Latent Space: The AI Engineer Podcast

• 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/

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• 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

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• 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.

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• 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

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• 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

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