Podcasts about gprs

Host Osama Aduib from ISS Facility Services sits down with his colleagues Paul Ratkovic and Amelia Ekus to discuss the “hidden menu” of facility management. The conversation explores how invisible systems, operational decisions and hospitality-focused thinking shape the workplace experience in ways occupants may never notice directly, from HVAC and lighting to food service, cleanliness and comfort. Paul and Amelia share insights on empathy in facility management, anticipatory service, workplace innovation and how FM teams can create seamless, people-centered environments through collaboration and intentional design. They also discuss the role of technology, AI and data-driven insights in supporting proactive building operations while emphasizing that hospitality, human connection and emotional intelligence remain at the center of exceptional workplace experiences. This episode is sponsored by SiteMap®, powered by GPRS. Learn more at sitemap.com/ifma Resources: Re-thinking the Purpose of the Workplace Experience The Enduring Significance of Place Timestamps: 0:00 - Introduction to the “Hidden Menu” of Facility Management 2:01 - How hospitality and facility management intersect 3:10 - Defining the hidden menu in workplace experiences 4:38 - Why engineering is one of the most hospitality-driven functions 6:18 - Frictionless experiences vs. “good friction” in the workplace 8:47 - The emotional impact of HVAC, comfort and building systems 11:10 - Emotional intelligence and empathy in facility management 14:28 - Innovation, anticipation and proactive workplace experiences 16:35 - AI, data and the future of anticipatory service 18:55 - How engineering teams create invisible, seamless experiences 20:35 - Building a culture of hospitality across FM teams 22:10 - Using sensors and data to improve occupant experiences 23:40 - Predictive analytics, occupancy insights and space behavior 25:35 - Practical ways FM leaders can activate the hidden menu 27:15 - Why “placemakers” mindset matters in FM 28:20 - Final thoughts on service, visibility and human-centered experiences 30:20 - Closing remarks Connect with Us:LinkedIn: https://www.linkedin.com/company/ifmaFacebook: https://www.facebook.com/InternationalFacilityManagementAssociation/Twitter: https://twitter.com/IFMAInstagram: https://www.instagram.com/ifma_hq/YouTube: https://youtube.com/ifmaglobalVisit us at https://ifma.org

What happens when a longtime facility manager steps beyond the built environment to help reshape it through technology? In this episode of Connected FM, host Brent Ward welcomes Billy Holder, Founder & CEO at Project Aidra, for a conversation about digital transformation, AI and the evolving role of facility management in a technology-driven world. Drawing from nearly 30 years of experience in facility management, Billy shares how recurring operational frustrations led him from the FM field into the PropTech space. He discusses why documentation overload, disconnected systems and inefficient workflows pushed him to search for smarter solutions powered by AI and automation. The conversation explores how facility managers can use technology to augment the workforce instead of replacing it, why human connection still matters in smart buildings, and what organizations should consider when evaluating PropTech platforms and AI-enabled tools. Whether you are a facility manager navigating digital transformation or simply curious about where the built environment is headed next, this episode offers practical insights into the future of FM and the people shaping it. This episode is sponsored by SiteMap®, powered by GPRS. Learn more at sitemap.com/ifma Timestamps: 00:00 Introduction 03:37 Documentation Pain Points 05:31 Winning Tech Buy In 07:15 AI As Workforce Boost 08:57 Human Metrics Matter 12:19 PropTech Stack Essentials 16:14 Training Next Gen FMs 18:55 ESG Reality Check 23:29 The Beautiful Mess Story 27:08 Hidden Wins Of Adoption 29:41 FM In Five Years 30:50 Start Small Advice 32:04 Closing Remarks Connect with Us:LinkedIn: https://www.linkedin.com/company/ifmaFacebook: https://www.facebook.com/InternationalFacilityManagementAssociation/Twitter: https://twitter.com/IFMAInstagram: https://www.instagram.com/ifma_hq/YouTube: https://youtube.com/ifmaglobalVisit us at https://ifma.org

THIS EPISODE IS BROUGHT TO YOU BY: GPRS Before you cut, core, drill, trench, or start guessing what is inside the slab, call GPRS. GPRS helps contractors locate what is hidden below the surface with ground penetrating radar, utility locating, concrete scanning, video pipe inspection, leak detection, and mapping services.They help keep your jobsite safer, reduce costly hits, and give your team better information before the work starts.Learn more here: https://www.concretelogicpodcast.com/gprs ON THIS EPISODE OF THE CONCRETE LOGIC PODCASTIs Type IL cement really the reason concrete started acting different, or did it just expose the problems we already had?Type I/II cement may be making a comeback because contractors, producers, and owners want concrete to act like concrete again.But Concrete Bob Higgins says we need to be careful.Because before Type IL showed up, the concrete industry still had scaling, dusting, cracking, bad curing, water problems, surface failures, and specs that cared more about 28-day strength than long-term durability.So if Type I/II comes back, will the problems go away? Or will we lose our favorite excuse?In this episode, Seth and Bob talk about what really changed in cement, why older concrete behaved differently, why today's concrete may be more sensitive than the standards admit, and what the industry needs to fix before it repeats the same mistakes.Type IL may have exposed the problem. But it may not be the whole problem. WHAT YOU'LL LEARN · Is Type IL cement really the problem, or did it expose bad habits? · Why Type I/II cement may be coming back · What concrete problems existed before Type IL became common · Why older cement was coarser, slower, and often more durable · How finer cement changed heat, curing demand, cracking, and permeability · Why Type I and Type III cement are closer than most people realize · What self-desiccation means and why it matters at the concrete surface · Why the top inch of concrete may be the weakest link · What contractors and producers should ask before switching back to Type I/II · Why going back to Type I/II cement does not fix bad concrete habits CHAPTERS 00:00 Is Type IL really the problem? 04:03 Why Bob says the industry needs this conversation 06:07 What cement was like before modern concrete problems 08:17 Same 28-day strength, but more permeability 09:25 Type I vs Type III cement 13:19 Why curing may not be protecting the top inch 16:47 What self-desiccation means in plain English 18:52 Why precast concrete can have a surface problem 21:50 What to ask before switching back to Type I/II 24:07 Bob's Type IL limestone float experiment 25:29 Why the industry cannot waste this opportunity 27:17 Next topic: are admixtures being mishandled? GUEST INFO Bob Higgins, Concrete Bob Concrete chemistry consultant and returning guest on the Concrete Logic Podcast. Guest link: https://www.concretelogicpodcast.com/guests/robert-higgins/ CONCRETE LOGIC ACADEMY The people who understand concrete are the people who get listened to. Not the loudest person in the meeting. Not the guy repeating what he heard ten years ago. Not the person blaming every problem on the latest material change. The person who understands the “why” behind the concrete usually has the most valuable voice in the room. That is what Concrete Logic Academy is built for. You get practical concrete education, PDH courses, and real-world lessons pulled from the same topics we cover on the Concrete Logic Podcast. Cement changes. Specs change. Admixtures change. Owners change their minds. Your knowledge needs to keep up. Start learning here: https://www.concretelogicpodcast.com/concreteschool SUPPORT THE PODCAST If the Concrete Logic Podcast gives you value, send a little value back. You can support the show here: https://www.concretelogicpodcast.com/support/ You can also support the show through our KUIU affiliate link: https://www.concretelogicpodcast.com/kuiu Interested in sponsoring the podcast or working with Concrete Logic Media? Email Seth: seth@concretelogicpodcast.com CREDITS Producers: Jodi Tandett and Concrete Logic Media Music by: Mike Dunton https://www.mdunton.com/ WHERE TO FIND SETH Concrete Logic Podcast: https://www.concretelogicpodcast.com/ YouTube: https://www.youtube.com/@concretelogicpodcast LinkedIn: https://www.linkedin.com/in/seth-tandett/ Concrete Logic Academy: https://www.concretelogicacademy.com/ Until next time, let's keep it concrete.

In this episode of Connected FM, host Dean Stanberry sits down with Melissa Kaan, Founder & CEO of NOVA IFM, to explore how facility teams can use CMMS platforms to drive smarter operational and capital decisions. They discuss the importance of quality data, why CMMS systems should function as decision engines rather than digital filing cabinets and how proactive maintenance strategies can improve response times, compliance and long-term asset performance. Melissa also shares practical insights on asset management, technician engagement, data governance and translating operational trends into meaningful capital planning conversations. The conversation highlights how facility leaders can improve CMMS adoption, strengthen reporting practices and use data more effectively to support both daily operations and long-term portfolio planning. This episode is sponsored by SiteMap®, powered by GPRS. Learn more at sitemap.com/ifma Timestamps: 00:00 Introduction 02:29 Minimum Viable CMMS Data 04:52 Must Have Data Fields 06:16 From Records to Decisions 09:16 First 90 Days Wins 11:00 Data to Capital Plans 13:27 Leadership Review Rhythm 14:59 One Step This Week 16:53 Data Quality Wrap Up   Connect with Us:LinkedIn: https://www.linkedin.com/company/ifmaFacebook: https://www.facebook.com/InternationalFacilityManagementAssociation/Twitter: https://twitter.com/IFMAInstagram: https://www.instagram.com/ifma_hq/YouTube: https://youtube.com/ifmaglobalVisit us at https://ifma.org

THIS EPISODE IS BROUGHT TO YOU BY: GPRSBefore you cut, core, drill, trench, or start guessing what is inside the slab, call GPRS.GPRS helps contractors locate what is hidden below the surface with ground penetrating radar, utility locating, concrete scanning, video pipe inspection, leak detection, and mapping services.They help keep your jobsite safer, reduce costly hits, and give your team better information before the work starts.Learn more here: https://www.concretelogicpodcast.com/gprsON THIS EPISODE OF THE CONCRETE LOGIC PODCASTThe concrete industry spent the last few years blaming Type IL cement for almost everything.Cracking. Scaling. Low breaks. Slow set times. Higher water demand.Now Type I/II cement may be making a comeback.So what happens when the “bad guy” leaves the room and the same concrete problems are still standing there?Rich Szecsy joins the show to explain what he is seeing in the Dallas-Fort Worth market, why cement suppliers are shifting, and why this move back to Type I/II may expose an uncomfortable truth.Maybe Type IL caused some problems.Maybe it didn't.But concrete was never problem-free before Type IL showed up.WHAT YOU'LL LEARNIs the cement market really shifting back to Type I/II?Why did Type IL become so common after 2020?What happens when one cement type gets blamed for every concrete problem?Will cracking, scaling, low breaks, and set delays disappear?Why the producer-contractor relationship matters more than internet argumentsHow ready-mix producers may handle Type IL and Type I/II at the same timeWhy the market, not the noise, decides which cement gets usedCHAPTERS 00:00 Introduction 01:02 The big topic: Type I/II cement coming back 01:26 How to support the Concrete Logic Podcast 03:34 Rich's view on the Type IL vs Type I/II shift 04:24 Why Type IL became more available after 2020 05:31 Rich's 100% placement rate during the supply crunch 06:44 Concrete complaints blamed on Type IL 07:45 What happens if Type I/II returns and problems continue? 09:33 Contractors adjusting to changing cement types 10:07 Micro business needs vs macro industry needs 10:59 Past material changes that caused industry panic 11:24 Why concrete has always had variability 12:28 The old Type I vs Type II confusion 12:43 What cement suppliers are telling customers 13:05 Is the market asking for Type I/II again? 14:00 Why the market decides which cement wins 14:58 How quickly Texas shifted from Type I/II to Type IL 16:08 How ready-mix producers may handle both cement types 16:47 Submittals that allow either Type IL or Type I/II 17:29 Rich's blunt definition of quality 18:35 Why the producer-contractor relationship matters most 19:51 Jobsite meetings, AI research, and “raspberry” 20:54 Is the Type I/II shift really happening? 21:28 Closing thoughtsGUEST INFORich Szecsy, CEO, Big Town Concrete https://www.concretelogicpodcast.com/guests/rich-szecsy/CONCRETE LOGIC ACADEMYThe people who understand concrete are the people who get listened to.Not the loudest person in the meeting.Not the guy repeating what he heard ten years ago.Not the person blaming every problem on the latest material change.The person who understands the “why” behind the concrete usually has the most valuable voice in the room.That is what Concrete Logic Academy is built for.You get practical concrete education, PDH courses, and real-world lessons pulled from the same topics we cover on the Concrete Logic Podcast.Cement changes. Specs change. Admixtures change. Owners change their minds.Your knowledge needs to keep up.Start learning here: https://www.concretelogicpodcast.com/concreteschoolSUPPORT THE PODCASTIf the Concrete Logic Podcast gives you value, send a little value back.You can support the show here: https://www.concretelogicpodcast.com/support/You can also support the show through our KUIU affiliate link: https://www.concretelogicpodcast.com/kuiuInterested in sponsoring the podcast or working with Concrete Logic Media?Email Seth: seth@concretelogicpodcast.comCREDITSProducers: Jodi Tandett and Concrete Logic MediaMusic by: Mike Dunton https://www.mdunton.com/WHERE TO FIND SETHConcrete Logic Podcast: https://www.concretelogicpodcast.com/YouTube: https://www.youtube.com/@concretelogicpodcastLinkedIn: https://www.linkedin.com/in/seth-tandett/Concrete Logic Academy: https://www.concretelogicacademy.com/Until next time, let's keep it concrete.

What does strong leadership really look like in facility management? In this episode of Connected FM, host Wayne Whitzell welcomes Kathryn Lopez for a candid conversation on leadership, resilience and the human side of facility management. Drawing from decades of experience in global FM leadership, Kathryn shares lessons on staying calm under pressure, creating psychological safety for teams and developing people through mentorship and trust. She explains why great leaders “take the bullets and give the credit,” how mistakes can become growth opportunities and why empathy is essential in today's workplace. The conversation also explores how AI is beginning to transform facility management workflows, from streamlining communication to delivering faster operational insights. Wayne and Kathryn discuss the growing role of data, digital tools and “smart buildings” in attracting the next generation of facility management professionals into the industry. Whether you lead a global portfolio, manage an in-house FM team or are growing into leadership for the first time, this episode offers practical insights on building trust, navigating challenges and leading with humanity. This episode is sponsored by SiteMap®, powered by GPRS. Learn more at sitemap.com/ifma Timestamps: 00:00 Introduction 02:17 Global FM Reality Check 03:20 IFMA Roots and Impact 04:30 Staying Calm Under Fire 07:30 Smoke Jumper Problem Solver 10:02 Psychological Safety in Leadership 15:17 Mentorship Mistakes Growth 18:31 Ad Break 18:59 Empathy Versus Dictatorship 20:39 AI That Actually Helps 22:39 Attracting the Next Gen 26:14 Kindness and Empathy 29:06 Closing Thanks and Outro Connect with Us:LinkedIn: https://www.linkedin.com/company/ifmaFacebook: https://www.facebook.com/InternationalFacilityManagementAssociation/Twitter: https://twitter.com/IFMAInstagram: https://www.instagram.com/ifma_hq/YouTube: https://youtube.com/ifmaglobalVisit us at https://ifma.org

THIS EPISODE IS BROUGHT TO YOU BY: GPRSGPRS helps keep your jobsite safer by locating what is hidden before you cut, core, trench, or drill.Click the GPRS image on the Concrete Logic Podcast website or go here:https://www.concretelogicpodcast.com/gprsON THIS EPISODE OF THE CONCRETE LOGIC PODCASTSpring is when concrete starts telling the truth.After months of cold weather, snow, ice, rain, deicers, and freeze-thaw abuse, your existing concrete may start showing what it went through all winter.In this episode, Dr. Jon Belkowitz joins the show to talk about what to look for when the weather warms up. Scaling. Flaking. Blotchy spots. Exposed aggregate. White staining. ASR gel. Rust bleeding from cracks. All of it is concrete trying to tell you something.Some of it may be surface damage.Some of it may be a sign of a much bigger problem inside the concrete.And if you wait until the damage is obvious, you may already be late.WHAT YOU'LL LEARNWhy spring and early summer act like a “lie detector” for existing concreteWhy damaged concrete often looks darker or blotchy after rainWhat scaling, flaking, and surface loss can tell you about winter damageWhy broom finish disappearance may be a warning signHow ASR cracks hold water and reveal themselves after rainWhat white staining and gel coming out of cracks may meanWhy some concrete problems cannot simply be cleaned off or sealed overHow wetting, drying, deicing salts, and outside contaminants can keep feeding deteriorationWhy extending the life of existing concrete may be one of the most practical “green” moves in constructionCHAPTERS00:00 Introduction 02:52 What warm weather reveals about existing concrete 03:39 Concrete as a springtime lie detector 04:26 Why newer concrete may look white, blotchy, or damaged 05:22 Scaling, flaking, and lost broom finish 06:01 Why rain makes concrete damage easier to see 07:10 Why damaged concrete holds water 08:25 Rust, staining, and visible cracks 09:02 Spillways, white streaks, and concrete exudation 10:36 Alkali-carbonate reaction and internal concrete problems 11:27 What “oozing” gel from cracks means 12:43 Why cleaning the surface does not fix internal damage 13:00 Slowing deterioration versus fixing it 14:21 The practical side of reducing concrete's carbon footprint 15:11 How ASR cracks grow and spread 16:15 ASR research and gel morphology 17:17 Protecting concrete from outside contaminants 18:21 Concrete Logic Academy and PDH reminder 19:39 Closing thoughtsGUEST INFODr. Jon Belkowitz Intelligent Concrete https://www.concretelogicpodcast.com/guests/dr-jon-belkowitz/CONCRETE LOGIC ACADEMYMost concrete problems do not show up out of nowhere.They start with bad assumptions, missed warning signs, and people not knowing what they are looking at until the problem is already expensive.Concrete Logic Academy was built for the people who want to catch those problems earlier.Practical concrete training. PDH courses. Real-world education from people who actually understand the work.If you want to get better at reading concrete, asking better questions, and spotting issues before they turn into claims, start here:https://www.concretelogicpodcast.com/concreteschoolSUPPORT THE PODCASTIf the Concrete Logic Podcast has helped you think differently about concrete, consider supporting the show.You can make a one-time donation, become a monthly supporter, or share the podcast with someone in the industry who needs to hear it.https://www.concretelogicpodcast.com/support/You can also support the show through the KUIU affiliate link:https://www.concretelogicpodcast.com/kuiuCREDITSProducers: Jodi Tandett & Concrete Logic Media Music by Mike Dunton: https://www.mdunton.com/WHERE TO FIND SETHConcrete Logic Podcast: https://www.concretelogicpodcast.com/ YouTube: https://www.youtube.com/@concretelogicpodcast Concrete Logic Academy: https://www.concretelogicacademy.com/ Until next time, let's keep it concrete.

THIS EPISODE IS BROUGHT TO YOU BY: GPRSBefore you cut, core, drill, or excavate, make sure you know what is inside the concrete.GPRS helps contractors locate rebar, conduit, post-tension cables, utilities, and other hidden hazards before they become expensive problems. Their scans help reduce hits, downtime, expenses, and keep your people safe. Learn more here: https://www.concretelogicpodcast.com/gprs ON THIS EPISODE OF THE CONCRETE LOGIC PODCAST Low-carbon cement sounds good on paper. But can it actually compete in the real concrete market without subsidies, mandates, or customers paying a “green premium”? That is the question Seth gets into with Ryan Gilliam, CEO of Fortera. Ryan explains how Fortera's approach differs from many other low-carbon cement companies by bolting onto existing cement plants, using limestone as the feedstock, and turning CO₂ back into a reactive cementitious product. This conversation gets into the hard part of low-carbon cement: economics, field performance, scaling, ready-mix adoption, policy risk, and whether these products can survive when the market stops caring about the carbon story. Ryan makes the case that the future of low-carbon cement will not be built on guilt, regulation, or good intentions. It has to perform. It has to be cost competitive. And it has to work in the field. WHAT YOU'LL LEARN • Why “green cement” usually makes contractors and producers assume there is a compromise • How Fortera's technology bolts onto existing cement plants instead of replacing them • Why limestone loses roughly 44% of its weight as CO₂ during traditional cement production • How Fortera claims to turn that CO₂ back into cementitious material • Whether Fortera's product should be thought of as an SCM, a cement replacement, or a new cement • Why ready-mix producers are skeptical of alternative cements • What field feedback Fortera has received on finishing, flow, pumping, set time, and cracking • Why Ryan does not believe customers will pay large green premiums • How policy changes could impact demand for low-carbon cement • Why carbon capture usually struggles economically • How Fortera's approach differs from traditional carbon capture and storage • What has to be true for low-carbon cement companies to scale • Why first commercial plants are such a hard step for new cement technologies • Why Ryan believes performance, not carbon marketing, will decide which technologies survive CHAPTERS 00:00 Introduction to Ryan Gilliam and Fortera 03:25 Ryan's background in materials engineering and cement research 05:20 Fortera's approach to low-carbon cement 08:28 Is Fortera's product an SCM or a new cement? 09:23 Blended cement use versus 100% product use 10:33 What is driving demand for low-carbon cement? 13:39 Scaling challenges for new cement technologies 15:43 Field feedback on alternative cement performance 18:58 Type IL rollout, skepticism, and contractor pushback 20:07 Policy risk and whether low-carbon demand depends on regulation 22:18 How Fortera captures CO₂ from limestone 23:07 Why the economics may work 24:41 How this differs from traditional carbon capture 25:45 What cement plants need to adopt the technology 28:07 Fortera's history and lessons from earlier attempts 29:00 How Fortera may go to market 30:20 Ryan's main takeaway for the concrete industry 32:09 How to contact Ryan Gilliam GUEST INFO Ryan Gilliam CEO, Fortera Profile: https://www.concretelogicpodcast.com/guests/ryan-gilliam/ CONCRETE LOGIC ACADEMY If you work in concrete and want practical education that actually connects to the jobsite, check out Concrete Logic Academy. This is not theory for the sake of theory. It is concrete education built around the stuff producers, contractors, engineers, and field leaders deal with every day. Specs. Mixes. Placement. Finishing. Troubleshooting. Materials. Durability. Bad assumptions. Costly mistakes. Get access here: https://www.concretelogicpodcast.com/concreteschool SUPPORT THE PODCAST Concrete Logic runs on a value-for-value model. If this episode helped you think through low-carbon cement, alternative cement technology, or what might actually work in the real market, send some value back. Donate here: https://www.concretelogicpodcast.com/support/ You can also support the show through KUIU: https://www.concretelogicpodcast.com/kuiu For sponsorship or media opportunities, contact: seth@concretelogicpodcast.com CREDITS Producers: Jodi Tandett & Concrete Logic Media Music: Mike Dunton https://www.mdunton.com/ WHERE TO FIND SETH Concrete Logic Podcast: https://www.concretelogicpodcast.com YouTube: https://www.youtube.com/@concretelogicpodcast LinkedIn: https://www.linkedin.com/in/seth-tandett/ Like, subscribe, comment, and share the episode with someone in the concrete industry who needs to hear it.(Correction: At the 33:42 mark, Ryan referenced testing that reported a compressive strength of 10,000 psi. After recording, the testing result was later determined to be incorrect. The corrected result was approximately 6,000 psi.)

THIS EPISODE IS BROUGHT TO YOU BY: GPRSBefore you cut, core, drill, or trench through concrete, know what is inside it.GPRS helps contractors locate rebar, conduit, post-tension cables, voids, and other hidden hazards before they become expensive problems. Their ground-penetrating radar scanning helps reduce hits, downtime, expenses, and keeps your people safe.Learn more here: https://www.concretelogicpodcast.com/gprsON THIS EPISODE OF THE CONCRETE LOGIC PODCASTEveryone wants concrete work done faster.But what if one of the biggest schedule killers is not the pour, the weather, or the labor shortage?What if it starts with the reinforcement choice?In this episode, Seth talks with TJ Lambert of Forta about how reinforcement decisions affect labor, sequencing, inspections, procurement, finishing, mix design, carbon reporting, and overall project speed.They discuss where fiber-reinforced concrete fits, where it does not, and why engineers, producers, and contractors need to think beyond “replace the bar and move on.”WHAT YOU'LL LEARNWhy reinforcement choices can slow a concrete project down before the first truck shows upHow fibers can reduce labor, congestion, inspection steps, and field coordinationThe difference between microfibers, macro synthetic fibers, and steel fibersWhere fiber reinforcement makes sense, and where traditional rebar still belongsHow concrete producers handle fiber dosing at the plant or into the truckWhy fiber-reinforced concrete can help contractors place more mud fasterHow EPDs, LCAs, GWP targets, and Buy America requirements are showing up in reinforcement decisionsWhy finishing fiber-reinforced slabs still depends on timing, mix design, vibration, and field conditionsHow Type IL cement may affect finishing timing, saw cutting, paste, and surface performanceWhat engineers need to know about ACI 544, ACI 360, ACI 330, and ACI 318 when considering fiber designsCHAPTERS00:00 Introduction: Can reinforcement choices kill your schedule? 04:02 What Forta does and how fiber-reinforced concrete fits 05:01 Why reinforcement choices affect schedule more than people realize 08:42 What fibers look like in the field 11:57 Matching fiber type to the right concrete application 13:26 What fiber use means for concrete producers 16:45 Fiber loading, truck mixing, and added time at the plant 17:17 How much time can contractors save by eliminating bar placement? 19:47 EPDs, GWP, and carbon reduction with fiber reinforcement 22:13 Why finishers struggle with some fiber-reinforced slabs 26:43 Type IL cement, paste, timing, and fiber finishing concerns 30:05 Fly ash, slag, mix design complexity, and owner expectations 31:54 What engineers need to understand before using fibers 35:03 TJ's main takeaway: Start with design, not just substitution 37:06 How to contact TJ LambertGUEST INFOTJ Lambert Sales Engineering Manager, Forta / Helix SteelProfile: https://www.concretelogicpodcast.com/guests/tj-lambert/Forta website: https://fortacorp.comHelix Steel website: https://helixsteel.comCONCRETE LOGIC ACADEMYConcrete Logic Academy is built for people who actually work with concrete.Not theory for theory's sake. Not another seminar full of recycled slides. Not some polished presentation from people who have not been near a pour in years.This is practical concrete education for contractors, producers, engineers, architects, QC teams, plant managers, finishers, and anyone else who has to make decisions before the mud hits the ground.If you want to better understand mixes, specs, reinforcement, troubleshooting, field problems, and how to make better calls on real projects, check it out.Start here: https://www.concretelogicpodcast.com/concreteschoolSUPPORT THE PODCASTIf the Concrete Logic Podcast helps you think differently, solve a problem, avoid a mistake, or make a better decision, send some value back.That could be $5. That could be $50. That could be more.The point is simple.If the show is worth something to you, help keep it going.Donate here: https://www.concretelogicpodcast.comYou can also support the show through our KUIU affiliate link: https://www.concretelogicpodcast.com/kuiuInterested in sponsoring the podcast or working with Concrete Logic Media? Email Seth: seth@concretelogicpodcast.comCREDITSProducers: Jodi Tandett & Concrete Logic Media Music by Mike Dunton: https://www.mdunton.com/WHERE TO FIND SETHConcrete Logic Podcast: https://www.concretelogicpodcast.comYouTube: https://www.youtube.com/@concretelogicpodcastLinkedIn: https://www.linkedin.com/in/seth-tandett/Like, subscribe, comment, and share the episode with someone who works around concrete.

This episode is brought to you by GPRS.GPRS helps keep your projects moving by locating what is hidden before you cut, core, drill, or trench. From ground-penetrating radar and utility locating to concrete scanning and 3D laser scanning, GPRS gives contractors better information before work starts.Learn more or request a quote here: https://www.concretelogicpodcast.com/gprsON THIS EPISODE OF THE CONCRETE LOGIC PODCASTReady-mix producers are being asked for EPDs more often, especially on data center and large infrastructure projects.But what are EPDs?Who is asking for them?And why should a small producer care?In this episode, Seth talks with Leise Sandeman, co-founder of Pathways, about Environmental Product Declarations, life cycle assessments, carbon reporting, and how these requirements are starting to affect concrete bids.Leise explains EPDs in plain language: what data goes into them, how cement, aggregate, admixtures, water, fuel, electricity, and transportation all get measured, and why producers should not assume this is only a “green building” paperwork exercise.The big point?EPDs are becoming part of how some owners, GCs, and hyperscale data center companies compare concrete producers.And for smaller ready-mix companies, the risk is not just the carbon number.It is being left out of the bid entirely because they do not have the documentation ready.WHAT YOU'LL LEARNWhat is an EPD?Why are data center owners asking concrete producers for EPDs?How does a life cycle assessment connect to a concrete mix?What data does a ready-mix producer need to create an EPD?Why can two plants from the same producer have different EPD numbers?How much of a concrete EPD is driven by cement?Are owners comparing concrete producers against each other?Why might simply having an EPD help a producer win work in some markets?How could EPDs affect smaller 2-to-10-plant ready-mix operations?Why does Leise think EPDs are becoming more about business than climate messaging?CHAPTERS00:00 - Intro and Concrete Logic Podcast support 03:15 - Who Leise Sandeman is and what Pathways does 03:52 - What is an EPD? 04:35 - Who is asking for EPDs? 05:54 - Where EPDs came from and how LCAs fit in 06:48 - Comparing concrete to other materials and other producers 07:36 - How cement and material supplier data affect EPDs 08:33 - Why EPDs involve a lot of math and manual work 09:07 - Generic EPDs vs producer-specific EPDs 10:09 - The three major data inputs for a concrete EPD 11:24 - Why utility and grid data matter 12:07 - What owners and hyperscalers compare 13:48 - How far the life cycle assessment goes 15:28 - How cement EPDs are built 16:12 - Does the EPD stop at placement? 17:16 - End-of-life questions and future standards 18:42 - Concrete's carbon footprint vs material volume 20:29 - Why supplier choices can change the EPD number 21:21 - Why smaller producers need a simpler path 23:42 - Where EPD requirements may be heading 24:04 - Why EPD publishing is expected to grow 25:21 - Future inputs, fuels, SCMs, and supplier options 26:34 - How to contact Leise and PathwaysGUEST INFOLeise Sandeman Guest Profile: https://www.concretelogicpodcast.com/guests/leise-sandeman/CONCRETE LOGIC ACADEMYTired of getting your concrete education from a PowerPoint presentation given by some guy who has probably never stepped in mud?Or someone who does not know what diesel smells like next to that first chute of concrete in the predawn darkness?That is why Concrete Logic Academy exists.It is built by people who understand the field, the plant, the jobsite, and the real problems concrete professionals deal with every day.The courses are practical, direct, and built for people who want to apply what they learn right away.Inside the Academy, you get access to PDH courses, quizzes, resources, live Q&A, early access to podcast episodes, and a place to ask concrete questions without throwing them out into the LinkedIn circus.For a limited time, get free access to the Concrete Logic Academy here: https://www.concreteschool.co SUPPORT THE PODCASTThe Concrete Logic Podcast runs on a value-for-value model.If the show gives you something useful, send some value back.Donate here: https://www.concretelogicpodcast.com/support/Want to support the show another way?Check out KUIU through the Concrete Logic link: https://www.concretelogicpodcast.com/kuiuInterested in sponsoring the podcast or working with Concrete Logic Media? Email: seth@concretelogicpodcast.comCREDITSHost: Seth Tandett Producers: Jodi Tandett & Concrete Logic Media Music: Mike Dunton https://www.mdunton.com/WHERE TO FIND SETHConcrete Logic Podcast Website: https://www.concretelogicpodcast.com/YouTube: https://www.youtube.com/@concretelogicpodcastLinkedIn: https://www.linkedin.com/in/seth-tandett/Like, subscribe, comment, and share the episode if it helped you understand where EPDs are headed in concrete.

PRESENTED BY: GPRS Construction professionals know that utilities and concrete reinforcements can cause big problems when you're on the job.GPRS helps you avoid them. We use ground penetrating radar to detect rebar, conduit, and post tension cables before you cut, core, or drill.And our concrete scans are 99.8% accurate - we guarantee it - helping you reduce hits, downtime, expenses, and keep your people safe.To keep your jobsite safer, visit: https://www.concretelogicpodcast.com/GPRSSUMMARYData centers may look like simple boxes, but the race to build them is changing everything for concrete.In this episode, Doug Mouton explains why concrete is still one of the most important materials in the data center boom, even if it is only a small slice of the total cost.He breaks down what is driving the explosion in hyperscale and AI data centers, why projects are moving into rural areas, and why concrete supply, logistics, and mix design need to be thought through much earlier than most teams are used to.This is a big-picture episode, but it gets practical fast.If you work in concrete and want to understand where the data center market is headed and what owners actually want, this one will help.WHAT YOU'LL LEARNWhy data centers are growing so fast right nowWhat a data center actually is and how it worksWhy AI is pushing demand far beyond traditional cloud computingWhy more data centers are being built in rural areasHow labor, materials, and logistics get harder as projects move farther outWhy concrete becomes a critical path item even if it is a small part of total project costWhat hyperscale owners want from concrete suppliers and contractorsWhy speed, cost, and lower embodied carbon are all being pushed at the same timeWhy leaner structural designs may be the easiest way to reduce concrete useWhy concrete supply needs to be planned much earlier on gigascale projectsHow power infrastructure is creating even more demand for concreteWhat future energy storage systems could mean for the concrete industryCHAPTERS00:00 - Intro and Doug Mouton's background01:20 - How to support the podcast03:20 - Why concrete matters so much to data center growth05:26 - What a data center actually is06:30 - Why cloud computing changed everything08:20 - How AI is driving a second wave of data center demand09:41 - Why more data centers are moving into rural areas11:20 - Rural pushback, trucking, roads, and local disruption12:32 - Why rural projects make labor and materials even harder13:15 - What owners and developers actually want from concrete15:05 - Speed, ESG pressure, and embodied carbon goals16:05 - Why concrete procurement is still too fragmented18:00 - Why concrete suppliers need a seat at the table earlier19:02 - How leaner design can cut carbon, cost, and schedule20:13 - Seth's skepticism on new low-carbon materials at scale21:39 - Why scale and supply chain reality still matter22:05 - Why concrete planning should start at the very beginning23:32 - How power infrastructure creates even more concrete demand24:05 - Massive towers, gravity batteries, and future energy storage ideas25:00 - Using AI to make steel and rebar design more efficient25:35 - Will data centers get smaller, denser, and stiffer?26:20 - Wrap-up and final thoughtsGUEST INFODouglas Mouton Mouton Advisory Serviceshttps://www.concretelogicpodcast.com/guests/douglas-mouton/ CONCRETE LOGIC ACADEMYNeed PDHs that are actually worth your time? The Concrete Logic Academy is built for engineers, contractors, and concrete professionals who want practical training they can actually use - not another boring seminar that gets forgotten by tomorrow. Use it for PDHs. Use it for lunch and learns. Use it to get smarter on concrete without wasting half your day. Real topics. Real field problems. Real conversations. Start your free trial here: https://www.concretelogicpodcast.com/pro SUPPORT THE PODCASTIf this episode helped you... If you learned something... If it made you think differently...Support the show here: https://www.concretelogicpodcast.com/donateThis podcast runs on Value for Value.Give whatever you think the episode was worth.PARTNERSKUIU (performance gear Seth actually uses): https://www.concretelogicpodcast.com/kuiuInterested in advertising or working with us? Email: seth@concretelogicpodcast.comCREDITSProducers: Jodi Tandett & Concrete Logic MediaMusic by Mike Dunton: https://www.mdunton.com/WHERE TO FIND CONCRETE LOGICWebsite: https://www.concretelogicpodcast.com YouTube: https://www.youtube.com/@concretelogicpodcast LinkedIn: https://www.linkedin.com/in/sethtandett/ Instagram: https://www.instagram.com/concretelogicpodcast/

PRESENTED BY: GPRS Construction professionals know that utilities and concrete reinforcements can cause big problems when you're on the job. GPRS helps you avoid them. We use ground penetrating radar to detect rebar, conduit, and post tension cables before you cut, core, or drill. And our concrete scans are 99.8% accurate - we guarantee it - helping you reduce hits, downtime, expenses, and keep your people safe. To keep your jobsite safer, visit: https://www.concretelogicpodcast.com/GPRS SUMMARY Last episode, Dr. Jon Belkowitz walked through how to build a concrete mix design on paper. This episode picks up where that left off. Because a design mix is not the same thing as a batch mix. Dr. Jon breaks down the final adjustments that have to happen before that mix can actually be used in production, including moisture corrections, free water, and admixture dosages. If you have ever wondered how a mix goes from a neat set of numbers on paper to something a plant can actually batch, this episode clears it up. WHAT YOU'LL LEARN Why a design mix is not the same as a batch mix How aggregate moisture changes your sand and rock weights What “free water” really means and why it matters How moisture in the aggregates affects water-cement ratio How to calculate the water coming from sand and stone How admixture dosage is calculated from cement content Why admixtures also add water to the mix How to calculate final batch water What the final adjusted batch mix looks like Why contractors and engineers should understand this math even if the plant computer does it for them CHAPTERS 00:00 - Intro and how to support the show 02:15 - Why people are actually using the podcast on the job 02:36 - Concrete Logic Academy and PDHs 03:36 - Picking up where the last mix design episode ended 04:22 - What has to change to move from design mix to batch mix 05:44 - The project assumptions for this slab mix 06:28 - Where moisture content and absorption values come from 08:13 - Adjusting sand weight for moisture 10:54 - Adjusting rock weight for moisture 12:05 - What free water is and why it affects your mix 13:24 - Calculating free water from the sand 14:33 - Calculating free water from the rock 15:24 - Admixture dosage explained 16:16 - Calculating admixture ounces per cubic yard 17:28 - Calculating how much water the admixture brings 18:47 - Final batch water calculation 19:34 - Final adjusted batch mix 20:38 - What happens next at the plant 21:03 - Why admixtures are mostly water 24:51 - Withholding water and adjusting slump at the plant 26:53 - How admixture dispensing systems work 29:25 - Wrap-up and what to cover next time GUEST INFODr. Jon Belkowitz Intelligent Concrete Website: https://www.concretelogicpodcast.com/intelligent-concrete CONCRETE LOGIC ACADEMY Got a concrete problem no one can explain? Bring it to the Concrete Logic Academy. Ask questions. Share what you're seeing. Get real answers you can use on the next pour. Simple as that. Free trial for pros here: https://www.concretelogicpodcast.com/pro SUPPORT THE PODCAST If this episode helped you... If you learned something... If it made you think differently... Support the show here: https://www.concretelogicpodcast.com This podcast runs on Value for Value. Give whatever you think the episode was worth. PARTNERS KUIU (performance gear Seth actually uses): https://www.concretelogicpodcast.com/kuiu Interested in advertising or working with us? Email: seth@concretelogicpodcast.com CREDITS Producers: Joseph Swann, Jodi Tandett & Concrete Logic Media Music by Mike Dunton: https://www.mdunton.com/ WHERE TO FIND CONCRETE LOGIC Website: https://www.concretelogicpodcast.com YouTube: https://www.youtube.com/@concretelogicpodcast LinkedIn: https://www.linkedin.com/in/sethtandett/ Instagram: https://www.instagram.com/concretelogicpodcast/

PRESENTED BY: GPRS Construction professionals know that utilities and concrete reinforcements can cause big problems when you're on the job. GPRS helps you avoid them. We use ground penetrating radar to detect rebar, conduit, and post tension cables before you cut, core, or drill. And our concrete scans are 99.8% accurate… we guarantee it—helping you reduce hits, downtime, expenses, and keep your people safe. To keep your jobsite safer, visit https://www.concretelogicpodcast.com/GPRS SUMMARYIf you've never built your own concrete mix design… this episode fixes that.Dr. Jon Belkowitz walks through the ACI 211 method step-by-step, showing exactly how a mix is built from scratch.By the end, you'll see that mix design isn't magic.It's just decisions… and a little math.WHAT YOU'LL LEARNWhat actually goes into a cubic yard of concrete — and why it mattersWhy mix design is basically a “choose your own adventure”How slump, aggregate size, and air content drive your entire mixThe simple math behind water-cement ratio (and why it controls everything)How to calculate cement, water, rock, and sand — step by stepWhy yield matters (and how getting it wrong costs real money)The difference between a design mix vs batch mixWhat changes when moisture shows up in your aggregates (this comes up near the end) CHAPTERS00:00 – Intro and how to support the show 02:20 – Shoutout to this episode's Producer 03:00 – Why PDHs should actually be useful 05:20 – What is a cubic yard of concrete? 06:30 – Absolute volume method explained 09:50 – ACI 211 and “choose your own adventure” 13:30 – Selecting slump, water, and air content 17:00 – Adjusting water for admixtures 19:00 – Water-cement ratio and strength 21:00 – Aggregate proportions and why they matter 25:30 – Start of the actual math 31:30 – Calculating coarse aggregate 34:00 – Converting weights to volumes 36:30 – Solving for sand (the missing piece) 39:30 – Final mix design breakdown 40:10 – Design mix vs batch mix (what's next) GUEST INFODr. Jon Belkowitz Intelligent Concrete Website: https://www.concretelogicpodcast.com/intelligent-concreteCONCRETE LOGIC ACADEMYGot a concrete problem no one can explain?Bring it to the Concrete Logic Academy.Ask questions. Share what you're seeing. Get real answers you can use on the next pour.Simple as that. Free trial for pros here: https://www.concretelogicpodcast.com/pro SUPPORT THE PODCASTIf this episode helped you…If you learned something…If it made you think differently…Support the show here: https://www.concretelogicpodcast.comThis podcast runs on Value for Value.Give whatever you think the episode was worth. PARTNERSKUIU (performance gear Seth actually uses): https://www.concretelogicpodcast.com/kuiuInterested in advertising or working with us? Email: seth@concretelogicpodcast.com CREDITSProducers: Joseph Swann, Jodi Tandett & Concrete Logic MediaMusic by Mike Dunton: https://www.mdunton.com/WHERE TO FIND CONCRETE LOGICWebsite: https://www.concretelogicpodcast.com YouTube: https://www.youtube.com/@concretelogicpodcast LinkedIn: https://www.linkedin.com/in/sethtandett/Instagram: https://www.instagram.com/concretelogicpodcast/

PRESENTED BY: GPRS Construction professionals know that utilities and concrete reinforcements can cause big problems when you're on the job. GPRS helps you avoid them. We use ground penetrating radar to detect rebar, conduit, and post tension cables before you cut, core, or drill. And our concrete scans are 99.8% accurate… we guarantee it—helping you reduce hits, downtime, expenses, and keep your people safe.To keep your jobsite safer, visit https://www.concretelogicpodcast.com/GPRS SUMMARYConcrete strength gets all the attention.But what if the real driver of concrete performance isn't strength at all?In this episode of the Concrete Logic Podcast, Bob Higgins returns to talk about alkalinity — the chemical environment inside concrete that may control moisture behavior, curing, permeability, and long-term durability.Bob explains why alkalinity is often confused with pH, why salts inside concrete can trap moisture that testing methods never see, and why modern cement chemistry may be quietly changing how concrete cures and performs.If you've ever wondered why concrete behaves differently today than it did decades ago, this conversation will make you think.WHAT YOU'LL LEARN· What alkalinity actually means in concrete chemistry · Why pH and alkalinity are not the same thing · The two alkaline salts that control moisture behavior in concrete · Why salts can trap moisture that RH tests can't detect · How high alkalinity can lead to permeable, weaker surface concrete · The difference between porosity and permeability · Why precast heat curing can change long-term durability · Why compressive strength often fails as a durability indicator · How cement kiln dust may have increased alkalinity in modern cementCHAPTERS00:00 Introduction 02:20 What alkalinity means in concrete 03:10 Why salts control moisture behavior 07:40 Why RH probes can miss trapped moisture 10:20 Calcium hydroxide vs sodium hydroxide 14:00 Self-desiccation and modern cement chemistry 18:00 Where alkalinity in concrete comes from 23:30 Signs of high alkalinity in concrete 24:00 Why precast surfaces can be more permeable 26:00 Porosity vs permeability explained 29:00 Why compressive strength can mislead durability 31:20 Why sealers often fail long term 33:00 Alkali-silica reaction explained 35:00 Why alkalinity isn't being studied enough 38:00 Why RH specifications often don't make sense 39:00 Preview: additional forms of moisture in concrete GUESTBob HigginsChief Scientisthttps://www.concretelogicpodcast.com/guests/robert-higgins/ CONCRETE LOGIC ACADEMYIf you like what you're learning on the podcast, the Concrete Logic Academy Unlimited Pro Membership is where it all comes together.We take the topics you hear on the show and turn them into structured courses—with real explanations, supporting material, and quizzes so you actually retain it.Most people in this industry learn by trial and error.This is how you get ahead without paying for mistakes.Many courses qualify for PDHs and CEUs.Start your free trial here: https://www.concretelogicpodcast.com/pro SUPPORT THE PODCASTIf the Concrete Logic Podcast has helped you learn something new or connect with someone in the industry, consider supporting the show.Donate here: https://www.concretelogicpodcast.com/supportLooking for great hunting or work gear: https://www.concretelogicpodcast.com/kuiuInterested in advertising or media services? https://www.concretelogicpodcast.com/partner-with-concrete-logic-podcast/ CREDITSProducers: Jodi Tandett, Concrete Logic MediaMusic by Mike Dunton https://www.mdunton.com/ WHERE TO FIND SETHWebsite: https://www.concretelogicpodcast.com LinkedIn: https://www.linkedin.com/in/sethtandett/ YouTube: https://www.youtube.com/@concretelogicpodcastUntil next time, let's keep it concrete!

Ein Festnetz, drei Mobilfunknetze und unendlich viele Telefonate. Die klassische fernmündliche Übermittlung von Informationen ist aus der modernen Welt nicht mehr wegzudenken. Oder steht sie bereits auf der Liste der bedrohten Arten?

In this People First Builders episode, host Fletcher Wimbush sits down with Matt Aston, founder and CEO of GPRS, a national leader in private utility locating, concrete scanning, and subsurface mapping. Matt shares how a single magazine ad in 2001 sparked an idea that grew from a one person startup into a coast to coast company with more than 900 team members and a reputation for accuracy, safety, and innovation. Matt explains the difference between public 811 locating and GPRS private utility work, why depth data changes decisions on site, and how early failures shaped a culture that aims to be right the first time, every time. He breaks down GPRS hiring and training systems, including SIM, the company's subsurface investigation methodology, a purpose built training center, ride alongs before offers, and a multi month ramp with ongoing assessments. The conversation also covers leadership development, a referral engine that drives nearly half of annual hires, incentive based compensation that can make up 30 to 50 percent of pay, and broad based employee ownership that tracks the same class of stock as leadership. Key takeaways Accuracy that scales: about 120,000 jobs in the last year with a reported error rate near 0.1 percent. Private vs public locating: 811 marks public rights of way, while GPRS supports contractors on private property and provides depth data that changes plans and prevents damage. Hire for character, teach the skill: referrals supply close to half of new hires, followed by structured interviews, ride alongs, and three to four months of training. Process over heroics: SIM gives field teams a consistent approach, supported by a full training lab and university partnerships for real world practice. Retention flywheel: once team members pass roughly 12 to 18 months, annual retention jumps into the mid 90s. Ownership and incentives: field compensation includes meaningful performance bonuses, and an opt in equity program opened in 2020 where everyone holds the same class of stock, aligned with private equity growth goals.

In this episode, Star Link Communication introduces the Bio Lynx Attendance Device, a breakthrough in centralized remote enrollment technology. Designed for enterprises managing employees across multiple locations, Bio Lynx eliminates the need for HR or IT teams to physically visit sites for employee enrollment.The episode explains how the system works – from granting remote permissions at the head office to local staff registering fingerprints on-site, with data instantly pushed to the central server via GPRS. It also highlights the key advantages of Bio Lynx, such as massive data capacity, real-time data transfer, and flexibility for remote environments.Listeners will also discover the industries that benefit the most, including manufacturing, construction, education, healthcare, logistics, and retail.Star Link's Bio Lynx Attendance Device offers organizations a smarter way to manage attendance – enhancing efficiency, reducing costs, and ensuring complete centralized control.

This week we're revisiting our 2023 interview with Matt Aston. The Founding of GPRS Today we welcome Matt Aston. Matt is the founder of GPRS. Although that is an acronym for ground-penetrating radar systems, the company does much more than that these days. Matt started his company in 2001, and now they employ almost 800 people in 54 cities. We discuss the ground-penetrating radar equipment. Matt walks us through some basics about how this equipment uses magnetic variations to help users create a map of the underground infrastructure. When he was starting GPRS, most of his work involved taking readings in concrete–sensing rebar, anchors, etc. As time passed, they shifted toward working with utility contractors before excavations. Matt shares about his dad's drilling and cutting business in Toledo and how a softball injury forced him to restructure his business. This led to substantial growth that led to a business he might have been interested in taking over. Building a Business on Young Technology, Equipment Overview Matt tells us about an early experience with the stress of the ground-penetrating radar business. On his way to the equipment-training session he was a little scared. On the way home, he was really scared. Eddie asks Matt to talk through ground-penetrating radar tools. He talks through the tools and the process that has enabled his team to reach a 99.87% accuracy record. It involves baby-buggy-like carts and converting screen data to the paint on the ground. A few GPR antennas, a couple for underground and one specifically for concrete. Then there are a few specialized tools, including the handheld wands and sewer cameras. The sewer cameras, along with a few other tools, enable the company to now provide leak-detection services. Training ProgramsMatt shares about his company's training programs. Matt explains that they now have 3 full-time trainers. These veteran project managers conduct their training in a facility with a custom-built floor full of all kinds of wire, pipes, and conduit. They also have a simulated gas station complete with tanks. Tyler asks Matt to share about his company's Trump Tower project in Chicago. This involved a demo and then some code upgrades. This required extensive time-consuming retrofits. He recounts a couple other incredibly ambitious projects. We discuss the increasing sensitivity and precision of the equipment involved. Matt shares about a time when he had to break some unfortunate news with the owners of a scientific facility where the concrete hadn't been poured to the proper thickness.  Unusual Projects and Big-City ProjectsTyler asks Matt to share about some of the unorthodox jobs they've been called to do. Matt shares a story about a mysterious old site where the client was looking for a large metal container. GPRS has even located a few murder victims. He's not confirming that one of them was Jimmy Hoffa, but he's not denying it either. Matt tells a few examples of the interesting variety of locations that this work takes his teams. He gives an example of one crew working in DC who was taken by the National Park Service to scan the lawn at the White House. Entrepreneurship Tyler asks Matt to talk about growing his business. Matt shares about early hires and the challenges of ensuring that the income exceeds the outflow. He discusses decisions that were especially influential, and he shares his thoughts about the role of the companies' CEOs in both successes and failures. As your company grows, Matt says, your potential also grows.   Matt recounts the stages of growth and the points at which you sense shifts in your perception of the business and your role in it. He set some ambitious goals and has found that they're achievable. He mentions the role that Toledo's size played in setting his company on a path for growth.Eddie and Matt compare notes on business-growth rewards and challenges. Matt shares about an unsettling conversation he had with a contractor who wanted to avoid knowing in advance about underground elements because he made more money when his equipment damaged them and then he needed to repair them. They agree that it's all about “meeting the need.” Matt's Megaphone MessageWe are capable of so, so much more than we realize. The world around us makes it really easy to be average. If you just show up and do what you said you were going to, you're already above average. But if you push yourself, you can move into that elite category. Find your why. You can be an elite performer. Find Matt on LinkedInCheck out the partners that make our show possible.Find Us Online: BrosPodcast.com - LinkedIn - Youtube - Instagram - Facebook - TikTok - Eddie's LinkedIn - Tyler's LinkedInIf you enjoy the podcast, please rate us on Apple Podcasts, Spotify, or wherever you listen to us! Thanks for listening

First and foremost DD is audio production challenged and apologizes for his shortcomings! Nonetheless...our good friend Joe Cherry, Project Manager at Cobb, Fendley & Associates, Inc. joined the conversation this week as PS takes yet another vacation. The guys welcomed Matt Aston, President of GPRS to the stage to talk all things GPRS! GPRS has been providing utility locating and mapping services since 2001 and believes that data control = damage control and they are committed to the pursuit of 100% subsurface damage prevention – on every job, in every market, nationwide. Just a few items we discussed include Subsurface Investigation Methodology (SIM), Subsurface Utility Engineering (SUE), Building Information Models (BIM), cross-technology training and SiteMap®! Music by Kid Rock!!  

In episode 136 of the Pro Series Podcast, we dive deep into the covert world of construction intelligence with Matt Aston, the ingenious Founder and CEO of GPRS. Join us as we explore how GPRS, what I say is the "spy's of the construction industry," utilizes cutting-edge technologies to revolutionize mapping, leak detection, concrete scanning, drone imagery, and beyond. Discover the clandestine operations behind ensuring project integrity, safety, and efficiency in the construction realm. Tune in for a revealing conversation that unveils the secrets beneath the surface of the construction industry.

In this episode, Matt Aston, founder of GPRS, shares his strategies for geographic expansion and raising awareness of GPRS services. He discusses the impact of partnering with a private equity firm, the importance of safety in construction, and implementation of the SIM process. Aston also talks about GPRS's hiring philosophy, the introduction of a subscription model, and offers advice to his younger self.

Matt Aston is the President at GPRS.   In this episode of Specified Growth Podcast, Matt talks about his entrepreneurial background and how he found his way into the ground penetrating radar industry. He also discusses how he expanded his business by working with the right team, the new technologies and services that GPRS is providing for its customers, some of the lessons he's learned along the way, and more. Don't miss this episode of Specified Growth Podcast!   Please reach out if you have any feedback or questions. Enjoy!    Twitter: @TatsuyaNakagawa Instagram: @tats_talks LinkedIn: Tatsuya Nakagawa  YouTube: Tats Talks www.tatstalk.com www.castagra.com Learn more about your ad choices. Visit megaphone.fm/adchoices

00:00 - The Founding of GPRS Today we welcome Matt Aston. Matt is the founder of GPRS. Although that is an acronym for ground-penetrating radar systems, the company does much more than that these days. Matt started his company in 2001, and now they employ almost 800 people in 54 cities. We discuss the ground-penetrating radar equipment. Matt walks us through some basics about how this equipment uses magnetic variations to help users create a map of the underground infrastructure. When he was starting GPRS, most of his work involved taking readings in concrete–sensing rebar, anchors, etc. As time passed, they shifted toward working with utility contractors before excavations. Matt shares about his dad's drilling and cutting business in Toledo and how a softball injury forced him to restructure his business. This led to substantial growth that led to a business he might have been interested in taking over. 06:08 - Building a Business on Young Technology, Equipment Overview Matt tells us about an early experience with the stress of the ground-penetrating radar business. On his way to the equipment-training session he was a little scared. On the way home, he was really scared. Eddie asks Matt to talk through ground-penetrating radar tools. He talks through the tools and the process that has enabled his team to reach a 99.87% accuracy record. It involves baby-buggy-like carts and converting screen data to the paint on the ground. A few GPR antennas, a couple for underground and one specifically for concrete. Then there are a few specialized tools, including the handheld wands and sewer cameras. The sewer cameras, along with a few other tools, enable the company to now provide leak-detection services. 15:33 - Training ProgramsMatt shares about his company's training programs. Matt explains that they now have 3 full-time trainers. These veteran project managers conduct their training in a facility with a custom-built floor full of all kinds of wire, pipes, and conduit. They also have a simulated gas station complete with tanks. Tyler asks Matt to share about his company's Trump Tower project in Chicago. This involved a demo and then some code upgrades. This required extensive time-consuming retrofits. He recounts a couple other incredibly ambitious projects. We discuss the increasing sensitivity and precision of the equipment involved. Matt shares about a time when he had to break some unfortunate news with the owners of a scientific facility where the concrete hadn't been poured to the proper thickness.  29:50 - Unusual Projects and Big-City ProjectsTyler asks Matt to share about some of the unorthodox jobs they've been called to do. Matt shares a story about a mysterious old site where the client was looking for a large metal container. GPRS has even located a few murder victims. He's not confirming that one of them was Jimmy Hoffa, but he's not denying it either. Matt tells a few examples of the interesting variety of locations that this work takes his teams. He gives an example of one crew working in DC who was taken by the National Park Service to scan the lawn at the White House. 37:24 - Entrepreneurship Tyler asks Matt to talk about growing his business. Matt shares about early hires and the challenges of ensuring that the income exceeds the outflow. He discusses decisions that were especially influential, and he shares his thoughts about the role of the companies' CEOs in both successes and failures. As your company grows, Matt says, your potential also grows.   Matt recounts the stages of growth and the points at which you sense shifts in your perception of the business and your role in it. He set some ambitious goals and has found that they're achievable. He mentions the role that Toledo's size played in setting his company on a path for growth.Eddie and Matt compare notes on business-growth rewards and challenges. Matt shares about an unsettling conversation he had with a contractor who wanted to avoid knowing in advance about underground elements because he made more money when his equipment damaged them and then he needed to repair them. They agree that it's all about “meeting the need.” 59:13 - Matt's Megaphone MessageWe are capable of so, so much more than we realize. The world around us makes it really easy to be average. If you just show up and do what you said you were going to, you're already above average. But if you push yourself, you can move into that elite category. Find your why. You can be an elite performer. Find Matt on LinkedInCheck out the partners that make our show possible.Find Us Online: BrosPodcast.com - LinkedIn - Youtube - Instagram - Facebook - TikTok - Eddie's LinkedIn - Tyler's LinkedInIf you enjoy the podcast, please rate us on Apple Podcasts, Spotify, or wherever you listen to us! Thanks for listening

The Robots are coming! We are talking about the latest in GPRs (general purpose humanoid robots), Apple cancelling their car project, Gemini 1.5 Pro testing, Biden's plan to ban voice impersonation, the decline of TV viewership, and Deep Mind CEO Demis Hassabis' views on AlphaZero sitting atop LLMs on the AGI stack.

Speaker CFPs and Sponsor Guides are now available for AIE World's Fair — join us on June 25-27 for the biggest AI Engineer conference of 2024!Soumith Chintala needs no introduction in the ML world — his insights are incredibly accessible across Twitter, LinkedIn, podcasts, and conference talks (in this pod we'll assume you'll have caught up on the History of PyTorch pod from last year and cover different topics). He's well known as the creator of PyTorch, but he's more broadly the Engineering Lead on AI Infra, PyTorch, and Generative AI at Meta.Soumith was one of the earliest supporters of Latent Space (and more recently AI News), and we were overjoyed to catch up with him on his latest SF visit for a braindump of the latest AI topics, reactions to some of our past guests, and why Open Source AI is personally so important to him.Life in the GPU-Rich LaneBack in January, Zuck went on Instagram to announce their GPU wealth: by the end of 2024, Meta will have 350k H100s. By adding all their GPU clusters, you'd get to 600k H100-equivalents of compute. At FP16 precision, that's ~1,200,000 PFLOPS. If we used George Hotz's (previous guest!) "Person of Compute" measure, Meta now has 60k humans of compute in their clusters. Occasionally we get glimpses into the GPU-rich life; on a recent ThursdAI chat, swyx prompted PaLM tech lead Yi Tay to write down what he missed most from Google, and he commented that UL2 20B was trained by accidentally leaving the training job running for a month, because hardware failures are so rare in Google.Meta AI's Epic LLM RunBefore Llama broke the internet, Meta released an open source LLM in May 2022, OPT-175B, which was notable for how “open” it was - right down to the logbook! They used only 16 NVIDIA V100 GPUs and Soumith agrees that, with hindsight, it was likely under-trained for its parameter size.In Feb 2023 (pre Latent Space pod), Llama was released, with a 7B version trained on 1T tokens alongside 65B and 33B versions trained on 1.4T tokens. The Llama authors included Guillaume Lample and Timothée Lacroix, who went on to start Mistral.July 2023 was Llama2 time (which we covered!): 3 model sizes, 7B, 13B, and 70B, all trained on 2T tokens. The three models accounted for a grand total of 3,311,616 GPU hours for all pre-training work. CodeLlama followed shortly after, a fine-tune of Llama2 specifically focused on code generation use cases. The family had models in the 7B, 13B, 34B, and 70B size, all trained with 500B extra tokens of code and code-related data, except for 70B which is trained on 1T.All of this on top of other open sourced models like Segment Anything (one of our early hits!), Detectron, Detectron 2, DensePose, and Seamless, and in one year, Meta transformed from a company people made fun of for its “metaverse” investments to one of the key players in the AI landscape and its stock has almost tripled since (about $830B in market value created in the past year).Why Open Source AIThe obvious question is why Meta would spend hundreds of millions on its AI efforts and then release them for free. Zuck has addressed this in public statements:But for Soumith, the motivation is even more personal:“I'm irrationally interested in open source. I think open source has that fundamental way to distribute opportunity in a way that is very powerful. Like, I grew up in India… And knowledge was very centralized, but I saw that evolution of knowledge slowly getting decentralized. And that ended up helping me learn quicker and faster for like zero dollars. And I think that was a strong reason why I ended up where I am. So like that, like the open source side of things, I always push regardless of like what I get paid for, like I think I would do that as a passion project on the side……I think at a fundamental level, the most beneficial value of open source is that you make the distribution to be very wide. It's just available with no friction and people can do transformative things in a way that's very accessible. Maybe it's open source, but it has a commercial license and I'm a student in India. I don't care about the license. I just don't even understand the license. But like the fact that I can use it and do something with it is very transformative to me……Like, okay, I again always go back to like I'm a student in India with no money. What is my accessibility to any of these closed source models? At some scale I have to pay money. That makes it a non-starter and stuff. And there's also the control issue: I strongly believe if you want human aligned AI, you want all humans to give feedback. And you want all humans to have access to that technology in the first place. And I actually have seen, living in New York, whenever I come to Silicon Valley, I see a different cultural bubble.We like the way Soumith put it last year: Closed AI “rate-limits against people's imaginations and needs”!What It Takes For Open Source AI to WinHowever Soumith doesn't think Open Source will simply win by popular demand. There is a tremendous coordination problem with the decentralized nature of the open source AI development right now: nobody is collecting the valuable human feedback in the way that OpenAI or Midjourney are doing.“Open source in general always has a coordination problem. If there's a vertically integrated provider with more resources, they will just be better coordinated than open source. And so now open source has to figure out how to have coordinated benefits. And the reason you want coordinated benefits is because these models are getting better based on human feedback. And if you see with open source models, like if you go to the /r/localllama subreddit, like there's so many variations of models that are being produced from, say, Nous research. I mean, like there's like so many variations built by so many people. And one common theme is they're all using these fine-tuning or human preferences datasets that are very limited and they're not sufficiently diverse. And you look at the other side, say front-ends like Oobabooga or like Hugging Chat or Ollama, they don't really have feedback buttons. All the people using all these front-ends, they probably want to give feedback, but there's no way for them to give feedback… So we're just losing all of this feedback. Maybe open source models are being as used as GPT is at this point in like all kinds of, in a very fragmented way, like in aggregate all the open source models together are probably being used as much as GPT is, maybe close to that. But the amount of feedback that is driving back into the open source ecosystem is like negligible, maybe less than 1% of like the usage. So I think like some, like the blueprint here I think is you'd want someone to create a sinkhole for the feedback… I think if we do that, if that actually happens, I think that probably has a real chance of the open source models having a runaway effect against OpenAI, I think like there's a clear chance we can take at truly winning open source.”If you're working on solving open source coordination, please get in touch!Show Notes* Soumith Chintala Twitter* History of PyTorch episode on Gradient Podcast* The Llama Ecosystem* Apple's MLX* Neural ODEs (Ordinary Differential Equations)* AlphaGo* LMSys arena* Dan Pink's "Drive"* Robotics projects:* Dobb-E* OK Robot* Yann LeCun* Yangqing Jia of Lepton AI* Ed Catmull* George Hotz on Latent Space* Chris Lattner on Latent Space* Guillaume Lample* Yannic Kilcher of OpenAssistant* LMSys* Alex Atallah of OpenRouter* Carlo Sferrazza's 3D tactile research* Alex Wiltschko of Osmo* Tangent by Alex Wiltschko* Lerrel Pinto - RoboticsTimestamps* [00:00:00] Introductions* [00:00:51] Extrinsic vs Intrinsic Success* [00:02:40] Importance of Open Source and Its Impact* [00:03:46] PyTorch vs TinyGrad* [00:08:33] Why PyTorch is the Switzerland of frameworks* [00:10:27] Modular's Mojo + PyTorch?* [00:13:32] PyTorch vs Apple's MLX* [00:16:27] FAIR / PyTorch Alumni* [00:18:50] How can AI inference providers differentiate?* [00:21:41] How to build good benchmarks and learnings from AnyScale's* [00:25:28] Most interesting unexplored ideas* [00:28:18] What people get wrong about synthetic data* [00:35:57] Meta AI's evolution* [00:38:42] How do you allocate 600,000 GPUs?* [00:42:05] Even the GPU Rich are GPU Poor* [00:47:31] Meta's MTIA silicon* [00:50:09] Why we need open source* [00:59:00] Open source's coordination problem for feedback gathering* [01:08:59] Beyond text generation* [01:15:37] Osmo and the Future of Smell Recognition TechnologyTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO in residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:15]: Hey, and today we have in the studio Soumith Chintala, welcome.Soumith [00:00:17]: Thanks for having me.Swyx [00:00:18]: On one of your rare visits from New York where you live. You got your start in computer vision at NYU with Yann LeCun. That was a very fortuitous start. I was actually listening to your interview on the Gradient podcast. So if people want to know more about the history of Soumith, history of PyTorch, they can go to that podcast. We won't spend that much time there, but I just was marveling at your luck, or I don't know if it's your luck or your drive to find AI early and then find the right quality mentor because I guess Yan really sort of introduced you to that world.Soumith [00:00:51]: Yeah, I think you're talking about extrinsic success, right? A lot of people just have drive to do things that they think is fun, and a lot of those things might or might not be extrinsically perceived as good and successful. I think I just happened to like something that is now one of the coolest things in the world or whatever. But if I happen, the first thing I tried to become was a 3D VFX artist, and I was really interested in doing that, but I turned out to be very bad at it. So I ended up not doing that further. But even if I was good at that, whatever, and I ended up going down that path, I probably would have been equally happy. It's just like maybe like the perception of, oh, is this person successful or not might be different. I think like after a baseline, like your happiness is probably more correlated with your intrinsic stuff.Swyx [00:01:44]: Yes. I think Dan Pink has this book on drive that I often refer to about the power of intrinsic motivation versus extrinsic and how long extrinsic lasts. It's not very long at all. But anyway, now you are an investor in Runway, so in a way you're working on VFX. Yes.Soumith [00:02:01]: I mean, in a very convoluted way.Swyx [00:02:03]: It reminds me of Ed Catmull. I don't know if you guys know, but he actually tried to become an animator in his early years and failed or didn't get accepted by Disney and then went and created Pixar and then got bought by Disney and created Toy Story. So you joined Facebook in 2014 and eventually became a creator and maintainer of PyTorch. And there's this long story there you can refer to on the gradient. I think maybe people don't know that you also involved in more sort of hardware and cluster decision affair. And we can dive into more details there because we're all about hardware this month. Yeah. And then finally, I don't know what else, like what else should people know about you on a personal side or professional side?Soumith [00:02:40]: I think open source is definitely a big passion of mine and probably forms a little bit of my identity at this point. I'm irrationally interested in open source. I think open source has that fundamental way to distribute opportunity in a way that is very powerful. Like, I grew up in India. I didn't have internet for a while. In college, actually, I didn't have internet except for GPRS or whatever. And knowledge was very centralized, but I saw that evolution of knowledge slowly getting decentralized. And that ended up helping me learn quicker and faster for zero dollars. And I think that was a strong reason why I ended up where I am. So the open source side of things, I always push regardless of what I get paid for, like I think I would do that as a passion project on the side.Swyx [00:03:35]: Yeah, that's wonderful. Well, we'll talk about the challenges as well that open source has, open models versus closed models. Maybe you want to touch a little bit on PyTorch before we move on to the sort of Meta AI in general.PyTorch vs Tinygrad tradeoffsAlessio [00:03:46]: Yeah, we kind of touched on PyTorch in a lot of episodes. So we had George Hotz from TinyGrad. He called PyTorch a CISC and TinyGrad a RISC. I would love to get your thoughts on PyTorch design direction as far as, I know you talk a lot about kind of having a happy path to start with and then making complexity hidden away but then available to the end user. One of the things that George mentioned is I think you have like 250 primitive operators in PyTorch, I think TinyGrad is four. So how do you think about some of the learnings that maybe he's going to run into that you already had in the past seven, eight years almost of running PyTorch?Soumith [00:04:24]: Yeah, I think there's different models here, but I think it's two different models that people generally start with. Either they go like, I have a grand vision and I'm going to build a giant system that achieves this grand vision and maybe one is super feature complete or whatever. Or other people say they will get incrementally ambitious, right? And they say, oh, we'll start with something simple and then we'll slowly layer out complexity in a way that optimally applies Huffman coding or whatever. Like where the density of users are and what they're using, I would want to keep it in the easy, happy path and where the more niche advanced use cases, I'll still want people to try them, but they need to take additional frictional steps. George, I think just like we started with PyTorch, George started with the incrementally ambitious thing. I remember TinyGrad used to be, like we would be limited to a thousand lines of code and I think now it's at 5,000. So I think there is no real magic to which why PyTorch has the kind of complexity. I think it's probably partly necessitated and partly because we built with the technology available under us at that time, PyTorch is like 190,000 lines of code or something at this point. I think if you had to rewrite it, we would probably think about ways to rewrite it in a vastly simplified way for sure. But a lot of that complexity comes from the fact that in a very simple, explainable way, you have memory hierarchies. You have CPU has three levels of caches and then you have DRAM and SSD and then you have network. Similarly, GPU has several levels of memory and then you have different levels of network hierarchies, NVLink plus InfiniBand or Rocky or something like that, right? And the way the flops are available on your hardware, they are available in a certain way and your computation is in a certain way and you have to retrofit your computation onto both the memory hierarchy and like the flops available. When you're doing this, it is actually a fairly hard mathematical problem to do this setup, like you find the optimal thing. And finding the optimal thing is, what is optimal depends on the input variables themselves. So like, okay, what is the shape of your input tensors and what is the operation you're trying to do and various things like that. Finding that optimal configuration and writing it down in code is not the same for every input configuration you have. Like for example, just as the shape of the tensors change, let's say you have three input tensors into a Sparstar product or something like that. The shape of each of these input tensors will vastly change how you do this optimally placing this operation onto the hardware in a way that will get you maximal throughput. So a lot of our complexity comes from writing out hundreds of configurations for each single PyTorch operator and templatizing these things and symbolically generating the final CUDA code or CPU code. There's no way to avoid it because mathematically we haven't found symbolic ways to do this that also keep compile time near zero. You can write a very simple framework, but then you also should be willing to eat the long compile time. So if searching for that optimal performance at runtime, but that's the trade off. There's no, like, I don't think unless we have great breakthroughs George's vision is achievable, he should be thinking about a narrower problem such as I'm only going to make this for work for self-driving car connets or I'm only going to make this work for LLM transformers of the llama style. Like if you start narrowing the problem down, you can make a vastly simpler framework. But if you don't, if you need the generality to power all of the AI research that is happening and keep zero compile time and in all these other factors, I think it's not easy to avoid the complexity.Pytorch vs MojoAlessio [00:08:33]: That's interesting. And we kind of touched on this with Chris Lattner when he was on the podcast. If you think about frameworks, they have the model target. They have the hardware target. They have different things to think about. He mentioned when he was at Google, TensorFlow trying to be optimized to make TPUs go brr, you know, and go as fast. I think George is trying to make especially AMD stack be better than ROCm. How come PyTorch has been such as Switzerland versus just making Meta hardware go brr?Soumith [00:09:00]: First, Meta is not in the business of selling hardware. Meta is not in the business of cloud compute. The way Meta thinks about funding PyTorch is we're funding it because it's net good for Meta to fund PyTorch because PyTorch has become a standard and a big open source project. And generally it gives us a timeline edge. It gives us leverage and all that within our own work. So why is PyTorch more of a Switzerland rather than being opinionated? I think the way we think about it is not in terms of Switzerland or not. We actually the way we articulate it to all hardware vendors and software vendors and all who come to us being we want to build a backend in core for PyTorch and ship it by default is we just only look at our user side of things. Like if users are using a particular piece of hardware, then we want to support it. We very much don't want to king make the hardware side of things. So as the MacBooks have GPUs and as that stuff started getting increasingly interesting, we pushed Apple to push some engineers and work on the NPS support and we spend significant time from Meta funded engineers on that as well because a lot of people are using the Apple GPUs and there's demand. So we kind of mostly look at it from the demand side. We never look at it from like oh which hardware should we start taking opinions on.Swyx [00:10:27]: Is there a future in which, because Mojo or Modular Mojo is kind of a superset of Python, is there a future in which PyTorch might use Mojo features optionally?Soumith [00:10:36]: I think it depends on how well integrated it is into the Python ecosystem. So if Mojo is like a pip install and it's readily available and users feel like they can use Mojo so smoothly within their workflows in a way that just is low friction, we would definitely look into that. Like in the same way PyTorch now depends on Triton, OpenAI Triton, and we never had a conversation that was like huh, that's like a dependency. Should we just build a Triton of our own or should we use Triton? It almost doesn't, like those conversations don't really come up for us. The conversations are more well does Triton have 10,000 dependencies and is it hard to install? We almost don't look at these things from a strategic leverage point of view. We look at these things from a user experience point of view, like is it easy to install? Is it smoothly integrated and does it give enough benefits for us to start depending on it? If so, yeah, we should consider it. That's how we think about it.Swyx [00:11:37]: You're inclusive by default as long as it meets the minimum bar of, yeah, but like maybe I phrased it wrongly. Maybe it's more like what problems would you look to solve that you have right now?Soumith [00:11:48]: I think it depends on what problems Mojo will be useful at.Swyx [00:11:52]: Mainly a performance pitch, some amount of cross compiling pitch.Soumith [00:11:56]: Yeah, I think the performance pitch for Mojo was like, we're going to be performant even if you have a lot of custom stuff, you're going to write arbitrary custom things and we will be performant. And that value proposition is not clear to us from the PyTorch side to consider it for PyTorch. So PyTorch, it's actually not 250 operators, it's like a thousand operators. PyTorch exposes about a thousand operators and people kind of write their ideas in the thousand operators of PyTorch. Mojo is like, well, maybe it's okay to completely sidestep those thousand operators of PyTorch and just write it in a more natural form. Just write raw Python, write for loops or whatever, right? So from the consideration of how do we intersect PyTorch with Mojo, I can see one use case where you have custom stuff for some parts of your program, but mostly it's PyTorch. And so we can probably figure out how to make it easier for say Torch.compile to smoothly also consume Mojo subgraphs and like, you know, the interoperability being actually usable, that I think is valuable. But Mojo as a fundamental front end would be replacing PyTorch, not augmenting PyTorch. So in that sense, I don't see a synergy in more deeply integrating Mojo.Pytorch vs MLXSwyx [00:13:21]: So call out to Mojo whenever they have written something in Mojo and there's some performance related thing going on. And then since you mentioned Apple, what should people think of PyTorch versus MLX?Soumith [00:13:32]: I mean, MLX is early and I know the folks well, Ani used to work at FAIR and I used to chat with him all the time. He used to be based out of New York as well. The way I think about MLX is that MLX is specialized for Apple right now. It has a happy path because it's defined its product in a narrow way. At some point MLX either says we will only be supporting Apple and we will just focus on enabling, you know, there's a framework if you use your MacBook, but once you like go server side or whatever, that's not my problem and I don't care. For MLS, it enters like the server side set of things as well. Like one of these two things will happen, right? If the first thing will happen, like MLX's overall addressable market will be small, but it probably do well within that addressable market. If it enters the second phase, they're going to run into all the same complexities that we have to deal with. They will not have any magic wand and they will have more complex work to do. They probably wouldn't be able to move as fast.Swyx [00:14:44]: Like having to deal with distributed compute?Soumith [00:14:48]: Distributed, NVIDIA and AMD GPUs, like just like having a generalization of the concept of a backend, how they treat compilation with plus overheads. Right now they're deeply assumed like the whole NPS graph thing. So they need to think about all these additional things if they end up expanding onto the server side and they'll probably build something like PyTorch as well, right? Like eventually that's where it will land. And I think there they will kind of fail on the lack of differentiation. Like it wouldn't be obvious to people why they would want to use it.Swyx [00:15:24]: I mean, there are some cloud companies offering M1 and M2 chips on servers. I feel like it might be interesting for Apple to pursue that market, but it's not their core strength.Soumith [00:15:33]: Yeah. If Apple can figure out their interconnect story, maybe, like then it can become a thing.Swyx [00:15:40]: Honestly, that's more interesting than the cars. Yes.Soumith [00:15:43]: I think the moat that NVIDIA has right now, I feel is that they have the interconnect that no one else has, like AMD GPUs are pretty good. I'm sure there's various silicon that is not bad at all, but the interconnect, like NVLink is uniquely awesome. I'm sure the other hardware providers are working on it, but-Swyx [00:16:04]: I feel like when you say it's uniquely awesome, you have some appreciation of it that the rest of us don't. I mean, the rest of us just like, you know, we hear marketing lines, but what do you mean when you say NVIDIA is very good at networking? Obviously they made the acquisition maybe like 15 years ago.Soumith [00:16:15]: Just the bandwidth it offers and the latency it offers. I mean, TPUs also have a good interconnect, but you can't buy them. So you have to go to Google to use it.PyTorch MafiaAlessio [00:16:27]: Who are some of the other FAIR PyTorch alumni that are building cool companies? I know you have Fireworks AI, Lightning AI, Lepton, and Yangqing, you knew since college when he was building Coffee?Soumith [00:16:40]: Yeah, so Yangqing and I used to be framework rivals, PyTorch, I mean, we were all a very small close-knit community back then. Caffe, Torch, Theano, Chainer, Keras, various frameworks. I mean, it used to be more like 20 frameworks. I can't remember all the names. CCV by Liu Liu, who is also based out of SF. And I would actually like, you know, one of the ways it was interesting is you went into the framework guts and saw if someone wrote their own convolution kernel or they were just copying someone else's. There were four or five convolution kernels that were unique and interesting. There was one from this guy out of Russia, I forgot the name, but I remembered who was awesome enough to have written their own kernel. And at some point there, I built out these benchmarks called ConNet benchmarks. They're just benchmarking all the convolution kernels that are available at that time. It hilariously became big enough that at that time AI was getting important, but not important enough that industrial strength players came in to do these kinds of benchmarking and standardization. Like we have MLPerf today. So a lot of the startups were using ConNet benchmarks in their pitch decks as like, oh, you know, on ConNet benchmarks, this is how we fare, so you should fund us. I remember Nirvana actually was at the top of the pack because Scott Gray wrote amazingly fast convolution kernels at that time. Very interesting, but separate times. But to answer your question, Alessio, I think mainly Lepton, Fireworks are the two most obvious ones, but I'm sure the fingerprints are a lot wider. They're just people who worked within the PyTorch Cafe2 cohort of things and now end up at various other places.Swyx [00:18:50]: I think as a, both as an investor and a people looking to build on top of their services, it's a uncomfortable slash like, I don't know what I don't know pitch. Because I've met Yang Tsing and I've met Lin Chao. Yeah, I've met these folks and they're like, you know, we are deep in the PyTorch ecosystem and we serve billions of inferences a day or whatever at Facebook and now we can do it for you. And I'm like, okay, that's great. Like, what should I be wary of or cautious of when these things happen? Because I'm like, obviously this experience is extremely powerful and valuable. I just don't know what I don't know. Like, what should people know about like these sort of new inference as a service companies?Soumith [00:19:32]: I think at that point you would be investing in them for their expertise of one kind. So if they've been at a large company, but they've been doing amazing work, you would be thinking about it as what these people bring to the table is that they're really good at like GPU programming or understanding the complexity of serving models once it hits a certain scale. You know, various expertise like from the infra and AI and GPUs point of view. What you would obviously want to figure out is whether their understanding of the external markets is clear, whether they know and understand how to think about running a business, understanding how to be disciplined about making money or, you know, various things like that.Swyx [00:20:23]: Maybe I'll put it like, actually I will de-emphasize the investing bit and just more as a potential customer. Oh, okay. Like, it's more okay, you know, you have PyTorch gods, of course. Like, what else should I know?Soumith [00:20:37]: I mean, I would not care about who's building something. If I'm trying to be a customer, I would care about whether...Swyx [00:20:44]: Benchmarks.Soumith [00:20:44]: Yeah, I use it and it's usability and reliability and speed, right?Swyx [00:20:51]: Quality as well.Soumith [00:20:51]: Yeah, if someone from some random unknown place came to me and say, user stuff is great. Like, and I have the bandwidth, I probably will give it a shot. And if it turns out to be great, like I'll just use it.Benchmark dramaSwyx [00:21:07]: Okay, great. And then maybe one more thing about benchmarks, since we already brought it up and you brought up Confident Benchmarks. There was some recent drama around AnyScale. AnyScale released their own benchmarks and obviously they look great on their own benchmarks, but maybe didn't give the other... I feel there are two lines of criticism. One, which is they didn't test some apples for apples on the kind of endpoints that the other providers, that they are competitors with, on their benchmarks and that is due diligence baseline. And then the second would be more just optimizing for the right thing. You had some commentary on it. I'll just kind of let you riff.Soumith [00:21:41]: Yeah, I mean, in summary, basically my criticism of that was AnyScale built these benchmarks for end users to just understand what they should pick, right? And that's a very good thing to do. I think what they didn't do a good job of is give that end user a full understanding of what they should pick. Like they just gave them a very narrow slice of understanding. I think they just gave them latency numbers and that's not sufficient, right? You need to understand your total cost of ownership at some reasonable scale. Not oh, one API call is one cent, but a thousand API calls are 10 cents. Like people can misprice to cheat on those benchmarks. So you want to understand, okay, like how much is it going to cost me if I actually subscribe to you and do like a million API calls a month or something? And then you want to understand the latency and reliability, not just from one call you made, but an aggregate of calls you've made over several various times of the day and times of the week. And the nature of the workloads, is it just some generic single paragraph that you're sending that is cashable? Or is it like testing of real world workload? I think that kind of rigor, like in presenting that benchmark wasn't there. It was a much more narrow sliver of what should have been a good benchmark. That was my main criticism. And I'm pretty sure if before they released it, they showed it to their other stakeholders who would be caring about this benchmark because they are present in it, they would have easily just pointed out these gaps. And I think they didn't do that and they just released it. So I think those were the two main criticisms. I think they were fair and Robert took it well.Swyx [00:23:40]: And he took it very well. And we'll have him on at some point and we'll discuss it. But I think it's important for, I think the market being maturing enough that people start caring and competing on these kinds of things means that we need to establish what best practice is because otherwise everyone's going to play dirty.Soumith [00:23:55]: Yeah, absolutely. My view of the LLM inference market in general is that it's the laundromat model. Like the margins are going to drive down towards the bare minimum. It's going to be all kinds of arbitrage between how much you can get the hardware for and then how much you sell the API and how much latency your customers are willing to let go. You need to figure out how to squeeze your margins. Like what is your unique thing here? Like I think Together and Fireworks and all these people are trying to build some faster CUDA kernels and faster, you know, hardware kernels in general. But those modes only last for a month or two. These ideas quickly propagate.Swyx [00:24:38]: Even if they're not published?Soumith [00:24:39]: Even if they're not published, the idea space is small. So even if they're not published, the discovery rate is going to be pretty high. It's not like we're talking about a combinatorial thing that is really large. You're talking about Llama style LLM models. And we're going to beat those to death on a few different hardware SKUs, right? Like it's not even we have a huge diversity of hardware you're going to aim to run it on. Now when you have such a narrow problem and you have a lot of people working on it, the rate at which these ideas are going to get figured out is going to be pretty rapid.Swyx [00:25:15]: Is it a standard bag of tricks? Like the standard one that I know of is, you know, fusing operators and-Soumith [00:25:22]: Yeah, it's the standard bag of tricks on figuring out how to improve your memory bandwidth and all that, yeah.Alessio [00:25:28]: Any ideas instead of things that are not being beaten to death that people should be paying more attention to?Novel PyTorch ApplicationsSwyx [00:25:34]: One thing I was like, you know, you have a thousand operators, right? Like what's the most interesting usage of PyTorch that you're seeing maybe outside of this little bubble?Soumith [00:25:41]: So PyTorch, it's very interesting and scary at the same time, but basically it's used in a lot of exotic ways, like from the ML angle, what kind of models are being built? And you get all the way from state-based models and all of these things to stuff nth order differentiable models, like neural ODEs and stuff like that. I think there's one set of interestingness factor from the ML side of things. And then there's the other set of interesting factor from the applications point of view. It's used in Mars Rover simulations, to drug discovery, to Tesla cars. And there's a huge diversity of applications in which it is used. So in terms of the most interesting application side of things, I think I'm scared at how many interesting things that are also very critical and really important it is used in. I think the scariest was when I went to visit CERN at some point and they said they were using PyTorch and they were using GANs at the same time for particle physics research. And I was scared more about the fact that they were using GANs than they were using PyTorch, because at that time I was a researcher focusing on GANs. But the diversity is probably the most interesting. How many different things it is being used in. I think that's the most interesting to me from the applications perspective. From the models perspective, I think I've seen a lot of them. Like the really interesting ones to me are where we're starting to combine search and symbolic stuff with differentiable models, like the whole AlphaGo style models is one example. And then I think we're attempting to do it for LLMs as well, with various reward models and search. I mean, I don't think PyTorch is being used in this, but the whole alpha geometry thing was interesting because again, it's an example of combining the symbolic models with the gradient based ones. But there are stuff like alpha geometry that PyTorch is used at, especially when you intersect biology and chemistry with ML. In those areas, you want stronger guarantees on the output. So yeah, maybe from the ML side, those things to me are very interesting right now.Swyx [00:28:03]: Yeah. People are very excited about the alpha geometry thing. And it's kind of like, for me, it's theoretical. It's great. You can solve some Olympia questions. I'm not sure how to make that bridge over into the real world applications, but I'm sure people smarter than me will figure it out.Synthetic Data vs Symbolic ModelsSoumith [00:28:18]: Let me give you an example of it. You know how the whole thing about synthetic data will be the next rage in LLMs is a thing?Swyx [00:28:27]: Already is a rage.Soumith [00:28:28]: Which I think is fairly misplaced in how people perceive it. People think synthetic data is some kind of magic wand that you wave and it's going to be amazing. Synthetic data is useful in neural networks right now because we as humans have figured out a bunch of symbolic models of the world or made up certain symbolic models because of human innate biases. So we've figured out how to ground particle physics in a 30 parameter model. And it's just very hard to compute as in it takes a lot of flops to compute, but it only has 30 parameters or so. I mean, I'm not a physics expert, but it's a very low rank model. We built mathematics as a field that basically is very low rank. Language, a deep understanding of language, like the whole syntactic parse trees and just understanding how language can be broken down and into a formal symbolism is something that we figured out. So we basically as humans have accumulated all this knowledge on these subjects, either synthetic, we created those subjects in our heads, or we grounded some real world phenomenon into a set of symbols. But we haven't figured out how to teach neural networks symbolic world models directly. The only way we have to teach them is generating a bunch of inputs and outputs and gradient dissenting over them. So in areas where we have the symbolic models and we need to teach all the knowledge we have that is better encoded in the symbolic models, what we're doing is we're generating a bunch of synthetic data, a bunch of input output pairs, and then giving that to the neural network and asking it to learn the same thing that we already have a better low rank model of in gradient descent in a much more over-parameterized way. Outside of this, like where we don't have good symbolic models, like synthetic data obviously doesn't make any sense. So synthetic data is not a magic wand where it'll work in all cases in every case or whatever. It's just where we as humans already have good symbolic models off. We need to impart that knowledge to neural networks and we figured out the synthetic data is a vehicle to impart this knowledge to. So, but people, because maybe they don't know enough about synthetic data as a notion, but they hear, you know, the next wave of data revolution is synthetic data. They think it's some kind of magic where we just create a bunch of random data somehow. They don't think about how, and then they think that's just a revolution. And I think that's maybe a gap in understanding most people have in this hype cycle.Swyx [00:31:23]: Yeah, well, it's a relatively new concept, so. Oh, there's two more that I'll put in front of you and then you can see what you respond. One is, you know, I have this joke that it's, you know, it's only synthetic data if it's from the Mistral region of France, otherwise it's just a sparkling distillation, which is what news research is doing. Like they're distilling GPT-4 by creating synthetic data from GPT-4, creating mock textbooks inspired by Phi 2 and then fine tuning open source models like Llama. And so I don't know, I mean, I think that's, should we call that synthetic data? Should we call it something else? I don't know.Soumith [00:31:57]: Yeah, I mean, the outputs of LLMs, are they synthetic data? They probably are, but I think it depends on the goal you have. If your goal is you're creating synthetic data with the goal of trying to distill GPT-4's superiority into another model, I guess you can call it synthetic data, but it also feels like disingenuous because your goal is I need to copy the behavior of GPT-4 and-Swyx [00:32:25]: It's also not just behavior, but data set. So I've often thought of this as data set washing. Like you need one model at the top of the chain, you know, unnamed French company that has that, you know, makes a model that has all the data in it that we don't know where it's from, but it's open source, hey, and then we distill from that and it's great. To be fair, they also use larger models as judges for preference ranking, right? So that is, I think, a very, very accepted use of synthetic.Soumith [00:32:53]: Correct. I think it's a very interesting time where we don't really have good social models of what is acceptable depending on how many bits of information you use from someone else, right? It's like, okay, you use one bit. Is that okay? Yeah, let's accept it to be okay. Okay, what about if you use 20 bits? Is that okay? I don't know. What if you use 200 bits? I don't think we as society have ever been in this conundrum where we have to be like, where is the boundary of copyright or where is the boundary of socially accepted understanding of copying someone else? We haven't been tested this mathematically before,Swyx [00:33:38]: in my opinion. Whether it's transformative use. Yes. So yeah, I think this New York Times opening eye case is gonna go to the Supreme Court and we'll have to decide it because I think we never had to deal with it before. And then finally, for synthetic data, the thing that I'm personally exploring is solving this great stark paradigm difference between rag and fine tuning, where you can kind of create synthetic data off of your retrieved documents and then fine tune on that. That's kind of synthetic. All you need is variation or diversity of samples for you to fine tune on. And then you can fine tune new knowledge into your model. I don't know if you've seen that as a direction for synthetic data.Soumith [00:34:13]: I think you're basically trying to, what you're doing is you're saying, well, language, I know how to parametrize language to an extent. And I need to teach my model variations of this input data so that it's resilient or invariant to language uses of that data.Swyx [00:34:32]: Yeah, it doesn't overfit on the wrong source documents.Soumith [00:34:33]: So I think that's 100% synthetic. You understand, the key is you create variations of your documents and you know how to do that because you have a symbolic model or like some implicit symbolic model of language.Swyx [00:34:48]: Okay.Alessio [00:34:49]: Do you think the issue with symbolic models is just the architecture of the language models that we're building? I think maybe the thing that people grasp is the inability of transformers to deal with numbers because of the tokenizer. Is it a fundamental issue there too? And do you see alternative architectures that will be better with symbolic understanding?Soumith [00:35:09]: I am not sure if it's a fundamental issue or not. I think we just don't understand transformers enough. I don't even mean transformers as an architecture. I mean the use of transformers today, like combining the tokenizer and transformers and the dynamics of training, when you show math heavy questions versus not. I don't have a good calibration of whether I know the answer or not. I, you know, there's common criticisms that are, you know, transformers will just fail at X. But then when you scale them up to sufficient scale, they actually don't fail at that X. I think there's this entire subfield where they're trying to figure out these answers called like the science of deep learning or something. So we'll get to know more. I don't know the answer.Meta AI and Llama 2/3Swyx [00:35:57]: Got it. Let's touch a little bit on just Meta AI and you know, stuff that's going on there. Maybe, I don't know how deeply you're personally involved in it, but you're our first guest with Meta AI, which is really fantastic. And Llama 1 was, you know, you are such a believer in open source. Llama 1 was more or less the real breakthrough in open source AI. The most interesting thing for us covering on this, in this podcast was the death of Chinchilla, as people say. Any interesting insights there around the scaling models for open source models or smaller models or whatever that design decision was when you guys were doing it?Soumith [00:36:31]: So Llama 1 was Guillaume Lample and team. There was OPT before, which I think I'm also very proud of because we bridged the gap in understanding of how complex it is to train these models to the world. Like until then, no one really in gory detail published.Swyx [00:36:50]: The logs.Soumith [00:36:51]: Yeah. Like, why is it complex? And everyone says, oh, it's complex. But no one really talked about why it's complex. I think OPT was cool.Swyx [00:37:02]: I met Susan and she's very, very outspoken. Yeah.Soumith [00:37:05]: We probably, I think, didn't train it for long enough, right? That's kind of obvious in retrospect.Swyx [00:37:12]: For a 175B. Yeah. You trained it according to Chinchilla at the time or?Soumith [00:37:17]: I can't remember the details, but I think it's a commonly held belief at this point that if we trained OPT longer, it would actually end up being better. Llama 1, I think, was Guillaume Lample and team Guillaume is fantastic and went on to build Mistral. I wasn't too involved in that side of things. So I don't know what you're asking me, which is how did they think about scaling loss and all of that? Llama 2, I was more closely involved in. I helped them a reasonable amount with their infrastructure needs and stuff. And Llama 2, I think, was more like, let's get to the evolution. At that point, we kind of understood what we were missing from the industry's understanding of LLMs. And we needed more data and we needed more to train the models for longer. And we made, I think, a few tweaks to the architecture and we scaled up more. And that was Llama 2. I think Llama 2, you can think of it as after Guillaume left, the team kind of rebuilt their muscle around Llama 2. And Hugo, I think, who's the first author is fantastic. And I think he did play a reasonable big role in Llama 1 as well.Soumith [00:38:35]: And he overlaps between Llama 1 and 2. So in Llama 3, obviously, hopefully, it'll be awesome.Alessio [00:38:42]: Just one question on Llama 2, and then we'll try and fish Llama 3 spoilers out of you. In the Llama 2 paper, the loss curves of the 34 and 70B parameter, they still seem kind of steep. Like they could go lower. How, from an infrastructure level, how do you allocate resources? Could they have just gone longer or were you just, hey, this is all the GPUs that we can burn and let's just move on to Llama 3 and then make that one better?Soumith [00:39:07]: Instead of answering specifically about that Llama 2 situation or whatever, I'll tell you how we think about things. Generally, we're, I mean, Mark really is some numbers, right?Swyx [00:39:20]: So let's cite those things again. All I remember is like 600K GPUs.Soumith [00:39:24]: That is by the end of this year and 600K H100 equivalents. With 250K H100s, including all of our other GPU or accelerator stuff, it would be 600-and-something-K aggregate capacity.Swyx [00:39:38]: That's a lot of GPUs.Soumith [00:39:39]: We'll talk about that separately. But the way we think about it is we have a train of models, right? Llama 1, 2, 3, 4. And we have a bunch of GPUs. I don't think we're short of GPUs. Like-Swyx [00:39:54]: Yeah, no, I wouldn't say so. Yeah, so it's all a matter of time.Soumith [00:39:56]: I think time is the biggest bottleneck. It's like, when do you stop training the previous one and when do you start training the next one? And how do you make those decisions? The data, do you have net new data, better clean data for the next one in a way that it's not worth really focusing on the previous one? It's just a standard iterative product. You're like, when is the iPhone 1? When do you start working on iPhone 2? Where is the iPhone? And so on, right? So mostly the considerations are time and generation, rather than GPUs, in my opinion.Alessio [00:40:31]: So one of the things with the scaling loss, like Chinchilla is optimal to balance training and inference costs. I think at Meta's scale, you would rather pay a lot more maybe at training and then save on inference. How do you think about that from infrastructure perspective? I think in your tweet, you say you can try and guess on like how we're using these GPUs. Can you just give people a bit of understanding? It's like, because I've already seen a lot of VCs say, Llama 3 has been trained on 600,000 GPUs and that's obviously not true, I'm sure. How do you allocate between the research, FAIR and the Llama training, the inference on Instagram suggestions that get me to scroll, like AI-generated stickers on WhatsApp and all of that?Soumith [00:41:11]: Yeah, we haven't talked about any of this publicly, but as a broad stroke, it's like how we would allocate resources of any other kinds at any company. You run a VC portfolio, how do you allocate your investments between different companies or whatever? You kind of make various trade-offs and you kind of decide, should I invest in this project or this other project, or how much should I invest in this project? It's very much a zero sum of trade-offs. And it also comes into play, how are your clusters configured, like overall, what you can fit of what size and what cluster and so on. So broadly, there's no magic sauce here. I mean, I think the details would add more spice, but also wouldn't add more understanding. It's just gonna be like, oh, okay, I mean, this looks like they just think about this as I would normally do.Alessio [00:42:05]: So even the GPU rich run through the same struggles of having to decide where to allocate things.Soumith [00:42:11]: Yeah, I mean, at some point I forgot who said it, but you kind of fit your models to the amount of compute you have. If you don't have enough compute, you figure out how to make do with smaller models. But no one as of today, I think would feel like they have enough compute. I don't think I've heard any company within the AI space be like, oh yeah, like we feel like we have sufficient compute and we couldn't have done better. So that conversation, I don't think I've heard from any of my friends at other companies.EleutherSwyx [00:42:47]: Stella from Eleuther sometimes says that because she has a lot of donated compute. She's trying to put it to interesting uses, but for some reason she's decided to stop making large models.Soumith [00:42:57]: I mean, that's a cool, high conviction opinion that might pay out.Swyx [00:43:01]: Why?Soumith [00:43:02]: I mean, she's taking a path that most people don't care to take about in this climate and she probably will have very differentiated ideas. I mean, think about the correlation of ideas in AI right now. It's so bad, right? So everyone's fighting for the same pie. In some weird sense, that's partly why I don't really directly work on LLMs. I used to do image models and stuff and I actually stopped doing GANs because GANs were getting so hot that I didn't have any calibration of whether my work would be useful or not because, oh yeah, someone else did the same thing you did. It's like, there's so much to do, I don't understand why I need to fight for the same pie. So I think Stella's decision is very smart.Making BetsAlessio [00:43:53]: And how do you reconcile that with how we started the discussion about intrinsic versus extrinsic kind of like accomplishment or success? How should people think about that especially when they're doing a PhD or early in their career? I think in Europe, I walked through a lot of the posters and whatnot, there seems to be mode collapse in a way in the research, a lot of people working on the same things. Is it worth for a PhD to not take a bet on something that is maybe not as interesting just because of funding and visibility and whatnot? Or yeah, what suggestions would you give?Soumith [00:44:28]: I think there's a baseline level of compatibility you need to have with the field. Basically, you need to figure out if you will get paid enough to eat, right? Like whatever reasonable normal lifestyle you want to have as a baseline. So you at least have to pick a problem within the neighborhood of fundable. Like you wouldn't wanna be doing something so obscure that people are like, I don't know, like you can work on it.Swyx [00:44:59]: Would a limit on fundability, I'm just observing something like three months of compute, right? That's the top line, that's the like max that you can spend on any one project.Soumith [00:45:09]: But like, I think that's very ill specified, like how much compute, right? I think that the notion of fundability is broader. It's more like, hey, are these family of models within the acceptable set of, you're not crazy or something, right? Even something like neural or DS, which is a very boundary pushing thing or states-based models or whatever. Like all of these things I think are still in fundable territory. When you're talking about, I'm gonna do one of the neuromorphic models and then apply image classification to them or something, then it becomes a bit questionable. Again, it depends on your motivation. Maybe if you're a neuroscientist, it actually is feasible. But if you're an AI engineer, like the audience of these podcasts, then it's more questionable. The way I think about it is, you need to figure out how you can be in the baseline level of fundability just so that you can just live. And then after that, really focus on intrinsic motivation and depends on your strengths, like how you can play to your strengths and your interests at the same time. Like I try to look at a bunch of ideas that are interesting to me, but also try to play to my strengths. I'm not gonna go work on theoretical ML. I'm interested in it, but when I want to work on something like that, I try to partner with someone who is actually a good theoretical ML person and see if I actually have any value to provide. And if they think I do, then I come in. So I think you'd want to find that intersection of ideas you like, and that also play to your strengths. And I'd go from there. Everything else, like actually finding extrinsic success and all of that, I think is the way I think about it is like somewhat immaterial. When you're talking about building ecosystems and stuff, slightly different considerations come into play, but that's a different conversation.Swyx [00:47:06]: We're gonna pivot a little bit to just talking about open source AI. But one more thing I wanted to establish for Meta is this 600K number, just kind of rounding out the discussion, that's for all Meta. So including your own inference needs, right? It's not just about training.Soumith [00:47:19]: It's gonna be the number in our data centers for all of Meta, yeah.Swyx [00:47:23]: Yeah, so there's a decent amount of workload serving Facebook and Instagram and whatever. And then is there interest in like your own hardware?MTIASoumith [00:47:31]: We already talked about our own hardware. It's called MTIA. Our own silicon, I think we've even showed the standard photograph of you holding the chip that doesn't work. Like as in the chip that you basically just get like-Swyx [00:47:51]: As a test, right?Soumith [00:47:52]: Yeah, a test chip or whatever. So we are working on our silicon and we'll probably talk more about it when the time is right, but-Swyx [00:48:00]: Like what gaps do you have that the market doesn't offer?Soumith [00:48:04]: Okay, I mean, this is easy to answer. So basically, remember how I told you about there's this memory hierarchy and like sweet spots and all of that? Fundamentally, when you build a hardware, you make it general enough that a wide set of customers and a wide set of workloads can use it effectively while trying to get the maximum level of performance they can. The more specialized you make the chip, the more hardware efficient it's going to be, the more power efficient it's gonna be, the more easier it's going to be to find the software, like the kernel's right to just map that one or two workloads to that hardware and so on. So it's pretty well understood across the industry that if you have a sufficiently large volume, enough workload, you can specialize it and get some efficiency gains, like power gains and so on. So the way you can think about everyone building, every large company building silicon, I think a bunch of the other large companies are building their own silicon as well, is they, each large company has a sufficient enough set of verticalized workloads that can be specialized that have a pattern to them that say a more generic accelerator like an NVIDIA or an AMD GPU does not exploit. So there is some level of power efficiency that you're leaving on the table by not exploiting that. And you have sufficient scale and you have sufficient forecasted stability that those workloads will exist in the same form, that it's worth spending the time to build out a chip to exploit that sweet spot. Like obviously something like this is only useful if you hit a certain scale and that your forecasted prediction of those kind of workloads being in the same kind of specializable exploitable way is true. So yeah, that's why we're building our own chips.Swyx [00:50:08]: Awesome.Open Source AIAlessio [00:50:09]: Yeah, I know we've been talking a lot on a lot of different topics and going back to open source, you had a very good tweet. You said that a single company's closed source effort rate limits against people's imaginations and needs. How do you think about all the impact that some of the Meta AI work in open source has been doing and maybe directions of the whole open source AI space?Soumith [00:50:32]: Yeah, in general, I think first, I think it's worth talking about this in terms of open and not just open source, because like with the whole notion of model weights, no one even knows what source means for these things. But just for the discussion, when I say open source, you can assume it's just I'm talking about open. And then there's the whole notion of licensing and all that, commercial, non-commercial, commercial with clauses and all that. I think at a fundamental level, the most benefited value of open source is that you make the distribution to be very wide. It's just available with no friction and people can do transformative things in a way that's very accessible. Maybe it's open source, but it has a commercial license and I'm a student in India. I don't care about the license. I just don't even understand the license. But like the fact that I can use it and do something with it is very transformative to me. Like I got this thing in a very accessible way. And then it's various degrees, right? And then if it's open source, but it's actually a commercial license, then a lot of companies are gonna benefit from gaining value that they didn't previously have, that they maybe had to pay a closed source company for it. So open source is just a very interesting tool that you can use in various ways. So there's, again, two kinds of open source. One is some large company doing a lot of work and then open sourcing it. And that kind of effort is not really feasible by say a band of volunteers doing it the same way. So there's both a capital and operational expenditure that the large company just decided to ignore and give it away to the world for some benefits of some kind. They're not as tangible as direct revenue. So in that part, Meta has been doing incredibly good things. They fund a huge amount of the PyTorch development. They've open sourced Llama and those family of models and several other fairly transformative projects. FICE is one, Segment Anything, Detectron, Detectron 2. Dense Pose. I mean, it's-Swyx [00:52:52]: Seamless. Yeah, seamless.Soumith [00:52:53]: Like it's just the list is so long that we're not gonna cover. So I think Meta comes into that category where we spend a lot of CapEx and OpEx and we have a high talent density of great AI people and we open our stuff. And the thesis for that, I remember when FAIR was started, the common thing was like, wait, why would Meta wanna start a open AI lab? Like what exactly is a benefit from a commercial perspective? And for then the thesis was very simple. It was AI is currently rate limiting Meta's ability to do things. Our ability to build various product integrations, moderation, various other factors. Like AI was the limiting factor and we just wanted AI to advance more and we didn't care if the IP of the AI was uniquely in our possession or not. However the field advances, that accelerates Meta's ability to build a better product. So we just built an open AI lab and we said, if this helps accelerate the progress of AI, that's strictly great for us. But very easy, rational, right? Still the same to a large extent with the Llama stuff. And it's the same values, but the argument, it's a bit more nuanced. And then there's a second kind of open source, which is, oh, we built this project, nights and weekends and we're very smart people and we open sourced it and then we built a community around it. This is the Linux kernel and various software projects like that. So I think about open source, like both of these things being beneficial and both of these things being different. They're different and beneficial in their own ways. The second one is really useful when there's an active arbitrage to be done. If someone's not really looking at a particular space because it's not commercially viable or whatever, like a band of volunteers can just coordinate online and do something and then make that happen. And that's great.Open Source LLMsI wanna cover a little bit about open source LLMs maybe. So open source LLMs have been very interesting because I think we were trending towards an increase in open source in AI from 2010 all the way to 2017 or something. Like where more and more pressure within the community was to open source their stuff so that their methods and stuff get adopted. And then the LLMs revolution kind of took the opposite effect OpenAI stopped open sourcing their stuff and DeepMind kind of didn't, like all the other cloud and all these other providers, they didn't open source their stuff. And it was not good in the sense that first science done in isolation probably will just form its own bubble where people believe their own b******t or whatever. So there's that problem. And then there was the other problem which was the accessibility part. Like, okay, I again always go back to I'm a student in India with no money. What is my accessibility to any of these closers models? At some scale I have to pay money. That makes it a non-starter and stuff. And there's also the control thing. I strongly believe if you want human aligned stuff, you want all humans to give feedback. And you want all humans to have access to that technology in the first place. And I actually have seen, living in New York, whenever I come to Silicon Valley, I see a different cultural bubble. Like all the friends I hang out with talk about some random thing like Dyson Spheres or whatever, that's a thing. And most of the world doesn't know or care about any of this stuff. It's definitely a bubble and bubbles can form very easily. And when you make a lot of decisions because you're in a bubble, they're probably not globally optimal decisions. So I think open source, the distribution of open source powers a certain kind of non-falsifiability that I think is very important. I think on the open source models, like it's going great in the fact that LoRa I think came out of the necessity of open source models needing to be fine-tunable in some way. Yeah, and I think DPO also came out of the academic open source side of things. So do any of the closed source labs, did any of them already have LoRa or DPO internally? Maybe, but that does not advance humanity in any way. It advances some companies probability of doing the winner takes all that I talked about earlier in the podcast.Open Source and TrustI don't know, it just feels fundamentally good. Like when people try to, you know, people are like, well, what are the ways in which it is not okay? I find most of these arguments, and this might be a little controversial, but I find a lot of arguments based on whether closed source models are safer or open source models are safer very much related to what kind of culture they grew up in, what kind of society they grew up in. If they grew up in a society that they trusted, then I think they take the closed source argument. And if they grew up in a society that they couldn't trust, where the norm was that you didn't trust your government, obviously it's corrupt or whatever, then I think the open source argument is what they take. I think there's a deep connection to like people's innate biases from their childhood and their trust in society and governmental aspects that push them towards one opinion or the other. And I'm definitely in the camp of open source is definitely going to actually have better outcomes for society. Closed source to me just means that centralization of power, which, you know, is really hard to trust. So I think it's going well

On the latest episode of Risk Management: Brick by Brick, Jason Reichl is joined by Matt Aston, President at GPRS, the nation's largest company specializing in the detection of underground utilities.

In this episode, Aaron is joined by Matt Aston, President at GPRS. GPRS is changing the construction game, using radar and electromagnetic signals to locate utilities. This technology is essential for spotting potential hazards (exp. underground power) in the ground and even through solid concrete! How effective is it you ask? Last year they completed roughly 120,000 jobs with an incident report rate of 0.13% saving potentially millions of dollars in damages, and keeping workers safe.  GPRS is now a major player in the industry with crews nationwide. Still, they have plans to grow and are always improving to get their margin for error down to zero. This all started because Matt was bold enough to spot a major problem he knew he could solve. So next time you have some iffy digging or treacherous cutting to do, consider GPRS. You could save a lot of time and money and potentially save a life! Learn more in this episode of Dirt Talk If you have any questions or feedback, email the Dirt Talk crew at dirttalk@buildwitt.com. Stay Dirty!

In this episode, I talk with Matt Aston, GPRS’ founder, about the company’s various services, including video pipe inspection, leak detection, laser scanning, and ground-penetrating radar. He shares the origin story of GPRS and its growth strategy, and also introduces the SiteMap initiative, which aims to create a comprehensive view of underground infrastructure for facilities. […] The post TCEP 254: Ground-Penetrating Radar: Cost-Effective Solutions for Infrastructure Visibility appeared first on Engineering Management Institute.

In this Concrete Logic Podcast episode, Seth interviews Dave Mulcahey from GPRS about concrete sawing and drilling technology and the importance of safety in the industry. They discuss the major technology for scanning concrete: ground penetrating radar (GPR). Dave explains that GPRS uses GPR to locate post-tension cables, electrical conduits, and rebar in or below concrete to ensure safe sawing and drilling. They also talk about Concrete Sawing and Drilling Safety Week, an annual event dedicated to promoting safety in the industry. Dave shares the key steps and precautions that workers should take to avoid accidents and injuries. They discuss the process of scanning concrete, the limitations and applications of GPR, and the integration of GPR data into building information models (BIM). Dave emphasizes the importance of communication and collaboration between project managers and contractors to ensure safe and efficient drilling and cutting. They also address common safety issues, such as slips, trips, and falls, and exposure to silica dust. Takeaways Ground penetrating radar (GPR) is a technology used to scan concrete and locate post-tension cables, electrical conduits, and rebar. Concrete Sawing and Drilling Safety Week is an annual event that promotes safety in the industry and educates workers on the dangers and precautions of concrete cutting and drilling. Communication plays a crucial role in scanning concrete, consulting contractors, and ensuring the safe execution of drilling and cutting. GPR technology provides a cross-sectional view of the concrete, allowing workers to identify targets and plan drilling and cutting accordingly. Silica dust exposure is a significant concern in the industry and measures should be taken to minimize exposure and protect workers' health.   Chapters 00:00 Introduction and Ways to Support the Podcast 02:29 Introduction to Concrete Sawing and Drilling Technology 04:24 Concrete Sawing and Drilling Safety Week 09:07 Process of Scanning and Safety Protocols 12:12 Overview of Ground Penetrating Radar (GPR) Technology 15:57 Limitations and Applications of GPR 17:19 Integration of GPR Data into Building Information Models (BIM) 20:32 Role of GPRS Project Managers 22:13 Recommendations for Cutting and Drilling 23:51 Concrete Safety Week and Common Safety Issues 25:48 Addressing Silica Dust Exposure 27:18 Participation in World of Concrete 28:37 Contact Information and Conclusion *** Did you learn something from this episode? If so, please consider donating to the show to help us continue to provide high-quality content for the concrete industry. Donate here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.concretelogicpodcast.com/support/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ***  Episode References Guest: Dave Mulcahey | GPRS | ⁠⁠⁠⁠⁠⁠⁠dave.mulcahey@gprsinc.com Guest Website:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.gprsinc.com, https://www.gp-radar.com/safety/concrete-safety-week   Producers: Jodi Tandett, Jace Stocker, Michael Butler Donate & Become a Producer: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.concretelogicpodcast.com/support/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Music: Mike Dunton | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.mikeduntonmusic.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠mikeduntonmusic@gmail.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | Instagram ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@Mike_Dunton⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠   Host: Seth Tandett, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠seth@concretelogicpodcast.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Host LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/seth-tandett/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.concretelogicpodcast.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/company/concrete-logic-podcast

On this episode of The Modern Facilities Management Podcast, Griffin interviews Matt Aston, the president of GPRS. Matt shares his story of growing GPRS from a one-person startup to nearly 800 employees across 50+ cities. Matt has 20+ years of experience in utility locating and concrete scanning, giving him an insider perspective on the industry, how it has developed, and how it will continue to innovate.TakeawaysGround penetrating radar (GPR) is a highly accurate technology used in facilities management to locate utilities and infrastructure.GPRS offers additional services like video pipe inspection and 3D laser scanning to provide a comprehensive solution for their clients.Proactive maintenance and regular inspections can help prevent unplanned downtime and costly repairs.The future of technology in facilities management includes the development of software programs like SiteMap, which provides quick visibility and access to accurate utility data.Chapters00:00Introduction and Background02:23The Use of Radar in Facilities Management04:17Comparison with Other Technologies07:40The Role of 811 and GPRS Services09:36Addition of Video Pipe Inspection Service13:06Proactive Maintenance and Inspection Frequency16:01The Impact of Unplanned Downtime18:54The Future of Technology in Facilities Management22:18Influential Figures in Matt Aston's Career24:44Conclusion and Contact Information

CCCT sat down with Matt Aston, Founder & CEO from Ground Penetrating Radar Systems LLC (GPRS), is the nation's largest company specializing in the detection of underground utilities, video pipe inspection, and the scanning of concrete structures. GPRS has an extensive nationwide network of highly trained and experienced Project Managers in every major U.S. market. When clients hire GPRS, they have the peace of mind of knowing that they have the most reliable scanning technology on their job site and they'll receive the assistance of a Project Manager who can provide them with the most accurate data. For over a decade, GPRS has been the industry leader by providing outstanding service and cutting edge technology, keeping projects on time, reducing safety risks, and putting our relationships with our clients before profit. Enjoy the conversation.https://www.gp-radar.com/#concretecutting  #underground #pipe #construction #detection #radar 

Mary Kym is a thought leader with a career spanning over two decades, marked by remarkable achievements in people management, Product Leadership, community leadership, autism advocacy, and human rights advancement. Renowned as a seasoned business coach, Mary is the driving force behind empowering women to reach and surpass multiple 6 figures in incomes. Her unwavering belief that there's no award for suffering has ignited transformative change for countless individuals. With academic foundations rooted in a Bachelor's degree in Management Information Systems (MIS) and a Master's Degree in Management and Leadership, Mary's expertise is further bolstered by a wealth of certifications in IT, management, leadership, and business coaching. Her magnetic charisma as a catalyst, motivator, and coach sets her apart, effortlessly guiding and encouraging women to unlock their boundless potential, all on their own terms. Residing in the heart of Tampa, Mary finds immense joy in her family, traversing the globe, exploring sun-kissed beaches, and savoring the rich tapestry of culinary experiences. Her professional odyssey serves as a resounding testament to resilience, empowerment, and relentless advocacy, inspiring all fortunate enough to cross her path to reach for the highest peaks of achievement. Contact: https://www.facebook.com/Mary.kimari Summary Entrepreneurship, business growth, and customer experience. 0:00 Mary Kay shares lessons learned from starting a fashion business after leaving a successful career in GPRS systems. She emphasizes the importance of providing excellent customer experience, treating all customers with respect and dignity, regardless of the price point of their purchases. Entrepreneurship, business lessons, and new launch. 5:15 New launch: Mary Kym coaching boutique for minority women in business. Starting and scaling a business for women entrepreneurs. 10:01 Mary Kym coaches women on how to start, launch, and scale their businesses, using her own experiences and skills to help them succeed. She provides personalized coaching to women from diverse backgrounds, including those from the African diaspora, helping them overcome challenges and achieve their business goals. Business coaching and mindset with a coach. 16:15 Coach Kym emphasizes the importance of creating a non-judgmental environment for clients to share their ideas and work towards their goals. Coach distinguishes between their role as a coach and that of a therapist, highlighting the need for a supportive environment for clients to explore their ideas without fear of judgment. Starting a business and coaching women. 24:05 Mary Kym aims to create a satellite business in Florida, with the goal of providing coaching services to as many people as possible, including women and men, in Kenya and beyond. She plans to hire coaches and train them to duplicate themselves, allowing the business to scale and reach a wider audience. Entrepreneurship, business growth, and personal development. 28:58 Mary Kim shares her success as a woman in business in diaspora, launching in November under her handle Mary Kay. #podcasts #sambaza #sambazapodcast #africanpodcasters #kenya #podcasting #africa #africanculture #podcast #podcastshow #podcastingfun #podcastlove #podcastcommunity #newpodcastepisode #podcasthost #PodcastHQ #podcastlife #podcastinterview #podcaster #podcastshow #PodcastRecommendations #podcasthost #podcastmovement #podcastnetwork #podcastaddict #podcastepisode #podcastjunkie #podcasttips #Podcastoftheyear #podbean #Podcasts

In this episode, we speak with Matt Aston, President and CEO of GPRS, who has built his company from an idea to over $160 million in annual revenue. GPRS or Ground Penetrating Radar Systems is the nation's largest company specializing in the detection of underground utilities, video pipe inspection, and the scanning of concrete structures. Matt founded GPRS in 2001 and the company has grown to be a great success in its market. Its 99.8%+ accuracy rating on over 250,000 projects leads the industry.  The company has worked on some of the largest and most significant construction projects in the country. GPRS is backed by CIVC Partners. Matt supports LifeWise Academy. To learn more about this organization click here. I am your host RJ Lumba.  We hope you enjoy the show.  If you like the episode, click to subscribe.

Episode #9 wikipedia: MS-DOS is an operating system for x86-based personal computers mostly developed by Microsoft. freedos: FreeDOS is a complete, free, DOS-compatible operating system. While we provide some utilities, you should be able to run any program intended for MS-DOS. wikipedia: Linux (/ˈliːnʊks/ (listen) LEE-nuuks or /ˈlɪnʊks/ LIN-uuks) is a family of open-source Unix-like operating systems based on the Linux kernel, an operating system kernel first released on September 17, 1991, by Linus Torvalds. wikipedia: Token Ring is a computer networking technology used to build local area networks. 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Y. Chu. wikipedia: Library (computing). wikipedia: Magnetic-core memory was the predominant form of random-access computer memory for 20 years between about 1955 and 1975. BASIC BASIC (Beginners' All-purpose Symbolic Instruction Code) is a family of general-purpose, high-level programming languages designed for ease of use. The original version was created by John G. Kemeny and Thomas E. Kurtz at Dartmouth College in 1963. wikipedia: Microsoft BASIC is the foundation software product of the Microsoft company and evolved into a line of BASIC interpreters and compiler(s) adapted for many different microcomputers. It first appeared in 1975 as Altair BASIC, which was the first version of BASIC published by Microsoft as well as the first high-level programming language available for the Altair 8800 microcomputer. wikipedia: A floppy disk or floppy diskette (casually referred to as a floppy, or a diskette) is an obsolescent type of disk storage composed of a thin and flexible disk of a magnetic storage medium in a square or nearly square plastic enclosure lined with a fabric that removes dust particles from the spinning disk. wikipedia: A tape drive is a data storage device that reads and writes data on a magnetic tape. wikipedia: In computer engineering, microarchitecture, also called computer organization and sometimes abbreviated as µarch or uarch, is the way a given instruction set architecture (ISA) is implemented in a particular processor. wikipedia: A microsleep is a sudden temporary episode of sleep or drowsiness which may last for a few seconds where an individual fails to respond to some arbitrary sensory input and becomes unconscious. clevo: We offer over 50 models from CLEVO. wikipedia: Clevo is a Taiwanese OEM/ODM computer manufacturer which produces laptop computers exclusively. wikipedia: Rapid transit or mass rapid transit (MRT), also known as heavy rail or metro, is a type of high-capacity public transport generally found in urban areas. wikipedia: Cracker Jack is an American brand of snack food that consists of molasses-flavored, caramel-coated popcorn, and peanuts, well known for being packaged with a prize of trivial value inside. gov: UK Driver's Licence. gov: Legal obligations of drivers and riders. sheilaswheels: We keep our Sheilas happy by supplying fabulous 5 Star Defaqto rated car and home insurance, and that's helped us to become one of the UK's leading direct insurers. nestle: Yorkie was launched in 1976 by Rowntree's of York hence the name. wikipedia: Joyriding refers to driving or riding in a stolen vehicle, most commonly a car, with no particular goal other than the pleasure or thrill of doing so or to impress other people. oggcamp: OggCamp is an unconference celebrating Free Culture, Free and Open Source Software, hardware hacking, digital rights, and all manner of collaborative cultural activities and is committed to creating a conference that is as inclusive as possible. ubuntu: Ubuntu is a Linux distribution based on Debian and composed mostly of free and open-source software. wikipedia: Ubuntu. wikipedia: Mark Shuttleworth. ubuntu: Ubuntu tablet press pack. stallman: Richard Stallman's Personal Site. elementary: The thoughtful, capable, and ethical replacement for Windows and macOS. slackware: The Slackware Linux Project. wikipedia: identi.ca was a free and open-source social networking and blogging service based on the pump.io software, using the Activity Streams protocol. wikipedia: GNU social (previously known as StatusNet and once known as Laconica) is a free and open source software microblogging server written in PHP that implements the OStatus standard for interoperation between installations. wikipedia: Friendica (formerly Friendika, originally Mistpark) is a free and open-source software distributed social network. lugcast: We are an open Podcast/LUG that meets every first and third Friday of every month using mumble. toastmasters Toastmasters International is a nonprofit educational organization that teaches public speaking and leadership skills through a worldwide network of clubs. wikipedia: Motorola, Inc. (/ˌmoʊtəˈroʊlə/) was an American multinational telecommunications company based in Schaumburg, Illinois, United States. volla: Volla Phone. ubports: We are building a secure & private operating system for your smartphone. sailfishos: The mobile OS with built-in privacy. calyxos: CalyxOS is an operating system for smartphones based on Android with mostly free and open-source software. wikipedia: WhatsApp. IRC IRC is short for Internet Relay Chat. It is a popular chat service still in use today. zoom: Unified communication and collaboration platform. jitsi: Jitsi Free & Open Source Video Conferencing Projects. joinmastodon: Mastodon is free and open-source software for running self-hosted social networking services. wikipedia: Karen Sandler is the executive director of the Software Freedom Conservancy, former executive director of the GNOME Foundation, an attorney, and former general counsel of the Software Freedom Law Center. fosdem: FOSDEM is a free event for software developers to meet, share ideas and collaborate. southeastlinuxfest: The SouthEast LinuxFest is a community event for anyone who wants to learn more about Linux and Open Source Software. olfconference: OLF (formerly known as Ohio LinuxFest) is a grassroots conference for the GNU/Linux/Open Source Software/Free Software community that started in 2003 as a large inter-LUG (Linux User Group) meeting and has grown steadily since. linuxfests: A home for educational programs focused on free and open source software & culture. wikipedia: Notacon (pronounced "not-a-con") was an art and technology conference which took place annually in Cleveland, Ohio from 2003 to 2014. penpalworld: a place where you can meet over 3,000,000 pen pals from every country on the planet. redhat: Red Hat Enterprise Linux. openssl: The OpenSSL Project develops and maintains the OpenSSL software - a robust, commercial-grade, full-featured toolkit for general-purpose cryptography and secure communication. STEM wikipedia: Obsessive–compulsive disorder. cdc: Autism. wikipedia: Asperger syndrome. askubuntu: Manual partitioning during installation. wikipedia: Colon cancer staging. cdc: Get Vaccinated Before You Travel. sqlite: SQLite is a C-language library that implements a small, fast, self-contained, high-reliability, full-featured, SQL database engine. wikipedia: Facial recognition system. wikipedia: Tribalism is the state of being organized by, or advocating for, tribes or tribal lifestyles. wikipedia: Southern hospitality. wikipedia: The Kroger Company, or simply Kroger, is an American retail company that operates (either directly or through its subsidiaries) supermarkets and multi-department stores throughout the United States. wikipedia: Prosopagnosia, more commonly known as face blindness, is a cognitive disorder of face perception in which the ability to recognize familiar faces, including one's own face, is impaired, while other aspects of visual processing and intellectual functioning remain intact. wikipedia: T-Mobile is the brand name used by some of the mobile communications subsidiaries of the German telecommunications company Deutsche Telekom AG in the Czech Republic, Poland, the United States and by the former subsidiary in the Netherlands. stackexchange: Where did the phrase "batsh-t crazy" come from? wikipedia: A conspiracy theory is an explanation for an event or situation that asserts the existence of a conspiracy by powerful and sinister groups, often political in motivation, when other explanations are more probable. brigs: At Brigs, we want everyone to get exactly what they're craving! papajohns: Papa Johns. dominos: Domino's Pizza, Inc., trading as Domino's, is a Michigan-based multinational pizza restaurant chain founded in 1960 and led by CEO Russell Weiner. wikipedia: Loitering is the act of remaining in a particular public place for a prolonged amount of time without any apparent purpose. wikipedia: Psychiatric hospitals, also known as mental health hospitals, behavioral health hospitals, are hospitals or wards specializing in the treatment of severe mental disorders, such as schizophrenia, bipolar disorder, eating disorders, dissociative identity disorder, major depressive disorder and many others. wikipedia: Therapist is a person who offers any kinds of therapy. Thanks To: Mumble Server: Delwin HPR Site/VPS: Joshua Knapp - AnHonestHost.com Streams: Honkeymagoo EtherPad: HonkeyMagoo Shownotes by: Sgoti and hplovecraft

Manja heeft misschien wel wat te veel honden. Een van deze schatjes heeft de neiging weg te lopen. Deze keer raakt de hond verstrikt in een afgerasterd weiland.

Our 1st episode is all about golden tips and advice for entrepreneurs and startup founders. You will hear some great stories about Samuel Lopez's previous businesses, lessons learned, and how he was able to pivot his current startup on its way to success. The 2nd episode is about Plantae's journey, challenges, opportunities, and what's next for the startup and the agribusiness sector. Plantae is a Software & Hardware as a Service company that connects plants through IoT devices based on radio frequency and GPRS technology. Samuel Lopez is an Industrial Engineer specialized in Business Development and a passionate entrepreneur. He's currently the CEO of Plantae.

Our 1st episode is all about golden tips and advice for entrepreneurs and startup founders. You will hear some great stories about Samuel Lopez's previous businesses, lessons learned, and how he was able to pivot his current startup on its way to success. The 2nd episode is about Plantae's journey, challenges, opportunities, and what's next for the startup and the agribusiness sector. Plantae is a Software & Hardware as a Service company that connects plants through IoT devices based on radio frequency and GPRS technology. Samuel Lopez is an Industrial Engineer specialized in Business Development and a passionate entrepreneur. He's currently the CEO of Plantae.

GPRS, THE TRAVELLERS GUIDE

GPRS, THE TRAVELLERS GUIDE

Internet awal-awal merebak 2005, sinyal masih bapuk, hanya GPRS, tidak ada paket internet, biaya per data. Situs yang awal-awal dikunjungi, friendster, facebook, primbon, kaskus, sukatoro dan lain-lain --- Send in a voice message: https://anchor.fm/absolut-vodkes/message

GPRS, JAGORAN TAFIYA

GPRS, JAGORAN TAFIYA

Momen-momen pertama kali Smouthies mengenal internet, wow seperti memasuki dunia baru! Dari yang hanya unduh ringtone dengan mengandalkan sinyal GPRS, meninggalkan testimoni di friendster, check in di foursquare, menghias friendster sampai ada lagunya dan mencari-cari jawaban tugas sekolah di blog, hampir semuanya pernah dilakuin. Semenjak melek teknologi udah ga bisa lepas pagi sama yang namanya internet, apa apa internet pokoknya, kalo bisa pilih provider cakep biar tetep online. Tapi penggunaan tentunya bukan cuma buat maen Onet, makanya lengkapi aplikasi kalian dengan spotify dan cari Smouthies ❤ --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/smouthies-podcast/support

Are you a transcended being? If so .. you won't need to follow the GPRS, but also .. please don't follow the mysterious banjo sounds. In this week's bonus episode, your co-hosts talk about colorful socks and their role in showcasing personality (plus hiding gangrenous afflictions) and how they've recently discovered their podcasting shavasana. Tim surprises Caleb with a blast from the past .. the one and only, Jon .. wait, who? Caleb then presents some wild Fake Florida Man cases to be solved including fish stuffing, traffic stop puddles, and how an ex-boyfriend makes a messy heartbreak even messier. The Bourbon Street Boys relive their memories of House Church, the trip to NOLA, and the infamous donut run 2.0. Tim and Jon share their summer experience in Panama City and how a glass of sake led to the Stud Muffins receiving more than just a mohawk .. yikes. Mr Fernandez catches the Ramblers up with where he went after leaving uni their senior year, his time as a journalist in the military, and where he is now with work and a family of rabbits (and humans). Email: podcast.theramble@gmail.com Instagram: @_theramblepodcast Facebook: @theramblepodcast Artwork Design: @indra.valdez --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/the-ramble/support

This week's guests move almost no dirt. They don't do demolition. They don't deal in aggregates. And yet, folks in their line of work visit hundreds of job sites per year. Welcome Chris Moore (SVP of Internal Operations) and Preston Higgins (Project Manager) of GPRS (Ground Penetrating Radar Systems) to the podcast. Among other things, GPRS is a great example of your friendly neighborhood utility locator. And boy, did these guys pique Aaron's interest. If you've ever worked a job and had to communicate with someone coming out to do a locate, then you might know that this kind of work is often looked at as an annoyance... and yet, people still hit utility lines! This conversation was a great basic rundown of what that work looks like from their perspective, and if you're a fan of tech talk, then you'll really connect with these guys. This week on Dirt Talk, host Aaron Witt chats with Chris Moore and Preston Higgins about ground penetrating radar/utility locating, why they (and companies like GPRS) can sometimes get a bad rap, and why GPRS likes to focus on developing their team members into good people, first and foremost. Don't forget you can watch or listen to Dirt Talk on the BuildWitt app! You can learn more about it at buildwitt.com/buildwitt-app. If you have questions/comments/concerns, reach out to DirtTalk@buildwitt.com. Stay Dirty!

GPRS, JAGORAN TAFIYA

GPRS, JAGORAN TAFIYA

GPRS, JAGORAN TAFIYA

GPRS, JAGORAN TAFIYA

In this episode of The IoT Unicorn Podcast, Rob Tiffany, VP and Head of IoT Strategy at Ericsson explores the development of 5G and LPWA technology for IoT solutions, what it looks like for Telco's to be successful in the IoT space, and how the Internet is playing the hero during the uncertainty of the Covid-19 pandemic. Download the Transcript Here   00:00 Pete Bernard: Great, so Rob, thanks for joining us today on the Unicorn, and really appreciate you taking the time. I was going to start by asking you a couple things about what your role is currently at Ericsson, kinda how you got there. I know that you and I did work together at Microsoft years ago back in the Windows Mobile days.   00:24 Rob Tiffany: Woo hoo.   00:25 PB: Good times, good times.   00:25 RT: Those were good times. Yep, absolutely. [chuckle]   00:28 PB: Yes. I thin, I think you were... Let's see, when did you stop working for Windows Mobile, like 2008 or something? Or is that...   00:38 RT: Yeah. And certainly by 2010 or around that timeframe I took an architect role in another group and probably started spending more time on Azure. I was at Microsoft for 12 years and so the first half was Mobile, Windows Mobile, CEE, Windows Phone. Second half was Azure, Azure IoT. And you know what? We had some good times in the Windows Mobile days when it was just us and BlackBerry slugging it out. We were making... When things like Exchange ActiveSync was a big deal to people.   01:21 PB: That's right, that was a big deal.   01:24 RT: Yeah, yeah, absolutely. And then no doubt, when we rebooted and did Windows Phone 7 and 7.5 and all that, I used to do so many EBCs for mobility and you noticed a difference and you had to get really thick skin. [chuckle]   01:42 PB: Yes, yes, yes, I know. Well, I peeled off after six... I think, so I went on to Zune incubation, I did Kin and I did all kinds of weird phone things and went off into the wilderness for a while on that while everyone else finished up with Windows Phone, but...   02:00 RT: Oh my gosh.   02:01 PB: And I also noticed on your LinkedIn profile. So you went to SUNY Albany. Are you from that area originally or...   02:07 RT: You know what? I finished college on board a submarine, so when I was in the Navy driving subs I had what, maybe 30 or so hours to go to graduate, and so I've actually never set foot on the SUNY Albany campus...   02:26 PB: Oh, wild.   02:27 RT: But the military has programs with lots of different universities around the country and to show how old I really am, I was able to take college courses underway on the submarine using Pioneer LaserDiscs.   02:42 PB: Wow.   02:43 RT: For college instruction, if anybody remembers what that was. [laughter]   02:47 PB: Yeah, that is old school, that's old school.   02:50 RT: That is fully old school.   02:52 PB: I actually just dropped my daughter off at Bard, which is a little south of Albany, so I was just there like a week ago, so that's why I asked.   02:58 RT: Oh, okay.   02:58 PB: I saw that on your profile and I was like, "Oh, yeah." It's a cool area, the Adirondacks, the whole upstate New York thing is cool.   03:04 RT: I know. Absolutely. Yeah, I just dropped my daughter off at Arizona State last week.   03:09 PB: Yeah.   03:10 RT: It was a little warm down there.   03:11 PB: Yeah, I could imagine, I could imagine.   03:14 RT: To say the least. But you know what? I think everything started back then with submarines and teaching myself how to code and do databases, and when you think about IoT, you're just remoting information that you had on these local sensors and we were surrounded by sensors on the submarine. There's the obvious things like sonar and things like that and this higher frequency one to see what your depth is below the keel, but inside you had CO2 radiation, all kinds of gas sensors and things like that to make sure we were still alive, which was kind of a thing. [chuckle]   04:02 PB: Yeah, it's kind of important.   04:04 RT: Yeah, yeah, absolutely.   04:06 PB: That's interesting. So you did the Microsoft thing and so you joined Ericsson a couple years ago, I think?   04:13 RT: Yeah, yeah, yeah. I did the Microsoft thing. I was recruited out of the Azure back when we were doing incubating Azure IT. There was that time... And actually Microsoft IoT stuff started in the embedded team with Intelligence System Service, but then I went to Hitachi actually to build an industrial IoT platform called Lumada, which was really interesting. But yes, I joined Ericsson a couple years ago. Up until recently, I split my time between Seattle and Stockholm. Normally I'd be in Kista, the Ericsson headquarters with the rest of my team. So yes, certainly disconnected these days.   05:00 PB: Yeah, interesting.   05:00 RT: And what Ericsson is doing in IoT is very different than my background both at Microsoft and Hitachi for sure, which was more data-focused, outcomes, analytics. Ericsson manages among... We have an IoT team. We have three products. Our big one is this IoT Accelerator, which is basically a global connection management platform. If you know what Jasper is, it's kinda like that in some ways. It spans about 35 or so mobile operators around the world and lots of enterprises. But the key thing, you know how we're always talking about that initial bootstrapping of devices to get them connected, right?   05:46 PB: Yep.   05:47 RT: In the event that you're using cellular for IoT, one of your options would be this IoT Accelerator thing we have at Ericsson, and so the narrative would be if a machine is being manufactured in Shenzhen and at manufacturer time, they're putting in the microcontroller and the software and the security keys and all that stuff, and there's also a cellular module, and if they're using our technology then when a customer buys that product and they turn it on the first time somewhere else in the world, maybe France, then it wakes up and connects to a local mobile operator to start sync telemetry.   06:24 PB: I see, so it's like a bootstrap profile kind of thing that phones home and then you guys connect it up to the right telco network.   06:35 RT: Yeah, and then it roams as well. But it's different than anybody who, if you... At least when IoT was getting hyped I was doing IoT-M to M in the '90s, but when it really started getting hyped after 2010, 2012, whatever, you started seeing these global SIMs and things like that that are just roaming all the time.   06:58 PB: Yes.   07:00 RT: But what the average person doesn't realize is mobile operators don't always want you roaming and just camped out on their network if you're from somewhere else.   07:08 PB: Yeah, yeah.   [laughter]   07:10 RT: And so our technology, aside from the technology and we're operating our own network, so even though Ericsson creates the technologies that mobile operators use, we actually manage our own network that spans the globe, that interfaces with all these other mobile operators, and then there's lots of contracts and everything. But the take away to make sure that it's all okay with them, that these devices... And we are also in the connected car space and we've been doing that for a long time. And so you can imagine a car manufactured in Japan and sold in Europe.   07:46 PB: Sure.   07:47 RT: And the whole infotainment, and then as we move forward, more and more IOT telemetry coming off, those cards may wanna roam from country to country, so we do a lot of stuff with those guys too.   08:00 PB: I noticed that recently I got an email this morning from account team in Finland talking about a telco, there seems to be this confluence of telco and IoT. And I've seen, and I think you might have had some commentary on that too or pointed some articles about 5G plus AI plus IoT, or there's something about... We're seeing some telcos have really... Forward leaning telcos, really investing and thinking about IoT as the next big wave for them. Ericsson is part of that story too. Is there some unnatural attraction between IoT and telco or what's going on there? Are you seeing the same thing?   08:40 RT: Yeah, I am. But of course, if you'll remember, we saw this before. When the IoT craze started taking off, you might remember a lot of the telcos built their own IoT platforms and waited for people to come...   08:54 PB: That's right.   08:54 RT: And people didn't always show up, and so it seems like most of the mobile operators actually took a stab at it back then. Of course, if we go back further in time, most mobile operators thought that it was their right to be the cloud as well and they gave a shot at that, but it didn't work out either. But you're right, there's a renewed effort. I think a lot of it's just numbers and money. We've saturated smartphones and people, and so we need... Lots of mobile operators for better or worse, think of the world in SIMs. [chuckle] Connected SIM endpoints, that's how they see the world. And so it's like, "Okay, we've maxed out all the SIMs on people. [laughter] Where are we gonna get some more SIMs?" And so they're thinking, "Oh, it's IoT." And so that's where a lot of it's coming. We've certainly seen some of them turning on, some of them like NB-IoT and CAT-M1, LTE-M networks to try to take a stab at that. And so that's kind of cruising along.   10:09 PB: I noticed that... And I love to buy all the gadgets and stuff and I'm also very invested in the whole LPWA space, I'm a big believer in that. And I'm curious and I see some things happening there, but it just seems like such a no-brainer for some of these WiFi connected things. Like I just installed a garage door opener in my house, I have a separate garage and it's WiFi connected for some reason, but I have to stand on a step ladder and scan a QR code and hold it next to it. I'm like, "Why doesn't it just turn on and connect through a little power cellular?" Just such a no-brainer, but it hasn't quite yet turned on.   10:49 RT: Yeah. No, you're right. Are you connected much with the SemTech guys doing LoRa?   10:56 PB: SemTech, not that much. No, no.   10:58 RT: Okay, okay. It's funny, so much of this is the people you work with over the years. When I went to Hitachi to build this industrial thing, I had a couple of compadres from Microsoft come along as well, but needless to say a couple of those guys are actually working for SemTech now and pushing hard on the whole LoRaWan thing.   11:23 PB: I see.   11:24 RT: And it looks like they're getting traction actually.   11:27 PB: Is LoRaWan, is that unlicensed or is that licensed? I think that's unlicensed.   11:31 RT: It's unlicensed, yeah.   11:32 PB: There's always those two camps, there's the licensed, which you got all your telcos with their spectrum and their 3GPP stuff, and then the unlicensed, which is probably a lot faster on the innovation side, but...   11:45 RT: Yes, they can get to market faster. You may remember, gosh, how many years ago was it when we were at Mobile World Congress and Sigfox launched out of nowhere. And they raised a bunch of money and they... But they weren't gonna do what the LoRaWan and guys did, they tried to be their own mobile operator as well. And so yeah, it's been interesting watching that. And you're right, they can get to market faster. They were using Sub-1 GHz and some rules, EU rules about how often you could send a signal and how big it could be, and they're like, "Hey, I think we can thread the needle here."   12:21 PB: Yes.   [laughter]   12:23 PB: Yeah, no, I'm looking forward to the LPWA stuff becoming more mainstream and just more turn key, if you will 'cause it just seems like it's such a low hanging fruit. There's the obvious metering and telemetry and that's parking meters and gas meters but even a lot of this current WiFi connected gear that people buy, it's just painful to get it all... I just installed a juice box level two charger for my house.   12:55 RT: Okay.   12:56 PB: And again, I had to download the app and the app... I had to connect the juice box to my phone and my phone to my WiFi and the blah, blah, blah. And I'm like, "What is happening?" It's just...   13:06 RT: Absolutely. You know what? It's so important, or at least from my perspective, to put yourself in the shoes of a developer and what they have to go through to get something connected, and I always think of the hassle factor. If I talk to people in the telco world and say, "Why is it cellular IoT is so far behind WiFi or other ways to connect?" And a big reason is actually what you just described. It's just such a hassle and it's expensive. A developer's like, "Oh, I gotta get some kind of SIM-based module thing and I gotta... Do I need to call a mobile operator and get a plan?" And you know what? The mobile operators, they still need to work on getting their prices down lower or at an appropriate amount for a IoT endpoint, because in many cases the prices are still too high.   14:01 PB: Yeah. Well, like my garage door opener, how much data is that sending? It's like either the garage door is open or closed. It's like one bit, plus 500K of overhead. A one or a zero, open or closed.   14:15 RT: Exactly. One or a zero, yeah. And so I think for telcos to be successful, while they would probably love to charge smartphone prices for plans for things, the reality is is no one's gonna use it unless they can still have an ROI. If I'm doing agriculture and I'm trying to put a weather station in a orchard and my plan with a mobile operators costing me $30 a month, I'm never gonna make any money on that deal. It's not worth doing.   14:48 PB: Yeah, I think you're right, there's the simplicity factor, the economics obviously drive the big deployments. But yeah, hopefully we'll start to see that take hold a little bit. I wanted to actually ask you a question about... I saw a post of yours the other day talking about 5G, and I'm sure you and I both get emails and questions about 5G on a daily basis or hourly basis, but you said that it's not just another G, which I thought was a good way of describing the other aspects of 5G. When people think of 5G, I just got this Samsung Ultra, Note Ultra 20 thing beautiful... It's a beautiful thing.   15:26 RT: How do you like it?   15:27 PB: Oh, it's fantastic. It's just like, it's hard to describe how awesome it is, but... And it's got 5G in it, and so fantastic, classic use case. And I work with Qualcomm all the time and Cristiano Amon and all these folks and they're all like, "5G all the way." But it's almost like the rest of 5G doesn't quite get the airtime about the high density and low latency. How do you see that impacting the IoT space?   15:56 RT: Yeah. Well, if the IoT space had actually been successful, 'cause we've massively underperformed across the board, it doesn't matter what company you are or what technology you built, everyone's massively underperformed, and so... But let's just assume for a second that we've been successful and we weren't in the trough of disillusionment right now, we would've found that we would've hit bottlenecks with lots of concurrently connected devices, if we were using cellular just over normal 4G networks and things like that. But we didn't hit those bottlenecks because IoT deployments haven't been that big yet. And so, the great thing about 5G is just with that same hardware, that same gear, all of the sudden you're getting more capacity. And you're right, that's what I wrote about, no one ever talks about the capacity angle. They talk about speed and they talk about the really low latency, and all that's super important, but for IoT capacity is gonna be the most important. And so the fact that it's a hundred times more capacity for the same cell tower, the same gear, is miraculous. And then that supporting a million devices per square kilometer is... That's how we're actually gonna have connected cars working well, smart cities, all those urban, a lot of those things that require a lot of density and a lot of devices all talking together over cellular networks, that's gonna make that real and make it happen.   17:29 PB: Yeah, I hear you. And yeah, you're right, we haven't really hit the bottlenecks yet so we're not quite appreciative of it, but when you think through how many billions of devices will be connected over the next few years, you just have to go there and you have to have that infrastructure. And then the ultra-low latency stuff, I think is fascinating. From the Microsoft side, we do a lot of commercial stuff, manufacturing, healthcare, a lot of things like that, and the ultra-low latency and some of those aspects of 5G are pretty fascinating, I think, and start to get more industry 4.0 type of scenarios.   18:06 RT: Yes, absolutely.   18:09 PB: I was curious what you think about... My next question around 5G and Release 16 for 3GPP. Do we need 3GPP Release 16 to really make this 5G thing work for IoT or do we need 17? Do you have any opinion on that or is that too esoteric of a question?   18:31 RT: It's a little esoteric, and the only reason I say that is I remember talking to folks in the past who would say ridiculous things to me like, "Oh, now that we're gonna get 5G, we can finally do IoT." And I'm like, "What are you talking about? We've done IoT forever and we've done it a million different ways, and we certainly did it over GPRS and it was fine [chuckle] and so I don't need 5G to do IoT." Is it gonna make it better and is it gonna help us with this capacity? Absolutely. And you're right, these subsequent releases, getting that ultra reliable, that low latency for mission critical stuff... 'Cause as you can imagine, you're talking about Microsoft being in the industrial world, Ericsson makes private LTE and private 5G technologies. And so that's complementary to what you're doing at Microsoft, 'cause we are certainly getting pinged on a lot by a lot of giant manufacturers around the world who, as they're heading into industry 4.0, they look at some of those use cases that require mass customization, flexibility around the factory...   19:47 PB: Sure.   19:48 RT: The notion of a fixed assembly line that doesn't change is gonna go away.   19:53 PB: Right, right, that's a novelty... That's Henry Ford style stuff. Yeah, that doesn't work.   19:55 RT: Yeah, and so therefore, they won't be able to use Ethernet anymore because it's gonna move around so they need wireless, they haven't had a lot of success with WiFi and so lots of people are piloting private 5G, private LTE inside factories, distribution centers, and so that's really interesting space there.   20:19 PB: Yeah. We've seen that as well, and we also see interest from transportation hubs.   20:24 RT: Yeah.   20:27 PB: Shipping ports, airports, places that have just a lot of acreage.   20:33 RT: Absolutely.   20:34 PB: So you're talking about oil refineries, places where there's 100 acres of space and they need a homogeneous, high speed network. You're not gonna stick WiFi repeaters out on poles down the runway.   20:49 RT: Right.   20:49 PB: So yeah, so I think that's another big area. We talked about the LPWA side is cool with the parking meters and garage door openers. And then the other side, you talked about there is gonna be this big wave of transformation going on with some of these big industrial players, I think using 5G or some kind of cell technology, private cell there.   21:12 RT: Yeah. And it's amazing 'cause I've seen it in action and the coverage is insane, the distance, the speed within a large building, instead of having zillions of WiFi access points trying to create coverage, you just have a few of these radio dots that we make and it just roams and it just works seamlessly all over. That's gonna be fun to watch.   21:37 PB: That'll be fun to watch, yes. Hey, I was gonna ask you kinda change gears a little bit, so we're recording this on August 25th so we've been in this pandemic mode for quite a while. What kind of insights have you gained from this pandemic?   21:56 RT: Yes. You know what? I think I put it together 'cause I have thought about it, I've kind of taken down notes, what's worked, what's not worked. And so I would say, succinctly, digital experiences delivered over connectivity is making remote things local and so whether it's you and I chatting here, the rest of the world on Zoom like you're seeing, it's kept people together. My wife is a school teacher and so she had to start teaching remotely and her school district uses Teams 'cause I'm right by Redmond, of course. [chuckle] So an Office 365 school district.   22:49 PB: Right.   22:50 RT: Yeah, as opposed to a Google classroom school district.   22:53 PB: Sure, sure.   22:54 RT: You've seen it in the stock price with certain tech companies, it's like, "Wow, we're really using this." But it certainly plays back to IoT and the taking an experience where I would normally be local in person and making it remote and I know it sounds really simple to say that but the hero in all of this is the internet.   23:20 PB: Right.   23:21 RT: It's held together.   23:22 PB: Yes.   23:23 RT: It keeps reaffirming that it's maybe one of the greatest creations ever and it's holding together for the whole planet, which is just miraculous.   23:33 PB: Yeah. The idea of remote everything, it sounds simple, but it's so complicated and...   23:39 RT: Yeah.   23:40 PB: We talk about latency and bandwidth and other things, and just... I think it's been a lifeline for so many people, to be honest with you.   23:49 RT: It has.   23:51 PB: Just with just the video conferencing, Satya talks about the acceleration, like two years worth of acceleration in two months, basically, just 'cause people have to start collaborating with these tools like Teams and Zoom and everything else, and so we've all fast forwarded a couple of years in our adoption of some of these technologies...   24:14 RT: Absolutely.   24:14 PB: And it'll be interesting to see what sticks. As we get out of this pandemic at some point, which of these habits will stick, that we'll get more used to, and then obviously... I think maybe also for me, I also now probably have more appreciation of the in person experiences than I probably did. And I did travel recently with my daughter to get her to school and I actually enjoy traveling, I enjoy being on an airplane, and these days it's a pretty high anxiety kind of thing with lots of face shields and wipes and things, but getting back to that mode, that's something that I'll probably, for the rest of my life really appreciate being able to just freely travel.   24:58 RT: Yes, absolutely.   25:00 PB: 'Cause of this situation we're in. So it will be interesting to see. I agree with you though, I think the internet has held together and that has been the hero amongst many heroes, but...   25:10 RT: Yeah. This internet infrastructure, fiber electricity beneath the cities and the country, and then little things popping up, either cell towers or WiFi access points, that let us roam around mobility and keeping us together. Obviously, we see a lot of stuff, there's been trends and things that we've had before that's just super accelerated, like you said, like tele-medicine, remote healthcare...   25:36 PB: Yeah.   25:36 RT: Just skyrocketed.   25:39 PB: Yeah. Well, I know that there...   25:40 RT: Out of necessity.   25:41 PB: Yeah, there was... I know there was a lot of rules in place for practitioners not being able to work across state lines and a lot of those rules were suspended during the pandemic to enable people to do tele-medicine, which I thought was fantastic, they were pretty... From a layman's perspective, they seemed anachronistic that you couldn't Zoom conference with a patient in another state and actually provide support or guidance.   26:09 RT: Yeah.   26:11 PB: And so yeah, things like that, where we just moved the whole ball forward, which is a good thing.   26:17 RT: Absolutely, absolutely. No, it's all good.   26:20 PB: Good stuff.   26:21 RT: I think you learned a lot. And I do miss traveling too. I complained about it when I'm flying every few weeks to Sweden or wherever...   26:30 PB: Sure, sure.   26:31 RT: But then that abrupt end of it and just the silence and being at home... You know it's weird, when you travel a lot and you're accustomed to all these international airports and maybe the place you go to get coffee or... This broad world, for a handful of us, it's like our comfort zone and then it just ended, and I miss it. No doubt about it.   26:54 PB: Cool, so, well, Rob, thanks a lot for the time, appreciate it. And good to see you again and...   27:01 RT: Absolutely.   27:02 PB: I see you pop up on LinkedIn on almost like a daily basis, so we'll keep communicating through LinkedIn and stuff.   27:10 RT: We're teachers.   27:11 PB: Yes, exactly, exactly.   27:14 RT: Spreading the word, absolutely.   27:16 PB: Exactly. Sounds good. Alright, Rob, well, take care stay safe.   27:19 RT: You do the same, it's great talking to you.   27:21 PB: Okay, thanks.   27:22 RT: Alright, bye bye.