Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

Don't waste 2 years learning to use LLMs like Claude & ChatGPT. Andrej Karpathy one of the clearest minds in AI just released a 2‑hour breakdown of how he actually uses LLMs every day. • 00:00 — LLMs, explained like you’re busy • 22:49 — picking the right model... show more

CyrilXBT

196,317 subscribers

51,835 görüntüleme • 24 gün önce •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

Don't waste 2 years learning to build LLMs like Claude & ChatGPT. MIT dropped a 3 hour course on building LLMs from scratch for free. Training → Architecture → Scale. It covers everything about LLMs. • 00:00 - training an LLM • 1:20:46 - LLM architecture from scratch • 2:34:56 - scaling laws for LLMs Anthropic pays $750,000/year to AI engineers in 2026 who master this exact knowledge. Bookmark this & skip Netflix. Give it 4 hours today. Then read the article below.

Don't waste 2 years learning to build LLMs like Claude & ChatGPT. MIT dropped a 3 hour course on building LLMs from scratch for free. Training → Architecture → Scale. It covers everything about LLMs. • 00:00 - training an LLM • 1:20:46 - LLM architecture from scratch • 2:34:56 - scaling laws for LLMs Anthropic pays $750,000/year to AI engineers in 2026 who master this exact knowledge. Bookmark this & skip Netflix. Give it 4 hours today. Then read the article below.

Roan

85,618 görüntüleme • 20 gün önce

Don't waste 2 years learning to build LLMs like Claude & ChatGPT. Stanford just dropped a 2 hour course on how to build LLMs from scratch. 00:00 - break text into tokens 25:44 - decode a user prompt 35:40 - train the model end to end 1:16:47 - build the architecture from scratch Anthropic pays $750,000/year to engineers who understand this exact knowledge of LLMs. Watch it, then go further with the step-by-step guide below on how to build a system that improves itself.

Don't waste 2 years learning to build LLMs like Claude & ChatGPT. Stanford just dropped a 2 hour course on how to build LLMs from scratch. 00:00 - break text into tokens 25:44 - decode a user prompt 35:40 - train the model end to end 1:16:47 - build the architecture from scratch Anthropic pays $750,000/year to engineers who understand this exact knowledge of LLMs. Watch it, then go further with the step-by-step guide below on how to build a system that improves itself.

Anatoli Kopadze

105,443 görüntüleme • 9 gün önce

Stanford just dropped a 1 hour course on how it teaches its AI engineers to train LLMs from scratch. This is the exact way LLMs like ChatGPT & Claude are trained. • 00:00 - LLM Policy Gradient basics • 23:24 - PPO for LLM training • 56:20 - how LLMs learn Chain of Thought • 1:03:50- LLM architecture explained Anthropic pays $750,000/year to engineers who understand this exact knowledge of LLMs. Bookmark this & give 1 hours today, no matter what. It'll be the most productive thing you do this week.

Stanford just dropped a 1 hour course on how it teaches its AI engineers to train LLMs from scratch. This is the exact way LLMs like ChatGPT & Claude are trained. • 00:00 - LLM Policy Gradient basics • 23:24 - PPO for LLM training • 56:20 - how LLMs learn Chain of Thought • 1:03:50- LLM architecture explained Anthropic pays $750,000/year to engineers who understand this exact knowledge of LLMs. Bookmark this & give 1 hours today, no matter what. It'll be the most productive thing you do this week.

Roan

37,436 görüntüleme • 27 gün önce

Met a guy making $1.6 million/year as an LLM engineer. I asked him how he learned LLMs from scratch. He sent me the exact video that got him in. A 1 hour course on how LLMs actually work. He shows how transformers inside LLMs like ChatGPT & Claude are actually built. I watched it last night. Halfway through, I realized LLM architecture is way simpler than they make it look. Bookmark this and read the article below. • 00:00 - LLM foundations • 04:21 - LLM tokenization • 05:43 - LLMs vector embeddings • 22:16 - attention mechanism of LLM • 43:42 - LLM multi head attention

Met a guy making $1.6 million/year as an LLM engineer. I asked him how he learned LLMs from scratch. He sent me the exact video that got him in. A 1 hour course on how LLMs actually work. He shows how transformers inside LLMs like ChatGPT & Claude are actually built. I watched it last night. Halfway through, I realized LLM architecture is way simpler than they make it look. Bookmark this and read the article below. • 00:00 - LLM foundations • 04:21 - LLM tokenization • 05:43 - LLMs vector embeddings • 22:16 - attention mechanism of LLM • 43:42 - LLM multi head attention

Roan

80,961 görüntüleme • 1 ay önce

Andrej Karpathy just rewrote the rules of using LLMs: "Prompting is going away. Delete everything, keep Graph." He dropped his full 2-hour course from Stanford on "Graph-Native Research" • 00:00 - Intro to Graph systems • 01:08:09 - LLMs architecture This Karpathy course can replace a $100K Yale LLM senior degree. Start watching right now, then read article below

Andrej Karpathy just rewrote the rules of using LLMs: "Prompting is going away. Delete everything, keep Graph." He dropped his full 2-hour course from Stanford on "Graph-Native Research" • 00:00 - Intro to Graph systems • 01:08:09 - LLMs architecture This Karpathy course can replace a $100K Yale LLM senior degree. Start watching right now, then read article below

Morty

31,683 görüntüleme • 12 gün önce

Andrej Karpathy just explained the 5 shifts turning LLMs into agentic systems. 00:00 - Memory turns chat into personal AI 06:41 - Multimodal AI reads the world 16:58 - Thinking models solve harder tasks 24:51 - Search makes LLMs live 30:58 - Tools turn LLMs into workers Most people are still treating LLMs like chatbots. Karpathy is showing the full stack: Memory → Vision → Reasoning → Search → Tools Prompting is the old workflow. Agentic systems are the new one. This 40-minute talk is worth more than most paid AI agent courses. Bookmark and watch it before everyone catches up. Then read how to turn LLMs into self-improving agent loops below

Andrej Karpathy just explained the 5 shifts turning LLMs into agentic systems. 00:00 - Memory turns chat into personal AI 06:41 - Multimodal AI reads the world 16:58 - Thinking models solve harder tasks 24:51 - Search makes LLMs live 30:58 - Tools turn LLMs into workers Most people are still treating LLMs like chatbots. Karpathy is showing the full stack: Memory → Vision → Reasoning → Search → Tools Prompting is the old workflow. Agentic systems are the new one. This 40-minute talk is worth more than most paid AI agent courses. Bookmark and watch it before everyone catches up. Then read how to turn LLMs into self-improving agent loops below

Lunar

354,047 görüntüleme • 28 gün önce

My friend applied to 200 tech jobs in two years. No PhD. No Stanford. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me a course that was never supposed to get out. A 3-hour video to build a full LLM from scratch. A developer teaches you exactly how LLMs like ChatGPT and Claude are actually built. I watched it last night. Halfway through, I realized it's embarrassingly simple to break into an AI lab. Bookmark this and read the article below. • 00:00 - intro to LLMs • 05:43 - LLM transformer architecture • 40:24 - training the LLM • 1:30:27 - modernizing the LLM • 2:33:53 - scaling the LLM

My friend applied to 200 tech jobs in two years. No PhD. No Stanford. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me a course that was never supposed to get out. A 3-hour video to build a full LLM from scratch. A developer teaches you exactly how LLMs like ChatGPT and Claude are actually built. I watched it last night. Halfway through, I realized it's embarrassingly simple to break into an AI lab. Bookmark this and read the article below. • 00:00 - intro to LLMs • 05:43 - LLM transformer architecture • 40:24 - training the LLM • 1:30:27 - modernizing the LLM • 2:33:53 - scaling the LLM

Roan

278,173 görüntüleme • 1 ay önce

My friend applied to 250 tech jobs in two years. No MIT. No Stanford. No PhD. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me the exact video that got him in. Andrej Karpathy's 4-hour course on "How to build a full LLM from scratch." Anthropic's own researcher gives up the exact playbook behind building LLMs like ChatGPT & Claude. I watched it last night. Halfway through, I realized I could break into an AI lab in months, not years. Bookmark this and read the article below. • 00:00 - LLMs as new OS • 40:58 - LLM tokenization architecture • 53:22 - LLM neural network internals • 1:04:20 - LLM pretraining • 3:21:37 - LLM RLHF & DeepSeek R1

My friend applied to 250 tech jobs in two years. No MIT. No Stanford. No PhD. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me the exact video that got him in. Andrej Karpathy's 4-hour course on "How to build a full LLM from scratch." Anthropic's own researcher gives up the exact playbook behind building LLMs like ChatGPT & Claude. I watched it last night. Halfway through, I realized I could break into an AI lab in months, not years. Bookmark this and read the article below. • 00:00 - LLMs as new OS • 40:58 - LLM tokenization architecture • 53:22 - LLM neural network internals • 1:04:20 - LLM pretraining • 3:21:37 - LLM RLHF & DeepSeek R1

Roan

124,260 görüntüleme • 1 ay önce

Don't waste 2 years learning to become an AI researcher in 2026. This is the exact 3 hour course on the math & architecture behind every LLM. • 00:00 - what an AI researcher does • 46:07 - the math behind LLMs • 1:54:00 - neural networks from scratch • 2:14:14 - LLM attention mechanism • 3:04:15 - LLM tokenization explained Anthropic pays $850,000/year to AI researchers who master this exact knowledge. Bookmark & give it 3 hours, no matter what. It will be the most productive thing you do this weekend.

Don't waste 2 years learning to become an AI researcher in 2026. This is the exact 3 hour course on the math & architecture behind every LLM. • 00:00 - what an AI researcher does • 46:07 - the math behind LLMs • 1:54:00 - neural networks from scratch • 2:14:14 - LLM attention mechanism • 3:04:15 - LLM tokenization explained Anthropic pays $850,000/year to AI researchers who master this exact knowledge. Bookmark & give it 3 hours, no matter what. It will be the most productive thing you do this weekend.

Roan

87,952 görüntüleme • 23 gün önce

My friend applied to 300 tech jobs in two years. No MIT. No Stanford. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me the exact video that got him in. Andrej Karpathy's 3-hour course on building a full LLM from scratch. Anthropic's own researcher shows you how LLMs like ChatGPT & Claude are actually built. I watched it last night. Halfway through, I realized it's embarrassingly simple to break into an AI lab. Bookmark this and read the article below. • 00:00 - intro to LLMs • 12:41 - LLM training pipeline • 31:58 - LLM tools & plugins • 41:14 - LLM transformer architecture • 2:19:02 - scaling the LLM

My friend applied to 300 tech jobs in two years. No MIT. No Stanford. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me the exact video that got him in. Andrej Karpathy's 3-hour course on building a full LLM from scratch. Anthropic's own researcher shows you how LLMs like ChatGPT & Claude are actually built. I watched it last night. Halfway through, I realized it's embarrassingly simple to break into an AI lab. Bookmark this and read the article below. • 00:00 - intro to LLMs • 12:41 - LLM training pipeline • 31:58 - LLM tools & plugins • 41:14 - LLM transformer architecture • 2:19:02 - scaling the LLM

Roan

286,556 görüntüleme • 1 ay önce

My friend applied to 150 tech jobs in two years. No MIT. No Stanford. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me the exact video that got him in. A 3-hour course to build a full LLM from scratch. A developer teaches you exactly how LLMs like ChatGPT and Claude are actually built. I watched it last night. Halfway through, I realized I could break into an AI lab in weeks, not years. Bookmark this and read the article below. • 00:00 - intro to building LLMs • 17:58 - LLM tokenization • 01:06:03 - LLM embedding vectors • 02:46:55 - LLM transformer architecture • 03:25:05 - GPT architecture

My friend applied to 150 tech jobs in two years. No MIT. No Stanford. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me the exact video that got him in. A 3-hour course to build a full LLM from scratch. A developer teaches you exactly how LLMs like ChatGPT and Claude are actually built. I watched it last night. Halfway through, I realized I could break into an AI lab in weeks, not years. Bookmark this and read the article below. • 00:00 - intro to building LLMs • 17:58 - LLM tokenization • 01:06:03 - LLM embedding vectors • 02:46:55 - LLM transformer architecture • 03:25:05 - GPT architecture

Roan

51,213 görüntüleme • 13 gün önce

Yann LeCun just raised $1.03 billion to build what comes after LLMs. Then he spent 4 hours explaining why: "There's literally hundreds of billions invested in an industry that is basically counting on the fact that LLMs is going to reach human level intelligence. It's complete bull shit." 1:03:40 00:00 - why machine learning still sucks, in his words 57:02 - what actually comes after LLMs 1:42:34 - what he's building at AMI Labs instead 2:43:08 - JEPA, and the road to human-level AI A Turing laureate who ran Meta's AI for 12 years, backed by Bezos and NVIDIA. Bookmark this & watch it before your feed starts repeating it.

Yann LeCun just raised $1.03 billion to build what comes after LLMs. Then he spent 4 hours explaining why: "There's literally hundreds of billions invested in an industry that is basically counting on the fact that LLMs is going to reach human level intelligence. It's complete bull shit." 1:03:40 00:00 - why machine learning still sucks, in his words 57:02 - what actually comes after LLMs 1:42:34 - what he's building at AMI Labs instead 2:43:08 - JEPA, and the road to human-level AI A Turing laureate who ran Meta's AI for 12 years, backed by Bezos and NVIDIA. Bookmark this & watch it before your feed starts repeating it.

Phosphen

27,249 görüntüleme • 1 ay önce

Don't waste 2 years learning Claude Code in 2026. This 1 hour course will teach you more about building AI agents than a 2 month internship at Anthropic. • 00:00 - build first AI agent • 15:03 - autonomous AI agent goals • 24:24 - self-improving Claude Skills • 1:02:44 - connect any tool to Claude if I got this a year ago, I would have shipped my first app in a day instead of 2 weeks. Bookmark this & give 1 hours today, no matter what. Then read the article below.

Don't waste 2 years learning Claude Code in 2026. This 1 hour course will teach you more about building AI agents than a 2 month internship at Anthropic. • 00:00 - build first AI agent • 15:03 - autonomous AI agent goals • 24:24 - self-improving Claude Skills • 1:02:44 - connect any tool to Claude if I got this a year ago, I would have shipped my first app in a day instead of 2 weeks. Bookmark this & give 1 hours today, no matter what. Then read the article below.

Roan

26,387 görüntüleme • 17 gün önce

Instead of watching 1 hour of Netflix today, watch this Stanford lecture by ex-GoogleBrain & OpenAI engineers. This is the best explanation of how LLMs like ChatGPT & Claude actually work, and how to unlock 100% of their potential. Worth watching whether you're a senior AI engineer or just taking your first steps in AI. I took the key ideas and turned them into a practical guide for getting 100% out of AI. You can find it below with ready-to-copy prompts and solutions.

Instead of watching 1 hour of Netflix today, watch this Stanford lecture by ex-GoogleBrain & OpenAI engineers. This is the best explanation of how LLMs like ChatGPT & Claude actually work, and how to unlock 100% of their potential. Worth watching whether you're a senior AI engineer or just taking your first steps in AI. I took the key ideas and turned them into a practical guide for getting 100% out of AI. You can find it below with ready-to-copy prompts and solutions.

Rohit

71,829 görüntüleme • 7 gün önce

Ex-Google Jeff Dean just dropped a 1-hour lecture on full AI engineering: from LLMs to Graphs 1:45 - LLM from scratch 17:22 - How to use AI models 30:03 - Prompt engineering 52:35 - One human coordinating 100 agents 27 years of building AI at Google, compressed into one hour. Watch it, then read the full guide on Graph engineering below.

Ex-Google Jeff Dean just dropped a 1-hour lecture on full AI engineering: from LLMs to Graphs 1:45 - LLM from scratch 17:22 - How to use AI models 30:03 - Prompt engineering 52:35 - One human coordinating 100 agents 27 years of building AI at Google, compressed into one hour. Watch it, then read the full guide on Graph engineering below.

Mahax

90,795 görüntüleme • 16 gün önce

Excited to present the LLM-Council skill. Initial idea by Karpathy. I just packaged it as a skill. You can easily spin up a council of LLMs or agents via Fireworks. Watch how the new GLM-5 model "deliberates" on other LLMs' thoughts on the big question, "Can LLMs reason?" Things worth paying attention to: New open models like GLM-5 have surprisingly improved on complex reasoning and long-running agentic tasks. The AskUserQuestion tool in Claude Code came in handy to select the council and chairperson. As Andrej Karpathy puts it, it's a really interesting way to get different perspectives from LLMs, which can lead to better decision-making on whatever task you are working on. You can use it for other agentic coding use cases, like evaluation, tool building, designing, and research.

Excited to present the LLM-Council skill. Initial idea by Karpathy. I just packaged it as a skill. You can easily spin up a council of LLMs or agents via Fireworks. Watch how the new GLM-5 model "deliberates" on other LLMs' thoughts on the big question, "Can LLMs reason?" Things worth paying attention to: New open models like GLM-5 have surprisingly improved on complex reasoning and long-running agentic tasks. The AskUserQuestion tool in Claude Code came in handy to select the council and chairperson. As Andrej Karpathy puts it, it's a really interesting way to get different perspectives from LLMs, which can lead to better decision-making on whatever task you are working on. You can use it for other agentic coding use cases, like evaluation, tool building, designing, and research.

elvis

39,593 görüntüleme • 6 ay önce

Don't waste 2 years figuring out how AI agents actually work. ex. Anthropic & Google engineers at Stanford just dropped a 1-hour lecture on self-improving AI agents: • 00:00 - Introduction to self-learning AI systems • 31:22 - inside a LLM: self-correction, backtracking • 42:24 - goal → loops → graphs→ feedback: the Agent loop • 45:47 - 6 agent orchestration patterns • 50:37 - the generator-verifier gap - why self-improvement stalls Planning, multi-step reasoning, self-correction. That's the entire agent roadmap for 2026. Bookmark this & give 70 minutes today. Then read the article below.

Don't waste 2 years figuring out how AI agents actually work. ex. Anthropic & Google engineers at Stanford just dropped a 1-hour lecture on self-improving AI agents: • 00:00 - Introduction to self-learning AI systems • 31:22 - inside a LLM: self-correction, backtracking • 42:24 - goal → loops → graphs→ feedback: the Agent loop • 45:47 - 6 agent orchestration patterns • 50:37 - the generator-verifier gap - why self-improvement stalls Planning, multi-step reasoning, self-correction. That's the entire agent roadmap for 2026. Bookmark this & give 70 minutes today. Then read the article below.

Movez

40,426 görüntüleme • 28 gün önce

Google just released a free 1-hour lecture from Chief Scientist Jeff Dean on full AI Engineering. How to go from one LLM to coordinating 100 AI agents: 01:45 - Build an LLM from scratch 17:22 - Learn how to actually use AI models 30:03 - Master prompt engineering 52:35 - Coordinate 100 agents as one human Most people are still learning how to prompt one model. Jeff Dean is showing the full stack: LLMs → Prompts → Agents → Agent Teams → Graphs Prompting is the old workflow. Coordinating agent systems is the new one. This is 27 years of Google AI compressed into one hour. Bookmark and watch it today Then read the full Graph Engineering guide below ↓

Google just released a free 1-hour lecture from Chief Scientist Jeff Dean on full AI Engineering. How to go from one LLM to coordinating 100 AI agents: 01:45 - Build an LLM from scratch 17:22 - Learn how to actually use AI models 30:03 - Master prompt engineering 52:35 - Coordinate 100 agents as one human Most people are still learning how to prompt one model. Jeff Dean is showing the full stack: LLMs → Prompts → Agents → Agent Teams → Graphs Prompting is the old workflow. Coordinating agent systems is the new one. This is 27 years of Google AI compressed into one hour. Bookmark and watch it today Then read the full Graph Engineering guide below ↓

Lunar

102,011 görüntüleme • 24 gün önce

.Richard Sutton, father of reinforcement learning, doesn’t think LLMs are bitter-lesson-pilled. My steel man of Richard’s position: we need some new architecture to enable continual (on-the-job) learning. And if we have continual learning, we don't need a special training phase - the agent just learns on-the-fly - like all humans, and indeed, like all animals. This new paradigm will render our current approach with LLMs obsolete. I did my best to represent the view that LLMs will function as the foundation on which this experiential learning can happen. Some sparks flew. 0:00:00 – Are LLMs a dead-end? 0:13:51 – Do humans do imitation learning? 0:23:57 – The Era of Experience 0:34:25 – Current architectures generalize poorly out of distribution 0:42:17 – Surprises in the AI field 0:47:28 – Will The Bitter Lesson still apply after AGI? 0:54:35 – Succession to AI

.Richard Sutton, father of reinforcement learning, doesn’t think LLMs are bitter-lesson-pilled. My steel man of Richard’s position: we need some new architecture to enable continual (on-the-job) learning. And if we have continual learning, we don't need a special training phase - the agent just learns on-the-fly - like all humans, and indeed, like all animals. This new paradigm will render our current approach with LLMs obsolete. I did my best to represent the view that LLMs will function as the foundation on which this experiential learning can happen. Some sparks flew. 0:00:00 – Are LLMs a dead-end? 0:13:51 – Do humans do imitation learning? 0:23:57 – The Era of Experience 0:34:25 – Current architectures generalize poorly out of distribution 0:42:17 – Surprises in the AI field 0:47:28 – Will The Bitter Lesson still apply after AGI? 0:54:35 – Succession to AI

Dwarkesh Patel

3,086,965 görüntüleme • 11 ay önce

New course! Generative AI with Large Language Models, created with Amazon Web Services and hosted on Coursera. This course goes deep into the technical foundations of LLMs and how to use them. You can sign up here: You’ll work through the full life-cycle of a generative AI project, and learn specific techniques like RLHF; zero-shot, one-shot, and few-shot learning with LLMs; advanced prompting frameworks like ReAct; even fine-tuning LLMs, and gain hands-on practice with all of these techniques. Instructors Antje Barth Chris Fregly Shelbee Eigenbrode and Mike G Chambers all do incredible Generative AI work at AWS, and have supported many companies to build creative LLM applications. They bring tremendous practical LLM expertise to this course. I'm confident you’ll finish this course with a deeper understanding of how LLMs work, and how to use them. I hope you enjoy the course!

New course! Generative AI with Large Language Models, created with Amazon Web Services and hosted on Coursera. This course goes deep into the technical foundations of LLMs and how to use them. You can sign up here: You’ll work through the full life-cycle of a generative AI project, and learn specific techniques like RLHF; zero-shot, one-shot, and few-shot learning with LLMs; advanced prompting frameworks like ReAct; even fine-tuning LLMs, and gain hands-on practice with all of these techniques. Instructors Antje Barth Chris Fregly Shelbee Eigenbrode and Mike G Chambers all do incredible Generative AI work at AWS, and have supported many companies to build creative LLM applications. They bring tremendous practical LLM expertise to this course. I'm confident you’ll finish this course with a deeper understanding of how LLMs work, and how to use them. I hope you enjoy the course!

Andrew Ng

467,986 görüntüleme • 3 yıl önce