Latent Space: The AI Engineer Podcast

• The Utility of Interpretability — Emmanuel Amiesen

  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

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• [AIEWF Preview] Containing Agent Chaos — Solomon Hykes

  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

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• [AIEWF Preview] CloudChef: Your Robot Chef - Michellin-Star food at $12/hr (w/ Kitchen tour!)

  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

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• The AI Coding Factory

  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

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• [AIEWF Preview] Multi-Turn RL for Multi-Hour Agents — with Will Brown, Prime Intellect

  In an otherwise heavy week packed with Microsoft Build, Google I/O, and OpenAI io, the worst kept secret in biglab land was the launch of Claude 4, particularly the triumphant return of Opus, which many had been clamoring for. We will leave the specific Claude 4 recap to AINews, however we think that both Gemini’s progress on Deep Think this week and Claude 4 represent the next frontier of progress on inference time compute/reasoning (at last until GPT5 ships this summer). Will Brown’s talk at AIE NYC and open source work on verifiers have made him one of the most prominent voices able to publicly discuss (aka without the vaguepoasting LoRA they put on you when you join a biglab) the current state of the art in reasoning models and where current SOTA research directions lead. We discussed his latest paper on Reinforcing Multi-Turn Reasoning in LLM Agents via Turn-Level Credit Assignment and he has previewed his AIEWF talk on Agentic RL for those with the temerity to power thru bad meetup audio. Chapters 00:00 Introduction and Episode Overview 02:01 Discussion on Cloud 4 and its Features 04:31 Reasoning and Tool Use in AI Models 07:01 Extended Thinking in Claude and Model Differences 09:31 Speculation on Claude's Extended Thinking 11:01 Challenges and Controversies in AI Model Training 13:31 Technical Highlights and Code Trustworthiness 16:01 Token Costs and Incentives in AI Models 18:31 Thinking Budgets and AI Effort 21:01 Safety and Ethics in AI Model Development 23:31 Anthropic's Approach to AI Safety 26:01 LLM Arena and Evaluation Challenges 28:31 Developing Taste and Direction in AI Research 31:01 Recent Research and Multi-Turn RL 33:31 Tools and Incentives in AI Model Development 36:01 Challenges in Evaluating AI Model Outputs 38:31 Model-Based Rewards and Future Directions 41:01 Wrap-up and Future Plans

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