Podcasts about Baseline

In episode 387 of The Physical Performance Show, host Tim Studley picks up part two of his conversation with fellow Pogo Physio physiotherapist Lewis Craig, continuing last week's deep dive into surf life saving and Ironman racing. This episode shifts focus from injury patterns to managing niggles, optimising performance, and the habits that separate top-tier surf ironman athletes from the rest. Tim and Lewis discuss the collaborative decision-making involved in managing a recurring niggle, the key training and screening recommendations every surf life saving athlete should have in place, the lessons Lewis has learned working with elite athletes in the sport, and his top tips for improving performance — from sand-specific training to fuelling properly for a demanding training load. SHOW SPONSORS:

On this episode of Fall Obsession Podcast, Sam sits down with Casey Chamberlin from Baseline Maps, one of the rapidly growing mapping apps making waves in the fishing and hunting community. Casey shares the story behind Baseline Maps, how the app has grown, and what makes the community surrounding it so unique. They dive into the vision behind the platform, the constant evolution of the app, and Casey's hands-on approach to listening to hunters, responding to feedback, and building Baseline around the people who actually use it in the field. They also talk about the role technology is playing in modern hunting, how Baseline is helping fishermen and hunters scout and understand the ground they hunt and fish, and why building a strong community is just as important as building a great product. From the early days of Baseline to where the app is headed next, Casey gives us an inside look at what it takes to grow an outdoor technology company while staying connected to the hunters who helped build it. The app is growing. The community is growing. And Baseline Maps is just getting started!Fall Obsession Podcast is sponsored by:Hoot Camo Company (https://hootcamo.com/) - use code "fallobsession15" to save with HootBear River Archery (https://www.bearriverarchery.com/) - use code "fallobsession" when shopping online with Bear RiverTactacam Reveal Cameras (https://www.tactacam.com/)The Outdoor Call Radio App (https://www.theoutdoorcallradio.com/)

Covert narcissism doesn't just create crisis moments — it leaves your nervous system stuck in a chronic state of hypervigilance long after the argument ends. You can get good at managing the spike, catching yourself mid-reaction, and still lie awake at night with your jaw clenched and your shoulders up around your ears for no reason at all. In this episode on narcissistic abuse recovery, Renee explains allostatic load — what happens when a nervous system adapts to years of walking on eggshells, gaslighting, and unpredictable moods in a covert narcissistic relationship, and never fully comes back down. She breaks down what chronic hypervigilance actually looks like from the inside, what it does to the body physically, and introduces eight new tools for lowering your baseline over time: Worry Spot, Villain Voice, Letter to Future You, The Doorway Reset, The Off-Duty Ritual, Compliment Jar, The Do-Nothing Timer, and The Petty Playlist. This is part two of a two-part series on covert narcissism and fight or flight. If you haven't heard part one, start there for eight in-the-moment crisis tools built for the spike itself. If you've been listening, learning, and starting to see the patterns of narcissistic abuse in your relationship — but still feel stuck in the same cycles of emotional dysregulation and self-doubt — that's not because you're missing information. It's because this isn't something you're meant to untangle alone. If you're ready to take the next step toward healing, I invite you to check out my coaching program at www.covertnarcissism.com. And if this episode helped, please subscribe so you don't miss what's next. The information provided by Renee Swanson, Covert Narcissism Podcast, and CNG Life Coaching is for educational purposes only and is not to be used for diagnosis purposes and is not intended to be a substitute for clinical care. Please consult a health care provider for guidance specific to your case. This material discusses narcissism in general. Renee shares stories from her personal experiences as well as from those she has talked with for several years. Her material does not claim that any specific person has narcissism and should not be used to refer to any specific person as having narcissism. Permission is not granted to link to or repost this material to support an allegation or support a claim that any specific person is a narcissist. That would be an unauthorized misuse of the material and information provided. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome back to another uplifting episode of the Creative Journey Podcast with Coach Kaila Allen! In this episode, we take a deep dive into the terrain of our physical bodies to explore how we can map out a personalized geography of safety, comfort, and joy. So often, we are conditioned to believe that true peace requires massive, external life shifts or complex circumstances. However, our nervous systems actually register safety through immediate, micro-level sensory inputs. We unpack the power of establishing a sensory baseline—the zero-point of stress where your body feels completely unthreatened—and share practical, somatic tools to help you identify the precise sights, sounds, smells, and textures that signal comfort to your physiology. Moving beyond passive relaxation, this episode explores the active art of dropping our structural armor and protecting our energetic peace. We examine how chronic stress causes us to hold tension in our jaws, shoulders, and breath, acting like thick static over our sensory channels. Through guided cognitive restructuring and auditory anchor practices, you will learn how to rewrite the limiting “Joy Scarcity” pattern and actively design your day around sensory boundaries. Pour yourself a refreshing glass of raspberry peach tea, settle into a cozy chair, and discover how to custom-tailor your world so that your body remains a true sanctuary. To dive deeper into our community archive and access session resources, visit our Official Website https://creativeguidancellc.com/ and explore our Buy Me A Coffee Membership Page https://buymeacoffee.com/creativeguf/membership

Gruppettoen optager i hver deres fyrstendømme - Mielke er på Bornholm med svigerfamilien og Matson er i Monaco, hvor han forsøger at trænee, kæmpe og svede sig til en plads på Vuelta a España-mandskabet. Hør mere om den varme kamp, Mielkes bornholmske eventyr og meget mere.  Gruppettoen på Forhjulslir er sponsoreret af Aioss.  Som ny kunde hos Aioss modtager du en "Back to Baseline"-gave – du modtager kreatin svarende til 6 måneders forbrug. Ved at købe Aioss støtter du ikke bare os – men vigtigst af alt dig selv, med mere fysisk og mentalt overskud i hverdagen. Brug koden "gruppettoen" og spar 100 kr på de tre første leveringer på dit aioss-abonnement. Læs mere på: https://aioss.dk/pages/gruppettoen

If you find yourself constantly resetting back to square one, you aren't building capacity—you are managing symptoms.True resilience isn't defined by how fast you bounce back from pressure; it is measured by how steady your baseline remains when pressure hits. Every experience creates a somatic-mindset feedback loop. When you autonomously self-regulate, either you leverage these moments to expand your baseline capacity, or you stagnate in a cycle of temporary recovery that perpetuates as is over time.In this episode, we unpack why conscious leaders, spiritual practitioners, and self-led individuals utilize somatic-mindset micro-habits to transcend the reset loop and move into an enlightening and intelligence growing loop instead. Discover how shifting from basic emotional control to mastery and dynamic self-leadership allows you to expand your capacity and navigate pressure from a grounded baseline. This foundation creates empowered composure, allowing you to uphold relational integrity and co-regulation.In This Episode, We Cover: The Symptom Management Trap: Why relying on repeated "resets" keeps your baseline static. The SMART MICRO-HABIT Somatic-Mindset Loop: How real-time somatic awareness and conscious mindset choices strengthen your baseline under pressure. Expanding Capacity vs. Managing Activation: Transitioning from reactive coping mechanisms to proactive baseline expansion. Somatic-Mindset Micro-Habits in Action: Practical, high-impact strategies to maintain emotional sovereignty and personal accountability.Episode Timestamps:(0:00) — Beyond Symptom Management: Somatic-Mindset Micro-Habits & Expanding Capacity (Window of Tolerance to Welcome)(5:10) — Emotional Sovereignty & Self-Leadership: Evolving from Control to Dynamic Regulation(7:14) — The Liberating Shift: Empowering Personal Sovereignty through Somatic Integration(7:40) — Recommended Reading for Nervous System Sovereignty & Mindset Mastery(7:59) — Moving Beyond the Reset: Aligning Intrinsic Motivation with Adaptive Self-LeadershipResources & ConnectSubscribe & Share: If you know spiritual and conscious, regulated leaders who want to navigate reactive survival loops with self-compassionate honesty and steady empowering composure using somatic-mindset micro-habits share this episode with them.Explore the Frameworks and digital resources on the Payhip store: Access dedicated somatic-mindset tools, guides, and workbooks designed to support baseline expansion: https://payhip.com/InspiringHumanPotentialShift from Reactive Survival Loops to Real-Time Empowered Composure—The SMART Way. Download Your FREE 10-Minute SMART MICRO-HABIT Check (PDF): https://payhip.com/b/r3sNW

Journalist. Podcaster. Network founder. Warren Shaw joins Kevin L. Warren to explain how 19 Media Group and The Baseline NBA Podcast were actually built — cold calls, credential hustles, and a COVID-era decision that changed everything.

GLP-1 medications have changed how we treat obesity and metabolic disease. But as their use has exploded, so have questions about side effects, muscle loss, long-term use, and whether patients are receiving the support they need to use them safely. In this episode, I reconnect with metabolic health and regenerative medicine expert Dr. Tyna Moore to revisit our conversation from two years ago and examine what we've learned since. We discuss: How to tell when your GLP-1 dose may be too high What you can do to protect your muscle and bone during weight loss Which metabolic and nutritional markers should you check before and during treatment Why weight can sometimes return after stopping a GLP-1 What emerging research suggests about GLP-1s beyond weight loss GLP-1s can be life-changing, but a lower number on the scale isn't the same as better health. Ultimately, how these medications are used—from dosing and monitoring to nutrition and strength training—matters just as much as whether they're used at all. Additional resources: Join Dr. Tyna Moore's community Listen to Dr. Tyna Moore's previous appearance on The Dr. Hyman Show 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 Seatopia, Perfect Amino, Cozy Earth, Timeline, Sunlighten, and Made In. Find a cleaner source of seafood. Check out seatopia.fish and use code HYMAN for free shipping on your first order. Get daily protein support at bodyhealth.com and use code HYMAN20 for 20% off. Head over to cozyearth.com to save 20% and upgrade all of your daily essentials today. Support healthy aging and get 20% at timeline.com/drhyman with code HYMAN. Discover why so many people are using sunlighten.com and use code HYMAN to save up to $2,100 today with free shipping. Upgrade your cookware at madeincookware.com and save 10% off your first order with code HYMAN-HIVE. (0:00) Introduction, Dr. Hyman's evolving views, and episode goals (0:43) Sponsor: Rose Nutrition Liposomal NAD (1:42) Sponsor: Seatopia clean seafood box (2:44) Disclaimers and Lyme disease preview (4:04) Guest Dr. Tina Moore reintroduced (4:30) GLP-1s: Effects after years and microdosing strategies (7:14) Risks of high-dose GLP-1s and misconceptions about muscle/bone loss (13:09) Functional deficiencies and microdosing approaches (17:05) Sponsor: Made In stainless clad cookware (18:02) Sponsor: Timeline with Mitopure (18:58) Broader and additional benefits of metabolic health and GLP-1s (21:48) GLP-1s for immune and brain health; genetic differences (27:59) Introduction to peptides and GLP-1 drugs (32:36) Gray market concerns and weight regain after stopping GLP-1s (37:14) Long-term safety, cost, and personalizing GLP-1 treatment (40:57) Emotional blunting and recent concerns about GLP-1s (46:30) Functional medicine approach: addressing root causes (46:46) Sponsor: Sunlighten Sauna (47:20) Sponsor: Magnesium Breakthrough from Bio Optimizers (48:17) Dr. Hyman's evolving perspective on GLP-1s (49:19) Hormonal effects of GLP-1s for men and women (55:02) Baseline lab markers and tests before GLP-1s (57:10) New and next-gen GLP-1 therapies (59:43) Telemedicine, gray market issues, and importance of reputable practitioners (1:05:57) Rapid fire: Alcohol, common mistakes, misconceptions, and eligibility for GLP-1s (1:08:16) Key lab tests and surprising non-weight benefits

After a week off, Josh and I are back with our football previews! We break down the Big Ten and the AFC North. Who will come out on top of the Big Ten? Will Indiana do it again or will Oregon come out on top? Then do the Browns have any chance to win the AFC North?

You wake up already reactive, responding to someone else's needs before you've taken a single conscious breath. And by 8am you're somehow already spent, already behind.That's not a discipline problem. The first thirty to sixty minutes of your day quietly set the cortisol curve and stress baseline you'll run on for the next twelve hours, whether you choose it or not.This week's Hi-Cap Move reframes the morning routine as nervous system strategy, not a productivity hack, and gives you five simple principles (no rigid 5am protocol) to start the day in regulation instead of survival.If your mornings currently belong to chaos and you're tired of being along for the ride, this is where you take that window back.--

Build Your Base - Lifting Plan to Build Capacity Needed for PoleConfusion to Clarity - Masterclass on How to Juggle All The ThingsInstructor Course Interest listAll my offers/services➡️ Online Summit that I'm a speaker inConnect with Dr. Emily:Website - i have tons of resources for you!Instagram

One of the greatest examples of basketball's "Special Teams" is your baseline out of bounds philosophy and execution on both sides of the ball. In today's podcast, we discuss goals that you should have for your playbook, who the ball should go to, formations, counters and selling the importance of baseline out of bounds to your team! We also have a great quote from USA Basketball, and we also discuss today in our Coaches Study one of my friends in the business who made his name nationally via USA Basketball!

Global water systems are facing an unprecedented challenge as climate change, resource depletion, and globalization place staggering pressure on our natural resources. Dr. Raha Hakimdavar, Senior Advisor to the Deans of Georgetown University in Qatar (GU-Q) and the Earth Commons & Founder & CEO - Zyon Space, unpacks the critical intersections of hydrology, space science, and environmental security. From analyzing how agricultural water waste and food imports impact the Arab world to detailing the vulnerabilities of desalination infrastructure, she offers a profound look at the cascading threats facing urban centers. By leveraging advanced satellite data collected from space, she reveals how tracking global groundwater levels can transform these invisible vulnerabilities into vital opportunities for international cooperation and policy reform.   00:00 Introduction 04:36 Energy, Oceans & the Cost of Innovation 06:22 Supermarket Aisles & Disconnected Systems 10:09 A Fifty-Year View from the Cosmos 14:09 Urban Budgets & Changing Taps 26:02 The Changing Anatomy of Regional Diets 30:47 Invisible Pipelines & Cascading Points 34:58 Distorting the Baseline of National Security 36:14 The Enabling Elements of Uprisings 38:06 A Landscape of Treaties   Dr. Raha Hakimdavar is a hydrologist, science policy expert, and space science leader with a proven record of innovation across government, academia, and industry. She is currently a Research Professor at the Earth Commons and Senior Advisor to the Deans at Georgetown University in Qatar and the Earth Commons, leading strategy and programs focused on environmental security, climate action, and sustainability research and education. She has active research projects in Greece (water scarcity), the Middle East (water and food security), and Indonesia (flood risk and nature based solutions), with a special focus on small islands. Dr. Hakimdavar has served as a technical consultant for UN Environment, the World Bank, and USAID on disaster risk reduction, water, and forestry projects for over a decade. Dr. Hakimdavar is also the Founder and CEO of Zyon Space, which supports emerging space agencies and organizations in the Global South in leveraging Earth observation technologies for climate and environmental resilience.   Connect with Raha Hakimdavar

Watch the full episode on YouTube:We first covered Baseten last year when DeepSeek mania was at peak hype. Now they have raised a monster $13B round and become one of the new cohort of AI Infra decacorns that are (with Nvidia, Intel, and the semis complex) chief beneficiaries of the Inference Inflection. We return to Baseten at the peak of the 2026 edition of Open Weights debate. Ali has published a viral breakdown of Kimi K3:And since you last saw him, Philip has spoken at AI Engineer and written the definitive book on Inference Engineering spotted all over SF:Three years ago, inference engineering barely existed as a category.Today, it is one of the most critical disciplines in AI. Inference engineering inherently tackles a different question than standard model training: “How do you turn those weights from training into a product that is fast, reliable, and affordable at scale?” Focusing on these creates an entirely new optimization problem.In one recent GLM-5.2 experiment, quantizing more of the model actually preserved its benchmark quality while increasing throughput by 20%, because the errors introduced in different layers could cancel each other out.Inference is no longer just the final step after training. It is becoming its own engineering discipline, with its own research problems, infrastructure, and increasingly specialized roles.In this episode, Baseten's Philip Kiely and Ali Taha join swyx and Vibhu to explain what actually happens after a new open model is released and what it takes to turn “we generated a token” into a fast, reliable, production-ready API.We go deep on cache-aware routing, disaggregated prefill and decode, quantization, speculative decoding, KV-cache movement, model parallelism, GPU kernels, and the race to make frontier models up to 10× faster. Philip and Ali explain why inference optimizations can still produce gains of 20%, 100%, or even 200%; how quantization errors can cancel one another out; why identical weights can behave differently across clusters; and how Baseten grafted a Kimi vision encoder onto GLM-5.2 without changing the underlying language model.The conversation then expands beyond LLMs into NVIDIA Dynamo, mega kernels, Rubin, AI-specific chips, local inference, video generation, diffusion versus autoregressive models, and the enormous compute barrier to generating coherent long-form video. Finally, we explore the convergence of training and inference, continual learning through persistent KV cache, and the emerging loop where models help optimize the infrastructure that runs them.We discuss:* What happens when a 200,000-token request enters an inference system* Cache-aware routing and reusing previously computed KV cache* Why prefill and decode are increasingly handled by different GPUs* When dedicated deployments become cheaper and more reliable than shared APIs* How speculative decoding uses a smaller model to accelerate a larger one* Tool calling, structured outputs, and what LLMs actually do* What it takes to support a new open model on day zero* Grafting Kimi's vision encoder onto GLM-5.2* Retrofitting inefficient model layers with components from other architectures* Why models sometimes collapse into repeating the same token* How hardware, kernels, and race conditions create nondeterministic failures* Preserving model fidelity while making inference faster* How quantization errors can cancel each other out* Why inference optimizations still deliver gains of 20%, 100%, and 200%* How optimized serving can make a model up to 10× faster* NVIDIA Dynamo, KV-aware routing, and distributed model serving* Speculative decoding the speculative decoder* Why local AI is about making models less dumb while data-center AI is about making them less slow* Tensor, expert, and pipeline parallelism across GPUs* Hardware-aware model design, auto-tuning, and the case against mega kernels* Rubin and why inference is becoming a systems problem* Whether modern GPUs are evolving into programmable AI ASICs* Why enormous models like Kimi K3 require GB300-class hardware* Why open-source video generation still trails Veo, Kling, and other closed models* The quadratic attention bottleneck behind long-form AI video* Autoregressive video, real-time generation, and compounding quality drift* Why future video systems may combine autoregressive and diffusion architectures* Training for inference and inference for training* Continuous post-training, deployment, evaluation, and improvement loops* How GLM-5.2 helped optimize the kernels serving GLM-5.2 itself* Why faster networking could unlock dramatically faster decoding* Continual learning, KV-cache compaction, and persistent model memoryShow Notes* How to build a day-0 API for Kimi K3* 22580: From GPT2 to Kimi3, ExplainedPhilip Kiely* LinkedIn: https://www.linkedin.com/in/philipkiely* X: https://x.com/philipkiely* Inference Engineering: https://www.baseten.co/inference-engineering/Ali Taha* LinkedIn: https://www.linkedin.com/in/aliestaha/* X: https://x.com/waterloointernTimestamps00:00:00 Introduction and the 200K-Token Prompt00:03:18 Dedicated Deployments, Speculative Decoding, and Tool Calling00:11:26 Launching Production-Ready Open Models00:19:06 Model Retrofits, Failure Modes, and Nondeterminism00:28:22 Quantization and Canceling Errors00:32:15 The Race to 10× Faster Inference00:40:48 Dynamo, Speculation, and Local vs. Data-Center AI00:50:18 Model Parallelism, Auto-Tuning, and Mega Kernels01:00:55 Rubin, GPUs vs. ASICs, and Custom AI Chips01:10:03 Giant Models and the Limits of GPU Memory01:12:42 AI Video, Quadratic Attention, and Autoregressive Generation01:21:47 Audio, Images, and Diffusion Models01:27:32 Training, Self-Optimizing Models, and Continual Learning01:40:06 Closing ThoughtsTranscriptIntroduction: Baseten, Waterloo Intern, and Inference EngineeringSwyx [00:00:00]: Okay, we're here in the studio with Philip, old friend from Inference Engineering, the book, as well as Baseten and everything that you've done, you and I have done before, as well as Ali. Welcome.Ali [00:00:15]: Pleasure to meet you.Swyx [00:00:15]: Waterloo intern.Ali [00:00:16]: Waterloo intern, always.Swyx [00:00:17]: When did you get “Waterloo intern” as a handle?Ali [00:00:19]: As a handle? Oh.Ali [00:00:20]: I think the rebranding happened mid-March. When I saw it was open, I was like, “I have to take it. Up for grabs.”Philip [00:00:26]: The problem is that Ali is really good at his job and is not gonna be an intern much longer.Philip [00:00:30]: So we have to figure out who's gonna get the handle.Ali [00:00:33]: Well, I'll pass the torch over to the next intern.Swyx [00:00:34]: Oh, okay. It can be, like, you just pass it to another Waterloo grad.Ali [00:00:37]: To another Waterloo intern. No, bruh.Philip [00:00:39]: Yeah.Ali [00:00:39]: Intern.Swyx [00:00:40]: Intern, yeah.Ali [00:00:40]: And no.Philip [00:00:41]: You gotta get an intern from Waterloo.Ali [00:00:42]: Yeah, I've gotta get an intern from Waterloo.Swyx [00:00:44]: Right.Ali [00:00:44]: But they have to follow the path.Swyx [00:00:45]: Oh, it could, but it could come from Baseten, so it's like whoever Baseten gets from Waterloo.Ali [00:00:48]: Right.Swyx [00:00:49]: Has the title of Waterloo.Ali [00:00:50]: It stays in the ecosystem.Philip [00:00:51]: Exactly.Ali [00:00:52]: Halfway through the internship, you either get it or you're out.Philip [00:00:55]: You should also do, like, a big graduation ceremony where you change the handle.Ali [00:00:59]: Just say it.Philip [00:00:59]: For everybody.Swyx [00:01:00]: You guys are good at ceremonies, clearly. We had a nice launch of the book, very successful. But before we get into all that, I wanna start off with a fun question for you. Okay, you're an expert inference engineer. What happens when I send a long query, say two hundred thousand tokens into Baseten's inference? What's the process of query through GPU model routing, balancing, all that? What is all the stuff that we don't think about?Long Context Requests, KV Cache, and Cache-Aware RoutingPhilip [00:01:26]: With a long query specifically, the first thing that I'm gonna ask is, “Have you sent me this query before, or at least part of it?” and I really hope you have, because it's gonna be a lot easier for me and a lot cheaper for you. So the first thing that we're gonna look at is some cache-aware routing, where we're going to see, we probably have a number of instances, a number of replicas up serving whatever model you're hitting. We want to send this one to something with, number one, available prefill workers, and number two, ideally some cached input already there so that we can skip prefill on at least part of these two hundred thousand tokens. If you're doing two hundred thousand tokens, it's probably coding or a multi-turn agent or something where you would expect to have that cached. If you don't, we're gonna have to send it to a prefill worker. We've at least on certain models disaggregated prefill and decode, so you're going to have one set of GPUs that's solely going to process the input, create the KV cache, and get you your first token, and then that's going to be passed over to a separate set of GPUs, which is going to run decode. We're going to iteratively make those tokens. We're probably going to have some speculator model in front of that. I'm going to assume that you're doing coding, and because of that, our speculator model, which assumes you're doing coding, is gonna have a high draft token acceptance rate. If I'm wrong and you're asking me to summarize every Harry Potter book, it's gonna be slower. And then we stream that output to you and account for it, charge you, a couple of pennies and say, “Hey, would you like to send another one?”Swyx [00:03:04]: Except Baseten doesn't charge by pennies.Philip [00:03:07]: Well, yeah, we charge. I'm assuming that we're talking about the public model APIs. If you are setting up a dedicated deployment, then yeah, it's not pennies.Public APIs vs. Dedicated DeploymentsSwyx [00:03:18]: Yeah, one of the key differentiators when I was talking with Baseten initially was that people who want very high volume just need to rent by the box, ‘cause then it's up to you to figure out how to saturate the box.Ali [00:03:31]: And more often than not, it's, like, way cheaper if you're pushing, like, millions of tokens per hour, if you just pay per hour instead of pay per token.Philip [00:03:37]: Yeah, they do. I think that we've increasingly seen a lot of demand for the pay per token APIs, just because everyone wants to try open models, and then once they find a use case that's really sticky, then they move over to dedicated.Swyx [00:03:51]: Is there a best practice on when it's time to swap over?Philip [00:03:54]: Couple reasons. Yeah, reliability, that's a big one, right?Ali [00:03:57]: Like, if they have a very specific use case, they want you to train something specifically for them, like they want their own spec dec, for instance, for their own traffic.Swyx [00:04:04]: Spec dec is speculative decoding.Speculative Decoding and Custom SpeculatorsAli [00:04:05]: Speculative decoding, yeah.Swyx [00:04:07]: You have to explain.Ali [00:04:07]: Sorry. Like, speculative decoding is like, if you have a huge model, right? And so the model is going to be generating one token at a time every single turn, every single forward pass. So we attach, like, this little, like, parasite, like this layer that goes on top of the model, and this model just has to predict. It does three very fast autoregressive forward passes, and it will predict, like, three certain tokens, and then you do one forward stage over the entire original model in order to see if those predictions were correct or not, and then you accept them or you reject them. Now, this draft model is traffic specific, so if you, like, Philip said, if you're summarizing Harry Potter books, I can train exclusively that draft model on Harry Potter books, and I can guarantee you that I'm gonna accept the three tokens every single time. And so with that case, I increase your decode speed. I wouldn't be able to provide this to you if you're a shared endpointSwyx [00:04:53]: YeahAli [00:04:53]: ‘cause I have no idea if you're doing Harry Potter, if you're doing coding, if you're doing English. We don't know. Also, there was a thing in the book that mentioned that if they really cared about a specific threshold, chapter four, I think. Do you remember that?Philip [00:05:06]: Yeah. The things that you can do is you can set a specific, like, batch sizing, a specific, like, parallelism strategy if you're trying to optimize for, like, throughput versus latency. You can. Maybe a NVFP4 quant doesn't pass your benchmarks and you wanna run a model at higher precision, you could do that. There's just a bunch of reasons why you might wanna have your own endpoint and the biggest one, of course, just being, like, you don't have to deal with someone else doing a hundred million tokens of benchmarking traffic at the endpoint when you happen to be trying to serve your users.Swyx [00:05:40]: Yeah. I think one thing that is. That is a classic journey. Like, it's people is asking the, what happens when you type Google into the browser. Tool calling, is that just, you're generating JSON or is there more complication beyond that?Tool Calling, JSON, and Structured OutputsAli [00:05:58]: Certain customers that we have, they have their own post-trained models, and so they demand a tool calling that's not just, like parse a file or go find the weather. It's something that's very specific and you have to do post-training on this. And if the post-training on the model is not good or if the quantization after the post-training to get the inference to be fast, the model will struggle reading the JSON file and reading the tool calling. But it doesn't require its own like sandbox. It's not like it's going to use that tool calling to like escape a sandbox or like it doesn't have to be contained. It can just be a normal dedicated deployment. The challenge with tool calling more and more seems to be that the companies want certain tool calling which is a very sensitive thing to train. And because you're dealing with all of the JSON outputs, if it doesn't like close the end of the request in a very certain manner, you end up with a model that did the tool calling and like the thinking and so as a result of that, it didn't see the result and just hallucinated the result as it decoded. That seems to be the most challenging thing with tool calling, not really the sandboxes model.Philip [00:06:56]: Yeah, that's a challenge on the training side and then on the inference side, there's work that you can do to scope the possible output. So we published this at this point close to two years ago, the solution to this problem which is you make a state machine and you use that to constrain the output to a specific format. So this is the structured output problem. If you remember backSwyx [00:07:27]: Yeah, the specific grammar is,Philip [00:07:29]: Yeah, exactlySwyx [00:07:30]: GML had this thing.Philip [00:07:31]: Yeah. So it's like the old-school “make sure this is only JSON”, return only JSON orSwyx [00:07:38]: YeahPhilip [00:07:38]: Grandma's gonna die type of prompts.Swyx [00:07:39]: Is it BNF grammar? At some point OpenAI had released a thing that was like, yeah, if you want to constrain your output, write BNF grammar, back as NOR.Philip [00:07:47]: In our inference system, it's just a specified output format. And you get the guarantee that your output's gonna be structured along that format. And so applying that to tool calls can like help cut down on. You can still call the wrong tool or call no tool. It doesn't solve the certainty problem but it at least solves the output structuring problemSwyx [00:08:10]: YeahPhilip [00:08:10]: Within tool calls.Swyx [00:08:12]: And MCP is just another form of tool, right.Philip [00:08:14]: Yeah, exactly.Swyx [00:08:15]: As far as there's no special thing there.Philip [00:08:16]: The thing I'm always like explaining to people is the LLM is not capable of doing anything. It's only capable of making suggestions of what to do and then if those suggestions are formatted in a certain way and applied to a system that knows what to do with them, then an action occurs.Swyx [00:08:32]: Yeah. Part of the fun stuff is, this is solved outside of tool calling too. Like in an agent loop if the output is not correct or you're right, like reasoning, tool calling was done in the reasoning trace, just be like, “Oh, I don't know what to do. Let me just try again.” And it might get there after a few tries. And on your point of training, sometimes this is harder in smaller models, so you don't have the same exact quality outputAli [00:08:56]: Right.Swyx [00:08:57]: When you just swap from a big model, right?Ali [00:08:59]: Yeah. I will say that, before, I think we need to go back to inference engineering proper.Ali [00:09:04]: But, I had expected that something would replace JSON because it's hard to stream JSON ‘cause JSON must be complete and you must have open and close brackets and everything. So it's hard to parse something or validate something while it's being streamed. So people invented all sorts of things that are like, I forget the name of some of these alternatives, but it's something like TOML, something like YAML. But JSON seems to be dominant still.Philip [00:09:30]: The JSON outputs aren't that long, right? Like you could have a long-- ‘cause tool calls also contain the arguments in them and perhaps for a certain tool you might pass like a very long argument. But my impression of the median tool call is that it's a relatively small number of tokens, right? So I would expect that speculators are generally fairly good at something as formatted as JSON. And so you would have like a pretty fast decode step there and that the streaming wouldn't be as valuable, but maybe I'm wrong about that.Ali [00:10:02]: I think you're also bounded by the software or that the model is gonna integrate with if the software is built with JSON for the tool calls or if the company that you'- if your customer says that this is how our software works and our tools are interfaced with JSON, you can ask them to like, change their software and say like, “Yeah, this is gonna be better for the model.” but like with the right training shouldn't be that much of a difference. Also more profitable if it outputs more tokens probably.Swyx [00:10:25]: Depends on your business model.Swyx [00:10:27]: It really depends. But I will say that, as a writer with like experience a lot with generated output, I do try to move from text to JSON text which is very long JSON, right? Like there's paragraphs in every field because I'm trying to structure it, right?Philip [00:10:44]: Right.Swyx [00:10:44]: I want you to first make factual statements, then make opinions then make bullet point summaries, have dates, have entity references have your sources for references, all these things. Anyway, so these are things that like I think people who really experiment with structural output have to really care about. But, let's, let's recurse up the stack a little bit. Before we started recording, you mentioned something really cool, which is that there's a lot of engineering that-- inference engineering that goes on when a new model provider releases a new model, right? So let's call it GLM-5.2, Kimi K3. I had previously assumed, especially if it's like, well, GLM 5 to 5.1 to GLM-5.2, like that you've supported them before. Is it that much work?What It Takes to Support a New Open ModelAli [00:11:26]: It's a lot of work.Swyx [00:11:28]: Yeah. Okay. So like, a lot of people, all you guys, right whenever a new model launch like, people rush to say like, “Oh, Hugging Face supports this, Fireworks supports this, Spacetime supports this,” and I'm like, “Yeah, of course we support it.” But what goes into that? What goes intoPhilip [00:11:40]: I think it's more than just support it too, right? It benefits the consumer a lot. Like I think it was with Kimi K2.5 or GLM-5.2 the latest, there was an inference war, right? X provider is at 90 tokens a second. The next day we're at 150. The nextSwyx [00:11:55]: I kinda kicked that off with the GLM-5.2.Swyx [00:11:58]: I wrote a Twitter article about. It got like half a million views,Ali [00:12:02]: Based on being numberSwyx [00:12:03]: YeahAli [00:12:04]: Or it's for something else.Swyx [00:12:05]: Yeah. Which,Ali [00:12:06]: Oh my GodSwyx [00:12:07]: Which then got everyone really excited about, hey, how can we, bend tracks a little bit further and,Philip [00:12:14]: There's a difference between support the model, as in I can make a token out of this model, and support a model, as in I have a production-ready API from this model.Philip [00:12:26]: Getting to the point of I can make a token out of this model is not that hard because generally the, open source inference engines, vLLM, SGLang of the world oftentimes even receive weights ahead of time, maintainers do, or the people making the model merge PRs to ensure support. So you generally can, just get it working on the standard open source stack without too much pain in most cases. The challenge is, every inference company is gonna have own proprietary stack. Some open source components, some in-house stuff. And for any arbitrary model, there's going to be some new stuff. Sometimes you get lucky, like K, two five to two six was, like, pretty similar.Quantization, Speculators, and Production ReadinessAli [00:13:16]: Yeah. It was pure continued post-trainingPhilip [00:13:18]: YeahAli [00:13:18]: If I remember correctly.Philip [00:13:19]: Even in those cases, there's still stuff you have to do. You have to redo the quantization work. You're taking the model from. Generally, these models are not released in NVFP4, and we want them to be in NVFP4 for maximum Blackwell compatibility. So we have to perform that quantization, and, calibrate the quantization to make sure that we're not causing any regression in the model's intelligence. And then we also have to train the speculator, as we've talked about. Generally, we have. We have ZDR, zero data retention on our model APIs, so we don't know exactly the traffic that people are sending us, but we know what's popular. We know that coding use cases are popular. We know that agents, agentic use cases are popular. So we can get public data sets that are representative of that traffic and train general speculators. Now, with speculators today, you need to train the speculator using the base model itself because you're getting hidden states out of the model from running inference on these specific prompts, and that is the training data you use to create the speculator. So there's that process which you need the real model weights for. And then there's of course just the process of, standing up all the infrastructure behind it, loading all this stuff, testing it. And then when there's a new model with a newer architecture, I think that, like, the DeepSeek models tend to be the most challenging as they have, like, the most novel architectural stuff going on, model after model. But every new model has something. Kimi K2 had. Oh, sorry, GLM-5.2 hadAli [00:14:53]: Sparse attention.Philip [00:14:54]: Yeah,Ali [00:14:54]: YeahPhilip [00:14:54]: the DSA.Ali [00:14:55]: Right. Which is brought from DeepSeek.Philip [00:14:57]: Yeah. AndAli [00:14:59]: So you can copy-paste then?Philip [00:15:01]: It kindAli [00:15:01]: I don't know how this works.Philip [00:15:02]: So, like we had to, like, build support for that into our runtime. And you're right, like it is really interesting the way that all of these open source labs borrow from each other. For example, like GLM-5.2 doesn't have vision. So something that, Haley, a guy on our team, if we could take a look at this, he, like, grafted the Kimi vision encoder onto GLM-5.2.Retrofitting Vision into GLM-5.2Ali [00:15:27]: We'll be training the projector.Philip [00:15:28]: Exactly. So if you think about, like, the encoder, there's the encoder, which is the part that looks at the image and turns it into latent information, and then there's the projector which likeAli [00:15:38]: You can say latent space. It's okay.Philip [00:15:41]: And then there's the projector that maps it onto, the model itself, and then there's the model weights. You don't wanna mess with the model weights because you run a chance of making the model dumber at something else for the purpose of giving it vision. So instead, Haley started with just a projector, which is only a handful of millions of parameters.Ali [00:16:02]: That would be, yeah.Philip [00:16:02]: Yeah.Ali [00:16:03]: Can you show the training one?Ali [00:16:04]: Like the way it groksPhilip [00:16:05]: YeahAli [00:16:06]: Very interesting.Philip [00:16:06]: And maybeAli [00:16:07]: That right therePhilip [00:16:07]: Maybe Ali, you should take it from here. You've got a betterAli [00:16:10]: Ooh, double the sandPhilip [00:16:11]: Understanding of this than I do.Ali [00:16:11]: Yeah. You can see, like, he. The way he trained this is really cool. At the beginning, he was training it using just like, “Here's a picture of a mountain. Can you describe what's in this mountain?” And that caused it just like the first, learning walls. Like here you can see this all we're trying to teach it is to translate the encoded. Like it's already taken the encoder from Kimi K. It's taken the image. It'Philip [00:16:31]: Yeah. FrozenAli [00:16:31]: FrozenPhilip [00:16:32]: With adapter.Ali [00:16:32]: Exactly.Philip [00:16:33]: Yeah.Ali [00:16:33]: So the brain is frozen and the eyes are frozen. It's just we're tryingPhilip [00:16:37]: AlignAli [00:16:38]: Interconnect between the eye and the brain, right? So the projector. And so you take the tokens and then he's like, “Oh, can you describe what's in this image?” And he's like, “Oh, it's a mountain,” or it's a person or it's a human, whatever the case is. But that didn't cause complete understanding. So he changed it such that every image was associated with a data set of questions. Like, does this image have a white male? Does this image have birds in the top corner? Does this image have a scientist in it? All of that stuff. And it would have to answer questions correctly. And using not just training on describing an image, but being able to answer question, another question, answer over time. Like you can see the grokking, which is like genuinely insane, that retrofitting vision into a large LLM can learn to that extent. And even for images that it doesn't perform well on, for instance, if you ask it a picture of like Stephen Hawking, “Who is this?” Maybe it doesn't get it, but it will say something like, “This is Albert Einstein.” Like it still understandsPhilip [00:17:25]: Close enoughAli [00:17:26]: That this is a scientist who is a man who has, some significant achievements, all that stuff. So that's like really cool.Philip [00:17:32]: Yeah. So, we've covered Hao Tian before, who the author of the LLaVA paper that did this, a while ago. And I think that's very foundational work for anyone who hasn't done vision work before.Ali [00:17:41]: Same with the CLIP and MetaCLIP, where you go from just captioning to building out questionsPhilip [00:17:47]: RightAli [00:17:47]: Off the image and how much better you can get performance.Philip [00:17:50]: Right. Right. Right. Yeah. But what's, what's so exciting about this is if you look at a model like this. Now, this is a little bit more of a research project. It's not. It got to 56% on MMLU Pro, I think. So not quite frontier. But if you're running this model, you haven't suffered any loss on your GLM-5.2 quality. If you don't have an image, it'll just behave exactly the way it used to. And ultimatelyAli [00:18:14]: Which in the inference code you literally do not include the other part, right?Philip [00:18:18]: Yeah. You would just skip the encoder if you don't have an image input.Ali [00:18:22]: Okay.Philip [00:18:22]: Just confirming.Philip [00:18:23]: YeahAli [00:18:23]: Does it affect a lot on the overall inference side? Like you're not adding much, you're adding a very small vision encoder. These are typically likePhilip [00:18:30]: They're super fineAli [00:18:31]: Less than a billion parameters, right?Philip [00:18:32]: Yeah. It's, - There's a little bit less standardization among vision encodersSwyx [00:18:37]: YeahPhilip [00:18:37]: So the support matrix can be a little bit, sparser. But overall, yeah, it's a pretty, it's a pretty minor component of the overall system. And ultimately what you get out of the system is all of a sudden you have Kimi Vision, GLM weights, and DeepSeek attention all in one model.Open Source Model Grafting and Franken-MergesPhilip [00:18:56]: And that's, I think, a lot of the power and beauty of open source, is that you can take all of these different components and combine them together into a system that's better than anyoneSwyx [00:19:05]: YeahPhilip [00:19:05]: Can be individually.Swyx [00:19:06]: People used to say that you would also do Franken-merges where you would take likePhilip [00:19:10]: YeahSwyx [00:19:10]: Layers from each model.Swyx [00:19:11]: Does anyone do that anymore?Ali [00:19:13]: Well, to your point previously when you were mentioning like, the work that goes into supporting a model when it first comes out, like GLM-5.2 or MiniMax M3 or whatever the case is. Sometimes you do have to like, you do have to switch out some things. Like, for instance, the MiniMax M3 head uses full attention, and with full attention you end up with this like insane bottleneck in spec dec ‘cause you're doing auto-regressive token generation for three tokens, and you're doing this like N squared over all of the tokens that are in your sequence. Your KV cache is like very large because it's not sparse, it's not top K. So we find it better to like, okay, we're gonna replace this, we're gonna replace this layer with a layer from another model that's using like GQA, for instance. And then just with the right training, you can get it to have the same acceptance rate. So it is very possible to retrofit layers from other models and very much needed. If a layer is like inefficient, the training just becomes the challenge, like how do you ensure that you train it properly? Which again to your earlier point is like the mesh between training and inference. As in like you need very good training in order to do fast inference. That's like, I feel like more and more becoming true.Swyx [00:20:21]: Yeah. Anything else on the support side when you say like get it to fully production ready?Loop Detection, Race Conditions, and Non-DeterminismPhilip [00:20:26]: Yeah. I think that there's also a question of just, we can test a model to a pretty extensive degree, but we're trying to get it out quickly and then you see a bunch of other people test it and you get interesting results. There was an issue with, GLM briefly where we had some like mode collapses where it would just output the same token over and over again for certain prompts on certain temperatures. Like once you expose an endpoint to the real world, there's going to be, so many more varieties of things given to it that you're able to, discover and patch things. So it's not just a, day zero process, it's then like for the first week, for the first month, if a model remains popular, like how do you both fix bugs and then continue to push the envelope on performance?Ali [00:21:21]: What do you mean you don't want your model outputting S?Swyx [00:21:24]: Is there loop detection on that stuff, by the way? It still happens like quite a lot, which is surprising.Ali [00:21:30]: We have like we, in our endpoint, like if a model was to output the same token like four plus times, we just cut the generation. We say like, “Oh, sorry, this-- Like try again,” or like we will reprocess the request. ‘Cause we know then, like if it, like if, yeah, it's four times the same token, it's probably collapsed.Swyx [00:21:45]: Yeah. Is there a way to opt out in case I really want that?Ali [00:21:48]: You want that?Ali [00:21:50]: I think there's a way that we have to handle it. I'm not exactly certain, but I feel like in certain models, like when they output something like you can imagine, like a table for instance, and so they want, they wanna draw like 12 dashes and 12 dashes. Yeah, I think there's a way for that to happen. I think we only do it on certain tokens. Like we exclude certain special characters.Swyx [00:22:07]: Yeah.Ali [00:22:07]: So we only do it on like certain like S is the most common almost. GLM-5.2Swyx [00:22:11]: OhAli [00:22:11]: And I think it was DSV 4 as well. Like you'd just have like looping issues where like you literallySwyx [00:22:17]: ItAli [00:22:17]: Just have like S.Swyx [00:22:18]: Yeah. Is there a special, something special about S? No, just randomlyAli [00:22:21]: It just seems to be the one token involved.Swyx [00:22:23]: Yeah. And it'Philip [00:22:24]: Is thereSwyx [00:22:24]: And it's only temperature 0Ali [00:22:27]: NoSwyx [00:22:27]: Even at other temperaturesAli [00:22:27]: Even at like 0.9 or whatever, it will still, it will still collapse.Swyx [00:22:30]: That's weird, right?Ali [00:22:30]: It's, it is an inference problem to be honest, like a software problem. Like oftentimes, the image you run will-- like NVIDIA will release an image for instance, and if we will upstream the changes from their latest TensorRT-LLM image into our stack, we'll find that it fixes it. Or oftentimes this will only happen in an inference engine that you're using like SGLang. But if you were to switch to vLLM, that isn't the case. So it seems to be like an extremely like deterministic software issue and not really a model issue. It's not like a weights problem. Like I'- we'll say like, “Oh, it's a problem with the quant. We did PTQ wrong,” right? But that isn't, that doesn't make sense because the same weights used with a different inference engine does not repeat the problem. And sometimes it's, the kernels that are being used in the backend have like these very subtle sometimes race conditions, where if you were to use this model hosted on one cluster, you will never get this problem.Swyx [00:23:19]: Oh my God.Ali [00:23:19]: But if you host it on a different cluster, you will. And the reason is the KV cache transfer from a node to node in that one cluster is using a slower interconnect than the node to node in another cluster. So that exposes the race, whereas in another cluster it doesn't. So then you end up just like, okay, this model is not gonna be hosted on this cluster. We're gonna host it on, another cluster because that cluster exposed that problem. But then it ends up with like, okay, is it the software? Is it the model weights or is it the hardware?Swyx [00:23:42]: There is a thing about this with temperature 0 still not being deterministic, right?Ali [00:23:46]: Right.Swyx [00:23:46]: Mostly because of hardware. Even at temperature 0 same model, you won't always get the same output.Swyx [00:23:52]: Even-- But I'm surprised by the race condition one because, I thought PyTorch was a graph that like guarantees that you at least, execute things in the right order.Ali [00:24:02]: Well, yeah, true. Like I'm not, I'm not saying that there is. Like well, you have things like PTL optimizations where like you can start a kernel before the end of the previous kernel, and that's like ‘cause you want to do that because there'sSwyx [00:24:12]: It's like pipeliningAli [00:24:12]: Expense. Exactly.Swyx [00:24:13]: Yeah.Ali [00:24:13]: But it'- But you don't do it cleanly. Like you overlap a little bit of the execution. No, it is very possible that the kernel itself, like that one block that is supposed to be running in this instance of time, that kernel itself has a race condition. For instance, like a missing barrier. Like often if you're designing a kernel and you want it to make it to be very fast, if you don't test it extensively, you'll, you'll have certain threads access data points from registers before they've been written to by other threadsSwyx [00:24:36]: YeahAli [00:24:36]: For example, because like your barrier is wrong or your synchronization was wrong. But yeah, like the testing itself is very difficult in those like, andSwyx [00:24:42]: And there's no like borrow checkerAli [00:24:45]: What does that mean?Swyx [00:24:46]: Like Rust. Like the. If you're trying to have like memory safety It sounds like a comparable problem.Ali [00:24:52]: Well, yes, but you're working in CUDA, right, NVIDIA GPUs. Like- You just need a higher level language like modular Maybe that's what modular is supposed to do. I don't know.Quantization Quality and Vendor FidelityVibhu [00:25:00]: How do you see keeping quality of the model? So you talked about all these steps of, okay, you gotta do quantization, train your own speculative decoderAli [00:25:07]: RightVibhu [00:25:07]: Run on different hardware. Looking at other model providers, okay, you kicked off a inference speed race on the consumer end. What goes into keeping quality the same across them, right? Sure, you can run benchmarksAli [00:25:22]: YeahVibhu [00:25:22]: But, like, how do you determine how much quantization are there standards? What goes intoPhilip [00:25:27]: There's a few things on quality. Most inference optimizations are lossless. KV caching, for example. You are just recomputing or preventing recomputing the same values. Speculation, of course, if a draft token is wrong, it gets rejected. The main lossy optimization is quantization. And that really comes down to, number one, data format, number two, which parts of the model you choose to quantize, which layers, and number three, like doing a lot of calibration on the quantized weights, to ensure that you're preserving all the outliers. There's other tricks that you can do, though. A big one is long context, ‘cause one thing you asked at, right at the beginning is, “Oh, what's gonna happen if I send a 200,000 token request in?” So with a long input sequence, you need to, store a lot more information. You need to process a lot more tokens. And so even if a model has a context of a certain length, you might, as an inference provider, choose to build an API with a shorter context length, and of course a full length one as well. Because if someone doesn't need the full million token context, for example, you can get them better performance. I don't know if that's exactly like quality of the model. The way that I think about quality is to what degree are we faithfully serving the original model? If you think of a golden implementation of a model that performs exactly the way the model is designed to perform, I think of quality as how close are we getting to that, 100% fidelity of the model.Philip [00:27:13]: You can also, of course, think about quality from the training side and how do you push yourself past 100%. But when I think about purely inference optimizations, it's getting faster while staying as close to that 100% fidelity mark as possible. And certainly our standard internally is that, like you should not be able to tell the difference between our API and a, official API. I think Kimi in particular does a good job of vendor benchmarking hereAli [00:27:41]: YesPhilip [00:27:41]: Where they haveAli [00:27:42]: They released an actual vendor benchmark.Philip [00:27:43]: Exactly, yeah.Ali [00:27:44]: ‘Cause they accused, some people, Amazon? There was some provider that was not doing very well on Kimi's benchmark.Philip [00:27:50]: Yeah.Philip [00:27:51]: So, with Reflect we probablyVibhu [00:27:52]: This was a long time ago, right?Philip [00:27:54]: No.Ali [00:27:54]: Yeah, like threeVibhu [00:27:55]: They alsoAli [00:27:55]: Four, five months agoVibhu [00:27:57]: This also happened with, I don't remember which model, but they pulled out quite a few, and then they started a whole chart about this. It might have beenPhilip [00:28:03]: Kimi Vendor Verifier.Ali [00:28:04]: Yeah.Philip [00:28:05]: Yeah.Ali [00:28:05]: Yeah, ‘cause you, ‘cause you'd be pissed, right? Like if you'Philip [00:28:07]: Yeah.Ali [00:28:07]: If like if I'm a consumer and I'm using like Amazon's endpoint for instance, and I've used Kimi and I'm like, “Oh my God, like this is bad,” I'm not gonna say, “Oh, Amazon quantized the model in a bad way.” I'm gonna say, “Oh, Kimi sucks.” Right?Philip [00:28:17]: Yeah.Ali [00:28:17]: So it seems like that makes sense.Philip [00:28:19]: Yeah, they care. They care.Vibhu [00:28:21]: Justifiably.Ali [00:28:21]: Yeah, justifiably.Vibhu [00:28:22]: This is probably a stupid question, but just checking, has anything improved from main quantization?Philip [00:28:28]: Yeah.Vibhu [00:28:28]: Like, is quantization always strictly worse?Ali [00:28:30]: Well technicallyVibhu [00:28:32]: NoAli [00:28:32]: It's a lossy. QuantizationPhilip [00:28:33]: YeahAli [00:28:33]: Is a lossy, it's a lossy implementation.Philip [00:28:36]: Speed improvesVibhu [00:28:36]: Speed improves.Ali [00:28:37]: It the number, likeVibhu [00:28:38]: No, I' always look for inverse scaling laws.Philip [00:28:40]: Yeah.Ali [00:28:40]: Yeah.Vibhu [00:28:40]: This is something I learned from Noam Brown, where like things that normally act in one direction sometimes do.Philip [00:28:45]: Well, technically when you run a benchmark, because these models are deterministic, sometimes your,Ali [00:28:52]: YeahPhilip [00:28:52]: NVFP4 quant is like, two basis points higher than yourAli [00:28:56]: No, it's noise. It's noise.Philip [00:28:57]: Yeah, exactly. I'm like, yeah, it's, it's within. That's why I always say within margin of error.Philip [00:29:01]: And I stopped saying that because everyone assumes that what is, well, within some margin of error, we're barely inside of that to the worst, so we're saying. But yeah, sometimes it's just like, gives you a higher output score. But like Ali said, that's noise. To my knowledge, you're not necessarily making the results better. You're just trying to, again, like keep your fidelity as close to 100% to the original model.Layer Selection, KL Divergence, and Better QuantizationAli [00:29:27]: There is, to your point, research that we did on MP. I don't know if you are able to pullPhilip [00:29:31]: YeahAli [00:29:32]: A tweet we did. One of our research interns, Joshua, I think it's a tweet on how we have 20% better quantized GLM-5.2 than NVIDIA. Essentially what we found throughout like this month research is, okay, quantization is a lossy. It's. You're compressing the data from, occupying 16 bits to occupying, four bits, for instance. And so you're losing some information, and you're trying to minimize that. And so when I say that I'm gonna quantize the model, my job becomes how do I find the layers that I can quantize, and how to find the layers to not. For instance, with image models, I don't quantize modulation layers, and I don't quantize out projections because those two are. Like out projection is what you see as the user. Modulation is what the model sees or understands. Right, exactly. And so to his paper, do you have the. It doesn't have the. Yeah. It's a long paper. I don't know if I can findVibhu [00:30:25]: If there's a part to search or it's probably in the thread.Ali [00:30:28]: It's probably in the thread.Vibhu [00:30:29]: Yeah.Ali [00:30:29]: But the long and the short is it is very possible that quantizing more of the model makes the results. Like if I have a model that I quantize layers one, five, and 10, and another model where I only quantize layers one and It is possible that the model in which I quantized more information is going to perform better because the quantization errors have canceled out. And so what Joshua showed in his mathematical proof where he had like a verifier in, is that you can predict which layers are going to have quantization errors that will cancel out with each other, and you choose to quantize those layers. And so the result of doing this mathematical quantization is you end up with a model that's 20% more quantized than another provider, so you get 20% more throughput of it because there's more layers than running an NVFP4, and your quality is better than that other quant because the layers that you chose to quantize have their errors cancel out, like one layer skewed to the right one layer skewed to the left, one layer skewed to the right. Your final logits distribution is more similar to the original distribution of the model, so you have better fidelity. And so the way we proved this was with KL divergence. So instead of just scoring on the benchmarks, we scored the KL divergence between the logit distribution of the quantized model and the logit distribution of the original full precision model, and we showed that with this technique we get. If your probability distribution on the logits which token it wants to select is more of the same as the original model, you're probably gonna end up staying true to the original model. So yeah, so it seems like previously before this, it seemed like the industry was, well, the more you quantize, the worse it's gonna be, ‘cause the more loss you introduce. That's not exactly, not necessarily true. So yeah, doesn't improve it, but can cancel out.Philip [00:31:57]: I think it might be this, but reminds me a good bit about pruning where you can prune off certain layers.Philip [00:32:03]: But very interesting. Didn't know this was a whole paper you guys put out.Ali [00:32:06]: It's. Fun fact, it was originally 72 pages, this paper, and then we decidedPhilip [00:32:11]: WowAli [00:32:11]: We can't tell. We couldn't release it. So it's now 45.Swyx [00:32:15]: Still 39 pages, so very substantive. We talked about evals and all these things and, like what's possible in terms of speedup? Like it's like probably like the numberInference Speedups and BenchmarkingSwyx [00:32:25]: Thing that people do wanna care about, and it's something that you wrote about in your post. Like official API is 70 tokens per second, and you push it up to 90. Is that like a normal thing?Philip [00:32:36]: So what's cool about working in inference, the reason that I think inference is going to be a useful place to do engineering for a long time, is that if you look at highly optimized domains like, say, finance, if you're in finance, you measure how much better you got in basis points. It's like, “Oh, I got five basis points better, like twentieth of 1% better,” that's huge news because everything is so optimized. When we publish optimizations, it's 20%, it's 100% it's 200%. So there's still probably like a lot further to go, honestly. Like you'll, you'll know that inference is pretty much solved when researchers start publishing about how they got 1% faster at something.Swyx [00:33:19]: Which by the way, because I am from the finance background, in the ‘70s, that was the margin at the time. When you did quantitative finance research, you would findAli [00:33:27]: And like 20%, tens of percent.Swyx [00:33:29]: That's. Yes.Philip [00:33:29]: Yeah.Swyx [00:33:30]: And now it'Philip [00:33:31]: Tiny fractionsSwyx [00:33:32]: For those people interested, look up Andrew Lo's paper. He had a really interesting illustration of quant, stat arb, distribution, narrowing down from like those kinds of 20% differences in the ‘70s, down to nothing today, which is very cool.Philip [00:33:48]: Exactly, and we're at the beginning of the same type of thing. Now benchmarking is hard. I think anyone will tell you that, and benchmarking provider speeds is hard because there's so many variables that go into it. What hardware are you using? How much load do you have on the system? What's the exact nature of the prompts and input and output sequence lengths? All that stuff. But overall, when you start stacking these improvements, you're looking at multiples. You can look at it. The most common form, of course, is TPS, tokens per second, which is bad naming by us in the industry, ‘cause there's two tokens per second. There's tokens per second, the throughput number, and the latency number.Ali [00:34:31]: TTMT, yeah.Philip [00:34:32]: Like total tokens per second out of the, out of the GPU as a throughput number. Most people only care about tokens per second as the latency number, which we should call ITL, intertoken latency, but we don't.Philip [00:34:44]: Anyway, so you can imagine a standard API without many optimizations for a 1 trillion parameter model operating somewhere in the 30 to 50 tokens per second range for reasonable traffic profile. And we generally see the goal of, pushing to 10X that. But, not necessarily day zero, but by stacking enough optimizations, if you have, say like four optimizations, each of which doubles performance. Or sorry, three optimizations, each of which doubles performance, then you stack that up, that's an 8X gain. That's the order of magnitude that we're working with in this space. We're trying to make things substantially faster, not just go from like 70 to 90.Swyx [00:35:38]: Are you saying you've. You have done that?Philip [00:35:40]: So let's say you have as a reasonable baseline, 30 or 40 tokens per second. You can achieve 10X that. So like on GLM-5.2, if you run it unquantized, perhaps on H100s even, and you're just using an off-the-shelf inference engine with no particular optimizations, no speculator, nothing extra around like KV routing, no disaggregation, you're, you're probably, yeah, looking at that like 30 to 40. You think that's like a reasonable baseline?Swyx [00:36:12]: Right. Right.Philip [00:36:12]: To get to something like 10X, there's a lot of trade-offs that you're making. If we're running at more like a 300, 400 tokens per second range, you are using the best hardware possible. You have a optimized speculator. You have done all of your quantization work. You are Seeing a pretty high cache hit rate. You are running with a reasonably small batch size and a parallelism configuration that is tuned for latency versus throughput, but it is possible. So the spreads that you see if you, like, go on artificial analysis or you go on OpenRouter and you look at, the worst provider to the best provider, oftentimes can hit that range. 10X is of course very aggressive. It's oftentimes maybe more of a four to six times improvement. But that's the performance that makes us really excited, is when we can get these huge gains, not just go from 70 to 90 tokens.Stacking Optimizations: NVFP4, Speculation, and DisaggregationAli [00:37:19]: It's also, like, hardware dependent. Like, ifPhilip [00:37:20]: YeahAli [00:37:20]: If you have a thing where you're serving it on just, like, a node of H100s and then you throw, like, you shard the model across, like, four nodes of B200s. Like, you can definitely increase the speed with just throwing more hardware at it. Like, normalizing for the same exact hardware and the same number of GPUs.Philip [00:37:35]: Yeah. Then you're looking at, like, a two to 4X improvementAli [00:37:38]: Right. RightPhilip [00:37:38]: Depending on the inference optimizations. So yeah, it's. Some of it's, what's the call, and some of it's who's the driver.Vibhu [00:37:46]: If you break down the two to 4X, say the example is run GLM-5.2Ali [00:37:51]: YeahVibhu [00:37:51]: On B200sAli [00:37:53]: YeahVibhu [00:37:53]: Single node, right? What's, like, the cost trade-off for effort to get, like, the last bit of juice out versus what should people just think of, right?Ali [00:38:01]: Spectre quantization. Yeah.Vibhu [00:38:03]: Spectre quantization.Ali [00:38:04]: That's, that's, that's like 95%. LikeVibhu [00:38:06]: And how far does that get you? And how easy is that for the average person to do? So say right I wanna throw the weights of GLM-5.2 on a node of B200s, how easy is it to find speculative decoder- decoder model or already quantized model? How much work goes into it?Philip [00:38:23]: If you're doing it up front, it's quite a lot of work. If you're doing it today, there's going to be people who have published things that you can just, you can just grab some NVFP4 weights. You can grab a speculator. Yeah, if we're thinking about, like, what are the 2Xs we're stacking, going from, BF16 to NVFP4 is, it's not quite a 2X, right? It's like. I think it's about, like, 30 to 40%, from 16 to 8, and then another 30 to 40% multiplied from, 8 to 4. So that doesn't quite get you a 2X, but, like, roughly a 2X. Speculator, roughly a 2X. Disagg on top of that if you're able to get enough hardware and put enough traffic through it, another roughly a 2X. And then you add in some, double-digit percent increase from having just a better runtime with, the latest kernels and stuff behind it. And that's how it stacks up.Ali [00:39:21]: YeahPhilip [00:39:21]: So building each of those, like, building the, quantized weights is, for someone who really knows what they're doing, hours to days of work. Building the speculator, again, like, hours to days of work. And the, disagg setup, hours to days. Well okay, but like once you haveAli [00:39:39]: Once set up. Once set up. YeahPhilip [00:39:40]: Yeah, getting disagg working for the first time, I'm saying, of course, is very difficult.Philip [00:39:44]: The marginal implementationAli [00:39:48]: Like, if you're just grabbing, like if you are a person, like just a normal consumer who has access to, like, a node of B200s and you're wondering, “How can I just host it myself?” You don't need to quantize the model yourself. There's always gonna be, like, an open source quantized checkpoint. NVIDIA's gonna push one out if no one else does. You. Usually, the providers will have their own spec dec that they've trained as well. You don't need to train your own spec dec. You can just use that as well.Philip [00:40:09]: Yeah. Like, GLM-5.2 has its own MTP.Ali [00:40:13]: Right. Right.Vibhu [00:40:14]: What's multi token prediction?Philip [00:40:15]: Yes.Ali [00:40:16]: I'm justVibhu [00:40:16]: Can you explain that?Ali [00:40:16]: I'm just an expert.Ali [00:40:18]: I can do it for you in case I get it wrong?Vibhu [00:40:20]: No.Vibhu [00:40:21]: Yeah, you should correct if we're wrong, but their multi-token prediction can be used for self-speculative decoding.Ali [00:40:27]: I'm not sure. I'm not gonna correct that.Vibhu [00:40:28]: Okay. I'm semi-confident in thatAli [00:40:30]: Okay. YeahVibhu [00:40:30]: But someone can check. But it's useful to paint the story of, okay, not just the average person, but say a company wants to switch from serverless inference I wanna throw this up on. I wanna rent some GPUs, throw it up. These are the steps you take to do significantly faster than just put it behind vLLM.Ali [00:40:48]: Right.Vibhu [00:40:49]: I was waiting for a mention of Dynamo.Vibhu [00:40:51]: I feel like, that's supposed to be the baseline that you measure against.Dynamo, KV Routing, and Disaggregation ToolkitsPhilip [00:40:55]: I would think of Dynamo as less of a box system and more of a toolkit for building with. So when we talk about doing aware routing, when we talk about doing KV offloading, when we talk about doing, PD disaggregation, Dynamo fundamentally is. By the way, Dynamo is an open source library from NVIDIA.Ali [00:41:17]: We've done a pod with KylePhilip [00:41:18]: OkayAli [00:41:19]: Kyle Cranin.Philip [00:41:19]: Cool. So then your listeners know then that it supports all the different inference frameworks. And it is multi hardware, which is interesting.Ali [00:41:28]: But it's just a router, it's not like an optimizer layer.Philip [00:41:30]: Yeah. All it does, like, what Dynamo is good at, it is a library for moving information around your cluster, around your hardware. So if you have, KV cache on one place and you need it to be somewhere else, Dynamo coordinates NIXL for you to move that around.Philip [00:41:49]: That doesn't mean that, like, out of the box, you just say, “Pip install Dynamo,” and then you get, like, a massive performance speed up. It's more of a developer toolkit.Ali [00:42:01]: Yeah. I would have said it would. It comes with a set of defaults that you can then swap out.Philip [00:42:06]: It does. If the industry at large, I think, was, like, rolling out all of these deployments, standard, then I think it would be, like, a credible baseline. But, we've got to, we've got to benchmark against, like, what we're seeing in the wild.Speculative Decoding Methods: Medusa, EAGLE, n-Gram, and Spec-SpecVibhu [00:42:23]: I did wanna talk a little bit more about PD disagg, because that is probably, like, number three after quantized and speculative decoding. In your book though, I was just gonna pull out the book.Philip [00:42:31]: Yeah.Vibhu [00:42:32]: Like section 522 on Medusa, 523 on EAGLEPhilip [00:42:35]: YeahVibhu [00:42:36]: 524 on gram.Philip [00:42:37]: It's 55, would be disaggregationAli [00:42:42]: Yeah. Well, no, I just wanted to dwell a little bitPhilip [00:42:44]: YeahAli [00:42:44]: The other. Like, so what do you choose to include? What do you choose to not to include? Because there was all these other techniques.Philip [00:42:51]: Yeah.Ali [00:42:51]: Are these still relevant? Because I think they came out, like, a year and a half ago maybe.Vibhu [00:42:55]: Medusa is quite old.Philip [00:42:56]: Yeah, Medusa's old.Ali [00:42:58]: It was old.Vibhu [00:42:58]: But is it in the book as a good, here'sPhilip [00:43:01]: BaselineVibhu [00:43:01]: Baseline vanilla understand it?Philip [00:43:02]: Like you should know this.Vibhu [00:43:03]: Like I read the paper, I'm like, “ it makes so much sense.”Philip [00:43:05]: Yeah.Philip [00:43:05]: So with the book, I had a couple goals. One was to give people just a working vocabulary for the space as a whole, and the other was to give them some intuition about how each of these techniques works. As I mentioned in my AI Engineer talk, which is the first public addendum to this, the speculation space has moved much faster than everything else. So yeah, even at the time that I wrote the book Medusa, I very much included as a way for people to understand how the space evolved rather than what the most modern technique is. And now of course, there's DFlash, dSpark. There's, there's newer techniques even than EAGLE, although EAGLE is still very commonly used.Ali [00:43:51]: SpecSpecta.Philip [00:43:52]: Yes. Speculative decoding.Vibhu [00:43:54]: What canAli [00:43:56]: Oh, it's a paper by Tri Dao and it's like, it's doing speculative decodingVibhu [00:44:00]: HuhAli [00:44:01]: For the speculative decoder.Philip [00:44:02]: Oh, in spec- oh my God.Ali [00:44:02]: It's literally just an another. It's like, yeah, that's the most simple way to explain it, and it seems like he got trivial speed ups there. But it seems that the complexity with training, it's almost like in our mind at least, it's almost as complex as training GANs. Like it's like a very delicate balance and oftentimes you, it's just but yeah, it's literally speculative decoding on speculative decoding.Vibhu [00:44:21]: Speculative.Ali [00:44:22]: Yeah. We saw this paper.Vibhu [00:44:24]: It's interesting, right?Ali [00:44:24]: Yeah.Vibhu [00:44:24]: I wouldn't even expect it to be very particular to train, I wouldAli [00:44:29]: Right.Vibhu [00:44:29]: The naive part of me is like, okay, train speculative decoder.Ali [00:44:32]: But like, and it makes sense, like the whole idea of speculative decoding is you. It's like, it's like almost like the iPhone auto predict version but for a normal model, right? Like you're just, you're just, generating three tokens and you're like, okay, I'll do prefill on them. And so you save those three turns for your original model. Now your speculative decoder is doing three turns of auto regression, so why not just have an even smaller model?Ali [00:44:53]: The other question there is what are the size of speculators? So say forPhilip [00:44:58]: Right. It's like a billion parameters.Ali [00:45:01]: Like for MiniMax, it's. Yeah. It's like one layer. It's like one 60th of the original model usually.Philip [00:45:06]: Yeah. I think we should do a paper when we get back to the office.Philip [00:45:10]: SpeculativeAli [00:45:11]: SpeculativePhilip [00:45:11]: Decoding.Ali [00:45:13]: No, it's, it does seem like how, when do you stop? But then it also seems like if you're able to train spec-spec decode for instance, right? Like if you're able to have a small model that is accurately predicts what the intermediate speculator is gonna predict, that is able to predict what the original target model's gonna predict, then why not just use that smallest model directly, right?Vibhu [00:45:34]: Yeah. This isAli [00:45:35]: Like it seems likeVibhu [00:45:35]: Adjacent to the routing problem.Ali [00:45:36]: Right.Vibhu [00:45:36]: Yeah.Ali [00:45:36]: Right.Philip [00:45:37]: The thing with speculators is one of the practical constraints on using them is that you do have to run a small model on the same hardware that you're running the big model on. There is a orchestration and resource competition problem inherent in that, and that is one of the constraints on speculation in general, is that draft tokens cost resources to create and cost software complexity to manage. And so if you have like infinitely recursive speculators, you add in quite a bit of that complexity on the actual implementation within the inference engine as well, not just in the training process.Vibhu [00:46:17]: I was gonna say, I would wonder if you could do similar, like distillation and pruning of, it's the same thing, it's just a model. Can we not just distill a lot of the weights, quantize the speculator, out of my domain? The question that also comes up is, this is all for big server workloads, right? How much of this applies to, say I have this MacBook, I wanna run Gemma really efficiently. Similar problems, not the same?Local AI vs. Data Center InferencePhilip [00:46:45]: Pretty different. I talked to Selo, about this on his podcast a couple weeks ago. The difference between inference engineering for the data center and for production workloads versus inference engineering for local AI, is that we start with fundamentally like different constraints and different goals. With local AI, it's how do I fit this model onto my hardware and then make it less dumb? And with data center influence, it's how do I load this model and then make it less slow? And we care about less dumb, and they care about less slow. But the local AI inference engineering ecosystem, I think has a lot for us to learn from in the data center space. They are experts in various forms of quantization, including dynamic quantization that we just don't touch, in the pruning, in the distillation, in the, layer removal. There'Ali [00:47:42]: Layer removal matters less.Philip [00:47:43]: Yeah. There'Ali [00:47:44]: No one loves pruning really.Philip [00:47:45]: Yeah. Well, but the, but they doVibhu [00:47:46]: Which is surprising, right? But that's, that's a whole different thingPhilip [00:47:48]: Just to fit something on the laptop.Ali [00:47:50]: Right.Philip [00:47:50]: So yeah, it's a, it's an interesting, it's an interesting space. Not necessarily that like their techniques make sense for us to do in the data center, because we have different resources and different goals, but more that the process as well as the openness of that field is something to, admire.Ali [00:48:12]: Yeah. Like to your point, like, certain optimizations that would. Like for instance, Turbo Quantum Sharper, like it made such huge hype on that and we did like a whole deep dive on Twitter and like said, what is it? How does it work? Why is it good or not? And it took off and it was implemented on local devices because your memory bandwidth is so slow on like a MacBook, for instance. But try putting the same thing on like an NVIDIA GPU on a B200 Turbo quant would not be. Like, it would not be used. Like, NVIDIA - Like, NVIDIA made it clear that this is not a good optimization, and we've seen it firsthand where the overhead of doing dequantization, quantization of, in the kernel itself with turbo quant kernel, each end is much slower than the time that you save from doing the bandwidth. ‘Cause on the B200s, you have like 3.5 terabytes per second. You don't need decrease the storage that much. You don't need to do, FP4 KV cache. You don't need to use a requant. There's, there's, there's better optimizations to be made. But on Edge devices, it's extremely important, it's extremely useful. So, seems to be, like, different optimizations there, but then they're all uniquely combined with like all you wanna quantize the model, you wanna do speculative decoding, like certain common prefixes with bothPhilip [00:49:18]: Principles.Ali [00:49:19]: Yeah, exactly. Exactly. Exactly.Philip [00:49:20]: They also do a lot of work on, model parallelism, especially over, heterogeneous topology, where you have, some sparks and they are wired together with, Ethernet, DGX sparks.Ali [00:49:35]: Yeah, this is the Exo Labs guys.Philip [00:49:36]: Yeah. You have, a nu

Episode 81 of the Unspoken Words podcast features Dr. Elisa Shipon-Blum and Dr. Jenna Blum returning to the foundation of it all — what S-CAT, or Social Communication Anxiety Treatment, really is, and just as importantly, what it's not. Prompted by families who reached out to the SMart Center still confused about the approach, Dr. E and Dr. Jenna make the case that the question so many parents ask — "How do I get my child to talk?" — is the wrong one. The better question is why is my child not communicating in this situation, because not speaking is only a symptom of something deeper.At the heart of the episode is a simple truth: this is a whole-person approach, not a Band-Aid for a single behavior. Using Dr. Jenna's "cake" analogy, they explain that every child is made of different ingredients — and that missing even one can mean missing a pivotal piece of the picture. That's why selective mutism is best understood not by its stigmatizing name, but as a social communication anxiety disorder with underlying "whys," and why the SM Evaluation is the essential first step in ruling those whys in or out.Dr. E and Dr. Jenna explain why S-CAT is a recipe of evidence-based approaches rather than any single therapy — blending CBT, behavioral exposures, motivational interviewing, insight-oriented work, and intensive parent management. They walk through the baseline stages of the Social Communication Bridge®, the feelings chart and the "sweet spot" that keeps kids growing without tipping into avoidance, and the parent's role as the driver of real progress. Along the way, real stories bring it to life — from a teen learning to get his own tickets at a Phillies game to a nine-year-old who needed structure long after her mutism resolved.The episode closes on a powerful reminder: the longer selective mutism lingers, the more likely it is that a "why" has been missed — and that with the right understanding, no one has to remain selectively mute.--Chapters: (03:20) Why "How Do I Get My Child to Talk?" Is the Wrong Question—And What to Ask Instead(10:33) The Bridge, the Baseline, and Why SCAT Is a Recipe—Not a Single Strategy(14:37) Honoring a Child's Feelings and the Parent's Role in Driving Real Progress(26:26) Motivation, the Hidden "Whys," and Why Courage Isn't Always Loud(34:16) The Whole Person Behind the Symptom—And Carrying the Gains Beyond SM- ADDITIONAL RESOURCES: https://selectivemutismcenter.org/resources/ Ask Dr. E a question of your own! Learn more about the host, Dr. Elisa Shipon-Blum Explore our SMart Center success stories! Get started at the SMart Center Listen to other Unspoken Words episodes here. For the best clips from every episode, follow the podcast on Instagram & YouTube Learn more about CommuniCamp, our 3+ day intensive group treatment and ALL DAY parent training & support programLearn more about our 6-week, virtual social skills series, which are skills-based groups designed to help children, teens, & young adults build social communication, comfort, and connection with similar aged-peers in a supportive setting.- For all podcast inquiries, please contact Dakota Hornak at ⁠dhornak@selectivemutismcenter.org⁠ This podcast was produced and published by New Edition Productions (neweditionconsulting.com)

On this Iron Sights episode recorded live on the vendor floor at TTPOA Conference, Scott Howell sits down for a collab with the TTPOA Podcast hosts Brandon Hernandez and Matt Smith. Their guests are Brad Ortiz, Director of Sales for the Law Enforcement Division at Silencer Shop and former officer, and Dewayne Manson, Project Manager at the American Warrior Association, to dig into the thing the Law Enforcement community has been slow to confront: the compounding weight of moral injury, and why the reactive system built to support officers is failing the people who need it most.The conversation goes deep on what moral injury actually is, how it differs from PTSD, and why the officer who has never been in a critical incident can be just as broken as the one who has. Brad and Dewayne walk through what a culturally competent clinician actually looks like, why baseline brain and blood health mapping belongs in the academy, and how the American Warrior Association's R3 program is embedding proactive resilience training directly into TTPOA's regional structure, the first partnership of its kind nationally.But this episode isn't just about mental health. It's about identity. What happens to the guy who IS the job when the job ends? Brad gets personal about his own struggles stepping away from full-time law enforcement, Dewayne shares the moment his teenage son had to pick him up after a DUI, and Scott connects it all to the broader truth that your calling and your identity are not the same thing, and that confusing the two is costing officers their health, their relationships, and sometimes their lives before and after retirement.The back half gets into the business world and how everything these men learned going through doors, managing teams, and serving their communities translates directly into relationship-based sales, leadership under pressure, and building something that outlasts the badge. If you've ever told yourself you're not capable of anything outside law enforcement, this one is going to challenge that story hard.In this episode:• Moral injury is NOT PTSD: it is the cumulative damage caused when you perpetrate something, fail to prevent something, or are ordered to act against your moral compass, and it compounds silently over an entire career without a single critical incident ever being the trigger• Culturally competent clinicians are practitioners who have lived the lifestyle or are married to someone who has: Dewayne walked out of his first VA appointment when the counselor opened with pronouns, then found a female therapist married to a 20-year infantry veteran and experienced a completely different outcome• The American Warrior Association's R3 program is building proactive resilience frameworks inside agencies, including regional resiliency coordinators, vetted clinician networks, and fully funded Warriors Refuge retreats where flights and lodging are free to eligible officers and spouses• Brad Ortiz's Sound Off program at Silencer Shop donates $2 per purchase to a vetted nonprofit fund, and every individual officer purchase routes through silencershop.com where buyers work directly with Brad and Chris• The inflection point analogy: moral injury deposits are made silently every shift, just like compounding interest, and what looks like a single critical incident blowing up is actually the hockey-stick moment on a years-long accumulation nobody was tracking• Social media pile-ons after a justified OIS caused Brad more lasting moral injury than the shooting itself, a blind spot no clinician or debrief ever addressed until he found the right cultural fit in counseling• Baseline blood panels and brain mapping done at the academy level would give officers a before-and-after reference for physiological change across a career: the greater endocrine system and brain health are being completely ignored while TRT gets all the attention• The law enforcement skill set transfers directly into business leadership: problem-solving under pressure, decision-making without perfect information, ownership of mistakes, and relationship-first sales are all things operators have already built on the job, they just don't recognize it as a resumeChapters:0:39 Iron Sights mission statement and welcome2:02 Introducing Brad Ortiz and Dewayne Manson2:49 Brad's background: The Uncommon Line podcast9:40 American Warrior Association programs overview11:43 Resilience training inside TTPOA: the first of its kind12:08 What moral injury actually is and why it's not PTSD14:20 Culturally competent clinicians: Dewayne's personal story31:52 Compounding critical incidents over a career38:37 Social media pile-ons as a hidden source of moral injury44:55 Identity vs. calling: challenging how officers define themselves1:12:18 Relationship-first sales, legacy brands losing ground, and the business parallel1:21:46 How to access AWA, R3, and SoundOff resourcesMentioned:Brad Ortiz — Director of Sales for the Law Enforcement Division at Silencer Shop, former law enforcement officer and fugitive investigator, and former host of The Uncommon Line podcast, joined as a guest to discuss the SoundOff program and the AWA partnership.Dewayne Manson — Representative of the American Warrior Association, infantry veteran who served in Afghanistan and contracted there through 2020, shared his personal journey through moral injury and recovery as context for AWA's R3 program.Matt Smith — Co-host of the TTPOA podcast with 21 years on SWAT, co-hosted this live conference episode alongside Scott Howell and competed in the TTPOA shooting competition the day prior.Brandon Hernandez — Region 7 Director and Director of Training for TTPOA, co-host of the TTPOA podcast, referred to on air as the celebrity of the group when a passerby stopped to greet him.Anna Heil — Associated with the American Warrior Association, attended a TTPOA show in St. Louis and briefed other state organizations on the AWA partnership afterward.Chris — Works alongside Brad Ortiz at Silencer Shop on the SoundOff individual officer program, mentioned as a direct point of contact buyers will reach at soundsoftshop.com.Neil Noakes — Former Chief of Police of the Fort Worth Police Department, cited as an example of a career officer who, when asked how many critical incidents he had been on, simply said he had lost count.Simon Sinek — Author and speaker known for the concept of finding your 'why,' referenced when Brad Ortiz challenged every officer in the room to examine the deeper reason behind their career and identity.

The episode identifies a core structural shift in the managed services industry: the decoupling of service measurement from observable work due to the adoption of autonomous service desk technologies. This shift is driven by the introduction of automation platforms—such as Atera's Robin, Acronis AI Service Desk, and NinjaOne's endpoint automations—that eliminate or obscure traditional service tickets, shifting operational baselines and the metrics used for client billing and value demonstration. Evidence of this shift includes Atera guaranteeing that within 90 days, its Robin system will autonomously resolve half of Tier 1 and complex Tier 2 tickets, enforced via commercial contract terms. The company builds baselines by requiring six months of client ticket history before implementation. Supporting data from Channel EDE and AT&T show that automation can suppress visible ticket volume while inflating claims of efficiency and avoided incidents, independent of provider-side measurement. According to Dave Sobel, AT&T tracked autonomous incident handling since 2018, but most MSPs lack comparable historical data. Further developments reinforce this transition: NinjaOne integrates with ServiceNow to create incidents without human intervention, while Ingram Micro channel feedback observes partners aiming to increase business without staff growth. Broader labor market data and user sentiment surveys reveal that AI-backed automation does not show aggregate productivity gains (Stanford Economic Policy Institute) and is generally viewed with skepticism: Gallup and Apistevist data highlight declining confidence in corporate AI deployments and increased worker nostalgia for pre-automation workflows. The operational impact for MSPs centers on data ownership, measurement accountability, and renewal risk. As traditional records like tickets are eliminated or fragmented, providers who lack their own carefully preserved baselines may find themselves forced to rely on vendor-generated claims for demonstrating avoided work or cost savings. This creates exposure to contract risk, compromised pricing leverage, and governance complexity—especially if ticket-level detail, taxonomy, or supporting operational notes are lost in platform migrations or poorly configured retention policies. According to Dave Sobel, preparing by exporting comprehensive ticket histories, freezing operational taxonomies, and independently counting non-ticket sources of demand are now urgent requirements to maintain accountability and defensible value in future client negotiations. 00:00 The Ticket Is Disappearing  03:45 You Can't Invoice an Absence  07:20 The Client Already Stopped Believing 11:18 Why Do We Care?    Supported by: ScalePad

Quick update episode before I head out to walk the Camino! A few episodes ago, I told you about coming off my medication and sleep aids, the brain zaps, and the nights I barely slept.Here's where things landed: I'm fully off everything, sleeping better than I have in months, and I'm sharing exactly what worked.I'm also telling you about the day I broke my own rule, paid for it until 12:30am, and what I did the next morning — because that part is the whole lesson. If you've ever blown a boundary with alcohol and used it as a reason to give up on the whole thing, this one's for you. In this episode, you'll learn: The full update on coming off sleep aids and medication, including the last little crutch I didn't even realize I was using The two simple changes that made the biggest difference in my sleep What happened the day I broke my own caffeine boundary (and why it felt exactly like drinking) How to get back to a boundary after you break it, without the shame spiral or the "I'll start Monday" trap Why your body knows how to get back to baseline when you stop interfering Quick reminder: this is my personal experience with medication. Always talk to your doctor before starting or stopping anything.Also, I recommend you listen to 'How to Stop Talking Yourself Out of It When It Gets Uncomfortable' before you listen to this one, to get the full context! Listen to that here: https://www.angelamascenik.com/podcasts/stop-over-drinking-and-start-living/episodes/2149227702Now accepting applications for the next cohort of the Transformation Program, check that out here: https://www.angelamascenik.com ABOUT ANGELA: Angela Mascenik is a certified stop over-drinking coach for women and the host of the Stop Over-Drinking and Start Living podcast. She helps high-achieving women get to the root of why they drink and change their relationship with alcohol from the inside out — without white-knuckling it or relying on willpower. Angela is the creator of the 6-month Transformation Program, the Alive AF! membership, and The Magic House Retreat Center in Lisbon, Portugal.

Dr. Peter Dionisopoulos is a physical therapist and performance rehabilitation specialist focused on injury prevention, rehabilitation, and performance optimization. He emphasizes the value of finding the right exercise threshold where discomfort exists but pain doesn't, and the importance of progressive exposure to stress in a safe manner to build resiliency.  He shares that different body types have genetic predispositions toward certain movement patterns, the need for individualized approaches rather than one-size-fits-all solutions, and the significance of maintaining fitness levels even during recovery from injuries or surgery. Pete explains his approach at Dynamic Performance Rehab, which bridges basic rehabilitation with performance enhancement to help people achieve optimal function rather than just returning to normal. To connect with Dr. Peter Dionisopoulos visit dynamicprri.com or follow on Instagram @dynamicpr.ri   Visit ConfidenceThroughHealth.com to find discounts to some of our favorite products.Follow me via All In Health and Wellness on Facebook or Instagram.Find my books on Amazon: No More Sugar Coating: Finding Your Happiness in a Crowded World and Confidence Through Health: Live the Healthy Lifestyle God DesignedProduction credit: Social Media Cowboys

A polished report, monthly invoice or TEM dashboard doesn't necessarily give customers a baseline they can trust. In complex technology environments, contracts, invoices, inventories and ownership records often drift apart as services change, sites move and suppliers bill differently. When the baseline is wrong, strategy, sourcing decisions, savings targets, and budgets are built on shaky ground. In this episode of Staying Connected, Tony Mangino is joined by TC2's Frank Zagrodnik to discuss how enterprise customers can move from invoice fire drills to a defensible and actionable baseline by connecting contracts, invoices, inventory and ownership. If you would like to learn more about our experience in this space, please visit our IT Cost Management webpage.

This past week, Josh was on vacation, so my brother Jared jumped on the show to fill in. We have our normal brotherly debates, but also some good discussion about the ACC and NFC East, with a little soccer sprinkled in.

This past week, Josh and breakdown one of the best conferences in football and one of the weakest divisions in football. Who will come out on top of the SEC? Will it be Georgia? Or will another team claim that prize? Then is the NFC South truly one of the worst divisions in football?

Episode Summary: To secure the growing U.S. interests in space, it is imperative we have a capable and ready Space Force that can meet the challenges of the future. The Future Operating Environment 2040 and the Objective Force Baseline are the Space Force's assessment of what to expect and what forces and capabilities it will need to preserve space superiority. To learn more about these documents we had an in-depth discussion with two of the principal authors, Col. Paul Latour and Lt. Col. Sean Frederick. Credits: Host: Heather "Lucky" Penney, Director of Research, The Mitchell Institute for Aerospace Studies Producer: Shane Thin Executive Producer: Douglas Birkey Guest: Charles Galbreath, Director & Senior Resident Fellow for Space Studies, The Mitchell Institute Spacepower Advantage Center of Excellence (MI-SPACE) Guest: Jennifer "Boots" Reeves, Senior Resident Fellow for Space Studies, MI-SPACE Guest: Col. Paul LaTour Guest: Lt. Col. Sean Frederick Links: Subscribe to our YouTube Channel: https://bit.ly/3GbA5Of Website: https://mitchellaerospacepower.org/ Twitter: https://twitter.com/MitchellStudies Facebook: https://www.facebook.com/Mitchell.Institute.Aerospace LinkedIn: https://bit.ly/3nzBisb Instagram: https://www.instagram.com/mitchellstudies/ #MitchellStudies #AerospaceAdvantage #Space #Military #Future

See how a single Azure Database for PostgreSQL Flexible Server scales read-heavy workloads using read replicas and virtual endpoints — the same pattern OpenAI relies on to run ChatGPT on Postgres. Scott and Paula show a live read workload saturating a primary, then offload it to a replica with a single hostname change, and finish with replicas across Europe staying within milliseconds of the primary. It's read scale-out without sharding or re-architecting your app. Chapters 00:30 - Introduction 01:55 - Why read replicas: read/write asymmetry 03:45 - Architecture: primary, replicas, and virtual endpoints 05:30 - Demo: the cluster and a read-only replica 10:28 - Baseline: saturating the primary 13:53 - The flip: offloading reads to a replica 16:48 - Wrap up Recommended resources Read replicas in Azure Database for PostgreSQL Azure Database for PostgreSQL Demo script Connect Scott Hanselman | Twitter/X: @SHanselman Paula Berenguel | LinkedIn: paulaberenguel Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

See how a single Azure Database for PostgreSQL Flexible Server scales read-heavy workloads using read replicas and virtual endpoints — the same pattern OpenAI relies on to run ChatGPT on Postgres. Scott and Paula show a live read workload saturating a primary, then offload it to a replica with a single hostname change, and finish with replicas across Europe staying within milliseconds of the primary. It's read scale-out without sharding or re-architecting your app. Chapters 00:30 - Introduction 01:55 - Why read replicas: read/write asymmetry 03:45 - Architecture: primary, replicas, and virtual endpoints 05:30 - Demo: the cluster and a read-only replica 10:28 - Baseline: saturating the primary 13:53 - The flip: offloading reads to a replica 16:48 - Wrap up Recommended resources Read replicas in Azure Database for PostgreSQL Azure Database for PostgreSQL Demo script Connect Scott Hanselman | Twitter/X: @SHanselman Paula Berenguel | LinkedIn: paulaberenguel Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

Dr. Nilesh Ambhore interviews Dr. Gerard H. Koppelman about his article, "Protocadherin-1 Deficiency Increases Baseline and Allergen-Induced Airway Hyperresponsiveness in Mice."

In this episode, Stuart is joined live at Groundswell by regen farmer and mentor Silas Hedley-Lawrence. Silas became a member of the Regenerate Outcomes mentoring team earlier this year. He is well known in the UK as a prominent trainer and communicator in soil health and regenerative agriculture. The conversation covers his progression from growing up on his family's farm in New Zealand to discovering new practices, learning from experts such as Nicole Masters and putting his knowledge into action to support other farmers. This podcast is brought to you by Regenerate Outcomes, which supports farmers to grow profits and improve crop and livestock performance by building functional soil.Receive one-on-one mentoring from experienced regenerative farmers to increase the productivity of your soil, cut costs and reduce external inputs.Baseline and measure changes in soil carbon to generate verified carbon credits which you can retain or sell for additional income.No cost to join. No cost to leave.For more information go to www.regenerateoutcomes.co.uk

Are your sleep trackers helping you sleep better? Or making you more anxious about your sleep? In this episode, we welcome back sleep expert Mollie Eastman, founder of Sleep Is A Skill, to explore how chasing the perfect sleep score can actually undermine restorative sleep, while sharing practical strategies for improving sleep through mindset shifts, Acceptance and Commitment Therapy (ACT), and smart use of wearable data. Mollie also discusses overcoming the "First Night Effect" while traveling, when to consider at-home sleep studies, and the foundational tests that matter most before investing in advanced biohacks. Finally, she shares her personal experience with psychedelic-assisted therapy at Beckley Retreats, revealing how addressing suppressed emotions and unresolved stress transformed both her sleep and overall well-being.Mollie Eastman is the creator of Sleep Is A Skill and the host of The Sleep Is A Skill Podcast. Sleep Is A Skill is a company that optimizes people's sleep through a unique blend of technology, accountability, and behavioral change. After navigating insomnia while traveling internationally, she created what she couldn't find - a place to go to learn the skill set of sleep. With a background in behavioral change from The Nonverbal Group, she became fascinated with chronobiology and its practical application to sleep and our overall experience of life. Knowing the difference between a life with sleep and without, she's now dedicated her life to sharing the forgotten skill set of sleep. In the spirit of that goal, she has created the #2 sleep podcast, where she has interviewed over 200 sleep experts, written a popular weekly sleep newsletter for over six years, partnered with luxury hotels & lifestyle brands, coached the world's top poker players, and has appeared on over 175 podcasts.SHOW NOTES:0:39 Welcome to the podcast!2:16 About Mollie Eastman3:19 Welcome her back to the show!4:48 Are wearables helping or harming?7:04 Placebo-Nocebo effect11:25 Getting relief from sleep data14:00 The ‘mindset' undermining sleep16:13 Acceptance & Commitment Theory (ACT) for Insomnia18:38 Using biohacks for “First Night Effect”25:05 Sleep disorders & at-home sleep studies32:14 Auditing sleep data36:13 Baseline tests to start with39:51 Her latest discovery on sleep support40:55 Beckley Retreats48:47 The benefits of psychedelics on her sleep54:24 The impact of suppressing emotions58:57 Where to find herRESOURCES:Website: Sleep Is a SkillSleep Obsessions' Monday Newsletter8-Week Wearable Group “Optimize Your Sleep” ProgramSleep Is A Skill PodcastLinkedInIG: @mollie.eastmanSleep Tests:SleepDoctor.comSleep Image RingHappy RingSleep Doctor Watch PAT - WatchPATOnera Sleep StudySupport this podcast at — https://redcircle.com/biohacker-babes-podcast/donationsAdvertising Inquiries: https://redcircle.com/brands

Today, I'm joined by Marco Suvilaakso, co-founder & co-CEO of Nucu.   Swapping wearables for "nearables," Nucu's low-touch ambient sleep monitoring platform gives guardians insight into kids' and teens' rest patterns.   In this episode, we discuss filling the market gap in pediatric sleep.   We also cover:   Building family-focused healthtech Benefits of real-time hypnogram data Avoiding obsession over metrics   Subscribe to the podcast → insider.fitt.co/podcast  Subscribe to our newsletter → insider.fitt.co/subscribe  Follow us on LinkedIn → linkedin.com/company/fittinsider    Nucu's Website: https://nucuhealth.com/  Nucu's Instagram: https://www.instagram.com/nucuhealth/  Nucu's LinkedIn: https://www.linkedin.com/company/nucu/    -   The Fitt Insider Podcast is brought to you by EGYM. Visit EGYM.com to learn more about its smart fitness ecosystem for fitness and health facilities.   Fitt Talent: https://talent.fitt.co/  Consulting: https://consulting.fitt.co/  Investments: https://capital.fitt.co/    Chapters: (00:00) Introduction (01:33) Marco background and Nucu overview (05:15) Kids/teens market gap  (06:30) Customer feedback and shift (09:54) Form factor redesign (11:20) Sleep consciousness building (15:03) Baseline and trend tracking (16:00) Room conditions correlation (18:31) Deep biometric insights (20:00) Training schedule impact (21:15) Lifespan data architecture (24:15) Ecosystem partnerships (26:30) Avoiding obsession over metrics (29:50) Real-time hypnogram data (33:40) Two target audiences (36:00) Research collaborations (37:07) Conclusion

In this episode, Hannah sits down with Taylor, a Special Education teacher at Little Light House, to explore a foundational approach to supporting Autism. Taylor walks parents through four key needs to check when dysregulation occurs: hydration, a full belly, rest, and health. She also unpacks the concept of sensory rest and why it looks different for neurodivergent children. If your child frequently struggles with dysregulation, this episode gives you a simple, powerful place to start. Every kid deserves the chance to just be a kid.At Little Light House, that belief drives everything we do. We provide tuition-free education and therapeutic services, rooted in Christ-centered care, for children with special needs and the families who love them.Learn More: https://www.littlelighthouse.orgLet's stay connected:Facebook: https://www.facebook.com/llhtulsaInstagram: https://www.instagram.com/llhtulsaBe part of the story:Give: https://www.littlelighthouse.org/give-helpJoin THECREW: https://www.littlelighthouse.org/the-crew

We were promised that more data would give us more control. Somewhere between the sleep score and the blood panel, many of us stopped asking how we actually feel. After stepping away from the demanding executive career that had shaped much of her identity, physician and healthcare leader Dr. Nasim Afsar was forced to confront a deeper question: Who are you when the role, schedule, and story that defined you are suddenly gone? In this conversation, Nasim and Marc explore identity beyond achievement, the danger of trying to fix every uncomfortable emotion, and the growing anxiety surrounding wearables, supplements, testing, peptides, and longevity protocols. They also examine how unified health data and artificial intelligence could create a more personalized and human healthcare system—provided that people remain the owners of their information and the centre of every decision. This is a conversation about becoming whole, trusting how you feel, and remembering that no technology can replace sleep, movement, purpose, community, and a life you genuinely want to live. Show Partners: Get your MENTAL FITNESS BLUEPRINT here! A special thanks to our mental fitness + sweat partner Sip Saunas Personal Socrates: Better Question, Better Life   Connect with Marc: https://konect.to/marcchampagne   Timestamps: 00:00 — The question that opens every interview: “Who are you?” 01:41 — Leaving the career that had become part of her identity 04:23 — What story are you telling yourself about who you are? 06:32 — Becoming a more whole human being 08:21 — What a Valentine's Day disappointment taught Nasim about pain 11:14 — Why fixing the problem can prevent us from understanding it 13:16 — Letting discomfort teach you instead of managing it away 15:38 — How medical training conditioned doctors to disconnect emotionally 18:36 — Feeling difficult emotions without becoming overwhelmed by them 20:03 — Why high-stress professions must teach people how to process trauma 22:17 — The connection between human context and intelligent health 23:47 — The patient labelled “non-compliant” whose real story was being missed 26:06 — Why clinical care represents only part of what shapes our health 26:44 — The three pillars of intelligent health 28:53 — Why modern health optimization has become overwhelming 30:12 — When wearable data disconnects you from how you actually feel 31:58 — The psychological cost of forcing yourself through “healthy” routines 33:34 — The anxiety and stress hiding inside the longevity movement 35:33 — A healthier, seasonal approach to wearables and tracking 37:56 — The simple fundamentals shared by people who live well 39:12 — The dangerous side of rushing into unverified health treatments 40:32 — Returning to the fundamentals before chasing the latest protocol 42:01 — Why fragmented health data prevents personalized care 45:15 — Where to begin when health information feels overwhelming 46:06 — Baseline testing, trusted practitioners, AI, and health-data privacy 47:38 — Why no peptide can replace the foundations of health 48:18 — The personalized health future that may be closer than we think 49:55 — Reclaiming autonomy without chasing every health trend * Special props

This past week, Josh and I started our annual football preview. This is where we preview every power 4 conference and NFL division. It takes us a while, so that is why we are starting this now. We give you our take on the wild Big 12 and the deep NFC West.

https://coachcollins.podia.com/funnel-down-defense https://teachhoops.com/ Today's episode is brought to you by TeachHoops.com, home to thousands of organized drill videos, defensive breakdowns, and course templates — everything in one click instead of scrubbing through random YouTube videos. In this episode, we install the LockLeft defensive framework: a simple, teachable system built on one ruthless idea — force every ball handler to his left hand, every possession, all game long. We cover the core rules: how on-ball defenders angle their stance to take away the right hand, how help defenders shade to shrink the left side of the floor, and how the whole scheme squeezes passing windows until offenses start throwing the ball to you. You'll learn the three most common breakdowns when teams first install it, how to rep it with simple shell drill constraints, and why this system is especially powerful at the high school and youth levels, where almost nobody can finish with their weak hand. If you've ever wanted a defensive identity your kids can master in two weeks and ride all season, this is the episode. Get the companion drill videos and full defensive install resources at https://teachhoops.com/. Learn more about your ad choices. Visit podcastchoices.com/adchoices

What does the data tell us about the real path to becoming an architect, and how is NCARB shifting its model to build a more adaptable, modular roadmap for future candidates?In this episode of Practice Disrupted, host Evelyn Lee breaks down the 15th edition of the NCARB by the Numbers report. She is joined by Jenny Kawecki, who leads data analytics and research at NCARB, and Gabriella Bermea, a licensed architect and NCARB volunteer who chaired the Experience Committee. Together, they explore the shifting dynamics of the architectural pipeline, unpacking why the median time to licensure has dropped to 12.3 years and what these statistics mean for the future of the profession.Jenny provides an insider's view of how NCARB uses this annual data report to track attrition points and actively improve its programs. She highlights major programmatic changes driven by historical data trends, such as retiring the rolling clock and revamping the Architectural Experience Program (AXP) reporting policy to give thousands of candidates their experience credits back. Gabriella adds an on-the-ground perspective, explaining how challenging past assumptions about rigid timelines helps eliminate systemic barriers and re-engages candidates who have stepped away from the path.The conversation also digs deeply into NCARB's massive "Pathways to Practice" initiative, a multi-year effort to pivot away from a single, rigid route toward a highly modular, competency-based framework. They address critical pipeline pinch points, including firm culture support during the AXP and the stark demographic pass-rate disparities within the Architect Registration Examination (ARE)."Instead of pushing everyone through the same path, how can that path meet the needs of the individual? How can it be customizable almost to the candidate so that it aligns with your background, your career path, your educational path, your social path. We want there to be a licensure path that exists for you." - Jenny KaweckiFinally, they look forward to how a more flexible, customizable licensure path can help mitigate the steep financial burdens of higher education and foster a significantly more diverse, representative body of architects.Guests:Jenny Kawecki is NCARB's Assistant Vice President of Data, Analytics, and Research, where she leads a team that analyzes industry trends and oversees publications like NCARB by the Numbers and Baseline on Belonging. Before joining NCARB in 2016, she wrote for Barnes & Noble and SparkNotes, and she holds a degree in Media, Culture, and the Arts from The King's College.Gabriella Bermea, AIA, is a Senior Associate and Architect at Perkins Eastman with more than eight years of experience in educational design, stakeholder engagement, and community-focused solutions. A fifth-generation Tejana, she is a passionate advocate for educational equity and community empowerment and currently serves as Chair of NCARB's Experience Committee.This episode is especially for you if:✅You want to know why the median time to licensure dropped and how policy changes like eliminating the rolling clock shifted the needle.✅You are curious about NCARB's "Pathways to Practice" initiative and how a modular, competency-based framework will offer diverse alternatives to the traditional degree path.✅ You want to explore the primary pinch points in the licensure pipeline, from firm culture barriers during the AXP to pass-rate disparities within the ARE.✅You want to learn how the revised AXP reporting requirements allow candidates to receive 100% credit for hours up to a year and 75% credit indefinitely.✅You are interested in how scholarship programs through NOMA chapters and state boards are helping candidates facing financial and institutional adversities stay on the path.What have you done to take action lately? Share your reflections with us on social and join the conversation.

Setting a Baseline for Cam Ward w/ Lucas Panzica | Football & Other F WordsSee omnystudio.com/listener for privacy information.

Welcome to the grand finale and weekly review of our master series. In this closing session, we explore the advanced neuro-somatic practice of permanent nervous system baseline integration, focusing our ultimate awareness on the 7th Chakra (The Crown Center). Discover the cutting-edge science of how a seven-day commitment to somatic tracking leverages neuroplasticity to structurally rewrite your default stress responses. By weaving the foundational elements of your week into our crowning, integrated protocol, you will learn to permanently anchor your nervous system on an inner throne of sovereign stillness. Turn off the noise of the outside world, claim your absolute crown, and drift effortlessly into a deep, restorative night of sleep.

Episode Summary In this episode of the Work at Home Rockstar Podcast, Tim Melanson sits down with Curtis McCullom, CEO and Clinical Hypnotherapist at Bespoke Human Potential Coaching. Curtis shares how his LEGIT Mindset framework, centered around learning, growing, expanding, and transforming, has shaped both his personal journey and the work he does with entrepreneurs looking to overcome the mental roadblocks holding them back. The conversation explores why setbacks are often valuable teachers, how knowing your numbers can change your business, and why authenticity is more important than perfection. Curtis also shares practical advice for building a productive work-from-home environment, finding the right clients, and creating a business that aligns with who you are. Whether you're just starting your entrepreneurial journey or looking for a fresh perspective on growth, this episode offers plenty of mindset shifts and practical lessons to help you keep moving forward. Who is Curtis McCullom? Curtis McCullom is the CEO and Clinical Hypnotherapist at Bespoke Human Potential Coaching. He helps high-achieving CEOs, founders, and entrepreneurs identify and release subconscious blind spots that may be limiting their performance, clarity, and income. Drawing from decades of business, sales, and coaching experience, Curtis combines mindset coaching with his LEGIT Mindset framework to help clients better understand themselves, build confidence, and move toward meaningful personal and professional growth. Connect with Curtis McCullom Website: http://www.bespokehumanpotentialcoaching.com/ Website: https://curtismccullom.com YouTube: https://youtube.com/@curtismccullom LinkedIn: https://www.linkedin.com/in/curtis-mccullom/ Facebook: https://www.facebook.com/BespokeHumanPotential Facebook: https://www.facebook.com/curtis.mccullom.BHPC/ Instagram: https://www.instagram.com/curtis.mccullom/ X: https://twitter.com/curtisBmccullom TikTok: https://tiktok.com/@curtismccullom Host Contact Details Website: https://workathomerockstar.com Facebook: https://www.facebook.com/workathomerockstar Instagram: https://www.instagram.com/workathomerockstar/ LinkedIn: https://www.linkedin.com/in/timmelanson/ YouTube: https://www.youtube.com/@WorkAtHomeRockStarPodcast Twitter: https://twitter.com/workathomestar Email: tim@workathomerockstar.com Timestamps 00:00 Meet Curtis McCullom 00:50 LEGIT Mindset Framework 02:27 Responding Not Reacting 04:39 Subconscious Lessons Loop 07:27 Better Questions Not Why 09:34 Early Sales Failure Turnaround 11:36 Asking for Help and 80 Percent 13:14 Know Your Numbers Game 16:05 Find Your Voice and Baseline 17:53 Bespoke Coaching and Niches 20:00 Choosing the Right Clients 20:40 Trial and Error Growth 22:34 Home Office Setup 25:07 Boundaries at Home 27:11 Tools Without Distraction 29:03 Authenticity Over Gear 30:04 Coaching Offer and Calls 31:24 Root Causes of Fear 32:30 Not Good Enough Belief 33:44 Mindset Beats Talent 36:29 Music Favorites and Listening 38:40 Final Thanks and Outro Disclaimers Mindset & Coaching Disclaimer This episode shares Curtis McCullom's personal coaching philosophy and professional experience. The ideas discussed are intended for educational and inspirational purposes and are not a substitute for professional medical, mental health, legal, or financial advice. Therapeutic Perspective Disclaimer This conversation includes discussions about subconscious patterns, hypnotherapy, NLP, and emotional growth. These topics are presented from the guest's professional perspective and should not be interpreted as medical or psychological treatment advice.

Episode Summary Most people believe they know how to use AI—but are they simply treating ChatGPT like a replacement for Google? In this episode of The EdTech Podcast, Philippa Wraithmell speaks with Joshua, Co-founder and CEO of Mindstone, about what genuine AI competency looks like and why successful AI transformation depends far more on people, behaviour and organisational culture than technology. Joshua shares his journey from launching his first company as a teenager to co-founding SuperAwesome, the child-safety technology company later acquired by Epic Games. He explains how this experience shaped his thinking about digital safeguarding, parental responsibility and the need to help children engage with technology gradually, rather than attempting to protect them through blanket bans. The conversation explores why simply inserting AI into existing tasks rarely delivers meaningful value. Joshua argues that the real opportunity lies in changing how we approach work: asking AI to question us, challenge our assumptions, identify blind spots and support our thinking rather than outsourcing it. They also examine the widening AI skills divide. As experienced users build years of personal context, workflows and AI agents, those who delay may find themselves increasingly disadvantaged. This has major implications for workplaces, schools and universities—particularly when many institutions still respond to AI through prohibition rather than education. From leadership and workforce transformation to safeguarding, sustainability misconceptions and the future of employment, this episode asks an important question: are we preparing people to use AI well, or simply giving them access to the tools?  Key Topics What genuine AI competency looks like Why most people are using generative AI incorrectly Using AI to enhance rather than replace human thinking Why AI transformation is a people and behaviour-change challenge The responsibility of senior leaders during AI adoption The growing inequality between experienced and inexperienced AI users Why banning AI in schools can encourage secretive and inappropriate use Building safer, age-appropriate pathways into AI and social media The role of parents, governments and technology companies in safeguarding Misconceptions surrounding AI's environmental impact How AI agents are changing productivity and cognitive workload Preparing people for disruption across employment and education Chapters 00:00 – Welcome and Introduction to Joshua Joshua introduces Mindstone and its work helping non-technical teams use generative AI more effectively. 01:04 – Building a Business at Sixteen From creating websites for family connections to managing a team of students building technology for private banks. 02:08 – Early Start-up Lessons Joshua reflects on a failed start-up, turning down investment and the lessons that led to his next venture. 03:17 – Building SuperAwesome and Protecting Children Online How SuperAwesome developed advertising, privacy and verified parental-consent technology for global children's brands. 04:47 – Safeguarding, Regulation and Parental Responsibility Why Joshua believes governments must avoid removing parental agency when introducing online-safety regulation. 06:46 – Education, Curiosity and Challenging Authority Joshua discusses his school experiences, constant questioning and learning across six different school models. 10:04 – Why Joshua Founded Mindstone The transition from Epic Games into education, learning science and the future of workforce skills. 13:07 – When Skills Expire Before Degrees Are Completed Why the declining half-life of workplace skills raises fundamental questions about traditional education. 14:04 – ChatGPT Changes the Direction of Mindstone How generative AI transformed both the learning process and the skills likely to matter in the future. 15:28 – Why Most People Do Not Know How to Use AI Joshua explains why frequent ChatGPT use does not necessarily equal AI competency. 17:24 – ChatGPT Is Not a Search Engine Why treating AI as a Google replacement represents one of its least valuable applications. 18:17 – AI, Water and Energy Misconceptions A discussion about environmental claims, data centres and the importance of understanding evidence and scale. 20:45 – Stop Asking AI for Answers Joshua shares a simple technique: ask AI to question you, uncover blind spots and strengthen your thinking. 22:09 – Treating AI as a Conversation Why users should refine, challenge and continue the interaction rather than abandoning AI after one poor response. 24:10 – AI as a Digital Chief of Staff How accumulated context allows AI to understand preferences, decisions and working patterns more effectively. 25:04 – The Emerging AI Inequality Gap Why users who have spent years building AI systems and context may gain a compounding advantage. 26:54 – Why School AI Bans Are Failing How prohibition can drive AI use underground and encourage learners to outsource rather than demonstrate their thinking. 27:23 – Making AI the Baseline for Learning Why students should be expected to show how they improved upon AI-generated work rather than pretend the technology does not exist. 30:47 – AI Transformation Begins with Leadership Why executive teams must understand and use AI before asking the rest of an organisation to change. 33:11 – AI Is a People Problem The case for moving AI transformation from the chief technology officer to the chief people officer. 33:40 – Why Online Training Alone Does Not Change Behaviour How live demonstrations, personalised learning and practical workplace applications support meaningful adoption. 36:19 – Champions, Laggards and Organisational Change The role of internal champions, visible success stories and sustained momentum. 38:05 – Reclaiming Time for Meaningful Work How AI can reduce administrative burden and allow people to focus on the work they value most. 39:12 – The Safeguarding Knife Edge Joshua considers how children can gradually gain independence while parents retain appropriate visibility and support. 42:38 – The Problem with Digital Cliffs Why completely restricting children until a set age may leave them unprepared for the realities of online life. 44:38 – Technology Cannot Replace Parenting Lessons from SuperAwesome about parental consent, responsibility and the limitations of technical safeguards. 47:44 – Jobs, Robotics and the Next Decade Joshua shares an optimistic but honest view of significant employment disruption and rapid technological progress. 49:15 – AI Agents and 'Brain Fry' Why delegating routine work to AI can leave humans completing only the most cognitively demanding tasks. 50:48 – Becoming More Human in an AI World The opportunity to rethink work, protect time and make greater space for creativity, relationships and life beyond productivity. Mindstone Want to boost productivity by training your non-technical staff to use AI? Sign up for Mindstone's AI Competency Programme, or join one of the world's largest practical AI communities, with in-person events taking place every month around the world. AI Competency Programme: mindstone.com/enterprise Community and Events: community.mindstone.com/events

Carl and Mike share thoughts on why it is hard for Carl "feel good" about Michael Penix Jr.'s status as he feels he should and discuss why they believe part of the challenge may be due to the fact the third-year quarterback has not established himself as a star in the league yet.

Unlock the hidden code behind human behavior—before it costs you opportunities, relationships, and influence.Most people think they're good at reading others. They're not. They project, assume, and misinterpret—then wonder why conversations stall, deals fall apart, and signals get missed. Behavioral Tells cuts through the noise and shows you what people are actually communicating beneath the surface. From bestselling author Patrick King, this is your field guide to decoding people in real time—without guesswork, overthinking, or relying on clichés like “just trust your gut.” You'll learn how perception quietly distorts everything you see—and how to fix it fast. How perceptual biases like the halo effect and projection silently sabotage your judgment The three perceptual positions that instantly sharpen your perspective in any interaction Why your expectations shape what you notice—and how to break that loop Practical ways to improve perceptual accuracy so you stop misreading people But reading people isn't just about what you see. It's about what you notice, how you interpret it, and how you respond in the moment. This book takes you deeper—into emotions, group dynamics, language patterns, and subtle behavioral signals most people completely miss. The ABC model for understanding why people behave the way they do Emotional granularity so you can distinguish nuance, not just “happy vs. angry” The SUE framework for asking questions that reveal truth without resistance How tone, word choice, and “meta-programs” expose hidden motivations Along the way, you'll learn why body language alone can mislead you, how attention flows in groups, and what everyday behaviors—like walking style, clothing, and even food choices—quietly reveal. Bottom line: This is not theory. It's a toolkit. Navigate conversations with precision Spot inconsistencies before they become problems Understand people faster than they understand themselves

Mickey Trescott introduces a modified autoimmune protocol that includes rice and coffee, making it more accessible and affordable than the core version. A successful transition requires tracking baseline symptoms and preparing the kitchen to handle the nutritional demands of the upcoming elimination and reintroduction phases. (11)1898 BRUSSELS

Mickey Trescott introduces a modified autoimmune protocol that includes rice and coffee, making it more accessible and affordable than the core version. A successful transition requires tracking baseline symptoms and preparing the kitchen to handle the nutritional demands of the upcoming elimination and reintroduction phases. (11)1900

SummaryStarting a health journey can feel overwhelming, especially if you think you have to change everything overnight. In this episode, Chase and Chris explain why the best place to start is with awareness. They talk about tracking your food, learning your current habits, and creating a realistic baseline that fits your life. They also explain why your baseline should grow over time as you make progress. You'll learn why building simple, repeatable habits creates confidence, helps you stay consistent during busy seasons, and leads to long-term success instead of another short-lived diet. Chapters(00:00) Why Every Health Journey Starts with Awareness(03:45) Tracking Food Is a Tool, Not a Diet(05:15) Building Your Baseline with Steps, Protein, and Strength Training(08:00) Why Your Baseline Should Continue to Grow(12:30) Building Confidence Through Small Wins(17:40) Why New Habits Feel Hard at First(20:00) The Truth About Being Active vs. Being Busy(25:45) Creating a Lifestyle You Can Always Return To(27:30) The Simple Formula for Long-Term SuccessSUBMIT YOUR QUESTIONS to be answered on the show:https://forms.gle/B6bpTBDYnDcbUkeD7How to Connect with Us:Chase's Instagram:https://www.instagram.com/changing_chase/Chris' Instagram:https://www.instagram.com/conquer_fitness2021/Facebook Group:https://www.facebook.com/groups/665770984678334/Interested in 1:1 Coaching:https://conquerfitnessandnutrition.com/1on1-coachingJoin The Fit Fam Collective:https://conquerfitnessandnutrition.com/fit-fam-collective

You have created a new baseline for managing your stress. If you frequently find yourself finishing your week feeling mentally fragmented, scattered, or stuck on an emotional rollercoaster between high-alert stress and total burnout, your system is missing integration. In this final session, we dive into the advanced science of neuro-somatic synthesis and introduce Somatic Stacking—layering postural alignment, the Pran Mudra, and Root Chakra grounding simultaneously to instantly drop cortisol, lock out environmental noise, and restore a state of sharp, effortless daily execution. In This Episode, You'll Discover: The Cost of Fragmentation: The biological reason why juggling multiple roles splits your attention and keeps your nervous system chemically unbalanced. The Architecture of Somatic Stacking: How layering physical, energetic, and breath anchors simultaneously creates a powerful synergistic off-switch for stress. The Equilibrium Protocol: A guided 10-minute integrated meditation to completely stabilize your heart rate, stop energy leaks, and anchor your mind in sovereign peace. A Daily Message for Your Heart You are balancing a mountain of moving parts, steering a large vision, and anchoring the spaces you occupy. It is completely natural to feel scattered, like you are a collection of tasks and timelines rather than a whole soul. Today, remember that balance is not something you find by fixing everything in the outside world—balance is an internal posture you claim right here in the immediate present. You do not have to wait for the storm to stop to find your peace. By taking these ten minutes to stack your physical tools, you are gathering your scattered power back into your own hands. Let the affirmation 'My nervous system is steady, my body is safe, and I am entirely at peace' act as your absolute standard of stability today. You are doing an extraordinary job. This is day 7 of a 7-day meditation series, 3514-3520 Somatic Stress Relief.   THIS WEEK'S MEDITATION JOURNEY   If you are tired of waking up already feeling overwhelmed, running on adrenaline, and dealing with that persistent background anxiety that makes it impossible to focus, you are in the exact right place.   This week, we are stepping into a profound, 7-day somatic experience designed to pull you out of survival mode and return you to a state of calm, unshakeable power.   We aren't just going to talk about peace; we are going to build it directly into your biology. Over the next seven days, we will layer powerful, science-backed tools—including ancient hand mudras like the Pran Mudra, deep rhythmic breathing protocols, target chakra alignments, and restorative physical resets—to train your body that it is safe to let go.   Whether you are navigating a high-velocity career, balancing a mountain of daily demands, or simply trying to quiet an overactive mind before bed, this series is your ultimate biological reset button. Get ready to lower your cortisol, drop your shoulders, and reclaim your inner sovereignty.   THIS WEEK'S CHALLENGE: THE 30-SECOND COLD SPLASH At the end of your morning shower, turn the handle to cold for just 30 seconds. Alternatively, fill a bowl with ice water and submerge your face up to your temples for 10 seconds.   MEDITATION TECHNIQUES: DAY 1: VISUALIZATION Visualize yourself seated next to people who calm you.   DAY 2: AFFIRMATION "My body is safe, and I am at peace."   DAY 3: THE EXTENDED EXHALE BREATHING TECHNIQUE Inhale through your nose for a count of 4, feeling your belly expand. Hold gently at the top for a count of 4. Exhale slowly through pursed lips (like breathing through a straw) for a count of 8. Repeat for 3 to 5 cycles to instantly lower your heart rate and drop cortisol levels. DAY 4: PRAN MUDRA (THE LIFE FORCE SEAL) This mudra acts like a grounding cord for a scattered, anxious mind while simultaneously replenishing drained energy reserves without overstimulating your system. How to do it: On both hands, bring the tips of your ring finger and pinky finger to touch the tip of your thumb. Keep your index and middle fingers extended straight out. Rest your hands on your lap, palms facing up. DAY 5: MULADHARA ROOT CHAKRA When the nervous system is overwhelmed, energy flies upward into a swirling vortex of overthinking. Dropping your awareness down to the Root Chakra—located at the base of your spine—anchors you. Visualize a rich, warm, steady crimson light grounding you deeply into the solid earth beneath you.   DAY 6: LAYER ALL THE TECHNIQUES TOGETHER   DAY 7: REFLECTION AND CELEBRATION   SHARE YOUR MEDITATION JOURNEY WITH YOUR FELLOW MEDITATORS Let's connect and inspire each other! Please share a little about how meditation has helped you by reaching out to me at Mary@SipandOm.com or better yet -- direct message me on https://www.instagram.com/sip.and.om. We'd love to hear about your meditation ritual!   WAYS TO SUPPORT THE DAILY MEDITATION PODCAST SUBSCRIBE so you don't miss a single episode. Consistency is the KEY to a successful meditation ritual. SHARE the podcast with someone who could use a little extra support.   I'd be honored if you left me a podcast review. If you do, please email me at Mary@sipandom.com and let me know a little about yourself and how meditation has helped you. I'd love to share your journey to inspire fellow meditators on the podcast! All meditations are created by Mary Meckley and are her original content. Please request permission to use any of Mary's content by sending an email to Mary@sipandom.com.   FOR DAILY EXTRA SUPPORT OUTSIDE THE PODCAST Each day's meditation techniques are shared at: sip.and.om Instagram https://www.instagram.com/sip.and.om/sip and om Facebookhttps://www.facebook.com/SipandOm/   SIP AND OM MEDITATION APP Looking for a little more support? If you're ready for a more in-depth meditation experience, allow Mary to guide you in daily 30-minute guided meditations on the Sip and Om meditation app. Give it a whirl for 7-days free! Receive access to 3,000+ 30-minute guided meditations customized around a weekly theme to help you manage emotions. Receive a Clarity Journal and a Slow Down Guide customized for each weekly theme.   2-Week's Free Access on iOS https://itunes.apple.com/us/app/sip-and-om/id1216664612?platform=iphone&preserveScrollPosition=true#platform/iphone   All meditations are created by Mary Meckley and are her original content. Please request permission to use any of Mary's content by sending an email to Mary@sipandom.com.Let go of repetitive negative thoughts.   Music composed by Christopher Lloyd Clark licensed by RoyaltyFreeMusic.com, and also by musician Greg Keller.

This AMA episode covers a variety of topics, including net position, formations, and strategy frameworks. A few of the lessons are from a tight mixed doubles match that I recently played, while the others are from Tennis Tribe Members.When is I-formation a bad idea?Why I recently told my partner to stand in two different positions on my return points.When should the baseline player change something? (including how to handle inactive partners at net)Members only: My backhand volley pops up and floats every time. How do I fix it?Members only: How and when to add new strokes to your game. In this case, a semi-western forehand.Members only: What to do against two tall players who rush the net.Members only: The deuce court returner has a great crosscourt forehand. What should we do?We only have 2 rooms left for the Rally Trip at the US Open!If you're interested and want to sign up for the Doubles Camp from September 3-4, you can email me: will@thetennistribe.com.If you're not a Tennis Tribe member and want this full episode, learn more and sign up here. -----**Join the #1 Doubles Strategy Newsletter for Club Tennis Players** New doubles strategy lessons weekly straight to your inbox**Become a Tennis Tribe Member**Tennis Tribe Members get access to premium video lessons, a monthly member-only webinar, doubles strategy Ebooks & Courses, exclusive discounts on tennis gear, and more.Learn More & Sign Up Here**Other Free Doubles Content**Serve Strategy CheatsheetReturn Strategy CheatsheetServe Strategy 101 - Video Course

If you enjoy this episode, we're sure you will enjoy more content like this on The Occult Rejects.  In fact, we have curated playlists on occult topics like grimoires, esoteric concepts and phenomena, occult history, analyzing true crime and cults with an occult lens, Para politics, and occultism in music. Whether you enjoy consuming your content visually or via audio, we've got you covered - and it will always be provided free of charge.  So, if you enjoy what we do and want to support our work of providing accessible, free content on various platforms, please consider making a donation to the links provided below.  Thank you and enjoy the episode!Links For The Occult Rejectshttps://linktr.ee/theoccultrejectsOccult Research Institutehttps://www.occultresearchinstitute.org/Substackhttps://substack.com/@theoccultrejects?r=7auau0&utm_campaign=profile&utm_medium=profile-pageCash Apphttps://cash.app/$theoccultrejectsVenmo@TheOccultRejectsBuy Me A Coffeebuymeacoffee.com/TheOccultRejectsPatreonhttps://www.patreon.com/TheOccultRejectsBIBLIOGRAPHYHidden Rooms, Holy Water, and the DeadWhite, L. Michael. The Social Origins of Christian Architecture, Volume I: Building God's House in the Roman World: Architectural Adaptation Among Pagans, Jews, and Christians. Trinity Press International, 1996. Key use: Essential source for early Christian architectural adaptation, especially the shift from domestic and semi-domestic gathering spaces toward more specialized Christian buildings. White's work is useful for showing that early Christian architecture develops inside a broader Roman social and architectural world, not in isolation.White, L. Michael. The Social Origins of Christian Architecture, Volume II: Texts and Monuments for the Christian Domus Ecclesiae in Its Environment. Trinity Press International, 1997. Key use: Companion volume for the textual and archaeological evidence behind the domus ecclesiae, early meeting spaces, and the built environment of pre-Constantinian Christianity.Yale University Art Gallery. “Christian Building.” Dura-Europos: Excavating Antiquity. Key use: Strong anchor for the Dura-Europos Christian building and its wall paintings. Yale notes that the Christian paintings were uncovered in 1932 and that Clark Hopkins described the murals as preserved from more than three-quarters of a century before Constantine recognized Christianity in 312.Yale News. “House Call: A New Study Rethinks Early Christian Landmark.” 2024. Key use: Useful cautionary source for not oversimplifying Dura-Europos as merely a domestic “house church.” The report highlights recent scholarship reexamining how domestic the Dura Christian building really was and why its architectural classification needs care.Smarthistory. “Dura-Europos.” Key use: Accessible overview of Dura-Europos as a multicultural Roman frontier site, including the adapted Christian building used as a meeting place and baptistery in the first half of the third century.Peppard, Michael. The World's Oldest Church: Bible, Art, and Ritual at Dura-Europos, Syria. Yale University Press, 2016. Key use: Major source for the Dura-Europos Christian building, its baptistery, biblical imagery, ritual use, and the danger of reading the site too simply through later church categories.Snyder, Graydon F. Ante Pacem: Archaeological Evidence of Church Life Before Constantine. Mercer University Press, revised edition, 2003. Key use: Important archaeological source for Christian life before Constantine, especially material evidence for worship, burial, symbols, and everyday Christian practice before public imperial privilege. Mercer University Press identifies the book as focused on archaeological evidence of church life before Constantine.Jensen, Robin M. Baptismal Imagery in Early Christianity: Ritual, Visual, and Theological Dimensions. Baker Academic, 2012. Key use: Core source for baptismal images, ritual meaning, water, initiation, death and rebirth, and the way visual programs frame baptismal practice.Jensen, Robin M. Understanding Early Christian Art. Routledge, 2000. Key use: Early Christian visual culture, catacomb imagery, baptismal scenes, Good Shepherd imagery, Jonah, Daniel, Lazarus, and the visual language of salvation and resurrection.Ferguson, Everett. Baptism in the Early Church: History, Theology, and Liturgy in the First Five Centuries. Eerdmans, 2009. Key use: Major historical and theological source for baptismal practice, initiation, immersion, anointing, catechesis, and the development of baptismal rites.Johnson, Maxwell E. The Rites of Christian Initiation: Their Evolution and Interpretation. Liturgical Press. Key use: Development of initiation rites, catechumenate, baptism, post-baptismal rites, and how Christian initiation becomes structured over time.Spinks, Bryan D. Early and Medieval Rituals and Theologies of Baptism: From the New Testament to the Council of Trent. Ashgate, 2006. Key use: Long-range ritual and theological development of baptism, useful for tracking how early baptismal space later becomes more formalized.Britannica. “Catacomb.” Key use: Baseline definition of catacombs as subterranean cemeteries composed of galleries or passages with recesses for tombs; useful for correcting the popular misconception that catacombs were primarily secret churches rather than burial landscapes.Stevenson, James. The Catacombs: Rediscovered Monuments of Early Christianity. Thames & Hudson, 1978. Key use: Classic overview of Roman catacombs, burial architecture, inscriptions, symbols, and early Christian memory.Rutgers, Leonard V. Subterranean Rome: In Search of the Roots of Christianity in the Catacombs of the Eternal City. Peeters, 2000. Key use: Catacombs as archaeological and social evidence, including burial practice, community identity, and the relationship between Jews, Christians, and Roman funerary culture.Fiocchi Nicolai, Vincenzo, Fabrizio Bisconti, and Danilo Mazzoleni. The Christian Catacombs of Rome: History, Decoration, Inscriptions. Schnell & Steiner, 2002. Key use: Detailed treatment of catacomb history, inscriptions, burial spaces, and visual programs.Brown, Peter. The Cult of the Saints: Its Rise and Function in Latin Christianity. University of Chicago Press, enlarged edition. Key use: Essential source for the holy dead, saint veneration, relics, tombs, pilgrimage, and the way corporeal remains became central to Christian religious life. The University of Chicago Press describes Brown's work as exploring how worship of saints and their corporeal remains became central to religious life in Western Europe.Brown, Peter. The Body and Society: Men, Women, and Sexual Renunciation in Early Christianity. Columbia University Press, 1988. Key use: Christian body theology, asceticism, holiness, discipline, and why the body is so central to late antique Christian imagination.Yasin, Ann Marie. Saints and Church Spaces in the Late Antique Mediterranean: Architecture, Cult, and Community. Cambridge University Press, 2009. Key use: Churches, saints, relics, cult practice, community identity, and how sacred spaces are organized around holy bodies and memory.Grabar, André. Martyrium: Recherches sur le culte des reliques et l'art chrétien antique. Key use: Classic work on martyr shrines, relic cult, and the relationship between architecture, art, and the holy dead.van Gennep, Arnold. The Rites of Passage. Key use: Separation, liminality, and incorporation. Crucial for baptism, catechumenate, thresholds, initiation, and the movement from outsider to insider.Turner, Victor. The Ritual Process: Structure and Anti-Structure. Key use: Liminality, threshold states, ritual transition, and communitas. Useful for baptism, catacomb descent, martyr devotion, and controlled access.Kilde, Jeanne Halgren. Sacred Power, Sacred Space: An Introduction to Christian Architecture and Worship. Oxford University Press, 2008. Key use: Christian buildings as arrangements of power, worship, divine presence, and embodied access. Useful for thresholds, sanctuary divisions, nave, altar, and congregation.Kieckhefer, Richard. Theology in Stone: Church Architecture from Byzantium to Berkeley. Oxford University Press, 2004. Key use: Church architecture as theology made spatial. Useful for altar, pulpit, nave, threshold, symbolic layout, and worship practice.Krautheimer, Richard. Early Christian and Byzantine Architecture. Yale University Press / Pelican History of Art. Key use: Classic architectural history for early Christian and Byzantine buildings, including the shift from pre-Constantinian spaces to basilicas, baptisteries, martyr shrines, and later monumental forms.Mathews, Thomas F. The Clash of Gods: A Reinterpretation of Early Christian Art. Princeton University Press, 1993. Key use: Early Christian imagery, visual conflict, ritual meaning, and the development of Christian art within the Roman world.Elsner, Jaś. Imperial Rome and Christian Triumph: The Art of the Roman Empire AD 100–450. Oxford University Press, 1998. Key use: Roman visual culture, Christian adaptation, imperial imagery, and the shift into Christian public art and architecture.MacMullen, Ramsay. Christianizing the Roman Empire: A.D. 100–400. Yale University Press, 1984. Key use: Social and historical context for Christian expansion before and after Constantine, useful for understanding how Christian space changes as Christianity grows.Mango, Cyril. Byzantine Architecture. Key use: LonAlso want to remind people about the website, if you're into reading we have tons of information by multiple contributors, and we got t-shirts up on the site if you're interested. Fun fact, the art is all based on the eyeball. A

The culture is obsessed with speed, scale, lean teams, massive output, and automation, but faster output does not automatically mean better direction. In part two of this connected conversation, the focus shifts to the missing human layer underneath AI-powered growth and the belief that more content, more automation, and more velocity always lead to better outcomes. Using S³ Growth Streams™ as the operating lens, you will learn how to strategize before accelerating, synergize before scaling, and systemize before sprinting forever. More importantly, you will see how to separate signal from noise, align tools with real human capacity, and build repeatable rhythms that support growth without requiring constant sacrifice. This episode is not anti-AI, anti-hustle, or anti-ambition. It is anti-default sacrifice, anti-permanent sprint, and anti-output without awareness. It also challenges the obsession with flow state by introducing five operating states that support sustainable growth: capture, clarity, commitment, flow, and reflection. Because flow is not the whole game. Sometimes the most productive thing you can do is capture the signal, get clarity, commit to the right action, recover, reflect, and return to baseline before sprinting again. The goal is not to become more machine-like. The goal is to become more intentionally human while using machines wisely. Beyond The Episode Gems: Buy My Book, Strategize Up: The Blueprint To Scale Your Business: StrategizeUpBook.com Discover All Podcasts On The HubSpot Podcast Network Get Free HubSpot Marketing Tools To Help You Grow Your Business Grow Your Business Faster Using HubSpot's CRM Platform Support The Podcast & Connect With Troy:  Rate & Review iDigress: iDigress.fm/Reviews Follow Troy's Socials @FindTroy: LinkedIn, Instagram, Threads, TikTok Subscribe to Troy's YouTube Channel For Strategy Videos & See Masterclass Episodes Need Growth Strategy, A Keynote Speaker, Or Want To Sponsor The Podcast? Go To FindTroy.com