Latent Space: The AI Engineer Podcast

swyx + Alessio

The podcast by and for AI Engineers! In 2024, over 2 million readers and listeners came to Latent Space to hear about news, papers and interviews in Software 3.0. We cover Foundation Models changing every domain in Code Generation, Multimodality, AI Agents, GPU Infra and more, directly from the founders, builders, and thinkers involved in pushing the cutting edge. Striving to give you both the definitive take on the Current Thing down to the first introduction to the tech you'll be using in the next 3 months! We break news and exclusive interviews from OpenAI, Anthropic, Gemini, Meta (Soumith Chintala), Sierra (Bret Taylor), tiny (George Hotz), Databricks/MosaicML (Jon Frankle), Modular (Chris Lattner), Answer.ai (Jeremy Howard), et al. Full show notes always on https://latent.space

  1. DevDay 2025: Apps SDK, Agent Kit, MCP, Codex and why Prompting is More Important than Ever

    قبل يوم واحد

    DevDay 2025: Apps SDK, Agent Kit, MCP, Codex and why Prompting is More Important than Ever

    At OpenAI DevDay, we sit down with Sherwin Wu and Christina Cai from the OpenAI Platform Team to discuss the launch of AgentKit - a comprehensive suite of tools for building, deploying, and optimizing AI agents. Christina walks us through the live demo she performed on stage, building a customer support agent in just 8 minutes using the visual Agent Builder, while Sherwin shares insights on how OpenAI is inverting the traditional website-chatbot paradigm by embedding apps directly within ChatGPT through the new Apps SDK. The conversation explores how OpenAI is tackling the challenges developers face when taking agents to production - from writing and optimizing prompts to building evaluation pipelines. They discuss the decision to adopt Anthropic's MCP protocol for tool connectivity, the importance of visual workflows for complex agent systems, and how features like human-in-the-loop approvals and automated prompt optimization are making agent development more accessible to a broader range of developers. Sherwin and Christina also reveal how OpenAI is dogfooding these tools internally, with their own customer support at openai.com already powered by AgentKit, and share candid insights about the evolution from plugins to GPTs to this new agent platform. They discuss the surprising persistence of prompting as a critical skill (contrary to predictions from two years ago), the challenges of serving custom fine-tuned models at scale, and why they believe visual agent builders are essential as workflows grow to span dozens of nodes. Guests: Sherwin Wu: Head of Engineering, OpenAI Platform https://www.linkedin.com/in/sherwinwu1/ https://x.com/sherwinwu?lang=en Christina Huang: Platform Experience, OpenAI https://x.com/christinaahuang https://www.linkedin.com/in/christinaahuang/ Thanks very much to Lindsay and Shaokyi for helping us set up this great deepdive into the new DevDay launches! Key Topics: • AgentKit launch: Agent SDK, Builder, Evals, and deployment tools • Apps SDK and the inversion of the app-chatbot paradigm • Adopting MCP protocol for universal tool connectivity • Visual agent building vs code-first approaches • Human-in-the-loop workflows and approval systems • Automated prompt optimization and "zero-gradient fine-tuning" • Service Health Dashboard and achieving five nines reliability • ChatKit as an embeddable, evergreen chat interface • The evolution from plugins to GPTs to agent platforms • Internal dogfooding with Codex and agent-powered support

  2. Taste is your Moat (Dylan Field of Figma)

    قبل ٦ أيام

    Taste is your Moat (Dylan Field of Figma)

    Dylan Field (CEO Figma) on how they are letting designers build with Figma Make, how Figma can be the context repository for aesthetic in the age of vibe coding, and why design is your only differentiator now. Full show notes: https://www.latent.space/p/figma 00:00 Figma’s Mission: Bridging Imagination and Reality 00:56 Becoming AI-Pilled 07:44 Figma Make 08:57 Language as the Interface for Design 13:37 Source of truth between design and code 18:15 Figma as a Context Repository 21:30 Understanding and Representing Design Diffs through AI 24:20 Figma’s Role in Shaping Visual Aesthetics 31:56 Fast Fashion in Software 36:04 Limitations of Prompt-Based Software Creation 39:43 Interfaces Beyond Chat 42:12 Lessons from the Thiel Fellowship 44:58 Using X for Product Feedback 48:10 Early-Stage Recruiting at Figma 53:11 Positioning Figma Make in the Prompt-to-App Landscape 55:19 Digital Scarcity & AI

  3. Amp: The Emperor Has No Clothes

    ٢٥ سبتمبر

    Amp: The Emperor Has No Clothes

    Quinn Slack (CEO) and Thorsten Ball (Amp Dictator) from SourceGraph join the show to talk about Amp Code, how they ship 15x/day with no code reviews, and why subagents and prompt optimizers aren’t a promising direction for coding agents. Amp Code: https://ampcode.com/ Latent Space: https://latent.space/ 00:00 Introduction 00:41 Transition from Cody to Amp 03:18 The Importance of Building the Best Coding Agent 06:43 Adapting to a Rapidly Evolving AI Tooling Landscape 09:36 Dogfooding at Sourcegraph 12:35 CLI vs. VS Code Extension 21:08 Positioning Amp in Coding Agent Market 24:10 The Diminishing Importance of Model Selectors 32:39 Tooling vs. Harness 37:19 Common Failure Modes of Coding Agents 47:33 Agent-Friendly Logging and Tooling 52:31 Are Subagents Real? 56:52 New Frameworks and Agent-Integrated Developer Tools 1:00:25 How Agents Are Encouraging Codebase and Workflow Changes 1:03:13 Evolving Outer Loop Tasks 1:07:09 Version Control and Merge Conflicts in an AI-First World 1:10:36 Rise of User-Generated Enterprise Software 1:14:39 Empowering Technical Leaders with AI 1:17:11 Evaluating Product Without Traditional Evals 1:20:58 Hiring

  4. Context Engineering for Agents - Lance Martin, LangChain

    ١١ سبتمبر

    Context Engineering for Agents - Lance Martin, LangChain

    Lance: https://www.linkedin.com/in/lance-martin-64a33b5/ How Context Fails: https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-how-to-fix-them.html How New Buzzwords Get Created: https://www.dbreunig.com/2025/07/24/why-the-term-context-engineering-matters.html Content Engineering: https://x.com/RLanceMartin/status/1948441848978309358 https://rlancemartin.github.io/2025/06/23/context_engineering/ https://docs.google.com/presentation/d/16aaXLu40GugY-kOpqDU4e-S0hD1FmHcNyF0rRRnb1OU/edit?usp=sharing Manus Post: https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus Cognition Post: https://cognition.ai/blog/dont-build-multi-agents Multi-Agent Researcher: https://www.anthropic.com/engineering/multi-agent-research-system Human-in-the-loop + Memory: https://github.com/langchain-ai/agents-from-scratch - Bitter Lesson in AI Engineering - Hyung Won Chung on the Bitter Lesson in AI Research: https://www.youtube.com/watch?v=orDKvo8h71o Bitter Lesson w/ Claude Code: https://www.youtube.com/watch?v=Lue8K2jqfKk&t=1s Learning the Bitter Lesson in AI Engineering: https://rlancemartin.github.io/2025/07/30/bitter_lesson/ Open Deep Research: https://github.com/langchain-ai/open_deep_research https://academy.langchain.com/courses/deep-research-with-langgraph Scaling and building things that "don't yet work": https://www.youtube.com/watch?v=p8Jx4qvDoSo - Frameworks - Roast framework at Shopify / standardization of orchestration tools: https://www.youtube.com/watch?v=0NHCyq8bBcM MCP adoption within Anthropic / standardization of protocols: https://www.youtube.com/watch?v=xlEQ6Y3WNNI How to think about frameworks: https://blog.langchain.com/how-to-think-about-agent-frameworks/ RAG benchmarking: https://rlancemartin.github.io/2025/04/03/vibe-code/ Simon's talk with memory-gone-wrong: https://simonwillison.net/2025/Jun/6/six-months-in-llms/

  5. A Technical History of Generative Media

    ٥ سبتمبر

    A Technical History of Generative Media

    Today we are joined by Gorkem and Batuhan from Fal.ai, the fastest growing generative media inference provider. They recently raised a $125M Series C and crossed $100M ARR. We covered how they pivoted from dbt pipelines to diffusion models inference, what were the models that really changed the trajectory of image generation, and the future of AI videos. Enjoy! 00:00 - Introductions 04:58 - History of Major AI Models and Their Impact on Fal.ai 07:06 - Pivoting to Generative Media and Strategic Business Decisions 10:46 - Technical discussion on CUDA optimization and kernel development 12:42 - Inference Engine Architecture and Kernel Reusability 14:59 - Performance Gains and Latency Trade-offs 15:50 - Discussion of model latency importance and performance optimization 17:56 - Importance of Latency and User Engagement 18:46 - Impact of Open Source Model Releases and Competitive Advantage 19:00 - Partnerships with closed source model developers 20:06 - Collaborations with Closed-Source Model Providers 21:28 - Serving Audio Models and Infrastructure Scalability 22:29 - Serverless GPU infrastructure and technical stack 23:52 - GPU Prioritization: H100s and Blackwell Optimization 25:00 - Discussion on ASICs vs. General Purpose GPUs 26:10 - Architectural Trends: MMDiTs and Model Innovation 27:35 - Rise and Decline of Distillation and Consistency Models 28:15 - Draft Mode and Streaming in Image Generation Workflows 29:46 - Generative Video Models and the Role of Latency 30:14 - Auto-Regressive Image Models and Industry Reactions 31:35 - Discussion of OpenAI's Sora and competition in video generation 34:44 - World Models and Creative Applications in Games and Movies 35:27 - Video Models’ Revenue Share and Open-Source Contributions 36:40 - Rise of Chinese Labs and Partnerships 38:03 - Top Trending Models on Hugging Face and ByteDance's Role 39:29 - Monetization Strategies for Open Models 40:48 - Usage Distribution and Model Turnover on FAL 42:11 - Revenue Share vs. Open Model Usage Optimization 42:47 - Moderation and NSFW Content on the Platform 44:03 - Advertising as a key use case for generative media 45:37 - Generative Video in Startup Marketing and Virality 46:56 - LoRA Usage and Fine-Tuning Popularity 47:17 - LoRA ecosystem and fine-tuning discussion 49:25 - Post-Training of Video Models and Future of Fine-Tuning 50:21 - ComfyUI Pipelines and Workflow Complexity 52:31 - Requests for startups and future opportunities in the space 53:33 - Data Collection and RedPajama-Style Initiatives for Media Models 53:46 - RL for Image and Video Models: Unknown Potential 55:11 - Requests for Models: Editing and Conversational Video Models 57:12 - VO3 Capabilities: Lip Sync, TTS, and Timing 58:23 - Bitter Lesson and the Future of Model Workflows 58:44 - FAL's hiring approach and team structure 59:29 - Team Structure and Scaling Applied ML and Performance Teams 1:01:41 - Developer Experience Tools and Low-Code/No-Code Integration 1:03:04 - Improving Hiring Process with Public Challenges and Benchmarks 1:04:02 - Closing Remarks and Culture at FAL

  6. Better Data is All You Need — Ari Morcos, Datology

    ٢٩ أغسطس

    Better Data is All You Need — Ari Morcos, Datology

    Our chat with Ari shows that data curation is the most impactful and underinvested area in AI. He argues that the prevailing focus on model architecture and compute scaling overlooks the "bitter lesson" that "models are what they eat." Effective data curation—a sophisticated process involving filtering, rebalancing, sequencing (curriculum), and synthetic data generation—allows for training models that are simultaneously faster, better, and smaller. Morcos recounts his personal journey from focusing on model-centric inductive biases to realizing that data quality is the primary lever for breaking the diminishing returns of naive scaling laws. Datology's mission is to automate this complex curation process, making state-of-the-art data accessible to any organization and enabling a new paradigm of AI development where data efficiency, not just raw scale, drives progress. Timestamps 00:00 Introduction 00:46 What is Datology? The mission to train models faster, better, and smaller through data curation. 01:59 Ari's background: From neuroscience to realizing the "Bitter Lesson" of AI. 05:30 Key Insight: Inductive biases from architecture become less important and even harmful as data scale increases. 08:08 Thesis: Data is the most underinvested area of AI research relative to its impact. 10:15 Why data work is culturally undervalued in research and industry. 12:19 How self-supervised learning changed everything, moving from a data-scarce to a data-abundant regime. 17:05 Why automated curation is superior to human-in-the-loop, citing the DCLM study. 19:22 The "Elephants vs. Dogs" analogy for managing data redundancy and complexity. 22:46 A brief history and commentary on key datasets (Common Crawl, GitHub, Books3). 26:24 Breaking naive scaling laws by improving data quality to maintain high marginal information gain. 29:07 Datology's demonstrated impact: Achieving baseline performance 12x faster. 34:19 The business of data: Datology's moat and its relationship with open-source datasets. 39:12 Synthetic Data Explain ed: The difference between risky "net-new" creation and powerful "rephrasing." 49:02 The Resurgence of Curriculum Learning: Why ordering data matters in the underfitting regime. 52:55 The Future of Training: Optimizing pre-training data to make post-training more effective. 54:49 Who is training their own models and why (Sovereign AI, large enterprises). 57:24 "Train Smaller": Why inference cost makes smaller, specialized models the ultimate goal for enterprises. 01:00:19 The problem with model pruning and why data-side solutions are complementary. 01:03:03 On finding the smallest possible model for a given capability. 01:06:49 Key learnings from the RC foundation model collaboration, proving that data curation "stacks." 01:09:46 Lightning Round: What data everyone wants & who should work at Datology. 01:14:24 Commentary on Meta's superintelligence efforts and Yann LeCun's role.

  7. Long Live Context Engineering - with Jeff Huber of Chroma

    ١٩ أغسطس

    Long Live Context Engineering - with Jeff Huber of Chroma

    Jeff Huber of Chroma joins us to talk about what actually matters in vector databases in 2025, why “modern search for AI” is different, and how to ship systems that don’t rot as context grows. Full show notes: https://www.latent.space/p/chroma 00:00 Introductions 00:48 Why Build Chroma 02:55 Information Retrieval vs. Search 04:29 Staying Focused in a Competitive AI Market 08:08 Building Chroma Cloud 12:15 Context Engineering and the Problems with RAG 16:11 Context Rot 21:49 Prioritizing Context Quality 27:02 Code Indexing and Retrieval Strategies 32:04 Chunk Rewriting and Query Optimization for Code 34:07 Transformer Architecture Evolution and Retrieval Systems 38:06 Memory as a Benefit of Context Engineering 40:13 Structuring AI Memory and Offline Compaction 45:46 Lessons from Previous Startups and Building with Purpose 47:32 Religion and Values in Silicon Valley 50:18 Company Culture, Design, and Brand Consistency 52:36 Hiring at Chroma: Designers, Researchers, and Engineers

  8. Greg Brockman on OpenAI's Road to AGI

    ١٥ أغسطس

    Greg Brockman on OpenAI's Road to AGI

    Greg Brockman, co-founder and president of OpenAI, joins us to talk about GPT-5 and GPT-OSS, the future of software engineering, why reinforcement learning is still scaling, and how OpenAI is planning to get to AGI. 00:00 Introductions 01:04 The Evolution of Reasoning at OpenAI 04:01 Online vs Offline Learning in Language Models 06:44 Sample Efficiency and Human Curation in Reinforcement Learning 08:16 Scaling Compute and Supercritical Learning 13:21 Wall clock time limitations in RL and real-world interactions 16:34 Experience with ARC Institute and DNA neural networks 19:33 Defining the GPT-5 Era 22:46 Evaluating Model Intelligence and Task Difficulty 25:06 Practical Advice for Developers Using GPT-5 31:48 Model Specs 37:21 Challenges in RL Preferences (e.g., try/catch) 39:13 Model Routing and Hybrid Architectures in GPT-5 43:58 GPT-5 pricing and compute efficiency improvements 46:04 Self-Improving Coding Agents and Tool Usage 49:11 On-Device Models and Local vs Remote Agent Systems 51:34 Engineering at OpenAI and Leveraging LLMs 54:16 Structuring Codebases and Teams for AI Optimization 55:27 The Value of Engineers in the Age of AGI 58:42 Current state of AI research and lab diversity 01:01:11 OpenAI’s Prioritization and Focus Areas 01:03:05 Advice for Founders: It's Not Too Late 01:04:20 Future outlook and closing thoughts 01:04:33 Time Capsule to 2045: Future of Compute and Abundance 01:07:07 Time Capsule to 2005: More Problems Will Emerge

مشاهدة الكل (١٥٢)

حول

The podcast by and for AI Engineers! In 2024, over 2 million readers and listeners came to Latent Space to hear about news, papers and interviews in Software 3.0. We cover Foundation Models changing every domain in Code Generation, Multimodality, AI Agents, GPU Infra and more, directly from the founders, builders, and thinkers involved in pushing the cutting edge. Striving to give you both the definitive take on the Current Thing down to the first introduction to the tech you'll be using in the next 3 months! We break news and exclusive interviews from OpenAI, Anthropic, Gemini, Meta (Soumith Chintala), Sierra (Bret Taylor), tiny (George Hotz), Databricks/MosaicML (Jon Frankle), Modular (Chris Lattner), Answer.ai (Jeremy Howard), et al. Full show notes always on https://latent.space

المعلومات

  • صناع العمل
    swyx + Alessio
  • سنوات النشاط
    ٢٠٢٣ - ٢٠٢٥
  • الحلقات
    ١٥٢
  • التقييم
    ملائم
  • حقوق النشر
    © Latent.Space
  • موقع البرنامج على الويب
    Latent Space: The AI Engineer Podcast

قد يعجبك أيضًا