Weaviate Podcast

Anand Kannappan, co-founder of Patronus AI, dives into the challenges of debugging complex AI agents. He introduces Percival, a game-changing tool that analyzes agent traces and identifies failures. Anand explains critical issues like 'context explosion' and the orchestration of multi-agent systems. The conversation shifts to the evolving landscape of AI evaluation, advocating for dynamic oversight over static methods. He envisions a future where AI systems monitor each other, providing insights on how to enhance agent performance and evaluation.

Leonard Tang, co-founder of Haize Labs, delves into innovative techniques for AI evaluation. He shares how stacking weaker models can enhance the performance of stronger ones through the revolutionary Verdict library, boasting a 10-20% improvement over traditional models. The conversation includes practical insights on creating contrastive evaluation sets and implementing debate-based judging systems. Tang discusses the balance between AI safety and user feedback, offering transformative strategies to ensure that AI systems meet enterprise needs effectively.

Ben Kus, CTO of Box, delves into the complexities of the company's three-layer infrastructure: managing millions of interactions, navigating multi-tenant security challenges, and ensuring AI adheres to intricate content permissions. He discusses the impact of vector embeddings on file sizes and emphasizes the continued relevance of RAG despite advancements in context windows. The conversation also highlights Box's development of AI agents aimed at streamlining cumbersome enterprise processes, creating a path to improved productivity in the workplace.

Join Will Kurt and Cameron Pfiffer, co-founders of .txt.ai, as they unveil the groundbreaking open-source library, Outlines. They discuss how constrained decoding enhances reliability in language model outputs, enabling capabilities like perfect JSON generation and guided reasoning. The duo shares insights on multitask inference, which boosts efficiency in AI systems, and the role of finite state machines in their innovations. Delve into practical applications, including knowledge graph creation and automated report generation, shaping the future of AI.

David Berenstein and Ben Burtenshaw from Hugging Face dive into the fascinating world of synthetic data generation. They discuss innovative methodologies like persona-driven data and integration tactics for enhancing quality and diversity. The duo highlights the importance of tools like DistilLabel and Argilla for smooth data augmentation and model fine-tuning. Excitingly, they explore the potential for synthetic image data and its impact on AI education, emphasizing accessibility and user-friendly solutions in AI's future.

In this captivating conversation, Sarah Wooders, co-founder and CTO of Letta AI, shares her revolutionary insights from the Berkeley Sky Computing Lab. She discusses the development of stateful AI agents that remember interactions, emphasizing the importance of memory management. Topics include context optimization, the evolution of AI personas, and innovative tools for enhancing developer experiences. Sarah also explores the integration of AI in coding workflows, shedding light on the future of conversational AI and its profound implications for tech.

Matt Biilmann, Co-founder and CEO of Netlify, brings his expertise in web platforms, joined by Sebastian Witalec from Weaviate. They dive into the fascinating concept of 'Agent Experience' and how it reshapes web development. The trio discusses the evolution of APIs with AI integration and the importance of tailored communication methods for agents. They also explore the challenges in designing user experiences for both developers and AI agents, emphasizing the need for open standards to enhance interactions and streamline workflows.

Shirley Wu, a PhD student at Stanford University, delves into AI agents and retrieval systems, bringing expertise from her work on the Avatar Optimizer and STaRK Benchmark. She describes how the Avatar Optimizer enhances LLM tool usage through contrastive reasoning and iterative feedback. The discussion also tackles the STaRK Benchmark's role in evaluating retrieval systems, highlighting challenges like unifying textual and relational data, multi-vector embeddings, and the future of human-centered language models in various applications.

Amanpreet Singh, Co-Founder and CTO of Contextual AI, dives into the revolutionary world of Retrieval Augmented Generation (RAG) 2.0. He discusses the seamless integration of generative and retrieval models and the challenges of prompt engineering. Amanpreet emphasizes the necessity of continual learning and updates to model weights to resolve knowledge conflicts. The conversation also highlights the potential of reinforcement learning algorithms and the importance of domain-specific data. Buckle up for insights on the future of AI and specialized agents!

Hey everyone! Thank you so much for watching the 113th episode of the Weaviate Podcast with Karan Goel from Cartesia AI! Cartesia AI is leading the AI world in text-to-speech models! As exciting as these new applications in speech generation are, Cartesia is also building around an incredibly exciting new neural network architecture that cuts across all of AI -- State Space Models. State Space Models (SSMs) present a new approach to modeling long sequences circumventing the quadratic attention bottlenecks of transformers. In the podcast, we discuss Karan's perspectives around end-to-end modeling, long context and Multimodal processing, building and deploying a new kind of model, and more! I hope you find the podcast interesting and useful! As always more than happy to discuss these ideas further with you! Thanks for listening!