Latent Space
Deep technical AI engineering content. The go-to podcast for AI builders.
187 episodes curated
Episodes
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://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-buil
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 brea
The RLVR Revolution — with Nathan Lambert (AI2, Interconnects.ai)
We first had Nathan on to give us his RLHF deep dive when he was joining AI2 , and now he’s back to help us catch up on the evolution to RLVR (Reinforcement Learning with Verifiable Rewards), first proposed in his Tulu 3 paper. While RLHF remains foundational, RLVR has emerged as a powerful approach for training models on tasks with clear success criteria and using verifiable, objective functions as reward signals—particularly useful in domains like math, code correctness, and instruction-following. Instead of relying solely on subjective human feedback, RLVR leverages deterministic signals to
AI is Eating Search
ChatGPT handles 2.5B prompts/day and is on track to match Google’s daily searches by end of 2026. AI agents don’t browse like us—they crave queryable, chunkable data for tools like ChatGPT & Perplexity. A new industry is being born, some are calling it AI SEO, others GEO, but what is clear is that it drives amazing results. Businesses are seeing 2-4x higher conversion from visitors coming from AI compared to traditional search. Robert McCloy is the co-founder of Scrunch AI (https://scrunchai.com/), a fast growing company that helps brands and businesses re-write their content on the fly based
Cline: the open source coding agent that doesn't cut costs
Saoud Rizwan and Pash from Cline joined us to talk about why fast apply models got bitter lesson’d, how they pioneered the plan + act paradigm for coding, and why non-technical people use IDEs to do marketing and generate slides. Full writeup: https://www.latent.space/p/cline X: https://x.com/latentspacepod Full Video Episode Timestamps 00:00 - Introductions 01:35 - Plan and Act Paradigm 05:37 - Model Evaluation and Early Development of Cline 08:14 - Use Cases of Cline Beyond Coding 09:09 - Why Cline is a VS Code Extension and Not a Fork 12:07 - Economic Value of Programming Agents 16:07 - Ear
Personalized AI Language Education — with Andrew Hsu, Speak
Speak (https://speak.com) may not be very well known to native English speakers, but they have come from a slow start in 2016 to emerge as one of the favorite partners of OpenAI , with their Startup Fund leading and joining their Series B and C as one of the new AI-native unicorns, noting that “Speak has the potential to revolutionize not just language learning, but education broadly”. Today we speak with Speak’s CTO, Andrew Hsu , on the journey of building the “3rd generation” of language learning software (with Rosetta Stone being Gen 1, and Duolingo being Gen 2). Speak’s premise is that spe
AI Video Is Eating The World — Olivia and Justine Moore, a16z
When the first video diffusion models started emerging, they were little more than just “moving pictures” - still frames extended a few seconds in either direction in time. There was a ton of excitement about OpenAI’s Sora on release through 2024, but so far only Sora-lite has been widely released. Meanwhile, other good videogen models like Genmo Mochi, Pika, MiniMax T2V, Tencent Hunyuan Video, and Kuaishou’s Kling have emerged, but the reigning king this year seems to be Google’s Veo 3 , which for the first time has added native audio generation into their model capabilities, eliminating the
Information Theory for Language Models: Jack Morris
Our last AI PhD grad student feature was Shunyu Yao , who happened to focus on Language Agents for his thesis and immediately went to work on them for OpenAI . Our pick this year is Jack Morris , who bucks the “hot” trends by -not- working on agents, benchmarks, or VS Code forks, but is rather known for his work on the information theoretic understanding of LLMs, starting from embedding models and latent space representations (always close to our heart). Jack is an unusual combination of doing underrated research but somehow still being to explain them well to a mass audience, so we felt this
Scaling Test Time Compute to Multi-Agent Civilizations — Noam Brown, OpenAI
Solving Poker and Diplomacy, Debating RL+Reasoning with Ilya, what’s *wrong* with the System 1/2 analogy, and where Test-Time Compute hits a wall Full Video Episode Timestamps 00:00 Intro – Diplomacy, Cicero & World Championship 02:00 Reverse Centaur: How AI Improved Noam’s Human Play 05:00 Turing Test Failures in Chat: Hallucinations & Steerability 07:30 Reasoning Models & Fast vs. Slow Thinking Paradigm 11:00 System 1 vs. System 2 in Visual Tasks (GeoGuessr, Tic-Tac-Toe) 14:00 The Deep Research Existence Proof for Unverifiable Domains 17:30 Harnesses, Tool Use, and Fragility in AI Agents 21:
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
[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 Full Video Episode Timestamps 00:00 Introduction & Guest Background 00:29 What is Dagger? Post-Development Automation 01:08 Dagger’s Community & Pl
[AIEWF Preview] Gemini in 2025 and Realtime Voice AI
As part of our AI Engineer World’s Fair preview , we’re releasing a special cross podcast recorded with Sam Charrington of TWiML AI at last week’s Google I/O! TUESDAY: Shrestha and Kwindla’s workshop: https://www.ai.engineer/schedule#milliseconds-to-magic-real-time-workflows-using-the-gemini-live-api-and-pipecat TUESDAY: Kwindla’s workshop: https://www.ai.engineer/schedule#building-voice-agents-with-gemini-and-pipecat WEDNESDAY: Shrestha and Kwindla’s talk: https://www.ai.engineer/schedule#milliseconds-to-magic-real-time-workflows-using-the-gemini-live-api-and-pipecat WEDNESDAY: Kwindla’s keyn
[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/pl
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 Full Video Episode Timestamps 00:00 Introductions 00:35 Meeting at Langchain Hackathon 04:02 Building Factory despite early model limitations 06:56 What is Factory AI? 08:55 Delegation vs Collaboration in AI Development Tools 10:06 Naming
[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 publ