moving to sf.Marc: Okay. Okay. Alright. Okay, there we go. And then, yeah, the PI guy, right?The PI guys are European.swyx: Yeah, they're also, they're buddies inAlessio: Australia. Mario's also there. Yeah.Marc: Right. And are they, yeah, they haven't announced yet. Any sort of change changed or have theyAlessio: No, they're, they have a company there.Marc: Okay. Got, okay. Good.Alessio: Good, good,good.Alessio: Um,Marc: yeah, good.swyx: Anyways, I think pie and open cloud very important software things and, and I just wanted you to just go off on what you think.Marc: Yeah. So I think in co the, the combination of the two of them I think is one of the 10 most important softwares. Openswyx: Claw got all the attention, but Right. Talk about pie,Marc: pi pie's, kind of the Yeah. PI's, PI's kind of the architectural breakthrough for those of us who are older. There was this whole thing that was very important in the world of software basically from like 1970 to, I don't know, it still is very important, but like 19, from 1973 to like basically the creation of Linux, which is basically this, this thing used to call like the Unix mindset.Like so, so, ‘cause there were all these different, you know, theories. There are all these different operating systems and mainframes and, and then you know, all these windows and Mac and all these things. And then there was this, but kind of behind it all was this idea of kind of the Unix mindset. And the Unix mindset was this thing where basically you don't have these, like, like in the old days, like, like the operating system that like made the computer industry really work, like in the 1960s mm-hmm.Was this thing called o os 360, which was this big operating system that IBM developed that was supposed to basically run everything. And it was this like giant monolithic architecture in the sky. It was like a, you know, it was like a giant castle. Um, of software. And, and by the way, it worked really well and they were very successful with it.But like, it was this huge castle in the sky, but it was this thing, it was almost unapproachable, which is like, you had to be kind of inside IBM or very close to IBM. And you had to really understand every aspect, how the system worked. And then the, the Unix sky is originally out of at and t and then out out of Berkeley, um, you know, came out and they said, no, let's have a completely different architecture.And the way architecture's gonna work is we're gonna have, we're gonna have a, a prompt and, and a, and a shell. And then, and then we're gonna, all, all the functionality is gonna be in the form of these discreet modules, and then you're gonna be able to chain the modules together. Mm-hmm. Yeah. And so like the, the, the op, it's almost like the operating, operating system itself is gonna be a programming language.Um, and then that led led to the, the, the sort of centrality of the shell. Um, and then that led to sort of, uh, you know, basically chaining together Unix tools. And then that led to the emergence of these, these scripting languages like Pearl, where you, you could basically kind of very easily do this, and then the shells got more sophisticated and then, and then, and then look like, you know, that, that, that number one, that worked and that, that was the world I grew up in.Like I was, I was a Unix guy. You know, sort of from, call it 1988 to, you know, kind of all, all the way through my work and it worked really well. It, it's in the background, um, you know, nor normal people don't need to, didn't need to necessarily know about it, but like, if you were doing like system architecture, application development, you, you, you knew all about it.Um, and then, you know, it's been in the background ever since. And, you know, look, your Mac still has a Unix shell, you know, kind of in there, and your iPhone still has a Unix shell kind of buried in there somewhere. So they're kind of in there. And then, you know, the Windows shell is kind of a, you know, sort of a weird derivative of that.But, um, you know, but look, the inter, the internet runs on Unix, um, and that smartphones, actually, both iOS and Android are Unix derivatives. And so, you know, kind of Unix did end up winning. But, but anyway, and then we just started taking that for granted. And then, and then so, so basically the, the way I think about what happened with Pie and then with Open Claw is basically what those guys figured out is, I always say the, the great breakthroughs are obvious in retrospect, right?Which is the best kind, the best kind. They weren't obvious at the time or somebody else would've done them already. Um, and so there is a, like a real conceptual leap, but then you look at it sort of the backwards looking and you're just like, oh, of course. Mm-hmm. Like the, the, to me those are always the best breakthroughs.Well, actually language models themselves are like that. It's just like, oh, next token completion. Oh, of course.swyx: Yeah. What other objective mattered?Marc: Yeah, exactly. But, but like it, right. But she's even saying it wasn't obvious until somebody actually did it. Right. And so the conceptual breakthrough is real and deep and powerful and, and very important.And so the way I think about pie and olaw is it's basically marrying the, the language model mindset to the un to the Unix, basically shell prompt mindset. And so it's, it's basically this idea that what, what, so what is an agent, right? And as, as, and as you know, like many smart people who have been trying to figure out what an agent is for, for, for decades, and they've had many architectures to build agents and the whole thing.And it turns out what is an agent. So it turns out what we now know is an agent is the following. It's, so it's a language model. And then above that, it's a ba, it's a bash shell. Um, so it's a, it's a Unix shell, and then it's, and then the agent has access, uh, has access to, to the shell. And, you know, hopeful, hopefully in a sandbox, maybe in, maybe in a sandbox.So it's, it's the model. Um, it's the shell. Um, and then it's a fi, it's a file system. Um, and then the state is stored in files. And then, you know, there's the markdown format for the, you know, for, for the files themselves. And then, and then there's basically what in Unix is called Aron job. There's a loop and then there's a heartbeat for the, there's heartbeat and, and the thing basically Wake Wakes up.Wakes up. So it's basically LLM plus shell, plus file system, plus markdown, plus kron. And it turns out that's an agent. And, and, and every part of that, other than the model is something that we already completely know and understand. And in fact, it turns out that like the latent power of the Unix shell is like extraordinary because basically like all, like, there's just like an, there's just enormous latent power in the shell.There's enormous numbers of Unix commands, there's enormous number of command line interfaces into all kinds of things already in the, you know, your entire, I mean your entire, just to start with, your computer runs on a shell. If you're running a Mac or a, or, or a phone, your computer, your computer's running on a shell, uh, already.And so like the full power of your computer is available at the command line level. Um, and then it turns out it's really easy to expose other functions as a command line interface. And so like this whole idea where we need like MCP and these like product mm-hmm. Fancy protocols, whatever, it's like, no, we don't, we just need like a command, command line thing.So that's the architecture. And then it turns out what is your agent? Your agent has a bunch of files starting a file system. And then there's the thing that just like completely blew my mind when I write my head around it as a result of this, which is like, okay. This means your agent is now actually independent of the model that it's running on.Because you can actually swap out a different LLM underneath your agent and your, your agent will change personality somewhat. ‘cause the model is different, but all of the state stored in the files will be retained.swyx: Yeah. Different instruction set, but you just compiledit.Marc: Right, exactly. And it's all right.It's like right. Swapping out a ship and recompiling, but it's, it's still, it's still your agent with all of its memories. Um, and with all of its capabilities. And then by the way, you can also swap out the shell, uh, so you can move it to a different execution environment that is also, is also a b shell, by the way, you can also switch out the file system, right.Uh, and you can, and you can, and you can swap out the, the, the heartbeat for the, the crown framework, the, the loop that the agent framework itself. And so your agent basically is ba basically at the end of the day, it's just. It's just, its files. Um, and then, and then there's of course it a openswyx: call.Marc: Yeah, it's, it's basically, it's, it's just the files.Um, and then by the way, as a consequence of that, the agent and then the agent itself, it turns out a couple important things. So one is it, it's, it, it can migrate itself, right? And so you're, you can instruct your agent, migrate yourself to a different, uh, runtime environment, migrate yourself to a different file system, migrate yourself to a different, you know, swap out the language model.Your agent will do all that stuff for you. And then there's the final thing, which is just amazing, which is the agent is the agent actually has full introspection. It actually, it actually knows about its own files and it could rewrite its own files. Right. Which by the way, is basically no widely deployed software system in history where the, the, the thing that you're using actually has full introspective knowledge of how it itself works and is able to modify itself.Like that, that, I mean, there have been toy systems that have had that, but there, there's never been a widely deployed system that has that capability and then that leads you to the capability. That just like completely blew my mind when I wrap my head around it, which is you can tell the agent to add new functions and features to itself and it can do that.Extend yourself. Yeah. Right? Extend, extend yourself. Like extend yourself. Give yourself a new capability. Right? And so, and so literally it's just like you run into somebody at a party and they're like, oh, I have my open claw, do whatever, connect to my eat, sleep bed, and it gives me better advice and sleep.And you go home at night and you tell your claw, or if they're at the party, by the way, you tell your claw, oh, add this capability to yourself. And your claw will say, oh, okay, no problem. And it'll go out on the internet and it'll figure out whatever it needs and then it'll go out to claw code or whatever.It'll write whatever it needs. And then the next thing you know, it has this new capability. And so you don't even have to, like, you can have it upgrade itself without even having to, without having to do anything other than tell it that you want it to do that. And so anyway, so the, the combination of all this is just, I mean, this is just like a massive, incredible, I mean, it's just incredible.Like if I, if I were, if I were 18, like this is a hundred, this is what I would be spending all of my time on. This is like such an incredible conceptual breakthrough. Yeah. And again, pe people are gonna look at it and they already get this response. People are gonna look at it and they're gonna say, oh, well, where's the breakthrough?‘cause these, the, all of these components were already known before. Mm-hmm. But, but this is the key, the key to the breakthrough was by using all these components that were known before, you get all of the underlying capability of that's buried in there. And so all, and so for example, computer use all of a sudden just kind of falls, trivi, trivial.Of course it's gonna be able to use your computer. It has full access to the shell. Right. And then, and then you just, you, you give it access to a browser, and then you've got the computer and the browser and, and often away it goes. And, and then you've got all the abilities of the browser also. Um, yeah.And so, and so the capability unlock here is profound. My friends who are, you know, deepest into this, are having their claw do like a, like, literally like a thousand things in their lives. They have new ideas every day. They're just like constantly throwing new challenges at the thing. And by the way, it's early and, you know, these are, you know, these are prototypes and there are, you know, as you guys know, there's security issues.Yeah. And, and so, you know, there's a bunch of stuff to be ironed out, but the, the unlock of capability is just incredible.swyx: Yeah.Marc: And I, I have absolutely no doubt that everybody in the world is gonna, is gonna have at least, you know, an agent like this, if not an entire family of agents. And w

Join Dr. Ben Holmes, CEO and Co-Founder of Nanochon, for a strategic look at the high-stakes world of medical device innovation. With a Ph.D. in biomedical engineering and a background in aerospace engineering, Ben has spent nearly a decade navigating the "long runway" of medtech. In this episode, we discuss how Nanochon is moving toward its first-in-human clinical trials with the Chondrograft™—a 3D-printed synthetic implant—and how founders can create tangible enterprise value years before their first dollar of commercial revenue.

Vera C. Rubin Searching for Planet XWormwoodThe Third Trumpet: Wormwood is the central feature of the third trumpet judgment, a series of plagues preceding the end times.The Burning Star: Revelation 8:11 describes a great star, blazing like a torch, falling from heaven onto the world's rivers and springs.Catastrophic Poisoning: The star's name is "Wormwood," and it turns one-third of all fresh water into bitter, deadly water, according to Bible Gateway. This event results in many deaths, notes this YouTube video.NibiruOrigin: The concept stems from interpretations by Zecharia Sitchin, who claimed ancient Sumerian texts described a 12th celestial body (Nibiru) with a 3,600-year elliptical orbit.Conspiracy Theory: Nancy Lieder, founder of ZetaTalk, popularized the idea in 1995, claiming extraterrestrials warned her of an impending pole shift caused by this planet.Scientific Refutation: Scientists point out that a large planet entering the inner solar system would be easily visible to the naked eye and would have caused observable, devastating gravitational disruptions to other planets.Confusion with Real Science: The myth is sometimes falsely linked to the legitimate scientific search for a "Planet Nine," which is believed to exist far beyond Pluto and poses no danger to Earth.Planet X, or Planet Nine, is a hypothetical Neptune-sized planet thought to exist in the extreme outer solar system, with a mass up to 10 times that of Earth and an orbit 20 times farther than Neptune. It is not directly observed but is suggested by gravitational anomalies in the orbits of distant Kuiper Belt objects.Location & Orbit: Located in the far outer solar system, it may take 15,000 to 20,000 Earth years to complete one orbit around the sun.Why It's Hard to Find: Due to its immense distance from the sun, it receives little light, making it nearly invisible and extremely difficult to detect with current telescopes.Vera C. Rubin Observatory'Revolutionary': Vera C. Rubin Observatory found 800,000 objects of interest in a single nightThe Vera C. Rubin Observatory sent scientists nearly 1 million astronomy alerts in one night, showing off changes in the sky. Eventually, the telescope is expected to reach 7 million alerts per night.The telescope, which scans the full sky from its perch atop Cerro Pachón mountain in Chile, produced the alerts to direct scientists to "new asteroids, exploding stars, and other changes in the night sky," representatives for the U.S. National Science Foundation (NSF) said in a statement."Scientists will have a greater ability to catch supernovae in their earliest moments, discover and track asteroids to assess potential threats to Earth, and spot rare interstellar objects as they race through the solar system," NSF representatives wrote in the statement.Rubin's alert system is starting up shortly before the observatory begins a 10-year program, known as the Legacy Survey of Space and Time (LSST), later this year. Rubin will do nightly sky scans to generate an image of the entire Southern Hemisphere sky every few nights, using the largest-ever digital camera to spot any changes in the view overhead.The observatory's debut images, released in June 2025, revealed more than 10 million galaxies in and around the Virgo ClusterBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-tempest-universe--4712510/support.Please follow the #podcast on YouTube: https://www.youtube.com/@TheTempestUniversePodcast?sub_confirmation=1

Vera C. Rubin Searching for Planet XWormwoodThe Third Trumpet: Wormwood is the central feature of the third trumpet judgment, a series of plagues preceding the end times.The Burning Star: Revelation 8:11 describes a great star, blazing like a torch, falling from heaven onto the world's rivers and springs.Catastrophic Poisoning: The star's name is "Wormwood," and it turns one-third of all fresh water into bitter, deadly water, according to Bible Gateway. This event results in many deaths, notes this YouTube video.NibiruOrigin: The concept stems from interpretations by Zecharia Sitchin, who claimed ancient Sumerian texts described a 12th celestial body (Nibiru) with a 3,600-year elliptical orbit.Conspiracy Theory: Nancy Lieder, founder of ZetaTalk, popularized the idea in 1995, claiming extraterrestrials warned her of an impending pole shift caused by this planet.Scientific Refutation: Scientists point out that a large planet entering the inner solar system would be easily visible to the naked eye and would have caused observable, devastating gravitational disruptions to other planets.Confusion with Real Science: The myth is sometimes falsely linked to the legitimate scientific search for a "Planet Nine," which is believed to exist far beyond Pluto and poses no danger to Earth.Planet X, or Planet Nine, is a hypothetical Neptune-sized planet thought to exist in the extreme outer solar system, with a mass up to 10 times that of Earth and an orbit 20 times farther than Neptune. It is not directly observed but is suggested by gravitational anomalies in the orbits of distant Kuiper Belt objects.Location & Orbit: Located in the far outer solar system, it may take 15,000 to 20,000 Earth years to complete one orbit around the sun.Why It's Hard to Find: Due to its immense distance from the sun, it receives little light, making it nearly invisible and extremely difficult to detect with current telescopes.Vera C. Rubin Observatory'Revolutionary': Vera C. Rubin Observatory found 800,000 objects of interest in a single nightThe Vera C. Rubin Observatory sent scientists nearly 1 million astronomy alerts in one night, showing off changes in the sky. Eventually, the telescope is expected to reach 7 million alerts per night.The telescope, which scans the full sky from its perch atop Cerro Pachón mountain in Chile, produced the alerts to direct scientists to "new asteroids, exploding stars, and other changes in the night sky," representatives for the U.S. National Science Foundation (NSF) said in a statement."Scientists will have a greater ability to catch supernovae in their earliest moments, discover and track asteroids to assess potential threats to Earth, and spot rare interstellar objects as they race through the solar system," NSF representatives wrote in the statement.Rubin's alert system is starting up shortly before the observatory begins a 10-year program, known as the Legacy Survey of Space and Time (LSST), later this year. Rubin will do nightly sky scans to generate an image of the entire Southern Hemisphere sky every few nights, using the largest-ever digital camera to spot any changes in the view overhead.The observatory's debut images, released in June 2025, revealed more than 10 million galaxies in and around the Virgo ClusterBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-tempest-universe--4712510/support.Please follow the #podcast on YouTube: https://www.youtube.com/@TheTempestUniversePodcast?sub_confirmation=1

This week, in honor of Women's History Month, we're presenting two stories from our archive about women in science and the unique challenges they face. Part 1: Alison Williams' blossoming passion for chemistry is sidetracked by a professor's thoughtless comment. Part 2: Climate scientist Sarah Myhre becomes embroiled in conflict after speaking out against a senior scientist's problematic statements about climate change. Alison Williams is the Associate Provost for Diversity and Intercultural Education at Denison University. She received her Ph.D. in biophysical chemistry from the University of Rochester where she was a NSF graduate fellow and winner of the graduate student teaching award. Prior to becoming an administrator first at Oberlin and now at Denison, she was a chemistry faculty member for 25 years, teaching at Swarthmore, Wesleyan, Princeton and Barnard College of Columbia University. Her research focused using spectroscopy to determine the role of ions in shaping the physical properties of nucleic acids. Dr. Williams has been active nationally to increase access, inclusion and equity, especially in the sciences. She has received numerous recognitions for her teaching, outreach and mentoring activities. She is a mother of two and a semi-professional oboist.Sarah Myhre Ph.D. is a Research Associate at the University of Washington and a board member of both 500 Women Scientists and the Center for Women and Democracy. She is actively investigating and publishing on the paleoceanographic history of the Pacific ocean, using ocean sediment cores and robots on the seafloor. She is a freelance writer, grass roots organizer, and a leading voice in the field science communication. She is also an uncompromising advocate for women's voices and leadership, both in science and society. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In Ghana, much as in other parts of the Global South, postcolonial leaders aimed for industrial growth through the establishment of affordable hydroelectric power. However, in the current rapidly changing climate, many nations face recurring droughts, which hinder electricity production just when demand is on the rise. This situation has led to challenges like load shedding and unplanned power outages, which have strained the bond between citizens and the government. Negotiating Power and Inequality in Ghana: Electricity and Citizenship as Reciprocity (Indiana UP, 2026) aims to unravel the puzzling reality that, despite enduring increasing difficulties from these electricity shortages, the Ghanaian citizens who suffer most harshly are also the least likely to demand political accountability from the state. Drawing on archival evidence, focus groups, qualitative interviews, survey data, and contemporary art and music, author Lauren M. MacLean explains how this disparity in experience—fueled by differences in income and geographical location—has led lower- and higher-income Ghanaians to form contrasting perspectives on their social rights regarding public services and to adopt varying approaches to political involvement. Rather than relying on a predetermined social contract, citizens in Ghana develop a more fluid relationship with the state, shaped by their histories, identities, and personal experiences. This reciprocity highlights their awareness of how climate change and the global shift toward green energy can significantly impact their lives while also underscoring the necessity for the government to take the lead and engage with Ghanaians to promote climate justice. Lauren M. MacLean is the Thomas P. O'Neill Chair of Public Life and Department Chair of Political Science at Northeastern University. Her research focuses on the politics of electricity access and the everyday practice of citizenship in Africa. She conducts fieldwork in Ghana and Kenya, collecting survey data from individuals, conducting focus group discussions, doing archival work, and carrying out qualitative interviews with politicians, policymakers, practitioners, and ordinary people. MacLean has published award-winning books and articles, including: Informal Institutions and Citizenship in Rural Africa (Cambridge, 2010), The Politics of Non-State Social Welfare in the Global South (Cornell, 2014), co-edited with Cammett, and Field Research in Political Science (Cambridge, 2015), coauthored with Kapiszewski and Read. Her research has been published in a wide range of journals and supported by grants, including NSF, SSRC, RWJ, Fulbright-Hays, and Carnegie. She was the recipient of the APSA QMMR 2016 David Collier Mid-Career Achievement Award. You can learn more about her work here. Afua Baafi Quarshie is a Ph.D. candidate in history at the Johns Hopkins University. Her research focuses on mothering and childhood in post-independence Ghana. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

In Ghana, much as in other parts of the Global South, postcolonial leaders aimed for industrial growth through the establishment of affordable hydroelectric power. However, in the current rapidly changing climate, many nations face recurring droughts, which hinder electricity production just when demand is on the rise. This situation has led to challenges like load shedding and unplanned power outages, which have strained the bond between citizens and the government. Negotiating Power and Inequality in Ghana: Electricity and Citizenship as Reciprocity (Indiana UP, 2026) aims to unravel the puzzling reality that, despite enduring increasing difficulties from these electricity shortages, the Ghanaian citizens who suffer most harshly are also the least likely to demand political accountability from the state. Drawing on archival evidence, focus groups, qualitative interviews, survey data, and contemporary art and music, author Lauren M. MacLean explains how this disparity in experience—fueled by differences in income and geographical location—has led lower- and higher-income Ghanaians to form contrasting perspectives on their social rights regarding public services and to adopt varying approaches to political involvement. Rather than relying on a predetermined social contract, citizens in Ghana develop a more fluid relationship with the state, shaped by their histories, identities, and personal experiences. This reciprocity highlights their awareness of how climate change and the global shift toward green energy can significantly impact their lives while also underscoring the necessity for the government to take the lead and engage with Ghanaians to promote climate justice. Lauren M. MacLean is the Thomas P. O'Neill Chair of Public Life and Department Chair of Political Science at Northeastern University. Her research focuses on the politics of electricity access and the everyday practice of citizenship in Africa. She conducts fieldwork in Ghana and Kenya, collecting survey data from individuals, conducting focus group discussions, doing archival work, and carrying out qualitative interviews with politicians, policymakers, practitioners, and ordinary people. MacLean has published award-winning books and articles, including: Informal Institutions and Citizenship in Rural Africa (Cambridge, 2010), The Politics of Non-State Social Welfare in the Global South (Cornell, 2014), co-edited with Cammett, and Field Research in Political Science (Cambridge, 2015), coauthored with Kapiszewski and Read. Her research has been published in a wide range of journals and supported by grants, including NSF, SSRC, RWJ, Fulbright-Hays, and Carnegie. She was the recipient of the APSA QMMR 2016 David Collier Mid-Career Achievement Award. You can learn more about her work here. Afua Baafi Quarshie is a Ph.D. candidate in history at the Johns Hopkins University. Her research focuses on mothering and childhood in post-independence Ghana. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/political-science

The Dark Energy Survey Collaboration collected information on hundreds of millions of galaxies across the Universe using the U.S. Department of Energy-fabricated Dark Energy Camera, mounted on the U.S. National Science Foundation Víctor M. Blanco 4-meter Telescope at CTIO, a Program of NSF NOIRLab. Their completed analysis combines all six years of data for the first time and yields constraints on the Universe's expansion history that are twice as tight as past analyses. In this podcast, Dr. Yuanyuan Zhang discusses the Dark Energy Survey results and how they inform the next steps in dark energy research.   Bios:  Rob Sparks is in the Communications, Education and Engagement group at NSF's NOIRLab in Tucson, Arizona. Dr. Yuanyuan Zhang is an Assistant Astronomer at NSF's NOIRLab. Her research interest is on galaxy clusters and large scale structures. She is heavily involved in the Dark Energy Survey (DES) and the LSST Dark Energy Science Collaborations.   We've added a new way to donate to 365 Days of Astronomy to support editing, hosting, and production costs.  Just visit: https://www.patreon.com/365DaysOfAstronomy and donate as much as you can! Share the podcast with your friends and send the Patreon link to them too!  Every bit helps! Thank you! ------------------------------------ Do go visit http://www.redbubble.com/people/CosmoQuestX/shop for cool Astronomy Cast and CosmoQuest t-shirts, coffee mugs and other awesomeness! http://cosmoquest.org/Donate This show is made possible through your donations.  Thank you! (Haven't donated? It's not too late! Just click!) ------------------------------------ The 365 Days of Astronomy Podcast is produced by the Planetary Science Institute. http://www.psi.edu Visit us on the web at 365DaysOfAstronomy.org or email us at info@365DaysOfAstronomy.org.

Science likes to call itself a meritocracy. Angela Anderson and Brandi Mattson know better. Both served as editors at elite journals (Cell and Neuron), where a single decision could determine who gets tenure, funding, or obscurity. They watched brilliant data get filtered out because the authors did not know the unwritten rules controlled by 5 dominant publishing houses with profit margins higher than Google.In 2020, amid pandemic shutdowns and national reckoning over racial injustice, they co-founded a nonprofit to expose that hidden curriculum. Through the JEDI program, they provide 10 hours of free editorial consulting to scientists who lack access to elite networks. In 1 year alone, 25 awards helped researchers salvage canceled grants, secure NSF career funding, and rebuild careers derailed by rejection.This episode pulls back the curtain on the multibillion dollar publishing engine that profits from taxpayer funded science and reveals who gets heard, who gets sidelined, and how insiders are choosing to redistribute power.RELATED LINKSAngela AndersonBrandy MattsonLife Science EditorsLife Science Editors FoundationCellNeuronNational Science FoundationFEEDBACKLike this episode? Rate and review Out of Patients on your favorite podcast platform. For guest suggestions or sponsorship email podcasts@matthewzachary.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Creatine used to be something people mainly associated with athletes or bodybuilders—but lately it's been having a major moment in the wellness space. And it turns out the benefits may go far beyond muscle. In today's episode, I sit down with Dr. Dan Pardi, PhD to explore what creatine actually is, how it works in the body, and why researchers are now studying its role in brain health, energy production, and cognitive performance.Dr. Pardi is the Chief Health Officer at Qualia Life Sciences, where he leads education around healthspan and human performance. He holds a PhD in Cognitive Neuroscience from Leiden University and Stanford, and has advised elite military units, Fortune 500 companies, and startups through his consultancy, Vivendi Health. If you've been hearing about creatine everywhere lately and wondering whether it's worth adding to your routine, this episode will give you the science and context to understand why it's getting so much attention.Suggested Resources:Qualia creatine (use the code wellnstrong for a special discount)Creatine before vs after workoutsSend me a text!This episode is proudly sponsored by Theralogix.If you're doing all the “right” things but your hormones still feel off, Ovasitol is a clinically studied 40:1 inositol blend designed to support healthy insulin signaling, cycle regularity, and metabolic health (especially for women with PCOS). It's NSF certified, filler-free, and research-backed.Visit Theralogix.com and use code This episode is proudly sponsored by: SizzlefishLet's talk about fueling your body with the best nature has to offer. If you're looking for premium, sustainable seafood delivered straight to your door, you need to check out Sizzlefish! Head to sizzlefish.com and use my code “wellnstrong” at checkout for an exclusive discount on your first order. Trust me, you're going to taste the difference with Sizzlefish!Join the WellnStrong mailing list for exclusive content here!Want more of The How To Be WellnStrong Podcast? Subscribe to the YouTube channel. Follow Jacqueline: Instagram Pinterest TikTok Youtube To access notes from the show & full transcripts, head over to WellnStrong's Podcast Page

Dr. Lauren Kim speaks with Adarsh Mallepady and Dr. Cory Trankle about their large-scale retrospective study of nearly 4 million patients examining the risk of nephrogenic systemic fibrosis after gadolinium-based contrast administration in those with advanced renal dysfunction. They discuss how modern group 2 and macrocyclic agents were associated with an exceedingly low incidence of NSF, offering important reassurance for evidence-based contrast use and evolving clinical practice. Nephrogenic Systemic Fibrosis in Patients with Advanced RenalDysfunction Following Gadolinium-based Contrast Agents. Mallepally et al. Radiology 2025; 317(3):e251794.   

Episode 2769 - Vinnie Tortorich and Anna Vocino discuss trusting product certifications as well as make some fun announcements. https://vinnietortorich.com/2026/03/trusting-product-certifications-episode-2769 PLEASE SUPPORT OUR SPONSORS Pure Vitamin Club Pure Coffee Club NSNG® Foods VILLA CAPPELLI EAT HAPPY KITCHEN YOU CAN WATCH THIS EPISODE ON YOUTUBE - @FitnessConfidential Podcast Vinnie's workout videos are available to purchase! Choose from a 2-day, 4-day, or 6-day workout–or buy all three at a discount! TO PURCHASE VINNIE'S WORKOUT VIDEOS, CLICK THIS LINK: https://vinnietortorich.com/workout Trusting Product Certifications Anna was just on the Mike Rowe podcast, "The Way I Heard It". (2:00) Vinnie is excited about his guest, D-D Breaux, who is a legendary gymnastics coach. (7:00) D-D's episode has already been posted so that you can enjoy it sooner. You can listen to it here: https://sites.libsyn.com/40024/building-excellence-with-d-d-breaux-episode-2768 Vinnie wants to be back into skiing for the first time in nine years! (13:00) Anna received her Jaspr air scrubber. (22:00) Certification of products—what is that about, and is it real? (32:00) Anna explains how it works, and they discuss olive oil as an example. Compliance and scaling up manufacturing needs are important. Vinnie won't work with a company unless it is GMP- and NSF-certified. GMP is Good Manufacturing Practices. NSF is the National Science Foundation. Supplements can be suspect if they are manufactured in a facility without GMP or NSF practices. Certifications can matter and can also be abused. (48:00) Anna has a fantastic announcement: she is stepping into the shoes of the late Estelle Harris and becoming the voice of Mrs. Potato Head in Toy Story 5! (50:00) They discuss a little of the darker side of being an actor in L.A. (1:00:00) Did you miss it?: The NSNG® VIP group closed, but you can get onto the waitlist for next time by signing up at https://www.nsngvip.com/join. A New Sponsor Jaspr Air Scrubbers has a discount code, VINNIE, that gets you $300 off for a limited time. Jaspr offers a lifetime warranty. Go to Jaspr.co for more information or to purchase. (1:05:00) You can book a consultation with Vinnie to get guidance on your goals. https://vinnietortorich.com/phone-consultation-2/ More News Serena has added some of her clothing suggestions and beauty product suggestions to Vinnie's Amazon Recommended Products link. Self Care, Beauty, and Grooming Products that Actually Work! https://www.amazon.com/shop/vinnietortorich/list/3GPVU29UHHPMY?ref_=aipsflist Don't forget to check out Serena Scott Thomas on Days of Our Lives on the Peacock channel. "Dirty Keto" is available on Amazon! You can purchase or rent it here.https://amzn.to/4d9agj1 Please make sure to watch, rate, and review it! Eat Happy Italian, Anna's next cookbook, is available! You can go to https://eathappyitalian.com You can order it from Vinnie's Book Club. https://amzn.to/3ucIXm Anna's recipes are in her cookbooks, on her website, and on Substack —they will spice up your day! https://annavocino.substack.com/ PURCHASE DIRTY KETO (2024) The documentary launched in August 2024! Order it TODAY! This is Vinnie's fourth documentary in just over five years. Visit my new Documentaries HQ to find my films everywhere: https://vinnietortorich.com/documentaries Then, please share my fact-based, health-focused documentary series with your friends and family. Additionally, the more views it receives, the better it ranks, so please watch it again with a new friend! REVIEWS: Please submit your REVIEW after you watch my films. Your positive REVIEW does matter! PURCHASE BEYOND IMPOSSIBLE (2022) Visit my new Documentaries HQ to find my films everywhere: https://vinnietortorich.com/documentaries FAT: A DOCUMENTARY 2 (2021) Visit my new Documentaries HQ to find my films everywhere: https://vinnietortorich.com/documentaries FAT: A DOCUMENTARY (2019) Visit my new Documentaries HQ to find my films everywhere: https://vinnietortorich.com/documentaries

Today, I'm joined by Kat Cole, CEO of AG1.   Evolving from a $160M single-SKU brand to $500M+ revenue, AG1's in hypergrowth mode — adding flavors and sleep aid AGZ, entering Costco, and investing $30M in clinical research.   In this episode, we discuss building foundational capabilities before scaling complexity.   We also cover:   Handling criticism and competition Insights from early operational mishaps Strategies for retail expansion and packaging redesign   Subscribe to the podcast → insider.fitt.co/podcast  Subscribe to our newsletter → insider.fitt.co/subscribe  Follow us on LinkedIn → linkedin.com/company/fittinsider    AG1's Website: www.drinkag1.com  Instagram: https://www.instagram.com/drinkag1  Tiktok: https://www.tiktok.com/@drinkag1  X (Twitter): https://x.com/drink_AG1  The Fitt Insider Podcast is brought to you by EGYM. Visit EGYM.com to learn more about its smart fitness ecosystem for fitness and health facilities. Fitt Talent: https://talent.fitt.co/  Consulting: https://consulting.fitt.co/  Investments: https://capital.fitt.co/  Chapters: (00:00) Introduction (01:50) Kat stepping into CEO role (02:30) Multi-product, multi-channel evolution (03:05) US blending capacity expansion (04:00) First flavors after 10+ years (04:45) AGZ launch: consolidating sleep stack (07:01) Operating gaps despite hypergrowth (09:00) New Zealand supply chain dependency (10:30) Leaky shaker bottles insight (13:00) Real growth at scale (14:20) Delaying retail expansion (16:00) Packaging redesign for shelf (18:20) NSF certification delays (20:10) D2C relationship advantages (22:05) $30M research commitment (23:15) Double-blind trials and skepticism (25:20) Larger studies: 100 vs. 30 participants (27:00) Why competitors don't invest in research (28:20) Handling criticism and competitors (30:00) Apple, Lululemon comparison (32:00) Using critique to improve (33:30) Marketing science strategy (35:00) Scientific advisory council (36:21) Podcast marketing reality (38:25) Referral and gifting conversion (40:00) Multi-channel integration (41:20) Retail as billboard (42:20) Costco untapped awareness (43:20) Slow operational work pays off (44:02) Future retail expansion (45:00) Stacking products customers request (45:39) Conclusion

→ My one stop shop for quality supplements: https://theswellscore.com/pages/drg Episode Description That Brita in your fridge? It's not doing what you think it is. You bought it to protect your family. You fill it up, watch the water drip through, and feel like you've done something good. But here's the reality: Brita is NSF-certified to remove five contaminants. The Environmental Working Group just found 324 in U.S. drinking water. That gap is the problem. PFAS (the forever chemicals) have been detected in the drinking water of over 200 million Americans. Hexavalent chromium, the chemical from Erin Brockovich, has no federal limit and is present in water systems across all 50 states. Nitrates. Microplastics. Pharmaceuticals. Fluoride. Brita addresses essentially none of them. In this episode, Dr. Christian Gonzalez breaks down exactly what's in your tap water, what Brita actually filters, and what it's leaving behind. Then he gives you six evidence-based alternatives across three price tiers—so you can make the best decision for your budget and your household. In this episode, Dr. G breaks down: • Why EPA regulations are decades out of date—and why that matters for your family • The six PFAS chemicals the EPA finally regulated in 2024—and why there are 5,000 more they don't touch • The best pitcher upgrade under $60 that removes over 365 contaminants Brita ignores • Under-the-sink options with 50x the filter life and clinical-grade PFAS removal • The reverse osmosis systems Dr. G actually uses—and why they're the gold standard This isn't about fear. It's about knowing what's real so you can take control of one of the biggest daily exposures most people never think about. Timestamps: 0:00 - Intro 1:34 - What's Really in Your Tap Water (324 Contaminants) 3:26 - PFAS Forever Chemicals: 200 Million Americans Affected 5:38 - What Brita Actually Removes (The Real NSF Data) 9:47 - What Brita Leaves Behind: PFAS, Fluoride, Arsenic & More 11:43 - 6 Cleaner Alternatives Across 3 Budget Tiers 12:05 - Tier 1: Best Budget Pitcher Upgrades (~$40–60) 1:40 - Tier 2: Under-the-Sink Carbon Filters (~$150–350) 15:02 - Tier 3: Reverse Osmosis Systems (Clinical Grade) 17:01 - Which Filter Tier Is Right for You? Learn more about your ad choices. Visit megaphone.fm/adchoices

Astronomy Cast Ep. 782: Luminous Fast Blue Optical Transients By Fraser Cain & Dr. Pamela Gay Streamed live on Feb 13, 2026. Modern astronomy has found that the Universe can surprise us. Here's one which astronomers have called Luminous Fast Blue Optical Transients. They're kinda like supernovas, they're kind of like gamma ray bursts, but they're not like them. So what are they? In the distant Universe, are blue light flashes, bright and hard to understand. These objects, uncreatively named "Luminous Fast Blue Optical Transients," are just the kind of puzzle astronomers love. In this episode, we look at their discovery and our current understanding of what they might be. Image credit: NASA, ESA, NSF's NOIRLab, Mark Garlick, Mahdi Zamani This show is supported through people like you on Patreon.com/AstronomyCast In this episode, we'd like to thank: Burry Gowen, Eric Lee, Jeanette Wink, Michael Purcell, Andrew Poelstra, David, David Rossetter, Ed, Gerhard Schwarzer, Jason Kwong, Joe McTee, Sergey Manouilov, Siggi Kemmler, Sergio Sancevero

→ My one stop shop for quality supplements: https://theswellscore.com/pages/drg Episode Description Is the most popular whey protein in America actually good for you? Or are you building muscle while unknowingly stressing your gut, spiking inflammation, and loading up on hidden ingredients? Dr. Christian Gonzalez breaks down Optimum Nutrition Gold Standard Whey—the protein powder stuffed in gym bags across the country—ingredient by ingredient, cross-referencing each one with the best available human studies. The problem: NSF certification sounds reassuring, but it doesn't give you access to actual test results. No heavy metal data. No pesticide levels. No certificate of analysis for the tub sitting in your kitchen right now. In this episode, Dr. G reveals: • Why factory-farmed whey spikes omega-6 fatty acids 2.5x more than grass-fed sources • The processing method that destroys up to 90% of cocoa's therapeutic benefits • Which ingredient requires 28% more insulin than regular table sugar • The "natural flavors" loophole hiding potentially hundreds of undisclosed chemicals • 6 cleaner whey protein alternatives that actually publish their third-party testing Timestamps: 0:00 - Introduction 1:36 - When Whey Protein Is Actually Incredible  2:14 - Grass-Fed vs Factory-Farmed Whey  3:29 - Breaking Down Every Ingredient  4:46 - The Hidden Danger in Chocolate Flavoring  5:23 - Maltodextrin, Artificial Sweeteners & Gut Health  7:25 - The Bottom Line on Optimum Nutrition  8:14 - 6 Better Whey Protein Brands Learn more about your ad choices. Visit megaphone.fm/adchoices

Last February, Sudip Parikh, CEO of the American Association for the Advancement of Science, issued a dire warning about federal cuts to science, saying the country was on its way to losing its status as a global science leader.Nearly a year later, where does the United States stand with science funding, and what happens next? Sudip Parikh joins Host Flora Lichtman once again to discuss.Guest: Dr. Sudip Parikh is CEO and Executive Publisher of the American Association for the Advancement of Science, based in Arlington, Virginia.Transcripts for each episode are available within 1-3 days at sciencefriday.com. Subscribe to this podcast. Plus, to stay updated on all things science, sign up for Science Friday's newsletters.