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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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81,356 Aufrufe • vor 2 Monaten •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

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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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Aakash Gupta

163,253 Aufrufe • vor 16 Tagen

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82,161 Aufrufe • vor 11 Monaten

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amit

96,116 Aufrufe • vor 1 Monat

.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

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Y Combinator

67,145 Aufrufe • vor 2 Monaten

“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

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

56,659 Aufrufe • vor 29 Tagen

tylercowen is bullish on AI education — here's why. 00:00 -- Preview 00:24 -- President Carlos Carvalho's AI-generated intro 03:21 -- Cowen reacts to UATX's campus 04:38 -- The AI revolution is here. Who will lose the most? 06:05 -- AI lawyers 07:17 -- Don't underestimate this 10:41 -- Changes to the "upper upper middle class" 12:38 -- How to be successful 13:43 -- The rise of managerial empires 14:02 -- When will we have the first billion dollar company with one employee? 16:05 -- 10-20 year forecast 16:19 -- Why education is so behind 17:01 -- Should you be bullish on UATX? 18:36 -- Should you still read Homer? 21:50 -- Write to think 25:01 -- Meet more people 25:42 -- How to get hired 26:54 -- Is AI your best mentor? 38:17 -- How to curb cheating 39:02 -- The new life of the mind 42:34 -- Q&A: Will there be more status associated with real education or AI education? 45:50 -- Q&A: Why do tech-savvy students need to practice using AI? 47:56 -- Q&A: Do LLMs atrophy your mind? 49:29 -- Q&A: How do you avoid AI-dependency? 51:05 -- Q&A: Isn't this vision lonely and isolating? 53:06 -- Q&A: Do students need teachers? 55:36 -- Q&A: What are the four most important courses for undergrads? 57:49 -- Q&A: Which AI company will win the AI race in the next five years and why? 59:22 -- Q&A: Can AI teach religion? 01:01:32 -- Q&A: Will AI narrow or widen our world? 01:04:37 -- Q&A: What makes us human? 01:05:42 -- Q&A: What is art? 01:08:33 -- Q&A: It's easy to catch cheaters

tylercowen is bullish on AI education — here's why. 00:00 -- Preview 00:24 -- President Carlos Carvalho's AI-generated intro 03:21 -- Cowen reacts to UATX's campus 04:38 -- The AI revolution is here. Who will lose the most? 06:05 -- AI lawyers 07:17 -- Don't underestimate this 10:41 -- Changes to the "upper upper middle class" 12:38 -- How to be successful 13:43 -- The rise of managerial empires 14:02 -- When will we have the first billion dollar company with one employee? 16:05 -- 10-20 year forecast 16:19 -- Why education is so behind 17:01 -- Should you be bullish on UATX? 18:36 -- Should you still read Homer? 21:50 -- Write to think 25:01 -- Meet more people 25:42 -- How to get hired 26:54 -- Is AI your best mentor? 38:17 -- How to curb cheating 39:02 -- The new life of the mind 42:34 -- Q&A: Will there be more status associated with real education or AI education? 45:50 -- Q&A: Why do tech-savvy students need to practice using AI? 47:56 -- Q&A: Do LLMs atrophy your mind? 49:29 -- Q&A: How do you avoid AI-dependency? 51:05 -- Q&A: Isn't this vision lonely and isolating? 53:06 -- Q&A: Do students need teachers? 55:36 -- Q&A: What are the four most important courses for undergrads? 57:49 -- Q&A: Which AI company will win the AI race in the next five years and why? 59:22 -- Q&A: Can AI teach religion? 01:01:32 -- Q&A: Will AI narrow or widen our world? 01:04:37 -- Q&A: What makes us human? 01:05:42 -- Q&A: What is art? 01:08:33 -- Q&A: It's easy to catch cheaters

University of Austin (UATX)

27,770 Aufrufe • vor 6 Monaten

Andrej Karpathy posted about the new programmable layer developers need to master: agents, subagents, prompts, contexts, memory, hooks, MCP. He said it came with no manual and everyone has to figure out how to hold it. This video is part of that manual. Hooks are one of the most underused features in Cursor and Droid, and they're what separate intentional AI-assisted development from hoping it works in production. After file edit: If the agent touches Rust code, hooks automatically regenerate TypeScript types for the frontend. If it touches TypeScript, ESLint runs with autofix. If it touches backend code, cargo clippy runs. On stop: When the agent finishes, it runs cargo check to validate the build before I even look at the output. 00:00 The problem: manually verifying AI-written code 00:31 Introducing hooks to automate tasks when files change 01:53 How hooks regenerate types for the frontend 02:24 How to set up the cursor.hooks.json file 04:44 Reviewing a more comprehensive hooks plan 05:48 Comparing FactoryDroid hooks vs Cursor hooks 06:45 Implementing the equivalent hooks in FactoryDroid 07:50 AI agent fixes a bug in the build command 08:27 How scaffolding makes the AI agent appear smarter 09:08 Demonstrating the successful hook implementation 09:53 Final insight: It's about providing guardrails, not a smarter AI

Andrej Karpathy posted about the new programmable layer developers need to master: agents, subagents, prompts, contexts, memory, hooks, MCP. He said it came with no manual and everyone has to figure out how to hold it. This video is part of that manual. Hooks are one of the most underused features in Cursor and Droid, and they're what separate intentional AI-assisted development from hoping it works in production. After file edit: If the agent touches Rust code, hooks automatically regenerate TypeScript types for the frontend. If it touches TypeScript, ESLint runs with autofix. If it touches backend code, cargo clippy runs. On stop: When the agent finishes, it runs cargo check to validate the build before I even look at the output. 00:00 The problem: manually verifying AI-written code 00:31 Introducing hooks to automate tasks when files change 01:53 How hooks regenerate types for the frontend 02:24 How to set up the cursor.hooks.json file 04:44 Reviewing a more comprehensive hooks plan 05:48 Comparing FactoryDroid hooks vs Cursor hooks 06:45 Implementing the equivalent hooks in FactoryDroid 07:50 AI agent fixes a bug in the build command 08:27 How scaffolding makes the AI agent appear smarter 09:08 Demonstrating the successful hook implementation 09:53 Final insight: It's about providing guardrails, not a smarter AI

Ray Fernando

60,884 Aufrufe • vor 7 Monaten