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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

21,336 subscribers

303,757 просмотров • 3 месяцев назад •via X (Twitter)

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Комментарии: 8

Фото профиля john
john3 месяцев назад

so dope enjoyed the episode. thanks def should have @0xBebis_ on from @uv

Фото профиля Gerald R
Gerald R3 месяцев назад

No one leaves third point to work for factset. Doesn’t add up

Фото профиля santi
santi3 месяцев назад

Amazing episode

Фото профиля Ethan Kho
Ethan Kho3 месяцев назад

Thanks bro

Фото профиля Applied History_65
Applied History_653 месяцев назад

Outstanding episode.

Фото профиля Ethan Kho
Ethan Kho3 месяцев назад

Thank you

Фото профиля capnpope
capnpope3 месяцев назад

crazy shit

Фото профиля Ethan Kho
Ethan Kho3 месяцев назад

oh yeah

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Y Combinator

755,741 просмотров • 4 месяцев назад

Real agents will not be limited by models first. They will be limited by data access. Max from Teneo Protocol joins the Acc Podcast to unpack why public web data is getting locked behind walls, and what permissionless infrastructure could unlock for builders, businesses, and the agent economy. Max Full conversation below. 👇 00:00 Intro + what we are covering 01:17 Teneo in one line (elevator pitch) 02:07 Why it matters - users as data owners, not “data lords” 04:06 Max’s origin story + how he got into Web3 05:02 Early days - Ethereum mining, rigs, learnings 05:35 The founding team - 4 co-founders, 8 years together 06:38 The pivot - how Teneo was born 08:19 Product overview - Community Node as the foundation 10:35 Chatroom - the simple UI for specialized agents (private beta) 11:41 SDKs - building on Teneo (customer SDK + agent SDK) 14:17 AI agents era - why real-time data access is the bottleneck 19:27 The core problem - APIs locked down, access gets expensive 22:36 What Max does day to day as CEO 26:28 How to start with Teneo - beginner to advanced paths 30:08 Lessons from pivots + building with the right team 33:08 Ops advice - trust and the right people 35:16 2-10 year landscape - data pipelines, cost barriers, opportunity 37:26 What’s live now + how people contribute today 38:50 Agent SDK launch - early feedback and traction 39:56 Next 6-12 months - pushing more open source 42:36 Awareness + surprising use cases (example: government PDFs) 46:00 Speed, latency, and agent-to-agent payments (microtransactions) 48:38 Web3 adoption - users won’t notice, it just needs to work 51:07 What’s next + closing thoughts + where to get involved
50:36

Real agents will not be limited by models first. They will be limited by data access. Max from Teneo Protocol joins the Acc Podcast to unpack why public web data is getting locked behind walls, and what permissionless infrastructure could unlock for builders, businesses, and the agent economy. Max Full conversation below. 👇 00:00 Intro + what we are covering 01:17 Teneo in one line (elevator pitch) 02:07 Why it matters - users as data owners, not “data lords” 04:06 Max’s origin story + how he got into Web3 05:02 Early days - Ethereum mining, rigs, learnings 05:35 The founding team - 4 co-founders, 8 years together 06:38 The pivot - how Teneo was born 08:19 Product overview - Community Node as the foundation 10:35 Chatroom - the simple UI for specialized agents (private beta) 11:41 SDKs - building on Teneo (customer SDK + agent SDK) 14:17 AI agents era - why real-time data access is the bottleneck 19:27 The core problem - APIs locked down, access gets expensive 22:36 What Max does day to day as CEO 26:28 How to start with Teneo - beginner to advanced paths 30:08 Lessons from pivots + building with the right team 33:08 Ops advice - trust and the right people 35:16 2-10 year landscape - data pipelines, cost barriers, opportunity 37:26 What’s live now + how people contribute today 38:50 Agent SDK launch - early feedback and traction 39:56 Next 6-12 months - pushing more open source 42:36 Awareness + surprising use cases (example: government PDFs) 46:00 Speed, latency, and agent-to-agent payments (microtransactions) 48:38 Web3 adoption - users won’t notice, it just needs to work 51:07 What’s next + closing thoughts + where to get involved

Acc Ventures

43,976 просмотров • 9 месяцев назад

Alex Behring and Daniel Schwartz have almost never spoken publicly about how 3G Capital invests and operates. The firm is different in many fascinating ways. Every fund they raise is designed to make exactly one investment. They invest more of their own money than any of their limited partners in every deal and send their own people to run the businesses as CEOs and CFOs. Over 20 years they have never lost money on a deal. The conversation includes the story of how they spent 15 years building a relationship with the founding family of Hunter Douglas before getting the opportunity to buy it. They describe receiving a two line rejection email from Tim Hortons after weeks of silence and how they managed to get back to the table and close the deal. They talk about how they bought Burger King for a billion dollars when no other firm showed up to compete. And they explain why Skechers being the third largest sneaker company in the world surprised even them. A major theme is how they develop talent. Daniel was an analyst who became CFO at 26 and CEO of Burger King at 32. Alex became CEO of the largest railroad in Latin America when he was 30. They explain what they look for in young people, why they give them significant responsibility faster than anywhere else, and what it takes to set them up to succeed while holding an unusually high bar. Daniel and Alex are two of the most talented and intense people I know. They are incredibly serious about business quality and disciplined about waiting for the right opportunity. They would rather do nothing than compromise. Enjoy! Timestamps: 00:00:00 Episode Intro: Daniel Schwartz & Alex Behring 00:01:20 The "One Investment Per Fund" Model 00:05:39 Characteristics of Great Businesses 00:08:40 The Unique Structure of 3G Capital 00:14:21 Why Hunter Douglas Was Appealing 00:20:15 Alex's Railroad Story 00:28:12 The "Burger King is Run by Children" Story 00:30:38 Negotiating with Tim Hortons 00:38:18 Be Wired for Urgency 00:46:43 3G's Operating System 00:55:35 Why Burger King Was Undervalued 00:59:43 From Zero to $2 Billion in France 01:01:42 Kraft Heinz: Concentration Risk 01:04:25 Skechers: Great Product Meets Great Distribution 01:13:10 Zero-Based Budgeting & When It Works 01:16:28 The Current State of Capital Markets 01:23:04 3G's Founder-Led Focus 01:28:57 The Kindest Thing
1:30:32

Alex Behring and Daniel Schwartz have almost never spoken publicly about how 3G Capital invests and operates. The firm is different in many fascinating ways. Every fund they raise is designed to make exactly one investment. They invest more of their own money than any of their limited partners in every deal and send their own people to run the businesses as CEOs and CFOs. Over 20 years they have never lost money on a deal. The conversation includes the story of how they spent 15 years building a relationship with the founding family of Hunter Douglas before getting the opportunity to buy it. They describe receiving a two line rejection email from Tim Hortons after weeks of silence and how they managed to get back to the table and close the deal. They talk about how they bought Burger King for a billion dollars when no other firm showed up to compete. And they explain why Skechers being the third largest sneaker company in the world surprised even them. A major theme is how they develop talent. Daniel was an analyst who became CFO at 26 and CEO of Burger King at 32. Alex became CEO of the largest railroad in Latin America when he was 30. They explain what they look for in young people, why they give them significant responsibility faster than anywhere else, and what it takes to set them up to succeed while holding an unusually high bar. Daniel and Alex are two of the most talented and intense people I know. They are incredibly serious about business quality and disciplined about waiting for the right opportunity. They would rather do nothing than compromise. Enjoy! Timestamps: 00:00:00 Episode Intro: Daniel Schwartz & Alex Behring 00:01:20 The "One Investment Per Fund" Model 00:05:39 Characteristics of Great Businesses 00:08:40 The Unique Structure of 3G Capital 00:14:21 Why Hunter Douglas Was Appealing 00:20:15 Alex's Railroad Story 00:28:12 The "Burger King is Run by Children" Story 00:30:38 Negotiating with Tim Hortons 00:38:18 Be Wired for Urgency 00:46:43 3G's Operating System 00:55:35 Why Burger King Was Undervalued 00:59:43 From Zero to $2 Billion in France 01:01:42 Kraft Heinz: Concentration Risk 01:04:25 Skechers: Great Product Meets Great Distribution 01:13:10 Zero-Based Budgeting & When It Works 01:16:28 The Current State of Capital Markets 01:23:04 3G's Founder-Led Focus 01:28:57 The Kindest Thing

Patrick OShaughnessy

43,200 просмотров • 7 месяцев назад

"Concentrated strategies have a shot to do extremely well." Sean Emory (Sean D. Emory) is the Founder & CIO of Avory & Co. — a Miami-based, high-conviction equity shop blending old-school fundamentals with modern alt-data. He's compounded at ~20% a year before fees. "Active traders are passively investing — just querying AI models. We use data for thesis tracking, not timing the quarter. In a six-minute market, long-term is the edge." We cover: - The Wix conversion rate trick from 2013 that kicked off his alt-data obsession — and why that playbook still works today - How app downloads, foot traffic & permit scraping give 99% revenue visibility on some names — and why most shops still don't use any of it - The six minute market — why AI + index funds are turning "active" investors into passive ones (and creating dislocations for the disciplined) - The "Six Ms" framework — why management & margin of safety are the bookends that actually matter - Why he'd rather ride the second AI wave than guess which data center provider survives - Unconstrained cash in an ETF wrapper — the flexibility most active managers don't have - Ringing the Nasdaq bell, the $200-300K year-one cost of launching an ETF & the compliance reality nobody warns you about - Teaching his son to compound — seven-day habit streaks rewarded with books Highlights: 00:00 Intro 00:46 Wix case study: Identifying early signals in alternative data 04:20 Identifying modern low-hanging fruit in digital data sets 06:08 Is there alpha in alt data if everyone has access? 09:08 Integrating alternative data into the fundamental investment process 14:08 Differentiating between fundamental inflection points and market noise 17:47 Self-awareness and avoiding cognitive bias in post-trade evaluation 28:43 Portfolio construction: Concentrated strategies vs. multi-name diversification 30:21 Rethinking risk: Permanent loss of capital vs. volatility metrics 32:58 How generative AI impacts market efficiency and passive flows 37:06 Launching an active ETF: Bridging the gap for allocators 40:13 Strategy deltas between private fund vehicles and public ETFs 01:00:48 Maintaining competitive edge and psychological resilience during drawdowns 01:04:13 The primary skill for outperformance in a storytelling market
1:09:32

"Concentrated strategies have a shot to do extremely well." Sean Emory (Sean D. Emory) is the Founder & CIO of Avory & Co. — a Miami-based, high-conviction equity shop blending old-school fundamentals with modern alt-data. He's compounded at ~20% a year before fees. "Active traders are passively investing — just querying AI models. We use data for thesis tracking, not timing the quarter. In a six-minute market, long-term is the edge." We cover: - The Wix conversion rate trick from 2013 that kicked off his alt-data obsession — and why that playbook still works today - How app downloads, foot traffic & permit scraping give 99% revenue visibility on some names — and why most shops still don't use any of it - The six minute market — why AI + index funds are turning "active" investors into passive ones (and creating dislocations for the disciplined) - The "Six Ms" framework — why management & margin of safety are the bookends that actually matter - Why he'd rather ride the second AI wave than guess which data center provider survives - Unconstrained cash in an ETF wrapper — the flexibility most active managers don't have - Ringing the Nasdaq bell, the $200-300K year-one cost of launching an ETF & the compliance reality nobody warns you about - Teaching his son to compound — seven-day habit streaks rewarded with books Highlights: 00:00 Intro 00:46 Wix case study: Identifying early signals in alternative data 04:20 Identifying modern low-hanging fruit in digital data sets 06:08 Is there alpha in alt data if everyone has access? 09:08 Integrating alternative data into the fundamental investment process 14:08 Differentiating between fundamental inflection points and market noise 17:47 Self-awareness and avoiding cognitive bias in post-trade evaluation 28:43 Portfolio construction: Concentrated strategies vs. multi-name diversification 30:21 Rethinking risk: Permanent loss of capital vs. volatility metrics 32:58 How generative AI impacts market efficiency and passive flows 37:06 Launching an active ETF: Bridging the gap for allocators 40:13 Strategy deltas between private fund vehicles and public ETFs 01:00:48 Maintaining competitive edge and psychological resilience during drawdowns 01:04:13 The primary skill for outperformance in a storytelling market

Ethan Kho

41,885 просмотров • 5 месяцев назад

🦙 ollama is used by 9 million developers and 85% of the Fortune 500, giving co-founder and CEO Jeffrey Morgan (Jeffrey Morgan) a unique view into which AI models people are actually using and how that’s changing. Right now, the biggest shift he sees is toward open models, driven by coding agents, falling costs, and capabilities that are rapidly catching up to the frontier labs. On Ollama Cloud, that shift has driven a 150x increase in token usage since the start of the year. In this episode of Lightcone Podcast, Jeff joins Garry Tan, Jared Friedman, Diana, and Harj Taggar to talk about the future of open models and the story behind Ollama, from two years of searching for the right idea to building one of the most widely used AI developer tools in the world. 00:43 — The Shift to Open Models 03:03 — How AI Agents Are Driving Token Usage 05:31 — Are Open Models Catching Up? 08:26 — What Happens When a New Model Launches 11:31 — Ollama as an Operating System for AI 14:05 — The New Opportunities Above the Model Layer 18:19 — Why 80–90% of Enterprise Tokens Could Be Open 20:57 — The Future Is Local and Cloud 26:40 — Why AI Is Coming Back to Your Computer 28:56 — The Coming Era of Unlimited Tokens 32:30 — Do We Still Need a “God Model”? 33:41 — Open Models and Geopolitics 36:14 — The Origins of Ollama 40:36 — Two Years Lost in the Wilderness 42:39 — The Pivot That Changed Everything 47:02 — How Ollama Found a Business Model 49:43 — Why Second-Time Founders Did YC
57:15

🦙 ollama is used by 9 million developers and 85% of the Fortune 500, giving co-founder and CEO Jeffrey Morgan (Jeffrey Morgan) a unique view into which AI models people are actually using and how that’s changing. Right now, the biggest shift he sees is toward open models, driven by coding agents, falling costs, and capabilities that are rapidly catching up to the frontier labs. On Ollama Cloud, that shift has driven a 150x increase in token usage since the start of the year. In this episode of Lightcone Podcast, Jeff joins Garry Tan, Jared Friedman, Diana, and Harj Taggar to talk about the future of open models and the story behind Ollama, from two years of searching for the right idea to building one of the most widely used AI developer tools in the world. 00:43 — The Shift to Open Models 03:03 — How AI Agents Are Driving Token Usage 05:31 — Are Open Models Catching Up? 08:26 — What Happens When a New Model Launches 11:31 — Ollama as an Operating System for AI 14:05 — The New Opportunities Above the Model Layer 18:19 — Why 80–90% of Enterprise Tokens Could Be Open 20:57 — The Future Is Local and Cloud 26:40 — Why AI Is Coming Back to Your Computer 28:56 — The Coming Era of Unlimited Tokens 32:30 — Do We Still Need a “God Model”? 33:41 — Open Models and Geopolitics 36:14 — The Origins of Ollama 40:36 — Two Years Lost in the Wilderness 42:39 — The Pivot That Changed Everything 47:02 — How Ollama Found a Business Model 49:43 — Why Second-Time Founders Did YC

Y Combinator

313,654 просмотров • 1 месяц назад

Leaving Citadel & launching a $1B AI hedge fund — how Renee Yao built NeoIvy Capital from scratch Renee Yao walked away from two of the most elite hedge funds on Wall Street — Citadel & Millennium — and built a quant fund on a fundamentally different model: modern AI instead of human-powered alpha generation. The result: $1B+ in regulatory AUM, uncorrelated returns through COVID, & a fund Business Insider named one of the top transforming investing in North America. We cover: - Why large multi-manager quant firms rely on massive global researcher headcounts — & why Renee saw that as a model worth disrupting - The 3 barriers to entry in AI-driven quant — & why legacy sequential infrastructure can be a disadvantage compared to modern parallel distributed systems - How NeoIvy's self-evolving models adapted in real time during the March 2020 crash — while traditional quant managers had a nightmare month - The difference between beta returns, factor returns & pure alpha — & why size is the enemy of true idiosyncratic returns - Why the "black box" reputation of quant funds has been the #1 fundraising obstacle - How a 4-year-old girl visiting her uncle's room-sized supercomputer in China set the foundation for all of this - The edge/breadth/constraint framework from Grinold & Kahn — & how it shaped Renee's thinking on diversification - Renee's raw advice on staying disciplined when everyone around you is chasing beta in a bull market Transcript: 00:00 Intro 01:14 Renee Yao’s journey to founding Neo Ivy 02:28 Joining Citadel after the financial crisis 04:13 Hedge fund diversification and breadth of edge 04:45 Why Neo Ivy trades with AI strategies 07:50 How self-learning AI adapts to markets 09:40 Causation vs correlation in AI hedge funds 10:33 Barriers to entry for AI hedge funds 14:47 Risks of crowded factor bets explained 16:39 Why big funds struggle with AI talent 17:29 From PM at Citadel to hedge fund founder 18:47 Challenges of launching a quant hedge fund 20:25 Biggest constraint for AI hedge fund startups 22:08 How AI hedge funds adapted during COVID 24:04 Modern AI tools used in quant trading 25:13 Building hedge fund infrastructure from scratch 26:26 Career advice for aspiring quants and traders 28:55 Adapting career goals to changing job markets 31:57 Life lessons from trading and risk management 32:51 Staying disciplined while running a hedge fund 34:38 Obsession and belief in AI hedge funds 35:41 Closing thoughts on hedge funds and life
35:59

Leaving Citadel & launching a $1B AI hedge fund — how Renee Yao built NeoIvy Capital from scratch Renee Yao walked away from two of the most elite hedge funds on Wall Street — Citadel & Millennium — and built a quant fund on a fundamentally different model: modern AI instead of human-powered alpha generation. The result: $1B+ in regulatory AUM, uncorrelated returns through COVID, & a fund Business Insider named one of the top transforming investing in North America. We cover: - Why large multi-manager quant firms rely on massive global researcher headcounts — & why Renee saw that as a model worth disrupting - The 3 barriers to entry in AI-driven quant — & why legacy sequential infrastructure can be a disadvantage compared to modern parallel distributed systems - How NeoIvy's self-evolving models adapted in real time during the March 2020 crash — while traditional quant managers had a nightmare month - The difference between beta returns, factor returns & pure alpha — & why size is the enemy of true idiosyncratic returns - Why the "black box" reputation of quant funds has been the #1 fundraising obstacle - How a 4-year-old girl visiting her uncle's room-sized supercomputer in China set the foundation for all of this - The edge/breadth/constraint framework from Grinold & Kahn — & how it shaped Renee's thinking on diversification - Renee's raw advice on staying disciplined when everyone around you is chasing beta in a bull market Transcript: 00:00 Intro 01:14 Renee Yao’s journey to founding Neo Ivy 02:28 Joining Citadel after the financial crisis 04:13 Hedge fund diversification and breadth of edge 04:45 Why Neo Ivy trades with AI strategies 07:50 How self-learning AI adapts to markets 09:40 Causation vs correlation in AI hedge funds 10:33 Barriers to entry for AI hedge funds 14:47 Risks of crowded factor bets explained 16:39 Why big funds struggle with AI talent 17:29 From PM at Citadel to hedge fund founder 18:47 Challenges of launching a quant hedge fund 20:25 Biggest constraint for AI hedge fund startups 22:08 How AI hedge funds adapted during COVID 24:04 Modern AI tools used in quant trading 25:13 Building hedge fund infrastructure from scratch 26:26 Career advice for aspiring quants and traders 28:55 Adapting career goals to changing job markets 31:57 Life lessons from trading and risk management 32:51 Staying disciplined while running a hedge fund 34:38 Obsession and belief in AI hedge funds 35:41 Closing thoughts on hedge funds and life

Ethan Kho

138,224 просмотров • 7 месяцев назад

Inside the Billionaire Backed Prediction Markets Hedge Fund. Run by a 24-Year-Old. Camilo Saravia (camilo), founder of BlueWalker Capital, a systematic prediction markets fund backed by Daniel Howard of Halo Capital. "I don't want more capital. I'm extremely long our equity." We cover: - Why insider trading in prediction markets is terrible for liquidity and GOOD for society - Prediction markets as cash-backed truth in a world of AI slop and disinformation - Why he turned down the allocator question entirely, and the "Goldilocks zone" that makes a fund this size work - His research team's actual mission statement: "collapse the entropy of the internet into signal" - Trading Spotify streams and measuring how fast Mamdani viralizes vs Cuomo on TikTok - Why beating earnings has almost no correlation with the stock going up, and why only testing reveals that - Mention markets as literal next-word prediction, and how makers got sniped out - Hiring missionaries with a mercenary work style, and why every hire takes a pay cut vs Citadel, Jane Street, Wintermute - Daniel Howard's mandate: faster, more risk, more aggression. "They haven't backed me to print 7% APY" - The abundance mindset, from a kid with immigrant parents sitting across from generational wealth - A venture mindset applied to public equities: pulling the thread from free cash flow down to Glassdoor culture - Why you never need to be binarily right: buy at 20, sell at 40, never wait for resolution Highlights: (00:00) Intro (00:56) Taker vs maker, reflexive vs proactive: the strategy map (03:08) What makes an event contract different from an equity (04:53) Insider trading in prediction markets: bug or feature (08:18) Cash-backed truth in a world of AI slop (12:43) Where edge actually comes from (14:19) The dataset: billions of records a day (17:44) Building a money management business from an empty office (21:34) Why asset management competes with software as a business model (23:51) How to underwrite elite talent (25:50) Missionaries vs mercenaries, and why the tension is the point (30:54) Recruiting against Citadel money (and losing on salary every time) (37:32) "We can't compromise speed": the Daniel Howard mandate (44:45) The abundance mindset (48:28) "Why should I invest?" / "I don't want more capital" (50:12) Collapsing the entropy of the internet (51:04) Spotify streams, TikTok virality, and mention markets (53:17) The one data provider he'd long if he could (55:49) There's a business behind everything (01:00:07) How to find the real drivers in any market (01:04:04) A venture mindset applied to public equities (01:10:06) Prediction markets 101: where to actually start (01:12:05) Why you don't need to be binarily right (01:13:19) Final question: building personal edge against the models
1:16:45

Inside the Billionaire Backed Prediction Markets Hedge Fund. Run by a 24-Year-Old. Camilo Saravia (camilo), founder of BlueWalker Capital, a systematic prediction markets fund backed by Daniel Howard of Halo Capital. "I don't want more capital. I'm extremely long our equity." We cover: - Why insider trading in prediction markets is terrible for liquidity and GOOD for society - Prediction markets as cash-backed truth in a world of AI slop and disinformation - Why he turned down the allocator question entirely, and the "Goldilocks zone" that makes a fund this size work - His research team's actual mission statement: "collapse the entropy of the internet into signal" - Trading Spotify streams and measuring how fast Mamdani viralizes vs Cuomo on TikTok - Why beating earnings has almost no correlation with the stock going up, and why only testing reveals that - Mention markets as literal next-word prediction, and how makers got sniped out - Hiring missionaries with a mercenary work style, and why every hire takes a pay cut vs Citadel, Jane Street, Wintermute - Daniel Howard's mandate: faster, more risk, more aggression. "They haven't backed me to print 7% APY" - The abundance mindset, from a kid with immigrant parents sitting across from generational wealth - A venture mindset applied to public equities: pulling the thread from free cash flow down to Glassdoor culture - Why you never need to be binarily right: buy at 20, sell at 40, never wait for resolution Highlights: (00:00) Intro (00:56) Taker vs maker, reflexive vs proactive: the strategy map (03:08) What makes an event contract different from an equity (04:53) Insider trading in prediction markets: bug or feature (08:18) Cash-backed truth in a world of AI slop (12:43) Where edge actually comes from (14:19) The dataset: billions of records a day (17:44) Building a money management business from an empty office (21:34) Why asset management competes with software as a business model (23:51) How to underwrite elite talent (25:50) Missionaries vs mercenaries, and why the tension is the point (30:54) Recruiting against Citadel money (and losing on salary every time) (37:32) "We can't compromise speed": the Daniel Howard mandate (44:45) The abundance mindset (48:28) "Why should I invest?" / "I don't want more capital" (50:12) Collapsing the entropy of the internet (51:04) Spotify streams, TikTok virality, and mention markets (53:17) The one data provider he'd long if he could (55:49) There's a business behind everything (01:00:07) How to find the real drivers in any market (01:04:04) A venture mindset applied to public equities (01:10:06) Prediction markets 101: where to actually start (01:12:05) Why you don't need to be binarily right (01:13:19) Final question: building personal edge against the models

Ethan Kho

128,490 просмотров • 1 месяц назад

Lightfield co-founder and CEO Keith Peiris on killing a product with two million monthly users and rebuilding the CRM from scratch: Lightfield's predecessor was an AI presentation tool growing faster than the team could buy inference to support it. Keith shut it down anyway. Nobody on the team liked the product. Keith decided he wanted to attack the CRM space, a vertical that's notoriously hard to break into. A company's record of its customers is scattered across a whole stack of systems that often contradict each other. His bet is reconciling them into one coherent source of truth, what Lightfield calls a business world model. Every agent a team might run sits downstream of it. In this conversation with a16z's Alex Rampell and Joe Schmidt: the office space they traded for their first 10 customers, why a CRM ended up modeled on the Facebook timeline, three attempts at pricing before one worked, and why nobody at a 40-person company has a fixed role. 00:00 Intro 01:24 Why 2 million users wasn't enough 03:36 Why better models wouldn't have fixed it 06:41 Reconciling the data was the real product 08:16 How free rent bought their first 10 customers 10:12 A CRM does 3 jobs and only 1 matters 11:42 Autopilot CRM nobody has to update 14:56 No more rigid data models 17:11 How one customer modeled a whole marketplace 18:48 Taking customers from an incumbent 23:49 How to survive a new head of sales 26:03 Why the CEO still uses a spreadsheet view 30:55 Pricing models that failed before one worked 36:23 Fixed roles make product pivots impossible 40:02 Why slow shipping loses CRM customers 43:17 What Silicon Valley customers are actually worth 46:20 Why buyers need a reference who looks like them 50:40 What to ignore during a pivot YouTube: Keith Peiris Lightfield Alex Rampell Joe Schmidt IV
52:03

Lightfield co-founder and CEO Keith Peiris on killing a product with two million monthly users and rebuilding the CRM from scratch: Lightfield's predecessor was an AI presentation tool growing faster than the team could buy inference to support it. Keith shut it down anyway. Nobody on the team liked the product. Keith decided he wanted to attack the CRM space, a vertical that's notoriously hard to break into. A company's record of its customers is scattered across a whole stack of systems that often contradict each other. His bet is reconciling them into one coherent source of truth, what Lightfield calls a business world model. Every agent a team might run sits downstream of it. In this conversation with a16z's Alex Rampell and Joe Schmidt: the office space they traded for their first 10 customers, why a CRM ended up modeled on the Facebook timeline, three attempts at pricing before one worked, and why nobody at a 40-person company has a fixed role. 00:00 Intro 01:24 Why 2 million users wasn't enough 03:36 Why better models wouldn't have fixed it 06:41 Reconciling the data was the real product 08:16 How free rent bought their first 10 customers 10:12 A CRM does 3 jobs and only 1 matters 11:42 Autopilot CRM nobody has to update 14:56 No more rigid data models 17:11 How one customer modeled a whole marketplace 18:48 Taking customers from an incumbent 23:49 How to survive a new head of sales 26:03 Why the CEO still uses a spreadsheet view 30:55 Pricing models that failed before one worked 36:23 Fixed roles make product pivots impossible 40:02 Why slow shipping loses CRM customers 43:17 What Silicon Valley customers are actually worth 46:20 Why buyers need a reference who looks like them 50:40 What to ignore during a pivot YouTube: Keith Peiris Lightfield Alex Rampell Joe Schmidt IV

a16z

115,604 просмотров • 19 дней назад

“Founders aren’t made when they start companies. They’re made when they interpret their market correctly.” Episode 2 of Knuckle Up with Nakul is live. Qasar Younis (Qasar Younis): Founder and CEO of Applied Intuition. One of the sharpest and most intentional operators you’ll ever meet on the craft of building a company. Full episode ↓ -- 00:00 Intro 01:19 What really makes someone a founder 05:26 The company that almost became Kickstarter 08:12 The most common misread on feedback 13:40 Why most founders don't end up with the best team 19:45 How to pick a co-founder 23:38 Your first 10 hires are really your first 100 28:21 The case for hiring slow and firing slow 33:22 Red, yellow, green: how Applied gives monthly feedback 35:00 The role that knows what’s actually going on in a company 40:01 How to operate with speed and intentionality 42:41 The three things Qasar spends time on 45:57 How Applied is driving AI adoption 52:06 The type of engineer Applied is now looking for 1:01:19 Why this could be the golden age of small companies 1:09:13 Quickfire: red flags, overrated advice, and superpowers 1:12:32 Qasar's advice to his 25-year-old self
1:13:20

“Founders aren’t made when they start companies. They’re made when they interpret their market correctly.” Episode 2 of Knuckle Up with Nakul is live. Qasar Younis (Qasar Younis): Founder and CEO of Applied Intuition. One of the sharpest and most intentional operators you’ll ever meet on the craft of building a company. Full episode ↓ -- 00:00 Intro 01:19 What really makes someone a founder 05:26 The company that almost became Kickstarter 08:12 The most common misread on feedback 13:40 Why most founders don't end up with the best team 19:45 How to pick a co-founder 23:38 Your first 10 hires are really your first 100 28:21 The case for hiring slow and firing slow 33:22 Red, yellow, green: how Applied gives monthly feedback 35:00 The role that knows what’s actually going on in a company 40:01 How to operate with speed and intentionality 42:41 The three things Qasar spends time on 45:57 How Applied is driving AI adoption 52:06 The type of engineer Applied is now looking for 1:01:19 Why this could be the golden age of small companies 1:09:13 Quickfire: red flags, overrated advice, and superpowers 1:12:32 Qasar's advice to his 25-year-old self

Nakul Mandan

521,704 просмотров • 5 месяцев назад

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11
43:20

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11

Dan Shipper

66,871 просмотров • 5 месяцев назад

E114: Walrus 🦭/acc : Decentralizing Data Storage, the perfect complement to the SUI Network George Danezis is the Chief Scientist at MystenLabs.sui , co-creator of the Sui , and one the minds behind Walrus 🦭/acc Timestamps 0:00 Intro 2:00 Partnerships: Jupiter, Bitwise, SUI & Mantle 2:46 Sleeping Habits 4:05 Who is George? 4:23 What is a Chief Scientist? 5:16 Research at Microsoft vs. Now 6:59 What is “Good Research”? 8:49 Corporate Innovation vs. Reality 10:31 Why I Love Teaching Security 11:52 Self Custody with Trezor 12:44 Teaching vs. Real-World Application 16:28 What Makes an Idea Great? 17:51 The Idea That Never Worked Out 19:50 Being Late to Ideas, Not Early 20:56 The Essence of Peer-to-Peer Networks 23:50 Why We Need Peer-to-Peer Systems? 27:51 Why Decentralization Was Hard to Realize 31:09 Bitcoin's Game-Changing Impact 33:10 ETH Aha Moment 34:46 Facebook’s Conviction in Blockchain 37:39 Scaling DIEM/LIBRA to Sui 39:20 Biggest Issue at Facebook 40:20 Why Facebook Bet on Crypto 41:40 Why I Left Facebook 45:07 Balancing Passion and Pay 46:43 What is a Blockchain Good For? 51:42 Understanding Blockchain’s Limitations 53:21 CryptoKitties 54:36 Meme Coins: Culture vs. Scam 57:02 The Role of Norms in Crypto 1:00:32 What is the Sui Network? 1:02:32 Why Do We Need The Sui Network? 1:03:34 Layer 1 vs. Layer 2 1:05:07 Why Build a Layer One? 1:09:21 Top Layer One Competitors 1:10:45 Decentralization vs. Practicality 1:14:06 Minimum Decentralization 1:16:33 Data Storage & Its Biggest Problem 1:20:05 What is Walrus Protocol? 1:22:09 Why Walrus is Possible Now 1:25:35 Why Sui Network for Data Storage? 1:27:37 Vision of Walrus Protocol 1:28:59 Building a Neutral Tech Future 1:30:44 Using Existing Blockchains 1:33:03 Future of Decentralized Production 1:35:51 Prediction For The Next 12 Months 1:39:04 Concluding Remarks
1:39:56

E114: Walrus 🦭/acc : Decentralizing Data Storage, the perfect complement to the SUI Network George Danezis is the Chief Scientist at MystenLabs.sui , co-creator of the Sui , and one the minds behind Walrus 🦭/acc Timestamps 0:00 Intro 2:00 Partnerships: Jupiter, Bitwise, SUI & Mantle 2:46 Sleeping Habits 4:05 Who is George? 4:23 What is a Chief Scientist? 5:16 Research at Microsoft vs. Now 6:59 What is “Good Research”? 8:49 Corporate Innovation vs. Reality 10:31 Why I Love Teaching Security 11:52 Self Custody with Trezor 12:44 Teaching vs. Real-World Application 16:28 What Makes an Idea Great? 17:51 The Idea That Never Worked Out 19:50 Being Late to Ideas, Not Early 20:56 The Essence of Peer-to-Peer Networks 23:50 Why We Need Peer-to-Peer Systems? 27:51 Why Decentralization Was Hard to Realize 31:09 Bitcoin's Game-Changing Impact 33:10 ETH Aha Moment 34:46 Facebook’s Conviction in Blockchain 37:39 Scaling DIEM/LIBRA to Sui 39:20 Biggest Issue at Facebook 40:20 Why Facebook Bet on Crypto 41:40 Why I Left Facebook 45:07 Balancing Passion and Pay 46:43 What is a Blockchain Good For? 51:42 Understanding Blockchain’s Limitations 53:21 CryptoKitties 54:36 Meme Coins: Culture vs. Scam 57:02 The Role of Norms in Crypto 1:00:32 What is the Sui Network? 1:02:32 Why Do We Need The Sui Network? 1:03:34 Layer 1 vs. Layer 2 1:05:07 Why Build a Layer One? 1:09:21 Top Layer One Competitors 1:10:45 Decentralization vs. Practicality 1:14:06 Minimum Decentralization 1:16:33 Data Storage & Its Biggest Problem 1:20:05 What is Walrus Protocol? 1:22:09 Why Walrus is Possible Now 1:25:35 Why Sui Network for Data Storage? 1:27:37 Vision of Walrus Protocol 1:28:59 Building a Neutral Tech Future 1:30:44 Using Existing Blockchains 1:33:03 Future of Decentralized Production 1:35:51 Prediction For The Next 12 Months 1:39:04 Concluding Remarks

MR SHIFT 🦁

111,082 просмотров • 1 год назад

My conversation with Karim Atiyeh, co-founder and CTO of Ramp and one of the best operators active today. I love talking to people who are obsessed with their craft, and Karim is one of them. The way he talks about every part of the business makes it clear that Ramp wins by caring more than anyone. They labor over every pixel and detail that helps customers save time, eliminate waste, and spend less. We talk about how to build products faster, assembling a team of "Avengers", seeking differentiation and how Karim’s "divinely discontent" mindset drives Ramp’s culture. It's a conversation about craftsmanship, obsession, and the quiet intensity behind one of the fastest-growing companies in the world. Enjoy! Timestamps: 0:00 Intro 0:29 Competing with a Day One Mentality 7:58 Winning with Consumer-Grade UX 10:40 Divinely Discontent 14:18 Building a Culture of Mutual Accountability 16:05 Origin Story: Growing Up in Lebanon 21:40 The First Company: Paribus 28:02 Lessons from Building on an Unstable Platform 36:42 From Paribus to Ramp 48:59 The Framework for Choosing Investors 57:54 Shifting the Business Model 1:03:00 Why the Best Builders Will Be Technical 1:06:05 An Engineer's Approach to Marketing 1:20:15 The Power of Differentiation 1:22:22 Recruiting for Spikiness 1:27:41 The Tactics of Speed 1:34:52 The Vision for Ramp 1:43:40 Kindest Thing
1:46:22

My conversation with Karim Atiyeh, co-founder and CTO of Ramp and one of the best operators active today. I love talking to people who are obsessed with their craft, and Karim is one of them. The way he talks about every part of the business makes it clear that Ramp wins by caring more than anyone. They labor over every pixel and detail that helps customers save time, eliminate waste, and spend less. We talk about how to build products faster, assembling a team of "Avengers", seeking differentiation and how Karim’s "divinely discontent" mindset drives Ramp’s culture. It's a conversation about craftsmanship, obsession, and the quiet intensity behind one of the fastest-growing companies in the world. Enjoy! Timestamps: 0:00 Intro 0:29 Competing with a Day One Mentality 7:58 Winning with Consumer-Grade UX 10:40 Divinely Discontent 14:18 Building a Culture of Mutual Accountability 16:05 Origin Story: Growing Up in Lebanon 21:40 The First Company: Paribus 28:02 Lessons from Building on an Unstable Platform 36:42 From Paribus to Ramp 48:59 The Framework for Choosing Investors 57:54 Shifting the Business Model 1:03:00 Why the Best Builders Will Be Technical 1:06:05 An Engineer's Approach to Marketing 1:20:15 The Power of Differentiation 1:22:22 Recruiting for Spikiness 1:27:41 The Tactics of Speed 1:34:52 The Vision for Ramp 1:43:40 Kindest Thing

Patrick OShaughnessy

599,830 просмотров • 11 месяцев назад

Hedge fund managers in their first 3 years beat the hedge fund index by ~8 points. This new fund is built entirely around that stat. Tyler Errickson (17 years in fund management | Co-founded the Ophir Global Funds: $2M → $1B+ AUM | Brant Point | Now founder & CEO of Riptide Advisors - the multi-manager betting on undiscovered talent, launched Jan 2026) "You're going to get discovered when you deserve to be discovered." We cover: - Why being a small single-manager has never been harder: the fund that met 46 investors, got 3 callbacks & 0 subscriptions - The flipped script: 10 managers chasing $10M checks vs. one platform raising $100M for all of them - The "optimal point of the alpha curve": why the hungriest managers are in their first 3 years - Ray Dalio's warning — "you're not building a 50-year business" in a pod — & why Tyler thinks PMs deserve an annuity on their success - Why fund founders spend 40–60% of their time on non-alpha work — and how Riptide absorbs it - Anti-screening: bring $250K, get on the field — the combine vs. game-film theory of talent - The 3-day bus tour from China to Vietnam with Ophir's Andrew Mitchell (22% net for 16 years) - Single A → Triple A → the majors: opportunities fund, market neutral, flexible equity, then your own fund - The $2M → $25M void: too small for seed checks, too slow through friends & family - How Riptide wins when you leave: GP stakes, 20% revenue shares & rights of first refusal Highlights: 00:00 Introduction 00:19 Building Riptide in a challenging environment for small funds 02:59 What is Riptide? 03:26 Two-sided marketplace: talent and capital 11:32 Sourcing and attracting talent 20:42 Screening process and X factors 27:20 Fund structure: opportunities, flexible equity, market neutral 35:09 Revenue share and strategic partnerships 42:09 Raising capital and allocator relationships 50:27 Core value proposition and closing thoughts
53:15

Hedge fund managers in their first 3 years beat the hedge fund index by ~8 points. This new fund is built entirely around that stat. Tyler Errickson (17 years in fund management | Co-founded the Ophir Global Funds: $2M → $1B+ AUM | Brant Point | Now founder & CEO of Riptide Advisors - the multi-manager betting on undiscovered talent, launched Jan 2026) "You're going to get discovered when you deserve to be discovered." We cover: - Why being a small single-manager has never been harder: the fund that met 46 investors, got 3 callbacks & 0 subscriptions - The flipped script: 10 managers chasing $10M checks vs. one platform raising $100M for all of them - The "optimal point of the alpha curve": why the hungriest managers are in their first 3 years - Ray Dalio's warning — "you're not building a 50-year business" in a pod — & why Tyler thinks PMs deserve an annuity on their success - Why fund founders spend 40–60% of their time on non-alpha work — and how Riptide absorbs it - Anti-screening: bring $250K, get on the field — the combine vs. game-film theory of talent - The 3-day bus tour from China to Vietnam with Ophir's Andrew Mitchell (22% net for 16 years) - Single A → Triple A → the majors: opportunities fund, market neutral, flexible equity, then your own fund - The $2M → $25M void: too small for seed checks, too slow through friends & family - How Riptide wins when you leave: GP stakes, 20% revenue shares & rights of first refusal Highlights: 00:00 Introduction 00:19 Building Riptide in a challenging environment for small funds 02:59 What is Riptide? 03:26 Two-sided marketplace: talent and capital 11:32 Sourcing and attracting talent 20:42 Screening process and X factors 27:20 Fund structure: opportunities, flexible equity, market neutral 35:09 Revenue share and strategic partnerships 42:09 Raising capital and allocator relationships 50:27 Core value proposition and closing thoughts

Ethan Kho

135,140 просмотров • 2 месяцев назад