Podcasts about RPA

Has communism ever worked? Conservatives say no. Democratic socialists point to countries like Sweden as proof that socialism has. To find the answer, we tell the story of one group that both sides of the argument fail to mention...a group that shows the answer is far more nuanced than either side often admits. Episode powered by: Ruff Greens - the premium supplement created to boost your dog's energy, digestion, and overall wellbeing. Use Discount Code “RPA” to claim your FREE JumpStart Trial Bag at https://www.RuffGreens.com. The Licorice Guy ...the delicious gourmet licorice made in America. Use promo code "RPA15" for 15% off at https://licoriceguy.com/ Join the FamBam by becoming a Red Pilled America backstage subscriber. Visit https://www.RedPilledAmerica.com and click join in the top menu! Follow us at: https://www.RedPilledAmerica.com https://x.com/RedPilled_USA https://www.instagram.com/redpilledamerica https://www.instagram.com/adryana_jadeSupport the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

Morrilton City Council discusses recycling center, trash collection; city seeking full-time parks director; Brewer Lake levels at 318.8 MSL; Dumas earns award from RPA; Atkins Fire Department awarded Rural Community Grants; Atkins Council to consider fireworks show at 150th celebration; MHS to hold organization and club fair separate from orientation; permits required to use WMAs by non-hunters; graduation requirements; Central Arkansas Devilettes accepting applications.

In Episode 111, Mike offers a "stream of consciousness" delivering observations and takeaways from leading recent cohort sessions for sales leadership teams implementing the RPA accountability framework. A senior executive asked:  "As we roll out the RPA meeting as prescribed, what are two pieces of advice (the two keys) that will guarantee success? Without hesitating, Mike responded with:   Cadence  Consistency Mike explains why the regular rhythm of the manager-salesperson 1:1 accountability meeting is what actually changes behavior (and culture), and why consistency across managers is critical. Throughout the organization, sales leaders must conduct the RPA meeting  with a similar tone, progression, and rigor. Mike also cautioned leaders about confusing a "fat" pipeline with a healthy one. A pipeline packed with stale, aging, going-dark deals may at first glance appear "full," but not one that will necessarily translate into closed business. He goes on to make the case that a "fresh" pipeline may actually be more valuable than a fat one filled with stalled, old opportunities that are growing mold! And that is exactly why Mike believes the single most important accountability question sales leaders can ask every rep is: "What NEW opportunities did you create last month? What new deals are in your pipeline that were not here during our last RPA meeting?" If you are serious about strengthening accountability, (without micromanaging or demotivating sellers), improving sales management effectiveness, and driving MORE New Sales, this episode is for you. ____________________________ The final Supercharge Your Sales Leadership Event of the year is October 7th at The Porsche Experience Center in Atlanta. Join Mike and 55 driven sales leaders tackling the biggest topics in sales management.   Learn more and register here:  www.mikeweinberg.com/atlanta2026 ____________________________ This episode is sponsored by Pursuit Sales Solutions. If you are looking for help adding A-player talent to your team, contact Mike's friends at pursuitsalessolutions.com/weinberg

Zach sits down with married couple A.D. and Courtney, who met on their first day at the University of North Texas and have been together, on and off, for over a decade. A.D. runs a coaching brand built around helping men value themselves inside marriage, and Courtney works in a corporate hybrid role while raising their three kids. Zach opens by asking what makes them tick, and gets a lot more than a love story. What follows is a conversation about inherited patterns, the cost of chasing validation, and what happens when two people in the same marriage see the same dynamic completely differently.A.D. traces his approach to relationships back to a chaotic, unstable childhood and a mother whose love felt conditional on performance. He built a framework he calls relationship mathematics, arguing that in any couple, one partner is always the pursuer and one is the pursued, and that most men undervalue themselves in ways that show up as resentment, moodiness, or letting a partner speak to them poorly. Courtney pushes back in real time, particularly on his claim that women need chaos to feel fulfilled. She also brings a sharper account of their two rounds of couples therapy, describing what it felt like to sit across from a therapist who did not stay neutral.What makes this one land is the disagreement itself. A.D. and Courtney do not perform a united front. They correct each other, laugh at each other's memory of their own meet cute, and land in different places on some of the biggest ideas in the episode. Listeners get a real look at how one couple negotiates whose theory of the relationship actually holds up, and why that negotiation might matter more than either theory on its own.Key TakeawaysA man who does not value himself cannot be treated better than he treats himself, no matter how much money or status he accumulatesGrowing up performing for love often turns into needing constant validation as an adultCouples therapy fails fast when a client feels the therapist has taken a side instead of staying neutralFraming a partner's directness as "bullying" can flatten a real need into a character flawNo two people in a relationship show up with equal levels of investment, and naming that imbalance out loud can be clarifying rather than threateningA shared metaphor, like a chessboard, can hold two different perspectives without either partner needing to be wrongHealing a strained parent relationship sometimes starts with a health scare or crisis, but the amends still matter even if the timing feels unfairBroad theories about "how women are" or "how men are" tend to break down the moment they meet an actual partner sitting next to youGuest InfoA.D. and Courtney are a married couple based in the North Dallas area. A.D. works as a relationship coach under the brand name "the Kingmaker," coaching men on self-worth and what he calls the desire equation, and previously worked as an RPA developer before moving into coaching. Courtney works a hybrid corporate role. They have three children together: a 14 year old son, a 3 year old son, and a 1 year old daughter.https://www.instagram.com/adthekingmaker/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

W sobotę Stany Zjednoczone kończą 250 lat. 4 lipca 1776 r. w Filadelfii ogłoszono deklarację niepodległości trzynastu wówczas brytyjskich kolonii. Bunt przeciw królowi Jerzemu III i wygrana wkrótce wojna doprowadziły do powstania państwa, które na sztandarach wypisało słowa: „wolność” i „równość” – i które przez niemal sto lat budowało swoją potęgę na niewolnictwie. Mity pogranicza i raju dla imigrantów, dwie wojny światowe, które uczyniły z kraju mocarstwo – te opowieści przez dekady kształtowały wyidealizowany obraz USA, państwa odpowiedzialnego także za wiele wojen i zamachów stanu. Swoje urodziny Ameryka obchodzi głęboko podzielona, a jej prezydent traktuje jubileusz jako okazję do promocji głównie siebie. Czym dziś dla Amerykanów są słowa deklaracji niepodległości oraz idee, które przyświecały Ojcom Założycielom? Czy Stany Zjednoczone są właśnie w najtrudniejszym momencie swoich dziejów?Czy Białoruś zaangażuje się bardziej w wojnę z Ukrainą? Na razie Łukaszenka ustępuje przed żądaniami Zełenskiego, jednak opozycja ostrzega przed militaryzacją kraju; Rosja zaś, borykająca się z niedoborami benzyny, pilnie potrzebuje białoruskich zasobów. Co zdecyduje Mińsk?W Wenezueli trwa żałoba narodowa po trzęsieniu ziemi, w wyniku którego zginęły co najmniej dwa i pół tysiąca osób. Jak radzą sobie mieszkańcy? W jaki sposób klęska żywiołowa wpłynęła na politykę?Aż 25 tysięcy przebywających nielegalnie w RPA cudzoziemców opuściło kraj w obawie przed konsekwencjami pozostania po terminie wyznaczonym przez kilka organizacji obywatelskich. Kto i co stoi za ruchem antyimigranckim?Ważą się losy międzynarodowej kontroli nad Bośnią i Hercegowiną. Czego chce Waszyngton, a czego Moskwa, w tym najmniej stabilnym państwie Bałkanów Zachodnich?Rozkład jazdy: (03:00) Marcin Fatalski: Urodziny USA(33:31) Hanna Liubakova: Łukaszenka znów pomiędzy(57:09) Podziękowania(1:04:12) Marcin Pośpiech: Wenezuela po trzęsieniu ziemi(1:19:23) Dobek Pater: RPA: marsze przeciw cudzoziemcom(1:42:06) Aleksandra Wojtaszek: Amerykanie w Bośni: najpierw inwestycje, potem pokój(2:03:48) Do usłyszenia---------------------------------------------Raport o stanie świata to audycja, która istnieje dzięki naszym Patronom, dołącz się do zbiórki ➡️ ⁠https://patronite.pl/DariuszRosiak⁠Subskrybuj newsletter Raportu o stanie świata ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠➡️ ⁠https://dariuszrosiak.substack.com⁠Koszulki i kubki Raportu ➡️ ⁠https://patronite-sklep.pl/kolekcja/raport-o-stanie-swiata/⁠ [Autopromocja]

As financial institutions move beyond AI experimentation, the primary bottleneck is no longer capability; it is governance. In this episode, Yoav Naveh, Co-Founder and Co-CEO at Reindeer AI, examines how enterprises can operationalize agentic AI inside regulated financial workflows, including the two-loop oversight model that keeps agents compliant, self-correcting, and continuously improving. The conversation covers what it means for agents to self-heal when they encounter exceptions, how decision logic should be governed in practice, and how to approach the build-versus-buy decision in a way that places accountability ahead of capability. This episode is sponsored by Reindeer AI. In this episode, we cover how enterprises in banking and financial services are operationalizing agentic AI inside regulated workflows — and what governance infrastructure actually makes it sustainable at scale. To go deeper on this topic and learn how banks are using RPA to reduce operational costs in repetitive workflows and applying anomaly detection to identify fraud in real time, download our free PDF report, "AI in Banking Executive Cheat Sheet," at emerj.com/bcs1

Czasem wiatr szumiący wśród łanów kukurydzy okazuje się być oddechem zła absolutnego, które z kawałkiem stali w dłoni czeka w mroku, gotowe uderzyć i odebrać życie. W 2005 roku okolice Kapsztadu w RPA terroryzowała bestia, która uwierzyła że jest bogiem, panem życia i śmierci. Pozbawiła życia co najmniej 16 osób, a kilkadziesiąt naznaczyła swym piętnem na zawsze. To historia mordercy zwanego "Jezusem z Philippi" - Jimmy'ego Maketty.

Domingo apasionante y viajero el que tenemos hoy día 28 de junio para cerrar mes y temporada en Un buen día para viajar, ya que nos vamos al concejo de Grao y os traemos un especial desde la Casa de Cultura de la capital moscona, hoy en Rpa especial Un buen día para viajar desde Grao!!!... iniciaremos el recorrido con el Alcalde, José Luis Trabanco, para hablar de muchas cosas de pasado presente y futuro del concejo y muchos proyectos sobre la mesa…a continuación Cristina Coalla concejala de turismo nos habla de retos en el ámbito turístico, y de muchas cuestiones más sobre Grao y su desarrollo…Gustavo Adolfo Fernández es el Cronista Oficial de Grao y por supuesto tenía que estar, y nos contará increíbles historias desde los orígenes del concejo, hasta la más cercana actualidad, de su toponimia, de sus maravillas artísticas, del camino de Santiago, de los indianos y por supuesto del mercado…en la segunda hora tendremos a Laura Iglesias técnica de turismo del ayuntamiento y a Daniel Menéndez que nos hablarán de proyectos interesantes de cara a los próximos meses, muchas actividades en rutas y recorridos por el concejo y sobre todo por una experiencia apasionante en unas recreaciones teatrales vinculadas con el Camino Primitivo en la zona del Alto del Fresno…nos acompañarán también José Álvaro García y Álvaro Peláez presidente y vicepresidente del Grupo Montañero Moscón, para contarnos su historia, las múltiples actividades que desarrollan y que dinamizan el concejo siempre con las montañas como protagonistas…y cerramos con José Manuel Arias al frente de la Asociación de Amigos del Camino de Santiago Primitivo, ya que los 9 km que recorren Grao de esta trascendental vía de peregrinaje vertebran el concejo sin género de dudas,nos hablará de las actividades que se desarrollan con el Camino Primitivo como eje de desarrollo…dos horas muy especiales para cerrar temporada desde la Casa de Cultura de Grao!!

Hey, it's Alex. Next month is my 40th b-day, and honestly, my wish for that month is to have a week like this week. A very chill, almost nothing announced week.This week started strong, with Sakana announcing FUGU (AI router) that can beat Fable (which we didn't get back yet), and then... quiet. The most important thing in AI this week from a release standpoint is that GLM 5.2 from Z.AI is having it's DeepSeek moment! Tons of new love for this model since last week! (+ we have the fastest GLM 5.2 deployment in the world with CW inference!) The rest we can quickly count on one hand, Anthropic added Claude to Slack (which made folks hate Andrej Karpathy), OpenAI announced their own inference chip, GPT 5.6 will be delayed and the US Gov will decide who gets it (yes really) and Sean Grove joined us to talk about Linzumi and his vision for running 10,000 agent hours per person per day. Oh and next week, is a special AI Engineer live stream from World's Fair! Don't miss itLet's get into it! Subscribe to never miss a beat! GLM 5.2 is having its DeepSeek moment (HF, CW Inference)We covered GLM 5.2 last week, but this week was when the rest verdict came in! We've never seen a better MIT licenced AI model! GLM 5.2 is scoring top scores on agentic benchmarks (Arena.ai), Design benchmarks, Legal tasks and full on software engineering tasks. The jump in generations from prevoius GLM is also massive and notable, as the lab is working on creating the next version of GLM (per the CEO's reply to Elon on X).Peter from Arena pulled up the Agent Arena numbers and they align with the vibe. GLM 5.2 sits above 5.1 but below Opus and Fable, which feels about right. Where it gets wild is Web Dev Arena: second place, right after Fable. Peter's take was that GLM has really good defaults. If you just say “give me a webpage” it gives you something nice. GPT models, by contrast, start off looking bad and need more steering.Last week, I asked my agents with GLM 5.2 to create a custom ThursdAI.news page for itself and it did a marvelous job! Look at that beautiful font, the castle it made... this is all just delignful. We also played Hassan's blind test on the show. It's a website that @nutlope built that lets you try and guess which webpage was built by which model. Nisten nailed it immediately by spotting Opus's circular buttons. Wolfram guessed right too. I got one wrong. The point isn't that GLM beats Opus, it's that you genuinely can't always tell which one costs 22 cents and which one costs 3 cents.Wolfram did flag that GLM is not good in German. First response already had mistakes. So if you're building for a non-English market, keep that in mind. It's a workhorse model, not a conversationalist. His approach: use GPT 5.5 for planning and discussion, GLM for the actual work, then GPT reviews. This weeks Buzz is all about GLM 5.2! First, we may have not been the fastest, but I'm glad to announce that we're the fastest provider to host GLM 5.2 on OpenRouter (at least at the time of writing this)! We're also not to shabby on the Artificial Analysis checks, clocking at #4 among the providers they tested for speed, TTFT and costAlso, Wolfram ran his WolfBench tests on GLM 5.2 and it's the best open model he's ever tested! In this new 3d view, wolfbench also shows the number of tokens it took for this test to run, and you can see that GLM 5.2 is fairly conservative with it's thinking budgets! Unsloth's 1-bit GLM 5.2 runs on a Mac Studio (X, HF)Shout out to Daniel Han and the Unsloth team, who took this 744B beast and quantized it down to a roughly 200GB GGUF that fits on a Mac Studio with 256GB of RAM. One bit still makes me laugh out loud. How does that even work. Nisten clarified it's a mixed quant, a true 1-bit would be under 100GB, but still.The wild part is the scores hold up. The 1-bit is within a point of GPT 5.5 on Frontier SWE, hits 62% on SWE-bench Pro, and 81% on Terminal-Bench. For a 1-bit quant that's incredible! AI's second-order effects: Apple is raising pricesThis one is AI news even though it doesn't look like it. Apple just raised prices across the board, base versions up around 20%, citing memory shortages. Same reason your RAM and SSDs cost two to three times what they did a year ago.We are so capacity constrained that memory is having its moment. Data center contracts are getting booked 18 months out, and here's the twist Nisten flagged: even open models you can run at home increase demand, because now a business says “great, we'll buy a rack of B200s and run it ourselves.” Sam Altman once said people saying “thank you” to ChatGPT costs them millions in generated “you're welcome” replies. Multiply that by a billion users. Even Intel is flying right now because anyone who can make a chip is winning.Is it worth it? I think yes. I love living in the era where Fable drops and we all get a taste of the future. But also I must admit this sucks and I hope that we'll unlock performance gains with the extra power all this AI is bringing to the world. But ask me again once the new iPhone hits and it's $300 more costly than the last one

RPA jeszcze niedawno było stawiane w roli outsidera, dziś świętuje historyczny sukces i pierwszy awans do fazy pucharowej mistrzostw świata.

Último fin de semana de junio y últimos dos programas de Un buen día para viajar en esta temporada, preparando ya nuevos temas para el mes de septiembre que será séptima temporada ya del programa…hoy 27 de junio las horas más cargadas de historia de Rpa!! Sara Moro nos lleva por el mundo del arte, con un artista asturiano, Ricardo Villoria, que expone en Oviedo una colección fotográfica de claras influencias de los bodegones pictóricos…A continuación Víctor Guerra nos lleva por los caminos Lenenses desde la Tercia hasta Busdongo en tierras de León previo paso a Asturias…. Faustino García cierra primera hora con Un buen día para viajar a las estrellas y nos hablará de los cometas, de su formación, características, órbitas y muchas más curiosidades…a continuación será el arqueólogo en el Instituto de Ciencias del Patrimonio del CSIC, Alfredo González Ruibal quien nos acompañe, su investigación se centra en arqueología contemporánea y arqueología del conflicto y ha hecho una tremenda investigación sobre las últimas trincheras de la Guerra Civil, las experiencias de los vencidos en los campos de concentración y los últimos momentos de las personas asesinadas por la dictadura franquista, además de las bases de la guerrilla, las casas de los campesinos y los barrios de chabolas en las grandes ciudades no olvidando lugares como el Pazo de Meiras y el Valle de los Caídos, y todo a través de la arqueología, apasionante…y cerramos con el periodista y escritor Bernardo Díaz Nosty que nos hablará de Mineros de Asturias, donde la figura de Manuel Llaneza y el Sindicato Minero son auténticos protagonistas…dos horas de radio e historia en Rpa!!!

In "The Dark Data Trap: Unlocking Logistics Documents with Tungsten Automation's Patrick Van Hull" Joe Lynch and Patrick Van Hull, Supply Chain Industry Consultant at Tungsten Automation, discuss how intelligent document processing eliminates manual data traps to drive logistics efficiency and cost savings. About Patrick Van Hull Patrick Van Hull, widely recognized as the Supply Chain Storyteller, helps organizations transform complexity into clarity. A multi-time "Top 25 Global Thought Leader and Influencer on Supply Chain" and Supply Chain Pro-to-Know, he focuses on supply chain digitalization and capability development, showing how operational details can drive resilience and performance across the value chain. Patrick's career spans more than two decades, with leadership and advisory roles at Apple, Dell, Rio Tinto, and CVS Health, Gartner, Deloitte, and SCM World, he became known for bridging practitioner expertise with executive-level insights to turn data and technology into impactful strategies and programs. Most recently, he has focused on helping enterprises use AI-powered intelligence to strengthen resilience and anticipate disruption. He holds degrees from the University of Michigan and Duke University Fuqua School of Business, and lectures on supply chain strategy at the University of Arkansas Walton School of Business. About Tungsten Automation Tungsten Automation, formerly Kofax, is the global leader in AI-powered document and workflow automation solutions, boasting a 40-year trusted legacy and a team of 2,200 employees across 40 countries, serving over 25,000 global customers. Our commitment to innovation and customer success has earned us industry recognition, including being named a Leader in the 2025 Gartner® Magic Quadrant™ for Intelligent Document Processing. Tungsten has also been recognized by other key analysts in areas such as Intelligent Automation and Process Orchestration. We are trusted to help businesses achieve unprecedented efficiencies and reduce costs through document and workflow automation, allowing them to scale and future-proof their business. Key Takeaways: The Dark Data Trap: Unlocking Logistics Documents In "The Dark Data Trap: Unlocking Logistics Documents with Tungsten Automation's Patrick Van Hull" Joe Lynch and Patrick Van Hull, Supply Chain Industry Consultant at Tungsten Automation, discuss how intelligent document processing eliminates manual data traps to drive logistics efficiency and cost savings. Tungsten Automation Profile: Formerly Kofax, Tungsten is a global leader in AI-powered Intelligent Document Processing (IDP) and advanced workflow automation. Backed by a 40-year legacy, 2,200 employees, and 25,000+ global customers, the company was named a Leader in the 2025 Gartner® Magic Quadrant™ for IDP. Their cloud-based platform sits cleanly over multiple, fragmented ERP and TMS networks to pull and push data seamlessly. Escaping the "Dark Data Trap": Moving a single international ocean container can require upwards of 30 separate documents. Because traditional TMS and ERP platforms can't read unstructured data (like dense PDFs, faxes, or Excel spreadsheets), this critical info becomes trapped "dark data." Tungsten uses IDP to automatically ingest, classify, and extract line-item data from over 40 different logistics document types, turning paper trails into structured, digital assets. Slashing AP Errors & Driving Revenue: Manual touchpoints in freight invoicing lead to constant billing discrepancies and human errors. Through automated multi-way matching, reconciliation, and automated exception handling, Tungsten drives "zero-touch" processing for order management and invoices. In one case study, acting as an automated "quality check" against contracted rates helped a major freight shipper capture an incremental $20 million in annual revenue. Preventing Customs and Shipment Delays: When data errors or missing documents hit customs or a port, shipments grind to a halt, triggering costly penalties, demurrage fees, and port congestion. Tungsten automates data extraction and email ingestion for customs clearance, validating regulatory documentation and compliance checks before shipments ever hit major bottlenecks. Accelerating Carrier and Supplier Onboarding: Traditional onboarding forces procurement teams into a weeks-long "paper chase" of manual risk assessments and compliance reviews. Tungsten uses automated self-service portals paired with automated risk assessments—such as using the platform to instantly verify the legitimacy of bank and credit statements—condensing onboarding timelines from weeks down to a matter of days. Bridging the Gap Between AI Hype and Reality: AI cannot solve supply chain issues without clean, unified data. Patrick notes that trying to run raw, messy documents entirely through an unguided Large Language Model (LLM) can cause the AI to run wild, exhausting months' worth of token allocations in a single week. Tungsten effectively solves this by embedding AI directly into workflows, blending traditional rules-based automation (RPA) for standard patterns with Generative and Agentic AI to manage highly complex exceptions. Elevating the Human Experience: Eliminating rudimentary data entry is ultimately a personal win for the workforce. Moving away from "swivel chair activity" and manual data chasing reduces friction and human error. By shifting repetitive tasks to automated workflows, logistics employees are freed up to use human ingenuity, focus on creative problem-solving, and ultimately enjoy more meaningful, higher-value work. Learn More About The Dark Data Trap: Unlocking Logistics Documents Patrick Van Hull | Linkedin Tungsten Automation | Linkedin Tungsten Automation Tungsten's Summits The Logistics of Logistics Podcast If you enjoy the podcast, please leave a positive review, subscribe, and share it with your friends and colleagues. The Logistics of Logistics Podcast: Google, Apple, Castbox, Spotify, Stitcher, PlayerFM, Tunein, Podbean, Owltail, Libsyn, Overcast Check out The Logistics of Logistics on Youtube

Introduction The rules that governed software pricing for a decade are breaking down. Per-user SaaS models made sense when users were people. Now that AI is automating those seats, the pricing logic collapses — and carriers, brokers, and insurtech vendors are all rethinking the economics at the same time. Michael Nadel is a partner and head of the global insurance practice at Simon-Kucher & Partners. He joined Josh Hollander for his second appearance on the show to dig into what the AI era is actually doing to monetization — from the pricing mistakes early movers keep making, to why outcome-based models are harder to execute than they are to sell, to a 50,000-session LLM shopping simulation that revealed what could become the next SEO arms race for insurance carriers.   Guest Bio Michael Nadel is a partner and head of the global insurance practice at Simon-Kucher & Partners, a global strategy consultancy focused on growth, pricing, and monetization. Before consulting, he spent time at CNA in strategy and innovation, and earlier led large-scale financial services implementation work at Accenture. He advises carriers, brokers, MGAs, and insurtech vendors on pricing strategy, and co-hosts an annual monetization masterclass at InsureTech Connect.   Key Topics From offense to defense — A year ago, clients asked how to monetize new AI features. Today the questions are more defensive: how does AI threaten my core product, my pricing model, and my existing revenue base? The two biggest AI pricing mistakes — Pricing before understanding what customers actually value, and jumping to outcome-based models before you can define or reliably deliver the outcome. Outcome-based pricing is the destination, not the shortcut — Buyers love paying only when value is delivered. The problem is definitional complexity — what counts as the outcome, who controls it, and what happens when results fall short. AI spend that looks like RPA in new packaging — Carriers running well-designed AI programs focus on workflow economics, not technology for its own sake. The recommended split: 70% on high-value workflow automation, 20% on data and governance, 10% on exploratory bets. GEO: the next SEO — Simon-Kucher simulated 50,000 insurance shopping sessions across 50 consumer personas and three major LLMs. What gets a carrier recommended by an LLM is not the same as what gets them ranked on Google. CMOs need to decide how to treat LLMs as a distribution channel now. Services businesses face the same disruption — Pure labor arbitrage is structurally challenged by AI. Firms that survive will combine domain expertise with AI-enabled delivery and evolve from vendors to partners.   Notable Quotes "You need to understand what people value before you prescribe a price. But very often, people build something, bring it to market, and then try to figure out why it isn't selling." "Automating a bad process simply creates a faster bad process." "It's not replacing system X with system Y — it's changing the way you work fundamentally. Orange juice to lemonade. Not a better way to make orange juice." "Last year, roughly one percent of our traffic came from LLMs. This year it was roughly five percent and increasing."   Resources Guest: Simon-Kucher & Partners: https://www.simon-kucher.com Michael Nadel on LinkedIn: (verify and add URL) Host & Organization: Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/ Horton International (USA): https://www.horton-usa.com/ Insurtech Leadership Podcast: https://www.linkedin.com/showcase/insurtech-leadership-show   Subscribe & Review If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Apple Podcasts, and Spotify.

Dziedzictwo apartheidu i nowa nienawiść. Sytuacja imigrantów w RPA. Zapraszam na rozmowę z prof. Andrzejem Polusem z Uniwersytetu Wrocławskiego o sytuacji społecznej w RPA.Partnerem Programu Afrykańskiego jest firma QUENDA. Infrastruktura z misją. Kompleksowe rozwiązania w zakresie bezpieczeństwa i obronności. Wejdź na www.quenda.plZapoznaj się z warunkami oprocentowania wolnych środków w OANDA TMS Brokers: https://go.tms.pl/bezpaszportuUM Inwestuj w fundusze ETF z OANDA TMS Brokers: https://go.tms.pl/bezpaszportuETF

ChatGPT tasks are back, Jack. ✅While we were collectively ping-ponging the Anthropic vs. U.S. government saga, the big tech AI players rolled out a TON of fresh AI features that are available today. ↳ Claude Design got a big upgrade↳ Google Vids got some serious AI sparkle↳ And there's a new Open Weights model king We'll break it all down. ChatGPT's Task Comeback, Claude's Design upgrade, Codex Copies your workflow and 7 other Fresh AI features you'll Want to use Today -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:ChatGPT Pulse Sunsetting and Tasks Comeback2. ChatGPT Scheduled Tasks Features and Access Tiers3. Claude Design June Update Overview4. WYSIWYG Editing and Design System Imports in Claude Design5. Claude Design Export Options and Third-Party Integrations6. Google Vids AI Avatars Upgrade with Veo 3.17. OpenRouter Fusion Multi-Model Synthesis Feature8. Claude Code Artifacts for Team and Enterprise Plans9. GLM 5.2 from ZAI Open Weights Model Overview10. GLM 5.2 Benchmarks and Enterprise Use Cases11. OpenAI Codex Record and Replay Feature Explained12. Codex Record and Replay vs. Traditional RPA ToolsTimestamps:00:00 Intro: 7 new AI features you can use today02:35 ChatGPT Tasks: Pulse is gone, Tasks are back04:31 Who has access to ChatGPT Tasks08:18 Claude Design June update overview09:14 WYSIWYG editing and Claude Code integration12:07 Claude Design export options and third-party integrations15:21 Google Vids AI Avatars upgrade18:15 OpenRouter Fusion multi-model synthesis21:54 Claude Code Artifacts for teams25:36 GLM 5.2 from ZAI open weights model29:02 OpenAI Codex Record and ReplayKeywords: ChatGPT Tasks, ChatGPT Pulse, OpenAI, scheduled tasks, proactive AI agent, Claude Design, WYSIWYG editor, Claude Code, design system import, PowerPoint export, Google Vids, AI avatars, Veo 3.1, Gemini 3.1 Flash, OpenRouter Fusion, model fusion, multi-model synthesis, Claude Code Artifacts, Claude Team plan, GLM 5.2, ZAI, open weights, MIT license, mixture of experts, Codex Record and Replay, RPA, workflow automation, Artificial Analysis, Hugging Face, Canva integrationSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

Where have all the conservative artists gone? Walt Disney. John Wayne. Bob Hope. Norman Rockwell. Not long ago, conservative artists were everywhere. Today they're remarkably hard to find. What happened? The answer isn't what most people think. We've had an epiphany on this topic that we wanted to share with you. Episode powered by: Ruff Greens - the premium supplement created to boost your dog's energy, digestion, and overall wellbeing. Use Discount Code “RPA” to claim your FREE JumpStart Trial Bag at https://www.RuffGreens.com. Follow us at: https://www.RedPilledAmerica.com https://x.com/RedPilled_USA https://www.instagram.com/redpilledamerica https://www.instagram.com/adryana_jade Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

Automation as Core Strategy: Aarin Bailey on RPA, AI, and Scaling MSP OperationsOn the Evolved Radio podcast, Todd interviews Aarin Bailey, COO at Webit Services and former COO at MSP Bots, about treating automation as a core MSP operating strategy. Aarin describes how his automation focus accelerated around COVID by chaining PowerShell scripts, later expanding into Python, GUIs, and modular systems connected via RESTful APIs, with much of the computation running outside the RMM on servers (including SQL and Python) while the RMM remains mainly a monitoring and job-push layer. They discuss whether RMM is a “zombie product,” the ongoing role of PSA/ticketing as a system of record, and managing complexity through separate modules and staff literacy in Python/RPA. Aarin explains build-vs-buy decisions driven by ROI and fit, cites automated triage/dispatch with ~98% accuracy and shifting token costs, argues AI should augment rather than replace humans, and emphasizes documentation, playbooks, and focusing on operational “bad” anomalies. They also cover client tolerance for AI, limiting client-facing AI after hallucinated ticket notes, skepticism about voice AI, and concerns about AI economics and subsidies.This episode is brought to you by Opsleader Pro. A place for MSP owners and managers to get the systems and tools they need to build a stable and growing MSP. Part group coaching, part peer group, everything you need to run a successful MSP. (00:00) - Automation First Mindset (01:10) - Aaron Origin Story (05:04) - From Scripts to Platforms (05:41) - Beyond the RMM Beehive (08:35) - Is RMM a Zombie (12:14) - Managing Complexity Safely (14:33) - Build vs Buy ROI (19:39) - Token Costs and Pair Coding (23:49) - AI Security Reality Check (27:34) - Scaling with Playbooks (30:12) - Hunt the Bad Stuff (30:59) - Blueprints Before Automation (32:46) - Ticket Volume and Vision (33:32) - Saying No as Integrator (35:44) - Healthy Disagreement Dynamics (37:08) - Client Facing vs Backend AI (40:05) - AI Hallucinations and Guardrails (43:05) - Voice AI and Live Answer (46:06) - Costs and Subsidized AI Era (49:26) - Outcome First and RPA Focus (51:36) - Wrap Up and Thanks

What’s an American? In Part Seven of our series, we tell the story of how Marxist intellectuals set the stage for a globalist dream – a United States where its citizens have a stronger allegiance with their ancestral lands than with America. What's An American? (Finale) airs Monday, June 15th, 2026. Episode powered by: The Licorice Guy ...the delicious gourmet licorice made in America. Use promo code "RPA15" for 15% off. Ethos - Protect your family with life insurance from Ethos. Get up to $3 million in coverage in as little as 10 minutes at ethos.com/rpa Ruff Greens - the premium supplement created to boost your dog's energy, digestion, and overall wellbeing. Use Discount Code “RPA” to claim your FREE JumpStart Trial Bag at RuffGreens.com.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

If your brokerage reps are still spending 10 minutes copying data from a shipper email, manually calculating lanes, and guessing on a spot rate, your business model is an operational failure. Today I am sitting down with Ricky Gonzalez, CEO of Tabi Connect, and tech strategist Rush Feldhacker to execute a forensic intervention on the massive technological velocity shift rewriting the rules of logistics. Tabi Connect has engineered an absolute powerhouse AI-driven Rate Management System (RMS) that completely automates freight quoting across shipper TMS platforms, digital bid boards, and unstructured email streams. With the truckload spot market hitting new peaks and primary carrier rejections sending urgent freight to the open market under intense time constraints, response velocity is the single greatest differentiator between winning a load and letting a competitor walk away with it. Ricky and Rush pull back the curtain on real-world case studies—including how Ryan Transportation doubled their spot bookings within 30 days of integrating Tabi Connect with Loadsmart's ShipperGuide platform. Discover how Tabi Connect replaces slow manual data entry with plain-English business rules, captures over 48 distinct operational data points per quote, and submits market-accurate pricing back to shippers in under two seconds flat. If you want to know how the top 100 transportation companies are protecting their margins and scaling quote volumes by 300% without adding overhead, this tactical briefing is your blueprint.   Inside this High-Stakes Briefing: The Velocity Imperative: Why API and RPA-connected brokers are capturing double the spot volume while manual entry shops get starved out of the routing guides. The Tabi Pricing Pressure Index: An exclusive breakdown of why broker spot margins are running hot at 18.3% and how to maintain strict pricing discipline using automated data. Killing Email Friction: How Tabi Connect's intelligent inbox plugins read free-form text quote requests and generate instant, compliant pricing. Plain English Governance: Updating complex algorithmic quoting models instantly using standard conversational English without writing a single line of code. The Carrier Rejection Playbook: Positioning your brokerage to automatically catch high-margin spot fallout the exact second a shipper's primary routing guide fails.   Connect with Ricky and Rush Website: https://tabiconnect.com/  LinkedIn: https://www.linkedin.com/company/tabi-connect/  

What’s an American? In Part Six of our series, we tell the story of the single piece of legislation that changed the face of the United States…literally. “Historians” often claim immigrants are what made America strong. But the U.S. once had a 40-year immigration pause that led to what’s been called the Golden Age of Capitalism. What's An American? (Part Seven) airs Friday, June 12th, 2026. Episode powered by: Ethos - Protect your family with life insurance from Ethos. Get up to $3 million in coverage in as little as 10 minutes at ethos.com/rpa Ruff Greens - the premium supplement created to boost your dog's energy, digestion, and overall wellbeing. Use Discount Code “RPA” to claim your FREE JumpStart Trial Bag at RuffGreens.com.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

What’s an American? In Part Five, we continue our journey by telling the story behind “the melting pot” concept of American assimilation…and the movement that rose to destroy it and replace it with “multiculturalism.” What's An American? (Part Six) airs Wednesday June 10th, 2026. Episode powered by: Life Insurance through Ethos & The Licorice Guy (promocode: RPA 15). Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

The Future of Tissue Renewal with Dr. Bankole Johnson. In this cutting-edge episode, Lucas Aoun sits down with the renowned Dr. Bankole Johnson to explore two of the most fascinating and underexplored frontiers in regenerative medicine — regenerative protein arrays and MUSE stem cells. Dr. Johnson unpacks the science behind how regenerative protein arrays are being leveraged to orchestrate cellular repair, signal tissue regeneration, and potentially redefine recovery at a biological level. The conversation dives deep into how these protein networks communicate within the body and what this means for the future of healing, longevity, and performance optimization. Relevant links:Official Website: https://genesisregenerative.com/ Learn more about RPA here: www.genesisregenerative.com/relgeanChapters:00:00 Introduction and Guest Introduction00:55 Personal Journey into Health and Optimization02:14 Principles of Naturopathic and Holistic Medicine03:52 Core Principles: Do No Harm and Individualized Care06:28 Focus on Gut Health and Its Central Role08:14 Evolution of Gut Health Strategies in Recent Years10:02 Food Intolerance Testing and Its Significance11:46 Understanding IgG vs IgE Food Reactions13:49 Genetics, Ethnicity, and Food Intolerances15:27 Practical Use of Food Intolerance Tests19:25 Personal Insights from Food Testing22:10 Common Pitfalls in Health Optimization24:44 The Role of Hormones and Foundation of Health27:36 High Effort Strategies and Their Impact30:50 Blood Work Analysis and Functional Medicine44:08 Thyroid Function and Its Impact on Health49:52 Holistic Principles: Body's Self-Regulation and Healing52:07 Practical Advice for Health Optimization52:49 Connecting with Dr. Alex Orton and Final ThoughtsDisclaimer:The information provided in this podcast episode is for entertainment purposes and is NOT MEDICAL ADVICE. If you have any questions about your health, contact a medical professional. This content is strictly the opinions of Lucas Aoun and is for informational and entertainment purposes only. It is not intended to provide medical advice or to take the place of medical advice or treatment from a personal physician. All viewers of this content are advised to consult with their doctors or qualified health professionals regarding specific health questions. Neither Lucas Aoun nor the publisher of this content takes responsibility for possible health consequences of any person or persons reading or following the information in this content. All consumers of this content especially taking prescription or over-the-counter medications should consult their physician before beginning any nutritional, supplement or lifestyle program. Hosted on Acast. See acast.com/privacy for more information.

À l'occasion de Sant'Expo 2026, Monique Sorrentino, directrice générale du CHU Grenoble-Alpes, détaille l'irruption concrète de l'intelligence artificielle dans le monde hospitalier. Entre prédiction des flux de patients, automatisation et exploitation massive des données de santé, elle décrit une transformation déjà en cours.

What’s an American? In Part Four, we continue our journey by telling the story behind the Supreme Court case that set the stage for the illegal immigration crisis. Many believe just being born on U.S. soil is enough to become an American citizen. But that narrative has been one of the biggest scams in American history. What's An American? (Part Five) airs Monday, June 8th, 2026. Episode powered by: Ethos - Protect your family with life insurance from Ethos. Get up to $3 million in coverage in as little as 10 minutes at ethos.com/rpa Ruff Greens - the premium supplement created to boost your dog's energy, digestion, and overall wellbeing. Use Discount Code “RPA” to claim your FREE JumpStart Trial Bag at RuffGreens.com.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

What’s an American? In Part Three, we continue our journey by telling the story of how American citizenship developed. So-called intellectuals claim the early days of American citizenship were based solely on racism. But the truth is, the people of America were primarily concerned with their future survival. What's An American? (Part Four) airs Wednesday, June 3rd, 2026. Episode powered by: Ethos - Protect your family with life insurance from Ethos. Get up to $3 million in coverage in as little as 10 minutes at ethos.com/rpa Ruff Greens - the premium supplement created to boost your dog's energy, digestion, and overall wellbeing. Use Discount Code “RPA” to claim your FREE JumpStart Trial Bag at RuffGreens.com. The Licorice Guy ...the delicious gourmet licorice made in America. Use promo code "RPA15" for 15% off. Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

What’s an American? In Part Two, we continue our journey by going way back…to a series of English kings that helped set the stage for the creation of America. Along the way we speak to William Federer, author of Who is the King in America? and purveyor of AmericanMinute.com. We also speak to Stanley Renshon – author, psychoanalyst and professor of political science at City University of New York. What's An American? (Part Three) airs Monday, June 1st, 2026. Episode powered by: The Licorice Guy ...the delicious gourmet licorice made in America. Use promo code "RPA15" for 15% off. Ethos - Protect your family with life insurance from Ethos. Get up to $3 million in coverage in as little as 10 minutes at ethos.com/rpa Alliance Defending Freedom - Learn more about how you can support free speech by texting RPA to 83848 or going to JoinADF.com/RPA.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

What’s an American? At first glance, the question seems simple…but it’s anything but. To celebrate the United States' 250th birthday, RPA takes a deep dive into the meaning of one of the most fundamental questions for the people of America. What's An American? (Part Two) airs Friday, May 29th, 2026. Episode powered by: The Licorice Guy ...the delicious gourmet licorice made in America. Use promo code "RPA15" for 15% off. Ethos - Protect your family with life insurance from Ethos. Get up to $3 million in coverage in as little as 10 minutes at ethos.com/rpa Alliance Defending Freedom - Learn more about how you can support free speech by texting RPA to 83848 or going to JoinADF.com/RPA.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

Why are conspiracy theories spreading? In the finale of our series, we tell the story behind Alex Jones' most famous "prediction" - the attacks of 9/11...a story that reveals just how persuasive, and deceptive, a conspiracy theorist can be. Episode powered by:RuffGreens - the premium supplement created to boost your dog's energy, digestion, and overall wellbeing. (promo code: RPA) The Licorice Guy ...the delicious gourmet licorice made in America. Visit: https://licoriceguy.comUse promocode "RPA15" for 15% off. Ethos - Protect your family with life insurance from Ethos. Get up to $3 million in coverage in as little as 10 minutes at ethos.com/rpa Alliance Defending Freedom - Learn more about how you can support free speech by texting RPA to 83848 or going to JoinADF.com/RPA.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!On the product side, everyone is getting Computer - Perplexity, Manus, Cursor, and so on. Meanwhile on the research side, agentic evals like TerminalBench and GDPVal are also assuming computer (Harbor). On both ends, the consolidating LLM OS stack has become a standard toolkit, and Daytona is one of a small set of AI Infra companies that are booming because of it.“The end of localhost” has been Ivan Burazin's obsession for more than a decade.Something that is all too familiar…Long before agents became the default way people talked about software development, Ivan was already chasing the idea that development should not depend on a fragile local machine. CodeAnywhere, one of the first browser-based IDEs, was an early attempt at that future: move the development environment into the cloud, make setup reproducible, and free developers from the endless “works on my machine” tax.The thesis was directionally right, but the market wasn't ready yet.However, agents changed that. They do not care about a laptop, desk setup, or favorite editor. They need a computer they can access through an API: something stateful enough to keep working, fast enough to spin up instantly, flexible enough to resize, isolated enough to be safe, and composable enough to run the messy real-world workflows that real software engineering actually requires.Daytona isn't just selling “sandboxes” in the narrow code-execution sense. It is the latest version of Ivan's original localhost thesis.In this episode, Daytona's CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs.We go deep on the new agent compute market: Daytona's hard pivot from human dev environments to AI sandboxes, the New Year's Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS.We discuss:* How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis* Why Daytona pivoted from human dev environments to AI sandboxes* Why agents need composable computers instead of disposable code execution boxes* The New Year's Eve MVP that customers chased API keys for* Why Daytona chose bare metal, stateful snapshots, and its own scheduler* How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds* Why Daytona's biggest customer runs ~850,000 sandboxes a day* How RL/eval workloads create zero-to-100,000 CPU spikes* Why RL workloads went from 0% to roughly 50% of Daytona usage* Why customers compare Daytona against EKS/GKS and say they're “never going back”* Why every AI agent may need a computer, including Windows and macOS environments* The Apple licensing constraints that make macOS sandboxes hard* Why CLI gives agents more power than MCP* How open source helps agents integrate Daytona* Why agent-generated PRs may break today's CI/CD assumptions* Why AI SaaS companies reselling tokens may face a cold shower* Why the AI cloud may look more like Stripe than AWSIvan Burazin* LinkedIn: https://www.linkedin.com/in/ivanburazin* X: https://x.com/ivanburazinDaytona* Website: https://www.daytona.io* X: https://x.com/daytonaioTimestamps* 00:00:00 Hook* 00:01:12 Introduction* 00:03:15 CodeAnywhere, Shift, and the end of localhost* 00:05:58 What Daytona is: composable computers for AI agents* 00:08:07 The pivot from dev environments to AI sandboxes* 00:10:17 The New Year's Eve MVP and customers begging for API keys* 00:12:56 Bare metal, stateful sandboxes, and Daytona's scheduler* 00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs* 00:21:53 Spiky RL/eval workloads and the new agent infra problem* 00:28:12 RL workloads, Kubernetes pain, and dynamic resizing* 00:33:31 Why every AI agent needs a computer* 00:38:48 macOS sandboxes and Apple's licensing problem* 00:44:28 Why CLI may matter more than MCP* 00:48:11 Open source, GitHub stars, and agent integration* 00:53:11 Git, CI/CD, and agent collaboration bottlenecks* 00:58:15 Founder life and building a 25-person infra company* 01:02:44 AI SaaS, token resale, and API-first business models* 01:06:10 GPU sandboxes, data centers, and compute growth* 01:09:48 Why the AI cloud may look more like Stripe than AWS* 01:11:26 Closing thoughtsTranscriptIntroduction: Daytona, CodeAnywhere, and the End of LocalhostSwyx [00:00:02]: Okay, we're in the studio with Ivan Burazin, CEO of Daytona. Welcome.Ivan [00:00:07]: Thanks for having me, man.Swyx [00:00:08]: Ivan, you and I go back.Ivan [00:00:10]: Way back.Swyx [00:00:11]: How I don't even know how, you found, did you reach out or, for Shift.Ivan [00:00:17]: I reached out to you. The reason was you - we were just - we were thinking about I was one of the co-founders of CodeAnywhere, the first browser-based IDE, and so we were thinking a long time of, localhost should die. And you had this article.Swyx [00:00:29]: End of localhost.Ivan [00:00:30]: Then I reached out to you because of that, and then we talked, and I was actually at a different job and learning about I was the head of, developer experience, and you were quite well-versed in that, and I actually reached out to you, among other people, how do we go about that? What are the key things and whatnot at this point in time? And you were nice enough to take the call, and I remember I was late on your call with you.Swyx [00:00:51]: I don't remember.Ivan [00:00:52]: I remember because I was with my then I'm thinking of a girlfriend or wife at that point in time, I'm not sure. It's the same person, so that's great, and I was late ‘cause we were, in, Italy on, vacation, and then I was late for something. I felt so bad, and you were so nice to be, good about.Swyx [00:01:10]: The reason I'm nice is because I'm also late to other people, so it's like, who's, who's without sin here, yeah, so I have to, for those who don't know, InfoBip Shift, there's this whole thing that, you did in the past, and, and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, “Oh, you can, you can build and sell conferences?”Ivan [00:01:34]: I remember you asked you asked me at the beginning to give me advisory shares, and I was so focused on what we were doing, I said no, and I should've took the advisory shares. So I'm sorry, dude. But anyway.Swyx [00:01:43]: We're not, we're not venture backed.Ivan [00:01:44]: No, it doesn't matter.Swyx [00:01:45]: It's Yeah, anyway, so I think what's impressive about you is that CodeAnywhere is the thing that you've been trying to build, and, you kind of put it on hold and then came back after InfoBip. Just give us the story, do you - the story and the origin story, going into Daytona.From CodeAnywhere and Shift to DaytonaIvan [00:02:05]: Sure. Like, really way back, me and my co-founder have been together. I say this, I've said this multiple times, it's like we were married and divorced and married. Some people actually ask me is my co-founder my partner. they thought it literally. It's not literally, but we have done multiple companies together, and to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift, and then back to, Daytona. We originally started stacking servers, doing like virtualization in the early 2000s and, routers and doing basically all these things, at a foundational level, and that was a services company which we sold to focus on what my co-founder actually invented, which was the very first browser-based IDE, right, I say the first. Before us was actually Heroku. They did it for a very short time until they became Heroku. But outside of them, we were the only one, and it was called.Swyx [00:02:55]: There was Cloud9.Ivan [00:02:57]: Cloud9 came out slightly after us. There was Replit, which came out when we stopped doing it, Replit came out, and they have been successful since then, which is great. There was Nitrous.io. There was quite a few that existed at the time, but it was like too early. But the interesting part is that we, at that point in time, because there was no VS Code, there was no Kubernetes, and Docker had just started when we Or I'm not sure if it was even public at that point in time. And so we had to build everything to the whole stack ourselves and that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about 3 million people used CodeAnywhere. It was slightly, it was angel-backed more than venture-backed. We ended up paying everyone back because it didn't have that sort of scale. But, three years ago, we started something similar with Daytona, which is not what we are today, but it was automating dev environments for human engineers, the basically the underlying stack of CodeAnywhere. And then we did a hard pivot last January to sandboxes. And so here we are.Swyx [00:04:01]: Historic pivot, yeah, and, it's one of those things where, I had independently invested in CodeAnywhere, but also in E2B, and then both of you pivoted into the same thing, and I'm like, “F**k.”Ivan [00:04:12]: You invested, you invested in Daytona. You invested in Daytona. But you were the first If we had not got your check, we wouldn't have done it.Swyx [00:04:18]: No way.Ivan [00:04:19]: No, it was like, “We have to get him on board first,” and you were that kicker that we, that got us off the ground.Swyx [00:04:23]: No, because you were putting me on your pitch deck, man. I was like, “Man, this is like a good trip if I don't invest.”Ivan [00:04:29]: That's because it was your quote. It's like we.Swyx [00:04:30]: Yeah. It's the end of localhost.Ivan [00:04:31]: Did a bunch of research about end of localhost and who was interested in that,.Swyx [00:04:34]: No, that's like, I put, I wrote that blog post, and every single company in that field reached out to me, and then every VC who was receiving those pitches then also had to call me and, talk it, talk through it with me.Ivan [00:04:47]: It's finally happening though.Swyx [00:04:48]: It was really super interesting.Ivan [00:04:48]: It's finally happening.Swyx [00:04:49]: It's finally happening.Ivan [00:04:49]: Yeah, it's finally.Swyx [00:04:49]: It's finally happening, with maybe sort of non-human users. Yeah, so what is Daytona today? Let's get like a quick description. I'm wearing the shirt.What Daytona Is Today: Composable Computers for AI AgentsIvan [00:04:58]: You're wearing the shirt. Yes,.Swyx [00:04:59]: It says, I think your branding is very good. Like, it's very consistent. It runs AI code. Like, it cannot be simpler.Ivan [00:05:05]: Exactly, but we're gonna probably have to change that.Swyx [00:05:07]: Oh, s**t.Ivan [00:05:07]: It's also a subset of what we do. Unfortunately, we really love this, Run AI Code is super simple. People interpret it different ways. I think we've given out 5,000, 6,000 of these shirts. People wear them with pride because it doesn't really market about us.Swyx [00:05:21]: Yeah, Daytona's on the back.Ivan [00:05:22]: It markets the back. It markets to the person itself, so I think we did a really good job on that one. But it is also a subset of what we do, because people, when they think about Run AI Code, they just think about these small, let's call it isolates, code execution boxes that, you send some code, you get an output. Whereas what Daytona is today is essentially composable computers for AI agents. It is, the market calls them sandboxes which can be misleading.Swyx [00:05:44]: All these things. All these things on.Ivan [00:05:45]: Yeah, exactly, ‘cause it can be misleading ‘cause people usually think about sandboxes as a demo or a test environment versus a production-grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, my wife is an architect, so she has like a Windows with a 3D graphics card inside to do 3D rendering. Like, as humans, we have different computers or different compositions of computers. And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks. And so we offer that basically through an API.Swyx [00:06:19]: Yeah, to give people - I'm trying to sort of front-load all the aha moments or the wow moments so that people can, stay engaged and click like and subscribe. the market is exploding, right? Like, you have been reporting 74% month-on-month growth, and it also, it's just been growing for a while. Like, it's been going like this. And every single - It's not just you guys. It's every single.Ivan [00:06:41]: Everyone, yeah.Swyx [00:06:42]: Sort of, compute provider. I don't know if you agree with me saying compute provider or not.Ivan [00:06:48]: It's fine.Swyx [00:06:48]: Yeah. So like organically PLG-driven growth, but also enterprise is doing super well, I think I wanna rewind to January of last year when you did the pivot. Like, so you obviously called this market early, and you were positioned for it, and you are now one of the market leaders. But what was the insight that made you do the pivot?The Pivot: From Human Dev Environments to Agent SandboxesIvan [00:07:06]: The insight that made us do this pivot is the quarter before that, so end of 2024, when we had - Basically, we did a demo with - I don't I think we discussed this as well, Devin was not public. You actually gave me access to Devin at that time. So Devin.Swyx [00:07:25]: I did?Ivan [00:07:26]: Yeah, you gave me access.Swyx [00:07:26]: I don't think I was supposed.Ivan [00:07:27]: Yeah, exactly.Swyx [00:07:28]: Yeah, I.Ivan [00:07:28]: So it doesn't matter. You.Swyx [00:07:29]: Yeah. I gave like three friends access.Ivan [00:07:31]: Yeah, or it was a call and you showed it to me. It doesn't matter. but OpenDevin was available, which is now called OpenHands. And so we're like, “Oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take, OpenDevin and launch that as a SaaS.” And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents, and they were like, “Hey, my agent needs a compute sandbox runtime,” whatever you wanna call it. I forgot what it was called at that point. And then we were like, “Oh, amazing. This is a new market. Here is our infrastructure. Here's our product, and go.” And what we found really fast, soon, was that people did not like what we had built. It didn't work. And I remember talking to people at the beginning when we're doing this, the sandbox we're building for agents. People were like, “Oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things.” But we saw that everyone we gave it to, it was like 20, 30 people, they all said, “No.” Like, “This is not what we need. This sort of breaks.” And basically, me and my co-founder not knowing a lot about - ‘cause we're infra people. We're not AI people. So I basically took it upon myself to like watch every single podcast that exists, including all of, all of these and all that, and sort of get up to date, read all the blogs, like get, understand what's going on.Swyx [00:08:45]: Do you wanna shout out who else was useful, just in case people are also looking.Ivan [00:08:49]: Generally we -, I looked at There's a few of podcast, different segments and different types. So there's you guys, No Priors, Bill Gurley's was great while.Swyx [00:09:04]: VG2, yeah.Ivan [00:09:05]: Yeah, while it was around. So there's a few. 20VC is interesting from a different dynamic, and some are different dynamic. But there was, also Red Points.Swyx [00:09:14]: We're not really about the compute market.Ivan [00:09:15]: It was also already - Sorry?Swyx [00:09:16]: You're, you want - You're looking at the agent infra market.Ivan [00:09:19]: I was looking at the agent market and the AI market in general and sort of understanding who are the players, what the perception, and how that goes. And like obviously you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening. And so when we figured, when we sort of had an idea of what we had to build, literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first MVP, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 AM or something like that. I was doing - I just put my like baby daughter and wife to sleep and, Happy New Year's, and go back to just, doing this. And I sent it to my co-founder, my CTO, and he saw it in the morning. He's like, “This is absolute garbage.” “Do not show this to anybody at all, but the idea is good.” And so he took two weeks, and he rebuilt it.Swyx [00:10:09]: Did it like look like that? Listen, I - It was rough idea.Ivan [00:10:12]: Oh, not even, not even close. Like it was it was way worse. But it was like a very - It was a simplistic view of what it should be. Like, it worked, but it was not ideal. And so he went, we went down the whole, which is his job as CTO, to go, and he came back with this version. We then called all the people that had said like, “This is garbage,” a quarter ago. And we set up these calls, and we gave it to - We just demoed it to everyone. And all the calls went long, every single one. They were 15-minute calls, and they all went to like 25, 30 minutes or whatnot. And everyone said, “We need, we want access.” There was no login, just an API key, ‘cause it was just a beta or an alpha. And they said, “Oh, we want access.” And we're like, “Sure, yeah. Okay, thank you very much.” But after like the next day, if we'd not send it, every single one, like every call that we did, everyone came back, “Where is my API key?” Like everyone wanted it. We're like, “S**t.” Like this is it. Like I've never felt So one, the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago it's not. We just didn't know what was the right primitive. And then when we came, and we can talk about what that is, and we gave it to these people, I've never seen, I've never experienced - I've done multiple companies in my life. I've never experienced this, that people literally call you if you do not give them access. Like they want access right now. And so it's like, okay, they don't want this. the thing that they want doesn't seem to exist, or they have not found it, and they really want what we want. And then when we understood that we're onto something, and then when you think about the size of the market, like the market for human engineers and enterprise is a very large market, so think GitLab or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, “We are all in on this.” And so that is where we made sort of the cut between the old product and the new one.Bare Metal, Stateful Sandboxes, and the Lambda + EC2 ModelSwyx [00:12:02]: Yeah. But it wasn't composable at the time?Ivan [00:12:05]: It was very - It was basically just a Linux box that you could change, that you could define number of CPUs, disk, and RAM. Like that is what you could do, but you couldn't have multiple operating systems, you couldn't resize it on the fly, you couldn't add a GPU, you couldn't do like all the things. It was just the, just the first sort of variation of that, yeah.Swyx [00:12:22]: Was it bare metal from the start?Ivan [00:12:24]: It was bare metal from the start. And so the interesting thing that we thought about right away, so our.Swyx [00:12:29]: Which, give people the background, what is the normal path?Ivan [00:12:32]: Yeah, so, basically most providers run this on top of VMs. And also.Swyx [00:12:37]: Firecracker.Ivan [00:12:38]: Yeah, they run on Firecracker and VM. And so we also fire - We can get - We have multiple isolation layers and we can do that. But the common way to do it is that they, one, that the state of the machine, or the hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever. So most of them are preemptible, like they can There's a time that they can live. And so our thought was when we were going into this is, agents will be like humans in the sense of you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid, it's the same state. So you - Agents would want that, like the pause and come back. They want those two things. But also agents really want speed, right? Can they get it? So when we thought about it's like we need something insanely fast, how to make it fast, how to make it long-running, and stateful. And so those two things, it's like combining a Lambda and an EC2, right? Those two things together. And so we didn't have an idea how others did it, ‘cause we didn't know too that there was a market around this. It was more like, okay, this is what we need, what they need. And we looked at Kubernetes, it wasn't wasn't good enough for that. We looked at Nomad, it didn't enable that. And so our history in rewriting our own scheduler at CodeAnywhere is basically what my CTO came up with. Like, he's like, “Oh, the learnings from there,” and he brought it. And the funny thing is, our third co-founder, when he saw it, he's like, “Dude, what is this? This is like 2008.” Like, we went back in time, and he's like, “Exactly.” And so the reason why Daytona is like super fast, and you see this on benchmarks, is we essentially, we run on bare metal. We have our own scheduler, we use the underlying, disk, CPU, and RAM of the underlying machine, which means your IOPS are insanely fast because there's no, there's no network between an EBS or something like that. But also the snapshot, the point in time, the templates, are also preloaded on the bare metal machines. So when you fire off a sandbox from a template or a snapshot, you're essentially directed to the bare metal machine where that snapshot is based on that NVMe drive, and then it literally just turns on that machine, and it's local. There's no network latency, anything on there. And so that is sort of the specificities that we, when we're thinking from first principles, what a computer would look like for an agent, that is what we came up with, and that's what we created.Benchmarks, 60ms Startup, and 50,000 SandboxesSwyx [00:15:02]: Yeah. I should maybe, I don't know if you endorse this, but there's someone that does compute SDK, you guys do very well on there, with like the TTI, right? I. is this a, is this a is this a relevant benchmark for you guys? I don't know.Ivan [00:15:16]: I don't know, and it changes every day. So today RKL is.Swyx [00:15:18]: I don't know what RKL is. Never heard of it.Ivan [00:15:20]: Yeah. RK, yeah, so it is there.Swyx [00:15:22]: You are, at least a third of the next tier of performance, and then, there's a lot of other better-known names that are very slow to start.Ivan [00:15:31]: Yeah. We've been the number one by far for a long time, and now there's different, there's different definitions also of sandboxes, different isolation patterns, different other things. So RKL runs it literally on the S3, the data, so it's very different, and they spin up a sandbox, spin up a container for that, so it's a different type of thing. So the definition of a sandbox is something that we can all, we all need to get along with. But yeah, we're insanely fast on getting these things, up and running. And so you can see even there that it's a zero point 0.10 to 0.11, so.Swyx [00:16:03]: Close enough. Yeah. what else do you need, right?Ivan [00:16:05]: Yeah. So the benchmarks itself, so, in this, in I don't think the benchmarks equate to market ownership or revenue or anything like that. and I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.Swyx [00:16:20]: It's table stakes. It's just like.Ivan [00:16:21]: Exactly. But it doesn't hurt.Swyx [00:16:22]: Just roughly check.Ivan [00:16:22]: Like you definitely have to be up there and you have to be competing so that people know that, oh, this is definitely one of the top. Because this is only one dimension of what customers look for. There's other things like how many can you spin up consecutively? There's a feature set, there's support, there's like all different things that people look at, but you definitely have to be there, on the benchmarks.Swyx [00:16:40]: How many people do people spin up consecutively?Ivan [00:16:43]: So we have.Swyx [00:16:43]: Or concurrently, is the Concurrency, right?Ivan [00:16:45]: There's three metrics that we look at. And so one is like time to spin up one, and so our time to spin up one is 60 milliseconds with network latency. So request, spin up, reply, 60, the whole thing, 60 milliseconds. That is one. But if you wanna spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently 50,000. Some others, there's public data around this, like take 2,000 seconds, which is 30 minutes. Like there's different variations of that. And then there is the so it is speed of one, speed of like multiple, and then how many can you consistently have up and running. And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they're, where they're just shy of a million every single day that they're running, we do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.Swyx [00:17:44]: They pay by like vCPU seconds.Ivan [00:17:47]: By seconds, yeah.Swyx [00:17:47]: Or whatever. Yeah. Okay, and so and then, and the other thing is, the sleeping and the resuming, ‘cause it's all the stateful resumption of all these things, how, what kind of workload are people putting through this, right? Like how is it Do we measure by gigabytes in memory, gigabytes in storage? I don't In like network attached storage. I, what are the costly ones of, out of all these features?Workload Economics: CPU, RAM, Network, and StorageIvan [00:18:15]: The most expensive thing are CPU.Swyx [00:18:18]: Okay. Yeah, of course.Ivan [00:18:18]: The second one, yeah Then it's RAM, then it's disk. We actually don't charge.Swyx [00:18:22]: Which is snapshotting, right?Ivan [00:18:23]: No, it's actually the, snapshotting's part of it, but basically the size of your hard disk, of your machine. So do you have 10 gigabytes, do you have 20, do you have 50, do you have whatever? And then the transference of that. Right now, currently we don't charge for, network at all at Polychron.Swyx [00:18:37]: Oh, you gotta, yeah, you gotta fix.Ivan [00:18:38]: Yeah. It is very much a it's a larger and larger part of our bill, so we're working around, that part there. Obviously, that is the least, expensive, so the hard disk is the least expensive, so it's basically CPU, RAM, for us network, ‘cause we don't charge the customer, and then hard disk, is how it's split up. But there's also different types of workloads, so we basically split it up into two types of workloads in Daytona. One is what we call background agents or long-running agents. and the other is, basically RLs and evals, which I put sort of together. And so they have very different patterns of usage, and if you look at the usage of a background And I'll just name names of companies, not specifically.Background Agents vs. RL/Evals: Two Usage ShapesSwyx [00:19:21]: Yeah, open, all hands.Ivan [00:19:23]: Yeah. So like a background agent's a Cognition, a Lovable, a like all these things are Harvey. These are all long-running, background agents. And so if you look at their usage patterns, their usage patterns are similar to human, which is like follow the sun. Basically, the usage patterns of that is like noon is probably the highest, and the midnight is the lowest, and then weekends are lower. weekday is higher.Swyx [00:19:42]: Yeah, that's a fun question. How global is it? Is it very US-centric or?Ivan [00:19:46]: The US is a large part, but we have currently, we have Asia, Europe, and the US regions.Swyx [00:19:52]: So it's quite global.Ivan [00:19:53]: Yeah, it's quite global. We have it all over. It's interesting that our I talked to you a bit about this. Our number one city by user.Swyx [00:20:01]: Hmm.Ivan [00:20:02]: Is Singapore.Swyx [00:20:04]: Oh, wow. Amazing.Ivan [00:20:05]: Which is an interesting one, right? Not by revenue, just by just like by individual head count.Swyx [00:20:09]: Really?Ivan [00:20:09]: Just like an interesting thing.Swyx [00:20:10]: Singapore is, Singapore is weirdly high in the adoption charts of AI for the population. It's like an, seven, eight million population. And it's like keeps showing up.Ivan [00:20:20]: No, it's quite interesting. We were quite shocked, and I was like, “Oh, this is interesting.” And also one that's up there.Swyx [00:20:24]: There's a reason I'm doing AI using Singapore. it's because I'm from there.Ivan [00:20:27]: We're there. We're gonna, we're gonna be there as well. and it's interesting that Japan is in the top or like Tokyo's in the top, which is in all the tech cycles it has never been. It has never been, so it's quite interesting that they're.Swyx [00:20:39]: I think the Japanese just love AI. Yeah. It's that, and then it's Brazil. That's it.Ivan [00:20:44]: Brazil has always been in.Swyx [00:20:45]: I think.Ivan [00:20:46]: Even when I look, if you look at like GitHub's data and ask historically with CodeAnywhere, it was always like US, Western Europe, and then you'd have like India, Brazil, China, like that would be there. But like Singapore was not in, specifically Japan was never in sort of that top, that top.Swyx [00:21:01]: Yeah. Weird pockets.Ivan [00:21:01]: Weird. Yeah, so it's very global.Swyx [00:21:02]: Okay, so actually that, but that's helps you to distribute your load through, all time?Ivan [00:21:08]: The interesting thing is like we have those kind of loads, but if you look at the researcher loads, they're quite different. So what they are is like if you give them concurrency of 10,000 or 50,000 or 100,000 CPUs at ARMb, when they fire off a run, it's just 100%. And then it just runs, and then it stops. So it's very, the usage pattern is squares basically, right? And it's also not follow the sun, because people will fire it off at midnight before they go to sleep but then wake up and so it's very unpredictable, so you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a follow the sun, even if you have a high growth company, you can sort of predict your usage patterns and have enough capacity for that, because it's sort of, it grows in a, in a way you can project. When you have companies doing sort of like evals and RL, they're super spiky. So they're gonna come in, it's like, “We're gonna use nothing, then can we have 100,000?” Right? And then go back down. And then 100,000, go back down. So it's very different, right? And.Swyx [00:22:09]: Do you want to lock them into commits so.Ivan [00:22:11]: Yeah, we do.Swyx [00:22:12]: Yeah, okay.Ivan [00:22:12]: We so we have to lock them into some sort of commits to have that capacity, because we have to have, basically we have to have the capacity for peak. Right? And so right now, Daytona's mean utilization is 15%, 1-5.Swyx [00:22:25]: Oh my God.Ivan [00:22:26]: So it's very low.Swyx [00:22:27]: Because it's very spiky.Ivan [00:22:27]: It's very spiky, but we get up to 90%. so we have these things. And so what we're, what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around, but that works really well for basically the background agent where it's follow the sun. But this, it's not. Like it's a very different shape. Obviously with scale you figure these things out, but that's an interesting new problem that we have, as a compute provider in the agent space. And when we were doing the conference recently, and so we talked to like Nikita from Neon and.Swyx [00:22:57]: I should bring it up.Ivan [00:22:58]: Parag from Parallel and whatnot, everyone has the same problem. Whereas the usage is super spiky, and this is something that has not happened before, that you have these types of like it was always, it the amplitudes were not this high, right? So it's quite interesting use case and problem solve.Compute Conference and Spiky Agent InfrastructureSwyx [00:23:12]: Yeah, I don't know if we're gonna bring this up again, but let's just talk about the conference, you had like 1,000 something people at the Warriors game, at the Sorry, where is it? What's.Ivan [00:23:22]: Chase Center.Swyx [00:23:23]: Chase Center.Ivan [00:23:23]: Chase Center.Swyx [00:23:24]: I went. It was, it was very impressive. Obviously, you can, how to throw a conference, what did you learn? you put, you pulled together all these impressive names.Ivan [00:23:33]: What I.Swyx [00:23:34]: What were you looking for?Ivan [00:23:35]: My thesis behind the Compute Conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user, what are the ergonomics and usage patterns of agents, and so we can do that. And what I found, this was a theory, it wasn't proven, is that we all have these problems, as I touched onto. And I was, as I was talking on stage, it was like we all have the same underlying infra problems, which is this spiky workloads, unpredictable workloads that we've never had before, in human, compute or human infrastructure. And it's, again, it's the same when I was talking to Parag or when I was talking.Swyx [00:24:20]: Lynn. Nikita.Ivan [00:24:21]: Lynn, Nikita. Lynn especially, I was talking to her the other day as well. Like the It is a very interesting type of problem to solve because I can touch on Cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a bunch of geos, and basically, as users work in different places, and depending on your tier, they can move you around the geos. And so that how, that's how they get the higher utilization. But you can sort of predict these, and it's If it's something in You'll rarely get a spike that is 10 orders of magnitude. Like you'll get a like let's say one of your customers has some like an exponential curve. What is that to I'm using Cloudflare as an example. 10%, 20%, whatever it is. I don't, I don't have this data, I'm just assessing. It's surely not 10x, right? It's surely not something there. And so how do you go out and solve this problem? And we're all solving this in different ways. So we have.Swyx [00:25:11]: She also has the same thing.Ivan [00:25:12]: Yeah, I know specifically that like Neon had that issue as well. Like how are we solving these spiky loads and things like that ‘cause we talked about it. And so the interesting thing for me to actually internalize was, yes, everyone that's building for agents first is going through this, and we're all solving similar problems, which is quite.Swyx [00:25:28]: Let me let me double-click on this. Okay. So for example, Neon, I happen to know that they're very sort of S3 oriented, right? so they're just like fully bet on S3. And you get to benefit from S3's distribution and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lynn maybe has to care a bit more because obviously she's doing GPU inference. And, for listeners, we did an episode with her, one and a half years ago. And you have to care. But like, right?Ivan [00:25:54]: Parag cares for sure, and Nikita.Swyx [00:25:58]: And Parag is C of, Parallel.Ivan [00:25:59]: Parallel, yeah.Swyx [00:26:00]: Former CTO of Twitter.Ivan [00:26:01]: Twitter, yeah.Swyx [00:26:02]: They are the search.Ivan [00:26:03]: Yeah, they're search, yeah.Swyx [00:26:03]: I You and I know but the listeners don't know.Ivan [00:26:08]: Yeah, we can put it down in the screen, and so ‘cause we, when we were talking.Swyx [00:26:11]: I'll put it up on the, on the screen.Ivan [00:26:12]: Yeah, right.Swyx [00:26:12]: People can look it up if they need.Ivan [00:26:14]: Look it up. And, yes, but they still have CPU and RAM, allocation that you have to have up and running. And so CPU and RAM, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either over-provision and you can handle the bursts, or two, you basically have, I don't know if this is a term, just-in-time compute, which is like as your load becomes, as your usage comes in, you can fire off requests for VMs or bare metals at other cloud providers and then get them up and running.Swyx [00:26:43]: This is if you go above 100%, right?Ivan [00:26:45]: Yeah, this is.Swyx [00:26:46]: Like your overflow.Ivan [00:26:46]: If your overflow, like spillage or whatever you do.Swyx [00:26:48]: You probably lose money on it, but it doesn't matter, right?Ivan [00:26:50]: It, not Well, you might, you might not That is a more cost-effective way to do it but it's a slower way to do it. Because basically what you have to do is you have to like queue your requests, spin up these just-in-time compute, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine, and you can do that. But if your customer, and especially for, let's say, the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs, right? So you want your GPU running at, what, 100% the entire time. And so when you're running runs on CPUs, when the when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down, right? And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait, and that's incurring our cost. So there's things that you have to try to solve for there.RL Workloads, Declarative Images, and Kubernetes ReplacementSwyx [00:27:43]: Yeah, let's talk about the different workload, right? You said that, what was it? A few months ago, you had zero RL workload and now it's 50%.Ivan [00:27:52]: It will be this one, 50%, yeah.Swyx [00:27:54]: Let's talk about how different it is, right? Like I imagine, for example, a lot less dynamic code generation of like arbitrary code. Like here, it's probably all the same code. You're just doing parallel runs or something, I don't know.Ivan [00:28:05]: Yeah. So you'll have multiple Depends on the like for each run, you'll have a snapshot. And they, for the most part, they actually do use our declarative image builder, which is like, “Oh, we, the agent wants these dependencies, these env vars.”Swyx [00:28:17]: These ones, yeah.Ivan [00:28:18]: Yeah, the declarative image builder, it.Swyx [00:28:20]: Which is a very modal like thing that they.Ivan [00:28:22]: Yeah. And so we build it on the fly and then we propagate that snapshot, and you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can, or like it could be also be automated. It's like, “Oh, now for the next run, we need to install these things or remove these things or whatever to get, a task done,” and then it goes off and runs that. So yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes. So EKS, GKE, whatever. That is what the vast majority run on. And anyone that has tried Daytona versus GKE, EKS is like, “I'm never going back.” That has always been. There's a few reasons. One is the ergonomics. So if you have, if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that. Daytona, although as a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is an AWS. Like you have an API, an SDK, it's quite like easy and seamless to get these things up and running, that's one. The other is the speed to which we spin up, which we mentioned earlier, which is much faster, and the scale to which we can go to. We haven't got into features, but an interesting feature is that it's very hard to OOM, or out of memory, our sandboxes, because we can dynamically on the fly.Swyx [00:29:48]: Resize.Ivan [00:29:49]: Resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when, the Terminal Revenge team is, brought us actually. So thank you, Alex and the team, that brought us into this whole space.Swyx [00:30:05]: It's just very rare that, a framework would just say, “Guys, just use Daytona.”Ivan [00:30:11]: Yeah, I think it says it somewhere. Yeah.Swyx [00:30:13]: Yeah. I was like, “What is this?”Ivan [00:30:15]: There's all, there's multiple there, but they also mention a few other places. and so Daytona specifically-We have, the, just jumping on themes here We, I don't know where it says Data Center.Swyx [00:30:27]: I, there.Ivan [00:30:27]: Doesn't matter.Swyx [00:30:28]: There's a very strong recommendation, which is, very unusual. Which is, it's.Ivan [00:30:33]: We do not pay them for this, just.Swyx [00:30:34]: I know, yeah. They just like you.Ivan [00:30:35]: Yeah, they like us. yeah, and also a thing, so, Data Center has multiple isolation sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container, that's hardened with Sysbox. So it's Docker's, isolation that is a security equivalent to a VM, but it's still a container. And that is the default, and they, especially in these training workloads, really like that as an interface to be able to use just a basic Docker container, and we enable Docker and Docker. Which for these RL runs, if you need to do a Docker compose or Kubernetes, you can spin up a K3S inside of these things, which unlocks a huge amount of workloads that you can do that you cannot do on other providers. So just on that part is much more interesting. And so we went that, through that. We showed them that we could do that, and they enjoyed that quite a bit. They being the general venture people.Swyx [00:31:28]: Those people, yeah.Ivan [00:31:29]: And Harbor people.Swyx [00:31:29]: Harbor people, do are they, are they a company yet?Ivan [00:31:33]: As far, I do not know.Customer Pull, Slack Connect, and the Computer Use BetSwyx [00:31:35]: Okay. All right. Yeah. It's like super obvious that like, there's a lot of excitement and success around these things, okay, so yeah, tell us more, right? Like, this is an exploding workload, Harbor adopted you, which helped speed things along. But what are you learning as this new workload comes online?Ivan [00:31:53]: There's a couple things that we learned, which we chat about in the beginning. We, and this has led our story, as we mentioned, we like talked to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And it's interesting that And I think it's that the ecosystem is so small and/or the models get smarter, where when we see one user come with a request, we know it goes on a roadmap if like three to five customers come with the same request in that week. It's like very bizarre. It happens so many times, which is.Swyx [00:32:27]: Because they're all friends.Ivan [00:32:28]: Sorry?Swyx [00:32:28]: They all, they're all friends. They're all in the same group chat.Ivan [00:32:30]: Yeah, probably, yeah. ‘Cause and they're like, “Oh, can you do this?” And I'm like, “Okay, this is interesting. We'll put it on a feature request.” And then the next one's like, “Oh, can you do this?” “Okay.” It's all the same, right? It's always the same. And so what we try to do, and I personally try to do, I try to be on as many call, quote-unquote “sales calls” I can. I'm in every Slack channel. We literally have about 1,000 Slack Connect channels, something like that. It's an interesting, there's so many interesting things you find out when you have all the Slack channels. You can also see where people, transfer between companies. You see leave Slack channel, enter Slack channel. It's an interesting thing. Also, just I digress, I feel that Slack Connect is literally LinkedIn what it should be. You have a list.Swyx [00:33:08]: LinkedIn charges you to, use your own connections, but Slack doesn't, right? Slack is like, do it for free. It's more lock-in. It's great.Ivan [00:33:15]: Yeah. It's amazing. Yeah. It's one of the reasons.Swyx [00:33:17]: You're gonna pay Slack for life.Ivan [00:33:18]: Exactly. You're there for life. So that's interesting. And so one of the things, the newer things we were talking about earlier is we made a big bet and put a lot of investment on computer use. that is not seen publicly the light of day. We haven't GA'd that yet, but we have.Swyx [00:33:32]: Is there a thing I can pull up?Ivan [00:33:33]: There is computer use there. It's right up a bit.Swyx [00:33:36]: Oh, yeah. Okay.Ivan [00:33:38]: What we have, what we talked about and what we've seen publicly is there's this theme now about, the human emulator where And Elon from XAI has talked about this publicly, and if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools. Like, I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, terminal or whatnot. But if we, if we look at knowledge work in general, there's about 100 million knowledge workers in the US, about a billion in the world, and knowledge workers, and the salaries of them aggregate to 10 trillion in the US 50 trillion worldwide.Swyx [00:34:24]: Wow.Ivan [00:34:25]: Something like that. And if we look at, the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US it's about 25 trillion, and most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very long time. Like, people just won't invest in that. How much of it? our assumption is the following: if, in the RPA market, which is similar market, well, not the same 25% of, these white collar, workers', work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's, 40%, right? And so if you take 40% of that, you get to essentially, $10 trillion a year.Swyx [00:35:17]: That's a TAM.Ivan [00:35:18]: That is a that is a TAM. So that's the TAM of the models, right? That's not our, essentially ours. But you get to that size, and to be able to do that, you essentially have to give agents these computers with the legacy. So computer use, either Mac or Windows or Linux. Linux we also obviously have and others have. But Windows specifically is something very new, and the only option right now is an EC2 with, Windows or on Azure. Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox, so it's a second instead of milliseconds, but you have, point in time snapshots, you have, forking, you have all the things that you have from a sandbox, but essentially enables you to hopefully unlock all this value. And so that's been our big push and bet, but we've sort of, kept our ear to the ground. What is sort of the next things in the market?RPA Returns: Why Agents Still Need ComputersSwyx [00:36:06]: Yeah, knowledge work, and building, and sort of RPA, the next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPO-ing. And it was, a very hot Isn't it, Eastern European?Ivan [00:36:20]: It is, Romanian.Swyx [00:36:21]: Romanian?Yeah, it might be the only Romanian, big unicorn okay, yeah. This I don't I don't, I don't have like a I think there's, I think there's a stage being set for the resurgence of RPA, ‘cause everyone understands that, yeah, no one wants to deal with these shitty apps and no one's gonna rewrite them. Like, you just have to do, a remote operation and programmatic operation of them.Ivan [00:36:45]: If you wanna unlock it, my own setup was basically the following. So I was doing a board deck recently, last month, whatever, and I'm like, “Okay, let's just, let's just do automated.” So, all our data's in, ClickHouse and PostHog and QuickBooks, where everyone else's is, and I'm basically, connected that all to, my Cloud code, like go off and go Cloud code whatever. Go off and, here's the integrations, go do that. It pulled out the first report, which was great. It connected to Brex and all these things, pulled it, which was great, and then I say, “Okay, now pull out this, and this,” and I kept getting, really well McKinsey-style design reports, but the data said partial data. all the missing data, partial data. Like, it can't access all the things, and I got so frustrated, and so I got, I got, my Mac Mini virtual sandbox with OpenClaw. I gave it its own account in our company, and then I went to all these services and created a read-only account, so literally like an intern in your company. And so I would say, “Now go and do this report,” and it would get the same, or like, “I can't via the MCP or the API or whatever. I can't get all the information.” I'm like, “Go log in.” And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed, and I to get value, I get immense value right now, but it has to be a computer usage, unfortunately, and so I spend a bunch of tokens just on that, but I get the job done. And so if even a startup like ours, and using all the hottest tools, still needs a computer agent what hope does, Goldman have to have a headless, right?Swyx [00:38:22]: Yeah, what a - Why isn't Microsoft doing this?Ivan [00:38:27]: I'm pretty sure, Satya had a post yesterday.Swyx [00:38:29]: Oh, okay. I see.Ivan [00:38:29]: Which was like, “Every agent needs a computer.”Swyx [00:38:31]: I see, I see.Ivan [00:38:32]: So they have launched something recently.Swyx [00:38:34]: Yeah, they have Microsoft Power Automate, I'm sure, I'm sure, they're gonna have their version.macOS Sandboxes, Apple Constraints, and the Windows OpportunityIvan [00:38:39]: Version of that, yeah.Swyx [00:38:39]: You're gonna try to do yours, and it - I always know there's always demand for Mac, but I know it's, tricky to host, macOS sandboxes.Ivan [00:38:49]: We will have macOS sandboxes fairly soon. The problem with macOS, OS sandboxes is, I'm deep in this, I don't know how much interesting is.Swyx [00:38:55]: No, it's.Ivan [00:38:56]: MacOS has this problem.Swyx [00:38:57]: It's a licensing thing, right?Ivan [00:38:58]: Licensing thing. So one, you're allowed to run only two parallel VMs per machine, so that's one. Two, you can only license to a different user every 24 hours. So if you come in and theoretically, if I wanna charge you per second and I charge you one second, I have to have it idle for the rest of the day. I can't have anyone else doing that. So the pricing will be different in the sense that I will have to - we would have to charge for 24 hours, and that's not even, that's not even the most difficult thing. But the, thing above that is, from a security perspective, they enable you to do memory snapshot, pause, resume, but only on the same physical drive, physical machine. And so what you can do in, Windows world or Linux world is that I can move in the background, your snapshot from one to the other and manage load, right? Here, if you wanna do that, you essentially have to have your.Swyx [00:39:49]: Yeah, snapshots. Yeah.Ivan [00:39:50]: Your.Swyx [00:39:51]: It's like.Ivan [00:39:51]: Physical machine.Swyx [00:39:52]: You can't break it up.Ivan [00:39:53]: You can't, you can't move things around that, and all of that is, that part is, from a security standpoint, if it is written. Like, I understand the security aspect of that, but it disables you from doing these agentic, like really scalable agentic workloads.Swyx [00:40:08]: You need to do a vibe-coded, clean room implementation on macOS that you can then - That's like Clean OS or something. I don't know.Ivan [00:40:17]: So. We have.Swyx [00:40:18]: ‘cause like Linux was originally like a clean room rewrite of Unix.Ivan [00:40:21]: Okay. Yeah.Swyx [00:40:21]: Or something like that, right? Like same thing to macOS. Someone needs to do it.Ivan [00:40:25]: Someone will do that, and someone will have some long-running agents for a few days to figure this stuff out. But yeah. So definitely we - we're really close to offering something ‘cause people do want it, but the pricing will be different, and the feature set will be sort of stringent.Swyx [00:40:38]: Yeah, nobody's gonna use this. like, the labs, the labs will because they want to automate macOS.Ivan [00:40:42]: They have to do RL. They have to do RL again. But even if you The - So the point is with the RL part, if you, if you do RL on macOS, then the next iteration of the model comes out, it will be able to use these tools significantly. Then you actually need to run those, that somewhere. So you're gonna have to have that, later on. And from, if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get if they would just enable a concurrency model similar to what you can get on a Windows and a, and Linux.Swyx [00:41:17]: Yeah. Yeah. And I'm sure they've heard this before. They just don't care. Yeah, it's And maybe they will change their mind with the new CEO.Ivan [00:41:24]: Yeah. We'll see.Swyx [00:41:25]: We'll see.Ivan [00:41:25]: High hopes.Swyx [00:41:26]: High hopes.Ivan [00:41:26]: High hopes.Swyx [00:41:27]: Okay. But I, it's very clear the market opportunity is huge in Windows, and you can go for a long time on just Windows, but your customers are gonna want both. and I think, it is interesting to me that, this is the sort of God application of agents, right? Like, I don't It was - How big was OpenClaw for you guys? Like, was it, was there, a significant bump.OpenClaw, Agent Labs, and the B2B2C Sandbox MarketIvan [00:41:54]: Not for us because we.Swyx [00:41:54]: Because you already.Ivan [00:41:55]: We're kind of positioned differently. Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C. Sort of it's either B2B or B2B2C. So, in the researcher world, it's B2B, so you're selling to, labs and neo labs and things like that. But on the long-running agents, it's mostly, from a scale revenue perspective, it's mostly B2B2C, where you have a app layer agent that uses you at a big scale.Swyx [00:42:26]: Like a Manus. Yeah.Ivan [00:42:28]: Like a Manus Lovable type of thing.Swyx [00:42:31]: Yeah. I think that's the question of, well how, um-Uh, yeah, B2B to C is basically to me what I've been calling an agent lab, which is kind of like you're not in a model lab, but you're making a very good wrapper that is a platform that other people can sign up so they don't have to code those things. Yeah, it sound, it sounds like a much better market than the direct OpenClaw market.Ivan [00:42:56]: I've like - We I've done multiple things. So the CodeAnywhere's part of our career path R in the calendar, was very much an end user developer product. And so that is great. It You can get a lot of developer love, and I feel that we do as a company have a bunch of developer love. But it's a different type, where it's people building these things. Again, it's more akin to a Twilio because you don't really run - As a person, you wouldn't run Twilio. I don't know how many people remember. It was like ask your developer billboard and whatnot. And people really love Twilio, but they only used it inside of like, “Oh, I'm building this app or service for thing.” And so we're very much directly to that. And you also know that I used to work for a competitor for Twilio, so it's kind of ingrained, in my DNA.Swyx [00:43:35]: People don't know InfoBip is that big.Ivan [00:43:38]: Yeah, it's.Swyx [00:43:39]: Because.Ivan [00:43:40]: It's a billion euro.Swyx [00:43:40]: They're all American. They're like, “Whatever's in Europe doesn't matter to me.” But like it's the, it's the same size or bigger? Same size?Ivan [00:43:46]: It's about half the size.Swyx [00:43:47]: Half the size?Ivan [00:43:48]: Yeah, about half the size.Swyx [00:43:48]: It's like, yeah.Ivan [00:43:48]: Still huge. Multiple billions a year. Yes.Swyx [00:43:51]: That's crazy.Ivan [00:43:51]: Exactly, and so that - These are like really interesting and large revenue-generating, very sticky businesses. Whereas when you're selling to the - When your focus is the end developer, it is a very hard sell because they're very price sensitive, very price conscious, very around that. And there's very It's very hard to scale. Your cap is the number of people that are willing to spin up - First of all, wanna spin that up, and then spin up multiple of these. Whereas if you're in the enterprise one, like we know everyone's talking about like how many tokens they're spending, I'm spending. Like a lot of companies today are like, “If this is our company, spend as much as you can.” Like basically that is where we're going. And so if you think about that paradigm, where you're selling to companies that say, “Spend as much as you can to generate, productivity,” versus, “Oh, I'm a single person. I have this much budget, and I'm doing this thing because it's fun or it's helping me out or whatever.” Like it is a different, it's a different go-to-market, I think, strategy.MCP, CLIs, and Sandboxes as the Agent RuntimeSwyx [00:44:50]: Yeah, there's a lot of discussion. I'm just kind of going through like the mental list of things that are in your favor, which is, for example, MCP versus CLI. Like obviously you want CLI. It's been very good for you. I feel like it's maybe a drop in the bucket or maybe it's huge. I'm just checking whether it's like these are big trends.Ivan [00:45:10]: Those things you - work well in our favor, to your point just because every.Swyx [00:45:13]: They're kind of drop in the bucket, right?Ivan [00:45:15]: I think it's like sort of all the things come together. And so there's so many things that impact that. To your point, like OpenClaw wasn't huge for us, but like having the agent SDK, from Anthropic, so or Cloud Claude Code was very interesting. The reason why it was interesting is that a lot of, let's call them app I don't know what to call them, app layer agent companies, essentially they are like, “Oh, I can create this new app, this new agent. All I need, I just use Claude Code, and I throw it into a sandbox, and then I have my interface to the human to that.” And so that enabled so many more companies to actually offer this, and then they would pull on sandbox. So that was, that was interesting. And to your point, like MCP, versus the CLI, the MCP is an interface against an API, whereas the CLI is like you can actually go do things. Like this is it. The difference between integrations and actually running scripts or data or analysis against a thing. So being able to use a CLI very well enables the agent to do more things, and it's because that people will invoke a sandbox, they'll run it in the CLI, and but it'll do anal-analysis on that data and then give you an actual result versus just, pulling data from an API source.Swyx [00:46:29]: Yeah, it's a layer of indirection basically, it's the same thing as agentic search versus RAG, which where you're.Ivan [00:46:34]: Exactly, yeah.Swyx [00:46:34]: Just like you just win whenever people put more agents into their workflow. And so like it doesn't really matter, but I'm just kinda teasing out like what else have people heard about that like it's sort of, “Oh yeah, this is another sandbox use case. Oh yeah, that's another one.” Am I, am I missing any big ones?Ivan [00:46:51]: The thing, the thing that people, which is the computer use stuff, which I think is probably the most interesting one, is, and to your point, we've talked to so many people over the last year. It's like, “Oh, like why do you need a sandbox? Why do you need this? Why this?” And to your point, it's like, “Oh, I need sandbox for this. I need sandbox for that. I need sandbox-” It's like, “Oh, I need it for every single thing.” And so basically what I, what I - and it sounds like a broken record, it's like you use a laptop every single day, right? And you are n of one. It's just you. But now imagine how And by the way, the laptop, the computer PC market, the PC market is about equal to the cloud market in total. So it's about 150, 180 billion a year. Something like that. It's about roughly the three cloud hyperscalers is about equal to like Apple, HP, Lenovo, whatever, It's a little bit less, but it's sort of like that. And now imagine And that's just like, so how big is the addressable market? What, how many people are there in the world now? What's the last data?Swyx [00:47:45]: Let's call it eight billion.Ivan [00:47:46]: Eight billion. And so let's say you can have two computer, like you have one personal and one business, whatever. Like so it's double that, right? and so that's 16 billion, right? How many agents are gonna be running in two years, in 10 years, in 100 years? Like And for every single task, they will need one of these. And so how big is that? That market is essentially quote unquote “infinite”. You will get to the point, and Dylan Patel was at the conference talking about, from SemiAnalysis, that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint. You won't be able to grow, or we won't be able to have enough of these because there won't be enough CPUs to basically do.Swyx [00:48:23]: Yeah. Well, I actually had a really good podcast with Doug Oliphant, who, which was his president at SemiAnalysis, where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to memory and now to CPUs.Ivan [00:48:35]: CPU, yeah.Swyx [00:48:35]: It-What's next? So networking. So, networking actually has been in shortage for a while if you're looking at, just GPU networking. But, yeah, it's really crazy the amount of computer use that's going on, yeah, cool. I, other questions are, just the one very big part is the open sourceness which you didn't have to do, your competitors don't do, like it's not, a lot of people are worried about keeping their projects open source because some competitor can just slot fork it. I don't know if there's any reflections on just being an open source company.Open Source, Trust, and Enterprise ProcurementIvan [00:49:15]: Yeah. There's a bunch. So we the original product that we did was open source.Swyx [00:49:19]: Yeah. CodeAnywhere.Ivan [00:49:20]: So doing that was actually very good for us. There's basically a saying of, What's the saying? Like, companies that are, that are doing really well, measure themselves against, free cashflow, that are kinda okay, it's EBITDA, then, it's, it goes all the way down.Swyx [00:49:36]: The worst is like GitHub stars.Ivan [00:49:37]: GitHub stars. GitHub stars are the worst, yeah. So you go all the way down to GitHub stars. And so our original one was GitHub stars. That's what we talked about, we're at the point we're talking about revenue, so we're we've gone up the stack on that. And so we started.Swyx [00:49:47]: No, profit.Ivan [00:49:48]: Yeah. We haven't, we're, we'll get there. We'll get there. But basically at that point we did stars and GitHub and it was useful, and the original variation that we did, it we split the core into its own repo and it was Apache 2.0, so very, permissive. And then we basically would bundl

In this episode, Swati Trehan, co-founder of Ema, breaks down what AI agents actually are, how “AI employees” work inside Fortune 500 companies, and why the future of enterprise software may look nothing like today's SaaS tools. Swati explains how Ema's platform orchestrates teams of AI agents that can autonomously handle HR, IT, finance, onboarding, payroll, employee support, and customer service workflows across massive organizations. She also reveals how companies like Hitachi are already deploying AI employees at scale, why traditional automation failed, and how enterprise AI is evolving beyond simple copilots into fully agentic systems. The conversation dives deep into the technical infrastructure behind agents, including memory, orchestration layers, knowledge graphs, model routing, and why Ema uses multiple LLMs simultaneously to optimize for cost, latency, and accuracy. Swati also shares why Excel remains one of AI's hardest unsolved problems, why video is the next frontier for agents, and how the “SaaS apocalypse” is reshaping software businesses. If you've been hearing terms like agents, autonomous workflows, AI employees, copilots, or agentic AI, this is one of the clearest explanations of where the technology is heading and what it means for the future of work. Key Topics Covered: What AI agents actually are (explained simply) The difference between copilots, agents, and AI employees Why traditional automation and RPA failed How Fortune 500 companies are deploying AI employees today Why HR is becoming the entry point for enterprise AI adoption How Ema orchestrates teams of agents across workflows The technical stack behind enterprise AI agents Why memory, context, and permissions are critical for agents The “mixture of experts” approach using multiple LLMs at once Why Excel remains surprisingly difficult for AI systems The next frontier: AI-generated video workflows The rise of the “SaaS apocalypse” Why solving business problems matters more than building features How AI is changing the way founders and engineers think Episode Timestamps: 00:00 - Intro 00:34 - What AI agents actually are 03:01 - The difference between agents and AI employees 03:25 - Liam's “light bulb” moment using agents 04:06 - Swati's realization that HR work could be automated 05:57 - The founding story behind Ema 08:20 - Why AI unlocks human creativity 09:20 - The technical infrastructure behind AI agents 12:12 - How Ema routes tasks across multiple LLMs 13:49 - Memory, context, and knowledge graphs for agents 16:35 - The biggest unsolved problems in AI agents 18:32 - Why video is the next frontier for AI 20:05 - Why Excel is still difficult for AI systems 21:00 - Who Ema's ideal customers are 23:27 - Why HR teams are leading enterprise AI adoption 24:25 - How enterprise AI implementation actually works 26:13 - Why modular agents matter 28:35 - What the employee experience looks like with AI agents 30:24 - Live demo of Ema's AI employee system 36:58 - How companies roll out AI agents internally 39:31 - Building AI employees in real time 44:01 - Ema's competitive moat in the AI race 47:46 - The “SaaS apocalypse” and future-proofing AI businesses 49:16 - Why Ema focused on product over hype 52:12 - How AI changed the way Swati thinks 55:07 - Why rapid problem-solving matters more than ever 57:27 - Living in London while building a global AI company 59:16 - Why Swati does what she does Swati Trehan's Socials: LinkedIn: https://www.linkedin.com/in/swati-trehan/ Ema: https://www.ema.co Partner Links Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: https://www.theaireport.ai/leaders-launch-guide Learn more about your ad choices. Visit megaphone.fm/adchoices

Christian Thordal: When Applying Scrum By The Book Fails, Understanding Context Before Changing The System Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.   "I treated Scrum like a military SOP — follow the book, execute the steps. But I failed to see that the context was really the tipping point. What looked like a problem was actually their solution." - Christian Thordal   Christian shares a hard-won lesson from his time coaching three RPA teams at one of Denmark's largest banks during the pandemic. He inherited teams running six-week sprints with half-hour planning sessions that amounted to little more than putting items on a calendar. As a former Danish Army officer, Christian's instinct was to fix the obvious deviation from the Scrum Guide — the sprint length. He advocated for shorter feedback loops and eventually convinced the Product Owner, who also served as the director, to try two-week sprints. The first planning session was a disaster. There was yelling and scolding, and it became clear that the real problem had nothing to do with sprint length. The teams had no proper backlog. The six-week sprints actually worked because they gave teams enough time to go out to the business, discover work, and deliver it within a single cycle. Christian realized he had been applying Scrum mechanically without understanding how work entered the system. He started attending business analyst and PO meetings, uncovered the backlog gap, and helped the teams build a proper one. His key insight: what looks like a symptom can actually be a pragmatic solution to real constraints. Understand the system before you change it.   In this episode, we refer to the book Scrum: The Art of Doing Twice the Work in Half the Time, by Jeff Sutherland.   Self-reflection Question: When was the last time you assumed a team's practice was wrong, only to discover it was a reasonable adaptation to their context? How might you investigate the "why" behind existing processes before proposing changes?   [The Scrum Master Toolbox Podcast Recommends]

May 15, 2026: The Guardian documents the tech industry's accelerating purge of middle managers — and history says companies have tried this exact bet before with Jack Welch and the Reengineering movement, with disastrous long-term results. The Wall Street Journal reports companies are drowning in ungoverned AI agents, raising a critical question: is agentic AI actually different from the RPA sprawl crisis of a decade ago, and is the difference showing up in real outcomes? And Yale's Jeffrey Sonnenfeld and NYU's Gary Marcus argue in Fortune that America's 1,200 AI bills have no shared test for what makes good policy — and the regulatory patchwork hardening in place rhymes uncomfortably with the conditions that produced the 2008 financial crisis.

As the Trump administration is in the process of revamping Penn Station, Tom Wright, CEO and president of the Regional Plan Association (RPA), talks about a new report that offers the RPA's ideas for how to increase capacity and make the transit hub work for commuters. Photo: A clock at Penn Station. (Credit: Boaventuravinicius via Wikimedia Commons CC BY 4.0) Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Is the conspiracy economy beginning to crack? In Part Two, we discuss Alex Jones signing off from InfoWars and some revealing Tucker Carlson Show statistics...put out by his network. We also analyze the reported layoffs at the Daily Wire.We tell you what the "influencers" will being talking about in six months! Alert: New three-part audio documentary series launching Monday, May 11th! This episode powered by: RuffGreens - the premium supplement created to boost your dog's energy, digestion, and overall wellbeing. (promo code: RPA) Ethos - Protect your family with life insurance from Ethos. Get up to $3 million in coverage in as little as 10 minutes at ethos.com/rpa Alliance Defending Freedom -Learn more about how you can support free speech by texting RPA to 83848 or going to JoinADF.com/RPA.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

Is the conspiracy economy beginning to crack? We take a deep dive into how a new defamation lawsuit related to the tragic events of Charlie Kirk shows some cracks in the business model of conspiracy theorists. Don't miss this episode! Alert: New three-part audio documentary series launching Monday, May 11th! This episode powered by: RuffGreens - the premium supplement created to boost your dog's energy, digestion, and overall wellbeing. (promo code: RPA) Ethos - Protect your family with life insurance from Ethos. Get up to $3 million in coverage in as little as 10 minutes at ethos.com/rpa Alliance Defending Freedom -Learn more about how you can support free speech by texting RPA to 83848 or going to JoinADF.com/RPA. Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

An interview with Robert Bean and Lance MacNevinIn this episode we unpack the rapidly transforming world of hydronic heating and cooling. We are joined by two seasoned veterans of the industry, Robert Bean and Lance MacNevin. With many decades of real-world experience and hard-earned perspective between them, they offer a thoughtful and engaging look into why hydronics is at the forefront of modern, highly efficient building practices. Robert is (attempting to be) a retired engineering technology professional and ASHRAE Fellow, while Lance brings his extensive background serving as the Director of Engineering at the Plastics Pipe Institute. This episode is packed with sound-bite worthy moments as our guests cut through the noise to discuss the realities of hydronic-based thermal comfort. Whether you are a homeowner, architect, or builder, you will find their independent, expert perspectives well worth listening to and holding on to. This is definitely an episode you will want to bookmark and share with anyone interested in the future of the HVAC industry!Robert BeanRobert Bean is a retired engineering technology professional who specialized in the design of indoor environments and high-performance building systems. Mr. Bean is an ASHRAE Fellow and ASHRAE Distinguished Lecturer, recipient of the Lou Flagg Award, Distinguished Service Award, and instructor for the ASHRAE Learning Institute. He has authored many papers, articles, and course curricula, and has served on numerous technical committees related to indoor environmental quality, building, and energy systems.Lance MacNevinLance MacNevin, P.Eng. is the senior director of engineering for the Plastics Pipe Institute's Building & Construction Division, with expertise on pressure pipes such as CPVC, HDPE, PEX, PE-RT, and PP. Lance has been in the plastic pipe industry since 1993, working as an R&D engineer, codes and standards specialist, and trainer for a major piping manufacturer for over twenty years before joining PPI in 2015. In this role, he focuses on plumbing and mechanical systems, coordinating research, education, and advocacy activities. He is an active member of ASHRAE, ASPE, ASTM, AWWA, CSA, IAPMO, ICC, IGSHPA, NSF, and RPA.TeamHosted by Kristof IrwinEdited by Nico MignardiProduced by M. Walker

You built a successful practice to serve clients, not to spend your evenings buried in repetitive paperwork. What if a software robot could handle those tedious tasks with perfect accuracy while you focused on work that actually requires your judgment?Gary sits down with Elisa Silverglade, Director of Intelligent Automation at Techromatic, to explore how Robotic Process Automation is transforming law firm operations. This is not another AI conversation. RPA follows strict rules, executes with near-perfect consistency, and never hallucinates. Elisa draws on 15 years of law office management to show attorneys where automation delivers the greatest return.Key TakeawaysUnderstand the difference between RPA and AI. Automation executes rule-based tasks with consistent accuracy. AI attempts to mimic human thinking but remains unpredictable. For zero-error processes, RPA wins.Recognize that automation elevates jobs rather than eliminating them. Staff shift from data entry to higher-value work requiring judgment and client connection.Start with your biggest bottleneck. Where is the most friction in your operations? That pain point is your automation starting point.Audit your existing tech stack before buying new tools. Many firms already pay for automation features they never activate.Seek expert guidance for the journey. The legal tech landscape is vast. Working with consultants and coaches who understand law firm operations helps you avoid costly mistakes and maximize results.Embrace personal freedom as a practice philosophy. Elisa defines freedom as having the power to make your own choices and believing you deserve everything you want.Continue the ConversationIf this episode got you thinking, subscribe and leave a review to help other attorneys find these conversations. Practice law with purpose. Live life with freedom.[00:00] Introduction and episode hook[02:03] What is RPA exactly?[05:50] RPA versus AI explained[10:01] Will automation eliminate jobs?[13:21] Real results from a PI firm[17:13] First steps toward automation[21:24] Automation and work-life balance[25:40] Why expert guidance matters[28:25] What personal freedom means[31:00] Final insight for law firmsElisa Silverglade is the Director of Intelligent Automation at Techromatic, where she partners with law firms to eliminate repetitive manual work through custom software robots. She helps legal teams identify operational bottlenecks and implement automation that saves time, reduces errors, and restores work-life balance. With over 15 years managing a law office and a background in finance, technology, and advocacy, Elisa brings rare empathy and operational insight to legal technology. She is also author of Meeting My Anxiety. Connect with Elisa on LinkedIn or at elisa@techromatic.com.Would you like to learn what it looks like to become a truly Free Lawyer? You can schedule a complimentary call here: https://calendly.com/garymiles-successcoach/one-one-discovery-callYou can find The Free Lawyer Assessment here- https://www.garymiles.net/the-free-lawyer-assessment

Proud to have Gary Washington on today's show. He is a former Air Force member, longtime federal IT leader, and former USDA CIO (eight years), now Chief Strategy Officer at ACT-IAC. Washington recounts his career across agencies including Treasury, HHS, FDA, OMB, and USDA, and explains how military discipline shaped his emphasis on documented plans, accountability, and trust. He discusses common resistance to change in large organizations, USDA's shift from decentralization toward centralization, and implementing the White House-driven IT Modernization Centers of Excellence through inclusive, business-driven governance, performance measurement, workforce education, RPA training, and results such as deactivating 37 data centers and consolidating networks and end-user support.https://www.actiac.org/act-iac-event/fellows-friends-day-domaine-fortier  ACT-IAC Gives Back: Wreaths Across America 2026 | ACT-IAC Small Business Alliance | ACT-IACSubscribe on your favorite podcast platform to never miss an episode! For more from ACT-IAC, follow us on LinkedIn or visit http://www.actiac.org.Learn more about membership at https://www.actiac.org/join.Donate to ACT-IAC at https://actiac.org/donate. Intro/Outro Music: See a Brighter Day/Gloria TellsCourtesy of Epidemic Sound(Episodes 1-159: Intro/Outro Music: Focal Point/Young CommunityCourtesy of Epidemic Sound)

With all the talk about today’s failing public schools…were yesterday’s any better? To find the answer, we tell the story of the time your humble co-host made the transition from elementary school to junior high…and what he learned about inner-city public schools along the way. This episode is powered by Ruff Greens (promo code: "RPA"). Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

With all the talk about today’s failing public schools…were yesterday’s any better? To find the answer, we tell the story of the time your humble co-host made the transition from elementary school to junior high…and what he learned about inner-city public schools along the way. Note: Some adult language. This episode is powered by Ruff Greens (promo code: "RPA"). Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

In this episode, we explore the rapidly evolving field of regenerative medicine, focusing on a cutting-edge approach known as RPA (Regenerative Protein Array). Joined by leading medical experts, we break down how RPA differs from traditional therapies like PRP and stem cells, and how it leverages a complex network of proteins to support healing, recovery, and performance. The discussion covers real-world applications across elite athletes, aging populations, and neurocognitive conditions, while also addressing key questions around treatment frequency, safety, and effectiveness. Overall, this episode bridges the gap between emerging science and practical outcomes in modern regenerative care.Relevant links:Genesis Contact Information1-855-320-7559www.genesisregenerative.compatient-inquiry@genesisregenerative.comclinician-inquiry@genesisregenerative.com Dr. Jacobson Contact InformationMark Jacobson, M.D.Genesis Medical Director – MusculoskeletalMedical Imaging & Therapeutics, Lady Lake, FloridaFounder & Medical Directorwww.mitflorida.comDr. Bregman Contact InformationPeter Bregman, DPMGenesis Medical Director – Foot, Ankle, NeuropathyBregman Foot-Ankle & Nerve Center, Las Vegas, NVFounder & Medical Directorwww.bregmanfance.com Prof. Dr. Bankole Johnson Contact InformationProfessor Bankole Johnson,DSc, MD, MBChB, MPhil, DFAPA, FRCPysch, DAASCPGenesis Medical Director – Neuro & CognitiveMiami Stem Cell Clinic, Miami, FLFounder & Medical Directorwww.miamistemcell.clinicDisclaimer:The information provided in this podcast episode is for entertainment purposes and is NOT MEDICAL ADVICE. If you have any questions about your health, contact a medical professional. This content is strictly the opinions of Lucas Aoun and is for informational and entertainment purposes only. It is not intended to provide medical advice or to take the place of medical advice or treatment from a personal physician. All viewers of this content are advised to consult with their doctors or qualified health professionals regarding specific health questions. Neither Lucas Aoun nor the publisher of this content takes responsibility for possible health consequences of any person or persons reading or following the information in this content. All consumers of this content especially taking prescription or over-the-counter medications should consult their physician before beginning any nutritional, supplement or lifestyle program.Timestamps 0:00 Intro0:52 Meet the Experts3:53 What is RPA?6:14 Limits of Current Treatments12:41 Steroids vs Healing13:42 Athletes vs Everyday Recovery16:50 Brain and Neuro Cases24:32 Evidence and Ethics25:57 Real Patient Results30:55 Autoimmune Effects32:10 Rapid Injury Recovery33:50 Avoiding Surgery Case35:50 How RPA is Delivered36:35 Broader Applications49:52 Vision Applications51:01 Safety vs Other Treatments56:00 Outro Hosted on Acast. See acast.com/privacy for more information.

Send us Fan MailFrom working COVID emergencies for medical gas compliance in New York City to working on code committees, fourth generation plumber John Mullen talks about his role as Director of Technical Services, IAMPO, and RPA Technical Liaison.Today's homes need more than a single energy source. Power key home systems like home heating, water heating, cooking, and backup power with propane to build high-performance homes ready for today's grid constraints and future demand. Propane delivers reliable whole-home performance while reducing electric load. Learn more at propane.com/residentialSubscribe to the Appetite for Construction podcast at any of your favorite streaming channels and don't forget about the other ways to interact with the Mechanical Hub Team!Follow Plumbing Perspective IG @plumbing_perspectiveFollow Mechanical Hub IG @mechanicalhubSign up for our newsletter at www.mechanical-hub.com/enewsletterVisit our websites at www.mechanical-hub.com and www.plumbingperspective.comSend John and Tim your feedback or topic ideas: @plumbing_perspective

Is global warming a hoax? To find the answer, we tell the story of the biggest science heist in history. This is episode is powered by The Licorice Guy. (promo code: RPA 15)Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

Can Hollywood be saved? We discuss some bright spots in Tinseltown, like Project Hail Mary, and the extraordinary opportunity that conservatives have to save America's most important export. Powered by Ruff Greens (promo code: RPA).Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

Can manufacturing jobs really come back to the United States? In Part Two, we tell the surprising story of how a long-haired hippy kicked off the chain of events that changed American manufacturing forever. American Icon (Part Three) airs Thursday, March 26th, 2026. Through the end of the month, get 50% off an RPA baseball cap. Just visit RedPilledAmerica.com.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

Can manufacturing jobs really come back to the United States? To find the answer, we tell the story of an American icon...the baseball cap. American Icon (Part Two) airs Wednesday, March 25th, 2026. Through the end of the month, get 50% off an RPA baseball cap. Just visit RedPilledAmerica.com.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

In Part Two, we continue our deep dive into the resignation of former RPA guest Joe Kent, and his contribution to what we call the conspiracy economy. We also discuss how Charlie Kirk's assassination changed us forever.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

In this episode of Famboogie, we take a deep dive into the resignation of former RPA guest Joe Kent, and his contribution to what we call the conspiracy economy. We also explain how conspiracy theories have become the victim ideology of a growing segment of the Right...and what needs to be done to stop it.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

Is America healing? We discuss how TPUSA exposed Candace Owens, and how Greenland and the price of precious metals may be signaling a global game of chess. This episode is powered by Ruff Greens...the supplement that makes your dog's food come alive! Use Discount Code “RPA” to claim your FREE JumpStart Trial Bag at RuffGreens.com.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

Is America healing? After Year One of the Trump Administration, we discuss their extraordinary accomplishments, and how CNN was caught trying to dupe the American public...again. This episode is powered by Ruff Greens...the supplement that makes your dog's food come alive! Use Discount Code “RPA” to claim your FREE JumpStart Trial Bag at RuffGreens.com.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.