Episodes

  • Canva Is Tackling AI Design’s Most Frustrating Problem with Stefano Corazza- #784

    Canva Is Tackling AI Design’s Most Frustrating Problem with Stefano Corazza- #784
    Nov 10 2026
    In this episode, Stefano Corazza, head of AI research at Canva, joins us to explore how his team is building custom models and agents around iterative design workflows. We discuss the tradeoffs between training models from scratch and adapting open weights models, and how proprietary design data supports specialization. Stefano explains how Craft separates layout planning from pixel generation and how Brush uses design tools and visual feedback to create and revise compositions. We examine the challenges of evaluating design quality beyond a single generation, including the use of vision language models as judges alongside human evaluation. We also discuss lessons from simplifying agent harnesses, why faster inference enables more iterations, and how model ownership creates opportunities to optimize serving costs. Finally, we explore how APIs and MCP are expanding access to these capabilities and what changes when software companies build tools for agents as well as people. 🗒️ Full show notes: ⁠⁠https://twimlai.com/go/784.
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    58 mins
  • Can AI Invent or Just Help Scientists Work Faster? with Richard Socher - #783

    Can AI Invent or Just Help Scientists Work Faster? with Richard Socher - #783
    Nov 3 2026
    AI can help scientists write code, analyze data, and draft papers. But can it generate the ideas that lead to new discoveries? Richard Socher, former chief scientist at Salesforce, founder of You.com, and co-founder of Recursive, joins us to explore the move from AI assisting researchers to AI conducting research. Starting with AI improving AI, we examine what it takes to propose ideas, run experiments, and validate results, and how those capabilities could eventually accelerate discovery across biology, chemistry, and physics. The conversation covers Recursive’s reported results in model training and GPU kernel optimization, why fast verification matters, and how reward hacking complicates the search for real improvements. We also discuss the data and experimental infrastructure needed for broader scientific discovery, whether commercial incentives can keep increasingly capable systems aligned, and Richard’s vision for a “Eureka machine.” 🗒️ Full show notes: ⁠https://twimlai.com/go/783.
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    49 mins
  • The Brutal Reality of Building Agentic Products Today with Daniel Jeffries - #782

    The Brutal Reality of Building Agentic Products Today with Daniel Jeffries - #782
    Oct 28 2026
    AI agents are becoming accessible to everyone, raising the bar for developers building the next generation of agent products. When capable, general-purpose agents are available out of the box, differentiation increasingly comes down to the architectural decisions and specialized experiences that make an agent uniquely valuable. In this episode, Dan Jeffries, creator of Nautilo, joins us to discuss what he's learned building a platform for personal and collaborative AI agents. We explore his approach to persistent memory, coordinating work across people and agents, managing private context, and designing deterministic tools that complement LLM capabilities. Dan also shares his views on multi-agent architectures, computer use, and the tradeoffs between local and cloud models. The conversation offers a practitioner's perspective on where the opportunities lie as AI agents become more capable, more accessible, and more difficult to differentiate. 🗒️ Full show notes: https://twimlai.com/go/782.
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    54 mins
  • Airbnb Is Building a ‘Company Brain’ for AI Agents with Ahmad Al-Dahle - #781

    Airbnb Is Building a ‘Company Brain’ for AI Agents with Ahmad Al-Dahle - #781
    Oct 21 2026
    AI agents need the institutional knowledge experienced employees rely on: why a system works a certain way, which policies apply, and what never made it into the documentation. Capturing that knowledge is hard. Keeping it useful as people and agents add more may be harder. In this episode, Ahmad Al-Dahle, CTO of Airbnb, shares how his team is building Project Everest, a “company brain” that captures knowledge from employees’ workflows for AI agents. We explore stale information and context poisoning, why too much context can backfire, and how the team evaluates whether shared knowledge improves engineering work. We also discuss measuring AI productivity beyond token usage and PR counts, the limits of chat-based interfaces, and deploying support agents in a marketplace where resolving disputes means mediating between conflicting interests. 🗒️ Full show notes: https://twimlai.com/go/781.
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    52 mins
  • Your Ranking Model Is Optimizing the Wrong Thing with Zoe Weil - #780

    Your Ranking Model Is Optimizing the Wrong Thing with Zoe Weil - #780
    Oct 15 2026
    In this episode, Zoe Weil, co-founder and CEO of Sequen, joins us to discuss how ranking systems can use live behavioral signals to optimize for purchases rather than clicks or search relevance alone. Zoe explains why approaches developed at data-rich social platforms don’t necessarily translate to retailers and how Sequen uses scroll speed, hover duration, and other in-session interactions to infer shopper intent without relying on user identity. We explore the company’s large event models and its architecture of specialized models that transform streaming events into features for downstream rankers. Zoe describes the infrastructure required to serve dynamic rankings within milliseconds, why the team chose Rust for its backend, and how evaluation harnesses connect model improvements to business outcomes. We also discuss the tradeoffs between advertising and organic revenue, continuous model improvement, and the boundary between useful personalization and surveillance. 🗒️ Full show notes: ⁠⁠https://twimlai.com/go/780.
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    53 mins
  • Why Jev Is Changing How We Build With AI with Diogo Almeida - #779

    Why Jev Is Changing How We Build With AI with Diogo Almeida - #779
    Oct 6 2026
    In this episode, Diogo Almeida, co-founder and CEO of TypeSafe, joins us to discuss Jev, TypeSafe’s recently released model for bringing fast, reliable intelligence directly into software. We explore the idea of “machine-native intelligence” and why Diogo believes models optimized for generating text are poorly suited to many of the decisions required for real-world automation. He explains how Jev differs from traditional classifiers and LLM-based approaches, the role of reinforcement learning from calibrated decisions (RLCD), and why calibration and reliability are central to making AI useful as a software primitive. We also discuss the relationship between models and code, why Diogo believes AI systems should become more engineered rather than relying on a single model to do everything, and how Jev-like models could reshape agents, tool use, and the architecture of AI-powered software. 🗒️ Full show notes: ⁠https://twimlai.com/go/779.
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    1 hr and 30 mins
  • From Math Olympiads to Navier-Stokes: How Fast Is AI Progressing? with Greg Burnham - #778

    From Math Olympiads to Navier-Stokes: How Fast Is AI Progressing? with Greg Burnham - #778
    Sep 29 2026
    AI systems have gone from struggling with grade-school math to helping solve research problems that have resisted mathematicians for decades, including Navier-Stokes. In this episode, Greg Burnham, who leads AI capabilities research at Epoch AI, joins us to examine what that progress says about where AI is going. We look at how these systems are solving hard math problems, how much they rely on persistence and prior human work, and whether they are starting to produce genuinely new ideas. We also discuss how to measure progress as traditional benchmarks become less useful, why capability gains appear surprisingly steady across model generations, and where models still struggle with open-ended work, learning from experience, and identifying promising new research directions. 🗒️ Full show notes: https://twimlai.com/go/778.
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    1 hr and 8 mins
  • From Voice Agents to AI Avatars with Alexander Smola - #777

    From Voice Agents to AI Avatars with Alexander Smola - #777
    Sep 17 2026
    Voice AI has gotten remarkably good, but natural conversation remains a high bar. Small delays, awkward interruptions, or the wrong tone can quickly break the illusion—and adding vision and visual presence only raises the stakes. In this episode, Alex Smola, co-founder and CEO of Boson AI, explores the path from today’s voice agents to audiovisual agents and AI avatars. We discuss the technical tradeoffs behind real-time voice, including audio tokenization, latency, model size, and inference cost, as well as what changes when these systems can both see and be seen. We also explore the role of emotional intelligence in AI, how agents can learn from human interactions, and what it will take to move beyond impressive demos toward interactions that actually feel natural. 🗒️ Full show notes: https://twimlai.com/go/777.
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    1 hr and 5 mins