Podcasts about Modal

View all cards mentioned in this episodeBy popular demand, Andy and Anthony rush back into their series exploring modal mechanics through Magic's history. Continuing where they left off, they talk about mechanics from Return to Ravnica to Fate Reforged. They argue over some mechanics fit a useful definition of “modal” and what the mechanics, and individual modal cards, offer to Cube designers.Discussed in this episode:Song-Poem DocumentaryModal Mechanics 001 — Some Nonzero Number of Mechanics Are KickerModal Mechanics 002 — Some Modal Mechanics Evoke a Strong ResponseReading Rainbow Cube100 OrnithoptersThe Design of Everyday ThingsLucky Paper PatreonIf you'd like to show your support for the show, please consider backing Lucky Paper on Patreon or leaving us a review on iTunes or wherever you listen.Check us out on Twitch and YouTube for paper Cube gameplay.You can find the hosts' Cubes on Cube Cobra:Andy's “Bun Magic” CubeAnthony's “Regular” CubeYou can find both your hosts in the MTG Cube Talk Discord. Send in questions to the show at mail@luckypaper.co or our p.o. box:Lucky PaperPO Box 4855Baltimore, MD 21211Musical production by DJ James Nasty.Timestamps0:00 - Intro3:03 - Modal Mechanics: Part 0034:44 - Scavenge9:03 - Bloodrush17:17 - Fuse27:30 - Bestow36:03 - Monstrosity37:59 - Will of the Council42:27 - Sieges48:41 - Planeswalker Addendum

View all cards mentioned in this episodeAndy and Anthony continue their roughly chronological journey through Magic's modal mechanics. From transmute, to evoke, to reinforce and everything in between they talk about every mechanic they'd categorize as “modal” from the prospective of gameplay and Cube design.But first, they have to revisit their age old question of whether or not Lightning Bolt counts as a modal spell.Discussed in this episode:Modal Mechanics 001Colin Furze on YouTubeDrive to Work PodcastTake Five Video with JaneTake Five Video with with Ryan OverturfIf you'd like to show your support for the show, please consider backing Lucky Paper on Patreon or leaving us a review on iTunes or wherever you listen.Check us out on Twitch and YouTube for paper Cube gameplay.You can find the hosts' Cubes on Cube Cobra:Andy's “Bun Magic” CubeAnthony's “Regular” CubeYou can find both your hosts in the MTG Cube Talk Discord. Send in questions to the show at mail@luckypaper.co or our p.o. box:Lucky PaperPO Box 4855Baltimore, MD 21211Musical production by DJ James Nasty.Timestamps0:00 - Intro1:04 - Main Topic, Modal Part 27:40 - Transmute15:06 - Forecast20:24 - Evoke27:59 - Planeswalkers33:54 - The Moment of Modality38:37 - Sagas vs Planeswalkers41:48 - Lorwyn Commands47:25 - Reinforce

In this episode Katrin Tomanek describes how she moved from climbing to kayaking and developed a style of multiimodal trips that combine sea kayaking and cycling to minimize driving. She shares how she learned skills, used careful planning and spreadsheets, and progressed from local trips to big expeditions. Katrin recounts standout journeys including a foggy, remote Lost Coast outing, a 52‑day mostly-solo paddle through the Inside Passage, and a counterclockwise circumnavigation of Vancouver Island that finished with a bike ride across the island. She discusses navigation in fog, wildlife encounters, food and gear choices, and how solo days and unexpected social connections shaped her experience. Enjoy today's discussion of learning  and discovery! Connect: Blog: Wildly Inside: A Visual Journey Through the Inside Passage by Susan Conrad Katrin's planning spreadsheet

Ilmu Sebagai Modal Utama Keshalihan Orang Tua merupakan kajian Islam ilmiah yang disampaikan oleh Ustadz Abu Ihsan Al-Atsaary dalam pembahasan Tarbiyah Jinsiyyah (Pendidikan Seksual Untuk Anak Dan Remaja Dalam Islam). Kajian ini disampaikan pada Selasa, 18 Jumadil Akhir 1447 H / 9 Desember 2025 M. Kajian Tentang Ilmu Sebagai Modal Utama Keshalihan Orang Tua Kita melanjutkan […] Tulisan Ilmu Sebagai Modal Utama Keshalihan Orang Tua ditampilkan di Radio Rodja 756 AM.

Ustadz Abu Ihsan Al-Maidaniy, Lc., M.A. - Bahagia Juga Perlu Modal

Gemini 3 combines language, vision, and logic seamlessly. Developers report faster and more accurate outputs. Analysts predict it will dominate AI applications.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, John and Arjun Arora from the University of Toronto present an in-depth critique of current psychological and folk theories on the meaning of life. They discuss the limitations of popular constructs such as purpose, coherence, significance, and mattering, and propose a shift towards concepts like 'orientation' and 'connectedness'. Arjun shares his personal journey from nihilism to understanding meaning, emphasizing the practical significance of this research. The discussion touches on the inadequacies of traditional semantic and personal subjective interpretations, advocating for a trans-objective perspective that integrates cognitive science, phenomenology, and wisdom cultivation. The conversation also explores the notion of deepening our connection to reality and the importance of rationality, culminating in a call for a new metaphysics of meaning that is both practically and academically robust. Arjun Arora is a cognitive science and physics scholar whose work bridges science, philosophy, and the search for meaning. As a student collaborator of Dr. John Vervaeke at the University of Toronto, Arora explores questions at the intersection of cognitive science, metaphysics, and existential psychology. His research focuses on the nature of meaning in life — how orientation, coherence, and connectedness to reality shape human flourishing. Drawing on insights from philosophy, neuroscience, and contemplative traditions, Arora examines how wisdom, rationality, and self-transcendence can counter modern nihilism and restore a deeper sense of purpose. Through his academic and public collaborations, he represents a new generation of thinkers committed to integrating rigorous science with timeless questions of meaning, being, and becoming.   The Blind Spot Meaning in Life: An Analytic Study  Charles Taylor Michael Levin   — 00:00 Welcome to the Lectern 01:16 "What makes a life worth living, even when we are faced by frustrations, failures, and our faults?" 01:30 Meet Arjun Arora 03:00 Critiquing the standard model of meaning in life 04:30 Purpose and orientation 06:00 Coherence and its challenges 08:00 Significance and mattering 11:30 Meaning in life vs. meaning of life 14:30 Worldviews and meta meaning systems 17:00 The role of affordance and adaptivity 22:00 The normativity of meaning in life 47:00 The developmental dimension of meaning in life 52:00 Exploring the connection between wisdom and meaning 52:30 Modal confusion and existential modes 53:00 The having mode vs. the being mode 53:30 The cost of modal confusion 55:00 Existential resilience and meaning in life 58:00 The normative aspect of meaning 01:04:00 The role of trust and belonging in meaning 01:08:00 The problem with the current meaning in life construct 01:12:30 The need for a new metaphysics 01:43:00 The importance of phenomenology in meaning 01:45:00 Concluding thoughts and future directions     —   The Vervaeke Foundation is committed to advancing the scientific pursuit of wisdom and creating a significant impact on the world. Become a part of our mission.    Join Awaken to Meaning to explore practices that enhance your virtues and foster deeper connections with reality and relationships.    —   Ideas, People, and Works Mentioned in this Episode Philosophical Silk Road Intersection of Neo-Platonism and Judaism Ibn Gabirol's philosophy of matter and form Fountain of Life and the concept of God Dialogical nature of reason Jewish mysticism and its influence Potentiality and actuality in Neo-Platonism Receptivity and creativity in philosophy Coupling of form and matter in existence Desire and the divine essence Logos as the "virtual engine" Purpose of mankind and knowledge Self-organization and complexity The mystery of the divine and analogy of language Ibn Gabirol (Avicebron) Sarah Pessin – scholar, Theology of Desire Zevi Slavin – host/interlocutor Andalusian thinkers Fountain of Life Selected Poems of Ibn Gabirol Theology of Desire Books and literature on Ibn Gabirol generally   —   Follow John Vervaeke: Website | Twitter | YouTube | Patreon —   Thank you for Listening!  

Este é só um trecho da aula completa da música "She" com Elvis Costello - tema do filme "Um Lugar Chamado Notting Hill", que você encontra aqui no podcast "Aprenda Inglês com Música". Use a lupa do podcast para encontrar a aula completa para ouvir ;) Quer dar aquele up no seu inglês com a Teacher Milena ?

Erik Bernhardsson is the Co-founder and CEO of Modal, building high-performance AI infrastructure.We talk about building what is essentially a new cloud provider, created from the ground up, optimized for AI. We also talk about what actually happened with the great GPU shortage, how Modal fixes the inference problem in AI, and why he thinks AI will lead to 10x more developers.He also shares lessons on culture from joining Spotify as the 40th employee, treating hiring like a prediction problem, what most people get wrong working with early customers, why more people should start companies in their 30's and 40's, and reflections on fundraising in a hot market.Thank you to Tim Chen at Essence Venture Capital and Erik's Co-founder Akshat for their help brainstorming topics for this conversation.Thank you to Meow and Hanover Park for supporting this episode.Meow: Get free bookkeeping for your startup at https://www.meow.comHanover Park: Modern, AI-native fund admin at https://www.hanoverpark.com/TurnerTimestamps:(4:26) Modal: AI-native infrastructure(9:02) Why its so hard to get GPU's(15:00) Hitting PMF with AI generated media(20:37) Competing in IOI competitions(23:09) 40th employee at Spotify(27:17) Lessons from Spotify(31:17) Starting Better[dot]com(34:05) Treating hiring like a prediction problem(36:12) Erik's favorite interview question(39:07) Sales + common design partner mistakes(42:02) Startups should solve hard problems(44:05) Evolution of Modal's product over time(50:15) Rise in importance of inference in AI(52:07) AI development post-GPU scarcity(58:51) Building a brand in dev tools(1:04:31) Fundraising from Seed to Series B(1:07:42) More 30+ year old's should start companies(1:10:00) Reducing developer tax, increasing productivity(1:20:37) Why Erik's bullish and bearish on AI(1:26:17) Bubbles, downsides to inappropriate valuations(1:34:58) High CO2 levels make you dumb(1:37:38) Difference between US and European startupsReferencedModal: https://modal.comCareers at Modal: https://jobs.ashbyhq.com/modalSuno: https://suno.comPlanet Scale: https://planetscale.comHow to hire smarter than the market: https://erikbern.com/2020/01/13/how-to-hire-smarter-than-the-market-a-toy-modelInterviewing is a noisy prediction problem: https://erikbern.com/2018/05/02/interviewing-is-a-noisy-prediction-problemCloud in 2030: https://erikbern.com/2021/11/30/storm-in-the-stratosphere-how-the-cloud-will-be-reshuffledFollow ErikTwitter: https://x.com/bernhardssonLinkedIn: https://www.linkedin.com/in/erikbernFollow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/

Modal angpao nikah untuk jualan kain kafan hingga sekarang jadi juragan kain wastra ternama, Sally Giovanny masih akan terus menguasai dunia. Kenali Sally yang melihat ide tanpa eksekusi adalah sebuah halusinasi.#cauldroncontent #andinieffendi #uncensoredwithandinieffendi #sallygiovanny #podcastindonesia #batiktrusmi #thekeranjangbali #batikdesigner #businessowner #womenpreneur #ladybatik— Uncensored bersama Andini Effendi ingin memulai percakapan mengenai isu yang kerap dianggap tabu. The Elephant in the Room adalah topik yang diketahui semua orang, namun tidak banyak yang berani membicarakannya. Dengan berdiskusi secara terbuka, kami harap masyarakat bisa lebih terbuka pikiran dan hatinya.୨♡୧ New episode drops every Thursday! ୨♡୧☆ Jangan lupa follow & Subscribe kami ☆https://www.instagram.com/cauldroncontent/https://www.youtube.com/playlist?list=PLZ3JpwVKQYqY6XA9E0ufQ6gWBL6H__NYw☆ Dengarkan juga podcast kami ☆https://open.spotify.com/show/6pHdBM4Jr0JMwBvbVCMiQI?si=cc66a009ea964c3a&nd=1&dlsi=4f0c2d2242694a34https://podcasts.apple.com/id/podcast/uncensored-with-andini-effendi/id1627192280☆ Host Andini Effendi ☆https://www.instagram.com/andinieffendi/☆ Sally Giovanny ☆https://www.instagram.com/sally.giovanny/☆ Love, Bonito ☆https://www.instagram.com/lovebonito/—Time stamp00:00 Intro01:30 Latar belakang Sally08.33 Usaha pertama: jualan kain kafan10:52 Jatuh cinta pada batik18:55 The Power of Kepepet24:10 Pelajaran dari ayah27:48 Suami, partner bisnisku32:08 Hubungan dengan mertua34:28 Pengalaman dikhianati karyawan45.34 Support UMKM50.05 Inovasi batik untuk Gen Z

Hari ini, banyak ahli kepemimpinan sepakat bahwa pengaruh sejati bukan karisma sesaat, melainkan tindakan jangka panjang yang berakar pada integritas. Kepemimpinan yang kokoh selalu dibangun di atas kepercayaan, sebuah fondasi yang perlu diuji dan dibuktikan melalui konsistensi tindakan. Kepercayaan ini menjadi modal utama seorang pemimpin. Maxwell merangkum ekspektasi pengikut melalui tiga pertanyaan: "Apakah kamu menyukaiku?," "Bisakah kamu membantuku?," dan yang paling fundamental, "Bisakah aku mempercayaimu?" Inti motivasi pemimpin haruslah pelayanan bagi pengikut, bukan sekadar kekuasaan atau keuntungan pribadi. Pemimpin yang melayani menggunakan keunggulannya untuk memastikan kemenangan tim, bukan kemenangan diri sendiri. Motif pelayanan sangat penting karena kepemimpinan yang berorientasi gelar hanya menarik individu yang belum matang secara emosional. Daniel Goleman menekankan bahwa kecerdasan emosional adalah kompetensi kunci bagi pemimpin yang efektif. Pemimpin wajib memiliki kesadaran diri yang tinggi agar mampu mengendalikan dorongan dan perilaku impulsif. Presencing (Presence dan Sensing) adalah praktik kepemimpinan transformatif yang dikembangkan oleh C. Otto Scharmer. Ini adalah kapasitas unik untuk merasakan dan mewujudkan potensi masa depan tertinggi yang ingin muncul melalui diri kita. Pemimpin yang ber-Presencing bertindak dari niat (intention) yang jernih, bukan sekadar mengulang pola pikir lama (downloading). Keaslian dimulai dari kejujuran diri, yaitu memahami sepenuhnya kekuatan dan kelemahan yang dimiliki. Meskipun demikian, Herminia Ibarra mengingatkan bahwa terlalu kaku pada satu "jati diri sejati" bisa menghambat kemauan untuk berkembang. Mengikuti saran Peter Drucker, keberhasilan maksimal hanya tercapai jika seseorang beroperasi dari kekuatan dan metode kerja alaminya. Mitos "ikuti gairah Anda" perlu dilengkapi dengan realitas bakat atau keahlian yang nyata. Jika tidak berbakat, jadikanlah gairah sebagai hobi agar kecintaan itu tidak hancur oleh kegagalan profesional. Marcus Buckingham menekankan bahwa manajer hebat berfokus pada kekuatan unik setiap karyawan untuk memaksimalkan kinerja tim secara keseluruhan. Beralih dari pemain terbaik menjadi seorang manajer adalah transisi yang sulit dan sering kali membingungkan. Linda A. Hill menjelaskan bahwa manajer baru harus menggeser fokus dari pencapaian individu ke keberhasilan yang dicapai melalui interdependensi tim. Mereka harus segera belajar mengelola konteks dan hubungan dalam lingkungan kerja secara menyeluruh. Manajer pemula sering keliru mengasumsikan bahwa kekuasaan mereka bersumber dari otoritas posisi. Padahal, pengaruh sejati lahir dari kredibilitas yang dibangun melalui karakter, kompetensi, dan keteladanan yang konsisten. Jika fondasi kepercayaan rapuh, upaya memaksakan kepatuhan pada staf berbakat hanya akan menemui kegagalan. Salah satu hambatan terbesar adalah delegasi, karena manajer pemula khawatir kehilangan kontrol atau terlihat tidak penting. Analogi "Monyet di Punggung" mengajarkan pemimpin untuk secara sadar mengembalikan inisiatif (agency) kepada staf, sehingga manajer dapat fokus pada waktu diskresionernya. Tindakan ini krusial untuk menumbuhkan akuntabilitas tim. Maxwell menegaskan bahwa promosi tidak seharusnya diberikan sebelum seseorang berhasil melatih pengganti mereka sendiri. Prinsip ini memastikan calon pemimpin memiliki kemampuan dasar untuk mengembangkan potensi orang lain. Carol Walker menyarankan agar perusahaan secara eksplisit memberikan penghargaan kepada manajer yang sukses dalam membina dan mempromosikan stafnya. Dalam komunikasi, prinsip Ramsey sangat jelas: "Menjadi tidak jelas berarti menjadi tidak baik," karena kejujuran menuntut keberanian untuk menyampaikan kebenaran. Kejelasan yang penuh kasih diperlukan agar semua orang memahami realitas, standar, dan arahan yang berlaku, mencegah ketidakjelasan menimbulkan kekacauan. Banyak insentif tradisional seperti kenaikan gaji atau fasilitas hanya menghasilkan pergerakan jangka pendek, didorong oleh imbalan eksternal. Frederick Herzberg berpendapat bahwa motivasi sejati berasal dari dalam, muncul dari rasa pencapaian, tanggung jawab, dan pekerjaan yang menantang. Pemimpin harus memperkaya peran kerja, bukan sekadar memperluas tugas, untuk mengaktifkan potensi ini. Pemimpin wajib memberikan umpan balik yang membangun secara teratur, lugas, dan tepat waktu, tidak menundanya hingga akhir tahun. Umpan balik bertujuan memberdayakan karyawan, meningkatkan keterampilan, dan mendukung jalur karier mereka. Kritik harus selalu berfokus pada perilaku yang dapat diubah dan bukan menyasar kepribadian. Kepemimpinan efektif memerlukan empati yang mendalam, secara sengaja mempertimbangkan perasaan anggota tim saat mengambil keputusan sulit. Lencioni menambahkan bahwa pemimpin harus berani "menderita lebih banyak" untuk kebaikan kolektif, siap terluka demi melindungi timnya. Pengorbanan adalah harga dari tanggung jawab kolektif. Membengkokkan sinar perhatian kembali ke diri sendiri adalah praktik sentral Presencing untuk menumbuhkan kesadaran (awareness) yang lebih luas. Pemimpin harus berlatih mendengarkan secara generatif, menangguhkan penilaian, dan membuka hati untuk merasakan dinamika sistem secara keseluruhan. Kualitas hasil kolektif berbanding lurus dengan kualitas "tanah sosial" yang ditanam oleh pemimpin. Kegagalan adalah bagian tak terhindarkan dari pertumbuhan, sebagaimana ditekankan oleh Maxwell. Respon adalah kuncinya: "Kegagalan yang Baik (Good Miss)" menghasilkan pembelajaran dan penyesuaian, sedangkan "Kegagalan yang Buruk (Bad Miss)" lahir dari penolakan tanggung jawab. Chris Argyris menyebut proses ini sebagai pembelajaran double-loop, kemampuan merefleksikan dan mengubah asumsi mendasar. Resiliensi adalah kemampuan krusial untuk bangkit dari kesulitan besar. Individu yang tangguh menunjukkan penerimaan realitas yang kuat, menolak optimisme buta yang menyesatkan. Resiliensi juga muncul dari penemuan makna di balik kesulitan, yang menciptakan jembatan harapan untuk membuat tantangan saat ini terasa tertahankan. Mengingat waktu adalah sumber daya terbatas, pemimpin efektif harus mengelola energi, bukan hanya waktu, melalui pembaruan yang sistematis. Dorongan untuk bekerja berlebihan menyebabkan kelelahan dan ketidakfokusan, merusak kinerja. Ritual pembaruan yang disengaja, seperti istirahat berkala dan nutrisi seimbang, adalah kunci untuk kinerja berkelanjutan. Transformasi sistem menuntut pemimpin melampaui ego-sistem (fokus pada diri) ke eko-sistem (fokus pada keseluruhan). Ini berarti beralih dari persaingan menuju ko-kreasi, mengintegrasikan perhatian, niat, dan tindakan pada tingkat keseluruhan, yang merupakan inti dari kepemimpinan eko-sistem Scharmer. Akhirnya, keberhasilan jangka panjang dibentuk oleh jangkar pribadi yang kuat, melampaui pencapaian profesional semata. Maxwell menegaskan definisi utamanya: dicintai dan dihormati oleh orang-orang terdekat. Investasi yang selaras dengan esensi batin dan tujuan hidup akan menjamin kebahagiaan yang mendalam dan abadi.

Interaksi sosial kini telah menjadi komoditas. Ia punya nilai tukar yang sangat fantastis. Hari ini, obrolan ringan dan sebuah sapaan bisa menghasilkan uang tunai. Dulu, hanya barang atau jasa yang bisa diperjualbelikan. Sekarang, yang dijual adalah perhatian dan keterlibatan publik. Itulah mengapa kita harus melihatnya sebagai revolusi ekonomi yang sunyi, namun mendasar. Di dunia digital, metrik interaksi adalah segalanya. Like, komentar, dan share diubah menjadi angka pendapatan. Semakin besar riak yang Anda ciptakan, semakin deras aliran uang yang datang. Ambil contoh para kreator konten di media sosial. Pekerjaan mereka adalah membangun komunitas yang loyal, bukan sekadar memamerkan hidup. Loyalitas dan kepercayaan komunitas inilah yang menjadi aset paling berharga untuk ditawarkan kepada merek. Para pembuat konten menjadi jembatan sempurna antara produk dan calon pembeli. Mereka tidak lagi sekadar beriklan, melainkan melakukan rekomendasi secara personal. Merek tahu, rekomendasi dari teman lebih mujarab daripada iklan di televisi. Monetisasi paling dasar datang dari bagi hasil iklan. Semakin banyak orang menonton, semakin banyak iklan yang tayang. Platform seperti YouTube dan TikTok membagi pendapatan ini kepada kreator berdasarkan volume interaksi yang terjadi. Model bisnis afiliasi bekerja dengan cara yang sangat cerdas. Anda cukup membagikan tautan pembelian produk di kolom komentar atau bio profil. Setiap transaksi yang berhasil dari klik tersebut akan diubah menjadi komisi yang masuk ke dompet Anda. Lalu ada skema langganan atau subscription berbayar. Audiens yang sangat terlibat bersedia membayar untuk konten eksklusif. Ini adalah bukti nyata bahwa interaksi yang intim memiliki harga jual premium. Bahkan fitur donasi atau tipping langsung menjadi umum. Penggemar memberikan hadiah virtual atau uang sebagai apresiasi saat siaran langsung. Ini menunjukkan bahwa interaksi satu arah bisa dikonversi langsung menjadi pendapatan. Tetapi monetisasi tidak hanya milik dunia digital saja. Interaksi sosial analog, atau yang kita sebut modal sosial, jauh lebih tua dan kuat. Kepercayaan yang dibangun melalui jabat tangan di dunia nyata adalah kunci pembuka pintu bisnis. Dalam lingkungan profesional, jaringan yang kuat adalah investasi tak ternilai. Koneksi baik Anda bisa mendatangkan pekerjaan, klien, atau bahkan investor baru. Modal sosial analog ini sering membuahkan proyek bernilai miliaran rupiah. Seorang konsultan menjual keahliannya melalui interaksi tatap muka. Ia menjual solusi, yang didasarkan pada kredibilitas dan reputasi yang dibangun selama bertahun-tahun. Nilai jual utama mereka adalah kualitas interaksi yang menumbuhkan keyakinan pada klien. Interaksi sosial pada dasarnya menghasilkan dua hal penting: informasi dan pengaruh.  Kedua hal ini adalah bahan bakar utama dari seluruh mekanisme monetisasi. Tanpa informasi yang relevan dan pengaruh yang kuat, interaksi tidak bernilai apa-apa. Namun, ada risiko besar di balik semua ini, yaitu objektivikasi hubungan. Ketika semua interaksi dihitung dalam mata uang, kehangatan asli hubungan itu bisa mendingin. Kita harus hati-hati agar tidak kehilangan esensi kemanusiaan dalam setiap transaksi. Pada akhirnya, konversi sosial menjadi uang adalah tentang nilai yang Anda berikan. Jualah kepercayaan, pengaruh, dan solusi, bukan sekadar selfie atau cerita kosong. Sebab, di pasar mana pun, integritas dan keaslian adalah capital tertinggi yang tak pernah tergerus inflasi.

Inizieremo la nostra rassegna di attualità con una discussione sulla necessità per l'Europa di rivedere le proprie strategie di difesa ai confini orientali di fronte all'aggressione russa. Le recenti incursioni di droni russi nello spazio aereo europeo sono considerate la minaccia più immediata per la NATO. Ci sposteremo in Svizzera, dove gli elettori hanno approvato, a stretta maggioranza, l'introduzione delle carte d'identità elettroniche (e-ID) in un referendum tenutosi domenica. La nostra sezione scientifica del programma è dedicata a uno studio pubblicato la scorsa settimana sulla rivista Science che sta ridefinendo la nostra comprensione dell'evoluzione umana. Concluderemo la prima parte della puntata con il caso del murale di Banksy, apparso sul muro dell'Alta Corte di Londra e rimosso poco dopo dalle autorità, scatenando accese polemiche.   L'argomento grammaticale di oggi è Combined Personal Pronouns with Modal and Reflexive Verbs . Ne troverete numerosi esempi nel dialogo che seguirà, ispirato ai successi italiani ai recenti Mondiali di atletica leggera disputati a Tokyo, in Giappone. Nel finale ci soffermeremo sull'espressione idiomatica di oggi: Stare/tenersi alla larga da. Il dialogo in cui la useremo prende spunto dalla notizia della scomparsa di Claudia Cardinale, uno dei volti simbolo del cinema italiano e attrice conosciuta e amata ben oltre i confini nazionali. - L'Europa rivaluta la difesa dei propri confini orientali di fronte all'aggressione russa - La Svizzera approva di misura un sistema nazionale di identificazione elettronica - Un nuovo studio suggerisce una storia evolutiva umana più complessa - Il murale di Banksy rimosso dalla facciata dell'Alta Corte di Londra - Il momento d'oro dell'atletica leggera italiana - Addio a Claudia Cardinale, leggenda del cinema italiano

Belum pernah kebobolan, sang calon juara liga musim ini, Arsenal. Melancong ke Utara Inggris untuk menghadapi Liverpool.Modal 2 gol Gyokeres nampaknya memberikan kepercayaan diri tinggi bagi The Gunners agar dapat mencuri poin di Anfield.Namun, mencuri poin dari Liverpool, di rumah mereka sendiri, bukan perkara membalikkan telapak tangan. Butuh keajaiban tersendiri untuk bisa mengalahkan kampiun Inggris musim lalu.

In this episode of Interlinks I'm joined by Howard Knott, Project Director at the Irish Exporters Association and columnist at Fleet Transport magazine—one of Ireland's most experienced commentators on freight, logistics, and trade. We unpack how recent shocks and policy shifts are reshaping Ireland–UK–EU connectivity and what that really means for shippers, carriers, and supply-chain leaders.In this episode we talk about the following:LHolyhead port's full reopening: timetable changes, capacity effects, and competitive responses (e.g., direct Ireland–EU services and CLdN's Liverpool frequencies)Landbridge vs. direct-to-continent: post-Brexit frictions, emerging UK–EU facilitation, and decision criteria for exportersStructural shift from Ro-Ro (driver-accompanied) to Lo-Lo (containerised): reliability, tracking tech, cost-to-serve, and reduced driver dependenceHGV driver shortages and how unaccompanied/container flows plus cross-border partnerships are bridging capability gapsRail freight in Ireland: new rolling stock, Waterford's intermodal traction, Limerick–Foynes reopening, current bottlenecks (drivers, terminals, Dublin rail access), and what it would take to lift modal sharePort strategy and resilience: Fishguard redevelopment, Rosslare/Waterford/Cork/Foynes roles, shore-power and alternative fuels readinessPractical new routings: Brittany Ferries + Cherbourg–Spain rail link as a viable Spain solution during peak ferry constraintsThis episode translates big shifts—Brexit recalibration, infrastructure disruptions, sustainability targets—into concrete choices: diversify routings and modes, lean into Lo-Lo with modern visibility, build continental partnerships, prepare for rail where feasible, and press for policy that prices carbon and supports rail economics. In short, it's a playbook for turning volatility into optionality—so Irish and international manufacturers can protect service levels, lower end-to-end cost and risk, and hit ESG goals without sacrificing competitiveness.Patrick Daly★ The Macro-to-Micro Strategist™╽ Turning Global Turbulence into Operational Clarity ╽ Helping Business Leaders Build Resilient, Profitable Supply Chains ╽ Multilingual Trusted Advisor ╽ Call/WhatsApp ☎️ +353868116030 ╽ Hosted on Acast. See acast.com/privacy for more information.

Anda pernah dengar mitos soal pasar modal seperti di bawah ini? ❌ Bisa untung cepat di pasar saham ❌ Hanya profesional yang bisa berhasil❌ Hanya yang modal besar yang bisa untung❌ Hanya yang pintar, cepat, dan canggih yang untung❌ Orang awam tidak bakal bisa survive, apa lagi untung di bursa❌ Hanya bandar yang mengendalikan harga saham❌ Si Anu jagoan karena dekat Anu. Kita gak bisa ngapa-ngapain.Di video edukasi terbaru ini kami akan menjelaskan apakah mitos-mitos seperti di atas benar atau salah.Penjelasan berdasarkan pengalaman kami aktif 15 tahun di bursa, baik sebagai pribadi dan periset saham, riset kinerja pengelola dana profesional, dan juga hasil pembuktian model portofolio Bolasalju.***Kinerja Bolasalju: https://www.bolasalju.com/kinerja/Riset Bolasalju: https://www.bolasalju.com/riset/

Hailing from a period in time that, to this day, continues to birth bands and artists all around the world, New York-based multi-instrumentalist, singer-songwriter, and one-half of perhaps one of the most influential bands of its time, The Holy Modal Rounders, Peter Stampfel has been creating music, and art for well over half a century. From the Rounders to the death-defying dynamics of The Fugs, and a million other lives inbetween, Stampfel's poetically prolific body of work is compelling to say the least, as the veteran musician takes us on a journey from one end of the galaxy to other end of the table in a cosmic conversation that ignites the spirit of creative change. His most recent effort, "Song Shards," carefully captures the essence of youth, eager advertisments of the past, and a friendly reminder to stop and appreciate the small things as the 46-track collection unfolds into a beautiful flower of friendliness. 

Modal is a serverless compute platform that's specifically focused on AI workloads. The company's goal is to enable AI teams to quickly spin up GPU-enabled containers, and rapidly iterate and autoscale. It was founded by Erik Bernhardsson who was previously at Spotify for 7 years where he built the music recommendation system and the popular The post Modal and Scaling AI Inference with Erik Bernhardsson appeared first on Software Engineering Daily.

Modal is a serverless compute platform that’s specifically focused on AI workloads. The company's goal is to enable AI teams to quickly spin up GPU-enabled containers, and rapidly iterate and autoscale. It was founded by Erik Bernhardsson who was previously at Spotify for 7 years where he built the music recommendation system and the popular The post Modal and Scaling AI Inference with Erik Bernhardsson appeared first on Software Engineering Daily.

En Centrafrique, la course de pirogues est un sport sacré chez les riverains, pratiqué en grande pompe dans les quartiers et villages situés au bord de la rivière Oubangui, au sud-ouest de la capitale Bangui. La course de pirogues permet généralement aux différentes communautés de se confronter pacifiquement sur l'eau. Chaque année, une compétition est organisée sur la rivière Oubangui pour commémorer le 1er décembre, la fête nationale du pays. De notre envoyé spécial à Modalé, Au village de Modalé, à 30 km de la capitale de Centrafrique, dans la Lobaye, le chef organise de temps en temps des courses pour sélectionner les meilleurs piroguiers en vue de représenter le village. Aux abords du quai de Lakéré, 10 pirogues en bois sont sur la ligne de départ. Chaque embarcation est composée de 12 rameurs, dont trois remplaçants.  Les équipes n'ont pas de gilets de sauvetage, mais se distinguent par leur maillot. Quand le top est donné, les pirogues s'élancent à vive allure, cadencées par des coups de pagaies. « La course de pirogue représente notre identité remarquable. Notre village a déjà enregistré 15 victoires aux compétitions nationales. Nous sommes nés et avons grandi au bord de l'eau. C'est pour dire que la course relève de notre domaine. Cette course locale est importante afin de préparer notre équipe aux compétitions à venir. Nous nous préparons depuis trois mois », explique avec enthousiasme Constant Charbardo, l'un des organisateurs. Les équipes doivent parcourir 500 mètres avant d'arriver au point de départ où les vainqueurs seront distingués. La compétition se déroule sous les regards attentifs d'une centaine de spectateurs curieux. Manuella Tondo est l'une d'entre eux. « La course de pirogue, c'est un vrai moment de bonheur et de partage. Il faut être là pour savourer ce qui se passe. Cela me donne aussi envie de participer. Regardez vous-même comment les gens sont mobilisés. C'est une source de cohésion, car presque tous les habitants du village sont là », se réjouit-elle. Les piroguiers sont encouragés par moult chants et acclamations. Finalement, c'est l'équipe des Martyrs de Modalé qui a remporté la manche. Âgée d'une trentaine d'années, Malcom Dagbia, l'un des piroguiers, mesure 1,95 mètre. Ses coéquipiers le surnomment Goliath. « À la base, je suis un pêcheur et c'est ce qui fait ma force. Les mots me manquent pour décrire ce que je ressens. Cette victoire représente le fruit de notre dur labeur. C'est un sport très technique et physique. On s'est bien entraîné et chacun de nous est conscient de son rôle », déclare-t-il, ému et encore essoufflé. Dans ce village, la plupart des piroguiers sont des pêcheurs. Bien que son équipe occupe la dernière place, Hector Nadji est sûr d'être sélectionné : « On fait cette course pour la fierté de notre village. Je félicite les équipes qui ont occupé les meilleures places, elles ont fait un bon départ. J'ai une équipe jeune et la plupart ne savent pas encore bien nager, c'est pourquoi elles ont ramé avec prudence. Mais ces jeunes représentent l'avenir de notre village. Je suis un guerrier, j'espère que les organisateurs vont me sélectionner. » Selon la direction des sports traditionnels, une fédération dédiée aux courses de pirogues sera créée cette année pour permettre à la Centrafrique de participer aux compétitions internationales. À lire aussiSports traditionnels: le festival Êlê, plus qu'une course de pirogues en Côte d'Ivoire [3/10]

Un brevísimo resumen sobre los modos griegos, la diferencia con los modos gregorianos, la importancia del surgimiento y auge de la armonía tonal y la forma en que recomiendo abordar el estudio del intercambio modal. Todo esto lo relaciono en esta historia "basada en hechos reales".

Please visit answersincme.com/VJW860 to participate, download slides and supporting materials, complete the post test, and obtain credit. In this activity, experts in cutaneous oncology discuss the role of emerging immunotherapeutic strategies in treating resectable cutaneous squamous cell carcinoma (CSCC). Upon completion of this activity, participants should be better able to: Review the current guideline-recommended use of immunotherapies for the management of resectable CSCC; Identify clinical factors that will guide the use of immunotherapeutic approaches for patients with resectable CSCC; Outline proactive strategies to enhance the benefit-to-risk profile of immunotherapy for patients with resectable CSCC; and Describe the evolving role of novel applications of immunotherapy in the treatment of resectable CSCC. This activity is intended for US healthcare professionals only.

In this episode of Radio Free Urbanism, the hosts discuss Vancouver's recent decision to lower speed limits on local streets to improve safety for pedestrians and cyclists. They explore the importance of traffic calming measures, such as modal filters, in creating safer urban environments. The conversation emphasizes the need for community engagement in urban planning and the role of public transit in enhancing mobility.Send us a question: radiofreeurbanism@gmail.comPatreon: patreon.com/RadioFreeUrbanism Sully: https://www.youtube.com/@SullyvilleEthan: https://www.youtube.com/@climateandtransitNic: https://www.youtube.com/@nicthedoor

Please visit answersincme.com/VJW860 to participate, download slides and supporting materials, complete the post test, and obtain credit. In this activity, experts in cutaneous oncology discuss the role of emerging immunotherapeutic strategies in treating resectable cutaneous squamous cell carcinoma (CSCC). Upon completion of this activity, participants should be better able to: Review the current guideline-recommended use of immunotherapies for the management of resectable CSCC; Identify clinical factors that will guide the use of immunotherapeutic approaches for patients with resectable CSCC; Outline proactive strategies to enhance the benefit-to-risk profile of immunotherapy for patients with resectable CSCC; and Describe the evolving role of novel applications of immunotherapy in the treatment of resectable CSCC. This activity is intended for US healthcare professionals only.

Read this Question of the Week Here: https://www.reasonablefaith.org/writings/question-answer/a-new-modal-argument-from-contingency

In this episode, the Creativity Department is joined by NAEA's President-Elect Amy Pfeiler-Wunder and her colleague Mark Kate Bergh for a discussion about the importance of play and multi-modal learning! explore topics such as: how multi-modal learning takes shape in an art classroom; what role play takes in nurturing students creativity and engagement; and how art educators can implement multi-modal learning in their own art rooms? Listen in to hear more from Amy and Mary Kate!

פרק מספר 496 של רברס עם פלטפורמה - באמפרס מספר 86: רן, דותן ואלון באולפן הוירטואלי (באמצעות Riverside.fm - תודה!) עם סדרה של קצרצרים שתפסו את תשומת הלב בתקופה האחרונה (והפעם קצת יותר) - בלוגים מעניינים, דברים מ- GitHub או Twitter וכל מיני דברים שראינו, לפני שהכל יתמלא ב-AI.

View all cards mentioned in this episodeAndy and Anthony walk through (some of) the history of modal mechanics: cards that offer players choices between multiple effects. Starting with alpha they look at the different manifestations of modality, how they compare and contrast, and how they impact gameplay and fit into their Cube design philosophy.Discussed in this episode:An 11 hour game of commander ends in a draw and irrecoverable footageRhymeZoneNeologismEpisode: 243: In which Parker shares his criticism of CyclingCheck us out on Twitch and YouTube for paper Cube gameplay.You can find the hosts' Cubes on Cube Cobra:Andy's “Bun Magic” CubeAnthony's “Regular” CubeYou can find both your hosts in the MTG Cube Talk Discord. Send in questions to the show at mail@luckypaper.co or our p.o. box:Lucky PaperPO Box 4855Baltimore, MD 21211If you'd like to show your support for the show, please leave us a review on iTunes or wherever you listen.Musical production by DJ James Nasty.Timestamps0:00 - Intro2:44 - Introduction to main topic5:40 - What is a modal card?8:15 - The history of modal effects9:36 - Charms, Commands, and Confluences22:41 - Bloomburrow's Pawprint Mechanic24:42 - Cycling33:31 - Kicker38:05 - Split Cards43:47 - Morph47:38 - Entwine49:36 - Overload53:35 - Channel

Wusstet ihr, dass jede 2. Frau einen Tchibo-BH zu Hause hat? Zumindest statistisch gesehen. In Teil 3 unserer Miniserie "Arbeiten im Kaffeeparadies" steht uns Wäscheexpertin Barbara Rede und Antwort: Sie hat seit 19 Jahren ihren Traumjob bei Tchibo als Produktmanagerin für Damen Lingerie und Nightwear. Mit ihr tauchen wir tief ein in die Welt der Materialien, die wir ganz nah auf der Haut tragen: Baumwolle, Microfaser, Modal, Viskose - Was sind die Unterschiede und welches Material ist das Richtige für euch? Barbara verrät, worauf es ankommt. Und wie schafft Tchibo es, dass Spitze nicht kratzt? Was haben die Italiener damit zu tun? …Auch dieses Geheimnis lüften wir in der Folge. BH-Größen sind für euch ein großes Mysterium? Wir entwirren das Chaos der Kreuzgrößen und erklären, warum erschreckende 70-80 % der Frauen einen BH tragen, der nicht richtig sitzt. Eine Zahl, der Tchibo den Kampf ansagt: Barbara erzählt vom integrierten Größenrechner im Tchibo Online Shop, mit dem alle die perfekte Größe finden können. Zeit für Veränderung! Ob im Nachthemd, Pyjama, Spaghetti Top oder nackt, auch nachts soll es gemütlich sein. Welche Rolle spielt Nicki dabei und gibt es überhaupt noch Frottee-Schlafanzüge? Wie ihr euch am besten bettet und warum Podcast-Moderator Ralf Podszus nicht mit Smoking ins Bett geht? All das und noch viel mehr erfahrt ihr in unserer neuen Podcastfolge. Barbaras Job klingt für euch spannend? Dann schaut euch alle aktuellen Jobmöglichkeiten bei uns auf der Tchibo Karriereseite an: https://www.tchibo-karriere.de/.

In this episode of the AI in Business Podcast, Brett Kiley, Executive Director of Customer Experience and Client Solutions at CVS Health, joins us for a compelling discussion on the evolving landscape of customer interactions. With over two decades of experience, Brett shares how CVS navigates the challenges of personalization, efficiency, and seamless service across their retail, pharmacy, and health services divisions. To access Emerj's frameworks for AI readiness, ROI, and strategy, visit Emerj Plus at emerj.com/p1. 

We are calling for the world's best AI Engineer talks for AI Architects, /r/localLlama, Model Context Protocol (MCP), GraphRAG, AI in Action, Evals, Agent Reliability, Reasoning and RL, Retrieval/Search/RecSys , Security, Infrastructure, Generative Media, AI Design & Novel AI UX, AI Product Management, Autonomy, Robotics, and Embodied Agents, Computer-Using Agents (CUA), SWE Agents, Vibe Coding, Voice, Sales/Support Agents at AIEWF 2025! Fill out the 2025 State of AI Eng survey for $250 in Amazon cards and see you from Jun 3-5 in SF!Coreweave's now-successful IPO has led to a lot of questions about the GPU Neocloud market, which Dylan Patel has written extensively about on SemiAnalysis. Understanding markets requires an interesting mix of technical and financial expertise, so this will be a different kind of episode than our usual LS domain.When we first published $2 H100s: How the GPU Rental Bubble Burst, we got 2 kinds of reactions on Hacker News:* “Ah, now the AI bubble is imploding!”* “Duh, this is how it works in every GPU cycle, are you new here?”We don't think either reaction is quite right. Specifically, it is not normal for the prices of one of the world's most important resources right now to swing from $1 to $8 per hour based on drastically inelastic demand AND supply curves - from 3 year lock-in contracts to stupendously competitive over-ordering dynamics for NVIDIA allocations — especially with increasing baseline compute needed for even the simplest academic ML research and for new AI startups getting off the ground.We're fortunate today to have Evan Conrad, CEO of SFCompute, one of the most exciting GPU marketplace startups, talk us through his theory of the economics of GPU markets, and why he thinks CoreWeave and Modal are well positioned, but Digital Ocean and Together are not.However, more broadly, the entire point of SFC is creating liquidity between GPU owners and consumers and making it broadly tradable, even programmable:As we explore, these are the primitives that you can then use to create your own, high quality, custom GPU availability for your time and money budget, similar to how Amazon Spot Instances automated the selective buying of unused compute.The ultimate end state of where all this is going is GPU that trade like other perishable, staple commodities of the world - oil, soybeans, milk. Because the contracts and markets are so well established, the price swings also are not nearly as drastic, and people can also start hedging and managing the risk of one of the biggest costs of their business, just like we have risk-managed commodities risks of all other sorts for centuries. As a former derivatives trader, you can bet that swyx doubleclicked on that…Show Notes* SF Compute* Evan Conrad* Ethan Anderson* John Phamous* The Curve talk* CoreWeave* Andromeda ClusterFull Video PodLike and subscribe!Timestamps* [00:00:05] Introductions* [00:00:12] Introduction of guest Evan Conrad from SF Compute* [00:00:12] CoreWeave Business Model Discussion* [00:05:37] CoreWeave as a Real Estate Business* [00:08:59] Interest Rate Risk and GPU Market Strategy Framework* [00:16:33] Why Together and DigitalOcean will lose money on their clusters* [00:20:37] SF Compute's AI Lab Origins* [00:25:49] Utilization Rates and Benefits of SF Compute Market Model* [00:30:00] H100 GPU Glut, Supply Chain Issues, and Future Demand Forecast* [00:34:00] P2P GPU networks* [00:36:50] Customer stories* [00:38:23] VC-Provided GPU Clusters and Credit Risk Arbitrage* [00:41:58] Market Pricing Dynamics and Preemptible GPU Pricing Model* [00:48:00] Future Plans for Financialization?* [00:52:59] Cluster auditing and quality control* [00:58:00] Futures Contracts for GPUs* [01:01:20] Branding and Aesthetic Choices Behind SF Compute* [01:06:30] Lessons from Previous Startups* [01:09:07] Hiring at SF ComputeTranscriptAlessio [00:00:05]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:12]: Hey, and today we're so excited to be finally in the studio with Evan Conrad from SF Compute. Welcome. I've been fortunate enough to be your friend before you were famous, and also we've hung out at various social things. So it's really cool to see that SF Compute is coming into its own thing, and it's a significant presence, at least in the San Francisco community, which of course, it's in the name, so you couldn't help but be. Evan: Indeed, indeed. I think we have a long way to go, but yeah, thanks. Swyx: Of course, yeah. One way I was thinking about kicking on this conversation is we will likely release this right after CoreWeave IPO. And I was watching, I was looking, doing some research on you. You did a talk at The Curve. I think I may have been viewer number 70. It was a great talk. More people should go see it, Evan Conrad at The Curve. But we have like three orders of magnitude more people. And I just wanted to, to highlight, like, what is your analysis of what CoreWeave did that went so right for them? Evan: Sell locked-in long-term contracts and don't really do much short-term at all. I think like a lot of people had this assumption that GPUs would work a lot like CPUs and the like standard business model of any sort of CPU cloud is you buy commodity hardware, then you lay on services that are mostly software, and that gives you high margins and pretty much all your value comes from those services. Not really the underlying. Compute in any capacity and because it's commodity hardware and it's not actually that expensive, most of that can be sort of on-demand compute. And while you do want locked-in contracts for folks, it's mostly just a sort of de-risk situation. It helps you plan revenue because you don't know if people are going to scale up or down. But fundamentally, people are like buying hourly and that's how your business is structured and you make 50 percent margins or higher. This like doesn't really work in GPUs. And the reason why it doesn't work is because you end up with like super price sensitive customers. And that isn't because necessarily it's just way more expensive, though that's totally the case. So in a CPU cloud, you might have like, you know, let's say if you had a million dollars of hardware in GPUs, you have a billion dollars of hardware. And so your customers are buying at much higher volumes than you otherwise expect. And it's also smaller customers who are buying at higher amounts of volume. So relative to what they're spending in general. But in GPUs in particular, your customer cares about the scaling law. So if you take like Gusto, for example, or Rippling or an HR service like this, when they're buying from an AWS or a GCP, they're buying CPUs and they're running web servers, those web servers, they kind of buy up to the capacity that they need, they buy enough, like CPUs, and then they don't buy any more, like, they don't buy any more at all. Yeah, you have a chart that goes like this and then flat. Correct. And it's like a complete flat. It's not even like an incremental tiny amount. It's not like you could just like turn on some more nodes. Yeah. And then suddenly, you know, they would make an incremental amount of money more, like Gusto isn't going to make like, you know, 5% more money, they're gonna make zero, like literally zero money from every incremental GPU or CPU after a certain point. This is not the case for anyone who is training models. And it's not the case for anyone who's doing test time inference or like inference that has scales at test time. Because like you, your scaling laws mean that you may have some diminishing returns, but there's always returns. Adding GPUs always means your model does actually get. And that actually does translate into revenue for you. And then for test time inference, you actually can just like run the inference longer and get a better performance. Or maybe you can run more customers faster and then charge for that. It actually does translate into revenue. Every incremental GPU translates to revenue. And what that means from the customer's perspective is you've got like a flat budget and you're trying to max the amount of GPUs you have for that budget. And it's very distinctly different than like where Augusto or Rippling might think, where they think, oh, we need this amount of CPUs. How do we, you know, reduce that? How do we reduce our amount of money that we're spending on this to get the same amount of CPUs? What that translates to is customers who are spending in really high volume, but also customers who are super price sensitive, who don't give a s**t. Can I swear on this? Can I swear? Yeah. Who don't give a s**t at all about your software. Because a 10% difference in a billion dollars of hardware is like $100 million of value for you. So if you have a 10% margin increase because you have great software, on your billion, the customers are that price sensitive. They will immediately switch off if they can. Because why wouldn't you? You would just take that $100 million. You'd spend $50 million on hiring a software engineering team to replicate anything that you possibly did. So that means that the best way to make money in GPUs was to do basically exactly what CoreWeave did, which is go out and sign only long-term contracts, pretty much ignore the bottom end of the market completely, and then maximize your long-term contracts. With customers who don't have credit risk, who won't sue you, or are unlikely to sue you for frivolous reasons. And then because they don't have credit risk and they won't sue you for frivolous reasons, you can go back to your lender and you can say, look, this is a really low risk situation for us to do. You should give me prime, prime interest rate. You should give me the lowest cost of capital you possibly can. And when you do that, you just make tons of money. The problem that I think lots of people are going to talk about with CoreWeave is it doesn't really look like a cloud platform. It doesn't really look like a cloud provider financially. It also doesn't really look like a software company financially.Swyx [00:05:37]: It's a bank.Evan [00:05:38]: It's a bank. It's a real estate company. And it's very hard to not be that. The problem of that that people have tricked themselves into is thinking that CoreWeave is a bad business. I don't think CoreWeave is explicitly a bad business. There's a bunch of people, there's kind of like two versions of the CoreWeave take at the moment. There's, oh my God, CoreWeave, amazing. CoreWeave is this great new cloud provider competitive with the hyperscalers. And to some extent, this is true from a structural perspective. Like, they are indeed a real sort of thing against the cloud providers in this particular category. And the other take is, oh my gosh, CoreWeave is this horrible business and so on and blah, blah, blah. And I think it's just like a set of perception or perspective. If you think CoreWeave's business is supposed to look like the traditional cloud providers, you're going to be really upset to learn that GPUs don't look like that at all. And in fact, for the hyperscalers, it doesn't look like this either. My intuition is that the hyperscalers are probably going to lose a lot of money, and they know they're going to lose a lot of money on reselling NVIDIA GPUs, at least. Hyperscalers, but I want to, Microsoft, AWS, Google. Correct, yeah. The Microsoft, AWS, and Google. Does Google resell? I mean, Google has TPUs. Google has TPUs, but I think you can also get H100s and so on. But there are like two ways they can make money. One is by selling to small customers who aren't actually buying in any serious volume. They're testing around, they're playing around. And if they get big, they're immediately going to do one of two things. They're going to ask you for a discount. Because they're not going to pay your crazy sort of margin that you have locked into your business. Because for CPUs, you need that. They're going to pay your massive per hour price. And so they want you to sign a long-term contract. And so that's your other way that you can make money, is you can basically do exactly what CoreWeave does, which is have them pay as much as possible upfront and lock in the contract for a long time. Or you can have small customers. But the problem is that for a hyperscaler, the GPUs to... To sell on the low margins relative to what your other business, your CPUs are, is a worse business than what you are currently doing. Because you could have spent the same money on those GPUs. And you could have trained model and you could have made a model on top of it and then turn that into a product and had high margins from your product. Or you could have taken that same money and you could have competed with NVIDIA. And you could have cut into their margin instead. But just simply reselling NVIDIA GPUs doesn't work like your CPU business. Where you're able to capture high margins from big customers and so on. And then they never leave you because your customers aren't actually price sensitive. And so they won't switch off if your prices are a little higher. You actually had a really nice chart, again, on that talk of this two by two. Sure. Of like where you want to be. And you also had some hot takes on who's making money and who isn't. Swyx: So CoreUv locked up long-term contracts. Get that. Yes. Maybe share your mental framework. Just verbally describe it because we're trying to help the audio listeners as well. Sure. People can look up the chart if they want to. Evan: Sure. Okay. So this is a graph of interest rates. And on the y-axis, it's a probability you're able to sell your GPUs from zero to one. And on the x-axis, it's how much they'll depreciate in cost from zero to one. And then you had ISO cost curves or ISO interest rate curves. Yeah. So they kind of shape in a sort of concave fashion. Yeah. The lowest interest rates enable the most aggressive. form of this cost curve. And the higher interest rates go, the more you have to push out to the top right. Yeah. And then you had some analysis of where every player sits in this, including CoreUv, but also Together and Modal and all these other guys. I thought that was super insightful. So I just wanted to elaborate. Basically, it's like a graph of risk and the genres of places where you can be and what the risk is associated with that. The optimal thing for you to do, if you can, is to lock in long-term contracts that are paid all up front or in with a situation in which you trust the other party to pay you over time. So if you're, you know, selling to Microsoft or something or OpenAI. Which are together 77% of the revenue of CoreUv. Yeah. So if you're doing that, that's a great business to be in because your interest rate that you can pitch for is really low because no one thinks Microsoft is going to default. And like maybe OpenAI will default, but the backing by Microsoft kind of doesn't. And I think there's enough, like, generally, it looks like OpenAI is winning that you can make it's just a much better case than if you're selling to the pre-seed startup that just raised $30 million or something pre-revenue. It's like way easier to make the case that the OpenAI is not going to default than the pre-seed startup. And so the optimal place to be is selling to the maximally low risk customer for as long as possible. And then you never have to worry about depreciation and you make lots of money. The less. Good. Good place to be is you could sell long-term contracts to people who might default on you. And then if you're not bringing it to the present, so you're not like saying, hey, you have to pay us all up front, then you're in this like more risky territory. So is it top left of the chart? If I have the chart right, maybe. Large contracts paid over time. Yeah. Large contracts paid over time is like top left. So it's more risky, but you could still probably get away with it. And then the other opportunity is that you could sell short-term contracts for really high prices. And so lots of people tried that too, because this is actually closer to the original business model that people thought would work in cloud providers for CPUs. It works for CPUs, but it doesn't really work for GPUs. And I don't think people were trying this because they were thinking about the risk associated with it. I think a lot of people are just come from a software background, have not really thought about like cogs or margins or inventory risk or things that you have to worry about in the physical world. And I think they were just like copy pasting the same business model onto CPUs. And also, I remember fundraising like a few years ago. And I know based on. Like what we knew other people were saying who were in a very similar business to us versus what we were saying. And we know that our pitch was way worse at the time, because in the beginning of SF Compute, we looked very similar to pretty much every other GPU cloud, not on purpose, but sort of accidentally. And I know that the correct pitch to give to an investor was we will look like a traditional CPU cloud with high margins and we'll sell to everyone. And that is a bad business model because your customers are price sensitive. And so what happens is if you. Sell at high prices, which is the price that you would need to sell it in order to de-risk your loss on the depreciation curve, and specifically what I mean by that is like, let's say you're selling it like $5 an hour and you're paying $1.50 an hour for the GPU under the hood. It's a little bit different than that, but you know, nice numbers, $5 an hour, $1.50 an hour. Great. Excellent. Well, you're charging a really high price per GPU hour because over time the price will go down and you'll get competed out. And what you need is to make sure that you never go under, or if you do go under your underlying cost. You've made so much money in the first part of it that the later end of it, like doesn't matter because from the whole structure of the deal, you've made money. The problem is that just, you think that you're going to be able to retain your customers with software. And actually what happens is your customers are super price sensitive and push you down and push you down and push you down and push you down, um, that they don't care about your software at all. And then the other problem that you have is you have, um, really big players like the hyperscalers who are looking to win the market and they have way more money than you, and they can push down on margin. Much better than you can. And so if they have to, and they don't, they don't necessarily all the time, um, I think they actually keep pride of higher margin, but if they needed to, they could totally just like wreck your margin at any point, um, and push you down, which meant that that quadrant over there where you're charging a high price, um, and just to make up for the risk completely got destroyed, like did not work at all for many places because of the price sensitivity, because people could just shove you down instead that pushed everybody up to the top right-hand corner of that, which is selling short-term. Contracts for low prices paid over time, which is the worst place to be in, um, the worst financial place to be in because it has the highest interest rate, um, which means that your, um, your costs go up at the same time, your, uh, your incoming cash goes down and squeezes your margins and squeezes your margins. The nice thing for like a core weave is that most of their business is over on the, on the other sides of those quadrants that the ones that survive. The only remaining question I have with core weave, and I promise I get to ask if I can compute, and I promise this is relevant to SOF Compute in general, because the framework is important, right? Sure. To understand the company. So why didn't NVIDIA or Microsoft, both of which have more money than core weave, do core weave, right? Why didn't they do core weave? Why have this middleman when either NVIDIA or Microsoft have more money than God, and they could have done an internal core weave, which is effectively like a self-funding vehicle, like a financial instrument. Why does there have to be a third party? Your question is like... Why didn't Microsoft, or why didn't NVIDIA just do core weave? Why didn't they just set up their own cloud provider? I think, and I don't know, and so correct me if I'm wrong, and lots of people will have different opinions here, or I mean, not opinions, they'll have actual facts that differ from my facts. Those aren't opinions. Those are actually indeed differences of reality, is that NVIDIA doesn't want to compete with their customers. They make a large amount of money by selling to existing clouds. If they launched their own core weave, then it would be a lot more money. It'd make it much harder for them to sell to the hyperscalers, and so they have a complex relationship with there. So not great for them. Second is that, at least for a while, I think they were dealing with antitrust concerns or fears that if they're going through, if they own too much layers of the stack, I could imagine that could be a problem for them. I don't know if that's actually true, but that's where my mind would go, I guess. Mostly, I think it's the first one. It's that they would be competing directly with their primary customers. Then Microsoft could have done it, right? That's the other question. Yeah, so Microsoft didn't do it. And my guess is that... NVIDIA doesn't want Microsoft to do it, and so they would limit the capacity because from NVIDIA's perspective, both they don't want to necessarily launch their own cloud provider because it's competing with their customers, but also they don't want only one customer or only a few customers. It's really bad for NVIDIA if you have customer concentration, and Microsoft and Google and Amazon, like Oracle, to buy up your entire supply, and then you have four or five customers or so who pretty much get to set prices. Monopsony. Yeah, monopsony. And so the optimal thing for you is a diverse set of customers who all are willing to pay at whatever price, because if you don't, somebody else will. And so it's really optimal for NVIDIA to have lots of other customers who are all competing against each other. Great. Just wanted to establish that. It's unintuitive for people who have never thought about it, and you think about it all day long. Yeah. Swyx: The last thing I'll call out from the talk, which is kind of cool, and then I promise we'll get to SF Compute, is why will DigitalOcean and Together lose money on their clusters? Why will DigitalOcean and Together lose money on their clusters?Evan [00:16:33]: I'm going to start by clarifying that all of these businesses are excellent and fantastic. That Together and DigitalOcean and Lambda, I think, are wonderful businesses who build excellent products. But my general intuition is that if you try to couple the software and the hardware together, you're going to lose money. That if you go out and you buy a long-term contract from someone and then you layer on services, or you buy the hardware yourself and you spin it up and you get a bunch of debt, you're going to run into the same problem that everybody else did, the same problem we did, same problem the hyperscalers did. And that's exactly what the hyperscalers are doing, which is you cannot add software and make high margins like a cloud provider can. You can pitch that into investors and it will totally make sense, and it's like the correct play in CPUs, but there isn't software you could make to make this occur. If you're spending a billion dollars on hardware, you need to make a billion dollars of software. There isn't a billion dollars of software that you can realistically make, and if you do, you're going to look like SAP. And that's not a knock on SAP. SAP makes a f**k ton of money, right? Right. Right. Right. Right. There aren't that many pieces of software that you could make, that you can realistically sell, like a billion dollars of software, and you're probably not going to do it to price-sensitive customers who are spending their entire budget already on compute. They don't have any more money to give you. It's a very hard proposition to do. And so many parties have been trying to do this, like, buy their own compute, because that's what a traditional cloud does. It doesn't really work for them. You know that meme where there's, like, the Grim Reaper? And he's, like, knocking on the door, and then he keeps knocking on the next door? We have just seen door after door after door of the Grim Reeker comes by, and the economic realities of the compute market come knocking. And so the thing we encourage folks to do is if you are thinking about buying a big GPU cluster and you are going to layer on software on top, don't. There are so many dead bodies in the wake there. We would recommend not doing that. And we, as SF Compute, our entire business is structured to help you not do that. It's helped disintegrate these. The GPU clouds are fantastic real estate businesses. If you treat them like real estate businesses, you will make a lot of money. The cloud services you can make on that, all the software you want to make on that, you can do that fantastically. If you don't own the underlying hardware, if you mix these businesses together, you get shot in the head. But if you combine, if you split them, and that's what the market does, it helps you split them, it allows you to buy, like, layer on services, but just buy from the market, you can make lots of money. So companies like Modal, who don't own the underlying compute, like they don't own it, lots of money, fantastic product. And then companies like Corbeave, who are functionally like really, really good real estate businesses, lots of money, fantastic product. But if you combine them, you die. That's the economic reality of compute. I think it also splits into trading versus inference, which are different kinds of workloads. Yeah. And then, yeah, one comment about the price sensitivity thing before we leave this. This topic, I want to credit Martin Casado for coining or naming this thing, which is like, you know, you said, you said this thing about like, you don't have room for a 10% margin on GPUs for software. Yep. And Martin actually played it out further. It's his first one I ever saw doing this at large enough runs. So let's say GPT-4 and O1 both had a total trading cost of like a $500 billion is the rough estimate. When you get the $5 billion runs, when you get the $50 billion runs, it is actually makes sense to build your own. You're going to have to get into chips, like for OpenEI to get into chip design, which is so funny. I would make an ASIC for this run. Yeah, maybe. I think a caveat of that that is not super well thought about is that only works if you're really confident. It only works if you really know which chip you're going to do. If you don't, then it's a little harder. So it makes in my head, it makes more sense for inference where you've already established it. But for training there's so much like experimentation. Any generality, yeah. Yeah. The generality is much more useful. Yeah. In some sense, you know, Google's like six generations into the CPUs. Yeah. Yeah. Okay, cool. Maybe we should go into SF Compute now. Sure. Yeah.Alessio [00:20:37]: Yeah. So you kind of talked about the different providers. Why did you decide to go with this approach and maybe talk a bit about how the market dynamics have evolved since you started a company?Evan [00:20:47]: So originally we were not doing this at all. We were definitely like forced into this to some extent. And SF Compute started because we wanted to go train models for music and audio in general. We were going to do a sort of generic audio model at some points, and then we were going to do a music model at some points. It was an early company. We didn't really spec down on a particular thing. But yeah, we were going to do a music model and audio model. First thing that you do when you start any AI lab is you go out and you buy a big cluster. The thing we had seen everybody else do was they went out and they raised a really big round and then they would get stuck. Because if you raise the amount of money that you need to train a model initially, like, you know, the $50 million pre-seed, pre-revenue, your valuation is so high or you get diluted so much that you can't raise the next round. And that's a very big ask to make. And also, I don't know, I felt like we just felt like we couldn't do it. We probably could have in retrospect, but I think one, we didn't really feel like we could do it. Two, it felt like if we did, we would have been stuck later on. We didn't want to raise the big round. And so instead, we thought, surely by now, we would be able to just go out. To any provider and buy like a traditional CPU cloud would sell offer you and just buy like on demand or buy like a month or so on. And this worked for like small incremental things. And I think this is where we were basing it off. We just like assumed we could go to like Lambda or something and like buy thousands of at the time A100s. And this just like was not at all the case. So we started doing all the sales calls with people and we said, OK, well, can we just get like month to month? Can we get like one month of compute or so on? Everyone told us at the time, no. You need to have a year long contract or longer or you're out of luck. Sorry. And at the time, we were just like pissed off. Like, why won't nobody sell us a month at a time? Nowadays, we totally understand why, because it's the same economic reason. Because if you if they had sold us the month to month or so on and we canceled or so on, they would have massive risk on that. And so the optimal thing to do was to only to just completely abandon the section of the market. We didn't like that. So our plan was we were going to buy a year long contract anyway. We would use a month. And then we would. At least the other 11 months. And we were locked in for a year, but we only had to pay on every individual month. And so we did this. But then immediately we said, oh, s**t, now we have a cloud provider, not a like training models company, not an AI lab, because every 30 days we owed about five hundred thousand dollars or so and we had about five hundred thousand dollars in the bank. So that meant that every single month, if we did not sell out our cluster, we would just go bankrupt. So that's what we did for the first year of the company. And when you're in that position. You try to think how in the world you get out of that position, what that transition to is, OK, well, we tend to be pretty good at like selling this cluster every month because we haven't died yet. And so what we should do is we should go basically be like this broker for other people and we will be more like a GPU real estate or like a GPU realtor. And so we started doing that for a while where we would go to other people who had who was trying to sell like a year long contract with somebody and we'd go to another person who like maybe this person wanted six months and somebody else on six months or something and we'd like combine all these people. Together to make the deal happen and we'd organize these like one off bespoke deals that looked like basically it ended up with us taking a bunch of customers, us signing with a vendor, taking some cut and then us operating the cluster for people typically with bare metal. And so we were doing this, but this was definitely like a oh, s**t, oh, s**t, oh, s**t. How do we get out of our current situation and less of a like a strategic plan of any sort? But while we were doing this, since like the beginning of the company, we had been thinking about how to buy GPU clusters, how to sell them effectively, because we'd seen every part of it. And what we ended up with was like a book of everybody who's trying to buy and everyone is trying to sell because we were these like GPU brokers. And so that turned into what is today SF Compute, which is a compute market, which we think we are the functionally the most liquid GPU market of any capacity. Honestly, I think we're the only thing that actually is like a real market that there's like bids and asks and there's like a like a trading engine that combines everything. And so. I think we're the only place where you can do things that a market should be able to do. Like you can go on SF Compute today and you get thousands of H100s for an hour if you want. And that's because there is a price for thousands of GPUs for an hour. That is like not a thing you can reasonably do on kind of any other cloud provider because nobody should realistically sell you thousands of GPUs for an hour. They should sell it to you for a year or so on. But one of the nice things about a market is that you can buy the year on SF Compute. But then if you need to sell. Back, you can sell back as well. And that opens up all these little pockets of liquidity where somebody who's just trying to buy for a little bit of time, some burst capacity. So people don't normally buy for an hour. That's not like actually a realistic thing, but it's like the range somebody who wants, who is like us, who needed to buy for a month can actually buy for a month. They can like place the order and there is actually a price for that. And it typically comes from somebody else who's selling back. Somebody who bought a longer term contract and is like they bought for some period of time, their code doesn't work, and now they need to like sell off a little bit.Alessio [00:25:49]: What are the utilization rates at which a market? What are the utilization rates at which a market? Like this works, what do you see the usual GPU utilization rate and like at what point does the market get saturated?Evan [00:26:00]: Assuming there are not like hardware problems or software problems, the utilization rate is like near 100 percent because the price dips until the utilization is 100 percent. So the price actually has to dip quite a lot in order for the utilization not to be. That's not always the case because you just have logistical problems like you get a cluster and parts of the InfiniBand fabric are broken. And there's like some issue with some switch somewhere and so you have to take some portion of the cluster offline or, you know, stuff like this, like there's just underlying physical realities of the clusters, but nominally we have better utilization than basically anybody because, but that's on utilization of the cluster, like that doesn't necessarily translate into, I mean, I actually do think we have much better overall money made for our underlying vendors than kind of anybody else. We work with the other GPU clouds and the basic pitch to the other GPU clouds is one. So we can sell your broker so we can we can find you the long term contracts that are at the prices that you want, but meanwhile, your cluster is idle and for that we can increase your utilization and get you more money because we can sell that idle cluster for you and then the moment we find the longer, the bigger customer and they come on, you can kick off those people and then go to the other ones. You get kind of the mix of like sell your cluster at whatever price you can get on the market and then sell your cluster at the big price that you want to do for long term contract, which is your ideal business model. And then the benefit of the whole thing being on the market. Is you can pitch your customer that they can cancel their long term contract, which is not a thing that you can reasonably do if you are just the GPU cloud, if you're just the GPU cloud, you can never cancel your contract, because that introduces so much risk that you would otherwise, like not get your cheap cost of capital or whatever. But if you're selling it through the market, or you're selling it with us, then you can say, hey, look, you can cancel for a fee. And that fee is the difference between the price of the market and then the price that they paid at, which means that they canceled and you have the ability to offer that flexibility. But you don't. You don't have to take the risk of it. The money's already there and like you got paid, but it's just being sold to somebody else. One of our top pieces from last year was talking about the H100 glut from all the long term contracts that were not being fully utilized and being put under the market. You have on here dollar a dollar per hour contracts as well as it goes up to two. Actually, I think you were involved. You were obliquely quoted in that article. I think you remember. I remember because this was hidden. Well, we hid your name, but then you were like, yeah, it's us. Yeah. Could you talk about the supply and demand of H100s? Was that just a normal cycle? Was that like a super cycle because of all the VC funding that went in in 2003? What was that like? GPU prices have come down. Yeah, GPU prices have come down. And there's some part that has normal depreciation cycle. Some part of that is just there were a lot of startups that bought GPUs and never used them. And now they're lending it out and therefore you exist. There's a lot of like various theories as to why. This happened. I dislike all of them because they're all kind of like they're often said with really high confidence. And I think just the market's much more complicated than that. Of course. And so everything I'm going to say is like very hedged. But there was a series of like places where a bunch of the orders were placed and people were pitching to their customers and their investors and just the broader market that they would arrive on time. And that is not how the world works. And because there was such a really quick build out of things, you would end up with bottlenecks in the supply chain somewhere that has nothing to do with necessarily the chip. It's like the InfiniBand cables or the NICs or like whatever. Or you need a bunch of like generators or you don't have data center space or like there's always some bottleneck somewhere else. And so a lot of the clusters didn't come online within the period of time. But then all the bottlenecks got sorted out and then they all came online all at the same time. So I think you saw a short. There was a shortage because supply chain hard. And then you saw a increase or like a glut because supply chain eventually figure itself out. And specifically people overordered in order to get the allocation that they wanted. Then they got the allocations and then they went under. Yeah, whatever. Right. There was just a lot of shenanigans. A caveat of this is every time you see somebody like overordered, there is this assumption that the problem was like the demand went down. I don't think that's the case at all. And so I want to clarify that. It definitely seems like a shortage. Like there's more demand for GPUs than there ever was. It's just that there was also more supply. So at the moment, I think there is still functionally a glut. But the difference that I think is happening is mostly the test time inference stuff that you just need way more chips for that than you did before. And so whenever you make a statement about the current market, people sort of take your words and then they assume that you're making a statement about the future market. And so if you say there's a glut now, people will continue to think there's a glut. But I think what is happening at the moment. My general prediction is that like by the winter, we will be back towards shortage. But then also, this very much depends on the rollout of future chips. And that comes with its own. I think I'm trying to give you like a good here's Evan's forecast. Okay. But I don't know if my forecast is right. You don't have to. Nobody is going to hold you to it. But like I think people want to know what's true and what's not. And there's a lot of vague speculations from people who are not that close to the market actually. And you are. I think I'm a closer. Close to the market, but also a vague speculator. Like I think there are a lot of really highly confident speculators and I am indeed a vague speculator. I think I have more information than a lot of other people. And this makes me more vague of a spectator because I feel less certain or less confident than I think a lot of other people do. The thing I do feel reasonably confident about saying is that the test time inference is probably going to quite significantly expand the amount of compute that was used for inference. So a caveat. This is like pretty much all the inference demand is in a few companies. A good example is like lots of bio and pharma was using H100s training sort of the bio models of sorts. And they would come along and they would buy, you know, thousands of H100s for training and then just like not a lot of stuff for inference. Not in any, not relative to like an opening iron anthropic or something because they like don't have a consumer product. Their inference event, if they can do it right. There's really like only one inference event that matters. And obviously I think they're going to run into it. And Batch and they're not going to literally just run one inference event. But like the one that produces the drug is the important one. Right. And I'm dumb and I don't know anything about biology, so I could be completely wrong here. But my understanding is that's kind of the gist. I can check that for you. You can check that for me. Check that for me. But my understanding is like the one that produces the sequence that is the drug that, you know, cures cancer or whatever. That's the important deal. But like a lot of models look like this where they're sort of more enterprising use cases or they're so prior to something that looks like test time inference. You got lots and lots of demand for training and then pretty much entirely fell off for inference. And I think like we looked at like Open Router, for example, the entirety of Open Router that was not anthropic or like Gemini or OpenAI or something. It was like 10 H100 nodes or something like that. It's just like not that much. It's like not that many GPUs actually to service that entire demand. But that's like a really sizable portion of the sort of open source market. But the actual amount of compute needed for it was not that much. But if you imagine like what an OpenAI needs for like GPT-4, it's like tremendously big. But that's because it's a consumer product that has almost all the inference demand. Yeah, that's a message we've had. Roughly open source AI compared to closed AI is like 5%. Yeah, it's like super small. Super small. It's super small. Super small. But test time inference changes that quite significantly. So I will... I will expect that to increase our overall demand. But my question on whether or not that actually affects your compute price is entirely based on how quickly do we roll out the next chips. The way that you burst is different for test time.Alessio [00:34:01]: Any thoughts on the third part of the market, which is the more peer-to-peer distributed, some are like crypto-enabled, like Hyperbolic, Prime Intellect, and all of that. Where do those fit? Like, do you see a lot of people will want to participate in a peer-to-peer market? Or just because of the capital requirements at the end of the day, it doesn't really matter?Evan [00:34:20]: I'm like wildly skeptical of these, to be frankly. The dream is like steady at home, right? I got this $15.90. Nobody has $15.90. $14.90 sitting at home. I can rent it out. Yeah. Like, I just don't really think this is going to ever be more efficient than a fully interconnected cluster with InfiniBand or, you know, whatever the sort of next spec might be. Like, I could be completely wrong. But speaking of... I mean, like, SpeedoLite is really hard to beat. And regardless of whatever you're using, you just like can't get around that physical limitation. And so you could like imagine a decentralized market that still has a lot of places where there's like co-location. But then you would get something that looks like SF Compute. And so that's what we do. That's why we take our general take is like on SF Compute, you're not buying from like random people. You're buying from the other GPU clouds, functionally. You're buying from data centers that are the same genre of people that you would work with already. And you can specify, oh, I want all these nodes to be co-located. And I don't think you're really going to get around that. And I think I buy crypto for the purposes of like transferring money. Like the financial system is like quite painful and so on. I can understand the uses of it to sort of incentivize an initial market or try to get around the cold start problem. We've been able to get around the cold start problem just fine. So it didn't actually need that at all. What I do think is totally possible is you could launch a token and then you could like subsidize the crypto. You could compute prices for a bit, but like maybe that will help you. I think that's what Nuus is doing. Yeah, I think there's lots of people who are trying to do things like this, but at some point that runs out. So I would, I think generally agree. I think the only thread in that model is very fine grained mixture of experts that can be like algorithms can shift to adapt to hardware realities. And the hardware reality is like, okay, it's annoying to do large co-located clusters. Then we'll just redesign attention or whatever in our architecture to distribute it more. There was a little bit buzz of block attention last year that Strong Compute made a big push on. But I think like, you know, in a world where we have 200 experts in MOE model, it starts to be a little bit better. Like, I don't disagree with this. I can imagine the world in which you have like, in which you've redesigned it to be more parallelizable, like across space.Evan [00:36:43]: But assuming without that, your hardware limitation is your speed of light limitation. And that's a very hard one to get around.Alessio [00:36:50]: Any customers or like stories that you want to shout out of like maybe things that wouldn't have been economically viable like others? I know there's some sensitivity on that.Evan [00:37:00]: My favorites are grad students, are folks who are trying to do things that would normally otherwise require the scale of a big lab. And the grad students are like the worst pilots. They're like the worst possible customer for the traditional GPU clouds because they will immediately turn if you sell them a thing because they're going to graduate and they're not going to go anywhere. They're not going to like, that project isn't continuing to spend lots of money. Like sometimes it does, but not if you're like working with the university or you're working with the lab of some sort. But a lot of times it's just like the ability for us to offer like big burst capacity, I think is lovely and wonderful. And it's like one of my favorite things to do because all those folks look like we did. And I have a special place in my heart for that. I have a special place in my heart for young hackers and young grad students and researchers who are trying to do the same genre of thing that we are doing. For the same reason, I have a special place in my heart for like the startups, the people who are just actively trying to compete on the same scale, but can't afford it time-wise, but can afford it spike-wise. Yeah, I liked your example of like, I have a grant of 100K and it's expiring. I got to spend it on that. That's really beautiful. Yeah. Interesting. Has there been interesting work coming out of that? Anything you want to mention? Yeah. So from like a startup perspective, like Standard Intelligence and Find, P-H-I-N-D. We've had them on the pod.Swyx [00:38:23]: Yeah. Yeah.Evan [00:38:23]: That was great. And then from grad students' perspective, we worked a lot with like the Schmidt Futures grantees of various sorts. My fear is if I talk about their research, I will be completely wrong to a sort of almost insulting degree because I am very dumb. But yeah. I think one thing that's maybe also relevant startups and GPUs-wise. Yeah. Is there was a brief moment where it kind of made sense that VCs provided GPU clusters. And obviously you worked at AI Grants, which set up Andromeda, which is supposedly a $100 million cluster. Yeah. I can explain why that's the case or why anybody would think that would be smart. Because I remember before any of that happened, we were asking for it to happen. Yeah. And the general reason is credit risk. Again, it's a bank. Yeah. I have lower risk than you due to credit transformation. I take your risk onto my balance sheet. Correct. Exactly. If you wanted to go for a while, if you wanted to go set up a GPU cluster, you had to be the one that actually bought the hardware and racked it and stacked it, like co-located it somewhere with someone. Functionally, it was like on your balance sheet, which means you had to get a loan. And you cannot get a loan for like $50 million as a startup. Like not really. You can get like venture debt and stuff, but like it's like very, very difficult to get a loan of any serious price for that. But it's like not that difficult to get a loan for $50 million. If you already have a fund or you already have like a million dollars under your assets somewhere or like you personally can like do a personal guarantee for it or something like this. If you have a lot of money, it is way easier for you to get a loan than if you don't have a lot of money. And so the hack of a VC or some capital partner offering equity for compute is always some arbitrage on the credit risk. That's amazing. Yeah. That's a hack. You should do that. I don't think people should do it right now. I think the market has like, I think it made sense at the time and it was helpful and useful for the people who did it at the time. But I think it was a one-time arbitrage because now there are lots of other sources that can do it. And also I think like it made sense when no one else was doing it and you were the only person who was doing it. But now it's like it's an arbitrage that gets competed down. Sure. So it's like super effective. I wouldn't totally recommend it. Like it's great that Andromeda did it. But the marginal increase of somebody else doing it is like not super helpful. I don't think that many people have followed in their footsteps. I think maybe Andreessen did it. Yeah. That's it. I think just because pretty much all the value like flows through Andromeda. What? That cannot be true. How many companies are in the air, Grant? Like 50? My understanding of Andromeda is it works with all the NFTG companies or like several of the NFTG companies. But I might be wrong about that. Again, you know, something something. Nat, don't kill me. I could be completely wrong. But the but you know, I think Andromeda was like an excellent idea to do at the right time in which it occurred. Perfect. His timing is impeccable. Timing. Yeah. Nat and Daniel are like, I mean, there's lots of people who are like... Sears? Yeah. Sears. Like S-E-E-R. Oh, Sears. Like Sears of the Valley. Yeah. They for years and years before any of the like ChatGPT moment or anything, they had fully understood what was going to happen. Like way, way before. Like. AI Grant is like, like five years old, six years old or something like that. Seven years old. When I, when it like first launched or something. Depends where you start. The nonprofit version. Yeah. The nonprofit version was like, like happening for a while, I think. It's going on for quite a bit of time. And then like Nat and Daniel are like the early investors in a lot of the sort of early AI labs of various sorts. They've been doing this for a bit.Alessio [00:41:58]: I was looking at your pricing yesterday. We're kind of talking about it before. And there's this weird thing where one week is more expensive of both one day and one month. Yeah. What are like some of the market pricing dynamics? What are things that like this to somebody that is not in the business? This looks really weird. But I'm curious, like if you have an explanation for it, if that looks normal to you. Yeah.Evan [00:42:18]: So the simple answer is preemptible pricing is cheaper than non-preemptible pricing. And the same economic principle is the reason why that's the case right now. That's not entirely true on SF Compute. SF Compute doesn't really have the concept of preemptible. Instead, what it has is very short reservations. So, you know, you go to a traditional cloud provider and you can say, hey, I want to reserve contract for a year. We will let you do a reserve contract for one hour, which is the part of SFC. But what you can do is you can just buy every single hour continuously. And you're reserving just for that hour. And then the next hour you reserve just for that next hour. And this is obviously like a built in. This is like an automation that you can do. But what you're seeing when you see the cheap price is you're seeing somebody who's buying the next hour, but maybe not necessarily buying an hour after that. So if the price goes up. Up too much. They might not get that next hour. And the underlying part of this of where that's coming from the market is you can imagine like day old milk or like milk that's about to be old. It might drop its price until it's expired because nobody wants to buy the milk that's in the past. Or maybe you can't legally sell it. Compute is the same way. No, you can't sell a block of compute that is not that is in the past. And so what you should do in the market and what people do do is they take. They take a block. A block of compute. And then they drop it and drop it and drop it and drop into a floor price right before it's about to expire. And they keep dropping it until it clears. And so anything that is idle drops until some point. So if you go and use on the website and you set that that chart to like a week from now, what you'll see is much more normal looking sort of curves. But if you say, oh, I want to start right now, that immediate instant, here's the compute that I want right now is the is functionally the preemptible price. It's where most people are getting the best compute or like the best compute prices from. The caveat of that is you can do really fun stuff on SFC if you want. So because it's not actually preemptible, it's it's reserved, but only reserved for an hour, which means that the optimal way to use as of compute is to just buy on the market price, but set a limit price that is much higher. So you can set a limit price for like four dollars and say, oh, if the market ever happens to spike up to four dollars, then don't buy. I don't want to buy that at that price for that price. I don't want to buy that at that price for that price for an hour. But otherwise, just buy at the cheapest price. And if you're comfortable with that of the volatility of it, you're actually going to get like really good prices, like close to a dollar an hour or so on, sometimes down to like 80 cents or whatever. You said four, though. Yeah. So that's the thing. You want to lower the limit. So four is your max price. Four is like where you basically want to like pull the plug and say don't do it because the actual average price is not or like the, you know, the preemptible price doesn't actually look like that. So what you're doing when you're saying four is always, always, always give me this compute. Like continue to buy every hour. Don't preempt me. Don't kick me off. And I want this compute and just buy at the preemptible price, but never kick me off. The only times in which you get kicked off is if there is a big price spike. And, you know, let's say one day out of the year, there's like a four dollar an hour price because of some weird fluke or something. If there are other periods of time, you're actually getting a much lower price than you. It makes sense. Your your average cost that you're actually paying is way better. And your trade off here is you don't literally know what price you're going to get. So it's volatile. But your actual average historically has been like everyone who's done this has gotten wildly better prices. And this is like one of the clever things you can do with the market. If you're willing to make those trade offs, you can get a lot of really good prices. You can also do other things like you can only buy at night, for example. So the price goes down at night. And so you can say, oh, I want to only buy, you know, if the price is lower than 90 cents. And so if you have some long running job, you can make it only run on 90 cents and then you recover back and so on. Yeah. So what you can kind of create as like a spot inst is what other the CPU world has. Yes. But you've created a system where you can kind of manufacture the exact profile that you want. Exactly. That is not just whatever the hyperscalers offer you, which is usually just one thing. Correct. SF Compute is like the power tool. The underlying primitives of like hourly compute is there. Correct. Yeah, it's pretty interesting. I've often asked OpenAI. So like, you know, all these guys. Cloud as well. They do batch APIs. So it's half off of whatever your thing is. Yeah. And the only contract is we'll return in 24 hours. Sure. Right. And I was like, 24 hours is good. But sometimes I want one hour. I want four hours. I want something. And so based off of SF Compute's system, you can actually kind of create that kind of guarantee. Totally. That would be like, you know, not 24, but within eight hours, within four hours, like the work half of a workday. Yes. I can return your results to you. And then I can return it to you. And if your latency requirements are like that low, actually it's fine. Yes. Correct. Yeah. You can carve out that. You can financially engineer that on SFC. Yeah. Yeah. I mean, I think to me that unlocks a lot of agent use cases that I want, which is like, yeah, I worked in a background, but I don't want you to take a day. Yeah. Correct. Take a couple hours or something. Yeah. This touches a lot of my like background because I used to be a derivatives trader. Yeah. And this is a forward market. Yeah. A futures forward market, whatever you call it. Not a future. Very explicitly not a future. Not yet a futures. Yes. But I don't know if you have any other points to talk about. So you recognize that you are a, you know, a marketplace and you've hired, I met Alex Epstein at your launch event and you're like, you're, you're building out the financialization of GPUs. Yeah. So part of that's legal. Mm-hmm. Totally. Part of that is like listing on an exchange. Yep. Maybe you're the exchange. I don't know how that works, but just like, talk to me about that. Like from the legal, the standardization, the like, where is this all headed? You know, is this like a full listed on the Chicago Mercantile Exchange or whatever? What we're trying to do is create an underlying spot market that gives you an index price that you can use. And then with that index price, you can create a cash settled future. And with a cash settled future, you can go back to the data centers and you can say, lock in your price now and de-risk your entire position, which lets you get cheaper cost of capital and so on. And that we think will improve the entire industry because the marginal cost of compute is the risk. It's risk as shown by that graph and basically every part of this conversation. It's risk that causes the price to be all sorts of funky. And we think a future is the correct solution to this. So that's the eventual goal. Right now you have to make the underlying spot market in order to make this occur. And then to make the spot market work, you actually have to solve a lot of technology problems. You really cannot make a spot market work if you don't run the clusters, if you don't have control over them, if you don't know how to audit them, because these are super computers, not soybeans. They have to work. In a way that like, it's just a lot simpler to deliver a soybean than it is to deliver it. I don't know. Talk to the soybean guys. Sure. You know? Yeah. But you have to have a delivery mechanism. Your delivery mechanism, like somebody somewhere has to actually get the compute at some point and it actually has to work. And it is really complicated. And so that is the other part of our business that we go and we build a bare metal infrastructure stack that goes. And then also we do auditing of all the clusters. You sort of de-risk the technical perspective and that allows you to eventually de-risk the financial perspective. And that is kind of the pitch of SF Compute. Yeah. I'll double click on the auditing on the clusters. This is something I've had conversations with Vitae on. He started Rika and I think he had a blog post which kind of shone the light a little bit on how unreliable some clusters are versus others. Correct. Yeah. And sometimes you kind of have to season them and age them a little bit to find the bad cards. You have to burn them in. Yeah. So what do you do to audit them? There's like a burn-in process, a suite of tests, and then active checking and passive checking. Burn-in process is where you typically run LINPACK. LINPACK is this thing that like a bunch of linear algebra equations that you're stress testing the GPUs. This is a proprietary thing that you wrote? No, no, no. LINPACK is like the most common form of burn-in. If you just type in burn-in, typically when people say burn-in, they literally just mean LINPACK. It's like an NVIDIA reference version of this. Again, NVIDIA could run this before they ship, but now the customers have to do it. It's annoying. You're not just checking for the GPU itself. You're checking like the whole component, all the hardware. And it's a lot of work. It's an integration test. It's an integration test. Yeah. So what you're doing when you're running LINPACK or burn-in in general is you're stress testing the GPUs for some period of time, 48 hours, for example, maybe seven days or so on. And you're just trying to kill all the dead GPUs or any components in the system that are broken. And we've had experiences where we ran LINPACK on a cluster and it rounds out, sort of comes offline when you run LINPACK. This is a pretty good sign that maybe there is a problem with this cluster. Yeah. So LINPACK is like the most common sort of standard test. But then beyond that, what you do is we have like a series of performance tests that replicate a much more realistic environment as well that we run just assuming if LINPACK works at all, then you run the next set of tests. And then while the GPUs are in operation, you're also going through and you're doing active tests and passive tests. Passive tests are things that are running in the background while somebody else is running, while like some other workload is running. And active tests are during like idle periods. You're running some sort of check that would otherwise sort of interrupt something. And then the active tests will take something offline, basically. Or a passive check might mark it to get taken offline later and so on. And then the thing that we are working on that we have working partially but not entirely is automated refunds, which is basically like, is the case that the hardware breaks so much. And there's only so much that we can do and it is the effect of pretty much the entire industry. So a pretty common thing that I think happens to kind of everybody in the space is a customer comes online, they experience your cluster, and your cluster has the same problem that like any cluster has, or it's I mean, a different problem every time, but they experience one of the problems of HPC. And then their experience is bad. And you have to like negotiate a refund or some other thing like this. It's always case by case. And like, yeah, a lot of people just eat the cost. Correct. So one of the nice things about a market that we can do as we get bigger and have been doing as we can bigger is we can immediately give you something else. And then also we can automatically refund you. And you're still gonna experience it like the hardware problems aren't going away until the underlying vendors fix things. But honestly, I don't think that's likely because you're always pushing the limits of HPC. This is the case of trying to build a supercomputer. that's one of the nice things that we can do is we can switch you out for somebody else somewhere, and then automatically refund you or prorate or whatever the correct move is. One of the things that you say in this conversation with me was like, you know, you know, a provider is good when they guarantee automatic refunds. Which doesn't happen. But yeah, that's, that's in our contact with all the underlying cloud providers. You built it in already. Yeah. So we have a quite strict SLA that we pass on to you. The reason why

As a Yoga teacher you've invested heavily to develop the skills to identify patterns and habits and offer mindful and compassionate modifications. As a Yoga teacher you're a champion of lifelong transformation and growth. As a Yoga teacher you're skilful at (and you LOVE!) creating safe and beautiful spaces. In this episode I'll explain why these skills set you up to be a powerful coach with the capacity to co-create lasting change with your clients.Curious about becoming a coach? Here's the link to read more: https://www.amymcdonald.com.au/coachingtrainingBook in to talk about working together here: https://scheduler.zoom.us/amymcdonald/coachingSign up for online Yoga classes with me here: https://app.moonclerk.com/pay/3znlrz7xsqy4A few years ago I caught myself being a TOTAL judgy bitch about someone's bio. Like, yuck! Then I realised I was doing it to myself, too. I'd created a FALSE story of what it meant to be ‘multi-passionate': flakey, crap at business, noncommittal. This week we're talking about heroes from Yoga who proudly back themselves with thiir variety of modalities and I've got 3 reflection questions for you if you might also be playing small and hiding your gifts.Curious about becoming a coach? Here's the link to read more: https://www.amymcdonald.com.au/coachingtrainingBook in to talk about working together here: https://scheduler.zoom.us/amymcdonald/coachingSign up for online Yoga classes with me here: https://app.moonclerk.com/pay/3znlrz7xsqy4

In this episode of the Meta Tech Podcast, host Pascal sits down with Shane, a research scientist at Meta, to explore the cutting-edge research behind Ray-Ban Meta glasses. Shane shares insights from his seven-year journey at Meta, where he focuses on computer vision and multimodal AI within the Wearables AI organization. Tune in to learn how Shane's team is pioneering foundational models for Ray-Ban Meta glasses, tackling unique challenges, and pushing the boundaries of AI-driven innovation. Discover how multimodal AI is transforming user experiences and get a glimpse into the future of wearable technology. Whether you're an engineer, a tech enthusiast, or simply curious about the latest advancements, there is something for everyone in this episode.  Got feedback? Send it to us on Threads (https://threads.net/@metatechpod), Instagram (https://instagram.com/metatechpod) and don't forget to follow our host Pascal (https://mastodon.social/@passy, https://threads.net/@passy_). Fancy working with us? Check out https://www.metacareers.com/. Links AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model - https://arxiv.org/abs/2309.16058  Be My Eyes Programme: https://www.forbes.com/sites/stevenaquino/2024/10/11/inside-the-be-my-eyes-meta-collaboration-and-the-allure-to--impact-humanity/  Meta Open Source on Threads: https://www.threads.net/@metaopensource  CacheLib: https://cachelib.org/  Meta's AI-Powered Ray-Bans Are Life-Enhancing for the Blind - Wall Street Journal: https://www.wsj.com/tech/ai/metas-ai-powered-ray-bans-are-life-enhancing-for-the-blind-3ae38026  Timestamps Intro 0:06 OSS News 0:56 Introduction Shane 1:30 The role of research scientist over time 3:03 What's Multi-Modal AI? 5:45 Applying Multi-Modal AI in Meta's products 7:21 Acoustic modalities beyond speech 9:17 AnyMAL 12:23 Encoder zoos 13:53 0-shot performance 16:25 Iterating on models 17:28 LLM parameter size 19:29 How do we process a request from the glasses? 21:53 Processing moving images 23:44 Scaling to billions of users 26:01 Where lies the optimisation potential? 28:12 Incorporating feedback 29:08 Open-source influence 31:30 Be My Eyes Programme 33:57 Working with industry experts at Meta 36:18 Outro 38:55

Osteosarcoma Webinar Series: Sowmya Ramesh, PhD joins us on OsteoBites to discuss how multi-modal omics analysis identifies targetable sensory neuron circuitry that promotes sarcoma disease progression.Bone pain is a hallmark of bone cancers, including osteosarcoma (OS), mediated by skeletal-innervating peripheral afferent neurons. However, the roles of tumor-associated sensory neurons in OS beyond pain perception remain poorly understood. To investigate their regulatory functions, a chemical-genetic approach was employed in mice carrying a knock-in allele for TrkA to perturb sensory nerve innervation during OS growth and progression. TrkA inhibition in these transgenic mice significantly reduced sarcoma-associated sensory innervation and vascularization, altered tumor-associated macrophage polarization, decreased tumor growth and metastasis, and extended overall survival.These findings suggest that targeting pathological innervation in OS may serve as an adjunctive therapy to enhance clinical outcomes and survival.Dr. Ramesh is a postdoctoral researcher from the James lab at Johns Hopkins. The lab's research focus lies at the intersection of skeletal pathophysiology and stem cell biology. The lab looks at the interaction of nerves in various musculoskeletal repair including bone repair and regeneration, osteoprogenitor cell characterization, and neoplastic bone. Dr. Ramesh specifically works on understanding how peripheral nerves regulate osteosarcoma disease progression.

If you want something done right, sometimes you have to do it yourself—and that's exactly why I created the Forged Base Series. A complete redesign from the ground up, this next-to-skin base layer system was built for unmatched comfort, durability, and performance in the backcountry.

After a decade of Bi-Modal IT discussion, we've now reached the era of Bi-Modal Cloud. Balancing traditional CPU-based cloud resources is now clashing with the demands of GPU-based AI cloud needs. Let's explore!SHOW: 896SHOW TRANSCRIPT: The Cloudcast #896 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW NOTES:Public cloud providers are missing the market with AI (David Linthicum, 2025)Why digital business needs bi-modal IT (Gartner, 2015)Microsoft / Azure earnings (Jan 2025)Alphabet / Google Cloud earnings (Feb 2024)Amazon / AWS earnings (Feb 2025)WHAT HAPPENS WHEN A BUSINESS HAS TO PRIORITIZE THE LOWER REVENUE PATHSWe've come full circle after 10+ yearsCan the clouds make all this CapEx spending work because of other businesses?Wait, is cloud interesting again?What is Bi-Modal Cloud?Is the cash cow still the cash cow?How do cloud providers prioritize cloud vs. AI?How do internal teams rationalize being profitable but unimportant?Stargate might be a crazy idea, but is an AI-cloud a crazy idea? FEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

Sam Lindley is a Reader in Programming Languages Design and Implementation at the University of Edinburgh. In this episode, he tells us how difficult naming is, the different kinds of effect systems and handlers, languages *much* purer than Haskell, and Modal logic.

In this episode, I sit down with Dr. Lance Cutsforth, an Army veteran, double board-certified Functional Medicine practitioner, and co-founder of Mons Sana, a healing community in Colorado. With decades of experience in mental wellness and optimization, Lance shares his personal and professional journey of addressing trauma, suicidal ideation, addiction, and TBI through a multi-modal approach. We dive into his work with alternative therapies and the transformative potential of entheogenic medicines. Lance's story of lived experience and his commitment to helping military, first responders, and their families transition from surviving to thriving will inspire and challenge how you think about healing. Don't miss this powerful conversation.

This week financial journalist Govindraj Ethiraj is joined by Vineet Agarwal, Managing Director at the Transport Corporation of India Limited. Agarwal discusses the current state of the logistics sector, from growth opportunities and profitability to the challenges and implications of supply chain disruption. You'll learn about all things supply chain management whether it be for quick commerce, warehousing, multi-modal logistics or the complexity of shifting supply chains. He also discusses the formalisation of sectors, the demands of compliance, and what affects the cost of logistics. Tune in for a 360 view of the logistics industry from one of its biggest players. (00:00) - Intro Logistics sector/industry (02:02) - Quick commerce, Kirana stores, Dark stores, (05:33) - Supply Chains of e-commerce vs Kirana stores (07:15) - Products coming from multiple factories (08:11) - Warehouse fulfilment level is where they operate. Delivery costs are the highest costs. (09:50) - History of the TCI. The business has changed, but the value system has not changed (11:41) - Working with all industries - auto, defence, etc. Offering a service without damages (12:47) - Domestic manufacturing, supply chain localisation due to supply chain disruption (14:49) - Complexity of shifting supply chains (17:20) - Multimodal logistics. Bringing down logistics cost (19:47) - The Shipping side of TCI (22:00) - What do Corporates want (23:00) - Tracking has become hygiene (24:21) - How does granular information add value (25:50) - Where is the growth opportunity (28:11) - Compliance needs (30:21) - Profit growth for TCI (32:30) - How do customers indicate growth opportunities (34:33) - Chocolate (35:53) - Speed increases cost (37:13) - What to look forward in the upcoming budget ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Listeners! We await your feedback....⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ The Core and The Core Report is ad supported and FREE for all readers and listeners. Write in to shiva@thecore.in for sponsorships and brand studio requirements. For more of our coverage check out ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠thecore.in⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join and Interact anonymously on our whatsapp channel⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow us on:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Youtube

Today on No Priors, Elad chats with Erik Bernhardsson, founder and CEO of Modal Labs, a platform simplifying ML workflows by providing a serverless infrastructure designed to streamline deployment, scaling, and development for AI engineers. Erik talks about his early work on Spotify's ML algorithms, what Modal offers today, and his vision for building an end-to-end solution for AI engineers. They dive into GPU trends, cloud vs on-premise setups, and when to train custom models vs use off-the-shelf solutions. Erik also shares his thoughts on the evolving role of AI in fields like coding, physics, and music. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Bernhardsson Show Notes: 0:00 Introduction  0:22 Erik's early interest in ML infra 1:22 Founding Modal Labs  4:17 State of GPU use today and what's to come 7:14 Modal's end-to-end vision  9:00 Differentiating amongst competition 10:20 Cloud vs on-premise  12:35 Popular AI models  13:20 Gaps in AI infrastructure 14:55 Insights on vector databases  16:48 Training models vs off-the-shelf models  17:47 AI's impact on coding and physics 22:14 AI's impact on music

Hugo Bowne-Anderson hosts a panel discussion from the MLOps World and Generative AI Summit in Austin, exploring the long-term growth of AI by distinguishing real problem-solving from trend-based solutions. If you're navigating the evolving landscape of generative AI, productionizing models, or questioning the hype, this episode dives into the tough questions shaping the field. The panel features: - Ben Taylor (Jepson) (https://www.linkedin.com/in/jepsontaylor/) – CEO and Founder at VEOX Inc., with experience in AI exploration, genetic programming, and deep learning. - Joe Reis (https://www.linkedin.com/in/josephreis/) – Co-founder of Ternary Data and author of Fundamentals of Data Engineering. - Juan Sequeda (https://www.linkedin.com/in/juansequeda/) – Principal Scientist and Head of AI Lab at Data.World, known for his expertise in knowledge graphs and the semantic web. The discussion unpacks essential topics such as: - The shift from prompt engineering to goal engineering—letting AI iterate toward well-defined objectives. - Whether generative AI is having an electricity moment or more of a blockchain trajectory. - The combinatorial power of AI to explore new solutions, drawing parallels to AlphaZero redefining strategy games. - The POC-to-production gap and why AI projects stall. - Failure modes, hallucinations, and governance risks—and how to mitigate them. - The disconnect between executive optimism and employee workload. Hugo also mentions his upcoming workshop on escaping Proof-of-Concept Purgatory, which has evolved into a Maven course "Building LLM Applications for Data Scientists and Software Engineers" launching in January (https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?utm_campaign=8123d0&utm_medium=partner&utm_source=instructor). Vanishing Gradient listeners can get 25% off the course (use the code VG25), with $1,000 in Modal compute credits included. A huge thanks to Dave Scharbach and the Toronto Machine Learning Society for organizing the conference and to the audience for their thoughtful questions. As we head into the new year, this conversation offers a reality check amidst the growing AI agent hype. LINKS Hugo on twitter (https://x.com/hugobowne) Hugo on LinkedIn (https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/) Vanishing Gradients on twitter (https://x.com/vanishingdata) "Building LLM Applications for Data Scientists and Software Engineers" course (https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?utm_campaign=8123d0&utm_medium=partner&utm_source=instructor).

Hugo speaks with Charles Frye, Developer Advocate at Modal and someone who really knows GPUs inside and out. If you're a data scientist, machine learning engineer, AI researcher, or just someone trying to make sense of hardware for LLMs and AI workflows, this episode is for you. Charles and Hugo dive into the practical side of GPUs—from running inference on large models, to fine-tuning and even training from scratch. They unpack the real pain points developers face, like figuring out: - How much VRAM you actually need. - Why memory—not compute—ends up being the bottleneck. - How to make quick, back-of-the-envelope calculations to size up hardware for your tasks. - And where things like fine-tuning, quantization, and retrieval-augmented generation (RAG) fit into the mix. One thing Hugo really appreciate is that Charles and the Modal team recently put together the GPU Glossary—a resource that breaks down GPU internals in a way that's actually useful for developers. We reference it a few times throughout the episode, so check it out in the show notes below.

Before he founded Modal, Erik Bernhardsson created Spotify's music recommendation system. Today he's bringing a consumer app approach to radically simplifying developer experience for data and AI projects on the Modal platform. In this episode, we dive into the broader AI compute landscape, discussing the roles of hyperscalers, GPU clouds, inference platforms, and the emergence of alternative AI cloud providers. Erik gives us a product tour of the Modal platform, provides insights into the AI industry's shift from training to inference as the primary use case, and speculates on the future of AI-native consumer applications. Learn about Modal's commitment to fast feedback loops, their cloud maximalist approach, their dedication to building a product that developers truly love, as well as founder lessons Erik learned along the way. Erik's blog: https://erikbern.com "It's hard to write code for humans": https://erikbern.com/2024/09/27/its-hard-to-write-code-for-humans Modal Website - https://modal.com Twitter - https://x.com/modal_labs Erik Bernhardsson LinkedIn - https://www.linkedin.com/in/erikbern Twitter - https://x.com/bernhardsson FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (01:35) What is Modal? (02:18) Current state of AI compute space (09:54) Erik's path to starting Modal (13:57) Core elements of the Modal platform (28:52) Is serverless the right level of abstraction for AI compute? (33:35) Balancing costs: GPU vendor fees vs. customer pricing (37:56) Designing products for humans (42:43) Modal's early go-to-market motion (45:32) Managing early engineering team (48:26) The only correct way to add a new function to the company (50:07) Building company in NYC (52:05) Modal's roadmap (54:04) Erik's predictions on AI

This episode is part of the upcoming online event called Behavior Connect. Behavior Connect is all about moving beyond traditional behavior strategies to foster authentic connections with children. In this episode, I am joined by Katelyn Kelps, an experienced SLP passionate about AAC and neurodiversity affirming practices, as we explore multimodal communication strategies in educational settings. Katelyn shares practical approaches for high and low support needs students, including the use of core boards, single message switches, break cards, and text to speech apps. She also discusses the importance of normalizing non-spoken communication and increasing school team collaboration to support neurodivergent students. To be part of Behavior Connect, grab your ⁠⁠FREE LIVE TICKET ⁠⁠now!

In this episode of Gradient Dissent, Erik Bernhardsson, CEO & Founder of Modal Labs, joins host Lukas Biewald to discuss the future of machine learning infrastructure. They explore how Modal is enhancing the developer experience, handling large-scale GPU workloads, and simplifying cloud execution for data teams. If you're into AI, data pipelines, or building robust ML systems, this episode is packed with valuable insights!

Send Steve a Text MessageEmbark on a journey through the harmonious realms of modal arpeggios as we unwrap the secrets of the G major scale's evocative power. Imagine the raised fourth of the Lydian or the soulful flat seventh of the Mixolydian coming alive under your fingertips; this episode is your guide to mastering these captivating sounds. We'll dissect the construction of seventh chords and their modal offspring - Ionian to Phrygian - and how recognizing and utilizing their distinct intervals can transform your musical expression. Whether it's the bright optimism of the Lydian mode or the exotic allure of Phrygian, get ready to add new depth to your playing with every strum.Gear up your guitar for an auditory adventure as we weave through the characteristic intervals of A Dorian, compare it to the natural minor scale, and even superimpose chords to foreshadow musical changes. Through hands-on demonstrations and insightful discussions, you'll learn to craft solos that resonate with the emotive quality of each mode and navigate chord progressions with newfound creativity. By the end of this episode, you'll not only have internalized the sound of each mode but also have the tools to make them sing through your strings, giving you an edge in your musical endeavors.Tune in now and learn more!Links:Steve's Channel → https://www.youtube.com/user/stinemus... GuitarZoom Channel → https://www.youtube.com/user/guitarz0... Songs Channel → https://www.youtube.com/user/GuitarSo... . Check out Steve's Guitar Membership and Courses: https://bit.ly/3rbZ3He Links: Steve's Channel → https://www.youtube.com/user/stinemus... GuitarZoom Channel → https://www.youtube.com/user/guitarz0... Songs Channel → https://www.youtube.com/user/GuitarSo... . Check out Steve's Guitar Membership and Courses: https://bit.ly/3rbZ3He

In this episode of HR Heretics, Kelli and Nolan sit down with Andrew Bartlow, a multi time VP of HR and co-founder of People Leader Accelerator. Andrew shares his extensive career from startups to Fortune 100 companies, the distinct challenges at each company stage across VC-backed startups and public companies, and the danger of being relegated to your HR island.*Email us your questions or topics for Kelli & Nolan: hrheretics@turpentine.coHR Heretics is a podcast from Turpentine.Support HR Heretics Sponsors:Metaview is the AI assistant for interviewing. Metaview completely removes the need for recruiters and hiring managers to take notes during interviews—because their AI is designed to take world-class interview notes for you. Team builders at companies like Brex, Hellofresh, and Quora say Metaview has changed the game—see the magic for yourself: https://www.metaview.ai/hereticsDiscover Workvivo, Zoom's employee experience platform, designed to delight your hybrid workforce. With dynamic and intuitive features people actually like using, Workvivo fosters a sense of belonging and boosts retention and productivity. Join Ryanair, Dollar General, and Virgin — head to https://bit.ly/hrhereticsworkvivo to book a demo (tell them Kelli and Nolan sent you).Attio is the next generation of CRM. It's powerful, flexible, and easily configures to the unique way your startup runs, whatever your go-to-market motion. The next era deserves a better CRM. Join ElevenLabs, Replicate, Modal, and more at https://bit.ly/AttioHRHereticsThis episode is brought to you by Warp. Don't let payroll and compliance hold your startup back: visit https://joinwarp.com/peel to get started and receive a $1000 gift card when your first run payroll.KEEP UP WITH ANDREW, NOLAN, + KELLI ON LINKEDIN Andrew: https://www.linkedin.com/in/bartlow/ Nolan: https://www.linkedin.com/in/nolan-church/ Kelli: https://www.linkedin.com/in/kellidragovich/—TIMESTAMPS:(00:00) Intro(01:06) Andrew's career journey(02:49) Differences between company types(06:57) Power structures and company culture(12:26) Talent assessment in private equity(17:47) Sponsors: Metaview | Workvivo(20:39) Private equity misconceptions(28:20) Sponsors: Attio | Warp(30:11) Changing reputation of private equity(33:11) CEO and Chief People Officer relationship(37:07) Assessing CEO's expectations(39:00) Concept of HR island(41:34) Engagement metrics: A critical view(44:58) Challenges in HR leadership(46:13) Engagement surveys: Pros and cons(54:56) Exec comp debate(01:00:51) Wrap This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hrheretics.substack.com

Scale AI's CEO sparks debate with "MEI" - is meritocracy a threat to diversity?Kelli and Nolan, together with Richard Ni, Scale AI's former Head of People, weigh in on the implications of this new hiring policy. Together, they discuss the role of meritocracy and company culture and offer actionable insights for HR professionals for talent acquisition and building a diverse workforce.*Email us your questions or topics for Kelli & Nolan: hrheretics@turpentine.coHR Heretics is a podcast from Turpentine.Support HR Heretics Sponsors:Metaview is the AI assistant for interviewing. Metaview completely removes the need for recruiters and hiring managers to take notes during interviews—because their AI is designed to take world-class interview notes for you. Team builders at companies like Brex, Hellofresh, and Quora say Metaview has changed the game—see the magic for yourself: https://www.metaview.ai/hereticsDiscover Workvivo, Zoom's employee experience platform, designed to delight your hybrid workforce. With dynamic and intuitive features people actually like using, Workvivo fosters a sense of belonging and boosts retention and productivity. Join Ryanair, Dollar General, and Virgin — head to https://bit.ly/hrhereticsworkvivo to book a demo (tell them Kelli and Nolan sent you).Attio is the next generation of CRM. It's powerful, flexible, and easily configures to the unique way your startup runs, whatever your go-to-market motion. The next era deserves a better CRM. Join ElevenLabs, Replicate, Modal, and more at https://bit.ly/AttioHRHereticsKEEP UP WITH RICHARD, NOLAN, + KELLI ON LINKEDINRichard: https://www.linkedin.com/in/richard-ni-a5605543Nolan: https://www.linkedin.com/in/nolan-church/Kelli: https://www.linkedin.com/in/kellidragovich/—TIMESTAMPS:(00:00) Introduction(02:09)  Richard's Perspective on MEI(04:16) Kelli's Take on Meritocracy vs. DEI(07:11) Nolan's Counterpoint: Meritocracy's Imperfections (09:06) The Importance of Transparency and Seeking Improvement(13:00) Richard's Analysis of Scale AI's Motivation(14:34) The Importance of Human Decision-Making and Corruption(19:00) The Imperfections of Humans and Systems(22:22) The Impact of Tech Giants on Expectations(24:40) The Impact of Economic Pressure on Companies(29:48) Wrap This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hrheretics.substack.com