Letta AI with Sarah Wooders - Weaviate Podcast #117!
Hey everyone! Thank you so much for watching the 117th episode of the Weaviate podcast! In this episode, we dive deep into the cutting edge of AI agent development with Sarah Wooders, co-founder and CTO of Letta AI. Emerging from Berkeley's Sky Computing Lab, Sarah and her team have pioneered a revolutionary approach to stateful agents - AI systems that genuinely remember both you and themselves across extended conversations. The conversation explores how the groundbreaking MemGPT project evolved into Letta's comprehensive Agent Development Environment (ADE), which empowers developers to build truly persistent AI experiences. Sarah shares powerful insights on context management, memory prioritization, and the critical role of databases in agent architecture. Whether you're building AI systems or simply curious about where conversational AI is heading, this episode illuminates how the future of agents depends not just on their reasoning capabilities, but on their ability to maintain coherent identity and memory over time.
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Agent Experience with Matt Biilmann, Sebastian Witalec, and Charles Pierse - Weaviate Podcast #116!
Hey everyone! Thank you so much for watching another episode of the Weaviate Podcast! I am SUPER excited to welcome Matt Biilmann, Co-Founder and CEO of Netlify, as well as Sebastian Witalec and Charles Pierse from Weaviate to discuss Agent Experience! You have probably heard about how you can connect LLMs to external software tools. This supercharges the capabilities of AI systems and what they can do. So what does that mean for you as a software developer?This podcast explores different ideas around designing software user experiences for Agents as well as Humans. How do we write documentation for Agents differently than Humans? How do we design REST or gRPC APIs, or programming languages clients, for Agents differently than Humans? llms.txt, JSON tool definitions, agents to agent, breaking changes, … there were so many interesting topics explored in this podcast! I really hope you find it useful! As always more than happy to discuss these ideas further with you!
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Optimizing Retrieval Agents with Shirley Wu - Weaviate Podcast #115!
Hey everyone! Thank you so much for watching the 115th episode of the Weaviate Podcast featuring Shirley Wu from Stanford University!We explore the innovative Avatar Optimizer—a novel framework that leverages contrastive reasoning to refine LLM agent prompts for optimal tool usage. Shirley explains how this self-improving system evolves through iterative feedback by contrasting positive and negative examples, enabling agents to handle complex tasks more effectively.We also dive into the STaRK Benchmark, a comprehensive testbed designed to evaluate retrieval systems on semi-structured knowledge bases. The discussion highlights the challenges of unifying textual and relational retrieval, exploring concepts such as multi-vector embeddings, relational graphs, and dynamic data modeling. Learn how these approaches help overcome information loss, enhance precision, and enable scalable, context-aware retrieval in diverse domains—from product recommendations to precision medicine.Whether you’re interested in advanced prompt optimization, multi-agent system design, or the future of human-centered language models, this episode offers a wealth of insights and a forward-looking perspective on integrating sophisticated AI techniques into real-world applications.
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Contextual AI with Amanpreet Singh - Weaviate Podcast #114!
Hey everyone! Thank you so much for watching the 114th episode of the Weaviate Podcast featuring Amanpreet Singh, Co-Founder and CTO of Contextual AI! Contextual AI is at the forefront of production-grade RAG agents! I learned so much from this conversation! We began by discussing the vision of RAG 2.0, jointly optimizing generative and retrieval models! This then lead us to discuss Agentic RAG and how the RAG 2.0 roadmap is evolving with emerging perspectives on tool use. Amanpreet continues to further motivate the importance of continual learning of the model and the prompt / few-shot examples -- discussing the limits of prompt engineering. Personally I have to admit I think I have been a bit too bullish on only tuning instructions / examples, Amanpreet made an excellent case for updating the weights of the models as well -- citing issues such as parametric knowledge conflicts, and later on discussing how Mechanistic Interpretability is used to audit models and their updates in enterprise settings. We then discussed Contextual AI's LMUnit for evaluating these systems. This then lead us into my favorite part of the podcast, a deep dive into RL algorithms for LLMs. I highly recommend checking out the links below to learn more about Contextual's innovations on APO and KTO! We then discuss the importance of domain specific data, Mechanistic Interpretability, return to another question on RAG 2.0, and conclude with Amanpreet's most exciting future directions for AI! I hope you enjoy the podcast!
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Cartesia AI with Karan Goel - Weaviate Podcast #113!
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!
Brasil