Podcasts about Hardware

Today, a look at the implications for a stronger JPY from coordinated intervention as the US has joined forces with Japan to force yen appreciation. As well, we wonder what the implications are for equities now that we trade with a much cleaner slate after reaching the other side of blowups in leveraged single-stock ETFs and the liquidation of the Situational Awareness fund late last week. A busy week ahead for earnings and macro and more also previewed on today's pod, which is hosted by Saxo Global Head of Macro Strategy John J. Hardy.  Links discussed on today's podcast and our Chart of the Day can be found on the John J. Hardy substack (within two to four hours from the time of the podcast release). Read daily in-depth market updates from the Saxo Market Call and the Saxo Strategy Team here. Please reach out to us at marketcall@saxobank.com for feedback and questions. Click here to open an account with Saxo. Intro music by AShamaluevMusic DISCLAIMER This content is marketing material. Trading financial instruments carries risks. Always ensure that you understand these risks before trading. This material does not contain investment advice or an encouragement to invest in a particular manner. Historic performance is not a guarantee of future results. The instrument(s) referenced in this content may be issued by a partner, from whom Saxo Bank A/S receives promotional fees, payment or retrocessions. While Saxo may receive compensation from these partnerships, all content is created with the aim of providing clients with valuable information and options.

Luke de Wolf is a cybersecurity professional and the author of ‘Defending Bitcoin: Industrial-Grade Cybersecurity for the Monetary Grid'.› https://x.com/lukedewolf› https://defendingbitcoin.comPARTNERS

The boys are back! We have news to talk about. ==== Special Thanks to Our Patrons! ==== https://thelinuxcast.org/patrons/ ===== Follow us

Irrfahrt Christopher Nolans The Odyssey wirkt wie ein Film, der von Anfang an auf große Bilder und starke Stimmung setzt. Schon die Idee des Stoffes passt zu Nolans Stil: eine Reise voller Gefahr, Zweifel und Spannung, erzählt nicht klein, sondern mit dem Anspruch auf echtes Kinoerlebnis. Bildgewaltig scheint hier das passende Wort zu sein. Man erwartet keine glatte, leichte Abenteuerreise, sondern monumentale Bilder, weite Räume und Szenen, die sich förmlich ins Gedächtnis brennen. Nolan nutzt solche Stoffe oft, um aus einzelnen Momenten etwas Großes zu machen, und genau das dürfte auch hier spürbar werden. Die Reise wird nicht nur erzählt, sie wird als visuelles Erlebnis inszeniert. Düster ist der Film vermutlich ebenso. Bei Nolan steckt hinter jeder großen Geschichte meist auch ein Gefühl von Bedrohung, Verlust oder innerem Konflikt. Das passt perfekt zu einem Stoff wie The Odyssey, in dem es nicht nur um Heimkehr, sondern auch um Prüfung, Irrweg und Überleben geht. Statt reiner Abenteuerromantik entsteht so eine schwerere, ernstere Atmosphäre. Gerade darin liegt die Stärke von großem Kino: wenn Bilder, Klang und Erzählung zusammenkommen und mehr erzeugen als bloße Handlung. The Odyssey dürfte genau so ein Film sein, der nicht einfach unterhält, sondern beeindruckt. Er will nicht nebenbei laufen, sondern den Raum füllen und das Publikum komplett in seine Welt ziehen. Das ist Kino mit Anspruch, Wucht und Nachhall. Kurz gesagt: Nolan macht aus The Odyssey vermutlich kein klassisches Abenteuer, sondern ein düsteres, visuell überwältigendes Epos, das zeigt, warum Kino auf der großen Leinwand am stärksten ist. Deswegen war Matze im weltweiten größten IMAX. Noch eine Irrfahrt Wer sich wohl auch ein wenig verfahren hat ist Apple. Die Vision pro war ja ne nette Idee aber nicht wirklich das, worauf die welt gewartet hat. Das hat wohl nun auch Apple festgestellt und konzentriert sich auf das, was wohl die größeren Chancen bei der breiten Masse hat – AR Brillen. Geplant sind wohl zwei Varianten, eine mit und eine ohne Displays. Und nochmal mit dem Schiff unterwegs Assassin's Creed Black Flag Resynced wirkt wie genau das Remake, auf das viele Fans gewartet haben. Schon das Original gehört für viele zu den stärksten Teilen der Reihe, und die neue Version bringt dieses Gefühl auf der PS5 Pro noch einmal deutlich eindrucksvoller zurück. Besonders stark ist, wie gut das Spiel den Charme des Originals bewahrt und gleichzeitig moderner wirkt. Die Seefahrten, die karibische Atmosphäre und das Leben als Pirat fühlen sich immer noch frei und abenteuerlich an, aber die überarbeitete Präsentation macht alles klarer, lebendiger und insgesamt deutlich hochwertiger. Genau so sollte ein gutes Remake funktionieren: vertraut bleiben und trotzdem neu begeistern. Auf der PS5 Pro kommt diese Welt offenbar besonders gut zur Geltung. Schärfere Details, saubere Bildqualität und eine insgesamt deutlich stärkere technische Präsentation sorgen dafür, dass man noch tiefer in die tropische Spielwelt eintaucht. Das Meer wirkt bedrohlicher, die Inseln stimmungsvoller und die Kämpfe spürbar intensiver. Dadurch bekommt das Spiel eine frische Energie, ohne seinen Charakter zu verlieren. Gerade bei einem Spiel wie Black Flag ist das entscheidend, weil nicht nur die Geschichte, sondern auch das Gefühl der Freiheit trägt. Das Remake macht aus diesem Kern kein anderes Spiel, sondern ein deutlich schöneres und moderneres Erlebnis. Wer das Original mochte, bekommt hier die Chance, es in einer Form zu erleben, die dem Klassiker wirklich gerecht wird. Am Ende zeigt Assassin's Creed Black Flag Resynced vor allem eines: Ein starkes Spiel wird durch ein gutes Remake nicht ersetzt, sondern neu glänzen gelassen. Spielen wird wohl etwas teurer Zuletzt bei der Diskussion über die Steam Machine haben wir ja gehört, dass Hardware immer knapper und dadurch teurer wird. Dass sich das auch auf die alten Konsolen auswirkt dürfte so aber recht neu sein. Microsoft plant ordentliche Preiserhöhungen bei der bestehenden Konsolengeneration. Borncity berichtet von: Die Xbox Series S mit 512 GB wird dann knapp 500 Euro kosten, die 1-TB-Version schlägt mit rund 600 Euro zu Buche. Noch teurer wird es bei der High-End-Xbox Series X: Die Digital Edition ohne Laufwerk kostet künftig etwa 750 Euro, das Modell mit Disc-Laufwerk sogar rund 800 Euro. Preise sollen ab dem 1.8. gelten, wir nehmen vorher auf, deshalb können wir das nicht überprüfen. Eigentlich sollten die alten Dinger doch billiger werden. Zum Vergleich: Preise auf der Microsoft Webseite am 27.07.2026: Series X Digital, weiß, 1TB 549,99€ Series X Laufwerk Galaxy Black, 2TB 699,99€ Series S All Digital weiß 512 349,99€ Series S All Digital weiß 1TB 399,99€ Mitternacht Mitternacht von Ferris MC, Afrob feat. Jan Delay ist ein fetter Deutschrap-Track, der sofort Druck macht und mit seiner Energie hängen bleibt. Der Song hat genau diese Mischung aus Selbstbewusstsein, Härte und Flow, die einen starken Rap-Track ausmacht. Besonders stark ist, wie die drei Künstler zusammen funktionieren. Ferris MC bringt Präsenz und Kante rein, Afrob liefert diesen markanten, souveränen Rap-Style, und Jan Delay setzt mit seiner unverwechselbaren Art einen eigenen Akzent. Zusammen entsteht ein Sound, der nicht glatt wirken will, sondern direkt, eigenständig und voller Haltung ist. Der Track hat dieses typische Gefühl von Nacht, Straße und Spannung, das perfekt zum Titel passt. Mitternacht klingt nicht nach Hintergrundmusik, sondern nach einem Song, der Lautstärke braucht und Raum einnimmt. Genau das macht ihn so stark: Er wirkt entschlossen, trocken und dennoch eingängig. Auch heute hat der Song noch diesen besonderen Reiz, weil er aus einer Zeit kommt, in der Deutschrap stark über Persönlichkeit, Wortwitz und Attitüde funktioniert hat. Das macht den Track nicht nur cool, sondern auch zeitlos auf seine eigene Art. Er steht für eine Phase, in der deutscher Rap Charakter hatte und klar nach vorne ging. Am Ende bleibt Mitternacht ein Track, der zeigt, wie gut Deutschrap sein kann, wenn Haltung, Flow und Energie zusammenkommen. Ein echter Banger, der seinen Platz in der deutschen Rap-Geschichte verdient hat. Kommt das lineare glotzen zurück? Manche (inkl. Peppi) hatten gehofft, dass mit dem Streaming das goldene Zeitalter des Medienkonsums anbricht. Am Anfang war es das auch. Günstig, gute Auswahl an Inhalten, überschaubare Kosten und Anbieter. Nun gibt es viel mehr Anbieter, alle sind teurer und man muss eigentlich für jede dritte Serie wechseln wenn man nicht doppelt bezahlen will. Netflix merkt das wohl auch an immer mehr Kündigungen. Da kam nun wohl die Idee auf einen ganz alten Weg zu gehen. Offenbar denkt Netflix über das einführen von linearen Sendern nach. Was wir davon halten und wir dazu stehen im Vergleich zu Amazons senderkonzept besprechen wir in der Episode. Sony Wut! Das Ende der physischen Disc-Produktion bei Sony für PlayStation ist für viele Kunden und Gamer ein ernstes Problem. Es geht dabei nicht nur um Nostalgie, sondern um echte Fragen von Besitz, Freiheit und Verfügbarkeit. Wer Spiele heute nur noch digital kauft, ist stärker von Plattformen, Preisen und Lizenzmodellen abhängig. Eine Disc kann man weiterverkaufen, verleihen oder einfach ins Regal stellen. Eine digitale Version bleibt dagegen an ein Konto und an die Regeln des Anbieters gebunden. Genau das macht viele Spieler unruhig, weil ihnen ein Stück Kontrolle über ihre eigene Sammlung verloren geht. Für Sammler und Vielspieler ist das besonders bitter. Physische Editionen haben einen klaren Wert, der über den reinen Spielstart hinausgeht. Sie sind greifbar, unabhängig und oft auch langfristig sicherer als digitale Käufe, die theoretisch jederzeit aus dem Shop verschwinden können. Wenn Sony diesen Weg weiter konsequent verlässt, wird das Gaming für viele weniger frei und mehr von Konzernentscheidungen bestimmt. Dazu kommt ein praktischer Nachteil: Nicht jeder hat schnelles oder unbegrenztes Internet. Große Downloads, Updates und Neuinstallationen sind schon heute ein Thema. Disc-Versionen waren für viele eine einfache und verlässliche Lösung. Wenn diese Möglichkeit wegfällt, trifft das vor allem Kunden, die auf Komfort, Beständigkeit und echte Wahlfreiheit angewiesen sind. Am Ende ist das Ende der Disc-Produktion kein kleiner Fortschritt in Richtung Zukunft, sondern für viele ein Rückschritt. Es schwächt den Gebrauchtmarkt, macht Spieler abhängiger und nimmt dem Medium ein wichtiges Stück Unabhängigkeit. Genau deshalb ist dieser Schritt für Kunden und Gamer ein großes Problem.

Irrfahrt Christopher Nolans The Odyssey wirkt wie ein Film, der von Anfang an auf große Bilder und starke Stimmung setzt. Schon die Idee des Stoffes passt zu Nolans Stil: eine Reise voller Gefahr, Zweifel und Spannung, erzählt nicht klein, sondern mit dem Anspruch auf echtes Kinoerlebnis. Bildgewaltig scheint hier das passende Wort zu sein. Man erwartet keine glatte, leichte Abenteuerreise, sondern monumentale Bilder, weite Räume und Szenen, die sich förmlich ins Gedächtnis brennen. Nolan nutzt solche Stoffe oft, um aus einzelnen Momenten etwas Großes zu machen, und genau das dürfte auch hier spürbar werden. Die Reise wird nicht nur erzählt, sie wird als visuelles Erlebnis inszeniert. Düster ist der Film vermutlich ebenso. Bei Nolan steckt hinter jeder großen Geschichte meist auch ein Gefühl von Bedrohung, Verlust oder innerem Konflikt. Das passt perfekt zu einem Stoff wie The Odyssey, in dem es nicht nur um Heimkehr, sondern auch um Prüfung, Irrweg und Überleben geht. Statt reiner Abenteuerromantik entsteht so eine schwerere, ernstere Atmosphäre. Gerade darin liegt die Stärke von großem Kino: wenn Bilder, Klang und Erzählung zusammenkommen und mehr erzeugen als bloße Handlung. The Odyssey dürfte genau so ein Film sein, der nicht einfach unterhält, sondern beeindruckt. Er will nicht nebenbei laufen, sondern den Raum füllen und das Publikum komplett in seine Welt ziehen. Das ist Kino mit Anspruch, Wucht und Nachhall. Kurz gesagt: Nolan macht aus The Odyssey vermutlich kein klassisches Abenteuer, sondern ein düsteres, visuell überwältigendes Epos, das zeigt, warum Kino auf der großen Leinwand am stärksten ist. Deswegen war Matze im weltweiten größten IMAX. Noch eine Irrfahrt Wer sich wohl auch ein wenig verfahren hat ist Apple. Die Vision pro war ja ne nette Idee aber nicht wirklich das, worauf die welt gewartet hat. Das hat wohl nun auch Apple festgestellt und konzentriert sich auf das, was wohl die größeren Chancen bei der breiten Masse hat – AR Brillen. Geplant sind wohl zwei Varianten, eine mit und eine ohne Displays. Und nochmal mit dem Schiff unterwegs Assassin's Creed Black Flag Resynced wirkt wie genau das Remake, auf das viele Fans gewartet haben. Schon das Original gehört für viele zu den stärksten Teilen der Reihe, und die neue Version bringt dieses Gefühl auf der PS5 Pro noch einmal deutlich eindrucksvoller zurück. Besonders stark ist, wie gut das Spiel den Charme des Originals bewahrt und gleichzeitig moderner wirkt. Die Seefahrten, die karibische Atmosphäre und das Leben als Pirat fühlen sich immer noch frei und abenteuerlich an, aber die überarbeitete Präsentation macht alles klarer, lebendiger und insgesamt deutlich hochwertiger. Genau so sollte ein gutes Remake funktionieren: vertraut bleiben und trotzdem neu begeistern. Auf der PS5 Pro kommt diese Welt offenbar besonders gut zur Geltung. Schärfere Details, saubere Bildqualität und eine insgesamt deutlich stärkere technische Präsentation sorgen dafür, dass man noch tiefer in die tropische Spielwelt eintaucht. Das Meer wirkt bedrohlicher, die Inseln stimmungsvoller und die Kämpfe spürbar intensiver. Dadurch bekommt das Spiel eine frische Energie, ohne seinen Charakter zu verlieren. Gerade bei einem Spiel wie Black Flag ist das entscheidend, weil nicht nur die Geschichte, sondern auch das Gefühl der Freiheit trägt. Das Remake macht aus diesem Kern kein anderes Spiel, sondern ein deutlich schöneres und moderneres Erlebnis. Wer das Original mochte, bekommt hier die Chance, es in einer Form zu erleben, die dem Klassiker wirklich gerecht wird. Am Ende zeigt Assassin's Creed Black Flag Resynced vor allem eines: Ein starkes Spiel wird durch ein gutes Remake nicht ersetzt, sondern neu glänzen gelassen. Spielen wird wohl etwas teurer Zuletzt bei der Diskussion über die Steam Machine haben wir ja gehört, dass Hardware immer knapper und dadurch teurer wird. Dass sich das auch auf die alten Konsolen auswirkt dürfte so aber recht neu sein. Microsoft plant ordentliche Preiserhöhungen bei der bestehenden Konsolengeneration. Borncity berichtet von: Die Xbox Series S mit 512 GB wird dann knapp 500 Euro kosten, die 1-TB-Version schlägt mit rund 600 Euro zu Buche. Noch teurer wird es bei der High-End-Xbox Series X: Die Digital Edition ohne Laufwerk kostet künftig etwa 750 Euro, das Modell mit Disc-Laufwerk sogar rund 800 Euro. Preise sollen ab dem 1.8. gelten, wir nehmen vorher auf, deshalb können wir das nicht überprüfen. Eigentlich sollten die alten Dinger doch billiger werden. Zum Vergleich: Preise auf der Microsoft Webseite am 27.07.2026: Series X Digital, weiß, 1TB 549,99€ Series X Laufwerk Galaxy Black, 2TB 699,99€ Series S All Digital weiß 512 349,99€ Series S All Digital weiß 1TB 399,99€ Mitternacht Mitternacht von Ferris MC, Afrob feat. Jan Delay ist ein fetter Deutschrap-Track, der sofort Druck macht und mit seiner Energie hängen bleibt. Der Song hat genau diese Mischung aus Selbstbewusstsein, Härte und Flow, die einen starken Rap-Track ausmacht. Besonders stark ist, wie die drei Künstler zusammen funktionieren. Ferris MC bringt Präsenz und Kante rein, Afrob liefert diesen markanten, souveränen Rap-Style, und Jan Delay setzt mit seiner unverwechselbaren Art einen eigenen Akzent. Zusammen entsteht ein Sound, der nicht glatt wirken will, sondern direkt, eigenständig und voller Haltung ist. Der Track hat dieses typische Gefühl von Nacht, Straße und Spannung, das perfekt zum Titel passt. Mitternacht klingt nicht nach Hintergrundmusik, sondern nach einem Song, der Lautstärke braucht und Raum einnimmt. Genau das macht ihn so stark: Er wirkt entschlossen, trocken und dennoch eingängig. Auch heute hat der Song noch diesen besonderen Reiz, weil er aus einer Zeit kommt, in der Deutschrap stark über Persönlichkeit, Wortwitz und Attitüde funktioniert hat. Das macht den Track nicht nur cool, sondern auch zeitlos auf seine eigene Art. Er steht für eine Phase, in der deutscher Rap Charakter hatte und klar nach vorne ging. Am Ende bleibt Mitternacht ein Track, der zeigt, wie gut Deutschrap sein kann, wenn Haltung, Flow und Energie zusammenkommen. Ein echter Banger, der seinen Platz in der deutschen Rap-Geschichte verdient hat. Kommt das lineare glotzen zurück? Manche (inkl. Peppi) hatten gehofft, dass mit dem Streaming das goldene Zeitalter des Medienkonsums anbricht. Am Anfang war es das auch. Günstig, gute Auswahl an Inhalten, überschaubare Kosten und Anbieter. Nun gibt es viel mehr Anbieter, alle sind teurer und man muss eigentlich für jede dritte Serie wechseln wenn man nicht doppelt bezahlen will. Netflix merkt das wohl auch an immer mehr Kündigungen. Da kam nun wohl die Idee auf einen ganz alten Weg zu gehen. Offenbar denkt Netflix über das einführen von linearen Sendern nach. Was wir davon halten und wir dazu stehen im Vergleich zu Amazons senderkonzept besprechen wir in der Episode. Sony Wut! Das Ende der physischen Disc-Produktion bei Sony für PlayStation ist für viele Kunden und Gamer ein ernstes Problem. Es geht dabei nicht nur um Nostalgie, sondern um echte Fragen von Besitz, Freiheit und Verfügbarkeit. Wer Spiele heute nur noch digital kauft, ist stärker von Plattformen, Preisen und Lizenzmodellen abhängig. Eine Disc kann man weiterverkaufen, verleihen oder einfach ins Regal stellen. Eine digitale Version bleibt dagegen an ein Konto und an die Regeln des Anbieters gebunden. Genau das macht viele Spieler unruhig, weil ihnen ein Stück Kontrolle über ihre eigene Sammlung verloren geht. Für Sammler und Vielspieler ist das besonders bitter. Physische Editionen haben einen klaren Wert, der über den reinen Spielstart hinausgeht. Sie sind greifbar, unabhängig und oft auch langfristig sicherer als digitale Käufe, die theoretisch jederzeit aus dem Shop verschwinden können. Wenn Sony diesen Weg weiter konsequent verlässt, wird das Gaming für viele weniger frei und mehr von Konzernentscheidungen bestimmt. Dazu kommt ein praktischer Nachteil: Nicht jeder hat schnelles oder unbegrenztes Internet. Große Downloads, Updates und Neuinstallationen sind schon heute ein Thema. Disc-Versionen waren für viele eine einfache und verlässliche Lösung. Wenn diese Möglichkeit wegfällt, trifft das vor allem Kunden, die auf Komfort, Beständigkeit und echte Wahlfreiheit angewiesen sind. Am Ende ist das Ende der Disc-Produktion kein kleiner Fortschritt in Richtung Zukunft, sondern für viele ein Rückschritt. Es schwächt den Gebrauchtmarkt, macht Spieler abhängiger und nimmt dem Medium ein wichtiges Stück Unabhängigkeit. Genau deshalb ist dieser Schritt für Kunden und Gamer ein großes Problem.

Zyadatar companies aaj AI ko bas ek marketing sticker ki tarah use kar rahi hain, asal problem koi nahi solve kar raha. Jab tak aap ye nahi samjhenge ki AI se sirf kaam fast karna hai ya koi aisi cheez karni hai jo pehle impossible thi, tab tak aap piche hi rahenge. Abhishek Rungta ji pichle kaafi samay se apni teams ke saath actual workflows mein AI integrate karke unhe automate kar rahe hain. Is episode mein hum dekhenge ki kaise bina apna data risk kiye ek personal "second brain" banaya jata hai.00:00:00 - 00:01:43 - Introduction00:01:43 - 00:04:36 - The evolution of the AI landscape and adoption00:04:36 - 00:07:13 - Integrating AI into business workflows00:07:13 - 00:13:00 - Data privacy risks and AI security00:13:00 - 00:17:21 - Hardware setup and using dedicated AI computers00:17:21 - 00:21:37 - Building a personal "second brain" with AI00:21:37 - 00:33:48 - AI's impact on jobs and the future of work00:33:48 - 00:46:04 - Advice for graduates and career growth00:46:04 - 1:00:20 - Insights on young founders and business pitfalls1:00:20 - 1:06:42 - AI use cases for studios and retail1:06:42 - 1:07:55 - Book recommendations Connect With Pritika -Podcast Related Emails - connect@pritika.coInstagram- https://www.instagram.com/pritika.looniaListen to the full podcast here - https://www.youtube.com/@PritikaLooniaOfficial Facebook - https://www.facebook.com/captainpritika/Learn From Me - www.pritika.co Listen to my podcast on - Jio saavn - https://www.jiosaavn.com/shows/sage-up-with-pritika-loonia/2/ZukCx7qhBVQ_ Spotify- https://open.spotify.com/show/7ErewAP263SgLXOUE8V0SI?si=f0c13ec52bb74062 Apple Podcast- https://podcasts.apple.com/in/podcast/sage-up-with-pritika-loonia/id1517629945

In this episode, Buddy and Ross discuss their recent experiences with outdoor activities, hunting, shooting competitions, and the technical aspects of tracking collars. They share insights on gear, training, and the challenges of outdoor sports, offering practical tips and personal stories. In this episode, Buddy and Ross delve into the complexities of GPS collar technology, troubleshooting, and repair challenges faced in the field. They share insights on signal synchronization, hardware issues, and the realities of fixing high-tech hunting gear, offering valuable tips for enthusiasts and professionals alike. Key Topics: Impact of weather on hunting and tracking Technical analysis of tracking collars and signal accuracy Training children and beginners in outdoor activities Strategies for competitive shooting and target practice Personal stories and lessons learned in outdoor sports GPS collar signal synchronization and interference Hardware repair challenges and solutions Impact of temperature and environment on device performance The importance of proper maintenance and troubleshooting Insights into Garmin and other collar brands' technology     We would like to thank those who support this podcast. Special thanks to Double U Hunting Supply for sponsoring this episode. www.dusupply.comhttps://www.youtube.com/@DoubleUHuntingSupply/podcasts

In today's Cloud Wars Minute, I explore OpenAI's bold move into AI-powered consumer hardware and what it reveals about the future of trust in artificial intelligence.   Highlights 00:03 — OpenAI is adding yet another string to its bow as it becomes increasingly recognized as more than just its flagship ChatGPT product. Now, the company is reportedly developing its first consumer hardware device: a human-like AI companion that lives in the home. 00:21 — The report suggests that OpenAI is creating a portable, screenless AI smart speaker, not the AI phone that many people are expecting. As you might expect, the device will integrate with ChatGPT and handle questions, send messages, play media, and enable smart home controls. 00:38 — All pretty comparable to the existing smart speakers on the market, like Amazon's Echo. However, OpenAI's device is also expected to include cameras and sensors to get a better understanding of where it is and the context. While mechanical moving elements will give it more presence. Over time, the AI will learn about its owner and become increasingly personalized. 01:01 — Now, beyond the obvious—and by that I mean OpenAI's potential foray into consumer electronics and all the potential battles that might start between the leaders in that space—there's something really interesting about the ambition here and what it says about where we are today. Not long ago, the discussion was all about trust. 01:21 — Could we get consumers to trust AI enough to get the most from it? Looking at this investment, I think the answer is yes. This is an AI that not only observes and listens to its owner, but actively changes to adapt to their likes, dislikes, and personalities, passively and without waiting for requests. This is a big jump. Visit Cloud Wars for more.

Computerwissen-Experte Martin Koch weiß, dass es viel Geschick braucht, die Hardware beim Handy zu reparieren. Aber "Zickereien" verursachen häufiger auch die Software oder der volle Speicher. • Sichern Sie sich jetzt die Geschenkprämie mit dem Smartphone-Service-Paket: https://lpm.computerwissen-verlag.de/1/7917/Android-1-2-3-Smartphone-Service-Paket/?campaignId=99397 • Kostenlos Computerwissen TV schauen: https://plus.computerwissen.de/cw-tv • Sie möchten die gratis Technikschau erhalten, um nichts zu verpassen? https://www.info.computerwissen-online.com/computerwissen-tv • Expertengeprüfte Artikel zu sämtlichen Technikthemen finden Sie hier: https://plus.computerwissen.de • www.Computerwissen.de

Seit 1. Juli firmiert der Schweinfurter IT-Refurbisher bb-net als Econocom Remarketing. Geschäftsführer Marco Kuhn erzählt, wie man Leasinggeräten einen zweiten Lebenszyklus schenkt, welchen Schwankungen der Markt ausgesetzt ist und dass man mit generalüberholter Hardware selten schlechter fährt als mit Neuware.

Fri, 31 Jul 2026 18:39:55 +0000 https://feed.neuezwanziger.de/link/21941/17397907/ea46c6b3-77f7-43b6-8790-ea471c6f67f2 aa8bbc25d497130750da57aef4dbf3e0 Stefan und Wolfgang besprechen den Juli 2026 Unterstützende Werbung Incogni Hol dir deine persönlichen Daten zurück mit Incogni! Nutzt den Code ZWANZIGER über den Link unten und erhaltet 60% Rabatt auf das Jahresabo: https://incogni.com/zwanziger# Saily Erhalte einen exklusiven Rabatt von 15 % auf die Datentarife von Saily! Verwende beim Bezahlen den Code „zwanziger“. Lade die Saily-App herunter oder besuche https://saily.com/zwanziger Links Tickets für den Sommersalon! Ausführliche Shownotes und Quellenverzeichnis: im Forum Komm' in den Salon. Es gibt ihn via Webplayer & RSS-Feed (zum Hören im Podcatcher deiner Wahl, auch bei Apple Podcasts und Spotify). Alle Infos dazu: neuezwanziger.de 00:00:00 Es war Juli 2026 — Begrüßung von Texel aus, direkt hinein in die Personalie des Monats. 00:00:53 Jens Spahn — Rücktritt als Minister mit Mandat; Doppelmoral-Kritik von SPD bis Grünen, gestürzt von den Frauen der Union. Vergleich mit Anne Spiegel: Familienthemen geben den Ausschlag. 00:11:28 Nolans Odyssee — IMAX-Fetisch und Professoren-Faktenchecks am Trailer; die ausgedachte Geschichte wäre so misstrauisch zu lesen wie Politikererzählungen. 00:15:51 Die Finals — 40 deutsche Meisterschaften zeitgleich in Hannover; junges ZDF, alte ARD-Fragen. 00:20:17 Sommersalon — Live-Salon am 22. August in Frankfurt, Karten über die Website. 00:21:51 Hitze und Brände — Waldbrände in Südeuropa, Dürre und Feuerverbot auf Texel. 00:23:42 Merz' Fleißarbeit — These: Merz arbeitet nicht, wo Merkel und Kohl Interessenausgleich betrieben; Politik-Podcasts als Deskription ohne Analyse, während Austerität und Aufrüstung unverhandelt bleiben. 00:32:13 Leihmutterschaft — Ethikrats-Chef Frister (Dlf) und Brosius-Gersdorf (ZDF) pro regulierte Leihmutterschaft; Wolfgang zieht eine rote Linie gegen Bauchvermietung, Stefan will altruistische Einzelfälle zulassen. 01:31:10 Mamdani zu Bregmans Frage — Regieren brauche Absurdität und Ehrlichkeit; erklärungsbedürftig ist danach die Unehrlichkeit von Rutte oder Merz. 01:36:54 Salon-Lektüre — Nächste Lektüre: Theo Bakers „How to Rule the World" über Stanford. 01:39:25 KI-Investmentblase — Tooze: Die Bewertungen tragen nur, wenn Amerika Rechenzentrum der nicht-chinesischen Welt wird. Zombie-Unicorns, einbrechende Cashflows, 40 Prozent des S&P 500 — Stefans Fazit: hochgefährlich. 02:25:41 Xi zu KI — Xis erste Rede auf der World AI Conference: Open Source, menschliche Kontrolle, UN-Governance. Zwischen benevolentem Führer und Dominanzanspruch; Parallelen zu Putin 2001 und Kluges Antikrieg. 03:01:03 KI-War zwischen China und Amerika — Open-Weight-Modelle als AI-Dumping: 87 Cent statt 50 Dollar pro Million Tokens. Der Destillationsvorwurf fällt auf die Ankläger zurück; am Ende die Verhinderung eines Lock-in-Kapitalismus. 03:41:01 Lokal AI — Modelle auf eigener Hardware als nächste Entwicklung, das Balkonkraftwerk für KI; Aufruf an die Hackerszene und den Chaos Communication Congress. 04:01:56 Kermanis Hinweise zum Gelöbnis — Kermani am 20. Juli: Pflicht zum Widerstand statt Gehorsam, Merz namentlich als wankelmütig benannt. 04:06:32 China und das Öl — Warum der Ölpreis nicht explodierte: China halbierte seine Importe — Vorbau gegen das Malacca-Dilemma und Hebel gegenüber Trump. 04:33:10 Kanzlergesetze — Schirachs Sieben-Jahre-Kanzler mit drei Gesetzen; dagegen Manows Abwahl-Argument und Stefans Gegenentwurf aus Gremien und ertüchtigtem Parlament. 04:44:30 The Farlands — TikToks Randgebiete zwischen Brainrot und Folklore-Horror, erschlossen über Zahlencodes; Bourdieu, Long Tail und die Entzauberung der Algorithmen. 05:02:03 Salon-Hinweise — Sommersalon-Tickets, Bakers Stanford-Buch als nächste Lektüre, Abo über Steady, Apple oder Patreon. full Stefan und Wolfgang besprechen den Juli 2026 no Stefan Schulz und Wolfgang M. Schmitt 18570

Catch up on all the headlines in NFL, College Football, NBA, MLB, Golf and NHL news with "What is Trending" for July 30, 2026.

Ray Schwetz gets business empowerment from Joey Costello, Director of Strategic Partnerships for Costello's Ace Hardware, your neighborhood store across Long Island for hardware, home-improvement, grilling, lawn & garden, and more.

On this episode of For Mac Eyes Only: With a long-standing commitment to accessibility, Apple develops their operating systems with everyone in mind, but we're discovering that users of Apple's devices are finding features that everyone can take advantage of. Join Mike and Darren as they explore a few of their favorite Accessibility settings in macOS and iOS and how they use them. Mike shares a FMEO Quick Tip for adding extensions to your contacts. We close the show with Mike's Essential App pick: Desktop Curtain!

Roku raises streaming device prices as YouTube Premium adds Peacock. Hear the latest on Xbox PC backward compatibility and Amazon Luna's Prime Video app. The post Entertainment 2.0 #715 – Roku Hardware Prices Soar as YouTube Premium Bundles Peacock appeared first on The Digital Media Zone.

Your AI bill just stopped behaving like a software bill. For twenty years, IT leaders got very good at counting seats: buy a hundred, pay for a hundred. Then AI swapped the seat for a meter, and the number stopped holding still.This episode follows the burn from three vantage points: a financial analyst rationing a $250-a-month token budget he tore through in two days; the tech executive who watched enterprise AI bills climb 7x, 10x, 20x; and the IT leader at a 300-person company who refused to solve it with a usage dashboard. Along the way: Meta's leaked internal token leaderboard, Uber blowing its entire annual AI budget by April, and the uncomfortable question of who profits when everyone's told to use more.In this episode:Why token-based pricing breaks the budgeting playbook IT has relied on for two decadesWhat happens to the people using the tool when the meter starts running — and why rationing has a hidden costWhy measuring usage is the wrong scoreboard, and who benefits when you keep score anywayThe mid-market move that beats policing: measure centrally, push the judgment to managers, and get clear on what you're optimizing forFeaturing Brian Elliott, CEO of Work Forward; Daryl Dore, Senior Director of IT & Information Security at Higher Logic; and Benjamin, a financial analyst who spoke with us on condition of anonymity.Support our sponsor:This episode is brought to you by Sophos MDR. Running Microsoft security tools and drowning in alerts? Sophos MDR's 24/7 experts investigate and stop the real threats. >>> Learn more at: https://www.sophos.com/en-us/solutions/use-cases/microsoft#ITLeadership  #AICostManagement  #SaaSManagement  #FinOps  #EnterpriseAI  #TokenBurn  #ITAMShow Notes & ResourcesReferenced in this episodeMeta's internal AI token leaderboard (Fortune) — 85,000 employees ranked by token consumption; shut down days after it leaked.Uber burns its 2026 AI budget in four months (Forbes; TechCrunch) — adoption jumps 32% to 84% in a month; spend later capped.Jensen Huang on token consumption as a productivity signal (Tom's Hardware).Gartner: worldwide AI spending forecast to grow 47% in 2026 (Gartner).Zylo 2026 SaaS Management Index — the scale of wasted SaaS spend (Zylo).Brian Elliott's newsletter, Work Forward.Guest: Daryl Dore — Higher Logic.This episode's sponsor: Sophos MDR, in partnership with Softchoice — 24/7 managed detection and response for Microsoft environments. https://www.sophos.com/en-us/solutions/use-cases/microsoft The Catalyst by Softchoice is the podcast dedicated to exploring the intersection of humans and technology. 

This week, I detail Comcast's Q2 earnings, which, for the first time in six years, showed Peacock had a positive quarter, with $189 million in EBITDA. This positive news was offset by the loss of 280,000 cable TV subscribers and 167,000 broadband residential customers, with total company revenue down 1.2% YoY.I discuss the news that Roku is raising the MSRP of its streaming sticks and boxes from $10-$50, depending on the model, noting that the largest portion of Roku's overall device unit volume comes from the sale of third-party Roku-made TVs by OEM partners, not Roku boxes.Finally, I detail the final FIFA World Cup viewership numbers from YouTube, Cazé TV, TikTok, Fox/FOX One, and Telemundo/Peacock, highlighting the different measurement methodologies used, and mention Telemundo becoming the exclusive U.S. Spanish-language rights holder for the next UEFA men's club competitions.Podcast produced by Security Halt Media

Apple has launched a new hardware leasing initiative called Apple Upgrade in partnership with the fintech company Klarna. This program allows consumers to acquire iPhones, iPads, Macs, and Apple Watches for a low monthly fee, with the option to swap for newer models or purchase the device eventually. While the collaboration simplifies the user experience by leveraging Klarna's specialized financial infrastructure, some analysts view it as a response to rising product costs driven by the AI boom. Experts are divided on whether this move signifies a validation of the buy-now, pay-later model or highlights growing financial stress among consumers who can no longer afford upfront prices. Ultimately, the shift helps Apple maintain a steady cycle of product turnover while keeping its expensive ecosystem accessible to a broader audience.

Smart Home fängt oft harmlos an. Eine smarte Lampe hier, ein Sensor da, vielleicht noch ein Staubsaugerroboter mit App. Ein paar Wochen später hängt Hardware an Fenstern, steckt in Unterputzdosen oder funkt über drei verschiedene Protokolle durchs Haus. Und plötzlich stellt sich die Frage: Automatisierst du eigentlich schon sinnvoll oder sammelst du gerade nur sehr teure Learnings? Genau an diesem Punkt setzen wir in dieser Episode an.Wir sprechen mit Andrej Friesen über die wichtigsten Entscheidungen in der Heimautomatisierung und über das, was man gern früher gewusst hätte. Es geht um lokale Steuerung versus Cloud-Abhängigkeit, Home Assistant, Matter, Zigbee, WLAN, Thread, Ausfallsicherheit, physische Taster, Backups, Infrastruktur-Komplexität und die Risiken von Third-Party-Plugins und Supply Chain Attacken. Andrej organisiert Home Assistant Meetups, baut mit ESPHome und Pokipow eigene Hardware und podcastet im Smart-Hütte-Podcast über Smart Home und Self-Hosting. Kurz gesagt: jemand, der die Theorie kennt und die Box of Shame wahrscheinlich trotzdem nicht ganz vermeiden konnte.Wenn du ein Smart Home aufbauen willst, das nicht nur cool wirkt, sondern auch im Alltag, bei Ausfällen und für andere Menschen im Haushalt funktioniert, bekommst du hier jede Menge praktische Denkanstöße. Vielleicht nimmst du am Ende keine fünf goldenen Regeln mit, sondern genau die eine, die dir später Zeit, Geld und Nerven spart.Bonus: Wir klären ganz nebenbei auch, warum eine App noch lange keine echte Automatisierung ist.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

These sources examine modern methods for improving the efficiency and performance of large-scale AI models throughout their lifecycle. Research on Mixture of Experts (MoE) and the Chinchilla study highlight how specialized internal architectures and balanced data scaling can achieve superior results with less computational power. New advancements like CompreSSM allow models to become leaner by removing unnecessary components while they are still learning, rather than after training is complete. Furthermore, the analysis of quantization demonstrates that reducing numerical precision to 8-bit or 4-bit formats can significantly lower memory requirements and increase speed with minimal loss in quality. Together, these texts provide a roadmap for developing high-performance AI that is more accessible and cost-effective to deploy on current hardware.

Marotta and Tim Ring talk Diamondbacks, go through Social Studies, and give out Hardware.

Vince, Tim, Sammy, and Jarrett hand out awards for the best and worst of the weekend.

You plug in a new monitor. Windows detects the hardware, pulls down a vendor companion app, and the first thing you see is… a McAfee ad. That's the story that kicks off this episode, but the bigger issue is not just one annoying popup.Hardware setup has become a software delivery channel. Drivers, companion apps, RGB utilities, printer suites, vendor dashboards, trialware, telemetry, ads, and startup apps can all arrive through a process most users think of as “just making the device work.” Tom, Scott, and Kevin discuss where convenience turns into bloatware, why user consent matters, and how these trusted installation paths could be abused for worse than advertising.The discussion also covers a related Krebs on Security story about LG smart TV apps that allowed televisions to be used as residential proxy nodes. If monitors, TVs, printers, keyboards, and other peripherals are really networked software platforms, then consumers need to treat them more like endpoints and less like harmless appliances.Practical advice: check what gets installed after connecting new hardware, review Windows Startup Apps, uninstall vendor utilities you do not need, dig through smart-TV privacy and ad settings, and segment smart devices away from the computers and phones you use for sensitive work.Special thanks to Guardsquare for sponsoring this episode! Guardsquare is the leader in mobile application security, with multi-layered protection for your Android and iOS apps. Learn more at Guardsquare.com.** Links mentioned on the show **Tom's Hardware: Companies are now using automatic Windows installers to display adware through the Microsoft Store when you install new hardware https://www.tomshardware.com/software/windows/companies-are-now-using-automatic-windows-installers-to-display-adware-through-the-microsoft-store-when-you-install-new-hardware-customer-immediately-gets-mcafee-ads-on-their-pc-after-connecting-new-lg-monitor-heres-how-to-block-the-new-adsKrebs on Security: LG to Ban Residential Proxies from Smart TV Apps https://krebsonsecurity.com/2026/07/lg-to-ban-residential-proxies-from-smart-tv-apps/Hackread: LG monitors installing adware-like app on Windows PCs https://hackread.com/lg-monitors-install-adware-app-windows-pcs/** Watch this episode on YouTube **https://youtu.be/E-lsZbkbmI8** Become a Shared Security Supporter **Get exclusive access to bonus episodes, listen to new episodes before they are released, receive a monthly shout-out on the show, and get a discount code for 15% off merch at the Shared Security store. Become a supporter today by going to our YouTube channel's membership section: https://www.youtube.com/channel/UCg9CCDIYkDDqwEZ3UYaxjnA/join** Thank you to our sponsors! **SLNTVisit https://slnt.com to check out SLNT's amazing line of Faraday bags and other products built to protect your privacy. As a listener of this podcast you receive 10% off your order at checkout using discount code "sharedsecurity".** Subscribe and follow the podcast **Subscribe on YouTube: https://www.youtube.com/c/SharedSecurityPodcastFollow us on Bluesky: https://bsky.app/profile/sharedsecurity.bsky.socialFollow us on Mastodon: https://infosec.exchange/@sharedsecurityJoin us on Reddit: https://www.reddit.com/r/SharedSecurityShow/Visit our website: https://sharedsecurity.netSubscribe on your favorite podcast app: https://sharedsecurity.net/subscribeSign-up for our email newsletter to receive updates about the podcast, contest announcements, and special offers from our sponsors: https://shared-security.beehiiv.com/subscribeLeave us a rating and review: https://ratethispodcast.com/sharedsecurityContact us: https://sharedsecurity.net/contact

Compass Hardware in Charlestown, RI, is the subject of this week's program with Skip Graf, General Manager. We talk about his role in this over 60-year-old, locally owned institution. It's an amazing story of what originally started as a store that mostly sold coal to what has become a very diversified establishment. For more information, you can go to www.compasshardware.com

Die Aktie des chinesischen Chip-Herstellers CXMT legt nach dem Börsengang rund 470 Prozent zu. Auch das chinesische KI-Unternehmen Moonshot AI möchte an die Börse. Laut SRF Shanghai-Korrespondent Lukas Messmer ist China den USA sowohl bei der Software als auch bei der Hardware auf den Fersen. SMI: +0.7%

Tesla held its quarterly earnings call for its huge second quarter. I've got highlight clips from what the Tesla executive team said on the call, plus all of the biggest news to come out of it, my FSD v14 Lite impressions now that the long-awaited update for the Hardware 3 cars has gone wide, and more! If you enjoy the podcast and would like to support my efforts, please check out my Patreon at https://www.patreon.com/teslapodcast and consider a monthly or (10% discounted!) annual pledge. Every little bit helps, and you can support for just $5 per month. And there are stacking bonuses in it for you at each pledge level, like early access to each episode at the $5 tier and the weekly Lightning Round bonus mini-episode (AND the early access!) at the $10 tier! And NO ADS at any Patreon tier! Also, don't forget to leave a message on the Ride the Lightning hotline anytime with a question, comment, or discussion topic for next week's show! The toll-free number to call is 1-888-989-8752. INTERESTED IN A FLEXIBLE EXTENDED WARRANTY FOR YOUR TESLA? Be a part of the future of transportation with XCare, the first extended warranty designed & built exclusively for EV owners, by EV owners. Use the code RTL250 to get $250 off your XCare Premium or Battery & Drive Unit Protection Plan using the one-time payment option! Offer expires July 31! Go to https://xcare.com to find the extended warranty policy that's right for you and your Tesla. P.S. Get 15% off your first order of awesome aftermarket Tesla accessories at AbstractOcean.com by using the code RTLpodcast at checkout. Grab the SnapPlate front license plate bracket for any Tesla at https://everyamp.com/RTL/ (don't forget the coupon code RTL too!). Enhance your car with cool carbon-fiber upgrades from RPMTesla.com. And make your garage door foolproof with the Infinity Shield – get yours at https://www.infinity-shield.com and use the promo code RTL at checkout for a $25 discount.

R.I.P. John C. Dvorak.In other news ... the Ryzen 7700X3D prices drop already, Nvidia N1X CUDA counts and an 88-core CPU (of course).  Windows on ARM gets a real boost with Nvidia GPU drivers, HP is just as bad as we all through regarding printer cartridges, and LG discovers a new low by auto-installing bloatware when you plug in a display.  Plenty of AMD good news in the datacenter and Open AI shows that unshackling their creations will probably be as bad as we thought.  Steam hardware sales and so much more in the show!  Enjoy.Timestamps:00:00 Intro01:09 Patreon02:21 Food with Josh06:08 RIP John C. Dvorak08:08 NVIDIA's 88-core CPU09:26 RTX Spark Win 11 driver and Arm dGPU support12:07 Josh leads us into more NVIDIA talk and later defends datacenters19:16 AMD gets two big datacenter wins22:43 Ryzen 7 7700X3D already had a price drop24:11 ADATA chairman warns RAM shortage will last another decade24:37 Samsung Unpacked29:21 HP fined for nefarious printer cartridge practices - in India34:44 LG adware38:18 (In)Security Corner48:46 Gaming Quick Hits58:44 Picks of the Week1:12:01 Outro ★ Support this podcast on Patreon ★

This week's podcast is about AgiBot, a fast rising full stack robot business in Shanghai.You can listen to this podcast here, which has the slides and graphics mentioned. Also available at iTunes and Google Podcasts.Here is the link to the TechMoat Consulting.Here is the link to our Tech Tours.Why I like AgiBot:Has rapidly expanded into a full suite of robotsGoing after all the humanoid industries and use casesBuilding an almost full AI tech stack. Everything except chips.Founded and run by Huawei executives. Moving very fast.A good strategy. It appears similar to Huawei.AgiBot's strategy is to flood the market with affordable humanoids while growing the entire ecosystem with open datasets and open models. They are scaling fast in manufacturing, usage and training.They mass-produce the hardware at scale so thousands of identical robots can collect real-world data in homes and factories.At the same time they open-source the entire stack: the million-trajectory AgiBot World dataset, the GO-1 foundation model, the training tools, and the GenieSim simulation twin—so researchers and developers can build skills and applications on top without paying anything.AgiBot's affordable humanoid platform as infrastructure. Hardware sales and ecosystem services become the revenue, not software licensing.Now in the top 1-2 in humanoid sales.AgiBot shipped approximately 5168 humanoid units in 2025. ---------I am a consultant & keynote speaker on how to increase digital growth and strengthen digital AI moats.I am the founder of TechMoat Consulting, a consulting firm specialized in increasing digital growth and strengthening digital AI moats. Get in contact here.I write (a lot) about digital growth and digital AI strategy (3 best selling books, +2.9M followers on LinkedIn). There is a free book and email newsletter below.My Moats and Marathons book series is a framework for building and measuring competitive advantages in digital businesses.This content (articles, podcasts, website info) is not investment, legal or tax advice. The information and opinions from me and any guests may be incorrect. The numbers and information may be wrong. The views expressed may no longer be relevant or accurate. This is not investment advice. Investing is risky. Do your own research.Support the show

In this one, we talk about the hardware we use. ==== Special Thanks to Our Patrons! ==== https://thelinuxcast.org/patrons/ ===== Follow us

Die Spielebranche steht am Scheideweg: Während Entwickler in generativer KI die Rettung vor explodierenden Kosten sehen, fürchten Programmierer und Kreative um ihre Arbeitsplätze und viele Gamer haben Angst vor einer Flut aus seelenlosem "KI-Slop". Lea spricht mit Entwickler Paul Redetzky, KI-Podcast Moderator Fritz Espenlaub und KI-Creator Andreas O. Loff über die aktuellen Entwicklungen der generativen KI im Gaming. Erleben wir gerade eine echte Revolution oder doch nur den Anfang vom Ende menschlicher Kreativität? Mehr Infos zu tic tac findet ihr unter: https://www.tictac.com/de/de/ (Werbung) Alle Links zum GameStar Podcast und unseren Werbepartnern: https://linktr.ee/gamestarpodcast

Anatoly Zak reports Roscosmos is currently developing the Russian Orbital Station (ROS) to replace its segment of the International Space Station after retirement. This modular station is intended to test hardware for future lunar exploration, including a habitat module similar to the American Gateway project. Russia still hopes for potential international cooperation with NASA and the ESA in lunar orbit. The ROS will typically host two to three cosmonauts for long-term deep space validation missions. (16)1952

Episode #612: This week on XoneBros, we talk about console generations, why hardware leaps don't feel as dramatic as they used to, how AI demand is affecting RAM, chips, and PC building, and why the next Xbox generation could be shaped by more than just raw power.We also get into Crimson Desert playtime, cooking mechanics, Gears of War nostalgia, motion sickness from old-school roadie running, Halo on a modern engine, and whether old games hit differently when you didn't grow up with them.Who are the XoneBros?We are your exclusive Xbox Series X & Game Pass weekly podcast. We are more than just a podcast though, we are a positive gaming and Xbox community. We are a group of friends who love gaming, comics, fantasizing about superpowers, and making lame jokes.We strive to bring you news, informative discussion, and rocking good times on a weekly basis all while discussing the world that is Xbox. We are the brothers you never had and the sisters you always wanted... we are the XoneBros. If you are looking for a positive gaming environment, you are always welcome here!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Support Us On YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our Discord⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X1TheGamer Daily Xbox News ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MrMcspicey Know Your Game

Topics: Leave yourself a good starting point LLM's and software/hardware projects Linear gage projects Vision system, Keyence vs DIY Blum laser install fixed Air compressors down

Governor Greg Abbott has pivoted his campaign rhetoric to a call for limits on data centers - but he fails to use the power he has right now to do anything to slow their explosive spread. Meanwhile, Ken Paxton is desperately fundraising outside the state as James Talarico out-raises him 3 to 1 at home - Paxton is apparently doing all he can to avoid a live debate.KXAN Austin: https://www.kxan.com/news/data-center-debate-grows-across-texas-as-leaders-push-for-more-oversight/Tom's Hardware: https://www.tomshardware.com/tech-industry/data-centers/bnef-nearly-doubles-its-us-data-center-power-forecast-to-194gwSan Antonio Current: https://www.sacurrent.com/news/san-antonio-news/ken-paxton-receives-unwelcome-surprise-at-washington-d-c-fundraiser/Texas Standard: https://texasstandard.org/stories/cornyn-donors-paxton-senate-race-talarico-fundraising/This election year, you can support the real free press by investing in pro-democracy, pro-justice, pro-gressive storytelling. Become a monthly donor or increase your giving TODAY for our Summer Sustaining Donor Drive today, and we'll thank you with "perks for progress" - plus you'll get a shoutout on the Daily Dispatch: huge thanks to new monthly supporter Carolyn Frawley! Join Carolyn and the rest of the Progress Texas family now at ⁠⁠⁠⁠⁠⁠⁠https://act.progresstexas.org/a/summersustainers26⁠⁠⁠⁠⁠⁠⁠.Check out the Substack version of the Daily Dispatch, which delivers each pod to your inbox and frequently includes extra video goodies: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://substack.com/@progresstexas⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Progress Texas is now part of the lineups at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠KPFT-FM in Houston,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Empower House Radio in San Antonio, and KZSM True Community Radio in San Marcos! Make a tax-deductible contribution to our radio initiative⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠HERE⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Find our web store and other ways to support our important work at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://progresstexas.org⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.⁠⁠

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

After Alphabet (GOOGL) raised CapEx expectations in earnings, investors are taking a step back despite strong numbers from Google's parent company. James Demmert argues Alphabet is a buy at current levels, pointing to a substantial bottom line beat and explosive cloud revenue growth as signs its CapEx is offering real ROI. He names Nvidia (NVDA) and ASML (ASML) as key hardware names to watch. As for Tesla (TSLA), James calls it a "great company, not a great stock" due to its high valuation. ======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Dave, Esmee, Rob and Marcel wrap up an incredible Season 5, reflecting on the biggest technology trends, the most memorable conversations, and the fantastic guests who joined us along the way. From AI and cybersecurity to quantum computing and digital transformation, it's been a season full of insights, innovation, and inspiration.Thank you to all our listeners, guests, and supporters for being part of the Realities Remixed journey. We wish you a fantastic summer and look forward to bringing you even more thought-provoking conversations when we return in September for Season 6!TLDR00:27 – Season 5 reflections and key trends02:38 – Summer observations and random interruptions05:01 – From Cloud Realities to Realities Remixed08:05 – Winning 3 Global Marketing Awards10:50 – Esmee's journey and what's next14:05 – Technology trends revisited15:20 – Cybersecurity and investment challenges18:34 – Scaling AI beyond pilots25:41 – The reality of business transformation 33:35 – AI governance, agents, ethics, and the future of work47:00 – Knowledge retention and collaboration49:20 – Hardware innovation for AI51:17 – Macro trends and standout guests57:45 – Digital sovereignty and resilience1:08:00 – Why systems thinking must change1:12:00 – The Octopus Organisation1:18:24 – Summer plans and what's aheadHostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Retail, restaurant, and warehouse owners often overlook POS hardware until something breaks. This episode unpacks the compatibility traps, barcode scanner choices, and installation mistakes that cost time and money, plus practical tips to keep your checkout running smoothly. Spartan POS City: Seminole Address: 7215 Bryan Dairy Road Website: https://spartanpos.com Phone: +1-888-895-4767

En el episodio de hoy repasamos las novedades más destacadas de la movilidad eléctrica: el futuro Audi A2 e-tron, la integración de Starlink en el Tesla Cybercab, las mejoras del FSD para Hardware 3, el nuevo MG 07, la oferta del Dongfeng MAGE PHEV y el BMW iX3, que ya es más barato que su equivalente de gasolina.

In this episode Ashley and Collin dive into the design details behind Smile Design Studio in Roseville. From the Pinterest board that started it all to the $200,000 lighting package that got vetoed, the $45,000 fireplace that never happened, and the cosmetic wing that patients cannot stop raving about.What You'll Hear In This Episode:The Design Vision If Tom Ford met dentistry, what would that look like? Ashley shares the Pinterest board of boutique hotels, restaurants, and resorts that inspired the entire aesthetic of Smile Design Studio. Neutrals, blacks, clean lines, and a vibe that is anything but a dental office. She also shares the one scent detail that had patients asking questions the moment they walked through the door.The $200,000 Lighting Reality Check Ashley wanted uplighting, downlighting, chandeliers, and statement fixtures throughout the entire space. The lighting package quote came back at $200,000. She scaled back. Tastefully. And the office still looks stunning.The Fireplace That Never Was A see-through fireplace separating the lounge from the consultation room was on the original design. The quote came in at $45,000. It did not make the cut. Neither did the full kitchen in the break room. Sometimes champagne taste has to meet the hotdog budget.The Design Box After flying to Denver to meet with the architect for the 3D walkthrough, Ashley received a large box of every material sample for the office. Flooring, tile, millwork, wallpaper, and hardware. She and Collin spent hours going through it and there is an unboxing video to prove it.Hardware, Furniture, and Finishing Touches Matte black hardware throughout. Chandeliers from Crate and Barrel, West Elm, and CB2. Sofas and lounge chairs from Rove Concepts. A white sofa in the patient lounge that the team Scotchguards on the regular and would do again in a heartbeat.The Coffee Bar A single serve machine that grinds beans fresh for every cup, sparkling waters, flavored syrups, and creamers. Ashley also shares the California ADA accessible counter height detail that catches a lot of practice owners off guard.The Cosmetic Wing The crown jewel of Smile Design Studio. A dedicated cosmetic photo studio with a black wall and light boxes, a private patient restroom with river rock flooring, a concierge desk, and Ashley's personal office with a couch and TV for patients during long procedures. It feels like a completely separate business within the practice and it is the single thing patients compliment most.The Advice Worth Writing Down Know your long game. Design for who you are and where you are going. And if this is your first location do not go balls to the wall. Save that for when you have already proven the concept.Resources Mentioned: Scent diffuser and espresso machine links coming in the show notes and newsletter soonThank You to Our PartnersNet32: The dental marketplace that helps practice owners stop overpaying for supplies. Compare and save at net32.com/themakingof.Studio 8E8 — Dentistry's story-driven growth agency for startups. s8e8.com/vslKasper Opportunity Finder: Fill those empty chairs and reclaim lost revenue with one click. Get it free at meetkasper.com/register.Support the showFind Out MoreThank you for listening to The Making Of podcast. If you enjoyed it, please share with anyone you think will gain value from the show by clicking on one of the sharing tabs above.SUBSCRIBE to our NEWSLETTER HEREAlso, please consider leaving an honest review on iTunes. It helps other listeners find the show, and I would be forever grateful.Questions or comments? Feel free to contact us at - themakingofadental@gmail.comFollow us on Instagram or Facebook and improve your dental practice every day!Have you subscribed? Don't miss a single episode!

In this episode of the HUNTR Podcast, we're joined by Gregg Farrell of Tactacam to take a deep dive into the technology behind the all-new Reveal 4.0 cellular trail camera and what it means for today's deer hunters. We explore the latest innovations in trail camera technology and discuss how they're changing the way hunters scout, gather information, and prepare for the season more efficiently than ever. We also discuss trail camera strategy, advancing technology, and where the future of whitetail hunting is headed. Whether you're looking to improve your trail camera setup or simply become a more effective hunter this season, this conversation is packed with valuable insights. Thanks for tuning in, and we'll catch you next week. Sit back, relax, and enjoy the show.0:00 Introduction & Early July Buck Activity3:39 The Origin of Tactacam & Reveal8:36 Reliability, Customer Service & Connectivity21:47 The Reveal 4.0 Battery Breakthrough26:52 Best Camera Settings for Performance40:46 The New Flex Pack Battery System1:08:07 Reveal Pro 4.0 Hardware & Dual-Lens Technology1:33:00 Summer to Fall Trail Camera Strategy1:54:27 The New Bluetooth Wind Sensor2:07:12 Hunting Ethics, Technology & Regulations2:45:21 Reveal 4.0 Pricing & AvailabilityCheck out the all new Reveal 4.0:https://www.tactacam.com/SUBSCRIBE TO THE CHANNEL:https://www.youtube.com/c/HUNTRTUBEShop HUNTR Merch:https://wearehuntr.com/HUNTR Podcast is presented by:Hoyt Archery: https://hoyt.com (Code HUNTR for 20% off apparel)DeerGro: https://www.deergro.com (Code HUNTR for 15% off)Predator Camo: https://www.predatorcamo.com/ (Code HUNTR for 20% off)Beast Broadheads: https://beastbroadheads.com/ (Code HUNTR for 10% off)Lone Wolf Custom Gear: https://www.lonewolfcustomgear.com/ (Code HUNTR for 10% off your first purchase)RackHub: https://www.rack-hub.com/huntr (Code HUNTR for 10% off)Pure Wildlife Blends: https://www.purewildlifeblends.com (Code HUNTR for 10% off)Primos: https://www.primos.com/ (Code HUNTR for 15% off)Bushnell: https://www.bushnell.com/ (Code HUNTR for 15% off)HHA: https://www.hhasports.com/

-Hoymiles released its HiFlow Pro microinverter, which allows for use of solar power drawn through a typical household outlet. -Anthropic's record-breaking $1.5 billion settlement for an AI copyright lawsuit filed by writers has been approved by a federal judge in San Francisco. -NVIDIA's Rubin generation of AI infrastructure is fully liquid-cooled. No fans, no cold aisles, no walking into a freezer when you're in the server room. Just liquid circulating quietly through a closed loop. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Gareth, Justin, and Michael at Skewed and Reviewed discuss San Diego Comic-Con, Marvel News, Call Of Duty Movie, And Fallout 5 on a new Skewedcast 00:00 Hardware/game News and Reviews 05:51 San Diego Comicon News 16:22 Kevin Feige Marvel announcement 26:28 Call of Duty Movie 32:47 Fallout Franchise news

Bickley, Marotta, Sammy, and Jarrett hand out awards for the best and worst of the weekend.

Bickley and Marotta talk Diamondbacks, go through Social Studies, and give out Hardware.

The guys are back, this time the wheel of doom returns! We're going to have to defend takes that are not ours. Should be fun. ==== Special Thanks to Our Patrons! ==== https://thelinuxcast.org/patrons/ ===== Follow us

Open source AI models just hit 41% of Hugging Face downloads — and the real cost gap is 90% or more. Here's what that means for your business.The numbers moved fast this week. Chinese open-weight models now dominate downloads, the quality gap versus closed models has shrunk to 3.3%, and a new repo opens on Hugging Face every seven seconds. Half of the Fortune 500 is already running open source models in production.Isar walks through the new frontier open models Kimi K3 and DeepSeek V4, Thinking Machines Lab's first release, Satya Nadella's Token Capital essay, the new state-level AI laws in New York and Illinois, BCG's AI at Work report, and the Apple lawsuit hanging over OpenAI's hardware plans.In this session, you'll discover:Why Chinese open-weight models now account for 41% of Hugging Face downloadsHow Kimi K3 and DeepSeek V4 price against top US closed models ($15 vs $50 per million output tokens, down to 87 cents)What Satya Nadella's "Token Capital" and reverse information paradox mean for your company's dataWhat New York's data center moratorium and Illinois Senate Bill 315 change for AI companiesWhy BCG found that AI strategy beats tool access — and 72% of CEOs now own the AI decisionBCG "AI at Work: Strategy Matters More Than Tools" — the 12,000-person study covered in this episode — https://www.bcg.com/publications/2026/ai-at-work-why-strategy-matters-more-than-toolsAI 2040 "Plan A" paper — the 90-page proposal to delay superintelligence until 2040 discussed in the rapid fire — https://ai-2040.com/About Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

As part of our summer replay series, we're revisiting one of our favorite conversations on the future of AI infrastructure. SemiAnalysis founder Dylan Patel joins Erin Price-Wright, Guido Appenzeller, and Erik Torenberg to examine the rapidly evolving economics of AI hardware, from GPUs and custom silicon to data centers, power, and the global race for compute. The conversation explores NVIDIA's competitive advantages, the rise of custom chips from Google, Amazon, and Meta, the economics of frontier AI models, and the infrastructure constraints shaping the industry's next phase. They also discuss AI startups, export controls, robotics, enterprise software, and why simply copying NVIDIA isn't enough to build a winning AI hardware company. Whether you're building AI products, investing in infrastructure, or trying to understand where the industry is headed, this conversation offers a practical look at the forces shaping the future of compute.   Resources: Follow Dylan Patel on X: https://x.com/dylan522p Follow Erin Price-Wright on X: https://x.com/espricewright Follow Guido Appenzeller on X: https://x.com/appenz Learn more about SemiAnalysis: https://semianalysis.com/dylan-patel/ Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

I got to experience the long-awaited FSD v14 Lite for the older Hardware 3 cars, and I want to tell you all about it. Plus: the Model Y gets a "cool" new accessory, California offers up a new EV incentive, the US market gets a very affordable new EV (but there's a catch), and more! Oh, and if you'd like to watch the FSD v14 (AI4) vs. FSD v14 Lite (HW3) comparison video I did with Tesla Raj, here it is: https://www.youtube.com/watch?v=31aHojmgMEc If you enjoy the podcast and would like to support my efforts, please check out my Patreon at https://www.patreon.com/teslapodcast and consider a monthly or (10% discounted!) annual pledge. Every little bit helps, and you can support for just $5 per month. And there are stacking bonuses in it for you at each pledge level, like early access to each episode at the $5 tier and the weekly Lightning Round bonus mini-episode (AND the early access!) at the $10 tier! And NO ADS at any Patreon tier! Also, don't forget to leave a message on the Ride the Lightning hotline anytime with a question, comment, or discussion topic for next week's show! The toll-free number to call is 1-888-989-8752. INTERESTED IN A FLEXIBLE EXTENDED WARRANTY FOR YOUR TESLA? Be a part of the future of transportation with XCare, the first extended warranty designed & built exclusively for EV owners, by EV owners. Use the code RTL250 to get $250 off your XCare Premium or Battery & Drive Unit Protection Plan using the one-time payment option! Offer expires July 31! Go to https://xcare.com to find the extended warranty policy that's right for you and your Tesla. P.S. Get 15% off your first order of awesome aftermarket Tesla accessories at AbstractOcean.com by using the code RTLpodcast at checkout. Grab the SnapPlate front license plate bracket for any Tesla at https://everyamp.com/RTL/ (don't forget the coupon code RTL too!). Enhance your car with cool carbon-fiber upgrades from RPMTesla.com. And make your garage door foolproof with the Infinity Shield – get yours at https://www.infinity-shield.com and use the promo code RTL at checkout for a $25 discount.