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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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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 Aufrufe • vor 5 Monaten

🎙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 Aufrufe • vor 6 Monaten

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 Aufrufe • vor 1 Jahr

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 Aufrufe • vor 6 Monaten

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 Aufrufe • vor 4 Monaten

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 Aufrufe • vor 5 Monaten

*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 Aufrufe • vor 9 Monaten