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Learn about Google’s new SOTA image model, Gemini 2.5 Flash, its key capabilities, and what’s next on the roadmap with some of the team behind the model Nicole Brichtova Kaushik Shivakumar Mostafa Dehghani Robert Riachi with Logan Kilpatrick. Timecodes: 0:37 New model introduction 01:21 Demo: Image editing 03:44 Text...

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Google AI Developers

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31,150 просмотров • 1 год назад •via X (Twitter)

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We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:
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We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:

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89,257 просмотров • 11 месяцев назад

How does an AI model actually learn to see? 🤖 Learn about the tech behind native multimodality, how models reason over visual data like documents and video, and the future of proactive AI assistants with Logan Kilpatrick and Gemini Model Behavior Product Lead, Ani Baddepudi. ↓ Timestamps: 01:12 Why Gemini is natively multimodal 02:23 The technology behind multimodal models 05:15 Video understanding with Gemini 2.5 09:25 Deciding what to build next 13:23 Building new product experiences with multimodal AI 17:15 The vision for proactive assistants 24:13 Improving video usability with variable FPS and frame tokenization 27:35 What’s next for Gemini’s multimodal development 31:47 Deep dive on Gemini’s document understanding capabilities 37:56 The teamwork and collaboration behind Gemini 40:56 What’s next with model behavior
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How does an AI model actually learn to see? 🤖 Learn about the tech behind native multimodality, how models reason over visual data like documents and video, and the future of proactive AI assistants with Logan Kilpatrick and Gemini Model Behavior Product Lead, Ani Baddepudi. ↓ Timestamps: 01:12 Why Gemini is natively multimodal 02:23 The technology behind multimodal models 05:15 Video understanding with Gemini 2.5 09:25 Deciding what to build next 13:23 Building new product experiences with multimodal AI 17:15 The vision for proactive assistants 24:13 Improving video usability with variable FPS and frame tokenization 27:35 What’s next for Gemini’s multimodal development 31:47 Deep dive on Gemini’s document understanding capabilities 37:56 The teamwork and collaboration behind Gemini 40:56 What’s next with model behavior

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We’re excited to announce the release and open-source of HunyuanImage 3.0 — the largest and most powerful open-source text-to-image model to date, with over 80 billion total parameters, of which 13 billion are activated per token during inference.The effect is completely comparable to the industry’s flagship closed-source model.🚀🚀🚀 HunyuanImage 3.0 originates from our internally developed native multimodal large language model, with fine-tuning and post-training focused on text-to-image generation. This unique foundation gives the model a powerful set of capabilities: ✅Reason with world knowledge ✅Understand complex, thousand-word prompts ✅Generate precise text within images Different from traditional DiT architecture image generation models, HunyuanImage 3.0’s MoE architecture uses a Transfusion-based approach to deeply couple Diffusion and LLM training for a single, powerful system. Built on Hunyuan-A13B, HunyuanImage 3.0 was trained on a massive dataset: 5 billion image-text pairs, video frames, interleaved image-text data, and 6 trillion tokens of text corpora. This hybrid training across multimodal generation, understanding, and LLM capabilities allows the model to seamlessly integrate multiple tasks. Whether you're an illustrator, designer, or creator, this is built to slash your workflow from hours to minutes. HunyuanImage 3.0 can generate intricate text, detailed comics, expressive emojis, and lively, engaging illustrations for educational content. The current release focuses solely on text-to-image generation and future updates will include image-to-image, image editing, multi-turn interaction, and more. 👉🏻Try it now: 🔗GitHub: 🤗Hugging Face:
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We’re excited to announce the release and open-source of HunyuanImage 3.0 — the largest and most powerful open-source text-to-image model to date, with over 80 billion total parameters, of which 13 billion are activated per token during inference.The effect is completely comparable to the industry’s flagship closed-source model.🚀🚀🚀 HunyuanImage 3.0 originates from our internally developed native multimodal large language model, with fine-tuning and post-training focused on text-to-image generation. This unique foundation gives the model a powerful set of capabilities: ✅Reason with world knowledge ✅Understand complex, thousand-word prompts ✅Generate precise text within images Different from traditional DiT architecture image generation models, HunyuanImage 3.0’s MoE architecture uses a Transfusion-based approach to deeply couple Diffusion and LLM training for a single, powerful system. Built on Hunyuan-A13B, HunyuanImage 3.0 was trained on a massive dataset: 5 billion image-text pairs, video frames, interleaved image-text data, and 6 trillion tokens of text corpora. This hybrid training across multimodal generation, understanding, and LLM capabilities allows the model to seamlessly integrate multiple tasks. Whether you're an illustrator, designer, or creator, this is built to slash your workflow from hours to minutes. HunyuanImage 3.0 can generate intricate text, detailed comics, expressive emojis, and lively, engaging illustrations for educational content. The current release focuses solely on text-to-image generation and future updates will include image-to-image, image editing, multi-turn interaction, and more. 👉🏻Try it now: 🔗GitHub: 🤗Hugging Face:

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Get the inside story on the development of Gemini's coding capabilities. Listen as the product and research leads for Gemini share their philosophy on what makes a great coding model, the impact of "vibe coding," and the future of programming languages with Logan Kilpatrick, Connie Fan and Danny Tarlow. Timecodes: 0:00 Intro 1:10 Defining Early Coding Goals 6:23 Ingredients of a Great Coding Model 9:28 Adapting to Developer Workflows 11:40 The Rise of Vibe Coding 14:43 Code as a Reasoning Tool 17:20 Code as a Universal Solver 20:47 Evaluating Coding Models 24:30 Leveraging Internal Googler Feedback 26:52 Winning Over AI Skeptics 28:04 Performance Across Programming Languages 33:05 The Future of Programming Languages 36:16 Strategies for Large Codebases 41:06 Hill Climbing New Benchmarks 42:46 Short-Term Improvements 44:42 Model Style and Taste 47:43 2.5 Pro’s Breakthrough 51:06 Early AI Coding Experiences 56:19 Specialist vs. Generalist Models

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83,323 просмотров • 2 месяцев назад

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
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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

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82,327 просмотров • 1 год назад

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My new interview with Lina Seiche about freedom + public safety + financial education in El Salvador, the truth about Nayib Bukele’s leadership, the Bitcoin Historico conference and much more. 🧡 Time codes: 00:00 The Birth of The Little Hodler 2:16 The President’s Coffee 4:15 From Germany to El Salvador 6:54 Gemini Bitcoin Credit Card | Bitwise | Ledn Bitcoin-backed Loans 8:14 Natalie's New Book "Bitcoin is For Everyone" 8:30 Teaching the Next Generation 10:30 Why Understanding Money Matters 12:54 Bitcoin Adoption in El Salvador 16:44 Beyond Stigma: Bitcoin Normalized 17:54 Bukele’s Global Image vs. Local Reality 21:50 Bitcoin Historico Conference 25:52 Speed Wallet | Bitkey | The Bitcoin Way | Genius Academy 27:43 El Salvador Safety and Misconceptions 32:33 Why Lina Stays 35:38 The Power of Purpose 40:12 Freedom Through Order 44:30 When Morality Becomes Relative 47:25 Hope and Broken Money

Natalie Brunell ⚡️

195,359 просмотров • 9 месяцев назад

Everyone is talking about Vibe Coding (Using AI to Create Apps Only using AI) This is the most Comprehensive Guide for Vibe Coding with Cursor (By Far) 250 Minutes, All the vibe code basics of cursor, plus 4 Projects in one video! This is how I, as someone who has never written a line of code, approach building apps (every day). Part 1A Intro to Cursor, Composer, and some basics --------------------- 00:00 Intro 03:41 Downloading Cursor 06:09 What the hell is Composer? 10:47 A Note on Context and Keeping Composer Threads Small 11:38 Simple Desings with Cursor Composer From Blank Project 14:04 Editing a Simple Animation With Cursor Composer 16:35 Setting Up The Voice to Talk to Cursor Composer Whispr Flow 17:54 Lets an Early 2000's Landing Page Part 1B AI Image Generator --------------------- 23:59 Using the GitHub Template to Create a NextJS App 26:43 Template is Open, Let's Edit it 28:55 Drawing Out My Idea With Whimsical 30:11 First Prompt Using Place Holders For Image Generation 32:10 Accept All Vs Save All and Restoring in Composer (Saving your work) 33:54 Adding AI Feature (Brief Teaser, Deep Dive Later) 35:15 What is an API 37:22 Perplexity the best place to learn about API's 40:21 Api keys and running prompt for first AI Feature 42:48 Debugging, Woohoo! Learn to love this :) 43:20 Inspect - Console, In Browser Debugging Hack 48:02 AI Image Generation Works! Lets add more Part 2: Landing Page ---------------------- 51:03 Pause and Reflect, What have we done so far? 53:41 Plan for rest of video 54:34 Ok Let's Talk about (1) Designs 56:19 GitHub is like --sref for those who do image gen 58:20 Starting Cursor project from a GitHub Repo we found on Perplexity 01:00:48 Yolo Mode... Wtf is that? 01:02:38 Inspecting GitHub Repo's Examples, to use in our landing page 01:02:58 The Project We're making - A landing page 01:03:56 Landing Page from Screenshot 01:06:17 Making Changes to Landing Page 01:11:42 Making a more epic section 01:13:42 The Essence of Vibe Coding 01:15:17 Creating Cool Testimonials Section From Screenshot 01:18:18 Deploy to Vercel! But First New Repo on GitHub 01:20:45 Ok it's on GitHub... Now lets do vercel 01:21:17 Untechnical Explanation of what Vercel is Lol 01:24:18 Connecting Custom Domain (Bought on Name Cheap) To Vercel Deployment Part 3: App With Database and Authentication ---------------------- 01:27:59 Recap and Prep For The Bigger Project! 01:35:13 Getting Started from Template (Again) 01:38:52 Setting Up Database and Authentication (Firebase) 01:44:01 Back To Cursor, Let's Set up The Auth in the app 01:48:35 Switching to mermaid because compatibility issues 01:51:13 Using AI (Claude) to Generate Mermaid Diagrams 01:52:19 Adding Docs to Cursor to use AI Features over and over again 01:54:38 Let's Troubleshoot 01:56:10 Adding View Button and EDIT WITH AI 02:01:45 AI Diagram Edit Feature is DOPE 02:03:17 Using Search Feature on Cursor to find text in Codebase 02:05:55 Lets add ability to save these to Database 02:09:33 What does saved to Google Firebase even mean? 02:13:00 We can Export as PDF! 02:15:48 GitHub and Vercel Again! 02:17:27 Vercel with CLI From Cursor 02:20:52 Setting Vercel Domain as an Authorized Domain 02:27:34 How To Learn More
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Everyone is talking about Vibe Coding (Using AI to Create Apps Only using AI) This is the most Comprehensive Guide for Vibe Coding with Cursor (By Far) 250 Minutes, All the vibe code basics of cursor, plus 4 Projects in one video! This is how I, as someone who has never written a line of code, approach building apps (every day). Part 1A Intro to Cursor, Composer, and some basics --------------------- 00:00 Intro 03:41 Downloading Cursor 06:09 What the hell is Composer? 10:47 A Note on Context and Keeping Composer Threads Small 11:38 Simple Desings with Cursor Composer From Blank Project 14:04 Editing a Simple Animation With Cursor Composer 16:35 Setting Up The Voice to Talk to Cursor Composer Whispr Flow 17:54 Lets an Early 2000's Landing Page Part 1B AI Image Generator --------------------- 23:59 Using the GitHub Template to Create a NextJS App 26:43 Template is Open, Let's Edit it 28:55 Drawing Out My Idea With Whimsical 30:11 First Prompt Using Place Holders For Image Generation 32:10 Accept All Vs Save All and Restoring in Composer (Saving your work) 33:54 Adding AI Feature (Brief Teaser, Deep Dive Later) 35:15 What is an API 37:22 Perplexity the best place to learn about API's 40:21 Api keys and running prompt for first AI Feature 42:48 Debugging, Woohoo! Learn to love this :) 43:20 Inspect - Console, In Browser Debugging Hack 48:02 AI Image Generation Works! Lets add more Part 2: Landing Page ---------------------- 51:03 Pause and Reflect, What have we done so far? 53:41 Plan for rest of video 54:34 Ok Let's Talk about (1) Designs 56:19 GitHub is like --sref for those who do image gen 58:20 Starting Cursor project from a GitHub Repo we found on Perplexity 01:00:48 Yolo Mode... Wtf is that? 01:02:38 Inspecting GitHub Repo's Examples, to use in our landing page 01:02:58 The Project We're making - A landing page 01:03:56 Landing Page from Screenshot 01:06:17 Making Changes to Landing Page 01:11:42 Making a more epic section 01:13:42 The Essence of Vibe Coding 01:15:17 Creating Cool Testimonials Section From Screenshot 01:18:18 Deploy to Vercel! But First New Repo on GitHub 01:20:45 Ok it's on GitHub... Now lets do vercel 01:21:17 Untechnical Explanation of what Vercel is Lol 01:24:18 Connecting Custom Domain (Bought on Name Cheap) To Vercel Deployment Part 3: App With Database and Authentication ---------------------- 01:27:59 Recap and Prep For The Bigger Project! 01:35:13 Getting Started from Template (Again) 01:38:52 Setting Up Database and Authentication (Firebase) 01:44:01 Back To Cursor, Let's Set up The Auth in the app 01:48:35 Switching to mermaid because compatibility issues 01:51:13 Using AI (Claude) to Generate Mermaid Diagrams 01:52:19 Adding Docs to Cursor to use AI Features over and over again 01:54:38 Let's Troubleshoot 01:56:10 Adding View Button and EDIT WITH AI 02:01:45 AI Diagram Edit Feature is DOPE 02:03:17 Using Search Feature on Cursor to find text in Codebase 02:05:55 Lets add ability to save these to Database 02:09:33 What does saved to Google Firebase even mean? 02:13:00 We can Export as PDF! 02:15:48 GitHub and Vercel Again! 02:17:27 Vercel with CLI From Cursor 02:20:52 Setting Vercel Domain as an Authorized Domain 02:27:34 How To Learn More

Riley Brown

368,936 просмотров • 1 год назад

College sports are going through massive changes, so Matt Brown (publisher, Extra Points & one of the top experts on the business of college athletics), joined the show to break it all down. We discuss: - Superconference realignment - Why the transfer portal is chaos right now - Why most NIL deals have terrible ROI - How “bag man” money moves - Which teams make the most money - Will athletes become employees? - The full history of college athletics - The class action lawsuit happening now (00:00) Meet Matt Brown: Expert in College Sports Business (03:09) The Origins of College Sports (06:31) The Evolution of College Sports Broadcasting (14:53) Title IX and Its Impact on College Athletics (17:53) The 1984 Supreme Court Decision and Its Aftermath (20:03) The SMU Death Penalty Scandal (22:19) Conference Realignment and the BCS Era (28:22) The Rise of Conference Television Networks (30:23) The Arms Race in College Sports Facilities (34:41) The Role of Boosters in College Sports (36:03) Financial Breakdown of Major College Sports Programs (37:04) Understanding Nonprofit Accounting in College Athletics (38:20) Revenue Generation in College Sports (40:34) Athletics as Enrollment Management (42:04) The Flutie Effect and University Applications (44:37) Conference Realignment and Financial Instability (48:58) The O'Bannon Case and Video Game Licensing (53:59) The Northwestern Unionization Attempt (58:19) The Alston Case and Educational Awards (01:02:11) Name, Image, and Likeness (NIL) Marketplaces (01:05:51) The Role of Collectives in College Sports (01:12:08) Dependability of Young Campaign Partners (01:13:03) Transfer Portal and Its Impact (01:15:56) Rise of NIL Agents and Handlers (01:17:40) Economic Incentives and Transfer Market (01:20:37) Challenges in NIL Enforcement (01:22:48) House Settlement and Future Implications (01:25:38) Allocation of NIL Funds by Universities (01:44:26) Potential Super Leagues and Investment Challenges (01:48:07) Concluding Thoughts on College Sports
1:49:43

College sports are going through massive changes, so Matt Brown (publisher, Extra Points & one of the top experts on the business of college athletics), joined the show to break it all down. We discuss: - Superconference realignment - Why the transfer portal is chaos right now - Why most NIL deals have terrible ROI - How “bag man” money moves - Which teams make the most money - Will athletes become employees? - The full history of college athletics - The class action lawsuit happening now (00:00) Meet Matt Brown: Expert in College Sports Business (03:09) The Origins of College Sports (06:31) The Evolution of College Sports Broadcasting (14:53) Title IX and Its Impact on College Athletics (17:53) The 1984 Supreme Court Decision and Its Aftermath (20:03) The SMU Death Penalty Scandal (22:19) Conference Realignment and the BCS Era (28:22) The Rise of Conference Television Networks (30:23) The Arms Race in College Sports Facilities (34:41) The Role of Boosters in College Sports (36:03) Financial Breakdown of Major College Sports Programs (37:04) Understanding Nonprofit Accounting in College Athletics (38:20) Revenue Generation in College Sports (40:34) Athletics as Enrollment Management (42:04) The Flutie Effect and University Applications (44:37) Conference Realignment and Financial Instability (48:58) The O'Bannon Case and Video Game Licensing (53:59) The Northwestern Unionization Attempt (58:19) The Alston Case and Educational Awards (01:02:11) Name, Image, and Likeness (NIL) Marketplaces (01:05:51) The Role of Collectives in College Sports (01:12:08) Dependability of Young Campaign Partners (01:13:03) Transfer Portal and Its Impact (01:15:56) Rise of NIL Agents and Handlers (01:17:40) Economic Incentives and Transfer Market (01:20:37) Challenges in NIL Enforcement (01:22:48) House Settlement and Future Implications (01:25:38) Allocation of NIL Funds by Universities (01:44:26) Potential Super Leagues and Investment Challenges (01:48:07) Concluding Thoughts on College Sports

The Logan Bartlett Show

23,210 просмотров • 1 год назад

Here's my conversation with Sundar Pichai, CEO of Google and Alphabet. I've gotten to know Sundar over the past 2 years. His life story and approach to leadership are truly inspiring. So this conversation was a pleasure and honor for me 🙏 We're living through the most exciting period of technological innovation in human history. And Google & Alphabet are at the center of it. This chat made me even more excited for the future! Oh, and they just now released a new model (gemini-2.5-pro-preview-06-05) that's the new state of the art at #1 on lmarena 🤯 Full conversation is here on X and is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 2:08 - Introduction 2:18 - Growing up in India 8:27 - Advice for young people 10:09 - Styles of leadership 14:29 - Impact of AI in human history 26:39 - Veo 3 and future of video 34:24 - Scaling laws 38:09 - AGI and ASI 44:33 - P(doom) 51:24 - Toughest leadership decisions 1:02:32 - AI mode vs Google Search 1:15:22 - Google Chrome 1:30:52 - Programming 1:37:37 - Android 1:42:49 - Questions for AGI 1:48:05 - Future of humanity 1:51:26 - Demo: Google Beam 1:59:09 - Demo: Google XR Glasses 2:01:54 - Biggest invention in human history
2:12:04

Here's my conversation with Sundar Pichai, CEO of Google and Alphabet. I've gotten to know Sundar over the past 2 years. His life story and approach to leadership are truly inspiring. So this conversation was a pleasure and honor for me 🙏 We're living through the most exciting period of technological innovation in human history. And Google & Alphabet are at the center of it. This chat made me even more excited for the future! Oh, and they just now released a new model (gemini-2.5-pro-preview-06-05) that's the new state of the art at #1 on lmarena 🤯 Full conversation is here on X and is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 2:08 - Introduction 2:18 - Growing up in India 8:27 - Advice for young people 10:09 - Styles of leadership 14:29 - Impact of AI in human history 26:39 - Veo 3 and future of video 34:24 - Scaling laws 38:09 - AGI and ASI 44:33 - P(doom) 51:24 - Toughest leadership decisions 1:02:32 - AI mode vs Google Search 1:15:22 - Google Chrome 1:30:52 - Programming 1:37:37 - Android 1:42:49 - Questions for AGI 1:48:05 - Future of humanity 1:51:26 - Demo: Google Beam 1:59:09 - Demo: Google XR Glasses 2:01:54 - Biggest invention in human history

Lex Fridman

939,144 просмотров • 1 год назад

SITUATION EXPLAINED: MiniMax released H3 with open weights, and it's already #1 in video editing. • Priced at $7.80 per minute of 2K video, against Seedance 2.0 at $22.45 and Kling 3.0 at $20.16 • It ranks #1 on Artificial Analysis for video editing, #2 for text-to-video behind Gemini Omni Flash, and #3 for image-to-video • H3 takes text, images, video, and audio in a single context and generates 5-15 second clips at native 2K, 24fps, with native stereo audio • MiniMax is releasing the weights under its Community License within days, which would make it the strongest open-weights video model by a wide margin • It launched the same week ByteDance rolled out Seedance 2.5, which now generates 30-second 4K video from a single prompt • Seedance 2.5's face consistency across a full 30 seconds is the visible jump, earlier models drifted into a different face partway through • Voices are still the obvious tell, the video reads as real long before the audio does • Smaller labs building purpose-built video models are now beating frontier labs at this specific task Theo Jaffee: "This is gonna be one of the biggest examples of things that people are gonna do with these models, historical simulations, basically. Nostalgia videos for zoomers."
7:17

SITUATION EXPLAINED: MiniMax released H3 with open weights, and it's already #1 in video editing. • Priced at $7.80 per minute of 2K video, against Seedance 2.0 at $22.45 and Kling 3.0 at $20.16 • It ranks #1 on Artificial Analysis for video editing, #2 for text-to-video behind Gemini Omni Flash, and #3 for image-to-video • H3 takes text, images, video, and audio in a single context and generates 5-15 second clips at native 2K, 24fps, with native stereo audio • MiniMax is releasing the weights under its Community License within days, which would make it the strongest open-weights video model by a wide margin • It launched the same week ByteDance rolled out Seedance 2.5, which now generates 30-second 4K video from a single prompt • Seedance 2.5's face consistency across a full 30 seconds is the visible jump, earlier models drifted into a different face partway through • Voices are still the obvious tell, the video reads as real long before the audio does • Smaller labs building purpose-built video models are now beating frontier labs at this specific task Theo Jaffee: "This is gonna be one of the biggest examples of things that people are gonna do with these models, historical simulations, basically. Nostalgia videos for zoomers."

MTS

55,187 просмотров • 1 месяц назад

🎥 Today we’re premiering Meta Movie Gen: the most advanced media foundation models to-date. Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. We’re excited for the potential of this line of research to usher in entirely new possibilities for casual creators and creative professionals alike. More details and examples of what Movie Gen can do ➡️ 🛠️ Movie Gen models and capabilities Movie Gen Video: 30B parameter transformer model that can generate high-quality and high-definition images and videos from a single text prompt. Movie Gen Audio: A 13B parameter transformer model that can take a video input along with optional text prompts for controllability to generate high-fidelity audio synced to the video. It can generate ambient sound, instrumental background music and foley sound — delivering state-of-the-art results in audio quality, video-to-audio alignment and text-to-audio alignment. Precise video editing: Using a generated or existing video and accompanying text instructions as an input it can perform localized edits such as adding, removing or replacing elements — or global changes like background or style changes. Personalized videos: Using an image of a person and a text prompt, the model can generate a video with state-of-the-art results on character preservation and natural movement in video. We’re continuing to work closely with creative professionals from across the field to integrate their feedback as we work towards a potential release. We look forward to sharing more on this work and the creative possibilities it will enable in the future.
0:30

🎥 Today we’re premiering Meta Movie Gen: the most advanced media foundation models to-date. Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. We’re excited for the potential of this line of research to usher in entirely new possibilities for casual creators and creative professionals alike. More details and examples of what Movie Gen can do ➡️ 🛠️ Movie Gen models and capabilities Movie Gen Video: 30B parameter transformer model that can generate high-quality and high-definition images and videos from a single text prompt. Movie Gen Audio: A 13B parameter transformer model that can take a video input along with optional text prompts for controllability to generate high-fidelity audio synced to the video. It can generate ambient sound, instrumental background music and foley sound — delivering state-of-the-art results in audio quality, video-to-audio alignment and text-to-audio alignment. Precise video editing: Using a generated or existing video and accompanying text instructions as an input it can perform localized edits such as adding, removing or replacing elements — or global changes like background or style changes. Personalized videos: Using an image of a person and a text prompt, the model can generate a video with state-of-the-art results on character preservation and natural movement in video. We’re continuing to work closely with creative professionals from across the field to integrate their feedback as we work towards a potential release. We look forward to sharing more on this work and the creative possibilities it will enable in the future.

AI at Meta

2,265,913 просмотров • 1 год назад

Okay, people. Chris Kemp and I got the band back together to talk Wild, Wild Space and Astra's $80m raise and return to the pad. Full episode of the pod here on X Timestamps 0:00:00 Introduction & Guest Chris Kemp 0:01:18 Astra's Journey and Current State 0:08:01 Taking Astra Private 0:10:17 New Leadership and Space Force Collaboration 0:12:18 Rocket Industry Landscape 0:14:22 Shifting to Larger Satellites 0:15:15 DoD Drone Launch Contract 0:21:19 Vision of Global Spaceports 0:24:25 Satellite Constellations and Dual Use 0:26:36 Global Telecom & Space Race 0:30:01 Open Standards & Space Interoperability 0:31:14 Life After Going Private 0:32:17 Lessons from the SPAC Era 0:34:03 Focusing on Rocket 4 0:36:15 Balancing Speed and Reliability 0:40:06 The Cost of Innovation 0:42:21 Fundraising and Customer Commitment 0:44:12 The Satellite Engine Business 0:45:09 Decentralized Constellation Vision 0:50:14 Future Plans and Series B 0:52:21 Why Kemp Keeps Going 0:54:20 What’s Next for Astra 0:56:06 Thoughts on the Competition 0:59:13 Sideways Rocket Explained 1:00:15 Vision for NASA's Future
1:12:26

Okay, people. Chris Kemp and I got the band back together to talk Wild, Wild Space and Astra's $80m raise and return to the pad. Full episode of the pod here on X Timestamps 0:00:00 Introduction & Guest Chris Kemp 0:01:18 Astra's Journey and Current State 0:08:01 Taking Astra Private 0:10:17 New Leadership and Space Force Collaboration 0:12:18 Rocket Industry Landscape 0:14:22 Shifting to Larger Satellites 0:15:15 DoD Drone Launch Contract 0:21:19 Vision of Global Spaceports 0:24:25 Satellite Constellations and Dual Use 0:26:36 Global Telecom & Space Race 0:30:01 Open Standards & Space Interoperability 0:31:14 Life After Going Private 0:32:17 Lessons from the SPAC Era 0:34:03 Focusing on Rocket 4 0:36:15 Balancing Speed and Reliability 0:40:06 The Cost of Innovation 0:42:21 Fundraising and Customer Commitment 0:44:12 The Satellite Engine Business 0:45:09 Decentralized Constellation Vision 0:50:14 Future Plans and Series B 0:52:21 Why Kemp Keeps Going 0:54:20 What’s Next for Astra 0:56:06 Thoughts on the Competition 0:59:13 Sideways Rocket Explained 1:00:15 Vision for NASA's Future

Ashlee Vance

31,431 просмотров • 1 год назад

Want world class research capabilities, but don’t have the resources of a big lab? At our recent Sovereign AI event, Gabe Pereyra shared Harvey ’s “moneyball” approach. Here’s the playbook: 00:00 Introduction 00:37 Building a research lab on a budget 02:28 Legal Agent Bench, contracting, and the diligence dataset 03:57 Domain experts guiding synthetic data generation 05:23 Why Harvey open sourced its datasets 06:55 Working with the neo labs – and why more than one 08:20 Post-training in-house: building "Associate 1" 09:44 The model serving matrix: 60 countries, fallbacks, SLAs 11:05 Deciding what stays in production 12:29 Simple open source switches and model routing 13:55 Moneyball: "If we win on this budget, we change the game" 14:53 Q&A: Training with sensitive data 17:16 Q&A: Competing for research talent 18:46 Q&A: Designing rubrics that actually challenge frontier models 20:19 Q&A: Where the pipeline breaks — data, research, or infra 22:59 Q&A: The tension in open sourcing a benchmark 25:02 Q&A: Biggest remaining open problems 27:10 Q&A: Competing with horizontal products
29:20

Want world class research capabilities, but don’t have the resources of a big lab? At our recent Sovereign AI event, Gabe Pereyra shared Harvey ’s “moneyball” approach. Here’s the playbook: 00:00 Introduction 00:37 Building a research lab on a budget 02:28 Legal Agent Bench, contracting, and the diligence dataset 03:57 Domain experts guiding synthetic data generation 05:23 Why Harvey open sourced its datasets 06:55 Working with the neo labs – and why more than one 08:20 Post-training in-house: building "Associate 1" 09:44 The model serving matrix: 60 countries, fallbacks, SLAs 11:05 Deciding what stays in production 12:29 Simple open source switches and model routing 13:55 Moneyball: "If we win on this budget, we change the game" 14:53 Q&A: Training with sensitive data 17:16 Q&A: Competing for research talent 18:46 Q&A: Designing rubrics that actually challenge frontier models 20:19 Q&A: Where the pipeline breaks — data, research, or infra 22:59 Q&A: The tension in open sourcing a benchmark 25:02 Q&A: Biggest remaining open problems 27:10 Q&A: Competing with horizontal products

Pat Grady

177,114 просмотров • 19 дней назад

I sat down a second time with Keith Rabois (Keith Rabois), General Partner at Khosla Ventures and CEO at OpenStore to talk operating. We spoke about the Hollywood model of startups, picking co-founders, identifying, attracting and assessing talent, raising capital, building a board, delegating, metrics, and knowing what to measure. 0:00 - Intro 1:56 - Great Founders and the Bottleneck to Innovation 4:35 - Vertical Integration 6:24 - The Hollywood Model of Startups 7:41 - The “Why Now?” in Company-Building 9:50 - Multi-Product Companies 10:58 - Iteration and Pivots 12:52 - Picking Co-Founders 14:51 - Identifying Mispriced Talent 17:20 - Attracting Talent 20:57 - Assessing Talent 24:02 - Doing References 25:56 - Closing Hires 28:28 - Thinking 6 Months Ahead 31:36 - How Long Should You Interview For? 33:28 - Creating a Monopoly on Talent 35:44 - Raising Capital 37:40 - Screening Investors 41:21 - Building a Board 44:11 - Triaging and Identifying Problems 47:59 - Writing vs Editing and Consistent Voice 49:34 - Creating Transparency 50:50 - Barrels and Ammunition 54:55 - Task-Relevant Maturity 56:40 - On Delegating 59:21 - Measuring Inputs vs Outputs 1:02:58 - Underrated Metrics 1:05:22 - What Should More People Be Thinking About?
1:06:37

I sat down a second time with Keith Rabois (Keith Rabois), General Partner at Khosla Ventures and CEO at OpenStore to talk operating. We spoke about the Hollywood model of startups, picking co-founders, identifying, attracting and assessing talent, raising capital, building a board, delegating, metrics, and knowing what to measure. 0:00 - Intro 1:56 - Great Founders and the Bottleneck to Innovation 4:35 - Vertical Integration 6:24 - The Hollywood Model of Startups 7:41 - The “Why Now?” in Company-Building 9:50 - Multi-Product Companies 10:58 - Iteration and Pivots 12:52 - Picking Co-Founders 14:51 - Identifying Mispriced Talent 17:20 - Attracting Talent 20:57 - Assessing Talent 24:02 - Doing References 25:56 - Closing Hires 28:28 - Thinking 6 Months Ahead 31:36 - How Long Should You Interview For? 33:28 - Creating a Monopoly on Talent 35:44 - Raising Capital 37:40 - Screening Investors 41:21 - Building a Board 44:11 - Triaging and Identifying Problems 47:59 - Writing vs Editing and Consistent Voice 49:34 - Creating Transparency 50:50 - Barrels and Ammunition 54:55 - Task-Relevant Maturity 56:40 - On Delegating 59:21 - Measuring Inputs vs Outputs 1:02:58 - Underrated Metrics 1:05:22 - What Should More People Be Thinking About?

Alex LaBossiere

139,220 просмотров • 1 год назад

Everything Gets Rebuilt: my conversation with Harrison Chase, CEO of LangChain about agent harnesses, evals, runtimes, sandboxes, MCP and the future of the agent stack 00:00 Intro - meet Harrison Chase - at the Chase Center for the Daytona Compute conference 01:32 What changed in agents over the last year 03:57 Why coding agents are ahead 06:26 Do models commoditize the framework layer? 08:27 Harnesses, in plain English 10:11 Why system prompts matter so much 13:11 The upside — and downside — of subagents 15:31 Why a useful agent needs a filesystem 18:13 Additional core primitives of modern agents 19:12 Skills: the new primitive 20:19 What context compaction actually means 23:02 How memory works in agents 25:16 One mega-agent or many specialized agents? 27:46 The future of MCP 29:38 Why agents need sandboxes 32:35 How sandboxes help with security 33:32 How Harrison Chase started LangChain 37:24 LangChain vs LangGraph vs Deep Agents 40:17 Why observability matters more for agents 41:48 Evals, no-code, and continuous improvement 44:41 What LangChain is building next 45:29 Where the real moat in AI lives
46:57

Everything Gets Rebuilt: my conversation with Harrison Chase, CEO of LangChain about agent harnesses, evals, runtimes, sandboxes, MCP and the future of the agent stack 00:00 Intro - meet Harrison Chase - at the Chase Center for the Daytona Compute conference 01:32 What changed in agents over the last year 03:57 Why coding agents are ahead 06:26 Do models commoditize the framework layer? 08:27 Harnesses, in plain English 10:11 Why system prompts matter so much 13:11 The upside — and downside — of subagents 15:31 Why a useful agent needs a filesystem 18:13 Additional core primitives of modern agents 19:12 Skills: the new primitive 20:19 What context compaction actually means 23:02 How memory works in agents 25:16 One mega-agent or many specialized agents? 27:46 The future of MCP 29:38 Why agents need sandboxes 32:35 How sandboxes help with security 33:32 How Harrison Chase started LangChain 37:24 LangChain vs LangGraph vs Deep Agents 40:17 Why observability matters more for agents 41:48 Evals, no-code, and continuous improvement 44:41 What LangChain is building next 45:29 Where the real moat in AI lives

Matt Turck

35,253 просмотров • 5 месяцев назад