Weaviate Podcast

In a fascinating discussion, John Yang from Stanford and Carlos E. Jimenez from Princeton, co-first authors of the SWE-bench papers, delve into the revolutionary SWE-bench project. They explore how AI enhances software engineering, addressing the challenges of integrating language models for coding tasks. The duo discusses resource allocation for software engineering agents in Docker and Kubernetes, and the future of AI in business, including potential advancements in virtual reality. Their insights reveal how AI can reshape the development landscape.

Rose E. Wang, a Ph.D. student at Stanford University, dives into her groundbreaking research on AI in education, particularly through the Tutor CoPilot project. She discusses one of the largest randomized control trials in this field, involving 900 students and 1800 tutors. Rose highlights the innovative blend of human expertise and AI, revealing how tools like Cursor enhance real-time tutoring experiences. She also addresses challenges in traditional education, the evolving role of AI, and the vital need for effective human-AI interactions in learning environments.

Philip Kiely, the leading developer relations at Baseten, shares insights on compound AI systems and their evolution. He discusses breaking tasks into multiple stages for better AI model performance. The conversation covers advancements in multimodal AI and strategies for deploying these systems efficiently. Kiely emphasizes the benefits of smaller models and constrained generation techniques, alongside architectural tips for Kubernetes deployment. Key comparisons are made between various model serving frameworks, focusing on optimizing AI performance while minimizing costs.

Sayash Kapoor and Benedikt Stroebl, co-first authors from Princeton Language and Intelligence, discuss their influential paper on AI agents. They explore the crucial balance between performance and cost in AI systems, emphasizing that amazing responses mean little if they are too expensive to produce. The duo introduces the DSPY framework to optimize accuracy and costs and debates the adapting challenges of AI benchmarks in dynamic environments. They also highlight the importance of human feedback in enhancing AI reliability and performance.

Krista Opsahl-Ong, a leading developer and scientist at Stanford University, is the mastermind behind MIPRO and DSPy. In this engaging discussion, she illuminates the world of Automated Prompt Engineering and its impact on language models. Krista dives into the challenges of manual prompt construction, highlighting innovative algorithms that streamline the process. The conversation also explores the intricacies of structured outputs, multi-stage language programs, and the revolutionary potential of Large Language Models in AI-driven solutions.

Guy Podjarny, co-founder of the successful cybersecurity firm Snyk, joins Bob van Luijt to dive into the world of AI-native development. They explore the critical differences between AI-native and AI-enabled applications, revolutionizing software development practices. Highlights include the shift from a code-centric approach to one focusing on user specifications, and how AI assists in the creative coding process. The duo also discusses the balance of trust and change in adopting AI, and the future of personalized media through innovative platforms like Tessle!

Devin Petersohn, creator of Modin and co-founder of Ponder, acquired by Snowflake, discusses optimizing pandas data frames, building Moden to match pandas API, query optimization in large language model systems, and handling CSV files efficiently in distributed systems on the Weaviate Podcast.

Former OpenAI member Lucas Negritto and Weaviate Co-founder Bob van Luijt discuss AI-native applications in a fascinating podcast. They explore building UIs within generative models, native multimodality, subjective feedback, and more. Topics include prototype creation, challenges of using natural language prompts, creativity in software writing, and the evolution of AI in game development.

Liana Patel is a Ph.D. student at Stanford University who is the lead author of ACORN, a breakthrough in Approximate Nearest Neighbor Search with Filters! Also joining the podcast is Abdel Rodriguez, a Vector Index Researcher and Engineer at Weaviate. This podcast dives into all sorts of details behind ACORN. Starting with how Liana developed her interest in Approximate Nearest Neighbor Search algorithms and then transitioning into how ACORN differs from previous approaches, the Two-Hop Neighborhood Heuristic, Predicate Subgraphs, Experimental Details, and many more topics! Major thank you to Liana and Abdel for joining the podcast, this was such a fun conversation packed with insights about Proximity Graph algorithms for Vector Search with Filtering!

Josh Engels is a Ph.D. student at MIT who has published several works advancing the state of the art in Vector Search. Josh has recently developed the Window Search Tree, a new algorithm particularly targeted for improving Filtered Vector Search. Even more particularly than that, the WST algorithm targets Filtered Search with continuous-valued filters such as "price" or "date", also known as range filters. This is a huge application for Vector Databases and it was incredible getting to pick Josh's brain on how this works and the state of Approximate Nearest Neighbor Search!