Podcasts about Alessio

Entrevista de Pablo Wende a Eduardo D´Alessio, presidente de la consultora D´Alessio IROL, a propósito del último informe del Monitor de Humor Social y Político.

Weekend ungherese con una lotta a tre per la vittoria che ci ha regalato una gara interessantissima sotto il profilo strategico! Purtroppo per la Ferrari l'ottima prestazione di Leclerc si è conclusa con un amarissimo quarto posto, con molte ombre e poche luci sui motivi che hanno visto la Rossa perdere improvvisamente performance nel terzo stint.Ad approfittare paradossalmente di una partenza sfortunata è Norris, che con una strategia a una sosta porta a scuola tutti quanti e vince un'altra gara, portandosi a pochi punti dal compagno prima della sosta estiva.Di questi e di altri temi parliamo ampiamente nella nuova puntata di ZonaDRS con Alessio, Angelo e Giacomo! Ci trovate anche su Youtube (CANALE YOUTUBE): buon ascolto!

Nous sommes l'eglise – Nouvelle série de messages

ENTREVISTA CON ALESSIO LISCI (31/07/2025)See omnystudio.com/listener for privacy information.

ENTREVISTA CON ALESSIO LISCI (31/07/2025)See omnystudio.com/listener for privacy information.

LA PIZARRA DE QUINTANA: LA GOLEADA DEL FC BARCELONA Y ENTREVISTA CON ALESSIO LISCI, ENTRENADOR DE CA OSASUNASee omnystudio.com/listener for privacy information.

Come si fa a non crollare nei momenti più duri della vita da imprenditore?In questa puntata di Confidenze Imprenditoriali parliamo di quei periodi in cui il peso delle responsabilità sembra schiacciarti: decisioni importanti, notti insonni e la sensazione costante di dover correre.Ti raccontiamo come Alessio ha affrontato uno di questi momenti, quali scelte hanno fatto la differenza e perché imparare a fermarsi, delegare e prendersi cura di sé non è un lusso… ma l'unico modo per non perdere la rotta.Se stai vivendo un periodo stressante, questa puntata è per te.-------------

In seiner Predigt zu Kolosser 4 fragt Alessio Passarella: „Was ist Kirche eigentlich?“ und zeigt auf, dass Kirche kein Event ist, sondern ein lebendiger Leib, in dem jeder Einzelne Verantwortung trägt. Anhand der vielen namentlich erwähnten Mitarbeiter des Paulus wird deutlich, wie wichtig jeder Beitrag ist – ob sichtbar oder im Hintergrund – um gemeinsam Christus ähnlicher zu werden und das Reich Gottes aktiv mitzugestalten. Die Botschaft lädt ein, Kirche nicht zu konsumieren, sondern sich mutig und liebevoll einzubringen – mit dem, was Gott jedem anvertraut hat. Bibelstellen: Kolosser 4

No doubt you've come across people using the analogy of a broken down machine to describe an osteoarthritic joint. Or perhaps that too much loading on the joint is responsible for wearing it out - assuming that each joint has a finite number of movements in its lifetime. You've probably also heard from patients who are concerned that the exercise therapy you prescribe might do more harm to their already worn-out joint. But is that true? Today, Dr Alessio Bricca (Centre for Muscle and Joint Health, University of Southern Denmark) explores the evidence and refutes these beliefs. ------------------------------ RESOURCES Exercise Therapy "Wears Down" My Knee Joint: Myth or Reality?: https://www.jospt.org/doi/10.2519/jospt.2025.13069

Dopo l'errore di Silverstone Piastri torna a vincere dimostrandosi perfetto in fase di partenza sotto Safety car; un Norris un po' addormentato perde lo spunto e si fa sorprendere sul Kemmel: è il momento decisivo, perché a quel punto Piastri avrà la priorità nei pit e la sua gara si farà in discesa.Torna a podio la Ferrari, con un Leclerc che approfitta soprattutto della partenza ritardata: il suo assetto scarico lo avrebbe messo in difficoltà contro Verstappen su Red Bull carica e invece, con gran parte del gp corsa su asciutto, il monegasco è riuscito a spuntarla, nonostante un errore sul finale.Verstappen a cui invece va una menzione doverosa per aver vinto la sprint al sabato, con una buona strategia di assetto che gli ha permesso di sviluppare una grande velocità, in grado di tenere dietro le agguerrite McLaren.Di questi e di altri temi parliamo ampiamente nella nuova puntata di ZonaDRS con Alessio, Angelo e Giacomo! Ci trovate anche su Youtube (CANALE YOUTUBE): buon ascolto!

Due giornate tra onde, chitarre distorte e passione vera: Michelangelo Rombi e Alessio Schirru raccontano l'anima del Beach Day Out, evento simbolo dell'underground sardo. Cagliari si prepara ad accogliere Beach Day Out Volume 19, il festival che da quasi vent'anni anima la spiaggia del Poetto con musica punk, rock e hardcore, proponendo un'esperienza che va oltre il semplice concerto. Un evento che nasce dal basso, dalla passione di chi vive la scena musicale indipendente e la vuole condividere. Michelangelo Rombi e Alessio Schirru, due dei principali organizzatori, ci hanno raccontato cosa c'è dietro le quinte di questo appuntamento sempre più atteso. L'idea del festival, spiegano, nasceva dall'esigenza di dare spazio alle band sarde che producevano musica originale. “All'inizio erano venti gruppi, si iniziava nel pomeriggio e si finiva all'alba”, ricorda Michelangelo. Un progetto che ha saputo crescere, aprendosi a ospiti italiani e internazionali, mantenendo però saldo il filo conduttore: musica autentica, non commerciale, fortemente identitaria. Tra scouting, passione e collaborazione: così nasce la line-up ogni anno La scelta delle band non è mai casuale. “Diamo spazio alle nuove uscite, a chi ha appena pubblicato un disco, ma anche ai gruppi giovani e emergenti dell'isola che si affacciano alla scena underground”, racconta Alessio. Fondamentale anche il lavoro di Stefano Panzeri, storico collaboratore del festival, e oggi anche l'apporto dei Cool Kids, collettivo di cui Schirru fa parte. Tra gli ospiti dell'edizione 2025 spiccano nomi come Discomostro da Milano e Burning heads dalla Francia, ma il valore aggiunto è sempre il mix tra il locale e il globale. La collaborazione con la webzine Punkadeka.it garantirà quest'anno anche la diretta streaming dell'evento, amplificando la portata nazionale e internazionale del festival. Un festival che nasce dalla sabbia, tra memoria e futuro Il Beach Day Out non è solo musica, ma anche atmosfera, libertà e condivisione. “Il palco guarda la Sella del Diavolo, il pubblico può godersi i concerti in costume, magari dopo un tuffo. È un'esperienza unica”, racconta Rombi. L'edizione di quest'anno parte al tramonto, con il calore del sole e delle chitarre distorte a colorare la spiaggia. E tra un'organizzazione sempre più rodata, amicizie che si trasformano in booking, e la voglia di rinnovarsi, si pensa già alla ventesima edizione. “Ogni anno è una sfida, ma anche un'enorme soddisfazione”, confermano entrambi. Un ricordo su tutti? “Portare in Sardegna band che ascoltavamo da adolescenti, sederci con loro a cena, condividere storie. È questo lo spirito del festival.”

In diesem zweiten Teil der Kolosser-Brief-Reihe erklärt Alessio Passarella, wie Paulus in Kolosser 2 eindringlich davor warnt, Jesus durch strikte Regeln, religiöse Rituale oder übersinnliche Schau-Erfahrungen zu ersetzen. Er betont, dass wahre geistliche Freiheit und Identität allein in der lebendigen Verbindung mit Christus liegen – nicht im Befolgen menschlicher Gebote. Das Video fordert dazu heraus, Jesus wirklich ins Zentrum zu stellen und sich von religiösem Schatten-Denken zu befreien. FÜR JESUS ENTSCHIEDEN ⁠⁠⁠⁠⁠| Wir wollen mit dir feiern ⁠⁠⁠⁠⁠SPENDEN⁠⁠⁠⁠⁠ | Vielen Dank für deine Unterstützung ⁠⁠⁠⁠⁠GEBET & HILFE⁠⁠⁠⁠⁠ | Wir sind für dich da ⁠⁠⁠⁠⁠PRAISE REPORT⁠⁠⁠⁠⁠ | Wie hat Gott in deinem leben gewirkt? 

Nel secondo episodio di A(Maze)ing Fantasy, Alessio ci guida alla scoperta di una saga forse poco conosciuta dal grande pubblico, ma che ha saputo custodire e rinnovare l'anima della letteratura fantasy epica: Il Ciclo dell'Eredità di Christopher Paolini.Partendo dalle radici culturali e geografiche dell'autore — cresciuto tra leggende, racconti fantastici e i paesaggi mozzafiato del Montana — Alessio ci racconta come questa serie sia riuscita a rendere omaggio ai grandi maestri del fantasy, pur distinguendosi per un sistema magico originale, solido e ben congegnato.Un episodio dedicato a chi ama il worldbuilding curato, le avventure che profumano di classico e la passione sincera per i draghi, gli elfi e i destini scritti nelle stelle.

Fatta fu!Non posso spiegarvi quanto io mi sia divertito sia a girare che a montare la nostra chiacchierata con Alessio Ciolino per EGOriferiti.La più grande forza di Alessio è senz'altro l'ironia, ma non ci ha risparmiato il suo lato fragile, dimostrandosi un fervente e sincero idealista. Probabilmente non lo voterò mai (pure perché vivo ad Isola delle Femmine), eppure non smetterò di seguire appassionatamente le sue gesta… e voi?

Episódio pedido com convidados especiais! Shin Koheo e Mario recebem Alessio e Load pra elencarem seus games preferidos neste Top Master System. Vivard como é Nintendista, ficou de fora.Tops do episódio (deve ter coisa errada aí, qualquer coisa dá um toque que a gente arruma haha)Load3- Wolfchild2- Kenseiden1- Spider-Man vs. the KingpinMenções Honrosas- Cheese Cat-astrophe (ligerinho)- Ghost House (chapolin)- Cloud Master - Tom and Jerry: The MovieAlessio3- Duck 2- Phantasy Star1- Alex Kidd in Shinobi WorldMenções Honrosas- Psyco Fox- Asterix- Y's- Doube DragonMenções horrorosas- Óculos 3D- Mônica no Castelo no DragãoShin Koheo3- Jogos de Verão2- Rampage1- The Luck Dime CapperMenções Honrosas- EminemMario3- Monica no Castelo do Dragão (Wonderboy in Monster Land)2- Psycho Fox1- Safari HuntMenções Honrosas- Altered Beast- Jogo do Labirinto

La copertina del nostro nuovo episodio va inevitabilmente e con grande gioia al primo podio in carriera di uno strepitoso Nico Hulkenberg, che sotto la pioggia agguanta un terzo posto figlio non solo di strategie azzeccate ma anche di una prestazione eccellente.Seconda doppietta di fila per la McLaren, con Norris che va a vincerla approfittando di un errore grossolano di Piastri al termine della SC. Verstappen, che aveva puntato tutto su un assetto da asciutto, paga scotto sull'acqua e si ritrova sesto dopo alcuni pasticci.Ferrari aveva promesso tanto al venerdì e al sabato ma poi si ritrova a fare il gambero sul bagnato.Di questi e di altri temi parliamo ampiamente nella nuova puntata di ZonaDRS con Alessio, Angelo e Giacomo! Ci trovate anche su Youtube (CANALE YOUTUBE): buon ascolto!

Prophetisch leben – wenn Gott zu dir spricht Was bedeutet es eigentlich, Gottes Stimme zu hören? Alessio Passarella nimmt dich mit hinein in das, was prophetisches Reden heute sein kann: kraftvoll, alltagstauglich und immer aufbauend. Eine Predigt über Mut zum Hören, das Prüfen prophetischer Worte – und warum ein einziges Wort zur rechten Zeit manchmal mehr bewirkt als tausend Sätze. Bibelstellen: 1. Korinther 14,1; 1. Korinther 14,3; Amos 3,7; Johannes 10,27; 1. Thessalonicher 5,21; Offenbarung 19,10; 2. Petrus 1,19; Apostelgeschichte 13,2; 1. Könige 19,12; 2. Samuel 12. ⁠FÜR JESUS ENTSCHIEDEN ⁠| Wir wollen mit dir feiern ⁠SPENDEN⁠ | Vielen Dank für deine Unterstützung ⁠GEBET & HILFE⁠ | Wir sind für dich da ⁠PRAISE REPORT⁠ | Wie hat Gott in deinem leben gewirkt? 

Si conferma il trend dei due piloti McLaren che non riescono a imporsi costantemente l'uno sull'altro: dopo l'erroraccio del Canada, Norris torna a vincere e si riporta a un podio di distanza dal compagno Piastri, che comunque ha fatto un weekend molto positivo, insidiando Lando fino all'ultimo giro.Ferrari porta a casa un 3°-4° posto positivo perché stacca Mercedes nettamente, accoglie bene gli aggiornamenti e non termina a distanze siderali dalla McLaren.Si ritira, non per colpa sua, dalla gara e crediamo anche dalla lotta mondiale il buon Max Verstappen, centrato in pieno da un Antonelli che fa il suo primo vero errore grave della stagione dopo ben 11 gare in cui si è, a onor del vero, comportato in maniera molto convincente.Di questi e di altri temi parliamo ampiamente nella nuova puntata di ZonaDRS con Alessio, Angelo e Giacomo! Ci trovate anche su Youtube (CANALE YOUTUBE): buon ascolto!Le musiche presenti nell'episodio sono free copyright e sono distribuite dai seguenti siti:- Heroes (EPIC): https://inaudio.org/track/heroes-epic/ - Airoso: "WombatNoisesAudio - Airoso" is under a Creative Commons (BY 3.0) license: https://creativecommons.org/licenses/...   / user-734462061   Music powered by BreakingCopyright

Prophetisch leben – wenn Gott zu dir sprichtWas bedeutet es eigentlich, Gottes Stimme zu hören? Alessio Passarella nimmt dich mit hinein in das, was prophetisches Reden heute sein kann: kraftvoll, alltagstauglich und immer aufbauend. Eine Predigt über Mut zum Hören, das Prüfen prophetischer Worte – und warum ein einziges Wort zur rechten Zeit manchmal mehr bewirkt als tausend Sätze. Bibelstellen: 1. Korinther 14,1; 1. Korinther 14,3; Amos 3,7; Johannes 10,27; 1. Thessalonicher 5,21; Offenbarung 19,10; 2. Petrus 1,19; Apostelgeschichte 13,2; 1. Könige 19,12; 2. Samuel 12. FÜR JESUS ENTSCHIEDEN | Wir wollen mit dir feiern SPENDEN | Vielen Dank für deine Unterstützung GEBET & HILFE | Wir sind für dich da PRAISE REPORT | Wie hat Gott in deinem leben gewirkt? 

Cosa significa oggi educare alla legalità? Alessio Pasquini, giornalista e Direttore Generale della Fondazione Scintille di futuro ETS, se lo chiede da anni. Con alle spalle un percorso tra istituzioni, scuola e impegno civile (dal Ministero dell'Istruzione alla Fondazione Falcone, fino al fianco di Pietro Grasso), Alessio ha fatto della cultura della legalità una missione da portare ai più giovani. In questa puntata ci racconta perché parlare di Costituzione, giustizia e responsabilità non è mai stato così urgente. E perché accendere una scintilla può fare la differenza. ▫️ Qui trovi tutti i dettagli sul Digital Detox Festival!

Quante volte ti sei dett* “non ho tempo”? Per poi ritrovarti ad aprire il telefono come prima cosa al mattino… Alessio Carciofi è il direttore artistico del Digital Detox Festival. Ma per arrivare a questa nuova strada (che si è costruito da solo) ha dovuto attraversare un burnout, con quattro mesi di insonnia. Questa è solo la prima delle tante interviste che realizzeremo durante questi tre giorni di detox a contatto con la natura, immersi nello splendido paesaggio di Sauris. Se non potrai essere presente di persona, ti porteremo con noi alla scoperta delle storie degli oltre 40 speaker di questo evento unico nel suo genere. ▫️ Qui trovi tutti i dettagli sul Digital Detox Festival!

In today's episode, we're joined by Alessio Monteleone, a true icon in the world of retro classic aesthetics. Alessio has become one of the most recognized and widely talked about creators whose work perfectly captures that nostalgic, vintage vibe we all love. From iconic imagery to timeless design influences, Alessio breaks down what makes the retro classic style so enduring and relevant even today. We explore Alessio's creative process, the inspiration behind his signature images, and how he brings the past into the present through his art. Whether you're a vintage enthusiast, a designer, or simply someone fascinated by retro culture, this episode offers a fascinating glimpse into a world where old-school charm meets modern creativity. Get ready to step back in time, appreciate the art of nostalgia, and hear firsthand how Alessio Monteleone has helped shape the conversation around what it means to be truly “retro classic.”

In questa puntata, Katia e Alessio propongono un modo originale e divertente per esercitare la pronuncia italiana: gli scioglilingua!Un episodio davvero speciale, diverso dal solito: non parleremo di grammatica, né di luoghi iconici o espressioni idiomatiche… ma vi sfideremo con un esercizio divertente e utilissimo per migliorare la vostra pronuncia!Siete pronti a sciogliere la lingua e mettere alla prova le vostre abilità fonetiche? Perché oggi parleremo… di scioglilingua! Quegli strani e simpatici tormentoni linguistici che, pur sembrando giochi di parole senza senso, sono perfetti per allenare i muscoli della nostra bocca e migliorare la dizione!Preparatevi a ripetere, sbagliare, ridere… e soprattutto imparare!

In questa puntata, Katia e Alessio ci portano alla scoperta di un tema... divino — anzi no, di-vino!

Shaun Escoffery - Days Like This / Beth Orton - Central Reservation /Miguel Migs - The Remedy /Meshell Ndegeocello - Earth /Solaris - Sunshine /Johnny Corporate - Sunday Shoutin' /Supersmack - Back In The Day /Johnny D & Nicki P - Revenge /United Future Organization ft Dee Dee Bridgewater- Flying Saucer /Finley Quaye - Spiritualized /Ruben Macias ft Maya Angelou - I Rise /The Supermen Lovers - Starlight /Black Science Orchestra - New Jersey Deep /Dusted off the turntables last Sunday afternoon and put together this mix of some favorite vinyl from back in the day. It has a jazzy mellow sunshine vibe, I hope U like it. Welcome to the newest podcast subscribers Marvin Collett, Alessio, and Sikhumbuzo! Thanks for listening!Deep House Episodes is among the Top Deep Podcasts on Goodpodshttps://goodpods.com/leaderboard/top-100-shows-by-category/other/deep?period=alltime#65812784Deep House Episodes was selected as one of the Top 100 Music Podcasts on the web by Feedspot! https://podcast.feedspot.com/music_podcasts/

Fluent Fiction - Italian: A Comic Knight: How Passion and Mistakes Create Success Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2025-06-02-22-34-02-it Story Transcript:It: Il sole risplendeva sulle strade acciottolate del villaggio medievale.En: The sun shone brightly on the cobblestone streets of the medieval village.It: Erano i primi giorni di giugno, e l'aria era piena di profumi di spezie e grida allegre.En: It was the beginning of June, and the air was filled with the scents of spices and cheerful cries.It: Era la Festa della Repubblica, e una fiera medievale animava il centro del villaggio.En: It was the Republic Day, and a medieval fair enlivened the village center.It: Tende colorate ondeggiavano al vento, musicisti suonavano melodie allegre e gli abitanti del villaggio si godevano la giornata.En: Colorful tents waved in the wind, musicians played joyful melodies, and the villagers enjoyed the day.It: Alessio, un giovane attore con grandi sogni, camminava tra la folla con gli occhi sgranati.En: Alessio, a young actor with big dreams, walked through the crowd with wide eyes.It: Da lontano, aveva visto un grande tabellone che annunciava un "torneo di giostra" e aveva subito pensato fosse un'audizione per attori.En: From afar, he had seen a large board advertising a "tournament of jousts" and immediately thought it was an audition for actors.It: "Finalmente, la mia occasione per brillare!"En: "Finally, my chance to shine!"It: pensò, senza alcun dubbio che la giostra fosse uno spettacolo teatrale.En: he thought, without any doubt that the joust was a theatrical performance.It: Nel frattempo, Bianca, la sua amica arguta e sempre pronta a sorridere, lo seguiva curvando le labbra in un sorriso divertito.En: Meanwhile, Bianca, his witty and always smiling friend, followed him with a playful smile.It: Conosceva bene Alessio e sapeva che anche una piccola confusione poteva trasformarsi in un'avventura.En: She knew Alessio well and knew that even a small misunderstanding could turn into an adventure.It: Arrivati al campo di giostra, Alessio avvicinò gli organizzatori con occhi brillanti.En: Arriving at the jousting field, Alessio approached the organizers with bright eyes.It: "Buongiorno!En: "Good morning!It: Sono qui per partecipare all'audizione," annunciò con fiducia.En: I'm here to participate in the audition," he announced confidently.It: Gli uomini si guardarono perplessi.En: The men looked at each other puzzled.It: In quel momento, Luciano, il cavaliere destinato a partecipare, entrò in scena in armatura completa.En: At that moment, Luciano, the knight destined to participate, entered the scene in full armor.It: Alessio, non scoraggiato, iniziò a spiegare il suo "ruolo" come un cavaliere che si era trasformato in attore.En: Alessio, undeterred, began to explain his "role" as a knight who had turned into an actor.It: Luciano, colpito dalla sua passione, decise di aiutarlo.En: Luciano, struck by his passion, decided to help him.It: "Un attore?En: "An actor?It: Interessante!En: Interesting!It: Mostrami cosa sai fare," disse con un sorriso.En: Show me what you can do," he said with a smile.It: Bianca, che ascoltava ogni parola, si coprì la bocca per non ridere.En: Bianca, who was listening to every word, covered her mouth to avoid laughing.It: Con grande teatralità, Alessio inventò una storia affascinante.En: With great theatricality, Alessio invented a fascinating story.It: "Sono Sir Alessio, il cavalier della scena!En: "I am Sir Alessio, the knight of the stage!It: Ho viaggiato molto, combattendo draghi sui palcoscenici e salvando principesse con le parole."En: I have traveled far, fighting dragons on stage, and saving princesses with words."It: Gli organizzatori, divertiti dalla performance di Alessio, gli concessero di partecipare.En: The organizers, amused by Alessio's performance, allowed him to participate.It: "Sarà interessante," dissero con occhi scintillanti.En: "It will be interesting," they said with sparkling eyes.It: Arrivato il momento del "torneo", Alessio era in sella a un cavallo per la prima volta.En: When the "tournament" time came, Alessio was on horseback for the first time.It: Tentò disperatamente di mantenere l'equilibrio, ma più si muoveva, più il cavallo sembrava confuso.En: He tried desperately to maintain balance, but the more he moved, the more the horse seemed confused.It: Avanzando maldestramente, Alessio finì per colpire accidentalmente un'asta con una bandiera, facendola cadere comicamente.En: Moving awkwardly forward, Alessio ended up accidentally hitting a pole with a flag, making it fall comically.It: Uno scoppio di risate esplose tra la folla.En: A burst of laughter erupted from the crowd.It: Invece di imbarazzo, Alessio si unì al divertimento, improvvisando un monologo umoristico sul suo improbabile alter-ego di cavaliere.En: Instead of embarrassment, Alessio joined in the fun, improvising a humorous monologue about his unlikely knight alter-ego.It: Quando la giostra finì, gli organizzatori, colpiti dall'intrattenimento che Alessio aveva regalato, lo invitarono a tenere uno spettacolo comico al festival.En: When the joust ended, the organizers, impressed by the entertainment Alessio had provided, invited him to hold a comedy show at the festival.It: Alessio accettò entusiasta, finalmente riconosciuto non come un cavaliere, ma come un artista in grado di far ridere.En: Alessio enthusiastically accepted, finally recognized not as a knight, but as an artist capable of making people laugh.It: E così, quel giorno, Alessio imparò che a volte, i sogni trovano strade inaspettate.En: And so, that day, Alessio learned that sometimes, dreams find unexpected paths.It: Il giusto mix di passione, humor e improvvisazione può trasformare qualsiasi occasione in una scena perfetta, anche in una giostra medievale.En: The right mix of passion, humor, and improvisation can turn any occasion into a perfect scene, even in a medieval joust.It: Bianca, a sua volta, prometteva di non lasciarsi mai sfuggire uno spettacolo di Alessio, certo che fosse il miglior attore del villaggio, anche senza spada e armatura.En: Bianca, in turn, promised never to miss an Alessio show, certain that he was the best actor in the village, even without a sword and armor. Vocabulary Words:the sun: il solethe scent: il profumoto shine: risplenderethe cobblestone: l'acciottolatomedieval: medievalethe village: il villaggiocheerful: allegrothe fair: la fierathe tent: la tendato wave: ondeggiarethe joust: la giostrathe audition: l'audizionethe organizer: l'organizzatorethe knight: il cavalierethe armor: l'armaturato deter: scoraggiarethe stage: il palcoscenicothe performance: la performancethe tournament: il torneoawkwardly: maldestramentethe pole: l'astato improvise: improvvisarethe monologue: il monologounexpected: inaspettatothe dream: il sognohumorous: umoristicothe alter-ego: l'alter-egoto recognize: riconoscerethe path: la stradathe sword: la spada

Discover the secrets behind scaling multimillion-dollar businesses as the founder of Skill for Impact shares his journey from Apple to marketing mastermind, helping top entrepreneurs achieve massive success.   In this episode of Sharkpreneur, Seth Greene speaks with Alessio Pieroni, the founder of Skill for Impact, who shares his journey from working at Apple to scaling a marketing agency that has helped entrepreneurs like Tony Robbins, Jordan Peterson, and Robert Kiyosaki achieve massive growth. With over a decade of experience, he led Mindvalley's expansion from $25 million to $75 million as CMO before launching his agency. Specializing in high-impact marketing strategies, he has masterminded successful webinars, challenges, and summits, including a book launch that became a New York Times bestseller with over 70,000 attendees.   Key Takeaways: → Learn how a career shift led to building a thriving marketing agency from the ground up. → Learn how funnels are used to scale businesses and achieve success without relying on referrals. → Discover the key elements that make webinars, challenges, and summits successful. → Find out how VIP upgrades boost conversions for high-ticket products. → Get insights into scaling from six-figure revenue to seven figures with tailored strategies.   Alessio Pieroni is a digital marketing consultant, expert, and speaker dedicated to scaling online education businesses from seven to eight figures. With over a decade of experience, he has helped generate more than $100 million in revenue for the companies he has worked with or consulted for. His expertise includes product marketing, growth marketing, data analytics, funnel marketing, and digital advertising. Alessio is known for creating high-impact content and campaigns that drive business growth. He is a firm believer in the power of online education to democratize learning and revolutionize the traditional education system. His work focuses on empowering businesses to achieve exceptional success through innovative digital marketing strategies.   Connect With Alessio: Alessio Pieroni Instagram Facebook LinkedIn   Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of The Functional Nurse Podcast, nurse coach Laura Alessio shares her journey from public health nursing to legal nurse consulting and ultimately to nurse coaching. The conversation explores integrating functional medicine with nurse coaching, celebrating client wins, and how personal health experiences shape professional practice. Topics include the differences between mindfulness and meditation, the challenges of building a coaching practice, and strategies for explaining the nurse coach role. The episode also highlights the importance of advocacy, networking, and community building within the functional medicine and nursing fields, encouraging aspiring nurse coaches to follow their passions. Ways to connect with Laura: ➡️ https://www.nursecoachalessio.com/www.nursecoachalessio.com   ➡️ https://www.facebook.com/share/16PC2fRegf/?mibextid=wwXIfr   ➡️ https://www.instagram.com/nursecoachalessio/   ➡️ https://www.linkedin.com/in/laura-alessiornmsnphnncbc/   ➡️ https://linktr.ee/nursecoachalessio   ➡️ www.youtube.com/@NurseCoachAlessio   ➡️ https://x.com/RNCoachAlessio     To stay up to date with the latest and upcoming, please sign up for my newsletter by visiting https://www.brigittesager.com/BrigitteSager.com. Hosted by Brigitte Sager, NP, a functional medicine nurse practitioner, nurse coach, and an RN and NP FM educator. Consider sharing this podcast with other nurses on your social media platforms, in a text, or listen together on this page or share this link to the website and podcast. We also now have video episodes on YouTube!

The Sound of Spaghetti with Alessio Tonin - 14.05.2025 by

The Sound of Spaghetti with Alessio Tonin - 02.04.2025 by

Con Pierpaolo Greco, Alessio Pianesani, Francesco Serino e Jacopo Di Giuli alla regia.In questa puntata si parla di recensioni "guidate" e libertà di critica: dal caso del film Ballerina – con embargo differenziato in base alla valutazione – alla strategia di Nvidia per la 5060, che ha sollevato dubbi sull'imparzialità dell'informazione tech.A seguire, un cambio totale di tono: si entra nel mondo delle abitudini igieniche internazionali, tra water americani, "bacio di Poseidone" e ansie di Alessio su relazioni e pulizia... il tutto condito dall'ironia dissacrante tipica del Cortocircuito.

Hablamos con el técnico del Mirandés después de depender de sí mismos para ascender a Primera y repasamos las mejores noticias polideportivas con el sueño de Juan Ayuso de vestirse la maglia rosa y ser campeón del Giro de Italia.

Hablamos con el técnico del Mirandés después de depender de sí mismos para ascender a Primera y repasamos las mejores noticias polideportivas con el sueño de Juan Ayuso de vestirse la maglia rosa y ser campeón del Giro de Italia.

Fluent Fiction - Italian: Facing Fears: Alessio's Journey to Embrace the Outside World Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2025-05-16-22-34-02-it Story Transcript:It: Alessio sedeva sulla sedia, i nervi tesi come corde di violino.En: Alessio sat on the chair, his nerves taut like violin strings.It: Le sue mani giocherellavano con un lembo del suo maglione, mentre guardava fuori dalla finestra.En: His hands played with a corner of his sweater, as he looked out the window.It: La luce del sole primaverile baciava il giardino fuori dalla clinica, suggerendo un mondo di possibilità che lui temeva.En: The spring sunlight kissed the garden outside the clinic, suggesting a world of possibilities that he feared.It: La porta si aprì delicatamente, e il viso sorridente di Giada illuminò la stanza.En: The door opened gently, and Giada's smiling face lit up the room.It: "Ciao, Alessio," disse rassicurante.En: "Hello, Alessio," she said reassuringly.It: Sedendosi, posò un taccuino sul tavolino tra di loro.En: Sitting down, she placed a notebook on the table between them.It: "Come ti senti oggi?"En: "How do you feel today?"It: Alessio alzò le spalle.En: Alessio shrugged.It: "Preoccupato," ammise a bassa voce.En: "Worried," he admitted softly.It: "Uscire… mi spaventa ancora."En: "Going out... still scares me."It: Giada annuì comprensiva.En: Giada nodded understandingly.It: "Parliamo della tua ultima esperienza al mercato," disse.En: "Let's talk about your last experience at the market," she said.It: "È stato un grande passo per te."En: "It was a big step for you."It: Riluttante, Alessio cominciò a raccontare.En: Reluctantly, Alessio began to recount.It: Immaginò di nuovo il mercato: bancarelle colorate, odori di spezie e frutta fresca, il brusio costante della folla.En: He imagined the market again: colorful stalls, the smells of spices and fresh fruit, the constant buzz of the crowd.It: Il caos lo aveva sopraffatto.En: The chaos had overwhelmed him.It: Sentiva la paura salire, come una marea implacabile.En: He felt the fear rising, like an unstoppable tide.It: Durante quella giornata, avevano camminato tra la gente, il cuore di Alessio batteva forte.En: During that day, they had walked among people, Alessio's heart beating fast.It: Poi, come un'onda improvvisa, il panico lo travolse.En: Then, like a sudden wave, panic swept over him.It: Si fermò, respirando a malapena.En: He stopped, barely breathing.It: "Ma ho fatto come mi hai detto," continuò Alessio, guardando Giada negli occhi.En: "But I did as you told me," Alessio continued, looking Giada in the eyes.It: "Ho usato la tecnica di respirazione."En: "I used the breathing technique."It: "Sì, l'hai fatto," annuì Giada sorridendo.En: "Yes, you did," Giada nodded smiling.It: "E ha funzionato, vero?"En: "And it worked, right?"It: Alessio si sentì leggermente rilassato.En: Alessio felt slightly relaxed.It: Annui.En: He nodded.It: La sensazione di ansia era diminuita abbastanza da permettergli di continuare fino a quando non si sono ritirati al sicuro nella clinica.En: The feeling of anxiety had diminished enough to let him continue until they retreated safely into the clinic.It: "La prossima volta sarà più facile," disse Giada con convinzione.En: "Next time will be easier," said Giada with conviction.It: "Ogni piccolo passo è una vittoria."En: "Every little step is a victory."It: Alessio sorrise debolmente.En: Alessio smiled faintly.It: Non era completamente convinto, ma sentiva una minuscola scintilla di fiducia in più.En: He wasn't completely convinced, but he felt a tiny spark of more confidence.It: Un gradino alla volta, pensò.En: One step at a time, he thought.It: Giada appoggiò una mano gentile sulla sua.En: Giada placed a gentle hand on his.It: "Sono orgogliosa di te.En: "I'm proud of you.It: Hai fatto grandi progressi."En: You've made great progress."It: Tornato nella sua stanza, Alessio guardò di nuovo fuori dalla finestra.En: Back in his room, Alessio looked out the window again.It: Il giardino sembrava meno minaccioso, più invitante.En: The garden seemed less threatening, more inviting.It: Era lontano dall'essere guarito, ma oggi aveva dimostrato a sé stesso che poteva affrontare le sue paure.En: He was far from healed, but today he had proven to himself that he could face his fears.It: Un passo dopo l'altro, il viaggio verso la libertà poteva continuare.En: One step after another, the journey towards freedom could continue.It: E in fondo, non era solo.En: And deep down, he was not alone.It: Giada era al suo fianco, e per la prima volta, Alessio credeva che ce l'avrebbe fatta.En: Giada was by his side, and for the first time, Alessio believed that he would make it.It: La primavera portava nuova vita, nuova speranza.En: Spring brought new life, new hope.It: Il sole brillava più caldo e il mondo, sebbene ancora spaventoso, sembrava più gestibile.En: The sun shone warmer, and the world, although still frightening, seemed more manageable. Vocabulary Words:the chair: la sediataut: tesithe strings: le cordethe corner: il lembothe sweater: il maglionesuggesting: suggerendofaintly: debolmentethe crowd: la follathe chaos: il caosunstoppable: implacabilethe garden: il giardinothe clinic: la clinicareassuringly: rassicuranteto shrink: ritrassithe sunlight: la luce del solethe market: il mercatobreathed: respirandogently: delicatamentethe notebook: il taccuinothe step: il passoreluctantly: riluttantethe panic: il panicothe fear: la paurato sweep over: travolseto nod: annuirefirmly: con convinzionethe smile: il sorrisoto diminish: diminuitathe victory: la vittoriato heal: guarito

In questa puntata di Start, un intoppo che ha fatto saltare i nervi a tanti professionisti e contribuenti; una vicenda che fa male raccontare; una scoperta incredibile che sembra uscita da un film di fantascienza; infine, la storia di Alessio. Se vuoi dirmi le difficoltà e le sfide che, come giovane, incontri nella tua vita quotidiana o, semplicemente, dirmi la tua opinione sulle notizie che hai ascoltato oggi, puoi mandare un'email a angelica.migliorisi@ilsole24ore.com Learn more about your ad choices. Visit megaphone.fm/adchoices

Appuntamento fisso del Venerdì pomeriggio dalle 16 alle 18, torna IL CORTOCIRCUITO con il solito trio delle meraviglie, ovvero Pierpaolo Greco, Alessio Pianesani e Francesco Serino (e pure Jacopo Di Giuli alla Regia), per 2 ore di scoppiettante intrattenimento a ruota libera, con il supporto anche del pubblico, grazie alle chiamate in tempo reale e agli immancabili vocali!

In questa puntata di Start, il costo della mensa scolastica italiana; la condanna di Gérard Depardieu; un libro che sta facendo tremare il Partito Democratico americano; infine, la storia di Alessio e sua moglie. Se vuoi dirmi le difficoltà e le sfide che, come giovane, incontri nella tua vita quotidiana o, semplicemente, dirmi la tua opinione sulle notizie che hai ascoltato oggi, puoi mandare un'email a angelica.migliorisi@ilsole24ore.com Learn more about your ad choices. Visit megaphone.fm/adchoices

Il Cortocircuito – Podcast Ufficiale di intrattenimento videoludico e non soloOgni venerdì pomeriggio dalle 16:00 alle 18:00 torna Il Cortocircuito, il talk-show live di Multiplayer.it con il trio delle meraviglie: Pierpaolo Greco, Alessio Pianesani e Francesco Serino, supportati dalla regia di Jacopo Di Giuli. Due ore di discussioni infuocate, ironia tagliente, analisi senza filtri e il contributo diretto del pubblico con chiamate e vocali in tempo reale.In questa puntata:

Alessio Caliandro"Gli incarnati"Rubbettino Editorehttps://www.store.rubbettinoeditore.it/catalogo/gli-incarnati/«La osservai ancora per un po', mi resi conto che quella figura non affiorava semplicemente dal passato, ma forse da un'altra vita, o da un sogno, o da un'esistenza solo possibile che non si era mai realizzata»In un presente distopico, sullo sfondo di una Roma grigia e alienante, il protagonista de Gli incarnati fugge dalle proprie frustrazioni quotidiane coltivando un'ossessione erotica per una giovane sconosciuta. Tale desiderio sarà così incontenibile da condurlo a mutare nel corpo. Nel suo testicolo destro si svilupperà un tumore dall'assurda morfologia cerebrale. La sua seconda intelligenza, quella della carne, lo guiderà alla liberazione da ogni convenzione sociale e familiare, consentendogli di sperimentare la pulsione pura del corpo. L'incontro, poi, con la Donna clitoride, suo pendant al femminile, metterà in pericolo l'intera civiltà.Alessio Caliandro (1977) è originario di Martina Franca e vive a Roma, dove si è laureato in Filosofia e in Studi storico-religiosi. Ha collaborato con “Nuovi Argomenti” e pubblicato il saggio di storia delle religioni Il Prete Gianni e la performatività del mito (Fallone 2022). Nel 2023, con Gli incarnati, è stato finalista della XXXVI edizione del Premio Italo CIL POSTO DELLE PAROLEascoltare fa pensarewww.ilpostodelleparole.itDiventa un supporter di questo podcast: https://www.spreaker.com/podcast/il-posto-delle-parole--1487855/support.

Alessio Torino"Il palio delle rane"Mondadori Editorewww.mondadori.itPerché a Luceoli, nel cuore dell'Appennino, si celebri come tutti gli anni il Palio delle Rane, sono necessarie regole, passione, dedizione. E non solo per trasformare la gara in una manifestazione in costume, colorata e insaporita da piatti “degni della festa”. Ci vuole qualcuno che abbia cura dei piccoli anfibi, che li nutra, che li prepari. E allora ecco, come in una fiaba bizzarra, crudele e dolcissima ci viene incontro la giovane Raniera, Gran Custode del Palio. Per lei, cuore semplice, incantata testimone, tutto cambia quando a terremotare le sue certezze arriva Das Lubbert, che di quelle rane è fratello. Nessuno degli abitanti di Luceoli – tutti incollati alle loro consuetudini – ha mai saputo leggere oltre la corsa degli scarriolanti, oltre il teatro della festa, dei banchetti, oltre i soprannomi che ciascuno si porta addosso. E invece. E invece non era tutto così semplice, neanche per il semplice cuore della Raniera. E adesso che fare? La storia si ribalta? La favola si incrina? In questo rito tribale, arcaico, favoloso, si avverte un confronto serrato con la natura umana e animale. La scrittura di Alessio Torino ci vola dentro a ritmi di ballata, e coglie – fra rane, ragni, topi, cicale cinesi, rondini – un sentimento del tempo che straripa come un torrente, e dice di noi.Alessio Torino è nato a Cagli nel 1975. Ha esordito con Undici decimi (Italic, 2010, premio Bagutta Opera Prima). In seguito ha pubblicato Tetano (2011), Urbino, Nebraska (2013) e Tina (2016), editi da minimum fax; Al centro del mondo (2020) e Cuori in piena (2023), editi da Mondadori. Ha vinto, fra gli altri, il premio Lo Straniero, il premio Frontino Montefeltro e il premio Mondello. Tetano e Urbino, Nebraska sono stati ristampati negli Oscar Mondadori (rispettivamente nel 2023 e 2025). Ha scritto Passare il fiume (Orecchio Acerbo, 2024), illustrato da Simone Massi con il quale ha collaborato in sede di sceneggiatura per il film di animazione Invelle (2024).IL POSTO DELLE PAROLEascoltare fa pensarewww.ilpostodelleparole.itDiventa un supporter di questo podcast: https://www.spreaker.com/podcast/il-posto-delle-parole--1487855/support.

Questo episodio è speciale perché per la prima volta i nostri due speaker, Katia e Alessio, si sono incontrati di persona e hanno registrato una puntata dal vivo! Questo ci ha ispirato l'argomento dell'episodio di oggi: come si dice in italiano quando si incontra una persona che non si conosce? Piacere! Ma il Galateo, cioè quell'insieme di regole che definiscono la buona educazione, non è d'accordo.Scopriamo perché e ascoltiamo il parere di alcuni ospiti presenti all'inaugurazione della nuova sede della Scuola Leonardo Da Vinci di Torino.

In questa puntata di Start, l'apertura del fronte dazi tra Europa e Stati Uniti con Giorgia Meloni che scende in campo; una notizia che mescola elezioni, troll e TikTok; una vicenda che ha a che fare con l'Italia, con l'estero... e con la pensione; infine, la storia di Alessio. Se vuoi raccontarmi le difficoltà e le sfide che, come giovane, incontri nella tua vita quotidiana o, semplicemente, dirmi la tua opinione sulle notizie che hai ascoltato oggi, puoi mandare un'email a angelica.migliorisi@ilsole24ore.com

The sound of Spaghetti with Alessio Tonin - 05.03.25 by

To unpack some of the most topical questions in AI, I'm joined by two fellow AI podcasters: Swyx and Alessio Fanelli, co-hosts of the Latent Space podcast. We've been wanting to do a cross-over episode for a while and finally made it happen.Swyx brings deep experience from his time at AWS, Temporal, and Airbyte, and is now focused on AI agents and dev tools. Alessio is an investor at Decibel, where he's been backing early technical teams pushing the boundaries of infrastructure and applied AI. Together they run Latent Space, a technical newsletter and podcast by and for AI engineers.To subscribe or learn more about Latent Space, click here: https://www.latent.space/ [0:00] Intro[1:08] Reflecting on AI Surprises of the Past Year[2:24] Open Source Models and Their Adoption[6:48] The Rise of GPT Wrappers[7:49] Challenges in AI Model Training[10:33] Over-hyped and Under-hyped AI Trends[24:00] The Future of AI Product Market Fit[30:27] Google's Momentum and Customer Support Insights[33:16] Emerging AI Applications and Market Trends[35:13] Challenges and Opportunities in AI Development[39:02] Defensibility in AI Applications[42:42] Infrastructure and Security in AI[50:04] Future of AI and Unanswered Questions[55:34] Quickfire With your co-hosts: @jacobeffron - Partner at Redpoint, Former PM Flatiron Health @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @ericabrescia - Former COO Github, Founder Bitnami (acq'd by VMWare) @jordan_segall - Partner at Redpoint

If you're in SF: Join us for the Claude Plays Pokemon hackathon this Sunday!If you're not: Fill out the 2025 State of AI Eng survey for $250 in Amazon cards!We are SO excited to share our conversation with Dharmesh Shah, co-founder of HubSpot and creator of Agent.ai.A particularly compelling concept we discussed is the idea of "hybrid teams" - the next evolution in workplace organization where human workers collaborate with AI agents as team members. Just as we previously saw hybrid teams emerge in terms of full-time vs. contract workers, or in-office vs. remote workers, Dharmesh predicts that the next frontier will be teams composed of both human and AI members. This raises interesting questions about team dynamics, trust, and how to effectively delegate tasks between human and AI team members.The discussion of business models in AI reveals an important distinction between Work as a Service (WaaS) and Results as a Service (RaaS), something Dharmesh has written extensively about. While RaaS has gained popularity, particularly in customer support applications where outcomes are easily measurable, Dharmesh argues that this model may be over-indexed. Not all AI applications have clearly definable outcomes or consistent economic value per transaction, making WaaS more appropriate in many cases. This insight is particularly relevant for businesses considering how to monetize AI capabilities.The technical challenges of implementing effective agent systems are also explored, particularly around memory and authentication. Shah emphasizes the importance of cross-agent memory sharing and the need for more granular control over data access. He envisions a future where users can selectively share parts of their data with different agents, similar to how OAuth works but with much finer control. This points to significant opportunities in developing infrastructure for secure and efficient agent-to-agent communication and data sharing.Other highlights from our conversation* The Evolution of AI-Powered Agents – Exploring how AI agents have evolved from simple chatbots to sophisticated multi-agent systems, and the role of MCPs in enabling that.* Hybrid Digital Teams and the Future of Work – How AI agents are becoming teammates rather than just tools, and what this means for business operations and knowledge work.* Memory in AI Agents – The importance of persistent memory in AI systems and how shared memory across agents could enhance collaboration and efficiency.* Business Models for AI Agents – Exploring the shift from software as a service (SaaS) to work as a service (WaaS) and results as a service (RaaS), and what this means for monetization.* The Role of Standards Like MCP – Why MCP has been widely adopted and how it enables agent collaboration, tool use, and discovery.* The Future of AI Code Generation and Software Engineering – How AI-assisted coding is changing the role of software engineers and what skills will matter most in the future.* Domain Investing and Efficient Markets – Dharmesh's approach to domain investing and how inefficiencies in digital asset markets create business opportunities.* The Philosophy of Saying No – Lessons from "Sorry, You Must Pass" and how prioritization leads to greater productivity and focus.Timestamps* 00:00 Introduction and Guest Welcome* 02:29 Dharmesh Shah's Journey into AI* 05:22 Defining AI Agents* 06:45 The Evolution and Future of AI Agents* 13:53 Graph Theory and Knowledge Representation* 20:02 Engineering Practices and Overengineering* 25:57 The Role of Junior Engineers in the AI Era* 28:20 Multi-Agent Systems and MCP Standards* 35:55 LinkedIn's Legal Battles and Data Scraping* 37:32 The Future of AI and Hybrid Teams* 39:19 Building Agent AI: A Professional Network for Agents* 40:43 Challenges and Innovations in Agent AI* 45:02 The Evolution of UI in AI Systems* 01:00:25 Business Models: Work as a Service vs. Results as a Service* 01:09:17 The Future Value of Engineers* 01:09:51 Exploring the Role of Agents* 01:10:28 The Importance of Memory in AI* 01:11:02 Challenges and Opportunities in AI Memory* 01:12:41 Selective Memory and Privacy Concerns* 01:13:27 The Evolution of AI Tools and Platforms* 01:18:23 Domain Names and AI Projects* 01:32:08 Balancing Work and Personal Life* 01:35:52 Final Thoughts and ReflectionsTranscriptAlessio [00:00:04]: Hey everyone, welcome back to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Small AI.swyx [00:00:12]: Hello, and today we're super excited to have Dharmesh Shah to join us. I guess your relevant title here is founder of Agent AI.Dharmesh [00:00:20]: Yeah, that's true for this. Yeah, creator of Agent.ai and co-founder of HubSpot.swyx [00:00:25]: Co-founder of HubSpot, which I followed for many years, I think 18 years now, gonna be 19 soon. And you caught, you know, people can catch up on your HubSpot story elsewhere. I should also thank Sean Puri, who I've chatted with back and forth, who's been, I guess, getting me in touch with your people. But also, I think like, just giving us a lot of context, because obviously, My First Million joined you guys, and they've been chatting with you guys a lot. So for the business side, we can talk about that, but I kind of wanted to engage your CTO, agent, engineer side of things. So how did you get agent religion?Dharmesh [00:01:00]: Let's see. So I've been working, I'll take like a half step back, a decade or so ago, even though actually more than that. So even before HubSpot, the company I was contemplating that I had named for was called Ingenisoft. And the idea behind Ingenisoft was a natural language interface to business software. Now realize this is 20 years ago, so that was a hard thing to do. But the actual use case that I had in mind was, you know, we had data sitting in business systems like a CRM or something like that. And my kind of what I thought clever at the time. Oh, what if we used email as the kind of interface to get to business software? And the motivation for using email is that it automatically works when you're offline. So imagine I'm getting on a plane or I'm on a plane. There was no internet on planes back then. It's like, oh, I'm going through business cards from an event I went to. I can just type things into an email just to have them all in the backlog. When it reconnects, it sends those emails to a processor that basically kind of parses effectively the commands and updates the software, sends you the file, whatever it is. And there was a handful of commands. I was a little bit ahead of the times in terms of what was actually possible. And I reattempted this natural language thing with a product called ChatSpot that I did back 20...swyx [00:02:12]: Yeah, this is your first post-ChatGPT project.Dharmesh [00:02:14]: I saw it come out. Yeah. And so I've always been kind of fascinated by this natural language interface to software. Because, you know, as software developers, myself included, we've always said, oh, we build intuitive, easy-to-use applications. And it's not intuitive at all, right? Because what we're doing is... We're taking the mental model that's in our head of what we're trying to accomplish with said piece of software and translating that into a series of touches and swipes and clicks and things like that. And there's nothing natural or intuitive about it. And so natural language interfaces, for the first time, you know, whatever the thought is you have in your head and expressed in whatever language that you normally use to talk to yourself in your head, you can just sort of emit that and have software do something. And I thought that was kind of a breakthrough, which it has been. And it's gone. So that's where I first started getting into the journey. I started because now it actually works, right? So once we got ChatGPT and you can take, even with a few-shot example, convert something into structured, even back in the ChatGP 3.5 days, it did a decent job in a few-shot example, convert something to structured text if you knew what kinds of intents you were going to have. And so that happened. And that ultimately became a HubSpot project. But then agents intrigued me because I'm like, okay, well, that's the next step here. So chat's great. Love Chat UX. But if we want to do something even more meaningful, it felt like the next kind of advancement is not this kind of, I'm chatting with some software in a kind of a synchronous back and forth model, is that software is going to do things for me in kind of a multi-step way to try and accomplish some goals. So, yeah, that's when I first got started. It's like, okay, what would that look like? Yeah. And I've been obsessed ever since, by the way.Alessio [00:03:55]: Which goes back to your first experience with it, which is like you're offline. Yeah. And you want to do a task. You don't need to do it right now. You just want to queue it up for somebody to do it for you. Yes. As you think about agents, like, let's start at the easy question, which is like, how do you define an agent? Maybe. You mean the hardest question in the universe? Is that what you mean?Dharmesh [00:04:12]: You said you have an irritating take. I do have an irritating take. I think, well, some number of people have been irritated, including within my own team. So I have a very broad definition for agents, which is it's AI-powered software that accomplishes a goal. Period. That's it. And what irritates people about it is like, well, that's so broad as to be completely non-useful. And I understand that. I understand the criticism. But in my mind, if you kind of fast forward months, I guess, in AI years, the implementation of it, and we're already starting to see this, and we'll talk about this, different kinds of agents, right? So I think in addition to having a usable definition, and I like yours, by the way, and we should talk more about that, that you just came out with, the classification of agents actually is also useful, which is, is it autonomous or non-autonomous? Does it have a deterministic workflow? Does it have a non-deterministic workflow? Is it working synchronously? Is it working asynchronously? Then you have the different kind of interaction modes. Is it a chat agent, kind of like a customer support agent would be? You're having this kind of back and forth. Is it a workflow agent that just does a discrete number of steps? So there's all these different flavors of agents. So if I were to draw it in a Venn diagram, I would draw a big circle that says, this is agents, and then I have a bunch of circles, some overlapping, because they're not mutually exclusive. And so I think that's what's interesting, and we're seeing development along a bunch of different paths, right? So if you look at the first implementation of agent frameworks, you look at Baby AGI and AutoGBT, I think it was, not Autogen, that's the Microsoft one. They were way ahead of their time because they assumed this level of reasoning and execution and planning capability that just did not exist, right? So it was an interesting thought experiment, which is what it was. Even the guy that, I'm an investor in Yohei's fund that did Baby AGI. It wasn't ready, but it was a sign of what was to come. And so the question then is, when is it ready? And so lots of people talk about the state of the art when it comes to agents. I'm a pragmatist, so I think of the state of the practical. It's like, okay, well, what can I actually build that has commercial value or solves actually some discrete problem with some baseline of repeatability or verifiability?swyx [00:06:22]: There was a lot, and very, very interesting. I'm not irritated by it at all. Okay. As you know, I take a... There's a lot of anthropological view or linguistics view. And in linguistics, you don't want to be prescriptive. You want to be descriptive. Yeah. So you're a goals guy. That's the key word in your thing. And other people have other definitions that might involve like delegated trust or non-deterministic work, LLM in the loop, all that stuff. The other thing I was thinking about, just the comment on Baby AGI, LGBT. Yeah. In that piece that you just read, I was able to go through our backlog and just kind of track the winter of agents and then the summer now. Yeah. And it's... We can tell the whole story as an oral history, just following that thread. And it's really just like, I think, I tried to explain the why now, right? Like I had, there's better models, of course. There's better tool use with like, they're just more reliable. Yep. Better tools with MCP and all that stuff. And I'm sure you have opinions on that too. Business model shift, which you like a lot. I just heard you talk about RAS with MFM guys. Yep. Cost is dropping a lot. Yep. Inference is getting faster. There's more model diversity. Yep. Yep. I think it's a subtle point. It means that like, you have different models with different perspectives. You don't get stuck in the basin of performance of a single model. Sure. You can just get out of it by just switching models. Yep. Multi-agent research and RL fine tuning. So I just wanted to let you respond to like any of that.Dharmesh [00:07:44]: Yeah. A couple of things. Connecting the dots on the kind of the definition side of it. So we'll get the irritation out of the way completely. I have one more, even more irritating leap on the agent definition thing. So here's the way I think about it. By the way, the kind of word agent, I looked it up, like the English dictionary definition. The old school agent, yeah. Is when you have someone or something that does something on your behalf, like a travel agent or a real estate agent acts on your behalf. It's like proxy, which is a nice kind of general definition. So the other direction I'm sort of headed, and it's going to tie back to tool calling and MCP and things like that, is if you, and I'm not a biologist by any stretch of the imagination, but we have these single-celled organisms, right? Like the simplest possible form of what one would call life. But it's still life. It just happens to be single-celled. And then you can combine cells and then cells become specialized over time. And you have much more sophisticated organisms, you know, kind of further down the spectrum. In my mind, at the most fundamental level, you can almost think of having atomic agents. What is the simplest possible thing that's an agent that can still be called an agent? What is the equivalent of a kind of single-celled organism? And the reason I think that's useful is right now we're headed down the road, which I think is very exciting around tool use, right? That says, okay, the LLMs now can be provided a set of tools that it calls to accomplish whatever it needs to accomplish in the kind of furtherance of whatever goal it's trying to get done. And I'm not overly bothered by it, but if you think about it, if you just squint a little bit and say, well, what if everything was an agent? And what if tools were actually just atomic agents? Because then it's turtles all the way down, right? Then it's like, oh, well, all that's really happening with tool use is that we have a network of agents that know about each other through something like an MMCP and can kind of decompose a particular problem and say, oh, I'm going to delegate this to this set of agents. And why do we need to draw this distinction between tools, which are functions most of the time? And an actual agent. And so I'm going to write this irritating LinkedIn post, you know, proposing this. It's like, okay. And I'm not suggesting we should call even functions, you know, call them agents. But there is a certain amount of elegance that happens when you say, oh, we can just reduce it down to one primitive, which is an agent that you can combine in complicated ways to kind of raise the level of abstraction and accomplish higher order goals. Anyway, that's my answer. I'd say that's a success. Thank you for coming to my TED Talk on agent definitions.Alessio [00:09:54]: How do you define the minimum viable agent? Do you already have a definition for, like, where you draw the line between a cell and an atom? Yeah.Dharmesh [00:10:02]: So in my mind, it has to, at some level, use AI in order for it to—otherwise, it's just software. It's like, you know, we don't need another word for that. And so that's probably where I draw the line. So then the question, you know, the counterargument would be, well, if that's true, then lots of tools themselves are actually not agents because they're just doing a database call or a REST API call or whatever it is they're doing. And that does not necessarily qualify them, which is a fair counterargument. And I accept that. It's like a good argument. I still like to think about—because we'll talk about multi-agent systems, because I think—so we've accepted, which I think is true, lots of people have said it, and you've hopefully combined some of those clips of really smart people saying this is the year of agents, and I completely agree, it is the year of agents. But then shortly after that, it's going to be the year of multi-agent systems or multi-agent networks. I think that's where it's going to be headed next year. Yeah.swyx [00:10:54]: Opening eyes already on that. Yeah. My quick philosophical engagement with you on this. I often think about kind of the other spectrum, the other end of the cell spectrum. So single cell is life, multi-cell is life, and you clump a bunch of cells together in a more complex organism, they become organs, like an eye and a liver or whatever. And then obviously we consider ourselves one life form. There's not like a lot of lives within me. I'm just one life. And now, obviously, I don't think people don't really like to anthropomorphize agents and AI. Yeah. But we are extending our consciousness and our brain and our functionality out into machines. I just saw you were a Bee. Yeah. Which is, you know, it's nice. I have a limitless pendant in my pocket.Dharmesh [00:11:37]: I got one of these boys. Yeah.swyx [00:11:39]: I'm testing it all out. You know, got to be early adopters. But like, we want to extend our personal memory into these things so that we can be good at the things that we're good at. And, you know, machines are good at it. Machines are there. So like, my definition of life is kind of like going outside of my own body now. I don't know if you've ever had like reflections on that. Like how yours. How our self is like actually being distributed outside of you. Yeah.Dharmesh [00:12:01]: I don't fancy myself a philosopher. But you went there. So yeah, I did go there. I'm fascinated by kind of graphs and graph theory and networks and have been for a long, long time. And to me, we're sort of all nodes in this kind of larger thing. It just so happens that we're looking at individual kind of life forms as they exist right now. But so the idea is when you put a podcast out there, there's these little kind of nodes you're putting out there of like, you know, conceptual ideas. Once again, you have varying kind of forms of those little nodes that are up there and are connected in varying and sundry ways. And so I just think of myself as being a node in a massive, massive network. And I'm producing more nodes as I put content or ideas. And, you know, you spend some portion of your life collecting dots, experiences, people, and some portion of your life then connecting dots from the ones that you've collected over time. And I found that really interesting things happen and you really can't know in advance how those dots are necessarily going to connect in the future. And that's, yeah. So that's my philosophical take. That's the, yes, exactly. Coming back.Alessio [00:13:04]: Yep. Do you like graph as an agent? Abstraction? That's been one of the hot topics with LandGraph and Pydantic and all that.Dharmesh [00:13:11]: I do. The thing I'm more interested in terms of use of graphs, and there's lots of work happening on that now, is graph data stores as an alternative in terms of knowledge stores and knowledge graphs. Yeah. Because, you know, so I've been in software now 30 plus years, right? So it's not 10,000 hours. It's like 100,000 hours that I've spent doing this stuff. And so I've grew up with, so back in the day, you know, I started on mainframes. There was a product called IMS from IBM, which is basically an index database, what we'd call like a key value store today. Then we've had relational databases, right? We have tables and columns and foreign key relationships. We all know that. We have document databases like MongoDB, which is sort of a nested structure keyed by a specific index. We have vector stores, vector embedding database. And graphs are interesting for a couple of reasons. One is, so it's not classically structured in a relational way. When you say structured database, to most people, they're thinking tables and columns and in relational database and set theory and all that. Graphs still have structure, but it's not the tables and columns structure. And you could wonder, and people have made this case, that they are a better representation of knowledge for LLMs and for AI generally than other things. So that's kind of thing number one conceptually, and that might be true, I think is possibly true. And the other thing that I really like about that in the context of, you know, I've been in the context of data stores for RAG is, you know, RAG, you say, oh, I have a million documents, I'm going to build the vector embeddings, I'm going to come back with the top X based on the semantic match, and that's fine. All that's very, very useful. But the reality is something gets lost in the chunking process and the, okay, well, those tend, you know, like, you don't really get the whole picture, so to speak, and maybe not even the right set of dimensions on the kind of broader picture. And it makes intuitive sense to me that if we did capture it properly in a graph form, that maybe that feeding into a RAG pipeline will actually yield better results for some use cases, I don't know, but yeah.Alessio [00:15:03]: And do you feel like at the core of it, there's this difference between imperative and declarative programs? Because if you think about HubSpot, it's like, you know, people and graph kind of goes hand in hand, you know, but I think maybe the software before was more like primary foreign key based relationship, versus now the models can traverse through the graph more easily.Dharmesh [00:15:22]: Yes. So I like that representation. There's something. It's just conceptually elegant about graphs and just from the representation of it, they're much more discoverable, you can kind of see it, there's observability to it, versus kind of embeddings, which you can't really do much with as a human. You know, once they're in there, you can't pull stuff back out. But yeah, I like that kind of idea of it. And the other thing that's kind of, because I love graphs, I've been long obsessed with PageRank from back in the early days. And, you know, one of the kind of simplest algorithms in terms of coming up, you know, with a phone, everyone's been exposed to PageRank. And the idea is that, and so I had this other idea for a project, not a company, and I have hundreds of these, called NodeRank, is to be able to take the idea of PageRank and apply it to an arbitrary graph that says, okay, I'm going to define what authority looks like and say, okay, well, that's interesting to me, because then if you say, I'm going to take my knowledge store, and maybe this person that contributed some number of chunks to the graph data store has more authority on this particular use case or prompt that's being submitted than this other one that may, or maybe this one was more. popular, or maybe this one has, whatever it is, there should be a way for us to kind of rank nodes in a graph and sort them in some, some useful way. Yeah.swyx [00:16:34]: So I think that's generally useful for, for anything. I think the, the problem, like, so even though at my conferences, GraphRag is super popular and people are getting knowledge, graph religion, and I will say like, it's getting space, getting traction in two areas, conversation memory, and then also just rag in general, like the, the, the document data. Yeah. It's like a source. Most ML practitioners would say that knowledge graph is kind of like a dirty word. The graph database, people get graph religion, everything's a graph, and then they, they go really hard into it and then they get a, they get a graph that is too complex to navigate. Yes. And so like the, the, the simple way to put it is like you at running HubSpot, you know, the power of graphs, the way that Google has pitched them for many years, but I don't suspect that HubSpot itself uses a knowledge graph. No. Yeah.Dharmesh [00:17:26]: So when is it over engineering? Basically? It's a great question. I don't know. So the question now, like in AI land, right, is the, do we necessarily need to understand? So right now, LLMs for, for the most part are somewhat black boxes, right? We sort of understand how the, you know, the algorithm itself works, but we really don't know what's going on in there and, and how things come out. So if a graph data store is able to produce the outcomes we want, it's like, here's a set of queries I want to be able to submit and then it comes out with useful content. Maybe the underlying data store is as opaque as a vector embeddings or something like that, but maybe it's fine. Maybe we don't necessarily need to understand it to get utility out of it. And so maybe if it's messy, that's okay. Um, that's, it's just another form of lossy compression. Uh, it's just lossy in a way that we just don't completely understand in terms of, because it's going to grow organically. Uh, and it's not structured. It's like, ah, we're just gonna throw a bunch of stuff in there. Let the, the equivalent of the embedding algorithm, whatever they called in graph land. Um, so the one with the best results wins. I think so. Yeah.swyx [00:18:26]: Or is this the practical side of me is like, yeah, it's, if it's useful, we don't necessarilyDharmesh [00:18:30]: need to understand it.swyx [00:18:30]: I have, I mean, I'm happy to push back as long as you want. Uh, it's not practical to evaluate like the 10 different options out there because it takes time. It takes people, it takes, you know, resources, right? Set. That's the first thing. Second thing is your evals are typically on small things and some things only work at scale. Yup. Like graphs. Yup.Dharmesh [00:18:46]: Yup. That's, yeah, no, that's fair. And I think this is one of the challenges in terms of implementation of graph databases is that the most common approach that I've seen developers do, I've done it myself, is that, oh, I've got a Postgres database or a MySQL or whatever. I can represent a graph with a very set of tables with a parent child thing or whatever. And that sort of gives me the ability, uh, why would I need anything more than that? And the answer is, well, if you don't need anything more than that, you don't need anything more than that. But there's a high chance that you're sort of missing out on the actual value that, uh, the graph representation gives you. Which is the ability to traverse the graph, uh, efficiently in ways that kind of going through the, uh, traversal in a relational database form, even though structurally you have the data, practically you're not gonna be able to pull it out in, in useful ways. Uh, so you wouldn't like represent a social graph, uh, in, in using that kind of relational table model. It just wouldn't scale. It wouldn't work.swyx [00:19:36]: Uh, yeah. Uh, I think we want to move on to MCP. Yeah. But I just want to, like, just engineering advice. Yeah. Uh, obviously you've, you've, you've run, uh, you've, you've had to do a lot of projects and run a lot of teams. Do you have a general rule for over-engineering or, you know, engineering ahead of time? You know, like, because people, we know premature engineering is the root of all evil. Yep. But also sometimes you just have to. Yep. When do you do it? Yes.Dharmesh [00:19:59]: It's a great question. This is, uh, a question as old as time almost, which is what's the right and wrong levels of abstraction. That's effectively what, uh, we're answering when we're trying to do engineering. I tend to be a pragmatist, right? So here's the thing. Um, lots of times doing something the right way. Yeah. It's like a marginal increased cost in those cases. Just do it the right way. And this is what makes a, uh, a great engineer or a good engineer better than, uh, a not so great one. It's like, okay, all things being equal. If it's going to take you, you know, roughly close to constant time anyway, might as well do it the right way. Like, so do things well, then the question is, okay, well, am I building a framework as the reusable library? To what degree, uh, what am I anticipating in terms of what's going to need to change in this thing? Uh, you know, along what dimension? And then I think like a business person in some ways, like what's the return on calories, right? So, uh, and you look at, um, energy, the expected value of it's like, okay, here are the five possible things that could happen, uh, try to assign probabilities like, okay, well, if there's a 50% chance that we're going to go down this particular path at some day, like, or one of these five things is going to happen and it costs you 10% more to engineer for that. It's basically, it's something that yields a kind of interest compounding value. Um, as you get closer to the time of, of needing that versus having to take on debt, which is when you under engineer it, you're taking on debt. You're going to have to pay off when you do get to that eventuality where something happens. One thing as a pragmatist, uh, so I would rather under engineer something than over engineer it. If I were going to err on the side of something, and here's the reason is that when you under engineer it, uh, yes, you take on tech debt, uh, but the interest rate is relatively known and payoff is very, very possible, right? Which is, oh, I took a shortcut here as a result of which now this thing that should have taken me a week is now going to take me four weeks. Fine. But if that particular thing that you thought might happen, never actually, you never have that use case transpire or just doesn't, it's like, well, you just save yourself time, right? And that has value because you were able to do other things instead of, uh, kind of slightly over-engineering it away, over-engineering it. But there's no perfect answers in art form in terms of, uh, and yeah, we'll, we'll bring kind of this layers of abstraction back on the code generation conversation, which we'll, uh, I think I have later on, butAlessio [00:22:05]: I was going to ask, we can just jump ahead quickly. Yeah. Like, as you think about vibe coding and all that, how does the. Yeah. Percentage of potential usefulness change when I feel like we over-engineering a lot of times it's like the investment in syntax, it's less about the investment in like arc exacting. Yep. Yeah. How does that change your calculus?Dharmesh [00:22:22]: A couple of things, right? One is, um, so, you know, going back to that kind of ROI or a return on calories, kind of calculus or heuristic you think through, it's like, okay, well, what is it going to cost me to put this layer of abstraction above the code that I'm writing now, uh, in anticipating kind of future needs. If the cost of fixing, uh, or doing under engineering right now. Uh, we'll trend towards zero that says, okay, well, I don't have to get it right right now because even if I get it wrong, I'll run the thing for six hours instead of 60 minutes or whatever. It doesn't really matter, right? Like, because that's going to trend towards zero to be able, the ability to refactor a code. Um, and because we're going to not that long from now, we're going to have, you know, large code bases be able to exist, uh, you know, as, as context, uh, for a code generation or a code refactoring, uh, model. So I think it's going to make it, uh, make the case for under engineering, uh, even stronger. Which is why I take on that cost. You just pay the interest when you get there, it's not, um, just go on with your life vibe coded and, uh, come back when you need to. Yeah.Alessio [00:23:18]: Sometimes I feel like there's no decision-making in some things like, uh, today I built a autosave for like our internal notes platform and I literally just ask them cursor. Can you add autosave? Yeah. I don't know if it's over under engineer. Yep. I just vibe coded it. Yep. And I feel like at some point we're going to get to the point where the models kindDharmesh [00:23:36]: of decide where the right line is, but this is where the, like the, in my mind, the danger is, right? So there's two sides to this. One is the cost of kind of development and coding and things like that stuff that, you know, we talk about. But then like in your example, you know, one of the risks that we have is that because adding a feature, uh, like a save or whatever the feature might be to a product as that price tends towards zero, are we going to be less discriminant about what features we add as a result of making more product products more complicated, which has a negative impact on the user and navigate negative impact on the business. Um, and so that's the thing I worry about if it starts to become too easy, are we going to be. Too promiscuous in our, uh, kind of extension, adding product extensions and things like that. It's like, ah, why not add X, Y, Z or whatever back then it was like, oh, we only have so many engineering hours or story points or however you measure things. Uh, that least kept us in check a little bit. Yeah.Alessio [00:24:22]: And then over engineering, you're like, yeah, it's kind of like you're putting that on yourself. Yeah. Like now it's like the models don't understand that if they add too much complexity, it's going to come back to bite them later. Yep. So they just do whatever they want to do. Yeah. And I'm curious where in the workflow that's going to be, where it's like, Hey, this is like the amount of complexity and over-engineering you can do before you got to ask me if we should actually do it versus like do something else.Dharmesh [00:24:45]: So you know, we've already, let's like, we're leaving this, uh, in the code generation world, this kind of compressed, um, cycle time. Right. It's like, okay, we went from auto-complete, uh, in the GitHub co-pilot to like, oh, finish this particular thing and hit tab to a, oh, I sort of know your file or whatever. I can write out a full function to you to now I can like hold a bunch of the context in my head. Uh, so we can do app generation, which we have now with lovable and bolt and repletage. Yeah. Association and other things. So then the question is, okay, well, where does it naturally go from here? So we're going to generate products. Make sense. We might be able to generate platforms as though I want a platform for ERP that does this, whatever. And that includes the API's includes the product and the UI, and all the things that make for a platform. There's no nothing that says we would stop like, okay, can you generate an entire software company someday? Right. Uh, with the platform and the monetization and the go-to-market and the whatever. And you know, that that's interesting to me in terms of, uh, you know, what, when you take it to almost ludicrous levels. of abstract.swyx [00:25:39]: It's like, okay, turn it to 11. You mentioned vibe coding, so I have to, this is a blog post I haven't written, but I'm kind of exploring it. Is the junior engineer dead?Dharmesh [00:25:49]: I don't think so. I think what will happen is that the junior engineer will be able to, if all they're bringing to the table is the fact that they are a junior engineer, then yes, they're likely dead. But hopefully if they can communicate with carbon-based life forms, they can interact with product, if they're willing to talk to customers, they can take their kind of basic understanding of engineering and how kind of software works. I think that has value. So I have a 14-year-old right now who's taking Python programming class, and some people ask me, it's like, why is he learning coding? And my answer is, is because it's not about the syntax, it's not about the coding. What he's learning is like the fundamental thing of like how things work. And there's value in that. I think there's going to be timeless value in systems thinking and abstractions and what that means. And whether functions manifested as math, which he's going to get exposed to regardless, or there are some core primitives to the universe, I think, that the more you understand them, those are what I would kind of think of as like really large dots in your life that will have a higher gravitational pull and value to them that you'll then be able to. So I want him to collect those dots, and he's not resisting. So it's like, okay, while he's still listening to me, I'm going to have him do things that I think will be useful.swyx [00:26:59]: You know, part of one of the pitches that I evaluated for AI engineer is a term. And the term is that maybe the traditional interview path or career path of software engineer goes away, which is because what's the point of lead code? Yeah. And, you know, it actually matters more that you know how to work with AI and to implement the things that you want. Yep.Dharmesh [00:27:16]: That's one of the like interesting things that's happened with generative AI. You know, you go from machine learning and the models and just that underlying form, which is like true engineering, right? Like the actual, what I call real engineering. I don't think of myself as a real engineer, actually. I'm a developer. But now with generative AI. We call it AI and it's obviously got its roots in machine learning, but it just feels like fundamentally different to me. Like you have the vibe. It's like, okay, well, this is just a whole different approach to software development to so many different things. And so I'm wondering now, it's like an AI engineer is like, if you were like to draw the Venn diagram, it's interesting because the cross between like AI things, generative AI and what the tools are capable of, what the models do, and this whole new kind of body of knowledge that we're still building out, it's still very young, intersected with kind of classic engineering, software engineering. Yeah.swyx [00:28:04]: I just described the overlap as it separates out eventually until it's its own thing, but it's starting out as a software. Yeah.Alessio [00:28:11]: That makes sense. So to close the vibe coding loop, the other big hype now is MCPs. Obviously, I would say Cloud Desktop and Cursor are like the two main drivers of MCP usage. I would say my favorite is the Sentry MCP. I can pull in errors and then you can just put the context in Cursor. How do you think about that abstraction layer? Does it feel... Does it feel almost too magical in a way? Do you think it's like you get enough? Because you don't really see how the server itself is then kind of like repackaging theDharmesh [00:28:41]: information for you? I think MCP as a standard is one of the better things that's happened in the world of AI because a standard needed to exist and absent a standard, there was a set of things that just weren't possible. Now, we can argue whether it's the best possible manifestation of a standard or not. Does it do too much? Does it do too little? I get that, but it's just simple enough to both be useful and unobtrusive. It's understandable and adoptable by mere mortals, right? It's not overly complicated. You know, a reasonable engineer can put a stand up an MCP server relatively easily. The thing that has me excited about it is like, so I'm a big believer in multi-agent systems. And so that's going back to our kind of this idea of an atomic agent. So imagine the MCP server, like obviously it calls tools, but the way I think about it, so I'm working on my current passion project is agent.ai. And we'll talk more about that in a little bit. More about the, I think we should, because I think it's interesting not to promote the project at all, but there's some interesting ideas in there. One of which is around, we're going to need a mechanism for, if agents are going to collaborate and be able to delegate, there's going to need to be some form of discovery and we're going to need some standard way. It's like, okay, well, I just need to know what this thing over here is capable of. We're going to need a registry, which Anthropic's working on. I'm sure others will and have been doing directories of, and there's going to be a standard around that too. How do you build out a directory of MCP servers? I think that's going to unlock so many things just because, and we're already starting to see it. So I think MCP or something like it is going to be the next major unlock because it allows systems that don't know about each other, don't need to, it's that kind of decoupling of like Sentry and whatever tools someone else was building. And it's not just about, you know, Cloud Desktop or things like, even on the client side, I think we're going to see very interesting consumers of MCP, MCP clients versus just the chat body kind of things. Like, you know, Cloud Desktop and Cursor and things like that. But yeah, I'm very excited about MCP in that general direction.swyx [00:30:39]: I think the typical cynical developer take, it's like, we have OpenAPI. Yeah. What's the new thing? I don't know if you have a, do you have a quick MCP versus everything else? Yeah.Dharmesh [00:30:49]: So it's, so I like OpenAPI, right? So just a descriptive thing. It's OpenAPI. OpenAPI. Yes, that's what I meant. So it's basically a self-documenting thing. We can do machine-generated, lots of things from that output. It's a structured definition of an API. I get that, love it. But MCPs sort of are kind of use case specific. They're perfect for exactly what we're trying to use them for around LLMs in terms of discovery. It's like, okay, I don't necessarily need to know kind of all this detail. And so right now we have, we'll talk more about like MCP server implementations, but We will? I think, I don't know. Maybe we won't. At least it's in my head. It's like a back processor. But I do think MCP adds value above OpenAPI. It's, yeah, just because it solves this particular thing. And if we had come to the world, which we have, like, it's like, hey, we already have OpenAPI. It's like, if that were good enough for the universe, the universe would have adopted it already. There's a reason why MCP is taking office because marginally adds something that was missing before and doesn't go too far. And so that's why the kind of rate of adoption, you folks have written about this and talked about it. Yeah, why MCP won. Yeah. And it won because the universe decided that this was useful and maybe it gets supplanted by something else. Yeah. And maybe we discover, oh, maybe OpenAPI was good enough the whole time. I doubt that.swyx [00:32:09]: The meta lesson, this is, I mean, he's an investor in DevTools companies. I work in developer experience at DevRel in DevTools companies. Yep. Everyone wants to own the standard. Yeah. I'm sure you guys have tried to launch your own standards. Actually, it's Houseplant known for a standard, you know, obviously inbound marketing. But is there a standard or protocol that you ever tried to push? No.Dharmesh [00:32:30]: And there's a reason for this. Yeah. Is that? And I don't mean, need to mean, speak for the people of HubSpot, but I personally. You kind of do. I'm not smart enough. That's not the, like, I think I have a. You're smart. Not enough for that. I'm much better off understanding the standards that are out there. And I'm more on the composability side. Let's, like, take the pieces of technology that exist out there, combine them in creative, unique ways. And I like to consume standards. I don't like to, and that's not that I don't like to create them. I just don't think I have the, both the raw wattage or the credibility. It's like, okay, well, who the heck is Dharmesh, and why should we adopt a standard he created?swyx [00:33:07]: Yeah, I mean, there are people who don't monetize standards, like OpenTelemetry is a big standard, and LightStep never capitalized on that.Dharmesh [00:33:15]: So, okay, so if I were to do a standard, there's two things that have been in my head in the past. I was one around, a very, very basic one around, I don't even have the domain, I have a domain for everything, for open marketing. Because the issue we had in HubSpot grew up in the marketing space. There we go. There was no standard around data formats and things like that. It doesn't go anywhere. But the other one, and I did not mean to go here, but I'm going to go here. It's called OpenGraph. I know the term was already taken, but it hasn't been used for like 15 years now for its original purpose. But what I think should exist in the world is right now, our information, all of us, nodes are in the social graph at Meta or the professional graph at LinkedIn. Both of which are actually relatively closed in actually very annoying ways. Like very, very closed, right? Especially LinkedIn. Especially LinkedIn. I personally believe that if it's my data, and if I would get utility out of it being open, I should be able to make my data open or publish it in whatever forms that I choose, as long as I have control over it as opt-in. So the idea is around OpenGraph that says, here's a standard, here's a way to publish it. I should be able to go to OpenGraph.org slash Dharmesh dot JSON and get it back. And it's like, here's your stuff, right? And I can choose along the way and people can write to it and I can prove. And there can be an entire system. And if I were to do that, I would do it as a... Like a public benefit, non-profit-y kind of thing, as this is a contribution to society. I wouldn't try to commercialize that. Have you looked at AdProto? What's that? AdProto.swyx [00:34:43]: It's the protocol behind Blue Sky. Okay. My good friend, Dan Abramov, who was the face of React for many, many years, now works there. And he actually did a talk that I can send you, which basically kind of tries to articulate what you just said. But he does, he loves doing these like really great analogies, which I think you'll like. Like, you know, a lot of our data is behind a handle, behind a domain. Yep. So he's like, all right, what if we flip that? What if it was like our handle and then the domain? Yep. So, and that's really like your data should belong to you. Yep. And I should not have to wait 30 days for my Twitter data to export. Yep.Dharmesh [00:35:19]: you should be able to at least be able to automate it or do like, yes, I should be able to plug it into an agentic thing. Yeah. Yes. I think we're... Because so much of our data is... Locked up. I think the trick here isn't that standard. It is getting the normies to care.swyx [00:35:37]: Yeah. Because normies don't care.Dharmesh [00:35:38]: That's true. But building on that, normies don't care. So, you know, privacy is a really hot topic and an easy word to use, but it's not a binary thing. Like there are use cases where, and we make these choices all the time, that I will trade, not all privacy, but I will trade some privacy for some productivity gain or some benefit to me that says, oh, I don't care about that particular data being online if it gives me this in return, or I don't mind sharing this information with this company.Alessio [00:36:02]: If I'm getting, you know, this in return, but that sort of should be my option. I think now with computer use, you can actually automate some of the exports. Yes. Like something we've been doing internally is like everybody exports their LinkedIn connections. Yep. And then internally, we kind of merge them together to see how we can connect our companies to customers or things like that.Dharmesh [00:36:21]: And not to pick on LinkedIn, but since we're talking about it, but they feel strongly enough on the, you know, do not take LinkedIn data that they will block even browser use kind of things or whatever. They go to great, great lengths, even to see patterns of usage. And it says, oh, there's no way you could have, you know, gotten that particular thing or whatever without, and it's, so it's, there's...swyx [00:36:42]: Wasn't there a Supreme Court case that they lost? Yeah.Dharmesh [00:36:45]: So the one they lost was around someone that was scraping public data that was on the public internet. And that particular company had not signed any terms of service or whatever. It's like, oh, I'm just taking data that's on, there was no, and so that's why they won. But now, you know, the question is around, can LinkedIn... I think they can. Like, when you use, as a user, you use LinkedIn, you are signing up for their terms of service. And if they say, well, this kind of use of your LinkedIn account that violates our terms of service, they can shut your account down, right? They can. And they, yeah, so, you know, we don't need to make this a discussion. By the way, I love the company, don't get me wrong. I'm an avid user of the product. You know, I've got... Yeah, I mean, you've got over a million followers on LinkedIn, I think. Yeah, I do. And I've known people there for a long, long time, right? And I have lots of respect. And I understand even where the mindset originally came from of this kind of members-first approach to, you know, a privacy-first. I sort of get that. But sometimes you sort of have to wonder, it's like, okay, well, that was 15, 20 years ago. There's likely some controlled ways to expose some data on some member's behalf and not just completely be a binary. It's like, no, thou shalt not have the data.swyx [00:37:54]: Well, just pay for sales navigator.Alessio [00:37:57]: Before we move to the next layer of instruction, anything else on MCP you mentioned? Let's move back and then I'll tie it back to MCPs.Dharmesh [00:38:05]: So I think the... Open this with agent. Okay, so I'll start with... Here's my kind of running thesis, is that as AI and agents evolve, which they're doing very, very quickly, we're going to look at them more and more. I don't like to anthropomorphize. We'll talk about why this is not that. Less as just like raw tools and more like teammates. They'll still be software. They should self-disclose as being software. I'm totally cool with that. But I think what's going to happen is that in the same way you might collaborate with a team member on Slack or Teams or whatever you use, you can imagine a series of agents that do specific things just like a team member might do, that you can delegate things to. You can collaborate. You can say, hey, can you take a look at this? Can you proofread that? Can you try this? You can... Whatever it happens to be. So I think it is... I will go so far as to say it's inevitable that we're going to have hybrid teams someday. And what I mean by hybrid teams... So back in the day, hybrid teams were, oh, well, you have some full-time employees and some contractors. Then it was like hybrid teams are some people that are in the office and some that are remote. That's the kind of form of hybrid. The next form of hybrid is like the carbon-based life forms and agents and AI and some form of software. So let's say we temporarily stipulate that I'm right about that over some time horizon that eventually we're going to have these kind of digitally hybrid teams. So if that's true, then the question you sort of ask yourself is that then what needs to exist in order for us to get the full value of that new model? It's like, okay, well... You sort of need to... It's like, okay, well, how do I... If I'm building a digital team, like, how do I... Just in the same way, if I'm interviewing for an engineer or a designer or a PM, whatever, it's like, well, that's why we have professional networks, right? It's like, oh, they have a presence on likely LinkedIn. I can go through that semi-structured, structured form, and I can see the experience of whatever, you know, self-disclosed. But, okay, well, agents are going to need that someday. And so I'm like, okay, well, this seems like a thread that's worth pulling on. That says, okay. So I... So agent.ai is out there. And it's LinkedIn for agents. It's LinkedIn for agents. It's a professional network for agents. And the more I pull on that thread, it's like, okay, well, if that's true, like, what happens, right? It's like, oh, well, they have a profile just like anyone else, just like a human would. It's going to be a graph underneath, just like a professional network would be. It's just that... And you can have its, you know, connections and follows, and agents should be able to post. That's maybe how they do release notes. Like, oh, I have this new version. Whatever they decide to post, it should just be able to... Behave as a node on the network of a professional network. As it turns out, the more I think about that and pull on that thread, the more and more things, like, start to make sense to me. So it may be more than just a pure professional network. So my original thought was, okay, well, it's a professional network and agents as they exist out there, which I think there's going to be more and more of, will kind of exist on this network and have the profile. But then, and this is always dangerous, I'm like, okay, I want to see a world where thousands of agents are out there in order for the... Because those digital employees, the digital workers don't exist yet in any meaningful way. And so then I'm like, oh, can I make that easier for, like... And so I have, as one does, it's like, oh, I'll build a low-code platform for building agents. How hard could that be, right? Like, very hard, as it turns out. But it's been fun. So now, agent.ai has 1.3 million users. 3,000 people have actually, you know, built some variation of an agent, sometimes just for their own personal productivity. About 1,000 of which have been published. And the reason this comes back to MCP for me, so imagine that and other networks, since I know agent.ai. So right now, we have an MCP server for agent.ai that exposes all the internally built agents that we have that do, like, super useful things. Like, you know, I have access to a Twitter API that I can subsidize the cost. And I can say, you know, if you're looking to build something for social media, these kinds of things, with a single API key, and it's all completely free right now, I'm funding it. That's a useful way for it to work. And then we have a developer to say, oh, I have this idea. I don't have to worry about open AI. I don't have to worry about, now, you know, this particular model is better. It has access to all the models with one key. And we proxy it kind of behind the scenes. And then expose it. So then we get this kind of community effect, right? That says, oh, well, someone else may have built an agent to do X. Like, I have an agent right now that I built for myself to do domain valuation for website domains because I'm obsessed with domains, right? And, like, there's no efficient market for domains. There's no Zillow for domains right now that tells you, oh, here are what houses in your neighborhood sold for. It's like, well, why doesn't that exist? We should be able to solve that problem. And, yes, you're still guessing. Fine. There should be some simple heuristic. So I built that. It's like, okay, well, let me go look for past transactions. You say, okay, I'm going to type in agent.ai, agent.com, whatever domain. What's it actually worth? I'm looking at buying it. It can go and say, oh, which is what it does. It's like, I'm going to go look at are there any published domain transactions recently that are similar, either use the same word, same top-level domain, whatever it is. And it comes back with an approximate value, and it comes back with its kind of rationale for why it picked the value and comparable transactions. Oh, by the way, this domain sold for published. Okay. So that agent now, let's say, existed on the web, on agent.ai. Then imagine someone else says, oh, you know, I want to build a brand-building agent for startups and entrepreneurs to come up with names for their startup. Like a common problem, every startup is like, ah, I don't know what to call it. And so they type in five random words that kind of define whatever their startup is. And you can do all manner of things, one of which is like, oh, well, I need to find the domain for it. What are possible choices? Now it's like, okay, well, it would be nice to know if there's an aftermarket price for it, if it's listed for sale. Awesome. Then imagine calling this valuation agent. It's like, okay, well, I want to find where the arbitrage is, where the agent valuation tool says this thing is worth $25,000. It's listed on GoDaddy for $5,000. It's close enough. Let's go do that. Right? And that's a kind of composition use case that in my future state. Thousands of agents on the network, all discoverable through something like MCP. And then you as a developer of agents have access to all these kind of Lego building blocks based on what you're trying to solve. Then you blend in orchestration, which is getting better and better with the reasoning models now. Just describe the problem that you have. Now, the next layer that we're all contending with is that how many tools can you actually give an LLM before the LLM breaks? That number used to be like 15 or 20 before you kind of started to vary dramatically. And so that's the thing I'm thinking about now. It's like, okay, if I want to... If I want to expose 1,000 of these agents to a given LLM, obviously I can't give it all 1,000. Is there some intermediate layer that says, based on your prompt, I'm going to make a best guess at which agents might be able to be helpful for this particular thing? Yeah.Alessio [00:44:37]: Yeah, like RAG for tools. Yep. I did build the Latent Space Researcher on agent.ai. Okay. Nice. Yeah, that seems like, you know, then there's going to be a Latent Space Scheduler. And then once I schedule a research, you know, and you build all of these things. By the way, my apologies for the user experience. You realize I'm an engineer. It's pretty good.swyx [00:44:56]: I think it's a normie-friendly thing. Yeah. That's your magic. HubSpot does the same thing.Alessio [00:45:01]: Yeah, just to like quickly run through it. You can basically create all these different steps. And these steps are like, you know, static versus like variable-driven things. How did you decide between this kind of like low-code-ish versus doing, you know, low-code with code backend versus like not exposing that at all? Any fun design decisions? Yeah. And this is, I think...Dharmesh [00:45:22]: I think lots of people are likely sitting in exactly my position right now, coming through the choosing between deterministic. Like if you're like in a business or building, you know, some sort of agentic thing, do you decide to do a deterministic thing? Or do you go non-deterministic and just let the alum handle it, right, with the reasoning models? The original idea and the reason I took the low-code stepwise, a very deterministic approach. A, the reasoning models did not exist at that time. That's thing number one. Thing number two is if you can get... If you know in your head... If you know in your head what the actual steps are to accomplish whatever goal, why would you leave that to chance? There's no upside. There's literally no upside. Just tell me, like, what steps do you need executed? So right now what I'm playing with... So one thing we haven't talked about yet, and people don't talk about UI and agents. Right now, the primary interaction model... Or they don't talk enough about it. I know some people have. But it's like, okay, so we're used to the chatbot back and forth. Fine. I get that. But I think we're going to move to a blend of... Some of those things are going to be synchronous as they are now. But some are going to be... Some are going to be async. It's just going to put it in a queue, just like... And this goes back to my... Man, I talk fast. But I have this... I only have one other speed. It's even faster. So imagine it's like if you're working... So back to my, oh, we're going to have these hybrid digital teams. Like, you would not go to a co-worker and say, I'm going to ask you to do this thing, and then sit there and wait for them to go do it. Like, that's not how the world works. So it's nice to be able to just, like, hand something off to someone. It's like, okay, well, maybe I expect a response in an hour or a day or something like that.Dharmesh [00:46:52]: In terms of when things need to happen. So the UI around agents. So if you look at the output of agent.ai agents right now, they are the simplest possible manifestation of a UI, right? That says, oh, we have inputs of, like, four different types. Like, we've got a dropdown, we've got multi-select, all the things. It's like back in HTML, the original HTML 1.0 days, right? Like, you're the smallest possible set of primitives for a UI. And it just says, okay, because we need to collect some information from the user, and then we go do steps and do things. And generate some output in HTML or markup are the two primary examples. So the thing I've been asking myself, if I keep going down that path. So people ask me, I get requests all the time. It's like, oh, can you make the UI sort of boring? I need to be able to do this, right? And if I keep pulling on that, it's like, okay, well, now I've built an entire UI builder thing. Where does this end? And so I think the right answer, and this is what I'm going to be backcoding once I get done here, is around injecting a code generation UI generation into, the agent.ai flow, right? As a builder, you're like, okay, I'm going to describe the thing that I want, much like you would do in a vibe coding world. But instead of generating the entire app, it's going to generate the UI that exists at some point in either that deterministic flow or something like that. It says, oh, here's the thing I'm trying to do. Go generate the UI for me. And I can go through some iterations. And what I think of it as a, so it's like, I'm going to generate the code, generate the code, tweak it, go through this kind of prompt style, like we do with vibe coding now. And at some point, I'm going to be happy with it. And I'm going to hit save. And that's going to become the action in that particular step. It's like a caching of the generated code that I can then, like incur any inference time costs. It's just the actual code at that point.Alessio [00:48:29]: Yeah, I invested in a company called E2B, which does code sandbox. And they powered the LM arena web arena. So it's basically the, just like you do LMS, like text to text, they do the same for like UI generation. So if you're asking a model, how do you do it? But yeah, I think that's kind of where.Dharmesh [00:48:45]: That's the thing I'm really fascinated by. So the early LLM, you know, we're understandably, but laughably bad at simple arithmetic, right? That's the thing like my wife, Normies would ask us, like, you call this AI, like it can't, my son would be like, it's just stupid. It can't even do like simple arithmetic. And then like we've discovered over time that, and there's a reason for this, right? It's like, it's a large, there's, you know, the word language is in there for a reason in terms of what it's been trained on. It's not meant to do math, but now it's like, okay, well, the fact that it has access to a Python interpreter that I can actually call at runtime, that solves an entire body of problems that it wasn't trained to do. And it's basically a form of delegation. And so the thought that's kind of rattling around in my head is that that's great. So it's, it's like took the arithmetic problem and took it first. Now, like anything that's solvable through a relatively concrete Python program, it's able to do a bunch of things that I couldn't do before. Can we get to the same place with UI? I don't know what the future of UI looks like in a agentic AI world, but maybe let the LLM handle it, but not in the classic sense. Maybe it generates it on the fly, or maybe we go through some iterations and hit cache or something like that. So it's a little bit more predictable. Uh, I don't know, but yeah.Alessio [00:49:48]: And especially when is the human supposed to intervene? So, especially if you're composing them, most of them should not have a UI because then they're just web hooking to somewhere else. I just want to touch back. I don't know if you have more comments on this.swyx [00:50:01]: I was just going to ask when you, you said you got, you're going to go back to code. What

Esta semana en Quizá hablemos de ti tenemos un episodio cargado de música, polémica y recuerdos. Hablamos de Silvia Pasquel y su eterna presencia en el espectáculo, el regreso de Shakira y Don Omar a los titulares, y el merecido homenaje a la Radio Mexicana. Además, Fey y Lupita D'Alessio siguen dando de qué hablar, La Cotorrisa y Adrián Marcelo encienden las redes, y analizamos Adolescencia, la serie de Netflix que está dando de qué hablar. ¡No te lo pierdas!

While everyone is now repeating that 2025 is the “Year of the Agent”, OpenAI is heads down building towards it. In the first 2 months of the year they released Operator and Deep Research (arguably the most successful agent archetype so far), and today they are bringing a lot of those capabilities to the API:* Responses API* Web Search Tool* Computer Use Tool* File Search Tool* A new open source Agents SDK with integrated Observability ToolsWe cover all this and more in today's lightning pod on YouTube!More details here:Responses APIIn our Michelle Pokrass episode we talked about the Assistants API needing a redesign. Today OpenAI is launching the Responses API, “a more flexible foundation for developers building agentic applications”. It's a superset of the chat completion API, and the suggested starting point for developers working with OpenAI models. One of the big upgrades is the new set of built-in tools for the responses API: Web Search, Computer Use, and Files. Web Search ToolWe previously had Exa AI on the podcast to talk about web search for AI. OpenAI is also now joining the race; the Web Search API is actually a new “model” that exposes two 4o fine-tunes: gpt-4o-search-preview and gpt-4o-mini-search-preview. These are the same models that power ChatGPT Search, and are priced at $30/1000 queries and $25/1000 queries respectively. The killer feature is inline citations: you do not only get a link to a page, but also a deep link to exactly where your query was answered in the result page. Computer Use ToolThe model that powers Operator, called Computer-Using-Agent (CUA), is also now available in the API. The computer-use-preview model is SOTA on most benchmarks, achieving 38.1% success on OSWorld for full computer use tasks, 58.1% on WebArena, and 87% on WebVoyager for web-based interactions.As you will notice in the docs, `computer-use-preview` is both a model and a tool through which you can specify the environment. Usage is priced at $3/1M input tokens and $12/1M output tokens, and it's currently only available to users in tiers 3-5.File Search ToolFile Search was also available in the Assistants API, and it's now coming to Responses too. OpenAI is bringing search + RAG all under one umbrella, and we'll definitely see more people trying to find new ways to build all-in-one apps on OpenAI. Usage is priced at $2.50 per thousand queries and file storage at $0.10/GB/day, with the first GB free.Agent SDK: Swarms++!https://github.com/openai/openai-agents-pythonTo bring it all together, after the viral reception to Swarm, OpenAI is releasing an officially supported agents framework (which was previewed at our AI Engineer Summit) with 4 core pieces:* Agents: Easily configurable LLMs with clear instructions and built-in tools.* Handoffs: Intelligently transfer control between agents.* Guardrails: Configurable safety checks for input and output validation.* Tracing & Observability: Visualize agent execution traces to debug and optimize performance.Multi-agent workflows are here to stay!OpenAI is now explicitly designs for a set of common agentic patterns: Workflows, Handoffs, Agents-as-Tools, LLM-as-a-Judge, Parallelization, and Guardrails. OpenAI previewed this in part 2 of their talk at NYC:Further coverage of the launch from Kevin Weil, WSJ, and OpenAIDevs, AMA here.Show Notes* Assistants API* Swarm (OpenAI)* Fine-Tuning in AI* 2024 OpenAI DevDay Recap with Romain* Michelle Pokrass episode (API lead)Timestamps* 00:00 Intros* 02:31 Responses API * 08:34 Web Search API * 17:14 Files Search API * 18:46 Files API vs RAG * 20:06 Computer Use / Operator API * 22:30 Agents SDKAnd of course you can catch up with the full livestream here:TranscriptAlessio [00:00:03]: Hey, everyone. Welcome back to another Latent Space Lightning episode. This is Alessio, partner and CTO at Decibel, and I'm joined by Swyx, founder of Small AI.swyx [00:00:11]: Hi, and today we have a super special episode because we're talking with our old friend Roman. Hi, welcome.Romain [00:00:19]: Thank you. Thank you for having me.swyx [00:00:20]: And Nikunj, who is most famously, if anyone has ever tried to get any access to anything on the API, Nikunj is the guy. So I know your emails because I look forward to them.Nikunj [00:00:30]: Yeah, nice to meet all of you.swyx [00:00:32]: I think that we're basically convening today to talk about the new API. So perhaps you guys want to just kick off. What is OpenAI launching today?Nikunj [00:00:40]: Yeah, so I can kick it off. We're launching a bunch of new things today. We're going to do three new built-in tools. So we're launching the web search tool. This is basically chat GPD for search, but available in the API. We're launching an improved file search tool. So this is you bringing your data to OpenAI. You upload it. We, you know, take care of parsing it, chunking it. We're embedding it, making it searchable, give you this like ready vector store that you can use. So that's the file search tool. And then we're also launching our computer use tool. So this is the tool behind the operator product in chat GPD. So that's coming to developers today. And to support all of these tools, we're going to have a new API. So, you know, we launched chat completions, like I think March 2023 or so. It's been a while. So we're looking for an update over here to support all the new things that the models can do. And so we're launching this new API. It is, you know, it works with tools. We think it'll be like a great option for all the future agentic products that we build. And so that is also launching today. Actually, the last thing we're launching is the agents SDK. We launched this thing called Swarm last year where, you know, it was an experimental SDK for people to do multi-agent orchestration and stuff like that. It was supposed to be like educational experimental, but like people, people really loved it. They like ate it up. And so we are like, all right, let's, let's upgrade this thing. Let's give it a new name. And so we're calling it the agents SDK. It's going to have built-in tracing in the OpenAI dashboard. So lots of cool stuff going out. So, yeah.Romain [00:02:14]: That's a lot, but we said 2025 was the year of agents. So there you have it, like a lot of new tools to build these agents for developers.swyx [00:02:20]: Okay. I guess, I guess we'll just kind of go one by one and we'll leave the agents SDK towards the end. So responses API, I think the sort of primary concern that people have and something I think I've voiced to you guys when, when, when I was talking with you in the, in the planning process was, is chat completions going away? So I just wanted to let it, let you guys respond to the concerns that people might have.Romain [00:02:41]: Chat completion is definitely like here to stay, you know, it's a bare metal API we've had for quite some time. Lots of tools built around it. So we want to make sure that it's maintained and people can confidently keep on building on it. At the same time, it was kind of optimized for a different world, right? It was optimized for a pre-multi-modality world. We also optimized for kind of single turn. It takes two problems. It takes prompt in, it takes response out. And now with these agentic workflows, we, we noticed that like developers and companies want to build longer horizon tasks, you know, like things that require multiple returns to get the task accomplished. And computer use is one of those, for instance. And so that's why the responses API came to life to kind of support these new agentic workflows. But chat completion is definitely here to stay.swyx [00:03:27]: And assistance API, we've, uh, has a target sunset date of first half of 2020. So this is kind of like, in my mind, there was a kind of very poetic mirroring of the API with the models. This, I kind of view this as like kind of the merging of assistance API and chat completions, right. Into one unified responses. So it's kind of like how GPT and the old series models are also unifying.Romain [00:03:48]: Yeah, that's exactly the right, uh, that's the right framing, right? Like, I think we took the best of what we learned from the assistance API, especially like being able to access tools very, uh, very like conveniently, but at the same time, like simplifying the way you have to integrate, like, you no longer have to think about six different objects to kind of get access to these tools with the responses API. You just get one API request and suddenly you can weave in those tools, right?Nikunj [00:04:12]: Yeah, absolutely. And I think we're going to make it really easy and straightforward for assistance API users to migrate over to responsive. Right. To the API without any loss of functionality or data. So our plan is absolutely to add, you know, assistant like objects and thread light objects to that, that work really well with the responses API. We'll also add like the code interpreter tool, which is not launching today, but it'll come soon. And, uh, we'll add async mode to responses API, because that's another difference with, with, uh, assistance. I will have web hooks and stuff like that, but I think it's going to be like a pretty smooth transition. Uh, once we have all of that in place. And we'll be. Like a full year to migrate and, and help them through any issues they, they, they face. So overall, I feel like assistance users are really going to benefit from this longer term, uh, with this more flexible, primitive.Alessio [00:05:01]: How should people think about when to use each type of API? So I know that in the past, the assistance was maybe more stateful, kind of like long running, many tool use kind of like file based things. And the chat completions is more stateless, you know, kind of like traditional completion API. Is that still the mental model that people should have? Or like, should you buy the.Nikunj [00:05:20]: So the responses API is going to support everything that it's at launch, going to support everything that chat completion supports, and then over time, it's going to support everything that assistance supports. So it's going to be a pretty good fit for anyone starting out with open AI. Uh, they should be able to like go to responses responses, by the way, also has a stateless mode, so you can pass in store false and they'll make the whole API stateless, just like chat completions. You're really trying to like get this unification. A story in so that people don't have to juggle multiple endpoints. That being said, like chat completions, just like the most widely adopted API, it's it's so popular. So we're still going to like support it for years with like new models and features. But if you're a new user, you want to or if you want to like existing, you want to tap into some of these like built in tools or something, you should feel feel totally fine migrating to responses and you'll have more capabilities and performance than the tech completions.swyx [00:06:16]: I think the messaging that I agree that I think resonated the most. When I talked to you was that it is a strict superset, right? Like you should be able to do everything that you could do in chat completions and with assistants. And the thing that I just assumed that because you're you're now, you know, by default is stateful, you're actually storing the chat logs or the chat state. I thought you'd be charging me for it. So, you know, to me, it was very surprising that you figured out how to make it free.Nikunj [00:06:43]: Yeah, it's free. We store your state for 30 days. You can turn it off. But yeah, it's it's free. And the interesting thing on state is that it just like makes particularly for me, it makes like debugging things and building things so much simpler, where I can like create a responses object that's like pretty complicated and part of this more complex application that I've built, I can just go into my dashboard and see exactly what happened that mess up my prompt that is like not called one of these tools that misconfigure one of the tools like the visual observability of everything that you're doing is so, so helpful. So I'm excited, like about people trying that out and getting benefits from it, too.swyx [00:07:19]: Yeah, it's a it's really, I think, a really nice to have. But all I'll say is that my friend Corey Quinn says that anything that can be used as a database will be used as a database. So be prepared for some abuse.Romain [00:07:34]: All right. Yeah, that's a good one. Some of that I've tried with the metadata. That's some people are very, very creative at stuffing data into an object. Yeah.Nikunj [00:07:44]: And we do have metadata with responses. Exactly. Yeah.Alessio [00:07:48]: Let's get through it. All of these. So web search. I think the when I first said web search, I thought you were going to just expose a API that then return kind of like a nice list of thing. But the way it's name is like GPD for all search preview. So I'm guessing you have you're using basically the same model that is in the chat GPD search, which is fine tune for search. I'm guessing it's a different model than the base one. And it's impressive the jump in performance. So just to give an example, in simple QA, GPD for all is 38% accuracy for all search is 90%. But we always talk about. How tools are like models is not everything you need, like tools around it are just as important. So, yeah, maybe give people a quick review on like the work that went into making this special.Nikunj [00:08:29]: Should I take that?Alessio [00:08:29]: Yeah, go for it.Nikunj [00:08:30]: So firstly, we're launching web search in two ways. One in responses API, which is our API for tools. It's going to be available as a web search tool itself. So you'll be able to go tools, turn on web search and you're ready to go. We still wanted to give chat completions people access to real time information. So in that. Chat completions API, which does not support built in tools. We're launching the direct access to the fine tuned model that chat GPD for search uses, and we call it GPD for search preview. And how is this model built? Basically, we have our search research team has been working on this for a while. Their main goal is to, like, get information, like get a bunch of information from all of our data sources that we use to gather information for search and then pick the right things and then cite them. As accurately as possible. And that's what the search team has really focused on. They've done some pretty cool stuff. They use like synthetic data techniques. They've done like all series model distillation to, like, make these four or fine tunes really good. But yeah, the main thing is, like, can it remain factual? Can it answer questions based on what it retrieves and get cited accurately? And that's what this like fine tune model really excels at. And so, yeah, so we're excited that, like, it's going to be directly available in chat completions along with being available as a tool. Yeah.Alessio [00:09:49]: Just to clarify, if I'm using the responses API, this is a tool. But if I'm using chat completions, I have to switch model. I cannot use 01 and call search as a tool. Yeah, that's right. Exactly.Romain [00:09:58]: I think what's really compelling, at least for me and my own uses of it so far, is that when you use, like, web search as a tool, it combines nicely with every other tool and every other feature of the platform. So think about this for a second. For instance, imagine you have, like, a responses API call with the web search tool, but suddenly you turn on function calling. You also turn on, let's say, structure. So you can have, like, the ability to structure any data from the web in real time in the JSON schema that you need for your application. So it's quite powerful when you start combining those features and tools together. It's kind of like an API for the Internet almost, you know, like you get, like, access to the precise schema you need for your app. Yeah.Alessio [00:10:39]: And then just to wrap up on the infrastructure side of it, I read on the post that people, publisher can choose to appear in the web search. So are people by default in it? Like, how can we get Latent Space in the web search API?Nikunj [00:10:53]: Yeah. Yeah. I think we have some documentation around how websites, publishers can control, like, what shows up in a web search tool. And I think you should be able to, like, read that. I think we should be able to get Latent Space in for sure. Yeah.swyx [00:11:10]: You know, I think so. I compare this to a broader trend that I started covering last year of online LLMs. Actually, Perplexity, I think, was the first. It was the first to say, to offer an API that is connected to search, and then Gemini had the sort of search grounding API. And I think you guys, I actually didn't, I missed this in the original reading of the docs, but you even give like citations with like the exact sub paragraph that is matching, which I think is the standard nowadays. I think my question is, how do we take what a knowledge cutoff is for something like this, right? Because like now, basically there's no knowledge cutoff is always live, but then there's a difference between what the model has sort of internalized in its back propagation and what is searching up its rag.Romain [00:11:53]: I think it kind of depends on the use case, right? And what you want to showcase as the source. Like, for instance, you take a company like Hebbia that has used this like web search tool. They can combine like for credit firms or law firms, they can find like, you know, public information from the internet with the live sources and citation that sometimes you do want to have access to, as opposed to like the internal knowledge. But if you're building something different, well, like, you just want to have the information. If you want to have an assistant that relies on the deep knowledge that the model has, you may not need to have these like direct citations. So I think it kind of depends on the use case a little bit, but there are many, uh, many companies like Hebbia that will need that access to these citations to precisely know where the information comes from.swyx [00:12:34]: Yeah, yeah, uh, for sure. And then one thing on the, on like the breadth, you know, I think a lot of the deep research, open deep research implementations have this sort of hyper parameter about, you know, how deep they're searching and how wide they're searching. I don't see that in the docs. But is that something that we can tune? Is that something you recommend thinking about?Nikunj [00:12:53]: Super interesting. It's definitely not a parameter today, but we should explore that. It's very interesting. I imagine like how you would do it with the web search tool and responsive API is you would have some form of like, you know, agent orchestration over here where you have a planning step and then each like web search call that you do like explicitly goes a layer deeper and deeper and deeper. But it's not a parameter that's available out of the box. But it's a cool. It's a cool thing to think about. Yeah.swyx [00:13:19]: The only guidance I'll offer there is a lot of these implementations offer top K, which is like, you know, top 10, top 20, but actually don't really want that. You want like sort of some kind of similarity cutoff, right? Like some matching score cuts cutoff, because if there's only five things, five documents that match fine, if there's 500 that match, maybe that's what I want. Right. Yeah. But also that might, that might make my costs very unpredictable because the costs are something like $30 per a thousand queries, right? So yeah. Yeah.Nikunj [00:13:49]: I guess you could, you could have some form of like a context budget and then you're like, go as deep as you can and pick the best stuff and put it into like X number of tokens. There could be some creative ways of, of managing cost, but yeah, that's a super interesting thing to explore.Alessio [00:14:05]: Do you see people using the files and the search API together where you can kind of search and then store everything in the file so the next time I'm not paying for the search again and like, yeah, how should people balance that?Nikunj [00:14:17]: That's actually a very interesting question. And let me first tell you about how I've seen a really cool way I've seen people use files and search together is they put their user preferences or memories in the vector store and so a query comes in, you use the file search tool to like get someone's like reading preferences or like fashion preferences and stuff like that, and then you search the web for information or products that they can buy related to those preferences and you then render something beautiful to show them, like, here are five things that you might be interested in. So that's how I've seen like file search, web search work together. And by the way, that's like a single responses API call, which is really cool. So you just like configure these things, go boom, and like everything just happens. But yeah, that's how I've seen like files and web work together.Romain [00:15:01]: But I think that what you're pointing out is like interesting, and I'm sure developers will surprise us as they always do in terms of how they combine these tools and how they might use file search as a way to have memory and preferences, like Nikum says. But I think like zooming out, what I find very compelling and powerful here is like when you have these like neural networks. That have like all of the knowledge that they have today, plus real time access to the Internet for like any kind of real time information that you might need for your app and file search, where you can have a lot of company, private documents, private details, you combine those three, and you have like very, very compelling and precise answers for any kind of use case that your company or your product might want to enable.swyx [00:15:41]: It's a difference between sort of internal documents versus the open web, right? Like you're going to need both. Exactly, exactly. I never thought about it doing memory as well. I guess, again, you know, anything that's a database, you can store it and you will use it as a database. That sounds awesome. But I think also you've been, you know, expanding the file search. You have more file types. You have query optimization, custom re-ranking. So it really seems like, you know, it's been fleshed out. Obviously, I haven't been paying a ton of attention to the file search capability, but it sounds like your team has added a lot of features.Nikunj [00:16:14]: Yeah, metadata filtering was like the main thing people were asking us for for a while. And I'm super excited about it. I mean, it's just so critical once your, like, web store size goes over, you know, more than like, you know, 5,000, 10,000 records, you kind of need that. So, yeah, metadata filtering is coming, too.Romain [00:16:31]: And for most companies, it's also not like a competency that you want to rebuild in-house necessarily, you know, like, you know, thinking about embeddings and chunking and, you know, how of that, like, it sounds like very complex for something very, like, obvious to ship for your users. Like companies like Navant, for instance. They were able to build with the file search, like, you know, take all of the FAQ and travel policies, for instance, that you have, you, you put that in file search tool, and then you don't have to think about anything. Now your assistant becomes naturally much more aware of all of these policies from the files.swyx [00:17:03]: The question is, like, there's a very, very vibrant RAG industry already, as you well know. So there's many other vector databases, many other frameworks. Probably if it's an open source stack, I would say like a lot of the AI engineers that I talk to want to own this part of the stack. And it feels like, you know, like, when should we DIY and when should we just use whatever OpenAI offers?Nikunj [00:17:24]: Yeah. I mean, like, if you're doing something completely from scratch, you're going to have more control, right? Like, so super supportive of, you know, people trying to, like, roll up their sleeves, build their, like, super custom chunking strategy and super custom retrieval strategy and all of that. And those are things that, like, will be harder to do with OpenAI tools. OpenAI tool has, like, we have an out-of-the-box solution. We give you the tools. We use some knobs to customize things, but it's more of, like, a managed RAG service. So my recommendation would be, like, start with the OpenAI thing, see if it, like, meets your needs. And over time, we're going to be adding more and more knobs to make it even more customizable. But, you know, if you want, like, the completely custom thing, you want control over every single thing, then you'd probably want to go and hand roll it using other solutions. So we're supportive of both, like, engineers should pick. Yeah.Alessio [00:18:16]: And then we got computer use. Which I think Operator was obviously one of the hot releases of the year. And we're only two months in. Let's talk about that. And that's also, it seems like a separate model that has been fine-tuned for Operator that has browser access.Nikunj [00:18:31]: Yeah, absolutely. I mean, the computer use models are exciting. The cool thing about computer use is that we're just so, so early. It's like the GPT-2 of computer use or maybe GPT-1 of computer use right now. But it is a separate model that has been, you know, the computer. The computer use team has been working on, you send it screenshots and it tells you what action to take. So the outputs of it are almost always tool calls and you're inputting screenshots based on whatever computer you're trying to operate.Romain [00:19:01]: Maybe zooming out for a second, because like, I'm sure your audience is like super, super like AI native, obviously. But like, what is computer use as a tool, right? And what's operator? So the idea for computer use is like, how do we let developers also build agents that can complete tasks for the users, but using a computer? Okay. Or a browser instead. And so how do you get that done? And so that's why we have this custom model, like optimized for computer use that we use like for operator ourselves. But the idea behind like putting it as an API is that imagine like now you want to, you want to automate some tasks for your product or your own customers. Then now you can, you can have like the ability to spin up one of these agents that will look at the screen and act on the screen. So that means able, the ability to click, the ability to scroll. The ability to type and to report back on the action. So that's what we mean by computer use and wrapping it as a tool also in the responses API. So now like that gives a hint also at the multi-turned thing that we were hinting at earlier, the idea that like, yeah, maybe one of these actions can take a couple of minutes to complete because there's maybe like 20 steps to complete that task. But now you can.swyx [00:20:08]: Do you think a computer use can play Pokemon?Romain [00:20:11]: Oh, interesting. I guess we tried it. I guess we should try it. You know?swyx [00:20:17]: Yeah. There's a lot of interest. I think Pokemon really is a good agent benchmark, to be honest. Like it seems like Claude is, Claude is running into a lot of trouble.Romain [00:20:25]: Sounds like we should make that a new eval, it looks like.swyx [00:20:28]: Yeah. Yeah. Oh, and then one more, one more thing before we move on to agents SDK. I know you have a hard stop. There's all these, you know, blah, blah, dash preview, right? Like search preview, computer use preview, right? And you see them all like fine tunes of 4.0. I think the question is, are we, are they all going to be merged into the main branch or are we basically always going to have subsets? Of these models?Nikunj [00:20:49]: Yeah, I think in the early days, research teams at OpenAI like operate with like fine tune models. And then once the thing gets like more stable, we sort of merge it into the main line. So that's definitely the vision, like going out of preview as we get more comfortable with and learn about all the developer use cases and we're doing a good job at them. We'll sort of like make them part of like the core models so that you don't have to like deal with the bifurcation.Romain [00:21:12]: You should think of it this way as exactly what happened last year when we introduced vision capabilities, you know. Yes. Vision capabilities were in like a vision preview model based off of GPT-4 and then vision capabilities now are like obviously built into GPT-4.0. You can think about it the same way for like the other modalities like audio and those kind of like models, like optimized for search and computer use.swyx [00:21:34]: Agents SDK, we have a few minutes left. So let's just assume that everyone has looked at Swarm. Sure. I think that Swarm has really popularized the handoff technique, which I thought was like, you know, really, really interesting for sort of a multi-agent. What is new with the SDK?Nikunj [00:21:50]: Yeah. Do you want to start? Yeah, for sure. So we've basically added support for types. We've made this like a lot. Yeah. Like we've added support for types. We've added support for guard railing, which is a very common pattern. So in the guardrail example, you basically have two things happen in parallel. The guardrail can sort of block the execution. It's a type of like optimistic generation that happens. And I think we've added support for tracing. So I think that's really cool. So you can basically look at the traces that the Agents SDK creates in the OpenAI dashboard. We also like made this pretty flexible. So you can pick any API from any provider that supports the ChatCompletions API format. So it supports responses by default, but you can like easily plug it in to anyone that uses the ChatCompletions API. And similarly, on the tracing side, you can support like multiple tracing providers. By default, it sort of points to the OpenAI dashboard. But, you know, there's like so many tracing providers. There's so many tracing companies out there. And we'll announce some partnerships on that front, too. So just like, you know, adding lots of core features and making it more usable, but still centered around like handoffs is like the main, main concept.Romain [00:22:59]: And by the way, it's interesting, right? Because Swarm just came to life out of like learning from customers directly that like orchestrating agents in production was pretty hard. You know, simple ideas could quickly turn very complex. Like what are those guardrails? What are those handoffs, et cetera? So that came out of like learning from customers. And it was initially shipped. It was not as a like low-key experiment, I'd say. But we were kind of like taken by surprise at how much momentum there was around this concept. And so we decided to learn from that and embrace it. To be like, okay, maybe we should just embrace that as a core primitive of the OpenAI platform. And that's kind of what led to the Agents SDK. And I think now, as Nikuj mentioned, it's like adding all of these new capabilities to it, like leveraging the handoffs that we had, but tracing also. And I think what's very compelling for developers is like instead of having one agent to rule them all and you stuff like a lot of tool calls in there that can be hard to monitor, now you have the tools you need to kind of like separate the logic, right? And you can have a triage agent that based on an intent goes to different kind of agents. And then on the OpenAI dashboard, we're releasing a lot of new user interface logs as well. So you can see all of the tracing UIs. Essentially, you'll be able to troubleshoot like what exactly happened. In that workflow, when the triage agent did a handoff to a secondary agent and the third and see the tool calls, et cetera. So we think that the Agents SDK combined with the tracing UIs will definitely help users and developers build better agentic workflows.Alessio [00:24:28]: And just before we wrap, are you thinking of connecting this with also the RFT API? Because I know you already have, you kind of store my text completions and then I can do fine tuning of that. Is that going to be similar for agents where you're storing kind of like my traces? And then help me improve the agents?Nikunj [00:24:43]: Yeah, absolutely. Like you got to tie the traces to the evals product so that you can generate good evals. Once you have good evals and graders and tasks, you can use that to do reinforcement fine tuning. And, you know, lots of details to be figured out over here. But that's the vision. And I think we're going to go after it like pretty hard and hope we can like make this whole workflow a lot easier for developers.Alessio [00:25:05]: Awesome. Thank you so much for the time. I'm sure you'll be busy on Twitter tomorrow with all the developer feedback. Yeah.Romain [00:25:12]: Thank you so much for having us. And as always, we can't wait to see what developers will build with these tools and how we can like learn as quickly as we can from them to make them even better over time.Nikunj [00:25:21]: Yeah.Romain [00:25:22]: Thank you, guys.Nikunj [00:25:23]: Thank you.Romain [00:25:23]: Thank you both. Awesome. Get full access to Latent.Space at www.latent.space/subscribe

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