Podcasts about Embedding

Insurance organizations unlock the greatest value from AI not by improving algorithms alone, but by embedding AI into customer education, data intake and analysis, and workflow guardrails that … Read More » The post Embedding AI Into Insurance Workflows: Where the Real Transformation Happens appeared first on Insurance Journal TV.

Insurance organizations unlock the greatest value from AI not by improving algorithms alone, but by embedding AI into customer education, data intake and analysis, and workflow guardrails that … Read More » The post Embedding AI Into Insurance Workflows: Where the Real Transformation Happens appeared first on Insurance Journal TV.

Embedding batteries into appliances to bypass big bottlenecks: home electrical upgrades. Instead of rewiring buildings, Copper turns induction stoves into distributed energy assets that can also support the grid.Copper is building appliances with integrated energy storage, starting with Charlie, a 30” induction stove with a built-in battery. The company focuses on making electrification cheaper, faster, and easier for multifamily buildings and older housing stock.They've received $60M in equity funding and government contracts so far.Before co-founding Copper, CEO Sam Calisch helped launch Rewiring America, was an Activate Fellow, co-authored Electrify, and previously founded Elmworks. He earned his PhD from MIT's Center for Bits and Atoms.Here's what we discussed:Installation arbitrage that changes adoption economics – Traditional induction stoves often require expensive 240V upgrades and panel work, while Charlie plugs into an existing 110V outlet behind most gas stoves using an onboard 5kWh LFP battery to deliver high-power cookingMultifamily as the wedge market – Buildings facing costly gas infrastructure repairs can avoid six-figure retrofit costs, with some projects saving over $100k by switching directly to Copper's battery-enabled electric appliancesAppliances as grid assets – Aggregated stoves participate in California's DSGS virtual power plant program, providing dispatchable capacity during peak demand and potentially offsetting future appliance costsLicensing instead of building everything alone – Copper is pursuing partnerships with incumbent appliance manufacturers rather than vertically integrating every product category itselfFounder operating system – Weekly written goals, deliberate “play time” for experimentation, outdoor activity, and separating business problems from personal identity to sustain long-term decision quality--Join our confidential CEO community.Private CEO group for VC/PE-backed climate tech founders navigating capital, strategy, and scale. Capped at 45 CEOs. See if you're a fit → entrepreneursforimpact.comJoin 40,000 professionals who get our newsletter.Climate tech finance, strategy, leadership. 2-min read. → entrepreneursforimpact.substack.comLeave a podcast review.If you got value, take 30 seconds and do the community a favor. It helps push more capital and talent toward scalable climate solutions.

Is your messaging making you memorable, or just visible?You could have the best content engine in your industry, be publishing consistently across multiple channels, and focusing on quality. But if it isn't memorable, none of it sticks.Core messaging and positioning is one of those things that sounds simple until you try to do it well. If you asked ten people in your organization what you are all about and why customers should choose to work with you, would they all broadly answer in the same way?In most organizations, the answer is no.That inconsistency shows up everywhere. In your content, on your website, in sales conversations, and in ways that are hard to trace back to the source.In part four of our seven-part B2B content strategy series, Amy Woods digs into what messaging is, what goes into a messaging framework, and how to know whether yours is working for you.Find out:What messaging is and why consistency is what makes it stickWhether you can have a B2B content strategy without clear messagingThe five components every B2B messaging framework needsHow to define your messaging and positioning through internal conversationsHow to use your messaging framework on a daily basis, including how to embed it into your AI toolsThe signals that tell you your messaging is workingWhen to review your messaging and how to update it without doing a hard pivotImportant links & mentions:Blog post about this episode: https://www.content10x.com/357Part one of the B2B content strategy series - What Is a B2B Content Strategy (And Why Does It Matter)?: https://www.content10x.com/354Part two of the B2B content strategy series — How Do You Align a B2B Content Strategy to Business Goals?: https://www.content10x.com/355Part three of the B2B content strategy series — How Does Competitor Analysis Fit Into Your B2B Content Strategy?: https://www.content10x.com/356B2B content strategy series on YouTube: https://youtube.com/playlist?list=PLVwaHzx-z4d4Rcnrh2VsUN9d47rrdMypp&si=NjYvp1Ilo26KnIF3Amy on LinkedIn: https://www.linkedin.com/in/amywoods2/Content 10x website: https://www.content10x.com/Amy's book: www.content10x.com/book (Content 10x: More Content, Less Time, Maximum Results)Timestamps:01:57 Free B2B Content Operations Benchmark Assessment02:35 What is messaging?03:44 Why a great content strategy can't exist without clear messaging04:33 Who owns messaging?05:10 Core components of a messaging framework06:55 How to define messaging through internal conversations08:35 Where messaging shows up in real-world execution09:16 Opinionated content and category positioning10:38 Embedding messaging into AI tools and workflows11:09 How to know your messaging is working (or not working)13:08 Quantitative vs qualitative signals from customers and sales13:47 Revisiting and adjusting your messaging framework16:07 Recap and what's next17:55 Wrap upAbout the host:Amy Woods is the CEO and founder of Content 10x, a creative agency that provides specialist content strategy, creation and repurposing support to B2B organizations.She's also a best-selling author, hosts two content marketing podcasts (The Content 10x Podcast and B2B Content Strategist), and speaks on stages all over the world about the power of content marketing.Join thousands of business owners, content creators and marketers and get the latest content marketing tips and advice delivered straight to your inbox every week https://www.content10x.com/newsletter

This year's Earth Day (22 April), the conversations pivot from carbon accounting to carbon action. While APAC CIOs have embedded sustainability dashboards, the rise of autonomous agents threatens to undo this progress. In 2026, an uncontrolled "agent sprawl" could exponentially increase compute, data storage, and energy use—directly conflicting with Net Zero pledges. True sustainability isn't just about reporting emissions; it's about embedding green governance into every autonomous decision. As agents become "digital coworkers," CIOs must treat energy efficiency and waste reduction as non-negotiable compliance metrics, ensuring AI acceleration doesn't come at the planet's expense.With us to understand what Earth Day means in the context of the exploding AI agent sprawl is Mr Liher Urbizu, present and MD of SAP Southeast Asia.Questions covered:1.       Give us the agentic sprawl in Southeast Asia in 2026.2.       How do you embed "carbon-aware" policies directly into agent workflows? This should force autonomous agents to defer non-urgent batch processing to times of renewable energy availability. (treat carbon data like financial data)3.       With Earth Day commitments tightening, what technical controls are required to mandate energy consumption caps per agent, treating efficiency as a governance rule rather than a post-execution report?4.       To ensure agents don't inadvertently increase waste, how do you establish trusted data lineage for Scope 3 emissions, enabling an agent to verify a supplier's carbon intensity before autonomously placing an order?5.       Given that poor data quality leads to redundant processing, what data governance rules are necessary to prevent agents from repeatedly querying or transforming the same inefficient datasets, wasting energy?6.       How do you build a "sustainability audit trail" for every autonomous decision, allowing CIOs to trace a specific agent's action back to its energy cost and carbon footprint for regulatory reporting?7.       As we manage agents like digital coworkers, what "retirement criteria" ensure that low-value, high-frequency agents are automatically decommissioned to prevent long-term energy leakage? (leanIX)8.       To avoid "shadow agent" sprawl doubling your infrastructure emissions undetected, what discovery tools can catalog every autonomous agent and calculate its real-time energy consumption against your Net Zero milestones?9.       With stakes higher than Shadow IT, how do you differentiate between essential agents that optimize sustainability (e.g., logistics routing) versus "rogue" agents that create unnecessary digital waste and technical carbon debt?10.   Where is the starting point for my organisation to move towards a more sustainable IT operation?

The ethics issues that arise in neuroscience research are usually novel, unresolved and understudied. Embedding ethicists in labs helps scientists navigate these challenges and develop strategies in real time to prevent harm.

In this episode, Kate Webber, Chief Solutions Officer at the PRI, is joined by Claudia Wearmouth, Global Head of Responsible Investment at Columbia Threadneedle Investments, and Travis Antoniono, Investment Director for Sustainable Investments at CalPERS.Together, they explore how responsible investment is being applied in practical, financially material ways, including how it is embedded into investment processes, how transparent dialogue between asset owners and managers supports long-term outcomes, and the role evidence plays in sustainable investment decision-making.Overview:Responsible investment is increasingly moving from a specialist function to a core part of investment decision-making. Across public and private markets, sustainability and governance considerations are being integrated into due diligence, portfolio construction, stewardship and long-term risk management.This episode explores how investors are building practical frameworks around financial materiality, balancing quantitative tools with qualitative judgement, and adapting to rapidly evolving risks such as climate change and AI disruption.Detailed coverage:Embedding sustainability into investment processesBoth guests explain how sustainability considerations are now integrated throughout the investment lifecycle, from initial due diligence through to ongoing monitoring and exit decisions.Financial materiality and fiduciary dutyThey explore how responsible investment supports long‑term, risk‑adjusted returns and helps meet fiduciary responsibilities to beneficiaries.The role of dedicated expertiseTravis Antoniono discusses embedding dedicated sustainability specialists directly into investment due diligence teams, while Claudia Wearmouth outlines how sustainable investment analysts can better work alongside fundamental research teams.Data, evidence and judgementThe conversation explores how responsible investment relies on a growing evidence base. While data is still evolving, investors increasingly combine quantitative tools with qualitative insight and real-world case studies.Explore real-world examples of how investors are combining data and judgement in practice in the PRI's investment case database: https://public.unpri.org/investment-tools/investment-case-databaseHow AI is changing investment researchAI is beginning to transform investment analysis itself, helping teams assess sector disruption, and emerging financial impacts more dynamically.Building organisational buy-inBoth guests highlight that embedding responsible investment depends on strong leadership and clear direction, with teams working together to apply it in practice.The importance of asset owner–manager relationshipsTransparency, trust and detailed communication are highlighted as essential for aligning investment objectives, stewardship expectations and long-term strategy execution.Practical lessons for investorsThe episode concludes with practical recommendations on how investors can improve governance and decision-making through more consistent use of evidence and ongoing dialogue.Chapters:00:08 - Introduction and the investment case for responsible investment01:29 - Embedding sustainability into investment processes05:14 - Sustainability, fiduciary duty and long-term returns10:56 - Building the evidence base for responsible investment13:39 - How AI is changing investment analysis20:15 - Creating organisational buy-in and investment alignment22:18 - Climate solutions, strategy and total portfolio thinking27:12 - Asset owner and investment manager collaboration35:15 - Key lessons on transparency, trust and detail37:04 - Practical recommendations for investorsDisclaimer:This podcast and material referenced herein is provided for information only. It is not intended to be investment, legal, tax or other advice, nor is it intended to be relied upon in making an investment or other decision. PRI Association is not responsible for any decision made or action taken based on information on this podcast. Listeners retain sole discretion over whether and how to use the information contained herein. PRI Association is not responsible for and does not endorse third parties featured on in this podcast or any third-party comments, content or other resources that may be included or referenced herein. Unless otherwise stated, podcast content does not necessarily represent the views of signatories to the Principles for Responsible Investment. All information is provided “as is” with no guarantee of completeness, accuracy or timeliness, or of the results obtained from the use of this information, and without warranty of any kind, expressed or implied. PRI Association is committed to compliance with all applicable laws. Copyright © PRI Association 2026. All rights reserved. This content may not be reproduced, or used for any other purpose, without the prior written consent of PRI Association.

Chinese consumer brands are rapidly expanding across Southeast Asia, moving beyond electronics and electric vehicles into sectors such as beauty, food service and home appliances, according to a report by Euromonitor International.市场研究机构欧睿国际的一份报告显示，中国消费品牌正在东南亚迅速扩张，其业务已从电子产品和电动汽车拓展至美妆、餐饮和家用电器等领域。Its “Rise of Chinese Brands in Southeast Asia” report found that the ASEAN economies of Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam account for 95 percent of the region‘s $4 trillion GDP. The region has become the largest and fastest-growing export destination for Chinese goods.该机构发布的《中国品牌在东南亚的崛起》报告指出，在东南亚地区4万亿美元的经济总量中，印度尼西亚、马来西亚、菲律宾、新加坡、泰国和越南这六个东盟经济体合计占95%。该地区已成为中国商品最大且增长最快的出口目的地。In 2024, China's exports to Southeast Asia reached $587 billion, up 12 percent year-on-year. More than 70 percent of Chinese companies operating in ASEAN plan further expansion, citing strong performance and untapped consumer demand, said the China Council for the Promotion of International Trade.据中国国际贸易促进委员会数据，2024年中国对东南亚出口额达到5870亿美元，同比增长12%。超过70%在东盟经营的中国企业表示将进一步扩大业务，这得益于其强劲的业绩表现以及尚未充分开发的消费市场。With a population exceeding 650 million, 63 percent under 40 and a median age of 31, Southeast Asia‘s consumer market is thriving. This demographic fuels demand for e-commerce, livestreaming shopping, fintech solutions and affordable premium products.东南亚人口超过6.5亿，其中63%在40岁以下，中位年龄为31岁，消费市场充满活力。这一人口结构推动了对电子商务、直播购物、金融科技解决方案以及高性价比优质产品的充分需求。Countries like Vietnam and Indonesia are outpacing China in GDP growth, offering Chinese brands a rapidly expanding consumer base with rising disposable incomes and accelerating urbanization, said the report.报告称，越南、印度尼西亚等国的经济增速已领先中国，这为中国品牌提供了一个蓬勃发展的消费市场——那里的居民收入不断增长，城市化步伐也在加快。Chinese companies have long dominated sectors such as EVs, consumer electronics and home appliances. In EVs, BYD is now the top brand in most Southeast Asian markets and the number-one car brand in Singapore, surpassing Toyota. In home appliances, Chinese brands‘ share of the air conditioner market rose from 9 percent in 2015 to 25 percent in 2024. Haier, Midea and Gree have become household names. In smartphones, Chinese brands' market share has increased from 21 percent in 2014 to over 60 percent today.长期以来，中国企业在电动汽车、消费电子产品和家用电器等领域占据主导地位。在电动汽车领域，比亚迪现已成为大多数东南亚市场的头号品牌，并在新加坡超越丰田成为第一大汽车品牌。在家电领域，中国品牌在空调市场的份额从2015年的9%上升至2024年的25%。海尔、美的和格力已成为家喻户晓的名字。在智能手机领域，中国品牌的市场份额已从2014年的21%提升至如今的60%以上。Now, Chinese companies are breaking into sectors once considered difficult for foreign entrants. In beauty and personal care, mass-market skincare brands achieved a 115 percent compound annual growth rate (CAGR) from 2019 to 2024. In the consumer food and beverage sector, chains such as Mixue, Luckin Coffee and Chagee are expanding aggressively. Mixue outlets grew 80 percent between 2019 and 2024, and by April 2026, Mixue had 4,153 overseas stores, while Chagee reached 262, said the China Chain Store and Franchise Association.如今，中国企业正挺进昔日外资难以进入的领域。美妆个护方面，大众护肤品品牌2019—2024年复合年增长率高达115%。餐饮消费方面，蜜雪冰城、瑞幸咖啡、霸王茶姬等品牌正加速扩张。中国连锁经营协会数据显示，2019至2024年，蜜雪冰城门店增长80%，截至2026年4月，其海外门店达4153家，霸王茶姬海外门店达262家。Nathanael Lim, APAC insight manager for beverages at Euromonitor International, said: “Chinese coffee and tea chains maintain consumer interest through relentless product innovation, often unveiling new menu items monthly. Significant investment in research and development and direct ingredient sourcing allows them to craft unique flavors that resonate with local palates.”欧睿国际亚太地区饮料行业洞察经理纳撒尼尔·林（音译）表示：“中国咖啡和茶饮连锁品牌通过不断的产品创新来维持消费者的兴趣，每月都会推出新品菜单。对研发和原材料直接采购的大量投入，使他们能够打造出与当地口味产生共鸣的独特风味。”Partnerships with local players are also central to expansion. In January, Eastroc Beverage signed a cooperation agreement with Indonesia‘s Salim Group to establish a joint venture, with investments of up to $200 million. Since 2021, Eastroc has exported products to 30 countries and regions.与当地企业建立合作伙伴关系对扩张同样至关重要。今年1月，东鹏饮料与印尼三林集团签署合作协议，共同成立合资公司，投资额高达2亿美元。自2021年以来，东鹏饮料已向30个国家和地区出口产品。Euromonitor said deep localization is key to Chinese brands' success, surpassing mere price competition. Many beauty and F&B companies register as local entities, adapt products for tropical climates and employ local teams for livestreaming and marketing activities.欧睿国际表示，深度本土化是中国品牌取得成功的关键，其重要性超越了单纯的价格竞争。许多美妆和餐饮企业在当地注册为本土实体，针对热带气候调整产品，并聘请本地团队从事直播带货和营销活动。“To move beyond transactional entry points, Chinese companies must transition from exporters to long-term ecosystem participants. Embedding within local value chains, adapting to cultural and economic contexts, and cultivating trust — through local manufacturing, customer service and community engagement — will be essential to sustaining growth,” the report said.报告指出：“为了超越交易性进入方式，中国企业必须从出口商转型为长期的生态系统参与者。通过本地制造、客户服务和社区参与等方式，融入当地价值链、适应文化与经济环境并建立信任，对于实现持续增长至关重要。”Euromonitor International /ˌjʊərəʊˈmɒnɪtər ˌɪntəˈnæʃənəl/欧睿国际untapped /ʌnˈtæpt/未开发的，未利用的fintech /ˈfɪntek/金融科技affordable premium products /əˈfɔːdəbəl ˈpriːmiəm ˈprɒdʌkts/高性价比优质产品disposable income /dɪˈspəʊzəbəl ˈɪnkʌm/可支配收入unveil /ʌnˈveɪl/推出，公布partnership /ˈpɑːtnəʃɪp/合作伙伴关系

Introduction What if the real bottleneck in commercial insurance isn't distribution or pricing—it's the workflow itself? Nearly $100 billion of SME P&C insurance is placed every year using manual processes, disconnected systems, and data that lives in spreadsheets and email threads. Hamesh Chawla has spent the last four years building the infrastructure to change that. Before founding Mulberri in 2021, Chawla led product and technology at Edelman Financial Engines and Asurion. He came to insurance not as a lifer but as a technologist who saw an industry still running on 20th-century tooling. Mulberri is his answer: an AI operations platform connecting PEOs, brokers, SMEs, and carriers—from smart submission and risk scoring to quote-and-bind and certificate of insurance. In this conversation, Josh Hollander and Chawla dig into why the MGA market was the right pivot, what AI governance looks like when binding decisions carry real capital risk, and why the SME segment is the most underserved frontier in commercial insurance. Guest Bio Hamesh Chawla is the Co-Founder and CEO of Mulberri, an AI operations platform for MGAs, PEOs, brokers, and carriers serving the SME market. Before Mulberri, he was EVP and Chief Product & Technology Officer at Edelman Financial Engines, with prior roles at Asurion and Zephyr (acquired by SmartBear). He holds an MS in Computer Science from Texas A&M University. Mulberri has raised $10.8M from Eos Venture Partners, Altamont Capital Partners, MS&AD Ventures, and Hanover Technology Management. Key Topics • The $100B manual workflow problem — Nearly $100B of SME P&C is placed annually using ACORD forms emailed back and forth, loss runs parsed by hand, and decisions made without the data that exists in the market. Mulberri automates this stack. • From embedded insurance to AI operating system — Chawla explains why he pivoted from embedded distribution to building the workflow layer MGAs actually run on—ingesting unstructured data, structuring it through a GenAI OS, and routing decisions with full context. • AI governance when capital is at stake — When AI is binding real policies, black-box models get rejected. Mulberri surfaces claim propensity, frequency, severity, and loss ratio so underwriters can interrogate and trust the output. • The PEO channel as data and distribution — PEOs sit on firmographic and workforce data directly predictive of workers' comp risk. Embedding into that channel is both a data strategy and a go-to-market strategy. • Building for carriers, brokers, and SMEs simultaneously — Carriers need loss ratio visibility, brokers need submission efficiency, SMEs need straightforward access. Aligning all three is the hardest product problem in the space. Notable Quotes "Our mission since day one has been to leverage technology to complement underwriters' expertise—simplifying and streamlining the business insurance process while ensuring transparency." "The Risk Engine puts the information underwriters need at their fingertips to make fast, accurate decisions—not replacing them, but making them dramatically more effective." Resources Guest: • Mulberri: https://www.mulberri.io • Hamesh Chawla on LinkedIn: https://www.linkedin.com/in/hameshchawla/ Host & Organization: • Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/ • Horton International (USA): https://www.horton-usa.com/ • Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show Subscribe & Review If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Apple Podcasts, and Spotify.

Clinicians do not have time to switch screens to search for medical evidence. Forcing them to open another application to find answers just adds to their cognitive load.Healthcare IT Today sat down with Derrick Leung from the BMJ Group. We discussed how his organization is rethinking the delivery of medical evidence. You will learn why they are moving their knowledge base directly into the clinical workflow via an API and using human curation to ground AI tools.

As sustainability conversations increasingly center on regulation, compliance deadlines and investment in new data systems, something risks getting lost: the need to invest in people who drive the change. Todd Corley's perspective emphasizes the importance of inspiring and developing individuals across all levels of an organization. Rooted in a career shaping social, philanthropic, sustainability and belonging initiatives in corporate workplaces, he now holds one of the most distinctive roles in the industry as Chief People and Impact Officer at Carhartt. From that vantage point, he builds strategy bottom-up, and asks a different question: What happens when you treat people and culture not as a support function to your people and impact strategy, but as its foundation?   Integrating this strategic framework within global organizations requires persistence, adaptability, and a willingness to accept that there is no one-size-fits-all approach to building a purpose-driven culture. In this webinar, we explored: - How Todd's journey to Chief People and Impact Officer shapes his approach to sustainability - What it looks like to structure teams and governance for real impact, and why capacity building is a strategic investment - Carhartt's people-first approach in practice: examples of community connection, skilled trade development and accessible circularity - Making the internal business case: navigating pushback and keeping purpose at the core - Reasons for optimism, and what the industry needs to do next to act on them

Joyce talks about how radical Islam are and have been quietly embedding themselves into American institutions with a plan to take over despite our values and culture. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Read the unfiltered memos I send my team as we scale Acquisition.com to $1B+:https://leilahormozi.com/subscribe Acceptance is power. Growth starts when people stop resisting discomfort and start moving with reality instead of against it. In this episode, Leila explains why anxiety, fear, and negative thoughts only control people who treat them like they were facts. She also shares the mental frameworks that helped her stop spiraling. Learning that thoughts are not facts might be the difference between staying trapped and finally moving forward.In this episode00:00 The power of active acceptance02:25 Why you must audit your beliefs04:06 Identifying and swapping irrational beliefs05:32 Embedding new beliefs with actionMore Value:Get your personalized $100m scaling roadmap: https://www.acquisition.com/roadmap Read the unfiltered memos I send my team as we scale Acquisition.com to $1B+: https://leilahormozi.com/subscribeReceive a curated set of internal memos from the past year at Acquisition.com: https://leilahormozi.com/acq Watch my latest YouTube videos: https://www.youtube.com/@leilahormozi/featuredLearn how to scale your business to millions of dollars in annual revenue: https://www.acquisition.com/ DISCLOSURE Information shared here is for educational purposes only. Individuals and business owners should evaluate their own business strategies, and identify any potential risks. The information shared here is not a guarantee of success. Your results may vary. Copyright © 2026.

In this episode, Paul Turner discusses his journey in integrating climate and nature education into the core school curriculum through the Ministry of Eco Education. Paul gives examples of places which are becoming more sustainable using practical measures. He also gives advice to students who are thinking of entering a green career.

“If you're not growing, you're dying.” – Brent W. RempeIn this week's episode, Carol Schultz sits down with Brent Rempe (President & CEO of First Alliance Credit Union) to unpack what actually drives workplace evolution—and why most companies fail to make their values meaningful. Brent shares how his team redefined their mission, vision, and values after realizing the old ones didn't resonate, and how simplifying them into something tangible changed the direction of the organization.Brent explains why having too many values makes them forgettable, how organizations can embed values into hiring and performance systems, and why alignment matters more than raw output. They explore how behavioral interviews reveal real character, why pairing HR with hiring managers improves decision-making, and how growth can expose weaknesses inside a team. The conversation also touches on leadership realities—like imposter syndrome—and why purpose and storytelling are critical to keeping employees engaged. The episode closes with a practical look at how companies can create workplaces where people feel connected to the impact of their work, not just the job itself.TakeawaysMission, vision, and values only work if they are simple and actionableToo many values make culture harder to understand and applyValues should be embedded into hiring, performance, and daily decisionsBehavioral interviews help uncover genuine alignment—not rehearsed answersHR involvement improves hiring consistency and reduces biasGrowth without alignment can create internal frictionEmployees stay engaged when they feel their work has real impactStorytelling helps teams connect to purpose and meaningEven experienced leaders deal with imposter syndromeStrong culture creates momentum, not just complianceChapters00:03 Intro: What it means to evolve a workplace04:13 Rethinking mission, vision, and values05:14 Why simplicity in values matters07:33 The three core tools: mission, vision, values08:17 Embedding values into hiring and performance09:10 How to interview for alignment10:28 The role of HR in better hiring decisions15:58 Defining the ideal member and growth focus18:05 Looking beyond credit scores: human-centered decisions20:26 Growth challenges and team development26:19 Imposter syndrome among leaders31:25 Purpose, storytelling, and employee motivationConnect With Host Carol SchultzFind more information about our host Carol Schultz and her company at Vertical Elevation, LinkedIn, YouTube, and Instagram.Want to be our next guest expert? Email cat.gloria@verticalelevation.com with your information.And of course, click "follow" to stay up-to-date on new episodes and leave an honest review/rating letting us know what you thought!

In this episode of the AI Agent & Copilot Podcast, host Giuseppe Ianni speaks with Parmesh Rajan, VP of Technology for HSO US, live from the AI Agent & Copilot Summit NA in San Diego, California.Rajan explains how organizations are moving beyond experimentation and embedding AI directly into business processes, implementation delivery, and workforce productivity. The discussion highlights a shift from standalone copilots toward operational, production-grade AI agents that deliver measurable outcomes. Key Takeaways AI Embedded in Core Operations, Not Add-On Tools: Organizations are moving from experimenting with AI to embedding it directly into enterprise systems and workflows. As Parmesh Rajan explains, the goal is to shorten complex ERP implementations and integrate AI into core business processes like finance, operations, and customer engagement rather than treating it as a standalone capability. Automation Across the Entire Implementation Lifecycle: HSO is applying AI across the full delivery stack—from gap-fit analysis and solution design to coding assistance and data transformation. This is helping reduce manual effort in traditionally lengthy 12–18 month implementations while improving accuracy and accelerating time to value for customers. Adoption Depends on Workflow Fit, Not Agent Quantity: Industry-specific AI agents are critical for real-world adoption because they align with how users actually work. Examples like automated timesheets and AI-driven expense processing show that success comes from embedding agents into daily workflows, with effectiveness ultimately measured by usage rather than the number of agents deployed. Visit Cloud Wars for more.

Employee engagement continues to be one of the most talked-about topics in HR, but also one of the most misunderstood. While many organisations measure it, far fewer truly understand what drives it or how to improve it in a meaningful way. In this episode of the HR Insights Podcast, Stuart Elliott sits down with industry experts Paul Knight, Group Chief People Officer at PA Media, and Rebecca Saunders Jones, Managing Director of Loopin, to explore what employee engagement really looks like today, why it may be stalling in many organisations, and what HR and business leaders can do differently. From leadership accountability to real-time data and shifting workforce expectations, the conversation offers a practical and honest view of where engagement stands today, and where it needs to go next. Key timestamps03:52 – What does employee engagement actually mean? 05:20 – Where is engagement today? 08:05 – What's ‘job hugging'? 10:29 – Why traditional engagement surveys fall short 16:36 – Who really owns engagement?24:42 – Transparency vs psychological safety28:28 – The biggest risk to engagement35:12 – Can you over-measure engagement?41:43 – Embedding engagement into cultureYou can listen to and download HR Insights from Apple Podcasts, Google Podcasts, Spotify and other popular podcast apps. Please subscribe so the latest episodes are directly available! You can also join our HR Community by following us on LinkedIn.Thank you for listening and please do review and rate us wherever you listen! 

In today's episode, we're speaking with Lisa Lawson, founder of Scotland's Dear Green Coffee Roasters.Lisa started in the coffee industry working alongside Toby Smith in the early days of creating Toby's Estate in Sydney, and went on to launch Dear Green in Glasgow in 2011, with a mission to bring sustainably sourced coffees to her local community.  Since then, Dear Green has become a cornerstone of the Scottish specialty coffee scene – not only producing the Glasgow Coffee Festival, but also setting the benchmark for what a responsible coffee business should look like.In this inspiring conversation, Lisa shares her philosophy of embedding sustainability into every aspect of her business. She also offers practical advice for greener operations – from measuring and reducing carbon emissions, to investing in renewable energy and working towards zero-to-landfill waste goals.Credits music: "Dust of a Star" by Daisy Chute in association with The Coffee Music Project and SEB Collective. Tune into the 5THWAVE Playlist on Spotify for more music from the showSign up for our newsletter to receive the latest coffee news at worldcoffeeportal.comSubscribe to 5THWAVE on Instagram @5thWaveCoffee and tell us what topics you'd like to hear

In this episode, I'm joined by Drs. Brandon May and Maggie Pavone, and Kate Heersink to talk about how we can better support healthier lifestyles for individuals with developmental disabilities. We start by digging into how each of them came to this work. Maggie shares some early experiences working as a direct support professional, where she began to notice patterns between food-related variables and challenging behavior. Brandon talks about coming into behavior analysis through the health and fitness world, and seeing firsthand how difficult it was to support individuals in building healthier routines without a clear behavioral framework. Kate adds her perspective from working with individuals with brain injury, where the connection between physical health and overall functioning is hard to ignore. We also spend some time acknowledging that this isn't entirely new territory. There's a solid body of work—both within and outside of behavior analysis—focused on physical activity and health for individuals with disabilities. At the same time, there's still a gap when it comes to practical, easy-to-implement tools that can be used by the people doing the day-to-day work. From there, we get into the early development of the Fit 4 All program and how it's currently being implemented in a day program setting for adults with developmental disabilities. Kate walks through what a typical session looks like, including: Starting the day by ensuring wearable tech (e.g., a Fitbit) is in place Using a token system tied to individualized goals (hydration, movement, functional fitness, and nutrition skills) Embedding physical activity throughout the day (walking, fitness videos, etc.) Teaching basic nutrition concepts using structured learning trials Incorporating functional skills like cooking where appropriate One of the things I appreciated about this approach is how integrated it is. Rather than treating exercise or nutrition as separate, isolated targets, they're woven into the flow of the day and supported through clear contingencies and reinforcement systems. We also talk about the importance of working within real-world environments. This isn't about creating tightly controlled, clinic-based interventions—it's about meeting people where they are and building systems that can be implemented by direct support staff, teachers, and caregivers in the settings where individuals actually live and spend their time. This is very much a "boots on the ground" application of behavior analysis—figuring out how to increase things like step count, heart rate, and water consumption in ways that are practical, sustainable, and individualized. And like a lot of good ABA work, it involves ongoing problem-solving—adjusting activities, testing different approaches, and using data to guide decisions. If you're a BCBA, or someone working directly with individuals with developmental disabilities, this conversation is a good reminder that health and wellness is an area where our science has a lot to offer—and probably more room to grow.

At kdc/one, learning is more than a support function—it's a driver of business performance. In this episode, kdc/one's Director of Learning and Development Sharron Northern shares how she's building a global strategy that simplifies complexity, aligns with business goals, and creates real demand for development.Show Notes:Kdc/one's Sharron Northern focuses on simplifying systems, focusing on leaders and creating meaningful learning experiences to drive engagement and performance. Her top takeaways include: Start with what leaders care about. Align learning initiatives to real business priorities to quickly build trust and demonstrate value.Create a “pull” for learning—not just push. When learning solves real problems, leaders actively seek it out, increasing engagement and impact.Simplify to scale. Breaking down complex systems and focusing on clear priorities enables global organizations to move faster and more effectively.Integrate learning into performance systems. Embedding development into performance management ensures learning is reinforced, measured, and sustained.Design for engagement and application. Interactive elements like role play, peer discussion, and even gamification—when used intentionally—drive retention and behavior change.Powered by Learning earned Awards of Distinction in the Podcast/Audio and Business Podcast categories from The Communicator Awards and a Gold and Silver Davey Award. The podcast is also named to Feedspot's Top 40 L&D podcasts and Training Industry's Ultimate L&D Podcast Guide. Learn more about d'Vinci at www.dvinci.com. Follow us on LinkedInLike us on Facebook

Favour Obasi-ike, MBA, MS breaks down why every business website needs an active, well-structured blog. He introduces content pillars — long-form foundational articles around 3,000 words — and content clusters, shorter supporting articles around 700 words that link back to the pillar to build semantic authority. The session also covers how embedding multimedia like YouTube videos and infographics increases "in-view" time and reduces bounce rates. It closes with Favour revealing his background as a music producer and playing an original instrumental track live.Who is this for?Business owners, content creators, and digital marketers who want to turn their website blog into a long-term traffic and authority asset — especially anyone publishing content inconsistently or without a proper content structure.Key Moments & Timestamps01:33 — Why every business website needs an active blog and a structured sitemap.04:21 — How embedding YouTube videos retains traffic and intellectual property on your domain.65:01 — Understanding content pillars (3,000 words) vs. content clusters (700 words).68:00 — How infographics increase content shares by up to 300% and lower bounce rates.143:10 — Favour reveals his music production background and plays an original instrumental track live.FAQsQ: Why embed a YouTube video instead of sharing the link?A: Embedding keeps traffic and intellectual property on your domain, increasing "time on page" and sending positive ranking signals to search engines.Q: What is the difference between a content pillar and a cluster?A: A pillar is a comprehensive long-form article on a broad topic. A cluster is a shorter article that links back to the pillar, building semantic authority over time.Q: Do people still read blogs in 2026?A: Yes. While many people skim, search engine bots read everything — and AI tools like ChatGPT, Siri, and Alexa pull answers directly from published blog content.Action StepsAudit Your Sitemap: Confirm your blog is active and properly indexed in your XML sitemap.Embed Your Media: Keep traffic on-site by embedding YouTube videos and podcast episodes directly into blog posts.Build Content Pillars: Write one comprehensive pillar article, then support it with 3–5 shorter cluster articles that link back to it.Use Infographics: Add visual elements to increase screen time and lower your bounce rate.Refresh Old Content: Update popular older posts with new information to keep them evergreen and re-indexable by search engines.Ready to Rank? Book Your SEO & Web Dev Services Today

Episode 131 How to design read aloud lessons that build understanding—not just engagement The difference between read aloud that supplements vs. supplants your instruction Using read aloud to teach reading skills like character motivation and author's craft How to connect knowledge building and accountable talk into one cohesive lesson Embedding learning science strategies like retrieval practice and interleaving into read aloud Designing literacy instruction so students remember and apply what they learn over timePractical Strategies Mentioned• Modeling character motivation during read aloud using sentence stems • Using repetition in a text to teach author's craft • Retrieval prompts like “What happened yesterday?” • Interleaving skills (character traits + motivation in one question) • Echo, choral, and partner reading followed by comprehension checks • Planning intentional stopping points and think-alouds • Using text sets (poems, articles, videos) to deepen understandingThese are all strategies grounded in the science of reading and learning science that help students move from understanding in the moment to learning that actually sticks.As you listen, consider this question:What is my read aloud actually doing in my literacy block?Is it:Filling time?Reinforcing skills?Or driving instruction and building understanding over time?Instructional leadership starts with teachers who are willing to move from doing the lesson to designing the learning experience.Earthquake Terror (used as a mentor text example for author's craft)Wonder by R.J. Palacio (used for text connections and deeper thinking)Episode 129: Why Read Aloud Still Matters in Upper Elementary Episode 130: How Accountable Talk Builds Thinking in Your Literacy ClassroomIf you're ready to strengthen your instruction and design literacy lessons that actually stick, you can learn more about coaching and professional development below:In This Episode We DiscussSelf-Leadership ReflectionResources MentionedPrevious Episodes ReferencedWork With EvaGrab my free guide: How to Keep Your Mini Lesson Mini  Book a discovery call for 1:1 coaching or school professional development

What does it really take to build a successful business that creates both financial returns and meaningful social impact?In this episode of Mirror Talk: Soulful Conversations, we sit down with Brent Freeman, Founder and President of Stealth Venture Labs, who has helped brands like Crocs, Poo-Pourri, and Home Chef generate over $500M in revenue.Brent shares the deeper principles behind sustainable entrepreneurship, including how to build a business rooted in purpose, why social impact should be part of a company's DNA, and how joy can become a real metric for success. He also opens up about his powerful Return Of Joy principle and how reconnecting with the things he truly loved transformed his health, mindset, impact, and results.If you are building something meaningful, navigating challenges, or trying to grow without losing yourself in the process, this conversation will give you wisdom, clarity, and practical encouragement.In this episode, you will learn:How to build a business that creates both profit and positive social impactWhat it truly means to be a social entrepreneurImportant habits every successful entrepreneur should buildCommon obstacles entrepreneurs face and how to overcome themHow to lead by example in business and in lifeBrent Freeman's Return Of Joy principle and how it can transform your lifeHow to create an abundance mindsetThe future of digital marketing in an AI-driven worldBrent's advice for anyone who wants to become truly successfulTimestamps:00:00 - Introduction to Brent Freeman02:22 - Brent's background and entrepreneurial journey05:26 - Embedding social impact into your business DNA09:09 - Scarcity mindset vs abundance mindset11:39 - The power of giving and community impact14:16 - Turning obstacles into opportunities for growth16:21 - Building resilience through hardship20:07 - Lessons from challenges and setbacks22:20 - Leading by example and building strong teams25:43 - Creating a high-performance culture with emotional intelligence27:01 - Brent's Return Of Joy principle30:33 - Reconnecting with joy through daily practices35:19 - The activities that bring Brent joy37:53 - Stealth Venture Labs and the future of digital marketing40:05 - AI and the evolution of marketing43:02 - Brent's advice for aspiring entrepreneursResources and Links:Stealth Venture LabsBrent Freeman on LinkedInBrent Freeman on InstagramThink and Grow Rich by Napoleon HillConnect with Brent Freeman:Website: https://www.stealthventurelabs.com/Profile: https://speakonpodcasts.com/brent-freeman/If this episode encouraged you, share it with someone building a business, pursuing purpose, or trying to grow without losing joy along the way.Ask what is on your heart. Mirror Talk will reflect back what may help you see more clearly. Try it here: https://mirrortalkpodcast.com/ask-mirror-talk/Thank you for joining me on this MIRROR TALK podcast journey. Please subscribe to any platform and remember to leave a review and rating.Stay connected: https://linktr.ee/mirrortalkpodcast More inspiring episodes and show notes are here: https://mirrortalkpodcast.com/podcast-episodes/ Your opinions, thoughts, suggestions, and comments are important to us. Please share them here: https://mirrortalkpodcast.com/your-opinion-matters/ Could you support us by becoming a Patreon? Please consider subscribing to one or more of our offerings at http://patreon.com/MirrorTalk All proceeds will help enhance the quality of our work and outreach, enabling us to serve you better.We use and trust these podcasting tools, software, and gear. We've partnered with amazing platforms to give our Mirror Talk community exclusive deals and discounts: https://mirrortalkpodcast.com/best-podcasting-tools/

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Watch every episode ad-free & uncensored on Patreon: https://patreon.com/dannyjones David Holthouse is a gonzo journalist, writer & filmmaker. His documentaries include Operation Odessa, the Last Narc, Sasquatch & Krishnas. https://davidholthouse.com SPONSORS https://amentara.com/go/dj - Use code DJ22 for 22% off your first order. https://shopify.com/dannyjones - Sign up for your one-dollar-per-month trial today. https://liquid-iv.com - Use code DANNY for 20% off your first order. https://mengotomars.com - Use code DANNY for 50% Off & 3 Free Gifts. https://whiterabbitenergy.com/?ref=DJP - Use code DJP for 20% off. EPISODE LINKS https://davidholthouse.com FOLLOW DANNY JONES https://www.instagram.com/dannyjones https://twitter.com/jonesdanny OUTLINE 00:00 - Operation Odessa 05:38 - Surveillance in Russia 12:07 - Cartel's access to technology & intel 14:44 - Cartel Influencers 18:13 - Why Chihuaua City, Mexico is terrifying 21:06 - Gonzo journalism 24:39 - The Last Narc & who killed Kiki Camarena 30:53 - Felix Rodriguez responds to Kiki Camerana rumors 35:59 - The trauma of Vietnam veterans 39:18 - More veterans die at home than at war 42:07 - Felix Rodriguez's relationship with CIA 45:03 - California's unsolved Sasquatch murder 49:13 - The scariest moment of filming Sasquatch documentary 53:32 - The scariest part of California 01:01:31 - "Mirroring" for good documentary filmmaking 01:04:51 - What Chinese cartels are up to 01:06:19 - Narco Mennonites 01:11:25 - Crazy story about El Chapo 01:15:14 - Staying up for 72 hours with meth heads 01:20:34 - Embedding with Skinheads 01:29:48 - Visiting Aryan Fest 01:37:59 - How to spot Scientologists 01:41:14 - The Hare Krishna movement 01:48:39 - David's production style 01:55:44 - David's secret to finding new projects 02:02:13 - Supernatural beliefs in Mendocino, CA 02:07:53 - Interdimensional portals in the woods 02:08:55 - David saw the Pheonix Lights 02:15:17 - Link between Epstein Files & UFOs 02:19:33 - California's energy policy relies on Iran oil 02:26:17 - Why we need nuclear power Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode, Sharona and Boz welcome back Matt Townsley to dig into a critical—and often overlooked—truth about grading reform: if leaders don't understand and support it, it simply won't scale. Drawing on both research and real-world experience, Matt explains why grading reform is a “second-order change” that requires deep philosophical commitment from administrators, not just technical adjustments from teachers. The conversation explores the upcoming Iowa based leadership-focused standards-based grading conference, the role of systems-level support, and emerging frameworks like multi-tiered support for teacher implementation. Along the way, the trio connects these ideas to broader challenges in both K–12 and higher education, from structural barriers to the growing urgency of reform in the age of AI. The takeaway is clear: isolated classroom innovation isn't enough—lasting change requires aligned leadership, intentional systems, and a shared purpose for what grades are meant to communicate.LinksPlease note - any books linked here are likely Amazon Associates links. Clicking on them and purchasing through them helps support the show. Thanks for your support!Standards-Based Grading Conference: The 3rd Annual Collaborative Assessment Conference for Leadership TeamsAll Things Standards-Based Grading, by Matt TownsleyGrading Reform Isn't Options Anymore - Here's Why, with Matt TownsleyTop 5 standards-based grading articles for 2025, by Matt TownsleyWhen standards-based grading feels dark…and reassessments become the flashlight everyone reaches for later, by Matt TownsleyWalking the talk: Embedding standards-based grading in an educational leadership course The 4 Common Myths about Grading Reform, Debunked, by Matt Townsley and Sarah MorrissPrevious Episodes MentionedEpisode 18 - Sportscaster of Alternative GradingEpisode 46 – Extinguishing the Fires within Assessment and Grading Reform: Welcoming Back Dr. Matt TownsleyEpisode 48 - Implementation Challenges and Opportunities: A Conversation with Becky Peppler and Don Smith on Working with K-12 School Districts to implement Alternative GradingEpisode 59 - Leaning Into ROI and Communication in Leading Grading Reform: An Interview with Dr. Chad LangEpisode 74 - Exploring Alt Grading in Physical Education (in more detail) with Josh OgilvieResourcesThe Center for Grading Reform - seeking to advance education in the United States by supporting effective grading reform at all levels through conferences, educational workshops, professional development, research and scholarship, influencing public policy, and community building.The Grading Conference - an annual, online conference exploring Alternative Grading in Higher Education & K-12.Some great resources to educate yourself about Alternative Grading:The Grading for Growth BlogThe Grading ConferenceThe Intentional Academia BlogRecommended Books on Alternative Grading:Grading for Growth, by Robert Talbert and David ClarkSpecifications Grading, by Linda NilsenUndoing the Grade, by Jesse StommelFollow us on Bluesky, Facebook and Instagram - @thegradingpod. To leave us a comment, please go to our website: www.thegradingpod.com and leave a comment on this episode's page.If you would like to be considered to be a guest on this show, please reach out using the Contact Us form on our website, www.thegradingpod.com.All content of this podcast and website are solely the opinions of the hosts and guests and do not necessarily represent the views of California State University Los Angeles or the Los Angeles Unified School District.MusicCountry Rock performed by Lite Saturation, licensed under a Attribution-NonCommercial-NoDerivatives 4.0 International License.

On this episode, Bob Morse, Co-Founder and Managing Partner at Strattam Capital, shares how founder-led vertical software companies can embed AI meaningfully into their products and avoid being left behind as the market shifts.Hear how Strattam approaches AI adoption across its portfolio—from replacing year-long custom development backlogs with 48-hour turnarounds, to tracking the share of deals where AI features are the deciding factor in the sale. Learn why successful AI adoption requires leadership change before technology change, how to identify AI projects worth pursuing by staying close to the customer problem and why the goal is to be in the AI spending bucket rather than the SaaS spending bucket.The information contained in this podcast is not intended to constitute, and should not be construed as, investment advice.

Healthcare doesn't have a technology problem; it has a workflow problem. In this episode, Kshitij Jaggi discusses why healthcare's digital tools have failed to improve efficiency and how agentic AI can transform operations by completing work rather than creating more of it. He explains the critical difference between task automation and system-level orchestration, and why administrative bottlenecks, such as prior authorizations, delay care. He also explores how governance, traceability, and new oversight roles are essential for responsible AI adoption. Finally, he shares how throughput should define ROI and unlock better outcomes across the healthcare system. Tune in to learn how AI can eliminate friction, improve access to care, and reshape the future of healthcare operations.  About Kshitij Jaggi: Co-founder and CEO of RISA Labs, Kshitij (KJ) leads the company's mission to accelerate oncology innovation through data-driven collaboration and transformative technology. Things You'll Learn: Most healthcare software fails because it adds work for users instead of reducing it, making time the primary barrier to adoption. True transformation in healthcare requires system-level orchestration rather than isolated task automation. Embedding clinical intelligence into administrative workflows reduces errors, delays, and inefficiencies in care delivery. Administrative processes are the biggest source of friction in healthcare systems. Integrating agentic AI with EMR systems can significantly increase throughput in a labor-constrained healthcare environment. Long-term success depends on platform-based solutions, governance and oversight of AI, and measuring ROI through throughput, timeliness of care, and reduced treatment leakage while enabling more seamless care and faster innovation. Resources: Connect with and follow Kshitij Jaggi on LinkedIn. Follow RISA Labs on LinkedIn and visit their website.

In this episode, Jeff Mains sits down with Stanley Leong — former IBM/Agilent engineer turned bestselling author and private wealth advisor — to explore what it truly means to engineer your finances. Stanley brings his analytical, systems-driven engineering background to personal wealth building, and the result is a refreshingly practical framework for tech founders and high-income professionals who are great at running businesses but often treat their personal finances as an afterthought.Stanley shares how getting laid off the day after buying his first house sent him on an unexpected 20-year journey into financial planning. He explains why concentration risk (too much wealth in one stock or one company) is the #1 mistake he sees among tech professionals, why investment management is really risk management, and how the key question every investor should ask first is "What if I'm wrong?" The conversation also dives deep into underutilized tax strategies — including the Mega Backdoor Roth and the HSA as a stealth retirement account — and wraps with a powerful discussion on aligning money with purpose and preparing emotionally for life after a liquidity event.Key Takeaways4:10 — From Chips to Cashflow: Stanley's Origin Story Stanley was laid off the day after buying his first house. Frustrated by conflicting advice and no clear answers, he pivoted from engineering to financial planning — and discovered he could serve others facing the same confusion.7:24 — What "Engineering Your Finances" Actually Means Stanley applies the same systematic, process-oriented thinking he used as an engineer to personal finance. His "Wealth Focus Model" structures client meetings around specific, scheduled topics — goal tracking, protection planning, taxes, and investment strategy.9:02 — Concentration Risk: The #1 Mistake Tech Founders Make Too much net worth tied up in a single stock, employer equity, or your own company is the most common and dangerous financial mistake. Tech founders are especially vulnerable — success can quietly become massive exposure.15:19 — How to Think About When to Diversify Start with your goal (e.g., retire at 60), work backward to determine how much you need to set aside in diversified investments, and then let the rest work harder in higher-risk/higher-reward vehicles. This keeps you on track even if the concentrated bet doesn't pay off.17:10 — Investment Management Is Really Risk Management Most people think investing is about making money. Stanley reframes it: the job is to manage risk first, then optimize returns. That mindset shift is what separates investors from gamblers.18:10 — The Investor's First Question: "What If I'm Wrong?" Before committing capital to anything, ask what happens if the investment doesn't go your way — and whether you can live with that outcome. Gamblers ask "How much can I make?" Investors ask "What's the downside?"20:34 — Tax Diversification: Build Three Buckets Prepare for an uncertain tax future by spreading wealth across three types of accounts: pre-tax (traditional 401k), after-tax Roth (tax-free growth and withdrawals), and taxable brokerage. Having optionality across tax buckets is just as important as investment diversification.22:44 — The Mega Backdoor Roth: A Largely Unknown Strategy High earners who can't contribute directly to a Roth IRA can use a little-known third 401k contribution type — after-tax contributions — to funnel an additional $20–40K/year into a Roth position. The key: don't forget to actually convert the after-tax contributions to Roth.27:45 — The HSA: The Most Tax-Efficient Account Nobody Maxes Out The Health Savings Account beats every other tax-advantaged vehicle: pre-tax contributions, tax-deferred growth, and tax-free withdrawals. The strategy: don't use it for current healthcare costs — let it grow, save your receipts, and reimburse yourself decades later tax-free.32:44 — The Retirement Tax Window Many Miss Many high earners experience a brief "tax valley" in early retirement — income drops before RMDs and Social Security kick in. Use that window to convert pre-tax retirement accounts to Roth at a very low (sometimes 0%) rate before required minimum distributions force higher taxes.36:19 — Money Without Purpose Has No Value Stanley's first question to every new client: "What is the purpose of this money?" Clear goals — not just "retire someday," but where, with whom, doing what — make risk evaluation real and decisions intentional.39:10 — Life After a Liquidity Event: The Emotional Preparation The financial transition is only part of the story. Founders who retire or exit without a clear vision for what comes next often struggle. Start forming that post-exit identity before the event — read, talk to others, explore — so you're moving toward something, not just away from work.42:17 — Financial Independence ≠ Retirement The better framing is "financial independence" — the freedom to work on your own terms. One of Stanley's clients realized he loved his job the moment he knew he didn't have to be there anymore. The ability to walk away is sometimes more valuable than walking away.Tweetable Quotes"You should want to pay more capital gains tax than anyone you know — because that means you've made more money than anyone you know." — Stanley Leong"Investment management sounds cooler, but we're really risk managers. The focus on risk is what defines an investor versus a gambler." — Stanley Leong"A gambler's first question is 'How much money am I going to make?' A good investor's first question is always 'What if I'm wrong?'" — Stanley Leong"Money without purpose has no value." — Stanley Leong"Success can quietly turn into massive exposure. Diversification isn't about fear — it's about freedom." — Stanley Leong"Don't be afraid to pay capital gains tax. It means you made money. The more you pay, the more you made." — Stanley Leong"Financial independence doesn't mean you stop. It means you're still living your life — just maybe in a different way." — Stanley Leong"Start forming your post-retirement vision while you're still working — it's a lot easier to dream when you're not already in it." — Stanley LeongSaaS Leadership Lessons1. Engineer Your Systems, Not Just Your Product The same discipline you apply to software architecture belongs in your financial life. Build repeatable, scheduled processes around your wealth — don't wing it. A systematic approach to finances compounds over time just like good code.2. Concentration Is a Silent Risk As founders, your identity and your net worth are often tied to one thing: your company. That's a risk management problem, not a success story. The most dangerous financial position isn't losing — it's winning so much in one place that you forget you're exposed.3. Reframe Risk Before You Reach for Returns Before you invest in anything — a new product line, a strategic hire, a side bet — ask "What if I'm wrong?" Not just "What's the upside?" Embedding this question into your leadership culture protects the company as much as the balance sheet.4. Build Optionality Into Everything — Including Taxes High-growth founders often optimize for today's tax savings and ignore tomorrow's flexibility. Diversifying across tax buckets (pre-tax, Roth, taxable) gives you options in an unpredictable future. The same principle applies to your cap table, your customer base, and your revenue streams.5. Purpose Drives Better Decisions at Every Stage Vague goals produce vague results. Whether you're managing a P&L or a portfolio, specificity creates accountability. "Retire at 60 to travel Europe with my family" is a strategy. "Someday retire" is a wish. Build toward something concrete.6. Financial Independence Is a Better Goal Than Exit The most underrated outcome of building a great company isn't the exit — it's the freedom to choose. Many founders discover they love the work once they no longer have to do it. Design your financial life so you work because you want to, not because you have to.Guest ResourcesStan@engineeringyourfinancesbook.comwww.engineeringyourfinancesbook.comEpisode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn -

Healthcare payments are often discussed as a transparency problem, but the deeper issue is structural fragmentation across contracts, claims, remittances, and workflows. In this episode, Ted Ferrin, Senior Vice President of Payments Innovation at Zelis, explains how the acquisition of Rivet is bringing provider-facing payment intelligence into Zelis's broader infrastructure. He discusses why achieving financial clarity between payers and providers has been so difficult due to fragmented systems and legacy technology. Ted highlights that true transparency goes beyond simply displaying data and requires meaningful, actionable insights. He also shares how tools like Claims Insights and Zap Edge embed intelligence into payment workflows to reduce rework, improve visibility, and create a smoother experience for providers, payers, and patients. Tune in and learn how better payment intelligence could help turn transparency from a buzzword into real operational trust.  About Ted Ferrin: Ted Ferrin is Senior Vice President of Payments Innovation at Zelis, where he focuses on building solutions that improve healthcare payments and strengthen financial clarity for providers. He joined Zelis through its acquisition of Rivet, the company he founded and led as CEO for more than eight years. Before Rivet, Ted held leadership and sales roles at Canopy, Instructure, and Qualtrics. His work has centered on building organizations, products, and customer-focused growth strategies, with a particular passion for making healthcare more efficient and easier to use for providers. He studied psychology and business management at Brigham Young University.  Things You'll Learn: Healthcare payment transparency breaks down when contracts, claims, remittances, and analytics all live in disconnected systems. True transparency requires clean, normalized data delivered in real time within workflows, not just static reporting. Providers still face a major administrative burden because the old payment infrastructure often forces manual reconciliation and rework. Shared financial clarity can improve trust by reducing disputes, errors, delays, and unnecessary administrative effort for both providers and payers. Embedding payment intelligence at the point of transaction can help organizations move from passive visibility to more actionable decision-making.  Resources: Connect with and follow Ted Ferrin on LinkedIn. Follow Zelis on LinkedIn and visit their website. 

Raul Parquet is the Director of Ecommerce at Princess Cruises, where he's helping to lead them into a more digital future where visa requirements, multi-destination itineraries, and endless customization options are something customers can actually complete online. In this episode, Raul shares: The unglamorous but vital elements of a complete eCommerce analytics stack, and the table-stakes things teams often skip Why an Analytics team embedded inside product is a requirement, and the deployment discipline that brings with it And how Princess Cruises is using AI behind the scenes to help their team work smarter — and why, when it comes to customers, simplicity will always matter more than technology Links LinkedIn: https://www.linkedin.com/in/raul-parquet/ Princess Cruises: https://www.princess.com/ Chapters 00:00 Introduction 02:00 Why cruises are one of the hardest ecommerce problems to solve 6:00 Embedding analytics teams into product 8:00 The analytics stack: What "table stakes" actually looks like 13:30 How AI is already helping analytics teams work smarter 15:00 The gaps most teams don't know they have 19:00 Simplifying complex bookings: The Tesla analogy 21:00 100% of Princess Cruisers have been on the website 25:00 Where AI actually fits in the customer journey 29:00 Outro Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Raul Parquet.

We've been on a bit of a mini World Models series over the last quarter: from introducing the topic with Yi Tay, to exploring Marble with World Labs' Fei-Fei Li and Justin Johnson, to previewing World Models learned from massive gaming datasets with General Intuition's Pim de Witte (who has now written down their approach to World Models with Not Boring), to discussing the Cosmos World Model with with Andrew White of Edison Scientific on our new Science pod, to writing up our own theses on Adversarial World Models. Meanwhile Nvidia, Waymo and Tesla have published their own approaches, Google has released Genie 3, and Yann LeCun has raised $1B for AMI and published LeWorldModel.Today's guests have a radically different approach to World Modeling to every player we just mentioned — while Genie 3 is impressive, its many flaws demonstrate the issues with their approach - terrain clipping, noninteractivity (single player, no physics/no objects other than the player move), and maximum of 60 second immersion. Moonlake AI (inspired by the Dreamworks logo) is the diametric opposite - immediately multiplayer, incredibly interactive, indefinite lifetime, capable of MANY different kinds of world models by simulating environments, predicting outcomes, and planning over long horizons. This is enabled by bootstrapping from game engines and training custom agents: In Towards Efficient World Models, Chris Manning and Ian Goodfellow join Fan-Yun in explaining why their approach to efficiency with structure and casuality instead of just blind scaling is sorely needed:SOTA models still show physical or spatial understanding glitches, such as solid objects floating in mid-air or moving “inside” other solid objects.If the goal is to plan for the next action, how often is a high-resolution pixel view necessary for modeling the world? Our bet is that there is a disproportionately large share of economically valuable tasks where such detail is not required. After all, humans with a wide variety of sensory limitations have little difficulty doing almost everything in the world. Furthermore, for a large number of purposes, describing a scene or a situation in a few words of language (“the car's tires squealed as it cornered sharply”) is sufficient for understanding and planning.Experiments also show that humans only partially process visual input in a top-down, task-directed way, often making use of abstracted object-level modeling. In almost all cases, partial representations combined with semantic understanding are sufficient.…If the goal is to facilitate the understanding of causality in multimodal environments, then the world model—whether it is used in the virtual world or the physical world—must prioritize properties such as spatial and physical state consistency maintained over long time periods, and an ability to evolve the world that accurately reflects the consequences of actions. That's what Moonlake is building.Game engines are the right starting point abstraction to efficiently extract causal relationships, and building the interfaces and community (including their new $30,000 Creator Cup) to kickstart the flywheel of actions-to-observations.We were fortunate enough to attend their sessions at GDC 2026 (the Mecca of Game Devs), and were impressed by the huge variety and flexibility of the worlds people were building with Moonlake's tools already! Live videos on the pod.Full Video Pod on YouTube!Timestamps00:00 Benchmarking Gets Hard00:47 Meet Moonlake Founders01:26 Why Build World Models03:12 Structure Not Just Scale05:37 Defining Action Conditioned Worlds07:32 Abstraction Versus Bitter Lesson14:39 Language Versus JEPA Debate20:27 Reasoning Traces And Rendering Layer37:00 Gameplay Over Graphics38:02 Fiction Rules And World Tweaks39:15 Code Engines Beat Learned Priors41:10 Diffusion Scaling Limits43:23 Symbolic Versus Diffusion Boundary46:14 Platform Vision Beyond Games50:24 Spatial Audio And Multimodal Latents54:23 NLP Roots Hiring And Moon Lake NameTranscript[00:00:00] Cold Open[00:00:00] Chris Manning: Think this whole space is extremely difficult as things are emerging now. And I mean, it's not only for world models, I think it's for everything including text-based models, right? ‘cause in the early days it seemed very easy to have good benchmarks ‘cause we could do things like question answering benchmarks.[00:00:20] But these days so much of what people are wanting to do is nothing like that, right? You're wanting to get some recommendations about which backpack would be best for you for your trip in Europe next month. It's not so easy to come up with a benchmark, and it's the same problem with these world models.[00:00:41] Meet the Founders[00:00:41] swyx: Okay. We're back in the studio with Moon Lake's, two leads. I, I guess there's other founders as well, but, sun and Chris Manning. Welcome to the studio.[00:00:54] Fan-yun Sun: Thanks. Thanks, Chris. Thanks for having us.[00:00:56] swyx: You've got, you guys have, come burst onto the scene with a really refreshing [00:01:00] new take of mold models.[00:01:01] I would just want to, I guess ask how you, the two of you came together. Chris, you're a legend in NLP and just AI in, in, in general. You're, you're his grad student, I guess[00:01:10] Fan-yun Sun: Actually my co-founder.[00:01:11] swyx: Oh, yeah.[00:01:12] Fan-yun Sun: I should give a lot of credit to my co-founder, Sharon. Yeah. She was, she was actually working with Professor Fe Androgyn and then she ended up working with, Ron and Chris Manning here.[00:01:22] And then, so I got connected through to Chris initially, actually through my co-founder,[00:01:26] What is Moon Lake?[00:01:26] swyx: what is Moon Lake? What, what is, actually, I'm also very curious about the name, but like why going into world models?[00:01:33] Fan-yun Sun: So I was working a lot. With actually Nvidia research during my PhD years on essentially generating interactive worlds to train reinforcement learning agents or embody EA agents.[00:01:44] And then there's two observations. One in academia and one in industry. An industry like folks at Nvidia are actually paying a lot of dollars to purchase these types of interactive worlds, whether it's for the sake of evaluation or training the robots, or policies or models. And [00:02:00] then, in academia, same thing is happening.[00:02:02] And more specifically, when I was actually working with Nvidia on the synthetic data foundation model training project, we were actually generating a lot of these synthetic data and showing that, hey, you can actually, these synthetic data are actually as useful as real world data when it comes to multimodal pre-training.[00:02:16] But then, like I said, there's a lot of dollars being paid out to like external vendors or, or like. Other folks to manually curate these types of data. It was very clear to us that, okay, on our way to, let's call it embody general intelligence models need to learn the consequences behind their actions, which means that they need interactive data and the demand for those types of data are growing exponentially.[00:02:38] But everybody's sort of thinking about it from a pure, say, video generation perspective or something else. But we feel like the true actually opportunity is actually building reasoning models that can do these things, like how humans do these things today. So that's a little bit on the genesis of Moon Lake, and I think the reason I got into world models was partly.[00:02:59] A philosophical [00:03:00] take of the on the world where I like, believe the simulation theory and stuff like that. But on the other, on the other hand, it's really just like, oh, like there's an opportunity there that I feel like nobody's doing it the way I think should be done.[00:03:10] Structure, Not Scale: The Vision[00:03:10] Chris Manning: I can say a little bit about that.[00:03:12] Yeah. So of the overall goal is the pursuit of artificial intelligence and most of my career has been doing that in the language space and that's been just extremely productive. As we all know, the story of the last few years, I don't have to tell about how much we've achieved with large language models, but, uh.[00:03:31] Although they have been extremely effective for ramping language and general intelligence, it's clearly not the whole world. There's this multimodal world of vision, sound, taste that you'd like to be dealing with more than just, language. And then the question is how to do it. And despite, a huge investment in the computer vision space, right, as the research field computer [00:04:00] vision has been for decades, far, far larger than the language space, actually.[00:04:05] I think it's fair. Say that, vision, understanding sort of stalled out, right? You got to object recognition and then progress just wasn't being made right? If you look at any of these, vision language models, it's the language that's doing 90% of the work and the vision barely works. And so there's really an interesting research question as to why that is and at heart, the ideas behind Moon Lake are an attempt to answer that, believing that there can be a really rich connection between a more symbolic layer of abstracted understanding of visual domains, which aren't in the mainstream vision models, which are still trying to operate on the surface level of pixels.[00:04:50] swyx: I think one of your blog posts, you put it as structure, not scale. Is that, a general thesis?[00:04:57] Chris Manning: Yeah. Well, scale is good too.[00:04:58] swyx: Yeah. Scale is good. Too[00:04:59] lot,[00:04:59] Chris Manning: [00:05:00] lots of data is good as well and scale, but nevertheless, you want the structure Yeah. To be able to much more efficiently learn.[00:05:07] swyx: Yeah. The other thing I really liked also is you put out an example of what your kind of reasoning traces look like.[00:05:12] Right. Which you would distill is the word that comes to mind. I don't even think that's a good, good description, but it would involve, for example, geometry, physics, affordances, symbolic logic, perceptual mappings, and what, what have you. But like that, that is the kind of example that involves, let's call it spatial reasoning, role model reasoning as as compared to normal LM reasoning.[00:05:35] Yeah.[00:05:36] Defining World Models vs Video Generation[00:05:36] Vibhu: But also like taking it a step back. So how do you guys define world models? A lot of people see okay, you can do diffusion, you can do video generation. But, you guys put out quite a few blog posts. You put out a essay recently, we can even pull it up about efficient world models. You have a pretty like structural definition here, but for the general audience that don't super follow the space, right.[00:05:55] What's, what's the difference in what we see from like a video generation model to [00:06:00] a world gen A simulator? How do you kind of paint that last[00:06:02] Chris Manning: year? Yeah, so I think this is actually a little bit subtle because, people look at these amazing generative AI video models, SAWA VO three, one of these things, and they think Genie, they think, oh, this is amazing.[00:06:17] This is we've solved understanding the world because you can produce these generative AI videos, but. The reality is that although the visuals do look fantastic, those visuals actually are accompanied by an understanding of the 3D world, understanding how objects can move, what the consequences of different actions are, and that's what's really needed for spatial intelligence.[00:06:49] So I mean, a term we sometimes use is that you need action condition, world models. That you only actually have a world model if you can predict, [00:07:00] given some action is taken, what is going to change in the world because of it. And in particular, that becomes hard over longer time scales. So if you're simply, trying to.[00:07:12] Predict the next video frame. That's not so difficult. But what you actually want to do is understand the consequences, likely consequences of actions minutes into the future. And to do that, you actually much more of an abstracted semantic model of the world.[00:07:32] The Bitter Lesson & Data Abstraction[00:07:32] swyx: Yeah, the question comes where you want to have more structure than is available in just predicting the next token.[00:07:41] And typically, well, let's, let's call it the experience of the last five years has been that is just washed away by scale, right? So what is the right middle ground here that, you don't ignore the bitter lesson, but also you. Can be more efficient than what we're doing today.[00:07:57] Chris Manning: One possibility [00:08:00] is, look, if we just collect masses and masses and masses and masses of video data, this problem will be solved.[00:08:11] Under certain assumptions that could be true, but there are sort of multiple avenues in which it could not be true. The first is what's really essential is understanding the, the consequences of actions producing an action conditioned world model. And if you are simply, collecting observational video data, which is the easy stuff to collect, when you're sort of mining online videos, you don't actually.[00:08:41] Know the actions that are being taken to see how the video is changing. And so if you are never collecting directly actions and you are having to try and infer them from what happened in the observed video, that's not impossible. But it's very [00:09:00] hard and it's not really established that you can get that to work at any scale yet.[00:09:05] And so there's a lot of premium on collecting action condition video data, which is part of why there's been a lot of interest in using simulation so that you can be collecting data where you do know the actions, which isn't quite limited supply, but there's also in the limit of as much data as you could possibly have.[00:09:28] Maybe the problem is eventually solvable, but. Even though we collect huge amounts of text data is always at a great level of abstraction, right? Language is a human designed, abstracted representation where there's meaning in each token and it's representing and abstraction of the world, right?[00:09:51] As soon as you are describing someone as a professor, and as soon as you are saying that they're condescending, right? These are very [00:10:00] abstracted descriptions of the world. It's not at what you're observing as pixel level, and to get to that kind of degree of abstraction, starting from pixels is orders and magnitude of extra data and processing.[00:10:14] And so, although, we absolutely want to exploit, get as much data as possible, use the bitter lesson. Nevertheless, if there are ways in which you can work with five orders of magnitude less data than people working purely from pixels, you're gonna be able to make a lot more progress, a lot more quickly.[00:10:34] And that's the bet here. And so you could just say that's only wanting to be able to, do it more efficiently, do it more quickly, do it more cheaply. But I think it's actually more than that, I think. One should be making the analogy to how human beings work at one level. You know? Yes, we have these high [00:11:00] resolution eyes and we can look and see a scene like a video, but all of the evidence from neuroscience and psychology is that most of what comes into people's eyes is never processed.[00:11:13] Right. That you are doing fairly fine ated processing of exactly what you're focusing on. But as soon as it's away from that of yeah, there's another guy over there that you've sort of only processing top down this very abstracted semantic description of the world around you. And so, that's what human beings are doing.[00:11:33] They're working with semantic abstractions and so. I think it is just the right representation. ‘cause we also have other goals we want to be able to do, real time worlds. So that means there's a limit to how much processing you can do and we want to do long-term planning and consistency. And again, that favors abstraction.[00:11:55] I mean, I guess there was actually a recent. Blog posts that [00:12:00] came out from our Friends of physical intelligence and, they were sort of heading in the same direction they were saying Oh, to the pay[00:12:06] swyx: pay model.[00:12:07] Chris Manning: Yeah. Yeah. To maintain a long term memory of what's happening in the world. So we can, do longer term we actually storing text of what is, been happening in the world.[00:12:19] Right. It is not such a successful strategy of trying to keep it all at a pixel level.[00:12:24] Vibhu: And yeah, I mean, you can see it in video models like that Temporal consistency. We're at a scale of train on, all the video data we have. We have it for maybe 30 seconds, a few minutes. That's not the same as a game state played for half an hour.[00:12:37] Right. I thought you guys break it down pretty well. You have a, you have a blog post about. Building multimodal worlds with an agent. I dunno if you guys wanna talk about this. This is one of the things I read, I[00:12:48] swyx: thought, yeah, it's the thing I talked about with the reasoning chain. Yeah.[00:12:51] Vibhu: So there's like different phases to this.[00:12:53] It seems like it's more of an agent, a scaffold, very different approach than just, type in a prompt and you, you don't have the same consistency. [00:13:00] It also, like, for people that are listening, I, I would highly recommend reading it. It breaks down the problem in a different light, right?[00:13:06] So like, what do you need to consider when you're talking about video, like world game models, right? How would, what do you need to consider? What are the factors? What are the elements? What's the state? So I don't know if you guys have stuff to talk about for this one.[00:13:19] Fan-yun Sun: Yeah. Actually, I wanted to add on a little bit Yeah.[00:13:22] On our previous point, which is just like, change topics so quickly. I, I do feel like sometimes people confuse like, oh, like we're taking an an, an method with abstraction. That means they don't believe in bitter lesson. Like that's just false, right? Like we are believed is a bitter lesson. But then I feel like the question that we always discuss is like, what is the right abstraction level today?[00:13:42] The analogy I like to make is like, let's just say we can encode and decode. Represent all of images, videos, audio and bytes. Then the most bitter lesson approached is to train a next byte prediction model as opposed to the next token prediction model where it's just like, okay, it's natively multimodal, can just, but it's like, yeah, like [00:14:00] to, to Chris's point, it's like the scale and computing you need to achieve that.[00:14:03] So that's why we always come back to like, okay, what is the most efficient way to do it? And reasoning models to the point of this blog post is a showcase of like, Hey, we're actually just like reasoning about the world and reasoning about. The aspects of the world that CAGR that matter for me to learn what I want to learn from this role model.[00:14:21] swyx: Yeah, it's like you're improving the en encoder of whatever you're, trying to model. And like a better representation would just represent the important things in less space. Yeah. Which would just be more efficient.[00:14:33] Fan-yun Sun: Yeah.[00:14:34] swyx: So yeah, I, I, I fully agree that it is not, antagonistic to, bitter lesson.[00:14:38] I do wanna wanna mention one more thing. Is there any philosophical differences with the JPA stuff that, Yun is working on? I gotta go there. You, you, you, you're, you're imagining like some latent abstraction. I'm like, okay, fine. Let's, let's talk about it, right? Like it's an elephant in the room.[00:14:52] Chris Manning: Yeah.[00:14:53] JEPA & Philosophical Differences with LeCun[00:14:53] Chris Manning: There are philosophical differences. Jan Lacoon is a dear friend of mine, but. [00:15:00] He has never appreciated the power of language in particular, or symbolic representations in general. Yarn is a very visual thinker. He always wants to claim that he thinks visually and there are no words, symbols, or math in his head.[00:15:21] Maybe that's true of yarn. It's certainly not the way I think. Um. But at any rate, the world according to yarn is the basic stuff of the, the world and of intelligence is visual and language is just. This low bit rate communication mechanism between humans and it doesn't have much other utility and it's far inferior to the high bit rate video, that comes into your eyes.[00:15:53] And I think he's fundamentally missing a number of important things [00:16:00] there. Think of this evolutionary argument looking at animals, right? That the closest analogies, the things with chimps, right? So chimpanzees, have fairly similar brains to human beings. They have great vision systems, they have great memory systems.[00:16:18] They've got, better memory than we do of short term memories. They can plan, they can build primitive tools that, humans. Massively ahead in what we understand about the world, what we can plan, what we can build. And essentially what took off for us was that humans managed to develop language and that gave a symbolic knowledge, representation, and reasoning level, which just, okay if this sort of vaulting of what could be done with the intelligence in brains.[00:16:59] So the [00:17:00] philosopher Dan de refers to language as a cognitive tool and argues that, humans unique among the creatures in the world have managed to build their own cognitive tools and language is the famous first example. But other things like, mathematics and programming languages are also cognitive tools.[00:17:21] They give you an ability to. Think in abstractions, in extended causal reasoning chains. And that allows you to do much more. And we use that for spatial representation and intelligence and planning and gameplay as well. So we believe, and this is, underlying the specific technologies that Moon Lake is making, that symbolic representations are powerful.[00:17:50] And you want to use that in your understanding of the visual world when you want a causal understanding, when you want to maintain long-term [00:18:00] consistency and prediction. And as I understand it, that's just not in ya Koon's worldview. So I think that's the fundamental philosophical difference. Then there's the specific model.[00:18:11] He's been advancing jpa, that's a reasonable. Research bed is a direction as to, to head for building out a model of the visual world. To my mind, it's sort of one reasonable research bed. It's not really established. It's the best one that everyone should be following,[00:18:32] swyx: at least developed at scale, at Meta.[00:18:34] But it's not just vision, right? Like, I mean, JPA is a, just joint admitting prediction can be applied to anything really. And people have done it. The argument is that there is a latent representation or that is probably more. Suited to the task, then why not let machines do it for us instead of predefining it at all?[00:18:50] And isn't something like a JPA shaped thing the right answer? And if not, why not?[00:18:55] Chris Manning: So I think there's a part of jpa that's right, which is [00:19:00] you do want to have a joint. Embedding that gives you a consistent model of the world. And Jan's argument is you can never get that from auto aggressive language models ‘cause they're sort of left to right churning out one token at a time.[00:19:22] I guess this is where we're the research arguments of the field, I'm not actually convinced that's right. ‘cause although the token production is this auto aggressive, process that's heading, left to right, I guess don't have to be left to right. But anyway, in sequence of tokens we could have right to left Arabic.[00:19:40] But although that's true, all of the weights of the model that are internal to the transformer, they are a joint model of the model's understanding of the world. And so I think you can think of the weights of the model as a form of. Joint representation, [00:20:00] and therefore it is plausible to think that could be the basis of a world model, which avoids, ya's objections.[00:20:10] swyx: I think I follow, and obviously that would touch on what Moon Lake eventually ends up doing as well. Right. Like, which it's hard to tell because you put out the end results, but we don't know the inputs that go into it. So it's, it's, that's something that we have to figure out over time.[00:20:25] Vibhu: Yeah. I mean, I guess this kind of breaks down some of the outputs. Do you wanna walk us through it?[00:20:31] Reasoning Traces & Interactive Worlds[00:20:31] Fan-yun Sun: Yeah. So this, this really just walks us through the reasoning traces of like, okay. So that just say, if we wanna build a world in this context, it's really just a game demo that, that shows the, the variety of interactions that this world model can build.[00:20:45] And yeah, it's really just a reasoning traces of like, okay it prompted to create a bowling game. Like how did it achieve what you saw? That level of causality, interaction and consistency, right? So yeah, this is almost just like a, an example of [00:21:00] like a reasoning traces. Very[00:21:01] swyx: detailed.[00:21:01] Fan-yun Sun: Yeah.[00:21:01] Vibhu: Very, very detailed.[00:21:02] You gotta you don't even realize it, right? Like when a video is generated, what happens when a ball strikes a pin, right? So first, like you, there's audio in that, like audio triggers happens, score increments, the world changes. Like pins have to start dropping. There's a timer that goes on. It's just like very similar to how now we're used to reasoning for language models.[00:21:20] There's a whole state of what happens. So geometry, physics, all this stuff. And then yeah, there's kind of that single prompt. So asset, ation all this stuff. It's like a, it's a nice view to see what's going on.[00:21:32] swyx: I think Sun is also too polite to point out that, both like Google's genie, demos as well as world Labs is marble, do not have interactive worlds.[00:21:41] Fan-yun Sun: That's the benefit of having a reasoning model, right? Like, because you can, you can say, oh, like maybe in this particular context, I want to learn how to bowl. And then you can say, okay, then what is it important when it comes to learning how to bowl? Okay, maybe it's like I need to understand the, the basic of like, physics and I want to throw it over [00:22:00] them.[00:22:00] I wanna know that when I, when it resets it's a new game. So I know that yeah, basically, you know to pick up the ball, you know that ball's gonna cause the pins to fall down. You know that what's important to this particular bowling game is to score and you know that the score corresponds to the number of pins that fell down.[00:22:19] So it's just like, if it's a model that sort of knows what it. Looks like, knows what a bowling game looks like, but doesn't actually allows you to practice over and over again and to understand that, oh, like what it takes to actually get a high score. Then it sort of doesn't actually allow you to learn what you set out to learn within the world model.[00:22:38] And I think this is really just one example of showing like the advantages of the approach that we're taking over most the, let's call it the zeitgeist, is today, when people talk about clinical role models,[00:22:51] Chris Manning: right? So it sort of seems like the question to ask when there's a world model is.[00:22:58] Can I not [00:23:00] only just wander around the world and look at the beautiful graphics, can I interact with the objects in the world and see the right consequences of actions?[00:23:11] Vibhu: And you also understand what the consequences would be if you do something right. So it's not just like, okay, there's one thing if I pick it up, something will happen.[00:23:19] But, there's 50 options and I know I can expect, I can infer what would happen if I do any of them. Right. So very different when you can actually see it play around with it.[00:23:28] swyx: There,[00:23:28] Beyond Unity: Cognitive Tools for World Building[00:23:31] swyx: there's two cheeky elements of that. I mean, the, the, the I guess, less ambitious one is, let's really establish for listeners, why is this fundamentally different than writing Unity code, right?[00:23:40] Like just creating a model to translate a prompt into Unity code[00:23:44] Fan-yun Sun: so there is an underlying physics engine. Yeah. In that sense, there's some overlapping things to Unity, but the way we think about it is like physics engine. Tools or code are cognitive tools like borrowing Chris's term, right? Like tools [00:24:00] that the model can employ as means to an end.[00:24:04] So today maybe you say, okay, in this particular context we care about physics, we care about the long-term causality consequences. Then yes, we deploy it, employ physics engine, and then maybe tomorrow we say, okay, we're we're training that. Just say drones where we only care about really fluid dynamics and the visual aspect of the world.[00:24:25] Then, then yeah, maybe we don't actually, the model actually doesn't have to use a physics engine. Or maybe it employs other types of representation or physics engine to achieve the task. So yes, writing code for Unity is sort of similar to a tool that our A model can employ, but our goal is for a model to take a representation conditioned reasoning.[00:24:46] Approach or process.[00:24:47] swyx: Yeah,[00:24:47] Fan-yun Sun: internally.[00:24:48] swyx: Yeah. Using these things as just like general two calls. Right. Which I think is very interesting. The other more ambitious one is, some kind of recursive element where it becomes multiplayer, right? Like here, there's a single player element, you're not [00:25:00] modeling any other people involved.[00:25:01] And that is a whole other thing.[00:25:04] Fan-yun Sun: But in fact, we can really do multiplayers. Oh yeah, okay. I haven't seen any double situations. So just actually just like prompt our, our model to say, Hey, like configure to multiplayer. Then it'll do like this. You'll be able to configure multiplayer[00:25:16] swyx: great[00:25:17] Fan-yun Sun: persistency database for you.[00:25:18] Easy. Yeah.[00:25:19] Vibhu: So what, what are like some of the current limitations in where we're at? So there's one approach of like, okay, scale up video predictors. Obviously there's data issues. With approaches like this, is it data constraints? What are like the next steps? Is it real time? Like, so there's one side of, write an agent to write Unity code, but okay, I want to be streaming a game real time.[00:25:38] I want to have characters being also like agent, but where, where do we kinda see this scaling up? Right?[00:25:44] Fan-yun Sun: Yeah, there's definitely a data constraint. Like the more data, the, the better. This reasoning model can almost basically act as humans to like operate a variety of tools and softwares to build whatever's necessary.[00:25:57] And then there's a sort [00:26:00] of fidelity constraint, which we're actually solving with another model, which we can talk about later. But it's like, it's not as easy to get to photorealism with the approach that we're taking. But we think there are better solutions to that, which is we can dive into later.[00:26:14] Later.[00:26:15] Vibhu: The one one thing you note here is it's a diffusion model, right? So there's, there's a few approaches, diffusion caution, splatting, yeah, so Ry diffusion model, you guys wanna[00:26:25] Fan-yun Sun: Yeah.[00:26:25] Vibhu: Introduce,[00:26:26] Fan-yun Sun: yeah, totally.[00:26:26] Rie: Neural Rendering & Skins for Worlds[00:26:26] Fan-yun Sun: So within our world modeling framework, we think there are two models that we train, right?[00:26:31] Like, there's the multimodal reasoning model that we just talked about that essentially handles. Mainly the, the causality, the persistency and logic determinism of the world. And then RY is our bet on saying, okay, like while all those model, can take care of all these things that we just talked about, it's limitations compared to existing, say, video models, is that it doesn't have as high of a pixel [00:27:00] ality right off the gate, right?[00:27:02] And EE is to say, Hey, we can actually take whatever persistent representation that we generate with our multimodal reasoning model and learn to restyle it into photo photorealistic styles or arbitrary styles you want. So this model is almost to say, Hey, I'm going to respect the persistency and interactivity of the world that you created, but my only job is to make sure that its pixel distribution is close to what we want.[00:27:29] Vibhu: Yeah.[00:27:30] swyx: Great example right there. You kept the KL divergence.[00:27:33] Fan-yun Sun: Oh. Where,[00:27:34] swyx: no, no. I mean this, this is a, a classic like, how you don't stray too far from the source material as you, you kept the kl, which is Oh yeah. Kind of cool. Yeah.[00:27:43] Fan-yun Sun: Yeah.[00:27:44] swyx: I mean, and the[00:27:44] Chris Manning: difference is, and I mean sun was pointing at this, where sort of saying it's in one way a more difficult path, but a better path that, typically the diffusion models are producing the whole scene and it looks lovely, [00:28:00] but there isn't spatial understanding behind it, which is allowing for the real time graphics gameplay, the spatial intelligence, understanding the consequences of worlds where this is, taking a path where it is assuming an abstracted semantic model of the world's state.[00:28:20] And then the diffusion model is then being used on top of that to produce the high quality graphics.[00:28:27] swyx: Is there an intended practical, or business use for this, or is it like a, like a demonstration of capabilities?[00:28:34] Fan-yun Sun: We actually believe that this is gonna be the next paradigm of rendering. So it's gonna replace how ra raizer, it's gonna replace DLSS today because it not only has these pixel prior that's learned from the world such that you can literally play any game in photo realistic styles, which is a lot of people's desire when they do GTA, right?[00:28:51] Like,[00:28:51] Vibhu: all the mods, all the people adding perfect lighting and all this.[00:28:54] swyx: So[00:28:54] Fan-yun Sun: skins[00:28:55] swyx: for worlds, let's call it[00:28:56] Fan-yun Sun: skins, let's call it skin for worlds. I,[00:28:58] Vibhu: it's also like, you can call it skin, you can call it [00:29:00] customization. You can play it how you want, right?[00:29:01] Fan-yun Sun: Yeah, exactly. And I think another thing that we really pointed out specific specifically in this blog is the programmability of it, right?[00:29:09] So what this means is that this render historically render is always a derivative of the game state, right? You're saying, oh, here's the game state, I'm rendering out a frame. But here I'm saying actually this render can be part of the gameplay loop. I can say something along the lines of, if upon getting 10.[00:29:26] Apples, I'm gonna, my weapon of choice, my bullet's gonna turn into apples. And that's, that's possible because we can say, we can basically dynamically have certain game state trigger the, the preconditions to the render such that the rendering is now part of the game loop too. One thing is to just say, okay, it's, it's, it's the appearance.[00:29:47] But the second thing is also to say there's these novel interactions that are possible because this render now has actually priors of the world.[00:29:57] swyx: It is up to the artist to figure out what to do with it.[00:29:59] Fan-yun Sun: It [00:30:00] is up to the creators. Yes.[00:30:01] swyx: Yeah.[00:30:01] Fan-yun Sun: And I also think that's actually another big argument that we're making and the reason that we're picking, taking the bet we're baking is that a lot of the times, whether it's for embody AI gaming, like you want a layer where human can inject their intentions.[00:30:15] So, for example, let's just say in the context of gaming, it's obviously like my creative intent, but maybe in the context of embodied ai, it's like, oh, like I take this foundational policy and I want to actually fine tune it to deploy in my house. So you want to almost say, inject, have a layer where human can say, oh, here's the distribution of things I want to create to achieve my goal.[00:30:35] And I think 3D graphics as it as it is today, is basic, the layer for people to say, Hey, what do I care about in this world? And it allows, basically human intent to be expressed in these worlds much more explicitly and distributionally as opposed to just saying, Hey, I'm gonna generate like, arbitrary.[00:30:54] And it's like just prompts,[00:30:55] swyx: it's one of those things where like, I think you, you're going to build up a series of models, right? [00:31:00] This is just one of, this is probably like the highest utility or heaviest, frequency one, I don't dunno what to call this. Where like you Yeah. You can immediately drop this in on any game and you don't need anything else that.[00:31:10] That you guys do. But, I, I could see, I could see that I think the, the human intent is something that people are not even used to because we're so used to static worlds or, worlds that just don't react, or, I don't know. It's, it, you're kind of blowing my mind right now with like, I'm, I wonder if you've talked to people at GDC Hmm.[00:31:27] And what are they gonna do with it?[00:31:30] Fan-yun Sun: Yeah. Now the stance that we take on this front is like, we're not gonna be more creative than our users to ship[00:31:35] swyx: it out.[00:31:35] Fan-yun Sun: Yeah. But we wanna make sure that we're building things in a way that really allows them to express their intent.[00:31:41] swyx: The thing that you said about, here's the distribution that I want.[00:31:45] I think text may be too low of a bandwidth to. To really demonstrate, because I, I, there, I'm, I'm probably just gonna want to drop in a bunch of, reference assets and then you can figure it out from[00:31:58] Vibhu: there. But you probably wanna do a, a mixture of [00:32:00] both, right? Like you throw in a few images. I wanted this style.[00:32:02] Yeah. I want it to look like this. So it, it's, it's a mixture, right?[00:32:05] Chris Manning: I, I think it's a mixture. I mean, yeah, I mean there's clearly a visual component of this, and it's not that, everything can be text. ‘cause of course you want to give a visual look, but there's also a massive amount of giving the overall picture of the look of the world and the behavior of things that you can express in a few words of text.[00:32:32] And it be very time consuming and difficult to do via visual means. So I think, yeah, you want a combination of both.[00:32:40] Evaluating World Models[00:32:40] Vibhu: So one question I kind of have is, how do we go about evaluating world models? So like, there's many axes, right? One is like, okay. I have preferences. How well do we adhere to prompts? One is the simulation.[00:32:50] One is like do things, is there core logic that's broken? So coming from we know how to evaluate diffusion, there's fidelity, there's [00:33:00] stuff like that. But what are some of the challenges that most people probably aren't thinking about?[00:33:04] Fan-yun Sun: Yeah, I think this is like a great question and probably one of the hardest questions in role models because like, I think it always comes back to what are you building this role model for?[00:33:13] And depending on your end goal and purpose, the evaluation should defer. So in the context of games, then the most direct way of measuring is how much behind are people actually spending in this world that you create? And if your goal is to say, for example, in the context that we just talked about, like, hey, deploying, deploying action in body, a agent, then your, your end.[00:33:33] Metric is then, okay, after training in these worlds that you generate how robust it is to when you actually deploy to the target environment. But then, it's, it's hard to measure these end metrics. So today people have like these proxy metrics that I call that basically try to measure what we really care about, which is the end metrics, but then frankly it's different for every use case.[00:33:57] Yeah,[00:33:57] Vibhu: which seems like quite a challenge, right? Like in [00:34:00] in language models or video models. Image models, your benchmarks are proxies, right? People aren't actually asking instruction, following tool use questions. They're proxies of how well it will do downstream. But for this, so like, should teams, should companies have their own individual benchmarks outside of games?[00:34:16] If you think of stuff like, okay, video production, movies, stuff like that, that also want to use world models. Should, should they sort of internalize like. Their own proxy. Is this something you guys do? Where, where does that connect[00:34:28] Chris Manning: go? Yeah, I think this whole space is extremely difficult as things are emerging now.[00:34:35] And I mean, it's not only for world models, I think it's for everything including text-based models, right? ‘cause in the early days it seemed very easy to have good benchmarks ‘cause we could do things like question answering benchmarks and could you answer the question based on these documents and the various other kinds of, do pieces of logical reasoning or math.[00:34:58] But again, these are sort of. [00:35:00] And there were sort of visual equivalents of things like object recognition, right? For these small component tasks. These days so much of what people are wanting to do also with language models is nothing like that, right? You're wanting to, have an interaction with the language model and get some recommendations about which backpack would be best for you for your trip in Europe next month.[00:35:25] And it's not the same kind of thing, right? And it's not so easy to come up with a benchmark as to does this large language model give you an effective interaction for guiding you in a good way for shopping, right? So, and it's the same problem with these world models. So if we take the game design case, well success is that a game designer can.[00:35:57] Produce what they are [00:36:00] imagining in a reasonable amount of time. And that's really the kind of macro task. That's a very hard thing to turn into a benchmark and I think a lot of this is actually going to turn into people walking, walking with their feet. Right? I mean, I guess that's what's happening, at the large language model level, right?[00:36:23] When people are choosing to use, GPT five or Gemini or clawed, individuals are trying out these different models and deciding, oh, I like the kind of answers that GT five gives me, or no, I feel like I get more accurate detail from Claude, right?[00:36:43] Vibhu: It's a lot of[00:36:43] Chris Manning: vitech, a lot of people just using it.[00:36:45] It's vibe checking. I realize that, but it's actually whether. People feel it's giving them utility in what they want. Right.[00:36:52] Vibhu: And the the interesting thing there is like a lot of people prefer the visual, right? This looks pretty, which is not the objective of what this is [00:37:00] for, right? It's if a, if a game designer is working on something, they care about the game engine, right?[00:37:04] The state, it's, it can look whatever. You can fix that up later. Or you can have a really good game state and you can quickly edit it to 20. 20 different versions, like Keep State,[00:37:14] Chris Manning: right?[00:37:14] Vibhu: So[00:37:14] Chris Manning: that's a really important distinction, for and for speaking to Moon Lake strength, right? So, yeah, great visuals are lovely to look at for a few seconds, but gains are really all about the concept, the game play.[00:37:33] And a lot of the time that doesn't actually even require great visuals. I mean, there are just lots of very successful games which have relatively primitive visuals, and there are other games where people have spent millions producing photo realistic, visuals, and the game sucks, right? So, keeping those two axes apart is really important in thinking about what's important in a [00:38:00] world model for different uses.[00:38:02] swyx: This conversation is reminding me of some game review and fiction discussions I've, had in my sort of non-AI related life. Some, for some people might know Brandon Sanderson, who's a very famous, fiction author, had, is is a big game reviewer. And he, he's a big fan of video games where you change one thing about a normal what you might assume about, about the world.[00:38:22] For example, Baba is you, I don't know if you might have come across that, where like the rules change as you play the game. And also like where, you can do things like reverse time selectively or like change gravity selectively. And I think this is also reminds, reminds me of other kinds of world models that are created by authors.[00:38:38] Where Ted Chang is, is my typical example where he'll take the world that, you know today, but change one thing about it and, but then create a consistent world based on that. Which is long-winded answer of me to, of. For me to say is it's it easy to create alternative roles that don't exist, but you change one thing and then let's, let's run a whole bunch of people through it to see if it works.[00:38:58] Chris Manning: My first dance will [00:39:00] be, that seems a lot easier and more conceivable to do using Techn technology like Moon Lakes than with some of the other world models out there, where the sun can actually make it happen. I'll let him give a second answer.[00:39:15] swyx: If I guess for you, you're constrained by the game engine tool, right?[00:39:18] Like at the end of the day, that's the, that's the thought, partner that you have. If I ask for something where like, if it never is allowed to reverse time or if gravity only ever works one way, then well that's it. But sometimes gravity might change,[00:39:33] Fan-yun Sun: but it's a lot easier to change with code as opposed to a model that is learned primarily on data of.[00:39:42] Real world and virtual worlds that are, I guess, like for example, junior, like there's actually trained on a lot of real world data and a lot of virtual gaming data, and it's hard to say maybe it's easier to say, okay, I wanna change the visuals in like the time period of, of the world. Like, you can't change gravity, for [00:40:00] example.[00:40:00] Vibhu: I feel like you can to light bounds, right? Everything comes down to like, code is a better way to execute it, but the models aren't that diverse and creative, right? You can say, okay, make gravity slower. It can do that, but it's limited to your representation of how you text it out, right? Like they're, they're only gonna do a few iterations, whereas programmatically, if there's a game engine under the hood, you can kind of go wild, right?[00:40:22] So one of the, I dunno, one of the limitations of most models is that they're very overtrained to one style. Right. And extracting diversity is pretty difficult. At least that's something we've seen.[00:40:35] Fan-yun Sun: I mean, are there examples you have in mind where you Existing models? Yeah. Like it would be easier to do that's not using code.[00:40:43] Certain types of creative intent or like transition state transitions,[00:40:47] swyx: Clipping, other models, other wo models are very good at clipping through things. Clipping my, my, my legs clipping through a rock because it's, it's just, it's just bad. [00:41:00] Like, you would have to struggle very hard with your stuff to actually make that happen.[00:41:04] Which I think is maybe a topic that you actually prepared on, Gian Splatting versus, the other stuff.[00:41:09] Vibhu: Yeah. Yeah. It's just for those not super familiar, right? There's a, there's gian splatting, there is diffusion. Like what works, what scales up. I feel like in February when Soro one came out the blog post was literally titled like,[00:41:21] swyx: you bring it up.[00:41:22] You never know.[00:41:23] Vibhu: World, world, video generation models are world simulators. It's super bitter lesson pilled. Yeah, emer, a lot of it is emergence, right? So, not to go through their blog post, basically their whole thing was as you scale up all this consistency, all this stuff just kind of solves, it's a very simple premise, right?[00:41:41] They just scaled up, diffusion, and from there, this is, this is Feb 2024, how much can we, it's already been two years, which is basically five years. How much more in AI time do we need to just scale up or, or do we hit a data cap? But I think we already talked about this a lot, right? Like this is back to the beginning discussion of what's [00:42:00] appropriate for the time.[00:42:01] And that seems like your approach, right?[00:42:03] Fan-yun Sun: Yeah. The point I'm trying to make is that they're very many, many different types of world simulators and like having a world simulator that can produce pixel coherency is very, very useful for games and, marketing and all these things, but it's not as useful as people think when it comes to causal reasoning.[00:42:25] When it comes to embodied ai. Yeah, like it this title is true. We're not saying that it's, it's like, not a great world simulator, but actually in the blog that we, we, we, we wrote, the bet is more so that there are gonna be disproportionately large share of value of real world tasks or, and virtual tasks where high resolution pixel fidelity is not needed.[00:42:47] Yes. Video models have their values.[00:42:50] swyx: Yeah. This is at the absolute limit of my physics understanding, but one example that comes to mind is basically having to solve like ba the equivalent of a three [00:43:00] body problem in a deterministic Well, where the video models, which is approximated good enough. Yeah.[00:43:08] Right. Like there's, there's some point at which your approach kind of runs into like the you now have to simulate the world. Please, thank you very much. And like you're trying to do that, but only to the extent that the game engine lets you and like game engines cannot do some things.[00:43:23] Fan-yun Sun: Yeah, no, I mean, I think the interesting or more technical question here actually is where do you draw the boundary between.[00:43:32] What's handled with, let's say, diffusion prior and what, when? What's handled with symbolic priors?[00:43:38] swyx: Yes.[00:43:38] Fan-yun Sun: Okay.[00:43:38] swyx: Okay.[00:43:39] Fan-yun Sun: Right. Let's go there. Because this, this boundary can actually be fluid. Like I think like maybe what you're trying to get at is like, okay, people are saying pixel prior, everything. But what we're saying is, okay, there's a boundary that we draw where this is where we think provides the most economical value for the domains and things that we care about today.[00:43:59] [00:44:00] And I actually do think, and it's something that we do internally all the time, which is like, okay, given new equations that we learn or new elements of the world and that we, we learn, or maybe some other knowledge that we acquire in the process of developing the models. Should we still be maintaining this line exactly as it is today?[00:44:22] Or should we move it a little bit left or a little bit right? Right. Like sometimes that we realize that, oh, like maybe customers or, or folks like want certain things that are better handled with preop pryor as opposed to, symbolic prior than,[00:44:34] swyx: yeah. Your, your skin thing is a, is a example moving it, right.[00:44:37] Yeah.[00:44:37] Or left. Yeah,[00:44:37] Fan-yun Sun: exactly.[00:44:38] swyx: I dunno what the, the left right is.[00:44:39] Fan-yun Sun: Yeah, yeah, yeah. No the, the model.[00:44:42] swyx: Yes.[00:44:42] Fan-yun Sun: Actually we have a few iterations of them. They're actually at slightly different[00:44:45] swyx: I know boundaries. You should, you should do that. That's a cool dimension to show.[00:44:49] Fan-yun Sun: Yeah.[00:44:50] swyx: Is quantum mechanics the diffusion prior of our world?[00:44:55] Right. It's like that's the boundary of classical mechanics versus quantum. Right? Like, that's it. At one [00:45:00] point God plays dice and the other point doesn't.[00:45:02] Fan-yun Sun: I dunno if Chris, you wanna say it, but I think, I think generally I feel like physics is better with symbol P priors.[00:45:08] Chris Manning: Even quantum physics.[00:45:09] Fan-yun Sun: Even quantum physics.[00:45:11] swyx: Yeah. This is starts against to, MLST territory is, is what I call it, where, he, he likes to get philosophical. We, we we're quite friendly.[00:45:18] Vibhu: I mean, we need to get, we need to get singularity. I heard some of that.[00:45:23] swyx: No, no, I think that is actually really helpful and man, I just want you to productize this like, as a product guy, I'm just like, oh, also[00:45:32] Vibhu: a gamer, I[00:45:33] swyx: wanna, it's like a researcher, like, it's cool.[00:45:35] Like this is a, the theoretical, like you have a very good, I don't know, like the way of thinking about these things, but I just wanna see you like, express it. I do think like your fundamentally things when, when you leave open new tools, like, okay, use, use human intent to incorporate it into how you render.[00:45:52] Artists are gonna have to take like two to three years to figure out what to do with this. And you just don't know.[00:45:57] Chris Manning: Right. But I think, this is, [00:46:00] gives a much more approachable and controllable world for the society, which is the beauty, the beauty of, NLP, that that will enable it to be adopted and used.[00:46:10] And we are very hopeful about that. Yeah,[00:46:13] Fan-yun Sun: yeah. Yeah. I mean, we are, we are very focused actually on commercialization in the sense that like we do, we do really believe in the data flywheel app approach. Yeah. Where, we put this in the hands of the creators and the users and then they will teach us when, what capability our model should improve.[00:46:27] And that's why we are, we are actually, like products and beta[00:46:31] swyx: Yeah. Focusing on gaming. What, what's like the adjacent thing to gaming[00:46:34] Fan-yun Sun: embody adjacent, basically. So maybe we can, we can I'll maybe start with where we see the platform in three years. Yeah. Which is like, okay. The users would tell us what they want to achieve.[00:46:45] The end goal could be, Hey, I just, I wanna make something to teach my kids the value of humility. Or it could be, Hey, I wanna fine tune my, drones to be really good at rescue situations. I could be vacuum robots. I want to like train [00:47:00] my manipulation or like vacuum robot to be very robust to my office, right?[00:47:04] But it's like, whatever it is, scenario robust to[00:47:06] swyx: my office[00:47:07] Fan-yun Sun: or like navigate very robustly in my office. But then it's like, whatever end goal that you want, our role model will say, okay, given what you want to achieve, let me generate a distribution of environments such that I can train and evaluate whatever it is you want.[00:47:24] Yeah. Right. Maybe for the purpose of games, it's just the end simulation and that's the end product for certain policies. It's like I can train it within these environments and then help you see where your policy is failing or not. Yeah. And then, so I think,[00:47:37] swyx: so in that case, much more of a training tool.[00:47:40] Than in other training[00:47:41] Vibhu: evaluation? Both. Right?[00:47:43] swyx: Sure. Same. Same thing.[00:47:43] Fan-yun Sun: Yeah, same thing. I think it's just this role model that allows people to train any policy that can act in any multimodal environments.[00:47:51] swyx: Would it be harder to reward hack? Is there an angle here where it is harder to reward hack? Like it's just, I'll just put it generally because I think that's a, that's obviously a key [00:48:00] problem that a lot of people face when in training agents in these environments, and I don't know, can you solve it?[00:48:07] Chris Manning: I think not necessarily. To the extent that there's a mis specified reward that. It seems like it could be hacked in a more symbolic world or in a more pixel based world. I dunno if Sun's got any thoughts, but I don't think that's really being solved.[00:48:26] swyx: The other thing that comes to mind is just you could just build a better sawa as a video generator model, right?[00:48:31] Because then you, you would move the diffusion, side a bit more further to the right. I think if I got the directionality correct. And that's it.[00:48:40] Vibhu: It's better on domains, right? Like on consistency over now, or for sure it exists versus something doesn't, right.[00:48:46] Chris Manning: So[00:48:46] swyx: yeah. Yeah. Is[00:48:49] Vibhu: is a question more like, like[00:48:51] swyx: I'm just riffing on like, how do you, what can you build, you know?[00:48:54] Oh, with the stuff that you have. I do think that the minor, the academic does go immediately to training [00:49:00] and in eval evaluation, but like art tends to take unusual directions. Like you might end up,[00:49:06] Chris Manning: okay. Yeah. But the question is, can you use this piece of software to develop compelling gameplay and. I don't think you can take SOAR and produce compelling gameplay, right?[00:49:19] If you want to have a world that you can wander around in a bit, you are good. But what are your abilities to have gameplay mechanics implemented the way you'd like them to be and to have things stay, with the long-term history of your gameplay that influences future actions. I think there's just nothing there for that.[00:49:39] swyx: Yeah, I do tend to agree. I, I'm just trying to sort of test the boundaries. I would also make the observation that as AAA games industry has developed the line between what is a movie and what is a game has blurred. And you, you, you do end up basically producing a two hour movie as part of your game.[00:49:57] Fan-yun Sun: No, honestly, there, there's so many actually [00:50:00] applications in adjacent markets that our world model can go into. Yeah. But yeah, it, it's sort of fun to riff, riff on. Although on the execution side, we we, we need to stay focused with like, okay, what are the capabilities we want to unlock over time?[00:50:11] And there's a roadmap for that. But yeah, if we're just riffing on sort of like the possibilities, I feel like, whether it's endless Yeah, it's like classic[00:50:18] swyx: and the embedding for a possibility and endless in my mind, it's very close. Yeah. I do wanna, focus on one, like weird choice. I, I don't know if it's weird.[00:50:28] Maybe I'm, I got something here. Audio, right? You could have just said no audio And audio in my mind has a lot of recursion, whereas in video you can just do recasting and that's much computationally much simpler. Audio just seems way harder. I don't know if you wanna just comment on just the special 3D audio.[00:50:46] Problem. Did you really have to do it? I guess you do to be immersive, but like a lot of people do treat it as like, well, you just stick a, a tt S model on top of[00:50:57] Vibhu: Well, there's a lot more to game audio than [00:51:00] just speech. Right. It's not just[00:51:01] swyx: tts. Yeah. Tts. S Fxt, GM Spatial in my mind Echoes[00:51:06] Chris Manning: Yeah.[00:51:06] swyx: And reflections.[00:51:07] And I, I don't even know what's, what else? I don't know what, what other problems in this space.[00:51:13] Fan-yun Sun: Yeah, I think this point like the, it's sort of a more, more pointing to the benefits of using an game engine as a tool that's available to the model, right? Because like part of the spatial audio is from the code that is underlying the simulation.[00:51:32] And while we do give our model access to other types of audio models as. Tools.[00:51:39] swyx: None of them would be spatial, I think.[00:51:41] Fan-yun Sun: But that's exactly sort of more 0.2. We're giving our model an abstraction or a suite of tools such that it's able to achieve that. And you can argue that sort of spatial is like a, like a emergence out of the, the tools that we and abstraction that we provide to the agents.[00:51:59] And I think that's the beauty of [00:52:00] this, this, this approach is like there's a lot of things kind of like how human's built technology and they're like Lego blocks that build on top of each other. And it's the same thing here. There's gonna be things that sort of just sort of emerges from being able to put these things together in like combinatorially interesting ways,[00:52:14] Chris Manning: right?[00:52:15] So this integrated audio model exploits the understanding and semantics of the Moon Lake world, right? And whereas in general for the Gen AI video models. There's no actual integration across to audio at all, right? That someone might stick some music or stick a soundscape or whatever else on top of their video.[00:52:44] So it's not a silent video, but they're in no way connected into a consistent world model. And there's nothing that's okay. An action is happening in the video. Therefore there should be a sound that's [00:53:00] coming from this part of the visual field.[00:53:03] swyx: Yeah.[00:53:03] Vibhu: Is that different than Sora too? Does it not have audio?[00:53:06] Not to say it's not like[00:53:08] swyx: amazing[00:53:08] Vibhu: isn't a spatial[00:53:09] swyx: audio.[00:53:09] Vibhu: It doesn't,[00:53:10] swyx: no. I've played around it with it enough. It just sounds like someone put an 11 laps voice on top of it and just tried to do the lip sync.[00:53:18] Vibhu: Oh, yeah. I've seen, okay. Generate a dog at the beach and reactions to big wave and move[00:53:23] swyx: around.[00:53:23] It's definitely like, so have the dog, have the dog move away from camera and see if the, the song goes down. It doesn't. ‘Cause they don't have facial audio.[00:53:32] Fan-yun Sun: We do want to basically like we, our moral model, like the one we're training is basically towards the goal of having a combined latent representation across all these different modalities.[00:53:42] Right? Such that it can like reason across these different modalities. So for example, if I close my eyes and like you play a video, you play a sound of like a car skidding away from me. I almost can like, visually extrapolate that trajectory in my mind. And I think that type of capability, we want our model to be able to reason, right?[00:53:59] And that's the reason that [00:54:00] we're sort of taking this multimodal reasoning approach. It's like we want this combine late in space that can[00:54:05] swyx: Yeah. Oh, you said late in space. We like that. Here we have to play the, the bell Every time that someone says late in space, no, you gotta train daredevil one. Where you, you, you, it's only audio, but you have to work out.[00:54:15] Where everything is.[00:54:19] Cool. I I think that that was, that was about it for our Moon Lake coverage. I do think that we have like a couple of, Chris Madden questions on, on IR and, just any, any other sort of attention topics or n NLP topics.[00:54:31] Vibhu: Okay.[00:54:31] swyx: Go ahead.[00:54:32] Chris Manning's Journey: From NLP to World Models[00:54:32] Vibhu: Well, no, I mean, yeah, it's just fun. We talked a bit about how you guys met, but you basically, you, you were like the godfather of NLP per se, right?[00:54:39] You spent the whole career from early embeddings, early early attention. You did 2015 attention for machine translation, everything. You, you had information retrieval, so RAG before rag, we just wanna shout that out and admire a lot of that. Right? So what prompted the switch over to world models?[00:54:56] How, how'd all that come about?[00:54:58] Chris Manning: To some answer it [00:55:00] is, the enthusiasms and creativity of students, but there's a bit of a history there, right? So, yeah. So clearly most of my career has been doing stuff with language and how I got into research was thinking, ah, this is just so amazing how humans can produce speech and understand each other in real time.[00:55:21] And somehow they managed to learn languages from their kids. How could this possibly happen? And so, yeah, starting off I was very focused on language, but as it sort of got into the 2000 and tens, I started, going, I'd been working on question answering, and then I started to get, interest in visual question answering.[00:55:42] And that was an area where it was very noticeable. That the visual understanding was bad. Right. These were the days when like, it sort of seemed like there's almost no visual [00:56:00] understanding. You were just getting answers that came from priors. So, if you asked how many people are sitting at the table, it'd always answer two regardless of how many, how many people you could see in the picture.[00:56:11] And so it seemed like, oh, these models actually aren't able to get semantic information outta

Text a Message to the ShowPolice Social Workers are not a new concept but until now there's not been a nationwide standard for how they are trained or how they are employed alongside police officers.  Caroline Ban teaches at Valparaiso University and she has started the first public safety social work certificate program in the nation for people who have their masters in social work or are getting their MSW.  Caroline talks about the how social workers can add something valuable to the police team that I know you'll appreciate.Music is by Chris HaugenHey Chaplain Podcast Episode 137Tags:Social Workers, Co-responders, Education, Embedding, Mental Health, Officer Wellness, Police, Professor, Training, University, St. Louis, Valparaiso, Indiana, Missouri Support the showThanks for Listening!  And, as always, pray for peace in our city.Subscribe/Follow here:Apple Podcasts: https://podcasts.apple.com/us/podcast/hey-chaplain/id1570155168Spotify: https://open.spotify.com/show/2CGK9A3BmbFEUEnx3fYZOYEmail us at: heychaplain44@gmail.comYou can help keep the show ad-free by buying me a virtual coffee!https://www.buymeacoffee.com/heychaplain

Cryptocurrency: for iGaming in the Republic of Ireland Cryptocurrencies have finally crossed into the financial mainstream, with numerous industries now considering them to be a legitimate payment option. The online gaming sector was an early adopter of crypto, bringing them into the equation alongside other options such as debit cards and digital wallets. Established iGaming markets such as the Republic of Ireland offer an excellent insight into how crypto has become firmly embedded in the sector. Crypto Payments and the Evolution of iGaming Ireland is a renowned gambling market worth around $1.5 billion. Its success has been built on a willingness to embrace innovative technological developments. Many of the reputable platforms listed on Casino.com Ireland offer players the opportunity to make deposits and withdrawals via crypto. The recognised its speed and cross-border relevance. Crypto transactions are faster than traditional bank transfers handled by Irish legacy institutions, typically being processed within a few minutes. Operational costs are also lower. Privacy is another key reason why players and operators have adopted crypto payments. Crypto allows players to transact without sharing extensive, personal or financial details. Blockchain technology can be also used to prove that online casino games are fair, giving users much greater confidence in the game outcomes they experience. True Digital Ownership and the Rise of In-Game Economies The concept of true ownership of crypto in gaming has been a serious topic of debate for the industry in Ireland and the rest of the world. Developers generally control all in-game items, such as skins, weapons, outfits and other collectables. This model never sat well with players who invested time and money to assemble assets but never truly owned them. Blockchain technology has changed that dynamic. Through tokenisation and non-fungible tokens (NFTs), players can now trade, own and sell their in-game items across different marketplaces. The widespread use of blockchain technology has given rise to player-driven economies in which virtual goods have real-world value. According to Grand View Research, global gaming revenue will grow from $184 billion in 2024 to a projected $205bn this year. In-game purchases covering microtransactions, downloadable content, battle passes and subscriptions are projected to contribute to 61 percent of that total in 2026. Crypto enables these trades in an actual, open, tradable financial ecosystem. It appeals to the younger, digitally-versed audience who want flexibility and control. Players can now monetise their time and assets with crypto, transforming the gaming experience into something remarkably more. Play-to-Earn Models and New Incentive Structures The emergence of crypto has introduced entirely new gaming models, the most popular of which is play-to-earn (P2E). They are unlike traditional games, where rewards are based on in-game progression. The P2E model allows players to earn cryptocurrencies or tokens that carry real-world value. This model gained traction recently, with millions of users participating in blockchain-based games. The market was projected to achieve a Compound Annual Growth Rate (CAGR) of 21.3% and reach an estimated market size of $1110.88 million by the end of last year. It has found a particular niche in emerging markets, providing a dual-purpose experience. Players are gaming for entertainment and income. The P2E model encourages players to play for longer periods. Developers also benefit. Embedding the token economy helps build an ecosystem where users contribute to growth, creating a feedback loop as more players drive more value, which then attracts more participants. P2E is still taking shape, but it is prompting a serious rethink of expectations about what gaming can offer. Interoperability of the Metaverse, Community Governance and Decentralised Gaming Models Interoperability is another key reason for crypto's ...

On this episode of The Jeff Dornik Show, Jeff Dornik and Karen Kingston warned that the Trump administration is fast tracking AI with no real guardrails, that Elon Musk, Peter Thiel, Alex Karp, Sam Altman, and the broader Silicon Valley machine are pushing a system that centralizes power, replaces workers, and strips away accountability when AI causes harm. They argued that Palantir is embedding itself across corporate America, that Americans are being forced to train the very AI that will replace them, and that the end result is a dystopian system where constitutional rights, human autonomy, and even access to the internet can be controlled by those who own the technology.Follow Karen Kingston on Pickax - https://pickax.com/karen_kingston Follow Jeff Dornik on Pickax - https://pickax.com/jeffdornikSPONSOR:The Deep State and the Globalists don't want you owning precious metals… which is exactly the reason you should get the FREE Gold and Silver Guide from My Gold Guy today to see if investing in gold and silver is right for you. https://mygoldguy.com/jeff Tune into The Jeff Dornik Show LIVE daily at 7pm ET on Rumble. Subscribe on Rumble and never miss a show. https://rumble.com/c/jeffdornikBig Tech is silencing truth while farming your data to feed the machine. That's why I built Pickax… a free speech platform that puts power back in your hands and your voice beyond their reach. Sign up today: https://pickax.com/?referralCode=y7wxvwq&refSource=copyBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-jeff-dornik-show--4788100/support.Follow The Jeff Dornik Show on Apple Podcasts and leave a 5-star review. That's how we reach more people and bypass Big Tech suppression.Watch LIVE daily at 7pm ET on Rumble and subscribe so you never miss a show:https://rumble.com/c/jeffdornikBig Tech is silencing truth while harvesting your data to feed the machine. That's why I built Pickax, a free speech platform where creators own their content and your voice isn't controlled. Join now:https://pickax.com/?referralCode=y7wxvwq&refSource=copy

Trust is difficult enough in an environment with strict controls and security; AI adds dimensions that make establishing trust even more challenging in the public sector. Today, we sat down with three experts who share insights into achieving this elusive trust. Leaders must evaluate how to trust three elements: the model, the data, and the monitoring processes. Tim Willging from Rocket software suggests that model choice matters for transparency.  A federal leader will need to document the system thoroughly, the training data, and known risks. One way to accomplish that is with a "model card."  This document provides details on AI's performance and training data. Model choice matters for transparency and risk documentation. Even if we assume the data we use to train a model is good, we must consider the concept of "context of use."   One data set may be safe for one context, but not another. Users need a deep understanding of data that includes hybrid governance, legal concerns, and ethical considerations. If we have learned anything in the past decade, it is that checkbox solutions never work. For example, if a data source is examined and deemed safe, this can change. Legacy data pipelines may not be secure, and the data may not be encrypted in transit or at rest. Continuous monitoring is mandatory for any valid AI application. The panel also explored the role of AI in improving collaboration, data integrity, and public service, as well as the need for continuous monitoring and agile governance to ensure trustworthy AI deployments.  

Education and our ability to respond to climate change are inexorably linked. Major international studies have shown that education is the single strongest predictor of whether or not someone is aware of climate change. In the US, while 74% of Americans support climate action, support is typically 10–20 points higher among those with a college education. It's not about perceptions on climate change; a more educated workforce is better able to innovate, accelerate the climate transition, and adapt in a less stable world – especially if that education builds climate resilient skills.One could almost imagine a university designed around this need – and that is exactly what the team at Unity Environmental University are building. Today, we're joined by Unity President Dr. Melik Khoury who is creating not just a new curriculum, but a new, more inclusive approach to higher ed. Dr. Khoury argues powerfully against the elitism that has underpinned our educational system and climate narratives. We spoke about his background, the role of education in addressing climate change, how Unity is different, and the influence it could have. Dr Khoury's energy is contagious and we're sure he'll get you thinking. Enjoy.On today's episode, we cover:03:06 – Dr. Khoury's upbringing in West Africa and awakening to environmental issues04:24 – Discovering the real impacts of resource exploitation06:01 – Choosing higher education transformation as the main lever08:32 – The core climate problem: beyond politics and single-issue framing09:04 – Climate as transdisciplinary: food, energy, people, commerce11:05 – History of Unity Environmental University13:07 – Transforming Unity's model and unbundling education15:14 – New operational model and rethinking the faculty role15:23 – Scaling Unity and redefining what a university is19:42 – Preparing students to operate in complex, uncertain systems20:15 – Embedding climate and sustainability across the curriculum23:14 – AI's challenge to traditional notions of knowledge and learning23:48 – What Unity is learning from its students and their needs28:14 – What success looks like for a climate-focused university28:30 – Influencing the broader higher-ed ecosystem31:47 – How AI is changing higher education and climate learning32:11 – Why Unity embraces rather than bans AI35:58 – Concrete AI experiments at Unity (UNA, tutors, automation)39:31 – Is climate momentum fading? Perception vs. reality39:57 – Climate's “brand problem” and the real enemy: ignorance42:58 – Depoliticizing climate and making the economic case43:16 – How we broadly attack ignorance through education reform45:52 – Call for partners and funders to back scalable climate education45:52 – Closing thanks and episode wrap-upResources MentionedUnity Environmental UniversityConnect with usDr. Melik KhouryJason RissmanKeep up with Invested In ClimateSign up for our NewsletterLinkedInInstagramIf you like what you hear, subscribe and rate to support the show! Have feedback or ideas for future episodes, events, or partnerships? Get in touch!

This week: Thomas Radal, global worker engagement expert at Ulula, talks with Innovation Forum's Ian Welsh about how worker empowerment programmes can really deliver at scale. Moving beyond traditional audits, they explore how companies can gather direct feedback from workers using accessible technologies. Plus: Innovation Forum's Ellen Atiyah chats with Liz Hershfield, executive director of COTTON USA™ and the U.S. Cotton Trust Protocol, ahead of an upcoming webinar on regenerative sourcing. And, Innovation Forum's Natasha Bodnar takes a look at how companies are moving from scope 3 commitments to real-world action, and how decarbonisation is increasingly linked to energy security and supply chain resilience.

Text a Message to the ShowThis is a special bonus episode of Hey Chaplain recorded on site in New Orleans, Louisiana, at the International Association of Chiefs of Police (IACP) Officer Safety and Wellness Conference.  Chaplain Phil Reeves from the Los Angeles Sheriff's Department was my partner in giving a presentation on embedding chaplains.  We recorded that talk and the Q&A that followed and I want to give you a few select cuts of what we did.  I also had the opportunity to hang out with some chaplains from California, Massachusetts, Colorado, and other states, so I'm going to throw in a few clips of those police chaplains saying hi to the Hey Chaplain audience.Music is by LesFMHey Chaplain Bonus Episode 49Tags:Chaplaincy, Chaplains, Culture, Embedding, Expectations, IACP, Leadership, Podcasting, Police, Relationships, Standards, Kansas City, New Orleans, California, Colorado, Kansas, Louisiana, MassachusettsSupport the showThanks for Listening!  And, as always, pray for peace in our city.Subscribe/Follow here:Apple Podcasts: https://podcasts.apple.com/us/podcast/hey-chaplain/id1570155168Spotify: https://open.spotify.com/show/2CGK9A3BmbFEUEnx3fYZOYEmail us at: heychaplain44@gmail.comYou can help keep the show ad-free by buying me a virtual coffee!https://www.buymeacoffee.com/heychaplain

Simon Cook is co-founder of Reset Scenery, a Scotland-based organisation working at the intersection of the creative industries and the circular economy. Simon has over 25 years in set construction and more than a decade focused on circular practice, and works to reduce waste in the creative industries by reclaiming, repurposing and rethinking scenic fabrication. Through Reset Scenery's circular programmes, material recovery systems and industry advocacy, Simon champions practical, scalable approaches to sustainable fabrication for stage, screen and live events. Reset Scenery supports the Stage, Screen & Events sectors through reclaimed material redistribution, circular material management strategies and lower-impact construction approaches aligned with initiatives such as the Theatre Green Book. Simon and his co-founder, Matt Doolan are focusing on how to change the whole system – how best can they intervene or educate, and where in the system; how do they help people see the benefits, and make the circular option more affordable and accessible than the scenery that's designed NOT to last. Simon explains some of the ways they embed circular principles directly into design and build processes — helping productions reduce embodied carbon, material waste and disposal costs without compromising technical standards. We'll also hear about Reset Scenery's circular design support for schools, helping build long-term skills and sector resilience through practical, hands-on sustainable practice.

In episode 179 of Cybersecurity Where You Are, Sean Atkinson and Tony Sager conclude their discussion of 2026 cybersecurity predictions from seven CIS experts, as shared on the CIS website.Here are some highlights from our episode:01:09. How threat actors' adoption of Agentic AI is reshaping the defender's dilemma06:28. Public confidence: The primary focus for attackers seeking to undermine U.S. elections10:43. The surge in threat actors targeting operational technology and critical infrastructure12:29. Responsibility and the cost of fixing flawed things instead of secure by design16:29. Secure by design: An invitation to rethink architecture and plan for future adaptability17:24. Meeting cloud service prioritization with a foundation of defense for all things25:44. Supporting state and local cybersecurity maturity with both competence and character41:00. Feedback: The key to adapting security controls to evolving threats and technology use50:23. Embedding security into the heart of a businessResourcesEpisode 169: 2026 Cybersecurity Predictions from CIS — Pt 1Episode 174: 2026 Cybersecurity Predictions from CIS — Pt 2Multi-State Information Sharing and Analysis Center®CIS Critical Security Controls®How to Defend Against Iran's Cyber Retaliation PlaybookEpisode 178: Appropriate Defense to Iranian Threat ActivitySecure by DesignCollective SLTT Cyber DefenseEpisode 144: Carrying on the MS-ISAC's Character and CultureMonitoring and Support During the CrowdStrike Falcon OutageEpisode 110: How Security Culture and Corporate Culture MeshIf you have some feedback or an idea for an upcoming episode of Cybersecurity Where You Are, let us know by emailing podcast@cisecurity.org.

Turbopuffer came out of a reading app.In 2022, Simon was helping his friends at Readwise scale their infra for a highly requested feature: article recommendations and semantic search. Readwise was paying ~$5k/month for their relational database and vector search would cost ~$20k/month making the feature too expensive to ship. In 2023 after mulling over the problem from Readwise, Simon decided he wanted to “build a search engine” which became Turbopuffer.We discuss:• Simon's path: Denmark → Shopify infra for nearly a decade → “angel engineering” across startups like Readwise, Replicate, and Causal → turbopuffer almost accidentally becoming a company • The Readwise origin story: building an early recommendation engine right after the ChatGPT moment, seeing it work, then realizing it would cost ~$30k/month for a company spending ~$5k/month total on infra and getting obsessed with fixing that cost structure • Why turbopuffer is “a search engine for unstructured data”: Simon's belief that models can learn to reason, but can't compress the world's knowledge into a few terabytes of weights, so they need to connect to systems that hold truth in full fidelity • The three ingredients for building a great database company: a new workload, a new storage architecture, and the ability to eventually support every query plan customers will want on their data • The architecture bet behind turbopuffer: going all in on object storage and NVMe, avoiding a traditional consensus layer, and building around the cloud primitives that only became possible in the last few years • Why Simon hated operating Elasticsearch at Shopify: years of painful on-call experience shaped his obsession with simplicity, performance, and eliminating state spread across multiple systems • The Cursor story: launching turbopuffer as a scrappy side project, getting an email from Cursor the next day, flying out after a 4am call, and helping cut Cursor's costs by 95% while fixing their per-user economics • The Notion story: buying dark fiber, tuning TCP windows, and eating cross-cloud costs because Simon refused to compromise on architecture just to close a deal faster • Why AI changes the build-vs-buy equation: it's less about whether a company can build search infra internally, and more about whether they have time especially if an external team can feel like an extension of their own • Why RAG isn't dead: coding companies still rely heavily on search, and Simon sees hybrid retrieval semantic, text, regex, SQL-style patterns becoming more important, not less • How agentic workloads are changing search: the old pattern was one retrieval call up front; the new pattern is one agent firing many parallel queries at once, turning search into a highly concurrent tool call • Why turbopuffer is reducing query pricing: agentic systems are dramatically increasing query volume, and Simon expects retrieval infra to adapt to huge bursts of concurrent search rather than a small number of carefully chosen calls • The philosophy of “playing with open cards”: Simon's habit of being radically honest with investors, including telling Lachy Groom he'd return the money if turbopuffer didn't hit PMF by year-end • The “P99 engineer”: Simon's framework for building a talent-dense company, rejecting by default unless someone on the team feels strongly enough to fight for the candidate —Simon Hørup Eskildsen• LinkedIn: https://www.linkedin.com/in/sirupsen• X: https://x.com/Sirupsen• https://sirupsen.com/aboutturbopuffer• https://turbopuffer.com/Full Video PodTimestamps00:00:00 The PMF promise to Lachy Groom00:00:25 Intro and Simon's background00:02:19 What turbopuffer actually is00:06:26 Shopify, Elasticsearch, and the pain behind the company00:10:07 The Readwise experiment that sparked turbopuffer00:12:00 The insight Simon couldn't stop thinking about00:17:00 S3 consistency, NVMe, and the architecture bet00:20:12 The Notion story: latency, dark fiber, and conviction00:25:03 Build vs. buy in the age of AI00:26:00 The Cursor story: early launch to breakout customer00:29:00 Why code search still matters00:32:00 Search in the age of agents00:34:22 Pricing turbopuffer in the AI era00:38:17 Why Simon chose Lachy Groom00:41:28 Becoming a founder on purpose00:44:00 The “P99 engineer” philosophy00:49:30 Bending software to your will00:51:13 The future of turbopuffer00:57:05 Simon's tea obsession00:59:03 Tea kits, X Live, and P99 LiveTranscriptSimon Hørup Eskildsen: I don't think I've said this publicly before, but I just called Lockey and was like, local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you. But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working.So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people. We're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards. Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before.Alessio: Hey everyone, welcome to the Leading Space podcast. This is Celesio Pando, Colonel Laz, and I'm joined by Swix, editor of Leading Space.swyx: Hello. Hello, uh, we're still, uh, recording in the Ker studio for the first time. Very excited. And today we are joined by Simon Eski. Of Turbo Farer welcome.Simon Hørup Eskildsen: Thank you so much for having me.swyx: Turbo Farer has like really gone on a huge tear, and I, I do have to mention that like you're one of, you're not my newest member of the Danish AHU Mafia, where like there's a lot of legendary programmers that have come out of it, like, uh, beyond Trotro, Rasmus, lado Berg and the V eight team and, and Google Maps team.Uh, you're mostly a Canadian now, but isn't that interesting? There's so many, so much like strong Danish presence.Simon Hørup Eskildsen: Yeah, I was writing a post, um, not that long ago about sort of the influences. So I grew up in Denmark, right? I left, I left when, when I was 18 to go to Canada to, to work at Shopify. Um, and so I, like, I've, I would still say that I feel more Danish than, than Canadian.This is also the weird accent. I can't say th because it, this is like, I don't, you know, my wife is also Canadian, um, and I think. I think like one of the things in, in Denmark is just like, there's just such a ruthless pragmatism and there's also a big focus on just aesthetics. Like, they're like very, people really care about like where, what things look like.Um, and like Canada has a lot of attributes, US has, has a lot of attributes, but I think there's been lots of the great things to carry. I don't know what's in the water in Ahu though. Um, and I don't know that I could be considered part of the Mafi mafia quite yet, uh, compared to the phenomenal individuals we just mentioned.Barra OV is also, uh, Danish Canadian. Okay. Yeah. I don't know where he lives now, but, and he's the PHP.swyx: Yeah. And obviously Toby German, but moved to Canada as well. Yes. Like this is like import that, uh, that, that is an interesting, um, talent move.Alessio: I think. I would love to get from you. Definition of Turbo puffer, because I think you could be a Vector db, which is maybe a bad word now in some circles, you could be a search engine.It's like, let, let's just start there and then we'll maybe run through the history of how you got to this point.Simon Hørup Eskildsen: For sure. Yeah. So Turbo Puffer is at this point in time, a search engine, right? We do full text search and we do vector search, and that's really what we're specialized in. If you're trying to do much more than that, like then this might not be the right place yet, but Turbo Buffer is all about search.The other way that I think about it is that we can take all of the world's knowledge, all of the exabytes and exabytes of data that there is, and we can use those tokens to train a model, but we can't compress all of that into a few terabytes of weights, right? Compress into a few terabytes of weights, how to reason with the world, how to make sense of the knowledge.But we have to somehow connect it to something externally that actually holds that like in full fidelity and truth. Um, and that's the thing that we intend to become. Right? That's like a very holier than now kind of phrasing, right? But being the search engine for unstructured, unstructured data is the focus of turbo puffer at this point in time.Alessio: And let's break down. So people might say, well, didn't Elasticsearch already do this? And then some other people might say, is this search on my data, is this like closer to rag than to like a xr, like a public search thing? Like how, how do you segment like the different types of search?Simon Hørup Eskildsen: The way that I generally think about this is like, there's a lot of database companies and I think if you wanna build a really big database company, sort of, you need a couple of ingredients to be in the air.We don't, which only happens roughly every 15 years. You need a new workload. You basically need the ambition that every single company on earth is gonna have data in your database. Multiple times you look at a company like Oracle, right? You will, like, I don't think you can find a company on earth with a digital presence that it not, doesn't somehow have some data in an Oracle database.Right? And I think at this point, that's also true for Snowflake and Databricks, right? 15 years later it's, or even more than that, there's not a company on earth that doesn't, in. Or directly is consuming Snowflake or, or Databricks or any of the big analytics databases. Um, and I think we're in that kind of moment now, right?I don't think you're gonna find a company over the next few years that doesn't directly or indirectly, um, have all their data available for, for search and connect it to ai. So you need that new workload, like you need something to be happening where there's a new workload that causes that to happen, and that new workload is connecting very large amounts of data to ai.The second thing you need. The second condition to build a big database company is that you need some new underlying change in the storage architecture that is not possible from the databases that have come before you. If you look at Snowflake and Databricks, right, commoditized, like massive fleet of HDDs, like that was not possible in it.It just wasn't in the air in the nineties, right? So you just didn't, we just didn't build these systems. S3 and and and so on was not around. And I think the architecture that is now possible that wasn't possible 15 years ago is to go all in on NVME SSDs. It requires a particular type of architecture for the database that.It's difficult to retrofit onto the databases that are already there, including the ones you just mentioned. The second thing is to go all in on OIC storage, more so than we could have done 15 years ago. Like we don't have a consensus layer, we don't really have anything. In fact, you could turn off all the servers that Turbo Buffer has, and we would not lose any data because we have all completely all in on OIC storage.And this means that our architecture is just so simple. So that's the second condition, right? First being a new workload. That means that every company on earth, either indirectly or directly, is using your database. Second being, there's some new storage architecture. That means that the, the companies that have come before you can do what you're doing.I think the third thing you need to do to build a big database company is that over time you have to implement more or less every Cory plan on the data. What that means is that you. You can't just get stuck in, like, this is the one thing that a database does. It has to be ever evolving because when someone has data in the database, they over time expect to be able to ask it more or less every question.So you have to do that to get the storage architecture to the limit of what, what it's capable of. Those are the three conditions.swyx: I just wanted to get a little bit of like the motivation, right? Like, so you left Shopify, you're like principal, engineer, infra guy. Um, you also head of kernel labs, uh, inside of Shopify, right?And then you consulted for read wise and that it kind of gave you that, that idea. I just wanted you to tell that story. Um, maybe I, you've told it before, but, uh, just introduce the, the. People to like the, the new workload, the sort of aha moment for turbo PufferSimon Hørup Eskildsen: For sure. So yeah, I spent almost a decade at Shopify.I was on the infrastructure team, um, from the fairly, fairly early days around 2013. Um, at the time it felt like it was growing so quickly and everything, all the metrics were, you know, doubling year on year compared to the, what companies are contending with today. It's very cute in growth. I feel like lot some companies are seeing that month over month.Um, of course. Shopify compound has been compounding for a very long time now, but I spent a decade doing that and the majority of that was just make sure the site is up today and make sure it's up a year from now. And a lot of that was really just the, um, you know, uh, the Kardashians would drive very, very large amounts of, of data to, to uh, to Shopify as they were rotating through all the merch and building out their businesses.And we just needed to make sure we could handle that. Right. And sometimes these were events, a million requests per second. And so, you know, we, we had our own data centers back in the day and we were moving to the cloud and there was so much sharding work and all of that that we were doing. So I spent a decade just scaling databases ‘cause that's fundamentally what's the most difficult thing to scale about these sites.The database that was the most difficult for me to scale during that time, and that was the most aggravating to be on call for, was elastic search. It was very, very difficult to deal with. And I saw a lot of projects that were just being held back in their ambition by using it.swyx: And I mean, self-hosted.Self-hosted. ‘causeSimon Hørup Eskildsen: it's, yeah, and it commercial, this is like 2015, right? So it's like a very particular vintage. Right. It's probably better at a lot of these things now. Um, it was difficult to contend with and I'm just like, I just think about it. It's an inverted index. It should be good at these kinds of queries and do all of this.And it was, we, we often couldn't get it to do exactly what we needed to do or basically get lucine to do, like expose lucine raw to, to, to what we needed to do. Um, so that was like. Just something that we did on the side and just panic scaled when we needed to, but not a particular focus of mine. So I left, and when I left, I, um, wasn't sure exactly what I wanted to do.I mean, it spent like a decade inside of the same company. I'd like grown up there. I started working there when I was 18.swyx: You only do Rails?Simon Hørup Eskildsen: Yeah. I mean, yeah. Rails. And he's a Rails guy. Uh, love Rails. So good. Um,Alessio: we all wish we could still work in Rails.swyx: I know know. I know, but some, I tried learning Ruby.It's just too much, like too many options to do the same thing. It's, that's my, I I know there's a, there's a way to do it.Simon Hørup Eskildsen: I love it. I don't know that I would use it now, like given cloud code and, and, and cursor and everything, but, um, um, but still it, like if I'm just sitting down and writing a teal code, that's how I think.But anyway, I left and I wasn't, I talked to a couple companies and I was like, I don't. I need to see a little bit more of the world here to know what I'm gonna like focus on next. Um, and so what I decided is like I was gonna, I called it like angel engineering, where I just hopped around in my friend's companies in three months increments and just helped them out with something.Right. And, and just vested a bit of equity and solved some interesting infrastructure problem. So I worked with a bunch of companies at the time, um, read Wise was one of them. Replicate was one of them. Um, causal, I dunno if you've tried this, it's like a, it's a spreadsheet engine Yeah. Where you can do distribution.They sold recently. Yeah. Um, we've been, we used that in fp and a at, um, at Turbo Puffer. Um, so a bunch of companies like this and it was super fun. And so we're the Chachi bt moment happened, I was with. With read Wise for a stint, we were preparing for the reader launch, right? Which is where you, you cue articles and read them later.And I was just getting their Postgres up to snuff, like, which basically boils down to tuning, auto vacuum. So I was doing that and then this happened and we were like, oh, maybe we should build a little recommendation engine and some features to try to hook in the lms. They were not that good yet, but it was clear there was something there.And so I built a small recommendation engine just, okay, let's take the articles that you've recently read, right? Like embed all the articles and then do recommendations. It was good enough that when I ran it on one of the co-founders of Rey's, like I found out that I got articles about, about having a child.I'm like, oh my God, I didn't, I, I didn't know that, that they were having a child. I wasn't sure what to do with that information, but the recommendation engine was good enough that it was suggesting articles, um, about that. And so there was, there was recommendations and uh, it actually worked really well.But this was a company that was spending maybe five grand a month in total on all their infrastructure and. When I did the napkin math on running the embeddings of all the articles, putting them into a vector index, putting it in prod, it's gonna be like 30 grand a month. That just wasn't tenable. Right?Like Read Wise is a proudly bootstrapped company and it's paying 30 grand for infrastructure for one feature versus five. It just wasn't tenable. So sort of in the bucket of this is useful, it's pretty good, but let us, let's return to it when the costs come down.swyx: Did you say it grows by feature? So for five to 30 is by the number of, like, what's the, what's the Scaling factor scale?It scales by the number of articles that you embed.Simon Hørup Eskildsen: It does, but what I meant by that is like five grand for like all of the other, like the Heroku, dinos, Postgres, like all the other, and this then storage is 30. Yeah. And then like 30 grand for one feature. Right. Which is like, what other articles are related to this one.Um, so it was just too much right to, to power everything. Their budget would've been maybe a few thousand dollars, which still would've been a lot. And so we put it in a bucket of, okay, we're gonna do that later. We'll wait, we will wait for the cost to come down. And that haunted me. I couldn't stop thinking about it.I was like, okay, there's clearly some latent demand here. If the cost had been a 10th, we would've shipped it and. This was really the only data point that I had. Right. I didn't, I, I didn't, I didn't go out and talk to anyone else. It was just so I started reading Right. I couldn't, I couldn't help myself.Like I didn't know what like a vector index is. I, I generally barely do about how to generate the vectors. There was a lot of hype about, this is a early 2023. There was a lot of hype about vector databases. There were raising a lot of money and it's like, I really didn't know anything about it. It's like, you know, trying these little models, fine tuning them.Like I was just trying to get sort of a lay of the land. So I just sat down. I have this. A GitHub repository called Napkin Math. And on napkin math, there's just, um, rows of like, oh, this is how much bandwidth. Like this is how many, you know, you can do 25 gigabytes per second on average to dram. You can do, you know, five gigabytes per second of rights to an SSD, blah blah.All of these numbers, right? And S3, how many you could do per, how much bandwidth can you drive per connection? I was just sitting down, I was like, why hasn't anyone build a database where you just put everything on O storage and then you puff it into NVME when you use the data and you puff it into dram if you're, if you're querying it alive, it's just like, this seems fairly obvious and you, the only real downside to that is that if you go all in on o storage, every right will take a couple hundred milliseconds of latency, but from there it's really all upside, right?You do the first go, it takes half a second. And it sort of occurred to me as like, well. The architecture is really good for that. It's really good for AB storage, it's really good for nvm ESSD. It's, well, you just couldn't have done that 10 years ago. Back to what we were talking about before. You really have to build a database where you have as few round trips as possible, right?This is how CPUs work today. It's how NVM E SSDs work. It's how as, um, as three works that you want to have a very large amount of outstanding requests, right? Like basically go to S3, do like that thousand requests to ask for data in one round trip. Wait for that. Get that, like, make a new decision. Do it again, and try to do that maybe a maximum of three times.But no databases were designed that way within NVME as is ds. You can drive like within, you know, within a very low multiple of DRAM bandwidth if you use it that way. And same with S3, right? You can fully max out the network card, which generally is not maxed out. You get very, like, very, very good bandwidth.And, but no one had built a database like that. So I was like, okay, well can't you just, you know, take all the vectors right? And plot them in the proverbial coordinate system. Get the clusters, put a file on S3 called clusters, do json, and then put another file for every cluster, you know, cluster one, do js O cluster two, do js ON you know that like it's two round trips, right?So you get the clusters, you find the closest clusters, and then you download the cluster files like the, the closest end. And you could do this in two round trips.swyx: You were nearest neighbors locally.Simon Hørup Eskildsen: Yes. Yes. And then, and you would build this, this file, right? It's just like ultra simplistic, but it's not a far shot from what the first version of Turbo Buffer was.Why hasn't anyone done thatAlessio: in that moment? From a workload perspective, you're thinking this is gonna be like a read heavy thing because they're doing recommend. Like is the fact that like writes are so expensive now? Oh, with ai you're actually not writing that much.Simon Hørup Eskildsen: At that point I hadn't really thought too much about, well no actually it was always clear to me that there was gonna be a lot of rights because at Shopify, the search clusters were doing, you know, I don't know, tens or hundreds of crew QPS, right?‘cause you just have to have a human sit and type in. But we did, you know, I don't know how many updates there were per second. I'm sure it was in the millions, right into the cluster. So I always knew there was like a 10 to 100 ratio on the read write. In the read wise use case. It's, um, even, even in the read wise use case, there'd probably be a lot fewer reads than writes, right?There's just a lot of churn on the amount of stuff that was going through versus the amount of queries. Um, I wasn't thinking too much about that. I was mostly just thinking about what's the fundamentally cheapest way to build a database in the cloud today using the primitives that you have available.And this is it, right? You just, now you have one machine and you know, let's say you have a terabyte of data in S3, you paid the $200 a month for that, and then maybe five to 10% of that data and needs to be an NV ME SSDs and less than that in dram. Well. You're paying very, very little to inflate the data.swyx: By the way, when you say no one else has done that, uh, would you consider Neon, uh, to be on a similar path in terms of being sort of S3 first and, uh, separating the compute and storage?Simon Hørup Eskildsen: Yeah, I think what I meant with that is, uh, just build a completely new database. I don't know if we were the first, like it was very much, it was, I mean, I, I hadn't, I just looked at the napkin math and was like, this seems really obvious.So I'm sure like a hundred people came up with it at the same time. Like the light bulb and every invention ever. Right. It was just in the air. I think Neon Neon was, was first to it. And they're trying, they're retrofitted onto Postgres, right? And then they built this whole architecture where you have, you have it in memory and then you sort of.You know, m map back to S3. And I think that was very novel at the time to do it for, for all LTP, but I hadn't seen a database that was truly all in, right. Not retrofitting it. The database felt built purely for this no consensus layer. Even using compare and swap on optic storage to do consensus. I hadn't seen anyone go that all in.And I, I mean, there, there, I'm sure there was someone that did that before us. I don't know. I was just looking at the napkin mathswyx: and, and when you say consensus layer, uh, are you strongly relying on S3 Strong consistency? You are. Okay.SoSimon Hørup Eskildsen: that is your consensus layer. It, it is the consistency layer. And I think also, like, this is something that most people don't realize, but S3 only became consistent in December of 2020.swyx: I remember this coming out during COVID and like people were like, oh, like, it was like, uh, it was just like a free upgrade.Simon Hørup Eskildsen: Yeah.swyx: They were just, they just announced it. We saw consistency guys and like, okay, cool.Simon Hørup Eskildsen: And I'm sure that they just, they probably had it in prod for a while and they're just like, it's done right.And people were like, okay, cool. But. That's a big moment, right? Like nv, ME SSDs, were also not in the cloud until around 2017, right? So you just sort of had like 2017 nv, ME SSDs, and people were like, okay, cool. There's like one skew that does this, whatever, right? Takes a few years. And then the second thing is like S3 becomes consistent in 2020.So now it means you don't have to have this like big foundation DB or like zookeeper or whatever sitting there contending with the keys, which is how. You know, that's what Snowflake and others have do so muchswyx: for goneSimon Hørup Eskildsen: Exactly. Just gone. Right? And so just push to the, you know, whatever, how many hundreds of people they have working on S3 solved and then compare and swap was not in S3 at this point in time,swyx: by the way.Uh, I don't know what that is, so maybe you wanna explain. Yes. Yeah.Simon Hørup Eskildsen: Yes. So, um, what Compare and swap is, is basically, you can imagine that if you have a database, it might be really nice to have a file called metadata json. And metadata JSON could say things like, Hey, these keys are here and this file means that, and there's lots of metadata that you have to operate in the database, right?But that's the simplest way to do it. So now you have might, you might have a lot of servers that wanna change the metadata. They might have written a file and want the metadata to contain that file. But you have a hundred nodes that are trying to contend with this metadata that JSON well, what compare and Swap allows you to do is basically just you download the file, you make the modifications, and then you write it only if it hasn't changed.While you did the modification and if not you retry. Right? Should just have this retry loops. Now you can imagine if you have a hundred nodes doing that, it's gonna be really slow, but it will converge over time. That primitive was not available in S3. It wasn't available in S3 until late 2024, but it was available in GCP.The real story of this is certainly not that I sat down and like bake brained it. I was like, okay, we're gonna start on GCS S3 is gonna get it later. Like it was really not that we started, we got really lucky, like we started on GCP and we started on GCP because tur um, Shopify ran on GCP. And so that was the platform I was most available with.Right. Um, and I knew the Canadian team there ‘cause I'd worked with them at Shopify and so it was natural for us to start there. And so when we started building the database, we're like, oh yeah, we have to build a, we really thought we had to build a consensus layer, like have a zookeeper or something to do this.But then we discovered the compare and swap. It's like, oh, we can kick the can. Like we'll just do metadata r json and just, it's fine. It's probably fine. Um, and we just kept kicking the can until we had very, very strong conviction in the idea. Um, and then we kind of just hinged the company on the fact that S3 probably was gonna get this, it started getting really painful in like mid 2024.‘cause we were closing deals with, um, um, notion actually that was running in AWS and we're like, trust us. You, you really want us to run this in GCP? And they're like, no, I don't know about that. Like, we're running everything in AWS and the latency across the cloud were so big and we had so much conviction that we bought like, you know, dark fiber between the AWS regions in, in Oregon, like in the InterExchange and GCP is like, we've never seen a startup like do like, what's going on here?And we're just like, no, we don't wanna do this. We were tuning like TCP windows, like everything to get the latency down ‘cause we had so high conviction in not doing like a, a metadata layer on S3. So those were the three conditions, right? Compare and swap. To do metadata, which wasn't in S3 until late 2024 S3 being consistent, which didn't happen until December, 2020.Uh, 2020. And then NVMe ssd, which didn't end in the cloud until 2017.swyx: I mean, in some ways, like a very big like cloud success story that like you were able to like, uh, put this all together, but also doing things like doing, uh, bind our favor. That that actually is something I've never heard.Simon Hørup Eskildsen: I mean, it's very common when you're a big company, right?You're like connecting your own like data center or whatever. But it's like, it was uniquely just a pain with notion because the, um, the org, like most of the, like if you're buying in Ashburn, Virginia, right? Like US East, the Google, like the GCP and, and AWS data centers are like within a millisecond on, on each other, on the public exchanges.But in Oregon uniquely, the GCP data center sits like a couple hundred kilometers, like east of Portland and the AWS region sits in Portland, but the network exchange they go through is through Seattle. So it's like a full, like 14 milliseconds or something like that. And so anyway, yeah. It's, it's, so we were like, okay, we can't, we have to go through an exchange in Portland.Yeah. Andswyx: you'd rather do this than like run your zookeeper and likeSimon Hørup Eskildsen: Yes. Way rather. It doesn't have state, I don't want state and two systems. Um, and I think all that is just informed by Justine, my co-founder and I had just been on call for so long. And the worst outages are the ones where you have state in multiple places that's not syncing up.So it really came from, from a a, like just a, a very pure source of pain, of just imagining what we would be Okay. Being woken up at 3:00 AM about and having something in zookeeper was not one of them.swyx: You, you're talking to like a notion or something. Do they care or do they just, theySimon Hørup Eskildsen: just, they care about latency.swyx: They latency cost. That's it.Simon Hørup Eskildsen: They just cared about latency. Right. And we just absorbed the cost. We're just like, we have high conviction in this. At some point we can move them to AWS. Right. And so we just, we, we'll buy the fiber, it doesn't matter. Right. Um, and it's like $5,000. Usually when you buy fiber, you buy like multiple lines.And we're like, we can only afford one, but we will just test it that when it goes over the public internet, it's like super smooth. And so we did a lot of, anyway, it's, yeah, it was, that's cool.Alessio: You can imagine talking to the GCP rep and it's like, no, we're gonna buy, because we know we're gonna turn, we're gonna turn from you guys and go to AWS in like six months.But in the meantime we'll do this. It'sSimon Hørup Eskildsen: a, I mean, like they, you know, this workload still runs on GCP for what it's worth. Right? ‘cause it's so, it was just, it was so reliable. So it was never about moving off GCP, it was just about honesty. It was just about giving notion the latency that they deserved.Right. Um, and we didn't want ‘em to have to care about any of this. We also, they were like, oh, egress is gonna be bad. It was like, okay, screw it. Like we're just gonna like vvc, VPC peer with you and AWS we'll eat the cost. Yeah. Whatever needs to be done.Alessio: And what were the actual workloads? Because I think when you think about ai, it's like 14 milliseconds.It's like really doesn't really matter in the scheme of like a model generation.Simon Hørup Eskildsen: Yeah. We were told the latency, right. That we had to beat. Oh, right. So, so we're just looking at the traces. Right. And then sort of like hand draw, like, you know, kind of like looking at the trace and then thinking what are the other extensions of the trace?Right. And there's a lot more to it because it's also when you have, if you have 14 versus seven milliseconds, right. You can fit in another round trip. So we had to tune TCP to try to send as much data in every round trip, prewarm all the connections. And there was, there's a lot of things that compound from having these kinds of round trips, but in the grand scheme it was just like, well, we have to beat the latency of whatever we're up against.swyx: Which is like they, I mean, notion is a database company. They could have done this themselves. They, they do lots of database engineering themselves. How do you even get in the door? Like Yeah, just like talk through that kind of.Simon Hørup Eskildsen: Last time I was in San Francisco, I was talking to one of the engineers actually, who, who was one of our champions, um, at, AT Notion.And they were, they were just trying to make sure that the, you know, per user cost matched the economics that they needed. You know, Uhhuh like, it's like the way I think about, it's like I have to earn a return on whatever the clouds charge me and then my customers have to earn a return on that. And it's like very simple, right?And so there has to be gross margin all the way up and that's how you build the product. And so then our customers have to make the right set of trade off the turbo Puffer makes, and if they're happy with that, that's great.swyx: Do you feel like you're competing with build internally versus buy or buy versus buy?Simon Hørup Eskildsen: Yeah, so, sorry, this was all to build up to your question. So one of the notion engineers told me that they'd sat and probably on a napkin, like drawn out like, why hasn't anyone built this? And then they saw terrible. It was like, well, it literally that. So, and I think AI has also changed the buy versus build equation in terms of, it's not really about can we build it, it's about do we have time to build it?I think they like, I think they felt like, okay, if this is a team that can do that and they, they feel enough like an extension of our team, well then we can go a lot faster, which would be very, very good for them. And I mean, they put us through the, through the test, right? Like we had some very, very long nights to to, to do that POC.And they were really our biggest, our second big customer off the cursor, which also was a lot of late nights. Right.swyx: Yeah. That, I mean, should we go into that story? The, the, the sort of Chris's story, like a lot, um, they credit you a lot for. Working very closely with them. So I just wanna hear, I've heard this, uh, story from Sole's point of view, but like, I'm curious what, what it looks like from your side.Simon Hørup Eskildsen: I actually haven't heard it from Sole's point of view, so maybe you can now cross reference it. The way that I remember it was that, um, the day after we launched, which was just, you know, I'd worked the whole summer on, on the first version. Justine wasn't part of it yet. ‘cause I just, I didn't tell anyone that summer that I was working on this.I was just locked in on building it because it's very easy otherwise to confuse talking about something to actually doing it. And so I was just like, I'm not gonna do that. I'm just gonna do the thing. I launched it and at this point turbo puffer is like a rust binary running on a single eight core machine in a T Marks instance.And me deploying it was like looking at the request log and then like command seeing it or like control seeing it to just like, okay, there's no request. Let's upgrade the binary. Like it was like literally the, the, the, the scrappiest thing. You could imagine it was on purpose because just like at Shopify, we did that all the time.Like, we like move, like we ran things in tux all the time to begin with. Before something had like, at least the inkling of PMF, it was like, okay, is anyone gonna hear about this? Um, and one of the cursor co-founders Arvid reached out and he just, you know, the, the cursor team are like all I-O-I-I-M-O like, um, contenders, right?So they just speak in bullet points and, and facts. It was like this amazing email exchange just of, this is how many QPS we have, this is what we're paying, this is where we're going, blah, blah, blah. And so we're just conversing in bullet points. And I tried to get a call with them a few times, but they were, so, they were like really writing the PMF bowl here, just like late 2023.And one time Swally emails me at like five. What was it like 4:00 AM Pacific time saying like, Hey, are you open for a call now? And I'm on the East coast and I, it was like 7:00 AM I was like, yeah, great, sure, whatever. Um, and we just started talking and something. Then I didn't know anything about sales.It was something that just comp compelled me. I have to go see this team. Like, there's something here. So I, I went to San Francisco and I went to their office and the way that I remember it is that Postgres was down when I showed up at the office. Did SW tell you this? No. Okay. So Postgres was down and so it's like they were distracting with that.And I was trying my best to see if I could, if I could help in any way. Like I knew a little bit about databases back to tuning, auto vacuum. It was like, I think you have to tune out a vacuum. Um, and so we, we talked about that and then, um, that evening just talked about like what would it look like, what would it look like to work with us?And I just said. Look like we're all in, like we will just do what we'll do whatever, whatever you tell us, right? They migrated everything over the next like week or two, and we reduced their cost by 95%, which I think like kind of fixed their per user economics. Um, and it solved a lot of other things. And we were just, Justine, this is also when I asked Justine to come on as my co-founder, she was the best engineer, um, that I ever worked with at Shopify.She lived two blocks away and we were just, okay, we're just gonna get this done. Um, and we did, and so we helped them migrate and we just worked like hell over the next like month or two to make sure that we were never an issue. And that was, that was the cursor story. Yeah.swyx: And, and is code a different workload than normal text?I, I don't know. Is is it just text? Is it the same thing?Simon Hørup Eskildsen: Yeah, so cursor's workload is basically, they, um, they will embed the entire code base, right? So they, they will like chunk it up in whatever they would, they do. They have their own embedding model, um, which they've been public about. Um, and they find that on, on, on their evals.It. There's one of their evals where it's like a 25% improvement on a very particular workload. They have a bunch of blog posts about it. Um, I think it works best on larger code basis, but they've trained their own embedding model to do this. Um, and so you'll see it if you use the cursor agent, it will do searches.And they've also been public around, um, how they've, I think they post trained their model to be very good at semantic search as well. Um, and that's, that's how they use it. And so it's very good at, like, can you find me on the code that's similar to this, or code that does this? And just in, in this queries, they also use GR to supplement it.swyx: Yeah.Simon Hørup Eskildsen: Um, of courseswyx: it's been a big topic of discussion like, is rag dead because gr you know,Simon Hørup Eskildsen: and I mean like, I just, we, we see lots of demand from the coding company to ethicsswyx: search in every part. Yes.Simon Hørup Eskildsen: Uh, we, we, we see demand. And so, I mean, I'm. I like case studies. I don't like, like just doing like thought pieces on this is where it's going.And like trying to be all macroeconomic about ai, that's has turned out to be a giant waste of time because no one can really predict any of this. So I just collect case studies and I mean, cursor has done a great job talking about what they're doing and I hope some of the other coding labs that use Turbo Puffer will do the same.Um, but it does seem to make a difference for particular queries. Um, I mean we can also do text, we can also do RegX, but I should also say that cursors like security posture into Tur Puffer is exceptional, right? They have their own embedding model, which makes it very difficult to reverse engineer. They obfuscate the file paths.They like you. It's very difficult to learn anything about a code base by looking at it. And the other thing they do too is that for their customers, they encrypt it with their encryption keys in turbo puffer's bucket. Um, so it's, it's, it's really, really well designed.swyx: And so this is like extra stuff they did to work with you because you are not part of Cursor.Exactly like, and this is just best practice when working in any database, not just you guys. Okay. Yeah, that makes sense. Yeah. I think for me, like the, the, the learning is kind of like you, like all workloads are hybrid. Like, you know, uh, like you, you want the semantic, you want the text, you want the RegX, you want sql.I dunno. Um, but like, it's silly to like be all in on like one particularly query pattern.Simon Hørup Eskildsen: I think, like I really like the way that, um, um, that swally at cursor talks about it, which is, um, I'm gonna butcher it here. Um, and you know, I'm a, I'm a database scalability person. I'm not a, I, I dunno anything about training models other than, um, what the internet tells me and what.The way he describes is that this is just like cash compute, right? It's like you have a point in time where you're looking at some particular context and focused on some chunk and you say, this is the layer of the neural net at this point in time. That seems fundamentally really useful to do cash compute like that.And, um, how the value of that will change over time. I'm, I'm not sure, but there seems to be a lot of value in that.Alessio: Maybe talk a bit about the evolution of the workload, because even like search, like maybe two years ago it was like one search at the start of like an LLM query to build the context. Now you have a gentech search, however you wanna call it, where like the model is both writing and changing the code and it's searching it again later.Yeah. What are maybe some of the new types of workloads or like changes you've had to make to your architecture for it?Simon Hørup Eskildsen: I think you're right. When I think of rag, I think of, Hey, there's an 8,000 token, uh, context window and you better make it count. Um, and search was a way to do that now. Everything is moving towards the, just let the agent do its thing.Right? And so back to the thing before, right? The LLM is very good at reasoning with the data, and so we're just the tool call, right? And that's increasingly what we see our customers doing. Um, what we're seeing more demand from, from our customers now is to do a lot of concurrency, right? Like Notion does a ridiculous amount of queries in every round trip just because they can't.And I'm also now, when I use the cursor agent, I also see them doing more concurrency than I've ever seen before. So a bit similar to how we designed a database to drive as much concurrency in every round trip as possible. That's also what the agents are doing. So that's new. It means just an enormous amount of queries all at once to the dataset while it's warm in as few turns as possible.swyx: Can I clarify one thing on that?Simon Hørup Eskildsen: Yes.swyx: Is it, are they batching multiple users or one user is driving multiple,Simon Hørup Eskildsen: one user driving multiple, one agent driving.swyx: It's parallel searching a bunch of things.Simon Hørup Eskildsen: Exactly.swyx: Yeah. Yeah, exactly. So yeah, the clinician also did, did this for the fast context thing, like eight parallel at once.Simon Hørup Eskildsen: Yes.swyx: And, and like an interesting problem is, well, how do you make sure you have enough diversity so you're not making the the same request eight times?Simon Hørup Eskildsen: And I think like that's probably also where the hybrid comes in, where. That's another way to diversify. It's a completely different way to, to do the search.That's a big change, right? So before it was really just like one call and then, you know, the LLM took however many seconds to return, but now we just see an enormous amount of queries. So the, um, we just see more queries. So we've like tried to reduce query, we've reduced query pricing. Um, this is probably the first time actually I'm saying that, but the query pricing is being reduced, like five x.Um, and we'll probably try to reduce it even more to accommodate some of these workloads of just doing very large amounts of queries. Um, that's one thing that's changed. I think the right, the right ratio is still very high, right? Like there's still a, an enormous amount of rights per read, but we're starting probably to see that change if people really lean into this pattern.Alessio: Can we talk a little bit about the pricing? I'm curious, uh, because traditionally a database would charge on storage, but now you have the token generation that is so expensive, where like the actual. Value of like a good search query is like much higher because they're like saving inference time down the line.How do you structure that as like, what are people receptive to on the other side too?Simon Hørup Eskildsen: Yeah. I, the, the turbo puffer pricing in the beginning was just very simple. The pricing on these on for search engines before Turbo Puffer was very server full, right? It was like, here's the vm, here's the per hour cost, right?Great. And I just sat down with like a piece of paper and said like, if Turbo Puffer was like really good, this is probably what it would cost with a little bit of margin. And that was the first pricing of Turbo Puffer. And I just like sat down and I was like, okay, like this is like probably the storage amp, but whenever on a piece of paper I, it was vibe pricing.It was very vibe price, and I got it wrong. Oh. Um, well I didn't get it wrong, but like Turbo Puffer wasn't at the first principle pricing, right? So when Cursor came on Turbo Puffer, it was like. Like, I didn't know any VCs. I didn't know, like I was just like, I don't know, I didn't know anything about raising money or anything like that.I just saw that my GCP bill was, was high, was a lot higher than the cursor bill. So Justine and I was just like, well, we have to optimize it. Um, and I mean, to the chagrin now of, of it, of, of the VCs, it now means that we're profitable because we've had so much pricing pressure in the beginning. Because it was running on my credit card and Justine and I had spent like, like tens of thousands of dollars on like compute bills and like spinning off the company and like very like, like bad Canadian lawyers and like things like to like get all of this done because we just like, we didn't know.Right. If you're like steeped in San Francisco, you're just like, you just know. Okay. Like you go out, raise a pre-seed round. I, I never heard a word pre-seed at this point in time.swyx: When you had Cursor, you had Notion you, you had no funding.Simon Hørup Eskildsen: Um, with Cursor we had no funding. Yeah. Um, by the time we had Notion Locke was, Locke was here.Yeah. So it was really just, we vibe priced it 100% from first Principles, but it wasn't, it, it was not performing at first principles, so we just did everything we could to optimize it in the beginning for that, so that at least we could have like a 5% margin or something. So I wasn't freaking out because Cursor's bill was also going like this as they were growing.And so my liability and my credit limit was like actively like calling my bank. It was like, I need a bigger credit. Like it was, yeah. Anyway, that was the beginning. Yeah. But the pricing was, yeah, like storage rights and query. Right. And the, the pricing we have today is basically just that pricing with duct tape and spit to try to approach like, you know, like a, as a margin on the physical underlying hardware.And we're doing this year, you're gonna see more and more pricing changes from us. Yeah.swyx: And like is how much does stuff like VVC peering matter because you're working in AWS land where egress is charged and all that, you know.Simon Hørup Eskildsen: We probably don't like, we have like an enterprise plan that just has like a base fee because we haven't had time to figure out SKU pricing for all of this.Um, but I mean, yeah, you can run turbo puffer either in SaaS, right? That's what Cursor does. You can run it in a single tenant cluster. So it's just you. That's what Notion does. And then you can run it in, in, in BYOC where everything is inside the customer's VPC, that's what an for example, philanthropic does.swyx: What I'm hearing is that this is probably the best CRO job for somebody who can come in and,Simon Hørup Eskildsen: I mean,swyx: help you with this.Simon Hørup Eskildsen: Um, like Turbo Puffer hired, like, I don't know what, what number this was, but we had a full-time CFO as like the 12th hire or something at Turbo Puffer, um, I think I hear are a lot of comp.I don't know how they do it. Like they have a hundred employees and not a CFO. It's like having a CFO is like a runningswyx: business man. Like, you know,Simon Hørup Eskildsen: it's so good. Yeah, like money Mike, like he just, you know, just handles the money and a lot of the business stuff and so he came in and just hopped with a lot of the operational side of the business.So like C-O-O-C-F-O, like somewhere in between.swyx: Just as quick mention of Lucky, just ‘cause I'm curious, I've met Lock and like, he's obviously a very good investor and now on physical intelligence, um, I call it generalist super angel, right? He invests in everything. Um, and I always wonder like, you know, is there something appealing about focusing on developer tooling, focusing on databases, going like, I've invested for 10 years in databases versus being like a lock where he can maybe like connect you to all the customers that you need.Simon Hørup Eskildsen: This is an excellent question. No, no one's asked me this. Um, why lockey? Because. There was a couple of people that we were talking to at the time and when we were raising, we were almost a little, we were like a bit distressed because one of our, one of our peers had just launched something that was very similar to Turbo Puffer.And someone just gave me the advice at the time of just choose the person where you just feel like you can just pick up the phone and not prepare anything. And just be completely honest, and I don't think I've said this publicly before, but I just called Lockey and was like local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you.But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working. So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people and we're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards and.Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before. As I said, I didn't even know what a seed or pre-seed round was like before, probably even at this time. So I was just like very honest with him. And I asked him like, Lockie, have you ever have, have you ever invested in database company?He was just like, no. And at the time I was like, am I dumb? Like, but I think there was something that just like really drew me to Lockie. He is so authentic, so honest, like, and there was something just like, I just felt like I could just play like, just say everything openly. And that was, that was, I think that that was like a perfect match at the time, and, and, and honestly still is.He was just like, okay, that's great. This is like the most honest, ridiculous thing I've ever heard anyone say to me. But like that, like that, whyswyx: is this ridiculous? Say competitor launch, this may not work out. It wasSimon Hørup Eskildsen: more just like. If this doesn't work out, I'm gonna close up shop by the end of the mo the year, right?Like it was, I don't know, maybe it's common. I, I don't know. He told me it was uncommon. I don't know. Um, that's why we chose him and he'd been phenomenal. The other people were talking at the, at the time were database experts. Like they, you know, knew a lot about databases and Locke didn't, this turned out to be a phenomenal asset.Right. I like Justine and I know a lot about databases. The people that we hire know a lot about databases. What we needed was just someone who didn't know a lot about databases, didn't pretend to know a lot about databases, and just wanted to help us with candidates and customers. And he did. Yeah. And I have a list, right, of the investors that I have a relationship with, and Lockey has just performed excellent in the number of sub bullets of what we can attribute back to him.Just absolutely incredible. And when people talk about like no ego and just the best thing for the founder, I like, I don't think that anyone, like even my lawyer is like, yeah, Lockey is like the most friendly person you will find.swyx: Okay. This is my most glow recommendation I've ever heard.Alessio: He deserves it.He's very special.swyx: Yeah. Yeah. Yeah. Okay. Amazing.Alessio: Since you mentioned candidates, maybe we can talk about team building, you know, like, especially in sf, it feels like it's just easier to start a company than to join a company. Uh, I'm curious your experience, especially not being n SF full-time and doing something that is maybe, you know, a very low level of detail and technical detail.Simon Hørup Eskildsen: Yeah. So joining versus starting, I never thought that I would be a founder. I would start with it, like Turbo Puffer started as a blog post, and then it became a project and then sort of almost accidentally became a company. And now it feels like it's, it's like becoming a bigger company. That was never the intention.The intentions were very pure. It's just like, why hasn't anyone done this? And it's like, I wanna be the, like, I wanna be the first person to do it. I think some founders have this, like, I could never work for anyone else. I, I really don't feel that way. Like, it's just like, I wanna see this happen. And I wanna see it happen with some people that I really enjoy working with and I wanna have fun doing it and this, this, this has all felt very natural on that, on that sense.So it was never a like join versus versus versus found. It was just dis found me at the right moment.Alessio: Well I think there's an argument for, you should have joined Cursor, right? So I'm curious like how you evaluate it. Okay, I should actually go raise money and make this a company versus like, this is like a company that is like growing like crazy.It's like an interesting technical problem. I should just build it within Cursor and then they don't have to encrypt all this stuff. They don't have to obfuscate things. Like was that on your mind at all orSimon Hørup Eskildsen: before taking the, the small check from Lockie, I did have like a hard like look at myself in the mirror of like, okay, do I really want to do this?And because if I take the money, I really have to do it right. And so the way I almost think about it's like you kind of need to ha like you kind of need to be like fucked up enough to want to go all the way. And that was the conversation where I was like, okay, this is gonna be part of my life's journey to build this company and do it in the best way that I possibly can't.Because if I ask people to join me, ask people to get on the cap table, then I have an ultimate responsibility to give it everything. And I don't, I think some people, it doesn't occur to me that everyone takes it that seriously. And maybe I take it too seriously, I don't know. But that was like a very intentional moment.And so then it was very clear like, okay, I'm gonna do this and I'm gonna give it everything.Alessio: A lot of people don't take it this seriously. But,swyx: uh, let's talk about, you have this concept of the P 99 engineer. Uh, people are 10 x saying, everyone's saying, you know, uh, maybe engineers are out of a job. I don't know.But you definitely see a P 99 engineer, and I just want you to talk about it.Simon Hørup Eskildsen: Yeah, so the P 99 engineer was just a term that we started using internally to talk about candidates and talk about how we wanted to build the company. And you know, like everyone else is, like we want a talent dense company.And I think that's almost become trite at this point. What I credit the cursor founders a lot with is that they just arrived there from first principles of like, we just need a talent dense, um, talent dense team. And I think I've seen some teams that weren't talent dense and like seemed a counterfactual run, which if you've run in been in a large company, you will just see that like it's just logically will happen at a large company.Um, and so that was super important to me and Justine and it's very difficult to maintain. And so we just needed, we needed wording for it. And so I have a document called Traits of the P 99 Engineer, and it's a bullet point list. And I look at that list after every single interview that I do, and in every single recap that we do and every recap we end with.End with, um, some version of I'm gonna reject this candidate completely regardless of what the discourse was, because I wanna see people fight for this person because the default should not be, we're gonna hire this person. The default should be, we're definitely not hiring this person. And you know, if everyone was like, ah, maybe throw a punch, then this is not the right.swyx: Do, do you operate, like if there's one cha there must have at least one champion who's like, yes, I will put my career on, on, on the line for this. You know,Simon Hørup Eskildsen: I think career on the line,swyx: maybe a chair, butSimon Hørup Eskildsen: yeah. You know, like, um, I would say so someone needs to like, have both fists up and be like, I'd fight.Right? Yeah. Yeah. And if one person said, then, okay, let's do it. Right?swyx: Yeah.Simon Hørup Eskildsen: Um. It doesn't have to be absolutely everyone. Right? And like the interviews are always the sign that you're checking for different attributes. And if someone is like knocking it outta the park in every single attribute, that's, that's fairly rare.Um, but that's really important. And so the traits of the P 99 engineer, there's lots of them. There's also the traits of the p like triple nine engineer and the quadruple nine engineer. This is like, it's a long list.swyx: Okay.Simon Hørup Eskildsen: Um, I'll give you some samples, right. Of what we, what we look for. I think that the P 99 engineer has some history of having bent, like their trajectory or something to their will.Right? Some moment where it was just, they just, you know, made the computer do what it needed to do. There's something like that, and it will, it will occur to have them at some point in their career. And, uh. Hopefully multiple times. Right.swyx: Gimme an example of one of your engineers that like,Simon Hørup Eskildsen: I'll give an eng.Uh, so we, we, we launched this thing called A and NV three. Um, we could, we're also, we're working on V four and V five right now, but a and NV three can search a hundred billion vectors with a P 50 of around 40 milliseconds and a p 99 of 200 milliseconds. Um, maybe other people have done this, I'm sure Google and others have done this, but, uh, we haven't seen anyone, um, at least not in like a public consumable SaaS that can do this.And that was an engineer, the chief architect of Turbo Puffer, Nathan, um, who more or less just bent this, the software was not capable of this and he just made it capable for a very particular workload in like a, you know, six to eight week period with the help of a lot of the team. Right. It's been, been, there's numerous of examples of that, like at, at turbo puff, but that's like really bending the software and X 86 to your will.It was incredible to watch. Um. You wanna see some moments like that?swyx: Isn't that triple nine?Simon Hørup Eskildsen: Um, I think Nathan, what's calledAlessio: group nine, that was only nine. I feel like this is too high forSimon Hørup Eskildsen: Nathan. Nathan is, uh, Nathan is like, yeah, there's a lot of nines. Okay. After that p So I think that's one trait. I think another trait is that, uh, the P 99 spends a lot of time looking at maps.Generally it's their preferred ux. They just love looking at maps. You ever seen someone who just like, sits on their phone and just like, scrolls around on a map? Or did you not look at maps A lot? You guys don't look atswyx: maps? I guess I'm not feeling there. I don't know, butSimon Hørup Eskildsen: you just dis What about trains?Do you like trains?swyx: Uh, I mean they, not enough. Okay. This is just like weapon nice. Autism is what I call it. Like, like,Simon Hørup Eskildsen: um, I love looking at maps, like, it's like my preferred UX and just like I, you know, I likeswyx: lotsAlessio: of, of like random places, soswyx: like,youswyx: know.Alessio: Yes. Okay. There you go. So instead of like random places, like how do you explore the maps?Simon Hørup Eskildsen: No, it's, it's just a joke.swyx: It's autism laugh. It's like you are just obsessed by something and you like studying a thing.Simon Hørup Eskildsen: The origin of this was that at some point I read an interview with some IOI gold medalistswyx: Uhhuh,Simon Hørup Eskildsen: and it's like, what do you do in your spare time? I was just like, I like looking at maps.I was like, I feel so seen. Like, I just like love, like swirling out. I was like, oh, Canada is so big. Where's Baffin Island? I don't know. I love it. Yeah. Um, anyway, so the traits of P 99, P 99 is obsessive, right? Like, there's just like, you'll, you'll find traits of that we do an interview at, at, at, at turbo puffer or like multiple interviews that just try to screen for some of these things.Um, so. There's lots of others, but these are the kinds of traits that we look for.swyx: I'll tell you, uh, some people listen for like some of my dere stuff. Uh, I do think about derel as maps. Um, you draw a map for people, uh, maps show you the, uh, what is commonly agreed to be the geographical features of what a boundary is.And it shows also shows you what is not doing. And I, I think a lot of like developer tools, companies try to tell you they can do everything, but like, let's, let's be real. Like you, your, your three landmarks are here, everyone comes here, then here, then here, and you draw a map and, and then you draw a journey through the map.And like that. To me, that's what developer relations looks like. So I do think about things that way.Simon Hørup Eskildsen: I think the P 99 thinks in offs, right? The P 99 is very clear about, you know, hey, turbo puffer, you can't run a high transaction workload on turbo puffer, right? It's like the right latency is a hundred milliseconds.That's a clear trade off. I think the P 99 is very good at articulating the trade offs in every decision. Um. Which is exactly what the map is in your case, right?swyx: Uh, yeah, yeah. My, my, my world. My world.Alessio: How, how do you reconcile some of these things when you're saying you bend the will the computer versus like the trade

Fixation on Histology: Understanding the Tools of Embedding Written based on the NSH Webinar: Embedding Basics & Troubleshooting To read the full blog, click here. 

What if being unmistakably human is your biggest competitive advantage in an AI-first world? In this episode, I sat down with Louis Carter, founder of Most Loved Workplace, to talk about why workplace culture is being tested and reshaped faster than ever. Louis shared how “most loved” isn't a slogan, it's a credibility signal grounded in real employee sentiment, and why being unmistakably human is becoming a serious competitive advantage in an AI-driven world. We also unpacked his idea of “inaction fatigue” (when leaders collect feedback but don't act), plus practical ways leaders can embed trust, respect, and emotional connection into the employee and customer experience so the world actually sees what's happening inside the company. Here are the highlights: -Culture as a competitive signal: “Most loved” works when it's validated by real employee sentiment, not just marketing. -Human advantage in an AI-first world: Being unmistakably human is becoming a standout differentiator as automation accelerates. -From feedback to follow-through: “Inaction fatigue” happens when employees share input but never see meaningful change. -The SPARK framework: Collaboration, shared vision, aligned values, respect, and outcomes create emotional connectedness at work. -Embedding love end-to-end: Culture should show up in onboarding, career paths, performance plans, and the customer experience.   About the guest: Louis Carter is a globally recognized organizational psychologist, author, speaker, and founder of Most Loved Workplace® and the Best Practice Institute (BPI). He created the Most Loved Workplace® certification and the Love of Workplace Index™, a data-driven methodology used by thousands of companies to build cultures where people feel deeply connected, respected, and engaged. Louis is the author of more than a dozen leadership and management books, including In Great Company: How to Spark Peak Performance by Creating an Emotionally Connected Workplace (McGraw-Hill), and his research has been featured in publications such as Forbes, Fast Company, Inc., and The Wall Street Journal. He has advised CEOs and executive teams from mid-sized firms to Fortune 500 organizations and is ranked among the top organizational culture thinkers in the world.   Connect with Louis: Website Business: http://www.mostlovedworkplace.com Personal: http://www.louiscarter.com LinkedIn Page: https://www.linkedin.com/in/louiscarter/ Facebook: https://www.facebook.com/louiscarter.bpi Instagram: https://www.instagram.com/louislcarter/ YouTube Channel: https://www.youtube.com/louiscarterchange X: x.com/louislcarter Books: https://louiscarter.com/leadership-books/   Connect with Allison: Feedspot has named Disruptive CEO Nation as one of the Top 25 CEO Podcasts on the web. LinkedIn: https://www.linkedin.com/in/allisonsummerschicago/ Website: https://www.disruptiveceonation.com/    #CEO #leadership #startup #founder #business #businesspodcast  Learn more about your ad choices. Visit megaphone.fm/adchoices

The host of episode 108 of Venture Everywhere is Harm-Julian Schumacher, co-founder and CEO of OneLot, a financing platform for used car dealers in the Philippines. He talks with Reto Bolliger, co-founder and CEO of Chaiz, an online marketplace for extended vehicle warranties. Reto shares how climbing Kilimanjaro led him to build a travel company, and how an investor in that business introduced him to the surprisingly profitable world of extended car warranties. He discusses how Chaiz challenges the industry consensus that warranties “must be sold” through aggressive tactics, instead building trust through transparency and offering consumers prices up to 40% cheaper than dealerships.In this episode, you will hear:Building the first online marketplace to compare and buy extended car warranties.Offering dealership products at 40% lower prices through digital channels.Replacing aggressive sales tactics with transparency and education.Leveraging AI for customer support and AI search optimization.Embedding warranty APIs for cross-selling through partner platforms.​​​​​​​​​​​​​​​​Learn more about Reto Bolliger | ChaizLinkedIn: https://www.linkedin.com/in/reto-bolligerWebsite: https://www.chaiz.comLearn more about Harm-Julian Schumacher | OneLotLinkedin: https://www.linkedin.com/in/harm-julian-schumacherWebsite: https://www.onelot.ph

Every leadership team craves alignment; no one wants the meetings. When executives hear "RevOps for Clients," they may picture more red tape and overhead. Courtney Baker, David DeWolf, and Mohan Rao argue that the right rigor doesn't slow business down—it slows bad decisions down. They unpack the "Minimum Viable Cadence," swapping hours of reactive fire drills for a single 30-minute triage, and discuss why exposing "dirty data" is the only path to shared accountability. Courtney also sits down with Alyssa Nolte of Ology to discuss AI in Customer Experience. Alyssa shares why the data you need is already there—just trapped in silos—and offers a "Kobe Bryant" approach to mastering the unsexy fundamentals of change management. All that, plus Pete Buer analyzes IBM's move to package its internal AI efficiency tool, IBM Consulting Advantage, as a client-facing product. Is it the ultimate example of productizing services? Get the resources to build your own RevOps for Clients discipline at our 2/25 webinar: www.knownwell.com/revops Watch the full episode on YouTube: https://www.youtube.com/watch?v=gwdcu54dfR4 

BONUS: Why Embedding Sales with Engineering in Stealth Mode Changed Everything for Snowflake In this episode, we talk about what it really takes to scale go-to-market from zero to billions. We interview Chris Degnan, a builder of one of the most iconic revenue engines in enterprise software at Snowflake. This conversation is grounded in the transformation described in his book Make It Snow—the journey from early-stage chaos to durable, aligned growth. Embedding Sales with Engineering While Still in Stealth "I don't expect you to sell anything for 2 years. What I really want you to do is get a ton of feedback and get customers to use the product so that when we come out of stealth mode, we have this world-class product."   Chris joined Snowflake when there were zero customers and the company was still in stealth mode. The counterintuitive move of embedding sales next to engineering so early wasn't about driving immediate revenue, it was about understanding product-market fit. Chris's job was to get customers to try the product, use it for free, and break it. And break it they did. This early feedback led to material changes in the product before general availability. The approach helped shape their ideal customer profile (ICP) and gave the engineering team real-world validation that shaped Snowflake's technical direction. In a world where startups are pressured to show revenue immediately, Snowflake's investors took the opposite approach: focus on building a product people cannot live without first. Why Sales and Marketing Alignment Is Existential "If we're not driving revenue, if the revenue is not growing, then how are we going to be successful? Revenue was king."   When Denise Persson joined as CMO, she shifted the conversation from marketing qualified leads (MQLs) to qualified meetings for the sales team. This simple reframe eliminated the typical friction between sales and marketing. Both leaders shared challenges openly and held each other accountable. When someone in either organization wasn't being respectful to the other team, they addressed it directly. Chris warns founders against creating artificial friction between sales and marketing: "A lot of founders who are engineers think that they want to create this friction between sales and marketing. And that's the opposite instinct you should have." The key insight is treating sales and marketing as a symbiotic system where revenue is the shared north star. Coaching Leaders Through Hypergrowth "If there's a problem in one of our organizations, if someone comes with a mentality that is not great for us, we're gonna give direct feedback to those people."   Chris and Denise maintained tight alignment at the top level of their organizations through four CEO transitions. Their partnership created a culture of accountability that cascaded through both teams. When either hired senior people who didn't fit the culture, they investigated and addressed it. The coaching approach wasn't about winning by authority—it was about maintaining partnership and shared accountability for results. This required unlearning traditional management approaches that pit departments against each other and instead fostering genuine collaboration. Cultural Behaviors That Scale (And Those That Don't) "We got dumb and lazy. We forgot about it. And then we decided, hey, we're gonna go get a little bit more fit, and figure out how to go get the new logos again."   Chris describes himself as a "velocity salesperson" with a hyper-focus on new customer acquisition. This focus worked brilliantly during Snowflake's growth phase—land customers, and the high net retention rate would drive expansion. However, as Snowflake prepared to go public, they took their foot off the gas on new logo acquisition, believing not all new logos were equal. This turned out to be a mistake. In his final year at Snowflake, working with CEO Sridhar Ramaswamy, they redesigned the sales team to reinvigorate the new logo acquisition machine. The lesson: the cultural behaviors that fuel early success must be consciously maintained and sometimes redesigned as you scale. Keeping the Message Narrow Before Going Platform "Eventually, I know you want to be a platform. But having a targeted market when you're initially launching the company, that people are spending money on, makes it easier for your sales team."   Snowflake intentionally positioned itself in the enterprise data warehousing market—a $10-12 billion annual market with 5,000-7,000 enterprise customers—rather than trying to sound "bigger" as a platform play. The strategic advantage was accessing existing budgets. When selling to large enterprises that go through annual planning processes, fitting into an existing budget means sales cycles of 3-6 months instead of 9-18 months. Yes, competition eventually tried to corner Snowflake as "just a cute data warehouse," but by then they had captured significant market share and could stretch their wings into the broader data cloud opportunity. Selling Consumption-Based Products to Fixed-Budget Buyers "Don't believe anything I say, try it."   One of Snowflake's hardest challenges was explaining their elastic, consumption-based architecture to procurement and legal teams accustomed to fixed budgets. In 2013-2015, many CIOs still believed data would stay in their data centers. Snowflake's model—where customers could spin up a thousand servers for 4 hours, load data, while analysts ran queries without performance impact—seemed impossible. Chris's approach was simple: set up proof of concepts and pilots. Let the technology speak for itself. The shift from fixed resources to elastic architecture required changing not just technology but entire mindsets about how data infrastructure could work.   About Chris Degnan Chris Degnan is a builder of one of the most iconic revenue engines in enterprise software. As the first sales hire at Snowflake, he helped scale the company from zero customers to billions in revenue. Chris co-authored Make It Snow: From Zero to Billions with Denise Persson, documenting their journey of building Snowflake's go-to-market organization. Today, Chris advises early-stage startups on building their go-to-market strategies and works with Iconiq Capital, the venture firm that led Snowflake's Series D round.   You can link with Chris Degnan on LinkedIn and learn more about the book at MakeItSnowBook.com.

The idea of embedding various forms of non-emergency care in the emergency department makes a WORLD of sense.  If an older adult comes into the ED with a fall, the minimum the ED has to do is address the fall injury and send them out. But many emergency providers realize this is often a band aid.  They see that patient again the next time they fall.  And again.  And again.  The same could be said for the patient who is malnourished and dehydrated and admitted for "failure to thrive," again. And again. Our two guests today, Liz Goldberg and Lauren Southerland, both emergency medicine physician-researchers, have had enough.  On our podcast today they discuss how these sorts of experiences led them to argue that other services that can address the underlying causes that lead to ED visits.  Liz Goldberg developed the GAPcare model to address falls, which includes a physical therapist and pharmacist seeing patients on the spot in the ED.  Lauren Southerland got Columbus Ohio Office of Aging staff to re-locate from their desks to the emergency department, where they could sign patients up for home delivered meals, medical transportation, adult day services, home modification such as grab bars, and utility assistance for electricity, gas, and water bills. With GAPcare, Liz saw a 66% drop in ED visits for fall over 6 months from her pilot (subsequent fall outcomes of the GAPcare II study will be linked here when published).  Remarkable, particularly in the context of the primary care STRIDE intervention, which did not reduce injurious falls (e.g. the type that would result in an ED visit). Maybe the ED is just a better place to intervene? Patients are motivated to change. Get the physical therapist and pharmacist in there! In a study published in JAGS, Lauren found 50% of participants were linked to a new Office of Aging service initiated during the ED visit, with no increase in ED length of stay or hospital admission rate.  See also this terrific JAGS editorial on Lauren's paper by Liz.  Putting on my JAGS editor hat - both the study and editorial have terrific color figures. A great way to increase your odds of review and acceptance at JAGS is to include one or more high-impact color figures that convey the main findings or points of your manuscript. We talk about the potential downsides, real and perceived in embedding care in the ED.  Should everything be embedded? We talk about how these interventions relate to geriatric ED certification. Lauren talks about a remarkable model in Australia that includes a geriatric RN embedded in the ED. Most encouraging is that Liz and Lauren are finding other adopting these interventions. Word is spreading. Other emergency providers have had enough of the endless cycle. Enough. And I got to belt out Gravity, by John Mayer! -Alex