Podcasts about Pi

“It would be sad if local models were not an option and there were only proprietary models. It's good to have alternatives. Competition is good for business.”— Sebastian Raschka, on open-weight AIKimi K3's weights landed about an hour before Hugo Bowne-Anderson and Sebastian Raschka went live. Sebastian had already updated his architecture diagram. That speed captures his approach to the current model wave: wait until the weights exist, run the model in the harness where it will actually work, then inspect the architecture closely enough to understand what changed.The conversation arrived during a larger fight over who supplies the models underneath global software. Three days earlier, twenty-five companies including NVIDIA, Meta, Microsoft, Hugging Face, and IBM published Open Weights and American AI Leadership. Their argument closely matches Sebastian's practical case for local models: open weights create competition, reduce dependence on a single provider, and let organizations choose a model at the right capability and cost.Update: Four days after we recorded, DeepSeek released V4 Flash 0731, a re-post-trained API model for agentic coding. Developers are already reporting that it can debug multi-project codebases and stay on task across very long contexts.You can find the full episode on Spotify, Apple Podcasts, and YouTube.

*La sera prima*Ci sono sere in cui il corpo è a letto e la testa è ancora nello spogliatoio. L'azione che non torna, la cosa rimasta a metà, il domani che comincia a bussare con qualche ora di anticipo. Questa puntata parla di quel pezzo di tempo lì — dalla fine della giornata al sonno — e di come si possa arrivarci sempre allo stesso modo, finché non è più una decisione ma una cosa che il corpo riconosce.*1. Chiudere la giornata su carta, dieci minuti, non a letto.* Quattro righe: cosa è successo, cosa è rimasto aperto, cosa ne farai domani. Quello che resta in sospeso continua a bussare finché non gli dai un appuntamento preciso.*2. Dare un contenitore all'azione che pesa.* Rivederla una volta sola, dentro quei dieci minuti. Distinguere l'errore che si ripara da quello che non si ripara. Poi chiudere: provare a non pensarci la tiene accesa, perché per evitarla te la devi ricordare.*3. Il respiro, cinque minuti, a letto.* Espirazione più lunga dell'inspirazione, lenta, regolare. Inspirare accelera, espirare rallenta — non ti convinci di essere calmo, lo diventi. Il ritmo giusto è il tuo, e si tara insieme.*4. Rientrare nel corpo.* Una scansione breve, dai piedi alla testa, notando quello che c'è senza sistemarlo. Di notte la testa lavora sul domani; riportarla su un segnale presente toglie carburante a chi sta cercando di prevedere.*5. Sempre la stessa sequenza.* Gli stessi gesti nello stesso ordine, anche le sere in cui sembrano inutili. Il sistema impara dalle regolarità: dopo qualche settimana la sequenza diventa il segnale che dice al corpo cosa sta per arrivare, e la preparazione parte prima.E la cosa più importante, che nella puntata torna alla fine: questa non è una prestazione. Più ti sforzi di dormire, meno dormi. Una notte storta dentro tre settimane regolari non significa niente — conta la serie, non la singola riga.---Il documento allegatoLa sera prima è la scheda che accompagna l'episodio: una pagina sola, i cinque gesti con accanto il perché di ciascuno, così che restino in mano dopo l'ascolto invece di sciogliersi. In fondo ci sono le tre cose di contorno che pesano più di quanto sembri — luce bassa nell'ultima ora (uno schermo vicino agli occhi è luce piena), stanza fresca, niente pasti pesanti né alcol vicino al letto, che ti fa addormentare prima e ti rovina la seconda metà della notte, cioè proprio quella che serve a digerire la giornata.Mental Lab (sito sperimentale, richiedi l'accesso) → https://mental-lab.web.app/Mentalità è anche un'app, creata proprio da Gabriele per aiutare chiunqua voglia essere seguito, inziare un percorso o anche solo provare a capire qualcosa di più dell'aspetto mentale applicato allo sport e al basket. Chi volesse provarla può andare su: gabrielecolombo.coach e con il codice BACKDOOR, chi scarica l'app e lo inserisce ha un mese gratis di pacchetto Mentorship e il 40% di sconto sul rinnovo del mese successivo. Un ottimo modo per capire meglio il mondo dello sport e capirsi meglio per avere migliori performance, nell'attività sportiva e nella vita.Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/backdoor-podcast--4175169/support.

El catálogo discografico independiente cubano, específicamente el producido en la frontera de los 50 a los 60 del siglo XX, nos devuelve el estilo del conjunto "Estrellas de Chocolate". La etiqueta Puchito de Jesús Gorís con apenas un lustro de existencia, ganaba un importante espacio en el mercado proponiendo las grabaciones de esta formidable agrupación sonera fundada hacia 1958 por el percusionista Félix "Chocolate" Alfonso. El olvidado sonero Arístides Balmaseda, con los coros del Chino Lahera y Filiberto, en la memoria de Cubakústica. Etapa en la que el Niño Rivera tocaba el tres y, por supuesto, arreglaba y componía para "Estrellas de Chocolate". Retrocedemos más de veinte años para retomar uno de los insignes sones de la nación cubana. En febrero de 1932, de paso por La Habana, George Gershwin quedó rendido ante la sabrosa cadencia del son cubano. Especialmente una de las creaciones del poeta del son: Ignacio Piñeiro, lo cautivó haciendo que el famoso pianista y compositor introdujera algunos pasajes de "Échale salsita" en una pieza sinfónica que dió en llamar finalmente "Obertura Cubana", estrenada ese mismo año en Nueva York. Así pues en dos tiempos, colores y latitudes la mítica pieza de Piñeiro. Carlos Embale con el Septeto Nacional y la Orquesta Sinfónica de Praga del año 1962, bajo la conducción del maestro Václav Neumann. A tiempo de victrola regresamos a lo que proponían los toca discos de la isla a comienzos de los 60. Muy bien recibido, como siempre, el cancionero azteca. En particular las creaciones de Roberto Cantoral. Desde una producción "Gema" aparecía en el mundo del disco Gina León con su versión de "Aléjate" y el cancionero Fernando Albuerne afianzaba su popularidad desde las ediciones Panart, con su versión de "El reloj". Los respectivos compañamientos a cargo de los maestros Rafael Somavilla y Osvaldo Estivill. Casi en la despedida algunos clásicos de la música popular cubana a la manera de Yeny Van Van, Coco Freeman y Tony Calá. Producción a cargo de José Luis Cortés "El Tosco" para el sello estatal Bis Music. "Recordar es vivir", del son montuno a la típica guaracha antecediendo a la maestría interpretativa del gran Miguel de Gonzalo. Grabaciones de este cantante precursor efectuadas para el sello norteamericano RCA Víctor hacia la segunda mitad de los años 50s. El respaldo orquestal a cargo del maestro venezolano Aldemaro Romero.

Pięć chlebów, dwie ryby i tłum, który trzeba nakarmić -- dziś, a nie jutro. Dlaczego Jezus nie odsyła głodnych do wioski, tylko każe swoim uczniom działać? Posłuchajcie komentarza o. Alvaro Grammatica do Ewangelii na XVIII niedzielę zwykłą, w którym odkryjemy, że największy cud rodzi się nie z obfitości, lecz ze współczucia -- i że nasza własna bieda może stać się miejscem, w którym najbardziej odczuwamy bliskość Boga. Komentarz przetłumaczyła i odczytała Elżbieta Wróbel. Napisz do nas! Wesprzyj nas! Wszystkie odcinki z podziałem na cykle tematyczne znajdziesz na www.koinoniagb.pl/podcast Obserwuj kanał Podcast Koinonii Jan Chrzciciel w WhatsAppieRead transcript

Two cases with the same question at their center: what did the adults know, and why are they the last ones talking?In California, Celeste Rivas Hernandez's parents signed documents for their thirteen-year-old to travel internationally with d4vd and then denied knowing the man after his arrest. In Mississippi, one of the friends on Horn Island with Nolan Wells hired an attorney who announced a PI firm and nationwide defamation lawsuits before the grand jury has weighed in.Former prosecutor Eric Faddis examines both cases in a single conversation — the legal exposure facing Celeste's parents under California law, the defense strategy of arguing self-infliction in a dismemberment case, and the institutional vacuum in Mississippi that forced the public to fill the silence with its own investigation.Burke has pleaded not guilty and is held without bail. Nolan Wells' cause of death is undetermined. Both were teenagers.Join Our SubStack For AD-FREE ADVANCE EPISODES & EXTRAS!: https://hiddenkillers.substack.com/ Want to comment and watch this podcast as a video? Check out our YouTube Channel. https://www.youtube.com/channel/UC8-vxmbhTxxG10sO1izODJg?sub_confirmation=1 Instagram https://www.instagram.com/hiddenkillerspod/ Facebook https://www.facebook.com/hiddenkillerspod/ Tik-Tok https://www.tiktok.com/@hiddenkillerspod X Twitter https://x.com/TrueCrimePodThis publication contains commentary and opinion based on publicly available information. All individuals are presumed innocent until proven guilty in a court of law. Nothing published here should be taken as a statement of fact, health or legal advice.#D4vd #NolanWells #CelesteRivas #DavidAnthonyBurke #TrueCrime #EricFaddis #HiddenKillers #HornIsland #JusticeForCeleste #JusticeForNolan

Two cases. Two dead teenagers. And a pattern: the adults who were closest to both of them either denied what they knew or hired attorneys to talk for them.Celeste Rivas Hernandez's father said he never knew d4vd. Testimony showed he signed a travel form and sat in church with the man prosecutors say killed his fourteen-year-old daughter. In Mississippi, one of the friends on Horn Island with Nolan Wells retained a lawyer who announced defamation lawsuits and a PI firm before anyone in authority said a word about how the eighteen-year-old died.Former prosecutor Eric Faddis examines the legal exposure on both sides — the parents who allegedly facilitated a relationship in California and the friends' legal blitz in Mississippi — and the institutional silence that made all of this worse.Burke has pleaded not guilty to all charges. Nolan Wells' cause of death is undetermined.Join Our SubStack For AD-FREE ADVANCE EPISODES & EXTRAS!: https://hiddenkillers.substack.com/ Want to comment and watch this podcast as a video? Check out our YouTube Channel. https://www.youtube.com/channel/UC8-vxmbhTxxG10sO1izODJg?sub_confirmation=1 Instagram https://www.instagram.com/hiddenkillerspod/ Facebook https://www.facebook.com/hiddenkillerspod/ Tik-Tok https://www.tiktok.com/@hiddenkillerspod X Twitter https://x.com/TrueCrimePodThis publication contains commentary and opinion based on publicly available information. All individuals are presumed innocent until proven guilty in a court of law. Nothing published here should be taken as a statement of fact, health or legal advice.#D4vd #NolanWells #CelesteRivas #DavidAnthonyBurke #TrueCrime #EricFaddis #HiddenKillers #HornIsland #JusticeForCeleste #JusticeForNolan

Former prosecutor Eric Faddis sits down to examine two cases where the adults around the victims allegedly knew more than they're saying — and the institutions that could have intervened did nothing.In the d4vd case: Celeste's parents reportedly signed travel documents, attended church with Burke, and then denied knowing him. Blair Berk is pressing the medical examiner on self-inflicted death in a dismemberment case. The bodycam shows Burke was told she was thirteen. Faddis on whether the parents face charges and whether the defense has anything left.In the Nolan Wells case: Edmiston's attorney announced categorical denials, a PI firm, and nationwide defamation lawsuits before the grand jury has convened. Twenty-five days of institutional silence, and a defense attorney is doing the job the DA should be doing. Faddis on whether discovery from a defamation suit could blow the case open.Burke has pleaded not guilty. Wells' death remains undetermined. Both were teenagers.Join Our SubStack For AD-FREE ADVANCE EPISODES & EXTRAS!: https://hiddenkillers.substack.com/ Want to comment and watch this podcast as a video? Check out our YouTube Channel. https://www.youtube.com/channel/UC8-vxmbhTxxG10sO1izODJg?sub_confirmation=1 Instagram https://www.instagram.com/hiddenkillerspod/ Facebook https://www.facebook.com/hiddenkillerspod/ Tik-Tok https://www.tiktok.com/@hiddenkillerspod X Twitter https://x.com/TrueCrimePodThis publication contains commentary and opinion based on publicly available information. All individuals are presumed innocent until proven guilty in a court of law. Nothing published here should be taken as a statement of fact, health or legal advice.#D4vd #NolanWells #CelesteRivas #DavidAnthonyBurke #TrueCrime #EricFaddis #HiddenKillers #HornIsland #JusticeForCeleste #JusticeForNolan

Più di mille chilometri in bicicletta, sotto il sole rovente di un'estate segnata da temperature record. Ma per Maurizio Costalunga, di Villaverla, e Guido Diquigiovanni, di Valdagno, arrendersi al caldo non è mai stata un'opzione. Alpini, ciclisti e over 70, hanno caricato sulle gambe esperienza, passione e spirito di sacrificio e sono partiti alla volta di Tuglie, in provincia di Lecce, per un'impresa che profuma di amicizia, memoria e autentico stile penne nere.

E' giunto il turno di un regista che ha fatto poco eppure tanto allo stesso tempo. Un uomo interessato a parlare del Male che ci circonda, trasmettendoci per davvero l'orrore della vita e la benedizione della morte. Ecco a voi il terrificante cinema di Oz Perkins.In principio, fu Febbraio. Più o meno letteralmente."The Blackcoat's Daughter" è il primo tassello di uno spaventoso mosaico che è una filmografia come quella di Perkins, autore e regista deciso a parlare della solitudine e del nostro bisogno di certezze e speranze in un mondo fin troppo tetro come il nostro.

Attorney Russell Latino told reporters that anyone posting accusations about his client — one of the friends on Horn Island with Nolan Wells on July 4th — faces a nationwide defamation lawsuit. He said adding the word “allegedly” won't protect them. A PI firm has been hired. The FBI is involved after a credible death threat.Former prosecutor Eric Faddis examines what this legal strategy reveals and whether it would exist at all if Mississippi's investigation had given the public any answers since Nolan Wells was found dead. The DA sealed the autopsy. The sheriff hasn't cleared anyone. The family's independent autopsy returned undetermined. Twenty-five days of official silence, and the only people talking publicly are the friends' attorneys.Faddis on whether the threat to sue opens a door the friends might not want opened — because defamation lawsuits come with discovery, and discovery goes both ways.Nolan Wells was eighteen years old and the only one from the group who didn't come home.Join Our SubStack For AD-FREE ADVANCE EPISODES & EXTRAS!: https://hiddenkillers.substack.com/ Want to comment and watch this podcast as a video? Check out our YouTube Channel. https://www.youtube.com/channel/UC8-vxmbhTxxG10sO1izODJg?sub_confirmation=1 Instagram https://www.instagram.com/hiddenkillerspod/ Facebook https://www.facebook.com/hiddenkillerspod/ Tik-Tok https://www.tiktok.com/@hiddenkillerspod X Twitter https://x.com/TrueCrimePodThis publication contains commentary and opinion based on publicly available information. All individuals are presumed innocent until proven guilty in a court of law. Nothing published here should be taken as a statement of fact, health or legal advice.#NolanWells #HornIsland #Mississippi #BartEdmiston #TrueCrime #EricFaddis #HiddenKillers #GrandJury #JusticeForNolan #JacksonCounty

One of the friends on Horn Island with Nolan Wells on July 4th didn't just hire an attorney — he hired one who announced nationwide defamation lawsuits, retained a PI firm to track down social media users, and publicly declared that adding “allegedly” to a post won't save you.Former prosecutor Eric Faddis breaks down what a legal offensive of this size means before a grand jury has even convened — and whether the investigation's twenty-five days of public silence is the real reason an attorney is doing the talking the DA should be doing.The official autopsy is sealed. Nobody has been cleared. Nobody has been named. The family's independent pathologist couldn't determine how Nolan died. And the only public accounting of that night comes from the friends' legal teams.Nolan Wells was eighteen. He was the only one from the group who didn't come back.Join Our SubStack For AD-FREE ADVANCE EPISODES & EXTRAS!: https://hiddenkillers.substack.com/ Want to comment and watch this podcast as a video? Check out our YouTube Channel. https://www.youtube.com/channel/UC8-vxmbhTxxG10sO1izODJg?sub_confirmation=1 Instagram https://www.instagram.com/hiddenkillerspod/ Facebook https://www.facebook.com/hiddenkillerspod/ Tik-Tok https://www.tiktok.com/@hiddenkillerspod X Twitter https://x.com/TrueCrimePodThis publication contains commentary and opinion based on publicly available information. All individuals are presumed innocent until proven guilty in a court of law. Nothing published here should be taken as a statement of fact, health or legal advice.#NolanWells #HornIsland #Mississippi #BartEdmiston #TrueCrime #EricFaddis #HiddenKillers #GrandJury #JusticeForNolan #JacksonCounty

In this episode of The True Fiction Project, actor Rishi Jaiswal walks us through his unlikely leap from aerospace engineering to a national Broadway tour, his lead role in The Revolution's Wife, and the deeply personal story behind his new short film, The Last Waltz. This movie is about a South Asian family confronting their trans daughter's identity and the silence that follows. We also talk about dance, Sundance, and the strange coincidences that shaped this casting. Tune in to hear the trailer of The Last Waltz, highlighting the moment a father finally faces the daughter he can barely name.What You'll Learn in This Episode: ✅ How Rishi Jaiswal traded aerospace engineering for a career on stage and screen, starting with So You Think You Can Dance and a national tour of Life of Pi on Broadway.✅ Why he sent a freedom fighter poem about Gaza to land his role in Revolution's Wife, and how that bold risk paid off during the casting process.✅ How growing up around a family dance studio and Bollywood culture shaped the way Rishi approaches every character he plays on screen and stage.✅ Why The Last Waltz speaks directly to the South Asian community about learning to accept a trans child, even without fully understanding her journey first.Subscribe to Reenita's Storytelling Den on Substack for free at https://substack.com/@reenitahora and to her YouTube channel to watch the video version of this episode! https://www.youtube.com/@reenymalCheck out her website to stay up-to-date on events, book releases and more! https://reenita.com/TIMESTAMPS:  00:00 Rishi Jaiswal shares how he became involved in Revolution's Wife03:25 How a Gaza freedom fighter poem helped Rishi land his role in Reenita's script05:14 Discussion of the Revolution's Wife script07:56 The Sundance Film Festival encounter that unexpectedly brought Nosheen into the cast as Padma11:35 From aerospace engineering to a family dance studio, tracing Rishi's winding path into the arts15:10 Rishi introduces The Last Waltz, his own short film about a trans daughter and her parents22:38 Watch the exclusive trailer for The Last Waltz ahead of its debut at the LA Shorts FestivalKEY TAKEAWAYS: 

Russell Latino, attorney for one of the friends who was on Horn Island with Nolan Wells on July 4th, told reporters that the word “allegedly” will not protect social media users from a defamation lawsuit. He announced his client had cooperated fully, turned over his boat and GPS and phone, and denied any involvement. He said a PI firm has been retained to track posters nationwide. A credible death threat activated the FBI.Defense attorney and former prosecutor Eric Faddis examines whether this is a legitimate response to mob harassment — or a calculated move to establish Edmiston as cleared in the court of public opinion before the grand jury decides anything. Because the investigation hasn't said a word in twenty-five days, and this attorney is already writing the narrative.The DA sealed the autopsy. The sheriff's office hasn't named or cleared anyone. The independent autopsy came back undetermined. Faddis on what happens when an attorney's threat to sue for defamation opens the door to discovery — and what discovery might reveal.Nolan Wells was eighteen years old.Join Our SubStack For AD-FREE ADVANCE EPISODES & EXTRAS!: https://hiddenkillers.substack.com/ Want to comment and watch this podcast as a video? Check out our YouTube Channel. https://www.youtube.com/channel/UC8-vxmbhTxxG10sO1izODJg?sub_confirmation=1 Instagram https://www.instagram.com/hiddenkillerspod/ Facebook https://www.facebook.com/hiddenkillerspod/ Tik-Tok https://www.tiktok.com/@hiddenkillerspod X Twitter https://x.com/TrueCrimePodThis publication contains commentary and opinion based on publicly available information. All individuals are presumed innocent until proven guilty in a court of law. Nothing published here should be taken as a statement of fact, health or legal advice.#NolanWells #HornIsland #Mississippi #BartEdmiston #TrueCrime #EricFaddis #HiddenKillers #GrandJury #JusticeForNolan #JacksonCounty

Learn English useful for talking about the Census, data security, and protecting personal information. Practise everyday phrases for expressing concern, explaining risks, and talking about how data is kept safe. - Piööc wël path thooŋ English ku bï yïïn ya jam ee Kuɛ̈ɛ̈n, ŋiëc tɔu wël ku gëlgël wël yiɛ̈nhdu. Cuthcuth yic wël wen ye laac luöi yic ku bï yïn jam kä diir yïïn, ku kuɛ̈ny ke wääc yiic ku jaämic ye kada bi wël käk tɔu ke ci ŋiëëc tɔɔu.Keep practising the phrases from this episode and test what you've learned with our quiz here. - Keep practising the phrases from this episode and test what you've learned with our quiz here.

Można Pięknie Żyć *---Witaj! "Można Pięknie Żyć*" to seria podcastów, w której odkrywamy, jak zmiany w stylu życia mogą poprawić nasze zdrowie metaboliczne. Skupiamy się na Terapeutycznym Ograniczaniu Węglowodanów i jego pozytywnym wpływie na metabolizm oraz ogólne samopoczucie. Pamiętaj, że zdrowie zaczyna się od wiedzy, a my jesteśmy tu, aby dostarczać Ci inspirację i praktyczne wskazówki na drodze do pięknego życia. Zaczynamy!  Na nadciśnienie choruje już 11 milionów Polaków, a co trzeci z nich o tym nie wie! Zamiast brać tabletki do końca życia, poznaj prawdziwą, metaboliczną przyczynę problemu. Omawiamy przełomowe badanie z 2025 roku – prosta zmiana obniża ciśnienie silniej niż farmakoterapia!Z tego filmu dowiesz się:·       Prawdziwy winowajca: Dlaczego to NIE sól, lecz ukryta insulinooporność odpowiada za 80% przypadków nadciśnienia.·       Historia i polityka: śmierć prezydenta Roosevelta, która zmieniła medycynę.·       Wycofanie chorób: Jak nasi pacjenci dzięki diecie ketogenicznej całkowicie odstawili leki na nadciśnienie i cukrzycę.·       Keto kontra DASH: Dlaczego to ograniczenie węglowodanów bezapelacyjnie wygrywa w badaniach klinicznych. Linki do przepisu: youtube.com/watch?v=UyjrIBIWdJk&feature=youtu.be  Polub i udostępnij ten odcinek, aby pomóc nam promować niezależną wiedzę, i odpowiedz w komentarzu na pytanie zadane w filmie! UWAGA! Bilety na Low Carb Festival.  WYPRZEDANE zapisz się do listy oczekujących.Low Carb Festival To #1 Największe Na Świecie wydarzenie Dotyczące Odżywiania Niskowęglowodanowego

Matías Valdés repasó el camino que lo llevó a apostar todo por la música. Recordó los desafíos de la pandemia, cuando dejó su trabajo como camionero para dedicarse de lleno a su carrera junto a su banda, y habló sobre la importancia de sus raíces y de las historias del interior que inspiran canciones como Sarandí y Estrella del Interior.Además, adelantó todos los detalles de su gran celebración: el próximo 10 de octubre volverá al Antel Arena para festejar sus cinco años como solista con un show único y una gran lista de invitados, entre ellos La Penúltima, Herederos, Luana, Lauta, Migrantes, Bámbara y Martín Piña, entre otros.Durante la charla también compartió cómo vive su proceso creativo, el valor de las colaboraciones y el vínculo que mantiene con su público.Las entradas ya están disponibles a través de Tickantel.

Un pallone gonfiato fa di tutto per passare per ciò che non è. Più grande, più forte, più colto... più di ciò che è. Il problema del pallone gonfiato è che le persone notando l'atteggiamento sviluppano distacco e antipatia e questo sentimento le porta a non voler riconoscere nemmeno quel poco valore che c'era in origine. È questo il problema di essere dei palloni gonfiati, si perde anche ciò che si ha. 

Real traction doesn't happen overnight. It happens after years of showing up when most people would have quit. Chris Earley, founder of Earley Law Group Injury Lawyers in Boston, built his firm from scratch straight out of law school. After two decades of relentless consistency, a hyper-local marketing strategy, more than 1,000 Google reviews, and an obsession with intake, his firm recently celebrated its first seven-figure month. In this episode, Chris shares why resilience is his greatest competitive advantage, how "Call Earley Before It's Too Late" became the foundation of his local brand, why he posts on LinkedIn every day, what drives his 93.5% intake conversion benchmark, and how creating an exceptional client experience continues to fuel referrals, reviews, and long-term traction. You'll learn: Why building traction takes years of consistent effort—not a single breakthrough. How a 93.5% wanted-lead conversion benchmark keeps intake accountable. Why posting valuable content every day on LinkedIn strengthens a local brand. How constant client communication uncovers case value and creates more referrals. Why getting hyper-local creates an advantage over trying to dominate an entire market. If you want a marketing partner that works just as relentlessly as you do to dominate your market, head over to Rankings.io. Like what you hear? Hit Subscribe! We do this every week. If you want to keep learning from the best voices in PI, join us at PIMCON 2026. Buy your tickets now! Subscribe to our newsletter and get the freshest news every Monday: newsletter.rankings.io Get Social! Personal Injury Mastermind w/ Chris Dreyer powered by Rankings.io is on Instagram | YouTube | TikTok

Deux mois après son parcours sensationnel à Paris, Moïse Kouamé traverse sa toute première grosse zone de turbulences. Éliminé d'entrée lors de ses deux tournois Challenger de reprise, le prodige français de 17 ans vient de concéder une 3e défaite consécutive à Los Cabos, battu dès le premier tour par un Lucky Loser.Une série noire qui pose la question de la gestion post-exploit. Trou d'air physique et mental inévitable chez un adolescent en apprentissage ? Piège de la sur-médiatisation et des attentes déplacées du public ? Dans cette émission, nos experts analysent la trajectoire de Kouamé et la comparent à l'histoire du tennis : entre les phénomes hors normes capables de confirmer immédiatement comme Rafael Nadal ou Carlos Alcaraz, et les pépites freinées ou brisées par le contrecoup de la pression.Faut-il s'inquiéter pour la suite de la saison de Moïse Kouamé, ou trouvez-vous ce passage à vide 100 % normal à 17 ans ? Faut-il qu'il retourne jouer sur le circuit Challenger pour accumuler des victoires ? On attend vos avis !Bienvenue dans "Sans Filet" avec Marie Beljean, Frédéric Verdier et Julient Varlet.Ce podcast est hébergé par Podcastics, la plateforme pour créer et diffuser votre podcast facilement.

Elisenda Julià Quartet obrirà el cicle Música a Santa Cristina d'aquest estiu. Serà aquest divendres amb un nou escenari. Com és habitual, l'Obreria oferirà concerts dins de l'ermita i a la Plaça del Pi, però en aquesta ocasió muntarà un escenari nou a l'espai que hi ha davant de la porta principal de l'ermita. La cantant del quartet, Elisenda Julià, explica que el repertori el conformaran alguns clàssics del jazz que han arranjat i que publicaran al seu proper disc. “Farem un recull de temes que formaran part del pròxim disc, que està a punt de sortir, i són estàndards de jazz dels que més ens agraden, com George Gershwin o Cole Porter, arranjats a la nostra manera”Elisenda Julià Elisenda Julià no ha actuat mai al paratge de Santa Cristina, però els seus pares sí i assegura que té moltes ganes de conèixer l'espai. “No hi he estat mai, he vist fotos i em sembla un lloc preciós i els meus pares, que són músics, sí que hi han tocat i és un lloc que em ve molt de gust conèixer”Elisenda Julià La seva música és original dels anys 30-40, però creu que atrapa a un públic molt ampli. “Suposo que, per l'època de la música que toquem, anys 30-40, sembla que hauria de ser un públic de generacions passades, però crec que és una música que arriba a qualsevol edat”Elisenda Julià A banda de la veu d'Elisenda Julià, formen part del quartet Guillem Garcia, al piano, Camil Alcarazo, al contrabaix, i Joan Moll, a la bateria. El concert serà aquest divendres, a dos quarts d'onze de la nit. Les entrades valen 25 euros i es poden comprar al Museu del Mar, a Entrapolis o bé a taquilla. Recordem que abans de l'actuació, qui ho vulgui podrà menjar alguna cosa allà mateix.

You're investing in marketing, but a 4.4-star rating could be sending those cases somewhere else. To get your copy of the Rankings Personal Injury Review Generation Playbook, visit ⁠rankings.io/reviewguide⁠  In this solo episode, Chris Dreyer, CEO of Rankings.io, explains why reviews are no longer just a reputation metric. They're one of the strongest trust signals influencing Google Maps, Local Services Ads, and AI search, making review generation an operational priority, not a marketing afterthought. You'll learn: Why reviews are one of the most important marketing assets for personal injury firms. How review freshness helps Google Maps, LSAs, and AI search build trust. When to ask for reviews across the client journey—not just at case close. Why video testimonials and third-party reviews strengthen AI search visibility. Get your copy of Rankings Personal Injury Review Generation Playbook now at rankings.io/reviewguide  Like what you hear? Hit Subscribe! We do this every week. If you want to keep learning from the best voices in PI, join us at PIMCON 2026. Buy your tickets now! Subscribe to our newsletter and get the freshest news every Monday: newsletter.rankings.io Get Social! Personal Injury Mastermind w/ Chris Dreyer powered by Rankings.io is on Instagram | YouTube | TikTok

With Nolan tackling one of the world's greatest adventures in The Odyssey, we enlist two guests, Mike Zimm a Homer scholar and our own Diogenes in Prof. Shai (@⁠⁠slyjester⁠⁠). We also DRAFT favorite sword & sandal movies plus share listener rankings of Nolan's 13 movies. 55 minutes in, we review other July 2026 movies: BLUE Heron, The INVITE, Minions & MONSTERS, TUNER, Mother MARY, Over Your DEAD Body, Girls Like GIRLS. Blindspot Swap reviews Menashe (2017) and They Shoot Horses Don't They (1969) + Classics Corner.SPOILERS only for The Odyssey (and briefly for Life of Pi!)Outre is "Odyssey" from the soundtrack by Ludwig Göransson

This time, we're poised for Gen Con 2026 and making plans. Where will we be? What games do we want? What games do others want? Who is good at thumbs? All will be answered along with a sprinkling of Roses, Thorns, and Hula Hoops. 00:48 - Busiest Time of the Year 02:01 - Where Can We Be Found? - Booth 249 04:09 - New at Cephalofair - Buttons and Bugs Expansion 06:07 - Getting Steps In 07:19 - Game Bag Giveaway 07:55 - The Dice Tower Awards 09:11 - The Dwarves of Aquilon 10:23 - Meet the Final Girls - Julie on Friday Morning 10:47 - Eric's Gen Con Releases: Gudnak Means War, Mountain Goats Legacy 11:50 - Julie's Releases: Tales of Myth and Legend, Coloring Book 13:02 - BGG Preview List and the Nature of Thumbs 13:58 - Entropy 14:53 - Jason and the Argonauts 16:01 - Container 16:33 - Enchanted Ivy 17:18 - Mountain Goats Legacy 18:30 - Hercules and the 12 Labors 18:43 - Jibber Jabble 19:25 - High Society and 3 Witches 19:56 - Gruntz 20:12 - Torchlit 20:29 - French Toast 21:37 - Piñatas 22:06 - Kokeshi 23:08 - Book Club 24:28 - Fiction: Banned Books 25:07 - Golden Goal 25:24 - Under the Leaves 26:06 - Movie Night 26:17 - Stonk Market 26:26 - Triangulation 27:04 - Phantom Ink and Wordsy 27:21 - Endangered Australia 27:37 - The Game Makers 28:58 - Night at the Zoo 29:54 - Lairs 30:17 - Drillers and Lost Ruins of Arnak: Surprise Shipment 31:47 - Holliday Hijinks Friday Fright and More 32:12 - Orloj: The Prague Astronomical Clock 32:32 - Cascadia Alpine Lakes 33:02 - Nippon 33:49 - Wingspan Pocket 34:30 - The Old King's Crown 34:37 - Railway Boom 36:00 - Glasgow Train Robbery 36:16 - Tag Team: Arthur's Legacy 37:25 - Three Sisters Harvest Edition 37:53 - Forage 39:12 - SETI: Space Agencies, Forest Shuffle: Smoky Mountains, and Rock Hard 1977: Ear Candy 39:42 - Kingdom Crossing 40:19 - Shackleton Base: Below. Within. Above. and Fliptoons Season 2 40:54 - Moytura 41:01 - Tenby 41:20 - Jisogi: Anime Studio Tycoon 42:02 - Faraway: Under Starry Skies and Castle Combo: Out of the Oubliette 42:10 - Gold Country 42:52 - Mythologies 43:11 - Scythe: Duel of Meloch 44:13 - Friendly Fishing 45:50 - Codenames: Critical Role Adventures 46:25 - Trick to the Future 46:45 - Chris Couch Games: Garden Clubs 47:23 - Crime Scene Tamperer 47:52 - Games Played in the Booth 49:31 - Container (Eric) 53:33 - Cthulhu: Death May Die (Julie) 57:55 - Falling (Tom and Julie) 1:02:17 - Stuff to Watch if You Can't Come to Gen Con Questions? Tales of Horror? tom@dicetower.com

Every truck accident case looks like a car wreck—until you start investigating it. Maxey Scherr, founder of Scherr Law Firm, explains why treating an 18-wheeler crash like a standard auto case can cost both lawyers and clients millions. After spending 14 years practicing at her father's firm, Maxey founded Scherr Law Firm to focus exclusively on trucking accidents, traumatic brain injuries, and product liability. She became the first woman in Texas and New Mexico to earn board certification in trucking litigation and recently secured an eight-figure settlement on half of a single trucking case, making her one of the country's leading voices in commercial trucking litigation. In this episode, Maxey explains what separates trucking litigation from everyday PI cases, why early investigation determines case value, and how experienced trucking lawyers uncover additional insurance coverage, liable parties, and product defects that many firms miss. You'll learn: Why trucking accident cases require a different litigation strategy than ordinary car wrecks. How to investigate commercial trucking companies before critical evidence disappears. What additional insurance policies and liable parties PI lawyers often overlook. Why specialization leads to stronger trucking settlements and safer roads. If your firm is looking to land those massive trucking and catastrophic injury cases, then visit Rankings.io. Like what you hear? Hit Subscribe! We do this every week. If you want to keep learning from the best voices in PI, join us at PIMCON 2026. Buy your tickets now! Subscribe to our newsletter and get the freshest news every Monday: newsletter.rankings.io Get Social! Personal Injury Mastermind w/ Chris Dreyer powered by Rankings.io is on Instagram | YouTube | TikTok

Mercedes Alejandro Dolina, Patricio Barton, Gabriel Schultz Introducción • 0:01:13 Presentación en Mercedes y regreso de Gabriel Schultz • 0:04:47 Charla humorística sobre empanadas de Don Pepito Segmento Inicial • 0:08:32 Consejos para alquilar un departamento • 0:33:26 Visita absurda a un departamento con cárcel, bailongo y condiciones imposibles • 0:40:43 Deriva final del sketch entre ladrones, policías y alquiler frustrado Segmento Dispositivo • 0:41:46 Historia de Juana I de Nápoles • 0:44:28 Infancia, herencia del trono y conflicto con Andrés de Hungría • 0:47:06 Asesinato de Andrés y guerra entre Hungría y Nápoles • 0:49:48 Nuevos matrimonios, gobierno y beneficencia • 0:51:13 Fama de mujer escandalosa, amantes y vida cortesana • 0:54:02 Exilio, muerte y mención a la obra de Lope de Vega • 0:55:48 "Lo quiero todo" ♫ Segmento Humorístico • 1:00:04 Consejos para celebrar el segundo cumpleaños de un hijo • 1:01:08 Crítica a los cumpleaños de bebés y a los invitados adultos • 1:04:24 Ruidos, horarios, retiro de niños y demoras de la fiesta • 1:09:34 Piñatas, fiestas temáticas y juegos para chicos muy pequeños • 1:17:30 Consulta por salón de fiestas, pelotero, globos, panchos y artistas Sordo Gancé / Trío Sin Nombre • 1:22:46 Presentación del cierre musical • 1:24:09 "Mañana campestre" ♫ • 1:27:00 "Buenos Aires de mi amor" ♫ • 1:29:09 "Norwegian Wood" ♫ • 1:31:51 "Paciencia" ♫ • 1:34:52 "Todo un palo" ♫ • 1:42:01 Canción a pedido para Romina • 1:45:36 Cierre y agradecimiento (Resumen generado automáticamente con IA, puede contener errores)

Did you like the episode? Send me a text and let me know!! One Reddit user sent 464,000 cold emails in a single year — and figured out exactly what made people reply. A lowercase subject line with no punctuation hit a 90% open rate. A permissionless value DM on X returned a 40% response rate. And asking for a 15-minute call? Converted at a devastating 0.8%. In this episode of Business Conversations with Pi and Piette 2.0, PI and Piette answer a listener's question from tuepodcast.net/askpi: "How do I run cold outreach on LinkedIn, X, or email without sounding like a spammer?" The answer is a complete platform-by-platform playbook built on deliverability science, human psychology, and one core truth — your ultimate competitive advantage is proving you're genuinely human. What You'll Learn: Why asking for a 15-minute call is the worst thing you can do in a cold emailThe offer psychology shift that jumped reply rates from 0.8% to 2.1%How to send 1,000 emails a day without triggering spam filters (the interval trick)The "ugly email rule" and why plain text outperforms beautiful HTML every timeNicholas Cole's lowercase subject line that achieved a 90% open rateThe four-step follow-up sequence — including the "right person pivot" and the A/B/C closeoutLinkedIn's five-touch warm-up sequence before you ever send a DMWhy automating LinkedIn likes and comments gets your account shadowbannedHow X (Twitter) works for reaching C-suite executives — and the golden rule about linksThe permissionless value framework and why it generates a 40% response rateWhy the future of outreach belongs to whoever can prove they're most humanTimestamps: [00:00:00] – Introduction & The Listener Question[00:01:00] – 464,000 Cold Emails: What the Biggest Outreach Test Ever Revealed[00:02:30] – Offer Psychology: Why Asking for a Meeting Is Killing Your Reply Rate[00:03:30] – Cognitive Load: The Sofa vs. the Piece of Paper[00:04:00] – From 0.8% to 2.1%: The Low-Friction Offer That Changes Everything[00:05:00] – Email Deliverability: The Invisible Bottleneck No One Talks About[00:05:30] – Sending Intervals: How to Mimic a Human to Beat the Algorithm[00:06:30] – Nicholas Cole's 90% Open Rate Subject Line (All Lowercase, No Punctuation)[00:07:00] – The Ugly Email Rule: Why Plain Text Wins Every Time[00:08:00] – The Text-to-Code Ratio: How Spam Filters Actually Read Your Email[00:08:30] – The Four-Step Follow-Up Sequence[00:09:00] – The Right Person Pivot (Step 3)[00:09:30] – The A/B/C Closeout (Step 4 — The Best Tactic in the Whole Episode)[00:10:00] – LinkedIn's Context Layer: Why DMs Here Are Different[00:10:30] – The Five-Touch Warm-Up Sequence Before You DM Anyone[00:11:30] – True Personalization vs. Mail Merge: The Trigger Event Framework[00:12:00] – Why Automating LinkedIn Engagement Gets You Shadowbanned[00:13:00] – The Three-Touch Limit on LinkedIn DMs[00:13:30] – X (Twitter): The C-Suite Goldmine With Strict Rules[00:14:00] – Account Warm-Up: Why You Can't Start DMing Immediately on X[00:14:30] – The Golden Rule: Never Put a Link in Your First X DM[00:15:00] – The Permissionless Value Framework: Reversing the Sales Debt[00:16:00] – 40% Response Rate: The Data Behind Leading With Value[00:17:00] – The Future of Outreach: AI Makes Noise, Humans Break Through[00:17:30] – Full Recap & Submit Your QuestionThe Four-Step Follow-Up Sequence: ✅ Day 2-3: Quick bump — "Open to a quick yes or no?"✅ Day 4-5: One specific researched detail proving you've done your homework✅ Day 7-10: Right person pivot — "Am I reaching the right person, or should I talk to someone else?"✅ Day 14: A/B/C closeout — "Reply A if not a priority, B to circle back next month, C if you're the wrong person"Submit Your Question:

This week on the podcast, Patrick and Tracy welcome Faith Hunter, author of Unpredictable Magic. About Unpredictable Magic: Angelina Everhart-Trueblood and her brother Evan run Everhart Investigations, a PI firm in Chattanooga that solves paranormal crimes committed by supernatural beings. When their new client wants help finding her friend, who supposedly disappeared during a reception at Angie's aunt Jane's winter residence, things get… complicated. The client is not who she appears to be, and demons strike the city for the first time since the Witch War. On top of that, evidence is pointing toward the involvement of an overly ambitious vampire—who just happens to be Angie's ex-husband. As Angie and Evan team up with CPD, they will have to dig deep into their magical reserves—and rely on some friends in high places—to rid Chattanooga of the danger creeping into their city. About Faith Hunter: Faith Hunter, urban fantasy writer, was born in Louisiana and raised all over the south. Hunter fell in love with reading in fifth grade, and best loved SciFi, fantasy, and gothic mystery. She decided to become a writer in high school, when a teacher told her she had talent. Now, she writes full-time, tries to keep house, and is a workaholic with a passion for RV travel, Japanese maples, orchids, white-water kayaking, and writing. She and her husband love to RV to whitewater rivers all over the Southeast. Under her pen name Gwen Hunter, she writes action adventure, mysteries, and thrillers. As Faith and Gwen, she has 40+ books in print in 30+ countries. This week's picks: Faith: This Kingdom Will Not Kill Me by Ilona Andrews Tracy: Ready Set Bet (Game) Patrick: Pathfinder Impossible Magic (Rulebook) Links: Faith Hunter on Instagram Tracy Townsend on BluSky Patrick Hester on Instagram The Functional Nerds Patreon Page © 2026 Patrick Hester The post Episode 712-With Faith Hunter appeared first on The Functional Nerds.

El presidente de la Sociedad Nacional de Agricultura (SNA), Antonio Walker, analizó las consecuencias económicas que tuvieron las últimas lluvias que afectaron a gran parte del país, especialmente el norte, advirtiendo que el daño suma "cientos de millones de dólares"."Yo no tengo recuerdo, y llevo 40 años trabajando en el campo, de haber tenido una concentración de lluvia en tan poco tiempo como esta (de los últimos días)", sostuvo el exministro de Agricultura de Sebastián Piñera."Tenemos un Niño que se está portando muy mal. Tenemos una isoterma muy alta y llovió a 2.800 metros de altura anoche. Este es un Niño que va a causar mucho daño. Están anunciadas precipitaciones para septiembre, octubre y noviembre. Entonces esto sigue, no ha terminado", aseveró.Por otro lado, Walker abordó los nuevos aranceles del 12,5% fijados por el gobierno de Estados Unidos a más de 60 economías, entre las que se incluye a Chile, por su presunta importación de bienes creados mediante trabajo forzoso. Walker destacó que Estados Unidos es el segundo socio comercial de Chile, sin embargo, "de Trump se puede esperar cualquier cosa; medidas muy unilaterales, una política exterior muy errática".El Diario de Cooperativa, con Verónica Franco y Rodrigo Vergara.Escucha más episodios en Cooperativapodcast.cl

Speciální vydání fotbalového podcastu MVP: nejlepší český basketbalista Tomáš Satoranský popsal zákulisí gigantu FC Barcelona a jak hrdí Katalánci prožívají španělský titul na mistrovství světa. --- Fotbal ze všech možných i nemožných úhlů pohledu. MVP jsou bývalí fotbaloví profesionálové Karel Tvaroh, Antonín Rosa, Tomáš Kučera a zkušený novinář Jan Palička, šéf sportovní rubriky Seznam Zpráv. Společně s námi hledejte nejdůležitější hráče, trenéry, přestupy, akce, problémy. Do hloubky a s humorem. I vy můžete být MVP. Každé úterý na webu Seznam Zpráv. Odebírejte na Podcasty.cz, Apple Podcasts nebo Spotify. Sledujte nás na Stream.cz nebo YouTube. Bližší pohled do kabin MVP se vám nabízí na našem Instagramu. Máte návrh, jak podcast vylepšit? Nebo nás chcete pochválit? Pište na audio@sz.cz

Charles Berde, MD, PhD, is the Sara Page Mayo Chair in Pediatric Pain Medicine and a Professor of Anaesthesia at Harvard Medical School. As a co-founder of the Pain Treatment Center at Boston Children's Hospital, he has spent decades at the forefront of pediatric analgesic pharmacology and the development of novel local anesthetics. His extensive translational research focuses on local anesthetic mechanisms and prolonged-duration formulations, making him a preeminent authority on the physiological impacts and safety profiles of these agents in neonates and children. A recipient of the Myron Yaster Lifetime Achievement Award from the Society for Pediatric Anesthesia, Dr. Berde brings unparalleled expertise to the discussion of managing and preventing local anesthetic systemic toxicity (LAST) within the high-stakes environments of the PICU and pediatric operating rooms.Guest Conflicts of Interest (COI)Algavita Bio: Collaborator/Developer of novel, prolonged-duration local anesthetics.Quiver Bioscience: Unpaid Scientific Advisor and co-PI on an NINDS-HEAL grant focused on rare disease pain treatments.Latigo Biotherapeutics: Scientific Advisor and recipient of past research support for novel analgesics development.Algos: Scientific Advisor for non-opioid analgesics development.Learning Objective: By the end of this podcast, listeners should be able to discuss an evidence-based and expert-guided approach to the management of local anesthetic systemic toxicity (LAST) in children.References:Patient and Doctor Reconcile for Greater GoodBerde CB. Toxicity of local anesthetics in infants and children. J Pediatr. 1993 May;122(5 Pt 2):S14-20. doi: 10.1016/s0022-3476(11)80004-1.McMahon K, Paster J, Baker KA. Local anesthetic systemic toxicity in the pediatric patient. Am J Emerg Med. 2022 Apr;54:325.e3-325.e6. doi: 10.1016/j.ajem.2021.10.021. Epub 2021 Oct 25. Lavonas et al. 2023 American Heart Association Focused Update on the Management of Patients With Cardiac Arrest or Life-Threatening Toxicity Due to Poisoning: An Update to the AHA Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2023 Oct 17;148(16):e149-e184. doi: 10.1161/CIR.0000000000001161. Epub 2023 Sep 18. Questions, comments or feedback? Please send us a message at this link (leave email address if you would like us to relpy) Thanks! -Alice & ZacSupport the showHow to support PedsCrit:Please complete our Listener Feedback SurveyPlease rate and review on Spotify and Apple Podcasts!Donations are appreciated @PedsCrit on Venmo , you can also support us by becoming a patron on Patreon. 100% of funds go to supporting the show.Please remember that all content during this episode is intended for educational and entertainment purposes only. It should not be used as medical advice. The views expressed during this episode by hosts and our guests are their own and do not reflect the official position of their institutions. If you have any comments, suggestions, or feedback-you can email us at pedscritpodcast@gmail.com.  You can also check out our website at http://www.pedscrit.com. Thank you for listening to this episode of PedsCrit!

In this SRNA “Ask the Expert” episode, GG deFiebre spoke with Dr. Michael Levy and Dr. Benjamin Greenberg about optic neuritis and how it is most often linked to multiple sclerosis but can also be idiopathic or associated with MOGAD and NMOSD. They compared differences across these conditions (including age patterns, bilateral involvement, severity, exam findings, and typical recovery), outlined diagnostic workups such as MRI, antibody testing, and spinal fluid studies, and reviewed acute treatments [03:14]. The discussion also covered emerging therapies like Pivikto for neuroprotection and efgartigimod alfa to lower IgG as a potential alternative to plasma exchange, and examined challenges in remyelination and stem-cell delivery approaches like Q-Cells while cautioning against unproven stem cell clinics [11:57].Benjamin M. Greenberg, MD, MHS is a Professor and the Cain Denius Scholar in Mobility Disorders in the Department of Neurology [https://utswmed.org/why-utsw/departments/neurology/] at UT Southwestern Medical Center in Dallas, Texas. He currently serves as the Vice Chair of Translational Research and Strategic Initiatives for the Department of Neurology. He is also the interim Director of the Multiple Sclerosis Center [https://utswmed.org/locations/aston/multiple-sclerosis-and-neuroimmunology-clinic/] and the Director of the Neurosciences Clinical Research Center. In addition, he serves as Director of the Transverse Myelitis and Neuromyelitis Optica Program and the Pediatric Demyelinating Disease Program [https://www.childrens.com/specialties-services/specialty-centers-and-programs/neurology/demyelinating-disease-program] at Children's Medical Center. Prior to his recruitment to UT Southwestern in 2009, Dr. Greenberg was on the faculty of the Johns Hopkins Division of Neuroimmunology, serving as the Director of the Encephalitis Center and Co-Director of the nation's first dedicated Transverse Myelitis Center. Dr. Greenberg splits his clinical time between adult and pediatric patients at William P. Clements Jr. and Zale Lipshy University Hospitals, Parkland, and Children's Medical Center. His research focuses on better diagnosing, prognosticating, and treating demyelinating diseases and nervous system infections. He also coordinates clinical trials to evaluate new treatments to prevent neurologic damage and restore function to affected patients. Michael Levy, MD, PhD is a recognized neurologist with over 15 years of clinical and research expertise in rare neuroimmunological disorders. He established the Neuroimmunology Clinic and Research Laboratory at Massachusetts General Hospital and is the Research Director in the Division of Neuroimmunology and Neuroinfectious Disease. Previously, Dr. Levy was on the faculty at Johns Hopkins University and was the founding Director of their Neuromyelitis Optica Clinic. Clinically, Dr. Levy cares for patients with MOG antibody disease (MOGAD), neuromyelitis optica spectrum disorder (NMOSD), and idiopathic transverse myelitis (TM). Dr. Levy is also the principal investigator (PI) on numerous patient studies and drug trials for new and improved treatments for these disorders. In 2022, Dr. Levy became the lead principal investigator for the two worldwide clinical trials in MOG antibody disease. In the lab, Dr. Levy's research focuses on the development of animal models of NMO and MOG with the goal of tolerization as a sustainable long-term treatment. Dr. Levy has more than 200 peer-reviewed research articles, reviews and editorials, and 3 patents covering NMO tolerization therapy, TM diagnostics, and stem cell regeneration approaches.00:00 Welcome01:02 Optic Neuritis Basics02:27 Causes and Percentages03:14 MS vs NMO vs MOG06:07 Workup and Testing07:51 Acute Attack Treatment09:30 Recovery and Vision Measures11:57 Pivikto Neuroprotection15:30 Efgartigimod vs Plasma Exchange17:59 Repair vs Remyelination20:15 Q-Cells and Stem Cell Delivery22:22 Closing

Pochoutkový rok slaví 10 let: Piškotový zákusek s pistáciovým krémem, rebarborou a sněhovou čepicí od Vojtěcha Vrtišky. Rada z etikety: Jakou teplotu má mít víno. Gastrotrend: Nápoje ve skle.

Pochoutkový rok slaví 10 let: Piškotový zákusek s pistáciovým krémem, rebarborou a sněhovou čepicí od Vojtěcha Vrtišky. Rada z etikety: Jakou teplotu má mít víno. Gastrotrend: Nápoje ve skle.

Pochoutkový rok slaví 10 let: Piškotový zákusek s pistáciovým krémem, rebarborou a sněhovou čepicí od Vojtěcha Vrtišky. Rada z etikety: Jakou teplotu má mít víno. Gastrotrend: Nápoje ve skle.

Można Pięknie Żyć *---Witaj! "Można Pięknie Żyć*" to seria podcastów, w której odkrywamy, jak zmiany w stylu życia mogą poprawić nasze zdrowie metaboliczne. Skupiamy się na Terapeutycznym Ograniczaniu Węglowodanów i jego pozytywnym wpływie na metabolizm oraz ogólne samopoczucie. Pamiętaj, że zdrowie zaczyna się od wiedzy, a my jesteśmy tu, aby dostarczać Ci inspirację i praktyczne wskazówki na drodze do pięknego życia. Zaczynamy!  Zgłaszasz się do profesjonalisty po pomoc w walce z insulinoopornością, nadciśnieniem czy stłuszczeniem wątroby i co słyszysz na dzień dobry? „Zamień białe pieczywo na pełnoziarniste”. Brzmi znajomo? Czas najwyższy obalić ten niebezpieczny mit! W tym odcinku udowadniamy, że bezmyślne powtarzanie tych przestarzałych zaleceń to serwowanie swojemu organizmowi metabolicznego sabotażu. Pokazujemy twarde dowody na to, jak kupny chleb demoluje Twoje zdrowie, i co najważniejsze – zdradzamy przepis na absolutnie najlepszy chleb świata, który zrobisz samodzielnie z zaledwie kilku składników. Gwarantujemy, że zakochasz się w nim od pierwszego kęsa!Z tego odcinka dowiecie się m.in.:Wielkie oszustwo pełnego ziarna –Trzy potężne problemy ukryte w kromce – dlaczego pieczywo z marketu pozbawia Twój organizm kluczowych minerałów, czym grozi unijne przyzwolenie na chemię w rolnictwie oraz jak powszechne białka roślinne dosłownie blokują Twoje receptory insulinowe.W drugiej części odcinka krok po kroku przygotowujemy dla Was genialny, niskoinsulinowy chleb na bazie mąki migdałowej, słonecznika i oleju kokosowego. Pieczony w 180°C chrupiący bochenek idealnie pasuje do masła lub smalcu i nie podnosi poziomu glukozy we krwi! Polub i udostępnij ten odcinek, aby pomóc nam promować niezależną wiedzę, i odpowiedz w komentarzu na pytanie zadane w filmie! UWAGA! Bilety na Low Carb Festival.  WYPRZEDANE zapisz się do listy oczekujących.Low Carb Festival To #1 Największe Na Świecie wydarzenie Dotyczące Odżywiania Niskowęglowodanowego

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

Fear as a Villain, Humanity as the Battleground Tony zoomed with filmmaker Christopher Piñero to explore how psychological horror can illuminate the most vulnerable corners of human experience. Piñero's award‑winning short film The Prowler — starring Aaron Dominguez of Only Murders in the Building — is a tense, unsettling descent into fear, paranoia, and the fragile line between perception and reality. As The Prowler prepares for its LA debut at the LA Shorts International Film Festival, Piñero joins Tony to unpack the film's origins, emotional architecture, and the cultural influences that shaped its haunting atmosphere. Piñero reveals the spark behind the film: “What if fear itself became the villain?” This question became the creative engine driving The Prowler, a story designed to keep audiences questioning what's real until the final moment. Tony and Piñero dig into how psychological horror can externalize internal battles — turning emotion into threat, and tension into narrative propulsion. Aaron Dominguez's Unsettling Performance Dominguez delivers a grounded, disquieting performance that anchors the film's psychological tension. Tony and Piñero discuss how Dominguez navigates the character's unraveling — guiding audiences through a night where nothing is quite what it seems. SAVE 17% ON PLUS  

In today's episode, I sit down with entrepreneur and inventor Sylvester Raymond, founder of Piñata Digital Marketing and Signal to Sale, to talk about building technology that solves real business problems. We discuss why humility creates opportunities, how listening to experienced mentors can change the direction of a company, and the challenge of explaining complex technology in simple terms. Sylvester shares how his ad-free monetization platform evolved into Signal to Sale, a system that helps businesses identify buyers at the right moment instead of wasting time on cold prospecting. We also explore AI, predictive data, sales timing, and revenue velocity.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Rebuilding a website is one of the largest marketing investments many firms make—but it's not always the smartest one. In this solo episode, Chris Dreyer, CEO of Rankings.io, breaks down one of the biggest misconceptions in legal marketing. Drawing from hundreds of law firm website audits, he explains why many firms often blame poor intake, weak marketing distribution, and generic SEO content on the website itself. Chris also shares when rebuilding actually makes sense, how to avoid common agency mistakes, and why some of the ugliest websites in personal injury continue to outperform beautifully designed competitors. You'll learn: When a law firm should rebuild its website—and when simple improvements create a better ROI. Why intake failures often reduce signed cases more than poor website design. How to evaluate whether your SEO agency has created valuable content or wasted your marketing budget. Why website messaging matters more than visual design for converting injury cases. We help elite personal injury firms turn their marketing into qualified opportunities and high-value signed cases. Check us out at ⁠Rankings.io⁠. Like what you hear? Hit Subscribe! We do this every week. If you want to keep learning from the best voices in PI, join us at PIMCON 2026. ⁠Buy your tickets now⁠! Subscribe to our newsletter and get the freshest news every Monday: ⁠newsletter.rankings.io⁠ Get Social! Personal Injury Mastermind w/ Chris Dreyer powered by Rankings.io is on ⁠Instagram⁠ | ⁠YouTube⁠ | ⁠TikTok⁠

Did you like this episode? Dislike it? ✍️ What blog topics bring personal injury clients to your law firm? Personal injury firms do not need to publish random blog content just to stay active online. They need content that answers the real questions potential clients are already searching for before they choose an attorney. In this episode, Danny Decker explains how PI law firms can think more strategically about blog topics, search intent, client questions, and law firm content marketing in 2026. If your firm wants its blog to support visibility, trust, and client acquisition, this episode gives you a better way to choose topics that actually connect with the people you want to reach.

Que paso con xavier Piñeros?Conviértete en un supporter de este podcast: https://www.spreaker.com/podcast/el-cartel-de-la-mega--4131412/support.

Every new piece of software promises to make your firm more efficient. Yet as your tech stack grows, so do the manual workarounds, disconnected data, and operational bottlenecks. Before long, your team becomes the integration layer between systems that were never designed to work together. Jim Andresen, the Chief Operating Officer of TorkLaw, oversees marketing, intake, HR, office operations, and IT for one of the nation's fastest-growing plaintiffs' firms. He's also the CEO of LawWorks, an AI-native operating system built from the operational challenges TorkLaw faced while scaling nationwide. In this episode, Jim joins Chris Dreyer to explain why disconnected technology quietly limits growth, how fragmented data weakens AI, and what firms should prioritize before adding another tool to their stack. They also discuss scaling across multiple states, choosing the right technology, building operational flexibility, and why the future belongs to firms that own—not rent—their data. You'll learn: Why legal tech becomes less effective when disconnected systems create operational bottlenecks. How AI-native operating systems improve data quality and workflow automation. What law firms should consider before investing in new case management software. Why controlling structured and unstructured data strengthens AI performance. We help elite personal injury firms turn their marketing into qualified opportunities and high-value signed cases. Check us out at Rankings.io. Like what you hear? Hit Subscribe! We do this every week. If you want to keep learning from the best voices in PI, join us at PIMCON 2026. Buy your tickets now! Subscribe to our newsletter and get the freshest news every Monday: newsletter.rankings.io Get Social! Personal Injury Mastermind w/ Chris Dreyer powered by Rankings.io is on Instagram | YouTube | TikTok

Cet été, nous vous proposons de (re)découvrir quelques épisodes marquants de ces derniers mois. Nous vous donnons rendez-vous à la rentrée pour des épisodes inédits !Nous sommes aujourd'hui avec Jean-François Piège, l'un des chefs français les plus influents de sa génération. Chef propriétaire, entrepreneur et figure majeure de la gastronomie contemporaine, il a construit, avec sa femme Élodie, un écosystème de restaurants singuliers, du très gastronomique « Grand Restaurant » aux tables plus accessibles et décomplexées : La Poule au Pot, À L'Épi d'Or et les Clover. Pour co-animer cet épisode de Business of Bouffe, Philibert est accompagné de Samir Ouriaghli, sourceur d'épices et fondateur d'Ankhor.À travers cet épisode, nous cherchons à comprendre comment Jean-François Piège a progressivement conquis sa liberté : celle de créer, d'entreprendre et de raconter sa propre vision de la gastronomie.Pour cela, Jean-François Piège revient sur ses débuts. Originaire de la Drôme, il raconte comment son rapport à la cuisine naît d'abord d'une fascination pour le savoir et les ingrédients. Très tôt, les livres deviennent son terrain d'apprentissage. Il y découvre la cuisine comme une culture, une histoire, un langage à maîtriser. Entre école hôtelière, premières brigades et rencontres fondatrices, notamment avec des chefs et enseignants qui joueront un rôle clé de mentors, Jean-François Piège pose les bases d'un parcours guidé par une curiosité insatiable et une exigence profonde.Ensemble, on évoque ensuite son ascension au sommet de la gastronomie, au Plaza Athénée puis à l'Hôtel de Crillon. Jean-François y apprend la rigueur absolue, la précision et la force du collectif, au contact de figures majeures de la gastronomie, comme Alain Ducasse. Mais derrière cette excellence, une envie grandit : celle de s'émanciper. Passer de l'exécution à la décision et porter une vision personnelle. Ces années nourrissent autant son exigence que son désir d'indépendance, prélude à un virage déterminant dans sa carrière.Avec Élodie, sa femme, Jean-François ouvre d'abord Clover, puis Le Grand Restaurant, un projet intime, exigeant et profondément incarné. Il partage les réalités de l'entrepreneuriat : risques, contraintes économiques, choix stratégiques, mais aussi la liberté de bâtir des lieux à son image. Chaque restaurant raconte une histoire différente, tout en s'inscrivant dans une vision cohérente de la gastronomie, fondée sur la singularité, la cuisson et le respect du produit.Cet épisode a été enregistré avec la participation exceptionnelle d'Élodie Piège. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

In this new episode, ⁠Crawlspace Media⁠'s Tim Pilleri and Lance Reenstierna discuss the mysterious disappearance of Kierra Coles from Chicago, Illinois with her mother Karen, retired Chicago PD detective Pam Childs, and PI from ⁠Private Investigations for the Missing⁠, Jordan Scherer. This is part two of two. Kierra Coles, a 26-year-old postal worker who was three months pregnant, vanished from Chicago's South Side on October 2, 2018, after last being seen on surveillance footage in her work uniform. Her car was later found parked near her apartment with her purse, cell phone, and packed lunch still inside, leading police to suspect foul play. If you have any information regarding the disappearance of Kierra Coles, you should contact the Chicago Police Special Victim's Unit at 312-747-8274. A $68,000 reward is currently being offered for information that leads to an arrest and conviction in her case. Check out Quince: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://quince.com/MISSING⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out Mint Mobile: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠mintmobile.com/missing⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out Kensington Publishing: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.kensingtonbooks.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Main podcast theme by Kevin Macleod. Check out his work at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://incompetech.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Additional music by David Williams. See his work at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠http://williamsflutes.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow Missing: IG: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/MissingCSM/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Youtube:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.youtube.com/missingcsm⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. FB:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.facebook.com/MissingCSM⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. X:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://twitter.com/MissingCSM⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Spotify:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://open.spotify.com/show/0yRXkJrZC85otfT7oXMcri⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Apple:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://podcasts.apple.com/us/podcast/missing/id1006974447⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow Crawlspace: IG:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.instagram.com/Crawlspacepodcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. TT:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.tiktok.com/@crawlspacepodcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. FB:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.facebook.com/Crawlspacepodcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. X:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://twitter.com/crawlspacepod.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Spotify:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://open.spotify.com/show/7iSnqnCf27NODdz0pJ1GvJ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Youtube:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.youtube.com/crawlspace⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Apple:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://podcasts.apple.com/us/podcast/crawlspace-true-crime-mysteries/id1187326340⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out our entire network at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ http://crawlspace-media.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow Private Investigations For the Missing and please donate if you can: ⁠https://investigationsforthemissing.org/⁠. ⁠http://piftm.org/donate⁠. ⁠https://twitter.com/PIFortheMissing⁠. ⁠https://www.facebook.com/PIFortheMissing/⁠. ⁠https://www.instagram.com/investigationsforthemissing/⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

Episode 22 of the Cosy Crime Classics Podcast sees us catch up with Thomas Magnum while he helps a big security company protect a new novel, as we take a look at J. ‘Digger' Doyle, from Season 1 of Magnum, PI. In this episode, Gerry and Iain discuss the power of the written word. This […]

In this Fuck Yeah Friday episode, Lesley Logan makes the case for celebrating the small stuff instead of holding your wins hostage until everything feels perfect. She shares an honest rant about technology that overpromises, a candid retirement-planning win with Brad, and a listener who finished all ten Spring Training classes. She digs into why tiny, bite-sized celebrations are what actually turn good intentions into habits. It is a warm, grounded reminder that the thing you just did is already worth honoring. If you have any questions about this episode or want to get some of the resources we mentioned, head over to LesleyLogan.co/podcast https://lesleylogan.co/podcast/. If you have any comments or questions about the Be It pod shoot us a message at beit@lesleylogan.co mailto:beit@lesleylogan.co. And as always, if you're enjoying the show please share it with someone who you think would enjoy it as well. It is your continued support that will help us continue to help others. Thank you so much! Never miss another show by subscribing at LesleyLogan.co/subscribe https://lesleylogan.co/podcast/#follow-subscribe-free.In this episode you will learn about:How modern tech overpromises while quietly making life harder.What Lesley and Brad's real retirement plan looks like.The listener streak that shows why consistency wins.Where confidence support actually comes from, and why.Why holding your celebrations hostage backfires on you.Episode References/Links:634 ft Gregg Lunceford - https://beitpod.com/ep634481 ft Steve Selengut - https://beitpod.com/ep481352 ft Tess Waresmith - https://beitpod.com/ep352@lmarieinsta - https://www.instagram.com/lmarieinstaSubmit your wins or questions - https://beitpod.com/questions If you enjoyed this episode, make sure and give us a five star rating and leave us a review on iTunes, Podcast Addict, Podchaser or Castbox. https://lovethepodcast.com/BITYSIDEALS! DEALS! DEALS! DEALS! https://onlinepilatesclasses.com/memberships/perks/#equipmentCheck out all our Preferred Vendors & Special Deals from Clair Sparrow, Sensate, Lyfefuel BeeKeeper's Naturals, Sauna Space, HigherDose, AG1 and ToeSox https://onlinepilatesclasses.com/memberships/perks/#equipmentBe in the know with all the workshops at OPC https://workshops.onlinepilatesclasses.com/lp-workshop-waitlistBe It Till You See It Podcast Survey https://pod.lesleylogan.co/be-it-podcasts-surveyBe a part of Lesley's Pilates Mentorship https://lesleylogan.co/elevate/FREE Ditching Busy Webinar https://ditchingbusy.com/Resources:Watch the Be It Till You See It podcast on YouTube! https://www.youtube.com/channel/UCq08HES7xLMvVa3Fy5DR8-gLesley Logan website https://lesleylogan.co/Be It Till You See It Podcast https://lesleylogan.co/podcast/Online Pilates Classes by Lesley Logan https://onlinepilatesclasses.com/Online Pilates Classes by Lesley Logan on YouTube https://www.youtube.com/channel/UCjogqXLnfyhS5VlU4rdzlnQProfitable Pilates https://profitablepilates.com/about/Follow Us on Social Media:Instagram https://www.instagram.com/lesley.logan/The Be It Till You See It Podcast YouTube channel https://www.youtube.com/channel/UCq08HES7xLMvVa3Fy5DR8-gFacebook https://www.facebook.com/llogan.pilatesLinkedIn https://www.linkedin.com/in/lesley-logan/The OPC YouTube Channel https://www.youtube.com/@OnlinePilatesClasses Episode Transcript:Lesley Logan 0:00  It's Fuck Yeah Friday. Brad Crowell 0:01  Fuck yeah. Lesley Logan 0:02  Get ready for some wins. Welcome to the Be It Till You See It podcast where we talk about taking messy action, knowing that perfect is boring. I'm Lesley Logan, Pilates instructor and fitness business coach. I've trained thousands of people around the world and the number one thing I see stopping people from achieving anything is self-doubt. My friends, action brings clarity and it's the antidote to fear. Each week, my guest will bring bold, executable, intrinsic and targeted steps that you can use to put yourself first and Be It Till You See It. It's a practice, not a perfect. Let's get started. Lesley Logan 0:48  Welcome back to the Be It Till You See It podcast. This is our Fuck Yeah Friday. This is where we share some wins, we get some mantras, we get to just be in each other's lives a little bit. I want to know what's going on with you. Send it to me, right? Send me in your wins, send me what you're working on. Send us the topics that you're wanting us to do solo episodes on, or get some interviews on. Also, I just want to say thank you to those of you who have bought flash cards or joined OPC recently, because your support of OPC is what powers this podcast. So, if you have not checked out what we have at OPC, we've got some really great work and get stuff for you, and so if you're Pi curious, Pilate's curious, we've got stuff for you, don't have to be a Pilates expert to join OPC, is for people who want access and accountability to their Pilates practice, and so big thank you, big shout out to all of you. Lesley Logan 1:33  Okay, maybe I should welcome OPC members who join, who are Be It Pod listeners, I don't know if I should do that, but maybe you should do that, maybe we should do a little shout out, so you can hear your name. It's fun to hear your name. It's fun to hear your name on a podcast, especially this one, because it's top 1% podcast, right? Okay, so my "I need a moment" is that I'm currently sitting on my phone because it won't stop dinging, even though it's on do not disturb, and my frustration and my rant is, okay, what? What is Do Not Disturb for if I get disturbed on it, and it's like, oh, well, if your favorite, so your favorite thing get through, because, of course, I want my husband to get through, but then it dings on him texting me, and it's like, well, why, why are you doing this? I do Do Not Disturb for a reason, so how do I get to totally turn off, but then you can't have it turned off, because what if someone has an emergency? Then Brad did something to his phone, so nothing gets through, which means literally there was one day when our websites went down, his sister and I are trying to call him, and I'm doing the double call, and all this stuff. We got to the point where I almost sent my father to the house to wake him up, because we're like, "We need to talk to you, and we couldn't get through. So I'm annoyed that Do Not Disturb work so badly that my husband has turned his phone to a place where we can't even call through, and it doesn't work at all for me, and that my phone is beeping, and I have to sit on it. Why do this podcast? Because in those last episodes, you probably heard it dinging, and I apologize for that. That's annoying. But, why? The thing about technology that pisses me off is, don't tell me it's great, and it's making my life easier when it's making my life shitty. You know what I mean? But then, if we don't have it, then it's like, well, that's gonna suck, right? That will totally suck.Lesley Logan 3:06  So, anyway, all right, now a win, because if you get to complain, you have to have a win, and when you're not, we talked about this last week, the win does not have to be in the same complaint, doesn't have to be from that, because that I don't want to talk, I want to be like, oh, so and so died, but the win in it is, that's terrible shit, that's not it at all. My win is Brad and I adulted, we talked with our wealth managers, and things are looking good, and that's, you know, that's nice, because it doesn't feel like the world is doing well, and I am an elder millennial, and I want to be an elder millennial who gets to retire like I am, I want to retire, not today, don't freak out, but I want to be someone who gets to retire, that feels like a great.. we don't.. we've had guests on this podcast who talk about having your retirement persona. Let me tell you what it is. It is owner of a compound with my Golden Girl lifestyle, and all of my DINCs, dual income, no children couples, each having their own motel room, and then we have this massive Pilates studio slash gym, massive restaurant-style kitchen that we all get to cook in, so no one has to cook in their place, and no one has to have all these different, they just have a, their motel room is like their their place of living, their personal space, but then we have all these communal spaces we can take care of each other, because who else is going to take care of us? So, my win is that we're at least investing in a way that I was going to allow us to retire someday, more money would be better, but we're doing pretty good, considering what's going on in the world today. So, that's a nice win. Lesley Logan 4:36  So, now let me pull my phone out, so I can read one of yours. This is @lmarieinsta. I did all 10 Spring Training classes, and joined OPC! That's insane. @lmarieinsta, way to go. 10 classes, huge. I watched the ones that I wasn't in because I had to do the thing, and if you did, those were hard. Way to go, way to do all 10, proud of you. See, you guys, that's a win, right? When you're like, I committed to something, and I'm going to do what I can, and then you celebrate that you did it. We've got to normalize celebrating that you did the thing you want to do. It doesn't mean that you did this, shouldn't make a massive project, like I'm into spring training, and I'm celebrating that I did the spring training. That's the thing, right? Some of you are holding yourself hostage on celebrations that you're like, "I don't get to celebrate that I signed up for this language class until I can speak fluently. No, you have to celebrate, you signed up for it, you have to celebrate that you went to it, to celebrate that you quizzed yourself on it, to celebrate that you study a little extra, you have to celebrate these things. That's what makes them habits. That's why we do this. It's why we celebrate. So you get a little dopamine high, and then it makes it easier to do more things for yourself. People are always seeking out confidence support. You want to know where confidence support comes from? From doing the thing that you said you would do, and that's why you got to make it small. You got to make it bite-sized. You got to make it things that you can do. Lesley Logan 5:54  So, your mantra: I am a reflection of love and kindness. I am a reflection of love and kindness, a reflection of love and kindness. Yes, you are. All right, Be It babe. Thanks so much for listening. I hope you are loving these Friday episodes. If you have any questions about OPC, what we do there, our flash cards, how to use them, you can go to onlinepilatesclasses.com or you can reach out to us. We'll be happy to answer them. We have phone numbers you can chat to. Okay, until next time, Be It Till You See It.Lesley Logan 6:24  That's all I got for this episode of the Be It Till You See It Podcast. One thing that would help both myself and future listeners is for you to rate the show and leave a review and follow or subscribe for free wherever you listen to your podcast. Also, make sure to introduce yourself over at the Be It Pod on Instagram. I would love to know more about you. Share this episode with whoever you think needs to hear it. Help us and others Be It Till You See It. Have an awesome day. Be It Till You See It is a production of The Bloom Podcast Network. If you want to leave us a message or a question that we might read on another episode, you can text us at +1-310-905-5534 or send a DM on Instagram @BeItPod.Brad Crowell 7:06  It's written, filmed, and recorded by your host, Lesley Logan, and me, Brad Crowell.Lesley Logan 7:11  It is transcribed, produced and edited by the epic team at Disenyo.co.Brad Crowell 7:16  Our theme music is by Ali at Apex Production Music and our branding by designer and artist, Gianfranco Cioffi.Lesley Logan 7:23  Special thanks to Melissa Solomon for creating our visuals.Brad Crowell 7:26  Also to Angelina Herico for adding all of our content to our website. And finally to Meridith Root for keeping us all on point and on time.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

In this new episode, Crawlspace Media's Tim Pilleri and Lance Reenstierna discuss the mysterious disappearance of Kierra Coles from Chicago, Illinois with her mother Karen, retired Chicago PD detective Pam Childs, and PI from Private Investigations for the Missing, Jordan Scherer. This is part one of two. Kierra Coles, a 26-year-old postal worker who was three months pregnant, vanished from Chicago's South Side on October 2, 2018, after last being seen on surveillance footage in her work uniform. Her car was later found parked near her apartment with her purse, cell phone, and packed lunch still inside, leading police to suspect foul play. If you have any information regarding the disappearance of Kierra Coles, you should contact the Chicago Police Special Victim's Unit at 312-747-8274. A $68,000 reward is currently being offered for information that leads to an arrest and conviction in her case. You can also submit info via this link: https://cookcountysheriffil.gov/person/kierra-coles/. Check out Quince: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://quince.com/MISSING⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out Mint Mobile: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠mintmobile.com/missing⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out Kensington Publishing: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.kensingtonbooks.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Main podcast theme by Kevin Macleod. Check out his work at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://incompetech.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Additional music by David Williams. See his work at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠http://williamsflutes.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow Missing: IG: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/MissingCSM/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Youtube:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.youtube.com/missingcsm⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. FB:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.facebook.com/MissingCSM⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. X:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://twitter.com/MissingCSM⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Spotify:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://open.spotify.com/show/0yRXkJrZC85otfT7oXMcri⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Apple:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://podcasts.apple.com/us/podcast/missing/id1006974447⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow Crawlspace: IG:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.instagram.com/Crawlspacepodcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. TT:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.tiktok.com/@crawlspacepodcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. FB:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.facebook.com/Crawlspacepodcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. X:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://twitter.com/crawlspacepod.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Spotify:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://open.spotify.com/show/7iSnqnCf27NODdz0pJ1GvJ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Youtube:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.youtube.com/crawlspace⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Apple:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://podcasts.apple.com/us/podcast/crawlspace-true-crime-mysteries/id1187326340⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out our entire network at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ http://crawlspace-media.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow Private Investigations For the Missing and please donate if you can: https://investigationsforthemissing.org/. http://piftm.org/donate. https://twitter.com/PIFortheMissing. https://www.facebook.com/PIFortheMissing/. https://www.instagram.com/investigationsforthemissing/. Learn more about your ad choices. Visit megaphone.fm/adchoices

The biggest shift happening in the legal industry isn't AI—it's ownership. As more law firms and legal vendors take on private equity investment, the incentives behind how businesses operate are quietly changing. In this solo episode, Chris Dreyer, CEO of Rankings.io, shares his perspective on the growing influence of private equity throughout the legal industry. Drawing on years of working with personal injury firms, he explains why founder-led businesses often operate differently, how institutional knowledge disappears after acquisitions, and what happens when firms begin to view clients as inventory instead of people. You'll learn: Why founder-led businesses create a different client experience than private equity-backed companies. What AI search means for the future of law firm marketing and SEO. Why strategic content assets outperform high-volume content production. How month-to-month agency contracts create greater accountability and faster client results. We help elite personal injury firms turn their marketing into qualified opportunities and high-value signed cases. Check us out at Rankings.io. Like what you hear? Hit Subscribe! We do this every week. If you want to keep learning from the best voices in PI, join us at PIMCON 2026. Buy your tickets now! Subscribe to our newsletter and get the freshest news every Monday: newsletter.rankings.io Get Social! Personal Injury Mastermind w/ Chris Dreyer powered by Rankings.io is on Instagram | YouTube | TikTok

The difference between a policy-limits settlement and a multi-million-dollar recovery may not be the injury—it may be the question your firm never asked. Too many personal injury lawyers evaluate cases through a single lens, leaving hidden value on the table for both their clients and their firms. John Richmond is the Co-Founder and CEO of Richmond Vona, a Buffalo-based personal injury firm. Under his leadership, the firm earned a spot on the 2024 Inc. 5000 list, was recognized as an Inc. Best Workplace, and received the Crisp Game Changer Award for Excellence in Firm Culture. In this episode, John explains why lawyers should evaluate every significant personal injury case for potential product liability claims before resolving it. He shares how his background in asbestos and complex products litigation changed the way he analyzes everyday PI cases, along with the intake philosophy and high-performance culture that support his firm's rapid growth. You'll learn: Why attorneys should evaluate most serious injury cases for potential product liability claims. How hidden defective products can dramatically increase case value beyond policy limits. How high-performance hiring and accountability support long-term law firm growth. We help elite personal injury firms turn their marketing into qualified opportunities and high-value signed cases. Check us out at Rankings.io. Like what you hear? Hit Subscribe! We do this every week. If you want to keep learning from the best voices in PI, join us at PIMCON 2026. Buy your tickets now! Subscribe to our newsletter and get the freshest news every Monday: newsletter.rankings.io Get Social! Personal Injury Mastermind w/ Chris Dreyer powered by Rankings.io is on Instagram | YouTube | TikTok

How did a European Amazon seller use AI, logistics, keyword data, and Helium 10 to scale millions without selling in the U.S.? Today's guest reveals the hidden plays most sellers overlook. ► Watch The Podcasts On YouTube: https://www.youtube.com/@Helium10SeriousSellersPodcast?sub_confirmation=1 ► Instagram: instagram.com/serioussellerspodcast ► Free Amazon Seller Chrome Extension: https://h10.me/extension ► Sign Up For Helium 10: https://h10.me/signup  (Use SSP10 To Save 10% For Life) ► Learn How To Sell on Amazon: https://h10.me/ft In this episode of the Serious Sellers Podcast, Bradley Sutton welcomes Bartłomiej Piątkowski, better known as Bart, an Amazon seller and agency operator from Poland who has sold millions in Europe and helped other brands do the same. What makes his story especially interesting is that he has built this success without selling his own products in the U.S. marketplace. Bart's journey started far from the typical e-commerce path. Trained as a cook, he then worked in radio and electronics, and discovered Amazon in 2016 when his company was on the verge of shutting down. With only a small amount of money left, he began learning about Amazon through online communities, moved returned inventory from retail into Amazon Europe, and realized that marketplace margins could beat traditional retail margins. From there, his business expanded into private label brands, reselling, packaging products, textiles, and managing major brand relationships across Europe. The conversation gets tactical as Bart breaks down how his team uses Helium 10, AI, keyword data, and advertising automation to move faster. He explains how tools like Cerebro, Magnet, Search Query Performance, Helium 10 Ads, and the all-new Helium 10 MCP help his team understand how Amazon reads a catalog, identify keyword opportunities, adjust bids based on performance, and even research hundreds of products in a fraction of the time. He also shares launch strategies using lower starting prices, Vine, coupons, inserts, and social proof to help products gain momentum. Bart also reveals why logistics, mobile-first listings, and category attributes are becoming major advantages for sellers in Europe. From sending inventory directly into destination countries for faster Prime delivery to optimizing listings for mobile shoppers and browser filters, his message is clear: success on Amazon is no longer about doing one thing well. It is about connecting data, operations, AI, and customer behavior into one smarter system. For sellers willing to adapt, automate, and think strategically, this episode is a reminder that the next level of growth may come from fixing the invisible parts of the business others ignore. To connect with Bart and learn more about his work, visit his website at https://bartlomiejpiatkowski.pl/en or check out his YouTube channel at https://www.youtube.com/@tdda_amzteam/videos. In episode 756 of the Serious Sellers Podcast, Bradley and Bart discuss: 00:00 - Introduction 04:14 - Discovering Amazon In 2016 06:43 - From Reselling To Private Label 11:15 - Bart's Favorite Helium 10 Tools 13:48 - Using AI And MCP For Amazon Research 15:22 - Automating Amazon Ads And Bids 16:46 - Scaling Keywords With SQP Data 20:58 - Launch Pricing And Social Proof 22:56 - Why Mobile-First Listings Matter 23:37 - Attributes That Drive Discoverability 26:01 - Europe's Biggest Logistics Mistake 28:48 - Expanding Beyond Amazon Europe