The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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.

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.

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.

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.

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.