Podcasts about Caffe

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Marco Galli presenta l'esclusivo podcast di Tutto Esaurito

Chi È Tommaso Starace: Perchè È Diventato Virale!Scopri la storia di Tommaso Starace, il magazziniere del Napoli che con passione e dedizione è diventato un'icona del calcio partenopeo. Ecco di chi si tratta!#breakingnews #ultimenotizie #notiziedelgiorno #notizie #cronaca #caffe #calcio #custode #magazziniere #maradona #moka #napoli #osimhen #simbolo #squadra #tommasostarace #virale

Joe Deuel is the guy in the back corner of Caffe Lena, huddled over a control console fiddling with the faders. And that fiddling makes the amazing sound we hear up there each night. And he's been doing that and taking pictures for five decades!

Το Discord κανάλι για να συμμετέχετε στον διαγωνισμό για ΤΡΕΙΣ ΣΥΝΔΡΟΜΕΣ TERROR 404 ΠΛΑΣ: https://discord.gg/JD54utR88DΌχι μία, όχι δύο, όχι τρεις - λογικά μέχρι τώρα πρέπει να έχετε καταλάβει που το πάμε - ΑΛΛΑ ΟΚΤΩ υποθέσεις δολοφονίας που έλαβαν χώρα κατά τη διάρκεια των Χριστουγέννων.Μπορεί όλοι μας να ανυπομονούμε για τις γιορτές (συνήθως), αλλά δυστυχώς κάποιοι συνάνθρωποί μας βρήκαν τραγικό τέλος κατά τη διάρκεια αυτών των ημερών που όλοι περιμένουμε για να ξεκουραστούμε και να περάσουμε οικογενειακές στιγμές.Είτε στην Αμερική, είτε στην Αυστραλία και την Αγγλία, κάποιοι άνθρωποι ξέχασαν τέτοιες μέρες να είναι ακριβώς αυτό... Άνθρωποι. Τις συνέπειες των πράξεων τους θα τις ακούσετε στο επεισόδιο. Αν εξαιρέσουμε αυτό, ευχόμαστε σε όλα τα τερροράκια τις ΚΑΛΥΤΕΡΕΣ γιορτές, γεμάτες καλοπέραση και όμορφες στιγμές με φίλους & οικογένεια κι αυτά που συζητάμε... μακριά απ' όλ@ μας!(00:00) - Εισαγωγή (20:19) - Intro (22:17) - 1.Οικογένεια Lawson (27:20) - 2.Το Δώρο (30:54) - 3.Συμμορία Εφήβων (38:44) - 4.Κάτω από τα Δώρα (42:54) - 5.Ω, media! Ω, media! (46:08) - 6.Bad Santa (49:10) - 7.Χριστουγεννιάτικος Εξορκισμός (50:59) - 8.Kill Bill (57:34) - 9.Κλείσιμο (01:00:02) - Συζήτηση με τη Μαρία από το Terror 404+ Click here to watch a video of this episode. -Terror 404 ΠΛΑΣ-+Μπορείτε να υποστηρίξετε την εκπομπή μας, είτε αφήνοντάς μας tips, είτε αγοράζοντας μια μηνιαία συνδρομή για το Terror 404+ στο www.ko-fi.com! Εκεί σας περιμένουν bonus επεισόδια σχεδόν κάθε εβδομάδα, πρόσβαση σε ειδικά channels στο Discord μας, κ.α.! Με την συνδρομή σας έχετε άμεσα πρόσβαση σε παραπάνω από 45 BONUS επεισόδια! ★ Support this podcast ★ Creators & Guests Lady Triggerou - Host Βασίλης Χάιντα - Host -Βρείτε μας online-+Για εύκολη πρόσβαση σε όλα μας τα links μπορείτε να μεταβείτε στο linktr.ee/terror404pod+Εγγραφείτε στο νέο μας κανάλι στο YouTube+Ελάτε κι εσείς στην παρέα μας στο Discord, όπου συζητάμε για το πιο πρόσφατο επεισόδιο, για true crime υποθέσεις & νέα, μας κάνετε προτάσεις για μελλοντικά επεισόδια, μας συστήνετε τα κατοικίδιά σας και πολλά ακόμα!+Αν περνάτε καλά ακούγοντας το Terror 404, βοηθήστε μας να μεγαλώσουμε τη μικρή μας κοινότητα! Δεν υπάρχει καλύτερος τρόπος για να το κάνετε από το να πείτε για εμάς στους αγαπημένους σας ανθρώπους που μοιράζεστε τα ίδια ενδιαφέροντα! Ακολουθήστε μας @terror404pod στο Instagram και το TikTok.+Μπορείτε να βρείτε όλα μας τα επεισόδια αλλά και τα links για όλες τις πλατφόρμες στις οποίες παίζει το Terror 404, μεταβαίνοντας στο terror404.transistor.fm------------------------ΣΥΜΠΑΡΑΓΩΓΟΙ ΕΠΕΙΣΟΔΙΟΥ:Vasiliki TouIoanna_ffPandora Elina DrakouDimitrismavrofidis

In questo episodio, Valeria Ferrero, Responsabile ESG Strategy Ersel, ospita Fabrizia Cimbali, Amministratrice Delegata di Cimbali Group, per raccontare una storia che coniuga sostenibilità, heritage e capacità di guardare al futuro. Fondata nel 1912, Cimbali Group è un simbolo autentico del made in Italy, specializzata nella progettazione e produzione di macchine professionali per caffè espresso e attrezzature dedicate alla caffetteria. Non è solo una storia di successo industriale; è un racconto che rivela come un'azienda possa evolversi, innovare e crescere restando fedele a valori ben radicati. Scopri cosa significa creare un “blend for the future” e lasciati ispirare. Resta aggiornato e buon ascolto!#comunicazionedimarketingIl presente podcast è destinato esclusivamente a scopi informativi/ di marketing non sostituendosi al prospetto informativo o ad altri documenti legali di prodotti finanziari ivi eventualmente richiamati. Nel caso, si prega di consultare il prospetto dell'OICVM/documento informativo e il documento contenente le informazioni chiave per gli investitori (KID) prima di prendere una decisione finale di investimento che può essere effettuata solo previa valutazione dell'adeguatezza del servizio o dello strumento finanziario rispetto al profilo individuato con il questionario MiFID. Solo la versione più recente del prospetto, dei regolamenti, del Documento chiave per gli investitori, delle relazioni annuali e semestrali del fondo può essere utilizzata come base per decisioni di investimento. Il presente podcast non costituisce né un'offerta né una sollecitazione all'acquisto, alla sottoscrizione o alla vendita di prodotti o strumenti finanziari o una sollecitazione all'effettuazione di investimenti. Ersel ha verificato con la massima attenzione tutte le informazioni rappresentate nel presente podcast e compiuto sforzi per garantire che il contenuto di questo podcast sia basato su informazioni e dati ottenuti da fonti affidabili, ma non garantisce della loro esattezza e completezza non assumendosi alcuna responsabilità. Ersel non si assume alcuna responsabilità circa le informazioni, le proiezioni o le opinioni contenute nel presente podcast e non risponde dell'uso che terzi potrebbero fare di tali informazioni, né di eventuali perdite o danni che possano verificarsi in seguito a tale uso. Il presente podcast può fare riferimento alla performance passata degli investimenti: i rendimenti passati non sono indicativi di quelli attuali o futuri. Le indicazioni e i dati relativi agli strumenti finanziari, forniti dalla Società, non costituiscono necessariamente un indicatore delle future prospettive dell'investimento o disinvestimento. È vietata la riproduzione e/o la distribuzione del presente podcast, non espressamente autorizzata.

Κάθε χρόνο όλο και περισσότερο βλέπουμε να αναφέρεται στην Ελλάδα το Halloween. Μια ημέρα που εμείς μπορεί να μην τη γιορτάζουμε, αλλά οφείλουμε να ανγνωρίσουμε πως έχει ένα ιδιαίτερο vibe. Αίματα, βρικόλακες, κολοκύθες και μάγισσες έχουν την τιμητική τους κάθε 31 Οκτωβρίου.Οι τελευταίες είναι που θα μας απασχολήσουν κι εμάς σήμερα. Η μαγεία και όσοι γνωρίζουν τα μυστικά της κυκλοφορούσαν πάντα αναμεσά μας, άλλες φορές ως σεβάσμια άτομα της κοινωνίας και άλλες... όπως στην Ευρώπη μετά τον Μεσαίωνα. Δηλαδή σα να μην άλλαξε τίποτα, πάλι Μεσαίωνα είχαμε. Κι αυτή τη φορά καίγαμε στην πυρά όποιο άτομο είχε την ατυχία να κατηγορηθεί από τους γείτονες ότι είχε... περίεργες ασχολίες.Φτιάξτε καφέ, ανεβείτε στη σκούπα σας (όχι) και πάμε για Terror Caffe από τα χεράκια του Βασίλη!Click here to watch a video of this episode. Μπορείτε να συνδράμετε οικονομικά για την εξόφληση της επέμβασης του Sparky εδώ:https://4fund.com/8zvj3aΒρείτε τον και στο Instagram!(00:00) - Εισαγωγή (17:12) - Intro (17:39) - Υπόθεση (01:02:19) - Κλείσιμο -Terror 404 ΠΛΑΣ-+Μπορείτε να υποστηρίξετε την εκπομπή μας, είτε αφήνοντάς μας tips, είτε αγοράζοντας μια μηνιαία συνδρομή για το Terror 404+ στο www.ko-fi.com! Εκεί σας περιμένουν bonus επεισόδια κάθε 2η εβδομάδα, πρόσβαση σε ειδικά channels στο Discord κ.α.! Με την συνδρομή σας έχετε άμεσα πρόσβαση σε 40+ bonus επεισόδια! ★ Support this podcast ★ Creators & Guests Lady Triggerou - Host Βασίλης Χάιντα - Host -Βρείτε μας online-+Για εύκολη πρόσβαση σε όλα μας τα links μπορείτε να μεταβείτε στο linktr.ee/terror404pod+Εγγραφείτε στο νέο μας κανάλι στο YouTube+Ελάτε κι εσείς στην παρέα μας στο Discord, όπου συζητάμε για το πιο πρόσφατο επεισόδιο, για true crime υποθέσεις & νέα, μας κάνετε προτάσεις για μελλοντικά επεισόδια και ό,τι άλλο θέλετε εσείς!+Αν περνάτε καλά ακούγοντας το Terror 404, βοηθήστε μας να μεγαλώσουμε τη μικρή μας κοινότητα! Δεν υπάρχει καλύτερος τρόπος για να το κάνετε από το να πείτε για εμάς στους αγαπημένους σας ανθρώπους που μοιράζεστε τα ίδια ενδιαφέροντα! Ακολουθήστε μας @terror404pod στο Instagram και το TikTok.+Μπορείτε να βρείτε όλα μας τα επεισόδια αλλά και τα links για όλες τις πλατφόρμες στις οποίες παίζει το Terror 404, μεταβαίνοντας στο terror404.transistor.fmΠΗΓΕΣ ΕΠΕΙΣΟΔΙΟΥ:https://courses.lumenlearning.com/atd-herkimer-westerncivilization/chapter/the-witch-trials/https://www.thecollector.com/european-witch-hunting/https://en.m.wikipedia.org/wiki/Witch_trials_in_the_early_modern_periodhttps://en.m.wikipedia.org/wiki/Witch_hunthttps://localhistories.org/a-history-of-the-witch-trials-in-europe/https://www.thecollector.com/early-modern-witch-hunts/https://en.m.wikipedia.org/wiki/Familiarhttps://en.m.wikipedia.org/wiki/Perrissona_Gappit_casehttps://en.m.wikipedia.org/wiki/Merga_Bienhttps://en.m.wikipedia.org/wiki/Pendle_witcheshttps://en.m.wikipedia.org/wiki/Bessie_Dunlop_of_Lynnhttps://en.m.wikipedia.org/wiki/Valais_witch_trialshttps://en.m.wikipedia.org/wiki/Trier_witch_trialshttps://en.m.wikipedia.org/wiki/Copenhagen_witch_trialshttps://en.m.wikipedia.org/wiki/Fulda_witch_trialshttps://en.m.wikipedia.org/wiki/Eichst%C3%A4tt_witch_trialshttps://en.m.wikipedia.org/wiki/W%C3%BCrzburg_witch_trialshttps://en.m.wikipedia.org/wiki/Bamberg_witch_trialshttps://en.m.wikipedia.org/wiki/North_Berwick_witch_trialshttps://en.m.wikipedia.org/wiki/Zaubererjackl_witch_trialshttps://en.m.wikipedia.org/wiki/Tors%C3%A5ker_witch_trialshttps://en.m.wikipedia.org/wiki/Doruch%C3%B3w_witch_trialΦωτογραφία Εξωφύλλου: "Witches' Sabbath" by Francisco Goya, 1797-8

Sarah Craig is the executive director of Caffe Lena, and was very excited to tell Caelan McPherson about this Folk Venue in Saratoga Springs . They chatted about the history of Caffe Lena, what makes the venue unique and what Caffe Lena has to offer other then live music.

Jill of Mark & Jill brought up the idea of doing a health clinic to help creatives in the Capital District connect with healthcare professionals and get tests, screenings, and more. The 3rd annual Clinic is Sunday, October 20th at Caffe Lena.

Popular musician/comedian Erin Harkes hosts a monthly showcase of emerging & established songwriters from the Capital Region at Caffe Lena on Wednesday night.

In today's episode, we're exploring the connection between coffee and music and how the right soundtrack can contribute to the success of a hospitality venue.How do you identify the perfect playlist? How do different genres, moods, and tempos shape the right customer experience throughout the entire week? And what are the latest trends in hospitality music?To answer these questions, we'll speak with Paul Ettinger, Business Development and Music Director, Caffè Nero, along with two experts from music curation companies - Magnus Linn, Music Consultant, Startle Music and Rey Day, Music and Communications Manager Playlister.Credits music: "The Coffee Song (I Like You A Latte)" by Nicole Zuraitis in association with The Coffee Music Project and SEB CollectiveTune into the 5THWAVE Playlist on Spotify for more music from the showSign up for our newsletter to receive the latest coffee news at worldcoffeeportal.comSubscribe to 5THWAVE on Instagram @5thWaveCoffee and tell us what topics you'd like to hear

From music at Berklee, to living in upstate, the duo Honeysuckle make a lovely folk pop sound. Hear my recent chat with Holly McGarry about their latest two singles and more.

In questa pillola di Buono a Sapersi parliamo degli effetti del caffè e di quanto rimane in circolo la caffeina. Segui la serie e recupera la puntata dedicata al CAFFE'.See omnystudio.com/listener for privacy information.

Offerta di ESCLUSIVA NORDVPN: Vai su https://nordvpn.com/dentrolastoria per acquistare NordVPN + 4 mesi Extra + 6 mesi da regalare a chi vuoi +30gg soddisfatti o rimborsati Il nostro canale Youtube: https://www.youtube.com/channel/UC1vziHBEp0gc9gAhR740fCw Sostieni DENTRO LA STORIA su Patreon: https://www.patreon.com/dentrolastoria Abbonati al canale: https://www.youtube.com/channel/UC1vziHBEp0gc9gAhR740fCw/join Il nostro store in Amazon: https://www.amazon.it/shop/dentrolastoria Sostienici su PayPal: https://paypal.me/infinitybeat Dentro La Storia lo trovi anche qui: https://linktr.ee/dentrolastoria Learn more about your ad choices. Visit megaphone.fm/adchoices

Jon Pousette-Dart has been making music since the 70's, a rich palette of sound from this singer-songwriter known for his classic folk rock sound. See him July 14 at Caffe Lena.

"I wanted to include the original field recording (unmodified) in this reimagined piece with a short composition using software instruments - I tried to aim for an operatic feel seeing as the original field recording was set in Rome, Italy, whilst attempting to complement the busy nature of the original recording. I'm not sure whether I achieved this or not, but it all seems to fit together reasonably well!" Pasticceria Regoli, Rome reimagined by Rod Dykeman.

What to say about my mate Linden? He built Caffe Dante, already a decades-old fixture in New York, into the #1 rated on the World's 50 Best Bars list, then opened Dante West Village in the teeth of the COVID pandemic, and recently opened Dante in the Maybourne Hotel in Beverly Hills.We talk about his origin story in Australia - I didn't know he was the son of a food critic - his forthcoming partnership with Neil Perry, where the fourth Dante will be located (spoiler alert: it's NY!) and a bunch more, all over a civilised martini to toast absent friends. Cheers! Get in touch with Duff!Podcast business enquiries: consulting@liquidsolutions.org (PR friends: we're only interested in having your client on if they can talk about OTHER things than their prepared speaking points or their new thing, whatever that is, for a few hours. They need to be able to hang. Oh, plus we don't edit, and we won't supply prepared or sample questions, or listener or “reach” stats, either.) Retain Philip's consulting firm, Liquid Solutions, specialised in on-trade engagement & education, brand creation and repositioning: philip@liquidsolutions.orgPhilip on Instagram: https://www.instagram.com/philipsduff/ Philip on Facebook: Philip Duff Philip on X/Twitter: Philip Duff (@philipduff) / Twitter Philip on LinkedIn: linkedin.com Old Duff Genever on Instagram: Old Duff Genever (@oldduffgenever) • Instagram photos and videos Old Duff Genever on Facebook: facebook.com Old Duff Genever on X/Twitter: https://twitter.com/oldduff...

Dwieście dwudziesty pierwszy odcinek z szybkiej serii ESPRESSO, w którym dowiesz się, dlaczego warto znać zwrot AL BANCO.Marzysz o nauce włoskiego?Sprawdź mój kurs WŁOSKI START: www.wloskistart.pl

Creating a fine dining level experience in a coffee shop is no easy task. I requires discipline, focus, and a view of coffee that assumes that it is not just a beverage but that it is truly a culinary art. This is the disposition and mission of of today's Founder Friday guest, John Piquet of Caffe d'bolla in Salt Lake City, UT. For nearly two decades, John Piquet has crafted his expertise as a coffee professional, roaster and siphon brewing specialist. As owner and operator, caffe d'bolla has won 7X Best of State, 3X Best of the Beehive, Zagat's Top 50 coffee shops in the United States. Les Roka captures John's role in a December 2022 feature article at Gastronomicslc.com. "John Piquet's journey as Salt Lake City's first artisan roaster and siphon coffee bar continues, always fine-tuning the matrix for producing an exceptional cup of coffee." John's expertise has been recognized internationally by both peers and culinary professionals and has been featured in The New York Times, Salt Lake Tribune, as well as Fox13, KSL, KUTV's Taste Utah, ChefsFeed, Sunset Magazine, Miles Away Travel Blog, and the aptly named, “100 Things to Do in Salt Lake City Before You Die.” In our conversation we discuss how John came into the world of coffee service, cafe operation, and roasting. We also explore his philosophy and discipline in focus as a one-man operator striving for the sublime in espresso, siphon coffee, and roasting.  I find John's consistent pursuits in coffee inspiring and it is obvious those who have experienced Caffe d'bolla agree.  Enjoy! Links: https://shop.caffedbolla.com/ https://www.instagram.com/caffedbolla/   Related Episodes: 401 : Founder Friday! w/ Jack Benchakul of Endorffiene in Los Angeles 293 : Thoughts on Defining Professionalism 274 : Crafting Specialty Drinks in your Shop w/ Matt Foster” 430 : Embracing “Unreasonable Hospitality” w/ Will Guidara | Thank You NYC + The Welcome Conference 447: Understanding the Business of Coffee w/ Maxwell Colonna-Dashwood     Want to run an amazing coffee shop? Hire Keys to the Shop Consulting to work with you 1:1 to transform your coffee shop operations, quality, and people. Custom consulting for your unique business. Schedule a free discovery call now! https://calendly.com/chrisdeferio/30min   Thank you to our amazing sponsors! Get the best brewer and tool for batch espresso, iced lattes, and 8 minute cold brew! www.groundcontrol.coffee   The world loves plant based beverages and baristas love the Barista Series! www.pacificfoodservice.com

Speaker CFPs and Sponsor Guides are now available for AIE World's Fair — join us on June 25-27 for the biggest AI Engineer conference of 2024!Soumith Chintala needs no introduction in the ML world — his insights are incredibly accessible across Twitter, LinkedIn, podcasts, and conference talks (in this pod we'll assume you'll have caught up on the History of PyTorch pod from last year and cover different topics). He's well known as the creator of PyTorch, but he's more broadly the Engineering Lead on AI Infra, PyTorch, and Generative AI at Meta.Soumith was one of the earliest supporters of Latent Space (and more recently AI News), and we were overjoyed to catch up with him on his latest SF visit for a braindump of the latest AI topics, reactions to some of our past guests, and why Open Source AI is personally so important to him.Life in the GPU-Rich LaneBack in January, Zuck went on Instagram to announce their GPU wealth: by the end of 2024, Meta will have 350k H100s. By adding all their GPU clusters, you'd get to 600k H100-equivalents of compute. At FP16 precision, that's ~1,200,000 PFLOPS. If we used George Hotz's (previous guest!) "Person of Compute" measure, Meta now has 60k humans of compute in their clusters. Occasionally we get glimpses into the GPU-rich life; on a recent ThursdAI chat, swyx prompted PaLM tech lead Yi Tay to write down what he missed most from Google, and he commented that UL2 20B was trained by accidentally leaving the training job running for a month, because hardware failures are so rare in Google.Meta AI's Epic LLM RunBefore Llama broke the internet, Meta released an open source LLM in May 2022, OPT-175B, which was notable for how “open” it was - right down to the logbook! They used only 16 NVIDIA V100 GPUs and Soumith agrees that, with hindsight, it was likely under-trained for its parameter size.In Feb 2023 (pre Latent Space pod), Llama was released, with a 7B version trained on 1T tokens alongside 65B and 33B versions trained on 1.4T tokens. The Llama authors included Guillaume Lample and Timothée Lacroix, who went on to start Mistral.July 2023 was Llama2 time (which we covered!): 3 model sizes, 7B, 13B, and 70B, all trained on 2T tokens. The three models accounted for a grand total of 3,311,616 GPU hours for all pre-training work. CodeLlama followed shortly after, a fine-tune of Llama2 specifically focused on code generation use cases. The family had models in the 7B, 13B, 34B, and 70B size, all trained with 500B extra tokens of code and code-related data, except for 70B which is trained on 1T.All of this on top of other open sourced models like Segment Anything (one of our early hits!), Detectron, Detectron 2, DensePose, and Seamless, and in one year, Meta transformed from a company people made fun of for its “metaverse” investments to one of the key players in the AI landscape and its stock has almost tripled since (about $830B in market value created in the past year).Why Open Source AIThe obvious question is why Meta would spend hundreds of millions on its AI efforts and then release them for free. Zuck has addressed this in public statements:But for Soumith, the motivation is even more personal:“I'm irrationally interested in open source. I think open source has that fundamental way to distribute opportunity in a way that is very powerful. Like, I grew up in India… And knowledge was very centralized, but I saw that evolution of knowledge slowly getting decentralized. And that ended up helping me learn quicker and faster for like zero dollars. And I think that was a strong reason why I ended up where I am. So like that, like the open source side of things, I always push regardless of like what I get paid for, like I think I would do that as a passion project on the side……I think at a fundamental level, the most beneficial value of open source is that you make the distribution to be very wide. It's just available with no friction and people can do transformative things in a way that's very accessible. Maybe it's open source, but it has a commercial license and I'm a student in India. I don't care about the license. I just don't even understand the license. But like the fact that I can use it and do something with it is very transformative to me……Like, okay, I again always go back to like I'm a student in India with no money. What is my accessibility to any of these closed source models? At some scale I have to pay money. That makes it a non-starter and stuff. And there's also the control issue: I strongly believe if you want human aligned AI, you want all humans to give feedback. And you want all humans to have access to that technology in the first place. And I actually have seen, living in New York, whenever I come to Silicon Valley, I see a different cultural bubble.We like the way Soumith put it last year: Closed AI “rate-limits against people's imaginations and needs”!What It Takes For Open Source AI to WinHowever Soumith doesn't think Open Source will simply win by popular demand. There is a tremendous coordination problem with the decentralized nature of the open source AI development right now: nobody is collecting the valuable human feedback in the way that OpenAI or Midjourney are doing.“Open source in general always has a coordination problem. If there's a vertically integrated provider with more resources, they will just be better coordinated than open source. And so now open source has to figure out how to have coordinated benefits. And the reason you want coordinated benefits is because these models are getting better based on human feedback. And if you see with open source models, like if you go to the /r/localllama subreddit, like there's so many variations of models that are being produced from, say, Nous research. I mean, like there's like so many variations built by so many people. And one common theme is they're all using these fine-tuning or human preferences datasets that are very limited and they're not sufficiently diverse. And you look at the other side, say front-ends like Oobabooga or like Hugging Chat or Ollama, they don't really have feedback buttons. All the people using all these front-ends, they probably want to give feedback, but there's no way for them to give feedback… So we're just losing all of this feedback. Maybe open source models are being as used as GPT is at this point in like all kinds of, in a very fragmented way, like in aggregate all the open source models together are probably being used as much as GPT is, maybe close to that. But the amount of feedback that is driving back into the open source ecosystem is like negligible, maybe less than 1% of like the usage. So I think like some, like the blueprint here I think is you'd want someone to create a sinkhole for the feedback… I think if we do that, if that actually happens, I think that probably has a real chance of the open source models having a runaway effect against OpenAI, I think like there's a clear chance we can take at truly winning open source.”If you're working on solving open source coordination, please get in touch!Show Notes* Soumith Chintala Twitter* History of PyTorch episode on Gradient Podcast* The Llama Ecosystem* Apple's MLX* Neural ODEs (Ordinary Differential Equations)* AlphaGo* LMSys arena* Dan Pink's "Drive"* Robotics projects:* Dobb-E* OK Robot* Yann LeCun* Yangqing Jia of Lepton AI* Ed Catmull* George Hotz on Latent Space* Chris Lattner on Latent Space* Guillaume Lample* Yannic Kilcher of OpenAssistant* LMSys* Alex Atallah of OpenRouter* Carlo Sferrazza's 3D tactile research* Alex Wiltschko of Osmo* Tangent by Alex Wiltschko* Lerrel Pinto - RoboticsTimestamps* [00:00:00] Introductions* [00:00:51] Extrinsic vs Intrinsic Success* [00:02:40] Importance of Open Source and Its Impact* [00:03:46] PyTorch vs TinyGrad* [00:08:33] Why PyTorch is the Switzerland of frameworks* [00:10:27] Modular's Mojo + PyTorch?* [00:13:32] PyTorch vs Apple's MLX* [00:16:27] FAIR / PyTorch Alumni* [00:18:50] How can AI inference providers differentiate?* [00:21:41] How to build good benchmarks and learnings from AnyScale's* [00:25:28] Most interesting unexplored ideas* [00:28:18] What people get wrong about synthetic data* [00:35:57] Meta AI's evolution* [00:38:42] How do you allocate 600,000 GPUs?* [00:42:05] Even the GPU Rich are GPU Poor* [00:47:31] Meta's MTIA silicon* [00:50:09] Why we need open source* [00:59:00] Open source's coordination problem for feedback gathering* [01:08:59] Beyond text generation* [01:15:37] Osmo and the Future of Smell Recognition TechnologyTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO in residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:15]: Hey, and today we have in the studio Soumith Chintala, welcome.Soumith [00:00:17]: Thanks for having me.Swyx [00:00:18]: On one of your rare visits from New York where you live. You got your start in computer vision at NYU with Yann LeCun. That was a very fortuitous start. I was actually listening to your interview on the Gradient podcast. So if people want to know more about the history of Soumith, history of PyTorch, they can go to that podcast. We won't spend that much time there, but I just was marveling at your luck, or I don't know if it's your luck or your drive to find AI early and then find the right quality mentor because I guess Yan really sort of introduced you to that world.Soumith [00:00:51]: Yeah, I think you're talking about extrinsic success, right? A lot of people just have drive to do things that they think is fun, and a lot of those things might or might not be extrinsically perceived as good and successful. I think I just happened to like something that is now one of the coolest things in the world or whatever. But if I happen, the first thing I tried to become was a 3D VFX artist, and I was really interested in doing that, but I turned out to be very bad at it. So I ended up not doing that further. But even if I was good at that, whatever, and I ended up going down that path, I probably would have been equally happy. It's just like maybe like the perception of, oh, is this person successful or not might be different. I think like after a baseline, like your happiness is probably more correlated with your intrinsic stuff.Swyx [00:01:44]: Yes. I think Dan Pink has this book on drive that I often refer to about the power of intrinsic motivation versus extrinsic and how long extrinsic lasts. It's not very long at all. But anyway, now you are an investor in Runway, so in a way you're working on VFX. Yes.Soumith [00:02:01]: I mean, in a very convoluted way.Swyx [00:02:03]: It reminds me of Ed Catmull. I don't know if you guys know, but he actually tried to become an animator in his early years and failed or didn't get accepted by Disney and then went and created Pixar and then got bought by Disney and created Toy Story. So you joined Facebook in 2014 and eventually became a creator and maintainer of PyTorch. And there's this long story there you can refer to on the gradient. I think maybe people don't know that you also involved in more sort of hardware and cluster decision affair. And we can dive into more details there because we're all about hardware this month. Yeah. And then finally, I don't know what else, like what else should people know about you on a personal side or professional side?Soumith [00:02:40]: I think open source is definitely a big passion of mine and probably forms a little bit of my identity at this point. I'm irrationally interested in open source. I think open source has that fundamental way to distribute opportunity in a way that is very powerful. Like, I grew up in India. I didn't have internet for a while. In college, actually, I didn't have internet except for GPRS or whatever. And knowledge was very centralized, but I saw that evolution of knowledge slowly getting decentralized. And that ended up helping me learn quicker and faster for zero dollars. And I think that was a strong reason why I ended up where I am. So the open source side of things, I always push regardless of what I get paid for, like I think I would do that as a passion project on the side.Swyx [00:03:35]: Yeah, that's wonderful. Well, we'll talk about the challenges as well that open source has, open models versus closed models. Maybe you want to touch a little bit on PyTorch before we move on to the sort of Meta AI in general.PyTorch vs Tinygrad tradeoffsAlessio [00:03:46]: Yeah, we kind of touched on PyTorch in a lot of episodes. So we had George Hotz from TinyGrad. He called PyTorch a CISC and TinyGrad a RISC. I would love to get your thoughts on PyTorch design direction as far as, I know you talk a lot about kind of having a happy path to start with and then making complexity hidden away but then available to the end user. One of the things that George mentioned is I think you have like 250 primitive operators in PyTorch, I think TinyGrad is four. So how do you think about some of the learnings that maybe he's going to run into that you already had in the past seven, eight years almost of running PyTorch?Soumith [00:04:24]: Yeah, I think there's different models here, but I think it's two different models that people generally start with. Either they go like, I have a grand vision and I'm going to build a giant system that achieves this grand vision and maybe one is super feature complete or whatever. Or other people say they will get incrementally ambitious, right? And they say, oh, we'll start with something simple and then we'll slowly layer out complexity in a way that optimally applies Huffman coding or whatever. Like where the density of users are and what they're using, I would want to keep it in the easy, happy path and where the more niche advanced use cases, I'll still want people to try them, but they need to take additional frictional steps. George, I think just like we started with PyTorch, George started with the incrementally ambitious thing. I remember TinyGrad used to be, like we would be limited to a thousand lines of code and I think now it's at 5,000. So I think there is no real magic to which why PyTorch has the kind of complexity. I think it's probably partly necessitated and partly because we built with the technology available under us at that time, PyTorch is like 190,000 lines of code or something at this point. I think if you had to rewrite it, we would probably think about ways to rewrite it in a vastly simplified way for sure. But a lot of that complexity comes from the fact that in a very simple, explainable way, you have memory hierarchies. You have CPU has three levels of caches and then you have DRAM and SSD and then you have network. Similarly, GPU has several levels of memory and then you have different levels of network hierarchies, NVLink plus InfiniBand or Rocky or something like that, right? And the way the flops are available on your hardware, they are available in a certain way and your computation is in a certain way and you have to retrofit your computation onto both the memory hierarchy and like the flops available. When you're doing this, it is actually a fairly hard mathematical problem to do this setup, like you find the optimal thing. And finding the optimal thing is, what is optimal depends on the input variables themselves. So like, okay, what is the shape of your input tensors and what is the operation you're trying to do and various things like that. Finding that optimal configuration and writing it down in code is not the same for every input configuration you have. Like for example, just as the shape of the tensors change, let's say you have three input tensors into a Sparstar product or something like that. The shape of each of these input tensors will vastly change how you do this optimally placing this operation onto the hardware in a way that will get you maximal throughput. So a lot of our complexity comes from writing out hundreds of configurations for each single PyTorch operator and templatizing these things and symbolically generating the final CUDA code or CPU code. There's no way to avoid it because mathematically we haven't found symbolic ways to do this that also keep compile time near zero. You can write a very simple framework, but then you also should be willing to eat the long compile time. So if searching for that optimal performance at runtime, but that's the trade off. There's no, like, I don't think unless we have great breakthroughs George's vision is achievable, he should be thinking about a narrower problem such as I'm only going to make this for work for self-driving car connets or I'm only going to make this work for LLM transformers of the llama style. Like if you start narrowing the problem down, you can make a vastly simpler framework. But if you don't, if you need the generality to power all of the AI research that is happening and keep zero compile time and in all these other factors, I think it's not easy to avoid the complexity.Pytorch vs MojoAlessio [00:08:33]: That's interesting. And we kind of touched on this with Chris Lattner when he was on the podcast. If you think about frameworks, they have the model target. They have the hardware target. They have different things to think about. He mentioned when he was at Google, TensorFlow trying to be optimized to make TPUs go brr, you know, and go as fast. I think George is trying to make especially AMD stack be better than ROCm. How come PyTorch has been such as Switzerland versus just making Meta hardware go brr?Soumith [00:09:00]: First, Meta is not in the business of selling hardware. Meta is not in the business of cloud compute. The way Meta thinks about funding PyTorch is we're funding it because it's net good for Meta to fund PyTorch because PyTorch has become a standard and a big open source project. And generally it gives us a timeline edge. It gives us leverage and all that within our own work. So why is PyTorch more of a Switzerland rather than being opinionated? I think the way we think about it is not in terms of Switzerland or not. We actually the way we articulate it to all hardware vendors and software vendors and all who come to us being we want to build a backend in core for PyTorch and ship it by default is we just only look at our user side of things. Like if users are using a particular piece of hardware, then we want to support it. We very much don't want to king make the hardware side of things. So as the MacBooks have GPUs and as that stuff started getting increasingly interesting, we pushed Apple to push some engineers and work on the NPS support and we spend significant time from Meta funded engineers on that as well because a lot of people are using the Apple GPUs and there's demand. So we kind of mostly look at it from the demand side. We never look at it from like oh which hardware should we start taking opinions on.Swyx [00:10:27]: Is there a future in which, because Mojo or Modular Mojo is kind of a superset of Python, is there a future in which PyTorch might use Mojo features optionally?Soumith [00:10:36]: I think it depends on how well integrated it is into the Python ecosystem. So if Mojo is like a pip install and it's readily available and users feel like they can use Mojo so smoothly within their workflows in a way that just is low friction, we would definitely look into that. Like in the same way PyTorch now depends on Triton, OpenAI Triton, and we never had a conversation that was like huh, that's like a dependency. Should we just build a Triton of our own or should we use Triton? It almost doesn't, like those conversations don't really come up for us. The conversations are more well does Triton have 10,000 dependencies and is it hard to install? We almost don't look at these things from a strategic leverage point of view. We look at these things from a user experience point of view, like is it easy to install? Is it smoothly integrated and does it give enough benefits for us to start depending on it? If so, yeah, we should consider it. That's how we think about it.Swyx [00:11:37]: You're inclusive by default as long as it meets the minimum bar of, yeah, but like maybe I phrased it wrongly. Maybe it's more like what problems would you look to solve that you have right now?Soumith [00:11:48]: I think it depends on what problems Mojo will be useful at.Swyx [00:11:52]: Mainly a performance pitch, some amount of cross compiling pitch.Soumith [00:11:56]: Yeah, I think the performance pitch for Mojo was like, we're going to be performant even if you have a lot of custom stuff, you're going to write arbitrary custom things and we will be performant. And that value proposition is not clear to us from the PyTorch side to consider it for PyTorch. So PyTorch, it's actually not 250 operators, it's like a thousand operators. PyTorch exposes about a thousand operators and people kind of write their ideas in the thousand operators of PyTorch. Mojo is like, well, maybe it's okay to completely sidestep those thousand operators of PyTorch and just write it in a more natural form. Just write raw Python, write for loops or whatever, right? So from the consideration of how do we intersect PyTorch with Mojo, I can see one use case where you have custom stuff for some parts of your program, but mostly it's PyTorch. And so we can probably figure out how to make it easier for say Torch.compile to smoothly also consume Mojo subgraphs and like, you know, the interoperability being actually usable, that I think is valuable. But Mojo as a fundamental front end would be replacing PyTorch, not augmenting PyTorch. So in that sense, I don't see a synergy in more deeply integrating Mojo.Pytorch vs MLXSwyx [00:13:21]: So call out to Mojo whenever they have written something in Mojo and there's some performance related thing going on. And then since you mentioned Apple, what should people think of PyTorch versus MLX?Soumith [00:13:32]: I mean, MLX is early and I know the folks well, Ani used to work at FAIR and I used to chat with him all the time. He used to be based out of New York as well. The way I think about MLX is that MLX is specialized for Apple right now. It has a happy path because it's defined its product in a narrow way. At some point MLX either says we will only be supporting Apple and we will just focus on enabling, you know, there's a framework if you use your MacBook, but once you like go server side or whatever, that's not my problem and I don't care. For MLS, it enters like the server side set of things as well. Like one of these two things will happen, right? If the first thing will happen, like MLX's overall addressable market will be small, but it probably do well within that addressable market. If it enters the second phase, they're going to run into all the same complexities that we have to deal with. They will not have any magic wand and they will have more complex work to do. They probably wouldn't be able to move as fast.Swyx [00:14:44]: Like having to deal with distributed compute?Soumith [00:14:48]: Distributed, NVIDIA and AMD GPUs, like just like having a generalization of the concept of a backend, how they treat compilation with plus overheads. Right now they're deeply assumed like the whole NPS graph thing. So they need to think about all these additional things if they end up expanding onto the server side and they'll probably build something like PyTorch as well, right? Like eventually that's where it will land. And I think there they will kind of fail on the lack of differentiation. Like it wouldn't be obvious to people why they would want to use it.Swyx [00:15:24]: I mean, there are some cloud companies offering M1 and M2 chips on servers. I feel like it might be interesting for Apple to pursue that market, but it's not their core strength.Soumith [00:15:33]: Yeah. If Apple can figure out their interconnect story, maybe, like then it can become a thing.Swyx [00:15:40]: Honestly, that's more interesting than the cars. Yes.Soumith [00:15:43]: I think the moat that NVIDIA has right now, I feel is that they have the interconnect that no one else has, like AMD GPUs are pretty good. I'm sure there's various silicon that is not bad at all, but the interconnect, like NVLink is uniquely awesome. I'm sure the other hardware providers are working on it, but-Swyx [00:16:04]: I feel like when you say it's uniquely awesome, you have some appreciation of it that the rest of us don't. I mean, the rest of us just like, you know, we hear marketing lines, but what do you mean when you say NVIDIA is very good at networking? Obviously they made the acquisition maybe like 15 years ago.Soumith [00:16:15]: Just the bandwidth it offers and the latency it offers. I mean, TPUs also have a good interconnect, but you can't buy them. So you have to go to Google to use it.PyTorch MafiaAlessio [00:16:27]: Who are some of the other FAIR PyTorch alumni that are building cool companies? I know you have Fireworks AI, Lightning AI, Lepton, and Yangqing, you knew since college when he was building Coffee?Soumith [00:16:40]: Yeah, so Yangqing and I used to be framework rivals, PyTorch, I mean, we were all a very small close-knit community back then. Caffe, Torch, Theano, Chainer, Keras, various frameworks. I mean, it used to be more like 20 frameworks. I can't remember all the names. CCV by Liu Liu, who is also based out of SF. And I would actually like, you know, one of the ways it was interesting is you went into the framework guts and saw if someone wrote their own convolution kernel or they were just copying someone else's. There were four or five convolution kernels that were unique and interesting. There was one from this guy out of Russia, I forgot the name, but I remembered who was awesome enough to have written their own kernel. And at some point there, I built out these benchmarks called ConNet benchmarks. They're just benchmarking all the convolution kernels that are available at that time. It hilariously became big enough that at that time AI was getting important, but not important enough that industrial strength players came in to do these kinds of benchmarking and standardization. Like we have MLPerf today. So a lot of the startups were using ConNet benchmarks in their pitch decks as like, oh, you know, on ConNet benchmarks, this is how we fare, so you should fund us. I remember Nirvana actually was at the top of the pack because Scott Gray wrote amazingly fast convolution kernels at that time. Very interesting, but separate times. But to answer your question, Alessio, I think mainly Lepton, Fireworks are the two most obvious ones, but I'm sure the fingerprints are a lot wider. They're just people who worked within the PyTorch Cafe2 cohort of things and now end up at various other places.Swyx [00:18:50]: I think as a, both as an investor and a people looking to build on top of their services, it's a uncomfortable slash like, I don't know what I don't know pitch. Because I've met Yang Tsing and I've met Lin Chao. Yeah, I've met these folks and they're like, you know, we are deep in the PyTorch ecosystem and we serve billions of inferences a day or whatever at Facebook and now we can do it for you. And I'm like, okay, that's great. Like, what should I be wary of or cautious of when these things happen? Because I'm like, obviously this experience is extremely powerful and valuable. I just don't know what I don't know. Like, what should people know about like these sort of new inference as a service companies?Soumith [00:19:32]: I think at that point you would be investing in them for their expertise of one kind. So if they've been at a large company, but they've been doing amazing work, you would be thinking about it as what these people bring to the table is that they're really good at like GPU programming or understanding the complexity of serving models once it hits a certain scale. You know, various expertise like from the infra and AI and GPUs point of view. What you would obviously want to figure out is whether their understanding of the external markets is clear, whether they know and understand how to think about running a business, understanding how to be disciplined about making money or, you know, various things like that.Swyx [00:20:23]: Maybe I'll put it like, actually I will de-emphasize the investing bit and just more as a potential customer. Oh, okay. Like, it's more okay, you know, you have PyTorch gods, of course. Like, what else should I know?Soumith [00:20:37]: I mean, I would not care about who's building something. If I'm trying to be a customer, I would care about whether...Swyx [00:20:44]: Benchmarks.Soumith [00:20:44]: Yeah, I use it and it's usability and reliability and speed, right?Swyx [00:20:51]: Quality as well.Soumith [00:20:51]: Yeah, if someone from some random unknown place came to me and say, user stuff is great. Like, and I have the bandwidth, I probably will give it a shot. And if it turns out to be great, like I'll just use it.Benchmark dramaSwyx [00:21:07]: Okay, great. And then maybe one more thing about benchmarks, since we already brought it up and you brought up Confident Benchmarks. There was some recent drama around AnyScale. AnyScale released their own benchmarks and obviously they look great on their own benchmarks, but maybe didn't give the other... I feel there are two lines of criticism. One, which is they didn't test some apples for apples on the kind of endpoints that the other providers, that they are competitors with, on their benchmarks and that is due diligence baseline. And then the second would be more just optimizing for the right thing. You had some commentary on it. I'll just kind of let you riff.Soumith [00:21:41]: Yeah, I mean, in summary, basically my criticism of that was AnyScale built these benchmarks for end users to just understand what they should pick, right? And that's a very good thing to do. I think what they didn't do a good job of is give that end user a full understanding of what they should pick. Like they just gave them a very narrow slice of understanding. I think they just gave them latency numbers and that's not sufficient, right? You need to understand your total cost of ownership at some reasonable scale. Not oh, one API call is one cent, but a thousand API calls are 10 cents. Like people can misprice to cheat on those benchmarks. So you want to understand, okay, like how much is it going to cost me if I actually subscribe to you and do like a million API calls a month or something? And then you want to understand the latency and reliability, not just from one call you made, but an aggregate of calls you've made over several various times of the day and times of the week. And the nature of the workloads, is it just some generic single paragraph that you're sending that is cashable? Or is it like testing of real world workload? I think that kind of rigor, like in presenting that benchmark wasn't there. It was a much more narrow sliver of what should have been a good benchmark. That was my main criticism. And I'm pretty sure if before they released it, they showed it to their other stakeholders who would be caring about this benchmark because they are present in it, they would have easily just pointed out these gaps. And I think they didn't do that and they just released it. So I think those were the two main criticisms. I think they were fair and Robert took it well.Swyx [00:23:40]: And he took it very well. And we'll have him on at some point and we'll discuss it. But I think it's important for, I think the market being maturing enough that people start caring and competing on these kinds of things means that we need to establish what best practice is because otherwise everyone's going to play dirty.Soumith [00:23:55]: Yeah, absolutely. My view of the LLM inference market in general is that it's the laundromat model. Like the margins are going to drive down towards the bare minimum. It's going to be all kinds of arbitrage between how much you can get the hardware for and then how much you sell the API and how much latency your customers are willing to let go. You need to figure out how to squeeze your margins. Like what is your unique thing here? Like I think Together and Fireworks and all these people are trying to build some faster CUDA kernels and faster, you know, hardware kernels in general. But those modes only last for a month or two. These ideas quickly propagate.Swyx [00:24:38]: Even if they're not published?Soumith [00:24:39]: Even if they're not published, the idea space is small. So even if they're not published, the discovery rate is going to be pretty high. It's not like we're talking about a combinatorial thing that is really large. You're talking about Llama style LLM models. And we're going to beat those to death on a few different hardware SKUs, right? Like it's not even we have a huge diversity of hardware you're going to aim to run it on. Now when you have such a narrow problem and you have a lot of people working on it, the rate at which these ideas are going to get figured out is going to be pretty rapid.Swyx [00:25:15]: Is it a standard bag of tricks? Like the standard one that I know of is, you know, fusing operators and-Soumith [00:25:22]: Yeah, it's the standard bag of tricks on figuring out how to improve your memory bandwidth and all that, yeah.Alessio [00:25:28]: Any ideas instead of things that are not being beaten to death that people should be paying more attention to?Novel PyTorch ApplicationsSwyx [00:25:34]: One thing I was like, you know, you have a thousand operators, right? Like what's the most interesting usage of PyTorch that you're seeing maybe outside of this little bubble?Soumith [00:25:41]: So PyTorch, it's very interesting and scary at the same time, but basically it's used in a lot of exotic ways, like from the ML angle, what kind of models are being built? And you get all the way from state-based models and all of these things to stuff nth order differentiable models, like neural ODEs and stuff like that. I think there's one set of interestingness factor from the ML side of things. And then there's the other set of interesting factor from the applications point of view. It's used in Mars Rover simulations, to drug discovery, to Tesla cars. And there's a huge diversity of applications in which it is used. So in terms of the most interesting application side of things, I think I'm scared at how many interesting things that are also very critical and really important it is used in. I think the scariest was when I went to visit CERN at some point and they said they were using PyTorch and they were using GANs at the same time for particle physics research. And I was scared more about the fact that they were using GANs than they were using PyTorch, because at that time I was a researcher focusing on GANs. But the diversity is probably the most interesting. How many different things it is being used in. I think that's the most interesting to me from the applications perspective. From the models perspective, I think I've seen a lot of them. Like the really interesting ones to me are where we're starting to combine search and symbolic stuff with differentiable models, like the whole AlphaGo style models is one example. And then I think we're attempting to do it for LLMs as well, with various reward models and search. I mean, I don't think PyTorch is being used in this, but the whole alpha geometry thing was interesting because again, it's an example of combining the symbolic models with the gradient based ones. But there are stuff like alpha geometry that PyTorch is used at, especially when you intersect biology and chemistry with ML. In those areas, you want stronger guarantees on the output. So yeah, maybe from the ML side, those things to me are very interesting right now.Swyx [00:28:03]: Yeah. People are very excited about the alpha geometry thing. And it's kind of like, for me, it's theoretical. It's great. You can solve some Olympia questions. I'm not sure how to make that bridge over into the real world applications, but I'm sure people smarter than me will figure it out.Synthetic Data vs Symbolic ModelsSoumith [00:28:18]: Let me give you an example of it. You know how the whole thing about synthetic data will be the next rage in LLMs is a thing?Swyx [00:28:27]: Already is a rage.Soumith [00:28:28]: Which I think is fairly misplaced in how people perceive it. People think synthetic data is some kind of magic wand that you wave and it's going to be amazing. Synthetic data is useful in neural networks right now because we as humans have figured out a bunch of symbolic models of the world or made up certain symbolic models because of human innate biases. So we've figured out how to ground particle physics in a 30 parameter model. And it's just very hard to compute as in it takes a lot of flops to compute, but it only has 30 parameters or so. I mean, I'm not a physics expert, but it's a very low rank model. We built mathematics as a field that basically is very low rank. Language, a deep understanding of language, like the whole syntactic parse trees and just understanding how language can be broken down and into a formal symbolism is something that we figured out. So we basically as humans have accumulated all this knowledge on these subjects, either synthetic, we created those subjects in our heads, or we grounded some real world phenomenon into a set of symbols. But we haven't figured out how to teach neural networks symbolic world models directly. The only way we have to teach them is generating a bunch of inputs and outputs and gradient dissenting over them. So in areas where we have the symbolic models and we need to teach all the knowledge we have that is better encoded in the symbolic models, what we're doing is we're generating a bunch of synthetic data, a bunch of input output pairs, and then giving that to the neural network and asking it to learn the same thing that we already have a better low rank model of in gradient descent in a much more over-parameterized way. Outside of this, like where we don't have good symbolic models, like synthetic data obviously doesn't make any sense. So synthetic data is not a magic wand where it'll work in all cases in every case or whatever. It's just where we as humans already have good symbolic models off. We need to impart that knowledge to neural networks and we figured out the synthetic data is a vehicle to impart this knowledge to. So, but people, because maybe they don't know enough about synthetic data as a notion, but they hear, you know, the next wave of data revolution is synthetic data. They think it's some kind of magic where we just create a bunch of random data somehow. They don't think about how, and then they think that's just a revolution. And I think that's maybe a gap in understanding most people have in this hype cycle.Swyx [00:31:23]: Yeah, well, it's a relatively new concept, so. Oh, there's two more that I'll put in front of you and then you can see what you respond. One is, you know, I have this joke that it's, you know, it's only synthetic data if it's from the Mistral region of France, otherwise it's just a sparkling distillation, which is what news research is doing. Like they're distilling GPT-4 by creating synthetic data from GPT-4, creating mock textbooks inspired by Phi 2 and then fine tuning open source models like Llama. And so I don't know, I mean, I think that's, should we call that synthetic data? Should we call it something else? I don't know.Soumith [00:31:57]: Yeah, I mean, the outputs of LLMs, are they synthetic data? They probably are, but I think it depends on the goal you have. If your goal is you're creating synthetic data with the goal of trying to distill GPT-4's superiority into another model, I guess you can call it synthetic data, but it also feels like disingenuous because your goal is I need to copy the behavior of GPT-4 and-Swyx [00:32:25]: It's also not just behavior, but data set. So I've often thought of this as data set washing. Like you need one model at the top of the chain, you know, unnamed French company that has that, you know, makes a model that has all the data in it that we don't know where it's from, but it's open source, hey, and then we distill from that and it's great. To be fair, they also use larger models as judges for preference ranking, right? So that is, I think, a very, very accepted use of synthetic.Soumith [00:32:53]: Correct. I think it's a very interesting time where we don't really have good social models of what is acceptable depending on how many bits of information you use from someone else, right? It's like, okay, you use one bit. Is that okay? Yeah, let's accept it to be okay. Okay, what about if you use 20 bits? Is that okay? I don't know. What if you use 200 bits? I don't think we as society have ever been in this conundrum where we have to be like, where is the boundary of copyright or where is the boundary of socially accepted understanding of copying someone else? We haven't been tested this mathematically before,Swyx [00:33:38]: in my opinion. Whether it's transformative use. Yes. So yeah, I think this New York Times opening eye case is gonna go to the Supreme Court and we'll have to decide it because I think we never had to deal with it before. And then finally, for synthetic data, the thing that I'm personally exploring is solving this great stark paradigm difference between rag and fine tuning, where you can kind of create synthetic data off of your retrieved documents and then fine tune on that. That's kind of synthetic. All you need is variation or diversity of samples for you to fine tune on. And then you can fine tune new knowledge into your model. I don't know if you've seen that as a direction for synthetic data.Soumith [00:34:13]: I think you're basically trying to, what you're doing is you're saying, well, language, I know how to parametrize language to an extent. And I need to teach my model variations of this input data so that it's resilient or invariant to language uses of that data.Swyx [00:34:32]: Yeah, it doesn't overfit on the wrong source documents.Soumith [00:34:33]: So I think that's 100% synthetic. You understand, the key is you create variations of your documents and you know how to do that because you have a symbolic model or like some implicit symbolic model of language.Swyx [00:34:48]: Okay.Alessio [00:34:49]: Do you think the issue with symbolic models is just the architecture of the language models that we're building? I think maybe the thing that people grasp is the inability of transformers to deal with numbers because of the tokenizer. Is it a fundamental issue there too? And do you see alternative architectures that will be better with symbolic understanding?Soumith [00:35:09]: I am not sure if it's a fundamental issue or not. I think we just don't understand transformers enough. I don't even mean transformers as an architecture. I mean the use of transformers today, like combining the tokenizer and transformers and the dynamics of training, when you show math heavy questions versus not. I don't have a good calibration of whether I know the answer or not. I, you know, there's common criticisms that are, you know, transformers will just fail at X. But then when you scale them up to sufficient scale, they actually don't fail at that X. I think there's this entire subfield where they're trying to figure out these answers called like the science of deep learning or something. So we'll get to know more. I don't know the answer.Meta AI and Llama 2/3Swyx [00:35:57]: Got it. Let's touch a little bit on just Meta AI and you know, stuff that's going on there. Maybe, I don't know how deeply you're personally involved in it, but you're our first guest with Meta AI, which is really fantastic. And Llama 1 was, you know, you are such a believer in open source. Llama 1 was more or less the real breakthrough in open source AI. The most interesting thing for us covering on this, in this podcast was the death of Chinchilla, as people say. Any interesting insights there around the scaling models for open source models or smaller models or whatever that design decision was when you guys were doing it?Soumith [00:36:31]: So Llama 1 was Guillaume Lample and team. There was OPT before, which I think I'm also very proud of because we bridged the gap in understanding of how complex it is to train these models to the world. Like until then, no one really in gory detail published.Swyx [00:36:50]: The logs.Soumith [00:36:51]: Yeah. Like, why is it complex? And everyone says, oh, it's complex. But no one really talked about why it's complex. I think OPT was cool.Swyx [00:37:02]: I met Susan and she's very, very outspoken. Yeah.Soumith [00:37:05]: We probably, I think, didn't train it for long enough, right? That's kind of obvious in retrospect.Swyx [00:37:12]: For a 175B. Yeah. You trained it according to Chinchilla at the time or?Soumith [00:37:17]: I can't remember the details, but I think it's a commonly held belief at this point that if we trained OPT longer, it would actually end up being better. Llama 1, I think, was Guillaume Lample and team Guillaume is fantastic and went on to build Mistral. I wasn't too involved in that side of things. So I don't know what you're asking me, which is how did they think about scaling loss and all of that? Llama 2, I was more closely involved in. I helped them a reasonable amount with their infrastructure needs and stuff. And Llama 2, I think, was more like, let's get to the evolution. At that point, we kind of understood what we were missing from the industry's understanding of LLMs. And we needed more data and we needed more to train the models for longer. And we made, I think, a few tweaks to the architecture and we scaled up more. And that was Llama 2. I think Llama 2, you can think of it as after Guillaume left, the team kind of rebuilt their muscle around Llama 2. And Hugo, I think, who's the first author is fantastic. And I think he did play a reasonable big role in Llama 1 as well.Soumith [00:38:35]: And he overlaps between Llama 1 and 2. So in Llama 3, obviously, hopefully, it'll be awesome.Alessio [00:38:42]: Just one question on Llama 2, and then we'll try and fish Llama 3 spoilers out of you. In the Llama 2 paper, the loss curves of the 34 and 70B parameter, they still seem kind of steep. Like they could go lower. How, from an infrastructure level, how do you allocate resources? Could they have just gone longer or were you just, hey, this is all the GPUs that we can burn and let's just move on to Llama 3 and then make that one better?Soumith [00:39:07]: Instead of answering specifically about that Llama 2 situation or whatever, I'll tell you how we think about things. Generally, we're, I mean, Mark really is some numbers, right?Swyx [00:39:20]: So let's cite those things again. All I remember is like 600K GPUs.Soumith [00:39:24]: That is by the end of this year and 600K H100 equivalents. With 250K H100s, including all of our other GPU or accelerator stuff, it would be 600-and-something-K aggregate capacity.Swyx [00:39:38]: That's a lot of GPUs.Soumith [00:39:39]: We'll talk about that separately. But the way we think about it is we have a train of models, right? Llama 1, 2, 3, 4. And we have a bunch of GPUs. I don't think we're short of GPUs. Like-Swyx [00:39:54]: Yeah, no, I wouldn't say so. Yeah, so it's all a matter of time.Soumith [00:39:56]: I think time is the biggest bottleneck. It's like, when do you stop training the previous one and when do you start training the next one? And how do you make those decisions? The data, do you have net new data, better clean data for the next one in a way that it's not worth really focusing on the previous one? It's just a standard iterative product. You're like, when is the iPhone 1? When do you start working on iPhone 2? Where is the iPhone? And so on, right? So mostly the considerations are time and generation, rather than GPUs, in my opinion.Alessio [00:40:31]: So one of the things with the scaling loss, like Chinchilla is optimal to balance training and inference costs. I think at Meta's scale, you would rather pay a lot more maybe at training and then save on inference. How do you think about that from infrastructure perspective? I think in your tweet, you say you can try and guess on like how we're using these GPUs. Can you just give people a bit of understanding? It's like, because I've already seen a lot of VCs say, Llama 3 has been trained on 600,000 GPUs and that's obviously not true, I'm sure. How do you allocate between the research, FAIR and the Llama training, the inference on Instagram suggestions that get me to scroll, like AI-generated stickers on WhatsApp and all of that?Soumith [00:41:11]: Yeah, we haven't talked about any of this publicly, but as a broad stroke, it's like how we would allocate resources of any other kinds at any company. You run a VC portfolio, how do you allocate your investments between different companies or whatever? You kind of make various trade-offs and you kind of decide, should I invest in this project or this other project, or how much should I invest in this project? It's very much a zero sum of trade-offs. And it also comes into play, how are your clusters configured, like overall, what you can fit of what size and what cluster and so on. So broadly, there's no magic sauce here. I mean, I think the details would add more spice, but also wouldn't add more understanding. It's just gonna be like, oh, okay, I mean, this looks like they just think about this as I would normally do.Alessio [00:42:05]: So even the GPU rich run through the same struggles of having to decide where to allocate things.Soumith [00:42:11]: Yeah, I mean, at some point I forgot who said it, but you kind of fit your models to the amount of compute you have. If you don't have enough compute, you figure out how to make do with smaller models. But no one as of today, I think would feel like they have enough compute. I don't think I've heard any company within the AI space be like, oh yeah, like we feel like we have sufficient compute and we couldn't have done better. So that conversation, I don't think I've heard from any of my friends at other companies.EleutherSwyx [00:42:47]: Stella from Eleuther sometimes says that because she has a lot of donated compute. She's trying to put it to interesting uses, but for some reason she's decided to stop making large models.Soumith [00:42:57]: I mean, that's a cool, high conviction opinion that might pay out.Swyx [00:43:01]: Why?Soumith [00:43:02]: I mean, she's taking a path that most people don't care to take about in this climate and she probably will have very differentiated ideas. I mean, think about the correlation of ideas in AI right now. It's so bad, right? So everyone's fighting for the same pie. In some weird sense, that's partly why I don't really directly work on LLMs. I used to do image models and stuff and I actually stopped doing GANs because GANs were getting so hot that I didn't have any calibration of whether my work would be useful or not because, oh yeah, someone else did the same thing you did. It's like, there's so much to do, I don't understand why I need to fight for the same pie. So I think Stella's decision is very smart.Making BetsAlessio [00:43:53]: And how do you reconcile that with how we started the discussion about intrinsic versus extrinsic kind of like accomplishment or success? How should people think about that especially when they're doing a PhD or early in their career? I think in Europe, I walked through a lot of the posters and whatnot, there seems to be mode collapse in a way in the research, a lot of people working on the same things. Is it worth for a PhD to not take a bet on something that is maybe not as interesting just because of funding and visibility and whatnot? Or yeah, what suggestions would you give?Soumith [00:44:28]: I think there's a baseline level of compatibility you need to have with the field. Basically, you need to figure out if you will get paid enough to eat, right? Like whatever reasonable normal lifestyle you want to have as a baseline. So you at least have to pick a problem within the neighborhood of fundable. Like you wouldn't wanna be doing something so obscure that people are like, I don't know, like you can work on it.Swyx [00:44:59]: Would a limit on fundability, I'm just observing something like three months of compute, right? That's the top line, that's the like max that you can spend on any one project.Soumith [00:45:09]: But like, I think that's very ill specified, like how much compute, right? I think that the notion of fundability is broader. It's more like, hey, are these family of models within the acceptable set of, you're not crazy or something, right? Even something like neural or DS, which is a very boundary pushing thing or states-based models or whatever. Like all of these things I think are still in fundable territory. When you're talking about, I'm gonna do one of the neuromorphic models and then apply image classification to them or something, then it becomes a bit questionable. Again, it depends on your motivation. Maybe if you're a neuroscientist, it actually is feasible. But if you're an AI engineer, like the audience of these podcasts, then it's more questionable. The way I think about it is, you need to figure out how you can be in the baseline level of fundability just so that you can just live. And then after that, really focus on intrinsic motivation and depends on your strengths, like how you can play to your strengths and your interests at the same time. Like I try to look at a bunch of ideas that are interesting to me, but also try to play to my strengths. I'm not gonna go work on theoretical ML. I'm interested in it, but when I want to work on something like that, I try to partner with someone who is actually a good theoretical ML person and see if I actually have any value to provide. And if they think I do, then I come in. So I think you'd want to find that intersection of ideas you like, and that also play to your strengths. And I'd go from there. Everything else, like actually finding extrinsic success and all of that, I think is the way I think about it is like somewhat immaterial. When you're talking about building ecosystems and stuff, slightly different considerations come into play, but that's a different conversation.Swyx [00:47:06]: We're gonna pivot a little bit to just talking about open source AI. But one more thing I wanted to establish for Meta is this 600K number, just kind of rounding out the discussion, that's for all Meta. So including your own inference needs, right? It's not just about training.Soumith [00:47:19]: It's gonna be the number in our data centers for all of Meta, yeah.Swyx [00:47:23]: Yeah, so there's a decent amount of workload serving Facebook and Instagram and whatever. And then is there interest in like your own hardware?MTIASoumith [00:47:31]: We already talked about our own hardware. It's called MTIA. Our own silicon, I think we've even showed the standard photograph of you holding the chip that doesn't work. Like as in the chip that you basically just get like-Swyx [00:47:51]: As a test, right?Soumith [00:47:52]: Yeah, a test chip or whatever. So we are working on our silicon and we'll probably talk more about it when the time is right, but-Swyx [00:48:00]: Like what gaps do you have that the market doesn't offer?Soumith [00:48:04]: Okay, I mean, this is easy to answer. So basically, remember how I told you about there's this memory hierarchy and like sweet spots and all of that? Fundamentally, when you build a hardware, you make it general enough that a wide set of customers and a wide set of workloads can use it effectively while trying to get the maximum level of performance they can. The more specialized you make the chip, the more hardware efficient it's going to be, the more power efficient it's gonna be, the more easier it's going to be to find the software, like the kernel's right to just map that one or two workloads to that hardware and so on. So it's pretty well understood across the industry that if you have a sufficiently large volume, enough workload, you can specialize it and get some efficiency gains, like power gains and so on. So the way you can think about everyone building, every large company building silicon, I think a bunch of the other large companies are building their own silicon as well, is they, each large company has a sufficient enough set of verticalized workloads that can be specialized that have a pattern to them that say a more generic accelerator like an NVIDIA or an AMD GPU does not exploit. So there is some level of power efficiency that you're leaving on the table by not exploiting that. And you have sufficient scale and you have sufficient forecasted stability that those workloads will exist in the same form, that it's worth spending the time to build out a chip to exploit that sweet spot. Like obviously something like this is only useful if you hit a certain scale and that your forecasted prediction of those kind of workloads being in the same kind of specializable exploitable way is true. So yeah, that's why we're building our own chips.Swyx [00:50:08]: Awesome.Open Source AIAlessio [00:50:09]: Yeah, I know we've been talking a lot on a lot of different topics and going back to open source, you had a very good tweet. You said that a single company's closed source effort rate limits against people's imaginations and needs. How do you think about all the impact that some of the Meta AI work in open source has been doing and maybe directions of the whole open source AI space?Soumith [00:50:32]: Yeah, in general, I think first, I think it's worth talking about this in terms of open and not just open source, because like with the whole notion of model weights, no one even knows what source means for these things. But just for the discussion, when I say open source, you can assume it's just I'm talking about open. And then there's the whole notion of licensing and all that, commercial, non-commercial, commercial with clauses and all that. I think at a fundamental level, the most benefited value of open source is that you make the distribution to be very wide. It's just available with no friction and people can do transformative things in a way that's very accessible. Maybe it's open source, but it has a commercial license and I'm a student in India. I don't care about the license. I just don't even understand the license. But like the fact that I can use it and do something with it is very transformative to me. Like I got this thing in a very accessible way. And then it's various degrees, right? And then if it's open source, but it's actually a commercial license, then a lot of companies are gonna benefit from gaining value that they didn't previously have, that they maybe had to pay a closed source company for it. So open source is just a very interesting tool that you can use in various ways. So there's, again, two kinds of open source. One is some large company doing a lot of work and then open sourcing it. And that kind of effort is not really feasible by say a band of volunteers doing it the same way. So there's both a capital and operational expenditure that the large company just decided to ignore and give it away to the world for some benefits of some kind. They're not as tangible as direct revenue. So in that part, Meta has been doing incredibly good things. They fund a huge amount of the PyTorch development. They've open sourced Llama and those family of models and several other fairly transformative projects. FICE is one, Segment Anything, Detectron, Detectron 2. Dense Pose. I mean, it's-Swyx [00:52:52]: Seamless. Yeah, seamless.Soumith [00:52:53]: Like it's just the list is so long that we're not gonna cover. So I think Meta comes into that category where we spend a lot of CapEx and OpEx and we have a high talent density of great AI people and we open our stuff. And the thesis for that, I remember when FAIR was started, the common thing was like, wait, why would Meta wanna start a open AI lab? Like what exactly is a benefit from a commercial perspective? And for then the thesis was very simple. It was AI is currently rate limiting Meta's ability to do things. Our ability to build various product integrations, moderation, various other factors. Like AI was the limiting factor and we just wanted AI to advance more and we didn't care if the IP of the AI was uniquely in our possession or not. However the field advances, that accelerates Meta's ability to build a better product. So we just built an open AI lab and we said, if this helps accelerate the progress of AI, that's strictly great for us. But very easy, rational, right? Still the same to a large extent with the Llama stuff. And it's the same values, but the argument, it's a bit more nuanced. And then there's a second kind of open source, which is, oh, we built this project, nights and weekends and we're very smart people and we open sourced it and then we built a community around it. This is the Linux kernel and various software projects like that. So I think about open source, like both of these things being beneficial and both of these things being different. They're different and beneficial in their own ways. The second one is really useful when there's an active arbitrage to be done. If someone's not really looking at a particular space because it's not commercially viable or whatever, like a band of volunteers can just coordinate online and do something and then make that happen. And that's great.Open Source LLMsI wanna cover a little bit about open source LLMs maybe. So open source LLMs have been very interesting because I think we were trending towards an increase in open source in AI from 2010 all the way to 2017 or something. Like where more and more pressure within the community was to open source their stuff so that their methods and stuff get adopted. And then the LLMs revolution kind of took the opposite effect OpenAI stopped open sourcing their stuff and DeepMind kind of didn't, like all the other cloud and all these other providers, they didn't open source their stuff. And it was not good in the sense that first science done in isolation probably will just form its own bubble where people believe their own b******t or whatever. So there's that problem. And then there was the other problem which was the accessibility part. Like, okay, I again always go back to I'm a student in India with no money. What is my accessibility to any of these closers models? At some scale I have to pay money. That makes it a non-starter and stuff. And there's also the control thing. I strongly believe if you want human aligned stuff, you want all humans to give feedback. And you want all humans to have access to that technology in the first place. And I actually have seen, living in New York, whenever I come to Silicon Valley, I see a different cultural bubble. Like all the friends I hang out with talk about some random thing like Dyson Spheres or whatever, that's a thing. And most of the world doesn't know or care about any of this stuff. It's definitely a bubble and bubbles can form very easily. And when you make a lot of decisions because you're in a bubble, they're probably not globally optimal decisions. So I think open source, the distribution of open source powers a certain kind of non-falsifiability that I think is very important. I think on the open source models, like it's going great in the fact that LoRa I think came out of the necessity of open source models needing to be fine-tunable in some way. Yeah, and I think DPO also came out of the academic open source side of things. So do any of the closed source labs, did any of them already have LoRa or DPO internally? Maybe, but that does not advance humanity in any way. It advances some companies probability of doing the winner takes all that I talked about earlier in the podcast.Open Source and TrustI don't know, it just feels fundamentally good. Like when people try to, you know, people are like, well, what are the ways in which it is not okay? I find most of these arguments, and this might be a little controversial, but I find a lot of arguments based on whether closed source models are safer or open source models are safer very much related to what kind of culture they grew up in, what kind of society they grew up in. If they grew up in a society that they trusted, then I think they take the closed source argument. And if they grew up in a society that they couldn't trust, where the norm was that you didn't trust your government, obviously it's corrupt or whatever, then I think the open source argument is what they take. I think there's a deep connection to like people's innate biases from their childhood and their trust in society and governmental aspects that push them towards one opinion or the other. And I'm definitely in the camp of open source is definitely going to actually have better outcomes for society. Closed source to me just means that centralization of power, which, you know, is really hard to trust. So I think it's going well

Neste episódio do Vivendo e Empreendendo, vamos conhecer uma história de empreendedorismo e superação. Foi exatamente o que ocorreu com a Karen Viegas. Ela era cozinheira e trabalhava em uma empresa que fornece serviços de alimentação - e que não vinha muito bem. Pois a Karen não apenas comprou o negócio do antigo dono, como deu uma virada na empresa, que hoje tem um café, presta serviços de catering e está ampliando com um novo empreendimento em Gravataí. É com ela, proprietária do Applause Caffe, o papo neste episódio. Saiba mais em:  https://www.instagram.com/sejavero/

Bliver du vred, euforisk, bange eller måske forundret, når du begiver dig ud på de finansielle markeder? Så er du helt normal. For selvom mange forsøger at holde sig i logikkens univers, når de investerer, kan ingen af os eliminere følelseslivet, og måske kan vi faktisk blive bedre investorer, hvis vi forstår vores følelser. Millionærklubben går helt ind i sjælen med de to bogaktuelle gæster, kognitiv neuroforsker Jon Wegener og mangeårig kommunikationsekspert i finansbranchen, Lene Nording-Groos, der i bogen “Caffe latte-reglen” beskriver, hvordan man kan bruge sin hjerne og sine følelser til at blive aktiemillionær. I studiet er aktiechef i HC Andersen Capital, Michael Friis Jørgensen, med til at udfordre perspektiverne og sætter desuden fokus på dagens aktuelle markeder og kursniveauer. Vært: Bodil Johanne Gantzel. See omnystudio.com/listener for privacy information.

Just in time for the 25th anniversary of The Sopranos, Jim welcomes Robert Funaro, Jason Cerbone, and Dan Grimaldi. The three discuss how the bond they formed working together led them to create Omerta Caffe's Sopranos-inspired coffee. They share interesting stories from the set, as well as the diverse range of roles they've had on stage and screen ever since. Tune in to hear about their lives and experiences in this fun and spirited episode.   New podcast episodes released weekly on Thursday. Follow along with the links below: Sign up for the Celebrations Chatter Newsletter: https://celebrationschatter.beehiiv.com/    Subscribe to Celebrations Chatter on YouTube: https://www.youtube.com/@celebrationschatter  Follow @CelebrationsChatter on Instagram: https://www.instagram.com/celebrationschatter/    Follow @CelebrationsChatter on Threads: https://www.threads.net/@celebrationschatter  Listen to more episodes of Celebrations Chatter on Apple Podcasts:  https://podcasts.apple.com/us/podcast/celebrations-chatter-with-jim-mccann/id1616689192    Listen to more episodes of Celebrations Chatter on Spotify: https://open.spotify.com/episode/5Yxfvb4qHGCwR5IgAmgCQX?si=ipuQC3-ATbKyqIk6RtPb-A    Listen to more episodes of Celebrations Chatter on Google Podcasts: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5saWJzeW4uY29tLzQwMzU0MS9yc3M?sa=X&ved=0CAMQ4aUDahcKEwio9KT_xJuBAxUAAAAAHQAAAAAQNg  Visit 1-800-Flowers.com: https://www.1800flowers.com/

“Everybody's so creative”!(In my Tamara Mallory voice). But no, seriously, EVERYBODY is creative, according to cultural creativity expert Genein Letford. Genein is an award winning speaker, best selling author, CEO of CAFFE strategies, Innovation Leadership Strategist, and corporate trainer on creative thinking and intercultural creativity. She believes that creative thinking is now the #1 skill needed in the workforce. In addition, she believes that creativity cannot thrive unless there is a culture of psychological safety and belonging. On this bonus episode of “Humble Beginnings”, we discuss Genein's journey from battling a speech impediment, taking on the National and global fight for creative justice, pioneering Intercultural and NueroSomatic creativity, co authoring a book with her son, and more! You can connect with Genein on Instagram @geneinletford and through her website: http://geneinletford.com/ --- Support this podcast: https://podcasters.spotify.com/pod/show/kristen-pough/support

Good Morning: Bosnian: Dobro jutro Pronunciation: DOH-bro YOO-tro Good Afternoon: Bosnian: Dobar dan Pronunciation: DOH-bar dan Good Evening: Bosnian: Dobra večer Pronunciation: DOH-bra VE-chehr How are you: Bosnian: Kako si? Pronunciation: KAH-ko see Good: Bosnian: Dobro Pronunciation: DOH-bro Not Bad: Bosnian: Nije loše Pronunciation: NEE-yeh LOH-sheh Hello: Bosnian: Zdravo Pronunciation: ZDRAH-vo How can I help you/What can I get you: Bosnian: Izvolite? Pronunciation: IZ-vo-lee-teh Can we get a table for two: Bosnian: Možemo li dobiti sto za dvoje? Pronunciation: MOH-zheh-mo lee DOH-bee-tee stoh zah DVOH-yeh Yes, you can: Bosnian: Da, može. Pronunciation: Dah, MOH-zheh. Can I have two coffees: Bosnian: Mogu li dobiti dvije kafe? Pronunciation: MOH-goo lee DOH-bee-tee DVYEH KAH-feh Can I get sugar: Bosnian: Mogu li dobiti šećer? Pronunciation: MOH-goo lee DOH-bee-tee SHEH-cher Can I get milk: Bosnian: Mogu li dobiti mlijeko? Pronunciation: MOH-goo lee DOH-bee-tee MLYEH-ko Can I get two beers: Bosnian: Mogu li dobiti dvije pive? Pronunciation: MOH-goo lee DOH-bee-tee DVYEH PEE-veh Can I get three portions of ćevapi: Bosnian: Mogu li dobiti tri porcije ćevapa? Pronunciation: MOH-goo lee DOH-bee-tee TREE POR-tsyeh CHAY-va-pa Can I get two coffees and three portions of ćevapi: Bosnian: Mogu li dobiti dvije kafe i tri porcije ćevapa? Pronunciation: MOH-goo lee DOH-bee-tee DVYEH KAH-feh ee TREE POR-tsyeh CHAY-va-pa

Your body is an instrument of thought. Top leaders understand how to connect with their body, listen to their body and control their body for optimal leadership and personal success. Today we have top thought leader and doctor in somatics, Dr. Robyn Tiger who is discussing why it is important to get reconnected to our bodies. Her work greatly aligns with CAFFE's NeuroSomatic Creativity®'s belief that we need to better link our mind, body, brain and spirit to have the best chance for a life well lived.Robyn Tiger, MD, DipABLM is a double board-certified physician in Diagnostic Radiology and Lifestyle Medicine. As founder of the wellness practice, StressFreeMD, she uniquely combines her trainings in medicine, yoga therapy, meditation & life coaching to teach others a whole person approach to elevate their overall health and happiness. Her innovative CME courses, coaching, presentations, retreats and podcast focus on the key topics of stress relief, nutrition, fitness, sleep and social connection for complete physical, mental, and emotional well-being, resilience and longevity. She is deeply passionate about successfully guiding others to become the best versions of themselves and live their healthiest most fulfilling lives!To learn more about Robyn, visit:Web: https://www.stressfreemd.net/ Web: https://www.stressfreemd.net/free-self-care-videosLinkedin: https://www.linkedin.com/in/robyntigermd/Instagram: https://www.instagram.com/stressfreemd/Facebook: https://www.facebook.com/robyn.frankel.tigerTwitter: https://twitter.com/robyntigermdTo learn more, visit:https://caffestrategies.com/Listen to more episodes on Mission Matters:https://missionmatters.com/author/genein-letford/

What is proximal Leadership? You'll find out today! Emotional intelligence affects leadership and is needed for team creativity. Kristen is a thought leader in the area of ERG support, leadership and organizational strategy in healthcare and other industries. She is also a top CAFFE facilitator in Prismatic Leadership and Intercultural Creativity. Listen in for great strategies to empower your leaders and team members for internal and external success!Kristen Isaac, MPH, Founder and CEO at Project Solved and Organizational Change Management Consultant. Kristen has nearly ten years of experience in project/program management. She has a background in business operations and process development and improvement – she has led numerous projects from conception to implementation and monitoring. Kristen is an analytical and conceptual thinker who effectively partners with stakeholders to accomplish goals. She has used these skills to develop and launch new workflows, planning and implementation of strategic plans, train teams on new technology, grow employee resource groups, impact institutional policy change, facilitate leadership development and has developed programming to support workforce development and diversification.Kristen holds a Bachelor of Science from Howard University and a Master of Public Health from Thomas Jefferson University, and most recently a Diversity and Inclusion Certificate from Cornell University. She is also a Certified Birth Doula and a Board Member for Spur Impact, an organization focused on connecting young professionals and inspiring them to get involved and make an impact in their career and community.To learn more, visit:https://caffestrategies.com/Listen to more episodes on Mission Matters:https://missionmatters.com/author/genein-letford/

Scopri miei corsi: https://bit.ly/3Z68xopScarica la trascrizione: https://bit.ly/45gxMpo

Want to help define the AI Engineer stack? Have opinions on the top tools, communities and builders? We're collaborating with friends at Amplify to launch the first State of AI Engineering survey! Please fill it out (and tell your friends)!If AI is so important, why is its software so bad?This was the motivating question for Chris Lattner as he reconnected with his product counterpart on Tensorflow, Tim Davis, and started working on a modular solution to the problem of sprawling, monolithic, fragmented platforms in AI development. They announced a $30m seed in 2022 and, following their successful double launch of Modular/Mojo

"The Sopranos" was one of the greatest TV series of all time, telling the story of Tony Soprano, his family and his mob gang. This episode features three of the great actors in the series: Robert Funaro who played Eugene Pontecorvo, Dan Grimaldi who played Patsy Parisi, and Jason Cerbone who played Jackie Aprile Jr. They talk about the series, including James Gandolfini, David Chase and Lorraine Bracco,  and about their new venture - Omerta Caffe And Cigars!---------------------------------------------The Follow Your Dream Podcast:Top 1% of all podcasts with Listeners in 200 countries!For more information and other episodes of the podcast click here. To subscribe to the podcast click here.To subscribe to our weekly Follow Your Dream Podcast email click here.To Rate and Review the podcast click here.“Dream With Robert”. Click here.—----------------------------------------“IT'S ALIVE!” is Robert's new Project Grand Slam album. Featuring 13 of the band's Greatest Hits performed “live” at festivals in Pennsylvania and Serbia.Reviews:"An instant classic!" (Melody Maker)"Amazing record...Another win for the one and only Robert Miller!" (Hollywood Digest)"Close to perfect!" (Pop Icon)"A Masterpiece!" (Big Celebrity Buzz)"Sterling effort!" (Indie Pulse)"Another fusion wonder for Project Grand Slam!" (MobYorkCity)Click here for all links.Click here for song videos—-----------------------------------------Audio production:Jimmy RavenscroftKymera Films Connect with Robert:Robert Funaro - Biography - IMDbConnect with Dan:Dan Grimaldi - Biography - IMDbConnect with Jason:Jason Cerbone - Biography - IMDbwww.caffeomerta.com Connect with the Follow Your Dream Podcast:Website - www.followyourdreampodcast.comEmail Robert - robert@followyourdreampodcast.com Follow Robert's band, Project Grand Slam, and his music:Website - www.projectgrandslam.comPGS Store - www.thePGSstore.comYouTubeSpotify MusicApple MusicEmail - pgs@projectgrandslam.com

Effectively training up an apprentice roaster is key not only to your succession, the growth of your business, and the consistency of your quality, -but providing a great learning environment and approach to education and coaching will push the whole industry forward.  The key is to not only deliver processes, rules, and systems, but also opportunity for contribution, conversation, and room for your trainee to grow in maturity.  These are just some of the powerful things discussed in this Rate of Rise episode featuring, Steve Lee! Steve Lee began his coffee career in 1996 as a barista at Peet's Coffee in the San Francisco Bay Area. After spending a number of years in the Training & Education Department of Peet's, he moved on to help open the Roasting and QC Department at Intelligentsia Coffee's Los Angeles Roasting Works, where he developed his love for the craft of roasting. Since then he has worked on a number of consultancy projects, worked as the Director of Coffee for Groundwork and the SPC Group in Korea, worked as a green importer for Odyssey Coffees, and has served as a judge for international coffee competitions. Throughout his coffee career, the main focus has been on craftsmanship, education, and fostering relationships across the supply chain for the betterment of the coffee industry as a whole. Steve spent two terms on the Roasters Guild Executive Council where he had served as the Vice Chair to the Education Committee and Vice Chair to the Events Committee, focusing on creating content for the RG Origin Trips.  Steve is currently the C.O.O. of Linea Caffe, a micro-roastery based in San Francisco, CA. In this conversation we discuss Steve's own now 25 year journey in being trained, becoming an educator, and the way he would love to see learning take place in roasteries that can deliver the most value for both the one being trained and the roasting company itself.  Links: https://www.pocketknifecoffee.com/ https://lineacaffe.com/   Related episodes: RoR Live! w/ Anne Cooper    Subscribe to Roast Magazine! https://www.roastmagazine.com/subscribe  

In this episode we will try to combine some of the words and phrases we used to chat at a caffe and order a coffee

If you live on the Westside of Los Angeles, there is a good chance you have passed by a Caffe Luxxe location. And, if you're lucky, you stopped inside and tried the coffee. Podcasts Editor Jack Garland sits down with Caffe Luxxe cofounder Gary Chau to discuss coffee, sustainable growth and navigating UCLA.

In this episode, we hear from thru-hiker founder, and soon to be triple-crowner, Cheer, as she continues her 2023 thru-hike on the Appalachian Trail! This episode covers weeks 11 and 12 on the AT through the state of Pennsylvania! Week 11 started in Pine Cove Furnace in Pennsylvania where Cheer enjoyed her best burger on trail so far! Southern Pennsylvania was pretty flat traveling through farmlands and a nice stop at Caffe 101 for lunch! Week 11 ended starting with a rough day on trail where Cheer got lost for a little bit and then took a rest staying at a nice AirBnB in Port Clinton. Week 12 begins in Northern Pennsylvania below the New Jersey border. As rocks got bigger and more prevalent, Cheer decides to pace herself a little reducing miles and taking more breaks. Good weather and views balanced things out. If you want to get the visual on this section, check out Cheer's YouTube, Week 11 and Week 12. Stay tuned for Cheer's week 13 trail update! If you loved this episode and our thru-hiker spotlights, remember to subscribe, rate & review, and share this podcast! You can also follow Cheer's adventures using the links below: ⁠⁠⁠⁠⁠⁠Cheer's YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠Cheer's Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠ Connect With Us / Join The THRU-r Community: ⁠⁠⁠⁠⁠⁠THRU-r Website⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠THRU-r Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠THRU-r Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠THRU-r Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠THRU-r TikTok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Episode Music: "Communicator" by Reed Mathis --- Send in a voice message: https://podcasters.spotify.com/pod/show/thru-r/message Support this podcast: https://podcasters.spotify.com/pod/show/thru-r/support

Can a music venue create space for civic life? Sarah Craig, Executive Director of Caffe Lena, the famous historic live music listening room, shares her insights and experiences about:-What music contributes to health, even if we don't fully understand why;-The special capacity of music to effect change, and why folk music has a particular relationship to this potential;-Her conclusion that civic engagement was a natural part of Caffe Lena's work;-The brilliant community-oriented programs that Caffe Lena has developed to fulfill their mission as a non-profit music organization.(See Episode Website link below for transcript, background info. & links)About Us Follow Us On InstagramContact Us: podcast@kinderpublic.com

Sarah Craig, Executive Director of Caffe Lena, the famous historic live music listening room, shares insights about the interdependent relationship of music and architecture at the oldest continuously operating folk music venue in the United States. We learn:-The uniquely American music type that was reinvigorated in response to over-commercialization, and influenced the development of a distinct architectural form;-5 details of Caffe Lena's interior architecture that meaningfully impact the relationship between audience members and performers;-The most terrifying question to consider when renovating a beloved historic music venue;-What the intensive building restoration taught Sarah Craig about the most important elements in the experience of live music and a thriving community.(See Episode Website link below for transcript, background info. & links)About Us Follow Us On InstagramContact Us: podcast@kinderpublic.com

Get the book, The Song of SignificanceGet the Audible version, The Song of SignificanceGet the Kindle version, The Song of SignificanceSeth Godin's WebsiteSeth Godin's Podcast, AkimboThe Carbon AlmanacOur Episode on YoutubeHolly Shannon's WebsiteZero To Podcast on AmazonHolly Shannon's new Youtube Channel, Subscribe here!Holly Shannon, InstagramHolly Shannon, LinkedinHolly Shannon, TwitterBuy Me a Coffee 

Greg in LA binge on Youtube here! Gregory Lay on InstagramGreg in LA series on InstagramEastside Cheesecakes Our Episode on YoutubeNFT episode for funding filmmakersHolly Shannon's WebsiteZero To Podcast on AmazonHolly Shannon's new Youtube Channel, Subscribe here!Holly Shannon, InstagramHolly Shannon, LinkedinHolly Shannon, TwitterBuy Me a Coffee 

Greg in LA binge on Youtube here! Sean Cunningham on InstagramLonesome Motel on InstagramOur Episode on YoutubeNFT episode for funding filmmakersHolly Shannon's WebsiteZero To Podcast on AmazonHolly Shannon's new Youtube Channel, Subscribe here!Holly Shannon, InstagramHolly Shannon, LinkedinHolly Shannon, TwitterBuy Me a Coffee 

Coco Nelson on InstagramOur Episode on YoutubeHolly Shannon's WebsiteZero To Podcast on AmazonHolly Shannon's new Youtube Channel, Subscribe here!Holly Shannon, InstagramHolly Shannon, LinkedinHolly Shannon, TwitterBuy Me a Coffee 

TikTok:  www.tiktok.com/@hiromiactsClubhouse: www.clubhouse.com/@hiromiactsIG: www.instagram.com/@hiromiactsTwitter: www.Twitter.com/hiromiactsLinkedIn: www.LinkedIn.com/hiromiokuyamaHer E-commerce LineOur Episode on YoutubeHolly Shannon's WebsiteZero To Podcast on AmazonHolly Shannon's new Youtube Channel, Subscribe here!Holly Shannon, InstagramHolly Shannon, LinkedinHolly Shannon, TwitterBuy Me a Coffee 

Jeff Gordinier on InstagramHungry: Eating, Road-Tripping, and Risking It All with the Greatest Chef in the WorldOur Episode on YoutubeHolly Shannon's WebsiteZero To Podcast on AmazonHolly Shannon's new Youtube Channel, Subscribe here!Holly Shannon, InstagramHolly Shannon, LinkedinHolly Shannon, TwitterBuy Me a Coffee 

Bizbros FacebookBizbros InstagramBizbros Linkedin Content is Profit PodcastHolly Shannon's WebsiteZero To Podcast on AmazonHolly Shannon's new Youtube Channel, Subscribe here!Holly Shannon, InstagramHolly Shannon, LinkedinHolly Shannon, TwitterBuy Me a Coffee 

Stash, My Life in Hiding (Paperback)Stash, My Life in Hiding (Audiobook)Laura Cathcart Robbins InstagramHolly Shannon's WebsiteZero To Podcast on AmazonHolly Shannon's new Youtube Channel, Subscribe here!Holly Shannon, InstagramHolly Shannon, LinkedinHolly Shannon, TwitterBuy Me a Coffee 

Classic ConversationsStampede SocialBuy Me a CoffeeHolly Shannon's WebsiteZero To Podcast on AmazonHolly Shannon's new Youtube Channel, Subscribe here!Holly Shannon, InstagramHolly Shannon, LinkedinHolly Shannon, Twitter 

Jeremy Falk's WebsiteJeremy Falk on InstagramThe Getaway, a Yoga Retreat for AllUse Promo Code COFFEECULTURE for $150 off!Www.everydaydose.comCode is “JEREMYFALK” for a free frother and 5 to-go coffees.Buy Me a CoffeeHolly Shannon's WebsiteZero To Podcast on AmazonHolly Shannon's new Youtube Channel, Subscribe here!Holly Shannon, InstagramHolly Shannon, LinkedinHolly Shannon, Twitter