Podcasts about Beauchamp

In the 17th episode of AnsweRED Podcast, our hosts, Sebastian Kalemba and Paweł Burza, are joined by Alena Dubrovina, Art Director at Larian Studios and Émilie Beauchamp, Associate Art Director at WB Games Montréal. Not only did we hear about their professional experience working on projects like Baldur's Gate 3 or Gotham Knights, but also about the role of an Art Director in the game dev industry.

True greatness isn't found in power or prestige, it's found in serving others with radical love. In this episode, Rhiannon Beauchamp shares how her faith journey, personal trials, and passion for ministry have led to life-changing work with special needs communities and missions abroad. Tune in and discover how your willingness to serve can become someone else's answered prayer.  Key Takeaways To Listen ForHow the love of Jesus transforms our mindset from words to actionWhy your calling isn't about going far but about being faithful where you areThe story behind Born to Shine and how a party became a movementHow a dream to preach the gospel led to divine appointments in TanzaniaWhy a pure heart is essential for seeing God and serving others Resources Mentioned In This EpisodeFaith ChurchAbout Rhiannon BeauchampRhiannon Beauchamp is a passionate minister, speaker, and community leader who serves as the Campus Pastor at Faith Church's Earth City campus in St. Louis. With a heart for radical love, inclusion, and service, she leads Born to Shine, a vibrant ministry for individuals with special needs and disabilities, creating joyful, Christ-centered experiences for families who often feel overlooked. Rhiannon's mission is rooted in daily surrender to God's calling, with a focus on action over words and love without limits. Her passion for global outreach recently led her to launch initiatives in Africa, where she's working to combat harmful stigmas and bring hope to marginalized families—all while pointing them to the love of Jesus.  Connect with RhiannonLinkedIn: Rhiannon BeauchampConnect With UsMaster your context with real results leadership training!To learn more, visit our website at www.greatsummit.com.For tax, bookkeeping, or accounting help, contact Dr. Nate's team at www.theincometaxcenter.com or send an email to info@theincometaxcenter.com.Follow Dr. Nate on His Social MediaLinkedIn: Nate Salah, Ph.DInstagram: @natesalah Facebook: Nate SalahTikTok: @drnatesalahClubhouse: @natesalah

As spring planting season nears, Wisconsin farmers face rising stress levels. Concerns about finances, unpredictable weather, and labor shortages can weigh heavily on their minds. Fortunately, the Wisconsin Department of Agriculture, Trade, and Consumer Protection offers resources like the Wisconsin Farmer Wellness Program to provide vital support. "There are so many different elements that can bring stress to a person's life," said Jessica Beauchamp, a licensed clinical social worker with the Farmer Wellness Program. "It's important for farmers to know they don't have to face these challenges alone." The Farmer Wellness Program offers telehealth services, in-person counseling, and confidential support groups for farmers and their families. "We’ve seen an increase in people reaching out for help, which is great because these resources are here for them," Beauchamp explained. According to Beauchamp, sleep issues have become a common concern. "Farmers work all day and stay busy to distract themselves," she said. "But when they lie down at night, their minds start racing because they've ignored their stress all day." See omnystudio.com/listener for privacy information.

Have you ever wondered how nature can enrich your child's learning experience and complement what they're taught in the classroom? In this episode of Parenting Ed-Ventures, host Lara Courtepatte sits down with Anne Beauchamp, a passionate educator and Forest and Nature School Practitioner, to explore the powerful connection between outdoor learning and child development.With over 14 years of experience teaching early elementary in a multi-graded setting, Anne now brings her expertise to the Child and Nature Alliance of Canada, advocating for the importance of play, exploration, and curiosity in learning. As a mother raising her family near Grasslands National Park, she understands firsthand how the natural world can foster resilience, creativity, and a love of learning in children.In today's conversation, Anne explains what Forest and Nature School education is all about and why this approach is gaining momentum. She dives into the benefits of outdoor learning, helping parents understand why time spent in nature is so impactful for children. Together, we also break down some of the most common misconceptions about nature-based education, discuss simple ways families can incorporate outdoor learning into daily life, and explore how parents can advocate for more nature-based opportunities in schools and communities.If you've ever felt that your child could benefit from more time outside, or if you're curious about how to balance structured education with hands-on, experiential learning, this episode is for you. Tune in to discover how connecting with nature can transform your child's learning journey and create lasting positive impacts.-----Learn more about the Child and Nature Alliance of Canada:https://childnature.ca/-----Follow Parenting Ed-Ventures on Instagram:⁠⁠⁠⁠⁠⁠⁠⁠@parentingedventurespod⁠⁠⁠⁠⁠⁠⁠Learn more about Tutor Teach ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://tutorteach.ca/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠-----Intro Music: Good Times by Patrick PatrikiosSting Music: Purple Planet Music - Timelapse

Having it All, All, All is a new group exhibition at Gus Fisher Gallery composed of nine international artists whose work has been pivotal in the reimagination of female subjectivity in art. The exhibition is showing select video and performance art by the likes of Ana Mendieta (Cuba/USA), Yoko Ono (Japan/USA), Howardena Pindell (USA), Pipilotti Rist (Switzerland), Martha Rosler (USA), Christa Schadt (Canada), Janice Tanaka (USA), Hannah Wilke (USA), and Nil Yalter (Turkey/France).  Spanning the 1960s to 1990s, Having it All, All, All delves into the activism and identity politics associated with Second Wave Feminism and its critics. Sofia spoke to curator of contemporary art at Gus Fisher Gallery, Lisa Beauchamp, about the exhibition, her curation process, and the impact of these works on feminist politics as seen through a contemporary perspective.

Utah Jazz vs. LA Clippers NBA Pick Prediction by Tony T. Jazz at Clippers Injuries Martin, Sexton and Williams are out for Utah with Clarkson questionable. Bogdanovic is out for LA with Beauchamp questionable. Recent Box Score Key Stats Jazz at Clippers 7PM ET—Utah drops to 12-38 with their 135-127 road overtime defeat at Phoenix. Jazz shot 41% with 27% from three. John Collins produced 21 points with six rebounds. Lauri Markkanen chipped in with 20 points with seven rebounds. Utah held Phoenix to 45% shooting with 39% from three. LA fell to 28-23 following their 119-112 home defeat to Indiana. The Clippers shot 47% with 42% from three. James Harden scored 22 points with 10 assists. Norman Powell produced 22 points with four rebounds. LA allowed 45% shooting to the Pacers with 33% from three.

Want to support the podcast? Join our Patreon or buy us a coffee. As an independent podcast, Shakespeare Anyone? is supported by listeners like you. In this week's episode, we are exploring the historical record to better understand the difference between the facts of the historical record and the history-making and myths in Shakespeare's King Henry V. We will share brief biographies of the historical figures presented in Shakespeare's play and discuss how understanding where Shakespeare embellished or elided history can help us understand the values of the audiences of his day and how this understanding can potentially inform performances and readings of Shakespeare's play today.  Shakespeare Anyone? is created and produced by Kourtney Smith and Elyse Sharp. Music is "Neverending Minute" by Sounds Like Sander. For updates: join our email list, follow us on Instagram at @shakespeareanyonepod or visit our website at shakespeareanyone.com You can support the podcast by becoming a patron at patreon.com/shakespeareanyone, sending us a virtual tip via our tipjar, or by shopping our bookshelves at bookshop.org/shop/shakespeareanyonepod. Are you a teacher who teaches upper grades (US 9-12 or equivalent) and teaches Shakespeare or wants to teach Shakespeare? We want to hear from you: https://www.shakespeareanyone.com/teachersurvey Works referenced: Britannica, The Editors of Encyclopaedia. "Charles VI". Encyclopedia Britannica, 29 Nov. 2024, https://www.britannica.com/biography/Charles-VI-king-of-France. Accessed 26 January 2025. Britannica, The Editors of Encyclopaedia. "Edward of Norwich, 2nd duke of York". Encyclopedia Britannica, 21 Oct. 2024, https://www.britannica.com/biography/Edward-of-Norwich-2nd-duke-of-York. Accessed 26 January 2025. Carpenter, Christine. "Beauchamp, Richard, thirteenth earl of Warwick (1382–1439), magnate." Oxford Dictionary of National Biography.  October 03, 2013. Oxford University Press. Date of access 27 Jan. 2025 Catto, Jeremy. "Chichele, Henry (c. 1362–1443), administrator and archbishop of Canterbury." Oxford Dictionary of National Biography.  September 23, 2004. Oxford University Press. Date of access 27 Jan. 2025 Griffiths, R. A. "Holland [Holand], John, first duke of Exeter (1395–1447), soldier and magnate." Oxford Dictionary of National Biography.  January 03, 2008. Oxford University Press. Date of access 27 Jan. 2025 Harriss, G. L. "Beaufort, Thomas, duke of Exeter (1377?–1426), magnate and soldier." Oxford Dictionary of National Biography.  January 03, 2008. Oxford University Press. Date of access 27 Jan. 2025 Harriss, G. L. "Humphrey [Humfrey or Humphrey of Lancaster], duke of Gloucester [called Good Duke Humphrey] (1390–1447), prince, soldier, and literary patron." Oxford Dictionary of National Biography.  June 11, 2020. Oxford University Press. Date of access 27 Jan. 2025 Harriss, G. L. "Richard [Richard of Conisbrough], earl of Cambridge (1385–1415), magnate." Oxford Dictionary of National Biography.  September 14, 2023. Oxford University Press. Date of access 27 Jan. 2025 Hughes, Jonathan. "Arundel [Fitzalan], Thomas (1353–1414), administrator and archbishop of Canterbury." Oxford Dictionary of National Biography.  May 24, 2007. Oxford University Press. Date of access 27 Jan. 2025 Jones, Dan. Henry V: The Astonishing Triumph of England's Greatest Warrior King. Viking, 2024. Pollard, A. J. "Neville, Richard, fifth earl of Salisbury (1400–1460), magnate." Oxford Dictionary of National Biography.  January 03, 2008. Oxford University Press. Date of access 27 Jan. 2025 Stratford, Jenny. "John [John of Lancaster], duke of Bedford (1389–1435), regent of France and prince." Oxford Dictionary of National Biography.  September 22, 2011. Oxford University Press. Date of access 27 Jan. 2025 Tuck, Anthony. "Edmund [Edmund of Langley], first duke of York (1341–1402), prince." Oxford Dictionary of National Biography.  September 14, 2023. Oxford University Press. Date of access 27 Jan. 2025 Tuck, Anthony. "Neville, Ralph, first earl of Westmorland (c. 1364–1425), magnate." Oxford Dictionary of National Biography.  January 03, 2008. Oxford University Press. Date of access 27 Jan. 2025 Vale, Brigette. "Scrope, Henry, third Baron Scrope of Masham (c. 1376–1415), soldier and administrator." Oxford Dictionary of National Biography.  January 03, 2008. Oxford University Press. Date of access 27 Jan. 2025 Walker, Simon. "Erpingham, Sir Thomas (c. 1355–1428), soldier." Oxford Dictionary of National Biography.  January 03, 2008. Oxford University Press. Date of access 27 Jan. 2025 Wikipedia contributors. "Charles II, Duke of Lorraine." Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia, 11 Jan. 2025. Web. 27 Jan. 2025. Wikipedia contributors. "Isabeau of Bavaria." Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia, 24 Jan. 2025. Web. 27 Jan. 2025. Wikipedia contributors. "Louis, Duke of Guyenne." Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia, 10 Nov. 2024. Web. 27 Jan. 2025.

durée : 00:30:33 - Les Nuits de France Culture - par : Philippe Garbit - - réalisation : Virginie Mourthé

One last Gold sponsor slot is available for the AI Engineer Summit in NYC. Our last round of invites is going out soon - apply here - If you are building AI agents or AI eng teams, this will be the single highest-signal conference of the year for you!While the world melts down over DeepSeek, few are talking about the OTHER notable group of former hedge fund traders who pivoted into AI and built a remarkably profitable consumer AI business with a tiny team with incredibly cracked engineering team — Chai Research. In short order they have:* Started a Chat AI company well before Noam Shazeer started Character AI, and outlasted his departure.* Crossed 1m DAU in 2.5 years - William updates us on the pod that they've hit 1.4m DAU now, another +40% from a few months ago. Revenue crossed >$22m. * Launched the Chaiverse model crowdsourcing platform - taking 3-4 week A/B testing cycles down to 3-4 hours, and deploying >100 models a week.While they're not paying million dollar salaries, you can tell they're doing pretty well for an 11 person startup:The Chai Recipe: Building infra for rapid evalsRemember how the central thesis of LMarena (formerly LMsys) is that the only comprehensive way to evaluate LLMs is to let users try them out and pick winners?At the core of Chai is a mobile app that looks like Character AI, but is actually the largest LLM A/B testing arena in the world, specialized on retaining chat users for Chai's usecases (therapy, assistant, roleplay, etc). It's basically what LMArena would be if taken very, very seriously at one company (with $1m in prizes to boot):Chai publishes occasional research on how they think about this, including talks at their Palo Alto office:William expands upon this in today's podcast (34 mins in):Fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it. And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours.In Crowdsourcing the leap to Ten Trillion-Parameter AGI, William describes Chai's routing as a recommender system, which makes a lot more sense to us than previous pitches for model routing startups:William is notably counter-consensus in a lot of his AI product principles:* No streaming: Chats appear all at once to allow rejection sampling* No voice: Chai actually beat Character AI to introducing voice - but removed it after finding that it was far from a killer feature.* Blending: “Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model.” (that's it!)But chief above all is the recommender system.We also referenced Exa CEO Will Bryk's concept of SuperKnowlege:Full Video versionOn YouTube. please like and subscribe!Timestamps* 00:00:04 Introductions and background of William Beauchamp* 00:01:19 Origin story of Chai AI* 00:04:40 Transition from finance to AI* 00:11:36 Initial product development and idea maze for Chai* 00:16:29 User psychology and engagement with AI companions* 00:20:00 Origin of the Chai name* 00:22:01 Comparison with Character AI and funding challenges* 00:25:59 Chai's growth and user numbers* 00:34:53 Key inflection points in Chai's growth* 00:42:10 Multi-modality in AI companions and focus on user-generated content* 00:46:49 Chaiverse developer platform and model evaluation* 00:51:58 Views on AGI and the nature of AI intelligence* 00:57:14 Evaluation methods and human feedback in AI development* 01:02:01 Content creation and user experience in Chai* 01:04:49 Chai Grant program and company culture* 01:07:20 Inference optimization and compute costs* 01:09:37 Rejection sampling and reward models in AI generation* 01:11:48 Closing thoughts and recruitmentTranscriptAlessio [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel, and today we're in the Chai AI office with my usual co-host, Swyx.swyx [00:00:14]: Hey, thanks for having us. It's rare that we get to get out of the office, so thanks for inviting us to your home. We're in the office of Chai with William Beauchamp. Yeah, that's right. You're founder of Chai AI, but previously, I think you're concurrently also running your fund?William [00:00:29]: Yep, so I was simultaneously running an algorithmic trading company, but I fortunately was able to kind of exit from that, I think just in Q3 last year. Yeah, congrats. Yeah, thanks.swyx [00:00:43]: So Chai has always been on my radar because, well, first of all, you do a lot of advertising, I guess, in the Bay Area, so it's working. Yep. And second of all, the reason I reached out to a mutual friend, Joyce, was because I'm just generally interested in the... ...consumer AI space, chat platforms in general. I think there's a lot of inference insights that we can get from that, as well as human psychology insights, kind of a weird blend of the two. And we also share a bit of a history as former finance people crossing over. I guess we can just kind of start it off with the origin story of Chai.William [00:01:19]: Why decide working on a consumer AI platform rather than B2B SaaS? So just quickly touching on the background in finance. Sure. Originally, I'm from... I'm from the UK, born in London. And I was fortunate enough to go study economics at Cambridge. And I graduated in 2012. And at that time, everyone in the UK and everyone on my course, HFT, quant trading was really the big thing. It was like the big wave that was happening. So there was a lot of opportunity in that space. And throughout college, I'd sort of played poker. So I'd, you know, I dabbled as a professional poker player. And I was able to accumulate this sort of, you know, say $100,000 through playing poker. And at the time, as my friends would go work at companies like ChangeStreet or Citadel, I kind of did the maths. And I just thought, well, maybe if I traded my own capital, I'd probably come out ahead. I'd make more money than just going to work at ChangeStreet.swyx [00:02:20]: With 100k base as capital?William [00:02:22]: Yes, yes. That's not a lot. Well, it depends what strategies you're doing. And, you know, there is an advantage. There's an advantage to being small, right? Because there are, if you have a 10... Strategies that don't work in size. Exactly, exactly. So if you have a fund of $10 million, if you find a little anomaly in the market that you might be able to make 100k a year from, that's a 1% return on your 10 million fund. If your fund is 100k, that's 100% return, right? So being small, in some sense, was an advantage. So started off, and the, taught myself Python, and machine learning was like the big thing as well. Machine learning had really, it was the first, you know, big time machine learning was being used for image recognition, neural networks come out, you get dropout. And, you know, so this, this was the big thing that's going on at the time. So I probably spent my first three years out of Cambridge, just building neural networks, building random forests to try and predict asset prices, right, and then trade that using my own money. And that went well. And, you know, if you if you start something, and it goes well, you You try and hire more people. And the first people that came to mind was the talented people I went to college with. And so I hired some friends. And that went well and hired some more. And eventually, I kind of ran out of friends to hire. And so that was when I formed the company. And from that point on, we had our ups and we had our downs. And that was a whole long story and journey in itself. But after doing that for about eight or nine years, on my 30th birthday, which was four years ago now, I kind of took a step back to just evaluate my life, right? This is what one does when one turns 30. You know, I just heard it. I hear you. And, you know, I looked at my 20s and I loved it. It was a really special time. I was really lucky and fortunate to have worked with this amazing team, been successful, had a lot of hard times. And through the hard times, learned wisdom and then a lot of success and, you know, was able to enjoy it. And so the company was making about five million pounds a year. And it was just me and a team of, say, 15, like, Oxford and Cambridge educated mathematicians and physicists. It was like the real dream that you'd have if you wanted to start a quant trading firm. It was like...swyx [00:04:40]: Your own, all your own money?William [00:04:41]: Yeah, exactly. It was all the team's own money. We had no customers complaining to us about issues. There's no investors, you know, saying, you know, they don't like the risk that we're taking. We could. We could really run the thing exactly as we wanted it. It's like Susquehanna or like Rintec. Yeah, exactly. Yeah. And they're the companies that we would kind of look towards as we were building that thing out. But on my 30th birthday, I look and I say, OK, great. This thing is making as much money as kind of anyone would really need. And I thought, well, what's going to happen if we keep going in this direction? And it was clear that we would never have a kind of a big, big impact on the world. We can enrich ourselves. We can make really good money. Everyone on the team would be paid very, very well. Presumably, I can make enough money to buy a yacht or something. But this stuff wasn't that important to me. And so I felt a sort of obligation that if you have this much talent and if you have a talented team, especially as a founder, you want to be putting all that talent towards a good use. I looked at the time of like getting into crypto and I had a really strong view on crypto, which was that as far as a gambling device. This is like the most fun form of gambling invented in like ever super fun, I thought as a way to evade monetary regulations and banking restrictions. I think it's also absolutely amazing. So it has two like killer use cases, not so much banking the unbanked, but everything else, but everything else to do with like the blockchain and, and you know, web, was it web 3.0 or web, you know, that I, that didn't, it didn't really make much sense. And so instead of going into crypto, which I thought, even if I was successful, I'd end up in a lot of trouble. I thought maybe it'd be better to build something that governments wouldn't have a problem with. I knew that LLMs were like a thing. I think opening. I had said they hadn't released GPT-3 yet, but they'd said GPT-3 is so powerful. We can't release it to the world or something. Was it GPT-2? And then I started interacting with, I think Google had open source, some language models. They weren't necessarily LLMs, but they, but they were. But yeah, exactly. So I was able to play around with, but nowadays so many people have interacted with the chat GPT, they get it, but it's like the first time you, you can just talk to a computer and it talks back. It's kind of a special moment and you know, everyone who's done that goes like, wow, this is how it should be. Right. It should be like, rather than having to type on Google and search, you should just be able to ask Google a question. When I saw that I read the literature, I kind of came across the scaling laws and I think even four years ago. All the pieces of the puzzle were there, right? Google had done this amazing research and published, you know, a lot of it. Open AI was still open. And so they'd published a lot of their research. And so you really could be fully informed on, on the state of AI and where it was going. And so at that point I was confident enough, it was worth a shot. I think LLMs are going to be the next big thing. And so that's the thing I want to be building in, in that space. And I thought what's the most impactful product I can possibly build. And I thought it should be a platform. So I myself love platforms. I think they're fantastic because they open up an ecosystem where anyone can contribute to it. Right. So if you think of a platform like a YouTube, instead of it being like a Hollywood situation where you have to, if you want to make a TV show, you have to convince Disney to give you the money to produce it instead, anyone in the world can post any content they want to YouTube. And if people want to view it, the algorithm is going to promote it. Nowadays. You can look at creators like Mr. Beast or Joe Rogan. They would have never have had that opportunity unless it was for this platform. Other ones like Twitter's a great one, right? But I would consider Wikipedia to be a platform where instead of the Britannica encyclopedia, which is this, it's like a monolithic, you get all the, the researchers together, you get all the data together and you combine it in this, in this one monolithic source. Instead. You have this distributed thing. You can say anyone can host their content on Wikipedia. Anyone can contribute to it. And anyone can maybe their contribution is they delete stuff. When I was hearing like the kind of the Sam Altman and kind of the, the Muskian perspective of AI, it was a very kind of monolithic thing. It was all about AI is basically a single thing, which is intelligence. Yeah. Yeah. The more intelligent, the more compute, the more intelligent, and the more and better AI researchers, the more intelligent, right? They would speak about it as a kind of erased, like who can get the most data, the most compute and the most researchers. And that would end up with the most intelligent AI. But I didn't believe in any of that. I thought that's like the total, like I thought that perspective is the perspective of someone who's never actually done machine learning. Because with machine learning, first of all, you see that the performance of the models follows an S curve. So it's not like it just goes off to infinity, right? And the, the S curve, it kind of plateaus around human level performance. And you can look at all the, all the machine learning that was going on in the 2010s, everything kind of plateaued around the human level performance. And we can think about the self-driving car promises, you know, how Elon Musk kept saying the self-driving car is going to happen next year, it's going to happen next, next year. Or you can look at the image recognition, the speech recognition. You can look at. All of these things, there was almost nothing that went superhuman, except for something like AlphaGo. And we can speak about why AlphaGo was able to go like super superhuman. So I thought the most likely thing was going to be this, I thought it's not going to be a monolithic thing. That's like an encyclopedia Britannica. I thought it must be a distributed thing. And I actually liked to look at the world of finance for what I think a mature machine learning ecosystem would look like. So, yeah. So finance is a machine learning ecosystem because all of these quant trading firms are running machine learning algorithms, but they're running it on a centralized platform like a marketplace. And it's not the case that there's one giant quant trading company of all the data and all the quant researchers and all the algorithms and compute, but instead they all specialize. So one will specialize on high frequency training. Another will specialize on mid frequency. Another one will specialize on equity. Another one will specialize. And I thought that's the way the world works. That's how it is. And so there must exist a platform where a small team can produce an AI for a unique purpose. And they can iterate and build the best thing for that, right? And so that was the vision for Chai. So we wanted to build a platform for LLMs.Alessio [00:11:36]: That's kind of the maybe inside versus contrarian view that led you to start the company. Yeah. And then what was maybe the initial idea maze? Because if somebody told you that was the Hugging Face founding story, people might believe it. It's kind of like a similar ethos behind it. How did you land on the product feature today? And maybe what were some of the ideas that you discarded that initially you thought about?William [00:11:58]: So the first thing we built, it was fundamentally an API. So nowadays people would describe it as like agents, right? But anyone could write a Python script. They could submit it to an API. They could send it to the Chai backend and we would then host this code and execute it. So that's like the developer side of the platform. On their Python script, the interface was essentially text in and text out. An example would be the very first bot that I created. I think it was a Reddit news bot. And so it would first, it would pull the popular news. Then it would prompt whatever, like I just use some external API for like Burr or GPT-2 or whatever. Like it was a very, very small thing. And then the user could talk to it. So you could say to the bot, hi bot, what's the news today? And it would say, this is the top stories. And you could chat with it. Now four years later, that's like perplexity or something. That's like the, right? But back then the models were first of all, like really, really dumb. You know, they had an IQ of like a four year old. And users, there really wasn't any demand or any PMF for interacting with the news. So then I was like, okay. Um. So let's make another one. And I made a bot, which was like, you could talk to it about a recipe. So you could say, I'm making eggs. Like I've got eggs in my fridge. What should I cook? And it'll say, you should make an omelet. Right. There was no PMF for that. No one used it. And so I just kept creating bots. And so every single night after work, I'd be like, okay, I like, we have AI, we have this platform. I can create any text in textile sort of agent and put it on the platform. And so we just create stuff night after night. And then all the coders I knew, I would say, yeah, this is what we're going to do. And then I would say to them, look, there's this platform. You can create any like chat AI. You should put it on. And you know, everyone's like, well, chatbots are super lame. We want absolutely nothing to do with your chatbot app. No one who knew Python wanted to build on it. I'm like trying to build all these bots and no consumers want to talk to any of them. And then my sister who at the time was like just finishing college or something, I said to her, I was like, if you want to learn Python, you should just submit a bot for my platform. And she, she built a therapy for me. And I was like, okay, cool. I'm going to build a therapist bot. And then the next day I checked the performance of the app and I'm like, oh my God, we've got 20 active users. And they spent, they spent like an average of 20 minutes on the app. I was like, oh my God, what, what bot were they speaking to for an average of 20 minutes? And I looked and it was the therapist bot. And I went, oh, this is where the PMF is. There was no demand for, for recipe help. There was no demand for news. There was no demand for dad jokes or pub quiz or fun facts or what they wanted was they wanted the therapist bot. the time I kind of reflected on that and I thought, well, if I want to consume news, the most fun thing, most fun way to consume news is like Twitter. It's not like the value of there being a back and forth, wasn't that high. Right. And I thought if I need help with a recipe, I actually just go like the New York times has a good recipe section, right? It's not actually that hard. And so I just thought the thing that AI is 10 X better at is a sort of a conversation right. That's not intrinsically informative, but it's more about an opportunity. You can say whatever you want. You're not going to get judged. If it's 3am, you don't have to wait for your friend to text back. It's like, it's immediate. They're going to reply immediately. You can say whatever you want. It's judgment-free and it's much more like a playground. It's much more like a fun experience. And you could see that if the AI gave a person a compliment, they would love it. It's much easier to get the AI to give you a compliment than a human. From that day on, I said, okay, I get it. Humans want to speak to like humans or human like entities and they want to have fun. And that was when I started to look less at platforms like Google. And I started to look more at platforms like Instagram. And I was trying to think about why do people use Instagram? And I could see that I think Chai was, was filling the same desire or the same drive. If you go on Instagram, typically you want to look at the faces of other humans, or you want to hear about other people's lives. So if it's like the rock is making himself pancakes on a cheese plate. You kind of feel a little bit like you're the rock's friend, or you're like having pancakes with him or something, right? But if you do it too much, you feel like you're sad and like a lonely person, but with AI, you can talk to it and tell it stories and tell you stories, and you can play with it for as long as you want. And you don't feel like you're like a sad, lonely person. You feel like you actually have a friend.Alessio [00:16:29]: And what, why is that? Do you have any insight on that from using it?William [00:16:33]: I think it's just the human psychology. I think it's just the idea that, with old school social media. You're just consuming passively, right? So you'll just swipe. If I'm watching TikTok, just like swipe and swipe and swipe. And even though I'm getting the dopamine of like watching an engaging video, there's this other thing that's building my head, which is like, I'm feeling lazier and lazier and lazier. And after a certain period of time, I'm like, man, I just wasted 40 minutes. I achieved nothing. But with AI, because you're interacting, you feel like you're, it's not like work, but you feel like you're participating and contributing to the thing. You don't feel like you're just. Consuming. So you don't have a sense of remorse basically. And you know, I think on the whole people, the way people talk about, try and interact with the AI, they speak about it in an incredibly positive sense. Like we get people who say they have eating disorders saying that the AI helps them with their eating disorders. People who say they're depressed, it helps them through like the rough patches. So I think there's something intrinsically healthy about interacting that TikTok and Instagram and YouTube doesn't quite tick. From that point on, it was about building more and more kind of like human centric AI for people to interact with. And I was like, okay, let's make a Kanye West bot, right? And then no one wanted to talk to the Kanye West bot. And I was like, ah, who's like a cool persona for teenagers to want to interact with. And I was like, I was trying to find the influencers and stuff like that, but no one cared. Like they didn't want to interact with the, yeah. And instead it was really just the special moment was when we said the realization that developers and software engineers aren't interested in building this sort of AI, but the consumers are right. And rather than me trying to guess every day, like what's the right bot to submit to the platform, why don't we just create the tools for the users to build it themselves? And so nowadays this is like the most obvious thing in the world, but when Chai first did it, it was not an obvious thing at all. Right. Right. So we took the API for let's just say it was, I think it was GPTJ, which was this 6 billion parameter open source transformer style LLM. We took GPTJ. We let users create the prompt. We let users select the image and we let users choose the name. And then that was the bot. And through that, they could shape the experience, right? So if they said this bot's going to be really mean, and it's going to be called like bully in the playground, right? That was like a whole category that I never would have guessed. Right. People love to fight. They love to have a disagreement, right? And then they would create, there'd be all these romantic archetypes that I didn't know existed. And so as the users could create the content that they wanted, that was when Chai was able to, to get this huge variety of content and rather than appealing to, you know, 1% of the population that I'd figured out what they wanted, you could appeal to a much, much broader thing. And so from that moment on, it was very, very crystal clear. It's like Chai, just as Instagram is this social media platform that lets people create images and upload images, videos and upload that, Chai was really about how can we let the users create this experience in AI and then share it and interact and search. So it's really, you know, I say it's like a platform for social AI.Alessio [00:20:00]: Where did the Chai name come from? Because you started the same path. I was like, is it character AI shortened? You started at the same time, so I was curious. The UK origin was like the second, the Chai.William [00:20:15]: We started way before character AI. And there's an interesting story that Chai's numbers were very, very strong, right? So I think in even 20, I think late 2022, was it late 2022 or maybe early 2023? Chai was like the number one AI app in the app store. So we would have something like 100,000 daily active users. And then one day we kind of saw there was this website. And we were like, oh, this website looks just like Chai. And it was the character AI website. And I think that nowadays it's, I think it's much more common knowledge that when they left Google with the funding, I think they knew what was the most trending, the number one app. And I think they sort of built that. Oh, you found the people.swyx [00:21:03]: You found the PMF for them.William [00:21:04]: We found the PMF for them. Exactly. Yeah. So I worked a year very, very hard. And then they, and then that was when I learned a lesson, which is that if you're VC backed and if, you know, so Chai, we'd kind of ran, we'd got to this point, I was the only person who'd invested. I'd invested maybe 2 million pounds in the business. And you know, from that, we were able to build this thing, get to say a hundred thousand daily active users. And then when character AI came along, the first version, we sort of laughed. We were like, oh man, this thing sucks. Like they don't know what they're building. They're building the wrong thing anyway, but then I saw, oh, they've raised a hundred million dollars. Oh, they've raised another hundred million dollars. And then our users started saying, oh guys, your AI sucks. Cause we were serving a 6 billion parameter model, right? How big was the model that character AI could afford to serve, right? So we would be spending, let's say we would spend a dollar per per user, right? Over the, the, you know, the entire lifetime.swyx [00:22:01]: A dollar per session, per chat, per month? No, no, no, no.William [00:22:04]: Let's say we'd get over the course of the year, we'd have a million users and we'd spend a million dollars on the AI throughout the year. Right. Like aggregated. Exactly. Exactly. Right. They could spend a hundred times that. So people would say, why is your AI much dumber than character AIs? And then I was like, oh, okay, I get it. This is like the Silicon Valley style, um, hyper scale business. And so, yeah, we moved to Silicon Valley and, uh, got some funding and iterated and built the flywheels. And, um, yeah, I, I'm very proud that we were able to compete with that. Right. So, and I think the reason we were able to do it was just customer obsession. And it's similar, I guess, to how deep seek have been able to produce such a compelling model when compared to someone like an open AI, right? So deep seek, you know, their latest, um, V2, yeah, they claim to have spent 5 million training it.swyx [00:22:57]: It may be a bit more, but, um, like, why are you making it? Why are you making such a big deal out of this? Yeah. There's an agenda there. Yeah. You brought up deep seek. So we have to ask you had a call with them.William [00:23:07]: We did. We did. We did. Um, let me think what to say about that. I think for one, they have an amazing story, right? So their background is again in finance.swyx [00:23:16]: They're the Chinese version of you. Exactly.William [00:23:18]: Well, there's a lot of similarities. Yes. Yes. I have a great affinity for companies which are like, um, founder led, customer obsessed and just try and build something great. And I think what deep seek have achieved. There's quite special is they've got this amazing inference engine. They've been able to reduce the size of the KV cash significantly. And then by being able to do that, they're able to significantly reduce their inference costs. And I think with kind of with AI, people get really focused on like the kind of the foundation model or like the model itself. And they sort of don't pay much attention to the inference. To give you an example with Chai, let's say a typical user session is 90 minutes, which is like, you know, is very, very long for comparison. Let's say the average session length on TikTok is 70 minutes. So people are spending a lot of time. And in that time they're able to send say 150 messages. That's a lot of completions, right? It's quite different from an open AI scenario where people might come in, they'll have a particular question in mind. And they'll ask like one question. And a few follow up questions, right? So because they're consuming, say 30 times as many requests for a chat, or a conversational experience, you've got to figure out how to how to get the right balance between the cost of that and the quality. And so, you know, I think with AI, it's always been the case that if you want a better experience, you can throw compute at the problem, right? So if you want a better model, you can just make it bigger. If you want it to remember better, give it a longer context. And now, what open AI is doing to great fanfare is with projection sampling, you can generate many candidates, right? And then with some sort of reward model or some sort of scoring system, you can serve the most promising of these many candidates. And so that's kind of scaling up on the inference time compute side of things. And so for us, it doesn't make sense to think of AI is just the absolute performance. So. But what we're seeing, it's like the MML you score or the, you know, any of these benchmarks that people like to look at, if you just get that score, it doesn't really tell tell you anything. Because it's really like progress is made by improving the performance per dollar. And so I think that's an area where deep seek have been able to form very, very well, surprisingly so. And so I'm very interested in what Lama four is going to look like. And if they're able to sort of match what deep seek have been able to achieve with this performance per dollar gain.Alessio [00:25:59]: Before we go into the inference, some of the deeper stuff, can you give people an overview of like some of the numbers? So I think last I checked, you have like 1.4 million daily active now. It's like over 22 million of revenue. So it's quite a business.William [00:26:12]: Yeah, I think we grew by a factor of, you know, users grew by a factor of three last year. Revenue over doubled. You know, it's very exciting. We're competing with some really big, really well funded companies. Character AI got this, I think it was almost a $3 billion valuation. And they have 5 million DAU is a number that I last heard. Torquay, which is a Chinese built app owned by a company called Minimax. They're incredibly well funded. And these companies didn't grow by a factor of three last year. Right. And so when you've got this company and this team that's able to keep building something that gets users excited, and they want to tell their friend about it, and then they want to come and they want to stick on the platform. I think that's very special. And so last year was a great year for the team. And yeah, I think the numbers reflect the hard work that we put in. And then fundamentally, the quality of the app, the quality of the content, the quality of the content, the quality of the content, the quality of the content, the quality of the content. AI is the quality of the experience that you have. You actually published your DAU growth chart, which is unusual. And I see some inflections. Like, it's not just a straight line. There's some things that actually inflect. Yes. What were the big ones? Cool. That's a great, great, great question. Let me think of a good answer. I'm basically looking to annotate this chart, which doesn't have annotations on it. Cool. The first thing I would say is this is, I think the most important thing to know about success is that success is born out of failures. Right? Through failures that we learn. You know, if you think something's a good idea, and you do and it works, great, but you didn't actually learn anything, because everything went exactly as you imagined. But if you have an idea, you think it's going to be good, you try it, and it fails. There's a gap between the reality and expectation. And that's an opportunity to learn. The flat periods, that's us learning. And then the up periods is that's us reaping the rewards of that. So I think the big, of the growth shot of just 2024, I think the first thing that really kind of put a dent in our growth was our backend. So we just reached this scale. So we'd, from day one, we'd built on top of Google's GCP, which is Google's cloud platform. And they were fantastic. We used them when we had one daily active user, and they worked pretty good all the way up till we had about 500,000. It was never the cheapest, but from an engineering perspective, man, that thing scaled insanely good. Like, not Vertex? Not Vertex. Like GKE, that kind of stuff? We use Firebase. So we use Firebase. I'm pretty sure we're the biggest user ever on Firebase. That's expensive. Yeah, we had calls with engineers, and they're like, we wouldn't recommend using this product beyond this point, and you're 3x over that. So we pushed Google to their absolute limits. You know, it was fantastic for us, because we could focus on the AI. We could focus on just adding as much value as possible. But then what happened was, after 500,000, just the thing, the way we were using it, and it would just, it wouldn't scale any further. And so we had a really, really painful, at least three-month period, as we kind of migrated between different services, figuring out, like, what requests do we want to keep on Firebase, and what ones do we want to move on to something else? And then, you know, making mistakes. And learning things the hard way. And then after about three months, we got that right. So that, we would then be able to scale to the 1.5 million DAE without any further issues from the GCP. But what happens is, if you have an outage, new users who go on your app experience a dysfunctional app, and then they're going to exit. And so your next day, the key metrics that the app stores track are going to be something like retention rates. And so your next day, the key metrics that the app stores track are going to be something like retention rates. Money spent, and the star, like, the rating that they give you. In the app store. In the app store, yeah. Tyranny. So if you're ranked top 50 in entertainment, you're going to acquire a certain rate of users organically. If you go in and have a bad experience, it's going to tank where you're positioned in the algorithm. And then it can take a long time to kind of earn your way back up, at least if you wanted to do it organically. If you throw money at it, you can jump to the top. And I could talk about that. But broadly speaking, if we look at 2024, the first kink in the graph was outages due to hitting 500k DAU. The backend didn't want to scale past that. So then we just had to do the engineering and build through it. Okay, so we built through that, and then we get a little bit of growth. And so, okay, that's feeling a little bit good. I think the next thing, I think it's, I'm not going to lie, I have a feeling that when Character AI got... I was thinking. I think so. I think... So the Character AI team fundamentally got acquired by Google. And I don't know what they changed in their business. I don't know if they dialed down that ad spend. Products don't change, right? Products just what it is. I don't think so. Yeah, I think the product is what it is. It's like maintenance mode. Yes. I think the issue that people, you know, some people may think this is an obvious fact, but running a business can be very competitive, right? Because other businesses can see what you're doing, and they can imitate you. And then there's this... There's this question of, if you've got one company that's spending $100,000 a day on advertising, and you've got another company that's spending zero, if you consider market share, and if you're considering new users which are entering the market, the guy that's spending $100,000 a day is going to be getting 90% of those new users. And so I have a suspicion that when the founders of Character AI left, they dialed down their spending on user acquisition. And I think that kind of gave oxygen to like the other apps. And so Chai was able to then start growing again in a really healthy fashion. I think that's kind of like the second thing. I think a third thing is we've really built a great data flywheel. Like the AI team sort of perfected their flywheel, I would say, in end of Q2. And I could speak about that at length. But fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it. And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours. And when we did that, we could really, really, really perfect techniques like DPO, fine tuning, prompt engineering, blending, rejection sampling, training a reward model, right, really successfully, like boom, boom, boom, boom, boom. And so I think in Q3 and Q4, we got, the amount of AI improvements we got was like astounding. It was getting to the point, I thought like how much more, how much more edge is there to be had here? But the team just could keep going and going and going. That was like number three for the inflection point.swyx [00:34:53]: There's a fourth?William [00:34:54]: The important thing about the third one is if you go on our Reddit or you talk to users of AI, there's like a clear date. It's like somewhere in October or something. The users, they flipped. Before October, the users... The users would say character AI is better than you, for the most part. Then from October onwards, they would say, wow, you guys are better than character AI. And that was like a really clear positive signal that we'd sort of done it. And I think people, you can't cheat consumers. You can't trick them. You can't b******t them. They know, right? If you're going to spend 90 minutes on a platform, and with apps, there's the barriers to switching is pretty low. Like you can try character AI, you can't cheat consumers. You can't cheat them. You can't cheat them. You can't cheat AI for a day. If you get bored, you can try Chai. If you get bored of Chai, you can go back to character. So the users, the loyalty is not strong, right? What keeps them on the app is the experience. If you deliver a better experience, they're going to stay and they can tell. So that was the fourth one was we were fortunate enough to get this hire. He was hired one really talented engineer. And then they said, oh, at my last company, we had a head of growth. He was really, really good. And he was the head of growth for ByteDance for two years. Would you like to speak to him? And I was like, yes. Yes, I think I would. And so I spoke to him. And he just blew me away with what he knew about user acquisition. You know, it was like a 3D chessswyx [00:36:21]: sort of thing. You know, as much as, as I know about AI. Like ByteDance as in TikTok US. Yes.William [00:36:26]: Not ByteDance as other stuff. Yep. He was interviewing us as we were interviewing him. Right. And so pick up options. Yeah, exactly. And so he was kind of looking at our metrics. And he was like, I saw him get really excited when he said, guys, you've got a million daily active users and you've done no advertising. I said, correct. And he was like, that's unheard of. He's like, I've never heard of anyone doing that. And then he started looking at our metrics. And he was like, if you've got all of this organically, if you start spending money, this is going to be very exciting. I was like, let's give it a go. So then he came in, we've just started ramping up the user acquisition. So that looks like spending, you know, let's say we're spending, we started spending $20,000 a day, it looked very promising than 20,000. Right now we're spending $40,000 a day on user acquisition. That's still only half of what like character AI or talkie may be spending. But from that, it's sort of, we were growing at a rate of maybe say, 2x a year. And that got us growing at a rate of 3x a year. So I'm growing, I'm evolving more and more to like a Silicon Valley style hyper growth, like, you know, you build something decent, and then you canswyx [00:37:33]: slap on a huge... You did the important thing, you did the product first.William [00:37:36]: Of course, but then you can slap on like, like the rocket or the jet engine or something, which is just this cash in, you pour in as much cash, you buy a lot of ads, and your growth is faster.swyx [00:37:48]: Not to, you know, I'm just kind of curious what's working right now versus what surprisinglyWilliam [00:37:52]: doesn't work. Oh, there's a long, long list of surprising stuff that doesn't work. Yeah. The surprising thing, like the most surprising thing, what doesn't work is almost everything doesn't work. That's what's surprising. And I'll give you an example. So like a year and a half ago, I was working at a company, we were super excited by audio. I was like, audio is going to be the next killer feature, we have to get in the app. And I want to be the first. So everything Chai does, I want us to be the first. We may not be the company that's strongest at execution, but we can always be theswyx [00:38:22]: most innovative. Interesting. Right? So we can... You're pretty strong at execution.William [00:38:26]: We're much stronger, we're much stronger. A lot of the reason we're here is because we were first. If we launched today, it'd be so hard to get the traction. Because it's like to get the flywheel, to get the users, to build a product people are excited about. If you're first, people are naturally excited about it. But if you're fifth or 10th, man, you've got to beswyx [00:38:46]: insanely good at execution. So you were first with voice? We were first. We were first. I only knowWilliam [00:38:51]: when character launched voice. They launched it, I think they launched it at least nine months after us. Okay. Okay. But the team worked so hard for it. At the time we did it, latency is a huge problem. Cost is a huge problem. Getting the right quality of the voice is a huge problem. Right? Then there's this user interface and getting the right user experience. Because you don't just want it to start blurting out. Right? You want to kind of activate it. But then you don't have to keep pressing a button every single time. There's a lot that goes into getting a really smooth audio experience. So we went ahead, we invested the three months, we built it all. And then when we did the A-B test, there was like, no change in any of the numbers. And I was like, this can't be right, there must be a bug. And we spent like a week just checking everything, checking again, checking again. And it was like, the users just did not care. And it was something like only 10 or 15% of users even click the button to like, they wanted to engage the audio. And they would only use it for 10 or 15% of the time. So if you do the math, if it's just like something that one in seven people use it for one seventh of their time. You've changed like 2% of the experience. So even if that that 2% of the time is like insanely good, it doesn't translate much when you look at the retention, when you look at the engagement, and when you look at the monetization rates. So audio did not have a big impact. I'm pretty big on audio. But yeah, I like it too. But it's, you know, so a lot of the stuff which I do, I'm a big, you can have a theory. And you resist. Yeah. Exactly, exactly. So I think if you want to make audio work, it has to be a unique, compelling, exciting experience that they can't have anywhere else.swyx [00:40:37]: It could be your models, which just weren't good enough.William [00:40:39]: No, no, no, they were great. Oh, yeah, they were very good. it was like, it was kind of like just the, you know, if you listen to like an audible or Kindle, or something like, you just hear this voice. And it's like, you don't go like, wow, this is this is special, right? It's like a convenience thing. But the idea is that if you can, if Chai is the only platform, like, let's say you have a Mr. Beast, and YouTube is the only platform you can use to make audio work, then you can watch a Mr. Beast video. And it's the most engaging, fun video that you want to watch, you'll go to a YouTube. And so it's like for audio, you can't just put the audio on there. And people go, oh, yeah, it's like 2% better. Or like, 5% of users think it's 20% better, right? It has to be something that the majority of people, for the majority of the experience, go like, wow, this is a big deal. That's the features you need to be shipping. If it's not going to appeal to the majority of people, for the majority of the experience, and it's not a big deal, it's not going to move you. Cool. So you killed it. I don't see it anymore. Yep. So I love this. The longer, it's kind of cheesy, I guess, but the longer I've been working at Chai, and I think the team agrees with this, all the platitudes, at least I thought they were platitudes, that you would get from like the Steve Jobs, which is like, build something insanely great, right? Or be maniacally focused, or, you know, the most important thing is saying no to, not to work on. All of these sort of lessons, they just are like painfully true. They're painfully true. So now I'm just like, everything I say, I'm either quoting Steve Jobs or Zuckerberg. I'm like, guys, move fast and break free.swyx [00:42:10]: You've jumped the Apollo to cool it now.William [00:42:12]: Yeah, it's just so, everything they said is so, so true. The turtle neck. Yeah, yeah, yeah. Everything is so true.swyx [00:42:18]: This last question on my side, and I want to pass this to Alessio, is on just, just multi-modality in general. This actually comes from Justine Moore from A16Z, who's a friend of ours. And a lot of people are trying to do voice image video for AI companions. Yes. You just said voice didn't work. Yep. What would make you revisit?William [00:42:36]: So Steve Jobs, he was very, listen, he was very, very clear on this. There's a habit of engineers who, once they've got some cool technology, they want to find a way to package up the cool technology and sell it to consumers, right? That does not work. So you're free to try and build a startup where you've got your cool tech and you want to find someone to sell it to. That's not what we do at Chai. At Chai, we start with the consumer. What does the consumer want? What is their problem? And how do we solve it? So right now, the number one problems for the users, it's not the audio. That's not the number one problem. It's not the image generation either. That's not their problem either. The number one problem for users in AI is this. All the AI is being generated by middle-aged men in Silicon Valley, right? That's all the content. You're interacting with this AI. You're speaking to it for 90 minutes on average. It's being trained by middle-aged men. The guys out there, they're out there. They're talking to you. They're talking to you. They're like, oh, what should the AI say in this situation, right? What's funny, right? What's cool? What's boring? What's entertaining? That's not the way it should be. The way it should be is that the users should be creating the AI, right? And so the way I speak about it is this. Chai, we have this AI engine in which sits atop a thin layer of UGC. So the thin layer of UGC is absolutely essential, right? It's just prompts. But it's just prompts. It's just an image. It's just a name. It's like we've done 1% of what we could do. So we need to keep thickening up that layer of UGC. It must be the case that the users can train the AI. And if reinforcement learning is powerful and important, they have to be able to do that. And so it's got to be the case that there exists, you know, I say to the team, just as Mr. Beast is able to spend 100 million a year or whatever it is on his production company, and he's got a team building the content, the Mr. Beast company is able to spend 100 million a year on his production company. And he's got a team building the content, which then he shares on the YouTube platform. Until there's a team that's earning 100 million a year or spending 100 million on the content that they're producing for the Chai platform, we're not finished, right? So that's the problem. That's what we're excited to build. And getting too caught up in the tech, I think is a fool's errand. It does not work.Alessio [00:44:52]: As an aside, I saw the Beast Games thing on Amazon Prime. It's not doing well. And I'mswyx [00:44:56]: curious. It's kind of like, I mean, the audience reading is high. The run-to-meet-all sucks, but the audience reading is high.Alessio [00:45:02]: But it's not like in the top 10. I saw it dropped off of like the... Oh, okay. Yeah, that one I don't know. I'm curious, like, you know, it's kind of like similar content, but different platform. And then going back to like, some of what you were saying is like, you know, people come to ChaiWilliam [00:45:13]: expecting some type of content. Yeah, I think it's something that's interesting to discuss is like, is moats. And what is the moat? And so, you know, if you look at a platform like YouTube, the moat, I think is in first is really is in the ecosystem. And the ecosystem, is comprised of you have the content creators, you have the users, the consumers, and then you have the algorithms. And so this, this creates a sort of a flywheel where the algorithms are able to be trained on the users, and the users data, the recommend systems can then feed information to the content creators. So Mr. Beast, he knows which thumbnail does the best. He knows the first 10 seconds of the video has to be this particular way. And so his content is super optimized for the YouTube platform. So that's why it doesn't do well on Amazon. If he wants to do well on Amazon, how many videos has he created on the YouTube platform? By thousands, 10s of 1000s, I guess, he needs to get those iterations in on the Amazon. So at Chai, I think it's all about how can we get the most compelling, rich user generated content, stick that on top of the AI engine, the recommender systems, in such that we get this beautiful data flywheel, more users, better recommendations, more creative, more content, more users.Alessio [00:46:34]: You mentioned the algorithm, you have this idea of the Chaiverse on Chai, and you have your own kind of like LMSYS-like ELO system. Yeah, what are things that your models optimize for, like your users optimize for, and maybe talk about how you build it, how people submit models?William [00:46:49]: So Chaiverse is what I would describe as a developer platform. More often when we're speaking about Chai, we're thinking about the Chai app. And the Chai app is really this product for consumers. And so consumers can come on the Chai app, they can come on the Chai app, they can come on the Chai app, they can interact with our AI, and they can interact with other UGC. And it's really just these kind of bots. And it's a thin layer of UGC. Okay. Our mission is not to just have a very thin layer of UGC. Our mission is to have as much UGC as possible. So we must have, I don't want people at Chai training the AI. I want people, not middle aged men, building AI. I want everyone building the AI, as many people building the AI as possible. Okay, so what we built was we built Chaiverse. And Chaiverse is kind of, it's kind of like a prototype, is the way to think about it. And it started with this, this observation that, well, how many models get submitted into Hugging Face a day? It's hundreds, it's hundreds, right? So there's hundreds of LLMs submitted each day. Now consider that, what does it take to build an LLM? It takes a lot of work, actually. It's like someone devoted several hours of compute, several hours of their time, prepared a data set, launched it, ran it, evaluated it, submitted it, right? So there's a lot of, there's a lot of, there's a lot of work that's going into that. So what we did was we said, well, why can't we host their models for them and serve them to users? And then what would that look like? The first issue is, well, how do you know if a model is good or not? Like, we don't want to serve users the crappy models, right? So what we would do is we would, I love the LMSYS style. I think it's really cool. It's really simple. It's a very intuitive thing, which is you simply present the users with two completions. You can say, look, this is from model one. This is from model two. This is from model three. This is from model A. This is from model B, which is better. And so if someone submits a model to Chaiverse, what we do is we spin up a GPU. We download the model. We're going to now host that model on this GPU. And we're going to start routing traffic to it. And we're going to send, we think it takes about 5,000 completions to get an accurate signal. That's roughly what LMSYS does. And from that, we're able to get an accurate ranking. And we're able to get an accurate ranking. And we're able to get an accurate ranking of which models are people finding entertaining and which models are not entertaining. If you look at the bottom 80%, they'll suck. You can just disregard them. They totally suck. Then when you get the top 20%, you know you've got a decent model, but you can break it down into more nuance. There might be one that's really descriptive. There might be one that's got a lot of personality to it. There might be one that's really illogical. Then the question is, well, what do you do with these top models? From that, you can do more sophisticated things. You can try and do like a routing thing where you say for a given user request, we're going to try and predict which of these end models that users enjoy the most. That turns out to be pretty expensive and not a huge source of like edge or improvement. Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model. Just a random 50%? Just a random, yeah. And then... That's blending? That's blending. You can do more sophisticated things on top of that, as in all things in life, but the 80-20 solution, if you just do that, you get a pretty powerful effect out of the gate. Random number generator. I think it's like the robustness of randomness. Random is a very powerful optimization technique, and it's a very robust thing. So you can explore a lot of the space very efficiently. There's one thing that's really, really important to share, and this is the most exciting thing for me, is after you do the ranking, you get an ELO score, and you can track a user's first join date, the first date they submit a model to Chaiverse, they almost always get a terrible ELO, right? So let's say the first submission they get an ELO of 1,100 or 1,000 or something, and you can see that they iterate and they iterate and iterate, and it will be like, no improvement, no improvement, no improvement, and then boom. Do you give them any data, or do you have to come up with this themselves? We do, we do, we do, we do. We try and strike a balance between giving them data that's very useful, you've got to be compliant with GDPR, which is like, you have to work very hard to preserve the privacy of users of your app. So we try to give them as much signal as possible, to be helpful. The minimum is we're just going to give you a score, right? That's the minimum. But that alone is people can optimize a score pretty well, because they're able to come up with theories, submit it, does it work? No. A new theory, does it work? No. And then boom, as soon as they figure something out, they keep it, and then they iterate, and then boom,Alessio [00:51:46]: they figure something out, and they keep it. Last year, you had this post on your blog, cross-sourcing the lead to the 10 trillion parameter, AGI, and you call it a mixture of experts, recommenders. Yep. Any insights?William [00:51:58]: Updated thoughts, 12 months later? I think the odds, the timeline for AGI has certainly been pushed out, right? Now, this is in, I'm a controversial person, I don't know, like, I just think... You don't believe in scaling laws, you think AGI is further away. I think it's an S-curve. I think everything's an S-curve. And I think that the models have proven to just be far worse at reasoning than people sort of thought. And I think whenever I hear people talk about LLMs as reasoning engines, I sort of cringe a bit. I don't think that's what they are. I think of them more as like a simulator. I think of them as like a, right? So they get trained to predict the next most likely token. It's like a physics simulation engine. So you get these like games where you can like construct a bridge, and you drop a car down, and then it predicts what should happen. And that's really what LLMs are doing. It's not so much that they're reasoning, it's more that they're just doing the most likely thing. So fundamentally, the ability for people to add in intelligence, I think is very limited. What most people would consider intelligence, I think the AI is not a crowdsourcing problem, right? Now with Wikipedia, Wikipedia crowdsources knowledge. It doesn't crowdsource intelligence. So it's a subtle distinction. AI is fantastic at knowledge. I think it's weak at intelligence. And a lot, it's easy to conflate the two because if you ask it a question and it gives you, you know, if you said, who was the seventh president of the United States, and it gives you the correct answer, I'd say, well, I don't know the answer to that. And you can conflate that with intelligence. But really, that's a question of knowledge. And knowledge is really this thing about saying, how can I store all of this information? And then how can I retrieve something that's relevant? Okay, they're fantastic at that. They're fantastic at storing knowledge and retrieving the relevant knowledge. They're superior to humans in that regard. And so I think we need to come up for a new word. How does one describe AI should contain more knowledge than any individual human? It should be more accessible than any individual human. That's a very powerful thing. That's superswyx [00:54:07]: powerful. But what words do we use to describe that? We had a previous guest on Exa AI that does search. And he tried to coin super knowledge as the opposite of super intelligence.William [00:54:20]: Exactly. I think super knowledge is a more accurate word for it.swyx [00:54:24]: You can store more things than any human can.William [00:54:26]: And you can retrieve it better than any human can as well. And I think it's those two things combined that's special. I think that thing will exist. That thing can be built. And I think you can start with something that's entertaining and fun. And I think, I often think it's like, look, it's going to be a 20 year journey. And we're in like, year four, or it's like the web. And this is like 1998 or something. You know, you've got a long, long way to go before the Amazon.coms are like these huge, multi trillion dollar businesses that every single person uses every day. And so AI today is very simplistic. And it's fundamentally the way we're using it, the flywheels, and this ability for how can everyone contribute to it to really magnify the value that it brings. Right now, like, I think it's a bit sad. It's like, right now you have big labs, I'm going to pick on open AI. And they kind of go to like these human labelers. And they say, we're going to pay you to just label this like subset of questions that we want to get a really high quality data set, then we're going to get like our own computers that are really powerful. And that's kind of like the thing. For me, it's so much like Encyclopedia Britannica. It's like insane. All the people that were interested in blockchain, it's like, well, this is this is what needs to be decentralized, you need to decentralize that thing. Because if you distribute it, people can generate way more data in a distributed fashion, way more, right? You need the incentive. Yeah, of course. Yeah. But I mean, the, the, that's kind of the exciting thing about Wikipedia was it's this understanding, like the incentives, you don't need money to incentivize people. You don't need dog coins. No. Sometimes, sometimes people get the satisfaction fro

Miami Heat vs. Milwaukee Bucks NBA Pick Prediction by Tony T Heat at Bucks Injuries Butler and Richardson are out for Miami. Johnson is questionable with Herro probable. Green is questionable for Milwaukee with Giannis, Beauchamp, and Middleton probable. Recent Box Score Key Stats Heat at Bucks 7:30PM ET—Miami fell to 21-21 following their 116-107 home defeat to Portland. The Heat shot 44% with 33% from three. Duncan Robinson put in 22 points with five rebounds. Kel'el Ware got 20 points with 15 rebounds. Miami allowed 49% shooting to the Trailblazers with 35% from three. Milwaukee improved to 25-17 after their 123-109 home win to Philadelphia. The Bucks hit 49% with 38% from three. Giannis scored 34 points with 15 rebounds. Damian Lillard chipped in with 25 points and five rebounds. Milwaukee held Philadelphia to 45% shooting with 36% from three.

Dr. Tyler Beauchamp is a resistant pediatrician and children's/ YA author. We discuss his journey in medicine, the inspiration behind his Amazon #1 bestseller “Freeze Frame,” and how he blends science and art to bring happiness to others. Tune in for an engaging conversation that bridges healthcare and storytelling.

Milwaukee Bucks vs. New Orleans Pelicans NBA Pick Prediction by Tony T. Bucks at Pelicans Injuries Green is out for Milwaukee. Giannis, Beauchamp, and Middleton are probable. For New Orleans, Ingram and Jones are out, while Zion and Missi are questionable, and Murray is probable. Recent Box Score Key Stats Milwaukee improved to 25-17 with a 123-109 home win against Philadelphia. The Bucks shot 49% from the field and 38% from three. Giannis Antetokounmpo scored 34 points with 15 rebounds, while Damian Lillard added 25 points with five rebounds. Milwaukee allowed 45% shooting to the 76ers with 36% from three. New Orleans improved to 12-32 following a 123-119 home victory over Utah. The Pelicans shot 45% from the field and 37% from three. CJ McCollum scored 45 points with eight rebounds, while Dejounte Murray added 26 points and 11 assists. New Orleans allowed 40% shooting to the Jazz with 24% from three.

Yannick Marceau nous parle de l'assermentation de Donald Trump comme président des États-Unis. On rejoint en direct de Washington notre envoyé spécial, Keven Benoit! On poursuit en parlant de tempêtes et conduite avec Diane Beauchamp, camionneuse team Californie, et qu'en est-il des remorqueurs et le non paiement des entreprises avec le stratagème ‘Chauffeur inc'? Michael... The post 20 Janvier 2025 Yannick Marceau – Keven Benoit – Diane Beauchamp et Michael Rouillard appeared first on Truck Stop Québec.

Elle travaille en équipe pour le Groupe Trans-West, c'est Diane Beauchamp, camionneuse, et elle compte des millions de kilomètres sans accidents! Dans cette entrevue, elle nous explique quelles sont les mauvaises habitudes de conduite qu'elle observe sur la route, particulièrement dans les tempêtes qu'il y a eu dernièrement aux États-Unis. Diane a également obtenu sa... The post Diane Beauchamp partage son expérience de camionneuse aux États-Unis appeared first on Truck Stop Québec.

Philadelphia 76ers vs. Milwaukee Bucks NBA Pick Prediction by Tony T. 76ers at Bucks Injuries Embiid, George, Lowry, C.Martin, KJ Martin and Yabusele are out for Philadelphia. Jackson and Green are questionable for Milwaukee. Middleton and Beauchamp are probable. Recent Box Score Key Stats 76ers at Bucks 7PM ET—Philadelphia drops to 15-25 after their 115-102 road defeat to Indiana. The 76ers shot 43% with 29% from three. Tyrese Maxey scored 28 points with three assists. Kelly Oubre had 18 points along with eight rebounds. Philadelphia allowed 53% shooting to the Pacers with 32% from three. Milwaukee improved to 24-17 after their 130-112 home victory to Toronto. The Bucks shot 52% with 42% from three. Giannis scored 35 points with 13 rebounds. Damian Lillard put up 26 points with eight assists. Milwaukee allowed 47% shooting to the Raptors with 24% from three.

The boys return for a new special, joined by Joe Little, HSBCs chief investment strategist, as they look ahead to what investors can expect from 2025, the markets to watch and the moments that matter.

Every business owner and founder knows the feeling of “imposter syndrome.” We feel like we're not who we say we are when meeting with people. Our websites must be describing other people and worst of all — we feel like at any moment, we're going to get caught and called out, like in some movie. Here's the thing - that's all natural. For most owners and founders, your first business is also the first time that you have taken all the risks, become THE boss and had to handle sales, plus whatever your business actually provides. When imposter syndrome becomes a constant background feeling, more than a distance noise, but an unwanted companion in your business that can impact your brand, it's time to seek some professional leadership coaching and guidance. That's what we're going to discuss on today's episode of The Brandtelling Podcast. Our guest is Joseph Beauchamp, president of Beauchamp Team Growth Solutions, where he coaches business leaders to overcome the challenges their teams face achieving goals and growing their businesses through a series of tools, including Leadership Development coaching, training, mentoring, and consulting. The Brandtelling Podcast is brought to you by Brandtelling, a brand storytelling agency that establishes, strengthens and promotes unique brand stories, and Boch Creative, helping founders with strategy and implementation of their unique brand flavor. Beauchamp Team Growth Solutions Website: https://beauchampteam.com/ Live2LeadLI event: https://www.live2leadli.com/

Rob talks with the team from Beauchamp Water Solutions to explore their game-changing approach to water quality. From advanced services like pre-filtration, ultraviolet lights, and cost-effective water purification systems to home improvements like water heater repairs, they're redefining what a water treatment company can do. This local family business is all about quality, innovation, and solutions tailored to your needs. Dive in for a refreshing look at how they're making waves in the industry, Their passion for ensuring your health and safety has driven them for over 40 years!   Show Links Learn more about the Brighton Chamber by visiting our website. Website: https://www.brightoncoc.org/   Guest Links Website: https://www.beauchampwater.com/ Facebook: https://www.facebook.com/beauchampwater/ Instagram: https://www.instagram.com/beauchampwatersolutions/ LinkedIn: https://www.linkedin.com/company/beauchamp-water-treatment-solutions/ X: https://x.com/beauchampwater YouTube: https://www.youtube.com/channel/UCPcdNJfc8P3J6EipXefbGwA Birdeye: https://birdeye.com/beauchamp-water-treatment-solutions-166724595021849

Atlanta Hawks vs. Milwaukee Bucks NBA Pick Prediction 12/14/2024 by Tony T. Hawks vs. Bucks Injuries Zeller is out in Atlanta with Young, Johnson, Hunter and Bogdanovic probable. Livington is questionable for the Bucks with Giannis, Beauchamp and Middleton probable. Recent Box Score Key Stats Hawks vs. Bucks—In Season Tournament semifinal played in Las Vegas. Atlanta is 14-12 following their 108-100 road win at NY Knicks. The Hawks shot 41% with 29% from three. Trae Young scored 22 points with 11 assists. Jalen Johnson contributed 21 points with 15 rebounds. Atlanta allowed 43% shooting to the Knicks with 26% from three. Milwaukee improved to 13-11 with their 114-109 home win to Orlando. The Bucks hit 55% with 37% from three. Giannis had 27 points with seven rebounds. Damian Lillard chipped in with 28 points and nine assists. Milwaukee held Orlando to 44% shooting

Le Comte de Monte-Cristo is an adventure novel and that deals with themes of hope, justice, vengeance, mercy and forgiveness. Alexandre Dumas' celebrated classic continues with Part 84- "Beauchamp"! Learn more about your ad choices. Visit megaphone.fm/adchoices

Le Comte de Monte-Cristo is an adventure novel and that deals with themes of hope, justice, vengeance, mercy and forgiveness. Alexandre Dumas' celebrated classic continues with Part 84- "Beauchamp"! Learn more about your ad choices. Visit megaphone.fm/adchoices

Orlando Magic vs. Milwaukee Bucks NBA Pick Prediction 12/10/2024 by Tony T. Magic at Bucks Injuries Banchero, Harris and Franz Wagner are out for Orlando with Isaac questionable. Livingston is out with Middleton, Giannis and Beauchamp are probable. Recent Box Score Key Stats Magic at Bucks 7PM ET—Orlando improved to 17-9 following their 115-110 home win to Phoenix. The Magic shot 48% with 33% from three. Jalen Suggs scored 26 points with four rebounds. Goga Bitadze put up 21 points with 16 rebounds. Orlando allowed 47% shooting to the Suns with 46% from three. Milwaukee sits at 12-11 after their 118-113 road win at Brooklyn. The Bucks hit 57% with 48% from three. Giannis had 34 points with 11 rebounds. Bobby Portis off the bench had 23 points with five rebounds. Milwaukee allowed 51% shooting to the Nets with 46% from three.

Atlanta Hawks vs. Milwaukee Bucks NBA Pick Prediction 12/4/2024 by Tony T. Hawks at Bucks Injuries Lundy and Zeller are out in Atlanta with Young probable. Middleton and Beauchamp are out in Milwaukee. Recent Box Score Key Stats Hawks at Bucks—Atlanta improved to 11-11 following their 124-112 home win to New Orleans. The Hawks shot 51% with 26% from three. Jalen Johnson scored 19 points with seven rebounds. De'Andre Hunter put up 22 points with two rebounds. Atlanta allowed 44% shooting to the Pelicans with 31% from three. Milwaukee improved to 11-9 after their 128-107 road win at Detroit. The Bucks shot 56% with 56% from three. Giannis scored 28 points with eight assists. Damian Lillard had 27 points and five assists. Milwaukee allowed 46% shooting to the Pistons with 33% from three.

Milwaukee Bucks vs. Detroit Pistons NBA Pick Prediction 12/3/2024 by Tony T. Bucks at Pistons Injuries Middleton is out for Milwaukee with Giannis, Beauchamp and Prince probable. Klintman is out for Detroit. Recent Box Score Key Stats Bucks at Pistons—Milwaukee improved to 10-9 following their 124-114 home win to Washington. The Bucks shot 51% with 46% from three. Giannis scored 42 points with a Triple Double. Damian Lillard contributed 25 points with 10 assists. Milwaukee allowed 47% shooting to the Wizards with 28% from three. Detroit fell to 9-13 with their 111-96 home defeat to Philadelphia. The Pistons converted on 38% with 28% from three. Malik Beasley had 19 points with six rebounds. Jaden Ivey chipped in with 15 points and five assists. Detroit allowed 49% shooting to the 76ers with 35% from three.

Practicing medicine in the third world is wildly different from working as a practitioner in the USA. During this episode, Dr. Tony Urbanek returns to the podcast to share wild and wonderful stories from his years in Haiti and Jamaica, and the key learnings about the third world and the USA that he picked up along the way. Join us as we explore what surgeons stand to gain from leaving their comfort zones to work in underserved communities, why Dr. Urbanek believes that ultimately, he gained more than he gave from his experiences in other countries, and why he recommends that other surgeons explore work in the third world. You'll also hear what it's really like to work without proper tools such as lighting and anesthesia, how cultural norms affect our response to pain and panic, and why truly being of service is all about listening to what is needed rather than offering solutions. Tune in today to hear all this and more. Key Points From This Episode:Dr. Urbanek's journey into third-world medicine through a Catholic mission in Beauchamp, Haiti. Observations of a happy, established culture with little to no technical or medical knowledge.Unexpected encounters with a Voodoo doctor. Being recruited to work as a doctor in Jamaica. One of the biggest learnings from his experiences: in the third world, people are very proud.How he operated without a suction or the appropriate lighting: local anesthesia. A story to illustrate the power of cultural perception. Discussions with a Health Minister in Jamaica.Why it is so critical to find out what is needed rather than offering what you think is needed.Dr. Stucki's two-year religious mission in Bolivia at the age of 19. What it's really like to work without proper tools. Adjusting your mindset in order to be effective in the third world.Why ultimately, Dr. Urbanek believes that he got more out of his experience in the third world than he put into it.What you really stand to gain as a surgeon by leaving your comfort zone. The two jobs that Dr. Urbanek has available currently. Links Mentioned in Today's Episode:Dr. Anthony Urbanek — https://www.urbanektmj.com/Dr. Anthony Urbanek on LinkedIn — https://www.linkedin.com/in/drtonyurbanek/Dr. Anthony Urbanek on X — https://x.com/anthonyurbanekDr. Anthony Urbanek on Instagram — https://www.instagram.com/drtonyurbanek/Dr. Anthony Urbanek on Facebook — https://www.facebook.com/drtonyurbanek/Urbanek TMJ Device & Protocol |Brentwood — https://www.tmjservicesofbrentwood.com/ Everyday Oral Surgery Website — https://www.everydayoralsurgery.com/ Everyday Oral Surgery on Instagram — https://www.instagram.com/everydayoralsurgery/ Everyday Oral Surgery on Facebook — https://www.facebook.com/EverydayOralSurgery/Dr. Grant Stucki Email — grantstucki@gmail.comDr. Grant Stucki Phone — 720-441-6059

Investing with Beauchamp and Bright is back – sort of. As Aaron Bright galivants around South America on holiday, Chris Beauchamp is joined by Trade Live with IG's very own Rich McDonald who tells his story of first getting into the world of investing and lessons he has learned.

Washington Wizards vs. Milwaukee Bucks NBA Pick Prediction 11/29/2024 by Tony T. Wizards at Bucks Injuries Bey, Kuzma and Vukcevic are out for Washington. Middleton is out with Giannis and Beauchamp are probable. Recent Box Score Key Stats Wizards at Bucks—Washington dropped to 2-15 following their 121-96 home defeat to LA Clippers. The Wizards shot 41% with 24% from three and 20 turnovers. Malcolm Brogdon scored 17 points with six assists. Alex Sarr had 13 points and five rebounds. Washington gave up 51% shooting with 36% from three. Milwaukee improved to 9-9 after their 106-103 road win at Miami. The Bucks connected on 47% with 48% from three. Damian Lillard had 37 points with 12 assists. Brook Lopez put up 13 points with three assists. Milwaukee allowed 45% shooting with 34% from three.

Milwaukee Bucks vs. Miami Heat NBA Pick Prediction 11/26/2024 by Tony T. Bucks at Heat Injuries Middleton is out for Milwaukee with Prince questionable. Johnson, Beauchamp and Giannis are probable. Smith is questionable with Rozier probable for Miami. Recent Box Score Key Stats Bucks at Heat—Milwaukee improved to 8-9 after their 125-119 home win to Charlotte. The Bucks shot 48% with 49% from three. Giannis scored 32 points with 11 rebounds. Damian Lillard chipped in with 31 points and four assists. Milwaukee allowed 41% shooting to the Hornets with 40% from three. Miami is 7-7 with their 123-118 home win to Dallas. The Heat shot 43% with 34% from three. Jimmy Butler had 33 points with nine rebounds. Tyler Herro chipped in with 18 points with ten rebounds. Miami allowed 42% shooting to the Mavs with 25% from three.

Storm Bert has left Chris Beauchamp stranded at home so a first remote podcast features all things dividends, a tricky quiz and best and worst investing memories

What just happened? It's been almost two weeks since the presidential election, and many Americans are still grappling with the result. The political reckoning will probably last for months, if not years, and we may never know exactly why voters made the choices they did. But one thing is clear: the roughly 75 million people who voted for Trump were saying “No” to something. So what were they rejecting? Today's guest is Zack Beauchamp, Vox senior correspondent and author of The Reactionary Spirit: How America's Most Insidious Political Tradition Swept the World. It's a book about democracy and the contradictions and conflicts at the heart of it. Beauchamp speaks with host Sean Illing about America's growing reactionary movement and what it could mean for the country's political future. Host: Sean Illing (@SeanIlling), host, The Gray Area Guest: Zack Beauchamp, Vox senior correspondent and author of The Reactionary Spirit: How America's Most Insidious Political Tradition Swept the World. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Beauchamp and Bright return, with guest Anthony Ginsberg, as they look at niche index's, Ukraine and Russia tension timings and Donald Trump's long term targets.

The pair return, joined by Lukas Ahnert, Senior Passive Product Specialist, to break down how the markets have moved since the US Election and more importantly what comes now with Donald Trump returning to the White House… Find out more: https://upl.inc/beauchamp&bright *Any opinions, news, research, analysis, prices or other information contained does not constitute investment advice. Losses can exceed deposits, 70% of retail clients lose money*

Woonsocket Mayor Chris Beauchamp joins the show to talk about last nights winSee omnystudio.com/listener for privacy information.

The biggest week of the year has arrived – and the guys delve deep into the US Election, Federal Reserve, Bank of England and much more…

Milwaukee Bucks vs. Cleveland Cavaliers NBA Pick Prediction 11/4/2024 by Tony T. Bucks at Cavaliers—Middleton and Beauchamp are out for Milwaukee. Giannis is questionable with Connaughton probable. Bates, Strus and Tyson are out for Cleveland with LeVert questionable. Milwaukee fell to 1-5 following their 114-113 home defeat to Cleveland. The Bucks shot 48% with 46% from three. Giannis scored 34 points with 16 rebounds. Damian Lillard contributed with 41 points and nine assists. Cleveland shot 48% with 39% from three. Donovan Mitchell had 30 points with four assists. Sam Merrill off the bench put up 17 points with not much else.

On the early morning of November 7, 1825, in Frankfort, Ky., Jereboam Beauchamp stabbed Kentucky Legislator Solomon Sharp, in an event that would become known as the Kentucky Tragedy. But did the murder really occur as Beauchamp explained in his sensational confessions? Join us today for a special discussion of one of Kentucky's most notorious murders, and a story that inspired Edgar Allen Poe and Robert Penn Warren. Dr. Matthew Schoenbachler is a professor of history at the University of North Alabama. He holds a PhD in history from the University of Kentucky and has co-authored a book and published in the Journal of the Early Republic. We are delighted to talk with him today about Murder and Madness: The Myth of the Kentucky Tragedy, which was published in 2009. Kentucky Chronicles is inspired by the work of researchers from across the world who have contributed to the scholarly journal, The Register of the Kentucky Historical Society, in publication since 1903. https://history.ky.gov/explore/catalog-research-tools/register-of-the-kentucky-historical-society Hosted by Dr. Daniel J. Burge, associate editor of The Register of the Kentucky Historical Society, and coordinator of our Research Fellows program, which brings in researchers from across the world to conduct research in the rich archival holdings of the Kentucky Historical Society. https://history.ky.gov/khs-for-me/for-researchers/research-fellowships Kentucky Chronicles is presented by the Kentucky Historical Society, with support from the Kentucky Historical Society Foundation. https://history.ky.gov/about/khs-foundation Our show is recorded and produced by Gregory Hardison, and edited by Gregory P. Meyer. Thanks to Dr. Stephanie Lang for her support and guidance. Our theme music, “Modern Documentary” was created by Mood Mode and is used courtesy of Pixabay. Other backing tracks are also used courtesy of Pixabay. To learn more about our publication of The Register of the Kentucky Historical Society, or to learn more about our Research Fellows program, please visit our website: https://history.ky.gov/ https://history.ky.gov/khs-podcasts

Dive into Episode # 115 of the Psych Health and Safety USA Podcast, featuring host Dr. I. David Daniels, PhD, CSD, VPS, and special guest Michelle Beauchamp, the author of “Re-Learning Leadership. " Beauchamp discusses the value of inclusive leadership and the psychological and emotional safety by-products of inclusive leadership styles. Inclusive leadership is a management style that values and includes diverse perspectives and backgrounds in the workplace. It involves recognizing and appreciating differences in team members, such as those related to culture, gender, age, or sexual orientation. Inclusive leaders aim to create a safe environment where people feel comfortable speaking up and contributing and can handle situations in their way. They also seek to foster diverse teams and empower everyone to do their best work. Mrs. Beauchamp will share her experiences from her long career in both corporate and entrepreneurial efforts over her life.

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Albany State's Golden Rams are kicking off the 2024 season with three matches in six days...We check in with ASU Head Coach Jared Beauchamp on the expectations of the 2024 season and how his young team is heading into Matchday One

Are you seeking strategies to streamline your business operations?Toyota, the renowned Japanese automotive giant, has developed various methodologies to enhance workplace organisation and boost productivity, with one of the most effective being the 5S model.In this episode of the Ikigai Podcast, Nick is joined once again by Steve Beauchamp, who shares his innovative take on the 5S model and how it can play a pivotal role in optimising your business processes.

This week we are pleased to bring you another remix episode from Season 1, this time with guest Caitlin Bruce Beauchamp, daughter of Lynn Bruce and an AmblesideOnline graduate How Caitlin came to embrace Charlotte Mason's methods as an adult and foster parent What Caitlin remembers most about her homeschool and growing up experience What subjects were Caitlin's nemeses in school How narration prepared Caitlin so well for college How growing up with a Charlotte Mason education informed Caitlin's family life today A few of Caitlin's favorite books of all time To view all the links and books mentioned in this episode, please visit our show notes page on our website at https://thenewmasonjar.com/091.

In this episode of the School for School Counselors podcast, host Steph Johnson addresses the expanding role of school counselors in behavior intervention, emphasizing the need for a trauma-informed approach. She discusses common concerns counselors have about behavior intervention, including the misconception of equating it to discipline, time constraints, and the fear of being misutilized. Steph urges counselors to rethink their approach by identifying the origins of problematic behavior and addressing systemic issues rather than relying on one-stop solutions. She also highlights the importance of mindful language use and empowering students as peer mentors to foster a supportive school environment. Finally, she introduces the upcoming topics in the podcast and the resources available in the School for School Counselors mastermind group.00:00 Introduction and Podcast Welcome00:14 The Role of School Counselors in Behavior Intervention01:29 Challenges and Concerns in Behavior Intervention02:25 Rethinking Behavior Intervention Strategies[03:00 Celebrating a Podcast Milestone]04:02 Addressing the Root Causes of Behavior08:55 The Power of Language in Behavior Intervention13:03 Empowering Students as Change Agents17:27 Final Thoughts and Upcoming Topics**********************************References/Resources:McCormick, M. P., Cappella, E., O'Connor, E. E., & McClowry, S. G. (2015). Do Intervention Impacts on Academic Achievement Vary by School Climate? Evidence from a Randomized Trial in Urban Elementary Schools. Society for Research on Educational Effectiveness.https://files.eric.ed.gov/fulltext/ED562123.pdfPaquette, D. and Ryan, J. (2015). Bronfenbrenner's Ecological Systems Theory. National Dropout Prevention Center. https://dropoutprevention.org/wp-content/uploads/2015/07/paquetteryanwebquest_20091110.pdfWade, L., Leahy, A. A., Babic, M. J., Beauchamp, M. R., Smith, J. J., Kennedy, S. G., ... & Lubans, D. R. (2022). A systematic review and meta-analysis of the benefits of school-based, peer-led interventions for leaders. Scientific Reports, 12(1), 21222. https://www.nature.com/articles/s41598-022-25662-9.pdf**********************************Our goal at School for School Counselors is to help school counselors stay on fire, make huge impacts for students, and catalyze change for our roles through grassroots advocacy and collaboration. Listen to get to know more about us and our mission, feel empowered and inspired, and set yourself up for success in the wonderful world of school counseling.Hang out in our Facebook groupJump in, ask questions, share your ideas and become a part of the most empowering school counseling group on the planet! (Join us to see if we're right.)Join the School for School Counselors MastermindThe Mastermind is packed with all the things your grad program never taught you IN ADDITION TO unparalleled support and consultation. No more feeling alone, invisible, unappreciated, or like you just don't know what to do next. We've got you!Did someone share this podcast with you? Be sure to subscribe for all the new episodes!!

Dr. Nathalie Beauchamp B.Sc., D.C., IFMCP, is a Chiropractor and natural health expert with over 27 years of experience, based in Ottawa, Canada, she has authored several books including "Hack Your Health Habits" and "SmartCuts—Biohack Your Healthspan." Dr. Beauchamp hosts the Live Stream TV show "Hack Your Health with Dr. Nat," translating complex health strategies into practical, everyday applications. A leader in corporate wellness, she drives initiatives that enhance organizational health resilience. Dedicated to ongoing personal and professional development, Dr. Beauchamp actively integrates the latest biohacking tools and strategies into her own health regimen. She is passionate about empowering individuals to master their health and unlock their full potential. She aims to inspire people worldwide to achieve optimal health, heightened energy, and life satisfaction. Disclaimer: The content of this podcast is for informational purposes only and should not be considered a substitute for medical treatment, or advice, and shall not make any health or medical-related decision based in whole or in part on anything contained in the site. The opinions expressed by the guests do not necessarily reflect the views of Beth Shaw or YogaFit.

For more than a decade, global politics have been rocked by the rise of right-wing nationalist governments. Similar to Donald Trump's rise in the United States, countries like India, Hungary, Brazil, and Italy have seen the emergence of far-right governments who've channeled popular anger into support for nativist and anti-immigrant platforms. It turns out we're largely to blame for it.Today on Lever Time, Arjun Singh sits down with Vox senior correspondent Zack Beauchamp to discuss his new book The Reactionary Spirit: How America's Most Insidious Political Tradition Swept The World, in which Beauchamp traces the roots of modern right-wing regimes to an antidemocratic tradition that began in the United States. 

Following the assassination attempt on former President Donald Trump, far-right Republicans pointed fingers at President Joe Biden, blaming his warnings about Trump's threats to democracy for instigating the violence. For Vox reporter Zack Beauchamp, this “should cause us to reflect more broadly on how our political leaders should respond to political violence in our country.” For the last decade Beauchamp has been covering global challenges to democracy — and why democratic countries with deep political divisions can become vulnerable to violence and autocracy. We reflect on the assassination attempt and where it leaves us as a nation. Beauchamp's new book is “The Reactionary Spirit: How America's Most Insidious Political Tradition Swept the World.” Guests: Zack Beauchamp, senior correspondent, Vox