Inside Nano Banana 🍌 and the Future of Vision-Language Models with Oliver Wang - #748
Today, we’re joined by Oliver Wang, principal scientist at Google DeepMind and tech lead for Gemini 2.5 Flash Image—better known by its code name, “Nano Banana.” We dive into the development and capabilities of this newly released frontier vision-language model, beginning with the broader shift from specialized image generators to general-purpose multimodal agents that can use both visual and textual data for a variety of tasks. Oliver explains how Nano Banana can generate and iteratively edit images while maintaining consistency, and how its integration with Gemini’s world knowledge expands creative and practical use cases. We discuss the tension between aesthetics and accuracy, the relative maturity of image models compared to text-based LLMs, and scaling as a driver of progress. Oliver also shares surprising emergent behaviors, the challenges of evaluating vision-language models, and the risks of training on AI-generated data. Finally, we look ahead to interactive world models and VLMs that may one day “think” and “reason” in images. The complete show notes for this episode can be found at https://twimlai.com/go/748.
23 Sep 1h 3min
Is It Time to Rethink LLM Pre-Training? with Aditi Raghunathan - #747
Today, we're joined by Aditi Raghunathan, assistant professor at Carnegie Mellon University, to discuss the limitations of LLMs and how we can build more adaptable and creative models. We dig into her ICML 2025 Outstanding Paper Award winner, “Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction,” which examines why LLMs struggle with generating truly novel ideas. We dig into the "Roll the dice" approach, which encourages structured exploration by injecting randomness at the start of generation, and the "Look before you leap" concept, which trains models to take "leaps of thought" using alternative objectives to create more diverse and structured outputs. We also discuss Aditi’s papers exploring the counterintuitive phenomenon of "catastrophic overtraining," where training models on more data improves benchmark performance but degrades their ability to be fine-tuned for new tasks, and dig into her lab's work on creating more controllable and reliable models, including the concept of "memorization sinks," an architectural approach to isolate and enable the targeted unlearning of specific information. The complete show notes for this episode can be found at https://twimlai.com/go/747.
16 Sep 58min
Building an Immune System for AI Generated Software with Animesh Koratana - #746
Today, we're joined by Animesh Koratana, founder and CEO of PlayerZero to discuss his team’s approach to making agentic and AI-assisted coding tools production-ready at scale. Animesh explains how rapid advances in AI-assisted coding have created an “asymmetry” where the speed of code output outpaces the maturity of processes for maintenance and support. We explore PlayerZero’s debugging and code verification platform, which uses code simulations to build a "memory bank" of past bugs and leverages an ensemble of LLMs and agents to proactively simulate and verify changes, predicting potential failures. Animesh also unpacks the underlying technology, including a semantic graph that analyzes code bases, ticketing systems, and telemetry to trace and reason through complex systems, test hypotheses, and apply reinforcement learning techniques to create an “immune system” for software. Finally, Animesh shares his perspective on the future of the software development lifecycle (SDLC), rethinking organizational workflows, and ensuring security as AI-driven tools continue to mature. The complete show notes for this episode can be found at https://twimlai.com/go/746.
9 Sep 1h 5min
Autoformalization and Verifiable Superintelligence with Christian Szegedy - #745
In this episode, Christian Szegedy, Chief Scientist at Morph Labs, joins us to discuss how the application of formal mathematics and reasoning enables the creation of more robust and safer AI systems. A pioneer behind concepts like the Inception architecture and adversarial examples, Christian now focuses on autoformalization—the AI-driven process of translating mathematical concepts from their human-readable form into rigorously formal, machine-verifiable logic. We explore the critical distinction between the informal reasoning of current LLMs, which can be prone to errors and subversion, and the provably correct reasoning enabled by formal systems. Christian outlines how this approach provides a robust path toward AI safety and also creates the high-quality, verifiable data needed to train models capable of surpassing human scientists in specialized domains. We also delve into his predictions for achieving this superintelligence and his ultimate vision for AI as a tool that helps humanity understand itself. The complete show notes for this episode can be found at https://twimlai.com/go/745.
2 Sep 1h 11min
Multimodal AI Models on Apple Silicon with MLX with Prince Canuma - #744
Today, we're joined by Prince Canuma, an ML engineer and open-source developer focused on optimizing AI inference on Apple Silicon devices. Prince shares his journey to becoming one of the most prolific contributors to Apple’s MLX ecosystem, having published over 1,000 models and libraries that make open, multimodal AI accessible and performant on Apple devices. We explore his workflow for adapting new models in MLX, the trade-offs between the GPU and Neural Engine, and how optimization methods like pruning and quantization enhance performance. We also cover his work on "Fusion," a weight-space method for combining model behaviors without retraining, and his popular packages—MLX-Audio, MLX-Embeddings, and MLX-VLM—which streamline the use of MLX across different modalities. Finally, Prince introduces Marvis, a real-time speech-to-speech voice agent, and shares his vision for the future of AI, emphasizing the move towards "media models" that can handle multiple modalities, and more. The complete show notes for this episode can be found at https://twimlai.com/go/744.
26 Aug 1h 10min
Genie 3: A New Frontier for World Models with Jack Parker-Holder and Shlomi Fruchter - #743
Today, we're joined by Jack Parker-Holder and Shlomi Fruchter, researchers at Google DeepMind, to discuss the recent release of Genie 3, a model capable of generating “playable” virtual worlds. We dig into the evolution of the Genie project and review the current model’s scaled-up capabilities, including creating real-time, interactive, and high-resolution environments. Jack and Shlomi share their perspectives on what defines a world model, the model's architecture, and key technical challenges and breakthroughs, including Genie 3’s visual memory and ability to handle “promptable world events.” Jack, Shlomi, and Sam share their favorite Genie 3 demos, and discuss its potential as a dynamic training environment for embodied AI agents. Finally, we will explore future directions for Genie research. The complete show notes for this episode can be found at https://twimlai.com/go/743.
19 Aug 1h 1min
Closing the Loop Between AI Training and Inference with Lin Qiao - #742
In this episode, we're joined by Lin Qiao, CEO and co-founder of Fireworks AI. Drawing on key lessons from her time building PyTorch, Lin shares her perspective on the modern generative AI development lifecycle. She explains why aligning training and inference systems is essential for creating a seamless, fast-moving production pipeline, preventing the friction that often stalls deployment. We explore the strategic shift from treating models as commodities to viewing them as core product assets. Lin details how post-training methods, like reinforcement fine-tuning (RFT), allow teams to leverage their own proprietary data to continuously improve these assets. Lin also breaks down the complex challenge of what she calls "3D optimization"—balancing cost, latency, and quality—and emphasizes the role of clear evaluation criteria to guide this process, moving beyond unreliable methods like "vibe checking." Finally, we discuss the path toward the future of AI development: designing a closed-loop system for automated model improvement, a vision made more attainable by the exciting convergence of open and closed-source model capabilities. The complete show notes for this episode can be found at https://twimlai.com/go/742.
12 Aug 1h 1min
Context Engineering for Productive AI Agents with Filip Kozera - #741
In this episode, Filip Kozera, founder and CEO of Wordware, explains his approach to building agentic workflows where natural language serves as the new programming interface. Filip breaks down the architecture of these "background agents," explaining how they use a reflection loop and tool-calling to execute complex tasks. He discusses the current limitations of agent protocols like MCPs and how developers can extend them to handle the required context and authority. The conversation challenges the idea that more powerful models lead to more autonomous agents, arguing instead for "graceful recovery" systems that proactively bring humans into the loop when the agent "knows what it doesn't know." We also get into the "application layer" fight, exploring how SaaS platforms are creating data silos and what this means for the future of interoperable AI agents. Filip also shares his vision for the "word artisan"—the non-technical user who can now build and manage a fleet of AI agents, fundamentally changing the nature of knowledge work. The complete show notes for this episode can be found at https://twimlai.com/go/741.
29 Juli 46min
