Latent Space: The AI Engineer Podcast

Chris Lattner of Modular (https://modular.com) joined us (again!) to talk about how they are breaking the CUDA monopoly, what it took to match NVIDIA performance with AMD, and how they are building a company of "elite nerds". X: https://x.com/latentspacepod Substack: https://latent.space 00:00:00 Introductions 00:00:12 Overview of Modular and the Shape of Compute 00:02:27 Modular’s R&D Phase 00:06:55 From CPU Optimization to GPU Support 00:11:14 MAX: Modular’s Inference Framework 00:12:52 Mojo Programming Language 00:18:25 MAX Architecture: From Mojo to Cluster-Scale Inference 00:29:16 Open Source Contributions and Community Involvement 00:32:25 Modular's Differentiation from VLLM and SGLang 00:41:37 Modular’s Business Model and Monetization Strategy 00:53:17 DeepSeek’s Impact and Low-Level GPU Programming 01:00:00 Inference Time Compute and Reasoning Models 01:02:31 Personal Reflections on Leading Modular 01:08:27 Daily Routine and Time Management as a Founder 01:13:24 Using AI Coding Tools and Staying Current with Research 01:14:47 Personal Projects and Work-Life Balance 01:17:05 Hiring, Open Source, and Community Engagement

Emmanuel Amiesen is lead author of “Circuit Tracing: Revealing Computational Graphs in Language Models” (https://transformer-circuits.pub/2025/attribution-graphs/methods.html ), which is part of a duo of MechInterp papers that Anthropic published in March (alongside https://transformer-circuits.pub/2025/attribution-graphs/biology.html ). We recorded the initial conversation a month ago, but then held off publishing until the open source tooling for the graph generation discussed in this work was released last week: https://www.anthropic.com/research/open-source-circuit-tracing This is a 2 part episode - an intro covering the open source release, then a deeper dive into the paper — with guest host Vibhu Sapra (https://x.com/vibhuuuus ) and Mochi the MechInterp Pomsky (https://x.com/mochipomsky ). Thanks to Vibhu for making this episode happen! While the original blogpost contained some fantastic guided visualizations (which we discuss at the end of this pod!), with the notebook and Neuronpedia visualization (https://www.neuronpedia.org/gemma-2-2b/graph ) released this week, you can now explore on your own with Neuronpedia, as we show you in the video version of this pod. CHAPTERS * 00:00 Intro & Guest Introductions * 01:00 Anthropic's Circuit Tracing Release * 06:11 Exploring Circuit Tracing Tools & Demos * 13:01 Model Behaviors and User Experiments * 17:02 Behind the Research: Team and Community * 24:19 Main Episode Start: Mech Interp Backgrounds * 25:56 Getting Into Mech Interp Research * 31:52 History and Foundations of Mech Interp * 37:05 Core Concepts: Superposition & Features * 39:54 Applications & Interventions in Models * 45:59 Challenges & Open Questions in Interpretability * 57:15 Understanding Model Mechanisms: Circuits & Reasoning * 01:04:24 Model Planning, Reasoning, and Attribution Graphs * 01:30:52 Faithfulness, Deception, and Parallel Circuits * 01:40:16 Publishing Risks, Open Research, and Visualization * 01:49:33 Barriers, Vision, and Call to Action

Solomon most famously created Docker and now runs Dagger… which has something special to share with you on Thursday. Catch Dagger at: - Tuesday: Dagger’s workshop https://www.ai.engineer/schedule#ship-agents-that-ship-a-hands-on-workshop-for-swe-agent-builders - Wednesday: Dagger’s talk: https://www.ai.engineer/schedule#how-to-trust-an-agent-with-software-delivery - Thursday: Solomon’s Keynote https://www.ai.engineer/schedule#containing-agent-chaos CHAPTERS * 00:00 Introduction & Guest Background * 00:29 What is Dagger? Post-Development Automation * 01:08 Dagger’s Community & Platform Engineers * 02:32 AI Agents and Developer Workflows * 03:40 Environment Isolation & The Power of Containers * 06:28 The Need for Standards in Agent Environments * 07:25 Design Constraints & Challenges for Dev Environments * 11:26 Limitations of Current Tools & Agent-Native UX * 14:11 Modularity, Customization, and the Lego Analogy * 16:24 Convergence of CICD and Agentic Systems * 17:41 Ephemeral Apps, Resource Constraints, and Local Execution * 21:01 Adoption, Ecosystem, and the Role of Open Source * 23:30 Dagger’s Modular Approach & Integration Philosophy * 25:38 Looking Ahead: Workshops, Keynotes, and the Future of Agentic Infrastructure

One of the new tracks at next week’s AI Engineer conference in SF is a new focus on LLMs + Robotics, ft. household names like Waymo and Physical Intelligence. However there are many other companies applying LLMs and VLMs in the real world! CloudChef, the first industrial-scale kitchen robotics company with one-shot demonstration learning and an incredibly simple business model, will be serving tasty treats all day with Zippy (https://www.cloudchef.co/zippy ) their AI Chef platform. This is a lightning pod with CEO Nikhil Abraham to preview what Zippy is capable of! https://www.cloudchef.co/platform See a real chef comparison: https://www.youtube.com/watch?v=INDhZ7LwSeo&t=64s See it in the AI Engineer Expo at SF next week: https://ai.engineer CHAPTERS * 00:00 Welcome and Introductions * 00:58 What is Cloud Chef? * 01:36 How the Robots Work: Culinary Intelligence * 05:57 Commercial Applications and Early Success * 07:02 The Software-First Approach * 10:09 Business Model and Pricing * 13:10 Demonstration Learning: Training the Robots * 16:03 Call to Action and Engineering Opportunities * 18:45 Final Thoughts and Technical Details

We are joined by Eno Reyes and Matan Grinberg, the co-founders of Factory.ai. They are building droids for autonomous software engineering, handling everything from code generation to incident response for production outages. After raising a $15M Series A from Sequoia, they just released their product in GA! https://factory.ai/ https://x.com/latentspacepod CHAPTERS * 00:00:00 Introductions * 00:00:35 Meeting at Langchain Hackathon * 00:04:02 Building Factory despite early model limitations * 00:06:56 What is Factory AI? * 00:08:55 Delegation vs Collaboration in AI Development Tools * 00:10:06 Naming Origins of 'Factory' and 'Droids' * 00:12:17 Defining Droids: Agent vs Workflow * 00:14:34 Live Demo * 00:17:37 Enterprise Context and Tool Integration in Droids * 00:20:26 Prompting, Clarification, and Agent Communication * 00:22:28 Project Understanding and Proactive Context Gathering * 00:24:10 Why SWE-Bench Is Dead * 00:28:47 Model Fine-tuning and Generalization Challenges * 00:31:07 Why Factory is Browser-Based, Not IDE-Based * 00:33:51 Test-Driven Development and Agent Verification * 00:36:17 Retrieval vs Large Context Windows for Cost Efficiency * 00:38:02 Enterprise Metrics: Code Churn and ROI * 00:40:48 Executing Large Refactors and Migrations with Droids * 00:45:25 Model Speed, Parallelism, and Delegation Bottlenecks * 00:50:11 Observability Challenges and Semantic Telemetry * 00:53:44 Hiring * 00:55:19 Factory's design and branding approach * 00:58:34 Closing Thoughts and Future of AI-Native Development