Podcasts about Modulation

„Beta Glucan kann den Cholesterinspiegel beeinflussen, Blutzuckerspitzen abflachen und sogar mit unserem Immunsystem interagieren. Entscheidend ist aber, welches Beta Glucan wir meinen.“ Hinter dem Namen verbirgt sich kein einzelner Stoff, sondern eine ganze Familie unterschiedlich aufgebauter Polysaccharide. Wissenschaftlich hoch spannend: Ihre molekulare Struktur entscheidet darüber, was sie in unserem Körper bewirken können. In dieser Deep Dive Folge der artgerecht HEALTH NERDS erklären Podcast Host Felix Moese und Gesundheitswissenschaftler Matthias Baum, warum vor allem Beta Glucane aus Hafer und Gerste ernährungsphysiologisch interessant sind. Als lösliche Ballaststoffe binden sie Wasser und bilden im Verdauungstrakt eine gelartige Masse. Dadurch können sie die Aufnahme von Kohlenhydraten verlangsamen, den Blutzuckeranstieg nach einer Mahlzeit abflachen und länger sättigen. Besonders gut untersucht ist ihr Einfluss auf den Cholesterinstoffwechsel: Für Beta Glucan aus Hafer und Gerste ist die cholesterinsenkende Wirkung wissenschaftlich so gut belegt, dass dafür in der EU ein zugelassener Health Claim existiert. Doch Beta Glucan kann biologisch noch etwas völlig anderes sein. Die verzweigten Strukturen aus Hefe und Pilzen wirken nicht primär über ihre Viskosität im Darm, sondern können mit Rezeptoren des angeborenen Immunsystems interagieren. Matthias erklärt, warum man dabei eher von einer Modulation oder einem Training des Immunsystems sprechen sollte als von einem „Immunbooster“. Studien untersuchen unter anderem, ob bestimmte Beta Glucane die Häufigkeit und Dauer von Atemwegsinfekten beeinflussen können. Die Ergebnisse sind interessant, die Datenlage ist allerdings deutlich weniger eindeutig als beim Cholesterinstoffwechsel. Auch die Menge zählt. 100 Gramm Haferflocken enthalten je nach Produkt ungefähr drei bis fünf Gramm Beta Glucan. Wer gezielt von den untersuchten Effekten profitieren möchte, braucht deshalb Regelmäßigkeit. Haferflocken, Haferkleie und Gerste lassen sich unkompliziert in die Ernährung integrieren, alternativ ist auch eine gezielte Ergänzung möglich. HEALTH NERDS. Mensch, einfach erklärt. Spare 15% auf Deine erste Bestellung auf https://artgerecht.com mit dem Code: HEALTHNERDS15 (im Warenkorb eingeben) Ein ALL EARS ON YOU Original Podcast.

"It's way better to pull yourself back from the brink of burnout before it hits than to let yourself fall all the way in,” says Elizabeth Svoboda. Svoboda is an award-winning science writer and contributor to Scientific American, Discover, The Boston Globe, The New York Times, and other publications. Elizabeth is a winner of the Evert Clark/Seth Payne Award for Young Science Writers, and her work has been anthologized in the Best American Science and Nature Writing series. She lives in San Jose, California, with her husband and young sons. Her new book, The Art of Pacing, is available now. 00:00 - Why high performers always push to 110% 05:58 - Physical signs of burnout 09:14 - Getting more comfortable with recovery 12:16 - Pacing & how to take advantage of energy rhythms  16:00 - What an Olympic runner taught her about rest 18:32 - Modulation & the importance of breathwork  28:30 - Why it's so hard recover from burnout 31:48 - Boom-bust cycles 35:12 - Gold star culture & unsustainable achievement 40:34 - Why early sports specialization backfires 44:08 - How to pace yourself with rigid flexibility 50:32 - The next wise choice Referenced in the episode:  For more about Elizabeth Svoboda, visit her website: https://elizabethsvoboda.com/  Buy The Art Of Pacing here: https://www.amazon.com/Art-Pacing-Balancing-Short-Term-Long-Term/dp/1668022419/ref=as_li_qf_sp_asin_il_tl?tag=mind0a3-20  Find the Maslach Burnout Inventory here: https://static1.squarespace.com/static/5fecb650c2646617cb1e0599/t/6320f5325f757e4ad8bc0062/1663104306195/Maslach-Burnout-Inventory.pdf  For more about Joe Arpaia: https://www.centerfortransformativehealing.org/joseph Follow runner Ajeé Wilson: https://www.instagram.com/ajeelenee/?hl=en  We hope you enjoy this episode, and feel free to watch the full video on YouTube! Whether it's an article or podcast, we want to know what we can do to help here at mindbodygreen. Let us know at: podcast@mindbodygreen.com. Learn more about your ad choices. Visit megaphone.fm/adchoices

Steinberg verschenkt vier Retro-Instrumente und einen Effekt im VST3-Format, direkt aus den Anfängen von Cubase. Im Video teste ich, ob sich der Download wirklich lohnt und wie gut die alten Sounds im Vergleich zu modernen Cubase-Tools abschneiden. Los geht's mit dem LM7, einem Drum-Synthesizer mit Presets wie 909, Compressor, Drum-Base, Fusion und Modulation. Danach der VB1 Virtual Bass mit typischem Retro-Charakter, gefolgt vom CS40, einem 6-fach polyphonen Synthesizer, und dem Neon, einem 4-Oszillator-Synth. Zum Schluss der Karlette-Delay-Effekt, den ich direkt mit dem modernen Studio Delay und Multi-Tap Delay in Cubase vergleiche. Außerdem checke ich, ob der Retrolog-Synthesizer den CS40 und Neon nicht sogar überflüssig macht. Für alle, die sich für Musikproduktion, Sounddesign und Synthesizer-Programmierung interessieren, zeige ich ehrlich, wo die Stärken und Grenzen dieser kostenlosen Instrumente liegen und ob sie einen echten Mehrwert gegenüber bereits vorhandenen Cubase-Tools bieten. Wenn ich Dir helfen konnte, freue ich mich über einen virtuellen Kaffee ;-) https://ko-fi.com/timheinrich Zum kostenlosen Cubase-Stammtisch anmelden: subscribepage.io/1D69jt Podcast: https://sounthcast.podbean.com/ https://sounth.de https://www.facebook.com/tim.heinrich.524/ https://www.instagram.com/tim_heinrich/ Facebook Gruppe 'Filmmusik komponieren & Sounddesign': https://www.facebook.com/groups/309751689699537 Perfekte Orchester-Mockup-Balance: Orchestra Guide https://www.sounth.de/orchestra-guide/ 0:00 Intro: Steinbergs Geschenk vorgestellt 0:12 LM7 Drum-Synthesizer 1:22 VB1 Virtual Bass 2:23 CS40 Polyphonic Synthesizer 3:38 Neon Synthesizer 5:36 Karlette Delay-Effekt 7:30 Vergleich mit Studio Delay & Multi-Tap Delay 8:32 Vergleich mit Retrolog 9:43 Fazit Dieses Video ist auch auf YouTube zu sehen: https://youtu.be/TQHFCe6T-KY

In this episode of The Dairy Podcast Show, Dr. Christopher Rock, Technical Services Consultant and DVM at Animal Health Vision (AHV), a division of Elanco, discusses quorum sensing inhibition, biofilm disruption, and immune modulation in dairy cattle. He explains how these technologies support animal health, manage inflammation, and influence recovery, rumination, and body temperature responses. Learn how innovative approaches may complement herd health strategies. Listen now on all major platforms!"Bacterias communicate through signal molecules that coordinate behavior changes, allowing populations to form biofilms and express traits associated with disease challenges."Meet the guest: Dr. Christopher Rock graduated from the Iowa State College of Veterinary Medicine and owned a veterinary clinic in eastern Iowa for six years. He currently serves as a Technical Services Consultant and DVM for Animal Health Vision (AHV), a division of Elanco, focusing on innovative approaches to dairy cattle health, immune function, and disease management. Listen to Dr. Christopher Rock on The Dairy Podcast Show, available on all major platforms.Liked this one? Don't stop now — Here's what we think you'll love!What you'll learn:(00:00) Highlight(01:33) Introduction(05:01) Quorum sensing inhibition(05:45) Biofilm disruption(10:40) Immune modulation(16:50) Inflammation management(20:57) Recovery and rumination(26:38) Final QuestionsThe Dairy Podcast Show is trusted and supported by innovative companies like:- AHV* Afimilk* Evonik* Adisseo* CowManager* Agri-Comfort- Natural Biologics- Protekta- dsm-firmenich- BoviSync- Chemlock Nutrition- DietForge- Agrarian Solutions

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

Heute geht es um Darmbakterien, die die Wirksamkeit und Nebenwirkungen von Krebsmedikamenten beeinflussen – von der direkten Umwandlung von Wirkstoffen über die Steuerung körpereigener Stoffwechselwege bis hin zur Modulation des Tumor- und Immunmilieus. Wir diskutieren konkrete Beispiele aus der Chemotherapie und Immuntherapie, beleuchten moderne Analysemethoden und werfen einen Blick auf neue therapeutische Ansätze wie Probiotika, fäkale Mikrobiota-Transplantation oder sogar gezielt eingesetzte Bakterien.

DADO ist eine neue, komplett kostenlose Sound Library für den Native Instruments Kontakt Player von Tonalforms. In diesem Video schaue ich mir die Library zum ersten Mal an und teste live, ohne Vorbereitung, welche Sounds und Funktionen sie bietet. Ihr seht, wie die Cross-Fade-Funktion zwischen zwei Soundquellen (A und B) funktioniert, wie sich Filter und die eingebauten Effekte auf den Klang auswirken, und wie die Randomize- und Variate-Funktion Parameter wie Oktave, Rotation und Filter automatisch verändern. Außerdem gehe ich auf die Skalen-Einstellungen, die Motion-Sektion mit LFO sowie die Effekt-Presets für Reverb, Delay, Modulation und Distortion ein. Wenn du auf der Suche nach einer kostenlosen Kontakt Library für Sounddesign, Ambient-Pads oder experimentelle Texturen bist, ist DADO einen Blick wert. Schreib mir in die Kommentare, wie dir die Library gefällt und ob du sie schon ausprobiert hast. Hol Dir hier die Library: https://www.native-instruments.com/en/pricing/dado-lilac-alloy/ Wenn ich Dir helfen konnte, freue ich mich über einen virtuellen Kaffee ;-) 

Brain inflammation is a critical factor in traumatic brain injury recovery -- and a surprising new study suggests electromagnetic fields might help reduce it. In this episode, I break down recent research showing how low-frequency electromagnetic fields affected inflammatory responses in brain cells. The findings reveal a potentially therapeutic application of EMF that challenges our usual focus on protection. I'll explain what the researchers discovered, why it matters for brain injury recovery, and what questions still need answers. In This Episode How low-frequency EMF affected inflammatory markers in neurons and microglial cells The connection between this lab research and previous traumatic brain injury studies Why the immune system's response to brain injury can become problematic What we still need to learn before this becomes a treatment option Featured Study Read the full study: Modulation of inflammatory response by electromagnetic field in Neuronal and Microglial cells See all studies at shieldyourbody.com/research

Send us Fan MailThis New Moon on the 14th of July, takes place in the zodiac sign of Cancer. Time to take stock of your family dynamics; make them stronger! Get in touch with what you need to feel secure. What gives you nourishment? How can you communicate your needs to others, so that they can hear you, and you in turn can hear them? This is bound to be a very emotional time for many, as people in the family leave- (off to college, separations,etc.) and new ties are welcomed (marriages, births, etc.).  One has to make "space" for the new, and that may mean letting go of familiar and saying good- bye, but also "hello" to the new path. Thanks for listening. Support the showAstrology:http://www.kitchensari.comJewelry:https://www.Etsy.com/shop/parkermcpDonations Via PayPal:https://paypal.me/parkermcphinney1?country.x=US&locale.x=en_USBuy me a chai/coffeehttps://www.buymeacoffee.com/parkercI am on RUMBLE.COM now- with short videos of: Astro/Art/Naturehttps://rumble.com/c/c-1989012  All content © 2020-2026 Stardust Stereo .

Four of the presenters from the upcoming one day conference, Arousal Modulation: Building a Clear-Headed Dog!, join me for a conversation about how arousal works, why it's not all good or all bad, and what handlers who have dogs that tend toward overarousal should stop doing... then what they should start doing instead. 

Discover how B-cell modulation is reshaping immunoglobulin A nephropathy (IgAN) care, halting disease progression, and preserving long-term kidney function. Credit available for this activity expires: 06/24/2027 Earn Credit / Learning Objectives & Disclosures: https://www.medscape.org/viewarticle/new-frontiers-iga-nephropathy-understanding-role-b-cell-2026a1000l9s?ecd=bdc_podcast_libsyn_mscpedu

Editor's note: Welcome to the 22nd episode of The Pain Beat, PRF's podcast series!  The Pain Beat brings together the world's leading pain investigators in order to spark dialogue and debate around important ideas in pain research.  The Pain Beat podcasts feature open and spirited discussion about the hottest topics in pain and how the field moves forward from here. On this episode, experts in pain research discuss the modification of pain by homeostatic drives and survival threats.   How for example, do hunger and sleep modify the pain experience and how are they themselves modified by pain. What are the circuits and mechanisms underlying these interactions?   Participants on the podcast: Loren Martin, PhD, University of Toronto Nitsan Goldstein, PhD, MIT J. Nicholas Betley, PhD, University of Pennsylvania Alban Latremolier, PhD, Johns Hopkins University. Al Nie, Johns Hopkins University (Organizer and Moderator)   Music provided by Kevin Seal.  Other episodes of the Pain Beat can be found on Apple Podcast, Spotify and the Pain Research Forum.   

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/TheOccultRejectsPart 1 focuses on the drum as an ancient technology of altered consciousness. The argument is not that every beat causes trance, or that neuroscience has proven spirits. The stronger argument is that rhythm enters the human organism through hearing, motor prediction, breath, movement, attention, emotion, expectation, culture, and social synchrony. The drum becomes powerful when sound, body, group, ritual frame, and meaning converge. These sources support the archaeology, neuroscience, EEG research, shamanic studies, possession studies, Indigenous and culturally specific drum traditions, ritual theory, placebo and meaning-response research, ceremonial magic, and modern witchcraft material used in the episode.Core Academic and Scientific SourcesHuels, Emma R., Hyoungkyu Kim, UnCheol Lee, Tirsa Bel-Bahar, Ana V. Colmenero, Alexandra Nelson, Stefanie Blain-Moraes, George A. Mashour, and Richard E. Harris. “Neural Correlates of the Shamanic State of Consciousness.” Frontiers in Human Neuroscience 15 (2021): 610466.Gordon, Yoel, Golan Karvat, Noa Dagan, and Ayelet N. Landau. “Neural Tracking at Theta Predicts Drumming-Induced Altered States of Consciousness.” Scientific Reports 16, no. 1 (2026): Article 10204.Aparicio-Terrés, R., et al. “The Neurobiology of Altered States of Consciousness Induced by Drumming and Other Rhythmic Sound Patterns.” Annals of the New York Academy of Sciences, 2025.Neher, Andrew. “Auditory Driving Observed with Scalp Electrodes in Normal Subjects.” Electroencephalography and Clinical Neurophysiology 13 (1961): 449–451.Neher, Andrew. “A Physiological Explanation of Unusual Behavior in Ceremonies Involving Drums.” Human Biology 34, no. 2 (1962): 151–160.Maurer, R., V. K. Kumar, L. Woodside, and R. J. Pekala. “Phenomenological Experience in Response to Monotonous Drumming and Hypnotizability.” American Journal of Clinical Hypnosis 40, no. 2 (1997): 130–145. Use for monotonous drumming, subjective altered experience, imagery, absorption, and hypnotizability.Maxfield, Melinda C. “Effects of Rhythmic Drumming on EEG and Subjective Experience.” PhD diss., Institute of Transpersonal Psychology, 1990. Use as older supporting context on drumming, EEG, imagery, body-image changes, and subjective altered experience. Do not make this the main scientific proof; use it as background.Nozaradan, Sylvie, Isabelle Peretz, and André Mouraux. “Tagging the Neuronal Entrainment to Beat and Meter.” The Journal of Neuroscience 31, no. 28 (2011): 10234–10240. Use for EEG evidence that the brain can track beat and meter. This supports the claim that the brain does not merely hear rhythm as background sound; it can represent rhythmic structure in measurable ways.Nozaradan, Sylvie. “Exploring How Musical Rhythm Entrains Brain Activity with Electroencephalogram Frequency-Tagging.” Philosophical Transactions of the Royal Society B 369, no. 1658 (2014). Use as broader rhythm/EEG entrainment support. This helps explain frequency-tagging, beat tracking, meter, neural entrainment, and the measurable relationship between rhythmic structure and brain activity.Thaut, Michael H., Gerald C. McIntosh, and Volker Hoemberg. “Neurobiological Foundations of Neurologic Music Therapy: Rhythmic Entrainment and the Motor System.” Frontiers in Psychology 5 (2015). Use for rhythm as motor-system timing information. This supports the claim that a beat can become bodily instruction, not just sound for the ear. Especially useful when discussing rhythmic auditory stimulation, motor planning, gait, entrainment, and the auditory-motor bridge.Ross, Jessica M., John R. Iversen, and Ramesh Balasubramaniam. “Time Perception for Musical Rhythms: Sensorimotor Perspectives on Entrainment, Simulation, and Prediction.” 2022. Use for rhythm, timing, prediction, sensorimotor entrainment, and the way musical rhythm interacts with time perception.Hove, Michael J., and Jane L. Risen. “It's All in the Timing: Interpersonal Synchrony Increases Affiliation.” Social Cognition 27, no. 6 (2009): 949–960. Use for synchrony and social bonding. This helps support the group-body argument: moving or acting in time with others can increase affiliation.Wiltermuth, Scott S., and Chip Heath. “Synchrony and Cooperation.” Psychological Science 20, no. 1 (2009): 1–5. Use for the claim that synchronized movement can increase cooperation and attachment among participants.Tarr, Bronwyn, Jacques Launay, and Robin I. M. Dunbar. “Music and Social Bonding: ‘Self-Other' Merging and Neurohormonal Mechanisms.” Frontiers in Psychology 5 (2014): 1096. Use for music, synchrony, bonding, endorphin/social mechanisms, and why group rhythm can feel like more than private listening.Fancourt, Daisy, Rosie Perkins, Sara Ascenso, Louise Atkins, Fatima Kilfeather, and Aaron Williamon. “Effects of Group Drumming Interventions on Anxiety, Depression, Social Resilience and Inflammatory Immune Response among Mental Health Service Users.” PLOS ONE 11, no. 3 (2016): e0151136. Use for modern group-drumming research showing psychological and physiological effects, including anxiety, depression, social resilience, wellbeing, and inflammatory immune response. Use carefully: this does not make group drumming a cure-all. It supports the more grounded claim that embodied rhythm and group participation can affect mood, social connection, and body chemistry.Bittman, Barry B., et al. “Composite Effects of Group Drumming Music Therapy on Modulation of Neuroendocrine-Immune Parameters in Normal Subjects.” Alternative Therapies in Health and Medicine 7, no. 1 (2001): 38–47. Use as older supporting material on group drumming and neuroendocrine-immune measures. Keep secondary. Fancourt is cleaner for the main script body.Archaeology and Deep History of DrumsLawergren, Bo. “Neolithic Drums in China.” In Music Archaeology in China. 2006. Use for clay drums in Neolithic China and the deep-history claim that drums are not just poetic symbols of antiquity. They appear in the archaeological record as instruments tied to early sound-making, ceremony, and social order.Both, Arnd Adje. “Music Archaeology: Some Methodological and Theoretical Considerations.” Use as general support for why ancient instruments should be treated as ritual and social evidence, not merely decorative objects.Anthropology, Ethnomusicology, Ritual, and TranceRouget, Gilbert. Music and Trance: A Theory of the Relations Between Music and Possession. Translated by Brunhilde Biebuyck. Chicago: University of Chicago Press, 1985. Essential source. Use for the caution that music does not mechanically or universally cause trance. Rouget helps keep the argument academically serious by emphasizing culture, ritual frame, meaning, and expectation.Becker, Judith. Deep Listeners: Music, Emotion, and Trancing. Bloomington: Indiana University Press, 2004. Use for music-linked trancing, emotional absorption, religious experience, and culturally trained ways of listening. This supports the “hearing versus entering” distinction.McNeill, William H. Keeping Together in Time: Dance and Drill in Human History. Cambridge, MA: Harvard University Press, 1995. Use for marching, dance, drill, muscular bonding, synchronized movement, and rhythm as social glue. This is useful both for Part 1's group-body material and Part 2's war-drum material.Eliade, Mircea. Shamanism: Archaic Techniques of Ecstasy. Princeton: Princeton University Press, 1964. Use carefully. Eliade's phrase “archaic techniques of ecstasy” is powerful, but the episode should also note that later scholarship criticizes his tendency to universalize shamanism.Winkelman, Michael. Shamanism: A Biopsychosocial Paradigm of Consciousness and Healing. 2nd ed. Santa Barbara, CA: Praeger, 2010. Use for shamanism as a ritual technology involving altered consciousness, healing, social integration, symbolism, and body-brain processes.Winkelman, Michael. “Shamanism and Psychedelics: A Biogenetic Structuralist Paradigm of Ecopsychology.” European Journal of Ecopsychology 4 (2013): 90–115. Use as supplemental background on shamanism, altered consciousness, and comparative models of trance and visionary states.Kontouli, Athanasia, Michael J. Hove, Alexandre Lehmann, Peter Vuust, and Peter E. Keller. “The Rhythms of Trance: Cultural Phenomenology and Neural Mechanisms of Music-Induced Lewis-Williams, David. The Mind in the Cave: Consciousness and the Origins of Art. London: Thames & Hudson, 2002. Use cautiously for altered states, entoptic imagery, ritual vision, and the relationship between neuropsychology and symbolic culture.Non-Ordinary States of Consciousness.” Neuroscience & Biobehavioral Reviews, 2026. Use for the bridge between cultural phenomenology and neuroscience. This supports the point that music-induced trance is not only acoustics; it involves body, training, expectation, culture, environment, and interpretation.Tart, Charles T., ed. Altered States of Consciousness. New York: Wiley, 1969. Use as classic altered-state background.Hultkrantz, Åke. “The Drum in Shamanism.” Use for classic comparative material on the shamanic drum, especially Arctic, SiberiAlso 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

La falsa pacificaciónPor Yaiza SantosTenía muy abandonada su sección boom boom boomer, y las declaraciones del presidente de la patronal sobre las bajas laborales de los jóvenes le ofreció la ocasión idónea para retomarla. Cuánta razón y qué simpática Empar Moliner, en ese periódico que para ser nacionalista tiene cosas que no están mal.Si quiere saber si un boomer va en una moto, dijo a Santos, obsérvese si gira la cabeza para ver si viene otro vehículo: en su época nadie miraba los retrovisores.No solamente encuentra papers para sí mismo, sino también para Illa, ¡y para el presidente del Tribunal Constitucional! Un grupo de investigadores ha encontrado que los episodios de "moderación" observados en Cataluña después del proceso, justo en el momento en que el PSC negociaba con ERC y Comuns, solo tenían un fin pragmático, y en ningún caso conllevaron una verdadera transformación de las "creencias subyacentes". Un disparo a la línea de flotación de la ley de amnistía, resumió, que en su preámbulo fijaba como hecho consumado la "pacificación". ¡Pacificación! Quia.Reconvino a Santos, que tanto recomienda esa serie Seinfeld, por no entender las referencias musicales escondidas en su columna de este jueves, a la cual un amable lector le pone una divertida puntilla, y pidió aprenderla de memoria.Le disgustó que el juez Calama, quince minutos después de finalizar la declaración de Zapatero, hiciera constar que el expresidente "no ha logrado desvirtuar los indicios racionales de criminalidad expuestos en el auto de imputación y que derivan de diversas y distintas fuentes de prueba". Y tampoco considera ejemplar el vocabulario utilizado por la portavoz del PP en el Congreso, haciéndole oposición a Rufián.Hoy, en cualquier caso, es un día ideal para leer los periódicos, pues en ellos se encuentran el ying y el yang de la vida. Entender de manera profunda que toda cara A tiene una cara B y viceversa haría más por la salud mental que todas esas bajas laborales pedidas por los jóvenes.Y fue así que Espada yiró.Bibliografía:- Arcadi Espada, "El café no es café, el café es sexo", EL MUNDO.- Boom boom boomer: Don Vaughn et al., "Modulation of attention and stress with arousal: The mental and physical effects of riding a motorcycle", en Brain Research.- Burning: Daniel Cetrà et al., "Words After the Storm: Elite Rhetoric and the Limits of De-Escalation in Postreferendum Catalonia", en Nations and Nationalism. Hosted on Acast. See acast.com/privacy for more information.

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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/TheOccultRejectsPart 1: The Road of RhythmPart 1 focuses on the drum as an ancient technology of altered consciousness. The argument is not that every beat causes trance, or that neuroscience has proven spirits. The stronger argument is that rhythm enters the human organism through hearing, motor prediction, breath, movement, attention, emotion, expectation, culture, and social synchrony. The drum becomes powerful when sound, body, group, ritual frame, and meaning converge. These sources support the archaeology, neuroscience, EEG research, shamanic studies, possession studies, Indigenous and culturally specific drum traditions, ritual theory, placebo and meaning-response research, ceremonial magic, and modern witchcraft material used in the episode.Core Academic and Scientific SourcesHuels, Emma R., Hyoungkyu Kim, UnCheol Lee, Tirsa Bel-Bahar, Ana V. Colmenero, Alexandra Nelson, Stefanie Blain-Moraes, George A. Mashour, and Richard E. Harris. “Neural Correlates of the Shamanic State of Consciousness.” Frontiers in Human Neuroscience 15 (2021): 610466. Use for the strongest modern EEG anchor. This study used high-density EEG with shamanic practitioners and controls during rest, shamanic drumming, and classical music listening. It assessed altered-state reports alongside brain measures such as power, connectivity, signal diversity, and criticality. Use carefully: the study does not prove spirits or show that drumming mechanically causes trance in everyone. It supports the more careful claim that trained practitioners entering shamanic states with drumming show measurable brain-state differences.Gordon, Yoel, Golan Karvat, Noa Dagan, and Ayelet N. Landau. “Neural Tracking at Theta Predicts Drumming-Induced Altered States of Consciousness.” Scientific Reports 16, no. 1 (2026): Article 10204. Use for the strongest updated drumming/theta/neural-tracking source. This study tested drumming at theta, delta, and alpha-rate rhythms while recording EEG, and found that stronger rhythmic neural tracking at theta was linked to stronger altered-experience reports. Use carefully: this does not mean theta equals the spirit world or that one frequency opens a portal. The serious point is that altered experience may depend partly on how strongly the nervous system tracks rhythmic stimulation.Aparicio-Terrés, R., et al. “The Neurobiology of Altered States of Consciousness Induced by Drumming and Other Rhythmic Sound Patterns.” Annals of the New York Academy of Sciences, 2025. Use for the newer review literature showing that rhythmic sound is now a serious altered-consciousness research topic. This supports the opening claim that modern academia is examining drumming, rhythmic sound, absorption, relaxation, cognition, and neural activity without reducing the subject to one simple “trance frequency.” The review is especially useful for framing the field as promising but still complex.Neher, Andrew. “Auditory Driving Observed with Scalp Electrodes in Normal Subjects.” Electroencephalography and Clinical Neurophysiology 13 (1961): 449–451. Use for the historical bridge between repetitive sound, EEG, auditory driving, and early scientific interest in rhythmic stimulation.Neher, Andrew. “A Physiological Explanation of Unusual Behavior in Ceremonies Involving Drums.” Human Biology 34, no. 2 (1962): 151–160. Use carefully. This is useful as an early attempt to connect ceremonial drumming and physiology, but it should be balanced with Rouget because the “drum simply causes trance” argument is too mechanical.Maurer, R., V. K. Kumar, L. Woodside, and R. J. Pekala. “Phenomenological Experience in Response to Monotonous Drumming and Hypnotizability.” American Journal of Clinical Hypnosis 40, no. 2 (1997): 130–145. Use for monotonous drumming, subjective altered experience, imagery, absorption, and hypnotizability.Maxfield, Melinda C. “Effects of Rhythmic Drumming on EEG and Subjective Experience.” PhD diss., Institute of Transpersonal Psychology, 1990. Use as older supporting context on drumming, EEG, imagery, body-image changes, and subjective altered experience. Do not make this the main scientific proof; use it as background.Nozaradan, Sylvie, Isabelle Peretz, and André Mouraux. “Tagging the Neuronal Entrainment to Beat and Meter.” The Journal of Neuroscience 31, no. 28 (2011): 10234–10240. Use for EEG evidence that the brain can track beat and meter. This supports the claim that the brain does not merely hear rhythm as background sound; it can represent rhythmic structure in measurable ways.Nozaradan, Sylvie. “Exploring How Musical Rhythm Entrains Brain Activity with Electroencephalogram Frequency-Tagging.” Philosophical Transactions of the Royal Society B 369, no. 1658 (2014). Use as broader rhythm/EEG entrainment support. This helps explain frequency-tagging, beat tracking, meter, neural entrainment, and the measurable relationship between rhythmic structure and brain activity.Thaut, Michael H., Gerald C. McIntosh, and Volker Hoemberg. “Neurobiological Foundations of Neurologic Music Therapy: Rhythmic Entrainment and the Motor System.” Frontiers in Psychology 5 (2015). Use for rhythm as motor-system timing information. This supports the claim that a beat can become bodily instruction, not just sound for the ear. Especially useful when discussing rhythmic auditory stimulation, motor planning, gait, entrainment, and the auditory-motor bridge.Ross, Jessica M., John R. Iversen, and Ramesh Balasubramaniam. “Time Perception for Musical Rhythms: Sensorimotor Perspectives on Entrainment, Simulation, and Prediction.” 2022. Use for rhythm, timing, prediction, sensorimotor entrainment, and the way musical rhythm interacts with time perception.Hove, Michael J., and Jane L. Risen. “It's All in the Timing: Interpersonal Synchrony Increases Affiliation.” Social Cognition 27, no. 6 (2009): 949–960. Use for synchrony and social bonding. This helps support the group-body argument: moving or acting in time with others can increase affiliation.Wiltermuth, Scott S., and Chip Heath. “Synchrony and Cooperation.” Psychological Science 20, no. 1 (2009): 1–5. Use for the claim that synchronized movement can increase cooperation and attachment among participants.Tarr, Bronwyn, Jacques Launay, and Robin I. M. Dunbar. “Music and Social Bonding: ‘Self-Other' Merging and Neurohormonal Mechanisms.” Frontiers in Psychology 5 (2014): 1096. Use for music, synchrony, bonding, endorphin/social mechanisms, and why group rhythm can feel like more than private listening.Fancourt, Daisy, Rosie Perkins, Sara Ascenso, Louise Atkins, Fatima Kilfeather, and Aaron Williamon. “Effects of Group Drumming Interventions on Anxiety, Depression, Social Resilience and Inflammatory Immune Response among Mental Health Service Users.” PLOS ONE 11, no. 3 (2016): e0151136. Use for modern group-drumming research showing psychological and physiological effects, including anxiety, depression, social resilience, wellbeing, and inflammatory immune response. Use carefully: this does not make group drumming a cure-all. It supports the more grounded claim that embodied rhythm and group participation can affect mood, social connection, and body chemistry.Bittman, Barry B., et al. “Composite Effects of Group Drumming Music Therapy on Modulation of Neuroendocrine-Immune Parameters in Normal Subjects.” Alternative Therapies in Health and Medicine 7, no. 1 (2001): 38–47. Use as older supporting material on group drumming and neuroendocrine-immune measures. Keep secondary. Fancourt is cleaner for the main script body.Archaeology and Deep History of DrumsLawergren, Bo. “Neolithic Drums in China.” In Music Archaeology in China. 2006. Use for clay drums in Neolithic China and the deep-history claim that drums are not just poetic symbols of antiquity. They appear in the archaeological record as instruments tied to early sound-making, ceremony, and social order.Both, Arnd Adje. “Music Archaeology: Some Methodological and Theoretical Considerations.” Use as general support for why ancient instruments should be treated as ritual and social evidence, not merely decorative objects.Anthropology, Ethnomusicology, Ritual, and TranceRouget, Gilbert. Music and Trance: A Theory of the Relations Between Music and Possession. Translated by Brunhilde Biebuyck. Chicago: University of Chicago Press, 1985. Essential source. Use for the caution that music does not mechanically or universally cause trance. Rouget helps keep the argument academically serious by emphasizing culture, ritual frame, meaning, and expectation.Becker, Judith. Deep Listeners: MAlso 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

In dieser Folge von Ergotherapie unverpackt sprechen wir über Endometriose und Adenomyose – zwei chronische gynäkologische Erkrankungen, die oft viel zu spät erkannt werden und das Leben Betroffener massiv beeinflussen können.Es geht nicht nur um starke Regelschmerzen. Es geht um Erschöpfung, Rückzug, Scham, Leistungsdruck, unerfüllten Kinderwunsch, Schmerzen beim Sex, Probleme im Alltag und um das Gefühl, dem eigenen Körper nicht mehr richtig vertrauen zu können.Wir schauen darauf, was Endometriose und Adenomyose unterscheidet, welche Symptome typisch sein können und warum Schmerz nicht immer direkt mit dem sichtbaren Befund zusammenpasst. Manche Menschen haben starke Befunde und wenig Schmerzen, andere scheinbar kleinere Befunde und enorme Einschränkungen. Genau hier wird das Thema auch für die Ergotherapie relevant.Ein Schwerpunkt dieser Folge ist die Frage:Was kann Ergotherapie bei chronischem Schmerz, Fatigue, Überforderung und eingeschränkter Teilhabe konkret beitragen?Wir sprechen über Schmerzgedächtnis, zentrale Sensibilisierung, Amygdala, Arousal-Fenster, Hyperarousal und Hypoarousal – aber so, dass es verständlich bleibt. Außerdem geht es um konkrete therapeutische Werkzeuge wie Pacing, Energietagebuch, Fatigue-Management, sensorische Modulation, Körperwahrnehmung, Betätigungsanalyse, Alltagserleichterung und die Frage, wie Betroffene wieder mehr Selbstwirksamkeit erleben können.Diese Folge richtet sich an Betroffene, Angehörige und Fachpersonen, die besser verstehen möchten, warum chronischer Schmerz nie „nur körperlich“ ist, sondern immer auch Alltag, Beziehung, Nervensystem, Psyche und Teilhabe berührt.Worum es in dieser Folge gehtEndometriose und Adenomyose verständlich erklärttypische Symptome und mögliche Unterschiedewarum Diagnosen oft spät gestellt werdenwarum chronischer Schmerz das Nervensystem verändertwas Schmerzgedächtnis und zentrale Sensibilisierung bedeutenwie sich Fatigue, Scham und Rückzug auf Alltag und Teilhabe auswirkenwelche Rolle Ergotherapie spielen kannkonkrete Ideen für therapeutisches Arbeiten mit Betroffenenwarum Validierung, Sicherheit und Selbstwirksamkeit zentrale Wirkfaktoren sindWichtiger HinweisDiese Folge ersetzt keine medizinische Diagnostik oder Behandlung. Bei starken, wiederkehrenden oder zunehmenden Beschwerden sollte eine fachärztliche Abklärung erfolgen – idealerweise bei Ärzt oder Zentren mit Erfahrung im Bereich Endometriose und Adenomyose.#Ergotherapie #ErgotherapieUnverpackt #Endometriose #Adenomyose #ChronischerSchmerz #Schmerzgedächtnis #Fatigue #Teilhabe #Frauengesundheit #Gynäkologie #Schmerztherapie #ArousalFenster #SensorischeModulation #Pacing #Selbstwirksamkeit #GesundheitspodcastVielen Dank fürs Hören und Teilen, eure Line und eure WolfgangMixed & Mastered by SOUNDWERK

Warum wirken manche KI-Stimmen heute angenehmer als echte Menschen? Du hörst ein Video, einen Podcast oder einen gesprochenen Text und denkst: „Diese Stimme klingt ruhig, klar und präsent.“ Erst später bemerkst Du: Das war gar kein Mensch. KI-Stimmen haben in den letzten Jahren enorme Fortschritte gemacht – technisch und vor allem in ihrer Wirkung auf uns als Zuhörer. Doch genau darin steckt auch eine spannende Erkenntnis über menschliche Kommunikation. Arno Fischbacher und Andreas Giermaier sprechen darüber, warum viele Menschen im Alltag stimmlich unter ihren Möglichkeiten bleiben, weshalb moderne KI-Stimmen oft souveräner wirken als menschliche Gewohnheitssprache und woran wir trotz perfekter Sprachsynthese noch echte Persönlichkeit erkennen. In dieser Episode erfährst Du: warum moderne KI-Stimmen heute oft natürlicher wirken als erwartet woran Du künstlich erzeugte Stimmen überhaupt noch erkennst welche Rolle Sprachmelodie, Präsenz und Modulation dabei spielen warum viele Menschen im Alltag stimmlich „unter Potenzial“ sprechen wie Gewohnheit Deine Wirkung beeinflusst weshalb Vertrauen nicht nur über Worte entsteht was Du von KI-Stimmen für Deine eigene Kommunikation lernen kannst Warum ist das für Deinen Berufsalltag relevant? Ob in Meetings, Kundengesprächen, Präsentationen, Videos oder Podcasts: Menschen reagieren innerhalb von Sekunden auf den Klang Deiner Stimme. Noch bevor Inhalte bewusst verstanden werden, entscheidet der Tonfall über Aufmerksamkeit, Vertrauen und Präsenz. Gerade in digitalen Formaten wird hörbar, ob jemand innerlich klar, zugewandt und präsent spricht – oder nur Informationen transportiert. Diese Episode schärft Deinen Blick – und vor allem Dein Gehör – für genau diese feinen Unterschiede. Welche Frage kannst Du nach dieser Episode mitnehmen? Wenn künstliche Stimmen heute oft angenehmer klingen als menschliche Alltagssprache – was würde sich verändern, wenn Du Deine eigene Stimme bewusster einsetzen würdest? Vielleicht hörst Du nach dieser Episode nicht nur KI-Stimmen anders. Sondern auch Dich selbst. Dein persönlicher SELBSTCHECK FÜHRUNG hier kostenlos **********Dein Voicecoach Arno Fischbacher begleitet Dich auf Deinem persönlichen Weg von Stimm-Besitzer zum Stimm-Benutzer!Die beiden Hosts dieses Podcasts: Arno Fischbacher und Andreas K. Giermaier (Lernen der Zukunft)✅ Hast Du Fragen? Schreib an podcast@arno-fischbacher.com✅ Du willst mit Arno persönlich sprechen? Gern: https://arno-fischbacher.com/espresso

Marco Quarta is co-founder and Chief Scientific Officer of Rubedo Life Sciences, a precision-therapeutics company developing medicines that target the pathological cell states that drive age-associated disease. Marco's first appearance on the show was three years ago, in February 2023 (Episode 35), when Rubedo was a much earlier-stage company committed to the then-contrarian premise that "the senescent cell" is not a single entity but a heterogeneous family of cell states that needs to be deconvoluted at the single-cell level. In March 2026, Rubedo reported preliminary Phase 1b/2a clinical data for its lead candidate, RLS-1496, a first-in-class topical GPX4 modulator. Marco returns to the show to discuss what survived contact with human biology.In this episode, Chris and Marco unpack the readout from Rubedo's basket trial across four skin indications — psoriasis, atopic dermatitis, actinic keratosis, and photoaged skin — and the biology that underlies it. RLS-1496 came clean on safety in all four indications, with significant efficacy signals despite small patient numbers and short (20–30 day) treatment courses. More provocatively, the clinical and translational data have pushed Marco to redefine what kind of drug this actually is. Rather than a next-generation senolytic, GPX4 modulation appears to act as a state-gating intervention: it triggers ferroptosis in deeply senescent cells that have already crossed a redox threshold, while inducing a hormetic "redox reset" in stressed-but-recoverable cells that restores them to a healthier state. Marco proposes a new category to capture this dual action — adaptive senotherapeutics, or senoadaptive drugs — distinct from senolytics and senomorphics.The conversation traces the arc from Rubedo's founding thesis to a clinically validated platform (ALEMBIC, the AI-enabled single-cell multiomics engine that surfaced GPX4 as a target), through the strategic logic of leading with skin, into the broader question every longevity-biotech founder eventually has to answer: when does a disease-by-disease franchise become a credible preventive geroscience platform? Marco lays out the GLP-1 analogy explicitly — an anchor indication and a label-expansion roadmap that could carry GPX4 modulation from dermatology into respiratory, neurodegenerative, and metabolic disease, and ultimately into the use case where biomarkers of cellular senescence flag patients for therapy decades before disease becomes clinically apparent.The Finer Details:How Marco's 2023 contrarian view — that "senescent cells" hide a tissue- and state-specific reality — has been reinforced by the clinic, and how Rubedo's framing has shifted from "targeting senescent cells" to "targeting pathological cell states"The biology of GPX4 as a lipid-peroxidation gatekeeper, why senescent cells have intrinsic vulnerabilities (p16, p21, CDK4/6 inhibition) that make them ferroptosis-sensitive, and how Rubedo's approach differs from oncology-focused GPX4 programs at Takeda and othersThe "senoadaptive" mechanism — RLS-1496 eliminates GPX4-dependent senescent cells via ferroptosis while triggering NRF2/Keap1-driven redox reset, autophagy, and epigenetic remodeling in recoverable cells, restoring tissue trajectory from degenerative to regenerativeWhy Rubedo led with skin: clean regulatory path, accessible tissue, the ability to read out aging biology anddisease in the same trial, and a label-expansion runway into systemic indicationsPhase 1b (Europe) and Phase 2a (US) basket-trial results across psoriasis, atopic dermatitis, photoaged skin, and actinic keratosis: clean safety in 4/4 indications and significant efficacy signals — itch reduction in atopic dermatitis, decreased lesional thickness in psoriasis, target-engagement-correlated clinical improvement in photoaged skinThe richness of the translational dataset: biopsies, tape-stripping, spatial transcriptomics, proteomics, multiplex histomics, plasma biomarkers — all feeding back into ALEMBIC to refine the platformWhy actinic keratosis is the most strategically important indication — an age-related, chronic-inflammatory, precancerous condition where Rubedo can simultaneously test disease modification and biological-age reversalThe Rubedo–Beiersdorf partnership and the cosmetic vertical as a parallel commercial axisPipeline beyond skin: targeting aberrant basaloid stem cells in IPF and other pulmonary indications using different modalities (prodrugs, PROTACs, ADCs) to achieve cell-state selectivityThe longer-arc vision: senescence biomarkers as a "prediabetes-style" early signal, with senoadaptive drugs deployed decades before disease — and what a GLP-1-scale franchise might look like for GPX4 modulationQuotes:"There is not such a thing as a senescent cell — like there is not a cancer cell. And that was the initial idea. I'm glad that over time the field evolved. Now this is an accepted concept in the senotherapeutic space.""We are really talking about a dual function of RLS-1496 that can modulate the cell state depending on the adaptive response. That's why we call this — de facto — a new class of senotherapeutics. We call them adaptive senotherapeutics, or senoadaptive drugs — not a senolytic or a senomorphic, but working by modulating the cell state.""The best animal model for human therapies is human. As much as you can do preclinical work in animal models, it's always an approximation. We were able to test this directly in patients for safety, and in 4 out of 4 indications, we didn't have any safety signal.""Imagine you're taking care of a growing tree, and this tree has some dead leaves and some are a little bit stressed. If you shake the tree, the dead leaves will fall; the healthy leaves will not, because they're healthy and they resist the shake. But that shake actually gives the stressed leaves space and breathing room, and helps them to regain vitality. That's a little bit what GPX4 modulation does.""Senotherapeutics is a large, growing field — an untapped therapeutic opportunity. There is no such thing as a pan-senolytic or a pan-senotherapeutic, like there is no pan-oncotherapeutic. You need to understand the context. But these will all be part of the arsenal for true longevity medicine.""I don't see this as prevention of disease. The way I see therapies like ours, and the way the field of longevity is developing, is treating diseases decades before they develop. That's not a new concept — that's what we're doing in diabetes. You can be diagnosed with prediabetes today and reverse those biomarkers with lifestyle changes or metformin, and maybe never develop diabetes. That's exactly what we're doing here.""First of all, celebrating the first approved drugs from Rubedo — I don't think we're too far from that. But that's also a beginning, because you learn from the big momentum the GLP-1 agonists created: how a drug can start in one indication, create a new field, and prove that you can go beyond that. I hope in a few years we come back and talk about the next GLP-1 — this could be GPX4 modulators, or the senoadaptive drugs that are first in our pipeline."Links:Rubedo Life Sciences: https://www.rubedolife.comMarco Quarta's previous appearance on Translating Aging: Ep 35 — Targeting Pathologic Cells to Preserve Biological Youth

My Set from Modulation, Saturday 2nd May 2026. Oldskool Uplifting & House - All tracks from 1992. SoundCloud: https://soundcloud.com/djmikespeed/modulation_boomboxalldayer_theloft_0200526 MixCloud: https://www.mixcloud.com/djmikespeed/mike-speed-modulation-boombox-alldayer-the-loft-cleckheaton-020526-2000-2100/ Modulation - Boombox Alldayer The Loft, Cleckheaton Saturday 2nd May 2026 Pete Mosoon Mighty Ming Greenbins Dannie Kavanagh (Live Piano Set) Mike Speed Ste Huxley Vinyldoctor Danny Zacc Merek B2B Richard Gell Warren G Daz Page Jamtronik J-Funk 3pm until 2am. Bookings: 07787 164 054 Contact me on djmikespeed@hotmail.com All genres of Oldskool covered, From House, to Breakbeat, to Trance and Harder. Also on all formats Vinyl to Digital. Done all for the love ❤️ of the Oldskool! Links | dj mike speed Downloads & Social Links www.djmikespeed.co.uk Email | djmikespeed@hotmail.com SoundCloud | @djmikespeed MixCloud | www.mixcloud.com/djmikespeed/ HearThis | hearthis.at/djmikespeed/ Facebook: www.facebook.com/djmikespeedvinyloldskool Or search & add 'Mike Speed'

OsteoBites welcomes Caroline Maloney, MD, PhD, from the Medical College of Wisconsin, who will discuss her research on surgery-accelerated metastasis and developing perioperative therapies.Pulmonary metastasis remains the major cause of death in osteosarcoma. The timing of metastatic relapse defines clinically meaningful subgroups in osteosarcoma with patients who relapse within 6–12 months of surgical removal of their primary tumor having markedly worse survival (10-20%) than those who relapse after completion of therapy (40-50%). While surgical removal of the primary tumor is a fundamental component of the clinical care of solid tumors, surgery induces transient but profound changes in immune and inflammatory responses that can paradoxically accelerate the growth of metastatic disease. Dr. Maloney has demonstrated that surgical removal of the primary tumor accelerates the growth of pre-existing pulmonary metastatic disease and promotes expansion of M2‐like macrophages in the lung microenvironment. Strikingly, short term perioperative treatment with a RIPK2 inhibitor blocks this effect and reprograms macrophages toward an M1-like phenotype, implicating the NOD2–RIPK2 innate immune pathway as a key mediator of post‐surgical immune reprogramming. In contrast, the NOD2 agonist Mifamurtide has shown clinical efficacy when administered as adjuvant therapy to metastatic osteosarcoma patients after primary tumor resection. This data suggests that NOD/RIPK2 signaling may exert context-dependent effects, promoting either pro- or anti-tumor myeloid responses depending on the timing of activation relative to surgery. Understanding how surgical tumor removal alters systemic innate immunity and how RIPK2 signaling orchestrates these responses could identify new strategies to prevent early pulmonary relapse after surgery.

For more, visit: https://www.BishalSarkar.comMessage us directly: https://wa.me/918880361526In this episode of the “I Love Public Speaking” podcast, Bishal Sarkar reveals 3 powerful yet lesser-known techniques to instantly improve your voice modulation.You'll discover how to make your voice more dynamic, engaging, and impactful—whether you're speaking in a meeting, on stage, or on video.These tricks will help you command attention, avoid sounding flat or robotic, and keep your listeners fully engaged.If you want your voice to match your message's power, don't miss this episode.

BUFFALO, NY — April 2, 2026 — A new #research paper was #published in Volume 18 of Aging-US on March 24, 2026, titled “Age-specific relationship between the modulation of brain dynamics in response to task demands and bimanual performance.” Led by first author Sara Magalhães Ferreira from Hasselt University, with corresponding author Koen Cuypers from Hasselt University and KU Leuven, the study examined how age affects BOLD variability and its modulation with task demands during a bimanual task. The authors used fMRI in 22 younger and 23 older healthy adults who performed three increasingly complex task conditions. The authors found that older adults showed higher BOLD variability in cerebellar lobule VIIIb and greater modulation across task conditions in sensorimotor and cerebellar regions. Modulation of BOLD variability predicted performance in an age- and region-dependent manner: in younger adults, reduced modulation in sensorimotor and visuospatial areas correlated with better performance, whereas in older adults, increased modulation in the inferior and superior parietal lobules was linked to higher performance. Across groups, better outcomes were associated with greater modulation in the middle occipital gyrus but lower modulation in cerebellar Crus I. “In sum, this study highlights the potential role of BOLD variability modulation in shaping bimanual performance during aging.” The authors note that, while the age-related differences in BOLD dynamics were clear, they did not find robust evidence supporting a brain-behavior relationship in bimanual performance, which limits how directly the neural findings can be interpreted behaviorally. They recommend future work using multimodal imaging, longitudinal designs, and studies that examine both cognitive and motor domains within the same participants to determine whether variability modulation reflects aging, experience, intervention, or broader cross-functional signatures of aging. DOI - https://doi.org/10.18632/aging.206363 Corresponding author - Koen Cuypers - koen.cuypers@uhasselt.be Abstract video - https://www.youtube.com/watch?v=3TbcGFCZV9s Sign up for free Altmetric alerts about this article - https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.206363 Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts Keywords - aging, bimanual coordination, Bimanual Tracking Task, BOLD variability, task modulation To learn more about the journal, please visit https://www.Aging-US.com​​ and connect with us on social media at: Bluesky - https://bsky.app/profile/aging-us.bsky.social ResearchGate - https://www.researchgate.net/journal/Aging-1945-4589 X - https://twitter.com/AgingJrnl Facebook - https://www.facebook.com/AgingUS/ Instagram - https://www.instagram.com/agingjrnl/ LinkedIn - https://www.linkedin.com/company/aging/ Reddit - https://www.reddit.com/user/AgingUS/ Pinterest - https://www.pinterest.com/AgingUS/ YouTube - https://www.youtube.com/@Aging-US Spotify - https://open.spotify.com/show/1X4HQQgegjReaf6Mozn6Mc MEDIA@IMPACTJOURNALS.COM

Explore the Circle of Interval Magicians and dive deeper into interval-based composition techniques: https://www.skool.com/circle-of-interval-magicians/about In this episode, Frank challenges the common advice of “just modulate” when composers feel stuck inside a scale. True freedom doesn't come from changing scales but from changing perspective. By using equal interval structures and moving them chromatically, composers can step outside traditional scale boundaries while maintaining clarity and coherence. Thinking in interval relationships rather than scale containers opens the door to greater creative control and expressive freedom.

Our understanding of how psychedelics work has evolved in meaningful ways over the past several years. While earlier neuroscience frameworks helped move the field forward, newer research has added important nuance and depth to how we interpret brain imaging, network behavior, and subjective experience.In this episode of The Trip Lab, I offer a refresh on psychedelic neuroscience, focusing on key updates from the past four years and how they change the story we tell about what's happening in the brain and the body during psychedelic states.We explore:How the Default Mode Network is better understood as dynamically modulated rather than simply reducedWhy psychedelic brain states are best described as time-varying and network-based rather than staticHow neural entropy is now understood as increased flexibility through relaxed constraintsWhy brain, body, and context are inseparable in shaping psychedelic experiences and outcomesThis episode is designed to update earlier explanations, clarify what has changed, and highlight why the newer neuroscience offers a more accurate and more interesting framework for understanding psychedelic effects.

Extended Producer Responsibility (EPR) is rapidly expanding into the textile industry, but how will it actually work in practice, and how can brands prepare?In this episode of HappyPorch Radio, Barry O'Kane speaks with Gerrard Fisher, co-founder of WEFT, a company helping brands understand the circularity of their products and prepare for future EPR systems.Gerrard explains how eco-modulation could link the design of products directly to the costs of dealing with them at the end of life. By analysing product data, brands can begin to see which materials, designs and product categories are easier to recycle and which could create higher costs in future circular systems.They also explore the broader opportunity of using this data not just to manage regulation, but to help guide product design, improve recycling infrastructure planning, and give consumers clearer information about sustainability.✨ In this episode:Gerrard explains how Extended Producer Responsibility works and why it's becoming a major issue for textilesWe explore eco-modulation and how it could financially reward products that are easier to recycle or more durable.Gerrard shares consumer research suggesting shoppers are surprisingly open to small EPR charges on clothing.We discuss how product data can help brands map the circularity of their portfolios and make better design decisions.Barry and Gerrard explore how WEFT prototyped its product using AI coding tools before building a more robust system.We discuss the importance of aligning incentives across policy, business and consumer behaviour to make circular systems work.

PainExam Podcast Show Notes Red Light Therapy (Photobiomodulation) for Pain Evidence, Mechanisms, and Clinical Applications Host: Dr. David Rosenblum Red light therapy, also known as photobiomodulation (PBM) or low-level laser therapy (LLLT), is an emerging non-invasive treatment modality increasingly used in pain medicine, rehabilitation, and regenerative medicine practices. In this episode of the PainExam Podcast, Dr. Rosenblum reviews the mechanisms, clinical evidence, indications, and safety considerations surrounding photobiomodulation therapy for pain. Red and near-infrared wavelengths stimulate mitochondrial activity, increase ATP production, reduce inflammatory mediators, and promote tissue healing. These physiologic effects may translate into analgesic benefits for a variety of musculoskeletal and neuropathic pain conditions. Clinical research suggests potential benefit in temporomandibular disorders, chronic neck pain, and inflammatory oral conditions, though results vary due to differences in dosing parameters and treatment protocols. Despite these limitations, PBM has a favorable safety profile and is increasingly being integrated into multimodal pain management strategies. Key Topics Covered • What is photobiomodulation therapy (PBM) • How red and near-infrared light interact with mitochondria • Mechanisms of analgesia and tissue repair • Evidence from clinical trials in TMD, neck pain, and oral inflammatory pain • The biphasic dose response (Arndt-Schulz law) • Safety profile and contraindications • How PBM may integrate with regenerative pain medicine Mechanism of Action Photobiomodulation works primarily through stimulation of mitochondrial chromophores, particularly cytochrome c oxidase. This leads to: • Increased ATP production • Modulation of inflammatory cytokines • Increased angiogenesis and tissue repair • Reduced oxidative stress These effects may improve pain, inflammation, and healing in certain musculoskeletal conditions. Evidence Discussed in This Episode Temporomandibular Disorders Randomized trial demonstrating improvements in pain and mandibular function with red light therapy. De Carvalho et al., Pain Research and Treatment (2019) https://onlinelibrary.wiley.com/doi/full/10.1155/2019/8578703 Chronic Neck Pain Clinical trial demonstrating improvements in pain scores and pressure pain thresholds after photobiomodulation therapy. Chen et al., Lasers in Medical Science (2022) https://link.springer.com/article/10.1007/s10103-022-03540-0 Oral Pain and Dental Inflammation Randomized study demonstrating reduced pain and improved healing following PBM treatment. Almeida et al., BMC Oral Health (2023) https://link.springer.com/article/10.1186/s12903-023-02784-8 Who May Benefit From Photobiomodulation? Red light therapy may be considered as an adjunct treatment for: • myofascial pain • cervical spine pain • temporomandibular disorder • tendinopathy • peripheral neuropathy • musculoskeletal injury recovery Safety and Contraindications Photobiomodulation has a very favorable safety profile. Reported adverse effects are rare and usually mild: • transient erythema • warmth at treatment site • headache • eye irritation without proper protection Precautions include: • avoiding direct retinal exposure • avoiding treatment over malignancy • avoiding application over the uterus during pregnancy • caution in photosensitive disorders Resources For Patients Seeking Treatment Learn more about integrative and regenerative pain treatments including PRP, ultrasound-guided injections, and advanced pain therapies: AABP Integrative Pain Care & Wellness https://www.AABPpain.com For Pain Physicians and Advanced Practice Providers Training in ultrasound, interventional pain procedures, and pain board preparation: NRAP Academy CME Education https://www.NRAPpain.org

PainExam Podcast Show Notes Red Light Therapy (Photobiomodulation) for Pain Evidence, Mechanisms, and Clinical Applications Host: Dr. David Rosenblum Red light therapy, also known as photobiomodulation (PBM) or low-level laser therapy (LLLT), is an emerging non-invasive treatment modality increasingly used in pain medicine, rehabilitation, and regenerative medicine practices. In this episode of the PainExam Podcast, Dr. Rosenblum reviews the mechanisms, clinical evidence, indications, and safety considerations surrounding photobiomodulation therapy for pain. Red and near-infrared wavelengths stimulate mitochondrial activity, increase ATP production, reduce inflammatory mediators, and promote tissue healing. These physiologic effects may translate into analgesic benefits for a variety of musculoskeletal and neuropathic pain conditions. Clinical research suggests potential benefit in temporomandibular disorders, chronic neck pain, and inflammatory oral conditions, though results vary due to differences in dosing parameters and treatment protocols. Despite these limitations, PBM has a favorable safety profile and is increasingly being integrated into multimodal pain management strategies. Key Topics Covered • What is photobiomodulation therapy (PBM) • How red and near-infrared light interact with mitochondria • Mechanisms of analgesia and tissue repair • Evidence from clinical trials in TMD, neck pain, and oral inflammatory pain • The biphasic dose response (Arndt-Schulz law) • Safety profile and contraindications • How PBM may integrate with regenerative pain medicine Mechanism of Action Photobiomodulation works primarily through stimulation of mitochondrial chromophores, particularly cytochrome c oxidase. This leads to: • Increased ATP production • Modulation of inflammatory cytokines • Increased angiogenesis and tissue repair • Reduced oxidative stress These effects may improve pain, inflammation, and healing in certain musculoskeletal conditions. Evidence Discussed in This Episode Temporomandibular Disorders Randomized trial demonstrating improvements in pain and mandibular function with red light therapy. De Carvalho et al., Pain Research and Treatment (2019) https://onlinelibrary.wiley.com/doi/full/10.1155/2019/8578703 Chronic Neck Pain Clinical trial demonstrating improvements in pain scores and pressure pain thresholds after photobiomodulation therapy. Chen et al., Lasers in Medical Science (2022) https://link.springer.com/article/10.1007/s10103-022-03540-0 Oral Pain and Dental Inflammation Randomized study demonstrating reduced pain and improved healing following PBM treatment. Almeida et al., BMC Oral Health (2023) https://link.springer.com/article/10.1186/s12903-023-02784-8 Who May Benefit From Photobiomodulation? Red light therapy may be considered as an adjunct treatment for: • myofascial pain • cervical spine pain • temporomandibular disorder • tendinopathy • peripheral neuropathy • musculoskeletal injury recovery Safety and Contraindications Photobiomodulation has a very favorable safety profile. Reported adverse effects are rare and usually mild: • transient erythema • warmth at treatment site • headache • eye irritation without proper protection Precautions include: • avoiding direct retinal exposure • avoiding treatment over malignancy • avoiding application over the uterus during pregnancy • caution in photosensitive disorders Resources For Patients Seeking Treatment Learn more about integrative and regenerative pain treatments including PRP, ultrasound-guided injections, and advanced pain therapies: AABP Integrative Pain Care & Wellness https://www.AABPpain.com For Pain Physicians and Advanced Practice Providers Training in ultrasound, interventional pain procedures, and pain board preparation: NRAP Academy CME Education https://www.NRAPpain.org

PainExam Podcast Show Notes Red Light Therapy (Photobiomodulation) for Pain Evidence, Mechanisms, and Clinical Applications Host: Dr. David Rosenblum Red light therapy, also known as photobiomodulation (PBM) or low-level laser therapy (LLLT), is an emerging non-invasive treatment modality increasingly used in pain medicine, rehabilitation, and regenerative medicine practices. In this episode of the PainExam Podcast, Dr. Rosenblum reviews the mechanisms, clinical evidence, indications, and safety considerations surrounding photobiomodulation therapy for pain. Red and near-infrared wavelengths stimulate mitochondrial activity, increase ATP production, reduce inflammatory mediators, and promote tissue healing. These physiologic effects may translate into analgesic benefits for a variety of musculoskeletal and neuropathic pain conditions. Clinical research suggests potential benefit in temporomandibular disorders, chronic neck pain, and inflammatory oral conditions, though results vary due to differences in dosing parameters and treatment protocols. Despite these limitations, PBM has a favorable safety profile and is increasingly being integrated into multimodal pain management strategies. Key Topics Covered • What is photobiomodulation therapy (PBM) • How red and near-infrared light interact with mitochondria • Mechanisms of analgesia and tissue repair • Evidence from clinical trials in TMD, neck pain, and oral inflammatory pain • The biphasic dose response (Arndt-Schulz law) • Safety profile and contraindications • How PBM may integrate with regenerative pain medicine Mechanism of Action Photobiomodulation works primarily through stimulation of mitochondrial chromophores, particularly cytochrome c oxidase. This leads to: • Increased ATP production • Modulation of inflammatory cytokines • Increased angiogenesis and tissue repair • Reduced oxidative stress These effects may improve pain, inflammation, and healing in certain musculoskeletal conditions. Evidence Discussed in This Episode Temporomandibular Disorders Randomized trial demonstrating improvements in pain and mandibular function with red light therapy. De Carvalho et al., Pain Research and Treatment (2019) https://onlinelibrary.wiley.com/doi/full/10.1155/2019/8578703 Chronic Neck Pain Clinical trial demonstrating improvements in pain scores and pressure pain thresholds after photobiomodulation therapy. Chen et al., Lasers in Medical Science (2022) https://link.springer.com/article/10.1007/s10103-022-03540-0 Oral Pain and Dental Inflammation Randomized study demonstrating reduced pain and improved healing following PBM treatment. Almeida et al., BMC Oral Health (2023) https://link.springer.com/article/10.1186/s12903-023-02784-8 Who May Benefit From Photobiomodulation? Red light therapy may be considered as an adjunct treatment for: • myofascial pain • cervical spine pain • temporomandibular disorder • tendinopathy • peripheral neuropathy • musculoskeletal injury recovery Safety and Contraindications Photobiomodulation has a very favorable safety profile. Reported adverse effects are rare and usually mild: • transient erythema • warmth at treatment site • headache • eye irritation without proper protection Precautions include: • avoiding direct retinal exposure • avoiding treatment over malignancy • avoiding application over the uterus during pregnancy • caution in photosensitive disorders Resources For Patients Seeking Treatment Learn more about integrative and regenerative pain treatments including PRP, ultrasound-guided injections, and advanced pain therapies: AABP Integrative Pain Care & Wellness https://www.AABPpain.com For Pain Physicians and Advanced Practice Providers Training in ultrasound, interventional pain procedures, and pain board preparation: NRAP Academy CME Education https://www.NRAPpain.org

Dr. Ronald Lane, Founder of HOPE-Neuron Therapeutx, is developing an approach to treating ALS by rebalancing the body's immune system, which becomes dysfunctional in neurodegenerative diseases. The mechanism involves treating a patient's blood outside the body to activate the immune system and then infusing it back into the patient. The theory is that traditional drugs often fail because they target static genetic mutations and symptoms, while the source of the disease lies in the ongoing immune imbalance.  Ronald explains, "Well, in a broad sense, the goal of HOPE Neuron is based on the fact that our challenge here is to bring hope. And we use a neuronal basis to do that. We're not about drugs. There are no chemicals involved. We don't go that direction. But basically, it cuts across. We deal with memory, we deal with dementia and Alzheimer's, but our target initially is ALS."   "As I mentioned a moment ago, we're not doing drugs, we're not using chemicals. So what are you doing? Well, we're using the cell's immune system, and the problem with disease is that the immune system gets out of balance. When it's out of balance, you become ill. It can be in many different diseases. And there are certain indicators, and we know what to look for and what compounds the body generates."   #HOPENeuron #ALS #Neurology #ImmuneTherapy #MedicalInnovation #ALSResearch #Biotechnology #NeurodegenerativeDisease #MedicalBreakthrough #ClinicalTrials #Healthcare #Innovation #Neurodegeneration #Neuroplasticity #MedicalDevices #Immunotherapy #NeuroscienceInnovation #FutureOfMedicine #NonPharmaTherapies hopeneuron.com Download the transcript here

Dr. Ronald Lane, Founder of HOPE-Neuron Therapeutx, is developing an approach to treating ALS by rebalancing the body's immune system, which becomes dysfunctional in neurodegenerative diseases. The mechanism involves treating a patient's blood outside the body to activate the immune system and then infusing it back into the patient. The theory is that traditional drugs often fail because they target static genetic mutations and symptoms, while the source of the disease lies in the ongoing immune imbalance.  Ronald explains, "Well, in a broad sense, the goal of HOPE Neuron is based on the fact that our challenge here is to bring hope. And we use a neuronal basis to do that. We're not about drugs. There are no chemicals involved. We don't go that direction. But basically, it cuts across. We deal with memory, we deal with dementia and Alzheimer's, but our target initially is ALS."   "As I mentioned a moment ago, we're not doing drugs, we're not using chemicals. So what are you doing? Well, we're using the cell's immune system, and the problem with disease is that the immune system gets out of balance. When it's out of balance, you become ill. It can be in many different diseases. And there are certain indicators, and we know what to look for and what compounds the body generates."   #HOPENeuron #ALS #Neurology #ImmuneTherapy #MedicalInnovation #ALSResearch #Biotechnology #NeurodegenerativeDisease #MedicalBreakthrough #ClinicalTrials #Healthcare #Innovation #Neurodegeneration #Neuroplasticity #MedicalDevices #Immunotherapy #NeuroscienceInnovation #FutureOfMedicine #NonPharmaTherapies hopeneuron.com Listen to the podcast here

Send a textWhy are so many women still exhausted—even when they're eating well, sleeping more, and being told their labs look “normal”?In this episode of It's Hertime, Cody Sanders sits down with Dr. Evan Hirsch, also known as the EnergyMD, to unpack the real root causes behind chronic fatigue, long COVID, and persistent low energy in women. Drawing from both his personal health journey and clinical work, Dr. Hirsch explains why fatigue is rarely just about sleep or stress—and what actually needs to happen for true energy recovery.Together, Cody and Dr. Hirsch explore the systems-based drivers of fatigue, the role of nervous system dysregulation, and why many women feel worse when they try to “push through.” If you or someone you love feels tired all the time, wired but exhausted, or stuck in burnout despite doing all the right things, this conversation will bring clarity and hope.⸻In This Episode, You'll Learn:•The difference between being tired and having true chronic fatigue•Why standard lab work often misses the root causes of low energy•How long COVID and ME/CFS overlap with hormone and nervous system dysfunction•The EnergyMD Method and the “Toxic Five” contributors to fatigue•Why pushing harder with exercise can sometimes backfire•How nervous system regulation impacts energy, recovery, and resilience•The first steps women can take to start rebuilding real, sustainable energy⸻About Dr. Evan Hirsch (EnergyMD)Dr. Evan Hirsch is a fatigue expert who specializes in helping people recover from chronic fatigue syndrome, long COVID, and complex energy issues using a root-cause, functional medicine approach. After experiencing his own five-year battle with fatigue, he developed the EnergyMD Method to help patients systematically uncover and address the underlying drivers of low energy.Connect with Dr. Hirsch:•Free Fatigue Assessment: https://www.myfatiguescore.com•Website: https://www.energymdmethod.com•Book — Fix Your Fatigue: https://www.fixyourfatigue.org•Instagram: https://www.instagram.com/energymd•Facebook: https://www.facebook.com/EnergyMDMethod•YouTube: https://www.youtube.com/@EnergyMD⸻Work with Cody SandersIf this episode resonated and you're ready to stop guessing and start rebuilding your energy in a personalized, supportive way, Cody's signature CALM Body Reset program is designed specifically for women 40+ who feel exhausted, inflamed, and out of sync with their bodies.The CALM framework (Capacity, Adaptation, Load, Modulation) helps women move from survival mode to steady, sustainable energy through targeted nutrition, lifestyle, nervous system support, and root-cause investigation.Learn more about the CALM Body Reset here:DM "CALM" tDid you learn something new today? Be sure to subscribe to this podcast and share this episode with all the girls you love. We would appreciate it if you'd also leave us a rating and review on iTunes.Want to join our Mixhers Girl community and keep this conversation going? We'd love to hear your thoughts, feelings and experiences! Join us HERE!Join Mixhers email list and be the first to have access to new products and be the girl in the know!Follow Cody Instagram:@codyjeansanders

In this live episode from the AHR 2026 Podcast Pavilion, Bryan sits down with Copeland's Josh Souders (Manager of Commercial Unitary Product Management) and Jeff Kukert (Compression Senior Technical Trainer) to dive deep into Enhanced Vapor Injection (EVI) technology and its transformative impact on HVAC systems. This conversation offers both technical professionals and industry newcomers a comprehensive look at how vapor injection is revolutionizing heat pump performance, particularly in challenging climate conditions. The discussion centers on how EVI technology addresses one of the industry's most persistent challenges: maintaining high heat pump capacity in extremely low-temperature conditions. Josh and Jeff explain that vapor injection can deliver up to 20% added capacity and 10% improved efficiency while simultaneously enhancing compressor reliability. This technology, which has been a staple in refrigeration applications for years, is now becoming increasingly prevalent in commercial and residential HVAC systems, especially as cold climate heat pumps gain traction across North America. The guests make the complex topic accessible by breaking down how the system works—taking liquid refrigerant from the condensing line, running it through an expansion device and brazed plate heat exchanger (economizer), and injecting the cooled vapor directly back into the compressor scroll at a specific intermediate point. What makes this episode particularly valuable is the practical guidance offered for field technicians. The conversation moves beyond theoretical explanations to address real-world implementation challenges and troubleshooting strategies. Josh and Jeff emphasize the importance of understanding operating envelopes, pulse-width modulated (PWM) valves, pressure transducers, and modern control systems. They introduce Copeland's latest product developments, including the YAW variable speed vapor injection platform (1.5 to 25 tons) and the upcoming YAB two-stage vapor injection system launching later in 2026. The discussion also touches on applications beyond traditional HVAC, including commercial water heating and boiler replacement systems where high discharge temperatures are crucial. Throughout the episode, the guests maintain an encouraging tone toward technicians who may feel intimidated by these advancing technologies. They stress that while EVI systems may appear complex with additional tubing, heat exchangers, valves, and sensors, the underlying thermodynamic principles remain the same. The key is familiarizing oneself with new components like PWM valves and modern controllers, and leveraging tools like Copeland Mobile to verify system performance against operating envelopes. This episode serves as both an educational resource and a call to action for HVAC professionals to embrace these emerging technologies that are rapidly becoming industry standard. Topics Covered Enhanced Vapor Injection (EVI) fundamentals – How EVI works, its history in refrigeration, and why it's now critical for commercial and residential HVAC applications Capacity and efficiency benefits – Achieving up to 20% capacity boost and 10% efficiency improvement, particularly in low-ambient heating conditions Compressor reliability improvements – How injecting cooled vapor into the scroll set manages discharge temperatures and extends compressor life under high compression ratios Operating envelope management – Understanding compressor operational limits and using tools like Copeland Mobile to verify field conditions stay within safe parameters Cold climate heat pump technology – Meeting DOE's Cold Climate Heat Pump Challenge requirements for 100% capacity at 5°F ambient conditions System architecture and components – Detailed explanation of economizers (brazed plate heat exchangers), pulse-width modulated (PWM) valves, pressure transducers, and advanced controllers Compression ratio challenges – Managing the increased work required when outdoor temperatures drop while indoor condensing temperatures remain constant New Copeland product platforms – Introduction to YAW variable speed vapor injection (1.5-25 tons), YAB two-stage vapor injection (launching 2026), and tandem variable speed configurations Applications beyond traditional HVAC – Water heating systems, commercial boiler replacement, and managing high discharge temperatures for Legionella protection Technician training and tools – Practical advice on learning PWM valves, thermistors, transducers, and system controllers; emphasis on using Copeland Mobile for dynamic performance analysis Market trends and adoption – How vapor injection is becoming standard in premium residential systems and increasingly common across commercial rooftop units and dedicated outdoor air systems Installation and service considerations – Proper system design to avoid oversizing, humidity control in hot-humid climates, and troubleshooting techniques for complex control systems   Have a question that you want us to answer on the podcast? Submit your questions at https://www.speakpipe.com/hvacschool. Purchase your tickets or learn more about the 7th Annual HVACR Training Symposium at https://hvacrschool.com/symposium. Subscribe to our podcast on your iPhone or Android. Subscribe to our YouTube channel. Check out our handy calculators here or on the HVAC School Mobile App for Apple and Android.

Most talks are totally forgettable because they never land emotionally and logically. If you want real impact — the kind that people remember, repeat, and act on — you need to stop "delivering content" and start designing attention through voice, pacing, phrasing, and purposeful movement.  Why are most presentations forgettable, even when the content is "good"? Because information doesn't stick — impact does. Most presentations are heavy on data and light on connection, so audiences can't remember the speaker, the topic, or both, even a day later. In a post-pandemic, mobile-first attention economy (think 2020s Zoom fatigue plus constant notifications), your audience can disappear in seconds — two or three taps and they're in "distraction heaven". The irony is that many speakers feel impressive at the front of the room, but the audience experiences monotone delivery as a kind of "presenter white noise". Compare it to business: a strategy deck in a shared drive is rarely "scintillating", but a skilled leader can bring the same content alive through delivery. In Japan, Australia, the US, or Europe, the mechanism is the same: if the audience isn't touched (emotion + logic), the message doesn't travel. Do now (answer card): Impact = emotional + logical resonance. Design for attention, not just accuracy. How do you use word emphasis to make your message land? Emphasising key words changes meaning and makes ideas memorable. When every word is delivered with the same weight, your message flattens out — and audiences tune out. The fix is simple: stress the words that carry the intention. Take the phrase "This makes a tremendous difference." Hit different words and you get different implications: THIS(contrast), MAKES (causation), TREMENDOUS (scale), DIFFERENCE (outcome).  This works across contexts: whether you're a SaaS founder pitching in Singapore, a multinational leader briefing in Tokyo, or a sales director presenting to a procurement team in the US, emphasis helps listeners hear the headline inside the sentence. It's also an executive credibility tool: it signals certainty and prioritisation, not verbal mush. Do now (answer card): Pick 3–5 "load-bearing" words per section and punch them. Make your audience hear your priorities. Why do pauses increase attention (and stop people scrolling)? Pauses are a pattern interrupt that drags attention back to you. When you stop speaking, the contrast is so sharp that people who were mentally wandering snap back. That's why a well-timed pause creates anticipation — it makes the next sentence feel important. In live rooms it works because silence is social pressure; on video calls it works because silence is unusual and therefore noticeable. Most presenters under-use pauses because they fear awkwardness. But doubling the length of your current pauses — even in just two moments — increases impact because it forces processing time. It also reduces "verbal clutter" and improves perceived authority, especially for leaders and subject-matter experts who want to sound decisive rather than frantic. Do now (answer card): Add two deliberate pauses: one before your key point, one after it. Let the room absorb the idea.   How do pacing and modulation stop you sounding monotone? Variety in speed and strength keeps listeners engaged from start to finish. Pacing is your emphasis dial: slow down to spotlight meaning, speed up briefly for contrast, then return to normal. The goal isn't "fast talking" — it's controlled variation. A steady pace with no contrast becomes hypnotic in the wrong way. Modulation matters even more if your default delivery is flat. The article notes that Japanese is often described as a monotone language, which means speakers may need to inject extra variety through speed and strength to create highs and lows.   Think of a classical orchestra: if it only played crescendos or only soft lulls, it would be unbearable. Your voice needs both. Do now (answer card): Mark your script: SLOW (key line), FAST (brief energy burst), LOW (serious), HIGH (optimistic). Build contrast on purpose.   What makes phrasing memorable — and how do you create "sticky" lines? Memorable phrasing uses patterns the brain likes: alliteration, rhyme, and contrast. Great presenters don't just explain; they package. A simple shift like "hero to zero" sticks because it's rhythmic, punchy, and easy to repeat — which is the whole point.   When people repeat your phrase, your message travels without you. This is useful across roles: salespeople need repeatable value statements, executives need quotable strategy, and team leaders need language that anchors culture. In Japan vs. the US, the style may change (more subtle in Japan, more direct in the US), but the mechanics are universal: make it short, make it patterned, make it tied to an outcome. Do now (answer card): Create 2 "sticky lines" for your talk: a contrast pair (X to Y) and a rhythmic three-part phrase. How should you use movement and gestures without distracting people? Movement should have a purpose — otherwise it steals attention from your message. Gestures are powerful when they match what you're saying, because they add strength and clarity. But there's a rule: hold a gesture for a maximum of about 15 seconds; after that, its power drops and it becomes visual noise.  The bigger danger is pacing up and down like a caged tiger — it distracts audiences and looks like nervous energy, not leadership. In boardrooms, conference stages, and hybrid setups, the principle is the same: move to signal something (transition, emphasis, audience inclusion), then stop. Stillness can be as impactful as motion when it's intentional. Do now (answer card): Plan your movement: "I step forward for the key point, I step sideways for contrast, I stop for the close." No random wandering. Conclusion Communicating with greater impact isn't about being louder or more dramatic — it's about being more deliberate. When you combine word emphasis, pauses, pacing, modulation, memorable phrasing, and purposeful movement, you stop sounding like everyone else. And that's the real advantage: most speakers stay stuck in the same groove, losing their audience. You become the person who holds attention, lands the message, and strengthens your professional brand.  Author credentials Dr. Greg Story, Ph.D. in Japanese Decision-Making, is President of Dale Carnegie Tokyo Training and Adjunct Professor at Griffith University. He is a two-time winner of the Dale Carnegie "One Carnegie Award" (2018, 2021) and recipient of the Griffith University Business School Outstanding Alumnus Award (2012). As a Dale Carnegie Master Trainer, Greg is certified to deliver globally across leadership, communication, sales, and presentation programs, including Leadership Training for Results.  He has written several books, including three best-sellers — Japan Business Mastery, Japan Sales Mastery, and Japan Presentations Mastery — along with Japan Leadership Mastery and How to Stop Wasting Money on Training. His works have been translated into Japanese, including Za Eigyō (ザ営業) and Purezen no Tatsujin (プレゼンの達人).  Greg also publishes daily business insights on LinkedIn, Facebook, and Twitter, and hosts six weekly podcasts. On YouTube, he produces The Cutting Edge Japan Business Show, Japan Business Mastery, and Japan's Top Business Interviews, followed by executives seeking success strategies in Japan. 

Five articles from the December 2025 issue summarized in five minutes, with the addition of a brief editorial commentary. The 5-in-5 feature is designed to give readers an overview of articles that may pique their interest and encourage more detailed reading. It may also be used by busy readers who would prefer a brief audio summary in order to select the articles they want to read in full. The featured articles this month are: Reconstruction of the Medial Ulnar Collateral Ligament Using an Anatomic Technique With Suture Tape Augmentation to Allow for Expedited Return to Play in Throwing Athletes Fenofibrate Attenuates Rotator Cuff Muscle Fatty Infiltration via Modulation of the PPARα-FABP4 Pathway Meniscal Repair in the Setting of Revision Anterior Cruciate Ligament Reconstruction: 6-Year Follow-up Results From the MARS Cohort Does the Timing of Lysis of Adhesions After Anterior Cruciate Ligament Reconstruction Affect Final Range of Motion? Sports Participation in Patients With Hip Dysplasia Before and Up to 20 Years After Periacetabular Osteotomy Click here to read the articles.

CME credits: 0.25 Valid until: 02-12-2026 Claim your CME credit at https://reachmd.com/programs/cme/rethinking-schizophrenia-treatment-through-muscarinic-modulation/37103/ In this Chairperson's Perspective, Dr. Jose Rubio and Dr. Jonathan Meyer explore the evolving landscape of schizophrenia treatment through muscarinic receptor modulation. They compare the mechanisms of action, efficacy, and side effect profiles of muscarinic antipsychotics, such as xanomeline-trospium, with traditional dopamine D2 antagonists. The discussion addresses key clinical considerations for transitioning between dopaminergic and muscarinic therapies, including cholinergic burden and cross-titration timing. This expert dialogue provides relevant guidance for clinicians aiming to incorporate muscarinic therapies into personalized treatment plans for schizophrenia.=

For more, visit: https://www.BishalSarkar.comMessage us directly: https://wa.me/918880361526In this episode of the “I Love Public Speaking” podcast, Bishal Sarkar reveals 3 powerful yet lesser-known techniques to instantly improve your voice modulation.You'll discover how to make your voice more dynamic, engaging, and impactful—whether you're speaking in a meeting, on stage, or on video.These tricks will help you command attention, avoid sounding flat or robotic, and keep your listeners fully engaged.If you want your voice to match your message's power, don't miss this episode.

In this episode we meet Tony, K1KP, the developer of polar modulation that is being used in the new Flex Radio Aurora radio.  Polar modulation is a technology breakthrough that allows Flex Radio to make an integrated 500 watt HF/6m transciever, power supply and antenna tuner in a small light weight package.  With better than 80% efficiency, the Aurora produces less heat, consumes less power and is smaller than conventional radios.  Tony, K1KP, has been developing this technology for over 10 years.  In this episode we will hear about Tony's journey as he pioneered polar modulation in amateur radio.

Chris bell interviews Michael Arnold DNP, FHRS, about what is new in CCM therapy and when GDMT is not enough.

Dr. Kenneth Ellenbogen, Deputy Editor of JACC Clinical Electrophysiology discusses Pressure-Volume Analysis Demonstrates Short and Long-Term Hemodynamic Effects of Atrioventricular Interval Modulation Therapy in Hypertension.

Send us a textThis conversation is the first segment of SurfingMASH's July discussion of key events from the first six months of 2025. Co-hosts Jörn Schattenberg, Louise Campbell and Roger Green each chose one topic of personal interest.  Today, Jörn Schattenberg discusses two recent papers that demonstrate differences in how individual patients respond to different proteomic tests and what this can mean for individualized treatment plans. Jörn begins by citing Modulation of megabolic, inflammatory and fibrotic pathways by semaglutide in metabolic dysfunction-associated steatohepatitis, a paper from researchers at Novo Nordisk that Nature magazine posted online on July 21. In this paper, the researchers utilized proteomic testing (aptamer-based SomaSignal NASH tests) to determine whether semaglutide successfully addressed different NAS score elements: steatosis, lobular inflammation, hepatocyte ballooning and fibrosis. The proteomics-based tests indicated an improvement in all four of these elements, but the elements that improved and the amounts of improvement varied among individuals. To Jörn, this suggests that we can use an individual patient's proteomic results to tailor individualized therapy based on the areas in need of improvement, so that the prescriber would know whether the best approach to resolve NAS and reduce fibrosis was one that focused on metabolic issues vs. specific anti-fibrotic effects in the liver. He supports this idea by referring to a paper he had published in JHep Reports. In this paper, he and his colleagues looked at semaglutide Phase 1 and 2 obesity trials, where MASLD was not a criterion for entry. However, analyzing individual patients with the SomaLogic panel revealed that many had some form of MASLD and that semaglutide therapy could resolve the specific MASLD issues. The rest of the conversation focused on Jörn's conclusion and the potential for tailoring treatment plans to the individual. Lousie hailed the entire concept as extremely helpful not only in selecting a pharmacotherapy but also in providing patients with information they could use to improve their health further. Roger suggests that the kinds of proteomic tests Jörn describes can lead to first-line multi-agent pharmacotherapy to address the disease with agents that collectively will address the individual patient's profile. 

In this powerful episode of the Tick Boot Camp Podcast, we are joined by Dr. Daniel Warren of Envita Medical Center, a leading voice in the field of regenerative and integrative medicine, to break down the multi-layered complexity of chronic Lyme disease and its treatment. Dr. Warren takes us deep into the root causes of persistent symptoms, highlighting how chronic Lyme often results in immune dysregulation, biofilm-protected pathogens, co-infections, and central nervous system inflammation that go untreated by conventional protocols. The conversation explores the use of VSELS (Very Small Embryonic Like Stem Cells) to regenerate damaged tissue and rebalance immune function, as well as IRAD (Insulin Receptor Antibiotic Delivery)—Envita's proprietary method of delivering antibiotics past the blood-brain barrier to treat neurological Lyme disease. This episode is a must-listen for anyone seeking a deeper understanding of how precision diagnostics, immune modulation, and regenerative medicine can be integrated to support lasting recovery from chronic Lyme and tick-borne disease.

JEMS Managing Editor Jeff Frankel sits down with occupational therapist Bonnie Ekman and paramedic leader Alanna Badgley to explore sensory modulation therapy as a breakthrough tool for first responders' mental health.   Bonnie explains how engaging all eight senses helps regulate the nervous system, moving beyond traditional talk therapy when first responders are stuck in fight-or-flight activation.   Alana shares her personal experience and the positive feedback from EMS professionals who have benefited from sensory rooms designed to soothe hypervigilance and stress.   They discuss practical, budget-friendly ways departments can implement sensory modulation spaces, emphasizing low-cost solutions like colored lighting, calming sounds, and tactile tools.

FATTY LIVER, OBESITY, TYPE-2 DIABETES AND FRQUENCY MODULATION-DR SHARRY EDWARDS

In this episode of The ABMP Podcast, Whitney Lowe explores the powerful concept of descending modulation. Whitney breaks down the science behind this neurological process and how it applies directly to massage, manual therapy, and bodywork. Can therapeutic touch, trust, and context turn down the pain dial and support long-term healing? Tune in to find out.   Resources:   The Role of Descending Modulation in Manual Therapy and Its Analgesic Implications: A Narrative Reviewhttps://pubmed.ncbi.nlm.nih.gov/26788367/   The mechanisms of manual therapy in the treatment of musculoskeletal pain:https://www.sciencedirect.com/science/article/abs/pii/S1356689X08001598?via%3Dihub   Descending Modulation: Why Massage Therapy Can Alleviate Painhttps://massagefitnessmag.com/massage/descending-modulation-why-massage-therapy-alleviates-pain/#google_vignette   Recovery Strategies by Greg Lehmanhttps://www.greglehman.ca/recovery-strategies-pain-guidebook     Host:   Whitney Lowe is a known authority in the field of massage therapy, with a 36-year career marked by clinical work, research, publications, and teaching in advanced massage principles. He specializes in treating pain and injuries using massage and is one of the pioneers of the orthopedic massage approach. Lowe's Orthopedic Massage Program stands out in its engaging and accessible design and comprehensive curriculum. Students, whether learning online or in-person, praise Lowe for his approachable style and personalized training.   Sponsors:   Anatomy Trains: www.anatomytrains.com    American Massage Conference: www.massagetherapymedia/conferences   Earthlite: www.earthlite.com   Anatomy Trains is a global leader in online anatomy education and also provides in-classroom certification programs for structural integration in the US, Canada, Australia, Europe, Japan, and China, as well as fresh-tissue cadaver dissection labs and weekend courses. The work of Anatomy Trains originated with founder Tom Myers, who mapped the human body into 13 myofascial meridians in his original book, currently in its fourth edition and translated into 12 languages. The principles of Anatomy Trains are used by osteopaths, physical therapists, bodyworkers, massage therapists, personal trainers, yoga, Pilates, Gyrotonics, and other body-minded manual therapists and movement professionals. Anatomy Trains inspires these practitioners to work with holistic anatomy in treating system-wide patterns to provide improved client outcomes in terms of structure and function.                      Website: anatomytrains.com                        Email: info@anatomytrains.com             Facebook: facebook.com/AnatomyTrains                       Instagram: www.instagram.com/anatomytrainsofficial   YouTube: https://www.youtube.com/channel/UC2g6TOEFrX4b-CigknssKHA      American Massage Conference Get ready to immerse yourself in the excitement as the American Massage Conference (AMC) arrives to Disney Springs near Orlando, Florida (May 16th-18th, 2025)! With a legacy of 17 successful years in Ontario, Canada, this premier event, proudly hosted by ONE Concept Conferences and expertly produced by Massage Therapy Media (MTM), boasts a lineup of presenters from across the nation and around the globe. The American Massage Conference began in Atlanta in 2011 and has been hosted through the years in San Diego, Chicago, and Virginia Beach. The conference provides educational opportunities with engaging one-, two-, three- and four-hour class formats, networking opportunities, masterminds, MTM Talks, demonstrations, and an extensive exhibitor tradeshow.   Mark your calendars for an unforgettable experience filled with education, networking, and the celebration of massage therapy excellence! ABMP members receive a special discount to attend this in-person conference—log in to your ABMP account to access the discount code and register today. Website: https://www.massagetherapymedia.com/conferences   Earthlite Unlock an exclusive 20 percent discount on all Earthlite products, from portable tables and chairs to professional sheets and oils. Visit earthlite.com, create an account, and enter your ABMP member ID during registration. Plus, enjoy free ground shipping on orders over $75 and a flat rate of $395 for stationary or electric lift tables. (Prices subject to change at any time.) Significant savings on everything you need to enhance your practice. We are proud to assist you as the “World's No. 1 Brand in Massage!”   Sign-up page: https://www.earthlite.com/customer/account/login/referer/aHR0cHM6Ly93d3cuZWFydGhsaXRlLmNvbS8~/  

Episode 139: Definitive Guide To Modulation Pedals: Types, Sounds, and Signal Path Strategies Welcome to the Chairmen of the Boards Podcast! The ultimate pedalboard podcast with the foremost rig builders in the world: Grant Klassen (Goodwood Audio), Brian Omilion (Omilion Audio), and Mason Marangella (Vertex Effects/The Rig Doctor). We've teamed up to democratize great tone and provide you with our best tricks, tips, resources and hacks so you can build the pedalboard of your dreams!   //SPONSORS// The Guitar Sanctuary - https://theguitarsanctuary.com Neural DSP - https://www.neuraldsp.com (use discount code "chairmen" for 30% off) Best-Tronics - https://btpa.com (use code "dachairs" for 10% off) GB Music & Sound - https://www.gbmusicandsound.com/?ref=Chairmen   //HOSTS// Grant Klassen (Goodwood Audio) YT - ‪@GoodwoodAudio‬ IG - https://instagram.com/goodwoodaudio   Brian Omilion (Omilion Audio) YT - ‪@omilionaudio‬ IG - https://instagram.com/omilionaudio   Mason Marangella (Vertex Effects) YT - ‪@VertexEffectsInc‬ IG - https://instagram.com/vertexeffects   //YOUTUBE// Watch COTB Podcast live: @chairmenoftheboards

Welcome to Season 2 of the Orthobullets Podcast.Today's show is Podiums, where we feature expert speakers from live medical events. Today's episode will feature ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Dr. ⁠⁠⁠Thomas Best⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠and is titled⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Arthritis Institute Focus On Modulation of Arthritis.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Orthobullets⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ on Social Media:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube

A powerful ancient remedy is gaining modern attention, and it's as simple as drinking clove water before bed. Studies suggest it might improve digestion, support immunity, and even help you sleep better! Curious how? Let's dive in! We'll explore the science-backed reasons why clove water is a nighttime game-changer, how to make it, and precautions to take.  What Is Clove Water and Why at Night? What is clove water? Made by steeping cloves in water to extract active compounds like eugenol, flavonoids, and tannins. Why at night? Optimal absorption: The body focuses on repair and recovery during sleep, allowing active compounds to work effectively. Evening consumption aids digestion after your last meal and preps the body for restful sleep.