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"If you're not tackling hard problems, why are you creating a startup?" Erik Bernhardsson was told to use Kubernetes. Instead he built Modal's file system and container runtime from the ground up, tuned for the speed AI workloads demand. With Modal, containers boot in under a second, and workloads...

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General Catalyst

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21,790 просмотров • 6 дней назад •via X (Twitter)

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We're honored to deepen our partnership with Modal and co-lead their $355M Series C! When Erik Bernhardsson and Akshat Bubna started Modal in 2021, they had conviction that building a truly great cloud for AI meant rebuilding the entire stack from the ground up. Five years later, that bet has paid off. Erik built the recommendation system behind Discover Weekly at Spotify. Akshat was an early engineer at Scale AI before joining Erik as co-founder and CTO. Together, they've assembled what one engineer affectionately calls "a monastery for super nerds" and it shows in the product. Modal has become the high-performance cloud that serious AI teams reach for when they need to ship. We couldn't be more excited to continue backing them. Huge congratulations to Erik, Akshat, and the entire Modal team!
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We're honored to deepen our partnership with Modal and co-lead their $355M Series C! When Erik Bernhardsson and Akshat Bubna started Modal in 2021, they had conviction that building a truly great cloud for AI meant rebuilding the entire stack from the ground up. Five years later, that bet has paid off. Erik built the recommendation system behind Discover Weekly at Spotify. Akshat was an early engineer at Scale AI before joining Erik as co-founder and CTO. Together, they've assembled what one engineer affectionately calls "a monastery for super nerds" and it shows in the product. Modal has become the high-performance cloud that serious AI teams reach for when they need to ship. We couldn't be more excited to continue backing them. Huge congratulations to Erik, Akshat, and the entire Modal team!

Redpoint

32,276 просмотров • 3 месяцев назад

In the first episode of our new series Full Stack, Conductor CEO and co-founder Charlie Holtz takes us into the details of how he sets up his workflow for coding and managing AI agents. 00:00 – Building Conductor With Conductor 01:05 – Managing a Team of Coding Agents 02:39 – Do You Still Write Code? 04:17 – Charlie's AI Stack and Setup 05:48 – “Slop-free” Zones 07:25 – Don’t Let the AI Be Your Architect 09:15 – The Future of Cloud Workspaces 10:40 – Claude vs Codex 12:01 – Tokenmaxxing 14:17 – The Future of Human–AI Collaboration 15:07 – "Code Is Becoming Sawdust"
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In the first episode of our new series Full Stack, Conductor CEO and co-founder Charlie Holtz takes us into the details of how he sets up his workflow for coding and managing AI agents. 00:00 – Building Conductor With Conductor 01:05 – Managing a Team of Coding Agents 02:39 – Do You Still Write Code? 04:17 – Charlie's AI Stack and Setup 05:48 – “Slop-free” Zones 07:25 – Don’t Let the AI Be Your Architect 09:15 – The Future of Cloud Workspaces 10:40 – Claude vs Codex 12:01 – Tokenmaxxing 14:17 – The Future of Human–AI Collaboration 15:07 – "Code Is Becoming Sawdust"

Y Combinator

156,937 просмотров • 3 месяцев назад

We're entering a new era of software where a single person, working with AI agents, can build products that previously required entire teams. In this episode of the Lightcone Podcast, they break down the rise of AI coding agents, "tokenmaxxing", and the emerging workflows behind tools like Claude Code and OpenClaw. They discuss why AI systems today feel less like productivity tools and more like collaborators, why the future of AI should be personal and user-controlled, and how founders are starting to build software in completely new ways. 00:00 — Will you control your AI? 00:47 — Coding again after 13 years 01:56 — Rebuilding a startup with Claude Code 05:50 — Software that thinks like a journalist 07:09 — The rise of “tokenmaxxing” 10:07 — The accidental creation of GStack 14:21 — The workflow behind 400x output 20:59 — Thin Harness, Fat Skills 24:35 — AI agents are like Ferraris 27:12 — The future of personal AI 38:37 — Buying back time with tokens
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We're entering a new era of software where a single person, working with AI agents, can build products that previously required entire teams. In this episode of the Lightcone Podcast, they break down the rise of AI coding agents, "tokenmaxxing", and the emerging workflows behind tools like Claude Code and OpenClaw. They discuss why AI systems today feel less like productivity tools and more like collaborators, why the future of AI should be personal and user-controlled, and how founders are starting to build software in completely new ways. 00:00 — Will you control your AI? 00:47 — Coding again after 13 years 01:56 — Rebuilding a startup with Claude Code 05:50 — Software that thinks like a journalist 07:09 — The rise of “tokenmaxxing” 10:07 — The accidental creation of GStack 14:21 — The workflow behind 400x output 20:59 — Thin Harness, Fat Skills 24:35 — AI agents are like Ferraris 27:12 — The future of personal AI 38:37 — Buying back time with tokens

Y Combinator

67,145 просмотров • 4 месяцев назад

Amjad Masad (Amjad Masad), co-founder & CEO of Replit, has spent nearly a decade making programming accessible to everyone. With AI, that vision is closer than ever. In his AI Startup School talk, Amjad traces computing’s arc from mainframes to PCs to a future where AI agents generate software on demand—driving the value of traditional software toward zero and reshaping how companies are built. 00:22 - Evolution of Computing 01:27 - The Rise of Software Engineering 02:16 - Replit's Vision: Making Programming Accessible 02:42 - The Role of AI in Software Development 04:32 - Challenges and Infrastructure for AI Agents 05:03 - Building a Scalable and Secure Environment 06:08 - Future of Software Engineering Agents 09:25 - Autonomy Levels in AI Agents 15:56 - Predictions for the Software Market 18:31 - Impact on Business and Employment 19:58 - The Role of Generalist Employees 26:00 - Audience Q&A
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Amjad Masad (Amjad Masad), co-founder & CEO of Replit, has spent nearly a decade making programming accessible to everyone. With AI, that vision is closer than ever. In his AI Startup School talk, Amjad traces computing’s arc from mainframes to PCs to a future where AI agents generate software on demand—driving the value of traditional software toward zero and reshaping how companies are built. 00:22 - Evolution of Computing 01:27 - The Rise of Software Engineering 02:16 - Replit's Vision: Making Programming Accessible 02:42 - The Role of AI in Software Development 04:32 - Challenges and Infrastructure for AI Agents 05:03 - Building a Scalable and Secure Environment 06:08 - Future of Software Engineering Agents 09:25 - Autonomy Levels in AI Agents 15:56 - Predictions for the Software Market 18:31 - Impact on Business and Employment 19:58 - The Role of Generalist Employees 26:00 - Audience Q&A

Y Combinator

224,320 просмотров • 1 год назад

Tony James on Costco, Starbucks, Blackstone, and Charlie Munger Tony James is an investor, Chairman of the Board at Costco, and former President and COO of Blackstone. In this conversation, he joins a16z GP David Haber for a conversation on scaling Blackstone from $14 billion to nearly $1 trillion, lessons from 38 years on the board of Costco, the future of private markets, and more. 00:00 Intro 00:45 Starting at DLJ: Getting in on the ground floor 03:05 The LBO revolution & building a merchant bank 09:09 Leading the Series A into Costco & lessons from Jim Sinegal 15:00 38 years on the Costco board & learning from Charlie Munger 25:17 Joining Blackstone: The partnership with Steve Schwarzman 33:44 Blackstone's journey from $14 billion to nearly $1 trillion 44:09 Investment committee culture & the art of robust debate 53:41 The IPO, retail distribution, and building competitive moats 01:05:39 Succession planning & knowing when to step away 01:10:00 The future of private markets 01:15:24 Advice for young people David Haber
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Tony James on Costco, Starbucks, Blackstone, and Charlie Munger Tony James is an investor, Chairman of the Board at Costco, and former President and COO of Blackstone. In this conversation, he joins a16z GP David Haber for a conversation on scaling Blackstone from $14 billion to nearly $1 trillion, lessons from 38 years on the board of Costco, the future of private markets, and more. 00:00 Intro 00:45 Starting at DLJ: Getting in on the ground floor 03:05 The LBO revolution & building a merchant bank 09:09 Leading the Series A into Costco & lessons from Jim Sinegal 15:00 38 years on the Costco board & learning from Charlie Munger 25:17 Joining Blackstone: The partnership with Steve Schwarzman 33:44 Blackstone's journey from $14 billion to nearly $1 trillion 44:09 Investment committee culture & the art of robust debate 53:41 The IPO, retail distribution, and building competitive moats 01:05:39 Succession planning & knowing when to step away 01:10:00 The future of private markets 01:15:24 Advice for young people David Haber

a16z

212,474 просмотров • 4 месяцев назад

In 2005, Sam Altman was a Stanford sophomore in YC’s first batch, building a startup in a little Cambridge office while Paul Graham cooked the founders dinner. Twenty years later, as co-founder & CEO of OpenAI, Sam closed Startup School 2026 in conversation with Garry Tan — on agents, ambition, and why the ceiling for what a startup can take on has never been higher. 00:07 — From YC’s First Batch to Today 03:04 — What PG Taught Sam 04:17 — Why Startups Matter More Than Ever 06:57 — The Coming Golden Age of Ambitious Startups 09:30 — Why Startups Win During Technology Shifts 11:46 — Building OpenAI When Nobody Believed in AGI 14:45 — Finding People Who Share Your Conviction 18:05 — Help People Before You Know Why 19:43 — Earnestness, Ambition, and Ignoring the Haters 24:04 — The AI Safety Incident That Changed the Stakes 26:58 — Preventing AI From Concentrating Power 30:41 — How Fast AI Models Will Improve 36:06 — The Best Version of an AI Future 37:47 — “It’s All Going to Work Out”
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In 2005, Sam Altman was a Stanford sophomore in YC’s first batch, building a startup in a little Cambridge office while Paul Graham cooked the founders dinner. Twenty years later, as co-founder & CEO of OpenAI, Sam closed Startup School 2026 in conversation with Garry Tan — on agents, ambition, and why the ceiling for what a startup can take on has never been higher. 00:07 — From YC’s First Batch to Today 03:04 — What PG Taught Sam 04:17 — Why Startups Matter More Than Ever 06:57 — The Coming Golden Age of Ambitious Startups 09:30 — Why Startups Win During Technology Shifts 11:46 — Building OpenAI When Nobody Believed in AGI 14:45 — Finding People Who Share Your Conviction 18:05 — Help People Before You Know Why 19:43 — Earnestness, Ambition, and Ignoring the Haters 24:04 — The AI Safety Incident That Changed the Stakes 26:58 — Preventing AI From Concentrating Power 30:41 — How Fast AI Models Will Improve 36:06 — The Best Version of an AI Future 37:47 — “It’s All Going to Work Out”

Y Combinator

69,478 просмотров • 1 месяц назад

My conversation with Jesse Zhang Decagon is building a customer service agent with AI, with plans for much more than that. It’s one of the more successful early AI applications, with tons of lessons for the rest of the market. I love how Jesse developed which problem to solve first, a set of lessons applicable to all entrepreneurs Enjoy Timestamps: 0:00 Intro 2:00 Competing in the AI Era 11:35 Finding Product-Market Fit 14:25 Anatomy of a Great Customer Interview 17:37 Everyone’s Wrong Until You Talk to Customers 19:25 AI Agents Replacing Human Labor 21:55 Why Customer Service 23:30 Coding vs. Customer Service 35:44 The Future of Voice and the Human UI 38:54 Why Voice-to-Voice Is So Hard 43:18 Learning from Customer Data 44:36 How CEOs Should Think About AI 47:06 The ROI of Coding and Productivity Tools 48:20 Every Brand Will Talk to You Through AI 56:00 Recruiting, Capital, and Demand 1:00:42 Investor Frenzy 1:22:17 The Long-Term Vision for Decagon 1:25:05 Kindest Thing
1:26:12

My conversation with Jesse Zhang Decagon is building a customer service agent with AI, with plans for much more than that. It’s one of the more successful early AI applications, with tons of lessons for the rest of the market. I love how Jesse developed which problem to solve first, a set of lessons applicable to all entrepreneurs Enjoy Timestamps: 0:00 Intro 2:00 Competing in the AI Era 11:35 Finding Product-Market Fit 14:25 Anatomy of a Great Customer Interview 17:37 Everyone’s Wrong Until You Talk to Customers 19:25 AI Agents Replacing Human Labor 21:55 Why Customer Service 23:30 Coding vs. Customer Service 35:44 The Future of Voice and the Human UI 38:54 Why Voice-to-Voice Is So Hard 43:18 Learning from Customer Data 44:36 How CEOs Should Think About AI 47:06 The ROI of Coding and Productivity Tools 48:20 Every Brand Will Talk to You Through AI 56:00 Recruiting, Capital, and Demand 1:00:42 Investor Frenzy 1:22:17 The Long-Term Vision for Decagon 1:25:05 Kindest Thing

Patrick OShaughnessy

49,418 просмотров • 11 месяцев назад

Claude Code cracked something open for us Every 🧱. Now I ship to codebases I barely know, every feature we ship makes the next one easier, and non-technical members of the team use the terminal. I’m genuinely grateful. So I brought its creators, Cat Wu (cat) and Boris Cherny (Boris Cherny) from Anthropic, on AI & I to say thank you—and to talk about everything they’ve learned from building Claude Code. We get into: • The workflows Anthropic’s smartest engineers use to push Claude Code to its limits. Why they pit subagents against each other to get cleaner results, how they turn past code into leverage, and the slash commands and MCPs they rely on most. • The product lessons behind one of the most loved AI agents in the world. How the team balances simplicity and power—building a tool that anyone can use, but that experts can bend to their will—and their philosophy of “unshipping,” or cutting back whenever there’s a simpler, more intuitive path to user intent. • A peek into the future of coding with AI. The new form factors they’re experimenting with to make Claude Code more autonomous, more reliable, and more accessible to non-technical users This is a must-watch for anyone—both technical and non-technical—who wants to learn how to use Claude Code like the people who built it. Watch below! Timestamps: Introduction: 00:01:26 Claude Code’s origin story: 00:02:25 How Anthropic dogfoods Claude Code: 00:07:03 Boris and Cat’s favorite slash commands: 00:14:06 How Boris uses Claude Code to plan feature development: 00:15:49 Everything Anthropic has learned about using sub-agents well: 00:21:53 Use Claude Code to turn past code into leverage: 00:26:16 The product decisions for building an agent that’s simple and powerful: 00:33:14 Making Claude Code accessible to the non-technical user: 00:36:38 The next form factor for coding with AI: 00:45:12
1:10:11

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

Dan Shipper 📧

57,619 просмотров • 10 месяцев назад

Boris Cherny, the creator of Claude Code at Anthropic, just explained how he runs thousands of agents from his phone while he sleeps in this talk he breaks down exactly how he manages thousands of agents from his phone: - the 14% you lose to CLAUDE.md before typing a word - the architecture behind running a few thousand agents overnight - why the system matters more than the tool - the knowledge structure that makes everything findable, connected, and useful if you've been using Claude for months and every conversation still starts from scratch with zero memory and zero context, you don't have a system. you have a chat history instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed the guide is in the article below
57:23

Boris Cherny, the creator of Claude Code at Anthropic, just explained how he runs thousands of agents from his phone while he sleeps in this talk he breaks down exactly how he manages thousands of agents from his phone: - the 14% you lose to CLAUDE.md before typing a word - the architecture behind running a few thousand agents overnight - why the system matters more than the tool - the knowledge structure that makes everything findable, connected, and useful if you've been using Claude for months and every conversation still starts from scratch with zero memory and zero context, you don't have a system. you have a chat history instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed the guide is in the article below

Khairallah AL-Awady

86,440 просмотров • 3 месяцев назад

We asked a dozen DevTool founders from companies like RevenueCat, Greptile, Firecrawl, Infisical, ollama, Resend, Mintlify, Unsloth AI, Porter, and recall.ai, about the state of AI agents and the future of software engineering. In this episode of Founder FAQ, we covered everything from agents as customers and the end of coding, to advice for founders starting out and what they're most excited about going forward. Their answers might surprise you. 00:00 – Meet the Founders 03:00 – Building for Agents First 04:22 – Biggest Early Mistakes 07:15 – Do Founders Still Write Code? 09:22 – Most Unexpected AI Discoveries 12:09 – What's Underrated Right Now 14:38 – Predictions & What's Next
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We asked a dozen DevTool founders from companies like RevenueCat, Greptile, Firecrawl, Infisical, ollama, Resend, Mintlify, Unsloth AI, Porter, and recall.ai, about the state of AI agents and the future of software engineering. In this episode of Founder FAQ, we covered everything from agents as customers and the end of coding, to advice for founders starting out and what they're most excited about going forward. Their answers might surprise you. 00:00 – Meet the Founders 03:00 – Building for Agents First 04:22 – Biggest Early Mistakes 07:15 – Do Founders Still Write Code? 09:22 – Most Unexpected AI Discoveries 12:09 – What's Underrated Right Now 14:38 – Predictions & What's Next

Y Combinator

27,672 просмотров • 3 месяцев назад

Engineers need to communicate effectively when building AI Systems. These terms will help you use a shared vocabulary. This is useful when discussing concepts, reading papers, or collaborating with teammates. Listed in order. 00:00 Agenda 00:28 1. Large Language Model 01:28 2. Tokenization 02:53 3. Vectorization 04:15 4. Attention 07:22 5. Self-Supervised Learning 12:07 6. Transformer 14:32 7. Fine-tuning 17:05 8. Few-shot Prompting 18:11 9. Retrieval Augmented Generation 20:33 10. Vector Database 23:03 11. Model Context Protocol 25:43 12. Context Engineering 28:17 13. Agents 29:19 14. Reinforcement Learning 34:42 15. Chain of Thought 35:55 16. Reasoning Models 36:36 17. Multi-modal Models 38:21 18. Small Language Models 40:24 19. Distillation 41:47 20. Quantization If you are a software engineer looking to transition to AI, click the link below. AI Engineering Course: #AI #SoftwareEngineering #Agents
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Engineers need to communicate effectively when building AI Systems. These terms will help you use a shared vocabulary. This is useful when discussing concepts, reading papers, or collaborating with teammates. Listed in order. 00:00 Agenda 00:28 1. Large Language Model 01:28 2. Tokenization 02:53 3. Vectorization 04:15 4. Attention 07:22 5. Self-Supervised Learning 12:07 6. Transformer 14:32 7. Fine-tuning 17:05 8. Few-shot Prompting 18:11 9. Retrieval Augmented Generation 20:33 10. Vector Database 23:03 11. Model Context Protocol 25:43 12. Context Engineering 28:17 13. Agents 29:19 14. Reinforcement Learning 34:42 15. Chain of Thought 35:55 16. Reasoning Models 36:36 17. Multi-modal Models 38:21 18. Small Language Models 40:24 19. Distillation 41:47 20. Quantization If you are a software engineer looking to transition to AI, click the link below. AI Engineering Course: #AI #SoftwareEngineering #Agents

Gaurav Sen

80,282 просмотров • 11 месяцев назад

AI INTERVIEW: OPENAI'S SECRET WEAPON AI agents are no longer just hype—they're here to revolutionize automation, Web3, and beyond. SwarmNode.ai is building a serverless AI agent platform for scalability, efficiency, and real-world impact. In this exclusive interview, he reveals how AI swarms can outperform single models, why OpenAI’s Operator is just the beginning, and how crypto is fueling AI innovation. Plus, he breaks down DeepSeek’s game-changing AI breakthrough, the future of agent monetization, and why serverless AI could be the next frontier in automation. 01:37 – From Engineering to AI: The journey into artificial intelligence. 02:43 – The GPT-3 Moment: How OpenAI’s tech pulled him in. 04:10 – AI’s Biggest Challenge: Why real-world use cases lag behind. 05:05 – OpenAI’s Operator: Why it’s “rudimentary” (for now). 06:25 – Crypto & AI: How tokens help bootstrap AI startups. 08:15 – Can You Bootstrap a Startup with a Token? The trade-offs. 09:56 – 90% of AI Token Holders Don’t Use the Product—Does It Matter? 11:18 – What is SwarmNode?: AI agents, hosted serverlessly. 14:23 – AI Swarms: Why multiple agents outperform single models. 16:08 – What is a Swarm? A simple definition of collaborative AI. 17:32 – “How Can I Make Money with AI?”: Real-world use cases. 18:41 – AI Bounties: Hiring devs to build your custom agent. 20:50 – The Future of AI Marketplaces: Monetizing pre-built agents. 23:15 – DeepSeek’s Disruption: Why it’s good news for AI. 24:46 – Is SwarmNode Compatible with DeepSeek? How it integrates. 26:17 – SwarmNode vs. AI Launchpads: What makes it different? 27:42 – Why Serverless Matters: Cost savings & efficiency. 29:53 – AI Agents in the Real World: Booking flights, managing workflows, and more. 31:11 – Building SwarmNode for Developers: Why it started as a personal project. 32:27 – Explosive Growth: 200,000 AI agent executions in 5 weeks. 34:41 – Why SwarmNode Agents Aren’t Visible on 𝕏 Yet. 36:46 – Startup Hiring Lessons: Finding top AI talent. 39:15 – Why SwarmNode is Built in Python (and What’s Next). 40:32 – Scaling AI Workloads: Handling traffic surges. 41:42 – AWS & Cost Challenges: The biggest monetization hurdle. 42:58 – 2025: The Year of Mass AI Adoption. 45:22 – Should We Be Worried About AI’s Rapid Growth? 46:46 – The Most Underrated AI Tools Right Now. 47:34 – What’s Next for SwarmNode?: Making AI accessible to everyone.
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AI INTERVIEW: OPENAI'S SECRET WEAPON AI agents are no longer just hype—they're here to revolutionize automation, Web3, and beyond. SwarmNode.ai is building a serverless AI agent platform for scalability, efficiency, and real-world impact. In this exclusive interview, he reveals how AI swarms can outperform single models, why OpenAI’s Operator is just the beginning, and how crypto is fueling AI innovation. Plus, he breaks down DeepSeek’s game-changing AI breakthrough, the future of agent monetization, and why serverless AI could be the next frontier in automation. 01:37 – From Engineering to AI: The journey into artificial intelligence. 02:43 – The GPT-3 Moment: How OpenAI’s tech pulled him in. 04:10 – AI’s Biggest Challenge: Why real-world use cases lag behind. 05:05 – OpenAI’s Operator: Why it’s “rudimentary” (for now). 06:25 – Crypto & AI: How tokens help bootstrap AI startups. 08:15 – Can You Bootstrap a Startup with a Token? The trade-offs. 09:56 – 90% of AI Token Holders Don’t Use the Product—Does It Matter? 11:18 – What is SwarmNode?: AI agents, hosted serverlessly. 14:23 – AI Swarms: Why multiple agents outperform single models. 16:08 – What is a Swarm? A simple definition of collaborative AI. 17:32 – “How Can I Make Money with AI?”: Real-world use cases. 18:41 – AI Bounties: Hiring devs to build your custom agent. 20:50 – The Future of AI Marketplaces: Monetizing pre-built agents. 23:15 – DeepSeek’s Disruption: Why it’s good news for AI. 24:46 – Is SwarmNode Compatible with DeepSeek? How it integrates. 26:17 – SwarmNode vs. AI Launchpads: What makes it different? 27:42 – Why Serverless Matters: Cost savings & efficiency. 29:53 – AI Agents in the Real World: Booking flights, managing workflows, and more. 31:11 – Building SwarmNode for Developers: Why it started as a personal project. 32:27 – Explosive Growth: 200,000 AI agent executions in 5 weeks. 34:41 – Why SwarmNode Agents Aren’t Visible on 𝕏 Yet. 36:46 – Startup Hiring Lessons: Finding top AI talent. 39:15 – Why SwarmNode is Built in Python (and What’s Next). 40:32 – Scaling AI Workloads: Handling traffic surges. 41:42 – AWS & Cost Challenges: The biggest monetization hurdle. 42:58 – 2025: The Year of Mass AI Adoption. 45:22 – Should We Be Worried About AI’s Rapid Growth? 46:46 – The Most Underrated AI Tools Right Now. 47:34 – What’s Next for SwarmNode?: Making AI accessible to everyone.

Mario Nawfal

338,311 просмотров • 1 год назад

In this special live event, Founders In Arms Podcast brings together a room full of founders at Mercury's San Francisco headquarters for an intimate conversation about building, investing, and the future of AI. Max Mullen is co-founder at Instacart, where he led product and grew it from a contrarian idea into the leader in online grocery delivery and eventually a publicly traded company. Now Max Mullen is an active investor and runs a founder community in San Francisco called Workshop. Max shares lessons from over a decade of building and his perspective on AI's transformative potential. In this episode, we cover: (00:00) Welcome to the first live Founders in Arms event at Mercury HQ (00:43) Introduction to Max Mullen and his early investment in Mercury (03:58) Max's origin story and first startup at Startup Weekend (04:36) Building a social network and getting acquihired (05:39) The path from acquihire to founding Instacart (06:19) Why Max wanted to solve real-world operational problems (07:17) Starting Instacart when there was no gig economy (09:15) Overcoming the "grocery delivery is dead" narrative (11:48) Market timing and why Webvan was too early (14:37) Being a non-technical co-founder in a technical role (17:45) The PARE framework for building company culture (22:09) Max's approach to M&A at Instacart (24:00) The Caper smart shopping cart acquisition story (25:31) How founders can position for acquisition (28:26) Why product-market fit attracts acquirers (29:46) Evaluating investments: market size vs. founder quality (32:54) Max and Immad's differing investment philosophies (35:39) The current state of VC funding and AI companies (36:51) The three best times to raise money as a founder (39:15) Instacart's seasonal business patterns and fundraising timing (39:59) The most interesting opportunities in AI right now (40:24) Why every profession will have AI agent co-pilots (41:44) What AI-native companies will look like for the next generation
42:26

In this special live event, Founders In Arms Podcast brings together a room full of founders at Mercury's San Francisco headquarters for an intimate conversation about building, investing, and the future of AI. Max Mullen is co-founder at Instacart, where he led product and grew it from a contrarian idea into the leader in online grocery delivery and eventually a publicly traded company. Now Max Mullen is an active investor and runs a founder community in San Francisco called Workshop. Max shares lessons from over a decade of building and his perspective on AI's transformative potential. In this episode, we cover: (00:00) Welcome to the first live Founders in Arms event at Mercury HQ (00:43) Introduction to Max Mullen and his early investment in Mercury (03:58) Max's origin story and first startup at Startup Weekend (04:36) Building a social network and getting acquihired (05:39) The path from acquihire to founding Instacart (06:19) Why Max wanted to solve real-world operational problems (07:17) Starting Instacart when there was no gig economy (09:15) Overcoming the "grocery delivery is dead" narrative (11:48) Market timing and why Webvan was too early (14:37) Being a non-technical co-founder in a technical role (17:45) The PARE framework for building company culture (22:09) Max's approach to M&A at Instacart (24:00) The Caper smart shopping cart acquisition story (25:31) How founders can position for acquisition (28:26) Why product-market fit attracts acquirers (29:46) Evaluating investments: market size vs. founder quality (32:54) Max and Immad's differing investment philosophies (35:39) The current state of VC funding and AI companies (36:51) The three best times to raise money as a founder (39:15) Instacart's seasonal business patterns and fundraising timing (39:59) The most interesting opportunities in AI right now (40:24) Why every profession will have AI agent co-pilots (41:44) What AI-native companies will look like for the next generation

Rajat Suri

144,197 просмотров • 7 месяцев назад

Nat Eliason’s (Nat Eliason) career arc is borderline absurd—but it works. He’ll spot a new tool or trend, master it, build a business around it, and move on. Nat’s pulled it off with the note-taking wave ($600k in sales from a Roam Research course), real estate (6x return flipping property in Austin), and crypto (published his insider story with Random House). Now it’s AI: he’s running a viral course on building apps with AI—$200k in pre-sales in just a week, 800 students and counting. I’ve known Nat for a long time and I think he has a great sense for where the puck is headed. He was one of the first guests I had on the podcast and I was delighted to have him on again. Here are a few takeaways from our conversation: - Coding with AI has become orders of magnitude easier for non-technical people over the last 2 years—Nat rarely has to help students fix bugs; they troubleshoot in Cursor on their own. - AI coding assistants are creating new behaviours in programming, like using a speech-to-text model to talk to an agent and having it write code for you. - The traditional learning curve of coding is flattening because AI tools let beginners build and iterate in faster feedback loops. - AI has given Nat leverage in spades—it increases his ability to be a creator while also building a robust business with as few people to manage as possible. He demos an AI book editor he coded for his sci-fi novel. - In the age of AI, software is becoming content and the barriers to create are lower than ever—but custom software for everything isn’t the answer. Nat’s model is that personalized tools make sense for that one thing you care the most about. - Nat believes that the future of writing with AI is a Cursor-style interface with a model that’s trained on your style and voice. This episode is a must-watch for writers, creators, and anyone interested in the future of product building. Watch below! Timestamps: Introduction: 00:01:45 The origins of Nat’s viral course on building apps with AI: 00:11:45 How coding with AI has evolved over the last two years: 00:18:46 Nat creates an app using Composer, Cursor’s AI assistant: 00:22:22 Tactical tips for coding with Cursor: 00:26:06 How coding with AI is creating new behaviours in programming: 00:29:06 What excites Nat the most about the future of AI: 00:32:41 A demo of Hubbard, the AI editor Nat built for his science fiction writing: 00:38:58 When does it makes sense to build custom software: 00:44:52 Nat’s take on the future of writing with AI: 00:49:18
1:00:52

Nat Eliason’s (Nat Eliason) career arc is borderline absurd—but it works. He’ll spot a new tool or trend, master it, build a business around it, and move on. Nat’s pulled it off with the note-taking wave ($600k in sales from a Roam Research course), real estate (6x return flipping property in Austin), and crypto (published his insider story with Random House). Now it’s AI: he’s running a viral course on building apps with AI—$200k in pre-sales in just a week, 800 students and counting. I’ve known Nat for a long time and I think he has a great sense for where the puck is headed. He was one of the first guests I had on the podcast and I was delighted to have him on again. Here are a few takeaways from our conversation: - Coding with AI has become orders of magnitude easier for non-technical people over the last 2 years—Nat rarely has to help students fix bugs; they troubleshoot in Cursor on their own. - AI coding assistants are creating new behaviours in programming, like using a speech-to-text model to talk to an agent and having it write code for you. - The traditional learning curve of coding is flattening because AI tools let beginners build and iterate in faster feedback loops. - AI has given Nat leverage in spades—it increases his ability to be a creator while also building a robust business with as few people to manage as possible. He demos an AI book editor he coded for his sci-fi novel. - In the age of AI, software is becoming content and the barriers to create are lower than ever—but custom software for everything isn’t the answer. Nat’s model is that personalized tools make sense for that one thing you care the most about. - Nat believes that the future of writing with AI is a Cursor-style interface with a model that’s trained on your style and voice. This episode is a must-watch for writers, creators, and anyone interested in the future of product building. Watch below! Timestamps: Introduction: 00:01:45 The origins of Nat’s viral course on building apps with AI: 00:11:45 How coding with AI has evolved over the last two years: 00:18:46 Nat creates an app using Composer, Cursor’s AI assistant: 00:22:22 Tactical tips for coding with Cursor: 00:26:06 How coding with AI is creating new behaviours in programming: 00:29:06 What excites Nat the most about the future of AI: 00:32:41 A demo of Hubbard, the AI editor Nat built for his science fiction writing: 00:38:58 When does it makes sense to build custom software: 00:44:52 Nat’s take on the future of writing with AI: 00:49:18

Dan Shipper 📧

27,207 просмотров • 1 год назад

Noah Brier (Noah Brier) uses Claude Code as his second brain—it’s the coolest notetaking setup I’ve ever seen. He has Claude running on a server in his basement hooked up to a VPN. It stores, reads, and writes to thousands of notes in his Obsidian (Obsidian) vault. He does it all from his phone. I had him on the show to tell us exactly how he’s pulling this off. We get into: - The nuts and bolts of the Claude Code-Obsidian setup: Noah set up Claude Code on top of his Obsidian root directory, and he walked me through how he uses it to prep for an upcoming speech—creating a project folder, pulling in relevant research from his notes, saving transcripts from chats with other LLMs, and generating daily progress updates. - The “thinking partner” that lives inside Noah’s second brain: Noah points out that in the hype around AI’s ability to write, the fact that it can read is overlooked. That’s why he has an agent inside Claude Code with strict guardrails to stay in “thinking mode.” It logs his questions, tracks insights, and catches him up on research if he returns to a project after a few days away. - How Noah does deep work on his phone: Noah rigged a home server in his basement, put his Obsidian vault in it—and then runs Claude Code on top. Noah says that being able to think, write, research, and ship code from his phone has fundamentally changed the way he works. This episode of Every 📧’s AI & I is a must-watch for anyone curious about who wants to learn how to use Claude Code to build a true second brain. Watch below! Timestamps: Introduction: 00:01:19 How you can do deep work on your phone: 00:04:28 Why Noah thinks Grok has the best voice AI: 00:06:14 The nuts and bolts of Noah’s Claude Code-Obsidian setup: 00:11:39 Using an agent in Claude Code as a “thinking partner”: 00:23:59 Noah’s Thomas’ English Muffin theory of AI: 00:35:07 The white space still left to explore in AI: 00:44:04 How Noah is preparing his kids for AI: 00:50:41 How he brought his Claude Code setup to mobile: 01:01:54
1:11:52

Noah Brier (Noah Brier) uses Claude Code as his second brain—it’s the coolest notetaking setup I’ve ever seen. He has Claude running on a server in his basement hooked up to a VPN. It stores, reads, and writes to thousands of notes in his Obsidian (Obsidian) vault. He does it all from his phone. I had him on the show to tell us exactly how he’s pulling this off. We get into: - The nuts and bolts of the Claude Code-Obsidian setup: Noah set up Claude Code on top of his Obsidian root directory, and he walked me through how he uses it to prep for an upcoming speech—creating a project folder, pulling in relevant research from his notes, saving transcripts from chats with other LLMs, and generating daily progress updates. - The “thinking partner” that lives inside Noah’s second brain: Noah points out that in the hype around AI’s ability to write, the fact that it can read is overlooked. That’s why he has an agent inside Claude Code with strict guardrails to stay in “thinking mode.” It logs his questions, tracks insights, and catches him up on research if he returns to a project after a few days away. - How Noah does deep work on his phone: Noah rigged a home server in his basement, put his Obsidian vault in it—and then runs Claude Code on top. Noah says that being able to think, write, research, and ship code from his phone has fundamentally changed the way he works. This episode of Every 📧’s AI & I is a must-watch for anyone curious about who wants to learn how to use Claude Code to build a true second brain. Watch below! Timestamps: Introduction: 00:01:19 How you can do deep work on your phone: 00:04:28 Why Noah thinks Grok has the best voice AI: 00:06:14 The nuts and bolts of Noah’s Claude Code-Obsidian setup: 00:11:39 Using an agent in Claude Code as a “thinking partner”: 00:23:59 Noah’s Thomas’ English Muffin theory of AI: 00:35:07 The white space still left to explore in AI: 00:44:04 How Noah is preparing his kids for AI: 00:50:41 How he brought his Claude Code setup to mobile: 01:01:54

Dan Shipper 📧

30,792 просмотров • 1 год назад

why agents need VMs, not containers with David Crawshaw, ex-CTO & co-founder of Tailscale now co-founder and CEO of exe - a new up-and-coming cloud provider Timestamps (0:00) why build a new cloud? (2:07) why Docker isn't enough for agents (12:28) why AI-friendly is developer-friendly (20:32) why VMs are the right abstraction (and the serendipity of just dropping an idea prompt from your phone) (28:30) the exorbitant price of IOPS in the cloud (32:21) Cloud Discounts (33:40) the rise of self hosting (41:25) Shelly and AI ops agents (48:10) the hard problem with AI SREs (53:00) parting thoughts and early EC2’s noisy neighbor shenanigans
56:55

why agents need VMs, not containers with David Crawshaw, ex-CTO & co-founder of Tailscale now co-founder and CEO of exe - a new up-and-coming cloud provider Timestamps (0:00) why build a new cloud? (2:07) why Docker isn't enough for agents (12:28) why AI-friendly is developer-friendly (20:32) why VMs are the right abstraction (and the serendipity of just dropping an idea prompt from your phone) (28:30) the exorbitant price of IOPS in the cloud (32:21) Cloud Discounts (33:40) the rise of self hosting (41:25) Shelly and AI ops agents (48:10) the hard problem with AI SREs (53:00) parting thoughts and early EC2’s noisy neighbor shenanigans

Stanislav Kozlovski

125,300 просмотров • 3 месяцев назад

Documentation used to eat my context window. Two MCPs cut that to 2,800 tokens. ref tools and exa ai 🕒 Key moments 00:00 Introducing ref tools and exa ai MCPs 02:08 Configuring ref tools in Claude Code 03:27 Configuring exa ai in Claude Code 04:37 Demo: Using MCPs to upgrade a Tailwind CSS project 07:22 How to configure MCPs in Cursor 10:14 Comparing Claude Code vs. Cursor performance 12:37 Setting up MCPs for Codex 15:41 Setting up MCPs for DroidFactory 16:40 Pro-tip: How to prompt agents to use MCPs 18:00 The future of AI coding and final thoughts
18:50

Documentation used to eat my context window. Two MCPs cut that to 2,800 tokens. ref tools and exa ai 🕒 Key moments 00:00 Introducing ref tools and exa ai MCPs 02:08 Configuring ref tools in Claude Code 03:27 Configuring exa ai in Claude Code 04:37 Demo: Using MCPs to upgrade a Tailwind CSS project 07:22 How to configure MCPs in Cursor 10:14 Comparing Claude Code vs. Cursor performance 12:37 Setting up MCPs for Codex 15:41 Setting up MCPs for DroidFactory 16:40 Pro-tip: How to prompt agents to use MCPs 18:00 The future of AI coding and final thoughts

Ray Fernando

17,919 просмотров • 9 месяцев назад

The end of coding? *Epic* conversation with Guillermo Rauch of Vercel on vibe coding, MCP, agents and building products in the AI era. (00:00) - Intro: Guillermo's Story (02:08) - What is v0 and Why Did It Take Off So Fast? (04:10) - Building a hit product with < people (07:51) - Cursor, Replit, etc: v0 vs Other AI Coding Tools (10:35) - Everyone can cook - What is Vibe Coding? (17:05) - Is v0 Just Frontend? Moving Toward Full Stack 19:40 - How to Become a Great Vibe Coder (23:35) - A GUI for AI: The Future of Interfaces (29:46) - Developer Love vs Agent Love (33:41) - The Importance of Taste in the AI Era (39:10) - MCP: The Protocol for AI-to-AI Communication (43:11) - Security and the Risks of Agentic Web (45:25) - Are Enterprises Ready for the Agentic Future? (49:42) - Building a Data Flywheel (56:06) - The Vercel AI Cloud: From Pixels to Tokens (1:10:14) - Adapting to a Non-Developer Persona (1:13:47) - Retention, Margins, and the Business of AI (1:16:51) - The Secret Behind Vercel Last Year Growth (1:24:15) - The importance of Online Presence (1:30:49) - Everything, Everywhere, All at Once: Keeping your Sanity as a CEO (1:34:59) - Guillermo's Advice to Younger Self
1:45:40

The end of coding? *Epic* conversation with Guillermo Rauch of Vercel on vibe coding, MCP, agents and building products in the AI era. (00:00) - Intro: Guillermo's Story (02:08) - What is v0 and Why Did It Take Off So Fast? (04:10) - Building a hit product with < people (07:51) - Cursor, Replit, etc: v0 vs Other AI Coding Tools (10:35) - Everyone can cook - What is Vibe Coding? (17:05) - Is v0 Just Frontend? Moving Toward Full Stack 19:40 - How to Become a Great Vibe Coder (23:35) - A GUI for AI: The Future of Interfaces (29:46) - Developer Love vs Agent Love (33:41) - The Importance of Taste in the AI Era (39:10) - MCP: The Protocol for AI-to-AI Communication (43:11) - Security and the Risks of Agentic Web (45:25) - Are Enterprises Ready for the Agentic Future? (49:42) - Building a Data Flywheel (56:06) - The Vercel AI Cloud: From Pixels to Tokens (1:10:14) - Adapting to a Non-Developer Persona (1:13:47) - Retention, Margins, and the Business of AI (1:16:51) - The Secret Behind Vercel Last Year Growth (1:24:15) - The importance of Online Presence (1:30:49) - Everything, Everywhere, All at Once: Keeping your Sanity as a CEO (1:34:59) - Guillermo's Advice to Younger Self

Matt Turck

86,153 просмотров • 1 год назад

AI coding is changing faster than almost anyone can keep up. It's impossible to predict exactly where we'll be a year from now. Claude Code's Thariq ( alum and 2x founder) came back to SPC to talk about what's on the frontier. (00:00) From Startup Founder to Anthropic (03:33) Building Better AI With Claude Code (07:21) The Capability Overhang (12:17) How Anthropic Builds AI Products (17:51) Prompting Claude More Effectively (30:58) How AI Is Changing Product Teams (41:11) Advice for AI Founders
46:55

AI coding is changing faster than almost anyone can keep up. It's impossible to predict exactly where we'll be a year from now. Claude Code's Thariq ( alum and 2x founder) came back to SPC to talk about what's on the frontier. (00:00) From Startup Founder to Anthropic (03:33) Building Better AI With Claude Code (07:21) The Capability Overhang (12:17) How Anthropic Builds AI Products (17:51) Prompting Claude More Effectively (30:58) How AI Is Changing Product Teams (41:11) Advice for AI Founders

Evan Tana

36,404 просмотров • 1 месяц назад