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Today's Headlines: The Trump–Musk bromance may be ending, with Elon blasting Trump's $2.3 trillion spending bill as a “disgusting abomination” and calling to oust lawmakers who supported it. Meanwhile, the Trump administration formally requested Congress to undo $9.4B in DOGE's previous cuts via a rescission process, still falling far short of Musk's promised $2T in cuts. New 50% tariffs on steel and aluminum go into effect tonight (except for the UK), but are subject to change next month. The administration also rescinded 2022 guidance protecting abortion access in emergency cases, exposing providers to prosecution in 13 states with total bans. FEMA's acting head reportedly didn't know the U.S. had a hurricane season, worsening concerns as meteorologists warn they're too understaffed to predict storm paths. In other news, DHS officers handcuffed a Nadler aide, accusing his office of “harboring rioters” after staff watched ICE detain migrants. The Supreme Court declined to hear gun rights cases, leaving Maryland and Rhode Island's bans intact. Just in time for Pride month, the Navy will rename the USNS Harvey Milk to align with a “warrior culture” vision. FInally, Russia carried out a successful hit on the strategic Crimea bridge following Ukraine's devastating drone strikes on Russian bombers as peace talks in Istanbul have collapsed. Resources/Articles mentioned in this episode: AP News: Musk calls GOP tax cut and spending bill a 'disgusting abomination' AP News: Trump formally asks Congress to claw back approved spending targeted by DOGE AP News: Musk calls GOP tax cut and spending bill a 'disgusting abomination' Axios: Trump administration strips federal protections for emergency abortion providers NYT: Acting FEMA Chief Told Staff He Didn't Know About U.S. Hurricane Season Axios: Dems seek to grill Kristi Noem on DHS handcuffing Nadler aide WaPo: Supreme Court leave Maryland's ban on assault rifles in place AP News: Musk calls GOP tax cut and spending bill a 'disgusting abomination' WaPo: As Russia reels from drone attack, Ukraine targets vital Crimean Bridge AP News: Putin's uncompromising demands emerge after the latest round of Russia-Ukraine peace talks Morning Announcements is produced by Sami Sage and edited by Grace Hernandez-Johnson Learn more about your ad choices. Visit megaphone.fm/adchoices
Więcej tutaj: sasana.wikidot.com/z-zewnatrz-i-od-srodkaKsiążkę można pobrać w całości w formie elektronicznej (PDF), można ją też zamówić w formie papierowej ZA DARMO ze strony dhamma.pl/ksiegarnia/z-zewnatrz-i-od-srodka/Możesz nas też posłuchać na tych serwisach podkastowych -Apple Podcast: podcasts.apple.com/us/podcast/sasa…1592163368?uo=4Spotify: open.spotify.com/show/039TDu6Pil0s4jutio5VeAGoogle Podcast: www.google.com/podcasts?feed=aHR…lcGlzb2Rlcy9mZWVkRSS: www.spreaker.com/show/5199898/episodes/feedWspomóż prace Sasany: patronite.pl/sasanaplPomóż nam tłumaczyć teksty źródłowe: patronite.pl/TheravadaPLZ ZEWNĄTRZ I OD ŚRODKAPytania i odpowiedzi dotyczące nauczania buddyzmu theravāda.© 2024 do wydania polskiego: Fundacja „Theravada”WITHOUT and WITHINQuestions and Answers on the Teachings of Theravada Buddhism By Ajahn JayasaroCopyright © Panyaprateep Foundation, 2013. All rights reserved.ISBN: 978-83-970795-0-2Tłumaczenie: Przemysław MajewskiRedakcja: Aneta Miklas, Piotr Jagodziński, Janusz PodkościelnySkład i łamanie tekstu: Joanna Grabowska, liliprojekt.pl, joannagrabowska.plZdjęcie na okładce: Viteethumb StudioProjekt okładki: Monika ZapisekCzyta: Aleksander Bromberek (lektor-online.pl/)
¿Cómo es el proceso de decisión cuando te compras una moto de trial? ¿Te fias de lo que cuentan los amigos? ¿La tienda? En este vídeo analizamos un problema recurrente: el aficionado no tiene información para acertar en la compra de su moto de trial. Nosotros abrimos ese melón y analizamos a fondo las diferencias de los motores de trial de 2T de 300cc y 250cc tanto con alimentación por carburador como por inyección. Nos ayuda en este vídeo Sam Xiberta, técnico de Sherco y mecánico personal de Adam Raga. En Trialworld somos concesionario oficial de SHERCO, BETA, TRRS, MONTESA, OSET, SCORPA, ELECTRIC MOTION y VERTIGO.
You've heard the headlines: “Bitcoin is for criminals.” But is that actually true?In this episode, we dig into the data, scandals, and hypocrisy behind the global money laundering industry. Spoiler: it's not Bitcoin moving $2 trillion a year — it's the traditional banking system.
MENOPAUSE:Conflicting medical advice, emotional volatility, and misunderstood symptoms.Personal and relational strain during a life phase most men don't understand.Plus — what every man needs to know (but was never taught) about menopause.GLOBAL POWER MOVES & DOMESTIC DEALSTrump takes on Big Pharma, Middle East relations, and economic nationalism.Massive global deals: $600B+ from Saudi Arabia, $1.2T from Qatar (including Boeing's biggest order ever).Qatar's influence in American institutions quietly reshaping policy and power.Klaus Schwab ousted from Davos power — globalist grip showing cracks.Plus — who's really pulling the strings in Washington?WEAPONIZED (Jeremy Corbell & George Knapp):Whistleblower Matthew Brown (former DoD/State Dept) reveals secret UAP program.Claims we're living in a controlled illusion — “left behind” by advanced beings.Final words: “God is real.”And... Mark's new AI Tip of the week!#TrumpNews #QatarInfluence #MenopauseAwareness #WeaponizedPodcast #UFOWhistleblower #MatthewBrown #DavosCollapse #O5OPodcast
Ukraine, Russia set for first direct talks in Istanbul in 3 years Israel snubs US envoy as Gaza offensive rages Gulf leaders call for ending Gaza war in summit with Trump President Erdogan has stood by Syrian people: Alsharaa Qatar hails Trump visit, inks $1.2T in deals
The Vatican shuts the doors by the Conclave as Catholics await the white smoke signaling a new Pope has been chosen. India strikes Pakistan in a retaliatory attack. A massive 12 foot plus-size black woman statue is erected in Times Square that is supposed to be a subtle nod to Michelangelo's David. The Supreme Court rules that Trump's transgender military ban can remain in effect. AG Bondi - claims there are tens of thousands of videos of Epstein with children. House Republicans send a letter to Speaker Johnson demanding a $2T reduction in spending including extending the 2017 tax cuts. Rep. Beth Van Duyne joins us to break news about the letter the House GOP is sending to Speaker Johnson to DEMAND a $2T reduction in spending and to make the 2017 tax cuts permanent. Vanity Fair says eating protein is MAGA and a sign of toxic masculinity. The FBI classifies the 2017 Congressional baseball shooting as a “Suicide by Cop”. Thank you for supporting our sponsors that make The Dana Show possible…All Family Pharmacyhttps://AllFamilyPharmacy.com/DanaCode Dana10 for 10% off your entire orderBeamhttp://shopbeam.com/DanashowSleep like never before—Beam has improved over 17.5 million nights of rest. Try it now with code Danashow for 40% off.Home Title Lockhttps://hometitlelock.com/danaProtect your home! Get a FREE title history report + 14 days of coverage with code DANA. Check out the Million Dollar TripleLock—terms apply.Byrnahttps://byrna.com/danaVisit Byrna and check out the New Byrna CL during their Mother's Day Promotion. Order by May 11th for your FREE Kinetic Projectiles with purchase. A $49.99 value. Patriot Mobilehttps://patriotmobile.com/DanaDana's personal cell phone provider is Patriot Mobile. Get a FREE MONTH of service code DANAHumanNhttps://humann.comSupport your metabolism and healthy blood sugar levels with Superberine by HumanN. Find it now at your local Sam's Club next to SuperBeets Heart Chews. KelTechttps://KelTecWeapons.comSee the third generation of the iconic SUB2000 and the NEW PS57 - Keltec Innovation & Performance at its bestRelief Factorhttps://relieffactor.comTurn the clock back on pain with Relief Factor. Get their 3-week Relief Factor Quick Start for only $19.95 today! Goldcohttps://DanaLikesGold.com My personal gold company - get your GoldCo 2025 Gold & Silver Kit. PLUS, you could qualify for up to 10% in BONUS silver
El panorama actual de tecnología de trial está atomizado. Motos 2T de carburador, motos 2T de inyección, con o sin arranque eléctrico, motos 4T de inyección, motos eléctricas... Las 2T siguen siendo las motos más vendidas. Ahora mismo, Sherco y Vertigo son fabricantes que solo comercializan modelos de inyección electrónica, mientras que TRRS acaba de presentar su nueva solución de inyección con la TRRS Gold, pero manteniendo en catálogo el carburador. ¿ Por qué?De la mano de Jordi Tarrés y David Araujo analizamos las ventas del carburador y la inyección, las diferencias de rendimiento, las claves de sus mantenimientos y posibilidades técnicas. Hablamos de homologación, tecnología y más cosas interesantes con dos profesionales del sector. La nueva TRRS ONE GOLD 2025 ya está disponible en Trialworld Store, la primera moto de trial 2T con inyección con posibilidad de arranque eléctrico, fabricada por TRRS.
Arthur Hayes says it's time to “long everything”—and in today's episode, NLW digs into why. Broadcasting from Token 2049 in Dubai, Hayes makes a bold case for an upcoming liquidity wave that could supercharge markets. NLW breaks down Hayes' forecast, the return of CZ to the public stage, and insights from BlackRock and Goldman on crypto's next chapter. Then it's a deep dive into troubling GDP numbers, the rising recession risk, and how the Fed may respond. Finally, we explore Bitcoin's strategic reserve deadline, wirehouse ETF adoption, and the Treasury's $2T stablecoin forecast. It's a full macro + crypto briefing you won't want to miss. Sponsored by: Crypto Tax Calculator Accurate Crypto Taxes. No Guesswork. Say goodbye to tax season headaches with Crypto Tax Calculator: Generate accurate, CPA-endorsed tax reports fully compliant with IRS rules. Seamlessly integrate with 3000+ wallets, exchanges, and on-chain platforms. Import reports directly into TurboTax or H&R Block, or securely share them with your accountant. Exclusive Offer: Use the code BW2025 to enjoy 30% off all paid plans. Don't miss out - offer expires 15 April 2025! Ledger Ledger, the world leader in digital asset security, proudly sponsors The Breakdown podcast. Celebrating 10 years of protecting over 20% of the world's crypto, Ledger ensures the security of your assets. For the best self-custody solution in the space, buy a LEDGER™ device and secure your crypto today. Buy now on Ledger.com. Enjoying this content? SUBSCRIBE to the Podcast: https://pod.link/1438693620 Watch on YouTube: https://www.youtube.com/nathanielwhittemorecrypto Subscribe to the newsletter: https://breakdown.beehiiv.com/ Join the discussion: https://discord.gg/VrKRrfKCz8 Follow on Twitter: NLW: https://twitter.com/nlw Breakdown: https://twitter.com/BreakdownNLW
Donate (no account necessary) | Subscribe (account required) Join Bryan Dean Wright, former CIA Operations Officer, for a Friday Headline Brief. Heavy on news, light on analysis. ICE Agents Targeted in California, Trump Admin Strikes Back – A left-wing activist doxing ICE agents and warning illegals of raids is now being hunted after evading arrest in Irvine, CA. The Trump administration signals a tougher stance against those threatening federal law enforcement. Judge Temporarily Blocks Alien Enemies Act Deportations – A federal judge rules Trump's order needs stronger language linking Venezuela's government to TdA gang violence. The White House is expected to revise and possibly declassify intel to support its case. FBI and Chile Take Down South American Theft Ring – A joint operation results in 23 arrests and the seizure of $1.3M in assets, targeting gangs notorious for burglarizing U.S. homes and fleeing abroad. National Security Shakeup: Rubio Replaces Waltz as NSA – Secretary of State Marco Rubio takes on dual roles after Trump reassigns Mike Waltz. Sources point to MAGA activist Laura Loomer's influence in the decision. CIA Courts Disillusioned Chinese Officials – New recruitment videos aim to exploit paranoia within the Chinese Communist Party. Bryan explains how just one defector could offer massive strategic gains. Tariff Fallout and Industrial Reshoring – Mercedes joins other automakers reshoring to the U.S. A bipartisan SHIPS Act hopes to revive U.S. shipbuilding. Elon Musk admits his cost-cutting team fell short, finding $160B in savings instead of $2T. Middle Class and Health Front Updates – April jobs report expected to show modest gains. Meanwhile, yogurt giant Danone commits to eliminating food dyes, and HHS declares youth gender-transition treatments dangerous, pushing talk therapy instead. Global Tensions Mount: Ukraine Stalemate, Iran Threats, Chinese Satellites Aid Houthis – Peace in Ukraine remains distant. Iran warns the U.S. after Trump threatens secondary sanctions on oil buyers. Trump's response to a $60M jet loss: economic pressure on Tehran and Beijing. Argentina Thrives Post-Socialism – Airline goes from deficit to profit without subsidies under President Javier Milei, highlighting the failure of socialist economics and the potential of reform. "And you shall know the truth, and the truth shall make you free." - John 8:32
In a powerful episode, @intheMatrixxx and @shadygrooove celebrate Trump's National Day of Prayer executive order, a bold stand for America's spiritual renewal against globalist secularism. They dive into Stephen Miller's fiery White House press conference, where he touts Trump's 100-day triumphs—140+ executive orders, record deportations, and crushing ActBlue's fraud schemes—proving America-First is unstoppable. This episode unveils the deep state's panic as faith and patriotism surge. With the constitution as your weapon, join the fight to reclaim America's soul. The truth is learned, never told—tune in to the MG Show at mg.show to ignite the revolution! Tune in weekdays at 12pm ET / 9am PST, hosted by @InTheMatrixxx and @Shadygrooove. Catch up on-demand on https://rumble.com/mgshow or via your favorite podcast platform. Where to Watch & Listen Live on https://rumble.com/mgshow https://mgshow.link/redstate X: https://x.com/inthematrixxx Backup: https://kick.com/mgshow PODCASTS: Available on PodBean, Apple, Pandora, and Amazon Music. Search for "MG Show" to listen. Engage with Us Join the conversation on https://t.me/mgshowchannel and participate in live voice chats at https://t.me/MGShow. Social & Support Follow us on X: @intheMatrixxx and @ShadyGrooove Join our listener group on X: https://mgshow.link/xgroup Support the show: Fundraiser: https://givesendgo.com/helpmgshow Donate: https://mg.show/support Merch: https://merch.mg.show MyPillow Special: Use code MGSHOW at https://mypillow.com/mgshow for savings! Crypto donations: Bitcoin: bc1qtl2mftxzv8cxnzenmpav6t72a95yudtkq9dsuf Ethereum: 0xA11f0d2A68193cC57FAF9787F6Db1d3c98cf0b4D ADA: addr1q9z3urhje7jp2g85m3d4avfegrxapdhp726qpcf7czekeuayrlwx4lrzcfxzvupnlqqjjfl0rw08z0fmgzdk7z4zzgnqujqzsf XLM: GAWJ55N3QFYPFA2IC6HBEQ3OTGJGDG6OMY6RHP4ZIDFJLQPEUS5RAMO7 LTC: ltc1qapwe55ljayyav8hgg2f9dx2y0dxy73u0tya0pu All Links Find everything on https://linktr.ee/mgshow Keywords National Day of Prayer, Stephen Miller, Trump, executive order, America First, border security, deportations, ActBlue fraud, deep state, spiritual renewal, globalism, truth, constitution, @intheMatrixxx, @shadygrooove, MG Show Filename mgshow-s7e082-national_day_of_prayer_press_conference_with_stephen_miller May 01 2025 TODAY'S HEADLINES IN THE NEWS: US ECONOMY POWERS THROUGH: Trump's tax cuts fuel small business boom, counter tariff concerns (CBS). | CORPORATE TAX CUTS PUSHED: Trump's bill offers 100% expensing, 15% rate for manufacturers (CNN). | VA PROGRAM SAVES VETERANS' HOMES: 20,000 avoid foreclosure, strengthening military families (NPR). | VETERANS RECEIVE VA UPGRADES: Trump's healthcare reforms honor sacrifice, expand access (NPR). | ICE ARRESTS HIT 7-YEAR HIGH: Trump's immigration policies drive record detentions (The Guardian). | DEPORTATIONS FACE LEGAL CHALLENGE: Venezuelans in El Salvador prison demand hearings (Reuters). | TRUMP ADVANCES UKRAINE PEACE DEAL: Ceasefire talks with Russia, Zelenskiy bolster U.S. leadership (AP). | UKRAINE-U.S. MINERALS DEAL SIGNED: U.S. gains resource access, Kyiv eyes peace (AP). | ELON MUSK CLEARS $2B HURDLE: Senate dismisses claims, DOGE boosts economic optimism (CNN). | ELON MUSK CUTS $160B FROM BUDGET: Falls short of $2T, plans Tesla return (CNN). | FARMERS SECURE $15B RELIEF: Trump's aid ensures rural economy, food independence (AP). | US JOB GROWTH HITS RECORD: Trump's policies add 80,000 manufacturing jobs, revive heartland (Reuters). | NEURALINK BREAKTHROUGH STUNS: Maine patient's mobility restored, U.S. tech leads (X trends). | EPA REFORMS BOOST INDUSTRY: Zeldin's deregulation saves billions, creates jobs (Reuters). | ACTBLUE FRAUD EO MOVES FORWARD: Trump's order stops donation scams, wins voters (X trends). | SPACE FORCE DEPLOYS NEW SATELLITE: Advanced tech enhances U.S. global security (AP). | COAL MINERS SEE JOB SURGE: Trump's coal revival adds 5,000 jobs, sparks debate (AP). | BLACK VOTERS GAIN IN ALABAMA: Redistricting boosts representation in Congress (NPR). | TENNESSEE CHALLENGES EDUCATION RULING: Legislature targets 1982 Supreme Court decision (NPR). | NORTH KOREA TESTS MISSILES: Kim Jong Un boosts navy's nuclear capabilities (ABC News). | VENEZUELA FACES OIL BLOCKADE: Trump's measures restrict exports, assert U.S. influence (Global Issues). | RUSSIA OPENS DOMINICAN EMBASSY: Lavrov calls Caribbean nation a “promising partner” (ABC News). | INDIA-PAKISTAN TENSIONS EASE: Rubio's diplomacy defuses Kashmir crisis (AP). | AI SHAPES SHOPPING FUTURE: Visa predicts consumer habits shift with AI integration (ABC News). | CANCER RESEARCH BREAKTHROUGHS SHINE: AACR 2025 highlights AI, new therapies (NPR).
En este episodio nos adentramos en un aspecto esencial pero muchas veces olvidado del mantenimiento de las motos: el aceite de la caja de cambios. Este fluido no solo se encarga de lubricar los componentes internos de la transmisión, sino que también juega un papel clave en el buen funcionamiento, la eficiencia y la durabilidad de nuestra moto. A lo largo del programa descubriremos por qué su papel es tan determinante y qué consecuencias puede tener no prestarle la atención adecuada. Hablaremos también de cómo funciona la transmisión en una moto, qué tipo de engranajes y mecanismos intervienen y por qué una correcta lubricación es tan importante para evitar el desgaste prematuro de piezas clave como rodamientos o embragues. Además, veremos cómo el tipo de aceite y su viscosidad varían según el tipo de moto: no es lo mismo una 2T, una 4T o una scooter, y cada una tiene necesidades específicas que conviene conocer. Profundizaremos en las propiedades fundamentales de los aceites de transmisión, desde su capacidad para reducir la fricción hasta su función refrigerante o su papel como protector contra la corrosión. También comentaremos cómo influyen en la suavidad del cambio de marchas y en el consumo de combustible, y qué ocurre si usamos un aceite inadecuado o no lo cambiamos cuando toca. Finalmente, daremos algunas claves para elegir el lubricante correcto para cada moto, siempre siguiendo las indicaciones del fabricante y atendiendo a normativas como las JASO. Porque al final, mantener una moto en buen estado es cuestión de atención, conocimiento y prevención. Y el aceite de transmisión es, sin duda, uno de esos elementos que marcan la diferencia entre una moto que va bien... y una que no. Tienes todos los podcasts de TotalEnergies en esta lista: https://www.ivoox.com/podcast-de-totalenergies_bk_list_11163903_1.html Todos los podcast: https://www.podcastmotor.es Twitter: @AutoFmRadio Instagram: https://www.instagram.com/autofmradio/ YouTube: https://www.youtube.com/@AutoFM Contacto: info@autofm.es
Welcome to another episode of the Private Equity Podcast, today I'm joined by Nayef Perry, Head of Direct Credit at Hamilton Lane. We dive into the state of the private credit market—where the opportunities are, how interest rates are shaping returns, and what investors need to watch out for. If you want a sharp, no-fluff breakdown of where private credit is heading and why it still has room to run, this one's for you.Breakdown:[00:00] Nayef Perry, Head of Direct Credit at Hamilton Lane, joins to discuss the private credit market, interest rates, and market outlook. [00:30] Background: Born in Miami, ex-consultant, GE Capital, joined Hamilton Lane in 2013. [01:13] Private credit's golden era isn't over—higher-for-longer rates mean higher yields for investors. [02:11] Despite growth since 2008, private credit isn't overcrowded—$1.4T credit gap vs equity. [03:36] Add $600B+ in upcoming maturities, and there's a $2T+ opportunity over next 3–5 years. [05:30] Hamilton Lane's deal drivers: add-ons, recapitalizations, and recovering LBO activity. [06:52] Credit lags LBO recovery but Hamilton Lane sees strong deal flow via LP relationships. [08:42] Democratization: retail access growing through evergreen funds—low minimums, high liquidity. [11:01] Biggest concern is defaults, but default rates and distress ratios remain below averages. [14:55] Credit shines across market cycles—positive performance every year since 1999. [16:51] Tight performance band and low volatility make credit an all-weather asset. [17:44] Investment discipline is key: big deal funnel + strict filters = consistent returns. [19:03] Four core criteria: top-tier sponsors, #1/#2 market leaders, recession-resistant sectors, strong capital structures. [21:01] Influences: WSJ daily, Poor Charlie's Almanack, Red Notice, industry reports. [23:54] Thanks for tuning in—subscribe and keep smashing it.Connect with Nayef Here.Thanks for tuning in!Subscribe for more episodes on iTunes & SpotifyGot feedback or questions? Email Alex at alex.rawlings@raw-selection.com. Until next time—keep smashing it!
En este episodio, desglosamos los temas más importantes que están marcando el pulso de los mercados: • Wall Street profundiza pérdidas: Los futuros caen con fuerza tras la respuesta de China a los aranceles de Trump. El $SPX baja -3.1%, $US100 -3.3% y $INDU -2.8%, luego de perder -4.8% el jueves. Inversionistas temen una recesión y una guerra comercial a gran escala. • China contraataca: China impone aranceles del 34% a todas las importaciones desde EE.UU. desde el 10 de abril. Además, lanza una ofensiva comercial: restricciones a tierras raras, investigación antidumping sobre tubos de rayos X, suspensión de importaciones avícolas y sanciones a 27 empresas de defensa y tecnología. • Mercados globales en rojo: Se han perdido más de $2T en valor de mercado desde los aranceles de Trump. El rendimiento del bono a 10 años cae por debajo de 4% por primera vez desde octubre. Ya se anticipan recortes de tasas por parte de la Fed este año, comenzando en junio. Acompáñanos para entender cómo esta nueva escalada comercial puede cambiar el rumbo económico global y desencadenar movimientos drásticos en los mercados financieros.
Chris rips Trump's ‘Liberation Day' tariff speech—$2T wiped from markets in 15 minutes! Is it a negotiation ploy or economic chaos? www.watchdogonwallstreet.com
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The Bitcoin Policy Institute releases a framework for the U.S. to buy $200B worth of BTC by issuing $2T worth of BitBonds. Learn more about your ad choices. Visit megaphone.fm/adchoices
WATER = warm 2 C- may end up being more depending uponsourdough starter thickness.DARK or whole RYE FLOUR 2 ½ CWHOLE GRAIN wheat flour 2 CSOURDOUGH STARTER 1CMolasses ¼Cs YEAST 1Tablespoon dry yeast or 1 packetMix, thick sponge, let sit for 1.5 hours Add WHITE BREAD FLOUR 2C with the salt below incorporated into it. SALT 1 TKnead for 8-10 minutes adjusting by adding in more whiteflour or water if needed, thicker denser dough than traditional breads but itshould hold shape, and start out very sticky and then the water will get absorbed. The whole grains take longerfor the water to absorb and rye is stickier than wheat. So, it should be messy at first. After you get a good dough, then add seeds. We use caraway and sunflower seeds.CARAWAY SEEDS 1TSUNFLOWER SEEDS 1/2C, 1T for the top. If you want a black bread then youcan add 2T of carmel color to the water. It doesn't add any flavor, just plenty of blackness. Divide in two, and shape like a football, seam side down.Egg wash and add your seeds to the top. Let rest about 1/2 hour, then score down the middle and bake in a 400 degree oven for 25-30 minutes.Happy Baking! For more informationabout House of Bread, please visit https://houseofbread.com/ and for more information about franchising, please visithttps://houseofbreadfranchise.com/. Youcan order a recipe book https://houseofbread.com/recipe-book/. If you want to take your bakingto the next level visually, we have on line video full baking classes that canbe found here https://houseofbread.com/product-category/online-baking-class/
Toph leads the discussion and review of Fine Young Cannibals' 1989 album ‘The Raw & The Cooked' – including, as always, albums + songs on our radar and much more! Stop the presses...we did two episodes in 8 days! Which makes us proud as pie. Tune in to hear the boys discuss the sweet sultry (I guess) voice of Roland Gift, provide the story of how this "spin off" band came about, and play the strangest game in the history of 2T&A. Will this episode be raw? Will it be cooked? Will it be a Gift? Will it be a good thing? Will we drive you crazy? Will you be satisfied? Or will you take what you can get? Regardless -- don't look back, and give episode 92 a listen. It's ok. It's alright.
Good Trump Bad Trump lives again! Segment 1: Gene Hackman Tribute* Context: Discussion sparked by the suspicious death of a 95-year-old man in Arizona (initially unclear if it's Hackman, later clarified as a tribute to his legacy).* Key Points:* Hackman's death at 95 is seen as a full life; hosts reflect on his cinematic impact.* Favorites: Hoosiers (Strong One's top pick for its underdog story), The Quick and the Dead (best villain role), Superman (best Lex Luthor), The Replacements, Young Frankenstein, The Firm, Royal Tenenbaums, Crimson Tide, Get Shorty.* Praise for Hackman as "the last true American tough guy" pre-"woke bull crap" (last credit 2004).* Memorable Quote: "Death, taxes, and whatever Gene Hackman's in is a good movie."Segment 2: Government Critique and DOGE* DOGE Revelations:* Hosts laud DOGE (Department of Government Efficiency) for exposing wasteful spending (e.g., $300 screws, $25K toilet seats).* Obama allegedly receives $2.6M annually from "Obamacare" royalties—hosts question its legitimacy.* Conspiracy Nod: References to Independence Day and Bourne Identity as examples of government corruption hiding in plain sight.* Critique: DOGE exposes financial waste but lacks power to stop it; hosts demand action over mere exposure.Segment 3: Epstein Files Disappointment* Update: "Part 1" of Epstein files released, but it's a "nothing burger"—same old phone records and flight logs, no new names.* Frustration: Hosts criticize Attorney General Pam Bondi for not acting despite having the files, suspect a cover-up by intelligence agencies (CIA, FBI, Mossad).* Hope: Speculation that "Part 2" might reveal more, but skepticism prevails based on historical inaction.* Bad Trump Moment: Trump's failure to push harder on this issue disappoints the hosts.Segment 4: Ukraine and American Empire* News: Trump negotiates $500B in Ukrainian mineral rights (uranium, titanium, coal, oil) in exchange for U.S. bases to secure the border against Russia.* Critique: Hosts decry it as empire-building, no different from Biden/Obama policies—contradicts "no boots on the ground" promises.* Irony: Both liberals (pro-war) and Republicans (pro-minerals) get what they want, exposing a unified agenda.Segment 5: Policy Updates* Tax Cuts: House passes $2T tax cut, $4.5T in breaks—no taxes on tips, overtime, or Social Security payments.* Immigration: Trump's "Golden Ticket"—$5M buys citizenship, bypassing legal struggles (hosts wary of corruption risks).* Maine Governor: Janet Mills defies Trump's ban on men in women's sports; Trump threatens to pull federal funding and college accreditation.* Ohio Governor Race: Vivek Ramaswamy emerges as a candidate, endorsed by J.D. Vance.* Florida Governor Race: Casey DeSantis runs, sparking dynasty talk with Ron DeSantis.Segment 6: Cultural Reflections* Kathleen Kennedy Retirement: Set for end of 2025—hosts cheer but lament she's not fired in disgrace for ruining Star Wars, Willow, and Indiana Jones.* Comedy as Reality: Past satire (Life of Brian, SNL's Pat) mirrors today's gender debates—hosts call it a mental illness, not reality.* Language Debate: Hosts discuss curse words' cultural weight vs. biblical purity, landing on personal conviction over strict rules.Final Thoughts* Strong One: Honor parents by avoiding their mistakes; only Jesus breaks generational curses.* Philosopher King: Navigating cultural lines (e.g., language) is complex—trust the Holy Spirit's conviction.* Dusty: Comedy of the past (e.g., Life of Brian, Pat) is today's absurd reality—trans issues are a mental health crisis, not a civil right.Closing* Verse: Zechariah 8:16—"Speak ye every man the truth to his neighbor; execute the judgment of truth and peace in your gates."* Prayer: Call for truth, vigilance, and drawing closer to Jesus.* Sign-Off: "You've been listening to the Kingsplaining Podcast… where the people are free, the taxes are voluntary, and your two kings serve the King of Kings, Christ Jesus."Where to Find Us* Website: Kingsplaining.com* Platforms: Anywhere you get podcasts* Call to Action: Like, share, subscribe, review on Apple Podcasts. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit kingsplaining.substack.com/subscribe
Today we are replaying my conversation with Phil Huber, the Head of Portfolio Solutions at Cliffwater. The private credit market has exploded recently, with Assets under management reaching a staggering $1.2T last year. I've been a little skeptical of this asset class, so I asked Phil to come on and convince me otherwise. We talk about the demand side of the equation, whether we are in a bubble, downside protection, due diligence, volatility and more. Phil also covers his backstory and his unique role at Cliffwater. Please enjoy this discussion with Phil Huber. For the full show notes, transcript, and links to the best content to learn more, check out the episode page HERE. ----- Making Markets is a property of Colossus, LLC. For more episodes of Making Markets, visit joincolossus.com/episodes. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @makingmkts | @ericgoldenx Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Show Notes (00:00:00) Welcome to Making Markets (00:01:04) Meet Phil Huber: Private Credit Expert (00:01:39) Phil Huber's Career Journey (00:05:34) The Evolution of Alternative Investments (00:10:32) The Growth and Demand for Private Credit (00:17:23) Private Credit Performance and Risks (00:22:36) Due Diligence in Alternative Investments (00:23:50) Emerging Trends in RIA Distribution (00:24:28) The Role of Intellectual Honesty in Investment Decisions (00:25:12) Understanding the Game of Access and Manager Selection (00:26:47) Volatility Laundering in Private Assets (00:28:47) The Journey of Writing and Sharing Knowledge (00:32:58) The Role and Responsibilities at Cliffwater (00:34:40) Exploring Interval Funds and Private Credit (00:40:43) The Importance of Secondaries in Private Equity (00:42:50) Current Sentiment in Private Credit (00:43:33) Conclusion and Resources Learn more about your ad choices. Visit megaphone.fm/adchoices
Episode 15 of Toph's Top Five, the 2T&A spinoff sweeping the nation, focuses on a quartet from Georgia who liked to put on their harborcoats and play some sort-of rock and roll. Mandolin sold separately.
A Response to The News Agents on BitcoinMain Criticisms & ResponsesBitcoin is Driven by Libertarian & Anarcho-Capitalist IdeologyWhile some Bitcoiners are libertarian, Bitcoin itself is neutral. It's used by people across the political spectrum, especially in countries with inflation and capital controls.Many Bitcoin advocates focus on financial freedom, not anarcho-capitalism. Its adoption by institutions (BlackRock, Fidelity) and nation-states (El Salvador) shows its mainstream relevance.Bitcoiners Want the Financial System to CollapseMost Bitcoin users see it as a hedge, not a weapon. They seek financial sovereignty amid inflation, reckless monetary policy, and overreach.Bitcoin is integrating with traditional finance, not replacing it. Major institutions hold Bitcoin, signaling coexistence, not destruction.Bitcoin is a Male-Dominated, Exclusive CommunityEarly adopters were mostly male, but adoption is diversifying. Women-led Bitcoin initiatives and education efforts are growing.Bitcoin is open to everyone, with grassroots adoption increasing in Africa, Latin America, and Southeast Asia.Bitcoin is a Bubble & Could CollapseBitcoin has survived multiple cycles over 15 years and remains a $2T asset. It's now embedded in global finance through ETFs and corporate treasuries.A Bitcoin collapse would impact investors but wouldn't trigger a systemic banking crisis.Bitcoin is Part of a Broader Tech Elitist MovementBitcoin's core purpose is financial independence, not AI-driven utopias.The majority of Bitcoin users are ordinary people in struggling economies, using it for remittances and savings.Get intouch with Myles at mylesdhillon@gmail.com
Episode 14 of Toph's Top Five, the 2T&A spinoff sweeping the nation, focuses on a Charlottesville, Virginia's favorite South African son, David J. Matthews and his reggae band. Don't burn the day away, listen to Toph's Top Five instead. It'll make you smile.
Khách mời số 1: Anh Phạm Sơn Tùng. Anh hiện là Phó Chủ tịch Hội đồng quản trị tập đoàn CF Holdings. Là một doanh nhân, một nhà quản trị thành công, anh Tùng cũng là chuyên gia trong lĩnh vực quản lý tài chính cá nhân.Khách mời số 2: Chị Mạch Khanh. Chị là cơ phó của hãng hàng không Pacific Airlines, thành viên của Vietnam Airlines và cũng là một Content Creator nổi bật. Chị Mạch Khanh còn có kinh nghiệm đầu tư đa dạng, nhưng không chỉ dừng lại ở lĩnh vực tài chính, mà còn thành công trong việc đầu tư vào chính bản thân mình.Trong EP3 Tiền Không Tệ mùa 2, anh Tùng & Mạch Khanh sẽ mang tới cho các những trải nghiệm và góc nhìn phong phú về lĩnh vực ĐẦU TƯ như: Nên nhìn nhận thế nào về nợ “đòn bẩy” trong đầu tư Ngoài tài sản, ta còn có thể đầu tư vào những gì khi còn trẻ? Đến khi nào thì nên chốt lời cho các khoản tích lũy? Nên mua nhà hay thuê nhà?Kết nối với khách mời của EP3 tại:Facebook anh Tùng: / nikkosontung Facebook Mạch Khanh: / fromkhanh Instagram Mạch Khanh: / machkhanh
Episode 13 of Toph's Top Five, the 2T&A spinoff sweeping the nation, focuses on a trio from Buffalo (go Chiefs) -- who once saved the day for a young, bored Nubs and Toph as an opening act before anyone knew who they were. True story. Enjoy.
Episode 12 of Toph's Top Five, the 2T&A spinoff sweeping the nation, focuses on a French-Canadian lady that could kinda sing. She had some tres bien songs. Tune in...maintenant!!
Episode 11 of Toph's Top Five, the 2T&A spinoff sweeping the nation, focuses on a band that at one point played their instruments, and now that seems fairly optional. So before I spend all my change on you...give this a listen. Will make you smile.
Record Trade Deficit Hits $1.2T, NJ Trucking Exec in $4.6M Fraud, and Tariff Hike Shakes Shein.We break down everything you need to know. Want it in your inbox? Subscribe at FreightCaviar.com.
[EP 25-051] It is being reported that one of the main issues federal workers have with returning to the office is that it will INTERFERE with their SECOND JOB. They have been ripping off America for 5 years. Taxpayers pay them & instead of working for us they go to a 2nd job. This will be easy to check as DOGE simply looks at the tax returns of employees in question. https://x.com/chamath The five alarm fire will happen AFTER the WH reveals some of the waste, fraud and corruption this team uncovers. This will be Iran Contra on steroids. I'd buckle up. Reducing the federal deficit from $2T to $1T in FY2026 requires cutting an average of ~$4B/day in projected 2026 spending from now to Sept 30. That would still result in a ~$1T deficit, but economic growth should be able to match that number, which would mean no inflation in 2026. Super big deal. Before even getting to timed scripts, the number of government jobs that could be replaced simply with a mouse macro is astounding!Become a supporter of this podcast: https://www.spreaker.com/podcast/the-kevin-jackson-show--2896352/support.
This week on The Nick Halaris Show we are welcoming back Darius Dale, the founder and CEO of 42 Macro, a leading Macro Risk Management serving investors around the world! Check out his first episode here for a master class introduction to the art of macro risk management in investing. Ready to dive in? Listen to this episode on Apple Podcasts, Spotify, Amazon Music and YouTube or on your favorite podcast platform.I wanted to have Darius on the show for a deep dive discussion on President Trump's highly anticipated Department of Governmental Efficiency initiative. While there's been a lot of hype and hyperbole around DOGE and its massive $2T target, Darius and his team at 42 Macro have actually sat down and done some real analysis. Of course, things are not as simple as advertised! As always, I hope you all enjoy this episode. Thanks for tuning in! Love this episode? Please rate, subscribe, and review on your favorite podcast platform to help more users find our show.
67 die in the US' deadliest air carrier crash since 2001, former rebel leader Ahmed al-Sharaa is announced as Syria's interim president, eight Gaza hostages are released in exchange for 110 Palestinian prisoners, Germany's parliament approves a motion to restrict migration, Meta agrees to pay Trump $25M for suspending his accounts, former US Sen. Bob Menendez is sentenced to 11 years in prison, OpenAI partners with US National Laboratories, as it accuses China's Deepseek of stealing its data, global clean energy investment crosses $2T, and scientists develop lab-grown muscle patches to treat heart failure. Sources: www.verity.news
Chuck Zodda and Marc Fandetti discuss the CPI report that came in better than expected and some of the key elements to watch for on inflation. What is Trump planning to do to achieve energy dominance? The $2T home insurance nightmare is only getting worse. Falling birth rates raise prospect of sharp decline in living standards. What are some of the less-known trends in inflation?
Applications close Monday for the NYC AI Engineer Summit focusing on AI Leadership and Agent Engineering! If you applied, invites should be rolling out shortly.The search landscape is experiencing a fundamental shift. Google built a >$2T company with the “10 blue links” experience, driven by PageRank as the core innovation for ranking. This was a big improvement from the previous directory-based experiences of AltaVista and Yahoo. Almost 4 decades later, Google is now stuck in this links-based experience, especially from a business model perspective. This legacy architecture creates fundamental constraints:* Must return results in ~400 milliseconds* Required to maintain comprehensive web coverage* Tied to keyword-based matching algorithms* Cost structures optimized for traditional indexingAs we move from the era of links to the era of answers, the way search works is changing. You're not showing a user links, but the goal is to provide context to an LLM. This means moving from keyword based search to more semantic understanding of the content:The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share... but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways.All of this is now powered by a $5M cluster with 144 H200s:This architectural choice enables entirely new search capabilities:* Comprehensive result sets instead of approximations* Deep semantic understanding of queries* Ability to process complex, natural language requestsAs search becomes more complex, time to results becomes a variable:People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned... But what if searches can take like a minute or 10 minutes or a whole day, what can you then do?Unlike traditional search engines' fixed-cost indexing, Exa employs a hybrid approach:* Front-loaded compute for indexing and embeddings* Variable inference costs based on query complexity* Mix of owned infrastructure ($5M H200 cluster) and cloud resourcesExa sees a lot of competition from products like Perplexity and ChatGPT Search which layer AI on top of traditional search backends, but Exa is betting that true innovation requires rethinking search from the ground up. For example, the recently launched Websets, a way to turn searches into structured output in grid format, allowing you to create lists and databases out of web pages. The company raised a $17M Series A to build towards this mission, so keep an eye out for them in 2025. Chapters* 00:00:00 Introductions* 00:01:12 ExaAI's initial pitch and concept* 00:02:33 Will's background at SpaceX and Zoox* 00:03:45 Evolution of ExaAI (formerly Metaphor Systems)* 00:05:38 Exa's link prediction technology* 00:09:20 Meaning of the name "Exa"* 00:10:36 ExaAI's new product launch and capabilities* 00:13:33 Compute budgets and variable compute products* 00:14:43 Websets as a B2B offering* 00:19:28 How do you build a search engine?* 00:22:43 What is Neural PageRank?* 00:27:58 Exa use cases * 00:35:00 Auto-prompting* 00:38:42 Building agentic search* 00:44:19 Is o1 on the path to AGI?* 00:49:59 Company culture and nap pods* 00:54:52 Economics of AI search and the future of search technologyFull YouTube TranscriptPlease like and subscribe!Show Notes* ExaAI* Web Search Product* Websets* Series A Announcement* Exa Nap Pods* Perplexity AI* Character.AITranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:10]: Hey, and today we're in the studio with my good friend and former landlord, Will Bryk. Roommate. How you doing? Will, you're now CEO co-founder of ExaAI, used to be Metaphor Systems. What's your background, your story?Will [00:00:30]: Yeah, sure. So, yeah, I'm CEO of Exa. I've been doing it for three years. I guess I've always been interested in search, whether I knew it or not. Like, since I was a kid, I've always been interested in, like, high-quality information. And, like, you know, even in high school, wanted to improve the way we get information from news. And then in college, built a mini search engine. And then with Exa, like, you know, it's kind of like fulfilling the dream of actually being able to solve all the information needs I wanted as a kid. Yeah, I guess. I would say my entire life has kind of been rotating around this problem, which is pretty cool. Yeah.Swyx [00:00:50]: What'd you enter YC with?Will [00:00:53]: We entered YC with, uh, we are better than Google. Like, Google 2.0.Swyx [00:01:12]: What makes you say that? Like, that's so audacious to come out of the box with.Will [00:01:16]: Yeah, okay, so you have to remember the time. This was summer 2021. And, uh, GPT-3 had come out. Like, here was this magical thing that you could talk to, you could enter a whole paragraph, and it understands what you mean, understands the subtlety of your language. And then there was Google. Uh, which felt like it hadn't changed in a decade, uh, because it really hadn't. And it, like, you would give it a simple query, like, I don't know, uh, shirts without stripes, and it would give you a bunch of results for the shirts with stripes. And so, like, Google could barely understand you, and GBD3 could. And the theory was, what if you could make a search engine that actually understood you? What if you could apply the insights from LLMs to a search engine? And it's really been the same idea ever since. And we're actually a lot closer now, uh, to doing that. Yeah.Alessio [00:01:55]: Did you have any trouble making people believe? Obviously, there's the same element. I mean, YC overlap, was YC pretty AI forward, even 2021, or?Will [00:02:03]: It's nothing like it is today. But, um, uh, there were a few AI companies, but, uh, we were definitely, like, bold. And I think people, VCs generally like boldness, and we definitely had some AI background, and we had a working demo. So there was evidence that we could build something that was going to work. But yeah, I think, like, the fundamentals were there. I think people at the time were talking about how, you know, Google was failing in a lot of ways. And so there was a bit of conversation about it, but AI was not a big, big thing at the time. Yeah. Yeah.Alessio [00:02:33]: Before we jump into Exa, any fun background stories? I know you interned at SpaceX, any Elon, uh, stories? I know you were at Zoox as well, you know, kind of like robotics at Harvard. Any stuff that you saw early that you thought was going to get solved that maybe it's not solved today?Will [00:02:48]: Oh yeah. I mean, lots of things like that. Like, uh, I never really learned how to drive because I believed Elon that self-driving cars would happen. It did happen. And I take them every night to get home. But it took like 10 more years than I thought. Do you still not know how to drive? I know how to drive now. I learned it like two years ago. That would have been great to like, just, you know, Yeah, yeah, yeah. You know? Um, I was obsessed with Elon. Yeah. I mean, I worked at SpaceX because I really just wanted to work at one of his companies. And I remember they had a rule, like interns cannot touch Elon. And, um, that rule actually influenced my actions.Swyx [00:03:18]: Is it, can Elon touch interns? Ooh, like physically?Will [00:03:22]: Or like talk? Physically, physically, yeah, yeah, yeah, yeah. Okay, interesting. He's changed a lot, but, um, I mean, his companies are amazing. Um,Swyx [00:03:28]: What if you beat him at Diablo 2, Diablo 4, you know, like, Ah, maybe.Alessio [00:03:34]: I want to jump into, I know there's a lot of backstory used to be called metaphor system. So, um, and it, you've always been kind of like a prominent company, maybe at least RAI circles in the NSF.Swyx [00:03:45]: I'm actually curious how Metaphor got its initial aura. You launched with like, very little. We launched very little. Like there was, there was this like big splash image of like, this is Aurora or something. Yeah. Right. And then I was like, okay, what this thing, like the vibes are good, but I don't know what it is. And I think, I think it was much more sort of maybe consumer facing than what you are today. Would you say that's true?Will [00:04:06]: No, it's always been about building a better search algorithm, like search, like, just like the vision has always been perfect search. And if you do that, uh, we will figure out the downstream use cases later. It started on this fundamental belief that you could have perfect search over the web and we could talk about what that means. And like the initial thing we released was really just like our first search engine, like trying to get it out there. Kind of like, you know, an open source. So when OpenAI released, uh, ChachBt, like they didn't, I don't know how, how much of a game plan they had. They kind of just wanted to get something out there.Swyx [00:04:33]: Spooky research preview.Will [00:04:34]: Yeah, exactly. And it kind of morphed from a research company to a product company at that point. And I think similarly for us, like we were research, we started as a research endeavor with a, you know, clear eyes that like, if we succeed, it will be a massive business to make out of it. And that's kind of basically what happened. I think there are actually a lot of parallels to, of w between Exa and OpenAI. I often say we're the OpenAI of search. Um, because. Because we're a research company, we're a research startup that does like fundamental research into, uh, making like AGI for search in a, in a way. Uh, and then we have all these like, uh, business products that come out of that.Swyx [00:05:08]: Interesting. I want to ask a little bit more about Metaforesight and then we can go full Exa. When I first met you, which was really funny, cause like literally I stayed in your house in a very historic, uh, Hayes, Hayes Valley place. You said you were building sort of like link prediction foundation model, and I think there's still a lot of foundation model work. I mean, within Exa today, but what does that even mean? I cannot be the only person confused by that because like there's a limited vocabulary or tokens you're telling me, like the tokens are the links or, you know, like it's not, it's not clear. Yeah.Will [00:05:38]: Uh, what we meant by link prediction is that you are literally predicting, like given some texts, you're predicting the links that follow. Yes. That refers to like, it's how we describe the training procedure, which is that we find links on the web. Uh, we take the text surrounding the link. And then we predict. Which link follows you, like, uh, you know, similar to transformers where, uh, you're trying to predict the next token here, you're trying to predict the next link. And so you kind of like hide the link from the transformer. So if someone writes, you know, imagine some article where someone says, Hey, check out this really cool aerospace startup. And they, they say spacex.com afterwards, uh, we hide the spacex.com and ask the model, like what link came next. And by doing that many, many times, you know, billions of times, you could actually build a search engine out of that because then, uh, at query time at search time. Uh, you type in, uh, a query that's like really cool aerospace startup and the model will then try to predict what are the most likely links. So there's a lot of analogs to transformers, but like to actually make this work, it does require like a different architecture than, but it's transformer inspired. Yeah.Alessio [00:06:41]: What's the design decision between doing that versus extracting the link and the description and then embedding the description and then using, um, yeah. What do you need to predict the URL versus like just describing, because you're kind of do a similar thing in a way. Right. It's kind of like based on this description, it was like the closest link for it. So one thing is like predicting the link. The other approach is like I extract the link and the description, and then based on the query, I searched the closest description to it more. Yeah.Will [00:07:09]: That, that, by the way, that is, that is the link refers here to a document. It's not, I think one confusing thing is it's not, you're not actually predicting the URL, the URL itself that would require like the, the system to have memorized URLs. You're actually like getting the actual document, a more accurate name could be document prediction. I see. This was the initial like base model that Exo was trained on, but we've moved beyond that similar to like how, you know, uh, to train a really good like language model, you might start with this like self-supervised objective of predicting the next token and then, uh, just from random stuff on the web. But then you, you want to, uh, add a bunch of like synthetic data and like supervised fine tuning, um, stuff like that to make it really like controllable and robust. Yeah.Alessio [00:07:48]: Yeah. We just have flow from Lindy and, uh, their Lindy started to like hallucinate recrolling YouTube links instead of like, uh, something. Yeah. Support guide. So. Oh, interesting. Yeah.Swyx [00:07:57]: So round about January, you announced your series A and renamed to Exo. I didn't like the name at the, at the initial, but it's grown on me. I liked metaphor, but apparently people can spell metaphor. What would you say are the major components of Exo today? Right? Like, I feel like it used to be very model heavy. Then at the AI engineer conference, Shreyas gave a really good talk on the vector database that you guys have. What are the other major moving parts of Exo? Okay.Will [00:08:23]: So Exo overall is a search engine. Yeah. We're trying to make it like a perfect search engine. And to do that, you have to build lots of, and we're doing it from scratch, right? So to do that, you have to build lots of different. The crawler. Yeah. You have to crawl a bunch of the web. First of all, you have to find the URLs to crawl. Uh, it's connected to the crawler, but yeah, you find URLs, you crawl those URLs. Then you have to process them with some, you know, it could be an embedding model. It could be something more complex, but you need to take, you know, or like, you know, in the past it was like a keyword inverted index. Like you would process all these documents you gather into some processed index, and then you have to serve that. Uh, you had high throughput at low latency. And so that, and that's like the vector database. And so it's like the crawling system, the AI processing system, and then the serving system. Those are all like, you know, teams of like hundreds, maybe thousands of people at Google. Um, but for us, it's like one or two people each typically, but yeah.Alessio [00:09:13]: Can you explain the meaning of, uh, Exo, just the story 10 to the 16th, uh, 18, 18.Will [00:09:20]: Yeah, yeah, yeah, sure. So. Exo means 10 to the 18th, which is in stark contrast to. To Google, which is 10 to the hundredth. Uh, we actually have these like awesome shirts that are like 10th to 18th is greater than 10th to the hundredth. Yeah, it's great. And it's great because it's provocative. It's like every engineer in Silicon Valley is like, what? No, it's not true. Um, like, yeah. And, uh, and then you, you ask them, okay, what does it actually mean? And like the creative ones will, will recognize it. But yeah, I mean, 10 to the 18th is better than 10 to the hundredth when it comes to search, because with search, you want like the actual list of, of things that match what you're asking for. You don't want like the whole web. You want to basically with search filter, the, like everything that humanity has ever created to exactly what you want. And so the idea is like smaller is better there. You want like the best 10th to the 18th and not the 10th to the hundredth. I'm like, one way to say this is like, you know how Google often says at the top, uh, like, you know, 30 million results found. And it's like crazy. Cause you're looking for like the first startups in San Francisco that work on hardware or something. And like, they're not 30 million results like that. What you want is like 325 results found. And those are all the results. That's what you really want with search. And that's, that's our vision. It's like, it just gives you. Perfectly what you asked for.Swyx [00:10:24]: We're recording this ahead of your launch. Uh, we haven't released, we haven't figured out the, the, the name of the launch yet, but what is the product that you're launching? I guess now that we're coinciding this podcast with. Yeah.Will [00:10:36]: So we've basically developed the next version of Exa, which is the ability to get a near perfect list of results of whatever you want. And what that means is you can make a complex query now to Exa, for example, startups working on hardware in SF, and then just get a huge list of all the things that match. And, you know, our goal is if there are 325 startups that match that we find you all of them. And this is just like, there's just like a new experience that's never existed before. It's really like, I don't know how you would go about that right now with current tools and you can apply this same type of like technology to anything. Like, let's say you want, uh, you want to find all the blog posts that talk about Alessio's podcast, um, that have come out in the past year. That is 30 million results. Yeah. Right.Will [00:11:24]: But that, I mean, that would, I'm sure that would be extremely useful to you guys. And like, I don't really know how you would get that full comprehensive list.Swyx [00:11:29]: I just like, how do you, well, there's so many questions with regards to how do you know it's complete, right? Cause you're saying there's only 30 million, 325, whatever. And then how do you do the semantic understanding that it might take, right? So working in hardware, like I might not use the words hardware. I might use the words robotics. I might use the words wearables. I might use like whatever. Yes. So yeah, just tell us more. Yeah. Yeah. Sure. Sure.Will [00:11:53]: So one aspect of this, it's a little subjective. So like certainly providing, you know, at some point we'll provide parameters to the user to like, you know, some sort of threshold to like, uh, gauge like, okay, like this is a cutoff. Like, this is actually not what I mean, because sometimes it's subjective and there needs to be a feedback loop. Like, oh, like it might give you like a few examples and you say, yeah, exactly. And so like, you're, you're kind of like creating a classifier on the fly, but like, that's ultimately how you solve the problem. So the subject, there's a subjectivity problem and then there's a comprehensiveness problem. Those are two different problems. So. Yeah. So you have the comprehensiveness problem. What you basically have to do is you have to put more compute into the query, into the search until you get the full comprehensiveness. Yeah. And I think there's an interesting point here, which is that not all queries are made equal. Some queries just like this blog post one might require scanning, like scavenging, like throughout the whole web in a way that just, just simply requires more compute. You know, at some point there's some amount of compute where you will just be comprehensive. You could imagine, for example, running GPT-4 over the internet. You could imagine running GPT-4 over the entire web and saying like, is this a blog post about Alessio's podcast, like, is this a blog post about Alessio's podcast? And then that would work, right? It would take, you know, a year, maybe cost like a million dollars, but, or many more, but, um, it would work. Uh, the point is that like, given sufficient compute, you can solve the query. And so it's really a question of like, how comprehensive do you want it given your compute budget? I think it's very similar to O1, by the way. And one way of thinking about what we built is like O1 for search, uh, because O1 is all about like, you know, some, some, some questions require more compute than others, and we'll put as much compute into the question as we need to solve it. So similarly with our search, we will put as much compute into the query in order to get comprehensiveness. Yeah.Swyx [00:13:33]: Does that mean you have like some kind of compute budget that I can specify? Yes. Yes. Okay. And like, what are the upper and lower bounds?Will [00:13:42]: Yeah, there's something we're still figuring out. I think like, like everyone is a new paradigm of like variable compute products. Yeah. How do you specify the amount of compute? Like what happens when you. Run out? Do you just like, ah, do you, can you like keep going with it? Like, do you just put in more credits to get more, um, for some, like this can get complex at like the really large compute queries. And like, one thing we do is we give you a preview of what you're going to get, and then you could then spin up like a much larger job, uh, to get like way more results. But yes, there is some compute limit, um, at, at least right now. Yeah. People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned, uh, to have search that takes 500 milliseconds. But like search engines like Google, right. No matter how complex your query to Google, it will take like, you know, roughly 400 milliseconds. But what if searches can take like a minute or 10 minutes or a whole day, what can you then do? And you can do very powerful things. Um, you know, you can imagine, you know, writing a search, going and get a cup of coffee, coming back and you have a perfect list. Like that's okay for a lot of use cases. Yeah.Alessio [00:14:43]: Yeah. I mean, the use case closest to me is venture capital, right? So, uh, no, I mean, eight years ago, I built one of the first like data driven sourcing platforms. So we were. You look at GitHub, Twitter, Product Hunt, all these things, look at interesting things, evaluate them. If you think about some jobs that people have, it's like literally just make a list. If you're like an analyst at a venture firm, your job is to make a list of interesting companies. And then you reach out to them. How do you think about being infrastructure versus like a product you could say, Hey, this is like a product to find companies. This is a product to find things versus like offering more as a blank canvas that people can build on top of. Oh, right. Right.Will [00:15:20]: Uh, we are. We are a search infrastructure company. So we want people to build, uh, on top of us, uh, build amazing products on top of us. But with this one, we try to build something that makes it really easy for users to just log in, put a few, you know, put some credits in and just get like amazing results right away and not have to wait to build some API integration. So we're kind of doing both. Uh, we, we want, we want people to integrate this into all their applications at the same time. We want to just make it really easy to use very similar again to open AI. Like they'll have, they have an API, but they also have. Like a ChatGPT interface so that you could, it's really easy to use, but you could also build it in your applications. Yeah.Alessio [00:15:56]: I'm still trying to wrap my head around a lot of the implications. So, so many businesses run on like information arbitrage, you know, like I know this thing that you don't, especially in investment and financial services. So yeah, now all of a sudden you have these tools for like, oh, actually everybody can get the same information at the same time, the same quality level as an API call. You know, it just kind of changes a lot of things. Yeah.Will [00:16:19]: I think, I think what we're grappling with here. What, what you're just thinking about is like, what is the world like if knowledge is kind of solved, if like any knowledge request you want is just like right there on your computer, it's kind of different from when intelligence is solved. There's like a good, I've written before about like a different super intelligence, super knowledge. Yeah. Like I think that the, the distinction between intelligence and knowledge is actually a pretty good one. They're definitely connected and related in all sorts of ways, but there is a distinction. You could have a world and we are going to have this world where you have like GP five level systems and beyond that could like answer any complex request. Um, unless it requires some. Like, if you say like, uh, you know, give me a list of all the PhDs in New York city who, I don't know, have thought about search before. And even though this, this super intelligence is going to be like, I can't find it on Google, right. Which is kind of crazy. Like we're literally going to have like super intelligences that are using Google. And so if Google can't find them information, there's nothing they could do. They can't find it. So, but if you also have a super knowledge system where it's like, you know, I'm calling this term super knowledge where you just get whatever knowledge you want, then you can pair with a super intelligence system. And then the super intelligence can, we'll never. Be blocked by lack of knowledge.Alessio [00:17:23]: Yeah. You told me this, uh, when we had lunch, I forget how it came out, but we were talking about AGI and whatnot. And you were like, even AGI is going to need search. Yeah.Swyx [00:17:32]: Yeah. Right. Yeah. Um, so we're actually referencing a blog post that you wrote super intelligence and super knowledge. Uh, so I would refer people to that. And this is actually a discussion we've had on the podcast a couple of times. Um, there's so much of model weights that are just memorizing facts. Some of the, some of those might be outdated. Some of them are incomplete or not. Yeah. So like you just need search. So I do wonder, like, is there a maximum language model size that will be the intelligence layer and then the rest is just search, right? Like maybe we should just always use search. And then that sort of workhorse model is just like, and it like, like, like one B or three B parameter model that just drives everything. Yes.Will [00:18:13]: I believe this is a much more optimal system to have a smaller LM. That's really just like an intelligence module. And it makes a call to a search. Tool that's way more efficient because if, okay, I mean the, the opposite of that would be like the LM is so big that can memorize the whole web. That would be like way, but you know, it's not practical at all. I don't, it's not possible to train that at least right now. And Carpathy has actually written about this, how like he could, he could see models moving more and more towards like intelligence modules using various tools. Yeah.Swyx [00:18:39]: So for listeners, that's the, that was him on the no priors podcast. And for us, we talked about this and the, on the Shin Yu and Harrison chase podcasts. I'm doing search in my head. I told you 30 million results. I forgot about our neural link integration. Self-hosted exit.Will [00:18:54]: Yeah. Yeah. No, I do see that that is a much more, much more efficient world. Yeah. I mean, you could also have GB four level systems calling search, but it's just because of the cost of inference. It's just better to have a very efficient search tool and a very efficient LM and they're built for different things. Yeah.Swyx [00:19:09]: I'm just kind of curious. Like it is still something so audacious that I don't want to elide, which is you're, you're, you're building a search engine. Where do you start? How do you, like, are there any reference papers or implementation? That would really influence your thinking, anything like that? Because I don't even know where to start apart from just crawl a bunch of s**t, but there's gotta be more insight than that.Will [00:19:28]: I mean, yeah, there's more insight, but I'm always surprised by like, if you have a group of people who are really focused on solving a problem, um, with the tools today, like there's some in, in software, like there are all sorts of creative solutions that just haven't been thought of before, particularly in the information retrieval field. Yeah. I think a lot of the techniques are just very old, frankly. Like I know how Google and Bing work and. They're just not using new methods. There are all sorts of reasons for that. Like one, like Google has to be comprehensive over the web. So they're, and they have to return in 400 milliseconds. And those two things combined means they are kind of limit and it can't cost too much. They're kind of limited in, uh, what kinds of algorithms they could even deploy at scale. So they end up using like a limited keyword based algorithm. Also like Google was built in a time where like in, you know, in 1998, where we didn't have LMS, we didn't have embeddings. And so they never thought to build those things. And so now they have this like gigantic system that is built on old technology. Yeah. And so a lot of the information retrieval field we found just like thinks in terms of that framework. Yeah. Whereas we came in as like newcomers just thinking like, okay, there here's GB three. It's magical. Obviously we're going to build search that is using that technology. And we never even thought about using keywords really ever. Uh, like we were neural all the way we're building an end to end neural search engine. And just that whole framing just makes us ask different questions, like pursue different lines of work. And there's just a lot of low hanging fruit because no one else is thinking about it. We're just on the frontier of neural search. We just are, um, for, for at web scale, um, because there's just not a lot of people thinking that way about it.Swyx [00:20:57]: Yeah. Maybe let's spell this out since, uh, we're already on this topic, elephants in the room are Perplexity and SearchGPT. That's the, I think that it's all, it's no longer called SearchGPT. I think they call it ChatGPT Search. How would you contrast your approaches to them based on what we know of how they work and yeah, just any, anything in that, in that area? Yeah.Will [00:21:15]: So these systems, there are a few of them now, uh, they basically rely on like traditional search engines like Google or Bing, and then they combine them with like LLMs at the end to, you know, output some power graphics, uh, answering your question. So they like search GPT perplexity. I think they have their own crawlers. No. So there's this important distinction between like having your own search system and like having your own cache of the web. Like for example, so you could create, you could crawl a bunch of the web. Imagine you crawl a hundred billion URLs, and then you create a key value store of like mapping from URL to the document that is technically called an index, but it's not a search algorithm. So then to actually like, when you make a query to search GPT, for example, what is it actually doing it? Let's say it's, it's, it could, it's using the Bing API, uh, getting a list of results and then it could go, it has this cache of like all the contents of those results and then could like bring in the cache, like the index cache, but it's not actually like, it's not like they've built a search engine from scratch over, you know, hundreds of billions of pages. It's like, does that distinction clear? It's like, yeah, you could have like a mapping from URL to documents, but then rely on traditional search engines to actually get the list of results because it's a very hard problem to take. It's not hard. It's not hard to use DynamoDB and, and, and map URLs to documents. It's a very hard problem to take a hundred billion or more documents and given a query, like instantly get the list of results that match. That's a much harder problem that very few entities on, in, on the planet have done. Like there's Google, there's Bing, uh, you know, there's Yandex, but you know, there are not that many companies that are, that are crazy enough to actually build their search engine from scratch when you could just use traditional search APIs.Alessio [00:22:43]: So Google had PageRank as like the big thing. Is there a LLM equivalent or like any. Stuff that you're working on that you want to highlight?Will [00:22:51]: The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share. And so if everyone is sharing some Paul Graham essay about fundraising, then like our model is more likely to predict it. So like inherent in our training objective is this, uh, a sense of like high canonicity and like high quality, but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways. That someone refers that Paul Graham, I say, while also learning how important that Paul Graham essay is. Um, so it's like, it's like PageRank on steroids kind of thing. Yeah.Alessio [00:23:26]: I think to me, that's the most interesting thing about search today, like with Google and whatnot, it's like, it's mostly like domain authority. So like if you get back playing, like if you search any AI term, you get this like SEO slop websites with like a bunch of things in them. So this is interesting, but then how do you think about more timeless maybe content? So if you think about, yeah. You know, maybe the founder mode essay, right. It gets shared by like a lot of people, but then you might have a lot of other essays that are also good, but they just don't really get a lot of traction. Even though maybe the people that share them are high quality. How do you kind of solve that thing when you don't have the people authority, so to speak of who's sharing, whether or not they're worth kind of like bumping up? Yeah.Will [00:24:10]: I mean, you do have a lot of control over the training data, so you could like make sure that the training data contains like high quality sources so that, okay. Like if you, if you're. Training data, I mean, it's very similar to like language, language model training. Like if you train on like a bunch of crap, your prediction will be crap. Our model will match the training distribution is trained on. And so we could like, there are lots of ways to tweak the training data to refer to high quality content that we want. Yeah. I would say also this, like this slop that is returned by, by traditional search engines, like Google and Bing, you have the slop is then, uh, transferred into the, these LLMs in like a search GBT or, you know, our other systems like that. Like if slop comes in, slop will go out. And so, yeah, that's another answer to how we're different is like, we're not like traditional search engines. We want to give like the highest quality results and like have full control over whatever you want. If you don't want slop, you get that. And then if you put an LM on top of that, which our customers do, then you just get higher quality results or high quality output.Alessio [00:25:06]: And I use Excel search very often and it's very good. Especially.Swyx [00:25:09]: Wave uses it too.Alessio [00:25:10]: Yeah. Yeah. Yeah. Yeah. Yeah. Like the slop is everywhere, especially when it comes to AI, when it comes to investment. When it comes to all of these things for like, it's valuable to be at the top. And this problem is only going to get worse because. Yeah, no, it's totally. What else is in the toolkit? So you have search API, you have ExaSearch, kind of like the web version. Now you have the list builder. I think you also have web scraping. Maybe just touch on that. Like, I guess maybe people, they want to search and then they want to scrape. Right. So is that kind of the use case that people have? Yeah.Will [00:25:41]: A lot of our customers, they don't just want, because they're building AI applications on top of Exa, they don't just want a list of URLs. They actually want. Like the full content, like cleans, parsed. Markdown. Markdown, maybe chunked, whatever they want, we'll give it to them. And so that's been like huge for customers. Just like getting the URLs and instantly getting the content for each URL is like, and you can do this for 10 or 100 or 1,000 URLs, wherever you want. That's very powerful.Swyx [00:26:05]: Yeah. I think this is the first thing I asked you for when I tried using Exa.Will [00:26:09]: Funny story is like when I built the first version of Exa, it's like, we just happened to store the content. Yes. Like the first 1,024 tokens. Because I just kind of like kept it because I thought of, you know, I don't know why. Really for debugging purposes. And so then when people started asking for content, it was actually pretty easy to serve it. But then, and then we did that, like Exa took off. So the computer's content was so useful. So that was kind of cool.Swyx [00:26:30]: It is. I would say there are other players like Gina, I think is in this space. Firecrawl is in this space. There's a bunch of scraper companies. And obviously scraper is just one part of your stack, but you might as well offer it since you already do it.Will [00:26:43]: Yeah, it makes sense. It's just easy to have an all-in-one solution. And like. We are, you know, building the best scraper in the world. So scraping is a hard problem and it's easy to get like, you know, a good scraper. It's very hard to get a great scraper and it's super hard to get a perfect scraper. So like, and, and scraping really matters to people. Do you have a perfect scraper? Not yet. Okay.Swyx [00:27:05]: The web is increasingly closing to the bots and the scrapers, Twitter, Reddit, Quora, Stack Overflow. I don't know what else. How are you dealing with that? How are you navigating those things? Like, you know. You know, opening your eyes, like just paying them money.Will [00:27:19]: Yeah, no, I mean, I think it definitely makes it harder for search engines. One response is just that there's so much value in the long tail of sites that are open. Okay. Um, and just like, even just searching over those well gets you most of the value. But I mean, there, there is definitely a lot of content that is increasingly not unavailable. And so you could get through that through data partnerships. The bigger we get as a company, the more, the easier it is to just like, uh, make partnerships. But I, I mean, I do see the world as like the future where the. The data, the, the data producers, the content creators will make partnerships with the entities that find that data.Alessio [00:27:53]: Any other fun use case that maybe people are not thinking about? Yeah.Will [00:27:58]: Oh, I mean, uh, there are so many customers. Yeah. What are people doing on AXA? Well, I think dating is a really interesting, uh, application of search that is completely underserved because there's a lot of profiles on the web and a lot of people who want to find love and that I'll use it. They give me. Like, you know, age boundaries, you know, education level location. Yeah. I mean, you want to, what, what do you want to do with data? You want to find like a partner who matches this education level, who like, you know, maybe has written about these types of topics before. Like if you could get a list of all the people like that, like, I think you will unblock a lot of people. I mean, there, I mean, I think this is a very Silicon Valley view of dating for sure. And I'm, I'm well aware of that, but it's just an interesting application of like, you know, I would love to meet like an intellectual partner, um, who like shares a lot of ideas. Yeah. Like if you could do that through better search and yeah.Swyx [00:28:48]: But what is it with Jeff? Jeff has already set me up with a few people. So like Jeff, I think it's my personal exit.Will [00:28:55]: my mom's actually a matchmaker and has got a lot of married. Yeah. No kidding. Yeah. Yeah. Search is built into the book. It's in your jeans. Yeah. Yeah.Swyx [00:29:02]: Yeah. Other than dating, like I know you're having quite some success in colleges. I would just love to map out some more use cases so that our listeners can just use those examples to think about use cases for XR, right? Because it's such a general technology that it's hard to. Uh, really pin down, like, what should I use it for and what kind of products can I build with it?Will [00:29:20]: Yeah, sure. So, I mean, there are so many applications of XR and we have, you know, many, many companies using us for very diverse range of use cases, but I'll just highlight some interesting ones. Like one customer, a big customer is using us to, um, basically build like a, a writing assistant for students who want to write, uh, research papers. And basically like XR will search for, uh, like a list of research papers related to what the student is writing. And then this product has. Has like an LLM that like summarizes the papers to basically it's like a next word prediction, but in, uh, you know, prompted by like, you know, 20 research papers that X has returned. It's like literally just doing their homework for them. Yeah. Yeah. the key point is like, it's, it's, uh, you know, it's, it's, you know, research is, is a really hard thing to do and you need like high quality content as input.Swyx [00:30:08]: Oh, so we've had illicit on the podcast. I think it's pretty similar. Uh, they, they do focus pretty much on just, just research papers and, and that research. Basically, I think dating, uh, research, like I just wanted to like spell out more things, like just the big verticals.Will [00:30:23]: Yeah, yeah, no, I mean, there, there are so many use cases. So finance we talked about, yeah. I mean, one big vertical is just finding a list of companies, uh, so it's useful for VCs, like you said, who want to find like a list of competitors to a specific company they're investigating or just a list of companies in some field. Like, uh, there was one VC that told me that him and his team, like we're using XR for like eight hours straight. Like, like that. For many days on end, just like, like, uh, doing like lots of different queries of different types, like, oh, like all the companies in AI for law or, uh, all the companies for AI for, uh, construction and just like getting lists of things because you just can't find this information with, with traditional search engines. And then, you know, finding companies is also useful for, for selling. If you want to find, you know, like if we want to find a list of, uh, writing assistants to sell to, then we can just, we just use XR ourselves to find that is actually how we found a lot of our customers. Ooh, you can find your own customers using XR. Oh my God. I, in the spirit of. Uh, using XR to bolster XR, like recruiting is really helpful. It is really great use case of XR, um, because we can just get like a list of, you know, people who thought about search and just get like a long list and then, you know, reach out to those people.Swyx [00:31:29]: When you say thought about, are you, are you thinking LinkedIn, Twitter, or are you thinking just blogs?Will [00:31:33]: Or they've written, I mean, it's pretty general. So in that case, like ideally XR would return like the, the really blogs written by people who have just. So if I don't blog, I don't show up to XR, right? Like I have to blog. well, I mean, you could show up. That's like an incentive for people to blog.Swyx [00:31:47]: Well, if you've written about, uh, search in on Twitter and we, we do, we do index a bunch of tweets and then we, we should be able to service that. Yeah. Um, I mean, this is something I tell people, like you have to make yourself discoverable to the web, uh, you know, it's called learning in public, but like, it's even more imperative now because otherwise you don't exist at all.Will [00:32:07]: Yeah, no, no, this is a huge, uh, thing, which is like search engines completely influence. They have downstream effects. They influence the internet itself. They influence what people. Choose to create. And so Google, because they're a keyword based search engine, people like kind of like keyword stuff. Yeah. They're, they're, they're incentivized to create things that just match a lot of keywords, which is not very high quality. Uh, whereas XR is a search algorithm that, uh, optimizes for like high quality and actually like matching what you mean. And so people are incentivized to create content that is high quality, that like the create content that they know will be found by the right person. So like, you know, if I am a search researcher and I want to be found. By XR, I should blog about search and all the things I'm building because, because now we have a search engine like XR that's powerful enough to find them. And so the search engine will influence like the downstream internet in all sorts of amazing ways. Yeah. Uh, whatever the search engine optimizes for is what the internet looks like. Yeah.Swyx [00:33:01]: Are you familiar with the term? McLuhanism? No, it's not. Uh, it's this concept that, uh, like first we shape tools and then the tools shape us. Okay. Yeah. Uh, so there's like this reflexive connection between the things we search for and the things that get searched. Yes. So like once you change the tool. The tool that searches the, the, the things that get searched also change. Yes.Will [00:33:18]: I mean, there was a clear example of that with 30 years of Google. Yeah, exactly. Google has basically trained us to think of search and Google has Google is search like in people's heads. Right. It's one, uh, hard part about XR is like, uh, ripping people away from that notion of search and expanding their sense of what search could be. Because like when people think search, they think like a few keywords, or at least they used to, they think of a few keywords and that's it. They don't think to make these like really complex paragraph long requests for information and get a perfect list. ChatGPT was an interesting like thing that expanded people's understanding of search because you start using ChatGPT for a few hours and you go back to Google and you like paste in your code and Google just doesn't work and you're like, oh, wait, it, Google doesn't do work that way. So like ChatGPT expanded our understanding of what search can be. And I think XR is, uh, is part of that. We want to expand people's notion, like, Hey, you could actually get whatever you want. Yeah.Alessio [00:34:06]: I search on XR right now, people writing about learning in public. I was like, is it gonna come out with Alessio? Am I, am I there? You're not because. Bro. It's. So, no, it's, it's so about, because it thinks about learning, like in public, like public schools and like focuses more on that. You know, it's like how, when there are like these highly overlapping things, like this is like a good result based on the query, you know, but like, how do I get to Alessio? Right. So if you're like in these subcultures, I don't think this would work in Google well either, you know, but I, I don't know if you have any learnings.Swyx [00:34:40]: No, I'm the first result on Google.Alessio [00:34:42]: People writing about learning. In public, you're not first result anymore, I guess.Swyx [00:34:48]: Just type learning public in Google.Alessio [00:34:49]: Well, yeah, yeah, yeah, yeah. But this is also like, this is in Google, it doesn't work either. That's what I'm saying. It's like how, when you have like a movement.Will [00:34:56]: There's confusion about the, like what you mean, like your intention is a little, uh. Yeah.Alessio [00:35:00]: It's like, yeah, I'm using, I'm using a term that like I didn't invent, but I'm kind of taking over, but like, they're just so much about that term already that it's hard to overcome. If that makes sense, because public schools is like, well, it's, it's hard to overcome.Will [00:35:14]: Public schools, you know, so there's the right solution to this, which is to specify more clearly what you mean. And I'm not expecting you to do that, but so the, the right interface to search is actually an LLM.Swyx [00:35:25]: Like you should be talking to an LLM about what you want and the LLM translates its knowledge of you or knowledge of what people usually mean into a query that excellent uses, which you have called auto prompts, right?Will [00:35:35]: Or, yeah, but it's like a very light version of that. And really it's just basically the right answer is it's the wrong interface and like very soon interface to search and really to everything will be LLM. And the LLM just has a full knowledge of you, right? So we're kind of building for that world. We're skating to where the puck is going to be. And so since we're moving to a world where like LLMs are interfaced to everything, you should build a search engine that can handle complex LLM queries, queries that come from LLMs. Because you're probably too lazy, I'm too lazy too, to write like a whole paragraph explaining, okay, this is what I mean by this word. But an LLM is not lazy. And so like the LLM will spit out like a paragraph or more explaining exactly what it wants. You need a search engine that can handle that. Traditional search engines like Google or Bing, they're actually... Designed for humans typing keywords. If you give a paragraph to Google or Bing, they just completely fail. And so Exa can handle paragraphs and we want to be able to handle it more and more until it's like perfect.Alessio [00:36:24]: What about opinions? Do you have lists? When you think about the list product, do you think about just finding entries? Do you think about ranking entries? I'll give you a dumb example. So on Lindy, I've been building the spot that every week gives me like the top fantasy football waiver pickups. But every website is like different opinions. I'm like, you should pick up. These five players, these five players. When you're making lists, do you want to be kind of like also ranking and like telling people what's best? Or like, are you mostly focused on just surfacing information?Will [00:36:56]: There's a really good distinction between filtering to like things that match your query and then ranking based on like what is like your preferences. And ranking is like filtering is objective. It's like, does this document match what you asked for? Whereas ranking is more subjective. It's like, what is the best? Well, it depends what you mean by best, right? So first, first table stakes is let's get the filtering into a perfect place where you actually like every document matches what you asked for. No surgeon can do that today. And then ranking, you know, there are all sorts of interesting ways to do that where like you've maybe for, you know, have the user like specify more clearly what they mean by best. You could do it. And if the user doesn't specify, you do your best, you do your best based on what people typically mean by best. But ideally, like the user can specify, oh, when I mean best, I actually mean ranked by the, you know, the number of people who visited that site. Let's say is, is one example ranking or, oh, what I mean by best, let's say you're listing companies. What I mean by best is like the ones that have, uh, you know, have the most employees or something like that. Like there are all sorts of ways to rank a list of results that are not captured by something as subjective as best. Yeah. Yeah.Alessio [00:38:00]: I mean, it's like, who are the best NBA players in the history? It's like everybody has their own. Right.Will [00:38:06]: Right. But I mean, the, the, the search engine should definitely like, even if you don't specify it, it should do as good of a job as possible. Yeah. Yeah. No, no, totally. Yeah. Yeah. Yeah. Yeah. It's a new topic to people because we're not used to a search engine that can handle like a very complex ranking system. Like you think to type in best basketball players and not something more specific because you know, that's the only thing Google could handle. But if Google could handle like, oh, basketball players ranked by like number of shots scored on average per game, then you would do that. But you know, they can't do that. So.Swyx [00:38:32]: Yeah. That's fascinating. So you haven't used the word agents, but you're kind of building a search agent. Do you believe that that is agentic in feature? Do you think that term is distracting?Will [00:38:42]: I think it's a good term. I do think everything will eventually become agentic. And so then the term will lose power, but yes, like what we're building is agentic it in a sense that it takes actions. It decides when to go deeper into something, it has a loop, right? It feels different from traditional search, which is like an algorithm, not an agent. Ours is a combination of an algorithm and an agent.Swyx [00:39:05]: I think my reflection from seeing this in the coding space where there's basically sort of classic. Framework for thinking about this stuff is the self-driving levels of autonomy, right? Level one to five, typically the level five ones all failed because there's full autonomy and we're not, we're not there yet. And people like control. People like to be in the loop. So the, the, the level ones was co-pilot first and now it's like cursor and whatever. So I feel like if it's too agentic, it's too magical, like, like a, like a one shot, I stick a, stick a paragraph into the text box and then it spits it back to me. It might feel like I'm too disconnected from the process and I don't trust it. As opposed to something where I'm more intimately involved with the research product. I see. So like, uh, wait, so the earlier versions are, so if trying to stick to the example of the basketball thing, like best basketball player, but instead of best, you, you actually get to customize it with like, whatever the metric is that you, you guys care about. Yeah. I'm still not a basketballer, but, uh, but, but, you know, like, like B people like to be in my, my thesis is that agents level five agents failed because people like to. To kind of have drive assist rather than full self-driving.Will [00:40:15]: I mean, a lot of this has to do with how good agents are. Like at some point, if agents for coding are better than humans at all tests and then humans block, yeah, we're not there yet.Swyx [00:40:25]: So like in a world where we're not there yet, what you're pitching us is like, you're, you're kind of saying you're going all the way there. Like I kind of, I think all one is also very full, full self-driving. You don't get to see the plan. You don't get to affect the plan yet. You just fire off a query and then it goes away for a couple of minutes and comes back. Right. Which is effectively what you're saying you're going to do too. And you think there's.Will [00:40:42]: There's a, there's an in-between. I saw. Okay. So in building this product, we're exploring new interfaces because what does it mean to kick off a search that goes and takes 10 minutes? Like, is that a good interface? Because what if the search is actually wrong or it's not exactly, exactly specified to what you mean, which is why you get previews. Yeah. You get previews. So it is iterative, but ultimately once you've specified exactly what you mean, then you kind of do just want to kick off a batch job. Right. So perhaps what you're getting at is like, uh, there's this barrier with agents where you have to like explain the full context of what you mean, and a lot of failure modes happen when you have, when you don't. Yeah. There's failure modes from the agent, just not being smart enough. And then there's failure modes from the agent, not understanding exactly what you mean. And there's a lot of context that is shared between humans that is like lost between like humans and, and this like new creature.Alessio [00:41:32]: Yeah. Yeah. Because people don't know what's going on. I mean, to me, the best example of like system prompts is like, why are you writing? You're a helpful assistant. Like. Of course you should be an awful, but people don't yet know, like, can I assume that, you know, that, you know, it's like, why did the, and now people write, oh, you're a very smart software engineer, but like, you never made, you never make mistakes. Like, were you going to try and make mistakes before? So I think people don't yet have an understanding, like with, with driving people know what good driving is. It's like, don't crash, stay within kind of like a certain speed range. It's like, follow the directions. It's like, I don't really have to explain all of those things. I hope. But with. AI and like models and like search, people are like, okay, what do you actually know? What are like your assumptions about how search, how you're going to do search? And like, can I trust it? You know, can I influence it? So I think that's kind of the, the middle ground, like before you go ahead and like do all the search, it's like, can I see how you're doing it? And then maybe help show your work kind of like, yeah, steer you. Yeah. Yeah.Will [00:42:32]: No, I mean, yeah. Sure. Saying, even if you've crafted a great system prompt, you want to be part of the process itself. Uh, because the system prompt doesn't, it doesn't capture everything. Right. So yeah. A system prompt is like, you get to choose the person you work with. It's like, oh, like I want, I want a software engineer who thinks this way about code. But then even once you've chosen that person, you can't just give them a high level command and they go do it perfectly. You have to be part of that process. So yeah, I agree.Swyx [00:42:58]: Just a side note for my system, my favorite system, prompt programming anecdote now is the Apple intelligence system prompt that someone, someone's a prompt injected it and seen it. And like the Apple. Intelligence has the words, like, please don't, don't hallucinate. And it's like, of course we don't want you to hallucinate. Right. Like, so it's exactly that, that what you're talking about, like we should train this behavior into the model, but somehow we still feel the need to inject into the prompt. And I still don't even think that we are very scientific about it. Like it, I think it's almost like cargo culting. Like we have this like magical, like turn around three times, throw salt over your shoulder before you do something. And like, it worked the last time. So let's just do it the same time now. And like, we do, there's no science to this.Will [00:43:35]: I do think a lot of these problems might be ironed out in future versions. Right. So, and like, they might, they might hide the details from you. So it's like, they actually, all of them have a system prompt. That's like, you are a helpful assistant. You don't actually have to include it, even though it might actually be the way they've implemented in the backend. It should be done in RLE AF.Swyx [00:43:52]: Okay. Uh, one question I was just kind of curious about this episode is I'm going to try to frame this in terms of this, the general AI search wars, you know, you're, you're one player in that, um, there's perplexity, chat, GPT, search, and Google, but there's also like the B2B side, uh, we had. Drew Houston from Dropbox on, and he's competing with Glean, who've, uh, we've also had DD from, from Glean on, is there an appetite for Exa for my company's documents?Will [00:44:19]: There is appetite, but I think we have to be disciplined, focused, disciplined. I mean, we're already taking on like perfect web search, which is a lot. Um, but I mean, ultimately we want to build a perfect search engine, which definitely for a lot of queries involves your, your personal information, your company's information. And so, yeah, I mean, the grandest vision of Exa is perfect search really over everything, every domain, you know, we're going to have an Exa satellite, uh, because, because satellites can gather information that, uh, is not available publicly. Uh, gotcha. Yeah.Alessio [00:44:51]: Can we talk about AGI? We never, we never talk about AGI, but you had, uh, this whole tweet about, oh, one being the biggest kind of like AI step function towards it. Why does it feel so important to you? I know there's kind of like always criticism and saying, Hey, it's not the smartest son is better. It's like, blah, blah, blah. What? You choose C. So you say, this is what Ilias see or Sam see what they will see.Will [00:45:13]: I've just, I've just, you know, been connecting the dots. I mean, this was the key thing that a bunch of labs were working on, which is like, can you create a reward signal? Can you teach yourself based on a reward signal? Whether you're, if you're trying to learn coding or math, if you could have one model say, uh, be a grading system that says like you have successfully solved this programming assessment and then one model, like be the generative system. That's like, here are a bunch of programming assessments. You could train on that. It's basically whenever you could create a reward signal for some task, you could just generate a bunch of tasks for yourself. See that like, oh, on two of these thousand, you did well. And then you just train on that data. It's basically like, I mean, creating your own data for yourself and like, you know, all the labs working on that opening, I built the most impressive product doing that. And it's just very, it's very easy now to see how that could like scale to just solving, like, like solving programming or solving mathematics, which sounds crazy, but everything about our world right now is crazy.Alessio [00:46:07]: Um, and so I think if you remove that whole, like, oh, that's impossible, and you just think really clearly about like, what's now possible with like what, what they've done with O1, it's easy to see how that scales. How do you think about older GPT models then? Should people still work on them? You know, if like, obviously they just had the new Haiku, like, is it even worth spending time, like making these models better versus just, you know, Sam talked about O2 at that day. So obviously they're, they're spending a lot of time in it, but then you have maybe. The GPU poor, which are still working on making Lama good. Uh, and then you have the follower labs that do not have an O1 like model out yet. Yeah.Will [00:46:47]: This kind of gets into like, uh, what will the ecosystem of, of models be like in the future? And is there room is, is everything just gonna be O1 like models? I think, well, I mean, there's definitely a question of like inference speed and if certain things like O1 takes a long time, because that's the thing. Well, I mean, O1 is, is two things. It's like one it's it's use it's bootstrapping itself. It's teaching itself. And so the base model is smarter. But then it also has this like inference time compute where it could like spend like many minutes or many hours thinking. And so even the base model, which is also fast, it doesn't have to take minutes. It could take is, is better, smarter. I believe all models will be trained with this paradigm. Like you'll want to train on the best data, but there will be many different size models from different, very many different like companies, I believe. Yeah. Because like, I don't, yeah, I mean, it's hard, hard to predict, but I don't think opening eye is going to dominate like every possible LLM for every possible. Use case. I think for a lot of things, like you just want the fastest model and that might not involve O1 methods at all.Swyx [00:47:42]: I would say if you were to take the exit being O1 for search, literally, you really need to prioritize search trajectories, like almost maybe paying a bunch of grad students to go research things. And then you kind of track what they search and what the sequence of searching is, because it seems like that is the gold mine here, like the chain of thought or the thinking trajectory. Yeah.Will [00:48:05]: When it comes to search, I've always been skeptical. I've always been skeptical of human labeled data. Okay. Yeah, please. We tried something at our company at Exa recently where me and a bunch of engineers on the team like labeled a bunch of queries and it was really hard. Like, you know, you have all these niche queries and you're looking at a bunch of results and you're trying to identify which is matched to query. It's talking about, you know, the intricacies of like some biological experiment or something. I have no idea. Like, I don't know what matches and what, what labelers like me tend to do is just match by keyword. I'm like, oh, I don't know. Oh, like this document matches a bunch of keywords, so it must be good. But then you're actually completely missing the meaning of the document. Whereas an LLM like GB4 is really good at labeling. And so I actually think like you just we get by, which we are right now doing using like LLM
Plus - Online spending grew 3% to a record $1.2T over holiday period; Samsung says its home robot will roll out this year Learn more about your ad choices. Visit podcastchoices.com/adchoices
On this episode of the podcast, Mark Meckler, President of Convention of States Action, discussed his organization's efforts to propose new amendments to the U.S. Constitution through Article V. He highlighted that 19 states have passes resolutions, with 29 states showing support. The proposed amendments include term limits, fiscal restraints, and jurisdictional limits on federal powers.Meckler emphasized the need for state-level action, as Congress may delay or obstruct the process. He also touched on the potential impact of Elon Musk's $2T cost-cutting plan and the importance of making changes permanent through constitutional amendments.Furthermore, Meckler shared his insights from a blockchain conference held in Argentina, where they focused on digital sovereignty and the governance of Cardano blockchain.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Argentina dismisses Tax Chief - Be Like Milei :: Milei dismisses tax authority over tax on streamers :: Musk comes out swinging against spending package, Republicans on board, sort of :: Yellen sounds panicky about Trump interfering with Banking Supervision (lol) :: Should there be competition in money? We say yes. :: Former libertarian says DOGE goal of cutting gvmt spending by $2T is a pipedream :: Sara talks Whole Foods and Amazon :: 2024-12-18 :: Hosts: Chris R., Stu
In this episode of the Brain & Life podcast, Dr. Daniel Correa is joined by Top Chef contestant and restaurant owner Dan Jacobs. Dan shares his Kennedy disease diagnosis and how despite his symptoms, he found his way to the Top Chef kitchen. He also discusses what led him to pursue a career in cooking and how he is planning for his future. Dr. Correa is then joined by Dr. Chris Grunseich, a neurologist and researcher with a focus on neurodegenerative disorders, working at the NIH in the National Institute of Neurological Disorders and Stroke. Dr. Grunseich explains Kennedy disease and other genetic disorders and how researchers are working to find exactly what patients need to thrive. Dan Jacobs, Dr. Correa, and Dr. Peters also shared some of their favorite holiday recipes in this episode! See the end of the show notes to learn more. We invite you to participate in our listener survey! By participating in the brief survey, you will have the opportunity to enter your name and email address for a chance to win one of five $100 Amazon gift cards. Additional Resources National Organization for Rare Diseases 8 Questions to Ask Before Undergoing Genetic Testing Genetic Testing and Disease Resources Other Brain & Life Episodes on Similar Topics Aaron Lazar on His ALS Journey and the Impossible Dream Author Tanita Allen on Existing with Huntington's Disease Master Chef Christine Ha on Adapting to Life with NMO We want to hear from you! Have a question or want to hear a topic featured on the Brain & Life Podcast? · Record a voicemail at 612-928-6206 · Email us at BLpodcast@brainandlife.org Social Media: Guests: Dan Jacobs @chefdanjacobs; Dr. Chris Grunseich @nihgov Hosts: Dr. Daniel Correa @neurodrcorrea; Dr. Katy Peters @KatyPetersMDPhD Recipes Dr. Peters' Favorite Turkey Risotto Dr. Correa's Favorite Mojo Verde Dan Jacobs' Smashed Cucumber Salad Ingredients • 6-8 persian cucmbers or 2 large seedless cucumbers • 1/4C mung bean sprouts • 1/4C sliced daikon radish • 1/4C scallions sliced as thin and long as you can • 1 bunch cilantro roughly picked (you can leave some stem on. The stems are full of flavor. • 1/4C dill • 1/4C roasted and salted peanuts chopped up in a food processor • 2T chili crunch (by whatever producer. Fly by Jing's is a nice one.) • 4T black vinegar • 1t salt • 2t sugar • 1/4C sesame oil Process 1. Put cucumbers in a gallon ziplock bag. Do not seal them. If using Persian cucumbers, you will need to chop them in. Thirds and then halve lengthwise. 2. Using a rolling pin or mallet, beat the cucumbers up a bit. The irregular crags and breaks will hold the vinaigrette perfectly. 3. Mix the chili crunch, black vinegar, sesame oil, salt, and sugar. 4. Mix all ingredients.
Mike Armstrong and Marc Fandetti discuss Trump's planned tariffs on Mexico, Canada, and China that could cripple trade. Trump fires salvo on North American trade pact. New home sales plunge 17% in October. Consumer confidence jumps to 16-month high as Americans see economy improving. Bessent will have to fix America's finances. Musk wants $2T of spending cuts.
"Jack Riccardi tracked the years leading up to Trump's victory, from wokeism to Kaepernick, from COVID to George Floyd, from Mika/Joe to the return to men and freedom to express your politics, plus can Vivek and Elon cut $2T and a university starts a "fatness" major.
Russia and Ukraine exchange their largest drone attacks of the war, Israel reports progress toward a Lebanon cease-fire, Trump names Thomas Homan as 'border czar,' The president-elect's allies advocate for Rick Scott to lead the Senate, Shigeru Ishiba is re-elected as prime minister of Japan, Haiti's transitional council replaces the prime minister, Keir Starmer attends Armistice Day with Emmanuel Macron, Māori groups march to New Zealand's capital against a controversial bill, Amsterdam police detain dozens of individuals for defying a protest ban, Wildfires burn on both US coasts, and extreme weather events are found to have cost the world $2T over the past decade. Sources: https://www.verity.news/
So Kamala's closing argument is effectively more weed and more young men watching videos of people having sex. On the other hand, Elon Musk, who's going to be working with President Trump, God willing, is promoting a $2 trillion spending cut. Would God really choose RFK Jr. to dismantle the so-called public health industry? Another attempt to imprison Trump, this time for something he legitimately did not say.Episode Links:Cardi B giving her speech at the Kamala rally tonight.Kamala's final pitch to young men: weed, crypto, porn, video games, and vasectomies, oh my…"I think we can rip out at least 2T out of the wasted 6.5T Harris/Biden budget. All government spending is taxation. Your money is being wasted and the DEPARTMENT OF GOVERNMENT EFFICIENCY is going to FIX that. We're going to get the Government off of your back, and out of your pocketbook.RFK Jr. with a powerful statement: “I asked God for 19 years to put me in a position where I could end the chronic disease epidemic and bring health back to our children, and in August, God sent me Donald Trump.”BREAKING: Arizona Attorney General Kris Mayes (D) is now INVESTIGATING Trump over his comments about Liz Cheney. She says he can be charged with a misdemeanor and/or a felony. This is all based on the Left purposely misrepresenting Trump's words. Unreal.Trump is talking abo ut Liz Cheney's stance on war. He is discussing how elites will send our kids to war but never do themselvesAlan's Soaps https://www.alansartisansoaps.comUse coupon code ‘TODD' to save an additional 10% off the bundle price.Bioptimizers https://bioptimizers.com/toddUse code TODD to unlock up to $100 in free gifts and save an additional 10% off the special 3-product bundle for a 25% savings.Bonefrog https://bonefrogcoffee.com/toddMake Bonefrog Cold Brew at home! Use code TODD at checkout to receive 10% off your first purchase and 15% on subscriptions.Bulwark Capital Bulwark Capital Management (bulwarkcapitalmgmt.com)Don't miss the next live Webinar November 21st 3:30pm pacific. Sign up today by calling 866-779-RISK or go to KnowYourRiskRadio.com.Renue Healthcare https://renue.healthcare/toddYour journey to a better life starts at Renue Healthcare. Visit renue.healthcare/Todd
En 2024, la búsqueda de metales preciosos ha alcanzado nuevas alturas, con el oro cerca de los $2.7k por onza y la plata superando al oro en rendimiento anual. El aumento en el valor de ambos metales, impulsado por factores como la incertidumbre geopolítica, la demanda de refugios seguros y las compras de bancos centrales, ha superado el crecimiento del índice S&P 500. La plata, que también tiene aplicaciones industriales, ha alcanzado su nivel más alto desde 2012, mientras que el oro se consolida como el segundo mayor activo de reserva global, respaldado por la creciente influencia de los países BRIC. Intel($INTC) está lista para finalizar un acuerdo con el gobierno de EE.UU. por $8.5 Billones en fondos directos para fines de este año bajo la Ley CHIPS. Si bien las conversaciones se encuentran en una etapa avanzada, cualquier adquisición de la empresa o de una parte de ella podría sabotear las discusiones. También ha habido mucha especulación sobre la adquisición de Intel, y el último informe de los medios de comunicación indica que el fabricante de chips rechazó un enfoque de Arm Holdings ($ARM) para su división de productos. Mientras tanto, se dice que Qualcomm (QCOM) está interesada en fusiones y adquisiciones, mientras que Apollo ofreció invertir hasta $5B de dólares en la empresa. El recorte de 50 puntos básicos de los tipos de interés de la Reserva Federal ha demostrado la confianza de los responsables políticos en que la inflación se dirige de forma sostenible hacia su objetivo del 2%. Esta mañana, obtendrán nuevos datos en el informe de ingresos y desembolsos personales de agosto sobre cualquier progreso adicional. "Todo se ve bien hasta ahora", dijo Christopher Clarke, profesor asistente de economía en la Universidad Estatal de Washington, a Seeking Alpha. "No veo ninguna razón por el lado de la inflación para más noticias negativas". La revisión del PIB del 2T del miércoles también fue alentadora, ya que las cifras del PCE del 2T no mostraron revisiones. La Administración de Alimentos y Medicamentos de Estados Unidos (FDA, por sus siglas en inglés) ha aprobado el novedoso tratamiento de Bristol-Myers Squibb ($BMY) para adultos con el trastorno psiquiátrico esquizofrenia. Las acciones de BMY subieron más de un 6% tras las noticias de las primeras operaciones del viernes. Cobenfy, anteriormente conocida como KarXT, se incorporó a la cartera del gigante farmacéutico tras la adquisición de Karuna Therapeutics por $14 Billones de dólares en marzo. "Este medicamento adopta el primer enfoque nuevo para el tratamiento de la esquizofrenia en décadas", declaró Tiffany Farchione, directora de la División de Psiquiatría de la Oficina de Neurociencia del Centro de Evaluación e Investigación de Medicamentos de la FDA. Citigroup ($C) y Apollo se unen para un programa de crédito privado de $25 Billones de dólares. El petróleo cae bruscamente tras los informes de que Arabia Saudita se prepara para aumentar la producción. El estímulo de China impulsa a las acciones de primera línea en su mejor semana desde 2008. Las acciones de Super Micro ($SMCI) caen ante la noticia de la investigación del Departamento de Justicia. El principal accionista de Trump Media ($DJT) se deshace de casi toda su participación.
Cops and MRIs don't Mix :: Judge approved plan to sell Alex Jones's Business :: Bitcoin Tech Bros want to create to countries that replace the existing :: Republican majority Congress passes $1.2T spending stopgap because they're "fiscally conservative" :: Crazy researchers want age restrictions on non-alcoholic beverages :: 2024-09-25 Hosts: Chris R., Riley Support Riley on Patreon: https://www.patreon.com/crblake86 Send Bitcoin: 1MnoYoPirXQHfhknDxbDHhLsF9u7kUggKy Send Bitcoin Cash: qpp62s8uupdqkrfew7vgp805pnsh5jk2ncnfkndwrd Dash: XpApo1jcPzTJyLLB6G8GJ7DoW9CGjcV5xT Ether: 0xFb1a23163bea743BB79B93849D864ad070597855 Lightcoin ltc1q6ygsamrkwl0at93datyqfh47z4crg4jkg4fx30
Cops and MRIs don't Mix :: Judge approved plan to sell Alex Jones's Business :: Bitcoin Tech Bros want to create to countries that replace the existing :: Republican majority Congress passes $1.2T spending stopgap because they're "fiscally conservative" :: Crazy researchers want age restrictions on non-alcoholic beverages :: 2024-09-25 Hosts: Chris R., Riley
Coming off a five-point (1G, 2T) display against the Waterdogs, Graham Bundy joins the show at the 53-minute mark to discuss a range of topics including using his backup stick since his last breakout performance against the Redwoods, the jump from the college game to the PLL ranks, the Outlaws team dynamic in the locker room, playing alongside Brennan O'Neill, Sam Handley and more. Also, Kevin and Rosie discuss Tom Schreiber's case for the best pro field lacrosse player of all time.
Amazon joins the likes of Microsoft, Apple, Nvidia and Alphabet above $2T. Who is least likely to stay there? (00:21) Jason Moser and Bill Mann discuss: - Tips for playing the long game with the 2024 election cycle ramping up - Amazon joining the $2T club, and which member is most likely to experience a big fall. - Disappointing earnings for Walgreen's and Nike, while McCormick keeps business zesty. (19:11) Author Nicola Twilley talks about her new book Frostbite, the development of modern refrigeration, and what its evolution can teach us about the development of other technologies today. (31:22) Jason and Bill break down two stocks on their radar: Disney and Itron. Stocks discussed: AMZN, RMD, WBA, NKE, NVDA, DIS, ITRI Host: Dylan Lewis Guests: Jason Moser, Bill Mann, Nicola Twilley, Ricky Mulvey Engineers: Tim Sparks, Dan Boyd Learn more about your ad choices. Visit megaphone.fm/adchoices
The robotaxi market is getting competitive even as many models and companies have consistent issues. With the makers of Bugatti throwing their hat in the ring, can Waymo, Cruise, Tesla, or Amazon come out on top? Plus: Uber offers $1k to ditch your car and Amazon hits a $2T valuation. Join our hosts Jon Weigell and Ben Berkley, as they take you through our most interesting stories of the day. Follow us on social media: TikTok: https://www.tiktok.com/@thehustle.co Instagram: https://www.instagram.com/thehustledaily/ Thank You For Listening to The Hustle Daily Show. Don't forget to hit Subscribe or Follow us on Apple Podcasts so you never miss an episode! If you want this news delivered to your inbox, join millions of others and sign up for The Hustle Daily newsletter, here: https://thehustle.co/email/ Plus! Your engagement matters to us. If you are a fan of the show, be sure to leave us a 5-Star Review on Apple Podcasts https://podcasts.apple.com/us/podcast/the-hustle-daily-show/id1606449047 (and share your favorite episodes with your friends, clients, and colleagues).
Today's blockchain and cryptocurrency news Bitcoin is down slightly at $67,689 Eth is down slightly at $3,331 Binance Coin, is up slightly at $581 Genesis buys over 32k BTC The Supreme Court of Montenegro flip flops re:extradition of Do Kwon Terraform Labs and its co-founder, were found liable for civil fraud charges. FTX sells $1.9B worth of SOL tokens at discount. Uniswap Labs crosses $2T trading volume. Learn more about your ad choices. Visit megaphone.fm/adchoices
Monday, March 25th, 2024Today, Tammy Murphy has dropped out of the Pennsylvania US Senate race clearing the way for Andy Kim; Republican House Rep Mike Gallagher is leaving early; former RNC chair serial liar and coup plotter Ronna McDaniel has been hired by NBC for $300K per year; the Senate has passed a $1.2T funding bill ending the threat of a shutdown; Texas AG Ken Paxton could see his criminal charges dropped; The Biden Administration's permanent ceasefire UN resolution was vetoed by Russia and China; Allies of Leonard Leo have mounted a monthslong offensive against DC AG Brian Schwalb; plus Allison and Dana deliver your good news.Promo Code:For 20% off all mattress orders AND two free pillows for our listeners! Go to https://www.helxsleep.com/dailybeans and use code HELIXPARTNER20.Our Guest Laura Packard:Twitter: https://twitter.com/lpackardThreads: https://www.threads.net/@laurapackardactivistFacebook: https://www.facebook.com/LauraPackardActivistInstagram: https://www.instagram.com/LauraPackardActivistLinkedin: https://www.linkedin.com/in/lpackardYouTube: https://www.youtube.com/c/LauraPackardTikTok: https://www.tiktok.com/@laurapackardactivisthttps://www.laurapackard.comFollow Adam Klasfeld for updates on the Manhattan DA's hearing this morning at 10 AM ET about the election interference hush money case.https://twitter.com/KlasfeldReportsRep. Mike Gallagher to leave Congress in April, giving GOP an even narrower majorityhttps://www.cnn.com/2024/03/22/politics/mike-gallagher-republican-retiring/index.htmlChuck Todd Questions His Network, NBC News, Over Hiring of Former R.N.C. Chairhttps://www.nytimes.com/2024/03/24/business/chuck-todd-ronna-mcdaniel-nbc-msnbc.htmlWhat did US Gaza ceasefire resolution say and why did Russia and China veto it?https://www.theguardian.com/world/2024/mar/22/us-gaza-ceasefire-resolution-explainerExclusive: Texas AG Ken Paxton could see criminal charges dropped in deal with prosecutorshttps://www.statesman.com/story/news/state/2024/03/22/ken-paxton-securities-fraud-case-felony-charges-texas-ag-plea-negotiation/72960517007What happens when an AG dares to investigate Leonard Leo's networkhttps://www.politico.com/news/2024/03/23/brian-schwalb-leonard-leo-investigation-00148385Subscribe to Lawyers, Guns, And MoneyAd-free premium feed: https://lawyersgunsandmoney.supercast.comSubscribe for free everywhere else:https://lawyersgunsandmoney.simplecast.com/episodes/1-miami-1985Check out other MSW Media podcastshttps://mswmedia.com/shows/Follow AG and Dana on Social MediaDr. Allison Gill Follow Mueller, She Wrote on Posthttps://post.news/@/MuellerSheWrote?utm_source=TwitterAG&utm_medium=creator_organic&utm_campaign=muellershewrote&utm_content=FollowMehttps://twitter.com/MuellerSheWrotehttps://www.threads.net/@muellershewrotehttps://www.tiktok.com/@muellershewrotehttps://instagram.com/muellershewroteDana Goldberghttps://twitter.com/DGComedyhttps://www.instagram.com/dgcomedyhttps://www.facebook.com/dgcomedyhttps://danagoldberg.comHave some good news; a confession; or a correction?Good News & Confessions - The Daily Beanshttps://www.dailybeanspod.com/confessional/From The Good News:Fine Mess Potteryhttps://www.patreon.com/FineMessPotteryUpcoming Live Show Dateshttps://allisongill.com (tickets and show dates)Sunday, June 2nd – Chicago IL – Schubas TavernFriday June 14th – Philadelphia PA – City WinerySaturday June 15th – New York NY – City WinerySunday June 16th – Boston MA – City WineryWednesday July 10th – Portland OR – Polaris Hall (with Dana!)Thursday July 11th – Seattle WA – The Triple Door (with Dana!) Live Show Ticket Links:Chicago, IL https://tinyurl.com/Beans-ChiPhiladelphia, PA https://tinyurl.com/Beans-PhillyNew York, NY https://tinyurl.com/Beans-NYCBoston, MA https://tinyurl.com/Beans-BosPortland, ORhttps://tinyurl.com/Beans-PDXSeattle, WAhttps://tinyurl.com/Beans-SEA Listener Survey:http://survey.podtrac.com/start-survey.aspx?pubid=BffJOlI7qQcF&ver=shortFollow the Podcast on Apple:The Daily Beans on Apple PodcastsWant to support the show and get it ad-free and early?Supercasthttps://dailybeans.supercast.com/OrPatreon https://patreon.com/thedailybeansOr subscribe on Apple Podcasts with our affiliate linkThe Daily Beans on Apple Podcasts
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