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TWIML AI

9 items in the feed. The texts are the sources' own descriptions — the content belongs to TWIML AI.

PodcastTWIML AI

The Brutal Reality of Building Agentic Products Today with Daniel Jeffries - #782

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.

28 Oct twimlai.com

PodcastTWIML AI

Airbnb Is Building a ‘Company Brain’ for AI Agents with Ahmad Al-Dahle - #781

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.

21 Oct twimlai.com

PodcastTWIML AI

Your Ranking Model Is Optimizing the Wrong Thing with Zoe Weil - #780

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.

15 Oct twimlai.com

PodcastTWIML AI

Why Jev Is Changing How We Build With AI with Diogo Almeida - #779

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.

6 Oct twimlai.com

PodcastTWIML AI

From Math Olympiads to Navier-Stokes: How Fast Is AI Progressing? with Greg Burnham - #778

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.

29 Sept twimlai.com

PodcastTWIML AI

From Voice Agents to AI Avatars with Alexander Smola - #777

The podcast explores the evolution from today's voice assistants to AI agents that can both speak and see—so-called audiovisual agents and AI avatars. Alexander Smola, founder of Boson AI and professor at Carnegie Mellon University, walks through the technical challenges: small delays, awkward interruptions, and wrong tone can quickly break the illusion. He explains technical tradeoffs such as audio tokenization (a way to split sound into units for AI), latency, and computational cost. A key insight is that when systems can both see and be seen, the demands become even higher—and emotional intelligence plus the ability to learn from human interaction become essential to move beyond impressive demos and achieve natural conversation.

16 Sept twimlai.com

PodcastTWIML AI

Do AI Tokenomics Matter More Than Model Benchmarks? with Chris Potts - #776

In this podcast episode, Stanford professor Chris Potts discusses the concept of "tokenflation" — the idea that AI models use more and more tokens (small units of text that the model processes) without providing corresponding increases in value or performance. Potts argues that when evaluating how good an AI model is, we often focus only on benchmarks — test results — but overlook what it actually costs to run the model. He explains that users skilled at iterating and challenging models get better results, and that more efficient architectures could change the economics of AI spending.

9 Sept twimlai.com

PodcastTWIML AI

World Models and the Future of Spatial AI with Justin Johnson - #775

The podcast discusses world models — AI systems that can understand, generate, and simulate environments around us. Justin Johnson from World Labs explains this is an important development frontier beyond just language. He describes various approaches to building world models, including explicit 3D representation and generative models, and notes that there is no established recipe yet. World Labs' system Marble can generate navigable 3D worlds from images, and Johnson discusses challenges in evaluating how well world models work, as well as how simulation, planning, and action play a role.

1 Sept twimlai.com

PodcastTWIML AI

Why the Next AI Breakthrough May Come from Physics with Max Welling - #774

Max Welling, professor and co-founder of CuspAI, argues that the next major breakthrough in AI may come from physics rather than solely from increased computing power and larger models. CuspAI uses generative AI to design new materials for semiconductors, batteries, and clean energy. Welling contends that foundation models for chemistry combined with automated experimentation dramatically accelerate the discovery of new materials. He also suggests that physics can teach AI researchers to build better systems by studying connections between machine learning and thermodynamics, exploring waves as a new computational method for neural networks, and applying symmetry concepts from statistical physics.

26 Aug twimlai.com

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