Latent Space: The AI Engineer Podcast

• Why Every Agent needs Open Source Cloud Sandboxes

  Vasek Mlejnsky from E2B joins us today to talk about sandboxes for AI agents. In the last 2 years, E2B has grown from a handful of developers building on it to being used by ~50% of the Fortune 500 and generating millions of sandboxes each week for their customers. As the “death of chat completions” approaches, LLMs workflows and agents are relying more and more on tool usage and multi-modality. The most common use cases for their sandboxes: - Run data analysis and charting (like Perplexity) - Execute arbitrary code generated by the model (like Manus does) - Running evals on code generation (see LMArena Web) - Doing reinforcement learning for code capabilities (like HuggingFace) Timestamps: 00:00:00 Introductions 00:00:37 Origin of DevBook -> E2B 00:02:35 Early Experiments with GPT-3.5 and Building AI Agents 00:05:19 Building an Agent Cloud 00:07:27 Challenges of Building with Early LLMs 00:10:35 E2B Use Cases 00:13:52 E2B Growth vs Models Capabilities 00:15:03 The LLM Operating System (LLMOS) Landscape 00:20:12 Breakdown of JavaScript vs Python Usage on E2B 00:21:50 AI VMs vs Traditional Cloud 00:26:28 Technical Specifications of E2B Sandboxes 00:29:43 Usage-based billing infrastructure 00:34:08 Pricing AI on Value Delivered vs Token Usage 00:36:24 Forking, Checkpoints, and Parallel Execution in Sandboxes 00:39:18 Future Plans for Toolkit and Higher-Level Agent Frameworks 00:42:35 Limitations of Chat-Based Interfaces and the Future of Agents 00:44:00 MCPs and Remote Agent Capabilities 00:49:22 LLMs.txt, scrapers, and bad AI bots 00:53:00 Manus and Computer Use on E2B 00:55:03 E2B for RL with Hugging Face 00:56:58 E2B for Agent Evaluation on LMArena 00:58:12 Long-Term Vision: E2B as Full Lifecycle Infrastructure for LLMs 01:00:45 Future Plans for Hosting and Deployment of LLM-Generated Apps 01:01:15 Why E2B Moved to San Francisco 01:05:49 Open Roles and Hiring Plans at E2B

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  1:06:38
• ⚡️GPT 4.1: The New OpenAI Workhorse

  We’ll keep this brief because we’re on a tight turnaround: GPT 4.1, previously known as the Quasar and Optimus models, is now live as the natural update for 4o/4o-mini (and the research preview of GPT 4.5). Though it is a general purpose model family, the headline features are: Coding abilities (o1-level SWEBench and SWELancer, but ok Aider) Instruction Following (with a very notable prompting guide) Long Context up to 1m tokens (with new MRCR and Graphwalk benchmarks) Vision (simply o1 level) Cheaper Pricing (cheaper than 4o, greatly improved prompt caching savings) We caught up with returning guest Michelle Pokrass and Josh McGrath to get more detail on each! Chapters 00:00:00 Introduction and Guest Welcome 00:00:57 GPC 4.1 Launch Overview 00:01:54 Developer Feedback and Model Names 00:02:53 Model Naming and Starry Themes 00:03:49 Confusion Over GPC 4.1 vs 4.5 00:04:47 Distillation and Model Improvements 00:05:45 Omnimodel Architecture and Future Plans 00:06:43 Core Capabilities of GPC 4.1 00:07:40 Training Techniques and Long Context 00:08:37 Challenges in Long Context Reasoning 00:09:34 Context Utilization in Models 00:10:31 Graph Walks and Model Evaluation 00:11:31 Real Life Applications of Graph Tasks 00:12:30 Multi-Hop Reasoning Benchmarks 00:13:30 Agentic Workflows and Backtracking 00:14:28 Graph Traversals for Agent Planning 00:15:24 Context Usage in API and Memory Systems 00:16:21 Model Performance in Long Context Tasks 00:17:17 Instruction Following and Real World Data 00:18:12 Challenges in Grading Instructions 00:19:09 Instruction Following Techniques 00:20:09 Prompting Techniques and Model Responses 00:21:05 Agentic Workflows and Model Persistence 00:22:01 Balancing Persistence and User Control 00:22:56 Evaluations on Model Edits and Persistence 00:23:55 XML vs JSON in Prompting 00:24:50 Instruction Placement in Context 00:25:49 Optimizing for Prompt Caching 00:26:49 Chain of Thought and Reasoning Models 00:27:46 Choosing the Right Model for Your Task 00:28:46 Coding Capabilities of GPC 4.1 00:29:41 Model Performance in Coding Tasks 00:30:39 Understanding Coding Model Differences 00:31:36 Using Smaller Models for Coding 00:32:33 Future of Coding in OpenAI 00:33:28 Internal Use and Success Stories 00:34:26 Vision and Multi-Modal Capabilities 00:35:25 Screen vs Embodied Vision 00:36:22 Vision Benchmarks and Model Improvements 00:37:19 Model Deprecation and GPU Usage 00:38:13 Fine-Tuning and Preference Steering 00:39:12 Upcoming Reasoning Models 00:40:10 Creative Writing and Model Humor 00:41:07 Feedback and Developer Community 00:42:03 Pricing and Blended Model Costs 00:44:02 Conclusion and Wrap-Up

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  41:52
• SF Compute: Commoditizing Compute

  Evan Conrad, co-founder of SF Compute, joined us to talk about how they started as an AI lab that avoided bankruptcy by selling GPU clusters, why CoreWeave financials look like a real estate business, and how GPUs are turning into a commodities market. Chapters: 00:00:05 - Introductions 00:00:12 - Introduction of guest Evan Conrad from SF Compute 00:00:12 - CoreWeave Business Model Discussion 00:05:37 - CoreWeave as a Real Estate Business 00:08:59 - Interest Rate Risk and GPU Market Strategy Framework 00:16:33 - Why Together and DigitalOcean will lose money on their clusters 00:20:37 - SF Compute's AI Lab Origins 00:25:49 - Utilization Rates and Benefits of SF Compute Market Model 00:30:00 - H100 GPU Glut, Supply Chain Issues, and Future Demand Forecast 00:34:00 - P2P GPU networks 00:36:50 - Customer stories 00:38:23 - VC-Provided GPU Clusters and Credit Risk Arbitrage 00:41:58 - Market Pricing Dynamics and Preemptible GPU Pricing Model 00:48:00 - Future Plans for Financialization? 00:52:59 - Cluster auditing and quality control 00:58:00 - Futures Contracts for GPUs 01:01:20 - Branding and Aesthetic Choices Behind SF Compute 01:06:30 - Lessons from Previous Startups 01:09:07 - Hiring at SF Compute Chapters 00:00:00 Introduction and Background 00:00:58 Analysis of GPU Business Models 00:01:53 Challenges with GPU Pricing 00:02:48 Revenue and Scaling with GPUs 00:03:46 Customer Sensitivity to GPU Pricing 00:04:44 Core Weave's Business Strategy 00:05:41 Core Weave's Market Perception 00:06:40 Hyperscalers and GPU Market Dynamics 00:07:37 Financial Strategies for GPU Sales 00:08:35 Interest Rates and GPU Market Risks 00:09:30 Optimal GPU Contract Strategies 00:10:27 Risks in GPU Market Contracts 00:11:25 Price Sensitivity and Market Competition 00:12:21 Market Dynamics and GPU Contracts 00:13:18 Hyperscalers and GPU Market Strategies 00:14:15 Nvidia and Market Competition 00:15:12 Microsoft's Role in GPU Market 00:16:10 Challenges in GPU Market Dynamics 00:17:07 Economic Realities of the GPU Market 00:18:03 Real Estate Model for GPU Clouds 00:18:59 Price Sensitivity and Chip Design 00:19:55 SF Compute's Beginnings and Challenges 00:20:54 Navigating the GPU Market 00:21:54 Pivoting to a GPU Cloud Provider 00:22:53 Building a GPU Market 00:23:52 SF Compute as a GPU Marketplace 00:24:49 Market Liquidity and GPU Pricing 00:25:47 Utilization Rates in GPU Markets 00:26:44 Brokerage and Market Flexibility 00:27:42 H100 Glut and Market Cycles 00:28:40 Supply Chain Challenges and GPU Glut 00:29:35 Future Predictions for the GPU Market 00:30:33 Speculations on Test Time Inference 00:31:29 Market Demand and Test Time Inference 00:32:26 Open Source vs. Closed AI Demand 00:33:24 Future of Inference Demand 00:34:24 Peer-to-Peer GPU Markets 00:35:17 Decentralized GPU Market Skepticism 00:36:15 Redesigning Architectures for New Markets 00:37:14 Supporting Grad Students and Startups 00:38:11 Successful Startups Using SF Compute 00:39:11 VCs and GPU Infrastructure 00:40:09 VCs as GPU Credit Transformators 00:41:06 Market Timing and GPU Infrastructure 00:42:02 Understanding GPU Pricing Dynamics 00:43:01 Market Pricing and Preemptible Compute 00:43:55 Price Volatility and Market Optimization 00:44:52 Customizing Compute Contracts 00:45:50 Creating Flexible Compute Guarantees 00:46:45 Financialization of GPU Markets 00:47:44 Building a Spot Market for GPUs 00:48:40 Auditing and Standardizing Clusters 00:49:40 Ensuring Cluster Reliability 00:50:36 Active Monitoring and Refunds 00:51:33 Automating Customer Refunds 00:52:33 Challenges in Cluster Maintenance 00:53:29 Remote Cluster Management 00:54:29 Standardizing Compute Contracts 00:55:28 Unified Infrastructure for Clusters 00:56:24 Creating a Commodity Market for GPUs 00:57:22 Futures Market and Risk Management 00:58:18 Reducing Risk with GPU Futures 00:59:14 Stabilizing the GPU Market 01:00:10 SF Compute's Anti-Hype Approach 01:01:07 Calm Branding and Expectations 01:02:07 Promoting San Francisco's Beauty 01:03:03 Design Philosophy at SF Compute 01:04:02 Artistic Influence on Branding 01:05:00 Past Projects and Burnout 01:05:59 Challenges in Building an Email Client 01:06:57 Persistence and Iteration in Startups 01:07:57 Email Market Challenges 01:08:53 SF Compute Job Opportunities 01:09:53 Hiring for Systems Engineering 01:10:50 Financial Systems Engineering Role 01:11:50 Conclusion and Farewell

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  1:12:01
• The Creators of Model Context Protocol

  Today’s guests, David Soria Parra and Justin Spahr-Summers, are the creators of Anthropic’s Model Context Protocol (MCP). When we first wrote Why MCP Won, we had no idea how quickly it was about to win. In the past 4 weeks, OpenAI and now Google have now announced the MCP support, effectively confirming our prediction that MCP was the presumptive winner of the agent standard wars. MCP has now overtaken OpenAPI, the incumbent option and most direct alternative, in GitHub stars (3 months ahead of conservative trendline): For protocol and history nerds, we also asked David and Justin to tell the origin story of MCP, which we leave to the reader to enjoy (you can also skim the transcripts, or, the changelogs of a certain favored IDE). It’s incredible the impact that individual engineers solving their own problems can have on an entire industry. Timestamps 00:00 Introduction and Guest Welcome 00:37 What is MCP? 02:00 The Origin Story of MCP 05:18 Development Challenges and Solutions 08:06 Technical Details and Inspirations 29:45 MCP vs Open API 32:48 Building MCP Servers 40:39 Exploring Model Independence in LLMs 41:36 Building Richer Systems with MCP 43:13 Understanding Agents in MCP 45:45 Nesting and Tool Confusion in MCP 49:11 Client Control and Tool Invocation 52:08 Authorization and Trust in MCP Servers 01:01:34 Future Roadmap and Stateless Servers 01:10:07 Open Source Governance and Community Involvement 01:18:12 Wishlist and Closing Remarks

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  1:19:56
• Unsupervised Learning x Latent Space Crossover Special

  Unsupervised Learning is a podcast that interviews the sharpest minds in AI about what’s real today, what will be real in the future and what it means for businesses and the world - helping builders, researchers and founders deconstruct and understand the biggest breakthroughs. Top guests: Noam Shazeer, Bob McGrew, Noam Brown, Dylan Patel, Percy Liang, David Luan https://www.latent.space/p/unsupervised-learning Timestamps 00:00 Introduction and Excitement for Collaboration 00:27 Reflecting on Surprises in AI Over the Past Year 01:44 Open Source Models and Their Adoption 06:01 The Rise of GPT Wrappers 06:55 AI Builders and Low-Code Platforms 09:35 Overhyped and Underhyped AI Trends 22:17 Product Market Fit in AI 28:23 Google's Current Momentum 28:33 Customer Support and AI 29:54 AI's Impact on Cost and Growth 31:05 Voice AI and Scheduling 32:59 Emerging AI Applications 34:12 Education and AI 36:34 Defensibility in AI Applications 40:10 Infrastructure and AI 47:08 Challenges and Future of AI 52:15 Quick Fire Round and Closing Remarks Chapters 00:00:00 Introduction and Collab Excitement 00:00:58 Open Source and Model Adoption 00:01:58 Enterprise Use of Open Source Models 00:02:57 The Competitive Edge of Closed Source Models 00:03:56 DeepSea and Open Source Model Releases 00:04:54 Market Narrative and DeepSea Impact 00:05:53 AI Engineering and GPT Wrappers 00:06:53 AI Builders and Low-Code Platforms 00:07:50 Innovating Beyond Existing Paradigms 00:08:50 Apple and AI Product Development 00:09:48 Overhyped and Underhyped AI Trends 00:10:46 Frameworks and Protocols in AI Development 00:11:45 Emerging Opportunities in AI 00:12:44 Stateful AI and Memory Innovation 00:13:44 Challenges with Memory in AI Agents 00:14:44 The Future of Model Training Companies 00:15:44 Specialized Use Cases for AI Models 00:16:44 Vertical Models vs General Purpose Models 00:17:42 General Purpose vs Domain-Specific Models 00:18:42 Reflections on Model Companies 00:19:39 Model Companies Entering Product Space 00:20:38 Competition in AI Model and Product Sectors 00:21:35 Coding Agents and Market Dynamics 00:22:35 Defensibility in AI Applications 00:23:35 Investing in Underappreciated AI Ventures 00:24:32 Analyzing Market Fit in AI 00:25:31 AI Applications with Product Market Fit 00:26:31 OpenAI's Impact on the Market 00:27:31 Google and OpenAI Competition 00:28:31 Exploring Google's Advancements 00:29:29 Customer Support and AI Applications 00:30:27 The Future of AI in Customer Support 00:31:26 Cost-Cutting vs Growth in AI 00:32:23 Voice AI and Real-World Applications 00:33:23 Scaling AI Applications for Demand 00:34:22 Summarization and Conversational AI 00:35:20 Future AI Use Cases and Market Fit 00:36:20 AI Education and Model Capabilities 00:37:17 Reforming Education with AI 00:38:15 Defensibility in AI Apps 00:39:13 Network Effects and AI 00:40:12 AI Brand and Market Positioning 00:41:11 AI Application Defensibility 00:42:09 LLM OS and AI Infrastructure 00:43:06 Security and AI Application 00:44:06 OpenAI's Role in AI Infrastructure 00:45:02 The Balance of AI Applications and Infrastructure 00:46:02 Capital Efficiency in AI Infrastructure 00:47:01 Challenges in AI DevOps and Infrastructure 00:47:59 AI SRE and Monitoring 00:48:59 Scaling AI and Hardware Challenges 00:49:58 Reliability and Compute in AI 00:50:57 Nvidia's Dominance and AI Hardware 00:51:57 Emerging Competition in AI Silicon 00:52:54 Agent Authentication Challenges 00:53:53 Dream Podcast Guests 00:54:51 Favorite News Sources and Startups 00:55:50 The Value of In-Person Conversations 00:56:50 Private vs Public AI Discourse 00:57:48 Latent Space and Podcasting 00:58:46 Conclusion and Final Thoughts

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