Sagar Batchu, CEO of Speakeasy, discusses the pivotal shift in API development as AI agents become primary consumers. The conversation covers the rise of the Model Context Protocol (MCP), the challenges in building agent-ready APIs, and how Speakeasy provides a toolchain for creating, managing, and securing MCP servers.
Pushmeet Kohli, head of AI for Science at DeepMind, discusses AlphaEvolve, an AI system that uses Large Language Models (LLMs) coupled with evolutionary search to discover novel, human-interpretable algorithms. He explains the architecture, from its predecessor FunSearch to the multi-agent "Co-scientist" system, and details breakthroughs in solving decades-old math problems and optimizing real-world systems like data center scheduling and chip design.
Jarek Kutylowski, CEO of DeepL, discusses the company's technical strategy for competing with large language models in the translation space. He covers their focus on specialized model architectures, the critical role of curated data, the engineering challenges of building custom GPU data centers and large-scale inference systems, and the future of AI-driven translation in enterprise workflows.
LG AI Research introduces its advanced financial forecasting framework, which powers a US equities market ETF (LQAI) and a new "Master Score with Commentary" product with LSEG. The system uniquely combines structured financial data with unstructured text from news and reports, using the proprietary Exaone LLM and a multi-agent architecture to deliver explainable, accurate, and actionable market predictions across the entire US stock market.
Shreya Shankar of UC Berkeley discusses DocETL, a MapReduce-style framework that leverages LLMs to extract, analyze, and structure insights from unstructured enterprise data. The conversation covers practical architecture patterns, the role of non-determinism, strategies for model selection (including fine-tuning and multi-LLM pipelines), and the importance of user experience in this emerging field.
Douwe Kiela, CEO of Contextual AI, explains the evolution from basic RAG to "RAG 2.0", an end-to-end, trainable system. He argues that this system-level approach, which integrates optimized document parsing, retrieval, reranking, and grounded models, is superior to relying on massive context windows alone and is a fundamental tool for next-generation AI agents.