Podcasts about backgrounds

Send us Fan MailThis week Mike discusses elevated backgrounds in ICP-OES when analyzing high total dissolved solids (TDS) samples such as brines, digests, excipients, salts, and starting materials. TDS increases free electron density in the plasma, producing Bremsstrahlung (braking) radiation and recombination radiation, which raise a broad continuum background across the spectrum. The elevated background degrades signal-to-noise, increases blank standard deviation, and worsens detection limits, especially for elements with poor ICP-OES sensitivity. Mitigation approaches include dilution, matrix-matched calibration or standard additions when dilution isn't feasible.

Best-selling author Tim Johnston was a master of the thriller. He passed away in May. To mark his passing, we're revisiting a conversation from 2025 with Johnston and author Anna Bruno. We talk about their books 'Distant Sons' and 'Fine Young People' and the art of writing suspense. Then, the Historical Society of Pottawattamie County reopened the Rails West Museum after a year and a half of renovations, where people can explore a historic train depot and train cars. We'll talk to the museum's site manager as well as the site manager of the Squirrel Cage Jail. (This show was originally produced July 21, 2025.)

AI, Sovereignty and the New Education Divide In this thought-provoking episode of The EdTech Podcast, Philippa Wraithmell speaks with Tom Orrell and Cameron Mirza about education, AI, global development and the growing divide between countries that can shape technological change — and those at risk of having it shaped for them. Tom, Deputy Director of Programs at Development Gateway, brings a background in human rights law, digital policy, sustainable development and humanitarian action. Cameron, Chief of Party for the Assas programme in Jordan, brings deep experience from the UK Department for Education and large-scale education reform across the Middle East. Together, they explore how their different professional routes have led to a shared focus on equity, ethics, implementation and meaningful system change. The conversation begins with the human story behind their work: family connections to teaching, personal motivations, and the values that have shaped their careers. Cameron reflects on how his mother's work as a primary school teacher continues to influence his commitment to early-grade education, while Tom shares how his own route through human rights, law and digital policy shaped his belief in critical thinking, rights and international cooperation. At the heart of the episode is the Assas programme in Jordan, which focuses on improving early-grade literacy and numeracy outcomes for young learners. Tom and Cameron discuss why foundational education matters not just for academic success, but for long-term life chances, economic development, health outcomes and social prosperity. The discussion then moves into the realities facing education systems globally. Cameron highlights the scale of the current learning crisis, teacher shortages, student debt, cost-of-living pressures and the increasing strain on public systems. Tom builds on this by exploring how AI and emerging technologies are accelerating change — but not always in a positive direction. AI, he argues, is not inherently good or bad; it is an acceleration force that can deepen harm or expand opportunity depending on the choices made around governance, policy and implementation. A key theme throughout the episode is sovereignty. Tom and Cameron unpack why AI sovereignty is no longer just about where data is stored. It is also about computing power, local infrastructure, culturally relevant datasets, language, regulation, national priorities and the ability of countries to make informed decisions about the tools they adopt. This raises difficult questions about global inequity, especially when most AI models are developed by a small number of countries and companies, while many lower-income nations lack the infrastructure or policy capacity to shape the direction of AI on their own terms. The episode also examines the danger of technology being treated as a quick fix for complex system problems. Cameron argues that the countries most likely to thrive in the next phase of innovation will not simply be those with the most money, but those able to build strong innovation governance systems — connecting government, regulators, universities, private sector partners, infrastructure, entrepreneurs and policymakers into trusted, coordinated systems. Tom and Cameron also discuss their work with sandboxes in Jordan, exploring how countries can safely test new technologies, understand trade-offs, and make choices that align with their own educational, cultural and national priorities. They emphasise that effective innovation is no longer just about technology; it is about coordination, trust, sequencing and the ability to cut through noise. The final part of the conversation turns to leadership. Cameron argues that leaders now need a systems mindset, ethical judgement, entrepreneurial thinking and the ability to operate in ambiguity while staying close to the realities on the ground. Tom reinforces the importance of emotional intelligence, empathy, communication and human connection in an age where technology is becoming increasingly dominant. This episode is a sharp, honest and deeply human conversation about the future of education. It challenges the hype around AI while recognising its potential, and asks what it will take to ensure that technological transformation strengthens education systems rather than widening the divide between them.   Chapters 00:00 Introduction and Backgrounds 04:13 Shared Vision and Collaboration 08:24 The Assas Program: Transforming Education in Jordan 12:56 Personal Motivations and Educational Impact 17:03 Current Challenges in Global Education 21:27 The Role of Technology in Education 25:56 Navigating Policy and Governance in Education 30:27 Leadership in Education: Skills for the Future 42:04 Sparkling_Chime_Transition.wav 42:10 NEWCHAPTER

What happens when schools see refugee students through a lens of deficit instead of possibility? In this episode of Our Classroom, Roberto Germán sits down with Dr. Yacoub Aljaffery, author of Narratives of Success and Resilience of Students from Refugee Backgrounds in U.S. Schools: From Invisible to Valuable, to discuss what educators need to understand about refugee students, multilingual learners, and the power of belonging. Drawing from his own journey as a refugee and educator, Dr. Aljaffery challenges common misconceptions and invites educators to move beyond trauma-centered narratives toward asset-based approaches that recognize resilience, brilliance, language, culture, and humanity. Together, Roberto and Dr. Aljaffery explore: • What makes students from refugee backgrounds feel invisible in schools • How deficit thinking harms students and limits possibilities • Why multilingualism should be viewed as an asset, not a problem • The difference between supporting students and “fixing” them • How current global conflicts and immigration debates impact students in classrooms today • Practical ways educators can foster belonging and dignity • What gives Dr. Aljaffery hope for the future This conversation is a reminder that refugee students do not need pity. They need opportunity, affirmation, and educators willing to see their full humanity. Connect with Us Teach in Truth. Lead with Courage. Belong to a Community That Gets It. Follow Multicultural Classroom and subscribe to Our Classroom for more conversations at the intersection of education, culture, identity, and belonging.

(1) Bob Zimmerman introduces the Apollo 8 crew—Frank Borman, Jim Lovell, and Bill Anders—noting their deep military backgrounds and commitment to American ideals. Borman is described as an intensely honest leader driven by duty, while Lovell is characterized as a "space cadet" eager for exploration. Zimmerman highlights the often-overlooked role of the astronauts' wives, such as Susan Borman and Valerie Anders, who managed households and intense media pressure while accepting the 50/50 survival odds of the mission. The guest also discusses the decision to use the Saturn 5rocket despite its previous mechanical vibration issues.(1) Bob Zimmerman introduces the Apollo 8 crew—Frank Borman, Jim Lovell, and Bill Anders—noting their deep military backgrounds and commitment to American ideals. Borman is described as an intensely honest leader driven by duty, while Lovell is characterized as a "space cadet" eager for exploration. Zimmerman highlights the often-overlooked role of the astronauts' wives, such as Susan Borman and Valerie Anders, who managed households and intense media pressure while accepting the 50/50 survival odds of the mission. The guest also discusses the decision to use the Saturn 5rocket despite its previous mechanical vibration issues.

What if your pet sitter had real veterinary training and could recognize health concerns before they became emergencies? Today on Scottsdale Vibes, we're talking with the founders of VetterCare about how they're changing pet care by bringing trusted veterinary professionals directly into your home. If you're a pet owner, you know how stressful it can be to leave your furry family members in someone else's care, especially if they're recovering from surgery, need medication, or have ongoing medical needs. Today's guests saw a major gap in the pet care world and decided to do something about it. I'm joined by Tony and Karen Chavez, the founders of VetterCare, a platform that connects pet owners with experienced veterinary professionals for in-home pet care. Unlike traditional pet sitting apps, VetterCare providers come from real veterinary backgrounds, including vet techs and veterinary nurses, giving pet owners peace of mind when it comes to medication administration, post surgical monitoring, and recognizing early signs of health issues. Tony and Karen, I'm excited to learn more about how this idea came to life and how you're helping pets and their people feel more supported.  

What if your pet sitter had real veterinary training and could recognize health concerns before they became emergencies? Today on Scottsdale Vibes, we're talking with the founders of VetterCare about how they're changing pet care by bringing trusted veterinary professionals directly into your home. If you're a pet owner, you know how stressful it can be to leave your furry family members in someone else's care, especially if they're recovering from surgery, need medication, or have ongoing medical needs. Today's guests saw a major gap in the pet care world and decided to do something about it. I'm joined by Tony and Karen Chavez, the founders of VetterCare, a platform that connects pet owners with experienced veterinary professionals for in-home pet care. Unlike traditional pet sitting apps, VetterCare providers come from real veterinary backgrounds, including vet techs and veterinary nurses, giving pet owners peace of mind when it comes to medication administration, post surgical monitoring, and recognizing early signs of health issues. Tony and Karen, I'm excited to learn more about how this idea came to life and how you're helping pets and their people feel more supported.  

Reiss is joined by Adam, Alex, Christian & Emma as they test their knowledge on Nottingham Forest's 25/26 campaign.Thanks to our sponsorswww.trentsidethreads.co.ukUse code RSOTT for 10% offVesta Blindswww.vestablinds.comBUY JUST CAN'T GET ENOUGH NOW!!https://tinyurl.com/bdz39zrnFollow us:Twitter/X: @redsidetrentFacebook: RedsideoftheTrentInstagram: @redsideofthetrentTikTok: @redsidetrentIntro animation@Jimmynffc 'Slept on it thoughts'Animation: @Jimmynffc Audio: @ianfinchtv Backgrounds: instagram: @jscomicsGraphics: @Ellismo17This Podcast has been created and uploaded by Red Side of the Trent. The views in this Podcast are not necessarily the views of talkSPORT. Hosted on Acast. See acast.com/privacy for more information.

Reiss, Adam, Christian & Lee look back at the 25/26 campaign as we give out our awards, look back at our pre-season predictions, grade the whole Forest squad & give a synopsis of the 25/26 campaign.Thanks to our sponsorswww.trentsidethreads.co.ukUse code RSOTT for 10% offVesta Blindswww.vestablinds.comBUY JUST CAN'T GET ENOUGH NOW!!https://tinyurl.com/bdz39zrnFollow us:Twitter/X: @redsidetrentFacebook: RedsideoftheTrentInstagram: @redsideofthetrentTikTok: @redsidetrentIntro animation@Jimmynffc 'Slept on it thoughts'Animation: @Jimmynffc Audio: @ianfinchtv Backgrounds: instagram: @jscomicsGraphics: @Ellismo17This Podcast has been created and uploaded by Red Side of the Trent. The views in this Podcast are not necessarily the views of talkSPORT. Hosted on Acast. See acast.com/privacy for more information.

See what the team at The Successful Bookkeeper has on right now → Sammy Mattingly and Fred Ott co-founded Mattingly & Ott Financial Accounting in their mid-twenties, with backgrounds in Big Four auditing and investment management — and a brief, memorable detour into portable sanitation. In Part 1 of this two-part conversation, they walk through the early decisions that shaped their firm: getting certified, landing first clients, and discovering that digital ads were no substitute for showing up in person. Chapters [00:00] Cold open and intro [01:18] Sammy's listener origin story [03:15] Backgrounds before bookkeeping [06:30] The porta potty adventure [11:00] Finding bookkeeping on YouTube [13:00] Five-week plan and first clients [16:00] Why paid ads flopped [18:30] Discovering networking as a strategy [21:00] Building one-to-one meeting habits [24:30] Shifting to strategic partnerships From Porta Potties to ProAdvisor Before bookkeeping, Sammy and Fred tried their hand at entrepreneurship the hard way — buying 20 used porta potties off a site called Crapper King, shipping them across the country on a semi-truck, and eventually moving them on Facebook Marketplace after a good pressure wash. The experience wasn't profitable, but it was formative. As Michael notes on the show, it gave them a layer of genuine empathy for clients: "They don't have all the answers, they're making mistakes, they're trying to figure it out." After a few more ideas, a YouTube video on starting a bookkeeping firm was all the spark Sammy needed. "I watched it, and I was like, well, if this guy can do it, Fred and I can do this." The Five-Week Launch Plan Once they committed to bookkeeping, Sammy and Fred moved fast. They built a five-week plan: get QuickBooks certified, become ProAdvisors, and land one client. Two large cleanup projects came through the QuickBooks ProAdvisor directory almost immediately — enough to justify going full-time. Fred describes those first weeks as equal parts doing the work and learning on the fly: "We were certified in QuickBooks, but it's like — we've got to figure out how this works. We've never done a QuickBooks cleanup for this type of company before." Why Paid Ads Weren't the Answer With their first projects underway, they turned to paid social media ads hoping to fill the pipeline. Six weeks and 15 or 16 leads later, the results were discouraging — contacts who were hard to reach and nowhere near ready to hire a bookkeeper. "We were finding they were all super unqualified," Fred says. That dead end turned out to be the pivot point. A conversation with a local small business attorney introduced a word they'd barely considered: networking. Networking as a Growth Engine Neither Sammy nor Fred would describe themselves as natural networkers — both lean introverted. But they committed fully, spending two to three months filling their days with open networking events and one-to-one coffee meetings. The accountability of working as a team made the difference: knowing the other person was putting in the effort kept each of them showing up. Fred's father, a career salesman, gave them the frame they needed: "Unseen, unheard, unsold." They tracked weekly one-to-one meeting goals, walked up to strangers, shook hands, and asked people to coffee — regardless of whether an obvious business connection was visible. Strategic Relationships Over Volume Over time, the approach evolved from broad networking to targeted relationship-building. Sammy describes the shift as following the data: "We took a step back and we were like, okay, what percentage of our referrals is coming from CPAs or whoever? And it's like, okay, well, if 80, 90% of our referrals are coming from these types of people, we need to go to rooms where there are these types of people." Tax preparers, business brokers, and other professionals who rarely attend networking events became the focus — making Mattingly & Ott's presence at those events even more valuable. Links mentioned Pure Bookkeeping — the system Sammy and Fred found through the podcast Pixie — practice management tool they discovered through The Successful Bookkeeper thesuccessfulbookkeeper.com — resources, episode search, and Ask the Show feature About the guests Sammy Mattingly and Fred Ott are co-founders of Mattingly & Ott Financial Accounting, LLC. High school friends turned business partners, they launched their bookkeeping firm roughly a year and a half ago and went full-time within the first few months. Sammy brings a background in Big Four audit; Fred comes from investment management. Together they serve small, service-based businesses and have built their client base almost entirely through in-person networking and strategic referral relationships. Part 2 of their conversation covers how those relationships translate into referral systems and scalable growth. About the hostMichael PalmerMichael Palmer is the host of The Successful Bookkeeper podcast and co-founder of Pure Bookkeeping and The Successful Bookkeeper. He started this work because of his father — a brilliant electrical contractor who worked twice as hard as he should have had to, because nobody on the financial side was in his corner. That gap is what The Successful Bookkeeper exists to close. His view: bookkeepers are the most undervalued force in small business — and every bookkeeper who builds a real business changes two families: theirs, and their clients'.

Getting comfortable on video is harder than most creators expect, especially when camera fears make every little detail feel bigger than it is. The morning show cast and crew talk with Katie Fawkes from Ecamm about the pressure to look polished, sound perfect, and have the ideal setup before showing your face online. The conversation keeps coming back to how confidence is built through repetition, not preparation, and why audiences connect more with real people than flawless production. There's also a reminder that most creators are way harder on themselves than viewers ever are. By the end, camera fears may start feeling less like a stop sign and more like part of learning how to show up. Episode Highlights:[02:31] Why Video Feels Scary[03:18] Meet Katie Fawkes[09:12] Katie's Camera Fear Story[12:52] Why We Freeze on Camera[15:18] Common Video Fears[17:28] Backgrounds and Human Connection[23:48] Live Streaming Builds Skills[25:56] Vertical Video Workflow[33:04] Reading Scripts on Camera[42:32] Audio Podcasts Going Video[45:27] What to Prioritize First[48:43] Affordable Gear Breakdown[53:16] Common Setup MistakesLinks & Resources:Check out Ecamm:https://www.ecamm.com/The Flow Podcast by Ecamm:https://flow.ecamm.com/The VHS Club Video Podcasthttps://www.thevhsclubpod.com/The Maker's Table:https://www.youtube.com/@themakerstableliveFeature Your Podcast on the Podcasting Morning Show:https://PodcastingMorningShow.com/spotlightThe Podcasting Morning Show:⁠⁠www.podcastingmorningshow.com⁠⁠Ways to Watch or Listen:⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠https://www.podcastingmorningshow.com/joinus/Meet the PMS Cast and Crew:⁠⁠https://podcastingmorningshow.com/people⁠⁠Join The Empowered Podcasting Facebook Group:⁠⁠www.facebook.com/groups/empoweredpodcasting⁠⁠⁠Book A Free Call With Marc:https://calendly.com/ironickmedia/freestrategycallApplication To Submit Your Show For Evaluation:⁠⁠⁠⁠⁠https://podcastingmorningshow.com/eval⁠⁠Join us every other Monday at 8 AM ET for the Obsession Worthy Podcasts:⁠⁠⁠http://podcastingmorningshow.com/owp/⁠⁠Join us LIVE every weekday morning at 8 am ET (US) on ⁠Clubhouse⁠: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://podcastingmorningshow.com/clubhouse⁠⁠EPC3 Speaker Application:⁠⁠ ⁠https://empoweredpodcasting.com/speakersPowered by⁠⁠⁠ ⁠iRonickMedia.com⁠⁠⁠⁠ and⁠ ⁠ContentCreatorsAccountant.com⁠⁠Send in your mailbag questions:⁠⁠⁠⁠ https://www.podcastingmorningshow.com/contact/⁠⁠⁠⁠ or ⁠marc@ironickmedia.com⁠Want to be a guest on The Podcasting Morning Show? Send me a message on PodMatch, here:https://podmatch.com/hostdetailpreview/1729879899384520035bad21b

*This Episode was originally published on 3/8/2026. This week, W. Tyler Sykora joins Jared Bumpers to discuss “Preaching and Biblical Backgrounds.” Dr. Sykora is the Chief of Staff in the The post Preaching and Biblical Backgrounds appeared first on Preaching and Preachers Institute.

Christian, Reiss and Lee discuss Nottingham Forest's 1-1 draw with AFC Bournemouth in the Premier League, which rounded off an eventful season for the Reds. Thanks to our sponsorswww.trentsidethreads.co.ukUse code RSOTT for 10% offVesta Blindswww.vestablinds.comBUY JUST CAN'T GET ENOUGH NOW!!https://tinyurl.com/bdz39zrnFollow us:Twitter/X: @redsidetrentFacebook: RedsideoftheTrentInstagram: @redsideofthetrentTikTok: @redsidetrentIntro animation@Jimmynffc 'Slept on it thoughts'Animation: @Jimmynffc Audio: @ianfinchtv Backgrounds: instagram: @jscomicsGraphics: @Ellismo17This Podcast has been created and uploaded by Red Side of the Trent. The views in this Podcast are not necessarily the views of talkSPORT.

**Special note to our listeners**Love the show? Help us keep the conversation going! Become a paid subscriber through our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Substack.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Your contributions help us continue to make content on issues related tothe Asian-American, immigrant, modern parent experience.THANK YOU to our super awesome listeners who have already signed up!--------------------------------------Mom, what happens to you and me when we die? Mom, what is God? Mom, why do you go to temple and Daddy doesn't? Mom, why do bad things happen?In this final episode of our Faith & Spirituality series, we turn our discussion from the spiritual/religious aspects of our childhoods to those of our relationships with our spouses and kiddos. Do you and your spouse share a common spiritual/ religious worldview or not? How has that played out in the early days of your relationship versus now? How does that impact how you raise your children? How are you answering questions from your kids that broach on the spiritual, metaphysical and moral? How does faith, uncertainty and community play out in those conversations?We will be the first to say we don't have clean answers to any of these questions. But we share the messy, honest view of how our own situations have played out in the hope that it sparks a connection, new questions and fresh energy.

This week, PhD candidate (Nursing) Azmat Jehan shines a light on older adults and their experiences in long-term care. How are they supported by family, or by other care partners? How do cultural practices intersect with the healthcare system to make individuals feel more heard, cared for, and respected? What are some of the potential barriers that remain, hindering the realization of what's been termed culturally safe care? Join Kelly Wang and Victor Lau as they learn more about Azmat's journey in interviewing and hearing stories from this age-friendly community. Discover a bit more about how communication can make a difference - both within, and outside of the time spent with individuals making up an important part of our community. You can find Azmat on LinkedIn: https://www.linkedin.com/in/azmat-jehan-928399309 Recorded on Tuesday, May 12th, 2026 Produced by Garth Casbourn Theme tune "Feelin Good" provided by FreeBeats.io (Produced by WhiteHot)

Reiss and Red Side regulars Danny & Premier Elliott reflect on our last away game of the 25/26 campaign as once again, we are involved in a highly controversial 3-2 game at Old Trafford.Thanks to our sponsorswww.trentsidethreads.co.ukUse code RSOTT for 10% offVesta Blindswww.vestablinds.comBUY JUST CAN'T GET ENOUGH NOW!!https://tinyurl.com/bdz39zrnFollow us:Twitter/X: @redsidetrentFacebook: RedsideoftheTrentInstagram: @redsideofthetrentTikTok: @redsidetrentIntro animation@Jimmynffc 'Slept on it thoughts'Animation: @Jimmynffc Audio: @ianfinchtv Backgrounds: instagram: @jscomicsGraphics: @Ellismo17This Podcast has been created and uploaded by Red Side of the Trent. The views in this Podcast are not necessarily the views of talkSPORT. Hosted on Acast. See acast.com/privacy for more information.

A strategic guide to risk, opportunity and adoption! $ BTC 78,461 Block Height 949,622 Today's guests are James Dewar, David Pool, and Darren Fremantle who join me to discuss their new "Bitcoin for Organisations" project, an open-source educational resource designed to help businesses understand Bitcoin as a critical risk management imperative. Key Topics: Introduction to the "Bitcoin for Organisations" idea Backgrounds and expertise of guests James Dewar, Darren Fremantle, and David Paul Framing Bitcoin as a crucial risk management issue for businesses The role of Bitcoin and Lightning in enabling "agentic payments" for AI Distinguishing Bitcoin from the broader "crypto" industry Challenges in educating traditional finance and corporate entities about Bitcoin Successes in engaging risk, compliance, legal, and academic sectors The open-source nature and collaborative development of the material Specific industry verticals and internal functions addressed in the materials Strategies for dispelling common myths and FUD surrounding Bitcoin The potential for "sleeper Bitcoiners" to advocate for adoption within their organisations The future landscape of fiat currency, central banks, and Bitcoin coexistence Connections: James Dewar - @Bitcoinshire David Pool - @exitingfiat Darren Freemantle - @freemantledj @MyFirstBitcoin_ My First Bitcoin website: ,https://programs.myfirstbitcoin.org/programs/bitcoin-for-organizations/ Check out my book ‘Choose Life' - https://bitcoinbook.shop/search?q=prince Pleb Service Announcements: Join 20 thousand Bitcoiners on @cluborange https://signup.cluborange.org/co/princey CONFERENCES: BITCOIN IRELAND - 22ND -25TH MAY 2026 - DUBLIN https://bitcoinireland.eu/ Use code BITTEN for - 10% BTC PRAGUE - 11th - 13th June 2026 http://btcprg.me/BITTEN - Use code BITTEN for - 10% BTC HEL - 25th - 26th September 2026. - Helsinki https://btchel.com/ Use code BITTEN for - 10% My First Bitcoin. https://myfirstbitcoin.org/ Shills and Mench's: BITBOX - SELF CUSTODY YOUR BITCOIN - www.bitbox.swiss/bitten Use Code BITTEN THE MEETUP BREAKDWON - BITCOIN EVENTS UK - https://www.themeetupbreakdown.com/ SWAN BITCOIN - www.swan.com/bitten PLEBEIAN MARKET - BUY AND SELL STUFF FOR SATS; https://plebeian.market/ @PlebeianMarket ZAPRITE - https://zaprite.com/bitten - Invoicing and accounting for Bitcoiners - Save $40 SATSBACK - Shop online and earn back sats! https://satsback.com/register/5AxjyPRZV8PNJGlM ALL FURTHER LINKS HERE - FOR DISCOUNTS AND OFFERS - https://vida.page/princey - https://linktr.ee/princey21m

This episode covers everything you need to know about the entertainer Background in the 2024 Player's Handbook for Dungeons & Dragons. Cold Open 0:00 Opening Theme & Intro 2:00 Themes & Lore 2:57 The Numbers 6:31 Feat 11:52 Gear 14:19 Missing Feature 18:08 Inspirations 20:56 Outro & Closing Theme 45:10 Post Credits (incl. Oblex Theatre)  DON'T FORGET TO LIKE & SUBSCRIBE! Patreon at https://www.patreon.com/user?u=84724626 Website: https://www.itsamimic.com Email at info@itsamimic.com Social: Instagram at https://www.instagram.com/itsamimic/?hl=en Threads at https://www.threads.net/@itsamimicpodcast Facebook at https://www.facebook.com/itsamimic/ Reddit at https://www.reddit.com/r/ItsaMimic/ Find Us On: Spotify at https://open.spotify.com/show/3Y19VxSxLKyfg0gY0yUeU1 Apple Podcasts https://podcasts.apple.com/us/podcast/its-a-mimic/id1450770037 Podbean at https://itsamimic.podbean.com/ YouTube at https://www.youtube.com/channel/UCzQmvEufzxPHWrFSZbB8uuw Dungeon Master 1:  Megan Lengle Dungeon Master 2:  Adam Nason Dungeon Master 3:  Brad McMann Narrator:  Pepperina Sparklegem Script By:  Adam Nason, Brad McMann, and Megan Lengle Produced By:  Megan Lengle Director:  Adam Nason Editor:  Adam Nason Executive Producer:  Adam Nason Main Theme:  Cory Wiebe Musical Scores:  Tyler Gibson Logo by:  Megan Lengle Other Artwork is owned by Wizards of the Coast. This episode is meant to be used as an inspirational supplement for Dungeons & Dragons 5th Edition and tabletop roleplaying games in general.  It's A Mimic! does not own the rights to any Wizards of the Coasts products.

In this episode, our hosts Alexis and Avery share the bird experiences that shaped their lives—from childhood memories of backyard birds and farm chickens to serious birding, museum research, conservation studies, and Costa Rica. They reflect on the experiences that led them to work with parrots today. They reflect on the moments that deepened their passion for birds and discuss why education, conservation, and better care for parrots both in our homes and in the wild matter more than ever. Plus, a round of Two Truths and a Lie! Don't forget our species spotlight featuring the Blue-Headed Pionus (Pionus menstruus)!Always Remember: Be kind to your Parrot, and it will be kind to you!—Links:Visit Us: https://www.parrotstars.comParrot Stars on Instagram: https://www.instagram.com/parrotstars/Parrot Stars on TikTok: https://www.tiktok.com/@parrot_starsParrot Stars on YouTube: https://www.youtube.com/@parrotstarsOSU Museum of Biological Diversity: https://mbd.osu.edu/North Central College: https://www.northcentralcollege.edu/Support the Parrot Stars Podcast! https://www.buzzsprout.com/2376122/supportFollow the Parrot Stars Podcast wherever you get your podcasts so you never miss an episode. Watch the video content on YouTube. Follow us on Instagram and TikTok for regular updates about all of the thrilling things happening at Parrot Stars!Enjoy the episode? Download each one and don't forget to like, subscribe, and review! Your support helps us with everything we do, and we genuinely appreciate it.Send us Fan MailSupport the showLearn more about Parrot Stars and shop online at parrotstars.com

Christian, Reiss and Red Side regular Matt discuss Nottingham Forest's 4-0 thumping away at Aston Villa in the second leg of the Europa League semi-finals, which sees the Reds crash out 4-1 on aggregate. Thanks to our sponsorswww.trentsidethreads.co.ukUse code RSOTT for 10% offVesta Blindswww.vestablinds.comBUY JUST CAN'T GET ENOUGH NOW!!https://tinyurl.com/bdz39zrnDonate to Christian's London marathon charity fundraiserhttps://2026tcslondonmarathon.enthuse.com/pf/christian-brownFollow us:Twitter/X: @redsidetrentFacebook: RedsideoftheTrentInstagram: @redsideofthetrentTikTok: @redsidetrentIntro animation@Jimmynffc 'Slept on it thoughts'Animation: @Jimmynffc Audio: @ianfinchtv Backgrounds: instagram: @jscomicsGraphics: @Ellismo17This Podcast has been created and uploaded by Red Side of the Trent. The views in this Podcast are not necessarily the views of talkSPORT. Hosted on Acast. See acast.com/privacy for more information.

An AP investigation finds that ICE has been hiring people with questionable qualifications. AP correspondent Donna Warder reports.

Richard O'Reilly steps in for William; he and guests discuss sport's unifying power.

Industrial Talk is onsite at PowerGen and talking to David Dickert and Frank Moyer with ANA, Inc. about "Traditional generation with battery technology". David Dickert and Frank Moyer discussed their roles and innovations at ANA, a company specializing in power solutions. David, with 38 years in the industry, leads sales, while Frank is the sector leader for North America data centers. They highlighted their E-Boss technology, a hybrid system combining diesel or natural gas generators with lithium titanate oxide (LTO) batteries, which offers 80,000 cycles compared to LFP's 6,000-7,000 cycles. E-BOSS can scale from 25 kVA to 50 MW, with a small footprint and high efficiency. They also introduced new extended battery packs for longer run times. Outline Introduction and Welcome Scott welcomes listeners to the number one industrial podcast, celebrating industry professionals.The podcast is broadcasting live from Power Gen in San Antonio, Texas.Scott introduces the guests, Frank and David, and sets the stage for the conversation. Backgrounds of Frank and David Frank Moyer introduces himself as the sector leader for North America data centers.David Dickert shares his background, leading sales at ANA and his extensive experience in the power industry.Both guests discuss their roles and the importance of their work in the industry.The conversation touches on the significance of data centers and their rapid growth. Introduction to E-BOSS Technology David explains the E-Boss technology, a hybrid system combining internal combustion and battery solutions.The E-Boss system makes diesel and natural gas generators 80% more efficient.Frank discusses the life expectancy of the battery component, highlighting the use of LTO technology.The E-Boss system is designed to optimize existing generators rather than replace them. Details of E-Boss Technology and Applications David elaborates on the E-Boss system's ability to parallel unlimited power systems.The system can scale from 25 kVA to 50 MW, with a small footprint.The E-Boss system is used in various applications, including data centers and construction sites.Frank emphasizes the safety and efficiency of the LTO technology, which is resistant to thermal runaway. Market Position and Future Plans David discusses the market position of ANA and their focus on data centers.The company is introducing new technologies, including extended battery packs, to meet growing demand.Frank highlights the unique inverter system that sets ANA's technology apart from competitors.The conversation concludes with a call to action for listeners to connect with ANA and explore their solutions. 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! DAVID DICKERT'S CONTACT INFORMATION: Personal LinkedIn: https://www.linkedin.com/in/david-dickert-8516a5/ Company LinkedIn: https://www.linkedin.com/company/anainc/ Company Website: https://anacorp.com/ FRANK MOYER'S CONTACT INFORMATION: Personal LinkedIn: https://www.linkedin.com/in/frank-moyer-a06981284/ PODCAST VIDEO: https://youtu.be/CvYmGukv83E 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? Live your business the way you want to live with the BUSINESS BEATITUDES...The Bridge connecting sacrifice to success. YOU NEED THE BUSINESS BEATITUDES! TAP INTO YOUR INDUSTRIAL SOUL, RESERVE YOUR COPY NOW! BE BOLD. BE BRAVE. DARE GREATLY AND CHANGE THE WORLD. GET THE BUSINESS BEATITUDES! Reserve My Copy and My 25% Discount

This episode covers everything you need to know about the Hermit Background in the 2025 Monster Manual for Dungeons & Dragons. Cold Open 0:00 Opening Theme & Intro 1:28 Themes & Lore 2:28 The Numbers 5:43 Feat 11:05 Gear 16:58 Missing Feature 19:27 Inspirations 22:29 Outro & Closing Theme 41:43 Post Credits (incl. An Ethereal Hut) 43:21 DON'T FORGET TO LIKE & SUBSCRIBE! Patreon at https://www.patreon.com/user?u=84724626 Website: https://www.itsamimic.com Email at info@itsamimic.com Social: Instagram at https://www.instagram.com/itsamimic/?hl=en Threads at https://www.threads.net/@itsamimicpodcast Facebook at https://www.facebook.com/itsamimic/ Reddit at https://www.reddit.com/r/ItsaMimic/ Find Us On: Spotify at https://open.spotify.com/show/3Y19VxSxLKyfg0gY0yUeU1 Apple Podcasts https://podcasts.apple.com/us/podcast/its-a-mimic/id1450770037 Podbean at https://itsamimic.podbean.com/ YouTube at https://www.youtube.com/channel/UCzQmvEufzxPHWrFSZbB8uuw Dungeon Master 1:  Megan Lengle Dungeon Master 2:  Kyle McQuaid Dungeon Master 3:  CB Dave Narrator:  Pepperina Sparklegem Script By:  CB Dave, Kyle McQuaid, and Megan Lengle Produced By:  Kyle McQuaid Director:  Megan Lengle Editor:  Adam Nason Executive Producer:  Adam Nason Main Theme:  Cory Wiebe Musical Scores:  Tyler Gibson Logo by:  Megan Lengle Other Artwork is owned by Wizards of the Coast. This episode is meant to be used as an inspirational supplement for Dungeons & Dragons 5th Edition and tabletop roleplaying games in general.  It's A Mimic! does not own the rights to any Wizards of the Coasts products.

1. Bob Zimmerman introduces the crew: Frank Borman, Jim Lovell, and Bill Anders. He highlights their intense dedication and military backgrounds. The discussion covers the high-stakes decision to launch the Saturn V rocket and the essential support provided by the astronauts' families in the NASA village. (1)1917

 In higher education over the past two centuries. He explores how the diversity principle—the belief that people with varied backgrounds, experiences, identities, and perspectives produce better outcomes by working together—first took root in the world's first modern research university, founded in Germany in 1810. This principle inspired John Stuart Mill's on Liberty, a cornerstone of academic freedom, and shaped Charles Eliot's transformation of Harvard in the late nineteenth century to encourage the “clash of ideas.” It also underpinned the twentieth-century equality efforts of figures like Thurgood Marshall, Ruth Bader Ginsburg, and Pauli Murray. By recounting the history of diversity through the lives and work of these and other influential thinkers, Oppenheimer makes a compelling case for embracing diversity as a central value in education and a vital ingredient for a vibrant intellectual and political culture. As contemporary backlash challenges diversity initiatives in government, business, and education, Oppenheimer—hailed by The New Yorker as America's “diversity detective”—reminds us that understanding the rich, two-hundred-year history of this idea is more important than ever.He is the author of “The Diversity Principle: The Story of a Transformative Idea." https://www.amazon.com/Diversity-Principle-Story-Transformative-Idea/dp/0300279892http://www.yourlotandparcel.org

In "Erigah", we've caught up to our plucky bandits, and one of them is in a bad way. Worse still: uncle has come calling! Meanwhile, the B team is trying to get to the bottom of the latest clue. And in both cases, we have a "Discovery" superpower: sudden backstory out of nowhere! Turns out Rayner has a long history with the Breen, while Jett gets a Poe Dameron style upgrade! Would've been nice if any of this had been mentioned earlier... Also this week: conclusion jumping, antagonist v. villain, and remaining side-love! [Erigah: 03:31; DS9/VOY/ENT minor romance: 58:33] [d'blog: https://sshbpodcast.tumblr.com/post/812714047765200896/hearts-stars-and-trek-other-trek-romances-part ]

I'd like to talk about something that is a struggle right now, something I'm hearing from associates, candidates, recruiters, and even hiring managers. Why is it so hard to find a job today? I had to pause for a second when this was brought up to me, that question doesn't always make sense. We drive down the road and see now hiring signs everywhere. We hear companies are short-staffed. Warehouses are expanding, it seems like there's new commercial complexes going up constantly, and we're hearing how production lines are growing, and distribution centers are moving more freight than ever. So, what is or where is the disconnect? Why does it feel like opportunities are everywhere, yet landing one is harder than ever? So, I wanted to look at it and break it down. And more importantly, I wanted to understand what's really happening so we can figure this out and make it work for us. I think first, we have to think about technology. Applicant Tracking Systems are being used, and they are much more detailed and programmable than ever before. Years ago, getting a job was a much more personal process. You walked in, shook a hand, filled out an application, maybe had a quick conversation with a supervisor or hiring manager. Today? Most of that first step happens through a computer system. Applicant Tracking Systems, or the ATS, are now the front door to us. You don't meet a person first. You meet a computer system first. And here's the challenge. These systems are scanning resumes for keywords, job titles, specific skills, and experience that matches exactly what was posted. If your resume doesn't match what the system is looking for, you may never get a call or be seen. And not because you're not qualified or because you couldn't do the job. But because your resume didn't speak the system's language. Now let's layer in something newer, AI. Artificial Intelligence is being used more and more in hiring. It helps companies sort through hundreds, sometimes thousands, of applicants. But here's the reality, or my opinion at least. AI doesn't understand potential and doesn't see attitude or personality or our confidence and it can't recognize work ethic. It looks for patterns or what it's been prompted to find. So, if your experience doesn't line up with the request and our job titles don't match exactly or even If your resume isn't structured correctly, oh, and, maybe the recruiter didn't write the prompt correctly or specific enough, you can get filtered out before a human ever lays eyes on your name. To me that's frustrating, but it's also something we need to understand and adapt to. Just being honest for a minute, we don't know what to put on our resume. This is one of the biggest struggles I see in light industrial recruiting, or for me anyway. A lot of great workers, solid, dependable, experienced associates, don't know how to present what they've done. We may say things like I worked in a warehouse, I loaded trucks, I picked orders. And while that's true, it's not enough in today's environment. Because the system and the employer want more detail. They want to know things like what type of equipment did you use, what were your productivity numbers, did you use RF scanners, and if so what kind? Did you work in a fast-paced environment? Were you meeting or exceeding goals? You may have years of experience, but if it's not clearly explained, It can look like you have very little. Now let's talk about something that doesn't always get said out loud and another thing that I land on the wrong side of, where you live matters. Employers today are looking closely at commute distance, reliability, and transportation challenges Why you may ask? Because attendance is critical. In warehousing, distribution, and manufacturing, if someone doesn't show up the line slows down, orders don't get out, and those trucks don't get unloaded or loaded. So companies often lean toward candidates who live closer. It's not always about fairness, it's about risk management. If two candidates have similar experience, the one with the shorter commute often gets the call. Another quick opinion, I tell people not to list their exact address, just the city. Of course, I guess that could hurt us also if the employer or hiring agent has a hundred others that listed theirs and they live withing 30 minutes of the facility! Next up, background checks, a real barrier sometimes. Backgrounds matter. And in today's world, they matter more than ever. Many companies have strict background requirements. And while there are still second chance employers out there, and I fully support them, the reality is, options can be more limited depending on the situation. This creates frustration for candidates who are ready to work and trying to move forward, and just looking for an opportunity, but keep hitting roadblocks. And that's something we, as an industry, continue to work through. And some states have their own ideas and laws concerning our backgrounds and employment. Now let's talk about another sensitive, but very real, topic. Workplace expectations around our appearance. We're seeing more face and neck tattoos, nose rings, ear spacers, and personal expression through style, and there's nothing wrong with individuality. But here's the challenge, not all workplaces have evolved at the same pace. Some environments, especially in food-grade facilities, manufacturing, and office or customer facing operations, still have policies around visible tattoos, jewelry and safety related appearance standards, and sometimes, those expectations can impact hiring decisions. It may not feel fair, but it is part of the current reality in many operations. Another factor? The competition has changed. You're no longer just competing with people in your neighborhood, or people who walk into the same office. You're competing with online applicants, candidates from across the city, sometimes even across regions that may be willing to move closer. And with easy apply buttons, hundreds of people can apply for the same role in minutes. That increases competition and makes standing out even more important. I think in many cases, it's not a skills gap. It's a communication gap. We have strong workers experienced on equipment that are comfortable in fast-paced environments, their reliable and capable. But they struggle to explain their experience or translate their skills onto paper and struggle to present themselves during the process, either on paper, on the phone or in the interview, and that gap can cost us opportunities. So What Can We Do About It? Now I don't want this to sound negative. Because while things have changed, they are not impossible. Far from it actually. We just have to adjust. I do think we have to learn to Speak Resume though. We have to break down our experience, list the equipment we've used, the tasks performed, what our productivity expectations were and any Systems (RF, WMS, etc.) we've used. And of course, we need to be honest and prepared. If you have challenges like transportation concerns, background, scheduling problems. Be upfront and look for employers who can align or work with them. We aren't going to change anybody's mind or their work shift. And we should remember, Sometimes the door in isn't necessarily your ideal job. But once you're in, Opportunities can open up for us. I want to say again that you're not just applying to a person anymore. You're applying to a process. And learning and accepting that process gives you an advantage. So yes, finding a job today can feel harder. Not because there aren't opportunities, but because the process has changed. Technology, employer expectations, the competition, and so many new policies, they've all evolved. And as workers, as leaders, as an industry, we have to evolve with it. And with all that being said, I'm going to get back to work myself. I'm Marty T Hawkins and I  appreciate your time and I hope you take another listen Warehouse and Operations as a Career next week. Until then work safe and live safe in everything you do.

Gm! In today's episode, we're joined by Kru and Cal, co-founders of Umbra, to discuss building privacy infrastructure on Solana using MPC and zero-knowledge technologies. We cover product design, compliance, SDK integrations, and how privacy could drive institutional adoption and improve crypto usability. Enjoy! -- Follow Lightspeed: ⁠https://twitter.com/Lightspeedpodhq⁠ Follow Umbra Privacy: https://x.com/UmbraPrivacy Follow Cal: https://x.com/typi_cal_ Follow Kru: https://x.com/kru_tweets?lang=en Follow Danny: https://x.com/defi_kay_ Join the Lightspeed Telegram: ⁠https://t.me/+QHlbNTNS4gc1ZTVh -- Get top market insights and the latest in crypto news. Subscribe to Blockworks Daily Newsletter: https://blockworks.co/newsletter/ -- Timestamps: (0:00) Introduction (1:12) Founders' Backgrounds (5:53) Why Privacy Matters Now (7:38) What Umbra Is Building (12:01) How Privacy Works (15:57) Expansion and Regulation (22:50) UX, Adoption, and Business Model (26:32) Token and Roadmap (32:07) Future of Privacy (41:57) Closing Comments -- Disclaimers: Lightspeed was kickstarted by a grant from the Solana Foundation. Nothing said on Lightspeed is a recommendation to buy or sell securities or tokens. This podcast is for informational purposes only, and any views expressed by anyone on the show are solely our opinions, not financial advice. Danny, and our guests may hold positions in the companies, funds, or projects discussed.

ITP - 142 Sofi explores the journey from classroom teaching to international school counseling in Busan, South Korea, where cultural awareness, student wellbeing, and community connection are central to daily life. Sofi shares how her experience during COVID led her toward counseling, how her interest in Korean language and culture helped shape her path, and why working in an international school has felt like the right fit from the start.The episode also dives into life in Korea as a parent, including the safety, support, and community spirit that have shaped her children's experience growing up abroad. From raising third culture kids and navigating cultural differences to thinking ahead about reverse culture shock during an upcoming return to the United States, this conversation highlights the personal and professional growth that comes with international education, family life overseas, and learning to feel at home in more than one place.Chapters:00:00) Introduction and Backgrounds(00:45) Sophie's Journey to Counseling(03:35) Cultural Connections and Language Learning(06:37) Life in Korea: First Impressions(09:32) Family Adventures in Korea(12:42) Community and Co-Parenting in Busan(15:46) Safety and Freedom for Kids in Korea(21:11) Cultural Adaptation and Third Culture Kids(30:26) Navigating School Life in Korea(37:47) Reverse Culture Shock and Safety Concerns(46:50) Language and Communication Challenges(47:21) Nostalgia for Home and Food(50:29) Cultural Connections and Small Talk(52:36) Reverse Culture Shock and Observations(55:02) Navigating Differences in Everyday Life(59:14) Embracing Change and Identity(01:01:01) Preparing for the Journey Home(01:04:58) Final Thoughts and Advice for Counselors-more information-The International Teacher Podcast is a bi-weekly discussion with experts in international education. New Teachers, burned out local teachers, local School Leaders, International school Leadership, current Overseas Teachers, and everyone interested in international schools can benefit from hearing stories and advice about living and teaching overseas.Additional Gems Related to Our Show:Greg's Favorite Video From Living Overseas - ⁠https://www.youtube.com/watch?v=UQWKBwzF-hw⁠Signup to be our guest  ⁠https://calendly.com/itpexpat/itp-interview?month=2025-01⁠Our Website⁠ -  ⁠https://www.itpexpat.com/⁠Our FaceBook Group - ⁠https://www.facebook.com/groups/itpexpat⁠⁠JPMint Consulting Website  - ⁠https://www.jpmintconsulting.com/⁠Greg's Personal YouTube Channel: ⁠https://www.youtube.com/playlist?list=PLs1B3Wc0wm6DR_99OS5SyzvuzENc-bBdO⁠Books By Gregory Lemoine:⁠International Teacher Guide: Finding the "Right Fit" 2nd Edition (2025)⁠ | by Gregory Lemoine M.Ed.⁠⁠"International Teaching: The Best-kept Secret in Education"⁠⁠ | by Gregory Lemoine M.Ed.Apps by Greg:https://apps.apple.com/app/6755244840  1. Who's That? Name & Face Trainer  Nov 21, 2025https://apps.apple.com/app/6756509803   2. Facetag | Memory Trainer   Dec 16, 2025

Austin shares why he avoided people with traditional backgrounds during his job search and instead preferred connecting with people who had non-traditional backgrounds!Time Stamped Show Notes:[0:25] - Why I Avoided People With Traditional Backgrounds In My Job Search[1:09] - Traditional vs. Non-Traditional[1:42] - What to do instead[2:23] - Positioning yourself for success[3:11] - How to find contacts in your dream company with non-traditional backgrounds[4:19] - Reach out to them and make your askWant To Level Up Your Job Search?Click here to learn more about 1:1 career coaching to help you land your dream job without applying online.Check out Austin's courses and, as a thank you for listening to the show, use the code PODCAST to get 5% off any digital course:The Interview Preparation System - Austin's proven, all-in-one process for turning your next job interview into a job offer.Value Validation Project Starter Kit - Everything you need to create a job-winning VVP that will blow hiring managers away and set you apart from the competition.No Experience, No Problem - Austin's proven framework for building the skills and experience you need to break into a new industry (even if you have *zero* experience right now).Try Austin's Job Search ToolsResyBuild.io - Build a beautiful, job-winning resume in minutes.ResyMatch.io - Score your resume vs. your target job description and get feedback.ResyBullet.io - Learn how to write attention grabbing resume bullets.Mailscoop.io - Find anyone's professional email in seconds.Connect with Austin for daily job search content:Cultivated CultureLinkedInTwitterThanks for listening!

WEISBERG1.mp3 Guest Barbara Weisberg introduces her book about Peter Strong and Mary Stevens, two elite New Yorkers who married in 1853. Despite their prominent backgrounds, Mary felt stifled living at Waverly, the Strong family estate in Queens, under her mother-in-law's roof, setting the stage for future conflict. (1)  1863 DRAFT RIOT

Two separate shootings this week raised national security concerns. At Old Dominion University in Virginia, authorities say the gunman was Mohamed Jalloh, a former Army National Guard member previously convicted in 2016 of attempting to provide material support to ISIS. He served about 11 years in federal prison and was released in 2024 before killing one person and wounding two others. In Michigan, an attack at Temple Israel synagogue was carried out by Ayman Ghazali, a naturalized U.S. citizen originally from Lebanon. Ghazali was killed by synagogue security during the attack. Please Like, Comment and Follow 'Broeske & Musson' on all platforms: --- The ‘Broeske & Musson Podcast’ is available on the KMJNOW app, Apple Podcasts, Spotify or wherever else you listen to podcasts. --- ‘Broeske & Musson' Weekdays 9-11 AM Pacific on News/Talk 580 AM & 105.9 FM KMJ | Facebook | Podcast| X | - Everything KMJ KMJNOW App | Podcasts | Facebook | X | InstagramSee omnystudio.com/listener for privacy information.

In this episode of The Uploft Interior Design Podcast, I answer listener design questions and focus on practical ways to improve everyday spaces. I start by helping Priya from Austin rethink her home office Zoom background, explaining how a balanced backdrop with subtle patterns, greenery, and good lighting can look polished without being distracting, and emphasizing that the background should support—not compete with—the person on screen. Then I help Caroline from Tennessee decide on lighting for her dining room, recommending one strong chandelier rather than multiple fixtures above a dining table and explaining how the size, shape, and texture of the light should complement the table and introduce a new material to the room. Throughout the episode, I share design principles about simplicity, balance, and texture while encouraging listeners to submit their own questions and experiment thoughtfully with their spaces. Timestamps: 0:00 – Intro & Listener Design Questions 1:10 – Priya's Home Office: Fixing a Boring Zoom Background 3:00 – Tips for Styling a Professional Zoom Wall 5:10 – Lighting and Camera Placement for Better Video Calls 7:00 – Caroline's Dining Room Lighting Dilemma 9:00 – Choosing the Right Chandelier & Using Texture in Design Links: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Uploft.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AffordableInteriorDesign.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Submit your design questions⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to be featured on the show ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Become a Premium Member⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and access the bonus episodes Click ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to become an interior designer with Uploft's Interior Design Academy. Get Betsy's book: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠betsyhelmuth.com/book⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠For more about our residential interior design services, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ModernInteriorDesign.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ For our commercial interior design services, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠OfficeInteriorDesign.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow Us: Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@uploftinteriordesign⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Facebook: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠facebook.com/UploftIntDes⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠tiktok.com/@uploftinteriordesign⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠linkedin.com/company/uploft-interior-design⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ If you enjoy the show, please spread the word and leave a review on iTunes! Learn more about your ad choices. Visit podcastchoices.com/adchoices

This week, W. Tyler Sykora joins Jared Bumpers to discuss “Preaching and Biblical Backgrounds.” Dr. Sykora is the Chief of Staff in the Office of the President and Assistant Professor The post Preaching and Biblical Backgrounds appeared first on Preaching and Preachers Institute.

Industrial Talk is onsite at SMRP 2025 and talking to Nancy Regan and Corey Dickens about "The future of operational excellence". Scott Mackenzie introduces Elevotec ERP, EAM, and business intelligence solutions on the Industrial Talk Podcast. The episode features Nancy and Corey, industry veterans discussing their careers in reliability and maintenance. Nancy, with 27 years of experience, emphasizes the importance of mentorship and simple explanations in reliability. Corey, with 13 years in the Navy and industry, highlights the need for leadership, mentorship, and workforce development. They stress the importance of overcoming comfort zones, embracing failure, and leveraging technology while maintaining foundational skills. Both advocate for inspiring the next generation and the value of conferences like SMRP for professional growth. Outline Introduction to Elevotec and Industrial Talk Podcast Scott introduces Elevotec, highlighting their ERP, EAM, and business intelligence solutions.Scott thanks listeners for joining the podcast, celebrating industrial professionals and their contributions.Scott mentions the SMRP 33 conference in Fort Worth, Texas, and introduces guests Nancy and Corey. Backgrounds of Nancy and Corey Scott asks Nancy and Corey to introduce themselves.Nancy shares her 27-year journey in reliability-centered maintenance (RCM) and her passion for the field.Corey discusses his 13-year career in maintenance and reliability, starting in the Navy and transitioning to the industrial sector.Both guests highlight their experiences and the impact of mentorship on their careers. Challenges and Opportunities in Mentorship Scott emphasizes the importance of inspiring the next generation and addressing the skills gap.Nancy stresses the significance of mentorship and the impact of her mentors, particularly John Mowbray.Corey talks about the need for vision, leadership, and ongoing support to develop talent.Both guests agree on the importance of mentorship and the role of experienced professionals in guiding the next generation. Inspiring the Next Generation Corey discusses the need for workforce development, including training and recruitment efforts.He highlights the importance of mentorship and the role of military veterans in the workforce.Nancy shares her approach to simplifying complex concepts to make them accessible to new professionals.Both guests emphasize the need for practical experience and the value of hands-on training. Overcoming Comfort Zones and Embracing Failure Nancy talks about the importance of getting out of one's comfort zone to achieve personal and professional growth.Corey shares his experience with failure and how it has shaped his approach to leadership and problem-solving.Both guests discuss the challenges of middle management and the need for effective leadership.They emphasize the importance of embracing failure as a learning opportunity and not being afraid to take risks. The Role of Technology and Certification Corey discusses the role of technology in training and developing the next generation of professionals.He highlights the importance of certification programs like the Certified Maintenance and Reliability Technician (CMRT).Nancy shares her approach to explaining complex concepts using simple analogies.Both guests agree on the need for a balanced approach to technology and traditional training methods. Final Thoughts and Contact Information Scott thanks Nancy and Corey for their insights and encourages listeners to reach out to them for mentorship and guidance.Nancy provides her contact information and mentions her availability on LinkedIn.Corey also encourages listeners to connect with him on LinkedIn for further discussions.Scott wraps up the podcast, emphasizing the importance of attending conferences like SMRP to network and learn from industry professionals. 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! NANCY REGAN'S CONTACT INFORMATION: Personal LinkedIn: https://www.linkedin.com/in/thenancyregan/ Company LinkedIn: https://www.linkedin.com/company/theforceinc/ Company Website: https://theforceinc.com/ COREY DICKENS' CONTACT INFORMATION: Personal LinkedIn: https://www.linkedin.com/in/coreydickens/ Company LinkedIn: https://www.linkedin.com/company/brightlysoftware/ Company Website: https://www.brightlysoftware.com/ PODCAST VIDEO: https://youtu.be/Fu54DdXmA9g 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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Not that long ago... in this exact galaxy... Aqua Teen art director Bob Pettitt and I talked about the best Star Wars parody of all time! Bob is back on the show to talk about his kidney-hurting Pawn Shop background, side-stepping legal loopholes with mustaches, and Bob's least favorite thing he had to draw for Aqua Teen Hunger Force.R E F E R E N C E S• Aqua Teen Xmas Fan Animation: https://www.instagram.com/reel/DSrFYdsjKcz/• Rabbot Rebuilt: https://www.youtube.com/watch?v=gx4sQBySJNE• Aqua Teen artist Todd Redner on Meat Kingdom: https://www.youtube.com/watch?v=yplpqIYP_Gc• Black Dahlia Murder drummer Alan Cassidy on Meat Kingdom: https://www.youtube.com/watch?v=wEi-1D5kP8QG U E S T

Neil Lanctot introduces Jane Addams, Theodore Roosevelt, and Woodrow Wilson in 1912, examining their distinct intellectual backgrounds and competing visions for America's reformist future during the Progressive era. 1

In this episode of The Gate 15 Interview, Andy Jabbour speaks with four Gate 15 analysts as Sadie-Anne Jones, Chase Snow, Mackenzie Gryder and Preston Wright share about their experiences, their work at Gate 15 and across critical infrastructure and faith-based organizations and more, including a rapid-fire round of Three Questions!Sadie-Anne on LinkedIn.Chase on LinkedIn.Mackenzie on LinkedIn.Preston on LinkedIn.In the podcast the team and Andy discuss:Backgrounds and paths to Gate 15.Surprising things the team has learned so far, and their ideas on threats, resilience, and what leaders may want to be thinking about today.The next hurdle they want to jump.We play 3 Questions! and talk late night snacks, secret skills, and where we love to chill and play.And more!

Go behind the scenes with the team building Atlas, a game-changing DeFi protocol on Cardano and Midnight that unlocks "trapped" staking rewards through yield tokenization. Learn how they are partnering with Midnight and Iagon to bring institutional-grade privacy and liquid yield strategies to everyone in 2026.Chapters00:00 Introduction to Atlas and the Guests03:04 Backgrounds in Crypto and Cardano06:06 Understanding Atlas and Its Purpose09:10 Yield Tokenization Explained11:45 Future of Atlas and Real World Assets15:02 Partnerships and Collaborations18:06 Development Timeline and Community EngagementDISCLAIMER: This content is for informational and educational purposes only and is not financial, investment, or legal advice. I am not affiliated with, nor compensated by, the project discussed—no tokens, payments, or incentives received. I do not hold a stake in the project, including private or future allocations. All views are my own, based on public information. Always do your own research and consult a licensed advisor before investing. Crypto investments carry high risk, and past performance is no guarantee of future results. I am not responsible for any decisions you make based on this content.

Ben and Carlos answer listener questions about how bat tracking technology and athletic testing metrics have changed high school scouting, how multi-sport athletes are valued and where some of the top 2027 draft prospects would slot into the 2026 class. —Time Stamps:(0:30) How has technology like bat tracking and force plates changed high school scouting?(14:20) Do multi-sport athletes project better than baseball-only players?(23:05) Where would high-end 2027 draft prospects fit in the 2026 draft class?Do you have feedback for the show or want to ask us a  question? Email us: futureprojection@baseballamerica.com.Future Projection Twitter: @FutureProPodBen's Twitter: @BenBadlerCarlos's Newsletter: Fringe AverageBaseball America WebsiteSupport this podcast at — https://redcircle.com/future-projection-a-baseball-america-podcast/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staff) and Myra Deng (Head of Product) are trying to turn “peeking inside the model” into a repeatable production workflow by shipping APIs, landing real enterprise deployments, and now scaling the bet with a recent $150M Series B funding round at a $1.25B valuation.In this episode, we go far beyond the usual “SAEs are cool” take. We talk about Goodfire's core bet: that the AI lifecycle is still fundamentally broken because the only reliable control we have is data and we post-train, RLHF, and fine-tune by “slurping supervision through a straw,” hoping the model picks up the right behaviors while quietly absorbing the wrong ones. Goodfire's answer is to build a bi-directional interface between humans and models: read what's happening inside, edit it surgically, and eventually use interpretability during training so customization isn't just brute-force guesswork.Mark and Myra walk through what that looks like when you stop treating interpretability like a lab demo and start treating it like infrastructure: lightweight probes that add near-zero latency, token-level safety filters that can run at inference time, and interpretability workflows that survive messy constraints (multilingual inputs, synthetic→real transfer, regulated domains, no access to sensitive data). We also get a live window into what “frontier-scale interp” means operationally (i.e. steering a trillion-parameter model in real time by targeting internal features) plus why the same tooling generalizes cleanly from language models to genomics, medical imaging, and “pixel-space” world models.We discuss:* Myra + Mark's path: Palantir (health systems, forward-deployed engineering) → Goodfire early team; Two Sigma → Head of Product, translating frontier interpretability research into a platform and real-world deployments* What “interpretability” actually means in practice: not just post-hoc poking, but a broader “science of deep learning” approach across the full AI lifecycle (data curation → post-training → internal representations → model design)* Why post-training is the first big wedge: “surgical edits” for unintended behaviors likereward hacking, sycophancy, noise learned during customization plus the dream of targeted unlearning and bias removal without wrecking capabilities* SAEs vs probes in the real world: why SAE feature spaces sometimes underperform classifiers trained on raw activations for downstream detection tasks (hallucination, harmful intent, PII), and what that implies about “clean concept spaces”* Rakuten in production: deploying interpretability-based token-level PII detection at inference time to prevent routing private data to downstream providers plus the gnarly constraints: no training on real customer PII, synthetic→real transfer, English + Japanese, and tokenization quirks* Why interp can be operationally cheaper than LLM-judge guardrails: probes are lightweight, low-latency, and don't require hosting a second large model in the loop* Real-time steering at frontier scale: a demo of steering Kimi K2 (~1T params) live and finding features via SAE pipelines, auto-labeling via LLMs, and toggling a “Gen-Z slang” feature across multiple layers without breaking tool use* Hallucinations as an internal signal: the case that models have latent uncertainty / “user-pleasing” circuitry you can detect and potentially mitigate more directly than black-box methods* Steering vs prompting: the emerging view that activation steering and in-context learning are more closely connected than people think, including work mapping between the two (even for jailbreak-style behaviors)* Interpretability for science: using the same tooling across domains (genomics, medical imaging, materials) to debug spurious correlations and extract new knowledge up to and including early biomarker discovery work with major partners* World models + “pixel-space” interpretability: why vision/video models make concepts easier to see, how that accelerates the feedback loop, and why robotics/world-model partners are especially interesting design partners* The north star: moving from “data in, weights out” to intentional model design where experts can impart goals and constraints directly, not just via reward signals and brute-force post-training—Goodfire AI* Website: https://goodfire.ai* LinkedIn: https://www.linkedin.com/company/goodfire-ai/* X: https://x.com/GoodfireAIMyra Deng* Website: https://myradeng.com/* LinkedIn: https://www.linkedin.com/in/myra-deng/* X: https://x.com/myra_dengMark Bissell* LinkedIn: https://www.linkedin.com/in/mark-bissell/* X: https://x.com/MarkMBissellFull Video EpisodeTimestamps00:00:00 Introduction00:00:05 Introduction to the Latent Space Podcast and Guests from Goodfire00:00:29 What is Goodfire? Mission and Focus on Interpretability00:01:01 Goodfire's Practical Approach to Interpretability00:01:37 Goodfire's Series B Fundraise Announcement00:02:04 Backgrounds of Mark and Myra from Goodfire00:02:51 Team Structure and Roles at Goodfire00:05:13 What is Interpretability? Definitions and Techniques00:05:30 Understanding Errors00:07:29 Post-training vs. Pre-training Interpretability Applications00:08:51 Using Interpretability to Remove Unwanted Behaviors00:10:09 Grokking, Double Descent, and Generalization in Models00:10:15 404 Not Found Explained00:12:06 Subliminal Learning and Hidden Biases in Models00:14:07 How Goodfire Chooses Research Directions and Projects00:15:00 Troubleshooting Errors00:16:04 Limitations of SAEs and Probes in Interpretability00:18:14 Rakuten Case Study: Production Deployment of Interpretability00:20:45 Conclusion00:21:12 Efficiency Benefits of Interpretability Techniques00:21:26 Live Demo: Real-Time Steering in a Trillion Parameter Model00:25:15 How Steering Features are Identified and Labeled00:26:51 Detecting and Mitigating Hallucinations Using Interpretability00:31:20 Equivalence of Activation Steering and Prompting00:34:06 Comparing Steering with Fine-Tuning and LoRA Techniques00:36:04 Model Design and the Future of Intentional AI Development00:38:09 Getting Started in Mechinterp: Resources, Programs, and Open Problems00:40:51 Industry Applications and the Rise of Mechinterp in Practice00:41:39 Interpretability for Code Models and Real-World Usage00:43:07 Making Steering Useful for More Than Stylistic Edits00:46:17 Applying Interpretability to Healthcare and Scientific Discovery00:49:15 Why Interpretability is Crucial in High-Stakes Domains like Healthcare00:52:03 Call for Design Partners Across Domains00:54:18 Interest in World Models and Visual Interpretability00:57:22 Sci-Fi Inspiration: Ted Chiang and Interpretability01:00:14 Interpretability, Safety, and Alignment Perspectives01:04:27 Weak-to-Strong Generalization and Future Alignment Challenges01:05:38 Final Thoughts and Hiring/Collaboration Opportunities at GoodfireTranscriptShawn Wang [00:00:05]: So welcome to the Latent Space pod. We're back in the studio with our special MechInterp co-host, Vibhu. Welcome. Mochi, Mochi's special co-host. And Mochi, the mechanistic interpretability doggo. We have with us Mark and Myra from Goodfire. Welcome. Thanks for having us on. Maybe we can sort of introduce Goodfire and then introduce you guys. How do you introduce Goodfire today?Myra Deng [00:00:29]: Yeah, it's a great question. So Goodfire, we like to say, is an AI research lab that focuses on using interpretability to understand, learn from, and design AI models. And we really believe that interpretability will unlock the new generation, next frontier of safe and powerful AI models. That's our description right now, and I'm excited to dive more into the work we're doing to make that happen.Shawn Wang [00:00:55]: Yeah. And there's always like the official description. Is there an understatement? Is there an unofficial one that sort of resonates more with a different audience?Mark Bissell [00:01:01]: Well, being an AI research lab that's focused on interpretability, there's obviously a lot of people have a lot that they think about when they think of interpretability. And I think we have a pretty broad definition of what that means and the types of places that can be applied. And in particular, applying it in production scenarios, in high stakes industries, and really taking it sort of from the research world into the real world. Which, you know. It's a new field, so that hasn't been done all that much. And we're excited about actually seeing that sort of put into practice.Shawn Wang [00:01:37]: Yeah, I would say it wasn't too long ago that Anthopic was like still putting out like toy models or superposition and that kind of stuff. And I wouldn't have pegged it to be this far along. When you and I talked at NeurIPS, you were talking a little bit about your production use cases and your customers. And then not to bury the lead, today we're also announcing the fundraise, your Series B. $150 million. $150 million at a 1.25B valuation. Congrats, Unicorn.Mark Bissell [00:02:02]: Thank you. Yeah, no, things move fast.Shawn Wang [00:02:04]: We were talking to you in December and already some big updates since then. Let's dive, I guess, into a bit of your backgrounds as well. Mark, you were at Palantir working on health stuff, which is really interesting because the Goodfire has some interesting like health use cases. I don't know how related they are in practice.Mark Bissell [00:02:22]: Yeah, not super related, but I don't know. It was helpful context to know what it's like. Just to work. Just to work with health systems and generally in that domain. Yeah.Shawn Wang [00:02:32]: And Mara, you were at Two Sigma, which actually I was also at Two Sigma back in the day. Wow, nice.Myra Deng [00:02:37]: Did we overlap at all?Shawn Wang [00:02:38]: No, this is when I was briefly a software engineer before I became a sort of developer relations person. And now you're head of product. What are your sort of respective roles, just to introduce people to like what all gets done in Goodfire?Mark Bissell [00:02:51]: Yeah, prior to Goodfire, I was at Palantir for about three years as a forward deployed engineer, now a hot term. Wasn't always that way. And as a technical lead on the health care team and at Goodfire, I'm a member of the technical staff. And honestly, that I think is about as specific as like as as I could describe myself because I've worked on a range of things. And, you know, it's it's a fun time to be at a team that's still reasonably small. I think when I joined one of the first like ten employees, now we're above 40, but still, it looks like there's always a mix of research and engineering and product and all of the above. That needs to get done. And I think everyone across the team is, you know, pretty, pretty switch hitter in the roles they do. So I think you've seen some of the stuff that I worked on related to image models, which was sort of like a research demo. More recently, I've been working on our scientific discovery team with some of our life sciences partners, but then also building out our core platform for more of like flexing some of the kind of MLE and developer skills as well.Shawn Wang [00:03:53]: Very generalist. And you also had like a very like a founding engineer type role.Myra Deng [00:03:58]: Yeah, yeah.Shawn Wang [00:03:59]: So I also started as I still am a member of technical staff, did a wide range of things from the very beginning, including like finding our office space and all of this, which is we both we both visited when you had that open house thing. It was really nice.Myra Deng [00:04:13]: Thank you. Thank you. Yeah. Plug to come visit our office.Shawn Wang [00:04:15]: It looked like it was like 200 people. It has room for 200 people. But you guys are like 10.Myra Deng [00:04:22]: For a while, it was very empty. But yeah, like like Mark, I spend. A lot of my time as as head of product, I think product is a bit of a weird role these days, but a lot of it is thinking about how do we take our frontier research and really apply it to the most important real world problems and how does that then translate into a platform that's repeatable or a product and working across, you know, the engineering and research teams to make that happen and also communicating to the world? Like, what is interpretability? What is it used for? What is it good for? Why is it so important? All of these things are part of my day-to-day as well.Shawn Wang [00:05:01]: I love like what is things because that's a very crisp like starting point for people like coming to a field. They all do a fun thing. Vibhu, why don't you want to try tackling what is interpretability and then they can correct us.Vibhu Sapra [00:05:13]: Okay, great. So I think like one, just to kick off, it's a very interesting role to be head of product, right? Because you guys, at least as a lab, you're more of an applied interp lab, right? Which is pretty different than just normal interp, like a lot of background research. But yeah. You guys actually ship an API to try these things. You have Ember, you have products around it, which not many do. Okay. What is interp? So basically you're trying to have an understanding of what's going on in model, like in the model, in the internal. So different approaches to do that. You can do probing, SAEs, transcoders, all this stuff. But basically you have an, you have a hypothesis. You have something that you want to learn about what's happening in a model internals. And then you're trying to solve that from there. You can do stuff like you can, you know, you can do activation mapping. You can try to do steering. There's a lot of stuff that you can do, but the key question is, you know, from input to output, we want to have a better understanding of what's happening and, you know, how can we, how can we adjust what's happening on the model internals? How'd I do?Mark Bissell [00:06:12]: That was really good. I think that was great. I think it's also a, it's kind of a minefield of a, if you ask 50 people who quote unquote work in interp, like what is interpretability, you'll probably get 50 different answers. And. Yeah. To some extent also like where, where good fire sits in the space. I think that we're an AI research company above all else. And interpretability is a, is a set of methods that we think are really useful and worth kind of specializing in, in order to accomplish the goals we want to accomplish. But I think we also sort of see some of the goals as even more broader as, as almost like the science of deep learning and just taking a not black box approach to kind of any part of the like AI development life cycle, whether that. That means using interp for like data curation while you're training your model or for understanding what happened during post-training or for the, you know, understanding activations and sort of internal representations, what is in there semantically. And then a lot of sort of exciting updates that were, you know, are sort of also part of the, the fundraise around bringing interpretability to training, which I don't think has been done all that much before. A lot of this stuff is sort of post-talk poking at models as opposed to. To actually using this to intentionally design them.Shawn Wang [00:07:29]: Is this post-training or pre-training or is that not a useful.Myra Deng [00:07:33]: Currently focused on post-training, but there's no reason the techniques wouldn't also work in pre-training.Shawn Wang [00:07:38]: Yeah. It seems like it would be more active, applicable post-training because basically I'm thinking like rollouts or like, you know, having different variations of a model that you can tweak with the, with your steering. Yeah.Myra Deng [00:07:50]: And I think in a lot of the news that you've seen in, in, on like Twitter or whatever, you've seen a lot of unintended. Side effects come out of post-training processes, you know, overly sycophantic models or models that exhibit strange reward hacking behavior. I think these are like extreme examples. There's also, you know, very, uh, mundane, more mundane, like enterprise use cases where, you know, they try to customize or post-train a model to do something and it learns some noise or it doesn't appropriately learn the target task. And a big question that we've always had is like, how do you use your understanding of what the model knows and what it's doing to actually guide the learning process?Shawn Wang [00:08:26]: Yeah, I mean, uh, you know, just to anchor this for people, uh, one of the biggest controversies of last year was 4.0 GlazeGate. I've never heard of GlazeGate. I didn't know that was what it was called. The other one, they called it that on the blog post and I was like, well, how did OpenAI call it? Like officially use that term. And I'm like, that's funny, but like, yeah, I guess it's the pitch that if they had worked a good fire, they wouldn't have avoided it. Like, you know what I'm saying?Myra Deng [00:08:51]: I think so. Yeah. Yeah.Mark Bissell [00:08:53]: I think that's certainly one of the use cases. I think. Yeah. Yeah. I think the reason why post-training is a place where this makes a lot of sense is a lot of what we're talking about is surgical edits. You know, you want to be able to have expert feedback, very surgically change how your model is doing, whether that is, you know, removing a certain behavior that it has. So, you know, one of the things that we've been looking at or is, is another like common area where you would want to make a somewhat surgical edit is some of the models that have say political bias. Like you look at Quen or, um, R1 and they have sort of like this CCP bias.Shawn Wang [00:09:27]: Is there a CCP vector?Mark Bissell [00:09:29]: Well, there's, there are certainly internal, yeah. Parts of the representation space where you can sort of see where that lives. Yeah. Um, and you want to kind of, you know, extract that piece out.Shawn Wang [00:09:40]: Well, I always say, you know, whenever you find a vector, a fun exercise is just like, make it very negative to see what the opposite of CCP is.Mark Bissell [00:09:47]: The super America, bald eagles flying everywhere. But yeah. So in general, like lots of post-training tasks where you'd want to be able to, to do that. Whether it's unlearning a certain behavior or, you know, some of the other kind of cases where this comes up is, are you familiar with like the, the grokking behavior? I mean, I know the machine learning term of grokking.Shawn Wang [00:10:09]: Yeah.Mark Bissell [00:10:09]: Sort of this like double descent idea of, of having a model that is able to learn a generalizing, a generalizing solution, as opposed to even if memorization of some task would suffice, you want it to learn the more general way of doing a thing. And so, you know, another. A way that you can think about having surgical access to a model's internals would be learn from this data, but learn in the right way. If there are many possible, you know, ways to, to do that. Can make interp solve the double descent problem?Shawn Wang [00:10:41]: Depends, I guess, on how you. Okay. So I, I, I viewed that double descent as a problem because then you're like, well, if the loss curves level out, then you're done, but maybe you're not done. Right. Right. But like, if you actually can interpret what is a generalizing or what you're doing. What is, what is still changing, even though the loss is not changing, then maybe you, you can actually not view it as a double descent problem. And actually you're just sort of translating the space in which you view loss and like, and then you have a smooth curve. Yeah.Mark Bissell [00:11:11]: I think that's certainly like the domain of, of problems that we're, that we're looking to get.Shawn Wang [00:11:15]: Yeah. To me, like double descent is like the biggest thing to like ML research where like, if you believe in scaling, then you don't need, you need to know where to scale. And. But if you believe in double descent, then you don't, you don't believe in anything where like anything levels off, like.Vibhu Sapra [00:11:30]: I mean, also tendentially there's like, okay, when you talk about the China vector, right. There's the subliminal learning work. It was from the anthropic fellows program where basically you can have hidden biases in a model. And as you distill down or, you know, as you train on distilled data, those biases always show up, even if like you explicitly try to not train on them. So, you know, it's just like another use case of. Okay. If we can interpret what's happening in post-training, you know, can we clear some of this? Can we even determine what's there? Because yeah, it's just like some worrying research that's out there that shows, you know, we really don't know what's going on.Mark Bissell [00:12:06]: That is. Yeah. I think that's the biggest sentiment that we're sort of hoping to tackle. Nobody knows what's going on. Right. Like subliminal learning is just an insane concept when you think about it. Right. Train a model on not even the logits, literally the output text of a bunch of random numbers. And now your model loves owls. And you see behaviors like that, that are just, they defy, they defy intuition. And, and there are mathematical explanations that you can get into, but. I mean.Shawn Wang [00:12:34]: It feels so early days. Objectively, there are a sequence of numbers that are more owl-like than others. There, there should be.Mark Bissell [00:12:40]: According to, according to certain models. Right. It's interesting. I think it only applies to models that were initialized from the same starting Z. Usually, yes.Shawn Wang [00:12:49]: But I mean, I think that's a, that's a cheat code because there's not enough compute. But like if you believe in like platonic representation, like probably it will transfer across different models as well. Oh, you think so?Mark Bissell [00:13:00]: I think of it more as a statistical artifact of models initialized from the same seed sort of. There's something that is like path dependent from that seed that might cause certain overlaps in the latent space and then sort of doing this distillation. Yeah. Like it pushes it towards having certain other tendencies.Vibhu Sapra [00:13:24]: Got it. I think there's like a bunch of these open-ended questions, right? Like you can't train in new stuff during the RL phase, right? RL only reorganizes weights and you can only do stuff that's somewhat there in your base model. You're not learning new stuff. You're just reordering chains and stuff. But okay. My broader question is when you guys work at an interp lab, how do you decide what to work on and what's kind of the thought process? Right. Because we can ramble for hours. Okay. I want to know this. I want to know that. But like, how do you concretely like, you know, what's the workflow? Okay. There's like approaches towards solving a problem, right? I can try prompting. I can look at chain of thought. I can train probes, SAEs. But how do you determine, you know, like, okay, is this going anywhere? Like, do we have set stuff? Just, you know, if you can help me with all that. Yeah.Myra Deng [00:14:07]: It's a really good question. I feel like we've always at the very beginning of the company thought about like, let's go and try to learn what isn't working in machine learning today. Whether that's talking to customers or talking to researchers at other labs, trying to understand both where the frontier is going and where things are really not falling apart today. And then developing a perspective on how we can push the frontier using interpretability methods. And so, you know, even our chief scientist, Tom, spends a lot of time talking to customers and trying to understand what real world problems are and then taking that back and trying to apply the current state of the art to those problems and then seeing where they fall down basically. And then using those failures or those shortcomings to understand what hills to climb when it comes to interpretability research. So like on the fundamental side, for instance, when we have done some work applying SAEs and probes, we've encountered, you know, some shortcomings in SAEs that we found a little bit surprising. And so have gone back to the drawing board and done work on that. And then, you know, we've done some work on better foundational interpreter models. And a lot of our team's research is focused on what is the next evolution beyond SAEs, for instance. And then when it comes to like control and design of models, you know, we tried steering with our first API and realized that it still fell short of black box techniques like prompting or fine tuning. And so went back to the drawing board and we're like, how do we make that not the case and how do we improve it beyond that? And one of our researchers, Ekdeep, who just joined is actually Ekdeep and Atticus are like steering experts and have spent a lot of time trying to figure out like, what is the research that enables us to actually do this in a much more powerful, robust way? So yeah, the answer is like, look at real world problems, try to translate that into a research agenda and then like hill climb on both of those at the same time.Shawn Wang [00:16:04]: Yeah. Mark has the steering CLI demo queued up, which we're going to go into in a sec. But I always want to double click on when you drop hints, like we found some problems with SAEs. Okay. What are they? You know, and then we can go into the demo. Yeah.Myra Deng [00:16:19]: I mean, I'm curious if you have more thoughts here as well, because you've done it in the healthcare domain. But I think like, for instance, when we do things like trying to detect behaviors within models that are harmful or like behaviors that a user might not want to have in their model. So hallucinations, for instance, harmful intent, PII, all of these things. We first tried using SAE probes for a lot of these tasks. So taking the feature activation space from SAEs and then training classifiers on top of that, and then seeing how well we can detect the properties that we might want to detect in model behavior. And we've seen in many cases that probes just trained on raw activations seem to perform better than SAE probes, which is a bit surprising if you think that SAEs are actually also capturing the concepts that you would want to capture cleanly and more surgically. And so that is an interesting observation. I don't think that is like, I'm not down on SAEs at all. I think there are many, many things they're useful for, but we have definitely run into cases where I think the concept space described by SAEs is not as clean and accurate as we would expect it to be for actual like real world downstream performance metrics.Mark Bissell [00:17:34]: Fair enough. Yeah. It's the blessing and the curse of unsupervised methods where you get to peek into the AI's mind. But sometimes you wish that you saw other things when you walked inside there. Although in the PII instance, I think weren't an SAE based approach actually did prove to be the most generalizable?Myra Deng [00:17:53]: It did work well in the case that we published with Rakuten. And I think a lot of the reasons it worked well was because we had a noisier data set. And so actually the blessing of unsupervised learning is that we actually got to get more meaningful, generalizable signal from SAEs when the data was noisy. But in other cases where we've had like good data sets, it hasn't been the case.Shawn Wang [00:18:14]: And just because you named Rakuten and I don't know if we'll get it another chance, like what is the overall, like what is Rakuten's usage or production usage? Yeah.Myra Deng [00:18:25]: So they are using us to essentially guardrail and inference time monitor their language model usage and their agent usage to detect things like PII so that they don't route private user information.Myra Deng [00:18:41]: And so that's, you know, going through all of their user queries every day. And that's something that we deployed with them a few months ago. And now we are actually exploring very early partnerships, not just with Rakuten, but with other people around how we can help with potentially training and customization use cases as well. Yeah.Shawn Wang [00:19:03]: And for those who don't know, like it's Rakuten is like, I think number one or number two e-commerce store in Japan. Yes. Yeah.Mark Bissell [00:19:10]: And I think that use case actually highlights a lot of like what it looks like to deploy things in practice that you don't always think about when you're doing sort of research tasks. So when you think about some of the stuff that came up there that's more complex than your idealized version of a problem, they were encountering things like synthetic to real transfer of methods. So they couldn't train probes, classifiers, things like that on actual customer data of PII. So what they had to do is use synthetic data sets. And then hope that that transfer is out of domain to real data sets. And so we can evaluate performance on the real data sets, but not train on customer PII. So that right off the bat is like a big challenge. You have multilingual requirements. So this needed to work for both English and Japanese text. Japanese text has all sorts of quirks, including tokenization behaviors that caused lots of bugs that caused us to be pulling our hair out. And then also a lot of tasks you'll see. You might make simplifying assumptions if you're sort of treating it as like the easiest version of the problem to just sort of get like general results where maybe you say you're classifying a sentence to say, does this contain PII? But the need that Rakuten had was token level classification so that you could precisely scrub out the PII. So as we learned more about the problem, you're sort of speaking about what that looks like in practice. Yeah. A lot of assumptions end up breaking. And that was just one instance where you. A problem that seems simple right off the bat ends up being more complex as you keep diving into it.Vibhu Sapra [00:20:41]: Excellent. One of the things that's also interesting with Interp is a lot of these methods are very efficient, right? So where you're just looking at a model's internals itself compared to a separate like guardrail, LLM as a judge, a separate model. One, you have to host it. Two, there's like a whole latency. So if you use like a big model, you have a second call. Some of the work around like self detection of hallucination, it's also deployed for efficiency, right? So if you have someone like Rakuten doing it in production live, you know, that's just another thing people should consider.Mark Bissell [00:21:12]: Yeah. And something like a probe is super lightweight. Yeah. It's no extra latency really. Excellent.Shawn Wang [00:21:17]: You have the steering demos lined up. So we were just kind of see what you got. I don't, I don't actually know if this is like the latest, latest or like alpha thing.Mark Bissell [00:21:26]: No, this is a pretty hacky demo from from a presentation that someone else on the team recently gave. So this will give a sense for, for technology. So you can see the steering and action. Honestly, I think the biggest thing that this highlights is that as we've been growing as a company and taking on kind of more and more ambitious versions of interpretability related problems, a lot of that comes to scaling up in various different forms. And so here you're going to see steering on a 1 trillion parameter model. This is Kimi K2. And so it's sort of fun that in addition to the research challenges, there are engineering challenges that we're now tackling. Cause for any of this to be sort of useful in production, you need to be thinking about what it looks like when you're using these methods on frontier models as opposed to sort of like toy kind of model organisms. So yeah, this was thrown together hastily, pretty fragile behind the scenes, but I think it's quite a fun demo. So screen sharing is on. So I've got two terminal sessions pulled up here. On the left is a forked version that we have of the Kimi CLI that we've got running to point at our custom hosted Kimi model. And then on the right is a set up that will allow us to steer on certain concepts. So I should be able to chat with Kimi over here. Tell it hello. This is running locally. So the CLI is running locally, but the Kimi server is running back to the office. Well, hopefully should be, um, that's too much to run on that Mac. Yeah. I think it's, uh, it takes a full, like each 100 node. I think it's like, you can. You can run it on eight GPUs, eight 100. So, so yeah, Kimi's running. We can ask it a prompt. It's got a forked version of our, uh, of the SG line code base that we've been working on. So I'm going to tell it, Hey, this SG line code base is slow. I think there's a bug. Can you try to figure it out? There's a big code base, so it'll, it'll spend some time doing this. And then on the right here, I'm going to initialize in real time. Some steering. Let's see here.Mark Bissell [00:23:33]: searching for any. Bugs. Feature ID 43205.Shawn Wang [00:23:38]: Yeah.Mark Bissell [00:23:38]: 20, 30, 40. So let me, uh, this is basically a feature that we found that inside Kimi seems to cause it to speak in Gen Z slang. And so on the left, it's still sort of thinking normally it might take, I don't know, 15 seconds for this to kick in, but then we're going to start hopefully seeing him do this code base is massive for real. So we're going to start. We're going to start seeing Kimi transition as the steering kicks in from normal Kimi to Gen Z Kimi and both in its chain of thought and its actual outputs.Mark Bissell [00:24:19]: And interestingly, you can see, you know, it's still able to call tools, uh, and stuff. It's um, it's purely sort of it's it's demeanor. And there are other features that we found for interesting things like concision. So that's more of a practical one. You can make it more concise. Um, the types of programs, uh, programming languages that uses, but yeah, as we're seeing it come in. Pretty good. Outputs.Shawn Wang [00:24:43]: Scheduler code is actually wild.Vibhu Sapra [00:24:46]: Yo, this code is actually insane, bro.Vibhu Sapra [00:24:53]: What's the process of training in SAE on this, or, you know, how do you label features? I know you guys put out a pretty cool blog post about, um, finding this like autonomous interp. Um, something. Something about how agents for interp is different than like coding agents. I don't know while this is spewing up, but how, how do we find feature 43, two Oh five. Yeah.Mark Bissell [00:25:15]: So in this case, um, we, our platform that we've been building out for a long time now supports all the sort of classic out of the box interp techniques that you might want to have like SAE training, probing things of that kind, I'd say the techniques for like vanilla SAEs are pretty well established now where. You take your model that you're interpreting, run a whole bunch of data through it, gather activations, and then yeah, pretty straightforward pipeline to train an SAE. There are a lot of different varieties. There's top KSAEs, batch top KSAEs, um, normal ReLU SAEs. And then once you have your sparse features to your point, assigning labels to them to actually understand that this is a gen Z feature, that's actually where a lot of the kind of magic happens. Yeah. And the most basic standard technique is look at all of your d input data set examples that cause this feature to fire most highly. And then you can usually pick out a pattern. So for this feature, If I've run a diverse enough data set through my model feature 43, two Oh five. Probably tends to fire on all the tokens that sounds like gen Z slang. You know, that's the, that's the time of year to be like, Oh, I'm in this, I'm in this Um, and, um, so, you know, you could have a human go through all 43,000 concepts andVibhu Sapra [00:26:34]: And I've got to ask the basic question, you know, can we get examples where it hallucinates, pass it through, see what feature activates for hallucinations? Can I just, you know, turn hallucination down?Myra Deng [00:26:51]: Oh, wow. You really predicted a project we're already working on right now, which is detecting hallucinations using interpretability techniques. And this is interesting because hallucinations is something that's very hard to detect. And it's like a kind of a hairy problem and something that black box methods really struggle with. Whereas like Gen Z, you could always train a simple classifier to detect that hallucinations is harder. But we've seen that models internally have some... Awareness of like uncertainty or some sort of like user pleasing behavior that leads to hallucinatory behavior. And so, yeah, we have a project that's trying to detect that accurately. And then also working on mitigating the hallucinatory behavior in the model itself as well.Shawn Wang [00:27:39]: Yeah, I would say most people are still at the level of like, oh, I would just turn temperature to zero and that turns off hallucination. And I'm like, well, that's a fundamental misunderstanding of how this works. Yeah.Mark Bissell [00:27:51]: Although, so part of what I like about that question is you, there are SAE based approaches that might like help you get at that. But oftentimes the beauty of SAEs and like we said, the curse is that they're unsupervised. So when you have a behavior that you deliberately would like to remove, and that's more of like a supervised task, often it is better to use something like probes and specifically target the thing that you're interested in reducing as opposed to sort of like hoping that when you fragment the latent space, one of the vectors that pops out.Vibhu Sapra [00:28:20]: And as much as we're training an autoencoder to be sparse, we're not like for sure certain that, you know, we will get something that just correlates to hallucination. You'll probably split that up into 20 other things and who knows what they'll be.Mark Bissell [00:28:36]: Of course. Right. Yeah. So there's no sort of problems with like feature splitting and feature absorption. And then there's the off target effects, right? Ideally, you would want to be very precise where if you reduce the hallucination feature, suddenly maybe your model can't write. Creatively anymore. And maybe you don't like that, but you want to still stop it from hallucinating facts and figures.Shawn Wang [00:28:55]: Good. So Vibhu has a paper to recommend there that we'll put in the show notes. But yeah, I mean, I guess just because your demo is done, any any other things that you want to highlight or any other interesting features you want to show?Mark Bissell [00:29:07]: I don't think so. Yeah. Like I said, this is a pretty small snippet. I think the main sort of point here that I think is exciting is that there's not a whole lot of inter being applied to models quite at this scale. You know, Anthropic certainly has some some. Research and yeah, other other teams as well. But it's it's nice to see these techniques, you know, being put into practice. I think not that long ago, the idea of real time steering of a trillion parameter model would have sounded.Shawn Wang [00:29:33]: Yeah. The fact that it's real time, like you started the thing and then you edited the steering vector.Vibhu Sapra [00:29:38]: I think it's it's an interesting one TBD of what the actual like production use case would be on that, like the real time editing. It's like that's the fun part of the demo, right? You can kind of see how this could be served behind an API, right? Like, yes, you're you only have so many knobs and you can just tweak it a bit more. And I don't know how it plays in. Like people haven't done that much with like, how does this work with or without prompting? Right. How does this work with fine tuning? Like, there's a whole hype of continual learning, right? So there's just so much to see. Like, is this another parameter? Like, is it like parameter? We just kind of leave it as a default. We don't use it. So I don't know. Maybe someone here wants to put out a guide on like how to use this with prompting when to do what?Mark Bissell [00:30:18]: Oh, well, I have a paper recommendation. I think you would love from Act Deep on our team, who is an amazing researcher, just can't say enough amazing things about Act Deep. But he actually has a paper that as well as some others from the team and elsewhere that go into the essentially equivalence of activation steering and in context learning and how those are from a he thinks of everything in a cognitive neuroscience Bayesian framework, but basically how you can precisely show how. Prompting in context, learning and steering exhibit similar behaviors and even like get quantitative about the like magnitude of steering you would need to do to induce a certain amount of behavior similar to certain prompting, even for things like jailbreaks and stuff. It's a really cool paper. Are you saying steering is less powerful than prompting? More like you can almost write a formula that tells you how to convert between the two of them.Myra Deng [00:31:20]: And so like formally equivalent actually in the in the limit. Right.Mark Bissell [00:31:24]: So like one case study of this is for jailbreaks there. I don't know. Have you seen the stuff where you can do like many shot jailbreaking? You like flood the context with examples of the behavior. And the topic put out that paper.Shawn Wang [00:31:38]: A lot of people were like, yeah, we've been doing this, guys.Mark Bissell [00:31:40]: Like, yeah, what's in this in context learning and activation steering equivalence paper is you can like predict the number. Number of examples that you will need to put in there in order to jailbreak the model. That's cool. By doing steering experiments and using this sort of like equivalence mapping. That's cool. That's really cool. It's very neat. Yeah.Shawn Wang [00:32:02]: I was going to say, like, you know, I can like back rationalize that this makes sense because, you know, what context is, is basically just, you know, it updates the KV cache kind of and like and then every next token inference is still like, you know, the sheer sum of everything all the way. It's plus all the context. It's up to date. And you could, I guess, theoretically steer that with you probably replace that with your steering. The only problem is steering typically is on one layer, maybe three layers like like you did. So it's like not exactly equivalent.Mark Bissell [00:32:33]: Right, right. There's sort of you need to get precise about, yeah, like how you sort of define steering and like what how you're modeling the setup. But yeah, I've got the paper pulled up here. Belief dynamics reveal the dual nature. Yeah. The title is Belief Dynamics Reveal the Dual Nature of Incompetence. And it's an exhibition of the practical context learning and activation steering. So Eric Bigelow, Dan Urgraft on the who are doing fellowships at Goodfire, Ekt Deep's the final author there.Myra Deng [00:32:59]: I think actually to your question of like, what is the production use case of steering? I think maybe if you just think like one level beyond steering as it is today. Like imagine if you could adapt your model to be, you know, an expert legal reasoner. Like in almost real time, like very quickly. efficiently using human feedback or using like your semantic understanding of what the model knows and where it knows that behavior. I think that while it's not clear what the product is at the end of the day, it's clearly very valuable. Thinking about like what's the next interface for model customization and adaptation is a really interesting problem for us. Like we have heard a lot of people actually interested in fine-tuning an RL for open weight models in production. And so people are using things like Tinker or kind of like open source libraries to do that, but it's still very difficult to get models fine-tuned and RL'd for exactly what you want them to do unless you're an expert at model training. And so that's like something we'reShawn Wang [00:34:06]: looking into. Yeah. I never thought so. Tinker from Thinking Machines famously uses rank one LoRa. Is that basically the same as steering? Like, you know, what's the comparison there?Mark Bissell [00:34:19]: Well, so in that case, you are still applying updates to the parameters, right?Shawn Wang [00:34:25]: Yeah. You're not touching a base model. You're touching an adapter. It's kind of, yeah.Mark Bissell [00:34:30]: Right. But I guess it still is like more in parameter space then. I guess it's maybe like, are you modifying the pipes or are you modifying the water flowing through the pipes to get what you're after? Yeah. Just maybe one way.Mark Bissell [00:34:44]: I like that analogy. That's my mental map of it at least, but it gets at this idea of model design and intentional design, which is something that we're, that we're very focused on. And just the fact that like, I hope that we look back at how we're currently training models and post-training models and just think what a primitive way of doing that right now. Like there's no intentionalityShawn Wang [00:35:06]: really in... It's just data, right? The only thing in control is what data we feed in.Mark Bissell [00:35:11]: So, so Dan from Goodfire likes to use this analogy of, you know, he has a couple of young kids and he talks about like, what if I could only teach my kids how to be good people by giving them cookies or like, you know, giving them a slap on the wrist if they do something wrong, like not telling them why it was wrong or like what they should have done differently or something like that. Just figure it out. Right. Exactly. So that's RL. Yeah. Right. And, and, you know, it's sample inefficient. There's, you know, what do they say? It's like slurping feedback. It's like, slurping supervision. Right. And so you'd like to get to the point where you can have experts giving feedback to their models that are, uh, internalized and, and, you know, steering is an inference time way of sort of getting that idea. But ideally you're moving to a world whereVibhu Sapra [00:36:04]: it is much more intentional design in perpetuity for these models. Okay. This is one of the questions we asked Emmanuel from Anthropic on the podcast a few months ago. Basically the question, was you're at a research lab that does model training, foundation models, and you're on an interp team. How does it tie back? Right? Like, does this, do ideas come from the pre-training team? Do they go back? Um, you know, so for those interested, you can, you can watch that. There wasn't too much of a connect there, but it's still something, you know, it's something they want toMark Bissell [00:36:33]: push for down the line. It can be useful for all of the above. Like there are certainly post-hocVibhu Sapra [00:36:39]: use cases where it doesn't need to touch that. I think the other thing a lot of people forget is this stuff isn't too computationally expensive, right? Like I would say, if you're interested in getting into research, MechInterp is one of the most approachable fields, right? A lot of this train an essay, train a probe, this stuff, like the budget for this one, there's already a lot done. There's a lot of open source work. You guys have done some too. Um, you know,Shawn Wang [00:37:04]: There's like notebooks from the Gemini team for Neil Nanda or like, this is how you do it. Just step through the notebook.Vibhu Sapra [00:37:09]: Even if you're like, not even technical with any of this, you can still make like progress. There, you can look at different activations, but, uh, if you do want to get into training, you know, training this stuff, correct me if I'm wrong is like in the thousands of dollars, not even like, it's not that high scale. And then same with like, you know, applying it, doing it for post-training or all this stuff is fairly cheap in scale of, okay. I want to get into like model training. I don't have compute for like, you know, pre-training stuff. So it's, it's a very nice field to get into. And also there's a lot of like open questions, right? Um, some of them have to go with, okay, I want a product. I want to solve this. Like there's also just a lot of open-ended stuff that people could work on. That's interesting. Right. I don't know if you guys have any calls for like, what's open questions, what's open work that you either open collaboration with, or like, you'd just like to see solved or just, you know, for people listening that want to get into McInturk because people always talk about it. What are, what are the things they should check out? Start, of course, you know, join you guys as well. I'm sure you're hiring.Myra Deng [00:38:09]: There's a paper, I think from, was it Lee, uh, Sharky? It's open problems and, uh, it's, it's a bit of interpretability, which I recommend everyone who's interested in the field. Read. I'm just like a really comprehensive overview of what are the things that experts in the field think are the most important problems to be solved. I also think to your point, it's been really, really inspiring to see, I think a lot of young people getting interested in interpretability, actually not just young people also like scientists to have been, you know, experts in physics for many years and in biology or things like this, um, transitioning into interp, because the barrier of, of what's now interp. So it's really cool to see a number to entry is, you know, in some ways low and there's a lot of information out there and ways to get started. There's this anecdote of like professors at universities saying that all of a sudden every incoming PhD student wants to study interpretability, which was not the case a few years ago. So it just goes to show how, I guess, like exciting the field is, how fast it's moving, how quick it is to get started and things like that.Mark Bissell [00:39:10]: And also just a very welcoming community. You know, there's an open source McInturk Slack channel. There are people are always posting questions and just folks in the space are always responsive if you ask things on various forums and stuff. But yeah, the open paper, open problems paper is a really good one.Myra Deng [00:39:28]: For other people who want to get started, I think, you know, MATS is a great program. What's the acronym for? Machine Learning and Alignment Theory Scholars? It's like the...Vibhu Sapra [00:39:40]: Normally summer internship style.Myra Deng [00:39:42]: Yeah, but they've been doing it year round now. And actually a lot of our full-time staff have come through that program or gone through that program. And it's great for anyone who is transitioning into interpretability. There's a couple other fellows programs. We do one as well as Anthropic. And so those are great places to get started if anyone is interested.Mark Bissell [00:40:03]: Also, I think been seen as a research field for a very long time. But I think engineering... I think engineers are sorely wanted for interpretability as well, especially at Goodfire, but elsewhere, as it does scale up.Shawn Wang [00:40:18]: I should mention that Lee actually works with you guys, right? And in the London office and I'm adding our first ever McInturk track at AI Europe because I see this industry applications now emerging. And I'm pretty excited to, you know, help push that along. Yeah, I was looking forward to that. It'll effectively be the first industry McInturk conference. Yeah. I'm so glad you added that. You know, it's still a little bit of a bet. It's not that widespread, but I can definitely see this is the time to really get into it. We want to be early on things.Mark Bissell [00:40:51]: For sure. And I think the field understands this, right? So at ICML, I think the title of the McInturk workshop this year was actionable interpretability. And there was a lot of discussion around bringing it to various domains. Everyone's adding pragmatic, actionable, whatever.Shawn Wang [00:41:10]: It's like, okay, well, we weren't actionable before, I guess. I don't know.Vibhu Sapra [00:41:13]: And I mean, like, just, you know, being in Europe, you see the Interp room. One, like old school conferences, like, I think they had a very tiny room till they got lucky and they got it doubled. But there's definitely a lot of interest, a lot of niche research. So you see a lot of research coming out of universities, students. We covered the paper last week. It's like two unknown authors, not many citations. But, you know, you can make a lot of meaningful work there. Yeah. Yeah. Yeah.Shawn Wang [00:41:39]: Yeah. I think people haven't really mentioned this yet. It's just Interp for code. I think it's like an abnormally important field. We haven't mentioned this yet. The conspiracy theory last two years ago was when the first SAE work came out of Anthropic was they would do like, oh, we just used SAEs to turn the bad code vector down and then turn up the good code. And I think like, isn't that the dream? Like, you know, like, but basically, I guess maybe, why is it funny? Like, it's... If it was realistic, it would not be funny. It would be like, no, actually, we should do this. But it's funny because we know there's like, we feel there's some limitations to what steering can do. And I think a lot of the public image of steering is like the Gen Z stuff. Like, oh, you can make it really love the Golden Gate Bridge, or you can make it speak like Gen Z. To like be a legal reasoner seems like a huge stretch. Yeah. And I don't know if that will get there this way. Yeah.Myra Deng [00:42:36]: I think, um, I will say we are announcing. Something very soon that I will not speak too much about. Um, but I think, yeah, this is like what we've run into again and again is like, we, we don't want to be in the world where steering is only useful for like stylistic things. That's definitely not, not what we're aiming for. But I think the types of interventions that you need to do to get to things like legal reasoning, um, are much more sophisticated and require breakthroughs in, in learning algorithms. And that's, um...Shawn Wang [00:43:07]: And is this an emergent property of scale as well?Myra Deng [00:43:10]: I think so. Yeah. I mean, I think scale definitely helps. I think scale allows you to learn a lot of information and, and reduce noise across, you know, large amounts of data. But I also think we think that there's ways to do things much more effectively, um, even, even at scale. So like actually learning exactly what you want from the data and not learning things that you do that you don't want exhibited in the data. So we're not like anti-scale, but we are also realizing that scale is not going to get us anywhere. It's not going to get us to the type of AI development that we want to be at in, in the future as these models get more powerful and get deployed in all these sorts of like mission critical contexts. Current life cycle of training and deploying and evaluations is, is to us like deeply broken and has opportunities to, to improve. So, um, more to come on that very, very soon.Mark Bissell [00:44:02]: And I think that that's a use basically, or maybe just like a proof point that these concepts do exist. Like if you can manipulate them in the precise best way, you can get the ideal combination of them that you desire. And steering is maybe the most coarse grained sort of peek at what that looks like. But I think it's evocative of what you could do if you had total surgical control over every concept, every parameter. Yeah, exactly.Myra Deng [00:44:30]: There were like bad code features. I've got it pulled up.Vibhu Sapra [00:44:33]: Yeah. Just coincidentally, as you guys are talking.Shawn Wang [00:44:35]: This is like, this is exactly.Vibhu Sapra [00:44:38]: There's like specifically a code error feature that activates and they show, you know, it's not, it's not typo detection. It's like, it's, it's typos in code. It's not typical typos. And, you know, you can, you can see it clearly activates where there's something wrong in code. And they have like malicious code, code error. They have a whole bunch of sub, you know, sub broken down little grain features. Yeah.Shawn Wang [00:45:02]: Yeah. So, so the, the rough intuition for me, the, why I talked about post-training was that, well, you just, you know, have a few different rollouts with all these things turned off and on and whatever. And then, you know, you can, that's, that's synthetic data you can kind of post-train on. Yeah.Vibhu Sapra [00:45:13]: And I think we make it sound easier than it is just saying, you know, they do the real hard work.Myra Deng [00:45:19]: I mean, you guys, you guys have the right idea. Exactly. Yeah. We replicated a lot of these features in, in our Lama models as well. I remember there was like.Vibhu Sapra [00:45:26]: And I think a lot of this stuff is open, right? Like, yeah, you guys opened yours. DeepMind has opened a lot of essays on Gemma. Even Anthropic has opened a lot of this. There's, there's a lot of resources that, you know, we can probably share of people that want to get involved.Shawn Wang [00:45:41]: Yeah. And special shout out to like Neuronpedia as well. Yes. Like, yeah, amazing piece of work to visualize those things.Myra Deng [00:45:49]: Yeah, exactly.Shawn Wang [00:45:50]: I guess I wanted to pivot a little bit on, onto the healthcare side, because I think that's a big use case for you guys. We haven't really talked about it yet. This is a bit of a crossover for me because we are, we are, we do have a separate science pod that we're starting up for AI, for AI for science, just because like, it's such a huge investment category and also I'm like less qualified to do it, but we actually have bio PhDs to cover that, which is great, but I need to just kind of recover, recap your work, maybe on the evil two stuff, but then, and then building forward.Mark Bissell [00:46:17]: Yeah, for sure. And maybe to frame up the conversation, I think another kind of interesting just lens on interpretability in general is a lot of the techniques that were described. are ways to solve the AI human interface problem. And it's sort of like bidirectional communication is the goal there. So what we've been talking about with intentional design of models and, you know, steering, but also more advanced techniques is having humans impart our desires and control into models and over models. And the reverse is also very interesting, especially as you get to superhuman models, whether that's narrow superintelligence, like these scientific models that work on genomics, data, medical imaging, things like that. But down the line, you know, superintelligence of other forms as well. What knowledge can the AIs teach us as sort of that, that the other direction in that? And so some of our life science work to date has been getting at exactly that question, which is, well, some of it does look like debugging these various life sciences models, understanding if they're actually performing well, on tasks, or if they're picking up on spurious correlations, for instance, genomics models, you would like to know whether they are sort of focusing on the biologically relevant things that you care about, or if it's using some simpler correlate, like the ancestry of the person that it's looking at. But then also in the instances where they are superhuman, and maybe they are understanding elements of the human genome that we don't have names for or specific, you know, yeah, discoveries that they've made that that we don't know about, that's, that's a big goal. And so we're already seeing that, right, we are partnered with organizations like Mayo Clinic, leading research health system in the United States, our Institute, as well as a startup called Prima Menta, which focuses on neurodegenerative disease. And in our partnership with them, we've used foundation models, they've been training and applied our interpretability techniques to find novel biomarkers for Alzheimer's disease. So I think this is just the tip of the iceberg. But it's, that's like a flavor of some of the things that we're working on.Shawn Wang [00:48:36]: Yeah, I think that's really fantastic. Obviously, we did the Chad Zuckerberg pod last year as well. And like, there's a plethora of these models coming out, because there's so much potential and research. And it's like, very interesting how it's basically the same as language models, but just with a different underlying data set. But it's like, it's the same exact techniques. Like, there's no change, basically.Mark Bissell [00:48:59]: Yeah. Well, and even in like other domains, right? Like, you know, robotics, I know, like a lot of the companies just use Gemma as like the like backbone, and then they like make it into a VLA that like takes these actions. It's, it's, it's transformers all the way down. So yeah.Vibhu Sapra [00:49:15]: Like we have Med Gemma now, right? Like this week, even there was Med Gemma 1.5. And they're training it on this stuff, like 3d scans, medical domain knowledge, and all that stuff, too. So there's a push from both sides. But I think the thing that, you know, one of the things about McInturpp is like, you're a little bit more cautious in some domains, right? So healthcare, mainly being one, like guardrails, understanding, you know, we're more risk adverse to something going wrong there. So even just from a basic understanding, like, if we're trusting these systems to make claims, we want to know why and what's going on.Myra Deng [00:49:51]: Yeah, I think there's totally a kind of like deployment bottleneck to actually using. foundation models for real patient usage or things like that. Like, say you're using a model for rare disease prediction, you probably want some explanation as to why your model predicted a certain outcome, and an interpretable explanation at that. So that's definitely a use case. But I also think like, being able to extract scientific information that no human knows to accelerate drug discovery and disease treatment and things like that actually is a really, really big unlock for science, like scientific discovery. And you've seen a lot of startups, like say that they're going to accelerate scientific discovery. And I feel like we actually are doing that through our interp techniques. And kind of like, almost by accident, like, I think we got reached out to very, very early on from these healthcare institutions. And none of us had healthcare.Shawn Wang [00:50:49]: How did they even hear of you? A podcast.Myra Deng [00:50:51]: Oh, okay. Yeah, podcast.Vibhu Sapra [00:50:53]: Okay, well, now's that time, you know.Myra Deng [00:50:55]: Everyone can call us.Shawn Wang [00:50:56]: Podcasts are the most important thing. Everyone should listen to podcasts.Myra Deng [00:50:59]: Yeah, they reached out. They were like, you know, we have these really smart models that we've trained, and we want to know what they're doing. And we were like, really early that time, like three months old, and it was a few of us. And we were like, oh, my God, we've never used these models. Let's figure it out. But it's also like, great proof that interp techniques scale pretty well across domains. We didn't really have to learn too much about.Shawn Wang [00:51:21]: Interp is a machine learning technique, machine learning skills everywhere, right? Yeah. And it's obviously, it's just like a general insight. Yeah. Probably to finance too, I think, which would be fun for our history. I don't know if you have anything to say there.Mark Bissell [00:51:34]: Yeah, well, just across the science. Like, we've also done work on material science. Yeah, it really runs the gamut.Vibhu Sapra [00:51:40]: Yeah. Awesome. And, you know, for those that should reach out, like, you're obviously experts in this, but like, is there a call out for people that you're looking to partner with, design partners, people to use your stuff outside of just, you know, the general developer that wants to. Plug and play steering stuff, like on the research side more so, like, are there ideal design partners, customers, stuff like that?Myra Deng [00:52:03]: Yeah, I can talk about maybe non-life sciences, and then I'm curious to hear from you on the life sciences side. But we're looking for design partners across many domains, language, anyone who's customizing language models or trying to push the frontier of code or reasoning models is really interesting to us. And then also interested in the frontier of modeling. There's a lot of models that work in, like, pixel space, as we call it. So if you're doing world models, video models, even robotics, where there's not a very clean natural language interface to interact with, I think we think that Interp can really help and are looking for a few partners in that space.Shawn Wang [00:52:43]: Just because you mentioned the keyword

Join Dr. Arun Seraphin and Dr. Jae Yu for a conversation that explores new data on Pentagon senior civilian leadership, illuminating the backgrounds of individuals serving in STEM leadership roles focused on Emerging Technologies. This discussion draws on the NDIA ETI report published by Dr. Yu, “Mapping Government Officials in Emerging Technologies Roles,” which examines how STEM education and prior STEM experience shape career pathways within the Pentagon.The report and conversation analyze leadership backgrounds across the 14 critical technology areas identified by the Under Secretary of Defense for Research and Engineering (USD(R&E)), highlighting where STEM expertise is concentrated and where gaps remain in the Pentagon's Emerging Technologies workforce. The discussion concludes with data-driven recommendations to strengthen the Pentagon's senior civilian STEM workforce.Be sure to follow us on social media for updates, inside scoops, & more:LinkedIn: https://bit.ly/4htROo0Twitter: https://bit.ly/48LHAx3Facebook: https://bit.ly/47vlht8 And for more podcasts, articles, & publications all things emerging tech, check out our website at: https://bit.ly/47oA5K1#EmergingTech #EmergingTechETI #USDR&E #Pentagon #STEM

Karl and Erum sit down with Amy Trejo and Jose Carlos Garcia Garcia from Procter & Gamble to uncover how one of the world's largest consumer goods companies is leveraging biotechnology to innovate at unprecedented scale. Founded 189 years ago as a bio-waste upcycling partnership between a candle maker and a soap maker, P&G has always been rooted in biomaterials innovation—from pioneering laundry enzymes in the 1960s to developing cold water enzyme technologies that have saved billions in energy costs. Amy and JC reveal what makes biotech innovations stick in the marketplace (hint: it's all about performance), share candid advice for startups hoping to partner with P&G, and explain why the company views biotech as a critical enabler of both sustainability and superior consumer experiences. They discuss common misconceptions about working with large CPG companies, the importance of reducing ideas to practice, and how P&G's connect-and-develop model creates win-win partnerships that can impact billions of consumers worldwide. Whether you're a biotech founder, investor, or enthusiast curious about how innovative materials make it from lab to everyday products, this conversation offers rare insights into the intersection of consumer goods, biotechnology, and global scale manufacturing.Grow Everything brings the bioeconomy to life. Hosts Karl Schmieder and Erum Azeez Khan share stories and interview the leaders and influencers changing the world by growing everything. Biology is the oldest technology. And it can be engineered. What are we growing?Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.messaginglab.com/groweverything⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Chapters:(00:00:00) - Introduction and Opening Remarks(00:01:00) - Erum's Article on Industrial Biomanufacturing for Lichen Ventures(00:04:00) - The Vision of Boom Towns and Interplanetary Innovation(00:07:00) - Introduction to Amy Trejo and JC Garcia Garcia from P&G(00:11:00) - Amy and JC's Backgrounds and Roles at P&G(00:13:00) - Biotech Innovations Throughout P&G's 189-Year History(00:19:00) - What Makes Biotech Innovations Stick: Performance Over Everything(00:22:00) - Biggest Misconceptions About Partnering with Large CPG Companies(00:29:00) - How to Approach P&G: Show Product, Generate Data, Demonstrate Performance(00:31:00) - The Power of Reapplication Across Product Categories(00:35:00) - Successful Biotech Partnerships: SK-II, Align, New Chapter, Base Camp Research(00:39:00) - What Catches P&G's Attention at Conferences and Trade Shows(00:42:00) - The Role of Storytelling in Biotech Innovation and Consumer Engagement(00:47:00) - Five-Year Vision: The Future of CPG and Biotech Partnerships(00:49:00) - One Piece of Advice for Biotech Innovators: Reduce Ideas to Practice(00:52:00) - Quickfire Questions with Amy and JC(00:53:00) - Closing Thoughts: Impacting Billions of Lives Through Partnership(00:54:00) - Karl and Erum's Recap and Key TakeawaysLinks and Resources:Procter & Gamble (P&G)P&G Connect + DevelopP&G PartnershipsStellar: A World Beyond Limits and How To Get ThereIndustrial Biomanufacturing Needs Its Manhattan Project Moment by Erum Azeez Khan107. Glow Big or Go Home: Andy Bass's Journey with Glowing Oceans17. Beauty and the Biome with Jasmina Aganovic of ArcaeaTopics Covered: biotech, industry, biomanufacturing, bioprocessing, agriculture, agritech, strain engineering, biotech R&D, feedstocks, chemical engineering, bioengineeringHave a question or comment? Message us here:Text or Call (804) 505-5553 Music by: Nihilore Production by:  Amplafy Media

Andrey Sabelnikov and Valeriy Pisarkov are the core devs of Zano: a privacy network which enables users to create tokens with privacy. The project's mix of Cryptonote and Zarcanum protocols can also enable private transfers of bridged BTC. In this episode, we talk about how this system works and we inquire about the tradeoffs involved. Time stamps: 00:01:51 – Introduction & Setting the Record Straight 00:02:51 – Val & Andre's Backgrounds, CryptoNote Origins 00:03:53 – Problems with Bytecoin & Monero's Launch 00:05:28 – Zano's Strategic Directions & AI Security Challenges 00:08:51 – Val's Role in Zano & Technical Evolution 00:13:45 – Network-Level Privacy Incident 00:19:15 – Proof-of-Work vs. Proof-of-Stake Privacy 00:24:11 – Zano vs. Monero: Not a Fork 00:29:22 – Monero Community Criticism & Scam Allegations 00:40:44 – Boolberry Project & Coin Swap 00:43:36 – Premine & Staking Controversy 00:48:29 – Tribalism & Ideology in Privacy Coins 00:53:21 – Differences Between Boolberry and Zano 00:57:32 – Wallet Support vs. Exchange Listings 01:01:03 – Exchange Listing Challenges & Gateway Addresses 01:04:20 – Gateway Addresses & Hard Fork 6 01:11:52 – Upcoming Roadmap: Proof-of-Stake & Full Chain Membership Proofs 01:17:40 – Bridging Bitcoin & Confidential Assets 01:27:52 – Asset Whitelisting & Stablecoin Risks 01:41:01 – Confidential Layer Bridge & Multi-Party Computation 01:51:21 – Zano's Scalability, Throughput, and Future Vision 01:59:08 – Full Chain Membership Proofs & Quantum Resistance 02:09:17 – Tech Stack Choices & Adaptability 02:27:07 – Why Be Bullish on Zano? 02:54:48 – DeFi Listings, Gateway Addresses, and Privacy Trade-offs 03:19:59 – Network-Level Privacy, Dandelion, and Mixnets 03:26:46 – VPNs, Network Privacy Tools, and Community Integration 03:44:03 – Personal Stories, Early Computing, and Closing Remarks

 Change feels different every time—but it never is. From John Henry to today, this episode explores the recurring moment when the world moves on… and where people still fit.Every generation feels it—the sense that this time, change is different.Faster. Bigger. Final.But history tells another story.From the legend of John Henry to the modern moment, this episode explores the recurring human experience that appears whenever progress accelerates: the quiet question of where people fit when the world moves on.This isn't a story about winning, resisting, or keeping up.It's about the moment that keeps returning—and the small space where choice still exists.If this perspective resonated, consider liking, subscribing, or sharing.And thanks for spending the time here.________________________________________

Industrial Talk is onsite at SMRP 2026 and talking to Greg Raglin and Bill Broderick with AssetWatch about "Bringing context to your asset management data". Scott MacKenzie hosts an industrial podcast featuring Greg RaglIn and Bill Broderick from AssetWatch, a company specializing in predictive maintenance and reliability solutions. Greg, a solutions architect, and Bill, a vibration analyst, discuss their experiences and the benefits of AssetWatch's technology, which integrates AI and human intelligence to provide actionable insights from condition-based monitoring of assets. They emphasize the importance of accurate data analysis to avoid false alarms and the need for continuous engagement with clients to ensure the success of predictive maintenance programs. The conversation highlights the evolving role of AI in industrial settings and the potential for future technological advancements. Action Items [ ] Reach out to Greg Raglin to discuss AssetWatch's solutions further.[ ] Connect with Bill Broderick on LinkedIn to stay updated on the company's developments. Outline Introduction and Welcome to Industrial Talk Podcast Scott MacKenzie introduces the Industrial Talk Podcast, emphasizing its focus on industry professionals and their innovations.Scott welcomes listeners and highlights the importance of celebrating industry heroes who solve daily problems.The podcast is broadcasting live from the SMRP conference in Fort Worth, Texas, where Scott has been discussing asset management, reliability, and maintenance.Scott introduces Greg and Bill from AssetWatch, who will share their experiences and insights from the conference. Backgrounds of Greg and Bill Greg Raglin shares his career journey, starting in psychology, moving to logistics, and eventually to fluid motion control and automation.Greg has been with AssetWatch for three years as a solutions architect, helping customers solve problems with practical solutions.Bill Broderick has been with AssetWatch for over a year, with a background in manufacturing automation and predictive maintenance.Bill emphasizes his passion for finding inefficiencies and optimizing processes to help companies save costs and improve efficiency. Overview of AssetWatch Greg explains that AssetWatch is a reliability partner, focusing on condition-based monitoring and using data, AI, and machine learning to provide actionable insights.The company has a team of 30+ dedicated engineers who analyze data and provide recommendations to fix issues.Bill adds that AssetWatch uses AI to monitor data and filter out false alarms, ensuring that plant-level teams receive accurate and timely information.The human element behind the technology is crucial for AssetWatch, as experienced engineers can communicate effectively with plant operators. Data Analysis and Integration Scott asks about the types of data AssetWatch can analyze, and Greg mentions that they focus on vibration and temperature data, with plans to expand to other modalities.Bill explains that AssetWatch integrates with other systems like CMS to provide a comprehensive solution for predictive maintenance.The company aims to be a one-stop shop for reliability, using data from various sources to reduce downtime and improve efficiency.AssetWatch manufactures their own devices, ensuring that all components are state-side and of high quality. Deployment and Training Greg discusses the deployment process, where AssetWatch's reliability...

In this episode, Donovan and Ken discuss what happens when a background goes bad and how BI's can make or break an investigation.--------------------For those who aren't subscribers: Have we helped you with our podcast content, or with a phone call or email advice? You can now show your love at buymeacoffee.com! Here are the links in the event you'd like to express your appreciation if we've made a difference:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠buymeacoffee.com/kenroybal⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠buymeacoffee.com/donovanheavener⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bonus: Our books are discounted 50% for podcast subscribers!! (Email us for your discount code.)You're going to love these great new podcast offerings!!Purchase your copies today:Ken's Book: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://payhip.com/b/BFYjg⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Donovan's Book: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://payhip.com/b/AVlRT⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Contact us:ken[atsign]policebackground.netdonovan[atsign]policebackground.netPolice candidate consultations: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.policebackground.net⁠⁠⁠⁠

In this debut episode of "Talk Fantasy to Me," hosts Chase and Kyle dive into the enchanting world of fantasy entertainment. From live-action epics and anime to animated classics and fantasy horror, they explore the genre's impact on culture and personal experiences. Join them as they discuss the latest fantasy news, upcoming releases, and their own journeys into the fantastical realms that have shaped their lives. 00:00:00 Introduction to Talk Fantasy to Me 00:03:00 Hosts' Backgrounds and Fantasy Journeys 00:09:00 Fantasy News and Upcoming Releases 00:18:00 Discussion on Fantasy's Cultural Impact 00:24:00 Marvel's Upcoming Timeline 00:30:00 Casting Choices in Fantasy Shows 00:36:00 Henry Cavill's Influence in Fantasy 00:42:00 Amazon's God of War Series 00:48:00 Highlander Reboot with Henry Cavill 00:54:00 Closing Thoughts and Future Episodes