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Geoffrey Litt is a design engineer at Notion. Previously, he was a senior researcher at the independent research lab Ink & Switch, and he got his PhD in HCI at MIT. Geoffrey’s work focuses on malleable software and designing environments for thinking. Outline: 0:00 - Introduction 0:47 - Design Is More Than Better Animation 3:32 - Malleable Software and Coding Agents 9:06 - Human-Software Interaction is a Full-Stack Problem 12:43 - Getting Users On Board and Deeper 15:53 - The Trap of Just Building More 20:27 - Primitive Design Is a Taste Problem 22:20 - Understanding Is the New Bottleneck 27:22 - How To Develop Taste? 34:49 - Productivity vs. Speed 38:10 - Will Better AI Hurt Human-Human Collaboration? 44:34 - Advice for Designing Better Products References: Geoffrey’s Homepage: https://www.geoffreylitt.com Malleable Software: https://www.inkandswitch.com/essay/malleable-software/ Understanding is the new bottleneck: https://www.geoffreylitt.com/2026/07/02/understanding-is-the-new-bottleneck Notion Workers: https://developers.notion.com/workers/get-started/overview Podcast Links: Podcast website: https://augmented-mind.github.io/ Apple Podcasts: https://podcasts.apple.com/us/podcast/augmented-mind-podcast/id1868102170 Spotify: https://open.spotify.com/show/40KculkYTe2tOpqJm6TAYr?si=PU_UncsMT4mXjVNCRwoXog&nd=1&dlsi=6d9bed7a43d64085 RSS: https://anchor.fm/s/10dbf5b7c/podcast/rss About the Hosts: The AM Podcast is hosted by Yijia Shao, Shannon Shen, and Michael Ryan, CS PhD students at Stanford University and MIT.

Zora Wang is a CS PhD student at Carnegie Mellon University. Zora’s work lies at the intersection of coding agents and human-ai collaboration.  In this episode we explore how agents work, how humans work, and how we can get more done together. Outline: 0:00 - Introduction 4:57 - Agent Workflow Memory 9:26 - From Program Synthesis to Agent Research 11:16 - Tools, Actions, and Skills 18:47 - Human Work vs. Agent Work 21:19 - Autonomous Agents vs. Collaborative Agents 29:47 - Picking Research Problems in the Age of AI Agents 34:09 - The Role of Coding Ability in Agent Development 36:49 - Cutting Through the Fear Mongering: Building Agents Responsibly 45:02 - What’s Next? (The Future of Human-Agent Collaboration) 52:42 - Final Takeaway References: Zora’s Homepage: https://zorazrw.github.io/  Agent Workflow Memory: https://arxiv.org/abs/2409.07429  How Do AI Agents Do Human Work? Comparing AI and Human Workflows Across Diverse Occupations: https://arxiv.org/abs/2510.22780  Podcast Links: Podcast website: https://augmented-mind.github.io/ Apple Podcasts: https://podcasts.apple.com/us/podcast/augmented-mind-podcast/id1868102170 Spotify: https://open.spotify.com/show/40KculkYTe2tOpqJm6TAYr?si=PU_UncsMT4mXjVNCRwoXog&nd=1&dlsi=6d9bed7a43d64085 RSS: https://anchor.fm/s/10dbf5b7c/podcast/rss About the Hosts: The AM Podcast is hosted by Yijia Shao, Shannon Shen, and Michael Ryan, CS PhD students at Stanford University and MIT.

Jeremy Avigad is a professor in the Department of Philosophy and the Department of Mathematical Sciences at Carnegie Mellon University. Jeremy is a pioneer in using AI for Mathematics and the co-creator of the Lean Theorem Prover. Currently, he is the director of the Hoskinson Center for Formal Mathematics at CMU, Dean's Chair in Logic and Philosophy of Mathematics, and the director of the newly-established Institute for Computer-Aided Reasoning in Mathematics under NSF. Outline: 0:00 - Teaser 1:04 - Monologue 2:50 - The Historical Landscape of AI for Mathematics 7:28 - Formalization and Computer-Aided Proof 11:56 - The Birth of the Lean Project 21:21 - Lean Blueprint, Model Training with Lean, Using Lean in Agentic Systems 29:48 - Making AI Actually Useful for Mathematicians 32:46 - How AI is Changing Mathematics 36:29 - "It's Our Mathematics, and Us Doing Mathematics" 43:04 - The Verification Gap in Human-AI Collaboration 47:46 - The Future of Math Education 52:23 - Capital, Startups, and the Mathematicians' Ecosystem 1:01:08 - Predictions References: Jeremy’s Homepage: https://www.andrew.cmu.edu/user/avigad/ The Lean Theorem Prover: https://lean-lang.org/papers/system.pdf Lean projects: https://leanprover-community.github.io/lean_projects.html Podcast Links: Podcast website: https://augmented-mind.github.io/ Apple Podcasts: https://podcasts.apple.com/us/podcast/augmented-mind-podcast/id1868102170 Spotify: https://open.spotify.com/show/40KculkYTe2tOpqJm6TAYr?si=PU_UncsMT4mXjVNCRwoXog&nd=1&dlsi=6d9bed7a43d64085 RSS: https://anchor.fm/s/10dbf5b7c/podcast/rss Special Thanks to Zixiao Jolene Wang and Rajarshi Mukherjee for their help with this episode! About the Hosts: The AM Podcast is hosted by Yijia Shao, Shannon Shen, and Michael Ryan, CS PhD students at Stanford University and MIT.

Ken Liu is a Stanford CS PhD student and founder of The Open Anonymity Project. Ken’s pioneering work explores the intersection between language models and data & user privacy. Outline: 0:00 - Teaser 1:08 - Prelude: Introducing Ken Liu 1:41 - Monologue: The Open Anonymity Project 3:41 - Ken’s Path to Privacy Research 6:31 - The Biggest Privacy Concern for LLM Users 9:39 - Three Perspectives on Tackling AI Privacy 10:57 - “AI presents a Uniquely Worse Privacy Problem” 13:44 - The Open Anonymity (OA) Project: Unlinkable Inference 17:50 - Blind Signatures as Unlinkable Authentication 20:52 - Secure Inference Proxies 28:31 - Threat Model in the OA Project 31:39 - What If People Give Away Information In Their Prompts 35:58 - OpenClaw, Privacy Nightmare In Agents 43:00 - The Stories Behind the OA Project 50:14 - Intelligence Neutrality 52:22 - Safety Concerns in a World with Private AI Inference References: Ken Liu’s Home Page: https://ai.stanford.edu/~kzliu/ The Open Anonymity Project: https://openanonymity.ai/ Unlinkable Inference as a User Privacy Architecture: https://openanonymity.ai/blog/unlinkable-inference/ Podcast Links: Podcast website: https://augmented-mind.github.io/ Apple Podcasts: https://podcasts.apple.com/us/podcast/augmented-mind-podcast/id186810217Spotify: https://open.spotify.com/show/40KculkYTe2tOpqJm6TAYr?si=PU_UncsMT4mXjVNCRwoXog&nd=1&dlsi=6d9bed7a43d6408RSS: https://anchor.fm/s/10dbf5b7c/podcast/rss About the Hosts: The AM Podcast is hosted by Yijia Shao, Shannon Shen, and Michael Ryan, CS PhD students at Stanford University and MIT.

Woosuk Kwon is CTO of Inferact and creator of the vLLM inference library. Woosuk shares what it takes to build the most popular open-source LLM inference engine from a human-centered perspective. Outline: 0:00 - Prelude: Introducing Woosuk and Inferact 3:00 - Woosuk’s First PhD Project 6:00 - How the vLLM Project Got Started 9:18 - AI Infra Needs More Than Just Efficiency 14:08 - How AI Infra and Human-centered AI Are Connected 15:01 - How to Prioritize Feature Requests for Popular AI Infra 18:18 - Streaming Requests and Realtime API 24:05 - Multi-turn, Agentic, Proactive LLMs 27:03 - How to Design AI Infra in a Principled Way 29:13 - How to Design an AI Inference Engine for Continue Learning with RL 35:05 - Would LoRA Training Affect RL Infra Design? 37:28 - Why Start an AI Inference Infra Startup? 40:46 - What Effortless Inference with Open-source Models Means for Developers 43:46 - A Vision for On-device AI Inference 46:19- Can Today’s Coding Agents Create vLLM? References: Inferact: https://inferact.ai/ Efficient Memory Management for Large Language Model Serving with PagedAttention: https://arxiv.org/abs/2309.06180 Streaming Requests & Realtime API in vLLM: https://vllm.ai/blog/streaming-realtime RL’s Razor: Why Online Reinforcement Learning Forget Less: https://arxiv.org/abs/2509.04259 Podcast Links: Podcast website: https://augmented-mind.github.io/ Apple Podcasts: https://podcasts.apple.com/us/podcast/augmented-mind-podcast/id1868102170 Spotify: https://open.spotify.com/show/40KculkYTe2tOpqJm6TAYr?si=PU_UncsMT4mXjVNCRwoXog&nd=1&dlsi=6d9bed7a43d64085 RSS: https://anchor.fm/s/10dbf5b7c/podcast/rss About the Hosts: The AM Podcast is hosted by Yijia Shao, Shannon Shen, and Michael Ryan, CS PhD students at Stanford University and MIT.

Sherry Wu is a professor at CMU whose research sits at the intersection of human-computer interaction and natural language processing. From making AI work for imperfect humans to making humans work better with AI — Sherry's work challenges us to rethink both sides of the equation. Outline: 0:00 - Teaser1:13 - Prelude: Introducing Sherry Wu2:30 - How the AI Field Has Changed in the Last Four Years4:22 - Making AI Systems Work for Imperfect Humans6:54 - Models vs. Scaffolding10:36 - Understanding Human Imperfection in Teaching Contexts19:28 - AI Literacy Skills22:04 - How AI Is Changing CS Education25:38 - Suppose We Have AGI, What Does It Mean to Be Human?29:14 - Training Models to be More Human-centered31:46 - Checklists Are Better Than Reward Models https://arxiv.org/abs/2507.1862436:56 - Challenge in Aligning Models43:22 - Advice for Interdisciplinary Research45:37 - Reflection on Her Own Research References: Sherry Wu’s Research Homepage: https://www.cs.cmu.edu/~sherryw/ Sherry Wu’s course page (PMDS, Spring 2025): https://www.cs.cmu.edu/~sherryw/courses/2025s-pmds.html AI Fluency Index: https://www.anthropic.com/research/AI-fluency-index Checklists Are Better Than Reward Models: https://arxiv.org/abs/2507.18624 Not Everyone Wins with LLMs: https://arxiv.org/pdf/2509.21890 Podcast Links: Podcast website: https://augmented-mind.github.io/ Apple Podcasts: https://podcasts.apple.com/us/podcast/augmented-mind-podcast/id1868102170 Spotify: https://open.spotify.com/show/40KculkYTe2tOpqJm6TAYr?si=PU_UncsMT4mXjVNCRwoXog RSS: https://anchor.fm/s/10dbf5b7c/podcast/rss About the Hosts: The AM Podcast is hosted by Yijia Shao, Shannon Shen, and Michael Ryan, CS PhD students at Stanford University and MIT.

Omar Shaikh is a Stanford PhD student, HCI and NLP researcher, and author of the award-winning UIST 2025 paper “Creating General User Models from Computer Use”. Omar’s pioneering work aims to bridge the Human-AI grounding gap. Outline: 0:00 - Teaser 1:21 - Prelude: Introducing Omar Shaikh 2:07 - Monologue: Better Context for AI 4:22 - Bridging the Human-AI Grounding Gap 6:14 - Confidence scores in General User Models (GUMs) 7:32 - Calibration of General User Models 13:20 - Uses of General User Models 15:01 - Mixed Initiative Interactions 22:10 - Motivation for GUM 25:31 - Tabracadabra: tab everywhere! 27:01 - Design decisions in GUM 28:26 - Designing Interactive Experiences 32:11 - DITTO: https://arxiv.org/abs/2406.00888 33:06 - Work on Domains without Existing Benchmarks 34:45 - Challenges of the GUM Project 37:26 - Privacy and Data Ownership 38:57 - Finetuning a User Model 44:09 - Mindblowing GUM Inferences 49:02 - Social Problems of GUMs 50:27 - GUM as a Reflection Tool References: Omar Shaikh’s research homepage: https://oshaikh.com/ Creating General User Models from Computer Use: https://arxiv.org/abs/2505.10831 Tabracadabra: https://x.com/oshaikh13/status/1967626897837494479 Aligning Language Models with Demonstrated Feedback: https://arxiv.org/abs/2406.00888 Principles of Mixed-Initiative User Interfaces: https://erichorvitz.com/chi99horvitz.pdf Verification of Forecasts Expressed in Terms of Probability: https://journals.ametsoc.org/view/journals/mwre/78/1/1520-0493_1950_078_0001_vofeit_2_0_co_2.xml  Podcast Links: Podcast website: https://augmented-mind.github.io/ Apple Podcasts: https://podcasts.apple.com/us/podcast/augmented-mind-podcast/id1868102170 Spotify: https://open.spotify.com/show/40KculkYTe2tOpqJm6TAYr?si=PU_UncsMT4mXjVNCRwoXog RSS: https://anchor.fm/s/10dbf5b7c/podcast/rss About the Hosts: The AM Podcast is hosted by Yijia Shao, Shannon Shen, and Michael Ryan, CS PhD students at Stanford University and MIT.

Introducing The Augmented Mind Podcast (The AM Podcast). We explore techniques for building AI models that collaborate with people and augment human intelligence. In Episode 0, we share who we are, why we started this podcast, and what we're looking forward to. Outline: 0:00 - Prelude: the problems we care about 1:48 - Host introduction 2:03 - Why we started the AM Podcast 2:31 - Hot takes on human-centered AI 2:45 - Hot take #1: learning on outcome rewards over long horizons will directly solve human-agent collaboration 3:00 - The Bitter Lesson 3:53 - How to define rewards is a human problem 4:50 - Empathetic AI 5:48 - Hot take #2: even with an automation-vs-augmentation view, as AI gets stronger, there will be less for us to work on 6:09 - Creative Destruction 7:21 - Task vs. goal 10:45 - Format of our podcast 11:28 - Unique technical challenges in human-centered AI 11:43 - Example #1: human variation 13:58 - Example #2: revolution of annotation and data collection 15:10 - Example #3: making sense of noisy data 16:45 - Let the journey begin! External Clips Referenced: Eric Horvitz; 1:02:38 - 1:03:07 https://www.youtube.com/watch?v=ddjNTxtyEnw Fei-Fei Li ; 12:40 - 12:58 https://www.youtube.com/watch?v=be0gLzeBX5w Podcast Links: Podcast website: https://augmented-mind.github.io/ Apple Podcasts: https://podcasts.apple.com/us/podcast/augmented-mind-podcast/id1868102170 Spotify: https://open.spotify.com/show/40KculkYTe2tOpqJm6TAYr?si=PU_UncsMT4mXjVNCRwoXog RSS: https://anchor.fm/s/10dbf5b7c/podcast/rss About the Hosts: The AM Podcast is hosted by Yijia Shao, Shannon Shen, and Michael Ryan, CS PhD students at Stanford University and MIT.