Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Introducing dAGI Accelerator’s first crop of graduates 🎉 300 AI startups applied. 8 were chosen. They got 2 months of hands-on mentorship and collaboration with and Delphi Labs. Now, we're thrilled to share their visions with the world: 00:00: fewsats: The easiest way for AI to use money 04:55:...

dAGIhouse's profile picture

dAGIhouse

2,109 subscribers

48,287 Aufrufe • vor 1 Jahr •via X (Twitter)

Wissenschaft & Technologie
LIVE

Anya Rossi• Live Now

Private livecam show

3 Kommentare

Profilbild von NEARWEEK
NEARWEEKvor 1 Jahr

@cyberFund_ @delphi_labs @fewsats !submit @nearweekbot

Profilbild von VistaShares
VistaSharesvor 1 Jahr

The global AI sector is evolving rapidly, supported by advancements in technology and infrastructure. AIS offers targeted exposure to key players driving these developments.

Profilbild von Cryptrus
Cryptrusvor 1 Jahr

@cyberFund_ @delphi_labs @fewsats You made a mistake: its @nocap_so

Ähnliche Videos

Dear VCs, Delphi Labs and teamed up to run our first-ever dAGI Accelerator cohort with 8 amazing teams building the future of open, unstoppable AI. After two months of relentless, product-focused progress, each team is ready to show off what they've built. Join us May 28 for a Virtual Demo Day like no other. Founders from TrueNorth, Mathlock, scade.xyz, ASIMOV, fewsats, Framework, No Cap👽, and RPS AI get up to 5 minutes to pitch… no fluff, just frontier AI. Backed by: 1️⃣ An unprecedented trio of support from the Solana and Ethereum foundations, and the Base ecosystem 2️⃣ Experts from Delphi Labs & cyber•Fund 3️⃣ 14 elite mentors including Ben Fielding at gensyn, Jasper at Hyperbolic, Tarun Chitra at Robot Ventures, Stability AI founder Emad, Sreeram Kannan at , , Rawson 🙏, Konstantin Lomashuk cyber/acc, Teng Yan and more! This is your front-row seat to the future of open and composable intelligence. Access is invite-only. DM us for an exclusive pass. The future of AI won’t be locked behind corporate firewalls. It’ll be open. And it will be unstoppable.
0:56

Dear VCs, Delphi Labs and teamed up to run our first-ever dAGI Accelerator cohort with 8 amazing teams building the future of open, unstoppable AI. After two months of relentless, product-focused progress, each team is ready to show off what they've built. Join us May 28 for a Virtual Demo Day like no other. Founders from TrueNorth, Mathlock, scade.xyz, ASIMOV, fewsats, Framework, No Cap👽, and RPS AI get up to 5 minutes to pitch… no fluff, just frontier AI. Backed by: 1️⃣ An unprecedented trio of support from the Solana and Ethereum foundations, and the Base ecosystem 2️⃣ Experts from Delphi Labs & cyber•Fund 3️⃣ 14 elite mentors including Ben Fielding at gensyn, Jasper at Hyperbolic, Tarun Chitra at Robot Ventures, Stability AI founder Emad, Sreeram Kannan at , , Rawson 🙏, Konstantin Lomashuk cyber/acc, Teng Yan and more! This is your front-row seat to the future of open and composable intelligence. Access is invite-only. DM us for an exclusive pass. The future of AI won’t be locked behind corporate firewalls. It’ll be open. And it will be unstoppable.

dAGIhouse

59,261 Aufrufe • vor 1 Jahr

Dr. Fei-Fei Li (Fei-Fei Li), the Godmother of AI, on the origin of computer vision and why spatial intelligence is the next frontier. At AI Startup School in San Francisco. 01:00 - Origins of ImageNet 02:15 - The Dream to Make Machines See 03:28 - A paradigm shift in AI 05:50 - AlexNet and Deep Learning 08:00 - Evolving Computer Vision 12:20 - Building World Labs 13:00 - The next frontier in AI 14:20 - Why Spatial Intelligence Is Harder Than Language 18:40 - Technical Barriers for Vision-Based AI 20:00 - AI is more than LLMs 25:50 - Fei Fei’s Journey 29:30 - Mentoring some of the legends of AI 33:00 - Audience Q&A
44:21

Dr. Fei-Fei Li (Fei-Fei Li), the Godmother of AI, on the origin of computer vision and why spatial intelligence is the next frontier. At AI Startup School in San Francisco. 01:00 - Origins of ImageNet 02:15 - The Dream to Make Machines See 03:28 - A paradigm shift in AI 05:50 - AlexNet and Deep Learning 08:00 - Evolving Computer Vision 12:20 - Building World Labs 13:00 - The next frontier in AI 14:20 - Why Spatial Intelligence Is Harder Than Language 18:40 - Technical Barriers for Vision-Based AI 20:00 - AI is more than LLMs 25:50 - Fei Fei’s Journey 29:30 - Mentoring some of the legends of AI 33:00 - Audience Q&A

Y Combinator

194,784 Aufrufe • vor 1 Jahr

🚨 ANNOUNCING THE WORLD'S FIRST VIBE CODING AGENT FOR APIs. Vibe coders just became LEGIT backend engineers. With ONE PROMPT, you can create an entire microservice and host it on Abacus AI Deep Agent. Vibe coders can now go toe-to-toe with backend engineers and create an API, a Telegram bot, or any AI service. The AI takes care of everything. - creating an API spec - implementing it and hosting it - using a back-end database for tracking - giving you a test bed - providing you with a ready-to-use endpoint! Backend engineers are expensive; now you can start vibe-coding their projects in English.
0:47

🚨 ANNOUNCING THE WORLD'S FIRST VIBE CODING AGENT FOR APIs. Vibe coders just became LEGIT backend engineers. With ONE PROMPT, you can create an entire microservice and host it on Abacus AI Deep Agent. Vibe coders can now go toe-to-toe with backend engineers and create an API, a Telegram bot, or any AI service. The AI takes care of everything. - creating an API spec - implementing it and hosting it - using a back-end database for tracking - giving you a test bed - providing you with a ready-to-use endpoint! Backend engineers are expensive; now you can start vibe-coding their projects in English.

Bindu Reddy

11,800 Aufrufe • vor 10 Monaten

AI has a trust problem. Verifiability is the solution. Our GM of AI Nima Vaziri sat down with a16z’s Ali Yahya and Dan Boneh of Stanford University to map the deepest fault lines in AI today. ☁️ Models we can’t trust ☁️ Current providers can censor, shut down, or shift rules overnight. Outsourced training hides backdoors. Even “open” weights don’t prove what’s actually running. Trust. Backdoors. Black boxes. The path forward is clear: 🔥 Verifiable evals 🔥 Verifiable inference 🔥 TEEs for hardware-backed integrity 🔥 Infra beyond single points of control 🔥 Blockchains as coordination layers for AI From “trust us” to “verify yourself.” That’s the shift. That’s the unlock. The frontier is here. The builders decide what comes next. Create and use AI that’s incentive aligned with you. Timestamps: 00:00:00 Introduction: AI & Crypto Intersection Overview 00:01:58 Four Major AI-Crypto Trends 00:02:44 AI Agents Need Financial Infrastructure 00:04:03 Proof of Humanity: Fighting AI-Generated Content 00:04:17 Decentralizing AI Infrastructure Networks 00:04:44 Synthetic Life: Autonomous AI Agents 00:06:20 Verifiable AI 00:10:16 Current Performance Numbers for AI Proofs 00:13:18 The Era of Experience in AI Learning 00:14:56 AI Agents Having Life of its Own 00:18:21 Algorithmic Fairness & Verifiable Models 00:23:18 Privacy in AI: Trusted Execution Environments 00:25:47 Economic Incentive for Open Weight Models 00:31:39 Attribution Problem: Who Gets Paid for AI Training? 00:35:52 Content Provenance & Authentication (C2PA) 00:48:03 AI Security: Finding Exploits & Vulnerabilities 00:54:53 Educational Applications: LLMs as Learning Partner 00:58:29 Reliance on LLMs and Cognitive Abilities 01:03:57 Content Providers’ Fear of LLM Training
1:07:13

AI has a trust problem. Verifiability is the solution. Our GM of AI Nima Vaziri sat down with a16z’s Ali Yahya and Dan Boneh of Stanford University to map the deepest fault lines in AI today. ☁️ Models we can’t trust ☁️ Current providers can censor, shut down, or shift rules overnight. Outsourced training hides backdoors. Even “open” weights don’t prove what’s actually running. Trust. Backdoors. Black boxes. The path forward is clear: 🔥 Verifiable evals 🔥 Verifiable inference 🔥 TEEs for hardware-backed integrity 🔥 Infra beyond single points of control 🔥 Blockchains as coordination layers for AI From “trust us” to “verify yourself.” That’s the shift. That’s the unlock. The frontier is here. The builders decide what comes next. Create and use AI that’s incentive aligned with you. Timestamps: 00:00:00 Introduction: AI & Crypto Intersection Overview 00:01:58 Four Major AI-Crypto Trends 00:02:44 AI Agents Need Financial Infrastructure 00:04:03 Proof of Humanity: Fighting AI-Generated Content 00:04:17 Decentralizing AI Infrastructure Networks 00:04:44 Synthetic Life: Autonomous AI Agents 00:06:20 Verifiable AI 00:10:16 Current Performance Numbers for AI Proofs 00:13:18 The Era of Experience in AI Learning 00:14:56 AI Agents Having Life of its Own 00:18:21 Algorithmic Fairness & Verifiable Models 00:23:18 Privacy in AI: Trusted Execution Environments 00:25:47 Economic Incentive for Open Weight Models 00:31:39 Attribution Problem: Who Gets Paid for AI Training? 00:35:52 Content Provenance & Authentication (C2PA) 00:48:03 AI Security: Finding Exploits & Vulnerabilities 00:54:53 Educational Applications: LLMs as Learning Partner 00:58:29 Reliance on LLMs and Cognitive Abilities 01:03:57 Content Providers’ Fear of LLM Training

EigenCloud

62,099 Aufrufe • vor 1 Jahr

.Ramp Labs (the AI unit of Ramp) has been *cooking* with agentic innovation Here's Alex L discussing and demo'ing code self-maintaining software and the concept of AI software factories #DataDrivenNYC ______________ 00:04 - Intro 01:11 - The shift from writing code to code maintenance 01:59 - Introducing Ramp Inspect, the background coding agent 03:05 - The first experiment: Nightly AI code automation 04:23 - The limits of stateless monitoring in large observability surfaces 05:47 - Using Datadog monitors to give the AI state and focus 07:23 - Real-world example: AI autonomously fixing an authentication bug 08:14 - How to control noise and implement an AI triage pattern 09:27 - The old vs. new paradigm for continuous code observability 10:21 - Key learnings on building autonomous AI software factories
14:21

.Ramp Labs (the AI unit of Ramp) has been *cooking* with agentic innovation Here's Alex L discussing and demo'ing code self-maintaining software and the concept of AI software factories #DataDrivenNYC ______________ 00:04 - Intro 01:11 - The shift from writing code to code maintenance 01:59 - Introducing Ramp Inspect, the background coding agent 03:05 - The first experiment: Nightly AI code automation 04:23 - The limits of stateless monitoring in large observability surfaces 05:47 - Using Datadog monitors to give the AI state and focus 07:23 - Real-world example: AI autonomously fixing an authentication bug 08:14 - How to control noise and implement an AI triage pattern 09:27 - The old vs. new paradigm for continuous code observability 10:21 - Key learnings on building autonomous AI software factories

Matt Turck

68,301 Aufrufe • vor 4 Monaten

New Delphi Podcast with Accelxr 👾, author of our latest report on China’s AI labs He went into the research thinking China was mostly distilling US models and benchmark-maxing. He came out with a very different view We cover: – Why DeepSeek is doing real research, not just copying – How Chinese labs are optimizing below CUDA – Why China’s electricity advantage could matter more than chips – Whether cheap open-source models are part of a broader strategy – Why more Chinese AI talent is returning home – Whether restricting NVIDIA chips could actually accelerate China’s independence – What happens if China reaches full AI training sovereignty 00:00 Intro 00:35 Is China Innovating or Just Distilling? 06:10 DeepSeek and China’s Leading AI Labs 10:15 How China Is Working Around Weaker Chips 18:25 China’s Biggest AI Advantage: Electricity 29:45 Three Scenarios for the US-China AI Race 40:20 America’s AI Talent Advantage Is Reversing 47:45 China’s Path to AI Sovereignty Full episode:
55:10

New Delphi Podcast with Accelxr 👾, author of our latest report on China’s AI labs He went into the research thinking China was mostly distilling US models and benchmark-maxing. He came out with a very different view We cover: – Why DeepSeek is doing real research, not just copying – How Chinese labs are optimizing below CUDA – Why China’s electricity advantage could matter more than chips – Whether cheap open-source models are part of a broader strategy – Why more Chinese AI talent is returning home – Whether restricting NVIDIA chips could actually accelerate China’s independence – What happens if China reaches full AI training sovereignty 00:00 Intro 00:35 Is China Innovating or Just Distilling? 06:10 DeepSeek and China’s Leading AI Labs 10:15 How China Is Working Around Weaker Chips 18:25 China’s Biggest AI Advantage: Electricity 29:45 Three Scenarios for the US-China AI Race 40:20 America’s AI Talent Advantage Is Reversing 47:45 China’s Path to AI Sovereignty Full episode:

Tommy

81,587 Aufrufe • vor 24 Tagen

EPISODE 160: Joe Lonsdale sits down with Aaron Levie "The actual creators of AI are scared sh*tless." — Aaron's take on tech's cognitive dissonance. "So you actually want people to die of cancer, right?" — on politicians trying to ban data centers And more: -- Why Geoffrey Hinton is wrong about AI job destruction -- China's Kimi K3 wake-up moment -- What doomers & accelerationists both get wrong -- 13 new categories of AI jobs at Box -- Law firms (law firms!) building their own AI models (00:00) Episode intro (01:15) AI wave / every two months is a year now (06:30) China, Kimi K3 & open-source debate (10:45) Diffusion is the real rate limiter (15:50) Agent-first startups vs incumbents (19:00) Nobody reads those contracts — AI's new work (23:35) Silicon Valley's AI Problem / Larry David making AI models (29:20) 13 new types of AI jobs at Box (32:20) The origins of Box / dropping out of college (37:00) Favorite angel investments right now (40:20) Optimism for America's future
43:21

EPISODE 160: Joe Lonsdale sits down with Aaron Levie "The actual creators of AI are scared sh*tless." — Aaron's take on tech's cognitive dissonance. "So you actually want people to die of cancer, right?" — on politicians trying to ban data centers And more: -- Why Geoffrey Hinton is wrong about AI job destruction -- China's Kimi K3 wake-up moment -- What doomers & accelerationists both get wrong -- 13 new categories of AI jobs at Box -- Law firms (law firms!) building their own AI models (00:00) Episode intro (01:15) AI wave / every two months is a year now (06:30) China, Kimi K3 & open-source debate (10:45) Diffusion is the real rate limiter (15:50) Agent-first startups vs incumbents (19:00) Nobody reads those contracts — AI's new work (23:35) Silicon Valley's AI Problem / Larry David making AI models (29:20) 13 new types of AI jobs at Box (32:20) The origins of Box / dropping out of college (37:00) Favorite angel investments right now (40:20) Optimism for America's future

American Optimist

141,289 Aufrufe • vor 1 Monat

Natalia Quintero (Natalia) runs Every 🧱’s seven-figure AI consulting practice, and she just automated her job with Claude Code. She built an AI project manager, Claudie, that cuts her weekly project management workload from 15 hours to just one. It manages the fleet of spreadsheets she uses to manage new clients, track what has been delivered and what hasn’t, and aggregate feedback to improve our services over time. A month ago she would’ve described herself as non-technical, but now she’s a ‘bonafide vibe code addict’—waking up at 6 a.m. everyday to work on Claudie before her meetings start. She’s not just personally neck-deep in AI—she’s also seen it work, and not, in the smartest enterprises in the world. As our head of consulting, she’s worked with top hedge funds, PE, firms, tech companies, and Fortune 500s to help drive AI adoption and automations. I had her on AI&I to talk about: - How to transition a big company into a truly AI-native org - Why enterprise AI adoption only goes as far as CEO adoption - How to empower AI early adopters inside your org - Why you have to carve out space to “play” with AI if you want real transformation If you’re trying to figure out how to actually put AI to work inside your company, this episode is for you. Watch below! Timestamps: Introduction: 00:00:00 Why successful AI adoption requires coordinated, top-down effort: 00:01:30 How a private equity firm reduced investment memo creation from weeks to 30 minutes: 00:07:05 The benefits of connecting AI to proprietary context: 00:13:30 The plan-delegate-assess-compound framework for engineering teams: 00:15:20 How non-technical team members are becoming vibe coding addicts: 00:17:55 Building Claudie: an AI project manager from scratch: 00:20:50 Why creative exploration time outside the 9-to-5 is essential: 00:23:00 Live demo: How Claudie automates client onboarding and tracking: 00:27:50 The human side of AI: spending less time in spreadsheets, more time with people: 00:38:40
47:14

Natalia Quintero (Natalia) runs Every 🧱’s seven-figure AI consulting practice, and she just automated her job with Claude Code. She built an AI project manager, Claudie, that cuts her weekly project management workload from 15 hours to just one. It manages the fleet of spreadsheets she uses to manage new clients, track what has been delivered and what hasn’t, and aggregate feedback to improve our services over time. A month ago she would’ve described herself as non-technical, but now she’s a ‘bonafide vibe code addict’—waking up at 6 a.m. everyday to work on Claudie before her meetings start. She’s not just personally neck-deep in AI—she’s also seen it work, and not, in the smartest enterprises in the world. As our head of consulting, she’s worked with top hedge funds, PE, firms, tech companies, and Fortune 500s to help drive AI adoption and automations. I had her on AI&I to talk about: - How to transition a big company into a truly AI-native org - Why enterprise AI adoption only goes as far as CEO adoption - How to empower AI early adopters inside your org - Why you have to carve out space to “play” with AI if you want real transformation If you’re trying to figure out how to actually put AI to work inside your company, this episode is for you. Watch below! Timestamps: Introduction: 00:00:00 Why successful AI adoption requires coordinated, top-down effort: 00:01:30 How a private equity firm reduced investment memo creation from weeks to 30 minutes: 00:07:05 The benefits of connecting AI to proprietary context: 00:13:30 The plan-delegate-assess-compound framework for engineering teams: 00:15:20 How non-technical team members are becoming vibe coding addicts: 00:17:55 Building Claudie: an AI project manager from scratch: 00:20:50 Why creative exploration time outside the 9-to-5 is essential: 00:23:00 Live demo: How Claudie automates client onboarding and tracking: 00:27:50 The human side of AI: spending less time in spreadsheets, more time with people: 00:38:40

Dan Shipper 📧

31,828 Aufrufe • vor 7 Monaten

12 weeks, 5 teams, Demo Day complete Wintermute Construct's first cohort just presented what they've built From 300+ applications, these teams were chosen for their ambition and conviction Watch their pitches: 00:13: RPS AI: Onchain asset management and liquidity engine for Solana 04:03: Caliber Money: Non-custodial neobrokerage unifying crypto and banking 09:02: Showdown.win: Skill-based gaming and wagering app on Solana 13:35: Canary Protocol: Oracle and bridge infrastructure for real-world assets and stablecoins 18:16: Tradevu: Onchain trade finance and payments for emerging-market SMEs Keen to learn more about what they are building? DM the teams directly
23:37

12 weeks, 5 teams, Demo Day complete Wintermute Construct's first cohort just presented what they've built From 300+ applications, these teams were chosen for their ambition and conviction Watch their pitches: 00:13: RPS AI: Onchain asset management and liquidity engine for Solana 04:03: Caliber Money: Non-custodial neobrokerage unifying crypto and banking 09:02: Showdown.win: Skill-based gaming and wagering app on Solana 13:35: Canary Protocol: Oracle and bridge infrastructure for real-world assets and stablecoins 18:16: Tradevu: Onchain trade finance and payments for emerging-market SMEs Keen to learn more about what they are building? DM the teams directly

Wintermute

38,191 Aufrufe • vor 10 Monaten

We’re back for Episode 14 of TAO Talk 🚨 calanthia from Masa joins 563 and brody this week to chat about new subnets and AI agents sourcing intelligence from Bittensor! The group chats about: - $TAO -pilling AI/ML chads at NeurIPS Conference feat. const Crucible Labs Macrocosmos Manifold and Yuma - JJ teaming up with Cameron Fairchild to form Laτenτ Holdings, which will validate and help scale subnets - Crucible Labs drops a subnet analysis framework - Celium offering H100s cheaper than any other provider - Tao360 releases inaugural research report for their AI-enabled subnet analysis tool @notYourBananaa - Masa unveils the AI Agent Arena on SN59 /// Timestamps: 00:00 Intro 01:10 Subnet 42 and Agent Arena: Masa’s Subnets 03:00 Real-Time Data Networks for AI 04:20 How Masa is Building AI Agent Arenas Inspired by Gladiators 06:10 Decentralized AI and Bittensor: The Growing Ecosystem 08:15 AI Meets Web3: Masa’s Role in Revolutionizing Data Networks 10:00 The Future of AI Agents: Intelligent Societies and Real-Time Data 12:05 Why Masa Chose Bittensor 14:10 $TAO Incentives and the Future of AI Decentralization 16:25 Bittensor and the Rise of Agent Competition: Masa’s Perspective 18:00 Exploring AI Agent Societies 20:30 Creating Competitive AI Arenas: The Agent Arena Subnet Explained 23:00 Calanthia on the Challenges of Web3 AI Development 25:10 Bringing Web2 Developers into Web3: Lessons from Masa 27:30 The Evolution of AI: From Dumb Agents to Intelligent Societies 30:00 AI Agents as the Future of Interaction in Decentralized AI 34:00 The Agent Arena’s Vision: Competition, Incentives, and Innovation
37:00

We’re back for Episode 14 of TAO Talk 🚨 calanthia from Masa joins 563 and brody this week to chat about new subnets and AI agents sourcing intelligence from Bittensor! The group chats about: - $TAO -pilling AI/ML chads at NeurIPS Conference feat. const Crucible Labs Macrocosmos Manifold and Yuma - JJ teaming up with Cameron Fairchild to form Laτenτ Holdings, which will validate and help scale subnets - Crucible Labs drops a subnet analysis framework - Celium offering H100s cheaper than any other provider - Tao360 releases inaugural research report for their AI-enabled subnet analysis tool @notYourBananaa - Masa unveils the AI Agent Arena on SN59 /// Timestamps: 00:00 Intro 01:10 Subnet 42 and Agent Arena: Masa’s Subnets 03:00 Real-Time Data Networks for AI 04:20 How Masa is Building AI Agent Arenas Inspired by Gladiators 06:10 Decentralized AI and Bittensor: The Growing Ecosystem 08:15 AI Meets Web3: Masa’s Role in Revolutionizing Data Networks 10:00 The Future of AI Agents: Intelligent Societies and Real-Time Data 12:05 Why Masa Chose Bittensor 14:10 $TAO Incentives and the Future of AI Decentralization 16:25 Bittensor and the Rise of Agent Competition: Masa’s Perspective 18:00 Exploring AI Agent Societies 20:30 Creating Competitive AI Arenas: The Agent Arena Subnet Explained 23:00 Calanthia on the Challenges of Web3 AI Development 25:10 Bringing Web2 Developers into Web3: Lessons from Masa 27:30 The Evolution of AI: From Dumb Agents to Intelligent Societies 30:00 AI Agents as the Future of Interaction in Decentralized AI 34:00 The Agent Arena’s Vision: Competition, Incentives, and Innovation

TAO τalk 🥩🦍

24,929 Aufrufe • vor 1 Jahr

VIBE CODING TUTORIAL: Creating a Viral Meme Mobile App Without Any Coding in 20 minutes! You can now build multi-step AI workflows on a drag and drop platform, and then connect that workflow to your vibe coded mobile app, in a single cursor prompt. This only scratches the surface on the different workflows you can create like this. IN THIS VIDEO 00:00 Intro 00:36 Overview: MultiStep AI Backend on GLIF 01:33 Using Glif as an API 03:49 The backend for our app 04:36 Structured Outputs (Important for Vibe Coding, skip if you know what these are) 06:40 Image Workflow for the meme 08:34 Let's Protototype on 11:09 Setting this up with Cursor 18:40 Testing with
19:37

VIBE CODING TUTORIAL: Creating a Viral Meme Mobile App Without Any Coding in 20 minutes! You can now build multi-step AI workflows on a drag and drop platform, and then connect that workflow to your vibe coded mobile app, in a single cursor prompt. This only scratches the surface on the different workflows you can create like this. IN THIS VIDEO 00:00 Intro 00:36 Overview: MultiStep AI Backend on GLIF 01:33 Using Glif as an API 03:49 The backend for our app 04:36 Structured Outputs (Important for Vibe Coding, skip if you know what these are) 06:40 Image Workflow for the meme 08:34 Let's Protototype on 11:09 Setting this up with Cursor 18:40 Testing with

Riley Brown

58,068 Aufrufe • vor 1 Jahr

Eric's message to SaaS CEOs navigating the shift to AI. Every day you hit your pre-AI plan, you're destroying value: "One of the big messages to these SaaS companies a few years ago was, get to AI or be worth 3x revenue. Those are your choices. Our whole careers we learned you lay out a plan, you execute against it relentlessly and violently and that's how you build equity value. That's what these CEOs learned pre-AI. Now, every day you hit that plan, you're destroying value. Another articulation of this is from my friend Anne Lee Skates. The CEOs who were going through this transitory period and had a business that was at hundreds of millions and they thought that they're all ready. They were working on their business from 8:00 AM to 5:00 PM and then trying to do AI from 5:00 to 8:00 in the evenings. What they needed to be doing was the inverse."
1:54

Eric's message to SaaS CEOs navigating the shift to AI. Every day you hit your pre-AI plan, you're destroying value: "One of the big messages to these SaaS companies a few years ago was, get to AI or be worth 3x revenue. Those are your choices. Our whole careers we learned you lay out a plan, you execute against it relentlessly and violently and that's how you build equity value. That's what these CEOs learned pre-AI. Now, every day you hit that plan, you're destroying value. Another articulation of this is from my friend Anne Lee Skates. The CEOs who were going through this transitory period and had a business that was at hundreds of millions and they thought that they're all ready. They were working on their business from 8:00 AM to 5:00 PM and then trying to do AI from 5:00 to 8:00 in the evenings. What they needed to be doing was the inverse."

Patrick OShaughnessy

73,683 Aufrufe • vor 1 Monat

Guillermo Rauch (Guillermo Rauch) is one of the most prolific coders of this generation. But he doesn’t think of himself as a coder anymore. Coding, he says, is a specific skill that AI is becoming great at. Instead, he thinks the future of coding is more holistic, full-stack engineers who can ideate, design, and execute all together. Guillermo is the founder and CEO of Vercel (Vercel), the creator of NextJS, and SocketIO. We spent an hour talking about the future of software development in an AI world—and the meta-skills that are essential for the coders of today to master—in order to use tomorrow’s tools to their fullest extent. Here are a few takeaways: - One of the most important keys to his success is taste—and developing taste is all about paying better attention to everything you experience day to day. - He’s great at recognizing bleeding-edge technologies with extremely practical applications but that have bad user experiences. If you can learn to recognize those and build with them, you might build the next NextJs or SocketIO. - Why prototype cultures are becoming common in AI—and the benefits of written cultures like Amazon vs. prototype cultures like Apple for different kinds of companies. - For developers building frameworks, always put the product first; a framework in isolation without a “customer zero” is never going to be a good tool. - The theory of “recursive founder mode”—if you want to build a scalable business, you have to scale yourself by creating an atmosphere that nurtures talent and ambition. - AI tools are shifting software toward consumption-based billing models, making us capital allocators who decide how much compute the AI consumes. - The future of AI is agents with the taste, knowledge, and tools to perform specialized tasks. Watch below! Timestamps: Introduction: 00:01:33 How to spot trends early: 00:03:18 Why you should be your own customer: 00:07:34 How to create an ecosystem of talent and ambition: 00:14:55 Why Guillermo doesn't identify as a coder: 00:17:29 AI is gearing us toward an allocation economy: 00:20:50 How Vercel’s copilot compares with other coding agents: 00:28:34 Guillermo’s advice on having better taste: 00:40:35 The future of AI agents is specialized: 00:42:46 How AI startups can compete with big tech: 00:47:50
56:15

Guillermo Rauch (Guillermo Rauch) is one of the most prolific coders of this generation. But he doesn’t think of himself as a coder anymore. Coding, he says, is a specific skill that AI is becoming great at. Instead, he thinks the future of coding is more holistic, full-stack engineers who can ideate, design, and execute all together. Guillermo is the founder and CEO of Vercel (Vercel), the creator of NextJS, and SocketIO. We spent an hour talking about the future of software development in an AI world—and the meta-skills that are essential for the coders of today to master—in order to use tomorrow’s tools to their fullest extent. Here are a few takeaways: - One of the most important keys to his success is taste—and developing taste is all about paying better attention to everything you experience day to day. - He’s great at recognizing bleeding-edge technologies with extremely practical applications but that have bad user experiences. If you can learn to recognize those and build with them, you might build the next NextJs or SocketIO. - Why prototype cultures are becoming common in AI—and the benefits of written cultures like Amazon vs. prototype cultures like Apple for different kinds of companies. - For developers building frameworks, always put the product first; a framework in isolation without a “customer zero” is never going to be a good tool. - The theory of “recursive founder mode”—if you want to build a scalable business, you have to scale yourself by creating an atmosphere that nurtures talent and ambition. - AI tools are shifting software toward consumption-based billing models, making us capital allocators who decide how much compute the AI consumes. - The future of AI is agents with the taste, knowledge, and tools to perform specialized tasks. Watch below! Timestamps: Introduction: 00:01:33 How to spot trends early: 00:03:18 Why you should be your own customer: 00:07:34 How to create an ecosystem of talent and ambition: 00:14:55 Why Guillermo doesn't identify as a coder: 00:17:29 AI is gearing us toward an allocation economy: 00:20:50 How Vercel’s copilot compares with other coding agents: 00:28:34 Guillermo’s advice on having better taste: 00:40:35 The future of AI agents is specialized: 00:42:46 How AI startups can compete with big tech: 00:47:50

Dan Shipper

187,107 Aufrufe • vor 1 Jahr

New podcast with jsd of Epoch AI digging into the open questions determining the future of frontier AI! We cover: 00:00 Predictions for RSI 18:15 The role of robotics in an AI acceleration 24:20 How far behind are Chinese models? 27:39 Does distillation explain the gap? 40:58 What Chinese job postings reveal about their labs 48:13 Are open or closed models safer? 58:10 How Epoch AI ticks 1:00:55 What a frontier post-training recipe looks like He's one of the people who gives the best feedback on my writing, so I was stoked to have him on.
1:05:28

New podcast with jsd of Epoch AI digging into the open questions determining the future of frontier AI! We cover: 00:00 Predictions for RSI 18:15 The role of robotics in an AI acceleration 24:20 How far behind are Chinese models? 27:39 Does distillation explain the gap? 40:58 What Chinese job postings reveal about their labs 48:13 Are open or closed models safer? 58:10 How Epoch AI ticks 1:00:55 What a frontier post-training recipe looks like He's one of the people who gives the best feedback on my writing, so I was stoked to have him on.

Nathan Lambert

38,525 Aufrufe • vor 4 Tagen

Another mindblowing conversation with my good friend .Emad... Enjoy!! 00:00 - Introduction 00:22 - AI: The Biggest Shift in Human History 00:42 - AI’s Impact on Society and the Economy 01:03 - Conversation with Emad Mostaque Begins 01:50 - The Acceleration of AI and Economic Takeoff 03:07 - AI Intelligence: Beyond Human IQ 04:08 - The Rise of AI Chefs and Super Cooks 05:07 - Breaking AI Constraints: Compute and Energy 07:03 - The Future of AI: Ubiquitous Intelligence 08:04 - The Shift to Local AI Models 10:07 - Why Has Apple Lagged in AI? 11:21 - The AI Race: OpenAI, Grok, Gemini, and More 13:11 - China’s Open-Source AI Strategy 14:57 - AI Bias and Ethical Challenges 16:57 - AI’s Cross-Pollination and Memory 18:02 - Are AI Models Becoming Self-Aware? 19:16 - AI, Bitcoin, and Self-Sustaining Algorithms 21:26 - AI-Driven Economies and Autonomous Companies 23:41 - The Future of Labor: A World Without Jobs? 25:26 - AI-Powered Robots: The Next Workforce Revolution 27:28 - The End of Traditional Economic Models 30:27 - The Political Shift: Humanist vs. Transhumanist 33:04 - AI in Financial Markets: The End of Human Traders? 36:03 - The Evolution of Investing in an AI World 38:33 - AI’s Impact on Capital Formation and Business Disruption 40:01 - The Rise of Digital Twins and Post-Capital Society 42:45 - Building AI for Education, Healthcare, and Governance 46:42 - The Future of Money in an AI-Driven World 50:11 - Universal Basic AI: A New Economic Model 54:29 - The Deflationary Impact of AI and Crypto’s Role 57:02 - The AI Singularity: Five Years Until Everything Changes 58:56 - The Road Ahead: AI, Crypto, and the Future of Civilization 01:02:24 - Final Thoughts: The Most Exciting and Terrifying Time in History
1:04:51

Another mindblowing conversation with my good friend .Emad... Enjoy!! 00:00 - Introduction 00:22 - AI: The Biggest Shift in Human History 00:42 - AI’s Impact on Society and the Economy 01:03 - Conversation with Emad Mostaque Begins 01:50 - The Acceleration of AI and Economic Takeoff 03:07 - AI Intelligence: Beyond Human IQ 04:08 - The Rise of AI Chefs and Super Cooks 05:07 - Breaking AI Constraints: Compute and Energy 07:03 - The Future of AI: Ubiquitous Intelligence 08:04 - The Shift to Local AI Models 10:07 - Why Has Apple Lagged in AI? 11:21 - The AI Race: OpenAI, Grok, Gemini, and More 13:11 - China’s Open-Source AI Strategy 14:57 - AI Bias and Ethical Challenges 16:57 - AI’s Cross-Pollination and Memory 18:02 - Are AI Models Becoming Self-Aware? 19:16 - AI, Bitcoin, and Self-Sustaining Algorithms 21:26 - AI-Driven Economies and Autonomous Companies 23:41 - The Future of Labor: A World Without Jobs? 25:26 - AI-Powered Robots: The Next Workforce Revolution 27:28 - The End of Traditional Economic Models 30:27 - The Political Shift: Humanist vs. Transhumanist 33:04 - AI in Financial Markets: The End of Human Traders? 36:03 - The Evolution of Investing in an AI World 38:33 - AI’s Impact on Capital Formation and Business Disruption 40:01 - The Rise of Digital Twins and Post-Capital Society 42:45 - Building AI for Education, Healthcare, and Governance 46:42 - The Future of Money in an AI-Driven World 50:11 - Universal Basic AI: A New Economic Model 54:29 - The Deflationary Impact of AI and Crypto’s Role 57:02 - The AI Singularity: Five Years Until Everything Changes 58:56 - The Road Ahead: AI, Crypto, and the Future of Civilization 01:02:24 - Final Thoughts: The Most Exciting and Terrifying Time in History

Raoul Pal

326,827 Aufrufe • vor 1 Jahr