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It's been 5 weeks since we learned about the OpenAI/Hugging Face breach, which revealed an awkward reality: defenders have to ask models the same questions attackers do. In this conversation, Cotool CEO Max Pollard and Neo CEO Nick Warner sit down with a16z's Joel de la Garza at Black... Hat to discuss how security tools were designed to stop people or malware (and how AI agents are neither), what breaks when half of enterprise software goes agentic, and how teams are routing around cyber refusals. 00:00 Intro 01:00 Models escaping containment & the Hugging Face breach 01:50 Why models refuse to help the good guys 05:45 Built to stop people and malware, but agents are neither 06:45 "The end justifies the means, in the mind of the model" 10:45 50% of enterprise apps agentic before 2027 14:20 When your honeypot becomes a false positive machine 15:25 Signatures are dead, and so is behavioral detection 18:55 Defending AI and defending from AI Max Pollard Cotool Neo Securityshow more

a16z

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34,855 просмотров • 24 дней назад •via X (Twitter)

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Amjad Masad and Marc Andreessen on AI agents, AGI, Creativity, and Reasoning Agents are getting better, fast. Replit Agent v1 could do only 2 minutes of coherent work. v2 could do 20 minutes. Today’s v3? 200 minutes. As agents get more and more reliable, we can make huge progress in coding without ever achieving “true” AGI. In this episode, Marc Andreessen and Erik Torenberg sit down with Replit ⠕ co-founder and CEO Amjad Masad to discuss the upward trajectory of agents, how RL unlocked reasoning for modern models, why verification loops changed everything, and whether LLMs are hitting diminishing returns. 00:00 Intro 03:00 The vision behind Replit 07:00 Building apps with AI agents 09:30 When the agent becomes the programmer 13:45 Reinforcement learning and problem solving 17:30 The verification loop and multi-agent systems 26:00 Why coding is advancing faster than other Fields 33:45 AGI debate 41:15 Functional AGI and automating labor 53:10 Creativity, reasoning, and finding truth in AI 57:30 The origins of Replit 01:03:00 Hacking his university and getting caught Amjad Masad Marc Andreessen 🇺🇸 Erik Torenberg

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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

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Matt Turck

33,694 просмотров • 4 месяцев назад

A lot of people are calling Hermes Agent the end of OpenClaw. BRUH! It's not... Nous Research trains actual models and they built an agent around that expertise. The local model routing is solid, but the part that matters for your business is that your conversations become fine-tuning data. You can train a model on how you actually work. 00:00 The Problem with Local AI Models 00:25 Introduction to Nous Research 01:04 Cross-Platform Agent Capabilities 01:44 Deep Local Model Integration 02:30 Routing Tasks to Different Models 03:06 Conversations as Training Data 03:50 Hermes Agent vs. OpenClaw 04:15 Future Plans and Series Overview

A lot of people are calling Hermes Agent the end of OpenClaw. BRUH! It's not... Nous Research trains actual models and they built an agent around that expertise. The local model routing is solid, but the part that matters for your business is that your conversations become fine-tuning data. You can train a model on how you actually work. 00:00 The Problem with Local AI Models 00:25 Introduction to Nous Research 01:04 Cross-Platform Agent Capabilities 01:44 Deep Local Model Integration 02:30 Routing Tasks to Different Models 03:06 Conversations as Training Data 03:50 Hermes Agent vs. OpenClaw 04:15 Future Plans and Series Overview

Ray Fernando

42,398 просмотров • 5 месяцев назад

"We are clearly entering a world where a product-minded engineer is now empowered to produce software without writing a line of code for it." In this conversation, Temporal CEO Samar Abbas joins a16z GPs Sarah Wang and Raghu Raghuram to cover: - Why agents are going from short-lived and interactive to long-running and async - Why the engineer of the future manages 15 parallel AI tasks at once - How OpenAI Codex runs millions of concurrent agent executions on Temporal - How real-time context engineering is exploding on Temporal - Why SaaS isn't dead, and value is migrating to APIs 00:00 Introduction 04:03 Temporal's origin story 11:14 Why agents raise the stakes 16:00 Specialized agents need durable RPC 25:20 Deep research agents 30:58 Execution histories as a superpower 39:04 Minimal viable long-running agent architecture 45:07 Context engineering at scale 52:40 Where value accrues: The "five-layer cake" and breakout AI applications Samar Abbas Sarah Wang Raghu Raghuram

"We are clearly entering a world where a product-minded engineer is now empowered to produce software without writing a line of code for it." In this conversation, Temporal CEO Samar Abbas joins a16z GPs Sarah Wang and Raghu Raghuram to cover: - Why agents are going from short-lived and interactive to long-running and async - Why the engineer of the future manages 15 parallel AI tasks at once - How OpenAI Codex runs millions of concurrent agent executions on Temporal - How real-time context engineering is exploding on Temporal - Why SaaS isn't dead, and value is migrating to APIs 00:00 Introduction 04:03 Temporal's origin story 11:14 Why agents raise the stakes 16:00 Specialized agents need durable RPC 25:20 Deep research agents 30:58 Execution histories as a superpower 39:04 Minimal viable long-running agent architecture 45:07 Context engineering at scale 52:40 Where value accrues: The "five-layer cake" and breakout AI applications Samar Abbas Sarah Wang Raghu Raghuram

a16z

46,280 просмотров • 6 месяцев назад

Jesse Genet on Agentic Parenting Jesse Genet joins a16z's Sarah Wang and Katherine Boyle to discuss her journey from founder to parent, how she's using agents in her household, and how AI could transform parenting for the better. 00:00 YC founder turned homeschool mom 03:00 Discovering Claude Code and agentic building 06:00 Building while homeschooling 4 kids under 5 11:00 How AI generates personalized lesson plans and logs progress 18:00 Jesse's 11-agents 27:05 Agent tech stack deep dive 33:56 How agents improve daily life 40:04 Letting kids interact with AI: values, risks, and the future of parenting Jesse Genet Katherine Boyle Sarah Wang

Jesse Genet on Agentic Parenting Jesse Genet joins a16z's Sarah Wang and Katherine Boyle to discuss her journey from founder to parent, how she's using agents in her household, and how AI could transform parenting for the better. 00:00 YC founder turned homeschool mom 03:00 Discovering Claude Code and agentic building 06:00 Building while homeschooling 4 kids under 5 11:00 How AI generates personalized lesson plans and logs progress 18:00 Jesse's 11-agents 27:05 Agent tech stack deep dive 33:56 How agents improve daily life 40:04 Letting kids interact with AI: values, risks, and the future of parenting Jesse Genet Katherine Boyle Sarah Wang

a16z

1,792,577 просмотров • 5 месяцев назад