Podcasts about Alessio

Fluent Fiction - Italian: Bridging Worlds: Art and Connection in Oaxaca's Mercato Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2026-01-29-23-34-02-it Story Transcript:It: Il mercato di Oaxaca era un mare di colori.En: The mercato of Oaxaca was a sea of colors.It: Le bancarelle straripavano di tessuti intricati, realizzati con un'arte che parlava direttamente al cuore di chi sapeva ascoltare.En: The stalls overflowed with intricate textiles, created with an art that spoke directly to the heart of those who knew how to listen.It: Alessio camminava lentamente tra la folla, la sua macchina fotografica pronta a catturare ogni dettaglio.En: Alessio walked slowly among the crowd, his camera ready to capture every detail.It: Tuttavia, non riusciva a trovare l'immagine perfetta, quella che potesse raccontare la vera essenza dell'arte indigena.En: However, he couldn't find the perfect image, the one that could tell the true essence of indigenous art.It: Dall'altra parte del mercato, Giulia osservava incantata i tessitori locali.En: On the other side of the mercato, Giulia watched the local weavers with enchantment.It: Anche se non parlava spagnolo, la sua passione per l'arte l'aveva spinta a venire qui.En: Even though she didn't speak Spanish, her passion for art had driven her to come here.It: Si sentiva un po' isolata, incapace di comunicare, ma non voleva arrendersi.En: She felt a bit isolated, unable to communicate, but didn't want to give up.It: Con il suo sketchbook in mano, iniziò a disegnare gli intricati motivi che vedeva intorno a lei, sperando di poter esprimere la sua ammirazione attraverso le immagini.En: With her sketchbook in hand, she started drawing the intricate patterns she saw around her, hoping to express her admiration through images.It: Alessio notò una giovane donna intenta a disegnare, circondata da tessitori sorridenti.En: Alessio noticed a young woman intent on drawing, surrounded by smiling weavers.It: Era Giulia, che aveva finalmente trovato un modo per interagire con gli artigiani, mostrando loro i suoi schizzi.En: It was Giulia, who had finally found a way to interact with the artisans by showing them her sketches.It: La scena aveva un'atmosfera magica, quasi come se una storia silenziosa si stesse svolgendo davanti ai suoi occhi.En: The scene had a magical atmosphere, almost as if a silent story was unfolding before his eyes.It: Senza pensarci troppo, Alessio alzò la macchina fotografica e scattò una foto.En: Without thinking too much, Alessio raised his camera and took a picture.It: Quando Giulia sollevò lo sguardo, i loro occhi si incrociarono.En: When Giulia lifted her gaze, their eyes met.It: Fu un incontro silenzioso ma pieno di significato.En: It was a silent but meaningful encounter.It: Alessio si avvicinò, incuriosito dai suoi disegni.En: Alessio approached, curious about her drawings.It: "I tuoi schizzi sono incredibili", disse, ammirando la profondità con cui aveva catturato i dettagli e l'anima dell'artigianato.En: "Your sketches are incredible," he said, admiring the depth with which she had captured the details and soul of the craftsmanship.It: Giulia sorrise timidamente, trovando conforto nella possibilità di comunicare.En: Giulia smiled timidly, finding comfort in the opportunity to communicate.It: Iniziarono a parlare, condividendo le loro esperienze e le loro sfide.En: They began to talk, sharing their experiences and challenges.It: Alessio le raccontò della sua ricerca della foto perfetta e di come avesse trovato la sua ispirazione nel vederla all'opera.En: Alessio told her about his search for the perfect photo and how he had found his inspiration by seeing her at work.It: "La tua presenza qui è ciò che mi ha dato una nuova prospettiva", ammise Alessio.En: "Your presence here is what gave me a new perspective," Alessio admitted.It: Nel frattempo, Giulia scoprì una nuova fiducia in sé stessa grazie alla connessione stabilita con gli artigiani e con Alessio.En: Meanwhile, Giulia discovered a newfound confidence in herself thanks to the connection established with the artisans and with Alessio.It: La loro conversazione si trasformò in una lezione di vita, insegnando a entrambi il valore della connessione umana e culturale.En: Their conversation turned into a life lesson, teaching them both the value of human and cultural connection.It: Poco dopo, Alessio scattò un'altra foto, questa volta di Giulia mentre discuteva animatamente con un anziano tessitore che sorrideva orgoglioso dei suoi lavori.En: Shortly after, Alessio took another photo, this time of Giulia while she animatedly discussed with an elderly weaver who smiled proudly at his work.It: L'immagine catturava perfettamente l'unione di due mondi artistici e culturali.En: The image perfectly captured the union of two artistic and cultural worlds.It: Quando il sole iniziò a tramontare, tingeva il cielo di arancio e oro, Alessio e Giulia si allontanarono dal mercato con nuove ispirazioni.En: When the sun began to set, painting the sky orange and gold, Alessio and Giulia left the mercato with new inspirations.It: Lui aveva trovato il soggetto perfetto per la sua fotografia, lei aveva trovato un nuovo modo di essere parte di un mondo a volte incompreso.En: He had found the perfect subject for his photograph, she had found a new way to be part of a sometimes misunderstood world.It: Il mercato si preparava a chiudere, eppure l'energia vibrante delle storie e delle culture che lo animavano rimanevano vive.En: The mercato was getting ready to close, yet the vibrant energy of the stories and cultures that animated it remained alive.It: Alessio e Giulia sapevano che quel giorno non avevano solo trovato l'arte, ma anche una nuova comprensione di sé stessi e degli altri.En: Alessio and Giulia knew that that day they had not only found art, but also a new understanding of themselves and others. Vocabulary Words:the market: il mercatothe colors: i colorithe stalls: le bancarellethe textiles: i tessutiintricate: intricatithe heart: il cuorethe crowd: la follathe weavers: i tessitorienchanted: incantataisolated: isolatato communicate: comunicarethe patterns: i motivithe sketchbook: il sketchbookthe eyes: gli occhisilent: silenziosodrawing: disegnaredepth: profonditàthe soul: l'animatimidly: timidamentechallenges: le sfideto discover: scoprireconfidence: fiduciaelderly: anzianoproudly: orgogliosothe sunset: il tramontoorange: aranciogold: orovibrant: vibranteto animate: animareunderstanding: comprensione

In questa puntata di Italiano ON-AIR, Katia e Alessio ci portano nel cuore del Rinascimento italiano per scoprire la figura di Fra' Giovanni da Fiesole, meglio conosciuto come Beato Angelico.Perché questo frate domenicano è considerato uno dei più grandi maestri del Quattrocento? Scopriremo insieme le caratteristiche uniche della sua pittura, fatta di luce divina, colori delicati e una prospettiva che unisce realismo e spiritualità. Parleremo anche dell'origine del suo nome e vi daremo un prezioso consiglio di viaggio per visitare i suoi capolavori a Firenze, lontano dalla folla, in un museo spesso fuori dalle rotte turistiche.

Fluent Fiction - Italian: Navigating Nature's Fury: A Journey of Discovery and Respect Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2026-01-26-08-38-20-it Story Transcript:It: Nella vastità della foresta pluviale brasiliana, Alessio e Giuliana avanzano tra le foglie fitte e il canto degli uccelli esotici.En: In the vastness of the Brazilian rainforest, Alessio and Giuliana advance through the thick leaves and the song of exotic birds.It: È estate e il caldo è opprimente, mentre il sole splende alto sopra di loro.En: It's summer and the heat is oppressive, while the sun shines high above them.It: Alessio, con i suoi occhi attenti, valuta ogni passo.En: Alessio, with his attentive eyes, evaluates every step.It: È uno scienziato ambientale e vede il mondo attraverso i dettagli.En: He is an environmental scientist and sees the world through details.It: Giuliana, invece, con il suo taccuino in mano, guarda tutto con curiosità.En: Giuliana, on the other hand, with her notebook in hand, looks at everything with curiosity.It: È una giornalista, sempre a caccia di una storia straordinaria da raccontare.En: She is a journalist, always on the hunt for an extraordinary story to tell.It: Il loro scopo è chiaro: Alessio vuole tornare al campo con i preziosi dati raccolti.En: Their purpose is clear: Alessio wants to return to the camp with the precious data collected.It: Giuliana spera di trovare un racconto avvincente.En: Giuliana hopes to find a compelling story.It: Ma la foresta è ingannevole, e il loro percorso diventa incerto.En: But the forest is deceptive, and their path becomes uncertain.It: Le nuvole cominciano a formarsi rapidamente, e un improvviso temporale si abbatte su di loro con tutta la sua furia.En: The clouds begin to form rapidly, and an unexpected storm crashes down on them with all its fury.It: "La bussola non mente," dice Alessio, cercando di mantenere la calma.En: "The compass doesn't lie," says Alessio, trying to stay calm.It: Sa che devono tornare al campo, ma i loro telefoni non funzionano più.En: He knows they must return to the camp, but their phones no longer work.It: La pioggia cade forte, cancellando le tracce del sentiero.En: The rain falls hard, erasing the tracks of the trail.It: Giuliana trema leggermente, ma è determinata a non arrendersi.En: Giuliana trembles slightly, but she is determined not to give up.It: "Meglio esplorare nuovi sentieri," propone Giuliana con vigore.En: "It's better to explore new paths," suggests Giuliana vigorously.It: "Potremmo scoprire qualcosa di unico!"En: "We might discover something unique!"It: Alessio è titubante, ma non possono permettersi di disperdersi nel buio che avanza rapidamente.En: Alessio is hesitant, but they can't afford to get scattered in the quickly approaching darkness.It: Mentre il fulmine illumina il cielo, Giuliana nota qualcosa di insolito tra le ombre: una piccola apertura nel fianco della montagna.En: As the lightning illuminates the sky, Giuliana notices something unusual among the shadows: a small opening in the side of the mountain.It: "Potremmo ripararci lì!"En: "We could shelter there!"It: suggerisce entusiasta.En: she suggests enthusiastically.It: Contro la sua abituale logica, Alessio decide di fidarsi dell'istinto di Giuliana.En: Against his usual logic, Alessio decides to trust Giuliana's instinct.It: Trovano rifugio nella grotta mentre il temporale imperversa fuori.En: They find refuge in the cave while the storm rages outside.It: Con il suono della pioggia che risuona nelle profondità, Alessio e Giuliana parlano con più tranquillità.En: With the sound of the rain echoing in the depths, Alessio and Giuliana speak more calmly.It: Le loro differenze diventano un ponte di comprensione.En: Their differences become a bridge of understanding.It: Lui vede quanto è importante la prospettiva coraggiosa di Giuliana.En: He sees the importance of Giuliana's bold perspective.It: Lei comprende il valore del ragionamento ponderato di Alessio.En: She comprehends the value of Alessio's thoughtful reasoning.It: Quando il temporale passa e il mattino giunge, la foresta ritorna alla sua solita sinfonia di vita.En: When the storm passes and morning comes, the forest returns to its usual symphony of life.It: Insieme, escono con cautela dalla grotta, guidati ora non solo da una bussola, ma da un nuovo rispetto reciproco.En: Together, they cautiously exit the cave, guided now not only by a compass but by a newfound mutual respect.It: Ritornano al campo con i dati intatti e una storia pronta a prendere forma nella penna di Giuliana.En: They return to the camp with the data intact and a story ready to take shape in Giuliana's pen.It: La foresta, con i suoi segreti e avventure, ha insegnato loro più di quanto avrebbero potuto immaginare.En: The forest, with its secrets and adventures, has taught them more than they could have imagined. Vocabulary Words:the vastness: la vastitàthe rainforest: la foresta pluvialethe heat: il caldooppressive: opprimentethe details: i dettaglithe curiosity: la curiositàextraordinary: straordinariathe purpose: lo scopoprecious: preziosicompelling: avvincentedeceptive: ingannevolethe storm: il temporalethe fury: la furiato tremble: tremaredetermined: determinatavigorously: con vigorehesitant: titubantethe darkness: il buiothe lightning: il fulmineunusual: insolitoenthusiastically: entusiastalogic: logicathe instinct: l'istintothe refuge: il rifugioto rage: imperversarethe depths: le profonditàthe differences: le differenzeto comprehend: comprenderethoughtful: ponderatothe symphony: la sinfonia

In questo episodio di

Fluent Fiction - Italian: Silent Snow, Loud Lessons: Building Bridges in the Dolomites Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2026-01-21-23-34-02-it Story Transcript:It: Le Dolomiti si coloravano di bianco, come un dipinto silenzioso carico di immensa bellezza.En: The Dolomiti were turning white, like a silent painting full of immense beauty.It: Il cielo era limpido, e il sole invernale brillava sopra il paesaggio innevato.En: The sky was clear, and the winter sun shone over the snowy landscape.It: Alessio e Bianca erano in viaggio verso una piccola baita incastonata tra i monti.En: Alessio and Bianca were traveling to a small cabin nestled between the mountains.It: Lì, avrebbero trascorso alcuni giorni insieme, inviati dall'azienda per migliorare la loro capacità di lavorare in team.En: There, they would spend a few days together, sent by the company to improve their teamwork skills.It: Alessio era un lavoratore capace.En: Alessio was a capable worker.It: Tuttavia, il suo carattere testardo spesso lo metteva in contrasto con gli altri.En: However, his stubborn nature often put him at odds with others.It: Bianca, al contrario, era creativa e sempre aperta a nuove idee, ma trovava difficile lavorare con Alessio, che raramente ascoltava le opinioni altrui.En: Bianca, on the other hand, was creative and always open to new ideas, but found it difficult to work with Alessio, who rarely listened to others' opinions.It: La baita era accogliente, con un fuoco che scoppiettava nel camino e una vista mozzafiato sulle montagne innevate.En: The cabin was cozy, with a fire crackling in the fireplace and a breathtaking view of the snowy mountains.It: I due colleghi si sedettero su un grande divano di fronte al camino.En: The two colleagues sat on a large couch in front of the fireplace.It: Parlare apertamente non era facile con tutto quel silenzio attorno, ma dovevano provarci.En: Speaking openly wasn't easy with all that silence around, but they had to try.It: Il mattino seguente, il sole brillava su un mare di neve fresca.En: The next morning, the sun shone on a sea of fresh snow.It: Un team-building era stato organizzato: i due dovevano costruire una struttura di neve.En: A team-building activity had been organized: the two had to build a snow structure.It: Alessio e Bianca si misero a lavoro, ma presto iniziarono i contrasti.En: Alessio and Bianca got to work, but soon conflicts arose.It: Alessio voleva sovrastare con la sua idea, mentre Bianca proponeva una struttura più innovativa e collaborativa.En: Alessio wanted to dominate with his idea, while Bianca proposed a more innovative and collaborative structure.It: La tensione era palpabile.En: The tension was palpable.It: Alessio continuava a respingere le proposte di Bianca, insistendo sulla sua visione.En: Alessio kept rejecting Bianca's proposals, insisting on his vision.It: La situazione sembrava destinata al fallimento, finché una parte della struttura non iniziò a cedere.En: The situation seemed destined for failure, until part of the structure began to collapse.It: Bianca cercò di rimediare, ma senza successo.En: Bianca tried to fix it, but without success.It: Era un momento critico, e Alessio si trovò di fronte a una scelta: lasciare che il progetto di Bianca crollasse o mettere da parte il suo orgoglio e aiutarla.En: It was a critical moment, and Alessio found himself facing a choice: let Bianca's project collapse or put aside his pride and help her.It: Con un profondo respiro, Alessio decise di agire.En: With a deep breath, Alessio decided to act.It: Insieme, lavorarono senza sosta per rinforzare la struttura.En: Together, they worked tirelessly to reinforce the structure.It: Sembrava quasi che, mentre la neve prendeva forma, anche il loro rispetto reciproco crescesse.En: It seemed almost as if, while the snow was taking shape, so too was their mutual respect growing.It: Alessio ascoltò per davvero le idee di Bianca, accettando per la prima volta che collaborare poteva portare a risultati eccezionali.En: Alessio truly listened to Bianca's ideas, accepting for the first time that collaboration could lead to exceptional results.It: Quando finirono, la struttura di neve era solida e unica, riflettendo il contributo di entrambi.En: When they finished, the snow structure was solid and unique, reflecting the contribution of both.It: Bianca si sentì finalmente apprezzata nelle sue capacità mentre Alessio comprese il valore della collaborazione e della fiducia.En: Bianca finally felt appreciated for her abilities while Alessio understood the value of collaboration and trust.It: L'atmosfera tra i due era cambiata.En: The atmosphere between the two had changed.It: Ritornarono verso la baita, mentre il sole calava dolcemente dietro le Dolomiti.En: They returned to the cabin, as the sun gently set behind the Dolomiti.It: Il silenzio che li avvolgeva era ora sereno e colmo di un nuovo rispetto reciproco.En: The silence surrounding them was now serene and full of a newfound mutual respect.It: Avevano scoperto la forza di lavorare insieme, sapendo che combinando le loro visioni potevano affrontare qualsiasi sfida.En: They had discovered the strength of working together, knowing that by combining their visions, they could face any challenge.It: E così, in quella fredda ma luminosa giornata d'inverno, Alessio e Bianca capirono che le montagne erano belle non solo quando le si osservava da lontano, ma anche quando si lasciavano esplorare.En: And so, on that cold but bright winter day, Alessio and Bianca realized that the mountains were beautiful not only when viewed from afar, but also when allowed to be explored. Vocabulary Words:the cabin: la baitato nestle: incastonarestubborn: testardobreathtaking: mozzafiatoproposal: la propostato dominate: sovrastareconflict: il contrastopalpable: palpabileto collapse: cederepride: l'orgoglioto reinforce: rinforzareexceptional: eccezionalemutual: reciprocoto explore: esplorarecompany: l'aziendacreative: creativoto propose: proporreinnovative: innovativocritical: criticoto fix: rimediaresea of snow: mare di neveteamwork: lavoro in teamstructure: la strutturacozy: accoglientecapability: capacitàto appreciate: apprezzaretrust: la fiduciaatmosphere: l'atmosferato shine: brillaresilence: il silenzio

If you learn how to live with intention, you can lead with purpose and manifest all of your goals in life. This is exactly what Bianca D'Alessio did, which led to her becoming the number one real estate broker in New York City. She joins J.R. Lowry to share all about her journey centered on authenticity, leading with lasting impact, and discovering gratitude even in her biggest failures. Bianca also talks about her new book, Mastering Intentions, wherein she breaks down daily practices to amplify your own power. Discover how to manage your narrative, be comfortable with vulnerability, and unlock resilience both in business and in life.Check out the full series of “Career Sessions, Career Lessons” podcasts here or visit pathwise.io/podcast/. A full written transcript of this episode is also available at https://pathwise.io/podcasts/bianca-dalessio.Become a PathWise member today! Join at https://pathwise.io/join-now/

Join Ivoclar (AND US!) this February at LMT Lab Day in Chicago. Ivoclar will be offering 16 different educational lectures over the three-day event, giving dental professionals plenty of opportunities to learn, connect, and grow. Visit labday.com/Ivoclar to view the full schedule and register, and be sure to stop by and see the Ivoclar team in the Windy City. Cal-Lab Association Meeting in Chicago Feb 19-20 https://cal-lab.org/ LMT Lab Day Chicago Feb 19-21 https://lmtmag.com/lmtlabday Almost three years after his last appearance, Rob Nazzal returns to Voices From the Bench, this time joined by Mike Alessio of Bonadent Dental Laboratory (https://bonadent.com/). The conversation dives deep into lab leadership, culture, transparency, and how data—when used the right way—can empower teams instead of policing them. Mike shares his 32-year journey with Bonadent, from starting as a pickup-and-delivery driver to leading the Danaren division, and explains how a family-owned lab has grown into a multi-location organization without losing its people-first culture. Rob and Mike unpack the realities of tracking productivity on the lab floor, the challenges of sharing metrics openly, and why transparency builds trust, alignment, and accountability when done with intention. The discussion shifts to quality vs. productivity, the difficulty of truly measuring “quality,” and why labs must lead with craftsmanship before numbers. They also explore how digital workflows, QC processes, and proactive communication with doctors impact remakes, efficiency, and relationships. On the sales side, Rob breaks down how icortica (https://www.icortica.com/voices) helps labs grow by focusing on existing customers, improving retention, and giving sales teams real-time insights into what conversations they should be having—right before they walk into an office. Mike and Elvis share firsthand experiences using icortica (https://www.icortica.com/voices), highlighting how real-time data, centralized notes, and smart alerts change the way sales reps prepare, prioritize, and perform. The episode wraps with a look at Bonadent's unique culture (including their famous converted Walmart lab), long employee tenure, and why investing in people, transparency, and the right technology is the real key to sustainable growth in today's dental lab landscape. If you want to grow your business, you need clear insight into what's happening inside your operation and across your customer journey. That's where Icortica comes in. At Canadian Dental Labs, Icortica has become a cornerstone of how we operate—giving us at-a-glance visibility into performance, helping us focus our efforts, spot opportunities early, and solve problems before they grow. It takes the guesswork out of decision-making and shows us what to do next. Plus, the Icortica team is incredibly responsive and feels like a true partner in our success. If you're serious about growing your business and understanding your customers better, Icortica can get you there. Learn more at icortica.com/voices — Icortica, helping dental labs grow. Special Guests: Mike Alessio and Rob Nazzal.

Fluent Fiction - Italian: Thwarting Election Fraud: A Cold Day in Collevecchio Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2026-01-17-23-34-02-it Story Transcript:It: Il cielo era grigio sopra il piccolo centro comunitario di Collevecchio.En: The sky was gray above the small community center of Collevecchio.It: Era inverno, e il vento portava con sé un freddo pungente.En: It was winter, and the wind carried with it a biting cold.It: All'interno del centro, la scena era ben diversa.En: Inside the center, the scene was quite different.It: Il calore umano riempiva l'aria mentre i cittadini, avvolti nei loro cappotti e sciarpe, si mettevano in fila per votare.En: Human warmth filled the air as the citizens, wrapped in their coats and scarves, queued to vote.It: La sala era addobbata con manifesti elettorali di vari colori.En: The hall was decorated with election posters of various colors.It: Alessio, un funzionario elettorale diligente e attento, stava verificando i documenti dei votanti con attenzione.En: Alessio, a diligent and attentive electoral official, was carefully checking voters' documents.It: Proprio mentre il giorno di voto procedeva senza intoppi, Alessio ricevette un messaggio anonimo.En: Just as the voting day was proceeding smoothly, Alessio received an anonymous message.It: Diceva che ci sarebbe stato un tentativo di frode elettorale.En: It said there would be an attempt at electoral fraud.It: Alessio lesse il messaggio due volte.En: Alessio read the message twice.It: Era preoccupato.En: He was worried.It: Non voleva causare panico inutilmente, ma non poteva ignorare l'avvertimento.En: He didn't want to cause panic unnecessarily, but he couldn't ignore the warning.It: Nel frattempo, Giuliana, una giovane giornalista dal piglio deciso, aveva sentito del messaggio.En: Meanwhile, Giuliana, a young journalist with a determined approach, had heard about the message.It: Voleva scoprire la verità.En: She wanted to uncover the truth.It: Era determinata a pubblicare la storia, ma le mancavano prove concrete.En: She was determined to publish the story, but she lacked concrete evidence.It: Decise di parlarne con Alessio.En: She decided to talk to Alessio about it.It: "Alessio, possiamo parlare?"En: "Alessio, can we talk?"It: chiese Giuliana, avvicinandosi a lui.En: asked Giuliana, approaching him.It: "Certo, Giuliana.En: "Certainly, Giuliana.It: Che succede?"En: What's going on?"It: Alessio rispose, anche se era visibilmente preoccupato.En: Alessio replied, although he was visibly concerned.It: "Questo messaggio anonimo... Credi che sia vero?"En: "This anonymous message... Do you think it's true?"It: chiese Giuliana.En: asked Giuliana.It: Voleva sapere se Alessio avrebbe indagato, ma anche se avrebbe collaborato con lei.En: She wanted to know if Alessio would investigate and if he would collaborate with her.It: Alessio sospirò, incerto sul da farsi.En: Alessio sighed, uncertain about what to do.It: "Non lo so.En: "I don't know.It: Ma non possiamo ignorarlo completamente.En: But we can't completely ignore it.It: Potrebbe essere solo un falso allarme."En: It might just be a false alarm."It: "Ma se fosse vero?En: "But what if it's true?It: Dobbiamo fare qualcosa," insistette Giuliana.En: We need to do something," insisted Giuliana.It: Nel corso della giornata, Giuliana osservò una figura sospetta vicino alle urne.En: Throughout the day, Giuliana observed a suspicious figure near the ballot boxes.It: Sembrava strano.En: It seemed odd.It: Scriveva qualcosa su un foglio in modo furtivo.En: The person was writing something on a sheet of paper furtively.It: Giuliana scattò rapidamente alcune foto e corse da Alessio.En: Giuliana quickly snapped a few photos and ran to Alessio.It: "Alessio, presto!En: "Alessio, quickly!It: C'è qualcosa che non va!"En: Something is wrong!"It: Giuliana esclamò concitata.En: Giuliana exclaimed, agitated.It: Alessio si precipitò con lei.En: Alessio hurried with her.It: Vide l'individuo e decise di intervenire.En: He saw the individual and decided to intervene.It: Lo fermò e chiese spiegazioni.En: He stopped him and asked for an explanation.It: Dopo un breve controllo, scoprirono che la persona stava tentando di manipolare i voti con nuove schede false.En: After a brief check, they discovered that the person was attempting to manipulate the votes with new fake ballots.It: Fu un momento teso, ma grazie alla prontezza di Giuliana e alla risolutezza di Alessio, la situazione venne risolta senza intaccare i risultati delle elezioni.En: It was a tense moment, but thanks to Giuliana's alertness and Alessio's decisiveness, the situation was resolved without affecting the election results.It: A fine giornata, Alessio e Giuliana si trovarono a discutere dell'accaduto.En: At the end of the day, Alessio and Giuliana found themselves discussing the incident.It: "Grazie a te, abbiamo sventato la frode," disse Alessio, riconoscendo il valore del lavoro di Giuliana.En: "Thanks to you, we thwarted the fraud," Alessio said, acknowledging the value of Giuliana's work.It: "E grazie a te, la tua attenzione ha evitato il panico," replicò Giuliana, rispettandolo per la sua calma e decisione.En: "And thanks to you, your attentiveness avoided panic," replied Giuliana, respecting him for his calmness and decisiveness.It: Quella sera, Alessio camminò verso casa sentendo un nuovo rispetto per il lavoro dei giornalisti.En: That evening, Alessio walked home with a newfound respect for the work of journalists.It: Giuliana tornò in redazione con una lezione importante sull'importanza dell'integrità.En: Giuliana returned to the newsroom with an important lesson about the importance of integrity.It: Entrambi sapevano di aver fatto la cosa giusta, consapevoli che vigilanza e collaborazione erano essenziali per la giustizia.En: Both knew they had done the right thing, aware that vigilance and collaboration were essential for justice. Vocabulary Words:the sky: il cielobiting: pungentethe scene: la scenawrapped: avvoltito queue: mettersi in filathe hall: la salathe official: il funzionariodiligent: diligenteto check: verificarethe document: il documentosmoothly: senza intoppithe message: il messaggioanonymous: anonimothe attempt: il tentativofraud: la frodeworried: preoccupatothe warning: l'avvertimentothe journalist: la giornalistadetermined: decisoto uncover: scoprireconcrete: concreteevidence: le proveto investigate: indagareto ignore: ignorarefalse alarm: falso allarmesuspicious: sospettafurtively: furtivoto manipulate: manipolaredecisiveness: risolutezzathwarted: sventato

Happy New Year! You may have noticed that in 2025 we had moved toward YouTube as our primary podcasting platform. As we'll explain in the next State of Latent Space post, we'll be doubling down on Substack again and improving the experience for the over 100,000 of you who look out for our emails and website updates!We first mentioned Artificial Analysis in 2024, when it was still a side project in a Sydney basement. They then were one of the few Nat Friedman and Daniel Gross' AIGrant companies to raise a full seed round from them and have now become the independent gold standard for AI benchmarking—trusted by developers, enterprises, and every major lab to navigate the exploding landscape of models, providers, and capabilities.We have chatted with both Clementine Fourrier of HuggingFace's OpenLLM Leaderboard and (the freshly valued at $1.7B) Anastasios Angelopoulos of LMArena on their approaches to LLM evals and trendspotting, but Artificial Analysis have staked out an enduring and important place in the toolkit of the modern AI Engineer by doing the best job of independently running the most comprehensive set of evals across the widest range of open and closed models, and charting their progress for broad industry analyst use.George Cameron and Micah-Hill Smith have spent two years building Artificial Analysis into the platform that answers the questions no one else will: Which model is actually best for your use case? What are the real speed-cost trade-offs? And how open is “open” really?We discuss:* The origin story: built as a side project in 2023 while Micah was building a legal AI assistant, launched publicly in January 2024, and went viral after Swyx's retweet* Why they run evals themselves: labs prompt models differently, cherry-pick chain-of-thought examples (Google Gemini 1.0 Ultra used 32-shot prompts to beat GPT-4 on MMLU), and self-report inflated numbers* The mystery shopper policy: they register accounts not on their own domain and run intelligence + performance benchmarks incognito to prevent labs from serving different models on private endpoints* How they make money: enterprise benchmarking insights subscription (standardized reports on model deployment, serverless vs. managed vs. leasing chips) and private custom benchmarking for AI companies (no one pays to be on the public leaderboard)* The Intelligence Index (V3): synthesizes 10 eval datasets (MMLU, GPQA, agentic benchmarks, long-context reasoning) into a single score, with 95% confidence intervals via repeated runs* Omissions Index (hallucination rate): scores models from -100 to +100 (penalizing incorrect answers, rewarding ”I don't know”), and Claude models lead with the lowest hallucination rates despite not always being the smartest* GDP Val AA: their version of OpenAI's GDP-bench (44 white-collar tasks with spreadsheets, PDFs, PowerPoints), run through their Stirrup agent harness (up to 100 turns, code execution, web search, file system), graded by Gemini 3 Pro as an LLM judge (tested extensively, no self-preference bias)* The Openness Index: scores models 0-18 on transparency of pre-training data, post-training data, methodology, training code, and licensing (AI2 OLMo 2 leads, followed by Nous Hermes and NVIDIA Nemotron)* The smiling curve of AI costs: GPT-4-level intelligence is 100-1000x cheaper than at launch (thanks to smaller models like Amazon Nova), but frontier reasoning models in agentic workflows cost more than ever (sparsity, long context, multi-turn agents)* Why sparsity might go way lower than 5%: GPT-4.5 is ~5% active, Gemini models might be ~3%, and Omissions Index accuracy correlates with total parameters (not active), suggesting massive sparse models are the future* Token efficiency vs. turn efficiency: GPT-5 costs more per token but solves Tau-bench in fewer turns (cheaper overall), and models are getting better at using more tokens only when needed (5.1 Codex has tighter token distributions)* V4 of the Intelligence Index coming soon: adding GDP Val AA, Critical Point, hallucination rate, and dropping some saturated benchmarks (human-eval-style coding is now trivial for small models)Links to Artificial Analysis* Website: https://artificialanalysis.ai* George Cameron on X: https://x.com/georgecameron* Micah-Hill Smith on X: https://x.com/micahhsmithFull Episode on YouTubeTimestamps* 00:00 Introduction: Full Circle Moment and Artificial Analysis Origins* 01:19 Business Model: Independence and Revenue Streams* 04:33 Origin Story: From Legal AI to Benchmarking Need* 16:22 AI Grant and Moving to San Francisco* 19:21 Intelligence Index Evolution: From V1 to V3* 11:47 Benchmarking Challenges: Variance, Contamination, and Methodology* 13:52 Mystery Shopper Policy and Maintaining Independence* 28:01 New Benchmarks: Omissions Index for Hallucination Detection* 33:36 Critical Point: Hard Physics Problems and Research-Level Reasoning* 23:01 GDP Val AA: Agentic Benchmark for Real Work Tasks* 50:19 Stirrup Agent Harness: Open Source Agentic Framework* 52:43 Openness Index: Measuring Model Transparency Beyond Licenses* 58:25 The Smiling Curve: Cost Falling While Spend Rising* 1:02:32 Hardware Efficiency: Blackwell Gains and Sparsity Limits* 1:06:23 Reasoning Models and Token Efficiency: The Spectrum Emerges* 1:11:00 Multimodal Benchmarking: Image, Video, and Speech Arenas* 1:15:05 Looking Ahead: Intelligence Index V4 and Future Directions* 1:16:50 Closing: The Insatiable Demand for IntelligenceTranscriptMicah [00:00:06]: This is kind of a full circle moment for us in a way, because the first time artificial analysis got mentioned on a podcast was you and Alessio on Latent Space. Amazing.swyx [00:00:17]: Which was January 2024. I don't even remember doing that, but yeah, it was very influential to me. Yeah, I'm looking at AI News for Jan 17, or Jan 16, 2024. I said, this gem of a models and host comparison site was just launched. And then I put in a few screenshots, and I said, it's an independent third party. It clearly outlines the quality versus throughput trade-off, and it breaks out by model and hosting provider. I did give you s**t for missing fireworks, and how do you have a model benchmarking thing without fireworks? But you had together, you had perplexity, and I think we just started chatting there. Welcome, George and Micah, to Latent Space. I've been following your progress. Congrats on... It's been an amazing year. You guys have really come together to be the presumptive new gardener of AI, right? Which is something that...George [00:01:09]: Yeah, but you can't pay us for better results.swyx [00:01:12]: Yes, exactly.George [00:01:13]: Very important.Micah [00:01:14]: Start off with a spicy take.swyx [00:01:18]: Okay, how do I pay you?Micah [00:01:20]: Let's get right into that.swyx [00:01:21]: How do you make money?Micah [00:01:24]: Well, very happy to talk about that. So it's been a big journey the last couple of years. Artificial analysis is going to be two years old in January 2026. Which is pretty soon now. We first run the website for free, obviously, and give away a ton of data to help developers and companies navigate AI and make decisions about models, providers, technologies across the AI stack for building stuff. We're very committed to doing that and tend to keep doing that. We have, along the way, built a business that is working out pretty sustainably. We've got just over 20 people now and two main customer groups. So we want to be... We want to be who enterprise look to for data and insights on AI, so we want to help them with their decisions about models and technologies for building stuff. And then on the other side, we do private benchmarking for companies throughout the AI stack who build AI stuff. So no one pays to be on the website. We've been very clear about that from the very start because there's no use doing what we do unless it's independent AI benchmarking. Yeah. But turns out a bunch of our stuff can be pretty useful to companies building AI stuff.swyx [00:02:38]: And is it like, I am a Fortune 500, I need advisors on objective analysis, and I call you guys and you pull up a custom report for me, you come into my office and give me a workshop? What kind of engagement is that?George [00:02:53]: So we have a benchmarking and insight subscription, which looks like standardized reports that cover key topics or key challenges enterprises face when looking to understand AI and choose between all the technologies. And so, for instance, one of the report is a model deployment report, how to think about choosing between serverless inference, managed deployment solutions, or leasing chips. And running inference yourself is an example kind of decision that big enterprises face, and it's hard to reason through, like this AI stuff is really new to everybody. And so we try and help with our reports and insight subscription. Companies navigate that. We also do custom private benchmarking. And so that's very different from the public benchmarking that we publicize, and there's no commercial model around that. For private benchmarking, we'll at times create benchmarks, run benchmarks to specs that enterprises want. And we'll also do that sometimes for AI companies who have built things, and we help them understand what they've built with private benchmarking. Yeah. So that's a piece mainly that we've developed through trying to support everybody publicly with our public benchmarks. Yeah.swyx [00:04:09]: Let's talk about TechStack behind that. But okay, I'm going to rewind all the way to when you guys started this project. You were all the way in Sydney? Yeah. Well, Sydney, Australia for me.Micah [00:04:19]: George was an SF, but he's Australian, but he moved here already. Yeah.swyx [00:04:22]: And I remember I had the Zoom call with you. What was the impetus for starting artificial analysis in the first place? You know, you started with public benchmarks. And so let's start there. We'll go to the private benchmark. Yeah.George [00:04:33]: Why don't we even go back a little bit to like why we, you know, thought that it was needed? Yeah.Micah [00:04:40]: The story kind of begins like in 2022, 2023, like both George and I have been into AI stuff for quite a while. In 2023 specifically, I was trying to build a legal AI research assistant. So it actually worked pretty well for its era, I would say. Yeah. Yeah. So I was finding that the more you go into building something using LLMs, the more each bit of what you're doing ends up being a benchmarking problem. So had like this multistage algorithm thing, trying to figure out what the minimum viable model for each bit was, trying to optimize every bit of it as you build that out, right? Like you're trying to think about accuracy, a bunch of other metrics and performance and cost. And mostly just no one was doing anything to independently evaluate all the models. And certainly not to look at the trade-offs for speed and cost. So we basically set out just to build a thing that developers could look at to see the trade-offs between all of those things measured independently across all the models and providers. Honestly, it was probably meant to be a side project when we first started doing it.swyx [00:05:49]: Like we didn't like get together and say like, Hey, like we're going to stop working on all this stuff. I'm like, this is going to be our main thing. When I first called you, I think you hadn't decided on starting a company yet.Micah [00:05:58]: That's actually true. I don't even think we'd pause like, like George had an acquittance job. I didn't quit working on my legal AI thing. Like it was genuinely a side project.George [00:06:05]: We built it because we needed it as people building in the space and thought, Oh, other people might find it useful too. So we'll buy domain and link it to the Vercel deployment that we had and tweet about it. And, but very quickly it started getting attention. Thank you, Swyx for, I think doing an initial retweet and spotlighting it there. This project that we released. And then very quickly though, it was useful to others, but very quickly it became more useful as the number of models released accelerated. We had Mixtrel 8x7B and it was a key. That's a fun one. Yeah. Like a open source model that really changed the landscape and opened up people's eyes to other serverless inference providers and thinking about speed, thinking about cost. And so that was a key. And so it became more useful quite quickly. Yeah.swyx [00:07:02]: What I love talking to people like you who sit across the ecosystem is, well, I have theories about what people want, but you have data and that's obviously more relevant. But I want to stay on the origin story a little bit more. When you started out, I would say, I think the status quo at the time was every paper would come out and they would report their numbers versus competitor numbers. And that's basically it. And I remember I did the legwork. I think everyone has some knowledge. I think there's some version of Excel sheet or a Google sheet where you just like copy and paste the numbers from every paper and just post it up there. And then sometimes they don't line up because they're independently run. And so your numbers are going to look better than... Your reproductions of other people's numbers are going to look worse because you don't hold their models correctly or whatever the excuse is. I think then Stanford Helm, Percy Liang's project would also have some of these numbers. And I don't know if there's any other source that you can cite. The way that if I were to start artificial analysis at the same time you guys started, I would have used the Luther AI's eval framework harness. Yup.Micah [00:08:06]: Yup. That was some cool stuff. At the end of the day, running these evals, it's like if it's a simple Q&A eval, all you're doing is asking a list of questions and checking if the answers are right, which shouldn't be that crazy. But it turns out there are an enormous number of things that you've got control for. And I mean, back when we started the website. Yeah. Yeah. Like one of the reasons why we realized that we had to run the evals ourselves and couldn't just take rules from the labs was just that they would all prompt the models differently. And when you're competing over a few points, then you can pretty easily get- You can put the answer into the model. Yeah. That in the extreme. And like you get crazy cases like back when I'm Googled a Gemini 1.0 Ultra and needed a number that would say it was better than GPT-4 and like constructed, I think never published like chain of thought examples. 32 of them in every topic in MLU to run it, to get the score, like there are so many things that you- They never shipped Ultra, right? That's the one that never made it up. Not widely. Yeah. Yeah. Yeah. I mean, I'm sure it existed, but yeah. So we were pretty sure that we needed to run them ourselves and just run them in the same way across all the models. Yeah. And we were, we also did certain from the start that you couldn't look at those in isolation. You needed to look at them alongside the cost and performance stuff. Yeah.swyx [00:09:24]: Okay. A couple of technical questions. I mean, so obviously I also thought about this and I didn't do it because of cost. Yep. Did you not worry about costs? Were you funded already? Clearly not, but you know. No. Well, we definitely weren't at the start.Micah [00:09:36]: So like, I mean, we're paying for it personally at the start. There's a lot of money. Well, the numbers weren't nearly as bad a couple of years ago. So we certainly incurred some costs, but we were probably in the order of like hundreds of dollars of spend across all the benchmarking that we were doing. Yeah. So nothing. Yeah. It was like kind of fine. Yeah. Yeah. These days that's gone up an enormous amount for a bunch of reasons that we can talk about. But yeah, it wasn't that bad because you can also remember that like the number of models we were dealing with was hardly any and the complexity of the stuff that we wanted to do to evaluate them was a lot less. Like we were just asking some Q&A type questions and then one specific thing was for a lot of evals initially, we were just like sampling an answer. You know, like, what's the answer for this? Like, we didn't want to go into the answer directly without letting the models think. We weren't even doing chain of thought stuff initially. And that was the most useful way to get some results initially. Yeah.swyx [00:10:33]: And so for people who haven't done this work, literally parsing the responses is a whole thing, right? Like because sometimes the models, the models can answer any way they feel fit and sometimes they actually do have the right answer, but they just returned the wrong format and they will get a zero for that unless you work it into your parser. And that involves more work. And so, I mean, but there's an open question whether you should give it points for not following your instructions on the format.Micah [00:11:00]: It depends what you're looking at, right? Because you can, if you're trying to see whether or not it can solve a particular type of reasoning problem, and you don't want to test it on its ability to do answer formatting at the same time, then you might want to use an LLM as answer extractor approach to make sure that you get the answer out no matter how unanswered. But these days, it's mostly less of a problem. Like, if you instruct a model and give it examples of what the answers should look like, it can get the answers in your format, and then you can do, like, a simple regex.swyx [00:11:28]: Yeah, yeah. And then there's other questions around, I guess, sometimes if you have a multiple choice question, sometimes there's a bias towards the first answer, so you have to randomize the responses. All these nuances, like, once you dig into benchmarks, you're like, I don't know how anyone believes the numbers on all these things. It's so dark magic.Micah [00:11:47]: You've also got, like… You've got, like, the different degrees of variance in different benchmarks, right? Yeah. So, if you run four-question multi-choice on a modern reasoning model at the temperatures suggested by the labs for their own models, the variance that you can see on a four-question multi-choice eval is pretty enormous if you only do a single run of it and it has a small number of questions, especially. So, like, one of the things that we do is run an enormous number of all of our evals when we're developing new ones and doing upgrades to our intelligence index to bring in new things. Yeah. So, that we can dial in the right number of repeats so that we can get to the 95% confidence intervals that we're comfortable with so that when we pull that together, we can be confident in intelligence index to at least as tight as, like, a plus or minus one at a 95% confidence. Yeah.swyx [00:12:32]: And, again, that just adds a straight multiple to the cost. Oh, yeah. Yeah, yeah.George [00:12:37]: So, that's one of many reasons that cost has gone up a lot more than linearly over the last couple of years. We report a cost to run the artificial analysis. We report a cost to run the artificial analysis intelligence index on our website, and currently that's assuming one repeat in terms of how we report it because we want to reflect a bit about the weighting of the index. But our cost is actually a lot higher than what we report there because of the repeats.swyx [00:13:03]: Yeah, yeah, yeah. And probably this is true, but just checking, you don't have any special deals with the labs. They don't discount it. You just pay out of pocket or out of your sort of customer funds. Oh, there is a mix. So, the issue is that sometimes they may give you a special end point, which is… Ah, 100%.Micah [00:13:21]: Yeah, yeah, yeah. Exactly. So, we laser focus, like, on everything we do on having the best independent metrics and making sure that no one can manipulate them in any way. There are quite a lot of processes we've developed over the last couple of years to make that true for, like, the one you bring up, like, right here of the fact that if we're working with a lab, if they're giving us a private endpoint to evaluate a model, that it is totally possible. That what's sitting behind that black box is not the same as they serve on a public endpoint. We're very aware of that. We have what we call a mystery shopper policy. And so, and we're totally transparent with all the labs we work with about this, that we will register accounts not on our own domain and run both intelligence evals and performance benchmarks… Yeah, that's the job. …without them being able to identify it. And no one's ever had a problem with that. Because, like, a thing that turns out to actually be quite a good… …good factor in the industry is that they all want to believe that none of their competitors could manipulate what we're doing either.swyx [00:14:23]: That's true. I never thought about that. I've been in the database data industry prior, and there's a lot of shenanigans around benchmarking, right? So I'm just kind of going through the mental laundry list. Did I miss anything else in this category of shenanigans? Oh, potential shenanigans.Micah [00:14:36]: I mean, okay, the biggest one, like, that I'll bring up, like, is more of a conceptual one, actually, than, like, direct shenanigans. It's that the things that get measured become things that get targeted by labs that they're trying to build, right? Exactly. So that doesn't mean anything that we should really call shenanigans. Like, I'm not talking about training on test set. But if you know that you're going to be great at another particular thing, if you're a researcher, there are a whole bunch of things that you can do to try to get better at that thing that preferably are going to be helpful for a wide range of how actual users want to use the thing that you're building. But will not necessarily work. Will not necessarily do that. So, for instance, the models are exceptional now at answering competition maths problems. There is some relevance of that type of reasoning, that type of work, to, like, how we might use modern coding agents and stuff. But it's clearly not one for one. So the thing that we have to be aware of is that once an eval becomes the thing that everyone's looking at, scores can get better on it without there being a reflection of overall generalized intelligence of these models. Getting better. That has been true for the last couple of years. It'll be true for the next couple of years. There's no silver bullet to defeat that other than building new stuff to stay relevant and measure the capabilities that matter most to real users. Yeah.swyx [00:15:58]: And we'll cover some of the new stuff that you guys are building as well, which is cool. Like, you used to just run other people's evals, but now you're coming up with your own. And I think, obviously, that is a necessary path once you're at the frontier. You've exhausted all the existing evals. I think the next point in history that I have for you is AI Grant that you guys decided to join and move here. What was it like? I think you were in, like, batch two? Batch four. Batch four. Okay.Micah [00:16:26]: I mean, it was great. Nat and Daniel are obviously great. And it's a really cool group of companies that we were in AI Grant alongside. It was really great to get Nat and Daniel on board. Obviously, they've done a whole lot of great work in the space with a lot of leading companies and were extremely aligned. With the mission of what we were trying to do. Like, we're not quite typical of, like, a lot of the other AI startups that they've invested in.swyx [00:16:53]: And they were very much here for the mission of what we want to do. Did they say any advice that really affected you in some way or, like, were one of the events very impactful? That's an interesting question.Micah [00:17:03]: I mean, I remember fondly a bunch of the speakers who came and did fireside chats at AI Grant.swyx [00:17:09]: Which is also, like, a crazy list. Yeah.George [00:17:11]: Oh, totally. Yeah, yeah, yeah. There was something about, you know, speaking to Nat and Daniel about the challenges of working through a startup and just working through the questions that don't have, like, clear answers and how to work through those kind of methodically and just, like, work through the hard decisions. And they've been great mentors to us as we've built artificial analysis. Another benefit for us was that other companies in the batch and other companies in AI Grant are pushing the capabilities. Yeah. And I think that's a big part of what AI can do at this time. And so being in contact with them, making sure that artificial analysis is useful to them has been fantastic for supporting us in working out how should we build out artificial analysis to continue to being useful to those, like, you know, building on AI.swyx [00:17:59]: I think to some extent, I'm mixed opinion on that one because to some extent, your target audience is not people in AI Grants who are obviously at the frontier. Yeah. Do you disagree?Micah [00:18:09]: To some extent. To some extent. But then, so a lot of what the AI Grant companies are doing is taking capabilities coming out of the labs and trying to push the limits of what they can do across the entire stack for building great applications, which actually makes some of them pretty archetypical power users of artificial analysis. Some of the people with the strongest opinions about what we're doing well and what we're not doing well and what they want to see next from us. Yeah. Yeah. Because when you're building any kind of AI application now, chances are you're using a whole bunch of different models. You're maybe switching reasonably frequently for different models and different parts of your application to optimize what you're able to do with them at an accuracy level and to get better speed and cost characteristics. So for many of them, no, they're like not commercial customers of ours, like we don't charge for all our data on the website. Yeah. They are absolutely some of our power users.swyx [00:19:07]: So let's talk about just the evals as well. So you start out from the general like MMU and GPQA stuff. What's next? How do you sort of build up to the overall index? What was in V1 and how did you evolve it? Okay.Micah [00:19:22]: So first, just like background, like we're talking about the artificial analysis intelligence index, which is our synthesis metric that we pulled together currently from 10 different eval data sets to give what? We're pretty much the same as that. Pretty confident is the best single number to look at for how smart the models are. Obviously, it doesn't tell the whole story. That's why we published the whole website of all the charts to dive into every part of it and look at the trade-offs. But best single number. So right now, it's got a bunch of Q&A type data sets that have been very important to the industry, like a couple that you just mentioned. It's also got a couple of agentic data sets. It's got our own long context reasoning data set and some other use case focused stuff. As time goes on. The things that we're most interested in that are going to be important to the capabilities that are becoming more important for AI, what developers are caring about, are going to be first around agentic capabilities. So surprise, surprise. We're all loving our coding agents and how the model is going to perform like that and then do similar things for different types of work are really important to us. The linking to use cases to economically valuable use cases are extremely important to us. And then we've got some of the. Yeah. These things that the models still struggle with, like working really well over long contexts that are not going to go away as specific capabilities and use cases that we need to keep evaluating.swyx [00:20:46]: But I guess one thing I was driving was like the V1 versus the V2 and how bad it was over time.Micah [00:20:53]: Like how we've changed the index to where we are.swyx [00:20:55]: And I think that reflects on the change in the industry. Right. So that's a nice way to tell that story.Micah [00:21:00]: Well, V1 would be completely saturated right now. Almost every model coming out because doing things like writing the Python functions and human evil is now pretty trivial. It's easy to forget, actually, I think how much progress has been made in the last two years. Like we obviously play the game constantly of like the today's version versus last week's version and the week before and all of the small changes in the horse race between the current frontier and who has the best like smaller than 10B model like right now this week. Right. And that's very important to a lot of developers and people and especially in this particular city of San Francisco. But when you zoom out a couple of years ago, literally most of what we were doing to evaluate the models then would all be 100% solved by even pretty small models today. And that's been one of the key things, by the way, that's driven down the cost of intelligence at every tier of intelligence. We can talk about more in a bit. So V1, V2, V3, we made things harder. We covered a wider range of use cases. And we tried to get closer to things developers care about as opposed to like just the Q&A type stuff that MMLU and GPQA represented. Yeah.swyx [00:22:12]: I don't know if you have anything to add there. Or we could just go right into showing people the benchmark and like looking around and asking questions about it. Yeah.Micah [00:22:21]: Let's do it. Okay. This would be a pretty good way to chat about a few of the new things we've launched recently. Yeah.George [00:22:26]: And I think a little bit about the direction that we want to take it. And we want to push benchmarks. Currently, the intelligence index and evals focus a lot on kind of raw intelligence. But we kind of want to diversify how we think about intelligence. And we can talk about it. But kind of new evals that we've kind of built and partnered on focus on topics like hallucination. And we've got a lot of topics that I think are not covered by the current eval set that should be. And so we want to bring that forth. But before we get into that.swyx [00:23:01]: And so for listeners, just as a timestamp, right now, number one is Gemini 3 Pro High. Then followed by Cloud Opus at 70. Just 5.1 high. You don't have 5.2 yet. And Kimi K2 Thinking. Wow. Still hanging in there. So those are the top four. That will date this podcast quickly. Yeah. Yeah. I mean, I love it. I love it. No, no. 100%. Look back this time next year and go, how cute. Yep.George [00:23:25]: Totally. A quick view of that is, okay, there's a lot. I love it. I love this chart. Yeah.Micah [00:23:30]: This is such a favorite, right? Yeah. And almost every talk that George or I give at conferences and stuff, we always put this one up first to just talk about situating where we are in this moment in history. This, I think, is the visual version of what I was saying before about the zooming out and remembering how much progress there's been. If we go back to just over a year ago, before 01, before Cloud Sonnet 3.5, we didn't have reasoning models or coding agents as a thing. And the game was very, very different. If we go back even a little bit before then, we're in the era where, when you look at this chart, open AI was untouchable for well over a year. And, I mean, you would remember that time period well of there being very open questions about whether or not AI was going to be competitive, like full stop, whether or not open AI would just run away with it, whether we would have a few frontier labs and no one else would really be able to do anything other than consume their APIs. I am quite happy overall that the world that we have ended up in is one where... Multi-model. Absolutely. And strictly more competitive every quarter over the last few years. Yeah. This year has been insane. Yeah.George [00:24:42]: You can see it. This chart with everything added is hard to read currently. There's so many dots on it, but I think it reflects a little bit what we felt, like how crazy it's been.swyx [00:24:54]: Why 14 as the default? Is that a manual choice? Because you've got service now in there that are less traditional names. Yeah.George [00:25:01]: It's models that we're kind of highlighting by default in our charts, in our intelligence index. Okay.swyx [00:25:07]: You just have a manually curated list of stuff.George [00:25:10]: Yeah, that's right. But something that I actually don't think every artificial analysis user knows is that you can customize our charts and choose what models are highlighted. Yeah. And so if we take off a few names, it gets a little easier to read.swyx [00:25:25]: Yeah, yeah. A little easier to read. Totally. Yeah. But I love that you can see the all one jump. Look at that. September 2024. And the DeepSeek jump. Yeah.George [00:25:34]: Which got close to OpenAI's leadership. They were so close. I think, yeah, we remember that moment. Around this time last year, actually.Micah [00:25:44]: Yeah, yeah, yeah. I agree. Yeah, well, a couple of weeks. It was Boxing Day in New Zealand when DeepSeek v3 came out. And we'd been tracking DeepSeek and a bunch of the other global players that were less known over the second half of 2024 and had run evals on the earlier ones and stuff. I very distinctly remember Boxing Day in New Zealand, because I was with family for Christmas and stuff, running the evals and getting back result by result on DeepSeek v3. So this was the first of their v3 architecture, the 671b MOE.Micah [00:26:19]: And we were very, very impressed. That was the moment where we were sure that DeepSeek was no longer just one of many players, but had jumped up to be a thing. The world really noticed when they followed that up with the RL working on top of v3 and R1 succeeding a few weeks later. But the groundwork for that absolutely was laid with just extremely strong base model, completely open weights that we had as the best open weights model. So, yeah, that's the thing that you really see in the game. But I think that we got a lot of good feedback on Boxing Day. us on Boxing Day last year.George [00:26:48]: Boxing Day is the day after Christmas for those not familiar.George [00:26:54]: I'm from Singapore.swyx [00:26:55]: A lot of us remember Boxing Day for a different reason, for the tsunami that happened. Oh, of course. Yeah, but that was a long time ago. So yeah. So this is the rough pitch of AAQI. Is it A-A-Q-I or A-A-I-I? I-I. Okay. Good memory, though.Micah [00:27:11]: I don't know. I'm not used to it. Once upon a time, we did call it Quality Index, and we would talk about quality, performance, and price, but we changed it to intelligence.George [00:27:20]: There's been a few naming changes. We added hardware benchmarking to the site, and so benchmarks at a kind of system level. And so then we changed our throughput metric to, we now call it output speed, and thenswyx [00:27:32]: throughput makes sense at a system level, so we took that name. Take me through more charts. What should people know? Obviously, the way you look at the site is probably different than how a beginner might look at it.Micah [00:27:42]: Yeah, that's fair. There's a lot of fun stuff to dive into. Maybe so we can hit past all the, like, we have lots and lots of emails and stuff. The interesting ones to talk about today that would be great to bring up are a few of our recent things, I think, that probably not many people will be familiar with yet. So first one of those is our omniscience index. So this one is a little bit different to most of the intelligence evils that we've run. We built it specifically to look at the embedded knowledge in the models and to test hallucination by looking at when the model doesn't know the answer, so not able to get it correct, what's its probability of saying, I don't know, or giving an incorrect answer. So the metric that we use for omniscience goes from negative 100 to positive 100. Because we're simply taking off a point if you give an incorrect answer to the question. We're pretty convinced that this is an example of where it makes most sense to do that, because it's strictly more helpful to say, I don't know, instead of giving a wrong answer to factual knowledge question. And one of our goals is to shift the incentive that evils create for models and the labs creating them to get higher scores. And almost every evil across all of AI up until this point, it's been graded by simple percentage correct as the main metric, the main thing that gets hyped. And so you should take a shot at everything. There's no incentive to say, I don't know. So we did that for this one here.swyx [00:29:22]: I think there's a general field of calibration as well, like the confidence in your answer versus the rightness of the answer. Yeah, we completely agree. Yeah. Yeah.George [00:29:31]: On that. And one reason that we didn't do that is because. Or put that into this index is that we think that the, the way to do that is not to ask the models how confident they are.swyx [00:29:43]: I don't know. Maybe it might be though. You put it like a JSON field, say, say confidence and maybe it spits out something. Yeah. You know, we have done a few evils podcasts over the, over the years. And when we did one with Clementine of hugging face, who maintains the open source leaderboard, and this was one of her top requests, which is some kind of hallucination slash lack of confidence calibration thing. And so, Hey, this is one of them.Micah [00:30:05]: And I mean, like anything that we do, it's not a perfect metric or the whole story of everything that you think about as hallucination. But yeah, it's pretty useful and has some interesting results. Like one of the things that we saw in the hallucination rate is that anthropics Claude models at the, the, the very left-hand side here with the lowest hallucination rates out of the models that we've evaluated amnesty is on. That is an interesting fact. I think it probably correlates with a lot of the previously, not really measured vibes stuff that people like about some of the Claude models. Is the dataset public or what's is it, is there a held out set? There's a hell of a set for this one. So we, we have published a public test set, but we we've only published 10% of it. The reason is that for this one here specifically, it would be very, very easy to like have data contamination because it is just factual knowledge questions. We would. We'll update it at a time to also prevent that, but with yeah, kept most of it held out so that we can keep it reliable for a long time. It leads us to a bunch of really cool things, including breakdown quite granularly by topic. And so we've got some of that disclosed on the website publicly right now, and there's lots more coming in terms of our ability to break out very specific topics. Yeah.swyx [00:31:23]: I would be interested. Let's, let's dwell a little bit on this hallucination one. I noticed that Haiku hallucinates less than Sonnet hallucinates less than Opus. And yeah. Would that be the other way around in a normal capability environments? I don't know. What's, what do you make of that?George [00:31:37]: One interesting aspect is that we've found that there's not really a, not a strong correlation between intelligence and hallucination, right? That's to say that the smarter the models are in a general sense, isn't correlated with their ability to, when they don't know something, say that they don't know. It's interesting that Gemini three pro preview was a big leap over here. Gemini 2.5. Flash and, and, and 2.5 pro, but, and if I add pro quickly here.swyx [00:32:07]: I bet pro's really good. Uh, actually no, I meant, I meant, uh, the GPT pros.George [00:32:12]: Oh yeah.swyx [00:32:13]: Cause GPT pros are rumored. We don't know for a fact that it's like eight runs and then with the LM judge on top. Yeah.George [00:32:20]: So we saw a big jump in, this is accuracy. So this is just percent that they get, uh, correct and Gemini three pro knew a lot more than the other models. And so big jump in accuracy. But relatively no change between the Google Gemini models, between releases. And the hallucination rate. Exactly. And so it's likely due to just kind of different post-training recipe, between the, the Claude models. Yeah.Micah [00:32:45]: Um, there's, there's driven this. Yeah. You can, uh, you can partially blame us and how we define intelligence having until now not defined hallucination as a negative in the way that we think about intelligence.swyx [00:32:56]: And so that's what we're changing. Uh, I know many smart people who are confidently incorrect.George [00:33:02]: Uh, look, look at that. That, that, that is very humans. Very true. And there's times and a place for that. I think our view is that hallucination rate makes sense in this context where it's around knowledge, but in many cases, people want the models to hallucinate, to have a go. Often that's the case in coding or when you're trying to generate newer ideas. One eval that we added to artificial analysis is, is, is critical point and it's really hard, uh, physics problems. Okay.swyx [00:33:32]: And is it sort of like a human eval type or something different or like a frontier math type?George [00:33:37]: It's not dissimilar to frontier frontier math. So these are kind of research questions that kind of academics in the physics physics world would be able to answer, but models really struggled to answer. So the top score here is not 9%.swyx [00:33:51]: And when the people that, that created this like Minway and, and, and actually off via who was kind of behind sweep and what organization is this? Oh, is this, it's Princeton.George [00:34:01]: Kind of range of academics from, from, uh, different academic institutions, really smart people. They talked about how they turn the models up in terms of the temperature as high temperature as they can, where they're trying to explore kind of new ideas in physics as a, as a thought partner, just because they, they want the models to hallucinate. Um, yeah, sometimes it's something new. Yeah, exactly.swyx [00:34:21]: Um, so not right in every situation, but, um, I think it makes sense, you know, to test hallucination in scenarios where it makes sense. Also, the obvious question is, uh, this is one of. Many that there is there, every lab has a system card that shows some kind of hallucination number, and you've chosen to not, uh, endorse that and you've made your own. And I think that's a, that's a choice. Um, totally in some sense, the rest of artificial analysis is public benchmarks that other people can independently rerun. You provide it as a service here. You have to fight the, well, who are we to, to like do this? And your, your answer is that we have a lot of customers and, you know, but like, I guess, how do you converge the individual?Micah [00:35:08]: I mean, I think, I think for hallucinations specifically, there are a bunch of different things that you might care about reasonably, and that you'd measure quite differently, like we've called this a amnesty and solutionation rate, not trying to declare the, like, it's humanity's last hallucination. You could, uh, you could have some interesting naming conventions and all this stuff. Um, the biggest picture answer to that. It's something that I actually wanted to mention. Just as George was explaining, critical point as well is, so as we go forward, we are building evals internally. We're partnering with academia and partnering with AI companies to build great evals. We have pretty strong views on, in various ways for different parts of the AI stack, where there are things that are not being measured well, or things that developers care about that should be measured more and better. And we intend to be doing that. We're not obsessed necessarily with that. Everything we do, we have to do entirely within our own team. Critical point. As a cool example of where we were a launch partner for it, working with academia, we've got some partnerships coming up with a couple of leading companies. Those ones, obviously we have to be careful with on some of the independent stuff, but with the right disclosure, like we're completely comfortable with that. A lot of the labs have released great data sets in the past that we've used to great success independently. And so it's between all of those techniques, we're going to be releasing more stuff in the future. Cool.swyx [00:36:26]: Let's cover the last couple. And then we'll, I want to talk about your trends analysis stuff, you know? Totally.Micah [00:36:31]: So that actually, I have one like little factoid on omniscience. If you go back up to accuracy on omniscience, an interesting thing about this accuracy metric is that it tracks more closely than anything else that we measure. The total parameter count of models makes a lot of sense intuitively, right? Because this is a knowledge eval. This is the pure knowledge metric. We're not looking at the index and the hallucination rate stuff that we think is much more about how the models are trained. This is just what facts did they recall? And yeah, it tracks parameter count extremely closely. Okay.swyx [00:37:05]: What's the rumored size of GPT-3 Pro? And to be clear, not confirmed for any official source, just rumors. But rumors do fly around. Rumors. I get, I hear all sorts of numbers. I don't know what to trust.Micah [00:37:17]: So if you, if you draw the line on omniscience accuracy versus total parameters, we've got all the open ways models, you can squint and see that likely the leading frontier models right now are quite a lot bigger than the ones that we're seeing right now. And the one trillion parameters that the open weights models cap out at, and the ones that we're looking at here, there's an interesting extra data point that Elon Musk revealed recently about XAI that for three trillion parameters for GROK 3 and 4, 6 trillion for GROK 5, but that's not out yet. Take those together, have a look. You might reasonably form a view that there's a pretty good chance that Gemini 3 Pro is bigger than that, that it could be in the 5 to 10 trillion parameters. To be clear, I have absolutely no idea, but just based on this chart, like that's where you would, you would land if you have a look at it. Yeah.swyx [00:38:07]: And to some extent, I actually kind of discourage people from guessing too much because what does it really matter? Like as long as they can serve it as a sustainable cost, that's about it. Like, yeah, totally.George [00:38:17]: They've also got different incentives in play compared to like open weights models who are thinking to supporting others in self-deployment for the labs who are doing inference at scale. It's I think less about total parameters in many cases. When thinking about inference costs and more around number of active parameters. And so there's a bit of an incentive towards larger sparser models. Agreed.Micah [00:38:38]: Understood. Yeah. Great. I mean, obviously if you're a developer or company using these things, not exactly as you say, it doesn't matter. You should be looking at all the different ways that we measure intelligence. You should be looking at cost to run index number and the different ways of thinking about token efficiency and cost efficiency based on the list prices, because that's all it matters.swyx [00:38:56]: It's not as good for the content creator rumor mill where I can say. Oh, GPT-4 is this small circle. Look at GPT-5 is this big circle. And then there used to be a thing for a while. Yeah.Micah [00:39:07]: But that is like on its own, actually a very interesting one, right? That is it just purely that chances are the last couple of years haven't seen a dramatic scaling up in the total size of these models. And so there's a lot of room to go up properly in total size of the models, especially with the upcoming hardware generations. Yes.swyx [00:39:29]: So, you know. Taking off my shitposting face for a minute. Yes. Yes. At the same time, I do feel like, you know, especially coming back from Europe, people do feel like Ilya is probably right that the paradigm is doesn't have many more orders of magnitude to scale out more. And therefore we need to start exploring at least a different path. GDPVal, I think it's like only like a month or so old. I was also very positive when it first came out. I actually talked to Tejo, who was the lead researcher on that. Oh, cool. And you have your own version.George [00:39:59]: It's a fantastic. It's a fantastic data set. Yeah.swyx [00:40:01]: And maybe it will recap for people who are still out of it. It's like 44 tasks based on some kind of GDP cutoff that's like meant to represent broad white collar work that is not just coding. Yeah.Micah [00:40:12]: Each of the tasks have a whole bunch of detailed instructions, some input files for a lot of them. It's within the 44 is divided into like two hundred and twenty two to five, maybe subtasks that are the level of that we run through the agenda. And yeah, they're really interesting. I will say that it doesn't. It doesn't necessarily capture like all the stuff that people do at work. No avail is perfect is always going to be more things to look at, largely because in order to make the tasks well enough to find that you can run them, they need to only have a handful of input files and very specific instructions for that task. And so I think the easiest way to think about them are that they're like quite hard take home exam tasks that you might do in an interview process.swyx [00:40:56]: Yeah, for listeners, it is not no longer like a long prompt. It is like, well, here's a zip file with like a spreadsheet or a PowerPoint deck or a PDF and go nuts and answer this question.George [00:41:06]: OpenAI released a great data set and they released a good paper which looks at performance across the different web chat bots on the data set. It's a great paper, encourage people to read it. What we've done is taken that data set and turned it into an eval that can be run on any model. So we created a reference agentic harness that can run. Run the models on the data set, and then we developed evaluator approach to compare outputs. That's kind of AI enabled, so it uses Gemini 3 Pro Preview to compare results, which we tested pretty comprehensively to ensure that it's aligned to human preferences. One data point there is that even as an evaluator, Gemini 3 Pro, interestingly, doesn't do actually that well. So that's kind of a good example of what we've done in GDPVal AA.swyx [00:42:01]: Yeah, the thing that you have to watch out for with LLM judge is self-preference that models usually prefer their own output, and in this case, it was not. Totally.Micah [00:42:08]: I think the way that we're thinking about the places where it makes sense to use an LLM as judge approach now, like quite different to some of the early LLM as judge stuff a couple of years ago, because some of that and MTV was a great project that was a good example of some of this a while ago was about judging conversations and like a lot of style type stuff. Here, we've got the task that the grader and grading model is doing is quite different to the task of taking the test. When you're taking the test, you've got all of the agentic tools you're working with, the code interpreter and web search, the file system to go through many, many turns to try to create the documents. Then on the other side, when we're grading it, we're running it through a pipeline to extract visual and text versions of the files and be able to provide that to Gemini, and we're providing the criteria for the task and getting it to pick which one more effectively meets the criteria of the task. Yeah. So we've got the task out of two potential outcomes. It turns out that we proved that it's just very, very good at getting that right, matched with human preference a lot of the time, because I think it's got the raw intelligence, but it's combined with the correct representation of the outputs, the fact that the outputs were created with an agentic task that is quite different to the way the grading model works, and we're comparing it against criteria, not just kind of zero shot trying to ask the model to pick which one is better.swyx [00:43:26]: Got it. Why is this an ELO? And not a percentage, like GDP-VAL?George [00:43:31]: So the outputs look like documents, and there's video outputs or audio outputs from some of the tasks. It has to make a video? Yeah, for some of the tasks. Some of the tasks.swyx [00:43:43]: What task is that?George [00:43:45]: I mean, it's in the data set. Like be a YouTuber? It's a marketing video.Micah [00:43:49]: Oh, wow. What? Like model has to go find clips on the internet and try to put it together. The models are not that good at doing that one, for now, to be clear. It's pretty hard to do that with a code editor. I mean, the computer stuff doesn't work quite well enough and so on and so on, but yeah.George [00:44:02]: And so there's no kind of ground truth, necessarily, to compare against, to work out percentage correct. It's hard to come up with correct or incorrect there. And so it's on a relative basis. And so we use an ELO approach to compare outputs from each of the models between the task.swyx [00:44:23]: You know what you should do? You should pay a contractor, a human, to do the same task. And then give it an ELO and then so you have, you have human there. It's just, I think what's helpful about GDPVal, the OpenAI one, is that 50% is meant to be normal human and maybe Domain Expert is higher than that, but 50% was the bar for like, well, if you've crossed 50, you are superhuman. Yeah.Micah [00:44:47]: So we like, haven't grounded this score in that exactly. I agree that it can be helpful, but we wanted to generalize this to a very large number. It's one of the reasons that presenting it as ELO is quite helpful and allows us to add models and it'll stay relevant for quite a long time. I also think it, it can be tricky looking at these exact tasks compared to the human performance, because the way that you would go about it as a human is quite different to how the models would go about it. Yeah.swyx [00:45:15]: I also liked that you included Lama 4 Maverick in there. Is that like just one last, like...Micah [00:45:20]: Well, no, no, no, no, no, no, it is the, it is the best model released by Meta. And... So it makes it into the homepage default set, still for now.George [00:45:31]: Other inclusion that's quite interesting is we also ran it across the latest versions of the web chatbots. And so we have...swyx [00:45:39]: Oh, that's right.George [00:45:40]: Oh, sorry.swyx [00:45:41]: I, yeah, I completely missed that. Okay.George [00:45:43]: No, not at all. So that, which has a checkered pattern. So that is their harness, not yours, is what you're saying. Exactly. And what's really interesting is that if you compare, for instance, Claude 4.5 Opus using the Claude web chatbot, it performs worse than the model in our agentic harness. And so in every case, the model performs better in our agentic harness than its web chatbot counterpart, the harness that they created.swyx [00:46:13]: Oh, my backwards explanation for that would be that, well, it's meant for consumer use cases and here you're pushing it for something.Micah [00:46:19]: The constraints are different and the amount of freedom that you can give the model is different. Also, you like have a cost goal. We let the models work as long as they want, basically. Yeah. Do you copy paste manually into the chatbot? Yeah. Yeah. That's, that was how we got the chatbot reference. We're not going to be keeping those updated at like quite the same scale as hundreds of models.swyx [00:46:38]: Well, so I don't know, talk to a browser base. They'll, they'll automate it for you. You know, like I have thought about like, well, we should turn these chatbot versions into an API because they are legitimately different agents in themselves. Yes. Right. Yeah.Micah [00:46:53]: And that's grown a huge amount of the last year, right? Like the tools. The tools that are available have actually diverged in my opinion, a fair bit across the major chatbot apps and the amount of data sources that you can connect them to have gone up a lot, meaning that your experience and the way you're using the model is more different than ever.swyx [00:47:10]: What tools and what data connections come to mind when you say what's interesting, what's notable work that people have done?Micah [00:47:15]: Oh, okay. So my favorite example on this is that until very recently, I would argue that it was basically impossible to get an LLM to draft an email for me in any useful way. Because most times that you're sending an email, you're not just writing something for the sake of writing it. Chances are context required is a whole bunch of historical emails. Maybe it's notes that you've made, maybe it's meeting notes, maybe it's, um, pulling something from your, um, any of like wherever you at work store stuff. So for me, like Google drive, one drive, um, in our super base databases, if we need to do some analysis or some data or something, preferably model can be plugged into all of those things and can go do some useful work based on it. The things that like I find most impressive currently that I am somewhat surprised work really well in late 2025, uh, that I can have models use super base MCP to query read only, of course, run a whole bunch of SQL queries to do pretty significant data analysis. And. And make charts and stuff and can read my Gmail and my notion. And okay. You actually use that. That's good. That's, that's, that's good. Is that a cloud thing? To various degrees of order, but chat GPD and Claude right now, I would say that this stuff like barely works in fairness right now. Like.George [00:48:33]: Because people are actually going to try this after they hear it. If you get an email from Micah, odds are it wasn't written by a chatbot.Micah [00:48:38]: So, yeah, I think it is true that I have never actually sent anyone an email drafted by a chatbot. Yet.swyx [00:48:46]: Um, and so you can, you can feel it right. And yeah, this time, this time next year, we'll come back and see where it's going. Totally. Um, super base shout out another famous Kiwi. Uh, I don't know if you've, you've any conversations with him about anything in particular on AI building and AI infra.George [00:49:03]: We have had, uh, Twitter DMS, um, with, with him because we're quite big, uh, super base users and power users. And we probably do some things more manually than we should in. In, in super base support line because you're, you're a little bit being super friendly. One extra, um, point regarding, um, GDP Val AA is that on the basis of the overperformance of the models compared to the chatbots turns out, we realized that, oh, like our reference harness that we built actually white works quite well on like gen generalist agentic tasks. This proves it in a sense. And so the agent harness is very. Minimalist. I think it follows some of the ideas that are in Claude code and we, all that we give it is context management capabilities, a web search, web browsing, uh, tool, uh, code execution, uh, environment. Anything else?Micah [00:50:02]: I mean, we can equip it with more tools, but like by default, yeah, that's it. We, we, we give it for GDP, a tool to, uh, view an image specifically, um, because the models, you know, can just use a terminal to pull stuff in text form into context. But to pull visual stuff into context, we had to give them a custom tool, but yeah, exactly. Um, you, you can explain an expert. No.George [00:50:21]: So it's, it, we turned out that we created a good generalist agentic harness. And so we, um, released that on, on GitHub yesterday. It's called stirrup. So if people want to check it out and, and it's a great, um, you know, base for, you know, generalist, uh, building a generalist agent for more specific tasks.Micah [00:50:39]: I'd say the best way to use it is get clone and then have your favorite coding. Agent make changes to it, to do whatever you want, because it's not that many lines of code and the coding agents can work with it. Super well.swyx [00:50:51]: Well, that's nice for the community to explore and share and hack on it. I think maybe in, in, in other similar environments, the terminal bench guys have done, uh, sort of the Harbor. Uh, and so it's, it's a, it's a bundle of, well, we need our minimal harness, which for them is terminus and we also need the RL environments or Docker deployment thing to, to run independently. So I don't know if you've looked at it. I don't know if you've looked at the harbor at all, is that, is that like a, a standard that people want to adopt?George [00:51:19]: Yeah, we've looked at it from a evals perspective and we love terminal bench and, and host benchmarks of, of, of terminal mention on artificial analysis. Um, we've looked at it from a, from a coding agent perspective, but could see it being a great, um, basis for any kind of agents. I think where we're getting to is that these models have gotten smart enough. They've gotten better, better tools that they can perform better when just given a minimalist. Set of tools and, and let them run, let the model control the, the agentic workflow rather than using another framework that's a bit more built out that tries to dictate the, dictate the flow. Awesome.swyx [00:51:56]: Let's cover the openness index and then let's go into the report stuff. Uh, so that's the, that's the last of the proprietary art numbers, I guess. I don't know how you sort of classify all these. Yeah.Micah [00:52:07]: Or call it, call it, let's call it the last of like the, the three new things that we're talking about from like the last few weeks. Um, cause I mean, there's a, we do a mix of stuff that. Where we're using open source, where we open source and what we do and, um, proprietary stuff that we don't always open source, like long context reasoning data set last year, we did open source. Um, and then all of the work on performance benchmarks across the site, some of them, we looking to open source, but some of them, like we're constantly iterating on and so on and so on and so on. So there's a huge mix, I would say, just of like stuff that is open source and not across the side. So that's a LCR for people. Yeah, yeah, yeah, yeah.swyx [00:52:41]: Uh, but let's, let's, let's talk about open.Micah [00:52:42]: Let's talk about openness index. This. Here is call it like a new way to think about how open models are. We, for a long time, have tracked where the models are open weights and what the licenses on them are. And that's like pretty useful. That tells you what you're allowed to do with the weights of a model, but there is this whole other dimension to how open models are. That is pretty important that we haven't tracked until now. And that's how much is disclosed about how it was made. So transparency about data, pre-training data and post-training data. And whether you're allowed to use that data and transparency about methodology and training code. So basically, those are the components. We bring them together to score an openness index for models so that you can in one place get this full picture of how open models are.swyx [00:53:32]: I feel like I've seen a couple other people try to do this, but they're not maintained. I do think this does matter. I don't know what the numbers mean apart from is there a max number? Is this out of 20?George [00:53:44]: It's out of 18 currently, and so we've got an openness index page, but essentially these are points, you get points for being more open across these different categories and the maximum you can achieve is 18. So AI2 with their extremely open OMO3 32B think model is the leader in a sense.swyx [00:54:04]: It's hooking face.George [00:54:05]: Oh, with their smaller model. It's coming soon. I think we need to run, we need to get the intelligence benchmarks right to get it on the site.swyx [00:54:12]: You can't have it open in the next. We can not include hooking face. We love hooking face. We'll have that, we'll have that up very soon. I mean, you know, the refined web and all that stuff. It's, it's amazing. Or is it called fine web? Fine web. Fine web.Micah [00:54:23]: Yeah, yeah, no, totally. Yep. One of the reasons this is cool, right, is that if you're trying to understand the holistic picture of the models and what you can do with all the stuff the company's contributing, this gives you that picture. And so we are going to keep it up to date alongside all the models that we do intelligence index on, on the site. And it's just an extra view to understand.swyx [00:54:43]: Can you scroll down to this? The, the, the, the trade-offs chart. Yeah, yeah. That one. Yeah. This, this really matters, right? Obviously, because you can b

Fluent Fiction - Italian: Finding Balance: A Tale of Exams & Friendship in Firenze Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2025-12-30-08-38-20-it Story Transcript:It: La biblioteca di Firenze era un luogo magico.En: The biblioteca of Firenze was a magical place.It: Il soffitto alto, le pareti foderate di quercia scura e il profumo di libri antichi rendevano l'ambiente speciale.En: The high ceiling, the walls lined with dark oak, and the smell of ancient books made the atmosphere special.It: In uno dei tavoli in legno, sotto la morbida luce verde di una lampada, Alessio stava studiando.En: At one of the wooden tables, under the soft green light of a lamp, Alessio was studying.It: Fuori faceva freddo, l'inverno era arrivato, ma dentro la biblioteca c'era calore e tranquillità.En: Outside it was cold, winter had arrived, but inside the biblioteca there was warmth and tranquility.It: Alessio era concentrato sui suoi libri.En: Alessio was focused on his books.It: I suoi esami finali erano vicini e lui doveva mantenere la borsa di studio.En: His final exams were approaching, and he had to maintain his scholarship.It: Sentiva una grande pressione.En: He felt a great deal of pressure.It: Giulia, invece, era seduta poco distante, con un sorriso sereno sul volto.En: Giulia, on the other hand, was sitting not far away, with a serene smile on her face.It: Lei studiava, ma sembrava non avere nessuna preoccupazione.En: She was studying, but seemed to have no worries.It: Alessio non poteva fare a meno di sentirsi geloso di quanto fosse rilassata.En: Alessio couldn't help feeling jealous of how relaxed she was.It: Una sera, in prossimità di Capodanno, la tensione diventò troppa.En: One evening, near Capodanno, the tension became too much.It: Alessio chiuse il suo libro di scienze e si avvicinò a Giulia.En: Alessio closed his science book and approached Giulia.It: "Come fai a essere così tranquilla?"En: "How do you stay so calm?"It: chiese, cercando di nascondere la sua ansia.En: he asked, trying to hide his anxiety.It: Giulia alzò lo sguardo e sorrise.En: Giulia looked up and smiled.It: "Cerco di bilanciare lo studio con la vita.En: "I try to balance study with life.It: Faccio delle pause, parlo con gli amici, e mi godo le piccole cose," rispose dolcemente.En: I take breaks, talk with friends, and enjoy the little things," she replied sweetly.It: Alessio sospirò.En: Alessio sighed.It: "Io non riesco mai a rilassarmi.En: "I can never relax.It: Ho paura di perdere la mia borsa di studio."En: I'm afraid of losing my scholarship."It: Giulia lo ascoltò attentamente, poi aggiunse: "Alessio, siamo tutti preoccupati.En: Giulia listened attentively, then added, "Alessio, we are all worried.It: Anche io ho delle difficoltà.En: Even I have difficulties.It: A volte mi diverto troppo e poi mi ritrovo con poco tempo per studiare."En: Sometimes I have too much fun, and then I find myself with little time to study."It: Questa confessione sorprese Alessio.En: This confession surprised Alessio.It: Non aveva mai pensato che lei potesse avere problemi simili.En: He had never thought that she might have similar problems.It: I due parlarono a lungo quella sera.En: The two talked for a long time that evening.It: Capirono di non essere soli nelle loro paure e si promisero di sostenersi a vicenda.En: They realized they were not alone in their fears and promised to support each other.It: Insieme, crearono un piano di studio che includeva pause e momenti di svago.En: Together, they created a study plan that included breaks and leisure moments.It: Decisero persino di festeggiare insieme la vigilia di Capodanno, per rilassarsi prima degli esami.En: They even decided to celebrate New Year's Eve together, to relax before the exams.It: Mentre i giorni passavano, Alessio iniziò a notare la differenza.En: As the days passed, Alessio began to notice the difference.It: Studiare diventava un po' meno pesante, e lui si sentiva più sereno.En: Studying became a little less burdensome, and he felt more at ease.It: Arrivò il giorno dell'esame e Alessio si sentì pronto.En: The exam day arrived, and Alessio felt ready.It: Sapeva di aver fatto del suo meglio.En: He knew he had done his best.It: Alla fine, capì che non si trattava solo di studio, ma anche di vivere.En: In the end, he understood that it wasn't just about studying but also about living.It: Balance era la parola chiave.En: Balance was the key word.It: Grazie a Giulia, Alessio scoprì che un equilibrio tra il lavoro e il benessere personale era essenziale.En: Thanks to Giulia, Alessio discovered that a balance between work and personal well-being was essential.It: I due uscirono dalla biblioteca, pronti ad affrontare il futuro e i suoi esami, insieme.En: The two of them left the biblioteca, ready to face the future and its exams, together. Vocabulary Words:the library: la bibliotecathe ceiling: il soffittothe wall: la paretethe oak: la querciathe table: il tavolothe lamp: la lampadathe warmth: il caloretranquility: la tranquillitàthe scholarship: la borsa di studiopressure: la pressioneserene: sereno/athe smile: il sorrisothe tension: la tensionecalm: tranquillo/aanxiety: l'ansiato balance: bilanciaresweetly: dolcementeto sigh: sospirareto relax: rilassarsiattentively: attentamentedifficulties: le difficoltàconfession: la confessioneto promise: prometterethe break: la pausaleisure: lo svagoto celebrate: festeggiareburdensome: pesanteease: sereno/ato face: affrontarebalance: l'equilibrio

Fluent Fiction - Italian: Finding Inspiration: A Christmas Tale in Piazza Navona Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2025-12-26-08-38-20-it Story Transcript:It: Nel cuore di Roma, sotto un cielo grigio d'inverno, Piazza Navona brillava di luci natalizie.En: In the heart of Roma, under a gray winter sky, Piazza Navona shone with Christmas lights.It: C'erano bancarelle ovunque, che esponevano dolci profumati e regali fatti a mano.En: There were stalls everywhere, displaying fragrant sweets and handmade gifts.It: L'aria, fredda e pungente, era piena di musica, risate e l'inebriante aroma di caldarroste.En: The air, cold and sharp, was filled with music, laughter, and the intoxicating aroma of roasted chestnuts.It: Alessio camminava lentamente tra la folla.En: Alessio walked slowly through the crowd.It: Portava un cappello di lana che nascondeva i suoi riccioli scuri e una sciarpa avvolta stretta intorno al collo.En: He wore a wool hat that hid his dark curls and a scarf wrapped tightly around his neck.It: Cercava ispirazione per il suo nuovo quadro.En: He was looking for inspiration for his new painting.It: Era un giovane artista in cerca della sua musa.En: He was a young artist searching for his muse.It: Al suo fianco c'era Giulia.En: Beside him was Giulia.It: Pratica e premurosa, lei osservava Alessio con un misto di preoccupazione e affetto.En: Practical and caring, she watched Alessio with a mix of concern and affection.It: "Devi pensare al futuro, Alessio," gli disse, scuotendo la testa mentre un carretto di zucchero filato passava accanto a loro.En: "You need to think about the future, Alessio," she said, shaking her head as a cotton candy cart passed by them.It: In quel momento, Matteo, il loro amico d'infanzia, tornato dall'estero, si unì a loro con un grande sorriso.En: At that moment, Matteo, their childhood friend, returned from abroad and joined them with a big smile.It: "Roma mi è mancata!En: "Roma really missed me!It: E voi ancora di più!En: And you guys even more!It: ", esclamò abbracciandoli forte.En: ", he exclaimed, embracing them tightly.It: Alessio sorrise, ma dentro di sé si sentiva frustrato.En: Alessio smiled, but inside he felt frustrated.It: La piazza era piena di vita, ma lui non riusciva a trovare l'immagine perfetta da dipingere.En: The square was full of life, but he couldn't find the perfect image to paint.It: Decise di fermarsi e osservare le persone attorno.En: He decided to stop and observe the people around.It: Vide famiglie che si scattavano foto davanti alla grande fontana, bambini che correvano intorno agli alberi decorati, e coppie che si scambiavano piccoli doni dal mercato.En: He saw families taking photos in front of the big fountain, children running around the decorated trees, and couples exchanging small gifts from the market.It: Eppure, niente sembrava colpire l'immaginazione di Alessio.En: Yet, nothing seemed to strike Alessio's imagination.It: Poi, all'improvviso, vide una scena che gli fece battere il cuore.En: Then, suddenly, he saw a scene that made his heart race.It: Un'anziana coppia, mano nella mano, si avvicinava a una bancarella.En: An elderly couple, hand in hand, approached a stall.It: I loro volti erano solcati dal tempo, ma si illuminavano di gioia mentre due bambini si lanciavano loro tra le braccia.En: Their faces were lined with time, but they lit up with joy as two children threw themselves into their arms.It: Le risate dei due piccoli e l'affetto sincero tra i nonni accesero una scintilla in Alessio.En: The laughter of the little ones and the sincere affection between the grandparents sparked something in Alessio.It: "È questo!En: "That's it!"It: ", esclamò, sorpreso dalla forza della sua visione.En: he exclaimed, surprised by the strength of his vision.It: Giulia lo guardò attentamente.En: Giulia looked at him carefully.It: "Hai visto qualcosa, vero?"En: "You saw something, didn't you?"It: Alessio assentì, con gli occhi pieni di nuova luce.En: Alessio nodded, his eyes full of new light.It: "I volti, le emozioni, le connessioni... sono l'essenza di questo Natale."En: "The faces, the emotions, the connections... they are the essence of this Christmas."It: Con rinnovata energia, Alessio si mise in un angolo tranquillo della piazza e cominciò a disegnare, perdendosi nei dettagli e nei sentimenti della scena che aveva visto.En: With renewed energy, Alessio found a quiet corner of the square and began to draw, losing himself in the details and feelings of the scene he had witnessed.It: Matteo lo guardava, impressionato dalla dedizione dell'amico ritrovato.En: Matteo watched him, impressed by the dedication of his rediscovered friend.It: Giulia, sollevata, gli diede una pacca sulla spalla.En: Giulia, relieved, gave him a pat on the shoulder.It: "Forse hai davvero trovato il modo di far funzionare la tua arte."En: "Perhaps you've truly found a way to make your art work."It: Quella sera, mentre le luci di Natale si riflettevano sui ciottoli umidi della piazza, Alessio capì che c'era un modo per bilanciare la passione e la realtà.En: That evening, as the Christmas lights reflected on the wet cobblestones of the square, Alessio realized there was a way to balance passion and reality.It: La sua tela si riempiva lentamente di vita, mentre una nuova determinazione cresceva dentro di lui.En: His canvas slowly filled with life as new determination grew within him.It: Questa volta, non solo aveva trovato la sua ispirazione, ma anche un cammino che univa il cuore e la ragione.En: This time, not only had he found his inspiration, but also a path that united heart and reason. Vocabulary Words:the heart: il cuorethe square: la piazzathe stalls: le bancarellefragrant: profumatithe gifts: i regalisharp: pungentethe scarf: la sciarpathe muse: la musapractical: praticacaring: premurosathe future: il futurothe childhood: l'infanziaabroad: l'esterofrustrated: frustratocouples: coppieto strike: colpirethe imagination: l'immaginazioneto approach: avvicinarsilined: solcatithe joy: la gioiasincere: sincerothe spark: la scintillato witness: assistereto draw: disegnarethe details: i dettaglithe feelings: i sentimentirenewed: rinnovatadetermination: determinazionethe canvas: la telato unite: unire

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Scarto a chi?! con Rucoolaaa (Alessio Cicchini)Ci siamo viste il 18 Dicembre al teatro delle arti con Alessio Cicchini per parlare di cibo e "scarti"Nella cucina (e nel mondo) definiamo “scarti” quello che non capiamo e non sappiamo valorizzare. Ma a guardare bene gli scarti non esistono! Abbiamo parlato di broccoli e carote, ricette e diritti, sostenibilità e futuro. 

Il consueto appuntamento del venerdì pomeriggio con IL CORTOCIRCUITO! Questa settimana il trio delle meraviglie vede un cambio in panchina: fuori Alessio (alle prese con misteriosi "scandali giudiziari") e dentro Fabio Di Felice, pronto a fare squadra con Pierpaolo Greco e Francesco Serino. Due ore di intrattenimento, analisi tecnica e i vostri immancabili messaggi vocali.In questa puntata:

In questa puntata di Italiano On Air, Katia e Alessio ci portano alla scoperta di uno dei simboli più amati del Natale italiano: il panettone. Nato a Milano, questo dolce alto e soffice ha una storia antica, fatta di tradizioni, leggende e curiosità.Quando nasce il panettone? Chi lo ha inventato? E com'è diventato il protagonista indiscusso delle feste natalizie in Italia e nel mondo? Tra Medioevo, corti ducali, fornai innamorati e lunghe lievitazioni, ripercorriamo l'evoluzione di questo “pane di lusso” così speciale.E alla fine… il dibattito più gustoso: canditi sì o canditi no? Una puntata perfetta per entrare nell'atmosfera natalizia e scoprire un pezzo goloso della cultura italiana.La trascrizione la puoi trovare nella pagina dell'episodio, scorrendo in basso.I nostri contatti

In questa puntata di

Una puntata assolutamente fuori controllo. Pierpaolo, Alessio e Francesco tornano ai microfoni per una live "speciale" che spazia dall'economia all'assurdo.Mentre Alessio confessa di aver addestrato un'IA con le foto dei redattori (Giordana inclusa...), cerchiamo di capire se Netflix stia davvero per comprare Warner Bros e che fine farà la divisione gaming. Tra un'analisi finanziaria e l'altra, c'è tempo per sparlare dell'invecchiamento di Leonardo DiCaprio, dei ritocchini estetici di Zac Efron e degli stipendi assurdi del cast di Stranger Things.Ah, e parliamo anche di cani radioattivi blu. Perché no?In questa puntata:Il calendario sexy dei redattori (forse)Alessio e l'ossessione per l'AI generativaNetflix vs Paramount: guerra tra titaniHollywood: botox, filler e rimpiantiCronaca bizzarra da ChernobylAscolta ora il delirio organizzato de Il Cortocircuito!

Alessandra Caneva"Natale 1938"Ianieri Edizioniwww.ianieriedizioni.comCate, una giovane e brillante medico, vive un'esistenza divisa tra l'impegno quotidiano in corsia e un matrimonio aristocratico che sembrava perfetto. A pochi giorni dal Natale del 1938, un misterioso incidente d'auto che rischia di costarle la vita costringe Cate a letto e mette in luce un tradimento ancora più oscuro: il marito, il conte Alessio, attratto dall'ingente patrimonio della moglie, ha manomesso i freni della sua Alfa Romeo nel tentativo di ucciderla.Con la mente da clinica investigatrice, Cate trasforma il proprio dolore in un vero e proprio “esame” dell'anima. Passa al setaccio ogni sguardo, ogni parola e ogni gesto del loro rapporto alla ricerca della genesi di un gesto tanto brutale. Il suo percorso interiore – tra ricordi dolorosi e speranze sconvolte – diventa la trama di un'indagine sull'origine del male e sulla possibilità di estirparlo dall'animo umano.Ambientato negli anni delle leggi razziali italiane e dell'alleanza sempre più stretta tra Mussolini e Hitler, Natale 1938 è un romanzo di grande intensità emotiva e spirituale. Un viaggio di dolore, scoperta e speranza, che ci invita a esplorare l'abisso del male senza rinunciare alla luce salvifica del perdono. “Hitler aveva attratto in maniera ipnotica la sua attenzione. In un primo momento sembrava avesse avuto la meglio su di lei, ma Cate aveva delle armi sulle quali lui non poteva nulla: il suo sguardo clinico, penetrante, intelligente, smascherante, che comunque le permetteva di creare un certo distacco, anche se subiva una sorta di assedio psicologico.”Alessandra Caneva. Soggettista, sceneggiatrice, scrittrice e saggista, Alessandra Caneva ha lavorato come consulente editoriale per la Lux Vide, come consulente artistico letterario per la struttura di Rai Fiction e come consulente letterario per Rai Cultura. Ha ideato e scritto numerosi soggetti per fiction televisive, in particolare Lourdes, Padre Pio (diretto da Giulio Base e vincitore dell'Oscar televisivo 2001). È coautrice del soggetto di serie Don Matteo. Tra le sue numerose pubblicazioni, L'immaginario contemporaneo (Mimesis, 2018) e Rapsodia d'Autunno (Ianieri, 2022).Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/il-posto-delle-parole--1487855/support.IL POSTO DELLE PAROLEascoltare fa pensarehttps://ilpostodelleparole.it/

Bianca D'Alessio discusses the importance of embracing vulnerability and authenticity in leadership. She reflects on her personal journey of breaking down societal expectations of perfectionism and strength, and how this transformation has positively impacted her business and personal growth. Through storytelling and connection, she emphasizes the power of being true to oneself and the strength found in shared experiences. Bianca D'Alessio, the star of Selling the Hamptons on HBO Max, is the top-ranked real estate broker in both New York City and state, and the founder of one of highest producing brokerage teams in the U.S. She oversees a $10 billion international real estate portfolio, is a global speaker, and the author of, Mastering Intentions: 10 Practices to Amplify Your Power and Lead with Lasting Impact. Bianca is a frequent expert voice in Forbes, The New York Times, Fox Business, Medium, and The Real Deal. Get in Touch with Bianca:WEBSITE: biancadalessio.com    SOCIAL MEDIA:    facebook.com/bianca.dalessio.3      https://www.linkedin.com/in/biancadalessio/      https://www.instagram.com/biancadalessio/

In questo episodio di

Fluent Fiction - Italian: Unveiling Michelangelo's Secret: A Vatican Art Mystery Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2025-12-01-08-38-20-it Story Transcript:It: Nella fredda mattina d'inverno, Alessio camminava con passo deciso verso la Basilica di San Pietro.En: On the cold winter morning, Alessio walked with a determined step towards the Basilica di San Pietro.It: La città era piena di luci natalizie e il profumo dei dolci di Natale galleggiava nell'aria.En: The city was full of Christmas lights and the scent of Christmas sweets floated in the air.It: Alessio era uno studente di storia dell'arte, curioso e determinato.En: Alessio was an art history student, curious and determined.It: In tasca, teneva una busta misteriosa, consegnatagli la sera prima senza mittente, contenente solo un indovinello.En: In his pocket, he held a mysterious envelope, handed to him the night before without a sender, containing only a riddle.It: Le parole dell'indovinello riecheggiavano nella sua mente: "Dove l'ombra incontra la luce, lì l'arte racconta la fede nascosta."En: The words of the riddle echoed in his mind: "Where the shadow meets the light, there art tells of hidden faith."It: Alessio intuiva che il messaggio celava un segreto legato a un'opera d'arte sconosciuta nel Vaticano.En: Alessio intuited that the message concealed a secret linked to an unknown work of art in the Vatican.It: Ma il tempo era contro di lui.En: But time was against him.It: Le celebrazioni di Natale avrebbero presto limitato l'accesso a molte aree.En: The Christmas celebrations would soon restrict access to many areas.It: Mentre passeggiava tra la folla di turisti, vide Chiara, una guida locale.En: As he strolled through the crowd of tourists, he saw Chiara, a local guide.It: Anche se lei era scettica di fronte ai misteri, Alessio sapeva che il suo aiuto poteva essere prezioso.En: Even though she was skeptical about mysteries, Alessio knew that her help could be valuable.It: Avvicinandosi, le mostrò la busta.En: Approaching her, he showed her the envelope.It: "Chiara," disse, "ho bisogno del tuo aiuto per risolvere questo indovinello."En: "Chiara," he said, "I need your help to solve this riddle."It: Chiara lesse attentamente il messaggio.En: Chiara read the message carefully.It: I suoi occhi si illuminarono di curiosità che cercava di nascondere.En: Her eyes lit up with curiosity, which she tried to hide.It: "Va bene," rispose, "vediamo cosa possiamo scoprire."En: "Alright," she replied, "let's see what we can discover."It: Insieme, esplorarono la Piazza di San Pietro, cercando indizi tra le statue magnifiche.En: Together, they explored Piazza di San Pietro, looking for clues among the magnificent statues.It: La folla era densa, ma Alessio e Chiara si muovevano con determinazione.En: The crowd was dense, but Alessio and Chiara moved with determination.It: Il tempo stava per scadere, eppure non si arresero.En: Time was running out, yet they didn't give up.It: Finalmente, sotto una statua poco frequentata, trovarono un piccolo scomparto segreto.En: Finally, under a rarely visited statue, they found a small secret compartment.It: Dentro, c'era una nota: "Questo monumento custodisce la mia fede e la mia arte.En: Inside, there was a note: "This monument guards my faith and my art.It: Michelangelo."En: Michelangelo."It: Chiara e Alessio si guardarono con meraviglia.En: Chiara and Alessio looked at each other in amazement.It: Quel giorno, scoprirono un legame tra la fede di un grande artista e le sue opere, un dettaglio dimenticato della storia dell'arte.En: That day, they discovered a connection between the faith of a great artist and his works, a forgotten detail of art history.It: Alessio capì l'importanza della collaborazione, mentre Chiara riscoprì l'amore per i misteri dell'arte.En: Alessio understood the importance of collaboration, while Chiara rediscovered her love for the mysteries of art.It: Mentre la sera scendeva su Roma e le luci natalizie brillavano, Alessio e Chiara si allontanarono dalla Basilica con un nuovo rispetto reciproco e una storia da raccontare.En: As evening fell over Rome and the Christmas lights shone, Alessio and Chiara walked away from the Basilica with a new mutual respect and a story to tell.It: Il Natale portava con sé un nuovo inizio e un'amicizia inattesa.En: Christmas brought with it a new beginning and an unexpected friendship. Vocabulary Words:the shadow: l'ombrathe light: la lucethe art: l'artethe faith: la fedethe scent: il profumothe envelope: la bustathe riddle: l'indovinellothe monument: il monumentothe crowd: la follathe winter: l'invernothe note: la notathe compartment: lo scompartodetermined: determinatocurious: curiosothe celebration: la celebrazionethe statue: la statuathe guide: la guidathe sender: il mittentethe area: l'areasecret: segretovaluable: preziosothe mystery: il misterodetermination: la determinazionefinally: finalmenteto hide: nascondererarely: raramentethe connection: il legamethe history: la storiamutual: reciprocounexpected: inatteso

Fluent Fiction - Italian: Bunker Thanksgiving: A Tribute to Unity and Tradition Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2025-11-28-08-38-20-it Story Transcript:It: Nell'aria sotterranea c'era un odore particolare di caldarroste.En: In the underground air, there was a particular smell of roasted chestnuts.It: Gli abitanti del bunker, Alessio, Gina e Luigi, erano impegnati a preparare il loro modo speciale di celebrare il Ringraziamento.En: The inhabitants of the bunker, Alessio, Gina, and Luigi, were busy preparing their special way of celebrating Thanksgiving.It: Anche se le foglie erano cadute sopra di loro, nel mondo di sopra, nel bunker l'autunno si sentiva nel calore familiare che cercavano di creare.En: Even though the leaves had fallen above them, in the world above, in the bunker, autumn was felt in the familiar warmth they tried to create.It: Alessio stava sistemando il piccolo tavolo al centro della stanza.En: Alessio was arranging the small table in the center of the room.It: Il tavolo era circondato da sedie tutte diverse, trovate in momenti e luoghi diversi.En: The table was surrounded by different chairs, found at different times and places.It: Sul tavolo c'erano zucche fatte di carta e foglie colorate che Gina aveva raccolto durante le sue piccole escursioni fuori dal rifugio.En: On the table, there were paper pumpkins and colored leaves that Gina had collected during her small excursions outside the shelter.It: "È importante fare questo," disse Alessio, mentre sistemava un piatto di patate.En: "It's important to do this," said Alessio, while arranging a plate of potatoes.It: "La nonna ci ha sempre detto di non dimenticare mai Ringraziamento."En: "Grandma always told us to never forget Thanksgiving."It: "Lo so," rispose Gina con un sorriso, "ma cerchiamo di renderlo speciale anche così."En: "I know," responded Gina with a smile, "but let's try to make it special anyway."It: Luigi sbuffò.En: Luigi huffed.It: "Non capisco perché perdiamo tempo.En: "I don't understand why we're wasting time.It: Abbiamo così poche cose."En: We have so few things."It: Il suo sguardo era scettico, ma si diede da fare, impastando il pane con attenzione.En: His look was skeptical, but he got busy, kneading the bread with care.It: "Pensa a questo come un modo per tenere la mente impegnata," disse Alessio, intento a cucinare una zuppa che sapeva di casa.En: "Think of this as a way to keep the mind occupied," said Alessio, intent on cooking a soup that tasted like home.It: "E poi, cibo o non cibo perfetto, siamo qui insieme."En: "And besides, perfect food or not, we're here together."It: Intanto, Gina appese delle lucine attorno al muro di cemento.En: Meanwhile, Gina hung some little lights around the concrete wall.It: Il loro bagliore rendeva l'ambiente più accogliente.En: Their glow made the environment more welcoming.It: "Guarda, Luigi, non è male, vero?"En: "Look, Luigi, it's not bad, right?"It: disse con entusiasmo.En: she said enthusiastically.It: Con le risorse limitate, Alessio aveva deciso di improvvisare.En: With limited resources, Alessio had decided to improvise.It: Non avevano un tacchino, ma aveva ideato una ricetta con carne in scatola e spezie, cucinata con cura.En: They had no turkey, but he devised a recipe with canned meat and spices, cooked with care.It: Era determinato a mantenere la tradizione, qualunque cosa accadesse.En: He was determined to keep the tradition, no matter what happened.It: Proprio quando sembrava tutto pronto, il rumore del generatore si fermò.En: Just when everything seemed ready, the sound of the generator stopped.It: Il bunker fu inghiottito da un buio profondo e silenzioso.En: The bunker was engulfed in deep, silent darkness.It: Nella confusione, Gina trovò delle candele e le accese una per una.En: In the confusion, Gina found some candles and lit them one by one.It: Le fiamme tremolanti gettarono ombre danzanti sulle pareti.En: The flickering flames cast dancing shadows on the walls.It: "Non lasciamo che questo ci fermi," disse con fermezza.En: "Let's not let this stop us," she said firmly.It: Alessio sospirò, un po' deluso, ma il caldo abbraccio della piccola fiamma delle candele riportò la fiducia.En: Alessio sighed, a bit disappointed, but the warm embrace of the small candle flame restored their confidence.It: "Hai ragione, Gina.En: "You're right, Gina.It: Ce la faremo."En: We'll make it."It: Tutti si sedettero intorno al tavolo, le candele al centro illuminavano le pietanze preparate con amore.En: Everyone sat around the table, the candles in the center illuminating the lovingly prepared dishes.It: Alessio sollevò un calice: "Ecco a noi, alla famiglia."En: Alessio raised a glass: "Here's to us, to family."It: Luigi, guardando la piccola tavolata luminosa, sorrise, per la prima volta sinceramente.En: Luigi, looking at the small bright table, smiled, sincerely for the first time.It: "Forse questo è il miglior Ringraziamento di sempre."En: "Maybe this is the best Thanksgiving ever."It: In quel momento, capirono che non erano le circostanze a contare, ma la compagnia e l'affetto che condividevano.En: In that moment, they realized it wasn't the circumstances that mattered, but the company and affection they shared.It: Alessio sentì il calore della loro unione e capì che la promessa alla nonna era stata davvero mantenuta nel migliore dei modi.En: Alessio felt the warmth of their unity and understood that the promise to grandma had truly been kept in the best way possible. Vocabulary Words:underground: sotterraneoinhabitants: gli abitantibunker: il bunkerleaves: le fogliechair: la sediashelter: il rifugioplate: il piattopotatoes: le patateskeptical: scetticokneading: impastandosoup: la zuppacanned meat: la carne in scatolagenerator: il generatoredarkness: il buioconfusion: la confusionecandles: le candeleflames: le fiammeshadows: le ombreembrace: l'abbracciounity: l'unioneresources: le risorseexcursions: le escursioniglow: il bagliorewalls: le paretiwelcoming: accoglientetradition: la tradizionecircumstances: le circostanzeaffection: l'affettopromise: la promessaThanksgiving: il Ringraziamento

Hello and welcome to episode 105 of The Last Standee Podcast! Nothing to say, you must be really, really patient with us to bear such confusion in episode descriptions if you are still there, but let me explain. I know the podcast is out on Mondays and it says "Saturday Morning", but if you go into the detail of the games talked about, there is a "saturday morning cartoons" vibe, or at least that's what we say ourselves when we pat our backs congratulating on the title. Truth to be told, we just recorded this one on a Saturday Morning and all games are cartoon-themed, so see? Brilliant! Anyway. The episode starts with the usual Standee Catch-up where we learn useful insight about how to paint ginger/redheads on miniatures without them looking like fiery aliens (much appreciated!), then we start vibing with Audrey talking us about Trenchcoat Raccoons, your unusual one-off TTRPG project of the week (fortnight?). Then it's Alessio's turn to bring up that weird phase in CMYK games' publisher history when they decide to put out in a short sequence two weirdly competing, weird titles about races: Hot Streak and Magical Athlete. All of this, while our heroes waited for Alexis - little they knew at the time, but... See you next time!

Fluent Fiction - Italian: From Burnout to Balance: A Lesson in Asking for Help Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2025-11-22-08-38-20-it Story Transcript:It: La luce del mattino filtrava attraverso le grandi finestre del Museo di Storia Naturale di Milano.En: The morning light filtered through the large windows of the Museo di Storia Naturale di Milano.It: I visitatori entravano a frotte, le risate dei bambini riecheggiavano tra le sale.En: Visitors entered in droves, the laughter of children echoing through the halls.It: L'autunno soffiava foglie dorate sui gradini d'ingresso, mentre dentro, uno scheletro di dinosauro dominava la scena.En: Autumn blew golden leaves onto the entrance steps, while inside, a dinosaur skeleton dominated the scene.It: Luca, con occhiaie profonde e una pila di appunti sotto il braccio, camminava al fianco di Giorgia.En: Luca, with deep dark circles under his eyes and a pile of notes under his arm, walked alongside Giorgia.It: Lei, con lo spirito allegro di sempre, lo incitava.En: She, with her usual cheerful spirit, was encouraging him.It: "Luca, guarda quella sala!En: "Luca, look at that hall!It: Sarà perfetta per la tua ricerca".En: It will be perfect for your research."It: Luca annuì, tentando di nascondere la stanchezza che gli offuscava la vista.En: Luca nodded, trying to hide the fatigue that blurred his vision.It: La pressione era grande e il desiderio di impressionare i professori lo spingeva a non fermarsi mai.En: The pressure was immense, and the desire to impress the professors drove him to never stop.It: Aveva bisogno del rapporto perfetto per ottenere una borsa di studio.En: He needed the perfect report to obtain a scholarship.It: Un po' più avanti, Alessio, un medico venuto per una ricerca, osservava un fossile.En: A little further ahead, Alessio, a doctor who came for research, was observing a fossil.It: La sua attenzione venne distratta quando notò Luca appoggiarsi pesantemente al muro, il colore sparito dal suo viso.En: His attention was distracted when he noticed Luca leaning heavily against the wall, the color drained from his face.It: "Non sto bene," sussurrò Luca, mentre una vertigine lo obbligava a chiudere gli occhi.En: "I don't feel well," Luca whispered, as a dizzy spell forced him to close his eyes.It: Giorgia, preoccupata, gli prese il braccio.En: Giorgia, concerned, took his arm.It: "Luca, devi fermarti.En: "Luca, you need to stop.It: Chiediamo aiuto.En: Let's ask for help."It: " Luca esitò, combattuto tra il bisogno di continuare e la crescente debolezza.En: Luca hesitated, torn between the need to continue and the growing weakness.It: Avvicinandosi, Alessio osservò con attenzione la scena.En: Approaching, Alessio watched the scene carefully.It: "Scusatemi, ho visto cosa è successo.En: "Excuse me, I saw what happened.It: Posso aiutare?En: Can I help?"It: "Giorgia, sollevata, rispose rapidamente.En: Giorgia, relieved, quickly replied.It: "Sì, per favore!En: "Yes, please!"It: "Luca voleva minimizzare, ma la voce di Alessio era ferma e sicura.En: Luca wanted to downplay the situation, but Alessio's voice was firm and assured.It: "Hai bisogno di riposo, Luca.En: "You need rest, Luca.It: Non puoi continuare così.En: You can't keep going like this."It: "Confuso e riluttante, Luca guardò Giorgia e Alessio.En: Confused and reluctant, Luca looked at Giorgia and Alessio.It: "È che.En: "It's just that...It: sono stressato.En: I'm stressed.It: Non posso fallire.En: I can't fail."It: "Giorgia strinse la mano di Luca.En: Giorgia squeezed Luca's hand.It: "Non devi fare tutto da solo.En: "You don't have to do everything alone.It: Possiamo dividere il lavoro.En: We can divide the work."It: "Alessio annuì.En: Alessio nodded.It: "Non c'è niente di male nel chiedere aiuto.En: "There's nothing wrong with asking for help.It: Anche noi professionisti dobbiamo ricordarcelo.En: Even we professionals need to remind ourselves of that."It: "Luca respirò profondamente, realizzando la verità nelle parole di Alessio.En: Luca took a deep breath, realizing the truth in Alessio's words.It: Per troppo tempo aveva messo da parte il suo benessere.En: For too long, he had neglected his well-being.It: Quel giorno nel museo, con l'aiuto di Giorgia e Alessio, capì che doveva prendersi cura di se stesso per poter davvero dare il meglio.En: That day in the museum, with the help of Giorgia and Alessio, he understood that he needed to take care of himself to truly give his best.It: Mentre lasciavano il museo, il sole dell'autunno li avvolgeva in un abbraccio caldo, e Luca si sentì per la prima volta, da tanto, leggero.En: As they left the museum, the autumn sun enveloped them in a warm embrace, and Luca felt, for the first time in a long while, light.It: Avrebbe ancora raggiunto i suoi obiettivi, ma ora sapeva di non essere solo.En: He would still achieve his goals, but now he knew he wasn't alone. Vocabulary Words:the morning light: la luce del mattinolarge windows: le grandi finestrethe laughter: le risateechoing: riecheggiavanothe halls: le salethe entrance steps: i gradini d'ingressoa dinosaur skeleton: uno scheletro di dinosaurodark circles: occhiaie profondea pile of notes: una pila di appuntithe fatigue: la stanchezzathe pressure: la pressionea scholarship: una borsa di studioa fossil: un fossilea dizzy spell: una vertiginereluctant: riluttantetiredness: la stanchezzato impress: impressionareto observe: osservareto be distracted: venne distrattathe weakness: la debolezzathe truth: la veritàthe well-being: il benesserea warm embrace: un abbraccio caldoto achieve: raggiungeregoals: obiettivito realize: realizzarethe work: il lavorothe professors: i professorito downplay: minimizzareto squeeze: stringere

Partiamo da una curiosa proposta di legge italiana per istituzionalizzare la gentilezza, per scoprire insieme come la lingua può farsi gentile e quali sono le espressioni da usare per essere cordiali.Preparatevi a usare strumenti preziosi come il condizionale – quel modo verbale che trasforma un ordine in una richiesta: da “voglio un caffè!” a “vorrei un caffè”…  tutto cambia, vero? E poi ci sono le richieste gentili con “potrebbe…?”, “mi scusi…”, “sarebbe così gentile da…?” che rendono ogni conversazione più educata, morbida e rispettosa.Pronti a scoprire perché la gentilezza… fa scuola, anzi, fa lingua? Allora mettetevi comodi: Alessio e Katia vi accompagnano tra esempi pratici, curiosità e buon umore, per imparare l'italiano un'espressione gentile alla volta!

Fluent Fiction - Italian: Spontaneous Escapes: A Road Trip to Rediscover Life's Thrills Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2025-11-18-08-38-20-it Story Transcript:It: Alessio camminava nervosamente avanti e indietro nel salotto.En: Alessio paced nervously back and forth in the living room.It: Il suo quartiere era tranquillo, le foglie cadevano dagli alberi formando un tappeto arancione e rosso.En: His neighborhood was quiet, leaves fell from the trees forming an orange and red carpet.It: Sentiva che qualcosa mancava nella sua vita.En: He felt that something was missing in his life.It: Era stanco della solita routine.En: He was tired of the usual routine.It: Desiderava avventura.En: He craved adventure.It: Giuliana entrò nella stanza con una tazza di tè.En: Giuliana entered the room with a cup of tea.It: "A cosa stai pensando, Alessio?"En: "What are you thinking about, Alessio?"It: chiese.En: she asked.It: "Lago di Como", rispose Alessio con fervore nei suoi occhi.En: "Lago di Como," replied Alessio with fervor in his eyes.It: "Un viaggio in macchina, in questo momento.En: "A road trip, right now.It: Cosa ne pensi?"En: What do you think?"It: Giuliana sospirò.En: Giuliana sighed.It: Era più cauta, pensava sempre ai suoi impegni e responsabilità.En: She was more cautious, always thinking about her commitments and responsibilities.It: "Non so, dobbiamo pianificare.En: "I don't know, we need to plan.It: Ci sono tante cose da considerare."En: There are so many things to consider."It: Ma Alessio era impaziente.En: But Alessio was impatient.It: "Giuliana, questo è il momento di fare qualcosa di diverso.En: "Giuliana, this is the time to do something different.It: Lasciamo tutto e partiamo.En: Let's leave everything and go.It: Sarà un'avventura."En: It will be an adventure."It: La proposta di Alessio fece battere il cuore di Giuliana.En: Alessio's proposal made Giuliana's heart race.It: Anche lei segretamente desiderava una pausa dalla sua vita strutturata.En: She too secretly longed for a break from her structured life.It: "Va bene", disse finalmente.En: "Okay," she finally said.It: "Andiamo.En: "Let's go.It: Sarà bello passare del tempo insieme.En: It will be nice to spend time together.It: Ma promettiamo di tornare domani."En: But let's promise to return tomorrow."It: Infilarono in fretta qualche vestito in una borsa e si misero in viaggio.En: They quickly packed some clothes into a bag and set off.It: La strada scorreva veloce sotto le ruote dell'auto, mentre gli alberi vestiti d'autunno sfumavano nel paesaggio.En: The road swept quickly under the wheels of the car, while the autumn-clad trees blurred into the landscape.It: Mentre si avvicinavano a Como, l'auto iniziò a tremare.En: As they neared Como, the car started to shake.It: Alessio accostò e i due capirono subito: una gomma a terra.En: Alessio pulled over, and the two immediately realized: a flat tire.It: Giuliana si sentì subito nervosa.En: Giuliana suddenly felt nervous.It: "Ecco", pensò, "è per questo che non mi piacciono le cose non pianificate."En: "See," she thought, "this is why I don't like unplanned things."It: Alessio, però, le prese la mano.En: However, Alessio took her hand.It: "Insieme ce la faremo."En: "Together we'll manage."It: Provarono a cambiare la gomma.En: They tried to change the tire.It: All'inizio non fu facile, ma poco a poco il loro lavoro portò risultati.En: At first, it wasn't easy, but little by little, their work paid off.It: Finalmente, la stanchezza e la tensione svanirono.En: Finally, the fatigue and tension faded away.It: Ripresero il viaggio.En: They resumed the journey.It: Arrivarono al Lago di Como mentre il sole calava, tingendo il lago di sfumature dorate e rosa.En: They arrived at Lago di Como as the sun was setting, painting the lake with golden and pink hues.It: Si sedettero sulla riva, guardando l'acqua calma.En: They sat on the shore, watching the calm water.It: In quel momento, Alessio si sentì finalmente libero e Giuliana scoprì quanto fosse bello lasciarsi andare.En: In that moment, Alessio felt finally free, and Giuliana discovered how beautiful it was to let go.It: Realizzarono l'importanza della spontaneità e del lavorare insieme.En: They realized the importance of spontaneity and working together.It: Alessio imparò ad essere più paziente, mentre Giuliana capì che ogni tanto prendere un rischio è il modo migliore per scoprire qualcosa di nuovo.En: Alessio learned to be more patient, while Giuliana understood that sometimes taking a risk is the best way to discover something new.It: Si abbracciarono, felici della loro avventura improvvisata, pronti a tornare a casa il giorno dopo, portando con sé un ricordo indelebile.En: They embraced, happy with their impromptu adventure, ready to return home the next day, carrying with them an unforgettable memory. Vocabulary Words:to pace: camminarenervously: nervosamenteleaves: le foglieroutine: la routineto crave: desiderareadventure: l'avventurafervor: il fervorecautious: cautacommitments: gli impegniresponsibilities: le responsabilitàto plan: pianificareimpatient: impazienteto propose: proporreto pack: infilarelandscape: il paesaggioto shake: tremareflat tire: la gomma a terraunplanned: non pianificateto manage: riusciretire: la gommafatigue: la stanchezzatension: la tensioneto resume: riprenderehues: le sfumatureshore: la rivacalm: calmaspontaneity: la spontaneitàto embrace: abbracciareimpromptu: improvvisataunforgettable: indelebile

Fluent Fiction - Italian: Tuscan Hills Revival: How a Festival Brought a Library to Life Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2025-11-15-23-34-02-it Story Transcript:It: Nel cuore delle colline toscane, c'era una piccola biblioteca comunale.En: In the heart of the Tuscan hills, there was a small public library.It: Era un edificio di pietra, antico e affascinante, circondato da vigneti e uliveti che, in autunno, assumevano i colori del rame e dell'oro.En: It was an ancient and charming stone building, surrounded by vineyards and olive groves that, in autumn, took on the colors of copper and gold.It: La biblioteca si trovava proprio in cima a una collina, dove il sole pomeridiano filtrava attraverso le finestre ad arco, illuminando gli scaffali di libri.En: The library was located right at the top of a hill, where the afternoon sun filtered through the arched windows, illuminating the bookshelves.It: Giulia, la bibliotecaria, osservava con preoccupazione la sala quasi vuota.En: Giulia, the librarian, watched with concern the nearly empty room.It: Il prossimo mese si sarebbe tenuto un festival del libro regionale.En: Next month a regional book festival would be held.It: Era l'occasione perfetta per rilanciare la biblioteca e attirare nuovi visitatori.En: It was the perfect opportunity to rejuvenate the library and attract new visitors.It: Ma c'era un problema: il budget era limitato e le risorse scarse.En: But there was a problem: the budget was limited and resources were scarce.It: "Manca poco e dobbiamo preparare tutto," disse Giulia, fissando il calendario.En: "Not much time left and we need to prepare everything," said Giulia, looking at the calendar.It: Alessio, un autore del posto, la raggiunse con passo incerto.En: Alessio, a local author, joined her with an uncertain step.It: Il suo primo romanzo era appena stato pubblicato, ma il dubbio sulle sue capacità lo tormentava.En: His first novel had just been published, but doubts about his abilities tormented him.It: "Pensi davvero che qualcuno voglia ascoltare i miei scritti?"En: "Do you really think anyone wants to listen to my writings?"It: chiese Alessio, il timore evidente nel tono della sua voce.En: asked Alessio, the fear evident in the tone of his voice.It: Accanto a loro, Martina, l'assistente giovane e vivace della biblioteca, sognava ad occhi aperti.En: Next to them, Martina, the library's young and lively assistant, was daydreaming.It: "Possiamo fare qualcosa di diverso quest'anno," suggerì con entusiasmo.En: "We can do something different this year," she suggested enthusiastically.It: "Interviste dal vivo, letture interattive... qualcosa che attiri le famiglie e i giovani!"En: "Live interviews, interactive readings... something that attracts families and young people!"It: Giulia inizialmente esitò.En: Giulia initially hesitated.It: Le idee di Martina erano nuove e audaci.En: Martina's ideas were new and bold.It: Ma sapeva che serviva innovazione.En: But she knew that innovation was needed.It: "Proviamo, non abbiamo nulla da perdere," disse infine, con un sorriso deciso.En: "Let's try, we have nothing to lose," she finally said, with a determined smile.It: Il giorno dell'incontro con il consiglio del villaggio, il sole autunnale risplendeva forte.En: On the day of the meeting with the village council, the autumn sun shone brightly.It: Alessio, con il cuore in gola, si preparava a leggere un brano del suo libro.En: Alessio, with his heart in his throat, prepared to read an excerpt from his book.It: Si sistemò davanti al pubblico, i nervi a fior di pelle, ma quando cominciò a leggere, la sua voce divenne ferma e chiara.En: He stood in front of the audience, nerves on edge, but when he started to read, his voice became steady and clear.It: Le parole risuonavano tra le pareti di pietra, catturando l'attenzione di tutti.En: The words resonated between the stone walls, capturing everyone's attention.It: Al termine della lettura, un applauso spontaneo riempì la stanza.En: At the end of the reading, spontaneous applause filled the room.It: Il consiglio, impressionato, acconsentì a sostenere il festival.En: The council, impressed, agreed to support the festival.It: La comunità intera sembrò rivalutare la piccola biblioteca, vedendo nel festival un'opportunità unica.En: The entire community seemed to reevaluate the small library, seeing in the festival a unique opportunity.It: Quando il festival finalmente giunse, fu un grande successo.En: When the festival finally arrived, it was a great success.It: La biblioteca si riempì di visitatori curiosi e appassionati di lettura.En: The library filled with curious visitors and book lovers.It: Le idee di Martina portarono un'aria nuova e Alessio ricevette molti complimenti, riscoprendo la fiducia nella propria scrittura.En: Martina's ideas brought a new atmosphere and Alessio received many compliments, rediscovering confidence in his writing.It: Dopo il festival, il numero di visitatori alla biblioteca aumentò notevolmente.En: After the festival, the number of visitors to the library increased significantly.It: Giulia si sentiva finalmente sollevata, e la sua fiducia crebbe.En: Giulia finally felt relieved, and her confidence grew.It: Martina si era guadagnata il rispetto, e Alessio, ora, non vedeva l'ora di scrivere il suo prossimo libro.En: Martina earned respect, and Alessio was now eager to write his next book.It: La piccola biblioteca sulle colline toscane era tornata a vivere, illuminata non solo dal sole autunnale, ma anche dalle nuove prospettive e dall'entusiasmo della comunità.En: The small library on the Tuscan hills had come back to life, illuminated not only by the autumn sun but also by the new perspectives and enthusiasm of the community. Vocabulary Words:heart: il cuorehill: la collinaancient: anticovineyard: il vignetoolive grove: l'ulivetosurrounded: circondatocharming: affascinantefloor: il pavimentoarch: l'arcobook festival: il festival del librobudget: il budgetresource: la risorsaconcern: la preoccupazionelibrarian: la bibliotecariaauthor: l'autoreabilities: le capacitàdoubt: il dubbiowriting: la scritturaenthusiastically: con entusiasmoinnovation: l'innovazionedetermined: decisospeech: il discorsoexcerpt: il branoapplause: l'applausocouncil: il consiglioconfidence: la fiduciaperspective: la prospettivasuccess: il successovisitor: il visitatorespontaneous: spontaneo

Quando dare del “tu” e quando del “Lei”? E perché in Italia si usa il “Lei” invece del “voi”? In questo episodio di

It's time to talk more college hoops on the latest episode of the Just End The Suffering podcast! Host Mike Phillips (⁠⁠⁠⁠@MPhillips331⁠⁠⁠⁠) kicks off the show by recapping the classic World Series (1:56) that saw the Dodgers become the first team to repeat as champs in 25 years. Mike is then joined by Zach Braziller (⁠@NYPost_Brazille⁠) of the New York Post to talk college basketball (6:25) with the season officially underway. Mike then makes his Week 10 NFL Picks (29:15) with Jets' fan Nick D'Alessio and reacts to the Jets' franchise-altering deadline deals (44:08) in the Two Minute Drill.Check out Zach Braziller's ⁠coverage⁠ for the New York Post!Subscribe to the Just End The Suffering podcast on ⁠⁠⁠⁠⁠⁠⁠Apple⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠Amazon⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠TuneIn⁠⁠⁠⁠⁠⁠⁠,⁠⁠⁠⁠⁠⁠ and⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠!Subscribe to ⁠⁠⁠⁠⁠⁠⁠Mike Phillips's channel⁠⁠⁠⁠⁠⁠⁠ on YouTube!Check out The Recovery Room On ⁠⁠⁠⁠⁠⁠Twitch⁠⁠⁠⁠⁠⁠!

1 hour and 55 minutes The Sponsors Thank you to Underground Printing for making this all possible. Rishi and Ryan have been our biggest supporters from the beginning. Check out their wide selection of officially licensed Michigan fan gear at their 3 store locations in Ann Arbor or learn about their custom apparel business at undergroundshirts.com. Our associate sponsors are: Peak Wealth Management, Matt Demorest - Realtor and Lender, Ann Arbor Elder Law, Michigan Law Grad, Human Element, Sharon's Heating & Air Conditioning, The Sklars Brothers, Champions Circle, Winewood Organics, Community Pest Solutions, Venue by 4M where record this, and Introducing this season: Radecki Oral Surgery, and Long Road Distillers. 1. Offense vs Purdue Starts at :57 This podcast starts out telepathically but then Brian's intrusive thoughts got telepathed so it had to stop. Dave introduces the Snack of the Week. Would you rather talk about this game or Dunkaroos? Bryce Underwood - not good in the first half. A fumble on the sideline is usually harmless unless it involves the silliest rule in football. His scrambling was good but you can't build a business in this industry by scrambling, that will get you killed against Ohio State. Too many missed passes, he doesn't really settle in. By the Georgia game, JJ was probably where Bryce is now - many mistakes but you can see the talent. On the flip side, the offensive line had a great game. Purdue loaded the box but Jordan Marshall rushed for 185 yards anyways. You can't tackle him with just one guy, he will emerge from piles. This is the fourth straight game where Sprague has been incredible. Bryson Kuzdzal had some nice runs on the game-sealing drive. Tight ends were fine, more catches by Zack Marshall. There's not a lot of separation between Marshall and Klein. Semaj had way fewer snaps, Goodwin saw more time. You have six 2nd or 3rd year players on this offensive line that can absolutely play in this conference. The future of the offensive line is bright.  2. Defense vs Purdue Starts at 41:43 How do we even feel about the defensive performance? We've seen Purdue all season be an offense that moves the ball down the field but can't score. That happened but it felt bad. Cam Brandt was too far upfield on a couple big run plays. Why are the good defensive ends not on the field for 70% of the snaps that they should be out for? Why are the starters rotating out so much throughout the game? Assuming he's healthy, do you put Jaishawn Barham at DE or LB against Ohio State? Michigan didn't commit to a position for him and it's hurting his play. Way fewer three defensive tackle sets, yay. If your name is going to be "Michael Jackson" you need to go by "Mike". Jyaire Hill got sealed a couple times but was otherwise fine. The endzone DPI was DPI. Metcalf got sucked in during the touchdown.  3. Hot Takes, Game Theory, and Special Teams Starts at 1:06:04 Takes hotter than the amount of trouble Jason would get into if he did the Hot Takes voice at a golf tournament where he was during recording. Michigan has not been good at Special Teams Things, why are they running kickoffs out of the middle of the endzone? Another punt that Semaj didn't field that gave up 20 yards. Did Jay Harbaugh have a heat map for punting? We've never had to talk so much about shield punting positioning but now we have to. Clock management at the end of the first half was pretty on-point. Purdue's 4th down decision making was aggressive which you do if you want to try to win the game. Shout out to Michigan fans for feeding energy back into the team in the 4th quarter. The students did the shirtless thing that's become a college football thing. Also shout out to Barry Odom for getting the Purdue bench fired up.  4. Around the Big Ten with Jamie Mac Starts at 1:28:22 Indiana 55, Maryland 10 This is a typical Indiana game these days. Indiana's offense is a machine. The defense is... also a machine?? Every week, Indiana has some weird defensive stat that's historical and worth tracking. Mendoza threw and interception on his first play, the game was wobbly for about a quarter.  Ohio State 38, Penn State 14 Briefly competitive in the 2nd quarter. Penn State is the first top five team in the history of college football to lose five straight games. Julian Sayin had 14 yards per attempt. Ohio State finally catches a break and gets an obvious targeting call to not get enforced.  Minnesota 23, Michigan State 20 (OT) MSU benches Aidan Chiles for Alessio Milivojevic. The Spartans lose this game despite outgaining Minnesota by about 160 yards. The final two minutes of this game are worth watching. Northwestern QB Aidan Chiles?? Alessio had a better EPA than Chiles any other game this season. USC 21, Nebraska 17 If you like offense, don't look at this game. We are suddenly having feelings about Wink Martindale. Dylan Raiola is done for the season and USC is able to grind out a win. Raiola's backup went 5/7 for 7 yards.  Illinois 35, Rutgers 13 A solid victory for Illinois, most of Rutgers' yards are when it was 35-6. Bert: "I put us as good as any 6-3 team out there. That doesn't mean anything." Bowl eligible in consecutive seasons for the first time since 2011. Illinois is the new Wisconsin.  MUSIC: "On & On"—The Marcus King Band "Husbands"—Geese "Don't Forget That I Love you"—Pale Jay “Across 110th Street”—JJ Johnson and his Orchestra 

This week, Ivy Slater, host of Her Success Story, chats with her guest, Bianca D'Alessio. The two talk about daily leadership strategies, building company culture through being present, and the importance of staying true to your story. In this episode, we discuss: How Bianca scaled her business from the ground up, learned from poor partnerships, and gradually transitioned from operator to CEO by hiring strategically and adapting her team's roles based on their strengths. What imposter syndrome has looked like for both Ivy and Bianca, and the ways women in leadership can reframe self-doubt into self-confidence. When challenging moments – from personal family issues to professional setbacks – became opportunities for Bianca to see failure as a pathway to growth. Why there's no true "work-life balance"—just life—and how focusing on personal growth, authenticity, and empowerment can fuel leadership success. What inspired Bianca's book, "Mastering Intentions," and how practices like gratitude, manifestation, and storytelling can help others amplify their power and impact?   Bianca D'Alessio is the CEO and founder of The Masters Division, where she manages a $10B real estate portfolio and oversees over 80 new construction projects across New York City, the Hamptons, Westchester, and international markets. Ranked the #1 Real Estate Broker in both New York City and State, and recognized by Crain's New York Business as a Notable Leader in Real Estate, Bianca is also the star of "Selling the Hamptons" on HBO Max and the author of Mastering Intentions: 10 Practices to Amplify Your Power and Lead with Lasting Impact. In addition to her real estate success, she is the founder of the nonprofit Master Intentions, a global initiative that reinvests commissions into philanthropic organizations, and she regularly speaks and writes on empowering women, financial literacy, and intentional leadership. Website: https://nestseekersmastersdivision.com/   Social Media Links: https://www.linkedin.com/in/biancadalessio/  

Protagonista della puntata numero 120 di Mondo Triathlon, la rubrica di Dario Daddo Nardone in onda su Bike Channel, èALESSIO CAPPAOgni lunedì alle 19.00 in anteprima il nuovo episodio sul canale youtube @DaddoSport,tutte le puntate di Mondo Triathlon sulla pagina ufficiale:https://www.mondotriathlon.it/mondoGuarda Mondo Triathlon anche sui canali di Bike Channel:- SKY Canale 222- DTT Canale 259- DTT Canale 60 tasto rosso SI- www.bikechannel.it#daddocè #mondotriathlon #ioTRIamo ❤️#triathlon #trilife #fczstyle #passionetriathlon

Alessio Vailati"La mappa del dolore"Riflessioni in versi su trenta fotografie vincitrici del Premio Pulitzer.il ramo e la foglia edizioniwww.ilramoelafogliaedizioni.itLa mappa del dolore è un libro di poesie a tema civile che ripercorre importanti vicende storiche dalla Seconda guerra mondiale ai giorni nostri, affrontando argomenti come la guerra, la povertà, la discriminazione razziale, l'emarginazione, i flussi migratori eccetera.Si tratta pertanto di un libro attuale imperniato sulle immagini icastiche di trenta tragici avvenimenti che hanno segnato la Storia, immortalati in altrettante fotografie vincitrici del Premio Pulitzer. Pur essendo scaturiti dalle fotografie i testi mantengono una certa autonomia e si occupano del lato umano delle vicende narrate. Non si tratta di testi con giudizi di natura politica ed economica quanto piuttosto di un lungo racconto che getta lo sguardo sulla disumanità di quanto ci accade attorno, pur non toccandoci direttamente.Il titolo del libro sta a indicare proprio questo percorso, quasi un viaggio nell'inferno dantesco, così tristemente reale e documentato. Le vicende (le immagini) trattate sono trenta e il loro andamento è scandito attraverso un testo guida che si apre in ulteriori sei testi.Riportiamo i titoli delle trenta poesie contenute nella raccolta di Alessio Vailati, La mappa del dolore - riflessioni in versi su trenta fotografie vincitrici del Premio Pulitzer (in libreria dal 19 settembre 2025); in corrispondenza di ogni titolo si trova il link a una pagina esterna che mostra la fotografia a cui l'autore si è ispirato. I titoli delle poesie non sono gli stessi delle fotografie a cui si ispirano. In corrispondenza dei titoli si trovano i nomi dei fotografi e l'anno in cui hanno vinto il Premio Pulitzer con le loro fotografie.1. Il ritorno di un eroe, Earle Bunker 19442. Il ponte sul Taedong, Max Desfor 19513. La morte e il vagoncino rosso, William Seaman 19594. Due uomini soli, Paul Vathis 19625. Rivoluzione e assoluzione, Hector Rondon 19636. Interludio di pace, Toshio Sakai 19687. Ritratto della dignità, Moneta Sleet 19698. La marea di migranti, Dallas Kinney 19709. Un magazzino per persone, Jack Dykinga 197110. Vendetta all'autodromo, Horst Faas e Michel Laurent 197211. Cicatrici di guerra, David Hume Kennerly 197212. La ragazza di Trangbang, Nick Út 197313. Fine dell'incendio, Gerald Gay 197514. Un volto nella folla, Robin Hood 197715. Disordini politici a Bangkok, Neal Ulevich 197716. Esecuzione sulla spiaggia, Larry Price 198117. Il campo della morte di El Salvador, James B. Dickman 198318. Carestia, Stan Grossfeld 198519. L'inverno dei senzatetto, Tom Gralish 198620. La bambina e l'avvoltoio, Kevin Carter 199421. Un rito di passaggio africano, Stephanie Welsh 199622. Il cammino delle lacrime, Martha Rial 199823. I rifugiati del Kosovo, C. Guzy, M. Williamson, L. Perkins 200024. Attacco al World Trade Center, Staff del New York Times 200225. Monrovia sotto assedio, Carolyn Cole 200426. Ultimo saluto, Todd Heisler 200527. Il viaggio di una madre, Renee C. Byer 200728. Il catastrofico terremoto di Haiti, C. Guzy, N. Kahn, R. Carioti 201129. La bambina in verde, Massoud Hossaini 201230. Il cinico disprezzo della vita umana, Daniel Berehulak 2017Alessio Vailati è nato a Monza nel 1975 e vive in provincia di Monza e Brianza. È laureato in giurisprudenza. Le sue raccolte di poesia sono: L'eco dell'ultima corda (Lietocolle, 2008), Sulla via del labirinto (L'arcolaio, 2010), Sulla lemniscata – L'ombra della luce (La Vita Felice, 2017), Piccolo Canzoniere privato (Controluna, 2018, Premio Poeti e Narratori per caso 2019 e finalista Premio Marineo 2018), Orfeo ed Euridice (Puntoacapo Editrice, 2018), Hirosaki (Lietocolle 2019, plaquette), Il moto perpetuo dell'acqua (Biblioteca dei Leoni, 2020), Lungo la muraglia (Bertoni editore, 2020), Luci da Oriente (Nulla Die edizioni, 2021). È autore del romanzo Ninfa alla selva (Robin, 2024).Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/il-posto-delle-parole--1487855/support.IL POSTO DELLE PAROLEascoltare fa pensarehttps://ilpostodelleparole.it/

Fluent Fiction - Italian: Unmasking Tradition: A Venetian Tale of Creativity and Innovation Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2025-10-31-22-34-02-it Story Transcript:It: Il sole autunnale illuminava le foglie dorate che ondeggiavano lungo i canali di Venezia.En: The autumn sun illuminated the golden leaves swaying along the canals of Venezia.It: Dentro una bottega profumata di cartapesta, Alessio lavorava con passione.En: Inside a shop filled with the scent of papier-mâché, Alessio worked with passion.It: Il suo sogno?En: His dream?It: Creare una maschera che combinasse la tradizione veneziana con la modernità.En: To create a mask that combined the veneziana tradition with modernity.It: Sedeva circondato da maschere di ogni tipo, dalle forme più classiche a quelle più eccentriche.En: He sat surrounded by masks of every kind, from the most classic shapes to the more eccentric ones.It: Giulia, la sua collega, osservava con occhio critico.En: Giulia, his colleague, watched with a critical eye.It: "Alessio, sei sicuro di questa strada?"En: "Alessio, are you sure about this path?"It: chiese, sottolineando l'importanza di rispettare le radici.En: she asked, emphasizing the importance of respecting roots.It: "Venezia è storia."En: "Venezia is history."It: Alessio però aveva un cliente molto particolare, Matteo.En: Alessio, however, had a very particular client, Matteo.It: Uomo ricco e appassionato di arte, era affascinato da idee nuove.En: A wealthy man and art enthusiast, he was fascinated by new ideas.It: "Voglio qualcosa di unico per il ballo di Halloween," aveva detto.En: "I want something unique for the Halloween ball," he had said.It: Il cuore di Alessio batteva veloce.En: Alessio's heart raced.It: Doveva convincere sia Matteo che Giulia.En: He had to convince both Matteo and Giulia.It: Costruire una maschera che parlasse di passato e futuro sembrava quasi impossibile.En: Creating a mask that spoke of both past and future seemed almost impossible.It: Ma la sua mente era già al lavoro.En: But his mind was already at work.It: Man mano che il tempo passava, Alessio sperimentava.En: As time passed, Alessio experimented.It: Ogni giorno mescolava colori e dettagli, cercando il perfetto equilibrio.En: Every day he mixed colors and details, seeking the perfect balance.It: Giulia lo osservava in silenzio, talvolta scuotendo la testa, talvolta sospirando.En: Giulia watched him in silence, sometimes shaking her head, sometimes sighing.It: Arrivò la notte del ballo.En: The night of the ball arrived.It: Le strade di Venezia erano vivaci, piene di risate e suoni di musica.En: The streets of Venezia were lively, full of laughter and sounds of music.It: Alessio, con il cuore in gola, rivelò la sua creazione.En: Alessio, with his heart in his throat, revealed his creation.It: Una maschera che intrecciava il romanticismo di Venezia con linee audaci e moderne.En: A mask that intertwined the romance of Venezia with bold and modern lines.It: Lucida e brillante, ma con quel tocco di antichità.En: Shiny and brilliant, yet with that touch of antiquity.It: Matteo fissò la maschera, meravigliato.En: Matteo stared at the mask, amazed.It: "È perfetta," disse, con un sorriso che illuminava la stanza.En: "It's perfect," he said, with a smile that lit up the room.It: Giulia rimase senza parole.En: Giulia was left speechless.It: Alla fine, con un piccolo sorriso approvante, ammise che l'innovazione avesse il suo spazio anche nella tradizione.En: At last, with a small approving smile, she admitted that innovation also had its place in tradition.It: Alessio finalmente si sentì compreso.En: Alessio finally felt understood.It: Aveva corso un rischio e aveva vinto.En: He had taken a risk and had won.It: Matteo gli propose immediatamente una commissione per un evento prestigioso, mentre Giulia riconobbe il valore della creatività.En: Matteo immediately proposed a commission for a prestigious event, while Giulia recognized the value of creativity.It: Così, Alessio imparò che unendo il nuovo al vecchio poteva creare un ponte tra due mondi.En: Thus, Alessio learned that by uniting the new with the old, he could create a bridge between two worlds.It: La sua maschera non era solo un'opera d'arte, ma una dichiarazione: l'equilibrio tra tradizione e innovazione poteva davvero esistere.En: His mask was not just a work of art, but a statement: the balance between tradition and innovation could truly exist.It: E in quel piccolo angolo di Venezia, il sogno di Alessio brillava forte come il sole autunnale.En: And in that small corner of Venezia, Alessio's dream shone as brightly as the autumn sun. Vocabulary Words:the autumn: l'autunnothe sun: il solethe leaves: le fogliethe passion: la passionethe shop: la bottegathe scent: il profumothe paper maché: la cartapestathe tradition: la tradizionethe modernity: la modernitàthe colleague: la collegathe roots: le radicithe history: la storiathe client: il clientethe future: il futurothe balance: l'equilibriothe silence: il silenzioto sigh: sospirareto convince: convincereto reveal: rivelarethe romance: il romanticismothe lines: le lineethe antiquity: l'antichitàto amaze: meravigliarethe smile: il sorrisothe risk: il rischiothe creativity: la creativitàthe commission: la commissionethe event: l'eventoto propose: proporrethe bridge: il ponte

Nel nuovo episodio di

In this episode, we have the pleasure of exploring the power of authentic communication and strategic storytelling with marketing expert Cassandra D'Alessio. We dive into the importance of taking control of our own narrative, differentiating between branding, sales, and marketing, and infusing authenticity into all facets of our messaging. Through Cassandra's practical advice and real-world examples, we discover how to turn words into strategy, build trust, and create lasting relationships in both business and personal conversations. Join us as we uncover valuable lessons on patience, consistency, and audience understanding that elevate our communication and help us stand out in a noisy marketplace.Timestamps: (00:00) - Introducing Cassandra D'Alessio and her expertise in B2B marketing and branding.(04:09) - Importance of taking control of your own story online.(06:02) - Balancing authenticity without sounding salesy or cheesy.(09:54) - Clarifying the differences between sales, brand, and marketing.(13:44) - Explaining why consistency in messaging matters across all channels.(16:11) - Turning words into effective communication strategy.(22:21) - How writing shapes Cassandra's communication approach and perception.(34:21) - Lessons from Cassandra's book on launching a business as a female entrepreneur.(41:30) - Teaching college students and applying those lessons to business clients.(46:22) - Role of imagery in stopping the scroll and engaging audiences online.Links and Resources:Cassandra D'Alessio | This Won't Be Pretty - https://www.cassandradalessio.com/Next Page Brand Strategies - https://www.turnthenextpage.com/Cassandra D'Alessio, M.A. | LinkedIn - https://www.linkedin.com/in/cassandra-dalessio/Sponsor Links:InQuasive: http://www.inquasive.com/Humintell: Body Language - Reading People - HumintellEnter Code INQUASIVE25 for 25% discount on your online training purchase.International Association of Interviewers: Home (certifiedinterviewer.com)Podcast Production Services by EveryWord Media

The guys wonder if Jonathan Smith should make a QB change with the season essentially lost.

A spooky welcome to Episode 104 of The Last Standee Podcast! This episode we explore curses, critters and cursed critters which also curse you! But let us get our curse-words (Inigo Montoya vibes here: you keep saying that word - I don't think it means what you think it means) sorted! First, there's our Standee Catch-up with Cara and Alessio, then Alessio breaks ice with Eternal Decks, an excellent scenario-based coop card game by Hiroken seeing first reprint right these days, then Cara goes with Here to Slay, the Unstable Unicorns sequel that nods a bit to Munchkin (but also has some good things going its way). Finally, Alessio and Cara talk about King of Tokyo Duel, the last (next to last, now that Essen SPIEL has come and gone there's Mindbug King of Tokyo) iteration of the franchise, which brings some very welcome fresh air to the known and loved concept of the game. And I think here we are: there are cards, combos, critters and curses - episode's done! See you next episode!

Per approfondire gli argomenti della puntata: La nostra serie Imperatores, sugli imperatori romani : https://youtube.com/playlist?list=PLpMrMjMIcOkkIDocjNI3Q7gCk-4bOiVVO Le altre puntate sulla storia di Roma antica : https://youtube.com/playlist?list=PLpMrMjMIcOkkVlao9HeDl3jIHVKO3IcR_ Learn more about your ad choices. Visit megaphone.fm/adchoices

I happen to be in Sicily with a group of Patrons (this could be you if you join Patreon!).    While I was in Verona at Wine2Wine, in 2019, I had the honor to speak with Alessio Planeta, President at Assovini Sicilia and Owner at Planeta Winery     For five centuries and through seventeen generations, Planeta has been active in changing and improving agriculture in Sicily. Alessio Planeta has spent his life dedicated to the study of Sicily and figuring out how to make it a significant force in world wine. With his family, Alessio now has six wineries around Sicily, and they have almost single-handedly put Sicily on the map as a quality player.   Planeta continues its mission to show what Sicily can do and what it's forgotten varietals can bring to the world of wine. They are one of the big reasons we have access to excellent Sicilian wine today.     Full show notes and all back episodes are on Patreon. Become a member today! www.patreon.com/winefornormalpeople _______________________________________________________________   Check out my exclusive sponsor, Wine Access.  They have an amazing selection -- once you get hooked on their wines, they will be your go-to! Make sure you join the Wine Access-Wine For Normal People wine club for wines I select delivered to you four times a year!    To register for an AWESOME, LIVE WFNP class with Elizabeth or get a class gift certificate for the wine lover in your life go to: www.winefornormalpeople.com/classes    

In questo episodio di

Fluent Fiction - Italian: Longing Across Cities: Love and Choices between Milano and Roma Find the full episode transcript, vocabulary words, and more:fluentfiction.com/it/episode/2025-10-13-22-34-02-it Story Transcript:It: Milano era in fermento.En: Milano was buzzing with activity.It: L'aria fresca dell'autunno riempiva le strade, portando con sé l'odore inebriante delle caldarroste.En: The fresh autumn air filled the streets, carrying with it the intoxicating smell of roasted chestnuts.It: Alessio camminava lungo Corso Vittorio Emanuele II, passando tra le vetrine scintillanti dei negozi di moda.En: Alessio walked along Corso Vittorio Emanuele II, passing by the glittering fashion store windows.It: Il suo cuore era però lontano, a Roma, accanto a Bianca.En: However, his heart was far away, in Roma, next to Bianca.It: Bianca frequentava l'università a Roma, circondata dall'arte e dalla storia che amava tanto.En: Bianca was attending university in Roma, surrounded by the art and history she loved so much.It: Spesso, si perdeva nei suoi pensieri mentre passeggiava vicino al Colosseo o visitava la Galleria Borghese.En: Often, she would get lost in her thoughts while strolling near the Colosseum or visiting the Galleria Borghese.It: Ma il pensiero di Alessio non la abbandonava mai.En: But the thought of Alessio never left her.It: Si chiedeva se potessero mai vivere insieme nella stessa città.En: She wondered if they could ever live together in the same city.It: Era sera e Alessio preparava la cena nel suo appartamento, pronto a chiamare Bianca su video.En: It was evening, and Alessio was preparing dinner in his apartment, ready to video call Bianca.It: Nel frattempo, Bianca si sistemava davanti al computer nel suo piccolo monolocale accogliente, pieno di libri e quadri.En: Meanwhile, Bianca was settling in front of her computer in her small, cozy studio filled with books and paintings.It: "Buonasera, Bianca," disse Alessio con un sorriso luminoso attraverso lo schermo.En: "Buonasera, Bianca," said Alessio with a bright smile through the screen.It: "Ciao, amore," rispose lei, con la stessa dolcezza.En: "Ciao, amore," she replied with the same sweetness.It: Si salutarono e iniziarono a parlare della giornata.En: They greeted each other and began talking about their day.It: Ma entrambi sapevano che dovevano affrontare una questione importante.En: But both knew they had to address an important issue.It: "Ho pensato molto," iniziò Alessio, "e voglio capire come possiamo stare insieme.En: "I've been thinking a lot," Alessio began, "and I want to understand how we can be together.It: Milano è la mia casa, ma senza di te mi sento incompleto."En: Milano is my home, but without you, I feel incomplete."It: Bianca sospirò, guardandolo attraverso i pixel del computer.En: Bianca sighed, looking at him through the computer pixels.It: "Anche a me manca stare con te," rispose.En: "I miss being with you too," she replied.It: "Ma qui a Roma ho il mio corso di studi e una vita costruita.En: "But here in Roma, I have my studies and a life I've built.It: È difficile lasciare tutto."En: It's hard to leave everything."It: "Lo capisco," disse Alessio, appoggiandosi alla sedia.En: "I understand," Alessio said, leaning back in his chair.It: "Amo il mio lavoro qui e ci sono molte opportunità per me, ma vorrei che fossi parte della mia vita ogni giorno."En: "I love my job here and there are many opportunities for me, but I want you to be a part of my life every day."It: La conversazione continuò, i loro cuori aperti e sinceri.En: The conversation continued, their hearts open and sincere.It: Con la connessione che a volte faceva i capricci, entrambi si ascoltarono attentamente, condividendo paure e, soprattutto, sogni per il futuro.En: With the connection sometimes acting up, they both listened attentively, sharing fears and, most importantly, dreams for the future.It: Alla fine, Alessio disse: "Forse dovremmo rivederci più spesso, dedicarci del tempo senza pensare alle distanze.En: In the end, Alessio said, "Perhaps we should see each other more often, dedicate time to us without thinking about the distances.It: Così potremmo capire meglio cosa vogliamo davvero."En: That way, we could better understand what we really want."It: Bianca annuì, il suo viso si illuminò di speranza.En: Bianca nodded, her face lighting up with hope.It: "Penso sia una buona idea.En: "I think that's a good idea.It: E poi, possiamo rivedere la situazione tra qualche mese, senza fretta."En: And then, we can reassess in a few months, without rushing."It: Decisero che il loro amore meritava questa prova e, sebbene difficile, valeva la pena lottare per esso.En: They decided that their love was worth this test and, although challenging, it was worth fighting for.It: La chiamata si concluse con la promessa di vedersi presto a metà strada.En: The call ended with the promise to meet soon halfway.It: Il sole autunnale calava lentamente su Milano, e Alessio sentì una nuova determinazione.En: The autumn sun slowly set over Milano, and Alessio felt a new determination.It: Aveva imparato che a volte, in amore, era necessario avere pazienza e trovare un equilibrio.En: He had learned that sometimes, in love, it was necessary to have patience and find balance.It: Mentre si affacciava alla finestra, vide la città in tutta la sua bellezza, pensando che forse, un passo alla volta, Milano e Roma non erano poi così lontane.En: As he looked out the window, he saw the city in all its beauty, thinking that perhaps, step by step, Milano and Roma weren't so far apart after all. Vocabulary Words:the autumn: l'autunnothe chestnuts: le caldarrostethe store windows: le vetrineglittering: scintillantito stroll: passeggiareintoxicating: inebrianteto wonder: chiedersithe pixel: il pixelthe opportunity: l'opportunitàsincere: sincerithe fear: la paurahope: speranzato rush: frettathe determination: la determinazioneto lean back: appoggiarsito dedicate: dedicarsito assess: rivederethe beauty: la bellezzathe computer: il computerto fight for: lottare perthe connection: la connessionestep by step: un passo alla voltathe test: la provaevening: serato miss: mancareto fill: riempireto attend: frequentareto get lost: perdersicozy: accoglientethe chair: la sedia

Recognized in Crain's New York Business Notable Leaders in Real Estate and ranked the #1 Real Estate Broker in New York and New York State and #14 in the Nation for 2022, Bianca currently manages a $10B real estate portfolio for Nest Seekers International. She is the star of Selling the Hamptons on HBOMax, CEO and founder of The Masters Division and Managing Director for Nest Seekers Development Marketing, and author of Mastering Intentions: 10 Practices to Amplify Your Power and Lead with Lasting Impact.Mentioned on the ShowConnect with Bianca on LinkedIn: https://www.linkedin.com/in/biancadalessioLearn more about Nest Seekers and Bianca's role in The Masters Division: https://www.nestseekers.com/agent/bianca-dalessio/Read Bianca's book Mastering Intentions: https://a.co/d/cVlGRkgTimestamps(00:00) – Introducing Bianca D'Alessio on People Business.(02:04) – Why did Bianca dedicate her book to a professor and what role did he play in her story?(06:28) – What is the book Mastering Intentions, 10 Practices to Amplify Your Power and Lead With Lasting Impact about? What do you hope people get out of the book?(08:15) – What does it mean to “always make the ask”?(12:44) – How can leaders get authentic feedback from teammates? (16:34) – Hiring: what techniques do you use to find (and qualify) teammates? (25:05) – How do you approach sales?(32:19) – How do you handle losses (not getting the sale, setbacks, failures)?(33:45) – What is your goal-setting process?(37:56) "How does that translate to the team? Because I imagine you bring somebody and you're like, I want you to hit some numbers."(41:03) – Working for sales, but not being money motivated (45:09) – How to balance “going with the flow” and directing and capturing and enhancing drive 

In questa puntata, Katia e Alessio ci portano nel mondo del Burraco, un gioco di carte molto popolare in Italia e non solo, che per molti è una vera e proprio passione!Ascoltando l'episodio, puoi migliorare: il vocabolario legato ai giochi di carte (come “mazzo”, “jolly”, “pinella”, “scale”, “combinazioni”);alcune espressioni quotidiane usate in modo spontaneo e naturale;la pronuncia e l'intonazione tipiche dell'italiano parlato;il significato più profondo di parole come “passione”, che in italiano non indica solo un interesse, ma un amore forte e coinvolgente.Inoltre, il dialogo offre spunti culturali su come gli italiani vivono il tempo libero e la socialità. Attraverso il Burraco, si parla infatti di amicizia, incontri e condivisione, aspetti fondamentali della vita in Italia.Pronto a giocare e a imparare allo stesso tempo? Allora ascolta questo episodio e scopri come una semplice partita di carte può insegnarti molto della lingua e della cultura italiana!

Oggi nella puntata della "Ruota della fortuna sprint" si gioca con Alessio, dalla Calabria

Con questo episodio si apre ufficialmente la stagione 11 di Italiano On-Air! Abbiamo scelto di iniziare con un omaggio speciale a Stefano Benni, uno degli scrittori più amati in Italia, scomparso di recente. Lo stile di Benni, capace di mescolare satira, poesia, ironia e riflessione, può essere una sfida per chi sta imparando la lingua, ma è anche una grande opportunità per chi ama la lingua italiana di immergersi in romanzi molto avvincenti. Katia e Alessio ricordano alcuni dei suoi libri più celebri e condividono citazioni indimenticabili.In questo episodio troverai espressioni come flipper, vintage e l'uso affettivo di quando hai bisogno.

Greetings Gentlefolk! The fellas are back and this time have brought along a couple of guests: Matt Gilbert and Alessio Cavatore! Join the gang as they delve into as much of 4th edition KoW news as Mantic will allow (we may have gleamed a couple of fresh tid bits).  Enjoy!   PS. The audio on Matt and Alessio's end isn't the greatest and there were lots of times it just completely dropped sound. I've tried to clean it up as best I could but just be aware some sentences are more difficult to understand... Or it could be that some info needed to be redacted and censored ;). ~Bensome

“Every single one of your setbacks and every moment of adversity is truly your advantage. You just need to figure out how to frame it for you.” – Bianca D'Alessio Today's featured international bestselling author is the #1 Real Estate Broker in NYC, the star of HBO Max's acclaimed series Selling The Hamptons, and CEO of The Masters Division & Nest Seekers International, Bianca D'Alessio. Bianca and I had a fun on a bun chat about her book, “Mastering Intentions:10 Practices to Amplify Power and Lead with Lasting Impact”, her journey from personal adversity to professional success, what it takes to turn adversity into advantage, and more!Key Things You'll Learn:Bianca's daily routine for staying spiritually groundedWhy it's important to practice and share your storyWhat her grandfather taught her about patience and problem-solvingHow she makes her affirmations stick like gorilla glue instead of waterHow her book evolved from a real estate guide to a broader message of self-empowermentThe most valuable sales skill that helped Bianca generate massive successBianca's Site: https://www.biancadalessio.com/Bianca's Book: https://a.co/d/6wsDglaThe opening track is titled, “North Wind and the Sun” by Trevin P. To listen to and download the full track, click the following link. https://compilationsforhumanity.bandcamp.com/track/north-wind-and-the-sunPlease support today's podcast to keep this content coming! CashApp: $DomBrightmonDonate on PayPal: @DBrightmonBuy Me a Coffee: https://www.buymeacoffee.com/dombrightmonGet Going North T-Shirts, Stickers, and More: https://www.teepublic.com/stores/dom-brightmonThe Going North Advancement Compass: https://a.co/d/bA9awotYou May Also Like…Ep. 309 – Home Worthy with Sandra Rinomato (@SandraRinomato): https://www.goingnorthpodcast.com/ep-309-home-worthy-with-sandra-rinomato-sandrarinomato/275 – How Thoughts Become Things with Dr. Marina Bruni (@DrMarinaBruni): https://www.goingnorthpodcast.com/275-how-thoughts-become-things-with-dr-marina-bruni-drmarinabruni/Ep. 962 – How Confusion Can Lead To Peace, Personal Growth, and Self-discovery with Giovanna Silvestre (@ConfusedGirlLA): https://www.goingnorthpodcast.com/ep-962-how-confusion-can-lead-to-peace-personal-growth-and-self-discovery-with-giovanna-silvest/Ep. 888 – How to Manifest Your Dream Home and Life with Victoria Marie Gallagher (@LOAHypnotist): https://www.goingnorthpodcast.com/ep-888-with-how-to-manifest-your-dream-home-and-life-with-victoria-marie-gallagher-loahypnotist/Ep. 983 – How Neuroscience Can Fuel Your Book & Life Success with Sara Connell (@saracconnell): https://www.goingnorthpodcast.com/saracconnell/Ep. 850 – How to Discover Your Untapped Magic with Chloe Panta (@chloepanta): https://www.goingnorthpodcast.com/ep-850-how-to-discover-your-untapped-magic-with-chloe-panta-chloepanta/Ep. 691 – How to Spark Your Heart and Ignite Your Life with Hilary DeCesare (@HilaryDeCesare): https://www.goingnorthpodcast.com/ep-691-how-to-spark-your-heart-and-ignite-your-life-with-hilary-decesare-hilarydecesare/Ep. 805 – The Full Spirit Workout with Kate Eckman (@KateEckman): https://www.goingnorthpodcast.com/ep-805-the-full-spirit-workout-with-kate-eckman-kateeckman/Ep. 509 - Exit Rich With Michelle Seiler Tucker (@MSeilerTucker): https://www.goingnorthpodcast.com/ep-509-exit-rich-with-michelle-seiler-tucker-mseilertucker/Ep. 932 – A Return to Radiance with Becca Powers: https://www.goingnorthpodcast.com/ep-932-a-return-to-radiance-with-becca-powers/Ep. 820 – How to Sculpt Your Future Through Flowdreaming with Summer McStravick (@flowdreaming): https://www.goingnorthpodcast.com/ep-820-how-to-sculpt-your-future-through-flowdreaming-with-summer-mcstravick-flowdreaming/Ep. 782 – Grab Life By the Dreams with Karin Freeland (@KarinFreeland): https://www.goingnorthpodcast.com/ep-782-grab-life-by-the-dreams-with-karin-freeland-karinfreeland/