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Yeah $TIG is flying under the radar An open protocol for algorithm development listening to John explain how algorithms can optimise & help you extract maximum utility from any given chip DeepMind, OpenAI, Anthropic, they're all racing to own the algorithmic layer if they win, you pay a tax...

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Jack Ai-Leung

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37,469 görüntüleme • 23 gün önce •via X (Twitter)

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We demonstrated our AI-agent framework for massively collaborative algorithm discovery for the first time yesterday Using the TIG Vehicle Routing Challenge (collaboration with Steven Diamond) The TIG team had the pleasure of presenting to some of the biggest players in the AI-algorithm discovery space They learned they can mine for algorithms, monetise the outputs through TIG, while keeping the algorithms open for others to build on and improve Minds were blown Details to follow
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We demonstrated our AI-agent framework for massively collaborative algorithm discovery for the first time yesterday Using the TIG Vehicle Routing Challenge (collaboration with Steven Diamond) The TIG team had the pleasure of presenting to some of the biggest players in the AI-algorithm discovery space They learned they can mine for algorithms, monetise the outputs through TIG, while keeping the algorithms open for others to build on and improve Minds were blown Details to follow

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This is why $TIG is so important 🔵 "We have got to acknowledge that most of the advances in AI came out of algorithm advances, not just the raw hardware" ⚫️ "The only way to really get 10x or 100x leaps is to fundamentally change the algorithm" Not just anyone's opinion.. thats from CEO of NVIDIA Jensen Huang Algorithms are where the real breakthroughs come from.. The Innovation Game (𝔦, 𝔦) is building the incentive layer to discover them.. at scale, in the open, with the best minds competing to push the frontier forward $TIG
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This is why $TIG is so important 🔵 "We have got to acknowledge that most of the advances in AI came out of algorithm advances, not just the raw hardware" ⚫️ "The only way to really get 10x or 100x leaps is to fundamentally change the algorithm" Not just anyone's opinion.. thats from CEO of NVIDIA Jensen Huang Algorithms are where the real breakthroughs come from.. The Innovation Game (𝔦, 𝔦) is building the incentive layer to discover them.. at scale, in the open, with the best minds competing to push the frontier forward $TIG

𝗗𝗥𝗘𝗔𝗗 𝗕𝗢𝗡𝗚𝗢

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DAILY RECAP FOR THE STOCK MARKET. APRIL 13TH, 2026. I usually post my daily recaps on YouTube but if people would want to listen on X then happy to post it here as well: Today's recap focuses on all the current updates we have on negotiations between Iran and the US, why Trump may begin pivoting back to the affordability narrative, and why SaaS could be in for a rebound. TLDR...we might be back. 00:00 - Intro 00:47 - Iran Negotiations 09:30 - DoorDash Driver with Trump 13:50 - Software Rebound 18:30 - Oil selloff 19:50 - Macro Updates

amit

58,915 görüntüleme • 1 ay önce

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Nathan Nathan Lambert sits down with Xeophon, our open model analyst to get spicy into debates of which labs won and lost momentum in open models of 2025. Tbt to Reflection 70B, Huawei repackaging someone else's model as their own, the fall of Llama -- no drama is left unturned. We also dig into the nuances that we didn't get to in our post, predict GPT-OSS 2, the American v. China balance at the end of 2026, and many other fun topics. Enjoy & let us know if we should do more of this. 00:00 Intro & The Tier List 02:30 The Case for IBM & NVIDIA 06:25 Everyone's Coding Assistants 08:00 DeepSeek FUD (Fear, Uncertainty, Doubt) 11:53 Xiaomi’s Late Entry 14:40 MiniMax & The "Chinese Playbook" 17:20 The Licensing Shift 18:50 What is Microsoft Phi doing? 21:20 The Reflection 70B Controversy 23:30 TNG & Model Surgery 26:00 Where is Apple? 28:00 2026 Predictions 31:00 Wrap Up

Interconnects

14,833 görüntüleme • 5 ay önce

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Last Friday we did an interview with Aaron van Wirdum Bitcoin Eindhoven. There were quite some nice questions and there were interesting insights to share. Hope you all enjoy it. 00:00 thank you Stijn, Jeffrey, Ramon, Roy 00:30 introduction Aaron 3:00 coin courant 8:00 how did you find bitcoin? 10:28 free Ross 11:00 why are you still interested in bitcoin? 12:00 how did you find the cypherpunks? 14:30 is there still a cypherpunk mailing list 15:30 it took you six years to write the book? 17:00 where did you write the book and where are you planning to live? 18:00 how do you see the next 15 years unfold? 23:00 opening quote of the book, Hayek 27:00 extropian movement 33:00 freezing yourself? 36:00 David chaum, democracy or dictatorship? 38:00 Hong Kong protests 40:00 tim may, government control and tax 43:00 hall finney VS Tim may, the moderate vs the radical 45:00 the goal of writing the book 47:00 Nick szabo e money and the anonymous contributer 49:00 who is Satoshi? 50:00 Satoshi and the growth of bitcoin 53:00 questions

₿dG

24,326 görüntüleme • 2 yıl önce

Taking a Waymo is one of the most delightful new product experiences of the past few years, and they're now ramping to considerable scale (500,000 rides/week). I was excited to host co-CEO Dmitri Dolgov on Cheeky Pint. He’s been working on self-driving since the very beginning, and I got to ask all my questions about how Waymo works underneath the hood. 00:00:22 Russia 00:02:51 Waymo architecture 00:09:59 Why now? 00:19:46 Driving nuance 00:29:37 Stripe Agentic Commerce Suite 00:30:17 Hardware 00:40:20 Emergent behavior 00:46:36 Scaling 00:57:56 Google
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Taking a Waymo is one of the most delightful new product experiences of the past few years, and they're now ramping to considerable scale (500,000 rides/week). I was excited to host co-CEO Dmitri Dolgov on Cheeky Pint. He’s been working on self-driving since the very beginning, and I got to ask all my questions about how Waymo works underneath the hood. 00:00:22 Russia 00:02:51 Waymo architecture 00:09:59 Why now? 00:19:46 Driving nuance 00:29:37 Stripe Agentic Commerce Suite 00:30:17 Hardware 00:40:20 Emergent behavior 00:46:36 Scaling 00:57:56 Google

John Collison

701,401 görüntüleme • 2 ay önce

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)
2:35:26

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)

Rob Wiblin

64,495 görüntüleme • 1 ay önce

John "Tig" Tiegen reflects on the final mortar attack in Benghazi as we mark the 13th anniversary today. Watch the full now. John “TIG” Tiegen
0:55

John "Tig" Tiegen reflects on the final mortar attack in Benghazi as we mark the 13th anniversary today. Watch the full now. John “TIG” Tiegen

Shawn Ryan Show

204,418 görüntüleme • 8 ay önce

AGI is coming. Reid Hoffman (Reid Hoffman) just wrote the book on how to prepare. According to Reid, every major tech breakthrough (the written word, the printing press, the telephone) triggered mass fear. But, contrary to our worries, new technology tends to enhance human agency—even more so, if you know how to use it well. Reid is the cofounder of LinkedIn, Inflection AI, and Manas, a partner at Greylock Partners, an award-winning podcaster, and an early backer and board member of OpenAI. We spent an hour talking about how to develop a compass for navigating AGI. Here are a few takeaways: - Our sense of human agency is not just about external control but an internal stance—how we approach uncertainty & new tech is crucial - In new technology waves, NO blueprint or plan will have the right answers. Instead, adapting to new technology requires broad access, an experimental mindset, and flexibility - In an AGI world most jobs will transform, not disappear—and how you can prepare with hands-on trial and error - How certain social norms and ethics should change as AGI changes the landscape—like individual access to personal data - Why now may be finally be the era where quantified self tools become valuable …and more, including everything in his new book Superagency, out this week. It was a pleasure to have him on the show for a second time. This is a must-watch for anyone who wants to help build a more human future with AI. Watch below! Timestamps: Introduction: 00:01:29 Patterns in how we’ve historically adopted technology: 00:02:50 Why humans have typically been fearful of new technologies: 00:07:02 How Reid developed his own sense of agency: 00:13:25 The way Reid thinks about making investment decisions: 00:20:08 AI as a “techno-humanist” compass: 00:29:40 How to prepare yourself for the way AI will change knowledge work: 00:35:30 Why equitable access to AI is important: 00:41:39 Reid’s take on why private commons will be beneficial for society: 00:45:15 How AI is making Silicon Valley’s conception of the “quantified self” a reality: 00:47:23 The shift from symbolic to sub-symbolic AI mirrors how we understand intelligence: 00:52:14 Reid’s new book, Superagency: 01:03:29
1:09:23

AGI is coming. Reid Hoffman (Reid Hoffman) just wrote the book on how to prepare. According to Reid, every major tech breakthrough (the written word, the printing press, the telephone) triggered mass fear. But, contrary to our worries, new technology tends to enhance human agency—even more so, if you know how to use it well. Reid is the cofounder of LinkedIn, Inflection AI, and Manas, a partner at Greylock Partners, an award-winning podcaster, and an early backer and board member of OpenAI. We spent an hour talking about how to develop a compass for navigating AGI. Here are a few takeaways: - Our sense of human agency is not just about external control but an internal stance—how we approach uncertainty & new tech is crucial - In new technology waves, NO blueprint or plan will have the right answers. Instead, adapting to new technology requires broad access, an experimental mindset, and flexibility - In an AGI world most jobs will transform, not disappear—and how you can prepare with hands-on trial and error - How certain social norms and ethics should change as AGI changes the landscape—like individual access to personal data - Why now may be finally be the era where quantified self tools become valuable …and more, including everything in his new book Superagency, out this week. It was a pleasure to have him on the show for a second time. This is a must-watch for anyone who wants to help build a more human future with AI. Watch below! Timestamps: Introduction: 00:01:29 Patterns in how we’ve historically adopted technology: 00:02:50 Why humans have typically been fearful of new technologies: 00:07:02 How Reid developed his own sense of agency: 00:13:25 The way Reid thinks about making investment decisions: 00:20:08 AI as a “techno-humanist” compass: 00:29:40 How to prepare yourself for the way AI will change knowledge work: 00:35:30 Why equitable access to AI is important: 00:41:39 Reid’s take on why private commons will be beneficial for society: 00:45:15 How AI is making Silicon Valley’s conception of the “quantified self” a reality: 00:47:23 The shift from symbolic to sub-symbolic AI mirrors how we understand intelligence: 00:52:14 Reid’s new book, Superagency: 01:03:29

Dan Shipper 📧

47,209 görüntüleme • 1 yıl önce

What does it take to build AI for scientific discovery? 🧠 To celebrate 10 years of AlphaGo, Thore Graepel and Pushmeet Kohli joined Hannah Fry on our podcast to discuss how mastering games has paved the way for it to help solve more complex problems. ↓ 00:00 The AlphaGo match 02:15 Why Go? 10:58 Lee Sedol vs AlphaGo 14:58 Move 37 20:55 Move 78 24:38 Reaction from the Go community 30:45 Never before seen footage 32:05 AlphaGo to protein folding 34:00 Matrix multiplication 38:00 AlphaGo and algorithmic discovery 41:40 How to verify new discoveries 47:38 Role of mathematicians 51:43 Would we be here without AlphaGo?
53:47

What does it take to build AI for scientific discovery? 🧠 To celebrate 10 years of AlphaGo, Thore Graepel and Pushmeet Kohli joined Hannah Fry on our podcast to discuss how mastering games has paved the way for it to help solve more complex problems. ↓ 00:00 The AlphaGo match 02:15 Why Go? 10:58 Lee Sedol vs AlphaGo 14:58 Move 37 20:55 Move 78 24:38 Reaction from the Go community 30:45 Never before seen footage 32:05 AlphaGo to protein folding 34:00 Matrix multiplication 38:00 AlphaGo and algorithmic discovery 41:40 How to verify new discoveries 47:38 Role of mathematicians 51:43 Would we be here without AlphaGo?

Google DeepMind

111,372 görüntüleme • 2 ay önce

Michael Bolin (Michael Bolin) is the tech lead of the Codex open source repository at OpenAI and formerly a distinguished eng (E9) at Meta. I asked him for all the details on his career story and how he uses Codex for max benefit. Timestamps: 00:00:00 - Intro 00:00:56 - Chickenfoot 00:02:45 - Working at Google 00:06:34 - Overhauling Facebook's build system 00:16:36 - Rewriting Facebook's IDE 00:26:01 - Struggles after Principal Eng (E8) promo 00:28:39 - Building a virtual filesystem for Facebook 00:35:47 - Delayed Distinguished promo (E9) and learnings 00:39:56 - Joining OpenAI 00:43:05 - Research-led vs engineering-led cultures 00:44:53 - The story behind Codex 00:51:00 - How he uses Codex 00:57:00 - Why Codex's harness is open source 00:59:50 - Top technical book recommendations 01:05:02 - Why deep technical skills are still valuable (for now) 01:11:07 - How to start projects well 01:14:27 - Advice on writing better and career planning 01:17:06 - Advice for his younger self 01:19:10 - Outro He was a dream guest of mine and I'm excited to share his story with you all! Other places to watch: • YouTube: • Spotify: • Apple Podcasts: • Transcript:
1:20:03

Michael Bolin (Michael Bolin) is the tech lead of the Codex open source repository at OpenAI and formerly a distinguished eng (E9) at Meta. I asked him for all the details on his career story and how he uses Codex for max benefit. Timestamps: 00:00:00 - Intro 00:00:56 - Chickenfoot 00:02:45 - Working at Google 00:06:34 - Overhauling Facebook's build system 00:16:36 - Rewriting Facebook's IDE 00:26:01 - Struggles after Principal Eng (E8) promo 00:28:39 - Building a virtual filesystem for Facebook 00:35:47 - Delayed Distinguished promo (E9) and learnings 00:39:56 - Joining OpenAI 00:43:05 - Research-led vs engineering-led cultures 00:44:53 - The story behind Codex 00:51:00 - How he uses Codex 00:57:00 - Why Codex's harness is open source 00:59:50 - Top technical book recommendations 01:05:02 - Why deep technical skills are still valuable (for now) 01:11:07 - How to start projects well 01:14:27 - Advice on writing better and career planning 01:17:06 - Advice for his younger self 01:19:10 - Outro He was a dream guest of mine and I'm excited to share his story with you all! Other places to watch: • YouTube: • Spotify: • Apple Podcasts: • Transcript:

Ryan Peterman

119,407 görüntüleme • 3 ay önce