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Full CludeAI $CLUDE interview w/seb Topics covered: → 2% hallucination rate vs 15% industry → Big labs are incentivized not to fix memory → ~100x token cost reduction -------------------------------- 0:00 Sovereign memory narrative 1:26 Founder background 3:28 The context window problem 9:08 Why big labs won't fix it 17:23...

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MCG

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13,370 просмотров • 5 месяцев назад •via X (Twitter)

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During an agentic session, a quick “hi” after a coffee break can cost a full dollar. But it’s not because of the “hi.” It’s the million tokens of context you’re repaying for the moment your cache goes cold. Here's the caching math from Evan: 0:00 Why you're billed for last turn's context, not just this one 0:17 Cache hits: ~10% of full price 0:41 Agentic AI = way more back-and-forth than chat 1:16 Paying full price for context even on a 1-token tool call 1:40 Cache hit vs. cache miss 2:13 The $1 "hi": walk away for an hour, come back to a cold cache 2:59 Cost grows by the square, even with caching 3:28 How context compaction helps 4:03 Why Claude Code/Codex may compact at ~200-300K, not the full 1M window
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During an agentic session, a quick “hi” after a coffee break can cost a full dollar. But it’s not because of the “hi.” It’s the million tokens of context you’re repaying for the moment your cache goes cold. Here's the caching math from Evan: 0:00 Why you're billed for last turn's context, not just this one 0:17 Cache hits: ~10% of full price 0:41 Agentic AI = way more back-and-forth than chat 1:16 Paying full price for context even on a 1-token tool call 1:40 Cache hit vs. cache miss 2:13 The $1 "hi": walk away for an hour, come back to a cold cache 2:59 Cost grows by the square, even with caching 3:28 How context compaction helps 4:03 Why Claude Code/Codex may compact at ~200-300K, not the full 1M window

Arena.ai

52,711 просмотров • 20 дней назад

Full interview w/Pop Punk 🇺🇸 01:05 - Founder background 01:34 - Pumpcade lore 03:15 - "Trenchifying" prediction markets 04:35 - Hackathon win and Pump.fun's support 06:40 - Advice to crypto founders 09:10 - Tokenized startups are the future 09:58 - Future of the token and common misconceptions 11:48 - VC pullback 14:08 - Token utility 17:19 - Preseed raise ($1m) 18:30 - The pitch 20:15 - Building on Solana (Solana_devs) 21:35 - Hiring talent (Steve + gnar) 23:00 - Platform redesign 24:00 - GTM & competition 26:00 - Infrastructure revamp 27:50 - User base and stats 29:58 - Tokenomics 31:00 - Fee & revenue model 31:35 - TAM 32:00 - Web2 native 35:25 - Inspo 37: 51 - Permissionless markets 43:00 - A thank you to the early supporters 43:15 - What's to come
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Full interview w/Pop Punk 🇺🇸 01:05 - Founder background 01:34 - Pumpcade lore 03:15 - "Trenchifying" prediction markets 04:35 - Hackathon win and Pump.fun's support 06:40 - Advice to crypto founders 09:10 - Tokenized startups are the future 09:58 - Future of the token and common misconceptions 11:48 - VC pullback 14:08 - Token utility 17:19 - Preseed raise ($1m) 18:30 - The pitch 20:15 - Building on Solana (Solana_devs) 21:35 - Hiring talent (Steve + gnar) 23:00 - Platform redesign 24:00 - GTM & competition 26:00 - Infrastructure revamp 27:50 - User base and stats 29:58 - Tokenomics 31:00 - Fee & revenue model 31:35 - TAM 32:00 - Web2 native 35:25 - Inspo 37: 51 - Permissionless markets 43:00 - A thank you to the early supporters 43:15 - What's to come

MCG

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I Made $4M, Here’s The AI Automation Stack We Use: 0:00 The AI stack behind $4M 0:50 Most businesses are bottlenecked by small tasks 1:42 Our old stack vs our current stack 2:35 Why Claude Code changed everything 3:21 Founder Funnel 6:26 Our clipping platform 8:00 Sales automation pipeline 9:06 What each tool is actually for 9:46 The $62/hour automation test 10:33 What I would not fully automate 11:40 What to build first
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I Made $4M, Here’s The AI Automation Stack We Use: 0:00 The AI stack behind $4M 0:50 Most businesses are bottlenecked by small tasks 1:42 Our old stack vs our current stack 2:35 Why Claude Code changed everything 3:21 Founder Funnel 6:26 Our clipping platform 8:00 Sales automation pipeline 9:06 What each tool is actually for 9:46 The $62/hour automation test 10:33 What I would not fully automate 11:40 What to build first

Sharbel

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Memory is eating the world When consumers feel the heat, you know its a problem. While big-3 memory companies are enjoying sweet-sweet profit margins…the rest of the electronics market is cracking under pricing pressure. - GoPro is more like GoingUnder - Apple products 10-30% pricier - Even music synths are more expensive Is it price gouging at this point? Is this revenge for all the memory cyclicality? When will this end? Chapters: 0:00 Memory crisis hits 1:20 AI impacting consumers 3:00 AI causing inflation 6:48 Consumer demand drop? 8:49 AI demand inelastic 10:56 Long-term memory outlook 11:02 GoPro's memory woes 12:24 Apple's pricing power 18:20 Apple seeks CXMT DRAM 21:38 Shrinkflation for phones 23:16 Korea's memory investment 26:10 Micron's killing profits 33:00 Why AI needs so much DRAM 40:27 Future of AI training 44:34 Cost-optimizing inference Get more of Austin and Vik daily, free! Sign up: Connect with Vik and Austin: Vik's Paid Substack: Austin's Paid Substack: Austin Lyons Vikram Sekar $MU
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Memory is eating the world When consumers feel the heat, you know its a problem. While big-3 memory companies are enjoying sweet-sweet profit margins…the rest of the electronics market is cracking under pricing pressure. - GoPro is more like GoingUnder - Apple products 10-30% pricier - Even music synths are more expensive Is it price gouging at this point? Is this revenge for all the memory cyclicality? When will this end? Chapters: 0:00 Memory crisis hits 1:20 AI impacting consumers 3:00 AI causing inflation 6:48 Consumer demand drop? 8:49 AI demand inelastic 10:56 Long-term memory outlook 11:02 GoPro's memory woes 12:24 Apple's pricing power 18:20 Apple seeks CXMT DRAM 21:38 Shrinkflation for phones 23:16 Korea's memory investment 26:10 Micron's killing profits 33:00 Why AI needs so much DRAM 40:27 Future of AI training 44:34 Cost-optimizing inference Get more of Austin and Vik daily, free! Sign up: Connect with Vik and Austin: Vik's Paid Substack: Austin's Paid Substack: Austin Lyons Vikram Sekar $MU

Semi Doped

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Ep. 88 - Eyad Gomaa τroy is building Quasar Subnet 24 Timestamps 1:24 - Introduction 2:08 - Long Context & Decentralized MoE Training 4:05 - Why Long Context Remains Unsolved 5:18 - Quasar Attention: Beyond Transformers 5:55 - Why Decentralized MoE Training Is Hard 8:53 - The MoE Router Problem 9:51 - How Quasar Found Bittensor 11:26 - Why Open Source & Decentralized AI 12:43 - The Joseph Jacks Rejection 13:47 - Subnet Design: CUDA Kernels & Distillation 15:38 - Quasar vs. Other Distillation Subnets 17:05 - Decentralized MoE Design: Miners & Validators 18:34 - Training Data & Upcoming Partnership 19:36 - Quality Over Size: The Real Goal 22:26 - Value Capture: Chutes, API & Quasar Copilot 23:28 - Hardest Part: Building the Incentive Mechanism 24:17 - Bittensor as YC for Real AI 25:24 - Decentralized Advantage vs. Centralized Labs 26:44 - Const: Subnet Acquisition & Mentorship 28:42 - Youngest Subnet Owner & Crypto Background 29:48 - Biggest Personal Growth in Bittensor 30:57 - State-of-the-Art from Bittensor 32:10 - Infinite Context: 10M Effective Window 33:16 - Open Source Inspiration & Model Persona 34:21 - Building Persona via Reinforcement Learning 36:04 - Advice for AI Nerds: Set Goals Before Joining
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Ep. 88 - Eyad Gomaa τroy is building Quasar Subnet 24 Timestamps 1:24 - Introduction 2:08 - Long Context & Decentralized MoE Training 4:05 - Why Long Context Remains Unsolved 5:18 - Quasar Attention: Beyond Transformers 5:55 - Why Decentralized MoE Training Is Hard 8:53 - The MoE Router Problem 9:51 - How Quasar Found Bittensor 11:26 - Why Open Source & Decentralized AI 12:43 - The Joseph Jacks Rejection 13:47 - Subnet Design: CUDA Kernels & Distillation 15:38 - Quasar vs. Other Distillation Subnets 17:05 - Decentralized MoE Design: Miners & Validators 18:34 - Training Data & Upcoming Partnership 19:36 - Quality Over Size: The Real Goal 22:26 - Value Capture: Chutes, API & Quasar Copilot 23:28 - Hardest Part: Building the Incentive Mechanism 24:17 - Bittensor as YC for Real AI 25:24 - Decentralized Advantage vs. Centralized Labs 26:44 - Const: Subnet Acquisition & Mentorship 28:42 - Youngest Subnet Owner & Crypto Background 29:48 - Biggest Personal Growth in Bittensor 30:57 - State-of-the-Art from Bittensor 32:10 - Infinite Context: 10M Effective Window 33:16 - Open Source Inspiration & Model Persona 34:21 - Building Persona via Reinforcement Learning 36:04 - Advice for AI Nerds: Set Goals Before Joining

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DeFi Dad ⟠ defidad.eth

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LIVE NOW - The Holy Grail of Crypto Privacy: Encrypted Ethereum, FHE & Living Forever Ethereum is transparent by design, and that’s a problem if you don’t want your entire financial life on display. Rand , co-founder of Zama, joins us to explain how fully homomorphic encryption (FHE) can turn Ethereum into an encrypted, confidential blockchain where contracts stay composable and UX feels exactly the same. -------------- TIMESTAMPS 0:00 Intro 0:24 Why Are Blockchains Public? 9:11 Privacy vs Confidentiality 11:13 Zama: Privacy on Ethereum 15:05 Fully Homomorphic Encryption (FHE) 19:34 Multi-Party Computation (MPC) 23:50 FHE vs ZK 37:25 FHE Critiques 40:08 Nation State Attacks 45:58 Combining ZK & FHE 49:20 Privacy Season 51:39 Compliance 1:00:18 Zama 1:04:03 Confidential Tokens 1:08:12 How it Works 1:11:30 Zama Token & Protocol 1:14:09 Privacy Competitors 1:17:36 Fhenix 1:19:46 Rand’s Background 1:23:07 AI Privacy 1:26:42 Longevity 1:33:01 Crypto & Privacy in 2026 1:33:33 Closing & Disclaimers
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LIVE NOW - The Holy Grail of Crypto Privacy: Encrypted Ethereum, FHE & Living Forever Ethereum is transparent by design, and that’s a problem if you don’t want your entire financial life on display. Rand , co-founder of Zama, joins us to explain how fully homomorphic encryption (FHE) can turn Ethereum into an encrypted, confidential blockchain where contracts stay composable and UX feels exactly the same. -------------- TIMESTAMPS 0:00 Intro 0:24 Why Are Blockchains Public? 9:11 Privacy vs Confidentiality 11:13 Zama: Privacy on Ethereum 15:05 Fully Homomorphic Encryption (FHE) 19:34 Multi-Party Computation (MPC) 23:50 FHE vs ZK 37:25 FHE Critiques 40:08 Nation State Attacks 45:58 Combining ZK & FHE 49:20 Privacy Season 51:39 Compliance 1:00:18 Zama 1:04:03 Confidential Tokens 1:08:12 How it Works 1:11:30 Zama Token & Protocol 1:14:09 Privacy Competitors 1:17:36 Fhenix 1:19:46 Rand’s Background 1:23:07 AI Privacy 1:26:42 Longevity 1:33:01 Crypto & Privacy in 2026 1:33:33 Closing & Disclaimers

Bankless

121,302 просмотров • 9 месяцев назад

Your OpenClaw🦞 agent works perfectly for 20 minutes. Then it silently forgets your instructions and goes rogue. The fix isn't better prompts. It's understanding how memory actually works. I'm an OpenClaw maintainer. After 2+ months of daily use, I ended up with a memory system where my agent remembers decisions from weeks ago, checks its notes before acting, and picks up exactly where it left off after every restart: 0:00 The real reason your agent forgets 1:59 Quick start: 3 things that matter most 3:01 The 4 "memories" people confuse 4:52 3 failure modes: why your agent forgets 6:42 Compaction vs pruning 8:05 Prove what's loaded: /context list 10:06 What compaction actually does 13:42 Layer 1: pre-compaction memory flush 16:27 Layer 2: manual saves + /compact trick 19:12 Layer 3: the file architecture 23:22 The memory protocol for AGENTS.md 26:06 Retrieval: Track A, A+, and QMD by tobi lutke 31:59 Cost and cache 33:33 The complete config (copy and go) 36:35 5 things to remember
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Your OpenClaw🦞 agent works perfectly for 20 minutes. Then it silently forgets your instructions and goes rogue. The fix isn't better prompts. It's understanding how memory actually works. I'm an OpenClaw maintainer. After 2+ months of daily use, I ended up with a memory system where my agent remembers decisions from weeks ago, checks its notes before acting, and picks up exactly where it left off after every restart: 0:00 The real reason your agent forgets 1:59 Quick start: 3 things that matter most 3:01 The 4 "memories" people confuse 4:52 3 failure modes: why your agent forgets 6:42 Compaction vs pruning 8:05 Prove what's loaded: /context list 10:06 What compaction actually does 13:42 Layer 1: pre-compaction memory flush 16:27 Layer 2: manual saves + /compact trick 19:12 Layer 3: the file architecture 23:22 The memory protocol for AGENTS.md 26:06 Retrieval: Track A, A+, and QMD by tobi lutke 31:59 Cost and cache 33:33 The complete config (copy and go) 36:35 5 things to remember

Radek Sienkiewicz

94,629 просмотров • 6 месяцев назад

My full interview with Roman Chernin, Co-founder & Chief Business Officer of Nebius Nebius just signed a $17 billion deal with Microsoft and a $3 billion deal with Meta. These are two of the biggest tech companies on Earth - and they're coming to Nebius for AI infrastructure. But the company does so much more than that: - Full-stack AI cloud (data centers → software → managed services) - Nebius Token Factory Nebius Token Factory for managed inference & post-training - Partners include Shopify, Higgsfield, Jetbrains My 5 takeaways with Roman Chernin below - thanks for the conversation! Full interview also available on YT ⬇️ Timestamps: 00:00 - Introduction 01:47 - Overview of Nebius $NBIS 03:23 - Roman's background and role at Nebius 04:10 - The $17B Microsoft & $3B Meta deals 05:29 - Why hyperscalers trust Nebius over building in-house 07:06 - Nebius's full-stack approach: Data centers to managed services 08:39 - Customer segmentation: Hyperscalers & Frontier AI Labs vs AI Startups vs Enterprises 14:41 - Token Factory explained: Managed inference & post-training 17:41 - When to switch from closed-source to open-source models 20:59 - Real results: Process achieves 26% cost reduction 25:05 - Why developers love building on Nebius 28:40 - AI agents and the future of infrastructure interaction 32:22 - NVIDIA's Groq acquisition: What it means for inference 35:38 - Why AI adoption will surprise us
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My full interview with Roman Chernin, Co-founder & Chief Business Officer of Nebius Nebius just signed a $17 billion deal with Microsoft and a $3 billion deal with Meta. These are two of the biggest tech companies on Earth - and they're coming to Nebius for AI infrastructure. But the company does so much more than that: - Full-stack AI cloud (data centers → software → managed services) - Nebius Token Factory Nebius Token Factory for managed inference & post-training - Partners include Shopify, Higgsfield, Jetbrains My 5 takeaways with Roman Chernin below - thanks for the conversation! Full interview also available on YT ⬇️ Timestamps: 00:00 - Introduction 01:47 - Overview of Nebius $NBIS 03:23 - Roman's background and role at Nebius 04:10 - The $17B Microsoft & $3B Meta deals 05:29 - Why hyperscalers trust Nebius over building in-house 07:06 - Nebius's full-stack approach: Data centers to managed services 08:39 - Customer segmentation: Hyperscalers & Frontier AI Labs vs AI Startups vs Enterprises 14:41 - Token Factory explained: Managed inference & post-training 17:41 - When to switch from closed-source to open-source models 20:59 - Real results: Process achieves 26% cost reduction 25:05 - Why developers love building on Nebius 28:40 - AI agents and the future of infrastructure interaction 32:22 - NVIDIA's Groq acquisition: What it means for inference 35:38 - Why AI adoption will surprise us

Elliot Garreffa

29,660 просмотров • 7 месяцев назад

The new Huberman Lab episode is out: How to Improve Your Memory & Cognitive Function at Any Age | Dr. Alan Castel 0:00 Dr. Alan Castel 2:41 What Is Memory?, Reconstruction & Metacognition 4:49 Mnemonics, Remembering Names & Deeper Learning 8:22 The Penny & Apple Logo, Noticing vs Seeing, Learning Through Mistakes 10:43 Sponsors: Wealthfront & Helix 14:05 Neuroplasticity, Frustration, Curiosity & Mindset 17:42 Maintaining vs Learning New Things, Habits, Novelty & Emotional Memory 24:28 "Mental Photographs," Photo-Taking & Imagining the Future 29:28 Eyewitness Memory, the Ronald Cotton Case, Confidence vs Accuracy 35:07 Medium-Term & Prospective Memory, Hotel Fire Exits 40:28 Sponsor: AG1 41:47 When Habits Turn Lethal, Aviation & Human Error 49:01 Why Memory Changes With Age; Alzheimer's & the Nun Study 52:34 Exercise & Hippocampal Volume, Falls & Balance 57:14 SuperAgers & Athletes; Regret, Balance & Being Driven 1:12:08 Sponsor: Function 1:13:45 Age Stereotypes, Subjective Age & Positive Age Beliefs 1:20:02 Goals & Plans, Scams; Anterior Midcingulate Cortex & SuperAgers 1:26:23 Culture, Resilience, Blue Zones & COVID 1:29:18 Adversity, the Positivity Effect & Intergenerational Learning 1:36:31 Sponsor: Lingo 1:38:00 Limitations & Purpose; Time, Family & Connection 1:44:58 Deliberately Building Memories; the ABCs of Successful Aging 1:51:02 Following Your Interests; Castel's Path & Older Adults 1:57:16 Mental Simulations, Curiosity Studies & Selectivity 2:01:19 Socioemotional Selectivity Theory; Steve Jobs & Lifespan 2:07:10 The Secret to Successful Aging; State vs Trait Curiosity 2:11:04 Scams & AI Voice Cloning 2:14:31 John Wooden, Wisdom, Love & Balance 2:17:41 Learning Through Mistakes; Does the Brain Get Better With Age? 2:25:00 Conclusion, Better With Age 2:26:00 Zero-Cost Support, YouTube, Spotify & Apple Follow, Reviews & Feedback, Sponsors, Protocols Book, Social Media, Neural Network Newsletter Includes paid partnerships.
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The new Huberman Lab episode is out: How to Improve Your Memory & Cognitive Function at Any Age | Dr. Alan Castel 0:00 Dr. Alan Castel 2:41 What Is Memory?, Reconstruction & Metacognition 4:49 Mnemonics, Remembering Names & Deeper Learning 8:22 The Penny & Apple Logo, Noticing vs Seeing, Learning Through Mistakes 10:43 Sponsors: Wealthfront & Helix 14:05 Neuroplasticity, Frustration, Curiosity & Mindset 17:42 Maintaining vs Learning New Things, Habits, Novelty & Emotional Memory 24:28 "Mental Photographs," Photo-Taking & Imagining the Future 29:28 Eyewitness Memory, the Ronald Cotton Case, Confidence vs Accuracy 35:07 Medium-Term & Prospective Memory, Hotel Fire Exits 40:28 Sponsor: AG1 41:47 When Habits Turn Lethal, Aviation & Human Error 49:01 Why Memory Changes With Age; Alzheimer's & the Nun Study 52:34 Exercise & Hippocampal Volume, Falls & Balance 57:14 SuperAgers & Athletes; Regret, Balance & Being Driven 1:12:08 Sponsor: Function 1:13:45 Age Stereotypes, Subjective Age & Positive Age Beliefs 1:20:02 Goals & Plans, Scams; Anterior Midcingulate Cortex & SuperAgers 1:26:23 Culture, Resilience, Blue Zones & COVID 1:29:18 Adversity, the Positivity Effect & Intergenerational Learning 1:36:31 Sponsor: Lingo 1:38:00 Limitations & Purpose; Time, Family & Connection 1:44:58 Deliberately Building Memories; the ABCs of Successful Aging 1:51:02 Following Your Interests; Castel's Path & Older Adults 1:57:16 Mental Simulations, Curiosity Studies & Selectivity 2:01:19 Socioemotional Selectivity Theory; Steve Jobs & Lifespan 2:07:10 The Secret to Successful Aging; State vs Trait Curiosity 2:11:04 Scams & AI Voice Cloning 2:14:31 John Wooden, Wisdom, Love & Balance 2:17:41 Learning Through Mistakes; Does the Brain Get Better With Age? 2:25:00 Conclusion, Better With Age 2:26:00 Zero-Cost Support, YouTube, Spotify & Apple Follow, Reviews & Feedback, Sponsors, Protocols Book, Social Media, Neural Network Newsletter Includes paid partnerships.

Andrew D. Huberman, Ph.D.

127,206 просмотров • 1 месяц назад

how to operate a company: complete breakdown 00:00 The goal of operations 01:23 A product is not a company 03:25 Why US investors won't fund foreign companies 05:38 Why everyone uses a Delaware C Corp 06:47 The 10M shares / 10% option pool standard 07:28 Why your strike price has to be tiny 07:49 The 83(b) election (file in 30 days) 08:38 The mistake that costs founders millions 09:28 External GC vs Stripe Atlas vs Clerky 11:39 Don't grift your metrics on Twitter 12:36 What investors actually want to see 13:17 Founder breakups all start with one question 14:14 Financials tell a story, what does yours say? 14:38 Real dilution numbers: pre-seed to Series A 16:08 Why advisor shares are usually a scam 17:34 Most "advisors" disappear after they sign 19:05 Incentivized customers aren't real customers 20:30 The right way to actually pay yourself 21:58 Don't pay unauthorized personnel 23:22 When do you actually need a board? 25:17 Stop hiring expensive lawyers 26:17 The 100x rule for early hires 27:15 Never hire someone you can't learn from 28:08 Equity over cash signals real belief 29:11 How to poach a Meta engineers 29:53 "Ask for advice" hiring trick 30:55 What a real trial offer looks like 32:16 Why working with friends is a trap
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how to operate a company: complete breakdown 00:00 The goal of operations 01:23 A product is not a company 03:25 Why US investors won't fund foreign companies 05:38 Why everyone uses a Delaware C Corp 06:47 The 10M shares / 10% option pool standard 07:28 Why your strike price has to be tiny 07:49 The 83(b) election (file in 30 days) 08:38 The mistake that costs founders millions 09:28 External GC vs Stripe Atlas vs Clerky 11:39 Don't grift your metrics on Twitter 12:36 What investors actually want to see 13:17 Founder breakups all start with one question 14:14 Financials tell a story, what does yours say? 14:38 Real dilution numbers: pre-seed to Series A 16:08 Why advisor shares are usually a scam 17:34 Most "advisors" disappear after they sign 19:05 Incentivized customers aren't real customers 20:30 The right way to actually pay yourself 21:58 Don't pay unauthorized personnel 23:22 When do you actually need a board? 25:17 Stop hiring expensive lawyers 26:17 The 100x rule for early hires 27:15 Never hire someone you can't learn from 28:08 Equity over cash signals real belief 29:11 How to poach a Meta engineers 29:53 "Ask for advice" hiring trick 30:55 What a real trial offer looks like 32:16 Why working with friends is a trap

Founders Inc

20,344 просмотров • 4 месяцев назад

.Dylan Patel gives a deep dive on the 3 big bottlenecks to scaling AI compute: logic, memory, and power. And walks through the economics of labs, hyperscalers, foundries, and fab equipment manufacturers. Learned a ton about every single level of the stack. 0:00:00 – Why an H100 is worth more today than 3 years ago 0:24:52 – Nvidia secured TSMC allocation early; Google is getting squeezed 0:34:34 – ASML will be the #1 constraint for AI compute scaling by 2030 0:56:06 – Can’t we just use TSMC’s older fabs? 1:05:56 – When will China outscale the West in semis? 1:16:20 – The enormous incoming memory crunch 1:42:53 – Scaling power in the US will not be a problem 1:55:03 – Space GPUs aren't happening this decade 2:14:26 – Why aren’t more hedge funds making the AGI trade? 2:18:49 – Will TSMC kick Apple out from N2? 2:24:35 – Robots and Taiwan risk Look up Dwarkesh Podcast on YouTube, Apple Podcasts, or Spotify. Enjoy!
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.Dylan Patel gives a deep dive on the 3 big bottlenecks to scaling AI compute: logic, memory, and power. And walks through the economics of labs, hyperscalers, foundries, and fab equipment manufacturers. Learned a ton about every single level of the stack. 0:00:00 – Why an H100 is worth more today than 3 years ago 0:24:52 – Nvidia secured TSMC allocation early; Google is getting squeezed 0:34:34 – ASML will be the #1 constraint for AI compute scaling by 2030 0:56:06 – Can’t we just use TSMC’s older fabs? 1:05:56 – When will China outscale the West in semis? 1:16:20 – The enormous incoming memory crunch 1:42:53 – Scaling power in the US will not be a problem 1:55:03 – Space GPUs aren't happening this decade 2:14:26 – Why aren’t more hedge funds making the AGI trade? 2:18:49 – Will TSMC kick Apple out from N2? 2:24:35 – Robots and Taiwan risk Look up Dwarkesh Podcast on YouTube, Apple Podcasts, or Spotify. Enjoy!

Dwarkesh Patel

489,541 просмотров • 5 месяцев назад

New interview: Reiner Pope, co-founder/CEO of MatX A counterintuitive throughput insight: “Low latency means small batch sizes. That is just Little’s law. Memory occupancy in HBM is proportional to batch size. So you can actually fit longer contexts than you could if the latency were larger. Low latency is not just a usability win, it improves throughput.” We get into: • The hybrid SRAM + HBM bet, and why pipeline parallelism finally works • Why sparse MoE drives MatX to “the most interconnect of any announced product” • Why frontier labs are willing to bet on an AI ASIC startup • Memory-bandwidth-efficient attention, numerics, and what MatX publishes (and what it does not) • Why 95% of model-side news is noise for chip design • The biggest challenges ahead 00:00 “We left Google one week before ChatGPT” 00:24 Intro: who is MatX 01:17 Origin story: leaving Google for LLM chips 02:21 GPT-3 and the “too expensive” problem 04:25 Why buy hardware that is not a GPU 05:52 Overcoming the CUDA moat 08:46 Early investors 09:35 The name MatX 09:59 The chip: matrix multiply + hybrid SRAM/HBM 12:11 Why pipeline parallelism finally works 14:22 Reading papers and Google going dark 15:20 Research agenda: attention and numerics 17:06 Five specs and meeting customers where they are 19:24 Why frontier labs are the natural first customer 20:32 Workloads: training, prefill, decode 22:18 Little’s law and the throughput case for low latency 24:29 Interconnect and MoE topology 26:35 Inside the team: 100 people, full stack 28:32 Agentic AI: 95% noise for hardware 30:35 KV cache sizing in an agentic world 32:11 How MatX uses AI for chip design (Verilog + BlueSpec) 34:23 Go to market: proving credibility under NDA 35:12 Porting effort for frontier labs 36:34 Biggest skepticism: manufacturing at gigawatt scale 37:32 Hiring plug Vikram Sekar
38:57

New interview: Reiner Pope, co-founder/CEO of MatX A counterintuitive throughput insight: “Low latency means small batch sizes. That is just Little’s law. Memory occupancy in HBM is proportional to batch size. So you can actually fit longer contexts than you could if the latency were larger. Low latency is not just a usability win, it improves throughput.” We get into: • The hybrid SRAM + HBM bet, and why pipeline parallelism finally works • Why sparse MoE drives MatX to “the most interconnect of any announced product” • Why frontier labs are willing to bet on an AI ASIC startup • Memory-bandwidth-efficient attention, numerics, and what MatX publishes (and what it does not) • Why 95% of model-side news is noise for chip design • The biggest challenges ahead 00:00 “We left Google one week before ChatGPT” 00:24 Intro: who is MatX 01:17 Origin story: leaving Google for LLM chips 02:21 GPT-3 and the “too expensive” problem 04:25 Why buy hardware that is not a GPU 05:52 Overcoming the CUDA moat 08:46 Early investors 09:35 The name MatX 09:59 The chip: matrix multiply + hybrid SRAM/HBM 12:11 Why pipeline parallelism finally works 14:22 Reading papers and Google going dark 15:20 Research agenda: attention and numerics 17:06 Five specs and meeting customers where they are 19:24 Why frontier labs are the natural first customer 20:32 Workloads: training, prefill, decode 22:18 Little’s law and the throughput case for low latency 24:29 Interconnect and MoE topology 26:35 Inside the team: 100 people, full stack 28:32 Agentic AI: 95% noise for hardware 30:35 KV cache sizing in an agentic world 32:11 How MatX uses AI for chip design (Verilog + BlueSpec) 34:23 Go to market: proving credibility under NDA 35:12 Porting effort for frontier labs 36:34 Biggest skepticism: manufacturing at gigawatt scale 37:32 Hiring plug Vikram Sekar

Semi Doped

19,797 просмотров • 5 месяцев назад

🎙EXCLUSIVE: 30-MINUTES WITH ASHTON (IGRA LABS, MOONBOUND) We sat down with a legend in the world of K.A.T. - Ashton of Igra Labs and Moonbound.gg. We discussed everything from the network's origins and the current state of the $KAS ecosystem... ... Through to what's coming next, which apps to watch, and what is set to be a MASSIVE 2026 for the network. Tune in, or get left behind by one of crypto's fastest-moving ecosystems ⬇️ Timestamps: (1:08) Introductions & Context (3:15) Ashton's journey into the Kaspa ecosystem (4:44) What makes Kaspa special? (9:18) The state of Kaspa in 2026 (15:00) What is Igra and why does it matter? (21:23) Why does Igra need a token? (26:35) What is Moonbound and why does it matter? (29:50) Which other Kaspa-based projects excite you? (36:54) Closing thoughts [NeverFinancialAdvice]
39:40

🎙EXCLUSIVE: 30-MINUTES WITH ASHTON (IGRA LABS, MOONBOUND) We sat down with a legend in the world of K.A.T. - Ashton of Igra Labs and Moonbound.gg. We discussed everything from the network's origins and the current state of the $KAS ecosystem... ... Through to what's coming next, which apps to watch, and what is set to be a MASSIVE 2026 for the network. Tune in, or get left behind by one of crypto's fastest-moving ecosystems ⬇️ Timestamps: (1:08) Introductions & Context (3:15) Ashton's journey into the Kaspa ecosystem (4:44) What makes Kaspa special? (9:18) The state of Kaspa in 2026 (15:00) What is Igra and why does it matter? (21:23) Why does Igra need a token? (26:35) What is Moonbound and why does it matter? (29:50) Which other Kaspa-based projects excite you? (36:54) Closing thoughts [NeverFinancialAdvice]

BSCN

30,862 просмотров • 6 месяцев назад

The alpha is not on Twitter, and most things in crypto will go to zero. Also, AI is gonna cook us. @countersignalpd episode 3 with my friend Addison, an acquired crypto founder & MIT student Timestamps: 0:00 Intro 0:38 Addison Origin Story 2:45 Founding Thunderhead at 16 13:45 Early to Hyperliquid in 2023 15:32 Why do high caliber people go to crypto... 17:01 Valantis Labs Acquisition of Thunderhead 22:08 Value creation in speculation as the world becomes more hopeless 26:29 How to not get cooked in the future 27:28 The alpha is NOT on Twitter, it's in... 30:23 Build generational wealth in the next 5-10 years or get cooked by AI 32:58 Media will become increasingly important 34:35 AI bubble vs crypto bubble 37:09 What isn't going to go to 0 in crypto? 45:45 Value creation outside of tech 48:28 Why not dropout of college?
52:47

The alpha is not on Twitter, and most things in crypto will go to zero. Also, AI is gonna cook us. @countersignalpd episode 3 with my friend Addison, an acquired crypto founder & MIT student Timestamps: 0:00 Intro 0:38 Addison Origin Story 2:45 Founding Thunderhead at 16 13:45 Early to Hyperliquid in 2023 15:32 Why do high caliber people go to crypto... 17:01 Valantis Labs Acquisition of Thunderhead 22:08 Value creation in speculation as the world becomes more hopeless 26:29 How to not get cooked in the future 27:28 The alpha is NOT on Twitter, it's in... 30:23 Build generational wealth in the next 5-10 years or get cooked by AI 32:58 Media will become increasingly important 34:35 AI bubble vs crypto bubble 37:09 What isn't going to go to 0 in crypto? 45:45 Value creation outside of tech 48:28 Why not dropout of college?

sracha

24,806 просмотров • 1 год назад

I've spent 2.54 BILLION tokens perfecting OpenClaw. The use cases I discovered have changed the way I live and work. ...and now I'm sharing them with the world. Here are 21 use cases I use daily: 0:00 Intro 0:50 What is OpenClaw? 1:35 MD Files 2:14 Memory System 3:55 CRM System 7:19 Fathom Pipeline 9:18 Meeting to Action Items 10:46 Knowledge Base System 13:51 X Ingestion Pipeline 14:31 Business Advisory Council 16:13 Security Council 18:21 Social Media Tracking 19:18 Video Idea Pipeline 21:40 Daily Briefing Flow 22:23 Three Councils 22:57 Automation Schedule 24:15 Security Layers 26:09 Databases and Backups 28:00 Video/Image Gen 29:14 Self Updates 29:56 Usage & Cost Tracking 30:15 Prompt Engineering 31:15 Developer Infrastructure 32:06 Food Journal
33:43

I've spent 2.54 BILLION tokens perfecting OpenClaw. The use cases I discovered have changed the way I live and work. ...and now I'm sharing them with the world. Here are 21 use cases I use daily: 0:00 Intro 0:50 What is OpenClaw? 1:35 MD Files 2:14 Memory System 3:55 CRM System 7:19 Fathom Pipeline 9:18 Meeting to Action Items 10:46 Knowledge Base System 13:51 X Ingestion Pipeline 14:31 Business Advisory Council 16:13 Security Council 18:21 Social Media Tracking 19:18 Video Idea Pipeline 21:40 Daily Briefing Flow 22:23 Three Councils 22:57 Automation Schedule 24:15 Security Layers 26:09 Databases and Backups 28:00 Video/Image Gen 29:14 Self Updates 29:56 Usage & Cost Tracking 30:15 Prompt Engineering 31:15 Developer Infrastructure 32:06 Food Journal

Matthew Berman

3,378,040 просмотров • 6 месяцев назад

SPECIAL: Cathie Wood (Cathie Wood) On Digital Assets, Stablecoins, and The Innovation Convergence Cathie Wood joins us to break down her innovation platforms thesis, the story behind her firm ARK Invest, and their foray into digital assets. She also unpacks the tokenization thesis, what 'good deflation' is, and her "Big Four" crypto assets. Full episode links below. Timestamps: 00:00 Intro 01:22 5 Innovation Platforms 04:53 Why ARK Started 08:11 Bitcoin's Institutional Journey 12:38 First Bitcoin Position 15:11 Institutional Adoption Today 17:33 Larry Fink's Conversion 19:30 Stablecoins Are Impactful 22:06 Cathie's Tokenization Thesis 23:07 DeFi Value Accrual 28:48 Bitcoin & The Big Four 32:39 Macro Liquidity Dynamics 34:24 Bitcoin vs Gold 35:38 ARK's Bitcoin Bull Case 38:00 The Fed & Good Deflation 41:15 New Fed Chair 45:05 Four Year Cycle Debate 46:10 Why Agentic AI Needs Blockchain 49:15 Self-Driving Labs & Healthcare
50:40

SPECIAL: Cathie Wood (Cathie Wood) On Digital Assets, Stablecoins, and The Innovation Convergence Cathie Wood joins us to break down her innovation platforms thesis, the story behind her firm ARK Invest, and their foray into digital assets. She also unpacks the tokenization thesis, what 'good deflation' is, and her "Big Four" crypto assets. Full episode links below. Timestamps: 00:00 Intro 01:22 5 Innovation Platforms 04:53 Why ARK Started 08:11 Bitcoin's Institutional Journey 12:38 First Bitcoin Position 15:11 Institutional Adoption Today 17:33 Larry Fink's Conversion 19:30 Stablecoins Are Impactful 22:06 Cathie's Tokenization Thesis 23:07 DeFi Value Accrual 28:48 Bitcoin & The Big Four 32:39 Macro Liquidity Dynamics 34:24 Bitcoin vs Gold 35:38 ARK's Bitcoin Bull Case 38:00 The Fed & Good Deflation 41:15 New Fed Chair 45:05 Four Year Cycle Debate 46:10 Why Agentic AI Needs Blockchain 49:15 Self-Driving Labs & Healthcare

The Rollup

70,113 просмотров • 4 месяцев назад

The bull case for crypto isn’t memes. It’s better market structure. buffalu on why onchain finance can be cheaper, faster, and more open than the brokerage model most people use today. 0:00 Intro 0:49 What is Jito and why does it exist? 1:11 Why Solana transactions are less than a penny 2:00 What attracted Lucas to Solana from Ethereum 2:39 How Jito is like Cloudflare for blockchains 5:02 The $1,500 Ethereum transaction fee problem 7:21 The vision: all of finance onchain 7:46 Why onchain beats opening a Robinhood account 8:37 Lucas's journey from robotics to crypto 9:55 Solana's rate of improvement and Anatoly's Law 10:44 The pitch for people new to crypto 15:00 Staying lean at 21 people 15:17 Nicotine as a productivity hack 15:58 Sleep, alcohol, and the Oura ring 16:25 Movie and show recommendations 16:40 Smallest hill you'll die on: littering and shopping carts
18:01

The bull case for crypto isn’t memes. It’s better market structure. buffalu on why onchain finance can be cheaper, faster, and more open than the brokerage model most people use today. 0:00 Intro 0:49 What is Jito and why does it exist? 1:11 Why Solana transactions are less than a penny 2:00 What attracted Lucas to Solana from Ethereum 2:39 How Jito is like Cloudflare for blockchains 5:02 The $1,500 Ethereum transaction fee problem 7:21 The vision: all of finance onchain 7:46 Why onchain beats opening a Robinhood account 8:37 Lucas's journey from robotics to crypto 9:55 Solana's rate of improvement and Anatoly's Law 10:44 The pitch for people new to crypto 15:00 Staying lean at 21 people 15:17 Nicotine as a productivity hack 15:58 Sleep, alcohol, and the Oura ring 16:25 Movie and show recommendations 16:40 Smallest hill you'll die on: littering and shopping carts

a16z crypto

19,954 просмотров • 5 месяцев назад

*Major* open source AI drop today. Can America win the Open AI race? My conversation with Nathan Lambert and Luca Soldaini 🎀 of Ai2 about the launch of Olmo 3 00:00 – Cold Open 00:39 – Welcome & today’s big announcement 01:18 – Introducing the Olmo 3 model family 02:07 – What “base models” really are (and why they matter) 05:51 – Dolma 3: the data behind Olmo 3 08:06 – Performance vs Qwen, Gemma, DeepSeek 10:28 – What true open source means (and why it’s rare) 12:51 – Intermediate checkpoints, transparency, and why AI2 publishes everything 16:37 – Why Qwen is everywhere (including U.S. startups) 18:31 – Why Chinese labs go open source (and why U.S. labs don’t) 20:28 – Inside ATOM: the U.S. response to China’s model surge 22:13 – The rise of “thinking models” and inference-time scaling 35:58 – The full Olmo pipeline, explained simply 46:52 – Pre-training: data, scale, and avoiding catastrophic spikes 50:27 – Mid-training (tail patching) and avoiding test leakage 52:06 – Why long-context training matters 55:28 – SFT: building the foundation for reasoning 1:04:53 – Preference tuning & why DPO still works 1:10:51 – The hard part: RLVR, long reasoning chains, and infrastructure pain 1:13:59 – Why RL is so technically brutal 1:18:17 – Complexity tax vs AGI hype 1:21:58 – How everyone can contribute to the future of AI 1:27:26 – Closing thoughts
1:28:10

*Major* open source AI drop today. Can America win the Open AI race? My conversation with Nathan Lambert and Luca Soldaini 🎀 of Ai2 about the launch of Olmo 3 00:00 – Cold Open 00:39 – Welcome & today’s big announcement 01:18 – Introducing the Olmo 3 model family 02:07 – What “base models” really are (and why they matter) 05:51 – Dolma 3: the data behind Olmo 3 08:06 – Performance vs Qwen, Gemma, DeepSeek 10:28 – What true open source means (and why it’s rare) 12:51 – Intermediate checkpoints, transparency, and why AI2 publishes everything 16:37 – Why Qwen is everywhere (including U.S. startups) 18:31 – Why Chinese labs go open source (and why U.S. labs don’t) 20:28 – Inside ATOM: the U.S. response to China’s model surge 22:13 – The rise of “thinking models” and inference-time scaling 35:58 – The full Olmo pipeline, explained simply 46:52 – Pre-training: data, scale, and avoiding catastrophic spikes 50:27 – Mid-training (tail patching) and avoiding test leakage 52:06 – Why long-context training matters 55:28 – SFT: building the foundation for reasoning 1:04:53 – Preference tuning & why DPO still works 1:10:51 – The hard part: RLVR, long reasoning chains, and infrastructure pain 1:13:59 – Why RL is so technically brutal 1:18:17 – Complexity tax vs AGI hype 1:21:58 – How everyone can contribute to the future of AI 1:27:26 – Closing thoughts

Matt Turck

37,531 просмотров • 9 месяцев назад