Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Can a vibe coder make a better app than a senior iOS engineer? - Both of us will use Claude 4.1 Opus - We each get 5 prompts - We each get to pick our stack vs @rileybrown_ai You can win $1000 worth of credits by voting who won...

72,246 görüntüleme • 11 ay önce •via X (Twitter)

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

Here's my conversation with Dario Amodei, CEO of Anthropic, the company that created Claude, one of the best AI systems in the world. We talk about scaling, AI safety, regulation, and a lot of super technical details about the present and future of AI and humanity. It's a 5+ hour conversation in total. Amanda Askell and Chris Olah (Chris Olah) join us for an hour each to talk about Claude's character and mechanistic interpretability, respectively. This was a fascinating, wide-ranging, super-technical, and fun conversation! First 4 hours are here on X (4 hours is current limit), and is up on everywhere else in full. Links in comment. Timestamps: 0:00 - Introduction 3:14 - Scaling laws 12:20 - Limits of LLM scaling 20:45 - Competition with OpenAI, Google, xAI, Meta 26:08 - Claude 29:44 - Opus 3.5 34:30 - Sonnet 3.5 37:50 - Claude 4.0 42:02 - Criticism of Claude 54:49 - AI Safety Levels 1:05:37 - ASL-3 and ASL-4 1:09:40 - Computer use 1:19:35 - Government regulation of AI 1:38:24 - Hiring a great team 1:47:14 - Post-training 1:52:39 - Constitutional AI 1:58:05 - Machines of Loving Grace 2:17:11 - AGI timeline 2:29:46 - Programming 2:36:46 - Meaning of life 2:42:53 - Amanda Askell - Philosophy 2:45:21 - Programming advice for non-technical people 2:49:09 - Talking to Claude 3:05:41 - Prompt engineering 3:14:15 - Post-training 3:18:54 - Constitutional AI 3:23:48 - System prompts 3:29:54 - Is Claude getting dumber? 3:41:56 - Character training 3:42:56 - Nature of truth 3:47:32 - Optimal rate of failure 3:54:43 - AI consciousness 4:09:14 - AGI 4:17:52 - Chris Olah - Mechanistic Interpretability 4:22:44 - Features, Circuits, Universality 4:40:17 - Superposition 4:51:16 - Monosemanticity 4:58:08 - Scaling Monosemanticity 5:06:56 - Macroscopic behavior of neural networks 5:11:50 - Beauty of neural networks

Lex Fridman

1,374,629 görüntüleme • 1 yıl önce

Cursor Complete Guide for AI Coding... 1. The Basics, Composer, Cursor 2.0, Why use Cursor? 2. Multiple Agent Testing, Adding Database, Deploying to Vercel 3. Comparing the big 4: v0, Replit, Lovable, Cursor And more... with Senior Software Engineer Kehan Zhang TIME STAMPS --------------- 1. BASICS: 00:00 Introduction 01:01 Overview of Cursor and Its Features 01:47 Getting Started with Cursor 02:39 Understanding IDE and Vibe Coding 06:00 Cursor For Mobile Apps 10:26 Downloading and Installing Cursor 11:17 Creating and Managing Projects in Cursor 15:14 Building a Simple Game with Cursor 19:10 Advanced Features and Customization 40:28 Fixing Styling Rules 40:53 Redesigning the App 42:17 Exploring Cursor 2.0 Features 43:22 Setting Up the Project Structure 44:17 Adding and Testing Meme Templates 46:08 Debugging Text Issues 2. ADVANCED 49:46 Using Multiple Agents 01:10:40 Creating Custom Commands 01:14:15 Creating Commands in Settings Tab 01:15:11 Introduction to Instant DB 01:16:04 Setting Up Instant DB in Your Project 01:18:24 Building a Full Stack Application 01:19:04 Using the Agent to Plan and Build 01:26:06 Testing and Debugging the Application 01:53:02 Deploying the Application with Vercel 01:55:35 Setting Up the CLI 01:56:15 Understanding Command Line Interfaces (CLI) 01:57:32 Deploying Code to Vercel 01:58:07 Handling Environment Variables 01:58:44 Interacting with the Vercel Deployment 02:00:34 Exploring Cursor's Capabilities 3. COMPARING VIBE CODING TOOLS 02:09:48 Comparing Vibe Coding Tools 02:31:04 Final Thoughts and Recommendations

Riley Brown

65,318 görüntüleme • 8 ay önce

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 📧

57,568 görüntüleme • 8 ay önce

64 Arguments Against Democratic Socialism Timestamps: 0:00 Definitions 2:28 Hyper-individualism 5:44 Universality 9:30 He Who Pays the Piper, Calls the Tune 12:37 Welfare State Turns Humans From Assets to Liabilities 15:18 Against Me 16:25 Trump Test 18:29 Zero Sum Thinking 21:05 Forcible Depravation 22:46 Monopoly Contradiction 25:27 Vilifying Producers 28:03 Competition Protects People 32:03 The Secret to Mass Consumption = Mass Production 34:30 Decreasing Prices in the Private Sector & Potential Competition 39:35 The Myth of the Rational Voter: Fake Power vs. Real Power 50:02 Social Contract Myth 53:29 "Which Happens First?" 58:45 Static Thinking Fallacy 1:03:20 Metrics for Standard of Living & Time Prices 1:07:58 Humans Are Inherently Self Interested 1:12:53 Recognizing Rights as Reciprocal 1:14:49 We Are Not the Government 1:17:29 More Power = Better Outcome Assumption 1:21:19 The Myth of "Free" 1:23:50 Iron Law of Oligarchy 1:26:50 Praxeology 1:37:55 Inequality & Trickle Down Economics 1:44:41 Domestic Imperialism 1:48:56 Regulatory Capture 1:50:48 Abolish the Police 1:53:02 The Empirical Case 1:54:08 "Me Being X Doesn't Affect You" 1:56:01 Material Mindset 1:59:16 Capital Financing Shift 2:00:30 The Value of the Capitalist 2:03:47 Opposition to Voluntary Solutions 2:07:45 Apartheid Entitlement 2:14:36 Democracy and Delegating Rights 2:17:02 The Expert Problem 2:23:45 Capitalist Time Preference 2:31:41 Capitalist Productivity 2:34:15 Self-Ownership 2:38:15 Who Defines What "Service X" is? 2:41:57 Inevitability of Scarcity 2:46:35 The Angel Argument 2:49:04 The Crimes of the 99% 2:51:09 "Poverty Causes Crime" 2:53:04 Intentions Over Results 2:57:41 Free Association = The Ultimate "Check and Balance" 3:00:31 Surplus Value 3:04:39 Firms Reduce Transaction Costs 3:08:21 Collective Action Problem 3:10:09 Empiricism, the High Cost of Experimentation, and the Difficulty in Isolating Variables 3:12:46 Each According to Their Ability? Need? 3:15:51 Disparities Prove Discrimination 3:23:58 Comparing Theory to Reality 3:31:42 Nirvana Fallacy 3:34:41 Which Collective? 3:39:20 Argument From Tolerance 3:39:50 Prices: Signal & Incentive 3:42:58 Catastrophizing 3:46:43 Stock Diversification 3:48:17 Press Conference Bias 3:50:20 Divorce and Discrimination 3:53:38 Discouraging Savings

Voluntaryist Keith

314,977 görüntüleme • 17 gün önce

I'm often asked for the best public example of AI evals done right for a real, production product. I finally have an answer. Teresa Torres shares how she shipped an AI interview coach, and used evals to rapidly squash bugs and improve the product. Teresa shows how she: 1. did error analysis FIRST to find real issues (instead of using generic metrics) 😍 2. used Jupyter notebooks to analyze errors 3. built custom annotation tools + custom widgets in notebooks 4. built a LLM-judge and assertions to test for specific errors 5. iterated through this feedback loop until it worked. 6. kept things simple the whole time It's also probably the best commercial for Jupyter notebooks you can imagine. 🥰 Chapter summary below. Link to YT in next thread 00:00:00 - Intro 00:01:45 - The Product: Building an AI Interview Coach 00:06:34 - The Problem: How Do I Know if My AI Coach is Any Good? 00:10:15 - Using Airtable for Traces and Annotation 00:12:15 - Discovering Jupyter Notebooks and Designing the First Evals 00:15:15 - Example Evals: LLM-as-Judge vs. Code-Based Assertions 00:21:00 - Learning Python with ChatGPT to Analyze Eval Results 00:31:00 - VS Code, Custom Tools, and an Eval Investigation Notebook 00:39:45 - Building a Custom Annotation Tool with Claude 00:41:00 - From Personal Project to Production App 00:46:02 - How Should PMs and Engineers Collaborate on AI Products? 00:55:45 - Q&A: Capturing Feedback and Annotations from End Users 00:58:11 - Q&A: Is a Technical Background Necessary to Build AI? 01:02:28 - Q&A: What's Next for Teresa? 01:03:13 - Q&A: Unpacking the Micro-Decisions of Building an AI App

Hamel Husain

51,376 görüntüleme • 11 ay önce