Podcasts about Differential

Hello Interactors,I've long been a fan of “regreening” cities, imagining replacing bits of asphalt and concrete with trees, plants, mini-parks, and green roofs to cool them down. In many cases these are indeed good interventions. But even these celebrated nature-based solutions to the “urban heat island” effect require closer inspection. It turns out any land cover change alters energy, water, and momentum exchanges between the Earth's surface and the atmosphere. Which are the right ones and where?To understand why cookie cutter greening plans can fail and how planners and policy makers can build cities that can handle climate change, we need to understand how land and the air interact. Environments vary from place to place, and this puts limits on what we can do to land to create urban climates that are both beautiful and healthy.To get a handle on what the research says, I found a literature review from 2025 that synthesized findings from 84 peer-reviewed studies. In was published in the journal Climate Risk Management.Let's see what they, and others, found.BEATING HEAT WITH BIOPHYSICAL FEATSWhat better place to start than science. Let's just delve right into how urban green spaces affect temperature. It starts with determining the energy balance at the surface of the earth. This comes from a budget equation found in physical geography. When sunlight hits the Earth's surface, it breaks down into three main fluxes, or ways heat moves around* Sensible heat flux: the heat that directly warms the surrounding air, which we measure as temperature.* Latent heat flux: the heat that's used to turn water from a liquid to a gas, like when we sweat or plants release water vapor (transpiration).* Ground heat flux: the heat stored in building materials, asphalt, and soil.In conventional urban environments dominated by concrete and asphalt, latent heat flux is minimal because we've built cities in ways that rapidly drain water away as ‘waste'. As a result, incoming solar energy is funneled into sensible heat. This raises daytime air temperatures that gets stored as ground heat, which is slowly radiated back into the city at night. Urban greening can reshuffle this thermodynamic budget through three interconnected physical mechanisms: shading, evapotranspiration, and albedo modification.Mechanism 1. Shading: Intercepting Solar RadiationShading — the most immediate and reliable cooling mechanism provided by vegetation — requires precise microclimatic quantification. Vegetative canopies act as physical shields, intercepting incoming shortwave solar radiation before it strikes impervious surfaces like concrete or asphalt. By preventing these dense materials from absorbing heat and emitting sensible heat flux back into the boundary layer, shading dramatically lowers surface temperatures and reduces the baseline thermal energy transferred to the surrounding air. (Bowler, D. E., et al. 2010)The cooling benefit of urban trees is non-linear, accelerating significantly once neighborhood canopy cover crosses a critical threshold of around 40%. Measuring daytime air temperatures across urban gradients, they found that canopy cover above this 40% mark can lower local air temperatures by up to 1.5 to 2.0 degrees Celsius, effectively offsetting the thermal burden created by surrounding roads and impervious surfaces. (Ziter, C. D., et al. 2019)Mechanism 2. Evapotranspiration: Trading Sensible Heat for Latent HeatWhile shading blocks heat absorption, evapotranspiration actively removes heat from the air. Plants absorb soil moisture through their root systems and release it as water vapor through tiny microscopic pores in their leaves called stomata. This process requires thermal energy that turns phase-changing liquid water into gas. This energy transfer absorbs sensible heat and converts it into latent heat.Evapotranspiration from well-watered urban green spaces can lower local air temperatures by 2-4 degrees celsius. (Coutts, et al., 2013) However, physical geographers emphasize a crucial physical prerequisite that is easy to overlook: evapotranspiration is entirely dependent on available water. When the soil is dry or the air is super dry, plants close their stomata to save water. When stomata close, evapotranspiration shuts down. This leaves shading as the sole remaining cooling mechanism.Mechanism 3. Albedo Dynamics: The Surface Reflectivity ParadoxAlbedo measures the reflectivity of a surface on a scale from 0.0 (total absorption) to 1.0 (total reflection). Urban asphalt has a low albedo (0.05 to 0.10), absorbing up to 95% of incoming solar energy. Vegetation's albedo can range from 0.11 to 0.25.In temperate climates, replacing low-albedo asphalt with greenery or reflective surfaces increases surface reflectivity, sending more solar radiation back into space before it can be absorbed as sensible heat. One study documented that increasing urban surface albedo typically lowers peak ambient air temperatures by 0.3 degres Celsius to 1.0 degree Celsius — alongside much larger drops in surface pavement temperatures. While a temperature change under one degree Celsius may sound modest at first glance, a baseline shift of this magnitude across a neighborhood is enough to redefine a local microclimate. (Santamouris, 2014)But swapping out light urban surfaces for dark vegetation can also have a negative effect. Paradoxically, when researchers in 2023 replaced dry, light-colored desert soil with darker vegetation in hot, arid regions it reduced overall surface reflectivity. The drop in albedo caused an increase in daytime surface warming. (Schlaerth, et al., 2023)Furthermore, dense tree canopies inside narrow urban street canyons can act as thermal blankets at night. While trees provide valuable shade during the day, their foliage reduces the sky view factor at street level, trapping outgoing longwave thermal radiation emitted by surrounding building facades and asphalt. When combined with reduced wind permeability, this canopy barrier restricts nocturnal radiative cooling and holds warm air near ground level (Lee et al., 2016).These are the physical realities that can demonstrate how urban greening is not a consistently inherent cooling practice. It is a complex thermodynamic intervention whose success relies entirely on local environmental conditions.PLACEMENT, PATCHES, AND PARK PATTERNSBecause thermodynamic processes happen across physical spaces, cooling urban green space can be uneven. The literature review reveals that a green space's cooling capacity depends on four spatial and biological variables: vegetation density, species selection, spatial configuration, and urban morphology. Let's break them down individually.Cooling Factor 1. Vegetation Density and Species TraitsNot all greenery cools equally. One way to measure this is through a Leaf Area Index (LAI). This is the total leaf area per unit of ground area and is a primary predictor of thermal performance. As you might imagine, dense foliage absorbs more from the sun and then produces higher cumulative transpiration (so long as there's adequate water).Botanical traits also play a big role. Broadleaf deciduous species (like oaks or maples) feature large surface areas that maximize summer transpiration, but that goes away when they drop their leaves in winter. Evergreen coniferous trees, however, maintain continuous canopy coverage through every season.In temperate rainforest environments like Seattle or Vancouver, researchers found that conifers cooled urban surroundings up to 1.7 degrees celsius more effectively than broadleaf trees. Dense needle canopies continuously block incoming solar radiation due to higher LAI and the fact that clusters of spiny needles better trap a stable buffer of calm air which moderates heat exchange with the surrounding urban environment. (Eyster & Beckage, 2022, 2023) Cooling factor 2. Spatial Configuration: Landscape Ecology PrinciplesEcologists and geographers commonly evaluate green spaces through two main attributes: composition (how much green space exists) and configuration (how those green patches are arranged across the landscape). Over 58% of the mechanism-focused studies in the literature review analyzed spatial pattern metrics. The consensus is the spatial layout of green space is just as important as its total area!Other empirical studies consistently demonstrate that cooling effects decay the further you get from green spaces. Research done in 2016 and 2023 shows how urban parks can produce a primary “cooling footprint” that extends typically 100 to 300 meters from the park boundaries. If you're lucky enough to live within this buffer, temperatures drop between 1.9 and 3.1 degrees celsius, but beyond 300 meters, the cooling influence quickly falls off. (Bao, et al. 2016, Shi et al. 2023)This spatial limit leads to a couple spatial layout choices. A few big parks or connected networks of smaller green spaces. Large, consolidated parks (>2 hectares) generate intense, stable “cool islands” at their core, but their benefits remain localized. For example, a study in Xalapa, Mexico, revealed that parks larger than 2.8 hectares with over 21% tree cover provided reliable local cooling of around 2 degrees celsius . (Lemoine-Rodriguez et al., 2022)Connected networks of smaller green spaces distributed across a city create a more equitable cooling effect. Three studies in 2019 and 2021 show that fragmented, isolated green patches — like big parks — perform poorly compared to continuous, linear green corridors. Linear “green belts” or street-tree networks (in the right environment) can act as ventilation channels, allowing cool air generated by vegetation to flow into adjacent built-up neighborhoods. (Masoudi et al., 2019, 2021; Pramanik, 2019)Cooling factor 3. Urban Morphology: The Built Environment MatrixObviously, green spaces don't exist in isolation. They're embedded within a three-dimensional hodge podge of buildings, streets, and other bits of infrastructure. Urban morphologists can quantify this urban morphological cacophony using building height-to-street-width ratio and sky view factor - the extent to which surrounding structures and canopies obstruct a location's view of the open sky.High-density urban cores with tall buildings create deep “urban canyons” that generate their own shade. In these settings, building shade can combine with tree shade during peak daylight hours to significantly lower temperatures.However, studies show that if tree canopies in narrow street canyons are too dense — particularly in humid environments — they can trap anthropogenic heat emitted by vehicle exhaust and air conditioning condenser units. They can also significantly reduce localized wind speeds. As a result, maximizing green space cooling efficiency requires aligning vegetation density and canopy architecture with prevailing wind corridors to preserve urban ventilation channels (Cheung & Jim, 2019; Morakinyo et al., 2019).Nothing is every as easy as it seems.BRIDGING GAPS WITH BETTER MAPSWhile academic literature can offer detailed insights into microclimatic processes, there remains a big gap between academia and urban planning and governmental policy. That gap may be self-fulfilling. The literature review of 84 papers revealed 61% of the papers simply advocate for expanding green space area, whereas only 26% focus on optimizing existing green infrastructure.Recommending that dense, historical cities “add more large parks” ignores real-world urban constraints. In modern, rapidly expanding cities, urban land is expensive, highly contested, and structurally constrained. Space dedicated to a new park often competes directly with housing, transit infrastructure, or commercial development. To move beyond idealistic slogans, urban planning will have to recon with three major implementation challenges.Challenge 1. The Water-Energy-Heat Nexus in Arid CitiesThe most significant implementation challenge facing nature-based solutions occurs precisely where urban heat stress is most severe — in hot, arid regions. Cities like Phoenix, Cairo, Tehran, or Riyadh already suffer from intense summer heatwaves. You don't have to live in or visit these places to know water there is extremely scarce.Maintaining green spaces in places like this requires pumping groundwater or desalinating seawater. Pumping and desalinating water requires massive amounts of electricity, which only increases greenhouse gas emissions if that power is coming from fossil fuels. Furthermore, if irrigation water runs out during a heatwave, non-adapted vegetation dries out, loses its cooling capacity, and can even become a wildfire risk.To solve this dilemma, physical geographers advocate evaluating urban greening through a standardized resource efficiency metric: evapotranspirative cooling per unit of water applied. In plain language, this ratio measures how many degrees of cooling you get for each liter (or gallon) of water that plants and soil release into the air through evaporation and transpiration.In dry climates — where municipal water is already tightly rationed and turfgrass is increasingly discouraged but rarely banned (I'm looking at you Arizona) — urban greening strategies are going to have pivot away from high-water lawns and non-native foliage. These cities have to move from simply prioritizing or incentivizing drought-tolerant species to requiring them. While drought-tolerant plants transpire less water than many other plants and trees — and still require water — when combined with drip-irrigation using treated municipal wastewater (greywater), they can provide a pretty reliable canopy shade and even modest evaporative cooling without draining water reserves.Challenge 2. Environmental Justice and Thermal EquityUrban heat exposure is rarely, if ever, distributed evenly across socio-economic groups. In many cities worldwide, low-income neighborhoods exhibit significantly lower tree canopy cover, higher proportions of impervious asphalt, and higher building densities than the more affluent suburbs and ex-urbs. This imbalance leaves more vulnerable populations exposed to extreme heat hazards.When municipalities undertake uncoordinated “regreening” projects, they risk triggering green gentrification. Installing fancy attractive parks can then inflate surrounding property values, displacing residents while still not reducing their heat related vulnerability.To address this, requires targeted interventions like deploying small-scale, distributed interventions (think pocket parks, vegetated bus stops, and road corridors) directly in high-vulnerability, low-canopy/vegetation neighborhoods. You could focus on functional shading over high-maintenance aesthetics. This could better ensure that at least transit stops, pedestrian walkways, and playgrounds are prioritized for canopy cover. Lastly, combining situated green infrastructure with social policy could create and/or protect more affordable housing around old and new greened public corridors and spaces.Challenge 3. A Multi-Benefit, Context-Specific Design FrameworkNo single cooling intervention works everywhere. Planners and policy makers need to adopt an integrated, multi-tiered approach that combines nature-based solutions with material interventions.In arid and semi-arid environments, planners should prioritize structural canopy shading and end an over-reliance on water-intensive lawn evapotranspiration. It's time to demand drought-tolerant trees with greywater irrigation networks, shade sails, and high-albedo “cool pavements” that can better reflect solar radiation without draining water resources.In contrast, humid and more temperate climates would likely benefit most from maximizing green spatial connectivity. By linking existing parks through linear street-tree corridors — with select broadleaf and/or coniferous species — summer evapotranspiration can be enhanced while maintaining year-round microclimate regulation.Finally, within high-density, built-up cores where ground space for new parks is limited, cities should leverage vertical green walls and green roofs paired with reflective building materials. But they best also preserve prevailing wind corridors while preventing nighttime heat traps through street canyons.“Regreening” is a compelling slogan, but as physical geography demonstrates, simplistic blanket policies can yield unpredictable thermodynamic results. Simply planting trees without considering local climate, available water, species traits, spatial configuration, and urban geometry can lead to unintended consequences.The exhaustive synthesis of decade-long research provided by Hadi Soltanifard and Majid Amani-Beni (2025) offers a clear path forward. Nature-based solutions are not off-the-shelf products that can be copy-pasted across different global cities. They are dynamic, living interventions that alter energy fluxes across urban surfaces.By moving beyond blanket acreage targets and framing urban greening as the strategic reorganization of surface-energy relations, geographers, urban planners, and policymakers will need to work together. When we design green infrastructure that respects local environmental constraints, honors spatial equity, and optimizes microclimatic processes, urban greening moves from a vague policy promise toward tools of experimentation that can pragmatically evolve our cities and megaregions into truly climate-resilient urbanscapes.ReferencesBowler, D. E., Buyung-Ali, L., Knight, T. M., & Pullin, A. S. (2010). Urban greening to cool towns and cities: A systematic review of the empirical evidence. Landscape and Urban Planning, 97(3), 147-155.Cheung, P.K., Jim, C.Y., 2019. Differential cooling effects of landscape parameters in humid-subtropical urban parks. Landscape and Urban Planning 192, 103651.Coutts, A.M., Tapper, N.J., Beringer, J., et al., 2013. Watering our cities: the capacity for Water Sensitive Urban Design to support urban cooling and improve human thermal comfort in the Australian context. Progress in Physical Geography 37(1), 2–28.Eyster, H. N., & Beckage, B. (2022). Conifers may ameliorate urban heat waves better than broadleaf trees: Evidence from Vancouver, Canada. Atmosphere, 13(5), 830.Eyster, H. N., & Beckage, B. (2023). Arboreal urban cooling is driven by leaf area index, leaf boundary layer resistance, and dry leaf mass per leaf area: Evidence from a system dynamics model. Atmosphere, 14(3), 552.Lee, H., Mayer, H., & Chen, L. (2016). Contribution of trees and grasslands to the mitigation of human heat stress in a residential district of Freiburg, Southwest Germany. Landscape and Urban Planning 148:37–50.Lemoine-Rodríguez, R., Inostroza, L., Falfán, I., & MacGregor-Fors, I. (2022). Too hot to handle? On the cooling capacity of urban green spaces in a Neotropical Mexican city. Urban Forestry & Urban Greening, 74, 127633.Masoudi, M., Tan, P.Y., 2019. Multi-year comparison of the effects of spatial pattern of urban green spaces on urban land surface temperature. Landscape and Urban Planning. 184, 44–58.Masoudi, M., Tan, P.Y., Fadaei, M., 2021. The effects of land use on spatial pattern of urban green spaces and their cooling ability. Urban. Clim 35, 100743.Masoudi, M., Tan, P.Y., Liew, S.C., 2019. Multi-city comparison of the relationships between spatial pattern and cooling effect of urban green spaces in four major Asian cities. Ecol. Indic 98, 200–213.Morakinyo, T.E., Ouyang, W., Lau, K.-K.-L., et al., 2020. Right tree, right place (urban canyon): Tree species selection approach for optimum urban heat mitigation-development and evaluation. Sci. Total. Environ 719, 137461.Pramanik, M. (2019). Impacts of urban expansion on land surface temperature and urban heat island in Kolkata Municipal Corporation, India. Environmental Monitoring and Assessment, 191(12), 738.Santamouris, M. (2014). Cooling the cities—a review of reflective and green roof mitigation technologies to fight heat island and improve comfort in urban environments. Solar Energy, 103, 682-703.Schlaerth, Hannah L., et al. "Albedo as a competing warming effect of urban greening." Journal of Geophysical Research: Atmospheres 128.24 (2023): e2023JD038764.Soltanifard, H., & Amani-Beni, M. (2025). The cooling effect of urban green spaces as nature-based solutions for mitigating urban heat: Insights from a decade-long systematic review. Climate Risk Management, 49, 100731.Ziter, C. D., Pedersen, E. J., Kucharik, C. J., & Turner, M. G. (2019). Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat. Proceedings of the National Academy of Sciences (PNAS), 116(15), 7575-7580. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit interplace.io

Join Rabbi Joey Rosenfeld as he guides us through the world and major works of Kabbalah, Hasidic masters, and Jewish philosophy, shedding light on the inner life of the soul. To learn more, visit JoeyRosenfeld.com

In this episode, Sam Ashoo, MD and Dr. T.R. Eckler, MD discuss the July 2026 Emergency Medicine Practice article, Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis: Diagnosis and Management in the Emergency Department.0:17 – Intro & sponsor promo1:09 – Episode introduction4:03 – Definitions: SJS vs. TEN vs. "overlap" by body surface area6:12 – Pathophysiology9:52 – Incidence and rarity of the disease10:53 – Differential diagnosis & clinical presentation clues12:08 – Prehospital care & fluid resuscitation considerations13:47 – ED history-taking: medications, infections, rash progression14:47 – Physical exam findings16:46 – Diagnostic studies18:12 – Scoring systems: SCORTEN and ABCD-1020:50 – Treatment goals23:57 – Wound care principles24:43 – Mechanical ventilation risk factors26:02 – Other possible therapies27:50 – Specialty consults & special populations29:06 – Ophthalmologic treatment31:17 – HIV patients and elevated risk31:55 – Emerging therapies & AI-based diagnostic tools32:57 – Five key clinical takeaways34:37 – Closing remarks & sponsor sign-offSubscribers, take the CME test here.Emergency Medicine Residents, get your free subscription by writing resident@ebmedicine.net

Living with chronic illness can feel like collecting diagnoses without getting real answers. Symptoms are treated one at a time, but the underlying causes can easily be missed. What if asking a different question is the first step toward feeling better? In this episode, I'm joined by Dr. Richard Horowitz, an internationally recognized Lyme disease expert and author of the new book Ending Chronic Illness. After caring for thousands of patients over more than four decades, he explains why many seemingly unrelated conditions share common underlying drivers—and why thinking like a medical detective may help uncover them. We discuss: Why treating the diagnosis isn't always the same as treating the problem—and how to start looking deeper How hidden infections, environmental toxins, gut health, and other overlooked factors can quietly drive chronic illness What new research may reveal about the links between infection, inflammation, and brain health How to begin uncovering what's really driving persistent symptoms Our bodies are constantly communicating with us. The challenge isn't that they're silent—it's that we haven't always known how to listen. Sometimes finding better answers begins with asking different questions. Continue Exploring If you'd like to explore Dr. Horowitz's work further, here are two great places to start: Take his Symptom Assessment Quiz to better understand what may be contributing to your symptoms. Subscribe to his Medical Detective Substack ****for ongoing insights into chronic illness, Lyme disease, and root-cause medicine. Interested in getting tested? Many of the lab tests discussed in this episode are available through Function, making it easier to get a more comprehensive picture of your health. View Show Notes From This Episode Sign up for Dr. Hyman's Brainshaping Academy to learn how to nourish the biological systems that support your mental, emotional, and cognitive health https://drhyman.com/products/brainshaping?utm_source=dr_hyman_show&utm_medium=newsletter&utm_campaign=may_27&utm_content=link Get Free Weekly Health Tips from Dr. Hymanhttps://drhyman.com/pages/picks?utm_campaign=shownotes&utm_medium=banner&utm_source=podcast Sign Up for Dr. Hyman's Weekly Longevity Journalhttps://drhyman.com/pages/longevity?utm_campaign=shownotes&utm_medium=banner&utm_source=podcast Join the 10-Day Detox to Reset Your Healthhttps://drhyman.com/pages/10-day-detox Join the Hyman Hive for Expert Support and Real Resultshttps://drhyman.com/pages/hyman-hive This episode is brought to you by Timeline, Cozy Earth, Seatopia, Perfect Amino, BON CHARGE, and Made In. Support healthy aging and get up to 20% off when you subscribe on top of the new starting price of $79 at timeline.com/drhyman. Upgrade your sleep setup with cozyearth.com and enjoy 20% off  with code HYMAN. Find a cleaner source of seafood. Check out seatopia.fish and use code HYMAN for free shipping on your first order. Help fill protein gaps at bodyhealth.com and use code HYMAN20 for 20% off. Explore red light products at boncharge.com/hyman  and enjoy 15% off with code HYMAN. Explore kitchen essentials at madeincookware.com and save 10% off your first order with code HYMAN-HIVE. (0:00) Infections and toxins as root causes of chronic illness; Dr. Horowitz's background and journey to functional medicine (5:37) Discovering and treating various infections, toxins, and Dr. Hyman's personal health story (9:16) Root causes of chronic disease, inflammation, and testing for infections and toxins (15:11) Sponsor: Seatopia and PerfectAmino (16:42) Importance of diet, addressing multiple health factors, and autoimmune misdiagnosis (19:26) Role of infections, toxins, microbiome, and leaky gut in autoimmune diseases and inflammation (21:06) Treating underlying causes and neuroinflammation in chronic diseases (24:37) Nutritional deficiencies, COVID-19, and the importance of sleep in chronic illness management (30:19) Plant medicines, supplements, and dapsone in treating neuroinflammation, Lyme, and Alzheimer's (41:30) Use of methylene blue in treatment protocols (44:56) Sponsor: Bon Charge (45:58) Sponsor: Made In (46:43) Evolution of treatment protocols and six principal causes of inflammation (50:18) Importance of adrenal function and hormone treatment in chronic illness (52:17) Differential diagnosis vs. root cause analysis and root cause testing (56:38) Horowitz's questionnaire and protocols for treating chronic infections (58:39) Addressing mold-related health issues and Dr. Hyman's personal experience (1:01:01) Mold testing, detoxification, and controversies in treatment (1:07:47) Insurance challenges and HHS innovation grant for chronic illness research (1:09:04) Importance of root cause medicine and Mark Hyman's upcoming book on longevity (1:11:56) Personalized medicine, reversing Alzheimer's biomarkers, and research funding priorities (1:15:02) Influence of Buddhist principles and personal experiences with chronic illness (1:16:01) Hope for patients, resources for learning, and closing remarks

Small businesses represent nearly half of all American jobs and 45% of all technology spend, yet less than 5% of venture capital goes to building technology for them.In this episode, Tim Metzner joins us to share how Fireroad, his Cincinnati-based early-stage venture firm, is betting that AI is changing that math. A serial entrepreneur who co-founded Coterie Insurance ($70M+ raised) and Differential (the studio behind Cincinnati's first unicorn, Astronomer), Tim returns to the show four years after his Episode 190 appearance with an entirely new chapter.We dig into the "silver tsunami" of retiring business owners with no succession plan, why Fireroad targets AI-resistant categories where technology supercharges rather than replaces, the flywheel of having business owners as LPs who become his founders' first customers, and why Tim believes staying small as a fund is the alpha most VCs are missing. Hosted by Logan JonesMiddle Tech is proudly supported by:KY Innovation → kyinnovation.comAwesome Inc → awesomeinc.org

In this solo episode, Coleman Ayers shares a "coaches' meeting" style breakdown on trainability in younger athletes, roughly beginners through 15-year-olds. He reframes the goal of youth development away from "what can we add to their game now" and toward priming athletes to learn, grow, and adapt for years down the line. The episode moves through the qualities and coaching practices Coleman's staff has been prioritizing this summer in their gym.Coleman opens with problem-solving as the foundation, breaking it into tactical, perceptual-motor, and social/emotional problems, and explains how a constraints-led approach naturally builds better problem solvers. From there he covers five more pillars of trainability: a healthy relationship with failure, curiosity through divergent training, adaptability to different environments, exposure to differential coaching styles, and teaching players to own their own development. He closes by tying it all back to general coordination, arguing that athletes who sample widely and learn to solve problems early become the ones who keep improving long after they leave the gym.00:00 – Welcome back and update on Coleman's summer training grind 01:22 – Topic intro: trainability in younger athletes 02:17 – Shifting focus from "what can we add now" to "how do we prime them for later" 02:45 – Area 1: Problem solving 04:20 – Three buckets of problems: tactical, perceptual-motor, social/emotional 07:41 – Using the constraints-led approach to build problem solvers 10:15 – Balancing prescription with problem-solving in motor development 11:02 – Building social and emotional problem-solving through daily challenges 13:41 – Area 2: Relationship with failure 15:40 – How coaches' reactions condition athletes' relationship with failure 16:25 – Area 3: Curiosity and divergent training 19:23 – Area 4: Adaptability to different environments 20:40 – Area 5: Differential coaching — exposing athletes to varied coaching styles 22:08 – Area 6: Teaching players to own their own development 23:20 – Using the optimal zone of improvement with players 26:19 – Closing thoughts: general coordination and building the late bloomerResources & LinksFree Resources: https://byanymeanscoaches.com/resources BAM Coaches Platform: https://platform.byanymeanscoaches.com/#/platform Books: https://byanymeanscoaches.com/blueprint-bookKeep ListeningWhat Exactly IS The Constraints-Led Approach (CLA)? Coleman leans heavily on CLA throughout this episode as the engine for problem-solving — this is the primer episode on what CLA actually is and isn't. https://www.buzzsprout.com/1911095/episodes/19251025Individualizing Group Workouts Another Coleman solo episode digging into player "North Stars" and individual constraints — a natural extension of the trainability framework covered here. https://www.buzzsprout.com/1911095/episodes/191112704 Player Development Concepts I've Been Using This Summer Coleman's real-time look at this same summer training block, covering fatigue shooting, hybrid games, and individual constraints in practice. https://www.buzzsprout.com/1911095/episodes/19331801

**Jeep Talk Show: Tire Tread Depth for Lockers, GoPro Struggles, Jeep Sales, Maserati Drama & Josh's Parking Rant!** Join Tony and Josh on the Jeep Talk Show for a fun, no-holds-barred conversation covering essential Jeep maintenance, industry news, and plenty of real-talk banter. From measuring tire tread depth with lockers in mind to GoPro's challenges, Stellantis/Jeep sales updates, Maserati's future, and Josh's epic rant on terrible parkers — this episode has it all. **Key Highlights:** - Tire tread depth guidelines for 4WD vehicles and lockers (quarter to half inch circumference difference tolerance) - Why deeper tread matters for off-road traction in mud, rocks, and snow - Practical replacement recommendations and the penny test - GoPro's financial troubles, competition from DJI/Insta360, and the rise of affordable alternatives + drones - Jeep brand sales under Stellantis (Q2 updates and broader industry trends) - Maserati partnership talks instead of sale — luxury EV strategy with Chinese firms - Show updates: Remastered classic XJ Talk Show episodes now on YouTube (audio-only with Grok reviews!) - Josh's "Get the [redacted] out the way" parking and driving frustration segment **Timestamps:** 00:00 Intro banter and rest‑stop musings 01:02 Tire tread depth and drivetrain lock 04:23 Differential heating, fluid limits, limited‑slip diffs 06:39 Tire wear limits and replacement advice 07:34 Dangers of shallow tread and traction loss 08:59 Common‑sense tire maintenance reminders 11:10 GoPro struggles amid competition and AI costs 12:36 Drone rise and phone camera alternatives 14:28 Multi‑angle off‑road video strategies 15:36 Show distribution, links, and new features 17:28 Video editing and GoPro sync challenges 18:36 DIY overhead camera mount with Hero 3 19:30 Early rigs before drones and concluding note 21:48 Jeep sales trends and Stellantis overview 25:12 EPA standards, diesel fluid, and regulations 28:49 Maserati's future and Stellantis partnerships 35:34 Wrap‑up and audience engagement 37:27 Show upgrades: Grok reviews and tech 40:44 Parking etiquette and driver responsibility 44:03 Call‑out for better parking behavior 46:01 Proposed windshield stickers for safe parking 46:25 Voicemail submissions and community interaction 48:55 Voicemail promotion, milestones, and thanks 50:23 Final farewell and friendship If you enjoy the Jeep Talk Show, please **like, subscribe, and hit the notification bell** so you never miss an episode! Drop a comment below with your thoughts on tire maintenance, GoPro alternatives, or your own parking horror stories.

Is There a Nexus Existing Between The Orchid Flower and The Elderly People? An Interdisciplinary, Theological, and Metaphorical Reflection. © 2026. ISBN 978-976-97997-3-8.mp3AbstractThis scholarly conversation explores whether a meaningful nexus exists between the orchid flower and elderly people. Rather than presenting a biological comparison, the discussion develops a metaphorical, theological, philosophical, and interdisciplinary framework informed by media arts, cultural theory, environmental appreciation, and Christian devotion. Orchids and elderly people occupy distinct kingdoms of life; however, both exemplify beauty, resilience, diversity, adaptation, and intrinsic worth. Through the integration of botanical history, gerontology, biblical theology, and cultural symbolism, this essay argues that orchids provide an illuminating metaphor for understanding aging with dignity. Ultimately, the comparison invites readers to reconsider cultural assumptions about age while affirming that every stage of life reflects God's creative wisdom. All things being considered it should be noted that the social constructs  orchids, elderly, aging, metaphor, theology, ecology, cultural theory, are media arts are critical to this discourse.Dr. William Anderson Gittens, Doctor of DivinityReferencesBoyce, W. T., & Ellis, B. J. (2005). Biological sensitivity to context: I. An evolutionary–developmental theory of the origins and functions of stress reactivity. Development and Psychopathology, 17(2), 271-301. https://doi.org/10.1017/S0954579405050145Duke Science & Society. (n.d.). Does race exist? https://scienceandsociety.duke.edu/does-race-exist/Ellis, B. J., Boyce, W. T., Belsky, J., Bakermans-Kranenburg, M. J., & van IJzendoorn, M. H. (2011). Differential susceptibility to the environment: An evolutionary–neurodevelopmental theory. Development and Psychopathology, 23(1), 7-28. https://doi.org/10.1017/S0954579410000611Gittens, W. A. (2026). The orchid flower and the elderly people. ISBN 978-976-97942-2-1.Harper, D. (2020).Online Etymology Dictionary. Retrieved from https://www.etymonline.comHistorical race concepts. (n.d.). Simple English Wikipedia. https://simple.wikipedia.org/wiki/Historical_race_conceptsHoly Bible, New International Version. (2011). Zondervan. (Original work published 1973)How many major races are there in the world? (2020). Academia.edu. https://www.academia.edu/40682024/How_many_major_races_are_there_in_the_worldKremen, W. S., Lachman, M. E., Pruessner, J. C., Sliwinski, M., Wilson, R., & Seeman, T. (2012). Mechanisms of age-related cognitive change and targets for intervention: Social interactions and cognitive reserve. Neuropsychology Review, 22(4), 466-475. https://doi.org/10.1007/s11065-012-9201-9Kremp Florist. (n.d.). Guide to the orchid flower family. https://www.kremp.com/pages/guide-orchid-flower-family-articlesOrchid. (n.d.). Wikipedia. https://en.wikipedia.org/wiki/OrchidOxford English Dictionary. (2023).Nexus. Oxford University Press.Pluess, M., & Belsky, J. (2013). Vantage sensitivity: Individual differences in response to positive experiences. Psychological Bulletin, 139(4), 901-916. https://doi.org/10.1037/a0030196Pridgeon, A. M. (2001).The Illustrated Encyclopedia of Orchids. Timber Press.Race and genetics. (n.d.). Wikipedia. https://en.wikipedia.org/wiki/Race_and_geneticsRoyal Botanic Gardens, Kew. (n.d.). Orchidaceae. https://powo.science.kew.org/Royal Horticultural Society. (n.d.). Orchid facts. https://www.rhs.org.uk/plants/types/houseplants/orchid-factsStearn, W. T. (1992).Botanical Latin. Timber Press.Stern, Y. (2012). Cognitive reserve in ageing and Alzheimer's disease. The Lancet Neurology, 11(11), 1006-1012. https://doi.org/10.1016/S1474-4422(12)70191-6Theophrastus. (1916). Enquiry into plants (A. Hort, Trans.). Harvard University Press. (Original work written ca. 300 BCE)World Health Organization. (2024). Ageing and health. https://www.who.int/news-room/fact-sheets/detail/ageing-and-healthSupport the showCultural Factors Influence Academic Achievements© 2024 ISBN978-976-97385-7-7 A_MEMOIR_OF_Dr_William_Anderson_Gittens_D_D_2024_ISBNISBN978_976_97385_0_8Academic.edu. Chief of Audio Visual Aids Officer Mr. Michael Owen Chief of Audio Visual Aids Officer Mr. Selwyn Belle Commissioner of Police Mr. Orville Durant Dr. William Anderson Gittens, D.D En.wikipedia.org/wiki/Lifelong_learning Hackett Philip Media Resource Development Officer Holder, B,Anthony Episcopal Priest,https://brainly.com/question/36353773https://en.wikipedia.org/wiki/Lifelong_learning#cite_note-19https://en.wikipedia.org/wiki/Lifelong_learning#cite_note-:2-18https://independent.academia.edu/WilliamGittens/Bookshttps://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=william+anderson+gittens+barbados&oq=william+anderson+gittenshttps://www.academia.edu/123754463/https://www.buzzsprout.com/429292/episodes. https://www.youtube.com/@williamandersongittens1714. Mr.Greene, Rupert

Michael Every, Global Strategist for Economics and Markets at Rabobank, presents a radical framework: everything is now about economic statecraft and geopolitics, not traditional monetary or fiscal policy, meaning central banks, interest rates, and economic structures are all subsets of national security objectives. Central bank models are broken because exogenous geopolitical supply shocks (Iran war, Ukraine, COVID) constantly disrupt equilibrium assumptions, and the old playbook of managing demand through one global interest rate no longer works in a fragmenting world with different sectors having different national security priorities. He warns the biggest risk is far more war ahead, specifically predicting Iran war will resume after the midterms because tolls, sanctions, uranium, and Lebanon remain unresolved—Iran is losing leverage as oil flows increase and the world moves on, so it will need to "rock the boat" to regain attention. Interest rates will trend higher due to massive fiscal pressures on defense spending, reshoring, supply chain security, and infrastructure investment, and differential interest rates will emerge where sectors critical to national security borrow cheaper than speculative sectors. He argues the private sector will be tasked with moonshot innovations (like AI and Manhattan Project-style programs) that governments can't afford alone, with government potentially taking stakes in critical companies like OpenAI and Intel. On the Strait of Hormuz, he dismisses markets pricing 45% chance of normalization before October 1 as too optimistic, noting ships run dark, ship-to-ship transfers hide traffic, and geopolitics will escalate after midterms—Hormuz will never fully normalize as countries build alternatives. America will retain primacy going forward but must completely reinvent itself economically and politically, with broader appeal to allies while accepting a world where other powers have their own sphere of influence, and whoever holds office will face the same underlying reality that American power projection equals American living standards.Thank you to our sponsors: Kalshi - download the Kalshi app and use code JULIA to get $10 when you trade $10. http://kalshi.com/r/JULIA Monetary Metals - learn more at https://www.monetary-metals.com/julia/Links: https://www.rabobank.com/knowledge/our-experts/011085368/michael-everyhttps://x.com/themichaeleveryTimestamps: 0:00 Everything now about geopolitics, not traditional economics2:00 Michael's background - 30 years, 9 countries, cross-border analyst5:20 Economic statecraft framework - national power is driving force6:06 Policymakers getting it, but many still don't understand8:16 Central bank models don't work, they never did8:40 Exogenous supply shocks (Iran, Ukraine, COVID) keep breaking models11:36 One interest rate doesn't work in fragmenting world15:33 Central banks being cagey about structural changes19:21 Geography matters - some countries will thrive, others fail23:20 Rates going higher, not lower for longer23:29 Massive fiscal pressures on defense, supply chains, infrastructure26:25 Differential interest rates by sector based on national security priority27:06 Biggest risk - far more war coming28:19 Iran war after midterms, not resolved yet31:59 Defense contractors won't make huge profits - government controls pricing34:40 AI is about national security, not making money35:31 Government may need private sector to fund moonshots they can't afford36:19 Government taking stakes in strategic companies (OpenAI, Intel, Trump)39:04 Strait of Hormuz assessment42:59 Iran needs to rock the boat, leverage slipping away44:19 Kalshi market too optimistic on Hormuz normalization45:08 Hormuz won't ever fully normalize again46:04 US still primary power but must reinvent itself49:15 America can retain primacy but it will look different50:09 Whoever's in office has to return to same arguments on American power

The only people that can truly advocate for the people ARE the people. The President's words are being changed by the media to fit their narrative. We seem to need martyrs, so thank you Tina Peters. Never relinquish your own thought process. Every day we should ask ourselves what we can do to help our country. A J6 miniseries is under consideration. Collective unity under one symbol is very powerful. The Goddess Columbia was worshiped. If Chy-nah messes with out elections, we may make other plans. The CCP is funding most of the anti-Trump opposition. Our President is surrounded by losers and grifters. Why do we still listen to influencers? Our trip to Guatemala was to showcase child trafficking. MSM thinks it's narratives that matter, not facts. How does your tracking data help them? Buying brand names could tag you. Evil will come wearing our own smiling face. Packaging is the entire game. Selling kindness for spying. SCOTUS has power because the people believe in it. That could change because ignorance is a choice. The next 250 years will be at least as hard as the last. It's still a young and incredible Republic. Let us all work hard to keep it.

In episode 379 of The Physical Performance Show, host Brad Beer and Pogo Physio physiotherapist Tim Studley continue their three-part expert series on bone stress injuries by shifting the focus from training errors to the biological factors that determine whether an athlete's skeleton can tolerate training demands. This episode explores the critical role of energy availability, bone health, recovery, and risk stratification in both preventing and diagnosing bone stress injuries. Brad shares practical screening strategies developed over two decades of clinical practice, explaining why identifying the underlying cause—not just treating the injury—is essential for long-term athletic performance and injury prevention. Show Sponsor: PILLAR Performance is leading a new frontier in sports nutrition through micronutrition innovation. Their new Collagen Repair formula, developed in collaboration with Gelita Laboratories, is designed to support tendon and ligament health while promoting collagen synthesis to help athletes stay healthy and perform at their best. For 15% off your first order, visit pillarperformance.shop (or thefeed.com for North American listeners) and use the discount code PHYSICALPERFORMANCE (one word, capitals) at checkout. In this episode, you'll hear Why biological factors may contribute more to bone stress injuries than training load alone The long-term consequences of early sport specialisation and why multi-sport participation builds a stronger skeleton Medical conditions and medications that can compromise bone health and increase injury risk Why low energy availability (RED-S) is one of the biggest contributors to bone stress injuries Practical screening questions clinicians should ask to identify athletes who may be under-fuelling The relationship between training volume, mood, gastrointestinal symptoms, sleep, life stress and bone health Female-specific indicators including menstrual irregularities and amenorrhoea Male-specific indicators including reduced libido and changes in morning erectile function Why blood tests, iron status, vitamin D and hormone profiles provide valuable clues to athlete health The importance of working with sports dietitians to optimise energy availability and performance Common locations of bone stress injuries throughout the lower limb and pelvis How injury location can provide clues to whether biomechanics or under-fuelling is the primary driver Understanding high-risk versus low-risk bone stress injury sites and why blood supply matters Practical assessment strategies including observation, palpation and functional loading tests When hop testing is appropriate—and when it should be avoided Clinical pearls for distinguishing bone stress injuries from other common running injuries Quotes / takeaways "Training loads the skeleton—but biology determines whether the skeleton can cope." "Eat more than you think you need." "The biggest driver of bone stress injuries is often invisible." "Above the knee, think fuelling. Below the knee, think biomechanics." "Treat the cause—not just the bone." Partners / links mentioned Show Sponsor: PILLAR Performance — 15% off your first order using code PHYSICALPERFORMANCE at checkout. North America: thefeed.com Timeline 00:00 – Introduction, sponsor and recap of the Bone Stress Injury mini-series 02:27 – Why biological risk factors deserve greater attention than ever before 03:51 – Early sport specialisation and athletic history as risk factors 04:50 – Medical conditions, medications and bone health 05:45 – Sleep, stress and the biological contributors to bone stress injuries 07:08 – Understanding low energy availability (RED-S) 08:32 – Practical screening questions for under-fuelling 09:02 – Female athlete indicators: menstrual health and amenorrhoea 10:29 – Male athlete indicators: libido and morning erectile function 11:51 – Blood tests, iron studies, vitamin D and hormone profiles 13:18 – Why even small daily energy deficits accumulate over time 14:16 – Working with sports dietitians and estimating energy requirements 15:40 – Brad's number one recommendation: "Eat more than you think you need" 16:08 – Common sites of bone stress injuries throughout the body 18:00 – Using injury location to distinguish biomechanics from under-fuelling 18:48 – High-risk versus low-risk bone stress injury locations 21:12 – The most commonly missed bone stress injuries in runners 22:37 – Clinical examination: observation, palpation and localisation of symptoms 24:19 – Differentiating shin splints from bone stress injuries through palpation 25:14 – Functional loading tests and when hop testing is appropriate 26:31 – Hop testing, femoral neck injuries and clinical safety considerations 27:24 – Differential diagnosis and key assessment principles 28:25 – Episode wrap-up and preview of Part 3 THE TEAM: Join The Physical Performance Show LEARNINGS membership through weekly podcasts here: https://www.patreon.com/TPPShow Our goal is to get you back to your Physical Best. Find out more about Telehealth Consultations and book online. Your Host:

This evening, we wrap up the day's market movements with Sanlam Investment Multi-Manager, unpack how Africa can take ownership of the critical minerals rush with Trade Collective, examine the latest municipal electricity tariff increases with Looksee, explore the acquisition of mining entities with Differential Capital, discuss how to identify value traps with Allan Gray, and, in this week's Executive Lounge, we follow the journey of Peet Cilliers from Legend Golf & Safari Resort. SAfm Market Update - Podcasts and live stream

Mark Salmon – Head of Special Situations Fund, Differential Capital SAfm Market Update - Podcasts and live stream

This week on Trending in Ed, host Mike Palmer is joined by Trending in Ed all-star Beth Rudden, CEO of Bast AI. From her roots digging in the dirt as an archaeologist to managing a $34 billion division as the Chief Data Officer of IBM Managed Services, Beth brings a deeply grounded, technical perspective to the artificial intelligence conversation. In this wide-ranging and insightful conversation, Mike and Beth skip the typical AI hype to explore what it actually takes to build explainable, trustworthy technology. Beth shares how Bast AI acts as an LLM-agnostic explainability layer—using a unique drinking chocolate analogy to demonstrate how they verify AI data rather than letting models hallucinate plausible narratives. They explore the practical application of using small language models (SLMs) for data enrichment, highlighted by Bast AI's meaningful work with Craig Hospital to translate complex neuro-spine outpatient procedures into accessible languages and analogies. KEY INSIGHTS: • Inverting the Chatbot Approach: Why defining what an AI can talk about is far more effective than building restrictive guardrails. • The Myth of "Human in the Loop": How shifting accountability to overworked humans can become a form of liability laundering. • Microservices vs. Agentic Harnesses: Looking at the risks of natural language agentic systems like Claude Code versus discrete, self-healing tasks. • Cognitive Offloading & Math Education: Why future technical skills should prioritize differential equations and the diversity prediction theorem over simple calculation. • Pattern Recognition vs. Choice: Defining true intelligence through the ability to choose wisely, rather than just matching mathematical patterns. They also cross paths with the Cynefin framework, explain how the human brain conserves energy by only holding two paradoxes at once, and unpack the cultural shifts reshaping modern engineering ethics. Stay ahead of the curve in education and technology! Please like and share this episode with your network, and follow the podcast on Apple Podcasts, Spotify, or your favorite player so you never miss an episode like this one. LINKS: Learn more about Bast AI: https://www.bast.ai Subscribe to Beth's Substack: https://bethrudden.substack.com TIMESTAMPS: 00:00 - Introduction and welcoming Beth Rudden back to the show 01:00 - The drinking chocolate analogy for Explainable AI 03:00 - Beth's lightning-round background: Archaeology to Chief Data Officer at IBM 05:00 - Getting "catfished by AI" and verifying facts with databases 07:00 - Mike on Gemini, RAG applications, and checking AI confabulation 09:00 - Enriched data and Small Language Models (SLMs) at Craig Hospital 12:00 - Epistemic security and inverting conversational technology 14:30 - Liability laundering and the illusion of "human in the loop" 15:30 - Agentic harnesses vs. self-healing microservices 20:00 - Understanding as labor and Conrad Wolfram's three-step math process 22:30 - Future human skills: Differential equations and jelly bean statistics 26:30 - Pattern recognition vs. true intelligence as the ability to choose 29:30 - Neurosymbolic systems and subjectivity in data science 34:30 - Shunting energy: The Cynefin framework and holding paradoxes 38:30 - Healthcare AI scribes and doctor burnout 44:30 - Trust architectures and building tech for the Maintenance Era 47:30 - Cultural devastation and the teleological suspension of ethics 49:00 - Final thoughts and wrapping up with Beth Rudden

Irritable bowel syndrome (IBS) is one of the most common—and often frustrating—conditions seen in primary care. Patients may come in with chronic abdominal pain, diarrhea, constipation, bloating, or a mix of symptoms, and many have already been told that their testing is “normal.” So how do you confidently evaluate IBS, rule out more serious conditions, and help patients move forward?In this episode, Liz talks with neurogastroenterologist Dr. Zach Spiritos about the real-world approach to IBS, including the underlying pathophysiology, common diagnostic pitfalls, practical workup strategies, and treatment options that go far beyond medication alone.Whether you're a new NP or an experienced clinician, this episode offers practical strategies to help you approach IBS with greater confidence and compassion.Timestamps:00:00 - Introduction to Dr. Zach Spiritos and episode overview02:16 - Pathway into neuro gastroenterology and training background05:06 - What is IBS? Symptoms, diagnosis, and underlying mechanisms07:36 - Factors contributing to IBS: trauma, antibiotics, triggers09:21 - Differential diagnosis: celiac, SIBO, bile acid malabsorption11:54 - When to consider endoscopy and testing strategies14:35 - History-taking tips for primary care clinicians17:54 - Managing expectations in chronic GI conditions20:12 - Overuse of endoscopy and its limited findings22:08 - Understanding what endoscopy can actually reveal24:23 - Communicating with patients who have “all tests normal”27:40 - The multitude of IBS treatment approaches: diet, psychological, medication32:12 - Role of diet and FODMAP in symptom management35:10 - Pharmacological options for IBSC and IBSD39:06 - Medications overview: Linzess, Amitiza, Viberzi, and others44:04 - The role and limitations of probiotics and fiber45:08 - Tips on managing patient expectations and chronicity46:54 - Setting goals and tracking progress with patients48:16 - Final advice: empower patients with knowledge, manage expectations, and tailor treatmentsFor a full transcript and conversation chapters, visit the blog www.realworldnp.com/blog/ibs______________________________© 2026 Real World NP. For educational and informational purposes only, see https://www.realworldnp.com/disclaimer for full details. Hosted on Acast. See acast.com/privacy for more information.

This episode with hip physiotherapist Mehmet Gem is a snippet taken from his Assessment of Lateral Hip Pain Practical live Q&A session. Held monthly, these sessions give Practicals members the chance to ask their clinical questions and get direct answers from expert presenters.In this episode, Mehmet discusses:Differential diagnosis in lateral hip painInterpretation of symptoms and clinical testsThe role of imaging in assessmentWhen to reassess and adapt your approach

This podcast by Dr. Ron Witteles reviews new data on transthyretin amyloid cardiomyopathy (ATTR‑CM), highlighting differences in transthyretin stabilization between acoramidis and tafamidis across wild-type and variant disease. The discussion underscores the clinical importance of TTR stabilization, emerging biomarkers such as serum transthyretin, and the expanding therapeutic landscape in cardiac amyloidosis.

In this episode of *PICU Doc on Call*, Dr. Monica Gray and Dr. Pradip Kamat are joined by fellow Dr. Hope Vancleve to discuss a complex case of a 12-year-old with MRSA septic shock requiring VA ECMO. The conversation covers sepsis-induced myocardial dysfunction, including its pathophysiology, diagnosis, and management. The hosts also explore differential hypoxia, or Harlequin syndrome, a serious VA ECMO complication causing upper body deoxygenation, and discuss monitoring strategies and circuit reconfiguration to prevent cerebral and myocardial ischemia.Show Highlights:Clinical case discussion of a 12-year-old male patient with MRSA septic shock.Complications of sepsis, including sepsis-induced myocardial dysfunction and refractory shock.Management strategies for septic shock, including antibiotic therapy and fluid resuscitation.Use of venoarterial ECMO support in pediatric patients with severe cardiac dysfunction.Pathophysiology of sepsis-induced myocardial dysfunction and its impact on cardiac function.Differential hypoxia (North-South syndrome) in patients on femoral VA ECMO.Diagnostic approaches for sepsis-induced myocardial dysfunction, including echocardiography and biomarkers.Importance of monitoring and managing end-organ function in septic patients.Strategies for addressing differential hypoxia in ECMO patients, including circuit reconfiguration.Discussion of the risks and benefits of various ECMO configurations and management techniques.References:Fuhrman & Zimmerman - Textbook of Pediatric Critical Care ChapterReference 1: Torre DE, Pirri C. Harlequin Syndrome in Venoarterial ECMO and ECPELLA: When ECMO and Native or Impella Circulations Collide - A Comprehensive Review. Rev Cardiovasc Med. 2025 Aug 26;26(8):39992. doi: 10.31083/RCM39992. PMID: 40927093; PMCID: PMC12415751.Reference 2 : Cove ME. Disrupting differential hypoxia in peripheral veno-arterial extracorporeal membrane oxygenation. Crit Care. 2015 Jul 22;19(1):280. doi: 10.1186/s13054-015-0997-3. PMID: 27391473; PMCID: PMC4511033.

This week we break down the upcoming games of the 2026 season! **Audio only episode**   Bears in the Neighborhood is your weekly insight into the Chicago Bears in under 30 minutes, a podcast hosted by Samir Patel and Chirag Rathod! Bears in the Neighborhood is part of the “Mr. Rathod's Neighborhood” network of podcasts! --- Music: “Juicy Booty” by Subpar Snatch – used with permission from the band Video: Created using Luma Dream Machine and Clipchamp Podcast Art: Created using Canva Opening Clip: https://x.com/mlfootball/status/2062236707617903002?s=46 --- Do you want to have your Bears thoughts heard on this podcast? Email us a 30 second clip of all your Bears feelings to bearsintheneighborhood@gmail.com and we may feature that clip in a future episode! --- *Samir Patel* Samir is a contributing author for On Tap Sports Net, co-host of the “Bears on Tap” podcast and a lifelong Chicago Bears fan. Website: https://www.mylifewithme.com/ Article/Podcast: https://www.youtube.com/live/5guudLbEk30?si=e74zF8Wj_e7Nu94m IG and X @smpatel06; @bearsontap   *Chirag Rathod* Chirag is the host of the podcast Mr Rathod's Neighborhood, an improvisor and a lifelong Chicago Bears fan. Website: https://www.chiragrathod.com/ IG @mrrathodsneighborhood In the Lab Cookin' with Me & Zazzle: https://youtu.be/MWPkDf2P0Co?si=MZbZhGDY0GJRLpn1 --- Check out the podcast Mr Rathod's Neighborhood on all streaming platforms to listen to episodes of “Bears in the Neighborhood”.   Spotify: https://open.spotify.com/show/6CWTRtL5dizA1iyS2O7qeN?si=573ff211b80c4d8a Apple Podcasts: https://podcasts.apple.com/us/podcast/mr-rathods-neighborhood/id1445766040 Video: Episodes available on YouTube! https://youtu.be/eUNjEiIwCF0 ------ChiragRathod.comIG: @mrrathodsneighborhoodYouTube: https://www.youtube.com/@chiragrathod9882

Send us Fan MailWe look at run differentials that reveal the good teams (Braves, Dodgers, Yankees, Brewers), and all the other teams.  The AL is weaker this season and that's no surprise. Should #brewers trade for Tarik Skubal? Mark and Gordon don't see eye-to-eye on this one.  Will #dodgers Shohei win the NL Cy Young award?  Cristopher Sanchez of the #phillies as a 44.2 inning scoreless streak.  And then there's #padres Mason Miller. Catch up on all the hot news from last week in under 30 minutes! Thanks again to Mercury Maid for the Intro & Outro music. Check them out on Spotify or Apple Music!  Please subscribe to our podcast and thanks for listening! If you can give us 4 or 5 star rating that means a lot. And if you have a suggestion for an episode please drop us a line via email at Almostcooperstown@gmail.com.  You can also follow us on X @almostcoop or visit the Almost Cooperstown Facebook page or YouTube channel.  And please tell your friends to check us out!www.almostcooperstown.com

In this episode, Tracey Davidoff, MD, Joe Toscano, MD, and Evan Nelson, MD, discuss the May 2026 Evidence-Based Urgent Care article, Urgent Care Evaluation and Management of Acute Bronchitis.0:08 Introduction1:08 Topic & guest introduction2:14 Differential diagnosis4:03 Respiratory virus testing6:42 Positive viral diagnosis & antibiotic prescribing8:00 Duration of cough & post-infectious cough9:41 Antibiotic stewardship12:07 Cough & cold medicines14:44 Narcotics & corticosteroids16:05 Steroid stewardship17:24 Radiologic stewardship & chest X-rays20:46 Sputum color21:53 Albuterol24:21 Special populations25:37 Take-home points & patient education27:08 Wrap-up and outroSubscribes, take the CME test here!Not a subscriber? Join here!

Darshan H. Brahmbhatt, Podcast Editor of JACC: Advances, discusses a recently published original research paper on Differential Association Between Surrounding Greenness and Mortality in Individuals With Coronary Heart Disease.

After 380 drama-filled matches, shocking upsets and FPL heartbreak, the 2025/26 Premier League season comes to an end, so Kelly Somers and the FPL Pod team are here to break down every major moment

This week on Herbal Radio, host Jiling Lin is joined by Richard Mandelbaum. Richard has been an avid student of our native flora for close to forty years. He has been practicing as an herbalist since 1999, blending Chinese and Western herbal traditions, with a private practice offering both online and in-person consultations in Forestburgh NY. He is also a founder of the ArborVitae School of Traditional Herbalism and is on the faculty at David Winston's Center for Herbal Studies and the Won Institute of Graduate Studies, and is co-coordinator of his local chapter of Herbalists Without Borders. Listen in as Richard and Jiling chat about: Integrating scientific and traditional herbalism Herbal energetics Sweet and bitter flavors Differential assessment method Importance of botany for herbalists Creative energy of anger in seasonal transitions Symbiosis between plants and people

Kevin and Alec analyze the New York Knicks' record-breaking point differential and Jalen Brunson's leadership during their playoff run against the Cavaliers. They also discuss the NHL conference finals, focusing on the Montreal Canadiens' dominant victory and the Carolina Hurricanes' struggles. 01:00 - NBA Playoff Super Segment 04:46 - Potential NBA Finals Matchups 12:02 - NHL Conference Finals Update

It all comes down to this, the last Gameweek of the season. And FPL managers will be keen for one final big points haul as they look to top their mini-leagues

In this episode with Beau Walker Tyrrell, we explore an interesting case study on a real patient of his – a Hyrox athlete, preparing for a marathon with bilateral shin pain. We cover:Differential diagnosis within the shin regionObjective testing related to bilateral shin painRole of acute:chronic work load ratiosRole of imaging with this patientInterdisciplinary management plan of this patientBeau's reflective reasoningThis episode is closely tied to Beau's case study he did with us. With case studies, you can see how top clinicians manage real-world cases and apply their strategies to get better results with your patients. 

Hugh Douglas and Joe Giglio analyze the Philadelphia Eagles' 2026 schedule, noting the significant rest disadvantage and the absence of a bye week following their game in London. They also examine the impact of coach Don Mattingly on the Phillies' recent performance and debate the competitive ceiling of rookie Quinyon Mitchell. Callers join the conversation to share elaborate personal lies as part of a ticket giveaway contest. 01:00 - Willie Nelson Debate 03:26 - Eagles Schedule Differential 07:24 - Quinyon Mitchell Comparison 10:21 - Don Mattingly Analysis 16:51 - Best Lie Contest

Subscribe to Boy Green's daily New York Jets-centric YouTube channel here! Follow Boy Green for daily New York Jets updates on X/Twitter! Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Arsenal have the title in sight but Man City are close behind, so will the Gunners be going all out attack or holding strong at the back?

In this podcast I react to all of the action from Gameweek 36, and look ahead to Gameweek 37. ━━━━━━━━━━━━━ Check out Fantasy Football Hub with a 7 day free trial

It's the final Double Gameweek of the season and FPL managers are looking to maximise their points for one last time

If you're enjoying the content, please like, subscribe, and comment! Philipp's Links:Soccer Instinct: https://www.amazon.ca/Soccer-Instinct-Differential-Learning-Neuro-Athletic/dp/B0GX2765H4Instagram: https://www.instagram.com/coach_philipp.wank/ Philipp Wank is a professional soccer coach, coach educator, and developer of the Soccer Instinct training methodology. Born and raised in Dortmund, Germany, he combines European coaching principles with modern neuro-athletic training and applied differential learning to develop adaptable, creative, and resilient players.In addition to his coaching career, Philipp holds two law degrees and brings a structured, analytical approach to performance development. His work focuses on reprogramming instinctive movement and decision-making through cognitively demanding, variable training environments that reflect the unpredictable nature of the game.Through Soccer Instinct, Philipp shares a practical framework for coaches who want to move beyond rigid systems and isolated drills, and instead develop players who think, adapt, and perform under real match conditions.Sponsored by Taelor. Most guys spend way too much time figuring out what to wear- and still end up in the same three outfits. Taelor fixes that. A personal stylist picks clothes in your size and style, ships them to you every 2 weeks- You wear them and return them. No shopping, no laundry, and no more spending on new clothes. Use code ERIC25 at Taelor.style for $25 off your first month._______________________Follow us!@worldxppodcast Instagram - https://bit.ly/3eoBwyr@worldxppodcast Twitter - https://bit.ly/2Oa7BzmSpotify - http://spoti.fi/3sZAUTGYouTube - http://bit.ly/3rxDvUL#soccer #soccercoach #coaching #coach #train #training #cognitivescience #instinct #soccerinstinct #football #entrepreneurship #subscribe #explore #explorepage #podcastshow #longformpodcast #podcasts #podcaster #podcasting #worldxppodcast #viralvideo #youtubeshorts

FPL managers have one more Double Gameweek to look forward to with Man City and Crystal Palace both doubling in GW36

Alynne is a woman who reports fatigue, intermittent joint pain, and a recurrent facial rash that becomes worse after spending time in the sun. Examination reveals a rash over the bridge of the nose and cheeks that spares the nasolabial folds. Which of the following conditions is MOST likely?A) PsoriasisB) Systemic lupus erythematosusC) EczemaD) RosaceaJoin the FREE Facebook Group: www.nptegroup.com

Most NBA comebacks are predictable, but the San Antonio Spurs just rewrote the playbook—down 15 at halftime and emerging victorious by 15 in the second half, they achieve the biggest comeback in playoff history. This isn't just a game; it's a statement that this young team has the grit, resilience, and swagger to challenge the status quo.Join Aaron Blackerby, Zach Montana, and Tom Petrini as they break down how the Spurs' strategic adjustments, bold psychological warfare, and Wemby's defensive dominance turned a near-disaster into NBA folklore. You'll discover the game-changing moments, including Wimby's jaw-dropping seven blocks and four steals, De'Aaron Fox's masterclass performance silenced in the second half, and the physical, mental chess match that defined this wild series.Get the inside scoop on a game that pushes the limits of playoff intensity, where momentum swings, subtle tactics, and legendary plays collide. Why does this victory matter beyond one game? Because it signals a franchise that refuses to back down, transforming adversity into victory in truly historic fashion.Perfect for NBA fans craving high-stakes drama, basketball strategists, and anyone who loves a gritty underdog story. If you believe in resilience, bold moves, and the roar of a young team rising, this episode is your front-row seat to NBA history in the making. Hit play and witness the future of basketball unfolding before your eyes.

The Double Gameweek delivered in style for FPL managers as many scored over 100 points, but now attention turns to Blank Gameweek 34

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

In this episode with Claire Patella we explore an interesting case study on a real patient of hers - a patient who presents suffering with bilateral Hoffa's Fat pain. We cover:Differential diagnosis of Hoffa's Fat pad painRole of the entire kinetic chainKey, expert tips for management of this conditionSpecific evidence-based exercises for treatmentHow to manage patient setbacksThis episode is closely tied to Claire's case study she did with us. With case studies, you can see how top clinicians manage real-world cases and apply their strategies to get better results with your patients.

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WHAT ABOUT BOB!?!?!?!?!?!? We try to answer that question in our official premiere episode of Season 9! We use Bob as a case study for how to use differential diagnosing. We also rip apart the portrayal of a therapist via Dr. Leo Marvin. Become a supporter of this podcast: https://www.spreaker.com/podcast/popcorn-psychology--3252280/support.

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In this video I take you through the latest news, hot-topics and answer your FPL questions ahead of Gameweek 33. ━━━━━━━━━━━━━ Check out Fantasy Football Hub with a 7 day free trial

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