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

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.

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.

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.