Podcasts about Airflow

- Chinese, Tesla Drive Strong EU Sales - 23% of New European Cars Now BEVs - Nissan Shareholders Not Happy - DRAM Chip Prices Up 450% - Investors Eager to Spend on Momenta - Chery Sees Real Growth in Pickups - China Defines True Solid-State Batteries - Chrysler Airflow Headed to Belvidere

- Chinese, Tesla Drive Strong EU Sales - 23% of New European Cars Now BEVs - Nissan Shareholders Not Happy - DRAM Chip Prices Up 450% - Investors Eager to Spend on Momenta - Chery Sees Real Growth in Pickups - China Defines True Solid-State Batteries - Chrysler Airflow Headed to Belvidere

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In this session from the 7th Annual HVACR Training Symposium in Florida, Eric "Elk" Kaiser delivers a comprehensive workshop on airflow testing and measurement. Eric opens by challenging technicians to think beyond simply pointing an instrument at a duct and reading a number. Before selecting any tool, he argues, professionals must understand exactly what they are measuring — whether that is velocity, pressure, volume (CFM), or the mass weight of air — and why each of those values matters for designing ductwork, sizing equipment, and delivering comfort to customers. The session sets the stage for a deeper technical conversation about the physics of air and how those physics affect measurement accuracy in the real world. A significant portion of the presentation focuses on air density and how it affects the accuracy of common industry formulas. Eric walks through the origin of the widely-used 1.08 and 4.5 airflow constants, explaining that they are derived from a theoretical "standard air" condition of sea level pressure (14.7 PSIA) and 0% relative humidity — conditions that virtually no technician encounters in the field. He demonstrates how changes in altitude, temperature, and humidity all shift air density, causing those constants to become variables. For technicians working at elevations above 2,500 feet, the density difference can exceed 10%, enough to significantly skew BTU calculations and equipment performance assessments if left uncorrected. Eric also walks through a real-world scenario involving measurements taken across an operating evaporator coil, where a 3.4% density shift between return and supply could easily be misread as duct leakage. The workshop then moves into a thorough survey of airflow measurement instruments and the specific conditions each one is best suited for. Eric covers vane anemometers (large and mini), hot wire anemometers, pitot tubes, flow hoods (passive and active/fan-powered), flow boxes, the temperature rise method, and the digital TrueFlow grid. For each tool, he discusses accuracy considerations, density correction requirements, velocity limitations, placement requirements, and common mistakes. He is candid about the limitations of manufacturer performance charts, sharing a behind-the-scenes look at how one manufacturer evaluated static pressure using a six-foot plenum and four averaging probes — conditions that bear no resemblance to a cramped residential closet with a coil slammed on top of the furnace. The takeaway is that no chart, regardless of source, should be trusted without understanding the conditions under which it was created. Throughout the session, Eric emphasizes a core professional philosophy: understand your instruments, understand their limitations, and understand what level of accuracy is truly needed for the job at hand. He introduces the concept of stacked inaccuracies — where instrument error combines with density correction error to produce readings that can mislead technicians into diagnosing problems that do not exist, or missing ones that do. He concludes with a strong endorsement of the digital TrueFlow grid for residential applications, highlighting its app-based forecasting feature that allows technicians to predict whether a new piece of equipment will work on an existing duct system before the installation begins. The session closes with audience Q&A covering topics such as using density-correcting instruments to compare supply and return readings, and measuring airflow in systems with multiple filter grilles. Topics Covered What airflow measurement actually captures: velocity, pressure, volume (CFM), and mass weight of air — and why the distinction matters The origin and limitations of the 1.08 and 4.5 airflow constants, and when technicians must correct for non-standard air conditions How air density changes with altitude, temperature, and humidity — including a 22% density drop from sea level to 5,000 feet elevation Real-world example: how a 3.4% density shift across an operating evaporator coil can be mistaken for duct leakage Instrument selection overview: large vane anemometers, mini vane anemometers, hot wire anemometers, pitot tubes, and in-duct flow devices Passive vs. active (fan-powered) flow hoods — accuracy differences and the importance of using residential hoods for residential applications Proper probe placement for in-duct measurements: ASHRAE guidelines, straight-run requirements, and how turbulence affects readings Duct traverses: log Chebyshev point averaging vs. timed traverse methods, and best practices for each Manufacturer performance charts and external static pressure testing: how lab conditions differ from field conditions and why charts can mislead Motor types (PSC, constant torque ECM, constant airflow ECM) and how motor behavior affects static pressure measurement and airflow setup Manometer selection: resolution, accuracy, auto-zeroing features, and why a precise-looking display does not equal an accurate reading Temperature rise method for estimating airflow: appropriate uses with electric heat, and limitations with gas furnaces Digital TrueFlow grid: application for residential retrofit work, CFM forecasting, and evaluating existing duct systems before equipment replacement Audience Q&A: density correction on supply vs. return readings, multi-grille TrueFlow workflows, and commercial system setup strategies   You can watch the flow hood comparison video by TruTech Tools HERE. You can also check out all of the great free downloads and other resources TruTech Tools has to offer at https://trutechtools.com/resources.  Have a question that you want us to answer on the podcast? Submit your questions at https://www.speakpipe.com/hvacschool. Purchase your tickets or learn more about the 7th Annual HVACR Training Symposium at https://hvacrschool.com/symposium. Subscribe to our podcast on your iPhone or Android. Subscribe to our YouTube channel. Check out our handy calculators here or on the HVAC School Mobile App for Apple and Android.

It's hot.The horses are sweating.And once again, the yearly horse-world civil war has begun: “To scrape or not to scrape?”Every summer this debate comes back around social media like clockwork. Hose the horse down and scrape the water off immediately… or leave the water on because it actually helps cool them?Apparently, according to some corners of the internet, if you leave water on your horse they will immediately boil alive.So naturally… we decided to talk about the physics.In this episode, Jen and Barbara get into: thermoregulation in horses  how horses manage heat in both summer and winter  convection, evaporation, airflow, and why moving air matters  why cold hosing works on injuries (and why that same logic matters here)  what humidity actually does to cooling  wet bulb vs dry bulb temperatures  why high humidity and no airflow is the real danger zone  climate change, hotter summers, and why Ireland and the UK are particularly bad at handling heat  misting systems, fans, and cooling strategies used in high-level competition horses  kangaroos licking their forearms  and somehow… the Titanic Because no episode stays on track for long around here. We  had to talk about one of the biggest myths around cooling horses:  that leaving water on the body somehow “heats” the horse up. Spoiler: that's not how thermodynamics works.The film of water absorbs heat from the horse, airflow helps remove heat through convection and evaporation, and moving water continuously increases cooling efficiency.Science. Not Facebook comments.This episode is part horse welfare, part biology lesson, part weather forecast, and part public service announcement during the Irish & UK heatwave.Stay hydrated.Mind yourselves.And maybe stop arguing with strangers on the internet about scrapers.—Some of the science we talk about in this episode:Thermoregulation The body's ability to maintain a stable internal temperature despite environmental changes.Thermoneutral Zone (TNZ) The temperature range where the horse does not need to use extra energy to stay warm or cool. For horses this is commonly estimated around 5°C–25°C, though this varies with breed, coat, age, body condition, and acclimation.Convection The transfer of heat through moving air or water. Airflow over wet skin helps remove heat from the horse's body.Evaporation When water changes from liquid to vapour, removing heat energy in the process. Sweating and water left on the horse both cool through evaporation.Humidity The amount of water vapour already present in the air. High humidity reduces evaporation efficiency, making it harder for both humans and horses to cool themselves.Wet Bulb Temperature A measure that combines heat and humidity to reflect how effectively evaporation can occur. High wet bulb temperatures are dangerous because sweating becomes less effective.Homeostasis The body's process of maintaining stable internal conditions, including temperature, hydration, and metabolism.Cold Hosing Using running water to remove heat and reduce inflammation in tissues by transferring heat away from the body.Got a question you are burning to asking us, nothing is off limitis, or do you have a behaviour issues you're trying to figure out? Send us a voice note. Your voice, your question, your community is here.Real cases. Real answers. All madness (guaranteed, the madness bit anyway).Voice note your questions on WhatsApp to +353 85 143 8688 to have your questions answered on the Podcast. Meet Your HostsBarbara Hardman (Bright Horse Equiation)www.brighthorse.ie

Send us Fan MailThis week on Hash Church, we dive into the art of the perfect pre-roll.The conversation explores what actually makes a pre-roll smoke properly from start to finish, including solventless infusion and hash holes, airflow, draw resistance, combustion, filtration, resin preservation, burn consistency, storage, and flavour expression.We get into:• Solventless infused pre-rolls• Kief coating, hash holes, hash crumbles, and rosin snakes• Tips for infused pre-roll manufacturing• Storage and moisture control• Airflow and draw resistance• Combustion and smoke quality• Flavour preservation and terpene expression• Common mistakes in pre-roll production• The future of premium smoking experiences This episode is sponsored by PureFlowe™, a patented hollow vortex filtration system designed specifically for cannabis pre-rolls. PureFlowe™ was developed to rethink the traditional filter by improving airflow, supporting a smoother draw, and helping preserve the flavour and character of the flower or infusion. As the pre-roll category continues to grow into one of the biggest segments in cannabis, we look at how better design, better materials, and better smoking mechanics can help brands create a more premium consumer experience.Filtration, combustion science, and pre-roll engineering are becoming a bigger part of the conversation, and this episode goes right into the centre of it.If you're into hash, solventless, rosin, infused joints, pre-roll manufacturing, or the craft of the smoking experience, this is a fun one! Learn more about PureFlowe™:www.stellarjs.comInstagram:@stellarjscoAt Hash Church, we talk a lot about ritual, respect for the plant, and elevating the experience. That's exactly why we're proud to be supported by Puffco.Puffco continues to set the standard for modern consumption with tools built for people who truly care about flavor, temperature, and intentional use.From the Puffco Peak Pro with the 3D XL Bowl — delivering consistent heat, bigger hits, and unmatched terp expression —to the Proxy, redefining modular, ritual-based consumption,and the Pivot, bringing true Puffco performance into a compact, everyday format…These aren't gadgets.They're purpose-built tools for hash and solventless.We're genuinely grateful for Puffco's continued support of Hash Church, our guests, and our community. Their belief in education, culture, and quality helps us keep these conversations alive.

In this episode of the HVAC Know It All Podcast, host Gary McCreadie continues his conversations with Gord Cooke, President at Building Knowledge Canada, and Ian Walker, Sales & Marketing Manager at Aeroseal, about duct sealing and building performance. They explain how Aeroseal works by sealing leaks at the source without coating the entire duct, clearing up common misconceptions. The discussion highlights how leaky duct systems can lose a large amount of airflow, affecting comfort, balance, and system efficiency in a home. Gord shares insights on why sealing ducts improves airflow control and helps deliver air where it is needed most. They also talk about return air design, common issues with panned returns, and why proper airflow measurement matters for HVAC performance. Gary, Ian, and Gord discuss how Aeroseal works to seal duct leaks using pressure and a targeted sealant that only sticks at gaps. They explain that the process does not coat the full duct system and instead seals leaks from the inside. The conversation covers how duct leakage can reduce airflow, comfort, and system balance in a home. They also talk about how sealing ducts can improve air delivery and make balancing systems more effective. Gord shares insights on return air design, explaining common issues with panned returns and why they often do not move much air. They finish by highlighting how proper sealing and airflow checks help improve overall HVAC performance and comfort. Expect to Learn: How Aeroseal seals duct leaks by targeting gaps without coating the full duct system. How duct leakage can reduce airflow, comfort, and system balance in a home. How sealing ducts can improve air delivery and make system balancing more effective. Why return air systems often move less air than expected, especially when not fully ducted. How proper sealing and airflow checks can improve overall HVAC performance and comfort.   Episode Highlights:  [00:00] - Sponsor: Factory Direct Filters ad [00:42] - Intro to Gord Cooke and Ian Walker in Part 02  [02:02] - Arrow Seal vs. Aero Barrier: Same tech, different application [02:49] - Myth busted: Sealant only plugs holes, doesn't coat duct walls [04:27] - Why seal ducts? Comfort, not just energy (30% typical leakage) [08:34] - How AeroSeal works: Pressurize, mist, seal in 20 – 30 min [09:59] - Cost estimate: 2,000 – 2,500 CAD for a 2,000 sq ft home [13:11] - Panned returns leak 100% – don't expect measurable flow [18:00] - Key difference: Stay home during AeroSeal, leave during AeroBarrier [19:57] - Target: 1.5 ACH50 for optimal building enclosure   This Episode is Kindly Sponsored by: Cintas: https://www.cintas.com/hvacknowitall Cool Air Products: https://www.coolairproducts.net/ Factory Direct Filters: https://www.factorydirectfilters.com/ SupplyHouse: https://www.supplyhouse.com/tm Use promo code HKIA5 to get 5% off your first order at Supplyhouse!   Follow the Guests Gord Cooke and Ian Walker on:  LinkedIn - Gord Cooke: https://www.linkedin.com/in/gord-cooke-4b9b3433   LinkedIn - Ian Walker: https://www.linkedin.com/in/ian-walker-930954101/   LinkedIn - Building Knowledge Canada: https://www.linkedin.com/company/building-knowledge-canada-inc./   LinkedIn - Ian Walker: https://www.linkedin.com/company/aeroseal-llc/    Follow the Host on: LinkedIn: https://www.linkedin.com/in/gary-mccreadie-38217a77/  Website: https://www.hvacknowitall.com Facebook: https://www.facebook.com/people/HVAC-Know-It-All-2/61569643061429/   Instagram: https://www.instagram.com/hvacknowitall1/    Follow the Podcast on:  YouTube: https://www.youtube.com/@HVACKnowItAll   Spotify: https://open.spotify.com/show/6LCBJGw0EHG03rdWHxUMce  Apple Podcast: https://podcasts.apple.com/us/podcast/hvac-know-it-all-podcast/id1359253455 

Episode #621 of EPARTRADE's Race Industry Now explores the engineering behind high-performance cooling systems for motorsports and performance vehicles with John Pairaktaridis, President of [Delta PAG](https://deltapag.com?utm_source=chatgpt.com). Hosted by Brad Gillie from SiriusXM, Ch. 90, Late Shift.This technical deep dive covers:• Oil cooling efficiency and pressure drop reduction• Brushless electric fan technology• Airflow management and thermal optimization• Water pump engineering• CFD and CAD cooling system development• Packaging constraints in modern race cars• Cooling reliability for high-horsepower applications• Heat exchanger performance and fan blade designLearn how advanced thermal management directly impacts engine reliability, power consistency, aerodynamic efficiency, and overall race performance.Whether you are a race engineer, engine builder, fabricator, crew chief, performance shop, or serious enthusiast, this webinar delivers valuable engineering insight into modern motorsports cooling technology.Presented by ARP, Inc., PEAK, Fifth Third Bank Motorsports, Ferrea Racing Components, CTech Manufacturing, & Race-Fan.#Motorsports #RaceCarEngineering #CoolingSystems #ThermalManagement #OilCooler #BrushlessFans #RaceEngineering #PerformanceEngineering #EngineCooling #AutomotiveEngineering #RaceCars #MotorsportTechnology #CFD #HeatExchanger #HighPerformance #EPARTRADE #DeltaPAG

Реальная миграция со Spot.io на Karpenter на EKS. Минус 40% на Airflow-compute, disruption budgets, каскадные нодопулы и bash-тула вместо UI. О ЧЁМ ВЫПУСК • Что нового в Karpenter за последний год: minNodes на уровне нод пула, операторы GT/LT, breaking changes v1.10 (IAM роли). • Spot.io (теперь Flexera): VNG-модель, 15% от сэкономленного, почему новые контракты стали хуже. • Реальные боли Spot.io: жирные споты на сутки, консолидация ломает Airflow-пайплайны в середине DAG. • Миграция step-by-step: POC (2 недели) → dev-кластер с двумя контроллерами через taints/affinity → Airflow последним. • Airflow: минус 40% на compute. Как считали через теги + Cost Explorer + Grafana. • Disruption budget в Karpenter (НЕ pod disruption, а свой). Schedule для бизнес-часов. Drift detection. • Каскадные нодопулы по приоритетам: priority-spot → fallback spot → priority-on-demand → wide on-demand. • Edge cases: Bahrain outage, security incidents, flapping типов инстансов. • EKS Auto Mode как альтернатива для маленьких команд. ГОСТЬ В гостях — Viktor Mikalayeu - https://www.linkedin.com/in/viktar-mikalayeu-mns/  ССЫЛКИ

This week, we sit down with Paul Assuncao, owner of Owner's Pride Canada, to explore the latest innovations in automotive protection.If you're in the detailing industry and serious about growth, this is a conversation you need to hear.In this episode, we break down Peel Clear. Peel clear is a product that sits right between traditional paint and paint protection film—and why it has the potential to reshape how detailers approach protection, wraps, and even full vehicle services.We didn't just talk about it… we tested it.From spraying a full panel on a 1955 Chevy to evaluating prep time, cost, durability, and real-world application, this episode gives you an honest, hands-on look at what this product can—and can't—do.We cover:What Peel Clear actually is and how it compares to PPF and paint• Real application process, including prep, spraying, and finishingCost breakdown vs traditional paint and wrap solutions Where this fits in your business (and where it doesn't)Why experienced shops should be paying attention right nowThis isn't for beginners—it's for operators looking to elevate their services, increase efficiency, and stay ahead of where the industry is going.If you're serious about staying competitive… this is your edge.⏱️ Timeline (Chapters)00:00 – Introduction & Episode Setup00:01 – Introducing Peel Clear00:02 – What Makes Peel Clear Different (Paint vs PPF)00:04 – First Test Application (1955 Chevy Panel)00:06 – Prep Process & Setup Explained00:08 – Paint Booth Setup & Safety Considerations00:10 – Application Process (Base Coat & Top Coat)00:12 – Spray Technique & Adjustments00:14 – Airflow, Equipment & Environment00:16 – Real Results & First Impressions00:18 – Cost Comparison vs Traditional Paint00:20 – Scratch Filling & Self-Leveling Results00:22 – Peeling, Scoring & Application Tips00:24 – Where This Product Wins (Bumpers, Curves, Complex Surfaces)00:26 – Limitations & Learning Curve00:28 – Business Opportunity for Detailers00:30 – Comparing Peel Clear vs Wraps & PPF00:32 – Material Cost vs Profit Potential00:34 – Longevity, Warranty & Durability00:36 – Who Should Use This Product 00:38 – Final Thoughts & Industry DirectionGuest:Paulo AssuncaoPeelClearhttps://peelclear.com/Hosts:Jody Sedrick and Rod PuzeyRoadFS / DetailBookie PodcastAuto Detailing CRMRoadFS - https://roadfs.comDetailBookie - Https://detailbookie.com

In this episode of the HVAC Know It All Podcast, host Gary McCreadie is joined by Chris Howells, Senior Training and Development Manager at AprilAire, to discuss the importance of dehumidification in indoor air quality and home comfort. Chris explains how air conditioners handle both temperature and moisture, and why many systems struggle to control humidity on their own. The conversation covers how standalone dehumidifiers work, including reheat and moisture removal, along with different installation methods and sizing considerations. Gary and Chris also explore common issues like oversized systems, seasonal humidity problems, and how proper humidity control can improve comfort, protect materials, and support overall health. In this conversation, Chris explains the importance of dehumidification in HVAC systems and how it supports comfort, health, and home protection. He describes how air conditioners remove some moisture but often cannot control humidity on their own, especially in certain conditions. Chris and Gary discuss how standalone dehumidifiers work using cooling and reheat, along with different installation methods and sizing factors. They also cover how issues like oversized systems, seasonal moisture, and poor humidity control can lead to discomfort and reduced system performance. Expect to Learn: How dehumidification improves indoor air quality, comfort, and home protection. How air conditioners handle moisture and why they often fall short. How standalone dehumidifiers work using cooling and reheating. How different installation methods affect system performance and airflow. How issues like oversized systems and seasonal humidity impact comfort and efficiency. Episode Highlights: [00:00] - Sponsor Ad: Factory Direct Filters [00:42] - Intro to Chris Howells [02:36] - Listener problem: High humidity (55% RH) with an oversized AC [04:14] - ACs prioritize sensible cooling; dehumidification is "leftover." [06:01] - Chris mentions that Airflow verification is step one [07:34] - How a dehumidifier works (reheat effect, no overcooling) [10:47] - Installation methods: Return-to-return vs. fully ducted [14:01] - 3 benefits of dehumidification: Health, comfort, home protection [18:22] - Model E100 (100 pints/day) as a standalone example [20:48] - Wet coil + dry supply air = humidification problem [23:32] - Checking sensible/latent ratios at different wet-bulb temps [25:18] - Chris: Inverters handle humidity; single-stage needs backup [28:01] - Sizing: E100 at 80/60 vs. real-world conditions (75/50) [31:34] - Hydroco: Venturi effect recirculates water (no pump) [34:32] - Gary: Shoulder season humidity + oversized AC = need dehumidifier This Episode is Kindly Sponsored by: Cintas: https://www.cintas.com/hvacknowitall Cool Air Products: https://www.coolairproducts.net/ Factory Direct Filters: https://www.factorydirectfilters.com/ SupplyHouse: https://www.supplyhouse.com/tm  Use promo code HKIA5 to get 5% off your first order at Supplyhouse! Follow the Guest Chris Howells on: LinkedIn: https://www.linkedin.com/in/chris-howells-5aa32b64 AprilAire: https://www.linkedin.com/company/aprilaire/  Website: AprilAire: https://www.aprilaire.com/ Follow the Host on: LinkedIn: https://www.linkedin.com/in/gary-mccreadie-38217a77/ Website: https://www.hvacknowitall.com Facebook: https://www.facebook.com/people/HVAC-Know-It-All-2/61569643061429/  Instagram: https://www.instagram.com/hvacknowitall1/  Follow the Podcast on: YouTube: https://www.youtube.com/@HVACKnowItAll Spotify: https://open.spotify.com/show/6LCBJGw0EHG03rdWHxUMce Apple Podcast: https://podcasts.apple.com/us/podcast/hvac-know-it-all-podcast/id1359253455 

As part of the April issue, the European Respiratory Journal presents the latest in its series of podcasts. Chief Editor James Chalmers interviews Associate Editor Sanja Stanojevic about the joint statement from the Global Initiative for Chronic Obstructive Lung Disease and the Global Lung Function Initiative regarding the use of spirometry to define airflow obstruction and diagnose COPD, published in this issue of the ERJ (https://doi.org/10.1183/13993003.02574-2025). Cite this podcast as: ERJ Podcast April 2026: Use of spirometry to define airflow obstruction and diagnose COPD. Eur Respir J 2026; 67: 26E6704. [https://doi.org/10.1183/13993003.E6704-2026]

How to tell if your transmission is in trouble. Keeping your fluids full. What to do with wheel bearings. What could make a knocking noise? When to get an oil change for a vehicle that does not get driven much. How vehicle technician school is changing. Replacing a mass airflow sensor. How to safely clean a radiator. Ask our car care expert Nick Stoffel of Lloyds Automotive. Visit lloydsautomotive.net 651-228-1316.

In this episode of the HVAC Know It All Podcast, host Gary McCreadie is joined by Eric Ruggles, Director of Engineering at Ritchie Engineering Co., Inc. (YELLOW JACKET), to discuss the key differences between airflow and static pressure and how to measure both correctly. Eric explains how airflow is calculated using air velocity and duct size, while static pressure is measured across system components to understand system performance. The conversation covers tools like hot wire anemometers and manometers, along with proper testing methods such as duct traversing and pressure tip placement. Gary and Eric also explore common issues like high static pressure caused by poor duct design, dirty filters, or oversized equipment, and how these problems can impact overall system efficiency. In this conversation, Eric explains the differences between airflow and static pressure in HVAC systems and how each one is measured. He describes how airflow is based on air speed and duct size, while static pressure shows how much resistance the system has. Eric and Gary discuss tools like hot wire anemometers and manometers, along with proper testing methods such as duct traversing and pressure tip placement. They also cover how issues like dirty filters, poor duct design, and system restrictions can raise static pressure and reduce overall system performance and airflow. Expect to Learn: How airflow and static pressure differ and why both matter in HVAC systems. How to use tools like hot wire anemometers and manometers for proper testing. How duct traversing helps get accurate airflow measurements. How static pressure testing can identify restrictions in filters and ductwork. How issues like dirty filters, poor duct design, and closed vents affect system performance. Episode Highlights: [00:00] - Sponsor Ad: Factory Direct Filters [00:42] - Intro to Eric Ruggles in Part 1 [02:34] - Static pressure and airflow are different, and need different tools [04:18] - Eric: Air speed units (ft/min, m/s) & calculating volume [07:15] - Hot wire anemometer: traverse duct, app calculates CFM [10:56] - Static pressure: positive on supply, negative on return [16:48] - Total external static: check return & supply sides separately [19:42] - Remove filter to test restriction; 1" high-MERV vs. 5" filter [23:61] - Plugging vents raises static, harms the system   This Episode is Kindly Sponsored by: Cintas: https://www.cintas.com/hvacknowitall Cool Air Products: https://www.coolairproducts.net/ Factory Direct Filters: https://www.factorydirectfilters.com/ SupplyHouse: https://www.supplyhouse.com/tm Use promo code HKIA5 to get 5% off your first order at Supplyhouse! Follow the Guest Eric Ruggles on: LinkedIn: https://www.linkedin.com/in/eric-ruggles-28a84424/ Ritchie Engineering Co., Inc. (YELLOW JACKET): https://www.linkedin.com/company/ritchie-engineering-co-yellow-jacket-/ Ritchie Engineering Co., Inc. (YELLOW JACKET) - Website: https://yellowjacket.com/ Follow the Host on: LinkedIn: https://www.linkedin.com/in/gary-mccreadie-38217a77/ Website: https://www.hvacknowitall.com Facebook: https://www.facebook.com/people/HVAC-Know-It-All-2/61569643061429/  Instagram: https://www.instagram.com/hvacknowitall1/  Follow the Podcast on: YouTube: https://www.youtube.com/@HVACKnowItAll Spotify: https://open.spotify.com/show/6LCBJGw0EHG03rdWHxUMce Apple Podcast: https://podcasts.apple.com/us/podcast/hvac-know-it-all-podcast/id1359253455

**Boys and Girls, We're Back! Tyler from MORRFlate Returns – Tire Inflation Wars, Copycats, Patents & Off-Road Innovation** We're back with another awesome episode of the Jeep Talk Show! Tyler from MORRFlate joins us again for a raw, honest conversation about building a business in the off-road world, dealing with big-time copycats, patent battles, and why competition (the right kind) actually drives innovation. Tyler has been wheeling Toyotas all over California since he was a kid, lives close to the Rubicon Trail, runs NorCal 4x4 Rescue, co-hosts the Snail Trail 4x4 Podcast, and has poured serious support into trail advocacy. In this return visit (first one was back in September 2025), he opens up about getting threatened by a major player, the reality of enforcing patents, and how a $3M company competes against $45M giants with deep marketing pockets. We dive deep into: - How MORRFlate's 4-tire inflation/deflation system works (connects all four tires as one big air tank, equalizes pressure automatically, saves massive time on the trail) - The frustration of direct copies (even stealing the signature neon green color) - Why patents aren't the silver bullet everyone thinks - Airflow limitations of Schrader valves and future ideas to fix the bottleneck - Building a company from a garage condo to a team of 8+ with a warehouse in Sacramento - Lifetime warranties, obsessive quality control, and hiring fellow off-roaders - AI chatbots on the website, robots in manufacturing, and how AI is changing marketing and product design Tyler also shares his 30-second elevator pitch: MORRFlate makes airing up and down your tires super convenient so you can actually enjoy the trail instead of babysitting a compressor. If you've ever deflated for traction, fought with individual tire hoses, or dreamed of faster air-ups on your Jeep, Toyota, or any rig — this one's for you. **Timestamps:** 00:00:00 Show Opening 00:00:10 Misnaming Moore Flat 00:00:31 Tyler's Background & Sales 00:01:19 Previous Interview & Threats 00:02:06 Acquisition Threat & Patent Journey 00:03:08 Patent Approval & Copycats 00:04:01 Company Name Confidential 00:04:33 Patent Enforcement Costs 00:05:21 Competition and Innovation 00:06:35 Amazon Listings & Pricing 00:08:03 RealTruck Ownership Impact 00:09:12 Podcast Monetization Issues 00:09:41 Morfleet Product Overview 00:11:07 Tire Deflation Benefits 00:12:59 Airflow Limits of Valves 00:13:28 Air Tank Volume Calculations 00:14:43 Limited Tank Capacity Demo 00:15:46 Compressor Performance Insights 00:16:24 Personal Projects & Ideas 00:17:40 Upcoming Valve Projects 00:19:07 Valve Interior Flow Restriction 00:21:41 Heavy Equipment Valve Solutions 00:23:48 Apex Rapid Valve Review 00:24:15 Challenges with Large Tires 00:24:59 Passion for Problem Solving 00:29:04 Warehouse & Quality Control 00:34:48 Lifetime Warranty Strategy 00:37:02 Customer Focus Assurance 00:38:22 Employee Attitude Culture 00:39:50 Testing Competitor Gear 00:40:51 People-Centric Philosophy 00:43:28 Corporate vs Small Business 00:44:07 Politics and Truth 00:45:47 Vendor Conflict Over Copying 00:49:05 AI Chatbot Deployment 00:50:30 AI, Quality Control & Robotics 00:53:25 AI in Product Design 00:54:51 AI Impact on Jobs 00:57:45 Minimum Wage Debate 00:58:49 Digital Media & AI 01:02:40 Show Recap & Future 01:04:25 Closing Thanks 01:06:38 Final Thanks & Friendship 01:09:02 Interview Conclusion **Links:** - MORRFlate Official Site: https://morrflate.com/ (Check out the Quad hose kits, Air Hub, and play with their AI chatbot!) - Tyler on Instagram/X: @4x4ToyotaTyler - MORRFlate on Social: @morrflate - Snail Trail 4x4 Podcast: Search "Snail Trail 4x4" on your favorite platform (830+ episodes!) If you're out at Overland Expo, off-road events, or shopping on Amazon — look for the real neon green MORRFlate gear. Drop a comment: Have you tried a multi-tire inflation system? Would you buy from the original innovator or a cheaper copy? What's your biggest air-up/down frustration on the trail? Thanks for watching! Hit LIKE if you enjoyed the convo, SUBSCRIBE for more Jeep/off-road stories, and turn on notifications so you never miss an episode. Support trail advocacy and small off-road businesses — they keep the trails open and the innovation coming. #Jeep #OffRoad #MORRFlate #Toyota #Rubicon #TireInflation #Overlanding #4x4 Visit our website: https://jeeptalkshow.com/ Watch/Listen on Spotify https://jeeptalkshow.com/spotify Join our Discord Server: https://jeeptalkshow.com/discord Subscribe to our newsletter: https://jeeptakshow.com/newsletter Help Support the show via Patreon: https://jeeptalkshow.com/patreon

Early bird discounts for the San Francisco World's Fair, the biggest AIE gathering of the year, end today - prices will go up by ~$500 tonight so do please lock in ASAP!From near-universal AI tool adoption inside Shopify to internal systems for ML experimentation, auto-research, customer simulation, and ultra-low-latency search, Mikhail Parakhin joins us for a deep dive into what it actually looks like when a 20-year-old, $200B software company goes all-in on AI. We cover why Shopify has become much more vocal about its internal stack, what changed after the December model-quality inflection, and why the real bottleneck in AI coding is no longer generation, but review, CI/CD, and deployment stability.We also go inside Tangle, Tangent, SimGym, which are three major AI initiatives that Shopify is doing to make experimentation reproducible, optimization automatic, customer behavior simulatable, and search and catalog intelligence faster and cheaper at scale. Along the way, Mikhail explains UCP, Liquid AI, and why token budgets are directionally right but often measured badly, why AI-written code can still increase bugs in production, what makes Shopify's customer simulation defensible, and what he learned from the Sydney era at Bing.We discuss:* Mikhail's path from running a major Microsoft business unit spanning Windows, Edge, Bing, and ads to becoming CTO of Shopify* Why Shopify is talking more publicly about AI now, and why staying at the frontier has become necessary for the company* Shopify's internal AI adoption curve, the December inflection, and why CLI-style tools are rising faster than traditional IDE-based tools* Why Jensen Huang is directionally right on token budgets, but raw token count is still the wrong way to evaluate engineering output* Why the real unlock is not more agents in parallel, but better critique loops, stronger models, and spending more on review than generation* Why AI coding can still lead to more bugs in production even if models write cleaner code on average than humans* Why Shopify built its own PR review flow, and why Mikhail thinks most off-the-shelf review tools miss the point* How PR volume, test failures, and deployment rollback are becoming the real bottlenecks in the agent era* Why Git, pull requests, and CI/CD may need a new metaphor once code is written at machine speed* What Tangle is, and how Shopify uses it to make ML and data workflows reproducible, collaborative, and production-ready from the start* Why Tangle is different from Airflow, and why content-addressed caching creates network effects across teams* What Tangent is, and how Shopify is using auto-research loops to optimize search, themes, prompt compression, storage, and more* Why Tangent is becoming a democratizing tool for PMs and domain experts, not just ML engineers* Why AutoML finally feels real in the LLM era, and where auto-research still falls short today* Why Tangle, Tangent, and SimGym become much more powerful when combined into one system* What SimGym is, why simulated customers only work if you have real historical behavior, and why Shopify's data gives it a moat* How SimGym evolved from comparing A/B variants to telling merchants what to change on a single live storefront to raise conversions* Why customer simulation is so expensive, from multimodal models to browser farms to serving and distillation costs* How Shopify models merchant and buyer trajectories, runs counterfactuals, and thinks about interventions like discounts, campaigns, and notifications* Why category-level behavior is so different across commerce, and why ideas like Chinese Restaurant Processes are showing up again in practice* Shopify's new UCP and catalog work, including runtime product search, bulk lookups, and identity linking* Why Shopify is using Liquid AI, and why Mikhail sees it as the first genuinely competitive non-transformer architecture he has used in practice* Where Liquid already works inside Shopify today, from low-latency query understanding to large-scale catalog and Sidekick Pulse workloads* Whether Liquid could become frontier-scale with enough compute, and why Shopify remains pragmatic and merit-based about model choice* Who Shopify is hiring right now across ML, data science, and distributed databases* The Sydney story at Bing, why its personality was not an accident, and what Mikhail learned from deliberately shaping AI character early onMikhail Parakhin* LinkedIn: https://www.linkedin.com/in/mikhail-parakhin/* X: https://x.com/MParakhinTimestamps00:00:00 Introduction: Mikhail Parakhin, Microsoft, and Shopify00:01:16 Why Shopify Is Talking More About AI00:02:29 Internal AI Adoption at Shopify and the December Inflection00:06:54 Token Budgets, Jensen Huang, and Why Usage Metrics Can Mislead00:10:55 Why Shopify Built Its Own AI PR Review System00:12:38 AI Coding, More Bugs, and the Real Deployment Bottleneck00:14:11 Why Git, PRs, and CI/CD May Need to Change for Agents00:18:24 Tangle: Shopify's Reproducible ML and Data Workflow Engine00:21:19 Why Tangle Is Different from Airflow00:26:14 Tangent: Auto Research for Optimization and Experimentation00:30:07 How Tangent Democratizes Experimentation Beyond ML Engineers00:33:06 The Limits of Auto Research00:36:36 Why Tangle, Tangent, and SimGym Compound Together00:37:20 SimGym: Simulating Customers with Shopify's Historical Data00:42:47 The Infra Behind SimGym00:46:00 Why SimGym Gets Better with Real Customer History00:47:30 Counterfactuals, HSTU, and Modeling Merchant Trajectories00:51:55 CRPs, Clustering, and Category-Level Customer Behavior00:53:30 UCP, Shopify Catalog, and Identity Linking00:55:07 Liquid AI: Why Shopify Uses Non-Transformer Models00:59:13 Real Shopify Use Cases for Liquid01:03:00 Can Liquid Scale into a Frontier Model?01:09:49 Hiring at Shopify: ML, Data Science, and Databases01:10:43 Sydney at Bing: Personality Shaping and AI Character01:13:32 Closing ThoughtsTranscript[00:00:00] swyx: Okay. We're here in the studio, a remote studio, with Mikhail Parakhin, CTO of Shopify. Welcome.[00:00:08] Mikhail Parakhin: Thank you. Welcome.[00:00:10] swyx: I don't even know if I should introduce you as CTO of Shopify. I feel like you have many identities. Uh, you led sort of the, the Bing ML team, I guess, uh, uh, or ads team. I, I don't know, I don't know, uh, you know, it's, uh, people va-variously refer you as like CEO or, or, uh, I don't know what that, that, that said previous role at Microsoft was.[00:00:29] Mikhail Parakhin: Uh, that was... Yeah, my previous role w- at Microsoft was the-- I actually was the CEO of one of Microsoft's business units, which included, as I, you know, as we discussed, all the things that people like to laugh about, uh, including Windows and Edge and Bing and ads and everything.[00:00:47] swyx: Yeah, yeah. What a, what a, what a wild time.You've obviously, uh, done a lot since you landed at Shopify. Uh, one of the reasons I reached out was because you started promoting more sort of internal tooling, uh, primarily Tangle, but also a lot of people have seen and adopted Tobi's QMD, uh, and obviously, I think, uh, Shopify has always been sort of leading in terms of, uh, engineering.I think more-- it's just more recent that you guys have been more vocal about your sort of AI adoption. Is that, is that true?[00:01:16] Mikhail Parakhin: Well, I think AI tools in general are fairly recent development, uh, and we've-- Shopify, you know, at this stage of its development, we're developing AI in-in-house and other, uh, building tools that use AI and, you know, interfacing with the wider AI community, uh, you know, are on the sort of the, uh, runaway trajectory.So it just did by sort of natural byproduct. We, we talk about it more also. We just, uh, just even yesterday, Andrej Karpathy was famous in tweeting about, oh, are there some, uh, ways, uh, that, that you can organize your agents to store the data and then, uh, look up the data so that you don't have to research or, or lose context every- Yestime. And a little bit tongue in cheek, I tweeted that, “Hey, we've, we've done it much earlier, and we even have different approaches, Tobi and I.” Tobi, of course, is a big fan of QMD, and I'm more of a SQL, SQLite fan. But, uh, yeah, very similar things that we've already done here. The point is, yeah, we're very dynamic, you know, explosively growing company, and we have to be at the forefront of AI adoption, obviously.[00:02:29] swyx: Yeah. Yeah. Um, you, your team kindly prepared some slides actually that we were gonna bring up on to, uh, the screen. I think I can, I can screen share, and then we can kind of go through some of the shocking stats that maybe, maybe put some numbers to what exactly is going on. So here we have, uh- An internal AI tool adoption chart.What are we looking at here? What ?[00:02:54] Mikhail Parakhin: Yeah, this is very interesting statistics. Uh, this is number of daily active workers, you know, think of, uh, DAO, basically the active users of-[00:03:05] swyx: Yeah ...[00:03:05] Mikhail Parakhin: AI tool as a percentage of all the people in the company, right? And then- Yeah ... different AI tools. And, uh, you could see two things here is that one is the green is total.Uh, green is just total. So you could see that it approaches really % by now. It's hard not to do your job now without interacting deeply, at least with one tool. You could see another interesting thing is just as many people commented in December was the phase transition when suddenly models gotten good enough that, that everything took off and started growing.Uh, it, it was many people noticed that the thing is that small improvements accumulated into this big change in Sep- December roughly timeframe.[00:03:52] swyx: Yeah.[00:03:52] Mikhail Parakhin: The other thing I would claim you could see is that, uh, CLI-based tools and tools that don't require you to look at the code becoming more popular, and you could see, yeah, various versions of, uh, Cloud Code and Codex and Pi and internal development tools taking off.Uh, exactly, yeah, uh, and blue is our River, just internal agent for coding, where tools, uh, that require IDEs such as, uh, GitHub, Copilot or Cursor, they're not exactly shrinking, but they're not growing as fast. Like, uh, red, red line is, is the IDE kind of tools. So you could see that they're, they're not experiencing as, as fast of a growth.[00:04:37] swyx: As I understand it, basically, every employee has their choice, right? Of choose whatever tool you use, and then you're just kind of doing a, a daily sur-survey or something.[00:04:47] Mikhail Parakhin: Exactly. And, uh, we- Yeah ... the, the push is to get your job done, you can use any tool, and we effectively fund unlimited tokens for everybody.Uh, we, we do, we do try to control the models that, uh, people use, but from the bottom, not from top. Like we basically say, “Hey, please don't use anything less than Opus four point six.”[00:05:09] swyx: Oh .[00:05:10] Mikhail Parakhin: Some people, some people end up using GPT five point four extra high. Some people use Opus four point six. Um, uh, you know, uh, there are some, uh, there are plus and minuses in going for full one million context window versus not.But, uh, we try to discourage people from using anything less than that.[00:05:28] swyx: Yeah, yeah. Got it, got it. Uh, I mean, uh, that's, you know... The, the next chart here, it really kind of shows the expansion and the sort of December twenty twenty-five inflection, right? That, uh, people are using a lot of tokens. I think it's also really interesting that no one was kind of abusing it in twenty twenty-five.Like it was- Had comparatively, uh, to this year, there was almost no growth. I mean, it's still like, you know, probably, probably gave fifty percent.[00:05:56] Mikhail Parakhin: Yeah. This is just a different scale. It's still exponential- Yeah, yeah ...growth at just a different- ...rate of expansion. Uh, there was inflection point, and Sean, I would claim the, the super interesting part here is that you could see that the distribution becoming more and more skewed.Yes. The top percentiles grow faster. So that means- Yeah ...the people in the top ten percentile, they, their consumption grows faster than seventy-five and so forth. So, uh, the distribution skews more and more towards the highest users, which is... I don't know what it tells me. It's like it feels not ideal, to be honest.Or maybe it's okay. We'll see.[00:06:36] swyx: Why does it feel not ideal? Is, is it because of, um, quantity over quality, or what's the concern?[00:06:42] Mikhail Parakhin: Because take it to the limit. That means, you know, if, if this rate of separation continued- Ah, yes ...a year, there will be one person consuming all the tokens. So it's just, it's kinda strange.[00:06:54] swyx: Yeah, I mean, um, uh, I, I think internal like teaching and all that, uh, will, will help sort of distribute things more widely. But in, in the early days, of course, the people who are sort of more AI-pilled will obviously find more ways to use it than the people who are less AI-pilled. Maybe let's, let's call it that.I'll just, I'll just kinda quickly, uh, pause from the, the... You know, we will go back to the rest of the slides, but I just wanna, um, review, you know, there are a lot of CTOs of, of large companies like yourself where they're all considering some kind of token budget, right? Like I think it's something, something that Jensen Huang has been talking about, where like if your 200K engineer is not using 100K of tokens every year, like they're, they're underutilizing coding agents.Of course, Jensen Huang would say that, but like it seems a very quantity over quality approach and like some, some people are basically saying like, well, is this comparable to judging engineer quality by lines of code, right? Which we also know is like kind of flawed, but better than nothing. So I, I don't know if you have like a sort of management take here on, on how to view this kind of, uh, metrics.[00:08:02] Mikhail Parakhin: Well, I mean, you're, you're baiting me. I, I like... This is my favorite topic. Uh, if you let me, I'll probably talk for two hours on just this. I have a lot of things to say. Like I do think Jensen gotten a lot of bad press saying, “Oh, of course you're, you know, this, uh, the- ...the cake seller says you don't need enough cakes.”You know? Like, of course. Uh, but, uh, I actually, uh, think that's undeserved. I think he, he's actually right. Uh, I do think- He,[00:08:33] swyx: he's directionally correct.[00:08:35] Mikhail Parakhin: Yeah. Yeah. He's directionally correct for sure. Uh-[00:08:37] swyx: Who knows what the right number is? Yeah.[00:08:39] Mikhail Parakhin: The thing that I do Uh, want to say, and this is something that we learned through trial and error and very important is like two things.One is that it's not about just consuming tokens. Uh, you can consume tokens and, and in fact, the anti-pattern is running multiple agents, too many agents in parallel that don't communicate with each other. That's almost useless, uh, compared to just fewer agents and burns tokens very efficiently. Uh, setting up the right critique loop, especially with the high quality models, where one agent does something, the other one, ideally with a different model, critiques it, uh, suggests ways to improve it, the agent redoes it with this critique and, and so it takes much longer.So people don't like it because latency goes up. You know, they, they have to wait until this debate is happening. But, uh, the quality of the code is much higher. And another thing, just since you mentioned like, look, uh, uh, yeah, the overall budget is just like, uh, lines of codes. Lines of codes are exploding for everybody right now, or partially because AI is really mover balls, but partially just because AI can write a lot more code, you know, doesn't get tired.And so you have to have to have a very strong narrow waist during PR review. Otherwise, just the number of bugs will go through the roof. It's, uh, it's this unexpected consequence of the just volume trumping everything. I would claim by now good model writes code on average with fewer bugs than, than the average human.But since they write so much more of it, like more of it will make it into production. So you have to- You still[00:10:26] swyx: have[00:10:26] Mikhail Parakhin: more bugs. Yeah. Have to have a very rigorous PR reviews, also automated of course. But, uh, yeah, that to spend a lot budget there. Like this, this for me, for me, actually, the important metric is the ratio of budget spent during code generation versus, uh, spent, uh, expensive tokens like GPT, uh, five point four Pro or, uh, uh, Deep Think from Gemini, you know, checking on PR reviews.[00:10:55] swyx: Yeah, totally. Uh, I noticed in your chart you didn't have any review tools. Do you just use like, like let's say a Claude code to review tools? Or do you have another set of review tools like the Greptiles, the Code Rabbits, uh, Devin Reviews has a review tool. I don't know if you've had those specialist review tools.[00:11:13] Mikhail Parakhin: You are a little bit jumping on my store tool right now because the graphs I was only showing public tools. Uh, uh, the-- I haven't found a good PR review tool that, that does what I think should be done. And, uh, partially my, my thinking is because it's so... It just goes against both what people feel like emotionally they prefer and, uh, some of the, uh, you know, frankly Even business models that, that the companies run.At peer review tool, uh, time, you want to run the largest models. That means, I don't know, Codex or, or, uh, Cloud Code is not gonna cut it. You need to have pro-level models if you really want to, uh, stand the tide of bots from going into production. And you need us to spend a lot of time, the models taking turns, but you don't want, like, a big swarm of, uh, of, uh, agents.So in fact, you end up in a different dual-dualistic world where you generate not that many tokens. You, in fact, generate few tokens, but it takes f-a long time because these are expensive models taking turns rather than many, many agents trying to do many things in parallel. So that's, that's why I feel like I haven't found good tools, so we are using our own for peer review for now.[00:12:33] swyx: Yeah. Yeah. I mean, uh, I think a lot of companies are building their own, uh, especially to their needs, right?[00:12:38] Mikhail Parakhin: Mm-hmm.[00:12:38] swyx: Um, I, uh, you also have a chart here going back to the slides on, uh, PR merge growth, where we're now at thirty percent, uh, month on month rather than ten percent. Uh, and also the, the estimated complexity is going up.You know, this is productivity, right? ‘Cause y- presumably there's more stuff going into the code base and more, more features getting worked on. I'm curious about the backlog, right? Like the, the, the-- I actually don't mind a pro-level model taking an hour or two hours to review my PR, because I've dealt with humans who take a week to review my PR, right?And I keep pinging them on Slack, “Hey, hey, review my PR.” So, you know, I think there's some trade-off here where, like, it still doesn't make sense.[00:13:18] Mikhail Parakhin: Exactly. That, that's exactly m-my point. Uh, that on one hand, you can tolerate longer latencies at, uh, PR. On the other hand, like right now, the real problem is not in spending time waiting for PR.It's real problem is since there's so much more code than- Yeah ... uh, probability of at least some tests failing going up, and then you, like, keep de-failing, then you have to find the offending PR, evict it, retest it without that PR, and so deployment cycle becomes much longer. Uh, so it actually, in terms of the overall time to deploy, it's total time savings if you spend more time on a longer model, like thinking for an hour, because then, then you, you don't have to spend all that time during testing and rolling, you know, rolling back the deployment.[00:14:03] swyx: Yeah, totally. That's still worth it. You know, you don't look at the individual, look at the aggregate, and look at the, the, the change in the aggregate system.[00:14:11] Mikhail Parakhin: Exactly.[00:14:11] swyx: I'm kind of curious if, like, there's this PR mentality and, like, c-- the, the, the CICD paradigm will be changed eventually. Some people are like, obviously a lot of people want new GitHub, but I even wonder if, like, Git is the problem, right?Like, is that the bottleneck? Is the concept of a PR a bottleneck? Do you guys use stack diffs? I don't know if, uh, that's a, like, a merge queue stack diff type of thing.[00:14:34] Mikhail Parakhin: We, we use, we use Stacks, we u- we use Graphite. We worked with, uh, Graphite a lot. Uh, so we use Stack, uh, PRs. I think, uh, like that's clearly the overall CICD in general, and the interaction with the code repository right now is the, clearly the sort of the, the main issue and the bottleneck for us, uh, and highest top of mind.I would say we probably need a different metaphor or different whole design of how to process it in new agentic world. I haven't seen anything dramatically better yet. I, I think everybody right now is just trying to keep their head above the water ‘cause, ‘cause there, there's so many PRs and then everybody's CICD pipelines start creaking, the, the times are increasing, the number of bugs slipping by increasing, and you have to, have to clap on down.And so we are a little bit in this situation when we need to first stabilize that story and then start thinking, hey, what, what it could be a completely different and new world, which I haven't... I know some people working on it. I haven't seen something, like anything super compelling yet, but clearly the old thing were designed for humans will need to be morphed into something new.[00:15:53] swyx: One of the thing that I, I think about is kind of like the merge conflict is basically a global mutex on the whole system, right? And in, in hu- in human organizations, we do have something like that. It's the company standup. But like, other than that, it's like it's actually fitting for us to be somewhat decentralized, somewhat plugged into one stream of information source, but somewhat lossy.Like it's okay, you know, that, that not every delivery is like atomic consistency. Like we're not dealing with a database sometimes.[00:16:27] Mikhail Parakhin: This is a very good point, uh, because since humans don't write code too fast, you know that global mutex is not too bad. Once you-[00:16:36] swyx: Yes ...[00:16:37] Mikhail Parakhin: start writing code at the speed of machine, it becomes the, you know, the bottleneck.Then what do you do? Maybe, and I can't believe I'm saying this because I, I'm long-- lifelong opponent of, uh, microservices, and I always thought that was, like, a really bad idea. And now that you're saying it, like, maybe in new guys like microservices will make a comeback, you know, because then you, you can ship things independently in tiny things and, and the managing all that complexity automatically will be much easier.I don't know. Like, we'll s-- we'll have to see.[00:17:10] swyx: Yeah. I mean, I don't know what the Microsoft or, or Shopify thing is, but I, I read this paper from Google where they have a monorepo that deploys into microservices, right? And then, uh, the other concept that I think about a lot is the Chaos Monkey concept from, from Netflix.Being able to create, like, this robust system where, um, uh, you know, you, you have the service discovery, you have the, uh, the independent, independent microservices discovery and, and, uh, you know, probably going to be a fair amount of duplication. That's how an organic system sort of scales, uh, that, that you have that...I don't know how you call it. Slack? Robustness? Depend-- uh, d-duplication. I, I, I forget the-- I, I'm-- And this-- those-- these are not exactly the terms- Hmm ... I'm looking for, but I c-can't really think of the words. Okay. I was gonna go into Tangent and Tangle. Uh, so, uh, we, we sort of discussed the overall stats that, uh, Shopify has.Uh, but, you know, I, I think some, some pretty cool stuff that you guys are working on is your ML experimentation, uh, and your, your sort of auto tr-research training pipeline. Presumably you're much closer to this one because it's, it's a sort of personal hobby of yours. How, how would you explain them in, together?I thought we have a slide that, like, uh, has the s- the system diagram.[00:18:24] Mikhail Parakhin: Yeah. Tangle first and then Tangent as a-[00:18:27] swyx: Yeah ...[00:18:28] Mikhail Parakhin: as a thing on top of Tangle. And, uh, Tangle is the third generation, I claim, of, uh, systems of, uh, running any data processing, but a bit with a skew for ML experiments, but not necessarily. Any sort of data processing tasks where you need to iterate, share, and you have scale so that you want maximum efficiency.You know how, like, normally you would work, you would-- Imagine you're a data scientist or an ML practitioner, you would get Jupiter notebooks or, or maybe you would get, uh, you know, Pyth- your Python scripts, and you would manage the data, and you produce those TSV files, and you put them in some JFS or something.Then you would notice that, oh, it has this, uh, weird missing values. You go and write another script that, uh, goes and replaces them with, uh-[00:19:20] swyx: Ah ...[00:19:21] Mikhail Parakhin: dash S. And then, then you, then you run some, some, uh, “Oh, I need to filter bots.” And so you run some light GBM model that, uh, removes the bots. And then, then you like-- And then you, you kind of like get into shape, and then you start experimenting, and you run multiple experiments, and then you're like, “Oh my God,” like, “this experiment is worse.”You undo, and you cannot get to previous result. And like, “Ah, what did I do?” Like that. Again, then, then you finally like get everything working. Then you like start throwing it over the fence to production. You, you replicate it, those things don't work, and then sometimes you like don't notice that you forgot some feature naming and the, the features don't match.But then, like imagine you, you did everything, and then six months later you're like, have to repeat it because now there's more data, or you wanted to do another pass, and you're like, “What, what did I do?” Or like, or like, “This script crashes now,” or the, “the path has changed.” And then, then you're trying to, like you spend another month just doing ar- digital archeology on your own, you know, history, right?Now multiply that by many, many teams. Now imagine you got an intern that you wanna ramp up. Now you have to show that intern, “Oh, you know, look, here's the folder, there's the scripts, you know, ask your cloud agent to do, and then, uh, to, to figure it out.” And then cloud agent does something, and then you're, “Ah, yeah, right, right, it was the wrong folder.I forgot to tell you, I actually have this other thing I forgot myself.” And, and that's, that's the, like, the daily life we all, uh, all know it, uh, if, if you're a data scientist, machine practitioner, ma- machine learning practitioner or, uh, or even like any data managing, uh, person.[00:21:00] swyx: Yeah. So I, I used to do this, uh, f- uh, on the quant finance side, uh, in, in my hedge fund.So we did this before Airflow, and then, uh, obviously Airflow came along and, uh, then more recently Dagster, uh, I would say is like, in my mind, what I would use for that shape of problem, uh, where you had to materialize assets and create a pipeline.[00:21:19] Mikhail Parakhin: And that's, that's very good segue because... So Airflow is great, but Airflow is more about you, you have something and you wanna repeatedly run it in production on schedule.It's less about you as a team developing things and being able to share, and you grabbing the standard pipeline and saying, “Hey, I wanna change this tiny little component in the huge sea of data processing, and I don't wanna-- I wanna run ten experiments on this, and I wanna do hyperparameter optimization.”All that is very hard to do with Airflow. It's very easy to do with Tango. Tango is m- more about, it's everything about group of people Running experiments, it might be agents too nowadays. Uh, running experiments cheaply, collaborating, sharing results. Uh, you don't need to understand fully. You, you grab-- you clone somebody else's experiment or somebody else's pipeline, uh, run, uh, change small piece, run it, be, like, get it to production state, and then ship in one click.So then the... You don't have to port it into any other system to, to run in production. You can just run the same experiment. It's, it's fully production ready. And, and it's, uh, it has lots of... Again, as I said, it's third generation system. The original one was, I would claim there was Ether and then, uh, at least in my career, Ether was the first, first, uh, that pioneered this type of approach.And then there was, uh, Nirvana, which, uh, uh, at Yandex, which did kind of sec-second take on this. And now this one aggregates the, the learnings from all of those and, and Airflow as well to, to get to the state where you try it, it, it feels kind of magical. Uh, ‘cause now everything is based on content, uh, hashes.So even if the version changed, but if the output didn't change, nothing is being rerun. It's very efficient. If you... Multiple people start experiment that needs the same sort of data preprocessing, it's not repeated multiple times. It's automatically done only once. If you start ten experiments that all require, you know, some, some data preparation first as the first step, and you don't have to coordinate for that.Like, you don't have to know that other people are starting it. You now, it's very easy compos-, uh, composability, any language you can u- uh, you wanna use, and it's very visual. So you can see immediately, you can edit it easily, you can assemble small things with just even mouse clicks if you want to, and, uh, share, clone.And everybody knows also it's fully kind of static in the sense that we rerun it second time, it will exactly have the same results. Like, you will never have to do digital archeology. So full versioning and everything is also there.[00:24:06] swyx: Uh, so, so people can, uh... It's open source. Go to the GitHub repo and, and, uh, check it out.Uh, and it is also a really good, uh, blog post about it. I think all these is, like, really appealing. The, the, the, the thing that I think sells me the most about it is that, um, sort of development to production transition, right? Which I think, um, a lot of people haven't really solved that, uh, strictly, right?Like, we develop really, really well in, in Python notebooks, but then, you know, that's obviously not a sort of production ready process. I think that, like, any way in which that is solved, I think is, is very appealing. Then the other thing that you mentioned, which also raised my eyebrows, was content-based caching, which you mentioned is, is, um, you know, is ve-very much, uh, um, a sort of efficiency measure about, uh, you know, just like recalculation only on, on sort of content addressing Which I think makes sense.Uh, it surprised me that the savings could be this much, but maybe I just haven't worked at your scale where there's so much duplication, uh, that people just rerun because they change a single ID upstream.[00:25:10] Mikhail Parakhin: It does, yeah. But it's not only you rerun. The, the main savings are coming from the fact that you ran it, you got your job done, and you moved on.Then- Yeah ... somebody else in some department you don't know existed runs the same task, but on a newer version.[00:25:27] swyx: Yeah.[00:25:27] Mikhail Parakhin: Like right now, you can't, in, in most of the organizations, you can't even find out about it so that you can't even measure that you're spending that time twice, right? Here- Yeah ... if everybody's on Tango, that's detected automatically and detected that the output is the same.And then for that person, all it looks like is like experiment just suddenly moved, jumped forward, right? Uh, uh- Yeah ... so that's because, because the, there's network effect of multiple people helping each other.[00:25:51] swyx: Yeah. This is one of those things where it's designed to be a platform from the beginning rather than an individual developer's tool from the beginning, right?And, and everything's gonna streams down from there. That is the sort of Tango, uh, orchestrator, and it's, it manages jobs. We've seen a few versions of this, and this is obviously, uh, uh, the sort of, uh, unique approaches that you guys have, have, uh, figured out. And then there's Tangent.[00:26:14] Mikhail Parakhin: Yeah. And Tangent is basically an automatic auto research loop that can help and kind of do your work for you.Uh- ... you know, uh, effectively, effectively, Andrej Karpathy recently popularized it with auto research. Yes. Remember he said like he was, uh, speed running this, uh... Yeah, uh, you know the story. The, here we're basically bringing the same capability into Tango so that, uh, the, uh, Tangent can analyze it. It's just an agent that can run multiple experiments, figure out what can be changed, and keep on rerunning it, keep on modifying until, uh, maximizing some goal, some loss function, whatever you need to, to achieve.And in general, I would say if you're not using auto research-like approach in whatever you do, like literally whatever you do, then you're missing out. We saw at Shopify that taking like a wildfire, anything where you can put measurements can be done dramatically better. Our-[00:27:19] swyx: Mm-hmm ...[00:27:20] Mikhail Parakhin: uh, speed of, uh, templatization HTML, uh, completely new UX tem- uh, templatization of, uh, reducing latency for liquid themes.Uh, we-- Our, uh, search, uh, recently we moved from It's hard even, uh, quote from eight hundred QPS to forty-two hundred QPS with the same quality just by pure optimizations and not a research loop that kept running and changing code in our index serve on the same number of machines, just increasing the throughput.We, we managed to improve the quality of gisting and machine learning process. Uh, you know, gisting is the prompt compression technique that[00:27:59] swyx: allows for[00:28:00] Mikhail Parakhin: lower latency and, and lower and, uh, actually higher quality slightly. So like literally whatever different walks of life, and it doesn't have to be AI related.Uh, we, we had a reduction in, uh, storage because the agents would go and find data sets that clearly are derivative, uh, and then you don't need to store things twice. You know, we, we, we found somewhat embarrassingly that it was one of the largest tables was hashing random IDs into another random ID, and we literally- Oofput only one. So it was translating, yeah, two random IDs hashed[00:28:36] swyx: into[00:28:37] Mikhail Parakhin: each. So, so[00:28:37] swyx: it has access to the code as well, so it can, it can check the, like what, what the hell is it doing?[00:28:42] Mikhail Parakhin: So there, there cou- it could be run in two levels. You, uh, you know, at the superficial level, it could just use ex-existing components and, uh, reshuffle them.Uh, you know, like you can grab- Yeah ... uh, XGBoost, and you can grab some, some Py- PyTorch module, and then can grab some, you know, grab another tools and, and combine them. At a deeper level, since Tangle is all sort of CLI based underneath you, every, every component is a wrapped really CLI, uh, call and a YAML file, it can analyze code and create new components and, and, uh, keep on iterating as well.So, so you can, you can both have quick modifications of existing t- uh, pipelines with the, with components that are already there pre-baked, or you can create new components, uh, and-[00:29:29] swyx: Yeah ...[00:29:29] Mikhail Parakhin: keep iterating on those. So auto research is, again, this is probably the, the thing I was excited the most in the last two months happening, and we see it taking like, like totally like a wildfire.Just, uh, everybody, every day, every... well, every day, every minute, I would, uh, have somebody Slack message saying, “Oh, look how much better I made it.” And, uh, it's all throughout the research.[00:29:53] swyx: Is this democratized in some way in, in the sense that like is it your ML, uh, engineers and researchers doing this, or is it your regular PMs and software engineers also have the ability to auto-- to use Tangent?[00:30:07] Mikhail Parakhin: This is an awesome question. Like, Tango in general and Tangent in particular are extremely democratizing. Like they- Yeah ... they are the main tools for- ‘Cause I don't[00:30:15] swyx: need the details.[00:30:16] Mikhail Parakhin: Yeah. Exactly. Initially used by ML and AI engineers, but then literally, as you said, PMs are like the highest user right now is one of PMs on our org, uh, Sartak and he was, he was number one by, by usage of, of this ‘cause they're just, uh, energetic and knowledgeable, and now it, it unlocks a lot of capability where you don't have to co-change code manually.[00:30:39] swyx: I mean, I mean, because it kind of cuts out the ML, ML engineer from the process because the, the, the PMs have the domain knowledge and the ability to think about, uh, from first principles about, okay, what, what results do I want? And they can-- they even have the access to the data that, that needs to go in.So it's like in some ways, like this is the magic black box that we've always wanted for, for training and, and for, uh, I guess, uh, uh, hill climbing, whatever.[00:31:04] Mikhail Parakhin: It's basically cloud code for your AI development- ... uh, situation, right? Like now, now you don't have to know exactly how algorithms work. You can just, uh, bring your domain knowledge and expertise and product knowledge and iterate within Tangent until you've gotten the results that you need.[00:31:21] swyx: In my previous roles, every time that someone has pitched AutoML, you know, I've always been like, “Uh, this is not, this is not gonna work. It's, you know, it's, it's always gonna be a flop.” Somehow it's working now. I mean, presumably the answer is now we have LLMs and it's good enough, right? It's, it's an emergent property that we can do auto research, but like, it doesn't feel that satisfying that how come we didn't do this before, right?Like we just did like parameter search and like, I don't know. That's maybe that's it.[00:31:48] Mikhail Parakhin: Yeah. Bayesian optimization and hyperparameter optimization was, was the one that, or facet of AutoML that was used very actively, which incidentally also built into, uh, Tango. But, you know, I know Patrice Simard very well, and, uh, he was such a, uh, such a proponent of AutoML, and he put, like literally spent careers trying to democratize it.Without LLMs, it just turned out to be very hard. Like it, you, you would have flexibility within certain narrow domain, but it was hard to wider scale, and now with LLMs suddenly it's like magic wand, and so suddenly everybody- ... is an AutoML expert.[00:32:28] swyx: Yeah, I, I think it's multiple things, right? Like I'm, I'm just gonna bring up the, the, the chart again, right?Like LLMs can do the monitoring very well. That is the very potentially unbounded, super unstructured. It can do the analysis very well, it can do the... Uh, and basically it is much more intelligence poured into every single step. Uh, there's maybe nothing structurally changed about AutoML, but this is just m-more intelligent and more unstructured.[00:32:53] Mikhail Parakhin: Exactly.[00:32:54] swyx: Any flaws that you've run into? Like everyone is like drinking the Kool-Aid, oh my God, time savings, uh, you know, performance improvements. Like what, what, uh, issues have you have, uh, come up?[00:33:06] Mikhail Parakhin: This is really cool. It's not a solution to all the world's problems for sure. The limitations are usually the ones I-- And this is where we get into a bit of a subjective territory.Uh, I can only share what I've, I've seen so far, and I'm sure the situation, uh, is changing, and, you know, maybe after I say it, like many people will reach out and say, “Hey, what about this?” And you don't know that, and then, then we'll be probably right. But what I've seen is auto research is very good at doing kind of obvious things that you don't have bandwidth to do or you didn't notice or maybe you're not aware of like the-- some standard practices.It is not good at doing something completely out of distribution, something that, you know, you have to think for, for multiple days, uh, and, and do something like none of this. So, so it's, uh, I, uh, set an experiment once, uh, on, on my sort of, uh, hobby thing, and I let it run for, uh, ended up, uh, several weeks run, uh, you know, it's like full production kind of scale, so it, you know, slow runs and, and it ex-- it performed in the end, uh, over four hundred experiments, and only one was successful.I'm like, “Okay, that's, that's good.” But-[00:34:18] swyx: But it saved time.[00:34:19] Mikhail Parakhin: Yeah, I saved time. Like it, it was the, that thing. Yeah, if I, if I were doing four hundred experiments myself, my betting average, as I said, would have been much higher, I'm sure. But also, first of all, it would take me like three years to do four hundred experiments.And, uh, I didn't have to do them. Like the machines were just, uh, the price of electricity did that. So, and I got one improvement, uh, that in, uh, my, my-- Honestly, when I was starting that experiment, my thinking was to go and show that, “Hey, Andre, maybe you just don't know how to optimize.” And I was super smart because in, in my pro-problem, it was optimized for many years, and it was like fully improved.Uh, and I didn't expect it, you know, auto research to find anything at all. Yet it did. So instead of making fun of Andre, I ended up, uh, a big, big supporter. Yeah, that's exactly the tweet. Yes.[00:35:10] swyx: You and Toby really, really go back and forth on-online a lot, which is really funny. Uh, think of it as, as an eval for the optimalness of the code it's running on.Uh, it's almost like it reminds me of like a Kolmogorov complexity thing, but, uh, I guess it's-- there's some optimal thing that you're trying to sort of reduce down to, I guess. Um, and so, so you, you, you know, you should congratulate yourself that you had, uh, you know, uh, ninety-nine percent, uh, optimality.[00:35:36] Mikhail Parakhin: Exactly, yeah. I think Andre really deserves a lot of credit for popularizing this approach. This is, uh, this is incredibly, I think, powerful and cool and You know, the, uh, even him, him just mentioning it led to a lot of gains in a lot of places in the industry, so we should be thankful.[00:35:56] swyx: Yeah. I think he also has a just...I don't know what it is. Like, um, you know, it, it is a simple self-contained project that people can take and apply to other things, which is, is, is one thing, but also just the name. Just like somehow no one, no one managed to call their thing auto research. It's just naming things is very important. I think that that is mostly, uh, our coverage of Tango and, and, uh, Tangents.I think obviously, you know, there's a lot of, uh, ML infra at, at Shopify that people can, uh, dive into. We're about to go into SimGym, but before I do that, any, any other sort of broader comments around this whole effort? Like where is it, where is it leading to?[00:36:36] Mikhail Parakhin: As a segue to SimGym, like all those things start composing strongly.And, uh, you could see a huge unlock when you can look at each one of the tools and, and you see, oh, they're extremely useful. Uh, Tango is useful by itself. Auto Research is useful by itself. SimGym is useful by itself. If you combine all three, you create like synergetic effect. I think that's why we wanted to even, uh, cover them today is because this is something that if you go back even, you know, five years ago, would've been unthinkable.Uh, replicating that, uh, would, would be either incredibly costly or impossible, right? With probably thousands of people are required.[00:37:20] swyx: Well, we have serverless human, uh, serverless intelligence, right? Like, uh, so yes, you do have thousands of hu-- of, of intelligences, not just, not humans. And that's, that's close enough, right?Even if they're not AGI, they're, they're close enough to do the, the task that you need them to do. And, and, you know, that's, there's plenty for, for a lot of routine work, knowledge work. Okay, let's get into SimGym. Um, this is one of those things I, I was surprised to see actually it's apparently your, uh, one of your most popular launches, and I think something that, uh, I think Sim AI, I think Yunjun Park, who did the Smallville thing, there's a very small cottage industry of people trying to do like the simulate customer thing.I think a lot of people maybe don't super trust this yet because they're like, well, obviously they would just do what you prompt them to do, right? But maybe just think, uh, tell us about the sort of inspiration or origin story.[00:38:10] Mikhail Parakhin: That's exactly actually the thing I wanted to cover, because if you don't have the historical data, all you can do is prompt a-agents in a vacuum, and they will do exactly what you prompt them to do.In fact, when I first proposed it, and this is a bit of, um, my brainchild initially, if I, I can boast, even Toby said like, “But wouldn't they, they just repeat what, what you tell them?” And, uh, but I'm like, “Yes, except Shopify has decades of history of how people made changes and what there is, uh, there, what it resulted in terms of sales.”So now what we can do is we can-- we have this... It's not, it's a noisy data. There's a small, usually websites, uh, you know, like things, things are never in isolation. It's almost never AB experiment. It's always AA experiment when there's has two meanings, but basically, you know, in different time you run two different things.But if you aggregate in general, uh, like everything together, and you apply, uh, denoising and collaborative filtering like approach, you can extract a very clear signal. And then you can optimize your agents. And that's why it took so long. It took almost a year of that optimization of just us sitting and fiddling, and, and we had this internal goals of correlation of hitting-- internal goal was to hit zero point seven correlation with, uh, add to cart events, for example.Like that, that if we run real AB test experiment, that it should, it should go and, and rep-uh, replicate, uh, same sort of success that, that humans had or lack thereof. And it, it took forever, and I don't think that's easily replicatable because, uh, like who else would have that data? You have to have this historic, you know, decades, uh, worth of data.And now, now the, like the other thing you need is in-infrastructure and the scale, right? Because, uh, w- again, what we found, uh, stat sig results, you need to run a lot of simulations, a lot of agents, and, and it's-- Those are expensive things. Like you're, you're making actions in the browser because you want a real friction.You want to, to be able to get the image like of what humans will see because you wanna, uh, detect effects like, “Hey, if I make my images larger, will I have more sales or l- uh, fewer sales?” And like usually people's intuition here, by the way, is that I increase my images, I will have more because they look nicer.You know, designers all look sparse and big images. Like usually your sales tank, right? But, but, uh, you know, from HTML, all the characters look the same only the, the size tag looks different, right? So it's very hard. So you have to take visual information, you have to run this in simulated browser environment on the big farm and, and of course, you have to have, uh, like very, very expensive model, good model with multi-model model.So all this it's-- is what's taken so long and, uh, to share my personal fail a little bit there, Sean, is like, you know, we always had this bias to-- for like large company bias. You know, we always, uh, whenever you-- we do, we're like, “Hey, we'll run an experiment,” right? We make, make a change, and we will run an experiment and then, uh, see, uh, see which one's better or like, “No, this is worse,” and most of them are worse, so you discard it and keep iterating, hill climbing.And we're like, “Oh, like smaller merchants, they cannot get stat sig results. They cannot really run experiments simply because, you know, in a week there would be not enough data for them.” So we thought from this perspective. What we didn't realize is that most people don't have A and B, they just have one thing, and they need suggestions of What A and B should be.So, uh, we first build this, hey, we run simulation on two separate teams and, and, uh, say, “Hey, which one is better?” We then morphed it into, and very recently just released it, when you have just your site, your theme, we run over it and we say, “Hey, here's what predicted values of, of, uh, uh, conversions are, and here's how we think you should modify it to increase your conversions.”And then circling back to what you started with, the proof is in the pudding. Like, if we are not correlating with reality, like, people will not be using it. And, uh, thankfully, we see literally every day more users than the previous day. So, so right now, uh, right now- It's working. Yeah. I'm-- Right now my problem is how to pay for it all because the so our major thing is how to optimize the LLMs, do distillation, how to run the headless browsers, uh, and handful browsers, uh, uh, cheaper so that we can accommodate the increase in traffic.[00:42:47] swyx: Yeah. I, I understand that you, uh, you published a lot of technical detail at GTC, so I was just gonna bring it up a little bit. I think s- was this in, in con-conjunction with some kind of GTC presentation? Or something like that, right?[00:42:59] Mikhail Parakhin: Well, we, yeah, we, we did it in several place, but yeah, we had the engineering- Yeahblog, uh, as well. Yeah.[00:43:05] swyx: Yeah. So you're running, uh, GPT OSS. Uh,[00:43:08] Mikhail Parakhin: the, this is an older version. You know, now we run multimodal model. But yeah- Yeah ... GPT OSS, we still run GPT OSS as well for[00:43:15] swyx: And then you have the VMs, and you also have browser-based. I really like this one where it you said, “It violates almost every assumption that standard LLM serving is designed for.”And then you had like, basically orders of magnitude differences between everything.[00:43:29] Mikhail Parakhin: Exactly. Which is, which, uh, which was, you know, a bit of a challenge to implement, like when, like even simple things. Uh, be- since it violates all the assumptions, for example, multi-instance GPUs, like MIGs don't work as well.But we needed, uh, to get MIG to work because, ‘cause otherwise it's way too expensive. And so we had to deal with the, yeah, with, uh, lots of infrastructure and, and, uh, work with, uh, uh, Fireworks and CentML, uh, you know, to help with optimizations and browser-based, as you mentioned. Yeah, like, takes a village.[00:44:04] swyx: Okay. So there's a lot of like, I guess, experimentation in the infrastructure so far, and you've published more or less what you have here. I guess I'm, I'm less familiar with CentML. I, I don't do, uh, that much work in this, this part of the stack. But why was it the sort of preferred instance platform?[00:44:22] Mikhail Parakhin: There are really three probably top companies. There used to be, uh, uh- Three top companies, uh, at least I was aware of that did, uh, LM optimization. You know, together Fireworks and Santa ML, not necessarily in that order. Santa ML recently got acquired by NVIDIA. Uh, what they did is if you have a model and you want to optimize it to a specific prof-- uh, profile of usage, uh, they would go and do it.And, uh, we work with, with those companies, uh, this was work particularly in with Santa ML and NVIDIA to get them the best possible results out of it. And, and sometimes you, you have to retune depending on, like sometimes you want the maximum throughput, sometimes you want minimal latency, sometimes you want like the cheapest, right?And, yeah, or some combination. And so yeah, these are people who would come and help you.[00:45:14] swyx: I see. I see. Yeah, yeah. I'm familiar with these people for the LLM, you know, autoregressive stack. But the other interesting category of these optimizers is also the diffusion people, whereas like Fel and, you know, uh, Pruna recently has come up a lot as well, which I think is like really underappreciated, uh, at least by myself, because I, I thought, oh, all the workload would be LLMs, but actually there's a lot of diffusion as well.[00:45:38] Mikhail Parakhin: Exactly.[00:45:38] swyx: There's a lot here, so I, I, I... it's, it's, uh, it's, it's, it's hard to cover. But I, I do think like people underappreciate the importance of customer simulation, basically. I think this is something that I'm candidly still getting to terms with. Uh, you know, uh, you also-- your team also like prepared this, like, really nice diagram.Uh, I, I assume this is AI generated.[00:46:00] Mikhail Parakhin: Yeah, it looks-[00:46:01] swyx: Maybe it's not.[00:46:01] Mikhail Parakhin: Yeah, it looks, uh, Gemini-ish. Yeah, but, uh, uh, honestly, I, I don't know where, where the hell they generated. It looks, look, uh, looks like it's, uh, Google. But the interesting part, John, that, that, uh, we haven't covered, but I, I wanted to mention is if your store had previous customers, rather than it's a new store, you're like new merchant just launching things, it helps tremendously in just correlation and forecast.Yeah, we take your previous, uh, customer's behavior, and we create agents that replicate those specific distribution of, of customers that you get, and then we a- we apply those to your changes, and then that, that raised raw, you know, the re-- uh, just correlation with the add to cart events or to-- with conversion or whatever it, it, it may be, uh, quite dramatically.So, uh, replicating humans in general seems like an interesting, cool challenge.[00:46:58] swyx: As a shareholder, I think this is the-- like if people are Shopify shareholders, they should really deeply understand this because this is basically the moat. The, the more you use Shopify, the more it will just automatically improve, right?Like you're, you're doing the job for them.[00:47:13] Mikhail Parakhin: Yeah, that's what we started with. Like, uh- ... uh, otherwise, if you're just a startup, I wouldn't do it if, uh, you know, if it was my startup because Without the data, it, yeah, as, as you said, it's, it's exactly the case that, uh, whatever you say in prompt, that's, that's what the agents will be doing.[00:47:30] swyx: The statistician in me wants to like really satisfy the sort of, um, statistical intuition, I guess. Um, to me it's kind of, uh, the, the word that comes to mind is, um, ergodicity. Uh, so let's say a, a customer takes this path, customer takes this path, customer takes this path, right? Um, the... In my mind, the way I explain it is like, okay, here, here's the ninety-five percentile, here's the five percentile, and here's the median, right?Um, but to me, what SimGym is potentially doing is that it can, uh, modify... It can sort of model the sort of in-between sort of journeys as well, that, that maybe are dependent on the previous states. This may be like a very RL-type conclusion where like basically the summary statistics, if you only did naive AB testing, you only have the, the statistics at, at, at a certain point, and you only judge based on the sort of overall summary statistics.But here you can actually model trajectories. Does that make sense? Or-[00:48:31] Mikhail Parakhin: That makes total sense because like, well, that, that makes even more sense that maybe even you realize bec- because-[00:48:38] swyx: Okay. Please,[00:48:38] Mikhail Parakhin: please. Yes ... we do-- Yeah. The, so internally, uh, we have this system, we talked about it briefly once at NeurIPS.We have a huge HSTU-based system that models the whole companies, uh, and their possible paths. And like- Yeah ... what you are, what you are showing, like actually at any point of time, you can either model the user's behavior or you mo- can also think about, uh, the whole merchant as a company, as the entity that acts in the world.You can model that as well. And then you can do, can do counterfactuals. In your graph, like in your blue graph, uh, if you're... Imagine in the center there, uh, somewhere in the middle, you would have an intervention. I give that person a coupon, or I don't know, I send a personal thank you card, or give a discount in some- somewhere.And then you can, uh, then you can do forward rollouts from that counterfactual. So what would have happened with that intervention or without the intervention? And you can even ch- change where that intervention, uh, in time can happen, right? Like some- where, where in this journey. So we, we do this at the Shopify scale for our merchants, and then if we notice that something that they can be fixing, like there's a strong counterfactual, like we have Shopify policy, they basically get a notification like, “Hey, we think your...something is wrong with your-” I don't know, Canadian sales. Like, uh, it looks like it's misconfigured. Here's what you need to do. Or do you think like, uh, you have to set up this campaign with these parameters? And we do that at the buyer level to literally offer discounts or cashback or, or things to buyers.So this is-- I'm getting very excited. Like this is my sort of area of, uh, interest, I guess, and, and hobby. But being able to m-model something complex as human beings or companies and model counterfactuals on it, where you can have interventions in the future and optimize when to make intervention, what kind inter-- uh, what kind of intervention to make.It's such an unlock that previously was completely impossible. Like the-- it was, it was always dreamed of, but never... Like how would you even simulate it without LLMs or HTUs? I think very, very exciting times.[00:50:59] swyx: I just wanted to, uh, to maybe illustrate this. I, I'm not the best illustrator, but I, I am a conceptual statistics guy.And y-you know, you cannot just do this. Like this is a dimensionality AB test doesn't do, right? Like, uh, because it doesn't have the, the, the change over time, uh, stochastic nature, uh, and it doesn't have the sort of contextual like... Here's all the context to this point. Um, okay, cool. Um, that's SimGym.You're, you're gonna burn a lot of tokens on this thing. But you're, you're one of the, the only scale platforms in the world that can, uh, that can do this across a huge variety of workloads, right? I'm even curious on a sort of human, uh, research level of like, well, do, does retail behave d-differently from like clothing sales?D-does that behave differently from electronic sales? I, I don't know. I don't know what else you guys... The Kardashian shoppers, do they differ from like people who buy, uh, I don't know, cars and, uh, whatever.[00:51:55] Mikhail Parakhin: Well, very different, and different sensitivities and different modes of, uh, shopping and, and different levels of what's important.Now, to-totally, you can do aggregations at, uh, at a store level. You can do aggregations at a different, uh, category level. I don't know if, uh, you know, for our statisticians among us, I couldn't believe, but we-- recently we're looking at it, and we had to bring back, uh, CRPs, you know, Chinese restaurant process.It's a, like, way of aggregating and, like, naturally grow clustering. So across... Specifically to answer questions that, uh, like you were just posing on how, how if, if buyers behave different categories. And I'm like, “I haven't seen CRP since two thousand and one.” It's[00:52:37] swyx: so What? It's so- What is... No, I haven't, I haven't seen this.No. This is not in my training. Uh,[00:52:44] Mikhail Parakhin: but, but yeah, it, uh, uh, it actually, like the, the-- there was a very popular kind of theory, popular neurips HTML circles in early two thousands, uh, kind of nice. And now, now it has practical applications, uh- Yeah ... that we were resurrecting.[00:53:03] swyx: Yeah, amazing. Uh, I, I can see, I can see how this is like a, uh, a fun job for you where you get to apply all these things.Um, yeah, yeah, so super cool. Super cool. So, okay, so, so anyone who, who knows what CRPs are and has always wanted to use them at work, uh, they should, they should definitely join Shopify. Okay, so w-we have a lot and but I, I'm, I'm being mindful of the time. I, I do wanted to, to sort of cover some other things.Um, I-I'll give you a choice, UCP or Liquid?[00:53:30] Mikhail Parakhin: Liquid. I think, I think on UCP, you know, like UCP is very important for us and, and it just we are-- UCP, we have a structured, uh, discussions, and you can read about them, and we have, uh, blog posts, and we have a big release this week, in fact, like with our catalog.Oh,[00:53:46] swyx: okay.[00:53:46] Mikhail Parakhin: Uh, yeah,[00:53:46] swyx: but- Le-I mean, we, we can, we can discuss the, the, the release briefly because we'll release this after the-- after it's already announced so whatever. There's a catalog that you guys are doing?[00:53:55] Mikhail Parakhin: Yeah. So we are, we are- Okay ... we are bringing in capabilities of a whole, uh, Shopify catalog.Basically, you now you can search for products, you can do lookups by specific ID, you can do bulk lookups when you need to bring m-multiple products. You don't need to know in ad-in advance what you're trying to show or to sell or check out. Like, you can now, you can now have this decided at, at runtime, and this big area for investment for us for both non-personalized and personalized searches, trying to provide basically a win-window into whole universe of products that are being sold everywhere in the world.And Shopify is really not exactly, but almost like a super set of any-anything being sold. Now we are bringing it into UCP and, uh, and, uh, identity linking is another big thing for us, uh, so that you, you can use, uh, like Google or whatever, whatever identity you have, uh, they're minimizing friction.[00:54:56] swyx: Yeah. So[00:54:57] Mikhail Parakhin: yeah, big release for us.But Liquid AI of course we never talk about, and the problem might be more, more aligned with what we d-discussed previously on this chat.[00:55:07] swyx: Sure. The main thing that everyone understands about Liquid is that it is inspired by Worm, and I still don't know why. I'm curious on your explanation. I think you, you, uh, you can make things very approachable.And also I think like what is the potential of like the, the level of efficiency that you get out of Liquid?[00:55:23] Mikhail Parakhin: You- we all familiar with transformer architectures. And, uh, for the longest time, there was a competing architecture, it's called the state space models. So, so Sams, uh, you know, Chris, Chris Reyes, one of the pioneers and, and lots of startups, uh, trying to make those realities.They have, uh, significant benefits being main being, uh, being much faster and, uh, lower footprint and not quadratic in length, you know, sort of, uh, linear in, in, uh, in your context length. But with state space models- They never quite made it. Like they're used-- They have, uh, certain niches when they thrive, their hybrid architectures are useful, but they never quite made it.And liquid neural networks are, you can think of them as a next step, like, uh, sort of, uh, state-space model square. It's non-transformer architecture that's more complicated than sta-state space and really difficult to code if you-- if I'm being honest. But it's, um, very efficient. It's, uh, subline-- sub, uh, quadratic in, in length of your context.Uh, it's very compact way to represent things, and that's a liquid AI company. They... Their goal is to productize it, and very often you have this need, uh, when you need to have long context and small model, and you want to have low latency. Like in general, it's basically on par with transformers, and if you do hybrids with transformers, it's, it's even better.That's why we at Shopify, when we tried multiple and we constantly try multiple models, multiple companies, we found that for small, particularly with low latency applications, when you have low latency and/or if you need longer context lengths, liquid was the best. And so we still use the whole zoo and always like obviously test and use everything, uh, every open source model and, you know, it feels l

Airflow, filtration efficiency, and engine protection—how do you balance all three without compromise?In this episode of Race Industry Now, the weekly webinar and podcast series from EPARTRADE, aFe POWER takes a deep technical dive into the engineering behind high-performance air filters.“Airflow Matters: Inside aFe POWER Performance Filters” features:Stuart Miyagishima, EVP, Sales & Marketing, Engineer & Co-FounderGeorge Chiang, Manager, Filtration ProductsHosted by Brad Gillie (SiriusXM, Ch. 90, Late Shift)

Most homeowners think HVAC systems just “stop working.” That's not how it happens.Breakdowns start long before the unit quits — with small warning signs that are easy to ignore. Airflow drops. Run times get longer. Energy bills creep up. And before you know it, you're dealing with a full system failure in the middle of the hottest week of the year.In this episode, we walk through what's really going on inside your system, the signs most people miss, and what it actually takes to stay ahead of expensive repairs and replacements. If you're planning to stay in your home for the next several years, this is one conversation you don't want to skip.Because the difference between a simple fix and a costly emergency usually comes down to timing.

In this episode of the HVAC Know It All Podcast, host Gary McCreadie talks with Matthew Waxer, HVAC/R Mechanic at Kilmer HVACR Services Inc, about the key factors that impact system performance and compressor life. In Part 3, they focus on the importance of proper maintenance, airflow, and system setup from installation through operation. Matthew explains how correct airflow, refrigerant charge, and fluid mixtures like glycol all play a role in system efficiency and reliability. They also discuss common field mistakes such as poor airflow around equipment, incorrect glycol ratios, and refrigerant loss during servicing. The conversation wraps up with practical tips on maintenance practices, using probes instead of gauges, and avoiding contamination to keep systems running properly over time. Gary and Matthew discuss the key steps needed to keep HVAC systems running properly, focusing on maintenance, airflow, and correct setup. They explain how proper airflow across indoor and outdoor units is critical for system performance and how poor installation choices can lead to long-term issues. Matthew highlights the importance of correct refrigerant charge, coil sizing, and fluid flow in systems using glycol, including how improper mixtures can reduce efficiency or cause freezing problems. They also cover common service mistakes like refrigerant loss and contamination when using gauges. They finish by sharing practical tips such as using probes, checking airflow, and following proper maintenance practices to protect system life.   Expect to Learn: How proper maintenance helps extend the life of HVAC systems and prevent costly breakdowns. How correct airflow across indoor and outdoor units affects system performance and reliability. Why proper refrigerant charge and coil sizing are important for efficient operation. How glycol mixtures impact heat transfer, flow, and freezing protection in fluid systems. How using probes and careful service methods can reduce refrigerant loss and system contamination.   Episode Highlights: [00:00] - Sponsor: Factory Direct Filters ad [00:42] - Intro to Matthew Waxer in Part 03  [02:13] - Why maintenance matters for compressor life  [03:30] - Airflow setup: Indoor coil sizing & outdoor obstructions  [05:32] - Glycol systems: Mixture %, freezing point, refractometer use  [10:07] - Manifold vs. probes: Refrigerant loss & contamination risks  [15:33] - Outro & next episode    This Episode is Kindly Sponsored by: Cintas: https://www.cintas.com/hvacknowitall Cool Air Products: https://www.coolairproducts.net/ Factory Direct Filters: https://www.factorydirectfilters.com/ SupplyHouse: https://www.supplyhouse.com/tm Use promo code HKIA5 to get 5% off your first order at Supplyhouse! Follow the Guest Matthew Waxer:  LinkedIn: https://www.linkedin.com/in/matthew-waxer-b4a62360/   Kilmer HVACR Service Inc - Website: https://kilmerservice.com/  Follow the Host on: LinkedIn: https://www.linkedin.com/in/gary-mccreadie-38217a77/  Website: https://www.hvacknowitall.com Facebook: https://www.facebook.com/people/HVAC-Know-It-All-2/61569643061429/   Instagram: https://www.instagram.com/hvacknowitall1/  Follow the Podcast on:  YouTube: https://www.youtube.com/@HVACKnowItAll   Spotify: https://open.spotify.com/show/6LCBJGw0EHG03rdWHxUMce  Apple Podcast: https://podcasts.apple.com/us/podcast/hvac-know-it-all-podcast/id1359253455 

Electricity is getting more expensive, and we need more of it to power our devices and appliances. At the same time, many organizations are looking to decarbonize. Dan Diehl, CEO of Thrive Buildings, joins the Buildings Podcast to discuss how to balance these competing factors.

Essential Industries, a family-owned polymer manufacturer with over 100 years of history, focuses on its Sport Kote PC gym floor coating line in this podcast. They explain how the innovative process eliminates sanding, significantly reducing refinishing time, with a full gym floor completed in approximately five hours. The conversation covers the transition from oil-based to water-based coatings, maintenance practices, and ongoing care options like burnishing. The hosts also announce upcoming Florida workshops in April, where attendees can watch live demonstrations and learn Essential Industries' efficient gym floor refinishing techniques firsthand. RSVP for the workshops & showcases here:  https://collections.humanitix.com/essential-industries-showcases-26   Preparation & Demonstration Details (00:02:45) Tools & Techniques (00:03:54) Company History & Product Durability (00:04:53) Old vs. New Methods (00:06:00) Water vs. Oil Base & Floor Types (00:07:04) Care & Upkeep (00:07:55) Auto Scrubber Use & Water Concerns (00:08:46) Dust, Tack Ragging, & Airflow (00:09:47) Drying Times & Workshop Schedule (00:10:41) Closing & Contact Information (00:11:47)   https://gemsupply.net FACEBOOK: https://www.facebook.com/GemSupply/ TWITTER: https://twitter.com/gemsupplyco INSTAGRAM: https://www.instagram.com/gemsupplyco

In this episode of the HVAC Know It All Podcast, host Gary McCreadie is joined by John Anderson, Senior Regional HVAC Technical Trainer at Sila Services, Formerly Service Manager and Technician at Burns & McBride Home Comfort, to discuss spring HVAC maintenance and the key checks technicians should perform before the cooling season begins. John shares practical methods for inspecting systems using visual checks and technician senses such as sight, sound, smell, and touch to identify problems early. The conversation covers preventive maintenance practices, the importance of clean equipment, and how technicians should verify system operation before taking detailed measurements. Gary and John also talk about airflow diagnostics, the role of static pressure testing, and why proper system checks help technicians find real problems instead of guessing. In this conversation, John explains how technicians should approach spring maintenance by first checking that the system is clean and operating correctly. He discusses using visual inspection and technician senses such as sight, sound, smell, and touch to quickly spot possible problems. John and Gary talk about how many issues can be noticed before any tools are used, including unusual noises, vibrations, or signs of damage. They also explain why confirming the system is running properly is important before taking measurements. The discussion also covers airflow diagnostics and static pressure testing, and why checking these values regularly helps technicians identify system restrictions and airflow problems early. Expect to Learn: How technicians can use sight, sound, smell, and touch to identify system issues during maintenance. Confirming the system is running properly is important before taking measurements. How checking for dirt in filters, coils, and condensate lines improves system performance. What static pressure readings reveal about airflow problems in HVAC systems. Regular airflow checks during maintenance help technicians find restrictions and system issues early. Episode Highlights: [00:00] - CMPX Show Announcement [00:34] - Intro to John Anderson in Part 1  [02:03] - John Returns & Last Episode Recap [03:17] - Starting Spring Maintenance Right [04:53] - Story: The Cost of Ignoring Bad Bearings [06:45] - Using Your 4 Senses Before Tools [13:57] - Why Static Pressure Matters Every Time [16:35] - How to Correctly Check Static Pressure [19:45] - Understanding Your Static Pressure Readings  Join us at the CMPX Show from March 25 to 27 in Toronto. Use code KNOWITALL to get your free pass. Don't miss it.

Key Takeaways for Cultivators The Week 3 Strategy: Preventive sprays for Botrytis must occur when the flowers are still small and "open." Once the bud swells (Weeks 6+), the pathogen is already locked inside. Airflow is Everything: Buds act as heat sinks. Use fans 24/7 to cool the internal temperature of the flower, which is significantly higher than your room's ambient temperature. Identify Your Mold: If your bud rot looks pink or white rather than grey, it may be Fusarium. This is a systemic issue that requires a different mitigation strategy and poses a mycotoxin risk. HLVd Survival: In living soil, HLVd may break down much faster than on sterile surfaces because the microbes decompose the host root tissue. Watch the Silica: Over-applying potassium silicate can cause the plant to excrete white crystals onto the leaves and stems—don't mistake this "silica vomit" for PM or resin! Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Monorepos -- you've heard the talks, you've read the blog posts, maybe you've seen a few tantalizing glimpses into how Google or Meta organize their massive codebases. But it's often in the abstract and behind closed doors. What if you could crack open a real, production monorepo, one with over a million lines of Python and over 100 of sub-packages, and actually see how it's built, step by step, using modern tools and standards? That's exactly what Apache Airflow gives us. On this episode, I sit down with Jarek Potiuk and Amogh Desai, two of Airflow's top contributors, to go inside one of the largest open-source Python monorepos in the world and learn how they manage it with uv, pyproject.toml, and the latest packaging standards, so you can apply those same patterns to your own projects. Episode sponsors Agentic AI Course Python in Production Talk Python Courses Links from the show Guests Amogh Desai: github.com Jarek's GitHub: github.com definition of a monorepo: monorepo.tools airflow: airflow.apache.org Activity: github.com OpenAI: airflowsummit.org Part 1. Pains of big modular Python projects: medium.com Part 2. Modern Python packaging standards and tools for monorepos: medium.com Part 3. Monorepo on steroids - modular prek hooks: medium.com Part 4. Shared “static” libraries in Airflow monorepo: medium.com PEP-440: peps.python.org PEP-517: peps.python.org PEP-518: peps.python.org PEP-566: peps.python.org PEP-561: peps.python.org PEP-660: peps.python.org PEP-621: peps.python.org PEP-685: peps.python.org PEP-723: peps.python.org PEP-735: peps.python.org uv: docs.astral.sh uv workspaces: blobs.talkpython.fm prek.j178.dev: prek.j178.dev your presentation at FOSDEM26: fosdem.org Tallyman: github.com Watch this episode on YouTube: youtube.com Episode #540 deep-dive: talkpython.fm/540 Episode transcripts: talkpython.fm Theme Song: Developer Rap

To watch a video version of this podcast, click here: https://youtu.be/eK-WIS7inMUIn this episode, Reuben Saltzman and Tessa Murry talk with Steve Rogers, President of The Energy Conservatory (TEC), about the science behind home performance and why so many HVAC systems fall short of expectations. Steve shares TEC's journey from early prototypes to industry‑standard testing tools and explains how comfort, moisture, and efficiency issues often stem from the building envelope. The conversation dives into blower doors, airflow testing, duct leakage, furnace short‑cycling, restrictive filters, oversized systems, and the key measurements inspectors and homeowners commonly miss, offering practical insights for homeowners, inspectors, HVAC technicians, and building‑science enthusiasts alike.You can check out The Energy Conservatory website here: www.energyconservatory.comTakeawaysTEC manufactures tools that measure building airtightness, duct leakage, and airflow—core metrics for diagnosing home performance issues.Blower door testing became essential because leaky houses waste energy, cause comfort issues, and contribute to attic moisture problems.Early blower door prototypes were expensive and slow; TEC revolutionized the field with affordable, efficient models.Airflow is one of the hardest HVAC metrics to measure accurately; TEC's TruFlow Grid helps techs commission systems properly.Most furnaces and ACs are never tested for correct airflow after installation, which leads to early equipment failure and poor efficiency.High temperature rise = low airflow. This often causes the furnace to hit its high‑limit switch and shut off prematurely.Oversizing is rampant—many homes have furnaces 1.5–2× larger than needed, increasing noise, inefficiency, and comfort issues.Filter restrictions depend on pressure drop, not just MERV rating. Pleat depth and surface area matter more than the number printed on the label.3M Filtrete filters maintain reasonable pressure drops because they add pleats as MERV levels increase.The most important starting point in energy upgrades is a blower door test, not HVAC replacement.Older homes—especially balloon‑framed houses—are extremely leaky and need targeted air‑sealing.Complicated house shapes (L‑shaped, multi‑level splits, many dormers) are typically leakier than simple rectangular designs.Many contractors still do not measure airflow or static pressure, causing repeat callbacks and inefficiency.TEC's tools and apps help HVAC techs commission systems properly—reducing callbacks and improving system performance.Homeowners can access subsidized energy audits through utilities, often including blower door and infrared inspections.Chapters00:00 — Introduction and Guest Welcome02:00 — Steve's Background & The Origin of The Energy Conservatory05:00 — How Blower Doors Were Invented & Early Challenges08:00 — Engineers, Inventors & TEC's Company Culture11:00 — Advances in Airflow Testing: TruFlow Grid Explained15:00 — Why Airflow Is Critical for Furnace & AC Efficiency17:00 — Temperature Rise, High‑Limit Switches & Furnace Cycling20:00 — Common Installation Issues & What Inspectors Should Look For22:00 — The Truth About Furnace Filters & Pressure Drop26:00 — Oversizing Problems & Proper Equipment Matching31:00 — Why Most Homes Have Comfort Problems (and How to Fix Them)35:00 — Blower Door Testing as the First Step in Home Performance38:00 — Moisture, Attic Frost & Air Leakage Pathways41:00 — Styles of Homes That Tend to Be Leakier44:00 — Balloon Framing vs. Platform Framing47:00 — Why the Industry Changes Slowly & The Role of Training52:00 — How Homeowners Can Learn More & Access Energy Audits53:00 — Closing Tho

Recorded live on the floor at the AHR Expo 2026, this episode of the podcast brings together host Bryan and his guest Jesse, National Training Manager at NAVAC, for a candid, high-energy conversation about professional best practices in the HVAC/R trade. The two have a long-standing friendship and professional rapport that makes the discussion feel both educational and genuinely entertaining. Jesse brings a unique background to the table — from underground coal mining in West Virginia to becoming a lineman, then pivoting to HVAC through vocational school and a contractor-sponsored apprenticeship program. His path to becoming a national trainer is a testament to the value of investing in yourself and being open to learning at every stage of a career. The core of this episode centers on refrigerant recovery and charging best practices — a topic that might sound routine but quickly reveals how many technicians, even experienced ones, are cutting corners that cost their clients and their companies money. Bryan and Jesse dig into the problems caused by unnecessarily opening sealed systems, the refrigerant lost every time a technician gauges up a system without need, and why the HVAC industry needs to shift its mindset to treat equipment more like a home refrigerator: a sealed system that should run for years without needing to be cracked open. Jesse makes a compelling case that many so-called "mysterious leaks" are actually caused by repeated unnecessary gauge hookups removing small amounts of refrigerant each time. A significant portion of the conversation focuses on the transition away from manifold gauges toward digital probes and modern recovery setups. Jesse isn't dismissive of manifolds — he acknowledges their place in the classroom and as a backup tool — but he makes a strong case that eliminating restrictions throughout the recovery and charging process is one of the single most impactful things a technician can do to improve efficiency, protect equipment, and deliver better results for customers. Topics like pulling Schrader cores, using 3/8" hoses, Rapid-Y fittings, and the importance of using a filter dryer inline with the recovery machine are all covered with practical, field-tested advice. Bryan and Jesse also tackle some timely and emerging issues facing the industry, including the equalization behavior of R-454B blends and the growing challenge of refrigerant recovery in extreme cold climates as cold-climate heat pumps become more widespread in northern markets. These aren't hypothetical — they're problems technicians are encountering right now, and Bryan's theory about refrigerant fractionation showing up on thermal imaging cameras offers a genuinely fascinating technical angle. The episode closes with Jesse's overarching message: eliminate restrictions wherever you can, take pride in your craft, and never stop learning. Topics Covered Jesse's background: coal mining, lineman work, HVAC vo-tech, contractor apprenticeship, and path to becoming a national trainer The sealed system philosophy: why unnecessarily opening refrigerant circuits causes more problems than it solves Manifold gauges — their appropriate role in training and as a backup vs. the case for moving to digital probes How repeated gauge hookups can introduce refrigerant loss and fake "mystery leaks" — the 3.5 oz. per hose problem Restrictions as the enemy of efficient recovery: pulling Schrader cores, using core removal tools, and proper hose sizing The importance of recovering liquid first and how restrictions cause flash gas that slows recovery and adds heat Hose size trade-offs: why 3/8" hoses are the recommended sweet spot between flow rate and refrigerant retention Using a filter dryer inline with the recovery machine as cheap insurance against acid contamination and machine damage Why recovered refrigerant should generally NOT be reused — dirty recovery tanks, fractionation, and the limits of a single filter pass Scales as a non-negotiable tool: weighing refrigerant in AND out, and why techs who estimate by feel are guessing Diagnosing overcharge and undercharge situations using scale data before making repairs Airflow first, charge second: the importance of confirming CFM before adding refrigerant to a struggling system The R-454B equalization issue: refrigerant fractionation in new blends and Bryan's thermal imaging theory Cold-climate heat pump recovery challenges at sub-zero temperatures and strategies for adding heat to the system Heat pump maintenance best practices: testing defrost cycles and what happens when they haven't been checked in years Word of mouth as the most powerful (and dangerous) form of advertising in the service industry Recovery cylinder safety: the dangers of overfilling tanks and the 80% rule   Learn more about NAVAC's products and resources at https://navacglobal.com/.  Have a question that you want us to answer on the podcast? Submit your questions at https://www.speakpipe.com/hvacschool. Purchase your tickets or learn more about the 7th Annual HVACR Training Symposium at https://hvacrschool.com/symposium. Subscribe to our podcast on your iPhone or Android. Subscribe to our YouTube channel. Check out our handy calculators here or on the HVAC School Mobile App for Apple and Android.

Have you ever walked into your home and felt the air was a little… stale? Maybe a bit heavy, dusty, or holding onto yesterday's dinner? You might not realise it, but your house needs to “burp.”House burping simply means opening doors and windows to create cross-flow ventilation so fresh air can move through your home. Not just a cracked window — real airflow. Even ten minutes a day can make a noticeable difference.When you live in a home (with humans, pets, cooking, showers and heaters running), moisture builds up. That moisture turns dust into grime, increases the risk of mould, and traps smells in soft furnishings and paint. If clutter is present, airflow is even more restricted — which means more stagnant air, more dust settling, and more odour lingering.When you open windows regularly, you improve air quality, reduce moisture, and make mould less likely. You also disturb settled dust while decluttering, which is exactly why ventilation matters when you're tidying.There's a psychological shift too. Fresh air and natural light change how you see your space. You notice dust on the mirror. You feel more motivated to wipe it down. Light reveals what's been hiding behind closed blinds. Airflow reduces that oppressive, boxed-in feeling clutter can create.Try opening several windows across your home for ten minutes in the morning. Let your house breathe. You might find it easier to clean, clearer to think, and lighter in your space.You may also like to listen to these episodes:Reducing VolumeBlame EntropyJoin my communityLeave a 5 Star Google ReviewFollow me on InstagramFollow me on FacebookJoin my Facebook groupThank you to my sound engineer, Jarred from Four4ty Studio Hosted on Acast. See acast.com/privacy for more information.

Vincent Heuschling reçoit Hayssam Saleh, créateur de **Starlake**, une plateforme data open source française née de la factorisation de projets clients depuis 2017-2018. L'épisode intervient dans un contexte de consolidation du marché (rachat de DBT et de SQLMesh par Fivetran), qui invite à challenger les solutions établies.Starlake se distingue par une approche **entièrement déclarative** (YAML + SQL natif, sans Jinja) couvrant toute la chaîne data engineering : ingestion, transformation, orchestration et qualité des données. L'outil s'appuie sur les moteurs sous-jacents des plateformes cibles (Snowflake, BigQuery, Spark) et génère automatiquement les DAGs pour les orchestrateurs du marché (Airflow, Dagster, Snowflake Tasks).Parmi les fonctionnalités marquantes : le **data branching** (branches de données à la manière de Git), l'inférence automatique de schémas YAML à partir de fichiers sources, un **transpiler SQL** multi-plateformes, et l'extraction du lineage depuis du SQL brut sans annotation. L'intégration récente de **DuckLake** ouvre la voie à des architectures on-premise souveraines à coût maîtrisé (sous 300 €/mois sur OVH, Scaleway, Clever Cloud).Le modèle économique repose sur le support, la formation, et le consulting : Starlake s'installe dans le cloud du client, avec mise à jour automatique gérée par l'équipe, sans accès aux données.**Chapitres****00:00:27** – Introduction : consolidation du marché data (rachat de DBT et SQLMesh par Fivetran) et présentation de l'épisode**00:03:13** – Hayssam et la genèse de Starlake : parcours Spark/Scala, POC à 4 000 formats de fichiers (2017-2018)**00:09:51** – Architecture et philosophie : load, transform, orchestration unifiés en déclaratif (YAML + SQL natif, pas de Jinja)**00:00:18:18** – Starlake vs DBT : différences philosophiques, composabilité, fonctionnalités 100 % open source**00:00:22:20** – Data branching, Starlake Labs (pipe syntax, transpiler SQL, lineage) et expérience développeur (DuckDB local, UI point-and-click)**00:36:35** – Modèle open source et économique : licence Apache, support, formation, marketplace cloud souveraine**00:43:42** – DuckLake : alternative on-premise/cloud souverain (OVH, Scaleway, Clever Cloud) et comment contribuer / démarrer**Le BigdataHebdo**Le BigdataHebdo est le podcast Francophone de la Data et de l'IA.Retrouvez plus de 200 épisodes https://bigdatahebdo.comRejoignez la communauté sur le Slack https://join.slack.com/t/bigdatahebdo/shared_invite/zt-a931fdhj-8ICbl9dbsZZbTcze61rr~Q

What if your data platform could power both critical business decisions and real-time product features at scale? In this episode, host Benjamin sits down with Magnus Dahlbäck, Senior Director of Data and Platform at Voi, to explore how a metrics-first approach and semantic layers transform data accessibility, why traditional ML and LLMs require different strategies for different problems, and how to balance FinOps costs while processing billions of IoT events daily. Whether you're building data infrastructure for a high-growth company or rethinking how your organization consumes data, this conversation is packed with practical strategies for unlocking data value and preparing your platform for AI. Tune in to discover how Voi ditched traditional BI tools and revolutionized their approach to enterprise analytics.

Rob Hoos is back! Three years after our first viral interview (50k+ views), we dive deep into his latest research on airflow, cultivars, and roasting physics.

Astronomer's Steven Hillion reveals how OpenAI, Anthropic, Uber, and Lyft use Apache Airflow to orchestrate AI and machine learning pipelines at scale on AWS.Topics Include:Steven Hillion leads data and AI at AstronomerApache Airflow surpassed Spark and Kafka in community metricsAstronomer coordinates data flow like conductor orchestrating instrumental platformsOrganizations with data engineering teams use Airflow at scaleCustomers already used Airflow for ML before official promotionUber and Lyft orchestrate pricing models using AirflowAstronomer runs on AWS with close integration partnershipsOpenAI Anthropic and GitHub Copilot use Airflow for operationsInternal data team uses Airflow creating feedback loopsEvolved from constrained AI reports to agentic workflowsPlatform monitors generative AI output quality at user interactionsMetadata and context increasingly critical for AI applicationsLearn more at Astronomer's Data FlowCast podcastParticipants:Steven Hillion – SVP, Data and AI, AstronomerSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Seed success starts long before the first tray is filled. We're laying down a practical, no‑nonsense prep plan that saves you time, cuts waste, and sets your early crops up for real momentum once daylight returns in mid‑February. From testing old packets on kitchen paper to choosing the right trays and compost, we go deep on the details that quietly deliver stronger seedlings and bigger harvests.We talk through the realities of germination rates, why seed vigour matters even when sprouts appear, and when to be ruthless about binning tired stock. You'll hear a clear comparison between open pollinated and F1 hybrid seed—where resilience, seed saving, and flavour meet reliability, pest tolerance, and uniformity—so you can choose with intent. On kit, we separate “nice to have” from “need”: rigid seed trays and modules earn their place; heated propagators help with tomatoes and peppers; grow lights are optional if you time sowings for rising natural light.Compost can make or break a sowing day. We weigh up peat's consistency against peat‑free variability, call out premium peat‑free options that perform, and share a simple DIY seed mix: fine, mature compost or leaf mould for structure, perlite for air, and a light nutrient lift from vermicompost and seaweed. Then it's technique: dense sowing with gentle pricking out, thinning to the strongest seedling, multi‑sowing spring onions for efficient beds, and watering that keeps media evenly moist without drowning roots. Airflow, patience, and timing bring it all together—wait until mid‑February and you'll have more light, steadier temperatures, and somewhere sensible to move plants on.Ready to start strong and skip the leggy mistakes? Listen now, get your seed box, trays, and compost lined up, and join us next week for the full February sowing guide. If this helped, follow the show, share it with a grower friend, and leave a quick review to help more gardeners find us.Why not come along to my Grow your own workshops where you will learn all about seed sowing and growing your own food. https://subscribepage.io/growyourownfoodworkshopSupport the showIf there is any topic you would like covered in future episodes, please let me know. Email: info@mastermygarden.com Check out Master My Garden on the following channels Facebook: https://www.facebook.com/mastermygarden/ Instagram @Mastermygarden https://www.instagram.com/mastermygarden/ Until next week Happy gardening John

Data as a Product: Was steckt dahinter?Warum ist AI überall, aber der Weg von der Datenbank zu "Wow, das Modell kann das" wirkt oft wie ein schwarzes Loch? Du loggst brav Events, die Daten landen in irgendwelchen Silos, und trotzdem bleibt die entscheidende Frage offen: Wer sorgt eigentlich dafür, dass aus Rohdaten ein zuverlässiges, verkaufbares Datenprodukt wird.In dieser Episode machen wir genau dort das Licht an. Gemeinsam mit Mario Müller, Director of Data Engineering bei Veeva Systems, schauen wir uns an, was Datenteams wirklich sind, wie "Data as a Product" in der Praxis funktioniert und warum Data Engineering mehr ist als nur ein paar CSVs über FTP zu schubsen. Wir sprechen über Teamstrukturen von der One-Man-Show bis zur cross-functional Squad, über Ownership auf den Daten, Data Governance und darüber, wie du Datenqualität wirklich misst, inklusive Monitoring, Alerts, SQL-Regeln und menschlicher Quality Control.Dazu gibt es eine ordentliche Portion Tech: Spark, AWS S3 als primärer Speicher, Delta Lake, Athena, Glue, Airflow, Push-Pull statt Event-Overkill und die Entscheidung für Batch Processing, obwohl alle Welt nach Streaming ruft.Und natürlich klären wir auch, was passiert, wenn KI an den Daten rumfummelt: Wo AI beim Bootstrapping hilft, warum Production und Scale tricky werden und wieso Verantwortlichkeit beim Commit nicht von einem LLM übernommen wird.Wenn du Datenteams aufbauen willst, Data Products liefern musst oder einfach verstehen willst, wie aus Daten verlässlicher Business-Impact wird, bist du hier genau richtig.Bonus: Batchjobs bekommen heute mal ein kleines Comeback.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

Brian Tooley and Rick Crawford join us at the PRI Tradeshow to their history, their current designs and upcoming projects, and of course the recent private equity deal for Brian Tooley Racing High Performance Academy: https://hpcdmy.co/Minnoxide Use code "MINNOX" for 55% off ANY course Use Code "MINVIP" for $300 of the MINVIP Package Tuned By Shawn: https://www.tunedbyshawn.com Code "Minnoxide" for 5% off! Ship with Sure Thing Logistics: https://www.surethinglogistics.net MORE BIGGER Turbo T-Shirts:  https://www.minnoxide.com/products/more-bigger-t-shirt  

In this episode, we review the high-yield topic of⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Airflow, Pressure, and Resistance⁠⁠⁠⁠⁠⁠⁠ ⁠from the Respiratory section.Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Medbullets⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ on social media:Facebook: www.facebook.com/medbulletsInstagram: www.instagram.com/medbulletsofficialTwitter: www.twitter.com/medbullets

In this episode of the HVAC School podcast, Bryan and Nathan dive deep into the challenges of humidity control in grocery stores and other refrigerated environments. While the conversation takes several entertaining detours (including discussions about morning radio shows, Indian weddings with elephants, and imaginary lava-heated homes), the core content provides valuable insights for HVAC and refrigeration technicians dealing with condensation and moisture issues in commercial refrigeration spaces. The hosts explain why humidity management is critical in grocery environments, where refrigerated cases and displays must maintain cold temperatures while preventing condensation on doors, frames, and floors. They discuss the evolution from traditional solutions—like energy-intensive frame heaters that kept surfaces above dew point—to modern strategies involving dedicated outdoor air systems (DOAS), strategic use of waste heat from refrigeration racks, and various dehumidification approaches. Nathan emphasizes that the key is maintaining proper dew point levels (typically targeting 45% relative humidity at around 72°F) while keeping the building under positive pressure to control moisture infiltration. A significant portion of the discussion focuses on airflow management and its impact on refrigeration equipment. The hosts explain how air curtains in display cases work on Bernoulli's principle to maintain cold temperatures, and why even minor disruptions to airflow patterns can cause product spoilage or increased energy consumption. They stress the importance of understanding building pressure dynamics, especially considering makeup air requirements for exhaust systems in sculleries and loading docks. The episode concludes with practical troubleshooting advice for technicians dealing with sweating cases and humidity problems. Nathan recommends systematically checking building pressure with a manometer, measuring dew point at multiple locations throughout the store, and verifying that door and frame heaters are functioning properly. He also suggests looking for intermittent fresh air sources and exhaust fans that might be disrupting the carefully balanced airflow patterns that keep moisture under control. Topics Covered: Dew Point vs. Relative Humidity: Why focusing on dew point (50-55°F typical target) is more important than relative humidity in grocery environments Condensation Prevention Strategies: Evolution from energy-intensive frame heaters to modern DOAS systems with reheat capabilities Airflow and Air Curtains: How Bernoulli's principle creates invisible barriers in refrigerated display cases and why disrupting these patterns causes problems Reheat Methods: Various approaches, including waste heat from refrigeration racks, electric reheat, and desiccant dehumidification systems Building Pressure Management: Importance of maintaining positive pressure while managing fresh air requirements and exhaust systems Radiant Heat Effects: How surface temperatures, not just air temperature, affect condensation on refrigerated cases Troubleshooting Humidity Issues: Systematic approach to diagnosing moisture problems, including pressure testing, dew point measurement, and identifying intermittent airflow sources Return Air Placement: Benefits of pulling return air from underneath cases to capture the most humid air for dehumidification   Have a question that you want us to answer on the podcast? Submit your questions at https://www.speakpipe.com/hvacschool. Purchase your tickets or learn more about the 7th Annual HVACR Training Symposium at https://hvacrschool.com/symposium. Subscribe to our podcast on your iPhone or Android. Subscribe to our YouTube channel. Check out our handy calculators here or on the HVAC School Mobile App for Apple and Android.

Send us a textIn this episode, recorded at the 2025 DSG Conference, Paul, Juan and Bryn talk with Doug Buch of the T.I.G.E.R. Team about the importance of airflow in an HVAC system.Thanks for listening! Please visit www.mitsubishicomfort.comContact us at metustechshow@hvac.mea.com

Researchers at NASA have found a novel paint-by-numbers method to measure experimental data faster and more accurately than ever before.

On episode 3 of Data Renegades, CL Kao and Dori Wilson sit down with Maxime Beauchemin. They explore the origins of Airflow and Superset, the evolution of open source in the data ecosystem, and how today's tooling reshapes the role of the data practitioner. Max also shares a forward-looking perspective on agentic workflows and how AI is accelerating everything from BI to pipeline development.

In this week's episode of Grow Guides, we're digging into four key topics that can seriously level up your next harvest: How to Boost Trichome Production Naturally: We break down the techniques, environmental tweaks, and plant-training methods that help your buds pack on more frost without gimmicks or snake oil. Airflow and Circulation: The Secret Weapon for Stronger Plants: Good airflow does way more than stop mould. It shapes stronger stems, improves nutrient flow, and drives explosive growth. We show you how to dial it in properly. How to Reset and Reuse Your Grow Setup Between Runs: Whether you're growing in soil, coco, or hydro, we explain how to clean, sterilise, and prep your space so every new grow starts fresh and problem-free. Understanding Plant Stress: When It Helps and When It Hurts: Not all stress is bad, some of it actually boosts terpenes and trichomes. But too much can wreck your yield. Learn how to tell the difference and use stress wisely. As always, we wrap up with listener mail and community questions!

On this episode of Recycled Idaho, Nick is joined by Eric Sonner to discuss his career transition from growing up on an Idaho farm and entering the HVAC trade to becoming a leader in building massive data centers for cryptocurrency mining and AI High Performance Computing (HPC). Sonner details the rapid, expensive scale of these projects, including a 527-megawatt AI HPC site under construction in New York, and emphasizes the personal philosophy of setting and achieving huge goals. Produced by Recycled Media.

Habitat Podcast #352 - In today's episode of The Habitat Podcast, we are back in the studio with co-host Andy and our good friends Doug and Matt Holmes of Downburst Seeders. We discuss: Dry conditions crushed deer movement and morale. Year-one plots exposed setup weaknesses. Customer rains highlight regional variability. Seeding rates adjusted for seed size and goals. Consistency in flow is key to accurate coverage. Airflow tuning prevents seed bounce and drift. On-site consulting offered for plot troubleshooting. Upgrades aim for precision and ease of use. Lesson learned: adapt plans; nature overrides. Adding bees to your property And So Much More! Shop the new Amendment Collection from Vitalize Seed here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://vitalizeseed.com/collections/new-natural-amendments ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ PATREON - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Patreon - Habitat Podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Brand new HP Patreon for those who want to support the Habitat Podcast. Good luck this Fall and if you have a question yourself, just email us @ info@habitatpodcast.com -------------------------------------------------------------------------- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Patreon - Habitat Podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Latitude Outdoors - Saddle Hunting: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/hplatitude⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Stealth Strips - Stealth Outdoors: Use code Habitat10 at checkout ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/stealthstripsHP⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Midwest Lifestyle Properties - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/3OeFhrm⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Vitalize Seed Food Plot Seed - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/vitalizeseed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Down Burst Seeders - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/downburstseeders⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ 10% code: HP10 Morse Nursery - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://bit.ly/MorseTrees⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ 10% off w/code: HABITAT10 Packer Maxx - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://bit.ly/PACKERMAXX⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ $25 off with code: HPC25 First Lite - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/3EDbG6P⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LAND PLAN Property Consultations – HP Land Plans: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LAND PLANS⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Leave us a review for a FREE DECAL - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://apple.co/2uhoqOO⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Morse Nursery Tree Dealer Pricing – info@habitatpodcast.com Habitat Podcast YOUTUBE - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/channel/UCmAUuvU9t25FOSstoFiaNdg⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Email us: info@habitatpodcast.com habitat management / deer habitat / food plots / hinge cut / food plot Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, we talk with Daniel, an astrophysicist turned machine learning engineer and AI ambassador. Daniel shares his journey bridging astronomy and data science, how he leveraged live courses and public knowledge sharing to grow his skills, and his experiences working on cutting-edge radio astronomy projects and AI deployments. He also discusses practical advice for beginners in data and astronomy, and insights on career growth through community and continuous learning.TIMECODES00:00 Lunar eclipse story and Daniel's astronomy career04:12 Electromagnetic spectrum and MEERKAT data explained10:39 Data analysis and positional cross-correlation challenges15:25 Physics behind radio star detection and observation limits16:35 Radio astronomy's advantage and machine learning potential20:37 Radio astronomy progress and Daniel's ML journey26:00 Python tools and experience with ZoomCamps31:26 Intel internship and exploring LLMs41:04 Sharing progress and course projects with orchestration tools44:49 Setting up Airflow 3.0 and building data pipelines47:39 AI startups, training resources, and NVIDIA courses50:20 Student access to education, NVIDIA experience, and beginner astronomy programs57:59 Skills, projects, and career advice for beginners59:19 Starting with data science or engineering1:00:07 Course sponsorship, data tools, and learning resourcesConnect with DanielLinkedin -   / egbodaniel  Connect with DataTalks.Club:Join the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/...Check other upcoming events - https://lu.ma/dtc-eventsGitHub: https://github.com/DataTalksClubLinkedIn -   / datatalks-club   Twitter -   / datatalksclub   Website - https://datatalks.club/

In this episode of the HVAC Know It All Podcast, host Gary McCreadie continues his discussion with John Ellis, a Product Ambassador at Dust Free, LP, an Instructor at Amana/Goodman/Dakin, and a Field Service Trainer at The New Flat Rate, Inc, on air quality, system design, and the role of UV lights in HVAC systems. In Part 02 of their talk, Gary and John explore the importance of air mixing, static pressure, and system performance. They break down how proper air distribution and good filtration can improve efficiency and comfort. They also dive into UV lamp use and its effectiveness in residential applications, offering practical insights for technicians on balancing technology with system design. John and Gary discuss the importance of air mixing in HVAC systems and how it affects air quality and comfort. They explain how poor air circulation can lead to stagnant pockets of air, causing pollutants to build up. John shares how using ceiling fans or proper system design can help improve air movement. They also talk about the impact of static pressure on airflow and how to balance it when designing systems. Finally, John gives his thoughts on UV lamps in HVAC systems, explaining why they can be useful but may not always be the best solution. This episode is packed with practical HVAC tips, air quality insights, and expert advice on system design. It helps techs understand the importance of air mixing, managing static pressure, and improving airflow. John and Gary also share their thoughts on UV lamps and how they can be useful in certain situations. This episode is all about using the right techniques to improve system efficiency and comfort while making informed decisions. Expect to Learn: The role of air mixing in improving HVAC comfort and air quality. How static pressure affects airflow and system performance. When UV lamps are useful and when they aren't ideal. The benefits of tight systems and good filtration for efficiency. Why system design should minimize the need for additional tech like UV lamps. Episode Highlights: [00:00] - Intro to John Ellis in Part 02 [01:40] - Importance of Ceiling Fans and Air Mixing in HVAC Design [05:26] - Real-World Experience in Pharmaceutical Industry [07:22] - Airflow vs. Static Pressure [09:46] - UV Lamps for Sanitizing Coils and Drain Pans [11:46] - Photocatalytic Oxidizers and the Dangers of Byproducts [16:05] - Final Thoughts on UV Use in HVAC [10:01] - Future Discussion on HVAC vs. Building Envelope This Episode is Kindly Sponsored by: Master: https://www.master.ca/ Cintas: https://www.cintas.com/ Cool Air Products: https://www.coolairproducts.net/ property.com: https://mccreadie.property.com SupplyHouse: https://www.supplyhouse.com/tm Use promo code HKIA5 to get 5% off your first order at Supplyhouse! Follow the Guest John Ellis on: LinkedIn: https://www.linkedin.com/in/john-ellis-b13b0411/ Dust Free, LP: https://www.linkedin.com/company/dust-free-lp-/ The New Flat Rate, Inc.: https://www.linkedin.com/company/the-new-flat-rate-inc-/ Follow the Host: LinkedIn: https://www.linkedin.com/in/gary-mccreadie-38217a77/ Website: https://www.hvacknowitall.com Facebook: https://www.facebook.com/people/HVAC-Know-It-All-2/61569643061429/ Instagram: https://www.instagram.com/hvacknowitall1/

Dean takes your calls and tackles real-life home dilemmas. He offers advice to a homeowner weighing the pros and cons of restoring a property to its original style versus relying on an assessor for resale. Dean also breaks down tricky property line concerns and offers clarity. Later, he dives into the world of e-pipes—what they are, how they work, and whether they're a smart choice for your home. Finally, a curious caller asks about steaming a room: Dean explores the risks, code issues, and how moisture, air flow, and mold can make or break your indoor environment.

Our BEST class of the year! OKCalyxx Natural Farming Immersive, Oct 11 & 12. Grab your tickets today! (00:00) Scissors vs Scalpels (05:03) Optimal Clone Defoliation (07:41) Cloning Gel and Alternatives (11:49) IBA and Optimal Moisture (18:32) Dome Vents and Airflow (29:15) Clone Tips, Tricks, and Pitfalls (33:04) How to Care for LOTS of Clones We pick up right at Part Two of our cloning exploration with Mike from GrowersHouse. Mike shares insights into his actual cloning technique where he emphasizes the need for proper clone placement and depth. He explains how plugs hold water at a certain "table level" in your clone dome, and where you should be placing your cuts to allow them to take up water and nutrients.  This leads a conversation about optimal moisture levels, and Mike. drops some tips on using weight as a metric of moisture. Mike also talks about using household alternatives to clone gels like aloe and willow bark extract- and why IBA is still the best choice for home growers. Mike wraps up the show by talking about caring for many many clones at once, and how the difficulty of cloning goes as the amount of genetics increases. Join GrowCast Membership TODAY! Connect with the most active, vibrant cannabis community in the entire world. Personal 24/7 garden support, Members Only content and discounts, and so much more! www.growcast.com/membership    GrowCast Seed Co KLM DROP IS LIVE! Members get $20 off per pack- this Key Lime Madness Drop is going fast so don't miss it! Code growcast15 now works with grow KITS from AC Infinity! www.acinfinity.com use promo code growcast15 for 15% off the BEST grow fans in the game, plus tents, pots, scissors, LED lights, and now REFILLABLE FILTERS!

In this enlightening presentation, Alex Meaney breaks down the fundamental concepts of airflow in HVAC systems using practical analogies and real-world examples. Rather than diving straight into complex mathematics, Alex focuses on helping technicians and contractors understand what's actually happening inside ductwork and why traditional design methods may be falling short in modern residential systems. Alex begins by addressing one of the most critical yet misunderstood aspects of ductwork: the exponential relationship between duct size and airflow capacity. He explains that the difference between a 6-inch and 7-inch duct isn't just 17% more capacity—it's actually 36% more, because airflow is determined by cross-sectional area (which increases geometrically) rather than linear measurements. This fundamental misunderstanding leads to significant underperformance in many HVAC installations. The presentation tackles the confusion surrounding pressure terminology in the HVAC industry, where the single word "pressure" is used to describe four distinct concepts: static pressure, velocity pressure, pressure loss, and external static pressure. Alex uses creative analogies, including a memorable demonstration with an inflatable tube dancer (referencing the "used car lot" in his title), to illustrate how static pressure and velocity pressure are always in balance—when one increases, the other decreases proportionally. A major focus of the discussion centers on why the traditional 0.1 inches of water column per 100 feet friction rate, long considered standard in residential duct design, is no longer adequate for modern systems. Alex explains that today's homes have evolved significantly: they're larger, use more restrictive filters for air quality, have more complex coil designs, and often place equipment in suboptimal locations. These factors combine to create much higher system resistance than the 0.1 standard was designed to handle. He advocates for using lower friction rates (like 0.06) and emphasizes that proper duct sizing is more critical than ever. The presentation concludes with practical insights about system design philosophy, emphasizing that while homeowners may not complain about poorly performing systems, HVAC professionals should use objective measurement tools rather than customer satisfaction as the primary indicator of system performance. Alex stresses that craftsmen in the field will make systems work regardless of design flaws, but this shouldn't excuse poor initial design practices. Key Topics Covered Duct Sizing Fundamentals The geometric relationship between duct diameter and airflow capacity Why linear measurements can be misleading when calculating system performance The critical importance of proper duct sizing in modern installations Pressure Concepts Demystified Static pressure vs. velocity pressure and their inverse relationship How pressure and friction work together in ductwork systems External static pressure as a measure of fan capability The role of pressure in airflow generation and control Friction and Resistance in Ductwork Understanding friction as the primary enemy of airflow How fittings create equivalent lengths of straight duct The impact of direction changes and system components on airflow Comparing flex duct vs. metal duct friction characteristics Modern System Design Challenges Why traditional 0.1 friction rates no longer work effectively The evolution of residential systems: larger homes, better filters, complex coils Equipment placement strategies and their impact on system performance The "war on sensible" and "war on blowers" affecting modern HVAC design Measurement and Verification Methods Tools for measuring static pressure and velocity pressure The importance of using objective measurement tools over customer satisfaction Available static pressure calculations and their practical applications Manual D design principles and their real-world limitations Practical Design Philosophy Working backwards from blower capacity rather than arbitrary friction rates Balancing system performance with budget constraints The importance of central equipment placement for optimal airflow Professional standards vs. "good enough" mentality in system design   Have a question that you want us to answer on the podcast? Submit your questions at https://www.speakpipe.com/hvacschool. Purchase your tickets or learn more about the 7th Annual HVACR Training Symposium at https://hvacrschool.com/symposium. Subscribe to our podcast on your iPhone or Android. Subscribe to our YouTube channel. Check out our handy calculators here or on the HVAC School Mobile App for Apple and Android  

In this short podcast episode, Bryan walks through some common thermostatic expansion valve (TXV) troubleshooting scenarios. Many of the same principles apply to troubleshooting electronic expansion valves (EEVs). These dynamic metering devices maintain a constant superheat. Troubleshooting does NOT start and end with the TXV. First, you need to inspect components (especially filters, ductwork, and filter-driers) and confirm the airflow and charge. You can use measureQuick to monitor superheat, subcooling, static pressure, and other key measurements, and the TrueFlow grid can give you a true idea of the CFM your system is moving. Keep in mind that superheat and subcooling values can vary by system. Airflow problems and filter-drier restrictions may mimic failed TXV conditions. Ideally, the liquid line filter-drier will be located indoors, and you can check for a pressure drop across it by looking for temperature differentials. You need a full column of liquid going into the filter-drier, and you can use a thermal imaging camera to see the desuperheating, condensing, and subcooling phases inside the condenser coil. The TXV has a bulb that can be loose, improperly mounted, or improperly insulated; when there is an issue with the bulb, there will likely be low superheat. The bulb should be on a clean and [ideally] horizontal portion of the suction line, and it should be strapped with copper or stainless steel straps. Insulating the bulb is especially important when it's externally located and when low superheat or flood back is a concern.    Have a question that you want us to answer on the podcast? Submit your questions at https://www.speakpipe.com/hvacschool. Purchase your tickets or learn more about the 6th Annual HVACR Training Symposium at https://hvacrschool.com/symposium. Subscribe to our podcast on your iPhone or Android. Subscribe to our YouTube channel. Check out our handy calculators here or on the HVAC School Mobile App for Apple and Android