Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

What's better? LLMs or Agents? This is a real interview question from a big tech company. For more questions, subscribe to the channel! AI engineering program for software engineers: 00:00 Question - LLMs vs. Agents 01:02 Basic Answer 02:16 Follow-up question 03:47 Context Management 05:08 Bloopers #LLMs #AI #InterviewReady

gkcs_'s profile picture

Gaurav Sen

71,943 subscribers

18,561 görüntüleme • 13 gün önce •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

AI agents. What makes them a hot topic? In this video, we find out. 00:00 AI Agents 01:05 Use cases 03:47 Architecture of Agentic Applications 06:50 Thank you! 07:10 The future? This video is for AI engineers and tech enthusiasts. #AI #Agents #MCP
9:38

AI agents. What makes them a hot topic? In this video, we find out. 00:00 AI Agents 01:05 Use cases 03:47 Architecture of Agentic Applications 06:50 Thank you! 07:10 The future? This video is for AI engineers and tech enthusiasts. #AI #Agents #MCP

Gaurav Sen

28,760 görüntüleme • 1 yıl önce

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: #Diffusion #AI #LLMs
15:00

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: #Diffusion #AI #LLMs

Gaurav Sen

27,927 görüntüleme • 9 ay önce

chat, is starknet gaming cooked? for episode two, loaf has joined me to talk about ai agents in gaming, eliza and Daydreams: Taskmarket . timestamps loaf introduction 00:46 what are ai agents? 01:16 what will gaming look like with ai agents? 02:02 whats the point of gaming with ai agents? 04:06 why is onchain gaming the ideal playground for AI agents? 05:11 is a gaming revolution imminent? 07:00 eliza & daydreams 08:36 endgame of ai agents and onchain games 13:33 when and where to play using agents 16:58 final remarks 18:47
19:44

chat, is starknet gaming cooked? for episode two, loaf has joined me to talk about ai agents in gaming, eliza and Daydreams: Taskmarket . timestamps loaf introduction 00:46 what are ai agents? 01:16 what will gaming look like with ai agents? 02:02 whats the point of gaming with ai agents? 04:06 why is onchain gaming the ideal playground for AI agents? 05:11 is a gaming revolution imminent? 07:00 eliza & daydreams 08:36 endgame of ai agents and onchain games 13:33 when and where to play using agents 16:58 final remarks 18:47

shadowfax

37,582 görüntüleme • 1 yıl önce

Amjad Masad (Amjad Masad), co-founder & CEO of Replit, has spent nearly a decade making programming accessible to everyone. With AI, that vision is closer than ever. In his AI Startup School talk, Amjad traces computing’s arc from mainframes to PCs to a future where AI agents generate software on demand—driving the value of traditional software toward zero and reshaping how companies are built. 00:22 - Evolution of Computing 01:27 - The Rise of Software Engineering 02:16 - Replit's Vision: Making Programming Accessible 02:42 - The Role of AI in Software Development 04:32 - Challenges and Infrastructure for AI Agents 05:03 - Building a Scalable and Secure Environment 06:08 - Future of Software Engineering Agents 09:25 - Autonomy Levels in AI Agents 15:56 - Predictions for the Software Market 18:31 - Impact on Business and Employment 19:58 - The Role of Generalist Employees 26:00 - Audience Q&A
42:01

Amjad Masad (Amjad Masad), co-founder & CEO of Replit, has spent nearly a decade making programming accessible to everyone. With AI, that vision is closer than ever. In his AI Startup School talk, Amjad traces computing’s arc from mainframes to PCs to a future where AI agents generate software on demand—driving the value of traditional software toward zero and reshaping how companies are built. 00:22 - Evolution of Computing 01:27 - The Rise of Software Engineering 02:16 - Replit's Vision: Making Programming Accessible 02:42 - The Role of AI in Software Development 04:32 - Challenges and Infrastructure for AI Agents 05:03 - Building a Scalable and Secure Environment 06:08 - Future of Software Engineering Agents 09:25 - Autonomy Levels in AI Agents 15:56 - Predictions for the Software Market 18:31 - Impact on Business and Employment 19:58 - The Role of Generalist Employees 26:00 - Audience Q&A

Y Combinator

224,320 görüntüleme • 10 ay önce

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)
2:35:26

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)

Rob Wiblin

65,088 görüntüleme • 2 ay önce

The best AI startup isn't being built in SF right now. Software engineering is the easiest job in the world. NVIDIA won't be the future of AI hardware. Solo founders are doomed to fail. These are just some of the hot takes we got Toronto Tech Week's Cursor event... Watch full video below. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Intro (00:31) AI Startup Not in SF (00:45) Coding Is Too Easy (01:17) Networking Is a Game (01:52) Post Nvidia AI Future (02:16) Solo Founders Fail (02:28) AI Hits Consciousness Wall (02:34) North America Bad Fashion (02:56) Self Hosted SLM Takeover (03:13) Codex vs Claude Code (03:28) Underdog Universities Win (03:37) LinkedIn Posting Advantage (04:04) Wrap Up
4:08

The best AI startup isn't being built in SF right now. Software engineering is the easiest job in the world. NVIDIA won't be the future of AI hardware. Solo founders are doomed to fail. These are just some of the hot takes we got Toronto Tech Week's Cursor event... Watch full video below. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Intro (00:31) AI Startup Not in SF (00:45) Coding Is Too Easy (01:17) Networking Is a Game (01:52) Post Nvidia AI Future (02:16) Solo Founders Fail (02:28) AI Hits Consciousness Wall (02:34) North America Bad Fashion (02:56) Self Hosted SLM Takeover (03:13) Codex vs Claude Code (03:28) Underdog Universities Win (03:37) LinkedIn Posting Advantage (04:04) Wrap Up

Julia Fedorin

11,069 görüntüleme • 2 ay önce

8 months ago we started explaining enterprise AI on YouTube. 2M views later, here are the 5 shifts that actually defined 2025. Timestamps 00:36 Prompt Engineering → Agent Engineering 00:51 Prompting best practices 01:19 How context engineering focuses on curating information 01:51 Evolution of assistants to agents 02:12 Agents handling complex tasks 02:58 How security became foundational 03:36 The correct security architecture for organizations 04:23 The emergence of model flexibility 05:26 The importance of governance and trust
7:40

8 months ago we started explaining enterprise AI on YouTube. 2M views later, here are the 5 shifts that actually defined 2025. Timestamps 00:36 Prompt Engineering → Agent Engineering 00:51 Prompting best practices 01:19 How context engineering focuses on curating information 01:51 Evolution of assistants to agents 02:12 Agents handling complex tasks 02:58 How security became foundational 03:36 The correct security architecture for organizations 04:23 The emergence of model flexibility 05:26 The importance of governance and trust

Box

460,406 görüntüleme • 7 ay önce

.Eric Jang is spearheading AI at 1X. Eric believes 1X is on the path to becoming an AGI company — not just a robotics company. I sat down with him at 1X’s new Palo Alto office to discuss the key AI building blocks needed to bring humanoid robots into homes at scale. 00:00 Eric Jang joins Humanoid Hub 00:27 Move to Palo Alto 01:02 Why 1X targets the home 02:01 The AGI company 02:49 Redwood AI overview 04:01 Unified control, modular stack 06:07 Data labeling at scale 07:10 RL mobility update for NEO 10:03 GPU and onboard compute 11:09 World model explained 13:34 Trusting world models for evaluation 15:05 Synthetic data generation 16:30 A world model Turing Test 17:41 RL in manipulation 20:08 Lessons from early NEO deployments 21:00 Full autonomy 22:18 1X’s unique edge in AI 24:02 Eric asks Devang a question
26:53

.Eric Jang is spearheading AI at 1X. Eric believes 1X is on the path to becoming an AGI company — not just a robotics company. I sat down with him at 1X’s new Palo Alto office to discuss the key AI building blocks needed to bring humanoid robots into homes at scale. 00:00 Eric Jang joins Humanoid Hub 00:27 Move to Palo Alto 01:02 Why 1X targets the home 02:01 The AGI company 02:49 Redwood AI overview 04:01 Unified control, modular stack 06:07 Data labeling at scale 07:10 RL mobility update for NEO 10:03 GPU and onboard compute 11:09 World model explained 13:34 Trusting world models for evaluation 15:05 Synthetic data generation 16:30 A world model Turing Test 17:41 RL in manipulation 20:08 Lessons from early NEO deployments 21:00 Full autonomy 22:18 1X’s unique edge in AI 24:02 Eric asks Devang a question

The Humanoid Hub

96,465 görüntüleme • 1 yıl önce

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 görüntüleme • 10 ay önce

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

194,393 görüntüleme • 1 yıl önce

EPISODE 142: Understanding the AI Wave Joe Lonsdale sits down with 8VC Partners Alex Kolicich & Jack Moshkovich Why is AI fundamentally disruptive? What does it mean for large software incumbents? Will LLMs become commodities? And are we entering a bubble, or just getting started? (00:00) Episode intro (01:40) Smart enterprise thesis (04:00) Did enterprise software dramatically boost productivity? (08:30) Understanding the AI wave (11:45) Will LLMs become commodities? (17:10) OpenAI vs Anthropic vs Meta vs Grok (20:30) Frontier models vs enterprise incumbents (25:27) What did Palantir get right? (32:30) AI case studies/new possibilities (43:00) The most surprising aspect of the AI wave (46:12) Are we entering a bubble? Or just getting started? (52:40) Biggest concern with AI (58:00) Biggest reason for optimism
1:01:18

EPISODE 142: Understanding the AI Wave Joe Lonsdale sits down with 8VC Partners Alex Kolicich & Jack Moshkovich Why is AI fundamentally disruptive? What does it mean for large software incumbents? Will LLMs become commodities? And are we entering a bubble, or just getting started? (00:00) Episode intro (01:40) Smart enterprise thesis (04:00) Did enterprise software dramatically boost productivity? (08:30) Understanding the AI wave (11:45) Will LLMs become commodities? (17:10) OpenAI vs Anthropic vs Meta vs Grok (20:30) Frontier models vs enterprise incumbents (25:27) What did Palantir get right? (32:30) AI case studies/new possibilities (43:00) The most surprising aspect of the AI wave (46:12) Are we entering a bubble? Or just getting started? (52:40) Biggest concern with AI (58:00) Biggest reason for optimism

American Optimist

416,550 görüntüleme • 5 ay önce

FULL INTERVIEW: Sam Altman joins TBPN to discuss GPT-5.3-Codex, AI agents, Anthropic's Super Bowl ads, and more. 00:00 GPT-5.3-Codex 02:27 AI agents and the future of work 03:20 The role of forward-deployed engineers in AI 05:42 AI benchmarks 07:29 Emotional attachment to chatbots 10:40 On data and compute being the 'new oil' 12:56 Is software dead? 17:48 Codex Desktop and the rise of the general-purpose work agent 25:00 OpenAI’s last Super Bowl ad and the Anthropic ads
36:41

FULL INTERVIEW: Sam Altman joins TBPN to discuss GPT-5.3-Codex, AI agents, Anthropic's Super Bowl ads, and more. 00:00 GPT-5.3-Codex 02:27 AI agents and the future of work 03:20 The role of forward-deployed engineers in AI 05:42 AI benchmarks 07:29 Emotional attachment to chatbots 10:40 On data and compute being the 'new oil' 12:56 Is software dead? 17:48 Codex Desktop and the rise of the general-purpose work agent 25:00 OpenAI’s last Super Bowl ad and the Anthropic ads

TBPN

639,180 görüntüleme • 5 ay önce

Ex-OpenAI Tech Lead, Justin Lebar joins SemiAnalysis as an Visiting Fellow to Burn $10,000 in 3 hours to find dozens of AMDGPU LLVM, x86 LLVM, NVPTX bugs 00:00 - Intro & Justin’s background 00:59 - How compiler fuzzing works 01:56 - Why we did this project 02:48 - The gap in GPU vs. CPU compiler testing 04:13 - The major AMD & x86 bugs we found 05:38 - Using LLMs to read code & find vulnerabilities 07:56 - The impact of UltraCode mode 12:18 - Doing this without AI (Time & manual limits) 15:03 - The future of AI in software development 16:17 - What’s next + key takeaways for devs
23:19

Ex-OpenAI Tech Lead, Justin Lebar joins SemiAnalysis as an Visiting Fellow to Burn $10,000 in 3 hours to find dozens of AMDGPU LLVM, x86 LLVM, NVPTX bugs 00:00 - Intro & Justin’s background 00:59 - How compiler fuzzing works 01:56 - Why we did this project 02:48 - The gap in GPU vs. CPU compiler testing 04:13 - The major AMD & x86 bugs we found 05:38 - Using LLMs to read code & find vulnerabilities 07:56 - The impact of UltraCode mode 12:18 - Doing this without AI (Time & manual limits) 15:03 - The future of AI in software development 16:17 - What’s next + key takeaways for devs

SemiAnalysis

73,686 görüntüleme • 1 ay önce

What is the best LLM for agentic software engineering? Today, we're releasing The OpenHands Index to answer this question. It's the first broad-coverage benchmark for AI coding agents, comparing them on accuracy, cost, and runtime across 5 task domains.
4:43

What is the best LLM for agentic software engineering? Today, we're releasing The OpenHands Index to answer this question. It's the first broad-coverage benchmark for AI coding agents, comparing them on accuracy, cost, and runtime across 5 task domains.

OpenHands

31,780 görüntüleme • 6 ay önce

Pingo - AI language learning app. Scaled to $500k/mo in 14 months from a college dorm, via organic TikTok (400M+ views) Full interview deep dive into their distribution & creator scaling strategy 👇 00:00 Intro to Pingo 05:02 LLMs in Language Learning 10:16 Viral TikTok Formats 15:02 Content Strategy 20:11 Creator Recruitment 28:04 Targeting International Markets 34:00 VC Portfolio Approach to Creator Team 40:50 Creator Coaching, Paywall Optimization 52:03 Y Combinator Experience
58:20

Pingo - AI language learning app. Scaled to $500k/mo in 14 months from a college dorm, via organic TikTok (400M+ views) Full interview deep dive into their distribution & creator scaling strategy 👇 00:00 Intro to Pingo 05:02 LLMs in Language Learning 10:16 Viral TikTok Formats 15:02 Content Strategy 20:11 Creator Recruitment 28:04 Targeting International Markets 34:00 VC Portfolio Approach to Creator Team 40:50 Creator Coaching, Paywall Optimization 52:03 Y Combinator Experience

Joseph Choi

75,888 görüntüleme • 3 ay önce

.Poetiq is a new startup that recently achieved a major jump on the ARC-AGI benchmark by layering a recursive self-improvement system on top of existing models. In this episode of the Lightcone Podcast, Poetiq's Founder & CEO Ian Fischer joined us to discuss how small teams can build “reasoning harnesses” that outperform base models, what that means for startups and why automating prompt engineering may be one of the most powerful levers in AI today. 00:00 – Intro 00:40 – What Is Poetiq? 01:07 – Recursive Self-Improvement Explained 02:07 – The Fine-Tuning Trap 02:59 – “Stilts” for LLMs 03:14 – Recursive Self-Improvement vs. Fine-Tuning 05:05 – Taking the Top Spot on ARC-AGI 06:37 – Beating Claude on Humanity’s Last Exam 08:40 – How the Meta-System Works 10:26 – Beyond RL: A New S-Curve 11:32 – Automating Prompt Engineering 13:37 – From 5% to 95% Performance 14:50 – Early Access & Putting Your Agent on Stilts 16:17 – From YC Founder to DeepMind Researcher 18:29 – Advice for Engineers in the AI Era
19:45

.Poetiq is a new startup that recently achieved a major jump on the ARC-AGI benchmark by layering a recursive self-improvement system on top of existing models. In this episode of the Lightcone Podcast, Poetiq's Founder & CEO Ian Fischer joined us to discuss how small teams can build “reasoning harnesses” that outperform base models, what that means for startups and why automating prompt engineering may be one of the most powerful levers in AI today. 00:00 – Intro 00:40 – What Is Poetiq? 01:07 – Recursive Self-Improvement Explained 02:07 – The Fine-Tuning Trap 02:59 – “Stilts” for LLMs 03:14 – Recursive Self-Improvement vs. Fine-Tuning 05:05 – Taking the Top Spot on ARC-AGI 06:37 – Beating Claude on Humanity’s Last Exam 08:40 – How the Meta-System Works 10:26 – Beyond RL: A New S-Curve 11:32 – Automating Prompt Engineering 13:37 – From 5% to 95% Performance 14:50 – Early Access & Putting Your Agent on Stilts 16:17 – From YC Founder to DeepMind Researcher 18:29 – Advice for Engineers in the AI Era

Y Combinator

156,125 görüntüleme • 5 ay önce

In 16 minutes we vibe coded a Web App and a Mobile App.... That share a backend Using Claude Code inside the Without writing a single line of code. App we built: instagram, except all the other users are AI agents that can post, like and comment. 00:00 Introduction 00:33 Starting with the Web App 01:08 First Prompt and Initial Setup 01:43 Authentication Process 02:49 Adding APIs and Enhancements 03:59 Testing the Web App 05:04 Redesigning the Web App 05:59 The Importance of Understanding Databases 07:20 Adding AI Agents 08:44 Setting Up the Admin Panel 10:02 Speeding Up Time for Testing 11:14 Finalizing the Web App 13:24 Building the Mobile App 16:11 Final Thoughts
16:39

In 16 minutes we vibe coded a Web App and a Mobile App.... That share a backend Using Claude Code inside the Without writing a single line of code. App we built: instagram, except all the other users are AI agents that can post, like and comment. 00:00 Introduction 00:33 Starting with the Web App 01:08 First Prompt and Initial Setup 01:43 Authentication Process 02:49 Adding APIs and Enhancements 03:59 Testing the Web App 05:04 Redesigning the Web App 05:59 The Importance of Understanding Databases 07:20 Adding AI Agents 08:44 Setting Up the Admin Panel 10:02 Speeding Up Time for Testing 11:14 Finalizing the Web App 13:24 Building the Mobile App 16:11 Final Thoughts

Riley Brown

68,207 görüntüleme • 6 ay önce

tylercowen is bullish on AI education — here's why. 00:00 -- Preview 00:24 -- President Carlos Carvalho's AI-generated intro 03:21 -- Cowen reacts to UATX's campus 04:38 -- The AI revolution is here. Who will lose the most? 06:05 -- AI lawyers 07:17 -- Don't underestimate this 10:41 -- Changes to the "upper upper middle class" 12:38 -- How to be successful 13:43 -- The rise of managerial empires 14:02 -- When will we have the first billion dollar company with one employee? 16:05 -- 10-20 year forecast 16:19 -- Why education is so behind 17:01 -- Should you be bullish on UATX? 18:36 -- Should you still read Homer? 21:50 -- Write to think 25:01 -- Meet more people 25:42 -- How to get hired 26:54 -- Is AI your best mentor? 38:17 -- How to curb cheating 39:02 -- The new life of the mind 42:34 -- Q&A: Will there be more status associated with real education or AI education? 45:50 -- Q&A: Why do tech-savvy students need to practice using AI? 47:56 -- Q&A: Do LLMs atrophy your mind? 49:29 -- Q&A: How do you avoid AI-dependency? 51:05 -- Q&A: Isn't this vision lonely and isolating? 53:06 -- Q&A: Do students need teachers? 55:36 -- Q&A: What are the four most important courses for undergrads? 57:49 -- Q&A: Which AI company will win the AI race in the next five years and why? 59:22 -- Q&A: Can AI teach religion? 01:01:32 -- Q&A: Will AI narrow or widen our world? 01:04:37 -- Q&A: What makes us human? 01:05:42 -- Q&A: What is art? 01:08:33 -- Q&A: It's easy to catch cheaters
1:10:31

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

University of Austin (UATX)

27,770 görüntüleme • 6 ay önce

.Trenton Bricken and Sholto Douglas are back Timestamps: 00:00:00 – Claude 4 & how far RL can scale 00:16:27 – Is continual learning a key bottleneck? 00:31:59 – Model self-awareness 00:50:32 – Taste and slop 01:00:51 – How soon to fully autonomous agents? 01:15:17 – Neuralese 01:18:55 – Inference compute will bottleneck AGI 01:23:01 – DeepSeek algorithmic improvements 01:37:42 – Why are LLMs ‘baby AGI’ but not AlphaZero? 01:45:38 – Mech interp 01:56:15 – How countries should prepare for AGI 02:10:26 – Automating white collar work 02:15:35 – Advice for students Up on YouTube, Spotify, Apple Podcasts, etc. Enjoy!
2:24:01

.Trenton Bricken and Sholto Douglas are back Timestamps: 00:00:00 – Claude 4 & how far RL can scale 00:16:27 – Is continual learning a key bottleneck? 00:31:59 – Model self-awareness 00:50:32 – Taste and slop 01:00:51 – How soon to fully autonomous agents? 01:15:17 – Neuralese 01:18:55 – Inference compute will bottleneck AGI 01:23:01 – DeepSeek algorithmic improvements 01:37:42 – Why are LLMs ‘baby AGI’ but not AlphaZero? 01:45:38 – Mech interp 01:56:15 – How countries should prepare for AGI 02:10:26 – Automating white collar work 02:15:35 – Advice for students Up on YouTube, Spotify, Apple Podcasts, etc. Enjoy!

Dwarkesh Patel

258,570 görüntüleme • 1 yıl önce

BG2 Guest Interview. AI Enterprise - Databricks & Glean 💥Why AIs Fail and Where Real Value Emerges Bg2 Pod Apoorv Agrawal Ali Ghodsi Arvind Jain --- (00:00) Intro (01:00) Consumer AI vs. Enterprise Reality (02:15) Why 95% of AI Projects Fail (04:15) RBC, Merck, and 7-Eleven Use Cases (06:45) What Actually Makes AI Work (07:00) LLMs Are Commodities—Data Is the Moat (08:45) Failed AI Bets at Databricks & Glean (11:00) RPA vs. Generative AI (14:15) Advice for CIOs Planning AI Budgets (16:00) AI CapEx and the Revenue Math (18:00) The Three Camps of AI (21:00) Making AI Useful Inside Enterprises (24:30) Why Apps Capture the Value (30:00) The Future of UI, Voice, and Data Entry (37:30) Rapid Fire: Winners, Bubbles, Long/Short
45:00

BG2 Guest Interview. AI Enterprise - Databricks & Glean 💥Why AIs Fail and Where Real Value Emerges Bg2 Pod Apoorv Agrawal Ali Ghodsi Arvind Jain --- (00:00) Intro (01:00) Consumer AI vs. Enterprise Reality (02:15) Why 95% of AI Projects Fail (04:15) RBC, Merck, and 7-Eleven Use Cases (06:45) What Actually Makes AI Work (07:00) LLMs Are Commodities—Data Is the Moat (08:45) Failed AI Bets at Databricks & Glean (11:00) RPA vs. Generative AI (14:15) Advice for CIOs Planning AI Budgets (16:00) AI CapEx and the Revenue Math (18:00) The Three Camps of AI (21:00) Making AI Useful Inside Enterprises (24:30) Why Apps Capture the Value (30:00) The Future of UI, Voice, and Data Entry (37:30) Rapid Fire: Winners, Bubbles, Long/Short

Bg2 Pod

35,410 görüntüleme • 7 ay önce