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Sintonía: "Hawaiian Eye" - ERNIE FREEMAN 1.- "Hawaiian Rock" - TOMMY SANDS 2.- "Coconut Girl" - AL TERRY 3.- "Little Grass Shack In Kealakekua" - FELIX MENDELSSOHN & HIS HAWAIIAN SERENADERS 4.- "Hawaiian Cha-Cha-Cha" - PEREZ PRADO & HIS ORCHESTRA 5.- "Yaaka Hula Hickey Dula" - HALELOKE KAUAALOPUA & ARTHUR GODFREY WITH THE MARINERS 6.- "Honolulu Rock & Roll" - EARTHA KITT 7.- "Hula-Rock" - BILL BROWNING 8.- "O hilo Hanakaki" - LANI McINTIRE & HIS ORCHESTRA KAULANA 9.- "Kookie Little Paradise" - FRANKIE VAUGHAN10.- "Two Ladies In De Shade Of Banana Tree" - JOSEPHINE PREMICE11.- "Ulili E" - THE SURFERS12.- "Rag of Rags" - DIE HULA HAWAIIANS13.- "On A Coconut Island" - LOUIS ARMSTRONG WITH THE POLYNESIANSTodas las músicas extraídas de la compilación (1xLP + CD gratis) "Ohana Hawaiiana - Hipshakin´& Fingersnappin´ Poolside Music with Hawaiian Flavor" (Jukebox Music Factory, 2024)14.- "I´m a Happy Hawaiian Cowboy" - HAL ALOMA AND HIS ORCHESTRA15.- "Hawaiian Cowboy" - SLIM WHITMAN16.- "Hawaiian War Chant" - THE AMES BROTHERS17.- "Hootchy Kootchy Henry" - MITCHELL TOROK18.- "Hapa Haole Hula Girl" - ALFRED APAKA19.- "Hukilau" - WEBLEY EDWARDSExtraídas de la compilación (1xLP + CD gratis) "Ohana Hawaiiana - Hipshakin´& Fingersnappin´ Poolside Music With Hawaiian Flavor Vol.2" (Jukebox Music Factory, 2025) Todas las músicas seleccionadas (singles a 45 rpm) y compiladas por Jens Galler (AKA Buddy Belpaso)Escuchar audio
Episode 12 is the proof-of-concept episode. Nigel Maine walks through the live RAG installation built on 1.67 million words of salesXchange IP — 708 documents, 4,590 retrieval chunks, 768-dimension embeddings running on Vertex AI Vector Search in Google Cloud's European region. The knowledge base is in. The closed-loop GTM system is operational. Then he reads the Manifesto.The Manifesto is forty minutes of the most direct argument Nigel has ever made on camera. Seven movements. Forty years of B2B sales observation combined with a decade of systematic research. It names the failure, presents the data — from 14,106 MarTech products to 43% average quota attainment — and makes the case for Broadcast B2B Selling as the only model built around how B2B buyers have always behaved.If you have privately suspected your GTM function is structurally broken, this episode is the forensic examination you've been waiting for. Watch the full episode, then follow the link to the sX Course below.What this episode coversThe RAG installation: what was built, how it works, and why the temp-vs-colleague analogy is a functional description, not a metaphorThe corpus: 1.67 million words, 708 documents, 4,590 retrieval chunks explainedWhat a B2B RAG system means for institutional knowledge, content production, and sales readinessThe Agentic AI shift — MCP, AI agents, and what Y Combinator and a16z are saying right nowThe closed-loop GTM system: content scheduling, performance analytics, and self-improving outputThe Manifesto — Movement 1: Nothing Changed Except the Door (1952 to 2026)Movement 2: The Crime Scene — the tool explosion that produced nothingMovement 3: The truth about how B2B buyers actually behaveMovement 4: Broadcast B2B Selling — the only logical responseMovement 5: The sX Operating System — a six-module commercial infrastructureMovement 6: Why the timing has never been betterMovement 7: The call to arms — two choices, one structural argumentWho should watchB2B technology and SaaS CEOs, founders, and revenue leaders who are spending £190,000 to £1 million annually on SaaS with diminishing returns, watching sales teams miss quota, and getting ready to ask whether there is a different model. This episode gives you the evidence base and the alternative.Take the next stepDownload the GTM Reset, GTM Landscape, or GTM Architecture Audit PDFs at salesxchange.co.uk — or email nigel@salesxchange.co.uk to talk about what this looks like in your business.
Send us Fan MailIn deze Data Expo-special van De Dataloog spreken we met Dennis Maas van Wolters Kluwer en Vincent Hoogsteder van Mozaik over de ontwikkeling van AI-toepassingen voor juridische informatievoorziening. Centraal staat de vraag: hoe bouw je een AI-oplossing die niet alleen slim klinkt, maar ook feitelijk juist, uitlegbaar en betrouwbaar genoeg is voor een domein waarin fouten grote gevolgen kunnen hebben?Dennis vertelt hoe het eerste “wow-moment” met ChatGPT leidde tot een strategische verkenning binnen Wolters Kluwer Schulinck. Vanuit een rijke bron aan juridische content ontstond de ambitie om AI in te zetten om professionals bij gemeenten sneller en beter naar relevante wetgeving, jurisprudentie en thematische duiding te leiden. Vincent legt uit hoe Mozaïek daarbij helpt met een technische architectuur waarin content, context en technologie samenkomen.De aflevering gaat diep in op de praktische kant van AI in complexe kennisdomeinen: van RAG, embeddings en reranking tot agents, evaluatiesets, juridische interpretatie en de rol van menselijke experts. Een belangrijk inzicht: technologie alleen is niet genoeg. Juist de domeincontext — het verschil tussen bijvoorbeeld “bijstand” en “bijzondere bijstand” — bepaalt of een AI-systeem bruikbaar en betrouwbaar wordt.Ook bespreken we waarom snelheid van leren cruciaal is, waarom kleine technische verbeteringen vaak meer opleveren dan de nieuwste hype, en waarom kwaliteit in juridische AI altijd boven snelheid moet gaan. Een aflevering voor iedereen die wil begrijpen hoe je AI verantwoord toepast in omgevingen waar je simpelweg gelijk móét zitten.De Dataloog is de onafhankelijke Nederlandstalige podcast over data & kunstmatige intelligentie.Hier hoor je alles wat je moet weten over de zin en onzin van data, de nieuwste ontwikkelingen en echte verhalen uit de praktijk. Onze hosts houden het altijd begrijpelijk, maar schuwen de diepgang niet.Vind je De Dataloog leuk? Abonneer je op de podcast en laat een review achter.
After the GM goes on a tirade about historical potash production and we do a live Resources & Teams turn, Eero leads VS, Ibn the Roaster, and Zaza in a continued exploration of the underlevel of the Abbey of St Clewyd. They figure out a tricky door and make contact with the monks inside the western portion of the underlevel, who are split into Cardinite and Loyalist factions and speak of something called "the Charge" that they refuse to let the PCs see. Abbott Lummingwyll convinces them to take a holy mace into the chaos rift to disrupt it. Ibn goes in three times, with different results, and the final time is nearly killed (but saved by the children of Mr Rag&Bone). Eero duels a resurrected monk, and they lower VS down a rope into a well, where he encounters Grandfather Eel, who seeks for worshipers to ascend to godhood. At close of play, the sun has risen on the ruins, but both Ibn and Tals are missing from the party. To be continued!
Stewart Alsop sat down with Michael Shackelford to discuss their experiences building applications through vibe coding—the practice of using AI to create software without traditional programming expertise. Stewart, who runs the AI Whispers community in Buenos Aires and hosts the Crazy Wisdom podcast (with over 660 interviews), shared how he went from teaching people prompt engineering to building his own video conferencing software as a Riverside.fm replacement, while Michael opened up about his year-long journey creating Genrupt Inc, an AI-powered content generation tool for e-commerce sellers. The conversation covered everything from the decline in quality of Claude's reasoning capabilities and how Chinese companies used distillation attacks to copy Anthropic's models, to the importance of spaced repetition systems for managing knowledge in the age of LLMs, with both sharing battle-tested prompting strategies like asking AI to "explain it to me in genius terms" and using deep research queries to reverse engineer how competitors build their products.Show Notes:- Dan Martell's book "Buy Back Your Time" was mentioned as one of the best business books for thinking about life and business- Check out John Vervaeke's "Awakening from the Meaning Crisis" for understanding relevance realization and why AI fundamentally cannot determine what's relevant to humans without being toldTimestamps00:00 Michael discusses being exhausted from getting his app ready for launch, working nonstop with AI to prepare landing page for podcast traffic driving beta signups05:00 Stewart explains starting AI Whispers in Buenos Aires after leaving OpenAI vendor company, meeting early adopters like Torin who was building mind-reading EEG technology10:00 Discussion of how corporations resist AI adoption due to political games and job security fears while some companies use AI as excuse for pandemic-era layoffs15:00 Stewart describes teaching workshops on using LLMs as linguistic tools rather than coding tools, noting technical people often lack humanities background needed for prompting20:00 Explaining chatbot wrappers, API calls, and how Anthropic's reasoning quality declined after Chinese distillation attacks copied their secret sauce developed with philosophers25:00 Technical discussion of model training, fine-tuning versus RAG for new information, and different approaches to updating AI knowledge beyond initial training30:00 Stewart describes building podcast recording software to replace expensive Riverside, struggling with syncing audio and video files across different computer clocks35:00 Discussion of critical factors in vibe coding, discovering unknown technical requirements, and how AIs don't automatically reveal missing information40:00 Stewart's reverse engineering process using deep research function to study competitors' hiring and technology stacks, separating planning agents from coding agents45:00 Prompting techniques including "explain like I know everything" and using spaced repetition systems to capture valuable prompts and technical knowledge50:00 Michael explains his Generux app for generating ecommerce content using Amazon review data analysis to inform high-converting listing images and videos55:00 Discussion of founder mentality involving self-delusion about project timelines, Michael working nine-plus hours daily for nine months on app development60:00 Comparing Amazon's expert software to prosumer software approach, discussing distribution challenges and future robotics applications for customized products65:00 Stewart demonstrates spaced repetition app for memory improvement and knowledge retention, explaining relevance realization problem that AI agents cannot solve without embodimentKey Insights1. Stewart Alsop started AI Whisperers in Buenos Aires after leaving his role at Invisible Technologies, which was OpenAI's largest vendor for RLHF work. He noticed that machine learning engineers at tech companies lacked the humanities background needed to properly interact with large language models, which are fundamentally linguistic tools. This led him to create weekly workshops teaching non-technical people how to use AI effectively, running events every Thursday for two years straight. The group attracted intense geeks from the start and eventually led to Stewart speaking right after Vitalik Buterin at DevConnect, marking a significant milestone for the community.2. Large corporations are resistant to AI adoption due to multiple factors including political dynamics within organizations and employees fearing job loss. Many companies that grew during the pandemic are now using AI as an excuse to downsize when the real issue is inefficiency from rapid expansion. Stewart observed that even technical people in machine learning often don't understand how to properly use AI tools because they lack linguistic and humanities training. The fundamental problem is educational, requiring companies to train people how to use these new tools while those same people resist learning them.3. Vibe coding has evolved significantly with Claude Code being a game changer that reduced the technical barrier to entry. Before Claude Code, developers needed substantial technical knowledge to work through constant doom loops and debugging cycles. The success of coding AI tools stems from thirty years of testing infrastructure that provides clear yes or no feedback on whether code works. This infrastructure doesn't exist in the same way for manufacturing, science, and other fields, which is why software became the dominant area for AI assistance initially.4. Claude's quality degradation over recent months resulted from multiple factors including distillation attacks by Chinese companies who reverse engineered Anthropic's reasoning capabilities. Anthropic had hired philosophers, sociologists, and psychologists to develop exceptional reasoning in Claude 4.5, but this was expensive to run. When Chinese models like Kimi copied these capabilities at one tenth the cost, and when mainstream users flooded the platform before Anthropic's planned IPO, the company had to reduce quality to manage computational costs. This represents a significant loss for power users who relied on Claude's superior reasoning abilities.5. Stewart built a podcast recording application to replace Riverside because he needed API access to automate workflows, which Riverside wanted one thousand dollars monthly to provide. The technical challenge involves syncing audio and video from local recordings on multiple computers with different clocks through a server, then merging them so voices match lip movements. This problem requires understanding complex timing issues across different network conditions and file formats. Stewart has been working through AI psychosis for months on this FFMPEG pipeline problem, illustrating how vibe coding still requires building intuition about technical problems even without traditional coding knowledge.6. The transition from expert software to prosumer software represents a major opportunity for AI-enabled tools. Expert software like Photoshop, Blender, and terminal interfaces have extreme complexity that intimidates beginners, but AI is making these capabilities accessible through natural language. The reign of specialists is ending as generalists with broad knowledge and curiosity can now build complete applications by leveraging AI to fill technical gaps. This shift particularly benefits entrepreneurs and founders who specialize in getting into difficult situations and figuring them out, even when they originally thought tasks would be easier than they turned out to be.7. Building applications with AI requires accepting massive time investments beyond initial estimates and developing strategies for overcoming knowledge gaps. Michael estimated his ecommerce content generation app would take months but spent nearly a year working over nine hours daily, while Stewart spent months solving audio-video sync issues. Success requires using tools like deep research to understand how competitors solve problems, maintaining separate planning and coding agents, and learning to ask the right questions. The key insight is that vibe coders can achieve ninety percent of functionality independently, but the final ten percent often requires understanding specific technical concepts that AI cannot intuit without proper context and domain knowledge.
01. The Chemical Brothers - Go (De Soffer Remix) 02. Disco Culture, Scotty - Show Me Love 03. Bl3ss, Camrinwatsin Feat Bbyclose - Kisses (Alex Caspian Remix) 04. Argy & Omiki - Wind 05. Zhu - Faded 06. Eelke Kleijn - Transmission (Joris Voorn Remix) 07. Doechii - Anxiety (Evermay Remix) 08. Anyma Feat Ellie Goulding - Hypnotized 09. DJ Nejtrino, DJ Peretse, Dima Kaminski - Feel 10. Tiësto, Mathame - Everlight 11. Snoop Dogg, David Guetta - Sweat 12. Paul Oakenfold, Planet Perfecto Knights & Kimmic - Resurection (Alex Caspian Edit) 13. Pbh & Jack, Alex Hosking - Lost In The Moment 14. Alok Feat. Jess Glynne - Summer's Back 15. By Индия, Xcho, Мот - Шадэ (Alexx Slam Remix) 16. Sam Feldt, Mc4d, Vize, Aloe Blacc - Hey Son 17. Dubdogz, Fezzo, Zaark - How Does It Feel 18. Cheat Codes, Jonita Gandhi - Last Night On Earth 19. Oneil & Kanvise & Ercodes - Every Single Day 20. Alesso Feat. Tove Lo - Heroes (We Could Be) 21. Felix Jaehn, Sarah Barrios - Now's A Good Time To Be 22. Felix Jaehn Feat. Alma - Bonfire (Holderz Remix) 23. Josh Fawaz - Like A Prayer 24. Modjo - Lady (Slim Tony & Slashy Disco Remix) 25. Onerepublic, Alesso - If I Lose Myself 26. Alesso Ft. Nico & Vinz - I Wanna Know (Manatee Remix) 27. Calvin Harris Feat Clementine Douglas - Blessings 28. Joel Corry & Pickle Feat Vula Stay Together - Baby Baby 29. DJ Louis, Sweetpower - Billie Jean 30. Lola Young - Messy (David Puentez Vip Edit) 31. Atyx, Adam Port, Stryv - Move (8one Re-Work 32. Avalan Rokston, Avalan, Rokston - Vertigo 33. Gotlucky, Mysterie, Afro Queen - La La La 34. Joachim Garraud, Jd Davis, David Guetta - The World Is Mine (Valeriy Smile Remix) 35. Oneil, Kanvise, Ercodes - Smalltown Boy 36. Meduza, Becky Hill, Goodboys - Lose Control 37. Nadia Ali, Starkillers, Alex Kenji - Pressure 38. Alok & Gryffin & Julia Church - Never Letting Go 39. Shouse - Love Tonight (Barthez Remix) 40. Diplo & Maren - Morris 41. Arei, DJ Lev - Du Hast 42. Gorilla Zippo - Танцую До Утра 43. Kddk, Alex Alta - 1&2 44. Pnau, The Warning - Tu Corazón (Your Heart) 45. Leony & Calum Scott - Stay 46. Oneil, Kanvise, Smola - Boys 47. Bobina, Marcus Dielen, Mario Cola - Need To Feel Loved 48. Armin Van Bureen - In And Out Of Love (Lilian Bilotta Remix) 49. DJ Snake Feat. Bipolar Sunshine - Paradise (Xavier Remix) 50. Ida Corr Vs Fedde Le Grand - Let Me Think About It 51. Kungs - Clap Your Hands (Robin Schulz 52. Hugel, Imael Angel, Ultra Naté - Movin' To The Sunremix 53. Green Velvet, Meduza, Genesi, Essentia - La La Land 54. Delerium - Silence (John Summit Remix) 55. Bunny Tunes - White Horse 56. David Guetta Sia - Beautiful People (Seth Hills Remix) 57. Kungs, Theophilus London - Galaxy 58. Calvin Harris & Rag'n'bone Man - Lovers In A Past Life 59. Jonas Blue, Malive - Edge Of Desire (Michaelbm & Jayie Remix) 60. Ofenbach - Miles Away (Valeriy Smile Remix) 61. Carla's Dreams - Sub Pielea Mea (Shnaps Remix) 62. Kaskade, Gryffin & Nu La - In My Head (Rscl Remix) 63. DJ Nejtrino, DJ Peretse - Road To Hell 64. Robin Schulz, Cyril, Sam Martin - World Gone Wild 65. Eric Mars - Popcorn (Valeriy Smile Bass Remix) 66. Ksu Sha, DJ Nejtrino, DJ Peretse - Кристаллы 67. Stardust - Music Sounds Better (Tim Grand Remix)
On the Rag, episode 100: Us Weekly- May 29, 2006 | Britney and Kevin's Secret Deal The Sherman Sisters
01. The Chemical Brothers - Go (De Soffer Remix) 02. Disco Culture, Scotty - Show Me Love 03. Bl3ss, Camrinwatsin Feat Bbyclose - Kisses (Alex Caspian Remix) 04. Argy & Omiki - Wind 05. Zhu - Faded 06. Eelke Kleijn - Transmission (Joris Voorn Remix) 07. Doechii - Anxiety (Evermay Remix) 08. Anyma Feat Ellie Goulding - Hypnotized 09. DJ Nejtrino, DJ Peretse, Dima Kaminski - Feel 10. Tiësto, Mathame - Everlight 11. Snoop Dogg, David Guetta - Sweat 12. Paul Oakenfold, Planet Perfecto Knights & Kimmic - Resurection (Alex Caspian Edit) 13. Pbh & Jack, Alex Hosking - Lost In The Moment 14. Alok Feat. Jess Glynne - Summer's Back 15. By Индия, Xcho, Мот - Шадэ (Alexx Slam Remix) 16. Sam Feldt, Mc4d, Vize, Aloe Blacc - Hey Son 17. Dubdogz, Fezzo, Zaark - How Does It Feel 18. Cheat Codes, Jonita Gandhi - Last Night On Earth 19. Oneil & Kanvise & Ercodes - Every Single Day 20. Alesso Feat. Tove Lo - Heroes (We Could Be) 21. Felix Jaehn, Sarah Barrios - Now's A Good Time To Be 22. Felix Jaehn Feat. Alma - Bonfire (Holderz Remix) 23. Josh Fawaz - Like A Prayer 24. Modjo - Lady (Slim Tony & Slashy Disco Remix) 25. Onerepublic, Alesso - If I Lose Myself 26. Alesso Ft. Nico & Vinz - I Wanna Know (Manatee Remix) 27. Calvin Harris Feat Clementine Douglas - Blessings 28. Joel Corry & Pickle Feat Vula Stay Together - Baby Baby 29. DJ Louis, Sweetpower - Billie Jean 30. Lola Young - Messy (David Puentez Vip Edit) 31. Atyx, Adam Port, Stryv - Move (8one Re-Work 32. Avalan Rokston, Avalan, Rokston - Vertigo 33. Gotlucky, Mysterie, Afro Queen - La La La 34. Joachim Garraud, Jd Davis, David Guetta - The World Is Mine (Valeriy Smile Remix) 35. Oneil, Kanvise, Ercodes - Smalltown Boy 36. Meduza, Becky Hill, Goodboys - Lose Control 37. Nadia Ali, Starkillers, Alex Kenji - Pressure 38. Alok & Gryffin & Julia Church - Never Letting Go 39. Shouse - Love Tonight (Barthez Remix) 40. Diplo & Maren - Morris 41. Arei, DJ Lev - Du Hast 42. Gorilla Zippo - Танцую До Утра 43. Kddk, Alex Alta - 1&2 44. Pnau, The Warning - Tu Corazón (Your Heart) 45. Leony & Calum Scott - Stay 46. Oneil, Kanvise, Smola - Boys 47. Bobina, Marcus Dielen, Mario Cola - Need To Feel Loved 48. Armin Van Bureen - In And Out Of Love (Lilian Bilotta Remix) 49. DJ Snake Feat. Bipolar Sunshine - Paradise (Xavier Remix) 50. Ida Corr Vs Fedde Le Grand - Let Me Think About It 51. Kungs - Clap Your Hands (Robin Schulz 52. Hugel, Imael Angel, Ultra Naté - Movin' To The Sunremix 53. Green Velvet, Meduza, Genesi, Essentia - La La Land 54. Delerium - Silence (John Summit Remix) 55. Bunny Tunes - White Horse 56. David Guetta Sia - Beautiful People (Seth Hills Remix) 57. Kungs, Theophilus London - Galaxy 58. Calvin Harris & Rag'n'bone Man - Lovers In A Past Life 59. Jonas Blue, Malive - Edge Of Desire (Michaelbm & Jayie Remix) 60. Ofenbach - Miles Away (Valeriy Smile Remix) 61. Carla's Dreams - Sub Pielea Mea (Shnaps Remix) 62. Kaskade, Gryffin & Nu La - In My Head (Rscl Remix) 63. DJ Nejtrino, DJ Peretse - Road To Hell 64. Robin Schulz, Cyril, Sam Martin - World Gone Wild 65. Eric Mars - Popcorn (Valeriy Smile Bass Remix) 66. Ksu Sha, DJ Nejtrino, DJ Peretse - Кристаллы 67. Stardust - Music Sounds Better (Tim Grand Remix)
1.The Chemical Brothers – Go (De Soffer Remix) 2.Disco Culture, Scotty – Show Me Love 3.Bl3ss, Camrinwatsin Feat Bbyclose – Kisses (Alex Caspian Remix) 4.Argy & Omiki – Wind 5.Zhu – Faded 6.Eelke Kleijn – Transmission (Joris Voorn Remix) 7.Doechii – Anxiety (Evermay Remix) 8.Anyma Feat Ellie Goulding – Hypnotized 9.DJ Nejtrino, DJ Peretse, Dima Kaminski – Feel 10.Tiësto, Mathame – Everlight 11.Snoop Dogg, David Guetta – Sweat 12.Paul Oakenfold, Planet Perfecto Knights & Kimmic – Resurection (Alex Caspian Edit) 13.Pbh & Jack, Alex Hosking – Lost In The Moment 14.Alok Feat. Jess Glynne – Summer's Back 15.By Индия, Xcho, Мот – Шадэ (Alexx Slam Remix) 16.Sam Feldt, Mc4d, Vize, Aloe Blacc – Hey Son 17.Dubdogz, Fezzo, Zaark – How Does It Feel 18.Cheat Codes, Jonita Gandhi – Last Night On Earth 19.Oneil & Kanvise & Ercodes – Every Single Day 20.Alesso Feat. Tove Lo – Heroes (We Could Be) 21.Felix Jaehn, Sarah Barrios – Now's A Good Time To Be 22.Felix Jaehn Feat. Alma – Bonfire (Holderz Remix) 23.Josh Fawaz – Like A Prayer 24.Modjo – Lady (Slim Tony & Slashy Disco Remix) 25.Onerepublic, Alesso – If I Lose Myself 26.Alesso Ft. Nico & Vinz – I Wanna Know (Manatee Remix) 27.Calvin Harris Feat Clementine Douglas – Blessings 28.Joel Corry & Pickle Feat Vula Stay Together – Baby Baby 29.DJ Louis, Sweetpower – Billie Jean 30.Lola Young – Messy (David Puentez Vip Edit) 31.Atyx, Adam Port, Stryv – Move (8one Re-Work 32.Avalan Rokston, Avalan, Rokston – Vertigo 33.Gotlucky, Mysterie, Afro Queen – La La La 34.Joachim Garraud, Jd Davis, David Guetta – The World Is Mine (Valeriy Smile Remix) 35.Oneil, Kanvise, Ercodes – Smalltown Boy 36.Meduza, Becky Hill, Goodboys – Lose Control 37.Nadia Ali, Starkillers, Alex Kenji – Pressure 38.Alok & Gryffin & Julia Church – Never Letting Go 39.Shouse – Love Tonight (Barthez Remix) 40.Diplo & Maren – Morris 41.Arei, DJ Lev – Du Hast 42.Gorilla Zippo – Танцую До Утра 43.Kddk, Alex Alta – 1&2 44.Pnau, The Warning- Tu Corazón (Your Heart) 45.Leony & Calum Scott – Stay 46.Oneil, Kanvise, Smola – Boys 47.Bobina, Marcus Dielen, Mario Cola – Need To Feel Loved 48.Armin Van Bureen – In And Out Of Love (Lilian Bilotta Remix) 49.DJ Snake Feat. Bipolar Sunshine – Paradise (Xavier Remix) 50.Ida Corr Vs Fedde Le Grand – Let Me Think About It 51.Kungs – Clap Your Hands (Robin Schulz 52.Hugel, Imael Angel, Ultra Naté – Movin' To The Sunremix 53.Green Velvet, Meduza, Genesi, Essentia – La La Land 54.Delerium – Silence (John Summit Remix) 55.Bunny Tunes – White Horse 56.David Guetta Sia – Beautiful People (Seth Hills Remix) 57.Kungs, Theophilus London – Galaxy 58.Calvin Harris & Rag'n'bone Man – Lovers In A Past Life 59.Jonas Blue, Malive – Edge Of Desire (Michaelbm & Jayie Remix) 60.Ofenbach – Miles Away (Valeriy Smile Remix) 61.Carla's Dreams – Sub Pielea Mea (Shnaps Remix) 62.Kaskade, Gryffin & Nu La – In My Head (Rscl Remix) 63.DJ Nejtrino, DJ Peretse – Road To Hell 64.Robin Schulz, Cyril, Sam Martin – World Gone Wild 65.Eric Mars – Popcorn (Valeriy Smile Bass Remix) 66.Ksu Sha, DJ Nejtrino, DJ Peretse – Кристаллы 67.Stardust – Music Sounds Better (Tim Grand Remix)
The Pure Report welcomes Dan Kent, Everpure's new Field CTO for Federal, to the studio to discuss the critical intersection of advanced technology and public services. Dan, who recently joined Everpure, brings decades of experience in the Federal space, including senior roles at companies like Cisco and as a CTO, where he developed a passion for leading teams and tackling challenging engineering problems. Our conversation kicks off by exploring the unique complexities and high stakes of working with government agencies, which range from managing the massive data sets of the Social Security Administration (supporting 300 million citizens) to deploying mission-critical IT components in the most extreme environments, such as on battleships, in military vehicles, and even in space. Dan asserts that the Federal AI tipping point has passed, driven by the competitive global landscape, executive orders, and the government's immense data holdings—which require AI to glean insights. With an estimated 4,000 AI use cases already in pilot across various agencies (from Air Force platform maintenance to IRS fraud detection), the biggest obstacles remain the outdated infrastructure and the pervasive challenge of data quality. Dan highlights that infrastructure is not yet generative AI-ready, with data locked in silos and complicated by time-sensitive, duplicated, or decades-old information, leading to self-induced mistakes and ethical concerns like misidentification. Our discussion shifts to how Everpure is positioned to solve these foundational issues. Dan explains the necessity of modern infrastructure that enables automated data pipelines for continuous cleaning, classification, and transformation into vector databases (RAG). This automation is key to ensuring AI applications have accurate, timely context, thereby eliminating security risks and self-inflicted errors. Finally, we address the critical human element, emphasizing that while a skills gap exists, the outlook is positive: AI should be treated as a co-worker to boost efficiency and help the federal workforce achieve its citizen-focused missions more effectively. To learn more, visit: https://www.everpuredata.com/solutions/industries/government/cost-efficiency.html Check out the new Everpure digital customer community to join the conversation with peers and Everpure experts: https://purecommunity.purestorage.com/ 00:00 Intro and Welcome 01:15 Dan's Career Journey 04:41 Supporting Federal Agencies 09:35 AI Tipping Point for Fed 13:31 State of Government Infrastructure 19:47 AI Trust and Compliance 25:25 Workforce Impacts of AI 33:11 Everpure for AI in Fed 36:45 Hot Takes Segment
On the Rag, episode 99: Us Weekly- May 22, 2006 | BRITNEY WANTS OUT! The Sherman Sisters
Much like Rag n' Bone Man, I'm only human after all. Enjoy the reimagines of Strange Presence, Slicked Oil, Weary Afterdawn, Whispering Filth, As the Evening Falls, Moon Dogs, and Shadows on the Roofscape. More to come soon! ----------------------- —--------------------- Want more 7th Valkyrie? Check out our Patreon to become a Hero of Edara, where you can shape the future of the series, decide on merch drops and incentives, get early access to new episodes, enjoy bonus features and content, and help us hit the major checkpoints on the Path of Heroes! https://www.patreon.com/7thvalkyrie
01. Green Velvet, Meduza, Genesi, Essentia - La La Land (Record Mix) 02. Oneil, Kanvise, Ercodes - Every Single Day (Record Mix) 03. Basto! - I Rave You (Record Mix) 04. Vintage Culture, Gabss - Lost (Record Mix) 05. Robin Schulz, Francesco Yates - Sugar (Record Mix) 06. Maesic, Marshall Jefferson, Salome Das - Life Is Simple (Record Mix) 07. Loreen, Denis First - Tattoo (Record Mix) 08. Anyma, Joji - Beautiful (Record Mix) 09. Tiesto, Don Diablo - Chemicals (Record Mix) 10. Akcent, Sera, Misha Miller - Don't Leave (Kylie) (Record Mix) 11. Clean Bandit, Anne-Marie, David Guetta - Cry Baby (Record Mix) 12. Kris Kross Amsterdam, Eyelar - Mr. Lie To Me (Record Mix) 13. Adam Port, Stryv, Malachii, Switch Disco - Move (Record Mix) 14. Eastblock Bitches, Ostblockschlampen - Sunglasses at Night (Record Mix) 15. Jerome Robins, Karsten Sollors - Don't Stop The Music (Record Mix) 16. Aize, Sasha - Waiting for Tonight (Record Mix) 17. R.I.O., Gonsu, Jenia Smile, Ser Twister - Shine On (Record Mix) 18. Ava Max - Don't Click Play (Record Mix) 19. Disco Culture, Scotty - Show Me Love (Record Mix) 20. Twocolors, Safri Duo, Chris De Sarandy - Head Above Water (Record Mix) 21. Alok, Ella Eyre, Kenny Dope, Never Dull - Deep Down (Record Mix) 22. Aaron Smith, Luvli, Krono - Dancin' (Record Mix) 23. Afrojack, Aloe Blacc - In My World 24. Lola Young, Ted Bear - Messy (Record Mix) 25. Zerb, Ty Dolla $Ign, Wiz Khalifa - Location (Record Mix) 26. Argy, Omiki - WIND (Record Mix) 27. Bob Sinclar, Kiesza - I Can't Wait (Record Mix) 28. Block & Crown, Atilla Cetin - How Many Nations (Record Mix) 29. Imanbek, Sofia Reyes, Luisa Sonza - NOT U (Record Mix) 30. Tim Berg - Seek Bromance (Record Mix) 31. Joel Corry, Rahh - Devotion (Sweetest Emotion) (Record Mix) 32. Hosh, 1979, Jalja, Slider & Magnit - Midnight (The Hanging Tree) (Record Mix) 33. Arei, Dj Lev - Du Hast (Record Mix) 34. Junior Jack - Stupidisco (Record Mix) 35. R3Hab - Right Here, Right Now (Record Mix) 36. Block & Crown - Mr DJ Give Me More 37. Morgan Page, Telykast - Dancing All Alone (Record Mix) 38. Garas, Eugenio Fico - Perfect 39. C Block, The Distance, Riddick - So Strung Out (Record Mix) 40. Hurts, Purple Disco Machine - Wonderful Life '25 (Record Mix) 41. Alessandro - Goes Deeper (Record Mix) 42. Shane Codd - Get Out My Head (Record Mix) 43. Richard Grey - At Night (Record Mix) 44. Leony, Calum Scott - Stay (Record Mix) 45. Trevor Daniel, Doublefast - Falling (Record Mix) 46. Cheat Codes, Jonita Gandhi - Last Night On Earth (Record Mix) 47. Skytech - The Rhythm (Record Mix) 48. Lady Gaga, Dj Dark - The Dead Dance (Record Mix) 49. Tiesto, Soaky Siren - Tantalizing (Record Mix) 50. David Guetta, Hypaton, Bonnie Tyler - Together (Record Mix) 51. Robin Schulz, Erika Sirola - Speechless (Record Mix) 52. Moby, Blond Ish, Kiko Franco - Natural Blues (Record Mix) 53. Jonas Blue, Jp Cooper - Perfect Strangers (Record Mix) 54. Joel Corry, Jennifer Lopez - Get Right 55. Gorgon City, Romans - Saving My Life (Record Mix) 56. Otnicka - Celebrate the Love (Record Mix) 57. Sebastian Ingrosso, Tommy Trash, John Martin - Reload (Record Mix) 58. Sebastian Ingrosso, Tommy Trash, John Martin - Reload (Record Mix) 59. Hugel, Ultra Nate - Free (You Got To Live) (Record Mix) 60. Keanu Silva, Don Diablo - King of My Castle (Record Mix) 61. Avalan Rokston, Alex Caspian - Something to Believe In (Record Mix) 62. Fisher, Aatig - Take It Off (Record Mix) 63. Mind Electric - Things You Say (Record Mix) 64. Playmen, Hadley - Luv You (Record Mix) 65. Alok, Khalid - Dive Into Me (Record Mix) 66. Firebeatz, Dubdogz - Give It Up (Record Mix) 67. Calvin Harris, Rag'N'Bone Man - Giant (Record Mix) 68. Dj Louis, Sweetpower - Billie Jean (Record Mix) 69. Basto! - Again & Again (Record Mix) 70. Oneil, Kanvise, Smola - The Riddle (Record Mix) 71. Diplo, Miguel - Don't Forget My Love (Record Mix) 72. Sam Feldt, Mc4D, Vize, Aloe Blacc - Hey Son (Record Mix) 73. Oliver Heldens, Djs From Mars, Jd Davis - Blue Monday (Record Mix) 74. Crazibiza - Fresh (Record Mix) 75. Annabell Kowalski - Hey Boy Hey Girl (Record Mix) 76. Bebe Rexha, Faithless - New Religion (Record Mix) 77. Joezi, Lizwi - Amathole (Record Mix) 78. Eben - Hollow (Record Mix) 79. Lost Culture, Morfi, Carine - Lean On (Record Mix) 80. Camelphat, Elderbrook - Cola (Record Mix) 81. Cozy Sky, Symono, Simon Riemann - Wicked Game (Record Mix) 82. Dj Feel, Desmind, Natalie Rise - Stereo Love (Record Mix) 83. Zerb, Sofiya Nzau, Izzy Bizu - Kumbaya (Record Mix) 84. Josh Fawaz - Like a Prayer (Record Mix) 85. Joel Corry, Pickle, Vula - Stay Together (Baby Baby) (Record Mix) 86. Darude, Glazur, Xm - Sandstorm (Record Mix) 87. Dj Quba, Sandra K, Ishnlv - Sexy Chick (Record Mix) 88. Bag Raiders - Shooting Stars (Record Mix) 89. All Things Break - Gravity (Record Mix) 90. Zhu - Faded (Record Mix) 91. Fisher, Flowdan - Boost Up (Record Mix) 92. Kungs - Never Going Home (Record Mix) 93. Yearboox - Graceland (Record Mix) 94. Armin Van Buuren - Es Vedra (Record Mix) 95. Hugel, Alleh, Yorghaki - una noche con hugel (Record Mix) 96. Maurizio Basilotta, Mf Productions - You're Not Alone (Record Mix) 97. Topcover - First Day (Record Mix) 98. Jaden Bojsen, David Guetta - Upside Down (Record Mix) 99. Filatov & Karas, Busy Reno - Au Revoir (Record Mix) 100. Martin Jensen, Fastboy - One Day (Record Mix) 101. Duke Dumont - Won't Look Back (Record Mix) 102. Lucas & Steve, Laura White - Are You Ready 103. Ben Delay - I Never Felt So Right (Record Mix) 104. Misha Miller, Alexvelea, Bodega - Bam Bam (Record Mix) 105. Axwell, Ingrosso - More Than You Know (Record Mix) 106. John Summit, Inez - crystallized (Record Mix) 107. Alok, Jess Glynne - Summer's Back (Record Mix) 108. Shouse, Vintage Culture - take me (to the sunrise) (Record Mix) 109. Tujamo, Azteck, Inna - Freak (Record Mix) 110. Claptone, Sea Girls, Henry Camamile - Put Your Love On Me 111. Dr Kucho!, Gregor Salto, Oliver Heldens - Can't Stop Playing (Record Mix) 112. Teriyaki Boyz, Hayat - Tokyo Drift (Record Mix) 113. Zerb, Sofiya Nzau - Mwaki (Record Mix) 114. The Chemical Brothers, De Soffer - Go (Record Mix) 115. Goodboys, Nu Aspect, Avaion - Blindspot (Record Mix) 116. Doechii, Dj Dark - Anxiety (Record Mix) 117. Lana Del Rey, Kevin Blanc - Young & Beautiful (Record Mix) 118. Weiss, Harry Romero - Where Do We Go (Record Mix) 119. Faros - Say It Right (Record Mix) 120. Zerb, The Chainsmokers, Ink - Addicted (Record Mix) 121. Pete Tong, Roro, Jules Buckley, The Essential Orchest - Rhythm Of The Night (Record Mix) 122. Alex Adair, Don Diablo, Cid - Make Me Feel Better (Record Mix) 123. Anyma, Joji - Beautiful (Record Mix) 124. Coldplay, Avicii - A Sky Full of Stars (Record Mix) 125. Adam Port, Stryv, Malachii, Switch Disco - Move (Record Mix) 126. Alesso, Calvin Harris - Under Control (Record Mix) 127. Alesso, Calvin Harris - Under Control (Record Mix) 128. Felix Jaehn, Polina - Book Of Love (Record Mix) 129. Ava Max - Don't Click Play (Record Mix) 130. Vize - Wait (Alibi Blue) (Record Mix) 131. One-T, Ywy, Nika - The Way To Love (Record Mix) 132. Killteq, D.Hash, Vallhee - I Like It (Record Mix) 133. Cristoph - String Thing 134. Basto!, Yves V - Cloud Breaker (Record Mix) 135. Twocolors, Safri Duo, Chris De Sarandy - Head Above Water (Record Mix) 136. Oneil, Kanvise, Murana - Redlight (Record Mix) 137. Roland Clark, Mark Knight, James Hurr - Get Deep 138. Oceana, Bodybangers - Endless Summer (Record Mix) 139. Argy, Omiki - WIND (Record Mix) 140. J Balvin, Willy William, Gonsu, Jenia Smile, Ser Twist - Mi Gente (Record Mix) 141. Gregory Porter, Jonas Blue - Liquid Spirit (Record Mix) 142. Robin Schulz, David Guetta - On Repeat (Record Mix) 143. Regard, Years & Years - Hallucination (Record Mix) 144. Hugel, Topic, Arash, Daecolm - I Adore You (Record Mix) 145. Dj Dimixer, Favia - One of Us (Record Mix) 146. Cassian, Yotto, Da Hool - Love Parade (Record Mix)
Podcast: Don't Panic! It's Just DataGuest: Michael Marolda, Senior Product Marketing Manager for Agentic RAG at Progress SoftwareHost: Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360TechGenerative AI has been brewing in the enterprise tech industry for at least three years now. AI pilots are launching every other day, internal copilots are deployed across enterprise divisions, and now teams themselves are experimenting with large language models (LLMs) to automate business workflows. Such additions have sped up research and notably improved productivity.While the excitement is valid, the truth beneath is often disregarded. Many enterprise AI systems produce answers that sound convincing, even when they are completely wrong.In the recent episode of the Don't Panic! It's Just Data podcast, Michael Marolda, Senior Product Marketing Manager for Agentic RAG at Progress Software, sat down with host Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech.Marolda argued that the problem is not necessarily with the AI models themselves. The real issue is with the enterprise data foundations supporting them.“Your AI is only as good as the knowledge it has access to,” Marolda explained during the conversation. The question is what the gap is alluded to in the AI enterprise tech space.Also Read: Build vs. Buy: The Reality of Production-Grade RAGWhat's the Hidden Risk Costing Enterprises?According to Marolda, around 80 per cent of enterprise data remains unstructured. This includes PDFs, contracts, emails, audio files, presentations, scanned documents, videos, and handwritten notes. This is the kind of information that traditional AI systems struggle to process reliably.While enterprises are heavily investing in AI infrastructure and model testing, many still do not have systems capable of organising, retrieving, and validating this scattered knowledge. The outcome often turns into a situation where AI tools begin to generate responses without the necessary business context, despite excellent prompt engineering.“We've seen enterprises rush into AI implementations,” Marolda said. “But many pilots fail to scale because the information isn't grounded in real business data.” It ultimately poses major operational risks for companies, especially in highly regulated industries.During the podcast, Marolda mentioned a high-profile case involving an airline chatbot that provided customers with incorrect policy information, leading to legal consequences for the company. The issue was not due to malicious intent or a technical failure at the model level — it was due to unreliable data grounding.For enterprises using AI in customer service, HR, legal operations, finance, or internal knowledge systems, such errors are not rare. In fact, they've become a governance issue.Is Modern RAG the Solution?Enterprises tend to rely on data lakes as centralised storage for vast amounts of information. However, Marolda makes a point about how storage is no longer enough in the age of AI. “A data lake is just cheap storage,” he explained. “A knowledge layer is what actually activates that information for AI.”This difference is increasingly important as enterprises move from testing to operational AI deployment. Traditional storage systems can hold documents, but they cannot interpret relationships between data points, retrieve context semantically, or validate AI-generated outputs against source material.An enterprise knowledge layer, on the other hand, is designed to fill that gap. Marolda tells Dua that modern retrieval-augmented generation (RAG) systems can process unstructured data, apply optical character recognition (OCR), convert speech to text from video and audio, and build semantic connections across enterprise content.This enables AI systems to retrieve not just documents, but highly specific pieces of contextual information, including paragraph-level citations and timestamped video references.For enterprise leaders, the implications are significant. Rather than viewing AI as a separate assistant, enterprises are increasingly seeing AI as a retrieval and reasoning layer built on top of their knowledge ecosystems.How Should Enterprises Prioritise Efficiency Over Hype?The economics of AI was a critical discussion Marolda had with Dua. He noted that while many AI providers continue to push for higher token consumption and larger workloads, enterprises such as Progress Software are now beginning to value efficiency instead.Unlike NVIDIA's enterprise philosophy, as proposed by its CEO Jensen Huang, is a new compensation model where engineers receive annual AI token budgets worth half their base salary on top of regular pay. During a live interview on the All-In Podcast, recorded in San Jose, California, in March 2026 at Nvidia's GPU Technology Conference (GTC), Huang stated: "If a $500,000 Engineer Did Not Consume At Least $250,000 Worth of Tokens, I'm Going To Be Deeply Alarmed."“We're actually trying to reduce token consumption,” he explained. Such an approach contrasts with broader industry trends focused on maximising AI use at scale. As enterprise AI budgets become more established, CIOs and CFOs are scrutinising infrastructure costs, energy consumption, and long-term operational sustainability.It's particularly relevant as enterprises pit multiple LLMs against each other for quality, relevance, and cost efficiency. According to Progress's Sr. Product Marketing Manager, the next phase of enterprise AI adoption won't be driven by model capability alone. It will be guided by practical governance, meaning identifying which systems produce the best results at reasonable costs.Overall, successful AI adoption is not just about selecting the right model but, in fact, pivoting towards building the right knowledge architecture.For instance, enterprises continue to invest in generative AI; the enterprises that thrive may be the ones that can effectively structure, govern, retrieve, and validate their institutional knowledge.Key TakeawaysEnterprise AI hallucinations increase without grounded enterprise data.Agentic RAG helps enterprises reduce AI hallucinations and improve accuracy.Unstructured data is the biggest challenge in enterprise AI adoption.Enterprise knowledge layers improve AI governance and traceability.AI token reduction lowers enterprise AI infrastructure costs.RAG architecture helps enterprises scale trustworthy AI systems.Chapters00:00 Introduction to Enterprise AI and Knowledge Layer02:13 Challenges with Unstructured Data in AI08:11 The Importance of a Knowledge Layer12:04 Trust and Governance in AI Solutions16:48 Progress's Unique Approach to AI Solutions19:15 Agentic RAG: A New Paradigm in AI Retrieval24:52 Real-World Applications of Agentic RAG26:39 Maintaining Quality and Performance in AI Systems28:01 Key Takeaways for IT Decision MakersFor more enterprise AI, Agentic RAG, data governance, and enterprise knowledge layer insights, follow Progress Software across its official channels:Website: Progress SoftwareYouTube: @ProgressSWLinkedIn: Progress SoftwareX: @ProgressSWFor more
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
Most AI conversations focus on models. The better conversation focuses on systems. In this episode, we continue our interview with Matt Levenhagen, exploring a practical challenge many developers are facing: integrating AI into business operations without creating costly chaos. The answer is not buying more AI tools. The answer is building an intentional AI Workflow Architecture. About Matt Levenhagen Matt is the founder and CEO of Unified Web Design, a web development agency focused on custom solutions, WordPress development, e-commerce, memberships, and business systems. His background as both a builder and agency owner gave him a unique perspective on where AI creates real leverage instead of superficial automation. Follow Matt on LinkedIn. AI Workflow Architecture Starts with Context Control One of the most important operational realities Matt discussed was token usage. Businesses rushing into AI often underestimate cost scaling. Every interaction with large models consumes resources, and poorly managed context windows dramatically increase operational expenses. Instead of treating AI like unlimited compute, Matt focused on controlling context intentionally. That included: Monitoring token usage Limiting unnecessary memory loading Structuring retrieval systems Using different models for different tasks Preventing oversized prompts This is a systems-thinking problem, not merely a coding problem. Developers who ignore architecture end up with bloated workflows that become financially unsustainable. The fastest way to make AI unprofitable is to send unnecessary context into every request. Why Retrieval Matters More Than Raw Memory A major breakthrough Matt discussed was implementing Retrieval-Augmented Generation (RAG). This matters because AI systems do not need all the information all the time. They need the right information at the right moment. That distinction completely changes system design. Without retrieval architecture: Costs increase Performance slows Outputs become less accurate Hallucinations increase Operational complexity grows RAG allows systems to retrieve semantically relevant information instead of dumping entire databases into prompts. This transforms AI from brute-force processing into intelligent retrieval. The future of AI operations will likely depend less on giant models and more on efficient information orchestration. AI Workflow Architecture Requires Layer Separation Another valuable concept from the conversation involved separating operational layers. Matt described balancing: Local storage Business memory External AI APIs Workflow automation SaaS integrations This layered architecture creates flexibility. Instead of locking the business into one AI provider, workflows remain adaptable. Different models can handle different workloads depending on cost, complexity, and accuracy requirements. This becomes increasingly important as pricing models fluctuate. Businesses relying entirely on one provider risk operational instability if pricing changes dramatically. Layer separation reduces that risk. The businesses that survive AI cost volatility will be the ones architected for flexibility instead of dependency. Why Embedded AI Features Often Disappoint Matt also discussed the growing wave of SaaS AI integrations. Every platform now markets AI capabilities: Project management tools Communication platforms CRM systems Design software Documentation systems Yet many users feel underwhelmed. The reason is architectural isolation. These tools only understand limited slices of operational context. They automate micro-tasks but rarely improve larger workflows. That creates a false impression that AI itself lacks value when the real issue is fragmented systems. AI becomes more useful as the organizational context becomes more connected. This is why developers building custom operational layers still maintain an enormous strategic advantage. AI Workflow Architecture Is an Operational Discipline The strongest insight from these episodes may be that AI implementation is becoming operational engineering. Success now depends on: Information structure Retrieval design Workflow sequencing Context prioritization Cost management Human oversight This moves AI away from novelty experimentation and toward infrastructure planning. Businesses that treat AI casually will likely accumulate technical debt quickly. Businesses that approach AI architecturally will build scalable operational leverage. AI is no longer just a development tool. It is becoming an operational systems discipline. Developers Must Learn Economic Thinking One overlooked topic in AI discussions is economics. Matt repeatedly referenced balancing capability with cost. This becomes critical because AI pricing models are still evolving rapidly. Businesses that ignore usage economics may accidentally build systems that become financially impossible to scale. Developers now need to think beyond: Can this be built? They also need to ask: Can this be sustained? Can this scale economically? Can context costs remain controlled? Can cheaper models handle simpler tasks? This represents a major evolution in modern software architecture. Review your current AI workflows and identify where unnecessary context or oversized prompts may be increasing costs. Conclusion AI Workflow Architecture is rapidly becoming one of the most important technical disciplines for modern developers. Matt Levenhagen's approach demonstrates that successful AI implementation is less about chasing the newest model and more about designing sustainable operational systems. The companies that gain long-term advantage from AI will not necessarily be the companies using the largest models. They will be the companies with the best architecture. 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Este episodio nos vamos a meter de lleno en el barro del cacharreo del bueno para hablar de algo que me tiene completamente entusiasmado y sin dormir de la emoción en los últimos días: el maravilloso e increíble mundo del futuro agéntico. Sí, sí, has oído bien. Vamos a desgranar cómo dar el salto definitivo de esos chats de Inteligencia Artificial tan aburridos en los que solo escribes una pregunta y esperas una respuesta, a tener un auténtico colaborador activo que haga tareas reales por ti en tu propia máquina.Seguro que te ha pasado alguna vez. Estás usando un modelo de lenguaje, le pides ayuda para tu proyecto personal o para organizar tus notas de Linux, y de repente te das cuenta de que la IA se ha quedado congelada en el tiempo. Su conocimiento es completamente estático, no tiene ni la más remota idea de tus datos, de tus notas en Markdown, de tus contenedores ni de tus flujos de trabajo. Y lo peor de todo: cuando no sabe algo, en lugar de callarse, ¡se lo inventa con una tranquilidad que asusta! Básicamente, alucina. Las IAs de hoy en día, tal y como nos las venden de fábrica, están completamente aisladas del entorno, del tiempo y de tus propios procesos de trabajo. Son como un trozo de corcho flotando a la deriva en mitad del océano: muy ligeras y con potencial, pero incapaces de hacer nada útil de forma autónoma.¿Y cuál es la solución para dejar de tener una IA "tonta" y aislada? No se trata de una única tecnología mágica, sino de combinar con cabeza tres piezas fundamentales que le darán superpoderes a tu asistente: el RAG (la memoria), los MCP (las manos) y las Skills (los manuales de instrucciones).Cuando consigues orquestar estas tres piezas en tu propio host local, la magia ocurre. Consigues crear un asistente de verdad, como mi querido Hermes, que es capaz de redactar los textos que necesito para este podcast, gestionar mis recordatorios y organizar mis notas de forma totalmente autónoma mientras yo me lo paso pipa programando.Capítulos del episodio:00:00:00 ¡Bienvenidos al futuro agéntico!00:01:21 Lo que se viene en este episodio (y en los próximos)00:02:42 ¿Por qué las IAs hoy en día son "tontas" e inútiles?00:04:36 La solución: Skills, RAG y MCP explicados fácil00:06:14 La analogía del nuevo empleado en tu empresa00:07:38 El agente de IA como el gran director de orquesta00:08:21 ¿Qué es el RAG? (Conocimiento en tiempo real sin fine-tuning)00:11:17 Mi RAG propio en Rust para archivos Markdown00:12:39 ¿Qué es el MCP? (La revolución de Anthropic)00:14:55 Cómo usar MCP para conectar tu IA con el mundo real00:16:14 Mis servidores MCP: SearXNG, Invidious y listas de tareas00:18:10 Skills: Ahorro de tokens y flujos de trabajo inteligentes00:20:11 La matriz definitiva: Memoria, Manos y Manuales00:22:04 De un chat reactivo a un colaborador activo (Mi asistente Hermes)00:23:54 Próximos pasos, descargas de código y despedidaMás información y enlaces en las notas del episodio
Send us Fan MailOn this episode of Embedded Insiders, Yaad Oren, Global Head of SAP Research & Innovation and Managing Director of SAP Labs US, joins the podcast to discuss how emerging technologies are influencing enterprise strategy. We also discuss the company's unified cloud ERP platform and investments in next-generation innovation. For more information about SAP's Research and Innovation, visit: SAP Innovation | About SAP SE and Generative AI | SAP Artificial Intelligence Innovations. Next, Ken sits down with Naama BAK, CEO & Founder of Understand Tech, to discuss an enterprise RAG platform designed to support mass market customers with enterprise-ready AI. But first, Ken and I recap my recent trip to Fort Worth, Texas, to attend NI Connect 2026. Read the news here.For more information, visit embeddedcomputing.com
I talk with Ramon Perez, Executive Director of the Digital Democracy Project this week. DDP is a nonpartisan nonprofit using secure mobile voting technology to give citizens a real-time voice in legislation. Ramon explains how the platform lets verified, registered voters weigh in on bills being debated in Congress and their state house—and then scores legislators on how closely their votes match what their districts wanted. We dig into how AI, including a RAG-powered chatbot called VoteBot, helps everyday citizens parse thousands of pages of complex legislative text. We also discuss digital security, participatory budgeting, and Ramon's ambitious goal of expanding the platform to all 50 state legislatures by 2027.Keywords: digital democracy, mobile voting, civic tech, legislative transparency, AI in government, Ramon Perez, Digital Democracy Project, VoteBot, participatory budgeting, voter engagement, legislator accountability, PolicyViz podcast, civic engagement, govtechSubscribe to the PolicyViz Podcast wherever you get your podcasts.Become a patron of the PolicyViz Podcast for as little as a buck a monthFollow Ramon Perez and the Digital Democracy Project at digitaldemocracyproject.org and download the Votes (VOATZ) app to participate.Follow me on Instagram, LinkedIn, Substack, Twitter, Website, YouTubeEmail: jon@policyviz.com
Logan Kilpatrick and Tulsee Doshi of Google DeepMind join for a first-ever in-person episode recorded just days before Google I/O, covering headline launches like Gemini 3.5 Flash, the Omni video generation model, and the new Gemini Spark agentic product. The conversation digs into Google's strategic decision to lead with cost-adjusted efficiency over raw capability, how DeepMind now ships a full agent harness rather than bare models, and technical questions around context window limits and knowledge cutoffs. They also explore how the team thinks about model psychology, AI welfare, and recursive self-improvement. Sponsors: Brave Search API: Brave Search API gives AI agents a fast, independent search index for research, RAG pipelines, images, places, and fewer hallucinations. Get $5 in free credits at https://brave.com/search/api/?mtm_campaign=q2-26-cognitive-revolution Sequence: Sequence handles the full revenue workflow for complex pricing, from quoting and metering to invoicing, revenue recognition, and collections. Book a public demo at https://sequencehq.com and use code COGNISM in the source field to save 20% off year one Roboflow: Roboflow is an end-to-end visual AI platform that lets you turn raw ideas into fully deployed applications in just hours, powering breakthroughs like Blueprint Pro's floor-plan understanding tool. Read the full Blueprint Pro story and see how over a million engineers are building the next wave of visual AI at https://roboflow.com Claude: Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr
Emily Angeloni co-founded WOW Recruitment in Sydney at 22. No salary for six months. Just her laptop, working from a building set for demolition. Nine years later: a team of 18 across every major Australian state, New Zealand, and the United States. One of the most respected boutique sales and marketing agencies in the country.The year she became a mother, her co-founder told her he was leaving. She came back from maternity leave to a business that had saved every problem for her return, hit a serious road block, and stood in a strategy offsite thinking she could not do it anymore. She needed to rediscover her why.She did. WOW has since had its strongest period in nine years. But then she had a valuation done. And as she says herself: valuations can be a pretty uncomfortable truth.This week on The RAG Podcast, Emily breaks down what it actually looks like to lose a business partner, build something you are not sure you even want to continue, find the clarity, and watch everything change.We cover:- How a co-founder exit right before maternity leave forced Emily into the driver's seat for the first time- Why she seriously considered walking away from nine years of work, and what made her stay- The rookie hiring model that cost WOW its billing average, its culture, and its margins- How she redesigned her role entirely around what she loves: external focus, 18 people, zero internal ops- The five-year plan to build an asset with freedom of choice, not a job she cannot leave- Why a recent valuation was an uncomfortable truth and what it sharpened her focus on- What AI is actually changing for WOW's delivery model right now-Why personal brand has been her competitive advantage since 2017 and how it converts to real revenueLosing a co-founder. Building something you are not sure you even want any more. Then getting clear on the why and watching everything change.More founders have been down this road than would ever admit it. Emily is just prepared to say it out loud. Nine years in, she is building something worth having on her own terms, and working out exactly what that is worth.If you've ever wondered what it looks like to rebuild a business around a life you actually want to live, this episode has the blueprint.--------------------------------------------------------------------------------------------Admin is a massive waste of time. That's why there's Atlas, the AI-first recruitment platform built for modern agencies.It doesn't only track CVs and calls. It remembers everything. Every email, every interview, every conversation. Instantly searchable, always available. And now, it's entering a whole new era.With Atlas 2.0, you can ask anything and it delivers. With Magic Search, you speak and it listens. It finds the right candidates using real conversations, not simply looking for keywords.Atlas 2.0 also makes business development easier than ever. With Opportunities, you can track, manage and grow client relationships, powered by generative AI and built right into your workflow.Need insights? Custom dashboards give you total visibility over your pipeline. And that's not theory. Atlas customers have reported up to 41% EBITDA growth and an 85% increase in monthly billings after adopting the platform.No admin. No silos. No lost info. Nothing but faster shortlists, better hires and more time to focus on what actually drives revenue.Atlas is your personal AI partner for modern recruiting.Don't miss the future of recruitment. Get started with Atlas today and unlock your exclusive RAG listener offer at https://recruitwithatlas.com/therag/--------------------------------------------------------------------------------------------Every recruitment founder is investing in LinkedIn.Spending thousands on Recruiter licences. Building connections. Posting content. Growing networks.But here's the question almost no one can answer:How much revenue is LinkedIn actually bringing into your business?Most founders have thousands of connections but no clear process to turn that attention into cash.That's the problem we solve.At Hoxo, we help recruitment founders build predictable revenue systems on LinkedIn, not just noise or vanity metrics.Our clients are turning LinkedIn into £100K-£300K in new billings within months, using their existing networks and a simple repeatable process.To show you how it works, we've created a short training video exclusively for RAG listeners.In less than 10 minutes, you'll learn:Why most recruiters are getting zero measurable ROI from LinkedInHow small, niche teams are generating consistent inbound demandThe 3X Revenue System we use to turn LinkedIn into a predictable cash-generating channelSo fill in the form today to see how this system could transform LinkedIn into your agency's most profitable channel: https://hubs.ly/Q03lBpYC0
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Joshua Bate, founder of Bonfires.ai and DeciWorld, for a wide-ranging conversation covering knowledge management, graph technology, ontologies, decentralized science, and the future of how humans organize and share information. They break down the differences between personal and enterprise knowledge management, explore why flat ontological graphs may be the key to making diverse knowledge bases interoperable, and get into why traditional RAG systems break down at scale and how graph RAG offers a more principled solution. The conversation expands into the philosophy of categorization, the slow death of basic "gentleman science" under institutional pressures, and how decentralized protocols might restore a kind of mycelial knowledge network connecting small groups of researchers, enthusiasts, and communities — much like the original spirit of the encyclopedia before it was co-opted by institutions. You can learn more about Joshua's work at bonfires.ai and deci.world or follow him on X at @Bonfiresai and @DeSciWorld.Timestamps00:00 - Stewart introduces Joshua Bate, founder of Bonfires.ai, discussing personal versus enterprise knowledge management and their fundamental differences at scale.05:00 - Joshua explains ontologies as classifiers for knowledge structures, describing their two-year search for a perfect ontology and ultimately building a flat, ontology-less graph protocol.10:00 - Stewart connects categorization to shamanic practice and intercategorical theory, noting how major companies like Netflix and Yahoo built graph-based ontologies while the discipline remains underappreciated philosophically.15:00 - Joshua traces Bonfires origins through decentralized science, explaining how NFT community excitement inspired redirecting capital toward funding unconventional researchers locked out of institutional systems.20:00 - Joshua describes building federated knowledge networks through hackathons and conferences, comparing the vision to what Wikipedia could have been with decentralized incentive structures.25:00 - Discussion shifts toward inevitable collapse of rigid scientific institutions, debating patchwork age theory, nation-state fragmentation, and rhizomatic versus arboreal knowledge structures.30:00 - Joshua articulates the mycelial network vision, enabling direct cross-cultural information access where individuals control their own narrative lens, warning against collective we thinking and authoritarianism.Key Insights1. Knowledge management exists on a spectrum from personal to enterprise, but the founder of Bonfires argues this split is artificial. He believes knowledge itself does not respect those boundaries, and that small groups, researchers, hobbyists, and large institutions all possess knowledge that can and should interoperate with each other.2. After two and a half years of searching for the perfect ontology to structure their knowledge graph, the team concluded that no perfect ontology exists. Their solution was to build the flattest possible graph structure with only events, entities, and edges, creating a base layer others can build specialized ontologies on top of.3. Graph-based knowledge systems are more efficient than traditional databases for AI traversal because once a graph is computed, it is relatively free to query. Graph RAG combines the discovery power of vector search with the structured precision of graph traversal, solving many hallucination problems associated with standard retrieval augmented generation.4. Basic scientific research, the soil from which applied discoveries grow, is deteriorating because institutional funding structures only reward commercially viable outcomes. The founder built his platform partly to redirect community-driven capital toward researchers who are doing important work without institutional support.5. The institutionalization of science has historically blocked the open exchange of ideas that drove the original scientific revolution. The human spirit for open inquiry has not changed, but people cannot pursue it without financial support, and building decentralized infrastructure could restore that possibility.6. A federated knowledge network would allow individuals to access information from any contributor and filter it through their own preferred lens, rather than receiving information pre-filtered by centralized platforms. This represents a form of information symmetry similar to how mycelial networks distribute nutrients across a forest.7. The concern is not whether current scientific and governmental institutions will change but in what direction the rebuilding goes. Those capitalizing on the transition carry the same incentives as the previous era, which risks reproducing the same problems inside new structures.
SEASON: 6 EPISODE: 27Episode Overview:Welcome to another episode of Becoming Preferred, the show where we dive deep into the strategies that help you level up your game and stay relevant in an ever-shifting marketplace.I am happy to introduce our guest today, a man who sits at the intersection of high-level software innovation and human-centric design. Ghazenfer Mansoor is the CEO of Technology Rivers, a powerhouse firm known for building everything from HIPAA-compliant healthcare tech to cutting-edge AI solutions.But Ghazenfer doesn't just build software; he understands the psychology of why we use it. He is the author of Beyond the Download, where he breaks down the alchemy of creating mobile apps that people actually love and share. Beyond his technical expertise, he's a fellow storyteller as the host of the Lessons from the Leap podcast, uncovering the raw failures and bold breakthroughs that define the entrepreneurial journey.Whether you're looking to scale your startup, automate your processes, or simply build a brand that resonates, you're going to want to take notes. Join me for my conversation with Ghazenfer Mansoor!Guest Bio: Ghazenfer Mansoor is the CEO of Technology Rivers, a software development firm recognized in Washington, D.C. for creating AI-powered solutions, innovative SaaS products, and HIPAA-compliant healthcare technologies. He helps startups and service businesses scale faster, automate processes, and build technology that drives real-world impact.As the author of Beyond the Download: How to Build Mobile Apps That People Love, Use, and Share Every Day, he shares practical insights and proven strategies to help app developers and entrepreneurs build mobile apps that not only acquire users but keep them engaged and growing.In addition to being a thought leader and speaker, Ghazenfer hosts the Lessons from the Leap podcast, where he uncovers the bold decisions, failures, and breakthroughs that shape entrepreneurial success.Resource Links:Website: https://ghazenfer.com/Business Website: https://technologyrivers.com/Product Link: https://www.amazon.com/Beyond-Download-Build-Mobile-People/dp/B0GP9T86DZ/Insight Gold Timestamps:03:17 Technology's changing with amazing speed04:58 To your point, it can be disruptive if we don't evolve05:07 In your latest book, Beyond the Download...08:03 How are you competing? Are you competing on people? Are you competing on pricing?09:21 You want to look at what are the bottlenecks in your business?13:44 I think security ought to be a feature, not a footnote16:02 The focus has to be building the product the right way the first time, so that you can focus on growing your business17:45 You build the foundation, you have the sketch, and then you gradually scale21:00 It's not easy for existing users of other products to just switch to your product22:56 So it's about what is missing in the industry26:04 Everybody is trying to get into the AI race, which is exciting, but at the same time, some people are scared to take that leap26:15 On our Lessons from the Leap (podcast)28:11 So I think as a society, we have to evolve our EQ...32:19 There's something called RAG, (Retrieval Augmented Generation)34:20 It's ghazenfer.com and technologyrivers.com is your company34:33 Beyond the Download: How to Build Mobile Apps that People Love, Use, and Share Every DayConnect Socially:LinkedIn: https://www.linkedin.com/in/gmansoor/Facebook: https://www.facebook.com/techriversYouTube: https://www.youtube.com/@technologyriversInstagram: https://www.instagram.com/techrivers/Lessons from the Leap Podcast: https://ghazenfer.com/lessons-from-the-leap/Email: GMansoor@TechnologyRivers.comSponsors: Rainmaker LeadGen Platform Demo: https://calendar.summit-learning.com/widget/booking/JKItVP7WErmCBjU2cCIxRainmaker Digital Solutions: https://www.rainmakerdigitalsolutions.com/
Tokenization. Context windows. Lost in the middle. Silent failures. RLHF. Anthropomorphism. Quantization. Top-P and Top-K. RAG. Deterministic checks.If you haven't heard of some of these topics, this podcast episode is for you.
¿Quieres usar agentes de IA para programar sin arruinarte? En este episodio de atareao con Linux comparo las dos opciones más interesantes para desarrolladores en 2026: OpenCode Go y OpenRouter.Durante las últimas semanas he estado completamente volcado con OpenCode, usándolo tanto para generar código como para revisar código existente. Y en el proceso me he encontrado con una pregunta clave: ¿cómo accedo a los modelos de IA sin arruinarme?La respuesta no es trivial. Tienes dos opciones clásicas: comprar hardware dedicado o pagar servicios en la nube como ChatGPT o Gemini. Pero hay una tercera vía: combinar herramientas open source con servicios de bajo coste.En este episodio te cuento:Qué es OpenCode Go y por qué $10/mes pueden ser suficienteQué es OpenRouter y cómo usar 400+ modelos (algunos gratis)Comparativa directa de precios, modelos, ventajas y desventajasCuál elegir según tu caso de usoCaso práctico: cómo mejoré mi herramienta Shul con skills de Rust y ReactPor qué las skills son el verdadero game-changer de los agentes de IATambién te adelanto lo que viene en los próximos episodios: flujo de trabajo completo con skills, RAG, MCPs... la cosa se pone muy caliente.Capítulos:00:00 — Introducción: el dilema de la IA y el dinero02:30 — ¿Qué es OpenCode?04:50 — OpenCode Go: la suscripción de $10/mes08:20 — OpenRouter: el agregador de 400+ modelos10:50 — Comparativa directa13:00 — Caso práctico: mejorando Shul con Skills16:00 — El poder de las Skills19:00 — Conclusiones y cuál elegir22:00 — Próximos episodiosMás información y enlaces en las notas del episodio
VS leads Tals, Ibn the Roaster, and ZaZa back to the Phantom Cloisters by the dark of the new moon. They look for Mr Rag-n-Bones and speak to the children, determining that Violet is missing from a noble family. They see the Ghost Crow Trappers and descend to the undercroft level. There, while they investigate some strange vines, a warp worm attacks and swallows Tals whole. He is expelled when the worm is killed but it disappears into another warp. Animated cherubs and a very bad cosmic rift, Ibn and ZaZa temporarily swapping heads, and Zaza's legs turn into grasshopper legs. A room of chattering skulls and fleshy walls is investigated. They briefly battle some undead abbots, and stop at a confluence of strange waters where corpses lie at the bottom of a pool.
Trouble In Paradise: Has the love between Corey Feldman and Adrien Skye ended? It appears she has her own apartment and is moving on singing break up songs. Or is this all for publicity?Corey and Stephen Baldwin: We delve more into the Corey Feldman appearance on the Stephen Baldwin podcast. We got hip hop stories, spyglasses and more.Corey's Twitter: Corey is on one again, accusing Michael Jackson of ripping him off and telling people to suck his dong.COREY FELDMAN!, SHOW STOPPER!, LET'S JUST TALK!, DON CHEADLE!, BOOGIE NIGHTS!, JIM AND THEM IS POP CULTURE!, REAL ONES!, MEMBERS!, STREAMATHON!, OBUNGA!, THOUSPISS!, ROAD TO 50K!, NMAN!, HEEL TURN!, HEEL NMAN!, TOXIC TOXIC TOXIC!, PACT BREAKING!, SELF REPORT!, PO BOX!, WEEBLES!, SUPERTIPS!, GOSSIP!, GOTHSIP!, RAG!, MINIMALIST!, ARIANA GRANDE!, SAD!, BREAKUP!, COZY!, ADRIEN SKYE!, ADRIEN AGE CHECK!, IN HONOR!, KISS THE RING!, APOLOGY!, PAY RESPECTS!, TRIPLE H!, FORGIVEN!, PAST MISDEEDS!, STEPHEN BALDWIN!, PODCAST!, RAW AUDIO!, AI MUSIC INTRO!, ONE BAD MOVIE!, DR. DRE!, ICE CUBE!, CURTIS YOUNG!, AYO!, PAUSE!, CAM'RON!, MASE!, ART!, LIVE!, VAPID!, I DIG MUSIC!, STORY OF HIS CAREER!, EXCITING!, WORLD TOUR!, LIES!, DOCUMENTARY!, CALLERS!, GUN UNDER MY PILLOW!, WHY GOD WHY!, COREY'S TWITTER!, NOTHING TO FEAR!, MICHAEL JACKSON!, EDDIE MURPHY!, WHAT'S UP WITH THE YOUTH!?, TELL THE TRUTH!, PUNCHLINE!, TWISTED!, SHE WAS AWFUL!, MADONNA!, TYLER!, DARK VIBE!, SUCK MY BIG DONG!You can find the videos from this episode at our Discord RIGHT HERE!
Andrew Lee, CEO of Tasklet, returns for his fourth appearance to share how his team has once again rewritten their entire agent stack, now emphasizing file system context, agentic search, and multi-resolution summarization. The conversation digs into the strategic tension of competing with your own supplier, as Anthropic's Claude Max accounts offer direct customers far more tokens than API partners get at the same price. Andrew also lays out his framework for the only three types of software companies that will survive the AI transition and discusses Tasklet's evolution toward becoming a model-agnostic horizontal platform. Sponsors: Brave Search API: Brave Search API gives AI agents a fast, independent search index for research, RAG pipelines, images, places, and fewer hallucinations. Get $5 in free credits at https://brave.com/search/api/?mtm_campaign=q2-26-cognitive-revolution Sequence: Sequence handles the full revenue workflow for complex pricing, from quoting and metering to invoicing, revenue recognition, and collections. Book a public demo at https://sequencehq.com and use code COGNISM in the source field to save 20% off year one Roboflow: Roboflow is an end-to-end visual AI platform that lets you turn raw ideas into fully deployed applications in just hours, powering breakthroughs like Blueprint Pro's floor-plan understanding tool. Read the full Blueprint Pro story and see how over a million engineers are building the next wave of visual AI at https://roboflow.com Claude: Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr
On the Rag, episode 98: Us Weekly- May 15, 2006 | Denise's side of the story The Sherman Sisters
Mock-интервью с Николаем Лебедевым - DevOps/SRE-инженер, 17 лет в Linux, 4 года AWS EKS. Stack: Terraform, Flux, Cassandra, Kafka, Vault, SOPS. Два часа - много практики, много каверзных вопросов. ЧТО СПРАШИВАЛИ ☁️ AWS: EKS и IRSA, VPC с нуля (CIDR, multi-AZ, multi-region), managed K8s vs self-hosted, Elasticache, Golden Signals и метрики SRE.
Vector search has risen to become a foundational tool in modern search and retrieval systems, including the RAG pipelines that power many AI applications. However, the demands on retrieval systems are growing more sophisticated, which is revealing the limits of relying on a single vector similarity score. Vespa is a popular open source search and The post Vespa AI and Surpassing the Limits of Vector Search appeared first on Software Engineering Daily.
Charlie Saffro started CS Recruiting by accident. Pregnant with her third child, out of a job overnight, doing free matchmaking from the school carpool line in Chicago. Her first placement fee was $1,200 with a year-long guarantee. Sixteen years later, she had built a $5 million logistics and supply chain recruitment firm with 48 employees at its peak. Inc. Best Workplaces honouree. TEDx speaker. A brand built entirely on the idea that business is personal. Then the market turned. Three brutal years, 2022 to 2024, forced her to halve the team. On Thanksgiving 2024, she took a walk with her husband and said the words out loud for the first time: "I'm done." She sold CS Recruiting on December 1st 2025. The same year her oldest son left for college. Within weeks, the woman who had been needed by everyone suddenly wasn't needed by anyone. "Who am I without these titles? Who am I if I'm not in charge of my kids, if I'm not in charge of my team?" This week on The RAG Podcast, Charlie opens up about the reality of selling a recruitment business and what comes after. We cover: How an advertising career in downtown Chicago accidentally led to a logistics recruitment firm Why her first big client needed 200 hires and she had zero recruiters How personal branding on LinkedIn generated more business than any marketing budget The moment on Thanksgiving that triggered the decision to sell after 16 years What it actually takes to sell a recruitment business that carries your name Why she describes life after the exit as "grieving" The parallel between sending your kid to college and handing over your company How she navigates being an employee inside someone else's business after being a founder What she wishes someone had told her before she signed the deal So many recruitment founders think about selling one day. Very few actually do it. And almost nobody talks about what happens after the champagne stops flowing. If you have ever wondered what it really feels like to sell the business you built from nothing, to hand over the team you hired and the brand you created, this episode is the most honest answer you will hear. __________________________________________ Episode Sponsor: Atlas Let's be honest. Admin is one of the biggest drains on growth in a recruitment business. That's where Atlas comes in. Atlas is the AI-first recruitment platform built for modern agencies that want to scale without adding more manual work. It doesn't just track CVs and calls. It captures every conversation - emails, interviews, client calls - and makes it fully searchable. With Magic Search, you can literally ask: Who mentioned they're open to relocating next year? Who talked about wanting a four-day week? Who's worried about their commute? Atlas searches across real conversations, not just keywords on a CV, and gives you answers instantly. Atlas 2.0 also makes business development easier. With Opportunities, you can track and grow client relationships using generative AI, all inside your existing workflow. And this isn't hypothetical. Atlas customers have reported up to 41% EBITDA growth and an 85% increase in monthly billings after adopting the platform. No admin. No silos. No lost information. Just faster shortlists, better hires, and more time spent on the work that actually drives revenue. If you want to see what the future of recruitment looks like, unlock your exclusive RAG listener offer at: https://recruitwithatlas.com/therag/ __________________________________________ Episode Sponsor: Hoxo Every recruitment founder is investing in LinkedIn. Spending thousands on Recruiter licences. Building connections. Posting content. Growing networks. But here's the question almost no one can answer: How much revenue is LinkedIn actually bringing into your business? Most founders have thousands of connections but no clear process to turn that attention into cash. That's the problem we solve. At Hoxo, we help recruitment founders build predictable revenue systems on LinkedIn, not just noise or vanity metrics. Our clients are turning LinkedIn into £100K–£300K in new billings within months, using their existing networks and a simple repeatable process. To show you how it works, we've created a short training video exclusively for RAG listeners. In less than 10 minutes, you'll learn: Why most recruiters are getting zero measurable ROI from LinkedIn How small, niche teams are generating consistent inbound demand The 3X Revenue System we use to turn LinkedIn into a predictable cash-generating channel So fill in the form today to see how this system could transform LinkedIn into your agency's most profitable channel: https://hubs.ly/Q03lBpYC0
Vector search has risen to become a foundational tool in modern search and retrieval systems, including the RAG pipelines that power many AI applications. However, the demands on retrieval systems are growing more sophisticated, which is revealing the limits of relying on a single vector similarity score. Vespa is a popular open source search and The post Vespa AI and Surpassing the Limits of Vector Search appeared first on Software Engineering Daily.
Si en los últimos episodios te he hablado de las bondades de Open Web UI, hoy vamos a ensuciarnos las manos (de forma figurada, claro) para exprimir esta herramienta al máximo. No se trata solo de instalar un contenedor y ver qué pasa; se trata de convertir a tu inteligencia artificial en un asistente que realmente te conoce y tiene "superpoderes" gracias a herramientas personalizadas.Seguro que te ha pasado alguna vez: estás hablando con una IA y, de repente, parece que se le ha olvidado lo que le dijiste hace cinco minutos. Esto es lo que conocemos como el problema de la ventana de contexto. Los modelos tienen un límite de información que pueden procesar a la vez. En este episodio, te muestro cómo Open Web UI gestiona la memoria para que el asistente recuerde quién eres, cuáles son tus aficiones y hasta tus lenguajes de programación preferidos. Es fascinante ver cómo, tras una búsqueda en GitHub o en redes sociales, la IA es capaz de guardar esos detalles en su "cerebro" local para usarlos más adelante.Pero lo que de verdad me ha volado la cabeza es la posibilidad de crear herramientas (Tools). Imagina que necesitas calcular la distancia exacta entre dos ciudades para planificar una ruta de entrenamiento. Normalmente, la IA haría una búsqueda web más o menos precisa, pero ¿y si le pudiéramos enseñar a usar un script de Python específico para eso? En el podcast te cuento cómo la propia IA es capaz de programar su propia herramienta, dándote el código y las instrucciones para que la integres en tu interfaz. ¡Es el sueño de cualquier amante de la automatización!Además, tocamos un tema fundamental en los tiempos que corren: la privacidad y el coste. Las grandes tecnológicas se están dando cuenta de que mantener estos modelos cuesta una fortuna y ya estamos empezando a ver cómo suben cuotas o limitan el uso. Al trabajar en local, no solo te ahorras suscripciones, sino que te aseguras de que tus datos no salen de tu casa. Es soberanía tecnológica en estado puro.Lo que vamos a tratar en este episodio:00:00:00 - Introducción: Exprimiendo Open Web UI00:00:45 - Presentándome a mi propia IA local00:01:38 - La importancia de la memoria y el contexto en los LLM00:02:11 - Herramientas de búsqueda: ¿Qué sabe internet de Atareao?00:04:37 - Guardando información relevante en la memoria (RAG)00:05:04 - Consultas en tiempo real: El tiempo y el pronóstico00:06:20 - Ahorrando tokens: La importancia de ser conciso00:07:24 - Planificando un entrenamiento basado en datos meteorológicos00:10:02 - Cálculos de distancia y búsquedas web inteligentes00:11:54 - Crea tus propias herramientas (Tools) con Python00:14:32 - Configuración de herramientas personalizadas paso a paso00:16:12 - Integración de APIs externas (Nominatim) en local00:17:18 - Poniendo a prueba la memoria a largo plazo de la IA00:19:53 - Análisis de perfil de GitHub y lenguajes preferidos00:21:55 - Privacidad y ahorro: El fin de las subvenciones de las grandes Big Tech00:23:44 - De Google AI Studio a Open Web UI: El flujo de trabajo00:24:45 - Conclusiones y adelanto del próximo episodio: ¡Open Code!Te cuento también mi flujo de trabajo actual, cómo he pasado de herramientas en la nube como Google AI Studio a tenerlo todo bajo mi control con Open Web UI. Y ojo, que esto es solo el principio. En el próximo episodio abandonaremos un poco la interfaz de chat para meternos de lleno en Open Code, buscando siempre esa independencia tecnológica que tanto nos gusta.Más información y enlaces en las notas del episodio
Send us Fan MailQuiet failures are the ones that scare me most, and enterprise AI creates a brand-new way for them to spread. If a chatbot becomes the “trusted employee” everyone relies on, a slow drip of bad documents, outdated procedures, or deliberately manipulated data can poison decisions for months without a single red flag. We break down what that looks like in real organizations, why it differs from the Hollywood version of a hack, and how the business impact shows up as confident misinformation rather than obvious outages.We also dig into the difference between data poisoning (deliberate manipulation) and data pollution (accidental garbage at scale), then connect it to retrieval augmented generation (RAG). RAG is powerful because it answers from your internal knowledge base, but that same knowledge base becomes the attack surface and the “source of truth” the model won't question. I share practical steps you can take right now: audit what your AI actually trusts, map the full AI contact surface across workflows and repositories, treat the AI pipeline like an untrusted vendor, and assign a named owner for accuracy and security.Then we shift into CISSP Domain 1 practice with exam-style questions that force real trade-offs: using annual loss expectancy (ALE) to recommend a risk treatment to the board, applying NIST RMF guidance even when controls are inherited through FedRAMP, handling an ethics dilemma under the ISC2 Code of Ethics, spotting the biggest BCP gap when RTO and RPO targets collide with backup frequency, and explaining why HIPAA compliance does not automatically equal GDPR compliance for EU citizen data.If you're studying for the CISSP or you're building security controls around AI and cloud systems, this one is built to sharpen both your judgement and your test readiness. Subscribe, share this with a friend who's deploying AI internally, and leave a quick review so more CISSP candidates can find the show.Gain exclusive access to 360 FREE CISSP Practice Questions at FreeCISSPQuestions.com and have them delivered directly to your inbox! Don't miss this valuable opportunity to strengthen your CISSP exam preparation and boost your chances of certification success. Join now and start your journey toward CISSP mastery today!
In dieser Folge ist das dynamische Duo Dietmar Deffner und Holger Zschäpitz wieder vereint – zumindest virtuell! Während Defffner noch den Geschmack von Tortellini di Ragù auf der Zunge und den Rhythmus der venezianischen Gondeln im Blut hat (für stolze 90 Euro die halbe Stunde!), berichtet Zschäpitz direkt aus dem Auge des Marketing-Sturms: von der OMR in Hamburg. Die Themen im Überblick: Italien-Update: Warum die italienische Bahn der Deutschen Bahn meilenweit voraus ist und weshalb die Campari-Aktie trotz „Spritz-Trend“ im roten Bereich dümpelt. OMR-Check: 67.000 Menschen, 590 Euro Eintritt – und trotzdem kein richtiges WLAN? Wir blicken mit der Investorenbrille auf die Marketing-Messe: Was taugen die Prognosen von Scott Galloway wirklich? Der Bulle der Woche: Wasserstoff-Power für die Verteidigung oder die Rückkehr zur Authentizität in einer KI-inflationierten Welt? Der Bär der Woche: Warum Deffner sein Magenta TV nach Jahren kündigt (Spoiler: Ein grottiger Chatbot ist schuld) und warum bei einem tschechischen Rüstungsriesen die Alarmglocken schrillen. Das Hauptthema: Ein Jahr neue Bundesregierung. Ist es eine Bilanz des Aufbruchs oder nur „Stagnation auf Pump“? Wir diskutieren über Investitionsbooster, den Iran-Krieg und die Frage, warum der Kanzler seine Worte manchmal nicht im Griff hat. Hört rein, wenn es wieder heißt: Bullen, Bären und die harte Wahrheit über euer Geld! DEFFNER & ZSCHÄPITZ sind wie das wahre Leben. Wie Optimist und Pessimist. Im wöchentlichen WELT-Podcast diskutieren und streiten die Journalisten Dietmar Deffner und Holger Zschäpitz über die wichtigen Wirtschaftsthemen des Alltags. Schreiben Sie uns an: wirtschaftspodcast@welt.de Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutzerklärung: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html
Blaine Daws started in data centres in 2008 with a stack of papers and a wired phone. By 25, he was billing £475k a year as a solo 360 recruiter. At a family Christmas party in December 2019, he met Karl Chatterjee, a man who had already sold two recruitment businesses for multi-millions. Karl said: "Why are you going to line someone else's pockets?" Four weeks later, WNTD was incorporated. Two weeks after that, the world went into lockdown.Like so many other founders back then, he dropped his niche, went after every market he could find, and started to make money. They grew to 20 people and had a huge office in the post-Covid boom. But lost a foothold in their niche and over a quarter of a million pounds doing it.Then he stripped everything back. Let everyone go. Rebuilt from scratch around the one market he knew inside out: the Nvidia ecosystem and the global data centre buildout powering the AI revolution.WNTD is now 75 people, generating £15 to 20 million in revenue, and running a £100 billion GPU infrastructure deployment across Europe. Blaine is 35, working 7 days a week, with two young children at home."I want to make as much money as possible. So then in five years time, when I'm 40, I don't have to do it anymore."This week on The RAG Podcast, Blaine Daws tells the real story behind one of the most unusual businesses in recruitment.We cover:How a family Christmas party sparked WNTD, launched two weeks before lockdownGoing mass market, losing £250K, and why he told Karl most weeks he wanted to quitWhy stripping back to one niche was the actual growth strategyWhat statement of work really means in AI infrastructure recruitmentRunning a £100 billion GPU build and what that looks like day to dayGetting equity stakes in the partner companies they work withWorking 7 days a week at 35 with two young kids, and what it's actually costingThe question Sean asked that changed the tone of the whole episodeBlaine went from nearly shutting the business down to running one of the most specialised agencies in the world's fastest growing market.If you've ever wondered what it actually costs to build something extraordinary... this episode has the answer.__________________________________________Episode Sponsor: Remote RecruitmentHiring shouldn't be slow, stressful, or expensive. That's why there's Remote Recruitment — the smart hiring partner for modern businesses.They don't just help you find great people. They help you access elite South African talent that's ready to deliver. No PAYE. No NI. No bloated overheads. Just trained, remote professionals who integrate seamlessly into your team.Their process handles everything: sourcing, shortlisting, onboarding, and retention. Fully managed. Fully supported. Fully remote.And now, Remote Recruitments has entered a new chapter. From ops to admin, sales to strategy, we're helping businesses scale smarter with people they trust, at a cost they can afford.Clients have seen:* Up to **60% productivity boosts*** **300% ROI** on BD roles* **30% faster completion** of operational tasksNo overhead burden. No talent shortage panic. Just growth-focused hiring that makes business sense.Remote Recruitment is your flexible hiring solution for the modern era.**RAG Listeners:** Get 5% off your first hire + a free strategy session at www.remoterecruitment.co.uk/rag__________________________________________Episode Sponsor: HoxoEvery recruitment founder is investing in LinkedIn.Spending thousands on Recruiter licences.Building connections. Posting content. Growing networks.But here's the question almost no one can answer:How much revenue is LinkedIn actually bringing into your business?Most founders have thousands of connections but no clear process to turn that attention into cash.That's the problem we solve.At Hoxo, we help recruitment founders build predictable revenue systems on LinkedIn, not just noise or vanity metrics.Our clients are turning LinkedIn into £100K–£300K in new billings within months, using their existing networks and a simple repeatable process.To show you how it works, we've created a short training video exclusively for RAG listeners.In less than 10 minutes, you'll learn:- Why most recruiters are getting zero measurable ROI from LinkedIn- How small, niche teams are generating consistent inbound demand- The 3X Revenue System we use to turn LinkedIn into a predictable cash-generating channelSo fill in the form today to see how this system could transform LinkedIn into your agency's most profitable channel: https://hubs.ly/Q03lBpYC0
Join the free weekly live session: parsity.io/aiHope I don't regret this.I'm giving away the exact AI engineering curriculum I teach at Parsity. The same stuff that helped change my own career trajectory and has recruiters sliding all in my DMs.How LLMs actually work (and why knowing this helps you push back on the hype)RAG from scratch — embeddings, vector databases, chunking strategiesBuilding with Pinecone, Weaviate, or QdrantStructured outputs with Zod + OpenAI/Anthropic SDKsObservability with LangSmithLLM-as-Judge evals so your agents don't silently degradeThere's a free project linked below where you build a LinkedIn writing clone using my actual posts and articles as training data. No fluff, no theory. Just build the thing.
¿Te has fijado en que el panorama de la inteligencia artificial está cambiando a pasos agigantados? Lo que hasta hace dos días era un campo de juegos gratuito, donde podíamos probarlo todo sin soltar un euro, se está transformando rápidamente en un servicio de suscripción más, como la luz o el teléfono. Pero no solo es una cuestión de dinero. Hay algo que me preocupa mucho más: tu privacidad y la propiedad de tus datos.En este episodio número 792 de Atareao con Linux, quiero invitarte a dar un paso adelante en tu camino hacia la soberanía digital. Vamos a hablar de cómo montar tu propio laboratorio de inteligencia artificial en casa, utilizando una herramienta que es, sencillamente, una maravilla: Open WebUI. Olvídate de depender de servidores externos para tareas sensibles; es hora de que el motor de la IA corra en tus propias máquinas.¿Por qué Open WebUI?Si ya has escuchado episodios anteriores, sabrás que soy un gran fan de Ollama para ejecutar modelos en local desde la terminal. Pero seamos sinceros: la terminal es fantástica para muchas cosas, pero para mantener una conversación fluida con un modelo de lenguaje, todos preferimos una interfaz visual. Open WebUI es ese "vestido elegante" que le ponemos a nuestros modelos locales. Es una interfaz web que, nada más verla, te va a resultar familiar porque se parece muchísimo a ChatGPT o Gemini, pero con una diferencia fundamental: tú tienes el control total.Personalización y diversión: El caso de Leslie WinkleUna de las cosas que más me apasiona de esta herramienta es su "Model Builder". No se trata solo de elegir un modelo como Llama 3 o Gemma y empezar a escribir. Puedes ir mucho más allá. En el podcast te cuento cómo he creado un modelo específico con la personalidad de Leslie Winkle, el personaje de Big Bang Theory. Le he dado instrucciones precisas sobre cómo comportarse, quiénes son sus aliados y quiénes sus enemigos. El resultado es una IA con la que puedo "pelear" intelectualmente y que me llama "cerebro de corcho". Es divertido, sí, pero también demuestra el poder de crear asistentes especializados para tareas concretas de tu trabajo o tu día a día.Características que marcan la diferenciaGestión de usuarios y grupos.Soberanía de datos con RAG.Búsqueda Web PrivadaSoporte para fórmulas y código.Capítulos del episodio:00:00:00 ¡Al rico modelo local! Introducción00:00:27 El fin de la era "gratis" en la IA comercial00:01:31 Privacidad y bolsillo: Las dos razones para el local00:02:42 Más allá de la terminal: Buscando la interfaz ideal00:03:47 Presentando Open WebUI: El cerebro de tu laboratorio00:05:21 ¿Qué es exactamente Open WebUI?00:06:28 Personalización extrema: Mi charla con Leslie Winkle00:08:11 Gestión de usuarios y permisos granulares00:09:48 PWA, Markdown y soporte para fórmulas matemáticas00:10:55 Model Builder: Crea tus propios expertos a medida00:12:12 Integrando Python y funciones avanzadas00:13:34 Buscando en la web de forma privada con SearXNG00:15:13 Integraciones en la nube y bases de datos vectoriales00:16:08 Un vistazo al panel de administración y consumo00:18:24 El arte del Prompting: Carpetas y roles de sistema00:20:38 Mi infraestructura: Podman, Traefik y contenedores00:22:56 Recursos, chuletas y el repositorio de GitHub00:24:12 Despedida y red de Sospechosos HabitualesMás información y enlaces en las notas del episodio
‘I've always been the youngest, the new kid, the underdog. It helps me not be intimidated'. Arvid Lindblad, the fourth-youngest F1 driver in history, is full of confidence. In his first race he fought wheel-to-wheel with World Champions. The Racing Bulls driver has made a fast start to his first season in Formula 1. He always believed he'd race at the top. He's living his childhood dream and he's loving it.Arvid tells Tom Clarkson how he went from watching F1 on TV aged 4 to racing in F1 aged 18, inspired and supported by his family, his coach – the racing driver Olly Rowland – and Helmut Marko, Red Bull's former Motorsport Advisor. He explains the coaching which helped him beat more experienced drivers as he progressed up the motorsport ladder in ever-faster cars. Arvid also looks back to his Formula 1 debut in Australia, where he overtook Lewis Hamilton and Lando Norris at the start and scored points at the end, and relives the stunning qualifying lap in Japan which saw him eliminate Max Verstappen.Listen to more Official F1 PodcastsListen to the latest episodes of F1 Nation and F1 ExplainsThis episode is sponsored by:Bitdefender: visit bitdefender.com to learn more about how Bitdefender supports Ferrari to stay ahead of cyber threats and how you can make your digital life safer Rag&Bone: for a limited time, our listeners get 20% off their entire order with code GRID at Rag-Bone.com CarGurus: go to cargurus.co.uk for complete vehicle details without any surprises
From A Glossary of the People, Places, and Events of Alkyon Rag and Bone— A loosely organized guild of bounty-men, assassins, hunters, mercenaries, and hounds with posts across the continent. Though plenty of credible people offer work through Rag and Bone, the organization is known first and foremost as a liaison organization between anyone who needs a job done without their name attached, and anyone willing to do the dirty work for a share of the gold. The guild has a headquarters in most major settlements, where those who have been granted membership can always find work. This week, on The Stormwrought Shores, The Razing of Aramyr. Support us on Patreon: https://www.patreon.com/unexploredcast Follow us on Tumblr: https://unexploredcast.tumblr.com/ Art by Ben Music by Andrew: https://andrewperricone.bandcamp.com/ Transcripts: https://unexploredcast.tumblr.com/transcripts
Andy Strong spent 10 years in retail selling mobile phones. Then 17 years building the biggest delivery team at a SaaS recruitment firm in Dorset. He became one of four divisional directors. His team placed 82% of the company's US hires from Poole. He drove 400% growth in North American placements.And then he went to therapy.Eight sessions later, he ended an eight-year relationship, resigned from the only recruitment business he had ever known, and started a company from a shed at the bottom of his garden.Strong Search launched with no brand, no pipeline, and no office. Four retained roles landed in month one. By the end of year one: $1 million in revenue, 39 placements, 100% placement retention, and a team of seven. He turned down work with one of the largest software companies in the world because it did not fit what he stands for. He hired an operations and marketing manager before he hired another recruiter. And he recently appointed a Managing Director for North America who previously held senior roles at Coupa, Icertis, Yahoo, and Clari."I spoke to a venture capitalist in May. She said, what you're gonna learn is what's more important is what you say no to than what you say yes to. She was right."This week on The RAG Podcast, Andy Strong breaks down how he built a seven-figure recruitment business in 12 months and why trust, not volume, is the only model that survives.We cover:Why Andy waited 17 years before going out on his own and what finally changedHow therapy became the catalyst for quitting his job, ending a relationship, and starting a businessThe four retained roles that landed in month one and how they happenedWhy he hired an operations and marketing manager before another recruiterHow his team in Dorset placed 82% of a firm's US hires and beat American-based competitorsWhy he turned down a massive revenue opportunity with SAPThe 100% placement retention stat and what it actually takes to maintain itHis views on AI in recruitment and where the human element cannot be replacedAndy built something from nothing in 12 months. Not through cold calls and volume. Through precision, trust, and the courage to finally back himself.If you have ever sat in someone else's business wondering whether it is your time to go... this episode has the blueprint.----------------------------------------------------------------------------------Episode Sponsor: Remote RecruitmentHiring shouldn't be slow, stressful, or expensive. That's why there's Remote Recruitment - the smart hiring partner for modern businesses.They don't just help you find great people. They help you access elite South African talent that's ready to deliver. No PAYE. No NI. No bloated overheads. Just trained, remote professionals who integrate seamlessly into your team.Their process handles everything: sourcing, shortlisting, onboarding, and retention. Fully managed. Fully supported. Fully remote.And now, Remote Recruitment has entered a new chapter. From ops to admin, sales to strategy, they're helping businesses scale smarter with people they trust, at a cost they can afford.Clients have seen:Up to 60% productivity boosts300% ROI on BD roles30% faster completion of operational tasksNo overhead burden. No talent shortage panic. Just growth-focused hiring that makes business sense.Remote Recruitment is your flexible hiring solution for the modern era.RAG Listeners: Get 5% off your first hire + a free strategy session at www.remoterecruitment.co.uk/rag----------------------------------------------------------------------------------Episode Sponsor: HoxoEvery recruitment founder is investing in LinkedIn. Spending thousands on Recruiter licences. Building connections. Posting content. Growing networks.But here's the question almost no one can answer:How much revenue is LinkedIn actually bringing into your business?Most founders have thousands of connections but no clear process to turn that attention into cash.That's the problem we solve.At Hoxo, we help recruitment founders build predictable revenue systems on LinkedIn, not just noise or vanity metrics.Our clients are turning LinkedIn into £100K–£300K in new billings within months, using their existing networks and a simple repeatable process.To show you how it works, we've created a short training video exclusively for RAG listeners.In less than 10 minutes, you'll learn:Why most recruiters are getting zero measurable ROI from LinkedInHow small, niche teams are generating consistent inbound demandThe 3X Revenue System we use to turn LinkedIn into a predictable cash-generating channelSo fill in the form today to see how this system could transform LinkedIn into your agency's most profitable channel: https://hubs.ly/Q03lBpYC0
This episode focuses on how to manage your game after a long layoff, particularly an offseason. Thanks to our sponsors PuttOUT, HackMotion, Rag & Bone, Warby Parker • PuttOut Golf offers some of the most intuitive putting training aids available, and the AirBreak putting green stands out for its ability to simulate real course conditions. Unlike standard flat mats, it uses adjustable pumps to create a variety of slopes, helping golfers better understand the relationship between speed and line while keeping practice engaging. The new AirBreak Max expands on the original with a larger surface and putts up to 10 feet, and Sweet Spot listeners can save $120 on the Max or $60 on the original at puttout.golf/sweetspot. • If you've listened to us for a while, you know we're big on meaningful practice and clubface control—and that's why we recommend HackMotion. It acts like a virtual coach, analyzing your swing, identifying issues like flipping or casting, and giving you personalized drills with real feedback. Used by over 70,000 golfers worldwide, it's one of the most effective tools we've seen for improving your swing. Right now, you can get up to $50 off the new HackMotion 4, or $70 off the previous version, while supplies last at https://hackmotion.com/sweetspot • Rag & Bone makes premium denim designed for both comfort and durability, with a wide range of fits and styles to suit different preferences. Their jeans feature built-in stretch and a proprietary multi-step process that creates rich color and a broken-in feel that improves over time. With over 20 years of craftsmanship behind their products—plus high-quality essentials like tees, knits, and jackets—Rag & Bone focuses on wardrobe staples made to be worn for years. Use promo code SWEETSPOT to get 20% off your first order at https://rag-bone.com/ • Warby Parker makes it easy to find stylish, well-fitting glasses without the usual hassle or high prices, with frames starting at $95. Their app allows you to virtually try on different styles from home, so you can see exactly how they'll look before ordering, while maintaining a premium feel and quality. With over 300 retail locations, options for glasses, sunglasses, contacts, and eye exams, plus coverage from many major insurance plans, Warby Parker offers a streamlined, flexible eyewear experience. Get 20% off any additional pair of prescription pairs at warbyparker.com/SWEETSPOT Learn more about your ad choices. Visit megaphone.fm/adchoices
A new episode of the Resilient Cyber Show just dropped, and this one is a conversation I've been looking forward to for a long time.I sat down with Tanya Janca, better known to most of the AppSec world as SheHacksPurple. Tanya is the best-selling author of Alice and Bob Learn Application Security and Alice and Bob Learn Secure Coding, an OWASP Lifetime Distinguished Member, CEO of She Hacks Purple Consulting, and one of the most recognized voices in application security and developer education on the planet.The timing of this conversation is hard to overstate. The OWASP Top 10 2025 was announced at the Global AppSec Conference last year, with two new categories, Software Supply Chain Failures and Mishandling of Exceptional Conditions, and SSRF folded into Broken Access Control. Recently, Anthropic released the Claude Mythos Preview system card, documenting a model that has already found thousands of high-severity zero-day vulnerabilities autonomously, including bugs in every major operating system and web browser, and a 27-year-old vulnerability in OpenBSD.In other words, AppSec is at a hinge moment, and Tanya is exactly the right person to think out loud with about it.Here's what we get into:What the OWASP Top 10 2025 got right, what it missed, and how teams should actually use itAI-generated code, “vibe coding,” and Tanya's brand-new free prompt library for secure coding with AI assistants, SecureMyVibe.caWhat Mythos-class capabilities mean for the offense/defense asymmetry AppSec has always lived withHow AI is genuinely changing the SDLC, where it creates lift, where it creates noise, and where it creates entirely new attack surfaceArchitecting real defenses at the prompt layer, across MCP servers, and inside RAG pipelines, not just bolting content filters onto the front doorWhy developers are the new attack surface, and why a lot of what gets labeled as “supply chain attacks” lately is really a developer compromise that cascaded into the supply chainTanya's threat model, defense framework, and maturity model for protecting developers themselvesDevSec Station, Tanya's new podcast delivering 5–10 minute secure coding lessons in a format built for how developers actually consume contentWhat she'd change tomorrow about how AppSec programs are built and run if she could change just one thingThis is one of those conversations that ranges from the practical (what to do Monday morning) to the philosophical (what does it even mean to “secure software” when an AI can find more zero-days in a weekend than a Red Team finds in a year). Tanya brings the rare combination of deep technical chops, real teaching ability, and genuine warmth that makes a hard subject feel approachable.If you lead an AppSec program, write code for a living, run a security team trying to keep up with AI-assisted development, or you're just trying to figure out where this whole industry is heading, this is the episode for you.Resources from the episode:SecureMyVibeDevSec Station Podcast (Tanya's new show)She Hacks Purple ConsultingAlice and Bob Learn Application Security and Alice and Bob Learn Secure CodingOWASP Top 10 2025 — https://owasp.org/Top10/2025/Claude Mythos Preview System Card — AnthropicThanks for being here. If this episode landed for you, the best thing you can do is share it with one person on your team who'd find it useful, that's how this newsletter and show grow.
Your organisation has probably spent years building a learning library. Courses, videos, SCORM files, PDFs — hundreds of them, living in the LMS or scattered across SharePoint. You can enrol in them. You can sit through them. What you can't do is ask them a question and get an answer in seconds, at the moment you actually need one. The knowledge is there. It just isn't retrievable. That's the problem Mike Alcock, founder of Talvi, has set out to solve. In this episode, Mike takes John through how Talvi works. They also cover Mike's own unlikely route into learntech: a Civil Engineering degree at Sheffield, a detour through an insulation factory in Newcastle, and three successive software businesses each arriving ahead of the market. And they have a searching conversation about what tools like Talvi mean for the LMS and for the instructional designer — neither of whom emerges entirely unscathed. Is the technology now genuinely good enough to make learning in the flow of work a practical reality, rather than a conference agenda perennial?. TIMESTAMPS 00:00 - Start 02:14 - Intro 04:15 - What is Talvi for? 16:20 - What's the journey for a learning leader adopting Talvi? 20:47 - Mike's story: from civil engineering to learntech 30:19 - What will tools like Talvi do to the LMS? 39:50 - Explanation of terms: RAG, vector databases… 49:01 - End CONNECT WITH LEARNING HACK LinkedIn: linkedin.com/in/johnhelmer X: @johnhelmer Threads: @jphelmer Bluesky: @johnhelmer.bsky.social Website: learninghackpodcast.com
SPONSORS: 1) RAG & BONE: Upgrade your denim game with rag & bone! Get 20% off sitewide with code JULIAN at https://www.rag-bone.com #ragandbonepod 2) GHOST BED: Get an extra 10% off GhostBed mattresses—built for cooling, comfort, and support—by going to https://GhostBed.com/julian and using promo code JULIAN at checkout. Some exclusions apply; see site for details. JOIN PATREON FOR EARLY UNCENSORED EPISODE RELEASES: https://www.patreon.com/JulianDorey CLIPPERS DISCORD: https://discord.gg/8QmWEKJ3BT (***TIMESTAMPS in description below) ~ Lauren Conlin is a crime and entertainment journalist. She is contributor to LA Magazine and has covered the Missing Scientists story from the beginning. LAUREN's LINKS: IG: https://www.instagram.com/laurenemilyconlin/ X: https://x.com/conlin_lauren FOLLOW JULIAN DOREY IG: https://www.instagram.com/julianddorey/ X: https://x.com/juliandorey JULIAN YT CHANNELS - SUBSCRIBE to Julian Dorey Clips YT: https://www.youtube.com/@juliandoreyclips - SUBSCRIBE to Julian Dorey Daily YT: https://www.youtube.com/@JulianDoreyDaily - SUBSCRIBE to Best of JDP: https://www.youtube.com/@bestofJDP ****TIMESTAMPS**** 0:00 - Lauren's “Dead Woman's Switch,” Missing Scientists Story explodes 13:01 - Parallels with Missing Scientists case, Pure Spec, Epstein Files 23:01 - Virginia Giuffre, Most Powerful people in the world 30:58 - Categorizing all Missing Scientists 36:11 - Caltech NASA Physicist Carl Grillmair Murder 52:24 - MIT Physicist Nuno Louriero Murder 1:06:52 - Epstein's Death 1:16:17 - Novartis Cancer Researcher Dies, Los Alamos EA Disappearance 1:26:41 - Kansas City Nukes Facility Security Manager Disappearance 1:34:44 - The “Distraction” of Missing Scientists Story, Fake Gov & Epstein, Trump 1:46:19 - The Epstein “Eating” Allegations 1:47:27 - The Sinister Utah Satanic P*** Ring Investigation 1:57:32 - Harvey Weinstein's 3rd Retrial Coverage 2:01:59 - Los Alamos Tech disappears, Amy Eskridge Death 2:16:58 - NASA Scientists Michael David Hicks & Frank Werner Maiwald 2:23:15 - Top of Pyramid: General McCasland & Physicist Monica Reza Disappearances 2:37:08 - General McCasland 911 Call, McCasland's history 2:48:58 - McCasland access to UFO Files, Bible passages showing Aliens 2:55:38 - Foreign Op possibility CREDITS: - Host, Editor & Producer: Julian Dorey - COO, Producer & Editor: Alessi Allaman - https://www.youtube.com/@UCyLKzv5fKxGmVQg3cMJJzyQ - In-Studio Producer: Joey Deef - https://www.instagram.com/joeydeef/ Julian Dorey Podcast Episode 413 - Lauren Conlin Music by Artlist.io Learn more about your ad choices. Visit podcastchoices.com/adchoices
In this Cloud Wars conversation, Bob Evans sits down with Shub Bhowmick, CEO and Founder of Tredence, alongside Yasmeen Ahmad from Google Cloud to explore how enterprises are moving from AI applications to AI agents. Their discussion focuses on what it takes to turn intelligence into action — covering data foundations, semantic layers, agentic architectures, and the operational shifts required for businesses to scale AI successfully. Turning AI Into Action The Big Themes: AI Agents Redefine Applications: Traditional AI apps assist by querying data, generating recommendations, and supporting limited workflows. AI agents, however, represent a much deeper operational shift. As Ahmad explains, agents are multi-step reasoning engines that can access multiple systems, coordinate actions, and execute entire business processes autonomously. Instead of simply helping humans decide, they can perform work across ERP systems, supply chains, and customer interactions. This changes the role of the database itself — from a storage and query engine into a reasoning engine with vector search, graph RAG, and semantic understanding. Examples like Home Depot and Danfoss show how this creates massive efficiency gains Why Questions Require Agentic Intelligence: Shub Bhowmick draws a critical distinction between “what” questions and “why” questions. A conversational BI system can answer what happened — such as how much sales dropped — but a “why” question demands deeper reasoning. Why did sales decline? Was it pricing pressure, competitor behavior, inventory constraints, or macroeconomic events? These problems require hypothesis-driven exploration. Tredence addresses this through business semantic layers, knowledge graphs, and hypothesis banks that support open-ended reasoning. Closed Systems Create Long-Term Risk: Bhowmick warns against enterprises rushing toward closed, inflexible platforms simply because they promise faster short-term value. While packaged solutions may accelerate deployment, they often restrict ownership, adaptability, and future innovation. In contrast, open architectures built with hyperscalers like Google Cloud allow customers to own the IP, customize solutions, and evolve as the market changes. The Big Quote: “Gone are the days when these migrations used to take 12 to 18 months. Nowadays, you have to complete these migrations in less than three to four months.” More from Tredence and Google Cloud: Learn about the partnership between Tredence and Google Cloud and AI agents on Gemini Enterprise. Visit Cloud Wars for more.
So Palantir weirded everyone out by posting a weird 22-point manifesto on twitter. We're going over the details of that and the ever-encroaching surveillance state we're all going to be stuck in before we know it. NEW MERCH OUT! Get 10% off when you sign up and also get bonus content, ad-free versions and more plus your first 7 days free at https://benandemilshow.com ***THE SOUTHWEST COMPANION PASS IS BACK GET IT HERE: https://www.cardratings.com/bestcards/featured-credit-cards?src=691608&shnq=520080,4028088,4048122,4028085,3006151,4048149,4028089,4048084&var2= The newest acid video is out now so check it out! https://youtu.be/7vkFY3f5kkw Give this video a thumbs up if you enjoyed it! And please leave us a comment! It helps us! ***Ben's new movies and tv podcast with Dillon is OUT NOW! GO WATCH the latest episode on our TOP MOVIES OF 2025: https://youtu.be/tbC-cMqcby8?si=tO0NK0PmpN2187ir **CHECK OUT EMIL'S LIVESTREAMS HERE: https://www.youtube.com/emilderosa __ SOME OTHER VIDEOS YOU MAY ENJOY: That's Cringe of Cody Ko: https://youtu.be/dTbEk0pVh2w Our AUSTIN VIDEO: https://youtu.be/yGSs56bFzRU Our episode with Kyla Scanlon: https://youtu.be/cIHWkY35cuc Big Tech is out of ideas (ft. ED ZITRON): https://youtu.be/zBvVGHZBpMw Arguing with a millionaire (ft. Chris Camillo): https://youtu.be/1ZUWTkWV_MM We bought suits HERE: https://youtu.be/_cM1XqA9n2U ***LINK TO OUR DISCORD: https://discord.gg/CjujBt8g ***Subscribe to Emil's Substack: https://substack.com/@emilderosa ***Trade with Ben at https://tradertreehouse.com __ RAG & BONE: Upgrade your denim game with rag & bone—get 20% off sitewide with code BAES at https://www.rag-bone.com #ragandbonepod HIMS HAIR: For simple, online access to personalized and affordable care for Hair Loss, ED, Weight Loss, and more, visit https://hims.com/baes TIMESTAMPS: 00:00-05:23 Intro, Ben's neighbors, boomer chain emails 05:23-15:35 Big tech's obligations 15:35-17:40 Rag & Bone ad 17:40-31:13 The draft, priests, be nice to politicians 31:13-33:00 Hims ad 33:00-53:00 Repeating history, defanging Germany, be nice to Elon Musk, Pokemon collectors 53:00-1:13:30 YEAAAAH, butthurt billionaire 1:13:30-1:22:09 Ben eats dog food, disproving accusations __ Follow us on instagram! @ benandemilshow @ bencahn @ emilderosa Learn more about your ad choices. Visit podcastchoices.com/adchoices
Randy returns from Hawaii, it's mutton bustin time, Russell Wilson took stepson Future on college visit to Texas, and Dillon is sort of in on 'Age of Attraction.' Support us on Patreon and receive weekly episodes for as low $5 per month: www.patreon.com/circlingbackpodcast Watch all of our full episodes on YouTube: www.youtube.com/washedmedia Shop Washed Merch: www.washedmedia.shop • (00:00) Fun & Easy Banter • (17:25) Randy is back from Hawaii • (33:10) Mutton Bustin Time • (44:40) Russell Wilson and Little Future Visit Texas • (53:05) Dillon Started ‘Age of Attraction' Support This Episode's Sponsors: - Rhoback: Go to https://rhoback.com/ and use code LUTES20 for 20% off your first order - Lucy: Go to https://lucy.co/steam and use promo code (STEAM) to get 20% off your first order. - Squarespace: Check out https://squarespace.com/steam for a free trial, and when you're ready to launch, use OFFER CODE: STEAM to save 10% off your first purchase of a website or domain. - Rag & Bone: Upgrade your denim game with Rag & Bone!. Get 20% off sitewide with code STEAM at https://www.rag-bone.com/ #ragandbonepod - Cheers: For a limited time our listeners are getting 20% off their entire order by using code STEAM at https://cheershealth.com/ Learn more about your ad choices. Visit megaphone.fm/adchoices
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