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First Principles Ep. 3 with Leslie Lamport Before blockchains could reach consensus, Leslie Lamport had to define what agreement even meant when computers fail, lie, or disappear. Hosted by Tim Roughgarden with Ittai Abraham. 00:00 The problem every blockchain is built to solve 02:52 Why concurrent systems are surprisingly... show more

a16z crypto

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First Principles Ep. 1 Bitcoin gets credited with inventing trustless consensus. It didn’t. The problem was named in the 1980s, and it took the research world years to even notice Satoshi had solved it. A new series hosted by Tim Roughgarden, with Ittai Abraham. 00:00 Introduction to First Principles: The Scientific Roots of Blockchain Technology 00:56 Why consensus matters for blockchains 02:30 Byzantine agreement: The old computer science problem Bitcoin made practical 04:34 Blockchains as a shared system of record: State machine replication and blockchain state 06:41 How two research worlds — distributed computing and crypto — began to converge 07:49 Proof of work vs. proof of stake 09:27 Why Ethereum’s move to proof-of-stake took years 11:08 When crypto rediscovered decades of distributed systems research 12:49 Throughput, latency, and modern consensus design 14:05 DAG-based protocols and faster blockchains 15:25 Peace time vs. war time: why modern blockchains need two modes 16:47 Theory, practice, and the future of blockchain research

First Principles Ep. 1 Bitcoin gets credited with inventing trustless consensus. It didn’t. The problem was named in the 1980s, and it took the research world years to even notice Satoshi had solved it. A new series hosted by Tim Roughgarden, with Ittai Abraham. 00:00 Introduction to First Principles: The Scientific Roots of Blockchain Technology 00:56 Why consensus matters for blockchains 02:30 Byzantine agreement: The old computer science problem Bitcoin made practical 04:34 Blockchains as a shared system of record: State machine replication and blockchain state 06:41 How two research worlds — distributed computing and crypto — began to converge 07:49 Proof of work vs. proof of stake 09:27 Why Ethereum’s move to proof-of-stake took years 11:08 When crypto rediscovered decades of distributed systems research 12:49 Throughput, latency, and modern consensus design 14:05 DAG-based protocols and faster blockchains 15:25 Peace time vs. war time: why modern blockchains need two modes 16:47 Theory, practice, and the future of blockchain research

a16z crypto

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First Principles Ep. 2 w/ Barbara Liskov Every blockchain alive today leans on replication ideas worked out in the 1980s, by a Turing Award winner who wasn’t thinking about how it might apply to money at all. Hosted by Tim Roughgarden with Ittai Abraham. 00:00 How do systems stay reliable when parts fail? 01:18 Barbara Liskov’s path from programming languages to distributed systems 05:45 Why modularity is “everything” 07:22 The replication problem: keeping data available across many machines 09:58 Viewstamped replication and the “ledger” before blockchains 16:32 Why good research starts with what you don’t understand 18:10 Leslie Lamport, Paxos, and the inevitability of ideas in the right time, in the right place 21:48 Practical Byzantine Fault Tolerance: what changes when replicas can lie 19:35 How PBFT bridged theory and practical systems 22:38 Why you should never trust an individual replica 28:39 Why blockchains are state machine replication in the wild 31:27 AI, verification, and the future of computer science

First Principles Ep. 2 w/ Barbara Liskov Every blockchain alive today leans on replication ideas worked out in the 1980s, by a Turing Award winner who wasn’t thinking about how it might apply to money at all. Hosted by Tim Roughgarden with Ittai Abraham. 00:00 How do systems stay reliable when parts fail? 01:18 Barbara Liskov’s path from programming languages to distributed systems 05:45 Why modularity is “everything” 07:22 The replication problem: keeping data available across many machines 09:58 Viewstamped replication and the “ledger” before blockchains 16:32 Why good research starts with what you don’t understand 18:10 Leslie Lamport, Paxos, and the inevitability of ideas in the right time, in the right place 21:48 Practical Byzantine Fault Tolerance: what changes when replicas can lie 19:35 How PBFT bridged theory and practical systems 22:38 Why you should never trust an individual replica 28:39 Why blockchains are state machine replication in the wild 31:27 AI, verification, and the future of computer science

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Leslie Lamport won a Turing award for his fundamental contributions to distributed systems. For instance, he invented the Paxos consensus algorithm that is a critical component of many distributed systems today. I interviewed him about his work and career. We discussed: • Why he never considered himself smart • The stories behind Paxos and Byzantine Generals Problem • Experiences working with Dijkstra • Paxos vs Raft Algorithms • How to improve your thinking Where to watch: • YouTube: • Spotify: • Apple Podcasts: • Transcript:

Leslie Lamport won a Turing award for his fundamental contributions to distributed systems. For instance, he invented the Paxos consensus algorithm that is a critical component of many distributed systems today. I interviewed him about his work and career. We discussed: • Why he never considered himself smart • The stories behind Paxos and Byzantine Generals Problem • Experiences working with Dijkstra • Paxos vs Raft Algorithms • How to improve your thinking Where to watch: • YouTube: • Spotify: • Apple Podcasts: • Transcript:

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Y Combinator

98,369 views • 7 months ago

Enough of the fluff. Time for some real advice. Launch your GPT 5.6 Sol Pro. Use the following prompt (feel free to swap the second and third lines for something of your choice): Prove for me that the group of rational points on the elliptic curve (y^2=x^3+17) is finitely generated. I want to tailor the general proof to this particular case to narrow down my understanding to a particular case. Write down a full mathematical proof, step by step, in Leslie Lamport style. Unpack all the definitions and notions so that it is fully self-contained. An example here: Notice how Sol is spawning all the subagents to complete the task. What is "Leslie Lamport style"? It's a technique where you split your proof into short, one-line sentences (claims) that are easy to justify. In a situation where your proof is not a 1-to-1 translation of, e.g., Python code, the Leslie Lamport-style proof can be treated as such. It tremendously improves the possibility of verifying each claim and helps the model structure the arguments in a very clear way. Leslie Lamport, a legendary mathematician and computer scientist, was a promoter of this way of structuring proofs. Partly because of that, he created an extension of Donald Knuth’s typesetting system TeX called LaTeX. Here you can read about "Leslie Lamport-style proofs":

Enough of the fluff. Time for some real advice. Launch your GPT 5.6 Sol Pro. Use the following prompt (feel free to swap the second and third lines for something of your choice): Prove for me that the group of rational points on the elliptic curve (y^2=x^3+17) is finitely generated. I want to tailor the general proof to this particular case to narrow down my understanding to a particular case. Write down a full mathematical proof, step by step, in Leslie Lamport style. Unpack all the definitions and notions so that it is fully self-contained. An example here: Notice how Sol is spawning all the subagents to complete the task. What is "Leslie Lamport style"? It's a technique where you split your proof into short, one-line sentences (claims) that are easy to justify. In a situation where your proof is not a 1-to-1 translation of, e.g., Python code, the Leslie Lamport-style proof can be treated as such. It tremendously improves the possibility of verifying each claim and helps the model structure the arguments in a very clear way. Leslie Lamport, a legendary mathematician and computer scientist, was a promoter of this way of structuring proofs. Partly because of that, he created an extension of Donald Knuth’s typesetting system TeX called LaTeX. Here you can read about "Leslie Lamport-style proofs":

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It started on a napkin. Turing Award winner Leslie Lamport on sketching a piece of modern cryptography in a Berkeley coffee shop, and how the fault model behind every blockchain came from research on keeping airplanes in the sky: "I was one of the few people in the world who happened to have heard about digital signatures… So I just wrote, literally on a napkin, a simple algorithm for writing digital signatures." "They had to assume that it could do anything, and therefore they were faced with the Byzantine generals problem… Once hackers started appearing on the scene, it was obvious that you really did need Byzantine fault tolerance."

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27,674 views • 5 months ago

My full conversation with Yat Siu, Chairman and Co-Founder of Animoca Brands "The people who win are the ones who learn to be divergent." 00:00:00 - Intro 00:01:43 - Growing Up in Austria: Music, Coding & Being an Outsider 00:07:40 - The Iron Curtain, Capitalism & What Shaped Yat's Worldview 00:14:53 - Building a Business from the Wreckage 00:18:43 - 3D Graphics, Early VR & the Metaverse Before Its Time 00:24:00 - Dot-Com Boom, Outblaze & Selling to IBM 00:28:17 - MUDs, Virtual Swords & the Origins of Digital Ownership 00:33:59 - The Animoca Origin Story 00:38:29 - Getting Deplatformed by Apple & Pivoting to Android 00:40:54 - CryptoKitties, NFTs & Going All-In During Crypto Winter 00:47:50 - Investing in De-Extinction: Colossal Biosciences & Real-Life Jurassic Park 00:50:41 - Agents, Tools & the 1% Problem 00:56:37 - Why Tokens & Blockchain Matter for AI 01:04:30 - Advice for the Young: Embrace Uncertainty & Be Divergent

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Y Combinator

151,332 views • 3 months ago

Full interview with the founder and CEO of Propr Lou sits down with me to discuss Hyperliquid and the future of prop trading Timestamps: 00:00:00 Intro 00:00:16 Meet Louis: from TradFi to crypto 00:01:52 How the crypto market has changed 00:03:49 Why build on Hyperliquid 00:06:52 How propr got started 00:08:40 Scaling a lean team (and the grind) 00:12:24 What a prop firm actually is 00:15:33 Agents, bots, and the future of trading 00:18:32 What Louis is most proud of 00:21:02 Thinking about the token and TGE 00:24:14 Where propr goes in 5 years 00:25:57 Best and worst trades 00:27:50 Life outside crypto + shoutouts

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ryandcrypto

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DeFi Dad ⟠ defidad.eth

21,341 views • 2 months ago

How does math research change when the cost of trying your first dumb idea goes to zero? Daniel Litt joins Greg Burnham and Anson Ho to discuss what today’s models can and can’t do in math, and how far they are from doing high-quality research. 0:00:00 What's the hardest math problem AI can solve today? 00:16:08 How helpful are today’s AI models for math research? 00:23:36 Junk papers, LLM-generated proofs, and the refereeing crisis 00:27:21 AI enables searching through problems at scale 00:33:49 When will AI be good enough to publish in top math journals? 00:42:15 What are the returns to intelligence? 00:59:50 Will AI solve Millennium problems? 01:11:54 Is math full of low-hanging fruit? 01:18:47 How Daniel has adapted his professional life to AI progress 01:25:28 What do AI math benchmarks actually measure? 01:33:05 Designing the Open Problems benchmark 01:56:35 Do mathematicians believe heuristic arguments about conjectures? 02:01:24 What if FrontierMath: Open Problems gets solved? 02:06:53 Is AI on the cusp of accelerating math progress?

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Epoch AI

178,703 views • 5 months ago

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:

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Shane Parrish

450,952 views • 2 months ago

Diffusion is the foundational ML framework behind state-of-the-art AI image and video generation, including Sora, Midjourney and Google Veo. In this episode of Decoded, Ankit Gupta sits down with Francois Chaubard to discuss how diffusion works, walk through a code sample, and explain why everyone training models should understand it. 00:00 Intro 00:33 What is diffusion? 02:50 What are applications of diffusion today? 04:06 Key innovations 07:01 Code examples 19:25 The "squint test" 22:27 Other areas diffusion is widely accessible 24:49 Outro

Diffusion is the foundational ML framework behind state-of-the-art AI image and video generation, including Sora, Midjourney and Google Veo. In this episode of Decoded, Ankit Gupta sits down with Francois Chaubard to discuss how diffusion works, walk through a code sample, and explain why everyone training models should understand it. 00:00 Intro 00:33 What is diffusion? 02:50 What are applications of diffusion today? 04:06 Key innovations 07:01 Code examples 19:25 The "squint test" 22:27 Other areas diffusion is widely accessible 24:49 Outro

Y Combinator

74,604 views • 5 months ago

👁‍🗨Not Dead Yet | S1E2 — Eli Ben-Sasson At long last, privacy is in vogue, particularly in crypto. Running parallel to the rise in interest has been the rise of Zcash 🛡️, now even with $ZEC treasury companies forming like Cypherpunk. In episode 2 of Not Dead Yet, Robert Baggs speaks with Eli Ben-Sasson | Starknet.io. Eli is a long-time researcher of zero-knowledge proofs (the technology that underpins Zcash), the co-inventor of Zerocash protocol and ZK_STARKS, and the co-founder and CEO of StarkWare (BTCFi arc) 🥷. Timestamps: 00:00 – Intro 01:23 – Why is privacy important to you? 03:24 – Why is a lack of privacy holding blockchains back? 04:05 – Privacy is normal, so why isn’t it already normal in crypto? 06:10 – The dangers of CBDCs 09:00 – “If you have nothing to hide, you have nothing to fear” 12:53 – What are Zero Knowledge Proofs (ZKPs) and why are they important? 19:26 – What is Zcash and how does it work? 21:14 – Why is privacy suddenly trending? 23:15 – What happens if Bitcoin doesn’t address its lack of privacy? 28:03 – Can we stop a dystopian future?

👁‍🗨Not Dead Yet | S1E2 — Eli Ben-Sasson At long last, privacy is in vogue, particularly in crypto. Running parallel to the rise in interest has been the rise of Zcash 🛡️, now even with $ZEC treasury companies forming like Cypherpunk. In episode 2 of Not Dead Yet, Robert Baggs speaks with Eli Ben-Sasson | Starknet.io. Eli is a long-time researcher of zero-knowledge proofs (the technology that underpins Zcash), the co-inventor of Zerocash protocol and ZK_STARKS, and the co-founder and CEO of StarkWare (BTCFi arc) 🥷. Timestamps: 00:00 – Intro 01:23 – Why is privacy important to you? 03:24 – Why is a lack of privacy holding blockchains back? 04:05 – Privacy is normal, so why isn’t it already normal in crypto? 06:10 – The dangers of CBDCs 09:00 – “If you have nothing to hide, you have nothing to fear” 12:53 – What are Zero Knowledge Proofs (ZKPs) and why are they important? 19:26 – What is Zcash and how does it work? 21:14 – Why is privacy suddenly trending? 23:15 – What happens if Bitcoin doesn’t address its lack of privacy? 28:03 – Can we stop a dystopian future?

Cointelegraph

120,257 views • 6 months ago

I sat down with Jordi Visser to discuss why bitcoin is the purest AI trade available in the market, breakdown of energy infrastructure, how to invest amid the government shutdown, the Fed/interest rates, and why prediction markets and tokenization are two big themes moving forward. Enjoy! YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 1:04 - Why bitcoin is the purest AI trade 11:43 - The impact of new energy infrastructure 16:53 - How Base Power is building a decentralized grid and other opportunities for investors 23:49 - Why retail spots themes before institutions 32:33 - Retail orange-pilling each other in public 37:25 - What is the impact of government shutdown on financial markets? 40:25 - The rise of prediction markets and tokenization 49:28 - Where to follow Jordi on the internet

I sat down with Jordi Visser to discuss why bitcoin is the purest AI trade available in the market, breakdown of energy infrastructure, how to invest amid the government shutdown, the Fed/interest rates, and why prediction markets and tokenization are two big themes moving forward. Enjoy! YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 1:04 - Why bitcoin is the purest AI trade 11:43 - The impact of new energy infrastructure 16:53 - How Base Power is building a decentralized grid and other opportunities for investors 23:49 - Why retail spots themes before institutions 32:33 - Retail orange-pilling each other in public 37:25 - What is the impact of government shutdown on financial markets? 40:25 - The rise of prediction markets and tokenization 49:28 - Where to follow Jordi on the internet

Anthony Pompliano 🌪

110,012 views • 9 months ago

Vivek Ramaswamy on how good just triumphed over evil. (0:00) Donald Trump’s Overwhelming Victory (7:38) Why Did Vivek Go All-In for Trump? (20:00) What Are the Effects of Trump’s Win? (27:04) Trump’s Government Efficiency Plan (45:33) The Oncoming Collapse of Corporate Media (52:42) How Foreign Wars Are Used to Expand Government Bureaucracy (56:12) The 2020 Election Is More Unexplainable Than Ever (1:03:37) Vivek’s Friendship With Trump (1:11:03) The Left’s Mission to Divide Us (1:16:03) What Happened to Joe Biden? (1:23:55) The Significance of Elon Musk and His Future With Trump Includes paid partnerships.

Vivek Ramaswamy on how good just triumphed over evil. (0:00) Donald Trump’s Overwhelming Victory (7:38) Why Did Vivek Go All-In for Trump? (20:00) What Are the Effects of Trump’s Win? (27:04) Trump’s Government Efficiency Plan (45:33) The Oncoming Collapse of Corporate Media (52:42) How Foreign Wars Are Used to Expand Government Bureaucracy (56:12) The 2020 Election Is More Unexplainable Than Ever (1:03:37) Vivek’s Friendship With Trump (1:11:03) The Left’s Mission to Divide Us (1:16:03) What Happened to Joe Biden? (1:23:55) The Significance of Elon Musk and His Future With Trump Includes paid partnerships.

Tucker Carlson

4,512,976 views • 1 year ago

Is it time to quantize time? Everything is quantum in quantum mechanics... almost. Time remains a mysterious flowing quantity slotted in to the equations. Surprisingly, there's a model for making time quantum, with a radical consequence: the overall universe is timeless. How is it possible to experience a flow of time in a timeless universe?! Find out in my latest episode of the Quantum Foundations Podcast with Simone Rijavec, Simone Rijavec! We discuss the Page-Wootters model for the emergence of time in quantum theory, and get an insight into Simone's research on the topic. Here's how physicists are solving the problem of time in quantum theory, explained in 60 minutes: 00:00 Coming Up 00:59 Intro 01:37 Simone's journey into quantum 05:04 The problem of time in quantum theory 07:07 Relational approach to time 11:12 Quantum measurement and time 17:53 Stationary universe and the Wheeler-DeWitt equation 30:18 Multiverse & block universe 31:11 How is time flowing? 34:20 Problems with wave-function collapse 36:11 What are clocks? 37:48 Resolution of time-steps 41:47 Debates on the timeless approach 45:08 Interactions with clocks 51:05 Recap 57:39 Making time fully quantum 1:00:43 Behind the Scenes

Is it time to quantize time? Everything is quantum in quantum mechanics... almost. Time remains a mysterious flowing quantity slotted in to the equations. Surprisingly, there's a model for making time quantum, with a radical consequence: the overall universe is timeless. How is it possible to experience a flow of time in a timeless universe?! Find out in my latest episode of the Quantum Foundations Podcast with Simone Rijavec, Simone Rijavec! We discuss the Page-Wootters model for the emergence of time in quantum theory, and get an insight into Simone's research on the topic. Here's how physicists are solving the problem of time in quantum theory, explained in 60 minutes: 00:00 Coming Up 00:59 Intro 01:37 Simone's journey into quantum 05:04 The problem of time in quantum theory 07:07 Relational approach to time 11:12 Quantum measurement and time 17:53 Stationary universe and the Wheeler-DeWitt equation 30:18 Multiverse & block universe 31:11 How is time flowing? 34:20 Problems with wave-function collapse 36:11 What are clocks? 37:48 Resolution of time-steps 41:47 Debates on the timeless approach 45:08 Interactions with clocks 51:05 Recap 57:39 Making time fully quantum 1:00:43 Behind the Scenes

Dr Maria Violaris

63,981 views • 8 months ago