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This guy literally broke down how to master Claude Code (even if you haven't coded before): 05:28 - Level 1: Why you start with Lovable 08:04 - Level 2: The Lovable + Claude Code bridge 28:37 - Level 3: Cursor + Vercel for real production 41:17 - Level 4:... show more

Aakash Gupta

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81,709 views • 3 months ago •via X (Twitter)

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💥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 views • 5 months ago

Anthropic dropped a free 59-minute Claude Code course From autocomplete to real agents: 00:00 - From autocomplete to agents 04:50 - How the agentic loop works 14:07 - CLAUDE.md as project memory 26:53 - Why Plan Mode comes first 33:31 - Live task from brief to commit 54:46 - Skills vs CLAUDE.md Most people still use Claude Code like a smarter autocomplete This shows how Anthropic uses it to plan, call tools, test, and ship real work Worth more than 99% of paid Claude Code tutorials Bookmark and watch it later

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Riley Brown

20,154 views • 3 months ago

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

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Aakash Gupta

150,362 views • 3 months ago

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42,646 views • 11 months ago

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424,191 views • 4 months ago

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Matt Turck

20,898 views • 17 days ago

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Aakash Gupta

178,026 views • 3 months ago

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

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Aakash Gupta

165,847 views • 1 month ago

Claude Cowork, Mythos, and the Future of Software: my conversation with Felix Rieseberg, who leads Cowork at Anthropic 00:00 Intro 01:53 Claude Mythos Preview and the “step-function change” 06:16 Why Anthropic is treating Mythos differently 11:19 The real story behind Claude Cowork’s “10-day” build 12:42 Why Anthropic realized Claude Code needed a non-technical version 15:44 What Claude Cowork actually is 17:03 Under the hood: virtual machines, tools, skills 18:36 Where Cowork’s memory actually lives 19:26 How Cowork connects to files, apps, and the internet 20:45 Why Felix thinks the local computer is under-appreciated 24:49 Trust: how do you get users comfortable with AI agents? 28:45 What UX actually means for AI agents 31:27 Anthropic Cowork's roadmap is only one month long 34:12 Building 100 prototypes 35:10 If execution is free, what becomes the bottleneck? 37:25 Does it come down to taste? 40:12 The hardest part of building Claude Cowork 41:43 Advice for founders building AI agents 44:21 SaaSpocalypse: what’s left for software startups 49:30 Where AI agents are going next 51:20 Regulated industries and enterprise adoption 54:15 Hot takes: what's underrated, overrated, and what Felix would build today

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80,718 views • 4 months ago

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Aakash Gupta

42,853 views • 1 year ago

Claude Code is a major (and accidental!) hit for Anthropic that surprised even its creator, Boris Cherny. Claude Code, an Agentic AI coding product that lives in the terminal. Most of the new code at Anthropic is created through it today. And in the last 5 months since it was launched publicly, Claude Code went from $0 to $400M in revenue run rate (as per The Information). 00:00 – Intro 01:15 – Did You Expect Claude Code’s Success? 04:22 – How Claude Code Works and Origins 08:05 – Command Line vs IDE: Why Start Claude Code in the Terminal? 11:31 – The Evolution of Programming: From Punch Cards to Agents 13:20 – Product Follows Model: Simple Interfaces and Fast Evolution 15:17 – Who Is Claude Code For? (Engineers, Designers, PMs & More) 17:46 – What Can Claude Code Actually Do? (Actions & Capabilities) 21:14 – Agentic Actions, Subagents, and Workflows 25:30 – Claude Code’s Awareness, Memory, and Knowledge Sharing 33:28 – Model Context Protocol (MCP) and Customization 35:30 – Safety, Human Oversight, and Enterprise Considerations 38:10 – UX/UI: Making Claude Code Useful and Enjoyable 40:44 – Pricing for Power Users and Subscription Models 43:36 – Real-World Use Cases: Debugging, Testing, and More 46:44 – How Does Claude Code Transform Onboarding? 49:36 – The Future of Coding: Agents, Teams, and Collaboration 54:11 – The AI Coding Wars: Competition & Ecosystem 57:27 – The Future of Coding as a Profession 58:41 – What’s Next for Claude Code

Claude Code is a major (and accidental!) hit for Anthropic that surprised even its creator, Boris Cherny. Claude Code, an Agentic AI coding product that lives in the terminal. Most of the new code at Anthropic is created through it today. And in the last 5 months since it was launched publicly, Claude Code went from $0 to $400M in revenue run rate (as per The Information). 00:00 – Intro 01:15 – Did You Expect Claude Code’s Success? 04:22 – How Claude Code Works and Origins 08:05 – Command Line vs IDE: Why Start Claude Code in the Terminal? 11:31 – The Evolution of Programming: From Punch Cards to Agents 13:20 – Product Follows Model: Simple Interfaces and Fast Evolution 15:17 – Who Is Claude Code For? (Engineers, Designers, PMs & More) 17:46 – What Can Claude Code Actually Do? (Actions & Capabilities) 21:14 – Agentic Actions, Subagents, and Workflows 25:30 – Claude Code’s Awareness, Memory, and Knowledge Sharing 33:28 – Model Context Protocol (MCP) and Customization 35:30 – Safety, Human Oversight, and Enterprise Considerations 38:10 – UX/UI: Making Claude Code Useful and Enjoyable 40:44 – Pricing for Power Users and Subscription Models 43:36 – Real-World Use Cases: Debugging, Testing, and More 46:44 – How Does Claude Code Transform Onboarding? 49:36 – The Future of Coding: Agents, Teams, and Collaboration 54:11 – The AI Coding Wars: Competition & Ecosystem 57:27 – The Future of Coding as a Profession 58:41 – What’s Next for Claude Code

Matt Turck

82,327 views • 1 year ago

Sat down with Aaron Levie, CEO of Box, to discuss where AI agents are going, how agentic workflows are transforming software, why agents will require much more memory, and how token budgets for enterprises are evolving. Thank you Aaron Levie for taking the time! 00:00 - Intro 01:30 - Where are we in AI 06:25 - The Role of Agents in Software 11:40 - The Role of Memory for Agents 14:45 - TokenMaxxing 24:54 - Why Are There AI Doomers? 31:50 - Aaron's Take on Nvidia

Sat down with Aaron Levie, CEO of Box, to discuss where AI agents are going, how agentic workflows are transforming software, why agents will require much more memory, and how token budgets for enterprises are evolving. Thank you Aaron Levie for taking the time! 00:00 - Intro 01:30 - Where are we in AI 06:25 - The Role of Agents in Software 11:40 - The Role of Memory for Agents 14:45 - TokenMaxxing 24:54 - Why Are There AI Doomers? 31:50 - Aaron's Take on Nvidia

amit

96,221 views • 1 month ago

.signüll and i went through some of my favorite posts and accounts of all time and attempted to explain why they are good. thank you a16z for having us. thank you for the golf cart rides. thank you for the vodka sodas 00:00 - Why we started posting 05:26 - Defining “banger” and what goes viral now 06:59 - @basedsrini reaction 12:18 - bowser reaction 15:27 - hope hopes hoping reaction 17:03 - Gabriel reaction 25:47 - The Murray Hill Guy format (listing things) 28:04 - Max Mönch (rip) 34:08 - Landshark reaction 36:28 - Marc Andreessen 🇺🇸 and the thread format 37:35 - HowlingMutant reaction 38:57 - roon reaction 46:25 - “paula” reaction 49:19 - Getting good and finding your audience 53:04 - “Imagine the smell” gate 56:26 - How to get an account started 01:02:59 - Ragebait 01:04:01 - Finding your edge 01:07:27 - How the algorithm works 01:10:59 - Crossposting 01:14:15 - Monetizing

.signüll and i went through some of my favorite posts and accounts of all time and attempted to explain why they are good. thank you a16z for having us. thank you for the golf cart rides. thank you for the vodka sodas 00:00 - Why we started posting 05:26 - Defining “banger” and what goes viral now 06:59 - @basedsrini reaction 12:18 - bowser reaction 15:27 - hope hopes hoping reaction 17:03 - Gabriel reaction 25:47 - The Murray Hill Guy format (listing things) 28:04 - Max Mönch (rip) 34:08 - Landshark reaction 36:28 - Marc Andreessen 🇺🇸 and the thread format 37:35 - HowlingMutant reaction 38:57 - roon reaction 46:25 - “paula” reaction 49:19 - Getting good and finding your audience 53:04 - “Imagine the smell” gate 56:26 - How to get an account started 01:02:59 - Ragebait 01:04:01 - Finding your edge 01:07:27 - How the algorithm works 01:10:59 - Crossposting 01:14:15 - Monetizing

atlas

78,979 views • 3 months ago

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

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 views • 3 months ago

“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 views • 1 year ago

Inside Nemotron and NVIDIA's AI lab: my conversation with Bryan Catanzaro (Bryan Catanzaro). NVIDIA is a chip company. So why does it put hundreds of researchers on building AI models - and then give them away for free? We go deep into the Nemotron models, what it takes to build a top AI lab, and the future of frontier AI. 01:33 - Is open source AI catching the frontier? 05:29 - Do closed labs blocking distillation slow open source down? 07:42 - Is the US falling behind China? 10:30 - Why companies actually choose open models 12:39 - A "crazy" 2008 bet: machine learning on GPUs 15:33 - Working with Andrew Ng and Dario Amodei at Baidu 17:41 - Coming back to NVIDIA: DLSS and the birth of Megatron 21:55 - The real reason NVIDIA builds its own models 24:28 - Is Moore's Law really dead? 33:37 - The Nemotron family: Nano, Super, Ultra 35:09 - Built for agents: why NVIDIA bets on speed 36:02 - How you train a 550B model in 4 bits 39:25 - Hybrid Mamba-Transformer, explained simply 42:31 - Mixture of experts, and why NVIDIA built NVL72 around it 47:26 - Why a 1-million-token context window matters 49:26 - Multi-token prediction: how the model predicts 5 tokens at once 52:47 - Multi-teacher distillation: teaching one model from many 58:01 - Where reinforcement learning goes next 01:00:16 - Inside NVIDIA's research org: "the mission is the boss" 01:04:03 - How NVIDIA decides who gets the GPUs 01:10:53 - Why NVIDIA still feels entrepreneurial after 33 years 01:12:58 - Why Bryan doesn't believe in the singularity 01:17:50 - The AI backlash 01:19:18 - The controversial case: open AI is safer than closed

Inside Nemotron and NVIDIA's AI lab: my conversation with Bryan Catanzaro (Bryan Catanzaro). NVIDIA is a chip company. So why does it put hundreds of researchers on building AI models - and then give them away for free? We go deep into the Nemotron models, what it takes to build a top AI lab, and the future of frontier AI. 01:33 - Is open source AI catching the frontier? 05:29 - Do closed labs blocking distillation slow open source down? 07:42 - Is the US falling behind China? 10:30 - Why companies actually choose open models 12:39 - A "crazy" 2008 bet: machine learning on GPUs 15:33 - Working with Andrew Ng and Dario Amodei at Baidu 17:41 - Coming back to NVIDIA: DLSS and the birth of Megatron 21:55 - The real reason NVIDIA builds its own models 24:28 - Is Moore's Law really dead? 33:37 - The Nemotron family: Nano, Super, Ultra 35:09 - Built for agents: why NVIDIA bets on speed 36:02 - How you train a 550B model in 4 bits 39:25 - Hybrid Mamba-Transformer, explained simply 42:31 - Mixture of experts, and why NVIDIA built NVL72 around it 47:26 - Why a 1-million-token context window matters 49:26 - Multi-token prediction: how the model predicts 5 tokens at once 52:47 - Multi-teacher distillation: teaching one model from many 58:01 - Where reinforcement learning goes next 01:00:16 - Inside NVIDIA's research org: "the mission is the boss" 01:04:03 - How NVIDIA decides who gets the GPUs 01:10:53 - Why NVIDIA still feels entrepreneurial after 33 years 01:12:58 - Why Bryan doesn't believe in the singularity 01:17:50 - The AI backlash 01:19:18 - The controversial case: open AI is safer than closed

Matt Turck

56,749 views • 1 month ago

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

155,794 views • 28 days ago