Avsnitt
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Help is out there
Almost everyone I know has had a horrible week and is looking for both allies and positive moments that will re-energize them. So rather than list all the terrible things from this week—from the truly heinous to the ridiculous (Gulf of America, anyone?), I want to be positive and point, first, to some allies, and then to the incredible embodiment of hope that is the Right Rev. Mariann Budde, the Episcopal bishop of Washington.
I’ve never understood the ethics of being a billionaire, but one thing I’ve always believed is that if you are one, you don’t have to submit to anyone. So I was really surprised when all the tech bros bent the knee for Trump. Yeah, he could affect government contracts, but loss of contracts won’t affect billionaires’ lifestyles, etc. They have the option that someone whose livelihood is threatened by not being ‘loyal’ doesn’t have—that is, they can tell absolutely anyone to fuck off.
I was talking to my friends and family about this because I still don’t understand it. I saw last week in Paul Krugman’s post “The Pathetic Billionaires’ Club” that I’m not the only person wondering about this:
Why is this self-owning by billionaires so extraordinary? Well, ask yourself: What’s the point of being rich?
Past a certain level of wealth, it can’t really be about material things. I very much doubt that billionaires have a significantly higher quality of life than mere multimillionaires.
To the extent that there’s a valid reason for accumulating a very large fortune, I’d say that it involves freedom, the ability to live your life more or less however you want. Indeed, one definition of true wealth is having “fuck you money” — enough money to walk away from unpleasant situations or distasteful individuals without suffering a big decline in your living standards. And some very wealthy men — most obviously Mark Cuban, but I’d at least tentatively include Bill Gates and Warren Buffett — do seem to exhibit the kind of independence wealth gives you if you choose to exercise it.
My favorite political cartoonist, Ann Telnaes, wasn’t allowed to criticize that billionaire self-owning in the Washington Post. And then one of my favorite columnists, Jennifer Rubin, left. So I left, too, canceled my subscription. Rubin, along with Norm Eisen started The Contrarian on Substack, so I got a paid subscription. (They have a lot of free posts, FYI.) And Ann Telnaes also has a Substack that I subscribed to. (I still subscribe to two newspapers, both of which have done some crappy stuff, too. Ugh.)
The Contrarian has many writers and guest writers. Here’s a good example of what it offers from the article “How to Drink from a Firehose” by David Litt. Litt discusses how not to despair and to work for the good. One of his suggestions is not to weigh in on Trump (don’t ask people who disagree with you to discuss his character), but rather to use specific examples of what is going on.
For right now, if a total stranger asked me to sum up this week, I’d say something like this:
“There’s a guy named Daniel Rodriguez. On January 5th, 2020, he texted his friends ‘There will be blood.’ On January 6th, when he stormed the Capitol, he grabbed a police officer and shocked him repeatedly in the neck with a stun gun. A jury of peers sentenced him to twelve years in prison for his violent crime. And less than 24 hours after taking office, Trump let Daniel Rodriguez back out on the street.”
If you're looking for actionable advice, subscribe to Hopium Chronicles By Simon Rosenberg. He always has the info on pending legislation (when to make calls to your representatives), campaigns around the country you can donate to, etc. He was too hopeful in his repeated assessment that he would rather be in the Harris camp than the Trump camp during the election campaign. (Yes, Trump was doing awful stuff and was becoming a convicted felon, but history—only eight years before—told us that America doesn’t care that a candidate is a sexual predator, grossly unethical, a failed businessman, etc. Competence and ethics are no longer the yardsticks.) Still, Rosenberg has good advice on where to focus your attention, week to week.
Good people rising
Okay, let’s focus our attention on the good people. Everyone I know is lit up by Bishop Budde right now. She’s the anti-dingus in lyz’s “Dingus of the Week” post.
The bishop presided over a prayer service held in the Washington Cathedral on January 21, which was attended by the president, the vice president, and their families.
During the sermon, Budde pleaded with the president to show mercy to the vulnerable — LGBTQ people, immigrants, and so many others who have been the target of the craven rhetoric and policies of the new president.
Her words were gentle as she stated, "Millions have put their trust in you. And as you told the nation yesterday, you have felt the providential hand of a loving God. In the name of our God, I ask you to have mercy upon the people in our country who are scared now. There are gay, lesbian and transgender children in Democratic, Republican, and Independent families, some who fear for their lives.”
Sean Hannity described the sermon as a "disgraceful prayer full of fear mongering and division."
Lawrence Jones, Fox & Friends co-host, called Budde a “radical leftist.”
Matt Walsh declared that "hell exists for people like Mariann" and called her "exhibit A for why women should not be pastors, priests, or bishops.”
And Rep. Mike Collins, a Republican from Georgia, said that Budde should be added to the deportation list.
And like, listen, I grew up Baptist. So I am going to tell you that in a Christian religious context, if you are the one trying to nail the soft-spoken religious leader to a cross, you are the bad guy.
Rebecca Solnit has cheered Budde in social media posts. My friends on social media are posting about her, too. Some are writing her thank you letters. I wrote her a thank you letter, on a card designed by my watercolor-artist friend Laura DeKloe.
Yeah, the bad guys are trying to take her down, but it’s not working. The reason I included the photo of my tea mug with the words “This nasty woman gets shit done” is because Trump called Budde “nasty.” (She has a ‘nasty tone.’) But then, that’s his favorite word for women who oppose him, isn’t it?
Some of the usual MAGA suspects just posted in socials like they are followers of the antiChrist. (See excerpt from Lyz Lenz, above.) Saying empathy is a sin? Are you fucking kidding me?
In his Book Club newsletter, Ron Charles discusses Budde’s most recent book, How We Learn to Be Brave.
And so, Budde cradles our doubts and reassures us that cowardice is natural but never final in the long arc of salvation. “The message throughout Scripture is that whenever God, or life itself, issues the summons, it’s normal to feel both unworthy and unprepared, but it doesn’t matter,” she writes. “We are to step into the gap between our current capacity and what’s needed anyway.
“This country needs leaders now, and citizens who can face things as they are, work to change what can be changed, and not give up hope for the future.” Amen.
I was delighted when, a few days ago, I saw that Budde had written this book. A high school friend posted that it was already sold out everywhere. True. And yay! I bought it as an audiobook. I could have gotten the ebook, but I have an unfortunate failing: my ebooks are out of sight, out of mind. I buy them, but I usually don’t read them.
Showing my gratitude
I’m grateful for the support I have received in bringing my novel Keep Sweet to life. It launches on June 21st—the first day of summer! Yes, good people are everywhere in life! Three of them wrote ‘blurbs’ (quick, positive comments for the back cover) and one made very helpful editorial suggestions. While blurbs must be brief, a person needs to read the book in order to write one. So it’s a solid ask of another author to do so.
I wanted to make something in return. I had a narrow piece of high-quality denim given to me by a friend as well as cotton fabric scraps from my own and my friends' projects. Scraps far too small for most projects. In the past, I posted about making bookmarks from them. (See this post for some bookmark photos.) I decided I could now make book totes. I didn’t have a pattern, but all the 12 pattern pieces would be rectangles. So I just needed to use some elementary math to plan. And, of course, I used the narrow strip left over from the front pocket design to make a bookmark for each. Yeah, I like matching stuff. Book tote matches bookmark? Happiness.
What I’m reading
I stopped reading The Bog Wife because I am going to write a blurb for an upcoming novel, Carlos Cortes’s Scout’s Honor. So far, it’s an incisive look back at mid-twentieth-century Boy Scouts, ages 12-14, when ‘boys will be boys’ excused all sorts of terrible behavior, including sexism, prejudice, and bullying. But when one of the boys turns his bullying against his own close friends, patrol members, and a scout leader, he ends up dead. (This is not a spoiler—he is dead in the first paragraph of the novel.) I’ll continue to read this week and find out ‘who done it’ and why.
I’ve been listening to the audio version of The Algebra of Wealth by Scott Galloway. It’s about how to plan and invest in order to be able to retire. It’s a general overview and probably not informative for those in the know. But I’m guessing readers here weren’t finance or accounting majors. (Except you, Lisa. And thank you for reading and supporting me!) You might be thinking, “Aren’t you a bit late, Vic?” And, yes, you are right. But I listened to see if it would be useful to my adult kids, who majored in humanities and science subjects. I think it is very useful. I didn’t want to buy the book twice, so I put a copy on hold in the library (Yay libraries!) I’m going to see if I can get my guys to read it.
I started listening to How We Learn to Be Brave, discussed above.
Part 2: Library and book ban news
Kootenai County library system approves adult-only room for books with mature content as 140 titles pulled from shelves for review from The Spokesman-Review
Books with mature themes will soon be relegated to an adults-only room in most public libraries in Kootenai County.
The private room is a requirement of an Idaho law passed last year, and the Idaho-based Community Library Network board voted Thursday to set up such a room at the Post Falls Library.
The library board also voted to stop children’s Community Library Network library cards from accessing other library systems that may not be subject to the Idaho law.
Utah students can no longer bring personal copies of banned books to school From kuer 90.1 (NPR)
While the 2024 law focuses on materials that schools own or use, one line in the state code explicitly states, “Sensitive materials are prohibited in the school setting.”
According to the state board’s updated FAQ, any books banned statewide are prohibited on any school property. Similarly, those banned by a local school district for being “sensitive material” are prohibited on any of that district’s school grounds.
“These titles should not be brought to school or used for classroom activities, assignments, or personal reading while on school property,” the FAQ page states.
Authors Guild Files Lawsuit Against Book Bans in Colorado School District From The Authors Guild
discontinuous excerpts:
The Authors Guild, the NAACP, and individual plaintiffs filed a federal lawsuit on December 19 against the Elizabeth School District in Colorado for removing books from school libraries based on their content and viewpoints. The lawsuit challenges the school board’s decision to permanently ban 19 books, many of which are highly acclaimed and widely taught across the country.
Beyond removing existing books, the board directed librarians not to order any new books for school libraries, banned classroom libraries entirely (causing teachers to cover their book collections with brown paper or take them home), and prohibited students from sharing books with each other in school. It switched from Scholastic to SkyTree Books, a vendor that promised book fairs without any LGBTQIA+ content, Critical Race Theory, foul language, explicit content, or dark magic. [Vic here—that’s a book fair that isn’t going to raise any money 🤣]
The board also implemented a system requiring parents to be notified whenever their child checks out a book on the “sensitive list,” with no way for parents to opt out of these notifications. These actions stigmatized books by and about racial minorities and LGBTQIA+ people by labeling them as inappropriate or dangerous, creating an environment where parents and teachers feel afraid to disagree with or challenge these policies publicly. The board threatened disciplinary action against staff members who provided “harsh feedback” about its decisions, compelled teachers to create inventories of their classroom libraries for review and potential further removals, and ultimately caused some families to withdraw their children from the district entirely.
To add insult to injury, board members admitted they had not fully read many of the banned books before deciding to remove them. The board’s actions appear politically motivated rather than educationally justified, with board members explicitly stating they were acting to impose “conservative values.”
Education Dept. Ends Book Ban Investigations from the New York Times (gift article—worth reading in its entirety)
The department said it would relinquish its role investigating schools that had received civil rights complaints after removing books dealing with sexual and racial identity.
By proclaiming that the department would not intervene in cases where students or parents felt they were harmed by the removal of certain titles, the announcement appeared to clear the way for states to enact more restrictive policies.
The headline for this change in the new Department of Education press release is:
U.S. Department of Education Ends Biden’s Book Ban Hoax
From Every Library:
If you worry about libraries and want to take action in helping them, go to Every Library (a registered 501(c)4 organization) and sign up for their newsletter. They will give you actionable ideas. Here are some of the things they are writing about now:
One of Trump's first actions was to rescind Biden's Executive Order 14084, titled "Promoting the Arts, the Humanities, and Museum and Library Services."
This action will dismantle the Committee on the Arts and Humanities and deliver a devasting blow to America's commitment to preserving the arts, the humanities, and museum and library services.
Trump's previous administration proposed eliminating all federal funding for libraries.
This action is the first step in dismantling the Institute of Museum and Library Services.
We know his supporters are enacting state legislation to arrest librarians, ban books, and defund libraries.
We'll have actions for you to take in the coming weeks, but we made it easy for you to write to your legislators today to ask them to support libraries.
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit victoriawaddle.substack.comEpisode: https://victoriawaddle.substack.com/p/lets-thank-bishop-budde-and-all-helpers
Podcast: https://victoriawaddle.substack.com/podcast
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Join us on this episode of "Create The Life You Want" where host Jeremy Ames dives deep with Janae Brown, co-founder of Proactive Prevention in Chicago, IL. Learn about their unique journey from corporate careers to creating a thriving family-owned handyman and preventative maintenance business. Discover the challenges and triumphs of entrepreneurship, including transitioning from technician work to full business development, leveraging personal experiences, and the critical role of business coaching.
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Want more empowering stories and entrepreneurial advice? Subscribe to the Create the Life You Want newsletter brought to you by Guidant Financial. Click the link below to sign up and start your journey today!
Sign Up for the Newsletter: https://www.guidantfinancial.com/newsletter
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Guidant Financial, sponsor of "Create the Life You Want," is America's leading provider of Rollovers for Business Startups (ROBS). Since 2003, Guidant has assisted over 30,000 small businesses across the U.S. in securing funding to start or grow their companies. With a strong focus on building the American entrepreneurial dream, Guidant Financial offers the tools and resources needed to launch a successful business.
Learn more about Guidant: https://www.guidantfinancial.com
Follow Jeremy on LinkedIn: https://www.linkedin.com/in/jeremyamesentrepreneur
Visit Jeremy's website: https://www.jeremyames.com
Episode: http://sites.libsyn.com/543487/building-a-business-with-family-the-journey-of-proactive-prevention
Podcast: https://jeremyames.com/podcast/
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Saknas det avsnitt?
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mailto:[email protected] In this episode of 'Need Some Introduction,' hosts Sona and Victor dive deep into Severance's second episode of the second season titled 'Goodbye, Mrs. Selvig.' They breakdown key scenes, character motivations, and speculate on future plot developments. From Heli's fascination with her innie's life to the mysterious intentions behind Mark's decisions, this discussion covers themes of duality, manipulation, and the emotional weight of personal traumas. Key points include Mark's confrontation with Ms. Cobell, Dylan's ill-fated job interview, and the revelation of how Lumen's insiders are controlling the narrative. Special focus is given to the new intro sequence and its potential clues, alongside an analysis of intrigue surrounding characters like Irv and Helena. The episode concludes with anticipation for further developments in next week's longer episode directed by Ben Stiller. 00:00 Introduction02:43 Streaming Analytics and the Value of AppleTV Programming07:37 Severance Theories and Speculations11:28 Character Dynamics and Emotional Moments21:30 Helena's Duality and Family Tensions36:40 New Intro Sequence Analysis40:19 Devin and Mark's Tense Interactions42:19 Milchik's Manipulative Visit46:29 Helena's Fascination with Her Innie50:28 Dylan's Job Interview Disaster54:06 Mark's Struggle with Grief and Denial58:08 Irv's Mysterious Actions01:07:47 Mark's Confrontation with Selvig01:10:57 Speculations and Final Thoughts
Episode: https://needssomeintroduction.podbean.com/e/severance-season-2-episode-2-goodbye-mrs-selvig/
Podcast: https://needssomeintroduction.podbean.com
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Minnesota kicks off 2025 with a flurry of new laws and significant updates across various sectors. The state legislature has been active, passing over a dozen new laws that took effect on January 1, 2025. Key among these are increased protections for concert ticket buyers, a requirement for employers to post salary ranges with job listings, and additional rights for renters[1][5].
In the realm of business and economy, the National Federation of Independent Business (NFIB) has outlined its 2025 legislative priorities, focusing on reforms to the Minnesota Paid Family & Medical Leave Program and providing small businesses with tax relief. The state's high corporate tax rate of 9.8% and individual income tax rate of 9.85% are highlighted as areas needing attention[2].
On the environmental front, Minnesota is bracing for a colder-than-normal January, with temperatures expected to be in the teens and single digits for the first half of the month. This forecast is welcome news for ice fishing tournaments and families with backyard ice skating rinks[3].
In education, the state has previously implemented free school meals for students and increased education funding, reflecting ongoing efforts to support students and schools[1].
Looking Ahead:
- The 2025 Minnesota Legislative Session is set to address various small business concerns, including reforms to the Paid Family & Medical Leave Program and tax relief.
- The state's weather outlook suggests a continued cold spell, which could impact outdoor activities and infrastructure projects.
- Upcoming events include ice fishing tournaments, which are expected to benefit from the cold weather.
- The legislative session will also focus on tackling "junk fees" and ensuring transparency in various consumer transactions, reflecting a broader effort to protect consumer rights[1][5].Episode: https://www.spreaker.com/episode/minnesota-launches-2025-with-bold-new-laws-tax-reforms-and-consumer-protections--63906702
Podcast: https://www.spreaker.com/podcast/minnesota-news-and-info-tracker--6235693
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New York has seen significant developments across various sectors in recent weeks. Governor Kathy Hochul delivered her 2025 State of the State Address, outlining over 200 initiatives aimed at making New York safer, healthier, and more affordable for its residents. Key proposals include an inflation refund to provide relief to 8.6 million New Yorkers, a major expansion of the child tax credit, and efforts to make child care more accessible and affordable[1].
In government and politics, the FY 2025 New York State Executive Budget has been released, detailing appropriations and legislation across various sectors including education, labor, family assistance, health, and mental hygiene[3]. Notable policy changes include measures to protect subway riders and transit workers, make rent and mortgages more affordable, and strengthen the criminal justice system.
On the business and economy front, the state's efforts to support working parents and expand food assistance programs are expected to boost local economies. Major business developments include initiatives to protect consumers shopping online and regulate emerging industries[1].
Community news highlights include the "Unplug and Play" initiative to promote kids' mental and physical health by funding community centers and recreational opportunities. Education and infrastructure projects are also underway, with a focus on building new child care facilities and repairing existing sites[1].
Environmental and weather updates indicate that New York is expected to experience above-normal winter precipitation, with frequent rain and snow, particularly in mountainous areas and interior regions. The Farmer’s Almanac predicts a season filled with "rapid-fire storms" that will bring both rain and snow, with little time between weather events[4].
Looking Ahead: Upcoming events and developing stories in New York include the implementation of the 2025 State of the State initiatives, further developments in the FY 2025 Executive Budget, and ongoing efforts to address environmental and weather challenges. Additionally, residents can expect updates on the state's economic indicators and major business developments. As New York continues to navigate its challenges and opportunities, residents and policymakers alike will be watching these developments closely.Episode: https://www.spreaker.com/episode/new-york-unveils-ambitious-2025-state-plan-inflation-refunds-child-care-expansion-and-economic-growth-set-to-transform-empire-state--63906483
Podcast: https://www.spreaker.com/podcast/new-york-state-news-and-info-tracker--6228896
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https://3speak.tv/watch?v=dragokazo/kdufomjj
– This time we're flying to Alderaan. It's going to be epic!That one sentence was enough to cause a real storm of emotions. Adrian, our unofficial general and self-proclaimed strategy expert, immediately said:– Obi-Wan will lead us to victory. Without it, you would get lost on the first map.Sara, feisty and confident as always, rolled her eyes, countering:– Sure, Adrian. Without Leia, you wouldn't even have plans for your mission.Victor, who was already running around the room, waving his arms and making Wookiee-like sounds, added his two cents:– Chewbacca has the biggest gun and he's doing a BOOM! That's all we need!Roni, our indefatigable pug, joined in the fun, running around the couch and barking at the plastic model of the Millennium Falcon that Alex had built out of bricks a few days earlier. Even Mija, who usually chooses to run to the windowsill in such moments, this time observed this commotion with clear interest, as if she was planning whether or not to jump on the TV at the key moment of the game."The Beginning of the Mission – Departure in the Millennium Falcon"Our adventure began in the Mos Eisley canteen, where the team was preparing for the start on Alderaan. The Millennium Falcon, a proud icon of the galactic smugler, stood in all its glory in the center of the hangar. Han Solo, as usual in his style, made a comment:– Look, if you want to get to Alderaan, stick to my rules.Sarah, with a sneer in her voice, replied:– Maybe someone will remind him that he wouldn't have gone anywhere without us.The first task was to load the equipment onto the ship. Simple in theory, but in our execution? Absolute chaos. Alex, instead of helping, decided to "check" each crate, destroying half of them.– Alex, stop it! These are our parts! Adrian shouted, trying to save the day.Victor, who was doing his own thing as usual, discovered the console that controlled the side door of the hangar. Before anyone could stop him, he activated the mechanism, which caused the door to open and a group of stormtroopers to burst in.– Wiktor! What have you done?! Alex shouted when the message appeared on the screen: Enemies nearby!"Hangar Combat – Chaos to the Fullest"What was supposed to be a peaceful loading quickly turned into an intense battle. Stormtroopers pressed from all sides, and our team, as always, operated in their unique style – complete chaos. Adrian, trying to take command, shouted:– Alex, Cover Me! Sarah, shoot at those on the left! Wiktor, stop throwing grenades at the walls!Sara, playing as Leia, fought with such passion that she looked like she wanted to jump into the game herself. Her character eliminated opponents after opponents, while she commented:– Leia will never let any stormtroopers defeat her. He is an icon!Wiktor, as always, did his job – he threw grenades blindly, which led to unexpected explosions.– Wiktor, you're throwing at us! Alex shouted, who lost half of his heart after one of the explosions.Even Roni started barking as if he wanted to join the fight, and Mija sat closer to the TV, watching the action with a face that said, "I don't know what's going on here, but it's interesting."After a few minutes of intense fighting, we managed to defeat our opponents and load the rest of the equipment. Han Solo, in his typical style, said:– Next time, try to do less and do more.Sarah, with a smile on her face, replied:– Next time you could do something yourself, Solo."Space Travel: A Meteoric Test of Patience"When the Millennium Falcon finally took off, it seemed that the hardest part of the mission was behind us. Nothing could be further from the truth. A flight through space quickly turned into a real cosmic obstacle course when giant meteorites appeared on our way.Adrian, as a co-driver, tried to control the situation:– Hold on tight! It will be difficult!Wiktor, delighted with the possibility of shooting, began to aim at every meteor he passed, which ended up with us hitting the rocks several times instead of avoiding them.– Victor, stop shooting meteorites! – said Alex, who was getting bigger and bigger with each collision.Sara, observing all this, commented:– This looks more like a cosmic version of our morning – chaos everywhere.After a few dramatic minutes, full of shouts, laughter and unsuccessful maneuvers, we managed to reach Alderaan."Alderaan – the calm before the storm"The planet Alderaan welcomed us with beautiful views – green meadows, blue skies and quiet towns. At first glance, it looked like a perfect place to rest. Sara immediately began to explore:– Someone needs to see if this planet has anything interesting to offer.Adrian, impatiently, said:– We don't have time to explore. Let's focus on the mission.Alex and Viktor, of course, had completely different plans. They ran around the map, searching every nook and cranny and collecting coins. In one of the buildings, Wiktor found a hidden chest with bonus items.– See? That's why it's worth exploring," he said triumphantly, looking at Adrian."Finale – Imperial Alert!"The peace did not last long. Suddenly, a message appeared on the screen: The Empire has located your position! Get ready to fight! Stormtroopers began to push from all sides, and we had to fight for survival again.Adrian tried to lead, but as always, everyone did their own thing. Sara fought with full commitment, Alex attacked with a lightsaber, and Wiktor... he threw grenades wherever he could.– Wiktor, stop it! You've hit us again! Alex shouted as he saw another explosion.After several minutes of intense fighting, we managed to repel the attack and finish the mission. Han Solo, as usual, threw his typical:– I knew we could do it. But I was the one who arranged everything.Alderaan saved, and we're ready for moreThe fourth night of LEGO Star Wars was full of laughter, excitement and absolute chaos – just the way we like it. The game showed that even in space battles, the most important thing is cooperation... Although in our case, cooperation was more chaos than strategy.Each of us had a unique contribution to the mission, from Adrian's command, through Sara's exploratory flair, to Wiktor's unpredictable actions. And while it wasn't always easy, every moment reminded us how important it is to spend time together and enjoy adventures together.What's next? The galaxy is waiting, and we're ready for the next challenge!POLISH:LEGO Star Wars: Saga Skywalkerów – „Ucieczka na Alderaan, czyli przygody w kosmicznym chaosie”Po trzech wieczorach pełnych śmiechu, epickich bitew i klockowych wyzwań, nikt w naszym domu nie spodziewał się, że czwarty wieczór z LEGO Star Wars przebije wszystko, co do tej pory przeżyliśmy. A jednak, Alex – nasz rodzinny fan Gwiezdnych Wojen – wszedł do salonu z kontrolerem w rękach i hasłem:– Tym razem lecimy na Alderaan. Będzie epicko!To jedno zdanie wystarczyło, by wywołać prawdziwą burzę emocji. Adrian, nasz nieoficjalny generał i samozwańczy ekspert od strategii, natychmiast rzucił:– Obi-Wan poprowadzi nas do zwycięstwa. Bez niego byście zgubili się na pierwszej mapie.Sara, jak zawsze zadziorna i pewna siebie, przewróciła oczami, odparowując:– Jasne, Adrian. Bez Leii nie mielibyście nawet planów na tę swoją misję.Wiktor, który już biegał po pokoju, machając rękoma i wydając z siebie dźwięki przypominające Wookiee, dorzucił swoje trzy grosze:– Chewbacca ma największą broń i robi BOOM! To wszystko, czego potrzebujemy!Roni, nasz niezmordowany mops, przyłączył się do zabawy, biegając wokół kanapy i szczekając na plastikowy model Sokoła Millennium, który Alex kilka dni wcześniej zbudował z klocków. Nawet Mija, która zazwyczaj w takich momentach wybiera ucieczkę na parapet, tym razem obserwowała to zamieszanie z wyraźnym zainteresowaniem, jakby planowała, czy nie wskoczyć na telewizor w kluczowym momencie rozgrywki.„Początek misji – Odlot Sokolem Millennium”Nasza przygoda rozpoczęła się w kantynie Mos Eisley, gdzie drużyna przygotowywała się do startu na Alderaan. Sokół Millennium, dumna ikona galaktycznego smuglera, stał w pełnej krasie na środku hangaru. Han Solo, jak zwykle w swoim stylu, rzucił komentarz:– Słuchajcie, jeśli chcecie dotrzeć na Alderaan, trzymajcie się moich zasad.Sara, z przekąsem w głosie, odpowiedziała:– Może ktoś mu przypomni, że bez nas w ogóle by nigdzie nie poleciał.Pierwszym zadaniem było załadowanie sprzętu na statek. Proste w teorii, ale w naszym wykonaniu? Absolutny chaos. Alex, zamiast pomóc, postanowił „sprawdzić” każdą skrzynię, niszcząc połowę z nich.– Alex, przestań! To nasze części! – krzyknął Adrian, próbując ratować sytuację.Wiktor, który jak zwykle działał po swojemu, odkrył konsolę sterującą bocznymi drzwiami hangaru. Zanim ktokolwiek zdążył go powstrzymać, aktywował mechanizm, co spowodowało, że drzwi otworzyły się, a do środka wpadła grupa szturmowców.– Wiktor! Coś ty narobił?! – krzyknął Alex, kiedy na ekranie pojawił się komunikat: Wrogowie w pobliżu!„Walka w hangarze – chaos na całego”To, co miało być spokojnym załadunkiem, szybko przerodziło się w intensywną bitwę. Szturmowcy napierali z każdej strony, a nasza drużyna, jak zawsze, działała w swoim unikalnym stylu – czyli kompletnym chaosie. Adrian, próbując przejąć dowodzenie, krzyczał:– Alex, osłaniaj mnie! Sara, strzelaj w tych z lewej! Wiktor, przestań rzucać granatami w ściany!Sara, grając jako Leia, walczyła z taką pasją, że wyglądała, jakby sama chciała wskoczyć do gry. Jej postać eliminowała kolejnych przeciwników, podczas gdy ona komentowała:– Leia nigdy nie pozwoli, żeby jacyś szturmowcy ją pokonali. Jest ikoną!Wiktor, jak zawsze, robił swoje – rzucał granaty na oślep, co prowadziło do niespodziewanych eksplozji.– Wiktor, rzucasz w nas! – krzyknął Alex, który stracił połowę serduszek po jednej z eksplozji.Nawet Roni zaczął szczekać, jakby chciał dołączyć do walki, a Mija przysiadła bliżej telewizora, obserwując całą akcję z miną mówiącą: „Nie wiem, co tu się dzieje, ale jest ciekawie”.Po kilku minutach intensywnej walki udało nam się pokonać przeciwników i załadować resztę sprzętu. Han Solo, w swoim typowym stylu, rzucił:– Następnym razem postarajcie się mniej rozwalać, a więcej robić.Sara, z uśmiechem na twarzy, odpowiedziała:– Następnym razem mógłbyś sam coś zrobić, Solo.„Podróż przez kosmos – meteorowy test cierpliwości”Kiedy Sokół Millennium w końcu wystartował, wydawało się, że najtrudniejsza część misji jest już za nami. Nic bardziej mylnego. Lot przez kosmos szybko zamienił się w prawdziwy kosmiczny tor przeszkód, gdy na naszej drodze pojawiły się gigantyczne meteoryty.Adrian, jako pilot, próbował zapanować nad sytuacją:– Trzymajcie się mocno! To będzie trudne!Wiktor, zachwycony możliwością strzelania, zaczął celować w każdy mijany meteor, co skończyło się tym, że kilka razy trafiliśmy w skały, zamiast ich unikać.– Wiktor, przestań strzelać w meteoryty! – powiedział Alex, który z każdą kolizją robił coraz większe oczy.Sara, obserwując to wszystko, skomentowała:– To bardziej wygląda jak kosmiczna wersja naszego poranka – chaos wszędzie.Po kilku dramatycznych minutach, pełnych okrzyków, śmiechu i nieudanych manewrów, udało nam się dotrzeć do Alderaan.„Alderaan – cisza przed burzą”Planeta Alderaan przywitała nas pięknymi widokami – zielone łąki, błękitne niebo i spokojne miasteczka. Na pierwszy rzut oka wyglądało to jak idealne miejsce na odpoczynek. Sara od razu zaczęła eksplorować:– Ktoś musi sprawdzić, czy ta planeta ma coś ciekawego do zaoferowania.Adrian, zniecierpliwiony, powiedział:– Nie mamy czasu na zwiedzanie. Skupmy się na misji.Alex i Wiktor oczywiście mieli zupełnie inne plany. Biegali po mapie, przeszukując każdy zakamarek i zbierając monety. W jednym z budynków Wiktor znalazł ukrytą skrzynię z bonusowymi przedmiotami.– Widzicie? To dlatego warto eksplorować – powiedział z triumfem, patrząc na Adriana.„Finał – alarm Imperium!”Spokój nie trwał długo. Nagle na ekranie pojawił się komunikat: Imperium zlokalizowało waszą pozycję! Przygotujcie się na walkę! Szturmowcy zaczęli napierać z każdej strony, a my znowu musieliśmy walczyć o przetrwanie.Adrian próbował dowodzić, ale jak zawsze każdy robił swoje. Sara walczyła z pełnym zaangażowaniem, Alex atakował mieczem świetlnym, a Wiktor… rzucał granatami, gdzie popadło.– Wiktor, przestań! Znowu trafiłeś w nas! – krzyknął Alex, widząc kolejną eksplozję.Po kilkunastu minutach intensywnej walki udało nam się odeprzeć atak i zakończyć misję. Han Solo, jak zwykle, rzucił swoje typowe:– Wiedziałem, że damy radę. Ale to ja wszystko załatwiłem.Alderaan uratowany, a my gotowi na więcejCzwarty wieczór z LEGO Star Wars był pełen śmiechu, emocji i absolutnego chaosu – dokładnie tak, jak lubimy. Gra pokazała, że nawet w kosmicznych bitwach najważniejsze jest współdziałanie… choć w naszym przypadku współdziałanie było bardziej chaosem niż strategią.Każdy z nas miał swój unikalny wkład w misję, od dowodzenia Adriana, przez eksploracyjne zacięcie Sary, po nieprzewidywalne akcje Wiktora. I choć nie zawsze było łatwo, każda chwila przypominała nam, jak ważne jest spędzanie czasu razem i cieszenie się wspólnymi przygodami.Co dalej? Galaktyka czeka, a my jesteśmy gotowi na kolejne wyzwania!
#### After three evenings full of laughter, epic battles and brick-based challenges, no one in our house expected that the fourth LEGO Star Wars evening would surpass anything we've experienced so far. And yet, Alex – our family Star Wars fan – walked into the living room with a controller in his hands and a password:Episode: https://3speak.tv/watch?v=dragokazo/kdufomjj
Podcast: https://3speak.tv/user/dragokazo
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In today's noisy marketplace, trying to speak to everyone often means reaching no one. But what if finding your business focus wasn't about limiting yourself, but rather about stepping into the space where your gifts can truly flourish?
In this fourth episode of the Sorceress CEO series, we're diving into how to identify and claim your unique corner of the market. You'll discover:
Why traditional "niching down" advice often misses the mark for spiritual entrepreneursHow to find the sweet spot between your soul's calling and market demandPractical steps to define your dream clients and their real needsCommon pitfalls to avoid when positioning your workSigns you've found the right territory for your businessWhether you're just starting out or ready to refine your focus, this episode will help you create more clarity, attract aligned clients, and build a business that feels truly yours. Join us for this powerful exploration of how to stand out authentically in today's crowded marketplace.
⭐ And, as a special gift, I want to offer you free access to one of my most popular programs, “The IFS Kickstarter Kit.” This will teach you the foundational concepts of the Internal Family Systems (IFS) model—a proven framework that has helped me and countless others navigate challenges, build inner clarity, and create thriving, soul-aligned businesses.
Through this 7-part video series, you’ll gain a deep understanding of how IFS works and how to apply it to your own life and business. These are the same principles I teach in my advanced programs, now made accessible in an introductory format.
To receive your complimentary “IFS Kickstarter Kit,” all you have to do is:
1) Leave a review of this podcast.
2) Email a screenshot of your review to [email protected]
It’s as simple as that!
In this Episode We Explore:
12:30 – Harness the Sacred Wisdom of Lunar Cycles
Discover how the moon’s rhythms illuminate a path to aligned business growth, offering a natural and powerful guide for entrepreneurs seeking flow and balance.
22:30 – Step Into Your Own Sacred Space
Break free from the noise of a crowded marketplace and claim your unique place. Here’s what becomes possible when you trust your one-of-a-kind journey.
30:00 – Reveal Hidden Barriers to Growth
Soul-stirring reflections and thought-provoking questions to uncover what’s truly holding your business back and spark profound clarity.
43:30 – The Art of Heart-Centered Connection
Master the shift from generic marketing to deeply serving and attracting your dream clients with authenticity and care.
48:45 – Your Magic Is Your Superpower
Let your unique brilliance be your strongest differentiator. Learn how to share it authentically and powerfully to stand out in your industry.
58:30 – Turn Confusion Into Magnetism
Uncover the hidden cost of unclear messaging. Discover how to transform vague communication into irresistible clarity that attracts aligned clients and consistent income.
Resources:
Free 3-Day Workshop: Step into Your Sorceress CEOOrder Sara’s new book: Handbook for the HeartbrokenSubscribe to Sara’s Sunday JournalConnect with Sara on InstagramMentioned in this episode:
Step into Your Sorceress...
Episode: http://www.SaraAvantStover.com
Podcast: http://www.SaraAvantStover.com
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Pete George delves into the contentious debate surrounding Australia Day, offering a personal perspective rooted in his family's diverse heritage. He explores the historical impact of the First Fleet's arrival in 1788 and urges for a focus on progress and unity. Highlighting overlooked stories, Pete acknowledges the struggles of both indigenous communities and European immigrants, advocating for a balanced narrative. This podcast prompts reflection on Australia’s evolving identity, celebrating resilience and diversity while challenging listeners to consider broader perspectives on national history and identity.
Episode: https://share.transistor.fm/s/8e0f2fa7
Podcast: https://heygeorgie.com.au
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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 evals
Remember 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 version
On 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 recruitment
Transcript
Alessio [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 chess
swyx [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 can
swyx [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 surprisingly
William [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 the
swyx [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 be
swyx [00:38:46]: insanely good at execution. So you were first with voice? We were first. We were first. I only know
William [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'm
swyx [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 Chai
William [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 super
swyx [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 from just seeing the correct thing. Number go up. Yeah, yeah. I mean, you do pay money for Chai vs. Weed. We've, we've paid out over $100,000 to model creators. But do you know what we saw? It's not motivating. We saw that it didn't really make a difference. Like if they were submitting models at a certain rate, if you pay them a bunch of money, they didn't change the rate. What the money let them do was if they wanted to fine tune Alarma 70B on eight H100s overnight, if you give them money, then they can do it. Or you could give them compute. Yeah. So, so I think the most exciting person we ever saw from interacting with Chai, Chai vs. was we gave some kid who was like, like 17 years old, I think we gave him $1,000 and he spent all the money on buying a physical computer. And he took a picture of it and said, this is what I bought. And I'm going to be training more models with it. So that's why, that's why I love platforms.
swyx [00:57:00]: Should you hire him or?
William [00:57:02]: That's the temptation. Yeah. That's the temptation. But you want to keep the team small? No, no. As a platform, we can't just hire every good content creator. We've got to build the systems and the best content creator today isn't going to be the best content creator next year.
Alessio [00:57:14]: What about Eva? So you've talked about reasoning and knowledge. Most of the benchmarks that people use want to mimic reasoning. Yep. I want to register, I disagree on the reasoning, but we have to keep going. Yeah, I'm curious, like how, how do you think about the evals that matter to you?
swyx [00:57:29]: So yeah, like Elo cannot be the only eval. You must have internal evals. You mentioned evals.
William [00:57:34]: I think Elo is a fantastic north star and the reason for it, or like it's the main one we want to see go up because it's this human feedback. The humans know what they want. It's beautiful because when you come up with an eval, you're further removing yourself away from the true problem. Right? So whatever it is you're trying to optimize or figure out, you kind of have to, have to slice it. And then you've got this, it's like a snapshot. Like as soon as you saturate one eval, you need to figure out a new eval. But with, by saying to humans, just which is better, A or B, it's super robust. It's super generalizable. It just keeps, keeps scaling. So we've in the past used evals to get through a, to get through a blocker. I mean, a great example is, you know, is like having like a safety filter or something. Yeah. Where you want to make sure your models, because listen, users find, you'll be shocked the correlation between not family friendly content, whether that's just like swearing, like people find it funny when the AI swears. So if you have two completions, A or B, like if you give me any LLM, I can make it 20% funnier just by training it to throw in swear words. So the issue with that is it's like, how are we measuring like quality improvements? Are we measuring superficial improvements? Right. And this actually links back to the LLM sys. They did a style control.
swyx [00:58:54]: We actually had them on the podcast.
William [00:58:56]: Yeah. Yeah. And so that's the way I, I would rather just lean on human feedback and just continue to make that more and more robust and more and more useful. And, you know, you can say some people are like GPU poor and GPU rich. We're like, we're feedback rich. Like when you've got one and a half million people a day, we get as much feedback from humans as we want. So we're not in a position where we needed to have the evals very much. Yeah. And when we do, we saturate them pretty quick. So a safety one, you know, within a month, we don't need to use it anymore because it's sort of, it's, you know, the issue has been addressed.
swyx [00:59:29]: I think one problem I have, and this is a broader products question maybe, is that the ELOs apply to the whole user population. That's right. Clearly the user behavior, there's segments that have like, I'm a role play person, I'm a therapy person, I'm a not safe for work person. You don't split them?
William [00:59:44]: This is why I say like, I think we're in year four of like a 20 year thing where it's like, at the end of the day, I'm a role play person. And I think if we all go on like Spotify or like, imagine if Spotify only had the top five musicians, I think it would retain over 85% of its existing users. Yeah. Right. And I think if YouTube, if YouTube only kept the top five content creators, it would be enough for the vast majority of people. The thing I'm just trying to share here is there's one surprising thing about humans is their preferences are pretty correlated. What you find funny and entertaining, I find funny and entertaining, and he finds funny and entertaining. There might be degrees of variation in it, I might find it super funny, you might find it only slightly funny, but optimizing to a global works very, very well. And for segmentation to be really powerful, segmentation will work amazing if you found a comment super boring, and I found it super fun. If we could segment that, then that would unlock really powerful stuff. But unfortunately, that's not the shape of human behavior, right? It's like, I might rank it 10 out of 10 funny, you might rank it 7 out of 10 funny. And it's like, it doesn't give you... It doesn't give you as much space to play as you would hope. It's an element of the diversity of content that AI can produce right now, which is it's not as diverse as if you consider a platform like YouTube, you can watch a Mr. Beast video, that's totally different to a makeup tutorial. So there's enough diversity there where if you go on my YouTube feed, it is totally different to my sister's one. My sister's one, it's all like women, and if you go on mine, it's all like bald, middle-aged men, either talking about MMA or, right? I think with AI, it's still a bit too early for that degree of segmentation. So I think it all comes, the recommender systems, the personalization. But this is why I like the, don't start with the technology, start with the problem. The problem is UGC. We must give users the tools to build more variety and more engaging content.
swyx [01:01:42]: Yeah. I feel like there's... I was surprised at how thin it was when I tried out Chai. Yeah. It's very thin. Haven't you been tempted? Like there's this ecosystem of Cobalt, Silly Tavern, those guys. They have model cards. It seems like an industry standard almost. Yeah, agreed. Can I just import those? I don't think I want to say.
William [01:02:01]: Oh, you're already working on it. No, it's like, I remember when Chai meant, Chai, Silly Tavern, and like Cobalt, Cobalt AI is basically as old as Chai. So when Chai was, when we just existed, they just existed. And both of us were using GPT. Chai, yeah, yeah, yeah. And I remember very early on, I was like, these guys shouldn't even exist. Because if we build a good enough platform, they should just be posting their content on our platform.
swyx [01:02:28]: Yeah, but they're open source. No, exactly.
William [01:02:30]: That was what I learned. Eventually, I learned like they're, what they're excited about is slightly different from a typical consumer. My answer is, it's kind of like a complex thing where it's really down to the content creator wants, typically they're building it for themselves. And typically they want to create an experience for themselves. So one content creator might have to write a thousand words describing, let's take a science fiction scenario. Let's say, okay, you're on a spaceship and you're going off into space and your crew, these are your crew members. You've got one that's really friendly, one that's really mean, and you're the new cadet and you want to rise to the top. And they can really go into great detail, right? And then you can give that to like a Lama 70B. And Lama 70B will do a pretty good job of adhering to the prompt and the user will have a good experience. Okay. Very few users will ever go to that level of content creation. If instead the user, we can really make the AI understand the user more so that rather than having to use a thousand characters or a thousand tokens to describe the scenario, we can just say, look, you're on a spaceship. You've got three crewmate. It's going to be dramatic and there should be some fighting. And then the AI gives you an even better experience. Then the content creator is happier. And so fundamentally, the way I'd kind of think about it. Is there's the sterability of the AI. And so a lot of the work we do at Chai is really about saying we want the AI to react to the user and react to the content creator in the way that they most want. One kind of like analog would be TikTok. I think the thing that TikTok did insanely good was they made it really easy for like anyone. If you make a video on TikTok, almost anyone can make a kind of fun video really easy. You just put some music on the top of it. You throw some of the. Animations on top and it's not hard to have a pretty fun thing. And I think that's much more like the Chai style where it's like users don't want to have to work. You know, if your content is only good, if you have like Shakespeare, it's better if, if just anyone at home can make the, can make the thing. So that's, that's kind of like my answer to the silly talent style. And I think the right answer is how do you get the silly time people fine tuning models that create a really special effect.
Alessio [01:04:46]: As we wrap this is kind of the call for action.
William [01:04:49]: Uh, part one, you have Chai Grant, which I think a lot of people don't know about, which is grants for open source projects, any ideas, any projects that you want to see people work on the should apply or let me think, I think, um, so we do try Chai Grant and fundamentally, you know, we give cash, no strings attached. It's kind of our way of doing two things. One, giving back and support in the community. We've benefited from a lot of open source packages. A lot of our developers and engineers are like. Really? Really pro open source. And then also it's a great way to just meet talented people and, and like expand connections. So with respect to Chai Grant, if anyone's got any sort of, um, GitHub project, any sort of thing they built that they're proud of, just apply, just apply. It's like no strings attached cash and people have a pretty high success rate. So that's the first thing. Other call to actions would be, I think Chai is this, you know, it's a startup. We're a small team. It's like 15 people. We work very intense. It's a very hardcore. Sort of environment, which we found that a lot of people don't like. They don't like the, you know, they'll ask us this concept of what life balance one time. A person said, they said something like, I can't get this done because I'm taking PTO on Friday. And I said, what is PTO? Okay. Um, it stands for paid time off and this, I know what it is and this person was gone. They didn't like, they were no longer in the company four weeks on legally. I think you have to, oh, it's true. There's no problem. Look, if you've got. You've got to take a day off, right? We all have personal lives, right? But it's about this idea of responsibility. If you're not in the office on Friday, you still have your responsibilities. So I don't care if you work hard Thursday to get it wrapped up. I don't care if you're working hard Saturday to get it wrapped up. It's not an excuse to, it's not an excuse. The way this individual spoke about it, it was like an excuse. I think it's an environment, very talented engineers working very hard in an intense space. It's the thing that gets me excited. It's, it's why I think, you know, I really love working at Chai is because it's a place of talent. It's a place of people working super hard. So yeah, I think people who have got, who've worked at startups and they, they love that. That's what they, they want the taste of, I think they should reach out, they should apply. And I think 90% of people can say that sounds terrible. Don't apply.
swyx [01:07:03]: It's not for them.
Alessio [01:07:03]: Yeah, it's exactly, exactly. Yeah. I just realized we skipped one important part. So you spent $10 million on compute last year. You say you're going to probably triple that. Yeah. I'm sure you're doing a lot of work on custom kernels, kind of like inference optimization, any cool stuff. Yeah. That you want to share there. Yeah.
William [01:07:20]: Lots of cool stuff. So really quickly, I think inference is very, very important. It's super important. It's massively underlooked and we can look at all the different foundation models and the techniques, the differences in the foundation models on how well they perform from a cost perspective with inference. Mixture of experts, for example, tend to do really, really good from like a cost perspective. We've worked with a very talented team called.
swyx [01:07:49]: MK1 and we, so I saw, I saw them in the Chaiverse logs. What are they?
William [01:07:54]: We were using, we were running VLLM for a while and VLLM is really fantastic. Absolutely amazing. The work that they've done and achieved. And at some point I got introduced to the founder's name is Paul Marola. And he was a co-founder at Neuralink, really, really expert in like hardware. He kind of explained to me, he was like, look, if you know, hardware really well, you can write the CUDA kernels really well. He said, you should check out our inference engine. And they kind of blew VLLM out the water when we evaluated it much, much, much faster. And I think the special thing that he was able to do with us is we love rejection sampling. So we do much more rejection sampling than maybe typical and, you know, generate it. So we, we never, ever, ever just generate a single completion, right? This is why we don't do streaming. A lot of people like ChatGPT used to do a lot of streaming. Like the completion would come out one thing at a time. I did. I didn't notice that in your UX. Normally chat, you have to stream. Exactly. But Chai has never done streaming because if you stream, you're unable to do rejection sampling. The benefit of that is you can serve a larger model. The reason why you can serve a larger model is because they're saying instead of generating a completion in four seconds, because the user gets the first token faster, you can generate in 10 seconds. Well, if you've got 10 seconds to generate completion, you can serve a much larger model. So typically the people that are streaming, the benefit that they're getting is they're, you know, serving a larger model with Chai, we give you, you know, the second answer comes, boom, you get the full completion. And the reason for that is because we want to generate 16 completions, see the entire response, and then we want to evaluate which one we think is the best.
swyx [01:09:34]: Do you have a separate LLM evaluator? Yes, we do. Yeah.
William [01:09:37]: So, um, typically they're referred to as a reward model and that's a, you know, that's like a term from reinforcement learning. And for that, you can start off with something very simple, which is, do you think the user is going to respond to it? That's a simple one. So you can, you can train, you can take 50 million messages and, and look at all the sorts of messages users reply to, which ones they don't. And then you can train this, this reward model to evaluate completions. And so it knows like, okay, if you say this, the user is not going to respond. So don't bother sending it to the user. If you say this, the user is definitely going to engage with it. So send them, send them that.
swyx [01:10:11]: There's an interesting parallel between MLAs and MLAs. I think we use at the top, spreading out to different experts and then at the bottom with rejection sampling, choosing from different paths.
William [01:10:21]: I totally agree. That's the stuff that is the future of AI. I think that's the exciting stuff. And there's a parallel between that. Why was AlphaGo able to be superhuman? Right. It's this ability to generate many different paths. Tree search. And tree search. Exactly. So I think if you want to talk about what would intelligence look like, it looks much more like tree search. Combining the generative nature of these LLMs with a really good tree search. And that's what opening I've done with O1 and O3.
swyx [01:10:51]: I don't know that they do tree search. They never said they do. It's implied. Yes. Okay. Yes. Yes. Are you comfortable with O1 being a reasoning engine? No, no, no, no.
William [01:11:01]: I'm saying it's better at reasoning because they leverage the tree search well. And the, the issue of the reasoning is they're saying, is this like they train, they have the models to say, is this logically correct? And what's the likelihood of it being logically correct? So you can build up the sophisticated mechanisms to get it less bad at reasoning, but you'll see like eventually what, what AI is really, really good at. People won't say it's, it's always going to be better at retrieving. It's always going to be better at storing knowledge, which is so highly correlated with intelligence that we often assume it's the same. What, what AI is truly special at and gets consumers really excited is it's generative. It can just make stuff. We've never had a technology. Before that can just make stuff simulate.
Alessio [01:11:45]: Yeah.
William [01:11:45]: Yeah. So that's the special, that's the exciting thing.
Alessio [01:11:48]: Awesome. Well, any parting parting thoughts?
William [01:11:51]: No, it's been, it's been a pleasure. I guess the only thing I'd add is like our office is in Palo Alto. So, um, yeah, you know, people with startup experience looking to join a fast growing high impact startup. Yeah.
swyx [01:12:03]: Uh, we'll find your culture deck, which is great. Fantastic. And then also, yeah. Yeah.
Alessio [01:12:07]: What's the story where if you made a hundred K trading, we'll fast track your application. Like, I mean, I kind of qualify.
William [01:12:15]: just looked at the team and it got to the point where almost every single person on the team you could point to, and they had done something special before joining the team. Like they, they had strong markers of like, there was something special about them. That's not to say it's like, like an exclusive thing. You have to have achieved something special, but it's just, uh, we got this one engineer and she, she started going to college. She went to CMU when she was like 15 years old or something. And it's like, that's a bit special. There's another engineer. He created a Git repo and I think he got like 1500 stars and it was like a repo for like, there was some drivers that he wrote. It was like a super low, low level thing. I was like, that's a bit special. We had this other guy, he joined the team and he'd, he had made a hundred K buying and selling sneakers, right? Trading. Yeah. So, so it's like, it's just this thing, like if you've been to Harvard, cool, that's great. It shows that you're really smart and you work really hard. Cool. That's good. But if you've actually built something and done something. I think there's a bit more tangible that gets us even more excited.
Alessio [01:13:16]: Cool. Well, thanks for having us at ChaiHQ. Yeah.
William [01:13:19]: Thanks guys.
Get full access to Latent Space at www.latent.space/subscribeEpisode: https://www.latent.space/p/chai
Podcast: https://www.latent.space/podcast
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HEADLINES
Ceasefire Violations Spark New Tensions in Gaza
Bibas Family Left Heartbroken Over Hostage Release
Female Soldiers Reveal Horrors of Captivity Experience
The time is now 08:00 PM in New York, I'm Noa Levi and this is the latest Israel Today: Ongoing War Report.
In the latest developments regarding the ongoing conflict involving Israel and Hamas, tensions have escalated following Hamas's breaches of the ceasefire agreement over the weekend. The Israeli Defense Forces have issued a stern warning to residents of Gaza, prohibiting them from approaching the Netzarim axis due to these violations. This directive comes in light of incidents where Hamas released female soldiers before freeing female civilian hostages, along with failing to provide updates on the status of remaining hostages, which has drawn considerable concern from Israeli officials.
The Bibas family, whose members have been held captive, expressed profound disappointment as their mother and children were not included in the latest release list, despite expectations set by previous agreements. This family’s plight highlights the emotional toll and complexity surrounding the hostage situation, evoking strong reactions from relatives advocating for their safe return.
Four female soldiers recently released from captivity shared harrowing details about their experiences, revealing the dire conditions they faced during their time held by Hamas. They described being moved frequently between locations, often under unsanitary conditions, and were forced to perform domestic duties for their captors. Their accounts of captivity paint a troubling picture of the psychological and physical challenges they endured.
In diplomatic news, Israeli Prime Minister Benjamin Netanyahu is set to meet with former President Donald Trump in February, with significant topics on the agenda including the situations in Gaza, Lebanon, and Iran. This meeting underscores the ongoing international dimensions of the conflict as Israel navigates its security concerns amidst shifting geopolitical alliances.
In other news, over a hundred dolphins have washed ashore in Somalia under mysterious circumstances, prompting investigations into the cause of this unusual occurrence. Meanwhile, the Israeli film scene is gearing up for the Smadar Festival, set to celebrate the cinema of Jerusalem with a series of screenings and cultural events. This festival aims to revitalize the Lev Smadar cinema and engage the community in a celebration of the city’s rich cinematic history.
As the situation continues to evolve, the focus remains on the humanitarian impacts of the conflict, the intricacies of the hostage negotiations, and the broader implications of regional security. The public is urged to stay informed as new developments emerge.
Thank you for tuning in to this Israel Today: Ongoing War Report update.
I'm Noa Levi. Stay safe and informed.
Keep in mind that this AI-generated report may contain occasional inaccuracies, so consult multiple sources for a comprehensive view. Find the code and more details in the podcast description.
SOURCES
https://t.me/newssil/135663
https://www.maariv.co.il/breaking-news/article-1167268
https://www.jpost.com/breaking-news/article-839232
https://t.me/newssil/135662
https://t.me/newssil/135661
https://worldisraelnews.com/bibas-family-world-came-crashing-down-when-hostage-mother-and-children-not-released/
https://worldisraelnews.com/no-food-terrifying-four-freed-female-soldiers-speak-about-captivity-in-gaza/
https://t.me/newssil/135660
https://t.me/newssil/135659
https://t.me/newssil/135658
https://t.me/newssil/135657
https://t.me/newssil/135656
https://t.me/newssil/135655
https://t.me/newssil/135654
https://t.me/newssil/135652
https://www.maariv.co.il/breaking-news/article-1167267
https://www.jpost.com/j-spot/article-839189
https://www.jpost.com/j-spot/article-839191
https://t.me/newssil/135651
https://t.me/newssil/135650Episode: https://www.spreaker.com/episode/israel-today-ongoing-war-report-update-from-2025-01-26-at-01-06--63902138
Podcast: https://babka.social/@noalevi
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On this week’s episode of the Escape Your Limits Podcast, we welcome back Kali Muscle, a powerhouse in the fitness world, entrepreneur and a motivational force.
Hailing from Oakland, California, Kali overcame a turbulent past, including prison time, to carve out a path of resilience and success. From bodybuilding champion to social media phenomenon, Kali’s journey is one of sheer determination, reinvention and grit.
After surviving a life-threatening heart attack, Kali took on new challenges, delving into extreme health practices like fasting, rediscovering his purpose and transforming the lives of countless others through his coaching and mentorship. Known for his “money and muscle” philosophy, he’s inspired millions to build not just physical strength, but financial stability and mental toughness.
In this compelling episode, Kali shares candid stories of his transformation, from battling demons to becoming a family man and role model. He opens up about the challenges of fame, navigating financial hurdles and the lessons learned from embracing a fruitarian lifestyle, entrepreneurship and fatherhood.
Packed with powerful insights, hard-hitting truths, and motivational moments, this conversation is one you don’t want to miss.
In this podcast, Kali discusses:
Watermelon and water fasting and a fruitarian diet. Diet and lifestyle changes that occurred when recovering from a heart attack. The impact of health on business. Entrepreneurial lessons and the importance of financial literacy. Overcoming adversity to build focus from prison to a life purpose. The role of a coach, trainer and mentor. Dealing with social media negativity and online haters. Legacy building to support his family, now and for the future. Faith and focus giving him the courage to change.To learn more about Kali Muscle, click here:
https://www.instagram.com/kalimuscle/p/DBr97N7N0_K/
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0:00 Intro
0:30 Watermelon fasting and health insights
14:50 Financial challenges and business setbacks
19:35 Growing health and wealth, muscle and money
25:21 Personal growth and mentorship
28:49 Turning points and personal transformation
32:46 Transforming a destructive headspace
34:37 Balancing personal and professional life
56:15 Crypto coin ventures and lessons learned
1:03:03 The importance of financial literacy
1:05:30 Family and future goals
1:15:45 Reflecting on personal limits and achievements
Episode: https://escapefitness.libsyn.com/ep-341-money-and-muscle-with-kali-muscle
Podcast: http://www.escapefitness.com/podcast
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Episode: 38: Crafting Your Year - Word of the Year WorkshopClickable Link to Resources Used
Key Highlights:
Introduction: Greg introduces Episode 38 and the ongoing theme of selecting a Word of the Year.
Workshop Introduction: Alessandra highlights the intimacy of the Creative Work Hour community and introduces Kara, a founding member and PhD holder in the creative and academic fields.
Kara's Workshop: Kara leads a workshop on choosing a guiding word or theme for the year. She emphasizes that the word can be a single term, a phrase, or a broader theme.
Mind Mapping Exercise: Participants engage in a mind mapping exercise to explore their chosen words. Arrows from the central word lead to questions, relationships, actions, and supporting words.
Participant Contributions:
Alessandra chooses "synchronous" and explores its implications for collaboration. Devin selects "suffering" with a focus on relieving it, particularly in relation to a supported school in India. Nate discusses "play" as a constant theme in various aspects of life. -
In this episode, we explore the inspiring journey of someone who transformed their life through resilience and smart decisions. Starting out feeling overlooked with a bachelor’s degree while others pursued higher-paying blue-collar jobs, they discovered a six-month software quality testing certification program that opened the doors to a new career.
After moving to a new state and excelling in the tech program, they returned to New York to be closer to family and friends. They didn't stop there—by tutoring others and launching their own tech program with better incentives, they’ve helped countless students leave blue-collar jobs behind and achieve financial freedom.
From living in the basement of their first multifamily property to saving aggressively and investing strategically, they’ve built significant passive income while continuing to expand their ventures. With the support of a like-minded spouse and a focus on empowering others, their story is a blueprint for success.
🚀 Learn how to break barriers and build a better future.
🏡 Discover software quality testing programs and real estate saving and investing strategies for wealth creation.
💡 Be inspired by their journey of helping others achieve financial independence.Episode: https://www.spreaker.com/episode/beyond-the-degree-embracing-new-opportunities-in-tech--63678352
Podcast: https://generationalicons.com/
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Summary
In this engaging conversation, the hosts and their guest discuss the expectations and goals for 2025, focusing on the evolution of business practices, the importance of core values, and the necessity of finding the right team. They explore the shift from individual coaching to corporate training, the significance of accountability in leadership, and the role of trust and delegation in fostering a productive work environment. The discussion emphasizes the need for clear processes and the power of repetition in retaining knowledge, ultimately highlighting the importance of transparency and learning from mistakes in business. In this conversation, the speakers delve into the complexities of leadership, accountability, and the challenges of entrepreneurship. They discuss the importance of setting expectations, the necessity of training and trust within teams, and the harsh realities of being a business owner. The dialogue emphasizes the need for preparation, understanding the costs associated with entrepreneurship, and the value of learning from experiences. Ultimately, they highlight the importance of building a sustainable business that can thrive beyond the owner's direct involvement. In this conversation, the speakers discuss the importance of setting realistic business expectations and goals, the necessity of continuous learning and implementation, and the personal and professional objectives they aim to achieve in 2025. They emphasize the role of family and personal fulfillment in the entrepreneurial journey, highlighting the balance between work and personal life.
Watch the YouTube Video Here
Takeaways
The importance of setting clear expectations for the year ahead.Unleash the Champ has shifted focus to corporate training.Core values drive the culture and success of a business.Finding the right team members is essential for growth.Repetition aids in retention of knowledge and skills.Trust must be established, while mistrust is earned through actions.Accountability is crucial in leadership roles.Mistakes should be addressed transparently to foster learning.Creating processes helps clarify roles and responsibilities.Being human and relatable as a leader builds trust with the team. Sometimes the king has to remind people why he's king.Accountability is crucial in leadership roles.Training is essential for trust and efficiency in teams.Entrepreneurs must count the cost before starting a business.Not everything in business is a financial benefit; knowledge is valuable.You need to earn your freedom as a business owner.Elevated standards lead to elevated expectations from customers.It's important to document processes to avoid losing knowledge.Many aspiring entrepreneurs lack the grit needed for success.The journey of entrepreneurship is not for everyone. It requires resilience and preparation. Research is crucial before starting a business.High-value services come with higher costs.Understanding customer expectations is key to success.Setting clear goals can drive motivation and focus.Continuous learning is essential for personal and professional growth.Implementation of learned concepts is as important as learning itself.Family support plays a significant role in entrepreneurial success.It's important to enjoy the journey, not just the destination.Saying no to distractions can help achieve goals.Entrepreneurship requires a balance between work and personal life.
Chapters
00:00 Setting the Stage for 2025 Goals
05:53 The Importance of Core Values in Business
11:46 The Role of Accountability in Leadership
18:04 The Power of Repetition in Retention
23:51 Addressing Mistakes: Transparency and Accountability
33:06 Expectations and Accountability in Leadership
39:27 The Importance of Training and Trust in Business
51:54 The Reality of Entrepreneurship vs. the Dream
01:05:08 Lessons Learned and the Cost of Knowledge
01:16:55 The Importance of Learning and Implementation
Become a supporter of this podcast: https://www.spreaker.com/podcast/brews-business--5630487/support.Episode: https://www.spreaker.com/episode/2025-expectations-with-kyle-sullivan--63733934
Podcast: https://www.spreaker.com/podcast/brews-business--5630487
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During this lesson I will be sharing with you the Third part of the meditation practice to help you live in harmony in our new reality. This part of the practice will focus on facilitating healing in your body.The Latest Episodes for the Our Galactic Family Podcast are Now Available as a Video Podcast on YouTube and Apple Podcast You Can Find My Full Podcast Library and More on my website Our Galactic Family Time Stamps0:00 Introduction2:43 Teaching Healing Movement5:22 Meditation Explanation7:57 Meditation Begins37:45 Thank-You https://ourgalacticfamily.com/
Episode: https://ourgalacticfamily.com/living-in-harmony-in-our-new-reality/
Podcast: https://ourgalacticfamily.com/
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Silver Johnson joins host Ron Aaron and cohost Carol Zernial to talk about everything from horses to caregiving to caregivers on this edition of Caregiver SOS.
About Silver
Silver Johnson is founder of The Wateringhole Resource, a multi-faceted Consulting service. She is a published author, Functional Communication Coach, Professional Nonprofit Consultant and A.W.& E. Project Representative and Content Creator for Artists, Writers and Entrepreneurs. Silver has over 50 years of success as a professional equestrian and Equine Assisted Services and Therapy coach, nonprofit executive and content creator. She was an early pioneer of E.A.S. in the United States and the second American to be Certified as an E.A.S. Instructor from the Chigwell Riding Trust, England. In 2001, Silver created The Lifehorse Research Project, an ongoing comparative study with a focus on how horses and humans are genetically programmed to deal with presentations of trauma and neurodegenerative presentations. Her teaching practice has been focused on Autism, Alzheimer's and other cognitive differences. One of her goals as a Communication Coach is to assist Caregivers in how to effectively communicate with family members, loved ones and the community at large dealing with cognitive impairment and communication differences. She also offers support and coaching to caregivers through a forthcoming video series 'Hi, My Name is ... and I'm a Caregiver.' She has teamed up with her coach and mentor of 25 years, Steve Gilmore of Jackson Hole, Wyoming, co-authoring their new podcast and soon to be published book, 'Coach2Coach.' Silver works and lives in Nashville and can be reached via email.
Hosts Ron Aaron and Carol Zernial, and their guests talk about Caregiving and how to best cope with the stresses associated with it. Learn about "Caregiver SOS" and the "Teleconnection Hotline" programs.
Listen every week for deep, inspiring, and helpful caregiving content on Caregiver SOS!
See omnystudio.com/listener for privacy information.
Episode: https://omny.fm/shows/caregiver-sos/horses-to-caregiving-to-caregivers-with-silver-joh
Podcast: http://soundcloud.com/cargiver-sos
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When Kim Rehir was in the hospital, paralyzed from the hips downwards, she felt like she had lost her power, dignity, and humanity. This totally shifted her perspective on what she valued in life.
Kim is a 60-year-old mother of three who was diagnosed with MS ten years ago and decided to ignore her doctor's advice and muscle her way back to a happy life. Last year she became European Champion in Masters Weightlifting in her age and weight category. Her journey inspired her to leave her career in journalism in her 50s and become a health coach for middle-aged women - with a big focus on reactivating and rebuilding muscle.
Today, she helps women tap into an abundant source of vitality. It works by reactivating and maintaining muscle and eating to nourish and flourish.Listen to her interview and start the Fabulous in 15 approach. Just fifteen minutes a day can change your life. Her approach helps women become lean, strong, and fabulous in fifteen minutes daily.
Episode: https://neverevergiveuphope.libsyn.com/how-15-minutes-a-day-can-change-your-life-and-your-health
Podcast: http://neverevergiveuphopenet.blogspot.com
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Iris Bohnet and Siri Chilazi are leading gender experts and Harvard researchers. Their work centers on data-driven evidence to create accessible strategies to bridge the gender gap at work and create environments based on fairness. What makes Iris and Siri’s work different is that they focus on changing behaviors around equality, not attitudes. As they show, this helps us move from virtuous intentions to visible action without trying to tackle people’s deeply held beliefs. Iris and Siri explore these issues in their upcoming book MAKE WORK FAIR: Data-Driven Design for Real Results (Harper Business; January 28, 2025; must-read nonfiction title by the Next Big Idea Club)
It’s cliché to say that the numbers don’t lie, but the fact is, they don’t:
· McKinsey’s latest Women in the Workplace report shows that while women today make up 29% of C-suite positions (compared with just 17% in 2015), women are still progressing slower earlier in the corporate pipeline, at the entry and manager levels.
· New research has also come to light that women leaders face 30 different kinds of discrimination in the workplace
Topics Iris and Siri can expand on include:
People will always be biased—why we need to focus on behaviors, not attitudes to achieve fairness How to move from virtual signaling to concrete action Why we need to make the workplace compatible to women, not the other way around Your gain does not equal my loss—debunking common myths about workplace fairness The hiring process is broken and how to fix it Bias from above, bias from below—the vicious cycle that keeps women from leadership positions The middle management gender gap and how it impacts women’s career trajectories How flexibility creates fairness and unleashes talents It’s possible to fix America’s parental leave problem—here’s how Why we need norm entrepreneurs and how to become one Why we can’t settle for moral outrage anymore—to truly eliminate bad behavior in the workplace we need to make the change we want to see**Next Big Idea Club pick for January 2025
Women have certainly come a long way in the workplace. When the U.S. Census Bureau first started including data on women-owned businesses in 1972, there were only about 400,000 female business owners in the country. Fast forward to 2024, and there are over 13 million women-owned businesses, and when it comes to the Fortune 500, women CEOs now account for 10.4% of the list. At the same time, the gender gap—in terms of pay and leadership positions—still hasn’t been bridged, despite the best intentions of many companies. How do we achieve equality in the workplace so that everyone has the opportunity to work better and smarter—and more fairly?
Iris Bohnet and Siri Chilazi, two leading gender experts and Harvard researchers, explore the concept of fairness in the workplace and offer novel solutions for professionals in their new book, MAKE WORK FAIR: Data-Driven Design for Real Results (Harper Business; January 28, 2025). As Bohnet and Chilazi explain, many well-meaning individuals and companies invest their time and resources in diversity, equity, and inclusion (DEI) initiatives with less than stellar results. Because inequity is built into the structures, processes, and environments of our workplaces, adding these programs has been ineffective and often becomes a burden passed off to the individuals they are meant to help.
In MAKE WORK FAIR, Bohnet and Chilazi offer data-backed, actionable solutions that build fairness into the very fabric of the workplace. Their methods—tested at many organizations and grounded in data proven to work in the real world—focus on changing behaviors, not attitudes. This helps us move from virtuous intentions to visible action without trying to tackle people’s deeply held beliefs.
Using Bohnet and Chilazi’s framework, employees at all levels can embed fairness into their everyday practices. “For us, making work fair means designing workplaces where everyone can thrive and perform at their best,” Bohnet and Chilazi write. “This means giving all people—regardless of gender, race, ethnicity, sexual orientation, or any other aspect of their identity or background—access to a playing field where some people are not unfairly advantaged in a way they didn’t earn.”
Offering an evidence-based blueprint, MAKE WORK FAIR shows you how to make fairness at work a reality, no matter your role, seniority, responsibilities, or where you are in the world. As Bohnet and Chilazi remind us, “To make change for good, we have to keep at it. The price of not persisting is simply too high—for each of us, companies and governments, and societies across the globe. Unfair work keeps the right people from doing the right job the right way in the right positions. That’s what we call a market failure in economics. So, let’s not keep failing.”
Episode: https://sites.libsyn.com/444312/make-work-fair-with-siri-chilazi
Podcast: http://carolinedowdhiggins.com
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In this episode, I sit down with the inspiring Angie and Dora Obwaka to discuss how rest, reflection, and God’s grace are essential for laying a strong foundation in life. As we navigate the new year, we explore the importance of aligning with God’s rhythm of grace and how intentional rest impacts our spiritual, personal, and professional growth.
Dora shares her vision for Jesus Girl Closet and how it was birthed out of moments of reflection and prayer. Angie brings her unique insights on capacity building and the practice of fasting, emphasizing the need for an intimate connection with God over the optics of religious rituals.
We also dive into family traditions, what Christmas taught them about slowing down, and how these lessons can help us approach the year with renewed purpose and focus. This episode is full of practical wisdom and heartfelt encouragement for anyone seeking to build a life centered on faith and grace.
📌Important Links:
1. Connect with Dora: https://www.instagram.com/doraobwaka?igsh=MWF1cWhtNWx1czMzMA==
2. Connect with Angie: https://www.instagram.com/angieobwaka?igsh=MXFxeGIzejhlZmcwMA==
3. Shop Jesus Girl Closet: https://www.instagram.com/jesusgirlcloset?igsh=Z3VheHVjc2RnZnkx
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This podcast and its content, including all audio, visuals, and written material, are the property of DJ Moz and The DJ Moz Podcast unless otherwise stated. Unauthorized use, reproduction, or distribution of this content without prior permission is prohibited. For permissions, please contact [email protected]
Episode: https://podcasters.spotify.com/pod/show/djmoz/episodes/EP-67-Rest--Reflection--and-Grace-with-Angie-and-Dora-Obwaka--Laying-the-Groundwork-Series-e2tvcvs
Podcast: https://podcasters.spotify.com/pod/show/djmoz
