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Ex-DeepMind researcher Misha Laskin on building superintelligence, coding as a shortcut, reinforcement learning, launching Reflection AI's first product, Asimov, and competing with the big AI labs 00:00 - Intro 01:42 - Reflection AI: Company Origins and Mission 04:14 - Making Superintelligence Concrete 06:04 - ASI vs. AGI: Why the...

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Matt Turck

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Google AI Developers

65,474 Aufrufe • vor 1 Jahr

Nobel laureate John Jumper (John Jumper) on AlphaFold and the future of science. At AI Startup School in San Francisco. 01:02 - Journey from Physics to Computational Biology 02:01 - Transition to Machine Learning and AI 02:59 - Joining Google DeepMind 03:47 - The Mission: Accelerating Scientific Discovery 04:54 - Understanding Protein Structures 07:44 - Challenges in Protein Structure Determination 11:21 - The Role of Data, Compute, and Research 13:28 - AlphaFold: A Breakthrough in Protein Prediction 14:09 - The Importance of Research and Ideas 17:48 - Real-World Applications and Impact 18:02 - Open Sourcing AlphaFold 21:07 - Unexpected Uses and Innovations 23:15 - Case Study: Molecular Syringe 25:28 - Future of AI in Science
27:12

Nobel laureate John Jumper (John Jumper) on AlphaFold and the future of science. At AI Startup School in San Francisco. 01:02 - Journey from Physics to Computational Biology 02:01 - Transition to Machine Learning and AI 02:59 - Joining Google DeepMind 03:47 - The Mission: Accelerating Scientific Discovery 04:54 - Understanding Protein Structures 07:44 - Challenges in Protein Structure Determination 11:21 - The Role of Data, Compute, and Research 13:28 - AlphaFold: A Breakthrough in Protein Prediction 14:09 - The Importance of Research and Ideas 17:48 - Real-World Applications and Impact 18:02 - Open Sourcing AlphaFold 21:07 - Unexpected Uses and Innovations 23:15 - Case Study: Molecular Syringe 25:28 - Future of AI in Science

Y Combinator

149,700 Aufrufe • vor 11 Monaten

.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

67,452 Aufrufe • vor 2 Monaten

How to build a beloved AI product, with Granola CEO Chris Pedregal Granola is the rare AI startup that slipped into a very crowded niche — meeting notes — and still managed to become the product founders (and VCs!) rave about. Plenty of product lessons in this episode. 00:00 - Introduction: The Granola Story 01:41 - Building a "Life-Changing" Product 04:31 - The "Second Brain" Vision 06:28 - Augmentation Philosophy (Engelbart), Tools That Shape Us 09:02 - Late to a Crowded Market: Why it Worked 13:43 - Two Product Founders, Zero ML PhDs 16:01 - London vs. SF: Building Outside the Valley 19:51 - One Year in Stealth: Learning Before Launch 22:40 - "Building For Us" & Finding First Users 25:41 - Key Design Choices: No Meeting Bot, No Stored Audio 29:24 - Simplicity is Hard: Cutting 50% of Features 32:54 - Intuition vs. Data in Making Product Decisions 36:25 - Continuous User Conversations: 4-6 Calls/Week 38:06 - Prioritizing the Future: Build for Tomorrow's Workflows 40:17 - Tech Stack Tour: Model Routing & Evals 42:29 - Context Windows, Costs & Inference Economics 45:03 - Audio Stack: Transcription, Noise Cancellation & Diarization Limits 48:27 - Guardrails & Citations: Building Trust in AI 50:00 - Growth Loops Without Virality Hacks 54:54 - Enterprise Compliance, Data Footprint & Liability Risk 57:07 - Retention & Habit Formation: The "500 Millisecond Window" 58:43 - Competing with OpenAI and Legacy Suites 1:01:27 - The Future: Deep Research Across Meetings & Roadmap 1:04:41 - Granola as Career Coach?
1:08:28

How to build a beloved AI product, with Granola CEO Chris Pedregal Granola is the rare AI startup that slipped into a very crowded niche — meeting notes — and still managed to become the product founders (and VCs!) rave about. Plenty of product lessons in this episode. 00:00 - Introduction: The Granola Story 01:41 - Building a "Life-Changing" Product 04:31 - The "Second Brain" Vision 06:28 - Augmentation Philosophy (Engelbart), Tools That Shape Us 09:02 - Late to a Crowded Market: Why it Worked 13:43 - Two Product Founders, Zero ML PhDs 16:01 - London vs. SF: Building Outside the Valley 19:51 - One Year in Stealth: Learning Before Launch 22:40 - "Building For Us" & Finding First Users 25:41 - Key Design Choices: No Meeting Bot, No Stored Audio 29:24 - Simplicity is Hard: Cutting 50% of Features 32:54 - Intuition vs. Data in Making Product Decisions 36:25 - Continuous User Conversations: 4-6 Calls/Week 38:06 - Prioritizing the Future: Build for Tomorrow's Workflows 40:17 - Tech Stack Tour: Model Routing & Evals 42:29 - Context Windows, Costs & Inference Economics 45:03 - Audio Stack: Transcription, Noise Cancellation & Diarization Limits 48:27 - Guardrails & Citations: Building Trust in AI 50:00 - Growth Loops Without Virality Hacks 54:54 - Enterprise Compliance, Data Footprint & Liability Risk 57:07 - Retention & Habit Formation: The "500 Millisecond Window" 58:43 - Competing with OpenAI and Legacy Suites 1:01:27 - The Future: Deep Research Across Meetings & Roadmap 1:04:41 - Granola as Career Coach?

Matt Turck

32,002 Aufrufe • vor 10 Monaten

In this episode of the Lightcone Podcast, we sat down with Mukund Jha and Madhav Jha, the founders of Emergent - an AI platform that lets anyone build and ship production-ready software. In just eight months, users have created more than 7 million apps on Emergent, with the number doubling in just the last 45 days. We discuss how they built one of the most powerful AI coding agents, why they focused on non-technical users and what it's like building for a global audience from India. 00:00 - Intro 01:06 - What Is Emergent? 01:18 - Founder Backstory 02:09 - From AI Testing to General Coding Agents 02:52 - Getting Ahead of the Market 04:18 - The Pivot to Non-Technical Users 05:22 - Why Second Movers Can Win in AI 09:04 - Building for Production, Not Just Prototypes 18:21 - Live Demo: Building Apps with Emergent 24:40 - How Emergent Hires and Runs a Lean Team 29:04 - Is SaaS Dead? The Rise of Personalized Software 34:04 - The Future: Niche Apps, Solo Builders and AI Agency
39:32

In this episode of the Lightcone Podcast, we sat down with Mukund Jha and Madhav Jha, the founders of Emergent - an AI platform that lets anyone build and ship production-ready software. In just eight months, users have created more than 7 million apps on Emergent, with the number doubling in just the last 45 days. We discuss how they built one of the most powerful AI coding agents, why they focused on non-technical users and what it's like building for a global audience from India. 00:00 - Intro 01:06 - What Is Emergent? 01:18 - Founder Backstory 02:09 - From AI Testing to General Coding Agents 02:52 - Getting Ahead of the Market 04:18 - The Pivot to Non-Technical Users 05:22 - Why Second Movers Can Win in AI 09:04 - Building for Production, Not Just Prototypes 18:21 - Live Demo: Building Apps with Emergent 24:40 - How Emergent Hires and Runs a Lean Team 29:04 - Is SaaS Dead? The Rise of Personalized Software 34:04 - The Future: Niche Apps, Solo Builders and AI Agency

Y Combinator

107,448 Aufrufe • vor 3 Monaten

We cloned an app that does $300M/yr... Using AI (Zero Coding) With (Founding Engineer at ) TIMESTAMPS 00:00 Intro 01:01 Setting Up the Competition 04:54 First Prompt in the 05:07 Generating Assets & Icons 08:07 Implementing Vibe Features 11:56 Oh My God that Song is Fire 22:06 API Integrations (OpenAI ) 27:36 Half Time and Feedback on the App 34:46 App Design and Aesthetic Improvements 35:50 Voice Customization and API Integration 40:21 Creating Onboarding with AI 41:53 Final Adjustments and Testing 56:15 Rileys App Final 59:52 Vishals App Walkthrough
1:05:41

We cloned an app that does $300M/yr... Using AI (Zero Coding) With (Founding Engineer at ) TIMESTAMPS 00:00 Intro 01:01 Setting Up the Competition 04:54 First Prompt in the 05:07 Generating Assets & Icons 08:07 Implementing Vibe Features 11:56 Oh My God that Song is Fire 22:06 API Integrations (OpenAI ) 27:36 Half Time and Feedback on the App 34:46 App Design and Aesthetic Improvements 35:50 Voice Customization and API Integration 40:21 Creating Onboarding with AI 41:53 Final Adjustments and Testing 56:15 Rileys App Final 59:52 Vishals App Walkthrough

Riley Brown

59,063 Aufrufe • vor 8 Monaten

Gemini 3, scaling laws and the 'finite data' era: my conversation with Sebastian Borgeaud, research engineer at Google DeepMind and a pre-training lead for Gemini 3 00:00 – Cold intro: “We’re ahead of schedule” + AI is now a system 00:58 – Oriol Vinyals's “secret recipe”: better pre- + post-training 02:09 – Why AI progress still isn’t slowing down 03:04 – Are models actually getting smarter? 04:36 – Two–three years out: what changes first? 06:34 – AI doing AI research: faster, not automated 07:45 – Frontier labs: same playbook or different bets? 10:19 – Post-transformers: will a disruption happen? 10:51 – DeepMind’s advantage: research × engineering × infra 12:26 – What a Gemini 3 pre-training lead actually does 13:59 – From Europe to Cambridge to DeepMind 18:06 – Why he left RL for real-world data 20:05 – From Gopher to Chinchilla to RETRO (and why it matters) 20:28 – “Research taste”: integrate or slow everyone down 23:00 – Fixes vs moonshots: how they balance the pipeline 24:37 – Research vs product pressure (and org structure) 26:24 – Gemini 3 under the hood: MoE in plain English 28:30 – Native multimodality: the hidden costs 30:03 – Scaling laws aren’t dead (but scale isn’t everything) 33:07 – Synthetic data: powerful, dangerous? 35:00 – Reasoning traces: what he can’t say (and why) 37:18 – Long context + attention: what’s next 38:40 – Retrieval vs RAG vs long context 41:49 – The real boss fight: evals (and contamination) 42:28 – Alignment: pre-training vs post-training 43:32 – Deep Think + agents + “vibe coding” 46:34 – Continual learning: updating models over time 49:35 – Advice for researchers + founders 53:35 – “No end in sight” for progress + closing
54:56

Gemini 3, scaling laws and the 'finite data' era: my conversation with Sebastian Borgeaud, research engineer at Google DeepMind and a pre-training lead for Gemini 3 00:00 – Cold intro: “We’re ahead of schedule” + AI is now a system 00:58 – Oriol Vinyals's “secret recipe”: better pre- + post-training 02:09 – Why AI progress still isn’t slowing down 03:04 – Are models actually getting smarter? 04:36 – Two–three years out: what changes first? 06:34 – AI doing AI research: faster, not automated 07:45 – Frontier labs: same playbook or different bets? 10:19 – Post-transformers: will a disruption happen? 10:51 – DeepMind’s advantage: research × engineering × infra 12:26 – What a Gemini 3 pre-training lead actually does 13:59 – From Europe to Cambridge to DeepMind 18:06 – Why he left RL for real-world data 20:05 – From Gopher to Chinchilla to RETRO (and why it matters) 20:28 – “Research taste”: integrate or slow everyone down 23:00 – Fixes vs moonshots: how they balance the pipeline 24:37 – Research vs product pressure (and org structure) 26:24 – Gemini 3 under the hood: MoE in plain English 28:30 – Native multimodality: the hidden costs 30:03 – Scaling laws aren’t dead (but scale isn’t everything) 33:07 – Synthetic data: powerful, dangerous? 35:00 – Reasoning traces: what he can’t say (and why) 37:18 – Long context + attention: what’s next 38:40 – Retrieval vs RAG vs long context 41:49 – The real boss fight: evals (and contamination) 42:28 – Alignment: pre-training vs post-training 43:32 – Deep Think + agents + “vibe coding” 46:34 – Continual learning: updating models over time 49:35 – Advice for researchers + founders 53:35 – “No end in sight” for progress + closing

Matt Turck

51,317 Aufrufe • vor 6 Monaten