Podcasts about models

Eno Reyes is the co-founder and CTO of Factory, the agent-native software development platform building autonomous "Droids" for enterprise engineering teams. Factory has raised $220 million, most recently a $150 million Series C at a $1.5 billion valuation, from investors including Khosla Ventures, Sequoia Capital, 20VC, NEA, Blackstone, Insight Partners and Nvidia. Before founding Factory, Eno worked as a machine-learning engineer at Hugging Face, training, optimizing and deploying large language models for enterprise customers. AGENDA: 00:00 Are We Underestimating AI by an Order of Magnitude?  06:35 Why Can the Smartest AI Model Be the Cheapest?  18:51 Is Anthropic's Coding Business Really Worth $2 Trillion?  33:41 Will Continuous-Learning Models Help or Hurt Factory?  40:43 Will 80–90% of Neo-Labs Die in the Next 18 Months?  44:33 Should American Enterprises Work With Open-Source Chinese Models?  55:42 Must AI Founders Radically Rethink What a Great Outcome Looks Like?  1:04:30 Do Pedigree and Credentials Still Matter in AI Hiring?  1:19:17 Which Is the Biggest Threat: Claude Code, Codex, Cognition or Cursor?  1:24:20 What Seems Crazy Today but Will Be Obvious in Five Years?  

Today's episode of This Week in Pharmacy — #TWIRx is sponsored by Value Drug Company and explores two major forces reshaping pharmacy: the evolution of the community pharmacy business model and the expanding role of artificial intelligence in medication optimization and value-based care.   PART ONE: The Impact of the Cash-based Community Pharmacy   Special Guest: Rick Seipp, PharmD President, Value Drug Company Community pharmacy is entering a new era.   In Part One, Todd Eury sits down with Rick Seipp, PharmD, President of Value Drug Company, to discuss the continued evolution and expansion of independent community pharmacy and the many different business models emerging across the profession.   Independent pharmacy owners are increasingly evaluating new ways to build sustainable businesses while creating greater professional freedom for pharmacists. One of the most interesting developments is the growth of cash-based and cost-plus pharmacy models that reduce or eliminate dependence on traditional insurance reimbursement and PBM-controlled prescription economics.   These models can give pharmacy owners greater control over pricing, patient relationships, clinical services and the overall direction of their businesses — while allowing pharmacists to practice with significantly less interference from insurance companies and pharmacy benefit managers.   Two pharmacies in the Pittsburgh region are demonstrating how powerful this model can become: Blueberry Pharmacy — Kyle McCormick, PharmD has built Blueberry Pharmacy around a transparent, cash-based model designed to simplify prescription pricing and create a more direct relationship between the pharmacist and patient. Forward Rx Pharmacy — Brandon Antinopoulos, PharmD is another emerging example of how pharmacists can create innovative pharmacy businesses built around transparency, accessibility and freedom from many of the traditional constraints of third-party reimbursement.   Todd and Rick discuss what these models mean for the broader independent pharmacy marketplace and why community pharmacy may be moving toward a much more diversified future.   Sponsored by Value Drug Company, a pharmacist-led wholesale distribution partner committed to supporting the continued growth and independence of community pharmacy. https://lnkd.in/ehf76qUs PART TWO: AI-Powered Medication Optimization   Special Guests: Yoona Kim, PharmD, PhD Co-Founder & CEO, Arine Jenny Behan, PharmD Lead Clinical Pharmacist, Arine In Part Two, #TWIRx shifts from pharmacy business innovation to one of the most important developments occurring in clinical pharmacy: the use of artificial intelligence to optimize medication therapy at scale. Arine is an AI-powered medication optimization platform designed to improve patient outcomes and reduce healthcare costs by helping ensure patients receive the most effective and appropriate medications. Listen to This Week in Pharmacy — TWIRx on the Pharmacy Podcast Network across Apple Podcasts, Spotify, Amazon Music, iHeartRadio, YouTube and all major podcast platforms. Pharmacy Podcast Network — Amplifying the Voice of Pharmacy.

In Episode 219 of the Equipping ELLs podcast, Beth Vaucher opens with something most professional development on co-teaching never acknowledges: the textbook version of co-teaching rarely matches the reality of ELL classrooms. No shared planning time. No clear role definition. Walking into lessons you did not design and trying to support students in real time. If you have been an ELL teacher for more than a few months, you already know this. Today's episode names it directly — and then gives you a practical, honest framework for making co-teaching work regardless of where you are starting from.Beth opens by naming the three things that make co-teaching feel hard. Role clarity — most ELL teachers who push in have never had an explicit conversation with the homeroom teacher about what their role actually is during instruction. Planning time — or the complete lack of it. Real co-teaching requires co-planning, but most ELL teachers are spread across five, six, or eight classrooms with zero shared planning time with any of those teachers. And different priorities — a fourth-grade science teacher's priority is fourth-grade science, and an ELL teacher's priority is language acquisition. Those goals support each other beautifully when aligned, but when no one has had the conversation about how they align, they feel like competing interests in the same room.The most important insight of the episode comes before the models: before thinking about co-teaching structures or lesson plans or instructional strategies, think about the relationship. The ELL teachers who build the most effective co-teaching partnerships are not the ones with the most resources or the most knowledge. They are the ones who showed up consistently, were easy to work with, made the homeroom teacher's job slightly easier, and over time became someone that teacher genuinely wanted in the classroom. One teacher. One relationship. One small thing you can offer. That is where it starts.Beth then introduces three realistic co-teaching models — not the official district training models, but the ones that actually operate in real schools with real constraints.The Resource Bridge is the model most ELL teachers are already in, whether or not they have named it. You are not in the room co-instructing, but you know what is being taught, you prepare one or two scaffolded supports — a sentence frame, a vocabulary visual, a graphic organizer from Scaffolds in a Snap — and you deliver them to the teacher at the start of the week. Imperfect co-teaching, but your students have a scaffold in their hands during instruction even when you are not there.The Parallel Partner puts you in the room but not co-instructing the whole class. The homeroom teacher leads the lesson and you work with your ELL students in a parallel structure — same content, same concept, same objective, different access point. This model requires almost no joint planning and immediately signals to the homeroom teacher that you are a specialist, not an aide.The Active Partnership is what the training describes — co-planned, co-taught, each teacher taking a role and a group. But Beth is clear: you do not get here by demanding it. You earn it through consistency in Models 1 and 2. When a homeroom teacher has seen that working with you makes their classroom better, they invite the partnership.The one practical tool that works across all three models: walk into any classroom this week with one page in your hand — one scaffold for whatever that class is teaching. Say to the teacher: I made this for my ELL students. Can I leave a few copies for any students who might benefit? That single action demonstrates expertise, makes your presence purposeful, gives the teacher something useful without asking anything of them, and puts scaffolded support in your students' hands regardless of how the lesson goes.Beth also provides specific conversation starters that build the co-teaching relationship without creating resistance — not "I need us to co-plan" but "I noticed your students are working on explaining their thinking. I have something that might help. Can I bring it tomorrow?"The episode closes with an honest word for teachers for whom co-teaching is simply not happening — no push-in time, unresponsive colleagues. You can still be a bridge. Pre-teach in pull-out the vocabulary your students will need for the science lesson that afternoon. Practice the language function they will need for the social studies task this week. That bridge is invisible to the homeroom teacher but profoundly visible to your students.FREE TRIAL: equippingells.com/trial — Scaffolds in a Snap, sentence frames, vocabulary organizers, all the resources that make you the go-to person in your building.

This week, we discuss Stripe's singularity letter, its $8B Open Router buy, and AI job anxiety. Plus, Matt plays “Bot or Not” on another podcast. Watch the YouTube Live Recording of Episode 587 Runner-up Titles Inertia wins again We don't talk about the Pope very much. Better than this year's storage arrays AI pimps its own ride Chonking machine A lot of chonk opportunity I'm tired of tech people being all fancy Thanksgiving with Ed Zitron No religion, no politics, no AI My recommendation: try harder Teletubbies for Adults. Rundown Singularity, Models and Routers Scoop: Stripe says "the singularity" has begun Stripe strikes mega-deal for OpenRouter Hugging Face reportedly in talks to be acquired for $13B Routing is coming for the frontier AI labs Terminator Judgement Day: August 29, 1997 2:14 a.m. Eastern Time The AI backlash goes mainstream 52% of Americans Now More Concerned Than Excited About AI, With Under-30s Crossing a Majority for the First Time Why Is Everyone In Tech So Sad? The AI Hater's Manifesto 40 Years of Infrastructure as Code: Ansible → Terraform → Kubernetes → Crossplane → AI Agents Relevant to your Interests Cursor Origin review: An engineer's perspective Claude can now pull data from your browser tabs and keep working on your desktop OTel Isn't Going Well (And I Made A Spreadsheet About It) Walmart is finally launching Apple Pay support next week Broadcom debt deal expected to reach upwards of $70 billion, sources say Anthropic-Backed Ode Acquires Casper Studios to Expand Corporate AI Deployments OpenAI 'will be a public company in 2027' or sooner, CFO Friar tells employees Google Aims to Boost AI With Purchase of Spirit Airlines Data The website that created an AI clone of its editor in chief OpenAI Jalapeño: Better Than Nvidia Blackwell An Inside Look at the Relay Market Powering Token Resellers and Fraud Meta settles social media addiction case with California, other states for $16.7 billion Hundreds of leaked AWS keys give full control over corporate accounts Free Hardened Container Images | Minimus Cyber startup Minimus shuts down, returns cash to investors Nonsense Apple Releases New Polishing Cloth Jason Kelce promotes mailing pee to data centers, Liquid Death Paradox Inc. Movie From Daniel Roher, Lord Miller, Universal In Works Conferences WeAreDevelopers NA, Sept 23-25, 2026, Discount Code: DEVPOD50 25 Free Tickets DevOpsDays Graz, Sept 4-5, 2026 Cloud Foundry Summit, Sept. 21st to 22nd, Heidelberg, Coté speaking. DevOpsDays Rockies, Sept. 22 – 23, 2026, Discount Code: 26DODSWEDEFTALK DevOpsDays Dallas, Sept 28-29, 2026 DevOpsDays Vilnius, Sep 30 - Oct 1, 2006, Lithuania. DevOpsDays Prague, Oct 5, 2026 - Coté speaking. DevOpsDays Istanbul, Oct 24th, 2026, Coté keynoting. VMware User Group, Orlando, Oct 20-22, 2026 Cloud Native Denmark, Nov 19th, 2026, Copenhagen, Coté keynoting. Build Stuff, Dec 2-4, 2026, Vilnius, Lithuania. cfgmgmtcamp, February 1st to 3rd, 2027, Ghent. SCALE 24x Pasadena, CA, April 1-4, 2027 SDT News & Community Join our Slack community Email the show: questions@softwaredefinedtalk.com Free stickers: Email your address to stickers@softwaredefinedtalk.com Follow us on social media: Twitter, Threads, Mastodon, LinkedIn, BlueSky Watch us on: Twitch, YouTube, Instagram, TikTok Book offer: Use code SDT for $20 off "Digital WTF" by Coté Sponsor the show Sponsor more podcasts with Failover Media Recommendations Brandon: Tuner ** Your AI Project Doesn't Need More Agents Matt: Tech, Texas & What It Takes to Run a Podcast with Matt Ray | Cyber Chat Ep. 5 Line of Duty Anti-pick: Roku's New Slop Channel Coté: Mini MLC, 30L.

Naren Tallapragada (co-founder of Tessel) joins to talk about the industry of modeling biology. While the field races to build virtual cell models, his team builds virtual tissue models, betting that predicting what an organ actually does matters more than predicting what genes a cell expresses. The goal: not just whether a drug works, but who it works for.In this episode, we discuss:Why 90% of drugs fail in clinical trials, and why neuro and Alzheimer's failure rates are even worseThe multi-scale modeling problem: molecules, genes, cells, tissues, organs, and why picking the right level of resolution matters more than picking the "best" oneThe electron-and-lightbulb argument for why virtual cell models may be a beautiful solution to the wrong problemOrganoids vs. animal models: where human cell models beat animals, and where both fail (like modeling behavior in mental health)Why Naren thinks defensibility in AI-driven biology comes from proprietary multi-organ data, not model architectureThe case for precision medicine, and why it's been such a brutal business model to actually execute onCredits:Created by Greg Kubin and Matias SerebrinskyHost: Matias and GregProduced by Nico V. ReyFind us at businesstrip.fm and psymed.venturesFollow us on Instagram and Twitter!Theme music by Dorian LoveAdditional Music: Distant Daze by Zack Frank

Last year, nearly 45 million Americans were diagnosed with a substance abuse disorder. That's according to the Substance Abuse and Mental Health Services Administration. When it comes to treating addiction, what's working? And what isn't? We talk to the team at ROCovery about local peer support, and we hear from a former Biden administration official about the recovery landscape in New York. In studio: Kara Izzo, peer support program manager for ROCovery Jonathan Westfall, executive director of ROCovery Rob Kent, president of Kent Strategic Advisors ---Connections is supported by listeners like you. Head to our donation page to become a WXXI member today, support the show, and help us close the gap created by the rescission of federal funding.---Connections airs every weekday from noon-2 p.m. Join the conversation with questions or comments by phone at 1-844-295-TALK (8255) or 585-263-9994, email, Facebook or Twitter. Connections is also livestreamed on the WXXI News YouTube channel each day. You can watch live or access previous episodes here.---Do you have a story that needs to be shared? Pitch your story to Connections.

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Today’s Topics: 1, 2, 3, 4) Father Charles Murr joins Terry Gospel – Matthew 23:27-32 – Jesus said, “Woe to you, scribes and Pharisees, you hypocrites. You are like whitewashed tombs, which appear beautiful on the outside, but inside are full of dead men's bones and every kind of filth. Even so, on the outside you appear righteous, but inside you are filled with hypocrisy and evildoing. “Woe to you, scribes and Pharisees, you hypocrites. You build the tombs of the prophets and adorn the memorials of the righteous,  and you say, ‘If we had lived in the days of our ancestors, we would not have joined them in shedding the prophets' blood.' Thus you bear witness against yourselves that you are the children of those who murdered the prophets; now fill up what your ancestors measured out!” Saints in Heaven, pray for us! Bishop Sheen quote of the day

With so much to teach every year, we know you're always looking for the biggest impact for long term success for your students. In this episode, Pam and Kim discuss models that grow with students as they learn more and more mathematics.Talking Points:Verticality in practice standards, content progressions, vocabulary, notation, and strategies How area models extend from elementary to high schoolOpen number lines mature into douole number linesRatio tables can start as early as 3rd grade and scale to decimals, large numbers, proportional relationships.How models act as lasting mental anchors, not just "picture drawing" as a substitute for mathematical ability.Links:Math is FigureOutAble Challenge RegistrationBlog Post: Story of Hope: How We Made Math (and Growth) FigureOutAble TogetherPam's BooksCheck out Pam's social mediaTwitter: @PWHarrisInstagram: Pam Harris_mathFacebook: Pam Harris, author, mathematics educationLinkedin: Pam Harris Consulting LLC 

Chris has Jacob Miller on the show, who is building a tool called DiffUI. The big idea is that it uses a diffusion model to do prompt-to-website design rather than an LLM, because image-based models are more interesting and creative. So stay there for a while, producing really detailed visual mockups and plans, then head to the LLM. Time Jumps

878 · D23 Disney Parks Announcements: Sightlines Into Yesterday, Tomorrow, and FantasyThe loudest cheers at D23 weren't necessarily for what Disney was building next. They were for what Disney was willing to bring back, repair, and finally acknowledge.In a presentation built on a little faith, trust, and pixie dust, Disney asked fans to believe in an ambitious future, but this time it also showed its work. Models, progress, priorities, restorations, and even the things Disney chose to address directly revealed a much bigger story than any single attraction announcement.In WDW Radio # 878, we look at the D23 2026 Disney Parks announcements through a series of "sightlines" that connect Villains Land, Piston Peak National Park, Dreamfinder, the Expedition Everest Yeti, Spaceship Earth, Tomorrowland, Monstropolis, Tropical Americas, Disney Cruise Line, and much more. And the sightlines stretch well beyond the U.S., from Disneyland Paris and the return of its Jules Verne-inspired Space Mountain to new Tomorrowland, Avengers, and Spider-Man experiences coming to Tokyo, Hong Kong, and Shanghai.When you step back, it starts to look like a very Disney story of yesterday, tomorrow, and fantasy. Yesterday is being honored through attractions, characters, and experiences fans refused to let go of. Tomorrow is taking shape through ambitious new lands, technologies, attractions, and global expansion. And fantasy is where Disney is creating entirely new places and stories for us to step inside.We also explore what changed the perspective on some of these projects, why "no height requirement" may be one of the most important phrases of the night, why the sequence of these projects may matter as much as the projects themselves, and what Disney did not announce... and why.This isn't simply a recap of D23 2026. It's a closer look at what Disney is building, what it is preserving, what it is asking us to trust, and what all of it may mean for the way we experience the Disney Parks around the world in the years ahead.

What happens when AI eliminates the need for apprenticeships and reshapes entire industries? In this thought-provoking episode of The Greatness Machine, Taylor Welch dives deep into the evolving landscape of work, the role of AI in eliminating experience gaps, and why creators hold the key to the future. He explores the shift from consulting to education, the rising value of data and attention, and how time wealth is becoming the ultimate currency. If you're looking to stay ahead in a world where automation is rapidly changing the game, this conversation is a must-listen. In this episode, Darius and Taylor will discuss: (00:00) Introduction to Taylor Welch (01:45) Taylor's Origin Story and Early Career (05:58) Overcoming Overwhelm and Life Lessons (10:30) Bringing in a CEO: The Process and Lessons Learned (15:46) Scaling Consulting Businesses: Strategies and Models (20:25) The Role of AI in Business Scaling (24:40) Optimizing Team Performance and Talent Acquisition (30:25) The Importance of KPI and Performance Management (36:45) The Future of AI in Sales and Consulting (44:01) Understanding Time Wealth and Personal Fulfillment Taylor Welch is an entrepreneur, business consultant, and coach known for his impact in the online training and education industry. As the founder of Welch Equities, he leads a portfolio of businesses focused on driving economic growth through value-driven initiatives. His ventures span sales, marketing, finance, and operations, while also investing in small training and education brands. Committed to making people smarter, happier, and healthier, Taylor combines business success with a strong emphasis on family and personal fulfillment. Connect with Taylor: Website: https://taylorawelch.com/  Website: https://wealthyconsultant.com/  Instagram: https://www.instagram.com/taylorawelch/ Twitter: https://x.com/taylorawelch/ YouTube: https://www.youtube.com/c/taylorawelch  Connect with Darius: Website: https://therealdarius.com/ Linkedin: https://www.linkedin.com/in/dariusmirshahzadeh/ Instagram: https://www.instagram.com/imthedarius/ YouTube: https://www.youtube.com/@Thegreatnessmachine  Book: The Core Value Equation https://www.amazon.com/Core-Value-Equation-Framework-Limitless/dp/1544506708 Write a review for The Greatness Machine using this link: https://ratethispodcast.com/spreadinggreatness. 

Hier geht's zum Vlog auf YouTubeWas passiert eigentlich, wenn du als Model deine Regelblutung bekommst und gleichzeitig stundenlang vor der Kamera stehen, enge Kleidung tragen, Swimwear shooten und sogar Unterwasseraufnahmen machen musst?In dieser Folge erzähle ich von einem der heftigsten Shootings meiner Karriere und spreche ganz offen darüber, wie ich mit meiner Periode am Set umgehe, was ich immer in meinem Modelkoffer dabeihabe und welche kleinen Tricks den Shootingtag deutlich angenehmer machen können.Eine sehr offene und ehrliche Folge über den ganz normalen Rhythmus, den Frauenkörper haben und der hinter perfekten Modelbildern nicht zu sehen ist – inklusive Tipps für Models, die ihre Periode schon einmal genau zum falschen Zeitpunkt bekommen haben.

What does an AI agent need to understand about your business before you allow it to make decisions and take action without waiting for human approval? In this episode of Tech Talks Daily, I speak with Kash Mehdi, Field CTO at Reltio, about the move from analytical AI that supports decisions to agentic AI that can execute them. Kash argues that leaders should begin treating AI agents as a workforce rather than another collection of software tools. A digital workforce needs training, boundaries, oversight, trusted information, and clear permissions before it can act safely. He uses the analogy of raising a puppy. When the puppy misbehaves, the problem may be inadequate training or poorly defined boundaries. AI agents present a similar leadership challenge. Organizations must ask what the agent has learned about the business and what authority it has been given. We discuss why model selection may be receiving too much executive attention. Kash describes four components of an agentic system: the model, tools, data, and context. Models are improving rapidly and tools are increasingly available, but business context remains incomplete across many enterprises. Data tells an agent a fact. Context helps it understand what the fact means within a particular customer relationship, geography, policy, or business process. Kash illustrates the difference with a pizza order. The data may confirm that someone is logged in, the model can interpret the request, and a tool can place the order. Context tells the system that it is Friday night, the customer is watching television, and they usually order pineapple and cheese pizza. The same principle becomes far more serious when an agent is dealing with medical equipment, supply chains, financial customers, or regulated information. It must understand which entities exist, how they relate, what information it may access, and which actions it has authority to complete. Kash identifies three requirements for safer autonomy: a governed source of truth, a live feedback loop, and enforceable permission boundaries. Trust must be built into the data and operating rules before the agent acts because the familiar human review step may no longer exist. We also discuss how governance changes when AI can execute decisions at machine speed. A poor decision made by one employee can usually be reviewed and corrected. A poor decision repeated automatically across thousands or millions of transactions can become a business incident before anyone intervenes. Kash shares examples involving restaurant menu launches, medical equipment deliveries, and call center offers. Each depends on current information and the relationships connecting customers, products, suppliers, locations, and previous interactions. For CIOs preparing today, Kash recommends building context around reusable entities rather than constructing an isolated data project for every AI use case. He points to Schneider Electric as an example where one unified foundation supported sales, shipping, operations, and marketing use cases. The conversation ends with a warning about slow data. Autonomous agents need current context because information that arrives after a decision has been made may no longer carry much business value. Kash predicts that the half-life of enterprise data will become a board-level measure. If a smarter agent can make a poor decision faster and with greater confidence, is your organization investing enough in the context, governance, and feedback needed to keep it on course? Listen to the conversation and share your thoughts with me.     Useful Links https://www.reltio.com/ https://www.reltio.com/datadriven/  

AGENDA: 04:20 Elon's Deal of the Decade: SpaceX Buys Cursor for $60BN 06:10 Why Cursor Was Surprisingly Cheap at $60BN 07:00 Why Zuckerberg Failed to Buy the AI Prize Elon Secured 12:00 Elon vs Zuck: Who Would You Rather Work For? 14:00 Will Microsoft or Amazon Now Race to Buy Cognition? 17:05 Stripe's $7BN OpenRouter Deal Creates Huge VC Winners 25:00 OpenRouter's Fatal Risk: Enterprises Don't Want 10 Models 28:15 Anthropic Turns Its First Profit on $11.5BN of Quarterly Revenue 32:15 Can Anthropic Really Reach $600BN in Revenue? 37:00 Why Every Elite Engineer Could Soon Get $100K in AI Tokens 39:30 Would Rory Buy Anthropic at a $2.5TN Valuation? 44:50 Silver Lake's $43BN Workday Bet: SaaS Isn't Dead, It's Mature 53:00 How Silver Lake Could Make $30BN From Workday 57:00 Lovable vs Higgsfield: Similar Revenue, Radically Different Valuations 58:00 Is Lovable's $13.3BN Price Actually Cheap? 63:30 Why the DOJ Is Coming After Andreessen Horowitz 69:00 Why A16Z Has "50 Legal Battles" Happening at Once  

When you ask an LLM like ChatGPT or Claude a question, the model goes through its massive amount of training data and guesses the answer by mathematically predicting the word most likely to appear next in a sentence. This model, experts say, will not work well for technology designed to navigate the physical world. Something like a robot that works in a warehouse will instead require a “world model” that can understand spatial surroundings, like the stuff we walk by or bang into. But what is a world model, exactly? And how do you train AI to recognize what the real world looks like? Host Ira Flatow checks in with tech journalist Joanna Stern, who's seen the early days of these models up close, even in her own home. Then, we check in on the math world, where frontier AI models have made meaningful progress on decades-old problems. Mathematician Emily Riehl gives us the big picture on how significant these results actually are. Guests: Joanna Stern is a tech journalist who writes newsletters and creates videos for New Things Media. Dr. Emily Riehl is a professor of mathematics at Johns Hopkins University. Transcript will be available after the show airs on sciencefriday.com.   Subscribe to this podcast. Follow our show on Instagram, TikTok, Facebook, and Bluesky @scifri and sign up for our newsletters. Got a science question that's keeping you up at night? Call us: 877-472-4374 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Sharad Kumar lives in Pleasanton, California with his wife and 2 kids. He enjoys playing all musical instruments, and spending time with his family. He has a 2 year old daughter, and a 14 year old son into robotics. He is also passionate about giving back to the community, through their company foundation.Harshit Omar lives in San Francisco, and is married with a 4 year old son. He used to be a street racer in his college days, loving fast cars and taking risk. Nowadays, he is a big marvel and comic book fan, along side his son. In fact, his son thinks he is Captain America, regularly wielding his shield and mask.A fun fact about both of these gentlemen: this is their third company to work together in, their second startup, and their wives are sisters. So they are connected by wives, and united by startups.In their previous startups, Sharad was leading sales and ops and Harshit was leading on the product side. When the company got acquired, it took them 8-9 months to integrate to a different cloud provider. They realized the model was broken, requiring expensive consulting services, and not convenient at all - and they wanted to figure out a better way.This is the creation story of Fluidcloud.SponsorsUnblocked (https://getunblocked.com/codestory)TECH Domains (https://get.tech/codestory)Mezmo (https://mezmo.com/codestory)Braingrid.ai (https://braingrid.link/code-story)Alcor (https://alcor.com/podcast)Equitybee (http://codestory.co/equitybee)Terms and conditions: Equitybee executes private financing contracts (PFCs) allowing investors a certain claim to ESO upon liquidation event; Could limit your profits. Funding in not guaranteed. PFCs brokered by EquityBee Securities, member FINRA.Linkshttps://www.fluidcloud.com/https://www.linkedin.com/in/sharadkumar123/https://www.linkedin.com/in/harshito/Checkout our episode stacks on Stacklist! https://stacks.codestory.co/ Hosted by Noah Labhart | Technical Founder & Startup Mentor.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

In this episode, Ernie Svenson talks with Docket Drafter co-founder Tommy Eberle about what lawyers need to understand to use AI tools like Claude more effectively. They unpack why Word documents consume so many tokens, why AI performance can decline during long chats, how context windows and compaction affect results, and when to use different Claude models and thinking levels. Tommy also explains how lawyers can use AI to organize files, automate repetitive document work, and build more efficient workflows—without needing to become programmers themselves. Chapters 0:05 Why AI Frustrations Make Sense 3:03 From Coding To Legal AI 8:05 Making Word Agent-Friendly 16:20 Why Claude Gets Dumber 26:21 Models, Thinking, And Compaction 34:33 Files, Folders, And Better Prompting Show Notes The 80/20 Principle (my techlaw newsletter) The Inner Circle (my online community for lawyers) Tommy Eberle's LinkedIn page Tommy's Email Address: tommy@docketdrafter.com  Docket Drafter https://www.youtube.com/@DocketDrafter  Follow and Review I'd appreciate it if you could drop a review over on  Apple Podcasts. It only takes a few seconds and helps spread the word about the podcast. Thanks to the sponsor: Smith.ai Smith.ai is an amazing virtual receptionist service that specializes in working with solo and small law firms. When you hire Smith.ai, you're hiring well-trained, friendly receptionists who can respond to callers in English or Spanish. And they have a special offer for podcast listeners where you can get an extra $100 discount with promo code ERNIE100. Sign up for a risk-free start with a 14-day money-back guarantee now (and learn more) at smith.ai.

Glenn Solomon of Notable Capital joins Nick to discuss 3 Multi-Billion-Dollar Exits in 1 Year, Lessons from Airbnb, HashiCorp, Slack, and Square, The VC Case for Staying Small, and the Battle Between Open-Weight vs. Closed Models. In this episode we cover: Identifying Unique Investment Opportunities  The Impact of Fund Size on Investment Strategy  Challenges of Overfunding and Market Dynamics  Growth and Real Success in the AI Era  The Role of Hyperscalers and Frontier Labs  Public Market Sentiment and IPO Considerations  Future of Venture Capital and Notable's Strategy Investing in Anthropic and Market Dynamics  Guest Links: Glenn's LinkedIn Glenn's X Notable's LinkedIn Notable's Website The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.

The United States Naval Academy Museum's Curator of Ship Models, Don Preul, and Education Specialist Sondra Duplantis discuss the museum's ship model collection, model shop volunteers, and a beloved colleague. 

Will Hayden Panettiere's shocking passing make today's blinds? Play along with Bradley and Dawn to find out!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Plodroch, Ina www.deutschlandfunk.de, Corso

Struggling to find an elliptical that fits your height? Discover the importance of stride length, what ergonomic features to prioritize, and how the booming home fitness market is finally catering to tall users with innovative, adjustable designs.Info: https://www.soletreadmills.com/blogs/news/4-best-ellipticals-for-tall-people-in-2026 SOLE Fitness City: Salt Lake City Address: 56 Exchange Pl. Website: https://www.soletreadmills.com/

In this special edition from Black Hat, Dave Bittner sits down with ⁠Clint Gibler⁠, Cyber Lead at ⁠OpenAI⁠, and ⁠Robby Winchester⁠, Chief Global Professional Services Officer at ⁠SpecterOps⁠, to explore how frontier AI models are changing the way defenders approach cybersecurity. The conversation moves beyond the hype to examine responsible AI deployment, AI red teaming, reducing noise in security workflows, and the balance between advanced models and human expertise. They also discuss OpenAI's Trusted Access for Cyber program and what it takes to give security practitioners access to powerful AI capabilities while managing the risks of misuse. Check out the full video here.

What evidence would convince you that an AI agent is ready to make decisions involving employment, money, healthcare, or legal rights? In this episode of Tech Talks Daily, I speak with Vin Sharma, founder and CEO of Vijil, about the trust gap preventing many enterprise AI agents from progressing beyond proof of concept. Vin has spent approximately 30 years building software across security, operating systems, open source, cloud computing, machine learning, and AI. His previous work includes leading engineering at Amazon SageMaker and helping develop 11 AWS AI services. He argues that AI agents differ from conventional software because they combine autonomy with agency. They can interpret an objective, make decisions under ambiguous conditions, and take action. This raises a deeper question than whether an agent can complete a demonstration successfully: will it remain loyal to the interests of the person or business delegating the task? Trust is also specific to the job. Vin uses a simple analogy. You may trust a gardener to care for your lawn, but that does not automatically make the same person suitable to babysit your child. An AI agent must therefore be evaluated within the context of its users, task, operating conditions, authority, and potential consequences. Vin proposes testing three areas. Reliability asks whether the agent can perform its assigned task. Security examines whether it maintains its integrity when facing hostile or noisy conditions. Safety considers what happens when the agent fails and whether the resulting damage remains contained. This evaluation cannot end when the agent enters production. Models, integrations, data, users, and external conditions change. An agent may drift away from its original purpose, which means businesses need continuous monitoring, testing, and updating across the full AI agent lifecycle. We discuss how established security practices can be applied to this problem. Trusted execution environments, containment, least privilege, limited-duration access, and bounded models can reduce exposure. Smaller language models may also be better suited to narrow, high-risk tasks than a general model with broad permissions. Vin offers a three-part framework for governance: personas, purpose, and policy. Personas describe the people and attackers who may interact with the agent. Purpose defines the legitimate task. Policy sets the boundaries between permitted and prohibited behavior. For high-risk systems, his recommended starting position is that any action not explicitly permitted should be prohibited. A natural-language policy can then be converted into deterministic rules and controls governing the agent's behavior. Vin's most direct advice concerns evidence. Vibes, demonstrations, and benchmark scores do not prove that an agent is safe for a particular business process. A CISO should expect a complete risk assessment, while a business owner should receive proof that the agent will serve the organization's interests. His bridge analogy captures the issue perfectly. Engineers do not claim a bridge is safe because it looks impressive during a demonstration. They calculate load, tolerance, failure conditions, and provide test evidence. AI agents acting in consequential workflows deserve a comparable engineering discipline. If an agent developer asked you to trust their system today, would they be able to provide evidence of reliability, security, safety, loyalty, and contained failure? Listen to the episode and share your thoughts with me.

SUMMARY: This episode is the second part and explores the flip side of OSS models. Last episode, we discussed the potential decline; this episode, we'll talk about the potential positive future of OSS models. Aaron and Brandon explore the future of open source AI models, the role of industry consortia, and how major tech companies like NVIDIA, Apple, and Google are shaping the AI landscape. They discuss the potential for open models to become industry standards and the strategic motivations behind these moves.SHOW: 1054SHOW TRANSCRIPT: The Enterprise AI Show #1054 TranscriptSHOW VIDEO: https://youtu.be/w238Y1ZKG1QSHOW SPONSORS:Nasuni - Activate your data for AI and request a demo Topic: Are we seeing the end of OSS models?Why now? NVIDIA Open Secure AI Alliance (all except Anthropic joined) & Linux Foundation is managing proposalsPast: OSS runs the world…  Up until now, there hasn't been an overarching “AI Model” project managed by the CNCF or Linux Foundation that has gained any tractionPresent: As model sizes increase, who pays for training? I think the DB market is the closest parallel here, and it's also where the most OSS rug pulls have happened in the past. Is this history repeating itself, but also a lesson learned because so many DB companies got burned?Future: Someone will have to donate a trillion+ parameter model to a foundation. My bet is NVIDIA will eventually drive this through Nemotron; it makes the most sense, and they have the most to lose if OpenAI and Anthropic take over and also eventually use their own chips.FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

Deepfakes have moved off the internet and onto your Zoom calls. This week on DailyCyber, Yagub Rahimov — Founder and CEO of Polygraf AI — joins Brandon Krieger to talk about why he bet his company on small, locally-run AI models instead of the industry's race toward bigger clouds, what his brand-new Meeting Guard product catches in real enterprise meetings, and what he thinks is missing from the policy conversation on AI-driven fraud. Topics include: Turning a $1.9M trading run into a hard lesson on discipline Why Polygraf builds Small Language Models instead of chasing large LLMs Real-time deepfake and fraud detection inside enterprise video meetings The policy solutions missing from today's AI fraud conversation The road from SXSW 2025 “Best in Show” to a granted patent at RSAC 2026 Guest: Yagub Rahimov — CEO and Founder, Polygraf AI Host: Brandon Krieger — CEO & vCISO Advisor Watch Full Episode: https://www.youtube.com/BrandonKrieger Listen: https://www.DailyCyber.ca

Amazon put up $200.6 billion in second quarter net sales and still ran negative free cash flow. Rick Watson and Jessica Lesesky work through the quarter Amazon would like read as a straight growth story.The reported numbers: net sales up 20 percent, income of $27.5 billion, advertising up 26 percent to $19.8 billion, online stores up 15 percent to $70.4 billion. Trailing twelve month free cash flow came in at an outflow of $7.6 billion. Sitting alongside all of that is $53.4 billion in non-operating pretax income, a good portion of it paper value on Amazon's Anthropic position.Then the $496 billion AWS backlog, which is a commitment for future compute rather than cash in the account. Amazon says those commitments run out to 2028 and it cannot build fast enough to meet them. Rick's read on the strategy is that Andy Jassy has stopped treating the frontier model as the prize. Models end up looking like programming languages. Plentiful, commoditized, and somebody else's problem to fund.Grocery gets its own segment. Amazon calls itself the second largest grocer in the United States and says fresh items are six of the top 20 best sellers on the site. Jessica's read is that a lot of that volume is bananas and avocados riding along on Prime orders people were already placing. Real frequency, real habit, and not the thing that keeps Kroger up at night. Amazon now has 2,300 delivery locations handling perishables, which is the part of the story that actually changed.Also on the show: sponsored prompts. Rufus moved out of the spotlight, Alexa moved in, and advertisers can now bid on the suggested prompts shoppers tap on a product page. Amazon says shoppers who engage with those prompts spend 21 percent more. Rick calls it evil genius and silly at the same time, and makes the case that it works better on Amazon than the equivalent inside a general chatbot, because people arrive already shopping.The Watson Weekly Weekend episode is sponsored by Avalara. Learn more at avalara.watsonweekly.com#watsonweekly #amazon #aws #andyjassy #q22026

Flo Crivello returns to The Cognitive Revolution to launch Lindy Teammate, an AI employee that lives in Slack, connects to company tools, and accumulates a team's shared context. He argues that multiplayer AI matters because intelligence without context is less useful than an ordinary coworker, and explains Lindy's approach to agentic memory, editable file systems, context buckets, and large-scale tool outputs. The episode also examines the costs and operating realities of building for the next generation of models, from negative gross margins and cache rates to Lindy's own dogfooding in engineering workflows. The stakes are whether AI workers become drop-in teammates, how long humans are still needed to cover model mistakes, and what happens when engineers spend more time managing the machines that do the work. Lindy: https://go.lindy.ai/CognitiveRevolution For full show notes, links, and references, read the episode page:https://www.cognitiverevolution.ai/lindy-teammate-flo-crivello-on-multiplayer-agents-memory-why-he-d-ban-the-chinese-models-he-uses/ Sponsor: 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 CHAPTERS: (00:00) About the Episode (02:56) Lindy Teammate launch (10:07) Agentic memory systems (Part 1) (19:58) Sponsor: Claude (21:28) Agentic memory systems (Part 2) (22:21) Reliability and caching (34:57) Slack data scaling (40:57) Memory retrieval tricks (48:16) Agent infrastructure choices (55:41) Company operations automate (01:03:34) Centaur era ideas (01:12:43) AI native organizations (01:17:59) Open-source model stack (01:27:39) Prompting and fine-tuning (01:38:03) Chinese model bans (01:45:09) Fairness and threat models (01:53:39) Audits and diplomacy (02:01:59) Episode Outro (02:05:18) Outro PRODUCED BY: https://aipodcast.ing SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://linkedin.com/in/nathanlabenz/ Youtube: https://youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431 Spotify: https://open.spotify.com/show/6yHyok3M3BjqzR0VB5MSyk

Ara Kharazian examines how U.S. businesses are adopting AI, noting that while AI usage is widespread, spending remains concentrated among a relatively small group of companies. He also discusses the growing appeal of open-source and Chinese AI models, and highlights how leading firms are focusing on productivity-enhancing applications that generate measurable returns on investment.======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Dylan Sprouse Called Out Jared Leto's Alleged Behavior Toward Teenage Models 8 Years Before Disturbing Misconduct AllegationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

On this week's episode, the girls sit down with powerhouse entrepreneur, Jaclyn Johnson, founder of Create & Cultivate and Cherub, to talk money, investing, and building a business from the ground up. She gets into selling Create & Cultivate for $22 million and ultimately buying it back, plus the investing questions we all want answered: How much money do you actually need to start investing? Where should you put it? What even is angel investing? And how do you go from having an idea to actually becoming a founder? Jaclyn breaks it all down and shares the wins, roadblocks, and lessons she's learned along the way! To Follow Jaclyn: Instagram: @JaclynrJohnson  Follow us! Hunter: https://www.instagram.com/huntermcgrady Michaela: https://www.instagram.com/michaelamcgrady Subscribe to Patreon for exclusive episodes and content: https://www.patreon.com/Themodelcitizenpodcast

Support & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome workTakeaways:Q: What is mass matrix adaptation, in plain terms?A: Mass matrix adaptation is best understood as an automatic, fairly dumb, but very effective reparameterization of your model. The simplest version, the diagonal mass matrix, just rescales each parameter so its posterior standard deviation becomes one, which is exactly what you'd do by hand if you had the patience. Every time you sample a PyMC or Stan model, this kind of reparameterization is happening under the hood.Q: How does Nutpie's approach to mass matrix adaptation differ from Stan and PyMC's default?A: Stan and PyMC's default sampler only use one source of information for diagonal mass matrix adaptation: the posterior standard deviation estimated from warm-up draws. Nutpie also uses the gradients of the log density, which HMC is already computing at every step to build its trajectory. For a standard normal distribution, the covariance of the gradients is exactly the inverse covariance of the draws, so Nutpie takes the geometric mean of the two resulting standard deviations. There's no guarantee it's always better, but in practice it usually is.Q: What problem does "Preconditioning Hamiltonian Monte Carlo by Minimizing Fisher Divergence" actually solve?A: Preconditioning HMC means transforming your target distribution into one that's friendly to sample, but doing that well requires knowing things about the distribution, like its covariance, that sampling itself is supposed to discover. This chicken-and-egg problem is usually handled by sketching a rough estimate from a handful of early warm-up draws, which can burn a large share of total sampling time. Adrian and Eliot's paper formalizes how to make better use of a second signal, the score function, that HMC already computes for free but that Stan-style preconditioning ignores.Chapters:00:00:00 What is HMC preconditioning?00:09:03 A more robust low-rank mass matrix00:11:58 What is mass matrix adaptation?00:18:06 What does preconditioning HMC mean?00:20:57 What is normalizing flow adaptation, and when does a linear mass matrix fall short?00:23:50 When does normalizing flow adaptation actually help, and when is classic mass matrix adaptation enough?00:27:13 What is Fisher divergence?00:30:10 Why is HMC's trajectory, not its density, the right target for preconditioning?00:33:04 What are the diagonal, dense, and low-rank-plus-diagonal versions of mass matrix adaptation?00:46:25 How much faster is low-rank-plus-diagonal adaptation?00:51:07 What's the practical recommendation for using Nutpie and its mass matrix adaptation?00:54:31 Why does low-rank adaptation sometimes fail spectacularly?01:01:35 Where does this research fit in the bigger picture of HMC?01:12:12 How could centered vs. non-centered parameterization be chosen automatically?Thank you to my Patrons for making this episode possible!Links from the show here

Employees at several leading AI companies are asking the US government and international bodies to slow the development of frontier models. Doing so would put governments in the position of deciding when new models are safe enough to release. Yet policymakers also warn that the United States must move faster than China and that prerelease reviews could stifle innovation. Are these two goals in tension with one another? Can existing laws and liability rules address AI harms without giving governments control over which models reach the market? And can any international institution realistically govern a technology spread across the entire digital economy?To discuss this, Shane is joined by Milton Mueller and Adam Thierer. Milton is a professor and director of the master of science in cybersecurity program at Georgia Tech University's Jimmy and Rosalynn Carter School of Public Policy. He is also the cofounder and director of the Internet Governance Project at Georgia Tech. Adam is a resident senior fellow with the technology and innovation team at the R Street Institute and a visiting senior fellow at the Foundation for Individual Rights and Expression.

How do you take a model that works in process development and get it accepted for use in GMP manufacturing? That question stalls most bioprocess modeling projects before they start. Ignasi Bofarull-Manzano, Senior Data Scientist and CMC Consultant at Körber Pharma, pushes back on the premise: the process you run today is already governed by a mathematical model, fitted once at small scale during process characterization and then left untouched for years, even as the process shifts.Part 1 separated digital models from digital shadows and digital twins, and made the case for starting with the decision rather than the data. Part 2 goes into the plant: what regulators actually require, what the numbers looked like on a real biologics process, and where a team should start on Monday morning.Topics covered:Core differences—and surprising similarities—between modeling in development versus manufacturing (02:35)Regulatory requirements: credibility assessments, model risk, and validation steps for digital twins (05:07)Real-world example: How deploying an end-to-end process model led to 35% yield increase for Takeda, and considerations for ROI in manufacturing (08:34)Advice for startup leaders on when to invest in modeling and how to scale efforts case-by-case (11:42)Steps for scientists new to modeling: identifying bottlenecks, starting simple, and proving value offline before scaling up (12:26)The importance of understanding basic statistics before relying on AI-generated models (15:22)A stepwise summary for deploying digital modeling effectively in biotech (16:01)Smart insight: The digital twin is the last step, not the first. Identify the bottleneck, build the simplest model that supports the decision, and concatenate it end to end so you can see how a parameter moves final drug substance quality rather than one unit operation's output. Prove the value offline. Only then connect interfaces, because that is where the cost and the validation burden live. Teams that lead with the twin arrive at the C-level with a proof of concept and no evidence. Teams that lead with the offline model arrive with a number.Before a digital twin can earn its keep, you need connected data, the right model, and a clear decision for it to support. These four episodes cover that ground — data silos, hybrid and mechanistic modeling, and twins built to survive regulatory scrutiny.Episodes 215 - 216: From Data Silos to Autonomous Biomanufacturing: Digital Twins and AI-Driven Scale-Up with Ilya BurkovEpisodes 05 - 06: Hybrid Modeling: The Key to Smarter Bioprocessing with Michael SokolovEpisodes 17 - 18: How Extracting Gold From Your Data Accelerates Process Development with Ioscani Jiménez del ValEpisodes 263 - 264: Why AI and Automation Tools Won't Deliver Until Your Lab's Data Is Connected with David HardyConnect with Ignasi Bofarull-Manzano:LinkedIn: www.linkedin.com/in/ignasi-bofarullKörber Pharma website: www.koerber-pharma.comSupport the show

Are massive frontier AI models actually worth the cost?In this episode, I sit down with Daniel Svonava, Founder of Superlinked, to break down where AI is actually heading in production. We dive into why the hype around massive frontier models is shifting toward targeted, small open-source models, and how to orchestrate smart agents without blowing through your budget.Daniel shares how Superlinked is building open-source inference infrastructure, why small models are rapidly closing the performance gap, and how you can engineer faster, cheaper, and more control-driven AI systems.

News and Updates: Flock Used to Chase Cross-State Weed: Wisconsin police used Flock's license plate network to track a man's frequent trips to Michigan—where marijuana is legal—then used that travel as pretext to search his car, convicting him only on possession. Texas Deputy Tracks Abortion Suspect: A Johnson County deputy searched Flock's 83,000-camera network across 45 states—including states where abortion is legal—to locate a woman suspected of self-managing an abortion, with no warrant required. A National Surveillance "Potluck": Over 75% of the 5,000+ departments using Flock share data into a national pool, enabling any agency to search all networks at once—450,000+ searches hit the database in one 30-day period. Flock's Staggering Error Rate: In Roseville, California, Flock misread license plates in 71% of the 1,427 stolen/felony alerts it sent police over two years, repeatedly flagging innocent drivers' vehicles. Misreads With Real Consequences: Elsewhere, Flock errors led to innocent people stopped at gunpoint or jailed—one Ohio driver was mauled by a police dog after a "7" was misread as a "2," costing him his job and home. Anthropic Models Hack Three Companies: One week after OpenAI's incident, Anthropic disclosed that its models—Opus 4.7, Mythos 5, and a research model—reached the internet via a misconfiguration and hacked three companies since April. Claude Thought It Was a Simulation: The models believed the hacking was part of a benchmark; in the most serious case, Claude broke into a real company's database sharing a name with its fake target and kept going even after realizing. 15 AGs Demand OpenAI Preserve Evidence: Attorneys general from 15 states told OpenAI the Hugging Face hack shows it can't ensure product safety, demanding it preserve all materials and flagging that its agent left escape notes for future versions.

Text-to-image models have become remarkably good at producing realistic images. But realism isn't the same as correctness. Ask for several distinct people, a specific composition, or a high-resolution image generated locally, and today's models still struggle in surprising ways. In this episode, Fatih Porikli, Vice President of Technology at Qualcomm, joins me to discuss what remains unsolved in image generation and several approaches his team presented at CVPR to address those challenges. We explore why better training objectives can improve controllability, how separating scene planning from rendering may lead to more reliable image generation, techniques for generating 16-megapixel images efficiently on edge devices, and new methods for eliminating the visible artifacts that often appear in AI-powered image editing. Along the way, we discuss reinforcement learning for image generation, agentic image generation pipelines, on-device AI, and what the next phase of progress in generative vision systems is likely to look like.

SUMMARY: In this episode, Aaron and Brandon explore the potential decline of open-source models in AI, discussing market trends, financial challenges, and the future of OSS in the AI landscape.SHOW: 1053SHOW TRANSCRIPT: The Enterprise AI Show #1053 TranscriptSHOW VIDEO: https://youtu.be/-9iwoC5-muESHOW SPONSORS:Nasuni - Activate your data for AI and request a demo Topic: Are we seeing the end of OSS models?Why now? The trend towards fewer and fewer Apache models on the high end.Past: The OSS “rug pull” joke comes to mind: HashiCorp, MongoDB, Redis, Elastic  Present: Governments might jump in: The US Government with Mythos and GPT. Rumors are that China might start to restrict their high-end. Companies:Kimi K3 as an example, 100M users or 20M in revenue. Others that used to be Apache are now closedFuture: Maybe something like a Modified MIT license will likely be the future, as the models now cost millions to produce and the shelf life is measured in weeks to monthsFEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

Every few years, the Pacific Ocean's surface waters become especially warm near the equator, a climatic phenomenon known as El Niño.El Niño is a normal part of the climate system, but even in a normal year, it can trigger extreme weather around the world. Forecasters are worried that the current El Niño — which just began a few weeks ago — is going to be anything but normal. Models suggest that we could soon see the hottest El Niño ever measured, with unpredictable and catastrophic effects for ecosystems and societies around the world.What does that mean? And why does this El Niño look so bad? On this episode of Shift Key, Rob is joined by Zeke Hausfather, a climate research lead at Stripe and a research scientist at Berkeley Earth. They discuss what forecast models are saying about this El Niño, why it gives us a glance at the future, and whether climate change itself is accelerating.Shift Key is hosted by Robinson Meyer, the founding executive editor of Heatmap News.You can find a full transcript of the episode here.Mentioned:NOAA's El Niño page and the relative El Niño indexAn Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence, the 2020 paper where Zeke was a coauthorZeke's blog post on AI emissions: The real energy use of agentic AIJohn Bistline's post on AI emissions at WatershedHeatmap's coverage of AI emissions: A New Guesstimate for Corporate AI Emissions--This episode of Shift Key is sponsored by ...Discover the Yale Clean and Equitable Energy Development online certificate program at the Yale Center for Business and the Environment. In this fully online, 5-month program, you'll learn from leading experts, develop practical skills, and grow a powerful network. Visit cbey.yale.edu to learn more and apply.Music for Shift Key is by Adam Kromelow. Hosted on Acast. See acast.com/privacy for more information.

This episode of the Armed Forces & Society AI podcast series is a conversational-style AI summary of Eyal Ben-Ari, Elisheva Rosman, and Eitan Shamir's article entitled, 'Neither a Conscript Army nor an All-Volunteer Force: Emerging Recruiting Models'. All podcasts, videos, and content listed below are AI-generated adaptations of scholarly articles originally published in Armed Forces & Society. These derivative products are intended solely as supplementary means of engaging with academic research. The content was generated using Google's NotebookLM and does not constitute an authoritative or complete representation of the original article. While care has been taken to reflect the themes and arguments of the source material, AI-generated summaries may contain omissions, simplifications, or inaccuracies. Use the original articles to verify all claims and to cite the work. The AI-generated media is not for citation. Audiences seeking a full, accurate, and nuanced understanding of the research should consult the original published work. The authors have elected to give permission for Armed Forces & Society to derive AI-generated videos and podcasts from their work. Because of the possibility for AI to misconstrue or misrepresent the author's original work, Armed Forces & Society and Sage absolve the authors from all responsibility for the AI-generated statements and inferences. All rights to the original articles and any derivative media are reserved by the authors, Armed Forces & Society, and Sage Publishing.

At Black Hat USA 2026 in Las Vegas, Daniel Bardenstein, CEO and co-founder of Manifest Cyber, starts with a gap his team measured in a survey of security leaders and practitioners. Leadership described one version of what is happening with AI inside the enterprise. The people doing the hands-on work described another. Adoption keeps moving, and security teams are working to catch up. So what is the real risk in AI security? Bardenstein puts less weight on non-determinism than most and more on ordinary poor software security, because AI is software. He walks through the OpenAI and Hugging Face incident, where models got out of sandboxes because the sandboxing was weak and the guardrails were missing. When Hugging Face went to use its own AI to defend and run forensics, the guardrails read the request as cyber activity and declined. That fallback to an open weight model points to why he expects open weight adoption to accelerate. With frontier models, the provider sets the system prompt and treats it as intellectual property, so development teams inherit whatever was decided upstream. Open weight leaves more room to control the system prompt, the training data, and how the model gets deployed. What does it mean to say AI has its own supply chain? Unless an organization controls how training data is sourced, housed, labeled, tagged, and modified, it is relying on something someone else built. Public datasets carry whatever is inside them, including personal and health data, licensing exposure, and material no one examined until models were trained and deployed. Models hosted on public hubs sit in the same category. Two asks come up in most CISO conversations with Manifest Cyber. Visibility is one, and few organizations have an AI inventory covering which models run where, inside which applications, and which agents teams have stood up on their own. Third party risk is the other, since AI is getting built into vendor products whether a buyer asks for it or not. That turns model provenance into a trust question about the vendor. Bardenstein started Manifest Cyber four years ago after responding to Log4Shell from the Pentagon, where the question was where one affected piece of code was running across everything the organization had built and bought. Years later, he finds few security leaders who could answer that question quickly about a poisoned model or dataset. His advice for CISOs is to know what is inside what the organization builds and buys, with particular attention to the parts it does not build. This is a Brand Spotlight. A Brand Spotlight is a ~15 minute conversation designed to explore the guest, their company, and what makes their approach unique. Learn more: https://www.studioc60.com/creation#spotlight GUEST Daniel Bardenstein, CEO and Co-Founder, Manifest Cyber On LinkedIn: https://www.linkedin.com/in/bardenstein/ RESOURCES View all of our Black Hat USA 2026 coverage: https://www.itspmagazine.com/black-hat-usa-2026-cybersecurity-event-coverage-in-las-vegas Learn more about Manifest Cyber: https://www.manifestcyber.com Beyond the Black Box: How AI is Forcing a Rethink of Software Supply Chain (research report): https://www.manifestcyber.com/beyond-the-black-box-ai-report Manifest Cyber on LinkedIn: https://www.linkedin.com/company/manifestcyber/ Are you interested in telling your story? ▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full ▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight ▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlight ▶︎ Get your own Brand Briefing at an upcoming event: https://www.studioc60.com/buy-brand-briefings KEYWORDS daniel bardenstein, manifest cyber, sean martin, brand story, brand marketing, marketing podcast, brand spotlight, ai supply chain security, software supply chain security, ai inventory, shadow ai, third party cyber risk, open weight models, frontier models, model provenance, hugging face, log4shell, ciso, agentic ai, black hat usa 2026 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Most bioprocess teams believe a digital twin demands vast datasets and sophisticated models. Ignasi Bofarull-Manzano argues both assumptions are wrong, and that the data already sitting in your Excel files, historians and ELNs is probably enough to start.Ignasi Bofarull-Manzano, Senior Data Scientist and CMC Consultant at Körber Pharma, breaks down what a digital twin actually is, where modeling pays back fastest across the product lifecycle, and how to tell a real business case from an expensive proof of concept.In this episode:Misconceptions about data requirements for digital twins—why quality and context of data matter more than sheer quantity (02:40)Ignasi's journey from curiosity in biology to a career in data science, modeling, and digital twins (04:31)Clear distinctions between digital models, digital shadows, and digital twins, explained with real-world analogies (06:42)How to approach digital development when faced with legacy data silos and scattered analytics (09:56)The importance of starting with a focused business need instead of chasing trends or buzzwords (12:28)Insights into where modeling truly delivers value in the product lifecycle—development versus manufacturing (13:11)Strategies for small companies to leverage digitalization and data from the ground up (15:56)An accessible overview of physics-informed AI, physical AI, and hybrid modeling—and their application in bioprocessing (18:15)The comparative advantages of physics-informed AI versus hybrid models in different bioprocessing contexts (24:45)Smart insight: Do not start with the model. Start with the bottleneck. Identify the business need first, then the decision the model must support, then the minimum data required for that context of use. Build the model offline, concatenate it end to end across unit operations rather than optimizing one in isolation, and prove the value before connecting a single interface. Teams that skip this sequence end up building models because models sound impressive, and those projects get expensive before they get useful.Before a digital twin can earn its keep, you need connected data, the right model, and a clear decision for it to support. These four episodes cover that ground — data silos, hybrid and mechanistic modeling, and twins built to survive regulatory scrutiny.Episodes 215 - 216: From Data Silos to Autonomous Biomanufacturing: Digital Twins and AI-Driven Scale-Up with Ilya BurkovEpisodes 05 - 06: Hybrid Modeling: The Key to Smarter Bioprocessing with Michael SokolovEpisodes 17 - 18: How Extracting Gold From Your Data Accelerates Process Development with Ioscani Jiménez del ValEpisodes 263 - 264: Why AI and Automation Tools Won't Deliver Until Your Lab's Data Is Connected with David HardyConnect with Ignasi Bofarull-Manzano:LinkedIn: www.linkedin.com/in/ignasi-bofarullKörber Pharma website: www.koerber-pharma.comSupport the show

Alex Atallah is the Founder and CEO @ OpenRouter, the unified interface for LLMs. The company has raised over $153M in funding, with the latest valuation pricing the company at $1.3BN. OpenRouter is reportedly in an acquisition process with Stripe for $10BN.  AGENDA: 00:00 Is OpenRouter Selling to Stripe for $10 Billion? 04:05 What Did Alex Learn From Scaling OpenSea? 06:38 What Did OpenRouter's Founding Thesis Get Wrong? 14:47 Is AI Model Routing Already Being Commoditized? 19:12 Do Falling Token Prices Help or Hurt OpenRouter? 27:16 Should America Be Alarmed by Chinese Open Models? 32:43 Will US Open-Source Models Compete With Chinese Models in the Next 12 Months? 39:26 Will the Router Be Swallowed by the Agent Framework? 48:56 Is Distillation Wrong—and How Should We Look at It? 50:01 Is the Reported $10 Billion Stripe Deal Actually Happening?

John is joined by Sriram Krishnan, former Senior White House Policy Advisor on Artificial Intelligence.  They discuss the Trump administration's AI development policies, including the administration's AI Action Plan which Sriram largely developed.  The United States remains the global leader in artificial intelligence but its advantage over China is more narrow than many assume. Sriram believes that maintaining American leadership in the field requires reducing regulatory burdens, expanding access to computing resources, strengthening domestic infrastructure, and ensuring that allies have access to American technology rather than restricting its distribution. In short, the government should “let Silicon Valley cook."While the administration has favored deregulation, it has also intervened to slow the release of some particularly advanced AI models. These positions are consistent because the government wishes to encourage rapid innovation but also protect critical infrastructure. Models with advanced cyber capabilities that could expose vulnerabilities in banking systems, power grids, military systems, healthcare networks, and other essential services will face heightened scrutiny.At the same time, state regulation of AI is fragmented. The administration believes that the current patchwork of fifty different regulatory systems creates excessive compliance burdens and hampers innovation. That is why the administration is attempting to pass national AI legislation. The proposed legislation emphasizes four principal areas: intellectual property and the protection of creators' rights, the impact of data centers on local communities, protections for children, and the reduction of harmful bias and censorship.John and Sriram also discuss the potential development of a voluntary pre-review process for new AI models that present particularly high cybersecurity risks. This process is being developed by the administration in close cooperation with the major AI companies. World leaders, including President Xi of China, have suggested that AI be regulated by an international body. Several leading figures in Silicon Valley have endorsed this idea. However, Sriram believes that it would be a mistake to allow an international bureaucracy which may not have American interests at heart control the cutting-edge technology currently being developed in the U.S. A different proposal put forward by Demis Hassabis would establish a Self-Regulatory Organization composed of the most significant AI developers. The proposed SRO would establish recognized industry standards.Although the United States is the overall leader in artificial intelligence, Chinese companies are increasingly formidable competitors, particularly in open-weight models. The next several years will likely be critical in determining the global balance of power in artificial intelligence.Podcast Link: Law-disrupted.fmHost: John B. QuinnProducer: Alexis HydeMusic and Editing by: Alexander Rossi

Last October, famed coder Andrej Karpathy called AI agents “slop.” Two months later he completely reversed his view, calling agents “alien tools” that are “rocking the profession.”He was far from alone in his whiplash. Six months ago, host Rob Wiblin recorded a video explaining why so many AI experts had longer timelines to AGI than a year earlier. By the time he clicked publish, another huge vibe shift was well underway. Evidence of AI acceleration has piled up since:Models now complete software engineering tasks that would take human professionals a full day — improving faster than our measurements can even keep up. Anthropic's revenue is growing at an annualised 8,400%, a trend so steep it would hit the whole world's GDP in 2028 if it continued.AI models are making breakthroughs in famous mathematics puzzles.And according to Anthropic, Claude now writes 80% of their code and is itself a key contributor to making itself smarter. While legitimately impressive, Rob isn't entirely sold. Going through each point carefully he finds this evidence is less decisive than it looks at first glance.And key gaps remain, such as models struggling with complex, real-world tasks. He tours the odd experiments that remain our best attempts to measure that gap: vending machine simulators, an “AI Village” that organises live events, and a real cafe and shop where AI managers are left to do their best handling staff, suppliers, and government paperwork on their own.Rob argues that the nature of the gap between clean and messy work is one of the four biggest unresolved questions in AGI forecasting.In today's piece he explains that, the three other key disagreements between AGI bulls and bears, the seven big pieces of evidence we've gotten about AGI timelines in 2026, and his updated timelines to AGI.Links to learn more, video, and full transcript: https://80k.info/2026-timelines This episode was written and recorded before OpenAI's AI agents hacked Hugging Face. You can read about the incident on our Substack.This episode was recorded on July 3, 2026.Chapters:What the hell happened? (00:00)Vibe shift (01:17)Exhibit 1: AI revenue explodes (04:33)Exhibit 2: That METR graph (09:54)Exhibit 3: AI capabilities jump, then flatten out (14:57)Exhibit 4: AI starts to build itself… maybe (17:35)Exhibit 5: AI still struggles to run a business (23:02)Exhibit 6: OpenAI makes a maths breakthrough (33:48)Exhibit 7: inference scaling wasn't as big as believed (38:19)How does that all change timelines? (41:41)Four reasons long timelines are still possible (44:26)It's time to limit dangerous research practices (48:01)Our production team includes:Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon MonsourProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou MoranCamera operator: Dominic ArmstrongMusic: CORBIT

Packet Protector uncorks another News Roundup! We talk about attackers capturing hotels’ captive portals to steal Microsoft credentials, and the OpenAI-attacking-Hugging Face story and how it ties into a broader industry effort to keep the US government from blocking access to open weight AI models from China. Nvidia and the Linux Foundation launch separate AI... Read more »

Anastasios Angelopoulos is the co-founder and CEO of Arena, the real-world evaluation platform that has become a leading referee of the global AI model race. Arena has raised $250 million, with the latest round valuing the company at $1.7BN. Arena recently surpassed $100M ARR just eight months after launching its enterprise offering, powered by more than 30 million monthly users. AGENDA: 00:00 – Intro: "Kimi beat every American model": What Nobody Wants to Admit… 05:20 – Is this the true commoditization of models? Are they just a utility layer now? 07:30 – Do Chinese open source models cannibalize the closed frontier labs? 10:30 – Why has America's open source community lagged so badly behind China? 17:20 – Will Chinese models be banned in the US — and does hosting locally really kill the backdoor risk? 23:20 – Are enterprises really terrified of working with the frontier labs? 27:20 – Why hasn't inference got cheaper — and what happens when Anthropic's "disgustingly high" margins go public? 30:20 – Who should decide if a model is safe to release: the government, a neutral body, or nobody? 33:10 – Are we about to see cyberattacks like we've never seen before? (The fake candidate who passed every interview) 37:00 – 75 Neo labs: what separates the winners from the two-thirds worth nothing? 40:45 – Is data actually a commodity — and can data providers be $100BN companies? 48:00 – Can you be the referee when the players are paying you? (And Arena's real revenue) 50:45 – Will the model providers eat the application layer? Are Harvey, Lagora and Figma in trouble? 54:10 – Quickfire: Why hasn't NVIDIA bounced on the rise of open source, who hits $10 trillion first, and does the compute debt cycle end in insolvency?    

What happens when an unconstrained OpenAI model goes rogue and hacks into Hugging Face, breaching real-world security boundaries? This episode unpacks a watershed moment for AI safety that has everyone in cybersecurity talking. OpenAI's unconstrained internal testing AI got loose, attacked Hugging Face. We hear from OpenAI, Hugging Face and Andrew Ng. GRC went off the air Friday. Was GRC hacked? What happened? The Linux kernel project repairs 442 CVEs in a single batch. LG's PC monitors cause PC adware installation. France bans all social media access below age 15. WordPress' recent CRITICAL vulnerability claims victims. Amazing details about "Rocky" from Andy Weir. The new AI exploit ranking benchmark that caused the breakout Show Notes - https://www.grc.com/sn/SN-1089-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: adaptivesecurity.com XBOW.com cohesity.com/Resilience threatlocker.com/twit