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One agentic workflow now does 1,000 hours of hedge fund analyst work. Aakarsh Ramchandi founded the data team @ Third Point, built screening engines @ FactSet, & now builds agentic research tools @ RavenPack. "There's gonna be a full convergence of quant and qual. Most discretionary analysts I know...

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Ethan Kho

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301,903 просмотров • 1 месяц назад •via X (Twitter)

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Ex-WorldQuant Head of Data Strategy Matt Ober on why quants don't care about the stock market, & why prediction markets become a new asset class. Matt Ober (Ex-Chief Data Scientist @ Third Point | Ex-Head of Data Strategy @ WorldQuant | Now GP @ Social Leverage) "Nobody actually really cared about the stock market. I bet if you asked people there when Nvidia was reporting, nobody had a clue." We cover: - The WorldQuant thesis: out-consume the world on data, out-manage the world on money - How a Chico State grad found the job on Craigslist — then walked into a room of PhDs - Revere, the alt-data goldmine only quants understood - Tracking CEOs' private jets to front-run M&A - WorldQuant's factory vs Third Point's fine-art boardroom - Selling data science to analysts who thought it was a joke - Why you sell beta, not alpha — alpha erodes, beta is sticky - The "degenerate economy," and why he can't hire interns anymore - Prediction markets as the next asset class — bigger than options and futures - The only edge left in an AI world: your network Thanks a ton Matt Ober for coming on the pod! Highlights: (00:00) Intro (01:18) The WorldQuant thesis - more data, more money (02:00) Building the alt-data stack (03:00) Revere - the dataset only quants understood (04:50) A finance major among PhDs (09:40) Trading datasets like stocks (10:30) Why quants don't watch the market (12:30) WorldQuant Ventures is born (16:30) Factory vs. boardroom (18:30) Data's wild west at Third Point (23:15) Tracking private jets to front-run M&A (24:20) Igor vs. Dan & the power of network (32:10) The degenerate economy (36:25) Patience & outworking everyone (38:55) Alpha vs. beta, markets & careers (46:10) Somebody's gotta sell (51:20) Prediction markets, the new asset class (53:30) The DoorDash earnings call (56:35) Going mainstream (01:03:10) The single source of all edge
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His team spent six weeks building an activist pitch. Dan Loeb found the hole with one question: Aakarsh Ramchandi, who founded the data team at Dan Loeb's Third Point: "We worked on an activist idea for a month and a half. A 45-page deck. All 26 points covered. Every base." "You sit in an office with Dan, and he's just flipping through the pages in complete silence." "He's seen this a million times. He has a fine-tuned process for looking at it." "He stops at page 26, looks at one number, and says: 'Why?'" "That was it. Your entire pitch breaks down on the one question you should have thought about and didn't." "World-class PMs have seen the pattern so many times, they can smell one tiny thing that's wrong with it." "Maybe 80% of the time, he'd point out something we had not thought about before."
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Anthropic Managed Agents Lead: "At Anthropic, >90% of our engineers are building with self-improving loops. In 4-6 months, it will be 100%. my agentic loops can run for hours without spending hundreds of dollars." in this 40-minute podcast, an Anthropic team lead reveals how to build effective agents from scratch. Agent → harness → loops → memory = modern agent This one video will replace 10 paid courses on vibe-coding. Watch it today, then explore the same setup in the article below.

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1/ New episode – Enabling User-Owned Data to Create User-Owned AI with Anna Kazlauskas from vana Host: Connor Lovely We discuss: - Why private data hasn't mattered much, until now - Building an EVM-compatible L1 for DataDAOs - $VANA tokenomics and how to get involved Timestamps: 00:00 - Introduction 01:45 - Impact of DeepSeek on AI and User Privacy 04:22 - User Awareness and Data Storage Concerns 06:28 - The Shift in AI Narrative 07:14 - Anna's Journey into User-Owned Data 10:45 - Vana's Technical Framework and Privacy 12:20 - The Need for Data Sovereignty in AI 13:27 - Building a Layer 1 Blockchain 15:51 - Data DAOs: A New Approach to Data Ownership 17:15 - Understanding Data DAOs and Their Function 22:47 - The Role of Data Ownership in Social Platforms 24:51 - Negotiating Better Terms for Data Ownership 26:40 - Bringing Data to Vana and AI Applications 28:09 - Privacy-Preserving Compute Solutions 29:22 - Tokenomics of Vana 30:55 - Data DAO Specific Tokens and Their Role 31:22 - Final Thoughts on Vana's Vision 32:30 - Where to Find More Information Watch on all platforms below ⬇️

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12,484 просмотров • 1 год назад

After many months, a lot of model releases and even more Amp releases, here it is: A new episode of Raising An Agent with Quinn Slack and Thorsten Ball. They discuss: - Why Gemini 3 might have been the inflection point, not Opus 4.5 - What changed over Christmas and why everybody talks about agents now - "The assistant is dead, long live the factory" - How codebases need to adapt for agents - Why small/local models don't give you an impression of the frontier - What does it mean for a codebase to be agent-friendly? - New things coming to Amp Enjoy! And happy hacking! Timestamps: 1:51 The Impact of Gemini 3 and Opus 4.5 5:28 Shifting Perceptions of AI Agents 10:17 Adapting Codebases for AI Efficiency 14:48 The Future of Software Development with AI Agents 19:29 Challenges and Opportunities Ahead 24:00 The Importance of Frontier Models 25:12 Adapting to AI-Driven Workflows 26:57 The Future of Coding Agents 28:07 Building an Agentic Code Base 30:22 Rethinking Development Tools 31:44 Amp's Evolution and Team Dynamics 33:00 Discord Q&A 39:18 Teasing New Features: Deep
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After many months, a lot of model releases and even more Amp releases, here it is: A new episode of Raising An Agent with Quinn Slack and Thorsten Ball. They discuss: - Why Gemini 3 might have been the inflection point, not Opus 4.5 - What changed over Christmas and why everybody talks about agents now - "The assistant is dead, long live the factory" - How codebases need to adapt for agents - Why small/local models don't give you an impression of the frontier - What does it mean for a codebase to be agent-friendly? - New things coming to Amp Enjoy! And happy hacking! Timestamps: 1:51 The Impact of Gemini 3 and Opus 4.5 5:28 Shifting Perceptions of AI Agents 10:17 Adapting Codebases for AI Efficiency 14:48 The Future of Software Development with AI Agents 19:29 Challenges and Opportunities Ahead 24:00 The Importance of Frontier Models 25:12 Adapting to AI-Driven Workflows 26:57 The Future of Coding Agents 28:07 Building an Agentic Code Base 30:22 Rethinking Development Tools 31:44 Amp's Evolution and Team Dynamics 33:00 Discord Q&A 39:18 Teasing New Features: Deep

Amp

36,150 просмотров • 5 месяцев назад

Inside Two Sigma & AQR with Bill Mann: How Early Quants Built Edge Before the Modern Tools Existed Bill Mann spent nearly 11 years across two of the world's most elite quant funds — AQR & Two Sigma — rising to Senior Vice President while building alpha models, establishing quantamental research teams, & designing the ML/AI systems that powered their forecasts. "A real edge you used to have 15 years ago was creating your own version of someone else's data." We cover: - How Two Sigma's fundamentals team built proprietary data pipelines before vendors existed - Why point-in-time databases were a secret weapon — & how look-ahead bias destroyed competitors - The crowding problem hiding inside everyone's favorite value factor - LLMs in quant research: what agents can already replace & what still requires human intuition - Why junior quants are at risk — & the one mindset that keeps senior researchers irreplaceable - How HarmoniQ Insights pivoted from advising buy-side firms to backing fintech startups with sweat equity - The New Barbarians thesis: crypto natives meeting old Wall Street, & why both sides need each other - Bill's one piece of advice for aspiring quants: build your own model, put real money behind it, learn from the losses Timestamps: 00:00 Intro 00:57 Life as a Quant at Two Sigma 02:36 Finding Edge in Fundamental Data 07:04 Creating a Creative Quant Research Culture 11:19 How LLMs Change Quantitative Trading 15:52 AI’s Impact on Junior Quant Careers 22:56 Using AI Tools for Learning 23:57 HarmoniQ Insights: Advising Fintech Startups 30:47 The New Barbarians Podcast Explained 33:26 Crypto and Market Makers vs TradFi 34:54 Career Advice for Aspiring Quants 38:46 Final Takeaways
39:09

Inside Two Sigma & AQR with Bill Mann: How Early Quants Built Edge Before the Modern Tools Existed Bill Mann spent nearly 11 years across two of the world's most elite quant funds — AQR & Two Sigma — rising to Senior Vice President while building alpha models, establishing quantamental research teams, & designing the ML/AI systems that powered their forecasts. "A real edge you used to have 15 years ago was creating your own version of someone else's data." We cover: - How Two Sigma's fundamentals team built proprietary data pipelines before vendors existed - Why point-in-time databases were a secret weapon — & how look-ahead bias destroyed competitors - The crowding problem hiding inside everyone's favorite value factor - LLMs in quant research: what agents can already replace & what still requires human intuition - Why junior quants are at risk — & the one mindset that keeps senior researchers irreplaceable - How HarmoniQ Insights pivoted from advising buy-side firms to backing fintech startups with sweat equity - The New Barbarians thesis: crypto natives meeting old Wall Street, & why both sides need each other - Bill's one piece of advice for aspiring quants: build your own model, put real money behind it, learn from the losses Timestamps: 00:00 Intro 00:57 Life as a Quant at Two Sigma 02:36 Finding Edge in Fundamental Data 07:04 Creating a Creative Quant Research Culture 11:19 How LLMs Change Quantitative Trading 15:52 AI’s Impact on Junior Quant Careers 22:56 Using AI Tools for Learning 23:57 HarmoniQ Insights: Advising Fintech Startups 30:47 The New Barbarians Podcast Explained 33:26 Crypto and Market Makers vs TradFi 34:54 Career Advice for Aspiring Quants 38:46 Final Takeaways

Ethan Kho

23,460 просмотров • 4 месяцев назад

EP:23 ft Daniele 🟧 ( Meme Quant ) Founder of Hey Anon! Discussing: Why did Wonderland fail, 1 to 10 SOL challenge, the future of AI. Timestamps: 1:45 How do you look back on the wonderland situation? 4:55 Do you believe a business can run efficiently as a DAO? 8:40 How do you avoid manipulation in DAOs? 11:40 How did you handle the tough times of Wonderland 13:50 Where is your vengeance fuelled from? 15:50 Can everyone technically win? 16:30 The memecoin challenge 20:00 Exploring the trenches 24:20 The different KOLs in memecoins 26:00 Simplify onchain data 29:30 Top 10/100 holders 37:00 Are you optimistic or pessimistic about AI 39:00 How do you see hallucinations in AI playing out? 40:20 What are you most excited about around AI?
43:05

EP:23 ft Daniele 🟧 ( Meme Quant ) Founder of Hey Anon! Discussing: Why did Wonderland fail, 1 to 10 SOL challenge, the future of AI. Timestamps: 1:45 How do you look back on the wonderland situation? 4:55 Do you believe a business can run efficiently as a DAO? 8:40 How do you avoid manipulation in DAOs? 11:40 How did you handle the tough times of Wonderland 13:50 Where is your vengeance fuelled from? 15:50 Can everyone technically win? 16:30 The memecoin challenge 20:00 Exploring the trenches 24:20 The different KOLs in memecoins 26:00 Simplify onchain data 29:30 Top 10/100 holders 37:00 Are you optimistic or pessimistic about AI 39:00 How do you see hallucinations in AI playing out? 40:20 What are you most excited about around AI?

Caleb

31,486 просмотров • 1 год назад

Amazon AI engineer just explained how to build agentic loops from scratch in under 10-minutes. this is one of the clearest explanations of AI agentic loops you’ll find anywhere. Loop + model + agent + hooks + MCP = an autonomous, self-improving system. People are paying $500 for courses that teach less than this. Watch it, then read the step-by-step guide below on building loops for your own agents.
8:29

Amazon AI engineer just explained how to build agentic loops from scratch in under 10-minutes. this is one of the clearest explanations of AI agentic loops you’ll find anywhere. Loop + model + agent + hooks + MCP = an autonomous, self-improving system. People are paying $500 for courses that teach less than this. Watch it, then read the step-by-step guide below on building loops for your own agents.

Codez

26,868 просмотров • 12 дней назад

DROPS E15: CUDIS : The Decentralized Operating System for Health Data Edison , the Founder of Cudis, closed his $20M crypto fund at Bitcoin's peak in 2021 and invested 90% of his life savings into a $349 smart ring that pays users for their health data. Timestamps 0:00 - Introduction 1:00 - Welcome to DROPS 1:38 - Believer in Ethereum? 3:11 - That Person for Solana 6:31 - What Do You Do 10:24 - Who Are You 11:05 - What Sparked Conviction 13:10 - Last Project Sold, Discipline? Luck? 15:48 - Finding Next Calling 18:08 - Big Successes With Problems 20:32 - Why Do We Need Web3 24:55 - Pay to See Data, Why? 27:31 - Explain Cudis to Mom 30:38 - Why a Ring Specifically 33:11 - Cudis the Centralized Wearable 36:53 - Why Not Become a Partner 38:14 - How Many Own Cudis Ring 39:15 - How to Participate in Community? 41:02 - What Must Happen Now 43:12 - Cudis as a Luxury Brand
46:21

DROPS E15: CUDIS : The Decentralized Operating System for Health Data Edison , the Founder of Cudis, closed his $20M crypto fund at Bitcoin's peak in 2021 and invested 90% of his life savings into a $349 smart ring that pays users for their health data. Timestamps 0:00 - Introduction 1:00 - Welcome to DROPS 1:38 - Believer in Ethereum? 3:11 - That Person for Solana 6:31 - What Do You Do 10:24 - Who Are You 11:05 - What Sparked Conviction 13:10 - Last Project Sold, Discipline? Luck? 15:48 - Finding Next Calling 18:08 - Big Successes With Problems 20:32 - Why Do We Need Web3 24:55 - Pay to See Data, Why? 27:31 - Explain Cudis to Mom 30:38 - Why a Ring Specifically 33:11 - Cudis the Centralized Wearable 36:53 - Why Not Become a Partner 38:14 - How Many Own Cudis Ring 39:15 - How to Participate in Community? 41:02 - What Must Happen Now 43:12 - Cudis as a Luxury Brand

MR SHIFT 🦁

73,885 просмотров • 10 месяцев назад

Over the past year, we've been building our own internal agent infrastructure at YC: over 350 tools, self-improving skill loops, and a shared organizational brain that gets smarter overnight. In this episode of the Lightcone Podcast, we sat down with YC General Partner Pete Pete Koomen to talk about how he led the effort from the ground up. We cover how giving agents unrestricted access to one database was the key unlock, the self-improving skill loops that get smarter overnight, and why he thinks we've arrived at the personal computer moment for AI. 00:39 — YC's AI Stack 02:15 — The Finance Team Problem That Started It All 05:07 — SQL Access Changes Everything 07:20 — One Database to Rule Them All 09:14 — Jevons Paradox 10:07 — Denormalizing for Agents 12:15 — The Single-Player Era of Agents 14:16 — 350 Tools and a Shared Registry 16:24 — Skillify, DRY, and MECE Resolvers 18:23 — The Self-Improving Dream Cycle 20:26 — The Two-Sentence Pitch Skill 23:06 — How Super Intelligence Compounds 25:10 — Recording Everything as a Building Layer 27:10 — The Shared Organizational Brain 29:18 — Trust-Default Culture as a Requirement 30:44 — Raising the Floor for New Employees 32:35 — Horseless Carriages 34:24 — Why Chat Is the Best Interface for Agents 38:50 — Just-in-Time Software 40:49 — Centralizing vs. Decentralizing AI 43:32 — The Personal AI Revolution
46:29

Over the past year, we've been building our own internal agent infrastructure at YC: over 350 tools, self-improving skill loops, and a shared organizational brain that gets smarter overnight. In this episode of the Lightcone Podcast, we sat down with YC General Partner Pete Pete Koomen to talk about how he led the effort from the ground up. We cover how giving agents unrestricted access to one database was the key unlock, the self-improving skill loops that get smarter overnight, and why he thinks we've arrived at the personal computer moment for AI. 00:39 — YC's AI Stack 02:15 — The Finance Team Problem That Started It All 05:07 — SQL Access Changes Everything 07:20 — One Database to Rule Them All 09:14 — Jevons Paradox 10:07 — Denormalizing for Agents 12:15 — The Single-Player Era of Agents 14:16 — 350 Tools and a Shared Registry 16:24 — Skillify, DRY, and MECE Resolvers 18:23 — The Self-Improving Dream Cycle 20:26 — The Two-Sentence Pitch Skill 23:06 — How Super Intelligence Compounds 25:10 — Recording Everything as a Building Layer 27:10 — The Shared Organizational Brain 29:18 — Trust-Default Culture as a Requirement 30:44 — Raising the Floor for New Employees 32:35 — Horseless Carriages 34:24 — Why Chat Is the Best Interface for Agents 38:50 — Just-in-Time Software 40:49 — Centralizing vs. Decentralizing AI 43:32 — The Personal AI Revolution

Y Combinator

752,311 просмотров • 1 месяц назад