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This video explains how diffusion models are overtaking Large Language Models for generation tasks like: 1. Code Generation 2. Image Generation 3. Video Generation 00:00 Agenda 00:20 How are they different from LLMs? 05:09 Internal Mechanism 10:09 How are vectors generated? 12:08 Conclusion 13:02 Opinion Piece AI Engineering Course:...

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Gaurav Sen

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28,027 görüntüleme • 11 ay önce •via X (Twitter)

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Don't waste 2 years learning to use LLMs like Claude & ChatGPT. Andrej Karpathy, the godfather of AI, dropped a 2 hour course on how he personally uses LLMs daily. • 00:00 - LLMs simplified • 22:49 - which LLM to actually use • 42:00 - one prompt for full research • 1:13:57 - how to code with LLMs • 1:37:04 - NotebookLM podcast generation This course will teach you more about using LLMs than most AI engineers learn in their entire career. Bookmark this & give 2 hours today, no matter what. Then read the article below.
2:11:07

Don't waste 2 years learning to use LLMs like Claude & ChatGPT. Andrej Karpathy, the godfather of AI, dropped a 2 hour course on how he personally uses LLMs daily. • 00:00 - LLMs simplified • 22:49 - which LLM to actually use • 42:00 - one prompt for full research • 1:13:57 - how to code with LLMs • 1:37:04 - NotebookLM podcast generation This course will teach you more about using LLMs than most AI engineers learn in their entire career. Bookmark this & give 2 hours today, no matter what. Then read the article below.

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Diffusion is the foundational ML framework behind state-of-the-art AI image and video generation, including Sora, Midjourney and Google Veo. In this episode of Decoded, Ankit Gupta sits down with Francois Chaubard to discuss how diffusion works, walk through a code sample, and explain why everyone training models should understand it. 00:00 Intro 00:33 What is diffusion? 02:50 What are applications of diffusion today? 04:06 Key innovations 07:01 Code examples 19:25 The "squint test" 22:27 Other areas diffusion is widely accessible 24:49 Outro
27:10

Diffusion is the foundational ML framework behind state-of-the-art AI image and video generation, including Sora, Midjourney and Google Veo. In this episode of Decoded, Ankit Gupta sits down with Francois Chaubard to discuss how diffusion works, walk through a code sample, and explain why everyone training models should understand it. 00:00 Intro 00:33 What is diffusion? 02:50 What are applications of diffusion today? 04:06 Key innovations 07:01 Code examples 19:25 The "squint test" 22:27 Other areas diffusion is widely accessible 24:49 Outro

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Wow, diffusion models (used in AI image generation) are also game engines - a type of world simulation. By predicting the next frame of the classic shooter DOOM, you get a playable game at 20 fps without any underlying real game engine. This video is from the diffusion model.
2:30

Wow, diffusion models (used in AI image generation) are also game engines - a type of world simulation. By predicting the next frame of the classic shooter DOOM, you get a playable game at 20 fps without any underlying real game engine. This video is from the diffusion model.

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How do we cache prompts with LLMs? Interview with Tanishq Singh. This is a real interview question from a big tech company, asked to a candidate in their technical interview round. The video explains the answer in roughly 6 minutes. 00:00 Question - Prompt Caching with LLMs 00:39 Exact Query Caching 02:04 Semantic Caching 04:14 Tradeoffs 05:52 Blooper :p AI engineering program for software engineers: #LLMs #AI #InterviewReady
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How do we cache prompts with LLMs? Interview with Tanishq Singh. This is a real interview question from a big tech company, asked to a candidate in their technical interview round. The video explains the answer in roughly 6 minutes. 00:00 Question - Prompt Caching with LLMs 00:39 Exact Query Caching 02:04 Semantic Caching 04:14 Tradeoffs 05:52 Blooper :p AI engineering program for software engineers: #LLMs #AI #InterviewReady

Gaurav Sen

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How do we run evals with LLMs? Interview with Tanishq Singh. This is a real interview question from a big tech company, asked to a candidate in their technical interview round. The video explains the answer in roughly 11 minutes. 00:00 Question - Reliability with LLMs 01:00 Observability & Evals 02:50 What are evals? 06:41 Who does evals? 08:11 Answer relevancy, groundedness, tool sequence 10:39 Bloopers :p AI engineering program for software engineers:
11:18

How do we run evals with LLMs? Interview with Tanishq Singh. This is a real interview question from a big tech company, asked to a candidate in their technical interview round. The video explains the answer in roughly 11 minutes. 00:00 Question - Reliability with LLMs 01:00 Observability & Evals 02:50 What are evals? 06:41 Who does evals? 08:11 Answer relevancy, groundedness, tool sequence 10:39 Bloopers :p AI engineering program for software engineers:

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35,790 görüntüleme • 2 ay önce

In this video, we break down the Model Context Protocol — a massively underrated concept in AI that's quietly redefining what large language models can do. While most people are distracted by the hype, MCP is solving a core problem: LLMs that can perform actions. We dive into: 1. The limitations of current AI systems 2. What makes MCP different 3. How MCP can reduce engineering overhead 4. Real-world scenarios where MCP shines 00:00 What is MCP? 01:48 Usecase - SEO 03:50 Usecase - RAG 04:50 Usecase - Apps 06:30 Conclusion 07:00 The future? #MCP #AI #LLM
9:17

In this video, we break down the Model Context Protocol — a massively underrated concept in AI that's quietly redefining what large language models can do. While most people are distracted by the hype, MCP is solving a core problem: LLMs that can perform actions. We dive into: 1. The limitations of current AI systems 2. What makes MCP different 3. How MCP can reduce engineering overhead 4. Real-world scenarios where MCP shines 00:00 What is MCP? 01:48 Usecase - SEO 03:50 Usecase - RAG 04:50 Usecase - Apps 06:30 Conclusion 07:00 The future? #MCP #AI #LLM

Gaurav Sen

41,154 görüntüleme • 1 yıl önce

Engineers need to communicate effectively when building AI Systems. These terms will help you use a shared vocabulary. This is useful when discussing concepts, reading papers, or collaborating with teammates. Listed in order. 00:00 Agenda 00:28 1. Large Language Model 01:28 2. Tokenization 02:53 3. Vectorization 04:15 4. Attention 07:22 5. Self-Supervised Learning 12:07 6. Transformer 14:32 7. Fine-tuning 17:05 8. Few-shot Prompting 18:11 9. Retrieval Augmented Generation 20:33 10. Vector Database 23:03 11. Model Context Protocol 25:43 12. Context Engineering 28:17 13. Agents 29:19 14. Reinforcement Learning 34:42 15. Chain of Thought 35:55 16. Reasoning Models 36:36 17. Multi-modal Models 38:21 18. Small Language Models 40:24 19. Distillation 41:47 20. Quantization If you are a software engineer looking to transition to AI, click the link below. AI Engineering Course: #AI #SoftwareEngineering #Agents
43:33

Engineers need to communicate effectively when building AI Systems. These terms will help you use a shared vocabulary. This is useful when discussing concepts, reading papers, or collaborating with teammates. Listed in order. 00:00 Agenda 00:28 1. Large Language Model 01:28 2. Tokenization 02:53 3. Vectorization 04:15 4. Attention 07:22 5. Self-Supervised Learning 12:07 6. Transformer 14:32 7. Fine-tuning 17:05 8. Few-shot Prompting 18:11 9. Retrieval Augmented Generation 20:33 10. Vector Database 23:03 11. Model Context Protocol 25:43 12. Context Engineering 28:17 13. Agents 29:19 14. Reinforcement Learning 34:42 15. Chain of Thought 35:55 16. Reasoning Models 36:36 17. Multi-modal Models 38:21 18. Small Language Models 40:24 19. Distillation 41:47 20. Quantization If you are a software engineer looking to transition to AI, click the link below. AI Engineering Course: #AI #SoftwareEngineering #Agents

Gaurav Sen

80,479 görüntüleme • 1 yıl önce

How Sui Plans to Beat Hyperliquid with evan.sui Timestamps: 00:00 Intro 01:32 Is Blockchain Just Finance? 02:40 Sui's Vertical Build Strategy 05:00 Who Benefits from Tokenization? 07:21 The Verticalization Shift 09:32 AI Meets Blockchain 11:10 Do We Need Billion TPS? 13:10 Regulatory Clarity Incoming 15:05 Competing with Hyper Liquid 20:02 L1 Fee Models Are Dead 21:32 Leaving Facebook's Libra 24:18 Institutional Appetite for Sui
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How Sui Plans to Beat Hyperliquid with evan.sui Timestamps: 00:00 Intro 01:32 Is Blockchain Just Finance? 02:40 Sui's Vertical Build Strategy 05:00 Who Benefits from Tokenization? 07:21 The Verticalization Shift 09:32 AI Meets Blockchain 11:10 Do We Need Billion TPS? 13:10 Regulatory Clarity Incoming 15:05 Competing with Hyper Liquid 20:02 L1 Fee Models Are Dead 21:32 Leaving Facebook's Libra 24:18 Institutional Appetite for Sui

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Live from Apsara Conference 2026, an immersive, multi-screen look at where AI meets creativity. Watch how our models are multimodal generation, from high-fidelity image and audio synthesis to seamless cinematic video and interactive virtual worlds.
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Live from Apsara Conference 2026, an immersive, multi-screen look at where AI meets creativity. Watch how our models are multimodal generation, from high-fidelity image and audio synthesis to seamless cinematic video and interactive virtual worlds.

Alibaba Group

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Andrej Karpathy explained the 5 shifts that turned LLMs from chatbots into agentic systems: 00:00 - Memory turns chat into personal AI 6:41- Multimodal AI can read the world 16:58 - Thinking models solve harder tasks 24:51 - Search makes LLMs live 30:58 - Tools turn LLMs into workers This is not another video about “prompt engineering.” It is a 40-minute roadmap for the next AI workflow: memory / vision / reasoning / search / tools. Watch today, then read the article below on how to turn LLMs into self-improving agent loops.
42:23

Andrej Karpathy explained the 5 shifts that turned LLMs from chatbots into agentic systems: 00:00 - Memory turns chat into personal AI 6:41- Multimodal AI can read the world 16:58 - Thinking models solve harder tasks 24:51 - Search makes LLMs live 30:58 - Tools turn LLMs into workers This is not another video about “prompt engineering.” It is a 40-minute roadmap for the next AI workflow: memory / vision / reasoning / search / tools. Watch today, then read the article below on how to turn LLMs into self-improving agent loops.

Morlex

37,456 görüntüleme • 3 ay 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
57:39

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

81,846 görüntüleme • 2 ay önce

Andrew Ng just dropped the best 2-hour prompt engineering course of 2026 From brainstorming and agentic prompt patterns To building prompts for images, video, apps, and data analysis: 00:00 - introduction to prompt engineering 38:12 - brainstorming prompt patterns 47:59 - context engineering 01:30:27 - prompts for media generation 01:53:43 - app building and data analysis prompts This 2-hour watch will change how you use LLMs Bookmark and watch it today Then read the article below
2:08:58

Andrew Ng just dropped the best 2-hour prompt engineering course of 2026 From brainstorming and agentic prompt patterns To building prompts for images, video, apps, and data analysis: 00:00 - introduction to prompt engineering 38:12 - brainstorming prompt patterns 47:59 - context engineering 01:30:27 - prompts for media generation 01:53:43 - app building and data analysis prompts This 2-hour watch will change how you use LLMs Bookmark and watch it today Then read the article below

rari

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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.
1:18:27

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

38,856 görüntüleme • 2 ay önce

., Co-founder of fal, on where AI image & video generation stands today: “Image and video models haven’t really generalized yet. There’s still a lot of model chaining and workflow generation." "Models are getting bigger and better, but unlike LLMs where you can write a prompt and expect it to handle various tasks, that doesn’t happen for media yet.” He notes that while chaining multiple models is still the norm, the opportunity lies in building hybrid systems that stitch workflows together reliably until true generalization arrives.
1:14

., Co-founder of fal, on where AI image & video generation stands today: “Image and video models haven’t really generalized yet. There’s still a lot of model chaining and workflow generation." "Models are getting bigger and better, but unlike LLMs where you can write a prompt and expect it to handle various tasks, that doesn’t happen for media yet.” He notes that while chaining multiple models is still the norm, the opportunity lies in building hybrid systems that stitch workflows together reliably until true generalization arrives.

TBPN

36,675 görüntüleme • 1 yıl önce

As someone who builds with LLMs and diffusion models, this 14-minute heat equation video is the closest thing to a "why noise reversal actually works" explainer I've ever seen released for free. Everyone thinks diffusion is a novel AI concept. It's the heat equation from 1822. The forward process spreads noise like heat, and the reverse process is what your model learns. Bookmark & watch this weekend. Same math, from 19th-century physics to 2025 image generation.
13:12

As someone who builds with LLMs and diffusion models, this 14-minute heat equation video is the closest thing to a "why noise reversal actually works" explainer I've ever seen released for free. Everyone thinks diffusion is a novel AI concept. It's the heat equation from 1822. The forward process spreads noise like heat, and the reverse process is what your model learns. Bookmark & watch this weekend. Same math, from 19th-century physics to 2025 image generation.

fintex

15,604 görüntüleme • 2 ay önce

Core Memory podcast with Suzanne Gildert on the hunt for AI consciousness in the quantum realm. The theory being that LLMs are too constrained to really get to robots and AIs that are really like us 00:00:00 – Consciousness: The First AI Training Model? 00:02:03 – Exploring the Quantum Mind with AI 00:04:42 – Will Robots Ever Have Rights? 00:07:54 – The AI Paradox: Fear vs. Progress 00:13:20 – Can Language Models Teach Robots? 00:17:29 – Why Today’s AI Feels Like a Zombie 00:23:47 – Quantum Computing Meets Artificial Consciousness 00:29:50 – Free Will, Entanglement, and Decision Making 00:38:21 – Teaching a Robot to Think, Not Just React 00:52:51 – Will Conscious AI Power Space Exploration?
1:10:42

Core Memory podcast with Suzanne Gildert on the hunt for AI consciousness in the quantum realm. The theory being that LLMs are too constrained to really get to robots and AIs that are really like us 00:00:00 – Consciousness: The First AI Training Model? 00:02:03 – Exploring the Quantum Mind with AI 00:04:42 – Will Robots Ever Have Rights? 00:07:54 – The AI Paradox: Fear vs. Progress 00:13:20 – Can Language Models Teach Robots? 00:17:29 – Why Today’s AI Feels Like a Zombie 00:23:47 – Quantum Computing Meets Artificial Consciousness 00:29:50 – Free Will, Entanglement, and Decision Making 00:38:21 – Teaching a Robot to Think, Not Just React 00:52:51 – Will Conscious AI Power Space Exploration?

Ashlee Vance

17,189 görüntüleme • 1 yıl önce

SPECIAL: How Erik Voorhees Built the Leading Private, Uncensored AI Venice Timestamps: 00:00 Intro & ShapeShift Origin Story 05:25 Did Crypto Play Out as Expected? 06:25 AI vs. Crypto: Are They Similar? 07:23 What Is Venice AI? 09:38 From ShapeShift to Venice 10:31 Censorship in AI & the Open Source Alternative 11:38 Hibachi, infiniFi Ads 12:00 Are AI Labs Too Censored? 13:24 Relay Ad 13:41 Geopolitics: Chinese Open Source vs. US Closed Models 14:15 SBF, the Anthropic Investment & Crypto Scammers 16:26 NearCon, Illia & the AI/Crypto Convergence
17:28

SPECIAL: How Erik Voorhees Built the Leading Private, Uncensored AI Venice Timestamps: 00:00 Intro & ShapeShift Origin Story 05:25 Did Crypto Play Out as Expected? 06:25 AI vs. Crypto: Are They Similar? 07:23 What Is Venice AI? 09:38 From ShapeShift to Venice 10:31 Censorship in AI & the Open Source Alternative 11:38 Hibachi, infiniFi Ads 12:00 Are AI Labs Too Censored? 13:24 Relay Ad 13:41 Geopolitics: Chinese Open Source vs. US Closed Models 14:15 SBF, the Anthropic Investment & Crypto Scammers 16:26 NearCon, Illia & the AI/Crypto Convergence

The Rollup

47,879 görüntüleme • 7 ay önce

New EP for my consumer ai series featuring MoemateAI $mates 00:00 introduction to moemate and its unique features 01:11 exploring moemate agent sandbox and customization 02:21 live streaming and image generation capabilities 03:45 user statistics and platform popularity 06:13 subscription plans and pricing 07:13 creating and customizing your own agents 09:44 advanced features 10:44 conclusion (disclaimer: I hold $MATES token)
10:56

New EP for my consumer ai series featuring MoemateAI $mates 00:00 introduction to moemate and its unique features 01:11 exploring moemate agent sandbox and customization 02:21 live streaming and image generation capabilities 03:45 user statistics and platform popularity 06:13 subscription plans and pricing 07:13 creating and customizing your own agents 09:44 advanced features 10:44 conclusion (disclaimer: I hold $MATES token)

Sandra

26,723 görüntüleme • 1 yıl önce

춤 챌린지 모음 #윈터 #WINTER 00:00 Kick Back 00:17 Weekend 00:38 Second 01:02 Sparkling 01:23 Sneakers 01:35 Candy 01:55 야야야 02:08 Unforgiven 02:25 Movie Star 02:34 이프푸 02:55 Hard 03:20 최애의 아이 03:37 Vujade 03:53 It is what it is 04:06 Get A Guitar 04:16 INVITATION 04:25 Guilty 05:10 Fact Check 05:34 Maniac 05:56 WOP 06:13 Chill Kill 06:34 Trance x I Know 06:45 Sweet Venom 07:03 Siren 07:16 Hot & Cold 07:41 Rockin' Around The Pole Again 08:03 Underneath the Tree 08:21 Be There For Me 08:42 뭣 같아 08:54 UNTOUCHABLE 09:09 Two Of Hearts 09:19 Boyfriend 09:42 Nee, 09:59 움파룸파 10:17 사랑스러워 10:38 Like I Do 10:53 Like I Do (골댕이ver.) 11:08 민정이가 저장해놓은.. 11:29 갈매기.. 11:45 날 바라바라봐 12:01 GGB 12:12 홀씨 12:56 시나모롤 13:10 띵띵땅땅 13:26 EENIE MEENIE 13:47 How Sweet
14:25

춤 챌린지 모음 #윈터 #WINTER 00:00 Kick Back 00:17 Weekend 00:38 Second 01:02 Sparkling 01:23 Sneakers 01:35 Candy 01:55 야야야 02:08 Unforgiven 02:25 Movie Star 02:34 이프푸 02:55 Hard 03:20 최애의 아이 03:37 Vujade 03:53 It is what it is 04:06 Get A Guitar 04:16 INVITATION 04:25 Guilty 05:10 Fact Check 05:34 Maniac 05:56 WOP 06:13 Chill Kill 06:34 Trance x I Know 06:45 Sweet Venom 07:03 Siren 07:16 Hot & Cold 07:41 Rockin' Around The Pole Again 08:03 Underneath the Tree 08:21 Be There For Me 08:42 뭣 같아 08:54 UNTOUCHABLE 09:09 Two Of Hearts 09:19 Boyfriend 09:42 Nee, 09:59 움파룸파 10:17 사랑스러워 10:38 Like I Do 10:53 Like I Do (골댕이ver.) 11:08 민정이가 저장해놓은.. 11:29 갈매기.. 11:45 날 바라바라봐 12:01 GGB 12:12 홀씨 12:56 시나모롤 13:10 띵띵땅땅 13:26 EENIE MEENIE 13:47 How Sweet

취향의 윈터

390,042 görüntüleme • 2 yıl önce