Podcasts about dashboards

We recently had the chance to sit down with Rajeeb Khatua, MD, Chief Operating Officer at ReMedi Health Solutions, and Sara Helvey, MD, Chief Clinical Information Officer at Care New England to talk about the Epic Go-Live experience at Care New England. In our discussion, we dive into some of the specialized training and support Remedi Health Solutions provided leading up to, during, and after their Epic Go-Live.Learn more about Care New England: https://www.carenewengland.org/Learn more about ReMedi Health Solutions: https://www.remedihs.com/Healthcare IT Community: https://www.healthcareittoday.com/

What if the most powerful tool in a company isn't the CEO, the strategy deck, or the financial model, but a handful of metrics on a dashboard?In this episode of Corporate Finance Explained, we explore the hidden world of executive dashboards, KPIs, and performance measurement systems that shape decision-making inside the world's largest organizations. From Amazon's famous driver trees to Airbnb's rapid dashboard transformation during the pandemic, we uncover how finance teams use data to focus attention, drive accountability, and guide strategy. We also examine what happens when metrics go wrong. Through the cautionary stories of Theranos and Wells Fargo, we show how poorly designed dashboards, vanity metrics, and misaligned incentives can create blind spots, encourage harmful behavior, and ultimately destroy value. 

Willkommen zur nächsten Folge von "Damit's bei dir Klick macht", dem True Online Marketing Podcast von Hanseranking! Abonniere jetzt und verpasse keine Episode! Mehr Daten, mehr Dashboards, mehr KI-Tools – und trotzdem treffen immer mehr Unternehmer keine Entscheidungen mehr. Ich zeige dir, warum Daten allein kein Kompass für die Zukunft sind, wie echte Intuition funktioniert (und was sie mit komprimierter Erfahrung zu tun hat) – und warum die Symbiose aus Analyse und unternehmerischem Gespür im KI-Zeitalter entscheidender ist denn je. Bleib außerdem dran für kommende Folgen, in der ich dir tiefere Einblicke in spezifische Online Marketing Strategien und Interviews mit weiteren Experten gebe.

Tech ist teuer? Nicht mehr überall. In dieser börse-at-home-Folge spricht Edda Vogt mit Tech-Investor Stefan Waldhauser über den Absturz vieler Software- und SaaS-Aktien, die einst mit zweistelligen Umsatz-Multiples gefeiert wurden und heute teilweise zu einstelligen Free-Cashflow-Multiples gehandelt werden. Waldhausers Kernthese: Oft frisst KI nicht das Geschäftsmodell, sondern die Angst vor KI frisst den Aktienkurs. Entscheidend sei deshalb der Blick unter die Software-Haube: Cashflow statt KGV, Rule of 40 statt Bauchgefühl, Geschäftsmodell statt KI-Panik. Besonders wichtig ist seine Einteilung in Systems of Record, Systems of Engagement und Systems of Intelligence. Während klassische Kernsysteme wie SAP oder ServiceNow schwerer angreifbar sind, geraten Tools für Webseiten, Dashboards oder kreative Inhalte stärker unter Druck. Bei UiPath sieht Waldhauser dagegen eher Ergänzung als Ersetzung durch KI-Agenten. Genannt werden außerdem Monday.com, Lyft, IAC, PayPal, TeamViewer und IONOS. Die Story des Podcasts: Tech-Value ist zurück, aber nicht als Schnäppchenjagd mit verbundenen Augen. Geduld, Cash-Reserve, weniger Klumpenrisiko und ein kühler Kopf sind wichtiger als der nächste Raketen-Hype. Weitere Links: Stefan Waldhauser: https://lp.aktien.guide Tobias Kramer zum SpaceX-Börsengang: https://youtu.be/dRqjfFQJAYI

AI investment is growing fast, but proving its value remains one of the biggest challenges facing data leaders today. Dashboards are built, models are deployed, and yet when the budget question arrives, most teams still can't clearly demonstrate return on investment.Speaking on Don't Panic, It's Just Data with host Christina Stathopoulos, Nadiem von Heydebrand, CEO and co-founder of Mindfuel, identified where most organisations go wrong: the interface between data teams and the business. According to von Heydebrand, the reason is straightforward: no use case, no value."We get a demand, we believe we've understood it, and we start executing immediately," he explained. Months pass, and nobody can answer why the project exists or what problem it was supposed to solve in the first place. The fix isn't more technology. It's better use case management.The 3 Pillars of Effective AI Use Case ManagementOne of von Heydebrand's core principles is straightforward: before you build anything, you need to really understand the business challenge you're trying to solve. "You have to fall in love with the problem, not with the solution," he said. This matters more than ever in the era of generative AI. With token costs attached to every AI interaction, building the wrong solution isn't just a wasted effort; it's an ongoing financial drain. Use case management has moved from being a nice-to-have to an operational necessity. Good use case management, according to Nadiem, rests on three pillars:Demand exploration: Don't assume you understand the problem. Engage stakeholders, ask deeper questions, and uncover the real business challenge before a single line of code is written.Value management: Every use case needs a value hypothesis. What outcome is expected if this problem is solved? As Nadiem puts it: "The solution itself has a value of zero. Value lives in the problem space."Value tracking: Once live, track performance against the original hypothesis. Define a realistic ROI timeframe and review it consistently.Adoption Metrics Are Not Proof of ValueOne of the most common mistakes? Measuring AI success through usage and adoption data alone. "I have enough examples where usage is high, and value is zero or even negative," von Heydebrand warned.Clicks and logins are a proxy. Business outcomes are the goal. If there's no correlation between the two, the metric is misleading.Output vs. Outcome: The Shift That MattersThe most important distinction in the conversation was the difference between output and outcome. Data teams have historically been measured on output like model accuracy, number of dashboards, and features delivered. But output without impact is just activity. Outcome means the value created for the recipient of your work. Organisations that make this mindset shift from measuring what they produce to measuring what they change are the ones that change their data functions from cost centres into genuine value generators.For leaders under pressure to prove ROI from AI initiatives, Mindfuel's CEO advises a pragmatic approach: start now, start small, and be honest. As Stathopoulos summarised: "It all comes back to being intentional about what you build and why." For more information, visit mindfuel.ai, the platform built to help data and AI teams demonstrate, manage, and maximise business value.Connect with the guest:Nadiem von Heydebrand: LinkedIn | MindfuelTakeawaysThe importance of structured use case managementLinking AI initiatives to business valueThe impact layer and value tracking in AI projectsChapters00:00 – Introduction to Data and AI Impact Management03:16 – The Challenge of Connecting AI to Business Outcomes11:38 – Understanding Use Case Management17:40 – The Missing Value Layer in Data and AI Initiatives22:23 – Evolving Mindsets in Data and AI27:36 – Advice for Leaders on Proving AI ROI

Send us Fan MailEvery company today says it's data-driven.Billions are spent on analytics. AI pilots are everywhere. Dashboards glow with real-time metrics.And yet, only a small fraction of organizations actually transform.In this episode of FUTUREPROOF., I sit down with Sebastian Wernicke — author of DATA INSPIRED: Building an Organizational Culture of Inquiry for Lasting Transformation—to unpack why.Sebastian argues that the problem isn't a lack of data. It's a lack of inquiry.Most companies use data to optimize what already exists. Few use it to question assumptions, rethink business models, or challenge leadership narratives. That's the difference between being data-driven and being data-inspired.We explore: Why data doesn't “speak for itself”  How organizations become excellent at staying the same  The dangers of data-resistant minds  Why psychological safety is foundational for real AI success  What “radical data integrity” actually requires  And how to navigate AI's “jagged frontier,” where human judgment still matters This isn't a conversation about tools; it's about whether your culture is equipped to learn — especially when the evidence is uncomfortable.Because AI won't transform your company. It will amplify whatever culture you already have.

AI adoption looks very different when mistakes can create legal, financial, and reputational risk.Vijay Gandra, Global CDO at Acrisure, joins The Tech Trek to talk about AI transformation inside a regulated industry, where explainability, data quality, governance, cost, and team readiness matter just as much as model capability.The conversation covers the trust gap in AI, how data teams are shifting from dashboard production to conversational data access, when to buy versus build, and why AI proof of concepts need to be judged by business value, operational efficiency, and customer impact.Practical Takeaways• Regulated industries cannot treat AI as a black box. Decisions need traceability, consistency, and often a human review layer.• Data quality has to be addressed from the start. AI can amplify bad data as easily as it can create value.• Data teams are moving beyond dashboard factories toward conversational data access and generative interfaces.• Most companies can likely use existing AI tools for many needs, but sensitive IP and core business logic may require internal capabilities.• AI cost will become a bigger production question as companies move from experimentation to scaled deployment.Timestamped Highlights00:47, Acrisure's shift from insurance brokerage toward fintech and financial tools.01:44, Why regulated industries face a trust gap with AI and need explainable decisions.04:41, How data teams are evolving from dashboards to conversational data enablement.08:28, The build versus buy question and where internal AI tools may still make sense.10:52, Why AI experimentation can get expensive before companies know what works.16:15, How to evaluate AI proof of concepts based on customer value, efficiency, and business impact.18:14, Why data governance and data quality need to be treated as day one requirements.One Line That Stuck“In an industry like this, a 5 percent deviation is not just a simple glitch. It is actually a legal liability.”Subscribe to The Tech Trek for more conversations with technical leaders building, operating, and adapting modern teams around AI, data, platform, product, and engineering execution.

This Week:  DAFs: 2026 Benchmark Report We return to our 2026 Nonprofit Technology Conference coverage with a discussion of the third annual report on Donor Advised Fund fundraising. Our panel shares DAF giving results; changed donor behaviors; illiquid assets; best … Continue reading →

In this episode, I talk with Jeremy Carney from Central Coast Analytics about how breweries can use better data, dashboards, and financial analytics to make smarter business decisions. We explore the biggest reporting blind spots breweries face, how to combine information from multiple software systems into one clear picture, and which KPIs brewery owners should actually pay attention to each week. Jeremy also shares real-world examples of how breweries are uncovering hidden profit opportunities through better reporting and visualization tools.Key Takeaways:Most breweries already have valuable data, but the challenge is organizing it into actionable insights that managers can actually use.Weekly KPI tracking can help breweries spot problems and opportunities long before month-end financial statements arrive.Dashboards and visual reporting tools simplify complex brewery data and improve team accountability and decision-making.Clean, accurate, and timely data is essential for meaningful financial analysis and forecasting.Better analytics can uncover hidden opportunities in pricing, product mix, labor efficiency, inventory management, and taproom performance.ResourcesConnect with Jeremy, jeremy@centralcoast-analytics.com Get the Brewery Profit Brief - tips, tactics and strategies to grow brewery cash flowThis episode is brought to you by Secret Hopper. One of the hardest things in the beer business is seeing your business through your customer's eyes. Secret Hopper helps breweries uncover what guests actually experience from first impressions and service to cleanliness, atmosphere, and whether guests are likely to come back. Their mystery shopping and customer feedback tools give you practical insights and action steps to improve the guest experience and drive repeat visits. To lReady to transform financial results in your beer business? Learn more about the Beer Business Finance Association, a network of owners and managers working together to build more profitable companies. 

What if your title company could instantly visualize performance problems before they become operational disasters? In this episode, Mo Choumil and Hope Ottoviani break down how they're using Claude, AI dashboards, and automation workflows to transform boring spreadsheets into real-time visual scorecards that track production, phone responsiveness, errors, compliance, and customer service performance across departments. If you're still managing your business through static Excel files and delayed reporting, this episode will completely change how you think about operational visibility. ​ What you'll learn from this episode How AI dashboards turn overwhelming spreadsheets into visual scorecards The key metrics that All Tech National Title is now measuring How RingCentral analytics and AI reporting expose customer service gaps Why automation is the next step in eliminating manual reporting How AI-powered compliance dashboards simplify legal tracking   Resources mentioned in this episode Anthropic RingCentral Microsoft Power Automate Microsoft Outlook Qualia   About Hope Ottoviani Hope Ottoviani has diverse work experience spanning various industries. Hope began their career in 2005 as a Dishwasher/Caterer at Nissan Pavilion/Jiffy Lube Live. Hope then worked as a Shift Runner at Domino's Pizza from 2005 to 2009. In 2009, they became a Crew Leader at Einstein's Café, where they stayed until 2011. During this time, in 2010, they also had an internship at the CBS Radio Station. In 2010, they joined Starbucks as a barista and remained there until 2011. In 2011, they joined Clearmind Events as a Trainer/Authorized Distributor for a few months. Since 2011, they have worked at Alltech National Title, starting as a Settlement Processor and becoming an Operations Manager. Currently, they hold the position of National Director of Operations. ​ Hope Ottoviani completed an AA degree in Communications from Lord Fairfax Community College from 2007 to 2009. Hope then pursued a BA in Communications at Christopher Newport University from 2009 to 2011. Additionally, they obtained certifications as an insurance producer and a title producer from ATG Title. ​ Connect with Hope Website: ATG Title LinkedIn: Hope Ottoviani   Connect With Us Love what you're hearing? Don't miss an episode! Follow us on our social media channels and stay connected.   Explore more on our website: www.alltechnational.com/podcast Stay updated with our newsletter: www.mochoumil.com Follow Mo on LinkedIn: Mo Choumil Stop waiting on underwriter emails or callbacks—TitleGPT.ai gives you instant, reliable answers to your title questions. Whether it's underwriting, compliance, or tricky closings, the information you need is just a click away. No more delays—work smarter, close faster. Try it now at www.TitleGPT.ai. Closing more deals starts with more appointments. At Alltech National Title, our inside sales team works behind the scenes to fill your pipeline, so you can focus on building relationships and closing business. No more cold calling—just real opportunities. Get started at AlltechNationalTitle.com. Extra hands without extra overhead—that's Safi Virtual. Our trained virtual assistants specialize in the title industry, handling admin work, client communication, and data entry so you can stay focused on closing deals. Scale smarter and work faster at SafiVirtual.com.  

In this episode of the Econ Dev Show Dane Carlson talks with Dr. Glenn Athey, author of The Local and Regional Economic Development Handbook, about what economic developers actually need to know to move from strategy to delivery. Glenn shares how growing up in northeast England during de-industrialization shaped his interest in regional economic development, why he wrote the book he wishes he had at the start of his career, and how practitioners can use international case studies without simply copying someone else's playbook. The conversation covers action-oriented strategies, evidence that informs decisions instead of burying teams in data, the importance of local capacity, entrepreneurship support that prioritizes high-growth potential, and how sustainability can run through every part of economic development rather than sit off to the side. Like this show? Please leave us a review here — even one sentence helps! 10 Actionable Takeaways for Economic Developers Keep a working reference shelf. Economic development is too broad to know everything cold. Have reliable resources you can dip into before meetings on unfamiliar topics. Read enough to participate intelligently. You do not have to become an expert overnight, but you should understand the basics well enough to ask good questions and add value. Turn strategy into an action plan. A useful strategy should say what the community will do, what it will keep doing, what happens next, and how success will be measured. Do not confuse data with analysis. Dashboards and tables are not the point. Ask, "So what does this mean, and what should we do differently?" Borrow proven ideas, then localize them. Most communities do not need to invent something brand new. Study what worked elsewhere, then adapt it to your own economy, assets, and constraints. Be more curious. Visit the neighboring community with the strong business center. Ask how their program works. Learn from people who are already doing the thing well. Know your community's real capacity. Big ambitions require people, skills, funding, and institutional ability. A plan that ignores delivery capacity is likely to become shelf art. Prioritize business support where you can add the most value. Lifestyle businesses, high-growth startups, exporters, and innovation-driven firms may all need help, but they do not all produce the same economic impact. Connect the functions. Investment attraction depends on workforce, sites, infrastructure, universities, entrepreneurship, planning, and policy. The best economic developers see how the pieces fit together. Build confidence across the whole field. Economic development touches strategy, business growth, workforce, sites, investment, inclusion, planning, and more. You do not need to know every topic perfectly, but you do need enough range to recognize how the pieces connect. Special Guest: Dr. Glenn Athey.

This week in search I covered, yep, heated Google search ranking volatility kicking in the middle of this week. Google updated its spam policies to say it also applies to Google's AI responses in Search. Google Discover...

Einfach hunderttausend Euro nehmen, ein KI-Tool ins Unternehmen stopfen und alles wird gut? Eher nicht! Heute hat Host Dr. Christian Krug den wunderbaren Jack Lampka zu Gast. Jack ist Keynote Speaker, AI Advisor und ein absoluter Pragmatiker, der Künstliche Intelligenz jenseits des Hypes betrachtet. Wir klären die drängende Frage: Bist du als Unternehmen "cooked", wenn du die letzten Jahre deine Hausaufgaben nicht gemacht hast? Jack und Christian sprechen darüber, warum die grassierende FOMO (Fear Of Missing Out) in den Chefetagen oft fehl am Platz ist und warum Chatbots nicht die magische Lösung für alles sind. Wir decken die 80/20-KI-Lüge auf: Warum das gehypte Generative AI oft nur 20 % des Mehrwerts bringt, während traditionelles Machine Learning seit Jahrzehnten die eigentlichen 80 % liefert. Erfahre, warum Data Teams endlich aus dem dunklen Keller raus ins Business müssen, warum Dashboards wie Witze funktionieren und wie du mit Data Storytelling deine Kolleginnen und Kollegen wirklich abholst. Am Ende des Tages macht immer noch der Mensch den Unterschied – mit oder ohne KI! ▬▬▬▬▬▬ Profile: ▬▬▬▬Zum LinkedIn-Profil von Jack: https://www.linkedin.com/in/jacklampka/Zum LinkedIn-Profil von Christian: https://www.linkedin.com/in/christian-krug/Christians Wonderlink: https://wonderl.ink/@christiankrugUnf*ck Your Data auf Linkedin: https://www.linkedin.com/company/unfck-your-data▬▬▬▬▬▬ Buchempfehlung: ▬▬▬▬Buchempfehlung von Jack: Daniel Kahnemann – Thinking fast and slowAlle Empfehlungen in Melenas Bücherladen: https://gunzenhausen.buchhandlung.de/unfuckyourdata▬▬▬▬▬▬ Hier findest Du Unf*ck Your Data: ▬▬▬▬Zum Podcast auf Spotify: https://open.spotify.com/show/6Ow7ySMbgnir27etMYkpxT?si=dc0fd2b3c6454bfaZum Podcast auf iTunes: https://podcasts.apple.com/de/podcast/unf-ck-your-data/id1673832019Zum Podcast auf Deezer: https://deezer.page.link/FnT5kRSjf2k54iib6Zum Podcast auf Youtube: https://www.youtube.com/@unfckyourdata▬▬▬▬▬▬ Merch: ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬https://unfckyourdata-shop.de/▬▬▬▬▬▬ Kontakt: ▬▬▬▬E-Mail: christian@uyd-podcast.com▬▬▬▬▬▬ Timestamps: ▬▬▬▬▬▬▬▬▬▬▬▬▬00:00 Intro: Muss ich KI jetzt sofort einsetzen oder bin ich erledigt? 02:20 Jack Lampka stellt sich vor: KI jenseits des Hypes und Vendor-Versprechen03:52 Das Excel-Problem und die KI-FOMO in der Chefetage07:11 Vertraue nicht jedem KI-Verkäufer: Warum internes Know-how der Schlüssel ist11:22 Sind Unternehmen ohne Daten-Historie jetzt völlig "cooked"? 15:22 Use Cases: Wie lange dauert ein KI-Projekt wirklich? (Next Best Action & Marketing Mix) 21:06 Warum Tech-Projekte ohne Business-User kläglich scheitern26:19 Data Storytelling: So verkaufst du KI-Lösungen intern richtig30:11 Vibe Coding & GitHub Copilot: Warum in jeder 16. Zeile ein Fehler steckt34:09 KI ist nur ein Verstärker: Am Ende entscheiden die Menschen39:10 Holt die Coder aus dem Keller! Warum Data Teams ins Business müssen45:42 Die Tesla-Regel: Warum dein Dashboard wie ein Witz funktionieren muss47:09 Der 80/20-Irrtum: Warum klassisches Machine Learning wichtiger ist als GenAI50:57 Outro: Deutsche Sprichwörter, Heavy Metal und Buchtipp

What if the dashboards you rely on today are already obsolete? In this forward-looking conversation, AirDNA CEO Rohit Bezewada joins Jamie Lane to unpack how AI is fundamentally reshaping the short-term rental industry—from how software is built to how hosts operate day-to-day.This episode goes beyond the hype. Rohit breaks down what “AI-native” actually means (and why most tools aren't there yet), how agents and MCPs are changing the way hosts interact with data, and why the future of STR tech may not live inside traditional platforms at all. The conversation also dives into AirDNA's latest moves—from launching an AI-powered pricing tool to rebuilding its entire data architecture to support a new generation of decision-making.For hosts, property managers, and investors, the takeaway is clear: AI isn't just another feature—it's redefining how you analyze deals, set pricing, and run operations. Those who learn how to leverage it effectively will have a meaningful edge in an increasingly competitive market.You don't want to miss this episode.Key Takeaways:Dashboards are giving way to AI interfaces: Instead of static reports, the future is dynamic—hosts can query their data directly and get tailored insights instantly through AI agents.“AI-native” tools require more than a chatbot: True AI-native platforms are built across multiple layers—data ownership, normalization, memory/context, model flexibility, and user interface. Most tools today only scratch the surface.Agents are becoming your operational co-pilot: From pricing adjustments to performance tracking, AI agents can handle repetitive, analytical tasks—freeing up hosts to focus on guest experience and hospitality.The STR tech stack is consolidating: Expect fewer point solutions and more all-in-one platforms that combine market data, pricing, listing optimization, and performance tracking into a single ecosystem.Human touch still wins in hospitality: While AI can automate operations and analytics, guest experience remains a key differentiator—personalization and service still drive reviews and repeat bookings.Sign up for AirDNA for FREE

Send us Fan MailEpisode Summary: In this insightful interview, social media strategist Tallie Proud shares her expertise on social listening, its misconceptions, and how organizations can better interpret online conversations to improve their communication strategies.Tallie's BIO: Tallie Proud is a UK-based freelance digital marketing consultant specializing in social media training, strategy, digital skills, and more. She previously worked for Social Simulator, a crisis preparedness agency, where she supported international organizations with crisis exercises and social media training. Before that, she was part of the award-winning digital team at The Church of England. You can find her on LinkedIn in/tallieproud or contact her via her website: www.tallieproud.com Support the showOur premiere sponsor, Social News Desk, has an exclusive offer for PIO Podcast listeners. Head over to socialnewsdesk.com/pio to get three months free when a qualifying agency signs up.

You can have the best systems, the best CRM, the best team — but if you're not excited and motivated, none of it moves.

You can have the best systems, the best CRM, the best team — but if you're not excited and motivated, none of it moves.

Two dashboards. Two stories. One broken relationship. This week on Lunch Hour Legal Marketing, Conrad Saam and Gyi Tsakalakis unpack a client loss that wasn't really about performance; it was about measurement. The agency saw success. The law firm saw zero cases. And both were looking at completely different versions of reality. We break down a $11.5K paid search campaign that generated 64 leads at a $181 CPL with strong engagement metrics but still ended in termination. Why? Because the agency optimized for cost per lead, while the firm measured success by signed clients. No shared definition of a “good lead” meant no shared understanding of success. So what actually went wrong? And more importantly, how do you prevent it? We dig into: Why cost per lead is the wrong scoreboard for a firm measuring signed cases How “wanted leads” become the missing feedback loop in legal marketing The role of intake, attribution, and CRM gaps in distorting performance Why even strong campaigns fail when dashboards don't match reality How fractional CMOs can either bridge—or widen—the measurement divide At the center of it all is a simple problem: agencies and law firms are often not just disagreeing… they're not even reading the same watch. Lunch Hour Legal Marketing will be covering Vista Consulting Team's Baltimore event, A Seat at the Table. Conrad and Gyi will be at the PILMMA Super Summit in May. Want to meet them and be on the pod? Reach out! Come to the Lunch Hour Legal Marketing Summit, and listen to John Henson school us on how to stay out of FTC jail, and eat a listicle on us! ------- Conferences & Mentions:

Welcome to episode #1033 of Thinking With Mitch Joel (formerly Six Pixels of Separation). We spent the last twenty-five years calling software "technology," which was convenient, profitable and just a little bit delusional. Apps got smarter. Feeds got stickier. Ads got creepier. Dashboards got dashboards. Meanwhile, the real world kept asking harder questions. How do we produce enough clean energy for eight billion people? How do we build things again? How do we manufacture without waste? How do we use AI for science, not just better chatbots and faster slop? Pablos Holman has spent his career living inside those questions. He is a hacker, inventor, venture capitalist, and founder of Deep Future, an invention capital firm backing the mad scientists, rogue engineers and maverick entrepreneurs trying to build technologies that actually matter. His career has touched everything from cryptocurrency in the 1990s and AI for financial markets to the early days of Blue Origin and the launch of Intellectual Ventures Lab, where the work included mosquito-killing lasers, malaria-diagnosing microscopes, vaccine coolers, advanced antennas and nuclear reactors powered by waste. His book, Deep Future - Creating Technology That Matters, argues that the next wave of innovation will not come from making software marginally more addictive. It will come from solving the physical, messy, expensive, essential problems that humanity depends on: energy, water, food, waste, construction, manufacturing, medicine and infrastructure. In this conversation, Pablos makes the case for optimism with teeth. Not optimism as a mood. Optimism as a discipline. A willingness to stare at massive problems without flinching… and then go build something. We talk about why software can't save the world by itself, why energy may be the root problem behind almost everything, why AI's most meaningful work may happen in science, and why the future belongs to people willing to work on problems big enough to scare everyone else. Enjoy the conversation… Running time: 1:04:47. Hello from beautiful Montreal. Listen and subscribe over at Apple Podcasts. Listen and subscribe over at Spotify. Please visit and leave comments on the blog - Thinking With Mitch Joel. Feel free to connect to me directly on LinkedIn. Check out ThinkersOne. Here is my conversation with Pablos Holman. Deep Future - Creating Technology That Matters. Check out Pablos on the Tim Ferriss Podcast: One of The Scariest Hackers I've Ever Met — Pablos Holman. Follow Pablos on LinkedIn. Follow Pablos on X. Follow Pablos on Instagram. Chapters: (00:00) - Introduction to Deep Future and Venture Capital. (02:47) - The Shift from Software to Deep Tech. (06:08) - Revolutionizing Manufacturing with Robotics. (08:49) - The Environmental Impact of Energy Production. (12:13) - Innovative Solutions for Energy Challenges. (15:08) - The Role of AI in Scientific Advancements. (17:53) - Cultural Shifts in Manufacturing and Education. (20:55) - The Future of Energy and Nuclear Solutions. (23:56) - The Importance of Long-Term Thinking. (26:45) - Connecting Work to Meaning and Purpose. (30:13) - The Role of Corporations in Infrastructure Investment. (33:03) - The Future of Jobs in an Automated World. (36:13) - AI's Role in Solving Global Problems. (39:01) - The Need for Optimism in Technology. (41:59) - Final Thoughts and the Future of Humanity.

We have more data than ever, and somehow we're still making people decisions that feel like they were made with a Magic 8-Ball and a prayer.  Make it make sense! Dashboards, engagement scores, predictive analytics…it's all there, and yet the decisions still feel completely disconnected from what the numbers are saying.  In this episode of Better Decisions, I sat down with Doug Melton, Global Chief Commercial Officer for Human Capital at Aon and a human capital analytics expert, to get into why orgs are so insight-rich but decision-poor, and what it looks like to close that gap. --- Aon's Human Capital capabilities help organizations make confident workforce decisions by connecting advisory, insights and data across health benefits, talent and retirement. By aligning people strategies to business outcomes, we enable leaders to drive engagement, manage program sustainability, and build a resilient workforce ready for what's next. Learn more at ⁠Aon.com⁠ --- 00:00:00 - Intro 00:02:57 - Why Orgs Struggle to Turn People into Data Decisions 00:06:20 - Why Some People Struggle to Even Understand the Data in Front of Them 00:08:32 - Ways Leaders Can Come to Agreements on Outcomes and How They're Being Measured 00:12:27 - The Difference Between Collecting Data and Making Better Decisions With the Data you Already Have 00:16:19 - What Does Good Judgment Look Like When Analytics Conflict With Intuition? 00:23:05 - One Analytics Decision Companies Keep Getting Wrong--- If you love I Hate It Here, sign up to Hebba's newsletter! It's for jaded, overworked, and emotionally burnt-out HR/People Operations professionals needing a little inspiration. ⁠https://workweek.com/discover-newsletters/i-hate-it-here-newsletter/⁠ And if you love the podcast, be sure to check out I Hate It Here on YouTube for even more exclusive insider content!   Follow Doug: LinkedIn: https://www.linkedin.com/in/doug-melton-a955a12/ Follow Hebba:  YouTube:⁠ https://www.youtube.com/@ihateit-here/videos⁠ LinkedIn:⁠ https://linkedin.com/in/hebba-youssef⁠ Twitter:⁠ https://twitter.com/hebbamyoussef⁠

Data Business Moats Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing. In building a startup, the founder should consider monetizing the data. Data can provide an additional range of moats for the business. Here is a list of data moats that are ineffective: Openly available and easily accessible data sets General analytics on the data Dashboards and reporting tools. Here's a list of the data moats can bring to the company: Turning your data into a standard data set used by the industry. This is called data currency, which the industry players use for data exchange. Extensive use of the data by many companies creates a de facto standard. Proprietary data. This data comes from a unique source that no other company has access to. Exclusive access to data In this case, the company has developed an exclusive arrangement for the use of data. Proprietary data exhaust This is the use of data from another source for a different purpose. For example, Whole Foods captures consumer product good sales data and then sells access to CPG companies that want to know how much is sold in each category. Consider these options for building a moat into your startup using data. Thank you for joining us for the Startup Funding Espresso where we help startups and investors connect for funding. Let's go startup something today. _______________________________________________________ For more episodes from Investor Connect, please visit the site at: http://investorconnect.org Check out our other podcasts here: https://investorconnect.org/ For Investors check out: https://tencapital.group/investor-landing/ For Startups check out: https://tencapital.group/company-landing/ For eGuides check out: https://tencapital.group/education/ For upcoming Events, check out https://tencapital.group/events/ For Feedback please contact info@tencapital.group Please follow, share, and leave a review. Music courtesy of Bensound.

Send us Fan MailWhat Dashboards and KPI's are You Using?  Episode 141 discusses Dashboards and KPI's with Nick Avaria. Nick Avaria is the Founder of Agency Acquisitions, a company that helps companies make top quartile profit and employees make top quartile wages. Nick has spent over a decade as an agency owner, and scaled multiple agencies past multiple 7 and 8-figures per year.  He has bought and sold 7 agencies and is no stranger to merging leadership teams and company cultures.  His philosophy is simple: Make more profit, re-invest that into training your staff, they perform better, you pay them more (above market preferably), hire top performers to inject new energy into your team, and repeat. Nick started consulting with other agency owners when he saw how burnt out his peers were while trying to scale and working 60+ hours per week.  He wants them to see their hard work pay off so they can grow fantastic workplaces where clients and employees can thrive, all while spending less time behind their desk.  Episode Benefits:  You can expect to gain actionable insights and strategies to help you Define your Dashboards and KPI's.   This Podcast series is targeted to Business Owners and C-Suite Executives.  It reflects my 34 years as a Business Owner and subsequent years as a Business Mentor and Consultant.  It focuses on the various subjects and topics to help you run a successful profitable business.  They are approximately 15-minutes long so you can listen while commuting.     Reach out to me to be put in contact with Nick.   The Business of Business, topics are divided into 5 Categories: Management, Operations, Sales, Financial, and Personal. Support the showHelping You Run a Successful Profitable Business !For Business Mentoring, Consulting, Schedule a Speaking Engagement, Help you with a Podcast, or to be a Podcast Guest - Contact me at:  www.bcforg.comLinkedIn:  https://www.linkedin.com/in/brian-fisher-72174413/

What if government buying felt fast, fair, and transparent—and actually powered economic growth? We sit down with New York State's Chief Procurement Officer, Dhanraj Singh, to unpack a bold modernization effort shaped by a clear mandate from elected officials. The goal is bigger than technology: build a people-first procurement ecosystem that cuts cycle times, scales innovation, and delivers better outcomes for residents, agencies, and suppliers.We go inside the pivot from siloed procurement shops to an enterprise approach with shared analytics, standard methods, and a statewide platform. We dig into the pain points that forced change—manual processes, fragmented data, and slow approvals—and the practical steps New York is taking to fix them. From automating repetitive tasks to deploying real-time vendor feedback with Procurated, the team is prioritizing tools that enable good judgment rather than replace it. We also talk about how dashboards and data literacy are improving performance oversight, risk management, and decision speed.At the center of it all is the workforce. New York is investing in skills for contract administration, negotiation, category management, and leadership, while putting change management up front through coaching, assessments, and strategic retreats. The aim is a resilient, energized profession that can respond to crises and raise the bar for public service. We also explore equity and access—making it easier for minority- and women-owned businesses and service-disabled veteran-owned businesses to compete and win—and why success will be measured by how the system feels for people, not by the tools alone.Subscribe, share with a colleague, and leave a review to help more practitioners find these stories. What's the one change you'd make to modernize procurement where you work?Follow & subscribe to stay up-to-date on NASPO!naspo.org | Pulse Blog | LinkedIn | Youtube | Facebook 

Looks Like Progress. It's Not.So I asked Adrian Stoch, CEO Americas at Hai Robotics:What's the mistake that looks right… but costs millions?Why do “working” systems still fail?What breaks first when you scale?He's led automation at massive scale.Inside Target.Inside GXO.Now leading robotics.He told me about one warehouse…Robots installed.Systems live.Dashboards green.It looked like progress.Until it didn't.30% of the products didn't even fit the system.$3M a year… gone.Here's what most leaders are missing:The failure didn't start with the robots.It started earlier.Bad inputs.Broken processes.Assumptions no one challenged.Automation didn't fix it.It exposed it.Fast.Expensive.Unavoidable.Amateurs scale chaos.Leaders fix the system first… then automate… then scale.If your systems look like they're working—but outcomes aren't—

We're proud to release this ahead of Ryan's keynote at AIE Europe. Hit the bell, get notified when it is live! Attendees: come prepped for Ryan's AMA with Vibhu after.Move over, context engineering. Now it's time for Harness engineering and the age of the token billionaires.Ryan Lopopolo of OpenAI is leading that charge, recently publishing a lengthy essay on Harness Eng that has become the talk of the town:In it, Ryan peeled back the curtains on how the recently announced OpenAI Frontier team have become OpenAI's top Codex users, running a >1m LOC codebase with 0 human written code and, crucially for the Dark Factory fans, no human REVIEWED code before merge. Ryan is admirably evangelical about this, calling it borderline “negligent” if you aren't using >1B tokens a day (roughly $2-3k/day in token spend based on market rates and caching assumptions):Over the past five months, they ran an extreme experiment: building and shipping an internal beta product with zero manually written code. Through the experiment, they adopted a different model of engineering work: when the agent failed, instead of prompting it better or to “try harder,” the team would look at “what capability, context, or structure is missing?”The result was Symphony, “a ghost library” and reference Elixir implementation (by Alex Kotliarskyi) that sets up a massive system of Codex agents all extensively prompted with the specificity of a proper PRD spec, but without full implementation:The future starts taking shape as one where coding agents stop being copilots and start becoming real teammates anyone can use and Codex is doubling down on that mission with their Superbowl messaging of “you can just build things”.Across Codex, internal observability stacks, and the multi-agent orchestration system his team calls Symphony, Ryan has been pushing what happens when you optimize an entire codebase, workflow, and organization around agent legibility instead of human habit.We sat down with Ryan to dig into how OpenAI's internal teams actually use Codex, why the real bottleneck in AI-native software development is now human attention rather than tokens, how fast build loops, observability, specs, and skills let agents operate autonomously, why software increasingly needs to be written for the model as much as for the engineer, and how Frontier points toward a future where agents can safely do economically valuable work across the enterprise.We discuss:* Ryan's background from Snowflake, Brex, Stripe, and Citadel to OpenAI Frontier Product Exploration, where he works on new product development for deploying agents safely at enterprise scale* The origin of “harness engineering” and the constraint that kicked off the whole experiment: Ryan deliberately refused to write code himself so the agent had to do the job end to end* Building an internal product over five months with zero lines of human-written code, more than a million lines in the repo, and thousands of PRs across multiple Codex model generations* Why early Codex was painfully slow at first, and how the team learned to decompose tasks, build better primitives, and gradually turn the agent into a much faster engineer than any individual human* The obsession with fast build times: why one minute became the upper bound for the inner loop, and how the team repeatedly retooled the build system to keep agents productive* Why humans became the bottleneck, and how Ryan's team shifted from reviewing code directly to building systems, observability, and context that let agents review, fix, and merge work autonomously* Skills, docs, tests, markdown trackers, and quality scores as ways of encoding engineering taste and non-functional requirements directly into context the agent can use* The shift from predefined scaffolds to reasoning-model-led workflows, where the harness becomes the box and the model chooses how to proceed* Symphony, OpenAI's internal Elixir-based orchestration layer for spinning up, supervising, reworking, and coordinating large numbers of coding agents across tickets and repos* Why code is increasingly disposable, why worktrees and merge conflicts matter less when agents can resolve them, and what it really means to fully delegate the PR lifecycle* “Ghost libraries”, spec-driven software, and the idea that a coding agent can reproduce complex systems from a high-fidelity specification rather than shared source code* The broader future of Frontier: safely deploying observable, governable agents into enterprises, and building the collaboration, security, and control layers needed for real-world agentic workRyan Lopopolo* X: https://x.com/_lopopolo* Linkedin: https://www.linkedin.com/in/ryanlopopolo/* Website: https://hyperbo.la/contact/Timestamps00:00:00 Introduction: Harness Engineering and OpenAI Frontier00:02:20 Ryan's background and the “no human-written code” experiment00:08:48 Humans as the bottleneck: systems thinking, observability, and agent workflows00:12:24 Skills, scaffolds, and encoding engineering taste into context00:17:17 What humans still do, what agents already own, and why software must be agent-legible00:24:27 Delegating the PR lifecycle: worktrees, merge conflicts, and non-functional requirements00:31:57 Spec-driven software, “ghost libraries,” and the path to Symphony00:35:20 Symphony: orchestrating large numbers of coding agents00:43:42 Skill distillation, self-improving workflows, and team-wide learning00:50:04 CLI design, policy layers, and building token-efficient tools for agents00:59:43 What current models still struggle with: zero-to-one products and gnarly refactors01:02:05 Frontier's vision for enterprise AI deployment01:08:15 Culture, humor, and teaching agents how the company works01:12:29 Harness vs. training, Codex model progress, and “you can just do things”01:15:09 Bellevue, hiring, and OpenAI's expansion beyond San FranciscoTranscriptRyan Lopopolo: I do think that there is an interesting space to explore here with Codex, the harness, as part of building AI products, right? There's a ton of momentum around getting the models to be good at coding. We've seen big leaps in like the task complexity with each incremental model release where if you can figure out how to collapse a product that you're trying to.Build a user journey that you're trying to solve into code. It's pretty natural to use the Codex Harness to solve that problem for you. It's done all the wiring and lets you just communicate in prompts. To let the model cook, you have to step back, right? Like you need to take a systems thinking mindset to things and constantly be asking, where is the Asian making mistakes?Where am I spending my time? How can I not spend that time going forward? And then build confidence in the automation that I'm putting in place. So I have solved this part of the SDLC.swyx: [00:01:00] All right.[00:01:03] Meet Ryan swyx: We're in the studio with Ryan from OpenAI. Welcome.Ryan Lopopolo: Hi,swyx: Thanks for visiting San Francisco and thanks for spending some time with us.Ryan Lopopolo: Yeah, thank you. I'm super excited to be here.swyx: You wrote a blockbuster article on harness engineering. It's probably going to be the defining piece of this emerging discipline, huh?Ryan Lopopolo: Thank you. It is it's been fun to feel like we've defined the discourse in some sense.swyx: Let's contextualize a little bit, this first podcast you've ever done. Yes. And thank you for spending with us. What is, where is this coming from? What team are you in all that jazz?Ryan Lopopolo: Sure, sure.Ryan Lopopolo: I work on Frontier Product Exploration, new product development in the space of OpenAI Frontier, which is our enterprise platform for deploying agents safely at scale, with good governance in any business. And. The role of VMI team has been to figure out novel ways to deploy our models into package and products that we can sell as solutions to enterprises.swyx: And you have a background, I'll just squeeze it in there. Snowflake, brick, [00:02:00] stripe, citadel.Ryan Lopopolo: Yes. Yes. Same. Any kind of customerswyx: entire life. Yes. The exact kind of customer that you want to,Vibhu: so I'll say, I was actually, I didn't expect the background when I looked at your Twitter, I'm seeing the opposite.Stuff like this. So you've got the mindset of like full send AI, coding stuff about slop, like buckling in your laptop on your Waymo's. Yes. And then I look at your profile, I'm like, oh, you're just like, you're in the other end too. Oh, perfect. Makes perfect.Ryan Lopopolo: I it's quite fun to be AI maximalist if you're gonna live that persona.Open eye is the place to do it. And it'sswyx: token is what you say.Ryan Lopopolo: Yeah. Certainly helps that we have no rate limits internally. And I can go, like you said, full send at this stay.swyx: Yeah. Yeah. So the Frontier, and you're a special team within O Frontier.Ryan Lopopolo: We had been given some space to cook, which has been super, super exciting.[00:02:47] Zero Code ExperimentRyan Lopopolo: And this is why I started with kind of a out there constraint to not write any of the code myself. I was figuring if we're trying to make agents that can be deployed into end to enterprises, they should be [00:03:00] able to do all the things that I do. And having worked with these coding models, these coding harnesses over 6, 7, 8 months, I do feel like the models are there enough, the harnesses are there enough where they're isomorphic to me in capability and the ability to do the job.So starting with this constraint of I can't write the code meant that the only way I could do my job was to get the agent to do my job.Vibhu: And like a, just a bit of background before that. This is basically the article. So what you guys did is five months of working on an internal tool, zero lines of code over a mi, a million lines of code in the total code base.You say it was cenex, more like it was cenex faster than you would've. If you had done it by end. SoRyan Lopopolo: yeah, thatVibhu: was the mindset going into this, right?Ryan Lopopolo: That's right.[00:03:46] Model Upgrades LessonsRyan Lopopolo: Started with some of the very first versions of Codex CLI, with the Codex Mini model, which was obviously much less capable than the ones we have today.Which was also a very good constraint, right? Quite a visceral feeling to ask the [00:04:00] model to build you a product feature. And it just not being able to assemble the pieces together.Which kind of defined one of the mindsets we had for going into this, which is whenever the model just cannot, you always pop open at the task, double click into it, and build smaller building blocks that then you can reassemble into the broader objective.And it was quite painful to do this. Honestly, the first month and a half was. 10 times slower than I would be. But because we paid that cost, we ended up getting to something much more productive than any one engineer could be because we built the tools, the assembly station for the agent to do the whole thing.[00:04:43] Model Generations, Build Systems & Background ShellsRyan Lopopolo: But yeah, so onward to G BT 5, 5, 1, 5, 2, 5, 3, 5 4. To go through all these model generations and see their kind of corks and different working styles also meant we had to adapt the code base to change things up when the model was revved. [00:05:00] One interesting thing here is five two, the Codex harness at the time did not have background shells in it, which means we were able to rely on blocking scripts to perform long horizon work.But with five, three and background shells, it became less patient, less willing to block. So we had to retool the entire build system to complete in under a minute and. This is not a thing I would expect to be able to do in a code base where people have opinions. But because the only goal was to make the Asian productive over the course of a week, we went from a bespoke make file build to Basil, to turbo to nx and just left it there because builds were fast at that point.swyx: Interesting. Talk more about Turbo TenX. That's interesting ‘cause that's the other direction that other people have been doing.Ryan Lopopolo: Ultimately I have. Not a lot of experience with actual frontend repo architecture.swyx: You're talking that Jessica built the sky. So I'm like, I know the NX team. I know Turbo from Jared [00:06:00] Palmer.And I'm like, yeah, that's an interesting comparison.[00:06:02] One Minute Build LoopRyan Lopopolo: The hill we were climbing right, was make it fast.swyx: Is there a micro front end involved? Is it how how complex reactRyan Lopopolo: electron base single app sort of thingswyx: And must be under a minute. That's an interesting limitation. I'm actually not super familiar with the background shelf stuff.Probably was talked about in the fight three release.Ryan Lopopolo: BA basically means that codex is able to spawn commands in the background and then go continue to work while it waits for them to finish. So it can spawn an expensive build and then continue reviewing the code, for example.swyx: Yeah.Ryan Lopopolo: And this helps it be more time efficient for the user invoking the harness.swyx: And I guess and just to really nail this, like what does one minute matter? Like why not five, okay, good. We want no. WeRyan Lopopolo: want the inner loop to be as fast as possible. Okay. One minute was just a nice round number and we were able to hit it.swyx: And if it doesn't complete, it kills it or some something,Ryan Lopopolo: No.We just take that as a signal that we need to stop what we're doing, double click, decompose a build graph a bit to get us to high back under so that we [00:07:00] can able the agent continue to operate.swyx: It's almost like you're, it's like a ratchet. It's like you're forcing build time discipline, because if you don't, it'll just grow and grow.That's right. And you mentioned that my current, like the software I work on currently is at 12 minutes. It sucks.Ryan Lopopolo: This has been my experience with platform teams in the past, where you have an envelope of acceptable build times and you let it go up to breach and then you spend two, three weeks to bring it back down to the lower end of the average low bed stop.But because tokens are so cheap Yeah. And we're so insanely parallel with the model, we can just constantly be gardening this thing to make sure that we maintain these in variants, which means. There's way less dispersion in the code and the SDLC, which means we can simplify in a way and rely on a lot more in variance as we write the software.[00:07:45] Observability, Traces & Local Dev StackVibhu: Lovely.[00:07:46] Humans Are BottleneckVibhu: You mentioned in your article, like humans became the bottleneck, right? You kicked off as a team of three people. You're putting out a million line of code, like 1500 prs, basically. What's the mindset there? So as much as code is disposable, you're doing a lot of review. A lot [00:08:00] of the article talks about how you wanna rephrase everything is prompting everything, is what the agent can't see.It's kind of garbage, right? You shouldn't have it in there. So what's like the high level of how you went about building it, and then how you address okay, humans are just PR review. Like how is human in the loop for this?Ryan Lopopolo: We've moved beyond even the humans reviewing the code as well.[00:08:19] Human Review, PR Automation & Agent Code ReviewRyan Lopopolo: Most of the human review is post merge at this point.But post, post merge, that's not even reviewed. That's justswyx: Oh, let's just make ourselves happy by YouRyan Lopopolo: haven't used fundamentally. The model is trivially paralyzable, right? As many GPUs and tokens as I am willing to spend, I can have capacity to work with my hood base.The only fundamentally scarce thing is the synchronous human attention of my team. There's only so many hours in the day we have to eat lunch. I would like to sleep, although it's quite difficult to, stop poking the machine because it makes me want to feed it. You have to step back, right?Like you need to take a systems thinking mindset to things and [00:09:00] constantly be asking where is the agent making mistakes? Where am I spending my time? How can I not spend that time going forward? And then build confidence in the automation that I'm putting in place. So I have solved this part of the SDLC, and usually what that has looked like is like we started needing to pay very close attention to the code because the agent did not have the right building blocks to produce.Modular software that decomposed appropriately that was reliable and observable and actually accrued a working front end in these things, right?[00:09:35] Observability First SetupRyan Lopopolo: So in order to not spend all of our time sitting in front of a terminal at most, doing one or two things at a time, invested in giving the model that observability, which is that that graph in the post here.swyx: Yeah. Let's walk through this traces and which existed firstRyan Lopopolo: we started with just the app and the whole rest of it. From vector through to all these login metrics, APIs was, I dunno, half an [00:10:00] afternoon of my time. We have intentionally chosen very high level fast developer tools. There's a ton of great stuff out there now.We use me a bunch, which makes it trivial to pull down all these go written Victoria Stack binaries in our local development. Tiny little bit of python glue to spin all these up. And off you go. One neat thing here is we have tried to invert things as much as possible, which is instead of setting up an environment to spawn the coding agent into, instead we spawn the coding agent, like that's the entry point.It's just Codex. And then we give Codex via skills and scripts the ability to boot the stack if it chooses to, and then tell it how to set some end variables. So the app and local Devrel points at this stack that it has chosen to spin up. And this I think is like the fundamental difference between reasoning models and the four ones and four ohs of the past, where these models could not think so you had to put them in [00:11:00] boxes with a predefined set of state transitions.Whereas here we have the model, the harness be the whole box. And give it a bunch of options for how to proceed with enough context for it to make intelligent choices. SoVibhu: sales, so like a lot of that is around scaffolding, right? Yes. Previous agents, you would define a scaffold. It would operate in that.Lube, try again. That's pivoted off from when we've had reasoning models. They're seeming to perform better when you don't have a scaffold, right? That's right.[00:11:28] Docs Skills GuardrailsVibhu: And you go into like niches here too, like your SPEC MD and like having a very short agent MG Agent md.swyx: Yes. Yes.Vibhu: Yeah. So you even lay out what it is here, but I likeswyx: the table contents.Vibhu: Yeah.swyx: Like stuff like this, it really helps guide people because everyone's trying to do this.Ryan Lopopolo: This structure also makes it super cheap to put new content into the repository to steer both the humans and the agents.swyx: You, you reinvented skills, right?Vibhu: One big agents andswyx: skills from first princip holdsRyan Lopopolo: all skills did not exist when we started doing this.Vibhu: You have a short [00:12:00] one 100 line overall table of contents and then you have little skills, right? Core beliefs, MD tech tracker. Yeah. Yeah. The scale is overRyan Lopopolo: The tech jet tracker and the quality score are pretty interesting because this is basically a tiny little scaffold, like a markdown table, which is a hook for Codex to review all the business logic that we have defined in the app, assess how it matches all these documented guardrails and propose follow up work for itself.Before beads and all these ticketing systems, we were just tracking follow up work as notes in a markdown file, which, we could spa an agent on Aron to burn down. There's this really neat thing that like the models fundamentally crave text. So a lot of what we have done here is figure out ways to inject textswyx: intoRyan Lopopolo: the system right when we get a page, because we're missing a timeout, for example.I can just add Codex in Slack on that page and say, I'm gonna fix this by adding a timeout. Please update our reliability documentation. To require that all network calls have [00:13:00] timeouts. So I have not only made a point in time fix, but also like durably encoded this process knowledge around what good looks like.swyx: Yeah.Ryan Lopopolo: And we give that to the root coding agent as it goes and does the thing. But you can also use that to distill tests out of, or a code review agent, which is pointed at the same things to narrow the acceptable universe of the code that's produced.swyx: I think one of the concerns I have with that kind of stuff is you think you're making the right call by making, it's persisted for all time across everything.Yes. But then you didn't think about the exceptions that you need to make, right? And that you have to roll it back.Vibhu: Part of it isswyx: also sometimes it can follow your s instructions too.Vibhu: It's somewhat a skill, right? So it determines when it uses the tools, right? Like it's not like it'll run outta every call.It'll determine when it wants to check quality score, right?Ryan Lopopolo: Yeah. And we do in the prompts we give these agents, allow them to push back,[00:13:51] Agent Code Review RulesRyan Lopopolo: When we first started adding code review agents to the pr, it would be Codex, CLI. Locally writes the change, pushes up a PR on [00:14:00] those PR synchronizations of review agent fires.It posts a comment. We instruct Codex that it has to at least acknowledge and respond to that feedback. And initially the Codex driving the code author was willing to be bullied by the PR reviewer, which meant you could end up in a situation where things were not converging. So yeah, we had to,swyx: he's just a thrash.Ryan Lopopolo: We had to add more optionality to the prompts on both of these things, right? The reviewer agents were instructed to bias toward merging the thing to not surface anything greater than a P two in priority. We didn't really define P two, but we gave it, youswyx: did define P two.Ryan Lopopolo: We gave it a framework within which to score its outputswyx: and then greater than P zero is worse, right?Yes. P two is very good.Ryan Lopopolo: P zero is you will mute the code place ifswyx: you merch thisRyan Lopopolo: thing, right?swyx: Yeah.Ryan Lopopolo: But also on the code authoring agent side, we also gave it the flexibility to either defer or push back against review feedback, right? This happens all the time, right? Like I happen to notice something and leave a code review, [00:15:00] which.Could blow up the scope by a factor of two. I usually don't mean for that to be addressed Exactly. In the moment. It's more of an FYI file it to the backlog, pick it up in the next fix it week sort of thing. And without the context that this is permissible, the coding agents are gonna bias toward what they do, which is following instructions.swyx: Yeah.[00:15:19] Autonomous Merging Flowswyx: I do wanted to check in on a couple things, right? Sure. All the coding review agent, it can merge autonomously. I think that's something that a lot of people aren't comfortable with. And you have a list here of how much agents do they do Product code and tests, CI configuration and release tooling, internal Devrel tools, documentation eval, harness review, comments, scripts that manage the repository itself, production dashboard definition files, like everything.Yes. And so they're just all churning at the same time, is there like a record that, that any human on the team pulls to stop everythingRyan Lopopolo: Because we are building a native application here. We're not doing continuous deploy. So there's still a human in the loop for cutting the release branch.I see. We require a blessed [00:16:00] human approved smoke test of the app before we promote it to distribution, these sort of things.swyx: So you're working on the app, you're not building like infrastructure where you have like nines of reliability, that kinda stuff?Ryan Lopopolo: That's correct. That's correct. Okay. And also like full recognition here that all of this activity took in a completely greenfield repository.There's. Should be no script that this applies generally toswyx: this is a production thing, you're gonna shipRyan Lopopolo: toswyx: customers. Of course. Yeah, of course. So this is realVibhu: And like one of the things there is, you mentioned you started this as a repo from scratch. The onboarding first month or so was pretty, it was like working backwards, right?Yeah. And then you had to work with the system and now you're at that point where you know, you're very autonomous. I'm curious like, okay, so what, how human in the loop is it? So what are the bottlenecks that you wish you could still automate? And part of that is also like, where do you see the model trajectory improving and offloading more human in the loop?We just got 5.4. It's a really good,Ryan Lopopolo: fantastic model, by the way.Vibhu: Yeah. Yeah. It's the first one that's merged. Top tier coding. So it's codex level coding and reasoning. So general reasoning both in one model. SoRyan Lopopolo: andVibhu: computer [00:17:00] use vision.Ryan Lopopolo: Now we now with five four, I can just have Codex write the blog post, whereas for this one I had to balance between chat.swyx: Oh, I need to, I might be out of a job. Oh my God.Ryan Lopopolo: Oh,swyx: I know. You just gave me an idea for a completely AI newsletter that five four could do. Yeah, I get it Now.Ryan Lopopolo: This sort of thing is just one example of closing the loop, right? Like the dashboard thing you mentioned. We have Codex authoring the Js ON, for the Grafana dashboards and publishing them and also responding to the pages, which means when it gets the page, it knows exactly which dashboards are defined and what alerts.What alert was triggered by which exact log in the code base. ‘cause all of this stuff is collated together.swyx: It has to own everything.Yes. Yeah. Yeah.Ryan Lopopolo: And it means that if we have an outage that did not result in a page. It has the existing set of dashboards available to it. It has the existing set of metrics and logs and can figure out where the gaps in the dashboard are or [00:18:00] in the underlying metrics and fix them in one go.In the same way, you would have a full stack engineer be able to drive a feature from the backend all the way to the front end.Vibhu: So it, it seems like a lot of the work you guys had to do was you as a small team are fully working for a way that the model wants the software to be written. It's like less human legible for better. Code legibility, agent legibility. How do you think that affects broader teams? So one at OpenAI, do liaison, like this is how software should be written. Like I can imagine, say you join a new team with this methodology, this mindset there's ways that, teams do code review, teams write code, like teams are structured and a lot of it is for human legibility.So should we all swap? Like how does this play back one broader into OpenAI and then like broader into the software engineering, right? Is it like teams that pick this up will it's pretty drastic, right? You have to make a pretty big switch. Should they just full send Yeah.Ryan Lopopolo: The mindset is very much that I'm removed from the process, right? I can't really have deep code level opinions about [00:19:00] things. It's as if I'm. Group tech leading a 500 person organization.Vibhu: Yeah.Ryan Lopopolo: Like it's not appropriate for me to be in the weeds on every pr. This is why that post merge code review thing is like a good analog here, right?Like I have some representative sample of the code as it is written, and I have to use that to infer what the teams are struggling with, where they could use help, where they're already moving quickly and I can pivot my focus elsewhere.Vibhu: Yeah.Ryan Lopopolo: So I don't really have too many opinions around the code as it is written.I do, however, have a command based class, which is used to have repeatable chunks of business logic that comes with tracing and metrics and observability for free. And the thing to focus on is not how that business logic is structured, but that it uses this primitive ‘cause I know that's gonna give leverage by default.Vibhu: Yeah.Ryan Lopopolo: Yeah, back to that sort of systems stinking,Vibhu: and you have part of that in your blog post, enforcing architecture and ta taste how you set boundaries for what's used. There's also a section on redefining [00:20:00] engineering and stuff, but yeah, it's just, it's interesting to hear,Ryan Lopopolo: and as the models have gotten better, they have gotten better at proposing these abstractions to unblock themselves, which again, lets me move higher and higher up the stack to look deeper into the future on what ultimately blocked the team from shipping.swyx: Yeah. You mentioned so you, this is primarily a, it is like a 1 million line of code base electron app. But it manages its own services as well, so it's like a backend for front end type thing.Ryan Lopopolo: We do have a backend in there, but that's hosted in the cloud.Yeah. This sort of structure is actually within the separate main and render processesWithin theswyx: electric.That's just how electronic works.Ryan Lopopolo: Yeah, of course. So have also treated like. MVC style decomposition with the same level of rigor, which has been very fun.swyx: I have a fun pun. This is a tangent, NVC is model view controller. Any sort of full stack web Devrel knows that.But my AI native version of this is Model view Claw, the clause the harness.Ryan Lopopolo: That's right. That's right. I do think that there is an interesting space to [00:21:00] explore here with Codex, the harness as part of building AI products, right? There's a ton of momentum around getting the models to be good at coding.We've seen big leaps in like the task complexity with each incremental model release where if you can figure out how to collapse a product that you're trying to build, a user journey that you're trying to solve into code, it's pretty natural to use the Codex Harness to solve that problem for you. It's done all the wiring and lets you just communicate and prompts to let the model cook.Yeah. It's been very fun. And there's also a very engineering legible way of increasing capabil. It's fantastic, right? Yeah. Just give you, just give the model scripts, the same scripts you would already build for yourself.swyx: Yeah.Yeah. So for listeners, this is Ryan saying that software engineering or coding against will eat knowledge work like the non-coding parts that you would normally think.Oh, you have to build a separate agent for it. No, start a coding agent and go out from there. Which open Claw has like it's pie Underhood.Ryan Lopopolo: [00:22:00] Yes.Vibhu: Basically define your task in code. Everything is a codingswyx: agent by the way. Since I brought it up, it's probably the only place we bring it up. Is any open claw usage from you?Any?Ryan Lopopolo: No. No. Not for me. I don't have any spare Mac Minis rattling around my house.swyx: You can afford it? No. I just, I'm curious if it's changed anything in opening eye yet, but it's probably early days. And then the other, the other thing I, I wanna pull on here is like you mentioned ticketing systems and you mentioned prs and I'm wondering if both those things have to go away or be reinvented for this kind of coding.So the git itself and is like very hostile to multi-agent.Ryan Lopopolo: Yeah. We make very heavy use of work trees.swyx: But like even then, like I just did a, dropped a podcast yesterday with Cursors saying, and they said they're getting rid of work trees ‘cause it still has too many merge conflicts.It's still un too un unintuitive. But go ahead.Ryan Lopopolo: The models are really great at resolving merge conflicts. Yeah. And to get to a state where I'm not synchronously in the loop in my terminal, I almost don't care that there are mergeswyx: with disposable.[00:23:00] Yeah.Ryan Lopopolo: We invoke a dollar land skill and that coaches codex to push the PR Wait for human and agent reviewers Wait for CI to be green.Fix the flakes if there are any merged upstream. If the PR comes into conflict, wait for everything to pass. Put it in the merge queue. Deal with flakes until it's in Maine. End. This is what it means to delegate fully, right? This is in a, very large model re probably a significant tax on humans to get PRS merged, but the agent is more than capable of doing this and I really don't have to think about it other than keep my laptop open.swyx: Yeah. I used to be much more of a control freak, but now I'm like, yeah, actually you could do a better job of this than me. Yeah. With the right context. Yes.[00:23:47] Encoding Requirementsswyx: Anything else in harness in general? Just this piece, I just wanna make sure we,Ryan Lopopolo: I think one thing that I maybe didn't make super clear in the article that I heard on Twitter as an interesting, that's respond [00:24:00]swyx: to them.What's the chatter and then what's your response?Ryan Lopopolo: Ultimately, all the things that we have encoded in docs and tests and review agents and all these things are ways to put all the non-functional requirements of building high scale, high quality, reliable software into a space that prompt injects the agent.We either write it down as docs, we add links where the error messages tell how to do the right thing. So the whole meta of the thing is to basically tease out of the heads of all the engineers on my team, what they think good looks like, what they would do by default, or what they would coach a new hire on the team to do to get things to merch.And that's why we pay attention to all the mistakes, mistakes that the agent makes, right? This is code being written that is misaligned with some as yet not written down, non-functional requirement.swyx: Sorry, what? Did the online people misunderstand orRyan Lopopolo: No,swyx: whatyouRyan Lopopolo: responded to? Somebody just literally said that.I was like, oh yeah,swyx: okay,Ryan Lopopolo: This is the [00:25:00] thing. This is what I've been doing. Oh, youswyx: agree? Yeah. I see. Interesting.Ryan Lopopolo: One other neat thing, which I did totally did not expect is folks were just. Taking the link to the article and giving it to pi or Codex and say, make my repo this,Vibhu: you achi a whole recursion.Ryan Lopopolo: And it was wildly effective. Really? It was wildly effective. NoVibhu: way. It just actually is something I tried with five, four yesterday. I didn't have time. Last time I was like out speaking of something, and this is one of my things, I was like, okay, I have this article. Can we just scaffold out what it would be like to run this?And I, I did it first as that and then I was like, okay, let me take another little side repo and say okay, if I was to fully automate this like this because I haven't written a line of code, it'sRyan Lopopolo: like over full, setVibhu: it right. The side thing I'm doing of voice. TTS I'm just like, slobbing out, whatever.It's nothing production. I'm like, how would I make this like this? And it's actually like a really good way. It's like a good way to learn what could be changed, what could be like, it's just a good analyzing, right? You give it all the codes, you give it all the context, you give it the article and it walks you through it very well.That's right. That's right.[00:25:57] Inlining Dependencies[00:25:57] Dependencies Going Away & Brett Taylor's Responseswyx: I guess one more thing before we go to Symphony is I wanted to cover [00:26:00] Brett Taylor's response. We had him on the show. He is your chairman, which is wild. Yeah. That he's reading your articles as well and like getting engaged in it. He says software dependencies are going away.Basically they can just be like vendored. Yes. Response.Ryan Lopopolo: Aswyx: hundred percent. A hundred percent agree. You still pro qr, you still pay Datadog. You still pay Temporal. Thank you.Ryan Lopopolo: Yep. The level of complexity of the dependencies that we can internalize is, I would say low, medium right now. Just based on model capability.What does the,swyx: what is medium?Ryan Lopopolo: I would say like a. A couple thousand line dependency is a thing that we could in-house No problem. Call in an afternoon of time. One neat thing about it is like probably most of that code you don't even need. Like by in-house and abstraction, you can strip away all the generic parts of it and only focus on what you need to enable the specific thing.Yes. You're building,swyx: I've been calling this the end of b******t plugins.Ryan Lopopolo: Yeah.swyx: Because there's so much when I published an open source thing, I want to accept everything, be liberal. I want to accept, this is post's law, but that means there's so much bloat. Yes. There's so much overhead.Ryan Lopopolo: One other neat thing about [00:27:00] this too is when we deploy Codex Security on the repo, it is able to deeply review and change. The internalized dependencies in a much lower friction way than it would be to like, push patches upstream, wait for them to be released, pull them down, make sure that's compatible with all the transitive I have in my repo and things like that.So it's also much lower friction to internalize some of these things if code is free. ‘cause the tokens are cheap sort of thing.swyx: Yeah. Yeah. I think like the only argument I have against this is basically scale testing, which obviously the larger pieces of software like Linux, MySQL, he calls up even the Datadog and Temporals and then maybe security testing where Yes.Classically, I think, is it linis tos, it said security open source is the best disinfectant.Ryan Lopopolo: Many eyes.swyx: Many eyes. And if inline your dependencies and code them up, you're gonna have to relearn mistakes from other people that Yep.Ryan Lopopolo: Yep. And to internalize that dependency, you're back to zero and you have to start.Reassembling all those bits and pieces to Yeah. Have [00:28:00] high confidence in the code as it is written. Yeah.Vibhu: Even part of the first intro of this, you basically mentioned like everything was written by codex, including internal tooling, right? So internal tooling, like when you're visualizing what's going on it's writing it for itself.swyx: Yeah. I'm built internal tools way I now, and like I just show them off and they're like, how long did you spend? And I didn't spend any time. I just prompted it,Ryan Lopopolo: very funny story here.swyx: Yeah, go ahead.Ryan Lopopolo: We had deployed our app to the first dozen users internally had some performance issues, so we asked them to export a trace for us get a tar ball, gave it to our on-call engineer, and he did a fantastic job of working with Codex to build this beautiful local Devrel tool, next JS app, the drag and drop the tar ball in, and it visualizes the entire trace.It's fantastic. Took an afternoon, but none of this was necessary. Because you could just spin up codex and give it the tar ball and ask the same thing and get the response immediately. So in a way, optimizing for human [00:29:00] legibility of that debugging process was wrong. It kept him in the loop unnecessarily when instead he could have just like Codex cooked for five minutes and gotten this same.swyx: Yeah, you verify your instincts here of this is how we used to do it. Or this is how I would have used to solve it.Ryan Lopopolo: Yeah. In this local observability stack. Like sure, you can de deploy Yeager to visualize the traces, but I wouldn't expect to be looking at the traces in the first place because I'm not gonna write the code to fix them.swyx: Yeah. So basically there needs to be like this kind of house stack and owning the whole loop. I think that is very well established. And it sounds like you might be like sharing more about that in the future, right?Ryan Lopopolo: Yeah. I think we're excited to do[00:29:36] Ghost Libraries Specs[00:29:36] Ghost Libraries & Distributing Software as SpecsRyan Lopopolo: We're gonna talk about Symphony in a little bit, but like the way we distribute it as a spec, which I think folks are calling Ghost Libraries on Twitter.This is like a such a cool name. It does mean it becomes much cheaper to share software with the world, right? You define a spec, how you could build your own specifying as much as is required for a coding agent to reassemble it [00:30:00] locally. The flow here is very cool. Like we have taken. All the scaffolding that has existed in our proprietary repo spun up a new one.Ask Codex with our repo as a reference. Write the spec. We tell it. Spin up a team ox spawn a disconnected codex to implement the spec. Wait for it to be done. Spawn another codex and another team ox to review the spec com or review the implementation compared to upstream and update the spec so it diverges less.And then you just loop over and over Ralph style until you get a spec that is with high fidelity able to reproduce the system as it is. It's fantastic.Vibhu: And you're basically, you're not really adding any of your human bias in there, right? That's correct. A lot of times people write a spec and be like, okay, I think it should be done this way, and you'll riff on something.And it's no, the agent could have just handled it like you're still scaffolding in a sense, right? I want it done this way. It can determine its spec better.swyx: That's right. That's right. Part of me it, I'm, I've been working a lot on evals recently, and part of me is wondering if [00:31:00] an agent can produce a spec that it cannot solve.Is it always capable of things that he can imagine or can you imagine things that it is impossible to do?Ryan Lopopolo: I think with Symphony, we, there's like this there's this axis where you have things that are easier, hard, or established or new, right? And I think things that are hard and new is still something that the models need humans.Yeah. Drive.swyx: Yeah. Yeah.Ryan Lopopolo: But I think those other quadrants are largely salt. Given the right scaffold and the right thing that's gonna drive the agent to completion,swyx: it's crazy that it solved,Ryan Lopopolo: but it means that the humans, the ones with limited time and attention get to work on the hardest stuff, like the problems where it's pure white space out in front. Or like the deepest refactorings where you don't know what the proper shape of the interfaces are. And this is where I wanna spend my time. ‘cause it lets me set up for the next level of scale.swyx: Yeah. Yeah. Amazing. Let's introduce Symphony.I think we've been mentioning it every now and then. Elixir. Interesting option.Ryan Lopopolo: Yeah.swyx: Yeah. I'm not,Ryan Lopopolo: again, like the [00:32:00] elixir manifestation here is just a derivative. Is it a modelswyx: chosen? Yeah.Ryan Lopopolo: Yeah. Yeah. And it chose that because the process supervision and the gen servers are super amenable to the type of process orchestration that we're doing here.You are essentially spinning up little Damons for every task that is in execution and driving it to completion, which. Means the mall gets a ton of stuff for free by using Elixir and the Beam.swyx: I had to go do a crash course in Beam and Elixir, and I think most people are not operating at that scale of concurrency where you need that.But it is a good mental model for Resum ability and all those things. And these are things I care about. But tell me the story, the origin story of Symphony. What do you use it for? Is this, how did it form maybe any abandoned paths that you didn't take?[00:32:46] Terminal Free Orchestration[00:32:46] Symphony: Removing Humans from the LoopRyan Lopopolo: At the end of December we were at about three and a half PRS per engineer per day.This was before five two came out in the beginning of January. Everyone gets back from holiday with five two and no other work [00:33:00] on the repository. We were up in the five to 10 PRS per day per engineer. And I don't know about y'all, but like it's very taxing to constantly be switching like that. Like I was pretty tapped out at the end of the day, again, where are the humans spending their time? They're spending their time context switching between all these active tmox pains to drive the agent forward.swyx: Yeah. No way. Yeah.Ryan Lopopolo: So let's again, build something to remove ourselves from the loop. And this is what frantic sprinted adapt here to find a way to remove the need for the human to sit in front of their terminal.So a lot of experimentation with Devrel boxes and, automatically spinning up agents, like it seems like a fantastic end state here, where my life is beach. I open live twice a day and say yes no to these things. Yeah. And this is again, a super, super interesting framing for how the work is done.Because I become more latency and sensitive. I have [00:34:00] way less attachment to the code as it is written. Like I've had close to zero investment in the actual authorship experience. So if it's garbage. I can just throw it away and not care too much about it. In Symphony, there's this like rework state where once the PR is proposed and it's escalated to the human for review, it should be a cheap review.It is either mergeable or it is not. And if it's not, you move it to rework. The elixir service will completely trash the entire work tree NPR and start it again from scratch. Okay. And this is that opportunity again to say, why was it trash right? What did the agent do that wasswyx: bad. Yeah.Ryan Lopopolo: Fix that before moving the ticket toswyx: endRyan Lopopolo: of progress again.swyx: Yeah. Why is this not in codex app? I guess this, you guys are ahead of Codex app,Ryan Lopopolo: yeah, so the way the team has been working is basically to be as AI pilled as possible and spread ahead. And a lot of the things we have worked on have fallen out [00:35:00] into a lot of the products that we have.Like we were in deep consultation with the Codex team to. Have the Codex app be a thing that exists, right? To have skills be a thing that Codex is able to use. So we didn't have to roll our own to put automations into the product. So all of our automatic refactoring agents didn't have to be these hand rolled control loops.It has been really fantastic to be, in a way, un anchored to the product development of Frontier and Codex and just very quickly try to figure out what works and then later find the scalable thing that can be deployed widely. It's been a very fun way to operate. It's certainly chaotic. I have lost track very often of what the actual state of the code looks like.‘cause I'm not in the loop. There was. One point where we had wired playwright directly up to the Electron app. With MCPM CCPs, I'm pretty bearish on because the harness forcibly injects all those tokens in the [00:36:00] context, and I don't really get a say over it. They mess with auto compaction. The agent can forget how to use the tool.There's probably only what three calls in playwright that I actually ever want to use. So I pay the cost for a ton of things. Somebody vibed a local Damon that boots playwright and exposes a tiny little shim CLI to drive it. And I had zero idea that this had occurred because to me, I run Codex and it's able to, it's oh, it's better.Yeah. Like no knowledge of this at all. Uhhuh.[00:36:30] Multi Human ChaosRyan Lopopolo: So we have had like in human space to spend a lot of time doing synchronous knowledge sharing. We have a daily standup that's 45 minutes long because we almost have to. Fan out the understanding of the current state.swyx: Yeah, I was gonna say this is good for a single human multi-agent, but multi human, multi-agent is a whole like po like explosion of stuff.Ryan Lopopolo: Yeah. And that this is fundamentally why we have such a rigid, like 10,000 [00:37:00] engineer level architecture in the app because we have to find ways to carve up the space so people are not trampling on each other.swyx: Sorry, I don't get the 10,000 thing. Did I miss that?Ryan Lopopolo: The structure of the repository is like 500 NPM packages.It's like architecture to the excess for what you would consider, I think normal for a seven person team. But if every person is actually like 10 to 50. Then the like numbers on being super, super deep into decomposition and sharding and like proper interface boundaries make a lot more sense.swyx: Yeah. To me, that's why I talked about Microfund ends and I, an anex is from that world, but Cool. It is just coming back to, to, to this I dunno if you have other, thoughts on. Orchestrating so much work coin going through this. Is this enough? Is this like any aha moments?Vibhu: It'll be interesting to see like where, okay, so right now you pick linear as your issue tracker, right?swyx: Or it's like a is it actually linear? This is actually linear.[00:37:55] Linear vs Slack WorkflowVibhu: Oh, that's linear. It's linear.swyx: Oh I never looked atVibhu: video. The demo video I had to download to [00:38:00] run.swyx: So I, because I'm a Slack maxie, but Yeah, linear. Linear is also really good. Yes,Ryan Lopopolo: we do make a good use of Slack. We we fire off codex to do all these lotion, elasticity, fix ups, the things that like sync that knowledge into the repository.It's super cheap. Yeah.swyx: Yeah.Ryan Lopopolo: Just do it in Codex.swyx: My biggest plug is OpenAI needs to build Slack. You need to own Slack. Build yours. Turn this into Slack.Ryan Lopopolo: I did read about it. Youswyx: did?Ryan Lopopolo: Yeah.[00:38:25] Collaboration Tools for AgentsRyan Lopopolo: I would say that if we think that we want these agents to do economically valuable work, which is like this is the mission, right?We want AI to be deployed widely, to do economically valuable work, then we need to find ways for them to naturally collaborate with humans, which means collaboration tooling, I think, is an interesting space to explore.swyx: Yeah, totally. Yeah. GitHub, slack, linear.Vibhu: Yeah, that was my thing. Okay, where do we see right now Codex has started Codex Model, then CLI, now there's an app, app can let me shoot off multiple Codex is in parallel, but there's no great team collaboration for Codex.And it [00:39:00] seems like your team had some say into what comes out, right? So you talked to ‘em, codex kind of was a thing. From there, if you guys are on the bound, what stuff that like, you might not focus on, but what do you expect other people to be building, right? So people that are like five x 50 Xing.Should you build stuff that's like very niche for your workflow, for your team? Should it be more general so other people can adopt? Is there a niche there? ‘Cause part of it is just okay, is everything just internal tooling? Do we have everything our own way? Like the way our team operates has our own ways that we like to communicate or is there a broader way to do it?Is it something like a issue tracker? Just thoughts if you wanna riff on that.[00:39:35] Standardizing Skills and CodeRyan Lopopolo: I think TBD we have not figured this out in a general way. I do think that there is leverage to be had in making the code and the processes as much the same as possible. If you think that code is context, code is prompts, it's better from the agent behavior perspective to be able to look in a package in directory X, Y, Z, and it not to have to page so [00:40:00] deeply into directory if you C, because they have the same structure, use the same language, they have the same patterns internally.And that same like leverage comes from aligning on a single set of skills that you're pouring every engineer's taste into to make sure that the agent is effective. So like in our code base, we have, I think, six skills. That's it. And if some part of the software development loop is not being covered, our first attempt is to encode it in one of the existing setup skills, which means that we can change the agent behavior.Yeah. More cheaply than changing the human driver behavior.swyx: Yeah.[00:40:39] Self Improvement via Logsswyx: Have you ever, have you experimented with agents changing their own behavior?Ryan Lopopolo: We do.swyx: Yeah. Or parent agent changing a subagents, behavior or something like that.Ryan Lopopolo: We have some bits for skill distillation. So for example, there's one neat thing you can do with Codex, which is just point it at its own session logs to ask it to tell you how you can use [00:41:00] the tool pedal better.swyx: It's like introspectionRyan Lopopolo: or ask it to do things. I useVibhu: this session better. What skills should Iswyx: high? I like the modification of, you can do, just do things to you can just ask agent to do things.Ryan Lopopolo: Yeah. You can just codex things. This is like a, this is like a silly emoji that we have, right? You can just codex things, you can just prompt things.It's really glorious future we live in, but okay, you can do that one-on-one. But we're actually slurping these up for the entire team into blob storage and. Running agent loops over them every day to figure out where as a team can we do better and how do we reflect that back into the repositories?Yes, though everybody benefits from everybody else's behavior for free. Same for like PR comments, right? These are all feedback. That means the code as written, deviated from what was good, a PR comment, a failed build. These are all signals that mean at some point the agent was missing context. We gotta figure out how toswyx: Yeah.Ryan Lopopolo: Slurp it up and put it back in the reboot.swyx: By the way, I do this exactly right. I used to, when I use cloud code for [00:42:00] knowledge work, cloud cowork is like a nice product, right? Yes. In I think you would agree. I always have it tell me what do I do better next time? And that's the meta programming reflection thing.So I almost think like you have six reflection extraction levels in symphony and almost like the zero of layer. So the six levels are PO policy, configuration, coordination, execution, integration, observability. We've talked about a couple of these, but the zero layer is like the, okay, are we working well?Can we improve how we work? Yes. Can I modify my own workflow without MD or something? I don't know.Ryan Lopopolo: Yeah, of course. Yeah, of course you can. Like this thing is also able to cut its own tickets ‘cause we give it full access.Yeah. Make it a ticket to have it cut. Tickets you can.Put in the ticket that you expect it to file as on follow up work,swyx: like Yeah. Self-modifying. Yeah.Ryan Lopopolo: Yeah.[00:42:44] Tool Access and CLI FirstRyan Lopopolo: Put, don't put the agent in a box. Give the agent full accessibility over it. Domain.swyx: I had a mental reaction when you said don't put the agent in a box. So I think you should put it in a box. Like it's just that you're giving the box everything it needs.Ryan Lopopolo: Yeah. Context and tools.swyx: But we're like, as developers, we're used to calling [00:43:00] out to different systems, but here you use the open source things like the Prometheus, whatever, and you run it locally so that you can have the full loop. I assume.Ryan Lopopolo: Yep.Vibhu: I think likeRyan Lopopolo: another, you wanna minimize cloud, cloud dependencies.Vibhu: You also want to make sure that you think about what the agent has access to. What does it see? Does it go back into the loop, like from the most basic sense of you let it see its own like calls, traces it can determine where it went wrong. But are you feeding that back in? So you know, just the most basic level of you wanna see exactly what's input output, like does the agent have access to.What is being outputted, right? It can self-improve a lot of these things. It's allRyan Lopopolo: text, right? My job is to figure out ways to funnel text from one agent to the other.swyx: It's so strange like way back at the start of this whole AI wave Andre was like, English is the hottest day programming language.It's here, it's just Yeah. The feature as well.Vibhu: A lot of, okay. Like a lot of software, a lot of stuff. There's a gui, it's made for the human. We're seeing the evolution of CLI for everything, right? All tools have CLIs. Your agents can use [00:44:00] them well, do we get good vision? Do we get good little sandboxes?Like right now? It's a really effective way, right? Models love to use tools. They love the best. They love to read through text. So slap a CLI let it go loose. That works for everything.Ryan Lopopolo: It does. Yeah. Yeah.[00:44:14] UI Perception and RasterizingRyan Lopopolo: We've also been adapting nont, textual things to that shape in order to improve model behavior in some ways, right?We want the agent to be able to see the UI agents do not perceive visually in the same way that we do. They don't see a red box, they see red box button, right? They see these things in latent space. So if we want, Hey, yeah, I do. We haveswyx: a ding if that goes off every time. Alien spaceRyan Lopopolo: ding.Anyway if we wanna actually make it see the layout, it's almost easier to rasterize that image to ask EOR and feed it in to the agent. Ha. And there's no reason you can't do both, right? To like further refine how the model perceives the object it's [00:45:00] manipulating.swyx: Cool. Could we, you wanna talk about a couple more of these layers that might bear more introspection or that you have personal passion for?[00:45:07] Coordination Layer with ElixirRyan Lopopolo: I will say that the coordination layer here was a really tricky piece to get right.swyx: Let's do it. Yep. I'm all about that. And this is Temporal core.Ryan Lopopolo: This is where when we turn the spec into Elixir, where like the model takes a shortcut, right? Like it's oh, I have all these primitives that I can make use of in this lovely runtime that has native process supervision.Which is I think, a neat way to have taken the spec and made it more choices achievable by making choices that naturally mapswyx: Yeah.Ryan Lopopolo: To the domain, right? In the same way that like you would prefer to have a TypeScript model repo if you are doing full stack web development, right? Because the ability to share types across the front end and backend reduces a lot of complexity.And becauseswyx: that's what graph kill used to be.Ryan Lopopolo: That's right. Andswyx: I don't know if it's still alive, butRyan Lopopolo: [00:46:00] no humans in the loop here. So like my own personal ability to write or not write elixir. Doesn't really have to bias us away from using the right tool for the job. It is just wild.swyx: Love it. I love it.Yeah. I wonder if any languages struggle more than others because of this? I feel like everyone has their own abstractions. That would make sense. But maybe it might be slower, it might be more faulty where like you'd have to just kick the server every now and then. I, I don't know. I think observability layer is really well understood.Integration layer, CP is dead. I think all these just like a really interesting hierarchy to travel up and down. It's common language for people working on the system to understandRyan Lopopolo: The policy stuff is really cool, right? Yeah. You don't really have to build a bunch of code to make sure the system wait for the, to passswyx: it's institutional knowledge.Ryan Lopopolo: Yeah. You just give it the G-H-C-L-I with some text that say CI has to pass. It makes the maintenance of these systems a lot easier.[00:46:57] Agent Friendly CLI Outputswyx: Do you think that CLI maintainers need to be [00:47:00] do anything special for agents or just as is? It's good because like I don't think when people made the G GitHub, CLI, they anticipated this happening.Ryan Lopopolo: That's correct. The GH CLI is fantastic. It's great super industry.swyx: Everyone go try GH repo create GH pull and then pull request number, right? GH HPR, like 1 53, whatever. And then it like pullsRyan Lopopolo: basically my only interaction with the GitHub web UI at this point is GH PR view dash web.Exactly. Glanceswyx: at the diffRyan Lopopolo: and be like Sure thing. Send it. Yeah. But the CLI are nice ‘cause they're super token efficient and they can be made more token efficient really easily. Like I'm sure you all have seen like I go to build Kite or Jenkins and I could just get this massive wall of build output.And in order to unblock the humans, your developer productivity team is almost certainly gonna write some code that parses the actual exception out of the build logs and sticks it in a sticky note at the top of the page. And you basically [00:48:00] want CLI to be structured in a similar way, right? You're gonna want to patch dash silent to prettier because the agent doesn't care that every file was already formatted.Just wants to know it's either formatted or not. So it can then go run a right command. Similarly, like in our PNPM distributed script runner, when we had one, when you do dash recursive, like it produces a absolute mountain of text. But all of that is for passing. Test suites. So we ended up wrapping all of this in another scriptswyx: to suppress the,Ryan Lopopolo: which you can vibe the channel only output the failing parts of the tests.swyx: You make a pipe errors versus the standard, standard out. I don't know. Okay. Whatever. Too much thinking have to do that. The CII used to maintain SCLI for my company and yeah, this is like core, very core to my heart. But you're vibing my job.Ryan Lopopolo: That's right.swyx: Cool. Any other things?This is a long spec. [00:49:00] I appreciate that. It's got a lot of strong opinions in here. Any other things that we should highlight? I think obviously you can spend the whole day going through some of these, but I do think that some of these have a lot of care or some of this you might wanna tell people, Hey, take this, but, make it your own.[00:49:15] Blueprint Spec and GuardrailsRyan Lopopolo: Fundamentally, software is made more flexible when it's able to adapt to the environment in which it is deployed, which means that things like linear or GitHub even are specified within the spec, but not required pieces of it. There's like a more platonic ideal of the thing that you could swap in like Jira or Bitbucket, for example.But being able to tightly specify things like the ID formats or how the Ralph Loop works for the individual agents. Basically means you can get up and running with a fully specified system quickly that you then evolve later on. I think we never intended for this to be a static spec that you can [00:50:00] never change.It's more like a blueprint to get something worth a starting point up and running.swyx: Yeah.Ryan Lopopolo: For you then to vibe later to your heart's content,swyx: you have like code and scripts in here where it's oh, I think this is a really good prompt. It's just a very long prompt.Ryan Lopopolo: Fundamentally, the agents are good at following instructions, so give them instructions.And it will, improve the reliability of the result. We, much like the way we use Symphony, we don't want folks to have to monitor the agent as it is vibing the system into existence. So being very opinionatedVery strict around what these success criteria are means that our deployment success rate goes up. Yeah. It means we don't have to get tickets on this thing.Vibhu: Think it all goes back to that like code to disposable, right? Like early on when you had CLI or you'd kick off a Codex run, it would take two hours. You would wanna monitor okay, I'm in the workflow of just using one.I don't want it to go down the wrong path. I'll cut it off and, just shoot off four, like that was my favorite thing of the Codex app, right? Yeah. Just Forex it like, [00:51:00] it's okay. One of them will probably be right, one of them might be better. Stop overthinking it. Like my first example was probably like deep research.When you put out deep research and I'd ask it something like, I asked it something about LLM, it thought it was legal something and spent an hour, came back with a report completely off the rails. And I was like, okay, I gotta monitor this thing a bit. No don't monitor it. Just you want to build it so it's that it, it goes the right way.And you don't wanna, you don't wanna sit there and babysit, right? You don't want to babysit your agentsRyan Lopopolo: with that deep research query that you made. Looking at the bad result, you probably figured out you needed to tweak your prompt Yeah. A bit, right? That's that guardrail that you fed back into the code base for the task, your prompt to further align the agent's execution.Same sort of concept supply there too.swyx: When you talk, how are the customers feelingRyan Lopopolo: for Symphony? I think we have none, right? This is a thing we have put out into theswyx: world. Symphony's internal, right? As long as you are happy, you are the customer. That'

In this week's episode, I talk with Melanie Tory, Professor of the Practice at Northeastern University, about how people actually use dashboards in the real world — and why that use often looks very different from what designers intend. Her research reveals that dashboards frequently serve as a starting point for accessing data rather than tools for answering questions directly, with many users simply exporting data to Excel to do their real analytical work. We also explore her work on AI-enabled healthcare systems designed to help clinicians monitor patient risk in intensive care units, including how to visualize uncertainty in ways that busy medical teams can process quickly. And we close with a look at her emerging research on how people are beginning to use generative AI tools for data visualization tasks. It's a thought-provoking conversation about the gap between the tools we build and the ways people actually work with data.Subscribe to the PolicyViz Podcast wherever you get your podcasts.Keywords: data visualization, dashboards, dashboard design, dashboard usability, data analysis workflows, Tableau dashboards, Power BI dashboards, human data interaction, Melanie Tory, data communication, dashboard research, analytics tools, business intelligence dashboards, data storytelling, data workflows, PolicyViz PodcastBecome a patron of the PolicyViz Podcast for as little as a buck a monthVisit Melanie's webpage at Northeastern UniversityFollow me on Instagram, LinkedIn, Substack, Twitter, Website, YouTubeEmail: jon@policyviz.com

A lot of companies say they “listen to the customer” and they do. They survey, they track NPS, they build dashboards, they share reports. But then nothing changes. Today we respond to a sharp question from a listener about what separates organizations that embed customer insights into everyday decision making from those where Voice of the Customer stays stuck as a feedback exercise.We walk through the mindset shift that turns VoC into real customer experience strategy: using your mission and goals as the lens for what you act on, getting aligned on expectations, and defining a clear customer experience mission statement so improvements aren't scattered across one-off complaints. When teams fix isolated issues without a unified view of the customer journey, customers feel the inconsistency and trust drops fast.Then we get practical about execution. Customer insights only matter when they influence decisions across product, operations, communication, and the partners you choose. That requires shared ownership, clear governance, and consistent processes for reporting what you're doing about the feedback and closing the loop with customers. We also address the “shoot the messenger” trap and how CX leaders can connect the dots to business value so teams understand what's in it for them.If you want to turn Voice of the Customer into decision infrastructure and measurable business outcomes, press play. Subscribe, share this with a CX leader on your team, and leave a rating and review so more people can find the show.Resources Mentioned:Order your copy of Experience Is Everything -- http://experienceiseverythingbook.comLearn more about CXI Membership™ and apply -- http://CXIMembership.comExperience Investigators Website -- https://experienceinvestigators.comEnjoyed the show? Subscribe, share with your team, and leave a quick review to help others find us. Leave your review at ratethispodcast.com/xact.Want to ask a question? Visit askjeannie.vip to leave Jeannie a voicemail! (And don't forget to follow Jeannie Walters, CCXP, CSP on LinkedIn!)

Hey Voices from the Bench community! Jessica Love here, sending a shoutout from Utah! If you're passionate about creating natural, beautiful smiles—but want to simplify your workflow without sacrificing aesthetics—this is for you. I'm honored to be part of Ivoclar's development team introducing a powerful new stain and glaze system featuring Structure Paste, IPS e.max Ceram Art. Create stunning depth and lifelike color in as little as one firing. Let's continue to innovate, simplify, and create meaningful change—one smile at a time. When it comes to digital dentures, design is easy—manufacturing is where things get messy. That's why the Elevate Denture Solution brings it all together. Built by Roland DGSHAPE, Ivoclar, and FOLLOW-ME! Technology Group, it combines machine, materials, and CAM into one fully optimized workflow—so you get consistent, high-quality results without the guesswork. Want to simplify production and scale with confidence? Check it out at rollanddga.com/elevate. "Live" from the Ivoclar ballroom at Lab Day 2026, Elvis and Barb dives into conversations that perfectly capture what this industry is all about—innovation, relationships, and a whole lot of nerding out. We kick things off with Frederic Rapp, who went from growing up in his dad's basement lab in France to scaling it into one of the largest labs in Europe. After selling the business, he found his way back into the industry through innovation—helping labs unlock the gold mine sitting inside their own data with icortica. From dashboards to AI-driven insights and even voice-activated notes in the parking lot, it's all about working smarter, not harder… and maybe not looking like an idiot when you walk into a doctor's office. Then things shift to a great partnership with Casey Baldwin and Darin Lockaby, where we get into a seriously cool collaboration between Ivoclar and DESS. Think plug-and-play workflows that let labs mill their own abutments in-house—FDA compliant, streamlined, and actually simple. With margins tighter than ever, this kind of control over production isn't just nice… it's becoming necessary. From scaling labs to scaling data, from implants to AI, this episode is packed with insight, laughs, and a clear message: the labs that embrace technology (without losing the human touch) are the ones that are going to win. Join us at exocad Insights 2026, happening April 30–May 1, 2026, on the stunning island of Mallorca, Spain. This two-day event features powerhouse keynotes, hands-on workshops, live software demos, and top-tier industry showcases—all in one unforgettable setting. Barb and Elvis will be on site bringing you exclusive interviews, plus don't miss the Women in Dentistry Lunch, celebrating career growth, wellbeing, and the real stories shaping our profession. And of course, cap it all off with the legendary exoGlam Night under the stars. Tickets are limited. Visit exocad.com/insights-2026 and use code VFTBPalma15 for 15% off.Special Guests: Casey Baldwin, Darin Lockaby, and Frederic Rapp.

If your CFO isn't producing a return, they're not an asset—they're an expense. In this episode, I break down what it really takes to get ROI from a fractional CFO and why so many business owners miss the value simply because they don't know how to use one effectively.We talk about the key shifts that happen as your business grows, why bad financial habits only get worse with scale, and how a CFO should help you actually keep more of what you make. I walk through the exact ways you should be working with a CFO—from communication and goal setting to dashboards and accountability—so you can turn that investment into real financial results in your business.Timeline Highlights:[0:00] Why a CFO must produce ROI or they're just an expense[0:50] Growth stages where financial problems become more visible[1:31] Why making more money often leads to keeping less[1:48] What triggers business owners to hire a fractional CFO[2:07] Why most owners don't know how to work with a CFO[2:45] The importance of open and honest communication about money[3:28] Understanding your money habits—spender vs saver[4:00] Why clear goals drive measurable ROI from a CFO[4:41] Tracking progress: reserves, owner pay, and financial outcomes[5:22] The role of dashboards in decision-making[6:06] The “sleep at night” factor and financial clarity[6:48] How a CFO creates systems instead of relying on hope[7:21] Managing your bookkeeper and CPA through a CFO[8:10] Turning tax strategies into real execution[9:04] Time savings, peace of mind, and true financial freedomKey TakeawaysA CFO should generate measurable ROI—not just reports.Scaling without fixing financial habits amplifies problems.Open communication about money is critical for success.Clear financial goals create measurable progress.Dashboards turn numbers into actionable decisions.A CFO provides systems, accountability, and leadership.Real ROI includes more money, less stress, and saved time.Links & ResourcesBook a free discovery call to see how a fractional CFO can create ROI in your business: profitrei.comClosingThanks for spending time with me today. If this episode helped you understand how to actually get a return from a CFO, make sure to follow the show, leave a review, and share it with another business owner who's growing but not keeping enough. And if you're ready to turn your finances into a system that produces real results, visit profitrei.com and book your free discovery call to start building clarity, confidence, and financial freedom.

Jay Caplan, a seasoned fractional CFO for real estate businesses, shares his expertise on turning financial guesswork into strategic clarity. Many investors, while handling millions in assets, struggle to track where their money goes. Jay's mission is to get clients "24 hours current" with their financial data, transforming reactive decision-making into proactive, informed planning. He dives into how he builds essential dashboards and tools, integrating data from various property management systems like Yardi and Appfolio with real-time banking information. This comprehensive approach empowers operators to forecast accurately, ensuring they have funds for both distributions and critical capital improvements, rather than facing unexpected cash shortfalls. Tune in to discover how to streamline your financial operations, gain unparalleled insight into your property portfolio, and build a robust financial framework for sustained real estate success.

Many adults with ADHD struggle with tools that seem simple at first but quickly become overwhelming. Dashboards full of icons, systems that require too many clicks, and constantly changing interfaces can quietly drain focus.In this episode of ADHD Skills Lab, Skye and Robbie explore practical ADHD work systems that reduce visual overload and make digital tools easier to navigate.Earlier this week, they explored research on object recognition memory in ADHD and why visual systems like software interfaces can create unexpected cognitive load.This episode focuses on what to do about it.They walk through practical ways to simplify work systems, stabilize digital environments, and design tools that support ADHD focus instead of constantly disrupting it.Start with Wednesday's research episode before this one. This conversation builds directly on the findings discussed there.What We CoverWhy constantly changing tools create friction for ADHD brainsDesigning stable digital systems that reduce cognitive loadHow visual clutter quietly drains focusPractical ways to simplify your work environment P.S. If your ADHD symptoms turn every business day into chaos—unfinished tasks piling up, revenue stuck, systems that don't stick—it's not you. It's your operating system. We help service business owners unblock their next $50-500k with simple systems that focus their brain. Watch this video to see how we do it, then take the program walkthrough.

Tired of hearing, 'this is the best AI model ever?'

Why do smart marketing teams keep optimizing for the wrong things?In Part 1 of this Sharp Cut series, we explored Goodhart's Law — when a measure becomes a target, it stops being a good measure.But the real problem doesn't start on the marketing dashboard.It starts two floors above it.In this episode of The Sharp Cut, Marc Binkley and Vassilis Douros trace the incentive problem all the way from the boardroom to the media buy, showing how the pressure to maximize shareholder value, hit revenue targets, and prove short-term ROI cascades through the organization — eventually shaping how marketing is measured.Drawing on insights from seven past Sleeping Barber guests, including Roger Martin, Peter Field, Avinash Kaushik, Dale Harrison, Herman Simon, Augustine Fou, and Koen Pauwels, this episode breaks down why marketing metrics often drift away from real business outcomes.We explore:Why shareholder value maximization may distort strategic decision-makingThe difference between revenue growth and real competitive growthHow efficiency metrics like ROI and ROAS can mislead organizationsWhy marketing dashboards are often 90% activity and only 10% outcomesWhy CPM may be one of the most dangerous metrics in media planningHow platform data quietly shapes the decisions marketers makeWhen incentives reward the wrong signals, even brilliant organizations can optimize themselves into decline.TakeawaysGoodheart's Law illustrates how metrics can become targets, leading to poor decision-making.Shareholder value maximization is a flawed approach that can harm long-term business health.Revenue growth does not equate to market growth; understanding this distinction is crucial.Short-term metrics can mislead organizations into making detrimental decisions.Effective marketing requires a balance between efficiency and effectiveness.Dashboards often reflect activity rather than meaningful outcomes, leading to misinterpretation of success.CPM is a dangerous metric that can create a false sense of accountability.Data reporting without context can lead to 'data puking' and poor decision-making.Organizations must evaluate whether their primary metrics truly reflect business health.Good measurement practices should focus on long-term outcomes rather than short-term gains.Chapters00:00 - Introduction to the Incentive Series01:00 - Understanding Goodheart's Law and Its Implications03:02 - The Shareholder Value Maximization Trap04:56 - Revenue vs. Growth: A Misunderstanding09:04 - The Dangers of Short-Term Metrics12:08 - The Role of Dashboards in Marketing Decisions14:59 - The Need for Better Measurement Practices

What if the reason you're not more profitable isn't effort, but clarity on what to fix next?As CEO of Lavu and the force behind Marty, an AI layer built for operators, Saleem isn't interested in prettier dashboards or yesterday's reports. He's focused on something far more dangerous—and far more valuable: telling you exactly what to fix today to put more cash in your bank account tomorrow.In this conversation, we get into why POS is already commoditized, why most data tools create the illusion of control, and how AI can surface the three decisions that actually move the needle. If you're tired of tracking problems instead of solving them, this conversation is your wake-up call.To learn more about Lavu, visit lavu.com._________________________________________________________Today's episode was brought to you by Square. If you want restaurant tech that actually supports how you run your restaurant, find out how Square can help at square.com/goodstuff.Free 5-Day Restaurant Marketing Masterclass – This is a live training where you'll learn the exact campaigns Josh has built and tested in real restaurants to attract new guests, increase visit frequency, and generate sales on demand. Save your spot at restaurantbusinessschool.com

CTO Sam Pierson explains how Qlik's associative engine and agentic AI are transforming the way businesses uncover insights and what's next on the data frontier.Topics Include:Qlik is a 30-year-old data analytics and AI company with global customers.Qlik's associative engine surfaces insights from data you aren't even examining.A paper manufacturer optimized supply chain routing and navigated tariff complexity.Generative AI can't easily query databases — Qlik's engine bridges that gap.Qlik built an agentic layer enabling natural language conversations with your data.MCP integration lets users access Qlik insights directly from tools like Claude Desktop.Qlik runs entirely on AWS, with global regions built around local compliance requirements.The AWS partnership prioritizes mutual success over transactional service relationships.Agents will mature in 2026; some agentic bets will succeed, others will be refactored.Fine-tuned, smaller language models will grow in importance alongside larger ones.AI adoption requires restructuring workflows end-to-end, from product spec to go-to-market.Qlik is hiring for curiosity and agency — people who experiment without waiting for permission.Participants:Sam Pierson – Chief Technology Officer, QlikSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

This was quite the episode! We return for Round 2 with Nelson Roque. He is Assistant Professor of Human Development and Family Studies at Penn State. He walked us through OpenClaw. Nelson is an incredibly useful guest because he is able to cover both podcasts I have run - The Behavioral Investor and this one, Geospatial FM. A significant message from The Behavioral Investor was the effect of delay discounting or hyperbolic discounting. This is the decay in appeal of even fantastic outcomes when there is a significant delay until they happen. A formula is available here and here. Applying the formula shows that receiving a billion dollars in 3 generations, 108 years from now, only feels like $10,000 right now. The feeling decays with delay.There are a couple of ways to defeat hyperbolic discounting. One is episodic future thinking. This is a way to juice the dopamine reward system by a multimodal, multisensory imagining of what one might see, hear, smell, think, touch and feel upon achieving a financial goal. Turns out that, of course, AI has been applied to help here. Another that I came up with is instant, constant feedback about the reduction in amount of money one can pass to the next generation with each spending decision you make. What do I mean?Let's work with the $600 you might splurge on a smartwatch. The US market has compounded at 7% annually, adjusted for inflation and including dividends, the past 150 years. Imagine you are 42 and have a life expectancy of 72. You've therefore got 30 years of compounding $600 at 7% annually. This becomes $4,500 if you invest it in a low cost US market index fund with a 0.05% fee. So, you could enjoy the $600 watch or consider that in splurging on it you are effectively saying to your child you don't want to give them $4,500 when you die. So what I'm proposing is an app to make the effect of long time periods on investing outcomes visible. Please watch the episode as Nelson walks through producing this live with an AGI agent orchestrator called OpenClaw. He also profiles some geospatial situation monitoring apps (e.g. here). These were inspired by the incredible Bilawal Sidhu. He was able to give a geospatial data replay of the Iran strikes: here and here. It is amazing how far we've come since Nelson's sceptical comments in the first episode with Nelson 9 months ago about whether or not we have AGI. Let's check back on his opinion of AGI in another 9 months.

I've worked with a lot of teams building analytics and insights products and decision-support systems. The pattern I keep seeing isn't that the math is wrong or the ML / AI models are weak. Much of the time, the technology is fine.     The challenge is that all that [not always artificial!] intelligence is not surfacing as value to your customer. Dashboards look impressive. AI features demo well. Pilots get strong reactions. And then… usage stalls. Sales cycles drag. Teams quietly revert to spreadsheets. Buyers, or rather, prospective buyers, say they “like the vision,” but deals don't move into the “closed” stage.     If your gut tells you the primary blocker is not your sales process, pricing/packaging, procurement, data quality, or risk/compliance, then you may be suffering from what I call the Invisible Intelligence Gap.      Your product's intelligence simply isn't visible to them. Three forces tend to amplify this gap. First, the value translation gap, which is when buyers and users can't easily connect insights to their own goals. Second is the workflow alignment gap resulting from the product not fitting how work actually gets done. Third, the trust and control gap involves users lacking confidence in how the system reaches conclusions. My frameworks like CED, FOWA, and MIRRR are designed to close these gaps by making value obvious, workflows smoother, and AI more trustworthy.     Highlights/ Skip to: The challenge of insights not providing value to buyers, end-users, and stakeholders (3:20) How the invisible intelligence gap manifests itself (6:42) Common symptoms of the invisible intelligence gap (8:10)  Examples of how changes in human behavior cause the gap (10:00) The (3) amplifiers of the invisible intelligence gap (11:47) The CED framework for addressing the intelligence gap problem (18:28) Addressing the invisible intelligence gap with FOWA (20:14) Using MIRRR to solve the invisible intelligence gap (21:25)

In this episode of Run the Numbers, CJ sits down with Superhuman's Head of Analytics Chris Byington. They break down where analytics should sit inside a company, why dashboards often fail, and how the best teams connect metrics, OKRs, and forecasting to real decisions. Chris also explains why “ship goals” can mislead teams and what CEOs and CFOs should expect from a truly decision-driving data function.—SPONSORS:Tabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.comRightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cj—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNChris: https://www.linkedin.com/in/chris-byington/Superhuman: https://superhuman.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:Matt Hudson Episodehttps://youtu.be/_FWGYkzhymQ—TIMESTAMPS:0:00 Preview and intro3:29 Centralized analytics team7:29 Start analytics with problems not tools9:41 Lead with the problem10:14 Align on growth model11:46 Pre-commit to decisions13:14 Sponsors — Tabs | Abacum | Brex16:35 Dashboards need growth context19:10 Where analytics should sit21:18 Pros and cons of analytics in finance23:18 Operations vs revenue org placement24:11 Hub-and-spoke analytics model25:18 What “embedded” actually means26:14 Sponsors — Metronome | RightRev | Rillet29:38 When self-service analytics works32:04 Self-serve pitfalls33:44 Buy vs build BI35:44 Analytics owns metrics38:26 Hero metric example41:41 Outcomes > shipping42:14 Set goals before build43:57 Metrics are outcome proxies46:40 Easy way to say no48:29 Start answers with yes52:17 Proving analytics impact56:19 Credits#RunTheNumbersPodcast

WHAT YOU'LL LEARN Why retail is now a demand chain, not a supply chain How AMRs deliver 6–12 month ROI in high-variability e-commerce Why robotics-as-a-service changes peak capacity planning The real bottleneck in AI adoption: structured WMS data Why dashboards are dying and exception-based orchestration is rising How consolidation will reshape 3PL economics Why operational excellence remains the ultimate differentiator HIGHLIGHTS 00:01–00:12 | Consumer expectations and the “fast + free + cheap” reality 00:12–00:15 | AMRs, ASRS, RaaS, and 6–12 month automation ROI 00:15–00:16 | Buy vs build: what's commodity vs “secret sauce” 00:16–00:19 | Agentic AI in warehouse ops: labor planning + execution 00:19–00:22 | AI proof, case studies, and demand planning as the next frontier 00:22–00:24 | Dashboards vs operators: turning analytics into actions 00:24–00:28 | Operator advice: efficiency, mechanization, and competition shifts 00:29–00:31 | Manifest trends: retail channels evolving + tech-driven 3PL future QUOTES  [00:04:10] “One of the biggest changes is you used to have a choice. You could either have it fast, you could have it free, or you could have it cheap. The consumer today wants all three.” – Jeff Wolpov [00:05:10] “We as logistics supply chain companies need to lean in and figure out how to do more with less. Today it's a necessity.” – Jeff Wolpov [00:07:30] “You need automation... We need to be faster and more flexible. Peaks have gotten much higher.” – Jeff Wolpov [00:16:00] "The hard part isn't building AI or using AI. It's what do you do with the results?" - Gary Allen [00:16:50] “Operators shouldn't hunt dashboards, they should get alerts, exception-based triggers. AI takes analytics to the next level.” – Gary Allen [00:23:00] "Reporting is the death of analytics." - Gary Allen  ABOUT THE GUESTS Jeff Wolpov Jeff Wolpov is Senior Vice President of E-commerce and Ryder Last Mile at Ryder System, Inc., where he leads the vision and strategy for omnichannel fulfillment and big & bulky home delivery. Previously, he served as CEO of Whiplash (formerly Port Logistics Group), achieving nearly 30% year-over-year revenue growth before its acquisition by Ryder in 2022. Earlier in his career, Jeff founded Distribution Solutions, scaling it from a startup into a $50 million regional logistics firm that became the foundation of Whiplash's national network. He holds a degree from the University of Michigan. Gary Allen Gary Allen is Vice President of Supply Chain Excellence at Ryder, overseeing Solution Design, Continuous Improvement, Data Analytics, and Automation across the supply chain organization. With more than 32 years of experience, he previously led EY's logistics consulting practice and held leadership roles at DHL and FedEx in product innovation, solution design, sustainability, and operations. Gary helped launch and co-author the “Annual Third Party Logistics Study” with Dr. John Langley of Penn State University and holds a Bachelor of Arts in Materials and Logistics Management from Michigan State University. LINKS MENTIONED Ryder report: https://www.ryder.com/en-us/insights/white-papers/e-comm/2025-ryder-e-commerce-consumer-study Ryder website: https://www.ryder.com/en-us Subscribe and Keep Learning!If you're a logistics leader looking to scale sustainably, don't miss out! Subscribe for more expert strategies on tackling modern supply chain challenges.Be sure to follow and tag the eCom Logistics Podcast on LinkedIn and YouTube

Welcome back to the Multifamily Collective with Mike Brewer.Today's operations tip delivers a clear message:Dashboards are tools — not operators.You can stare at data all day, but until a human takes action, your property won't move an inch.Mike explores:Why dashboards only highlight, not solveThe danger of outsourcing leadership judgment to softwareHow dashboards support prioritization, but not decision-makingWhy context, nuance, and empathy still belong to humansHow engaged leadership turns dashboards into leverage — not crutchesIt's tempting to say, “That's what the numbers said.”But smart operators stay curious, ask better questions, and take responsibility for the outcomes.Dashboards are only as powerful as the leaders interpreting them.This is one of many tips being compiled into a 2027 desk reference for multifamily professionals who are serious about leading with intention.MultifamilyCollective Blog: https://www.multifamilycollective.comThe Daily Collective Book: https://amzn.to/3YI6BDaHosted by: https://www.multifamilymedianetwork.com

What's on your mind? Let CX Passport know...Technology is loud. Agentic AI. Automation. Platforms. Dashboards.Meanwhile, the person who greets the customer at the door still determines whether that experience succeeds.April Sabral spent more than 30 years leading over 350 physical retail stores. In this episode, she explains why the answers to better customer experience aren't sitting in a conference room … they're already in the field.And y'all will not want to miss the Starbucks name-on-the-cup story … including how it started in a Miami Beach store and solved a real operational problem overnight.What You'll LearnWhy frontline managers … not technology … determine store performanceHow the Starbucks “name on the cup” idea started as a simple operational fixThe danger of promoting high performers without leadership trainingWhy skill-building beats “just be friendly” every timeThe leadership test: If you left your job … would your team follow you?CHAPTERS00:00 Introduction to April Sabral02:00 Where the CX conversation misses reality05:00 The Starbucks name-on-the-cup origin story08:20 Why simple solutions outperform complex tech10:40 Bridging the gap between field and head office14:15 What actually makes in-store CX work19:40 Process discipline vs. customer experience23:40 Would your team follow you?26:00 Why retail promotes too fast29:00 The future of physical retail in an AI world31:00 How to connect with AprilGuest LinksWebsite: https://www.aprilsabral.com/Listen: https://www.cxpassport.comWatch: https://www.youtube.com/@cxpassportNewsletter: https://cxpassport.kit.com/signupI'm Rick Denton and I believe the best meals are served outside and require a passport.Disclaimer: This podcast is for informational and entertainment purposes only. The views and opinions expressed are those of the hosts and guests and should not be taken as legal, financial, or professional advice. Always consult with a qualified attorney, financial advisor, or other professional regarding your specific situation. The opinions expressed by guests are solely theirs and do not necessarily represent the views or positions of the host(s).

There's an easy button for hard conversations now, and it's dangerously good. You've got something complicated to say. It needs nuance. It needs empathy. It probably needs a little courage. The AI will draft the whole thing in seconds. It sounds smart. It sounds reasonable. You skim it. You send it. And most of the time, nothing bad happens. The problem is that the time it does go bad is the exact situation where you thought you were being thoughtful. This week's Raw Data walks straight through one of those moments, from both sides of the exchange, and it's a reminder that outsourcing the structure of your thinking is not the same thing as being clear. Then there's the part that's almost more interesting. Thirteen years ago, the first real client engagement couldn't get traction around dashboards. The connection between "this is my business" and "data should change how I run it" just didn't stick. Same people, same company, different conversation recently around AI. Immediate traction. Leaning forward. Connecting dots in real time. That difference isn't about better slides or better storytelling. Dashboards improved a slice of the business. AI shows up in the messy motion of the whole thing. In workflows. In manual processes. In strategic questions leaders don't have time to chase down. That shift in surface area changes everything. AI isn't a toy and it isn't a ghostwriter. It's leverage. Real leverage. The kind that can remove friction across an organization faster than dashboards ever could. But leverage only works if you're still the one steering. That's really what this episode comes down to. Listen in, then decide where AI belongs in your workflow and where it needs to stay out of your head.

Organizations are moving fast on AI. New tools are being piloted. Hackathons are being hosted. Dashboards are lighting up with sentiment data, productivity metrics, collaboration trends, and predictive signals.But most companies are missing the harder question: who owns the output?AI can surface cultural risk, burnout signals, innovation pockets, communication breakdowns, pipeline friction, and brand perception shifts. That part is getting easier by the day. What remains rare is structured accountability for turning those signals into operational change.This conversation challenges HR and executive leaders to rethink AI adoption beyond installation. It explores why many AI initiatives lose momentum after the demo, why insight without ownership becomes noise, and why every meaningful AI deployment requires a defined six-month execution layer tied to measurable outcomes.The future of AI in large organizations won't be defined by model sophistication. It will be defined by whether leaders build the infrastructure, roles, and decision authority required to translate intelligence into behavior change.The tool is not the transformation. The operational discipline behind it is.

In this episode, I talk with Amanda Makulec about what it really takes to design dashboards and data products that people can understand and use. We dig into why so many dashboards fail, how designers and analysts often misjudge their audiences, and what it means to take a truly human-centered approach to data visualization. Amanda shares insights from her work leading the Data Visualization Society and from her book, including practical ways to think about context, cognition, and decision-making. We also discuss common misconceptions about dashboards, stakeholder expectations, and the gap between technical correctness and real-world usefulness. This conversation is packed with ideas for anyone building data tools meant to inform decisions, not just look impressive.Subscribe to the PolicyViz Podcast wherever you get your podcasts.Become a patron of the PolicyViz Podcast for as little as a buck a monthPick up the new book, Dashboards That Deliver.Follow me on Instagram, LinkedIn, Substack, Twitter, Website, YouTubeEmail: jon@policyviz.com

This episode is a re-air of one of our most popular conversations, featuring insights worth revisiting. Thank you for being part of the Data Stack community. Stay up to date with the latest episodes at datastackshow.com.This week on The Data Stack Show, John welcomes back repeat-guest Ben Rogojan, Owner and Data Consultant of Seattle Data Guy. John and Ben discuss the evolving relationship between data teams and businesses, highlighting the challenges of proving value in a cost-conscious environment. Ben explores the impact of technological advancements, the rise of AI, and the critical skills data professionals need to succeed. Key insights include the importance of understanding business context, being proactive, and focusing on delivering tangible outcomes rather than just producing dashboards. Ben also emphasizes the need for data teams to communicate value effectively, show rather than tell, and be willing to take calculated risks. The conversation provides practical advice for data professionals looking to advance their careers, with a focus on developing business skills, understanding organizational needs, creating meaningful impact beyond technical expertise, and so much more. Technical Freelancer Academy & Consulting Community (1:21)Evolution of Data Teams and Technology (2:52)Data Team Growth and Output vs. Outcome (4:47)Internal Optimization vs. Client-Facing Data Work (7:23)Audience, Delivery Mechanisms, and Actionability (12:40)Proving ROI and Prioritizing Work (15:27)Practical Tips for Data Team-Business Alignment (18:31)Dealing with Vanity and Security Blanket Metrics (23:39)AI's Impact on Data Workflows (27:07)BI Tools, AI Integration, and Dashboards (32:25)Top Skills for Data Professionals (37:27)Career Growth: Technical, Communication, and Business Skills (42:02)Show, Don't Tell: Prototyping and Feedback (44:37)Taking Initiative and Risk in Data Roles (50:21)Parting Advice and Closing Thoughts (51:16)The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it's needed to power smarter decisions and better customer experiences. Each week, we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, we explore why high ad performance numbers don't always lead to a profitable business. Matt Raminick, Founder and CEO of Sunnyside, explains how brands can grow themselves into a corner by following the wrong data. He shares why traditional metrics like ROAS can be misleading and how looking at your total bank account balance is the ultimate truth. You will learn how to use better tools to track real profit and why a brand-first approach is the secret to scaling a lifestyle business.Topics discussed in this episode:  Why a 3x ROAS might still mean losing money.How vanity metrics point brands in the wrong direction.What makes MER a cleaner way to track impact.Why ROAS is easy for media buyers to inflate.How to sync Shopify and Meta for better tracking.What "A-plus players" with brand experience offer.How a 12-month forecast ensures future profitability.Why lifestyle brands keep creative close to home.What CFO-grade tools reveal about true contribution. Links & Resources Website: https://www.sunnysidecalifornia.com/LinkedIn: https://www.linkedin.com/in/mattraminick/ Instagram: https://www.instagram.com/sunnysidecaliforniaGet access to more free resources by visiting the show notes at https://tinyurl.com/5enfjcrb______________________________________________________ LOVE THE SHOW? HERE ARE THE NEXT STEPS! Follow the podcast to get every bonus episode. Tap follow now and don't miss out! Rate & Review: Help others discover the show by rating the show on Apple Podcasts at https://tinyurl.com/ecb-apple-podcasts Join our Free Newsletter: https://newsletter.ecommercecoffeebreak.com/ Support The Show On Patreon: https://www.patreon.com/EcommerceCoffeeBreak Partner with us: https://ecommercecoffeebreak.com/partner-with-us/

Chase Warrington, Head of Operations at Doist, joined us on The Modern People Leader to break down how async-first work enables faster decision-making, stronger culture, and scalable operations. We talked about building trust without offices, the systems and rituals behind Doist's execution velocity, and why async workflows are foundational to effective AI adoption.----  Downloadable PDF with top takeaways: https://modernpeopleleader.kit.com/episode280Sponsor Links:

Research is everywhere in HR. Surveys. Dashboards. Trend reports. Predictions for what comes next.  But having more research available does not automatically mean leaders feel more confident using it. HR and compensation teams are under pressure to make decisions that are fair, competitive, and explainable, often while the market feels uncertain and noisy.  In this episode of Comp and Coffee, Ruth Thomas sits down with Stacey Harris from Sapient Insights Group and Amy Stewart from Payscale to move beyond headlines and rankings and talk about what the research is actually telling us right now.  Episode resources: Sapient Insights Group HR Systems Survey Report: https://bit.ly/4rB1zos Payscale 2026 Salary Increase Preview Report: https://bit.ly/3NWIQ8a Payscale Salary Budget Survey: https://bit.ly/4qYwlaD Payscale Pay Trends and Market Pricing Research: https://bit.ly/4kj5hk4 Email: coffee@payscale.com for listener questions and suggestions 

On this episode of the Shared Practices Podcast, George sits down with Dr. Aditi Agarwal, co-founder of Practice by Numbers and practicing dentist, to unpack how AI and data are transforming private practice ownership. Aditi shares the origin story of PBN, practical examples of Operations AI, revenue finding, and call analytics, and why embracing technology is becoming essential for private practices competing with DSOs. They close with a candid look at team-led tech adoption and how to reduce stress for your front office while increasing performance.