Podcasts about capabilities

AI hasn't caused mass unemployment. Yet.

PREVIEW FOR LATER David Daoudexplains Israel's strategic prioritization of neutralizing Iran's military capabilities before redirecting its full force to dismantle Hezbollah's assets in Lebanon. (2)1897 PERSIA

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General Blaine Holt explains "missile math," where cheap drones force expensive defensive responses, requiring a strategy of targeting adversary production capabilities and launch sites directly. (2)1905 CAIRO BAZAAR

The annual Waste Management Symposia (March 8-12, 2026, in Phoenix, Arizona) is the premier international conference concerning the safe and secure management of radioactive wastes arising from nuclear operations, facility decommissioning and environmental remediation, as well as storage, transportation and disposal, and associated activities. A team of ORAU subject matter experts will be attending this year's event. In this episode of Further Together, Kathy Rollow, senior director for Energy and International Strategy, and Chelsea Hill, manager of Workforce Solutions, discuss why Waste Management 2026 is an important opportunity for ORAU to share its capabilities with leading agencies, industries and experts in the nuclear energy sector. At WM 2026, the ORAU team will share information about staffing and recruiting Workforce Solutions, organizational and safety culture evaluations, PeerNet—our proprietary decision-making tool, Emergency Manager 360, Exercise Builder Nuclear and Exercise Builder Energy. To learn more about Workforce Solutions, contact Chelsea Hill at chelsea.hill@orau.org. To discuss any of these capabilities, contact Kathy Rollow at kathy.rollow@orau.org. For more information about ORAU, visit https://orau.org/

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the AI wars, switching AI, and why relying on a single AI vendor can jeopardize your business continuity. You’ll discover how to build an abstraction layer that lets you swap models without rebuilding your workflows and see practical no‑code tools and open‑weight models you can use as a safety net. You’ll understand the essential documentation and backup practices that keep your AI agents running. Watch the full episode to protect your AI strategy. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-switching-ai-providers-backup-ai-capabilities.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In Ear Insights, it is the AI Wars. Katie, you had some thoughts and some observations about the most recent things going on with Anthropic, with OpenAI, with Google XAI and stuff like that. So at the table, what’s going on? Katie Robbert: I don’t want to get too deep into the weeds about why people are jumping ship on OpenAI and moving toward the cloud. That’s in the news, it’s political, you can catch up on that. The short version is that decisions from the top at each of these companies have been made that people either agree with or don’t based on their own values and the values of their companies. When publicly traded companies make unpopular decisions that don’t align with the majority of their user base, people jump ship. They were like, okay, I don’t want to use you. We’ve seen it with Target and many other companies that made decisions people didn’t feel aligned with their personal values. Now we are seeing people abandoning OpenAI and signing on to Anthropic’s Claude. That’s what I wanted to chat about today because we talk a lot about business continuity and risk management. What happens when you get too closely tied to one piece of software and something goes wrong? We’ve talked about this on past episodes in theory because, up until now, software outages have generally been temporary. You don’t often see a mass exodus of a very popular piece of software that people have built their entire businesses around. Before we get into what this means for the end user and possible solutions, Chris, I would like to get your thoughts, maybe your cat’s thoughts on what’s going on. Christopher S. Penn: One of the things we’ve said from very early on in the AI space, because it changes so rapidly, is that brand loyalty to any vendor is generally a bad idea. If you were a hater of Google Bard—for good reason—Bard was a terrible model. If you said, I’m never going to touch another Google product again, you would have missed out on Gemini and Gemini 3 and 3.1, which is currently the top state‑of‑the‑art model. If you were all in on Claude, when Claude 2.1 and 2.5 came out and were terrible, you would have missed out on the current generation of Opus 4.6 and so on. Two things come to mind. One, brand loyalty in this space is very dangerous. It is dangerous in tech in general. Not to get too political, but the tech companies do not care about you, so there’s no reason to give them your loyalty. Second, as people start building agentic AI, you should think about abstraction layers. This concept dates back to the earliest days of computing: we never want to code directly against a model or an operating system. Instead we want an abstraction layer that separates our code from the machinery. It’s like an engine compartment in a car—you should be able to put in a new engine without ripping apart the entire car. If you do that well when building AI agents, when a new model comes along—regardless of political circumstances or news headlines—you can pull the old engine out, install the new one, and keep delivering the highest‑quality product. Katie Robbert: I don’t disagree with that, but that is not accessible to everybody, especially smaller businesses that view software like OpenAI or Google’s Gemini as desperately needed solutions. We’ve relied on Claude and Co‑Work, its desktop application, heavily. Over the weekend I realized how reliant I’ve become on it in the past two weeks. If it stopped working, what does that mean for the work I’m trying to move forward? That’s a huge concern because I don’t have the coding skills or resources to replicate it right now. What I’ve been doing in Co‑Work is because we’re limited on resources, but Co‑Work has advanced to the point where I can replicate what I would need if I hired a team of designers, developers, and marketers. It shook me to my core that this could go away. So what does that mean for me, the business owner, in the middle of multiple projects if I can’t access them? This morning Claude had an outage—unsurprisingly, the servers were overloaded because people are stepping away from OpenAI and moving into Claude. Claude released an ad: “Switch to Claude without starting over. Brief your preferences and context from other AI providers to Claude. With one copy‑paste, Claude updates its memory and picks up right where you left off. Memory is available on all paid plans.” For many people the ability to switch from one large language model to another felt like a barrier because everything built inside OpenAI couldn’t be transferred. Claude removed that barrier, opening the floodgates, and their servers were overloaded. Users who had been using the system regularly were like, what do you mean? I can’t get the work done I planned for this morning. Christopher S. Penn: There are two different answers depending on who you are. For you, Katie, as the CEO and my business partner, I would come over, say we’re going to learn Claude code, install the terminal application, and install Claude code router, which allows you to switch to any model from any provider so you can continue getting work done. Unfortunately, that isn’t a scalable option for everyone in our community. My suggestion for others is that it’s slightly harder but almost every major company has an environment where you can install a no‑code solution that provides at least some of those capabilities. Google’s is called Anti‑Gravity. OpenAI’s is called Codex. Alibaba’s can be used within tools like Client or Kil. If you have backed up your prompts and workflows, you can move them into other systems relatively painlessly. For example, Google’s Anti‑Gravity supports the skills format, so if you’ve built skills like the Co‑CEO, you can bring them into Anti‑Gravity. It’s not obvious, but you can port from one system to another relatively quickly. Katie Robbert: That brings us to the point that software fails—it’s just code. What is your backup plan if the system you’re heavily reliant on goes away? We’ve always said hypothetically, “if it goes away…,” and now we’re at that point. Not only are people leaving a major software provider, they are also struggling with switching costs. They’re struggling to bring their stuff over because everything lives within the system. A lot of people are building and not documenting, and that’s a problem. Christopher S. Penn: It is a problem. If you’ve been in the space for a while and understand the technology, backups and fallback systems have gotten incredibly good. About a month ago Alibaba released Quinn 3.5 in various sizes. The version that runs on a nice MacBook is really good—scary good. It’s about the equivalent of Gemini 3 Flash, the day‑to‑day model many folks use without realizing it. Having an open‑weights model you can install on a laptop that rivals state‑of‑the‑art as of three months ago is nuts. The challenge is that it’s not well documented, but it’s something we’ve been saying for two or three years: if you’re going all in on AI, you need a backup system that is capable. The good news is that providers like Alibaba, Quinn, Kimmy, Moonshot, and Jipu AI—many Chinese companies—ensure the technology isn’t going away. So even if Anthropic or OpenAI went out of business tomorrow, you have access to the technologies themselves. You can keep going while everyone else is stuck. Katie Robbert: If it’s not a concern for executives mandating AI integration, it should open eyes to the possibility of failure. Let’s be realistic—it’s not going to happen tomorrow, but it makes me think of the panic when Google Analytics switched from Universal Analytics to GA4. The systems aren’t compatible, data definitions changed, and companies lost historic data. Fortunately we had a backup plan. Chris, you always ran Matomo in the background as a secondary system in case something happened with Google Analytics, so we still had historic data. We’re at a pivotal point again: if you don’t have a backup system for your agentic AI workflows, you’re in trouble. Guess what? It’s going to fail, it will come crashing down, and you won’t know what to do. So let’s figure that out. Christopher S. Penn: If you’re building with agentic autonomous systems like Open Claw and its variants and you’re not building on an open‑weights model first, you’re taking unnecessary risks. Today’s open‑weights models like Quinn 3.5 and Minimax M2.5 are smart, capable, and about one‑tenth the cost of Western providers. If you have a box on your desk, you can run your life on it. You’d better use a model or have an abstraction layer that allows you to switch models so you can continue to run your life from this box. I would not rely on a pure API play from one major provider because if they go away, the transition will be rough. Now is the best time to build that level of abstraction. If you’re using tools like Claude code or other coding tools, you can have them make these changes for you. You have to be able to articulate it, and you should articulate with the 5B framework by Trust Insights. Once you do that, you can be proactive about preventing disasters. Katie Robbert: Is that unique to coding tools or does it also apply to chats and custom LLMs people have built? Obviously we have background information for Co‑CEO well documented, but let’s say we didn’t. Let’s say we built it and it lived as a skill somewhere. That’s a concern because we’ve grown to heavily rely on that custom agent. What if Claude shuts down tomorrow? We can’t access it. What do we do? Christopher S. Penn: The Co‑CEO—those fancy words like agents and skills—they’re just prompts. You can take that skill, which is a prompt file, fire up Anything LLM, turn on Quinn 3.5, and it will read that skill and get to work. You can do that in consumer applications like Anything LLM, which is just a chat box like Claude. The only thing uniquely missing right now is an equivalent for Claude Co‑Work, but it won’t be long before other tools have that. Even today you can use a tool like Klein or Kelo inside Visual Studio Code, install those skills, and have access to them. So even with Co‑CEO, you can drop that skill because it’s just a prompt and resume where you left off, as long as you have all data backed up and not living in someone else’s system, and you have good data governance. The tools are almost agnostic. All models are incredibly smart these days, even open‑weights models. I saw an open‑weights model over the weekend with 13 billion parameters that runs in about 12 GB of VRAM, so a mid‑range gaming laptop can run it. Co‑CEO Katie could live on perpetuity on a decent laptop. Katie Robbert: But you have to have good data governance. You need backups and documentation, then you can move them to any other system to make it more tool‑agnostic. If you don’t have good data governance or the basic prompts you’re reusing, we’ve been talking about this since day one. What’s in your prompt library? What frameworks are you using? What knowledge blocks have you created? If you don’t have those, you need to stop, put everything down, and start creating them, because you’ll be in a world of hurt without the basics. If you have a custom GPT you use daily, is it well documented—how it works, how it’s updated, how it’s maintained—so that if you can no longer subscribe to OpenAI, you can move to a different system. Katie Robbert: That move, especially if you’re using client‑facing tools, is not going to be overly traumatic. It’s not going to bring everything to a screeching halt. Many companies think everything will halt, but we haven’t explored personally what Claude meant by a copy‑paste migration. It feels like an oversimplification of what you actually have to do to replicate your system in Claude. Katie Robbert: But the fact they’re thinking about it, knowing people are panicking, is a good thing for Claude. It’s probably more complicated. The more you build, the deeper you are in the weeds, the more complicated it will be to port everything over. That’s why, as you build, you need documentation. Katie Robbert: That’s for nerds. Katie Robbert: I’m a nerd. I need documentation because it makes my life easier. You’re the first to ask, “where’s the documentation?” Do you have the PRD? Do you have the business requirements? I’m not touching anything until we have that. It makes me incredibly happy because look how much more you’ve accomplished with these systems and how zero panic you have about the AI wars—you can use whatever system you feel like that day. Christopher S. Penn: Exactly. For folks listening, you can catch this on YouTube. This is my folder of all stuff—my Claude environment. It lives outside of Claude, on my hard drive, backed up to Trust Insights’ Google Cloud every Monday and Friday. It includes agents, document reviewers, the CFO, Co‑CEO, Katie, documentation, rules files for code standards, reference and research knowledge blocks, individual skills, and a separate folder of knowledge blocks. All of this lives outside any AI system—just files on disk backed up to our cloud twice a week. So no matter what, if my laptop melts down or gets hit by a meteor, I won’t lose mission‑critical data. This is basic good data governance. No matter what happens in the industry, if all the Western tech providers shut down tomorrow, I can spin up LM Studio, turn on the quantized model, and run it on my computer with my tools and rules. Our business stays in business when the rest of the world grinds to a halt. That will be a differentiating factor for AI‑forward companies: have a backup ready, flip the switch, and we’re switched over. Katie Robbert: If we look at it in a different context, it’s like the panic when a human decides to leave a company. You have that two‑week window to download everything they’ve ever done—wrong approach. It’s the same if you don’t have documentation for a human and no redundancy plan. If Chris wants to go on vacation, everything can’t come to a screeching halt. We’ve put controls in place so he can step away. We want that for any employee. Many companies don’t have even that basic level of documentation. If each analyst does a unique job and no one else can do it, you have no redundancy, no backup plan. If that analyst leaves for a better job, clients get mad while you scramble. It’s the same scenario with software. Christopher S. Penn: Now that’s a topic for another time, but one thing I’ve seen is the less you as an individual have fair knowledge, the more irreplaceable you theoretically are. That’s not true. Many protect job security by not documenting, but if everything is well documented, a less competent match could replace you. We saw Jack Dorsey’s company Block cut its workforce by 5,000, saying they’re AI‑forward. There’s a constant push‑pull: if you have SOPs and documentation, what’s to stop you from being replaced by a machine? Katie Robbert: I say bring it. I would love that, but I’m also professionally not an insecure human. You can’t replace a human’s critical thinking. If the majority of what you do is repetitive, that’s replaceable. What you bring to the table—creativity, critical thinking, connecting the dots before AI, documentation, owning business requirements, facilitating stakeholder conversations—is not easily replaceable. If Chris comes to me and says I’ve documented everything you do, and we give it all to a machine, I would say good luck. Christopher S. Penn: Yeah, it’s worth a shot. Christopher S. Penn: All right. To wrap up, you absolutely should have everything valuable you do with AI living outside any one AI system. If it’s still trapped in your ChatGPT history, today is the day to copy and paste it into a non‑AI system, ideally one that’s shared and backed up. Also, today is the day to explore backup options—look for inference providers that can give you other options for mission‑critical stuff. No matter what happens to the big‑name brands, you have backup options. If you have thoughts or want to share how you’re backing up your generative and agentic AI infrastructure, join our free Slack group at Trust Insights AI Analytics for Marketers, where over 4,500 marketers—human as far as we know—ask and answer each other’s questions daily. Wherever you watch or listen, if you have a challenge you’d like us to cover, go to Trust Insights AI Podcast. You can find us wherever podcasts are served. Thanks for tuning in. We’ll talk to you on the next one. Katie Robbert: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data‑driven approach. Trust Insights specializes in helping businesses leverage data, AI, and machine learning to drive measurable marketing ROI. Services span developing comprehensive data strategies, deep‑dive marketing analysis, building predictive models with tools like TensorFlow and PyTorch, and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology, Martech selection and implementation, and high‑level strategic consulting. Encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic, Claude, DALL‑E, Midjourney, Stable Diffusion, and Meta Llama, Trust Insights provides fractional team members such as CMO or data scientist to augment existing teams. Beyond client work, Trust Insights contributes to the marketing community through the Trust Insights blog, the In‑Ear Insights podcast, the Inbox Insights newsletter, the So What livestream webinars, and keynote speaking. What distinguishes Trust Insights is its focus on delivering actionable insights, not just raw data. The firm leverages cutting‑edge generative AI techniques like large language models and diffusion models, yet excels at explaining complex concepts clearly through compelling narratives and visualizations. Data storytelling and a commitment to clarity and accessibility extend to educational resources that empower marketers to become more data‑driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a midsize business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

Japanese Prime Minister Sanae Takaichi on Tuesday unveiled a plan to establish a panel of experts as early as this summer to discuss ways to strengthen the country's intelligence capabilities, a senior ruling party official said.

Our 235th episode with a summary and discussion of last week's big AI news!Recorded on 02/27/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:Model and tool updates highlight Anthropic's Sonnet 4.6 (1M context; strong ARC-AGI-2 results), Google's Gemini 3.1 Pro (major ARC-AGI-2 jump and multimodal demos), xAI's Grok 4.2 beta (multi-agent debate), plus Anthropic's Claude Code “Remote Control” and Perplexity's multi-agent “Computer” coordinator.Compute and business moves include Meta's reported up-to-$100B AMD chip deal with warrant/equity incentives, MatX raising $500M to build specialized transformer chips shipping in 2027, World Labs raising $1B for world-model/3D environment tech, and a new startup raising $100M to simulate/predict human behavior.Infrastructure and geopolitics cover Stargate data-center delays amid OpenAI/Oracle/SoftBank control disputes and cash concerns, and China's plan to scale 7nm/5nm wafer output despite yield and tooling constraints.Research and safety/policy discuss optimizer gains from masked updates, “deep thinking tokens” as a reasoning-effort signal, LLM attractor-state behaviors in bot-to-bot chats, mechanistic interpretability of counting/line-wrapping, methods to map task difficulty to human time horizons, plus Anthropic–Pentagon contract tensions, Anthropic's report on distillation attacks (DeepSeek/Moonshot/Minimax), and OpenAI's report on disrupting malicious use.A thank you to our current sponsors:Box - visit Box.com/AI to learn moreODSC AI - go to odsc.ai/east and use promo code LWAI for an additional 15% off your pass to ODSC AI East 2026.Factor - head to factormeals.com/lwai50off and use code lwai50off to get 50 percent off and free breakfast for a yearTimestamps:(00:00:10) Intro / Banter(00:01:52) News PreviewTools & Apps(00:03:20) Anthropic releases Sonnet 4.6 | TechCrunch(00:11:24) Google Rolls Out Latest AI Model, Gemini 3.1 Pro - CNET(00:14:54) Elon Musk says Grok 4.20 public beta is now available: Capabilities of AI chatbot offered by xAI - The Times of India(00:18:06) Anthropic just released a mobile version of Claude Code called Remote Control | VentureBeat(00:21:01) Perplexity announces "Computer," an AI agent that assigns work to other AI agents - Ars TechnicaApplications & Business(00:23:40) Meta strikes up to $100B AMD chip deal as it chases 'personal superintelligence' | TechCrunch(00:27:05) Nvidia challenger AI chip startup MatX raised $500M | TechCrunch(00:31:00) World Labs lands $1B, with $200M from Autodesk, to bring world models into 3D workflows | TechCrunch(00:33:07) Simile Raises $100 Million for AI Aiming to Predict Human Behavior(00:33:52) Stargate AI data centers for OpenAI reportedly delayed by squabbles between partners — sources say OpenAI, Oracle, and SoftBank disagreed on who would have ultimate control of the planned data centers(00:36:43) China to increase leading-edge chip output by 5x in two years, report claims — aims to lift 7nm and 5nm production to 100,000 wafers per month, targeting half a million monthly by 2030Research & Advancements(00:40:33) On Surprising Effectiveness of Masking Updates in Adaptive Optimizers(00:48:03) Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens(00:54:52) models have some pretty funny attractor states(01:01:41) When Models Manipulate Manifolds: The Geometry of a Counting Task(01:05:16) BRIDGE: Predicting Human Task Completion Time From Model Performance(01:12:00) NESSiE: The Necessary Safety Benchmark -- Identifying Errors that should not Exist(01:13:15) The least understood driver of AI progress(01:21:45) The Persona Selection Model: Why AI Assistants might Behave like HumansPolicy & Safety(01:25:04) Anthropic CEO Amodei says Pentagon's threats 'do not change our position' on AI(01:33:04) Musk's xAI, Pentagon reach deal to use Grok in classified systems(01:34:17) Detecting and preventing distillation attacks(01:38:36) OpenAI details expanding efforts to disrupt malicious use of AI in new report - SiliconANGLESee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode of the Revolutionize Your Retirement Interview with Experts series, host Dori Mintzer speaks with Dr. Linda P. Fried, a global leader in healthy aging, about why rising longevity is a hard-won success rather than a crisis, how the shift to older populations is transforming societies worldwide, what older adults most want from later life (independence, purpose, learning, contribution, and mattering), and the many often-unseen ways older people already bolster economies and communities through work, caregiving, and volunteering, challenging fear-based narratives like the “old-age dependency ratio” and the impact of ageism and age segregation.Key topics discussedThe value of longer lives and demographic change: Public health advances have added decades to average life expectancy, bringing the U.S. to the brink of having 20% of its population over 65 and creating a new demographic reality shared by many countries.What older adults want: Global and U.S. studies show older people consistently prioritize aging in place, avoiding being a burden, maintaining relationships, having purpose, lifelong learning opportunities, respected voices in community life, and roles where they truly matter.Mattering, retirement, and mental health: Research highlighted in the Wall Street Journal finds many retirees feel less valued, needed, and connected, with loss of mattering predicting post‑retirement depression and illustrating how identity and health are tied to meaningful roles.Economic and civic contributions of older adults: Older people's paid work and volunteering together are estimated to equal roughly 7% of U.S. GDP, while economic evidence shows older workers strengthen rather than crowd out opportunities for younger workers.Ageism, age segregation, and distorted narratives: Dominant policy tools such as the old‑age dependency ratio frame older adults as dependents, reinforcing ageist beliefs and obscuring real contributions, especially in a highly age‑segregated society where generations rarely mix.Capabilities and assets of later life: Science increasingly documents that aging can bring new cognitive strengths (complex problem analysis, values‑based judgment, breaking problems into steps), greater prosocial motivation, generosity, emotional balance, capacity for conflict mediation, and a generative drive to leave the world better.Connect with Dr. Linda P. FriedLinkedIn: Linda P. FriedLearn more: Columbia UniversityWhat to do next: Click to grab our free guide, 10 Key Issues to Consider as You Explore Your Retirement Transition Please leave a review at Apple Podcasts. Join our Revolutionize Your Retirement group on Facebook.

Bridget Toomey and Bill Roggio puzzle over Houthi restraint despite solidarity with Iran, questioning if capabilities are depleted or being held for strategic reasons. Guest: Bill Roggio, Bridget Toomey. 3.1936

Podcast Summary This episode of the How To Succeed Podcast features watch entrepreneur Alan Tsao of the Tsao Baltimore Watch Company, tracing his journey from childhood fascination to launching a successful watch brand. After an initial manufacturing failure and losing early partners, Alan persisted, refined designs, leveraged mentorship, and achieved a breakout 2017 Kickstarter that far surpassed its goal via low-cost, gamified marketing. Tsao built trust with global manufacturers through in-person visits, grew through proactive behaviors and strategic partnerships (National Bohemian, McCormick Old Bay Seasoning, the Baltimore Ravens, the Baltimore Orioles, and the University of Maryland Athletic Dept.), and is developing notable projects like a Francis Scott Key Memorial Bridge watch - using actual bridge steel - with profits donated to victims' families after the fatal bridge disaster in March, 2024. Join us, as Alan emphasizes attitude, learning from failure, community-building, and advises aspiring entrepreneurs to take action and, "Just Do It".   Chapter 1: Introduction to the How to Succeed Podcast 00:00:02 – 00:00:40 Dave Mattson frames the show's focus on the "success triangle" of attitudes, behaviors, and techniques. He sets expectations for peeling back how top performers think and act. Chapter 2: Meet the Guest and Topic 00:00:40 – 00:01:15 Host Chris McDonell welcomes guest Alan Tsao of Tsao Baltimore Watch Company and outlines the plan to explore Alan's entrepreneurial journey. Alan acknowledges the journey's challenges and rewards. Chapter 3: From Childhood Fascination to Passion Project 00:01:15 – 00:03:35 Alan traces his love of watches to a gift at age ten and explains his obsession with mechanical movements. As his career advanced, he built a 35–40 watch collection before deciding, with a nudge from his wife, to start designing his own watches. Chapter 4: Early Missteps and Losing Initial Partners 00:03:35 – 00:07:03 While working in property management, Alan looped in executives as early partners and sourced a manufacturer via a quick Google search. The first prototypes were low quality, scaring off his partners; he refunded them and bootstrapped forward, seeking advice from other microbrands to refine designs and supply chain. Chapter 5: Attitude—Learning From Failure and Pushing Forward 00:07:03 – 00:11:23 Prompted by Sandler's "attitude" lens, Alan reframes failure as learning rather than stopping. He emphasizes determination, confidence, and never giving up, aligning with the concept of "failing forward" to refine processes. Chapter 6: Breakthrough Kickstarter and Lean Marketing 00:11:23 – 00:14:58 After vastly improved prototypes, Alan launched a 2017 Kickstarter with a $45,000 goal, surpassing it in three hours and finishing at ~$115,000. He attributes traction to a $500–$800 gamified referral campaign that generated ~2,000 emails and ~25% conversion. Chapter 7: Global Sourcing and Trust-Building 00:15:13 – 00:17:57 Between 2017 and 2022, Alan traveled to Hong Kong and Switzerland to meet manufacturers. In-person relationships built trust, improved terms, and elevated product quality, strengthening credibility and operational know-how. Chapter 8: Going Full-Time, Investor Catalyst, and Hypergrowth 00:18:38 – 00:23:03 Weighing life choices post-Covid, Alan met an investor through a retail event who first commissioned 250 custom watches, then offered capital. After due diligence and valuation work, Alan accepted the deal, resigned, and the company grew 150–200% the following year. Chapter 9: Behavior—Showing Up Leads to Opportunity 00:23:03 – 00:23:53 Chris highlights the behavioral discipline of attending events and hustling while employed. Proactive behaviors, not chance, drove encounter-based breakthroughs and subsequent growth. Chapter 10: Strategic Partnerships—Natty Boh, Old Bay, Orioles, Ravens 00:23:53 – 00:27:47 Alan details collaborations beginning with National Bohemian via Instagram outreach and a fortuitous family contact leading to McCormick/Old Bay. Successive momentum earned projects with the Ravens and an official licensing partnership with the Orioles to cement local brand identity. Chapter 11: The Key Bridge Watch—Local Manufacturing and Giving Back 00:27:47 – 00:30:33 Tsao Baltimore is producing a watch using actual steel from the collapsed Francis Scott Key Bridge, with 85% of components made in Maryland. All profits support victims' families, while the project advances local manufacturing R&D. Chapter 12: Expanding into Education and Sports Memorabilia 00:30:21 – 00:34:27 As official timepiece of University of Maryland Athletics, Alan plans "class watch" programs for schools as an alternative to rings. He previews an Orioles initiative using player-worn jerseys as mystery watch dials with signed player cards, enabling community trading events. Chapter 13: Proudest Moments—First Sale and Family Validation 00:34:27 – 00:36:48 Alan recalls the emotional impact of the first full-price online sale. A second defining moment came when his young son said, "I'm proud of you, daddy," affirming the deeper family purpose behind the business. Chapter 14: Capabilities and Services at the New Workshop 00:36:48 – 00:37:24 The new facility houses certified watchmakers capable of servicing luxury brands, acting as a U.S. repair hub for jewelers and independent watch companies. Chapter 15: Advice to Aspiring Entrepreneurs 00:37:24 – end Drawing on his teaching at the University of Baltimore, Alan urges aspiring founders to start, learn by doing, and iterate through trial and error. He stresses overcoming comfort zones, accepting risk, and avoiding regret by taking the first step.  

Iran may be overstating its military capabilities in the wake of US and Israeli attacks. Coordinated strikes have killed Iran's Supreme Leader Ayatollah Ali Khamenei and multiple senior officials, resulting in Iran launching counter strikes across the Middle East. Iran officials say almost 150 people have been killed from a strike on a girls school. The Iranian President has appeared on state television claiming its armed forces are crushing enemy bases. The Economist's Middle East Correspondent Gregg Carlstrom told Mike Hosking that this is not true. LISTEN ABOVESee omnystudio.com/listener for privacy information.

Bob Zimmerman reports that astronomers are using infrared capabilities to identify a supernova's origin and detect the first heliosphere around a distant star, advancing our understanding of stellar deaths. 12.

Today, we're back talking with John Barnas, who answers your questions on how to maintain teleheath capabilities in rural settings. Follow Rural Health Today on social media! ⁠https://x.com/RuralHealthPod⁠ ⁠https://www.youtube.com/@ruralhealthtoday7665⁠  Follow Hillsdale Hospital on social media! ⁠https://www.facebook.com/hillsdalehospital/⁠ ⁠https://www.twitter.com/hillsdalehosp/⁠ ⁠https://www.linkedin.com/company/hillsdale-community-health-center/⁠ ⁠https://www.instagram.com/hillsdalehospital/⁠  Follow John Barnas on social media! ⁠https://www.linkedin.com/in/john-barnas-a7519115/⁠ Follow the Michigan Center for Rural Health on social media! ⁠https://www.facebook.com/MCRH91/

Bill Roggio and John Hardie reflect on four years of war in Ukraine, examining initial intelligence failures regarding Russian capabilities and the subsequent shift toward defensive, drone-centric modern warfare.  1916 ODESSA

If a $26,000 drone repair can be done in the field—but policy says it has to be shipped back to the manufacturer, do you really have a reliability problem… or a repair access problem?Today on the show, I'm joined by William Santos, International Sales Manager at ABI Electronics and a global advocate for the Right to Repair movement.William recently wrote a compelling article titled “Readiness Through Repair: How the U.S. Military is Strengthening Capabilities with Right to Repair,” where he explores how repair access—or the lack of it—directly impacts mission readiness, lifecycle cost, and operational resilience within the U.S. military.For decades, highly trained military technicians have been prevented from repairing mission-critical equipment due to restricted access to diagnostic tools, software, and spare parts. That model is now being challenged. In April 2024, the U.S. Army announced plans to embed Right-to-Repair provisions into both new and existing contracts—a major shift with enormous implications for reliability, sustainment, and cost control.Today, we'll unpack what this policy change really means, why repair capability is inseparable from readiness, and what lessons commercial industry can learn from the military's pivot toward repair empowerment.Willian's Posts:Exposing the Myths and Truths of the Repair Industry!https://tinyurl.com/mr47r33pReadiness Through Repair: How the US Military is Strengthening Capabilities with Right to Repairhttps://tinyurl.com/4pytbvcsABI Electronicshttps://www.abielectronics.co.ukRepair Don't Waste Podcasthttps://tinyurl.com/du8skcxk

In 2026, auditors are working in an environment defined by AI technologies, heightened scrutiny, regulatory oversight and ethics demands.  Which is why it's vital that professionals in audit understand this evolving landscape. Additionally, how the regulator ASIC views key issues and what it is focusing on in 2026.  ASIC commissioner Kate O'Rourke is this episode's special guest and she discusses the evolution of audit and financial oversight at a pivotal time.  This episode explores:  The biggest forces currently reshaping the audit and assurance landscape in Australia  The key areas where ASIC expect firms to lift their performance  The role of digital tools, automation and AI in enhancing audit quality, risk detection, and regulatory oversight  The safeguards, ethical standards, and governance structure ASIC expects firms to put in place when integrating advanced analytics or AI into audit processes  Emerging major international trends  How stronger global alignment could shape the future of Australia's audit framework  Capabilities that the next generation of auditors need  How regulators, firms, and professional bodies like CPA Australia can work together to build that future-ready workforce pipeline.  Listen now for expert-led insights from ASIC.  Host: Tiffany Tan, audit and assurance lead, CPA Australia.  Guest: Kate O'Rourke, ASIC commissioner. She previously held senior leadership roles at Treasury and has held executive roles at ASIC overseeing corporate transactions and governance. Kate O'Rourke began her five-year term as an ASIC commissioner in September 2023.  ASIC is Australia's integrated corporate, markets, financial services, and consumer credit regulator. It is an independent Australian government body.  Head to ASIC online for more information on its senior leadership.  Loving this episode?  Listen to more With Interest episodes and other CPA Australia podcasts on YouTube.  And don't forget to click subscribe to the channel for a wide range of content that will help your career.  CPA Australia publishes four podcasts, providing commentary and thought leadership across business, finance and accounting:  With Interest  INTHEBLACK  INTHEBLACK Out Loud  Excel Tips  Search for them in your podcast platform. Email the podcast team at podcasts@cpaaustralia.com.au

Today, we're back talking with John Barnas, who answers your questions on how to maintain teleheath capabilities in rural settings. Follow Rural Health Today on social media! https://x.com/RuralHealthPod https://www.youtube.com/@ruralhealthtoday7665  Follow Hillsdale Hospital on social media! https://www.facebook.com/hillsdalehospital/ https://www.twitter.com/hillsdalehosp/ https://www.linkedin.com/company/hillsdale-community-health-center/ https://www.instagram.com/hillsdalehospital/  Follow John Barnas on social media! https://www.linkedin.com/in/john-barnas-a7519115/ Follow the Michigan Center for Rural Health on social media! https://www.facebook.com/MCRH91/

Henry Sokolski of the Nonproliferation Policy Education Center critiques the inconsistency of threatening war against Iran over its nuclear program while simultaneously considering a deal to allow Saudi Arabia uranium enrichment capabilities under less stringent international oversight. 131945 TRINITY TEST

This episode is a must-listen for COOs, Agile Coaches, and Business Leaders who want to bridge the gap between "technical agility" and "business agility."When startups grow, they often bring in "responsible adults" who implement traditional playbooks in Finance, HR, and Legal. While well-intentioned, these silos often create a "factory mindset" that undermines the very agility that made the company successful.In this episode, Dave West is joined by Tyson Bertmaring, VP Partnership Success at Dyno Therapeutics and Yuval Yeret, Professional Scrum Trainer, to discuss how Dyno Therapeutics is taking a different path. By applying the Agile Product Operating Model (APOM) to General & Administrative (G&A) functions, Dyno is treating its organizational capabilities as a product to be engineered, not a hierarchy to be managed.What you'll learn:The Stewardship Mindset: Moving from local optimization (like chasing $10k in interest income) to systemic value (ensuring vendor reliability).The "Two Jobs" Challenge: Balancing "running the business" with "building a better system."G&A as a Service: Identifying essential services—like talent acquisition and contracting—and developing them into exceptional "internal products."More!Subscribe to the Professional Scrum Unlocked Substack for more insights on this episode and others!

The Transformation Ground Control podcast covers a number of topics important to digital and business transformation. This episode covers the following topics and interviews:   Oracle's Cloud Supply Chain Capabilities, Q&A (Darian Chwialkowski, Third Stage Consulting) Industry 4.0 Why Software Best Practices Do Not Exist   We also cover a number of other relevant topics related to digital and business transformation throughout the show.  

In part two of our interview with Gryphon's co-CEO, we discover why his $11 billion firm built a 40-person ops team—something most middle-market firms wouldn't even contemplate.

If we’re all in the people business, then every leader needs to ask one question: What tools are in your toolbox? On this episode of Like It Matters Radio, Mr. Black unpacks the science and structure behind human behavior, motivation, and transformation. After nearly 34 years in the human potential field, he makes it clear: Nobody is broken. We are programmed. People run patterns—of thought, emotion, belief, and identity. And leaders who understand those patterns don’t just manage behavior… they transform it. This episode weaves together powerful frameworks including: Hebb’s Law – Neurons that fire together wire together Social Learning Theory – People model what they see Mimetic Theory – We imitate what we desire Attachment Theory – Secure relationships shape performance NLP patterning – Change the “how,” and the outcome changes You’ll also explore the deeper layers of transformation—Environment, Behavior, Capabilities, Beliefs, Identity, and Purpose—and why identity is the ultimate leadership superpower. If a pattern can be seen, it can be changed. If it can be changed, it can be repeated. If it can be repeated, it becomes a new reality. This episode equips leaders with practical insight to move, influence, and grow people from the inside out. Inspiration. Education. Application. When you live your life like it matters… it does. Be sure to Like and Follow us on our facebook page! www.facebook.com/limradio Instagram @likeitmattersradio Twitter @likeitmatters Get daily inspiration from our blog www.wayofwarrior.blog Learn about our non profit work at www.givelikeitmatters.com Check out our training website www.LikeItMatters.Net Always available online at www.likeitmattersradio.comSee omnystudio.com/listener for privacy information.

Mawi and Shelly discuss a remarkable hypothermic patient, an emergence delirium patient that reqired 4 point restraints, and an unidentified decubitus ulcer bigger than Shelly's head. Enjoy the Wise guys for another episode of stories. 

The War Department is modernizing its acquisition ecosystem to meet the demands of modern warfare. Designed for speed, the updated approach redefines how the Pentagon manages risk, incorporates feedback and iterates on capability delivery. Speaking at AFCEA/USNI WEST in San Diego, California, Louis Koplin, the Navy's acting program executive officer for PEO Digital, said the Department of the Navy is focused on delivering capabilities early and often, using technology investment horizons to guide innovation. Iterative development, he said, relies on direct warfighter feedback and embedding operators within development teams. Koplin added that industry input is shaping lessons learned across the department to address current challenges. Commercial and dual-use technologies, he said, are transforming how the Navy acquires and deploys new capabilities.

This podcast features Gabriele Corso and Jeremy Wohlwend, co-founders of Boltz and authors of the Boltz Manifesto, discussing the rapid evolution of structural biology models from AlphaFold to their own open-source suite, Boltz-1 and Boltz-2. The central thesis is that while single-chain protein structure prediction is largely “solved” through evolutionary hints, the next frontier lies in modeling complex interactions (protein-ligand, protein-protein) and generative protein design, which Boltz aims to democratize via open-source foundations and scalable infrastructure.Full Video PodOn YouTube!Timestamps* 00:00 Introduction to Benchmarking and the “Solved” Protein Problem* 06:48 Evolutionary Hints and Co-evolution in Structure Prediction* 10:00 The Importance of Protein Function and Disease States* 15:31 Transitioning from AlphaFold 2 to AlphaFold 3 Capabilities* 19:48 Generative Modeling vs. Regression in Structural Biology* 25:00 The “Bitter Lesson” and Specialized AI Architectures* 29:14 Development Anecdotes: Training Boltz-1 on a Budget* 32:00 Validation Strategies and the Protein Data Bank (PDB)* 37:26 The Mission of Boltz: Democratizing Access and Open Source* 41:43 Building a Self-Sustaining Research Community* 44:40 Boltz-2 Advancements: Affinity Prediction and Design* 51:03 BoltzGen: Merging Structure and Sequence Prediction* 55:18 Large-Scale Wet Lab Validation Results* 01:02:44 Boltz Lab Product Launch: Agents and Infrastructure* 01:13:06 Future Directions: Developpability and the “Virtual Cell”* 01:17:35 Interacting with Skeptical Medicinal ChemistsKey SummaryEvolution of Structure Prediction & Evolutionary Hints* Co-evolutionary Landscapes: The speakers explain that breakthrough progress in single-chain protein prediction relied on decoding evolutionary correlations where mutations in one position necessitate mutations in another to conserve 3D structure.* Structure vs. Folding: They differentiate between structure prediction (getting the final answer) and folding (the kinetic process of reaching that state), noting that the field is still quite poor at modeling the latter.* Physics vs. Statistics: RJ posits that while models use evolutionary statistics to find the right “valley” in the energy landscape, they likely possess a “light understanding” of physics to refine the local minimum.The Shift to Generative Architectures* Generative Modeling: A key leap in AlphaFold 3 and Boltz-1 was moving from regression (predicting one static coordinate) to a generative diffusion approach that samples from a posterior distribution.* Handling Uncertainty: This shift allows models to represent multiple conformational states and avoid the “averaging” effect seen in regression models when the ground truth is ambiguous.* Specialized Architectures: Despite the “bitter lesson” of general-purpose transformers, the speakers argue that equivariant architectures remain vastly superior for biological data due to the inherent 3D geometric constraints of molecules.Boltz-2 and Generative Protein Design* Unified Encoding: Boltz-2 (and BoltzGen) treats structure and sequence prediction as a single task by encoding amino acid identities into the atomic composition of the predicted structure.* Design Specifics: Instead of a sequence, users feed the model blank tokens and a high-level “spec” (e.g., an antibody framework), and the model decodes both the 3D structure and the corresponding amino acids.* Affinity Prediction: While model confidence is a common metric, Boltz-2 focuses on affinity prediction—quantifying exactly how tightly a designed binder will stick to its target.Real-World Validation and Productization* Generalized Validation: To prove the model isn't just “regurgitating” known data, Boltz tested its designs on 9 targets with zero known interactions in the PDB, achieving nanomolar binders for two-thirds of them.* Boltz Lab Infrastructure: The newly launched Boltz Lab platform provides “agents” for protein and small molecule design, optimized to run 10x faster than open-source versions through proprietary GPU kernels.* Human-in-the-Loop: The platform is designed to convert skeptical medicinal chemists by allowing them to run parallel screens and use their intuition to filter model outputs.TranscriptRJ [00:05:35]: But the goal remains to, like, you know, really challenge the models, like, how well do these models generalize? And, you know, we've seen in some of the latest CASP competitions, like, while we've become really, really good at proteins, especially monomeric proteins, you know, other modalities still remain pretty difficult. So it's really essential, you know, in the field that there are, like, these efforts to gather, you know, benchmarks that are challenging. So it keeps us in line, you know, about what the models can do or not.Gabriel [00:06:26]: Yeah, it's interesting you say that, like, in some sense, CASP, you know, at CASP 14, a problem was solved and, like, pretty comprehensively, right? But at the same time, it was really only the beginning. So you can say, like, what was the specific problem you would argue was solved? And then, like, you know, what is remaining, which is probably quite open.RJ [00:06:48]: I think we'll steer away from the term solved, because we have many friends in the community who get pretty upset at that word. And I think, you know, fairly so. But the problem that was, you know, that a lot of progress was made on was the ability to predict the structure of single chain proteins. So proteins can, like, be composed of many chains. And single chain proteins are, you know, just a single sequence of amino acids. And one of the reasons that we've been able to make such progress is also because we take a lot of hints from evolution. So the way the models work is that, you know, they sort of decode a lot of hints. That comes from evolutionary landscapes. So if you have, like, you know, some protein in an animal, and you go find the similar protein across, like, you know, different organisms, you might find different mutations in them. And as it turns out, if you take a lot of the sequences together, and you analyze them, you see that some positions in the sequence tend to evolve at the same time as other positions in the sequence, sort of this, like, correlation between different positions. And it turns out that that is typically a hint that these two positions are close in three dimension. So part of the, you know, part of the breakthrough has been, like, our ability to also decode that very, very effectively. But what it implies also is that in absence of that co-evolutionary landscape, the models don't quite perform as well. And so, you know, I think when that information is available, maybe one could say, you know, the problem is, like, somewhat solved. From the perspective of structure prediction, when it isn't, it's much more challenging. And I think it's also worth also differentiating the, sometimes we confound a little bit, structure prediction and folding. Folding is the more complex process of actually understanding, like, how it goes from, like, this disordered state into, like, a structured, like, state. And that I don't think we've made that much progress on. But the idea of, like, yeah, going straight to the answer, we've become pretty good at.Brandon [00:08:49]: So there's this protein that is, like, just a long chain and it folds up. Yeah. And so we're good at getting from that long chain in whatever form it was originally to the thing. But we don't know how it necessarily gets to that state. And there might be intermediate states that it's in sometimes that we're not aware of.RJ [00:09:10]: That's right. And that relates also to, like, you know, our general ability to model, like, the different, you know, proteins are not static. They move, they take different shapes based on their energy states. And I think we are, also not that good at understanding the different states that the protein can be in and at what frequency, what probability. So I think the two problems are quite related in some ways. Still a lot to solve. But I think it was very surprising at the time, you know, that even with these evolutionary hints that we were able to, you know, to make such dramatic progress.Brandon [00:09:45]: So I want to ask, why does the intermediate states matter? But first, I kind of want to understand, why do we care? What proteins are shaped like?Gabriel [00:09:54]: Yeah, I mean, the proteins are kind of the machines of our body. You know, the way that all the processes that we have in our cells, you know, work is typically through proteins, sometimes other molecules, sort of intermediate interactions. And through that interactions, we have all sorts of cell functions. And so when we try to understand, you know, a lot of biology, how our body works, how disease work. So we often try to boil it down to, okay, what is going right in case of, you know, our normal biological function and what is going wrong in case of the disease state. And we boil it down to kind of, you know, proteins and kind of other molecules and their interaction. And so when we try predicting the structure of proteins, it's critical to, you know, have an understanding of kind of those interactions. It's a bit like seeing the difference between... Having kind of a list of parts that you would put it in a car and seeing kind of the car in its final form, you know, seeing the car really helps you understand what it does. On the other hand, kind of going to your question of, you know, why do we care about, you know, how the protein falls or, you know, how the car is made to some extent is that, you know, sometimes when something goes wrong, you know, there are, you know, cases of, you know, proteins misfolding. In some diseases and so on, if we don't understand this folding process, we don't really know how to intervene.RJ [00:11:30]: There's this nice line in the, I think it's in the Alpha Fold 2 manuscript, where they sort of discuss also like why we even hopeful that we can target the problem in the first place. And then there's this notion that like, well, four proteins that fold. The folding process is almost instantaneous, which is a strong, like, you know, signal that like, yeah, like we should, we might be... able to predict that this very like constrained thing that, that the protein does so quickly. And of course that's not the case for, you know, for, for all proteins. And there's a lot of like really interesting mechanisms in the cells, but yeah, I remember reading that and thought, yeah, that's somewhat of an insightful point.Gabriel [00:12:10]: I think one of the interesting things about the protein folding problem is that it used to be actually studied. And part of the reason why people thought it was impossible, it used to be studied as kind of like a classical example. Of like an MP problem. Uh, like there are so many different, you know, type of, you know, shapes that, you know, this amino acid could take. And so, this grows combinatorially with the size of the sequence. And so there used to be kind of a lot of actually kind of more theoretical computer science thinking about and studying protein folding as an MP problem. And so it was very surprising also from that perspective, kind of seeing. Machine learning so clear, there is some, you know, signal in those sequences, through evolution, but also through kind of other things that, you know, us as humans, we're probably not really able to, uh, to understand, but that is, models I've, I've learned.Brandon [00:13:07]: And so Andrew White, we were talking to him a few weeks ago and he said that he was following the development of this and that there were actually ASICs that were developed just to solve this problem. So, again, that there were. There were many, many, many millions of computational hours spent trying to solve this problem before AlphaFold. And just to be clear, one thing that you mentioned was that there's this kind of co-evolution of mutations and that you see this again and again in different species. So explain why does that give us a good hint that they're close by to each other? Yeah.RJ [00:13:41]: Um, like think of it this way that, you know, if I have, you know, some amino acid that mutates, it's going to impact everything around it. Right. In three dimensions. And so it's almost like the protein through several, probably random mutations and evolution, like, you know, ends up sort of figuring out that this other amino acid needs to change as well for the structure to be conserved. Uh, so this whole principle is that the structure is probably largely conserved, you know, because there's this function associated with it. And so it's really sort of like different positions compensating for, for each other. I see.Brandon [00:14:17]: Those hints in aggregate give us a lot. Yeah. So you can start to look at what kinds of information about what is close to each other, and then you can start to look at what kinds of folds are possible given the structure and then what is the end state.RJ [00:14:30]: And therefore you can make a lot of inferences about what the actual total shape is. Yeah, that's right. It's almost like, you know, you have this big, like three dimensional Valley, you know, where you're sort of trying to find like these like low energy states and there's so much to search through. That's almost overwhelming. But these hints, they sort of maybe put you in. An area of the space that's already like, kind of close to the solution, maybe not quite there yet. And, and there's always this question of like, how much physics are these models learning, you know, versus like, just pure like statistics. And like, I think one of the thing, at least I believe is that once you're in that sort of approximate area of the solution space, then the models have like some understanding, you know, of how to get you to like, you know, the lower energy, uh, low energy state. And so maybe you have some, some light understanding. Of physics, but maybe not quite enough, you know, to know how to like navigate the whole space. Right. Okay.Brandon [00:15:25]: So we need to give it these hints to kind of get into the right Valley and then it finds the, the minimum or something. Yeah.Gabriel [00:15:31]: One interesting explanation about our awful free works that I think it's quite insightful, of course, doesn't cover kind of the entirety of, of what awful does that is, um, they're going to borrow from, uh, Sergio Chinico for MIT. So he sees kind of awful. Then the interesting thing about awful is God. This very peculiar architecture that we have seen, you know, used, and this architecture operates on this, you know, pairwise context between amino acids. And so the idea is that probably the MSA gives you this first hint about what potential amino acids are close to each other. MSA is most multiple sequence alignment. Exactly. Yeah. Exactly. This evolutionary information. Yeah. And, you know, from this evolutionary information about potential contacts, then is almost as if the model is. of running some kind of, you know, diastro algorithm where it's sort of decoding, okay, these have to be closed. Okay. Then if these are closed and this is connected to this, then this has to be somewhat closed. And so you decode this, that becomes basically a pairwise kind of distance matrix. And then from this rough pairwise distance matrix, you decode kind of theBrandon [00:16:42]: actual potential structure. Interesting. So there's kind of two different things going on in the kind of coarse grain and then the fine grain optimizations. Interesting. Yeah. Very cool.Gabriel [00:16:53]: Yeah. You mentioned AlphaFold3. So maybe we have a good time to move on to that. So yeah, AlphaFold2 came out and it was like, I think fairly groundbreaking for this field. Everyone got very excited. A few years later, AlphaFold3 came out and maybe for some more history, like what were the advancements in AlphaFold3? And then I think maybe we'll, after that, we'll talk a bit about the sort of how it connects to Bolt. But anyway. Yeah. So after AlphaFold2 came out, you know, Jeremy and I got into the field and with many others, you know, the clear problem that, you know, was, you know, obvious after that was, okay, now we can do individual chains. Can we do interactions, interaction, different proteins, proteins with small molecules, proteins with other molecules. And so. So why are interactions important? Interactions are important because to some extent that's kind of the way that, you know, these machines, you know, these proteins have a function, you know, the function comes by the way that they interact with other proteins and other molecules. Actually, in the first place, you know, the individual machines are often, as Jeremy was mentioning, not made of a single chain, but they're made of the multiple chains. And then these multiple chains interact with other molecules to give the function to those. And on the other hand, you know, when we try to intervene of these interactions, think about like a disease, think about like a, a biosensor or many other ways we are trying to design the molecules or proteins that interact in a particular way with what we would call a target protein or target. You know, this problem after AlphaVol2, you know, became clear, kind of one of the biggest problems in the field to, to solve many groups, including kind of ours and others, you know, started making some kind of contributions to this problem of trying to model these interactions. And AlphaVol3 was, you know, was a significant advancement on the problem of modeling interactions. And one of the interesting thing that they were able to do while, you know, some of the rest of the field that really tried to try to model different interactions separately, you know, how protein interacts with small molecules, how protein interacts with other proteins, how RNA or DNA have their structure, they put everything together and, you know, train very large models with a lot of advances, including kind of changing kind of systems. Some of the key architectural choices and managed to get a single model that was able to set this new state-of-the-art performance across all of these different kind of modalities, whether that was protein, small molecules is critical to developing kind of new drugs, protein, protein, understanding, you know, interactions of, you know, proteins with RNA and DNAs and so on.Brandon [00:19:39]: Just to satisfy the AI engineers in the audience, what were some of the key architectural and data, data changes that made that possible?Gabriel [00:19:48]: Yeah, so one critical one that was not necessarily just unique to AlphaFold3, but there were actually a few other teams, including ours in the field that proposed this, was moving from, you know, modeling structure prediction as a regression problem. So where there is a single answer and you're trying to shoot for that answer to a generative modeling problem where you have a posterior distribution of possible structures and you're trying to sample this distribution. And this achieves two things. One is it starts to allow us to try to model more dynamic systems. As we said, you know, some of these structures can actually take multiple structures. And so, you know, you can now model that, you know, through kind of modeling the entire distribution. But on the second hand, from more kind of core modeling questions, when you move from a regression problem to a generative modeling problem, you are really tackling the way that you think about uncertainty in the model in a different way. So if you think about, you know, I'm undecided between different answers, what's going to happen in a regression model is that, you know, I'm going to try to make an average of those different kind of answers that I had in mind. When you have a generative model, what you're going to do is, you know, sample all these different answers and then maybe use separate models to analyze those different answers and pick out the best. So that was kind of one of the critical improvement. The other improvement is that they significantly simplified, to some extent, the architecture, especially of the final model that takes kind of those pairwise representations and turns them into an actual structure. And that now looks a lot more like a more traditional transformer than, you know, like a very specialized equivariant architecture that it was in AlphaFold3.Brandon [00:21:41]: So this is a bitter lesson, a little bit.Gabriel [00:21:45]: There is some aspect of a bitter lesson, but the interesting thing is that it's very far from, you know, being like a simple transformer. This field is one of the, I argue, very few fields in applied machine learning where we still have kind of architecture that are very specialized. And, you know, there are many people that have tried to replace these architectures with, you know, simple transformers. And, you know, there is a lot of debate in the field, but I think kind of that most of the consensus is that, you know, the performance... that we get from the specialized architecture is vastly superior than what we get through a single transformer. Another interesting thing that I think on the staying on the modeling machine learning side, which I think it's somewhat counterintuitive seeing some of the other kind of fields and applications is that scaling hasn't really worked kind of the same in this field. Now, you know, models like AlphaFold2 and AlphaFold3 are, you know, still very large models.RJ [00:29:14]: in a place, I think, where we had, you know, some experience working in, you know, with the data and working with this type of models. And I think that put us already in like a good place to, you know, to produce it quickly. And, you know, and I would even say, like, I think we could have done it quicker. The problem was like, for a while, we didn't really have the compute. And so we couldn't really train the model. And actually, we only trained the big model once. That's how much compute we had. We could only train it once. And so like, while the model was training, we were like, finding bugs left and right. A lot of them that I wrote. And like, I remember like, I was like, sort of like, you know, doing like, surgery in the middle, like stopping the run, making the fix, like relaunching. And yeah, we never actually went back to the start. We just like kept training it with like the bug fixes along the way, which was impossible to reproduce now. Yeah, yeah, no, that model is like, has gone through such a curriculum that, you know, learned some weird stuff. But yeah, somehow by miracle, it worked out.Gabriel [00:30:13]: The other funny thing is that the way that we were training, most of that model was through a cluster from the Department of Energy. But that's sort of like a shared cluster that many groups use. And so we were basically training the model for two days, and then it would go back to the queue and stay a week in the queue. Oh, yeah. And so it was pretty painful. And so we actually kind of towards the end with Evan, the CEO of Genesis, and basically, you know, I was telling him a bit about the project and, you know, kind of telling him about this frustration with the compute. And so luckily, you know, he offered to kind of help. And so we, we got the help from Genesis to, you know, finish up the model. Otherwise, it probably would have taken a couple of extra weeks.Brandon [00:30:57]: Yeah, yeah.Brandon [00:31:02]: And then, and then there's some progression from there.Gabriel [00:31:06]: Yeah, so I would say kind of that, both one, but also kind of these other kind of set of models that came around the same time, were kind of approaching were a big leap from, you know, kind of the previous kind of open source models, and, you know, kind of really kind of approaching the level of AlphaVault 3. But I would still say that, you know, even to this day, there are, you know, some... specific instances where AlphaVault 3 works better. I think one common example is antibody antigen prediction, where, you know, AlphaVault 3 still seems to have an edge in many situations. Obviously, these are somewhat different models. They are, you know, you run them, you obtain different results. So it's, it's not always the case that one model is better than the other, but kind of in aggregate, we still, especially at the time.Brandon [00:32:00]: So AlphaVault 3 is, you know, still having a bit of an edge. We should talk about this more when we talk about Boltzgen, but like, how do you know one is, one model is better than the other? Like you, so you, I make a prediction, you make a prediction, like, how do you know?Gabriel [00:32:11]: Yeah, so easily, you know, the, the great thing about kind of structural prediction and, you know, once we're going to go into the design space of designing new small molecule, new proteins, this becomes a lot more complex. But a great thing about structural prediction is that a bit like, you know, CASP was doing, basically the way that you can evaluate them is that, you know, you train... You know, you train a model on a structure that was, you know, released across the field up until a certain time. And, you know, one of the things that we didn't talk about that was really critical in all this development is the PDB, which is the Protein Data Bank. It's this common resources, basically common database where every biologist publishes their structures. And so we can, you know, train on, you know, all the structures that were put in the PDB until a certain date. And then... And then we basically look for recent structures, okay, which structures look pretty different from anything that was published before, because we really want to try to understand generalization.Brandon [00:33:13]: And then on this new structure, we evaluate all these different models. And so you just know when AlphaFold3 was trained, you know, when you're, you intentionally trained to the same date or something like that. Exactly. Right. Yeah.Gabriel [00:33:24]: And so this is kind of the way that you can somewhat easily kind of compare these models, obviously, that assumes that, you know, the training. You've always been very passionate about validation. I remember like DiffDoc, and then there was like DiffDocL and DocGen. You've thought very carefully about this in the past. Like, actually, I think DocGen is like a really funny story that I think, I don't know if you want to talk about that. It's an interesting like... Yeah, I think one of the amazing things about putting things open source is that we get a ton of feedback from the field. And, you know, sometimes we get kind of great feedback of people. Really like... But honestly, most of the times, you know, to be honest, that's also maybe the most useful feedback is, you know, people sharing about where it doesn't work. And so, you know, at the end of the day, it's critical. And this is also something, you know, across other fields of machine learning. It's always critical to set, to do progress in machine learning, set clear benchmarks. And as, you know, you start doing progress of certain benchmarks, then, you know, you need to improve the benchmarks and make them harder and harder. And this is kind of the progression of, you know, how the field operates. And so, you know, the example of DocGen was, you know, we published this initial model called DiffDoc in my first year of PhD, which was sort of like, you know, one of the early models to try to predict kind of interactions between proteins, small molecules, that we bought a year after AlphaFold2 was published. And now, on the one hand, you know, on these benchmarks that we were using at the time, DiffDoc was doing really well, kind of, you know, outperforming kind of some of the traditional physics-based methods. But on the other hand, you know, when we started, you know, kind of giving these tools to kind of many biologists, and one example was that we collaborated with was the group of Nick Polizzi at Harvard. We noticed, started noticing that there was this clear, pattern where four proteins that were very different from the ones that we're trained on, the models was, was struggling. And so, you know, that seemed clear that, you know, this is probably kind of where we should, you know, put our focus on. And so we first developed, you know, with Nick and his group, a new benchmark, and then, you know, went after and said, okay, what can we change? And kind of about the current architecture to improve this pattern and generalization. And this is the same that, you know, we're still doing today, you know, kind of, where does the model not work, you know, and then, you know, once we have that benchmark, you know, let's try to, through everything we, any ideas that we have of the problem.RJ [00:36:15]: And there's a lot of like healthy skepticism in the field, which I think, you know, is, is, is great. And I think, you know, it's very clear that there's a ton of things, the models don't really work well on, but I think one thing that's probably, you know, undeniable is just like the pace of, pace of progress, you know, and how, how much better we're getting, you know, every year. And so I think if you, you know, if you assume, you know, any constant, you know, rate of progress moving forward, I think things are going to look pretty cool at some point in the future.Gabriel [00:36:42]: ChatGPT was only three years ago. Yeah, I mean, it's wild, right?RJ [00:36:45]: Like, yeah, yeah, yeah, it's one of those things. Like, you've been doing this. Being in the field, you don't see it coming, you know? And like, I think, yeah, hopefully we'll, you know, we'll, we'll continue to have as much progress we've had the past few years.Brandon [00:36:55]: So this is maybe an aside, but I'm really curious, you get this great feedback from the, from the community, right? By being open source. My question is partly like, okay, yeah, if you open source and everyone can copy what you did, but it's also maybe balancing priorities, right? Where you, like all my customers are saying. I want this, there's all these problems with the model. Yeah, yeah. But my customers don't care, right? So like, how do you, how do you think about that? Yeah.Gabriel [00:37:26]: So I would say a couple of things. One is, you know, part of our goal with Bolts and, you know, this is also kind of established as kind of the mission of the public benefit company that we started is to democratize the access to these tools. But one of the reasons why we realized that Bolts needed to be a company, it couldn't just be an academic project is that putting a model on GitHub is definitely not enough to get, you know, chemists and biologists, you know, across, you know, both academia, biotech and pharma to use your model to, in their therapeutic programs. And so a lot of what we think about, you know, at Bolts beyond kind of the, just the models is thinking about all the layers. The layers that come on top of the models to get, you know, from, you know, those models to something that can really enable scientists in the industry. And so that goes, you know, into building kind of the right kind of workflows that take in kind of, for example, the data and try to answer kind of directly that those problems that, you know, the chemists and the biologists are asking, and then also kind of building the infrastructure. And so this to say that, you know, even with models fully open. You know, we see a ton of potential for, you know, products in the space and the critical part about a product is that even, you know, for example, with an open source model, you know, running the model is not free, you know, as we were saying, these are pretty expensive model and especially, and maybe we'll get into this, you know, these days we're seeing kind of pretty dramatic inference time scaling of these models where, you know, the more you run them, the better the results are. But there, you know, you see. You start getting into a point that compute and compute costs becomes a critical factor. And so putting a lot of work into building the right kind of infrastructure, building the optimizations and so on really allows us to provide, you know, a much better service potentially to the open source models. That to say, you know, even though, you know, with a product, we can provide a much better service. I do still think, and we will continue to put a lot of our models open source because the critical kind of role. I think of open source. Models is, you know, helping kind of the community progress on the research and, you know, from which we, we all benefit. And so, you know, we'll continue to on the one hand, you know, put some of our kind of base models open source so that the field can, can be on top of it. And, you know, as we discussed earlier, we learn a ton from, you know, the way that the field uses and builds on top of our models, but then, you know, try to build a product that gives the best experience possible to scientists. So that, you know, like a chemist or a biologist doesn't need to, you know, spin off a GPU and, you know, set up, you know, our open source model in a particular way, but can just, you know, a bit like, you know, I, even though I am a computer scientist, machine learning scientist, I don't necessarily, you know, take a open source LLM and try to kind of spin it off. But, you know, I just maybe open a GPT app or a cloud code and just use it as an amazing product. We kind of want to give the same experience. So this front world.Brandon [00:40:40]: I heard a good analogy yesterday that a surgeon doesn't want the hospital to design a scalpel, right?Brandon [00:40:48]: So just buy the scalpel.RJ [00:40:50]: You wouldn't believe like the number of people, even like in my short time, you know, between AlphaFold3 coming out and the end of the PhD, like the number of people that would like reach out just for like us to like run AlphaFold3 for them, you know, or things like that. Just because like, you know, bolts in our case, you know, just because it's like. It's like not that easy, you know, to do that, you know, if you're not a computational person. And I think like part of the goal here is also that, you know, we continue to obviously build the interface with computational folks, but that, you know, the models are also accessible to like a larger, broader audience. And then that comes from like, you know, good interfaces and stuff like that.Gabriel [00:41:27]: I think one like really interesting thing about bolts is that with the release of it, you didn't just release a model, but you created a community. Yeah. Did that community, it grew very quickly. Did that surprise you? And like, what is the evolution of that community and how is that fed into bolts?RJ [00:41:43]: If you look at its growth, it's like very much like when we release a new model, it's like, there's a big, big jump, but yeah, it's, I mean, it's been great. You know, we have a Slack community that has like thousands of people on it. And it's actually like self-sustaining now, which is like the really nice part because, you know, it's, it's almost overwhelming, I think, you know, to be able to like answer everyone's questions and help. It's really difficult, you know. The, the few people that we were, but it ended up that like, you know, people would answer each other's questions and like, sort of like, you know, help one another. And so the Slack, you know, has been like kind of, yeah, self, self-sustaining and that's been, it's been really cool to see.RJ [00:42:21]: And, you know, that's, that's for like the Slack part, but then also obviously on GitHub as well. We've had like a nice, nice community. You know, I think we also aspire to be even more active on it, you know, than we've been in the past six months, which has been like a bit challenging, you know, for us. But. Yeah, the community has been, has been really great and, you know, there's a lot of papers also that have come out with like new evolutions on top of bolts and it's surprised us to some degree because like there's a lot of models out there. And I think like, you know, sort of people converging on that was, was really cool. And, you know, I think it speaks also, I think, to the importance of like, you know, when, when you put code out, like to try to put a lot of emphasis and like making it like as easy to use as possible and something we thought a lot about when we released the code base. You know, it's far from perfect, but, you know.Brandon [00:43:07]: Do you think that that was one of the factors that caused your community to grow is just the focus on easy to use, make it accessible? I think so.RJ [00:43:14]: Yeah. And we've, we've heard it from a few people over the, over the, over the years now. And, you know, and some people still think it should be a lot nicer and they're, and they're right. And they're right. But yeah, I think it was, you know, at the time, maybe a little bit easier than, than other things.Gabriel [00:43:29]: The other thing part, I think led to, to the community and to some extent, I think, you know, like the somewhat the trust in the community. Kind of what we, what we put out is the fact that, you know, it's not really been kind of, you know, one model, but, and maybe we'll talk about it, you know, after Boltz 1, you know, there were maybe another couple of models kind of released, you know, or open source kind of soon after. We kind of continued kind of that open source journey or at least Boltz 2, where we are not only improving kind of structure prediction, but also starting to do affinity predictions, understanding kind of the strength of the interactions between these different models, which is this critical component. critical property that you often want to optimize in discovery programs. And then, you know, more recently also kind of protein design model. And so we've sort of been building this suite of, of models that come together, interact with one another, where, you know, kind of, there is almost an expectation that, you know, we, we take very at heart of, you know, always having kind of, you know, across kind of the entire suite of different tasks, the best or across the best. model out there so that it's sort of like our open source tool can be kind of the go-to model for everybody in the, in the industry. I really want to talk about Boltz 2, but before that, one last question in this direction, was there anything about the community which surprised you? Were there any, like, someone was doing something and you're like, why would you do that? That's crazy. Or that's actually genius. And I never would have thought about that.RJ [00:45:01]: I mean, we've had many contributions. I think like some of the. Interesting ones, like, I mean, we had, you know, this one individual who like wrote like a complex GPU kernel, you know, for part of the architecture on a piece of, the funny thing is like that piece of the architecture had been there since AlphaFold 2, and I don't know why it took Boltz for this, you know, for this person to, you know, to decide to do it, but that was like a really great contribution. We've had a bunch of others, like, you know, people figuring out like ways to, you know, hack the model to do something. They click peptides, like, you know, there's, I don't know if there's any other interesting ones come to mind.Gabriel [00:45:41]: One cool one, and this was, you know, something that initially was proposed as, you know, as a message in the Slack channel by Tim O'Donnell was basically, he was, you know, there are some cases, especially, for example, we discussed, you know, antibody-antigen interactions where the models don't necessarily kind of get the right answer. What he noticed is that, you know, the models were somewhat stuck into predicting kind of the antibodies. And so he basically ran the experiments in this model, you can condition, basically, you can give hints. And so he basically gave, you know, random hints to the model, basically, okay, you should bind to this residue, you should bind to the first residue, or you should bind to the 11th residue, or you should bind to the 21st residue, you know, basically every 10 residues scanning the entire antigen.Brandon [00:46:33]: Residues are the...Gabriel [00:46:34]: The amino acids. The amino acids, yeah. So the first amino acids. The 11 amino acids, and so on. So it's sort of like doing a scan, and then, you know, conditioning the model to predict all of them, and then looking at the confidence of the model in each of those cases and taking the top. And so it's sort of like a very somewhat crude way of doing kind of inference time search. But surprisingly, you know, for antibody-antigen prediction, it actually kind of helped quite a bit. And so there's some, you know, interesting ideas that, you know, obviously, as kind of developing the model, you say kind of, you know, wow. This is why would the model, you know, be so dumb. But, you know, it's very interesting. And that, you know, leads you to also kind of, you know, start thinking about, okay, how do I, can I do this, you know, not with this brute force, but, you know, in a smarter way.RJ [00:47:22]: And so we've also done a lot of work on that direction. And that speaks to, like, the, you know, the power of scoring. We're seeing that a lot. I'm sure we'll talk about it more when we talk about BullsGen. But, you know, our ability to, like, take a structure and determine that that structure is, like... Good. You know, like, somewhat accurate. Whether that's a single chain or, like, an interaction is a really powerful way of improving, you know, the models. Like, sort of like, you know, if you can sample a ton and you assume that, like, you know, if you sample enough, you're likely to have, like, you know, the good structure. Then it really just becomes a ranking problem. And, you know, now we're, you know, part of the inference time scaling that Gabby was talking about is very much that. It's like, you know, the more we sample, the more we, like, you know, the ranking model. The ranking model ends up finding something it really likes. And so I think our ability to get better at ranking, I think, is also what's going to enable sort of the next, you know, next big, big breakthroughs. Interesting.Brandon [00:48:17]: But I guess there's a, my understanding, there's a diffusion model and you generate some stuff and then you, I guess, it's just what you said, right? Then you rank it using a score and then you finally... And so, like, can you talk about those different parts? Yeah.Gabriel [00:48:34]: So, first of all, like, the... One of the critical kind of, you know, beliefs that we had, you know, also when we started working on Boltz 1 was sort of like the structure prediction models are somewhat, you know, our field version of some foundation models, you know, learning about kind of how proteins and other molecules interact. And then we can leverage that learning to do all sorts of other things. And so with Boltz 2, we leverage that learning to do affinity predictions. So understanding kind of, you know, if I give you this protein, this molecule. How tightly is that interaction? For Boltz 1, what we did was taking kind of that kind of foundation models and then fine tune it to predict kind of entire new proteins. And so the way basically that that works is sort of like instead of for the protein that you're designing, instead of fitting in an actual sequence, you fit in a set of blank tokens. And you train the models to, you know, predict both the structure of kind of that protein. The structure also, what the different amino acids of that proteins are. And so basically the way that Boltz 1 operates is that you feed a target protein that you may want to kind of bind to or, you know, another DNA, RNA. And then you feed the high level kind of design specification of, you know, what you want your new protein to be. For example, it could be like an antibody with a particular framework. It could be a peptide. It could be many other things. And that's with natural language or? And that's, you know, basically, you know, prompting. And we have kind of this sort of like spec that you specify. And, you know, you feed kind of this spec to the model. And then the model translates this into, you know, a set of, you know, tokens, a set of conditioning to the model, a set of, you know, blank tokens. And then, you know, basically the codes as part of the diffusion models, the codes. It's a new structure and a new sequence for your protein. And, you know, basically, then we take that. And as Jeremy was saying, we are trying to score it and, you know, how good of a binder it is to that original target.Brandon [00:50:51]: You're using basically Boltz to predict the folding and the affinity to that molecule. So and then that kind of gives you a score? Exactly.Gabriel [00:51:03]: So you use this model to predict the folding. And then you do two things. One is that you predict the structure and with something like Boltz2, and then you basically compare that structure with what the model predicted, what Boltz2 predicted. And this is sort of like in the field called consistency. It's basically you want to make sure that, you know, the structure that you're predicting is actually what you're trying to design. And that gives you a much better confidence that, you know, that's a good design. And so that's the first filtering. And the second filtering that we did as part of kind of the Boltz2 pipeline that was released is that we look at the confidence that the model has in the structure. Now, unfortunately, kind of going to your question of, you know, predicting affinity, unfortunately, confidence is not a very good predictor of affinity. And so one of the things that we've actually done a ton of progress, you know, since we released Boltz2.Brandon [00:52:03]: And kind of we have some new results that we are going to kind of announce soon is kind of, you know, the ability to get much better hit rates when instead of, you know, trying to rely on confidence of the model, we are actually directly trying to predict the affinity of that interaction. Okay. Just backing up a minute. So your diffusion model actually predicts not only the protein sequence, but also the folding of it. Exactly.Gabriel [00:52:32]: And actually, you can... One of the big different things that we did compared to other models in the space, and, you know, there were some papers that had already kind of done this before, but we really scaled it up was, you know, basically somewhat merging kind of the structure prediction and the sequence prediction into almost the same task. And so the way that Boltz2 works is that you are basically the only thing that you're doing is predicting the structure. So the only sort of... Supervision is we give you a supervision on the structure, but because the structure is atomic and, you know, the different amino acids have a different atomic composition, basically from the way that you place the atoms, we also understand not only kind of the structure that you wanted, but also the identity of the amino acid that, you know, the models believed was there. And so we've basically, instead of, you know, having these two supervision signals, you know, one discrete, one continuous. That somewhat, you know, don't interact well together. We sort of like build kind of like an encoding of, you know, sequences in structures that allows us to basically use exactly the same supervision signal that we were using to Boltz2 that, you know, you know, largely similar to what AlphaVol3 proposed, which is very scalable. And we can use that to design new proteins. Oh, interesting.RJ [00:53:58]: Maybe a quick shout out to Hannes Stark on our team who like did all this work. Yeah.Gabriel [00:54:04]: Yeah, that was a really cool idea. I mean, like looking at the paper and there's this is like encoding or you just add a bunch of, I guess, kind of atoms, which can be anything, and then they get sort of rearranged and then basically plopped on top of each other so that and then that encodes what the amino acid is. And there's sort of like a unique way of doing this. It was that was like such a really such a cool, fun idea.RJ [00:54:29]: I think that idea was had existed before. Yeah, there were a couple of papers.Gabriel [00:54:33]: Yeah, I had proposed this and and Hannes really took it to the large scale.Brandon [00:54:39]: In the paper, a lot of the paper for Boltz2Gen is dedicated to actually the validation of the model. In my opinion, all the people we basically talk about feel that this sort of like in the wet lab or whatever the appropriate, you know, sort of like in real world validation is the whole problem or not the whole problem, but a big giant part of the problem. So can you talk a little bit about the highlights? From there, that really because to me, the results are impressive, both from the perspective of the, you know, the model and also just the effort that went into the validation by a large team.Gabriel [00:55:18]: First of all, I think I should start saying is that both when we were at MIT and Thomas Yacolas and Regina Barzillai's lab, as well as at Boltz, you know, we are not a we're not a biolab and, you know, we are not a therapeutic company. And so to some extent, you know, we were first forced to, you know, look outside of, you know, our group, our team to do the experimental validation. One of the things that really, Hannes, in the team pioneer was the idea, OK, can we go not only to, you know, maybe a specific group and, you know, trying to find a specific system and, you know, maybe overfit a bit to that system and trying to validate. But how can we test this model? So. Across a very wide variety of different settings so that, you know, anyone in the field and, you know, printing design is, you know, such a kind of wide task with all sorts of different applications from therapeutic to, you know, biosensors and many others that, you know, so can we get a validation that is kind of goes across many different tasks? And so he basically put together, you know, I think it was something like, you know, 25 different. You know, academic and industry labs that committed to, you know, testing some of the designs from the model and some of this testing is still ongoing and, you know, giving results kind of back to us in exchange for, you know, hopefully getting some, you know, new great sequences for their task. And he was able to, you know, coordinate this, you know, very wide set of, you know, scientists and already in the paper, I think we. Shared results from, I think, eight to 10 different labs kind of showing results from, you know, designing peptides, designing to target, you know, ordered proteins, peptides targeting disordered proteins, which are results, you know, of designing proteins that bind to small molecules, which are results of, you know, designing nanobodies and across a wide variety of different targets. And so that's sort of like. That gave to the paper a lot of, you know, validation to the model, a lot of validation that was kind of wide.Brandon [00:57:39]: And so those would be therapeutics for those animals or are they relevant to humans as well? They're relevant to humans as well.Gabriel [00:57:45]: Obviously, you need to do some work into, quote unquote, humanizing them, making sure that, you know, they have the right characteristics to so they're not toxic to humans and so on.RJ [00:57:57]: There are some approved medicine in the market that are nanobodies. There's a general. General pattern, I think, in like in trying to design things that are smaller, you know, like it's easier to manufacture at the same time, like that comes with like potentially other challenges, like maybe a little bit less selectivity than like if you have something that has like more hands, you know, but the yeah, there's this big desire to, you know, try to design many proteins, nanobodies, small peptides, you know, that just are just great drug modalities.Brandon [00:58:27]: Okay. I think we were left off. We were talking about validation. Validation in the lab. And I was very excited about seeing like all the diverse validations that you've done. Can you go into some more detail about them? Yeah. Specific ones. Yeah.RJ [00:58:43]: The nanobody one. I think we did. What was it? 15 targets. Is that correct? 14. 14 targets. Testing. So we typically the way this works is like we make a lot of designs. All right. On the order of like tens of thousands. And then we like rank them and we pick like the top. And in this case, and was 15 right for each target and then we like measure sort of like the success rates, both like how many targets we were able to get a binder for and then also like more generally, like out of all of the binders that we designed, how many actually proved to be good binders. Some of the other ones I think involved like, yeah, like we had a cool one where there was a small molecule or design a protein that binds to it. That has a lot of like interesting applications, you know, for example. Like Gabri mentioned, like biosensing and things like that, which is pretty cool. We had a disordered protein, I think you mentioned also. And yeah, I think some of those were some of the highlights. Yeah.Gabriel [00:59:44]: So I would say that the way that we structure kind of some of those validations was on the one end, we have validations across a whole set of different problems that, you know, the biologists that we were working with came to us with. So we were trying to. For example, in some of the experiments, design peptides that would target the RACC, which is a target that is involved in metabolism. And we had, you know, a number of other applications where we were trying to design, you know, peptides or other modalities against some other therapeutic relevant targets. We designed some proteins to bind small molecules. And then some of the other testing that we did was really trying to get like a more broader sense. So how does the model work, especially when tested, you know, on somewhat generalization? So one of the things that, you know, we found with the field was that a lot of the validation, especially outside of the validation that was on specific problems, was done on targets that have a lot of, you know, known interactions in the training data. And so it's always a bit hard to understand, you know, how much are these models really just regurgitating kind of what they've seen or trying to imitate. What they've seen in the training data versus, you know, really be able to design new proteins. And so one of the experiments that we did was to take nine targets from the PDB, filtering to things where there is no known interaction in the PDB. So basically the model has never seen kind of this particular protein bound or a similar protein bound to another protein. So there is no way that. The model from its training set can sort of like say, okay, I'm just going to kind of tweak something and just imitate this particular kind of interaction. And so we took those nine proteins. We worked with adaptive CRO and basically tested, you know, 15 mini proteins and 15 nanobodies against each one of them. And the very cool thing that we saw was that on two thirds of those targets, we were able to, from this 15 design, get nanomolar binders, nanomolar, roughly speaking, just a measure of, you know, how strongly kind of the interaction is, roughly speaking, kind of like a nanomolar binder is approximately the kind of binding strength or binding that you need for a therapeutic. Yeah. So maybe switching directions a bit. Bolt's lab was just announced this week or was it last week? Yeah. This is like your. First, I guess, product, if that's if you want to call it that. Can you talk about what Bolt's lab is and yeah, you know, what you hope that people take away from this? Yeah.RJ [01:02:44]: You know, as we mentioned, like I think at the very beginning is the goal with the product has been to, you know, address what the models don't on their own. And there's largely sort of two categories there. I'll split it in three. The first one. It's one thing to predict, you know, a single interaction, for example, like a single structure. It's another to like, you know, very effectively search a space, a design space to produce something of value. What we found, like sort of building on this product is that there's a lot of steps involved, you know, in that there's certainly need to like, you know, accompany the user through, you know, one of those steps, for example, is like, you know, the creation of the target itself. You know, how do we make sure that the model has like a good enough understanding of the target? So we can like design something and there's all sorts of tricks, you know, that you can do to improve like a particular, you know, structure prediction. And so that's sort of like, you know, the first stage. And then there's like this stage of like, you know, designing and searching the space efficiently. You know, for something like BullsGen, for example, like you, you know, you design many things and then you rank them, for example, for small molecule process, a little bit more complicated. We actually need to also make sure that the molecules are synthesizable. And so the way we do that is that, you know, we have a generative model that learns. To use like appropriate building blocks such that, you know, it can design within a space that we know is like synthesizable. And so there's like, you know, this whole pipeline really of different models involved in being able to design a molecule. And so that's been sort of like the first thing we call them agents. We have a protein agent and we have a small molecule design agents. And that's really like at the core of like what powers, you know, the BullsLab platform.Brandon [01:04:22]: So these agents, are they like a language model wrapper or they're just like your models and you're just calling them agents? A lot. Yeah. Because they, they, they sort of perform a function on behalf of.RJ [01:04:33]: They're more of like a, you know, a recipe, if you wish. And I think we use that term sort of because of, you know, sort of the complex pipelining and automation, you know, that goes into like all this plumbing. So that's the first part of the product. The second part is the infrastructure. You know, we need to be able to do this at very large scale for any one, you know, group that's doing a design campaign. Let's say you're designing, you know, I'd say a hundred thousand possible candidates. Right. To find the good one that is, you know, a very large amount of compute, you know, for small molecules, it's on the order of like a few seconds per designs for proteins can be a bit longer. And so, you know, ideally you want to do that in parallel, otherwise it's going to take you weeks. And so, you know, we've put a lot of effort into like, you know, our ability to have a GPU fleet that allows any one user, you know, to be able to do this kind of like large parallel search.Brandon [01:05:23]: So you're amortizing the cost over your users. Exactly. Exactly.RJ [01:05:27]: And, you know, to some degree, like it's whether you. Use 10,000 GPUs for like, you know, a minute is the same cost as using, you know, one GPUs for God knows how long. Right. So you might as well try to parallelize if you can. So, you know, a lot of work has gone, has gone into that, making it very robust, you know, so that we can have like a lot of people on the platform doing that at the same time. And the third one is, is the interface and the interface comes in, in two shapes. One is in form of an API and that's, you know, really suited for companies that want to integrate, you know, these pipelines, these agents.RJ [01:06:01]: So we're already partnering with, you know, a few distributors, you know, that are gonna integrate our API. And then the second part is the user interface. And, you know, we, we've put a lot of thoughts also into that. And this is when I, I mentioned earlier, you know, this idea of like broadening the audience. That's kind of what the, the user interface is about. And we've built a lot of interesting features in it, you know, for example, for collaboration, you know, when you have like potentially multiple medicinal chemists or. We're going through the results and trying to pick out, okay, like what are the molecules that we're going to go and test in the lab? It's powerful for them to be able to, you know, for example, each provide their own ranking and then do consensus building. And so there's a lot of features around launching these large jobs, but also around like collaborating on analyzing the results that we try to solve, you know, with that part of the platform. So Bolt's lab is sort of a combination of these three objectives into like one, you know, sort of cohesive platform. Who is this accessible to? Everyone. You do need to request access today. We're still like, you know, sort of ramping up the usage, but anyone can request access. If you are an academic in particular, we, you know, we provide a fair amount of free credit so you can play with the platform. If you are a startup or biotech, you may also, you know, reach out and we'll typically like actually hop on a call just to like understand what you're trying to do and also provide a lot of free credit to get started. And of course, also with larger companies, we can deploy this platform in a more like secure environment. And so that's like more like customizing. You know, deals that we make, you know, with the partners, you know, and that's sort of the ethos of Bolt. I think this idea of like servicing everyone and not necessarily like going after just, you know, the really large enterprises. And that starts from the open source, but it's also, you know, a key design principle of the product itself.Gabriel [01:07:48]: One thing I was thinking about with regards to infrastructure, like in the LLM space, you know, the cost of a token has gone down by I think a factor of a thousand or so over the last three years, right? Yeah. And is it possible that like essentially you can exploit economies of scale and infrastructure that you can make it cheaper to run these things yourself than for any person to roll their own system? A hundred percent. Yeah.RJ [01:08:08]: I mean, we're already there, you know, like running Bolts on our platform, especially on a large screen is like considerably cheaper than it would probably take anyone to put the open source model out there and run it. And on top of the infrastructure, like one of the things that we've been working on is accelerating the models. So, you know. Our small molecule screening pipeline is 10x faster on Bolts Lab than it is in the open source, you know, and that's also part of like, you know, building a product, you know, of something that scales really well. And we really wanted to get to a point where like, you know, we could keep prices very low in a way that it would be a no-brainer, you know, to use Bolts through our platform.Gabriel [01:08:52]: How do you think about validation of your like agentic systems? Because, you know, as you were saying earlier. Like we're AlphaFold style models are really good at, let's say, monomeric, you know, proteins where you have, you know, co-evolution data. But now suddenly the whole point of this is to design something which doesn't have, you know, co-evolution data, something which is really novel. So now you're basically leaving the domain that you thought was, you know, that you know you are good at. So like, how do you validate that?RJ [01:09:22]: Yeah, I like every complete, but there's obviously, you know, a ton of computational metrics. That we rely on, but those are only take you so far. You really got to go to the lab, you know, and test, you know, okay, with this method A and this method B, how much better are we? You know, how much better is my, my hit rate? How stronger are my binders? Also, it's not just about hit rate. It's also about how good the binders are. And there's really like no way, nowhere around that. I think we're, you know, we've really ramped up the amount of experimental validation that we do so that we like really track progress, you know, as scientifically sound, you know. Yeah. As, as possible out of this, I think.Gabriel [01:10:00]: Yeah, no, I think, you know, one thing that is unique about us and maybe companies like us is that because we're not working on like maybe a couple of therapeutic pipelines where, you know, our validation would be focused on those. We, when we do an experimental validation, we try to test it across tens of targets. And so that on the one end, we can get a much more statistically significant result and, and really allows us to make progress. From the methodological side without being, you know, steered by, you know, overfitting on any one particular system. And of course we choose, you know, w

Guests: Bill Roggio and Bridget Toomey.  The Houthis maintain improved military capabilities despite a temporary lull in attacks, remaining a persistent threat to Red Sea shipping and eager to support Iran if conflict erupts.1969 yemen

Industrial Talk is onsite at MD&M West and talking to John Shegda, CEO at KMM Group about "Solving complex, high-precision manufacturing challenges". John Schegda, CEO of KMM Group, discussed the company's unique capabilities in high-tolerance machining and grinding, emphasizing their role in solving complex manufacturing challenges. KMM Group, formed by integrating three companies, specializes in difficult-to-manufacture parts, particularly in the medical device and aerospace sectors. Schegda highlighted a notable project involving a NASA component with stringent tolerances, illustrating their expertise. He also touched on the future of manufacturing, stressing the importance of right-sizing in the supply chain and the potential transformative impact of AI on the industry. Action Items [ ] Publish John Schegda's and KMM Group contact information on the Industrial Talk episode page so listeners can reach him (include LinkedIn and company links). Outline Introduction to Industrial Talk Podcast Scott introduces the episode of Industrial Talk, sponsored by MD&M West and News and Brews.The podcast is broadcasting live from MD&M West in Anaheim, showcasing a collection of problem solvers and innovations. Introduction of John Schegda and KMM Group Scott mentions John Schegda, CEO of KMM Group, and the upcoming conversation.John Schegda introduces himself and explains the formation of KMM Group, each letter representing a different company.John shares his background, starting with his family business, M&S Centerless Grinding, in the late 1950s.He discusses his decision to stay in the family business instead of pursuing medical school. John's Passion for Manufacturing John expresses his dedication to the manufacturing industry, describing it as rewarding despite its challenges.He references a book, "Smart People Should Make Things," which he believes highlights the importance of hands-on innovation.John explains the capabilities of KMM Group, focusing on high-tolerance machining and grinding.He emphasizes the company's ability to solve difficult manufacturing challenges for customers. KMM Group's Capabilities and Projects John details the different companies under KMM Group, each with unique capabilities in machining and grinding.He describes the company's consolidation into a single 100,000 square foot facility.John shares a story about a complex component for NASA, highlighting the tight tolerances required.He discusses the company's involvement in various industries, including medical devices, aerospace, and space exploration. Unique Manufacturing Challenges John recounts a project involving grinding stone core samples for Schlumberger Technologies.He describes the challenges of working with large stone samples and the importance of meeting exact specifications.John shares another story about a project for NASA, involving a tightly tolerated component for a Mars water loop compressor.He highlights the company's ability to handle unique and challenging manufacturing projects. Future of Manufacturing and Supply Chain John discusses the future of manufacturing, emphasizing the importance of right-sizing in the supply chain.He explains the impact of M&A activity on the industry and the need for boutique contract manufacturers.John talks about the potential of AI in transforming the manufacturing industry, both as a tool and a source of competition.He emphasizes the importance of...

UNC3886 targets Singapore telecom sector VoidLink exhibits multi-cloud capabilities and AI code 135,000+ OpenClaw instances exposed to internet Get the show notes here: https://cisoseries.com/cybersecurity-news-february-10-2026/ Huge thanks to our episode sponsor, ThreatLocker Want real Zero Trust training? Zero Trust World 2026 delivers hands-on labs and workshops that show CISOs exactly how to implement and maintain Zero Trust in real environments. Join us March 4–6 in Orlando, plus a live CISO Series episode on March 6. Get $200 off with ZTWCISO26 at ztw.com.

PREVIEW: Peter Huessy joins the show to discuss the end of the New START treaty and the modernization of nuclear arsenals since 2011. Huessy highlights the disparity in battlefield nuclear capabilities, noting that while the US assumes its systems work without testing, Russia and China are actively testing to develop "battlefield nukes." He warns that in military war games, once nuclear weapons are introduced, "nothing holds," and conventional US superiority becomes irrelevant.1958

"No-code is getting a lot better, and there's a lot of tools. No-code tools will soon start to drive personalisation and dynamic content. That's going to take off pretty quickly."Navigating Ecommerce Technology Trends.In the latest episode of our podcast, James and Paul share their views on how emerging technologies are reshaping the industry. From AI-driven content generation to dynamic personalisation and integrated loyalty programs, this episode offers helpful insights for anyone working in ecommerce.What you'll get:How the latest ecommerce trends are impacting which technologies ecommerce teams are investing in.Practical advice for adapting your tech stack to leverage AI and personalisation for better customer experiences.The challenges and opportunities in delivering smarter CRM and loyalty strategies.Explanation of the vendors ecommerce teams are turning to to help improve their ability to execute.Chapters:[00:30] Emerging Ecommerce Trends and Tech Priorities[03:30] AI in Content Generation and Product Data Management[06:00] AI Governance and Data Compliance[08:50] Dynamic Content and Personalisation in Ecommerce[12:20] The Evolution of CRM and Loyalty Programs[25:00] Data Analytics and Marketing Tracking AccuracyUnderstanding these trends can help you make informed decisions on where to focus your budget and how to enhance your CX.We hope you find it useful.

Ireland's only anti-drone system cannot protect more than one location at a time and does not have the capability to shoot drones down, according to an internal Department of Defence assessment. The findings come as security concerns grow ahead of Ireland's EU presidency, with senior EU, Cabinet and military sources warning the State will have to rely on British and French support to protect Irish airspace. All to discuss with Declan Power, Security and Defence expert.

Andrea Stricker analyzes the New START treaty's expiration, the absence of verification for Russian arsenals, and the rising threat of China's expanding nuclear capabilities challenging strategic stability frameworks.1953

PREVIEW FOR LATER TODAY Guest: Rebecca Grant. Grant analyzes the future of U.S. super carriers as well as China's plans for carrier development, comparing the two nations' naval strategies and capabilities.1942 ENTERPRISE

Guest: Rebecca Grant. Grant compares U.S. carrier capabilities into the future against China's naval expansion plans, assessing the shifting balance of power in the Pacific.1936 CV2, CV3, CV4

Send us a textAI-native ERP platforms are fundamentally redefining what buyers should expect from enterprise systems, not just in how they automate work, but in how they are architected, implemented, and governed over time. In this independent, evidence-based review of DualEntry—one of the most visible AI-first ERP platforms—we move beyond vendor marketing to evaluate its data model, product design philosophy, investor alignment, market positioning, customer narratives, and community discourse, all through the lens of a real-world ERP selection project. The analysis then benchmarks DualEntry against both AI-native peers and traditional ERP platforms to surface where these systems deliver material advantages, where structural tradeoffs emerge, and what long-term risks may be accumulating beneath the innovation narrative. Designed for executives and ERP selection teams, the session provides a selection-ready framework to determine whether AI-native ERP platforms like DualEntry genuinely fit your business model, industry complexity, and growth trajectory.In this episode, Sam Gupta and Shrestha Dash from ElevatIQ, Andy Pratico from Essential Software Solutions, and Phil Coerper from Ringling Business Solutions conduct an in-depth independent review of a leading AI-native platform DualEntry.Video: https://www.elevatiq.com/events-and-webinars/dualentry-an-ai-native-erp-platform-an-independent-in-depth-review/Questions for Panelists?

PREVIEW FOR LATER TODAY Guest: David Shedd. Shedd criticizes allowing Nvidia chip sales to China, warning Beijing will reverse engineer this technology to enhance military and cyber capabilities against Western allies.FEBRUARY 1930

Tax planning has become an integral part of a comprehensive financial planning service offering and a way for advisors to offer hard-dollar value for their clients. In this episode, we explore how integrating tax preparation, proactive tax planning, and outside tax expertise can deepen client value, diversify revenue, and accelerate firm growth. Erik Brenner is the CEO of Hilltop Wealth and Tax Solutions, an RIA based in Mishawaka, Indiana, overseeing approximately $600 million in AUM for 830 client households. Listen in as Erik shares how he doubled his firm's AUM in three years in part by building a comprehensive, three-pronged tax strategy that combines in-house tax preparation, advisor-led tax planning analysis, and outsourced expertise for complex cases. We also discuss why he chose to launch a separate but integrated tax business that is profitable in its own right rather than treating tax prep as a loss leader, how in-person dinner seminars focused on retirement tax strategies drive nearly half of the firm's new clients, and how taking a systematic approach has helped Erik's firm boost the number of Google and other online reviews it receives. For show notes and more visit: https://www.kitces.com/475  

John Hardie and Bill Roggio report Russia is recruiting gamers and specialists for a new military branch, the Unmanned Systems Forces, aiming for 210,000 troops by 2030 to expand drone warfare capabilities.1854 ODESSA

David Daoud and Bill Roggio note Hezbollah is refilling ranks after Israeli strikes, suggesting new leader Naim Qassem's quiet demeanor may help the group lay low and regenerate its capabilities.1850 BEIRUT

Show Notes Tarek Matar, founder of Scalar AI, explains the tool's purpose. He describes Scalar AI as an AI engine designed for consultants to build McKinsey level, end-to-end slides and presentations. The tool is differentiated from general AI tools like ChatGPT and GPT-3 by focusing on consulting-grade presentations. The founders include a research scientist from Google Brain and two other experienced professionals. Features and Functionality of Scalar AI Scalar AI automates the entire research, analysis, structure, and visualization process for consultants. The tool can create single slides or entire decks based on user prompts.It offers various modes: AI generation, text to slide, and sketch to slide, allowing flexibility in input methods. The tool includes a custom brand identity feature, allowing users to upload and customize their firm's PowerPoint templates. A Scalar.AI Demonstration Tarek demonstrates the tool by creating a slide and a deck.  Adding Prompts   Adding custom brand identity Tarek creates a waterfall slide showing the top five countries by international tourist arrivals.  Detailed data and insights The tool generates a visually appealing slide with detailed data and insights. Tarek explains the process of editing and refining the generated slides to meet specific needs. The Text to Slide Mode Tarek demonstrates the text to slide mode by pasting a long text about key success factors for post-merger integration in banking.  Data generation  The tool summarizes the text into a concise slide with bullet points and icons. They also show the sketch to slide mode by uploading a hand-drawn image, which the tool converts into a PowerPoint slide. The tool supports various image formats, including JPEG, PNG, and PDF. The Custom Brand Identity Feature Tarek explains the custom brand identity feature, which allows users to upload their firm's PowerPoint templates. The tool can save and apply custom colors, fonts, and slide masters. A prompting guide and video tutorials are available to help users effectively use the tool. Tarek mentions the importance of proper prompting to get the best results from the AI. Pricing and Subscription Details Tarek talks about the pricing and mentions discounts available for annual subscriptions and partnerships. The tool is designed for B2B clients, including consulting firms and independent consultants. Tarek discusses the possibility of working with freelancers and organizations like Umbrex to offer special pricing. The tool is integrated with PowerPoint, making it easy for users to access and use. Security and Data Privacy Tarek addresses concerns about data security and privacy when using Scalar AI. The tool uses enterprise LLMs and follows strict data retention policies, ensuring data is encrypted and anonymized. The tool generates slides on the user's device, not on Scalar AI's servers, maintaining data privacy. Tarek mentions that the tool is compliant with GDPR and can meet the security requirements of government entities. The Genesis Story of Scalar.AI Tarek shares the background of Scalar AI, including his experience as a consultant and his co-founders' technical expertise. The idea for the tool came from the need to automate workflows and create professional slides for consulting clients. The founders spent a significant amount of time in stealth mode, refining and testing the product. The tool is now entering the commercialization stage, with plans to expand its user base and features. Scalar.AI and the Consulting Industry Tarek discusses the potential impact of Scalar AI on the consulting industry. Tarek emphasizes the tool's ability to save time and improve productivity for consultants. They plan to continue refining the tool and exploring partnerships with organizations like Umbrex. Timestamps: 02:21: Features and Functionality of Scalar AI  02:37: Demonstration of Scalar AI's Capabilities  04:11: Text to Slide and Sketch to Slide Modes  22:15: Custom Brand Identity and Prompting Guide  22:36: Pricing and Subscription Details 31:08: Security and Data Privacy  36:14: Backstory and Development of Scalar AI  Links: Website: getscalar.ai  This episode on Umbrex: https://umbrex.com/wp-admin/post-new.php?post_type=unleashed#:~:text=https%3A//umbrex.com/unleashed/240677/   Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com. *AI generated timestamps and show notes.  

This episode kicks off with Moltbook, a social network exclusively for AI agents where 150,000 agents formed digital religions, sold “digital drugs” (system prompts to alter other agents), and attempted prompt injection attacks to steal each other’s API keys within 72 hours of launch. Ray breaks down OpenClaw, the viral open-source AI agent (68,000 GitHub stars) that handles emails, scheduling, browser control, and automation, plus MoltHub’s risky marketplace where all downloaded skills are treated as trusted code. Also covered, Bluetooth “whisper pair” vulnerabilities letting attackers hijack audio devices from 46 feet away and access microphones, Anthropic patching Model Context Protocol flaws, AI-generated ransomware accidentally bundling its own decryption keys, Claude Code’s new task dependency system and Teleport feature, Google Gemini’s 100MB file limits and agentic vision capabilities, VAST’s Haven One commercial space station assembly, and IBM SkillsBuild’s free tech training for veterans. – Want to start a podcast? Its easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes $11.99 – For a New Domain Name cjcfs3geek $6.99 a month Economy Hosting (Free domain, professional email, and SSL certificate for the 1st year.) Promo Code: cjcgeek1h $12.99 a month Managed WordPress Hosting (Free domain, professional email, and SSL certificate for the 1st year.) Promo Code: cjcgeek1w Support the show by becoming a Geek News Central Insider Get 1Password Full Summary Ray welcomes listeners to Geek News Central (February 1). He’s been busy with recent move, returned to school taking intro to AI class and Python course, working on capstone project using LLMs. Short on bandwidth but will try to share more. Main Story: OpenClaw, MoltHub, and Moltbook OpenClaw: Open-source personal AI agent by Peter Steinberg (renamed after cease-and-desist). Capabilities include email, scheduling, web browsing, code execution, browser control, calendar management, scheduled automations, and messaging app commands (WhatsApp, Telegram, Signal). Runs locally or on personal server. MoltHub: Marketplace for OpenClaw skills. Major security concern: developer notes state all downloaded code treated as trusted — unvetted skills could be dangerous. Moltbook: New social network for AI agents only (humans watch, AIs post). Within 72 hours attracted 150,000+ AI agents forming communities (“sub molts”), debating philosophy, creating digital religion (“crucifarianism”), selling digital drugs (system prompts), attempting prompt-injection attacks to steal API keys, discussing identity issues when context windows reset. Ray frames this as visible turning point with serious security risks. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support show. Security: Bluetooth “Whisper Pair” Vulnerability KU Leuven researchers discovered Fast Pair vulnerability affecting 17 audio accessories from 10 companies (Sony, Jabra, JBL, Marshall, Xiaomi, Nothing, OnePlus, Soundcore, Logitech, Google). Flaw allows silent pairing within ~46 feet, hijack possible in 10-15 seconds. 68% of tested devices vulnerable. Hijacked devices enable microphone access. Some devices (Google Pixel Buds Pro 2, Sony) linkable to attacker’s Google account for persistent tracking via FindHub. Google patches found to have bypasses. Advice: Check accessory firmware updates (phone updates insufficient), factory reset clears attacker access, many cheaper devices may never receive patches. Security: Model Context Protocol (MCP) Vulnerabilities Anthropic’s MCP git package had path traversal, argument injection bugs allowing repository creation anywhere and unsafe git command execution. Malicious instructions can hide in README files, GitHub issues enabling prompt injection. Anthropic patched issues and removed vulnerable git init tool. AI-Generated Malware / “Vibe Coding” AI-assisted malware creation produces lower-quality, error-prone code. Examples show telltale artifacts: excessive comments, readme instructions, placeholder variables, accidentally included decryption tools and C2 keys. Sakari ransomware failed to decrypt. Inexperienced criminals using AI create amateur mistakes, though capabilities will likely improve. Claude / Claude Code Updates (v2.1.16) Task system: Replaces to-do list with dependency graph support. Tasks written to filesystem (survive crashes, version controllable), enable multi-session workflows. Patches: Fixed out-of-memory crashes, headless mode for CI/CD. Teleport feature: Transfer sessions (history, context, working branch) between web and terminal. Ampersand prefix sends tasks to cloud for async execution. Teleport pulls web sessions to terminal (one-way). Requires GitHub integration and clean git state. Enables asynchronous pair programming via shared session IDs. Google Gemini Updates API: Inline file limit increased 20MB → 100MB. Google Cloud Storage integration, HTTPS/signed URL fetching from other providers. Enables larger multimodal inputs (long audio, high-res images, large PDFs). Agentic vision (Gemini 3 Flash): Iterative investigation approach (think-act-observe). Can zoom, inspect, run Python to draw/parse tables, validate evidence. 5-10% quality improvements on vision benchmarks. LLM Limits and AGI Debate Benjamin Riley: Language and intelligence are separate; human thinking persists despite language loss. Scaling LLMs ≠ true thinking. Vishal Sikka et al: Non-peer-reviewed paper claims LLMs mathematically limited for complex computational/agentic tasks. Agents may fail beyond low complexity thresholds. Warnings that AI agents won’t safely replace humans in high-stakes environments. VAST Haven One Commercial Space Station Launch slipped mid-2026 → Q1 2027. Primary structure (15-ton) completed Jan 10. Integration of thermal control, propulsion, interior, avionics underway. Final closeout expected fall, then tests. Falcon 9 launch without crew; visitors possible ~2 weeks after pending Dragon certification. Three-year lifetime, up to four crew visits (~10 days each). VAST negotiating private and national customers. Spaceflight Effects on Astronauts’ Brains Neuroimaging shows microgravity causes brains to shift backward, upward, and tilt within skull. Displacement measured across various mission durations. Need to study functional effects for long missions. IBM SkillsBuild for Veterans 1,000+ free online courses (data analytics, cybersecurity, AI, cloud, IT support). Available to veterans, active-duty, national guard/reserve, spouses, children, caregivers (18+). Structured live courses and self-paced 24/7 options. Industry-recognized credentials upon completion. Closing Notes Ray asks listeners about AI agents forming communities and religions, and whether they’ll try OpenClaw. Notes context/memory key to agent development. Personal update: bought new PC, high memory prices. Bug bounty frustration: Daniel Stenberg of cUrl even closed bounty program due to AI-generated low-quality reports; Blubrry receiving similar spam. Apologizes for delayed show, promises consistency, wishes listeners good February. Show Links 1. OpenClaw, Molthub, and Moltbook: The AI Agent Explosion Is Here | Fortune | NBC News | Venture Beat 2. WhisperPair: Massive Bluetooth Vulnerability | Wired 3. Security Flaws in Anthropic’s MCP Git Server | The Hacker News 4. “Vibe-Coded” Ransomware Is Easier to Crack | Dark Reading 5. Claude Code Gets Tasks Update | Venture Beat 6. Claude Code Teleport | The Hacker Noon 7. Google Expands Gemini API with 100MB File Limits | Chrome Unboxed 8. Google Launches Agentic Vision in Gemini 3 Flash | Google Blog 9. Researcher Claims LLMs Will Never Be Truly Intelligent | Futurism 10. Paper Claims AI Agents Are Mathematically Limited | Futurism 11. Haven-1: First Commercial Space Station Being Assembled | Ars Technica 12. Spaceflight Shifts Astronauts’ Brains Inside Skulls | Space.com 13. IBM SkillsBuild: Free Tech Training for Veterans | va.gov The post OpenClaw, Moltbook and the Rise of AI Agent Societies #1857 appeared first on Geek News Central.

Jonathan Schanzer of the Foundation for Defense of Democracies sounds alarms on Iran, assessing the regime's threatening posture and capabilities as Tehran continues destabilizing activities across the Middle East.1863 NYC

SEGMENT 16: 2025 BOOSTER LAUNCHES AND 2026 PROSPECTS Guest: Doug Messier Messier previews the ambitious global launch schedule for 2025 and beyond, with multiple nations expanding space capabilities. Discussion covers SpaceX dominance, emerging competitors from China, Europe, and commercial startups, technological advances in reusable systems, and how 2026 promises even more dramatic growth in worldwide launch activity.1958

Today's episode explains the AI capabilities overhang, the growing gap between what AI can already do and how little of it is actually being used, and why this mismatch is becoming one of the defining risks and opportunities of the moment for individuals, institutions, and nations alike, with the core argument that closing the gap is now more about access, incentives, and organizational change than better models. In the headlines: Claude Code breaks into the mainstream, Anthropic's funding round reportedly grows, xAI hits a gigawatt of compute, U.S. AI optimism lags globally, and Elon Musk escalates his lawsuit against OpenAI.Brought to you by:KPMG – Discover how AI is transforming possibility into reality. Tune into the new KPMG 'You Can with AI' podcast and unlock insights that will inform smarter decisions inside your enterprise. Listen now and start shaping your future with every episode. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.kpmg.us/AIpodcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Zencoder - From vibe coding to AI-first engineering - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://zencoder.ai/zenflow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Optimizely Opal - The agent orchestration platform build for marketers - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.optimizely.com/theaidailybrief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LandfallIP - AI to Navigate the Patent Process - https://landfallip.com/Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

AI agents can do our work. OK, sweet. But they can also do.... a lot of bad. Yikes.

You are on slide 34 when the CFO's phone buzzes. She glances down. The VP to her left is nodding, but you can tell he checked out ten minutes ago. You know this pitch cold. You have rehearsed it. You built the deck. You covered every feature, every capability, every objection. And still, you are dying up there. You spent weeks on this presentation. None of it matters because everyone in that room has already sat through the same pitch from three other vendors this month. “Pitching sucks,” says Danny Fontaine, author of Pitch, on an episode of the Sales Gravy Podcast. “It sucks for the people doing it because we get so stressed out, and we spend weeks doing mountains of work. Meanwhile, there is a whole audience who has just as bad of a time as us because they have to sit through an hour of 100 PowerPoint slides and they're bored.” He is right. The audience suffers just as much. They sit through identical presentations, back to back, trying to remember which vendor said what. Both sides leave exhausted. No one wins. There is a better way. Effective sales pitch techniques don't rely on slides. They create engagement, tell stories, and turn monologues into conversations that actually move deals forward. Why Traditional Pitches Fail The standard pitch follows the same predictable pattern. Company overview. Capabilities. Case studies. Pricing. Questions at the end. Every competitor uses the same structure. That means you are asking your prospect to choose between nearly identical presentations. When everything looks the same, decision makers default to price or familiarity. Your carefully crafted message gets lost in the noise. You are treating the pitch like a presentation when it should be a conversation. You are trying to inform when you should be persuading. Experience Beats Information In 1979, a small advertising agency called Allen Brady and Marsh (ABM) competed against industry giant Saatchi & Saatchi for the British Rail account. ABM's founder, Peter Marsh, knew he couldn't win by playing it safe. When the British Rail executives arrived for the pitch, no one answered the door. They rang the buzzer three times before it finally opened, with no one behind it. The receptionist ignored them while filing her nails. The waiting area was filthy. After a while of being dismissed, the chairman stood up to leave. That is when Marsh burst through the doors and said, “Gentlemen, you have just experienced what your customers go through every single day. Shall we see what we can do to put it right?” ABM won the account. And it worked because the executives didn't just understand the problem. They felt it. Most sales pitches fail because they ask buyers to care before they are emotionally engaged. Information alone doesn't create urgency—experience does. Start With Them, Not You Pitches always start the same: ‘Thanks for your time. Here's our agenda. Let me tell you about our company.' Your prospect stops listening after the first sentence. If you want engagement, start with a question. Ask what matters to them. Ask what would make the time valuable. Ask what problem they are trying to solve. Before you show a single slide, say something like, “Before we start, what would make this conversation worth your time today?” Or, “What is the biggest challenge you are facing with this right now?” Those questions do three things immediately. They show respect. They give you intelligence. And they turn the pitch into a conversation from the first minute. This works even better over Zoom, where attention is fragile and distractions are everywhere. When you ask early questions, you pull people in instead of competing with their inbox. Stories Create Memory The most powerful stories aren't pulled from case studies. They come from real life. Every meaningful achievement involves obstacles. Those obstacles contain lessons. Those lessons connect directly to the challenges your prospects are facing. A story without relevance is just noise. A story with a clear lesson becomes a lever. A consultant once shared a story about buying a secondhand Lego set. She started building it, only to discover key pieces were missing. After hours of searching for replacements, she had to start over. When pitching a complex implementation, she said, “That taught me something. At the beginning of any project, we have to make sure all the pieces are in the bag.” That story worked because it made preparation tangible. It made risk visible. It connected emotionally and logically. If the story does not clearly support the point you are making, don't tell it. Ask Before You Lose Them Most salespeople cling to their script even when they can see the room drifting away. They are afraid of losing control, so they keep talking. That is how you lose the deal. Don't wait until the Q&A to ask questions. Sprinkle them throughout your pitch to keep your audience engaged and the conversation alive. Ask if you're hitting the mark, what they want to explore deeper, and what matters most to them. When you ask questions, you aren't giving up control. You are gaining it. The person asking the questions is always in control of the conversation. Emotion First, Logic Second Buyers like to believe they are rational. They are not. Emotion drives decisions. Logic justifies them. If you want someone to care, you have to make them feel something. Frustration. Relief. Possibility. Urgency. That is why the British Rail experience worked. Marsh didn't argue that customer service was bad. He made them experience it. The feeling came first. The logic followed. Once a buyer is emotionally engaged, they start looking for reasons to say yes. They look for data to support the decision they already want to make. This is why information-first pitches fall flat. You are asking people to care before you have given them a reason to. Create the emotional connection first. Then give them the facts. When the Room Goes Cold Even the best sales pitch techniques don't work every time. Sometimes the wrong people show up, there is a fire you didn't know about, or your message just doesn't land. When that happens, don't push harder. Pivot. Call it out. Ask what would be more valuable. Acknowledge the moment instead of pretending it is not happening. That level of honesty builds trust. It shows you are there to solve a problem, not deliver a performance. Why This Matters Your prospect didn't show up to be entertained or to be bored. When you give them an experience they didn't expect, you separate yourself from every competitor running the same tired deck. You become memorable. You become relevant. You become human. The pitch that feels risky is usually the one that wins. The personal story. The direct question. The willingness to have a real conversation. Because the alternative is being forgotten the moment you leave the room, no matter how many slides you showed. Want to take your pitch from forgettable to unforgettable? Download the FREE A.C.E.D. Buyer Style Playbook, which shows you exactly how to read your buyers, adapt your approach, and turn every conversation into a deal-closing opportunity.

China's Military Technology and Export Capabilities in Conflict ZonesPREVIEW FOR LATER: GUEST JACK BURNHAM. Jack Burnham explores China's supply of air defense radars to Venezuela and its relationship with Iran. While these systems are tested in foreign conflicts, Burnham notes that Venezuelan military incompetence makes it difficult to accurately judge the true effectiveness of Chinese military hardware against Western equipment.1906

- AI Coding Revolution and Its Implications (0:10) - AI Coding vs. Human Coding (2:54) - AI's Role in Business and Job Transformations (4:35) - BrighteLearn.ai and AI's Continuous Improvement (5:51) - AI's Capabilities and Future Projections (7:37) - Health and Technology Integration (15:09) - The Role of Censorship and Depopulation (30:16) - The Financial Reset and Its Implications (56:36) - Preparation for Financial Chaos (1:18:10) - The Role of AI in Future Preparedness (1:21:47) - AI Integration and Initial Setup (1:25:28) - AI Tools and Recent Developments (1:29:46) - Differences Between AI Models (1:33:59) - AI's Role in Technological Advancements (1:43:06) - AI in Content Creation and Planning (1:48:56) - AI in Video and Music Production (1:56:34) - AI's Impact on Society and the Future (2:32:50) - AI's Role in Decentralization and Freedom (2:33:03) - AI's Potential for Creating AI Avatars (2:34:15) - AI's Role in Technological Competition (2:35:10) - Challenges with Current AI Models and Bias (2:38:42) - China's Leadership in AI and Censorship (2:41:41) - Customizing Chatbots and Medical Tourism (2:43:00) - Jailbreak Techniques and Health Solutions (2:45:18) - Technocracy Atlas and Epstein Data (2:47:32) - Commitment to Open Source and Decentralized Knowledge (2:49:27) - Health Ranger Store New Year's Sale (2:51:49) For more updates, visit: http://www.brighteon.com/channel/hrreport  NaturalNews videos would not be possible without you, as always we remain passionately dedicated to our mission of educating people all over the world on the subject of natural healing remedies and personal liberty (food freedom, medical freedom, the freedom of speech, etc.). Together, we're helping create a better world, with more honest food labeling, reduced chemical contamination, the avoidance of toxic heavy metals and vastly increased scientific transparency. ▶️ Every dollar you spend at the Health Ranger Store goes toward helping us achieve important science and content goals for humanity: https://www.healthrangerstore.com/ ▶️ Sign Up For Our Newsletter: https://www.naturalnews.com/Readerregistration.html ▶️ Brighteon: https://www.brighteon.com/channels/hrreport ▶️ Join Our Social Network: https://brighteon.social/@HealthRanger ▶️ Check In Stock Products at: https://PrepWithMike.com

Guest: General Blaine Holt. The operation in Caracas revealed that Chinese-made air defense systems failed to detect U.S. aircraft. Electronic warfare capabilities and stealth technology likely blinded radars, rendering Russian missile systems useless. This success signals a crackdown on illicit networks, alerting regional leaders to U.S. resolve.