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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 次观看 • 3 个月前 •via X (Twitter)

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8 条评论

john 的头像
john3 个月前

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

Gerald R 的头像
Gerald R3 个月前

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

santi 的头像
santi3 个月前

Amazing episode

Ethan Kho 的头像
Ethan Kho3 个月前

Thanks bro

Applied History_65 的头像
Applied History_653 个月前

Outstanding episode.

Ethan Kho 的头像
Ethan Kho3 个月前

Thank you

capnpope 的头像
capnpope3 个月前

crazy shit

Ethan Kho 的头像
Ethan Kho3 个月前

oh yeah

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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

Ethan Kho

584,212 次观看 • 4 个月前

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Matt Turck

86,153 次观看 • 1 年前

Ex-Balyasny PM Ying Hua (Ying Hua) on why automation will increase demand for hedge fund talent, the quant/fundamental convergence, & why quant is blackjack but fundamental is poker. Ying Hua (PM @ Balyasny — built & led a quantamental team covering US insurance, capital markets & fintech | ~5 yrs @ Citadel running a long/short insurance book | Equity research @ Goldman Sachs | MS in Data Science @ UC Berkeley | Now founder & CEO of Implied Implied) "One of the best-kept secrets: fundamental investors are not good at sizing. Quant funds are really good at sizing." We cover: - The only real line between quant and fundamental: historical pattern matching vs. "how is this time different" — and the alpha neither group is looking at - Why she rebuilt her process so every model updated within 2 minutes of a print - Scraping highway patrol data from 15 states to track auto insurance losses live, every single day - The Malibu wildfire: mapping burned mansions from celebrity tweets to estimate losses before any industry consultant published a number - Her automation math: data gathering ~100% automatable, processing ~80%, judgment still 100% human - AI is quant for words — next-token prediction is pattern matching, which makes this just the next automation wave after quant and indexing - The proof differentiated views pay more: insurance stocks moved 2-3% on earnings in 2010; by the time she left, 15-20% intraday - Why "hook Claude Code up to data and let it rip" fails: BloombergGPT losing to a smaller open-source model, & why horizontal models are college grads - Quant is blackjack with card counting; multi-manager investing is poker — your hand, others' perception of it, your seat, everyone's stack - Most PMs are playing the wrong game: the positioning game hiding inside "fundamental" sectors with no new money coming in - Her hiring bar at BAM: every fundamental analyst learns Python — and the one skill she says can't be trained - The only two truly meritocratic jobs: hedge fund PM & sales Highlights: (00:00) Intro (00:40) How a quantamental PM actually puts on a position (02:05) The only real line between quant and fundamental (04:45) Why quantamental lowers the burden on your brain (06:40) Scraping 15 states of highway patrol data to nowcast insurance losses (09:25) The Malibu wildfire: estimating losses from celebrity tweets (11:55) How much of fundamental investing can be automated (13:45) Quantifying intuition: when a CFO's filler words jump 8% to 20% (16:25) The contrarian case: automation expands demand for talent (18:15) Earnings vol exploded — differentiated views pay more (20:25) Why Claude Code can't run your book (23:40) Horizontal models are college grads with no domain knowledge (30:50) Why chat is the wrong interface for investors (36:20) Will AI make markets more or less efficient? (39:10) Two things every fundamental PM should do today (41:50) The moat that expands: talent, redefined (45:50) Sometimes the game is positioning, not fundamentals (48:25) Blackjack vs. poker vs. surfing: matching the game to your horizon (52:00) Should young analysts chase the hottest sector? (57:55) Munger vs. Musk: two philosophies of wealth (1:01:05) Self-awareness in investing is bimodal (1:07:05) The only two truly meritocratic jobs: hedge funds & sales (1:08:50) The one skill for every regime: reconstruct the narrative
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Ex-Balyasny PM Ying Hua (Ying Hua) on why automation will increase demand for hedge fund talent, the quant/fundamental convergence, & why quant is blackjack but fundamental is poker. Ying Hua (PM @ Balyasny — built & led a quantamental team covering US insurance, capital markets & fintech | ~5 yrs @ Citadel running a long/short insurance book | Equity research @ Goldman Sachs | MS in Data Science @ UC Berkeley | Now founder & CEO of Implied Implied) "One of the best-kept secrets: fundamental investors are not good at sizing. Quant funds are really good at sizing." We cover: - The only real line between quant and fundamental: historical pattern matching vs. "how is this time different" — and the alpha neither group is looking at - Why she rebuilt her process so every model updated within 2 minutes of a print - Scraping highway patrol data from 15 states to track auto insurance losses live, every single day - The Malibu wildfire: mapping burned mansions from celebrity tweets to estimate losses before any industry consultant published a number - Her automation math: data gathering ~100% automatable, processing ~80%, judgment still 100% human - AI is quant for words — next-token prediction is pattern matching, which makes this just the next automation wave after quant and indexing - The proof differentiated views pay more: insurance stocks moved 2-3% on earnings in 2010; by the time she left, 15-20% intraday - Why "hook Claude Code up to data and let it rip" fails: BloombergGPT losing to a smaller open-source model, & why horizontal models are college grads - Quant is blackjack with card counting; multi-manager investing is poker — your hand, others' perception of it, your seat, everyone's stack - Most PMs are playing the wrong game: the positioning game hiding inside "fundamental" sectors with no new money coming in - Her hiring bar at BAM: every fundamental analyst learns Python — and the one skill she says can't be trained - The only two truly meritocratic jobs: hedge fund PM & sales Highlights: (00:00) Intro (00:40) How a quantamental PM actually puts on a position (02:05) The only real line between quant and fundamental (04:45) Why quantamental lowers the burden on your brain (06:40) Scraping 15 states of highway patrol data to nowcast insurance losses (09:25) The Malibu wildfire: estimating losses from celebrity tweets (11:55) How much of fundamental investing can be automated (13:45) Quantifying intuition: when a CFO's filler words jump 8% to 20% (16:25) The contrarian case: automation expands demand for talent (18:15) Earnings vol exploded — differentiated views pay more (20:25) Why Claude Code can't run your book (23:40) Horizontal models are college grads with no domain knowledge (30:50) Why chat is the wrong interface for investors (36:20) Will AI make markets more or less efficient? (39:10) Two things every fundamental PM should do today (41:50) The moat that expands: talent, redefined (45:50) Sometimes the game is positioning, not fundamentals (48:25) Blackjack vs. poker vs. surfing: matching the game to your horizon (52:00) Should young analysts chase the hottest sector? (57:55) Munger vs. Musk: two philosophies of wealth (1:01:05) Self-awareness in investing is bimodal (1:07:05) The only two truly meritocratic jobs: hedge funds & sales (1:08:50) The one skill for every regime: reconstruct the narrative

Ethan Kho

248,035 次观看 • 2 个月前

The Asymmetric Operator ==================== The operators building agents right now are mostly stuck solving the wrong problem. I ran a live briefing on this the other week for a small room of operators already building with agents. Full video attached. Short version below. Almost every agent platform is within a rounding error of the others on raw capability. The agents can do the work. Most operators have figured out the real bottleneck is coordination, so they're switching orchestration tools every other month chasing a fix. Twelve-month read: coordination is closing at the platform layer. The platforms you're using have been quietly collecting your fix-it decisions all year, and that data is the training signal for agents that self-correct. If your edge is managing agent failures, you have nine months before the default tools absorb it. The layer that compounds past that is underneath the agents. A small set of files your whole system reads from, structured as a single source of truth instead of fifteen agents each guessing from a fresh prompt. Inside that layer, the piece almost nobody is building is thought patterns, your reasoning about the work logged as you make decisions. Ten minutes a day per cohort, voice-to-text is fine. At the end of the week pull out the five or six patterns that kept showing up and drop them into the cohort's master context file. Repeat for a month and the agents' outputs start reading like a sharper version of your own judgment rather than a generic model's best guess. Six months in, the operators I advise have agents making sharper calls than any competitor's agents, because those agents have been trained on half a year of their captured reasoning. No competitor can buy that. Living through the same six months is the only way to produce it. If you want more like this every week, see the next post or profile. **Chapters:** 00:00 Why this isn't about prompts or agents 04:25 The agent landscape right now 07:45 Why agent capability is commoditized 08:30 The e-commerce friend running 80% of his business on one agent platform 10:30 What the one-person billion-dollar company actually proves 14:25 The coordination problem and why it gets solved this year 19:00 How agent platforms are quietly learning from your fixes 22:30 Scattered vs. coordinated vs. redesigned businesses 24:45 What cognitive architecture really means 28:00 Thinking in cognitive labor instead of org charts 31:10 The file layer: claude.md, agents.md, skills, memory, context, heartbeat 38:30 What makes an architecture cognitive: thought patterns 40:10 The 10-minute daily decision journal that feeds your agents 42:30 Why your thought patterns don't need to be perfect 44:40 Where to start if you don't have agents yet 46:20 Thought patterns per agent team, not per whole business 48:20 Case study: SaaS consultancy with 7 agents and 3x capacity 51:15 Decomposing work into 7 units of cognitive labor 53:30 Month-by-month build timeline 56:00 The data set no competitor can copy 1:01:20 The maturity ladder: reactive, automated, attentive, autonomous 1:03:35 What's coming over the next 12 months 1:06:40 Your job when agents handle execution 1:08:00 Why now is the cheapest this will ever be Want more? See next post
1:09:18

The Asymmetric Operator ==================== The operators building agents right now are mostly stuck solving the wrong problem. I ran a live briefing on this the other week for a small room of operators already building with agents. Full video attached. Short version below. Almost every agent platform is within a rounding error of the others on raw capability. The agents can do the work. Most operators have figured out the real bottleneck is coordination, so they're switching orchestration tools every other month chasing a fix. Twelve-month read: coordination is closing at the platform layer. The platforms you're using have been quietly collecting your fix-it decisions all year, and that data is the training signal for agents that self-correct. If your edge is managing agent failures, you have nine months before the default tools absorb it. The layer that compounds past that is underneath the agents. A small set of files your whole system reads from, structured as a single source of truth instead of fifteen agents each guessing from a fresh prompt. Inside that layer, the piece almost nobody is building is thought patterns, your reasoning about the work logged as you make decisions. Ten minutes a day per cohort, voice-to-text is fine. At the end of the week pull out the five or six patterns that kept showing up and drop them into the cohort's master context file. Repeat for a month and the agents' outputs start reading like a sharper version of your own judgment rather than a generic model's best guess. Six months in, the operators I advise have agents making sharper calls than any competitor's agents, because those agents have been trained on half a year of their captured reasoning. No competitor can buy that. Living through the same six months is the only way to produce it. If you want more like this every week, see the next post or profile. **Chapters:** 00:00 Why this isn't about prompts or agents 04:25 The agent landscape right now 07:45 Why agent capability is commoditized 08:30 The e-commerce friend running 80% of his business on one agent platform 10:30 What the one-person billion-dollar company actually proves 14:25 The coordination problem and why it gets solved this year 19:00 How agent platforms are quietly learning from your fixes 22:30 Scattered vs. coordinated vs. redesigned businesses 24:45 What cognitive architecture really means 28:00 Thinking in cognitive labor instead of org charts 31:10 The file layer: claude.md, agents.md, skills, memory, context, heartbeat 38:30 What makes an architecture cognitive: thought patterns 40:10 The 10-minute daily decision journal that feeds your agents 42:30 Why your thought patterns don't need to be perfect 44:40 Where to start if you don't have agents yet 46:20 Thought patterns per agent team, not per whole business 48:20 Case study: SaaS consultancy with 7 agents and 3x capacity 51:15 Decomposing work into 7 units of cognitive labor 53:30 Month-by-month build timeline 56:00 The data set no competitor can copy 1:01:20 The maturity ladder: reactive, automated, attentive, autonomous 1:03:35 What's coming over the next 12 months 1:06:40 Your job when agents handle execution 1:08:00 Why now is the cheapest this will ever be Want more? See next post

Sam Woods

164,293 次观看 • 5 个月前

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 次观看 • 7 个月前

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1:06:52

Databricks' Ali Ghodsi on AI risk and adoption: Ali isn't losing sleep over the existential risk debate. He says a number of conditions would all have to be true simultaneously to enable an actual runaway takeoff scenario, and currently several opposite conditions exist. Each frontier training run requires significantly more resources. Power, GPUs, engineers - and some attempts fail, burning up huge piles of money with them. Until that reverses, he doesn't see the self-improving loop happening. Cyber is what he's watching most closely and where he anticipates real impact. Most orgs are not equipped for the coming change in agentic capabilities. The time between a vulnerability being published and being weaponized has collapsed from years to hours. On adoption, he believes most companies don't need a smarter model. The models are already smart enough. The gap is context they don't absorb - the things an employee who's worked at a company for five years learned by osmosis. If the frontier stopped advancing today, he thinks it wouldn't meaningfully change the value most are extracting from AI anyway. In conversation with a16z's Martin Casado and Sarah Wang: 00:00 Intro 00:48 Why Ali places the AI risk near zero 05:05 The word "pacing" was a mistake 10:50 What 10k agents and $100m can do 12:20 What would change his mind on AI risk 14:20 More GPUs, more ways to fail 18:05 US export controls on PlayStations 20:10 The damage everyone expected by now 24:30 Public vulnerabilities weaponized in hours 30:15 Why labs can't grade each other 37:15 Why most of RSI isn't actually RSI 40:05 Why nobody really needs a smarter model 41:50 The AI use cases nobody argues about 47:30 Google Search solved this 25 years ago 50:55 Nobody has privileged knowledge now 55:10 Same model, new harness, 2x cost 58:15 Open source: 5% of spend, 60% of tokens 1:05:30 90% of new databases are created by agents YouTube: Databricks martin_casado Sarah Wang

a16z

334,158 次观看 • 17 天前

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

755,741 次观看 • 4 个月前

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 次观看 • 9 个月前

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 次观看 • 7 个月前

"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 次观看 • 5 个月前

🦙 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 次观看 • 1 个月前

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 次观看 • 7 个月前

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 次观看 • 1 个月前

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 次观看 • 19 天前

“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 次观看 • 5 个月前

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 次观看 • 5 个月前

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 次观看 • 1 年前

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 次观看 • 11 个月前

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 次观看 • 2 个月前