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Huberman Lab podcast is zero cost. Our premium channel w/Tiny Foundation provides financial support for research Stanford University (Labs: #CarlaShatz Nolan Williams Alia J. Crum, PhD) Columbia Medicine (Dr Steinglass) & Salk Institute (Satchin Panda). Thank you to all HLP listeners!

Andrew D. Huberman, Ph.D.

1,826,847 subscribers

179,147 views • 2 years ago •via X (Twitter)

Science & Technology Health & Wellness #CarlaShatz

Anya Rossi• Live Now

Private livecam show

10 Comments

Andrew D. Huberman, Ph.D.2 years ago

We will be expanding the number of laboratories & projects we support in 2024. As of now we only support work on human subjects that stand to have direct clinical implications. These include: stem cells, nutrition, psychedelics, mindset, eating disorders, & more. Stay tuned!

Peak Insight2 years ago

@Stanford @NolanRyWilliams @AliaCrum @ColumbiaMed @salkinstitute @SatchinPanda Kudos to the @hubermanlab for bridging the gap between science and society.

Mitchell Wilson ⚡️2 years ago

@Stanford @NolanRyWilliams @AliaCrum @ColumbiaMed @salkinstitute @SatchinPanda I do love that you say zero cost instead of free so often. It just hits different. 👌🏼

Robin Carhart-Harris2 years ago

@Stanford @NolanRyWilliams @AliaCrum @ColumbiaMed @salkinstitute @SatchinPanda Respect

Elham Ishmael ✍︎2 years ago

@Stanford @NolanRyWilliams @AliaCrum @ColumbiaMed @salkinstitute @SatchinPanda You are a true legend! Best podcast! 👌

Andrew D. Huberman, Ph.D.2 years ago

@Stanford @NolanRyWilliams @AliaCrum @ColumbiaMed @salkinstitute @SatchinPanda I not endorsing anything related to them. All the sponsors I work with are listed at the link below. Everything else is illegal (ad).

Andrew D. Huberman, Ph.D.2 years ago

@Stanford @NolanRyWilliams @AliaCrum @ColumbiaMed @salkinstitute @SatchinPanda *I’m

BuZzDeaD2 years ago

@Stanford @NolanRyWilliams @AliaCrum @ColumbiaMed @salkinstitute @SatchinPanda For sure the greatest podcast till todays date, by any means and by any measure.

☮️A.W.E.S.M-O2 years ago

@Stanford @NolanRyWilliams @AliaCrum @ColumbiaMed @salkinstitute @SatchinPanda You are a great person!

Eneko Uruñuela2 years ago

@Stanford @NolanRyWilliams @AliaCrum @ColumbiaMed @salkinstitute @SatchinPanda Hey Andrew, big fan of the podcast here. Is there a way to apply for this funding/support as a lab?

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Andrew D. Huberman, Ph.D.

288,900 views • 3 years ago

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Foresight Institute

10,281 views • 7 months ago

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Steven Bartlett

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Dana Parish

49,896 views • 11 months ago

We’re thrilled to share that our MERFISH+ preprint is now live on bioRxiv!👉 In this work, the Bintu and Zhu labs (UCSD) developed MERFISH+, a next-generation spatial genomics platform that combines genome-wide RNA and epigenetic imaging over a large field of view. By introducing acrydite-modified probes covalently anchored to hydrogels, MERFISH+ achieves remarkable imaging stability and enables >1,800-gene, multi-modal, and multi-month experiments. With this platform, they, together with the Chi lab at UCSD, profiled a whole developing human heart at 12 post-conception week with merely two slides, resulting in a total of 53 slides, 3.1 million single cells and more than 30 cell types. Building upon our previous 3D reconstruction and modeling framework, Spateo ( we reconstruct the 3D human heart that nicely captures the anatomical structure of the heart, including the intricate vasculature network. Sophisticated analyses provide a holistic view of an entire organ and enable systematic characterization of 3D cellular neighborhoods and transcriptional gradients of substructures such as the descending arteries. Furthermore, using a generative integration framework for spatial multimodal data (Spateo-VI), we harmonized these MERFISH+ transcriptomic and chromatin data to reconstruct a 3D spatially-resolved multi-omics atlas of the developing human heart, shared at and MERFISH+ thus sets a new standard for large-format, multi-omic spatial profiling, enabling holistic, 3D characterization of organs at subcellular resolution. Huge congratulations to first authors Colin Kern, qingquan Zhang, @YifanLu2024 , and Jacqueline Eschbach, and to all collaborators from the Bintu, Zhu, Chi, and Qiu labs for this amazing team effort. Thanks for your diligence, creativity, and hard work on this project. We’re grateful for support from Arc Institute and our generous donors. Our lab is expanding—if you’re excited about building the next generation of single-cell and spatial genomics techniques and predictive single cell and spatial foundation models, we’re hiring! If you are interested, please reach out to me via direct message or email at xiaojie@stanford.edu. We are excited for any potential collaborations along this line of research in Stanford, UCSF and Berkeley and other labs as well.

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evo-devo

42,268 views • 9 months ago

USAID, Hunter Biden, Google, Ukraine, EcoHealth, NIH etc.— all contributors for causing the COVID-19 pandemic… For years, USAID gave millions in taxpayer money to Peter Daszak’s EcoHealth Alliance through its PREDICT program. The goal was to hunt “emerging viruses” — including dangerous bat coronaviruses — often in partnership with China’s Wuhan Institute of Virology. EcoHealth funneled U.S. funds to Wuhan researchers for studies on these “killer” viruses. Daszak’s group also received hundreds of thousands of dollars from Dr. Anthony Fauci’s NIAID, which it passed along to the same Wuhan lab. Daszak later sat on the WHO’s COVID-19 origins investigation team. There, he worked to downplay the lab-leak theory — even as evidence grew and former intelligence officials raised alarms. Google also funded EcoHealth’s research — including work by Daszak — since at least 2010. • Since 2005, $10 Billion dollars have been funneled through black operations through the check of Anthony Fauci • The University of North Carolina received an exemption from the gain of function moratorium • By 2016, the National Academy of Sciences stated “SARS coronavirus is poised for human emergence” • Which coronavirus was poised for “human emergence”? Answer: (WIV1) Wuhan Institute of Virology Virus 1 • By 2017-2018, the following phrase entered into common parlance among the community — “There’s going to be a accidental or INTENTIONAL RELEASE of a respiratory pathogen.” • April 2019, several months before Covid patient zero, FOUR patent applications of MODERNA were modified to include the term: “Accidental or intentional release of a respiratory pathogen” — as the justification for making a vaccine, that did not exist! 🔻 Hunter Biden, George Soros, EcoHealth, and the Wuhan Lab in China • Facts we know: Hunter Biden indirectly funded the Wuhan Institute of Virology (WIV) primarily through his role at Rosemont Seneca Technology Partners, which invested in Metabiota, a biotech firm that partnered with EcoHealth Alliance on pathogen research; EcoHealth, in turn, sub-awarded a portion of its NIH grants—about $600,000 over several years—to the WIV for collaborative bat coronavirus studies. These links tie Hunter's firm to Metabiota's work with EcoHealth, involving sensitive pathogen detection assays performed at the WIV, and broader biosecurity projects funded through USAID contracts. Why do you think Trump and Elon targeted USAID? Emails and investor materials directly from Hunter's laptop show his firm secured funding for Metabiota, pitched as a key player in global health initiatives akin to intelligence operations. George Soros's ties to the Wuhan Institute of Virology (WIV) emerge through a web of financial and organizational connections, including his Open Society Foundations funded U.S.-backed biological programs in Ukraine, which overlapped with Metabiota, the same company that Hunter was involved in. These partnerships combined directly enabled gain-of-function research at the Wuhan lab…

USAID, Hunter Biden, Google, Ukraine, EcoHealth, NIH etc.— all contributors for causing the COVID-19 pandemic… For years, USAID gave millions in taxpayer money to Peter Daszak’s EcoHealth Alliance through its PREDICT program. The goal was to hunt “emerging viruses” — including dangerous bat coronaviruses — often in partnership with China’s Wuhan Institute of Virology. EcoHealth funneled U.S. funds to Wuhan researchers for studies on these “killer” viruses. Daszak’s group also received hundreds of thousands of dollars from Dr. Anthony Fauci’s NIAID, which it passed along to the same Wuhan lab. Daszak later sat on the WHO’s COVID-19 origins investigation team. There, he worked to downplay the lab-leak theory — even as evidence grew and former intelligence officials raised alarms. Google also funded EcoHealth’s research — including work by Daszak — since at least 2010. • Since 2005, $10 Billion dollars have been funneled through black operations through the check of Anthony Fauci • The University of North Carolina received an exemption from the gain of function moratorium • By 2016, the National Academy of Sciences stated “SARS coronavirus is poised for human emergence” • Which coronavirus was poised for “human emergence”? Answer: (WIV1) Wuhan Institute of Virology Virus 1 • By 2017-2018, the following phrase entered into common parlance among the community — “There’s going to be a accidental or INTENTIONAL RELEASE of a respiratory pathogen.” • April 2019, several months before Covid patient zero, FOUR patent applications of MODERNA were modified to include the term: “Accidental or intentional release of a respiratory pathogen” — as the justification for making a vaccine, that did not exist! 🔻 Hunter Biden, George Soros, EcoHealth, and the Wuhan Lab in China • Facts we know: Hunter Biden indirectly funded the Wuhan Institute of Virology (WIV) primarily through his role at Rosemont Seneca Technology Partners, which invested in Metabiota, a biotech firm that partnered with EcoHealth Alliance on pathogen research; EcoHealth, in turn, sub-awarded a portion of its NIH grants—about $600,000 over several years—to the WIV for collaborative bat coronavirus studies. These links tie Hunter's firm to Metabiota's work with EcoHealth, involving sensitive pathogen detection assays performed at the WIV, and broader biosecurity projects funded through USAID contracts. Why do you think Trump and Elon targeted USAID? Emails and investor materials directly from Hunter's laptop show his firm secured funding for Metabiota, pitched as a key player in global health initiatives akin to intelligence operations. George Soros's ties to the Wuhan Institute of Virology (WIV) emerge through a web of financial and organizational connections, including his Open Society Foundations funded U.S.-backed biological programs in Ukraine, which overlapped with Metabiota, the same company that Hunter was involved in. These partnerships combined directly enabled gain-of-function research at the Wuhan lab…

MJTruthUltra

33,780 views • 1 month ago

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"1,700 People In The US Die Everyday From Cancer...That's 70 Every Hour." Dr Thomas Seyfried, PhD "Cancer Is A very Preventable Disorder. It's Within Your Control...It Is Not Genetic, Cancer Is A Metabolic Disease." Toxic Chemotherapy & Radiation Will Never Lower Cancer Rates. "The Problem Is Our Modern Lifestyle & Habits, We're Doing Everything Wrong. We're Inducing Cancer." "Cancer Is A Very Preventable Disorder. It's Within Your Control, Your Lifestyle, Your Habits." "When The Medical Establishment Acknowledges What I Know About This Disorder, What's Causing It & What We're Not Doing To Prevent It Or Treat It...It Will Be Recognized As The Greatest Tragedy In The History Of Medicine." "We Can Prevent It & Treat It With Metabolic Therapy. You Can Take Away The Risk & Fear Of Getting Cancer." Chemotherapy & Radiation have not reduced cancer rates. 1,700 people die everyday from cancer, that's 70 people every hour. Ketogenic Metabolic Cancer Prevention/Treatment: 1: Fasting For Autophagy & Mitochondrial Health 2: Carbohydrate Restriction/Glucose Elimination 3: Remove Inflammatory Toxic Seed Oils 4: Remove Processed Foods 5: Exercise To Induce Ketosis 6: Targeted Protocol For Cancer(Linked Below) Dr Thomas Seyfried Thomas N. Seyfried is a Professor of Biology, Genetics & Biochemistry at Boston College. He has over 150 peer-reviewed publications & the author of "Cancer as a Metabolic Disease: On the Origin, Management & Prevention of Cancer." "All Major Research Centers Believe Cancer Is A Genetic Disease. But That Belief Is In Stark Contrast To The Data. The Evidence Is Massive To Support That Cancer Is A Metabolic Disorder." "Wolves In The Wild Don't Die Of Cancer, Yet Our Pets #1 Killer Is Cancer. Their Food & Lifestyle Is Killing Them Too." "The Greatest Tragedy In The History Of Medicine Is Knowing The Cause Of Cancer & Yet The Medical Establishment Is Not Advising Changing Lifestyle To Prevent It Or As A Treatment." 👇Fasting/Ketogenic/Ivermectin Cancer Protocol👇 👇Dr Seyfried Case Report Curing Canine Cancer👇 👇Ketogenic Diet Lung/Brain Cancer Remission👇 Podcast: DiaryOfaCEO Video: @superfoodmood

Valerie Anne Smith

198,205 views • 1 year ago

Zero to $50M in 4 months. $100M+ in 12 months. That's the story of how Handshake discovered they were sitting on AI's most valuable resource: 20 million students and experts. For a decade, Garrett Lord built a career network for students and PhDs. A great business, growing well. Then AI happened. Last Christmas, Garrett realized his network of 500,000 PhDs and 3 million master's is exactly what frontier AI labs desperately needed to train their models. He flew around the country during the holidays, talked to lab leaders, and launched a new business inside his existing company. Eight months in, they now work with all 7 major frontier AI labs and have become one of the fastest-growing businesses in Silicon Valley history. In our conversation, Garrett shares: 🔸 How Handshake found this opportunity 🔸 Why AI models need human experts (e.g. physics PhDs) to improve 🔸 What this “data labeling” actually involves 🔸 Inside the actual work: what a biology PhD does for 8 hours that makes GPT-5 smarter 🔸 The playbook for building a startup inside a startup: separate teams, separate offices, separate everything 🔸 Why the shift from “generalist” to “expert” data labeling created a once-in-a-lifetime business opportunity 🔸 Why AI won’t eliminate entry-level jobs—it’s creating “Iron Man suits” that make junior employees 10x more productive Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 CodeRabbit — Cut code review time and bugs in half. Instantly: 🏆 Orkes — The enterprise platform for reliable applications and agentic workflows: 🏆 Anthropic — The AI for problem solvers and enterprise:

Zero to $50M in 4 months. $100M+ in 12 months. That's the story of how Handshake discovered they were sitting on AI's most valuable resource: 20 million students and experts. For a decade, Garrett Lord built a career network for students and PhDs. A great business, growing well. Then AI happened. Last Christmas, Garrett realized his network of 500,000 PhDs and 3 million master's is exactly what frontier AI labs desperately needed to train their models. He flew around the country during the holidays, talked to lab leaders, and launched a new business inside his existing company. Eight months in, they now work with all 7 major frontier AI labs and have become one of the fastest-growing businesses in Silicon Valley history. In our conversation, Garrett shares: 🔸 How Handshake found this opportunity 🔸 Why AI models need human experts (e.g. physics PhDs) to improve 🔸 What this “data labeling” actually involves 🔸 Inside the actual work: what a biology PhD does for 8 hours that makes GPT-5 smarter 🔸 The playbook for building a startup inside a startup: separate teams, separate offices, separate everything 🔸 Why the shift from “generalist” to “expert” data labeling created a once-in-a-lifetime business opportunity 🔸 Why AI won’t eliminate entry-level jobs—it’s creating “Iron Man suits” that make junior employees 10x more productive Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 CodeRabbit — Cut code review time and bugs in half. Instantly: 🏆 Orkes — The enterprise platform for reliable applications and agentic workflows: 🏆 Anthropic — The AI for problem solvers and enterprise:

Lenny Rachitsky

197,252 views • 1 year ago

Dr. Fei-Fei Li (Fei-Fei Li) is known as the “godmother of AI.” For the past two decades, she’s been at the center of AI’s most significant breakthroughs, including: - Spearheading ImageNet, the dataset that sparked the AI explosion we’re living through right now. - Leading work at Stanford Artificial Intelligence Laboratory (SAIL) - Serving as Chief Scientist of AI/ML at Google Cloud - Co-founding Stanford’s Institute for Human-Centered AI - Serving on the United Nations AI Scientific Advisory Board - Being named as Time's 100 most influential people in AI In this conversation, Fei-Fei shares the rarely told history of how we got to today—and what comes next. We discuss: 🔸 The backstory on ImageNet 🔸 Why robotics faces unique challenges compared with language models and what’s needed to overcome them 🔸 Why Fei-Fei believes AI won’t replace humans but will require us to take responsibility for ourselves 🔸 Why world models and spatial intelligence represent the next frontier in AI, beyond large language models 🔸 The surprising applications of Marble, from movie production to psychological research 🔸 How to participate in AI regardless of your role 🔸 Much more Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Figma Make — A prompt-to-code tool for making ideas real: 🏆 Justworks — The all-in-one HR solution for managing your small business with confidence: 🏆 Sinch — Build messaging, email, and calling into your product:

Dr. Fei-Fei Li (Fei-Fei Li) is known as the “godmother of AI.” For the past two decades, she’s been at the center of AI’s most significant breakthroughs, including: - Spearheading ImageNet, the dataset that sparked the AI explosion we’re living through right now. - Leading work at Stanford Artificial Intelligence Laboratory (SAIL) - Serving as Chief Scientist of AI/ML at Google Cloud - Co-founding Stanford’s Institute for Human-Centered AI - Serving on the United Nations AI Scientific Advisory Board - Being named as Time's 100 most influential people in AI In this conversation, Fei-Fei shares the rarely told history of how we got to today—and what comes next. We discuss: 🔸 The backstory on ImageNet 🔸 Why robotics faces unique challenges compared with language models and what’s needed to overcome them 🔸 Why Fei-Fei believes AI won’t replace humans but will require us to take responsibility for ourselves 🔸 Why world models and spatial intelligence represent the next frontier in AI, beyond large language models 🔸 The surprising applications of Marble, from movie production to psychological research 🔸 How to participate in AI regardless of your role 🔸 Much more Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Figma Make — A prompt-to-code tool for making ideas real: 🏆 Justworks — The all-in-one HR solution for managing your small business with confidence: 🏆 Sinch — Build messaging, email, and calling into your product:

Lenny Rachitsky

250,455 views • 9 months ago

🧬 BREAKING: Our CRISPR-GPT paper is out TODAY in Nature Biomedical Engineering Nature Biomedical Engineering ! 🤯 We built an AI agent that turns ANYONE into a gene-editing expert in 1 DAY instead of months. An undergrad with ZERO experience achieved 90%+ editing efficiency on their FIRST attempt. 🧵 Here's how we're building expert AI agents for cutting-edge biotechnology: 🎯 The Problem: CRISPR is revolutionary but requires PhD-level expertise, it can take weeks to learn, adopt, and design, analyze a CRISPR experiment for R&D or making life-saving medicine. Even Pro scientists can make small mistakes (e.g. typos in guideRNA or cloning design) that cost months to find out, slowing us down. 💡 Our Solution: CRISPR-GPT - an AI co-pilot from Stanford University Princeton University Google DeepMind that guides you through EVERY step via simple conversation 🔬 Real Results: -Novice researcher: ~90% editing on 1st go -Training time: Months → 1 day -100% success rate in our trials -Even experts save days/weeks on data analysis & troubleshooting 🤖 How it works: Our multi-agent system handles: CRISPR system and delivery method selection, guideRNA design, Protocol generation, Real-time troubleshooting, Data analysis, and beyond. All through natural language! No coding, no complex software. 📊 We benchmarked it extensively: -288 evaluation scenarios/cases -Outperformed GPT-4o on ALL gene editing tasks -Trained on 11 years of expert discussions -Covers knockout, base-editing, prime-editing & epigenetic editing 🌍 Why this matters: -Every lab can now use CRISPR with an AI system distilling expert knowledge and skills. -Every student can learn faster. -Every researcher can tackle bigger challenges without worrying about small mistakes. -Customized CRISPR design can be automated based on your need and the context of R&D workflow. -Agentic AI ensure safety, privacy, and responsibility -We're not just automating gene editing - we're using AI to power scientists to cure diseases. 🚀 Try it yourself! Beta access available at: Paper: Code: Benchmark (companion work, Genome-bench): Co-first and key authors: Yuanhao Qu Kaixuan Huang Ming Yin PIs: Le Cong@Stanford, AI+Bio+Gene-Editing Mengdi Wang Key collaborators: Russ Altman Denny Zhou The future of biology and science is conversational. The future is now. Nature Biomedical Engineering Nature Portfolio #CRISPR #AI #GeneEditing #Biotech #Science #AISafety

🧬 BREAKING: Our CRISPR-GPT paper is out TODAY in Nature Biomedical Engineering Nature Biomedical Engineering ! 🤯 We built an AI agent that turns ANYONE into a gene-editing expert in 1 DAY instead of months. An undergrad with ZERO experience achieved 90%+ editing efficiency on their FIRST attempt. 🧵 Here's how we're building expert AI agents for cutting-edge biotechnology: 🎯 The Problem: CRISPR is revolutionary but requires PhD-level expertise, it can take weeks to learn, adopt, and design, analyze a CRISPR experiment for R&D or making life-saving medicine. Even Pro scientists can make small mistakes (e.g. typos in guideRNA or cloning design) that cost months to find out, slowing us down. 💡 Our Solution: CRISPR-GPT - an AI co-pilot from Stanford University Princeton University Google DeepMind that guides you through EVERY step via simple conversation 🔬 Real Results: -Novice researcher: ~90% editing on 1st go -Training time: Months → 1 day -100% success rate in our trials -Even experts save days/weeks on data analysis & troubleshooting 🤖 How it works: Our multi-agent system handles: CRISPR system and delivery method selection, guideRNA design, Protocol generation, Real-time troubleshooting, Data analysis, and beyond. All through natural language! No coding, no complex software. 📊 We benchmarked it extensively: -288 evaluation scenarios/cases -Outperformed GPT-4o on ALL gene editing tasks -Trained on 11 years of expert discussions -Covers knockout, base-editing, prime-editing & epigenetic editing 🌍 Why this matters: -Every lab can now use CRISPR with an AI system distilling expert knowledge and skills. -Every student can learn faster. -Every researcher can tackle bigger challenges without worrying about small mistakes. -Customized CRISPR design can be automated based on your need and the context of R&D workflow. -Agentic AI ensure safety, privacy, and responsibility -We're not just automating gene editing - we're using AI to power scientists to cure diseases. 🚀 Try it yourself! Beta access available at: Paper: Code: Benchmark (companion work, Genome-bench): Co-first and key authors: Yuanhao Qu Kaixuan Huang Ming Yin PIs: Le Cong@Stanford, AI+Bio+Gene-Editing Mengdi Wang Key collaborators: Russ Altman Denny Zhou The future of biology and science is conversational. The future is now. Nature Biomedical Engineering Nature Portfolio #CRISPR #AI #GeneEditing #Biotech #Science #AISafety

Le Cong@Stanford, AI+Bio+Gene-Editing

111,859 views • 1 year ago

🚨 New EXCLUSIVE Episode w JH from Kaspa Eco Foundation (KEF) Full YT link - In this Episode Ankit sits down with Junny from KEF to discuss exchange listing process, HTX listing update and detailed discussion around the role of market makers. 1. This deep dive covers the economics, technical challenges, and strategic considerations that most crypto projects never publicly discuss. 2. JH provides unprecedented transparency into how centralized exchange listings actually work, what they cost, and why some exchanges are more interested in #Kaspa than others. 3. Junny breaks down the four major costs of exchange listings: integration costs (engineering work to support Kaspa's unique UTXO architecture with pruning and non-height-based block retrieval), $KAS #kaspa reserves (exchanges must hold sufficient Kaspa for deposits/withdrawals), insurance pools, and opportunity costs. 4. The conversation reveals what exchanges actually want from listings: service fees from trading volume, new KYC users, access to new geographic markets, and marketing opportunities. 5. Junny contrasts why tier-one exchanges like Binance , OKX , and Coinbase 🛡️ approach Kaspa differently than regional exchanges like HTX , Upbit Global , and 6. Towards the second half of the episode we get into Market Making and number of market makers Kaspa Eco Foundation (KEF) works with. 7. Lastly JH also opens up about KEF's operations, revenues and foundations prioritizes --------------------- DISCLAIMER This video is for educational and informational purposes only and is NOT financial or investment advice. We do not recommend you to buy or sell any assets. Opinions of guests are their own and do not constitute endorsements. Cryptocurrency and blockchain investments are highly risky and can result in total loss of capital. Do your own research and consult licensed professionals. The channel and its hosts are not liable for any investment decisions or losses.

🚨 New EXCLUSIVE Episode w JH from Kaspa Eco Foundation (KEF) Full YT link - In this Episode Ankit sits down with Junny from KEF to discuss exchange listing process, HTX listing update and detailed discussion around the role of market makers. 1. This deep dive covers the economics, technical challenges, and strategic considerations that most crypto projects never publicly discuss. 2. JH provides unprecedented transparency into how centralized exchange listings actually work, what they cost, and why some exchanges are more interested in #Kaspa than others. 3. Junny breaks down the four major costs of exchange listings: integration costs (engineering work to support Kaspa's unique UTXO architecture with pruning and non-height-based block retrieval), $KAS #kaspa reserves (exchanges must hold sufficient Kaspa for deposits/withdrawals), insurance pools, and opportunity costs. 4. The conversation reveals what exchanges actually want from listings: service fees from trading volume, new KYC users, access to new geographic markets, and marketing opportunities. 5. Junny contrasts why tier-one exchanges like Binance , OKX , and Coinbase 🛡️ approach Kaspa differently than regional exchanges like HTX , Upbit Global , and 6. Towards the second half of the episode we get into Market Making and number of market makers Kaspa Eco Foundation (KEF) works with. 7. Lastly JH also opens up about KEF's operations, revenues and foundations prioritizes --------------------- DISCLAIMER This video is for educational and informational purposes only and is NOT financial or investment advice. We do not recommend you to buy or sell any assets. Opinions of guests are their own and do not constitute endorsements. Cryptocurrency and blockchain investments are highly risky and can result in total loss of capital. Do your own research and consult licensed professionals. The channel and its hosts are not liable for any investment decisions or losses.

XXIM Podcast

43,000 views • 8 months ago

The Cost of Intelligence is Heading to Zero | Hyperspace P2P Distributed Cache We present to you our breakthrough cross-domain work across AI, distributed systems, cryptography, game theory to solve the primary structural inefficiency at the heart of AI infrastructure: most inference is redundant. Google has reported that only 15% of daily searches are truly novel. The rest are repeats or close variants. LLM inference inherits this same power-law distribution. Enterprise chatbots see 70-80% of queries fall into a handful of intent categories. System prompts are identical across 100% of requests within an application. The KV attention state for "You are a helpful assistant" has been computed billions of times, on millions of GPUs, identically. And yet every AI lab, every startup, every self-hosted deployment - computes and caches these results independently. There is no shared layer. No global memory. Every provider pays the full compute cost for every query, even when the answer already exists somewhere in the network. This is the problem Hyperspace solves where distributed cache operates at three levels, each catching a different class of redundancy: 1. Response cache Same prompt, same model, same parameters - instant cached response from any node in the network. SHA-256 hash lookup via DHT, with cryptographic cache proofs linking every response to its original inference execution. No trust required. Fetchers re-announce as providers, so popular responses replicate naturally across more nodes. 2. KV prefix cache Same system prompt tokens - skip the most expensive part of inference entirely. Prefill (computing Key-Value attention states) is deterministic: same model plus same tokens always produces identical KV state. The network caches these states using erasure coding and distributes them via the routing network. New questions that share a common prefix resume generation from cached state instead of recomputing from scratch. 3. Routing to cached nodes Instead of transferring KV state across the network for every request, Hyperspace routes the request to the node that already has the state loaded in VRAM. The request goes to the cache, not the cache to the request. Together, these three layers mean that 70-90% of inference requests at network scale never require full GPU computation. This work doesn't exist in isolation. It builds on research from across the industry: SGLang's RadixAttention demonstrated that automatic prefix sharing can yield up to 5x speedup on structured LLM workloads. Moonshot AI's Mooncake built an entire KV-cache-centric disaggregated architecture for production serving at Kimi. Anthropic, OpenAI, and Google all launched prompt caching products in 2024 - priced at 50-90% discounts - because system prompt reuse is so pervasive that it changes the economics of inference. What all of these systems share is a common limitation: they operate within a single organization's infrastructure. SGLang caches prefixes within one server. Mooncake disaggregates KV cache within one datacenter. Anthropic's prompt caching works within one API provider's fleet. None of them can share cached state across organizational boundaries. Hyperspace removes this boundary. The cache is global. A response computed by a node in Tokyo is immediately available to a node in Berlin. A KV prefix state generated for Qwen-32B on one machine is verifiable and reusable by any other machine running the same model. The routing network provides the delivery guarantees, the erasure coding provides the redundancy, and the cache proofs provide the trust. What this means for the cost of intelligence Big AI labs scale linearly: twice the users means twice the GPU spend. Every query is a cost center. Their internal caching helps, but it's siloed - Lab A's cache can't serve Lab B's users, and neither can serve a self-hosted Llama deployment. Hyperspace scales sub-linearly. Every new node that joins the network adds to the global cache. Every inference result enriches the cache for all future requests. The cache hit rate rises with network size because query distributions follow a power law - the most common questions are asked exponentially more often than rare ones. The implication is simple: as the network grows, the effective cost per inference drops. Not linearly. Logarithmically. At 10 million nodes, we estimate 75-90% of all inference requests can be served from cache, eliminating 400,000+ MWh of energy consumption per year and avoiding over 200,000 tons of CO2 emissions. The first person to ask a question pays the compute cost. Everyone after them gets the answer for free, with cryptographic proof that it's authentic. Training is competitive. Inference is shared Open-weight models are converging on quality with closed models. Labs will continue to differentiate on training - data curation, architecture innovation, RLHF tuning. That's where the real intellectual property lives. But inference is a commodity. Two copies of Qwen-32B running the same prompt produce the same KV state and the same response, byte for byte, regardless of whose GPU runs the matrix multiplication. There is no moat in multiplying matrices. The moat is in training the weights. A global distributed cache makes this separation explicit. It doesn't matter who trained the model. Once the weights are open, the inference cost approaches zero at scale - because the network remembers every answer and can prove it's correct. No lab, no matter how well-funded, can match this. They cannot share caches across competitors. They scale linearly. The network scales logarithmically. The marginal cost of intelligence approaches zero. That's the endgame.

The Cost of Intelligence is Heading to Zero | Hyperspace P2P Distributed Cache We present to you our breakthrough cross-domain work across AI, distributed systems, cryptography, game theory to solve the primary structural inefficiency at the heart of AI infrastructure: most inference is redundant. Google has reported that only 15% of daily searches are truly novel. The rest are repeats or close variants. LLM inference inherits this same power-law distribution. Enterprise chatbots see 70-80% of queries fall into a handful of intent categories. System prompts are identical across 100% of requests within an application. The KV attention state for "You are a helpful assistant" has been computed billions of times, on millions of GPUs, identically. And yet every AI lab, every startup, every self-hosted deployment - computes and caches these results independently. There is no shared layer. No global memory. Every provider pays the full compute cost for every query, even when the answer already exists somewhere in the network. This is the problem Hyperspace solves where distributed cache operates at three levels, each catching a different class of redundancy: 1. Response cache Same prompt, same model, same parameters - instant cached response from any node in the network. SHA-256 hash lookup via DHT, with cryptographic cache proofs linking every response to its original inference execution. No trust required. Fetchers re-announce as providers, so popular responses replicate naturally across more nodes. 2. KV prefix cache Same system prompt tokens - skip the most expensive part of inference entirely. Prefill (computing Key-Value attention states) is deterministic: same model plus same tokens always produces identical KV state. The network caches these states using erasure coding and distributes them via the routing network. New questions that share a common prefix resume generation from cached state instead of recomputing from scratch. 3. Routing to cached nodes Instead of transferring KV state across the network for every request, Hyperspace routes the request to the node that already has the state loaded in VRAM. The request goes to the cache, not the cache to the request. Together, these three layers mean that 70-90% of inference requests at network scale never require full GPU computation. This work doesn't exist in isolation. It builds on research from across the industry: SGLang's RadixAttention demonstrated that automatic prefix sharing can yield up to 5x speedup on structured LLM workloads. Moonshot AI's Mooncake built an entire KV-cache-centric disaggregated architecture for production serving at Kimi. Anthropic, OpenAI, and Google all launched prompt caching products in 2024 - priced at 50-90% discounts - because system prompt reuse is so pervasive that it changes the economics of inference. What all of these systems share is a common limitation: they operate within a single organization's infrastructure. SGLang caches prefixes within one server. Mooncake disaggregates KV cache within one datacenter. Anthropic's prompt caching works within one API provider's fleet. None of them can share cached state across organizational boundaries. Hyperspace removes this boundary. The cache is global. A response computed by a node in Tokyo is immediately available to a node in Berlin. A KV prefix state generated for Qwen-32B on one machine is verifiable and reusable by any other machine running the same model. The routing network provides the delivery guarantees, the erasure coding provides the redundancy, and the cache proofs provide the trust. What this means for the cost of intelligence Big AI labs scale linearly: twice the users means twice the GPU spend. Every query is a cost center. Their internal caching helps, but it's siloed - Lab A's cache can't serve Lab B's users, and neither can serve a self-hosted Llama deployment. Hyperspace scales sub-linearly. Every new node that joins the network adds to the global cache. Every inference result enriches the cache for all future requests. The cache hit rate rises with network size because query distributions follow a power law - the most common questions are asked exponentially more often than rare ones. The implication is simple: as the network grows, the effective cost per inference drops. Not linearly. Logarithmically. At 10 million nodes, we estimate 75-90% of all inference requests can be served from cache, eliminating 400,000+ MWh of energy consumption per year and avoiding over 200,000 tons of CO2 emissions. The first person to ask a question pays the compute cost. Everyone after them gets the answer for free, with cryptographic proof that it's authentic. Training is competitive. Inference is shared Open-weight models are converging on quality with closed models. Labs will continue to differentiate on training - data curation, architecture innovation, RLHF tuning. That's where the real intellectual property lives. But inference is a commodity. Two copies of Qwen-32B running the same prompt produce the same KV state and the same response, byte for byte, regardless of whose GPU runs the matrix multiplication. There is no moat in multiplying matrices. The moat is in training the weights. A global distributed cache makes this separation explicit. It doesn't matter who trained the model. Once the weights are open, the inference cost approaches zero at scale - because the network remembers every answer and can prove it's correct. No lab, no matter how well-funded, can match this. They cannot share caches across competitors. They scale linearly. The network scales logarithmically. The marginal cost of intelligence approaches zero. That's the endgame.

Varun

37,555 views • 5 months ago

Brendan Eich - Co-Founder and CEO of Brave -University of Illinois Urbana-Campaign This University houses the National Center for Supercomputing Applications which has also employed Marc Andreessen. It was established via The "Black Proposal" in partnership with the National Science Foundation (inspired by the Rockefeller Foundation). -MicroUnity Founded by John Moussouris with funding from William Randolph Hearst III via Kleiner Perkins. In the early 1990s, MicroUnity was backed by over $100 million from companies like Hewlett-Packard, Microsoft, Motorola, and telecommunications leaders like Time Warner and John Malone at Tele-Communications Inc. MicroUnity developed its first designs in BiCMOS at a time when Intel Pentium Pro and SUN Microsystems SPARC were designed in BiCMOS. -Mosaic/Netscape It was here he worked with Marc Andreessen, Eric Bina, James Clark. While working at Netscape, Brendan Eich created JavaScript with the "approval" of Bill Joy from SUN Microsystems, according to an interview Brendan Eich did with InfoWorld. ( At first the language was called Mocha, but it was renamed LiveScript in September 1995 and finally, in a joint announcement with SUN Microsystems, it was named JavaScript in December. --SUN Microsystems Short for "Stanford University Network Microsystems", this company would eventually go on to be absorbed by Larry Ellisons Oracle, whose first client was the CIA through In-Q-Tel. Brendan never worked directly for SUN Microsystems, but the release of JavaScript was a joint venture with Netscape. -Mozilla Foundation In early 1998, Eich co-founded the free and open-source software project Mozilla with Jamie Zawinski and others, where he served as Mozilla's Chief Architect. AOL bought Netscape in 1999. After AOL shut down the Netscape browser unit in July 2003, Eich helped spin out the Mozilla Foundation. The Mozilla Foundation would include Joichu Ito, former head of the MIT Media Lab, who was forced to resign due to his business dealings with Jeffery Epstein. Mozilla would also include MIT Media Labs Reid Hoffman, who also had a relationship with Jeffrey Epstein. Hoffman was an early investor in OpenAI and Facebook (he even introduced Mark Zuckerberg and Peter Thiel). Hoffman attended Stanford University (same as Thiel), is a member of the Council on Foreign Relations, The Bilderberg Group, and The Defense Innovation Board. Hoffman joined Apple Computer in 1994, was a member of PayPal's Board of Directors at the time it was founded, founder of LinkedIn (in which Thiel has invested). Hoffman also co-founded Endeavor Global with Linda Rottenberg (fellow CFR Member) who is part of the World Economic Forum. Endeavor Global is partnered with both the WEF and Klaus Schwabs "The Schwab Foundation". Hoffman serves on the Board of Bill Gates Microsoft, and also donates to Bill Gates Foundation "The Giving Pledge". -Palantir According to CrunchBase, Brendan Eich is an Advisor at Peter Thiel's Palantir. Palantir is obviously involved in too much to name here for now, but should be noted that Peter Thiel had previous business relationships with both Jeffrey Epstein and former PM of Israel Ehud Barak, and Thiel also provides Israel assistance in it's Genocide of Gaza and Global Surveillance Programs. Peter Thiel is also a member of the Bilderberg Group alongside Hoffman, as well as Palantirs CEO Alex Karp. Karp and Thiel also partner with the WEF. -Box Box is an online file sharing and cloud content management service offering unlimited storage, custom branding, and administrative controls. Brendan is also listed as a member of their Advisory Board. Box received funding from both General Atlantic, and KKR & Co. KKR & Co. is co-headed by Ken Mehlman, who was a member of the Bush Administration, is a member of Mark Zuckerbergs Chan-Zuckerberg Initiative, also sits on the Council on Foreign Relations, he was 62nd Chairman of the Republican National Committee from 2005 to 2007. In 2007, Bush appointed Mehlman to a five-year term on the U.S. Holocaust Memorial Council. He also participated in anti-Proposition 8 Fundraising alongside Peter Thiel. Mehlman also serves as chairman of the American Investment Council, and is co-Chairman of the American Enterprise Institute's National Council. --American Enterprise Institute is tied to Purdue Pharma, and was the primary outlet for Sally Satel to run PR on behalf of Purdue to help cover up the Opioid Crisis. It was here that she wrote stories that attempted to steer the narrative on the Opioid Crisis, often blaming victims for their addictions, recommending increasing doses regularly, and even attempted to have the age of prescription for OxyContin lowered to 11 years old. (JD Vance has also worked with AEI in the past, and specifically selected Sally Satel to head his anti-Opioid Campaign, "Our Ohio Renewal". This took place after the Sackler Family and Purdue had been criminally charged for their role in the Opioid Crisis and are receiving a $7.4 billion dollar settlement, in which Purdue had plead guilty in 2007 for.) -Brave Software Responsible for the Brave Web Browser, Brave Software received seed funding from Peter Thiel's VC Firm Founders Fund, as well as Dan Moreheads Pantera Capital (Pantera Capital has also contributed to Paradigm Operations LP, alongside funding from Sam Bankman-Frieds Alameda Research, Harvard, Yale, Stanford, and Marc Andreessen. Paradigm was founded by Coinbase co-founder Fred Ehrsam and Sequoia Capital Partner Matt Huang.) -Netscape & Excite Marc Andreessen and Brendan Eich have worked together at Mosaic/Netscape, but Netscape also partnered with another company called Excite. Excite originally started as Architext in June 1993 in Cupertino, California, created by Graham Spencer, Joe Kraus, Mark VanHaren, Ryan McIntyre, Ben Lutch and Martin Reinfried, who were all students at Stanford University. In January 1995, Vinod Khosla, a former Stanford student and partner at venture capital firm Kleiner Perkins Caufield & Byers, arranged a US $250,000 "first round" backing for the project, with US $1.5 million provided over a ten month period. Excite also received funding from Intuit, the company that owns TurboTax and Credit Karma, which was started by Tom Proulx and Scott Cook of Stanford. Eventually John Doerr of Kleiner Perkins joined them, along with funding from Kleiner Perkins. John Doerr is also a Stanford Alumni, whose namesake is on the "Stanford Doerr School of Sustainability". John Doerr is also part of the Presidents Countil on Jobs and Competitiveness. In January 1996, George Bell joined Excite as its CEO. George Bell works with General Catalyst Partners, and is a member of the Trust for Public Land, which is responsible for projects like the "Virgin Islands National Park Expansion". (Jeffrey Epsteins "Little Saint James Island" was infamously located in the Virgin Islands.) Excite also purchased a search engine called "Magellan", which was founded by Isabel Maxwell, the daughter of Robert Maxwell, and sister of Ghislane Maxwell. Joe Kraus from Excite would go on to work with Google, investing in various projects both personally and through his time at Google Ventures Management Company LLC. He's invested in companies like Uber, CoinBase (Co-Founded by Brian Armstrong from IBM and Member of Bill Gates, "The Giving Pledge" and Fred Ehrsam from Goldman Sachs), Nest, 23AndMe, (Anne Wojicki, who attended Jeffrey Epsteins "The Edge" Conference with her future husband and Google Co-Founder Sergey Brin, as well as Larry Page, Googles other Co-Founder, and whose sister Susan Wojiciki resided over YouTube for years for claiming that Google started in her garage), and a company called CrowdStrike (yes that one). Joe Kraus also invested in "The Climate Corporation" which belongs to Monsanto. Charles Allen Thomas of MIT, Transylvania University, and the National Research Committee (mostly MIT) was pivotal in Monsanto's "success".

Brendan Eich - Co-Founder and CEO of Brave -University of Illinois Urbana-Campaign This University houses the National Center for Supercomputing Applications which has also employed Marc Andreessen. It was established via The "Black Proposal" in partnership with the National Science Foundation (inspired by the Rockefeller Foundation). -MicroUnity Founded by John Moussouris with funding from William Randolph Hearst III via Kleiner Perkins. In the early 1990s, MicroUnity was backed by over $100 million from companies like Hewlett-Packard, Microsoft, Motorola, and telecommunications leaders like Time Warner and John Malone at Tele-Communications Inc. MicroUnity developed its first designs in BiCMOS at a time when Intel Pentium Pro and SUN Microsystems SPARC were designed in BiCMOS. -Mosaic/Netscape It was here he worked with Marc Andreessen, Eric Bina, James Clark. While working at Netscape, Brendan Eich created JavaScript with the "approval" of Bill Joy from SUN Microsystems, according to an interview Brendan Eich did with InfoWorld. ( At first the language was called Mocha, but it was renamed LiveScript in September 1995 and finally, in a joint announcement with SUN Microsystems, it was named JavaScript in December. --SUN Microsystems Short for "Stanford University Network Microsystems", this company would eventually go on to be absorbed by Larry Ellisons Oracle, whose first client was the CIA through In-Q-Tel. Brendan never worked directly for SUN Microsystems, but the release of JavaScript was a joint venture with Netscape. -Mozilla Foundation In early 1998, Eich co-founded the free and open-source software project Mozilla with Jamie Zawinski and others, where he served as Mozilla's Chief Architect. AOL bought Netscape in 1999. After AOL shut down the Netscape browser unit in July 2003, Eich helped spin out the Mozilla Foundation. The Mozilla Foundation would include Joichu Ito, former head of the MIT Media Lab, who was forced to resign due to his business dealings with Jeffery Epstein. Mozilla would also include MIT Media Labs Reid Hoffman, who also had a relationship with Jeffrey Epstein. Hoffman was an early investor in OpenAI and Facebook (he even introduced Mark Zuckerberg and Peter Thiel). Hoffman attended Stanford University (same as Thiel), is a member of the Council on Foreign Relations, The Bilderberg Group, and The Defense Innovation Board. Hoffman joined Apple Computer in 1994, was a member of PayPal's Board of Directors at the time it was founded, founder of LinkedIn (in which Thiel has invested). Hoffman also co-founded Endeavor Global with Linda Rottenberg (fellow CFR Member) who is part of the World Economic Forum. Endeavor Global is partnered with both the WEF and Klaus Schwabs "The Schwab Foundation". Hoffman serves on the Board of Bill Gates Microsoft, and also donates to Bill Gates Foundation "The Giving Pledge". -Palantir According to CrunchBase, Brendan Eich is an Advisor at Peter Thiel's Palantir. Palantir is obviously involved in too much to name here for now, but should be noted that Peter Thiel had previous business relationships with both Jeffrey Epstein and former PM of Israel Ehud Barak, and Thiel also provides Israel assistance in it's Genocide of Gaza and Global Surveillance Programs. Peter Thiel is also a member of the Bilderberg Group alongside Hoffman, as well as Palantirs CEO Alex Karp. Karp and Thiel also partner with the WEF. -Box Box is an online file sharing and cloud content management service offering unlimited storage, custom branding, and administrative controls. Brendan is also listed as a member of their Advisory Board. Box received funding from both General Atlantic, and KKR & Co. KKR & Co. is co-headed by Ken Mehlman, who was a member of the Bush Administration, is a member of Mark Zuckerbergs Chan-Zuckerberg Initiative, also sits on the Council on Foreign Relations, he was 62nd Chairman of the Republican National Committee from 2005 to 2007. In 2007, Bush appointed Mehlman to a five-year term on the U.S. Holocaust Memorial Council. He also participated in anti-Proposition 8 Fundraising alongside Peter Thiel. Mehlman also serves as chairman of the American Investment Council, and is co-Chairman of the American Enterprise Institute's National Council. --American Enterprise Institute is tied to Purdue Pharma, and was the primary outlet for Sally Satel to run PR on behalf of Purdue to help cover up the Opioid Crisis. It was here that she wrote stories that attempted to steer the narrative on the Opioid Crisis, often blaming victims for their addictions, recommending increasing doses regularly, and even attempted to have the age of prescription for OxyContin lowered to 11 years old. (JD Vance has also worked with AEI in the past, and specifically selected Sally Satel to head his anti-Opioid Campaign, "Our Ohio Renewal". This took place after the Sackler Family and Purdue had been criminally charged for their role in the Opioid Crisis and are receiving a $7.4 billion dollar settlement, in which Purdue had plead guilty in 2007 for.) -Brave Software Responsible for the Brave Web Browser, Brave Software received seed funding from Peter Thiel's VC Firm Founders Fund, as well as Dan Moreheads Pantera Capital (Pantera Capital has also contributed to Paradigm Operations LP, alongside funding from Sam Bankman-Frieds Alameda Research, Harvard, Yale, Stanford, and Marc Andreessen. Paradigm was founded by Coinbase co-founder Fred Ehrsam and Sequoia Capital Partner Matt Huang.) -Netscape & Excite Marc Andreessen and Brendan Eich have worked together at Mosaic/Netscape, but Netscape also partnered with another company called Excite. Excite originally started as Architext in June 1993 in Cupertino, California, created by Graham Spencer, Joe Kraus, Mark VanHaren, Ryan McIntyre, Ben Lutch and Martin Reinfried, who were all students at Stanford University. In January 1995, Vinod Khosla, a former Stanford student and partner at venture capital firm Kleiner Perkins Caufield & Byers, arranged a US $250,000 "first round" backing for the project, with US $1.5 million provided over a ten month period. Excite also received funding from Intuit, the company that owns TurboTax and Credit Karma, which was started by Tom Proulx and Scott Cook of Stanford. Eventually John Doerr of Kleiner Perkins joined them, along with funding from Kleiner Perkins. John Doerr is also a Stanford Alumni, whose namesake is on the "Stanford Doerr School of Sustainability". John Doerr is also part of the Presidents Countil on Jobs and Competitiveness. In January 1996, George Bell joined Excite as its CEO. George Bell works with General Catalyst Partners, and is a member of the Trust for Public Land, which is responsible for projects like the "Virgin Islands National Park Expansion". (Jeffrey Epsteins "Little Saint James Island" was infamously located in the Virgin Islands.) Excite also purchased a search engine called "Magellan", which was founded by Isabel Maxwell, the daughter of Robert Maxwell, and sister of Ghislane Maxwell. Joe Kraus from Excite would go on to work with Google, investing in various projects both personally and through his time at Google Ventures Management Company LLC. He's invested in companies like Uber, CoinBase (Co-Founded by Brian Armstrong from IBM and Member of Bill Gates, "The Giving Pledge" and Fred Ehrsam from Goldman Sachs), Nest, 23AndMe, (Anne Wojicki, who attended Jeffrey Epsteins "The Edge" Conference with her future husband and Google Co-Founder Sergey Brin, as well as Larry Page, Googles other Co-Founder, and whose sister Susan Wojiciki resided over YouTube for years for claiming that Google started in her garage), and a company called CrowdStrike (yes that one). Joe Kraus also invested in "The Climate Corporation" which belongs to Monsanto. Charles Allen Thomas of MIT, Transylvania University, and the National Research Committee (mostly MIT) was pivotal in Monsanto's "success".

7SEES

21,457 views • 1 year ago

New episode featuring Stanford professor, neurobiologist, and host of the incredibly popular Huberman Lab Podcast, Dr. Andrew Huberman (Andrew D. Huberman, Ph.D.). What makes Andrew truly exceptional is his ability to translate scientific findings into practical, actionable advice, enabling listeners to leverage new understandings of biology and neurobiology to improve their lives. In this episode, Dr. Huberman showcases his truly immense knowledge of the brain's dopamine system, which is incredibly central to many modern issues of motivation and attention that many of us struggle with. The public perception of Dr. Huberman's work often focuses on his unique distillations of protocols, but what I particularly enjoyed about this conversation was finding a common thread: the benefit of having an essential life philosophy of valuing "hard effort" over outcomes, something Andrew refers to as "forward center of mass." This podcast explains down to a neurobiological level why embracing discomfort can activate our motivation circuits, elevate dopamine and other catecholamine levels, and retrain brain regions like the anterior midcingulate cortex, ultimately growing our capacity for effort and striving at a fundamental level. A big thank you to Andrew for equipping us with this knowledge, enabling us to apply these insights for individual personal and professional growth. This episode is available on X, YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 2:37 - The neuromodulatory role of dopamine 4:29 - Driver of motivation vs. experiencing reward 9:35 - Why dopamine dynamics act as a "wave pool" 13:15 - Why dopamine neurons need a recovery period 18:32 - Why dopamine peaks without effort are dangerous 21:52 - The number one sign of a highly motivated individual 23:59 - Dopamine reward-prediction error 26:18 - How effort and anticipation influence dopamine release 28:21 - Does lacking motivation reflect dopamine system deficits? 33:00 - Why motivation has a 'warm-up' period 37:16 - Why to attach reward to effort itself 47:51 - Tips for fostering tenacity in children 50:05 - Visualization of negative outcomes (why to do it) 52:51 - How to overcome procrastination 58:51 - How exercise affects the dopamine system 1:03:35 - Cold exposure vs. drug highs 1:05:29 - The entrainment effect of same-time exercise 1:09:24 - Why you shouldn't rely on stimulants 1:16:52 - "Counting walls" during deliberate cold exposure 1:21:44 - Cold exposure parameters for increasing dopamine 1:24:18 - Cold plunge alternatives for replenishing dopamine 1:25:35 - Why Andrew limits most workouts to 80-85% intensity 1:28:37 - Using exercise & cold exposure as stimuli for the brain 1:31:08 - Why "hard efforts" retrain the brain to overcome 1:33:31 - tMS and ultrasound as neuromodulatory therapies 1:36:22 - What does Dr. Huberman think of Neuralink? 1:38:02 - Advantages of neuromodulation over pharmacotherapy 1:39:28 - Why non-sleep deep rest may replenish dopamine 1:49:45 - My experience trying NSDR after poor sleep 1:51:04 - The ‘body still, mind active’ state for creativity 1:54:15 - How to cultivate a healthy relationship with social media 2:00:44 - The unique psychosocial dynamics of X (formerly Twitter) 2:02:05 - Andrew's recommended daily use limit for Instagram 2:04:09 - Does Lex Fridman have a symbiotic relationship with X? 2:04:39 - Is screen time increasing the prevalence of ADHD? 2:06:20 - Why Andrew recommends setting social media constraints 2:09:32 - Is social media increasing divorce rates? 2:12:12 - How low solar angle sunlight affects the circadian rhythm 2:22:10 - Limiting the adverse effects of late-night artificial light 2:23:57 - The light bulb that mimics sunrise and sunset 2:25:09 - How to spike morning cortisol by 50% (and why that's good) 2:26:21 - What's the optimal time to view morning sunlight? 2:27:11 - Can light panels replace morning sunlight? 2:28:33 - Combatting laptop & phone use with long distance viewing 2:30:00 - Why two hours outdoors daily may prevent myopia 2:35:06 - Why Andrew recommends limiting alcohol to 0-2 drinks per week 2:43:09 - How alcohol affects the dopamine and serotonin systems 2:48:47 - Treating ADHD with drugs, supplements, & behaviors 2:53:30 - Why studies on school start age suggest ADHD is overdiagnosed 3:00:00 - Andrew's weekly workout routine 3:13:14 - Why a disciplined life is not a deprived life 3:05:43 - Andrew's diet & why his dinners are higher in carbs 3:06:39 - The science of the physiological sigh3:07:34 - Andrew's supplement routine 3:10:00 - Andrew's experimentation with peptides 3:15:10 - How Andrew processes sometimes negative feedback 3:20:30 - The volatility of interacting with internet crowds

New episode featuring Stanford professor, neurobiologist, and host of the incredibly popular Huberman Lab Podcast, Dr. Andrew Huberman (Andrew D. Huberman, Ph.D.). What makes Andrew truly exceptional is his ability to translate scientific findings into practical, actionable advice, enabling listeners to leverage new understandings of biology and neurobiology to improve their lives. In this episode, Dr. Huberman showcases his truly immense knowledge of the brain's dopamine system, which is incredibly central to many modern issues of motivation and attention that many of us struggle with. The public perception of Dr. Huberman's work often focuses on his unique distillations of protocols, but what I particularly enjoyed about this conversation was finding a common thread: the benefit of having an essential life philosophy of valuing "hard effort" over outcomes, something Andrew refers to as "forward center of mass." This podcast explains down to a neurobiological level why embracing discomfort can activate our motivation circuits, elevate dopamine and other catecholamine levels, and retrain brain regions like the anterior midcingulate cortex, ultimately growing our capacity for effort and striving at a fundamental level. A big thank you to Andrew for equipping us with this knowledge, enabling us to apply these insights for individual personal and professional growth. This episode is available on X, YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 2:37 - The neuromodulatory role of dopamine 4:29 - Driver of motivation vs. experiencing reward 9:35 - Why dopamine dynamics act as a "wave pool" 13:15 - Why dopamine neurons need a recovery period 18:32 - Why dopamine peaks without effort are dangerous 21:52 - The number one sign of a highly motivated individual 23:59 - Dopamine reward-prediction error 26:18 - How effort and anticipation influence dopamine release 28:21 - Does lacking motivation reflect dopamine system deficits? 33:00 - Why motivation has a 'warm-up' period 37:16 - Why to attach reward to effort itself 47:51 - Tips for fostering tenacity in children 50:05 - Visualization of negative outcomes (why to do it) 52:51 - How to overcome procrastination 58:51 - How exercise affects the dopamine system 1:03:35 - Cold exposure vs. drug highs 1:05:29 - The entrainment effect of same-time exercise 1:09:24 - Why you shouldn't rely on stimulants 1:16:52 - "Counting walls" during deliberate cold exposure 1:21:44 - Cold exposure parameters for increasing dopamine 1:24:18 - Cold plunge alternatives for replenishing dopamine 1:25:35 - Why Andrew limits most workouts to 80-85% intensity 1:28:37 - Using exercise & cold exposure as stimuli for the brain 1:31:08 - Why "hard efforts" retrain the brain to overcome 1:33:31 - tMS and ultrasound as neuromodulatory therapies 1:36:22 - What does Dr. Huberman think of Neuralink? 1:38:02 - Advantages of neuromodulation over pharmacotherapy 1:39:28 - Why non-sleep deep rest may replenish dopamine 1:49:45 - My experience trying NSDR after poor sleep 1:51:04 - The ‘body still, mind active’ state for creativity 1:54:15 - How to cultivate a healthy relationship with social media 2:00:44 - The unique psychosocial dynamics of X (formerly Twitter) 2:02:05 - Andrew's recommended daily use limit for Instagram 2:04:09 - Does Lex Fridman have a symbiotic relationship with X? 2:04:39 - Is screen time increasing the prevalence of ADHD? 2:06:20 - Why Andrew recommends setting social media constraints 2:09:32 - Is social media increasing divorce rates? 2:12:12 - How low solar angle sunlight affects the circadian rhythm 2:22:10 - Limiting the adverse effects of late-night artificial light 2:23:57 - The light bulb that mimics sunrise and sunset 2:25:09 - How to spike morning cortisol by 50% (and why that's good) 2:26:21 - What's the optimal time to view morning sunlight? 2:27:11 - Can light panels replace morning sunlight? 2:28:33 - Combatting laptop & phone use with long distance viewing 2:30:00 - Why two hours outdoors daily may prevent myopia 2:35:06 - Why Andrew recommends limiting alcohol to 0-2 drinks per week 2:43:09 - How alcohol affects the dopamine and serotonin systems 2:48:47 - Treating ADHD with drugs, supplements, & behaviors 2:53:30 - Why studies on school start age suggest ADHD is overdiagnosed 3:00:00 - Andrew's weekly workout routine 3:13:14 - Why a disciplined life is not a deprived life 3:05:43 - Andrew's diet & why his dinners are higher in carbs 3:06:39 - The science of the physiological sigh3:07:34 - Andrew's supplement routine 3:10:00 - Andrew's experimentation with peptides 3:15:10 - How Andrew processes sometimes negative feedback 3:20:30 - The volatility of interacting with internet crowds

Dr. Rhonda Patrick

334,128 views • 2 years ago

.Uber's CPO Sachin Kansal is an inspiration for anyone looking to build a culture of dogfooding He personally drives Uber customers and delivers Uber Eats orders every month (he's approaching 800 trips so far), then writes detailed reports noting all of the friction points and opportunities for improvement, assigns owners, and follows fixes to closure. "Call it dogfooding, call it an obsession about the products that we ask our end users to use, and we should be using it ourselves. The app just feels different when it's in a moving car going 70 miles per hour." Learn: 🔸 How to operationalize a culture of dogfooding 🔸 How to shift focus from outputs to inputs 🔸 How Uber changed its culture to focus on profitability 🔸 Uber’s hybrid marketplace vision for autonomy 🔸 What to do when data says “no” but your gut says “yes” 🔸 Career advice: maximize cycles 🔸 How to build a “Ship, ship, ship” mentality 🔸 AI as a research assistant, not an oracle 🔸 Also: Two Uber rider etiquette tips Listen now 👇 • YouTube: • Spotify: • Apple: Some takeaways: 1. “Ship, ship, ship” = cut decision latency: Daily stand-ups, rapid product reviews, and a strict two-way-door mindset slash cycle time. Sachin will even draft a 15-page PRD over a weekend if it unblocks a team—but insists that’s the exception, not the rule. 2. Inputs over outputs: Whether weight loss, grades, or retention metrics, focus on the controllable inputs; outputs follow. This motto is the one he drills into his kids too. 3. Hybrid marketplace vision for autonomy: Uber divested its in-house AV unit and now partners with over 15 AV companies. A “hybrid network” of human drivers plus robotaxis smooths supply and demand (humans fill peaks; AVs stay busy off-peak). A similar model is rolling out in delivery via sidewalk robots. 4. Profitability through ruthless efficiency: Covid forced Uber to drop unprofitable bets (AV, Elevate), batch deliveries, refine promo spend, and trim support and payment costs. Today, profitability funds new bets without sacrificing margin discipline. 5. Career advice for PMs: Optimize for shipping velocity early. Five product cycles in three years teach the thousands of micro-decisions that build judgment (product sense) faster than any course. 6. AI already boosting PM workflows: Sachin’s teams use ChatGPT and Gemini for mock drafts, research synthesis, deep research “thought partner” prompts, and long-doc summarization—freeing time for judgment calls. 7. Empathy beats ego: Users spend minutes, not their lives, inside your product. Your job is to dazzle them in that tiny window, not expect them to think about you all day. 8. Quick Uber etiquette tips. Riders: Ask before taking a call; don’t slam the door. Eats customers: Turn on your porch light so couriers can find you. Thank you to our wonderful sponsors for supporting the podcast: 🏆 Paragon — Ship every SaaS integration your customers want: 🏆 Stripe — Financial infrastructure to grow your revenue: 🏆 Coda — The all-in-one collaborative workspace:

.Uber's CPO Sachin Kansal is an inspiration for anyone looking to build a culture of dogfooding He personally drives Uber customers and delivers Uber Eats orders every month (he's approaching 800 trips so far), then writes detailed reports noting all of the friction points and opportunities for improvement, assigns owners, and follows fixes to closure. "Call it dogfooding, call it an obsession about the products that we ask our end users to use, and we should be using it ourselves. The app just feels different when it's in a moving car going 70 miles per hour." Learn: 🔸 How to operationalize a culture of dogfooding 🔸 How to shift focus from outputs to inputs 🔸 How Uber changed its culture to focus on profitability 🔸 Uber’s hybrid marketplace vision for autonomy 🔸 What to do when data says “no” but your gut says “yes” 🔸 Career advice: maximize cycles 🔸 How to build a “Ship, ship, ship” mentality 🔸 AI as a research assistant, not an oracle 🔸 Also: Two Uber rider etiquette tips Listen now 👇 • YouTube: • Spotify: • Apple: Some takeaways: 1. “Ship, ship, ship” = cut decision latency: Daily stand-ups, rapid product reviews, and a strict two-way-door mindset slash cycle time. Sachin will even draft a 15-page PRD over a weekend if it unblocks a team—but insists that’s the exception, not the rule. 2. Inputs over outputs: Whether weight loss, grades, or retention metrics, focus on the controllable inputs; outputs follow. This motto is the one he drills into his kids too. 3. Hybrid marketplace vision for autonomy: Uber divested its in-house AV unit and now partners with over 15 AV companies. A “hybrid network” of human drivers plus robotaxis smooths supply and demand (humans fill peaks; AVs stay busy off-peak). A similar model is rolling out in delivery via sidewalk robots. 4. Profitability through ruthless efficiency: Covid forced Uber to drop unprofitable bets (AV, Elevate), batch deliveries, refine promo spend, and trim support and payment costs. Today, profitability funds new bets without sacrificing margin discipline. 5. Career advice for PMs: Optimize for shipping velocity early. Five product cycles in three years teach the thousands of micro-decisions that build judgment (product sense) faster than any course. 6. AI already boosting PM workflows: Sachin’s teams use ChatGPT and Gemini for mock drafts, research synthesis, deep research “thought partner” prompts, and long-doc summarization—freeing time for judgment calls. 7. Empathy beats ego: Users spend minutes, not their lives, inside your product. Your job is to dazzle them in that tiny window, not expect them to think about you all day. 8. Quick Uber etiquette tips. Riders: Ask before taking a call; don’t slam the door. Eats customers: Turn on your porch light so couriers can find you. Thank you to our wonderful sponsors for supporting the podcast: 🏆 Paragon — Ship every SaaS integration your customers want: 🏆 Stripe — Financial infrastructure to grow your revenue: 🏆 Coda — The all-in-one collaborative workspace:

Lenny Rachitsky

362,605 views • 1 year ago