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Qwen, Muse Glimmer, Ollama, llama.cpp, quantization, mixture-of-experts, RAG, tokens per second. On Hands-On AI, those aren't buzzwords — Mikah Sargent will explain exactly what they mean, then put them to work on hardware you already own. No cloud, no account, no data, leaving your desk. Host: Mikah Sargent Download or subscribe to Hands-On AI at https://twit.tv/shows/hands-on-ai Want access to the ad-free audio and video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.
Qwen, Muse Glimmer, Ollama, llama.cpp, quantization, mixture-of-experts, RAG, tokens per second. On Hands-On AI, those aren't buzzwords — Mikah Sargent will explain exactly what they mean, then put them to work on hardware you already own. No cloud, no account, no data, leaving your desk. Host: Mikah Sargent Download or subscribe to Hands-On AI at https://twit.tv/shows/hands-on-ai Want access to the ad-free audio and video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.
Qwen, Muse Glimmer, Ollama, llama.cpp, quantization, mixture-of-experts, RAG, tokens per second. On Hands-On AI, those aren't buzzwords — Mikah Sargent will explain exactly what they mean, then put them to work on hardware you already own. No cloud, no account, no data, leaving your desk. Host: Mikah Sargent Download or subscribe to Hands-On AI at https://twit.tv/shows/hands-on-ai Want access to the ad-free audio and video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.
How to Assess Skillful Use of AI The way we assess engineering talent is undergoing a fundamental shift. With AI now capable of generating functional code in seconds, the old evaluation playbook - whiteboard algorithms, take-home tests, "does it compile?"- is looking increasingly obsolete. The real question for hiring teams isn't whether a candidate can write syntax; it's whether they can exercise judgment, orchestrate multiple AI agents, and ship production-quality systems in a world where code generation is commoditised. G2i has been on the front lines of this shift, spending the last two years embedded inside frontier AI labs building reinforcement learning environments, human evaluation workflows, and training data infrastructure. That hands-on experience has completely redefined how they vet engineers - not for what they can type, but for how they think, direct, and evaluate AI-generated output. In this session, we'll unpack how reinforcement learning principles are reshaping technical assessment and what it means for anyone hiring engineering talent right now. Key Themes We'll Cover: Why Syntax Is No Longer the Differentiator – How AI code generation has flattened the playing field and why "can they code?" is the wrong question to ask in 2026 From Code Tests to Judgment Tests – What G2i's live technical evaluations actually measure now: agentic development, RL pipeline design, RAG architecture, LLM engineering, and the ability to evaluate AI output quality Inside the Frontier Lab – What G2i learned from two years building RL environments and human evaluation systems for cutting-edge AI models, and how that experience rewired their vetting philosophy Reinforcement Learning as Evaluation Framework – How RLHF-style feedback loops and reward modelling concepts are being applied to assess candidate decision-making under uncertainty The Orchestration Skill Gap – Why the engineers worth hiring today are the ones who can direct, critique, and refine the output of multiple AI agents—not just prompt them What "Taste" Looks Like in Practice – How G2i distinguishes between engineers who ship AI-assisted code and engineers who ship good AI-assisted code Building Multi-Agent Workflows – How G2i developed internal orchestration tools to coordinate complex agentic pipelines, and what that reveals about the skills they now screen for The Recruiter's Translation Challenge – How TA teams can bridge the gap between technical hiring managers and engineering candidates when the evaluation criteria have shifted this dramatically What Comes Next for Technical Vetting – How assessment is likely to evolve as AI capabilities accelerate, and why the platforms still testing for LeetCode-style problems are falling behind Whether you're a recruiter struggling to keep up with how engineering assessment is changing, a TA leader rethinking your technical interview process, or a hiring manager wondering why your last three "great on paper" hires couldn't ship reliable code, this session will give you a clear-eyed view of where evaluation is heading. G2i isn't talking theory—they've been building the systems that train the models. Join Hung Lee and David House as we explore what reinforcement learning reveals about real engineering judgment, and why the best hires in the agentic era will be defined by taste, not syntax. We're on Thursday 17th September, 10am PT / 1pm ET Follow the Recruiting Brainfood channel here (recomended) and register now - this is one skill shift you need to understand before your next technical search
Mercury is the smallest planet in our solar system, closest to the sun, taking a magical 88 days for a year. It travels fast but spins slowly – a day being 1.5 times as long as a year – with a quirky, egg-shaped orbit.Small with a big character. That kind of sums up mythical Hermes (Mercury) who, on the first day of his life, invents music, fire and ritual, steals Apollo's cattle and snuggles back into his blanket like nothing happened. To get in touch with this endlessly paradoxical being we begin with his Orphic Hymn: an invocation of the god. Only, of course, there isn't one hymn but two. Already he has found a way to 'twin' himself. The first hymn gives us a sense of his role as messenger – his 'day job' – the second as psychopomp: guide of souls who assists us through the many deaths of our lives. That's the thing about Hermes/Mercury. We meet him at the threshold. Even astrologically he is the third in a set of three. He revises where we've been and sets us up for where we're going next. Of course, being an episode about words themselves, there had to be some poetry involved. Dylan Thomas takes us in with On the Words in Poetry – essentially a love song dedicated to the magic of words themselves. And then, to close us out, a poem called, if this isn't too on the nose, Magic Words. ReferencesHomeric Hymn to Hermes — Theoi Classical Texts Library Orphic Hymns — Apostolos N. Athanassakis & Benjamin M. Wolkow, The Orphic Hymns — Johns Hopkins University PressThe Rag and Bone Shop of the Heart — Robert Bly, James Hillman & Michael Meade — Open LibraryDylan Thomas — “On the Words in Poetry” — from The Rag and Bone Shop of the Heart“Magic Words” — from The Rag and Bone Shop of the HeartTim Minchin — “Prejudice” — YouTubeTim Minchin — “If You Open Your Mind Too Much Your Brain Will Fall Out” — Tim MinchinSigmund Freud / “Freudian slip” — APA Dictionary of Psychology Arnold Mindell — “Discovering the World in the Individual: The World Channel in Psychotherapy” — Process Work Hermes — Theoi Greek Mythology — Hermes Hermes, the herma, fire, lyre and psychopomp — Theoi: HermesJoin the Newsletter!Podcast Musician: Marlia CoeurPlease consider becoming a Patron to support the show!Go to OnTheSoulsTerms.com for more.
Kolme tekoälytyöryhmän jäsentä vertailee omia tekoälytyöntekijöitään ja sitä, mitä agenttinen työ käytännössä vaatii. Anssi Nurminen kertoo Anneli-agentista, joka tekee WhatsApp-kuvista ja tekstistä konseptoituja videoita ja oppii saamastaan palautteesta, ja Lasse Mikkonen avaa Ossia ja Dudea, jotka hoitavat kuittirumban, ylläpitävät keissimuistia ja hakevat tiedot sähköpostista. Jaksossa käydään läpi MCP-yhteydet, skillit, harness joka pitää agentin ruodussa, sekä muistirakenteet obsidiaanista graafimuistiin ja RAG-järjestelmiin. Mukana on myös Meta Ray-Ban -lasien demo, keskustelu jatkuvan videotallennuksen ja yksityisyyden rajapinnasta, pseudonymisoinnista ennen pilveen lähettämistä ja siitä, onko Euroopalla vielä toinen mahdollisuus mallikisassa. Lopuksi puhutaan siitä, miksi kahdenkymmenen euron kuukausitilaus on halvin tapa investoida omaan osaamiseen ja miksi tietoturva ratkaisee, kun mallien kilpajuoksu Amerikan ja Kiinan välillä kiihtyy.0:00 Vieraat Anssi Nurminen ja Lasse Mikkonen0:09 Tekoälytyöryhmä ja 14 tekoälytyöntekijää0:48 Anssin tausta ja Anneli-agentti1:31 Lassen tausta DevOpsista agenttiseen tekemiseen2:33 Ossi ja Dude hoitavat kuittirumban3:51 Miten Anneli tekee videoita WhatsAppista5:12 Annelin opettaminen palautteen kautta6:32 Mikä MCP oikeastaan on6:56 Skills eli taidot tekoälytyöntekijälle7:46 Videot ravintola-alan innostajana8:48 Meta Ray-Ban -lasit käytössä10:05 Kuvahaku lasien kameralla10:39 Digitaalinen jalanjälki ja anonymiteetti11:57 Videoiden tekeminen lasit päässä12:38 Jatkuva nauhoitus ja muistin vahvistaminen13:47 Suomen kielen tuki laseissa puuttuu16:21 Miksi lisätty todellisuus kiinnostaa enemmän17:07 Videomallit ja Kiinan etumatka18:21 Suomalainen alustatalouskeskustelu on jäljessä19:07 Kannattaako odottaa valmista pöytää21:34 Vibekoodaus versus oikea osaaminen23:19 Agenttien delegointi ja rikkoutumisen opetus24:47 Harness valvoo että agentti pysyy uralla26:19 Kun agentti vuoti ajatteluprosessinsa ryhmään27:49 Mitä muita skillejä on rakennettu28:39 Duden keissimuisti ja palaverimuistiinpanot30:23 Ravintolakannattavuuden skilli yhteisprojektina31:01 Pitkä videotuotanto ja hahmojen konsistenssi33:28 Piilaakson aikakäsitys versus Eurooppa35:04 Miksi kannattaa kokeilla tasosto riippumatta36:19 Tietoturva ja Red Queen -ilmiö39:03 Mac Mini, Nvidia ja paikallinen laskenta41:13 Pseudonymisointi ennen pilveen lähettämistä43:00 Euroopan toinen mahdollisuus malleissa44:21 Kuukausitilaus on halpa investointi itseensä44:56 Agentin vieminen yrityksen IT-osastolle47:05 Hello Humans -podcastsimulaattori48:29 Joukkoistaminen ja skillien treidaus50:03 Muistirakenne vuoden ykkösteemana52:00 Graphify ja räätälöity keissitemplate54:35 Muisti toimii jo ChatGPT:ssä ja Claudessa56:13 Skillipäivitykset keskustelun pohjalta57:01 Suomen kielen skilli poistaa sloppia57:50 Botit keskustelevat keskenään liminaalitilassa58:48 Keskon AI-strategia ja koulutukset59:50 Amerikan ja Kiinan kilpajuoksu malleissa1:01:15 Loppusanat ja Sisäpiiri
A year ago, Palo Alto Networks built dashboards to track AI spend. Today those dashboards are useless, and the team that built them thinks that's the whole story.Recorded at FinOps X in San Diego, this conversation brings together Abhinav Lad, who leads cloud and AI finance at Palo Alto Networks, and Kuntal Patel, who runs the cloud engineering function behind it. They explain what happened when agents entered the picture, and AI stopped behaving like a service anyone could forecast.The short version: consumption went from linear to exponential almost overnight. Agents are goal-oriented rather than task-oriented, so they plan, call tools, verify, fail, retry, and keep looping until they hit the outcome, and every iteration is billable. So how do you run finance on top of that? Abhinav and Kuntal walk through the metrics that replaced their old forecasts: adoption rate, cost per user, AI as a percentage of revenue - and the budget limits that let engineering leaders choose between the newest model and a longer runway. They get into the open question of whether a cheaper model saves money or just burns more tokens thinking. They explain why an AI gateway became the control plane for cost and security at the same time, why retry caps belong in the design phase instead of the postmortem, and how FinOps starts to resemble product QA once the bill becomes the clearest signal that something is broken.They close on a warning worth sitting with: cost per million tokens is a number that means almost nothing on its own, and a value story built on it will point you somewhere you don't want to go.Palo Alto Networks: https://www.paloaltonetworks.comAbhinav Lad: https://www.linkedin.com/in/abhinav-ladKuntal Patel: https://www.linkedin.com/in/kuntalpatel35Alex Salkever: https://www.linkedin.com/in/alexsalkeverTimestamps:[0:00] Intro[1:00] Who runs FinOps for AI at Palo Alto Networks[2:10] Last year's AI dashboards are already useless[4:26] Agents turned linear forecasts exponential[7:27] Three traits that make agents expensive[8:34] The hidden bill: RAG, vectors and egress[9:16] Cost per user and adoption rate[11:21] Giving engineering leaders a budget[12:07] Using DORA metrics to prove value[13:53] Where DORA stops fitting AI[16:20] Does the cheaper model actually save money[17:57] Why you need an AI gateway[20:05] Inside Prisma AIRS[21:00] Three cost models for three use cases[22:52] Forecasting lessons from Electronic Arts[24:03] Runaway agents and endless loops[25:59] Capping retries before they burn cash[28:06] Writing cost policy at design time[29:01] When FinOps becomes product QA[32:17] Explaining AI spend to the C-suite[34:51] Valuing AI beyond engineering[37:04] Crawl, walk, run: where they are today[38:20] Why cost per million tokens is meaningless[39:26] Closing thoughts
In this solo episode, Bilal flips the script to pull back the curtain on his personal research workflow, addressing a surge of questions from peers on how to effectively apply AI to financial market analysis using six core principles. Some key points include: Machines Speaking Human The "CLEAR" Prompting Framework Gold-Standard Human Evaluation Converting Prompts to "Skills" Custom Session Memory & Feedback Loops Delegating to "Co-Work" Agents Reliable Market Analysis via RAG
Le groupe vocal a cappella Voxset fête ses 20 ans de carrière et reprend à sa sauce Rag'nʹBone Man, comme Aznavour ou Stromae, au Kubus à Cheseaux-sur-Lausanne, dès le 12 septembre. Wintershome se produit prochainement avec le Sinfonia Wallis à lʹéglise de Zermatt. Un événement pour le Zermatt Music Festival & Academy.
Unsloth Studio riunisce modelli locali, RAG, MCP e API. Filippo lo prova su Mac con Qwen3.8 e Gemma 4.Link alle note:https://www.avvocati-e-mac.it/podcast/88
On the Rag, episode 108: Us Weekly- September 4, 2006 | Kate Hudson TORN BETWEEN TWO LOVES The Sherman Sisters
“If you feel that a product is hard to eval, or you feel like, ‘I don't even know how to eval this,' it's a strong smell that your product isn't good.”— Hamel Husain, on AI product designAn AI data agent tells you last quarter's net revenue. It doesn't show the metric definition, source tables, filters, query, intermediate calculations, or assumptions. You can't trust the answer without asking a data scientist to reproduce it.Hamel Husain argues that the eval problem is evidence of bad product design. If the user can't inspect the work well enough to decide whether the answer is right, another scoring pipeline won't rescue the experience. The fix begins by exposing the evidence and checks a domain expert actually uses.Follow that problem far enough and you arrive at a broader argument: AI isn't killing data science. It's creating more noisy, black-box systems that need hypotheses, experimentation, search expertise, and judgment. Hamel suggests that the people doing this work may eventually be called AI scientists.This episode connects those two ideas. Building AI products people can verify and understanding whether those products work are becoming part of the same job.“AI has made data science way more valuable than ever before, because now you have way more data and way more noisy signals that you need to reason about and debug.”— Hamel Husain, on the rise of the AI scientistYou can also find the full episode on Spotify, Apple Podcasts, and YouTube.
Retrieval has become one of the central problems in building useful AI systems. The standard approach to grounding a model in one’s own data has been retrieval augmented generation, or RAG, where an agent searches a vector database for relevant information at query time. That pattern works, but it has limitations, such as retrieving information that’s not truly relevant, repeating the same lookup work on every query, and producing inconsistent answers to the same question. Pinecone is a vector database that’s widely used to power semantic search and RAG at scale. The team recently developed Nexus, which is a knowledge engine that reframes context as a first-class, precomputed asset rather than something reassembled on the fly. The approach borrows the database concept of a materialized view, and curates context once into a versioned artifact that carries its own schema, metadata, permissions, and lineage. Jörg Schad is the VP of Engineering at Pinecone. In this episode, he joins Kevin Ball for an in-depth conversation about the frontier of retrieval technology. They discuss precompiled context, how context artifacts are curated and versioned much like code, how metadata and semantic layers help agents choose the right information, and much more.Sponsorship inquiries:sponsor@softwareengineeringdaily.com The post Moving Beyond RAG with Precomputed Context appeared first on Software Engineering Daily.
Retrieval has become one of the central problems in building useful AI systems. The standard approach to grounding a model in one’s own data has been retrieval augmented generation, or RAG, where an agent searches a vector database for relevant information at query time. That pattern works, but it has limitations, such as retrieving information that’s not truly relevant, repeating the same lookup work on every query, and producing inconsistent answers to the same question. Pinecone is a vector database that’s widely used to power semantic search and RAG at scale. The team recently developed Nexus, which is a knowledge engine that reframes context as a first-class, precomputed asset rather than something reassembled on the fly. The approach borrows the database concept of a materialized view, and curates context once into a versioned artifact that carries its own schema, metadata, permissions, and lineage. Jörg Schad is the VP of Engineering at Pinecone. In this episode, he joins Kevin Ball for an in-depth conversation about the frontier of retrieval technology. They discuss precompiled context, how context artifacts are curated and versioned much like code, how metadata and semantic layers help agents choose the right information, and much more.Sponsorship inquiries:sponsor@softwareengineeringdaily.com The post Moving Beyond RAG with Precomputed Context appeared first on Software Engineering Daily.
Retrieval has become one of the central problems in building useful AI systems. The standard approach to grounding a model in one’s own data has been retrieval augmented generation, or RAG, where an agent searches a vector database for relevant information at query time. That pattern works, but it has limitations, such as retrieving information that’s not truly relevant, repeating the same lookup work on every query, and producing inconsistent answers to the same question. Pinecone is a vector database that’s widely used to power semantic search and RAG at scale. The team recently developed Nexus, which is a knowledge engine that reframes context as a first-class, precomputed asset rather than something reassembled on the fly. The approach borrows the database concept of a materialized view, and curates context once into a versioned artifact that carries its own schema, metadata, permissions, and lineage. Jörg Schad is the VP of Engineering at Pinecone. In this episode, he joins Kevin Ball for an in-depth conversation about the frontier of retrieval technology. They discuss precompiled context, how context artifacts are curated and versioned much like code, how metadata and semantic layers help agents choose the right information, and much more.Sponsorship inquiries:sponsor@softwareengineeringdaily.com The post Moving Beyond RAG with Precomputed Context appeared first on Software Engineering Daily.
Este episodio 828 es la carta de presentación de la Temporada 9 de atareao con Linux. Treinta y cuatro episodios ya guionizados, siete etapas, y un objetivo claro: construir tu cerebro digital sobre Linux con herramientas locales, sin depender de nubes ni suscripciones.Pero antes de mirar adelante, toca hacer balance. La T08 empezó prometiendo Docker, selfhosting y Android, y sí, hablé de todo eso. Pero en abril de 2025 la IA local irrumpió con fuerza y la temporada viró hacia Ollama, modelos locales, RAG, MCP. Fue un giro desordenado, lo reconozco. Pero también fue el germen de todo lo que viene ahora.De eso va esta T09: de poner orden al caos. Siete etapas, de menos a más, para que sigas el hilo hagas el nivel que hagas.Etapa 1 — Recursos básicos: los cimientos de tu laboratorio de IA. Skills para tu agente, herramientas de publicación, el botiquín del explorador.Etapa 2 — Skills y MCPs: el pegamento. El Model Context Protocol ha madurado hasta ser un estándar abierto — lo soportan Claude, ChatGPT, VS Code, Cursor. Ya no es un experimento, es el USB-C de la IA. Y de paso, herramientas del ecosistema atareao como watchbeat (monitor de uptime en Rust) y alloy (dashboard Docker con OIDC).Etapa 3 — GraphRAG, el gran hito: de RAG vectorial a grafos de conocimiento. Mientras el RAG clásico devuelve fragmentos sueltos y tú unes los puntos, GraphRAG construye un grafo con entidades y relaciones. Preguntas como "qué contenedores están detrás de Traefik" pasan a ser una consulta directa a tu mapa de conocimiento. Usaremos LightRAG, que con 39.000 estrellas ya superó al Microsoft GraphRAG original. Esto ocupa tres episodios.Etapa 4 — Multimedia: Whisper para speech-to-text, TTS local, ffmpeg, visión artificial, y el pipeline de YouTube a conocimiento con yt-dlp. Rematamos con RAG multimodal.Etapa 5 — Orquestación: systemd timers, asyncio, just, y CrewAI para montar equipos de agentes.Etapa 6 — Proyecto final: dos episodios para construir El Asistente que te Conoce y ponerlo en producción con Quadlets.Etapa 7 — El futuro: mantenimiento de tu cerebro digital y hacia dónde va todo esto.Entre medias, herramientas Linux: shuul, sqlite-utils, yq + jq, Rust en el kernel, Wayland vs X11, la guerra de los filesystems.No necesitas una GPU de 3000 euros ni un doctorado. Con 16 GB de RAM y un CPU decente ejecutas modelos de 7B a 14B. Esto es IA local, en tu máquina, con tus datos.Capítulos del episodio:00:00 — Introducción y bienvenida a la Temporada 901:47 — Balance T08: de Docker y Selfhosting al boom de la IA04:37 — El momento adecuado para cada tecnología06:46 — El gran objetivo: tu cerebro digital08:44 — Roadmap T09: 30 episodios ya guionizados11:28 — Skills y MCPs imprescindibles12:43 — GraphRAG: de RAG a grafos de conocimiento14:01 — RAG vs GraphRAG: el mapa de tu conocimiento17:28 — Herramientas del ecosistema: alloy, populater, watchbeat20:08 — ¿Para quién es esto? De veteranos a escépticos22:08 — No es hype: es un cambio de paradigma24:30 — El momento perfecto para el linuxeroToda la info y el roadmap completo en atareao.es/828.Más información y enlaces en las notas del episodio
Retrieval has become one of the central problems in building useful AI systems. The standard approach to grounding a model in one’s own data has been retrieval augmented generation, or RAG, where an agent searches a vector database for relevant information at query time. That pattern works, but it has limitations, such as retrieving information that’s not truly relevant, repeating the same lookup work on every query, and producing inconsistent answers to the same question. Pinecone is a vector database that’s widely used to power semantic search and RAG at scale. The team recently developed Nexus, which is a knowledge engine that reframes context as a first-class, precomputed asset rather than something reassembled on the fly. The approach borrows the database concept of a materialized view, and curates context once into a versioned artifact that carries its own schema, metadata, permissions, and lineage. Jörg Schad is the VP of Engineering at Pinecone. In this episode, he joins Kevin Ball for an in-depth conversation about the frontier of retrieval technology. They discuss precompiled context, how context artifacts are curated and versioned much like code, how metadata and semantic layers help agents choose the right information, and much more.Sponsorship inquiries:sponsor@softwareengineeringdaily.com The post Moving Beyond RAG with Precomputed Context appeared first on Software Engineering Daily.
Retrieval has become one of the central problems in building useful AI systems. The standard approach to grounding a model in one’s own data has been retrieval augmented generation, or RAG, where an agent searches a vector database for relevant information at query time. That pattern works, but it has limitations, such as retrieving information that’s not truly relevant, repeating the same lookup work on every query, and producing inconsistent answers to the same question. Pinecone is a vector database that’s widely used to power semantic search and RAG at scale. The team recently developed Nexus, which is a knowledge engine that reframes context as a first-class, precomputed asset rather than something reassembled on the fly. The approach borrows the database concept of a materialized view, and curates context once into a versioned artifact that carries its own schema, metadata, permissions, and lineage. Jörg Schad is the VP of Engineering at Pinecone. In this episode, he joins Kevin Ball for an in-depth conversation about the frontier of retrieval technology. They discuss precompiled context, how context artifacts are curated and versioned much like code, how metadata and semantic layers help agents choose the right information, and much more.Sponsorship inquiries:sponsor@softwareengineeringdaily.com The post Moving Beyond RAG with Precomputed Context appeared first on Software Engineering Daily.
Hocus Focus Mix met Calvin Harris, Rag'n'Bone Man, Shakira, Burna Boy, Maan, Goldband, Justė, Jaxstyle, Jon, Armin van Buuren & Kensington
Cerramos la octava temporada con un episodio que me apetecía grabar desde hace meses. Igual te ha pasado como a mí: empecé hablando de un laboratorio de IA para cualquiera, y terminé recomendando GPUs de 3000 euros. Me fui creciendo, pero no hace falta. Te cuento cómo montar un laboratorio de IA local con el equipo que ya tienes. Da igual si tienes 8 GB de RAM o 16, CPU modesta o sin GPU. La clave está en elegir los modelos adecuados. Muchas veces nos perdemos buscando el modelo más grande, cuando con uno pequeño y bien cuantizado tenemos de sobra para el 80% de las tareas.Te hablo de Ollama, el gestor de modelos estándar para ejecutar modelos locales. Más de 180.000 estrellas en GitHub, API compatible con OpenAI, modelos para todos los presupuestos: desde Phi 3.5 con 3.8B parámetros hasta Qwen 1.5B que ocupa 1 GB. También la cuantización: reduces la precisión numérica de los pesos para que ocupen menos y vayan más rápido. El punto dulce es Q4_K_M, que reduce el tamaño a menos de un tercio. Para 8 GB de RAM, Q3_K_S puede ser tu salvación.También te hablo de Open WebUI, la interfaz que le da mil vueltas a ChatGPT. No solo chateas: tiene RAG local, Whisper integrado para transcribir voz (75 MB en CPU), TTS con Kokoro-82M para que el modelo te hable en tiempo real, búsqueda web, plugins y memoria persistente. Todo en un contenedor Docker que levantas con un solo comando.Y de SQLite Vec, extensión de SQLite sponsorizada por Mozilla para búsqueda semántica sin servidores vectoriales. Ni ChromaDB, ni Qdrant, ni Milvus. C puro que funciona hasta en Raspberry Pi. Creas tablas virtuales para vectores de 768 dimensiones, generas embeddings con nomic-embed-text, y buscas por similitud coseno en milisegundos. RAG local sin complicaciones.Y te explico cómo organizarlo todo con Docker o Podman. Un docker-compose.yml que levanta Ollama y Open WebUI en segundos, con healthchecks, redes separadas y volúmenes persistentes. También a limitar recursos con --memory y --cpus. He preparado scripts: inicialización que comprueba requisitos, crea directorios y descarga modelos; otro para descargar por niveles según tu hardware (nivel 1 para 8 GB, nivel 2 para 16 GB, nivel 3 para 32 GB); y uno de respaldo.Y la estrategia híbrida local + nube, que es lo que realmente tiene sentido. El enfoque Minions del Stanford Hazy Research Lab: el modelo local hace el trabajo pesado, y solo consulta al grande en la nube para tareas complejas. El 90% de las consultas se resuelven localmente. Ahorras dinero, mantienes privacidad de tus datos, y cuando necesitas potencia, la tienes.Con 16 GB de RAM y un SSD te sobra para el 80% de las tareas: traducciones, resúmenes, código, asistentes, RAG, transcripción de audio, texto a voz... Todo en tu máquina, sin enviar datos a servidores, sin suscripciones, sin depender de internet. Con 8 GB también puedes, con modelos más pequeños. Cerramos temporada, la novena arranca en el episodio 828. Capítulos del episodio:0:00 - Introducción — cierre de temporada 8 y replanteamiento2:30 - Hardware mínimo: 8-16 GB RAM + SSD obligatorio5:00 - Software base: instalar Ollama en tu distribución7:30 - Contenedores: Docker vs Podman para el laboratorio10:00 - Modelos pequeños: Phi 3.5, Qwen 1.5B y cuantización13:00 - Herramientas complementarias: SQLite Vec, Whisper, TTS16:00 - Organización del laboratorio: script y estructura de directorios19:00 - Demo: probando Ollama en local con modelos ligeros22:00 - Combinación local + nube: lo mejor de ambos mundos24:30 - Cierre, avance temporada 9 y despedidaMás información y enlaces en las notas del episodio
On the Rag, episode 107: Us Weekly- August 28, 2006 | Kate and Owen's Secret Affair The Sherman Sisters
SUMMARY: Brandon speaks with Rich Ziade, co-founder and CEO of Aboard, about why enterprise AI projects fail without real discovery, why "agents" have been oversold as a headcount play, and why organizational urgency, not new tooling, is what actually makes digital transformation succeed.SHOW: 1057SHOW TRANSCRIPT: The Enterprise AI Show #1057 TranscriptSHOW VIDEO: https://youtu.be/RMgycbmuXGsSHOW SPONSORS:Nasuni - Activate your data for AI and request a demoSHOW LINKS:https://www.aboard.com/https://aboard.com/podcast/Topic 1 - From lawyer to digital transformation CEO: Rich's path through Postlight (with co-founder Paul Ford), the sale in 2021, and how Aboard was already incubating inside Postlight Labs before AI "landed like a spaceship."Topic 1a - The six months after ChatGPT arrived: why Aboard resisted rushing a prompt-based fix into messy, political, human organizations, and why "vibe coding" convinced them to hang back rather than parachute AI into a company.Topic 2 - The doctor/patient analogy: executives walk in asking for a specific AI "medicine" instead of describing the underlying pain, and why real engagements start with tests and diagnosis, not the prescription the client thinks they want.Topic 2a - Why discovery hasn't fundamentally changed in the AI era — still in-person interviews and observation, with AI mainly useful for note-taking and summarizing documentation, not for skipping the hard thinking.Topic 3 - The agent hype cycle: why Rich thinks the "millions of agents" narrative (including Anthropic's Boris Cherny running swarms of planning/implementation agents) reflects an engineering-execution worldview rather than a product or organizational one — and why he sees the agent narrative cooling off.Topic 3a - The "spreadsheet problem" vs. targeted AI: most of Aboard's actual delivery work (90%+) isn't agents — it's modernizing spreadsheet-run processes and building narrow RAG/vector tools so people can query their own data in plain English.Topic 4 - "Forward deployed" as the new name for an old idea — going on-site, listening, and understanding a client's world before proposing a solution.Topic 5 - Why no successful digital transformation starts without a real, externally imposed deadline or crisis — and why "innovation labs" without urgency rarely ship anything.Topic 6 - Lightning round: Is AI a bubble? Should GPUs be securitized assets? Three things to do in NYC in one day.FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow
AI hallucinations get all the attention. But hallucinations are relatively easy to catch because the output is obviously wrong. The failure mode that should worry data scientists more is when the agent uses facts that are true to draw conclusions that are false, producing outputs that look perfectly fine. This is known as silent correctness.In this Value Boost episode, Jia Huang joins Dr Genevieve Hayes to explore why silent correctness is the most dangerous failure mode in agentic AI systems and what data scientists can do to catch it before it causes serious harm.You'll discover:Why silent correctness is harder to catch than a hallucination [04:17]Why sampling and auditing are non-negotiable in agentic systems [07:09]Four techniques data scientists can use to catch silent failures [09:28]The one safeguard every agentic AI system should have [11:10]Guest BioJia Huang is a lead research engineer at A*STAR, Singapore's Agency for Science, Technology and Research, and is the author of multiple books on AI engineering and agent design, including Designing AI Agents and RAG from First Principles. His work focuses on turning agentic AI from impressive demos into reliable, auditable, and value-producing engineering systems.LinksConnect with Jia on LinkedInFollow Jia on SubstackAgent Design Pattern Society (ADPS) websiteJia's AI agent design position paperConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
As the 2026-27 athletics calendar begins, Ben Weiner Director of Athletics David Harris sits down with Corey for a wide-ranging, extensive conversation on the Wave. A new era begins for Tulane football under Will Hall, with the Wave garnering major attention for the City Edition Mardi Gras uniforms and the return of The Battle for the Rag in 2030. As college athletics changes rapidly, David addresses Tulane's approach to new eligibility rules, increasing scholarship numbers and more. Plus, his goals for the 2026-27 season.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
On the Rag, episode 106: Us Weekly- August 21, 2006 | VINCE PROPOSES! + Simpsons strike back at Dad The Sherman Sisters
Software Engineering Radio - The Podcast for Professional Software Developers
Sathiesh Veera, a GenAI Solutions Architect at At&T, speaks with host Brijesh Ammanath about the data-protection guardrails required when using LLMs. The core issue is that LLMs sit outside the cloud tenant in most enterprise AI deployments, which means that data leaves the company's perimeter with every prompt, RAG retrieval, and tool call. Contractual agreements can restrict the data that LLM vendors are allowed to use for training and audits, but they don't stop prompt injection or unintended exposure as company data is often shared to LLMs via natural language queries, APIs, tool and function calls, and MCPs. Sathiesh discusses ways to employ security measures and data filtering at each layer to conform to data security policies and protect the data.
The shift to agentic AI doesn't make data science skills obsolete. But it does require data scientists to rewire how they think about familiar concepts, such as uncertainty, model evaluation and accountability, in their work.In this episode, Jia Huang joins Dr Genevieve Hayes to explore what that rewiring actually looks like, and why data scientists are better placed than almost any other profession to make it.You'll discover:Why data scientists are better prepared for the agentic AI era than they might think [03:00]How the data scientist's role is shifting from analyst to system designer [06:37]The three types of uncertainty in agentic AI systems [11:58]Why context engineering is the new feature engineering [23:19]Guest BioJia Huang is a lead research engineer at A*STAR, Singapore's Agency for Science, Technology and Research, and is the author of multiple books on AI engineering and agent design, including Designing AI Agents and RAG from First Principles. His work focuses on turning agentic AI from impressive demos into reliable, auditable, and value-producing engineering systems. LinksConnect with Jia on LinkedInFollow Jia on SubstackAgent Design Pattern Society (ADPS) websiteJia's AI agent design position paperConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
On the Rag, episode 105: Us Weekly- August 14, 2006 | Why Nick chose Vanessa The Sherman Sisters
A Developer Advocate for Deepgram with over ten years of experience in advocacy, Ed Charbeneau focuses on fostering engagement with the developer community while supporting the development of innovative AI-Native applications. With a diverse technical skill set, Ed contributes to building impactful solutions using a wide range of technologies including: C#, Asp.Net, HTML/CSS, JavaScript, Microsoft Extensions AI, RAG, and ML.NET technologies. Ed leverages his role as a developer advocate to find new and innovative ways to expand the boundaries of product through customer feedback, research and hands-on development. Ed has built proof of concept frameworks, components, and SDKs that have gone on to production. These initiatives have future-proofed the Progress portfolio, empowered customers, and enabled product integration. Ed is passionate about empowering developers to explore opportunities in full-stack development, AI, machine learning, and prompt engineering. Ed facilitates discussions on emerging technology trends, fostering collaboration and knowledge-sharing within the tech community.You can find Ed on the following sites:WebsiteXGitHubMastodonTwitchYouTubeHere are some links provided by Ed:Deepgram PLEASE SUBSCRIBE TO THE PODCASTSpotifyApple PodcastsYouTube MusicAmazon MusicRSS FeedYou can check out more episodes of Coffee and Open Source on https://www.coffeeandopensource.comCoffee and Open Source is hosted by Isaac Levin
Sign up for Practi, a new platform that helps law firms use subscription billing.Here are the top 5 takeaways from this episode:* Gemini Notebook (formerly, Notebook LM) is the standout tool. The speaker calls it “the best value in Google Workspace.” It uses retrieval-augmented generation (RAG) to answer questions only from sources you provide, making it ideal for analyzing contracts, running playbooks against briefs, and even as a client-facing lead generation tool.* Listen to your documents. Google Docs has a built-in text-to-speech feature (the play button next to the editing mode toggle) that lets you listen to documents at variable speeds. It's a significant time-saver for document review, especially for auditory learners, though it's currently limited to ~6 pages at a time.* Google Workspace's subscription tier matters. The Business Standard plan (~$14/month) unlocks Gemini Notebook Pro, advanced Gemini features, Google Cloud Search (search across your entire Google ecosystem at once), and HIPAA compliance options via a free Business Associate Agreement (BAA). The starter tier is insufficient for serious law practice.* Underused productivity features. Chrome tab groups (organized per client, synced across devices), email aliases/alternates (use multiple domain emails without extra seats), and Google Cloud Search are powerful built-in tools most attendees hadn't fully explored. Google Sheets' Gemini integration can also auto-build tracking spreadsheets by pulling from existing email/payment data.* AI-powered video and podcast generation from legal documents. Gemini Notebook's Video Overview and Audio Overview features can turn a client handbook, brief, or contract into an explainer video or debate-style podcast. This lets lawyers deliver client-facing explanations without recording themselves, and the debate feature can surface gaps in legal arguments before filing.__________________________Want your question to be answered on a future show? Fill out this short survey.Have subscription model question? Check out this free resource to ask all of your questions at notebook.practi.ai.Check out Practi.Sign up for Paxton, my all-in-one AI legal assistant, helping me with legal research, analysis, drafting, and enhancing existing legal work product.Get Connected with SixFifty, a business and employment legal document automation tool.Sign up for Gavel, an automation platform for law firms.Visit Law Subscribed to subscribe to the weekly newsletter to listen from your web browser.Prefer monthly updates? Sign up for the Law Subscribed Monthly Digest on LinkedIn.Check out Mathew Kerbis' law firm Subscription Attorney LLC.Want to use the subscription model for your law firm? Click here to sign up for a new platform that helps law firms use subscription billing. Get full access to Law Subscribed at www.lawsubscribed.com/subscribe
Stewart Alsop sits down with Juan Verhook, founder of Tender Market, for a second conversation that ranges from the mechanics of European public tenders to the future of how we organize digital information. They cover how Tender Market helps smaller companies work around barriers like SOC 2 and ISO certification requirements, the surprising scale of public procurement (roughly 20% of GDP), and how AI and machine learning are reshaping the bidding process. From there the conversation opens up into bigger territory: the changing tolerance for being wrong in an AI-saturated information landscape, how language and culture shape perception, the reverse Turing test and the challenge of verifying human versus AI identity online, and Juan's daily workflow running eight or nine MCP servers through Claude Code. They close out talking about whether the folder and file system will survive the shift to AI-native interfaces, tying back to Stewart's own Stewart Squared episodes on the history of the PC. You can visit Tender Market at tendermarket.eu.Timestamps05:00 — Tender Market's origin story and how they help smaller companies work around SOC 2 and ISO certificate barriers.10:00 — Public procurement and its scale, roughly 20% of GDP, plus a look at public-private partnerships.15:00 — Local LLMs on a plane with no Wi-Fi, and comparing local model performance to frontier models.20:00 — Supply versus demand in AI infrastructure and whether hyperscaler token efficiency is quietly improving.25:00 — Whether AI will replace knowledge work tasks, and the shifting reality of what lawyers and other professionals actually do.30:00 — Reverse Turing test, digital identity verification, and the idea of a "pre-AI internet."35:00 — Model poisoning, RLHF, and the difference between pretraining and post-training.40:00 — Interleaved tool calling and how Tender Market ties pricing to task deliverables instead of billable hours.45:00 — RAG versus fine-tuning, prompt engineering, and when context windows actually matter.50:00 — Deterministic programming versus probabilistic agents, and when to build custom tools versus buy existing ones.55:00 — Juan's daily MCP stack (Supabase, GitHub, Calendly, CRM), and whether the folder-and-file system will survive the shift to AI-native interfaces.Key InsightsCertification requirements aren't dead ends—they're routing problems. When smaller companies got rejected from tenders for lacking SOC 2 or ISO certificates, Juan didn't turn them away. He found that EU procurement rules allow bidding as a consortium or subcontracting to a certified partner, turning a disqualifier into a workaround that builds trust with clients.Public procurement is a massive, underexamined market. Roughly 20% of GDP flows through public purchasing of private-sector goods and services, yet most people have no visibility into how tenders work or how governments post and award these contracts.Being wrong has become more socially acceptable. Juan traced this shift to the falling cost of information: in the Stack Overflow era, giving a wrong answer was costly, but now that answers are instant and abundant, both mistakes and corrections happen faster, changing how people learn and communicate.Task-based pricing beats hourly billing for AI-era services. Rather than charging per hour, Tender Market prices around the deliverable, winning a tender, which avoids the perverse incentive of hourly billing to be inefficient and instead rewards actually solving the client's problem.RAG and fine-tuning solve different problems. RAG helps a model reference large documents without hitting context limits, while fine-tuning changes a model's internal weights so it learns new behavior or style. Juan noted that true RAG use cases needing thousands of pages of context are rarer than the hype suggests.Deterministic code should replace repeated LLM calls once a pattern is found. Stewart described his own workflow: solve a task with an LLM a handful of times, then convert the repeated pattern into deterministic software so tokens are no longer spent on it, freeing the model for genuinely new problems.AI agents are never truly autonomous. Both hosts agreed that no matter how many steps an agent chains together, a human operator always initiates the first prompt, meaning accountability and intent trace back to a person even in multi-agent systems.
In today's Cloud Wars AI Minute, I break down the rise of agentic reasoning loops and why their impact on AI token consumption and costs could be significant. Highlights 00:12 — Today's topic is going to be agentic reasoning loops. Everyone's moving to this concept where we can have a planning, act, observe, and reflect type of process, which allows us to be able to get much deeper analysis and much more resilient implementations across AI, across the world. 01:03 — Now, the other thing is with this, we're seeing that about 33% of enterprise software will run on this kind of approach by 2028. Also, the other part of this is that because these different loops are taking place, we're actually seeing that tokens are going to get more and more consumption happening off the back end. 01:38 — So one of the driving factors of this is going to be the increase that we're seeing, and just to give you perspective of what we're starting to see, it's about a 17x on a single chapter that we're starting to see happen as a result of this. So what it used to do when we just did simple RAG patterns. 02:01 — Now we're seeing about 17 times the amount of tokens being consumed, and when we start seeing that level of token consumption, we're going to see that while we're getting better answers, there's also going to be a higher cost that's associated to it. 02:28 — But it doesn't matter that the tokens are necessarily coming down in cost because of the fact that what we used to see is that an average transaction would be about three cents, and now we're seeing it move to about 15 cents across the board, and so this is going to be one of those things that we really need to be thinking about as you start to build out your solutions. Visit Cloud Wars for more.
Nicolas Decavel, senior AI engineer with Candidly, joins co-hosts Andrew Miller and Meighan Berberich to discuss the progress organizations are making with customizing LLMs using RAG - inlcuding the importance of data preparation, selecting the best LLMs for RAG, and evaluating performance. Please visit RAG Bootcamp for AI Engineering for more information on the October seminar and check out Hands-On: Customizing LLMs with RAG: Using GenAI with Business Data for details on Nicolas' course at TDWI Transform 2026 Data and AI Conference. ____________ More information: · TDWI Conference: https://bit.ly/3XqBhGH · TDWI Virtual Summits: https://bit.ly/31HJ2xr · Seminars: https://bit.ly/3WxQPr4 · More Speaking of Data Episodes: https://bit.ly/3JsQPWo Follow Us on: · LinkedIn - https://bit.ly/42zCZZB · Facebook - https://bit.ly/49uej7j · Instagram - https://bit.ly/3HM8x57 · X - https://bit.ly/3SsYu9P
Ronald, Marco en Jelle zijn terug van de zomerstop. Marco nam zijn HackRF mee naar Noorwegen maar de verkeerde antenne, Jelle hielp Marktplaats met het verwijderen van 1.800 nepadvertenties en Ronald bouwde een eigen president's daily brief voor zijn Kindle. Daarna Patch Tuesday. Microsoft repareerde in augustus 419 kwetsbaarheden, waaronder drie zero-days. Een daarvan werd volgens Check Point actief gebruikt door Lazarus. Ook kwam de eerder aangekondigde Legacy Hive-exploit van Nightmare Eclipse voorbij, al bleek de beloofde "bone-shattering" zero-day iets minder spectaculair dan aangekondigd. Marco bespreekt vervolgens onderzoek van Unit 42 naar gesynchroniseerde Google-passkeys op Windows. De cryptografie zelf bleef overeind, maar malware op het apparaat kon misbruik maken van het vertrouwen tussen Chrome, de TPM en Googles cloud-authenticator. De onderzoekers vonden zelfs de master key waarmee alle gesynchroniseerde passkeys kunnen worden ontsleuteld eerst in logging en later in het geheugen van Chrome. Jelle kijkt mee op een AI-werkstation van Kimsuky. De Noord-Koreaanse spionagegroep experimenteerde volgens Genians met lokale modellen, RAG, transcriptie en AI-ondersteunde softwareontwikkeling. Niet het trainen van een eigen supermodel, maar bestaande open-sourcemodellen dicht bij de aanvalsinfrastructuur inzetten. Marco blijft nog even in Noord-Korea. Onderzoeker Vangelis Stykas keek 22 maanden mee in de infrastructuur van Noord-Koreaanse hackers nadat enkele operators zichzelf met hun eigen malware hadden besmet. Hij vond sporen van 1.640 organisaties in 57 landen. Opvallend: ondanks toegang tot allerlei gevoelige gegevens lag de nadruk vooral op cryptovaluta, terwijl veel gewaarschuwde organisaties niet reageerden. Het eerste grote verhaal gaat over een Poolse warmte-krachtcentrale. Een aanvaller kwam via een gecompromitteerde FortiGate bij een mobiele router die al toegang had tot een private APN. Omdat locaties binnen dat mobiele bedrijfsnetwerk niet van elkaar waren gescheiden, kon de aanvaller door naar een WAGO-controller en drie Siemens-PLC's in STOP zetten. De centrale bediende ongeveer 50.000 inwoners, maar operators herstelden de installatie voordat er maatschappelijke gevolgen waren. Tot slot: waar bewaar je eigenlijk je bitcoins? Bij Coldcard zorgde een firmwarefout ervoor dat sommige hardwarewallets seeds maakten met een voorspelbare softwarematige randomgenerator. Een aanvaller hoefde de wallets niet aan te raken, maar kon mogelijke seeds berekenen, adressen op de openbare blockchain controleren en de gevonden wallets leegtrekken. De omweg langs Cloudflares muur van lavalampen maakt meteen duidelijk waarom echte onvoorspelbaarheid zo belangrijk is. Bronnen: - Microsoft Patch Tuesday: https://www.bleepingcomputer.com/news/microsoft/microsoft-august-2026-patch-tuesday-fixes-400-flaws-3-zero-days/ - Unit 42 over Google-passkeys: https://unit42.paloaltonetworks.com/passwordless-authentication-security-risks/ - Genians over Kimsuky's lokale AI-stack: https://www.genians.co.kr/en/blog/threat_intelligence/kimsuky_ai_llm - Wired over de Noord-Koreaanse hackers: https://www.wired.com/story/a-security-pro-hacked-north-korean-hackers-he-found-theyd-breached-hundreds-of-networks-worldwide/ - BleepingComputer over de Poolse centrale: https://www.bleepingcomputer.com/news/security/hackers-breached-a-small-polish-energy-plant-via-private-apn-last-year/ - CERT Polska, follow-uprapport: https://cert.pl/uploads/docs/CERT_Polska_Energy_Sector_Incident_Follow_up_Report_2025.pdf - Coldcard, technische analyse: https://blog.coinkite.com/entropy-technical-backgrounder/ - Coldcard, beveiligingswaarschuwing: https://blog.coinkite.com/coldcard-mk3-seed-generation-warning/ - Cloudflare over lavalampen en entropie: https://www.cloudflare.com/learning/ssl/lava-lamp-encryption/
Send us Fan MailWhat if AI could tap into live operational data — without ETL or RAG? In this episode, Deepti Srivastava, founder of Snow Leopard, reveals how her company is transforming enterprise data access with intelligent data retrieval, semantic intelligence, and a governance-first approach. Tune in for a fresh perspective on the future of AI and the startup journey behind it.We explore how companies are revolutionizing their data access and AI strategies. Deepti Srivastava, founder of Snow Leopard, shares her insights on bridging the gap between live operational data and generative AI — and how it's changing the game for enterprises worldwide.We dive into Snow Leopard's innovative approach to data retrieval, semantic intelligence, and governance-first architecture.04:54 Meeting Deepti Srivastava 14:06 AI with No ETL, no RAG 17:11 Snow Leopard's Intelligent Data Fetching 19:00 Live Query Challenges 21:01 Snow Leopard's Secret Sauce 22:14 Latency 23:48 Schema Changes 25:02 Use Cases 26:06 Snow Leopard's Roadmap 29:16 Getting Started 33:30 The Startup Journey 34:12 A Woman in Technology 36:03 The Contrarian View
Send us Fan MailWhat if AI could tap into live operational data — without ETL or RAG? In this episode, Deepti Srivastava, founder of Snow Leopard, reveals how her company is transforming enterprise data access with intelligent data retrieval, semantic intelligence, and a governance-first approach. Tune in for a fresh perspective on the future of AI and the startup journey behind it.We explore how companies are revolutionizing their data access and AI strategies. Deepti Srivastava, founder of Snow Leopard, shares her insights on bridging the gap between live operational data and generative AI — and how it's changing the game for enterprises worldwide.We dive into Snow Leopard's innovative approach to data retrieval, semantic intelligence, and governance-first architecture.04:54 Meeting Deepti Srivastava 14:06 AI with No ETL, no RAG 17:11 Snow Leopard's Intelligent Data Fetching 19:00 Live Query Challenges 21:01 Snow Leopard's Secret Sauce 22:14 Latency 23:48 Schema Changes 25:02 Use Cases 26:06 Snow Leopard's Roadmap 29:16 Getting Started 33:30 The Startup Journey 34:12 A Woman in Technology 36:03 The Contrarian View
A great copy isn't just a “nice to have” anymore, it's the difference between traffic that prints money and traffic that quietly bleeds your ad budget dry. In a world where attention is short, feeds are noisy, and AI-generated fluff is everywhere, only truly persuasive words cut through and convert. Today's guest, Jon Benson of BNSN.AI, is the copywriter credited with inventing the Video Sales Letter (VSL), a format that has generated billions of dollars in digital sales and reshaped online direct response marketing.In this episode of Marketer of the Day, Jon breaks down the evolution of the “ugly” VSL, from plain text on a black screen with voiceover, to hypnotic slide patterns, NLP command phrases, and innovations like magic buy buttons, no-pause video players, and timed call-to-action reveals. He explains why words on the screen plus spoken words outperform talking-head videos, how dual modalities (reading + listening) supercharge retention, and why removing distractions can multiply conversions even in today's muted, mobile-first world. Jon then pulls back the curtain on Benson (BNSN.AI), his AI-driven copy platform built specifically for high-converting VSLs and funnels. Instead of generic prompts and AI “stink,” Benson uses promptless AI, pre-prompting, frameworks, and RAG to train multiple LLMs on proven copy patterns. You'll hear how his system breaks a VSL into specialized sections, uses different models for different copy tasks, and rewrites behind the scenes so what you see sounds like a top-tier human copywriter, not a template-spitting chatbot. A big theme of this conversation is mindset: you are not your avatar. Jon explains why writing “how you talk” usually kills conversions, why your copy shouldn't sound exactly like you, and how to instead sound like the best version of you that your market will respond to. He shares lessons from decades as a hired gun copywriter and ghostwriter, the importance of writing for cold traffic, and why the words are still 80% of the funnel, even if you use ClickFunnels, GoHighLevel, or any other page builder. https://youtu.be/deBPX78TpHQ?si=wuWrOAUu9AaXn9b- If you've ever been frustrated by weak AI copy, low-converting VSLs, or the feeling that your offer “should” be selling better than it is, this episode will recalibrate how you think about persuasion, funnels, and AI-powered copywriting. You'll walk away with a deeper understanding of what truly moves cold prospects to buy, and how tools like BNSN.AI can help you scale high-quality copy across your entire funnel without sacrificing human-level persuasion. Quotes: “It's not enough to just give AI a good VSL and say ‘you figure it out.' You have to break it into components and get very specific.” “What people call ‘AI stink' is really just AI pulling from the wrong training. If you don't feed it the right patterns, you'll never get what you're wanting.” “The words are the most important things that you have to sell anything… if you get the words that sell, you are 80% there.” Contact Details: Turn AI Into Your Best Copywriter: Start with BNSN Today Master the Words That Sell: Learn from Jon Benson Connect with Jon Benson on LinkedIn and Master Copy That Sells Subscribe to Jon Benson's YouTube Channel for Sales Copy Secrets Follow Jon Benson on Instagram for Daily Copywriting Tips Follow Jon Benson for AI & Copywriting Strategies Master Copywriting: Follow Jon Benson on Facebook
Government software development pipelines are increasingly integrating AI coding assistants, but Air Force Sustainment Center Software Directorate CTO Kurt Jarvis said success depends on improving how developers use existing code, not just generating new code faster. Speaking with GovCIO Media & Research at the Carahsoft DevSecOps Conference, Jarvis outlined an engineer-first approach that uses retrieval-augmented generation (RAG), vector databases and established DevSecOps pipelines to safely accelerate software development. Jarvis said one of the biggest opportunities for AI is helping developers discover and reuse decades of trusted software already maintained by the Air Force. The goal, he said, is to make AI a code‑reuse engine, not a code‑generation machine. He also emphasized that AI has not changed the Air Force's software engineering standards. Existing CI/CD pipelines are still the foundation for evaluating AI-generated code. AI can accelerate development, Jarvis said, but engineering discipline and human oversight remain essential to producing mission-ready software.
AI isn't just transforming the way we work, but also the way we write the software that people use for work. In this episode, we talk to two engineering productivity leads at Dropbox: Uma Namasivayam, senior director of software engineering productivity, and Anuradha Agarwal, director of software engineering. Whether it's writing tests, fixing bugs, tackling tech debt, or accelerating migrations, they explain how Dropbox engineers are using agentic AI—including in-house tools like Nova—to build the future of Dropbox, and create more space to do impactful work. ~ ~ ~ Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck. Our theme song was composed by Doug Stuart. Working Smarter is hosted by Matthew Braga. Thanks for listening!
Join Andreas Walbrodt, CEO and Co-Founder of enclaive, for an essential exploration of cybersecurity, data sovereignty, and business confidentiality in an increasingly cloud-reliant world. While enterprises have spent decades securing data at rest (in storage) and in transit (over networks), sensitive workloads remain completely unencrypted the moment they are processed in memory. As organizations rush to adopt public cloud platforms, scale Industry 4.0 smart manufacturing, and deploy proprietary AI models, this "in-use" vulnerability creates catastrophic exposure to cloud operator access, third-party subpoenas, and cyber threats. Drawing on over 30 years of enterprise IT leadership at IBM and TÜV Rheinland, Andreas explains how confidential computing closes this gap—allowing businesses to leverage hyper-scale cloud power without sacrificing sovereignty, compliance, or core intellectual property.
On the Rag, episode 104: Us Weekly- August 7, 2006 | Tori and Candy Spelling are BEEFING The Sherman Sisters
Public Domain AI Flipping: Build & Monetize Custom GPTs With 19th‑Century Books (Before the Window Closes) Make money online by flipping public domain AI content—the overlooked side hustle for busy parents. Learn how to find, repurpose, and sell digital products without creating anything from scratch. This contrarian strategy saves time, cuts through the noise of traditional side gigs, and generates income faster than you think. Perfect for the AI entrepreneur who wants results without the grind. https://DarkHorseEntrepreneur.com The episode explains a “public domain AI flipping” strategy: training custom GPTs on 19th-century public domain books (including Project Gutenberg and the Internet Archive) to create niche, highly specific tools that can be monetized quickly via subscriptions or products. It argues most custom GPT monetization fails due to content creation costs or licensing, but public domain works published on or before 1930 can be used and monetized without copyright barriers, with the public domain expanding yearly. The script outlines a four-step process: pick a narrow vertical with an unmet need, curate matching public domain texts, build a custom GPT using prompts and retrieval-augmented generation (RAG), then monetize via recurring revenue using platforms like GPT Plus, Gumroad, or API interfaces. It recommends verticals such as historical accuracy consulting, academic supplementation, content engines for podcasters, and archival tools, while warning about uncertain platform rules, limited precedent, and the need for distribution over technical execution. 00:00 Intro to public domain AI flipping 01:05 Why Most Custom GPT Monetization Fails 02:10 Clarify U.S. Public-Domain Cutoff 03:40 4-step Mechanism 06:20 RAG & Tightly Sourced Corpus > GPT 08:05 Monetization Options 09:40 Necessary Callouts 11:10 4 Promising Verticals 14:30 Real Trap Warning 17:10 Whiskered Wisdom Custom GPT monetization, Public domain,AI, Project Gutenberg, training data, AI side hustle 2026, ChatGPT, passive income, Public domain, Train GPT on books, RAG, custom chatbot,AI entrepreneur, niche GPT, recurring revenue, how to make money with chatgpt, ChatGPT, GPT-4o, OpenAI, Custom GPT, GPT Store, how to make money online, make money online, ai side gig, digital products for beginners, side hustles, income growth hacks https://DarkHorseEntrepreneur.com Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
In this episode, we reflect on the most impactful themes from season eleven of the HR Data Labs Podcast, emphasizing the transformative role of artificial intelligence (AI) in the world of work. Host David Turetsky discusses how AI has become a central focus across industries, influencing regulation, data governance, organizational change, and HR practices. Tune in for a comprehensive summary of trends, predictions, and key conversations that are shaping HR's future. Key Topics Covered: The dominance of artificial intelligence in season eleven and its influence on HR. The importance of data governance for AI integration, highlighted by insights from Talenode. Practical applications of AI, including the use of large language models and local AI hosting solutions. Discussions with industry experts on AI ethics, security, talent acquisition, and leadership. Predictions and insights about AI's role and regulation in HR over the next six months and beyond. Notable interviews with HR thought leaders discussing organizational change, burnout, women in leadership, and HR technology innovation. Future episodes focusing on pay equity, county-level HR issues, and machine learning use cases. Timestamps: 00:00 - Welcome and season eleven wrap-up overview 00:33 - Changes in HR over the past years and current transformations 01:31 - Introduction of chickens and personal updates 02:29 - Recap of season eleven and its focus on artificial intelligence 03:12 - AI's influence on organizational change, trust, and data governance 03:57 - The importance of data foundations for AI initiatives 04:27 - The increasing integration of AI into daily work and business practices 04:56 - The evolution from early computer use to AI today 05:54 - The proliferation of AI tools in corporate technology stacks 06:24 - Environmental and infrastructure challenges of AI data centers 07:10 - Advances in small form factor AI hardware and local hosting options 08:07 - Real-world AI applications at the desk and secure data practices 09:34 - Building AI models with retrieval-augmented generation (RAG) techniques 10:03 - Resources available at hrdatalabs.ai and hrdatalabs.com 11:02 - Notable interviews, including legal, security, talent acquisition, and leadership insights 12:29 - Reflection on season eleven, personal connections, and upcoming season twelve topics 13:00 - Highlights from upcoming episodes, including county HR challenges and practical AI applications 14:26 - Reviewing past predictions and setting new forecasts for the next six months 15:25 - Using AI to evaluate predictions and industry trends 16:17 - The shift from AI experimentation to measurable ROI in HR 17:44 - Current state of HR AI use cases, human-in-the-loop importance, and regulatory outlook 19:11 - Predicted regulation developments at the state and federal levels 20:44 - Host's forecast: increased progress in AI adoption and ROI validation in HR 22:36 - The impact of AI-related layoffs, rehiring, and reputational considerations 25:02 - Exciting plans for future episodes, conferences, and continued industry insights 26:26 - Appreciation for listeners and partners, and encouragement to follow the show This episode offers a deep dive into the evolving landscape of HR technology, emphasizing AI's transformative potential and ongoing challenges. Whether you're an HR professional, a business leader, or an enthusiast of HR innovation, these insights provide vital context for navigating the future of work.
Eric Chou welcomes Juulia Santala, a Solutions Engineer at Cisco, to discuss her transition from traditional network programmability to cutting-edge AI research. They explore how she moved beyond simple RAG to make AI truly agentic, while solving some hallucination problems in production configurations. They also cover the number one skill network engineers need by 2027,... Read more »
Eric Chou welcomes Juulia Santala, a Solutions Engineer at Cisco, to discuss her transition from traditional network programmability to cutting-edge AI research. They explore how she moved beyond simple RAG to make AI truly agentic, while solving some hallucination problems in production configurations. They also cover the number one skill network engineers need by 2027,... Read more »
Max Learmonth was a professional rugby player who fell into recruitment by accident. He answered one trainee ad at Robert Half, started two weeks later, and never looked back.He climbed fast. £180,000 in his first year, £300,000 in his second, then off the tools rebuilding offices for a global giant. But somewhere in the grind, he lost himself. 'I'd become an arsehole,' he says. 'A typical Wolf of Wall Street recruitment dickhead. All I cared about was what my P&L looked like.'In May 2023, with a young daughter at home, he finally made the move and started again from his kitchen table. He turned down over £500,000 of investment to keep it 100% his. The plan was narrow on purpose: finance and accounting recruitment in the North West, and nothing else.Three years on, Forge Talent is a team of 16 that did £2.4 million in net fee income last year, entirely bootstrapped, built on a single stubborn belief: relationships, not transactions.On this episode of The RAG Podcast, Max breaks down exactly how he did it, the one KPI his whole business runs on each week, and why he thinks the average recruiter is finished while the specialist is only getting started.Max is not building the biggest recruitment business in the country. He is building the most dominant finance and accounting firm the North West has ever seen, and he already has a date on the exit.The difference is that he is doing it in the open, on his own terms, with a team of 16 who all know exactly what the 10-year goal is.If you have ever wondered whether going smaller and more specific is actually the smartest way to grow in the age of AI, this episode is evidence of that idea working right now.The Season 9 finale with Max Learmonth of Forge Talent is available now.----------------------------------------------Episode Sponsor: AtlasAdmin is a massive waste of time. That's why there's Atlas, a CRM that actually understands context.Atlas captures everything you say, hear, read and write. Every interview, every client call, every LinkedIn message, email and WhatsApp, automatically. Not because you typed it up, but because Atlas was listening. Then it goes one step further and tells you the next action to take.So when you need to fill a role, People Search ranks your best candidates and tells you why, no digging required. That same memory turns your BD into a shortlist of exactly who to chase and why, so you win more clients. And when you want to see your pipeline, you just speak to your dashboards and Atlas builds the view for you in real time, tracking only what you care about.Whether you place permanent or contract, Atlas covers both. Its Contract Suite means you never chase a timesheet again, with live margin visibility across every placement. And with its new MCP and API, Atlas plugs straight into your LLMs and the rest of your stack, so the low-value admin that keeps you from billing gets done for you.This is not theory. Atlas customers are seeing 50% higher candidate response rates, a 35% increase in new clients won, 15+ hours saved every week, and monthly billings jumping by 85%. Some agencies are hitting 130% of their annual revenue target after building their business around Atlas.So if you're thinking you need to bolt AI onto your CRM, don't bother. Take a look at Atlas instead.Head to https://recruitwithatlas.com/therag/ to find out more.----------------------------------------------Episode Sponsor: HoxoEvery recruitment founder is investing in LinkedIn, but AI has turned templated posts and outreach into a commodity. When everyone sounds the same, the market stops listening. The recruiters winning now are the ones the market trusts.At Hoxo we help recruitment founders become the most influential name in their niche, using AI to multiply output while trust stays the product. Our clients turn their existing networks into £100K to £300K in new billings within months. Watch the free RAG listener training to see how: https://hubs.ly/Q03lBpYC0
מה קורה כשאתם שולטים טוב בLLM, אבל רוצים לעלות לשלב הבא וללמוד איך להטמיע במודל את כל הידע, ה־Best Practices וההקשר שלכם, בלי להסביר את עצמכם מחדש בכל שיחה?אירחתי היום את דנה ממן, מייסדת Salted Mind, לשיחה על Second Brain, LLM Wiki ואיך הופכים את מודלי השפה למערכת הפעלה אישית ועסקית.איך פורמטים כמו OKF של גוגל שומרים על סדר ואחידות? איך מונעים ממידע להירקב, מוודאים שהמודל מתבסס על עובדות ולא על ניחושים, ומתי מגיע הרגע שבו חייבים לעבור ל־RAG? בפרק נגענו בכל אלה, וגם באיך רותמים את המערכת לרפלקציה עצמית ולזיהוי פערים בלמידה בזמן אמת.
What parts of yourself have you learned to hide in order to be taken seriously? My guest on this episode is Mark Heywood, a friend of the show, returning guest and someone I've collaborated with on a number of projects. Episode Summary & Guest ProfileMark has spent more than twenty years working in crisis management and operational resilience, while simultaneously building a successful career as a bestselling novelist, screenwriter and, more recently, stand-up comedian.On the morning we recorded this conversation, Mark published an article marking his fifty-first birthday. In it, he reflected on the pressure many of us feel to present only one version of ourselves professionally, arguing that his creative and corporate lives were never really separate. As he puts it, they were 'not two jobs, but one obsession wearing two different lanyards.' That simple idea becomes the starting point for a much wider discussion about the stories we tell ourselves, and the stories organisations tell themselves.Along the way we explore:Why "stay in your lane" may say more about other people's expectations than your own potential.Why organisations often encourage people to bring their whole selves to work, while rewarding conformity.What interviews, dress codes and organisational culture reveal about human behaviour.Why stories often influence behaviour more effectively than rules, policies and frameworks.What films can teach us about operational resilience, cybersecurity and crisis management.Why the Titanic remains one of the most powerful lessons in organisational failure.How imagination is a strategic capability rather than a soft skill.Why thinking through "severe but plausible" scenarios requires organisations to become better storytellers.LinksMark's birthday article: "A Birthday Declaration from Someone Who Spent Over Twenty Years Pretending to Be Half of Himself" (LinkedIn)Mark's Behind The Spine podcastThe Writing SalonBehind The Spine podcastMark on LinkedInMark's previous appearances on the show talking about The Creative Industries under COVID, and Human Risk in the Creative IndustriesFilms, Stories & People MentionedDie Hard Apollo 13 The Martian The Odyssey (Christopher Nolan)Don't Look UpSnakes on a Plane Sharknado Noah's Ark RMS TitanicRachel Heyhoe Flint Jeremy King AI-Generated Timestamped Summary00:00 – Why do we hide parts of ourselves to be taken seriously?03:40 – Mark explains why he stopped separating his creative and corporate identities.08:00 – Which hobbies and outside interests are considered professionally acceptable—and why?10:30 – Job interviews, authenticity and bringing your whole self to work.16:00 – Jeremy King on why behaviour matters more than dress codes.19:00 – Rachel Heyhoe Flint, the MCC and what institutional rules reveal about culture.23:00 – Why stories influence behaviour more effectively than rules.24:00 – The Trojan Horse and using films to teach cybersecurity and resilience.29:00 – The Titanic as a lesson in organisational resilience rather than maritime history.32:00 – Conduct versus outcomes: why testing matters more than compliance.36:00 – Using stories and popular culture to make risk real.38:00 – Noah's Ark, history and the enduring power of storytelling.40:30 – CrowdStrike, PPE and why organisations keep repeating the same mistakes.46:00 – Stories aren't predictions—they're a way of exploring possibility.49:00 – A ransomware exercise and the danger of relying on fixed policies.52:00 – Returning to the Titanic: preparing for the inquiry before the crisis.55:00 – Thinking like an attacker and challenging assumptions about cyber risk.58:00 – Taylor Swift, insurance and why seemingly ordinary data can become highly sensitive.1:02:00 – The idea of "risk inflation" and why yesterday's rules aren't enough.1:10:00 – Why memorable stories change behaviour more effectively than realistic scenarios.1:15:00 – Reverse stress testing and imagining what could really break an organisation.1:20:00 – RAG ratings, reporting and the danger of looking resilient instead of being resilient.1:23:00 – Final reflections on creativity, identity and why being more than one thing is a strength.
01. David Guetta, Sia, Morten - Titanium (Record Mix) 02. Bob Sinclar, Kiesza - I Can't Wait (Record Mix) 03. Calvin Harris, Ellie Goulding - I Need Your Love (Record Mix) 04. Alok, Gryffin, Julia Church - Never Letting Go (Record Mix) 05. Ofenbach, Norma Jean Martine - Overdrive (Record Mix) 06. Kddk, Alex Alta - 1&2 (Record Mix) 07. Diplo, Maren Morris - 42 (Record Mix) 08. Don Diablo, R3hab, Neeka - Disco Marathon 09. Oneil, Kanvise, Ercodes - Smalltown Boy (Record Mix) 10. Anyma, Joji - Beautiful (Record Mix) 11. Zhu - In the Morning! (Record Mix) 12. Argy, Omnya - Aria (Record Mix) 13. Dr Kucho!, Gregor Salto, Oliver Heldens - Can't Stop Playing (Record Mix) 14. DJ Louis - Let Me Blow Ya Mind 15. Avicii - Levels (Record Mix) 16. ONE-T, Ywy, Nika - The Way To Love (Record Mix) 17. Robin Schulz, Ilsey - Headlights (Record Mix) 18. Zerb, Ty Dolla $ign, Wiz Khalifa - Location (Record Mix) 19. Sebastian Ingrosso, Tommy Trash, John Martin - Reload (Record Mix) 20. Pupa Nas T, James Hype, Denise Belfon - Work (Record Mix) 21. Tujamo, Azteck, Inna - Freak (Record Mix) 22. Sean Paul, Odd Mob - Get Busy 23. Fisher - Losing It (Record Mix) 24. Topcover - First Day (Record Mix) 25. Ben Delay - I Never Felt So Right (Record Mix) 26. Kygo, Ava Max, Tiesto - Whatever (Record Mix) 27. DJ Snake, Bipolar Sunshine - Paradise (Record Mix) 28. Junior Jack - Stupidisco (Record Mix) 29. Mind Electric - Things You Say (Record Mix) 30. Klaas - The Way (Record Mix) 31. Disclosure - She's Gone, Dance On 32. Jonas Blue, Jp Cooper - Perfect Strangers (Record Mix) 33. Fedde Le Grand - Got Your Money (Record Mix) 34. Swedish House Mafia, Pharell - One (Your Name) (Record Mix) 35. Hugel, Topic, Arash, Daecolm - I Adore You (Record Mix) 36. Maruv, Boosin - Drunk Groove (Record Mix) 37. Garas, Eugenio Fico - Perfect 38. Shane Codd - Get Out My Head (Record Mix) 39. Oceana, Bodybangers - Endless Summer (Record Mix) 40. The Chemical Brothers, De Soffer - Go (Record Mix) 41. Adam Lambert - Ghost Town (Record Mix) 42. Rue Jay - I Want Your Love 43. Lunax, Mia Amare - Sweet Harmony (Record Mix) 44. Bebe Rexha, David Guetta - Sad Girls (Record Mix) 45. Bausa - Magnetic (Record Mix) 46. Tony Igy, Vicetone - Astronomia (Record Mix) 47. Alle Farben, Rene Miller - Body Talk (Record Mix) 48. Avalan Rokston, Alex Caspian - Something to Believe In (Record Mix) 49. Dynoro, Gigi D'agostino - In My Mind... (Record Mix) 50. Lola Young, Ted Bear - Messy (Record Mix) 51. Faul, Wad, Pnau - Changes (Record Mix) 52. Bob Sinclar, Steve Edwards, Fisher - World, Hold On (Record Mix) 53. Shouse, Vintage Culture - take me (to the sunrise) (Record Mix) 54. Alok, Jess Glynne - Summer's Back (Record Mix) 55. Block & Crown, Daisy - Mr Vain (Record Mix) 56. Global Deejays, Jenia Smile, Ser Twister - The Sound Of San Francisco (Record Mix) 57. Calvin Harris, Benny Blanco - I Found You (Record Mix) 58. Don Diablo - The Way I Are (Record Mix) 59. Armin Van Buuren - Es Vedra (Record Mix) 60. Alesso, Sacha - Destiny (Record Mix) 61. Philip George - Wish You Were Mine (Record Mix) 62. Vize - Wait (Alibi Blue) (Record Mix) 63. Lady Gaga, DJ Dark - The Dead Dance (Record Mix) 64. Killteq, D.hash, Vallhee - I Like It (Record Mix) 65. Afrojack, Aloe Blacc - In My World 66. Avicii - Fade into Darkness (Record Mix) 67. Anyma, Ejae - Out Of My Body (Record Mix) 68. Oliver Heldens, Riton, Vula - Turn Me On (Record Mix) 69. Ava Max - Don't Click Play (Record Mix) 70. Imany, Ivan Spell, Daniel Magre - You Will Never Know (Record Mix) 71. Daft Punk - Around The World (Record Mix) 72. Meduza, Henry Camamile - Don't Wanna Go Home (Record Mix) 73. Zerb, The Chainsmokers, Ink - Addicted (Record Mix) 74. Duke Dumont - Won't Look Back (Record Mix) 75. Richard Grey, Kamafro - No Scrubs (Record Mix) 76. Lost Frequencies, Janieck Devy - Reality (Record Mix) 77. Kygo, Max Mcnown - Take Me Back (Record Mix) 78. James Hype, Kim Petras - Drums (Record Mix) 79. Baauer - Calling Out For U (Record Mix) 80. Faul & Wad, Nico & Vinz, Altego, Old Jim - How I Feel (Am I Wrong) (Record Mix) 81. Amor - Tell Me (Record Mix) 82. Leony, Calum Scott - Stay (Record Mix) 83. Hypaton, David Guetta, La Bouche - Be My Lover (Record Mix) 84. Bassjackers, KSHMR, Sirah - Memories (Record Mix) 85. Don Diablo - Momentum (Record Mix) 86. Shouse, Cub Sport - Only You (Record Mix) 87. Bebe Rexha, Faithless - New Religion (Record Mix) 88. Basto!, Yves V - Cloud Breaker (Record Mix) 89. Hugel, Alleh, Yorghaki - una noche con hugel (Record Mix) 90. Showtek, Smack, Sam Gray - Take My Heart Away (Record Mix) 91. Otnicka - Celebrate the Love (Record Mix) 92. Imanbek, Sofia Reyes, Luisa Sonza - NOT U (Record Mix) 93. Hurts, Purple Disco Machine - Wonderful Life '25 (Record Mix) 94. Lost Culture, Morfi, Carine - Lean On (Record Mix) 95. Doechii, DJ Dark - Anxiety (Record Mix) 96. Joezi, Lizwi - Amathole (Record Mix) 97. Argy, Omiki - WIND (Record Mix) 98. Relanium, Deen West - Leel Lost (Record Mix) 99. Adam Port, Stryv, Malachii, Switch Disco - Move (Record Mix) 100. Diplo, Miguel - Don't Forget My Love (Record Mix) 101. Aaron Smith, Luvli, Krono - Dancin' (Record Mix) 102. Goodboys, Nu Aspect, Avaion - Blindspot (Record Mix) 103. Gorgon City, Mk - There for You (Record Mix) 104. DJ Louis, Sweetpower - Billie Jean (Record Mix) 105. Calvin Harris, Rag'n'bone Man - Giant (Record Mix) 106. Sonny Fodera, D.O.D, Poppy Baskcomb - Think About Us (Record Mix) 107. Rain Radio, DJ Craig Gorman - Talk About (Record Mix)
Flight Instructor Suicide: A flight instructor jumps from a plane to leave his student to fend for herself. Could this be the greatest teacher we've ever seen?AI Robot Fails: As they keep trying to get us to work with robots, one of our favorite things is robot fails. Speaking of robot fails, here are a couple of new robot butlers on the horizon.Diarrhea Epidemic: Cyclosporiasis is spreading the country meaning that it's a diarrhea summer.FUCK YOU WATCH THIS!, THE BEAR!, TIP YOUR BARTENDER!, GLASSJAW!, DARYL!, NEW SPECIAL!, SHELL TV!, QR CODE!, SUPERTIPS!, PLATITUDES!, ZEN FELDMAN!, BOOPAC!, TIM TAMS!, FLIGHT INSTRUCTOR!, SUICIDE!, STUDENT PILOT!, NEWS!, MISHAP!, JUMPS FROM PLANE!, TALLER!, BIG!, YOU KNOW WHAT TO DO!, LET'S ROLL!, SIMPSONS!, GAG!, SUICIDE HERO!, KRISPY KREME!, PUT IT IN DRIVE!, BOYZ N DA HOOD!, RICKY!, PHONE THAT TELLS THE FUTURE!, DRIVE!, OLD LADY NEWS!, DISGUISE!, FREAK OUT!, GEEKING!, FALL!, KUNG FU!, DANCING!, BALLET!, GET UP!, GRABBED!, BRUCE LEE!, ROBOT FIGHTS!, AMERICA'S FUNNIEST HOME VIDEOS!, BABIES PUKING!, WALKING THROUGH GLASS!, WEAVE!, SEGWAY!, ISAAC!, STARTUP!, NORI!, REMOTE TASK ROBOTS!, MOP!, RAG!, ROBOT CAMERA!, SKATEBOARDING!, KICKSTARTER!, HORROR UNLEASHED!, DIARRHEA!, INFECTION!, PARASITE!, VEGETABLES!, IMMIGRANTS SHITTING ON LETTUCE!, ICE!, AUSTRALIAN!, GOLDEN CORRAL!, FOREIGNERS!, WORLD CUP!, TOURISTS!, CUT DICK OFF!, SET FIRE!, MUTILATING!, BOOKIE!, PEDEN!, LOAN SHARK!You can find the videos from this episode at our Discord RIGHT HERE!
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