Podcasts about hmi

Industrial Talk is talking to Zohar Kantor, Co-Founder at PLCs.ai about "Converting PLC code into plain-language operational intelligence". The conversation highlights the importance of the Barcelona Cybersecurity Congress from November 3-5, 2023, and the need for cybersecurity professionals to attend. Scott Mackenzie emphasizes the significance of industrial professionals telling their stories and leveraging AI to enhance their operations. Zohar Kantor from PLCs AI discusses their platform, which simplifies PLC programming and diagnostics, reducing downtime and improving efficiency. Kantor stresses the importance of trust in AI solutions and the need to empower employees rather than replace them. The platform offers code optimization and documentation, aiming to support manufacturers in maintaining and optimizing their automation systems. Outline Barcelona Cybersecurity Congress Announcement Scott introduces the Barcelona Cybersecurity Congress, emphasizing its importance for cybersecurity professionals.The event is scheduled for November 3-5 in Barcelona, with networking opportunities and expert discussions.Scott mentions their own participation and encourages listeners to mark their calendars.The event aims to provide valuable insights and connections within the cybersecurity industry. Introduction to Industrial Talk Podcast Scott praises industrial professionals for their boldness, bravery, and problem-solving skills.The conversation shifts to Zohar Kantor and his company, PLCs AI, which simplifies PLC programming and provides greater insights.Scott shares their experience teaching PLCs at a technical college and expresses support for Zohar's solution. Importance of Telling Your Story Scott emphasizes the need for companies to tell their stories through AI platforms.They discuss the critical role of storytelling in business success and social presence.Scott shares their experience of countless conversations with industry professionals and the importance of amplifying messages.The conversation highlights the challenges of finding, retaining, and inspiring people in the industry. Challenges in the Industry and the Role of AI Scott identifies people-related challenges as the real impact on the industry, including finding, retaining, and inspiring talent.They discuss the importance of creating a resilient business culture and the role of leaders in inspiring their teams.Scott introduces the concept of a "high beer factor," emphasizing the need for authenticity and relatability in communication.The conversation touches on the importance of persistence and the role of media companies like Industrial Talk in supporting industrial professionals. Zohar Kantor's Background and PLCs AI Zohar Kantor, introduces himself and his company, PLCs AI, which focuses on simplifying PLC programming and providing greater insights.Zohar shares his experience joining a startup in 2017 that brought defect inspection based on AI to industrial manufacturing.He discusses the challenges of integrating AI with legacy PLC technology and the need to simplify the process for engineers.Zohar explains the concept of PLCs AI and its mission to open up the "black box" of PLCs, allowing engineers to understand and diagnose issues more effectively. PLCs AI's Solutions and Use Cases Zohar describes how PLCs AI helps maintenance engineers diagnose and resolve issues by guiding them through the process.The platform allows engineers to query and diagnose issues from the HMI in plain language, reducing downtime and costs.Zohar explains the concept of "explain" and "generate" use cases, where the platform helps engineers understand and resolve issues quickly.The platform also provides recommendations for code optimization and ensures compliance with industry standards. PLCs AI's Impact on New Lines and Commissioning Zohar discusses the role of PLCs AI in the commissioning process, helping manufacturers document and optimize new lines.The platform ensures that documentation is generated throughout the code on a line basis, making it easier for maintenance and operation teams to manage the line over its lifespan.Zohar highlights the importance of documentation in the life cycle of a line, where 85-90% of the costs are incurred in maintenance and retrofitting.The platform aims to support manufacturers in maintaining their brownfield with legacy PLCs, ensuring smooth operations and reducing downtime. Challenges and Opportunities in the Market Zohar identifies the need to gain trust from engineers and experts in the conservative manufacturing industry.He emphasizes the importance of trust in human-to-platform connections and the role of PLCs AI in empowering employees.The conversation touches on the broader impact of AI on the workforce, with a focus on efficiency and productivity rather than job replacement.Zohar shares insights from top-notch manufacturers who approach PLCs AI for solutions to their automation challenges. Conclusion and Contact Information Scott wraps up the conversation, expressing admiration for Zohar's passion and the potential of PLCs AI.Zohar provides contact information for PLCs AI, encouraging listeners to reach out and try the platform.Scott reiterates the importance of telling one's story and the role of Industrial Talk in supporting industrial professionals.The conversation ends with a reminder of the upcoming Barcelona Cybersecurity Congress and the importance of staying connected and informed. If interested in being on the Industrial Talk show, simply contact us and let's have a quick conversation. Finally, get your exclusive free access to the Industrial Academy and a series on “Why You Need To Podcast” for Greater Success in 2026. All links designed for keeping you current in this rapidly changing Industrial Market. Learn! Grow! Enjoy! ZOHAR KANTOR'S CONTACT INFORMATION: Personal LinkedIn: https://www.linkedin.com/in/zohar-kantor/ Company LinkedIn: https://www.linkedin.com/company/plcs-ai/ Company Website: https://www.plcs.ai/ PODCAST VIDEO: https://youtu.be/L7_rht_jTOc THE STRATEGIC REASON "WHY YOU NEED TO PODCAST": OTHER GREAT INDUSTRIAL RESOURCES: NEOM: https://www.neom.com/en-us Hexagon: https://hexagon.com/ Arduino: https://www.arduino.cc/ Fictiv: https://www.fictiv.com/ Hitachi Vantara: https://www.hitachivantara.com/en-us/home.html Industrial Marketing Solutions:  https://industrialtalk.com/industrial-marketing/ Industrial Academy: https://industrialtalk.com/industrial-academy/ Industrial Dojo: https://industrialtalk.com/industrial_dojo/ We the 15: https://www.wethe15.org/ YOUR INDUSTRIAL DIGITAL TOOLBOX: LifterLMS: Get One Month Free for $1 – https://lifterlms.com/ Active Campaign: Active Campaign Link Social Jukebox: https://www.socialjukebox.com/ Business Beatitude the Book Do you desire a more joy-filled, deeply-enduring sense of accomplishment and success? 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When hurricanes, tornadoes, ice storms, and other catastrophic (CAT) events strike, tree removal becomes more complex than clearing a single fallen tree. In this episode of The Savvy Adjuster Podcast, host Chris Nichols is joined by HMI Senior Vice President Doug Cowles and HMI Director of Program Services Gretchen Piechottka to discuss how CAT events affect emergency tree removal operations, response timelines, accessibility, equipment needs, and pricing. Discussed in This Episode How CAT events could impact emergency tree removal and create dangerous tree removal scenarios Considerations for tree damage cleanup and removal affected by flooding, damaged infrastructure, and claim volume Why tree species, terrain, and regional differences influence crew and equipment requirements, such as needing cranes and aerial lifts Why emergency tree removal costs often rise following catastrophic events and how to distinguish legitimate increases from potentially inflated pricing How invoice reviews help evaluate tree removal pricing after services have been completed Real-world stories from major CAT events, including hurricanes and ice storms HMI's invoice-review services are technical and informational only. HMI does not determine coverage, establish the amount payable under a policy, or negotiate settlements on behalf of the carrier, insured, or contractor. Additional Resources Alpine Intel Resource Library: https://bit.ly/4gldQuK HMI: https://bit.ly/44dJDqj More Tree Removal Resources Guide: HMI's 2026 Tree Removal Cost Guide: https://bit.ly/4wwIzK9 Article: Understanding Tree and Debris Removal Costs https://alpineintel.com/resource/understanding-tree-and-debris-removal-costs/ Article: 4 Qualities Insurance Carriers Need in a Tree Service Partner https://alpineintel.com/resource/4-qualities-insurance-carriers-need-in-a-tree-service-partner/ Article: Handling Tree Claims: What Adjusters Can Expect https://alpineintel.com/resource/handling-tree-claims-what-adjusters-can-expect/ Guide: How Weather-Related Perils Contribute to Tree Damage https://alpineintel.com/resource/how-weather-related-perils-contribute-to-tree-damage-guide/

Podcast: PrOTect It All (LS 27 · TOP 10% what is this?)Episode: OT Pen Testing: Why Trust, Culture & Hands-On Experience Matter MostPub date: 2026-08-03Get Podcast Transcript →powered by Listen411 - fast audio-to-text and summarizationEffective OT penetration testing isn't just about finding vulnerabilities, it's about building trust. In this episode of Protect It All, host Aaron Crow is joined by Oren Niskin and Reynaldo Gonzalez for an engaging discussion on the evolving role of penetration testing in operational technology (OT) environments. Drawing from years of experience in industrial operations, IT, and OT cybersecurity, Oren and Reynaldo explain why successful OT security assessments require far more than technical expertise. They explore how trust, communication, and collaboration help bridge the gap between security teams and operations, making penetration testing a valuable business tool rather than something to fear. The conversation also highlights the importance of diverse career paths, hands-on learning, and mentoring the next generation of cybersecurity professionals. From network segmentation and risk communication to creating realistic lab environments, this episode offers practical lessons for anyone responsible for securing critical infrastructure. Key Learnings:  Why trust is essential for successful OT penetration testing How IT and operational experience strengthen OT security teams The value of network segmentation in reducing cyber risk Why "doing nothing" is no longer an acceptable cybersecurity strategy How organizations can bridge the gap between safety, operations, and security Practical ways to develop the next generation of OT cybersecurity professionals Key Moments:  07:00 Oren's technology and security approach 15:04 Discussing network security measures 19:16 Building Trust in Business Relationships 26:17 Understanding and planning security architecture 30:16 Network management and rule assessment 33:39 Managing and Assessing Tool Overload 38:39 Analyzing tool overlap and complementarity 48:41 Building a trusting team culture 49:48 Encouraging Open Communication 56:34 Getting into cybersecurity without IT experience 01:04:56 Entry-level ICS training courses Whether you're an OT engineer, penetration tester, security leader, or just beginning your cybersecurity journey, this episode delivers practical insights into protecting industrial environments while building stronger relationships across teams. Tune in to discover why the most successful OT penetration tests don't just identify vulnerabilities they build trust, strengthen teams, and improve security for the long term. About the guests:  Oren Niskin is Principal OT Security Engineer at GuidePoint Security. His 20-year arc runs from the US Navy as a Nuclear Electrician's Mate, to Electronics Technician on offshore drilling rigs, to office OT/IT management, to OT cybersecurity consulting at EY, and now into OT cyber engineering at GuidePoint. He also runs Packets Or It Didn't Happen, a YouTube livestream where he builds a factory-realistic PLC and HMI training kit on camera (the open-source PLC Trainer Kit), aimed at helping newcomers get hands-on with OT for under $500. Link to connect Oren Niskin: LinkedIn: https://www.linkedin.com/in/orenniskin/ YouTube: https://www.youtube.com/@PacketsOrItDidntHappen PLC Trainer Kit (GitHub): https://github.com/oniskin/PLC-Trainer-Kit LinkedIn Group: https://www.linkedin.com/groups/17708001/ Reynaldo Gonzalez is a recognized cybersecurity thought leader with 18+ years of experience helping organizations strengthen cyber resilience and secure critical infrastructure. His expertise spans cybersecurity strategy, IT/OT security, AI security, risk management, and digital transformation, complemented by a passion for education, innovation, and industry collaboration. Link to connect Reynaldo Gonzalez:  LinkedIn: https://www.linkedin.com/in/reynaldoglz/ Learn more about PrOTect IT All: Email: info@protectitall.co  Website: https://protectitallpod.com/ep117 X: https://twitter.com/protectitall  YouTube: https://www.youtube.com/@PrOTectITAll  FaceBook:  https://facebook.com/protectitallpodcast To be a guest or suggest a guest/episode, please email us at info@protectitall.co Please leave us a review on Apple/Spotify Podcasts: Apple   - https://podcasts.apple.com/us/podcast/protect-it-all/id1727211124 Spotify - https://open.spotify.com/show/1Vvi0euj3rE8xObK0yvYi4The podcast and artwork embedded on this page are from Aaron Crow | Operational Technology & Cybersecurity Host, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.

Çetin Ünsalan'ın hazırlayıp sunduğu Reel Piyasalar programına Mitsubishi Electric Türkiye Fabrika Otomasyon Sistemleri SCADA ve HMI Ürün Müdürü Burcu Çöpür konuk oldu.

Çetin Ünsalan'ın hazırlayıp sunduğu Reel Piyasalar programına Mitsubishi Electric Türkiye Fabrika Otomasyon Sistemleri SCADA ve HMI Ürün Müdürü Burcu Çöpür konuk oldu.

While many astrologers dream of being on television, Edward O stumbled into the spotlight while trying to get his brother — a doctor, newly arrived in the US — airtime on local TV stations.Over the next few years he'd take on one TV astrology role after another, working a series of day jobs and spending his nights poring over astrology books at the famed Bodhi Tree Bookstore in West Hollywood.In 2005 all that reading found an international audience: he was hired as cohost and astrologer for 12 Corazones, Telemundo's astrological dating show. For a decade he was on a mission to show the world astrology is about more than sun signs — pulling, by his own estimate, some 41,000 charts along the way and creating a signature style of reading that expanded beyond astrology to include palmistry and graphology.I knew none of this until Edward O reached out to me after attending the LA Astro Fest Kick-Off party.Fate works in mysterious ways indeed.Edward O recently joined me to TALKTALKTALK an astrological life well lived, where we explore all sorts of terrain — ghosts, ancestry, relationship astrology, Shakira and the Royal Family. May this be the first of many wild conversations!Support the Podcast & Learn Astrology!Ready to take a deep dive into astrology? I invite you to join⁠ Club Astro⁠ by becoming a paid subscriber to ART of the ZODIAC on Substack.Your membership offers:Exclusive Cohort: Access to a dedicated community of astro seekers.Weekly ZOOM Sessions: Bring your chart, ask me questions directly, and connect with fellow astrology enthusiasts in intimate gatherings.Secret Invites & Discounts: Including discounted tickets to online workshops, LA Astro Fest, and my monthly in-person gathering, The Los Angeles Astro Salon.More than just benefits, your membership directly supports me and this work, allowing me to continue creating content like this.Join Club Astro here:⁠ https://vivihenriette.memberful.com/⁠Other Ways to Support (No Funds Required!)Even if you can't join Club Astro right now, your time and listenership are invaluable. If you enjoy this work, please consider:Tell a friend: Word-of-mouth is incredibly helpful!Leave a review: A review wherever you listen to podcasts truly helps new listeners find the showAbout Edward OEdward O is a Nicaraguan astrologer based in Los Angeles. Born in Managua, he emigrated to Glendale at twelve and came to astrology sideways — working in the medical field by day, haunting the astrology section of the Bodhi Tree by night, with the rest of his education supplied by dating and long-term relationships. He studied with Zip Dobyns and Marion March and spent time at the Philosophical Research Society, and holds a BA in Latin American Studies, an MA in Spiritual Psychology from USM, and a clinical hypnotherapy certification from HMI. His television career spans four decades, beginning with an astrotherapy segment he pitched to a morning news program and expanding to networks across the U.S. and abroad. For ten years he co-hosted Telemundo's 12 Corazones, casting roughly 41,000 quick charts and teaching a mass audience to care about the ascendant, Moon, Venus, and Mars.Follow him on IG at @Edward_o_astrocoachAbout ViviVivi Henriette is an LA-based astrologer and tarot reader whose practice centers on storytelling, mythology, and collaborative divination. She creates a space for clients to reclaim their personal narratives through the lens of ancient archetypes. Vivi produces⁠ LA Astro Fest⁠, hosts the Los Angeles Astro Salon, and is the creator of the podcast⁠ TalkTalkTalk⁠. You can find her weekly writing on ritual and meaning right here on⁠ ART of the ZODIAC⁠.

After several weeks away from the podcast, Dave Griffith and Vladimir Romanov return to Manufacturing Hub to unpack their experiences at Automate 2026 and discuss what the event revealed about the current state of industrial automation.Automate showcased an enormous range of robotics, industrial AI, machine vision, software, cloud connectivity, and emerging automation technology. However, some of the most revealing conversations were not about futuristic factories. They were about PLC 5 migrations, aging SLC systems, obsolete PanelView terminals, industrial networks, basic data collection, and how platforms such as Ignition actually connect to plant floor equipment.Vlad shares what he learned from demonstrating a complete packaging line environment built around a CompactLogix PLC, an industrial computer, Ignition, and production performance data. The demonstration was designed to show how manufacturers can use OEE, downtime information, and machine states to identify production bottlenecks and determine where capital investment could deliver the greatest return. Instead, many attendees wanted to understand the underlying architecture, where Ignition runs, how it connects to PLCs, what protocols are required, and whether it can replace traditional HMI and SCADA platforms.Dave discusses his experience inside the Ignition ecosystem booth, the FactoryStack cloud demonstration, the advantages of MQTT in a difficult trade show network environment, and his Automate panel on software defined automation and the factory of the future. He also introduces Elephant, an industrial log analysis and contextualization tool being developed to help users identify meaningful patterns across Ignition gateways, reduce system noise, compare facilities, and diagnose intermittent problems.The conversation then moves across the industrial automation stack. Dave and Vlad examine virtual PLCs from Siemens, Phoenix Contact, and other vendors, including where software based control may provide value and where it may add unnecessary organizational complexity. They discuss AI generated PLC code, the limitations of translating functional specifications into reliable control applications, and why tools that produce 80 or 90 percent of an automation solution still require experienced engineers to validate the final result.They also explore AI assisted HMI and SCADA development, Ignition 8.3, MCP servers, high performance HMI design, and the risks of providing AI agents with uncontrolled access to production systems. At the MES layer, they question whether manufacturers should build custom applications through vibe coding or focus instead on creating clean, contextualized, well governed data that can support many future applications.The central conclusion is that AI tools, virtual controllers, cloud platforms, and dynamically generated applications will continue to improve. However, manufacturers still need reliable controls, secure networks, maintainable architectures, experienced people, and ownership of their operational data. The companies that establish those foundations today will have the greatest freedom to adopt whatever technologies emerge next.Dave and Vlad also preview the 2026 Ignition Community Conference in Sacramento, upcoming Manufacturing Hub conversations, new demonstrations, and several projects the community will see throughout the remainder of the year.Join us for a detailed and candid discussion about Automate 2026, Ignition, industrial AI, virtual PLCs, HMI and SCADA development, MES, MQTT, data architecture, and what manufacturers should prioritize next.

Die Car.HMI in Berlin gilt seit Jahren als die wichtigste Konferenz für Automotive Human-Machine Interfaces. 2026 stand nicht das nächste Display, die nächste Interaktionsoberfläche oder die nächste KI-Demo im Mittelpunkt. Stattdessen zeichnet sich ein grundlegender Paradigmenwechsel ab: Fahrzeuge entwickeln sich von isolierten Produkten zu intelligent vernetzten Ökosystemen. Orchestrierung wird zur neuen Designdisziplin, KI übernimmt zunehmend die Rolle eines unsichtbaren Dirigenten und Human-Centered Design wird strategischer denn je. In dieser Folge ordne ich die wichtigsten Trends der Car.HMI 2026 ein und beantworte die Frage, warum die Zukunft des Automotive HMI nicht auf dem Bildschirm entschieden wird, sondern im intelligenten Zusammenspiel von Mensch, Fahrzeug, Software und digitalen Diensten. Außerdem gibt es einen persönlichen Blick hinter die Kulissen der Konferenz und meine fünf wichtigsten Takeaways für alle, die sich mit der Zukunft von Human-Machine Interfaces beschäftigen.

https://youtu.be/Ji1OZYu1r1Y Charles Fry, Founder and CEO of CODE Éxitos, is helping businesses hire AI in enterprise to transform software engineering, modernize product development, and build intelligent connected systems.  In this conversation, Charles introduces The Agentic Org Chart Framework: Hire Systems Thinker, Look for Failed Entrepreneurs, and Have AI Replace “Trade Skills”. He explains why AI is fundamentally changing software development, how organizations must redesign their structures for an AI-first workforce, and why systems thinking is becoming more valuable than technical specialization. Charles also discusses how the rise of agentic software development is reshaping the future of SaaS, why combining AI with connected hardware creates a stronger competitive advantage, and what business leaders must do to successfully navigate AI-driven transformation. — Hire AI in Enterprises with Charles Fry  Good day. Steve Preda here, and I’m talking with Charles Fry, the Founder and CEO of CODE Éxitos, building cyber-physical systems for mid-market and enterprise companies, as well as full-stack web, mobile, and SaaS development. Charles, welcome back to the show.  Hey, I’m happy to be here. It’s always great to see you, and I’m looking forward to our chat. Yeah. It’s so interesting to talk to you because the last time we had you on the show, three or four years ago, it was still before the AI age was fully upon us.  Right.  Your business was kind of a different business. I’ve been following you on LinkedIn, and I see that you’ve evolved your approach, and now you’re an AI-first company. So tell me a little bit about how that came about, this whole evolution, and how you found your new focus?  Yeah. Wow. It’s been that long since we were on the show. It was a lot of fun, but here we are. You’re right. AI really sort of came out of left field. I’ll skip all the technical things that suddenly made AI an achievable thing. But really, at the beginning of 2024—and somebody can fact-check my timeline—ChatGPT, if you were aware of it, was kind of passing the Turing test. It was giving pretty reasonable answers to natural-language questions.  And we were like, “Wow, this is interesting.” At first, we were helping our clients think about how to build those capabilities into their products, something we still do.Share on X But by mid-’24, late ’24, it became pretty obvious that one of the best applications of large language models—and expert systems, we used to call them that—is writing software. And so by early to mid-’25, the systems were suddenly not novelties. They were credible at what they were doing, and they were gaining momentum in the quality and credibility of the software they were producing.  Now, CODE Éxitos was started largely to create an opportunity for entrepreneurs and enterprises that needed, let’s call it, garden-variety, well-done software. We built that through the Americas to arbitrage labor rates in Latin America and leverage the time zones. So it was essentially an offshore, blended, hybrid-team model. Honestly, by the middle of 2025, the AI tools for software engineering were as good as, or better than, 50 percent of the human developers that we employed.  And at some point, as a business owner, when you’re out there representing yourself, and your product is yourself, and you’re representing that to clients, you have a moral and ethical obligation to say, “Hey, I think I can still give you the best product that you’re looking for, but I’m going to do it in a different way.” So beginning in late 2025, we were hard into the pivot. Today, all of our software development is done agentically. There are still people. It’s not a complete dark factory. But our mid-level performers and below, we exited them from the business, which caused a lot of human turmoil. I mean, we were at around 100 people. A lot of human turmoil. Our clients were going through the same thing. We were watching what was happening in their organizations and what the leadership demands were.  We just made the decision to lean into it. And here we are, almost mid-’26 now, and it’s actually going really well. Now, AI, of course, anyone who opens an internet browser anymore sees it. AI is everywhere. You read the newspaper. AI is going to change everything.  Every application has an AI layer to it.  Yeah.  Right? Every SaaS application has a button that says, “Use AI here.”  My view, at least—and these are the things people should probably give me some credibility on—I’m going to keep my views focused on how AI applies to the software industry, my industry, and its direct impact. We see, and we have clients working on, things like AI in customer service, the legal department, and the finance department. We do all of those things internally—agentic first, AI first. But those really aren’t my industry. I’m not ready to make a sweeping prognosis about how AI is going to change capitalism in the United States. But in the software industry, it’s a fundamental change. And it’s not done yet.  So what’s your vision? Where is everything going? What’s it going to look like three years from now?  Well, I’m not smart enough to know that. But I think the patterns we've seen—and again, within the world of writing software, and making that a broad category of activitiesShare on X —are going to continue to consolidate and converge to where humans are important, but they might be only 10 to 20 percent of the input to the process. I really do think we’re going to see a day, sometime in the three-to-five-year time horizon, where a large amount of software will be written and managed by other software systems.  There’s no technical reason to prevent that. For example, I was talking to one of our clients today, a CIO at a great company. A couple hundred million dollars in revenue. A really well-run business. A sizable internal IT team. But there’s a lot of ongoing maintenance and attention required. Building the software is just the beginning of a five-to-ten-year life cycle. So I think in the near term we’re going to see that building the software becomes, “Okay, we got that figured out.” That’s a largely solved problem.  We’ll then progress to the problem of: “Hey, this software has been in production for five years.” “It needs updates.” “It needs attention.” “It needs maintenance.” “It needs to scale.” More software systems will take care of that. Fewer and fewer humans will be involved in that kind of work. So I think that’s where the software industry is headed. I think it’s going to be 60 to 80 percent smaller in human capital than it is today. Sometime soon. Yeah. I really do think it’s about as close as I want to get to calling it an extinction event. Let’s put it that way. Some people say SaaS companies are going to go out of business, and it’s all going to be agents running around doing things for us. But other people say SaaS companies are actually the SOP for whatever activity is out there, and you need that structure. The SaaS application provides that structure. You don’t want agents running in an unstructured way. You’d rather have these SaaS applications. What’s your view?  I think that’s a good way of looking at it. If you’re a dinosaur like I am, back in the late 1980s or early 1990s, when you wrote software for a company, everything was custom software because there were no packaged software products, no SaaS platforms. But over the last 20 years, I think SaaS companies have become, for a lot of businesses, exactly what you said. They’re the embodiment of best practices. If you take something like HubSpot, which I’m sure you and many of your audience are familiar with, you really don’t need to customize it.  You just need to follow its baseline processes because they have thousands of customers who have helped refine the sales motions that work. So I think there’s some truth to that. The problem SaaS systems face is that the barrier to competitive entry is much, much lower. If you look at a company like Salesforce—and I think I’ve led three different Salesforce deployments back when I was a CIO or CTO—that software really shows its age. It’s layers and layers of complexity built to serve a wide audience. It’s great. It’s expensive. Emerging companies don’t need that. They can effectively vibe-code their own CRM system, and it works. I think the threat for big SaaS companies is twofold.  One is that the next generation of customers is going to onboard very differently into those systems than the previous generation.Share on X I don’t know what that onboarding ramp is going to look like. The second problem is there’s very little defensibility in having a pure software product. And the other part of our intro—you talked about cyber-physical systems. We’re spending more and more of our product development cycles on hardware-related products, things that have a nexus in the physical world. Here’s a good example. I’m wearing one of these health rings. This Oura Ring. Oura, yeah.  There’s a lot of amazing hardware in here that justifies my monthly subscription for the app. The app we could recreate pretty easily. But the development, manufacturing, and distribution of this physical item create a much higher barrier for a competitor to overcome. So more and more of our clients are companies that have a physical product they either want to make smarter or make more connected.  That’s really what it comes down to. And that’s a pretty exciting space. But for a pure-play SaaS company, I think it’s going to get tough. The competitive pressure is going to be intense. And the barrier to entry is going to be really low. It’s not even about engineering cost anymore because the cost of engineering has dropped so much with AI. It’s almost like it went full circle. You had all these product businesses that wanted to become service businesses to create recurring revenue. And now the service businesses—the SaaS businesses—want to become product businesses to create a barrier to entry, improve retention, or reduce disruption. Isn’t that interesting?  Yeah. I hadn’t thought about it exactly that way. Maybe the pushback would be that these professional services businesses wanted to have a technology play or a platform. That’s interesting. But I think we’re going to see AI, at least in technology, continue to lower the barrier to entry. It will allow much faster experimentation with pure software ideas. And we’re focused on the things where the AI robots can’t play. They’re not going to cut your grass.  They might guide the machine that cuts your grass, but they’re not going to cut your grass. So I think that while the turmoil is still sorting itself out in the pure software world, we’re going to see a whole new set of opportunities open up. We’ll be able to build truly smart devices. Devices that think for themselves. Devices that are aware of the world around them. They can participate with us in our day-to-day work. That’ll be a lot of fun. I think we still have some rough sailing ahead of us as AI sorts itself out.  Isn’t it true that people prefer to interact with a purpose-designed device rather than a software product? And maybe an app is kind of a device that is software, or maybe that’s the overlap there. But I know there are some things I’d rather have on my phone, even though it’s complicated because there are so many other things on it. But if I have a single-purpose device, like you have your Oura Ring, it’s easier to interact with. There’s no complexity, and then it lowers the accessibility.  Yeah. An area of active study is something called HMI, or Human-Machine Interface. Again, back in the ’80s and ’90s, it meant things like: Are the buttons big enough for someone to push? Does a red light always mean a bad thing, and a green light always mean a good thing? But now that’s expanded into the kind of research in psychology and sociology that you’re talking about, Steve. Some of that is really amazing.  I’m sure you’ve seen them, and your audience has seen them. You can find these videos on YouTube. There are humanoid robots. It took a while for researchers to figure out that a robot doesn’t actually need a head. It can have what is essentially a torso with arms and legs. The head doesn’t really need to be there. But a robot with no head freaks people out. Yes. People don’t like it. So the robotics engineers put heads on them. Then they thought, “Well, if we’ve got a head here, we’ll just put a face on it.” But if the face is too realistic, it gives people the creeps.  Yeah.  So people didn’t like faces on them. If you look at the current generation of humanoid autonomous robots, they have these, I don’t know, sort of pseudo-faces. They kind of look like Halloween jack-o’-lanterns or something. They’re not scary, but they’re somewhere in the middle. Anyway, the things you’re talking about are really fascinating.  I mean, Isaac Asimov wrote about humanoid robots and all the challenges that come with them. What happens when they have a human-like appearance? What happens when people think they are actually people, but they just don’t age? All those things have been explored. But listen, I’d like to switch gears here and ask you this. Right now, in this AI age, what drives your business? What drives growth in your business?  This part is truly fascinating to me. Everyone is working off the same timeline now, which isn’t a very long timeline. We don’t have a lot of experience to draw on. Much more quickly than when the internet became commercially available—I was there when that happened too— the adoption of AI as a fundamental change happened in a matter of months, compared to several years for the internet.Share on X Some people also compare it to the adoption of mobile phones, which you mentioned.  But this has happened very fast. A year ago, we were talking to sophisticated technical buyers who said, “Yeah, I’m still on the fence about whether I like agentic software development.” That doesn’t happen anymore. Everybody says, “Yeah, we’re using it too.” What we’re seeing now is that it’s evolved so quickly and had such a fundamental impact that people don’t know not only how to manage it inside their business, but also how to deploy it. It’s really disruptive. And this is where you’re a pro.  It’s really disruptive to organizations. So in less than a year, we’ve gone from, “Hey, should I let my developers use AI?” to now everybody using AI. And the leading teams, including ours, can produce high-quality commercial code faster than organizations can absorb it, and faster than org charts can adapt to the change. Our engineering teams have to adapt to the pace of the business, not the other way around. Because we get clients saying, “Hey, you guys are producing too much.” “We can’t check everything.”  “We haven’t finished testing last week’s new features and capabilities yet.” “We can’t take another batch of features and capabilities this week.” So we’re seeing a lot of organizational behavior change starting to come out of this. When we’re talking to C-level executives and senior leaders, that’s where most of the conversations are today. “How do I retool my organization to capture the benefits?”  Yeah. Because what I see is that the more AI you apply, the faster decision velocity becomes. And the complexity of understanding the whole picture, connecting the dots, increases. So you need a different kind of person who can operate at that higher level of contextualization. Do you see the same thing?  We do. Software engineering and product development had matured into a pretty predictable set of job descriptions and capabilities. The business processes were really well worn. We knew what the product owner did. We knew what a project manager did. We knew what a tech lead did. Et cetera, et cetera. A lot of these, frankly, became trade skills. “Hey, I’m really good at writing code.” Or, “I’m really good at doing QA, but I’m not really a systems thinker.” “I’m not an entrepreneur.” “I’m not a creator.” Pick your fuzzy lens of choice.  That’s really what AI displaces right now. AI displaces those trade skills and, frankly, does them better and cheaper. There’s no way to dispute that. What we look for now, and I think where the trend is going with our clients, is systems thinkers. We have enough agentic tooling built on our own internal platform that the people operating and building products for our clientsShare on X —we refer to our team as digital creators—come from a variety of backgrounds. You don’t have to be a computer science major. You do have to have some domain experience. You do have to be a systems thinker. You do have to understand what business value you’re trying to create. But as far as actually writing really good code, nobody’s really doing that now. It’s being done automatically.  If you have a couple of gray hairs like I do, you’ll remember back 20 years ago when we talked about the war for talent. That’s what everybody was looking for. They wanted people with these highly specialized engineering skills. I think there’s a new war for talent. It’s going to be harder to pin down because we’re going to be looking for whole-systems thinkers as opposed to technical specialists. Because AI will be the technical specialist we need, regardless of the business domain. So what do you do to infuse systems thinking in your business?  Wow. I wish I had a good answer for that. I don’t even know how to recruit these kinds of people right now. I’ll be that candid with you and your listeners. I was talking to a couple of my senior people, and I said, “Maybe we should go look for failed entrepreneurs.” Which is kind of a heretical thing to say. But as an entrepreneur, I know firsthand that it doesn’t always work. The fact that the business fails doesn’t necessarily mean you, as an entrepreneur, are a personal failure.  Entrepreneurs are about the only, I don’t know, primary source I can think of for people who have done a little bit of everything. They’re systems thinkers. Maybe they didn’t get it right, but they could. So we talked about that. I don’t know if Disney still does it, but Disney was phenomenal at producing these kinds of people through its internal training programs. We’re not big enough to compete with Disney. But to answer your question, how do we teach it? I can’t honestly say that we do. Because we’re still figuring out what it is that we would even teach.  Well, it’s a new type of SOP that’s needed in the business. So what are the best practices for building an AI workforce? That still needs to be defined.  Yeah. For larger organizations, our clients that are running larger organizations have the same problem. All of a sudden, their org chart is broken. What I mean by that is, if you look at the way we’ve traditionally built and scaled businesses, you have this pretty large cadre of managers who give you what Eliyahu Goldratt called the span of control, your degree of leverage. When you’re younger, you hear things like, “Ah, my manager doesn’t even do anything.” You’ve probably heard that before. “Oh, my manager doesn’t really do any work.” “He just comes in and bugs me.”  It’s not entirely wrong because we rely on that manager’s experience to be spread across six, eight, or ten other people and supervise their work. So managers don’t really do a whole lot of delivering the work themselves. I think AI is going to change that. I know AI is already changing that inside technical teams. All of a sudden, we have clients saying, “My org chart doesn’t translate to the way my business operates under this new agentic model.” That’s problem number one. Problem number two is, “I have people on my team who are good people and good contributors, but there’s no box for them in the new org chart that I think works with an agentic workflow.” Does that make sense?  Yeah.  So two things have happened suddenly. We see a lot of press—although I think it’s moderating a little bit now—about how kids coming out of university are having trouble getting entry-level jobs. True enough. I think the other area where org charts are collapsing is managers who don’t actually produce any output. Supervising other managers and compiling the weekly report of reports just isn’t a valuable job function, even at the best of times. And now—I hate to say this—but it’s useless. So I think there’s a lot more work we’re going to have to do with our clients, and ourselves, on what an org chart should look like. And what the expectations are for people doing work inside a company that’s moving aggressively toward leveraging AI capabilities in what we would normally call white-collar job functions. Yeah. Some years ago, I thought about the org chart being broken. The top-down hierarchical org chart—I think it’s completely broken. In my head, the org chart is more like an amoeba, where you have the entrepreneur and the manager in the middle as yin and yang. Different departments report to different people. And you’ve got these pizza teams all over, actually delivering teamwork. But that’s fascinating. Yeah, fascinating topic. So who are the ideal customers for you? If they have the right kind of projects, what are the right kinds of customers and the right kinds of projects for CODE Éxitos that would be interesting to look at?  There are two types of clients that we focus on and that we can help quite a bit. The first type of client is one that has a large, established internal software development and product development process, and they’re trying to figure out how to adapt, modernize it, and harness AI. We can come in, and we have a very opinionated point of view. We can have a couple of conversations, and they either like the direction our telescope is pointed in and want us to help, or they say, “No, we think we’re going to do it a different way.”  And that’s okay, too. Because right now, nobody really knows the final answer. We call those engineering transformation projects. Somebody says, “Hey, we have a team. Can you help us get better?” The second type of client we like is one that has this physical connection challenge. I’ll give you an example. We have a client that primarily makes pumps and motors. They put those pumps and motors into very specific industrial applications across North America. They wanted those pumps and motors connected to a network.  They wanted to collect data from those pumps and motors to help their customers. Once we built the data collection, the electronics, and the connectivity, the data started coming in. Now there are a lot of AI-related things we can do with that data. We’re beginning to work on what you can think of as supervisory agents that watch what’s going on. They’re much more robust than the old filters that just looked for exceptions and red lights. Those are the kinds of clients we really help on the product side.  Sometimes they come to us with an idea scribbled on a napkin. It’s like, “Hey, we have this system or this process that we envision, and we need somebody to help us build it.” We’ll do the electronics, the AI, and the software engineering. And that becomes a complete system. So, more systems thinking. Yeah. And then you combine software with hardware. Then you have AI agents doing much of the coding, management, and maintenance of these systems.  Yeah, that’s right. This is not a client of ours, by the way, but the story I’m going to tell is fascinating. It’s a second-degree connection that I chatted with. He runs a $100 million-a-year manufacturing company. I think he’s second or third generation—I can’t remember which. He knows the business. He grew up in the industry. He’s in the process of transforming the company so that he can basically run the whole business from his phone.  He’s applying AI to his internal business functions like finance and accounting. He’s done some really amazing stuff. He’s automating the manufacturing process, the machines, and the feedback systems. It’s just stunning what this guy is already able to do. He just picks up his phone and says, “Yeah, I want to run my company from here.” I think he’s going to be able to do it. I think we’re going to see more of that emerge. Yeah. That’s fascinating. The race is for the first one-person unicorn, right?  I think that’s already happened. I don’t know if you’ve seen this. I can send it to you. There was a New York Times article a couple of months ago about a guy—not a tech guy, a marketing guy. He and his brother run an online company that generates $1.8 billion a year in sales. And it’s just the two of them. Wow!  Spoiler alert: As I remember, they sell weight-loss drugs. He’s a marketing guy with a tech background. He figured out all these marketing channels where, if you order Ozempic or whatever online, it basically drop-ships from the pharmaceutical company to you, and he gets a cut. But he said in the article, “At one point, we were doing $300 million a month in sales.” He goes, “Well, technically, I’m not a one-man company because I had to hire my brother to help me out.” So it’s the two of them.  That’s pretty great. Yeah. That’s definitely a unicorn. So if you’re listening to this and you have an enterprise company, and you want to harness AI in your business, create agentic systems, streamline your operations, and make your company more efficient and productive, then reach out to Charles Fry at CODE Éxitos. Any last thoughts for listeners who are thinking about building a business in the AI age? What advice would you give them? I don’t know that it’s changed a whole lot. Building a business is always hard work. For any listener who wants to chat about any of these topics or see if we’re the right fit, they can always reach out and contact me. The conversation is free, and I usually learn something from it. But no, I would say that things are different. It doesn’t mean they’re wrong or better. I think they’re just different. It’ll be interesting to see how all of this unfolds over the next few years.  Yeah.  I’m in it for the journey.  We’re living in interesting times. So, Charles Fry, Founder and CEO of CODE Éxitos, thanks for coming on the show. And if you enjoyed this show, make sure you follow us on YouTube and Apple Podcasts. Give us a review, and stay tuned because every week I bring an amazing entrepreneur onto the show. Thanks for coming. Thanks for listening.  Thanks, Steve. Important Links: Charles's LinkedIn Charles's Website

Chinesische Automobilhersteller erobern den europäischen Markt und mit ihren Fahrzeugen kommen neue HMI-Konzepte nach Europa. In dieser Folge schaue ich hinter Displays, Sprachassistenten und digitale Ökosysteme und diskutieren, warum Nutzer in Shanghai oft andere Erwartungen an Technologie haben als Nutzer in Stuttgart oder Paris. Wir sprechen über Cultural UX, Vertrauen, Transparenz, Automatisierung und die Frage, warum Human-Centered Design immer auch den kulturellen Kontext berücksichtigen muss. Denn die Zukunft erfolgreicher HMIs liegt nicht in globalen Einheitslösungen, sondern in Technologien, die sich lokal menschgerecht anfühlen.

What if the biggest bottleneck in hardware design isn't your engineer's skill — it's the sheer volume of manual work standing between a great idea and a working schematic? And once you decide to embrace AI-assisted design, how do you make sure the output is actually trustworthy enough to build from? What you'll learn… (00:12) Why fragmented workflows hit SMB hardware teams hardest (02:39) The real cost of going from requirements to prototype without specialist support (07:33) How functional block-level design changes early decisions — including when a SOM beats building from scratch (12:04) Why system-level abstraction catches wrong-path decisions before they reach the schematic (14:39) The "rubber duck debugging" effect: how AI clarifies requirements before they become costly mistakes (17:54) The biggest AI misconception in hardware design — and why the engineer must own every decision (20:30) How CELUS's NXP collaboration delivers manufacturer-validated, human-in-the-loop solutions (25:05) Why abstraction-first tools help SMBs take on projects that would otherwise be out of reach (28:19) The CELUS Success Program: high-touch onboarding for SMBs on the Siemens instance More about the episode… In this episode of the Printed Circuit Podcast, host Steph Chavez welcomes back Antonio Becerra Esteban, VP of Customer Success at CELUS — a physicist-turned-engineer with experience at Infineon and Altium who now leads the team ensuring customers extract real value from the platform. The conversation tackles the fragmented, manual journey from requirements to schematic that burdens small hardware teams. Antonio explains how CELUS's functional block-level design approach lets engineers define system architectures, navigate component trade-offs with an AI assistant, and output fully-interconnected schematics — illustrating the point with a Linux-based HMI example where the right abstraction layer turns a complex MPU build into a simple SOM selection. On the trust question, Antonio is direct: the engineer must own every decision. CELUS backs this up with manufacturer-validated design blocks, transparent sourcing, and a human-in-the-loop process — putting engineers in the driving seat rather than asking them to ship whatever the model produces. SMBs can join CELUS' Success Program by sending an email to cs@celus.io. Connect with Steph Chavez: LinkedIn Website Connect with Antonio Becerra Esteban: LinkedIn CELUS Website

In this Fluid Power Forum episode, NFPA host Eric Lanke shares a recorded NFPA webinar on advancing technologies for controls, IoT, and data—featuring Scanreco Director of Sales Andy Gray.   Gray explains how Scanreco's professional radio remote controls serve OEMs and system integrators for mobile and industrial machinery in harsh environments, translating operator intent into hydraulic actuator motion from simple on/off to highly proportional control to improve safety, precision, and productivity. Discussion covers operating at safer distances, integrating camera feedback and Human Machine Interface, HMI, information onto remotes, and the growing role of assisted operation and autonomy across applications like agriculture, forklifts, drilling, demolition, forestry, and fire equipment.   Gray describes field research with operators, integration via CAN and software tools, OEM preference for open integration ecosystems, and progress to unify remote control, advanced displays, and onboard computing amid trends of digitalization, electrification, automation, and usability. Subscribe to the Fluid Power Forum today to never miss an episode. The podcast is available on all of your favorite podcast platforms, including YouTube, Apple Podcasts, Spotify, and iHeart Radio.   Connect with our host, Eric Lanke, at elanke@nfpa.com.   Connect with our guest, Andy Gray, at andy.gray@scanreco.com.   Learn more about the company at www.scanreco.com.   Find and share more interesting fluid power technologies and unique applications using #onlyfluidpowercan and follow podcast and other fluid power industry-related updates at @TheNFPA.   #FluidPowerForum #Controls #Safety #Autonomy #CID

This week Shawn Tierney meets up with Maria Santella and Robert Murao of ICP DAS USA to learn about their new Modbus RTU, TCP, and other products in this episode of #TheAutomationPodcast. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast from The Automation Blog: Listen to The Automation Podcast from The Automation Blog: The Automation Podcast, Episode 274 Show Notes: Special thanks to Maria and Robert for coming on the show, and to ICP DAS USA for sponsoring this episode. To learn more about these products, please see the below links: Modbus Gateways  Modbus Touch Screen Controllers   Modbus RTU DAQ Serial Port Sharing Devices  Modbus over Cellular Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

Industrial network protocols decide whether a machine talks or stays silent. Chuck from Horner Automation breaks down how they win, fade, and converge.Chuck has spent 36 years at Horner Automation and lived through what the industry once called the fieldbus wars. Before Horner became known for its all in one controllers, it spent a decade building specialty IO modules for GE Fanuc during the era of DeviceNet, SDS, InterBus S, PROFIBUS, and CANopen. His core argument is that most of those early protocols were technically fine. The ones that became standards won on the commercial weight of the companies backing them, not on superior specifications, with EtherCAT a rare exception that succeeded largely on technical merit.Trust is the recurring theme. Industry adopts slowly, and for years Ethernet was dismissed as too unreliable and not deterministic enough for control until Ethernet/IP, PROFINET, and Modbus TCP proved themselves. Today the market has settled around a big four set of protocols, and Chuck does not expect it to narrow further. For high speed motion he points to EtherCAT and PROFINET IRT as the implementations he most respects, since both step away from standard Ethernet at the device level to reach submillisecond timing.The episode is also a reality check on building your own hardware. Chuck and Dave describe how custom development routinely costs teams hundreds of thousands to millions of dollars, and how the real trap is obsolescence and maintenance rather than the first build. On the product side, the standout is FPD-Link, a serialization technology borrowed from automotive that carries video, touch, and power over one coaxial cable. Working with Safe Fleet, a maker of ambulances and fire trucks, Horner now mounts rugged displays up to seven meters from the PLC while still programming everything as one device.Looking ahead, Chuck argues that every PLC should now be treated as a data device first, because digitizing the process is the prerequisite for doing anything useful with AI. He also flags cybersecurity as the next burden for application engineers, with new mandates forcing both manufacturers and integrators to implement protections that were once optional. At Automate, Horner is showing HMI Connect and a 300 dollar CPU 151 that packs 18 IO points, wireless connectivity, and edge capability into a micro PLC.About Chuck and Horner AutomationChuck is a technical brand ambassador at Horner Automation, where he has spent 36 years across applications, product management, and education. An electrical engineer who started in the automotive industry, he now produces in depth tutorials on industrial protocols for the Horner APG YouTube channel. Horner Automation is a privately held controls manufacturer best known for its all in one PLC and HMI controllers, edge ready PLCs, and rugged hardware for industrial and mobile applications.Timestamps0:00 Introduction2:20 Chuck's Background and 36 Years at Horner Automation9:20 End User Engineer vs OEM Manufacturer Perspective13:20 New at Automate: HMI Connect and the CPU 151 Edge PLC21:30 The Fieldbus Wars and the History of Industrial Protocols24:20 What It Takes to Implement a Protocol Stack29:30 Why Protocols Win: Commercial Force vs Technical Merit32:40 Will Industrial Protocols Ever Converge?40:30 High Speed Motion: EtherCAT, PROFINET IRT, and Ethernet/IP44:40 FPD-Link: Rugged Remote HMI for Ambulances and Fire Trucks55:00 PLCs as Data Devices and the Push Toward AI1:02:40 Cybersecurity Mandates Coming for Application EngineersReferencesHorner Automation: https://www.hornerautomation.comAbout Your HostsVladimir Romanov is a co-host of The Manufacturing Hub Podcast and the founder of Joltek, an independent manufacturing and industrial automation consulting firm specializing in modernization strategy, digital transformation, and workforce development. Joltek works with manufacturers and investors to de-risk modernization and build the internal capability to sustain results.Connect with Vlad: https://www.linkedin.com/in/vladromanov/Want to go deeper? Vlad and the team at Joltek have covered related topics here:Understanding Plant Networks: https://www.joltek.com/blog/understanding-plant-networks-how-industrial-connectivity-evolvedIndustrial Ethernet Reliability: https://www.joltek.com/blog/industrial-ethernet-reliabilityDave Griffith is a co-host of The Manufacturing Hub Podcast and founder of Capelin Solutions, an industrial automation firm helping manufacturers adopt smart manufacturing technology. He brings 15 years of experience in industrial automation and digital transformation.Connect with Dave: https://www.linkedin.com/in/davegriffith23/Subscribe to Manufacturing Hub: https://www.manufacturinghub.liveLinkedIn: https://www.linkedin.com/company/manufacturing-hub-networkYouTube: https://www.youtube.com/@ManufacturingHub

Das Fahrzeug war lange Zeit das Zentrum der Mensch-Maschine-Interaktion. Doch diese Welt verändert sich rasant. Automotive HMIs verlassen das Cockpit, verteilen sich über Smartphones, Tablets, Smartwatches und Cloud-Dienste und werden Teil eines umfassenden digitalen Ökosystems. In dieser Episode des Mensch-Technik Podcasts analysiere ich die nächste Evolutionsstufe der Automotive User Experience: Liquid HMIs, vernetzte Ökosysteme und die digitale Identität des Nutzers. Warum verlieren Interfaces ihre festen Grenzen? Weshalb wird das Fahrzeug zunehmend zu einem Knotenpunkt innerhalb eines größeren digitalen Netzwerks? Und warum kämpfen OEMs, Apple, Google & Co. letztlich um dieselbe Ressource: die Beziehung zum Nutzer? Die Zukunft des Automotive HMI liegt nicht in mehr Displays oder mehr Features – sondern in der intelligenten Organisation eines digitalen Ökosystems rund um den Menschen.

This week Shawn Tierney meets up with Nick Rosner of Schneider Electric to discuss VFD and HVAC Applications in this episode of #TheAutomationPodcast. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast from The Automation Blog: Listen to The Automation Podcast from The Automation Blog: The Automation Podcast, Episode 273 Show Notes: Special thanks goes out to Nick Rosner of Schneider Electric for coming on the show, and to Schneider Electric for sponsoring this episode. Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

Shawn Tierney meets up with Rylan Pyciak of Cleveland Automation Systems to discuss Control System Migrations and more in this episode of The Automation Podcast. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast from The Automation Blog: Listen to The Automation Podcast from The Automation Blog: The Automation Podcast, Episode 272 Show Notes: Special thanks to Rylan for coming on the show, and to Cleveland Automation Systems for sponsoring this episode! Below you’ll find more information on what we discussed: Read “A Decade in the Automation Industry: Reflecting on 10 Years of Change” Read “The Lost Art of Industrial Troubleshooting: Why Real Problem Solving Still Matters” Explore “The ROI Calculator” – A Free Resource for Maximizing Your Investments with Data-Driven Insights Explore “How to Write a URS” – A Free Guide for Writing Specifications That Drive Automation Success Explore CAS Services – How Our Engineers Help You Keep Pace with Industry Changes Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

This week Shawn Tierney meets up with Phillip Swinson and Mike Stoup of ISA Tampa to discuss the upcoming Florida Automation Expo in this episode of #TheAutomationPodcast. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast from The Automation Blog: Listen to The Automation Podcast from The Automation Blog: The Automation Podcast, Episode 271 Show Notes: Special thanks to our Members who support our work! To learn more about memberships, checkout this link. Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

Change management decides whether your MES or digital transformation project lasts, or quietly gets shut off six months after go live.Vlad Romanov and Dave Griffith sit down with Logan Terry, who leads digital transformation at LSI, to dig into change management as the deciding factor in any automation or MES rollout. Logan defines change management as a methodical approach to moving an individual, team, or organization from a current state to a desired future state. The closer a system sits to where decisions are actually made, the more change management it requires, which is why MES is the single hardest place to land a project successfully.Much of the episode digs into why change management is rarely scoped properly. In competitive RFPs, the integrator who includes a robust change management line item often loses to the lowest bid, and end users frequently do not know how to evaluate that line item even when it is offered. Logan starts every client engagement with a direct question: what does your continuous improvement practice look like internally? If the client cannot sustain the change after handover, the project is on borrowed time no matter how clean the FAT and SAT looked.Logan walks through one of the most useful failure stories on the show this year. His team delivered a technically perfect OEE dashboard for a production line. Six to nine months later, every terminal was shut off. The postmortem surfaced two missed details. Maintenance was never folded into the design, and a single failed photo eye broke throughput calculations with no manual reconciliation path, which destroyed operator trust in the data. The second miss was behavioral. Showing a 30 percent OEE against a 90 percent ideal demotivates the floor, while reframing the same number as 80 percent of a realistic 36 percent target turned out to be a cleaner motivator.Looking forward, Logan sees vendors moving away from monolithic 14 function MES suites toward modular, use case specific deployments, which compresses change management scope from twenty five workflows to five or six. On AI, he argues that managing generative agents in production is closer to managing a team of people than managing software, with continuous validation replacing one time qualification. He cites the line that AI does not make bad data worse, it makes it more convincing. LSI now uses AI assisted coding agents and React based prototypes to shrink design cycles from three or four weeks of Figma work down to three or four days.About Logan TerryLogan Terry leads digital transformation at LSI, a multinational systems integrator with roughly 400 resources across 13 North American locations and offices in Asia Pacific. A mechanical engineer by training, Logan spent a decade in PLC, HMI, and SCADA development before moving into digital transformation consulting and joining LSI in late 2024. His work spans advanced SCADA, MES, analytics, and BI integrations.LSI: https://www.logicalsysinc.com/Timestamps0:00 Introduction2:15 Logan's background and the LSI digital transformation practice7:25 Defining change management9:00 Why MES requires the most change management13:00 How young engineers stumble into change management24:30 Starting with decisions and workflows before technology35:00 Internal CI capability as a project gating factor43:30 OEE dashboard turned off six months after go live46:30 Behavioral psychology of how operators read numbers54:50 Modular MES replacing monolithic platforms58:00 Generative AI and continuous validation1:11:00 AI assisted prototyping shrinking design cyclesAbout Your HostsVladimir Romanov is a co-host of The Manufacturing Hub Podcast and the founder of Joltek, an independent manufacturing and industrial automation consulting firm specializing in modernization strategy, digital transformation, and workforce development. Joltek works with manufacturers and investors to de-risk modernization and build the internal capability to sustain results.Connect with Vlad: https://www.linkedin.com/in/vladimirromanov/Want to go deeper? Vlad and the team at Joltek have covered related topics here:Digital Transformation in Manufacturing: https://www.joltek.com/blog/digital-transformation-in-manufacturingManufacturing Execution Systems and Business Strategy: https://www.joltek.com/blog/manufacturing-execution-systems-business-strategyDave Griffith is a co-host of The Manufacturing Hub Podcast and founder of Capelin Solutions, an industrial automation firm helping manufacturers adopt smart manufacturing technology. He brings 15 years of experience in industrial automation and digital transformation.Connect with Dave: https://www.linkedin.com/in/davegriffith23/Subscribe to Manufacturing Hub: https://www.manufacturinghub.liveLinkedIn: https://www.linkedin.com/company/manufacturing-hub-networkYouTube: https://www.youtube.com/@ManufacturingHub

This week Shawn Tierney meets up with Christine Bush of Schneider Electric to discuss What’s New and Next in Robotics in this episode of #TheAutomationPodcast. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast from The Automation Blog: Listen to The Automation Podcast from The Automation Blog: The Automation Podcast, Episode 270 Show Notes: Special thanks goes out to Christine Bush of Schneider Electric for coming on the show, and to Schneider Electric for sponsoring this episode. Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

This week Shawn Tierney meets up with Chuck Ridgeway of Horner Automation to learn about their new Modular PLCs in this episode of #TheAutomationPodcast. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast from The Automation Blog: Listen to The Automation Podcast from The Automation Blog: The Automation Podcast, Episode 269 Show Notes: Special thanks to Chuck Ridgeway of Horner Automation for coming on the show, and to Horner Automation for sponsoring this episode. To learn more about these products, please see the below links: Horner Automation’s YouTube Channel Horner Automation’s LinkedIn Horner Automation’s Modular Controllers Horner Automation’s OCS360 Cloud Service Horner Automation’s Academic Program Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

From building Applied Intuition from YC-era autonomy tooling into a $15B physical AI company, Qasar Younis and Peter Ludwig have spent the last decade living through the full arc of autonomy: from simulation and data infrastructure for robotaxi companies, to operating systems for safety-critical machines, to deploying AI onto cars, trucks, mining equipment, construction vehicles, agriculture, defense systems, and driverless L4 trucks running in Japan today. They join us to explain why “physical AI” is not just LLMs on wheels, why the real bottleneck is no longer model intelligence but deployment onto constrained hardware, and why the future of autonomy may look less like one-off demos and more like Android for every moving machine.We discuss:* Applied Intuition's mission: building physical AI for a safer, more prosperous world, powering cars, trucks, construction and mining equipment, agriculture, defense, and other moving machines* Why physical AI is different from screen-based AI: learned systems can make mistakes in chat or coding, but safety-critical machines like driverless trucks, autonomous vehicles, and robots need much higher reliability* The evolution from autonomy tooling to a broad physical AI platform: starting with simulation and data infrastructure for robotaxi companies, then expanding into 30+ products across simulation, operating systems, autonomy, and AI models* Why tooling companies came back into fashion: Qasar on why developer tooling looked unfashionable in 2016, why Applied Intuition still bet on it, and how the AI boom made workflows and tools central again* The three core buckets of Applied Intuition's technology: simulation and RL infrastructure, true operating systems for vehicles and machines, and fundamental AI models for autonomy and world understanding* Why vehicles need a real AI operating system: real-time control, sensor streaming, latency, memory management, fail-safes, reliable updates, and why “bricking a car” is much worse than bricking an iPad* Physical machines as “phones before Android and iOS”: Peter explains why today's vehicle and machine software stack is fragmented across many operating systems, and why Applied Intuition wants to consolidate the platform layer* Coding agents inside Applied Intuition: Cursor, Claude Code, internal adoption leaderboards, and how AI tools are changing engineering workflows even in embedded systems and safety-critical software* Verification and validation for physical AI: why evals get harder as models improve, how end-to-end autonomy changes simulation requirements, and why neural simulation has to be fast and cheap enough to make RL practical* From deterministic tests to statistical safety: why autonomy validation is shifting from binary pass/fail requirements toward “how many nines” of reliability and mean time between failures* Cruise, Waymo, and public trust: Qasar and Peter discuss why autonomy failures are not just technical issues, how companies interact with regulators, and why Waymo is setting a high bar for the industry* Simulation vs. reality: why no simulator perfectly represents the real world, how sim-to-real validation works, and why real-world testing will never disappear* World models for physical AI: hydroplaning, construction equipment, visual cues, cause-and-effect learning, and where world models help versus where they are not enough* Onboard vs. offboard AI: why data-center models can be huge and slow, but onboard vehicle models need millisecond-level latency, low power, small size, and distillation-like efficiency* Why physical AI is not constrained by model intelligence alone: the hard part is deploying models onto real hardware, under safety, latency, power, cost, and reliability constraints* Legacy autonomy vs. intelligent autonomy: RTK GPS in mining and agriculture, why hand-coded path-following worked for decades, and why modern systems need perception and dynamic intelligence* Planning for physical systems: how “plan mode” applies to robotaxis, mining, defense, and multi-step physical tasks where actions change the state of the world* Why robotics demos are not production: the brittle last 1%, humanoid reliability, DARPA Grand Challenge-style prize policy, and the advanced engineering gap between research and deployment* Applied Intuition's hard-earned lessons: after nearly a decade, Peter says they can look at a robotics demo and predict the next 20 problems the company will hit* Qasar's advice to founders: constrain the commercial problem, avoid copying mature-company strategies too early, and remember that compounding technology only matters if you survive long enough to see it compound* Why 2014 YC advice may not apply in 2026: capital markets, AI company dynamics, and the difference between building in stealth with a deep network versus building as a new founder today* What Applied is hiring for: operating systems, autonomy, dev tooling, model performance, evals, safety-critical systems, hardware/software boundaries, and engineers with deep curiosity about how things workApplied Intuition:* YouTube: https://www.youtube.com/@AppliedIntuitionInc* X: https://x.com/AppliedInt* LinkedIn: https://www.linkedin.com/company/applied-intuition-incQasar Younis:* X: https://x.com/qasar* LinkedIn: https://www.linkedin.com/in/qasar/Peter Ludwig:* LinkedIn: https://www.linkedin.com/in/peterwludwig/Timestamps00:00:00 Introduction: Applied Intuition, Physical AI, and 10 Years of Building00:01:37 Physical AI vs. Screen AI: Why Safety-Critical Changes Everything00:02:51 The Origin Story: Tooling, YC, and the Scale AI Comparison00:05:41 The Three Buckets: Simulation, Operating Systems, and Autonomy Models00:11:10 Hardware, Sensors, and the LiDAR Question00:14:26 The Operating System Layer: Why Vehicles Are Like Pre-Android Phones00:19:13 Customers, Licensing, and the Better-Together Stack00:21:19 AI Coding Adoption: Cursor, Claude Code, and the Bimodal Engineer00:26:41 Verifiable Rewards, Evals, and Neural Simulation00:31:04 Statistical Validation, Regulators, and the Cruise Lesson00:40:25 World Models, Hydroplaning, and Cause-Effect Learning00:43:34 Onboard vs. Offboard: Latency, Embedded ML, and Distillation00:50:57 Plan Mode for Physical Systems and Next-Token Prediction Universally00:53:04 Productionization: The 20 Problems Every Robotics Demo Will Hit00:58:00 Founder Advice: Constraints, Compounding Tech, and Mature-Company Mimicry01:05:41 Hiring Philosophy: Hardware/Software Boundary and Engineering Mindset01:08:50 General Motors Institute, Education, and the Curiosity MindsetTranscriptIntroduction: Applied Intuition, Physical AI, and 10 Years of BuildingAlessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: And today we're very honored to have the founders of Applied Intuition, Qasar and Peter. Welcome.Qasar [00:00:17]: You guys really know how to turn it on to podcast mode. That was, you guys are real pros at this.Qasar [00:00:23]: They were just joking around right before this, and then they flipped it pretty quick.Alessio [00:00:29]: Oh, yeah, it's good to have you guys. Maybe you just wanna introduce yourself so people know the voice on the mic and they'll know what they're hearing.Peter [00:00:33]: Oh, sure. Yeah, I'm Peter Ludwig. I'm the co-founder and CTO of Applied Intuition.Qasar [00:00:38]: And my name is Qasar Younis. I am the CEO and co-founder with Peter.Alessio [00:00:42]: Nice. Can you guys give the high-level overview of what Applied Intuition is? And I was reading through some of the Congress files, when you went out there, Peter, and eighteen of the top twenty global non-Chinese automakers, you two guys, you have customers in agriculture, defense, construction. I think most people have heard of Applied Intuition tied to YC when it was first started, and then you were kinda in stealth for a long time, so maybe just give people the high-level overview of what it is today, and then we'll dive into the different pieces.Peter [00:01:10]: Yeah. So at Applied Intuition, our mission is to build physical AI for a safer, more prosperous world. And so we work on physical AI for all different types of moving systems, everything from cars to trucks to construction and mining equipment, to defense technologies. And we're a true technology company, so we build and sell the technology, and we sell it to the companies that make the machines. We sell it to the government, really anyone that wants to buy a technology to make machines smart.Physical AI vs. Screen AI: Why Safety-Critical Changes EverythingQasar [00:01:38]: Yeah. And I think in the broader AI landscape, a lot of the focus, rightfully so in the last, three years has been on large language models, and so everything fits in a screen. Like, whether it's code complete products or things like that. And what's different about us is we're deploying intelligence onto a lot of things that don't have screens. they're physical machines. There are sometimes screens within the cabin or for example of a car or a truck or something like that, but most of the value we provide is putting intelligence that is in safety critical environments. So that those two words are really important because learn systems can make mistakes if you're asking for, like, some, so something like, “Tell me about these podcast hostsQasar [00:02:28]: that I'm about to go meet.” But you can't do that obviously when you run, like, as an example, we run driverless trucks in Japan right now, as we speak. We can't have errors. Those are L4 trucks. Yeah.Alessio [00:02:40]: Yeah. Was that always the mission? I remember initially, I think people put you and Scale AI very similarly for some things about being kinda like on the data infrastructure side of things. What was the evolution of the company?The Origin Story: Tooling, YC, and the Scale AI ComparisonPeter [00:02:51]: Well, from the very beginning, we always wanted to, really be a technology company that helped generally push forward the industrial sector. And so we started off working in autonomy. Our very first customers were robotaxi companies. And we started off doing a lot of work in simulation and data infrastructure. And then over the years, we've expanded our portfolios. Now we have, over thirty products, and it's a pretty broad technology play within the landscape of physical AI.Qasar [00:03:19]: Yeah, I think the Scale reason is because we're all YC Universe companies. But it was a very different company. Scale, was, is more of a services company, data labeling company fundamentally. We started and still are, do a lot of tooling. So like, you think developer tooling is now in vogue again, thanks to the AI boom. But honestly, ten years ago, it was out of vogue. It w Like, doing a tooling company in 2016, 2017 was not, like, the thing to do because, I don't know if you remember, the VCs generally, their views was that toolings are They're just workflows, and workflows ultimately are not really interesting. And we've gone and come, full circle with that. But when we started the company, our kind of it's kinda like in the periphery of what the company wants to be. It was like, from our earliest days, like, we wanna deploy software on physical machines, like on cars and on trucks and things like that. And obviously, we didn't know that the transformer boom was gonna happen. We didn't know that autonomy systems would become end-to-end. Those things we didn't know. And why that's important when autonomy systems become end-to-end, it is just now those models can be generalized to, multiple form factors. And so back nine, ten years ago, tooling was a great way, and still is a great way to, build the technology and sell technology to our end customers, a lot of them who wanna build this stuff themselves. And so we just offer like a spectrum of solutions from you can just use like one part of a development suite of tools all the way to buying the full thing. The way to think about the company, or at least the way we think about the company is, as Peter said, a technology provider. It's kinda like, what NVIDIA does or what an AMD, but we just don't do chips.Qasar [00:05:06]: We don't do silicon. But we're a technology provider fundamentally. And I think even, we used to joke when we started the company, like, we're not the guys to build, like, Instagram. Like that was just towards That's not our That's just not us in a most fundamental way. IAlessio [00:05:20]: You have thoughts.Qasar [00:05:21]: Yes.Qasar [00:05:22]: Well, it's, it's I mean, I think it's just like what And I mean, we worked on Maps and stuff, Google Maps. Consumer products are extremely difficult for a lot of different reasons. It just, I think doesn't scratch the itch. I think we're like Michigan guys who are kind of more of that traditional engineering kind of a realm, or lineage. we used to jokeThe Three Buckets: Simulation, Operating Systems, and Autonomy ModelsPeter [00:05:41]: I gotta say, though, what was clear ten years ago was that there was so much more that was possible with software and AI in vehiclesPeter [00:05:47]: and that was generally the space that we started in ten years ago.Peter [00:05:51]: And the precise path that we've taken over the years, I think we've been strategic, and we've adjusted to make sure that we're actually building stuff that's valuable to the market. And like, the technology has changed so much. Like our own technology stack has completely changed, I would say, roughly every two years. And so now we've probably done, let's say, four complete evolutions of our own technology stack. And I sort of see that cadence roughly keeping up.Peter [00:06:13]: And so the way even we think about engineering is almost on this two-year horizon, we're preparing ourselves that, hey, like, we wanna invest the appropriate amount, but then also be very dynamic as the research gets published and as our research team figures out new advancements and adapting to that.Qasar [00:06:27]: Yeah. One thing that has been consistent is the type of people we've, we've recruited. It's engineers who are fall into the sometimes very traditional, like, GoogleQasar [00:06:38]: -gen suite, but way different from, other companies. We are hiring folks who really know the intersection of hardware and software, who know really low-level systems. Obviously, traditional ML researchers and folks who've, actually, put ML systems into production. That's been pretty consistent. I think that, like, you look at the mix of our engineering, eighty-three percent of the company is engineering, so it's, like, a giant list.Qasar [00:07:05]: A lot of engineers.Alessio [00:07:06]: Which, by the way, a thousand engineersQasar [00:07:07]: Yeah. A thousand engineers.Alessio [00:07:08]: that's on your website, so I imagine it's up to date.Qasar [00:07:11]: It is, it is up to date, yes. Yes.Alessio [00:07:12]: okay. And then forty-plus founders.Qasar [00:07:15]: Yeah. We would tend to also, This was more luck than strategy. But we've recruited a lot of ex-founders. It's been a great place for founders, YC and non, ‘cause obviously I know a lot of the YC folks. It's kind of like we recruit a lot of Google people.Qasar [00:07:33]: For them to exercise both their technical and non-technical skills because, we're, we're, we're on the applied side. We have a research team that we do fundamental research, we publish, and we've, we've had great traction there. But fundamentally, the business wants to take this intelligence and deploy it into production and there's, like, a certain type of person that's more interested in that.Alessio [00:07:54]: Yeah. You mentioned the tech stack, Peter, so I just wanted to give you some rein to just go into it. I'm interested in where Wayve Nutrition, starts and ends in some sense, what won't you do? What, do you do that's common among all the verticals that you cover?Peter [00:08:10]: There's a few buckets of work that we do, and we've been at this for almost ten years now, so the technology's pretty broad. But we got startedQasar [00:08:17]: Yeah, with a thousand engineers, like, you could work on lots of things.Peter [00:08:19]: There's lots of stuff, yeah, espe-especially with AI tools to help.Peter [00:08:22]: So we got our start in simulation and simulation tooling and infrastructure. And so generally, if you're trying to build a very complex software system that involves moving machines, you need to test that, and the best way to test it is it's a combination of virtual developments, a simulation, and then also obviously real world testing.Peter [00:08:39]: And then there's a very careful process of that correlation between the simulation results and the real world results and ensuring that the simulator is in fact accurate to that. Simulation's a very deep topic.Peter [00:08:49]: We have a whole suite of products in that, and we could talk for many hours about that specifically. But that is one part of what we do as a company. Reinforcement learning as a subpart of that is also super critical. I think a lot of the a lot of the best advancements happening in a lot of these AI systems right now in some way relate to reinforcement learning, and with now we have lots of compute, and you can do tons of interesting things for reinforcement learning. The second bucket of work that we do is on operating systems technology. true operating systems. Like, think about, schedulers and memory management and middleware and message passing and highly reliable networking and data links. Like, the reality is, if you want to deploy AI onto vehicles, you need a really good operating system. And when we were getting deeper into that space, there wasn't really anything that we were happy with.Peter [00:09:39]: Like, things existed, absolutely, and we were using what was available in the market, and as an engineering organization, we roughly realized these things aren't great. We think we can do this better, and so let's, let's build something. And that was then the that was the moment of inspiration that started our operating systems business, which is now a very real business for us. And in order to write and run great AI, you need a great operating system, and so that-that's what got us into that. And then the third bucket that we work on, it's, it's true fundamental AI technology. Models, we do a lot of work in, as mentioned, the foundational research, but then the also the world models and the actual autonomy models that are running on these physical machines, and that's across cars, trucks, mining, construction, agriculture, and defense, and so that's both land, air, and sea.Qasar [00:10:31]: And also, a smaller subsector of that third bucket is the interaction of humans with those machines.Qasar [00:10:38]: So that's a multimodal, experience. Historically, if you're moving a dirt mover or any of these machines, there are, like, buttons you press, whether they're actual physical tactile buttons or something like a touch screen. That's just That fundamentally is changing to where you're just talking to the machine and the machine and you're teaming with the machine.Alessio [00:10:58]: Voice?Qasar [00:10:59]: Yeah, voice, absolutely, yeah.Alessio [00:11:00]: Oh.Qasar [00:11:00]: And also the machine just being aware of who is in the cabin, what their state is. you can think from a safety systems perspective, the most simple version of this is, like, the driver is tired, right? They're, they're if you get those alerts when you're driving your car and saysHardware, Sensors, and the LiDAR QuestionQasar [00:11:15]: -maybe take a coffee break, that take that times, a couple of order of magnitudes up. But this concept of teaming man and machine is important. When you think about running agents or just running, different instances of, Claude and doing work for you in the background, you can take that analogy out, almost copy and paste and put it into, like, a farm, where you have a farmer who's running a number of machines. So where they interact with the machine is where there's maybe a critical decision or a disengagement or something like that, but generally speaking, the agent on the physical machine is running and making decisions on the behalf of the farmer until there's something maybe critical. And that's also what we work on. So that's not pure autonomy. It's a little bit of a mix, but it falls under, autonomy. In the automotive sense, that's typically defined in SAE levels as an L2++ systemQasar [00:12:05]: -with a human in the loop. But just take that idea, to other verticals.Alessio [00:12:09]: Yeah. You've not mentioned hardware at all, like sensors or obviously we you mentioned you don't do chips. I think even in AV there's, like, a big, cameras versus lidars. Like, what are, like, in your space maybe some of those design decisions that you made, and are they driven by the OEM's ability to put things on the machinery? And like, how much influence do you guys have on co-designing those?Peter [00:12:32]: Yeah. So we don't make sensors. Like, we're, we're not a manufacturer. Obviously, we use a lot of sensors in our autonomy products. in terms of what actually goes on the vehicles, we have a preferred set of sensors that we, let's say fully support, and then our customers, they can sort of choose from those. And obviously if there's a very strong opinion on supporting something else, we'll add that to the platform as well. And the lidar question is at this point sort of the age-old,Peter [00:12:59]: topic in autonomy, and the state of the industry right now is lidar is hands down a useful sensor, specifically for data collection and the R&D phase of autonomy development. if you see, for example, a Tesla R&D vehicle, it actually has lidar on itPeter [00:13:17]: to this day, right? In the Bay Area we see these. you'll see, like, Model Ys or Cybercab that have lidars on them just driving around. So it's, it's useful because it gives you per pixel depth information. So if you can pair a lidar with a camerand you can say that, well, this camera's looking this direction, this lidar's looking this direction, and now for each pixel of the camera I can see how far away is that pixel. you can actually then use that as a part of your model training, and then the that depth information then becomes a learned, a learned state of the camera data. And then when you're doing the production system, you can now remove the lidarPeter [00:13:52]: and now you can actually get depth with just the camera. And so that difference between, like, a highly sensored R&D vehicle and then the down-costed production vehicle, we use that across our whole portfolio of products. And of course the end goal is you want super low cost and super reliable.Peter [00:14:08]: And then in certain use cases you have some more, bespoke things. Like in defense as an example, you do things at night oftentimes, and so you care about sensors like infrared, more so than And you don't, you don't wanna be putting energy out, so you don't wanna use lidar or radar.Peter [00:14:23]: but you still need to be able to see at nighttime. So yeah, we work the whole gamut.The Operating System Layer: Why Vehicles Are Like Pre-Android PhonesAlessio [00:14:27]: Cool. So that's kinda like on the hardware level. Then on the OS level, how does that look like? What is, like, unique? my drive- I drive a Tesla. Whenever I drive some other car that has a screen, it always sucks.Alessio [00:14:38]: It's on, like, cheap Android tablet. It's like, it's laggy and all of that. What does the OS of, like, the autonomy future look like?Peter [00:14:46]: When most people, it's really what you just described. When you think about operating system in a vehicle, you're thinking about the HMI, right? The human machine interface, and absolutely that's a an important part of it, but that's actually only one thin layer on top. So when we talk about operating systems for, like, AI in vehicles, there's many layers that go deep into the CPU critical realm and embedded systems, and you're talking about the real time control ofPeter [00:15:13]: let's say the electric motors or the engine and the actuators, and you have different redundancies for different, let's say, the steering actuation in the vehicle. And all of these things, need very core support in the in the operating system. And then of course for autonomy you have real time sensor data that's streaming in, and the latencies there are really important, right? If you try to Imagine you try to run Microsoft WindowsPeter [00:15:35]: like streaming your sensor data in or controlling the vehicle. Like, the latencies are gonna be absurd. Like, you can never do that. And so what's special about what we do is we really have this system level thinking, right? So we're looking at, we care about every performance characteristics of the entire system, and then we also, because we're doing a lot of the software or all of that software, we can fine-tune and control all of those things. So we can very carefully tune in the latencies for every aspect of the system. We can carefully tune in the memory management. We can have the right, fail-safes and fallbacks, for different things. ‘Cause you have to account for what if, what if there is a critical failure? What if there's a cosmic ray that flipsPeter [00:16:14]: a bit in the middle of the processor that causes some, malfunction? And you have to have a fail-safe to all of that, and so the core operating system is a part of that. And then the one last thing, which is a lot less exciting but is, actually a very big topic, is reliability of updates.Peter [00:16:30]: so the I have a Tesla and you get updates fairly frequently, right?Peter [00:16:36]: Once a month. Most companies that are making vehiclesPeter [00:16:40]: are basically never doing updates, and they're And even if they are doing updates, they're usually only updating maybe one module. Maybe they're updating the HMI module. But they're not able to update, let's say, the CPU critical parts of the system.Peter [00:16:51]: You have to go into the dealer for that. And so with our operating system now we can actually enable highly reliable updates of any system in the vehicle, and that's way easier said than done. Like, there's lots of technical, technically deep stuff, in the tech stack to do that in a way that you're not going to accidentally brick a vehicle.Peter [00:17:08]: And right? If, imagine yourAlessio [00:17:10]: That would be bad.Alessio [00:17:11]: Bad.Peter [00:17:11]: Bricking a car is a very expensivePeter [00:17:13]: and honestly, like across the industry maybe one of the most just pure impactful things that we've done is we've just, we're, we're now enabling the industry to actually do software updates.Alessio [00:17:22]: Just to clarify as well, who is the customer for this? Like, I assume a lot of hardware manufacturers have their own firmware, and I'm sure some of them would just have you write it for them because you're experts. And others would have their own. Like, who pays for this? Who invites you into the house? Is it, is it the end user, or is it, is it the manufacturer?Peter [00:17:41]: Yeah. So let me make an analogy firstly on the on the fragmentation of software. So physical machines today are more akin to the state of the phone market before Android and iOS existed, right? So I worked on Android at Google by the way many years ago, and part of the reason that Larry at Google decided to get into Android was they wanted to run Google products on a bunch of phones, and they bought all of these phones from the industry, and it turned out they had like 50 different operating systems on these phones. And it was virtually impossiblePeter [00:18:17]: for Google to make their app run on all 50 devices equally well. And so the solution was, well, actually what if, what if they created-A really great operating system and made it attractive to all of these phone makers, and that was sort of the genesis for what Android was and why Android existed. It was a way for Google to get their products onto really wide diversity of devices. The state of the physical, industry right now, it's a little bit like that. Like, there's yes, these companies have firmware, but they have so many different operating systems, it's so fragmented, and to actually get a modern AI application to run on these vehicles, you actually, you first have to consolidate the operating system, and so that's, that's why we've done that. And then, your specific question was who are our customers? It's, it's, generally it's the companies that are making these machines.Peter [00:19:06]: And we're, we're, we're selling our technology to them to really simplify the architecture and then enable these AI applications to run on them.Customers, Licensing, and the Better-Together StackSwyx [00:19:13]: How much is reusable across? Like, do you have, like, one OS that is just configured for everything, or is there some more customization that is needed?Peter [00:19:22]: Yeah, highly reusable. So the fundamental technology is quite universal, right? So things that we do have to think about though are, like, chipset support. And so if you're, if you're coding, let's say, an LLM and you have start with an assumption that, “Hey, oh, I'm gonna, I'm gonna use CUDA, and I'm gonna run this, on an NVIDIA chip,” then you don't really have to think about the hardware in that sense. Like, you're just, “Okay, I'm just I'm in the CUDA/NVIDIA ecosystem, and I'm, I'm going to use that.” But the hardware, especially in safety critical systems, it's a lot more diverse. There's not one or one or two players. There's a bunch of different chipsets that we have to support. And so our operating system doesn't just run on, like, the equivalent of X86. It has to, it has to run on a number of different architectures from chips from a bunch of different companies. But again, we've been working on this for a long time now, so we have, we have support for all of those chipsets. And then when you want to then run the AI applications, we can then do that reliably across now a variety of providers.Qasar [00:20:19]: And I think that is, like, heavily inspired by Android, right? Android has a huge suite of testing and it's a reliable operating system that runs on thousands of devices. And we think we can, we can do the same in all these physical moving machines, with the difference that we're really in a safety critical realm. Android isn't.Alessio [00:20:40]: So on Android, I don't need to use Gmail, I can use Superhuman. Like, what about your machinery? Like, can people bring somebody else's automation to it, or is it kinda like all-in-one?Qasar [00:20:50]: You have to use us. No. Yeah. we're If, Yeah. Yeah, it's totally open. Yeah.Peter [00:20:56]: Yeah. our philosophy is that we are a technology company, and so we license our technology to customers to use how they want. And so if a customer wants to If they wanna license our autonomy tech and our operating system, then great, we'll license those. If they just wanna license the operating system and then use different autonomy tech, that's fine also, and we have great documentation andSwyx [00:21:17]: Or if they wanna use developer tooling.Peter [00:21:18]: Yeah, exactly.AI Coding Adoption: Cursor, Claude Code, and the Bimodal EngineerSwyx [00:21:19]: It's, like, a better together if, obviously, if you, if they work together. Is it all C++ I assume is with different compile targets?Peter [00:21:27]: We use a lot of C++.Peter [00:21:28]: Rust is sort of a hot, the new hot kid on the blockPeter [00:21:32]: for a bunch of things as well. But yeah, the lower level you get, especially when you get to real-time constraints, you hit C++ at some point, and at some point maybe you work your way into assembly when needed.Swyx [00:21:44]: Oh, damn.Alessio [00:21:46]: I'm curious about the coding agent adoption, just, like, since you're mentioning more esoteric languages. Like, what's the adoption internally? What have you learned?Peter [00:21:55]: Yeah. We use everything. So Cursor was, I think the hottest tool in the company for a good while. Now Claude Code, I think has taken the reign on that. We have a internal leader, leaderboard that we use just to sort of encourage adoptionPeter [00:22:09]: with-within the company. And yeah, it's, they're phenomenally useful. it's, Honestly, we take inspiration from some of those tools also in how we're adapting some of that mindset of thinking to the physical realm. Like if it's so easy to build an app for this or that thing that lives just on a screen, we can We're taking now a lot of the same ideas and applying that to, “Okay, well, if you wanted a physical machine to do something, how easy can we make that, using our own tooling and platform as well?”Alessio [00:22:40]: Are you changing any of, like, the OS architecture, kinda like the way you expose services to, like, be more AI friendly or?Peter [00:22:48]: Yeah, absolutely. The in the early days of our tools infrastructure work, it was a lot about, You had engineers that were experts in certain topics, but the things that you're dealing with, they're oftentimes more mathematical or more abstract, where actually GUI tools are very useful for certain things. Like as an example, we have a product we call Sensor Studio, which is, it helps you design the sensor suite for your autonomous vehicle, whether, again, it could be a car, it could be a drone, could be a mining equipment, could be a robot. And you place sensors in different places. You There's different, There's a library. You can understand what are the trade-offs that you're making in the design of that system, and that was, like, a very, a very GUI intensive, thing ‘cause it's a little more like a CAD tool in that senseSwyx [00:23:37]: YepPeter [00:23:37]: if you've seen CAD tools. Nowadays, though, right, we expose all of the underlying APIs for that and now using, AI agents, you can actually configure a sensor suite with just text and likely reach a better result than you could've through the GUI in the past, and we're taking that thinking now through the whole product portfolio.Swyx [00:23:57]: Another thing I was thinking about is just in terms of, like, AI, adoption, does it change your hiring at least a little bit, or how do you, how do you sort of manage engineers, differently?Peter [00:24:08]: Yeah. absolutely, it does. we, I think like every company in the Valley right now, are evolving our hiring practicesPeter [00:24:16]: because the skills required to be effective are changing so fast, right? you used to really select for just rote implementation ability and now it is more the AI engineer skill set, right? Where it's like, yeah, how to implement, but actually-Just banging out code is no longer the core job, right? It's, it's actually knowing what questions to ask, knowing how to tie, how to tie together these different AI tools. And so the interviews that we give now I think are way harder than they've ever been.Peter [00:24:46]: But we also allow, right, selective use of AI tools to solve the problems. And I think in that you start to see more of a bimodal distribution of engineers, right? You start to see like wow, there's, there's this subset of people that they really get it. Like they're, they're all in and they've, they've clearly invested the hours needed to learn these tools and how to be effective.Peter [00:25:09]: And then there's sort of the group of people that haven't done that, and that the productivity gap is just enormous. And so we're, we're trying to obviously select for the people that are really into this.Qasar [00:25:20]: I first wrote the my AI engineer piece three years ago, and when I first wrote about it, I was like, “Actually, not everyone should be an AI engineer,” ‘cause I think there's a there's an extremist stance where well, every software is an engineer is an AI engineer. And my actual example of people who should not be adopting AI was embedded systems and operating systems, and database people. Are they adopting AI?Peter [00:25:41]: I think it's the classic bitter lesson, topic, which is the Six months ago I would've said the same thing, but it's, it's becoming super useful for every domain.Qasar [00:25:53]: I'm sure.Peter [00:25:54]: Right? Like,Peter [00:25:56]: there was, I think six months ago, or maybe a year ago, if you tried to use, let's say the latest Claude model for writing shaders, GPU shaders, the results were probably underwhelming. And if you use the latest model now to do that kind of task, you're a little bit blown away, like, “Wow, that actually worked. That's amazing.” And we see the same thing in the embedded realm. No question though, especially when you get into safety critical systems, the human validation isPeter [00:26:25]: is 100% key. Like I You're not gonna trust your life to a an AI written software that's, that's not been very carefully, checked by humans. And so I think now the really the challenge is about that appropriate level of human validation for these safety critical systems.Verifiable Rewards, Evals, and Neural SimulationAlessio [00:26:41]: How do you think about, yeah, touching on the simulation side, I think verifiable reward and reinforcement learning is, like, the hottest thing. What have you done internally to build around that? And like, what gives you What makes you sleep at night? Like, if somebody's like, just web coding something or likeAlessio [00:26:57]: wants to try something new, you have like a good enough system. Because I think the opposite is also true, is like if it's super easy to write anythingAlessio [00:27:04]: then it puts a lot of work on like the verifiableAlessio [00:27:07]: side of it. Like, what does that look like for people?Peter [00:27:10]: Yeah. So verifiability, a broader bucket of like evaluations, right? Like how do you evaluate the results that you're, you're getting? I think this is probably the hardest problem right now, because the As the models get better, it can be harder and harder to find the faults on the system.Peter [00:27:29]: And so like the problem of doing proper eval to find those faults, like that problem also keeps getting harder as the models get better. But it's no less important than it's ever been, right? You still there are still going to be edge cases that are not met and whatnot. And so it's, it's a big area of investment for us. On the reinforcement learning topic, the key thing is there's all these new requirements that come to be in the latest generation of these technologies. So for example, end-to-end is the big thing right now in autonomy and physical AI, which is you can now train these models that can effectively take sensor data in and then put control signals out, and get really good results out of that. But the way that you train and improve those models is really different from the previous generations. And so to do reinforcement learning on an end-to-end model, you now need to actually simulate all the sensor data, right? So then this becomes a we call our, work in this neural simulation, but it'sPeter [00:28:26]: think of it like a hybrid of Gaussian, splatting and diffusion methods, and where you really care about performance. Like performance is everything. If you can't do enough simulation fast enough and cheap enough, you actually can't get results that are worthwhile, in the end. It also gets to a lot of our work in embedded systems, which is like performance critical work, and that performance optimization, performance criticality, it carries over to a lot of the model training work. because, like, the only way to make it affordable is it has to be really fast.Qasar [00:28:58]: I think it's worth a few minutes talking about our own, evolving thoughts on verification and validation withinQasar [00:29:05]: kind of, traditional simulators, which are, you can think of like vehicle dynamics or something like that, which you're just taking textbooks and taking those formulasQasar [00:29:13]: and putting them into software, to like now this neural sim/world model universe. I think that's an interesting topic.Peter [00:29:20]: Yeah. So in more traditional development, right, you oftentimes would have, more black-and-white answers to questions.Peter [00:29:28]: And so the in Europe as an example, there's, a regulatory, system, it's called Euro NCAP. It's the European New Car Assessment Program, and as part of that, the vehicles have to pass a bunch of tests, and those tests actually, include, safety systems. So automatic emergency braking for a child that runs in front of a carPeter [00:29:51]: or let's say an occluded child that runs out and you hit it. And so you have You end up with sort of these binary answers of like, well, did the car under test pass this specific test? And there's a very well-known set of test casesPeter [00:30:05]: that the vehicle has to pass. And that was how the industry worked, let's say, until 10-ish years ago. But what's changed now is with these models, everything is statistics, right? Like you no longer have a black-and-white answer, but it's like, well, how many orders of magnitude or how many nines of reliability can I get in the system, and how can I, how can I prove that to be true? And the big unlock honestly for physical AI as an industry is that these models are just becoming much more reliable. Right? Things like things actually work a lot better. It's like the number of nines you can get out of these systems are now good enough that it actually becomes cost effective to really deploy these things. And so the big shift in, so verification and validation has been from a little bit more of a Again the past it was strictly requirements, and are you meeting or not? And now it's more of a statistical, verification and validation case where it's all about how many nines of reliability and meantime between failures, that sort of thing.Statistical Validation, Regulators, and the Cruise LessonSwyx [00:31:04]: And is the target audience regulators or even the customers are yeah, if you I imagine the customers are bought in, and it's mostly regulators that need to be satisfied.Peter [00:31:15]: We do work with the US government, we do work of course with the European governments and the government of Japan, and the government is not like an AI lab by any means.Peter [00:31:25]: So Swyx [00:31:26]: They just care about the outcome.Peter [00:31:27]: They care about the outcome.Peter [00:31:28]: And so we do education, in that regard, and like so sort of teaching about, “Hey, this is how we think validation should be done, and this is an approach that we think is reasonable,” and how to think about like when is a driverless system actually safe enough to go on the roads and that sort of thing. But I wouldn't say that the government is asking for it. It's like we're more teaching the government in that, in that sense. It's honestly, it's more so for our own, our own comfort, right? Like, we want to build very safe systems, and then of course our customers care deeply about that as well. But in that context we're also typically educating our customers.Qasar [00:32:01]: Yeah. Our first, our first core value is on round safety. So I think we can't underline enough that, us also verifying and validating that the systems that we're deploying are safe to us is probably as important as, like, some regulator or a customer saying,Swyx [00:32:19]: Of course. Okay. Yeah.Swyx [00:32:20]: You have to satisfy yourselves.Peter [00:32:22]: As I say, as a whole across the world, regulation oftentimes it's like a almost lowest common denominator. But like, you really have to substantially exceed what the regulators are expecting to make good products.Swyx [00:32:33]: Yeah. One thing I often talk about, I think and I try to make this relatable to the audience also, is Cruise, where they had an accident that basically ended the company. I wonder if people overreact to single incidents, because incidents are going to happen regardless, right? ‘Cause it's a statistical thing, but as long I don't know if regulators understand that, you cannot extrapolate from a single incident, but we do because that's all we have to go on. And your sample sizes are necessarily gonna be lower than, I don't knowSwyx [00:33:00]: consumer driving.Qasar [00:33:01]: Yeah. I think the Cruise example wasn't a technology failure. there was The real, compounding issue there was just how did the company talk to the regulators and what was their kind of behavior, and I think that became more of the issue. If you look,Peter [00:33:19]: It isn't It definitely was a technology failure, but it was made much worse by theSwyx [00:33:23]: Put the car back on the woman.Qasar [00:33:25]: Yeah. And let me put it another way. There is a version where Cruise still exists.Swyx [00:33:29]: right. Right.Qasar [00:33:30]: Right. It'sSwyx [00:33:30]: It was like the last strawQasar [00:33:31]: ItSwyx [00:33:31]: in like a long chain ofSwyx [00:33:33]: like issues.Qasar [00:33:33]: So do you feel like ATG had that horrific accident or someone actually dying, because, that was a homeless person crossing the street? So yeah, I think we can't understate enough that ultimately, like, statistical validation of something, that's one part of it, but it's not the only part of it. Like, consumer and let's say, mainstream adoption of these technologies is also gonna be part of that conversation. I think companies like Waymo are doing a lot of service positively to the industry in the sense of they're, they're setting a high benchmark and they're showing, kind of in a very responsible way how to, how to deal with these. There have been Waymo incidences as well. They've just not been as significant as the Cruise one that you mentioned. But yeah, so I think you'll just continue to see that. I think probably the long term question is really gonna be, again, around Like it is very clear humans are way worse drivers statistically.Qasar [00:34:29]: Like, there's no, there's no debate. And so at what point But we're emotional animals.Swyx [00:34:34]: Yeah. So my thing is, like, we have to get to a point as a society where we accept horrific accidents that would never happen by a human because statistically we understand that it is safer overall. In the same way that planes, they're safer, than I think they're the safest mode of transport that we have.Qasar [00:34:50]: Yeah. it's more dangerous to drive to the airport than it is to get on a flight.Qasar [00:34:53]: So if you're everQasar [00:34:54]: if you're ever getting nervous about getting on a plane, just think “I just gotta get to the airport.”Swyx [00:34:58]: Yes, we're flying.Qasar [00:34:59]: If I get to the airportQasar [00:35:00]: I'll be good.Swyx [00:35:00]: But then it's, planes also concentrate the tail risk if planesQasar [00:35:03]: Yeah. AndPeter [00:35:04]: And I was, I don't think we honestly have to worry about there ever being, accidents from these systems that are like much worse than what humans would cause, ‘cause humans do terrible things.Peter [00:35:14]: Like, people fall asleep at the wheel all the time.Swyx [00:35:16]: I have.Swyx [00:35:17]: Like, I'll call, I've been a drowsy driver.Peter [00:35:19]: Kinda drunk drivers, and that'sPeter [00:35:20]: that's the extreme end of the example. But these AI systems, you have redundancies, you have fallbacks. Like, there's many things have to go wrong for there to actually be a something catastrophic because there's, there's so many, fallbacks that these systems have.Alessio [00:35:36]: your simulation is like so vast because there's so many use cases. What are, like, maybe things that worked in a simulation and then you put it out and it's like, “F**k, this isAlessio [00:35:45]: this just did not work at all?”Peter [00:35:47]: Yes.Alessio [00:35:47]: IsPeter [00:35:47]: That's maybe a bit of a misconception, about simulation there. So let me go a little bit, more technical on this. So at first go, no simulation is going to represent the real world. There's always a process of this, sim to real matchingPeter [00:36:02]: where you actually, you need the real world feedback to basically feed into the parameters that are being used in the simulator, and you have to do that, it's like this validation flow, a number of times until you can get some confidence that, like I think the simulator is now accurately representingPeter [00:36:19]: what's gonna happen in the real world. Now, if you have a situation where you've done that full validation and you thought that it was accurate and then there's something different, those are much trickier cases, and that's, that absolutely can happen, but really I think the validation process is a really important part. You can never skip the simulation validation process, like where you're actually ensuring that, hey, the actual, my sim to real gap here is small enough that I can trust these simulation results. And there's, there's so many fun things that you can do when you get into it. Like, I'll, I'll give one fun example that came up recently is like in these humanoid robotics, systemsOverheating actuators is a real problem, right? So obviously phenomenal demos. IPeter [00:37:01]: The most amazingAlessio [00:37:02]: For 10 minutes.Peter [00:37:03]: The most amazing I can get. I love, I love watching robots do acrobatics like everybody but the these systems actually overheat, right? If, like, And one of the ways you can use simulation though is you can actually have that, the temperature of those actuators be one of the parameters that's representedPeter [00:37:18]: in the simulation. And if you're doing reinforcement learning over a certain task, then the robot can actually adjust its motions in the simulation to account for the fact that, oh, it knows that as it's moving, it's actually beginning to overheat this motor. But if you didn't have that parameter of, let's say, the heat of that motor represented in the simulation initially, then your RL policy might It will disregard that. And now you run that on the robot and the robot will overheat and fail.Alessio [00:37:43]: I guess the question is, like, how do you have all of these parameters taken care of while also understanding the deployment environment? Like, temperature is like a great example, right? WellAlessio [00:37:53]: why did you make my robot worse when it runs in like a freezer?Alessio [00:37:57]: So it actually shouldn't worry about that. it's like, yeah, how do you design these simulations?Peter [00:38:02]: This is honestly the This is what makes simulation so hard, right? it's because you Simulation is fundamentally about you're trying to optimize the development of a system, right? Like, how can I build this system faster and better and cheaper and what are all the levers that I have to actually accomplish that? And because simulation's just a software program, you can, you can change it a lot more easily than you can hardware systems. And then what's particularly awesome about the let's say, world models and using that as a part of simulation is now the simulation doesn't just scale with, let's say, adding new math equations inPeter [00:38:36]: but we can actually scale the simulation environment now with additional real world data and that also unlocks a whole new field of robotics.Qasar [00:38:46]: There is a meniscus line where you cross where still doing real world testing is better. there's, in this, sim-to-real gap, you can reproduce reality at exceedingly expensive costs and this So nothing is free. So really you have to you're finding that line where you're getting great performance, you're getting great feedback, whether it's on the training side or on the eval side, but it's way cheaper than doing it in the real world. At some point it, that doesn't make sense. And so even, from our earliest days in autonomy, our view was you're still gonna do real world testing. You There's, there's not, there's not this, magical land where you're not gonna do that. And maybe even like a more nuanced version of this in like traditional software development is, most of your testing for software in a vehicle, 95% of that can be like traditional CI/CD kind of, flows that you would have in traditional web development. But once you have Now you, let's say you have a truck. Well, you can do like 4% of those in like a rig which has all the components, the electrical and electronics of a truck, but doesn't have, it doesn't have the tires and it doesn't have the And then you have the 1%, which is actually the vehicle. There's something There's a similar analogy in terms of using simulation for intelligent systems. You can do a lot in a simulator, but in using world models, but ultimately it's, it's physical AI. So you're gonna deploy it on physical machines andQasar [00:40:17]: the freezer example comes to, comes to light.Alessio [00:40:20]: The world model thing has been to me the hardest thing toAlessio [00:40:22]: wrap my head around. Like we have Faith Eliyon on the podcast.World Models, Hydroplaning, and Cause-Effect LearningQasar [00:40:25]: We've been doing a small series with like another Intuition company, General Intuition as well.Qasar [00:40:31]: yeah, and I mean, lots of, lots of coverage on NeRFs and yes.Alessio [00:40:34]: Yeah. It feels like we talk with about, the heliocentric system, right? It's like in a world model, if you just feed visual data, the model might learn that the sun spins around the Earth. It makes sense, right? And it's like, well, not really. And I think what are like some of these other things that like hydroplaning is one thing I think about, is like can a world model understand hydroplaning and like what amount of water like causes it to happen? And it's like, yeah, to me it's like I don't understand how you guys do it. I guess it's like the real thing is like when you're doing both cars and the highway in Japan versus the excavator in a mine in,Qasar [00:41:13]: ArizonaAlessio [00:41:13]: wherever you're Arizona, wherever you're deploying them.Alessio [00:41:15]: How much of it are you relying on the world models to like generate the simulations for you and then try and close the gap after versus like giving the world models as a tool to your engineers to like curate the simulations if that makes sense?Peter [00:41:28]: Yeah, totally. So yeah, I can say at a pure engineering level, I think if you're hoping to do real world deploys and you're purely relying on a world model approach, you probably won't get to something that works, before you go bankrupt. So there is just a very practical mindset of like, world models are amazing and they're extremely useful for a lot of use cases, but there are a lot of other things that you need to do to actually get something started and something deployed and working. most fundamentally, world models are all about It's understanding the world, but also understanding what's going to happen. It's like the cause-effect relationship.Peter [00:42:01]: Right? And so like it, right, if you have a take some sort of construction tool, and that construction tool is gonna be doing some work on the Earth in some way, it's gonna be moving earth, the world model needs to understand that cause-effect relationship. Like, okay, when I, when I take this material from here and put it over there and now I have things that are over here and not over there anymore and that cause-effect, relationship. data obviously is a is a big problem. The hydroplaningPeter [00:42:26]: one is actually a really great example because it's actually quite non-obvious sometimes. Right? It's like, well, it's, it's raining and well this road, has, let's say the appropriate curvature to it so the water is running off the road and cars are driving faster here and then you approach a road that's very flat and water is now puddling on that road and all of a sudden cars are driving slower because when they were driving faster they were starting to lose control. And there are a lot of visual nuance, very nuanced visual cues in the scene and so I do think in the world model concept there's a good chance that the model actually would learn that you should just drive slower when these visual cues exist, and that's obviously the beautiful-The beauty of, these kinds of models where they just, they learn these non-obvious things.Swyx [00:43:14]: It doesn't need to know about hydroplaning to know that it needs to drive slower.Peter [00:43:17]: Yes.Swyx [00:43:17]: I guess it's Yeah. I wanna ask questions about, also deploying models. I presume, like, you use a lot of these world models for training data and simulation, but what about deploying it onto the systems in production? Presumably you have you have, like, GPUs on deviceOnboard vs. Offboard: Latency, Embedded ML, and DistillationSwyx [00:43:36]: but they're I keep saying on device. What's the what's the right term for that?Peter [00:43:40]: On machine.Swyx [00:43:41]: On machine.Peter [00:43:41]: Or embedded, yeah.Swyx [00:43:42]: Yeah. What is the embedded world like? because for people who are not used to that world, this is very alien.Peter [00:43:49]: Yeah. So it's actually We call it onboard and off board.Peter [00:43:52]: So like, onboard software and off board software.Peter [00:43:54]: And the great thing about off board software is you don't have to care about time, and you can run really large models, right? So you can, you can say, “Well, this model, I don't care if it takes one second for it to give me a result or 10 seconds for it to give me a result, because we have time.” And the models can be really big, and they can run, in a data center or on a on a huge GPU and you can obviously have distribute to compute, et cetera. But onboard you don't have any of those benefits. You're like, “Well, I need I have this many milliseconds where I need an answer from this model.” And so a lot more of the energy then is about, think of it more like distillation and it's like truly efficiency and like, literally every fraction of a millisecond counts. And you can't have a situation where the model takes too long because then the vehicle can't actually function.Peter [00:44:42]: And so you can, you can still use a lot of the same techniques, and the models themselves you can think of as like a derivative of larger models that you can run offline, and then you're, you're trying to just get a model that is still performs really well but it's, it's a it's smaller, small enough version that you can then run on this embedded system where you care about latency and power.Qasar [00:45:03]: Yeah. And I think like, the broader point I think which, maybe is not obvious but it's worth saying is in physical AI world, we're not really constrained right now by, like, the intelligence of the models. It's actually what Peter's talking about, it's actually deploying them inSwyx [00:45:19]: The hardware they give you.Qasar [00:45:21]: Yeah. On the hardware you give you.Qasar [00:45:22]: And so And there's just a reality is of safety critical systems. So those end up being the your limiting factorsQasar [00:45:29]: rather than, let's say, a limiting factor for, a foundation model companyQasar [00:45:34]: is gonna be just capital maybe or researchers.Qasar [00:45:38]: So we're, we're in that way dealing with, for us as people who kind of come in that realm with like a very interesting Those constraints force creativity.Swyx [00:45:47]: And I imagine, nobody was deploying or giving you the hardware for transformers back in 2018, whatever, but now they are. What's the evolution like? just peel back the curtains a little bit.Peter [00:45:59]: Yeah. Transformers first off, I think the paper was originally published in 2017.Swyx [00:46:02]: 2017.Swyx [00:46:02]: So there's no time.Peter [00:46:04]: And ISwyx [00:46:05]: But I'm just saying I guess I'm saying, like, embedded ML systems usually, like, a lot less parameters, a lot less compute, and now, like, orders of magnitude more.Peter [00:46:14]: Yeah. absolutely. what I was gonna say though was I think in the in the original paper in 2017, maybe it's in the last paragraph, somewhere in the paper they talk about, like, “Oh, by the way, this technique might be useful for, like, images and videos as well.”Peter [00:46:30]: These last subjects.Peter [00:46:31]: And it took a few years for that impact to really hit. But like, now, we're seeing transformers are everywhere.Swyx [00:46:39]: Yeah. Vision transformers.Peter [00:46:40]: And then then the compute just keeps getting better and better. But you do have this fundamental trade-off, right? It's like you have power, you have cost, and performance and like, getting the right, getting the right mix of those things in an embedded package that can also be, like, shaken and baked in all thePeter [00:47:00]: conditions that these things have to have to operate in. But yeah, I think that they're only going to keep getting better and so we also try to plan our strategy understanding that, we know the rate of improvements of these systems.Swyx [00:47:11]: Yeah. So like, Google just released the Gemma 2B modelSwyx [00:47:15]: that effective 2B model. Is that useful to you guys or is that too big?Peter [00:47:18]: You can run that model on an embedded system, definitely.Peter [00:47:21]: the So yes, it's, it's useful in that regard. The bigger question is, like, what do you use it for in an embedded system? Like, you actually need to customize it quite a bit to make it useful for something. But yeah, you could run a two billion parameter model, definitely.Swyx [00:47:35]: It also interesting, like, what percent is a custom ML model that only does that thing versus a generalist LLMSwyx [00:47:41]: which probably is not that useful actually for your context.Peter [00:47:46]: Like, you, like, you can imagine different use cases, right?Peter [00:47:48]: So theSwyx [00:47:49]: The voice stuff, yes.Peter [00:47:49]: Yeah, the voice test. Totally, yes.Peter [00:47:51]: So for the actual, autonomy elements, that's 100% in-house. We do every bit of that, the data simulation, the model, everything. But when you get into the more generic use cases like voice or voice assistant kind of thing, that's where these more generalist models like Gemma actually can be quite, can be quite useful.Swyx [00:48:09]: Yeah. And then there's also obviously a trade-off between, like, what percent must you do on machine, versus just call home.Peter [00:48:16]: Yeah. It's all about latency.Swyx [00:48:17]: Latency.Peter [00:48:17]: It's all about latency. Yeah.Swyx [00:48:18]: Yeah. Well, like, I think actually in a lot of contexts, especially in the US, you can just have a connection to the web.Qasar [00:48:26]: Yeah. I think though most of our universe is everything has to be fairly, embedded and local because just the nature of Even in the US there's a lot of likeSwyx [00:48:39]: PatchinessQasar [00:48:40]: don't haveQasar [00:48:41]: have coverage, right? And if you look at, like, the old world of autonomy within mining, which is, like, long before transformers and kind of, neural networks, in the like CNN and kind of a universe, they were really just hand-coded, systems. They were just like, this machine is gonna run to that place with thisPeter [00:49:03]: That was our GPS, like very accurate GPS.Qasar [00:49:05]: Yeah. And so that worked, and that worked for 20 years, so why would we actually need to use transformers or kind of more modern end-to-end systems? Mainly because you can only really run a path and run backwards. That provided a lot of value, but m-Not as much as you get when the machine is actually intelligent. It's, it's seeing, it's perceiving, it's acting in a dynamic world.Alessio [00:49:28]: I looked up RTK, real-time kinematic, one to two-centimeter accuracy.Qasar [00:49:32]: Yeah. Fantastic. But the and fantastic in faraway lands where there's not gonna be cell phone coverage.Peter [00:49:39]: Yeah, so it's widely used on the legacy mining and agricultural autonomy systems today. So like, for example, a combine that can be precise within one or two centimeters as it's driving down the field, they use RTK.Qasar [00:49:53]: Yes.Peter [00:49:53]: But it's, it's expensive.Qasar [00:49:54]: Yeah. And it's, it's, it's autonomy, but it's not intelligent in the way that I think all of usQasar [00:49:58]: if in twenty-six we'd be talking about intelligence.Alessio [00:50:00]: In one of your blog posts, you mentioned research on large scale transformers that are similar to those doing modern generative AI. What are, like, the big differences other than, “You're absolutely right. I should steer the car, so you probably wanna remove that?”Peter [00:50:14]: We have a diversified bet strategy internally, and the reason we've done that is because we operate in now a bunch of industries, a bunch of geographies, and each of the approaches has, obviously a different risk to them.Peter [00:50:27]: And so like, we're not going to put all of our eggs in a single basket for a single approach because that approach may no

This week Shawn Tierney meets up with Felipe Polido of Reframe Systems to learn how they are using Automation to change the process of building homes in this episode of #TheAutomationPodcast. Unlock access to the ad free EXTENDED EDITION by joining our channel at https://TheAutomationBlog.com/join or https://youtube.com/@InsightsIA/join. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast, Free Edition: Note: Below member’s will also find an ad-free and extended edition of this episode. To unlock the ad free extended episode, you can become a member here. Watch the Members’ Extended Edition: Listen to The Automation Podcast from The Automation Blog: Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

Discover industrial AI at Hannover Messe with SAP's Matthias Deindl, covering embodied AI, productivity-boosting agents, and demos like ginger-shot packaging, digital twins, warehouse robots and partner integrations. Download the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠episode transcript⁠⁠⁠⁠===== This episode explores industrial AI at Hannover Messe (20–24 April) with SAP's Matthias Deindl. Key topics include embodied AI (robots in production, logistics, asset management) and AI agents that enhance productivity and reduce errors. The SAP booth at HMI features a ginger-shot packaging demonstrator, digital twins, humanoid warehouse handling, CNC machining, and partner integrations. Matthias emphasises the importance of accurate, timely data, using SAP Business Data Cloud to enable autonomous tasks like AI-assisted tendering and robot-led inspections. The SAP booth at HMI features a ginger-shot packaging demonstrator, digital twins, humanoid warehouse handling, CNC machining, and partner integrations. The episode envisions seamless disturbance response, improved productivity for an ageing workforce, and stronger human–AI collaboration.  ===== Guest: Matthias DeindlMatthias Deindl is a digital transformation leader focused on discrete industries and supply chains, with more than 17 years of leadership in dynamic, cross-functional environments. At SAP, he currently leads end-to-end product management for Discrete Industries. Previously, he headed supply chain management initiatives across the global SAP Experience Centers network and oversaw the SAP S.Factory Walldorf, helping customers in process and discrete industries accelerate their digital transformation. Before joining SAP, Matthias served as a Group Leader and Product Owner in a corporate IoT startup at Bosch and worked as an Innovation Manager in Corporate Logistics. Earlier in his career, he was a Project Lead and Head of Department in R&D at RWTH Aachen. He has collaborated with customers across automotive, aviation, pharmaceuticals, mechanical engineering, and logistics. Matthias holds a Diplom in industrial engineering from the Karlsruhe Institute of Technology and a doctorate in mechanical engineering from RWTH Aachen.Host 1: Richard Howells⁠⁠⁠⁠Richard Howells⁠⁠⁠⁠ has been working in the Supply Chain Management and Manufacturing space for over 30 years. He is responsible for driving the thought leadership and awareness of SAP's ERP, Finance, and Supply Chain solutions and is an active writer, podcaster, and thought leader on the topics of supply chain, Industry 4.0, digitization, and sustainability.Host 2: Sin ToSin brings over 15 years of experience in the digital media and technology industry – primarily in marketing, business development, thought leadership, and editorial. At SAP, they ensure that SAP's supply chain solutions are properly visible with a focus on future trends and sustainable innovations as part of the Thought Leadership & Awareness Supply Chain Team.===== Show Links:SAP Digital Supply Chain: www.sap.com/scm Visit us at Hannover Messe (HMI): Hall 15, Booth F08Follow Us on Social Media : Matthias Deindl:LinkedIn: https://www.linkedin.com/in/mdeindl/   Richard Howells:LinkedIn: www.linkedin.com/in/richardjhowells Sin To: LinkedIn: www.linkedin.com/in/sin-to-5334208 SAP Digital Supply Chain:LinkedIn: www.linkedin.com/showcase/sapdsc/ Please give us a like, share, and subscribe to stay up-to-date on future episodes!  ===== Chapters: 00:00:00 Vision for AI Supply Chains00:01:51 Meet Matthias and Industrial AI Today00:02:14 Two AI Tracks Robots and Assistants00:03:30 Data Foundations and Business Data Cloud00:05:04 Deployment Challenges and Quick Win Use Cases00:06:46 Embodied AI Inspection Robots00:09:51 Resilience Roadmap Transparency to Agents00:12:25 Human Machine Collaboration at the SAP Booth00:15:41 Ecosystems and Partner Integration00:19:17 Closing and How to Find SAP at Hannover Messe

Nikki Gonzales has built a career at the intersection of industrial automation, software, and systems thinking, and in this episode, she makes the case that the next chapter of manufacturing won't be defined by AI alone. It will be defined by how well people understand process, data, machines, and the interfaces that connect them. The future of automation is as much about human judgment and lifelong learning as it is about smarter technology. A big part of that story runs through the human-machine interface. The HMI has evolved from a control screen into a communication layer between machines, operators, plant systems, and increasingly, AI-enabled tools. The conversation explores how open standards, AI assistants, scripting support, and emerging protocols like MCP could expand what industrial systems can do, while also lowering the barrier for more people to work with them. But the episode is not a story about technology replacing expertise. We also discuss technology raising the premium on real understanding. Gonzales argues that even as AI becomes more capable, foundational knowledge of physics, process, controls, and manufacturing systems still matters. She also makes the case that careers in this space are built not just through technical skill, but through curiosity, relationships, mentorship, and the willingness to keep learning.In this episode:How NVIDIA's Inception program is helping a 30-year-old HMI company innovate like a startupWhy HMIs are a great starting point for applied AI projectsWhat MCP can make possible in industrial automation that a standard API connection cannotHow AI could lower the barrier to entry in automation while raising the bar for process knowledgeWill the future of skills be more specialized, or more generalized?3 Big Takeaways:The HMI may be one of the best places to start with applied AI in manufacturing. The HMI already sits at the intersection of the machine, the PLC, plant systems, and operator decision-making, which makes it a natural place to aggregate data and connect AI tools. In that sense, the future of applied AI in manufacturing is about smarter interfaces that can translate, contextualize, and move information where it needs to go. AI will make automation more accessible, but not less demanding. Nikki argues that AI can reduce the barrier to entry by helping newer users with scripting, debugging, and development workflows, especially on the HMI side. But she is equally clear that these tools raise the premium on people who understand process, physics, controls, and how manufacturing systems actually work, because the consequences of getting it wrong are too high. The future automation workforce will be built as much through community as through technology. Through Automation Ladies and OT SCADA CON, Nikki makes the case that technical careers are shaped not only by tools and training, but also by mentorship, relationships, and exposure to the full range of roles in the industry.Resources in this Episode:Connect with Nikki on LinkedInLearn more about Automation LadiesMore links & resources: https://techeWe want to hear from you! Send us a text.Instagram - Facebook - YouTube - TikTok - Twitter - LinkedIn

This week Shawn Tierney meets up with Mohua Ghosh of Schneider Electric to learn about their AI Assistant for PLC Code Generation in this episode of #TheAutomationPodcast. Unlock access to the ad free EXTENDED EDITION by joining our channel at https://TheAutomationBlog.com/join or https://youtube.com/@InsightsIA join. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast, Free Edition: Note: Below member’s will also find an ad-free and extended edition of this episode. To unlock the ad free extended episode, you can become a member here. Listen to The Automation Podcast from The Automation Blog: Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

This week Shawn Tierney meets up with Steven Mustard to talk about his new book on Operational Technology (OT) System Risk Management in this episode of #TheAutomationPodcast. To learn more about becoming a member, visit https://TheAutomationBlog.com/join or https://youtube.com/@InsightsIA/join. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast: Note: To support our work and keep new episodes coming, consider becoming a member here. Listen to The Automation Podcast from The Automation Blog: Links to Steven’s Book: Amazon Link Publisher Link Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

Carl Gould, CTO and co-founder of Inductive Automation, joins hosts Phil Seboa and Ed Fuentes for an in-person conversation recorded in Australia ahead of the Ignition Everywhere event in Brisbane.Carl traces Ignition's journey from FactorySQL in 2003 to the 8.3 release, which introduces file-based configuration, Git and GitOps compatibility, Perspective offline mode, and a new architecture for managing distributed OT systems at scale. He breaks down the three design principles that have guided the platform from day one (cost, convenience, and capability), shares his evolving take on AI in industrial automation, and explains why he calls the IT/OT divide "a fictional line."In this episode, we discuss:The 8.3 release: file-based config, GitOps, deployment modes, and Perspective offlineScaling from thousands of tags to millions with distributed, decoupled architecturesWhy AI in industrial automation is a means to an end, not a product in itselfThe community and culture behind Ignition's worldwide growth---------------------------This episode is proudly made possible by PLCnext TechnologyPLCnext Technology is the ecosystem for industrial automation consisting of open hardware, modular engineering software, a global community, and a digital software marketplace.Learn more at:⁠⁠⁠https://www.plcnext-community.net/news/synergy-edge-cloud/---------------------------FlowFuse at Hannover Messe 2026Discover how FlowFuse empowers you to build, deploy, and scale industrial automation -- your way. Visit FlowFuse at Hall 014, Stand K26 during Hannover Messe (April 20-24, 2026) and experience live demonstrations of FlowFuse connecting the entire industrial stack -- from PLCs on the shop floor to MES, ERP, and cloud services -- enabling real-time industrial connectivity, data integration, and AI-powered operations.Let's transform industrial data together -- live, integrated, and in real time.Claim your free pass and learn more: https://flowfuse.com/events/hannover-messe-2026/---------------------------Carl Gould is the CTO and co-founder of Inductive Automation. He has been building and guiding the Ignition platform since 2003. Under his leadership, Ignition has grown from a SQL connectivity tool into a comprehensive platform used across industries worldwide for SCADA, HMI, MES, and IIoT applications.Connect with Carl Gould on LinkedIn: https://www.linkedin.com/in/carl-gouldLearn more about Inductive Automation: https://inductiveautomation.comConnect with Phil on LinkedIn: ⁠https://www.linkedin.com/in/philseboa/⁠Connect with Ed on LinkedIn: ⁠https://www.linkedin.com/in/edfuentes/

This week Shawn Tierney meets up with Johann Kotze of Rockwell Automation to learn what’s new in FactoryTalk View 15 & 16 in this episode of #TheAutomationPodcast. Unlock access to the ad free EXTENDED EDITION by joining our channel at https://TheAutomationBlog.com/join or https://youtube.com/@InsightsIA join. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast, Free Edition: Note: Below member’s will also find an ad-free and extended edition of this episode. To unlock the ad free extended episode, you can become a member here. Listen to The Automation Podcast from The Automation Blog: Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

In this episode of Manufacturing Hub, Vlad and Dave sit down with Travis Cox and Kevin McCluskey from Inductive Automation to unpack what was actually proven at ProveIt and why it matters for teams trying to modernize plants without building a fragile mess of point to point integrations. If you have ever looked at a shiny demo and wondered what the real architecture looks like, how it scales beyond a single line, and what it takes to roll out across multiple sites without turning every change into a high risk event, this conversation is for you.Travis and Kevin walk through their ProveIt Enterprise B build and the thinking behind it. The core idea is simple but powerful: treat the factory like a system that needs a shared digital infrastructure, built on open standards, where data is contextualized and reusable. They break down how they used Ignition Edge close to PLCs for resiliency, local HMIs, and disciplined data modeling, then moved data through MQTT into a Unified Namespace so multiple applications can consume the same trusted signals and context. This is the difference between “we can connect to anything” and “we can scale without rewriting everything every time the business changes.” Open standards show up repeatedly in the conversation because ProveIt is specifically designed to force interoperability and practical implementation tradeoffs. Inductive Automation has also written about ProveIt as a place where MQTT, OPC UA, and SQL show up as real foundations rather than slogans.From there, the episode gets into the part that should make both OT and IT teams pay attention: modern deployment practices applied to industrial applications. Kevin outlines a clear maturity path from a single designer workflow to version control, then to containerized deployments, and finally to full GitOps style promotion across dev, staging, and production using tools like Argo CD, Helm, Kubernetes, and release promotion concepts that look like what the software world has used for years. Argo CD is explicitly built around Git repositories as the source of truth for desired state, which is exactly why it fits this style of deployment. The live portion of the conversation demonstrates how fast this can get when the infrastructure is treated as code: they spin up a brand new “site four” by submitting a form, generating a pull request, merging it, and letting the pipeline do the rest.Timestamps00:00 Welcome back and why this ProveIt recap matters01:35 Meet Travis Cox and Kevin McCluskey from Inductive Automation03:10 What ProveIt is and the key vendor questions it forces05:20 Enterprise B architecture overview from PLC to Edge to site to enterprise07:30 HMI walkthrough across liquid processing, filling, packaging, palletizing09:05 Why deploy Ignition Edge instead of only a centralized site gateway12:05 Design once, reuse everywhere and what that means for scaling quickly14:35 On prem realities versus cloud infrastructure in the ProveIt environment17:10 MCP, n8n workflows, and bringing live operational context into AI20:40 i3X style API access to models, history, and alarms for interoperability23:15 GitHub, Docker Compose, Helm, Kubernetes, Argo CD, Cargo and GitOps promotion36:55 Spinning up a new site live and what it changes for multi site rolloutsAbout the hostsVlad Romanov is an electrical engineer and MBA who has spent over a decade building and modernizing manufacturing systems across industrial automation, controls, and plant operations. Through Joltek, Vlad works with manufacturers to assess current state OT foundations, reduce modernization risk, improve reliability, and build internal capability through practical training and standards that stick.Dave Griffith co hosts Manufacturing Hub and brings a practitioner lens focused on what works on the plant floor, how architectures survive real constraints, and how industrial teams can modernize without breaking production.About the guestsTravis Cox is Chief Technology Evangelist at Inductive Automation and has spent over two decades helping customers and partners design scalable architectures, apply best practices, and deliver real solutions with Ignition.Kevin McCluskey is Chief Technology Architect at Inductive Automation and works with organizations on architecture decisions, platform direction, and enabling the next generation of industrial applications.Learn more about Joltekhttps://www.joltek.com/serviceshttps://www.joltek.com/book-a-modernization-consultation

In this episode, we dive deep into the fascinating world of neurocardiology with Jeff Goelitz, Director of Education at the HeartMath Institute. For over 35 years, HeartMath has been at the forefront of researching the "heart-brain" connection, proving that the heart is far more than just a mechanical pump. Jeff explains the revolutionary concept of "coherence"—a state of physiological balance where the heart, brain, and nervous system work in harmony to reduce stress and improve mental clarity. We explore the practical applications of HeartMath techniques for high-stress professions, including first responders, professional athletes, and the military. Jeff shares insights into how "heart rate variability" (HRV) serves as a key measure of our resilience and how simple breathing techniques can help us reset our nervous systems after trauma or daily stress. Whether you are a parent, an educator, or someone looking to manage anxiety, this episode provides the scientific foundation and actionable tools to tap into your heart's intelligence. Key Takeaways: ·         Beyond the Pump: Understand why the heart is now classified as an endocrine gland and a sensory organ with its own "brain" of 40,000 neurons. ·         The Power of Coherence: Learn how a state of heart coherence facilitates better decision-making and "cortical facilitation" in the brain. ·         Resilience for First Responders: Discover how HeartMath is being used to help firefighters and soldiers manage PTSD and maintain a "baseline of calm" in dangerous situations. ·         Measuring Inner Balance: An overview of HRV technology and how biofeedback sensors like the "Inner Balance" help track emotional stability. ·         The Heart-Brain Dialogue: Why the heart sends more neural traffic to the brain than the brain sends to the heart. About Jeff Goelitz   Jeff Goelitz has gained vast knowledge, experience and understanding of behavior and social and emotional learning in the many years he has been with HMI. His 35 years of experience, including five as a private school teacher, have helped him become a leader in the development of stress management and education solutions for children and adults. He has spent two decades practicing and teaching the HeartMath System of tools and technology, and his expertise has helped HMI find practical solutions for lowering student test anxiety and improving test score   Social links for Jeff Goelitz https://www.facebook.com/jeff.goelitz.1/ https://www.facebook.com/HeartMathMyKids https://www.heartmath.org/   Social links for Doug Beitz Facebook: https://www.facebook.com/dougbeitz/ Instagram: https://www.instagram.com/dougbeitz/ Website: https://buymeacoffee.com/dougbeitz Spotify: https://open.spotify.com/show/6mQ258nugC3lyw3SpvYuoK?si=7cec409527d34438 Apple Podcasts: https://podcasts.apple.com/au/podcast/intuitive-conversations-with-doug/id1593172364 LinkedIn: https://www.linkedin.com/in/doug-beitz-472a4b338/ TikTok: https://www.tiktok.com/@dougbeitz178  

This week Shawn Tierney meets up with Hany Fouda of Schneider Electric to discuss What’s Driving Open Automation initiatives in this episode of #TheAutomationPodcast. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast from The Automation Blog: Listen to The Automation Podcast from The Automation Blog: The Automation Podcast, Episode 264 Show Notes: Special thanks goes out to Hany Fouda of Schneider Electric for coming on the show, and to Schneider Electric for sponsoring this episode. Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

In this episode of the Harvest Growth Podcast, Jon LaClare sits down with Scott Brown, founder of Snax Depot and VendingAd.com, to explore a surprising but powerful advertising opportunity hiding in plain sight: vending machines.Most advertising today competes in speed mode — scrolling, swiping, skipping, and digital fatigue. But what happens when you reach people in a true moment of pause?Scott shares how modern, AI-powered vending machines equipped with 15-inch HMI screens are transforming everyday snack stops into high-attention marketing moments. With 10-second visual ads placed directly at eye level, local businesses can reach a captive audience in gyms, warehouses, apartment complexes, and office spaces — where repetition, frequency, and exclusivity drive real brand recall.This isn't about chasing clicks. It's about being remembered.You'll learn why physical-world visibility still matters in a digital-first era, how QR codes bridge real-world impressions with measurable online results, and why limiting each machine to just 10 advertisers dramatically increases brand impact.If you serve local customers and want repeated, real-world exposure in places where people actually pause — this conversation may change how you think about marketing.In today's episode of the Harvest Growth Podcast, we cover:Why “paused attention” is more valuable than endless digital impressionsThe psychology of repetition and frequency in local advertisingHow vending machine screens create credibility in physical spacesWhy limiting advertisers per screen increases impactHow QR codes make offline ads measurable and retargetableWhich types of businesses benefit most from vending machine advertisingWhy exclusivity (no competitors per machine) strengthens brand recallHow physical and digital marketing can work together seamlesslyVisit VendingAd.com to learn more or schedule a strategy call to see if this opportunity is a fit for your business — especially if you serve customers in the Boston market.To be a guest on our next podcast, contact us today!Do you have a brand you'd like to launch or grow? Visit HarvestGrowth.com and set up a free consultation with our team.

This week Shawn Tierney meets up with John DeTellem of Siemens to walk through the steps of migrating an existing S7 PLC and its Program to TIA Portal v21 in this episode of #TheAutomationPodcast. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast from The Automation Blog: Listen to The Automation Podcast from The Automation Blog: The Automation Podcast, Episode 263 Show Notes: Special thanks to John DeTellem of Siemens for coming on the show, and to Siemens for sponsoring this episode. For more information please see the below links: TIA Portal V21 Sales & Delivery Release TIA Portal V21 Technical Slides TIA Portal V21 Trial Download TIA Portal in the Cloud TIA Portal Documentations Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

This week Shawn Tierney meets up with Soroush Karimzadeh of Novarc to discuss their AI-Powered Autonomous Welding Robotics in this episode of #TheAutomationPodcast. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast from The Automation Blog: Listen to The Automation Podcast from The Automation Blog: The Automation Podcast, Episode 262 Show Notes: Special thanks goes out to Soroush Karimzadeh for coming on the show, and to Novarc for sponsoring this episode. To learn more about their AI-Powered Autonomous Robotic Welding solution, see the below links: Soroush Karimzadeh, LinkedIn, CEO & CoFounder, Novarc Technologies Inc.: https://www.linkedin.com/in/soroushkarimzadeh Novarc Technologies, LinkedIn: https://www.linkedin.com/company/novarc-technologies-inc- Novarc Technologies Website: https://www.novarctech.com/ NovAI™ – Adaptive Welding: The full power of AI and machine vision in welding automation: https://www.novarctech.com/products/novai/ Spool Welding Robot (SWR™): https://www.novarctech.com/products/spool-welding-robot/ Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

【节目简介】大屏，算力，HMI已然成为汽车的兵家必争之地然而，无论你再喜欢你的车，你每天用它的时间也不过2小时。有些人，却会在他的车上一呆就是一天，甚至好几天。他们的头衔是卡车司机然而又有多少卡车升级了自己的仪表和车机呢？本期《孤岛车谈》新熠和三位HMI专家聊聊这个使用时间最长、需求最特殊、被车企了解却最少的群体的诉求该如何满足的问题。美国一卡车司机的五个屏美国一卡车司机的五个屏美国一卡车司机的五个屏2022 Tesla Semi内饰2022 Tesla Semi内饰2025 Peterbilt 579EV 内饰查尔姆斯大学的低科技卡车HMI提议查尔姆斯大学的低科技卡车HMI提议 查尔姆斯大学的低科技卡车HMI提议苇渡卡车的内饰吉利远程星瀚卡车的内饰起亚PV5的屏幕基于Google Automobile【话题成员】高峰 商用车智能座舱和交互软件开发经理June 交互设计师侯依萱 人因工程师剪辑 PSC，猫又，许新熠，罗新雨片尾曲 All I do is drive by Johnny Cash (1974)【时刻文稿】26:38 特斯拉Semi的内饰49:45 HMI的一致性是基操1:17:57 收集用户需求1:28:43 如何用HMI给卡车司机情感慰藉1:39:12 卡车的大屏该如何分配空间1:46:12 功能的门槛不能太高1:49:10 账号体系打通【参考链接】【【专属】乘用车底盘系统开发 车辆动力学原理应用与正向开发工程实践 吴旭亭 系统构建车身动力学底盘知识体系书籍】#小程序://机械工业出版社旗舰店/商品/I4N8mLuPmjWkmRt【官网 车用动力电池系统设计与制造 中国汽车工程学会 电芯产品设计 电池系统产品设计 动力电池产品设计制造方法技术书籍】#小程序://机械工业出版社旗舰店/商品/P8isKji8jO5DkNc【汽车创新：前沿技术背后的科技原理】#小程序://机械工业出版社旗舰店/商品/7tltQzCQfJUWRVi【官网 广义车规级电子元器件可靠性设计与开发实践 左成钢 系统介绍汽车电子零部件的可靠性设计与开发 汽车电子 汽车工业技术书籍】#小程序://机械工业出版社旗舰店/商品/dBujAN68sEk1Rzl【智能驾驶：产品设计与评价】#小程序://机械工业出版社旗舰店/商品/Q8KWriuNDGdzlSs【官网 智能底盘关键技术及应用 线控执行 融合控制 失效运行 张俊智 智能底盘核心线控执行系统关键技术书籍】#小程序://机械工业出版社旗舰店/商品/5R5ZjdGhScib14ALow-Tech HMI for trucks - A study in driver-centered interaction. (Chalmers University of Technology, 2025): https://odr.chalmers.se/items/53fad8f4-c580-4436-b2cc-667f91610c86

This week Shawn Tierney meets up with Karim Kozman of Schneider Electric to discuss What’s Next for Industrial Automation in this episode of #TheAutomationPodcast. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast from The Automation Blog: Listen to The Automation Podcast from The Automation Blog: The Automation Podcast, Episode 261 Show Notes: Special thanks goes out to Karim Kozman of Schneider Electric for coming on the show, and to Schneider Electric for sponsoring this episode. Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

This week Shawn Tierney meets up with Manish Sharma of Emerson to learn about the Next Generation of PACSystems Industrial PCs in this episode of #TheAutomationPodcast. Unlock access to the ad free EXTENDED EDITION by joining our channel at https://youtube.com/@InsightsIA/join or https://TheAutomationBlog.com/join For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast: Note: Below is an ad-free extended edition of the show that’s a member perk. To unlock the extended episode, become a member here. Listen to The Automation Podcast from The Automation Blog: Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

This week Shawn Tierney meets up with John DeTellem of Siemens to learn what’s new in TIA Portal v21 in this episode of #TheAutomationPodcast. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast from The Automation Blog: Listen to The Automation Podcast from The Automation Blog: The Automation Podcast, Episode 259 Show Notes: Special thanks to John DeTellem of Siemens for coming on the show, and to Siemens for sponsoring this episode. For more information please see the below links: TIA Portal V21 Sales & Delivery Release TIA Portal V21 Technical Slides TIA Portal V21 Trial Download TIA Portal in the Cloud TIA Portal Documentations Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

Technology modernization in manufacturing is not a list of shiny tools. It is a sequencing problem. In this episode of Manufacturing Hub, Vlad Romanov and Dave Griffith break down why the executive vision for AI often collides with the reality of the plant floor, and what a practical path forward actually looks like when you account for data quality, legacy controls, networking, and the true cost of integration.A core theme in this conversation is imperfect information. Leaders often believe the data already exists because reports exist. But a stack of paper, a few spreadsheets, or a single counter value is not the same as contextualized, trustworthy history that can drive decisions or support advanced analytics. Vlad and Dave walk through why foundational work matters, what teams usually miss during modernization, and how quickly the bill grows when you discover your architecture is outdated, undocumented, or full of dependencies you cannot see until you open panels and start tracing signals.You will also hear a grounded debate on how to think about SCADA, MES, historians, dashboards, and what it would actually mean to “feed data into AI” in a manufacturing context. The takeaway is simple. If you want better outcomes, you need a better understanding of your current state, a clear business case, and a roadmap that prioritizes what matters operationally. Modernization is not one big upgrade. It is a series of decisions that either reduce friction or create it.About the hostsVlad Romanov is an industrial automation and manufacturing expert focused on plant assessments, controls and data architecture, IT and OT integration, and workforce upskilling. Vlad has over 10 years of experience across large manufacturers and complex multi site environments, working from PLC and HMI layers up through SCADA, MES, and ERP integration programs. He is the founder of Joltek, where the mission is to help manufacturers modernize safely, build internal capability, and deliver results that actually survive handoff to operations.Learn more about Joltekhttps://www.joltek.comhttps://www.joltek.com/servicesDave Griffith is an industrial automation practitioner and consultant who works closely with manufacturers to modernize legacy environments, improve reliability, and build practical systems that operators and maintenance teams can support. Dave brings a strong perspective on what is feasible in real plants, where uptime, risk, budget, and organizational readiness drive every decision.Timestamps00:00:00 Welcome and why this month is about technology modernization00:02:10 The real problem with “just add AI” in manufacturing00:04:15 Quick background on Vlad and Dave and the work they do00:05:25 The disconnect between the perfect factory vision and the plant floor00:06:25 Vlad on business cases, integration reality, and infrastructure gaps00:09:05 Dave on imperfect information and why reports are not data00:14:35 What executives actually want from AI and why it is often about people constraints00:20:25 How to get there, hardware first, data normalization, and context00:22:05 Vlad on assessments, legacy hardware, and why upgrades get complicated fast00:39:00 New facility planning mistakes and why early decisions lock you in00:45:10 You have the data, now what, OEE baselines, bottlenecks, and root causes00:58:10 Final takeaways, inventory your architecture and treat data like an assetReferences and links mentionedManufacturing Hub Podcasthttps://www.manufacturinghub.liveProveIt Conferencehttps://www.proveitconference.comAutomate Showhttps://www.automateshow.comIgnition Community Conferencehttps://icc.inductiveautomation.comIf you are watching on YouTube, subscribe so you do not miss the rest of this month's deep dives on hardware, data teams, and practical applications that actually work on real plant floors.

This week Shawn Tierney meets up with Dante Vaccaro of Schneider Electric to discuss Digital Transformations and Trends in Industrial Automation in this episode of #TheAutomationPodcast. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast from The Automation Blog: Listen to The Automation Podcast from The Automation Blog: The Automation Podcast, Episode 258 Show Notes: Special thanks goes out to Dante Vaccaro of Schneider Electric for coming on the show, and to Schneider Electric for sponsoring this episode. Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

Send us a textOn this episode of Embedded Insiders, Jim McElroy, Senior Director Sales and Market Development, and Janez Ulcakar, R&D Manager at TASKING, discuss what developers, especially in mission-critical spaces such as aerospace and automotive, need to do to produce efficient and productive safety and security-critical applications. TASKING recently acquired LDRA, and the companies have consolidated under the TASKING brand. Next, Rich and Vin are back with another Dev Talk discussing what it takes to properly design a great HMI, as well as how much effort needs to go into it. They also get some assistance from Renesas, who are making HMIs one of the company's core enabling technologies. Renesas will host a webinar on this topic on February 25th. But first, Editor-in-Chief Ken Briodagh is back from CES 2026. He's giving us a recap on the top trends and technologies on show at the event. For more information, visit embeddedcomputing.com

This week Shawn Tierney meets up with Matthew Dulcey of PRONETIQS, and Stefan Hild of Spur Insights, to learn how PRONETIQS helps Measure, Monitor, and Maintain Control Systems in this episode of #TheAutomationPodcast. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast from The Automation Blog: Listen to The Automation Podcast from The Automation Blog: The Automation Podcast, Episode 257 Show Notes: Special thanks goes out to Matthew Dulcey of PRONETIQS, and Stefan Hild of Spur Insights, for coming on the show, and to PRONETIQS for sponsoring this episode. If you’d like to learn more, please visit the below links: Follow on LinkedIn: https://www.linkedin.com/company/pronetiqs SID 5: https://pronetiqs.com/sid5 IntraVUE: https://pronetiqs.com/intravue Service & Support: https://pronetiqs.com/service-and-support Trainings: https://pronetiqs.com/trainings Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

Finding the root cause of an erratic signal can feel like chasing a ghost, especially when your trend lines show noise that shouldn't be there. In this session, we walk through a real-world troubleshooting scenario involving a Yamaha robot simulation where the A3 axis exhibited mysterious spikes. While it initially looked like signal noise or a math error in Studio 5000, the investigation led deep into the interaction between the PLC and the Ignition SCADA system. We explore why a standard cross-reference in the PLC didn't reveal the culprit and how switching to read-only communications finally exposed a hidden bidirectional tag write.We also discuss the common trap of "patching" problems with software filters instead of identifying the source. Whether it is a bad shield on an analog line or an accidental setting in your HMI, understanding the "why" behind the spike is what separates a technician from a parts changer. Additionally, we touch on the challenges of modern Ethernet troubleshooting, the limitations of Wireshark without port mirroring, and why the "View Diagnostics" tool in Ignition is a game-changer for identifying communication conflicts.Helping you become a better technician so you will always be in demandNot sure what video to watch next? Enhance your skills and track your progress at https://controls.tw/yt-courses!Items used in this video:PLC Trainer https://controls.tw/yt-plc-trainersThe above links make these videos possible. Please use them!

Shawn Tierney meets up with Drew Allen of Grace Technologies to discuss Automation, Safety, the history of Grace, and more in this episode of The Automation Podcast. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast from The Automation Blog: Listen to The Automation Podcast from The Automation Blog: The Automation Podcast, Episode 256 Show Notes: To learn about becoming a member and unlocking hundreds of our “member’s only” videos, click here. Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

Shawn Tierney meets up with Erik Nieves of Plus One Robotics to discuss Robotics in Warehouse Automation in this episode of The Automation Podcast. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast from The Automation Blog: Note: As mentioned above, this episode was not sponsored so the video edition is a “member only” perk. The below audio edition (also available on major podcasting platforms) is available to the public and supported by ads. To learn more about our membership/supporter options and benefits, click here. Listen to The Automation Podcast from The Automation Blog: The Automation Podcast, Episode 255 Show Notes: To learn about becoming a member and unlocking hundreds of our “member’s only” videos, click here. Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

Industrial Talk is talking to Nikki Gonzales, Director of Business Development at Weintek USA about "HMI - Human Machine Interface". Scott Mackenzie hosts Nikki Gonzales on the Industrial Talk Podcast to discuss the human-machine interface (HMI). Nikki shares her background, including her Icelandic roots and career journey in sales engineering and AI startups. She highlights Win Tech, a Taiwanese company specializing in HMIs, which manufactures over 2 million HMIs annually. Win Tech's HMIs are known for their durability, connectivity, and cost-effectiveness. Nikki emphasizes the importance of continuous learning and the challenges in industrial B2B sales. She also mentions her podcast, Automation Ladies, and encourages listeners to connect with her on LinkedIn. Action Items [ ] Check out the Wintec website at automation.io[ ] Listen to Nikki's podcast "Automation Ladies"[ ] Reach out to Nikki Gonzales Outline Introduction and Welcome Scott Mackenzie introduces the Industrial Talk Podcast, emphasizing its focus on industry professionals and innovations.Scott welcomes listeners and expresses gratitude for their support, highlighting the importance of continuous learning in the industry.Scott introduces Nikki Gonzales , the guest for the episode, and mentions the topic of discussion: the human-machine interface (HMI).Scott shares his personal experience with learning Spanish and the importance of passion and desire for continuous learning in the industry. Scott's Journey and Podcast Insights Scott discusses his journey into podcasting, starting eight years ago to understand marketing better.He mentions the importance of communicating company stories in an approachable and human way.Scott introduces two of his podcasts: "Ask Molly" and "Business Beatitudes," highlighting their focus on marketing insights and the soul of the industrial sector, respectively.Scott encourages listeners to check out these podcasts for valuable industry insights. Nikki Gonzales's Background and Career Journey Nikki shares her background, mentioning her move from Iceland to the U.S. in middle school and her father's career as an electrical engineer.She describes her early work experiences, starting with her father's small business and progressing through various roles in sales engineering and marketing.Nikki discusses her career path, including her work with sensor manufacturers, machine vision, motion control, software design, and AI startups.She highlights her recent role with a startup focused on supply chain software and inventory management, and her current position with Win Tech, an HMI manufacturer. Challenges in Industrial B2B Sales Scott and Nikki discuss the challenges of industrial B2B sales, particularly the complex landscape shaped by historical laws and regulations.Nikki explains the historical context of industrial sales, including the restrictions on manufacturers selling directly to consumers and the reliance on regional distributors.They discuss the differences in sales practices between the U.S. and Europe, where such restrictions are considered anti-competitive.Nikki shares her experiences with the complexities of industrial B2B sales, including the difficulties in digitalizing and simplifying the buying process. Win Tech and HMI Technology Nikki provides an overview of Win Tech, a Taiwanese company specializing in HMIs, and its history of innovation in touchscreen technology.She explains the role of HMIs in industrial automation, describing them as the interface between humans and machines.Nikki highlights Win Tech's...

Shawn Tierney recaps his trip to Schneider Electric’s Innovation Summit Las Vegas in this episode of The Automation Podcast. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Podcast from The Automation Blog: Listen to The Automation Podcast from The Automation Blog: The Automation Podcast, Episode 254 Show Notes: Special thanks to Schneider Electric and their Marketing Partners for sponsoring my trip to this year’s Innovation Summit! Below are all the links I mentioned in this episode: Innovation Summit 2025 – All Videos and Images Modicon Edge I/O NTS: Unbox, Setup & Use with Logix First Time Programming a Modicon M262 PLC Schneider's Altivar Solar ATV320 VSD (P218) Pro-face HMIs & IPCs by Schneider Electric (P195) Schneider Altivar Machine Drives (P187) Harmony HMIs and iPCs from Schneider Electric (P176) TeSys Island: Smart Motor Starters from Schneider Electric (P170) Modicon Machine Level PLC Product Line Update (P161) Next Generation Automation with Schneider Electric (P96) Read the transcript on The Automation Blog: (automatically generated) Shawn Tierney (Host): coming Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

Shawn meets up with Travis Cox of Inductive Automation to learn about the new features found in Ignition 8.3 in this episode of The Automation Show. For any links related to this episode, check out the “Show Notes” located below the video. Watch The Automation Show from The Automation Blog: Listen to The Automation Show on The Automation Blog: The Automation Show, Season 2 Episode 37 Show Notes: Special thanks to Travis for coming on the show, and to Inductive Automation for sponsoring this episode so we could release it ad free! To learn more about Ignition, please see the below links: What’s New in Ignition 8.3 Download Ignition 8.3 Ignition User Manual 8.3 Documentation Learn Ignition and earn a free credential   Schedule an Ignition demo Travis’ first appearance back in episode TAP 124 Read the transcript on The Automation Blog: (automatically generated) Shawn Tierney (Host): coming later Vendors: Would you like your product featured on the Podcast, Show or Blog? If you would, please contact me at: https://theautomationblog.com/contact Until next time, Peace ✌️ If you enjoyed this content, please give it a Like, and consider Sharing a link to it as that is the best way for us to grow our audience, which in turn allows us to produce more content

Factories don't run on magic. They run on control panels that turn raw sensor data into precise, safe action—and we're kicking off a three‑part series to show exactly how. We break down the essentials in plain language so you can open a panel door and actually know what you're seeing, from the disconnect and power supplies to the PLC logic and the HMI screens operators trust.We start with the PLC, the rugged brain that reads inputs, executes logic, and drives outputs with millisecond timing. You'll learn why modern controllers are networked, how modular I/O scales with your process, and what clean wiring and accurate channel maps do for uptime. Then we shift to the HMI, the operator window into the process. A well‑built screen mirrors the machine, makes status obvious, and keeps routine actions outside the enclosure for safer work. Clear colors, readable values, trends, and alarms turn data into smart, fast decisions.Power is the quiet foundation. We walk through the pathway: visible disconnects, fuses and breakers sized for protection, control transformers that step down voltage, and 24 VDC power supplies that feed sensors, relays, and PLC cards. Grounding, spacing, and heat management guard both people and electronics. Along the way, we share practical tips to read a panel like a map: trace power first, find the PLC and I/O, compare HMI values to the machine, and rely on current drawings stored on the door. These habits, backed by UL 508A and NFPA 79 principles, create safer, more reliable systems that technicians can troubleshoot under pressure.With nearly a century of experience supporting automation across industries, we believe craftsmanship and documentation are force multipliers. If you're new to automation, mentoring someone who is, or just want a refresher, this guide will raise your confidence on the plant floor. Subscribe for the next parts of the series, share this with a colleague who needs it, and leave a quick review to help more pros find the show.Keep Asking Why...Read our latest article on Industrial Manufacturing herehttps://eecoonline.com/inspire/panels_101 Online Account Registration:Video Explanation of Registering for an AccountRegister for an AccountOther Resources to help with your journey:Installed Asset Analysis SupportSystem Planning SupportSchedule your Visit to a Lab in North or South CarolinaSchedule your Visit to a Lab in VirginiaSubmit your questions and feedback to: podcast@eecoaskwhy.comFollow EECO on LinkedInHost: Chris Grainger