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She literally explained how her $100M AI startup runs completely out of an operating system in Claude Code: 2:04 - The Company OS GitHub structure 5:40 - The 1% vs 99% problem 9:00 - 3 steps to build your own Company OS 12:30 - Slack automation demo: feature request... show more

Aakash Gupta

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387,124 Aufrufe • vor 3 Monaten •via X (Twitter)

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She literally showed how she uses adversarial agents in Claude Code to win hackathons and ship features: 2:47 - How she won her hackathon 4:30 - The 5-layer Claude stack 7:17 - Which model to actually use 9:55 - Chat vs Desktop vs Chrome 14:43 - Cowork automations 26:18 - Skills that beat prompts 34:28 - The AI chief of staff build 59:24 - MCPs every PM needs 1:02:16 - Claude Design is here 1:08:53 - Adversarial agents, live 1:13:01 - The new AI builder role 1:15:57 - The 2026 AI PM interview 1:29:06 - Self-improving product loop

She literally showed how she uses adversarial agents in Claude Code to win hackathons and ship features: 2:47 - How she won her hackathon 4:30 - The 5-layer Claude stack 7:17 - Which model to actually use 9:55 - Chat vs Desktop vs Chrome 14:43 - Cowork automations 26:18 - Skills that beat prompts 34:28 - The AI chief of staff build 59:24 - MCPs every PM needs 1:02:16 - Claude Design is here 1:08:53 - Adversarial agents, live 1:13:01 - The new AI builder role 1:15:57 - The 2026 AI PM interview 1:29:06 - Self-improving product loop

Aakash Gupta

165,847 Aufrufe • vor 2 Monaten

Hash Rate - Ep 88: Virtuals Protocol ai16zdao Agentic AI + Crypto - Explained Simply 🧙‍♂️ Guest: 0xBebis of Big Tony (medium arc) & Cod3x | Win More Trades 00:00 Introduction to Agentic AI + Crypto 02:51 Understanding Virtuals 08:59 Tokenomics and Market Dynamics of Virtuals 15:10 Creating and Deploying AI Agents 20:52 Eliza OS and AI16Z: A New Paradigm 24:08 The Cathedral vs. The Bazaar in AI Development 29:51 The Economic Implications of AI Agents 34:42 Monetizing the Agentic Economy 37:30 The Role of Stablecoins in Transactions 46:08 The Future of Specialized AI Agents 53:29 Introducing Big Tony: The AI Trading Agent YouTube: Hit Subscribe! Sponsored by Ledger:

Hash Rate - Ep 88: Virtuals Protocol ai16zdao Agentic AI + Crypto - Explained Simply 🧙‍♂️ Guest: 0xBebis of Big Tony (medium arc) & Cod3x | Win More Trades 00:00 Introduction to Agentic AI + Crypto 02:51 Understanding Virtuals 08:59 Tokenomics and Market Dynamics of Virtuals 15:10 Creating and Deploying AI Agents 20:52 Eliza OS and AI16Z: A New Paradigm 24:08 The Cathedral vs. The Bazaar in AI Development 29:51 The Economic Implications of AI Agents 34:42 Monetizing the Agentic Economy 37:30 The Role of Stablecoins in Transactions 46:08 The Future of Specialized AI Agents 53:29 Introducing Big Tony: The AI Trading Agent YouTube: Hit Subscribe! Sponsored by Ledger:

Mark Jeffrey

28,209 Aufrufe • vor 1 Jahr

This guy literally broke down everything to build a 21 agent dev team in Claude Code: 0:00 - Why one prompt = AI slop 1:55 - Claude Code as your full startup 2:43 - The 21-agent team 4:57 - Inside the system analyst agent 5:52 - Live demo: 0 → TestFlight 8:42 - Define a "good" system analyst first 11:53 - The system analyst workflow 12:17 - Why Confluence + Jira + MCPs matter 15:30 - Classic PM skills > vibe coding 19:15 - The scaffolding that kills spaghetti code 22:10 - Setting up agents inside Claude Code 26:19 - The longest dictation prompt this podcast has seen 35:29 - Why dictation beats typing for AI specs 47:30 - Brand guidelines in Figma Make 55:59 - Idea → prompt → design → app 1:06:27 - Claude Code builds the Figma screens 1:23:59 - Frontend tickets appear in Jira (with Figma links) 1:48:49 - The hockey rules AI app goes live 1:53:56 - Full recap: Claude, Confluence, Figma, Jira, Simulator, TestFlight 2:03:17 - Should PMs get AI PM certificates? 2:08:15 - How to build a PM portfolio that lands FAANG offers 2:13:25 - How to get started this week

Aakash Gupta

178,026 Aufrufe • vor 4 Monaten

Dianne Penn is Head of Product for Research and Labs at Anthropic. She joined in 2023 as their first technical PM (when there were just five product engineers) and has helped ship every Claude model from Claude 2 to Mythos. We discuss: 02:31 Early Anthropic 08:55 The two biggest inflections 13:50 Inside the exponential 20:02 Token maxing 23:30 Anthropic Labs and the incubation model 27:30 How the AI research role works 31:35 How to become a top AI researcher 35:18 Frontier model safeguards 39:38 Hiring in the AI era 44:16 Building evals 47:48 Evals are the new PRDs 49:55 The importance of hands-on leadership 52:46 Finding joy in AI 58:10 How Dianne uses Claude 01:01:05 Avoiding overreliance on AI 01:03:50 The constitution that makes Claude better 01:07:11 AI writing and verification 01:11:40 Where human brains will continue to be valuable 01:14:10 Navigating AI with kids 01:16:26 Alignment, the future of the PM role, and burnout 01:21:54 Lightning round and final thoughts

Dianne Penn is Head of Product for Research and Labs at Anthropic. She joined in 2023 as their first technical PM (when there were just five product engineers) and has helped ship every Claude model from Claude 2 to Mythos. We discuss: 02:31 Early Anthropic 08:55 The two biggest inflections 13:50 Inside the exponential 20:02 Token maxing 23:30 Anthropic Labs and the incubation model 27:30 How the AI research role works 31:35 How to become a top AI researcher 35:18 Frontier model safeguards 39:38 Hiring in the AI era 44:16 Building evals 47:48 Evals are the new PRDs 49:55 The importance of hands-on leadership 52:46 Finding joy in AI 58:10 How Dianne uses Claude 01:01:05 Avoiding overreliance on AI 01:03:50 The constitution that makes Claude better 01:07:11 AI writing and verification 01:11:40 Where human brains will continue to be valuable 01:14:10 Navigating AI with kids 01:16:26 Alignment, the future of the PM role, and burnout 01:21:54 Lightning round and final thoughts

Lenny Rachitsky

157,470 Aufrufe • vor 1 Monat

I turned the Google Gemma Gemma 4 12B model into an enterprise RFP agent. Running locally in the Hugging Face Llama app and connected to Box, the agent: - pulls the RFP + approved source docs - drafts source-cited responses - flags gaps instead of hallucinating - pauses for human approval - writes the final draft back to Box The model runs locally while Box keeps the enterprise content governed. 00:22 - Demo overview 00:48 - RFP + company sources stored in Box 02:00 - Why local AI works well for confidential document workflows 02:28 - Setting up local AI with the Llama app 04:50 - Walkthrough of AGENTS.md + the script 08:07 - Running the workflow 11:12 - Conclusion

I turned the Google Gemma Gemma 4 12B model into an enterprise RFP agent. Running locally in the Hugging Face Llama app and connected to Box, the agent: - pulls the RFP + approved source docs - drafts source-cited responses - flags gaps instead of hallucinating - pauses for human approval - writes the final draft back to Box The model runs locally while Box keeps the enterprise content governed. 00:22 - Demo overview 00:48 - RFP + company sources stored in Box 02:00 - Why local AI works well for confidential document workflows 02:28 - Setting up local AI with the Llama app 04:50 - Walkthrough of AGENTS.md + the script 08:07 - Running the workflow 11:12 - Conclusion

Chris Kim

150,387 Aufrufe • vor 1 Monat

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, January 29th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (1570+ registered):

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, January 29th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (1570+ registered):

Matt Dancho (Business Science)

242,084 Aufrufe • vor 1 Jahr

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Data Science Agents): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, May 21st, I'm sharing one of my best AI Projects: AI Customer Segmentation Agent with Python Register here (570+ registered):

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Data Science Agents): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, May 21st, I'm sharing one of my best AI Projects: AI Customer Segmentation Agent with Python Register here (570+ registered):

Matt Dancho (Business Science)

35,452 Aufrufe • vor 1 Jahr

💥NEW AI CODING TUTORIAL: 12 Months of Claude Code Lessons in 46 Minutes Claude Code just turned 1. So here's 52 lessons on how to get the most out of Claude Code, organized into 3 levels: Beginner, Intermediate, and Master. TIMESTAMPS 00:00 12 Months of building with Claude Code 02:01 LEVEL 1: Beginner 02:09 Installation and Setup 04:55 Models and Modes 09:04 CLAUDE.md (and what to put in) 10:30 Context Management 11:31 Debugging with Claude 12:22 Version control 14:12 Prompting, Just ask Claude 16:01 LEVEL 2: Intermediate 16:24 Customizations (Skills, Plugins, MCPs) 24:42 Workflows: Plan, Build, Test 29:52 Verifying and Documenting with Claude 33:10 LEVEL 3: Master 34:17 Multi-Agent Development (multi-clauding with git worktrees) 37:07 Self-improving Systems 38:45 Ralph Wiggum Agent Loops 40:11 Async Task Delegation 42:00 Agent Teams, Pre-configure permissions, Hooks 44:42 Continue your learning

💥NEW AI CODING TUTORIAL: 12 Months of Claude Code Lessons in 46 Minutes Claude Code just turned 1. So here's 52 lessons on how to get the most out of Claude Code, organized into 3 levels: Beginner, Intermediate, and Master. TIMESTAMPS 00:00 12 Months of building with Claude Code 02:01 LEVEL 1: Beginner 02:09 Installation and Setup 04:55 Models and Modes 09:04 CLAUDE.md (and what to put in) 10:30 Context Management 11:31 Debugging with Claude 12:22 Version control 14:12 Prompting, Just ask Claude 16:01 LEVEL 2: Intermediate 16:24 Customizations (Skills, Plugins, MCPs) 24:42 Workflows: Plan, Build, Test 29:52 Verifying and Documenting with Claude 33:10 LEVEL 3: Master 34:17 Multi-Agent Development (multi-clauding with git worktrees) 37:07 Self-improving Systems 38:45 Ralph Wiggum Agent Loops 40:11 Async Task Delegation 42:00 Agent Teams, Pre-configure permissions, Hooks 44:42 Continue your learning

Avthar

62,263 Aufrufe • vor 6 Monaten

Deeply thoughtful conversation with Zico Kolter, board member at OpenAI and head of the machine learning department at Carnegie Mellon University, about AI safety, AI security, agents and frontier AI 00:00 Intro 01:32 OpenAI board role and Safety & Security Committee 03:53 How OpenAI reviews major model releases 05:33 OpenAI’s preparedness framework explained 09:46 Are frontier AI models getting safer? 12:33 Why AI safety does not come from scale 15:23 The four categories of AI risk 19:38 Doomerism vs accelerationism in AI 24:11 The six-month AI pause debate 26:20 AI safety as a global effort 28:04 How Zico Kolter got into machine learning 31:05 OpenAI in the early days 34:14 Why Carnegie Mellon became an AI powerhouse 38:43 What Gray Swan does in AI security 40:44 AI safety vs AI security 43:15 The GCG jailbreak paper 49:19 How AI labs responded to jailbreak research 50:19 State-of-the-art AI defenses 52:32 State-of-the-art AI attacks 54:22 Why AI agents expand the attack surface 58:39 Are AI agents ready for production? 59:40 Mechanistic interpretability explained 1:02:31 Will AI be safer in two years? 1:03:46 Reinforcement learning and self-improving models 1:08:09 Do post-transformer architectures matter 1:09:29 Best research directions in AI now 1:11:00 Zico Kolter’s Intro to Modern AI course 1:14:53 Why modern AI is simpler than people think

Deeply thoughtful conversation with Zico Kolter, board member at OpenAI and head of the machine learning department at Carnegie Mellon University, about AI safety, AI security, agents and frontier AI 00:00 Intro 01:32 OpenAI board role and Safety & Security Committee 03:53 How OpenAI reviews major model releases 05:33 OpenAI’s preparedness framework explained 09:46 Are frontier AI models getting safer? 12:33 Why AI safety does not come from scale 15:23 The four categories of AI risk 19:38 Doomerism vs accelerationism in AI 24:11 The six-month AI pause debate 26:20 AI safety as a global effort 28:04 How Zico Kolter got into machine learning 31:05 OpenAI in the early days 34:14 Why Carnegie Mellon became an AI powerhouse 38:43 What Gray Swan does in AI security 40:44 AI safety vs AI security 43:15 The GCG jailbreak paper 49:19 How AI labs responded to jailbreak research 50:19 State-of-the-art AI defenses 52:32 State-of-the-art AI attacks 54:22 Why AI agents expand the attack surface 58:39 Are AI agents ready for production? 59:40 Mechanistic interpretability explained 1:02:31 Will AI be safer in two years? 1:03:46 Reinforcement learning and self-improving models 1:08:09 Do post-transformer architectures matter 1:09:29 Best research directions in AI now 1:11:00 Zico Kolter’s Intro to Modern AI course 1:14:53 Why modern AI is simpler than people think

Matt Turck

33,764 Aufrufe • vor 4 Monaten

AI is an extinction event for the traditional product playbook. Geoff Charles explains why Ramp is replacing classic PMs and burning their own code to maintain legendary velocity. (0:00) How AI Destroyed the Product Playbook? (1:08) What Are the 3 Levels of AI Proficiency? (3:55) When Should You Rip Up Your Own Code? (6:57) Why Did Charles Change Ramp’s Org Design? (8:21) How Did One Hackathon Change Everything? (13:08) Does the Product Manager Role Still Exist? (18:18) Why Are There Only 22 PMs at Ramp? (22:25) Is Middle Management Dead in the AI Era? (26:36) How Do You Pass an AI Product Interview? (31:13) Should New Grads Avoid Product Management? (36:16) Will Agents Replace Software QA Testing? (38:31) Is Traditional User Research Obsolete? (42:16) Overrated or Underrated: Product Specs? (46:36) Why Hasn't Ramp Logged Into Figma? (49:04) Are Stablecoins the Future of Agents?

AI is an extinction event for the traditional product playbook. Geoff Charles explains why Ramp is replacing classic PMs and burning their own code to maintain legendary velocity. (0:00) How AI Destroyed the Product Playbook? (1:08) What Are the 3 Levels of AI Proficiency? (3:55) When Should You Rip Up Your Own Code? (6:57) Why Did Charles Change Ramp’s Org Design? (8:21) How Did One Hackathon Change Everything? (13:08) Does the Product Manager Role Still Exist? (18:18) Why Are There Only 22 PMs at Ramp? (22:25) Is Middle Management Dead in the AI Era? (26:36) How Do You Pass an AI Product Interview? (31:13) Should New Grads Avoid Product Management? (36:16) Will Agents Replace Software QA Testing? (38:31) Is Traditional User Research Obsolete? (42:16) Overrated or Underrated: Product Specs? (46:36) Why Hasn't Ramp Logged Into Figma? (49:04) Are Stablecoins the Future of Agents?

Rex Salisbury

15,814 Aufrufe • vor 2 Monaten

When AI builds AI: my conversation with Richard Socher about RSI and scientific progress. 00:00 Intro: AI That Improves Itself 00:55 Why Scientific Progress Is Slowing 03:08 The Labyrinth of Human Knowledge 05:59 Can AI Put Science Back Together? 07:57 How LLMs Learn Biology and Proteins 10:56 Next-Token Prediction as a World Model 16:44 Can AI Generate Truly Original Ideas? 17:32 Simulations, Verifiers and Superhuman AI 22:18 The Path to Recursive Self-Improvement 24:49 Why AI Hallucinations Can Drive Discovery 27:42 From Reading Biology to Writing It 31:31 Can AI Accelerate Drug Discovery? 33:31 Will AI Help Cure Cancer? 38:03 AI Breakthroughs in Biology, Energy and Materials 40:19 Will Some Societies Reject AI? 45:07 Building the AI Economist 52:22 The Scientific Data Bottleneck 53:41 The Four Pillars of the Eureka Machine 55:01 Teaching AI the Rules of Reality 57:44 Simulations and Virtual Cells 1:00:40 Self-Driving Robotic Laboratories 1:02:51 Agent Swarms and Open-Ended Discovery 1:04:30 The Compute Bottleneck 1:05:44 Inside Recursive 1:07:33 What Recursive Will Build First 1:10:10 How Do We Define Intelligence? 1:11:32 How Far Can Intelligence Go?

When AI builds AI: my conversation with Richard Socher about RSI and scientific progress. 00:00 Intro: AI That Improves Itself 00:55 Why Scientific Progress Is Slowing 03:08 The Labyrinth of Human Knowledge 05:59 Can AI Put Science Back Together? 07:57 How LLMs Learn Biology and Proteins 10:56 Next-Token Prediction as a World Model 16:44 Can AI Generate Truly Original Ideas? 17:32 Simulations, Verifiers and Superhuman AI 22:18 The Path to Recursive Self-Improvement 24:49 Why AI Hallucinations Can Drive Discovery 27:42 From Reading Biology to Writing It 31:31 Can AI Accelerate Drug Discovery? 33:31 Will AI Help Cure Cancer? 38:03 AI Breakthroughs in Biology, Energy and Materials 40:19 Will Some Societies Reject AI? 45:07 Building the AI Economist 52:22 The Scientific Data Bottleneck 53:41 The Four Pillars of the Eureka Machine 55:01 Teaching AI the Rules of Reality 57:44 Simulations and Virtual Cells 1:00:40 Self-Driving Robotic Laboratories 1:02:51 Agent Swarms and Open-Ended Discovery 1:04:30 The Compute Bottleneck 1:05:44 Inside Recursive 1:07:33 What Recursive Will Build First 1:10:10 How Do We Define Intelligence? 1:11:32 How Far Can Intelligence Go?

Matt Turck

10,645 Aufrufe • vor 12 Tagen

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

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 Aufrufe • vor 10 Monaten

Fresh off the launch of Opus 5, Claude Code creator Boris Cherny (Boris Cherny) joins YC's Diana at Startup School 2026 to talk about what the newest models can do, how Claude Code came to be, and what it means to build products when the underlying capabilities keep accelerating. 00:07 — What Makes Opus 5 Different 02:06 — Solving Prompt Injection 03:21 — Why Claude Code Deleted 80% of Its System Prompt 06:37 — Press Delete on Your AI Product 07:20 — How to Rebuild Your System Prompt 10:30 — Product Overhang and “Unhobbling” AI 14:26 — Give Claude Harder Problems 19:32 — Prompt Engineering Is Changing 21:57 — The Two-Week Claude Code Prompt 24:42 — Running Thousands of AI Agents 30:15 — Coding Is (Almost) Solved 32:20 — What Every CS Student Should Still Learn Anthropic Claude

Fresh off the launch of Opus 5, Claude Code creator Boris Cherny (Boris Cherny) joins YC's Diana at Startup School 2026 to talk about what the newest models can do, how Claude Code came to be, and what it means to build products when the underlying capabilities keep accelerating. 00:07 — What Makes Opus 5 Different 02:06 — Solving Prompt Injection 03:21 — Why Claude Code Deleted 80% of Its System Prompt 06:37 — Press Delete on Your AI Product 07:20 — How to Rebuild Your System Prompt 10:30 — Product Overhang and “Unhobbling” AI 14:26 — Give Claude Harder Problems 19:32 — Prompt Engineering Is Changing 21:57 — The Two-Week Claude Code Prompt 24:42 — Running Thousands of AI Agents 30:15 — Coding Is (Almost) Solved 32:20 — What Every CS Student Should Still Learn Anthropic Claude

Y Combinator

85,554 Aufrufe • vor 1 Monat

“Product Management isn’t going to exist in 5 years” - 2x CPO @ Qualtrics and Apollo That's just one of the bold takes Shek, my former manager, shares in today's episode. If you're observing the future of the PM role, this episode is a must-listen. Designers in the AI-Driven Era – 09:19 AI Tools for Rapid Prototyping – 17:44 Why PMs Must Be Technical with AI – 30:13 Product Engineers Taking on PM Roles – 40:50 The Two Categories of Product Engineers – 45:33 The Future of Product Management – 51:15 How Technical PMs Impact a Company – 57:11 How Companies Will Win by Embracing This Shift – 01:04:38 The Rise of the Feature Factory Era – 01:08:13 Why the Top Two Jobs of a PM Are the Same – 01:14:13 The Expanding Role of Product Engineers Beyond Building – 01:20:23 Lessons from Failures as a Product Builder – 01:27:19 Balancing Work and Execution as a Product Leader – 01:34:55 Paid Promotions Included

“Product Management isn’t going to exist in 5 years” - 2x CPO @ Qualtrics and Apollo That's just one of the bold takes Shek, my former manager, shares in today's episode. If you're observing the future of the PM role, this episode is a must-listen. Designers in the AI-Driven Era – 09:19 AI Tools for Rapid Prototyping – 17:44 Why PMs Must Be Technical with AI – 30:13 Product Engineers Taking on PM Roles – 40:50 The Two Categories of Product Engineers – 45:33 The Future of Product Management – 51:15 How Technical PMs Impact a Company – 57:11 How Companies Will Win by Embracing This Shift – 01:04:38 The Rise of the Feature Factory Era – 01:08:13 Why the Top Two Jobs of a PM Are the Same – 01:14:13 The Expanding Role of Product Engineers Beyond Building – 01:20:23 Lessons from Failures as a Product Builder – 01:27:19 Balancing Work and Execution as a Product Leader – 01:34:55 Paid Promotions Included

Aakash Gupta

34,037 Aufrufe • vor 1 Jahr

Data Models - Better AI Code in 5 Minutes Your AI isn't consistent because you're missing one file. I had the same problem until I started using a data model file. In this video, I'm showing you the exact two prompts I use: one generates the data model, the second uses it to generate features without breaking my app. Testing this with AnimeLeak (my actual production app) adding an admin approval feature. Using Droid with Sonnet 4.5 in spec mode, but this works in Cursor, Claude, or whatever you're using. KEY TIMESTAMPS 00:00 - The One File Your AI is Missing 00:44 - Prompt 1: Generate a Data Model 01:12 - The Result: Data Model Documentation 02:12 - Prompt 2: Implement a New Feature 03:00 - Reviewing the AI-Generated Spec 04:17 - Why This Workflow is So Powerful 04:32 - Your 3-Step Action Plan 05:12 - Next Video: Live Code Generation

Data Models - Better AI Code in 5 Minutes Your AI isn't consistent because you're missing one file. I had the same problem until I started using a data model file. In this video, I'm showing you the exact two prompts I use: one generates the data model, the second uses it to generate features without breaking my app. Testing this with AnimeLeak (my actual production app) adding an admin approval feature. Using Droid with Sonnet 4.5 in spec mode, but this works in Cursor, Claude, or whatever you're using. KEY TIMESTAMPS 00:00 - The One File Your AI is Missing 00:44 - Prompt 1: Generate a Data Model 01:12 - The Result: Data Model Documentation 02:12 - Prompt 2: Implement a New Feature 03:00 - Reviewing the AI-Generated Spec 04:17 - Why This Workflow is So Powerful 04:32 - Your 3-Step Action Plan 05:12 - Next Video: Live Code Generation

Ray Fernando

15,479 Aufrufe • vor 11 Monaten

New episode of This Week in AI is live @jason sits down with Tanay Kothari (Wispr Flow) and Richard Socher ( to break down the AI doom post that crashed markets, how to build an AI-first team, and why the agent era is already here. 00:00:00 - Welcome & Guest Introductions 00:09:32 - The Citrini doom post explained 00:14:28 - Why it's completely wrong 00:26:18 - Open source vs. closed models 00:38:05 - Building AI-first teams 00:43:42 - From 200 support agents to 4 00:50:34 - Producer Oliver's OpenClaw demo 01:16:29 - Apple on-shoring & the Taiwan chip risk 🎥 Watch the full episode here 👇

New episode of This Week in AI is live @jason sits down with Tanay Kothari (Wispr Flow) and Richard Socher ( to break down the AI doom post that crashed markets, how to build an AI-first team, and why the agent era is already here. 00:00:00 - Welcome & Guest Introductions 00:09:32 - The Citrini doom post explained 00:14:28 - Why it's completely wrong 00:26:18 - Open source vs. closed models 00:38:05 - Building AI-first teams 00:43:42 - From 200 support agents to 4 00:50:34 - Producer Oliver's OpenClaw demo 01:16:29 - Apple on-shoring & the Taiwan chip risk 🎥 Watch the full episode here 👇

This Week in AI

11,116 Aufrufe • vor 6 Monaten

Perplexity co-founder and CEO Aravind Srinivas (Aravind Srinivas) on the race to build the AI browser of the future. At AI Startup School in San Francisco. 00:48 - Perplexity's Current Status and Challenges 01:16 - Future Plans: The Browser Bet 03:18 - Competition and Market Dynamics 04:43 - The Importance of Speed and Innovation 05:51 - Founding Perplexity: The Early Days 09:18 - Product Evolution and User Experience 19:44 - Running the Company and Future Outlook 20:15 - AI Tools in Software Engineering 20:31 - Adoption and Implementation of AI Coding Tools 21:11 - Challenges and Benefits of AI in Development 22:35 - Brand Value and Competition 23:56 - Network Effects and Integrations 27:40 - Business Model and Revenue Streams 29:54 - Advice for Founders 31:33 - Audience Q&A

Y Combinator

236,501 Aufrufe • vor 1 Jahr

.Poetiq is a new startup that recently achieved a major jump on the ARC-AGI benchmark by layering a recursive self-improvement system on top of existing models. In this episode of the Lightcone Podcast, Poetiq's Founder & CEO Ian Fischer joined us to discuss how small teams can build “reasoning harnesses” that outperform base models, what that means for startups and why automating prompt engineering may be one of the most powerful levers in AI today. 00:00 – Intro 00:40 – What Is Poetiq? 01:07 – Recursive Self-Improvement Explained 02:07 – The Fine-Tuning Trap 02:59 – “Stilts” for LLMs 03:14 – Recursive Self-Improvement vs. Fine-Tuning 05:05 – Taking the Top Spot on ARC-AGI 06:37 – Beating Claude on Humanity’s Last Exam 08:40 – How the Meta-System Works 10:26 – Beyond RL: A New S-Curve 11:32 – Automating Prompt Engineering 13:37 – From 5% to 95% Performance 14:50 – Early Access & Putting Your Agent on Stilts 16:17 – From YC Founder to DeepMind Researcher 18:29 – Advice for Engineers in the AI Era

.Poetiq is a new startup that recently achieved a major jump on the ARC-AGI benchmark by layering a recursive self-improvement system on top of existing models. In this episode of the Lightcone Podcast, Poetiq's Founder & CEO Ian Fischer joined us to discuss how small teams can build “reasoning harnesses” that outperform base models, what that means for startups and why automating prompt engineering may be one of the most powerful levers in AI today. 00:00 – Intro 00:40 – What Is Poetiq? 01:07 – Recursive Self-Improvement Explained 02:07 – The Fine-Tuning Trap 02:59 – “Stilts” for LLMs 03:14 – Recursive Self-Improvement vs. Fine-Tuning 05:05 – Taking the Top Spot on ARC-AGI 06:37 – Beating Claude on Humanity’s Last Exam 08:40 – How the Meta-System Works 10:26 – Beyond RL: A New S-Curve 11:32 – Automating Prompt Engineering 13:37 – From 5% to 95% Performance 14:50 – Early Access & Putting Your Agent on Stilts 16:17 – From YC Founder to DeepMind Researcher 18:29 – Advice for Engineers in the AI Era

Y Combinator

156,393 Aufrufe • vor 6 Monaten

I asked Dan Martell to walk me through every level of making money with AI. He gave me the most simple, practical advice I've ever heard on this subject. Level 1 - Making $0 - $100k Level 2 - Making $1m - $10m Level 3 - Building a $10m++ enterprise. 0:00 Only 5% of the World Has Ever Paid for AI 0:46 The Easiest Thing to Sell With AI Right Now 1:56 The Marcus and Sophie Framework 4:24 Theory of Constraints (Right Problem to Solve) 5:33 What Is the Number One Business Constraint 7:13 How to Leave Your Job and Go All In 8:27 Business Is Simple Find a Problem and Solve It 9:08 Stop Getting Ready to Get Ready 9:33 The Sarah Story One Text and $10K 9:53 Pull Up Your Phone and Message Your Contacts 11:05 Dan's Son Gets His First Client at $800/Month 12:41 Best Employee vs. Best Employer 13:59 What Other Services Can You Sell With AI 14:44 Sales Is Not Talking It's Asking 17:01 What to Do When You Hate Your Business 18:40 Pain and Pleasure Are the Only Two Motivators 19:13 They Haven't Made It a Must Yet 20:29 Make It a Must Not a Nice to Have 21:06 The Jen Story and the Gasping Moment 22:17 How to Find Your First 10 to 15 Clients 28:38 The Personal Brand Play 33:06 Vision Is What AI Cannot Do 34:55 Hard for Computers Easy for Humans 36:13 Level 2 Making Your First Million With AI 37:18 The Replacement Ladder Framework 37:39 Admin First Then Delivery Then Marketing 39:09 Why Marketing Is the Biggest AI Category 39:32 Why You Should Keep Sales for Yourself 40:00 Level 5 Leadership and AI Agents 41:41 What a Fully AI Systems Business Looks Like 43:13 The Gym Owner With Three Locations 46:16 Shutting Down the Company for Two Days 46:37 Teaching the Whole Team to Code in Claude 49:28 Wayne the 62 Year Old Who Made $12K a Month 52:38 I Only Share What Actually Works 53:21 Whisper Flow and Talking to Your AI 56:41 Claude Chat Claude Coworker and Claude Code 57:57 The Claude Browser Extension 58:49 Claude Code Is Not Just for Developers 1:00:06 How to Migrate Your AI Memory Across Tools 1:01:08 Level 3 $1M to $10M and the Brand Play 1:02:05 Nobody Buys AI They Buy Trust 1:03:25 Brand Is Association and Association Is Trust 1:05:12 A Million Followers Is $10M in Activated Revenue 1:07:03 How to Keep AI From Becoming Slop 1:07:42 Human in the Loop 1:08:16 The 10 80 10 Rule and Why AI Is Now the 80 1:10:01 The Team FIRED Themselves 1:11:45 Dan's Free AI Curriculum for Your Team

Grant

168,385 Aufrufe • vor 3 Monaten