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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...

38,463 görüntüleme • 3 ay önce •via X (Twitter)

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From a Thai prison cell to a fintech empire processing $1.6B in international payments across 40+ banks and 250M+ users. Jonathan Low (Jonathan Low). Forbes 40 Under 40. Author of "Cell to CEO." We covered prison, banking for AI agents, RWA projects, the future of crypto in banking, vibe coding for trading, and the businesses that win the next 5 years. ⏱ Timestamps: 00:00:00 — Teaser 00:00:46 — Who is Jonathan Low 00:01:35 — What Jonathan's life was like before prison 00:02:13 — How and why Jonathan ended up in prison 00:02:57 — Prison conditions: expectations vs reality 00:07:33 — Prison became the greatest blessing 00:09:12 — How the entrepreneurial journey began after prison 00:10:34 — Why social capital matters 00:11:06 — Launched own club and took it to the top in 3 months 00:12:09 — Built an Axie Infinity gaming guild during COVID 00:13:49 — The beginning of the BipTap Group journey 00:17:23 — How Jonathan built his own banking system 00:20:07 — How to get a crypto card 00:22:03 — How much it costs to launch a white-label solution with BipTap 00:22:58 — Banking for AI agents 00:25:57 — How to build an RWA project 00:27:45 — Future of cryptocurrencies in banking 00:32:08 — The business verticals within Empire Group 00:32:48 — What Jonathan invests his money in 00:33:54 — How relationships with regulators are built 00:34:43 — Implementing AI in business 00:36:28 — Vibe coding in trading 00:40:06 — Advice for first-time founders 00:43:36 — From construction to trading: Ruslan Khairullin's journey 00:44:57 — Inner peace: why calmness is essential for founders 00:50:39 — Work-life balance for entrepreneurs 00:53:47 — $1.5 million in 24 hours on TST coin 00:55:11 — The best way to capture a market 00:55:57 — Banking for nations Watch the full conversation and let me know which part you liked the most 👇

Ruslan Khairullin

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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:

Shane Parrish

450,952 görüntüleme • 4 ay önce

Claude Code cracked something open for us Every 🧱. Now I ship to codebases I barely know, every feature we ship makes the next one easier, and non-technical members of the team use the terminal. I’m genuinely grateful. So I brought its creators, Cat Wu (cat) and Boris Cherny (Boris Cherny) from Anthropic, on AI & I to say thank you—and to talk about everything they’ve learned from building Claude Code. We get into: • The workflows Anthropic’s smartest engineers use to push Claude Code to its limits. Why they pit subagents against each other to get cleaner results, how they turn past code into leverage, and the slash commands and MCPs they rely on most. • The product lessons behind one of the most loved AI agents in the world. How the team balances simplicity and power—building a tool that anyone can use, but that experts can bend to their will—and their philosophy of “unshipping,” or cutting back whenever there’s a simpler, more intuitive path to user intent. • A peek into the future of coding with AI. The new form factors they’re experimenting with to make Claude Code more autonomous, more reliable, and more accessible to non-technical users This is a must-watch for anyone—both technical and non-technical—who wants to learn how to use Claude Code like the people who built it. Watch below! Timestamps: Introduction: 00:01:26 Claude Code’s origin story: 00:02:25 How Anthropic dogfoods Claude Code: 00:07:03 Boris and Cat’s favorite slash commands: 00:14:06 How Boris uses Claude Code to plan feature development: 00:15:49 Everything Anthropic has learned about using sub-agents well: 00:21:53 Use Claude Code to turn past code into leverage: 00:26:16 The product decisions for building an agent that’s simple and powerful: 00:33:14 Making Claude Code accessible to the non-technical user: 00:36:38 The next form factor for coding with AI: 00:45:12

Dan Shipper 📧

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This week, Colin M. McDonald joins us on The Arena. We discuss the Trump Administration’s fight against fraud, U.S. Department of Justice’s brand-new Fraud Division, and the most shocking fraud schemes DOJ has uncovered. 00:00 - Intro 00:36 - Colin McDonald Joins the Show 00:59 - Who Are You and What Do You Do? 02:22 - Why Public Service? 03:25 - Why Be a Prosecutor? 05:50 - What Does a Prosecutor Actually Do? 09:21 - No Prosecution Without Law Enforcement 11:08 - Relentless: Cases That Last Years 12:02 - Turning to Fraud Enforcement 13:06 - Why No One Tackled Fraud Before 13:41 - The Old Binary Choice: Violent Crime vs. Fraud 15:21 - Tallying the True Cost of Fraud 17:10 - Wasteful Spending vs. Outright Theft 19:17 - The Goal Is Deterrence 21:25 - Building Roots, Not a Potted Plant 23:18 - No Fraud Too Big, No Fraud Too Small 25:28 - Fraud Wounds the Soul, Not Just the Wallet 27:36 - Quarterbacking the Fight 28:26 - One Front Door to DOJ 29:38 - A 500-Person Division in Two Weeks 31:13 - A Career Home for Young Lawyers 33:26 - The Ripple Effects of Unchecked Crime 34:24 - Changing the Incentive Structure 35:23 - Which Statutes Do You Enforce? 36:05 - Healthcare Fraud: The Biggest Target 37:00 - The Student Athlete Who Died 38:03 - The Skin Graft Scheme 39:28 - Fraud Robs Dignity, Too 40:49 - Benefit Programs and the Public Trust Section 42:06 - Global Trade and Commerce Enforcement 43:43 - The Many Inspectors General Offices 45:05 - The Most Shocking Cases 45:56 - “Everybody’s Doing This” 46:48 - The Home Care Scam 48:25 - Billing Medicaid From Egypt and Prison 50:22 - Minnesota’s Looted Housing Program 51:27 - Clearing the Decks for the Truly Needy 52:44 - DOJ Is Here to Help 54:14 - Outro

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446,720 görüntüleme • 21 gün 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)

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Dan Shipper 📧

47,229 görüntüleme • 1 yıl önce

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Chris Powers

64,474 görüntüleme • 3 yıl önce

A Physics That Could Finally Unify Reality (Part 2/2) - James Ellias, DemystifySci #385 A quiet tremor sounds beneath the floorboards of physics, the still-living heartbeat of the forgotten search for the hidden substance that carries every wave and whisper of the universe. We walk through the fog of equations and theories of fundamental physics with James Ellias of James Ellias, and ask if there is a material truth lies beneath the symbols, or if we have to be satisfied with the short-sighted vision of mathematics alone. 00:00 Go! 00:04:37 Central Equations in Physics 00:08:20 The Importance of a Medium in Physics 00:10:00 Empowerment Through Understanding Physics 00:12:36 Rationality and Truth in Society 00:16:31 Existence vs Consciousness 00:20:30 Discussion on Existence and Consciousness 00:24:25 Role of Imagination in Existence 00:28:10 Properties and Entities in Physics 00:30:15 The Nature of Aether and Physical Mediums 00:36:08 Clarity and Understanding in Physics 00:40:26 Discussion on Force and Aether 00:44:57 Relationship of Entities and Actions 00:49:30 Theoretical Framework for Aether 00:58:04 Exploration of Aether Theories 01:00:40 Importance of Conciseness in Communication 01:02:00 Understanding Physics Before Proposing Hypotheses 01:05:15 Reevaluation of Flawed Theories 01:09:35 The Evolution of Key Physics Concepts 01:15:19 Context-Specific Nature of Constants 01:19:43 Historical Context in Physics 01:21:36 The Nature of Electrons 01:25:53 J.J. Thomson’s Evolving Perspective 01:29:24 Upcoming Work and Philosophical Frameworks 01:32:11 Collaborations

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